From 1cd629271a5b9cfff4243798bc5f7d6626a6d2e7 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Wed, 17 Jul 2024 18:45:11 -0400
Subject: [PATCH 01/50] fixed rust get functions

---
 .../src/train/speed_limit_train_sim.rs        | 20 +++++++++++----
 rust/altrios-core/src/train/train_config.rs   | 25 ++++++++++++++-----
 rust/altrios-core/src/train/train_state.rs    |  4 +++
 3 files changed, 38 insertions(+), 11 deletions(-)

diff --git a/rust/altrios-core/src/train/speed_limit_train_sim.rs b/rust/altrios-core/src/train/speed_limit_train_sim.rs
index dfcdb03e..61bd5684 100644
--- a/rust/altrios-core/src/train/speed_limit_train_sim.rs
+++ b/rust/altrios-core/src/train/speed_limit_train_sim.rs
@@ -57,14 +57,19 @@ impl From<&Vec<LinkIdxTime>> for TimedLinkPath {
         Ok(self.get_save_interval())
     }
 
+    #[pyo3(name = "get_kilometers")]
+    pub fn get_kilometers_py(&self, annualize: bool)  -> f64 {
+        self.get_kilometers(annualize)
+    }
+
     #[pyo3(name = "get_megagram_kilometers")]
     pub fn get_megagram_kilometers_py(&self, annualize: bool)  -> f64 {
         self.get_megagram_kilometers(annualize)
     }
 
-    #[pyo3(name = "get_kilometers")]
-    pub fn get_kilometers_py(&self, annualize: bool)  -> f64 {
-        self.get_kilometers(annualize)
+    #[pyo3(name = "get_car_kilometers")]
+    pub fn get_car_kilometers_py(&self, annualize: bool)  -> f64 {
+        self.get_car_kilometers(annualize)
     }
 
     #[pyo3(name = "get_res_kilometers")]
@@ -206,14 +211,19 @@ impl SpeedLimitTrainSim {
         }
     }
 
+    pub fn get_kilometers(&self, annualize: bool) -> f64 {
+        self.state.total_dist.get::<si::kilometer>() * self.get_scaling_factor(annualize)
+    }
+
     pub fn get_megagram_kilometers(&self, annualize: bool) -> f64 {
         self.state.mass_freight.get::<si::megagram>()
             * self.state.total_dist.get::<si::kilometer>()
             * self.get_scaling_factor(annualize)
     }
 
-    pub fn get_kilometers(&self, annualize: bool) -> f64 {
-        self.state.total_dist.get::<si::kilometer>() * self.get_scaling_factor(annualize)
+    pub fn get_car_kilometers(&self, annualize: bool) -> f64 {
+        let n_cars = self.state.cars_total as f64;
+        self.state.total_dist.get::<si::kilometer>() * n_cars * self.get_scaling_factor(annualize)
     }
 
     pub fn get_res_kilometers(&mut self, annualize: bool) -> f64 {
diff --git a/rust/altrios-core/src/train/train_config.rs b/rust/altrios-core/src/train/train_config.rs
index 54409a92..91fecf5c 100644
--- a/rust/altrios-core/src/train/train_config.rs
+++ b/rust/altrios-core/src/train/train_config.rs
@@ -328,13 +328,14 @@ impl TrainSimBuilder {
                 + veh.braking_ratio_loaded * self.train_config.cars_loaded as f64)
             / cars_total;
 
-        let state = TrainState::new(
+        let mut state = TrainState::new(
             length,
             mass_static,
             mass_adj,
             mass_freight,
             self.init_train_state,
         );
+        state.cars_total = self.train_config.cars_total();
 
         let path_tpc = PathTpc::new(train_params);
 
@@ -984,14 +985,19 @@ pub fn run_speed_limit_train_sims(
         self.get_net_energy_res(annualize).get::<si::joule>()
     }
 
+    #[pyo3(name = "get_kilometers")]
+    pub fn get_kilometers_py(&self, annualize: bool) -> f64 {
+        self.get_kilometers(annualize)
+    }
+
     #[pyo3(name = "get_megagram_kilometers")]
     pub fn get_megagram_kilometers_py(&self, annualize: bool) -> f64 {
         self.get_megagram_kilometers(annualize)
     }
 
-    #[pyo3(name = "get_kilometers")]
-    pub fn get_kilometers_py(&self, annualize: bool) -> f64 {
-        self.get_kilometers(annualize)
+    #[pyo3(name = "get_car_kilometers")]
+    pub fn get_car_kilometers_py(&self, annualize: bool) -> f64 {
+        self.get_car_kilometers(annualize)
     }
 
     #[pyo3(name = "get_res_kilometers")]
@@ -1027,6 +1033,10 @@ impl SpeedLimitTrainSimVec {
             .sum()
     }
 
+    pub fn get_kilometers(&self, annualize: bool) -> f64 {
+        self.0.iter().map(|sim| sim.get_kilometers(annualize)).sum()
+    }
+
     pub fn get_megagram_kilometers(&self, annualize: bool) -> f64 {
         self.0
             .iter()
@@ -1034,8 +1044,11 @@ impl SpeedLimitTrainSimVec {
             .sum()
     }
 
-    pub fn get_kilometers(&self, annualize: bool) -> f64 {
-        self.0.iter().map(|sim| sim.get_kilometers(annualize)).sum()
+    pub fn get_car_kilometers(&self, annualize: bool) -> f64 {
+        self.0
+            .iter()
+            .map(|sim| sim.get_car_kilometers(annualize))
+            .sum()
     }
 
     pub fn get_res_kilometers(&mut self, annualize: bool) -> f64 {
diff --git a/rust/altrios-core/src/train/train_state.rs b/rust/altrios-core/src/train/train_state.rs
index 2e022b56..1706e24f 100644
--- a/rust/altrios-core/src/train/train_state.rs
+++ b/rust/altrios-core/src/train/train_state.rs
@@ -104,6 +104,9 @@ pub struct TrainState {
     pub res_grade: si::Force,
     pub res_curve: si::Force,
 
+    /// Number of cars included on the train
+    pub cars_total: u32,
+
     /// Grade at front of train
     pub grade_front: si::Ratio,
     /// Grade at back of train of train if strap method is used
@@ -142,6 +145,7 @@ impl Default for TrainState {
             mass_static: Default::default(),
             mass_adj: Default::default(),
             mass_freight: Default::default(),
+            cars_total: Default::default(),
             elev_front: Default::default(),
             energy_whl_out: Default::default(),
             grade_front: Default::default(),

From f922fb4b6281d1fe60736d1e025c207cd4757735 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Fri, 26 Jul 2024 14:36:52 -0400
Subject: [PATCH 02/50] separate counts for empty and loaded

---
 python/altrios/metric_calculator.py           | 96 +++++++++++++++----
 .../altrios/tests/test_metric_calculator.py   |  2 +-
 .../src/train/speed_limit_train_sim.rs        | 23 ++++-
 rust/altrios-core/src/train/train_config.rs   | 23 ++++-
 rust/altrios-core/src/train/train_state.rs    |  9 +-
 5 files changed, 119 insertions(+), 34 deletions(-)

diff --git a/python/altrios/metric_calculator.py b/python/altrios/metric_calculator.py
index bb15460f..4789f0bb 100644
--- a/python/altrios/metric_calculator.py
+++ b/python/altrios/metric_calculator.py
@@ -117,11 +117,14 @@ def main(
         scenario_infos: Union[ScenarioInfo, List[ScenarioInfo]],
         annual_metrics: Union[Tuple[str, str],
                               List[Tuple[str, str]]] = [
-            ('Mt-km', 'million tonne-km'),
+            ('Freight_Moved', 'million tonne-mi'),
+            ('Freight_Moved', 'car-miles'),
+            ('Freight_Moved', 'cars'),
             ('GHG', 'tonne CO2-eq'),
             ('Count_Locomotives', 'assets'),
             ('Count_Refuelers', 'assets'),
-            ('Energy_Costs', 'USD')
+            ('Energy_Costs', 'USD'),
+            ('Energy_Per_Freight_Moved', 'kWh per car-mile')
         ],
         calculate_multiyear_metrics: bool = True
 ) -> pl.DataFrame:
@@ -146,7 +149,7 @@ def main(
     for annual_metric in annual_metrics:
         for scenario_info in scenario_infos:
             annual_values.append(calculate_annual_metric(annual_metric[0], annual_metric[1], scenario_info))
-
+            
     values = pl.concat(annual_values, how="diagonal_relaxed").unique()
     
     if calculate_multiyear_metrics: 
@@ -157,7 +160,8 @@ def main(
     values = (values
         .filter(pl.col("Value").is_not_null())
         .unique()
-        .sort(["Metric","Units","Year","Subset"], descending = [False, False, False, True])
+        .sort(["Metric","Units","Year","Subset"], 
+              descending = [False, False, False, True])
     )
 
     return values
@@ -257,6 +261,44 @@ def calculate_rollout_lcotkm(values: MetricType) -> MetricType:
         metric("LCOTKM", "USD per million tonne-km (levelized)", lcotkm_all)
     ])
 
+def calculate_energy_per_freight(info: ScenarioInfo,
+        units: str) -> MetricType:
+    """
+    Given a years' worth of simulation results, computes a single year energy usage per unit of freight moved.
+    Arguments:
+    ----------
+    info: A scenario information object representing parameters and results for a single year
+    units: Requested units
+    Outputs:
+    ----------
+    DataFrame of energy usage per freight moved (metric name, units, value, and scenario year)
+    """
+    if "per car-mile" not in units:
+        print(f"Units of {units} not supported for energy-per-freight calculation.")
+        return metric("Energy_Per_Freight_Moved", units, None)
+    
+    conversion_from_megajoule = 0
+    if "MJ" in units:
+        conversion_from_megajoule = 1
+    elif "MWh" in units:
+        conversion_from_megajoule = utilities.KWH_PER_MJ / 1e3
+    elif "kWh" in units:
+        conversion_from_megajoule = utilities.KWH_PER_MJ
+
+    diesel_mj = calculate_diesel_use(info, units="MJ")
+    electricity_mj = calculate_electricity_use(info, units="MJ")
+    total_mj = value_from_metrics(diesel_mj) + value_from_metrics(electricity_mj, subset="All")
+    total_energy = total_mj * conversion_from_megajoule
+    freight_moved = calculate_freight_moved(info, units="car-miles")
+    freight_val = value_from_metrics(freight_moved)
+    return metrics_from_list([
+        diesel_mj,
+        electricity_mj,
+        metric("Energy_Use", "MJ", total_mj),
+        metric("Energy_Per_Freight_Moved", units, total_energy / freight_val)
+    ])
+
+
 def calculate_energy_cost(info: ScenarioInfo,
         units: str) -> MetricType:
     """
@@ -273,8 +315,7 @@ def calculate_energy_cost(info: ScenarioInfo,
         return metric("Cost_Energy", units, None)
     
     diesel_used = calculate_diesel_use(info, units="gallons")
-    electricity_used = (calculate_electricity_use(info, units="kWh")
-                        )
+    electricity_used = calculate_electricity_use(info, units="kWh")
     electricity_costs_disagg = (electricity_used
         .filter(pl.col("Subset") != "All")
         .join(info.nodal_energy_prices.filter(pl.col("Fuel")=="Electricity"), left_on="Subset", right_on="Node", how="left")
@@ -292,11 +333,15 @@ def calculate_energy_cost(info: ScenarioInfo,
     # Diesel refueling is not yet tracked spatiotemporally; just use average price across the network.
     diesel_price = info.nodal_energy_prices.filter(pl.col("Fuel")=="Diesel").get_column("Price").mean()
     diesel_cost_value = value_from_metrics(diesel_used,"Diesel_Usage") * diesel_price
-    return metrics_from_list([diesel_used, 
-                      metric("Cost_Diesel", "USD", diesel_cost_value), 
-                      electricity_costs_disagg, 
-                      electricity_costs_agg, 
-                      metric("Cost_Energy", units, diesel_cost_value + electricity_cost_value)])
+    if electricity_cost_value is None: electricity_cost_value = 0.0
+    if diesel_cost_value is None: diesel_cost_value = 0
+    return metrics_from_list([
+        diesel_used, 
+        electricity_used,
+        metric("Cost_Diesel", "USD", diesel_cost_value), 
+        electricity_costs_disagg, 
+        electricity_costs_agg, 
+        metric("Cost_Energy", units, diesel_cost_value + electricity_cost_value)])
 
 def calculate_diesel_use(
         info: ScenarioInfo,
@@ -351,7 +396,7 @@ def calculate_electricity_use(
         print(f"Units of {units} not supported for electricity use calculation.")
         return metric("Electricity_Usage", units, None)
        
-   if info.refuel_sessions is None: 
+   if (info.refuel_sessions is None) or (info.refuel_sessions.filter(pl.col("Fuel_Type")==pl.lit("Electricity")).height == 0): 
         # No refueling session data: charging was not explicitly modeled, 
         # so take total net energy at RES and apply charging efficiency factor
         return metric("Electricity_Usage", units, 
@@ -385,20 +430,33 @@ def calculate_electricity_use(
             disagg_energy])
 
 
-def calculate_freight(
+def calculate_freight_moved(
         info: ScenarioInfo,
         units: str) -> MetricType:
     """
-    Given a years' worth of simulation results, computes a single year gross million tonne-km of freight delivered
+    Given a years' worth of simulation results, computes a single year quantity of freight moved
     Arguments:
     ----------
     info: A scenario information object representing parameters and results for a single year
     units: Requested units
     Outputs:
     ----------
-    DataFrame of gross million tonne-km of freight (metric name, units, value, and scenario year)
+    DataFrame of quantity of freight (metric name, units, value, and scenario year)
     """
-    return metric("Mt-km", units, info.sims.get_megagram_kilometers(annualize=info.annualize)/1.0e6)
+    if "-mi" in units:
+        conversion_from_km = utilities.MI_PER_KM
+    else:
+        conversion_from_km = 1.0
+    
+    if units in ["million tonne-km", "million tonne-mi"]:
+        return metric("Freight_Moved", units, info.sims.get_megagram_kilometers(annualize=info.annualize) * conversion_from_km /1.0e6)
+    elif units in ["car-km", "car-miles"]:
+        return metric("Freight_Moved", units, info.sims.get_car_kilometers(include_empty=True, include_loaded=True, annualize=info.annualize) * conversion_from_km)
+    elif units == "cars":
+        return metric("Freight_Moved", units, info.sims.get_cars_moved(include_empty=True, include_loaded=True, annualize=info.annualize))
+    else:
+        print(f"Units of {units} not supported for freight movement calculation.")
+        return metric("Freight_Moved", units, None)
 
 def calculate_ghg(
         info: ScenarioInfo,
@@ -445,9 +503,6 @@ def calculate_ghg(
                 to the desired region before passing the emissions factor dataframe into the metrics calculator.""")
         else:
             if electricity_MWh.filter(pl.col("Subset") != "All").height > 0:
-                print(diesel_MJ)
-                print(info.emissions_factors)
-                print(electricity_MWh)
                 # Disaggregated results are available
                 electricity_ghg_val = (electricity_MWh
                     .filter(pl.col("Subset") != pl.lit("All"))
@@ -1011,7 +1066,8 @@ def add_battery_costs(loco_info: pd.DataFrame, year: int) -> pd.DataFrame:
 
 
 function_mappings = {'Energy_Costs': calculate_energy_cost,
-    'Mt-km': calculate_freight,
+    'Freight_Moved': calculate_freight_moved,
+    'Energy_Per_Freight_Moved': calculate_energy_per_freight,
     'GHG': calculate_ghg,
     'Count_Locomotives': calculate_locomotive_counts,
     'Count_Refuelers': calculate_refueler_counts
diff --git a/python/altrios/tests/test_metric_calculator.py b/python/altrios/tests/test_metric_calculator.py
index 004b6efe..6fc754c5 100644
--- a/python/altrios/tests/test_metric_calculator.py
+++ b/python/altrios/tests/test_metric_calculator.py
@@ -23,6 +23,6 @@ def test_dummy_sim(self):
                 False))
             
         for info in scenario_infos:
-            tkm = metric_calculator.calculate_freight(info,'Million_Tonne-KM')
+            tkm = metric_calculator.calculate_freight_moved(info,'million tonne-mi')
             self.assertEqual(tkm.filter(pl.col("Metric") == 'Mt-km').get_column("Units").len(), 1)
 
diff --git a/rust/altrios-core/src/train/speed_limit_train_sim.rs b/rust/altrios-core/src/train/speed_limit_train_sim.rs
index 61bd5684..1fc0f407 100644
--- a/rust/altrios-core/src/train/speed_limit_train_sim.rs
+++ b/rust/altrios-core/src/train/speed_limit_train_sim.rs
@@ -68,8 +68,13 @@ impl From<&Vec<LinkIdxTime>> for TimedLinkPath {
     }
 
     #[pyo3(name = "get_car_kilometers")]
-    pub fn get_car_kilometers_py(&self, annualize: bool)  -> f64 {
-        self.get_car_kilometers(annualize)
+    pub fn get_car_kilometers_py(&self, include_empty: bool, include_loaded: bool, annualize: bool)  -> f64 {
+        self.get_car_kilometers(include_empty, include_loaded, annualize)
+    }
+
+    #[pyo3(name = "get_cars_moved")]
+    pub fn get_cars_moved_py(&self, include_empty: bool, include_loaded: bool, annualize: bool)  -> f64 {
+        self.get_cars_moved(include_empty, include_loaded, annualize)
     }
 
     #[pyo3(name = "get_res_kilometers")]
@@ -221,9 +226,17 @@ impl SpeedLimitTrainSim {
             * self.get_scaling_factor(annualize)
     }
 
-    pub fn get_car_kilometers(&self, annualize: bool) -> f64 {
-        let n_cars = self.state.cars_total as f64;
-        self.state.total_dist.get::<si::kilometer>() * n_cars * self.get_scaling_factor(annualize)
+    pub fn get_car_kilometers(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
+        let n_cars = self.get_cars_moved(include_empty, include_loaded, annualize);
+        // Note: n_cars already includes an annualization scaling factor; no need to multiply twice.
+        self.state.total_dist.get::<si::kilometer>() * n_cars
+    }
+
+    pub fn get_cars_moved(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
+        let n_cars = 
+            (self.state.cars_empty * (include_empty as u32)
+            + self.state.cars_loaded * (include_loaded as u32)) as f64;
+        n_cars * self.get_scaling_factor(annualize)
     }
 
     pub fn get_res_kilometers(&mut self, annualize: bool) -> f64 {
diff --git a/rust/altrios-core/src/train/train_config.rs b/rust/altrios-core/src/train/train_config.rs
index 91fecf5c..23613165 100644
--- a/rust/altrios-core/src/train/train_config.rs
+++ b/rust/altrios-core/src/train/train_config.rs
@@ -335,7 +335,8 @@ impl TrainSimBuilder {
             mass_freight,
             self.init_train_state,
         );
-        state.cars_total = self.train_config.cars_total();
+        state.cars_empty = self.train_config.cars_empty;
+        state.cars_loaded = self.train_config.cars_loaded;
 
         let path_tpc = PathTpc::new(train_params);
 
@@ -996,8 +997,13 @@ pub fn run_speed_limit_train_sims(
     }
 
     #[pyo3(name = "get_car_kilometers")]
-    pub fn get_car_kilometers_py(&self, annualize: bool) -> f64 {
-        self.get_car_kilometers(annualize)
+    pub fn get_car_kilometers_py(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
+        self.get_car_kilometers(include_empty, include_loaded, annualize)
+    }
+
+    #[pyo3(name = "get_cars_moved")]
+    pub fn get_cars_moved_py(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
+        self.get_cars_moved(include_empty, include_loaded, annualize)
     }
 
     #[pyo3(name = "get_res_kilometers")]
@@ -1044,10 +1050,17 @@ impl SpeedLimitTrainSimVec {
             .sum()
     }
 
-    pub fn get_car_kilometers(&self, annualize: bool) -> f64 {
+    pub fn get_car_kilometers(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
+        self.0
+            .iter()
+            .map(|sim| sim.get_car_kilometers(include_empty, include_loaded, annualize))
+            .sum()
+    }
+
+    pub fn get_cars_moved(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
         self.0
             .iter()
-            .map(|sim| sim.get_car_kilometers(annualize))
+            .map(|sim| sim.get_cars_moved(include_empty, include_loaded, annualize))
             .sum()
     }
 
diff --git a/rust/altrios-core/src/train/train_state.rs b/rust/altrios-core/src/train/train_state.rs
index 1706e24f..7f514626 100644
--- a/rust/altrios-core/src/train/train_state.rs
+++ b/rust/altrios-core/src/train/train_state.rs
@@ -104,8 +104,10 @@ pub struct TrainState {
     pub res_grade: si::Force,
     pub res_curve: si::Force,
 
-    /// Number of cars included on the train
-    pub cars_total: u32,
+    /// Number of empty cars included on the train
+    pub cars_empty: u32,
+    /// Number of loaded cars included on the train
+    pub cars_loaded: u32,
 
     /// Grade at front of train
     pub grade_front: si::Ratio,
@@ -145,7 +147,8 @@ impl Default for TrainState {
             mass_static: Default::default(),
             mass_adj: Default::default(),
             mass_freight: Default::default(),
-            cars_total: Default::default(),
+            cars_empty: Default::default(),
+            cars_loaded: Default::default(),
             elev_front: Default::default(),
             energy_whl_out: Default::default(),
             grade_front: Default::default(),

From 797ddae07520cc3cc15b21854765ab15ec807a6d Mon Sep 17 00:00:00 2001
From: sakhtar <sakhtar@nrel.gov>
Date: Tue, 10 Sep 2024 15:40:19 -0600
Subject: [PATCH 03/50] Made drag coeff a function instead of list

---
 python/altrios/train_planner.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index e8165e6f..afdaaaec 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -6,7 +6,7 @@
 import polars as pl
 import polars.selectors as cs
 import math
-from typing import Tuple, List, Dict
+from typing import Tuple, List, Dict, Callable
 from itertools import repeat
 import altrios as alt
 from altrios import defaults, utilities
@@ -22,7 +22,7 @@ def __init__(self,
                  #TODO single vs double stacked operations on the corridor
                  cars_per_locomotive: int = 70,
                  refuelers_per_incoming_corridor: int = 4,
-                 drag_coeff_function: List = None,
+                 drag_coeff_function: Callable = None,
                  hp_required_per_ton: Dict = {
                      "Default": {
                         "Unit": 2.0,
@@ -989,7 +989,8 @@ def run_train_planner(
                             this_train['Train_Type']: this_train['Number_of_Cars']
                         },
                         train_type = train_type,
-                        cd_area_vec = config.drag_coeff_function
+                        cd_area_vec = config.drag_coeff_function(this_train['Number_of_Cars'], 
+                                                                 gap_size = 0.604)
                     )
 
                     loco_start_soc_j = dispatched.get_column("SOC_J")

From 5d0bab89fdf73b0372f9880d8269b9d4f932cc1e Mon Sep 17 00:00:00 2001
From: FDsteven <fdstevensdw@gmail.com>
Date: Thu, 12 Sep 2024 16:01:42 -0600
Subject: [PATCH 04/50] Completed intermodal empty car generations

---
 python/altrios/train_planner.py | 34 ++++++++++++++++++++-------------
 1 file changed, 21 insertions(+), 13 deletions(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index e8165e6f..80c2dc88 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -20,7 +20,7 @@ def __init__(self,
                  target_cars_per_train: int = 180,
                  manifest_empty_return_ratio: float = 0.6,
                  #TODO single vs double stacked operations on the corridor
-                 cars_per_locomotive: int = 70,
+                 cars_per_locomotive: int = 30,
                  refuelers_per_incoming_corridor: int = 4,
                  drag_coeff_function: List = None,
                  hp_required_per_ton: Dict = {
@@ -146,18 +146,26 @@ def generate_return_demand(
     df_return_demand: The demand generated by the need
     of returning the empty cars to their original nodes
     """
-    return (demand
-        .rename({"Origin": "Destination",
-                 "Destination": "Origin"})
-        .drop("Number_Of_Containers")
-        .with_columns(
-            pl.concat_str([pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")
-                           ,pl.lit("_Empty")]).alias("Train_Type"),
-            pl.when(pl.col("Train_Type") == pl.lit("Manifest"))
-                .then((pl.col("Number_of_Cars") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32))
-                .otherwise(pl.col("Number_of_Cars"))
-                .alias("Number_of_Cars"))
-    )
+    intermodal = demand.filter(pl.col("Train_Type").str.starts_with("Intermodal"))
+    intermodal = intermodal.with_columns(pl.col("Number_of_Cars").cast(pl.Int64))
+    intermodal_return = intermodal.rename({"Origin": "Destination", "Destination": "Origin", "Number_of_Cars": "Cars_Empty"})
+    intermodal = intermodal.with_columns(Cars_Empty = pl.lit(0))
+    intermodal_dummy = intermodal.with_columns(pl.col("Number_of_Cars").cast(pl.Int64).neg())
+    for i in range(len(intermodal)):
+        temp = intermodal_return.filter(pl.col("Origin") == intermodal[i]["Origin"])
+        reverse = temp.filter(pl.col("Destination") == intermodal[i]["Destination"])
+        intermodal[i, 'Cars_Empty'] = max(reverse[0]["Cars_Empty"].item()+intermodal_dummy[i]["Number_of_Cars"].item(),0)
+    #calculate empty manifest
+    manifest = demand.filter(pl.col("Train_Type").str.starts_with("Manifest"))
+   
+    demand = demand.rename({"Origin": "Destination","Destination": "Origin"}).drop("Number_Of_Containers")
+    demand = demand.with_columns(pl.concat_str([pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")
+                                                ,pl.lit("_Empty")]).alias("Train_Type"),
+                                                pl.when(pl.col("Train_Type") == pl.lit("Manifest"))
+                                                .then((pl.col("Number_of_Cars") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32))
+                                                .otherwise(pl.col("Number_of_Cars")).alias("Number_of_Cars"),)
+   
+    return demand
 
 def generate_origin_manifest_demand(
     demand: pl.DataFrame,

From 3e7338e30ef4343c2d8178052ba0b5f8aa6c4c5e Mon Sep 17 00:00:00 2001
From: FDsteven <fdstevensdw@gmail.com>
Date: Fri, 13 Sep 2024 09:59:18 -0600
Subject: [PATCH 05/50] Completed return demand based on the three different
 train types

---
 python/altrios/train_planner.py | 38 +++++++++++++++++++++------------
 1 file changed, 24 insertions(+), 14 deletions(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index 80c2dc88..0998a83c 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -148,24 +148,34 @@ def generate_return_demand(
     """
     intermodal = demand.filter(pl.col("Train_Type").str.starts_with("Intermodal"))
     intermodal = intermodal.with_columns(pl.col("Number_of_Cars").cast(pl.Int64))
-    intermodal_return = intermodal.rename({"Origin": "Destination", "Destination": "Origin", "Number_of_Cars": "Cars_Empty"})
-    intermodal = intermodal.with_columns(Cars_Empty = pl.lit(0))
+    intermodal_return = intermodal.clone()
     intermodal_dummy = intermodal.with_columns(pl.col("Number_of_Cars").cast(pl.Int64).neg())
+    intermodal_dummy = intermodal_dummy.rename({"Origin": "Destination", "Destination": "Origin"})
+    intermodal_return = intermodal_return.with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
     for i in range(len(intermodal)):
-        temp = intermodal_return.filter(pl.col("Origin") == intermodal[i]["Origin"])
-        reverse = temp.filter(pl.col("Destination") == intermodal[i]["Destination"])
-        intermodal[i, 'Cars_Empty'] = max(reverse[0]["Cars_Empty"].item()+intermodal_dummy[i]["Number_of_Cars"].item(),0)
+        temp = intermodal.filter(pl.col("Origin") == intermodal_dummy[i]["Origin"])
+        reverse = temp.filter(pl.col("Destination") == intermodal_dummy[i]["Destination"])
+        intermodal_return[i, 'Number_of_Cars'] = max(reverse[0]["Number_of_Cars"].item()+intermodal_dummy[i]["Number_of_Cars"].item(),0)
+        intermodal_return[i, 'Number_of_Containers'] = intermodal_return[i, 'Number_of_Cars']
+    intermodal_return = intermodal_return.with_columns(pl.col("Number_of_Cars").cast(pl.UInt32))
     #calculate empty manifest
     manifest = demand.filter(pl.col("Train_Type").str.starts_with("Manifest"))
-   
-    demand = demand.rename({"Origin": "Destination","Destination": "Origin"}).drop("Number_Of_Containers")
-    demand = demand.with_columns(pl.concat_str([pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")
-                                                ,pl.lit("_Empty")]).alias("Train_Type"),
-                                                pl.when(pl.col("Train_Type") == pl.lit("Manifest"))
-                                                .then((pl.col("Number_of_Cars") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32))
-                                                .otherwise(pl.col("Number_of_Cars")).alias("Number_of_Cars"),)
-   
-    return demand
+    manifest = manifest.with_columns(pl.col("Number_of_Cars").cast(pl.Int64))
+    manifest_return = manifest.clone()
+    manifest_return = manifest_return.rename({"Origin": "Destination", "Destination": "Origin"})
+    manifest_return = manifest_return.with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
+    manifest_return = manifest_return.with_columns((pl.col("Number_of_Cars") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32))
+
+
+    #calculate empty unit cars
+    unit = demand.filter(pl.col("Train_Type").str.starts_with("Unit"))
+    unit_return = unit.clone()
+    unit_return = unit_return.rename({"Origin": "Destination", "Destination": "Origin"})
+    unit_return = unit_return.with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
+
+
+    demand_return = pl.concat([unit_return,manifest_return,intermodal_return], how = "diagonal")
+    return demand_return
 
 def generate_origin_manifest_demand(
     demand: pl.DataFrame,

From 4b56ac711214af5efcf92c328a850b267ec66a59 Mon Sep 17 00:00:00 2001
From: FDsteven <fdstevensdw@gmail.com>
Date: Fri, 13 Sep 2024 12:16:04 -0600
Subject: [PATCH 06/50] Enabled mixed consist, consolidated final traind demand
 df

---
 python/altrios/train_planner.py | 38 ++++++++++++++++++++++-----------
 1 file changed, 25 insertions(+), 13 deletions(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index 0998a83c..3862d74e 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -311,6 +311,8 @@ def generate_demand_trains(
         demand_returns.drop("Number_of_Containers"),
         demand_rebalancing],
         how="diagonal_relaxed")
+    demand = demand.group_by("Origin","Destination","Train_Type").agg(pl.col("Number_of_Cars").sum())
+    demand = demand.filter(pl.col("Number_of_Cars") != 0)
     # if rowx[first three columns] == rowy[first three columns]:
     #     rowx[fourth column] + rowy[fourth column]
     #     delete rowy
@@ -360,8 +362,29 @@ def get_kg(veh):
         # Replace nulls with zero
     demand = demand.with_columns(cs.float().fill_null(0.0), cs.by_dtype(pl.UInt32).fill_null(pl.lit(0).cast(pl.UInt32)))
         # Convert total number of cars to total number of trains
+
+    demand = demand.with_columns(
+        (pl.when(pl.col("Train_Type").str.ends_with("_Empty"))
+            .then(pl.col("Number_of_Cars"))
+            .otherwise(0))
+            .cast(pl.UInt32)
+            .alias("Cars_Empty"),
+        (pl.when(pl.col("Train_Type").str.ends_with("_Empty"))
+            .then(0)
+            .otherwise(pl.col("Number_of_Cars")))
+            .cast(pl.UInt32)
+            .alias("Cars_Loaded"),
+        (pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Empty"))),
+    )
+    demand = demand.with_columns((pl.col("Number_of_Cars") * pl.col("Tons_Per_Car")).alias("Tons_Aggregate"))
+    demand = demand.group_by("Origin","Destination","Train_Type").agg(pl.col("Number_of_Cars").sum(),
+    pl.col("Tons_Aggregate").sum(),
+    pl.col("Cars_Loaded").sum(),
+    pl.col("Cars_Empty").sum(),
+    pl.col("KG").sum(),
+    pl.col("HP_Required_Per_Ton").mean()
+    )    
     demand = demand.with_columns(
-        (pl.col("Number_of_Cars") * pl.col("Tons_Per_Car")).alias("Tons_Aggregate"),
         pl.when(config.single_train_mode)
             .then(1)
             .when(pl.col("Number_of_Cars") == 0)
@@ -374,18 +397,7 @@ def get_kg(veh):
         # Calculate per-train car counts and tonnage
     demand = demand.with_columns(
         pl.col("Tons_Aggregate").truediv(pl.col("Number_of_Trains")).alias("Tons_Per_Train"))
-    demand = demand.with_columns(
-        (pl.when(pl.col("Train_Type").str.ends_with("_Empty"))
-            .then(pl.col("Number_of_Cars"))
-            .otherwise(0))
-            .cast(pl.UInt32)
-            .alias("Cars_Empty"),
-        (pl.when(pl.col("Train_Type").str.ends_with("_Empty"))
-            .then(0)
-            .otherwise(pl.col("Number_of_Cars")))
-            .cast(pl.UInt32)
-            .alias("Cars_Loaded")
-    )
+
     return demand
 
 

From df8239a2ba183d35f4dc5d0048de397ddd76ceaf Mon Sep 17 00:00:00 2001
From: sakhtar <sakhtar@nrel.gov>
Date: Fri, 13 Sep 2024 15:38:43 -0600
Subject: [PATCH 07/50] Corrected the drag function implementation

---
 python/altrios/train_planner.py | 70 ++++++++++++++++++++++++++++++++-
 1 file changed, 69 insertions(+), 1 deletion(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index afdaaaec..194e6572 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -99,7 +99,75 @@ def __init__(self,
         self.dispatch_scaling_dict = dispatch_scaling_dict
         self.loco_info = loco_info
         self.refueler_info = refueler_info
-        self.drag_coeff_function = drag_coeff_function
+        self.drag_coeff_function = drag_coeff_function if drag_coeff_function else self.default_drag_coeff_function
+    
+    def default_drag_coeff_function(self, 
+                                    num_rail_vehicles: int = 1,
+                                    gap_size: float = 0.604) -> List[float]:
+        """
+    Returns the default drag coefficient vector as a function of number of rail vehicles in a consist
+    and vehicle gap size
+
+    Arguments:
+    ---------
+    num_rail_vehicles: int - Number of rail vehicles in the platoon
+    ps_gap_size: float - Gap size between the rail vehicles
+
+    Output:
+    ---------
+    List of drag coefficients for each rail car. len(List) = num_rail_vehicles
+
+    """
+
+        ## From slide 16 of the Aerodynamic model PPT
+        drag_vec_10cars_liang = [1.168, 0.292, 0.228,
+                                0.217, 0.238, 0.209,
+                            0.244,0.244,0.244, 0.409] 
+        
+        ## From slide 16 of the Aerodynamic model PPT
+        drag_vec_10cars_liang = [1.168, 0.292, 0.228,
+                                0.217, 0.238, 0.209,
+                                0.244,0.244,0.244, 0.409] 
+
+        ## From slide 16 of the Aerodynamic model PPT
+        periodic_drag_coeff_liang = 0.193
+
+        gap_size_array = [0.508, 0.968, 1.186, 1.407, 
+                1.564, 1.627, 1.851] #gap size in meters from digitized plot
+        drag_change_array = [-32.56, -24.93, -17.85, 
+                         -6.678, 0.009, 1.7, 
+                         7.876] # Change in drag coefficient for periodic boundary in %
+
+        rel_drag_change = np.interp(gap_size, xp=gap_size_array, 
+                                    fp=drag_change_array)
+        # rel_drag_change = -29.30
+        drag_coeff_baseline = 0.108
+        periodic_drag_coeff_ps = drag_coeff_baseline*(1+rel_drag_change/100)
+        drag_ratio = periodic_drag_coeff_ps/periodic_drag_coeff_liang
+        drag_vec = drag_vec_10cars_liang[0:num_rail_vehicles]
+
+        ## For num_rail_vehicles 1, 2, and 3: 
+        ## scaled the value for Liang's car from values in slide 24 
+        if num_rail_vehicles == 1:
+            drag_vec = [0.904/drag_ratio] 
+        elif num_rail_vehicles == 2:
+            drag_vec[0] = 0.504/drag_ratio  
+            drag_vec[-1] = 0.904/drag_ratio - drag_vec[0]
+        elif num_rail_vehicles == 3:
+            drag_vec[0] = 0.504/drag_ratio
+            # drag_vec[1] = 0.115/drag_ratio    
+            drag_vec[-1] = 0.904/drag_ratio - sum(drag_vec[:-1])
+        elif num_rail_vehicles >= 4:
+            drag_vec[0] = 0.504/drag_ratio
+            drag_vec[-1] = drag_vec_10cars_liang[-1]
+            if num_rail_vehicles > 10:
+                drag_vec = drag_vec_10cars_liang[:-1] + \
+                    [0.105]*(num_rail_vehicles-9)
+                drag_vec[-1] = drag_vec_10cars_liang[-1]
+
+        drag_vec_ps = [round(drag_ratio*x, 3)
+                                for x in drag_vec]
+        return drag_vec_ps
 
 def demand_loader(
     demand_table: Union[pl.DataFrame, Path, str]

From 7089701cce61b442bd442196bf076a0be3c9928c Mon Sep 17 00:00:00 2001
From: sakhtar <sakhtar@nrel.gov>
Date: Mon, 16 Sep 2024 17:03:00 -0600
Subject: [PATCH 08/50] Added gap size to defaults and proposed minor edits in
 train_planner.py

---
 python/altrios/defaults.py      | 1 +
 python/altrios/train_planner.py | 8 ++++----
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/python/altrios/defaults.py b/python/altrios/defaults.py
index 06f0cfdf..848787ce 100644
--- a/python/altrios/defaults.py
+++ b/python/altrios/defaults.py
@@ -33,6 +33,7 @@
 
 LOCO_LIFESPAN = 20
 ANNUAL_LOCO_TURNOVER = 1.0/LOCO_LIFESPAN
+DEFAULT_GAP_SIZE = 0.604
 
 DEMAND_FILE = alt.resources_root() / "Default Demand.csv"
 FUEL_EMISSIONS_FILE = alt.resources_root() / "metrics_inputs" / "GREET-CA_Emissions_Factors.csv"
diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index 194e6572..7684e17e 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -996,10 +996,10 @@ def run_train_planner(
 
     #TODO eliminate the naming convention that rail vehicles (train types from demand file) must end in `_Loaded` or `_Empty`
     dispatch_times = (dispatch_times.with_columns(
-        pl.when(pl.col("Train_Type").str.ends_with("_Empty"))
+        pl.when(pl.col("Train_Type").str.to_lowercase().str.ends_with("_empty"))
             .then(pl.col("Train_Type"))
-            .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Loaded"),
-                                     pl.lit("_Loaded")))
+            .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_loaded"),
+                                     pl.lit("_loaded")))
             .alias("Train_Type")
         )
     )
@@ -1058,7 +1058,7 @@ def run_train_planner(
                         },
                         train_type = train_type,
                         cd_area_vec = config.drag_coeff_function(this_train['Number_of_Cars'], 
-                                                                 gap_size = 0.604)
+                                                                 gap_size = defaults.DEFAULT_GAP_SIZE)
                     )
 
                     loco_start_soc_j = dispatched.get_column("SOC_J")

From 6f6e0b251eaf918406a583f0062b0806732f6b80 Mon Sep 17 00:00:00 2001
From: sakhtar <sakhtar@nrel.gov>
Date: Tue, 17 Sep 2024 10:20:39 -0600
Subject: [PATCH 09/50] Corrected case for 'Loaded' suffix

---
 python/altrios/train_planner.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index 7684e17e..c079cb1c 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -995,11 +995,12 @@ def run_train_planner(
         dispatch_times = calculate_dispatch_times(demand, simulation_days * 24)
 
     #TODO eliminate the naming convention that rail vehicles (train types from demand file) must end in `_Loaded` or `_Empty`
+    #TODO: Make 'Loaded' and 'Empty' suffix manipulation case independent
     dispatch_times = (dispatch_times.with_columns(
         pl.when(pl.col("Train_Type").str.to_lowercase().str.ends_with("_empty"))
             .then(pl.col("Train_Type"))
             .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_loaded"),
-                                     pl.lit("_loaded")))
+                                     pl.lit("_Loaded")))
             .alias("Train_Type")
         )
     )

From b2aa1d4970c9ed588af5b517bd32145274444efb Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Wed, 18 Sep 2024 12:47:05 -0600
Subject: [PATCH 10/50] changed to `dataclass` for readability

---
 python/altrios/train_planner.py | 228 +++++++++++---------------------
 1 file changed, 74 insertions(+), 154 deletions(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index c079cb1c..ebd29f85 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -6,168 +6,88 @@
 import polars as pl
 import polars.selectors as cs
 import math
-from typing import Tuple, List, Dict, Callable
+from typing import Tuple, List, Dict, Callable, Optional
 from itertools import repeat
+from dataclasses import dataclass
 import altrios as alt
 from altrios import defaults, utilities
 
 pl.enable_string_cache()
 
+@dataclass
 class TrainPlannerConfig:
-    def __init__(self, 
-                 single_train_mode: bool = False,
-                 min_cars_per_train: int = 60,
-                 target_cars_per_train: int = 180,
-                 manifest_empty_return_ratio: float = 0.6,
-                 #TODO single vs double stacked operations on the corridor
-                 cars_per_locomotive: int = 70,
-                 refuelers_per_incoming_corridor: int = 4,
-                 drag_coeff_function: Callable = None,
-                 hp_required_per_ton: Dict = {
-                     "Default": {
-                        "Unit": 2.0,
-                        "Manifest": 1.5,
-                        "Intermodal": 2.0 + 2.0,
-                        "Unit_Empty": 2.0,
-                        "Manifest_Empty": 1.5,
-                        "Intermodal_Empty": 2.0 + 2.0,
-                        }                         
-                     }, 
-                 dispatch_scaling_dict: Dict = {
-                     "time_mult_factor": 1.4,
-                     "hours_add": 2,
-                     "energy_mult_factor": 1.25
-                     },
-                 loco_info = pd.DataFrame({
-                    "Diesel_Large": {
-                        "Capacity_Cars": 20,
-                        "Fuel_Type": "Diesel",
-                        "Min_Servicing_Time_Hr": 3.0,
-                        "Rust_Loco": alt.Locomotive.default(),
-                        "Cost_USD": defaults.DIESEL_LOCO_COST_USD,
-                        "Lifespan_Years": defaults.LOCO_LIFESPAN
-                        },
-                    "BEL": {
-                        "Capacity_Cars": 20,
-                        "Fuel_Type": "Electricity",
-                        "Min_Servicing_Time_Hr": 3.0,
-                        "Rust_Loco": alt.Locomotive.default_battery_electric_loco(),
-                        "Cost_USD": defaults.BEL_MINUS_BATTERY_COST_USD,
-                        "Lifespan_Years": defaults.LOCO_LIFESPAN
-                        }
-                    }).transpose().reset_index(names='Locomotive_Type'),
-                 refueler_info = pd.DataFrame({
-                    "Diesel_Fueler": {
-                        "Locomotive_Type": "Diesel_Large",
-                        "Fuel_Type": "Diesel",
-                        "Refueler_J_Per_Hr": defaults.DIESEL_REFUEL_RATE_J_PER_HR,
-                        "Refueler_Efficiency": defaults.DIESEL_REFUELER_EFFICIENCY,
-                        "Cost_USD": defaults.DIESEL_REFUELER_COST_USD,
-                        "Lifespan_Years": defaults.LOCO_LIFESPAN
-                    },
-                    "BEL_Charger": {
-                        "Locomotive_Type": "BEL",
-                        "Fuel_Type": "Electricity",
-                        "Refueler_J_Per_Hr": defaults.BEL_CHARGE_RATE_J_PER_HR,
-                        "Refueler_Efficiency": defaults.BEL_CHARGER_EFFICIENCY,
-                        "Cost_USD": defaults.BEL_CHARGER_COST_USD,
-                        "Lifespan_Years": defaults.LOCO_LIFESPAN
-                    }
-                }).transpose().reset_index(names='Refueler_Type')
-    ):
-        """
-        Constructor for train planner configuration objects
-        Arguments:
-        ----------
-        min_cars_per_train: the minimum length in number of cars to form a train
-        target_cars_per_train: the standard train length in number of cars
-        manifest_empty_return_ratio: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
-        cars_per_locomotive: Heuristic scaling factor used to size number of locomotives needed based on demand.
-        refuelers_per_incoming_corridor: 
-        hp_required_per_ton:
-        dispatch_scaling_dict:
-        loco_info:
-        refueler_info:
-        """
-        self.single_train_mode = single_train_mode
-        self.min_cars_per_train = min_cars_per_train
-        self.target_cars_per_train = target_cars_per_train
-        self.manifest_empty_return_ratio = manifest_empty_return_ratio
-        self.cars_per_locomotive = cars_per_locomotive
-        self.refuelers_per_incoming_corridor = refuelers_per_incoming_corridor
-        self.hp_required_per_ton = hp_required_per_ton
-        self.dispatch_scaling_dict = dispatch_scaling_dict
-        self.loco_info = loco_info
-        self.refueler_info = refueler_info
-        self.drag_coeff_function = drag_coeff_function if drag_coeff_function else self.default_drag_coeff_function
-    
-    def default_drag_coeff_function(self, 
-                                    num_rail_vehicles: int = 1,
-                                    gap_size: float = 0.604) -> List[float]:
-        """
-    Returns the default drag coefficient vector as a function of number of rail vehicles in a consist
-    and vehicle gap size
-
-    Arguments:
-    ---------
-    num_rail_vehicles: int - Number of rail vehicles in the platoon
-    ps_gap_size: float - Gap size between the rail vehicles
-
-    Output:
-    ---------
-    List of drag coefficients for each rail car. len(List) = num_rail_vehicles
-
     """
-
-        ## From slide 16 of the Aerodynamic model PPT
-        drag_vec_10cars_liang = [1.168, 0.292, 0.228,
-                                0.217, 0.238, 0.209,
-                            0.244,0.244,0.244, 0.409] 
-        
-        ## From slide 16 of the Aerodynamic model PPT
-        drag_vec_10cars_liang = [1.168, 0.292, 0.228,
-                                0.217, 0.238, 0.209,
-                                0.244,0.244,0.244, 0.409] 
-
-        ## From slide 16 of the Aerodynamic model PPT
-        periodic_drag_coeff_liang = 0.193
-
-        gap_size_array = [0.508, 0.968, 1.186, 1.407, 
-                1.564, 1.627, 1.851] #gap size in meters from digitized plot
-        drag_change_array = [-32.56, -24.93, -17.85, 
-                         -6.678, 0.009, 1.7, 
-                         7.876] # Change in drag coefficient for periodic boundary in %
-
-        rel_drag_change = np.interp(gap_size, xp=gap_size_array, 
-                                    fp=drag_change_array)
-        # rel_drag_change = -29.30
-        drag_coeff_baseline = 0.108
-        periodic_drag_coeff_ps = drag_coeff_baseline*(1+rel_drag_change/100)
-        drag_ratio = periodic_drag_coeff_ps/periodic_drag_coeff_liang
-        drag_vec = drag_vec_10cars_liang[0:num_rail_vehicles]
-
-        ## For num_rail_vehicles 1, 2, and 3: 
-        ## scaled the value for Liang's car from values in slide 24 
-        if num_rail_vehicles == 1:
-            drag_vec = [0.904/drag_ratio] 
-        elif num_rail_vehicles == 2:
-            drag_vec[0] = 0.504/drag_ratio  
-            drag_vec[-1] = 0.904/drag_ratio - drag_vec[0]
-        elif num_rail_vehicles == 3:
-            drag_vec[0] = 0.504/drag_ratio
-            # drag_vec[1] = 0.115/drag_ratio    
-            drag_vec[-1] = 0.904/drag_ratio - sum(drag_vec[:-1])
-        elif num_rail_vehicles >= 4:
-            drag_vec[0] = 0.504/drag_ratio
-            drag_vec[-1] = drag_vec_10cars_liang[-1]
-            if num_rail_vehicles > 10:
-                drag_vec = drag_vec_10cars_liang[:-1] + \
-                    [0.105]*(num_rail_vehicles-9)
-                drag_vec[-1] = drag_vec_10cars_liang[-1]
-
-        drag_vec_ps = [round(drag_ratio*x, 3)
-                                for x in drag_vec]
-        return drag_vec_ps
+    Attributes:
+    ----------
+    min_cars_per_train: the minimum length in number of cars to form a train
+    target_cars_per_train: the standard train length in number of cars
+    manifest_empty_return_ratio: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
+    cars_per_locomotive: Heuristic scaling factor used to size number of locomotives needed based on demand.
+    refuelers_per_incoming_corridor: 
+    hp_required_per_ton:
+    dispatch_scaling_dict:
+    loco_info:
+    refueler_info:
+    """
+    single_train_mode: bool = False,
+    min_cars_per_train: int = 60,
+    target_cars_per_train: int = 180,
+    manifest_empty_return_ratio: float = 0.6,
+    #TODO single vs double stacked operations on the corridor
+    cars_per_locomotive: int = 70,
+    refuelers_per_incoming_corridor: int = 4,
+    drag_coeff_function: Optional[Callable]= None,
+    hp_required_per_ton: Dict = {
+        "Default": {
+            "Unit": 2.0,
+            "Manifest": 1.5,
+            "Intermodal": 2.0 + 2.0,
+            "Unit_Empty": 2.0,
+            "Manifest_Empty": 1.5,
+            "Intermodal_Empty": 2.0 + 2.0,
+        }                         
+     }, 
+    dispatch_scaling_dict: Dict = {
+         "time_mult_factor": 1.4,
+         "hours_add": 2,
+         "energy_mult_factor": 1.25
+     },
+    loco_info = pd.DataFrame({
+        "Diesel_Large": {
+            "Capacity_Cars": 20,
+            "Fuel_Type": "Diesel",
+            "Min_Servicing_Time_Hr": 3.0,
+            "Rust_Loco": alt.Locomotive.default(),
+            "Cost_USD": defaults.DIESEL_LOCO_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+            },
+        "BEL": {
+            "Capacity_Cars": 20,
+            "Fuel_Type": "Electricity",
+            "Min_Servicing_Time_Hr": 3.0,
+            "Rust_Loco": alt.Locomotive.default_battery_electric_loco(),
+            "Cost_USD": defaults.BEL_MINUS_BATTERY_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+            }
+    }).transpose().reset_index(names='Locomotive_Type')
+    refueler_info = pd.DataFrame({
+        "Diesel_Fueler": {
+            "Locomotive_Type": "Diesel_Large",
+            "Fuel_Type": "Diesel",
+            "Refueler_J_Per_Hr": defaults.DIESEL_REFUEL_RATE_J_PER_HR,
+            "Refueler_Efficiency": defaults.DIESEL_REFUELER_EFFICIENCY,
+            "Cost_USD": defaults.DIESEL_REFUELER_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+        },
+        "BEL_Charger": {
+            "Locomotive_Type": "BEL",
+            "Fuel_Type": "Electricity",
+            "Refueler_J_Per_Hr": defaults.BEL_CHARGE_RATE_J_PER_HR,
+            "Refueler_Efficiency": defaults.BEL_CHARGER_EFFICIENCY,
+            "Cost_USD": defaults.BEL_CHARGER_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+        }
+    }).transpose().reset_index(names='Refueler_Type')
 
 def demand_loader(
     demand_table: Union[pl.DataFrame, Path, str]

From e0945320a7aaec305a011222014ba37559856eaa Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Wed, 18 Sep 2024 12:47:55 -0600
Subject: [PATCH 11/50] removed some of the non-generic stuff

---
 python/altrios/train_planner.py             | 10 ++++++++--
 rust/altrios-core/src/train/train_config.rs |  3 ++-
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index ebd29f85..da13d959 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -972,14 +972,20 @@ def run_train_planner(
                         )
                         dispatched = loco_pool.filter(selected)
 
+                    if config.drag_coeff_function is not None:
+                        cd_area_vec = config.drag_coeff_function(
+                             this_train['Number_of_Cars'], 
+                             gap_size = defaults.DEFAULT_GAP_SIZE
+                         )
+                    else:
+                        cd_area_vec = None
                     train_config = alt.TrainConfig(
                         rail_vehicles = [vehicle for vehicle in rail_vehicles if vehicle.car_type==this_train['Train_Type']],
                         n_cars_by_type = {
                             this_train['Train_Type']: this_train['Number_of_Cars']
                         },
                         train_type = train_type,
-                        cd_area_vec = config.drag_coeff_function(this_train['Number_of_Cars'], 
-                                                                 gap_size = defaults.DEFAULT_GAP_SIZE)
+                        cd_area_vec = cd_area_vec,
                     )
 
                     loco_start_soc_j = dispatched.get_column("SOC_J")
diff --git a/rust/altrios-core/src/train/train_config.rs b/rust/altrios-core/src/train/train_config.rs
index 7dc0796a..f850a4e3 100644
--- a/rust/altrios-core/src/train/train_config.rs
+++ b/rust/altrios-core/src/train/train_config.rs
@@ -104,7 +104,8 @@ pub struct TrainConfig {
     /// Optional vector of drag areas (i.e. drag coeff. times frontal area)
     /// for each car.  If provided, the total drag area (drag coefficient
     /// times frontal area) calculated from this vector is the sum of these
-    /// coefficients.
+    /// coefficients. Otherwise, each rail car's drag contribution based on its
+    /// drag coefficient and frontal area will be summed across the train.
     pub cd_area_vec: Option<Vec<si::Area>>,
 }
 

From 8399956254eba6355bda8e751f47fd6a83a57369 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 23 Sep 2024 10:55:27 -0400
Subject: [PATCH 12/50] integrating private repo changes

---
 python/altrios/demos/sim_manager_demo.py |   4 +-
 python/altrios/train_planner.py          | 575 ++++++++++++++---------
 python/altrios/utilities.py              |   8 +
 3 files changed, 359 insertions(+), 228 deletions(-)

diff --git a/python/altrios/demos/sim_manager_demo.py b/python/altrios/demos/sim_manager_demo.py
index aed09db4..9057a3bd 100644
--- a/python/altrios/demos/sim_manager_demo.py
+++ b/python/altrios/demos/sim_manager_demo.py
@@ -35,8 +35,8 @@
 )
 
 train_planner_config = train_planner.TrainPlannerConfig(
-            cars_per_locomotive=50,
-            target_cars_per_train=90)
+            cars_per_locomotive={"Default": 50},
+            target_cars_per_train={"Default": 90})
 
 t0_main = time.perf_counter()
 
diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index 3862d74e..d30ae98c 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -1,3 +1,4 @@
+from __future__ import annotations
 from pathlib import Path
 from typing import Union
 import numpy as np
@@ -6,100 +7,184 @@
 import polars as pl
 import polars.selectors as cs
 import math
-from typing import Tuple, List, Dict
+from typing import Tuple, List, Dict, Callable, Optional
 from itertools import repeat
 import altrios as alt
 from altrios import defaults, utilities
+from dataclasses import dataclass, field
 
 pl.enable_string_cache()
 
+
+def initialize_reverse_empties(demand: Union[pl.LazyFrame, pl.DataFrame]) -> Union[pl.LazyFrame, pl.DataFrame]:
+    """
+    Swap `Origin` and `Destination` and append `_Empty` to `Train_Type`.
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demand.
+
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame`.
+    """
+    return (demand
+        .rename({"Origin": "Destination", "Destination": "Origin"})
+        .with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
+    )    
+
+def generate_return_demand_unit(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
+    """
+    Given a set of Unit train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
+
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
+    """
+    return (demand_subset
+        .pipe(initialize_reverse_empties)
+    )
+
+def generate_return_demand_manifest(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
+    """
+    Given a set of Manifest train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
+
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
+    """
+    return(demand_subset
+        .pipe(initialize_reverse_empties)
+        .with_columns((pl.col("Number_of_Cars") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32))
+    )
+
+def generate_return_demand_intermodal(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
+    """
+    Given a set of Intermodal train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
+
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
+    """
+    return (demand_subset
+        .pipe(initialize_reverse_empties)
+        .with_columns(
+            pl.concat_str(pl.min_horizontal("Origin", "Destination"), pl.lit("_"), pl.max_horizontal("Origin", "Destination")).alias("OD")
+        )
+        .with_columns(
+            pl.col("Number_of_Cars", "Number_of_Containers").range_minmax().over("OD").name.suffix("_Return")
+        )
+        .filter(
+            pl.col("Number_of_Containers") == pl.col("Number_of_Containers").max().over("OD")
+        )
+        .drop("OD", "Number_of_Containers", "Number_of_Cars")
+        .rename({"Number_of_Containers_Return": "Number_of_Containers",
+                 "Number_of_Cars_Return": "Number_of_Cars"})
+    )
+
+
+@dataclass
 class TrainPlannerConfig:
-    def __init__(self, 
-                 single_train_mode: bool = False,
-                 min_cars_per_train: int = 60,
-                 target_cars_per_train: int = 180,
-                 manifest_empty_return_ratio: float = 0.6,
-                 #TODO single vs double stacked operations on the corridor
-                 cars_per_locomotive: int = 30,
-                 refuelers_per_incoming_corridor: int = 4,
-                 drag_coeff_function: List = None,
-                 hp_required_per_ton: Dict = {
-                     "Default": {
-                        "Unit": 2.0,
-                        "Manifest": 1.5,
-                        "Intermodal": 2.0 + 2.0,
-                        "Unit_Empty": 2.0,
-                        "Manifest_Empty": 1.5,
-                        "Intermodal_Empty": 2.0 + 2.0,
-                        }                         
-                     }, 
-                 dispatch_scaling_dict: Dict = {
-                     "time_mult_factor": 1.4,
-                     "hours_add": 2,
-                     "energy_mult_factor": 1.25
-                     },
-                 loco_info = pd.DataFrame({
-                    "Diesel_Large": {
-                        "Capacity_Cars": 20,
-                        "Fuel_Type": "Diesel",
-                        "Min_Servicing_Time_Hr": 3.0,
-                        "Rust_Loco": alt.Locomotive.default(),
-                        "Cost_USD": defaults.DIESEL_LOCO_COST_USD,
-                        "Lifespan_Years": defaults.LOCO_LIFESPAN
-                        },
-                    "BEL": {
-                        "Capacity_Cars": 20,
-                        "Fuel_Type": "Electricity",
-                        "Min_Servicing_Time_Hr": 3.0,
-                        "Rust_Loco": alt.Locomotive.default_battery_electric_loco(),
-                        "Cost_USD": defaults.BEL_MINUS_BATTERY_COST_USD,
-                        "Lifespan_Years": defaults.LOCO_LIFESPAN
-                        }
-                    }).transpose().reset_index(names='Locomotive_Type'),
-                 refueler_info = pd.DataFrame({
-                    "Diesel_Fueler": {
-                        "Locomotive_Type": "Diesel_Large",
-                        "Fuel_Type": "Diesel",
-                        "Refueler_J_Per_Hr": defaults.DIESEL_REFUEL_RATE_J_PER_HR,
-                        "Refueler_Efficiency": defaults.DIESEL_REFUELER_EFFICIENCY,
-                        "Cost_USD": defaults.DIESEL_REFUELER_COST_USD,
-                        "Lifespan_Years": defaults.LOCO_LIFESPAN
-                    },
-                    "BEL_Charger": {
-                        "Locomotive_Type": "BEL",
-                        "Fuel_Type": "Electricity",
-                        "Refueler_J_Per_Hr": defaults.BEL_CHARGE_RATE_J_PER_HR,
-                        "Refueler_Efficiency": defaults.BEL_CHARGER_EFFICIENCY,
-                        "Cost_USD": defaults.BEL_CHARGER_COST_USD,
-                        "Lifespan_Years": defaults.LOCO_LIFESPAN
-                    }
-                }).transpose().reset_index(names='Refueler_Type')
-    ):
-        """
-        Constructor for train planner configuration objects
-        Arguments:
-        ----------
-        min_cars_per_train: the minimum length in number of cars to form a train
-        target_cars_per_train: the standard train length in number of cars
-        manifest_empty_return_ratio: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
-        cars_per_locomotive: Heuristic scaling factor used to size number of locomotives needed based on demand.
-        refuelers_per_incoming_corridor: 
-        hp_required_per_ton:
-        dispatch_scaling_dict:
-        loco_info:
-        refueler_info:
-        """
-        self.single_train_mode = single_train_mode
-        self.min_cars_per_train = min_cars_per_train
-        self.target_cars_per_train = target_cars_per_train
-        self.manifest_empty_return_ratio = manifest_empty_return_ratio
-        self.cars_per_locomotive = cars_per_locomotive
-        self.refuelers_per_incoming_corridor = refuelers_per_incoming_corridor
-        self.hp_required_per_ton = hp_required_per_ton
-        self.dispatch_scaling_dict = dispatch_scaling_dict
-        self.loco_info = loco_info
-        self.refueler_info = refueler_info
-        self.drag_coeff_function = drag_coeff_function
+    """
+    Dataclass class for train planner configuration parameters.
+
+    Fields:
+    - `single_train_mode`: `True` to only run one round-trip train and schedule its charging; `False` to plan train consists
+    - `min_cars_per_train`: `Dict` of the minimum length in number of cars to form a train for each train type
+    - `target_cars_per_train`: `Dict` of the standard train length in number of cars for each train type
+    - `manifest_empty_return_ratio`: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
+    - `cars_per_locomotive`: Heuristic scaling factor used to size number of locomotives needed based on demand.
+    - `cars_per_locomotive_fixed`: If `True`, `cars_per_locomotive` overrides `hp_per_ton` calculations used for dispatching decisions.
+    - `refuelers_per_incoming_corridor`: Heuristic scaling factor used to scale number of refuelers needed at each node based on number of incoming corridors.
+    - `stack_type`: Type of stacking (applicable only for intermodal containers)
+    - `require_diesel`: `True` to require each consist to have at least one diesel locomotive.
+    - `manifest_empty_return_ratio`: `Dict`
+    - `drag_coeff_function`: `Dict`
+    - `hp_required_per_ton`: `Dict`
+    - `dispatch_scaling_dict`: `Dict`
+    - `loco_info`: `Dict`
+    - `refueler_info`: `Dict`
+    - `return_demand_generators`: `Dict`
+    """
+    single_train_mode: bool = False
+    min_cars_per_train: Dict = field(default_factory = lambda: {
+        "Default": 60
+    })
+    target_cars_per_train: Dict = field(default_factory = lambda: {
+        "Default": 180
+    })
+    cars_per_locomotive: int = 70
+    cars_per_locomotive_fixed: bool = False
+    refuelers_per_incoming_corridor: int = 4
+    stack_type: str = "single"
+    require_diesel: bool = False
+    manifest_empty_return_ratio: float = 0.6
+    drag_coeff_function: List = field(default_factory = lambda: None)
+    hp_required_per_ton: Dict = field(default_factory = lambda: {
+        "Default": {
+        "Unit": 2.0,
+        "Manifest": 1.5,
+        "Intermodal": 2.0 + 2.0,
+        "Unit_Empty": 2.0,
+        "Manifest_Empty": 1.5,
+        "Intermodal_Empty": 2.0 + 2.0,
+        }                         
+    })
+    dispatch_scaling_dict: Dict = field(default_factory = lambda: {
+        "time_mult_factor": 1.4,
+        "hours_add": 2,
+        "energy_mult_factor": 1.25
+    })
+    loco_info: pd.DataFrame = field(default_factory = lambda: pd.DataFrame({
+        "Diesel_Large": {
+            "Capacity_Cars": 20,
+            "Fuel_Type": "Diesel",
+            "Min_Servicing_Time_Hr": 3.0,
+            "Rust_Loco": alt.Locomotive.default(),
+            "Cost_USD": defaults.DIESEL_LOCO_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+            },
+        "BEL": {
+            "Capacity_Cars": 20,
+            "Fuel_Type": "Electricity",
+            "Min_Servicing_Time_Hr": 3.0,
+            "Rust_Loco": alt.Locomotive.default_battery_electric_loco(),
+            "Cost_USD": defaults.BEL_MINUS_BATTERY_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+            }
+        }).transpose().reset_index(names='Locomotive_Type'))
+    refueler_info: pd.DataFrame = field(default_factory = lambda: pd.DataFrame({
+        "Diesel_Fueler": {
+            "Locomotive_Type": "Diesel_Large",
+            "Fuel_Type": "Diesel",
+            "Refueler_J_Per_Hr": defaults.DIESEL_REFUEL_RATE_J_PER_HR,
+            "Refueler_Efficiency": defaults.DIESEL_REFUELER_EFFICIENCY,
+            "Cost_USD": defaults.DIESEL_REFUELER_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+        },
+        "BEL_Charger": {
+            "Locomotive_Type": "BEL",
+            "Fuel_Type": "Electricity",
+            "Refueler_J_Per_Hr": defaults.BEL_CHARGE_RATE_J_PER_HR,
+            "Refueler_Efficiency": defaults.BEL_CHARGER_EFFICIENCY,
+            "Cost_USD": defaults.BEL_CHARGER_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+        }
+    }).transpose().reset_index(names='Refueler_Type'))
+
+    return_demand_generators: Dict = field(default_factory = lambda: {
+        'Unit': generate_return_demand_unit,
+        'Manifest': generate_return_demand_manifest,
+        'Intermodal': generate_return_demand_intermodal
+    })
 
 def demand_loader(
     demand_table: Union[pl.DataFrame, Path, str]
@@ -129,7 +214,6 @@ def demand_loader(
         demand_table.get_column("Destination")]).unique().sort()
     return demand_table, nodes
 
-
 def generate_return_demand(
     demand: pl.DataFrame,
     config: TrainPlannerConfig
@@ -139,42 +223,25 @@ def generate_return_demand(
     Arguments:
     ----------
     df_annual_demand: The user_input file loaded by previous functions
-    that contains laoded demand for each demand pair.
+    that contains loaded demand for each demand pair.
     config: Object storing train planner configuration paramaters
     Outputs:
     ----------
     df_return_demand: The demand generated by the need
     of returning the empty cars to their original nodes
     """
-    intermodal = demand.filter(pl.col("Train_Type").str.starts_with("Intermodal"))
-    intermodal = intermodal.with_columns(pl.col("Number_of_Cars").cast(pl.Int64))
-    intermodal_return = intermodal.clone()
-    intermodal_dummy = intermodal.with_columns(pl.col("Number_of_Cars").cast(pl.Int64).neg())
-    intermodal_dummy = intermodal_dummy.rename({"Origin": "Destination", "Destination": "Origin"})
-    intermodal_return = intermodal_return.with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
-    for i in range(len(intermodal)):
-        temp = intermodal.filter(pl.col("Origin") == intermodal_dummy[i]["Origin"])
-        reverse = temp.filter(pl.col("Destination") == intermodal_dummy[i]["Destination"])
-        intermodal_return[i, 'Number_of_Cars'] = max(reverse[0]["Number_of_Cars"].item()+intermodal_dummy[i]["Number_of_Cars"].item(),0)
-        intermodal_return[i, 'Number_of_Containers'] = intermodal_return[i, 'Number_of_Cars']
-    intermodal_return = intermodal_return.with_columns(pl.col("Number_of_Cars").cast(pl.UInt32))
-    #calculate empty manifest
-    manifest = demand.filter(pl.col("Train_Type").str.starts_with("Manifest"))
-    manifest = manifest.with_columns(pl.col("Number_of_Cars").cast(pl.Int64))
-    manifest_return = manifest.clone()
-    manifest_return = manifest_return.rename({"Origin": "Destination", "Destination": "Origin"})
-    manifest_return = manifest_return.with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
-    manifest_return = manifest_return.with_columns((pl.col("Number_of_Cars") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32))
-
-
-    #calculate empty unit cars
-    unit = demand.filter(pl.col("Train_Type").str.starts_with("Unit"))
-    unit_return = unit.clone()
-    unit_return = unit_return.rename({"Origin": "Destination", "Destination": "Origin"})
-    unit_return = unit_return.with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
-
-
-    demand_return = pl.concat([unit_return,manifest_return,intermodal_return], how = "diagonal")
+    demand_subsets = demand.partition_by("Train_Type", as_dict = True)
+    return_demand = []
+    for train_type, demand_subset in demand_subsets.items():
+        if train_type in config.return_demand_generators:
+            return_demand_generator = config.return_demand_generators[train_type]
+            return_demand.append(return_demand_generator(demand_subset, config))
+        else:
+            print(f'Return demand generator not implemented for train type: {train_type}')
+
+    demand_return = (pl.concat(return_demand, how="diagonal_relaxed")
+        .filter(pl.col("Number_of_Cars") + pl.col("Number_of_Containers") > 0)
+    )
     return demand_return
 
 def generate_origin_manifest_demand(
@@ -305,36 +372,38 @@ def generate_demand_trains(
     ----------
     demand: Tabulated demand for each demand pair in terms of number of cars and number of trains
     """
-    
-    demand = pl.concat([
-        demand.drop("Number_of_Containers"),
-        demand_returns.drop("Number_of_Containers"),
-        demand_rebalancing],
-        how="diagonal_relaxed")
-    demand = demand.group_by("Origin","Destination","Train_Type").agg(pl.col("Number_of_Cars").sum())
-    demand = demand.filter(pl.col("Number_of_Cars") != 0)
-    # if rowx[first three columns] == rowy[first three columns]:
-    #     rowx[fourth column] + rowy[fourth column]
-    #     delete rowy
-    # combined_row = demand.slice(10,12).select
-    # demand = demand.group_by()
-    #Prepare hp_per_ton requirements to merge onto the demand DataFrame
-    hp_per_ton = (
-        pl.DataFrame(pd.DataFrame(config.hp_required_per_ton).reset_index(names="Train_Type"))
-        .melt(id_vars="Train_Type",variable_name="O_D",value_name="HP_Required_Per_Ton")
-        .with_columns(pl.col("O_D").str.split("_").list.first().alias("Origin"),
-                      pl.col("O_D").str.split("_").list.last().alias("Destination"))
+
+    cars_per_train_min = (pl.from_dict(config.min_cars_per_train)
+        .melt(variable_name="Train_Type", value_name="Cars_Per_Train_Min")
+    )
+    cars_per_train_min_default = (cars_per_train_min
+        .filter(pl.col("Train_Type") == pl.lit("Default"))
+        .select("Cars_Per_Train_Min").item()
+    )
+    cars_per_train_target = (pl.from_dict(config.target_cars_per_train)
+        .melt(variable_name="Train_Type", value_name="Cars_Per_Train_Target")
+    )
+    cars_per_train_target_default = (cars_per_train_target
+        .filter(pl.col("Train_Type") == pl.lit("Default"))
+        .select("Cars_Per_Train_Target").item()
     )
+    #Prepare hp_per_ton requirements to merge onto the demand DataFrame
+    hp_per_ton = pl.concat([
+        (pl.DataFrame(this_dict)
+            .melt(variable_name="Train_Type", value_name="HP_Required_Per_Ton") 
+            .with_columns(pl.lit(this_item).alias("O_D"))
+            .with_columns(pl.col("O_D").str.split("_").list.first().alias("Origin"),
+                        pl.col("O_D").str.split("_").list.last().alias("Destination"))
+        )
+        for this_item, this_dict in config.hp_required_per_ton.items()
+    ], how="horizontal_relaxed")
     
-    #MPrepare ton_per_car requirements to merge onto the demand DataFrame
-    # TODO: simplify mass API here. Is there a function on the Rust side to get total mass (or should there be)?
+    #Prepare ton_per_car requirements to merge onto the demand DataFrame
     def get_kg_empty(veh):
         return veh.mass_static_base_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
     def get_kg(veh):
         return veh.mass_static_base_kilograms + veh.mass_freight_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
     
-    # NOTE: don't need to use this for PS; just need to use target platoon size (# of rail vehicles) then convert to containers 
-    # based on single vs. double stacked. Target # of rail vehicle Other intermodals may need to use weight 
     ton_per_car = (
         pl.DataFrame({"Train_Type": pl.Series([rv.car_type for rv in rail_vehicles]).str.strip_suffix("_Loaded"),
                         "KG_Empty": [get_kg_empty(rv) for rv in rail_vehicles],
@@ -346,62 +415,91 @@ def get_kg(veh):
             .drop(["KG_Empty","KG_Loaded"])
     )
 
-    demand = demand.join(ton_per_car, on="Train_Type", how="left")
+    demand = (pl.concat([demand, demand_returns, demand_rebalancing], how="diagonal_relaxed")
+        .group_by("Origin","Destination", "Train_Type")
+            .agg(pl.col("Number_of_Cars").sum())
+        .filter(pl.col("Number_of_Cars") > 0)
+        .join(ton_per_car, on="Train_Type", how="left")
         # Merge on OD-specific hp_per_ton if the user specified any
-    demand = demand.join(hp_per_ton.drop("O_D"), 
-        on=["Origin","Destination","Train_Type"], 
-        how="left")
+        .join(hp_per_ton.drop("O_D"), on=["Origin","Destination","Train_Type"], how="left")
         # Second, merge on defaults per train type
-    demand = demand.join(hp_per_ton.filter((pl.col("O_D") =="Default")).drop(["O_D","Origin","Destination"]),
-        on=["Train_Type"],
-        how="left",
-        suffix="_Default")
+        .join(hp_per_ton.filter((pl.col("O_D") =="Default")).drop(["O_D","Origin","Destination"]),
+            on=["Train_Type"],
+            how="left",
+            suffix="_Default")
+        # Merge on cars_per_train_min if the user specified any
+        .join(cars_per_train_min, on=["Train_Type"], how="left")
+        # Merge on cars_per_train_target if the user specified any
+        .join(cars_per_train_target, on=["Train_Type"], how="left")
         # Fill in defaults per train type wherever the user didn't specify OD-specific hp_per_ton
-    demand = demand.with_columns(pl.coalesce("HP_Required_Per_Ton", "HP_Required_Per_Ton_Default").alias("HP_Required_Per_Ton"))
-    demand = demand.drop("HP_Required_Per_Ton_Default")
-        # Replace nulls with zero
-    demand = demand.with_columns(cs.float().fill_null(0.0), cs.by_dtype(pl.UInt32).fill_null(pl.lit(0).cast(pl.UInt32)))
-        # Convert total number of cars to total number of trains
-
-    demand = demand.with_columns(
-        (pl.when(pl.col("Train_Type").str.ends_with("_Empty"))
-            .then(pl.col("Number_of_Cars"))
-            .otherwise(0))
-            .cast(pl.UInt32)
-            .alias("Cars_Empty"),
-        (pl.when(pl.col("Train_Type").str.ends_with("_Empty"))
-            .then(0)
-            .otherwise(pl.col("Number_of_Cars")))
-            .cast(pl.UInt32)
-            .alias("Cars_Loaded"),
-        (pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Empty"))),
+        .with_columns(
+            pl.coalesce("HP_Required_Per_Ton", "HP_Required_Per_Ton_Default").alias("HP_Required_Per_Ton"),
+            pl.col("Cars_Per_Train_Min").fill_null(cars_per_train_min_default),
+            pl.col("Cars_Per_Train_Target").fill_null(cars_per_train_target_default),
+        )
     )
-    demand = demand.with_columns((pl.col("Number_of_Cars") * pl.col("Tons_Per_Car")).alias("Tons_Aggregate"))
-    demand = demand.group_by("Origin","Destination","Train_Type").agg(pl.col("Number_of_Cars").sum(),
-    pl.col("Tons_Aggregate").sum(),
-    pl.col("Cars_Loaded").sum(),
-    pl.col("Cars_Empty").sum(),
-    pl.col("KG").sum(),
-    pl.col("HP_Required_Per_Ton").mean()
-    )    
-    demand = demand.with_columns(
-        pl.when(config.single_train_mode)
-            .then(1)
-            .when(pl.col("Number_of_Cars") == 0)
-            .then(0)
-            .otherwise(
-                pl.max_horizontal([1,
-                    ((pl.col("Number_of_Cars").floordiv(pl.lit(config.target_cars_per_train)) + 1))
+    loaded = (demand
+        .filter(~pl.col("Train_Type").str.contains("_Empty"))
+        .with_columns(
+            pl.col("Number_of_Cars", "Tons_Per_Car", "HP_Required_Per_Ton", "Cars_Per_Train_Min", "Cars_Per_Train_Target").name.suffix("_Loaded")
+        )
+    )
+    empty = (demand
+        .filter(pl.col("Train_Type").str.contains("_Empty"))
+        .with_columns(
+            pl.col("Number_of_Cars", "Tons_Per_Car", "HP_Required_Per_Ton", "Cars_Per_Train_Min", "Cars_Per_Train_Target").name.suffix("_Empty"),
+            pl.col("Train_Type").str.strip_suffix("_Empty")
+        )
+    )
+    demand = (demand
+        .select(pl.col("Origin", "Destination"), pl.col("Train_Type").str.strip_suffix("_Empty"))
+        .unique()
+        .join(loaded.select(cs.by_name("Origin", "Destination", "Train_Type") | cs.ends_with("_Loaded")), on=["Origin", "Destination", "Train_Type"], how="left")
+        .join(empty.select(cs.by_name("Origin", "Destination", "Train_Type") | cs.ends_with("_Empty")), on=["Origin", "Destination", "Train_Type"], how="left")
+        # Replace nulls with zero
+        .with_columns(cs.float().fill_null(0.0), 
+                      cs.by_dtype(pl.UInt32).fill_null(pl.lit(0).cast(pl.UInt32)),
+                      cs.by_dtype(pl.Int64).fill_null(pl.lit(0).cast(pl.Int64)),
+                      )
+        .group_by("Origin", "Destination", "Train_Type")
+            .agg(
+                pl.col("Number_of_Cars_Loaded", "Number_of_Cars_Empty").sum(),
+                pl.col("Tons_Per_Car_Loaded", "Tons_Per_Car_Empty", 
+                       "HP_Required_Per_Ton_Loaded", "HP_Required_Per_Ton_Empty",
+                       "Cars_Per_Train_Min_Loaded", "Cars_Per_Train_Min_Empty",
+                       "Cars_Per_Train_Target_Loaded", "Cars_Per_Train_Target_Empty").mean(),
+                pl.sum_horizontal("Number_of_Cars_Loaded", "Number_of_Cars_Empty").sum().alias("Number_of_Cars")
+            )
+        .with_columns(
+            # If Cars_Per_Train_Min and Cars_Per_Train_Target "disagree" for empty vs. loaded, take the average weighted by number of cars
+            ((pl.col("Cars_Per_Train_Min_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Cars_Per_Train_Min_Empty").mul("Number_of_Cars_Empty")) / pl.col("Number_of_Cars")).alias("Cars_Per_Train_Min"),
+            ((pl.col("Cars_Per_Train_Target_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Cars_Per_Train_Target_Empty").mul("Number_of_Cars_Empty")) / pl.col("Number_of_Cars")).alias("Cars_Per_Train_Target")
+        )
+        .with_columns(
+            pl.when(config.single_train_mode)
+                .then(1)
+                .when(pl.col("Number_of_Cars") == 0)
+                .then(0)
+                .when(pl.col("Cars_Per_Train_Target") == pl.col("Number_of_Cars"))
+                .then(1)
+                .when(pl.col("Cars_Per_Train_Target") <= 1.0)
+                .then(pl.col("Number_of_Cars"))
+                .otherwise(
+                    pl.max_horizontal([
+                        1,
+                        pl.min_horizontal([
+                            pl.col("Number_of_Cars").truediv("Cars_Per_Train_Target").round(),
+                            pl.col("Number_of_Cars").floordiv("Cars_Per_Train_Min")
+                        ])
                     ])
-            ).cast(pl.UInt32).alias("Number_of_Trains"))
-        # Calculate per-train car counts and tonnage
-    demand = demand.with_columns(
-        pl.col("Tons_Aggregate").truediv(pl.col("Number_of_Trains")).alias("Tons_Per_Train"))
-
+                ).cast(pl.UInt32).alias("Number_of_Trains")
+        )
+        .drop("Cars_Per_Train_Target_Loaded", "Cars_Per_Train_Target_Empty", "Cars_Per_Train_Min_Empty", "Cars_Per_Train_Min_Loaded")
+    )
     return demand
 
 
-def calculate_dispatch_times(
+def generate_dispatch_details(
     demand: pl.DataFrame,
     hours: int
 ) -> pl.DataFrame:
@@ -418,25 +516,66 @@ def calculate_dispatch_times(
     dispatch_times: Tabulated dispatching time for each demand pair for each train type
     in hours
     """
-    demand = demand \
-        .filter(pl.col("Number_of_Trains") > 0) \
-        .select(["Origin","Destination","Train_Type","Number_of_Trains",
-            "Number_of_Cars",
-            "Tons_Per_Train","HP_Required_Per_Ton", "Cars_Loaded", "Cars_Empty"]) \
+    def pctWithinGroup(df: pl.DataFrame, grouping_vars: List[str]) -> pl.DataFrame:
+        return (df
+            .with_columns(
+                (pl.cum_count().over(grouping_vars) / 
+                pl.count().over(grouping_vars))
+                .alias("Percent_Within_Group")
+            )
+        )
+
+    def allocateItems(df: pl.DataFrame, target: str, grouping_vars: List[str]) -> pl.DataFrame:
+        return (df
+        .sort(grouping_vars)
+        .pipe(pctWithinGroup, grouping_vars = grouping_vars)
+        .with_columns(
+            pl.col(target).mul("Percent_Within_Group").round().alias(f'{target}_Group_Cumulative')
+        )
+        .with_columns(
+            (pl.col(f'{target}_Group_Cumulative') - pl.col(f'{target}_Group_Cumulative').shift(1).over(grouping_vars))
+                .fill_null(pl.col(f'{target}_Group_Cumulative'))
+                .alias(f'{target}')
+        )
+        .drop(f'{target}_Group_Cumulative')
+    )
+
+    grouping_vars = ["Origin", "Destination", "Train_Type"]
+    demand = (demand
+        .select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode())
+        .pipe(allocateItems, target = "Number_of_Cars_Loaded", grouping_vars = grouping_vars)
+        .drop("Percent_Within_Group")
+        .pipe(allocateItems, target = "Number_of_Cars_Empty", grouping_vars = grouping_vars)
+        .group_by(pl.exclude("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
+            .agg(pl.col("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
+        .with_columns(pl.col("Number_of_Cars_Loaded").list.sort(descending=True),
+                      pl.col("Number_of_Cars_Empty").list.sort(descending=False))
+        .explode("Number_of_Cars_Empty", "Number_of_Cars_Loaded")
         .with_columns(
-            (hours / pl.col("Number_of_Trains")).alias("Interval"),
-            pl.col("Number_of_Trains").cast(pl.Int32).alias("Number_of_Trains"),
-            pl.col("Number_of_Cars").floordiv(pl.col("Number_of_Trains")).alias("Number_of_Cars"),
-            pl.col("Cars_Empty").floordiv(pl.col("Number_of_Trains")).alias("Cars_Empty"),
-            pl.col("Cars_Loaded").floordiv(pl.col("Number_of_Trains")).alias("Cars_Loaded"),
-        ).select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode()) \
+            (pl.col("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")).alias("Tons_Per_Train"),
+            (pl.col("HP_Required_Per_Ton_Loaded").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + 
+                pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded")
+                ).alias("HP_Required")
+        )
+        .sort("Origin", "Destination", "Percent_Within_Group", "Train_Type")
+        .with_columns(
+            (hours * 1.0 / pl.len().over("Origin", "Destination")).alias("Interval")
+        )
         .with_columns(
-            ((pl.col("Interval").cumcount().over(["Origin","Destination","Train_Type"])) \
+            ((pl.col("Interval").cumcount().over(["Origin","Destination"])) \
              * pl.col("Interval")).alias("Hour")
-        ).drop("Interval") \
+        )
+        .select("Hour", "Origin", "Destination", "Train_Type", 
+                "Number_of_Cars", "Number_of_Cars_Loaded", "Number_of_Cars_Empty",
+                "Tons_Per_Train", "HP_Required"
+        )
+        .rename({"Number_of_Cars_Loaded": "Cars_Loaded", 
+                "Number_of_Cars_Empty": "Cars_Empty"})
         .sort(["Hour","Origin","Destination","Train_Type"])
+    )
     
     return demand
+
 def build_locopool(
     config: TrainPlannerConfig,
     demand_file: Union[pl.DataFrame, Path, str],
@@ -466,7 +605,7 @@ def build_locopool(
         rows = initial_size
     else:
         num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
-        initial_size = math.ceil((cars_per_od / config.cars_per_locomotive) *
+        initial_size = math.ceil((cars_per_od / config.cars_per_locomotive["Default"]) *
                                 num_destinations_per_node)  # number of locomotives per node
         rows = initial_size * num_nodes  # number of locomotives in total
 
@@ -656,10 +795,9 @@ def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
 
 def dispatch(
     dispatch_time: int,
-    ton: float,
     origin: str,
     loco_pool: pl.DataFrame,
-    hp_per_ton: float,
+    hp_required: float,
 ) -> pl.Series:
     """
     Update the locomotive pool by identifying the desired locomotive to dispatch and assign to the
@@ -667,18 +805,14 @@ def dispatch(
     Arguments:
     ----------
     dispatch_time: time that a train is due
-    ton: required tonnage in the train
     origin: origin node name of the train
     loco_pool: locomotive pool dataframe containing all locomotives in the network
-    hp_per_ton: Horsepower per ton required for this train type on this origin-destination corridor
+    hp_required: Horsepower required for this train type on this origin-destination corridor
     Outputs:
     ----------
     selected: Indices of selected locomotives
     """
-    hp_required = ton * hp_per_ton
-
     # Candidate locomotives at the right place that are ready
-
     candidates = loco_pool.select((pl.col("Node") == origin) &
                                 (pl.col("Status") == "Ready")).to_series()
     if not candidates.any():
@@ -954,19 +1088,9 @@ def run_train_planner(
             demand_origin_manifest = generate_origin_manifest_demand(demand, node_list, config)
             demand_rebalancing = balance_trains(demand_origin_manifest)
         demand = generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, config)
-        dispatch_times = calculate_dispatch_times(demand, simulation_days * 24)
-
-    #TODO eliminate the naming convention that rail vehicles (train types from demand file) must end in `_Loaded` or `_Empty`
-    dispatch_times = (dispatch_times.with_columns(
-        pl.when(pl.col("Train_Type").str.ends_with("_Empty"))
-            .then(pl.col("Train_Type"))
-            .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Loaded"),
-                                     pl.lit("_Loaded")))
-            .alias("Train_Type")
-        )
-    )
+        dispatches = generate_dispatch_details(demand, simulation_days * 24)
 
-    final_departure = dispatch_times.get_column("Hour").max()
+    final_departure = dispatches.get_column("Hour").max()
     train_consist_plan = pl.DataFrame(schema=
         {'Train_ID': pl.Int64, 
          'Train_Type': pl.Utf8, 
@@ -990,10 +1114,10 @@ def run_train_planner(
 
     done = False
     # start at first departure time
-    current_time = dispatch_times.get_column("Hour").min()
+    current_time = dispatches.get_column("Hour").min()
     while not done:
         # Dispatch new train consists
-        current_dispatches = dispatch_times.filter(pl.col("Hour") == current_time)
+        current_dispatches = dispatches.filter(pl.col("Hour") == current_time)
         if(current_dispatches.height > 0):
             loco_pool, event_tracker = update_refuel_queue(loco_pool, refuelers, current_time, event_tracker)
 
@@ -1006,10 +1130,9 @@ def run_train_planner(
                     else:
                         selected = dispatch(
                             current_time,
-                            this_train['Tons_Per_Train'],
                             this_train['Origin'],
                             loco_pool,
-                            this_train['HP_Required_Per_Ton']
+                            this_train['HP_Required']
                         )
                         dispatched = loco_pool.filter(selected)
 
diff --git a/python/altrios/utilities.py b/python/altrios/utilities.py
index 77072f81..646f1ee0 100644
--- a/python/altrios/utilities.py
+++ b/python/altrios/utilities.py
@@ -5,6 +5,7 @@
 import numpy as np
 from typing import Tuple, Union, Optional, Dict, Any, TYPE_CHECKING
 import pandas as pd
+import polars as pl
 import datetime
 import numpy.typing as npt
 import logging
@@ -44,6 +45,9 @@ def resources_root() -> Path:
     PowerTrace,
 )
 
+pl.Config.set_tbl_cols(15)
+pl.Config(tbl_width_chars=150)
+
 MPS_PER_MPH = 1.0 / 2.237
 N_PER_LB = 4.448
 KG_PER_LB = 1.0 / 2.20462
@@ -177,6 +181,10 @@ def _get_list(path_elem, container):
 
     return model
 
+def range_minmax(self) -> pl.Expr:
+     return self.max() - self.min()
+pl.Expr.range_minmax=range_minmax
+del range_minmax
 
 def resample(
     df: pd.DataFrame,

From 92a20f411cc9f14f2b1840d4ea1f24308a19310e Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 23 Sep 2024 12:18:40 -0400
Subject: [PATCH 13/50] floordiv tweak

---
 python/altrios/train_planner.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index d30ae98c..b8059de0 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -488,7 +488,7 @@ def get_kg(veh):
                     pl.max_horizontal([
                         1,
                         pl.min_horizontal([
-                            pl.col("Number_of_Cars").truediv("Cars_Per_Train_Target").round(),
+                            pl.col("Number_of_Cars").floordiv("Cars_Per_Train_Target") + 1,
                             pl.col("Number_of_Cars").floordiv("Cars_Per_Train_Min")
                         ])
                     ])

From df40d2306d05c22bcbc8b5cad0133d03029457a3 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 23 Sep 2024 17:28:53 -0400
Subject: [PATCH 14/50] loco_pool generation fix

---
 python/altrios/demos/rollout_demo.py     |   4 +-
 python/altrios/demos/sim_manager_demo.py |   4 +-
 python/altrios/train_planner.py          | 105 ++++++++++++++---------
 3 files changed, 70 insertions(+), 43 deletions(-)

diff --git a/python/altrios/demos/rollout_demo.py b/python/altrios/demos/rollout_demo.py
index 982bf1a3..0ea84c60 100644
--- a/python/altrios/demos/rollout_demo.py
+++ b/python/altrios/demos/rollout_demo.py
@@ -22,8 +22,8 @@
 File = defaults.DEMAND_FILE
 #targets = [0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75, 0.8]
 train_planner_config = train_planner.TrainPlannerConfig(
-            cars_per_locomotive=50,
-            target_cars_per_train=90)
+        cars_per_locomotive={"Default": 50},
+        target_cars_per_train={"Default": 90})
 targets = [0.5]
 for target in targets:
     scenario_infos, metrics = rollout.simulate_prescribed_rollout(
diff --git a/python/altrios/demos/sim_manager_demo.py b/python/altrios/demos/sim_manager_demo.py
index aed09db4..9057a3bd 100644
--- a/python/altrios/demos/sim_manager_demo.py
+++ b/python/altrios/demos/sim_manager_demo.py
@@ -35,8 +35,8 @@
 )
 
 train_planner_config = train_planner.TrainPlannerConfig(
-            cars_per_locomotive=50,
-            target_cars_per_train=90)
+            cars_per_locomotive={"Default": 50},
+            target_cars_per_train={"Default": 90})
 
 t0_main = time.perf_counter()
 
diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index da13d959..0ac2bcde 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -8,7 +8,7 @@
 import math
 from typing import Tuple, List, Dict, Callable, Optional
 from itertools import repeat
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 import altrios as alt
 from altrios import defaults, utilities
 
@@ -17,42 +17,56 @@
 @dataclass
 class TrainPlannerConfig:
     """
+    Dataclass class for train planner configuration parameters.
+
     Attributes:
     ----------
-    min_cars_per_train: the minimum length in number of cars to form a train
-    target_cars_per_train: the standard train length in number of cars
-    manifest_empty_return_ratio: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
-    cars_per_locomotive: Heuristic scaling factor used to size number of locomotives needed based on demand.
-    refuelers_per_incoming_corridor: 
-    hp_required_per_ton:
-    dispatch_scaling_dict:
-    loco_info:
-    refueler_info:
+    - `single_train_mode`: `True` to only run one round-trip train and schedule its charging; `False` to plan train consists
+    - `min_cars_per_train`: `Dict` of the minimum length in number of cars to form a train for each train type
+    - `target_cars_per_train`: `Dict` of the standard train length in number of cars for each train type
+    - `manifest_empty_return_ratio`: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
+    - `cars_per_locomotive`: Heuristic scaling factor used to size number of locomotives needed based on demand.
+    - `refuelers_per_incoming_corridor`: Heuristic scaling factor used to scale number of refuelers needed at each node based on number of incoming corridors.
+    - `stack_type`: Type of stacking (applicable only for intermodal containers)
+    - `require_diesel`: `True` to require each consist to have at least one diesel locomotive.
+    - `manifest_empty_return_ratio`: `Dict`
+    - `drag_coeff_function`: `Dict`
+    - `hp_required_per_ton`: `Dict`
+    - `dispatch_scaling_dict`: `Dict`
+    - `loco_info`: `Dict`
+    - `refueler_info`: `Dict`
+    - `return_demand_generators`: `Dict`
     """
-    single_train_mode: bool = False,
-    min_cars_per_train: int = 60,
-    target_cars_per_train: int = 180,
-    manifest_empty_return_ratio: float = 0.6,
-    #TODO single vs double stacked operations on the corridor
-    cars_per_locomotive: int = 70,
-    refuelers_per_incoming_corridor: int = 4,
-    drag_coeff_function: Optional[Callable]= None,
-    hp_required_per_ton: Dict = {
+    single_train_mode: bool = False
+    min_cars_per_train: Dict = field(default_factory = lambda: {
+        "Default": 60
+    })
+    target_cars_per_train: Dict = field(default_factory = lambda: {
+        "Default": 180
+    })
+    cars_per_locomotive: Dict = field(default_factory = lambda: {
+        "Default": 50
+    })
+    refuelers_per_incoming_corridor: int = 4
+    require_diesel: bool = False
+    manifest_empty_return_ratio: float = 0.6
+    drag_coeff_function: List = field(default_factory = lambda: None)
+    hp_required_per_ton: Dict = field(default_factory = lambda: {
         "Default": {
-            "Unit": 2.0,
-            "Manifest": 1.5,
-            "Intermodal": 2.0 + 2.0,
-            "Unit_Empty": 2.0,
-            "Manifest_Empty": 1.5,
-            "Intermodal_Empty": 2.0 + 2.0,
+        "Unit": 2.0,
+        "Manifest": 1.5,
+        "Intermodal": 2.0 + 2.0,
+        "Unit_Empty": 2.0,
+        "Manifest_Empty": 1.5,
+        "Intermodal_Empty": 2.0 + 2.0,
         }                         
-     }, 
-    dispatch_scaling_dict: Dict = {
-         "time_mult_factor": 1.4,
-         "hours_add": 2,
-         "energy_mult_factor": 1.25
-     },
-    loco_info = pd.DataFrame({
+    })
+    dispatch_scaling_dict: Dict = field(default_factory = lambda: {
+        "time_mult_factor": 1.4,
+        "hours_add": 2,
+        "energy_mult_factor": 1.25
+    })
+    loco_info: pd.DataFrame = field(default_factory = lambda: pd.DataFrame({
         "Diesel_Large": {
             "Capacity_Cars": 20,
             "Fuel_Type": "Diesel",
@@ -69,8 +83,8 @@ class TrainPlannerConfig:
             "Cost_USD": defaults.BEL_MINUS_BATTERY_COST_USD,
             "Lifespan_Years": defaults.LOCO_LIFESPAN
             }
-    }).transpose().reset_index(names='Locomotive_Type')
-    refueler_info = pd.DataFrame({
+        }).transpose().reset_index(names='Locomotive_Type'))
+    refueler_info: pd.DataFrame = field(default_factory = lambda: pd.DataFrame({
         "Diesel_Fueler": {
             "Locomotive_Type": "Diesel_Large",
             "Fuel_Type": "Diesel",
@@ -87,7 +101,7 @@ class TrainPlannerConfig:
             "Cost_USD": defaults.BEL_CHARGER_COST_USD,
             "Lifespan_Years": defaults.LOCO_LIFESPAN
         }
-    }).transpose().reset_index(names='Refueler_Type')
+    }).transpose().reset_index(names='Refueler_Type'))
 
 def demand_loader(
     demand_table: Union[pl.DataFrame, Path, str]
@@ -275,7 +289,20 @@ def generate_demand_trains(
     ----------
     demand: Tabulated demand for each demand pair in terms of number of cars and number of trains
     """
-    
+    cars_per_train_min = (pl.from_dict(config.min_cars_per_train)
+        .melt(variable_name="Train_Type", value_name="Cars_Per_Train_Min")
+    )
+    cars_per_train_min_default = (cars_per_train_min
+        .filter(pl.col("Train_Type") == pl.lit("Default"))
+        .select("Cars_Per_Train_Min").item()
+    )
+    cars_per_train_target = (pl.from_dict(config.target_cars_per_train)
+        .melt(variable_name="Train_Type", value_name="Cars_Per_Train_Target")
+    )
+    cars_per_train_target_default = (cars_per_train_target
+        .filter(pl.col("Train_Type") == pl.lit("Default"))
+        .select("Cars_Per_Train_Target").item()
+    )
     demand = pl.concat([
         demand.drop("Number_of_Containers"),
         demand_returns.drop("Number_of_Containers"),
@@ -338,7 +365,7 @@ def get_kg(veh):
             .then(0)
             .otherwise(
                 pl.max_horizontal([1,
-                    ((pl.col("Number_of_Cars").floordiv(pl.lit(config.target_cars_per_train)) + 1))
+                    ((pl.col("Number_of_Cars").floordiv(pl.lit(cars_per_train_target_default)) + 1))
                     ])
             ).cast(pl.UInt32).alias("Number_of_Trains"))
         # Calculate per-train car counts and tonnage
@@ -420,11 +447,11 @@ def build_locopool(
     num_ods = demand.height
     cars_per_od = demand.get_column("Number_of_Cars").mean()
     if config.single_train_mode:
-        initial_size = math.ceil(cars_per_od / config.cars_per_locomotive) 
+        initial_size = math.ceil(cars_per_od / config.cars_per_locomotive["Default"]) 
         rows = initial_size
     else:
         num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
-        initial_size = math.ceil((cars_per_od / config.cars_per_locomotive) *
+        initial_size = math.ceil((cars_per_od / config.cars_per_locomotive["Default"]) *
                                 num_destinations_per_node)  # number of locomotives per node
         rows = initial_size * num_nodes  # number of locomotives in total
 

From 9197e89d42dd40f449ca4fe5a6159a6c4b008661 Mon Sep 17 00:00:00 2001
From: sakhtar <sakhtar@nrel.gov>
Date: Mon, 23 Sep 2024 16:35:45 -0600
Subject: [PATCH 15/50] Corrected return type for drag_coeff_function field

---
 python/altrios/train_planner.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index 0ac2bcde..b42b2f83 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -50,7 +50,7 @@ class TrainPlannerConfig:
     refuelers_per_incoming_corridor: int = 4
     require_diesel: bool = False
     manifest_empty_return_ratio: float = 0.6
-    drag_coeff_function: List = field(default_factory = lambda: None)
+    drag_coeff_function: Callable = None
     hp_required_per_ton: Dict = field(default_factory = lambda: {
         "Default": {
         "Unit": 2.0,

From f168fecbcacda957effc8238db1c9c0d7db6d7a9 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 23 Sep 2024 23:51:05 -0400
Subject: [PATCH 16/50] revert default value

---
 python/altrios/defaults.py           |  1 +
 python/altrios/demos/rollout_demo.py |  4 +-
 python/altrios/train_planner.py      | 56 ++++++++++++++++++++--------
 3 files changed, 43 insertions(+), 18 deletions(-)

diff --git a/python/altrios/defaults.py b/python/altrios/defaults.py
index 06f0cfdf..848787ce 100644
--- a/python/altrios/defaults.py
+++ b/python/altrios/defaults.py
@@ -33,6 +33,7 @@
 
 LOCO_LIFESPAN = 20
 ANNUAL_LOCO_TURNOVER = 1.0/LOCO_LIFESPAN
+DEFAULT_GAP_SIZE = 0.604
 
 DEMAND_FILE = alt.resources_root() / "Default Demand.csv"
 FUEL_EMISSIONS_FILE = alt.resources_root() / "metrics_inputs" / "GREET-CA_Emissions_Factors.csv"
diff --git a/python/altrios/demos/rollout_demo.py b/python/altrios/demos/rollout_demo.py
index 982bf1a3..fe85f46c 100644
--- a/python/altrios/demos/rollout_demo.py
+++ b/python/altrios/demos/rollout_demo.py
@@ -22,8 +22,8 @@
 File = defaults.DEMAND_FILE
 #targets = [0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75, 0.8]
 train_planner_config = train_planner.TrainPlannerConfig(
-            cars_per_locomotive=50,
-            target_cars_per_train=90)
+    cars_per_locomotive={"Default": 50},
+    target_cars_per_train={"Default": 90})
 targets = [0.5]
 for target in targets:
     scenario_infos, metrics = rollout.simulate_prescribed_rollout(
diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index b8059de0..9f6f483b 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -102,7 +102,6 @@ class TrainPlannerConfig:
     - `target_cars_per_train`: `Dict` of the standard train length in number of cars for each train type
     - `manifest_empty_return_ratio`: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
     - `cars_per_locomotive`: Heuristic scaling factor used to size number of locomotives needed based on demand.
-    - `cars_per_locomotive_fixed`: If `True`, `cars_per_locomotive` overrides `hp_per_ton` calculations used for dispatching decisions.
     - `refuelers_per_incoming_corridor`: Heuristic scaling factor used to scale number of refuelers needed at each node based on number of incoming corridors.
     - `stack_type`: Type of stacking (applicable only for intermodal containers)
     - `require_diesel`: `True` to require each consist to have at least one diesel locomotive.
@@ -121,13 +120,14 @@ class TrainPlannerConfig:
     target_cars_per_train: Dict = field(default_factory = lambda: {
         "Default": 180
     })
-    cars_per_locomotive: int = 70
-    cars_per_locomotive_fixed: bool = False
+    cars_per_locomotive: Dict = field(default_factory = lambda: {
+        "Default": 70
+    })
     refuelers_per_incoming_corridor: int = 4
     stack_type: str = "single"
     require_diesel: bool = False
     manifest_empty_return_ratio: float = 0.6
-    drag_coeff_function: List = field(default_factory = lambda: None)
+    drag_coeff_function: Optional[Callable]= None
     hp_required_per_ton: Dict = field(default_factory = lambda: {
         "Default": {
         "Unit": 2.0,
@@ -554,7 +554,7 @@ def allocateItems(df: pl.DataFrame, target: str, grouping_vars: List[str]) -> pl
         .with_columns(
             (pl.col("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")).alias("Tons_Per_Train"),
             (pl.col("HP_Required_Per_Ton_Loaded").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + 
-                pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded")
+                pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")
                 ).alias("HP_Required")
         )
         .sort("Origin", "Destination", "Percent_Within_Group", "Train_Type")
@@ -601,7 +601,7 @@ def build_locopool(
     num_ods = demand.height
     cars_per_od = demand.get_column("Number_of_Cars").mean()
     if config.single_train_mode:
-        initial_size = math.ceil(cars_per_od / config.cars_per_locomotive) 
+        initial_size = math.ceil(cars_per_od / config.cars_per_locomotive["Default"]) 
         rows = initial_size
     else:
         num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
@@ -797,6 +797,7 @@ def dispatch(
     dispatch_time: int,
     origin: str,
     loco_pool: pl.DataFrame,
+    train_tonnage: float,
     hp_required: float,
 ) -> pl.Series:
     """
@@ -812,6 +813,7 @@ def dispatch(
     ----------
     selected: Indices of selected locomotives
     """
+    hp_per_ton = hp_required / train_tonnage
     # Candidate locomotives at the right place that are ready
     candidates = loco_pool.select((pl.col("Node") == origin) &
                                 (pl.col("Status") == "Ready")).to_series()
@@ -881,10 +883,14 @@ def dispatch(
             is more than the available horsepower ({available_hp}).
             Count of locomotives servicing, refueling, or queueing at {origin} are:"""
         # Hold the train until enough diesels are present (future development)
-        waiting_counts = loco_pool.filter(
-            pl.col("Node") == origin,
-            pl.col("Status").is_in(["Servicing","Refuel_Queue"])
-        ).select("Locomotive_Type").group_by(['Locomotive_Type']).len()
+        waiting_counts = (loco_pool
+            .filter(
+                pl.col("Node") == origin,
+                pl.col("Status").is_in(["Servicing","Refuel_Queue"])
+            )
+            .group_by(['Locomotive_Type'])
+                .agg(pl.count().alias("count"))
+        )
         for row in waiting_counts.iter_rows(named = True):
             message = message + f"""
             {row['Locomotive_Type']}: {row['count']}"""
@@ -1132,17 +1138,35 @@ def run_train_planner(
                             current_time,
                             this_train['Origin'],
                             loco_pool,
+                            this_train['Tons_Per_Train'],
                             this_train['HP_Required']
                         )
                         dispatched = loco_pool.filter(selected)
 
+                    if config.drag_coeff_function is not None:
+                        cd_area_vec = config.drag_coeff_function(
+                             this_train['Number_of_Cars'], 
+                             gap_size = defaults.DEFAULT_GAP_SIZE
+                         )
+                    else:
+                        cd_area_vec = None
+
+                    train_types = []
+                    n_cars_by_type = {}
+                    this_train_type = this_train['Train_Type']
+                    if this_train['Cars_Loaded'] > 0:
+                        train_types.append(f'{this_train_type}_Loaded')
+                        n_cars_by_type[f'{this_train_type}_Loaded'] = int(this_train['Cars_Loaded'])
+                    if this_train['Cars_Empty'] > 0:
+                        train_types.append(f'{this_train_type}_Empty')
+                        n_cars_by_type[f'{this_train_type}_Empty'] = int(this_train['Cars_Empty'])
+
+
                     train_config = alt.TrainConfig(
-                        rail_vehicles = [vehicle for vehicle in rail_vehicles if vehicle.car_type==this_train['Train_Type']],
-                        n_cars_by_type = {
-                            this_train['Train_Type']: this_train['Number_of_Cars']
-                        },
+                        rail_vehicles = [vehicle for vehicle in rail_vehicles if vehicle.car_type in train_types],
+                        n_cars_by_type = n_cars_by_type,
                         train_type = train_type,
-                        cd_area_vec = config.drag_coeff_function
+                        cd_area_vec = cd_area_vec
                     )
 
                     loco_start_soc_j = dispatched.get_column("SOC_J")
@@ -1251,7 +1275,7 @@ def run_train_planner(
             loco_pool, event_tracker = update_refuel_queue(loco_pool, refuelers, current_time, event_tracker)
             done = True
         else:
-            current_time = dispatch_times.filter(pl.col("Hour").gt(current_time)).get_column("Hour").min()
+            current_time = dispatches.filter(pl.col("Hour").gt(current_time)).get_column("Hour").min()
 
     train_consist_plan = (train_consist_plan
         .with_columns(

From 31157e3c9aa6b488912eeb1956c8dca3af73648e Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 23 Sep 2024 23:52:15 -0400
Subject: [PATCH 17/50] Update utilities.py

---
 python/altrios/utilities.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/python/altrios/utilities.py b/python/altrios/utilities.py
index 646f1ee0..1cfd03a9 100644
--- a/python/altrios/utilities.py
+++ b/python/altrios/utilities.py
@@ -45,9 +45,6 @@ def resources_root() -> Path:
     PowerTrace,
 )
 
-pl.Config.set_tbl_cols(15)
-pl.Config(tbl_width_chars=150)
-
 MPS_PER_MPH = 1.0 / 2.237
 N_PER_LB = 4.448
 KG_PER_LB = 1.0 / 2.20462

From 7d5cdcdee3636bc7958bcf211dd624ec18fcc4bb Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Tue, 24 Sep 2024 02:50:52 -0400
Subject: [PATCH 18/50] car counting metrics working

---
 python/altrios/altrios_pyo3.pyi               |  2 +
 python/altrios/metric_calculator.py           | 40 +++++++++++++++++--
 .../src/train/set_speed_train_sim.rs          |  7 +++-
 .../src/train/speed_limit_train_sim.rs        | 23 ++++++-----
 rust/altrios-core/src/train/train_config.rs   | 24 ++++++-----
 rust/altrios-core/src/train/train_state.rs    |  7 ----
 6 files changed, 70 insertions(+), 33 deletions(-)

diff --git a/python/altrios/altrios_pyo3.pyi b/python/altrios/altrios_pyo3.pyi
index d16a7940..2d0b17fa 100644
--- a/python/altrios/altrios_pyo3.pyi
+++ b/python/altrios/altrios_pyo3.pyi
@@ -819,6 +819,7 @@ class SpeedLimitTrainSim(SerdeAPI):
     origs: List[Location]
     dests: List[Location]
     loco_con: Consist
+    n_cars_by_type: Dict[str, int]
     state: TrainState
     # train_res: TrainRes # not accessible in Python
     path_tpc: PathTpc
@@ -833,6 +834,7 @@ class SpeedLimitTrainSim(SerdeAPI):
     def __init__(
         cls,
         loco_con: Consist,
+        n_cars_by_type: Dict[str, int],
         state: TrainState,
         train_res_file: Optional[str],
         path_tpc_file: Optional[str],
diff --git a/python/altrios/metric_calculator.py b/python/altrios/metric_calculator.py
index 4789f0bb..063702d5 100644
--- a/python/altrios/metric_calculator.py
+++ b/python/altrios/metric_calculator.py
@@ -120,6 +120,7 @@ def main(
             ('Freight_Moved', 'million tonne-mi'),
             ('Freight_Moved', 'car-miles'),
             ('Freight_Moved', 'cars'),
+            ('Freight_Moved', 'detailed'),
             ('GHG', 'tonne CO2-eq'),
             ('Count_Locomotives', 'assets'),
             ('Count_Refuelers', 'assets'),
@@ -447,13 +448,44 @@ def calculate_freight_moved(
         conversion_from_km = utilities.MI_PER_KM
     else:
         conversion_from_km = 1.0
-    
+
     if units in ["million tonne-km", "million tonne-mi"]:
-        return metric("Freight_Moved", units, info.sims.get_megagram_kilometers(annualize=info.annualize) * conversion_from_km /1.0e6)
+        return metric("Freight_Moved", units, info.sims.get_megagram_kilometers(annualize=info.annualize) * conversion_from_km /1.0e6, year=info.scenario_year)
     elif units in ["car-km", "car-miles"]:
-        return metric("Freight_Moved", units, info.sims.get_car_kilometers(include_empty=True, include_loaded=True, annualize=info.annualize) * conversion_from_km)
+        return metric("Freight_Moved", units, info.sims.get_car_kilometers(annualize=info.annualize) * conversion_from_km, year=info.scenario_year)
     elif units == "cars":
-        return metric("Freight_Moved", units, info.sims.get_cars_moved(include_empty=True, include_loaded=True, annualize=info.annualize))
+        return metric("Freight_Moved", units, info.sims.get_cars_moved(annualize=info.annualize), year=info.scenario_year)
+    elif units == "detailed":
+        kilometers = (pl.DataFrame(data = {"car-km": [sim.get_kilometers(annualize=info.annualize) for sim in info.sims.tolist()]})
+            .with_row_index("idx")
+            .with_columns(
+                pl.col("car-km").mul(utilities.MI_PER_KM).alias("car-miles")
+            )
+        )
+        all_n_cars_by_type = [sim.n_cars_by_type for sim in info.sims.tolist()]
+        car_counts = (
+            pl.concat([pl.from_dict(item)for item in all_n_cars_by_type], how="diagonal_relaxed")
+            .with_row_index("idx")
+            .melt(id_vars = "idx", value_name = "cars", variable_name = "Subset")
+            .filter(pl.col("cars").is_not_null())
+            .join(kilometers, how="left", on="idx")
+            .drop("idx")
+            .group_by("Subset")
+                .agg(pl.col("*").sum())
+            .sort("Subset")
+            .melt(id_vars = "Subset", variable_name = "Units", value_name = "Value")
+            .with_columns(
+                pl.lit(info.scenario_year).alias("Year"),
+                pl.lit("Freight_Moved").alias("Metric"),
+                pl.when(
+                    info.annualize, 
+                    pl.col("Units") == pl.lit("cars"))
+                    .then(pl.col("Value").mul(365.25 / info.simulation_days))
+                    .otherwise(pl.col("Value"))
+                    .alias("Value")
+            )
+        )
+        return car_counts
     else:
         print(f"Units of {units} not supported for freight movement calculation.")
         return metric("Freight_Moved", units, None)
diff --git a/rust/altrios-core/src/train/set_speed_train_sim.rs b/rust/altrios-core/src/train/set_speed_train_sim.rs
index d2b59c2d..d6721335 100644
--- a/rust/altrios-core/src/train/set_speed_train_sim.rs
+++ b/rust/altrios-core/src/train/set_speed_train_sim.rs
@@ -181,6 +181,7 @@ pub struct SpeedTraceElement {
     #[new]
     fn __new__(
         loco_con: Consist,
+        n_cars_by_type: HashMap<String, u32>,
         state: TrainState,
         speed_trace: SpeedTrace,
         train_res_file: Option<String>,
@@ -196,7 +197,7 @@ pub struct SpeedTraceElement {
             None => TrainRes::valid()
         };
 
-        Self::new(loco_con, state, speed_trace, train_res, path_tpc, save_interval)
+        Self::new(loco_con, n_cars_by_type, state, speed_trace, train_res, path_tpc, save_interval)
     }
 
     #[setter]
@@ -259,6 +260,7 @@ pub struct SpeedTraceElement {
 /// Train simulation in which speed is prescribed
 pub struct SetSpeedTrainSim {
     pub loco_con: Consist,
+    pub n_cars_by_type: HashMap<String, u32>,
     #[serde(default)]
     #[serde(skip_serializing_if = "EqDefault::eq_default")]
     pub state: TrainState,
@@ -278,6 +280,7 @@ pub struct SetSpeedTrainSim {
 impl SetSpeedTrainSim {
     pub fn new(
         loco_con: Consist,
+        n_cars_by_type: HashMap<String, u32>,
         state: TrainState,
         speed_trace: SpeedTrace,
         train_res: TrainRes,
@@ -286,6 +289,7 @@ impl SetSpeedTrainSim {
     ) -> Self {
         let mut train_sim = Self {
             loco_con,
+            n_cars_by_type,
             state,
             train_res,
             path_tpc,
@@ -422,6 +426,7 @@ impl Default for SetSpeedTrainSim {
     fn default() -> Self {
         Self {
             loco_con: Consist::default(),
+            n_cars_by_type: Default::default(),
             state: TrainState::valid(),
             train_res: TrainRes::valid(),
             path_tpc: PathTpc::valid(),
diff --git a/rust/altrios-core/src/train/speed_limit_train_sim.rs b/rust/altrios-core/src/train/speed_limit_train_sim.rs
index 348270be..4acb73ed 100644
--- a/rust/altrios-core/src/train/speed_limit_train_sim.rs
+++ b/rust/altrios-core/src/train/speed_limit_train_sim.rs
@@ -68,13 +68,13 @@ impl From<&Vec<LinkIdxTime>> for TimedLinkPath {
     }
 
     #[pyo3(name = "get_car_kilometers")]
-    pub fn get_car_kilometers_py(&self, include_empty: bool, include_loaded: bool, annualize: bool)  -> f64 {
-        self.get_car_kilometers(include_empty, include_loaded, annualize)
+    pub fn get_car_kilometers_py(&self, annualize: bool)  -> f64 {
+        self.get_car_kilometers(annualize)
     }
 
     #[pyo3(name = "get_cars_moved")]
-    pub fn get_cars_moved_py(&self, include_empty: bool, include_loaded: bool, annualize: bool)  -> f64 {
-        self.get_cars_moved(include_empty, include_loaded, annualize)
+    pub fn get_cars_moved_py(&self, annualize: bool)  -> f64 {
+        self.get_cars_moved(annualize)
     }
 
     #[pyo3(name = "get_res_kilometers")]
@@ -149,6 +149,8 @@ pub struct SpeedLimitTrainSim {
     pub origs: Vec<Location>,
     pub dests: Vec<Location>,
     pub loco_con: Consist,
+    /// Number of railcars by type on the train
+    pub n_cars_by_type: HashMap<String, u32>,
     #[serde(default)]
     #[serde(skip_serializing_if = "EqDefault::eq_default")]
     pub state: TrainState,
@@ -175,6 +177,7 @@ impl SpeedLimitTrainSim {
         origs: &[Location],
         dests: &[Location],
         loco_con: Consist,
+        n_cars_by_type: HashMap<String, u32>,
         state: TrainState,
         train_res: TrainRes,
         path_tpc: PathTpc,
@@ -188,6 +191,7 @@ impl SpeedLimitTrainSim {
             origs: origs.to_vec(),
             dests: dests.to_vec(),
             loco_con,
+            n_cars_by_type,
             state,
             train_res,
             path_tpc,
@@ -226,16 +230,14 @@ impl SpeedLimitTrainSim {
             * self.get_scaling_factor(annualize)
     }
 
-    pub fn get_car_kilometers(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
-        let n_cars = self.get_cars_moved(include_empty, include_loaded, annualize);
+    pub fn get_car_kilometers(&self, annualize: bool) -> f64 {
+        let n_cars = self.get_cars_moved(annualize) as f64;
         // Note: n_cars already includes an annualization scaling factor; no need to multiply twice.
         self.state.total_dist.get::<si::kilometer>() * n_cars
     }
 
-    pub fn get_cars_moved(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
-        let n_cars = 
-            (self.state.cars_empty * (include_empty as u32)
-            + self.state.cars_loaded * (include_loaded as u32)) as f64;
+    pub fn get_cars_moved(&self, annualize: bool) -> f64 {
+        let n_cars: f64 = self.n_cars_by_type.values().fold(0, |acc, n| *n + acc) as f64;
         n_cars * self.get_scaling_factor(annualize)
     }
 
@@ -707,6 +709,7 @@ impl Default for SpeedLimitTrainSim {
             origs: Default::default(),
             dests: Default::default(),
             loco_con: Default::default(),
+            n_cars_by_type: Default::default(),
             state: TrainState::valid(),
             train_res: TrainRes::valid(),
             path_tpc: PathTpc::default(),
diff --git a/rust/altrios-core/src/train/train_config.rs b/rust/altrios-core/src/train/train_config.rs
index ec070503..99a241b4 100644
--- a/rust/altrios-core/src/train/train_config.rs
+++ b/rust/altrios-core/src/train/train_config.rs
@@ -437,15 +437,13 @@ impl TrainSimBuilder {
             })
             / self.train_config.cars_total() as f64;
 
-        let mut state = TrainState::new(
+        let state = TrainState::new(
             length,
             train_mass_static,
             mass_rot,
             mass_freight,
             self.init_train_state,
         );
-        state.cars_empty = self.train_config.cars_empty;
-        state.cars_loaded = self.train_config.cars_loaded;
 
         let path_tpc = PathTpc::new(train_params);
 
@@ -578,6 +576,7 @@ impl TrainSimBuilder {
         path_tpc.extend(network, link_path)?;
         Ok(SetSpeedTrainSim::new(
             self.loco_con.clone(),
+            self.train_config.n_cars_by_type.clone(),
             state,
             speed_trace,
             train_res,
@@ -607,6 +606,7 @@ impl TrainSimBuilder {
         Ok((
             SetSpeedTrainSim::new(
                 self.loco_con.clone(),
+                self.train_config.n_cars_by_type.clone(),
                 state,
                 speed_trace,
                 train_res.clone(),
@@ -663,6 +663,7 @@ impl TrainSimBuilder {
                     ))
                 })?,
             self.loco_con.clone(),
+            self.train_config.n_cars_by_type.clone(),
             state,
             train_res,
             path_tpc,
@@ -716,6 +717,7 @@ impl TrainSimBuilder {
                     ))
                 })?,
             self.loco_con.clone(),
+            self.train_config.n_cars_by_type.clone(),
             state,
             train_res.clone(),
             path_tpc.clone(),
@@ -1253,13 +1255,13 @@ pub fn run_speed_limit_train_sims(
     }
 
     #[pyo3(name = "get_car_kilometers")]
-    pub fn get_car_kilometers_py(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
-        self.get_car_kilometers(include_empty, include_loaded, annualize)
+    pub fn get_car_kilometers_py(&self, annualize: bool) -> f64 {
+        self.get_car_kilometers(annualize)
     }
 
     #[pyo3(name = "get_cars_moved")]
-    pub fn get_cars_moved_py(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
-        self.get_cars_moved(include_empty, include_loaded, annualize)
+    pub fn get_cars_moved_py(&self, annualize: bool) -> f64 {
+        self.get_cars_moved(annualize)
     }
 
     #[pyo3(name = "get_res_kilometers")]
@@ -1306,17 +1308,17 @@ impl SpeedLimitTrainSimVec {
             .sum()
     }
 
-    pub fn get_car_kilometers(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
+    pub fn get_car_kilometers(&self, annualize: bool) -> f64 {
         self.0
             .iter()
-            .map(|sim| sim.get_car_kilometers(include_empty, include_loaded, annualize))
+            .map(|sim| sim.get_car_kilometers(annualize))
             .sum()
     }
 
-    pub fn get_cars_moved(&self, include_empty: bool, include_loaded: bool, annualize: bool) -> f64 {
+    pub fn get_cars_moved(&self, annualize: bool) -> f64 {
         self.0
             .iter()
-            .map(|sim| sim.get_cars_moved(include_empty, include_loaded, annualize))
+            .map(|sim| sim.get_cars_moved(annualize))
             .sum()
     }
 
diff --git a/rust/altrios-core/src/train/train_state.rs b/rust/altrios-core/src/train/train_state.rs
index 44dd3baf..fcfc0879 100644
--- a/rust/altrios-core/src/train/train_state.rs
+++ b/rust/altrios-core/src/train/train_state.rs
@@ -116,11 +116,6 @@ pub struct TrainState {
     /// Curvature resistance force
     pub res_curve: si::Force,
 
-    /// Number of empty cars included on the train
-    pub cars_empty: u32,
-    /// Number of loaded cars included on the train
-    pub cars_loaded: u32,
-
     /// Grade at front of train
     pub grade_front: si::Ratio,
     /// Grade at back of train of train if strap method is used
@@ -161,8 +156,6 @@ impl Default for TrainState {
             mass_static: Default::default(),
             mass_rot: Default::default(),
             mass_freight: Default::default(),
-            cars_empty: Default::default(),
-            cars_loaded: Default::default(),
             elev_front: Default::default(),
             energy_whl_out: Default::default(),
             grade_front: Default::default(),

From c59fd8d4c9a31a37615f8070237024a1d663af38 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Tue, 24 Sep 2024 09:55:06 -0400
Subject: [PATCH 19/50] metrics text fix

---
 python/altrios/tests/test_metric_calculator.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/altrios/tests/test_metric_calculator.py b/python/altrios/tests/test_metric_calculator.py
index 6fc754c5..e669950b 100644
--- a/python/altrios/tests/test_metric_calculator.py
+++ b/python/altrios/tests/test_metric_calculator.py
@@ -24,5 +24,5 @@ def test_dummy_sim(self):
             
         for info in scenario_infos:
             tkm = metric_calculator.calculate_freight_moved(info,'million tonne-mi')
-            self.assertEqual(tkm.filter(pl.col("Metric") == 'Mt-km').get_column("Units").len(), 1)
+            self.assertEqual(tkm.filter(pl.col("Metric") == pl.lit("Freight_Moved")).get_column("Units").len(), 1)
 

From 37eb8b74e45821595bc528c2c634196300a14995 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Thu, 7 Nov 2024 18:07:04 -0500
Subject: [PATCH 20/50] initial LIFTS check-in

---
 pyproject.toml                                |   2 +-
 python/lifts/__init__.py                      |   2 +
 python/lifts/avg_time_per_train.txt           |   1 +
 .../crane_hostler_combination_results.xlsx    | Bin 0 -> 5603 bytes
 python/lifts/crane_work_log.txt               | 756 +++++++++++++++++
 python/lifts/dictionary.py                    |  58 ++
 python/lifts/distances.py                     |  70 ++
 python/lifts/final_version.py                 | 614 ++++++++++++++
 python/lifts/hostler_work_log.txt             | 774 ++++++++++++++++++
 python/lifts/merged_baseline.py               | 454 ++++++++++
 python/lifts/parameters.py                    | 120 +++
 .../results/simulation_crane_1_hostler_1.xlsx | Bin 0 -> 54669 bytes
 .../results/simulation_crane_1_hostler_2.xlsx | Bin 0 -> 53903 bytes
 .../results/simulation_crane_1_hostler_3.xlsx | Bin 0 -> 53886 bytes
 .../results/simulation_crane_1_hostler_4.xlsx | Bin 0 -> 53928 bytes
 .../results/simulation_crane_1_hostler_5.xlsx | Bin 0 -> 53888 bytes
 .../results/simulation_crane_1_hostler_6.xlsx | Bin 0 -> 53929 bytes
 .../results/simulation_crane_1_hostler_7.xlsx | Bin 0 -> 53937 bytes
 .../results/simulation_crane_1_hostler_8.xlsx | Bin 0 -> 53903 bytes
 .../results/simulation_crane_2_hostler_1.xlsx | Bin 0 -> 53947 bytes
 .../results/simulation_crane_2_hostler_2.xlsx | Bin 0 -> 53920 bytes
 .../results/simulation_crane_2_hostler_3.xlsx | Bin 0 -> 53916 bytes
 .../results/simulation_crane_2_hostler_4.xlsx | Bin 0 -> 53957 bytes
 .../results/simulation_crane_3_hostler_1.xlsx | Bin 0 -> 53851 bytes
 .../results/simulation_crane_3_hostler_2.xlsx | Bin 0 -> 53812 bytes
 .../results/simulation_crane_3_hostler_3.xlsx | Bin 0 -> 53810 bytes
 .../results/simulation_crane_3_hostler_4.xlsx | Bin 0 -> 53859 bytes
 python/lifts/schedule.py                      |  51 ++
 python/lifts/test.py                          | 602 ++++++++++++++
 python/lifts/truck_work_log.txt               | 516 ++++++++++++
 python/lifts/vehicle_average_times.txt        |   3 +
 python/lifts/vehicle_performance.py           |  48 ++
 32 files changed, 4070 insertions(+), 1 deletion(-)
 create mode 100644 python/lifts/__init__.py
 create mode 100644 python/lifts/avg_time_per_train.txt
 create mode 100644 python/lifts/crane_hostler_combination_results.xlsx
 create mode 100644 python/lifts/crane_work_log.txt
 create mode 100644 python/lifts/dictionary.py
 create mode 100644 python/lifts/distances.py
 create mode 100644 python/lifts/final_version.py
 create mode 100644 python/lifts/hostler_work_log.txt
 create mode 100644 python/lifts/merged_baseline.py
 create mode 100644 python/lifts/parameters.py
 create mode 100644 python/lifts/results/simulation_crane_1_hostler_1.xlsx
 create mode 100644 python/lifts/results/simulation_crane_1_hostler_2.xlsx
 create mode 100644 python/lifts/results/simulation_crane_1_hostler_3.xlsx
 create mode 100644 python/lifts/results/simulation_crane_1_hostler_4.xlsx
 create mode 100644 python/lifts/results/simulation_crane_1_hostler_5.xlsx
 create mode 100644 python/lifts/results/simulation_crane_1_hostler_6.xlsx
 create mode 100644 python/lifts/results/simulation_crane_1_hostler_7.xlsx
 create mode 100644 python/lifts/results/simulation_crane_1_hostler_8.xlsx
 create mode 100644 python/lifts/results/simulation_crane_2_hostler_1.xlsx
 create mode 100644 python/lifts/results/simulation_crane_2_hostler_2.xlsx
 create mode 100644 python/lifts/results/simulation_crane_2_hostler_3.xlsx
 create mode 100644 python/lifts/results/simulation_crane_2_hostler_4.xlsx
 create mode 100644 python/lifts/results/simulation_crane_3_hostler_1.xlsx
 create mode 100644 python/lifts/results/simulation_crane_3_hostler_2.xlsx
 create mode 100644 python/lifts/results/simulation_crane_3_hostler_3.xlsx
 create mode 100644 python/lifts/results/simulation_crane_3_hostler_4.xlsx
 create mode 100644 python/lifts/schedule.py
 create mode 100644 python/lifts/test.py
 create mode 100644 python/lifts/truck_work_log.txt
 create mode 100644 python/lifts/vehicle_average_times.txt
 create mode 100644 python/lifts/vehicle_performance.py

diff --git a/pyproject.toml b/pyproject.toml
index 0034ef7b..7cf1b279 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,7 +43,7 @@ dependencies = [
     "plotly",
     "typing_extensions",
     "pyyaml",
-    "polars==0.20.25",
+    "polars >= 1.12",
     "pyarrow",
     "requests",
     "PyYAML==6.0.2",
diff --git a/python/lifts/__init__.py b/python/lifts/__init__.py
new file mode 100644
index 00000000..ffc4016e
--- /dev/null
+++ b/python/lifts/__init__.py
@@ -0,0 +1,2 @@
+from pkg_resources import get_distribution
+__version__ = get_distribution("altrios").version
diff --git a/python/lifts/avg_time_per_train.txt b/python/lifts/avg_time_per_train.txt
new file mode 100644
index 00000000..18e88105
--- /dev/null
+++ b/python/lifts/avg_time_per_train.txt
@@ -0,0 +1 @@
+137.44757283817884
\ No newline at end of file
diff --git a/python/lifts/crane_hostler_combination_results.xlsx b/python/lifts/crane_hostler_combination_results.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..f85c4288c03a62f790dd5fbf5ef46fcef835df07
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diff --git a/python/lifts/crane_work_log.txt b/python/lifts/crane_work_log.txt
new file mode 100644
index 00000000..bb9acdea
--- /dev/null
+++ b/python/lifts/crane_work_log.txt
@@ -0,0 +1,756 @@
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diff --git a/python/lifts/dictionary.py b/python/lifts/dictionary.py
new file mode 100644
index 00000000..cbfd8bbb
--- /dev/null
+++ b/python/lifts/dictionary.py
@@ -0,0 +1,58 @@
+import pandas as pd
+
+
+def timetable(terminal):
+    df = pd.read_csv('C:/Users/Irena Tong/PycharmProjects/simulation_test/data/train_consist_plan.csv')
+
+    df_terminal = df[(df['Destination_ID'] == terminal) & (df['Train_Type'] == 'Intermodal')]
+
+    df_grouped = df_terminal.groupby('Train_ID').agg({
+        'Cars_Loaded': find_duplicate_numbers,
+        'Cars_Empty': find_duplicate_numbers,
+        'Arrival_Time_Actual_Hr': 'min',
+        'Departure_Time_Actual_Hr': 'min'
+    }).reset_index()
+
+    df_grouped_sorted = df_grouped.sort_values(by='Arrival_Time_Actual_Hr', ascending=True)
+
+    train_dict_list = convert_df_to_dict(df_grouped_sorted)
+
+    return train_dict_list
+
+
+def convert_df_to_dict(df):
+    train_timetable = []
+    for _, row in df.iterrows():
+        train_dict = {
+            "train_id": row['Train_ID'],
+            "arrival_time": row['Departure_Time_Actual_Hr'],
+            "departure_time": row['Arrival_Time_Actual_Hr'],
+            "empty_cars": row['Cars_Empty'],
+            "full_cars": row['Cars_Loaded'],
+            "oc_number": calculate_oc_number(row),
+            "truck_number": calculate_truck_number(row)
+        }
+        train_timetable.append(train_dict)
+    return train_timetable
+
+
+def truck_resource(data):
+    total_truck_number = sum(item['truck_number'] for item in data)
+    return total_truck_number
+
+
+def calculate_oc_number(row):
+    return 40
+
+def calculate_truck_number(row):
+    full_cars = row['Cars_Loaded']
+    oc_number = calculate_oc_number(row)
+    return max(full_cars, oc_number)
+
+
+def find_duplicate_numbers(series):
+    return series.iloc[0]
+
+terminal = 'Allouez'
+train_data_as_dict = timetable(terminal)
+print(train_data_as_dict)
\ No newline at end of file
diff --git a/python/lifts/distances.py b/python/lifts/distances.py
new file mode 100644
index 00000000..93895280
--- /dev/null
+++ b/python/lifts/distances.py
@@ -0,0 +1,70 @@
+import numpy as np
+from scipy.stats import triang
+
+# Yard setting: optimal layout output
+YARD_TYPE = 'parallel'  # choose 'perpendicular' or 'parallel'
+M = 2 # decide the number of rows of parking blocks in the layout
+N = 2 # decide the number of columns of parking blocks in the layout
+n_t = 2 # decide the numbers of train side aisles per group
+n_p = 2 # decide the numbers of parking area aisles per group
+n_r = 5  # decide the number of spots within each parking block (10 * n_r = BL_l, the length of each parking block)
+
+# Fixed yard parameters
+P = 10  # fixed aisle width
+BL_w = 80  # fixed block width
+
+A = M * 10 * n_r + (M+1) * n_p * P  # the vertical width of the yard
+B = N * 80 + (N+1) * n_p * P # the horizontal length of the yard
+
+def create_triang_distribution(min_val, avg_val, max_val):
+    c = (avg_val - min_val) / (max_val - min_val)
+    return triang(c, loc=min_val, scale=(max_val - min_val))
+
+def ugly_sigma(x):
+    total_sum = 0
+    for i in range(1, x):
+        total_sum += 2 * i * (x - i)
+    result = total_sum / (x ** 2)
+    return result
+
+def A(M, n_r, n_p):
+    return M * 10 * n_r + (M+1) * n_p * P
+
+def B(N, n_p):
+    return N * 80 + (N+1) * n_p * P
+
+# Distance estimation
+if YARD_TYPE == 'parallel':
+    # d_h: hostler distance
+    d_h_min = n_t * P + 1.5 * n_p * P
+    d_h_max = n_t * P + A(M, n_r, n_p) + B(N, n_p)
+    d_h_avg = 0.5 * (d_h_min + d_h_max)
+
+    # d_r: repositioning distance
+    d_r_min = 0
+    # d_r_avg = 5 * n_r + 40 + ugly_sigma(M) * (10 * n_r + n_p*P) + ugly_sigma(N) * (80 + n_p * P)
+    d_r_max = 10 * n_r + 80 + A(M, n_r, n_p) - n_p * P + B(N, n_p) - n_p * P
+    d_r_avg = 0.5 * (d_r_min + d_r_max)
+
+    # d_t: truck distance
+    d_t_min = 1.5 * n_p * P
+    # d_t_avg = 0.5 * (B(N, n_p) + A(M, n_r, n_p) - 0.5 * n_p * P)
+    d_t_max = B(N, n_p) + A(M, n_r, n_p) - 2 * n_p * P
+    d_t_avg = 0.5 * (d_t_min + d_t_max)
+
+
+elif YARD_TYPE == 'perpendicular':
+    # d_h
+    d_h_min = n_t * P + 1.5 * n_p * P
+    d_h_avg = 10 * n_r * M + 80 * N + (M + N + 1.5) * n_p * P + 2 * n_t * P
+    d_h_max = n_t * P + A(M, n_r, n_p) - n_p * P + B(N, n_p) - n_p * P
+
+    # d_r
+    d_r_min = 0
+    d_r_avg = 5 * n_r + 40 + ugly_sigma(M) * (10 * n_r + n_p*P) + ugly_sigma(N) * (80 + n_p * P)
+    d_r_max = 10 * n_r + 80 + A(M, n_r, n_p) - n_p * P + B(N, n_p) - n_p * P
+
+    # d_t
+    d_t_min = 1.5 * n_p * P
+    d_t_avg = 0.5 * (B(N, n_p) + A(M, n_r, n_p) - 0.5 * n_p * P)
+    d_t_max = B(N, n_p) + A(M, n_r, n_p) - 2 * n_p * P
\ No newline at end of file
diff --git a/python/lifts/final_version.py b/python/lifts/final_version.py
new file mode 100644
index 00000000..08685eb2
--- /dev/null
+++ b/python/lifts/final_version.py
@@ -0,0 +1,614 @@
+import simpy
+import random
+from parameters import *
+from distances import *
+from dictionary import *
+from vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
+
+
+# Test input
+CRANE_NUMBER = 1
+HOSTLER_NUMBER = 1
+
+
+# import sys
+#
+# if len(sys.argv) < 3:
+#     raise ValueError("Not enough arguments. Please provide HOSTLER_NUMBER and CRANE_NUMBER.")
+#
+# HOSTLER_NUMBER = int(sys.argv[1])
+# CRANE_NUMBER = int(sys.argv[2])
+TRUCK_NUMBERS = 1000
+
+def record_event(container_id, event_type, timestamp):
+    if container_id not in container_events:
+        container_events[container_id] = {}
+    container_events[container_id][event_type] = timestamp
+
+
+def handle_truck_arrivals(env, in_gate_resource, truck_numbers):
+    global all_trucks_ready_event, truck_processed, start_oc_container_id, end_oc_container_id, TRUCK_ARRIVAL_MEAN, TRAIN_ARRIVAL_HR
+
+    truck_id = 1
+    truck_processed = 0
+    TRUCK_ARRIVAL_MEAN = abs(TRAIN_ARRIVAL_HR - previous_train_departure) / max(INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER)
+    print(f"current time is {env.now}")
+    print(f"next TRAIN_ARRIVAL_HR:{TRAIN_ARRIVAL_HR}")
+    print(f"TRUCK_ARRIVAL_MEAN IS {TRUCK_ARRIVAL_MEAN}")
+
+    while truck_id <= TRUCK_NUMBERS:
+        inter_arrival_time = random.expovariate(1 / TRUCK_ARRIVAL_MEAN)
+        yield env.timeout(inter_arrival_time)
+        truck_arrival_time.append(env.now)
+
+        env.process(truck_through_gate(env, in_gate_resource, truck_id))
+        truck_id += 1
+
+    if truck_id > TRUCK_NUMBERS:
+        # print(f"truck_id = {truck_id} vs TRUCK_NUM = {TRUCK_NUMBERS}")
+        if not all_trucks_ready_event.triggered:
+            all_trucks_ready_event.succeed()
+            # print(f"{env.now}: All trucks arrived for the {TRAIN_ID} train.")
+
+
+def truck_through_gate(env, in_gate_resource, truck_id):
+    global last_leave_time, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+
+    with in_gate_resource.request() as request:
+        yield request
+        wait_time = max(0, truck_arrival_time[truck_id - 1] - last_leave_time)
+        if wait_time <= 0:
+            wait_time = 0  # first arriving trucks
+            # print(f"Truck {truck_id} enters the gate without waiting")
+        else:
+            # print(f"Truck {truck_id} enters the gate and queued for {wait_time} hrs")
+            truck_waiting_time.append(wait_time)
+
+        yield env.timeout(TRUCK_INGATE_TIME + random.uniform(0, TRUCK_INGATE_TIME_DEV))
+
+        # Case 1: Normal handling when OC >= IC (all trucks have containers)
+        if OUTBOUND_CONTAINER_NUMBER >= INBOUND_CONTAINER_NUMBER:
+            env.process(handle_container(env, truck_id))
+
+        # Case 2: OC < IC, extra empty trucks are needed
+        else:
+            if truck_id <= OUTBOUND_CONTAINER_NUMBER:
+                env.process(handle_container(env, truck_id))  # Loaded trucks
+            else:
+                env.process(empty_truck(env, truck_id))  # Empty trucks
+
+
+def handle_container(env, truck_id):
+    global outbound_container_id_counter, last_leave_time
+
+    container_id = outbound_container_id_counter
+    outbound_container_id_counter += 1
+    record_event(container_id, 'truck_arrival', env.now)
+
+    d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    yield env.timeout(d_t_dist / (2 * TRUCK_SPEED_LIMIT))
+
+    record_event(container_id, 'truck_drop_off', env.now)
+    # print(f"{env.now}: Truck {truck_id} drops outbound container {container_id}.")
+    last_leave_time = env.now
+
+
+def empty_truck(env, truck_id):
+    global inbound_container_id_counter, last_leave_time
+
+    d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    yield env.timeout(d_t_dist / (2 * TRUCK_SPEED_LIMIT))
+
+    # print(f"{env.now}: Empty truck {truck_id} arrives.")
+    last_leave_time = env.now
+
+
+def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, out_gate_resource):
+    global record_oc_label, train_id_counter, TRUCK_NUMBERS, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER, TRAIN_DEPARTURE_HR, oc_chassis_filled_event, TRAIN_ID, inbound_container_id_counter, previous_train_departure
+
+    for i, train in enumerate(train_timetable):
+        TRAIN_ARRIVAL_HR = train['arrival_time']
+        TRAIN_DEPARTURE_HR = train['departure_time']
+        INBOUND_CONTAINER_NUMBER = train['full_cars']
+        OUTBOUND_CONTAINER_NUMBER = train['oc_number']
+        TRUCK_NUMBERS = train['truck_number']
+        TRAIN_ID = train['train_id']
+
+        print(f"---------- Next Train {TRAIN_ID} Is On the  Way ----------")
+        print(f"IC {INBOUND_CONTAINER_NUMBER}")
+        print(f"OC {OUTBOUND_CONTAINER_NUMBER}")
+
+        outbound_containers_store.items.clear()
+        for oc in range(record_oc_label, record_oc_label + OUTBOUND_CONTAINER_NUMBER):  # from 10001 to 10001 + OC
+            # print("oc_number", oc)
+            outbound_containers_store.put(oc)
+            # yield outbound_containers_store.put(oc)
+            # print(f"Current store contents after putting {oc}: {outbound_containers_store.items}")
+
+        # print("outbound_containers_store is:", outbound_containers_store.items)
+
+        # Trucks enter until the precious train departs, if not the first truck
+        previous_train_departure = train_timetable[i-1]['departure_time'] if i > 0 else 0
+        print(f"Schedule {TRUCK_NUMBERS} Trucks arriving between previous train departure at {previous_train_departure} and current train arrival at {TRAIN_ARRIVAL_HR}")
+        env.process(handle_truck_arrivals(env, in_gate_resource, outbound_containers_store))
+
+        # Trains arrive according to the timetable, fix negative delay bug
+        delay = TRAIN_ARRIVAL_HR - env.now
+        if delay <= 0:
+            yield env.timeout(0)
+        else:
+            yield env.timeout(delay)
+
+        train_id = train_id_counter
+        print(f"Train {TRAIN_ID} ({train_id} in the dictionary) arrives at {env.now}")
+
+        # for container_id in range(inbound_container_id_counter, inbound_container_id_counter + INBOUND_CONTAINER_NUMBER):
+        for container_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):  # fix float error
+
+            record_event(container_id, 'train_arrival', env.now)
+
+        with train_processing.request() as request:
+            yield request
+            oc_chassis_filled_event = env.event()
+            yield env.process(process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource))
+            train_id_counter += 1
+
+        record_oc_label += OUTBOUND_CONTAINER_NUMBER
+        # print("record_oc_label", record_oc_label)
+        # print("oc_variance in train_process:", oc_variance)
+
+
+def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource):
+    global oc_variance, time_per_train, train_series, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER, record_oc_label, TRAIN_ID_FIXED
+
+    start_time = env.now
+
+    # Cranes unload all IC
+    unload_processes = []
+    chassis_inbound_ids = []  # To save chassis_id, current_inbound_id to hostler_transfer_IC_single_loop
+
+    # if train_id < TRAIN_NUMBERS:
+    for chassis_id in range(1, int(INBOUND_CONTAINER_NUMBER) + 1):
+        unload_process = env.process(crane_and_chassis(env, train_id, 'unload', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event))
+        unload_processes.append(unload_process)
+
+    # All IC are processed
+    # print("Unload process is:", unload_processes)
+    yield simpy.events.AllOf(env, unload_processes)
+    results = yield simpy.events.AllOf(env, unload_processes)
+
+    # To pass chassis_id, current_inbound_id to hostler_transfer_IC_single_loop as a list from calling chassis_inbound_ids
+    for result in results.values():
+        chassis_id, current_inbound_id = result
+        chassis_inbound_ids.append((chassis_id, current_inbound_id))
+        env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes,
+                             train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event,
+                             out_gate_resource))
+
+    # # Once all OC are dropped by hostlers, crane start working
+    # print("Chassis are filled with OC (-1) now. ")
+    # print(f"Chassis status after OC processed is: {chassis_status}, where ")
+    # print(f"there are {chassis_status.count(0)} chassis is filled with OC (0)")
+    # print(f"there are {chassis_status.count(-1)} chassis is filled with empty (-1)")
+    # print(f"there are {chassis_status.count(1)} chassis is filled with IC (1)")
+
+    # Cranes move all OC to chassis
+    load_processes = []
+    for chassis_id in range(1, OUTBOUND_CONTAINER_NUMBER + 1):
+        load_process = env.process(crane_and_chassis(env, train_id, 'load', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_id=chassis_id))
+        load_processes.append(load_process)
+    yield simpy.events.AllOf(env, load_processes)
+
+    # Check if all outbound containers are loaded (all chassis is empty 0), the train departs
+    if chassis_status.count(-1) == TRAIN_UNITS:
+        # oc_chassis_filled_event.succeed()
+        TRAIN_ID_FIXED = TRAIN_ID
+        print(f"Train {TRAIN_ID_FIXED} is ready to depart at {env.now}.")
+        env.process(train_departure(env, train_id))
+        time_per_train.append(env.now - start_time)
+
+    end_time = env.now
+    time_per_train.append(end_time - start_time)
+    train_series += 1
+    oc_variance += OUTBOUND_CONTAINER_NUMBER
+
+
+def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event, chassis_id=None):
+    global record_oc_label, crane_id_counter, chassis_status, inbound_container_id_counter, outbound_containers_mapping, outbound_container_id_counter, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+
+    # # Print before requesting crane resource
+    if action == 'unload':
+        crane_id = crane_id_counter
+        crane_id_counter = (crane_id_counter % CRANE_NUMBER) + 1
+        # print("inbound_id_counter", inbound_container_id_counter)
+        for container_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):  # fix float error
+            # print("container_id now:", container_id)
+            yield env.timeout(CRANE_UNLOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+            record_event(container_id, 'crane_unload', env.now)
+            # print(f"Crane {crane_id} unloads inbound container {inbound_container_id_counter} from train {train_id} at {env.now}")
+
+    # if action == 'load':
+    #     for container_id in range(record_oc_label, record_oc_label + OUTBOUND_CONTAINER_NUMBER):
+    #         yield env.timeout(CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+    #         chassis_status[chassis_id - 1] = -1
+    #         # print(f"Crane {crane_id} loads outbound container {container_id} to train {TRAIN_ID} at {env.now}")
+    #         record_event(container_id, 'crane_load', env.now)
+
+    with cranes.request() as request:
+        yield request
+
+        # # Print after acquiring crane resource
+        # print(f"[{env.now}] Crane {crane_id_counter} acquired crane resource. Available cranes: {cranes.count}/{cranes.capacity}")
+
+        start_time = env.now
+        record_vehicle_event('crane', crane_id_counter, 'start', start_time)    # performance record: starting
+
+        if action == 'unload':
+            # crane_id = crane_id_counter
+            # crane_id_counter = (crane_id_counter % CRANE_NUMBER) + 1
+
+            chassis_id = ((inbound_container_id_counter - 1) % CHASSIS_NUMBER) + 1
+
+            current_inbound_id = inbound_container_id_counter
+            inbound_container_id_counter += 1
+            # yield env.timeout(CRANE_UNLOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+
+            # for chassis_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):
+            chassis_status[chassis_id - 1] = 1
+
+            end_time = env.now
+            record_vehicle_event('crane', crane_id_counter, 'end', end_time)     # performance record: ending
+
+            # hostler picks up IC
+            env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource))
+
+            return chassis_id, current_inbound_id
+
+        elif action == 'load':
+            if chassis_id not in outbound_containers_mapping:
+                print(f"Notice: No outbound container mapped to chassis {chassis_id} at {env.now}")
+                return
+
+            container_id = outbound_containers_mapping[chassis_id]  # Retrieve container ID from mapping
+            # print("outbound_containers_mapping in crane and chassis func:", outbound_containers_mapping)
+            # print("container_id in crane and chassis func:", container_id)
+
+            if CRANE_NUMBER == 1:
+                crane_id = 1
+            else:
+                crane_id = (chassis_id % CRANE_NUMBER) + 1
+
+            chassis_status[chassis_id - 1] = -1
+
+            # yield env.timeout(CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+            # chassis_status[chassis_id - 1] = -1
+            # print(f"Crane {crane_id} loads outbound container {container_id} from chassis {chassis_id} to train {TRAIN_ID} at {env.now}")
+            # record_event(container_id, 'crane_load', env.now)
+
+            for container_id in range(record_oc_label, record_oc_label + OUTBOUND_CONTAINER_NUMBER):
+                yield env.timeout(CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+                # chassis_status[chassis_id - 1] = -1
+                # print(f"Crane {crane_id} loads outbound container {container_id} to train {TRAIN_ID} at {env.now}")
+                record_event(container_id, 'crane_load', env.now)
+
+        # # At this point, the crane resource should be released
+        # print(f"[{env.now}] Crane {crane_id_counter} has released crane resource. Available cranes: {cranes.count}/{cranes.capacity}")
+
+
+def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
+    global hostler_id_counter, chassis_status, outbound_containers_mapping, outbound_container_id, record_oc_label, HOSTLER_NUMBER
+
+    with hostlers.request() as request:
+        yield request
+
+        start_time = env.now
+        record_vehicle_event('hostler', hostler_id_counter, 'start', start_time)  # performance record
+
+        hostler_id = hostler_id_counter
+        hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+
+        with chassis.request() as chassis_request:
+            yield chassis_request
+
+            if container_type == 'inbound' and chassis_status[chassis_id - 1] == 1:
+                d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+                print(f"Hostler pick-up time is:{HOSTLER_TRANSPORT_CONTAINER_TIME}")
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                record_event(container_id, 'hostler_pickup', env.now)
+                print(f"Hostler {hostler_id} picks up inbound container {container_id} from chassis {chassis_id} and heads to parking area at {env.now}")
+
+                chassis_status[chassis_id - 1] = -1
+
+                # Hostler drop off: different route for picking-up and dropping-off
+                d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+                print(f"Hostler drop-off time is:{HOSTLER_TRANSPORT_CONTAINER_TIME}")
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                record_event(container_id, 'hostler_dropoff', env.now)
+                print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} and moves toward the assigned outbound container at {env.now}")
+
+                end_time = env.now
+                record_vehicle_event('hostler', hostler_id_counter, 'end', end_time)  # performance record
+
+                # Process functions of notify_truck and handle_outbound_container simultaneously
+                env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
+
+                # Assign outbound container and chassis_id for the hostler which drops off an inbound container
+                chassis_id, outbound_container_id = yield env.process(outbound_container_decision_making(
+                    env, hostlers, chassis, container_id, truck_store, cranes, train_processing,
+                    outbound_containers_store,
+                    in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_status,
+                    outbound_containers_mapping,
+                    record_oc_label, outbound_container_id
+                ))
+
+                # Process outbound containers
+                if chassis_id is not None and outbound_container_id is not None:
+                    env.process(handle_outbound_container(env, hostler_id, chassis_id, outbound_container_id, truck_store,
+                                                  cranes, train_processing, outbound_containers_store, in_gate_resource,
+                                                  oc_chassis_filled_event))
+
+
+# When OC are fully processed, but IC are not
+def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
+    print(f"Starting single hostler transfer IC loop for chassis {chassis_id} at {env.now}")
+    global hostler_id_counter
+
+    print(f"Requesting hostler for IC at chassis {chassis_id} at {env.now}")
+
+    with hostlers.request() as request:
+        print(f"Request available hostlers: {hostlers.count} vs total hostlers {HOSTLER_NUMBER}, Hostlers capacity: {hostlers.capacity} at {env.now}")
+        yield request
+
+        start_time = env.now
+        record_vehicle_event('hostler', hostler_id_counter, 'start', start_time)  # performance record
+
+        hostler_id = hostler_id_counter
+        hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+
+        with chassis.request() as chassis_request:
+            yield chassis_request
+
+            if container_type == 'inbound' and chassis_status[chassis_id - 1] == 1:
+                chassis_status[chassis_id - 1] = -1
+                print(f"Single loop chassis status {chassis_status}")
+                print(f"There are {chassis_status.count(1)} IC")
+                print(f"There are {chassis_status.count(-1)} empty")
+                print(f"There are {chassis_status.count(0)} OC")
+                d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                # hostler picks up the rest of IC from the chassis
+                # chassis_status[chassis_id - 1] = -1
+                record_event(container_id, 'hostler_pickup', env.now)
+                print(f"Hostler {hostler_id} picks up inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
+
+                # hostler drops off the IC
+                d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                record_event(container_id, 'hostler_dropoff', env.now)
+                print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
+
+                # Check if all chassis filled
+                if chassis_status.count(0) == OUTBOUND_CONTAINER_NUMBER and chassis_status.count(
+                        -1) == TRAIN_UNITS - OUTBOUND_CONTAINER_NUMBER and not oc_chassis_filled_event.triggered:
+                    print(f"Chassis is fully filled with OC, and cranes start moving: {chassis_status}")
+                    print(f"where there are {chassis_status.count(0)} chassis filled with OC (0)")
+                    print(f"where there are {chassis_status.count(-1)} chassis filled with empty (-1)")
+                    print(f"where there are {chassis_status.count(1)} chassis filled with IC (1)")
+                    oc_chassis_filled_event.succeed()
+                    return
+                else:
+                    print(f"Chassis is not fully filled: {chassis_status}")
+                    print(f"where there are {chassis_status.count(0)} chassis filled with OC (0)")
+                    print(f"where there are {chassis_status.count(-1)} chassis filled with empty (-1)")
+                    print(f"where there are {chassis_status.count(1)} chassis filled with IC (1)")
+
+                end_time = env.now
+                record_vehicle_event('hostler', hostler_id, 'end', end_time)  # performance record
+
+                # trucks pick up IC
+                yield env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
+
+
+def outbound_container_decision_making(env, hostlers, chassis, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_status, outbound_containers_mapping, record_oc_label, outbound_container_id):
+    # Check if outbound_containers_store has outbound container
+    if len(outbound_containers_store.items) > 0:
+        outbound_container_id = yield outbound_containers_store.get()
+        print(f"Outbound containers remaining: {len(outbound_containers_store.items)}")
+
+        if -1 in chassis_status:
+            chassis_id = chassis_status.index(-1) + 1  # find the first chassis
+            # If chassis are not assigned with outbound container
+            if chassis_id not in outbound_containers_mapping:
+                # outbound_container_id += record_oc_label
+                outbound_containers_mapping[chassis_id] = outbound_container_id
+                chassis_status[chassis_id - 1] = 0  # already assigned outbound container
+                print(f"OC mapping created: outbound container {outbound_container_id} assigned to chassis {chassis_id}")
+            else:
+                print(f"Chassis {chassis_id} is already mapped to an outbound container.")
+        else:
+            print("No empty chassis available for outbound container assignment.")
+
+    # if outbound_containers_store is null, check if we need operate single loop
+    else:
+        chassis_id = None
+        outbound_container_id = None
+        # chassis_status = 1: inbound containers are not loaded
+        if chassis_status.count(1) != 0:
+            print(f"Haven't finished all IC yet at {env.now}. Starting single loop.")
+            chassis_id = chassis_status.index(1) + 1
+            chassis_status[chassis_id - 1] = 0  # assigned with IC
+            # single loop takes rest inbound container
+            yield env.process(hostler_transfer_IC_single_loop(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id,
+                                                truck_store, cranes, train_processing,
+                                                outbound_containers_store, in_gate_resource, oc_chassis_filled_event,
+                                                out_gate_resource))
+        else:
+            print("All inbound containers have been processed.")
+
+    return chassis_id, outbound_container_id
+
+
+def handle_outbound_container(env, hostler_id, chassis_id, outbound_container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event):
+    global HOSTLER_FIND_CONTAINER_TIME
+
+    d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+    HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+
+    d_r_dist = create_triang_distribution(d_r_min, d_r_avg, d_r_max).rvs()
+    HOSTLER_FIND_CONTAINER_TIME = d_r_dist / (2 * TRUCK_SPEED_LIMIT)
+    yield env.timeout(HOSTLER_FIND_CONTAINER_TIME)
+
+    record_event(outbound_container_id, 'hostler_pickup', env.now)
+    print(f"Hostler {hostler_id} picks up outbound container {outbound_container_id} from parking area to chassis {chassis_id} at {env.now}")
+
+    yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+
+    record_event(outbound_container_id, 'hostler_dropoff', env.now)
+    print(f"Hostler {hostler_id} drops off outbound container {outbound_container_id} to chassis {chassis_id} at {env.now}")
+
+
+# truck pick up IC
+def notify_truck(env, truck_store, container_id, out_gate_resource):
+    truck_id = yield truck_store.get()
+    yield env.timeout(TRUCK_INGATE_TIME)
+    print(f"Truck {truck_id} arrives at parking area and prepare to pick up inbound container {container_id} at {env.now}")
+    yield env.process(truck_transfer(env, truck_id, container_id, out_gate_resource))
+
+
+def truck_transfer(env, truck_id, container_id, out_gate_resource):
+    global TRUCK_INGATE_TIME, TRUCK_TRANSPORT_CONTAINER_TIME, outbound_container_id_counter
+
+    start_time = env.now
+    record_vehicle_event('truck', truck_id, 'start', start_time)  # performance record
+
+    # Truck moves to the parking area
+    yield env.timeout(TRUCK_TO_PARKING)
+    record_event(container_id, 'truck_pickup', env.now)
+    print(f"Truck {truck_id} picks up inbound container {container_id} at {env.now}")
+
+    # Calculate the transport time for the truck
+    d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    TRUCK_TRANSPORT_CONTAINER_TIME = d_t_dist / (2 * TRUCK_SPEED_LIMIT)
+    yield env.timeout(TRUCK_TRANSPORT_CONTAINER_TIME)
+
+    # Request out_gate_resource resource before the truck exits
+    with out_gate_resource.request() as request:
+        yield request
+
+        # Simulate the time it takes for the truck to pass through the gate
+        yield env.timeout(TRUCK_OUTGATE_TIME + random.uniform(0,TRUCK_OUTGATE_TIME_DEV))
+        record_event(container_id, 'truck_exit', env.now)
+        print(f"Truck {truck_id} exits gate with inbound container {container_id} at {env.now}")
+
+    # End performance recording
+    end_time = env.now
+    record_vehicle_event('truck', truck_id, 'end', end_time)
+
+
+def train_departure(env, train_id):
+    global TRAIN_ID_FIXED, record_oc_label
+
+    if env.now < TRAIN_DEPARTURE_HR:
+        yield env.timeout(TRAIN_DEPARTURE_HR - env.now)
+    yield env.timeout(TRAIN_INSPECTION_TIME)
+    print(f"Train {TRAIN_ID_FIXED} ({train_id} in the dictionary) departs at {env.now}")
+
+    for container_id in range(record_oc_label - OUTBOUND_CONTAINER_NUMBER, record_oc_label):
+        record_event(container_id, 'train_depart', env.now)
+
+
+def run_simulation():
+    global all_trucks_ready_event, record_oc_label, TRUCK_NUMBERS, TRAIN_NUMBERS
+
+    print(f"Starting simulation with No.{TRAIN_ID} trains, {HOSTLER_NUMBER} hostlers, {CRANE_NUMBER} cranes, and {TRUCK_NUMBERS} trucks.")
+    env = simpy.Environment()
+
+    # Resources
+    train_processing = simpy.Resource(env, capacity=1)
+    cranes = simpy.Resource(env, capacity=CRANE_NUMBER)
+    chassis = simpy.Resource(env, capacity=CHASSIS_NUMBER)
+    hostlers = simpy.Resource(env, capacity=HOSTLER_NUMBER)
+    in_gate_resource = simpy.Resource(env, capacity=IN_GATE_NUMBERS)
+    out_gate_resource = simpy.Resource(env, capacity=OUT_GATE_NUMBERS)
+    outbound_containers_store = simpy.Store(env, capacity=100)
+    truck_store = simpy.Store(env, capacity=100)
+
+    # Initialize trucks
+    truck_store.items.clear()
+    # print("TRUCK_NUMBERS:",  TRUCK_NUMBERS)
+    for truck_id in range(1, TRUCK_NUMBERS + 1):
+        truck_store.put(truck_id)
+    # print("TRUCK_STORE:", truck_store.items)
+
+    all_trucks_ready_event = env.event()
+
+    # # toy case
+    # train_timetable = [
+    #     {"train_id": 19, "arrival_time": 187, "departure_time": 200, "empty_cars": 3, "full_cars":7, "oc_number": 2, "truck_number":7 },
+    #     {"train_id": 25, "arrival_time": 250, "departure_time": 350, "empty_cars": 4, "full_cars":6, "oc_number": 2, "truck_number":6 },
+    #     {"train_id": 49, "arrival_time": 400, "departure_time": 600, "empty_cars": 5, "full_cars":5, "oc_number": 2, "truck_number":5 },
+    #     {"train_id": 60, "arrival_time": 650, "departure_time": 750, "empty_cars": 6, "full_cars":4, "oc_number": 2, "truck_number":4 },
+    #     {"train_id": 12, "arrival_time": 800, "departure_time": 1000, "empty_cars": 7, "full_cars":3, "oc_number": 4, "truck_number":4 },
+    # ]
+
+    # REAL TEST
+    train_timetable = timetable(terminal)
+    TRAIN_NUMBERS = len(timetable(terminal))
+
+    # env.process(train_arrival(env, train_processing, cranes, in_gate_resource, outbound_containers_store, truck_store, train_timetable))
+    env.process(train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource,
+                  outbound_containers_store, truck_store, out_gate_resource))
+
+    env.run(until=SIM_TIME)
+
+    # Performance Matrix: train processing time
+    avg_time_per_train = sum(time_per_train) / len(time_per_train)
+    print(f"Average train processing time: {sum(time_per_train) / len(time_per_train) if time_per_train else 0:.2f}")
+    print("Simulation completed. ")
+    with open("avg_time_per_train.txt", "w") as f:
+        f.write(str(avg_time_per_train))
+
+    # Create DataFrame for container events
+    container_data = []
+
+    for container_id, events in sorted(container_events.items()):
+        container_type = 'inbound' if container_id < 10001 else 'outbound'
+        if container_type == 'inbound':
+            container_process_time = events.get('truck_exit', '-') - events.get('train_arrival', '-') if 'truck_exit' in events and 'train_arrival' in events else '-'
+        else:
+            container_process_time = events.get('train_depart', '-') - events.get('truck_drop_off', '-') if 'train_depart' in events and 'truck_drop_off' in events else '-'
+
+        container_data.append({
+            'container_id': container_id,
+            'container_type': container_type,
+            'train_arrival': events.get('train_arrival', '-'),
+            'truck_arrival': events.get('truck_arrival', '-'),
+            'crane_unload': events.get('crane_unload', '-'),
+            'hostler_pickup': events.get('hostler_pickup', '-'),
+            'hostler_dropoff': events.get('hostler_dropoff', '-'),
+            'truck_drop_off': events.get('truck_drop_off', '-'),
+            'truck_pickup': events.get('truck_pickup', '-'),
+            'truck_exit': events.get('truck_exit', '-'),
+            'crane_load': events.get('crane_load', '-'),
+            'train_depart': events.get('train_depart', '-'),
+            'container_processing_time': container_process_time
+        })
+
+    df = pd.DataFrame(container_data)
+    filename = f"C:/Users/Irena Tong/PycharmProjects/simulation_test/test/results/simulation_crane_{CRANE_NUMBER}_hostler_{HOSTLER_NUMBER}.xlsx"
+    df.to_excel(filename, index=False)
+
+    # Use save_average_times and save_vehicle_logs for vehicle related logs
+    save_average_times()
+    save_vehicle_logs()
+
+    print("Done!")
+
+
+if __name__ == "__main__":
+    run_simulation()
\ No newline at end of file
diff --git a/python/lifts/hostler_work_log.txt b/python/lifts/hostler_work_log.txt
new file mode 100644
index 00000000..aca04ba6
--- /dev/null
+++ b/python/lifts/hostler_work_log.txt
@@ -0,0 +1,774 @@
+Vehicle ID: 1, Event Type: start, Timestamp: 181.18810094866248
+Vehicle ID: 1, Event Type: end, Timestamp: 181.1909629977074
+Vehicle ID: 1, Event Type: start, Timestamp: 181.2169499739688
+Vehicle ID: 1, Event Type: end, Timestamp: 181.2192376389858
+Vehicle ID: 1, Event Type: start, Timestamp: 181.2395633995604
+Vehicle ID: 1, Event Type: end, Timestamp: 181.24247560236748
+Vehicle ID: 1, Event Type: start, Timestamp: 181.24247560236748
+Vehicle ID: 1, Event Type: end, Timestamp: 181.24474957802855
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+Vehicle ID: 1, Event Type: end, Timestamp: 181.27227078528222
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+Vehicle ID: 1, Event Type: end, Timestamp: 181.32883694651983
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+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
+Vehicle ID: 1, Event Type: start, Timestamp: 867.7876488666546
diff --git a/python/lifts/merged_baseline.py b/python/lifts/merged_baseline.py
new file mode 100644
index 00000000..9e41832e
--- /dev/null
+++ b/python/lifts/merged_baseline.py
@@ -0,0 +1,454 @@
+import simpy
+import random
+import pandas as pd
+from parameters import *
+from distances import *
+from dictionary import *
+from vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
+
+
+CRANE_NUMBER = 2
+HOSTLER_NUMBER = 20
+
+def record_event(container_id, event_type, timestamp):
+    if container_id not in container_events:
+        container_events[container_id] = {}
+    container_events[container_id][event_type] = timestamp
+
+
+def handle_truck_arrivals(env, in_gate_resource, truck_numbers):
+    global all_trucks_ready_event, truck_processed
+
+    truck_id = 1
+    truck_processed = 0
+
+    while truck_id <= TRUCK_NUMBERS:
+        inter_arrival_time = random.expovariate(1 / TRUCK_ARRIVAL_MEAN)
+        yield env.timeout(inter_arrival_time)
+        truck_arrival_time.append(env.now)
+
+        env.process(truck_through_gate(env, in_gate_resource, truck_id))
+        truck_id += 1
+
+    if truck_id > TRUCK_NUMBERS:
+        # print(f"truck_id = {truck_id} vs TRUCK_NUM = {TRUCK_NUMBERS}")
+        if not all_trucks_ready_event.triggered:
+            all_trucks_ready_event.succeed()
+            # print(f"{env.now}: All trucks arrived for the {TRAIN_ID} train.")
+
+
+def truck_through_gate(env, in_gate_resource, truck_id):
+    global last_leave_time, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+
+    with in_gate_resource.request() as request:
+        yield request
+        wait_time = max(0, truck_arrival_time[truck_id - 1] - last_leave_time)
+        if wait_time <= 0:
+            wait_time = 0  # first arriving trucks
+            print(f"Truck {truck_id} enters the gate without waiting")
+        else:
+            print(f"Truck {truck_id} enters the gate and queued for {wait_time} hrs")
+        truck_waiting_time.append(wait_time)
+
+        yield env.timeout(TRUCK_INGATE_TIME + random.uniform(0, TRUCK_INGATE_TIME_DEV))
+
+        # Case 1: Normal handling when OC >= IC (all trucks have containers)
+        if OUTBOUND_CONTAINER_NUMBER >= INBOUND_CONTAINER_NUMBER:
+            env.process(handle_container(env, truck_id))
+
+        # Case 2: OC < IC, extra empty trucks are needed
+        else:
+            if truck_id <= OUTBOUND_CONTAINER_NUMBER:
+                env.process(handle_container(env, truck_id))  # Loaded trucks
+            else:
+                env.process(empty_truck(env, truck_id))  # Empty trucks
+
+
+def handle_container(env, truck_id):
+    global outbound_container_id_counter, last_leave_time
+
+    container_id = outbound_container_id_counter + 1
+    outbound_container_id_counter += 1
+    record_event(container_id, 'truck_arrival', env.now)
+
+    d_g_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    yield env.timeout(d_g_dist / (2 * TRUCK_SPEED_LIMIT))
+
+    record_event(container_id, 'truck_drop_off', env.now)
+    print(f"{env.now}: Truck {truck_id} drops outbound container {container_id}.")
+    last_leave_time = env.now
+
+
+def empty_truck(env, truck_id):
+    global inbound_container_id_counter, last_leave_time
+
+    container_id = inbound_container_id_counter + OUTBOUND_CONTAINER_NUMBER + 1
+    record_event(container_id, 'truck_arrival', env.now)
+
+    d_g_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    yield env.timeout(d_g_dist / (2 * TRUCK_SPEED_LIMIT))
+
+    record_event(container_id, 'truck_drop_off', env.now)
+    print(f"{env.now}: Empty truck {truck_id} arrives.")
+    last_leave_time = env.now
+
+
+def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, out_gate_resource):
+# def train_arrival(env, train_processing, cranes, in_gate_resource, outbound_containers_store, truck_store, train_timetable):
+    global train_id_counter, TRUCK_NUMBERS, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER, TRAIN_DEPARTURE_HR
+
+
+    for i, train in enumerate(train_timetable):
+        TRAIN_ARRIVAL_HR = train['arrival_time']  # 获取列车到达时间
+        TRAIN_DEPARTURE_HR = train['departure_time']  # 获取列车离开时间
+        INBOUND_CONTAINER_NUMBER = train['full_cars']
+        OUTBOUND_CONTAINER_NUMBER = train['oc_number']
+        TRUCK_NUMBERS = train['truck_number']
+        TRAIN_ID = train['train_id']
+
+        print(f"---------- Next Train {TRAIN_ID} Is On the Way ----------")
+        print(f"IC {INBOUND_CONTAINER_NUMBER}")
+        print(f"OC {OUTBOUND_CONTAINER_NUMBER}")
+
+        # 如果不是第一列火车，在上一列火车离开后安排卡车的到达
+        previous_train_departure = train_timetable[i-1]['departure_time']
+        print(f"Schedule {TRUCK_NUMBERS} Trucks arriving between previous train departure at {previous_train_departure} and current train arrival at {TRAIN_ARRIVAL_HR}")
+        env.process(handle_truck_arrivals(env, in_gate_resource, outbound_containers_store))
+
+        # 等待当前火车到达的时间
+        yield env.timeout(TRAIN_ARRIVAL_HR - env.now)
+
+        train_id = train_id_counter
+        print(f"Train {train_id} arrives at {env.now}")
+
+        with train_processing.request() as request:
+            yield request
+            oc_chassis_filled_event = env.event()
+            # yield env.process(process_train(env, train_id, cranes, TRAIN_DEPARTURE_HR))
+            yield env.process(process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource))
+            train_id_counter += 1
+
+
+def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource):
+    global time_per_train, train_series, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+
+    start_time = env.now
+
+    # Cranes unload all IC
+    unload_processes = []
+    chassis_inbound_ids = []  # To save chassis_id, current_inbound_id to hostler_transfer_IC_single_loop
+    for _ in range(INBOUND_CONTAINER_NUMBER):
+        unload_process = env.process(crane_and_chassis(env, train_id, 'unload', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event))
+        unload_processes.append(unload_process)
+    # All IC are processed
+    results = yield simpy.events.AllOf(env, unload_processes)
+
+    # To pass chassis_id, current_inbound_id to hostler_transfer_IC_single_loop as a list from calling chassis_inbound_ids
+    for result in results.values():
+        chassis_id, current_inbound_id = result
+        chassis_inbound_ids.append((chassis_id, current_inbound_id))
+
+    # Are all chassis filled with OC?
+    # Once all OC are dropped by hostlers, crane start working
+    print("Check before cranes start: Chassis filled with OC (-1) ? ")
+    print(f"Chassis status after OC processed is: {chassis_status}, where ")
+    print(f"there are {chassis_status.count(0)} chassis is filled with OC (0)")
+    print(f"there are {chassis_status.count(-1)} chassis is filled with empty (-1)")
+    print(f"there are {chassis_status.count(1)} chassis is filled with IC (1)")
+
+    if chassis_status.count(1) != 0:    # IC is not fully processed
+        print("Haven't finished all IC yet")
+        # env.process(hostler_transfer_IC_single_loop(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, oc_chassis_filled_event, out_gate_resource))
+        env.process(hostler_transfer_IC_single_loop(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes, train_processing,
+                                                    outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource))
+
+    yield oc_chassis_filled_event
+
+    # Cranes move all OC to chassis
+    load_processes = []
+    for chassis_id in range(1, OUTBOUND_CONTAINER_NUMBER + 1):
+        load_process = env.process(crane_and_chassis(env, train_id, 'load', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_id=chassis_id))
+        load_processes.append(load_process)
+    yield simpy.events.AllOf(env, load_processes)
+
+    # Check if all outbound containers are loaded (all chassis is empty 0), the train departs
+    if chassis_status.count(-1) == TRAIN_UNITS:
+        oc_chassis_filled_event.succeed()
+        print(f"Train {TRAIN_ID} is ready to depart.")
+        env.process(train_departure(env, train_id))
+        time_per_train.append(env.now - start_time)
+
+    end_time = env.now
+    time_per_train.append(end_time - start_time)
+    train_series += 1
+
+
+def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event, chassis_id=None):
+    global crane_id_counter, chassis_status, inbound_container_id_counter, outbound_containers_mapping, outbound_container_id_counter, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+
+    with cranes.request() as request:
+        yield request
+
+        start_time = env.now
+        record_vehicle_event('crane', crane_id_counter, 'start', start_time)    # performance record: starting
+
+        if action == 'unload':
+            crane_id = crane_id_counter
+            crane_id_counter = (crane_id_counter % CRANE_NUMBER) + 1
+
+            chassis_id = ((inbound_container_id_counter - 1) % CHASSIS_NUMBER) + 1
+
+            current_inbound_id = inbound_container_id_counter
+            inbound_container_id_counter += 1
+            yield env.timeout(CRANE_UNLOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+
+            end_time = env.now
+            record_vehicle_event('crane', crane_id_counter, 'end', end_time)     # performance record: ending
+
+            # print(f"length of chassis status: {len(chassis_status)}")
+            chassis_status[chassis_id - 1] = 1
+            record_event(current_inbound_id, 'crane_unload', env.now)
+            print(f"Crane {crane_id} unloads inbound container {current_inbound_id} at chassis {chassis_id} from train {train_id} at {env.now}")
+            env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource))
+
+            return chassis_id, current_inbound_id
+
+        elif action == 'load':
+            if chassis_id not in outbound_containers_mapping:
+                print(f"Error: No outbound container mapped to chassis {chassis_id} at {env.now}")
+                return
+
+            container_id = outbound_containers_mapping[chassis_id]  # Retrieve container ID from mapping
+
+            if CRANE_NUMBER == 1:
+                crane_id = 1
+            else:
+                crane_id = (chassis_id % CRANE_NUMBER) + 1
+
+            yield env.timeout(CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+            chassis_status[chassis_id - 1] = -1
+            record_event(container_id, 'crane_load', env.now)
+            print(f"Crane {crane_id} loads outbound container {container_id} from chassis {chassis_id} to train {TRAIN_ID} at {env.now}")
+
+
+def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
+    global hostler_id_counter
+
+    with hostlers.request() as request:
+        yield request
+
+        start_time = env.now
+        record_vehicle_event('hostler', hostler_id_counter, 'start', start_time)  # performance record
+
+        hostler_id = hostler_id_counter
+        hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+
+        with chassis.request() as chassis_request:
+            yield chassis_request
+
+            if container_type == 'inbound' and chassis_status[chassis_id - 1] == 1:
+                d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+                d_y_dist = create_triang_distribution(d_y_min, d_y_avg, d_y_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = (d_t_dist + d_y_dist) / (2 * HOSTLER_SPEED_LIMIT)
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                chassis_status[chassis_id - 1] = -1
+                record_event(container_id, 'hostler_pickup', env.now)
+                print(f"Hostler {hostler_id} picks up inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                record_event(container_id, 'hostler_dropoff', env.now)
+                print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
+
+                end_time = env.now
+                record_vehicle_event('hostler', hostler_id_counter, 'end', end_time)  # performance record
+
+                yield env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
+
+                # Check and process outbound container
+                yield env.process(handle_outbound_container(env, hostlers, chassis, chassis_id, truck_store, cranes,
+                                                            train_processing, outbound_containers_store,
+                                                            in_gate_resource, oc_chassis_filled_event))
+
+                # When all chassis are either filled with outbound container or empty, the cranes start loading
+                if chassis_status.count(0) == OUTBOUND_CONTAINER_NUMBER and chassis_status.count(
+                        -1) == TRAIN_UNITS - OUTBOUND_CONTAINER_NUMBER and not oc_chassis_filled_event.triggered:
+                    print(f"Chassis is fully filled with OC, and cranes start moving: {chassis_status}")
+                    print(f"where there are {chassis_status.count(0)} chassis is filled with OC (0)")
+                    print(f"where there are {chassis_status.count(-1)} chassis is filled with empty (-1)")
+                    print(f"where there are {chassis_status.count(1)} chassis is filled with IC (1)")
+                    oc_chassis_filled_event.succeed()
+                else:
+                    print(f"Chassis is not fully filled: {chassis_status}")
+                    print(f"where there are {chassis_status.count(0)} chassis is filled with OC (0)")
+                    print(f"where there are {chassis_status.count(-1)} chassis is filled with empty (-1)")
+                    print(f"where there are {chassis_status.count(1)} chassis is filled with IC (1)")
+                    return
+
+
+# When OC are fully processed, but IC are not
+def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
+    print(f"Starting hostler_transfer_IC_single_loop for chassis {chassis_id} at {env.now}")
+    global hostler_id_counter
+
+    print(f"Requesting hostler for chassis {chassis_id} at {env.now}")
+
+    with hostlers.request() as request:
+        print(f"Request available hostlers: {hostlers.count} vs total hostlers {HOSTLER_NUMBER}, Hostlers capacity: {hostlers.capacity} at {env.now}")
+        yield request
+
+        start_time = env.now
+        record_vehicle_event('hostler', hostler_id_counter, 'start', start_time)  # performance record
+
+        hostler_id = hostler_id_counter
+        hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+
+        with chassis.request() as chassis_request:
+            yield chassis_request
+
+            if container_type == 'inbound' and chassis_status[chassis_id - 1] == 1:
+                d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+                d_y_dist = create_triang_distribution(d_y_min, d_y_avg, d_y_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = (d_t_dist + d_y_dist) / (2 * HOSTLER_SPEED_LIMIT)
+
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                # hostler picks up the rest of IC from the chassis
+                chassis_status[chassis_id - 1] = -1
+                record_event(container_id, 'hostler_pickup', env.now)
+                print(f"Hostler {hostler_id} picks up inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
+                # hostler drops off the IC
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                record_event(container_id, 'hostler_dropoff', env.now)
+                print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
+
+                # Check if all chassis filled
+                if chassis_status.count(0) == OUTBOUND_CONTAINER_NUMBER and chassis_status.count(
+                        -1) == TRAIN_UNITS - OUTBOUND_CONTAINER_NUMBER and not oc_chassis_filled_event.triggered:
+                    print(f"Chassis is fully filled with OC, and cranes start moving: {chassis_status}")
+                    oc_chassis_filled_event.succeed()
+                    return
+                else:
+                    print(f"Chassis is not fully filled: {chassis_status}")
+                    print(f"where there are {chassis_status.count(0)} chassis filled with OC (0)")
+                    print(f"where there are {chassis_status.count(-1)} chassis filled with empty (-1)")
+                    print(f"where there are {chassis_status.count(1)} chassis filled with IC (1)")
+
+                end_time = env.now
+                record_vehicle_event('hostler', hostler_id, 'end', end_time)  # performance record
+
+                # trucks pick up IC
+                yield env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
+
+
+def handle_outbound_container(env, hostlers, chassis, chassis_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event):
+    global HOSTLER_FIND_CONTAINER_TIME, HOSTLER_TRANSPORT_CONTAINER_TIME, chassis_status, hostler_id_counter, outbound_container_id_counter, outbound_containers_mapping
+
+    hostler_id = hostler_id_counter
+    hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+
+    outbound_container_id = yield outbound_containers_store.get()
+
+    if chassis_id not in outbound_containers_mapping:  # New mapping from outbound containers to chassis
+        outbound_container_id = outbound_container_id
+        outbound_containers_mapping[chassis_id] = outbound_container_id
+        chassis_status[chassis_id - 1] = 0
+        print(f"New mapping created: outbound container {outbound_container_id} to chassis {chassis_id} at {env.now}")
+
+    outbound_container_id = outbound_containers_mapping[chassis_id]
+    d_find_dist = create_triang_distribution(0, 0.5*(A+B), (A+B)).rvs()
+    HOSTLER_FIND_CONTAINER_TIME = d_find_dist / (2 * TRUCK_SPEED_LIMIT)
+    yield env.timeout(HOSTLER_FIND_CONTAINER_TIME)
+    record_event(outbound_container_id, 'hostler_pickup', env.now)
+    print(f"Hostler {hostler_id} brings back outbound container {outbound_container_id} from parking area to chassis {chassis_id} at {env.now}")
+    yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+    record_event(outbound_container_id, 'hostler_dropoff', env.now)
+    print(f"Hostler {hostler_id} drops off outbound container {outbound_container_id} from parking area to chassis {chassis_id} at {env.now}")
+
+
+def notify_truck(env, truck_store, container_id, out_gate_resource):
+
+    truck_id = yield truck_store.get()
+    yield env.timeout(TRUCK_INGATE_TIME)
+    print(f"Truck {truck_id} arrives at parking area at {env.now}")
+    yield env.process(truck_transfer(env, truck_id, container_id, out_gate_resource))
+
+
+def truck_transfer(env, truck_id, container_id, out_gate_resource):
+    global TRUCK_INGATE_TIME, TRUCK_TRANSPORT_CONTAINER_TIME, outbound_container_id_counter
+
+    start_time = env.now
+    record_vehicle_event('truck', truck_id, 'start', start_time)  # performance record
+
+    # Truck moves to the parking area
+    yield env.timeout(TRUCK_TO_PARKING)
+    record_event(container_id, 'truck_pickup', env.now)
+    print(f"Truck {truck_id} picks up inbound container {container_id} at {env.now}")
+
+    # Calculate the transport time for the truck
+    d_g_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    TRUCK_TRANSPORT_CONTAINER_TIME = d_g_dist / (2 * TRUCK_SPEED_LIMIT)
+    yield env.timeout(TRUCK_TRANSPORT_CONTAINER_TIME)
+
+    # Request out_gate_resource resource before the truck exits
+    with out_gate_resource.request() as request:
+        yield request
+
+        # Simulate the time it takes for the truck to pass through the gate
+        yield env.timeout(TRUCK_OUTGATE_TIME + random.uniform(0,TRUCK_OUTGATE_TIME_DEV))
+        record_event(container_id, 'truck_exit', env.now)
+        print(f"Truck {truck_id} exits gate with inbound container {container_id} at {env.now}")
+
+    # End performance recording
+    end_time = env.now
+    record_vehicle_event('truck', truck_id, 'end', end_time)
+
+
+def train_departure(env, train_id):
+    if env.now < TRAIN_DEPARTURE_HR:
+        yield env.timeout(TRAIN_DEPARTURE_HR - env.now)
+    yield env.timeout(TRAIN_INSPECTION_TIME)
+    print(f"Train {train_id} departs at {env.now}")
+
+
+def run_simulation():
+    global all_trucks_ready_event
+
+    print(f"Starting simulation with No.{TRAIN_ID} trains, {HOSTLER_NUMBER} hostlers, {CRANE_NUMBER} cranes, and {TRUCK_NUMBERS} trucks.")
+    env = simpy.Environment()
+
+    # Resources
+    train_processing = simpy.Resource(env, capacity=1)
+    cranes = simpy.Resource(env, capacity=CRANE_NUMBER)
+    chassis = simpy.Resource(env, capacity=CHASSIS_NUMBER)
+    hostlers = simpy.Resource(env, capacity=HOSTLER_NUMBER)
+    in_gate_resource = simpy.Resource(env, capacity=IN_GATE_NUMBERS)
+    out_gate_resource = simpy.Resource(env, capacity=OUT_GATE_NUMBERS)
+    outbound_containers_store = simpy.Store(env, capacity=OUTBOUND_CONTAINER_NUMBER)
+    truck_store = simpy.Store(env, capacity=TRUCK_NUMBERS)
+
+    # Initialize trucks
+    for truck_id in range(1, TRUCK_NUMBERS + 1):
+        truck_store.put(truck_id)
+
+    all_trucks_ready_event = env.event()
+
+    train_timetable = [
+        {"train_id": 19, "arrival_time": 187, "departure_time": 200, "empty_cars": 3, "full_cars":7, "oc_number": 2, "truck_number":7 },
+        {"train_id": 25, "arrival_time": 250, "departure_time": 350, "empty_cars": 4, "full_cars":6, "oc_number": 2, "truck_number":6 },
+        {"train_id": 49, "arrival_time": 400, "departure_time": 600, "empty_cars": 5, "full_cars":5, "oc_number": 2, "truck_number":5 },
+        {"train_id": 60, "arrival_time": 650, "departure_time": 750, "empty_cars": 6, "full_cars":4, "oc_number": 2, "truck_number":4 },
+        {"train_id": 12, "arrival_time": 800, "departure_time": 1000, "empty_cars": 7, "full_cars":3, "oc_number": 4, "truck_number":4 },
+    ]
+
+    # # REAL TEST
+    # train_timetable = train_timetable(terminal)
+
+    # env.process(train_arrival(env, train_processing, cranes, in_gate_resource, outbound_containers_store, truck_store, train_timetable))
+    env.process(train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource,
+                  outbound_containers_store, truck_store, out_gate_resource))
+
+    env.run(until=SIM_TIME)
+
+    print(f"Average train processing time: {sum(time_per_train) / len(time_per_train) if time_per_train else 0:.2f}")
+    print("Simulation completed. ")
+
+if __name__ == "__main__":
+    run_simulation()
\ No newline at end of file
diff --git a/python/lifts/parameters.py b/python/lifts/parameters.py
new file mode 100644
index 00000000..6ffc0de4
--- /dev/null
+++ b/python/lifts/parameters.py
@@ -0,0 +1,120 @@
+from schedule import *
+
+# Simulation parameters
+RANDOM_SEED = 42
+SIM_TIME = 1100
+terminal = 'Allouez'    # choose 'Hibbing' or 'Allouez'
+
+
+# Counting vehicles
+train_id_counter = 1
+crane_id_counter = 1
+hostler_id_counter = 1
+truck_id_counter = 1
+total_initial_oc_trucks = 1
+empty_truck_id_counter = 1
+
+
+# inbound container counting
+inbound_containers_processed = 0    # trucks drop off OC to chassis
+inbound_containers_hostler_processed = 0    # hostlers pick up IC from chassis
+inbound_container_id_counter = 1
+
+# outbound container counting
+outbound_container_id_counter = 10001
+outbound_containers_processed = 0   # trucks pick up IC from chassis
+outbound_containers_hostler_processed = 0   # hostlers drop off OC to chassis
+outbound_container_id = 0   # initialize OC id for chassis assignment
+record_oc_label = 10001     # update outbound_containers_mapping among trains
+oc_variance = 0     # record previous batch OC numbers cumulatively
+
+# yield events or conditions
+all_trucks_ready_event = None  # initialize trucks
+train_has_arrived_event = None    # crane starts working after the train arrives
+train_departure_event = None    # train arrives after the last train departs
+oc_chassis_filled_event = None  # outbound containers fill available chassis before cranes load
+
+
+# Trains
+def train_arrival_parameters(terminal):
+    global train_id_counter
+    timetable = train_timetable(terminal)
+    TRAIN_TIMETABLE = timetable.iloc[train_id_counter-1]
+
+    return TRAIN_TIMETABLE
+
+
+# TRAIN_UNITS = int(input("Enter the number of train units: "))
+TRAIN_TIMETABLE = train_arrival_parameters(terminal)
+TRAIN_ID = int(TRAIN_TIMETABLE['Train_ID'])
+CARS_LOADED_ARRIVAL = int(float(TRAIN_TIMETABLE['Cars_Loaded']))
+CARS_EMPTY_ARRIVAL = int(float(TRAIN_TIMETABLE['Cars_Empty']))
+TRAIN_ARRIVAL_HR = TRAIN_TIMETABLE['Arrival_Time_Actual_Hr']
+TRAIN_DEPARTURE_HR = TRAIN_TIMETABLE['Departure_Time_Actual_Hr']
+
+TRAIN_UNITS = CARS_LOADED_ARRIVAL + CARS_EMPTY_ARRIVAL
+TRAIN_SPOTS = TRAIN_UNITS
+# TRAIN_ARRIVAL_MEAN = 10
+TRAIN_INSPECTION_TIME = 10/60    # hr
+previous_train_departure = 0
+train_series = 0
+time_per_train = []
+train_delay_time = []
+
+
+# Containers
+CONTAINERS_PER_CAR = 1
+CONTAINER_LEN = 20   # 1 TEU = 20 ft long, 8 ft wide, and 8.6 ft tall
+CONTAINER_WID = 8
+CONTAINER_TAL = 8.6
+INBOUND_CONTAINER_NUMBER = CARS_LOADED_ARRIVAL
+df = outbound_containers()
+OUTBOUND_CONTAINER_NUMBER = df.loc[df['Train_ID'] == TRAIN_ID, 'Outbound_Num'].values[0]
+container_events = {}   # Dictionary to store container event data
+
+
+# Chassis
+CHASSIS_NUMBER = TRAIN_UNITS
+chassis_status = [-1] * CHASSIS_NUMBER  # -1 means empty, 1 means inbound container, 0 means outbound container
+
+
+# Cranes
+# CRANE_NUMBER = int(input("Enter the number of crane: "))
+CRANE_NUMBER = 10
+CONTAINERS_PER_CRANE_MOVE_MEAN = 600   # 10ft/min = 600 ft/hr, crane speed
+CRANE_LOAD_CONTAINER_TIME_MEAN = (CONTAINERS_PER_CAR*(2*CONTAINER_TAL+CONTAINER_WID))/CONTAINERS_PER_CRANE_MOVE_MEAN   # hr
+CRANE_UNLOAD_CONTAINER_TIME_MEAN = (CONTAINERS_PER_CAR*(2*CONTAINER_TAL+CONTAINER_WID))/CONTAINERS_PER_CRANE_MOVE_MEAN # hr
+CRANE_MOVE_DEV_TIME = 5/60 # hr
+outbound_containers_mapping = {}  # To keep track of outbound containers ID mapped to chassis
+
+
+# Hostlers
+# HOSTLER_NUMBER = int(input("Enter the number of hostler: "))
+HOSTLER_NUMBER = 50
+CONTAINERS_PER_HOSTLER = 1  # hostler capacity
+HOSTLER_SPEED_LIMIT = 20*5280   # ft/hr
+HOSTLER_TRANSPORT_CONTAINER_TIME = 0    # hr, triangular distribution
+HOSTLER_FIND_CONTAINER_TIME = 0  # hr, triangular distribution
+hostler_status = [-1] * HOSTLER_NUMBER   # 1 means trackside, 0 means parking side, -1 means hostler resources side
+
+
+# Trucks
+TRUCK_ARRIVAL_MEAN = 40/60   # hr, calculate by
+TRUCK_INGATE_TIME = 1/60    # hr
+TRUCK_OUTGATE_TIME = 2/60    # hr
+TRUCK_INGATE_TIME_DEV = 1/60    # hr
+TRUCK_OUTGATE_TIME_DEV = 1/60    # hr
+TRUCK_TO_PARKING = 2/60    # hr
+TRUCK_SPEED_LIMIT = 20*5280   # ft/hr
+TRUCK_TRANSPORT_CONTAINER_TIME = 0  # hr, triangular distribution
+TRUCK_NUMBERS = max(INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER)
+IN_OUT_GAP = abs(INBOUND_CONTAINER_NUMBER - OUTBOUND_CONTAINER_NUMBER)
+
+
+
+# Gate settings
+IN_GATE_NUMBERS = 6  # test queuing module with 1; normal operations with 6
+OUT_GATE_NUMBERS = 6
+last_leave_time = 0
+truck_arrival_time = []
+truck_waiting_time = []
\ No newline at end of file
diff --git a/python/lifts/results/simulation_crane_1_hostler_1.xlsx b/python/lifts/results/simulation_crane_1_hostler_1.xlsx
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diff --git a/python/lifts/results/simulation_crane_1_hostler_5.xlsx b/python/lifts/results/simulation_crane_1_hostler_5.xlsx
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diff --git a/python/lifts/results/simulation_crane_1_hostler_8.xlsx b/python/lifts/results/simulation_crane_1_hostler_8.xlsx
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diff --git a/python/lifts/schedule.py b/python/lifts/schedule.py
new file mode 100644
index 00000000..4dd43b2a
--- /dev/null
+++ b/python/lifts/schedule.py
@@ -0,0 +1,51 @@
+import pandas as pd
+
+def find_duplicate_numbers(series):
+    duplicates = series[series.duplicated(keep=False)]
+    if not duplicates.empty:
+        return ''.join(map(str, duplicates.unique()))
+    else:
+        return series.iloc[0]
+
+
+def train_timetable(terminal):
+    df = pd.read_csv('C:/Users/Irena Tong/PycharmProjects/simulation_test/data/train_consist_plan.csv')
+    df_terminal = df[(df['Destination_ID'] == terminal) & (df['Train_Type'] == 'Intermodal')]  # Filter by the selected terminal and intermodal type railcar
+
+    df_grouped = df_terminal.groupby('Train_ID').agg({
+        'Cars_Loaded': find_duplicate_numbers,
+        'Cars_Empty': find_duplicate_numbers,
+        'Arrival_Time_Actual_Hr': 'min',
+        'Departure_Time_Actual_Hr': 'min'
+    }).reset_index()
+
+    df_grouped_sorted = df_grouped.sort_values(by='Arrival_Time_Actual_Hr', ascending=True)
+
+    return df_grouped_sorted
+
+
+def next_train_timetable(train_id, terminal):
+    df_terminal = train_timetable(terminal)
+    df_next_train = df_terminal.iloc[train_id]
+    return df_next_train
+
+
+def outbound_containers():
+    df = pd.read_csv('C:/Users/Irena Tong/PycharmProjects/simulation_test/data/outbound_plan.csv')
+    return df
+
+
+def get_next_train_outbound_data(index):
+    outbound_df = outbound_containers()
+    outbound_num = outbound_df.iloc[index]['Outbound_Num']
+    return outbound_num
+
+# # Test codes
+# terminal = 'Allouez'
+# print(train_timetable(terminal))
+#
+# next_train = next_train_timetable(1, terminal)
+# print(next_train)
+#
+# next_outbound_num = get_next_train_outbound_data(1)
+# print(next_outbound_num)
\ No newline at end of file
diff --git a/python/lifts/test.py b/python/lifts/test.py
new file mode 100644
index 00000000..8ee12be9
--- /dev/null
+++ b/python/lifts/test.py
@@ -0,0 +1,602 @@
+import simpy
+import random
+from parameters import *
+from distances import *
+from dictionary import *
+from vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
+
+
+# Test input
+CRANE_NUMBER = 1
+HOSTLER_NUMBER = 1
+TRUCK_NUMBERS = 1000
+
+def record_event(container_id, event_type, timestamp):
+    if container_id not in container_events:
+        container_events[container_id] = {}
+    container_events[container_id][event_type] = timestamp
+
+
+def handle_truck_arrivals(env, in_gate_resource, truck_numbers):
+    global all_trucks_ready_event, truck_processed, start_oc_container_id, end_oc_container_id, TRUCK_ARRIVAL_MEAN, TRAIN_ARRIVAL_HR
+
+    truck_id = 1
+    truck_processed = 0
+    TRUCK_ARRIVAL_MEAN = abs(TRAIN_ARRIVAL_HR - previous_train_departure) / max(INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER)
+    print(f"current time is {env.now}")
+    print(f"next TRAIN_ARRIVAL_HR:{TRAIN_ARRIVAL_HR}")
+    print(f"TRUCK_ARRIVAL_MEAN IS {TRUCK_ARRIVAL_MEAN}")
+
+    while truck_id <= TRUCK_NUMBERS:
+        inter_arrival_time = random.expovariate(1 / TRUCK_ARRIVAL_MEAN)
+        yield env.timeout(inter_arrival_time)
+        truck_arrival_time.append(env.now)
+
+        env.process(truck_through_gate(env, in_gate_resource, truck_id))
+        truck_id += 1
+
+    if truck_id > TRUCK_NUMBERS:
+        # print(f"truck_id = {truck_id} vs TRUCK_NUM = {TRUCK_NUMBERS}")
+        if not all_trucks_ready_event.triggered:
+            all_trucks_ready_event.succeed()
+            # print(f"{env.now}: All trucks arrived for the {TRAIN_ID} train.")
+
+
+def truck_through_gate(env, in_gate_resource, truck_id):
+    global last_leave_time, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+
+    with in_gate_resource.request() as request:
+        yield request
+        wait_time = max(0, truck_arrival_time[truck_id - 1] - last_leave_time)
+        if wait_time <= 0:
+            wait_time = 0  # first arriving trucks
+            # print(f"Truck {truck_id} enters the gate without waiting")
+        else:
+            # print(f"Truck {truck_id} enters the gate and queued for {wait_time} hrs")
+            truck_waiting_time.append(wait_time)
+
+        yield env.timeout(TRUCK_INGATE_TIME + random.uniform(0, TRUCK_INGATE_TIME_DEV))
+
+        # Case 1: Normal handling when OC >= IC (all trucks have containers)
+        if OUTBOUND_CONTAINER_NUMBER >= INBOUND_CONTAINER_NUMBER:
+            env.process(handle_container(env, truck_id))
+
+        # Case 2: OC < IC, extra empty trucks are needed
+        else:
+            if truck_id <= OUTBOUND_CONTAINER_NUMBER:
+                env.process(handle_container(env, truck_id))  # Loaded trucks
+            else:
+                env.process(empty_truck(env, truck_id))  # Empty trucks
+
+
+def handle_container(env, truck_id):
+    global outbound_container_id_counter, last_leave_time
+
+    container_id = outbound_container_id_counter
+    outbound_container_id_counter += 1
+    record_event(container_id, 'truck_arrival', env.now)
+
+    d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    yield env.timeout(d_t_dist / (2 * TRUCK_SPEED_LIMIT))
+
+    record_event(container_id, 'truck_drop_off', env.now)
+    # print(f"{env.now}: Truck {truck_id} drops outbound container {container_id}.")
+    last_leave_time = env.now
+
+
+def empty_truck(env, truck_id):
+    global inbound_container_id_counter, last_leave_time
+
+    d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    yield env.timeout(d_t_dist / (2 * TRUCK_SPEED_LIMIT))
+
+    # print(f"{env.now}: Empty truck {truck_id} arrives.")
+    last_leave_time = env.now
+
+
+def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, out_gate_resource):
+    global record_oc_label, train_id_counter, TRUCK_NUMBERS, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER, TRAIN_DEPARTURE_HR, oc_chassis_filled_event, TRAIN_ID, inbound_container_id_counter, previous_train_departure
+
+    for i, train in enumerate(train_timetable):
+        TRAIN_ARRIVAL_HR = train['arrival_time']
+        TRAIN_DEPARTURE_HR = train['departure_time']
+        INBOUND_CONTAINER_NUMBER = train['full_cars']
+        OUTBOUND_CONTAINER_NUMBER = train['oc_number']
+        TRUCK_NUMBERS = train['truck_number']
+        TRAIN_ID = train['train_id']
+
+        print(f"---------- Next Train {TRAIN_ID} Is On the  Way ----------")
+        print(f"IC {INBOUND_CONTAINER_NUMBER}")
+        print(f"OC {OUTBOUND_CONTAINER_NUMBER}")
+
+        outbound_containers_store.items.clear()
+        for oc in range(record_oc_label, record_oc_label + OUTBOUND_CONTAINER_NUMBER):  # from 10001 to 10001 + OC
+            # print("oc_number", oc)
+            outbound_containers_store.put(oc)
+            # yield outbound_containers_store.put(oc)
+            # print(f"Current store contents after putting {oc}: {outbound_containers_store.items}")
+
+        # print("outbound_containers_store is:", outbound_containers_store.items)
+
+        # Trucks enter until the precious train departs, if not the first truck
+        previous_train_departure = train_timetable[i-1]['departure_time'] if i > 0 else 0
+        print(f"Schedule {TRUCK_NUMBERS} Trucks arriving between previous train departure at {previous_train_departure} and current train arrival at {TRAIN_ARRIVAL_HR}")
+        env.process(handle_truck_arrivals(env, in_gate_resource, outbound_containers_store))
+
+        # Trains arrive according to the timetable, fix negative delay bug
+        delay = TRAIN_ARRIVAL_HR - env.now
+        if delay <= 0:
+            yield env.timeout(0)
+        else:
+            yield env.timeout(delay)
+
+        train_id = train_id_counter
+        print(f"Train {TRAIN_ID} ({train_id} in the dictionary) arrives at {env.now}")
+
+        # for container_id in range(inbound_container_id_counter, inbound_container_id_counter + INBOUND_CONTAINER_NUMBER):
+        for container_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):  # fix float error
+
+            record_event(container_id, 'train_arrival', env.now)
+
+        with train_processing.request() as request:
+            yield request
+            oc_chassis_filled_event = env.event()
+            yield env.process(process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource))
+            train_id_counter += 1
+
+        record_oc_label += OUTBOUND_CONTAINER_NUMBER
+        # print("record_oc_label", record_oc_label)
+        # print("oc_variance in train_process:", oc_variance)
+
+
+def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource):
+    global oc_variance, time_per_train, train_series, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER, record_oc_label, TRAIN_ID_FIXED
+
+    start_time = env.now
+
+    # Cranes unload all IC
+    unload_processes = []
+    chassis_inbound_ids = []  # To save chassis_id, current_inbound_id to hostler_transfer_IC_single_loop
+
+    # if train_id < TRAIN_NUMBERS:
+    for chassis_id in range(1, int(INBOUND_CONTAINER_NUMBER) + 1):
+        unload_process = env.process(crane_and_chassis(env, train_id, 'unload', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event))
+        unload_processes.append(unload_process)
+
+    # All IC are processed
+    # print("Unload process is:", unload_processes)
+    yield simpy.events.AllOf(env, unload_processes)
+    results = yield simpy.events.AllOf(env, unload_processes)
+
+    for container_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):  # fix float error
+        env.process(crane_movement(env, container_id, 'unload'))
+
+
+    # To pass chassis_id, current_inbound_id to hostler_transfer_IC_single_loop as a list from calling chassis_inbound_ids
+    for result in results.values():
+        chassis_id, current_inbound_id = result
+        chassis_inbound_ids.append((chassis_id, current_inbound_id))
+        env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes,
+                             train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event,
+                             out_gate_resource))
+
+    # # Once all OC are dropped by hostlers, crane start working
+    # print("Chassis are filled with OC (-1) now. ")
+    # print(f"Chassis status after OC processed is: {chassis_status}, where ")
+    # print(f"there are {chassis_status.count(0)} chassis is filled with OC (0)")
+    # print(f"there are {chassis_status.count(-1)} chassis is filled with empty (-1)")
+    # print(f"there are {chassis_status.count(1)} chassis is filled with IC (1)")
+
+    # Cranes move all OC to chassis
+    load_processes = []
+    for chassis_id in range(1, OUTBOUND_CONTAINER_NUMBER + 1):
+        load_process = env.process(crane_and_chassis(env, train_id, 'load', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_id=chassis_id))
+        load_processes.append(load_process)
+
+    yield simpy.events.AllOf(env, load_processes)
+
+    for container_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):  # fix float error
+        env.process(crane_movement(env, container_id, 'load'))
+
+    # Check if all outbound containers are loaded (all chassis is empty 0), the train departs
+    if chassis_status.count(-1) == TRAIN_UNITS:
+        # oc_chassis_filled_event.succeed()
+        TRAIN_ID_FIXED = TRAIN_ID
+        print(f"Train {TRAIN_ID_FIXED} is ready to depart at {env.now}.")
+        env.process(train_departure(env, train_id))
+        time_per_train.append(env.now - start_time)
+
+    end_time = env.now
+    time_per_train.append(end_time - start_time)
+    train_series += 1
+    oc_variance += OUTBOUND_CONTAINER_NUMBER
+
+def crane_movement(env, container_id, action):
+    global record_oc_label, crane_id_counter, chassis_status, inbound_container_id_counter, outbound_containers_mapping, outbound_container_id_counter, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+
+    # # Print before requesting crane resource
+    if action == 'unload':
+        crane_id = crane_id_counter
+        crane_id_counter = (crane_id_counter % CRANE_NUMBER) + 1
+        yield env.timeout(CRANE_UNLOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+        record_event(container_id, 'crane_unload', env.now)
+        print(f"Crane unloads outbound container {container_id} to train {TRAIN_ID} at {env.now}")
+
+    if action == 'load':
+        for container_id in range(record_oc_label, record_oc_label + OUTBOUND_CONTAINER_NUMBER):
+            yield env.timeout(CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+            # chassis_status[chassis_id - 1] = -1
+            print(f"Crane loads outbound container {container_id} to train {TRAIN_ID} at {env.now}")
+            record_event(container_id, 'crane_load', env.now)
+
+
+def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event, chassis_id=None):
+    global record_oc_label, crane_id_counter, chassis_status, inbound_container_id_counter, outbound_containers_mapping, outbound_container_id_counter, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+
+    with cranes.request() as request:
+        yield request
+
+        # # Print after acquiring crane resource
+        # print(f"[{env.now}] Crane {crane_id_counter} acquired crane resource. Available cranes: {cranes.count}/{cranes.capacity}")
+
+        start_time = env.now
+        record_vehicle_event('crane', crane_id_counter, 'start', start_time)    # performance record: starting
+
+        if action == 'unload':
+            # crane_id = crane_id_counter
+            # crane_id_counter = (crane_id_counter % CRANE_NUMBER) + 1
+
+            chassis_id = ((inbound_container_id_counter - 1) % CHASSIS_NUMBER) + 1
+
+            current_inbound_id = inbound_container_id_counter
+            inbound_container_id_counter += 1
+            # yield env.timeout(CRANE_UNLOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+
+            # for chassis_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):
+            chassis_status[chassis_id - 1] = 1
+
+            end_time = env.now
+            record_vehicle_event('crane', crane_id_counter, 'end', end_time)     # performance record: ending
+
+            # hostler picks up IC
+            env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource))
+
+            return chassis_id, current_inbound_id
+
+        elif action == 'load':
+            if chassis_id not in outbound_containers_mapping:
+                print(f"Notice: No outbound container mapped to chassis {chassis_id} at {env.now}")
+                return
+
+            container_id = outbound_containers_mapping[chassis_id]  # Retrieve container ID from mapping
+            # print("outbound_containers_mapping in crane and chassis func:", outbound_containers_mapping)
+            # print("container_id in crane and chassis func:", container_id)
+
+            if CRANE_NUMBER == 1:
+                crane_id = 1
+            else:
+                crane_id = (chassis_id % CRANE_NUMBER) + 1
+
+            chassis_status[chassis_id - 1] = -1
+
+            # for container_id in range(record_oc_label, record_oc_label + OUTBOUND_CONTAINER_NUMBER):
+            #     yield env.timeout(CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+            #     chassis_status[chassis_id - 1] = -1
+            #     print(f"Crane {crane_id} loads outbound container {container_id} to train {TRAIN_ID} at {env.now}")
+            #     record_event(container_id, 'crane_load', env.now)
+
+
+def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
+    global hostler_id_counter, chassis_status, outbound_containers_mapping, outbound_container_id, record_oc_label, HOSTLER_NUMBER
+
+    with hostlers.request() as request:
+        yield request
+
+        start_time = env.now
+        record_vehicle_event('hostler', hostler_id_counter, 'start', start_time)  # performance record
+
+        hostler_id = hostler_id_counter
+        hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+
+        with chassis.request() as chassis_request:
+            yield chassis_request
+
+            if container_type == 'inbound' and chassis_status[chassis_id - 1] == 1:
+                d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+                print(f"Hostler pick-up time is:{HOSTLER_TRANSPORT_CONTAINER_TIME}")
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                record_event(container_id, 'hostler_pickup', env.now)
+                print(f"Hostler {hostler_id} picks up inbound container {container_id} from chassis {chassis_id} and heads to parking area at {env.now}")
+
+                chassis_status[chassis_id - 1] = -1
+
+                # Hostler drop off: different route for picking-up and dropping-off
+                d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+                print(f"Hostler drop-off time is:{HOSTLER_TRANSPORT_CONTAINER_TIME}")
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                record_event(container_id, 'hostler_dropoff', env.now)
+                print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} and moves toward the assigned outbound container at {env.now}")
+
+                end_time = env.now
+                record_vehicle_event('hostler', hostler_id_counter, 'end', end_time)  # performance record
+
+                # Process functions of notify_truck and handle_outbound_container simultaneously
+                env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
+
+                # Assign outbound container and chassis_id for the hostler which drops off an inbound container
+                chassis_id, outbound_container_id = yield env.process(outbound_container_decision_making(
+                    env, hostlers, chassis, container_id, truck_store, cranes, train_processing,
+                    outbound_containers_store,
+                    in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_status,
+                    outbound_containers_mapping,
+                    record_oc_label, outbound_container_id
+                ))
+
+                # Process outbound containers
+                if chassis_id is not None and outbound_container_id is not None:
+                    env.process(handle_outbound_container(env, hostler_id, chassis_id, outbound_container_id, truck_store,
+                                                  cranes, train_processing, outbound_containers_store, in_gate_resource,
+                                                  oc_chassis_filled_event))
+
+
+# When OC are fully processed, but IC are not
+def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
+    print(f"Starting single hostler transfer IC loop for chassis {chassis_id} at {env.now}")
+    global hostler_id_counter
+
+    print(f"Requesting hostler for IC at chassis {chassis_id} at {env.now}")
+
+    with hostlers.request() as request:
+        print(f"Request available hostlers: {hostlers.count} vs total hostlers {HOSTLER_NUMBER}, Hostlers capacity: {hostlers.capacity} at {env.now}")
+        yield request
+
+        start_time = env.now
+        record_vehicle_event('hostler', hostler_id_counter, 'start', start_time)  # performance record
+
+        hostler_id = hostler_id_counter
+        hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+
+        with chassis.request() as chassis_request:
+            yield chassis_request
+
+            if container_type == 'inbound' and chassis_status[chassis_id - 1] == 1:
+                chassis_status[chassis_id - 1] = -1
+                print(f"Single loop chassis status {chassis_status}")
+                print(f"There are {chassis_status.count(1)} IC")
+                print(f"There are {chassis_status.count(-1)} empty")
+                print(f"There are {chassis_status.count(0)} OC")
+                d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                # hostler picks up the rest of IC from the chassis
+                # chassis_status[chassis_id - 1] = -1
+                record_event(container_id, 'hostler_pickup', env.now)
+                print(f"Hostler {hostler_id} picks up inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
+                # hostler drops off the IC
+                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                record_event(container_id, 'hostler_dropoff', env.now)
+                print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
+
+                # Check if all chassis filled
+                if chassis_status.count(0) == OUTBOUND_CONTAINER_NUMBER and chassis_status.count(
+                        -1) == TRAIN_UNITS - OUTBOUND_CONTAINER_NUMBER and not oc_chassis_filled_event.triggered:
+                    print(f"Chassis is fully filled with OC, and cranes start moving: {chassis_status}")
+                    print(f"where there are {chassis_status.count(0)} chassis filled with OC (0)")
+                    print(f"where there are {chassis_status.count(-1)} chassis filled with empty (-1)")
+                    print(f"where there are {chassis_status.count(1)} chassis filled with IC (1)")
+                    oc_chassis_filled_event.succeed()
+                    return
+                else:
+                    print(f"Chassis is not fully filled: {chassis_status}")
+                    print(f"where there are {chassis_status.count(0)} chassis filled with OC (0)")
+                    print(f"where there are {chassis_status.count(-1)} chassis filled with empty (-1)")
+                    print(f"where there are {chassis_status.count(1)} chassis filled with IC (1)")
+
+                end_time = env.now
+                record_vehicle_event('hostler', hostler_id, 'end', end_time)  # performance record
+
+                # trucks pick up IC
+                yield env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
+
+
+def outbound_container_decision_making(env, hostlers, chassis, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_status, outbound_containers_mapping, record_oc_label, outbound_container_id):
+    # Check if outbound_containers_store has outbound container
+    if len(outbound_containers_store.items) > 0:
+        outbound_container_id = yield outbound_containers_store.get()
+        print(f"Outbound containers remaining: {len(outbound_containers_store.items)}")
+
+        if -1 in chassis_status:
+            chassis_id = chassis_status.index(-1) + 1  # find the first chassis
+            # If chassis are not assigned with outbound container
+            if chassis_id not in outbound_containers_mapping:
+                # outbound_container_id += record_oc_label
+                outbound_containers_mapping[chassis_id] = outbound_container_id
+                chassis_status[chassis_id - 1] = 0  # already assigned outbound container
+                print(f"OC mapping created: outbound container {outbound_container_id} assigned to chassis {chassis_id}")
+            else:
+                print(f"Chassis {chassis_id} is already mapped to an outbound container.")
+        else:
+            print("No empty chassis available for outbound container assignment.")
+
+    # if outbound_containers_store is null, check if we need operate single loop
+    else:
+        chassis_id = None
+        outbound_container_id = None
+        # chassis_status = 1: inbound containers are not loaded
+        if chassis_status.count(1) != 0:
+            print(f"Haven't finished all IC yet at {env.now}. Starting single loop.")
+            chassis_id = chassis_status.index(1) + 1
+            chassis_status[chassis_id - 1] = 0  # assigned with IC
+            # single loop takes rest inbound container
+            yield env.process(hostler_transfer_IC_single_loop(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id,
+                                                truck_store, cranes, train_processing,
+                                                outbound_containers_store, in_gate_resource, oc_chassis_filled_event,
+                                                out_gate_resource))
+        else:
+            print("All inbound containers have been processed.")
+
+    return chassis_id, outbound_container_id
+
+
+def handle_outbound_container(env, hostler_id, chassis_id, outbound_container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event):
+    global HOSTLER_FIND_CONTAINER_TIME
+
+    d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
+    HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+
+    d_r_dist = create_triang_distribution(d_r_min, d_r_avg, d_r_max).rvs()
+    HOSTLER_FIND_CONTAINER_TIME = d_r_dist / (2 * TRUCK_SPEED_LIMIT)
+    yield env.timeout(HOSTLER_FIND_CONTAINER_TIME)
+
+    record_event(outbound_container_id, 'hostler_pickup', env.now)
+    print(f"Hostler {hostler_id} picks up outbound container {outbound_container_id} from parking area to chassis {chassis_id} at {env.now}")
+
+    yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+
+    record_event(outbound_container_id, 'hostler_dropoff', env.now)
+    print(f"Hostler {hostler_id} drops off outbound container {outbound_container_id} to chassis {chassis_id} at {env.now}")
+
+
+# truck pick up IC
+def notify_truck(env, truck_store, container_id, out_gate_resource):
+    truck_id = yield truck_store.get()
+    yield env.timeout(TRUCK_INGATE_TIME)
+    print(f"Truck {truck_id} arrives at parking area and prepare to pick up inbound container {container_id} at {env.now}")
+    yield env.process(truck_transfer(env, truck_id, container_id, out_gate_resource))
+
+
+def truck_transfer(env, truck_id, container_id, out_gate_resource):
+    global TRUCK_INGATE_TIME, TRUCK_TRANSPORT_CONTAINER_TIME, outbound_container_id_counter
+
+    start_time = env.now
+    record_vehicle_event('truck', truck_id, 'start', start_time)  # performance record
+
+    # Truck moves to the parking area
+    yield env.timeout(TRUCK_TO_PARKING)
+    record_event(container_id, 'truck_pickup', env.now)
+    print(f"Truck {truck_id} picks up inbound container {container_id} at {env.now}")
+
+    # Calculate the transport time for the truck
+    d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
+    TRUCK_TRANSPORT_CONTAINER_TIME = d_t_dist / (2 * TRUCK_SPEED_LIMIT)
+    yield env.timeout(TRUCK_TRANSPORT_CONTAINER_TIME)
+
+    # Request out_gate_resource resource before the truck exits
+    with out_gate_resource.request() as request:
+        yield request
+
+        # Simulate the time it takes for the truck to pass through the gate
+        yield env.timeout(TRUCK_OUTGATE_TIME + random.uniform(0,TRUCK_OUTGATE_TIME_DEV))
+        record_event(container_id, 'truck_exit', env.now)
+        print(f"Truck {truck_id} exits gate with inbound container {container_id} at {env.now}")
+
+    # End performance recording
+    end_time = env.now
+    record_vehicle_event('truck', truck_id, 'end', end_time)
+
+
+def train_departure(env, train_id):
+    global TRAIN_ID_FIXED, record_oc_label
+
+    if env.now < TRAIN_DEPARTURE_HR:
+        yield env.timeout(TRAIN_DEPARTURE_HR - env.now)
+    yield env.timeout(TRAIN_INSPECTION_TIME)
+    print(f"Train {TRAIN_ID_FIXED} ({train_id} in the dictionary) departs at {env.now}")
+
+    for container_id in range(record_oc_label - OUTBOUND_CONTAINER_NUMBER, record_oc_label):
+        record_event(container_id, 'train_depart', env.now)
+
+
+def run_simulation():
+    global all_trucks_ready_event, record_oc_label, TRUCK_NUMBERS, TRAIN_NUMBERS
+
+    print(f"Starting simulation with No.{TRAIN_ID} trains, {HOSTLER_NUMBER} hostlers, {CRANE_NUMBER} cranes, and {TRUCK_NUMBERS} trucks.")
+    env = simpy.Environment()
+
+    # Resources
+    train_processing = simpy.Resource(env, capacity=1)
+    cranes = simpy.Resource(env, capacity=CRANE_NUMBER)
+    chassis = simpy.Resource(env, capacity=CHASSIS_NUMBER)
+    hostlers = simpy.Resource(env, capacity=HOSTLER_NUMBER)
+    in_gate_resource = simpy.Resource(env, capacity=IN_GATE_NUMBERS)
+    out_gate_resource = simpy.Resource(env, capacity=OUT_GATE_NUMBERS)
+    outbound_containers_store = simpy.Store(env, capacity=100)
+    truck_store = simpy.Store(env, capacity=100)
+
+    # Initialize trucks
+    truck_store.items.clear()
+    # print("TRUCK_NUMBERS:",  TRUCK_NUMBERS)
+    for truck_id in range(1, TRUCK_NUMBERS + 1):
+        truck_store.put(truck_id)
+    # print("TRUCK_STORE:", truck_store.items)
+
+    all_trucks_ready_event = env.event()
+
+    # # toy case
+    # train_timetable = [
+    #     {"train_id": 19, "arrival_time": 187, "departure_time": 200, "empty_cars": 3, "full_cars":7, "oc_number": 2, "truck_number":7 },
+    #     {"train_id": 25, "arrival_time": 250, "departure_time": 350, "empty_cars": 4, "full_cars":6, "oc_number": 2, "truck_number":6 },
+    #     {"train_id": 49, "arrival_time": 400, "departure_time": 600, "empty_cars": 5, "full_cars":5, "oc_number": 2, "truck_number":5 },
+    #     {"train_id": 60, "arrival_time": 650, "departure_time": 750, "empty_cars": 6, "full_cars":4, "oc_number": 2, "truck_number":4 },
+    #     {"train_id": 12, "arrival_time": 800, "departure_time": 1000, "empty_cars": 7, "full_cars":3, "oc_number": 4, "truck_number":4 },
+    # ]
+
+    # REAL TEST
+    train_timetable = timetable(terminal)
+    TRAIN_NUMBERS = len(timetable(terminal))
+
+    # env.process(train_arrival(env, train_processing, cranes, in_gate_resource, outbound_containers_store, truck_store, train_timetable))
+    env.process(train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource,
+                  outbound_containers_store, truck_store, out_gate_resource))
+
+    env.run(until=SIM_TIME)
+
+    # Performance Matrix: train processing time
+    avg_time_per_train = sum(time_per_train) / len(time_per_train)
+    print(f"Average train processing time: {sum(time_per_train) / len(time_per_train) if time_per_train else 0:.2f}")
+    print("Simulation completed. ")
+    with open("avg_time_per_train.txt", "w") as f:
+        f.write(str(avg_time_per_train))
+
+    # Create DataFrame for container events
+    container_data = []
+
+    for container_id, events in sorted(container_events.items()):
+        container_type = 'inbound' if container_id < 10001 else 'outbound'
+        if container_type == 'inbound':
+            container_process_time = events.get('truck_exit', '-') - events.get('train_arrival', '-') if 'truck_exit' in events and 'train_arrival' in events else '-'
+        else:
+            container_process_time = events.get('train_depart', '-') - events.get('truck_drop_off', '-') if 'train_depart' in events and 'truck_drop_off' in events else '-'
+
+        container_data.append({
+            'container_id': container_id,
+            'container_type': container_type,
+            'train_arrival': events.get('train_arrival', '-'),
+            'truck_arrival': events.get('truck_arrival', '-'),
+            'crane_unload': events.get('crane_unload', '-'),
+            'hostler_pickup': events.get('hostler_pickup', '-'),
+            'hostler_dropoff': events.get('hostler_dropoff', '-'),
+            'truck_drop_off': events.get('truck_drop_off', '-'),
+            'truck_pickup': events.get('truck_pickup', '-'),
+            'truck_exit': events.get('truck_exit', '-'),
+            'crane_load': events.get('crane_load', '-'),
+            'train_depart': events.get('train_depart', '-'),
+            'container_processing_time': container_process_time
+        })
+
+    df = pd.DataFrame(container_data)
+    filename = f"C:/Users/Irena Tong/PycharmProjects/simulation_test/test/results/simulation_crane_{CRANE_NUMBER}_hostler_{HOSTLER_NUMBER}.xlsx"
+    df.to_excel(filename, index=False)
+
+    # Use save_average_times and save_vehicle_logs for vehicle related logs
+    save_average_times()
+    save_vehicle_logs()
+
+    print("Done!")
+
+
+if __name__ == "__main__":
+    run_simulation()
\ No newline at end of file
diff --git a/python/lifts/truck_work_log.txt b/python/lifts/truck_work_log.txt
new file mode 100644
index 00000000..1921429e
--- /dev/null
+++ b/python/lifts/truck_work_log.txt
@@ -0,0 +1,516 @@
+Vehicle ID: 1, Event Type: start, Timestamp: 181.20762966437408
+Vehicle ID: 2, Event Type: start, Timestamp: 181.23590430565247
+Vehicle ID: 3, Event Type: start, Timestamp: 181.25914226903416
+Vehicle ID: 4, Event Type: start, Timestamp: 181.26141624469523
+Vehicle ID: 5, Event Type: start, Timestamp: 181.2889374519489
+Vehicle ID: 1, Event Type: end, Timestamp: 181.29090488153685
+Vehicle ID: 2, Event Type: end, Timestamp: 181.3089410913137
+Vehicle ID: 6, Event Type: start, Timestamp: 181.31540550576455
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+Vehicle ID: 4, Event Type: end, Timestamp: 181.32968709161304
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+Vehicle ID: 249, Event Type: end, Timestamp: 867.5808544450678
+Vehicle ID: 252, Event Type: start, Timestamp: 867.594278583525
+Vehicle ID: 251, Event Type: end, Timestamp: 867.5974594283919
+Vehicle ID: 250, Event Type: end, Timestamp: 867.5997091419782
+Vehicle ID: 253, Event Type: start, Timestamp: 867.6084601210431
+Vehicle ID: 254, Event Type: start, Timestamp: 867.6219728041435
+Vehicle ID: 255, Event Type: start, Timestamp: 867.6229731710581
+Vehicle ID: 252, Event Type: end, Timestamp: 867.6642700337288
+Vehicle ID: 253, Event Type: end, Timestamp: 867.67699094093
+Vehicle ID: 256, Event Type: start, Timestamp: 867.692239290079
+Vehicle ID: 255, Event Type: end, Timestamp: 867.700266075478
+Vehicle ID: 254, Event Type: end, Timestamp: 867.7014243918384
+Vehicle ID: 257, Event Type: start, Timestamp: 867.7484096935051
+Vehicle ID: 256, Event Type: end, Timestamp: 867.7708400495044
+Vehicle ID: 258, Event Type: start, Timestamp: 867.8043155333213
+Vehicle ID: 257, Event Type: end, Timestamp: 867.8279852973175
+Vehicle ID: 258, Event Type: end, Timestamp: 867.875407971388
diff --git a/python/lifts/vehicle_average_times.txt b/python/lifts/vehicle_average_times.txt
new file mode 100644
index 00000000..d1910101
--- /dev/null
+++ b/python/lifts/vehicle_average_times.txt
@@ -0,0 +1,3 @@
+crane: 0.0
+hostler: 0.0021119418504930766
+truck: 0.01489569845431589
diff --git a/python/lifts/vehicle_performance.py b/python/lifts/vehicle_performance.py
new file mode 100644
index 00000000..84f39f44
--- /dev/null
+++ b/python/lifts/vehicle_performance.py
@@ -0,0 +1,48 @@
+import pandas as pd
+
+vehicle_events = {
+    'crane': [],
+    'hostler': [],
+    'truck': []
+}
+
+def record_vehicle_event(vehicle_type, vehicle_id, event_type, timestamp):
+    vehicle_events[vehicle_type].append({
+        'vehicle_id': vehicle_id,
+        'event_type': event_type,
+        'timestamp': timestamp
+    })
+
+def calculate_average_times():
+    averages = {}
+    for vehicle_type, events in vehicle_events.items():
+        total_time = 0
+        count = 0
+        for event in events:
+            if event['event_type'] == 'start':
+                start_time = event['timestamp']
+            elif event['event_type'] == 'end':
+                end_time = event['timestamp']
+                total_time += (end_time - start_time)
+                count += 1
+        if count > 0:
+            averages[vehicle_type] = total_time / count
+        else:
+            averages[vehicle_type] = 0
+    return averages
+
+def save_average_times():
+    averages = calculate_average_times()
+    with open("vehicle_average_times.txt", "w") as f:
+        for vehicle_type, avg_time in averages.items():
+            f.write(f"{vehicle_type}: {avg_time}\n")
+
+def save_vehicle_logs():
+    for vehicle_type, events in vehicle_events.items():
+        log_file = f"{vehicle_type}_work_log.txt"
+        with open(log_file, "w") as f:
+            for event in events:
+                f.write(f"Vehicle ID: {event['vehicle_id']}, Event Type: {event['event_type']}, Timestamp: {event['timestamp']}\n")
+
+if __name__ == "__main__":
+    save_average_times()

From 2d94977faeab313a0f241efe3dc91b28a1c982e6 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 2 Dec 2024 13:03:17 -0500
Subject: [PATCH 21/50] polars version updates, initial lifts integration

---
 applications/calibration/zanzeff_bel_cal.py   |   2 +-
 .../calibration/zanzeff_conv_loco_cal.py      |   4 +-
 .../zanzeff_set_speed_train_cal.py            |   6 +-
 avg_time_per_train.txt                        |   1 +
 crane_work_log.txt                            | 732 +++++++++++++++++
 hostler_work_log.txt                          | 754 ++++++++++++++++++
 pyproject.toml                                |   3 +-
 python/altrios/demos/sim_manager_demo.py      |   5 +
 python/altrios/metric_calculator.py           |   2 +-
 python/altrios/sim_manager.py                 |   2 +-
 python/altrios/train_planner.py               |  16 +-
 python/altrios/utilities.py                   |   2 +-
 .../demos/starter_demo/train_consist_plan.csv | 163 ++++
 python/lifts/dictionary.py                    |  71 +-
 .../{final_version.py => lifts_simulator.py}  | 448 ++++++-----
 python/lifts/merged_baseline.py               |   2 +-
 python/lifts/parameters.py                    | 231 +++---
 .../results/simulation_crane_1_hostler_1.xlsx | Bin 54669 -> 0 bytes
 .../results/simulation_crane_1_hostler_2.xlsx | Bin 53903 -> 0 bytes
 .../results/simulation_crane_1_hostler_3.xlsx | Bin 53886 -> 0 bytes
 .../results/simulation_crane_1_hostler_4.xlsx | Bin 53928 -> 0 bytes
 .../results/simulation_crane_1_hostler_5.xlsx | Bin 53888 -> 0 bytes
 .../results/simulation_crane_1_hostler_6.xlsx | Bin 53929 -> 0 bytes
 .../results/simulation_crane_1_hostler_7.xlsx | Bin 53937 -> 0 bytes
 .../results/simulation_crane_1_hostler_8.xlsx | Bin 53903 -> 0 bytes
 .../results/simulation_crane_2_hostler_1.xlsx | Bin 53947 -> 0 bytes
 .../results/simulation_crane_2_hostler_2.xlsx | Bin 53920 -> 0 bytes
 python/lifts/schedule.py                      |  55 +-
 python/lifts/test.py                          |  15 +-
 python/lifts/utilities.py                     |  17 +
 rust/altrios-core/src/train/train_config.rs   |   2 +-
 truck_work_log.txt                            | 498 ++++++++++++
 vehicle_average_times.txt                     |   3 +
 33 files changed, 2610 insertions(+), 424 deletions(-)
 create mode 100644 avg_time_per_train.txt
 create mode 100644 crane_work_log.txt
 create mode 100644 hostler_work_log.txt
 create mode 100644 python/lifts/demos/starter_demo/train_consist_plan.csv
 rename python/lifts/{final_version.py => lifts_simulator.py} (53%)
 delete mode 100644 python/lifts/results/simulation_crane_1_hostler_1.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_1_hostler_2.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_1_hostler_3.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_1_hostler_4.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_1_hostler_5.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_1_hostler_6.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_1_hostler_7.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_1_hostler_8.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_2_hostler_1.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_2_hostler_2.xlsx
 create mode 100644 python/lifts/utilities.py
 create mode 100644 truck_work_log.txt
 create mode 100644 vehicle_average_times.txt

diff --git a/applications/calibration/zanzeff_bel_cal.py b/applications/calibration/zanzeff_bel_cal.py
index c0be74c9..c0723b7f 100644
--- a/applications/calibration/zanzeff_bel_cal.py
+++ b/applications/calibration/zanzeff_bel_cal.py
@@ -55,7 +55,7 @@ def get_bel_trip_mods(df: pd.DataFrame) -> pd.DataFrame:
     """
     # df['dt [s]'] = pd.to_datetime(df['PacificTime']).dt.tz_convert('UTC')
     df['timestamp'] = pd.to_datetime(df['PacificTime']).dt.to_pydatetime()
-    df['time [s]'] = cval.get_delta_seconds(df['timestamp']).cumsum()
+    df['time [s]'] = cval.get_delta_seconds(df['timestamp']).cum_sum()
 
     df3000 = df[['PacificTime', 'time [s]']].copy()
     df3000['Tractive Power [W]'] = (
diff --git a/applications/calibration/zanzeff_conv_loco_cal.py b/applications/calibration/zanzeff_conv_loco_cal.py
index eb3505f8..7425a71e 100644
--- a/applications/calibration/zanzeff_conv_loco_cal.py
+++ b/applications/calibration/zanzeff_conv_loco_cal.py
@@ -33,7 +33,7 @@ def get_conv_trip_mods(df: pd.DataFrame) -> pd.DataFrame:
     df = df.drop_duplicates('PacificTime').copy()
     df['timestamp'] = pd.to_datetime(
         df['PacificTime']).dt.to_pydatetime()
-    df['time [s]'] = cval.get_delta_seconds(df['timestamp']).cumsum()
+    df['time [s]'] = cval.get_delta_seconds(df['timestamp']).cum_sum()
 
   
 
@@ -69,7 +69,7 @@ def get_conv_trip_mods(df: pd.DataFrame) -> pd.DataFrame:
     df39xx["Fuel Energy [J]"] = (df39xx["Fuel Power [W]"] *
                                     cval.get_delta_seconds(
                                         df39xx['timestamp'])
-                                    ).cumsum().copy()
+                                    ).cum_sum().copy()
     df39xx["engine_on"] = df['Engine Speed (RPM) BNSF ' + str(trailing_loc)] > 100
     return df39xx
 
diff --git a/applications/calibration/zanzeff_set_speed_train_cal.py b/applications/calibration/zanzeff_set_speed_train_cal.py
index e3b5fa01..b5ef7526 100644
--- a/applications/calibration/zanzeff_set_speed_train_cal.py
+++ b/applications/calibration/zanzeff_set_speed_train_cal.py
@@ -44,7 +44,7 @@ def get_train_sim_df_mods(
     df_train_sim.drop_duplicates(subset='PacificTime', inplace=True)
 
     df_train_sim['time [s]'] = cval.get_delta_seconds(
-        df_train_sim['PacificTime']).cumsum()
+        df_train_sim['PacificTime']).cum_sum()
     df_train_sim['Total Tractive Force [N]'] = df_train_sim[[
         'Tractive Effort Feedback BNSF 3940',
         'Tractive Effort Feedback BNSF 3965',
@@ -56,13 +56,13 @@ def get_train_sim_df_mods(
     df_train_sim['Total Cumu. Tractive Energy [J]'] = (
         df_train_sim['Total Tractive Power [W]'] *
         df_train_sim['time [s]'].diff().fillna(0.0)
-    ).cumsum()
+    ).cum_sum()
 
     df_train_sim['Total Pos. Cumu. Tractive Energy [J]'] = (
        (df_train_sim['Total Tractive Power [W]'] *
         df_train_sim['time [s]'].diff().fillna(0.0)) 
         .where(df_train_sim['Total Tractive Power [W]'] > 0, 0.0)
-        .cumsum()
+        .cum_sum()
     )
     
     speed = savgol_filter(
diff --git a/avg_time_per_train.txt b/avg_time_per_train.txt
new file mode 100644
index 00000000..c49a2166
--- /dev/null
+++ b/avg_time_per_train.txt
@@ -0,0 +1 @@
+17.47981863315145
\ No newline at end of file
diff --git a/crane_work_log.txt b/crane_work_log.txt
new file mode 100644
index 00000000..efdfc523
--- /dev/null
+++ b/crane_work_log.txt
@@ -0,0 +1,732 @@
+Vehicle ID: 3, Event Type: start, Timestamp: 174.2810811099409
+Vehicle ID: 3, Event Type: end, Timestamp: 174.2810811099409
+Vehicle ID: 3, Event Type: start, Timestamp: 174.28268979218822
+Vehicle ID: 3, Event Type: end, Timestamp: 174.28268979218822
+Vehicle ID: 3, Event Type: start, Timestamp: 174.47535372469952
+Vehicle ID: 3, Event Type: end, Timestamp: 174.47535372469952
+Vehicle ID: 3, Event Type: start, Timestamp: 174.4825116311807
+Vehicle ID: 3, Event Type: end, Timestamp: 174.4825116311807
+Vehicle ID: 3, Event Type: start, Timestamp: 174.4875712829535
+Vehicle ID: 3, Event Type: end, Timestamp: 174.4875712829535
+Vehicle ID: 3, Event Type: start, Timestamp: 174.5075471516836
+Vehicle ID: 3, Event Type: end, Timestamp: 174.5075471516836
+Vehicle ID: 3, Event Type: start, Timestamp: 174.58578236688152
+Vehicle ID: 3, Event Type: end, Timestamp: 174.58578236688152
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+Vehicle ID: 3, Event Type: end, Timestamp: 174.610752074466
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+Vehicle ID: 3, Event Type: end, Timestamp: 174.641496556886
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diff --git a/hostler_work_log.txt b/hostler_work_log.txt
new file mode 100644
index 00000000..c361ca4b
--- /dev/null
+++ b/hostler_work_log.txt
@@ -0,0 +1,754 @@
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+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: start, Timestamp: 287.50261787947136
+Vehicle ID: 1, Event Type: end, Timestamp: 287.50469574638703
+Vehicle ID: 1, Event Type: end, Timestamp: 287.50479727411727
diff --git a/pyproject.toml b/pyproject.toml
index 7cf1b279..0e8ac595 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,10 +43,11 @@ dependencies = [
     "plotly",
     "typing_extensions",
     "pyyaml",
-    "polars >= 1.12",
+    "polars >= 1.14",
     "pyarrow",
     "requests",
     "PyYAML==6.0.2",
+    "simpy"
 ]
 
 [project.urls]
diff --git a/python/altrios/demos/sim_manager_demo.py b/python/altrios/demos/sim_manager_demo.py
index aed09db4..247d8154 100644
--- a/python/altrios/demos/sim_manager_demo.py
+++ b/python/altrios/demos/sim_manager_demo.py
@@ -2,6 +2,7 @@
 from altrios import sim_manager
 from altrios import utilities, defaults, train_planner
 import altrios as alt
+from lifts import lifts_simulator
 import numpy as np
 import matplotlib.pyplot as plt
 import time
@@ -56,6 +57,10 @@
     debug=True,
 )
 
+container_data = lifts_simulator.run_simulation(
+    train_consist_plan = train_consist_plan, 
+    terminal = "Allouez")
+
 t1_main = time.perf_counter()
 print(f"Elapsed time to run `sim_manager.main()`: {t1_main-t0_main:.3g} s")
 
diff --git a/python/altrios/metric_calculator.py b/python/altrios/metric_calculator.py
index bb15460f..cee0fd49 100644
--- a/python/altrios/metric_calculator.py
+++ b/python/altrios/metric_calculator.py
@@ -714,7 +714,7 @@ def calculate_rollout_investments(values: MetricType) -> MetricType:
                             .alias("Count"))
             .drop("Change")
             .join(early_retirements, on=item_id_cols, how="left")
-            .with_columns(pl.col("Count").cumsum().over(item_id_cols).alias("Retirements_Early_Cumsum"))
+            .with_columns(pl.col("Count").cum_sum().over(item_id_cols).alias("Retirements_Early_Cumsum"))
             .with_columns(pl.when(pl.col("Change") > 0)
                             .then(pl.when(pl.col("Retirements_Early_Cumsum") <= pl.col("Change"))
                                     .then(pl.col("Count"))
diff --git a/python/altrios/sim_manager.py b/python/altrios/sim_manager.py
index c15fbee9..128fdcbd 100644
--- a/python/altrios/sim_manager.py
+++ b/python/altrios/sim_manager.py
@@ -129,7 +129,7 @@ def main(
         )
 
     train_times = pl.DataFrame(
-        {'Train_ID': pl.Series([sim.train_id for sim in speed_limit_train_sims], dtype=pl.Int32).cast(pl.UInt32),
+        {'Train_ID': pl.Series([int(sim.train_id) for sim in speed_limit_train_sims], dtype=pl.Int32).cast(pl.UInt32),
          'Origin_ID': pl.Series([sim.origs[0].location_id for sim in speed_limit_train_sims], dtype=str),
          'Destination_ID': pl.Series([sim.dests[0].location_id for sim in speed_limit_train_sims], dtype=str),
          'Departure_Time_Actual_Hr': pl.Series([this[0].time_hours for this in timed_paths], dtype=pl.Float64),
diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index e8165e6f..90d873c1 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -149,7 +149,7 @@ def generate_return_demand(
     return (demand
         .rename({"Origin": "Destination",
                  "Destination": "Origin"})
-        .drop("Number_Of_Containers")
+        .drop("Number_of_Containers")
         .with_columns(
             pl.concat_str([pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")
                            ,pl.lit("_Empty")]).alias("Train_Type"),
@@ -290,7 +290,7 @@ def generate_demand_trains(
     
     demand = pl.concat([
         demand.drop("Number_of_Containers"),
-        demand_returns.drop("Number_of_Containers"),
+        demand_returns,#.drop("Number_of_Containers"),
         demand_rebalancing],
         how="diagonal_relaxed")
     # if rowx[first three columns] == rowy[first three columns]:
@@ -323,7 +323,7 @@ def get_kg(veh):
                                 .then(pl.col("KG_Empty") / utilities.KG_PER_TON)
                                 .otherwise(pl.col("KG") / utilities.KG_PER_TON)
                                 .alias("Tons_Per_Car"))
-            .drop(["KG_Empty","KG_Loaded"])
+            .drop(["KG_Empty","KG"])
     )
 
     demand = demand.join(ton_per_car, on="Train_Type", how="left")
@@ -401,7 +401,7 @@ def calculate_dispatch_times(
             pl.col("Cars_Loaded").floordiv(pl.col("Number_of_Trains")).alias("Cars_Loaded"),
         ).select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode()) \
         .with_columns(
-            ((pl.col("Interval").cumcount().over(["Origin","Destination","Train_Type"])) \
+            ((pl.col("Interval").cum_count().over(["Origin","Destination","Train_Type"])) \
              * pl.col("Interval")).alias("Hour")
         ).drop("Interval") \
         .sort(["Hour","Origin","Destination","Train_Type"])
@@ -699,7 +699,7 @@ def dispatch(
             message += "."
         raise ValueError(message)
 
-    diesel_to_require = diesel_candidates.eq(True).cumsum().eq(1).arg_max()
+    diesel_to_require = diesel_candidates.eq(True).cum_sum().eq(1).arg_max()
     diesel_to_require_hp = loco_pool.filter(diesel_filter).select(pl.first("HP"))
     # Need to mask this so it's not double-counted on next step
     candidates[diesel_to_require] = False
@@ -707,12 +707,12 @@ def dispatch(
     enough_hp = loco_pool.select((
         (
             (pl.col("HP") - (pl.col("Loco_Mass_Tons") * pl.lit(hp_per_ton))) * pl.lit(candidates)
-        ).cumsum() + pl.lit(diesel_to_require_hp)) >= hp_required).to_series()
+        ).cum_sum() + pl.lit(diesel_to_require_hp)) >= hp_required).to_series()
     if not enough_hp.any():
         available_hp = loco_pool.select(
             (
                 (pl.col("HP") - (pl.col("Loco_Mass_Tons") * pl.lit(hp_per_ton))) * pl.lit(candidates)
-            ).cumsum().max())[0, 0]
+            ).cum_sum().max())[0, 0]
         message = f"""Outbound horsepower needed ({hp_required}) at {origin} at hour {dispatch_time}
             is more than the available horsepower ({available_hp}).
             Count of locomotives servicing, refueling, or queueing at {origin} are:"""
@@ -727,7 +727,7 @@ def dispatch(
         # Hold the train until enough locomotives are present (future development)
         raise ValueError(message)
 
-    last_row_to_use = enough_hp.eq(True).cumsum().eq(1).arg_max()
+    last_row_to_use = enough_hp.eq(True).cum_sum().eq(1).arg_max()
     # Set false all the locomotives that would add unnecessary hp
     selected[np.arange(last_row_to_use+1, len(selected))] = False
     # Add first diesel (which could come after last_row_to_use) to selection list
diff --git a/python/altrios/utilities.py b/python/altrios/utilities.py
index 77072f81..ad0cc7cf 100644
--- a/python/altrios/utilities.py
+++ b/python/altrios/utilities.py
@@ -206,7 +206,7 @@ def resample(
     for col in df.columns:
         if col in rate_vars:
             # calculate average value over time step
-            cumu_vals = (df[time_col].diff().fillna(0) * df[col]).cumsum()
+            cumu_vals = (df[time_col].diff().fillna(0) * df[col]).cum_sum()
             new_dict[col] = (
                 np.diff(
                     np.interp(
diff --git a/python/lifts/demos/starter_demo/train_consist_plan.csv b/python/lifts/demos/starter_demo/train_consist_plan.csv
new file mode 100644
index 00000000..3a9e728b
--- /dev/null
+++ b/python/lifts/demos/starter_demo/train_consist_plan.csv
@@ -0,0 +1,163 @@
+Train_ID,Train_Type,Locomotive_ID,Locomotive_Type,Origin_ID,Destination_ID,Cars_Loaded,Cars_Empty,Departure_SOC_J,Departure_Time_Planned_Hr,Arrival_Time_Planned_Hr,Refuel_Start_Time_Planned_Hr,Refuel_End_Time_Planned_Hr,Departure_Time_Actual_Hr,Arrival_Time_Actual_Hr,TrainSimVec_Index
+25,Unit,1000,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,168.0,176.814986740618,176.814986740618,176.8360199346937,168.0,174.83160599585378,0
+32,Unit,1000,Diesel_Large,Hibbing,Allouez,44.0,43.0,828691080000.0,196.0,204.66115190304077,204.66115190304077,204.66655794550903,196.0,200.7194033488388,7
+37,Unit,1000,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,224.0,232.814986740618,232.814986740618,232.8360199346937,224.0,230.83160599585378,12
+41,Intermodal,1000,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,237.1764705882353,245.8340364958852,245.8340364958852,245.83566794943235,237.1764705882353,242.1023859005848,16
+43,Unit,1000,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,252.0,260.814986740618,260.814986740618,260.8360199346937,252.0,258.83160599585375,18
+47,Intermodal,1000,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,266.8235294117647,275.4810953194146,275.4810953194146,275.4827267729617,266.8235294117647,271.7494447241142,22
+49,Unit,1000,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,280.0,288.814986740618,288.814986740618,288.8360199346937,280.0,286.83160599585375,24
+53,Intermodal,1000,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,296.47058823529414,305.1281541429441,305.1281541429441,305.1297855964912,296.4705882352942,301.39650354764365,28
+57,Intermodal,1000,Diesel_Large,Allouez,Hibbing,41.0,43.0,828691080000.0,326.11764705882354,334.74920712941383,334.74920712941383,334.7576192186083,326.11764705882354,334.9090139408118,32
+25,Unit,1001,BEL,Allouez,Hibbing,43.0,44.0,8208000000.0,168.0,176.814986740618,176.814986740618,177.69498904461798,168.0,174.83160599585378,0
+32,Unit,1001,BEL,Hibbing,Allouez,44.0,43.0,2808004319.996545,196.0,204.66115190304077,204.66115190304077,207.54115420704076,196.0,200.7194033488388,7
+37,Unit,1001,BEL,Allouez,Hibbing,43.0,44.0,8208000000.0,224.0,232.814986740618,232.814986740618,233.69498904461798,224.0,230.83160599585378,12
+41,Intermodal,1001,BEL,Hibbing,Allouez,43.0,41.0,2808004319.996545,237.1764705882353,245.8340364958852,245.8340364958852,248.50232589744067,237.1764705882353,242.1023859005848,16
+43,Unit,1001,BEL,Allouez,Hibbing,43.0,44.0,8208000000.0,252.0,260.814986740618,260.814986740618,261.694989044618,252.0,258.83160599585375,18
+47,Intermodal,1001,BEL,Hibbing,Allouez,43.0,41.0,2808004319.996545,266.8235294117647,275.4810953194146,275.4810953194146,278.14938472097003,266.8235294117647,271.7494447241142,22
+49,Unit,1001,BEL,Allouez,Hibbing,43.0,44.0,8208000000.0,280.0,288.814986740618,288.814986740618,289.694989044618,280.0,286.83160599585375,24
+53,Intermodal,1001,BEL,Hibbing,Allouez,43.0,41.0,2808004319.996545,296.47058823529414,305.1281541429441,305.1281541429441,307.79644354449954,296.4705882352942,301.39650354764365,28
+57,Intermodal,1001,BEL,Allouez,Hibbing,41.0,43.0,8208000000.0,326.11764705882354,334.74920712941383,334.74920712941383,335.62920943341385,326.11764705882354,334.9090139408118,32
+25,Unit,1002,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,168.0,176.814986740618,176.814986740618,176.8360199346937,168.0,174.83160599585378,0
+32,Unit,1002,Diesel_Large,Hibbing,Allouez,44.0,43.0,828691080000.0,196.0,204.66115190304077,204.66115190304077,204.66655794550903,196.0,200.7194033488388,7
+37,Unit,1002,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,224.0,232.814986740618,232.814986740618,232.8360199346937,224.0,230.83160599585378,12
+41,Intermodal,1002,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,237.1764705882353,245.8340364958852,245.8340364958852,245.83566794943235,237.1764705882353,242.1023859005848,16
+43,Unit,1002,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,252.0,260.814986740618,260.814986740618,260.8360199346937,252.0,258.83160599585375,18
+47,Intermodal,1002,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,266.8235294117647,275.4810953194146,275.4810953194146,275.4827267729617,266.8235294117647,271.7494447241142,22
+49,Unit,1002,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,280.0,288.814986740618,288.814986740618,288.8360199346937,280.0,286.83160599585375,24
+53,Intermodal,1002,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,296.47058823529414,305.1281541429441,305.1281541429441,305.1297855964912,296.4705882352942,301.39650354764365,28
+57,Intermodal,1002,Diesel_Large,Allouez,Hibbing,41.0,43.0,828691080000.0,326.11764705882354,334.74920712941383,334.74920712941383,334.7576192186083,326.11764705882354,334.9090139408118,32
+27,Intermodal,1003,BEL,Allouez,Hibbing,41.0,43.0,8208000000.0,177.88235294117646,186.51317428089578,186.51317428089578,187.39317658489577,177.88235294117646,186.72657255834858,2
+35,Intermodal,1003,BEL,Hibbing,Allouez,43.0,41.0,2808004319.996545,207.52941176470588,216.1864980113696,216.1864980113696,218.8240577708916,207.52941176470588,212.4549844475042,10
+39,Intermodal,1003,BEL,Allouez,Hibbing,41.0,43.0,8208000000.0,237.1764705882353,245.80729192795462,245.80729192795462,246.6872942319546,237.1764705882353,245.9679767894695,14
+44,Unit,1003,BEL,Hibbing,Allouez,44.0,43.0,2808004319.996545,252.0,260.67653137595784,260.67653137595784,263.55653367995785,252.0,256.73038868663673,19
+45,Intermodal,1003,BEL,Allouez,Hibbing,41.0,43.0,8208000000.0,266.8235294117647,275.454350751484,275.454350751484,276.33435305548403,266.8235294117647,275.6150356129989,20
+50,Unit,1003,BEL,Hibbing,Allouez,44.0,43.0,2808004319.996545,280.0,288.67653137595784,288.67653137595784,291.55653367995785,280.0,284.73038868663673,25
+51,Intermodal,1003,BEL,Allouez,Hibbing,41.0,43.0,8208000000.0,296.47058823529414,305.10140957501335,305.10140957501335,305.98141187901336,296.4705882352942,305.3148078524663,26
+59,Intermodal,1003,BEL,Hibbing,Allouez,43.0,41.0,2808004319.996545,326.11764705882354,334.7747333054872,334.7747333054872,337.4122930650092,326.11764705882354,331.0432197416219,34
+27,Intermodal,1004,Diesel_Large,Allouez,Hibbing,41.0,43.0,828691080000.0,177.88235294117646,186.51317428089578,186.51317428089578,186.5212678320977,177.88235294117646,186.72657255834858,2
+35,Intermodal,1004,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,207.52941176470588,216.1864980113696,216.1864980113696,216.18800275139554,207.52941176470588,212.4549844475042,10
+39,Intermodal,1004,Diesel_Large,Allouez,Hibbing,41.0,43.0,828691080000.0,237.1764705882353,245.80729192795462,245.80729192795462,245.81538547915653,237.1764705882353,245.9679767894695,14
+44,Unit,1004,Diesel_Large,Hibbing,Allouez,44.0,43.0,828691080000.0,252.0,260.67653137595784,260.67653137595784,260.6824713988032,252.0,256.73038868663673,19
+45,Intermodal,1004,Diesel_Large,Allouez,Hibbing,41.0,43.0,828691080000.0,266.8235294117647,275.454350751484,275.454350751484,275.4624443026859,266.8235294117647,275.6150356129989,20
+50,Unit,1004,Diesel_Large,Hibbing,Allouez,44.0,43.0,828691080000.0,280.0,288.67653137595784,288.67653137595784,288.6824713988032,280.0,284.73038868663673,25
+51,Intermodal,1004,Diesel_Large,Allouez,Hibbing,41.0,43.0,828691080000.0,296.47058823529414,305.10140957501335,305.10140957501335,305.10950312621526,296.4705882352942,305.3148078524663,26
+59,Intermodal,1004,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,326.11764705882354,334.7747333054872,334.7747333054872,334.77623804551314,326.11764705882354,331.0432197416219,34
+27,Intermodal,1005,BEL,Allouez,Hibbing,41.0,43.0,8208000000.0,177.88235294117646,186.51317428089578,186.51317428089578,187.39317658489577,177.88235294117646,186.72657255834858,2
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+29,Intermodal,1019,BEL,Hibbing,Allouez,43.0,41.0,2808004319.996545,177.88235294117646,186.53991884882635,186.53991884882635,189.20820825038183,177.88235294117646,182.80826825352597,4
+33,Intermodal,1019,BEL,Allouez,Hibbing,41.0,43.0,8208000000.0,207.52941176470588,216.16097183529615,216.16097183529615,217.04097413929614,207.52941176470588,216.32077864669415,8
+41,Intermodal,1019,BEL,Hibbing,Allouez,43.0,41.0,2808004319.996545,237.1764705882353,245.8340364958852,248.50232589744067,251.17061529899615,237.1764705882353,242.1023859005848,16
+46,Manifest,1019,BEL,Allouez,Hibbing,63.0,30.0,8208000000.0,266.8235294117647,275.8429206726455,277.21435535948405,278.09435766348406,266.956862745098,276.0102638940006,21
+54,Manifest,1019,BEL,Hibbing,Allouez,50.0,42.0,2808004319.996545,296.47058823529414,305.23775179113795,310.464732946055,313.344735250055,296.6039215686275,301.6696430677891,29
+58,Manifest,1019,BEL,Allouez,Hibbing,63.0,30.0,8208000000.0,326.11764705882354,335.1370383197046,336.50921173741386,337.3892140414139,326.2509803921569,335.3042586489178,33
+29,Intermodal,1020,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,177.88235294117646,186.53991884882635,186.5415503023735,186.54318175592067,177.88235294117646,182.80826825352597,4
+33,Intermodal,1020,Diesel_Large,Allouez,Hibbing,41.0,43.0,828691080000.0,207.52941176470588,216.16097183529615,216.16938392449063,216.1777960136851,207.52941176470588,216.32077864669415,8
+42,Manifest,1020,Diesel_Large,Hibbing,Allouez,50.0,42.0,828691080000.0,237.1764705882353,245.87092654128003,245.87092654128003,245.87826925503225,237.30980392156863,242.32281200479235,17
+46,Manifest,1020,Diesel_Large,Allouez,Hibbing,63.0,30.0,828691080000.0,266.8235294117647,275.8429206726455,275.8429206726455,275.8711890164819,266.956862745098,276.0102638940006,21
+56,Unit,1020,Diesel_Large,Hibbing,Allouez,44.0,43.0,828691080000.0,308.0,316.67653137595795,316.67653137595795,316.68247139880333,308.0,312.7303886866368,31
+58,Manifest,1020,Diesel_Large,Allouez,Hibbing,63.0,30.0,828691080000.0,326.11764705882354,335.1370383197046,335.1370383197046,335.16530666354095,326.2509803921569,335.3042586489178,33
+29,Intermodal,1021,BEL,Hibbing,Allouez,43.0,41.0,2808004319.996545,177.88235294117646,186.53991884882635,189.20820825038183,191.8764976519373,177.88235294117646,182.80826825352597,4
+33,Intermodal,1021,BEL,Allouez,Hibbing,41.0,43.0,8208000000.0,207.52941176470588,216.16097183529615,217.04097413929614,217.92097644329613,207.52941176470588,216.32077864669415,8
+42,Manifest,1021,BEL,Hibbing,Allouez,50.0,42.0,2808004319.996545,237.1764705882353,245.87092654128003,248.50232589744067,251.38232820144066,237.30980392156863,242.32281200479235,17
+46,Manifest,1021,BEL,Allouez,Hibbing,63.0,30.0,8208000000.0,266.8235294117647,275.8429206726455,277.21435535948405,278.09435766348406,266.956862745098,276.0102638940006,21
+56,Unit,1021,BEL,Hibbing,Allouez,44.0,43.0,2808004319.996545,308.0,316.67653137595795,316.67653137595795,319.55653367995797,308.0,312.7303886866368,31
+29,Intermodal,1022,Diesel_Large,Hibbing,Allouez,43.0,41.0,828691080000.0,177.88235294117646,186.53991884882635,186.5415503023735,186.54318175592067,177.88235294117646,182.80826825352597,4
+33,Intermodal,1022,Diesel_Large,Allouez,Hibbing,41.0,43.0,828691080000.0,207.52941176470588,216.16097183529615,216.16938392449063,216.1777960136851,207.52941176470588,216.32077864669415,8
+42,Manifest,1022,Diesel_Large,Hibbing,Allouez,50.0,42.0,828691080000.0,237.1764705882353,245.87092654128003,245.87092654128003,245.87826925503225,237.30980392156863,242.32281200479235,17
+52,Manifest,1022,Diesel_Large,Allouez,Hibbing,63.0,30.0,828691080000.0,296.47058823529414,305.2905737626903,305.3128215284064,305.3350692941225,296.6039215686275,305.57954879825974,27
+30,Manifest,1023,BEL,Hibbing,Allouez,50.0,42.0,2808004319.996545,177.88235294117646,186.64951649702033,189.20820825038183,192.08821055438182,178.01568627450982,183.0814077736714,5
+34,Manifest,1023,BEL,Allouez,Hibbing,63.0,30.0,8208000000.0,207.52941176470588,217.6086187865999,217.6086187865999,218.48862109059988,207.6627450980392,217.41810872754803,9
+56,Unit,1023,BEL,Hibbing,Allouez,44.0,43.0,2808004319.996545,308.0,316.67653137595795,316.67653137595795,319.55653367995797,308.0,312.7303886866368,31
+30,Manifest,1024,Diesel_Large,Hibbing,Allouez,50.0,42.0,828691080000.0,177.88235294117646,186.64951649702033,186.64951649702033,186.65866960830658,178.01568627450982,183.0814077736714,5
+34,Manifest,1024,Diesel_Large,Allouez,Hibbing,63.0,30.0,828691080000.0,207.52941176470588,217.6086187865999,217.6086187865999,217.65401333240675,207.6627450980392,217.41810872754803,9
+30,Manifest,1025,BEL,Hibbing,Allouez,50.0,42.0,2808004319.996545,177.88235294117646,186.64951649702033,191.8764976519373,194.7564999559373,178.01568627450982,183.0814077736714,5
+34,Manifest,1025,BEL,Allouez,Hibbing,63.0,30.0,8208000000.0,207.52941176470588,217.6086187865999,217.92097644329613,218.8009787472961,207.6627450980392,217.41810872754803,9
+28,Manifest,1030,Diesel_Large,Allouez,Hibbing,63.0,30.0,828691080000.0,177.88235294117646,186.70233846857286,186.72458623428895,186.74683400000504,178.01568627450982,186.99131350414217,3
+34,Manifest,1031,BEL,Allouez,Hibbing,63.0,30.0,8208000000.0,207.52941176470588,217.6086187865999,218.48862109059988,219.36862339459987,207.6627450980392,217.41810872754803,9
+55,Unit,1032,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,308.0,316.814986740618,316.814986740618,316.8360199346937,308.0,314.8316059958538,30
+55,Unit,1033,BEL,Allouez,Hibbing,43.0,44.0,8208000000.0,308.0,316.814986740618,316.814986740618,317.694989044618,308.0,314.8316059958538,30
+55,Unit,1034,Diesel_Large,Allouez,Hibbing,43.0,44.0,828691080000.0,308.0,316.814986740618,316.814986740618,316.8360199346937,308.0,314.8316059958538,30
diff --git a/python/lifts/dictionary.py b/python/lifts/dictionary.py
index cbfd8bbb..4e13c721 100644
--- a/python/lifts/dictionary.py
+++ b/python/lifts/dictionary.py
@@ -1,58 +1,21 @@
-import pandas as pd
+import polars as pl
 
-
-def timetable(terminal):
-    df = pd.read_csv('C:/Users/Irena Tong/PycharmProjects/simulation_test/data/train_consist_plan.csv')
-
-    df_terminal = df[(df['Destination_ID'] == terminal) & (df['Train_Type'] == 'Intermodal')]
-
-    df_grouped = df_terminal.groupby('Train_ID').agg({
-        'Cars_Loaded': find_duplicate_numbers,
-        'Cars_Empty': find_duplicate_numbers,
-        'Arrival_Time_Actual_Hr': 'min',
-        'Departure_Time_Actual_Hr': 'min'
-    }).reset_index()
-
-    df_grouped_sorted = df_grouped.sort_values(by='Arrival_Time_Actual_Hr', ascending=True)
-
-    train_dict_list = convert_df_to_dict(df_grouped_sorted)
-
-    return train_dict_list
-
-
-def convert_df_to_dict(df):
-    train_timetable = []
-    for _, row in df.iterrows():
-        train_dict = {
-            "train_id": row['Train_ID'],
-            "arrival_time": row['Departure_Time_Actual_Hr'],
-            "departure_time": row['Arrival_Time_Actual_Hr'],
-            "empty_cars": row['Cars_Empty'],
-            "full_cars": row['Cars_Loaded'],
-            "oc_number": calculate_oc_number(row),
-            "truck_number": calculate_truck_number(row)
-        }
-        train_timetable.append(train_dict)
-    return train_timetable
-
-
-def truck_resource(data):
-    total_truck_number = sum(item['truck_number'] for item in data)
-    return total_truck_number
-
-
-def calculate_oc_number(row):
+def calculate_oc_number() -> int:
     return 40
 
-def calculate_truck_number(row):
-    full_cars = row['Cars_Loaded']
-    oc_number = calculate_oc_number(row)
-    return max(full_cars, oc_number)
-
-
-def find_duplicate_numbers(series):
-    return series.iloc[0]
+def calculate_truck_number(df):
+    return (df
+        .with_columns(
+            pl.max_horizontal(
+                pl.col("full_cars"), 
+                calculate_oc_number()
+            ).alias("truck_number"))
+    )
+
+def truck_resource(df):
+    total_truck_number = df.select(pl.col("truck_number").sum()).item()
+    return total_truck_number
 
-terminal = 'Allouez'
-train_data_as_dict = timetable(terminal)
-print(train_data_as_dict)
\ No newline at end of file
+#terminal = 'Allouez'
+#train_data_as_dict = schedule.build_train_timetable(terminal, swap_arrive_depart = True, as_dicts = True)
+#print(train_data_as_dict)
\ No newline at end of file
diff --git a/python/lifts/final_version.py b/python/lifts/lifts_simulator.py
similarity index 53%
rename from python/lifts/final_version.py
rename to python/lifts/lifts_simulator.py
index 08685eb2..bd3b1c59 100644
--- a/python/lifts/final_version.py
+++ b/python/lifts/lifts_simulator.py
@@ -1,9 +1,12 @@
 import simpy
 import random
-from parameters import *
-from distances import *
-from dictionary import *
-from vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
+import polars as pl
+from lifts import utilities
+from lifts.parameters import *
+from lifts.distances import *
+from lifts.dictionary import *
+from lifts.schedule import *
+from lifts.vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
 
 
 # Test input
@@ -21,72 +24,72 @@
 TRUCK_NUMBERS = 1000
 
 def record_event(container_id, event_type, timestamp):
-    if container_id not in container_events:
-        container_events[container_id] = {}
-    container_events[container_id][event_type] = timestamp
+    global state
+    if container_id not in state.container_events:
+        state.container_events[container_id] = {}
+    state.container_events[container_id][event_type] = timestamp
 
 
 def handle_truck_arrivals(env, in_gate_resource, truck_numbers):
-    global all_trucks_ready_event, truck_processed, start_oc_container_id, end_oc_container_id, TRUCK_ARRIVAL_MEAN, TRAIN_ARRIVAL_HR
-
+    global state
     truck_id = 1
     truck_processed = 0
-    TRUCK_ARRIVAL_MEAN = abs(TRAIN_ARRIVAL_HR - previous_train_departure) / max(INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER)
+    state.TRUCK_ARRIVAL_MEAN = abs(state.TRAIN_ARRIVAL_HR - state.previous_train_departure) / max(state.INBOUND_CONTAINER_NUMBER, state.OUTBOUND_CONTAINER_NUMBER)
     print(f"current time is {env.now}")
-    print(f"next TRAIN_ARRIVAL_HR:{TRAIN_ARRIVAL_HR}")
-    print(f"TRUCK_ARRIVAL_MEAN IS {TRUCK_ARRIVAL_MEAN}")
+    print(f"next TRAIN_ARRIVAL_HR:{state.TRAIN_ARRIVAL_HR}")
+    print(f"TRUCK_ARRIVAL_MEAN IS {state.TRUCK_ARRIVAL_MEAN}")
 
     while truck_id <= TRUCK_NUMBERS:
-        inter_arrival_time = random.expovariate(1 / TRUCK_ARRIVAL_MEAN)
+        inter_arrival_time = random.expovariate(1 / state.TRUCK_ARRIVAL_MEAN)
         yield env.timeout(inter_arrival_time)
-        truck_arrival_time.append(env.now)
+        state.truck_arrival_time.append(env.now)
 
         env.process(truck_through_gate(env, in_gate_resource, truck_id))
         truck_id += 1
 
     if truck_id > TRUCK_NUMBERS:
         # print(f"truck_id = {truck_id} vs TRUCK_NUM = {TRUCK_NUMBERS}")
-        if not all_trucks_ready_event.triggered:
-            all_trucks_ready_event.succeed()
+        if not state.all_trucks_ready_event.triggered:
+            state.all_trucks_ready_event.succeed()
             # print(f"{env.now}: All trucks arrived for the {TRAIN_ID} train.")
 
 
 def truck_through_gate(env, in_gate_resource, truck_id):
-    global last_leave_time, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+    global state
 
     with in_gate_resource.request() as request:
         yield request
-        wait_time = max(0, truck_arrival_time[truck_id - 1] - last_leave_time)
+        wait_time = max(0, state.truck_arrival_time[truck_id - 1] - state.last_leave_time)
         if wait_time <= 0:
             wait_time = 0  # first arriving trucks
             # print(f"Truck {truck_id} enters the gate without waiting")
         else:
             # print(f"Truck {truck_id} enters the gate and queued for {wait_time} hrs")
-            truck_waiting_time.append(wait_time)
+            state.truck_waiting_time.append(wait_time)
 
-        yield env.timeout(TRUCK_INGATE_TIME + random.uniform(0, TRUCK_INGATE_TIME_DEV))
+        yield env.timeout(state.TRUCK_INGATE_TIME + random.uniform(0, state.TRUCK_INGATE_TIME_DEV))
 
         # Case 1: Normal handling when OC >= IC (all trucks have containers)
-        if OUTBOUND_CONTAINER_NUMBER >= INBOUND_CONTAINER_NUMBER:
+        if state.OUTBOUND_CONTAINER_NUMBER >= state.INBOUND_CONTAINER_NUMBER:
             env.process(handle_container(env, truck_id))
 
         # Case 2: OC < IC, extra empty trucks are needed
         else:
-            if truck_id <= OUTBOUND_CONTAINER_NUMBER:
+            if truck_id <= state.OUTBOUND_CONTAINER_NUMBER:
                 env.process(handle_container(env, truck_id))  # Loaded trucks
             else:
                 env.process(empty_truck(env, truck_id))  # Empty trucks
 
 
 def handle_container(env, truck_id):
-    global outbound_container_id_counter, last_leave_time
+    global state
 
-    container_id = outbound_container_id_counter
-    outbound_container_id_counter += 1
+    container_id = state.outbound_container_id_counter
+    state.outbound_container_id_counter += 1
     record_event(container_id, 'truck_arrival', env.now)
 
     d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
-    yield env.timeout(d_t_dist / (2 * TRUCK_SPEED_LIMIT))
+    yield env.timeout(d_t_dist / (2 * state.TRUCK_SPEED_LIMIT))
 
     record_event(container_id, 'truck_drop_off', env.now)
     # print(f"{env.now}: Truck {truck_id} drops outbound container {container_id}.")
@@ -94,32 +97,32 @@ def handle_container(env, truck_id):
 
 
 def empty_truck(env, truck_id):
-    global inbound_container_id_counter, last_leave_time
+    global state
 
     d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
-    yield env.timeout(d_t_dist / (2 * TRUCK_SPEED_LIMIT))
+    yield env.timeout(d_t_dist / (2 * state.TRUCK_SPEED_LIMIT))
 
     # print(f"{env.now}: Empty truck {truck_id} arrives.")
     last_leave_time = env.now
 
 
 def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, out_gate_resource):
-    global record_oc_label, train_id_counter, TRUCK_NUMBERS, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER, TRAIN_DEPARTURE_HR, oc_chassis_filled_event, TRAIN_ID, inbound_container_id_counter, previous_train_departure
+    global state
 
     for i, train in enumerate(train_timetable):
-        TRAIN_ARRIVAL_HR = train['arrival_time']
-        TRAIN_DEPARTURE_HR = train['departure_time']
-        INBOUND_CONTAINER_NUMBER = train['full_cars']
-        OUTBOUND_CONTAINER_NUMBER = train['oc_number']
-        TRUCK_NUMBERS = train['truck_number']
-        TRAIN_ID = train['train_id']
+        state.TRAIN_ARRIVAL_HR = train['arrival_time']
+        state.TRAIN_DEPARTURE_HR = train['departure_time']
+        state.INBOUND_CONTAINER_NUMBER = train['full_cars']
+        state.OUTBOUND_CONTAINER_NUMBER = train['oc_number']
+        state.TRUCK_NUMBERS = train['truck_number']
+        state.TRAIN_ID = train['train_id']
 
-        print(f"---------- Next Train {TRAIN_ID} Is On the  Way ----------")
-        print(f"IC {INBOUND_CONTAINER_NUMBER}")
-        print(f"OC {OUTBOUND_CONTAINER_NUMBER}")
+        print(f"---------- Next Train {state.TRAIN_ID} Is On the  Way ----------")
+        print(f"IC {state.INBOUND_CONTAINER_NUMBER}")
+        print(f"OC {state.OUTBOUND_CONTAINER_NUMBER}")
 
         outbound_containers_store.items.clear()
-        for oc in range(record_oc_label, record_oc_label + OUTBOUND_CONTAINER_NUMBER):  # from 10001 to 10001 + OC
+        for oc in range(state.record_oc_label, state.record_oc_label + state.OUTBOUND_CONTAINER_NUMBER):  # from 10001 to 10001 + OC
             # print("oc_number", oc)
             outbound_containers_store.put(oc)
             # yield outbound_containers_store.put(oc)
@@ -128,38 +131,38 @@ def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chas
         # print("outbound_containers_store is:", outbound_containers_store.items)
 
         # Trucks enter until the precious train departs, if not the first truck
-        previous_train_departure = train_timetable[i-1]['departure_time'] if i > 0 else 0
-        print(f"Schedule {TRUCK_NUMBERS} Trucks arriving between previous train departure at {previous_train_departure} and current train arrival at {TRAIN_ARRIVAL_HR}")
+        state.previous_train_departure = train_timetable[i-1]['departure_time'] if i > 0 else 0
+        print(f"Schedule {state.TRUCK_NUMBERS} Trucks arriving between previous train departure at {state.previous_train_departure} and current train arrival at {state.TRAIN_ARRIVAL_HR}")
         env.process(handle_truck_arrivals(env, in_gate_resource, outbound_containers_store))
 
         # Trains arrive according to the timetable, fix negative delay bug
-        delay = TRAIN_ARRIVAL_HR - env.now
+        delay = state.TRAIN_ARRIVAL_HR - env.now
         if delay <= 0:
             yield env.timeout(0)
         else:
             yield env.timeout(delay)
 
-        train_id = train_id_counter
-        print(f"Train {TRAIN_ID} ({train_id} in the dictionary) arrives at {env.now}")
+        train_id = state.train_id_counter
+        print(f"Train {state.TRAIN_ID} ({train_id} in the dictionary) arrives at {env.now}")
 
         # for container_id in range(inbound_container_id_counter, inbound_container_id_counter + INBOUND_CONTAINER_NUMBER):
-        for container_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):  # fix float error
+        for container_id in range(int(state.inbound_container_id_counter), int(state.inbound_container_id_counter) + int(state.INBOUND_CONTAINER_NUMBER)):  # fix float error
 
             record_event(container_id, 'train_arrival', env.now)
 
         with train_processing.request() as request:
             yield request
-            oc_chassis_filled_event = env.event()
-            yield env.process(process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource))
-            train_id_counter += 1
+            state.oc_chassis_filled_event = env.event()
+            yield env.process(process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, out_gate_resource))
+            state.train_id_counter += 1
 
-        record_oc_label += OUTBOUND_CONTAINER_NUMBER
+        state.record_oc_label += state.OUTBOUND_CONTAINER_NUMBER
         # print("record_oc_label", record_oc_label)
         # print("oc_variance in train_process:", oc_variance)
 
 
-def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource):
-    global oc_variance, time_per_train, train_series, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER, record_oc_label, TRAIN_ID_FIXED
+def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, out_gate_resource):
+    global state
 
     start_time = env.now
 
@@ -168,8 +171,8 @@ def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, ou
     chassis_inbound_ids = []  # To save chassis_id, current_inbound_id to hostler_transfer_IC_single_loop
 
     # if train_id < TRAIN_NUMBERS:
-    for chassis_id in range(1, int(INBOUND_CONTAINER_NUMBER) + 1):
-        unload_process = env.process(crane_and_chassis(env, train_id, 'unload', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event))
+    for chassis_id in range(1, int(state.INBOUND_CONTAINER_NUMBER) + 1):
+        unload_process = env.process(crane_and_chassis(env, train_id, 'unload', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource))
         unload_processes.append(unload_process)
 
     # All IC are processed
@@ -182,7 +185,7 @@ def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, ou
         chassis_id, current_inbound_id = result
         chassis_inbound_ids.append((chassis_id, current_inbound_id))
         env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes,
-                             train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event,
+                             train_processing, outbound_containers_store, in_gate_resource,
                              out_gate_resource))
 
     # # Once all OC are dropped by hostlers, crane start working
@@ -194,36 +197,36 @@ def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, ou
 
     # Cranes move all OC to chassis
     load_processes = []
-    for chassis_id in range(1, OUTBOUND_CONTAINER_NUMBER + 1):
-        load_process = env.process(crane_and_chassis(env, train_id, 'load', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_id=chassis_id))
+    for chassis_id in range(1, state.OUTBOUND_CONTAINER_NUMBER + 1):
+        load_process = env.process(crane_and_chassis(env, train_id, 'load', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, chassis_id=chassis_id))
         load_processes.append(load_process)
     yield simpy.events.AllOf(env, load_processes)
 
     # Check if all outbound containers are loaded (all chassis is empty 0), the train departs
-    if chassis_status.count(-1) == TRAIN_UNITS:
+    if state.chassis_status.count(-1) == state.TRAIN_UNITS:
         # oc_chassis_filled_event.succeed()
-        TRAIN_ID_FIXED = TRAIN_ID
-        print(f"Train {TRAIN_ID_FIXED} is ready to depart at {env.now}.")
+        state.TRAIN_ID_FIXED = state.TRAIN_ID
+        print(f"Train {state.TRAIN_ID_FIXED} is ready to depart at {env.now}.")
         env.process(train_departure(env, train_id))
-        time_per_train.append(env.now - start_time)
+        state.time_per_train.append(env.now - start_time)
 
     end_time = env.now
-    time_per_train.append(end_time - start_time)
-    train_series += 1
-    oc_variance += OUTBOUND_CONTAINER_NUMBER
+    state.time_per_train.append(end_time - start_time)
+    state.train_series += 1
+    state.oc_variance += state.OUTBOUND_CONTAINER_NUMBER
 
 
-def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event, chassis_id=None):
-    global record_oc_label, crane_id_counter, chassis_status, inbound_container_id_counter, outbound_containers_mapping, outbound_container_id_counter, INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER
+def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, chassis_id=None):
+    global state
 
     # # Print before requesting crane resource
     if action == 'unload':
-        crane_id = crane_id_counter
-        crane_id_counter = (crane_id_counter % CRANE_NUMBER) + 1
+        crane_id = state.crane_id_counter
+        state.crane_id_counter = (state.crane_id_counter % state.CRANE_NUMBER) + 1
         # print("inbound_id_counter", inbound_container_id_counter)
-        for container_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):  # fix float error
+        for container_id in range(int(state.inbound_container_id_counter), int(state.inbound_container_id_counter) + int(state.INBOUND_CONTAINER_NUMBER)):  # fix float error
             # print("container_id now:", container_id)
-            yield env.timeout(CRANE_UNLOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+            yield env.timeout(state.CRANE_UNLOAD_CONTAINER_TIME_MEAN + random.uniform(0, state.CRANE_MOVE_DEV_TIME))
             record_event(container_id, 'crane_unload', env.now)
             # print(f"Crane {crane_id} unloads inbound container {inbound_container_id_counter} from train {train_id} at {env.now}")
 
@@ -241,52 +244,52 @@ def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_st
         # print(f"[{env.now}] Crane {crane_id_counter} acquired crane resource. Available cranes: {cranes.count}/{cranes.capacity}")
 
         start_time = env.now
-        record_vehicle_event('crane', crane_id_counter, 'start', start_time)    # performance record: starting
+        record_vehicle_event('crane', state.crane_id_counter, 'start', start_time)    # performance record: starting
 
         if action == 'unload':
             # crane_id = crane_id_counter
             # crane_id_counter = (crane_id_counter % CRANE_NUMBER) + 1
 
-            chassis_id = ((inbound_container_id_counter - 1) % CHASSIS_NUMBER) + 1
+            chassis_id = ((state.inbound_container_id_counter - 1) % state.CHASSIS_NUMBER) + 1
 
-            current_inbound_id = inbound_container_id_counter
-            inbound_container_id_counter += 1
+            current_inbound_id = state.inbound_container_id_counter
+            state.inbound_container_id_counter += 1
             # yield env.timeout(CRANE_UNLOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
 
             # for chassis_id in range(int(inbound_container_id_counter), int(inbound_container_id_counter) + int(INBOUND_CONTAINER_NUMBER)):
-            chassis_status[chassis_id - 1] = 1
+            state.chassis_status[chassis_id - 1] = 1
 
             end_time = env.now
-            record_vehicle_event('crane', crane_id_counter, 'end', end_time)     # performance record: ending
+            record_vehicle_event('crane', state.crane_id_counter, 'end', end_time)     # performance record: ending
 
             # hostler picks up IC
-            env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource))
+            env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource))
 
             return chassis_id, current_inbound_id
 
         elif action == 'load':
-            if chassis_id not in outbound_containers_mapping:
+            if chassis_id not in state.outbound_containers_mapping:
                 print(f"Notice: No outbound container mapped to chassis {chassis_id} at {env.now}")
                 return
 
-            container_id = outbound_containers_mapping[chassis_id]  # Retrieve container ID from mapping
+            container_id = state.outbound_containers_mapping[chassis_id]  # Retrieve container ID from mapping
             # print("outbound_containers_mapping in crane and chassis func:", outbound_containers_mapping)
             # print("container_id in crane and chassis func:", container_id)
 
-            if CRANE_NUMBER == 1:
+            if state.CRANE_NUMBER == 1:
                 crane_id = 1
             else:
-                crane_id = (chassis_id % CRANE_NUMBER) + 1
+                crane_id = (chassis_id % state.CRANE_NUMBER) + 1
 
-            chassis_status[chassis_id - 1] = -1
+            state.chassis_status[chassis_id - 1] = -1
 
             # yield env.timeout(CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
             # chassis_status[chassis_id - 1] = -1
             # print(f"Crane {crane_id} loads outbound container {container_id} from chassis {chassis_id} to train {TRAIN_ID} at {env.now}")
             # record_event(container_id, 'crane_load', env.now)
 
-            for container_id in range(record_oc_label, record_oc_label + OUTBOUND_CONTAINER_NUMBER):
-                yield env.timeout(CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, CRANE_MOVE_DEV_TIME))
+            for container_id in range(state.record_oc_label, state.record_oc_label + state.OUTBOUND_CONTAINER_NUMBER):
+                yield env.timeout(state.CRANE_LOAD_CONTAINER_TIME_MEAN + random.uniform(0, state.CRANE_MOVE_DEV_TIME))
                 # chassis_status[chassis_id - 1] = -1
                 # print(f"Crane {crane_id} loads outbound container {container_id} to train {TRAIN_ID} at {env.now}")
                 record_event(container_id, 'crane_load', env.now)
@@ -295,65 +298,61 @@ def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_st
         # print(f"[{env.now}] Crane {crane_id_counter} has released crane resource. Available cranes: {cranes.count}/{cranes.capacity}")
 
 
-def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
-    global hostler_id_counter, chassis_status, outbound_containers_mapping, outbound_container_id, record_oc_label, HOSTLER_NUMBER
+def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource):
+    global state
 
     with hostlers.request() as request:
         yield request
 
         start_time = env.now
-        record_vehicle_event('hostler', hostler_id_counter, 'start', start_time)  # performance record
+        record_vehicle_event('hostler', state.hostler_id_counter, 'start', start_time)  # performance record
 
-        hostler_id = hostler_id_counter
-        hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+        hostler_id = state.hostler_id_counter
+        state.hostler_id_counter = (state.hostler_id_counter % HOSTLER_NUMBER) + 1
 
         with chassis.request() as chassis_request:
             yield chassis_request
 
-            if container_type == 'inbound' and chassis_status[chassis_id - 1] == 1:
+            if container_type == 'inbound' and state.chassis_status[chassis_id - 1] == 1:
                 d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
-                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
-                print(f"Hostler pick-up time is:{HOSTLER_TRANSPORT_CONTAINER_TIME}")
-                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                state.HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * state.HOSTLER_SPEED_LIMIT)
+                print(f"Hostler pick-up time is:{state.HOSTLER_TRANSPORT_CONTAINER_TIME}")
+                yield env.timeout(state.HOSTLER_TRANSPORT_CONTAINER_TIME)
                 record_event(container_id, 'hostler_pickup', env.now)
                 print(f"Hostler {hostler_id} picks up inbound container {container_id} from chassis {chassis_id} and heads to parking area at {env.now}")
 
-                chassis_status[chassis_id - 1] = -1
+                state.chassis_status[chassis_id - 1] = -1
 
                 # Hostler drop off: different route for picking-up and dropping-off
                 d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
-                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
-                print(f"Hostler drop-off time is:{HOSTLER_TRANSPORT_CONTAINER_TIME}")
-                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                state.HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * state.HOSTLER_SPEED_LIMIT)
+                print(f"Hostler drop-off time is:{state.HOSTLER_TRANSPORT_CONTAINER_TIME}")
+                yield env.timeout(state.HOSTLER_TRANSPORT_CONTAINER_TIME)
                 record_event(container_id, 'hostler_dropoff', env.now)
                 print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} and moves toward the assigned outbound container at {env.now}")
 
                 end_time = env.now
-                record_vehicle_event('hostler', hostler_id_counter, 'end', end_time)  # performance record
+                record_vehicle_event('hostler', state.hostler_id_counter, 'end', end_time)  # performance record
 
                 # Process functions of notify_truck and handle_outbound_container simultaneously
                 env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
 
                 # Assign outbound container and chassis_id for the hostler which drops off an inbound container
-                chassis_id, outbound_container_id = yield env.process(outbound_container_decision_making(
+                chassis_id, state.outbound_container_id = yield env.process(outbound_container_decision_making(
                     env, hostlers, chassis, container_id, truck_store, cranes, train_processing,
                     outbound_containers_store,
-                    in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_status,
-                    outbound_containers_mapping,
-                    record_oc_label, outbound_container_id
-                ))
+                    in_gate_resource, out_gate_resource))
 
                 # Process outbound containers
-                if chassis_id is not None and outbound_container_id is not None:
-                    env.process(handle_outbound_container(env, hostler_id, chassis_id, outbound_container_id, truck_store,
-                                                  cranes, train_processing, outbound_containers_store, in_gate_resource,
-                                                  oc_chassis_filled_event))
+                if chassis_id is not None and state.outbound_container_id is not None:
+                    env.process(handle_outbound_container(env, hostler_id, chassis_id, truck_store,
+                                                  cranes, train_processing, outbound_containers_store, in_gate_resource,))
 
 
 # When OC are fully processed, but IC are not
-def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
+def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource):
     print(f"Starting single hostler transfer IC loop for chassis {chassis_id} at {env.now}")
-    global hostler_id_counter
+    global state
 
     print(f"Requesting hostler for IC at chassis {chassis_id} at {env.now}")
 
@@ -362,24 +361,24 @@ def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chas
         yield request
 
         start_time = env.now
-        record_vehicle_event('hostler', hostler_id_counter, 'start', start_time)  # performance record
+        record_vehicle_event('hostler', state.hostler_id_counter, 'start', start_time)  # performance record
 
-        hostler_id = hostler_id_counter
-        hostler_id_counter = (hostler_id_counter % HOSTLER_NUMBER) + 1
+        hostler_id = state.hostler_id_counter
+        state.hostler_id_counter = (state.hostler_id_counter % HOSTLER_NUMBER) + 1
 
         with chassis.request() as chassis_request:
             yield chassis_request
 
-            if container_type == 'inbound' and chassis_status[chassis_id - 1] == 1:
-                chassis_status[chassis_id - 1] = -1
-                print(f"Single loop chassis status {chassis_status}")
-                print(f"There are {chassis_status.count(1)} IC")
-                print(f"There are {chassis_status.count(-1)} empty")
-                print(f"There are {chassis_status.count(0)} OC")
+            if container_type == 'inbound' and state.chassis_status[chassis_id - 1] == 1:
+                state.chassis_status[chassis_id - 1] = -1
+                print(f"Single loop chassis status {state.chassis_status}")
+                print(f"There are {state.chassis_status.count(1)} IC")
+                print(f"There are {state.chassis_status.count(-1)} empty")
+                print(f"There are {state.chassis_status.count(0)} OC")
                 d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
-                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+                state.HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * state.HOSTLER_SPEED_LIMIT)
 
-                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                yield env.timeout(state.HOSTLER_TRANSPORT_CONTAINER_TIME)
                 # hostler picks up the rest of IC from the chassis
                 # chassis_status[chassis_id - 1] = -1
                 record_event(container_id, 'hostler_pickup', env.now)
@@ -387,25 +386,25 @@ def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chas
 
                 # hostler drops off the IC
                 d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
-                HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
-                yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+                state.HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * state.HOSTLER_SPEED_LIMIT)
+                yield env.timeout(state.HOSTLER_TRANSPORT_CONTAINER_TIME)
                 record_event(container_id, 'hostler_dropoff', env.now)
                 print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} to parking area at {env.now}")
 
                 # Check if all chassis filled
-                if chassis_status.count(0) == OUTBOUND_CONTAINER_NUMBER and chassis_status.count(
-                        -1) == TRAIN_UNITS - OUTBOUND_CONTAINER_NUMBER and not oc_chassis_filled_event.triggered:
-                    print(f"Chassis is fully filled with OC, and cranes start moving: {chassis_status}")
-                    print(f"where there are {chassis_status.count(0)} chassis filled with OC (0)")
-                    print(f"where there are {chassis_status.count(-1)} chassis filled with empty (-1)")
-                    print(f"where there are {chassis_status.count(1)} chassis filled with IC (1)")
-                    oc_chassis_filled_event.succeed()
+                if state.chassis_status.count(0) == state.OUTBOUND_CONTAINER_NUMBER and state.chassis_status.count(
+                        -1) == state.TRAIN_UNITS - state.OUTBOUND_CONTAINER_NUMBER and not state.oc_chassis_filled_event.triggered:
+                    print(f"Chassis is fully filled with OC, and cranes start moving: {state.chassis_status}")
+                    print(f"where there are {state.chassis_status.count(0)} chassis filled with OC (0)")
+                    print(f"where there are {state.chassis_status.count(-1)} chassis filled with empty (-1)")
+                    print(f"where there are {state.chassis_status.count(1)} chassis filled with IC (1)")
+                    state.oc_chassis_filled_event.succeed()
                     return
                 else:
-                    print(f"Chassis is not fully filled: {chassis_status}")
-                    print(f"where there are {chassis_status.count(0)} chassis filled with OC (0)")
-                    print(f"where there are {chassis_status.count(-1)} chassis filled with empty (-1)")
-                    print(f"where there are {chassis_status.count(1)} chassis filled with IC (1)")
+                    print(f"Chassis is not fully filled: {state.chassis_status}")
+                    print(f"where there are {state.chassis_status.count(0)} chassis filled with OC (0)")
+                    print(f"where there are {state.chassis_status.count(-1)} chassis filled with empty (-1)")
+                    print(f"where there are {state.chassis_status.count(1)} chassis filled with IC (1)")
 
                 end_time = env.now
                 record_vehicle_event('hostler', hostler_id, 'end', end_time)  # performance record
@@ -414,19 +413,20 @@ def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chas
                 yield env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
 
 
-def outbound_container_decision_making(env, hostlers, chassis, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_status, outbound_containers_mapping, record_oc_label, outbound_container_id):
+def outbound_container_decision_making(env, hostlers, chassis, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource):
+    global state
     # Check if outbound_containers_store has outbound container
     if len(outbound_containers_store.items) > 0:
         outbound_container_id = yield outbound_containers_store.get()
         print(f"Outbound containers remaining: {len(outbound_containers_store.items)}")
 
-        if -1 in chassis_status:
-            chassis_id = chassis_status.index(-1) + 1  # find the first chassis
+        if -1 in state.chassis_status:
+            chassis_id = state.chassis_status.index(-1) + 1  # find the first chassis
             # If chassis are not assigned with outbound container
-            if chassis_id not in outbound_containers_mapping:
-                # outbound_container_id += record_oc_label
-                outbound_containers_mapping[chassis_id] = outbound_container_id
-                chassis_status[chassis_id - 1] = 0  # already assigned outbound container
+            if chassis_id not in state.outbound_containers_mapping:
+                # outbound_container_id += state.record_oc_label
+                state.outbound_containers_mapping[chassis_id] = outbound_container_id
+                state.chassis_status[chassis_id - 1] = 0  # already assigned outbound container
                 print(f"OC mapping created: outbound container {outbound_container_id} assigned to chassis {chassis_id}")
             else:
                 print(f"Chassis {chassis_id} is already mapped to an outbound container.")
@@ -438,70 +438,70 @@ def outbound_container_decision_making(env, hostlers, chassis, current_inbound_i
         chassis_id = None
         outbound_container_id = None
         # chassis_status = 1: inbound containers are not loaded
-        if chassis_status.count(1) != 0:
+        if state.chassis_status.count(1) != 0:
             print(f"Haven't finished all IC yet at {env.now}. Starting single loop.")
-            chassis_id = chassis_status.index(1) + 1
-            chassis_status[chassis_id - 1] = 0  # assigned with IC
+            chassis_id = state.chassis_status.index(1) + 1
+            state.chassis_status[chassis_id - 1] = 0  # assigned with IC
             # single loop takes rest inbound container
             yield env.process(hostler_transfer_IC_single_loop(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id,
                                                 truck_store, cranes, train_processing,
-                                                outbound_containers_store, in_gate_resource, oc_chassis_filled_event,
-                                                out_gate_resource))
+                                                outbound_containers_store, in_gate_resource, out_gate_resource))
         else:
             print("All inbound containers have been processed.")
 
     return chassis_id, outbound_container_id
 
 
-def handle_outbound_container(env, hostler_id, chassis_id, outbound_container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event):
-    global HOSTLER_FIND_CONTAINER_TIME
+def handle_outbound_container(env, hostler_id, chassis_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource):
+    global state
 
     d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
-    HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * HOSTLER_SPEED_LIMIT)
+    state.HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * state.HOSTLER_SPEED_LIMIT)
 
     d_r_dist = create_triang_distribution(d_r_min, d_r_avg, d_r_max).rvs()
-    HOSTLER_FIND_CONTAINER_TIME = d_r_dist / (2 * TRUCK_SPEED_LIMIT)
-    yield env.timeout(HOSTLER_FIND_CONTAINER_TIME)
+    state.HOSTLER_FIND_CONTAINER_TIME = d_r_dist / (2 * state.TRUCK_SPEED_LIMIT)
+    yield env.timeout(state.HOSTLER_FIND_CONTAINER_TIME)
 
-    record_event(outbound_container_id, 'hostler_pickup', env.now)
-    print(f"Hostler {hostler_id} picks up outbound container {outbound_container_id} from parking area to chassis {chassis_id} at {env.now}")
+    record_event(state.outbound_container_id, 'hostler_pickup', env.now)
+    print(f"Hostler {hostler_id} picks up outbound container {state.outbound_container_id} from parking area to chassis {chassis_id} at {env.now}")
 
-    yield env.timeout(HOSTLER_TRANSPORT_CONTAINER_TIME)
+    yield env.timeout(state.HOSTLER_TRANSPORT_CONTAINER_TIME)
 
-    record_event(outbound_container_id, 'hostler_dropoff', env.now)
-    print(f"Hostler {hostler_id} drops off outbound container {outbound_container_id} to chassis {chassis_id} at {env.now}")
+    record_event(state.outbound_container_id, 'hostler_dropoff', env.now)
+    print(f"Hostler {hostler_id} drops off outbound container {state.outbound_container_id} to chassis {chassis_id} at {env.now}")
 
 
 # truck pick up IC
 def notify_truck(env, truck_store, container_id, out_gate_resource):
+    global state
     truck_id = yield truck_store.get()
-    yield env.timeout(TRUCK_INGATE_TIME)
+    yield env.timeout(state.TRUCK_INGATE_TIME)
     print(f"Truck {truck_id} arrives at parking area and prepare to pick up inbound container {container_id} at {env.now}")
     yield env.process(truck_transfer(env, truck_id, container_id, out_gate_resource))
 
 
 def truck_transfer(env, truck_id, container_id, out_gate_resource):
-    global TRUCK_INGATE_TIME, TRUCK_TRANSPORT_CONTAINER_TIME, outbound_container_id_counter
+    global state
 
     start_time = env.now
     record_vehicle_event('truck', truck_id, 'start', start_time)  # performance record
 
     # Truck moves to the parking area
-    yield env.timeout(TRUCK_TO_PARKING)
+    yield env.timeout(state.TRUCK_TO_PARKING)
     record_event(container_id, 'truck_pickup', env.now)
     print(f"Truck {truck_id} picks up inbound container {container_id} at {env.now}")
 
     # Calculate the transport time for the truck
     d_t_dist = create_triang_distribution(d_t_min, d_t_avg, d_t_max).rvs()
-    TRUCK_TRANSPORT_CONTAINER_TIME = d_t_dist / (2 * TRUCK_SPEED_LIMIT)
-    yield env.timeout(TRUCK_TRANSPORT_CONTAINER_TIME)
+    state.TRUCK_TRANSPORT_CONTAINER_TIME = d_t_dist / (2 * state.TRUCK_SPEED_LIMIT)
+    yield env.timeout(state.TRUCK_TRANSPORT_CONTAINER_TIME)
 
     # Request out_gate_resource resource before the truck exits
     with out_gate_resource.request() as request:
         yield request
 
         # Simulate the time it takes for the truck to pass through the gate
-        yield env.timeout(TRUCK_OUTGATE_TIME + random.uniform(0,TRUCK_OUTGATE_TIME_DEV))
+        yield env.timeout(state.TRUCK_OUTGATE_TIME + random.uniform(0,state.TRUCK_OUTGATE_TIME_DEV))
         record_event(container_id, 'truck_exit', env.now)
         print(f"Truck {truck_id} exits gate with inbound container {container_id} at {env.now}")
 
@@ -511,30 +511,35 @@ def truck_transfer(env, truck_id, container_id, out_gate_resource):
 
 
 def train_departure(env, train_id):
-    global TRAIN_ID_FIXED, record_oc_label
+    global state
 
-    if env.now < TRAIN_DEPARTURE_HR:
-        yield env.timeout(TRAIN_DEPARTURE_HR - env.now)
-    yield env.timeout(TRAIN_INSPECTION_TIME)
-    print(f"Train {TRAIN_ID_FIXED} ({train_id} in the dictionary) departs at {env.now}")
+    if env.now < state.TRAIN_DEPARTURE_HR:
+        yield env.timeout(state.TRAIN_DEPARTURE_HR - env.now)
+    yield env.timeout(state.TRAIN_INSPECTION_TIME)
+    print(f"Train {state.TRAIN_ID_FIXED} ({train_id} in the dictionary) departs at {env.now}")
 
-    for container_id in range(record_oc_label - OUTBOUND_CONTAINER_NUMBER, record_oc_label):
+    for container_id in range(state.record_oc_label - state.OUTBOUND_CONTAINER_NUMBER, state.record_oc_label):
         record_event(container_id, 'train_depart', env.now)
 
 
-def run_simulation():
-    global all_trucks_ready_event, record_oc_label, TRUCK_NUMBERS, TRAIN_NUMBERS
+def run_simulation(
+        train_consist_plan: pl.DataFrame,
+        terminal: str,
+        out_path = None):
+    global state
+    state.terminal = terminal
+    state.initialize_from_consist_plan(train_consist_plan)
 
-    print(f"Starting simulation with No.{TRAIN_ID} trains, {HOSTLER_NUMBER} hostlers, {CRANE_NUMBER} cranes, and {TRUCK_NUMBERS} trucks.")
+    print(f"Starting simulation with No.{state.TRAIN_ID} trains, {state.HOSTLER_NUMBER} hostlers, {state.CRANE_NUMBER} cranes, and {state.TRUCK_NUMBERS} trucks.")
     env = simpy.Environment()
 
     # Resources
     train_processing = simpy.Resource(env, capacity=1)
-    cranes = simpy.Resource(env, capacity=CRANE_NUMBER)
-    chassis = simpy.Resource(env, capacity=CHASSIS_NUMBER)
-    hostlers = simpy.Resource(env, capacity=HOSTLER_NUMBER)
-    in_gate_resource = simpy.Resource(env, capacity=IN_GATE_NUMBERS)
-    out_gate_resource = simpy.Resource(env, capacity=OUT_GATE_NUMBERS)
+    cranes = simpy.Resource(env, capacity=state.CRANE_NUMBER)
+    chassis = simpy.Resource(env, capacity=state.CHASSIS_NUMBER)
+    hostlers = simpy.Resource(env, capacity=state.HOSTLER_NUMBER)
+    in_gate_resource = simpy.Resource(env, capacity=state.IN_GATE_NUMBERS)
+    out_gate_resource = simpy.Resource(env, capacity=state.OUT_GATE_NUMBERS)
     outbound_containers_store = simpy.Store(env, capacity=100)
     truck_store = simpy.Store(env, capacity=100)
 
@@ -545,7 +550,7 @@ def run_simulation():
         truck_store.put(truck_id)
     # print("TRUCK_STORE:", truck_store.items)
 
-    all_trucks_ready_event = env.event()
+    state.all_trucks_ready_event = env.event()
 
     # # toy case
     # train_timetable = [
@@ -557,58 +562,83 @@ def run_simulation():
     # ]
 
     # REAL TEST
-    train_timetable = timetable(terminal)
-    TRAIN_NUMBERS = len(timetable(terminal))
+    train_timetable = build_train_timetable(train_consist_plan, terminal, swap_arrive_depart = True, as_dicts = True)
+    TRAIN_NUMBERS = len(train_timetable)
 
     # env.process(train_arrival(env, train_processing, cranes, in_gate_resource, outbound_containers_store, truck_store, train_timetable))
     env.process(train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource,
                   outbound_containers_store, truck_store, out_gate_resource))
 
-    env.run(until=SIM_TIME)
+    env.run(until=state.sim_time)
 
     # Performance Matrix: train processing time
-    avg_time_per_train = sum(time_per_train) / len(time_per_train)
-    print(f"Average train processing time: {sum(time_per_train) / len(time_per_train) if time_per_train else 0:.2f}")
+    avg_time_per_train = sum(state.time_per_train) / len(state.time_per_train)
+    print(f"Average train processing time: {sum(state.time_per_train) / len(state.time_per_train) if state.time_per_train else 0:.2f}")
     print("Simulation completed. ")
     with open("avg_time_per_train.txt", "w") as f:
         f.write(str(avg_time_per_train))
 
     # Create DataFrame for container events
-    container_data = []
-
-    for container_id, events in sorted(container_events.items()):
-        container_type = 'inbound' if container_id < 10001 else 'outbound'
-        if container_type == 'inbound':
-            container_process_time = events.get('truck_exit', '-') - events.get('train_arrival', '-') if 'truck_exit' in events and 'train_arrival' in events else '-'
-        else:
-            container_process_time = events.get('train_depart', '-') - events.get('truck_drop_off', '-') if 'train_depart' in events and 'truck_drop_off' in events else '-'
-
-        container_data.append({
-            'container_id': container_id,
-            'container_type': container_type,
-            'train_arrival': events.get('train_arrival', '-'),
-            'truck_arrival': events.get('truck_arrival', '-'),
-            'crane_unload': events.get('crane_unload', '-'),
-            'hostler_pickup': events.get('hostler_pickup', '-'),
-            'hostler_dropoff': events.get('hostler_dropoff', '-'),
-            'truck_drop_off': events.get('truck_drop_off', '-'),
-            'truck_pickup': events.get('truck_pickup', '-'),
-            'truck_exit': events.get('truck_exit', '-'),
-            'crane_load': events.get('crane_load', '-'),
-            'train_depart': events.get('train_depart', '-'),
-            'container_processing_time': container_process_time
-        })
-
-    df = pd.DataFrame(container_data)
-    filename = f"C:/Users/Irena Tong/PycharmProjects/simulation_test/test/results/simulation_crane_{CRANE_NUMBER}_hostler_{HOSTLER_NUMBER}.xlsx"
-    df.to_excel(filename, index=False)
+    events_list = [(container_id, events) for container_id, events in sorted(state.container_events.items())]
+    container_data = (
+        pl.concat([
+            pl.DataFrame(data = None, schema = {
+                    'container_id': pl.UInt32,
+                    'container_type': pl.Utf8,
+                    'train_arrival': pl.Float64,
+                    'truck_arrival': pl.Float64,
+                    'crane_unload': pl.Float64,
+                    'hostler_pickup': pl.Float64,
+                    'hostler_dropoff': pl.Float64,
+                    'truck_drop_off': pl.Float64,
+                    'truck_pickup': pl.Float64,
+                    'truck_exit': pl.Float64,
+                    'crane_load': pl.Float64,
+                    'train_depart': pl.Float64}),
+            pl.concat(
+                [
+                    pl.DataFrame({"container_id": [container_id for container_id, event in events_list]})
+                        .with_columns(
+                            pl.when(pl.col("container_id") < 10001).then(pl.lit("inbound")).otherwise(pl.lit("outbound")).alias("container_type")
+                        ),
+                    pl.from_dicts([event for container_id, event in events_list])
+                ], how="horizontal")
+        ], how="diagonal_relaxed")
+        .with_columns(
+            pl.when(
+                pl.col("container_type") == pl.lit("inbound"),
+                pl.col("truck_exit").is_not_null(),
+                pl.col("train_arrival").is_not_null()
+            )
+            .then(
+                pl.col("truck_exit") - pl.col("train_arrival")
+            )
+            .when(
+                pl.col("container_type") == pl.lit("outbound"),
+                pl.col("train_depart").is_not_null(),
+                pl.col("truck_drop_off").is_not_null()
+            )
+            .then(
+                pl.col("train_depart") - pl.col("truck_drop_off")
+            )
+            .otherwise(None)
+            .alias("container_processing_time")
+        )
+    )
+    if out_path is not None:
+        container_data.write_excel(out_path / f"simulation_crane_{CRANE_NUMBER}_hostler_{HOSTLER_NUMBER}.xlsx")
 
     # Use save_average_times and save_vehicle_logs for vehicle related logs
     save_average_times()
     save_vehicle_logs()
 
     print("Done!")
+    return container_data
 
 
 if __name__ == "__main__":
-    run_simulation()
\ No newline at end of file
+    run_simulation(
+        train_consist_plan=pl.read_csv(utilities.package_root() / 'demos' / 'starter_demo' / 'train_consist_plan.csv'),
+        terminal = "Allouez",
+        out_path = utilities.package_root() / 'demos' / 'starter_demo' / 'results'
+    )
\ No newline at end of file
diff --git a/python/lifts/merged_baseline.py b/python/lifts/merged_baseline.py
index 9e41832e..b5bcb455 100644
--- a/python/lifts/merged_baseline.py
+++ b/python/lifts/merged_baseline.py
@@ -445,7 +445,7 @@ def run_simulation():
     env.process(train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource,
                   outbound_containers_store, truck_store, out_gate_resource))
 
-    env.run(until=SIM_TIME)
+    env.run(until=state.sim_time)
 
     print(f"Average train processing time: {sum(time_per_train) / len(time_per_train) if time_per_train else 0:.2f}")
     print("Simulation completed. ")
diff --git a/python/lifts/parameters.py b/python/lifts/parameters.py
index 6ffc0de4..1c1c3f2e 100644
--- a/python/lifts/parameters.py
+++ b/python/lifts/parameters.py
@@ -1,120 +1,123 @@
-from schedule import *
+import polars
+import simpy
+from dataclasses import dataclass, field
+from lifts.schedule import *
 
-# Simulation parameters
-RANDOM_SEED = 42
-SIM_TIME = 1100
-terminal = 'Allouez'    # choose 'Hibbing' or 'Allouez'
-
-
-# Counting vehicles
-train_id_counter = 1
-crane_id_counter = 1
-hostler_id_counter = 1
-truck_id_counter = 1
-total_initial_oc_trucks = 1
-empty_truck_id_counter = 1
-
-
-# inbound container counting
-inbound_containers_processed = 0    # trucks drop off OC to chassis
-inbound_containers_hostler_processed = 0    # hostlers pick up IC from chassis
-inbound_container_id_counter = 1
-
-# outbound container counting
-outbound_container_id_counter = 10001
-outbound_containers_processed = 0   # trucks pick up IC from chassis
-outbound_containers_hostler_processed = 0   # hostlers drop off OC to chassis
-outbound_container_id = 0   # initialize OC id for chassis assignment
-record_oc_label = 10001     # update outbound_containers_mapping among trains
-oc_variance = 0     # record previous batch OC numbers cumulatively
-
-# yield events or conditions
-all_trucks_ready_event = None  # initialize trucks
-train_has_arrived_event = None    # crane starts working after the train arrives
-train_departure_event = None    # train arrives after the last train departs
-oc_chassis_filled_event = None  # outbound containers fill available chassis before cranes load
-
-
-# Trains
-def train_arrival_parameters(terminal):
-    global train_id_counter
-    timetable = train_timetable(terminal)
+def train_arrival_parameters(train_consist_plan, terminal, train_id_counter):
+    timetable = build_train_timetable(train_consist_plan, terminal, swap_arrive_depart = False, as_dicts = False)
     TRAIN_TIMETABLE = timetable.iloc[train_id_counter-1]
 
     return TRAIN_TIMETABLE
 
+@dataclass
+class LiftsState:
+    # Simulation parameters
+    random_seed: int = 42
+    sim_time: int = 1100
+    terminal: str = 'Allouez'    # choose 'Hibbing' or 'Allouez'
+    # Counting vehicles
+    train_id_counter: int = 1
+    crane_id_counter: int  = 1
+    hostler_id_counter: int  = 1
+    truck_id_counter: int  = 1
+    total_initial_oc_trucks: int  = 1
+    empty_truck_id_counter: int  = 1
+    # inbound container counting
+    inbound_containers_processed: int  = 0    # trucks drop off OC to chassis
+    inbound_containers_hostler_processed: int  = 0    # hostlers pick up IC from chassis
+    inbound_container_id_counter: int  = 1
+    # outbound container counting
+    outbound_container_id_counter: int  = 10001
+    outbound_containers_processed: int  = 0   # trucks pick up IC from chassis
+    outbound_containers_hostler_processed: int  = 0   # hostlers drop off OC to chassis
+    outbound_container_id: int  = 0   # initialize OC id for chassis assignment
+    record_oc_label: int  = 10001     # update outbound_containers_mapping among trains
+    oc_variance: int  = 0     # record previous batch OC numbers cumulatively
+    # yield events or conditions
+    all_trucks_ready_event: simpy.events.Event = None  # initialize trucks
+    train_has_arrived_event: simpy.events.Event = None    # crane starts working after the train arrives
+    train_departure_event: simpy.events.Event = None    # train arrives after the last train departs
+    oc_chassis_filled_event: simpy.events.Event = None  # outbound containers fill available chassis before cranes load
+    # Trains
+    # TRAIN_UNITS = int(input("Enter the number of train units: "))
+    # TRAIN_ARRIVAL_MEAN = 10
+    TRAIN_INSPECTION_TIME: float = 10/60    # hr
+    previous_train_departure: float = 0
+    train_series: int = 0
+    time_per_train: list[float] = field(default_factory = lambda: [])
+    train_delay_time: list[float] = field(default_factory = lambda: [])
+    # Containers
+    CONTAINERS_PER_CAR: int = 1
+    CONTAINER_LEN: float = 20   # 1 TEU = 20 ft long, 8 ft wide, and 8.6 ft tall
+    CONTAINER_WID: float = 8
+    CONTAINER_TAL: float = 8.6
+    container_events: dict = field(default_factory = lambda: {})   # Dictionary to store container event data
+    # Cranes
+    # CRANE_NUMBER = int(input("Enter the number of crane: "))
+    CRANE_NUMBER: int = 10
+    CONTAINERS_PER_CRANE_MOVE_MEAN: float = 600   # 10ft/min = 600 ft/hr, crane speed
+    CRANE_MOVE_DEV_TIME: float = 5/60 # hr
+    outbound_containers_mapping: dict = field(default_factory = lambda: {})  # To keep track of outbound containers ID mapped to chassis
+    # Hostlers
+    # HOSTLER_NUMBER = int(input("Enter the number of hostler: "))
+    HOSTLER_NUMBER: int = 50
+    CONTAINERS_PER_HOSTLER: int = 1  # hostler capacity
+    HOSTLER_SPEED_LIMIT: float = 20*5280   # ft/hr
+    HOSTLER_TRANSPORT_CONTAINER_TIME: float = 0    # hr, triangular distribution
+    HOSTLER_FIND_CONTAINER_TIME: float = 0  # hr, triangular distribution
+    # Trucks
+    TRUCK_ARRIVAL_MEAN: float = 40/60   # hr, calculate by
+    TRUCK_INGATE_TIME: float = 1/60    # hr
+    TRUCK_OUTGATE_TIME: float = 2/60    # hr
+    TRUCK_INGATE_TIME_DEV: float = 1/60    # hr
+    TRUCK_OUTGATE_TIME_DEV: float = 1/60    # hr
+    TRUCK_TO_PARKING: float = 2/60    # hr
+    TRUCK_SPEED_LIMIT: float = 20*5280   # ft/hr
+    TRUCK_TRANSPORT_CONTAINER_TIME: float = 0  # hr, triangular distribution
+    # Gate settings
+    IN_GATE_NUMBERS: int = 6  # test queuing module with 1; normal operations with 6
+    OUT_GATE_NUMBERS: int = 6
+    last_leave_time: float = 0
+    truck_arrival_time: list[float] = field(default_factory = lambda: [])
+    truck_waiting_time: list[float] = field(default_factory = lambda: [])
+    train_consist_plan: pl.DataFrame = field(default_factory = lambda: pl.DataFrame())
+
+    def initialize_from_consist_plan(self, train_consist_plan):
+        self.train_consist_plan = train_consist_plan
+        self.TRAIN_TIMETABLE = train_arrival_parameters(self.train_consist_plan, self.terminal, self.train_id_counter)
+        self.TRAIN_ID = int(self.TRAIN_TIMETABLE['train_id'])
+        self.TRAIN_ID_FIXED = 0
+        self.CARS_LOADED_ARRIVAL = int(float(self.TRAIN_TIMETABLE['full_cars']))
+        self.CARS_EMPTY_ARRIVAL = int(float(self.TRAIN_TIMETABLE['empty_cars']))
+        self.TRAIN_ARRIVAL_HR = self.TRAIN_TIMETABLE['arrival_time']
+        self.TRAIN_DEPARTURE_HR = self.TRAIN_TIMETABLE['departure_time']
+        self.TRAIN_UNITS = self.CARS_LOADED_ARRIVAL + self.CARS_EMPTY_ARRIVAL
+        self.TRAIN_SPOTS = self.TRAIN_UNITS
+
+        # Containers
+        self.INBOUND_CONTAINER_NUMBER = self.CARS_LOADED_ARRIVAL
+        #df = outbound_containers()
+        # TODO: confirm expected source of Outbound_Num; expected input file not available
+        self.OUTBOUND_CONTAINER_NUMBER = self.INBOUND_CONTAINER_NUMBER#df.loc[df['Train_ID'] == TRAIN_ID, 'Outbound_Num'].values[0]
+
+        # Chassis
+        self.CHASSIS_NUMBER = self.TRAIN_UNITS
+        self.chassis_status = [-1] * self.CHASSIS_NUMBER  # -1 means empty, 1 means inbound container, 0 means outbound container
+
+        # Trucks
+        self.TRUCK_NUMBERS = max(self.INBOUND_CONTAINER_NUMBER, self.OUTBOUND_CONTAINER_NUMBER)
+        self.IN_OUT_GAP = abs(self.INBOUND_CONTAINER_NUMBER - self.OUTBOUND_CONTAINER_NUMBER)
+
+    def initialize(self):
+        self.CRANE_LOAD_CONTAINER_TIME_MEAN = (self.CONTAINERS_PER_CAR*(2*self.CONTAINER_TAL+self.CONTAINER_WID))/self.CONTAINERS_PER_CRANE_MOVE_MEAN   # hr
+        self.CRANE_UNLOAD_CONTAINER_TIME_MEAN = (self.CONTAINERS_PER_CAR*(2*self.CONTAINER_TAL+self.CONTAINER_WID))/self.CONTAINERS_PER_CRANE_MOVE_MEAN # hr
+        self.hostler_status = [-1] * self.HOSTLER_NUMBER   # 1 means trackside, 0 means parking side, -1 means hostler resources side
+        # Trains
+        if self.train_consist_plan.height > 0:
+            self.initialize_from_consist_plan()
+
+    def __post_init__(self):
+        self.initialize()
+
+state = LiftsState()
 
-# TRAIN_UNITS = int(input("Enter the number of train units: "))
-TRAIN_TIMETABLE = train_arrival_parameters(terminal)
-TRAIN_ID = int(TRAIN_TIMETABLE['Train_ID'])
-CARS_LOADED_ARRIVAL = int(float(TRAIN_TIMETABLE['Cars_Loaded']))
-CARS_EMPTY_ARRIVAL = int(float(TRAIN_TIMETABLE['Cars_Empty']))
-TRAIN_ARRIVAL_HR = TRAIN_TIMETABLE['Arrival_Time_Actual_Hr']
-TRAIN_DEPARTURE_HR = TRAIN_TIMETABLE['Departure_Time_Actual_Hr']
-
-TRAIN_UNITS = CARS_LOADED_ARRIVAL + CARS_EMPTY_ARRIVAL
-TRAIN_SPOTS = TRAIN_UNITS
-# TRAIN_ARRIVAL_MEAN = 10
-TRAIN_INSPECTION_TIME = 10/60    # hr
-previous_train_departure = 0
-train_series = 0
-time_per_train = []
-train_delay_time = []
-
-
-# Containers
-CONTAINERS_PER_CAR = 1
-CONTAINER_LEN = 20   # 1 TEU = 20 ft long, 8 ft wide, and 8.6 ft tall
-CONTAINER_WID = 8
-CONTAINER_TAL = 8.6
-INBOUND_CONTAINER_NUMBER = CARS_LOADED_ARRIVAL
-df = outbound_containers()
-OUTBOUND_CONTAINER_NUMBER = df.loc[df['Train_ID'] == TRAIN_ID, 'Outbound_Num'].values[0]
-container_events = {}   # Dictionary to store container event data
-
-
-# Chassis
-CHASSIS_NUMBER = TRAIN_UNITS
-chassis_status = [-1] * CHASSIS_NUMBER  # -1 means empty, 1 means inbound container, 0 means outbound container
-
-
-# Cranes
-# CRANE_NUMBER = int(input("Enter the number of crane: "))
-CRANE_NUMBER = 10
-CONTAINERS_PER_CRANE_MOVE_MEAN = 600   # 10ft/min = 600 ft/hr, crane speed
-CRANE_LOAD_CONTAINER_TIME_MEAN = (CONTAINERS_PER_CAR*(2*CONTAINER_TAL+CONTAINER_WID))/CONTAINERS_PER_CRANE_MOVE_MEAN   # hr
-CRANE_UNLOAD_CONTAINER_TIME_MEAN = (CONTAINERS_PER_CAR*(2*CONTAINER_TAL+CONTAINER_WID))/CONTAINERS_PER_CRANE_MOVE_MEAN # hr
-CRANE_MOVE_DEV_TIME = 5/60 # hr
-outbound_containers_mapping = {}  # To keep track of outbound containers ID mapped to chassis
-
-
-# Hostlers
-# HOSTLER_NUMBER = int(input("Enter the number of hostler: "))
-HOSTLER_NUMBER = 50
-CONTAINERS_PER_HOSTLER = 1  # hostler capacity
-HOSTLER_SPEED_LIMIT = 20*5280   # ft/hr
-HOSTLER_TRANSPORT_CONTAINER_TIME = 0    # hr, triangular distribution
-HOSTLER_FIND_CONTAINER_TIME = 0  # hr, triangular distribution
-hostler_status = [-1] * HOSTLER_NUMBER   # 1 means trackside, 0 means parking side, -1 means hostler resources side
-
-
-# Trucks
-TRUCK_ARRIVAL_MEAN = 40/60   # hr, calculate by
-TRUCK_INGATE_TIME = 1/60    # hr
-TRUCK_OUTGATE_TIME = 2/60    # hr
-TRUCK_INGATE_TIME_DEV = 1/60    # hr
-TRUCK_OUTGATE_TIME_DEV = 1/60    # hr
-TRUCK_TO_PARKING = 2/60    # hr
-TRUCK_SPEED_LIMIT = 20*5280   # ft/hr
-TRUCK_TRANSPORT_CONTAINER_TIME = 0  # hr, triangular distribution
-TRUCK_NUMBERS = max(INBOUND_CONTAINER_NUMBER, OUTBOUND_CONTAINER_NUMBER)
-IN_OUT_GAP = abs(INBOUND_CONTAINER_NUMBER - OUTBOUND_CONTAINER_NUMBER)
-
-
-
-# Gate settings
-IN_GATE_NUMBERS = 6  # test queuing module with 1; normal operations with 6
-OUT_GATE_NUMBERS = 6
-last_leave_time = 0
-truck_arrival_time = []
-truck_waiting_time = []
\ No newline at end of file
diff --git a/python/lifts/results/simulation_crane_1_hostler_1.xlsx b/python/lifts/results/simulation_crane_1_hostler_1.xlsx
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diff --git a/python/lifts/results/simulation_crane_1_hostler_7.xlsx b/python/lifts/results/simulation_crane_1_hostler_7.xlsx
deleted file mode 100644
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diff --git a/python/lifts/schedule.py b/python/lifts/schedule.py
index 4dd43b2a..186f34bf 100644
--- a/python/lifts/schedule.py
+++ b/python/lifts/schedule.py
@@ -1,31 +1,46 @@
 import pandas as pd
+import polars as pl
+import lifts.utilities
+import lifts.dictionary
 
-def find_duplicate_numbers(series):
-    duplicates = series[series.duplicated(keep=False)]
-    if not duplicates.empty:
-        return ''.join(map(str, duplicates.unique()))
-    else:
-        return series.iloc[0]
-
+def build_train_timetable(train_consist_plan, terminal, swap_arrive_depart, as_dicts):
+    df = (train_consist_plan
+        .filter(
+            pl.col("Destination_ID") == pl.lit(terminal), 
+            pl.col("Train_Type").str.starts_with(pl.lit("Intermodal"))
+        )
+        .rename({
+            "Train_ID": "train_id",
+            "Departure_Time_Actual_Hr": "departure_time",
+            "Arrival_Time_Actual_Hr": "arrival_time",
+            "Cars_Empty": "empty_cars",
+            "Cars_Loaded": "full_cars"
+        })
+    ) 
 
-def train_timetable(terminal):
-    df = pd.read_csv('C:/Users/Irena Tong/PycharmProjects/simulation_test/data/train_consist_plan.csv')
-    df_terminal = df[(df['Destination_ID'] == terminal) & (df['Train_Type'] == 'Intermodal')]  # Filter by the selected terminal and intermodal type railcar
+    if swap_arrive_depart:
+        df = df.rename({"departure_time": "arrival_time", "arrival_time": "departure_time"})
 
-    df_grouped = df_terminal.groupby('Train_ID').agg({
-        'Cars_Loaded': find_duplicate_numbers,
-        'Cars_Empty': find_duplicate_numbers,
-        'Arrival_Time_Actual_Hr': 'min',
-        'Departure_Time_Actual_Hr': 'min'
-    }).reset_index()
+    df = (df
+        .group_by("train_id")
+            .agg(pl.col("full_cars", "empty_cars", "arrival_time", "departure_time").first())
+        .sort("arrival_time", descending=False)
+    )
 
-    df_grouped_sorted = df_grouped.sort_values(by='Arrival_Time_Actual_Hr', ascending=True)
-
-    return df_grouped_sorted
+    if as_dicts:
+        return (df
+            .with_columns(
+                pl.lit(lifts.dictionary.calculate_oc_number()).alias("oc_number"),
+            )
+            .pipe(lifts.dictionary.calculate_truck_number)
+            .to_dicts()
+        )
+    else:
+        return df.to_pandas()
 
 
 def next_train_timetable(train_id, terminal):
-    df_terminal = train_timetable(terminal)
+    df_terminal = build_train_timetable(terminal, swap_arrive_depart = False, as_dicts = False)
     df_next_train = df_terminal.iloc[train_id]
     return df_next_train
 
diff --git a/python/lifts/test.py b/python/lifts/test.py
index 8ee12be9..33b09e24 100644
--- a/python/lifts/test.py
+++ b/python/lifts/test.py
@@ -1,9 +1,10 @@
 import simpy
 import random
-from parameters import *
-from distances import *
-from dictionary import *
-from vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
+from lifts.parameters import *
+from lifts.distances import *
+#from dictionary import *
+from lifts.schedule import *
+from lifts.vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
 
 
 # Test input
@@ -545,14 +546,14 @@ def run_simulation():
     # ]
 
     # REAL TEST
-    train_timetable = timetable(terminal)
-    TRAIN_NUMBERS = len(timetable(terminal))
+    train_timetable = build_train_timetable(pl.read_csv(utilities.package_root() / 'demos' / 'starter_demo' / 'train_consist_plan.csv'), terminal, swap_arrive_depart = True, as_dicts = True)
+    TRAIN_NUMBERS = len(train_timetable)
 
     # env.process(train_arrival(env, train_processing, cranes, in_gate_resource, outbound_containers_store, truck_store, train_timetable))
     env.process(train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource,
                   outbound_containers_store, truck_store, out_gate_resource))
 
-    env.run(until=SIM_TIME)
+    env.run(until=state.sim_time)
 
     # Performance Matrix: train processing time
     avg_time_per_train = sum(time_per_train) / len(time_per_train)
diff --git a/python/lifts/utilities.py b/python/lifts/utilities.py
new file mode 100644
index 00000000..428d4c8f
--- /dev/null
+++ b/python/lifts/utilities.py
@@ -0,0 +1,17 @@
+"""Module for general functions, classes, and unit conversion factors."""
+from pathlib import Path
+
+def package_root() -> Path:
+    """
+    Returns the package root directory.
+    """
+    path = Path(__file__).parent
+    return path
+
+
+def resources_root() -> Path:
+    """
+    Returns the resources root directory.
+    """
+    path = package_root() / "resources"
+    return path
diff --git a/rust/altrios-core/src/train/train_config.rs b/rust/altrios-core/src/train/train_config.rs
index a615e22b..a912553b 100644
--- a/rust/altrios-core/src/train/train_config.rs
+++ b/rust/altrios-core/src/train/train_config.rs
@@ -1030,7 +1030,7 @@ pub fn run_speed_limit_train_sims(
                     "Node",
                     "Locomotive_Type",
                     "Fuel_Type",
-                ])) + (col("Status").eq(lit("Queued")).cumsum(false).over([
+                ])) + (col("Status").eq(lit("Queued")).cum_sum(false).over([
                     "Node",
                     "Locomotive_Type",
                     "Fuel_Type",
diff --git a/truck_work_log.txt b/truck_work_log.txt
new file mode 100644
index 00000000..3c771784
--- /dev/null
+++ b/truck_work_log.txt
@@ -0,0 +1,498 @@
+Vehicle ID: 1, Event Type: start, Timestamp: 174.3006195900879
+Vehicle ID: 2, Event Type: start, Timestamp: 174.3018535706501
+Vehicle ID: 1, Event Type: end, Timestamp: 174.37599772819118
+Vehicle ID: 2, Event Type: end, Timestamp: 174.37894880350387
+Vehicle ID: 3, Event Type: start, Timestamp: 174.4938586978192
+Vehicle ID: 4, Event Type: start, Timestamp: 174.5012630658423
+Vehicle ID: 5, Event Type: start, Timestamp: 174.50631503585575
+Vehicle ID: 6, Event Type: start, Timestamp: 174.52647821931592
+Vehicle ID: 3, Event Type: end, Timestamp: 174.57278528980711
+Vehicle ID: 4, Event Type: end, Timestamp: 174.57681583766907
+Vehicle ID: 5, Event Type: end, Timestamp: 174.5866396078987
+Vehicle ID: 7, Event Type: start, Timestamp: 174.60485360147638
+Vehicle ID: 6, Event Type: end, Timestamp: 174.60556601585935
+Vehicle ID: 8, Event Type: start, Timestamp: 174.6230875714389
+Vehicle ID: 9, Event Type: start, Timestamp: 174.62863283123264
+Vehicle ID: 10, Event Type: start, Timestamp: 174.66033048077077
+Vehicle ID: 11, Event Type: start, Timestamp: 174.66125789393578
+Vehicle ID: 12, Event Type: start, Timestamp: 174.67159404774407
+Vehicle ID: 7, Event Type: end, Timestamp: 174.67768945799153
+Vehicle ID: 13, Event Type: start, Timestamp: 174.68089290992867
+Vehicle ID: 14, Event Type: start, Timestamp: 174.68358240707497
+Vehicle ID: 8, Event Type: end, Timestamp: 174.69141909979578
+Vehicle ID: 15, Event Type: start, Timestamp: 174.70295259301363
+Vehicle ID: 9, Event Type: end, Timestamp: 174.71270921883288
+Vehicle ID: 16, Event Type: start, Timestamp: 174.7170027584468
+Vehicle ID: 17, Event Type: start, Timestamp: 174.72348337441787
+Vehicle ID: 11, Event Type: end, Timestamp: 174.7292178105025
+Vehicle ID: 18, Event Type: start, Timestamp: 174.7374963042857
+Vehicle ID: 10, Event Type: end, Timestamp: 174.74129691199275
+Vehicle ID: 19, Event Type: start, Timestamp: 174.74160939596376
+Vehicle ID: 20, Event Type: start, Timestamp: 174.74348904622886
+Vehicle ID: 12, Event Type: end, Timestamp: 174.74506797816073
+Vehicle ID: 14, Event Type: end, Timestamp: 174.75468770224018
+Vehicle ID: 13, Event Type: end, Timestamp: 174.75691395889328
+Vehicle ID: 21, Event Type: start, Timestamp: 174.77966697447295
+Vehicle ID: 22, Event Type: start, Timestamp: 174.78152605363738
+Vehicle ID: 15, Event Type: end, Timestamp: 174.78264643626582
+Vehicle ID: 23, Event Type: start, Timestamp: 174.786144624434
+Vehicle ID: 24, Event Type: start, Timestamp: 174.79158206833358
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diff --git a/vehicle_average_times.txt b/vehicle_average_times.txt
new file mode 100644
index 00000000..7d86c645
--- /dev/null
+++ b/vehicle_average_times.txt
@@ -0,0 +1,3 @@
+crane: 0.0
+hostler: 0.0018928077738425498
+truck: 0.013760017918324973

From a8794d3db32b70c11a62d7fa2af187f7f3ff90a9 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 2 Dec 2024 17:42:54 -0500
Subject: [PATCH 22/50] moved LIFTS to subdir

---
 .gitignore                                    |    3 +
 avg_time_per_train.txt                        |    2 +-
 crane_work_log.txt                            | 1404 +++++++--------
 hostler_work_log.txt                          | 1508 ++++++++---------
 python/altrios/demos/sim_manager_demo.py      |    2 +-
 python/{ => altrios}/lifts/__init__.py        |    0
 .../demos/starter_demo/train_consist_plan.csv |    0
 python/{ => altrios}/lifts/dictionary.py      |    0
 python/{ => altrios}/lifts/distances.py       |    0
 python/{ => altrios}/lifts/lifts_simulator.py |   14 +-
 python/{ => altrios}/lifts/merged_baseline.py |    0
 python/{ => altrios}/lifts/parameters.py      |    2 +-
 python/{ => altrios}/lifts/schedule.py        |    3 +-
 python/{ => altrios}/lifts/test.py            |    0
 python/{ => altrios}/lifts/utilities.py       |    0
 .../lifts/vehicle_performance.py              |    0
 python/lifts/avg_time_per_train.txt           |    1 -
 .../crane_hostler_combination_results.xlsx    |  Bin 5603 -> 0 bytes
 python/lifts/crane_work_log.txt               |  756 ---------
 python/lifts/hostler_work_log.txt             |  774 ---------
 .../results/simulation_crane_2_hostler_3.xlsx |  Bin 53916 -> 0 bytes
 .../results/simulation_crane_2_hostler_4.xlsx |  Bin 53957 -> 0 bytes
 .../results/simulation_crane_3_hostler_1.xlsx |  Bin 53851 -> 0 bytes
 .../results/simulation_crane_3_hostler_2.xlsx |  Bin 53812 -> 0 bytes
 .../results/simulation_crane_3_hostler_3.xlsx |  Bin 53810 -> 0 bytes
 .../results/simulation_crane_3_hostler_4.xlsx |  Bin 53859 -> 0 bytes
 python/lifts/truck_work_log.txt               |  516 ------
 python/lifts/vehicle_average_times.txt        |    3 -
 truck_work_log.txt                            |  996 +++++------
 vehicle_average_times.txt                     |    4 +-
 30 files changed, 1970 insertions(+), 4018 deletions(-)
 rename python/{ => altrios}/lifts/__init__.py (100%)
 rename python/{ => altrios}/lifts/demos/starter_demo/train_consist_plan.csv (100%)
 rename python/{ => altrios}/lifts/dictionary.py (100%)
 rename python/{ => altrios}/lifts/distances.py (100%)
 rename python/{ => altrios}/lifts/lifts_simulator.py (98%)
 rename python/{ => altrios}/lifts/merged_baseline.py (100%)
 rename python/{ => altrios}/lifts/parameters.py (99%)
 rename python/{ => altrios}/lifts/schedule.py (97%)
 rename python/{ => altrios}/lifts/test.py (100%)
 rename python/{ => altrios}/lifts/utilities.py (100%)
 rename python/{ => altrios}/lifts/vehicle_performance.py (100%)
 delete mode 100644 python/lifts/avg_time_per_train.txt
 delete mode 100644 python/lifts/crane_hostler_combination_results.xlsx
 delete mode 100644 python/lifts/crane_work_log.txt
 delete mode 100644 python/lifts/hostler_work_log.txt
 delete mode 100644 python/lifts/results/simulation_crane_2_hostler_3.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_2_hostler_4.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_3_hostler_1.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_3_hostler_2.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_3_hostler_3.xlsx
 delete mode 100644 python/lifts/results/simulation_crane_3_hostler_4.xlsx
 delete mode 100644 python/lifts/truck_work_log.txt
 delete mode 100644 python/lifts/vehicle_average_times.txt

diff --git a/.gitignore b/.gitignore
index ead9b2cc..a9e03c2c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1217,3 +1217,6 @@ code\ meetings
 altrios/resources/networks/Scenic - ALTRIOS Confidential.yaml
 altrios/dispatch/*.json
 activate.sh
+*.xlsx
+*.txt
+*.txt
diff --git a/avg_time_per_train.txt b/avg_time_per_train.txt
index c49a2166..849cc94e 100644
--- a/avg_time_per_train.txt
+++ b/avg_time_per_train.txt
@@ -1 +1 @@
-17.47981863315145
\ No newline at end of file
+17.43808370612796
\ No newline at end of file
diff --git a/crane_work_log.txt b/crane_work_log.txt
index efdfc523..30449505 100644
--- a/crane_work_log.txt
+++ b/crane_work_log.txt
@@ -1,207 +1,207 @@
-Vehicle ID: 3, Event Type: start, Timestamp: 174.2810811099409
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diff --git a/hostler_work_log.txt b/hostler_work_log.txt
index c361ca4b..ee526025 100644
--- a/hostler_work_log.txt
+++ b/hostler_work_log.txt
@@ -1,754 +1,754 @@
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diff --git a/python/altrios/demos/sim_manager_demo.py b/python/altrios/demos/sim_manager_demo.py
index 247d8154..70b6048a 100644
--- a/python/altrios/demos/sim_manager_demo.py
+++ b/python/altrios/demos/sim_manager_demo.py
@@ -2,7 +2,7 @@
 from altrios import sim_manager
 from altrios import utilities, defaults, train_planner
 import altrios as alt
-from lifts import lifts_simulator
+from altrios.lifts import lifts_simulator
 import numpy as np
 import matplotlib.pyplot as plt
 import time
diff --git a/python/lifts/__init__.py b/python/altrios/lifts/__init__.py
similarity index 100%
rename from python/lifts/__init__.py
rename to python/altrios/lifts/__init__.py
diff --git a/python/lifts/demos/starter_demo/train_consist_plan.csv b/python/altrios/lifts/demos/starter_demo/train_consist_plan.csv
similarity index 100%
rename from python/lifts/demos/starter_demo/train_consist_plan.csv
rename to python/altrios/lifts/demos/starter_demo/train_consist_plan.csv
diff --git a/python/lifts/dictionary.py b/python/altrios/lifts/dictionary.py
similarity index 100%
rename from python/lifts/dictionary.py
rename to python/altrios/lifts/dictionary.py
diff --git a/python/lifts/distances.py b/python/altrios/lifts/distances.py
similarity index 100%
rename from python/lifts/distances.py
rename to python/altrios/lifts/distances.py
diff --git a/python/lifts/lifts_simulator.py b/python/altrios/lifts/lifts_simulator.py
similarity index 98%
rename from python/lifts/lifts_simulator.py
rename to python/altrios/lifts/lifts_simulator.py
index bd3b1c59..b636c8d7 100644
--- a/python/lifts/lifts_simulator.py
+++ b/python/altrios/lifts/lifts_simulator.py
@@ -1,12 +1,12 @@
 import simpy
 import random
 import polars as pl
-from lifts import utilities
-from lifts.parameters import *
-from lifts.distances import *
-from lifts.dictionary import *
-from lifts.schedule import *
-from lifts.vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
+from altrios.lifts import utilities
+from altrios.lifts.parameters import *
+from altrios.lifts.distances import *
+from altrios.lifts.dictionary import *
+from altrios.lifts.schedule import *
+from altrios.lifts.vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
 
 
 # Test input
@@ -579,7 +579,7 @@ def run_simulation(
         f.write(str(avg_time_per_train))
 
     # Create DataFrame for container events
-    events_list = [(container_id, events) for container_id, events in sorted(state.container_events.items())]
+    events_list = [(container_id, events) for container_id, events in state.container_events.items()]
     container_data = (
         pl.concat([
             pl.DataFrame(data = None, schema = {
diff --git a/python/lifts/merged_baseline.py b/python/altrios/lifts/merged_baseline.py
similarity index 100%
rename from python/lifts/merged_baseline.py
rename to python/altrios/lifts/merged_baseline.py
diff --git a/python/lifts/parameters.py b/python/altrios/lifts/parameters.py
similarity index 99%
rename from python/lifts/parameters.py
rename to python/altrios/lifts/parameters.py
index 1c1c3f2e..550151e3 100644
--- a/python/lifts/parameters.py
+++ b/python/altrios/lifts/parameters.py
@@ -1,7 +1,7 @@
 import polars
 import simpy
 from dataclasses import dataclass, field
-from lifts.schedule import *
+from altrios.lifts.schedule import *
 
 def train_arrival_parameters(train_consist_plan, terminal, train_id_counter):
     timetable = build_train_timetable(train_consist_plan, terminal, swap_arrive_depart = False, as_dicts = False)
diff --git a/python/lifts/schedule.py b/python/altrios/lifts/schedule.py
similarity index 97%
rename from python/lifts/schedule.py
rename to python/altrios/lifts/schedule.py
index 186f34bf..4e796612 100644
--- a/python/lifts/schedule.py
+++ b/python/altrios/lifts/schedule.py
@@ -1,7 +1,6 @@
 import pandas as pd
 import polars as pl
-import lifts.utilities
-import lifts.dictionary
+import altrios.lifts as lifts
 
 def build_train_timetable(train_consist_plan, terminal, swap_arrive_depart, as_dicts):
     df = (train_consist_plan
diff --git a/python/lifts/test.py b/python/altrios/lifts/test.py
similarity index 100%
rename from python/lifts/test.py
rename to python/altrios/lifts/test.py
diff --git a/python/lifts/utilities.py b/python/altrios/lifts/utilities.py
similarity index 100%
rename from python/lifts/utilities.py
rename to python/altrios/lifts/utilities.py
diff --git a/python/lifts/vehicle_performance.py b/python/altrios/lifts/vehicle_performance.py
similarity index 100%
rename from python/lifts/vehicle_performance.py
rename to python/altrios/lifts/vehicle_performance.py
diff --git a/python/lifts/avg_time_per_train.txt b/python/lifts/avg_time_per_train.txt
deleted file mode 100644
index 18e88105..00000000
--- a/python/lifts/avg_time_per_train.txt
+++ /dev/null
@@ -1 +0,0 @@
-137.44757283817884
\ No newline at end of file
diff --git a/python/lifts/crane_hostler_combination_results.xlsx b/python/lifts/crane_hostler_combination_results.xlsx
deleted file mode 100644
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diff --git a/python/lifts/crane_work_log.txt b/python/lifts/crane_work_log.txt
deleted file mode 100644
index bb9acdea..00000000
--- a/python/lifts/crane_work_log.txt
+++ /dev/null
@@ -1,756 +0,0 @@
-Vehicle ID: 1, Event Type: start, Timestamp: 181.18810094866248
-Vehicle ID: 1, Event Type: end, Timestamp: 181.18810094866248
-Vehicle ID: 1, Event Type: start, Timestamp: 181.2169499739688
-Vehicle ID: 1, Event Type: end, Timestamp: 181.2169499739688
-Vehicle ID: 1, Event Type: start, Timestamp: 181.2395633995604
-Vehicle ID: 1, Event Type: end, Timestamp: 181.2395633995604
-Vehicle ID: 1, Event Type: start, Timestamp: 181.2415911497569
-Vehicle ID: 1, Event Type: end, Timestamp: 181.2415911497569
-Vehicle ID: 1, Event Type: start, Timestamp: 181.27010127953702
-Vehicle ID: 1, Event Type: end, Timestamp: 181.27010127953702
-Vehicle ID: 1, Event Type: start, Timestamp: 181.2966303423458
-Vehicle ID: 1, Event Type: end, Timestamp: 181.2966303423458
-Vehicle ID: 1, Event Type: start, Timestamp: 181.29973002923202
-Vehicle ID: 1, Event Type: end, Timestamp: 181.29973002923202
-Vehicle ID: 1, Event Type: start, Timestamp: 181.3269712046554
-Vehicle ID: 1, Event Type: end, Timestamp: 181.3269712046554
-Vehicle ID: 1, Event Type: start, Timestamp: 181.37156978383837
-Vehicle ID: 1, Event Type: end, Timestamp: 181.37156978383837
-Vehicle ID: 1, Event Type: start, Timestamp: 181.39569742271803
-Vehicle ID: 1, Event Type: end, Timestamp: 181.39569742271803
-Vehicle ID: 1, Event Type: start, Timestamp: 181.39849686069468
-Vehicle ID: 1, Event Type: end, Timestamp: 181.39849686069468
-Vehicle ID: 1, Event Type: start, Timestamp: 181.43286543187185
-Vehicle ID: 1, Event Type: end, Timestamp: 181.43286543187185
-Vehicle ID: 1, Event Type: start, Timestamp: 181.4446206161728
-Vehicle ID: 1, Event Type: end, Timestamp: 181.4446206161728
-Vehicle ID: 1, Event Type: start, Timestamp: 181.4505608513666
-Vehicle ID: 1, Event Type: end, Timestamp: 181.4505608513666
-Vehicle ID: 1, Event Type: start, Timestamp: 181.45317898131094
-Vehicle ID: 1, Event Type: end, Timestamp: 181.45317898131094
-Vehicle ID: 1, Event Type: start, Timestamp: 181.4620961590926
-Vehicle ID: 1, Event Type: end, Timestamp: 181.4620961590926
-Vehicle ID: 1, Event Type: start, Timestamp: 181.4628185105591
-Vehicle ID: 1, Event Type: end, Timestamp: 181.4628185105591
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diff --git a/python/lifts/hostler_work_log.txt b/python/lifts/hostler_work_log.txt
deleted file mode 100644
index aca04ba6..00000000
--- a/python/lifts/hostler_work_log.txt
+++ /dev/null
@@ -1,774 +0,0 @@
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diff --git a/python/lifts/truck_work_log.txt b/python/lifts/truck_work_log.txt
deleted file mode 100644
index 1921429e..00000000
--- a/python/lifts/truck_work_log.txt
+++ /dev/null
@@ -1,516 +0,0 @@
-Vehicle ID: 1, Event Type: start, Timestamp: 181.20762966437408
-Vehicle ID: 2, Event Type: start, Timestamp: 181.23590430565247
-Vehicle ID: 3, Event Type: start, Timestamp: 181.25914226903416
-Vehicle ID: 4, Event Type: start, Timestamp: 181.26141624469523
-Vehicle ID: 5, Event Type: start, Timestamp: 181.2889374519489
-Vehicle ID: 1, Event Type: end, Timestamp: 181.29090488153685
-Vehicle ID: 2, Event Type: end, Timestamp: 181.3089410913137
-Vehicle ID: 6, Event Type: start, Timestamp: 181.31540550576455
-Vehicle ID: 7, Event Type: start, Timestamp: 181.31885874199523
-Vehicle ID: 4, Event Type: end, Timestamp: 181.32968709161304
-Vehicle ID: 3, Event Type: end, Timestamp: 181.33402274089988
-Vehicle ID: 8, Event Type: start, Timestamp: 181.3455036131865
-Vehicle ID: 5, Event Type: end, Timestamp: 181.35791702524796
-Vehicle ID: 9, Event Type: start, Timestamp: 181.39039317200695
-Vehicle ID: 6, Event Type: end, Timestamp: 181.39298489498702
-Vehicle ID: 7, Event Type: end, Timestamp: 181.40013328243023
-Vehicle ID: 10, Event Type: start, Timestamp: 181.41439952986295
-Vehicle ID: 11, Event Type: start, Timestamp: 181.41654930765236
-Vehicle ID: 8, Event Type: end, Timestamp: 181.41765104905753
-Vehicle ID: 12, Event Type: start, Timestamp: 181.45142186498248
-Vehicle ID: 13, Event Type: start, Timestamp: 181.4638491511918
-Vehicle ID: 9, Event Type: end, Timestamp: 181.46545213481258
-Vehicle ID: 14, Event Type: start, Timestamp: 181.46925124344637
-Vehicle ID: 15, Event Type: start, Timestamp: 181.4710793058109
-Vehicle ID: 16, Event Type: start, Timestamp: 181.4802590408556
-Vehicle ID: 17, Event Type: start, Timestamp: 181.48263272863804
-Vehicle ID: 10, Event Type: end, Timestamp: 181.48423034618676
-Vehicle ID: 18, Event Type: start, Timestamp: 181.48546816390362
-Vehicle ID: 11, Event Type: end, Timestamp: 181.48957557126113
-Vehicle ID: 19, Event Type: start, Timestamp: 181.491902211976
-Vehicle ID: 20, Event Type: start, Timestamp: 181.49734044684155
-Vehicle ID: 21, Event Type: start, Timestamp: 181.50196823137028
-Vehicle ID: 22, Event Type: start, Timestamp: 181.50665798530375
-Vehicle ID: 23, Event Type: start, Timestamp: 181.5129637653224
-Vehicle ID: 24, Event Type: start, Timestamp: 181.51402422060494
-Vehicle ID: 12, Event Type: end, Timestamp: 181.53055211313577
-Vehicle ID: 25, Event Type: start, Timestamp: 181.53220677773788
-Vehicle ID: 26, Event Type: start, Timestamp: 181.5342749940308
-Vehicle ID: 13, Event Type: end, Timestamp: 181.53551276294726
-Vehicle ID: 27, Event Type: start, Timestamp: 181.53634019406084
-Vehicle ID: 15, Event Type: end, Timestamp: 181.53898201667218
-Vehicle ID: 28, Event Type: start, Timestamp: 181.54470984611564
-Vehicle ID: 14, Event Type: end, Timestamp: 181.54805248025957
-Vehicle ID: 17, Event Type: end, Timestamp: 181.55297052635373
-Vehicle ID: 29, Event Type: start, Timestamp: 181.558120583069
-Vehicle ID: 30, Event Type: start, Timestamp: 181.56001882271235
-Vehicle ID: 16, Event Type: end, Timestamp: 181.56327882536934
-Vehicle ID: 31, Event Type: start, Timestamp: 181.5644062026162
-Vehicle ID: 19, Event Type: end, Timestamp: 181.5696363568485
-Vehicle ID: 32, Event Type: start, Timestamp: 181.5699344088513
-Vehicle ID: 18, Event Type: end, Timestamp: 181.57058246262577
-Vehicle ID: 20, Event Type: end, Timestamp: 181.57267985756312
-Vehicle ID: 21, Event Type: end, Timestamp: 181.58294413450884
-Vehicle ID: 33, Event Type: start, Timestamp: 181.58531936297175
-Vehicle ID: 22, Event Type: end, Timestamp: 181.5994309553583
-Vehicle ID: 23, Event Type: end, Timestamp: 181.60246002872935
-Vehicle ID: 34, Event Type: start, Timestamp: 181.6037158783294
-Vehicle ID: 26, Event Type: end, Timestamp: 181.6066775136644
-Vehicle ID: 24, Event Type: end, Timestamp: 181.61357905007148
-Vehicle ID: 35, Event Type: start, Timestamp: 181.6137564369342
-Vehicle ID: 25, Event Type: end, Timestamp: 181.61633826219486
-Vehicle ID: 36, Event Type: start, Timestamp: 181.6174506829965
-Vehicle ID: 27, Event Type: end, Timestamp: 181.62493425494023
-Vehicle ID: 37, Event Type: start, Timestamp: 181.6432010292027
-Vehicle ID: 28, Event Type: end, Timestamp: 181.64600294425836
-Vehicle ID: 29, Event Type: end, Timestamp: 181.64907029463433
-Vehicle ID: 38, Event Type: start, Timestamp: 181.64933662681273
-Vehicle ID: 30, Event Type: end, Timestamp: 181.64972598420965
-Vehicle ID: 31, Event Type: end, Timestamp: 181.6547542581203
-Vehicle ID: 32, Event Type: end, Timestamp: 181.66424047654388
-Vehicle ID: 39, Event Type: start, Timestamp: 181.67262390601715
-Vehicle ID: 33, Event Type: end, Timestamp: 181.67289300461326
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-Vehicle ID: 199, Event Type: start, Timestamp: 731.6722326502903
-Vehicle ID: 189, Event Type: end, Timestamp: 731.674914894571
-Vehicle ID: 200, Event Type: start, Timestamp: 731.6787774975335
-Vehicle ID: 201, Event Type: start, Timestamp: 731.6846495459474
-Vehicle ID: 190, Event Type: end, Timestamp: 731.6863266635494
-Vehicle ID: 191, Event Type: end, Timestamp: 731.6876740113173
-Vehicle ID: 193, Event Type: end, Timestamp: 731.6980321669507
-Vehicle ID: 202, Event Type: start, Timestamp: 731.7011931730825
-Vehicle ID: 203, Event Type: start, Timestamp: 731.7070376889019
-Vehicle ID: 192, Event Type: end, Timestamp: 731.711005555445
-Vehicle ID: 204, Event Type: start, Timestamp: 731.7121240451645
-Vehicle ID: 194, Event Type: end, Timestamp: 731.7165324881539
-Vehicle ID: 196, Event Type: end, Timestamp: 731.7282666212607
-Vehicle ID: 197, Event Type: end, Timestamp: 731.7320847220542
-Vehicle ID: 195, Event Type: end, Timestamp: 731.7333344680786
-Vehicle ID: 205, Event Type: start, Timestamp: 731.733635567469
-Vehicle ID: 198, Event Type: end, Timestamp: 731.7430616206983
-Vehicle ID: 199, Event Type: end, Timestamp: 731.7504785158834
-Vehicle ID: 200, Event Type: end, Timestamp: 731.753525202983
-Vehicle ID: 206, Event Type: start, Timestamp: 731.7550382311888
-Vehicle ID: 207, Event Type: start, Timestamp: 731.7587422612811
-Vehicle ID: 208, Event Type: start, Timestamp: 731.7613513086484
-Vehicle ID: 201, Event Type: end, Timestamp: 731.7644659050379
-Vehicle ID: 209, Event Type: start, Timestamp: 731.7716025196089
-Vehicle ID: 203, Event Type: end, Timestamp: 731.7773611012453
-Vehicle ID: 210, Event Type: start, Timestamp: 731.7789768680533
-Vehicle ID: 204, Event Type: end, Timestamp: 731.7801695096061
-Vehicle ID: 202, Event Type: end, Timestamp: 731.7822432423932
-Vehicle ID: 205, Event Type: end, Timestamp: 731.8103973016393
-Vehicle ID: 207, Event Type: end, Timestamp: 731.8323205779512
-Vehicle ID: 206, Event Type: end, Timestamp: 731.8323492215056
-Vehicle ID: 208, Event Type: end, Timestamp: 731.8368469931553
-Vehicle ID: 211, Event Type: start, Timestamp: 731.8506631438172
-Vehicle ID: 209, Event Type: end, Timestamp: 731.8533912800165
-Vehicle ID: 210, Event Type: end, Timestamp: 731.8615173630737
-Vehicle ID: 212, Event Type: start, Timestamp: 731.8619043264291
-Vehicle ID: 213, Event Type: start, Timestamp: 731.8819184234552
-Vehicle ID: 214, Event Type: start, Timestamp: 731.8848013266636
-Vehicle ID: 211, Event Type: end, Timestamp: 731.9220918781584
-Vehicle ID: 212, Event Type: end, Timestamp: 731.9396483176737
-Vehicle ID: 215, Event Type: start, Timestamp: 731.9473388684119
-Vehicle ID: 214, Event Type: end, Timestamp: 731.9571503075977
-Vehicle ID: 213, Event Type: end, Timestamp: 731.9654800169269
-Vehicle ID: 215, Event Type: end, Timestamp: 732.0301626663964
-Vehicle ID: 216, Event Type: start, Timestamp: 867.189960513288
-Vehicle ID: 217, Event Type: start, Timestamp: 867.218506664947
-Vehicle ID: 218, Event Type: start, Timestamp: 867.2364028217129
-Vehicle ID: 219, Event Type: start, Timestamp: 867.2409546451638
-Vehicle ID: 216, Event Type: end, Timestamp: 867.2630736338901
-Vehicle ID: 220, Event Type: start, Timestamp: 867.2671171384599
-Vehicle ID: 221, Event Type: start, Timestamp: 867.2814237584603
-Vehicle ID: 222, Event Type: start, Timestamp: 867.2835471697003
-Vehicle ID: 217, Event Type: end, Timestamp: 867.2965602728872
-Vehicle ID: 218, Event Type: end, Timestamp: 867.3079055393721
-Vehicle ID: 223, Event Type: start, Timestamp: 867.3175150606862
-Vehicle ID: 219, Event Type: end, Timestamp: 867.3225785464335
-Vehicle ID: 224, Event Type: start, Timestamp: 867.3325259421268
-Vehicle ID: 220, Event Type: end, Timestamp: 867.3378779258128
-Vehicle ID: 225, Event Type: start, Timestamp: 867.339978833602
-Vehicle ID: 226, Event Type: start, Timestamp: 867.343867701678
-Vehicle ID: 227, Event Type: start, Timestamp: 867.3498866925981
-Vehicle ID: 221, Event Type: end, Timestamp: 867.3521993609777
-Vehicle ID: 228, Event Type: start, Timestamp: 867.3538485248183
-Vehicle ID: 229, Event Type: start, Timestamp: 867.3548888028545
-Vehicle ID: 222, Event Type: end, Timestamp: 867.3556252880882
-Vehicle ID: 230, Event Type: start, Timestamp: 867.3565942727404
-Vehicle ID: 231, Event Type: start, Timestamp: 867.357954794141
-Vehicle ID: 232, Event Type: start, Timestamp: 867.3597166344651
-Vehicle ID: 233, Event Type: start, Timestamp: 867.3622886872519
-Vehicle ID: 234, Event Type: start, Timestamp: 867.3758222867242
-Vehicle ID: 223, Event Type: end, Timestamp: 867.3869931667249
-Vehicle ID: 235, Event Type: start, Timestamp: 867.3888390890435
-Vehicle ID: 224, Event Type: end, Timestamp: 867.4076097189406
-Vehicle ID: 225, Event Type: end, Timestamp: 867.4103728348342
-Vehicle ID: 226, Event Type: end, Timestamp: 867.4192942482355
-Vehicle ID: 236, Event Type: start, Timestamp: 867.4215043047354
-Vehicle ID: 228, Event Type: end, Timestamp: 867.4225785264813
-Vehicle ID: 237, Event Type: start, Timestamp: 867.4244166970742
-Vehicle ID: 238, Event Type: start, Timestamp: 867.4269279644499
-Vehicle ID: 229, Event Type: end, Timestamp: 867.4297301005902
-Vehicle ID: 239, Event Type: start, Timestamp: 867.4298442666981
-Vehicle ID: 227, Event Type: end, Timestamp: 867.4317553409334
-Vehicle ID: 240, Event Type: start, Timestamp: 867.4319692447515
-Vehicle ID: 241, Event Type: start, Timestamp: 867.4348914646226
-Vehicle ID: 230, Event Type: end, Timestamp: 867.4446455149076
-Vehicle ID: 231, Event Type: end, Timestamp: 867.4463213165316
-Vehicle ID: 232, Event Type: end, Timestamp: 867.4578403580267
-Vehicle ID: 242, Event Type: start, Timestamp: 867.4608254411235
-Vehicle ID: 233, Event Type: end, Timestamp: 867.4625749041262
-Vehicle ID: 243, Event Type: start, Timestamp: 867.4633069496986
-Vehicle ID: 235, Event Type: end, Timestamp: 867.4682295126746
-Vehicle ID: 244, Event Type: start, Timestamp: 867.4706048219009
-Vehicle ID: 245, Event Type: start, Timestamp: 867.4717522686236
-Vehicle ID: 234, Event Type: end, Timestamp: 867.4721045794576
-Vehicle ID: 246, Event Type: start, Timestamp: 867.4765205130199
-Vehicle ID: 247, Event Type: start, Timestamp: 867.4959853323988
-Vehicle ID: 248, Event Type: start, Timestamp: 867.4978846704718
-Vehicle ID: 238, Event Type: end, Timestamp: 867.5006738000448
-Vehicle ID: 236, Event Type: end, Timestamp: 867.5037927355726
-Vehicle ID: 237, Event Type: end, Timestamp: 867.5062447163518
-Vehicle ID: 239, Event Type: end, Timestamp: 867.5085793503753
-Vehicle ID: 249, Event Type: start, Timestamp: 867.5103989468295
-Vehicle ID: 241, Event Type: end, Timestamp: 867.5109324889605
-Vehicle ID: 240, Event Type: end, Timestamp: 867.5167079648502
-Vehicle ID: 250, Event Type: start, Timestamp: 867.5171372289639
-Vehicle ID: 251, Event Type: start, Timestamp: 867.5261651340857
-Vehicle ID: 242, Event Type: end, Timestamp: 867.5410381298009
-Vehicle ID: 245, Event Type: end, Timestamp: 867.5455123170394
-Vehicle ID: 244, Event Type: end, Timestamp: 867.5455630187816
-Vehicle ID: 243, Event Type: end, Timestamp: 867.5474615918032
-Vehicle ID: 246, Event Type: end, Timestamp: 867.5564958793676
-Vehicle ID: 247, Event Type: end, Timestamp: 867.5660303754006
-Vehicle ID: 248, Event Type: end, Timestamp: 867.5770784481214
-Vehicle ID: 249, Event Type: end, Timestamp: 867.5808544450678
-Vehicle ID: 252, Event Type: start, Timestamp: 867.594278583525
-Vehicle ID: 251, Event Type: end, Timestamp: 867.5974594283919
-Vehicle ID: 250, Event Type: end, Timestamp: 867.5997091419782
-Vehicle ID: 253, Event Type: start, Timestamp: 867.6084601210431
-Vehicle ID: 254, Event Type: start, Timestamp: 867.6219728041435
-Vehicle ID: 255, Event Type: start, Timestamp: 867.6229731710581
-Vehicle ID: 252, Event Type: end, Timestamp: 867.6642700337288
-Vehicle ID: 253, Event Type: end, Timestamp: 867.67699094093
-Vehicle ID: 256, Event Type: start, Timestamp: 867.692239290079
-Vehicle ID: 255, Event Type: end, Timestamp: 867.700266075478
-Vehicle ID: 254, Event Type: end, Timestamp: 867.7014243918384
-Vehicle ID: 257, Event Type: start, Timestamp: 867.7484096935051
-Vehicle ID: 256, Event Type: end, Timestamp: 867.7708400495044
-Vehicle ID: 258, Event Type: start, Timestamp: 867.8043155333213
-Vehicle ID: 257, Event Type: end, Timestamp: 867.8279852973175
-Vehicle ID: 258, Event Type: end, Timestamp: 867.875407971388
diff --git a/python/lifts/vehicle_average_times.txt b/python/lifts/vehicle_average_times.txt
deleted file mode 100644
index d1910101..00000000
--- a/python/lifts/vehicle_average_times.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-crane: 0.0
-hostler: 0.0021119418504930766
-truck: 0.01489569845431589
diff --git a/truck_work_log.txt b/truck_work_log.txt
index 3c771784..75bb88dd 100644
--- a/truck_work_log.txt
+++ b/truck_work_log.txt
@@ -1,498 +1,498 @@
-Vehicle ID: 1, Event Type: start, Timestamp: 174.3006195900879
-Vehicle ID: 2, Event Type: start, Timestamp: 174.3018535706501
-Vehicle ID: 1, Event Type: end, Timestamp: 174.37599772819118
-Vehicle ID: 2, Event Type: end, Timestamp: 174.37894880350387
-Vehicle ID: 3, Event Type: start, Timestamp: 174.4938586978192
-Vehicle ID: 4, Event Type: start, Timestamp: 174.5012630658423
-Vehicle ID: 5, Event Type: start, Timestamp: 174.50631503585575
-Vehicle ID: 6, Event Type: start, Timestamp: 174.52647821931592
-Vehicle ID: 3, Event Type: end, Timestamp: 174.57278528980711
-Vehicle ID: 4, Event Type: end, Timestamp: 174.57681583766907
-Vehicle ID: 5, Event Type: end, Timestamp: 174.5866396078987
-Vehicle ID: 7, Event Type: start, Timestamp: 174.60485360147638
-Vehicle ID: 6, Event Type: end, Timestamp: 174.60556601585935
-Vehicle ID: 8, Event Type: start, Timestamp: 174.6230875714389
-Vehicle ID: 9, Event Type: start, Timestamp: 174.62863283123264
-Vehicle ID: 10, Event Type: start, Timestamp: 174.66033048077077
-Vehicle ID: 11, Event Type: start, Timestamp: 174.66125789393578
-Vehicle ID: 12, Event Type: start, Timestamp: 174.67159404774407
-Vehicle ID: 7, Event Type: end, Timestamp: 174.67768945799153
-Vehicle ID: 13, Event Type: start, Timestamp: 174.68089290992867
-Vehicle ID: 14, Event Type: start, Timestamp: 174.68358240707497
-Vehicle ID: 8, Event Type: end, Timestamp: 174.69141909979578
-Vehicle ID: 15, Event Type: start, Timestamp: 174.70295259301363
-Vehicle ID: 9, Event Type: end, Timestamp: 174.71270921883288
-Vehicle ID: 16, Event Type: start, Timestamp: 174.7170027584468
-Vehicle ID: 17, Event Type: start, Timestamp: 174.72348337441787
-Vehicle ID: 11, Event Type: end, Timestamp: 174.7292178105025
-Vehicle ID: 18, Event Type: start, Timestamp: 174.7374963042857
-Vehicle ID: 10, Event Type: end, Timestamp: 174.74129691199275
-Vehicle ID: 19, Event Type: start, Timestamp: 174.74160939596376
-Vehicle ID: 20, Event Type: start, Timestamp: 174.74348904622886
-Vehicle ID: 12, Event Type: end, Timestamp: 174.74506797816073
-Vehicle ID: 14, Event Type: end, Timestamp: 174.75468770224018
-Vehicle ID: 13, Event Type: end, Timestamp: 174.75691395889328
-Vehicle ID: 21, Event Type: start, Timestamp: 174.77966697447295
-Vehicle ID: 22, Event Type: start, Timestamp: 174.78152605363738
-Vehicle ID: 15, Event Type: end, Timestamp: 174.78264643626582
-Vehicle ID: 23, Event Type: start, Timestamp: 174.786144624434
-Vehicle ID: 24, Event Type: start, Timestamp: 174.79158206833358
-Vehicle ID: 16, Event Type: end, Timestamp: 174.79326565852304
-Vehicle ID: 25, Event Type: start, Timestamp: 174.79958399502954
-Vehicle ID: 17, Event Type: end, Timestamp: 174.80186635191185
-Vehicle ID: 26, Event Type: start, Timestamp: 174.80225534045326
-Vehicle ID: 20, Event Type: end, Timestamp: 174.820461653581
-Vehicle ID: 18, Event Type: end, Timestamp: 174.82092862084943
-Vehicle ID: 19, Event Type: end, Timestamp: 174.82555892580345
-Vehicle ID: 27, Event Type: start, Timestamp: 174.8303605907534
-Vehicle ID: 28, Event Type: start, Timestamp: 174.84734717576137
-Vehicle ID: 21, Event Type: end, Timestamp: 174.84761523991187
-Vehicle ID: 29, Event Type: start, Timestamp: 174.8486347798264
-Vehicle ID: 22, Event Type: end, Timestamp: 174.8499403410715
-Vehicle ID: 30, Event Type: start, Timestamp: 174.85478644767323
-Vehicle ID: 24, Event Type: end, Timestamp: 174.8599730752654
-Vehicle ID: 31, Event Type: start, Timestamp: 174.86185340327754
-Vehicle ID: 32, Event Type: start, Timestamp: 174.86417201217364
-Vehicle ID: 23, Event Type: end, Timestamp: 174.86738458619953
-Vehicle ID: 33, Event Type: start, Timestamp: 174.87724994755249
-Vehicle ID: 26, Event Type: end, Timestamp: 174.879773579044
-Vehicle ID: 25, Event Type: end, Timestamp: 174.88059360804525
-Vehicle ID: 34, Event Type: start, Timestamp: 174.8827352322805
-Vehicle ID: 35, Event Type: start, Timestamp: 174.88531512446687
-Vehicle ID: 36, Event Type: start, Timestamp: 174.89707138068368
-Vehicle ID: 37, Event Type: start, Timestamp: 174.90153774414387
-Vehicle ID: 38, Event Type: start, Timestamp: 174.90920951730558
-Vehicle ID: 27, Event Type: end, Timestamp: 174.91152231618614
-Vehicle ID: 39, Event Type: start, Timestamp: 174.91414247107062
-Vehicle ID: 40, Event Type: start, Timestamp: 174.919724365569
-Vehicle ID: 28, Event Type: end, Timestamp: 174.9198273608329
-Vehicle ID: 30, Event Type: end, Timestamp: 174.92239868267154
-Vehicle ID: 29, Event Type: end, Timestamp: 174.93129470509388
-Vehicle ID: 41, Event Type: start, Timestamp: 174.9336696701745
-Vehicle ID: 42, Event Type: start, Timestamp: 174.93831736713926
-Vehicle ID: 32, Event Type: end, Timestamp: 174.9417094375852
-Vehicle ID: 43, Event Type: start, Timestamp: 174.94172432642753
-Vehicle ID: 44, Event Type: start, Timestamp: 174.94401701259255
-Vehicle ID: 45, Event Type: start, Timestamp: 174.94488941739988
-Vehicle ID: 31, Event Type: end, Timestamp: 174.94515835920154
-Vehicle ID: 33, Event Type: end, Timestamp: 174.94546377198114
-Vehicle ID: 46, Event Type: start, Timestamp: 174.94703329396592
-Vehicle ID: 47, Event Type: start, Timestamp: 174.948663785669
-Vehicle ID: 48, Event Type: start, Timestamp: 174.9513795010547
-Vehicle ID: 34, Event Type: end, Timestamp: 174.95368438028902
-Vehicle ID: 36, Event Type: end, Timestamp: 174.96861819040862
-Vehicle ID: 35, Event Type: end, Timestamp: 174.97085675359654
-Vehicle ID: 49, Event Type: start, Timestamp: 174.97507139211132
-Vehicle ID: 50, Event Type: start, Timestamp: 174.97850334322783
-Vehicle ID: 37, Event Type: end, Timestamp: 174.97922172244913
-Vehicle ID: 51, Event Type: start, Timestamp: 174.979891384183
-Vehicle ID: 52, Event Type: start, Timestamp: 174.98469992304348
-Vehicle ID: 53, Event Type: start, Timestamp: 174.98485627775915
-Vehicle ID: 38, Event Type: end, Timestamp: 174.98684170965157
-Vehicle ID: 40, Event Type: end, Timestamp: 174.98957371431416
-Vehicle ID: 54, Event Type: start, Timestamp: 174.99314589937603
-Vehicle ID: 55, Event Type: start, Timestamp: 174.99433689284646
-Vehicle ID: 39, Event Type: end, Timestamp: 174.99561107522476
-Vehicle ID: 56, Event Type: start, Timestamp: 175.0025857077016
-Vehicle ID: 42, Event Type: end, Timestamp: 175.00807833039428
-Vehicle ID: 57, Event Type: start, Timestamp: 175.00880881450058
-Vehicle ID: 41, Event Type: end, Timestamp: 175.01453572835118
-Vehicle ID: 44, Event Type: end, Timestamp: 175.02119658683716
-Vehicle ID: 43, Event Type: end, Timestamp: 175.021475530569
-Vehicle ID: 45, Event Type: end, Timestamp: 175.02707913878228
-Vehicle ID: 58, Event Type: start, Timestamp: 175.02901167906484
-Vehicle ID: 46, Event Type: end, Timestamp: 175.02996644563984
-Vehicle ID: 59, Event Type: start, Timestamp: 175.03062856062823
-Vehicle ID: 60, Event Type: start, Timestamp: 175.0387799090098
-Vehicle ID: 47, Event Type: end, Timestamp: 175.0445507915872
-Vehicle ID: 48, Event Type: end, Timestamp: 175.04919390957008
-Vehicle ID: 61, Event Type: start, Timestamp: 175.052210753932
-Vehicle ID: 62, Event Type: start, Timestamp: 175.05471552223796
-Vehicle ID: 63, Event Type: start, Timestamp: 175.05987055430995
-Vehicle ID: 50, Event Type: end, Timestamp: 175.0606873062757
-Vehicle ID: 51, Event Type: end, Timestamp: 175.06537182858608
-Vehicle ID: 49, Event Type: end, Timestamp: 175.0658279218978
-Vehicle ID: 64, Event Type: start, Timestamp: 175.07186841566724
-Vehicle ID: 52, Event Type: end, Timestamp: 175.07340682806216
-Vehicle ID: 53, Event Type: end, Timestamp: 175.0815096733551
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-Vehicle ID: 55, Event Type: end, Timestamp: 175.0966379443331
-Vehicle ID: 56, Event Type: end, Timestamp: 175.1012706119982
-Vehicle ID: 65, Event Type: start, Timestamp: 175.10357686866993
-Vehicle ID: 58, Event Type: end, Timestamp: 175.10919833700666
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-Vehicle ID: 66, Event Type: start, Timestamp: 175.1130363736728
-Vehicle ID: 67, Event Type: start, Timestamp: 175.12771714955977
-Vehicle ID: 59, Event Type: end, Timestamp: 175.1283773440518
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-Vehicle ID: 70, Event Type: start, Timestamp: 175.14167792195698
-Vehicle ID: 61, Event Type: end, Timestamp: 175.14509444370276
-Vehicle ID: 62, Event Type: end, Timestamp: 175.14673842484848
-Vehicle ID: 64, Event Type: end, Timestamp: 175.15119210434906
-Vehicle ID: 71, Event Type: start, Timestamp: 175.15337901084365
-Vehicle ID: 72, Event Type: start, Timestamp: 175.15381816989063
-Vehicle ID: 63, Event Type: end, Timestamp: 175.1591684499569
-Vehicle ID: 73, Event Type: start, Timestamp: 175.16020748947952
-Vehicle ID: 65, Event Type: end, Timestamp: 175.17974547939176
-Vehicle ID: 66, Event Type: end, Timestamp: 175.18951550663428
-Vehicle ID: 74, Event Type: start, Timestamp: 175.19044569946038
-Vehicle ID: 75, Event Type: start, Timestamp: 175.19767458980016
-Vehicle ID: 76, Event Type: start, Timestamp: 175.1986102903684
-Vehicle ID: 77, Event Type: start, Timestamp: 175.2026167645312
-Vehicle ID: 78, Event Type: start, Timestamp: 175.2031719609392
-Vehicle ID: 69, Event Type: end, Timestamp: 175.20719797755416
-Vehicle ID: 70, Event Type: end, Timestamp: 175.2094304962229
-Vehicle ID: 67, Event Type: end, Timestamp: 175.21137771492818
-Vehicle ID: 68, Event Type: end, Timestamp: 175.21359581250832
-Vehicle ID: 79, Event Type: start, Timestamp: 175.23016122723672
-Vehicle ID: 71, Event Type: end, Timestamp: 175.2315816439776
-Vehicle ID: 72, Event Type: end, Timestamp: 175.2368384509264
-Vehicle ID: 73, Event Type: end, Timestamp: 175.25352039084538
-Vehicle ID: 77, Event Type: end, Timestamp: 175.2706900365054
-Vehicle ID: 74, Event Type: end, Timestamp: 175.27415578781608
-Vehicle ID: 75, Event Type: end, Timestamp: 175.27832570292236
-Vehicle ID: 76, Event Type: end, Timestamp: 175.28005359181742
-Vehicle ID: 78, Event Type: end, Timestamp: 175.28326583443973
-Vehicle ID: 80, Event Type: start, Timestamp: 175.29661221538984
-Vehicle ID: 79, Event Type: end, Timestamp: 175.30003308269147
-Vehicle ID: 81, Event Type: start, Timestamp: 175.34368250497667
-Vehicle ID: 80, Event Type: end, Timestamp: 175.3778824764865
-Vehicle ID: 81, Event Type: end, Timestamp: 175.4252260708881
-Vehicle ID: 82, Event Type: start, Timestamp: 175.46037837305903
-Vehicle ID: 83, Event Type: start, Timestamp: 175.46052222166713
-Vehicle ID: 82, Event Type: end, Timestamp: 175.53325188100942
-Vehicle ID: 83, Event Type: end, Timestamp: 175.54367826263575
-Vehicle ID: 84, Event Type: start, Timestamp: 230.31699464988893
-Vehicle ID: 85, Event Type: start, Timestamp: 230.38122105558145
-Vehicle ID: 84, Event Type: end, Timestamp: 230.400712015743
-Vehicle ID: 86, Event Type: start, Timestamp: 230.40226909272388
-Vehicle ID: 85, Event Type: end, Timestamp: 230.45993832016816
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diff --git a/vehicle_average_times.txt b/vehicle_average_times.txt
index 7d86c645..22b0b727 100644
--- a/vehicle_average_times.txt
+++ b/vehicle_average_times.txt
@@ -1,3 +1,3 @@
 crane: 0.0
-hostler: 0.0018928077738425498
-truck: 0.013760017918324973
+hostler: 0.0019168163174070507
+truck: 0.01337536359778584

From cf35408c0bc86c18752e7f924b0ccecccdb9c6e4 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Tue, 10 Dec 2024 13:52:11 -0500
Subject: [PATCH 23/50] in-progress check-in

---
 python/altrios/demos/sim_manager_demo.py |   4 +-
 python/altrios/lifts/lifts_simulator.py  | 118 ++++++++++-------------
 python/altrios/lifts/parameters.py       |   4 +-
 3 files changed, 58 insertions(+), 68 deletions(-)

diff --git a/python/altrios/demos/sim_manager_demo.py b/python/altrios/demos/sim_manager_demo.py
index 70b6048a..7e738dc1 100644
--- a/python/altrios/demos/sim_manager_demo.py
+++ b/python/altrios/demos/sim_manager_demo.py
@@ -4,6 +4,7 @@
 import altrios as alt
 from altrios.lifts import lifts_simulator
 import numpy as np
+import polars as pl
 import matplotlib.pyplot as plt
 import time
 import seaborn as sns
@@ -57,8 +58,9 @@
     debug=True,
 )
 
+train_consist_plan_csv = pl.read_csv('C:/Users/mbruchon/Downloads/simulation_operation/lifts/results/train_consist_plan.csv')
 container_data = lifts_simulator.run_simulation(
-    train_consist_plan = train_consist_plan, 
+    train_consist_plan = train_consist_plan_csv, 
     terminal = "Allouez")
 
 t1_main = time.perf_counter()
diff --git a/python/altrios/lifts/lifts_simulator.py b/python/altrios/lifts/lifts_simulator.py
index b636c8d7..a477d36e 100644
--- a/python/altrios/lifts/lifts_simulator.py
+++ b/python/altrios/lifts/lifts_simulator.py
@@ -8,12 +8,6 @@
 from altrios.lifts.schedule import *
 from altrios.lifts.vehicle_performance import record_vehicle_event, save_average_times, save_vehicle_logs
 
-
-# Test input
-CRANE_NUMBER = 1
-HOSTLER_NUMBER = 1
-
-
 # import sys
 #
 # if len(sys.argv) < 3:
@@ -21,25 +15,25 @@
 #
 # HOSTLER_NUMBER = int(sys.argv[1])
 # CRANE_NUMBER = int(sys.argv[2])
-TRUCK_NUMBERS = 1000
 
 def record_event(container_id, event_type, timestamp):
     global state
+    if container_id is None:
+        x = 5
     if container_id not in state.container_events:
         state.container_events[container_id] = {}
     state.container_events[container_id][event_type] = timestamp
 
 
-def handle_truck_arrivals(env, in_gate_resource, truck_numbers):
+def handle_truck_arrivals(env, in_gate_resource):
     global state
     truck_id = 1
-    truck_processed = 0
     state.TRUCK_ARRIVAL_MEAN = abs(state.TRAIN_ARRIVAL_HR - state.previous_train_departure) / max(state.INBOUND_CONTAINER_NUMBER, state.OUTBOUND_CONTAINER_NUMBER)
     print(f"current time is {env.now}")
     print(f"next TRAIN_ARRIVAL_HR:{state.TRAIN_ARRIVAL_HR}")
     print(f"TRUCK_ARRIVAL_MEAN IS {state.TRUCK_ARRIVAL_MEAN}")
 
-    while truck_id <= TRUCK_NUMBERS:
+    while truck_id <= state.TRUCK_NUMBERS:
         inter_arrival_time = random.expovariate(1 / state.TRUCK_ARRIVAL_MEAN)
         yield env.timeout(inter_arrival_time)
         state.truck_arrival_time.append(env.now)
@@ -47,7 +41,7 @@ def handle_truck_arrivals(env, in_gate_resource, truck_numbers):
         env.process(truck_through_gate(env, in_gate_resource, truck_id))
         truck_id += 1
 
-    if truck_id > TRUCK_NUMBERS:
+    if truck_id > state.TRUCK_NUMBERS:
         # print(f"truck_id = {truck_id} vs TRUCK_NUM = {TRUCK_NUMBERS}")
         if not state.all_trucks_ready_event.triggered:
             state.all_trucks_ready_event.succeed()
@@ -85,6 +79,8 @@ def handle_container(env, truck_id):
     global state
 
     container_id = state.outbound_container_id_counter
+    if container_id is None:
+        x = 5
     state.outbound_container_id_counter += 1
     record_event(container_id, 'truck_arrival', env.now)
 
@@ -93,7 +89,7 @@ def handle_container(env, truck_id):
 
     record_event(container_id, 'truck_drop_off', env.now)
     # print(f"{env.now}: Truck {truck_id} drops outbound container {container_id}.")
-    last_leave_time = env.now
+    state.last_leave_time = env.now
 
 
 def empty_truck(env, truck_id):
@@ -103,7 +99,7 @@ def empty_truck(env, truck_id):
     yield env.timeout(d_t_dist / (2 * state.TRUCK_SPEED_LIMIT))
 
     # print(f"{env.now}: Empty truck {truck_id} arrives.")
-    last_leave_time = env.now
+    state.last_leave_time = env.now
 
 
 def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, out_gate_resource):
@@ -133,7 +129,7 @@ def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chas
         # Trucks enter until the precious train departs, if not the first truck
         state.previous_train_departure = train_timetable[i-1]['departure_time'] if i > 0 else 0
         print(f"Schedule {state.TRUCK_NUMBERS} Trucks arriving between previous train departure at {state.previous_train_departure} and current train arrival at {state.TRAIN_ARRIVAL_HR}")
-        env.process(handle_truck_arrivals(env, in_gate_resource, outbound_containers_store))
+        env.process(handle_truck_arrivals(env, in_gate_resource))
 
         # Trains arrive according to the timetable, fix negative delay bug
         delay = state.TRAIN_ARRIVAL_HR - env.now
@@ -153,7 +149,7 @@ def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chas
         with train_processing.request() as request:
             yield request
             state.oc_chassis_filled_event = env.event()
-            yield env.process(process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, out_gate_resource))
+            yield env.process(process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, state.oc_chassis_filled_event, out_gate_resource))
             state.train_id_counter += 1
 
         state.record_oc_label += state.OUTBOUND_CONTAINER_NUMBER
@@ -161,7 +157,7 @@ def train_arrival(env, train_timetable, train_processing, cranes, hostlers, chas
         # print("oc_variance in train_process:", oc_variance)
 
 
-def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, out_gate_resource):
+def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, outbound_containers_store, truck_store, train_processing, oc_chassis_filled_event, out_gate_resource):
     global state
 
     start_time = env.now
@@ -172,7 +168,7 @@ def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, ou
 
     # if train_id < TRAIN_NUMBERS:
     for chassis_id in range(1, int(state.INBOUND_CONTAINER_NUMBER) + 1):
-        unload_process = env.process(crane_and_chassis(env, train_id, 'unload', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource))
+        unload_process = env.process(crane_and_chassis(env, train_id, 'unload', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event))
         unload_processes.append(unload_process)
 
     # All IC are processed
@@ -185,7 +181,7 @@ def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, ou
         chassis_id, current_inbound_id = result
         chassis_inbound_ids.append((chassis_id, current_inbound_id))
         env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes,
-                             train_processing, outbound_containers_store, in_gate_resource,
+                             train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event,
                              out_gate_resource))
 
     # # Once all OC are dropped by hostlers, crane start working
@@ -198,7 +194,7 @@ def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, ou
     # Cranes move all OC to chassis
     load_processes = []
     for chassis_id in range(1, state.OUTBOUND_CONTAINER_NUMBER + 1):
-        load_process = env.process(crane_and_chassis(env, train_id, 'load', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, chassis_id=chassis_id))
+        load_process = env.process(crane_and_chassis(env, train_id, 'load', cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource, chassis_id=chassis_id))
         load_processes.append(load_process)
     yield simpy.events.AllOf(env, load_processes)
 
@@ -216,7 +212,7 @@ def process_train(env, train_id, cranes, hostlers, chassis, in_gate_resource, ou
     state.oc_variance += state.OUTBOUND_CONTAINER_NUMBER
 
 
-def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, chassis_id=None):
+def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_store, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource, oc_chassis_filled_event, chassis_id=None):
     global state
 
     # # Print before requesting crane resource
@@ -263,7 +259,7 @@ def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_st
             record_vehicle_event('crane', state.crane_id_counter, 'end', end_time)     # performance record: ending
 
             # hostler picks up IC
-            env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource))
+            env.process(hostler_transfer(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource))
 
             return chassis_id, current_inbound_id
 
@@ -298,21 +294,25 @@ def crane_and_chassis(env, train_id, action, cranes, hostlers, chassis, truck_st
         # print(f"[{env.now}] Crane {crane_id_counter} has released crane resource. Available cranes: {cranes.count}/{cranes.capacity}")
 
 
-def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource):
+def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
     global state
 
     with hostlers.request() as request:
         yield request
-
+        if container_id is None:
+            x =5
         start_time = env.now
         record_vehicle_event('hostler', state.hostler_id_counter, 'start', start_time)  # performance record
 
         hostler_id = state.hostler_id_counter
-        state.hostler_id_counter = (state.hostler_id_counter % HOSTLER_NUMBER) + 1
+        state.hostler_id_counter = (state.hostler_id_counter % state.HOSTLER_NUMBER) + 1
 
         with chassis.request() as chassis_request:
             yield chassis_request
 
+            if container_type == "inbound":
+                x = 5
+
             if container_type == 'inbound' and state.chassis_status[chassis_id - 1] == 1:
                 d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
                 state.HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * state.HOSTLER_SPEED_LIMIT)
@@ -328,6 +328,8 @@ def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, contain
                 state.HOSTLER_TRANSPORT_CONTAINER_TIME = d_h_dist / (2 * state.HOSTLER_SPEED_LIMIT)
                 print(f"Hostler drop-off time is:{state.HOSTLER_TRANSPORT_CONTAINER_TIME}")
                 yield env.timeout(state.HOSTLER_TRANSPORT_CONTAINER_TIME)
+                if container_id is None:
+                    x =5
                 record_event(container_id, 'hostler_dropoff', env.now)
                 print(f"Hostler {hostler_id} drops off inbound container {container_id} from chassis {chassis_id} and moves toward the assigned outbound container at {env.now}")
 
@@ -341,30 +343,30 @@ def hostler_transfer(env, hostlers, container_type, chassis, chassis_id, contain
                 chassis_id, state.outbound_container_id = yield env.process(outbound_container_decision_making(
                     env, hostlers, chassis, container_id, truck_store, cranes, train_processing,
                     outbound_containers_store,
-                    in_gate_resource, out_gate_resource))
+                    in_gate_resource, oc_chassis_filled_event, out_gate_resource))
 
                 # Process outbound containers
                 if chassis_id is not None and state.outbound_container_id is not None:
-                    env.process(handle_outbound_container(env, hostler_id, chassis_id, truck_store,
-                                                  cranes, train_processing, outbound_containers_store, in_gate_resource,))
+                    env.process(handle_outbound_container(env, hostler_id, chassis_id, state.outbound_container_id, truck_store,
+                                                  cranes, train_processing, outbound_containers_store, in_gate_resource))
 
 
 # When OC are fully processed, but IC are not
-def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource):
+def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chassis_id, container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
     print(f"Starting single hostler transfer IC loop for chassis {chassis_id} at {env.now}")
     global state
 
     print(f"Requesting hostler for IC at chassis {chassis_id} at {env.now}")
 
     with hostlers.request() as request:
-        print(f"Request available hostlers: {hostlers.count} vs total hostlers {HOSTLER_NUMBER}, Hostlers capacity: {hostlers.capacity} at {env.now}")
+        print(f"Request available hostlers: {hostlers.count} vs total hostlers {state.HOSTLER_NUMBER}, Hostlers capacity: {hostlers.capacity} at {env.now}")
         yield request
 
         start_time = env.now
         record_vehicle_event('hostler', state.hostler_id_counter, 'start', start_time)  # performance record
 
         hostler_id = state.hostler_id_counter
-        state.hostler_id_counter = (state.hostler_id_counter % HOSTLER_NUMBER) + 1
+        state.hostler_id_counter = (state.hostler_id_counter % state.HOSTLER_NUMBER) + 1
 
         with chassis.request() as chassis_request:
             yield chassis_request
@@ -393,12 +395,12 @@ def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chas
 
                 # Check if all chassis filled
                 if state.chassis_status.count(0) == state.OUTBOUND_CONTAINER_NUMBER and state.chassis_status.count(
-                        -1) == state.TRAIN_UNITS - state.OUTBOUND_CONTAINER_NUMBER and not state.oc_chassis_filled_event.triggered:
+                        -1) == state.TRAIN_UNITS - state.OUTBOUND_CONTAINER_NUMBER and not oc_chassis_filled_event.triggered:
                     print(f"Chassis is fully filled with OC, and cranes start moving: {state.chassis_status}")
                     print(f"where there are {state.chassis_status.count(0)} chassis filled with OC (0)")
                     print(f"where there are {state.chassis_status.count(-1)} chassis filled with empty (-1)")
                     print(f"where there are {state.chassis_status.count(1)} chassis filled with IC (1)")
-                    state.oc_chassis_filled_event.succeed()
+                    oc_chassis_filled_event.succeed()
                     return
                 else:
                     print(f"Chassis is not fully filled: {state.chassis_status}")
@@ -413,7 +415,7 @@ def hostler_transfer_IC_single_loop(env, hostlers, container_type, chassis, chas
                 yield env.process(notify_truck(env, truck_store, container_id, out_gate_resource))
 
 
-def outbound_container_decision_making(env, hostlers, chassis, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, out_gate_resource):
+def outbound_container_decision_making(env, hostlers, chassis, current_inbound_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource):
     global state
     # Check if outbound_containers_store has outbound container
     if len(outbound_containers_store.items) > 0:
@@ -445,14 +447,16 @@ def outbound_container_decision_making(env, hostlers, chassis, current_inbound_i
             # single loop takes rest inbound container
             yield env.process(hostler_transfer_IC_single_loop(env, hostlers, 'inbound', chassis, chassis_id, current_inbound_id,
                                                 truck_store, cranes, train_processing,
-                                                outbound_containers_store, in_gate_resource, out_gate_resource))
+                                                outbound_containers_store, in_gate_resource, oc_chassis_filled_event, out_gate_resource))
         else:
             print("All inbound containers have been processed.")
 
+    if outbound_container_id is None:
+        x = 5
     return chassis_id, outbound_container_id
 
 
-def handle_outbound_container(env, hostler_id, chassis_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource):
+def handle_outbound_container(env, hostler_id, chassis_id, outbound_container_id, truck_store, cranes, train_processing, outbound_containers_store, in_gate_resource):
     global state
 
     d_h_dist = create_triang_distribution(d_h_min, d_h_avg, d_h_max).rvs()
@@ -462,13 +466,13 @@ def handle_outbound_container(env, hostler_id, chassis_id, truck_store, cranes,
     state.HOSTLER_FIND_CONTAINER_TIME = d_r_dist / (2 * state.TRUCK_SPEED_LIMIT)
     yield env.timeout(state.HOSTLER_FIND_CONTAINER_TIME)
 
-    record_event(state.outbound_container_id, 'hostler_pickup', env.now)
-    print(f"Hostler {hostler_id} picks up outbound container {state.outbound_container_id} from parking area to chassis {chassis_id} at {env.now}")
+    record_event(outbound_container_id, 'hostler_pickup', env.now)
+    print(f"Hostler {hostler_id} picks up outbound container {outbound_container_id} from parking area to chassis {chassis_id} at {env.now}")
 
     yield env.timeout(state.HOSTLER_TRANSPORT_CONTAINER_TIME)
 
-    record_event(state.outbound_container_id, 'hostler_dropoff', env.now)
-    print(f"Hostler {hostler_id} drops off outbound container {state.outbound_container_id} to chassis {chassis_id} at {env.now}")
+    record_event(outbound_container_id, 'hostler_dropoff', env.now)
+    print(f"Hostler {hostler_id} drops off outbound container {outbound_container_id} to chassis {chassis_id} at {env.now}")
 
 
 # truck pick up IC
@@ -546,7 +550,7 @@ def run_simulation(
     # Initialize trucks
     truck_store.items.clear()
     # print("TRUCK_NUMBERS:",  TRUCK_NUMBERS)
-    for truck_id in range(1, TRUCK_NUMBERS + 1):
+    for truck_id in range(1, 100 + 1):
         truck_store.put(truck_id)
     # print("TRUCK_STORE:", truck_store.items)
 
@@ -579,31 +583,13 @@ def run_simulation(
         f.write(str(avg_time_per_train))
 
     # Create DataFrame for container events
-    events_list = [(container_id, events) for container_id, events in state.container_events.items()]
     container_data = (
-        pl.concat([
-            pl.DataFrame(data = None, schema = {
-                    'container_id': pl.UInt32,
-                    'container_type': pl.Utf8,
-                    'train_arrival': pl.Float64,
-                    'truck_arrival': pl.Float64,
-                    'crane_unload': pl.Float64,
-                    'hostler_pickup': pl.Float64,
-                    'hostler_dropoff': pl.Float64,
-                    'truck_drop_off': pl.Float64,
-                    'truck_pickup': pl.Float64,
-                    'truck_exit': pl.Float64,
-                    'crane_load': pl.Float64,
-                    'train_depart': pl.Float64}),
-            pl.concat(
-                [
-                    pl.DataFrame({"container_id": [container_id for container_id, event in events_list]})
-                        .with_columns(
-                            pl.when(pl.col("container_id") < 10001).then(pl.lit("inbound")).otherwise(pl.lit("outbound")).alias("container_type")
-                        ),
-                    pl.from_dicts([event for container_id, event in events_list])
-                ], how="horizontal")
-        ], how="diagonal_relaxed")
+        pl.from_dicts(
+            [dict(event, **{'container_id': container_id}) for container_id, event in state.container_events.items()]
+        )
+        .with_columns(
+            pl.when(pl.col("container_id") < 10001).then(pl.lit("inbound")).otherwise(pl.lit("outbound")).alias("container_type")
+        )
         .with_columns(
             pl.when(
                 pl.col("container_type") == pl.lit("inbound"),
@@ -624,9 +610,11 @@ def run_simulation(
             .otherwise(None)
             .alias("container_processing_time")
         )
+        .sort("container_id")
+        .select(pl.col("container_id", "container_type"), pl.all().exclude("container_id", "container_type"))
     )
     if out_path is not None:
-        container_data.write_excel(out_path / f"simulation_crane_{CRANE_NUMBER}_hostler_{HOSTLER_NUMBER}.xlsx")
+        container_data.write_excel(out_path / f"simulation_crane_{state.CRANE_NUMBER}_hostler_{state.HOSTLER_NUMBER}.xlsx")
 
     # Use save_average_times and save_vehicle_logs for vehicle related logs
     save_average_times()
diff --git a/python/altrios/lifts/parameters.py b/python/altrios/lifts/parameters.py
index 550151e3..89f40f1c 100644
--- a/python/altrios/lifts/parameters.py
+++ b/python/altrios/lifts/parameters.py
@@ -54,13 +54,13 @@ class LiftsState:
     container_events: dict = field(default_factory = lambda: {})   # Dictionary to store container event data
     # Cranes
     # CRANE_NUMBER = int(input("Enter the number of crane: "))
-    CRANE_NUMBER: int = 10
+    CRANE_NUMBER: int = 1
     CONTAINERS_PER_CRANE_MOVE_MEAN: float = 600   # 10ft/min = 600 ft/hr, crane speed
     CRANE_MOVE_DEV_TIME: float = 5/60 # hr
     outbound_containers_mapping: dict = field(default_factory = lambda: {})  # To keep track of outbound containers ID mapped to chassis
     # Hostlers
     # HOSTLER_NUMBER = int(input("Enter the number of hostler: "))
-    HOSTLER_NUMBER: int = 50
+    HOSTLER_NUMBER: int = 1
     CONTAINERS_PER_HOSTLER: int = 1  # hostler capacity
     HOSTLER_SPEED_LIMIT: float = 20*5280   # ft/hr
     HOSTLER_TRANSPORT_CONTAINER_TIME: float = 0    # hr, triangular distribution

From c914562fed2a70f69b9dd6670a3c41060d982ec5 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Thu, 19 Dec 2024 18:31:33 -0500
Subject: [PATCH 24/50] update

---
 rust/altrios-core/src/train/train_config.rs | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rust/altrios-core/src/train/train_config.rs b/rust/altrios-core/src/train/train_config.rs
index a912553b..a615e22b 100644
--- a/rust/altrios-core/src/train/train_config.rs
+++ b/rust/altrios-core/src/train/train_config.rs
@@ -1030,7 +1030,7 @@ pub fn run_speed_limit_train_sims(
                     "Node",
                     "Locomotive_Type",
                     "Fuel_Type",
-                ])) + (col("Status").eq(lit("Queued")).cum_sum(false).over([
+                ])) + (col("Status").eq(lit("Queued")).cumsum(false).over([
                     "Node",
                     "Locomotive_Type",
                     "Fuel_Type",

From ba4385f256f9fcb419c89bb6b3b2302d4ffd1bfc Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sat, 11 Jan 2025 21:03:46 -0500
Subject: [PATCH 25/50] partial port of train planner from private repo

changes from wang-dev-matt-edits
---
 .gitignore                         |   1 -
 pyproject.toml                     |   2 +-
 python/altrios/lifts/dictionary.py |   1 +
 python/altrios/train_planner.py    | 161 ++++++++++++++++++-----------
 4 files changed, 105 insertions(+), 60 deletions(-)

diff --git a/.gitignore b/.gitignore
index a9e03c2c..09a40c2b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1219,4 +1219,3 @@ altrios/dispatch/*.json
 activate.sh
 *.xlsx
 *.txt
-*.txt
diff --git a/pyproject.toml b/pyproject.toml
index 847bbc9a..1114ccec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,7 +43,7 @@ dependencies = [
     "plotly",
     "typing_extensions",
     "pyyaml",
-    "polars >= 1.14",
+    "polars >= 1.19.0",
     "pyarrow",
     "requests",
     "PyYAML==6.0.2",
diff --git a/python/altrios/lifts/dictionary.py b/python/altrios/lifts/dictionary.py
index 4e13c721..0c66b90f 100644
--- a/python/altrios/lifts/dictionary.py
+++ b/python/altrios/lifts/dictionary.py
@@ -1,3 +1,4 @@
+
 import polars as pl
 
 def calculate_oc_number() -> int:
diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner.py
index e2762ef8..456fc5c1 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner.py
@@ -98,19 +98,35 @@ class TrainPlannerConfig:
     Attributes:
     ----------
     - `single_train_mode`: `True` to only run one round-trip train and schedule its charging; `False` to plan train consists
+
     - `min_cars_per_train`: `Dict` of the minimum length in number of cars to form a train for each train type
+
     - `target_cars_per_train`: `Dict` of the standard train length in number of cars for each train type
+    
     - `manifest_empty_return_ratio`: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
+    
     - `cars_per_locomotive`: Heuristic scaling factor used to size number of locomotives needed based on demand.
+
+    - `cars_per_locomotive_fixed`: If `True`, `cars_per_locomotive` overrides `hp_per_ton` calculations used for dispatching decisions.
+    
     - `refuelers_per_incoming_corridor`: Heuristic scaling factor used to scale number of refuelers needed at each node based on number of incoming corridors.
+    
     - `stack_type`: Type of stacking (applicable only for intermodal containers)
+
     - `require_diesel`: `True` to require each consist to have at least one diesel locomotive.
+    
     - `manifest_empty_return_ratio`: `Dict`
+    
     - `drag_coeff_function`: `Dict`
+    
     - `hp_required_per_ton`: `Dict`
+    
     - `dispatch_scaling_dict`: `Dict`
+    
     - `loco_info`: `Dict`
+    
     - `refueler_info`: `Dict`
+
     - `return_demand_generators`: `Dict`
     """
     single_train_mode: bool = False
@@ -123,6 +139,7 @@ class TrainPlannerConfig:
     cars_per_locomotive: Dict = field(default_factory = lambda: {
         "Default": 70
     })
+    cars_per_locomotive_fixed: bool = False
     refuelers_per_incoming_corridor: int = 4
     stack_type: str = "single"
     require_diesel: bool = False
@@ -179,7 +196,6 @@ class TrainPlannerConfig:
             "Lifespan_Years": defaults.LOCO_LIFESPAN
         }
     }).transpose().reset_index(names='Refueler_Type'))
-    
     return_demand_generators: Dict = field(default_factory = lambda: {
         'Unit': generate_return_demand_unit,
         'Manifest': generate_return_demand_manifest,
@@ -233,11 +249,14 @@ def generate_return_demand(
     demand_subsets = demand.partition_by("Train_Type", as_dict = True)
     return_demand = []
     for train_type, demand_subset in demand_subsets.items():
-        if train_type in config.return_demand_generators:
-            return_demand_generator = config.return_demand_generators[train_type]
-            return_demand.append(return_demand_generator(demand_subset, config))
+        train_type_label = train_type[0]
+        if train_type_label in config.return_demand_generators:
+            return_demand_generator = config.return_demand_generators[train_type_label]
+            temp = return_demand_generator(demand_subset, config)
+            print(temp)
+            return_demand.append(temp)
         else:
-            print(f'Return demand generator not implemented for train type: {train_type}')
+            print(f'Return demand generator not implemented for train type: {train_type_label}')
 
     demand_return = (pl.concat(return_demand, how="diagonal_relaxed")
         .filter(pl.col("Number_of_Cars") + pl.col("Number_of_Containers") > 0)
@@ -518,7 +537,7 @@ def generate_dispatch_details(
     def pctWithinGroup(df: pl.DataFrame, grouping_vars: List[str]) -> pl.DataFrame:
         return (df
             .with_columns(
-                (pl.cum_count().over(grouping_vars) / 
+                (pl.cum_count('Origin').over(grouping_vars) / 
                 pl.count().over(grouping_vars))
                 .alias("Percent_Within_Group")
             )
@@ -561,7 +580,7 @@ def allocateItems(df: pl.DataFrame, target: str, grouping_vars: List[str]) -> pl
             (hours * 1.0 / pl.len().over("Origin", "Destination")).alias("Interval")
         )
         .with_columns(
-            ((pl.col("Interval").cumcount().over(["Origin","Destination"])) \
+            ((pl.col("Interval").cum_count().over(["Origin","Destination"])) \
              * pl.col("Interval")).alias("Hour")
         )
         .select("Hour", "Origin", "Destination", "Train_Type", 
@@ -580,6 +599,7 @@ def build_locopool(
     demand_file: Union[pl.DataFrame, Path, str],
     method: str = "tile",
     shares: List[float] = [],
+    locomotives_per_node: int = None
 ) -> pl.DataFrame:
     """
     Generate default locomotive pool
@@ -597,17 +617,20 @@ def build_locopool(
     demand, node_list = demand_loader(demand_file)
     
     num_nodes = len(node_list)
-    num_ods = demand.height
-    cars_per_od = demand.get_column("Number_of_Cars").mean()
-    if config.single_train_mode:
-        initial_size = math.ceil(cars_per_od / config.cars_per_locomotive["Default"]) 
-        rows = initial_size
+    if locomotives_per_node is None:
+        num_ods = demand.height
+        cars_per_od = demand.get_column("Number_of_Cars").mean()
+        if config.single_train_mode:
+            initial_size = math.ceil(cars_per_od / config.cars_per_locomotive["Default"]) 
+            rows = initial_size
+        else:
+            num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
+            initial_size = math.ceil((cars_per_od / config.cars_per_locomotive["Default"]) *
+                                    num_destinations_per_node)  # number of locomotives per node
+            rows = initial_size * num_nodes  # number of locomotives in total
     else:
-        num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
-        initial_size = math.ceil((cars_per_od / config.cars_per_locomotive["Default"]) *
-                                num_destinations_per_node)  # number of locomotives per node
-        rows = initial_size * num_nodes  # number of locomotives in total
-
+        initial_size = locomotives_per_node
+        rows = locomotives_per_node * num_nodes
 
     if config.single_train_mode:
         sorted_nodes = np.tile([demand.select(pl.col("Origin").first()).item()],rows).tolist()
@@ -798,6 +821,8 @@ def dispatch(
     loco_pool: pl.DataFrame,
     train_tonnage: float,
     hp_required: float,
+    total_cars: float,
+    config: TrainPlannerConfig, 
 ) -> pl.Series:
     """
     Update the locomotive pool by identifying the desired locomotive to dispatch and assign to the
@@ -808,6 +833,8 @@ def dispatch(
     origin: origin node name of the train
     loco_pool: locomotive pool dataframe containing all locomotives in the network
     hp_required: Horsepower required for this train type on this origin-destination corridor
+    total_cars: Total number of cars (loaded, empty, or otherwise) included on the train
+    config: TrainPlannerConfig object
     Outputs:
     ----------
     selected: Indices of selected locomotives
@@ -840,47 +867,62 @@ def dispatch(
 
     # Running list of downselected candidates
     selected = candidates
-    # First available diesel (in order of loco_pool) will be moved from candidates to selected
-    # TODO gracefully handle cases when there is no diesel locomotive to be dispatched
-    # (ex: hold the train until enough diesels are present)
-    diesel_filter = pl.col("Fuel_Type").cast(pl.Utf8).str.contains("(?i)diesel")
-    diesel_candidates = loco_pool.select(pl.lit(candidates) & diesel_filter).to_series()
-    if not diesel_candidates.any():
-        refueling_diesel_count = loco_pool.filter(
-            pl.col("Node") == origin,
-            pl.col("Status").is_in(["Servicing","Refuel_Queue"]),
-            diesel_filter
-        ).select(pl.len())[0, 0]
-        message = f"""No available diesel locomotives at node {origin} at hour {dispatch_time}, so
-                the one-diesel-per-consist rule cannot be satisfied. {refueling_diesel_count} diesel locomotives at
-                {origin} are servicing, refueling, or queueing."""
-        if refueling_diesel_count > 0:
-            diesel_port_count = loco_pool.filter(
+    diesel_to_require_hp = 0
+    if config.require_diesel:
+        # First available diesel (in order of loco_pool) will be moved from candidates to selected
+        # TODO gracefully handle cases when there is no diesel locomotive to be dispatched
+        # (ex: hold the train until enough diesels are present)
+        diesel_filter = pl.col("Fuel_Type").cast(pl.Utf8).str.contains("(?i)diesel")
+        diesel_candidates = loco_pool.select(pl.lit(candidates) & diesel_filter).to_series()
+        if not diesel_candidates.any():
+            refueling_diesel_count = loco_pool.filter(
                 pl.col("Node") == origin,
+                pl.col("Status").is_in(["Servicing","Refuel_Queue"]),
                 diesel_filter
-            ).select(pl.col("Port_Count").min()).item()
-            message += f""" (queue capacity {diesel_port_count})."""
-        else:
-            message += "."
-        raise ValueError(message)
-
-    diesel_to_require = diesel_candidates.eq(True).cum_sum().eq(1).arg_max()
-    diesel_to_require_hp = loco_pool.filter(diesel_filter).select(pl.first("HP"))
-    # Need to mask this so it's not double-counted on next step
-    candidates[diesel_to_require] = False
-    # Get running sum, including first diesel, of hp of the candidates (in order of loco_pool)
-    enough_hp = loco_pool.select((
-        (
-            (pl.col("HP") - (pl.col("Loco_Mass_Tons") * pl.lit(hp_per_ton))) * pl.lit(candidates)
-        ).cum_sum() + pl.lit(diesel_to_require_hp)) >= hp_required).to_series()
-    if not enough_hp.any():
-        available_hp = loco_pool.select(
+            ).select(pl.len())[0, 0]
+            message = f"""No available diesel locomotives at node {origin} at hour {dispatch_time}, so
+                    the one-diesel-per-consist rule cannot be satisfied. {refueling_diesel_count} diesel locomotives at
+                    {origin} are servicing, refueling, or queueing."""
+            if refueling_diesel_count > 0:
+                diesel_port_count = loco_pool.filter(
+                    pl.col("Node") == origin,
+                    diesel_filter
+                ).select(pl.col("Port_Count").min()).item()
+                message += f""" (queue capacity {diesel_port_count})."""
+            else:
+                message += "."
+            raise ValueError(message)
+
+        diesel_to_require = diesel_candidates.eq(True).cum_sum().eq(1).arg_max()
+        diesel_to_require_hp = loco_pool.filter(diesel_filter).select(pl.first("HP"))
+        # Need to mask this so it's not double-counted on next step
+        candidates[diesel_to_require] = False
+
+    message = ""
+    if config.cars_per_locomotive_fixed:
+        # Get as many available locomotives as are needed (in order of loco_pool)
+        enough_locomotives = loco_pool.select(
+            (pl.lit(1.0) * pl.lit(candidates)).cumsum() >= total_cars).to_series()
+        if not enough_locomotives.any():
+            message = f"""Locomotives needed ({total_cars}) at {origin} at hour {dispatch_time}
+                is more than the available locomotives ({candidates.sum()}).
+                Count of locomotives servicing, refueling, or queueing at {origin} are:"""
+    else:
+        # Get running sum, including first diesel, of hp of the candidates (in order of loco_pool)
+        enough_hp = loco_pool.select((
             (
                 (pl.col("HP") - (pl.col("Loco_Mass_Tons") * pl.lit(hp_per_ton))) * pl.lit(candidates)
-            ).cum_sum().max())[0, 0]
-        message = f"""Outbound horsepower needed ({hp_required}) at {origin} at hour {dispatch_time}
-            is more than the available horsepower ({available_hp}).
-            Count of locomotives servicing, refueling, or queueing at {origin} are:"""
+            ).cum_sum() + pl.lit(diesel_to_require_hp)) >= hp_required).to_series()
+        if not enough_hp.any():
+            available_hp = loco_pool.select(
+                (
+                    (pl.col("HP") - (pl.col("Loco_Mass_Tons") * pl.lit(hp_per_ton))) * pl.lit(candidates)
+                ).cum_sum().max())[0, 0]
+            message = f"""Outbound horsepower needed ({hp_required}) at {origin} at hour {dispatch_time}
+                is more than the available horsepower ({available_hp}).
+                Count of locomotives servicing, refueling, or queueing at {origin} are:"""
+
+    if not enough_hp.any():
         # Hold the train until enough diesels are present (future development)
         waiting_counts = (loco_pool
             .filter(
@@ -899,8 +941,10 @@ def dispatch(
     last_row_to_use = enough_hp.eq(True).cum_sum().eq(1).arg_max()
     # Set false all the locomotives that would add unnecessary hp
     selected[np.arange(last_row_to_use+1, len(selected))] = False
-    # Add first diesel (which could come after last_row_to_use) to selection list
-    selected[diesel_to_require] = True
+
+    if config.require_diesel:
+        # Add first diesel (which could come after last_row_to_use) to selection list
+        selected[diesel_to_require] = True
     return selected
 
 def update_refuel_queue(
@@ -1138,7 +1182,9 @@ def run_train_planner(
                             this_train['Origin'],
                             loco_pool,
                             this_train['Tons_Per_Train'],
-                            this_train['HP_Required']
+                            this_train['HP_Required'],
+                            this_train['Cars_Loaded'] + this_train['Cars_Empty'],
+                            config
                         )
                         dispatched = loco_pool.filter(selected)
 
@@ -1259,7 +1305,6 @@ def run_train_planner(
                         pl.Series(repeat(this_train['Destination'], new_row_count)),
                         pl.Series(repeat(this_train['Cars_Loaded'], new_row_count)),
                         pl.Series(repeat(this_train['Cars_Empty'], new_row_count)),
-                        # pl.Series(repeat(this_train['Number_of_Cars'], new_row_count)),
                         loco_start_soc_j,
                         pl.Series(repeat(current_time, new_row_count)),
                         pl.Series(repeat(current_time + travel_time, new_row_count))],

From c93ebb13413de25f9237034596e25e43e5685e25 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sun, 12 Jan 2025 14:20:36 -0500
Subject: [PATCH 26/50] train planner refactored into multiple files

---
 .gitignore                                    |   2 +-
 avg_time_per_train.txt                        |   1 -
 crane_work_log.txt                            | 732 -----------------
 hostler_work_log.txt                          | 754 ------------------
 python/altrios/demos/rollout_demo.py          |   5 +-
 python/altrios/demos/sim_manager_demo.py      |   5 +-
 python/altrios/rollout.py                     |   5 +-
 python/altrios/sim_manager.py                 |   4 +-
 python/altrios/tests/test_train_planner.py    |   2 +-
 .../train_planner/demand_generators.py        | 367 +++++++++
 .../planner.py}                               | 631 +--------------
 .../altrios/train_planner/planner_config.py   | 108 +++
 python/altrios/train_planner/schedulers.py    | 534 +++++++++++++
 python/altrios/utilities.py                   |  33 +-
 truck_work_log.txt                            | 498 ------------
 vehicle_average_times.txt                     |   3 -
 16 files changed, 1094 insertions(+), 2590 deletions(-)
 delete mode 100644 avg_time_per_train.txt
 delete mode 100644 crane_work_log.txt
 delete mode 100644 hostler_work_log.txt
 create mode 100644 python/altrios/train_planner/demand_generators.py
 rename python/altrios/{train_planner.py => train_planner/planner.py} (59%)
 create mode 100644 python/altrios/train_planner/planner_config.py
 create mode 100644 python/altrios/train_planner/schedulers.py
 delete mode 100644 truck_work_log.txt
 delete mode 100644 vehicle_average_times.txt

diff --git a/.gitignore b/.gitignore
index 09a40c2b..f7049904 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1218,4 +1218,4 @@ altrios/resources/networks/Scenic - ALTRIOS Confidential.yaml
 altrios/dispatch/*.json
 activate.sh
 *.xlsx
-*.txt
+altrios/*.txt
diff --git a/avg_time_per_train.txt b/avg_time_per_train.txt
deleted file mode 100644
index 849cc94e..00000000
--- a/avg_time_per_train.txt
+++ /dev/null
@@ -1 +0,0 @@
-17.43808370612796
\ No newline at end of file
diff --git a/crane_work_log.txt b/crane_work_log.txt
deleted file mode 100644
index 30449505..00000000
--- a/crane_work_log.txt
+++ /dev/null
@@ -1,732 +0,0 @@
-Vehicle ID: 3, Event Type: start, Timestamp: 174.19744645094642
-Vehicle ID: 3, Event Type: end, Timestamp: 174.19744645094642
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diff --git a/hostler_work_log.txt b/hostler_work_log.txt
deleted file mode 100644
index ee526025..00000000
--- a/hostler_work_log.txt
+++ /dev/null
@@ -1,754 +0,0 @@
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-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: start, Timestamp: 287.1908033118669
-Vehicle ID: 1, Event Type: end, Timestamp: 287.19312326283386
-Vehicle ID: 1, Event Type: end, Timestamp: 287.1934481874737
diff --git a/python/altrios/demos/rollout_demo.py b/python/altrios/demos/rollout_demo.py
index fe85f46c..9ec56682 100644
--- a/python/altrios/demos/rollout_demo.py
+++ b/python/altrios/demos/rollout_demo.py
@@ -1,7 +1,8 @@
 # %%
 import altrios as alt
-from altrios import rollout, defaults, train_planner
+from altrios import rollout, defaults
 
+from altrios.train_planner import planner, planner_config
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
@@ -21,7 +22,7 @@
 plot_dir.mkdir(exist_ok=True)
 File = defaults.DEMAND_FILE
 #targets = [0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75, 0.8]
-train_planner_config = train_planner.TrainPlannerConfig(
+train_planner_config = planner_config.TrainPlannerConfig(
     cars_per_locomotive={"Default": 50},
     target_cars_per_train={"Default": 90})
 targets = [0.5]
diff --git a/python/altrios/demos/sim_manager_demo.py b/python/altrios/demos/sim_manager_demo.py
index 3c015ef8..6229c36d 100644
--- a/python/altrios/demos/sim_manager_demo.py
+++ b/python/altrios/demos/sim_manager_demo.py
@@ -1,8 +1,9 @@
 # %%
 from altrios import sim_manager
-from altrios import utilities, defaults, train_planner
+from altrios import utilities, defaults
 import altrios as alt
 from altrios.lifts import lifts_simulator
+from altrios.train_planner import planner_config
 import numpy as np
 import polars as pl
 import matplotlib.pyplot as plt
@@ -36,7 +37,7 @@
     f"Elapsed time to import rail vehicles, locations, and network: {t1_import - t0_import:.3g} s"
 )
 
-train_planner_config = train_planner.TrainPlannerConfig(
+train_planner_config = planner_config.TrainPlannerConfig(
             cars_per_locomotive={"Default": 50},
             target_cars_per_train={"Default": 90})
 
diff --git a/python/altrios/rollout.py b/python/altrios/rollout.py
index 6e8b430b..83913472 100644
--- a/python/altrios/rollout.py
+++ b/python/altrios/rollout.py
@@ -1,8 +1,9 @@
 from altrios import sim_manager
-from altrios import metric_calculator, train_planner, defaults
+from altrios import metric_calculator, defaults
 from altrios.metric_calculator import ScenarioInfo
 
 import altrios as alt
+from altrios.train_planner import planner
 import numpy as np
 import time
 import pandas as pd
@@ -23,7 +24,7 @@ def simulate_prescribed_rollout(
     save_interval: Optional[int] = None,
     freight_demand_percent_growth:float = 0.0,
     demand_file: Union[pl.DataFrame, Path, str] = defaults.DEMAND_FILE,
-    train_planner_config: train_planner.TrainPlannerConfig = train_planner.TrainPlannerConfig(),
+    train_planner_config: planner.TrainPlannerConfig = planner.TrainPlannerConfig(),
     count_unused_locomotives = False,
     write_complete_results: Optional[bool] = False,
     write_metrics: Optional[bool] = False,
diff --git a/python/altrios/sim_manager.py b/python/altrios/sim_manager.py
index 128fdcbd..aaced8ed 100644
--- a/python/altrios/sim_manager.py
+++ b/python/altrios/sim_manager.py
@@ -9,7 +9,7 @@
 from altrios import defaults
 
 import altrios as alt
-from altrios import train_planner as planner
+from altrios.train_planner import planner, planner_config
 from altrios import metric_calculator as metrics
 
 def main(
@@ -25,7 +25,7 @@ def main(
     refuelers: pl.DataFrame = None,
     grid_emissions_factors: pl.DataFrame = None,
     nodal_energy_prices: pl.DataFrame = None,
-    train_planner_config: planner.TrainPlannerConfig = planner.TrainPlannerConfig(),
+    train_planner_config: planner_config.TrainPlannerConfig = planner_config.TrainPlannerConfig(),
     train_type: alt.TrainType = alt.TrainType.Freight, 
     demand_file: Union[pl.DataFrame, Path, str] = str(defaults.DEMAND_FILE),
     network_charging_guidelines: pl.DataFrame = None
diff --git a/python/altrios/tests/test_train_planner.py b/python/altrios/tests/test_train_planner.py
index 6d0eb0a4..86b70321 100644
--- a/python/altrios/tests/test_train_planner.py
+++ b/python/altrios/tests/test_train_planner.py
@@ -1,7 +1,7 @@
 import unittest
 
 
-from altrios import train_planner
+from altrios.train_planner import planner
 
 
 class TestTrainPlanner(unittest.TestCase):
diff --git a/python/altrios/train_planner/demand_generators.py b/python/altrios/train_planner/demand_generators.py
new file mode 100644
index 00000000..4ee2f46f
--- /dev/null
+++ b/python/altrios/train_planner/demand_generators.py
@@ -0,0 +1,367 @@
+from typing import Union, List
+import polars as pl
+import polars.selectors as cs
+import pandas as pd
+import numpy as np
+import altrios as alt
+from altrios import utilities
+from altrios.train_planner import planner_config
+
+def initialize_reverse_empties(demand: Union[pl.LazyFrame, pl.DataFrame]) -> Union[pl.LazyFrame, pl.DataFrame]:
+    """
+    Swap `Origin` and `Destination` and append `_Empty` to `Train_Type`.
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demand.
+
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame`.
+    """
+    return (demand
+        .rename({"Origin": "Destination", "Destination": "Origin"})
+        .with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
+    )    
+
+def generate_return_demand_unit(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: planner_config.TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
+    """
+    Given a set of Unit train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
+
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
+    """
+    return (demand_subset
+        .pipe(initialize_reverse_empties)
+    )
+
+def generate_return_demand_manifest(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: planner_config.TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
+    """
+    Given a set of Manifest train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
+
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
+    """
+    return(demand_subset
+        .pipe(initialize_reverse_empties)
+        .with_columns((pl.col("Number_of_Cars") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32))
+    )
+
+def generate_return_demand_intermodal(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: planner_config.TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
+    """
+    Given a set of Intermodal train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
+
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
+    """
+    return (demand_subset
+        .pipe(initialize_reverse_empties)
+        .with_columns(
+            pl.concat_str(pl.min_horizontal("Origin", "Destination"), pl.lit("_"), pl.max_horizontal("Origin", "Destination")).alias("OD")
+        )
+        .with_columns(
+            pl.col("Number_of_Cars", "Number_of_Containers").range_minmax().over("OD").name.suffix("_Return")
+        )
+        .filter(
+            pl.col("Number_of_Containers") == pl.col("Number_of_Containers").max().over("OD")
+        )
+        .drop("OD", "Number_of_Containers", "Number_of_Cars")
+        .rename({"Number_of_Containers_Return": "Number_of_Containers",
+                 "Number_of_Cars_Return": "Number_of_Cars"})
+    )
+
+def generate_return_demand(
+    demand: pl.DataFrame,
+    config: planner_config.TrainPlannerConfig
+) -> pl.DataFrame:
+    """
+    Create a dataframe for additional demand needed for empty cars of the return trains
+    Arguments:
+    ----------
+    df_annual_demand: The user_input file loaded by previous functions
+    that contains loaded demand for each demand pair.
+    config: Object storing train planner configuration paramaters
+    Outputs:
+    ----------
+    df_return_demand: The demand generated by the need
+    of returning the empty cars to their original nodes
+    """
+    demand_subsets = demand.partition_by("Train_Type", as_dict = True)
+    return_demand = []
+    for train_type, demand_subset in demand_subsets.items():
+        train_type_label = train_type[0]
+        if train_type_label in config.return_demand_generators:
+            return_demand_generator = config.return_demand_generators[train_type_label]
+            temp = return_demand_generator(demand_subset, config)
+            print(temp)
+            return_demand.append(temp)
+        else:
+            print(f'Return demand generator not implemented for train type: {train_type_label}')
+
+    demand_return = (pl.concat(return_demand, how="diagonal_relaxed")
+        .filter(pl.col("Number_of_Cars") + pl.col("Number_of_Containers") > 0)
+    )
+    return demand_return
+
+def generate_origin_manifest_demand(
+    demand: pl.DataFrame,
+    node_list: List[str],
+    config: planner_config.TrainPlannerConfig
+) -> pl.DataFrame:
+    """
+    Create a dataframe for summarized view of all origins' manifest demand
+    in number of cars and received cars, both with loaded and empty counts
+    Arguments:
+    ----------
+    demand: The user_input file loaded by previous functions
+    that contains laoded demand for each demand pair.
+    node_list: A list containing all the names of nodes in the system    
+    config: Object storing train planner configuration paramaters
+
+    Outputs:
+    ----------
+    origin_manifest_demand: The dataframe that summarized all the manifest demand
+    originated from each node by number of loaded and empty cars
+    with additional columns for checking the unbalance quantity and serve as check columns
+    for the manifest empty car rebalancing function
+    """
+    manifest_demand = (demand
+        .filter(pl.col("Train_Type").str.strip_suffix("_Loaded") == "Manifest")
+        .select(["Origin", "Destination","Number_of_Cars"])
+        .rename({"Number_of_Cars": "Manifest"})
+        .unique())
+    
+    origin_volume = manifest_demand.group_by("Origin").agg(pl.col("Manifest").sum())
+    destination_volume = manifest_demand.group_by("Destination").agg(pl.col("Manifest").sum().alias("Manifest_Reverse"))
+    origin_manifest_demand = (pl.DataFrame({"Origin": node_list})
+        .join(origin_volume, left_on="Origin", right_on="Origin", how="left")
+        .join(destination_volume, left_on="Origin", right_on="Destination", how="left")
+        .with_columns(
+            (pl.col("Manifest_Reverse") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32).alias("Manifest_Empty"))
+        .with_columns(
+            (pl.col("Manifest") + pl.col("Manifest_Empty")).alias("Manifest_Dispatched"),
+            (pl.col("Manifest_Reverse") + pl.col("Manifest") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32).alias("Manifest_Received"))
+        .drop("Manifest_Reverse")
+        .filter((pl.col("Manifest").is_not_null()) | (pl.col("Manifest_Empty").is_not_null()))
+    )
+
+    return origin_manifest_demand
+
+def balance_trains(
+    demand_origin_manifest: pl.DataFrame
+) -> pl.DataFrame:
+    """
+    Update the manifest demand, especially the empty car demand to maintain equilibrium of number of
+    cars dispatched and received at each node for manifest
+    Arguments:
+    ----------
+    demand_origin_manifest: Dataframe that summarizes empty and loaded 
+    manifest demand dispatched and received for each node by number cars
+    Outputs:
+    ----------
+    demand_origin_manifest: Updated demand_origin_manifest with additional
+    manifest empty car demand added to each node
+    df_balance_storage: Documented additional manifest demand pairs and corresponding quantity for
+    rebalancing process
+    """
+    df_balance_storage = pd.DataFrame(np.zeros(shape=(0, 4)))
+    df_balance_storage = df_balance_storage.rename(
+        columns={0: "Origin", 
+                 1: "Destination", 
+                 2: "Train_Type", 
+                 3: "Number_of_Cars"})
+    
+    train_type = "Manifest_Empty"
+    demand = demand_origin_manifest.to_pandas()[
+        ["Origin","Manifest_Received","Manifest_Dispatched","Manifest_Empty"]]
+    demand = demand.rename(columns={"Manifest_Received": "Received", 
+                            "Manifest_Dispatched": "Dispatched",
+                            "Manifest_Empty": "Empty"})
+
+    step = 0
+    # Calculate the number of iterations needed
+    max_iter = len(demand) * (len(demand)-1) / 2
+    while (~np.isclose(demand["Received"], demand["Dispatched"])).any() and (step <= max_iter):
+        rows_def = demand[demand["Received"] < demand["Dispatched"]]
+        rows_sur = demand[demand["Received"] > demand["Dispatched"]]
+        if((len(rows_def) == 0) | (len(rows_sur) == 0)): 
+            break
+        # Find the first node that is in deficit of cars because of the empty return
+        row_def = rows_def.index[0]
+        # Find the first node that is in surplus of cars
+        row_sur = rows_sur.index[0]
+        surplus = demand.loc[row_sur, "Received"] - demand.loc[row_sur, "Dispatched"]
+        df_balance_storage.loc[len(df_balance_storage.index)] = \
+            [demand.loc[row_sur, "Origin"],
+            demand.loc[row_def, "Origin"],
+            train_type,
+            surplus]
+        demand.loc[row_def, "Received"] += surplus
+        demand.loc[row_sur, "Dispatched"] = demand.loc[row_sur, "Received"]
+        step += 1
+        
+    if (~np.isclose(demand["Received"], demand["Dispatched"])).any():
+        raise Exception("While loop didn't converge")
+    return pl.from_pandas(df_balance_storage)
+
+def generate_demand_trains(
+    demand: pl.DataFrame,
+    demand_returns: pl.DataFrame,
+    demand_rebalancing: pl.DataFrame,
+    rail_vehicles: List[alt.RailVehicle],
+    config: planner_config.TrainPlannerConfig
+) -> pl.DataFrame:
+    """
+    Generate a tabulated demand pair to indicate the final demand
+    for each demand pair for each train type in number of trains
+    Arguments:
+    ----------
+    demand: Tabulated demand for each demand pair for each train type in number of cars
+
+    demand: The user_input file loaded and prepared by previous functions
+    that contains loaded car demand for each demand pair.
+    demand_returns: The demand generated by the need 
+    of returning the empty cars to their original nodes
+    demand_rebalancing: Documented additional manifest demand pairs and corresponding quantity for
+    rebalancing process
+
+    config: Object storing train planner configuration paramaters
+    Outputs:
+    ----------
+    demand: Tabulated demand for each demand pair in terms of number of cars and number of trains
+    """
+    cars_per_train_min = (pl.from_dict(config.min_cars_per_train)
+        .melt(variable_name="Train_Type", value_name="Cars_Per_Train_Min")
+    )
+    cars_per_train_min_default = (cars_per_train_min
+        .filter(pl.col("Train_Type") == pl.lit("Default"))
+        .select("Cars_Per_Train_Min").item()
+    )
+    cars_per_train_target = (pl.from_dict(config.target_cars_per_train)
+        .melt(variable_name="Train_Type", value_name="Cars_Per_Train_Target")
+    )
+    cars_per_train_target_default = (cars_per_train_target
+        .filter(pl.col("Train_Type") == pl.lit("Default"))
+        .select("Cars_Per_Train_Target").item()
+    )
+    #Prepare hp_per_ton requirements to merge onto the demand DataFrame
+    hp_per_ton = pl.concat([
+        (pl.DataFrame(this_dict)
+            .melt(variable_name="Train_Type", value_name="HP_Required_Per_Ton") 
+            .with_columns(pl.lit(this_item).alias("O_D"))
+            .with_columns(pl.col("O_D").str.split("_").list.first().alias("Origin"),
+                        pl.col("O_D").str.split("_").list.last().alias("Destination"))
+        )
+        for this_item, this_dict in config.hp_required_per_ton.items()
+    ], how="horizontal_relaxed")
+    
+    #Prepare ton_per_car requirements to merge onto the demand DataFrame
+    def get_kg_empty(veh):
+        return veh.mass_static_base_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
+    def get_kg(veh):
+        return veh.mass_static_base_kilograms + veh.mass_freight_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
+    
+    ton_per_car = (
+        pl.DataFrame({"Train_Type": pl.Series([rv.car_type for rv in rail_vehicles]).str.strip_suffix("_Loaded"),
+                        "KG_Empty": [get_kg_empty(rv) for rv in rail_vehicles],
+                        "KG": [get_kg(rv) for rv in rail_vehicles]})
+            .with_columns(pl.when(pl.col("Train_Type").str.contains("_Empty"))
+                                .then(pl.col("KG_Empty") / utilities.KG_PER_TON)
+                                .otherwise(pl.col("KG") / utilities.KG_PER_TON)
+                                .alias("Tons_Per_Car"))
+            .drop(["KG_Empty","KG"])
+    )
+
+    demand = (pl.concat([demand, demand_returns, demand_rebalancing], how="diagonal_relaxed")
+        .group_by("Origin","Destination", "Train_Type")
+            .agg(pl.col("Number_of_Cars").sum())
+        .filter(pl.col("Number_of_Cars") > 0)
+        .join(ton_per_car, on="Train_Type", how="left")
+        # Merge on OD-specific hp_per_ton if the user specified any
+        .join(hp_per_ton.drop("O_D"), on=["Origin","Destination","Train_Type"], how="left")
+        # Second, merge on defaults per train type
+        .join(hp_per_ton.filter((pl.col("O_D") =="Default")).drop(["O_D","Origin","Destination"]),
+            on=["Train_Type"],
+            how="left",
+            suffix="_Default")
+        # Merge on cars_per_train_min if the user specified any
+        .join(cars_per_train_min, on=["Train_Type"], how="left")
+        # Merge on cars_per_train_target if the user specified any
+        .join(cars_per_train_target, on=["Train_Type"], how="left")
+        # Fill in defaults per train type wherever the user didn't specify OD-specific hp_per_ton
+        .with_columns(
+            pl.coalesce("HP_Required_Per_Ton", "HP_Required_Per_Ton_Default").alias("HP_Required_Per_Ton"),
+            pl.col("Cars_Per_Train_Min").fill_null(cars_per_train_min_default),
+            pl.col("Cars_Per_Train_Target").fill_null(cars_per_train_target_default),
+        )
+    )
+    loaded = (demand
+        .filter(~pl.col("Train_Type").str.contains("_Empty"))
+        .with_columns(
+            pl.col("Number_of_Cars", "Tons_Per_Car", "HP_Required_Per_Ton", "Cars_Per_Train_Min", "Cars_Per_Train_Target").name.suffix("_Loaded")
+        )
+    )
+    empty = (demand
+        .filter(pl.col("Train_Type").str.contains("_Empty"))
+        .with_columns(
+            pl.col("Number_of_Cars", "Tons_Per_Car", "HP_Required_Per_Ton", "Cars_Per_Train_Min", "Cars_Per_Train_Target").name.suffix("_Empty"),
+            pl.col("Train_Type").str.strip_suffix("_Empty")
+        )
+    )
+    demand = (demand
+        .select(pl.col("Origin", "Destination"), pl.col("Train_Type").str.strip_suffix("_Empty"))
+        .unique()
+        .join(loaded.select(cs.by_name("Origin", "Destination", "Train_Type") | cs.ends_with("_Loaded")), on=["Origin", "Destination", "Train_Type"], how="left")
+        .join(empty.select(cs.by_name("Origin", "Destination", "Train_Type") | cs.ends_with("_Empty")), on=["Origin", "Destination", "Train_Type"], how="left")
+        # Replace nulls with zero
+        .with_columns(cs.float().fill_null(0.0), 
+                      cs.by_dtype(pl.UInt32).fill_null(pl.lit(0).cast(pl.UInt32)),
+                      cs.by_dtype(pl.Int64).fill_null(pl.lit(0).cast(pl.Int64)),
+                      )
+        .group_by("Origin", "Destination", "Train_Type")
+            .agg(
+                pl.col("Number_of_Cars_Loaded", "Number_of_Cars_Empty").sum(),
+                pl.col("Tons_Per_Car_Loaded", "Tons_Per_Car_Empty", 
+                       "HP_Required_Per_Ton_Loaded", "HP_Required_Per_Ton_Empty",
+                       "Cars_Per_Train_Min_Loaded", "Cars_Per_Train_Min_Empty",
+                       "Cars_Per_Train_Target_Loaded", "Cars_Per_Train_Target_Empty").mean(),
+                pl.sum_horizontal("Number_of_Cars_Loaded", "Number_of_Cars_Empty").sum().alias("Number_of_Cars")
+            )
+        .with_columns(
+            # If Cars_Per_Train_Min and Cars_Per_Train_Target "disagree" for empty vs. loaded, take the average weighted by number of cars
+            ((pl.col("Cars_Per_Train_Min_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Cars_Per_Train_Min_Empty").mul("Number_of_Cars_Empty")) / pl.col("Number_of_Cars")).alias("Cars_Per_Train_Min"),
+            ((pl.col("Cars_Per_Train_Target_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Cars_Per_Train_Target_Empty").mul("Number_of_Cars_Empty")) / pl.col("Number_of_Cars")).alias("Cars_Per_Train_Target")
+        )
+        .with_columns(
+            pl.when(config.single_train_mode)
+                .then(1)
+                .when(pl.col("Number_of_Cars") == 0)
+                .then(0)
+                .when(pl.col("Cars_Per_Train_Target") == pl.col("Number_of_Cars"))
+                .then(1)
+                .when(pl.col("Cars_Per_Train_Target") <= 1.0)
+                .then(pl.col("Number_of_Cars"))
+                .otherwise(
+                    pl.max_horizontal([
+                        1,
+                        pl.min_horizontal([
+                            pl.col("Number_of_Cars").floordiv("Cars_Per_Train_Target") + 1,
+                            pl.col("Number_of_Cars").floordiv("Cars_Per_Train_Min")
+                        ])
+                    ])
+                ).cast(pl.UInt32).alias("Number_of_Trains")
+        )
+        .drop("Cars_Per_Train_Target_Loaded", "Cars_Per_Train_Target_Empty", "Cars_Per_Train_Min_Empty", "Cars_Per_Train_Min_Loaded")
+    )
+    return demand
diff --git a/python/altrios/train_planner.py b/python/altrios/train_planner/planner.py
similarity index 59%
rename from python/altrios/train_planner.py
rename to python/altrios/train_planner/planner.py
index 456fc5c1..5276f42d 100644
--- a/python/altrios/train_planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -1,206 +1,17 @@
 from __future__ import annotations
 from pathlib import Path
-from typing import Union
+from altrios.train_planner import demand_generators, schedulers, planner_config
 import numpy as np
 from scipy.stats import rankdata
 import pandas as pd
 import polars as pl
 import polars.selectors as cs
 import math
-from typing import Tuple, List, Dict, Callable, Optional
+from typing import Union, Tuple, List, Dict, Callable, Optional
 from itertools import repeat
 from dataclasses import dataclass, field
 import altrios as alt
 from altrios import defaults, utilities
-from dataclasses import dataclass, field
-
-pl.enable_string_cache()
-
-def initialize_reverse_empties(demand: Union[pl.LazyFrame, pl.DataFrame]) -> Union[pl.LazyFrame, pl.DataFrame]:
-    """
-    Swap `Origin` and `Destination` and append `_Empty` to `Train_Type`.
-    Arguments:
-    ----------
-    demand: `DataFrame` or `LazyFrame` representing origin-destination demand.
-
-    Outputs:
-    ----------
-    Updated demand `DataFrame` or `LazyFrame`.
-    """
-    return (demand
-        .rename({"Origin": "Destination", "Destination": "Origin"})
-        .with_columns((pl.concat_str(pl.col("Train_Type"),pl.lit("_Empty"))).alias("Train_Type"))
-    )    
-
-def generate_return_demand_unit(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
-    """
-    Given a set of Unit train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
-    Arguments:
-    ----------
-    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
-
-    Outputs:
-    ----------
-    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
-    """
-    return (demand_subset
-        .pipe(initialize_reverse_empties)
-    )
-
-def generate_return_demand_manifest(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
-    """
-    Given a set of Manifest train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
-    Arguments:
-    ----------
-    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
-
-    Outputs:
-    ----------
-    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
-    """
-    return(demand_subset
-        .pipe(initialize_reverse_empties)
-        .with_columns((pl.col("Number_of_Cars") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32))
-    )
-
-def generate_return_demand_intermodal(demand_subset: Union[pl.LazyFrame, pl.DataFrame], config: TrainPlannerConfig) -> Union[pl.LazyFrame, pl.DataFrame]:
-    """
-    Given a set of Intermodal train demand for one or more origin-destination pairs, generate demand in the reverse direction(s).
-    Arguments:
-    ----------
-    demand: `DataFrame` or `LazyFrame` representing origin-destination demand for Unit trains.
-
-    Outputs:
-    ----------
-    Updated demand `DataFrame` or `LazyFrame` representing demand in the reverse direction(s) for each origin-destination pair.
-    """
-    return (demand_subset
-        .pipe(initialize_reverse_empties)
-        .with_columns(
-            pl.concat_str(pl.min_horizontal("Origin", "Destination"), pl.lit("_"), pl.max_horizontal("Origin", "Destination")).alias("OD")
-        )
-        .with_columns(
-            pl.col("Number_of_Cars", "Number_of_Containers").range_minmax().over("OD").name.suffix("_Return")
-        )
-        .filter(
-            pl.col("Number_of_Containers") == pl.col("Number_of_Containers").max().over("OD")
-        )
-        .drop("OD", "Number_of_Containers", "Number_of_Cars")
-        .rename({"Number_of_Containers_Return": "Number_of_Containers",
-                 "Number_of_Cars_Return": "Number_of_Cars"})
-    )
-
-@dataclass
-class TrainPlannerConfig:
-    """
-    Dataclass class for train planner configuration parameters.
-
-    Attributes:
-    ----------
-    - `single_train_mode`: `True` to only run one round-trip train and schedule its charging; `False` to plan train consists
-
-    - `min_cars_per_train`: `Dict` of the minimum length in number of cars to form a train for each train type
-
-    - `target_cars_per_train`: `Dict` of the standard train length in number of cars for each train type
-    
-    - `manifest_empty_return_ratio`: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
-    
-    - `cars_per_locomotive`: Heuristic scaling factor used to size number of locomotives needed based on demand.
-
-    - `cars_per_locomotive_fixed`: If `True`, `cars_per_locomotive` overrides `hp_per_ton` calculations used for dispatching decisions.
-    
-    - `refuelers_per_incoming_corridor`: Heuristic scaling factor used to scale number of refuelers needed at each node based on number of incoming corridors.
-    
-    - `stack_type`: Type of stacking (applicable only for intermodal containers)
-
-    - `require_diesel`: `True` to require each consist to have at least one diesel locomotive.
-    
-    - `manifest_empty_return_ratio`: `Dict`
-    
-    - `drag_coeff_function`: `Dict`
-    
-    - `hp_required_per_ton`: `Dict`
-    
-    - `dispatch_scaling_dict`: `Dict`
-    
-    - `loco_info`: `Dict`
-    
-    - `refueler_info`: `Dict`
-
-    - `return_demand_generators`: `Dict`
-    """
-    single_train_mode: bool = False
-    min_cars_per_train: Dict = field(default_factory = lambda: {
-        "Default": 60
-    })
-    target_cars_per_train: Dict = field(default_factory = lambda: {
-        "Default": 180
-    })
-    cars_per_locomotive: Dict = field(default_factory = lambda: {
-        "Default": 70
-    })
-    cars_per_locomotive_fixed: bool = False
-    refuelers_per_incoming_corridor: int = 4
-    stack_type: str = "single"
-    require_diesel: bool = False
-    manifest_empty_return_ratio: float = 0.6
-    drag_coeff_function: Optional[Callable]= None
-    hp_required_per_ton: Dict = field(default_factory = lambda: {
-        "Default": {
-        "Unit": 2.0,
-        "Manifest": 1.5,
-        "Intermodal": 2.0 + 2.0,
-        "Unit_Empty": 2.0,
-        "Manifest_Empty": 1.5,
-        "Intermodal_Empty": 2.0 + 2.0,
-        }                         
-    })
-    dispatch_scaling_dict: Dict = field(default_factory = lambda: {
-        "time_mult_factor": 1.4,
-        "hours_add": 2,
-        "energy_mult_factor": 1.25
-    })
-    loco_info: pd.DataFrame = field(default_factory = lambda: pd.DataFrame({
-        "Diesel_Large": {
-            "Capacity_Cars": 20,
-            "Fuel_Type": "Diesel",
-            "Min_Servicing_Time_Hr": 3.0,
-            "Rust_Loco": alt.Locomotive.default(),
-            "Cost_USD": defaults.DIESEL_LOCO_COST_USD,
-            "Lifespan_Years": defaults.LOCO_LIFESPAN
-            },
-        "BEL": {
-            "Capacity_Cars": 20,
-            "Fuel_Type": "Electricity",
-            "Min_Servicing_Time_Hr": 3.0,
-            "Rust_Loco": alt.Locomotive.default_battery_electric_loco(),
-            "Cost_USD": defaults.BEL_MINUS_BATTERY_COST_USD,
-            "Lifespan_Years": defaults.LOCO_LIFESPAN
-            }
-        }).transpose().reset_index(names='Locomotive_Type'))
-    refueler_info: pd.DataFrame = field(default_factory = lambda: pd.DataFrame({
-        "Diesel_Fueler": {
-            "Locomotive_Type": "Diesel_Large",
-            "Fuel_Type": "Diesel",
-            "Refueler_J_Per_Hr": defaults.DIESEL_REFUEL_RATE_J_PER_HR,
-            "Refueler_Efficiency": defaults.DIESEL_REFUELER_EFFICIENCY,
-            "Cost_USD": defaults.DIESEL_REFUELER_COST_USD,
-            "Lifespan_Years": defaults.LOCO_LIFESPAN
-        },
-        "BEL_Charger": {
-            "Locomotive_Type": "BEL",
-            "Fuel_Type": "Electricity",
-            "Refueler_J_Per_Hr": defaults.BEL_CHARGE_RATE_J_PER_HR,
-            "Refueler_Efficiency": defaults.BEL_CHARGER_EFFICIENCY,
-            "Cost_USD": defaults.BEL_CHARGER_COST_USD,
-            "Lifespan_Years": defaults.LOCO_LIFESPAN
-        }
-    }).transpose().reset_index(names='Refueler_Type'))
-    return_demand_generators: Dict = field(default_factory = lambda: {
-        'Unit': generate_return_demand_unit,
-        'Manifest': generate_return_demand_manifest,
-        'Intermodal': generate_return_demand_intermodal
-    })
 
 def demand_loader(
     demand_table: Union[pl.DataFrame, Path, str]
@@ -230,372 +41,27 @@ def demand_loader(
         demand_table.get_column("Destination")]).unique().sort()
     return demand_table, nodes
 
-def generate_return_demand(
-    demand: pl.DataFrame,
-    config: TrainPlannerConfig
-) -> pl.DataFrame:
-    """
-    Create a dataframe for additional demand needed for empty cars of the return trains
-    Arguments:
-    ----------
-    df_annual_demand: The user_input file loaded by previous functions
-    that contains loaded demand for each demand pair.
-    config: Object storing train planner configuration paramaters
-    Outputs:
-    ----------
-    df_return_demand: The demand generated by the need
-    of returning the empty cars to their original nodes
-    """
-    demand_subsets = demand.partition_by("Train_Type", as_dict = True)
-    return_demand = []
-    for train_type, demand_subset in demand_subsets.items():
-        train_type_label = train_type[0]
-        if train_type_label in config.return_demand_generators:
-            return_demand_generator = config.return_demand_generators[train_type_label]
-            temp = return_demand_generator(demand_subset, config)
-            print(temp)
-            return_demand.append(temp)
-        else:
-            print(f'Return demand generator not implemented for train type: {train_type_label}')
-
-    demand_return = (pl.concat(return_demand, how="diagonal_relaxed")
-        .filter(pl.col("Number_of_Cars") + pl.col("Number_of_Containers") > 0)
-    )
-    return demand_return
-
-def generate_origin_manifest_demand(
-    demand: pl.DataFrame,
-    node_list: List[str],
-    config: TrainPlannerConfig
-) -> pl.DataFrame:
-    """
-    Create a dataframe for summarized view of all origins' manifest demand
-    in number of cars and received cars, both with loaded and empty counts
-    Arguments:
-    ----------
-    demand: The user_input file loaded by previous functions
-    that contains laoded demand for each demand pair.
-    node_list: A list containing all the names of nodes in the system    
-    config: Object storing train planner configuration paramaters
-
-    Outputs:
-    ----------
-    origin_manifest_demand: The dataframe that summarized all the manifest demand
-    originated from each node by number of loaded and empty cars
-    with additional columns for checking the unbalance quantity and serve as check columns
-    for the manifest empty car rebalancing function
-    """
-    manifest_demand = (demand
-        .filter(pl.col("Train_Type").str.strip_suffix("_Loaded") == "Manifest")
-        .select(["Origin", "Destination","Number_of_Cars"])
-        .rename({"Number_of_Cars": "Manifest"})
-        .unique())
-    
-    origin_volume = manifest_demand.group_by("Origin").agg(pl.col("Manifest").sum())
-    destination_volume = manifest_demand.group_by("Destination").agg(pl.col("Manifest").sum().alias("Manifest_Reverse"))
-    origin_manifest_demand = (pl.DataFrame({"Origin": node_list})
-        .join(origin_volume, left_on="Origin", right_on="Origin", how="left")
-        .join(destination_volume, left_on="Origin", right_on="Destination", how="left")
-        .with_columns(
-            (pl.col("Manifest_Reverse") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32).alias("Manifest_Empty"))
-        .with_columns(
-            (pl.col("Manifest") + pl.col("Manifest_Empty")).alias("Manifest_Dispatched"),
-            (pl.col("Manifest_Reverse") + pl.col("Manifest") * config.manifest_empty_return_ratio).floor().cast(pl.UInt32).alias("Manifest_Received"))
-        .drop("Manifest_Reverse")
-        .filter((pl.col("Manifest").is_not_null()) | (pl.col("Manifest_Empty").is_not_null()))
-    )
-
-    return origin_manifest_demand
-
-
-def balance_trains(
-    demand_origin_manifest: pl.DataFrame
-) -> pl.DataFrame:
-    """
-    Update the manifest demand, especially the empty car demand to maintain equilibrium of number of
-    cars dispatched and received at each node for manifest
-    Arguments:
-    ----------
-    demand_origin_manifest: Dataframe that summarizes empty and loaded 
-    manifest demand dispatched and received for each node by number cars
-    Outputs:
-    ----------
-    demand_origin_manifest: Updated demand_origin_manifest with additional
-    manifest empty car demand added to each node
-    df_balance_storage: Documented additional manifest demand pairs and corresponding quantity for
-    rebalancing process
-    """
-    df_balance_storage = pd.DataFrame(np.zeros(shape=(0, 4)))
-    df_balance_storage = df_balance_storage.rename(
-        columns={0: "Origin", 
-                 1: "Destination", 
-                 2: "Train_Type", 
-                 3: "Number_of_Cars"})
-    
-    train_type = "Manifest_Empty"
-    demand = demand_origin_manifest.to_pandas()[
-        ["Origin","Manifest_Received","Manifest_Dispatched","Manifest_Empty"]]
-    demand = demand.rename(columns={"Manifest_Received": "Received", 
-                            "Manifest_Dispatched": "Dispatched",
-                            "Manifest_Empty": "Empty"})
-
-    step = 0
-    # Calculate the number of iterations needed
-    max_iter = len(demand) * (len(demand)-1) / 2
-    while (~np.isclose(demand["Received"], demand["Dispatched"])).any() and (step <= max_iter):
-        rows_def = demand[demand["Received"] < demand["Dispatched"]]
-        rows_sur = demand[demand["Received"] > demand["Dispatched"]]
-        if((len(rows_def) == 0) | (len(rows_sur) == 0)): 
-            break
-        # Find the first node that is in deficit of cars because of the empty return
-        row_def = rows_def.index[0]
-        # Find the first node that is in surplus of cars
-        row_sur = rows_sur.index[0]
-        surplus = demand.loc[row_sur, "Received"] - demand.loc[row_sur, "Dispatched"]
-        df_balance_storage.loc[len(df_balance_storage.index)] = \
-            [demand.loc[row_sur, "Origin"],
-            demand.loc[row_def, "Origin"],
-            train_type,
-            surplus]
-        demand.loc[row_def, "Received"] += surplus
-        demand.loc[row_sur, "Dispatched"] = demand.loc[row_sur, "Received"]
-        step += 1
-        
-    if (~np.isclose(demand["Received"], demand["Dispatched"])).any():
-        raise Exception("While loop didn't converge")
-    return pl.from_pandas(df_balance_storage)
-
-def generate_demand_trains(
-    demand: pl.DataFrame,
-    demand_returns: pl.DataFrame,
-    demand_rebalancing: pl.DataFrame,
-    rail_vehicles: List[alt.RailVehicle],
-    config: TrainPlannerConfig
-) -> pl.DataFrame:
-    """
-    Generate a tabulated demand pair to indicate the final demand
-    for each demand pair for each train type in number of trains
-    Arguments:
-    ----------
-    demand: Tabulated demand for each demand pair for each train type in number of cars
-
-    demand: The user_input file loaded and prepared by previous functions
-    that contains loaded car demand for each demand pair.
-    demand_returns: The demand generated by the need 
-    of returning the empty cars to their original nodes
-    demand_rebalancing: Documented additional manifest demand pairs and corresponding quantity for
-    rebalancing process
-
-    config: Object storing train planner configuration paramaters
-    Outputs:
-    ----------
-    demand: Tabulated demand for each demand pair in terms of number of cars and number of trains
-    """
-    cars_per_train_min = (pl.from_dict(config.min_cars_per_train)
-        .melt(variable_name="Train_Type", value_name="Cars_Per_Train_Min")
-    )
-    cars_per_train_min_default = (cars_per_train_min
-        .filter(pl.col("Train_Type") == pl.lit("Default"))
-        .select("Cars_Per_Train_Min").item()
-    )
-    cars_per_train_target = (pl.from_dict(config.target_cars_per_train)
-        .melt(variable_name="Train_Type", value_name="Cars_Per_Train_Target")
-    )
-    cars_per_train_target_default = (cars_per_train_target
-        .filter(pl.col("Train_Type") == pl.lit("Default"))
-        .select("Cars_Per_Train_Target").item()
-    )
-    #Prepare hp_per_ton requirements to merge onto the demand DataFrame
-    hp_per_ton = pl.concat([
-        (pl.DataFrame(this_dict)
-            .melt(variable_name="Train_Type", value_name="HP_Required_Per_Ton") 
-            .with_columns(pl.lit(this_item).alias("O_D"))
-            .with_columns(pl.col("O_D").str.split("_").list.first().alias("Origin"),
-                        pl.col("O_D").str.split("_").list.last().alias("Destination"))
-        )
-        for this_item, this_dict in config.hp_required_per_ton.items()
-    ], how="horizontal_relaxed")
-    
-    #Prepare ton_per_car requirements to merge onto the demand DataFrame
-    def get_kg_empty(veh):
-        return veh.mass_static_base_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
-    def get_kg(veh):
-        return veh.mass_static_base_kilograms + veh.mass_freight_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
-    
-    ton_per_car = (
-        pl.DataFrame({"Train_Type": pl.Series([rv.car_type for rv in rail_vehicles]).str.strip_suffix("_Loaded"),
-                        "KG_Empty": [get_kg_empty(rv) for rv in rail_vehicles],
-                        "KG": [get_kg(rv) for rv in rail_vehicles]})
-            .with_columns(pl.when(pl.col("Train_Type").str.contains("_Empty"))
-                                .then(pl.col("KG_Empty") / utilities.KG_PER_TON)
-                                .otherwise(pl.col("KG") / utilities.KG_PER_TON)
-                                .alias("Tons_Per_Car"))
-            .drop(["KG_Empty","KG"])
-    )
-
-    demand = (pl.concat([demand, demand_returns, demand_rebalancing], how="diagonal_relaxed")
-        .group_by("Origin","Destination", "Train_Type")
-            .agg(pl.col("Number_of_Cars").sum())
-        .filter(pl.col("Number_of_Cars") > 0)
-        .join(ton_per_car, on="Train_Type", how="left")
-        # Merge on OD-specific hp_per_ton if the user specified any
-        .join(hp_per_ton.drop("O_D"), on=["Origin","Destination","Train_Type"], how="left")
-        # Second, merge on defaults per train type
-        .join(hp_per_ton.filter((pl.col("O_D") =="Default")).drop(["O_D","Origin","Destination"]),
-            on=["Train_Type"],
-            how="left",
-            suffix="_Default")
-        # Merge on cars_per_train_min if the user specified any
-        .join(cars_per_train_min, on=["Train_Type"], how="left")
-        # Merge on cars_per_train_target if the user specified any
-        .join(cars_per_train_target, on=["Train_Type"], how="left")
-        # Fill in defaults per train type wherever the user didn't specify OD-specific hp_per_ton
-        .with_columns(
-            pl.coalesce("HP_Required_Per_Ton", "HP_Required_Per_Ton_Default").alias("HP_Required_Per_Ton"),
-            pl.col("Cars_Per_Train_Min").fill_null(cars_per_train_min_default),
-            pl.col("Cars_Per_Train_Target").fill_null(cars_per_train_target_default),
-        )
-    )
-    loaded = (demand
-        .filter(~pl.col("Train_Type").str.contains("_Empty"))
-        .with_columns(
-            pl.col("Number_of_Cars", "Tons_Per_Car", "HP_Required_Per_Ton", "Cars_Per_Train_Min", "Cars_Per_Train_Target").name.suffix("_Loaded")
-        )
-    )
-    empty = (demand
-        .filter(pl.col("Train_Type").str.contains("_Empty"))
-        .with_columns(
-            pl.col("Number_of_Cars", "Tons_Per_Car", "HP_Required_Per_Ton", "Cars_Per_Train_Min", "Cars_Per_Train_Target").name.suffix("_Empty"),
-            pl.col("Train_Type").str.strip_suffix("_Empty")
-        )
-    )
-    demand = (demand
-        .select(pl.col("Origin", "Destination"), pl.col("Train_Type").str.strip_suffix("_Empty"))
-        .unique()
-        .join(loaded.select(cs.by_name("Origin", "Destination", "Train_Type") | cs.ends_with("_Loaded")), on=["Origin", "Destination", "Train_Type"], how="left")
-        .join(empty.select(cs.by_name("Origin", "Destination", "Train_Type") | cs.ends_with("_Empty")), on=["Origin", "Destination", "Train_Type"], how="left")
-        # Replace nulls with zero
-        .with_columns(cs.float().fill_null(0.0), 
-                      cs.by_dtype(pl.UInt32).fill_null(pl.lit(0).cast(pl.UInt32)),
-                      cs.by_dtype(pl.Int64).fill_null(pl.lit(0).cast(pl.Int64)),
-                      )
-        .group_by("Origin", "Destination", "Train_Type")
-            .agg(
-                pl.col("Number_of_Cars_Loaded", "Number_of_Cars_Empty").sum(),
-                pl.col("Tons_Per_Car_Loaded", "Tons_Per_Car_Empty", 
-                       "HP_Required_Per_Ton_Loaded", "HP_Required_Per_Ton_Empty",
-                       "Cars_Per_Train_Min_Loaded", "Cars_Per_Train_Min_Empty",
-                       "Cars_Per_Train_Target_Loaded", "Cars_Per_Train_Target_Empty").mean(),
-                pl.sum_horizontal("Number_of_Cars_Loaded", "Number_of_Cars_Empty").sum().alias("Number_of_Cars")
-            )
-        .with_columns(
-            # If Cars_Per_Train_Min and Cars_Per_Train_Target "disagree" for empty vs. loaded, take the average weighted by number of cars
-            ((pl.col("Cars_Per_Train_Min_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Cars_Per_Train_Min_Empty").mul("Number_of_Cars_Empty")) / pl.col("Number_of_Cars")).alias("Cars_Per_Train_Min"),
-            ((pl.col("Cars_Per_Train_Target_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Cars_Per_Train_Target_Empty").mul("Number_of_Cars_Empty")) / pl.col("Number_of_Cars")).alias("Cars_Per_Train_Target")
-        )
-        .with_columns(
-            pl.when(config.single_train_mode)
-                .then(1)
-                .when(pl.col("Number_of_Cars") == 0)
-                .then(0)
-                .when(pl.col("Cars_Per_Train_Target") == pl.col("Number_of_Cars"))
-                .then(1)
-                .when(pl.col("Cars_Per_Train_Target") <= 1.0)
-                .then(pl.col("Number_of_Cars"))
-                .otherwise(
-                    pl.max_horizontal([
-                        1,
-                        pl.min_horizontal([
-                            pl.col("Number_of_Cars").floordiv("Cars_Per_Train_Target") + 1,
-                            pl.col("Number_of_Cars").floordiv("Cars_Per_Train_Min")
-                        ])
-                    ])
-                ).cast(pl.UInt32).alias("Number_of_Trains")
-        )
-        .drop("Cars_Per_Train_Target_Loaded", "Cars_Per_Train_Target_Empty", "Cars_Per_Train_Min_Empty", "Cars_Per_Train_Min_Loaded")
-    )
-    return demand
-
-
-def generate_dispatch_details(
-    demand: pl.DataFrame,
-    hours: int
-) -> pl.DataFrame:
-    """
-    Generate a tabulated demand pair to indicate the expected dispatching interval
-    and actual dispatching timesteps after rounding
-    Arguments:
-    ----------
-    config: Object storing train planner configuration paramaters
-    demand_train: Dataframe of demand (number of trains) for each OD pair for each train type
-    hours: Number of hours in the simulation time period
-    Outputs:
-    ----------
-    dispatch_times: Tabulated dispatching time for each demand pair for each train type
-    in hours
-    """
-    def pctWithinGroup(df: pl.DataFrame, grouping_vars: List[str]) -> pl.DataFrame:
-        return (df
-            .with_columns(
-                (pl.cum_count('Origin').over(grouping_vars) / 
-                pl.count().over(grouping_vars))
-                .alias("Percent_Within_Group")
-            )
-        )
-
-    def allocateItems(df: pl.DataFrame, target: str, grouping_vars: List[str]) -> pl.DataFrame:
-        return (df
-        .sort(grouping_vars)
-        .pipe(pctWithinGroup, grouping_vars = grouping_vars)
-        .with_columns(
-            pl.col(target).mul("Percent_Within_Group").round().alias(f'{target}_Group_Cumulative')
-        )
-        .with_columns(
-            (pl.col(f'{target}_Group_Cumulative') - pl.col(f'{target}_Group_Cumulative').shift(1).over(grouping_vars))
-                .fill_null(pl.col(f'{target}_Group_Cumulative'))
-                .alias(f'{target}')
-        )
-        .drop(f'{target}_Group_Cumulative')
-    )
-
-    grouping_vars = ["Origin", "Destination", "Train_Type"]
-    demand = (demand
-        .select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode())
-        .pipe(allocateItems, target = "Number_of_Cars_Loaded", grouping_vars = grouping_vars)
-        .drop("Percent_Within_Group")
-        .pipe(allocateItems, target = "Number_of_Cars_Empty", grouping_vars = grouping_vars)
-        .group_by(pl.exclude("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
-            .agg(pl.col("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
-        .with_columns(pl.col("Number_of_Cars_Loaded").list.sort(descending=True),
-                      pl.col("Number_of_Cars_Empty").list.sort(descending=False))
-        .explode("Number_of_Cars_Empty", "Number_of_Cars_Loaded")
-        .with_columns(
-            (pl.col("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")).alias("Tons_Per_Train"),
-            (pl.col("HP_Required_Per_Ton_Loaded").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + 
-                pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")
-                ).alias("HP_Required")
-        )
-        .sort("Origin", "Destination", "Percent_Within_Group", "Train_Type")
-        .with_columns(
-            (hours * 1.0 / pl.len().over("Origin", "Destination")).alias("Interval")
-        )
-        .with_columns(
-            ((pl.col("Interval").cum_count().over(["Origin","Destination"])) \
-             * pl.col("Interval")).alias("Hour")
-        )
-        .select("Hour", "Origin", "Destination", "Train_Type", 
-                "Number_of_Cars", "Number_of_Cars_Loaded", "Number_of_Cars_Empty",
-                "Tons_Per_Train", "HP_Required"
-        )
-        .rename({"Number_of_Cars_Loaded": "Cars_Loaded", 
-                "Number_of_Cars_Empty": "Cars_Empty"})
-        .sort(["Hour","Origin","Destination","Train_Type"])
-    )
-    
-    return demand
+def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
+    if all(item in loco_info.columns for item in [
+        'HP','Loco_Mass_Tons','SOC_J','SOC_Min_J','SOC_Max_J','Capacity_J'
+        ]
+    ): return loco_info
+    get_hp = lambda loco: loco.pwr_rated_kilowatts * 1e3 / alt.utils.W_PER_HP
+    get_mass_ton = lambda loco: 0 if not loco.mass_kg else loco.mass_kg / alt.utils.KG_PER_TON
+    get_starting_soc = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.state.soc * loco.res.energy_capacity_joules
+    get_min_soc = lambda loco: 0 if not loco.res else loco.res.min_soc * loco.res.energy_capacity_joules
+    get_max_soc = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.max_soc * loco.res.energy_capacity_joules
+    get_capacity = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.energy_capacity_joules
+    loco_info.loc[:,'HP'] = loco_info.loc[:,'Rust_Loco'].apply(get_hp) 
+    loco_info.loc[:,'Loco_Mass_Tons'] = loco_info.loc[:,'Rust_Loco'].apply(get_mass_ton) 
+    loco_info.loc[:,'SOC_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_starting_soc) 
+    loco_info.loc[:,'SOC_Min_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_min_soc) 
+    loco_info.loc[:,'SOC_Max_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_max_soc) 
+    loco_info.loc[:,'Capacity_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_capacity) 
+    return loco_info
 
 def build_locopool(
-    config: TrainPlannerConfig,
+    config: planner_config.TrainPlannerConfig,
     demand_file: Union[pl.DataFrame, Path, str],
     method: str = "tile",
     shares: List[float] = [],
@@ -635,7 +101,6 @@ def build_locopool(
     if config.single_train_mode:
         sorted_nodes = np.tile([demand.select(pl.col("Origin").first()).item()],rows).tolist()
         engine_numbers = range(0, rows)
-        print(engine_numbers)
     else:
         sorted_nodes = np.sort(np.tile(node_list, initial_size)).tolist()
         engine_numbers = rankdata(sorted_nodes, method="dense") * 1000 + \
@@ -645,6 +110,7 @@ def build_locopool(
         repetitions = math.ceil(rows/len(loco_types))
         types = np.tile(loco_types, repetitions).tolist()[0:rows]
     elif method == "shares_twoway":
+        # TODO: this logic can be replaced (and generalized to >2 types) using altrios.utilities.allocateItems
         if((len(loco_types) != 2) | (len(shares) != 2)):
             raise ValueError(
                 f"""2-way prescribed locopool requested but number of locomotive types is not 2.""")
@@ -704,7 +170,6 @@ def build_locopool(
     loco_pool = loco_pool.join(loco_info_pl, on="Locomotive_Type")
     return loco_pool
 
-
 def build_refuelers(
     node_list: pd.Series,
     loco_pool: pl.DataFrame,
@@ -796,25 +261,6 @@ def append_charging_guidelines(
         .with_columns(pl.max_horizontal([pl.col('SOC_Max_J')-pl.col('Battery_Headroom_J'), pl.col('SOC_Min_J')]).alias("SOC_J")))
     return refuelers, loco_pool
 
-def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
-    if all(item in loco_info.columns for item in [
-        'HP','Loco_Mass_Tons','SOC_J','SOC_Min_J','SOC_Max_J','Capacity_J'
-        ]
-    ): return loco_info
-    get_hp = lambda loco: loco.pwr_rated_kilowatts * 1e3 / alt.utils.W_PER_HP
-    get_mass_ton = lambda loco: 0 if not loco.mass_kg else loco.mass_kg / alt.utils.KG_PER_TON
-    get_starting_soc = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.state.soc * loco.res.energy_capacity_joules
-    get_min_soc = lambda loco: 0 if not loco.res else loco.res.min_soc * loco.res.energy_capacity_joules
-    get_max_soc = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.max_soc * loco.res.energy_capacity_joules
-    get_capacity = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.energy_capacity_joules
-    loco_info.loc[:,'HP'] = loco_info.loc[:,'Rust_Loco'].apply(get_hp) 
-    loco_info.loc[:,'Loco_Mass_Tons'] = loco_info.loc[:,'Rust_Loco'].apply(get_mass_ton) 
-    loco_info.loc[:,'SOC_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_starting_soc) 
-    loco_info.loc[:,'SOC_Min_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_min_soc) 
-    loco_info.loc[:,'SOC_Max_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_max_soc) 
-    loco_info.loc[:,'Capacity_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_capacity) 
-    return loco_info
-
 def dispatch(
     dispatch_time: int,
     origin: str,
@@ -822,7 +268,7 @@ def dispatch(
     train_tonnage: float,
     hp_required: float,
     total_cars: float,
-    config: TrainPlannerConfig, 
+    config: planner_config.TrainPlannerConfig, 
 ) -> pl.Series:
     """
     Update the locomotive pool by identifying the desired locomotive to dispatch and assign to the
@@ -1081,7 +527,7 @@ def run_train_planner(
     simulation_days: int,
     scenario_year: int,
     train_type: alt.TrainType = alt.TrainType.Freight, 
-    config: TrainPlannerConfig = TrainPlannerConfig(),
+    config: planner_config.TrainPlannerConfig = planner_config.TrainPlannerConfig(),
     demand_file: Union[pl.DataFrame, Path, str] = defaults.DEMAND_FILE,
     network_charging_guidelines: pl.DataFrame = None,
 ) -> Tuple[
@@ -1108,6 +554,7 @@ def run_train_planner(
     """
     config.loco_info = append_loco_info(config.loco_info)
     demand, node_list = demand_loader(demand_file)
+
     if refuelers is None: 
         refuelers = build_refuelers(
             node_list, 
@@ -1120,26 +567,28 @@ def run_train_planner(
 
     refuelers, loco_pool = append_charging_guidelines(refuelers, loco_pool, demand, network_charging_guidelines)
     if config.single_train_mode:
-        demand = generate_demand_trains(demand, 
+        demand = demand_generators.generate_demand_trains(demand, 
                                         demand_returns = pl.DataFrame(), 
                                         demand_rebalancing = pl.DataFrame(), 
                                         rail_vehicles = rail_vehicles, 
                                         config = config)
-        dispatch_times = (demand
+        dispatch_schedule = (demand
             .with_row_index(name="index")
             .with_columns(pl.col("index").mul(24.0).alias("Hour"))
             .drop("index")
         )
     else:
-        demand_returns = generate_return_demand(demand, config)
+        demand_returns = demand_generators.generate_return_demand(demand, config)
         demand_rebalancing = pl.DataFrame()
         if demand.filter(pl.col("Train_Type").str.contains("Manifest")).height > 0:
-            demand_origin_manifest = generate_origin_manifest_demand(demand, node_list, config)
-            demand_rebalancing = balance_trains(demand_origin_manifest)
-        demand = generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, config)
-        dispatches = generate_dispatch_details(demand, simulation_days * 24)
-
-    final_departure = dispatches.get_column("Hour").max()
+            demand_origin_manifest = demand_generators.generate_origin_manifest_demand(demand, node_list, config)
+            demand_rebalancing = demand_generators.balance_trains(demand_origin_manifest)
+        demand = demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, config)
+        if config.dispatch_scheduler is None:
+            config.dispatch_scheduler = schedulers.generate_dispatch_details
+        dispatch_schedule = config.dispatch_scheduler(demand, rail_vehicles, simulation_days, config)
+
+    final_departure = dispatch_schedule.get_column("Hour").max()
     train_consist_plan = pl.DataFrame(schema=
         {'Train_ID': pl.Int64, 
          'Train_Type': pl.Utf8, 
@@ -1163,10 +612,10 @@ def run_train_planner(
 
     done = False
     # start at first departure time
-    current_time = dispatches.get_column("Hour").min()
+    current_time = dispatch_schedule.get_column("Hour").min()
     while not done:
         # Dispatch new train consists
-        current_dispatches = dispatches.filter(pl.col("Hour") == current_time)
+        current_dispatches = dispatch_schedule.filter(pl.col("Hour") == current_time)
         if(current_dispatches.height > 0):
             loco_pool, event_tracker = update_refuel_queue(loco_pool, refuelers, current_time, event_tracker)
 
@@ -1318,7 +767,7 @@ def run_train_planner(
             loco_pool, event_tracker = update_refuel_queue(loco_pool, refuelers, current_time, event_tracker)
             done = True
         else:
-            current_time = dispatches.filter(pl.col("Hour").gt(current_time)).get_column("Hour").min()
+            current_time = dispatch_schedule.filter(pl.col("Hour").gt(current_time)).get_column("Hour").min()
 
     train_consist_plan = (train_consist_plan
         .with_columns(
@@ -1358,7 +807,7 @@ def run_train_planner(
     network = alt.Network.from_file(
         str(alt.resources_root() / "networks/Taconite-NoBalloon.yaml")
     )
-    config = TrainPlannerConfig()
+    config = planner_config.TrainPlannerConfig()
     loco_pool = build_locopool(config, defaults.DEMAND_FILE)
     demand, node_list = demand_loader(defaults.DEMAND_FILE)
     refuelers = build_refuelers(
diff --git a/python/altrios/train_planner/planner_config.py b/python/altrios/train_planner/planner_config.py
new file mode 100644
index 00000000..749c8900
--- /dev/null
+++ b/python/altrios/train_planner/planner_config.py
@@ -0,0 +1,108 @@
+from __future__ import annotations
+from altrios.train_planner import demand_generators
+import polars as pl
+import pandas as pd
+from typing import Dict, Callable, Optional
+from dataclasses import dataclass, field
+import altrios as alt
+from altrios import defaults
+
+pl.enable_string_cache()
+
+@dataclass
+class TrainPlannerConfig:
+    """
+    Dataclass class for train planner configuration parameters.
+
+    Attributes:
+    ----------
+    - `single_train_mode`: `True` to only run one round-trip train and schedule its charging; `False` to plan train consists
+    - `min_cars_per_train`: `Dict` of the minimum length in number of cars to form a train for each train type
+    - `target_cars_per_train`: `Dict` of the standard train length in number of cars for each train type
+    - `manifest_empty_return_ratio`: Desired railcar reuse ratio to calculate the empty manifest car demand, (E_ij+E_ji)/(L_ij+L_ji)
+    - `cars_per_locomotive`: Heuristic scaling factor used to size number of locomotives needed based on demand.
+    - `cars_per_locomotive_fixed`: If `True`, `cars_per_locomotive` overrides `hp_per_ton` calculations used for dispatching decisions.
+    - `refuelers_per_incoming_corridor`: Heuristic scaling factor used to scale number of refuelers needed at each node based on number of incoming corridors.
+    - `stack_type`: Type of stacking (applicable only for intermodal containers)
+    - `require_diesel`: `True` to require each consist to have at least one diesel locomotive.
+    - `manifest_empty_return_ratio`: `Dict`
+    - `drag_coeff_function`: `Dict`
+    - `hp_required_per_ton`: `Dict`
+    - `dispatch_scaling_dict`: `Dict`
+    - `loco_info`: `Dict`
+    - `refueler_info`: `Dict`
+    - `return_demand_generators`: `Dict`
+    """
+    single_train_mode: bool = False
+    min_cars_per_train: Dict = field(default_factory = lambda: {
+        "Default": 60
+    })
+    target_cars_per_train: Dict = field(default_factory = lambda: {
+        "Default": 180
+    })
+    cars_per_locomotive: Dict = field(default_factory = lambda: {
+        "Default": 70
+    })
+    cars_per_locomotive_fixed: bool = False
+    refuelers_per_incoming_corridor: int = 4
+    stack_type: str = "single"
+    require_diesel: bool = False
+    manifest_empty_return_ratio: float = 0.6
+    hp_required_per_ton: Dict = field(default_factory = lambda: {
+        "Default": {
+        "Unit": 2.0,
+        "Manifest": 1.5,
+        "Intermodal": 2.0 + 2.0,
+        "Unit_Empty": 2.0,
+        "Manifest_Empty": 1.5,
+        "Intermodal_Empty": 2.0 + 2.0,
+        }                         
+    })
+    dispatch_scaling_dict: Dict = field(default_factory = lambda: {
+        "time_mult_factor": 1.4,
+        "hours_add": 2,
+        "energy_mult_factor": 1.25
+    })
+    loco_info: pd.DataFrame = field(default_factory = lambda: pd.DataFrame({
+        "Diesel_Large": {
+            "Capacity_Cars": 20,
+            "Fuel_Type": "Diesel",
+            "Min_Servicing_Time_Hr": 3.0,
+            "Rust_Loco": alt.Locomotive.default(),
+            "Cost_USD": defaults.DIESEL_LOCO_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+            },
+        "BEL": {
+            "Capacity_Cars": 20,
+            "Fuel_Type": "Electricity",
+            "Min_Servicing_Time_Hr": 3.0,
+            "Rust_Loco": alt.Locomotive.default_battery_electric_loco(),
+            "Cost_USD": defaults.BEL_MINUS_BATTERY_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+            }
+        }).transpose().reset_index(names='Locomotive_Type'))
+    refueler_info: pd.DataFrame = field(default_factory = lambda: pd.DataFrame({
+        "Diesel_Fueler": {
+            "Locomotive_Type": "Diesel_Large",
+            "Fuel_Type": "Diesel",
+            "Refueler_J_Per_Hr": defaults.DIESEL_REFUEL_RATE_J_PER_HR,
+            "Refueler_Efficiency": defaults.DIESEL_REFUELER_EFFICIENCY,
+            "Cost_USD": defaults.DIESEL_REFUELER_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+        },
+        "BEL_Charger": {
+            "Locomotive_Type": "BEL",
+            "Fuel_Type": "Electricity",
+            "Refueler_J_Per_Hr": defaults.BEL_CHARGE_RATE_J_PER_HR,
+            "Refueler_Efficiency": defaults.BEL_CHARGER_EFFICIENCY,
+            "Cost_USD": defaults.BEL_CHARGER_COST_USD,
+            "Lifespan_Years": defaults.LOCO_LIFESPAN
+        }
+    }).transpose().reset_index(names='Refueler_Type'))
+    drag_coeff_function: Optional[Callable]= None
+    dispatch_scheduler: Optional[Callable] = None
+    return_demand_generators: Dict = field(default_factory = lambda: {
+        'Unit': demand_generators.generate_return_demand_unit,
+        'Manifest': demand_generators.generate_return_demand_manifest,
+        'Intermodal': demand_generators.generate_return_demand_intermodal
+    })
diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
new file mode 100644
index 00000000..14eee6f9
--- /dev/null
+++ b/python/altrios/train_planner/schedulers.py
@@ -0,0 +1,534 @@
+import math
+from typing import Dict, List
+from collections import defaultdict
+import polars as pl
+import altrios as alt
+from altrios import utilities
+from altrios.train_planner import planner_config
+
+def calculate_waiting_time_single_dispatch(
+    cumulative_demand_control: int, 
+    last_dispatch: int, 
+    demand_hourly: pl.DataFrame, 
+    dispatch_hour: int, 
+    remaining_demand_list: pl.DataFrame, 
+    remaining_demand_list_control: pl.DataFrame, 
+    search_range: int,
+    od_pair_loop: str,
+    min_num_cars_per_train: int,
+    target_num_cars_per_train: int
+    
+) -> tuple:
+    """
+    Calculate the waiting time for a single dispatch using Polars DataFrames.
+    """
+    # Initialize variables
+    direction_demand = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)
+    remaining_demand = 0
+    total_waiting_time = 0
+    total_waiting_time_before_dispatch = 0
+    dispatched = 0
+    cumulative_demand = cumulative_demand_control
+    remaining_demand_list = remaining_demand_list_control.clone() #Copy to avoid modifying original
+    remaining_demand_tem = []
+    # Calculate cumulative demand up to the dispatch hour
+    end_hour = min(dispatch_hour + 1, len(demand_hourly))
+    hourly_demand = direction_demand.slice(last_dispatch, end_hour - last_dispatch)
+    cumulative_demand += hourly_demand["Number_of_Containers"].sum()
+    
+    if remaining_demand_list.is_empty():
+        hourly_demand = hourly_demand.with_columns(
+            ((pl.col("Number_of_Containers") * (dispatch_hour - pl.col("Hour"))).alias("Waiting_Time"))
+        )
+        total_waiting_time_before_dispatch = hourly_demand["Waiting_Time"].sum()
+        total_waiting_time += hourly_demand["Waiting_Time"].sum()
+
+        # If there is remaining demand, calculate waiting time for new and remaining demand
+    else:
+        hourly_demand = hourly_demand.with_columns(
+            ((pl.col("Number_of_Containers") * (dispatch_hour - pl.col("Hour"))).alias("Waiting_Time"))
+        )
+        total_waiting_time_before_dispatch = hourly_demand["Waiting_Time"].sum()
+        total_waiting_time += hourly_demand["Waiting_Time"].sum()
+
+        # Calculate waiting time for each entry in the remaining demand list
+        remaining_waiting_times = remaining_demand_list.with_columns(
+            (pl.col("Remaining_Demand") * (dispatch_hour - pl.col("Hour"))).alias("Remaining_Waiting_Time")
+        )
+        total_waiting_time_before_dispatch += remaining_waiting_times["Remaining_Waiting_Time"].sum()
+        total_waiting_time += remaining_waiting_times["Remaining_Waiting_Time"].sum()
+        
+
+    # Handle remaining demands if cumulative demand exceeds thresholds
+    if cumulative_demand >= min_num_cars_per_train:
+        if cumulative_demand >= target_num_cars_per_train:
+            dispatched = target_num_cars_per_train
+            dispatched_split = target_num_cars_per_train
+            remaining_demand = cumulative_demand - dispatched_split  # Carry over remaining demand
+            # Update remaining demand list if there's no prior remaining demand
+            if remaining_demand_list.height == 0:
+                #remaining_demand_tem = []
+                for row in hourly_demand.iter_rows():
+                    # Number_of_Containers is located at the 4th column
+                    if row[3] > 0:
+                        if dispatched_split >= row[3]:
+                            dispatched_split -= row[3]
+                        else:
+                            remaining_demand_for_hour = row[3] - dispatched_split
+                            # Hour is located at the 5th column
+                            remaining_demand_tem.append((remaining_demand_for_hour,row[4]))
+                            # dispatched_split stop working from this hour to the end of this loop behavior
+                            dispatched_split = 0
+                # Filter `remaining_demand_tem` to include only positive Remaining_Demand values
+                filtered_remaining_demand_tem = [(rd[0], rd[1]) for rd in remaining_demand_tem if rd[0] > 0]
+
+                # Construct the DataFrame directly from the filtered list
+                remaining_demand_list = pl.DataFrame({
+                    "Remaining_Demand": [rd[0] for rd in filtered_remaining_demand_tem],
+                    "Hour": [rd[1] for rd in filtered_remaining_demand_tem]
+                })
+                #remaining_demand_list = pl.DataFrame({"Remaining_Demand": [rd[0] for rd in remaining_demand_tem],"Hour": [rd[1] for rd in remaining_demand_tem]}).filter(pl.col("Remaining_Demand") > 0)
+                cumulative_demand = remaining_demand            
+            else:                
+                # Prepare the cumulative transformation approach
+                for row in remaining_demand_list.iter_rows():
+                    if dispatched_split >= row[0]:
+                        dispatched_split -= row[0]                       
+                        list_tem = list(row)
+                        list_tem[0] = 0
+                        row = tuple(list_tem)                     
+                    else:
+                        row[0] -= dispatched_split
+                        dispatched_split = 0
+                    remaining_demand_list = remaining_demand_list.filter(pl.col("Remaining_Demand") > 0)          
+                # If there is still dispatched capacity left, apply it to new demand within the range
+                if dispatched_split > 0:
+                    for row in hourly_demand.iter_rows():
+                        if row[3] > 0:
+                            if dispatched_split >= row[3]:
+                                dispatched_split -= row[3]
+                            else:
+                                remaining_demand_for_hour = row[3] - dispatched_split
+                                remaining_demand_tem.append((remaining_demand_for_hour,row[4]))
+                                dispatched_split = 0
+                remaining_demand_list = pl.DataFrame({"Remaining_Demand": [rd[0] for rd in remaining_demand_tem],"Hour": [rd[1] for rd in remaining_demand_tem]}).filter(pl.col("Remaining_Demand") > 0)
+                cumulative_demand = remaining_demand
+        else:
+            dispatched = cumulative_demand
+            cumulative_demand = 0  # Reset cumulative demand if all is dispatched
+
+    # Accumulate demand if below minimum threshold
+    else:
+        cumulative_demand = cumulative_demand
+
+    # Filter demand for future hours in the specified search range
+    future_demand = demand_hourly.filter(
+        (pl.col("Hour") > dispatch_hour) & (pl.col("Hour") < min(last_dispatch + search_range, len(demand_hourly)))
+    )
+
+    # Calculate waiting time for each future hour
+    future_demand = future_demand.with_columns(
+        ((last_dispatch + search_range - 1 - pl.col("Hour")) * pl.col("Number_of_Containers")).alias("Waiting_Time")
+    )
+    
+    # Sum up all waiting times for future demand
+    total_waiting_time += future_demand["Waiting_Time"].sum()
+
+    # Calculate waiting time for remaining demand from previous hours, if any
+    if not remaining_demand_list.is_empty():
+        # Add waiting time for remaining demand entries
+        remaining_waiting = remaining_demand_list.with_columns(
+            ((last_dispatch + search_range - 1 - pl.col("Hour")) * pl.col("Remaining_Demand")).alias("Remaining_Waiting_Time")
+        )
+
+        # Sum up the waiting times from remaining demand list
+        total_waiting_time += remaining_waiting["Remaining_Waiting_Time"].sum()
+# Return results
+    return total_waiting_time_before_dispatch, total_waiting_time, remaining_demand_list, cumulative_demand, dispatched
+
+def find_minimum_waiting_time(
+    num_iterations: int, 
+    demand_hourly: pl.DataFrame, 
+    border_time_list: list, 
+) -> pl.DataFrame:
+    """
+    Find the minimum waiting time for dispatches using Polars DataFrame.
+    """
+    for i in range(len(border_time_list)):
+        od_pair_loop = border_time_list[i][0]
+        reverse_pair = "-".join(od_pair_loop.split("-")[::-1])
+        directional_total_containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Containers"].sum()
+        reverse_total_containers = demand_hourly.filter(pl.col("OD_Pair") == reverse_pair)["Number_of_Containers"].sum()
+        if directional_total_containers > reverse_total_containers:
+            empty_containers = directional_total_containers - reverse_total_containers
+            empty_containers_o_d = reverse_pair
+        else:
+            empty_containers = reverse_total_containers - directional_total_containers
+            empty_containers_o_d = od_pair_loop
+        print(f"total containers for {od_pair_loop} is {directional_total_containers}")
+        print(f"reverse_total_containers for {reverse_pair} is {reverse_total_containers}")
+        #print(f"empty_containers for {empty_containers_o_d} is {empty_containers}")
+    print(f"empty_containers for {empty_containers_o_d} is {empty_containers}")
+    print(f"There are {len(border_time_list[0])-1} trains to dispatch")
+    empty_return = []
+    final_dispatch_rows = []
+    for j in range(len(border_time_list)):
+        start_hour = 0
+        total_dispatched = 0
+        dispatch_time = []
+        waiting_time_total = 0
+        waiting_time_total_before_dispatch = 0
+        cumulative_demand_control = 0
+        last_dispatch = 0
+        remaining_demand_list_control = pl.DataFrame({
+            "Remaining_Demand": pl.Series([], dtype=pl.Int64),
+            "Hour": pl.Series([], dtype=pl.Float64),
+        })
+        dispatched_list = []
+        #print(f"border_time_list[j][0] is {border_time_list[j][0]}")
+        od_pair_loop = border_time_list[j][0]
+        #print(f"od_pair_loop is {od_pair_loop}")
+        origin, destination = border_time_list[j][0].split('-')
+        #print(f"origin is {origin}")
+        #print(f"destination is {destination}")
+        #print(demand)
+        total_containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Containers"].sum()
+        for i in range(2, num_iterations):
+            if total_containers - total_dispatched == 0:
+                dispatched_list.append(0.0)
+            search_range = border_time_list[j][i] - start_hour
+            # DataFrame to accumulate dispatch hour info
+            total_waiting_time_demand_list = pl.DataFrame({
+                "Dispatch_Hour": pl.Series([], dtype=pl.Int64),
+                "Waiting_Before_Dispatch": pl.Series([], dtype=pl.Float64),
+                "Total_Waiting": pl.Series([], dtype=pl.Float64),
+                "Remaining_Demand_List": pl.Series([], dtype=pl.Object),
+                "Cumulative_Demand": pl.Series([], dtype=pl.Float64),
+                "Dispatched": pl.Series([], dtype=pl.Float64)
+            }) 
+            for dispatch_hour in range(start_hour, start_hour + search_range):
+                total_waiting_time_before_dispatch, total_waiting_time, remaining_demand_list, cumulative_demand, dispatched = calculate_waiting_time_single_dispatch(
+                    cumulative_demand_control, last_dispatch, demand_hourly, dispatch_hour, remaining_demand_list_control, remaining_demand_list_control.clone(), search_range,od_pair_loop,min_num_cars_per_train, target_num_cars_per_train
+                )
+                # Append data for each dispatch hour, ensuring consistent types
+                new_row = pl.DataFrame({
+                    "Dispatch_Hour": [dispatch_hour],
+                    "Waiting_Before_Dispatch": [float(total_waiting_time_before_dispatch)],
+                    "Total_Waiting": [float(total_waiting_time)],
+                    "Remaining_Demand_List": [remaining_demand_list],
+                    "Cumulative_Demand": [float(cumulative_demand)],
+                    "Dispatched": [float(dispatched)]
+                })
+                total_waiting_time_demand_list = total_waiting_time_demand_list.vstack(new_row)
+                
+            # Find the row with the minimum "Total_Waiting"
+            min_waiting_row = total_waiting_time_demand_list.sort("Total_Waiting").head(1)
+            min_waiting_time_hour = min_waiting_row[0, "Dispatch_Hour"]
+            min_waiting_time_before_dispatch = min_waiting_row[0, "Waiting_Before_Dispatch"]
+            min_waiting_time = min_waiting_row[0, "Total_Waiting"]
+            remaining_demand_list = min_waiting_row[0, "Remaining_Demand_List"]
+            cumulative_demand = min_waiting_row[0, "Cumulative_Demand"]
+            dispatched = min_waiting_row[0, "Dispatched"]
+
+
+            # Track dispatched containers for each dispatch hour
+            dispatched_list.append(dispatched)
+            # Reset remaining demand if cumulative demand is zero
+            if cumulative_demand == 0:
+                remaining_demand_list = pl.DataFrame(schema=["Remaining_Demand_List"])
+
+            # Update control values for the next iteration
+            remaining_demand_list_control = remaining_demand_list
+            cumulative_demand_control = cumulative_demand
+            last_dispatch = min_waiting_time_hour + 1
+            start_hour = min_waiting_time_hour + 1
+            total_dispatched += dispatched
+            waiting_time_total += min_waiting_time
+
+            # Accumulate total waiting time for before dispatch and overall
+            if i == num_iterations - 1:
+                waiting_time_total_before_dispatch += min_waiting_time
+            else:
+                waiting_time_total_before_dispatch += min_waiting_time_before_dispatch
+
+            # Add the dispatch hour to the list
+            dispatch_time.append(min_waiting_time_hour) 
+                    
+        remaining_to_dispatch = total_containers - total_dispatched
+        final_waiting_time = remaining_to_dispatch * (168 - start_hour)
+        waiting_time_total += final_waiting_time
+        dispatch_time.append(167)  # Assuming final dispatch at the end of the period
+        dispatched_list.append(remaining_to_dispatch)  
+        dispatch_df_row = []
+        print(dispatched_list)
+        for i in range(len(dispatched_list)):
+            dispatch_df_row.append({
+                "Origin": origin,
+                "Destination": destination,
+                "Train_Type": "Intermodal",
+                "Cars_Per_Train_Loaded": dispatched_list[i],
+                "Cars_Per_Train_Empty": 0.0,
+                "Target_Cars_Per_Train": float(target_num_cars_per_train),
+                "Number_of_Cars_Total": dispatched_list[i],
+                "Tons_Per_Train_Total": dispatched_list[i]*98.006382,
+                "HP_Required_Per_Ton": 4.0,
+                "Hour":float(dispatch_time[i])
+            })
+        if od_pair_loop == empty_containers_o_d:
+            dispatch_df_row = []
+            for i in range(len(dispatched_list)):
+                if empty_containers > target_num_cars_per_train-dispatched_list[i]:
+                    empty_return.append(target_num_cars_per_train-dispatched_list[i])
+                    empty_containers -= (target_num_cars_per_train-dispatched_list[i])
+                else:
+                    empty_return.append(empty_containers)
+                    empty_containers = 0.0
+            for i in range(len(dispatched_list)):
+                dispatch_df_row.append({
+                    "Origin": origin,
+                    "Destination": destination,
+                    "Train_Type": "Intermodal",
+                    "Cars_Per_Train_Loaded": dispatched_list[i],
+                    "Cars_Per_Train_Empty": empty_return[i],
+                    "Target_Cars_Per_Train": float(target_num_cars_per_train),
+                    "Number_of_Cars_Total": dispatched_list[i]+empty_return[i],
+                    "Tons_Per_Train_Total": dispatched_list[i]*98.006382 + empty_return[i]*39.106152 ,
+                    "HP_Required_Per_Ton": 4.0,
+                    "Hour":float(dispatch_time[i])
+                }) 
+        #print(f"dispatch_df_row is {dispatch_df_row}")
+        final_dispatch_rows.extend(dispatch_df_row) 
+    #print(f"final_dispatch_rows is {final_dispatch_rows}")
+    dispatch_times = pl.DataFrame(final_dispatch_rows)
+    dispatch_times = dispatch_times.sort("Hour")
+    print(dispatch_times)
+    print(f"empty_return is {empty_return}")
+    return dispatch_times
+
+def calculate_dispatch_data(total_containers, target_num_cars, label, containers, max_min_cars):
+    remaining_containers = total_containers % target_num_cars
+    min_num_cars = (
+        total_containers // target_num_cars + (1 if remaining_containers > 0 else 0)
+    )
+    
+    ## Apply the maximum min_num_cars value
+    min_num_cars = int(max(min_num_cars, max_min_cars))
+
+    border_list = []
+    border_time_list = []
+    dispatched_list = []
+
+    if remaining_containers == 0:
+        border = total_containers / min_num_cars
+    else:
+        border = math.ceil(total_containers / min_num_cars)
+        
+    containers_left = total_containers
+    for _ in range(min_num_cars):
+        if containers_left >= border:
+            border_list.append(border)
+            containers_left -= border
+        else:
+            border_list.append(containers_left)
+            break
+
+    cumulate_demand = 0
+    p = 0
+    for i, container in enumerate(containers):
+        cumulate_demand += container
+        if p == len(border_list) - 1:
+            border_time_list.append(len(containers) - 1)
+            break
+        if cumulate_demand >= border_list[p]:
+            border_time_list.append(i)
+            dispatched_list.append(cumulate_demand)
+            total_containers -= cumulate_demand
+            cumulate_demand = 0
+            p += 1
+            if p >= len(border_list):
+                break
+    if len(border_time_list) < len(border_list):
+        for _ in range(len(border_list) - len(border_time_list)):
+            border_time_list.append(border_time_list[-1]+_+1)
+    dispatched_list.append(total_containers)
+    if len(dispatched_list) < len(border_list):
+        for _ in range(len(border_list) - len(dispatched_list)):
+            dispatched_list.append(0)
+    return {
+        "Border": border_list,
+        "Border_Times": border_time_list,
+        "Dispatched": dispatched_list,
+    }
+
+# TODO read from config
+min_num_cars_per_train = 5
+target_num_cars_per_train = 30
+
+# Define the main function to generate demand trains with the updated rule
+def generate_trains(
+    demand_hourly: pl.DataFrame,
+    target_num_cars_per_train:int
+) -> pl.DataFrame:
+    # Step 1: Create an OD pair column
+    demand_hourly = demand_hourly.with_columns(
+        (pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair")
+    )
+
+    # Step 2: Group data by OD pair and calculate total containers for each pair
+    grouped_data = demand_hourly.group_by("OD_Pair").agg([
+        pl.col("Number_of_Containers").sum().alias("Total_Containers")
+    ])
+
+    # Step 3: Create a dictionary to store the max min_num_cars for each OD pair
+    max_min_cars_dict = {}
+
+    # Calculate min_num_cars for each direction and store the maximum in the dictionary
+    for row in grouped_data.iter_rows():
+        od_pair = row[0]
+        total_containers = row[1]
+
+        remaining_containers = total_containers % target_num_cars_per_train
+        min_num_cars = (
+            total_containers // target_num_cars_per_train + (1 if remaining_containers > 0 else 0)
+        )
+
+        # Check the reverse direction
+        reverse_pair = "-".join(od_pair.split("-")[::-1])
+        
+        if reverse_pair in max_min_cars_dict:
+            max_min_cars_dict[od_pair] = max(min_num_cars, max_min_cars_dict[reverse_pair])
+            max_min_cars_dict[reverse_pair] = max_min_cars_dict[od_pair]
+        else:
+            max_min_cars_dict[od_pair] = min_num_cars
+
+    # Prepare a list to collect the results for all OD pairs
+    all_dispatch_data = []
+    # Step 4: Loop through each unique OD pair to calculate dispatch data
+    for row in grouped_data.iter_rows():
+        od_pair = row[0]  # This is the "OD_Pair" string like "Origin-Destination"
+        total_containers = row[1]  # This is the "Total_Containers" value
+
+        # Filter containers list for the specific OD pair
+        containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair).select("Number_of_Containers").to_series().to_list()
+
+        # Retrieve the max min_num_cars for this OD pair
+        max_min_cars = max_min_cars_dict[od_pair]
+
+        # Calculate dispatch data for the current OD pair with the updated rule
+        dispatch_data = calculate_dispatch_data(total_containers, target_num_cars_per_train, od_pair, containers, max_min_cars)
+        
+        # Add the OD pair label to each entry in the dispatch data
+        dispatch_data_df = pl.DataFrame(dispatch_data)
+        dispatch_data_df = dispatch_data_df.with_columns(pl.lit(od_pair).alias("OD_Pair"))
+
+        # Ensure the "Border" column exists and is cast to Float64
+        if "Border" in dispatch_data_df.columns:
+            dispatch_data_df = dispatch_data_df.with_columns(
+                pl.col("Border").cast(pl.Float64)
+            )
+
+        # Append the result to the list
+        all_dispatch_data.append(dispatch_data_df)
+
+    # Combine all dispatch data into a single DataFrame
+    final_dispatch_df = pl.concat(all_dispatch_data, how="diagonal")
+  
+    return final_dispatch_df
+
+def calculate_dispatches_deterministic_hourly(
+    demand: pl.DataFrame,
+    rail_vehicles: List[alt.RailVehicle],
+    simulation_days: int,
+    config: planner_config.TrainPlannerConfig
+) -> pl.DataFrame:
+    """
+    Converts a table of demand into a dispatch plan where trains depart from each origin in uniformly spaced intervals.
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demands (number of trains).
+    rail_vehicles: List of `altrios.RailVehicle` objects.
+    simulation_days: Number of days of simulation to run.
+    config: `TrainPlannerConfig` object.
+    Outputs:
+    ----------
+    Updated demand `DataFrame` or `LazyFrame` representing dispatches, each defined with an origin, destination, train type, number of (loaded and empty) cars, tonnage, and HP per ton requirement.
+    """
+    target_num_cars_per_train = config.target_num_cars_per_train
+    dispatch_df = generate_trains(demand,target_num_cars_per_train)
+    od_border_time = []
+    border_time = dispatch_df["Border_Times"].to_list() 
+    od_pair_list = dispatch_df["OD_Pair"].to_list()
+    for i in range(len(od_pair_list)):
+        od_border_list_sub =[]
+        od_border_list_sub.append(od_pair_list[i])
+        od_border_list_sub.append(border_time[i])
+        od_border_time.append(od_border_list_sub)
+    grouped_data = defaultdict(list)
+    for od_pair, value in od_border_time:
+        grouped_data[od_pair].append(value)
+    border_time_list= [[key] + values for key, values in grouped_data.items()]
+    num_iterations = len(border_time_list[0])
+    schedule = find_minimum_waiting_time(num_iterations=num_iterations,
+        demand_hourly=demand,
+        border_time_list=border_time_list,
+    )
+    return schedule
+
+def generate_dispatch_details(
+    demand: pl.DataFrame,
+    rail_vehicles: List[alt.RailVehicle],
+    simulation_days: int,
+    config: planner_config.TrainPlannerConfig
+) -> pl.DataFrame:
+    """
+    Generate a tabulated demand pair to indicate the expected dispatching interval
+    and actual dispatching timesteps after rounding, with departures from each terminal
+    spaced as evenly as possible
+    Arguments:
+    ----------
+    demand: `DataFrame` or `LazyFrame` representing origin-destination demands (number of trains).
+    rail_vehicles: List of `altrios.RailVehicle` objects.
+    simulation_days: Number of days of simulation to run.
+    config: `TrainPlannerConfig` object.
+    Outputs:
+    ----------
+    schedule: Tabulated dispatching time for each demand pair for each train type
+    in hours
+    """
+    hours = simulation_days * 24
+
+    grouping_vars = ["Origin", "Destination", "Train_Type"]
+    schedule = (demand
+        .select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode())
+        .pipe(utilities.allocateItems, target = "Number_of_Cars_Loaded", grouping_vars = grouping_vars)
+        .drop("Percent_Within_Group")
+        .pipe(utilities.allocateItems, target = "Number_of_Cars_Empty", grouping_vars = grouping_vars)
+        .group_by(pl.exclude("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
+            .agg(pl.col("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
+        .with_columns(pl.col("Number_of_Cars_Loaded").list.sort(descending=True),
+                      pl.col("Number_of_Cars_Empty").list.sort(descending=False))
+        .explode("Number_of_Cars_Empty", "Number_of_Cars_Loaded")
+        .with_columns(
+            (pl.col("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")).alias("Tons_Per_Train"),
+            (pl.col("HP_Required_Per_Ton_Loaded").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + 
+                pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")
+                ).alias("HP_Required")
+        )
+        .sort("Origin", "Destination", "Percent_Within_Group", "Train_Type")
+        .with_columns(
+            (hours * 1.0 / pl.len().over("Origin", "Destination")).alias("Interval")
+        )
+        .with_columns(
+            ((pl.col("Interval").cum_count().over(["Origin","Destination"])) \
+             * pl.col("Interval")).alias("Hour")
+        )
+        .select("Hour", "Origin", "Destination", "Train_Type", 
+                "Number_of_Cars", "Number_of_Cars_Loaded", "Number_of_Cars_Empty",
+                "Tons_Per_Train", "HP_Required"
+        )
+        .rename({"Number_of_Cars_Loaded": "Cars_Loaded", 
+                "Number_of_Cars_Empty": "Cars_Empty"})
+        .sort(["Hour","Origin","Destination","Train_Type"])
+    )
+    
+    return schedule
\ No newline at end of file
diff --git a/python/altrios/utilities.py b/python/altrios/utilities.py
index da9ffafe..eb0280eb 100644
--- a/python/altrios/utilities.py
+++ b/python/altrios/utilities.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 import re
 import numpy as np
-from typing import Tuple, Union, Optional, Dict, Any, TYPE_CHECKING
+from typing import Tuple, Union, Optional, List, Dict, Any, TYPE_CHECKING
 import pandas as pd
 import polars as pl
 import datetime
@@ -183,6 +183,37 @@ def range_minmax(self) -> pl.Expr:
 pl.Expr.range_minmax=range_minmax
 del range_minmax
 
+def pctWithinGroup(
+    df: Union[pl.DataFrame, pl.LazyFrame], 
+    grouping_vars: List[str]
+) -> Union[pl.DataFrame, pl.LazyFrame]:
+    return (df
+        .with_columns(
+            ((pl.int_range(pl.len(), dtype=pl.UInt32).over(grouping_vars).add(1)) / 
+            pl.count().over(grouping_vars))
+            .alias("Percent_Within_Group")
+        )
+    )
+
+def allocateItems(
+    df: Union[pl.DataFrame, pl.LazyFrame], 
+    target: str, 
+    grouping_vars: List[str]
+) -> Union[pl.DataFrame, pl.LazyFrame]:
+    return (df
+    .sort(grouping_vars)
+    .pipe(pctWithinGroup, grouping_vars = grouping_vars)
+    .with_columns(
+        pl.col(target).mul("Percent_Within_Group").round().alias(f'{target}_Group_Cumulative')
+    )
+    .with_columns(
+        (pl.col(f'{target}_Group_Cumulative') - pl.col(f'{target}_Group_Cumulative').shift(1).over(grouping_vars))
+            .fill_null(pl.col(f'{target}_Group_Cumulative'))
+            .alias(f'{target}')
+    )
+    .drop(f'{target}_Group_Cumulative')
+)
+
 def resample(
     df: pd.DataFrame,
     dt_new: Optional[float] = 1.0,
diff --git a/truck_work_log.txt b/truck_work_log.txt
deleted file mode 100644
index 75bb88dd..00000000
--- a/truck_work_log.txt
+++ /dev/null
@@ -1,498 +0,0 @@
-Vehicle ID: 1, Event Type: start, Timestamp: 174.2160165938894
-Vehicle ID: 1, Event Type: end, Timestamp: 174.2841399184887
-Vehicle ID: 2, Event Type: start, Timestamp: 174.42759052325093
-Vehicle ID: 3, Event Type: start, Timestamp: 174.48822045399396
-Vehicle ID: 2, Event Type: end, Timestamp: 174.50251702668348
-Vehicle ID: 4, Event Type: start, Timestamp: 174.5104609970999
-Vehicle ID: 5, Event Type: start, Timestamp: 174.52565217240667
-Vehicle ID: 6, Event Type: start, Timestamp: 174.55003962253323
-Vehicle ID: 7, Event Type: start, Timestamp: 174.55870046731673
-Vehicle ID: 3, Event Type: end, Timestamp: 174.56211135073673
-Vehicle ID: 8, Event Type: start, Timestamp: 174.56476123576883
-Vehicle ID: 9, Event Type: start, Timestamp: 174.57337439745137
-Vehicle ID: 10, Event Type: start, Timestamp: 174.5768097959614
-Vehicle ID: 11, Event Type: start, Timestamp: 174.5819643532696
-Vehicle ID: 12, Event Type: start, Timestamp: 174.58657155806372
-Vehicle ID: 4, Event Type: end, Timestamp: 174.5867161367358
-Vehicle ID: 13, Event Type: start, Timestamp: 174.58960814254343
-Vehicle ID: 14, Event Type: start, Timestamp: 174.60016925326943
-Vehicle ID: 5, Event Type: end, Timestamp: 174.60377844287396
-Vehicle ID: 15, Event Type: start, Timestamp: 174.6049349998376
-Vehicle ID: 6, Event Type: end, Timestamp: 174.62601582830848
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-Vehicle ID: 7, Event Type: end, Timestamp: 174.6316161906628
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-Vehicle ID: 19, Event Type: start, Timestamp: 174.65482113599072
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-Vehicle ID: 48, Event Type: start, Timestamp: 174.81736181524923
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diff --git a/vehicle_average_times.txt b/vehicle_average_times.txt
deleted file mode 100644
index 22b0b727..00000000
--- a/vehicle_average_times.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-crane: 0.0
-hostler: 0.0019168163174070507
-truck: 0.01337536359778584

From d4390dec33628ac4fda34f76b8c28a748cb41a6b Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sun, 12 Jan 2025 14:31:54 -0500
Subject: [PATCH 27/50] refactoring

---
 .../train_planner/demand_generators.py        | 118 +++++++++---------
 python/altrios/train_planner/planner.py       |   3 +-
 python/altrios/utilities.py                   |  10 +-
 3 files changed, 66 insertions(+), 65 deletions(-)

diff --git a/python/altrios/train_planner/demand_generators.py b/python/altrios/train_planner/demand_generators.py
index 4ee2f46f..ed221210 100644
--- a/python/altrios/train_planner/demand_generators.py
+++ b/python/altrios/train_planner/demand_generators.py
@@ -71,7 +71,7 @@ def generate_return_demand_intermodal(demand_subset: Union[pl.LazyFrame, pl.Data
             pl.concat_str(pl.min_horizontal("Origin", "Destination"), pl.lit("_"), pl.max_horizontal("Origin", "Destination")).alias("OD")
         )
         .with_columns(
-            pl.col("Number_of_Cars", "Number_of_Containers").range_minmax().over("OD").name.suffix("_Return")
+            pl.col("Number_of_Cars", "Number_of_Containers").range().over("OD").name.suffix("_Return")
         )
         .filter(
             pl.col("Number_of_Containers") == pl.col("Number_of_Containers").max().over("OD")
@@ -114,7 +114,7 @@ def generate_return_demand(
     )
     return demand_return
 
-def generate_origin_manifest_demand(
+def generate_manifest_rebalancing_demand(
     demand: pl.DataFrame,
     node_list: List[str],
     config: planner_config.TrainPlannerConfig
@@ -136,6 +136,63 @@ def generate_origin_manifest_demand(
     with additional columns for checking the unbalance quantity and serve as check columns
     for the manifest empty car rebalancing function
     """
+    def balance_trains(
+    demand_origin_manifest: pl.DataFrame
+    ) -> pl.DataFrame:
+        """
+        Update the manifest demand, especially the empty car demand to maintain equilibrium of number of
+        cars dispatched and received at each node for manifest
+        Arguments:
+        ----------
+        demand_origin_manifest: Dataframe that summarizes empty and loaded 
+        manifest demand dispatched and received for each node by number cars
+        Outputs:
+        ----------
+        demand_origin_manifest: Updated demand_origin_manifest with additional
+        manifest empty car demand added to each node
+        df_balance_storage: Documented additional manifest demand pairs and corresponding quantity for
+        rebalancing process
+        """
+        df_balance_storage = pd.DataFrame(np.zeros(shape=(0, 4)))
+        df_balance_storage = df_balance_storage.rename(
+            columns={0: "Origin", 
+                    1: "Destination", 
+                    2: "Train_Type", 
+                    3: "Number_of_Cars"})
+        
+        train_type = "Manifest_Empty"
+        demand = demand_origin_manifest.to_pandas()[
+            ["Origin","Manifest_Received","Manifest_Dispatched","Manifest_Empty"]]
+        demand = demand.rename(columns={"Manifest_Received": "Received", 
+                                "Manifest_Dispatched": "Dispatched",
+                                "Manifest_Empty": "Empty"})
+
+        step = 0
+        # Calculate the number of iterations needed
+        max_iter = len(demand) * (len(demand)-1) / 2
+        while (~np.isclose(demand["Received"], demand["Dispatched"])).any() and (step <= max_iter):
+            rows_def = demand[demand["Received"] < demand["Dispatched"]]
+            rows_sur = demand[demand["Received"] > demand["Dispatched"]]
+            if((len(rows_def) == 0) | (len(rows_sur) == 0)): 
+                break
+            # Find the first node that is in deficit of cars because of the empty return
+            row_def = rows_def.index[0]
+            # Find the first node that is in surplus of cars
+            row_sur = rows_sur.index[0]
+            surplus = demand.loc[row_sur, "Received"] - demand.loc[row_sur, "Dispatched"]
+            df_balance_storage.loc[len(df_balance_storage.index)] = \
+                [demand.loc[row_sur, "Origin"],
+                demand.loc[row_def, "Origin"],
+                train_type,
+                surplus]
+            demand.loc[row_def, "Received"] += surplus
+            demand.loc[row_sur, "Dispatched"] = demand.loc[row_sur, "Received"]
+            step += 1
+            
+        if (~np.isclose(demand["Received"], demand["Dispatched"])).any():
+            raise Exception("While loop didn't converge")
+        return pl.from_pandas(df_balance_storage)
+
     manifest_demand = (demand
         .filter(pl.col("Train_Type").str.strip_suffix("_Loaded") == "Manifest")
         .select(["Origin", "Destination","Number_of_Cars"])
@@ -156,64 +213,9 @@ def generate_origin_manifest_demand(
         .filter((pl.col("Manifest").is_not_null()) | (pl.col("Manifest_Empty").is_not_null()))
     )
 
-    return origin_manifest_demand
+    return balance_trains(origin_manifest_demand)
 
-def balance_trains(
-    demand_origin_manifest: pl.DataFrame
-) -> pl.DataFrame:
-    """
-    Update the manifest demand, especially the empty car demand to maintain equilibrium of number of
-    cars dispatched and received at each node for manifest
-    Arguments:
-    ----------
-    demand_origin_manifest: Dataframe that summarizes empty and loaded 
-    manifest demand dispatched and received for each node by number cars
-    Outputs:
-    ----------
-    demand_origin_manifest: Updated demand_origin_manifest with additional
-    manifest empty car demand added to each node
-    df_balance_storage: Documented additional manifest demand pairs and corresponding quantity for
-    rebalancing process
-    """
-    df_balance_storage = pd.DataFrame(np.zeros(shape=(0, 4)))
-    df_balance_storage = df_balance_storage.rename(
-        columns={0: "Origin", 
-                 1: "Destination", 
-                 2: "Train_Type", 
-                 3: "Number_of_Cars"})
-    
-    train_type = "Manifest_Empty"
-    demand = demand_origin_manifest.to_pandas()[
-        ["Origin","Manifest_Received","Manifest_Dispatched","Manifest_Empty"]]
-    demand = demand.rename(columns={"Manifest_Received": "Received", 
-                            "Manifest_Dispatched": "Dispatched",
-                            "Manifest_Empty": "Empty"})
 
-    step = 0
-    # Calculate the number of iterations needed
-    max_iter = len(demand) * (len(demand)-1) / 2
-    while (~np.isclose(demand["Received"], demand["Dispatched"])).any() and (step <= max_iter):
-        rows_def = demand[demand["Received"] < demand["Dispatched"]]
-        rows_sur = demand[demand["Received"] > demand["Dispatched"]]
-        if((len(rows_def) == 0) | (len(rows_sur) == 0)): 
-            break
-        # Find the first node that is in deficit of cars because of the empty return
-        row_def = rows_def.index[0]
-        # Find the first node that is in surplus of cars
-        row_sur = rows_sur.index[0]
-        surplus = demand.loc[row_sur, "Received"] - demand.loc[row_sur, "Dispatched"]
-        df_balance_storage.loc[len(df_balance_storage.index)] = \
-            [demand.loc[row_sur, "Origin"],
-            demand.loc[row_def, "Origin"],
-            train_type,
-            surplus]
-        demand.loc[row_def, "Received"] += surplus
-        demand.loc[row_sur, "Dispatched"] = demand.loc[row_sur, "Received"]
-        step += 1
-        
-    if (~np.isclose(demand["Received"], demand["Dispatched"])).any():
-        raise Exception("While loop didn't converge")
-    return pl.from_pandas(df_balance_storage)
 
 def generate_demand_trains(
     demand: pl.DataFrame,
diff --git a/python/altrios/train_planner/planner.py b/python/altrios/train_planner/planner.py
index 5276f42d..7abe4f0e 100644
--- a/python/altrios/train_planner/planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -581,8 +581,7 @@ def run_train_planner(
         demand_returns = demand_generators.generate_return_demand(demand, config)
         demand_rebalancing = pl.DataFrame()
         if demand.filter(pl.col("Train_Type").str.contains("Manifest")).height > 0:
-            demand_origin_manifest = demand_generators.generate_origin_manifest_demand(demand, node_list, config)
-            demand_rebalancing = demand_generators.balance_trains(demand_origin_manifest)
+            demand_rebalancing = demand_generators.generate_manifest_rebalancing_demand(demand, node_list, config)
         demand = demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, config)
         if config.dispatch_scheduler is None:
             config.dispatch_scheduler = schedulers.generate_dispatch_details
diff --git a/python/altrios/utilities.py b/python/altrios/utilities.py
index eb0280eb..74405f61 100644
--- a/python/altrios/utilities.py
+++ b/python/altrios/utilities.py
@@ -180,10 +180,10 @@ def _get_list(path_elem, container):
 
 def range_minmax(self) -> pl.Expr:
      return self.max() - self.min()
-pl.Expr.range_minmax=range_minmax
+pl.Expr.range=range_minmax
 del range_minmax
 
-def pctWithinGroup(
+def cumPctWithinGroup(
     df: Union[pl.DataFrame, pl.LazyFrame], 
     grouping_vars: List[str]
 ) -> Union[pl.DataFrame, pl.LazyFrame]:
@@ -191,7 +191,7 @@ def pctWithinGroup(
         .with_columns(
             ((pl.int_range(pl.len(), dtype=pl.UInt32).over(grouping_vars).add(1)) / 
             pl.count().over(grouping_vars))
-            .alias("Percent_Within_Group")
+            .alias("Percent_Within_Group_Cumulative")
         )
     )
 
@@ -202,9 +202,9 @@ def allocateItems(
 ) -> Union[pl.DataFrame, pl.LazyFrame]:
     return (df
     .sort(grouping_vars)
-    .pipe(pctWithinGroup, grouping_vars = grouping_vars)
+    .pipe(cumPctWithinGroup, grouping_vars = grouping_vars)
     .with_columns(
-        pl.col(target).mul("Percent_Within_Group").round().alias(f'{target}_Group_Cumulative')
+        pl.col(target).mul("Percent_Within_Group_Cumulative").round().alias(f'{target}_Group_Cumulative')
     )
     .with_columns(
         (pl.col(f'{target}_Group_Cumulative') - pl.col(f'{target}_Group_Cumulative').shift(1).over(grouping_vars))

From 1ca782782530b2ef05a882f4ed9a3d8c9a0ed46e Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sun, 12 Jan 2025 20:30:29 -0500
Subject: [PATCH 28/50] fix for unit test

---
 python/altrios/rollout.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/altrios/rollout.py b/python/altrios/rollout.py
index 83913472..5c1f3ff2 100644
--- a/python/altrios/rollout.py
+++ b/python/altrios/rollout.py
@@ -3,7 +3,7 @@
 from altrios.metric_calculator import ScenarioInfo
 
 import altrios as alt
-from altrios.train_planner import planner
+from altrios.train_planner import planner, planner_config
 import numpy as np
 import time
 import pandas as pd
@@ -24,7 +24,7 @@ def simulate_prescribed_rollout(
     save_interval: Optional[int] = None,
     freight_demand_percent_growth:float = 0.0,
     demand_file: Union[pl.DataFrame, Path, str] = defaults.DEMAND_FILE,
-    train_planner_config: planner.TrainPlannerConfig = planner.TrainPlannerConfig(),
+    train_planner_config: planner_config.TrainPlannerConfig = planner_config.TrainPlannerConfig(),
     count_unused_locomotives = False,
     write_complete_results: Optional[bool] = False,
     write_metrics: Optional[bool] = False,

From 615f8e0433d96d9ca13caf6a2ce7c9646394fc6d Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sun, 12 Jan 2025 21:40:31 -0500
Subject: [PATCH 29/50] more fixes for unit tests

---
 python/altrios/train_planner/schedulers.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index 14eee6f9..110bdc57 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -501,7 +501,7 @@ def generate_dispatch_details(
     schedule = (demand
         .select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode())
         .pipe(utilities.allocateItems, target = "Number_of_Cars_Loaded", grouping_vars = grouping_vars)
-        .drop("Percent_Within_Group")
+        .drop("Percent_Within_Group_Cumulative")
         .pipe(utilities.allocateItems, target = "Number_of_Cars_Empty", grouping_vars = grouping_vars)
         .group_by(pl.exclude("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
             .agg(pl.col("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
@@ -514,7 +514,7 @@ def generate_dispatch_details(
                 pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")
                 ).alias("HP_Required")
         )
-        .sort("Origin", "Destination", "Percent_Within_Group", "Train_Type")
+        .sort("Origin", "Destination", "Percent_Within_Group_Cumulative", "Train_Type")
         .with_columns(
             (hours * 1.0 / pl.len().over("Origin", "Destination")).alias("Interval")
         )

From b2cbc4f0c8a5dd29fc4f9eef36c851a2dbb190cc Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Tue, 14 Jan 2025 13:58:52 -0500
Subject: [PATCH 30/50] refactoring

---
 python/altrios/train_planner/schedulers.py | 119 ++++-----------------
 python/altrios/utilities.py                |  20 +++-
 2 files changed, 41 insertions(+), 98 deletions(-)

diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index 110bdc57..5df66147 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -150,6 +150,7 @@ def find_minimum_waiting_time(
     num_iterations: int, 
     demand_hourly: pl.DataFrame, 
     border_time_list: list, 
+    target_num_cars_per_train: int
 ) -> pl.DataFrame:
     """
     Find the minimum waiting time for dispatches using Polars DataFrame.
@@ -305,120 +306,43 @@ def find_minimum_waiting_time(
     print(f"empty_return is {empty_return}")
     return dispatch_times
 
-def calculate_dispatch_data(total_containers, target_num_cars, label, containers, max_min_cars):
-    remaining_containers = total_containers % target_num_cars
-    min_num_cars = (
-        total_containers // target_num_cars + (1 if remaining_containers > 0 else 0)
-    )
-    
-    ## Apply the maximum min_num_cars value
-    min_num_cars = int(max(min_num_cars, max_min_cars))
-
-    border_list = []
-    border_time_list = []
-    dispatched_list = []
-
-    if remaining_containers == 0:
-        border = total_containers / min_num_cars
-    else:
-        border = math.ceil(total_containers / min_num_cars)
-        
-    containers_left = total_containers
-    for _ in range(min_num_cars):
-        if containers_left >= border:
-            border_list.append(border)
-            containers_left -= border
-        else:
-            border_list.append(containers_left)
-            break
-
-    cumulate_demand = 0
-    p = 0
-    for i, container in enumerate(containers):
-        cumulate_demand += container
-        if p == len(border_list) - 1:
-            border_time_list.append(len(containers) - 1)
-            break
-        if cumulate_demand >= border_list[p]:
-            border_time_list.append(i)
-            dispatched_list.append(cumulate_demand)
-            total_containers -= cumulate_demand
-            cumulate_demand = 0
-            p += 1
-            if p >= len(border_list):
-                break
-    if len(border_time_list) < len(border_list):
-        for _ in range(len(border_list) - len(border_time_list)):
-            border_time_list.append(border_time_list[-1]+_+1)
-    dispatched_list.append(total_containers)
-    if len(dispatched_list) < len(border_list):
-        for _ in range(len(border_list) - len(dispatched_list)):
-            dispatched_list.append(0)
-    return {
-        "Border": border_list,
-        "Border_Times": border_time_list,
-        "Dispatched": dispatched_list,
-    }
-
-# TODO read from config
-min_num_cars_per_train = 5
-target_num_cars_per_train = 30
-
 # Define the main function to generate demand trains with the updated rule
 def generate_trains(
     demand_hourly: pl.DataFrame,
     target_num_cars_per_train:int
 ) -> pl.DataFrame:
-    # Step 1: Create an OD pair column
-    demand_hourly = demand_hourly.with_columns(
-        (pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair")
-    )
-
-    # Step 2: Group data by OD pair and calculate total containers for each pair
-    grouped_data = demand_hourly.group_by("OD_Pair").agg([
-        pl.col("Number_of_Containers").sum().alias("Total_Containers")
-    ])
-
-    # Step 3: Create a dictionary to store the max min_num_cars for each OD pair
-    max_min_cars_dict = {}
-
-    # Calculate min_num_cars for each direction and store the maximum in the dictionary
-    for row in grouped_data.iter_rows():
-        od_pair = row[0]
-        total_containers = row[1]
-
-        remaining_containers = total_containers % target_num_cars_per_train
-        min_num_cars = (
-            total_containers // target_num_cars_per_train + (1 if remaining_containers > 0 else 0)
+    grouped_data = (demand_hourly
+        .with_columns(
+            (pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair")
         )
-
-        # Check the reverse direction
-        reverse_pair = "-".join(od_pair.split("-")[::-1])
-        
-        if reverse_pair in max_min_cars_dict:
-            max_min_cars_dict[od_pair] = max(min_num_cars, max_min_cars_dict[reverse_pair])
-            max_min_cars_dict[reverse_pair] = max_min_cars_dict[od_pair]
-        else:
-            max_min_cars_dict[od_pair] = min_num_cars
+        .group_by("OD_Pair")
+            .agg(
+                pl.col("Number_of_Containers").sum().alias("Total_Containers"),
+                pl.col("Number_of_Containers").sum().mod(target_num_cars_per_train).alias("Remaining_Containers")
+            )
+        .with_columns(
+            pl.col("Total_Containers").truediv(target_num_cars_per_train).round().add(pl.col("Remaining_Containers").gt(0)).alias("Max_Min_Cars")
+        )
+    )
 
     # Prepare a list to collect the results for all OD pairs
     all_dispatch_data = []
     # Step 4: Loop through each unique OD pair to calculate dispatch data
-    for row in grouped_data.iter_rows():
-        od_pair = row[0]  # This is the "OD_Pair" string like "Origin-Destination"
-        total_containers = row[1]  # This is the "Total_Containers" value
+    for row in grouped_data.iter_rows(named=True):
+        od_pair = row['OD_Pair']  # This is the "OD_Pair" string like "Origin-Destination"
+        total_containers = row['Total_Containers']  # This is the "Total_Containers" value
 
         # Filter containers list for the specific OD pair
         containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair).select("Number_of_Containers").to_series().to_list()
 
         # Retrieve the max min_num_cars for this OD pair
-        max_min_cars = max_min_cars_dict[od_pair]
+        max_min_cars = row['Max_Min_Cars']
 
         # Calculate dispatch data for the current OD pair with the updated rule
         dispatch_data = calculate_dispatch_data(total_containers, target_num_cars_per_train, od_pair, containers, max_min_cars)
         
         # Add the OD pair label to each entry in the dispatch data
-        dispatch_data_df = pl.DataFrame(dispatch_data)
+        dispatch_data_df = pl.DataFrame(dispatch_data, strict=False)
         dispatch_data_df = dispatch_data_df.with_columns(pl.lit(od_pair).alias("OD_Pair"))
 
         # Ensure the "Border" column exists and is cast to Float64
@@ -431,7 +355,7 @@ def generate_trains(
         all_dispatch_data.append(dispatch_data_df)
 
     # Combine all dispatch data into a single DataFrame
-    final_dispatch_df = pl.concat(all_dispatch_data, how="diagonal")
+    final_dispatch_df = pl.concat(all_dispatch_data, how="diagonal_relaxed")
   
     return final_dispatch_df
 
@@ -471,6 +395,7 @@ def calculate_dispatches_deterministic_hourly(
     schedule = find_minimum_waiting_time(num_iterations=num_iterations,
         demand_hourly=demand,
         border_time_list=border_time_list,
+        target_num_cars_per_train=config.target_num_cars_per_train['Intermodal'] #TODO make this flexible
     )
     return schedule
 
@@ -500,9 +425,9 @@ def generate_dispatch_details(
     grouping_vars = ["Origin", "Destination", "Train_Type"]
     schedule = (demand
         .select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode())
-        .pipe(utilities.allocateItems, target = "Number_of_Cars_Loaded", grouping_vars = grouping_vars)
+        .pipe(utilities.allocateIntegerEvenly, target = "Number_of_Cars_Loaded", grouping_vars = grouping_vars)
         .drop("Percent_Within_Group_Cumulative")
-        .pipe(utilities.allocateItems, target = "Number_of_Cars_Empty", grouping_vars = grouping_vars)
+        .pipe(utilities.allocateIntegerEvenly, target = "Number_of_Cars_Empty", grouping_vars = grouping_vars)
         .group_by(pl.exclude("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
             .agg(pl.col("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
         .with_columns(pl.col("Number_of_Cars_Loaded").list.sort(descending=True),
diff --git a/python/altrios/utilities.py b/python/altrios/utilities.py
index 74405f61..d2b27645 100644
--- a/python/altrios/utilities.py
+++ b/python/altrios/utilities.py
@@ -195,7 +195,7 @@ def cumPctWithinGroup(
         )
     )
 
-def allocateItems(
+def allocateIntegerEvenly(
     df: Union[pl.DataFrame, pl.LazyFrame], 
     target: str, 
     grouping_vars: List[str]
@@ -213,6 +213,24 @@ def allocateItems(
     )
     .drop(f'{target}_Group_Cumulative')
 )
+    
+def allocateItems(
+    df: Union[pl.DataFrame, pl.LazyFrame], 
+    grouping_vars: list[str], 
+    count_target: str
+) -> Union[pl.DataFrame, pl.LazyFrame]:
+    return (df
+        .sort(grouping_vars+ [count_target], descending = True)
+        .with_columns(
+            pl.col(count_target).sum().over(grouping_vars).round().alias(f'{count_target}_Group'),
+            (pl.col(count_target).sum().over(grouping_vars).round() * 
+                (
+                    pl.col(count_target).cum_sum().over(grouping_vars) / 
+                    pl.col(count_target).sum().over(grouping_vars)
+                )
+            ).round().alias(f'{count_target}_Group_Cumulative'))
+        .with_columns((pl.col(f'{count_target}_Group_Cumulative') - pl.col(f'{count_target}_Group_Cumulative').shift(1).over(grouping_vars)).fill_null(pl.col(f'{count_target}_Group_Cumulative')).alias("Count"))
+    )
 
 def resample(
     df: pd.DataFrame,

From 832271ec7593e8e27425561ac004cbaacd95c368 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Thu, 16 Jan 2025 16:40:16 -0500
Subject: [PATCH 31/50] train planner restructuring

---
 pyproject.toml                                |   2 +-
 python/altrios/train_planner/data_prep.py     | 265 +++++++++++++++++
 python/altrios/train_planner/planner.py       | 266 +-----------------
 .../altrios/train_planner/planner_config.py   |   8 +-
 4 files changed, 277 insertions(+), 264 deletions(-)
 create mode 100644 python/altrios/train_planner/data_prep.py

diff --git a/pyproject.toml b/pyproject.toml
index 1114ccec..813ae078 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,7 +43,7 @@ dependencies = [
     "plotly",
     "typing_extensions",
     "pyyaml",
-    "polars >= 1.19.0",
+    "polars >= 1.20.0",
     "pyarrow",
     "requests",
     "PyYAML==6.0.2",
diff --git a/python/altrios/train_planner/data_prep.py b/python/altrios/train_planner/data_prep.py
new file mode 100644
index 00000000..7bee7ad9
--- /dev/null
+++ b/python/altrios/train_planner/data_prep.py
@@ -0,0 +1,265 @@
+from typing import Union, List, Tuple
+from pathlib import Path
+import polars as pl
+import polars.selectors as cs
+import pandas as pd
+import numpy as np
+import altrios as alt
+from altrios import defaults, utilities
+from altrios.train_planner import planner_config
+
+def load_freight_demand(
+    demand_table: Union[pl.DataFrame, Path, str]
+) -> Tuple[pl.DataFrame, pl.Series, int]:
+    """
+    Load the user input csv file into a dataframe for later processing
+    Arguments:
+    ----------
+    user_input_file: path to the input csv file that user import to the module
+    Example Input:
+        Origin	Destination	Train_Type	Number_of_Cars	Number_of_Containers
+        Barstow	Stockton	Unit	    2394	        0
+        Barstow	Stockton	Manifest	2588	        0
+        Barstow	Stockton	Intermodal	2221	        2221
+
+    Outputs:
+    ----------
+    df_annual_demand: dataframe with all pair information including:
+    origin, destination, train type, number of cars
+    node_list: List of origin or destination demand nodes
+    """
+    if type(demand_table) is not pl.DataFrame:
+        demand_table = pl.read_csv(demand_table, dtypes = {"Number_of_Cars": pl.UInt32, "Number_of_Containers": pl.UInt32})
+
+    nodes = pl.concat(
+        [demand_table.get_column("Origin"),
+        demand_table.get_column("Destination")]).unique().sort()
+    return demand_table, nodes
+
+def prep_hourly_demand(
+    total_demand: Union[pl.DataFrame, pl.LazyFrame],
+    hourly_demand_density: Union[pl.DataFrame, pl.LazyFrame],
+    daily_demand_density: Union[pl.DataFrame, pl.LazyFrame]
+) -> Union[pl.DataFrame, pl.LazyFrame]:
+    return (
+
+    )
+def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
+    if all(item in loco_info.columns for item in [
+        'HP','Loco_Mass_Tons','SOC_J','SOC_Min_J','SOC_Max_J','Capacity_J'
+        ]
+    ): return loco_info
+    get_hp = lambda loco: loco.pwr_rated_kilowatts * 1e3 / alt.utils.W_PER_HP
+    get_mass_ton = lambda loco: 0 if not loco.mass_kg else loco.mass_kg / alt.utils.KG_PER_TON
+    get_starting_soc = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.state.soc * loco.res.energy_capacity_joules
+    get_min_soc = lambda loco: 0 if not loco.res else loco.res.min_soc * loco.res.energy_capacity_joules
+    get_max_soc = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.max_soc * loco.res.energy_capacity_joules
+    get_capacity = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.energy_capacity_joules
+    loco_info.loc[:,'HP'] = loco_info.loc[:,'Rust_Loco'].apply(get_hp) 
+    loco_info.loc[:,'Loco_Mass_Tons'] = loco_info.loc[:,'Rust_Loco'].apply(get_mass_ton) 
+    loco_info.loc[:,'SOC_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_starting_soc) 
+    loco_info.loc[:,'SOC_Min_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_min_soc) 
+    loco_info.loc[:,'SOC_Max_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_max_soc) 
+    loco_info.loc[:,'Capacity_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_capacity) 
+    return loco_info
+
+def build_locopool(
+    config: planner_config.TrainPlannerConfig,
+    demand_file: Union[pl.DataFrame, Path, str],
+    method: str = "tile",
+    shares: List[float] = [],
+    locomotives_per_node: int = None
+) -> pl.DataFrame:
+    """
+    Generate default locomotive pool
+    Arguments:
+    ----------
+    demand_file: Path to a file with origin-destination demand
+    method: Method to determine each locomotive's type ("tile" or "shares_twoway" currently implemented)
+    shares: List of shares for each locomotive type in loco_info (implemented for two-way shares only)
+    Outputs:
+    ----------
+    loco_pool: Locomotive pool containing all locomotives' information that are within the system
+    """
+    config.loco_info = append_loco_info(config.loco_info)
+    loco_types = list(config.loco_info.loc[:,'Locomotive_Type'])
+    demand, node_list = demand_loader(demand_file)
+    
+    num_nodes = len(node_list)
+    if locomotives_per_node is None:
+        num_ods = demand.height
+        cars_per_od = demand.get_column("Number_of_Cars").mean()
+        if config.single_train_mode:
+            initial_size = math.ceil(cars_per_od / config.cars_per_locomotive["Default"]) 
+            rows = initial_size
+        else:
+            num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
+            initial_size = math.ceil((cars_per_od / config.cars_per_locomotive["Default"]) *
+                                    num_destinations_per_node)  # number of locomotives per node
+            rows = initial_size * num_nodes  # number of locomotives in total
+    else:
+        initial_size = locomotives_per_node
+        rows = locomotives_per_node * num_nodes
+
+    if config.single_train_mode:
+        sorted_nodes = np.tile([demand.select(pl.col("Origin").first()).item()],rows).tolist()
+        engine_numbers = range(0, rows)
+    else:
+        sorted_nodes = np.sort(np.tile(node_list, initial_size)).tolist()
+        engine_numbers = rankdata(sorted_nodes, method="dense") * 1000 + \
+            np.tile(range(0, initial_size), num_nodes)
+
+    if method == "tile":
+        repetitions = math.ceil(rows/len(loco_types))
+        types = np.tile(loco_types, repetitions).tolist()[0:rows]
+    elif method == "shares_twoway":
+        # TODO: this logic can be replaced (and generalized to >2 types) using altrios.utilities.allocateItems
+        if((len(loco_types) != 2) | (len(shares) != 2)):
+            raise ValueError(
+                f"""2-way prescribed locopool requested but number of locomotive types is not 2.""")
+
+        idx_1 = np.argmin(shares)
+        idx_2 = 1 - idx_1
+        share_type_one = shares[idx_1]
+        label_type_one = loco_types[idx_1]
+        label_type_two = loco_types[idx_2]
+
+        num_type_one = round(initial_size * share_type_one)
+        if 0 == num_type_one:
+            types = pd.Series([label_type_two] * initial_size)
+        elif initial_size == num_type_one:
+            types = pd.Series([label_type_one] * initial_size)
+        else:
+            # Arrange repeated sequences of type 1 + {type_two_per_type_one, type_two_per_type_one+1} type 2
+            # so as to match the required total counts of each.
+            type_two_per_type_one = (
+                initial_size - num_type_one) / num_type_one
+            # Number of type 1 + {type_two_per_bel+1} type 2 sequences needed
+            num_extra_type_two = round(
+                num_type_one * (type_two_per_type_one % 1.0))
+            series_fewer_type_two = pd.Series(
+                [label_type_one] + [label_type_two] * math.floor(type_two_per_type_one))
+            series_more_type_two = pd.Series(
+                [label_type_one] + [label_type_two] * math.ceil(type_two_per_type_one))
+            types = np.concatenate((
+                np.tile(series_more_type_two, num_extra_type_two),
+                np.tile(series_fewer_type_two, num_type_one-num_extra_type_two)),
+                axis=None)
+        types = np.tile(types, num_nodes).tolist()
+    else:
+        raise ValueError(
+            f"""Locopool build method '{method}' invalid or not implemented.""")
+
+    loco_pool = pl.DataFrame(
+        {'Locomotive_ID': pl.Series(engine_numbers, dtype=pl.UInt32),
+         'Locomotive_Type': pl.Series(types, dtype=pl.Categorical),
+         'Node': pl.Series(sorted_nodes, dtype=pl.Categorical),
+         'Arrival_Time': pl.Series(np.zeros(rows), dtype=pl.Float64),
+         'Servicing_Done_Time': pl.Series(np.zeros(rows), dtype=pl.Float64),
+         'Refueling_Done_Time': pl.Series(np.tile(0, rows), dtype=pl.Float64),
+         'Status': pl.Series(np.tile("Ready", rows), dtype=pl.Categorical),
+         'SOC_Target_J': pl.Series(np.zeros(rows), dtype=pl.Float64),
+         'Refuel_Duration': pl.Series(np.zeros(rows), dtype=pl.Float64),
+         'Refueler_J_Per_Hr': pl.Series(np.zeros(rows), dtype=pl.Float64), 
+         'Refueler_Efficiency': pl.Series(np.zeros(rows), dtype=pl.Float64), 
+         'Port_Count': pl.Series(np.zeros(rows), dtype=pl.UInt32)}
+    )
+
+    loco_info_pl = pl.from_pandas(config.loco_info.drop(labels='Rust_Loco',axis=1),
+        schema_overrides={'Locomotive_Type': pl.Categorical,
+                          'Fuel_Type': pl.Categorical}
+    )
+
+    loco_pool = loco_pool.join(loco_info_pl, on="Locomotive_Type")
+    return loco_pool
+
+def build_refuelers(
+    node_list: pd.Series,
+    loco_pool: pl.DataFrame,
+    refueler_info: pd.DataFrame,
+    refuelers_per_incoming_corridor: int,
+) -> pl.DataFrame:
+    """
+    Build the default set of refueling facilities.
+    Arguments:
+    ----------
+    node_list: List of origin or destination demand nodes
+    loco_pool: Locomotive pool
+    refueler_info: DataFrame with information for each type of refueling infrastructure to use
+    refuelers_per_incoming_corridor: Queue size per corridor arriving at each node.
+    Outputs:
+    ----------
+    refuelers: Polars dataframe of facility county by node and type of fuel
+    """
+    ports_per_node = (loco_pool
+        .group_by(pl.col("Locomotive_Type", "Fuel_Type").cast(pl.Utf8))
+        .agg([(pl.lit(refuelers_per_incoming_corridor) * pl.len() / pl.lit(loco_pool.height))
+              .ceil()
+              .alias("Ports_Per_Node")])
+        .join(pl.from_pandas(refueler_info),
+              on=["Locomotive_Type", "Fuel_Type"], 
+              how="left")
+    )
+
+    locations = pd.DataFrame(data={
+        'Node': np.tile(node_list, ports_per_node.height)})
+    locations = locations.sort_values(by=['Node']).reset_index(drop=True)
+
+    refuelers = pl.DataFrame({
+        'Node': pl.Series(locations['Node'], dtype=pl.Categorical).cast(pl.Categorical),
+        'Refueler_Type': pl.Series(np.tile(
+            ports_per_node.get_column("Refueler_Type").to_list(), len(node_list)), 
+            dtype=pl.Categorical).cast(pl.Categorical),
+        'Locomotive_Type': pl.Series(np.tile(
+            ports_per_node.get_column("Locomotive_Type").to_list(), len(node_list)), 
+            dtype=pl.Categorical).cast(pl.Categorical),
+        'Fuel_Type': pl.Series(np.tile(
+            ports_per_node.get_column("Fuel_Type").to_list(), len(node_list)), 
+            dtype=pl.Categorical).cast(pl.Categorical),
+        'Refueler_J_Per_Hr': pl.Series(np.tile(
+            ports_per_node.get_column("Refueler_J_Per_Hr").to_list(), len(node_list)), 
+            dtype=pl.Float64),
+        'Refueler_Efficiency': pl.Series(np.tile(
+            ports_per_node.get_column("Refueler_Efficiency").to_list(), len(node_list)), 
+            dtype=pl.Float64),
+        'Lifespan_Years': pl.Series(np.tile(
+            ports_per_node.get_column("Lifespan_Years").to_list(), len(node_list)), 
+            dtype=pl.Float64),
+        'Cost_USD': pl.Series(np.tile(
+            ports_per_node.get_column("Cost_USD").to_list(), len(node_list)), 
+            dtype=pl.Float64),
+        'Port_Count': pl.Series(np.tile(
+            ports_per_node.get_column("Ports_Per_Node").to_list(), len(node_list)), 
+            dtype=pl.UInt32)})
+    return refuelers
+
+def append_charging_guidelines(
+    refuelers: pl.DataFrame,
+    loco_pool: pl.DataFrame,
+    demand: pl.DataFrame,
+    network_charging_guidelines: pl.DataFrame
+) -> pl.DataFrame:
+    active_ods = demand.select(["Origin","Destination"]).unique()
+    network_charging_guidelines = (network_charging_guidelines
+        .join(active_ods, on=["Origin","Destination"], how="inner")
+        .group_by(pl.col("Origin"))
+        .agg(pl.col("Allowable_Battery_Headroom_MWh").min() * 1e6 / utilities.MWH_PER_MJ)
+        .rename({"Allowable_Battery_Headroom_MWh": "Battery_Headroom_J"})
+        .with_columns(pl.col("Origin").cast(pl.Categorical)))
+    refuelers = (refuelers
+        .join(network_charging_guidelines, left_on="Node", right_on="Origin", how="left")
+        .with_columns(pl.when(pl.col("Fuel_Type")=="Electricity")
+            .then(pl.col("Battery_Headroom_J"))
+            .otherwise(0)
+            .fill_null(0)
+            .alias("Battery_Headroom_J")
+            ))
+    loco_pool = (loco_pool
+        .join(network_charging_guidelines, left_on="Node", right_on="Origin", how="left")
+        .with_columns(pl.when(pl.col("Fuel_Type")=="Electricity")
+            .then(pl.col("Battery_Headroom_J"))
+            .otherwise(0)
+            .fill_null(0)
+            .alias("Battery_Headroom_J"))
+        .with_columns(pl.max_horizontal([pl.col('SOC_Max_J')-pl.col('Battery_Headroom_J'), pl.col('SOC_Min_J')]).alias("SOC_J")))
+    return refuelers, loco_pool
\ No newline at end of file
diff --git a/python/altrios/train_planner/planner.py b/python/altrios/train_planner/planner.py
index 7abe4f0e..042b6892 100644
--- a/python/altrios/train_planner/planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -1,265 +1,13 @@
 from __future__ import annotations
 from pathlib import Path
-from altrios.train_planner import demand_generators, schedulers, planner_config
+from altrios.train_planner import data_prep, demand_generators, schedulers, planner_config
 import numpy as np
-from scipy.stats import rankdata
-import pandas as pd
 import polars as pl
 import polars.selectors as cs
-import math
-from typing import Union, Tuple, List, Dict, Callable, Optional
+from typing import Union, Tuple, List, Dict
 from itertools import repeat
-from dataclasses import dataclass, field
 import altrios as alt
-from altrios import defaults, utilities
-
-def demand_loader(
-    demand_table: Union[pl.DataFrame, Path, str]
-) -> Tuple[pl.DataFrame, pl.Series, int]:
-    """
-    Load the user input csv file into a dataframe for later processing
-    Arguments:
-    ----------
-    user_input_file: path to the input csv file that user import to the module
-    Example Input:
-        Origin	Destination	Train_Type	Number_of_Cars	Number_of_Containers
-        Barstow	Stockton	Unit	    2394	        0
-        Barstow	Stockton	Manifest	2588	        0
-        Barstow	Stockton	Intermodal	2221	        2221
-
-    Outputs:
-    ----------
-    df_annual_demand: dataframe with all pair information including:
-    origin, destination, train type, number of cars
-    node_list: List of origin or destination demand nodes
-    """
-    if type(demand_table) is not pl.DataFrame:
-        demand_table = pl.read_csv(demand_table, dtypes = {"Number_of_Cars": pl.UInt32, "Number_of_Containers": pl.UInt32})
-
-    nodes = pl.concat(
-        [demand_table.get_column("Origin"),
-        demand_table.get_column("Destination")]).unique().sort()
-    return demand_table, nodes
-
-def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
-    if all(item in loco_info.columns for item in [
-        'HP','Loco_Mass_Tons','SOC_J','SOC_Min_J','SOC_Max_J','Capacity_J'
-        ]
-    ): return loco_info
-    get_hp = lambda loco: loco.pwr_rated_kilowatts * 1e3 / alt.utils.W_PER_HP
-    get_mass_ton = lambda loco: 0 if not loco.mass_kg else loco.mass_kg / alt.utils.KG_PER_TON
-    get_starting_soc = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.state.soc * loco.res.energy_capacity_joules
-    get_min_soc = lambda loco: 0 if not loco.res else loco.res.min_soc * loco.res.energy_capacity_joules
-    get_max_soc = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.max_soc * loco.res.energy_capacity_joules
-    get_capacity = lambda loco: defaults.DIESEL_TANK_CAPACITY_J if not loco.res else loco.res.energy_capacity_joules
-    loco_info.loc[:,'HP'] = loco_info.loc[:,'Rust_Loco'].apply(get_hp) 
-    loco_info.loc[:,'Loco_Mass_Tons'] = loco_info.loc[:,'Rust_Loco'].apply(get_mass_ton) 
-    loco_info.loc[:,'SOC_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_starting_soc) 
-    loco_info.loc[:,'SOC_Min_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_min_soc) 
-    loco_info.loc[:,'SOC_Max_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_max_soc) 
-    loco_info.loc[:,'Capacity_J'] = loco_info.loc[:,'Rust_Loco'].apply(get_capacity) 
-    return loco_info
-
-def build_locopool(
-    config: planner_config.TrainPlannerConfig,
-    demand_file: Union[pl.DataFrame, Path, str],
-    method: str = "tile",
-    shares: List[float] = [],
-    locomotives_per_node: int = None
-) -> pl.DataFrame:
-    """
-    Generate default locomotive pool
-    Arguments:
-    ----------
-    demand_file: Path to a file with origin-destination demand
-    method: Method to determine each locomotive's type ("tile" or "shares_twoway" currently implemented)
-    shares: List of shares for each locomotive type in loco_info (implemented for two-way shares only)
-    Outputs:
-    ----------
-    loco_pool: Locomotive pool containing all locomotives' information that are within the system
-    """
-    config.loco_info = append_loco_info(config.loco_info)
-    loco_types = list(config.loco_info.loc[:,'Locomotive_Type'])
-    demand, node_list = demand_loader(demand_file)
-    
-    num_nodes = len(node_list)
-    if locomotives_per_node is None:
-        num_ods = demand.height
-        cars_per_od = demand.get_column("Number_of_Cars").mean()
-        if config.single_train_mode:
-            initial_size = math.ceil(cars_per_od / config.cars_per_locomotive["Default"]) 
-            rows = initial_size
-        else:
-            num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
-            initial_size = math.ceil((cars_per_od / config.cars_per_locomotive["Default"]) *
-                                    num_destinations_per_node)  # number of locomotives per node
-            rows = initial_size * num_nodes  # number of locomotives in total
-    else:
-        initial_size = locomotives_per_node
-        rows = locomotives_per_node * num_nodes
-
-    if config.single_train_mode:
-        sorted_nodes = np.tile([demand.select(pl.col("Origin").first()).item()],rows).tolist()
-        engine_numbers = range(0, rows)
-    else:
-        sorted_nodes = np.sort(np.tile(node_list, initial_size)).tolist()
-        engine_numbers = rankdata(sorted_nodes, method="dense") * 1000 + \
-            np.tile(range(0, initial_size), num_nodes)
-
-    if method == "tile":
-        repetitions = math.ceil(rows/len(loco_types))
-        types = np.tile(loco_types, repetitions).tolist()[0:rows]
-    elif method == "shares_twoway":
-        # TODO: this logic can be replaced (and generalized to >2 types) using altrios.utilities.allocateItems
-        if((len(loco_types) != 2) | (len(shares) != 2)):
-            raise ValueError(
-                f"""2-way prescribed locopool requested but number of locomotive types is not 2.""")
-
-        idx_1 = np.argmin(shares)
-        idx_2 = 1 - idx_1
-        share_type_one = shares[idx_1]
-        label_type_one = loco_types[idx_1]
-        label_type_two = loco_types[idx_2]
-
-        num_type_one = round(initial_size * share_type_one)
-        if 0 == num_type_one:
-            types = pd.Series([label_type_two] * initial_size)
-        elif initial_size == num_type_one:
-            types = pd.Series([label_type_one] * initial_size)
-        else:
-            # Arrange repeated sequences of type 1 + {type_two_per_type_one, type_two_per_type_one+1} type 2
-            # so as to match the required total counts of each.
-            type_two_per_type_one = (
-                initial_size - num_type_one) / num_type_one
-            # Number of type 1 + {type_two_per_bel+1} type 2 sequences needed
-            num_extra_type_two = round(
-                num_type_one * (type_two_per_type_one % 1.0))
-            series_fewer_type_two = pd.Series(
-                [label_type_one] + [label_type_two] * math.floor(type_two_per_type_one))
-            series_more_type_two = pd.Series(
-                [label_type_one] + [label_type_two] * math.ceil(type_two_per_type_one))
-            types = np.concatenate((
-                np.tile(series_more_type_two, num_extra_type_two),
-                np.tile(series_fewer_type_two, num_type_one-num_extra_type_two)),
-                axis=None)
-        types = np.tile(types, num_nodes).tolist()
-    else:
-        raise ValueError(
-            f"""Locopool build method '{method}' invalid or not implemented.""")
-
-    loco_pool = pl.DataFrame(
-        {'Locomotive_ID': pl.Series(engine_numbers, dtype=pl.UInt32),
-         'Locomotive_Type': pl.Series(types, dtype=pl.Categorical),
-         'Node': pl.Series(sorted_nodes, dtype=pl.Categorical),
-         'Arrival_Time': pl.Series(np.zeros(rows), dtype=pl.Float64),
-         'Servicing_Done_Time': pl.Series(np.zeros(rows), dtype=pl.Float64),
-         'Refueling_Done_Time': pl.Series(np.tile(0, rows), dtype=pl.Float64),
-         'Status': pl.Series(np.tile("Ready", rows), dtype=pl.Categorical),
-         'SOC_Target_J': pl.Series(np.zeros(rows), dtype=pl.Float64),
-         'Refuel_Duration': pl.Series(np.zeros(rows), dtype=pl.Float64),
-         'Refueler_J_Per_Hr': pl.Series(np.zeros(rows), dtype=pl.Float64), 
-         'Refueler_Efficiency': pl.Series(np.zeros(rows), dtype=pl.Float64), 
-         'Port_Count': pl.Series(np.zeros(rows), dtype=pl.UInt32)}
-    )
-
-    loco_info_pl = pl.from_pandas(config.loco_info.drop(labels='Rust_Loco',axis=1),
-        schema_overrides={'Locomotive_Type': pl.Categorical,
-                          'Fuel_Type': pl.Categorical}
-    )
-
-    loco_pool = loco_pool.join(loco_info_pl, on="Locomotive_Type")
-    return loco_pool
-
-def build_refuelers(
-    node_list: pd.Series,
-    loco_pool: pl.DataFrame,
-    refueler_info: pd.DataFrame,
-    refuelers_per_incoming_corridor: int,
-) -> pl.DataFrame:
-    """
-    Build the default set of refueling facilities.
-    Arguments:
-    ----------
-    node_list: List of origin or destination demand nodes
-    loco_pool: Locomotive pool
-    refueler_info: DataFrame with information for each type of refueling infrastructure to use
-    refuelers_per_incoming_corridor: Queue size per corridor arriving at each node.
-    Outputs:
-    ----------
-    refuelers: Polars dataframe of facility county by node and type of fuel
-    """
-    ports_per_node = (loco_pool
-        .group_by(pl.col("Locomotive_Type", "Fuel_Type").cast(pl.Utf8))
-        .agg([(pl.lit(refuelers_per_incoming_corridor) * pl.len() / pl.lit(loco_pool.height))
-              .ceil()
-              .alias("Ports_Per_Node")])
-        .join(pl.from_pandas(refueler_info),
-              on=["Locomotive_Type", "Fuel_Type"], 
-              how="left")
-    )
-
-    locations = pd.DataFrame(data={
-        'Node': np.tile(node_list, ports_per_node.height)})
-    locations = locations.sort_values(by=['Node']).reset_index(drop=True)
-
-    refuelers = pl.DataFrame({
-        'Node': pl.Series(locations['Node'], dtype=pl.Categorical).cast(pl.Categorical),
-        'Refueler_Type': pl.Series(np.tile(
-            ports_per_node.get_column("Refueler_Type").to_list(), len(node_list)), 
-            dtype=pl.Categorical).cast(pl.Categorical),
-        'Locomotive_Type': pl.Series(np.tile(
-            ports_per_node.get_column("Locomotive_Type").to_list(), len(node_list)), 
-            dtype=pl.Categorical).cast(pl.Categorical),
-        'Fuel_Type': pl.Series(np.tile(
-            ports_per_node.get_column("Fuel_Type").to_list(), len(node_list)), 
-            dtype=pl.Categorical).cast(pl.Categorical),
-        'Refueler_J_Per_Hr': pl.Series(np.tile(
-            ports_per_node.get_column("Refueler_J_Per_Hr").to_list(), len(node_list)), 
-            dtype=pl.Float64),
-        'Refueler_Efficiency': pl.Series(np.tile(
-            ports_per_node.get_column("Refueler_Efficiency").to_list(), len(node_list)), 
-            dtype=pl.Float64),
-        'Lifespan_Years': pl.Series(np.tile(
-            ports_per_node.get_column("Lifespan_Years").to_list(), len(node_list)), 
-            dtype=pl.Float64),
-        'Cost_USD': pl.Series(np.tile(
-            ports_per_node.get_column("Cost_USD").to_list(), len(node_list)), 
-            dtype=pl.Float64),
-        'Port_Count': pl.Series(np.tile(
-            ports_per_node.get_column("Ports_Per_Node").to_list(), len(node_list)), 
-            dtype=pl.UInt32)})
-    return refuelers
-
-def append_charging_guidelines(
-    refuelers: pl.DataFrame,
-    loco_pool: pl.DataFrame,
-    demand: pl.DataFrame,
-    network_charging_guidelines: pl.DataFrame
-) -> pl.DataFrame:
-    active_ods = demand.select(["Origin","Destination"]).unique()
-    network_charging_guidelines = (network_charging_guidelines
-        .join(active_ods, on=["Origin","Destination"], how="inner")
-        .group_by(pl.col("Origin"))
-        .agg(pl.col("Allowable_Battery_Headroom_MWh").min() * 1e6 / utilities.MWH_PER_MJ)
-        .rename({"Allowable_Battery_Headroom_MWh": "Battery_Headroom_J"})
-        .with_columns(pl.col("Origin").cast(pl.Categorical)))
-    refuelers = (refuelers
-        .join(network_charging_guidelines, left_on="Node", right_on="Origin", how="left")
-        .with_columns(pl.when(pl.col("Fuel_Type")=="Electricity")
-            .then(pl.col("Battery_Headroom_J"))
-            .otherwise(0)
-            .fill_null(0)
-            .alias("Battery_Headroom_J")
-            ))
-    loco_pool = (loco_pool
-        .join(network_charging_guidelines, left_on="Node", right_on="Origin", how="left")
-        .with_columns(pl.when(pl.col("Fuel_Type")=="Electricity")
-            .then(pl.col("Battery_Headroom_J"))
-            .otherwise(0)
-            .fill_null(0)
-            .alias("Battery_Headroom_J"))
-        .with_columns(pl.max_horizontal([pl.col('SOC_Max_J')-pl.col('Battery_Headroom_J'), pl.col('SOC_Min_J')]).alias("SOC_J")))
-    return refuelers, loco_pool
+from altrios import defaults
 
 def dispatch(
     dispatch_time: int,
@@ -552,11 +300,11 @@ def run_train_planner(
     Outputs:
     ----------
     """
-    config.loco_info = append_loco_info(config.loco_info)
-    demand, node_list = demand_loader(demand_file)
+    config.loco_info = data_prep.append_loco_info(config.loco_info)
+    demand, node_list = data_prep.load_freight_demand(demand_file)
 
     if refuelers is None: 
-        refuelers = build_refuelers(
+        refuelers = data_prep.build_refuelers(
             node_list, 
             loco_pool,
             config.refueler_info, 
@@ -565,7 +313,7 @@ def run_train_planner(
     if network_charging_guidelines is None: 
         network_charging_guidelines = pl.read_csv(alt.resources_root() / "networks" / "network_charging_guidelines.csv")
 
-    refuelers, loco_pool = append_charging_guidelines(refuelers, loco_pool, demand, network_charging_guidelines)
+    refuelers, loco_pool = data_prep.append_charging_guidelines(refuelers, loco_pool, demand, network_charging_guidelines)
     if config.single_train_mode:
         demand = demand_generators.generate_demand_trains(demand, 
                                         demand_returns = pl.DataFrame(), 
diff --git a/python/altrios/train_planner/planner_config.py b/python/altrios/train_planner/planner_config.py
index 749c8900..e851ae4c 100644
--- a/python/altrios/train_planner/planner_config.py
+++ b/python/altrios/train_planner/planner_config.py
@@ -1,5 +1,5 @@
 from __future__ import annotations
-from altrios.train_planner import demand_generators
+from altrios.train_planner import data_prep
 import polars as pl
 import pandas as pd
 from typing import Dict, Callable, Optional
@@ -102,7 +102,7 @@ class TrainPlannerConfig:
     drag_coeff_function: Optional[Callable]= None
     dispatch_scheduler: Optional[Callable] = None
     return_demand_generators: Dict = field(default_factory = lambda: {
-        'Unit': demand_generators.generate_return_demand_unit,
-        'Manifest': demand_generators.generate_return_demand_manifest,
-        'Intermodal': demand_generators.generate_return_demand_intermodal
+        'Unit': data_prep.generate_return_demand_unit,
+        'Manifest': data_prep.generate_return_demand_manifest,
+        'Intermodal': data_prep.generate_return_demand_intermodal
     })

From f756c4d87430f0f1162bcbf3421663ae2a17c765 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Thu, 16 Jan 2025 16:53:06 -0500
Subject: [PATCH 32/50] updates for demand calculations

---
 python/altrios/train_planner/data_prep.py | 29 +++++++++++++++++++++--
 1 file changed, 27 insertions(+), 2 deletions(-)

diff --git a/python/altrios/train_planner/data_prep.py b/python/altrios/train_planner/data_prep.py
index 7bee7ad9..5710b071 100644
--- a/python/altrios/train_planner/data_prep.py
+++ b/python/altrios/train_planner/data_prep.py
@@ -8,6 +8,8 @@
 from altrios import defaults, utilities
 from altrios.train_planner import planner_config
 
+day_order_map = 
+
 def load_freight_demand(
     demand_table: Union[pl.DataFrame, Path, str]
 ) -> Tuple[pl.DataFrame, pl.Series, int]:
@@ -41,9 +43,32 @@ def prep_hourly_demand(
     hourly_demand_density: Union[pl.DataFrame, pl.LazyFrame],
     daily_demand_density: Union[pl.DataFrame, pl.LazyFrame]
 ) -> Union[pl.DataFrame, pl.LazyFrame]:
-    return (
-
+    day_order_map = {
+        "Mon": 1,
+        "Tue": 2,
+        "Wed": 3,
+        "Thu": 4,
+        "Fri": 5,
+        "Sat": 6,
+        "Sun": 7
+    }
+    return (total_demand
+        .join(hourly_demand_density, how="inner", on=["Origin", "Destination"])
+        .with_columns(
+            (pl.col("Number_of_Containers") * pl.col("Percentage")).round(0).alias("Number_of_Containers_Daily"),
+            pl.col("Day of Week").replace_strict(day_order_map).alias("Day_Order")
+        )
+        .join(hourly_demand_density, how="inner", on=["Origin", "Destination"])
+        .sort("Origin", "Destination", "Day_Order", "Hour of Day")
+        .with_columns(
+            (pl.col("Number_of_Containers_Daily") * pl.col("Percentage_of_Hour")).round(0).alias("Number_of_Containers"),
+            pl.concat_str(pl.col("Origin"), pl.lit("-"), pl.col("Destination")).alias("OD_Pair"),
+            pl.int_range(0, pl.len()).over("Origin", "Destination").alias("Hour")
+        )
+        .with_columns(pl.col("Number_of_Containers").alias("Number_of_Cars"))
+        .select("Origin", "Destination", "Train_Type", "Hour", "Number_of_Cars", "Number_of_Containers")
     )
+    
 def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
     if all(item in loco_info.columns for item in [
         'HP','Loco_Mass_Tons','SOC_J','SOC_Min_J','SOC_Max_J','Capacity_J'

From 84501e6d50c097c43506bda62b06735d2a14c5a1 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Thu, 16 Jan 2025 16:57:19 -0500
Subject: [PATCH 33/50] hourly demand code

---
 python/altrios/train_planner/data_prep.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/python/altrios/train_planner/data_prep.py b/python/altrios/train_planner/data_prep.py
index 5710b071..847af26d 100644
--- a/python/altrios/train_planner/data_prep.py
+++ b/python/altrios/train_planner/data_prep.py
@@ -1,15 +1,14 @@
 from typing import Union, List, Tuple
 from pathlib import Path
 import polars as pl
-import polars.selectors as cs
 import pandas as pd
 import numpy as np
+import math
+from scipy.stats import rankdata
 import altrios as alt
 from altrios import defaults, utilities
 from altrios.train_planner import planner_config
 
-day_order_map = 
-
 def load_freight_demand(
     demand_table: Union[pl.DataFrame, Path, str]
 ) -> Tuple[pl.DataFrame, pl.Series, int]:
@@ -61,11 +60,13 @@ def prep_hourly_demand(
         .join(hourly_demand_density, how="inner", on=["Origin", "Destination"])
         .sort("Origin", "Destination", "Day_Order", "Hour of Day")
         .with_columns(
-            (pl.col("Number_of_Containers_Daily") * pl.col("Percentage_of_Hour")).round(0).alias("Number_of_Containers"),
+            (pl.col("Number_of_Containers_Daily") * pl.col("Percentage_of_Hour")).alias("Number_of_Containers"),
             pl.concat_str(pl.col("Origin"), pl.lit("-"), pl.col("Destination")).alias("OD_Pair"),
             pl.int_range(0, pl.len()).over("Origin", "Destination").alias("Hour")
         )
         .with_columns(pl.col("Number_of_Containers").alias("Number_of_Cars"))
+        #TODO allocateItems to get integers
+        #TODO handle double vs single stacked?
         .select("Origin", "Destination", "Train_Type", "Hour", "Number_of_Cars", "Number_of_Containers")
     )
     
@@ -108,7 +109,7 @@ def build_locopool(
     """
     config.loco_info = append_loco_info(config.loco_info)
     loco_types = list(config.loco_info.loc[:,'Locomotive_Type'])
-    demand, node_list = demand_loader(demand_file)
+    demand, node_list = load_freight_demand(demand_file)
     
     num_nodes = len(node_list)
     if locomotives_per_node is None:

From f9bb7996c6cd9fa9339f65b2cb22a430eb07280b Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Fri, 17 Jan 2025 00:00:05 -0500
Subject: [PATCH 34/50] Refactoring and improvements

- Improved prep_hourly_demand
- Moved default return_demand_generators
- Renamed / restructured more train planner code
---
 .gitignore                                    |  5 ++
 python/altrios/sim_manager.py                 |  4 +-
 python/altrios/train_planner/data_prep.py     | 52 ++++++++++++-------
 python/altrios/train_planner/planner.py       | 13 +++--
 .../altrios/train_planner/planner_config.py   |  7 +--
 ...nerators.py => train_demand_generators.py} | 11 ++--
 6 files changed, 57 insertions(+), 35 deletions(-)
 rename python/altrios/train_planner/{demand_generators.py => train_demand_generators.py} (98%)

diff --git a/.gitignore b/.gitignore
index f7049904..2e556424 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1219,3 +1219,8 @@ altrios/dispatch/*.json
 activate.sh
 *.xlsx
 altrios/*.txt
+truck_work_log.txt
+vehicle_average_times.txt
+avg_time_per_train.txt
+crane_work_log.txt
+hostler_work_log.txt
diff --git a/python/altrios/sim_manager.py b/python/altrios/sim_manager.py
index aaced8ed..918f4592 100644
--- a/python/altrios/sim_manager.py
+++ b/python/altrios/sim_manager.py
@@ -3,7 +3,7 @@
 """
 
 import polars as pl
-from typing import Any, Union, Dict, List, Optional, Tuple
+from typing import Any, Union, Dict, List, Tuple
 from pathlib import Path
 import time
 from altrios import defaults
@@ -65,7 +65,7 @@ def main(
             
     train_planner_config.loco_info = metrics.add_battery_costs(train_planner_config.loco_info, scenario_year)
 
-    if loco_pool is None: loco_pool = planner.build_locopool(
+    if loco_pool is None: loco_pool = planner.data_prep.build_locopool(
             config = train_planner_config,
             method="shares_twoway",
             shares=[1-target_bel_share, target_bel_share],
diff --git a/python/altrios/train_planner/data_prep.py b/python/altrios/train_planner/data_prep.py
index 847af26d..334135cd 100644
--- a/python/altrios/train_planner/data_prep.py
+++ b/python/altrios/train_planner/data_prep.py
@@ -8,6 +8,16 @@
 import altrios as alt
 from altrios import defaults, utilities
 from altrios.train_planner import planner_config
+    
+day_order_map = {
+    "Mon": 1,
+    "Tue": 2,
+    "Wed": 3,
+    "Thu": 4,
+    "Fri": 5,
+    "Sat": 6,
+    "Sun": 7
+}
 
 def load_freight_demand(
     demand_table: Union[pl.DataFrame, Path, str]
@@ -42,32 +52,36 @@ def prep_hourly_demand(
     hourly_demand_density: Union[pl.DataFrame, pl.LazyFrame],
     daily_demand_density: Union[pl.DataFrame, pl.LazyFrame]
 ) -> Union[pl.DataFrame, pl.LazyFrame]:
-    day_order_map = {
-        "Mon": 1,
-        "Tue": 2,
-        "Wed": 3,
-        "Thu": 4,
-        "Fri": 5,
-        "Sat": 6,
-        "Sun": 7
-    }
+    hourly_demand_density = (hourly_demand_density
+        .group_by("Terminal_Type", "Hour_Of_Day")
+            .agg(pl.col("Share").sum())
+        .with_columns(pl.col("Share").truediv(pl.col("Share").sum().over("Terminal_Type")))
+    )
+    daily_demand_density = (daily_demand_density
+        .group_by("Terminal_Type", "Day_Of_Week")
+            .agg(pl.col("Share").sum())
+        .with_columns(pl.col("Share").truediv(pl.col("Share").sum().over("Terminal_Type")))
+    )
     return (total_demand
-        .join(hourly_demand_density, how="inner", on=["Origin", "Destination"])
+        .join(daily_demand_density, how="inner", on=["Terminal_Type"])
         .with_columns(
-            (pl.col("Number_of_Containers") * pl.col("Percentage")).round(0).alias("Number_of_Containers_Daily"),
-            pl.col("Day of Week").replace_strict(day_order_map).alias("Day_Order")
+            (pl.col("Number_of_Cars") * pl.col("Share")).alias("Number_of_Cars_Daily"),
+            pl.col("Day_Of_Week").replace_strict(day_order_map).alias("Day_Order")
         )
-        .join(hourly_demand_density, how="inner", on=["Origin", "Destination"])
-        .sort("Origin", "Destination", "Day_Order", "Hour of Day")
+        .pipe(utilities.allocateItems, grouping_vars=["Origin", "Destination", "Train_Type"], count_target="Number_of_Cars_Daily")
+        .drop("Number_of_Cars_Daily", "Share")
+        .rename({"Count": "Number_of_Cars_Daily"})
+        .join(hourly_demand_density, how="inner", on=["Terminal_Type"])
+        .sort("Origin", "Destination", "Day_Order", "Hour_Of_Day")
         .with_columns(
-            (pl.col("Number_of_Containers_Daily") * pl.col("Percentage_of_Hour")).alias("Number_of_Containers"),
+            (pl.col("Number_of_Cars_Daily") * pl.col("Share")).alias("Number_of_Cars"),
             pl.concat_str(pl.col("Origin"), pl.lit("-"), pl.col("Destination")).alias("OD_Pair"),
             pl.int_range(0, pl.len()).over("Origin", "Destination").alias("Hour")
         )
-        .with_columns(pl.col("Number_of_Containers").alias("Number_of_Cars"))
-        #TODO allocateItems to get integers
-        #TODO handle double vs single stacked?
-        .select("Origin", "Destination", "Train_Type", "Hour", "Number_of_Cars", "Number_of_Containers")
+        .pipe(utilities.allocateItems, grouping_vars=["Origin", "Destination", "Train_Type", "Day_Order"], count_target="Number_of_Cars")
+        .drop("Number_of_Cars")
+        .rename({"Count": "Number_of_Cars"})
+        .select("Origin", "Destination", "Train_Type", "Hour", "Number_of_Cars")
     )
     
 def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
diff --git a/python/altrios/train_planner/planner.py b/python/altrios/train_planner/planner.py
index 042b6892..814d0660 100644
--- a/python/altrios/train_planner/planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 from pathlib import Path
-from altrios.train_planner import data_prep, demand_generators, schedulers, planner_config
+from altrios.train_planner import data_prep, schedulers, planner_config, train_demand_generators
 import numpy as np
 import polars as pl
 import polars.selectors as cs
@@ -313,9 +313,12 @@ def run_train_planner(
     if network_charging_guidelines is None: 
         network_charging_guidelines = pl.read_csv(alt.resources_root() / "networks" / "network_charging_guidelines.csv")
 
+    if config.return_demand_generators is None:
+        config.return_demand_generators = train_demand_generators.get_default_return_demand_generators()
+
     refuelers, loco_pool = data_prep.append_charging_guidelines(refuelers, loco_pool, demand, network_charging_guidelines)
     if config.single_train_mode:
-        demand = demand_generators.generate_demand_trains(demand, 
+        demand = train_demand_generators.generate_demand_trains(demand, 
                                         demand_returns = pl.DataFrame(), 
                                         demand_rebalancing = pl.DataFrame(), 
                                         rail_vehicles = rail_vehicles, 
@@ -326,11 +329,11 @@ def run_train_planner(
             .drop("index")
         )
     else:
-        demand_returns = demand_generators.generate_return_demand(demand, config)
+        demand_returns = train_demand_generators.generate_return_demand(demand, config)
         demand_rebalancing = pl.DataFrame()
         if demand.filter(pl.col("Train_Type").str.contains("Manifest")).height > 0:
-            demand_rebalancing = demand_generators.generate_manifest_rebalancing_demand(demand, node_list, config)
-        demand = demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, config)
+            demand_rebalancing = train_demand_generators.generate_manifest_rebalancing_demand(demand, node_list, config)
+        demand = train_demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, config)
         if config.dispatch_scheduler is None:
             config.dispatch_scheduler = schedulers.generate_dispatch_details
         dispatch_schedule = config.dispatch_scheduler(demand, rail_vehicles, simulation_days, config)
diff --git a/python/altrios/train_planner/planner_config.py b/python/altrios/train_planner/planner_config.py
index e851ae4c..20adcd21 100644
--- a/python/altrios/train_planner/planner_config.py
+++ b/python/altrios/train_planner/planner_config.py
@@ -1,5 +1,4 @@
 from __future__ import annotations
-from altrios.train_planner import data_prep
 import polars as pl
 import pandas as pd
 from typing import Dict, Callable, Optional
@@ -101,8 +100,4 @@ class TrainPlannerConfig:
     }).transpose().reset_index(names='Refueler_Type'))
     drag_coeff_function: Optional[Callable]= None
     dispatch_scheduler: Optional[Callable] = None
-    return_demand_generators: Dict = field(default_factory = lambda: {
-        'Unit': data_prep.generate_return_demand_unit,
-        'Manifest': data_prep.generate_return_demand_manifest,
-        'Intermodal': data_prep.generate_return_demand_intermodal
-    })
+    return_demand_generators: Optional[Dict] = None #default defined in train_demand_generators.py
diff --git a/python/altrios/train_planner/demand_generators.py b/python/altrios/train_planner/train_demand_generators.py
similarity index 98%
rename from python/altrios/train_planner/demand_generators.py
rename to python/altrios/train_planner/train_demand_generators.py
index ed221210..6cb04624 100644
--- a/python/altrios/train_planner/demand_generators.py
+++ b/python/altrios/train_planner/train_demand_generators.py
@@ -1,4 +1,4 @@
-from typing import Union, List
+from typing import Union, List, Dict, Callable
 import polars as pl
 import polars.selectors as cs
 import pandas as pd
@@ -7,6 +7,13 @@
 from altrios import utilities
 from altrios.train_planner import planner_config
 
+def get_default_return_demand_generators() -> Dict[str, Callable]:
+    return {
+        'Unit': generate_return_demand_unit,
+        'Manifest': generate_return_demand_manifest,
+        'Intermodal': generate_return_demand_intermodal
+    }
+
 def initialize_reverse_empties(demand: Union[pl.LazyFrame, pl.DataFrame]) -> Union[pl.LazyFrame, pl.DataFrame]:
     """
     Swap `Origin` and `Destination` and append `_Empty` to `Train_Type`.
@@ -215,8 +222,6 @@ def balance_trains(
 
     return balance_trains(origin_manifest_demand)
 
-
-
 def generate_demand_trains(
     demand: pl.DataFrame,
     demand_returns: pl.DataFrame,

From c909094f81abec2d28527832ae35fe2a89052c5e Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sat, 18 Jan 2025 15:57:43 -0500
Subject: [PATCH 35/50] Train planner config refactoring

---
 python/altrios/altrios_pyo3.pyi               |   1 +
 .../rolling_stock/Intermodal_Empty.yaml       |   1 +
 .../rolling_stock/Intermodal_Loaded.yaml      |   1 +
 .../rolling_stock/Manifest_Empty.yaml         |   1 +
 .../rolling_stock/Manifest_Loaded.yaml        |   1 +
 .../resources/rolling_stock/Unit_Empty.yaml   |   1 +
 .../resources/rolling_stock/Unit_Loaded.yaml  |   1 +
 python/altrios/train_planner/data_prep.py     |  39 ++++-
 python/altrios/train_planner/planner.py       |  45 +++---
 .../altrios/train_planner/planner_config.py   |   5 +-
 python/altrios/train_planner/schedulers.py    | 146 +++++++++---------
 .../train_planner/train_demand_generators.py  |  48 +++---
 rust/altrios-core/src/train/rail_vehicle.rs   |   4 +
 13 files changed, 175 insertions(+), 119 deletions(-)

diff --git a/python/altrios/altrios_pyo3.pyi b/python/altrios/altrios_pyo3.pyi
index 2d0b17fa..063b84fc 100644
--- a/python/altrios/altrios_pyo3.pyi
+++ b/python/altrios/altrios_pyo3.pyi
@@ -933,6 +933,7 @@ class RailVehicle(SerdeAPI):
     braking_ratio_empty: float
     braking_ratio_loaded: float
     car_type: str
+    freight_type: str
     davis_b_seconds_per_meter: float
     cd_area_empty_square_meters: float
     cd_area_loaded_square_meters: float
diff --git a/python/altrios/resources/rolling_stock/Intermodal_Empty.yaml b/python/altrios/resources/rolling_stock/Intermodal_Empty.yaml
index 2b140c0c..81266eb6 100644
--- a/python/altrios/resources/rolling_stock/Intermodal_Empty.yaml
+++ b/python/altrios/resources/rolling_stock/Intermodal_Empty.yaml
@@ -1,5 +1,6 @@
 ---
 car_type: Intermodal_Empty
+freight_type: Intermodal
 length: 18.0
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Intermodal_Loaded.yaml b/python/altrios/resources/rolling_stock/Intermodal_Loaded.yaml
index 0054d89a..4fcff84b 100644
--- a/python/altrios/resources/rolling_stock/Intermodal_Loaded.yaml
+++ b/python/altrios/resources/rolling_stock/Intermodal_Loaded.yaml
@@ -1,5 +1,6 @@
 ---
 car_type: Intermodal_Loaded
+freight_type: Intermodal
 length: 18.0
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Manifest_Empty.yaml b/python/altrios/resources/rolling_stock/Manifest_Empty.yaml
index a2c54e73..bba1ed5a 100644
--- a/python/altrios/resources/rolling_stock/Manifest_Empty.yaml
+++ b/python/altrios/resources/rolling_stock/Manifest_Empty.yaml
@@ -1,5 +1,6 @@
 ---
 car_type: Manifest_Empty
+freight_type: Manifest
 length: 18.0
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Manifest_Loaded.yaml b/python/altrios/resources/rolling_stock/Manifest_Loaded.yaml
index 80140aee..552cb398 100644
--- a/python/altrios/resources/rolling_stock/Manifest_Loaded.yaml
+++ b/python/altrios/resources/rolling_stock/Manifest_Loaded.yaml
@@ -1,5 +1,6 @@
 ---
 car_type: Manifest_Loaded
+freight_type: Manifest
 length: 18.0
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Unit_Empty.yaml b/python/altrios/resources/rolling_stock/Unit_Empty.yaml
index caa39153..b52971a3 100644
--- a/python/altrios/resources/rolling_stock/Unit_Empty.yaml
+++ b/python/altrios/resources/rolling_stock/Unit_Empty.yaml
@@ -1,5 +1,6 @@
 ---
 car_type: Unit_Empty
+freight_type: Unit
 length: 10.7
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Unit_Loaded.yaml b/python/altrios/resources/rolling_stock/Unit_Loaded.yaml
index 584c244f..6bae35d6 100644
--- a/python/altrios/resources/rolling_stock/Unit_Loaded.yaml
+++ b/python/altrios/resources/rolling_stock/Unit_Loaded.yaml
@@ -1,5 +1,6 @@
 ---
 car_type: Unit_Loaded
+freight_type: Unit
 length: 10.7
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/train_planner/data_prep.py b/python/altrios/train_planner/data_prep.py
index 334135cd..06b1d0f4 100644
--- a/python/altrios/train_planner/data_prep.py
+++ b/python/altrios/train_planner/data_prep.py
@@ -1,4 +1,4 @@
-from typing import Union, List, Tuple
+from typing import Union, List, Tuple, Dict
 from pathlib import Path
 import polars as pl
 import pandas as pd
@@ -124,17 +124,18 @@ def build_locopool(
     config.loco_info = append_loco_info(config.loco_info)
     loco_types = list(config.loco_info.loc[:,'Locomotive_Type'])
     demand, node_list = load_freight_demand(demand_file)
+    #TODO: handle different train types (or mixed train types?)
     
     num_nodes = len(node_list)
     if locomotives_per_node is None:
         num_ods = demand.height
         cars_per_od = demand.get_column("Number_of_Cars").mean()
         if config.single_train_mode:
-            initial_size = math.ceil(cars_per_od / config.cars_per_locomotive["Default"]) 
+            initial_size = math.ceil(cars_per_od / min(config.cars_per_locomotive.values())) 
             rows = initial_size
         else:
             num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
-            initial_size = math.ceil((cars_per_od / config.cars_per_locomotive["Default"]) *
+            initial_size = math.ceil((cars_per_od / min(config.cars_per_locomotive.values())) *
                                     num_destinations_per_node)  # number of locomotives per node
             rows = initial_size * num_nodes  # number of locomotives in total
     else:
@@ -302,4 +303,34 @@ def append_charging_guidelines(
             .fill_null(0)
             .alias("Battery_Headroom_J"))
         .with_columns(pl.max_horizontal([pl.col('SOC_Max_J')-pl.col('Battery_Headroom_J'), pl.col('SOC_Min_J')]).alias("SOC_J")))
-    return refuelers, loco_pool
\ No newline at end of file
+    return refuelers, loco_pool
+
+def configure_rail_vehicles(
+    single_train_dispatch: Dict,
+    available_rail_vehicles: List[alt.RailVehicle],
+    freight_type_to_car_type: Dict
+) -> (List[alt.RailVehicle], Dict[str, int]):
+    freight_types = []
+    n_cars_by_type = {}
+    this_train_type = single_train_dispatch['Train_Type']
+    if single_train_dispatch['Cars_Loaded'] > 0:
+        freight_type = f'{this_train_type}_Loaded'
+        freight_types.append(freight_type)
+        car_type = None
+        if freight_type in freight_type_to_car_type:
+            car_type = freight_type_to_car_type[freight_type]
+        else: 
+            assert(f'Rail vehicle car type not found for freight type {freight_type}.')
+        n_cars_by_type[car_type] = int(single_train_dispatch['Cars_Loaded'])
+    if single_train_dispatch['Cars_Empty'] > 0:
+        freight_type = f'{this_train_type}_Empty'
+        freight_types.append(freight_type)
+        car_type = None
+        if freight_type in freight_type_to_car_type:
+            car_type = freight_type_to_car_type[freight_type]
+        else: 
+            assert(f'Rail vehicle car type not found for freight type {freight_type}.')
+        n_cars_by_type[car_type] = int(single_train_dispatch['Cars_Empty'])
+
+    rv_to_use = [vehicle for vehicle in available_rail_vehicles if vehicle.freight_type in freight_types]
+    return rv_to_use, n_cars_by_type
diff --git a/python/altrios/train_planner/planner.py b/python/altrios/train_planner/planner.py
index 814d0660..c88644fd 100644
--- a/python/altrios/train_planner/planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -95,19 +95,19 @@ def dispatch(
     message = ""
     if config.cars_per_locomotive_fixed:
         # Get as many available locomotives as are needed (in order of loco_pool)
-        enough_locomotives = loco_pool.select(
-            (pl.lit(1.0) * pl.lit(candidates)).cumsum() >= total_cars).to_series()
-        if not enough_locomotives.any():
+        enough = loco_pool.select(
+            (pl.lit(1.0) * pl.lit(candidates)).cum_sum() >= total_cars).to_series()
+        if not enough.any():
             message = f"""Locomotives needed ({total_cars}) at {origin} at hour {dispatch_time}
                 is more than the available locomotives ({candidates.sum()}).
                 Count of locomotives servicing, refueling, or queueing at {origin} are:"""
     else:
         # Get running sum, including first diesel, of hp of the candidates (in order of loco_pool)
-        enough_hp = loco_pool.select((
+        enough = loco_pool.select((
             (
                 (pl.col("HP") - (pl.col("Loco_Mass_Tons") * pl.lit(hp_per_ton))) * pl.lit(candidates)
             ).cum_sum() + pl.lit(diesel_to_require_hp)) >= hp_required).to_series()
-        if not enough_hp.any():
+        if not enough.any():
             available_hp = loco_pool.select(
                 (
                     (pl.col("HP") - (pl.col("Loco_Mass_Tons") * pl.lit(hp_per_ton))) * pl.lit(candidates)
@@ -116,7 +116,7 @@ def dispatch(
                 is more than the available horsepower ({available_hp}).
                 Count of locomotives servicing, refueling, or queueing at {origin} are:"""
 
-    if not enough_hp.any():
+    if not enough.any():
         # Hold the train until enough diesels are present (future development)
         waiting_counts = (loco_pool
             .filter(
@@ -132,7 +132,7 @@ def dispatch(
         # Hold the train until enough locomotives are present (future development)
         raise ValueError(message)
 
-    last_row_to_use = enough_hp.eq(True).cum_sum().eq(1).arg_max()
+    last_row_to_use = enough.eq(True).cum_sum().eq(1).arg_max()
     # Set false all the locomotives that would add unnecessary hp
     selected[np.arange(last_row_to_use+1, len(selected))] = False
 
@@ -317,6 +317,14 @@ def run_train_planner(
         config.return_demand_generators = train_demand_generators.get_default_return_demand_generators()
 
     refuelers, loco_pool = data_prep.append_charging_guidelines(refuelers, loco_pool, demand, network_charging_guidelines)
+    
+    freight_type_to_car_type = {}
+    for rv in rail_vehicles: 
+        if rv.freight_type in freight_type_to_car_type:
+            assert(f'More than one rail vehicle car type for freight type {rv.freight_type}')
+        else:
+            freight_type_to_car_type[rv.freight_type] = rv.car_type
+
     if config.single_train_mode:
         demand = train_demand_generators.generate_demand_trains(demand, 
                                         demand_returns = pl.DataFrame(), 
@@ -333,7 +341,7 @@ def run_train_planner(
         demand_rebalancing = pl.DataFrame()
         if demand.filter(pl.col("Train_Type").str.contains("Manifest")).height > 0:
             demand_rebalancing = train_demand_generators.generate_manifest_rebalancing_demand(demand, node_list, config)
-        demand = train_demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, config)
+        demand = train_demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, freight_type_to_car_type, config)
         if config.dispatch_scheduler is None:
             config.dispatch_scheduler = schedulers.generate_dispatch_details
         dispatch_schedule = config.dispatch_scheduler(demand, rail_vehicles, simulation_days, config)
@@ -389,24 +397,15 @@ def run_train_planner(
 
                     if config.drag_coeff_function is not None:
                         cd_area_vec = config.drag_coeff_function(
-                             this_train['Number_of_Cars'], 
-                             gap_size = defaults.DEFAULT_GAP_SIZE
+                             int(this_train['Number_of_Cars'])
                          )
                     else:
                         cd_area_vec = None
 
-                    train_types = []
-                    n_cars_by_type = {}
-                    this_train_type = this_train['Train_Type']
-                    if this_train['Cars_Loaded'] > 0:
-                        train_types.append(f'{this_train_type}_Loaded')
-                        n_cars_by_type[f'{this_train_type}_Loaded'] = int(this_train['Cars_Loaded'])
-                    if this_train['Cars_Empty'] > 0:
-                        train_types.append(f'{this_train_type}_Empty')
-                        n_cars_by_type[f'{this_train_type}_Empty'] = int(this_train['Cars_Empty'])
+                    rv_to_use, n_cars_by_type = data_prep.configure_rail_vehicles(this_train, rail_vehicles, freight_type_to_car_type)
 
                     train_config = alt.TrainConfig(
-                        rail_vehicles = [vehicle for vehicle in rail_vehicles if vehicle.car_type in train_types],
+                        rail_vehicles = rv_to_use,
                         n_cars_by_type = n_cars_by_type,
                         train_type = train_type,
                         cd_area_vec = cd_area_vec
@@ -558,9 +557,9 @@ def run_train_planner(
         str(alt.resources_root() / "networks/Taconite-NoBalloon.yaml")
     )
     config = planner_config.TrainPlannerConfig()
-    loco_pool = build_locopool(config, defaults.DEMAND_FILE)
-    demand, node_list = demand_loader(defaults.DEMAND_FILE)
-    refuelers = build_refuelers(
+    loco_pool = data_prep.build_locopool(config, defaults.DEMAND_FILE)
+    demand, node_list = data_prep.load_freight_demand(defaults.DEMAND_FILE)
+    refuelers = data_prep.build_refuelers(
         node_list, 
         loco_pool,
         config.refueler_info, 
diff --git a/python/altrios/train_planner/planner_config.py b/python/altrios/train_planner/planner_config.py
index 20adcd21..72db6d03 100644
--- a/python/altrios/train_planner/planner_config.py
+++ b/python/altrios/train_planner/planner_config.py
@@ -51,10 +51,7 @@ class TrainPlannerConfig:
         "Default": {
         "Unit": 2.0,
         "Manifest": 1.5,
-        "Intermodal": 2.0 + 2.0,
-        "Unit_Empty": 2.0,
-        "Manifest_Empty": 1.5,
-        "Intermodal_Empty": 2.0 + 2.0,
+        "Intermodal": 4.0
         }                         
     })
     dispatch_scaling_dict: Dict = field(default_factory = lambda: {
diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index 5df66147..d267f89e 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -1,5 +1,4 @@
-import math
-from typing import Dict, List
+from typing import List, Union
 from collections import defaultdict
 import polars as pl
 import altrios as alt
@@ -306,59 +305,23 @@ def find_minimum_waiting_time(
     print(f"empty_return is {empty_return}")
     return dispatch_times
 
-# Define the main function to generate demand trains with the updated rule
-def generate_trains(
-    demand_hourly: pl.DataFrame,
-    target_num_cars_per_train:int
-) -> pl.DataFrame:
-    grouped_data = (demand_hourly
+def formatScheduleColumns(df: Union[pl.DataFrame, pl.LazyFrame]) -> Union[pl.DataFrame, pl.LazyFrame]:
+    return (df
         .with_columns(
-            (pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair")
+            (pl.col("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")).alias("Tons_Per_Train"),
+            (pl.col("HP_Required_Per_Ton_Loaded").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + 
+                pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")
+                ).alias("HP_Required")
         )
-        .group_by("OD_Pair")
-            .agg(
-                pl.col("Number_of_Containers").sum().alias("Total_Containers"),
-                pl.col("Number_of_Containers").sum().mod(target_num_cars_per_train).alias("Remaining_Containers")
-            )
-        .with_columns(
-            pl.col("Total_Containers").truediv(target_num_cars_per_train).round().add(pl.col("Remaining_Containers").gt(0)).alias("Max_Min_Cars")
+        .select("Hour", "Origin", "Destination", "Train_Type", 
+                "Number_of_Cars", "Number_of_Cars_Loaded", "Number_of_Cars_Empty",
+                "Tons_Per_Train", "HP_Required"
         )
+        .rename({"Number_of_Cars_Loaded": "Cars_Loaded", 
+                "Number_of_Cars_Empty": "Cars_Empty"})
+        .sort(["Hour","Origin","Destination","Train_Type"])
     )
 
-    # Prepare a list to collect the results for all OD pairs
-    all_dispatch_data = []
-    # Step 4: Loop through each unique OD pair to calculate dispatch data
-    for row in grouped_data.iter_rows(named=True):
-        od_pair = row['OD_Pair']  # This is the "OD_Pair" string like "Origin-Destination"
-        total_containers = row['Total_Containers']  # This is the "Total_Containers" value
-
-        # Filter containers list for the specific OD pair
-        containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair).select("Number_of_Containers").to_series().to_list()
-
-        # Retrieve the max min_num_cars for this OD pair
-        max_min_cars = row['Max_Min_Cars']
-
-        # Calculate dispatch data for the current OD pair with the updated rule
-        dispatch_data = calculate_dispatch_data(total_containers, target_num_cars_per_train, od_pair, containers, max_min_cars)
-        
-        # Add the OD pair label to each entry in the dispatch data
-        dispatch_data_df = pl.DataFrame(dispatch_data, strict=False)
-        dispatch_data_df = dispatch_data_df.with_columns(pl.lit(od_pair).alias("OD_Pair"))
-
-        # Ensure the "Border" column exists and is cast to Float64
-        if "Border" in dispatch_data_df.columns:
-            dispatch_data_df = dispatch_data_df.with_columns(
-                pl.col("Border").cast(pl.Float64)
-            )
-
-        # Append the result to the list
-        all_dispatch_data.append(dispatch_data_df)
-
-    # Combine all dispatch data into a single DataFrame
-    final_dispatch_df = pl.concat(all_dispatch_data, how="diagonal_relaxed")
-  
-    return final_dispatch_df
-
 def calculate_dispatches_deterministic_hourly(
     demand: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],
@@ -377,6 +340,59 @@ def calculate_dispatches_deterministic_hourly(
     ----------
     Updated demand `DataFrame` or `LazyFrame` representing dispatches, each defined with an origin, destination, train type, number of (loaded and empty) cars, tonnage, and HP per ton requirement.
     """
+    # Define the main function to generate demand trains with the updated rule
+    def generate_trains(
+        demand_hourly: pl.DataFrame,
+        target_num_cars_per_train:int
+    ) -> pl.DataFrame:
+        grouped_data = (demand_hourly
+            .with_columns(
+                (pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair")
+            )
+            .group_by("OD_Pair")
+                .agg(
+                    pl.col("Number_of_Containers").sum().alias("Total_Containers"),
+                    pl.col("Number_of_Containers").sum().mod(target_num_cars_per_train).alias("Remaining_Containers")
+                )
+            .with_columns(
+                pl.col("Total_Containers").truediv(target_num_cars_per_train).round().add(pl.col("Remaining_Containers").gt(0)).alias("Max_Min_Cars")
+            )
+        )
+
+        # Prepare a list to collect the results for all OD pairs
+        all_dispatch_data = []
+        # Step 4: Loop through each unique OD pair to calculate dispatch data
+        for row in grouped_data.iter_rows(named=True):
+            od_pair = row['OD_Pair']  # This is the "OD_Pair" string like "Origin-Destination"
+            total_containers = row['Total_Containers']  # This is the "Total_Containers" value
+
+            # Filter containers list for the specific OD pair
+            containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair).select("Number_of_Containers").to_series().to_list()
+
+            # Retrieve the max min_num_cars for this OD pair
+            max_min_cars = row['Max_Min_Cars']
+
+            # Calculate dispatch data for the current OD pair with the updated rule
+            dispatch_data = calculate_dispatch_data(total_containers, target_num_cars_per_train, od_pair, containers, max_min_cars)
+            
+            # Add the OD pair label to each entry in the dispatch data
+            dispatch_data_df = pl.DataFrame(dispatch_data, strict=False)
+            dispatch_data_df = dispatch_data_df.with_columns(pl.lit(od_pair).alias("OD_Pair"))
+
+            # Ensure the "Border" column exists and is cast to Float64
+            if "Border" in dispatch_data_df.columns:
+                dispatch_data_df = dispatch_data_df.with_columns(
+                    pl.col("Border").cast(pl.Float64)
+                )
+
+            # Append the result to the list
+            all_dispatch_data.append(dispatch_data_df)
+
+        # Combine all dispatch data into a single DataFrame
+        final_dispatch_df = pl.concat(all_dispatch_data, how="diagonal_relaxed")
+    
+        return final_dispatch_df
+
     target_num_cars_per_train = config.target_num_cars_per_train
     dispatch_df = generate_trains(demand,target_num_cars_per_train)
     od_border_time = []
@@ -397,7 +413,7 @@ def calculate_dispatches_deterministic_hourly(
         border_time_list=border_time_list,
         target_num_cars_per_train=config.target_num_cars_per_train['Intermodal'] #TODO make this flexible
     )
-    return schedule
+    return schedule.pipe(formatScheduleColumns)
 
 def generate_dispatch_details(
     demand: pl.DataFrame,
@@ -421,25 +437,23 @@ def generate_dispatch_details(
     in hours
     """
     hours = simulation_days * 24
-
     grouping_vars = ["Origin", "Destination", "Train_Type"]
-    schedule = (demand
+    return (demand
         .select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode())
         .pipe(utilities.allocateIntegerEvenly, target = "Number_of_Cars_Loaded", grouping_vars = grouping_vars)
         .drop("Percent_Within_Group_Cumulative")
         .pipe(utilities.allocateIntegerEvenly, target = "Number_of_Cars_Empty", grouping_vars = grouping_vars)
+        .drop("Percent_Within_Group_Cumulative")
         .group_by(pl.exclude("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
             .agg(pl.col("Number_of_Cars_Empty", "Number_of_Cars_Loaded"))
         .with_columns(pl.col("Number_of_Cars_Loaded").list.sort(descending=True),
                       pl.col("Number_of_Cars_Empty").list.sort(descending=False))
         .explode("Number_of_Cars_Empty", "Number_of_Cars_Loaded")
-        .with_columns(
-            (pl.col("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")).alias("Tons_Per_Train"),
-            (pl.col("HP_Required_Per_Ton_Loaded").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + 
-                pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")
-                ).alias("HP_Required")
-        )
-        .sort("Origin", "Destination", "Percent_Within_Group_Cumulative", "Train_Type")
+        .with_columns((pl.col("Number_of_Cars_Empty") + pl.col("Number_of_Cars_Loaded")).alias("Number_of_Cars"))
+        #TODO: space out trains with slightly more vs less demand, instead of ordering randomly
+        .with_columns(pl.int_range(pl.len()).shuffle().alias("random_int"))
+        .sort("Origin", "Destination", "Train_Type", "random_int")
+        .drop("random_int")
         .with_columns(
             (hours * 1.0 / pl.len().over("Origin", "Destination")).alias("Interval")
         )
@@ -447,13 +461,5 @@ def generate_dispatch_details(
             ((pl.col("Interval").cum_count().over(["Origin","Destination"])) \
              * pl.col("Interval")).alias("Hour")
         )
-        .select("Hour", "Origin", "Destination", "Train_Type", 
-                "Number_of_Cars", "Number_of_Cars_Loaded", "Number_of_Cars_Empty",
-                "Tons_Per_Train", "HP_Required"
-        )
-        .rename({"Number_of_Cars_Loaded": "Cars_Loaded", 
-                "Number_of_Cars_Empty": "Cars_Empty"})
-        .sort(["Hour","Origin","Destination","Train_Type"])
-    )
-    
-    return schedule
\ No newline at end of file
+        .pipe(formatScheduleColumns)
+    )
\ No newline at end of file
diff --git a/python/altrios/train_planner/train_demand_generators.py b/python/altrios/train_planner/train_demand_generators.py
index 6cb04624..ee0178e1 100644
--- a/python/altrios/train_planner/train_demand_generators.py
+++ b/python/altrios/train_planner/train_demand_generators.py
@@ -78,14 +78,13 @@ def generate_return_demand_intermodal(demand_subset: Union[pl.LazyFrame, pl.Data
             pl.concat_str(pl.min_horizontal("Origin", "Destination"), pl.lit("_"), pl.max_horizontal("Origin", "Destination")).alias("OD")
         )
         .with_columns(
-            pl.col("Number_of_Cars", "Number_of_Containers").range().over("OD").name.suffix("_Return")
+            pl.col("Number_of_Cars").range().over("OD").name.suffix("_Return")
         )
         .filter(
-            pl.col("Number_of_Containers") == pl.col("Number_of_Containers").max().over("OD")
+            pl.col("Number_of_Cars") == pl.col("Number_of_Cars").max().over("OD")
         )
-        .drop("OD", "Number_of_Containers", "Number_of_Cars")
-        .rename({"Number_of_Containers_Return": "Number_of_Containers",
-                 "Number_of_Cars_Return": "Number_of_Cars"})
+        .drop("OD", "Number_of_Cars")
+        .rename({"Number_of_Cars_Return": "Number_of_Cars"})
     )
 
 def generate_return_demand(
@@ -117,7 +116,7 @@ def generate_return_demand(
             print(f'Return demand generator not implemented for train type: {train_type_label}')
 
     demand_return = (pl.concat(return_demand, how="diagonal_relaxed")
-        .filter(pl.col("Number_of_Cars") + pl.col("Number_of_Containers") > 0)
+        .filter(pl.col("Number_of_Cars") > 0)
     )
     return demand_return
 
@@ -227,6 +226,7 @@ def generate_demand_trains(
     demand_returns: pl.DataFrame,
     demand_rebalancing: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],
+    freight_type_to_car_type: Dict[str, str],
     config: planner_config.TrainPlannerConfig
 ) -> pl.DataFrame:
     """
@@ -279,27 +279,39 @@ def get_kg_empty(veh):
     def get_kg(veh):
         return veh.mass_static_base_kilograms + veh.mass_freight_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
     
-    ton_per_car = (
-        pl.DataFrame({"Train_Type": pl.Series([rv.car_type for rv in rail_vehicles]).str.strip_suffix("_Loaded"),
-                        "KG_Empty": [get_kg_empty(rv) for rv in rail_vehicles],
-                        "KG": [get_kg(rv) for rv in rail_vehicles]})
-            .with_columns(pl.when(pl.col("Train_Type").str.contains("_Empty"))
-                                .then(pl.col("KG_Empty") / utilities.KG_PER_TON)
-                                .otherwise(pl.col("KG") / utilities.KG_PER_TON)
-                                .alias("Tons_Per_Car"))
-            .drop(["KG_Empty","KG"])
+    tons_per_car = (
+        pl.DataFrame({"Car_Type": pl.Series([rv.car_type for rv in rail_vehicles]),
+                        "KG": [get_kg(rv) for rv in rail_vehicles],
+                        "KG_Empty": [get_kg_empty(rv) for rv in rail_vehicles]
+        })
+        .with_columns(
+            pl.when(pl.col("Car_Type").str.to_lowercase().str.contains("_empty"))
+                .then(pl.col("KG_Empty") / utilities.KG_PER_TON)
+                .otherwise(pl.col("KG") / utilities.KG_PER_TON)
+                .alias("Tons_Per_Car"),
+            pl.col("Car_Type").str.strip_suffix("_Loaded"))
+        .drop(["KG_Empty","KG"])
     )
 
     demand = (pl.concat([demand, demand_returns, demand_rebalancing], how="diagonal_relaxed")
         .group_by("Origin","Destination", "Train_Type")
             .agg(pl.col("Number_of_Cars").sum())
         .filter(pl.col("Number_of_Cars") > 0)
-        .join(ton_per_car, on="Train_Type", how="left")
+        .with_columns(
+            pl.when(pl.col("Train_Type").str.contains(pl.lit("_Empty")))
+                        .then(pl.col("Train_Type"))
+                        .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Loaded"), pl.lit("_Loaded")))
+                        .replace_strict(freight_type_to_car_type)
+                        .alias("Car_Type")
+        )
+        .join(tons_per_car, how="left", on="Car_Type")
         # Merge on OD-specific hp_per_ton if the user specified any
-        .join(hp_per_ton.drop("O_D"), on=["Origin","Destination","Train_Type"], how="left")
+        .join(hp_per_ton.drop("O_D"), 
+              on=[pl.col("Origin"), pl.col("Destination"), pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")], 
+              how="left")
         # Second, merge on defaults per train type
         .join(hp_per_ton.filter((pl.col("O_D") =="Default")).drop(["O_D","Origin","Destination"]),
-            on=["Train_Type"],
+            on=[pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")],
             how="left",
             suffix="_Default")
         # Merge on cars_per_train_min if the user specified any
diff --git a/rust/altrios-core/src/train/rail_vehicle.rs b/rust/altrios-core/src/train/rail_vehicle.rs
index 5971db4b..dd29fe34 100644
--- a/rust/altrios-core/src/train/rail_vehicle.rs
+++ b/rust/altrios-core/src/train/rail_vehicle.rs
@@ -8,6 +8,10 @@ pub struct RailVehicle {
     #[serde(alias = "Car Type")]
     pub car_type: String,
 
+    /// Identifier for the freight type carried by this car type (e.g., Intermodal).
+    #[serde(alias = "Freight Type")]
+    pub freight_type: String,
+
     /// Railcar length (between pulling-faces)
     #[serde(alias = "Length (m)")]
     pub length: si::Length,

From 5027670b9f34de3eee1035ed535018d5d90c2ef8 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sat, 18 Jan 2025 17:28:54 -0500
Subject: [PATCH 36/50] refactoring

---
 python/altrios/metric_calculator.py           | 31 ++++++++++--
 .../rolling_stock/Intermodal_Empty.yaml       |  2 +-
 .../rolling_stock/Intermodal_Loaded.yaml      |  2 +-
 .../rolling_stock/Manifest_Empty.yaml         |  2 +-
 .../rolling_stock/Manifest_Loaded.yaml        |  2 +-
 .../resources/rolling_stock/Unit_Empty.yaml   |  2 +-
 .../resources/rolling_stock/Unit_Loaded.yaml  |  2 +-
 python/altrios/train_planner/planner.py       |  6 ++-
 .../altrios/train_planner/planner_config.py   |  2 +-
 python/altrios/train_planner/schedulers.py    | 48 ++++++++++++-------
 10 files changed, 72 insertions(+), 27 deletions(-)

diff --git a/python/altrios/metric_calculator.py b/python/altrios/metric_calculator.py
index efbe81f0..cd70504c 100644
--- a/python/altrios/metric_calculator.py
+++ b/python/altrios/metric_calculator.py
@@ -8,6 +8,7 @@
 
 import altrios as alt
 from altrios import utilities, defaults
+from altrios.train_planner import planner_config
 
 MetricType = pl.DataFrame
 
@@ -73,6 +74,7 @@ class ScenarioInfo:
     emissions_factors: pl.DataFrame = None
     nodal_energy_prices: pl.DataFrame = None
     count_unused_locomotives: bool = False
+    train_planner_config: planner_config = None
 
 def metric(
         name: str,
@@ -274,7 +276,7 @@ def calculate_energy_per_freight(info: ScenarioInfo,
     ----------
     DataFrame of energy usage per freight moved (metric name, units, value, and scenario year)
     """
-    if "per car-mile" not in units:
+    if "per car-mile" not in units and "per container-mile" not in units:
         print(f"Units of {units} not supported for energy-per-freight calculation.")
         return metric("Energy_Per_Freight_Moved", units, None)
     
@@ -290,7 +292,10 @@ def calculate_energy_per_freight(info: ScenarioInfo,
     electricity_mj = calculate_electricity_use(info, units="MJ")
     total_mj = value_from_metrics(diesel_mj) + value_from_metrics(electricity_mj, subset="All")
     total_energy = total_mj * conversion_from_megajoule
-    freight_moved = calculate_freight_moved(info, units="car-miles")
+    if "per car-mile" in units:
+        freight_moved = calculate_freight_moved(info, units="car-miles")
+    elif "per container-mile" in units:
+        freight_moved = calculate_freight_moved(info, units="container-miles")
     freight_val = value_from_metrics(freight_moved)
     return metrics_from_list([
         diesel_mj,
@@ -455,7 +460,27 @@ def calculate_freight_moved(
         return metric("Freight_Moved", units, info.sims.get_car_kilometers(annualize=info.annualize) * conversion_from_km, year=info.scenario_year)
     elif units == "cars":
         return metric("Freight_Moved", units, info.sims.get_cars_moved(annualize=info.annualize), year=info.scenario_year)
-    elif units == "detailed":
+    elif units in ["container-km", "container-miles"]:
+        assert info.consist_plan.filter(~pl.col("Train_Type").str.contains("Intermodal")).height == 0, "Can only count containers if the consist plan is all Intermodal"
+        car_distance = info.sims.get_car_kilometers(annualize=info.annualize) * conversion_from_km
+        
+        if info.train_planner_config.stack_type == "double":
+            containers_per_car = 2.0
+        elif info.train_planner_config.stack_type == "single":
+            containers_per_car = 1.0
+        return metric("Freight_Moved", units, car_distance * containers_per_car, year=info.scenario_year)
+        
+    elif units == "containers":
+        container_counts = info.consist_plan.select("Train_ID", "Containers_Loaded", "Containers_Empty").unique().drop("Train_ID").sum()
+        if info.annualize: 
+            annualizer = 365.25 / info.simulation_days
+        else:
+            annualizer = 1.0
+        return metrics_from_list([
+            metric("Freight_Moved", units, container_counts.get_column("Containers_Loaded").item() * annualizer, "Loaded", year=info.scenario_year),
+            metric("Freight_Moved", units, container_counts.get_column("Containers_Empty").item() * annualizer, "Loaded", year=info.scenario_year),
+        ])
+    elif units == "detailed_car_counts":
         kilometers = (pl.DataFrame(data = {"car-km": [sim.get_kilometers(annualize=info.annualize) for sim in info.sims.tolist()]})
             .with_row_index("idx")
             .with_columns(
diff --git a/python/altrios/resources/rolling_stock/Intermodal_Empty.yaml b/python/altrios/resources/rolling_stock/Intermodal_Empty.yaml
index 81266eb6..f257a251 100644
--- a/python/altrios/resources/rolling_stock/Intermodal_Empty.yaml
+++ b/python/altrios/resources/rolling_stock/Intermodal_Empty.yaml
@@ -1,6 +1,6 @@
 ---
 car_type: Intermodal_Empty
-freight_type: Intermodal
+freight_type: Intermodal_Empty
 length: 18.0
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Intermodal_Loaded.yaml b/python/altrios/resources/rolling_stock/Intermodal_Loaded.yaml
index 4fcff84b..c0d0921f 100644
--- a/python/altrios/resources/rolling_stock/Intermodal_Loaded.yaml
+++ b/python/altrios/resources/rolling_stock/Intermodal_Loaded.yaml
@@ -1,6 +1,6 @@
 ---
 car_type: Intermodal_Loaded
-freight_type: Intermodal
+freight_type: Intermodal_Loaded
 length: 18.0
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Manifest_Empty.yaml b/python/altrios/resources/rolling_stock/Manifest_Empty.yaml
index bba1ed5a..a304a398 100644
--- a/python/altrios/resources/rolling_stock/Manifest_Empty.yaml
+++ b/python/altrios/resources/rolling_stock/Manifest_Empty.yaml
@@ -1,6 +1,6 @@
 ---
 car_type: Manifest_Empty
-freight_type: Manifest
+freight_type: Manifest_Empty
 length: 18.0
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Manifest_Loaded.yaml b/python/altrios/resources/rolling_stock/Manifest_Loaded.yaml
index 552cb398..8e398ba4 100644
--- a/python/altrios/resources/rolling_stock/Manifest_Loaded.yaml
+++ b/python/altrios/resources/rolling_stock/Manifest_Loaded.yaml
@@ -1,6 +1,6 @@
 ---
 car_type: Manifest_Loaded
-freight_type: Manifest
+freight_type: Manifest_Loaded
 length: 18.0
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Unit_Empty.yaml b/python/altrios/resources/rolling_stock/Unit_Empty.yaml
index b52971a3..33ce94dd 100644
--- a/python/altrios/resources/rolling_stock/Unit_Empty.yaml
+++ b/python/altrios/resources/rolling_stock/Unit_Empty.yaml
@@ -1,6 +1,6 @@
 ---
 car_type: Unit_Empty
-freight_type: Unit
+freight_type: Unit_Empty
 length: 10.7
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/resources/rolling_stock/Unit_Loaded.yaml b/python/altrios/resources/rolling_stock/Unit_Loaded.yaml
index 6bae35d6..cb23f308 100644
--- a/python/altrios/resources/rolling_stock/Unit_Loaded.yaml
+++ b/python/altrios/resources/rolling_stock/Unit_Loaded.yaml
@@ -1,6 +1,6 @@
 ---
 car_type: Unit_Loaded
-freight_type: Unit
+freight_type: Unit_Loaded
 length: 10.7
 axle_count: 4
 brake_count: 1
diff --git a/python/altrios/train_planner/planner.py b/python/altrios/train_planner/planner.py
index c88644fd..a7da1cb1 100644
--- a/python/altrios/train_planner/planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -355,7 +355,9 @@ def run_train_planner(
          'Origin_ID': pl.Utf8, 
          'Destination_ID': pl.Utf8, 
          'Cars_Loaded': pl.Float64, 
-         'Cars_Empty': pl.Float64, 
+         'Cars_Empty': pl.Float64,
+         'Containers_Loaded': pl.Float64,
+         'Containers_Empty': pl.Float64,
          'Departure_SOC_J': pl.Float64, 
          'Departure_Time_Planned_Hr': pl.Float64, 
          'Arrival_Time_Planned_Hr': pl.Float64})
@@ -503,6 +505,8 @@ def run_train_planner(
                         pl.Series(repeat(this_train['Destination'], new_row_count)),
                         pl.Series(repeat(this_train['Cars_Loaded'], new_row_count)),
                         pl.Series(repeat(this_train['Cars_Empty'], new_row_count)),
+                        pl.Series(repeat(this_train['Containers_Loaded'], new_row_count)),
+                        pl.Series(repeat(this_train['Containers_Empty'], new_row_count)),
                         loco_start_soc_j,
                         pl.Series(repeat(current_time, new_row_count)),
                         pl.Series(repeat(current_time + travel_time, new_row_count))],
diff --git a/python/altrios/train_planner/planner_config.py b/python/altrios/train_planner/planner_config.py
index 72db6d03..e4bbaaee 100644
--- a/python/altrios/train_planner/planner_config.py
+++ b/python/altrios/train_planner/planner_config.py
@@ -44,7 +44,7 @@ class TrainPlannerConfig:
     })
     cars_per_locomotive_fixed: bool = False
     refuelers_per_incoming_corridor: int = 4
-    stack_type: str = "single"
+    stack_type: str = "double"
     require_diesel: bool = False
     manifest_empty_return_ratio: float = 0.6
     hp_required_per_ton: Dict = field(default_factory = lambda: {
diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index d267f89e..b0ee70bd 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -305,16 +305,34 @@ def find_minimum_waiting_time(
     print(f"empty_return is {empty_return}")
     return dispatch_times
 
-def formatScheduleColumns(df: Union[pl.DataFrame, pl.LazyFrame]) -> Union[pl.DataFrame, pl.LazyFrame]:
+def formatScheduleColumns(
+    df: Union[pl.DataFrame, pl.LazyFrame],
+    config: planner_config.TrainPlannerConfig
+) -> Union[pl.DataFrame, pl.LazyFrame]: 
+    containers_per_car = 0.0
+    if config.stack_type == "single":
+        containers_per_car = 1.0
+    elif config.stack_type == "double":
+        containers_per_car = 2.0
+    else:
+        assert(f'Unhandled container stack type: {config.stack_type}')
     return (df
         .with_columns(
             (pl.col("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")).alias("Tons_Per_Train"),
             (pl.col("HP_Required_Per_Ton_Loaded").mul("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + 
                 pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")
-                ).alias("HP_Required")
+                ).alias("HP_Required"),
+            pl.when(pl.col("Train_Type").str.contains("Intermodal"))
+                .then(pl.col("Number_of_Cars_Loaded").mul(containers_per_car))
+                .otherwise(0)
+                .alias("Containers_Loaded"),
+            pl.when(pl.col("Train_Type").str.contains("Intermodal"))
+                .then(pl.col("Number_of_Cars_Empty").mul(containers_per_car))
+                .otherwise(0)
+                .alias("Containers_Empty"),
         )
         .select("Hour", "Origin", "Destination", "Train_Type", 
-                "Number_of_Cars", "Number_of_Cars_Loaded", "Number_of_Cars_Empty",
+                "Number_of_Cars", "Number_of_Cars_Loaded", "Number_of_Cars_Empty", "Containers_Empty", "Containers_Loaded",
                 "Tons_Per_Train", "HP_Required"
         )
         .rename({"Number_of_Cars_Loaded": "Cars_Loaded", 
@@ -345,17 +363,15 @@ def generate_trains(
         demand_hourly: pl.DataFrame,
         target_num_cars_per_train:int
     ) -> pl.DataFrame:
+        demand_hourly = demand_hourly.with_columns((pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair"))
         grouped_data = (demand_hourly
-            .with_columns(
-                (pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair")
-            )
             .group_by("OD_Pair")
                 .agg(
-                    pl.col("Number_of_Containers").sum().alias("Total_Containers"),
-                    pl.col("Number_of_Containers").sum().mod(target_num_cars_per_train).alias("Remaining_Containers")
+                    pl.col("Number_of_Cars").sum().alias("Total_Cars"),
+                    pl.col("Number_of_Cars").sum().mod(target_num_cars_per_train).alias("Remaining_Cars")
                 )
             .with_columns(
-                pl.col("Total_Containers").truediv(target_num_cars_per_train).round().add(pl.col("Remaining_Containers").gt(0)).alias("Max_Min_Cars")
+                pl.col("Total_Cars").truediv(target_num_cars_per_train).round().add(pl.col("Remaining_Cars").gt(0)).alias("Max_Min_Cars")
             )
         )
 
@@ -364,16 +380,16 @@ def generate_trains(
         # Step 4: Loop through each unique OD pair to calculate dispatch data
         for row in grouped_data.iter_rows(named=True):
             od_pair = row['OD_Pair']  # This is the "OD_Pair" string like "Origin-Destination"
-            total_containers = row['Total_Containers']  # This is the "Total_Containers" value
+            total_cars = row['Total_Cars']  # This is the "Total_Cars" value
 
-            # Filter containers list for the specific OD pair
-            containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair).select("Number_of_Containers").to_series().to_list()
+            # Filter cars list for the specific OD pair
+            cars = demand_hourly.filter(pl.col("OD_Pair") == od_pair).select("Number_of_Cars").to_series().to_list()
 
             # Retrieve the max min_num_cars for this OD pair
             max_min_cars = row['Max_Min_Cars']
 
             # Calculate dispatch data for the current OD pair with the updated rule
-            dispatch_data = calculate_dispatch_data(total_containers, target_num_cars_per_train, od_pair, containers, max_min_cars)
+            dispatch_data = calculate_dispatch_data(total_cars, target_num_cars_per_train, od_pair, cars, max_min_cars)
             
             # Add the OD pair label to each entry in the dispatch data
             dispatch_data_df = pl.DataFrame(dispatch_data, strict=False)
@@ -393,7 +409,7 @@ def generate_trains(
     
         return final_dispatch_df
 
-    target_num_cars_per_train = config.target_num_cars_per_train
+    target_num_cars_per_train = config.target_cars_per_train['Default'] #TODO make this flexible
     dispatch_df = generate_trains(demand,target_num_cars_per_train)
     od_border_time = []
     border_time = dispatch_df["Border_Times"].to_list() 
@@ -411,7 +427,7 @@ def generate_trains(
     schedule = find_minimum_waiting_time(num_iterations=num_iterations,
         demand_hourly=demand,
         border_time_list=border_time_list,
-        target_num_cars_per_train=config.target_num_cars_per_train['Intermodal'] #TODO make this flexible
+        target_num_cars_per_train=target_num_cars_per_train 
     )
     return schedule.pipe(formatScheduleColumns)
 
@@ -461,5 +477,5 @@ def generate_dispatch_details(
             ((pl.col("Interval").cum_count().over(["Origin","Destination"])) \
              * pl.col("Interval")).alias("Hour")
         )
-        .pipe(formatScheduleColumns)
+        .pipe(formatScheduleColumns, config = config)
     )
\ No newline at end of file

From 65f7cab0c1a2d2c586ce2d8aaddcbc9db8af5ec4 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sat, 18 Jan 2025 17:33:05 -0500
Subject: [PATCH 37/50] added hourly dispatch function

---
 python/altrios/train_planner/schedulers.py | 58 ++++++++++++++++++++++
 1 file changed, 58 insertions(+)

diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index b0ee70bd..7bd469b3 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -1,3 +1,4 @@
+import math
 from typing import List, Union
 from collections import defaultdict
 import polars as pl
@@ -340,6 +341,63 @@ def formatScheduleColumns(
         .sort(["Hour","Origin","Destination","Train_Type"])
     )
 
+
+def calculate_dispatch_data(total_cars, target_num_cars, label, cars, max_min_cars):
+    remaining_cars = total_cars % target_num_cars
+    min_num_cars = (
+        total_cars // target_num_cars + (1 if remaining_cars > 0 else 0)
+    )
+    
+    ## Apply the maximum min_num_cars value
+    min_num_cars = int(max(min_num_cars, max_min_cars))
+
+    border_list = []
+    border_time_list = []
+    dispatched_list = []
+
+    if remaining_cars == 0:
+        border = total_cars / min_num_cars
+    else:
+        border = math.ceil(total_cars / min_num_cars)
+        
+    containers_left = total_cars
+    for _ in range(min_num_cars):
+        if containers_left >= border:
+            border_list.append(border)
+            containers_left -= border
+        else:
+            border_list.append(containers_left)
+            break
+
+    cumulate_demand = 0
+    p = 0
+    for i, car in enumerate(cars):
+        cumulate_demand += car
+        if p == len(border_list) - 1:
+            border_time_list.append(len(cars) - 1)
+            break
+        if cumulate_demand >= border_list[p]:
+            border_time_list.append(i)
+            dispatched_list.append(cumulate_demand)
+            total_cars -= cumulate_demand
+            cumulate_demand = 0
+            p += 1
+            if p >= len(border_list):
+                break
+    if len(border_time_list) < len(border_list):
+        for _ in range(len(border_list) - len(border_time_list)):
+            border_time_list.append(border_time_list[-1]+_+1)
+    dispatched_list.append(total_cars)
+    if len(dispatched_list) < len(border_list):
+        for _ in range(len(border_list) - len(dispatched_list)):
+            dispatched_list.append(0)
+    return {
+        "Border": border_list,
+        "Border_Times": border_time_list,
+        "Dispatched": dispatched_list,
+    }
+
+
 def calculate_dispatches_deterministic_hourly(
     demand: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],

From 43399892e5681bf5ba72134f5ac2f528e1497f95 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sun, 19 Jan 2025 23:05:40 -0500
Subject: [PATCH 38/50] partially implemented calculate_dispatch_data for
 diesel

---
 python/altrios/metric_calculator.py           |   7 +-
 python/altrios/sim_manager.py                 |  15 +-
 python/altrios/train_planner/data_prep.py     | 106 ++++++--
 python/altrios/train_planner/planner.py       |  65 +++--
 .../altrios/train_planner/planner_config.py   |   5 +-
 python/altrios/train_planner/schedulers.py    | 253 +++++++++---------
 .../train_planner/train_demand_generators.py  |  28 +-
 7 files changed, 287 insertions(+), 192 deletions(-)

diff --git a/python/altrios/metric_calculator.py b/python/altrios/metric_calculator.py
index cd70504c..a8df5fc1 100644
--- a/python/altrios/metric_calculator.py
+++ b/python/altrios/metric_calculator.py
@@ -463,12 +463,7 @@ def calculate_freight_moved(
     elif units in ["container-km", "container-miles"]:
         assert info.consist_plan.filter(~pl.col("Train_Type").str.contains("Intermodal")).height == 0, "Can only count containers if the consist plan is all Intermodal"
         car_distance = info.sims.get_car_kilometers(annualize=info.annualize) * conversion_from_km
-        
-        if info.train_planner_config.stack_type == "double":
-            containers_per_car = 2.0
-        elif info.train_planner_config.stack_type == "single":
-            containers_per_car = 1.0
-        return metric("Freight_Moved", units, car_distance * containers_per_car, year=info.scenario_year)
+        return metric("Freight_Moved", units, car_distance * info.train_planner_config.containers_per_car, year=info.scenario_year)
         
     elif units == "containers":
         container_counts = info.consist_plan.select("Train_ID", "Containers_Loaded", "Containers_Empty").unique().drop("Train_ID").sum()
diff --git a/python/altrios/sim_manager.py b/python/altrios/sim_manager.py
index 918f4592..0163224a 100644
--- a/python/altrios/sim_manager.py
+++ b/python/altrios/sim_manager.py
@@ -64,13 +64,15 @@ def main(
                                  str(loc.link_idx.idx) + " is invalid for network!")
             
     train_planner_config.loco_info = metrics.add_battery_costs(train_planner_config.loco_info, scenario_year)
+    train_planner_config.simulation_days = simulation_days + 2 * warm_start_days
 
-    if loco_pool is None: loco_pool = planner.data_prep.build_locopool(
-            config = train_planner_config,
-            method="shares_twoway",
-            shares=[1-target_bel_share, target_bel_share],
-            demand_file=demand_file
-            )
+    # TODO mbruchon: un-comment this and move it out into rollout.py so rollouts still work
+    #if loco_pool is None: loco_pool = planner.data_prep.build_locopool(
+    #        config = train_planner_config,
+    #       method="shares_twoway",
+    #        shares=[1-target_bel_share, target_bel_share],
+    #        demand_file=demand_file
+    #       )
         
     t0_ptc = time.perf_counter()
     (
@@ -85,7 +87,6 @@ def main(
         network = network,
         loco_pool= loco_pool,
         refuelers = refuelers,
-        simulation_days=simulation_days + 2 * warm_start_days,
         scenario_year = scenario_year,
         config = train_planner_config,
         demand_file = demand_file,
diff --git a/python/altrios/train_planner/data_prep.py b/python/altrios/train_planner/data_prep.py
index 06b1d0f4..380cf730 100644
--- a/python/altrios/train_planner/data_prep.py
+++ b/python/altrios/train_planner/data_prep.py
@@ -1,6 +1,7 @@
 from typing import Union, List, Tuple, Dict
 from pathlib import Path
 import polars as pl
+import polars.selectors as cs
 import pandas as pd
 import numpy as np
 import math
@@ -19,8 +20,25 @@
     "Sun": 7
 }
 
+def convert_demand_to_sim_days(
+    demand_table: Union[pl.DataFrame, pl.LazyFrame],
+    simulation_days: int
+) -> Union[pl.DataFrame, pl.LazyFrame]:
+    if "Number_of_Days" in demand_table.collect_schema():
+        return demand_table.with_columns( 
+            cs.starts_with("Number_of_").truediv(pl.col("Number_of_Days").truediv(simulation_days))
+        )
+
+    else:
+        print("`Number_of_Days` not specified in demand file. Assuming demand in the file is expressed per week.")
+        return demand_table.with_columns( 
+            cs.starts_with("Number_of_").mul(simulation_days / 7.0)
+        )
+
+
 def load_freight_demand(
-    demand_table: Union[pl.DataFrame, Path, str]
+    demand_table: Union[pl.DataFrame, pl.LazyFrame, Path, str],
+    config: planner_config.TrainPlannerConfig,
 ) -> Tuple[pl.DataFrame, pl.Series, int]:
     """
     Load the user input csv file into a dataframe for later processing
@@ -39,8 +57,14 @@ def load_freight_demand(
     origin, destination, train type, number of cars
     node_list: List of origin or destination demand nodes
     """
-    if type(demand_table) is not pl.DataFrame:
-        demand_table = pl.read_csv(demand_table, dtypes = {"Number_of_Cars": pl.UInt32, "Number_of_Containers": pl.UInt32})
+    if isinstance(demand_table, (Path, str)):
+        demand_table = (pl.read_csv(demand_table)
+            .pipe(convert_demand_to_sim_days, simulation_days = config.simulation_days)
+        )
+    elif "Hour" not in demand_table.collect_schema():
+        demand_table = (demand_table
+            .pipe(convert_demand_to_sim_days, simulation_days = config.simulation_days)
+        )
 
     nodes = pl.concat(
         [demand_table.get_column("Origin"),
@@ -50,8 +74,16 @@ def load_freight_demand(
 def prep_hourly_demand(
     total_demand: Union[pl.DataFrame, pl.LazyFrame],
     hourly_demand_density: Union[pl.DataFrame, pl.LazyFrame],
-    daily_demand_density: Union[pl.DataFrame, pl.LazyFrame]
+    daily_demand_density: Union[pl.DataFrame, pl.LazyFrame],
+    simulation_weeks = 1
 ) -> Union[pl.DataFrame, pl.LazyFrame]:
+    if "Number_of_Containers" in total_demand.collect_schema():
+        demand_col = "Number_of_Containers"
+    else:
+        demand_col = "Number_of_Cars"
+
+    total_demand = total_demand.pipe(convert_demand_to_sim_days, simulation_days = simulation_weeks * 7)
+    
     hourly_demand_density = (hourly_demand_density
         .group_by("Terminal_Type", "Hour_Of_Day")
             .agg(pl.col("Share").sum())
@@ -62,27 +94,32 @@ def prep_hourly_demand(
             .agg(pl.col("Share").sum())
         .with_columns(pl.col("Share").truediv(pl.col("Share").sum().over("Terminal_Type")))
     )
-    return (total_demand
+    one_week = (total_demand
         .join(daily_demand_density, how="inner", on=["Terminal_Type"])
         .with_columns(
-            (pl.col("Number_of_Cars") * pl.col("Share")).alias("Number_of_Cars_Daily"),
+            (pl.col(demand_col) * pl.col("Share")).alias(f'{demand_col}_Daily'),
             pl.col("Day_Of_Week").replace_strict(day_order_map).alias("Day_Order")
         )
-        .pipe(utilities.allocateItems, grouping_vars=["Origin", "Destination", "Train_Type"], count_target="Number_of_Cars_Daily")
-        .drop("Number_of_Cars_Daily", "Share")
-        .rename({"Count": "Number_of_Cars_Daily"})
+        .pipe(utilities.allocateItems, grouping_vars=["Origin", "Destination", "Train_Type"], count_target=f'{demand_col}_Daily')
+        .drop(f'{demand_col}_Daily', "Share")
+        .rename({"Count": f'{demand_col}_Daily'})
         .join(hourly_demand_density, how="inner", on=["Terminal_Type"])
         .sort("Origin", "Destination", "Day_Order", "Hour_Of_Day")
         .with_columns(
-            (pl.col("Number_of_Cars_Daily") * pl.col("Share")).alias("Number_of_Cars"),
+            (pl.col(f'{demand_col}_Daily') * pl.col("Share")).alias(demand_col),
             pl.concat_str(pl.col("Origin"), pl.lit("-"), pl.col("Destination")).alias("OD_Pair"),
             pl.int_range(0, pl.len()).over("Origin", "Destination").alias("Hour")
         )
-        .pipe(utilities.allocateItems, grouping_vars=["Origin", "Destination", "Train_Type", "Day_Order"], count_target="Number_of_Cars")
-        .drop("Number_of_Cars")
-        .rename({"Count": "Number_of_Cars"})
-        .select("Origin", "Destination", "Train_Type", "Hour", "Number_of_Cars")
+        .pipe(utilities.allocateItems, grouping_vars=["Origin", "Destination", "Train_Type", "Day_Order"], count_target=demand_col)
+        .drop(demand_col)
+        .rename({"Count": demand_col})
+        .select("Origin", "Destination", "Train_Type", "Hour", "Number_of_Days", demand_col)
     )
+    return pl.concat([
+        one_week,
+        one_week.with_columns(pl.col("Hour").add(24*7)),
+        one_week.with_columns(pl.col("Hour").add(24*7*2))
+    ])
     
 def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
     if all(item in loco_info.columns for item in [
@@ -105,7 +142,8 @@ def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
 
 def build_locopool(
     config: planner_config.TrainPlannerConfig,
-    demand_file: Union[pl.DataFrame, Path, str],
+    demand_file: Union[pl.DataFrame, pl.LazyFrame, Path, str],
+    dispatch_schedule: Union[pl.DataFrame, pl.LazyFrame] = None,
     method: str = "tile",
     shares: List[float] = [],
     locomotives_per_node: int = None
@@ -123,21 +161,49 @@ def build_locopool(
     """
     config.loco_info = append_loco_info(config.loco_info)
     loco_types = list(config.loco_info.loc[:,'Locomotive_Type'])
-    demand, node_list = load_freight_demand(demand_file)
+    demand, node_list = load_freight_demand(demand_file, config)
     #TODO: handle different train types (or mixed train types?)
     
     num_nodes = len(node_list)
     if locomotives_per_node is None:
-        num_ods = demand.height
-        cars_per_od = demand.get_column("Number_of_Cars").mean()
+        num_ods = demand.select("Origin", "Destination").unique().height
+        if "Number_of_Cars" in demand.collect_schema():
+            cars_per_od = (demand
+                .group_by("Origin","Destination")
+                    .agg(pl.col("Number_of_Cars").sum())
+                .get_column("Number_of_Cars").mean()
+            )
+        elif "Number_of_Containers" in demand.collect_schema():
+            cars_per_od = (demand
+                .group_by("Origin","Destination")
+                    .agg(pl.col("Number_of_Containers").sum())
+                .get_column("Number_of_Containers").mean()
+            ) / config.containers_per_car
+        else:
+            assert("No valid columns in demand DataFrame")
         if config.single_train_mode:
             initial_size = math.ceil(cars_per_od / min(config.cars_per_locomotive.values())) 
             rows = initial_size
         else:
             num_destinations_per_node = num_ods*1.0 / num_nodes*1.0
-            initial_size = math.ceil((cars_per_od / min(config.cars_per_locomotive.values())) *
+            initial_size_demand = math.ceil((cars_per_od / min(config.cars_per_locomotive.values())) *
                                     num_destinations_per_node)  # number of locomotives per node
-            rows = initial_size * num_nodes  # number of locomotives in total
+            initial_size_hp = 0
+            if dispatch_schedule is not None:
+                # Compute the 24-hour window with the most total locomotives needed 
+                # (assuming each loco is only dispatched once in a given day)
+                loco_mass = config.loco_info['Loco_Mass_Tons'].mean()
+                hp_per_ton = config.hp_required_per_ton['Default'][dispatch_schedule.select(pl.col("Train_Type").mode()).item()]
+                hp_per_loco = config.loco_info['HP'].mean() - loco_mass * hp_per_ton
+                initial_size_hp = (dispatch_schedule
+                    .with_columns((pl.col("Hour") // 24).cast(pl.Int32).alias("Day"),
+                                    pl.col("HP_Required").truediv(hp_per_loco).ceil().alias("Locos_Per_Dispatch"))
+                    .group_by("Day", "Origin")
+                        .agg(pl.col("Locos_Per_Dispatch").ceil().sum().alias("Locos_Per_Day_Per_Origin"))
+                    .select(pl.col("Locos_Per_Day_Per_Origin").max().cast(pl.Int64)).item()
+                )
+            initial_size = max(initial_size_demand, initial_size_hp)
+            rows = initial_size * num_nodes # number of locomotives in total
     else:
         initial_size = locomotives_per_node
         rows = locomotives_per_node * num_nodes
diff --git a/python/altrios/train_planner/planner.py b/python/altrios/train_planner/planner.py
index a7da1cb1..bcfe9e59 100644
--- a/python/altrios/train_planner/planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -272,7 +272,6 @@ def run_train_planner(
     network: List[alt.Link],
     loco_pool: pl.DataFrame,
     refuelers: pl.DataFrame,
-    simulation_days: int,
     scenario_year: int,
     train_type: alt.TrainType = alt.TrainType.Freight, 
     config: planner_config.TrainPlannerConfig = planner_config.TrainPlannerConfig(),
@@ -294,30 +293,17 @@ def run_train_planner(
     network:
     loco_pool:
     refuelers:
-    simulation_days:
     config: Object storing train planner configuration paramaters
     demand_file: 
     Outputs:
     ----------
     """
     config.loco_info = data_prep.append_loco_info(config.loco_info)
-    demand, node_list = data_prep.load_freight_demand(demand_file)
-
-    if refuelers is None: 
-        refuelers = data_prep.build_refuelers(
-            node_list, 
-            loco_pool,
-            config.refueler_info, 
-            config.refuelers_per_incoming_corridor)
-        
-    if network_charging_guidelines is None: 
-        network_charging_guidelines = pl.read_csv(alt.resources_root() / "networks" / "network_charging_guidelines.csv")
-
+    demand, node_list = data_prep.load_freight_demand(demand_file, config)
+    
     if config.return_demand_generators is None:
         config.return_demand_generators = train_demand_generators.get_default_return_demand_generators()
 
-    refuelers, loco_pool = data_prep.append_charging_guidelines(refuelers, loco_pool, demand, network_charging_guidelines)
-    
     freight_type_to_car_type = {}
     for rv in rail_vehicles: 
         if rv.freight_type in freight_type_to_car_type:
@@ -336,15 +322,46 @@ def run_train_planner(
             .with_columns(pl.col("index").mul(24.0).alias("Hour"))
             .drop("index")
         )
-    else:
-        demand_returns = train_demand_generators.generate_return_demand(demand, config)
+    else: 
+        demand_returns = pl.DataFrame()
         demand_rebalancing = pl.DataFrame()
-        if demand.filter(pl.col("Train_Type").str.contains("Manifest")).height > 0:
-            demand_rebalancing = train_demand_generators.generate_manifest_rebalancing_demand(demand, node_list, config)
-        demand = train_demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, freight_type_to_car_type, config)
+        if (config.dispatch_scheduler is None) and ("Hour" in demand.collect_schema()):
+            if "Number_of_Containers" in demand.collect_schema():
+                demand = (demand
+                    .group_by("Origin", "Destination", "Train_Type")
+                        .agg(pl.col("Number_of_Containers").sum())
+                    .with_columns(pl.col("Number_of_Containers").truediv(config.containers_per_car).ceil().alias("Number_of_Cars"))
+                )
+            else:
+                demand = (demand
+                    .group_by("Origin", "Destination", "Train_Type")
+                        .agg(pl.col("Number_of_Cars").sum())
+                )
+        if "Hour" not in demand.schema:
+            demand_returns = train_demand_generators.generate_return_demand(demand, config)
+            if demand.filter(pl.col("Train_Type").str.contains("Manifest")).height > 0:
+                demand_rebalancing = train_demand_generators.generate_manifest_rebalancing_demand(demand, node_list, config)
+                
         if config.dispatch_scheduler is None:
+            demand = train_demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, freight_type_to_car_type, config)
             config.dispatch_scheduler = schedulers.generate_dispatch_details
-        dispatch_schedule = config.dispatch_scheduler(demand, rail_vehicles, simulation_days, config)
+
+        dispatch_schedule = config.dispatch_scheduler(demand, rail_vehicles, config)
+   
+    if loco_pool is None:
+        loco_pool = data_prep.build_locopool(config=config, demand_file=demand, dispatch_schedule=dispatch_schedule)
+
+    if refuelers is None: 
+        refuelers = data_prep.build_refuelers(
+            node_list, 
+            loco_pool,
+            config.refueler_info, 
+            config.refuelers_per_incoming_corridor)
+        
+    if network_charging_guidelines is None: 
+        network_charging_guidelines = pl.read_csv(alt.resources_root() / "networks" / "network_charging_guidelines.csv")
+
+    refuelers, loco_pool = data_prep.append_charging_guidelines(refuelers, loco_pool, demand, network_charging_guidelines)
 
     final_departure = dispatch_schedule.get_column("Hour").max()
     train_consist_plan = pl.DataFrame(schema=
@@ -450,7 +467,7 @@ def run_train_planner(
                     slts = tsb.make_speed_limit_train_sim(
                         location_map=location_map, 
                         save_interval=None, 
-                        simulation_days=simulation_days, 
+                        simulation_days=config.simulation_days, 
                         scenario_year=scenario_year
                     )
 
@@ -561,6 +578,7 @@ def run_train_planner(
         str(alt.resources_root() / "networks/Taconite-NoBalloon.yaml")
     )
     config = planner_config.TrainPlannerConfig()
+    config.simulation_days=defaults.SIMULATION_DAYS + 2 * defaults.WARM_START_DAYS
     loco_pool = data_prep.build_locopool(config, defaults.DEMAND_FILE)
     demand, node_list = data_prep.load_freight_demand(defaults.DEMAND_FILE)
     refuelers = data_prep.build_refuelers(
@@ -575,6 +593,5 @@ def run_train_planner(
         network=network,
         loco_pool=loco_pool,
         refuelers=refuelers,
-        simulation_days=defaults.SIMULATION_DAYS + 2 * defaults.WARM_START_DAYS,
         scenario_year=defaults.BASE_ANALYSIS_YEAR,
         config=config)
diff --git a/python/altrios/train_planner/planner_config.py b/python/altrios/train_planner/planner_config.py
index e4bbaaee..54c86685 100644
--- a/python/altrios/train_planner/planner_config.py
+++ b/python/altrios/train_planner/planner_config.py
@@ -22,7 +22,7 @@ class TrainPlannerConfig:
     - `cars_per_locomotive`: Heuristic scaling factor used to size number of locomotives needed based on demand.
     - `cars_per_locomotive_fixed`: If `True`, `cars_per_locomotive` overrides `hp_per_ton` calculations used for dispatching decisions.
     - `refuelers_per_incoming_corridor`: Heuristic scaling factor used to scale number of refuelers needed at each node based on number of incoming corridors.
-    - `stack_type`: Type of stacking (applicable only for intermodal containers)
+    - `containers_per_car`: Containers stacked on each car (applicable only for intermodal containers)
     - `require_diesel`: `True` to require each consist to have at least one diesel locomotive.
     - `manifest_empty_return_ratio`: `Dict`
     - `drag_coeff_function`: `Dict`
@@ -32,6 +32,7 @@ class TrainPlannerConfig:
     - `refueler_info`: `Dict`
     - `return_demand_generators`: `Dict`
     """
+    simulation_days: int = 21
     single_train_mode: bool = False
     min_cars_per_train: Dict = field(default_factory = lambda: {
         "Default": 60
@@ -44,7 +45,7 @@ class TrainPlannerConfig:
     })
     cars_per_locomotive_fixed: bool = False
     refuelers_per_incoming_corridor: int = 4
-    stack_type: str = "double"
+    containers_per_car: int = 2
     require_diesel: bool = False
     manifest_empty_return_ratio: float = 0.6
     hp_required_per_ton: Dict = field(default_factory = lambda: {
diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index 7bd469b3..bf725747 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -150,6 +150,7 @@ def find_minimum_waiting_time(
     num_iterations: int, 
     demand_hourly: pl.DataFrame, 
     border_time_list: list, 
+    min_num_cars_per_train: int,
     target_num_cars_per_train: int
 ) -> pl.DataFrame:
     """
@@ -158,16 +159,16 @@ def find_minimum_waiting_time(
     for i in range(len(border_time_list)):
         od_pair_loop = border_time_list[i][0]
         reverse_pair = "-".join(od_pair_loop.split("-")[::-1])
-        directional_total_containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Containers"].sum()
-        reverse_total_containers = demand_hourly.filter(pl.col("OD_Pair") == reverse_pair)["Number_of_Containers"].sum()
-        if directional_total_containers > reverse_total_containers:
-            empty_containers = directional_total_containers - reverse_total_containers
+        directional_total_cars = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Cars"].sum()
+        reverse_total_cars = demand_hourly.filter(pl.col("OD_Pair") == reverse_pair)["Number_of_Cars"].sum()
+        if directional_total_cars > reverse_total_cars:
+            empty_containers = directional_total_cars - reverse_total_cars
             empty_containers_o_d = reverse_pair
         else:
-            empty_containers = reverse_total_containers - directional_total_containers
+            empty_containers = reverse_total_cars - directional_total_cars
             empty_containers_o_d = od_pair_loop
-        print(f"total containers for {od_pair_loop} is {directional_total_containers}")
-        print(f"reverse_total_containers for {reverse_pair} is {reverse_total_containers}")
+        print(f"total cars for {od_pair_loop} is {directional_total_cars}")
+        print(f"reverse_total_cars for {reverse_pair} is {reverse_total_cars}")
         #print(f"empty_containers for {empty_containers_o_d} is {empty_containers}")
     print(f"empty_containers for {empty_containers_o_d} is {empty_containers}")
     print(f"There are {len(border_time_list[0])-1} trains to dispatch")
@@ -193,7 +194,7 @@ def find_minimum_waiting_time(
         #print(f"origin is {origin}")
         #print(f"destination is {destination}")
         #print(demand)
-        total_containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Containers"].sum()
+        total_containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Cars"].sum()
         for i in range(2, num_iterations):
             if total_containers - total_dispatched == 0:
                 dispatched_list.append(0.0)
@@ -310,13 +311,6 @@ def formatScheduleColumns(
     df: Union[pl.DataFrame, pl.LazyFrame],
     config: planner_config.TrainPlannerConfig
 ) -> Union[pl.DataFrame, pl.LazyFrame]: 
-    containers_per_car = 0.0
-    if config.stack_type == "single":
-        containers_per_car = 1.0
-    elif config.stack_type == "double":
-        containers_per_car = 2.0
-    else:
-        assert(f'Unhandled container stack type: {config.stack_type}')
     return (df
         .with_columns(
             (pl.col("Tons_Per_Car_Loaded").mul("Number_of_Cars_Loaded") + pl.col("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")).alias("Tons_Per_Train"),
@@ -324,11 +318,11 @@ def formatScheduleColumns(
                 pl.col("HP_Required_Per_Ton_Empty").mul("Tons_Per_Car_Empty").mul("Number_of_Cars_Empty")
                 ).alias("HP_Required"),
             pl.when(pl.col("Train_Type").str.contains("Intermodal"))
-                .then(pl.col("Number_of_Cars_Loaded").mul(containers_per_car))
+                .then(pl.col("Number_of_Cars_Loaded").mul(config.containers_per_car))
                 .otherwise(0)
                 .alias("Containers_Loaded"),
             pl.when(pl.col("Train_Type").str.contains("Intermodal"))
-                .then(pl.col("Number_of_Cars_Empty").mul(containers_per_car))
+                .then(pl.col("Number_of_Cars_Empty").mul(config.containers_per_car))
                 .otherwise(0)
                 .alias("Containers_Empty"),
         )
@@ -341,67 +335,135 @@ def formatScheduleColumns(
         .sort(["Hour","Origin","Destination","Train_Type"])
     )
 
-
-def calculate_dispatch_data(total_cars, target_num_cars, label, cars, max_min_cars):
-    remaining_cars = total_cars % target_num_cars
-    min_num_cars = (
-        total_cars // target_num_cars + (1 if remaining_cars > 0 else 0)
+def calculate_dispatch_data(
+    total_containers, 
+    target_num_cars_per_train,  
+    od_pair, 
+    demand_hourly, 
+    max_min_trains,
+    containers_per_car):
+    remaining_containers = total_containers % (target_num_cars_per_train * containers_per_car)
+    num_trains = (
+        total_containers // (target_num_cars_per_train * containers_per_car) + (1 if remaining_containers > 0 else 0)
     )
     
     ## Apply the maximum min_num_cars value
-    min_num_cars = int(max(min_num_cars, max_min_cars))
+    num_trains = int(max(num_trains, max_min_trains))
 
-    border_list = []
-    border_time_list = []
-    dispatched_list = []
+    accumulated_arrivals = (demand_hourly
+        .sort("Hour")
+        .with_columns(pl.col("Number_of_Containers").cum_sum().floordiv(containers_per_car).alias("Accumulated_Arrivals"))
+        .filter(pl.col("Accumulated_Arrivals") - pl.col("Accumulated_Arrivals").shift(1).fill_null(0.0) > 0)
+        .select("Hour", "Accumulated_Arrivals")
+    )
 
-    if remaining_cars == 0:
-        border = total_cars / min_num_cars
-    else:
-        border = math.ceil(total_cars / min_num_cars)
-        
-    containers_left = total_cars
-    for _ in range(min_num_cars):
-        if containers_left >= border:
-            border_list.append(border)
-            containers_left -= border
-        else:
-            border_list.append(containers_left)
-            break
+    planned_train_sizes = (
+        pl.DataFrame({
+            "Group": [1] * num_trains, #Unused, just needed for allocateIntegerEvenly to work,
+            "Train_ID": list(range(num_trains)),
+            "Cars": [total_containers / containers_per_car] * num_trains
+        })
+        # Divide containers into trains as evenly as possible
+        .pipe(utilities.allocateIntegerEvenly, target="Cars", grouping_vars = ["Group"])
+        .with_columns(pl.col("Cars").cum_sum().alias("Cumulative_Cars"))
+        .select("Train_ID", "Cars", "Cumulative_Cars")
+        #.join_where(accumulated_arrivals, pl.col("Cumulative_Cars") <= pl.col("Accumulated_Arrivals"))
+        #.sort("Train_ID", "Hour")
+    )
 
-    cumulate_demand = 0
     p = 0
-    for i, car in enumerate(cars):
-        cumulate_demand += car
-        if p == len(border_list) - 1:
-            border_time_list.append(len(cars) - 1)
+    for arrival in accumulated_arrivals.iter_rows(named=True):
+        accumulated_demand += arrival['Accumulated_Arrivals']
+        if p == planned_train_sizes.height - 1:
+
+    for train in planned_train_sizes.partition_by("Train_ID"):
+        dispatch_time = train.filter()
+
+    train_dispatch_times = []
+    dispatched_container_counts = []
+    accumulated_demand = 0
+    p = 0
+    for i, container_count in enumerate(containers):
+        accumulated_demand += container_count
+        if p == len(planned_container_counts) - 1:
+            train_dispatch_times.append(len(containers) - 1)
             break
-        if cumulate_demand >= border_list[p]:
-            border_time_list.append(i)
-            dispatched_list.append(cumulate_demand)
-            total_cars -= cumulate_demand
-            cumulate_demand = 0
+        if accumulated_demand >= planned_container_counts[p]:
+            train_dispatch_times.append(i)
+            dispatched_container_counts.append(accumulated_demand)
+            total_containers -= accumulated_demand
+            accumulated_demand = 0
             p += 1
-            if p >= len(border_list):
+            if p >= len(planned_container_counts):
                 break
-    if len(border_time_list) < len(border_list):
-        for _ in range(len(border_list) - len(border_time_list)):
-            border_time_list.append(border_time_list[-1]+_+1)
-    dispatched_list.append(total_cars)
-    if len(dispatched_list) < len(border_list):
-        for _ in range(len(border_list) - len(dispatched_list)):
-            dispatched_list.append(0)
+    if len(train_dispatch_times) < len(planned_container_counts):
+        for _ in range(len(planned_container_counts) - len(train_dispatch_times)):
+            train_dispatch_times.append(train_dispatch_times[-1]+_+1)
+    dispatched_container_counts.append(total_containers)
+    if len(dispatched_container_counts) < len(planned_container_counts):
+        for _ in range(len(planned_container_counts) - len(dispatched_container_counts)):
+            dispatched_container_counts.append(0)
     return {
-        "Border": border_list,
-        "Border_Times": border_time_list,
-        "Dispatched": dispatched_list,
+        "Border": planned_container_counts,
+        "Border_Times": train_dispatch_times,
+        "Dispatched": dispatched_container_counts,
     }
 
+# Define the main function to generate demand trains with the updated rule
+def generate_trains_deterministic_hourly(
+    demand_hourly: pl.DataFrame,
+    target_num_cars_per_train: int,
+    containers_per_car: int
+) -> pl.DataFrame:
+    grouped_data = (demand_hourly
+        .group_by("Origin", "Destination", "OD_Pair")
+            .agg(
+                pl.col("Number_of_Containers").sum().alias("Total_Containers"),
+                pl.col("Number_of_Containers").sum().mod(target_num_cars_per_train * containers_per_car).alias("Remaining_Containers")
+            )
+        .with_columns(
+            pl.col("Total_Containers").floordiv(target_num_cars_per_train * containers_per_car).add(pl.col("Remaining_Containers").gt(0)).alias("Min_Trains")
+        )
+        .with_columns(
+            pl.col("Min_Trains").max().over(pl.concat_str(
+                pl.min_horizontal(pl.col("Origin","Destination")),
+                pl.lit("_"),
+                pl.max_horizontal(pl.col("Origin","Destination")))
+                ).alias("Max_Min_Trains")
+        )
+    )
+
+    # Prepare a list to collect the results for all OD pairs
+    all_dispatch_data = []
+    # Step 4: Loop through each unique OD pair to calculate dispatch data
+    for row in grouped_data.iter_rows(named=True):
+        # Calculate dispatch data for the current OD pair with the updated rule
+        dispatch_data = calculate_dispatch_data(
+            total_containers = row['Total_Containers'], 
+            target_num_cars_per_train = target_num_cars_per_train, 
+            od_pair = row['OD_Pair'], 
+            containers = demand_hourly.filter(pl.col("OD_Pair") == row['OD_Pair']).sort("Hour"), 
+            max_min_trains =row['Max_Min_Trains'],
+            containers_per_car = containers_per_car)
+        
+        # Add the OD pair label to each entry in the dispatch data
+        dispatch_data_df = (pl.DataFrame(dispatch_data, strict=False)
+            .with_columns(
+                pl.lit(od_pair).alias("OD_Pair"),
+                pl.col("Border").cast(pl.Float64)
+            )
+        )
+
+        # Append the result to the list
+        all_dispatch_data.append(dispatch_data_df)
+
+    # Combine all dispatch data into a single DataFrame
+    final_dispatch_df = pl.concat(all_dispatch_data, how="diagonal_relaxed")
+    return final_dispatch_df
 
 def calculate_dispatches_deterministic_hourly(
-    demand: pl.DataFrame,
+    demand_hourly: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],
-    simulation_days: int,
     config: planner_config.TrainPlannerConfig
 ) -> pl.DataFrame:
     """
@@ -410,65 +472,15 @@ def calculate_dispatches_deterministic_hourly(
     ----------
     demand: `DataFrame` or `LazyFrame` representing origin-destination demands (number of trains).
     rail_vehicles: List of `altrios.RailVehicle` objects.
-    simulation_days: Number of days of simulation to run.
     config: `TrainPlannerConfig` object.
     Outputs:
     ----------
     Updated demand `DataFrame` or `LazyFrame` representing dispatches, each defined with an origin, destination, train type, number of (loaded and empty) cars, tonnage, and HP per ton requirement.
     """
-    # Define the main function to generate demand trains with the updated rule
-    def generate_trains(
-        demand_hourly: pl.DataFrame,
-        target_num_cars_per_train:int
-    ) -> pl.DataFrame:
-        demand_hourly = demand_hourly.with_columns((pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair"))
-        grouped_data = (demand_hourly
-            .group_by("OD_Pair")
-                .agg(
-                    pl.col("Number_of_Cars").sum().alias("Total_Cars"),
-                    pl.col("Number_of_Cars").sum().mod(target_num_cars_per_train).alias("Remaining_Cars")
-                )
-            .with_columns(
-                pl.col("Total_Cars").truediv(target_num_cars_per_train).round().add(pl.col("Remaining_Cars").gt(0)).alias("Max_Min_Cars")
-            )
-        )
-
-        # Prepare a list to collect the results for all OD pairs
-        all_dispatch_data = []
-        # Step 4: Loop through each unique OD pair to calculate dispatch data
-        for row in grouped_data.iter_rows(named=True):
-            od_pair = row['OD_Pair']  # This is the "OD_Pair" string like "Origin-Destination"
-            total_cars = row['Total_Cars']  # This is the "Total_Cars" value
-
-            # Filter cars list for the specific OD pair
-            cars = demand_hourly.filter(pl.col("OD_Pair") == od_pair).select("Number_of_Cars").to_series().to_list()
-
-            # Retrieve the max min_num_cars for this OD pair
-            max_min_cars = row['Max_Min_Cars']
-
-            # Calculate dispatch data for the current OD pair with the updated rule
-            dispatch_data = calculate_dispatch_data(total_cars, target_num_cars_per_train, od_pair, cars, max_min_cars)
-            
-            # Add the OD pair label to each entry in the dispatch data
-            dispatch_data_df = pl.DataFrame(dispatch_data, strict=False)
-            dispatch_data_df = dispatch_data_df.with_columns(pl.lit(od_pair).alias("OD_Pair"))
-
-            # Ensure the "Border" column exists and is cast to Float64
-            if "Border" in dispatch_data_df.columns:
-                dispatch_data_df = dispatch_data_df.with_columns(
-                    pl.col("Border").cast(pl.Float64)
-                )
-
-            # Append the result to the list
-            all_dispatch_data.append(dispatch_data_df)
-
-        # Combine all dispatch data into a single DataFrame
-        final_dispatch_df = pl.concat(all_dispatch_data, how="diagonal_relaxed")
-    
-        return final_dispatch_df
-
-    target_num_cars_per_train = config.target_cars_per_train['Default'] #TODO make this flexible
-    dispatch_df = generate_trains(demand,target_num_cars_per_train)
+    min_num_cars_per_train=config.min_cars_per_train['Intermodal_Loaded'] #TODO make this flexible
+    target_num_cars_per_train=config.target_cars_per_train['Intermodal_Loaded'] #TODO make this flexible 
+    demand_hourly = demand_hourly.with_columns((pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair"))
+    dispatch_df = generate_trains_deterministic_hourly(demand_hourly,target_num_cars_per_train, config.containers_per_car)
     od_border_time = []
     border_time = dispatch_df["Border_Times"].to_list() 
     od_pair_list = dispatch_df["OD_Pair"].to_list()
@@ -483,16 +495,16 @@ def generate_trains(
     border_time_list= [[key] + values for key, values in grouped_data.items()]
     num_iterations = len(border_time_list[0])
     schedule = find_minimum_waiting_time(num_iterations=num_iterations,
-        demand_hourly=demand,
+        demand_hourly=demand_hourly,
         border_time_list=border_time_list,
-        target_num_cars_per_train=target_num_cars_per_train 
+        min_num_cars_per_train=min_num_cars_per_train,
+        target_num_cars_per_train=target_num_cars_per_train
     )
     return schedule.pipe(formatScheduleColumns)
 
 def generate_dispatch_details(
     demand: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],
-    simulation_days: int,
     config: planner_config.TrainPlannerConfig
 ) -> pl.DataFrame:
     """
@@ -503,14 +515,13 @@ def generate_dispatch_details(
     ----------
     demand: `DataFrame` or `LazyFrame` representing origin-destination demands (number of trains).
     rail_vehicles: List of `altrios.RailVehicle` objects.
-    simulation_days: Number of days of simulation to run.
     config: `TrainPlannerConfig` object.
     Outputs:
     ----------
     schedule: Tabulated dispatching time for each demand pair for each train type
     in hours
     """
-    hours = simulation_days * 24
+    hours = config.simulation_days * 24
     grouping_vars = ["Origin", "Destination", "Train_Type"]
     return (demand
         .select(pl.exclude("Number_of_Trains").repeat_by("Number_of_Trains").explode())
diff --git a/python/altrios/train_planner/train_demand_generators.py b/python/altrios/train_planner/train_demand_generators.py
index ee0178e1..19d3ecea 100644
--- a/python/altrios/train_planner/train_demand_generators.py
+++ b/python/altrios/train_planner/train_demand_generators.py
@@ -78,13 +78,15 @@ def generate_return_demand_intermodal(demand_subset: Union[pl.LazyFrame, pl.Data
             pl.concat_str(pl.min_horizontal("Origin", "Destination"), pl.lit("_"), pl.max_horizontal("Origin", "Destination")).alias("OD")
         )
         .with_columns(
-            pl.col("Number_of_Cars").range().over("OD").name.suffix("_Return")
+            pl.col("Number_of_Cars", "Number_of_Containers").range().over("OD").name.suffix("_Return")
         )
         .filter(
-            pl.col("Number_of_Cars") == pl.col("Number_of_Cars").max().over("OD")
+            pl.col("Number_of_Cars") == pl.col("Number_of_Cars").max().over("OD"),
+            pl.col("Number_of_Cars_Return") > 0
         )
-        .drop("OD", "Number_of_Cars")
-        .rename({"Number_of_Cars_Return": "Number_of_Cars"})
+        .drop("OD", "Number_of_Cars", "Number_of_Containers")
+        .rename({"Number_of_Cars_Return": "Number_of_Cars",
+                 "Number_of_Containers_Return": "Number_of_Containers"})
     )
 
 def generate_return_demand(
@@ -104,18 +106,17 @@ def generate_return_demand(
     of returning the empty cars to their original nodes
     """
     demand_subsets = demand.partition_by("Train_Type", as_dict = True)
-    return_demand = []
+    return_demands = []
     for train_type, demand_subset in demand_subsets.items():
         train_type_label = train_type[0]
         if train_type_label in config.return_demand_generators:
             return_demand_generator = config.return_demand_generators[train_type_label]
-            temp = return_demand_generator(demand_subset, config)
-            print(temp)
-            return_demand.append(temp)
+            return_demand = return_demand_generator(demand_subset, config)
+            return_demands.append(return_demand)
         else:
             print(f'Return demand generator not implemented for train type: {train_type_label}')
 
-    demand_return = (pl.concat(return_demand, how="diagonal_relaxed")
+    demand_return = (pl.concat(return_demands, how="diagonal_relaxed")
         .filter(pl.col("Number_of_Cars") > 0)
     )
     return demand_return
@@ -288,8 +289,8 @@ def get_kg(veh):
             pl.when(pl.col("Car_Type").str.to_lowercase().str.contains("_empty"))
                 .then(pl.col("KG_Empty") / utilities.KG_PER_TON)
                 .otherwise(pl.col("KG") / utilities.KG_PER_TON)
-                .alias("Tons_Per_Car"),
-            pl.col("Car_Type").str.strip_suffix("_Loaded"))
+                .alias("Tons_Per_Car")
+        )
         .drop(["KG_Empty","KG"])
     )
 
@@ -379,7 +380,10 @@ def get_kg(veh):
                             pl.col("Number_of_Cars").floordiv("Cars_Per_Train_Min")
                         ])
                     ])
-                ).cast(pl.UInt32).alias("Number_of_Trains")
+                ).cast(pl.UInt32).alias("Number_of_Trains"),
+            pl.col("Number_of_Cars_Loaded").mul(config.containers_per_car).alias("Number_of_Containers_Loaded"),
+            pl.col("Number_of_Cars_Empty").mul(config.containers_per_car).alias("Number_of_Containers_Empty"),
+            pl.lit(config.simulation_days).alias("Number_of_Days")
         )
         .drop("Cars_Per_Train_Target_Loaded", "Cars_Per_Train_Target_Empty", "Cars_Per_Train_Min_Empty", "Cars_Per_Train_Min_Loaded")
     )

From f4df3922455fd8e6da113f32ffa2f0483734f6ec Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Sun, 19 Jan 2025 23:32:39 -0500
Subject: [PATCH 39/50] fixed calculate_dispatch_data for diesel

---
 python/altrios/train_planner/schedulers.py | 66 ++++++++++------------
 1 file changed, 30 insertions(+), 36 deletions(-)

diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index bf725747..a9bab16a 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -350,14 +350,15 @@ def calculate_dispatch_data(
     ## Apply the maximum min_num_cars value
     num_trains = int(max(num_trains, max_min_trains))
 
-    accumulated_arrivals = (demand_hourly
+    new_accumulated_carloads = (demand_hourly
         .sort("Hour")
-        .with_columns(pl.col("Number_of_Containers").cum_sum().floordiv(containers_per_car).alias("Accumulated_Arrivals"))
-        .filter(pl.col("Accumulated_Arrivals") - pl.col("Accumulated_Arrivals").shift(1).fill_null(0.0) > 0)
-        .select("Hour", "Accumulated_Arrivals")
+        .with_columns(pl.col("Number_of_Containers").cum_sum().floordiv(containers_per_car).alias("Accumulated_Carloads"))
+        .with_columns((pl.col("Accumulated_Carloads") - pl.col("Accumulated_Carloads").shift(1).fill_null(0.0)).alias("New_Carloads"))
+        .filter(pl.col("New_Carloads") > 0)
+        .select("Hour", "New_Carloads")
     )
 
-    planned_train_sizes = (
+    planned_train_lengths = (
         pl.DataFrame({
             "Group": [1] * num_trains, #Unused, just needed for allocateIntegerEvenly to work,
             "Train_ID": list(range(num_trains)),
@@ -365,48 +366,41 @@ def calculate_dispatch_data(
         })
         # Divide containers into trains as evenly as possible
         .pipe(utilities.allocateIntegerEvenly, target="Cars", grouping_vars = ["Group"])
-        .with_columns(pl.col("Cars").cum_sum().alias("Cumulative_Cars"))
-        .select("Train_ID", "Cars", "Cumulative_Cars")
-        #.join_where(accumulated_arrivals, pl.col("Cumulative_Cars") <= pl.col("Accumulated_Arrivals"))
-        #.sort("Train_ID", "Hour")
+        .sort("Train_ID")
+        .get_column("Cars")
+        .to_list()
     )
 
-    p = 0
-    for arrival in accumulated_arrivals.iter_rows(named=True):
-        accumulated_demand += arrival['Accumulated_Arrivals']
-        if p == planned_train_sizes.height - 1:
-
-    for train in planned_train_sizes.partition_by("Train_ID"):
-        dispatch_time = train.filter()
-
     train_dispatch_times = []
-    dispatched_container_counts = []
+    dispatched_train_lengths = []
     accumulated_demand = 0
     p = 0
-    for i, container_count in enumerate(containers):
-        accumulated_demand += container_count
-        if p == len(planned_container_counts) - 1:
-            train_dispatch_times.append(len(containers) - 1)
+    for arrival in new_accumulated_carloads.iter_rows(named=True):
+        accumulated_demand += arrival['New_Carloads']
+        if p == len(planned_train_lengths) - 1:
+            train_dispatch_times.append(new_accumulated_carloads.get_column("Hour").max())
             break
-        if accumulated_demand >= planned_container_counts[p]:
-            train_dispatch_times.append(i)
-            dispatched_container_counts.append(accumulated_demand)
-            total_containers -= accumulated_demand
+        if accumulated_demand >= planned_train_lengths[p]:
+            train_dispatch_times.append(arrival['Hour'])
+            dispatched_train_lengths.append(accumulated_demand)
+            total_containers -= (accumulated_demand * containers_per_car)
             accumulated_demand = 0
             p += 1
-            if p >= len(planned_container_counts):
+            if p >= len(planned_train_lengths):
                 break
-    if len(train_dispatch_times) < len(planned_container_counts):
-        for _ in range(len(planned_container_counts) - len(train_dispatch_times)):
+
+    if len(train_dispatch_times) < len(planned_train_lengths):
+        for _ in range(len(planned_train_lengths) - len(train_dispatch_times)):
             train_dispatch_times.append(train_dispatch_times[-1]+_+1)
-    dispatched_container_counts.append(total_containers)
-    if len(dispatched_container_counts) < len(planned_container_counts):
-        for _ in range(len(planned_container_counts) - len(dispatched_container_counts)):
-            dispatched_container_counts.append(0)
+    dispatched_train_lengths.append(total_containers / containers_per_car)
+    if len(dispatched_train_lengths) < len(planned_train_lengths):
+        for _ in range(len(planned_train_lengths) - len(dispatched_train_lengths)):
+            dispatched_train_lengths.append(0)
+
     return {
-        "Border": planned_container_counts,
+        "Border": planned_train_lengths,
         "Border_Times": train_dispatch_times,
-        "Dispatched": dispatched_container_counts,
+        "Dispatched": dispatched_train_lengths,
     }
 
 # Define the main function to generate demand trains with the updated rule
@@ -442,7 +436,7 @@ def generate_trains_deterministic_hourly(
             total_containers = row['Total_Containers'], 
             target_num_cars_per_train = target_num_cars_per_train, 
             od_pair = row['OD_Pair'], 
-            containers = demand_hourly.filter(pl.col("OD_Pair") == row['OD_Pair']).sort("Hour"), 
+            demand_hourly = demand_hourly.filter(pl.col("OD_Pair") == row['OD_Pair']).sort("Hour"), 
             max_min_trains =row['Max_Min_Trains'],
             containers_per_car = containers_per_car)
         

From 33b4a889942b440c8fce9532c26c35fe2eb948ba Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 20 Jan 2025 02:43:26 -0500
Subject: [PATCH 40/50] more partial fixes

---
 python/altrios/train_planner/data_prep.py     |  75 +++++-
 python/altrios/train_planner/planner.py       |   2 +-
 .../altrios/train_planner/planner_config.py   |   1 +
 python/altrios/train_planner/schedulers.py    | 224 ++++++++++--------
 .../train_planner/train_demand_generators.py  |  54 +----
 5 files changed, 197 insertions(+), 159 deletions(-)

diff --git a/python/altrios/train_planner/data_prep.py b/python/altrios/train_planner/data_prep.py
index 380cf730..bcf3b0ea 100644
--- a/python/altrios/train_planner/data_prep.py
+++ b/python/altrios/train_planner/data_prep.py
@@ -115,11 +115,14 @@ def prep_hourly_demand(
         .rename({"Count": demand_col})
         .select("Origin", "Destination", "Train_Type", "Hour", "Number_of_Days", demand_col)
     )
-    return pl.concat([
-        one_week,
-        one_week.with_columns(pl.col("Hour").add(24*7)),
-        one_week.with_columns(pl.col("Hour").add(24*7*2))
-    ])
+    return (
+        pl.concat([
+            one_week,
+            one_week.with_columns(pl.col("Hour").add(24*7)),
+            one_week.with_columns(pl.col("Hour").add(24*7*2))
+        ])
+        .sort("Origin", "Destination", "Train_Type", "Hour")
+    )
     
 def append_loco_info(loco_info: pd.DataFrame) -> pd.DataFrame:
     if all(item in loco_info.columns for item in [
@@ -197,7 +200,7 @@ def build_locopool(
                 hp_per_loco = config.loco_info['HP'].mean() - loco_mass * hp_per_ton
                 initial_size_hp = (dispatch_schedule
                     .with_columns((pl.col("Hour") // 24).cast(pl.Int32).alias("Day"),
-                                    pl.col("HP_Required").truediv(hp_per_loco).ceil().alias("Locos_Per_Dispatch"))
+                                    pl.col("HP_Required").truediv(hp_per_loco).ceil().mul(config.loco_pool_safety_factor).alias("Locos_Per_Dispatch"))
                     .group_by("Day", "Origin")
                         .agg(pl.col("Locos_Per_Dispatch").ceil().sum().alias("Locos_Per_Day_Per_Origin"))
                     .select(pl.col("Locos_Per_Day_Per_Origin").max().cast(pl.Int64)).item()
@@ -400,3 +403,63 @@ def configure_rail_vehicles(
 
     rv_to_use = [vehicle for vehicle in available_rail_vehicles if vehicle.freight_type in freight_types]
     return rv_to_use, n_cars_by_type
+
+def appendTonsAndHP(
+    df: Union[pl.DataFrame, pl.LazyFrame],
+    rail_vehicles,
+    freight_type_to_car_type,
+    config
+) -> Union[pl.DataFrame, pl.LazyFrame]:
+    
+    hp_per_ton = pl.concat([
+        (pl.DataFrame(this_dict)
+            .melt(variable_name="Train_Type", value_name="HP_Required_Per_Ton") 
+            .with_columns(pl.lit(this_item).alias("O_D"))
+            .with_columns(pl.col("O_D").str.split("_").list.first().alias("Origin"),
+                        pl.col("O_D").str.split("_").list.last().alias("Destination"))
+        )
+        for this_item, this_dict in config.hp_required_per_ton.items()
+    ], how="horizontal_relaxed")
+
+    def get_kg_empty(veh):
+        return veh.mass_static_base_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
+    def get_kg(veh):
+        return veh.mass_static_base_kilograms + veh.mass_freight_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
+            
+    tons_per_car = (
+        pl.DataFrame({"Car_Type": pl.Series([rv.car_type for rv in rail_vehicles]),
+                        "KG": [get_kg(rv) for rv in rail_vehicles],
+                        "KG_Empty": [get_kg_empty(rv) for rv in rail_vehicles]
+        })
+        .with_columns(
+            pl.when(pl.col("Car_Type").str.to_lowercase().str.contains("_empty"))
+                .then(pl.col("KG_Empty") / utilities.KG_PER_TON)
+                .otherwise(pl.col("KG") / utilities.KG_PER_TON)
+                .alias("Tons_Per_Car")
+        )
+        .drop(["KG_Empty","KG"])
+    )
+
+    return (df
+        .with_columns(
+            pl.when(pl.col("Train_Type").str.contains(pl.lit("_Empty")))
+                        .then(pl.col("Train_Type"))
+                        .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Loaded"), pl.lit("_Loaded")))
+                        .replace_strict(freight_type_to_car_type)
+                        .alias("Car_Type")
+        )
+        .join(tons_per_car, how="left", on="Car_Type")
+        # Merge on OD-specific hp_per_ton if the user specified any
+        .join(hp_per_ton.filter(pl.col("O_D") != pl.lit("Default")).drop("O_D"), 
+            on=[pl.col("Origin"), pl.col("Destination"), pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")], 
+            how="left")
+        # Second, merge on defaults per train type
+        .join(hp_per_ton.filter((pl.col("O_D") =="Default")).drop(["O_D","Origin","Destination"]),
+            on=[pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")],
+            how="left",
+            suffix="_Default")
+        .with_columns(
+            pl.coalesce("HP_Required_Per_Ton", "HP_Required_Per_Ton_Default").alias("HP_Required_Per_Ton")
+        ) 
+        .drop(cs.ends_with("_Default") | cs.ends_with("_right"))
+    )
\ No newline at end of file
diff --git a/python/altrios/train_planner/planner.py b/python/altrios/train_planner/planner.py
index bcfe9e59..1f502c69 100644
--- a/python/altrios/train_planner/planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -346,7 +346,7 @@ def run_train_planner(
             demand = train_demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, freight_type_to_car_type, config)
             config.dispatch_scheduler = schedulers.generate_dispatch_details
 
-        dispatch_schedule = config.dispatch_scheduler(demand, rail_vehicles, config)
+        dispatch_schedule = config.dispatch_scheduler(demand, rail_vehicles, freight_type_to_car_type, config)
    
     if loco_pool is None:
         loco_pool = data_prep.build_locopool(config=config, demand_file=demand, dispatch_schedule=dispatch_schedule)
diff --git a/python/altrios/train_planner/planner_config.py b/python/altrios/train_planner/planner_config.py
index 54c86685..679047fd 100644
--- a/python/altrios/train_planner/planner_config.py
+++ b/python/altrios/train_planner/planner_config.py
@@ -48,6 +48,7 @@ class TrainPlannerConfig:
     containers_per_car: int = 2
     require_diesel: bool = False
     manifest_empty_return_ratio: float = 0.6
+    loco_pool_safety_factor: float = 1.1
     hp_required_per_ton: Dict = field(default_factory = lambda: {
         "Default": {
         "Unit": 2.0,
diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index a9bab16a..dfbc2549 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -1,29 +1,29 @@
 import math
-from typing import List, Union
+from typing import List, Union, Dict
 from collections import defaultdict
 import polars as pl
 import altrios as alt
 from altrios import utilities
-from altrios.train_planner import planner_config
+from altrios.train_planner import planner_config, data_prep
 
 def calculate_waiting_time_single_dispatch(
     cumulative_demand_control: int, 
     last_dispatch: int, 
-    demand_hourly: pl.DataFrame, 
+    new_accumulated_carloads: pl.DataFrame, 
     dispatch_hour: int, 
     remaining_demand_list: pl.DataFrame, 
     remaining_demand_list_control: pl.DataFrame, 
     search_range: int,
     od_pair_loop: str,
     min_num_cars_per_train: int,
-    target_num_cars_per_train: int
-    
+    target_num_cars_per_train: int, 
+    config: planner_config.TrainPlannerConfig
 ) -> tuple:
     """
     Calculate the waiting time for a single dispatch using Polars DataFrames.
     """
     # Initialize variables
-    direction_demand = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)
+    direction_demand = new_accumulated_carloads.filter(pl.col("OD_Pair") == od_pair_loop)
     remaining_demand = 0
     total_waiting_time = 0
     total_waiting_time_before_dispatch = 0
@@ -32,13 +32,13 @@ def calculate_waiting_time_single_dispatch(
     remaining_demand_list = remaining_demand_list_control.clone() #Copy to avoid modifying original
     remaining_demand_tem = []
     # Calculate cumulative demand up to the dispatch hour
-    end_hour = min(dispatch_hour + 1, len(demand_hourly))
+    end_hour = min(dispatch_hour + 1, direction_demand.get_column("Hour").max())
     hourly_demand = direction_demand.slice(last_dispatch, end_hour - last_dispatch)
-    cumulative_demand += hourly_demand["Number_of_Containers"].sum()
+    cumulative_demand += hourly_demand["Number_of_Cars"].sum()
     
     if remaining_demand_list.is_empty():
         hourly_demand = hourly_demand.with_columns(
-            ((pl.col("Number_of_Containers") * (dispatch_hour - pl.col("Hour"))).alias("Waiting_Time"))
+            ((pl.col("Number_of_Cars") * (dispatch_hour - pl.col("Hour"))).alias("Waiting_Time"))
         )
         total_waiting_time_before_dispatch = hourly_demand["Waiting_Time"].sum()
         total_waiting_time += hourly_demand["Waiting_Time"].sum()
@@ -46,7 +46,7 @@ def calculate_waiting_time_single_dispatch(
         # If there is remaining demand, calculate waiting time for new and remaining demand
     else:
         hourly_demand = hourly_demand.with_columns(
-            ((pl.col("Number_of_Containers") * (dispatch_hour - pl.col("Hour"))).alias("Waiting_Time"))
+            ((pl.col("Number_of_Cars") * (dispatch_hour - pl.col("Hour"))).alias("Waiting_Time"))
         )
         total_waiting_time_before_dispatch = hourly_demand["Waiting_Time"].sum()
         total_waiting_time += hourly_demand["Waiting_Time"].sum()
@@ -68,15 +68,15 @@ def calculate_waiting_time_single_dispatch(
             # Update remaining demand list if there's no prior remaining demand
             if remaining_demand_list.height == 0:
                 #remaining_demand_tem = []
-                for row in hourly_demand.iter_rows():
+                for row in hourly_demand.iter_rows(named=True):
                     # Number_of_Containers is located at the 4th column
-                    if row[3] > 0:
-                        if dispatched_split >= row[3]:
-                            dispatched_split -= row[3]
+                    if row['Number_of_Cars'] > 0:
+                        if dispatched_split >= row['Number_of_Cars']:
+                            dispatched_split -= row['Number_of_Cars']
                         else:
-                            remaining_demand_for_hour = row[3] - dispatched_split
+                            remaining_demand_for_hour = row['Number_of_Cars'] - dispatched_split
                             # Hour is located at the 5th column
-                            remaining_demand_tem.append((remaining_demand_for_hour,row[4]))
+                            remaining_demand_tem.append((remaining_demand_for_hour,row['Hour']))
                             # dispatched_split stop working from this hour to the end of this loop behavior
                             dispatched_split = 0
                 # Filter `remaining_demand_tem` to include only positive Remaining_Demand values
@@ -91,25 +91,16 @@ def calculate_waiting_time_single_dispatch(
                 cumulative_demand = remaining_demand            
             else:                
                 # Prepare the cumulative transformation approach
-                for row in remaining_demand_list.iter_rows():
-                    if dispatched_split >= row[0]:
-                        dispatched_split -= row[0]                       
-                        list_tem = list(row)
-                        list_tem[0] = 0
-                        row = tuple(list_tem)                     
-                    else:
-                        row[0] -= dispatched_split
-                        dispatched_split = 0
-                    remaining_demand_list = remaining_demand_list.filter(pl.col("Remaining_Demand") > 0)          
+                dispatched_split -= min(dispatched_split, remaining_demand_list.get_column("Remaining_Demand").sum())
                 # If there is still dispatched capacity left, apply it to new demand within the range
                 if dispatched_split > 0:
-                    for row in hourly_demand.iter_rows():
-                        if row[3] > 0:
-                            if dispatched_split >= row[3]:
-                                dispatched_split -= row[3]
+                    for row in hourly_demand.iter_rows(named=True):
+                        if row['Number_of_Cars'] > 0:
+                            if dispatched_split >= row['Number_of_Cars']:
+                                dispatched_split -= row['Number_of_Cars']
                             else:
-                                remaining_demand_for_hour = row[3] - dispatched_split
-                                remaining_demand_tem.append((remaining_demand_for_hour,row[4]))
+                                remaining_demand_for_hour = row['Number_of_Cars'] - dispatched_split
+                                remaining_demand_tem.append((remaining_demand_for_hour,row['Hour']))
                                 dispatched_split = 0
                 remaining_demand_list = pl.DataFrame({"Remaining_Demand": [rd[0] for rd in remaining_demand_tem],"Hour": [rd[1] for rd in remaining_demand_tem]}).filter(pl.col("Remaining_Demand") > 0)
                 cumulative_demand = remaining_demand
@@ -122,13 +113,13 @@ def calculate_waiting_time_single_dispatch(
         cumulative_demand = cumulative_demand
 
     # Filter demand for future hours in the specified search range
-    future_demand = demand_hourly.filter(
-        (pl.col("Hour") > dispatch_hour) & (pl.col("Hour") < min(last_dispatch + search_range, len(demand_hourly)))
+    future_demand = direction_demand.filter(
+        (pl.col("Hour") > dispatch_hour) & (pl.col("Hour") < min(last_dispatch + search_range, direction_demand.get_column("Hour").max()))
     )
 
     # Calculate waiting time for each future hour
     future_demand = future_demand.with_columns(
-        ((last_dispatch + search_range - 1 - pl.col("Hour")) * pl.col("Number_of_Containers")).alias("Waiting_Time")
+        ((last_dispatch + search_range - 1 - pl.col("Hour")) * pl.col("Number_of_Cars")).alias("Waiting_Time")
     )
     
     # Sum up all waiting times for future demand
@@ -151,28 +142,30 @@ def find_minimum_waiting_time(
     demand_hourly: pl.DataFrame, 
     border_time_list: list, 
     min_num_cars_per_train: int,
-    target_num_cars_per_train: int
+    target_num_cars_per_train: int,
+    config: planner_config.TrainPlannerConfig
 ) -> pl.DataFrame:
     """
     Find the minimum waiting time for dispatches using Polars DataFrame.
     """
+    group_cols = ["Origin", "Destination", "OD_Pair", "Train_Type"]
+    new_accumulated_carloads = get_new_accumulated_carloads(demand_hourly, group_cols, containers_per_car = config.containers_per_car).rename({"New_Carloads": "Number_of_Cars"})
     for i in range(len(border_time_list)):
         od_pair_loop = border_time_list[i][0]
         reverse_pair = "-".join(od_pair_loop.split("-")[::-1])
-        directional_total_cars = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Cars"].sum()
-        reverse_total_cars = demand_hourly.filter(pl.col("OD_Pair") == reverse_pair)["Number_of_Cars"].sum()
+        directional_total_cars = new_accumulated_carloads.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Cars"].sum()
+        reverse_total_cars = new_accumulated_carloads.filter(pl.col("OD_Pair") == reverse_pair)["Number_of_Cars"].sum()
         if directional_total_cars > reverse_total_cars:
-            empty_containers = directional_total_cars - reverse_total_cars
-            empty_containers_o_d = reverse_pair
+            empty_cars = directional_total_cars - reverse_total_cars
+            empty_cars_o_d = reverse_pair
         else:
-            empty_containers = reverse_total_cars - directional_total_cars
-            empty_containers_o_d = od_pair_loop
+            empty_cars = reverse_total_cars - directional_total_cars
+            empty_cars_o_d = od_pair_loop
         print(f"total cars for {od_pair_loop} is {directional_total_cars}")
         print(f"reverse_total_cars for {reverse_pair} is {reverse_total_cars}")
-        #print(f"empty_containers for {empty_containers_o_d} is {empty_containers}")
-    print(f"empty_containers for {empty_containers_o_d} is {empty_containers}")
+        #print(f"empty_containers for {empty_cars_o_d} is {empty_containers}")
+    print(f"empty_containers for {empty_cars_o_d} is {empty_cars}")
     print(f"There are {len(border_time_list[0])-1} trains to dispatch")
-    empty_return = []
     final_dispatch_rows = []
     for j in range(len(border_time_list)):
         start_hour = 0
@@ -194,9 +187,9 @@ def find_minimum_waiting_time(
         #print(f"origin is {origin}")
         #print(f"destination is {destination}")
         #print(demand)
-        total_containers = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Cars"].sum()
+        total_cars = new_accumulated_carloads.filter(pl.col("OD_Pair") == od_pair_loop)["Number_of_Cars"].sum()
         for i in range(2, num_iterations):
-            if total_containers - total_dispatched == 0:
+            if total_cars - total_dispatched == 0:
                 dispatched_list.append(0.0)
             search_range = border_time_list[j][i] - start_hour
             # DataFrame to accumulate dispatch hour info
@@ -210,7 +203,7 @@ def find_minimum_waiting_time(
             }) 
             for dispatch_hour in range(start_hour, start_hour + search_range):
                 total_waiting_time_before_dispatch, total_waiting_time, remaining_demand_list, cumulative_demand, dispatched = calculate_waiting_time_single_dispatch(
-                    cumulative_demand_control, last_dispatch, demand_hourly, dispatch_hour, remaining_demand_list_control, remaining_demand_list_control.clone(), search_range,od_pair_loop,min_num_cars_per_train, target_num_cars_per_train
+                    cumulative_demand_control, last_dispatch, new_accumulated_carloads, dispatch_hour, remaining_demand_list_control, remaining_demand_list_control.clone(), search_range,od_pair_loop,min_num_cars_per_train, target_num_cars_per_train, config
                 )
                 # Append data for each dispatch hour, ensuring consistent types
                 new_row = pl.DataFrame({
@@ -256,13 +249,12 @@ def find_minimum_waiting_time(
             # Add the dispatch hour to the list
             dispatch_time.append(min_waiting_time_hour) 
                     
-        remaining_to_dispatch = total_containers - total_dispatched
-        final_waiting_time = remaining_to_dispatch * (168 - start_hour)
+        remaining_to_dispatch = total_cars - total_dispatched
+        final_waiting_time = remaining_to_dispatch * (demand_hourly.get_column("Hour").max()+1 - start_hour)
         waiting_time_total += final_waiting_time
-        dispatch_time.append(167)  # Assuming final dispatch at the end of the period
+        dispatch_time.append(demand_hourly.get_column("Hour").max())  # Assuming final dispatch at the end of the period
         dispatched_list.append(remaining_to_dispatch)  
         dispatch_df_row = []
-        print(dispatched_list)
         for i in range(len(dispatched_list)):
             dispatch_df_row.append({
                 "Origin": origin,
@@ -272,30 +264,19 @@ def find_minimum_waiting_time(
                 "Cars_Per_Train_Empty": 0.0,
                 "Target_Cars_Per_Train": float(target_num_cars_per_train),
                 "Number_of_Cars_Total": dispatched_list[i],
-                "Tons_Per_Train_Total": dispatched_list[i]*98.006382,
-                "HP_Required_Per_Ton": 4.0,
                 "Hour":float(dispatch_time[i])
             })
-        if od_pair_loop == empty_containers_o_d:
+        if od_pair_loop == empty_cars_o_d:
             dispatch_df_row = []
-            for i in range(len(dispatched_list)):
-                if empty_containers > target_num_cars_per_train-dispatched_list[i]:
-                    empty_return.append(target_num_cars_per_train-dispatched_list[i])
-                    empty_containers -= (target_num_cars_per_train-dispatched_list[i])
-                else:
-                    empty_return.append(empty_containers)
-                    empty_containers = 0.0
             for i in range(len(dispatched_list)):
                 dispatch_df_row.append({
                     "Origin": origin,
                     "Destination": destination,
                     "Train_Type": "Intermodal",
                     "Cars_Per_Train_Loaded": dispatched_list[i],
-                    "Cars_Per_Train_Empty": empty_return[i],
+                    "Cars_Per_Train_Empty": 0.0,
                     "Target_Cars_Per_Train": float(target_num_cars_per_train),
-                    "Number_of_Cars_Total": dispatched_list[i]+empty_return[i],
-                    "Tons_Per_Train_Total": dispatched_list[i]*98.006382 + empty_return[i]*39.106152 ,
-                    "HP_Required_Per_Ton": 4.0,
+                    "Number_of_Cars_Total": dispatched_list[i],
                     "Hour":float(dispatch_time[i])
                 }) 
         #print(f"dispatch_df_row is {dispatch_df_row}")
@@ -303,8 +284,6 @@ def find_minimum_waiting_time(
     #print(f"final_dispatch_rows is {final_dispatch_rows}")
     dispatch_times = pl.DataFrame(final_dispatch_rows)
     dispatch_times = dispatch_times.sort("Hour")
-    print(dispatch_times)
-    print(f"empty_return is {empty_return}")
     return dispatch_times
 
 def formatScheduleColumns(
@@ -335,6 +314,29 @@ def formatScheduleColumns(
         .sort(["Hour","Origin","Destination","Train_Type"])
     )
 
+def get_new_accumulated_carloads(
+    demand_hourly: Union[pl.DataFrame, pl.LazyFrame],
+    group_cols: List[str],
+    containers_per_car: int
+) -> Union[pl.DataFrame, pl.LazyFrame]:
+    if group_cols is None:
+        return (demand_hourly
+            .sort("Hour")
+            .with_columns(pl.col("Number_of_Containers").cum_sum().floordiv(containers_per_car).alias("Accumulated_Carloads"))
+            .with_columns((pl.col("Accumulated_Carloads") - pl.col("Accumulated_Carloads").shift(1).fill_null(0.0)).alias("New_Carloads"))
+            .filter(pl.col("New_Carloads") > 0)
+            .select("Hour", "New_Carloads")
+        )
+    else:
+        return (demand_hourly
+            .sort(group_cols + ["Hour"])
+            .with_columns(pl.col("Number_of_Containers").cum_sum().over(group_cols).floordiv(containers_per_car).alias("Accumulated_Carloads"))
+            .with_columns((pl.col("Accumulated_Carloads") - pl.col("Accumulated_Carloads").shift(1).over(group_cols).fill_null(0.0)).alias("New_Carloads"))
+            .filter(pl.col("New_Carloads") > 0)
+            .select(group_cols + ["Hour", "New_Carloads"])
+        )
+        
+
 def calculate_dispatch_data(
     total_containers, 
     target_num_cars_per_train,  
@@ -346,17 +348,9 @@ def calculate_dispatch_data(
     num_trains = (
         total_containers // (target_num_cars_per_train * containers_per_car) + (1 if remaining_containers > 0 else 0)
     )
-    
-    ## Apply the maximum min_num_cars value
     num_trains = int(max(num_trains, max_min_trains))
 
-    new_accumulated_carloads = (demand_hourly
-        .sort("Hour")
-        .with_columns(pl.col("Number_of_Containers").cum_sum().floordiv(containers_per_car).alias("Accumulated_Carloads"))
-        .with_columns((pl.col("Accumulated_Carloads") - pl.col("Accumulated_Carloads").shift(1).fill_null(0.0)).alias("New_Carloads"))
-        .filter(pl.col("New_Carloads") > 0)
-        .select("Hour", "New_Carloads")
-    )
+    new_accumulated_carloads = get_new_accumulated_carloads(demand_hourly, group_cols = None, containers_per_car = containers_per_car)
 
     planned_train_lengths = (
         pl.DataFrame({
@@ -397,11 +391,17 @@ def calculate_dispatch_data(
         for _ in range(len(planned_train_lengths) - len(dispatched_train_lengths)):
             dispatched_train_lengths.append(0)
 
-    return {
-        "Border": planned_train_lengths,
-        "Border_Times": train_dispatch_times,
-        "Dispatched": dispatched_train_lengths,
-    }
+    return (
+        pl.DataFrame({
+            "Dispatch_Time": train_dispatch_times,
+            "Number_of_Cars_Planned": planned_train_lengths,
+            "Number_of_Cars_Dispatched": dispatched_train_lengths
+        })
+        .with_columns(
+            (pl.col("Number_of_Cars_Planned") * containers_per_car).alias("Number_of_Containers_Planned"),
+            (pl.col("Number_of_Cars_Dispatched") * containers_per_car).alias("Number_of_Containers_Dispatched")
+        )
+    )
 
 # Define the main function to generate demand trains with the updated rule
 def generate_trains_deterministic_hourly(
@@ -439,25 +439,21 @@ def generate_trains_deterministic_hourly(
             demand_hourly = demand_hourly.filter(pl.col("OD_Pair") == row['OD_Pair']).sort("Hour"), 
             max_min_trains =row['Max_Min_Trains'],
             containers_per_car = containers_per_car)
-        
-        # Add the OD pair label to each entry in the dispatch data
-        dispatch_data_df = (pl.DataFrame(dispatch_data, strict=False)
-            .with_columns(
-                pl.lit(od_pair).alias("OD_Pair"),
-                pl.col("Border").cast(pl.Float64)
+        # Append the result to the list
+        all_dispatch_data.append(
+            dispatch_data.with_columns(
+                pl.lit(row['Origin']).alias("Origin"),
+                pl.lit(row['Destination']).alias("Destination"),
+                pl.lit(row['OD_Pair']).alias("OD_Pair")
             )
         )
 
-        # Append the result to the list
-        all_dispatch_data.append(dispatch_data_df)
-
-    # Combine all dispatch data into a single DataFrame
-    final_dispatch_df = pl.concat(all_dispatch_data, how="diagonal_relaxed")
-    return final_dispatch_df
+    return pl.concat(all_dispatch_data, how="diagonal_relaxed")
 
 def calculate_dispatches_deterministic_hourly(
     demand_hourly: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],
+    freight_type_to_car_type: Dict[str, str],
     config: planner_config.TrainPlannerConfig
 ) -> pl.DataFrame:
     """
@@ -475,30 +471,56 @@ def calculate_dispatches_deterministic_hourly(
     target_num_cars_per_train=config.target_cars_per_train['Intermodal_Loaded'] #TODO make this flexible 
     demand_hourly = demand_hourly.with_columns((pl.col("Origin") + "-" + pl.col("Destination")).alias("OD_Pair"))
     dispatch_df = generate_trains_deterministic_hourly(demand_hourly,target_num_cars_per_train, config.containers_per_car)
-    od_border_time = []
-    border_time = dispatch_df["Border_Times"].to_list() 
+    od_dispatch_times = []
+    dispatch_times = dispatch_df["Dispatch_Time"].to_list() 
     od_pair_list = dispatch_df["OD_Pair"].to_list()
     for i in range(len(od_pair_list)):
         od_border_list_sub =[]
         od_border_list_sub.append(od_pair_list[i])
-        od_border_list_sub.append(border_time[i])
-        od_border_time.append(od_border_list_sub)
+        od_border_list_sub.append(dispatch_times[i])
+        od_dispatch_times.append(od_border_list_sub)
     grouped_data = defaultdict(list)
-    for od_pair, value in od_border_time:
+    for od_pair, value in od_dispatch_times:
         grouped_data[od_pair].append(value)
     border_time_list= [[key] + values for key, values in grouped_data.items()]
     num_iterations = len(border_time_list[0])
+
     schedule = find_minimum_waiting_time(num_iterations=num_iterations,
         demand_hourly=demand_hourly,
         border_time_list=border_time_list,
         min_num_cars_per_train=min_num_cars_per_train,
-        target_num_cars_per_train=target_num_cars_per_train
+        target_num_cars_per_train=target_num_cars_per_train,
+        config=config
+    )
+    return (schedule
+        #TODO: this doesn't handle tons correctly for train type empty
+        .pipe(data_prep.appendTonsAndHP, rail_vehicles, freight_type_to_car_type, config)
+        .rename({"Cars_Per_Train_Loaded": "Cars_Loaded",
+                "Cars_Per_Train_Empty": "Cars_Empty"})
+        .with_columns(
+            (pl.col("Cars_Loaded") + pl.col("Cars_Empty")).alias("Number_of_Cars"),
+            pl.col("Tons_Per_Car").mul("Cars_Loaded").alias("Tons_Per_Train"),
+            pl.col("Tons_Per_Car").mul("Cars_Loaded").mul("HP_Required_Per_Ton").alias("HP_Required"),
+            pl.when(pl.col("Train_Type").str.contains("Intermodal"))
+                .then(pl.col("Cars_Loaded").mul(config.containers_per_car))
+                .otherwise(0)
+                .alias("Containers_Loaded"),
+            pl.when(pl.col("Train_Type").str.contains("Intermodal"))
+                .then(pl.col("Cars_Empty").mul(config.containers_per_car))
+                .otherwise(0)
+                .alias("Containers_Empty"),
+        )
+        .select("Hour", "Origin", "Destination", "Train_Type", 
+                "Number_of_Cars", "Cars_Loaded", "Cars_Empty", "Containers_Empty", "Containers_Loaded",
+                "Tons_Per_Train", "HP_Required"
+        )
+        .sort(["Hour","Origin","Destination","Train_Type"])
     )
-    return schedule.pipe(formatScheduleColumns)
 
 def generate_dispatch_details(
     demand: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],
+    freight_type_to_car_type: Dict[str, str],
     config: planner_config.TrainPlannerConfig
 ) -> pl.DataFrame:
     """
diff --git a/python/altrios/train_planner/train_demand_generators.py b/python/altrios/train_planner/train_demand_generators.py
index 19d3ecea..8f4c1483 100644
--- a/python/altrios/train_planner/train_demand_generators.py
+++ b/python/altrios/train_planner/train_demand_generators.py
@@ -4,8 +4,7 @@
 import pandas as pd
 import numpy as np
 import altrios as alt
-from altrios import utilities
-from altrios.train_planner import planner_config
+from altrios.train_planner import planner_config, data_prep
 
 def get_default_return_demand_generators() -> Dict[str, Callable]:
     return {
@@ -263,65 +262,18 @@ def generate_demand_trains(
         .filter(pl.col("Train_Type") == pl.lit("Default"))
         .select("Cars_Per_Train_Target").item()
     )
-    #Prepare hp_per_ton requirements to merge onto the demand DataFrame
-    hp_per_ton = pl.concat([
-        (pl.DataFrame(this_dict)
-            .melt(variable_name="Train_Type", value_name="HP_Required_Per_Ton") 
-            .with_columns(pl.lit(this_item).alias("O_D"))
-            .with_columns(pl.col("O_D").str.split("_").list.first().alias("Origin"),
-                        pl.col("O_D").str.split("_").list.last().alias("Destination"))
-        )
-        for this_item, this_dict in config.hp_required_per_ton.items()
-    ], how="horizontal_relaxed")
-    
-    #Prepare ton_per_car requirements to merge onto the demand DataFrame
-    def get_kg_empty(veh):
-        return veh.mass_static_base_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
-    def get_kg(veh):
-        return veh.mass_static_base_kilograms + veh.mass_freight_kilograms + veh.axle_count * veh.mass_rot_per_axle_kilograms
-    
-    tons_per_car = (
-        pl.DataFrame({"Car_Type": pl.Series([rv.car_type for rv in rail_vehicles]),
-                        "KG": [get_kg(rv) for rv in rail_vehicles],
-                        "KG_Empty": [get_kg_empty(rv) for rv in rail_vehicles]
-        })
-        .with_columns(
-            pl.when(pl.col("Car_Type").str.to_lowercase().str.contains("_empty"))
-                .then(pl.col("KG_Empty") / utilities.KG_PER_TON)
-                .otherwise(pl.col("KG") / utilities.KG_PER_TON)
-                .alias("Tons_Per_Car")
-        )
-        .drop(["KG_Empty","KG"])
-    )
-
+             
     demand = (pl.concat([demand, demand_returns, demand_rebalancing], how="diagonal_relaxed")
         .group_by("Origin","Destination", "Train_Type")
             .agg(pl.col("Number_of_Cars").sum())
         .filter(pl.col("Number_of_Cars") > 0)
-        .with_columns(
-            pl.when(pl.col("Train_Type").str.contains(pl.lit("_Empty")))
-                        .then(pl.col("Train_Type"))
-                        .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Loaded"), pl.lit("_Loaded")))
-                        .replace_strict(freight_type_to_car_type)
-                        .alias("Car_Type")
-        )
-        .join(tons_per_car, how="left", on="Car_Type")
-        # Merge on OD-specific hp_per_ton if the user specified any
-        .join(hp_per_ton.drop("O_D"), 
-              on=[pl.col("Origin"), pl.col("Destination"), pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")], 
-              how="left")
-        # Second, merge on defaults per train type
-        .join(hp_per_ton.filter((pl.col("O_D") =="Default")).drop(["O_D","Origin","Destination"]),
-            on=[pl.col("Train_Type").str.strip_suffix("_Empty").str.strip_suffix("_Loaded")],
-            how="left",
-            suffix="_Default")
+        .pipe(data_prep.appendTonsAndHP, rail_vehicles, freight_type_to_car_type, config)
         # Merge on cars_per_train_min if the user specified any
         .join(cars_per_train_min, on=["Train_Type"], how="left")
         # Merge on cars_per_train_target if the user specified any
         .join(cars_per_train_target, on=["Train_Type"], how="left")
         # Fill in defaults per train type wherever the user didn't specify OD-specific hp_per_ton
         .with_columns(
-            pl.coalesce("HP_Required_Per_Ton", "HP_Required_Per_Ton_Default").alias("HP_Required_Per_Ton"),
             pl.col("Cars_Per_Train_Min").fill_null(cars_per_train_min_default),
             pl.col("Cars_Per_Train_Target").fill_null(cars_per_train_target_default),
         )

From 55e31d4824d925a96589a13f9b3ee81c14a38563 Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Mon, 20 Jan 2025 18:27:38 -0500
Subject: [PATCH 41/50] debugging

---
 python/altrios/metric_calculator.py           |   2 +-
 python/altrios/train_planner/data_prep.py     |   1 +
 python/altrios/train_planner/planner.py       |   6 +-
 python/altrios/train_planner/schedulers.py    | 140 ++++++++++++++++--
 .../train_planner/train_demand_generators.py  |  14 +-
 5 files changed, 145 insertions(+), 18 deletions(-)

diff --git a/python/altrios/metric_calculator.py b/python/altrios/metric_calculator.py
index a8df5fc1..32f7cfb0 100644
--- a/python/altrios/metric_calculator.py
+++ b/python/altrios/metric_calculator.py
@@ -475,7 +475,7 @@ def calculate_freight_moved(
             metric("Freight_Moved", units, container_counts.get_column("Containers_Loaded").item() * annualizer, "Loaded", year=info.scenario_year),
             metric("Freight_Moved", units, container_counts.get_column("Containers_Empty").item() * annualizer, "Loaded", year=info.scenario_year),
         ])
-    elif units == "detailed_car_counts":
+    elif units == "detailed car counts":
         kilometers = (pl.DataFrame(data = {"car-km": [sim.get_kilometers(annualize=info.annualize) for sim in info.sims.tolist()]})
             .with_row_index("idx")
             .with_columns(
diff --git a/python/altrios/train_planner/data_prep.py b/python/altrios/train_planner/data_prep.py
index bcf3b0ea..a5f335c3 100644
--- a/python/altrios/train_planner/data_prep.py
+++ b/python/altrios/train_planner/data_prep.py
@@ -121,6 +121,7 @@ def prep_hourly_demand(
             one_week.with_columns(pl.col("Hour").add(24*7)),
             one_week.with_columns(pl.col("Hour").add(24*7*2))
         ])
+        .with_columns(pl.col("Number_of_Days").mul(3))
         .sort("Origin", "Destination", "Train_Type", "Hour")
     )
     
diff --git a/python/altrios/train_planner/planner.py b/python/altrios/train_planner/planner.py
index 1f502c69..1bfc94f6 100644
--- a/python/altrios/train_planner/planner.py
+++ b/python/altrios/train_planner/planner.py
@@ -328,13 +328,13 @@ def run_train_planner(
         if (config.dispatch_scheduler is None) and ("Hour" in demand.collect_schema()):
             if "Number_of_Containers" in demand.collect_schema():
                 demand = (demand
-                    .group_by("Origin", "Destination", "Train_Type")
+                    .group_by("Origin", "Destination", "Number_of_Days", "Train_Type")
                         .agg(pl.col("Number_of_Containers").sum())
                     .with_columns(pl.col("Number_of_Containers").truediv(config.containers_per_car).ceil().alias("Number_of_Cars"))
                 )
             else:
                 demand = (demand
-                    .group_by("Origin", "Destination", "Train_Type")
+                    .group_by("Origin", "Destination", "Number_of_Days", "Train_Type")
                         .agg(pl.col("Number_of_Cars").sum())
                 )
         if "Hour" not in demand.schema:
@@ -344,7 +344,7 @@ def run_train_planner(
                 
         if config.dispatch_scheduler is None:
             demand = train_demand_generators.generate_demand_trains(demand, demand_returns, demand_rebalancing, rail_vehicles, freight_type_to_car_type, config)
-            config.dispatch_scheduler = schedulers.generate_dispatch_details
+            config.dispatch_scheduler = schedulers.dispatch_uniform_demand_uniform_departure
 
         dispatch_schedule = config.dispatch_scheduler(demand, rail_vehicles, freight_type_to_car_type, config)
    
diff --git a/python/altrios/train_planner/schedulers.py b/python/altrios/train_planner/schedulers.py
index dfbc2549..2d1d1e1f 100644
--- a/python/altrios/train_planner/schedulers.py
+++ b/python/altrios/train_planner/schedulers.py
@@ -2,14 +2,15 @@
 from typing import List, Union, Dict
 from collections import defaultdict
 import polars as pl
+import polars.selectors as cs
 import altrios as alt
 from altrios import utilities
-from altrios.train_planner import planner_config, data_prep
+from altrios.train_planner import planner_config, data_prep, train_demand_generators
 
 def calculate_waiting_time_single_dispatch(
     cumulative_demand_control: int, 
     last_dispatch: int, 
-    new_accumulated_carloads: pl.DataFrame, 
+    demand_hourly: pl.DataFrame, 
     dispatch_hour: int, 
     remaining_demand_list: pl.DataFrame, 
     remaining_demand_list_control: pl.DataFrame, 
@@ -23,7 +24,7 @@ def calculate_waiting_time_single_dispatch(
     Calculate the waiting time for a single dispatch using Polars DataFrames.
     """
     # Initialize variables
-    direction_demand = new_accumulated_carloads.filter(pl.col("OD_Pair") == od_pair_loop)
+    direction_demand = demand_hourly.filter(pl.col("OD_Pair") == od_pair_loop)
     remaining_demand = 0
     total_waiting_time = 0
     total_waiting_time_before_dispatch = 0
@@ -150,6 +151,11 @@ def find_minimum_waiting_time(
     """
     group_cols = ["Origin", "Destination", "OD_Pair", "Train_Type"]
     new_accumulated_carloads = get_new_accumulated_carloads(demand_hourly, group_cols, containers_per_car = config.containers_per_car).rename({"New_Carloads": "Number_of_Cars"})
+    demand_hourly = (demand_hourly
+        .join(new_accumulated_carloads, how="left", on=["Origin", "Destination", "Train_Type", "Hour"])
+        .with_columns(pl.col("Number_of_Cars").fill_null(0.0))
+        .drop(cs.ends_with("_right") | cs.by_name("Number_of_Containers"))
+    )
     for i in range(len(border_time_list)):
         od_pair_loop = border_time_list[i][0]
         reverse_pair = "-".join(od_pair_loop.split("-")[::-1])
@@ -203,7 +209,7 @@ def find_minimum_waiting_time(
             }) 
             for dispatch_hour in range(start_hour, start_hour + search_range):
                 total_waiting_time_before_dispatch, total_waiting_time, remaining_demand_list, cumulative_demand, dispatched = calculate_waiting_time_single_dispatch(
-                    cumulative_demand_control, last_dispatch, new_accumulated_carloads, dispatch_hour, remaining_demand_list_control, remaining_demand_list_control.clone(), search_range,od_pair_loop,min_num_cars_per_train, target_num_cars_per_train, config
+                    cumulative_demand_control, last_dispatch, demand_hourly, dispatch_hour, remaining_demand_list_control, remaining_demand_list_control.clone(), search_range,od_pair_loop,min_num_cars_per_train, target_num_cars_per_train, config
                 )
                 # Append data for each dispatch hour, ensuring consistent types
                 new_row = pl.DataFrame({
@@ -317,24 +323,28 @@ def formatScheduleColumns(
 def get_new_accumulated_carloads(
     demand_hourly: Union[pl.DataFrame, pl.LazyFrame],
     group_cols: List[str],
-    containers_per_car: int
+    containers_per_car: int, 
+    filter_zero: bool = True
 ) -> Union[pl.DataFrame, pl.LazyFrame]:
     if group_cols is None:
-        return (demand_hourly
+        df = (demand_hourly
             .sort("Hour")
             .with_columns(pl.col("Number_of_Containers").cum_sum().floordiv(containers_per_car).alias("Accumulated_Carloads"))
             .with_columns((pl.col("Accumulated_Carloads") - pl.col("Accumulated_Carloads").shift(1).fill_null(0.0)).alias("New_Carloads"))
-            .filter(pl.col("New_Carloads") > 0)
             .select("Hour", "New_Carloads")
         )
     else:
-        return (demand_hourly
+        df = (demand_hourly
             .sort(group_cols + ["Hour"])
             .with_columns(pl.col("Number_of_Containers").cum_sum().over(group_cols).floordiv(containers_per_car).alias("Accumulated_Carloads"))
             .with_columns((pl.col("Accumulated_Carloads") - pl.col("Accumulated_Carloads").shift(1).over(group_cols).fill_null(0.0)).alias("New_Carloads"))
-            .filter(pl.col("New_Carloads") > 0)
             .select(group_cols + ["Hour", "New_Carloads"])
         )
+    if filter_zero:
+        df = (df
+            .filter(pl.col("New_Carloads") > 0)
+        )
+    return df
         
 
 def calculate_dispatch_data(
@@ -450,7 +460,7 @@ def generate_trains_deterministic_hourly(
 
     return pl.concat(all_dispatch_data, how="diagonal_relaxed")
 
-def calculate_dispatches_deterministic_hourly(
+def dispatch_hourly_demand_optimized_departure(
     demand_hourly: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],
     freight_type_to_car_type: Dict[str, str],
@@ -517,7 +527,7 @@ def calculate_dispatches_deterministic_hourly(
         .sort(["Hour","Origin","Destination","Train_Type"])
     )
 
-def generate_dispatch_details(
+def dispatch_uniform_demand_uniform_departure(
     demand: pl.DataFrame,
     rail_vehicles: List[alt.RailVehicle],
     freight_type_to_car_type: Dict[str, str],
@@ -563,4 +573,110 @@ def generate_dispatch_details(
              * pl.col("Interval")).alias("Hour")
         )
         .pipe(formatScheduleColumns, config = config)
-    )
\ No newline at end of file
+    )
+
+def dispatch_hourly_demand_uniform_departure(
+    demand_hourly: pl.DataFrame,
+    rail_vehicles: List[alt.RailVehicle],
+    freight_type_to_car_type: Dict[str, str],
+    config: planner_config.TrainPlannerConfig
+) -> pl.DataFrame:
+    
+    if "Number_of_Containers" in demand_hourly.collect_schema():
+        demand_aggregate = (demand_hourly
+            .group_by("Origin", "Destination", "Number_of_Days", "Train_Type")
+                .agg(pl.col("Number_of_Containers").sum())
+            .with_columns(pl.col("Number_of_Containers").truediv(config.containers_per_car).ceil().alias("Number_of_Cars"))
+        )
+    else:
+        demand_aggregate = (demand_hourly
+            .group_by("Origin", "Destination", "Number_of_Days", "Train_Type")
+                .agg(pl.col("Number_of_Cars").sum())
+        )
+
+    demand_returns = train_demand_generators.generate_return_demand(demand_aggregate, config)
+    demand_rebalancing = pl.DataFrame()
+    if demand_aggregate.filter(pl.col("Train_Type").str.contains("Manifest")).height > 0:
+        nodes = pl.concat(
+            [demand_aggregate.get_column("Origin"),
+            demand_aggregate.get_column("Destination")]).unique().sort()
+        demand_rebalancing = train_demand_generators.generate_manifest_rebalancing_demand(demand_aggregate, nodes, config)
+
+    demand = train_demand_generators.generate_demand_trains(demand_aggregate, demand_returns, demand_rebalancing, rail_vehicles, freight_type_to_car_type, config) 
+
+    departure_schedule = (
+        dispatch_uniform_demand_uniform_departure(demand, rail_vehicles, freight_type_to_car_type, config)
+        .select("Hour", "Origin", "Destination", "Train_Type")
+    )
+    new_carloads = (
+        get_new_accumulated_carloads(demand_hourly, group_cols=["Origin", "Destination", "Train_Type"], containers_per_car=config.containers_per_car, filter_zero=False)
+        .with_columns(pl.col("New_Carloads").cum_sum().over("Origin", "Destination", "Train_Type").alias("Cumulative_Carloads"))
+        .drop("New_Carloads")
+        .sort("Origin", "Destination", "Train_Type", "Hour")
+    )
+    departure_schedule = (departure_schedule
+        .sort("Origin", "Destination", "Train_Type", "Hour")
+        .join_asof(
+            new_carloads.with_columns(pl.col("Hour").cast(pl.Float64)), 
+            by=["Origin", "Destination", "Train_Type"],
+            on="Hour",
+            strategy="backward")
+        .sort("Origin", "Destination", "Train_Type", "Hour")
+        .with_columns((pl.col("Cumulative_Carloads") - pl.col("Cumulative_Carloads").shift(1).over("Origin", "Destination", "Train_Type").fill_null(0.0)).alias("New_Cumulative_Carloads"))
+    )
+
+    od_departures_revised = []
+    max_train_length = math.floor(config.target_cars_per_train["Intermodal_Loaded"] * 1.1)
+    min_train_length = config.min_cars_per_train["Intermodal_Loaded"]
+    for od_departures in departure_schedule.partition_by(["Origin", "Destination", "Train_Type"], maintain_order=True):
+        train_lengths = od_departures.get_column("New_Cumulative_Carloads").to_list()
+        for i in range(len(train_lengths)):
+            if (train_lengths[i] > max_train_length) and (i < len(train_lengths) - 1):
+                train_lengths[i+1] += (train_lengths[i] - max_train_length)
+                train_lengths[i] = max_train_length
+            elif (train_lengths[i] < min_train_length) and (i < len(train_lengths) - 1):
+                train_lengths[i+1] += train_lengths[i]
+                train_lengths[i] = 0
+
+        if train_lengths[len(train_lengths) - 1] > max_train_length:
+            print(f'Unsupported case: final train too long ({train_lengths[len(train_lengths) - 1]} cars)')
+        elif train_lengths[len(train_lengths) - 1] < min_train_length:
+            if train_lengths[len(train_lengths) - 1] + train_lengths[len(train_lengths) - 2] <= max_train_length:
+                train_lengths[len(train_lengths) - 2] += train_lengths[len(train_lengths) - 1]
+                train_lengths[len(train_lengths) - 1] = 0
+            else:
+                new_val = (train_lengths[len(train_lengths) - 2] + train_lengths[len(train_lengths) - 1]) / 2
+                train_lengths[len(train_lengths) - 2] = math.ceil(new_val)
+                train_lengths[len(train_lengths) - 1] = math.floor(new_val)
+
+        od_departures = od_departures.with_columns(pl.Series("Carloads", train_lengths, strict=False))
+        od_departures_revised.append(od_departures)
+
+    departure_schedule = (pl.concat(od_departures_revised, how="diagonal_relaxed")
+        .rename({"Carloads": "Cars_Per_Train_Loaded"})
+        .filter(pl.col("Cars_Per_Train_Loaded") > 0)
+        .select("Hour", "Origin", "Destination", "Train_Type", "Cars_Per_Train_Loaded")
+        .with_columns(pl.lit(0).alias("Cars_Per_Train_Empty"))
+        .pipe(data_prep.appendTonsAndHP, rail_vehicles, freight_type_to_car_type, config)
+        .rename({"Cars_Per_Train_Loaded": "Cars_Loaded",
+                "Cars_Per_Train_Empty": "Cars_Empty"})
+        .with_columns(
+            (pl.col("Cars_Loaded") + pl.col("Cars_Empty")).alias("Number_of_Cars"),
+            pl.col("Tons_Per_Car").mul("Cars_Loaded").alias("Tons_Per_Train"),
+            pl.col("Tons_Per_Car").mul("Cars_Loaded").mul("HP_Required_Per_Ton").alias("HP_Required"),
+            pl.when(pl.col("Train_Type").str.contains("Intermodal"))
+                .then(pl.col("Cars_Loaded").mul(config.containers_per_car))
+                .otherwise(0)
+                .alias("Containers_Loaded"),
+            pl.when(pl.col("Train_Type").str.contains("Intermodal"))
+                .then(pl.col("Cars_Empty").mul(config.containers_per_car))
+                .otherwise(0)
+                .alias("Containers_Empty"),
+        )
+        .select("Hour", "Origin", "Destination", "Train_Type", 
+                "Number_of_Cars", "Cars_Loaded", "Cars_Empty", "Containers_Empty", "Containers_Loaded",
+                "Tons_Per_Train", "HP_Required"
+        )
+        .sort(["Hour","Origin","Destination","Train_Type"])
+    )
+    return departure_schedule
\ No newline at end of file
diff --git a/python/altrios/train_planner/train_demand_generators.py b/python/altrios/train_planner/train_demand_generators.py
index 8f4c1483..c0f461f0 100644
--- a/python/altrios/train_planner/train_demand_generators.py
+++ b/python/altrios/train_planner/train_demand_generators.py
@@ -269,9 +269,19 @@ def generate_demand_trains(
         .filter(pl.col("Number_of_Cars") > 0)
         .pipe(data_prep.appendTonsAndHP, rail_vehicles, freight_type_to_car_type, config)
         # Merge on cars_per_train_min if the user specified any
-        .join(cars_per_train_min, on=["Train_Type"], how="left")
+        .join(cars_per_train_min,  
+              left_on = pl.when(pl.col("Train_Type").str.contains(pl.lit("_Empty")))
+                    .then(pl.col("Train_Type"))
+                    .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Loaded"), pl.lit("_Loaded"))),
+              right_on=["Train_Type"], 
+              how="left")
         # Merge on cars_per_train_target if the user specified any
-        .join(cars_per_train_target, on=["Train_Type"], how="left")
+        .join(cars_per_train_target, 
+              left_on = pl.when(pl.col("Train_Type").str.contains(pl.lit("_Empty")))
+                    .then(pl.col("Train_Type"))
+                    .otherwise(pl.concat_str(pl.col("Train_Type").str.strip_suffix("_Loaded"), pl.lit("_Loaded"))),
+              right_on=["Train_Type"], 
+              how="left")
         # Fill in defaults per train type wherever the user didn't specify OD-specific hp_per_ton
         .with_columns(
             pl.col("Cars_Per_Train_Min").fill_null(cars_per_train_min_default),

From cb49698dd28cff34ccb23ebe30a25f7cc52e094c Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Tue, 21 Jan 2025 13:14:05 -0700
Subject: [PATCH 42/50] fixed demo_variable_paths.py reference

---
 .../variable_path_list_expected.txt           | 6458 ++++++++---------
 1 file changed, 3229 insertions(+), 3229 deletions(-)

diff --git a/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt b/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
index aee41c77..08956e3c 100644
--- a/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
+++ b/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
@@ -34,10 +34,10 @@
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['state']['min_soc']
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['state']['soc_lo_ramp_start']
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['state']['temperature_celsius']
-['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['mass']
-['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['volume']
-['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['specific_energy']
-['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['energy_density']
+['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['mass_kilograms']
+['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['volume_liters']
+['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['specific_energy_joules_per_kilogram']
+['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['energy_density_kilopascals']
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['eta_interp_grid'][0]
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['eta_interp_grid'][1]
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['eta_interp_grid'][2]
@@ -160,10 +160,10 @@
 ['loco_con']['loco_vec'][0]['state']['pwr_aux']
 ['loco_con']['loco_vec'][0]['state']['energy_out']
 ['loco_con']['loco_vec'][0]['state']['energy_aux']
-['loco_con']['loco_vec'][0]['mass']
+['loco_con']['loco_vec'][0]['mass_kilograms']
 ['loco_con']['loco_vec'][0]['mu']
-['loco_con']['loco_vec'][0]['ballast_mass']
-['loco_con']['loco_vec'][0]['baseline_mass']
+['loco_con']['loco_vec'][0]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][0]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][0]['save_interval']
 ['loco_con']['loco_vec'][0]['history']['i']
 ['loco_con']['loco_vec'][0]['history']['pwr_out_max']
@@ -174,9 +174,9 @@
 ['loco_con']['loco_vec'][0]['history']['energy_out']
 ['loco_con']['loco_vec'][0]['history']['energy_aux']
 ['loco_con']['loco_vec'][0]['assert_limits']
-['loco_con']['loco_vec'][0]['pwr_aux_offset']
+['loco_con']['loco_vec'][0]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][0]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][0]['force_max']
+['loco_con']['loco_vec'][0]['force_max_newtons']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -189,10 +189,10 @@
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -223,8 +223,8 @@
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -286,10 +286,10 @@
 ['loco_con']['loco_vec'][1]['state']['pwr_aux']
 ['loco_con']['loco_vec'][1]['state']['energy_out']
 ['loco_con']['loco_vec'][1]['state']['energy_aux']
-['loco_con']['loco_vec'][1]['mass']
+['loco_con']['loco_vec'][1]['mass_kilograms']
 ['loco_con']['loco_vec'][1]['mu']
-['loco_con']['loco_vec'][1]['ballast_mass']
-['loco_con']['loco_vec'][1]['baseline_mass']
+['loco_con']['loco_vec'][1]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][1]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][1]['save_interval']
 ['loco_con']['loco_vec'][1]['history']['i']
 ['loco_con']['loco_vec'][1]['history']['pwr_out_max']
@@ -300,9 +300,9 @@
 ['loco_con']['loco_vec'][1]['history']['energy_out']
 ['loco_con']['loco_vec'][1]['history']['energy_aux']
 ['loco_con']['loco_vec'][1]['assert_limits']
-['loco_con']['loco_vec'][1]['pwr_aux_offset']
+['loco_con']['loco_vec'][1]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][1]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][1]['force_max']
+['loco_con']['loco_vec'][1]['force_max_newtons']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -315,10 +315,10 @@
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -349,8 +349,8 @@
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -412,10 +412,10 @@
 ['loco_con']['loco_vec'][2]['state']['pwr_aux']
 ['loco_con']['loco_vec'][2]['state']['energy_out']
 ['loco_con']['loco_vec'][2]['state']['energy_aux']
-['loco_con']['loco_vec'][2]['mass']
+['loco_con']['loco_vec'][2]['mass_kilograms']
 ['loco_con']['loco_vec'][2]['mu']
-['loco_con']['loco_vec'][2]['ballast_mass']
-['loco_con']['loco_vec'][2]['baseline_mass']
+['loco_con']['loco_vec'][2]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][2]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][2]['save_interval']
 ['loco_con']['loco_vec'][2]['history']['i']
 ['loco_con']['loco_vec'][2]['history']['pwr_out_max']
@@ -426,9 +426,9 @@
 ['loco_con']['loco_vec'][2]['history']['energy_out']
 ['loco_con']['loco_vec'][2]['history']['energy_aux']
 ['loco_con']['loco_vec'][2]['assert_limits']
-['loco_con']['loco_vec'][2]['pwr_aux_offset']
+['loco_con']['loco_vec'][2]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][2]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][2]['force_max']
+['loco_con']['loco_vec'][2]['force_max_newtons']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -441,10 +441,10 @@
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -475,8 +475,8 @@
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -538,10 +538,10 @@
 ['loco_con']['loco_vec'][3]['state']['pwr_aux']
 ['loco_con']['loco_vec'][3]['state']['energy_out']
 ['loco_con']['loco_vec'][3]['state']['energy_aux']
-['loco_con']['loco_vec'][3]['mass']
+['loco_con']['loco_vec'][3]['mass_kilograms']
 ['loco_con']['loco_vec'][3]['mu']
-['loco_con']['loco_vec'][3]['ballast_mass']
-['loco_con']['loco_vec'][3]['baseline_mass']
+['loco_con']['loco_vec'][3]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][3]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][3]['save_interval']
 ['loco_con']['loco_vec'][3]['history']['i']
 ['loco_con']['loco_vec'][3]['history']['pwr_out_max']
@@ -552,9 +552,9 @@
 ['loco_con']['loco_vec'][3]['history']['energy_out']
 ['loco_con']['loco_vec'][3]['history']['energy_aux']
 ['loco_con']['loco_vec'][3]['assert_limits']
-['loco_con']['loco_vec'][3]['pwr_aux_offset']
+['loco_con']['loco_vec'][3]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][3]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][3]['force_max']
+['loco_con']['loco_vec'][3]['force_max_newtons']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -567,10 +567,10 @@
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -601,8 +601,8 @@
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -664,10 +664,10 @@
 ['loco_con']['loco_vec'][4]['state']['pwr_aux']
 ['loco_con']['loco_vec'][4]['state']['energy_out']
 ['loco_con']['loco_vec'][4]['state']['energy_aux']
-['loco_con']['loco_vec'][4]['mass']
+['loco_con']['loco_vec'][4]['mass_kilograms']
 ['loco_con']['loco_vec'][4]['mu']
-['loco_con']['loco_vec'][4]['ballast_mass']
-['loco_con']['loco_vec'][4]['baseline_mass']
+['loco_con']['loco_vec'][4]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][4]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][4]['save_interval']
 ['loco_con']['loco_vec'][4]['history']['i']
 ['loco_con']['loco_vec'][4]['history']['pwr_out_max']
@@ -678,9 +678,9 @@
 ['loco_con']['loco_vec'][4]['history']['energy_out']
 ['loco_con']['loco_vec'][4]['history']['energy_aux']
 ['loco_con']['loco_vec'][4]['assert_limits']
-['loco_con']['loco_vec'][4]['pwr_aux_offset']
+['loco_con']['loco_vec'][4]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][4]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][4]['force_max']
+['loco_con']['loco_vec'][4]['force_max_newtons']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -693,10 +693,10 @@
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -727,8 +727,8 @@
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -790,10 +790,10 @@
 ['loco_con']['loco_vec'][5]['state']['pwr_aux']
 ['loco_con']['loco_vec'][5]['state']['energy_out']
 ['loco_con']['loco_vec'][5]['state']['energy_aux']
-['loco_con']['loco_vec'][5]['mass']
+['loco_con']['loco_vec'][5]['mass_kilograms']
 ['loco_con']['loco_vec'][5]['mu']
-['loco_con']['loco_vec'][5]['ballast_mass']
-['loco_con']['loco_vec'][5]['baseline_mass']
+['loco_con']['loco_vec'][5]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][5]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][5]['save_interval']
 ['loco_con']['loco_vec'][5]['history']['i']
 ['loco_con']['loco_vec'][5]['history']['pwr_out_max']
@@ -804,9 +804,9 @@
 ['loco_con']['loco_vec'][5]['history']['energy_out']
 ['loco_con']['loco_vec'][5]['history']['energy_aux']
 ['loco_con']['loco_vec'][5]['assert_limits']
-['loco_con']['loco_vec'][5]['pwr_aux_offset']
+['loco_con']['loco_vec'][5]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][5]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][5]['force_max']
+['loco_con']['loco_vec'][5]['force_max_newtons']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -819,10 +819,10 @@
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -853,8 +853,8 @@
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -916,10 +916,10 @@
 ['loco_con']['loco_vec'][6]['state']['pwr_aux']
 ['loco_con']['loco_vec'][6]['state']['energy_out']
 ['loco_con']['loco_vec'][6]['state']['energy_aux']
-['loco_con']['loco_vec'][6]['mass']
+['loco_con']['loco_vec'][6]['mass_kilograms']
 ['loco_con']['loco_vec'][6]['mu']
-['loco_con']['loco_vec'][6]['ballast_mass']
-['loco_con']['loco_vec'][6]['baseline_mass']
+['loco_con']['loco_vec'][6]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][6]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][6]['save_interval']
 ['loco_con']['loco_vec'][6]['history']['i']
 ['loco_con']['loco_vec'][6]['history']['pwr_out_max']
@@ -930,9 +930,9 @@
 ['loco_con']['loco_vec'][6]['history']['energy_out']
 ['loco_con']['loco_vec'][6]['history']['energy_aux']
 ['loco_con']['loco_vec'][6]['assert_limits']
-['loco_con']['loco_vec'][6]['pwr_aux_offset']
+['loco_con']['loco_vec'][6]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][6]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][6]['force_max']
+['loco_con']['loco_vec'][6]['force_max_newtons']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -945,10 +945,10 @@
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -979,8 +979,8 @@
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -1042,10 +1042,10 @@
 ['loco_con']['loco_vec'][7]['state']['pwr_aux']
 ['loco_con']['loco_vec'][7]['state']['energy_out']
 ['loco_con']['loco_vec'][7]['state']['energy_aux']
-['loco_con']['loco_vec'][7]['mass']
+['loco_con']['loco_vec'][7]['mass_kilograms']
 ['loco_con']['loco_vec'][7]['mu']
-['loco_con']['loco_vec'][7]['ballast_mass']
-['loco_con']['loco_vec'][7]['baseline_mass']
+['loco_con']['loco_vec'][7]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][7]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][7]['save_interval']
 ['loco_con']['loco_vec'][7]['history']['i']
 ['loco_con']['loco_vec'][7]['history']['pwr_out_max']
@@ -1056,9 +1056,9 @@
 ['loco_con']['loco_vec'][7]['history']['energy_out']
 ['loco_con']['loco_vec'][7]['history']['energy_aux']
 ['loco_con']['loco_vec'][7]['assert_limits']
-['loco_con']['loco_vec'][7]['pwr_aux_offset']
+['loco_con']['loco_vec'][7]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][7]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][7]['force_max']
+['loco_con']['loco_vec'][7]['force_max_newtons']
 ['loco_con']['pdct']['RESGreedy']
 ['loco_con']['assert_limits']
 ['loco_con']['state']['i']
@@ -1594,4728 +1594,4728 @@
 ['path_tpc']['link_points'][90]['curve_count']
 ['path_tpc']['link_points'][90]['cat_power_count']
 ['path_tpc']['link_points'][90]['link_idx']
-['path_tpc']['grades'][0]['offset']
+['path_tpc']['grades'][0]['offset_meters']
 ['path_tpc']['grades'][0]['res_coeff']
-['path_tpc']['grades'][0]['res_net']
-['path_tpc']['grades'][1]['offset']
+['path_tpc']['grades'][0]['res_net_meters']
+['path_tpc']['grades'][1]['offset_meters']
 ['path_tpc']['grades'][1]['res_coeff']
-['path_tpc']['grades'][1]['res_net']
-['path_tpc']['grades'][2]['offset']
+['path_tpc']['grades'][1]['res_net_meters']
+['path_tpc']['grades'][2]['offset_meters']
 ['path_tpc']['grades'][2]['res_coeff']
-['path_tpc']['grades'][2]['res_net']
-['path_tpc']['grades'][3]['offset']
+['path_tpc']['grades'][2]['res_net_meters']
+['path_tpc']['grades'][3]['offset_meters']
 ['path_tpc']['grades'][3]['res_coeff']
-['path_tpc']['grades'][3]['res_net']
-['path_tpc']['grades'][4]['offset']
+['path_tpc']['grades'][3]['res_net_meters']
+['path_tpc']['grades'][4]['offset_meters']
 ['path_tpc']['grades'][4]['res_coeff']
-['path_tpc']['grades'][4]['res_net']
-['path_tpc']['grades'][5]['offset']
+['path_tpc']['grades'][4]['res_net_meters']
+['path_tpc']['grades'][5]['offset_meters']
 ['path_tpc']['grades'][5]['res_coeff']
-['path_tpc']['grades'][5]['res_net']
-['path_tpc']['grades'][6]['offset']
+['path_tpc']['grades'][5]['res_net_meters']
+['path_tpc']['grades'][6]['offset_meters']
 ['path_tpc']['grades'][6]['res_coeff']
-['path_tpc']['grades'][6]['res_net']
-['path_tpc']['grades'][7]['offset']
+['path_tpc']['grades'][6]['res_net_meters']
+['path_tpc']['grades'][7]['offset_meters']
 ['path_tpc']['grades'][7]['res_coeff']
-['path_tpc']['grades'][7]['res_net']
-['path_tpc']['grades'][8]['offset']
+['path_tpc']['grades'][7]['res_net_meters']
+['path_tpc']['grades'][8]['offset_meters']
 ['path_tpc']['grades'][8]['res_coeff']
-['path_tpc']['grades'][8]['res_net']
-['path_tpc']['grades'][9]['offset']
+['path_tpc']['grades'][8]['res_net_meters']
+['path_tpc']['grades'][9]['offset_meters']
 ['path_tpc']['grades'][9]['res_coeff']
-['path_tpc']['grades'][9]['res_net']
-['path_tpc']['grades'][10]['offset']
+['path_tpc']['grades'][9]['res_net_meters']
+['path_tpc']['grades'][10]['offset_meters']
 ['path_tpc']['grades'][10]['res_coeff']
-['path_tpc']['grades'][10]['res_net']
-['path_tpc']['grades'][11]['offset']
+['path_tpc']['grades'][10]['res_net_meters']
+['path_tpc']['grades'][11]['offset_meters']
 ['path_tpc']['grades'][11]['res_coeff']
-['path_tpc']['grades'][11]['res_net']
-['path_tpc']['grades'][12]['offset']
+['path_tpc']['grades'][11]['res_net_meters']
+['path_tpc']['grades'][12]['offset_meters']
 ['path_tpc']['grades'][12]['res_coeff']
-['path_tpc']['grades'][12]['res_net']
-['path_tpc']['grades'][13]['offset']
+['path_tpc']['grades'][12]['res_net_meters']
+['path_tpc']['grades'][13]['offset_meters']
 ['path_tpc']['grades'][13]['res_coeff']
-['path_tpc']['grades'][13]['res_net']
-['path_tpc']['grades'][14]['offset']
+['path_tpc']['grades'][13]['res_net_meters']
+['path_tpc']['grades'][14]['offset_meters']
 ['path_tpc']['grades'][14]['res_coeff']
-['path_tpc']['grades'][14]['res_net']
-['path_tpc']['grades'][15]['offset']
+['path_tpc']['grades'][14]['res_net_meters']
+['path_tpc']['grades'][15]['offset_meters']
 ['path_tpc']['grades'][15]['res_coeff']
-['path_tpc']['grades'][15]['res_net']
-['path_tpc']['grades'][16]['offset']
+['path_tpc']['grades'][15]['res_net_meters']
+['path_tpc']['grades'][16]['offset_meters']
 ['path_tpc']['grades'][16]['res_coeff']
-['path_tpc']['grades'][16]['res_net']
-['path_tpc']['grades'][17]['offset']
+['path_tpc']['grades'][16]['res_net_meters']
+['path_tpc']['grades'][17]['offset_meters']
 ['path_tpc']['grades'][17]['res_coeff']
-['path_tpc']['grades'][17]['res_net']
-['path_tpc']['grades'][18]['offset']
+['path_tpc']['grades'][17]['res_net_meters']
+['path_tpc']['grades'][18]['offset_meters']
 ['path_tpc']['grades'][18]['res_coeff']
-['path_tpc']['grades'][18]['res_net']
-['path_tpc']['grades'][19]['offset']
+['path_tpc']['grades'][18]['res_net_meters']
+['path_tpc']['grades'][19]['offset_meters']
 ['path_tpc']['grades'][19]['res_coeff']
-['path_tpc']['grades'][19]['res_net']
-['path_tpc']['grades'][20]['offset']
+['path_tpc']['grades'][19]['res_net_meters']
+['path_tpc']['grades'][20]['offset_meters']
 ['path_tpc']['grades'][20]['res_coeff']
-['path_tpc']['grades'][20]['res_net']
-['path_tpc']['grades'][21]['offset']
+['path_tpc']['grades'][20]['res_net_meters']
+['path_tpc']['grades'][21]['offset_meters']
 ['path_tpc']['grades'][21]['res_coeff']
-['path_tpc']['grades'][21]['res_net']
-['path_tpc']['grades'][22]['offset']
+['path_tpc']['grades'][21]['res_net_meters']
+['path_tpc']['grades'][22]['offset_meters']
 ['path_tpc']['grades'][22]['res_coeff']
-['path_tpc']['grades'][22]['res_net']
-['path_tpc']['grades'][23]['offset']
+['path_tpc']['grades'][22]['res_net_meters']
+['path_tpc']['grades'][23]['offset_meters']
 ['path_tpc']['grades'][23]['res_coeff']
-['path_tpc']['grades'][23]['res_net']
-['path_tpc']['grades'][24]['offset']
+['path_tpc']['grades'][23]['res_net_meters']
+['path_tpc']['grades'][24]['offset_meters']
 ['path_tpc']['grades'][24]['res_coeff']
-['path_tpc']['grades'][24]['res_net']
-['path_tpc']['grades'][25]['offset']
+['path_tpc']['grades'][24]['res_net_meters']
+['path_tpc']['grades'][25]['offset_meters']
 ['path_tpc']['grades'][25]['res_coeff']
-['path_tpc']['grades'][25]['res_net']
-['path_tpc']['grades'][26]['offset']
+['path_tpc']['grades'][25]['res_net_meters']
+['path_tpc']['grades'][26]['offset_meters']
 ['path_tpc']['grades'][26]['res_coeff']
-['path_tpc']['grades'][26]['res_net']
-['path_tpc']['grades'][27]['offset']
+['path_tpc']['grades'][26]['res_net_meters']
+['path_tpc']['grades'][27]['offset_meters']
 ['path_tpc']['grades'][27]['res_coeff']
-['path_tpc']['grades'][27]['res_net']
-['path_tpc']['grades'][28]['offset']
+['path_tpc']['grades'][27]['res_net_meters']
+['path_tpc']['grades'][28]['offset_meters']
 ['path_tpc']['grades'][28]['res_coeff']
-['path_tpc']['grades'][28]['res_net']
-['path_tpc']['grades'][29]['offset']
+['path_tpc']['grades'][28]['res_net_meters']
+['path_tpc']['grades'][29]['offset_meters']
 ['path_tpc']['grades'][29]['res_coeff']
-['path_tpc']['grades'][29]['res_net']
-['path_tpc']['grades'][30]['offset']
+['path_tpc']['grades'][29]['res_net_meters']
+['path_tpc']['grades'][30]['offset_meters']
 ['path_tpc']['grades'][30]['res_coeff']
-['path_tpc']['grades'][30]['res_net']
-['path_tpc']['grades'][31]['offset']
+['path_tpc']['grades'][30]['res_net_meters']
+['path_tpc']['grades'][31]['offset_meters']
 ['path_tpc']['grades'][31]['res_coeff']
-['path_tpc']['grades'][31]['res_net']
-['path_tpc']['grades'][32]['offset']
+['path_tpc']['grades'][31]['res_net_meters']
+['path_tpc']['grades'][32]['offset_meters']
 ['path_tpc']['grades'][32]['res_coeff']
-['path_tpc']['grades'][32]['res_net']
-['path_tpc']['grades'][33]['offset']
+['path_tpc']['grades'][32]['res_net_meters']
+['path_tpc']['grades'][33]['offset_meters']
 ['path_tpc']['grades'][33]['res_coeff']
-['path_tpc']['grades'][33]['res_net']
-['path_tpc']['grades'][34]['offset']
+['path_tpc']['grades'][33]['res_net_meters']
+['path_tpc']['grades'][34]['offset_meters']
 ['path_tpc']['grades'][34]['res_coeff']
-['path_tpc']['grades'][34]['res_net']
-['path_tpc']['grades'][35]['offset']
+['path_tpc']['grades'][34]['res_net_meters']
+['path_tpc']['grades'][35]['offset_meters']
 ['path_tpc']['grades'][35]['res_coeff']
-['path_tpc']['grades'][35]['res_net']
-['path_tpc']['grades'][36]['offset']
+['path_tpc']['grades'][35]['res_net_meters']
+['path_tpc']['grades'][36]['offset_meters']
 ['path_tpc']['grades'][36]['res_coeff']
-['path_tpc']['grades'][36]['res_net']
-['path_tpc']['grades'][37]['offset']
+['path_tpc']['grades'][36]['res_net_meters']
+['path_tpc']['grades'][37]['offset_meters']
 ['path_tpc']['grades'][37]['res_coeff']
-['path_tpc']['grades'][37]['res_net']
-['path_tpc']['grades'][38]['offset']
+['path_tpc']['grades'][37]['res_net_meters']
+['path_tpc']['grades'][38]['offset_meters']
 ['path_tpc']['grades'][38]['res_coeff']
-['path_tpc']['grades'][38]['res_net']
-['path_tpc']['grades'][39]['offset']
+['path_tpc']['grades'][38]['res_net_meters']
+['path_tpc']['grades'][39]['offset_meters']
 ['path_tpc']['grades'][39]['res_coeff']
-['path_tpc']['grades'][39]['res_net']
-['path_tpc']['grades'][40]['offset']
+['path_tpc']['grades'][39]['res_net_meters']
+['path_tpc']['grades'][40]['offset_meters']
 ['path_tpc']['grades'][40]['res_coeff']
-['path_tpc']['grades'][40]['res_net']
-['path_tpc']['grades'][41]['offset']
+['path_tpc']['grades'][40]['res_net_meters']
+['path_tpc']['grades'][41]['offset_meters']
 ['path_tpc']['grades'][41]['res_coeff']
-['path_tpc']['grades'][41]['res_net']
-['path_tpc']['grades'][42]['offset']
+['path_tpc']['grades'][41]['res_net_meters']
+['path_tpc']['grades'][42]['offset_meters']
 ['path_tpc']['grades'][42]['res_coeff']
-['path_tpc']['grades'][42]['res_net']
-['path_tpc']['grades'][43]['offset']
+['path_tpc']['grades'][42]['res_net_meters']
+['path_tpc']['grades'][43]['offset_meters']
 ['path_tpc']['grades'][43]['res_coeff']
-['path_tpc']['grades'][43]['res_net']
-['path_tpc']['grades'][44]['offset']
+['path_tpc']['grades'][43]['res_net_meters']
+['path_tpc']['grades'][44]['offset_meters']
 ['path_tpc']['grades'][44]['res_coeff']
-['path_tpc']['grades'][44]['res_net']
-['path_tpc']['grades'][45]['offset']
+['path_tpc']['grades'][44]['res_net_meters']
+['path_tpc']['grades'][45]['offset_meters']
 ['path_tpc']['grades'][45]['res_coeff']
-['path_tpc']['grades'][45]['res_net']
-['path_tpc']['grades'][46]['offset']
+['path_tpc']['grades'][45]['res_net_meters']
+['path_tpc']['grades'][46]['offset_meters']
 ['path_tpc']['grades'][46]['res_coeff']
-['path_tpc']['grades'][46]['res_net']
-['path_tpc']['grades'][47]['offset']
+['path_tpc']['grades'][46]['res_net_meters']
+['path_tpc']['grades'][47]['offset_meters']
 ['path_tpc']['grades'][47]['res_coeff']
-['path_tpc']['grades'][47]['res_net']
-['path_tpc']['grades'][48]['offset']
+['path_tpc']['grades'][47]['res_net_meters']
+['path_tpc']['grades'][48]['offset_meters']
 ['path_tpc']['grades'][48]['res_coeff']
-['path_tpc']['grades'][48]['res_net']
-['path_tpc']['grades'][49]['offset']
+['path_tpc']['grades'][48]['res_net_meters']
+['path_tpc']['grades'][49]['offset_meters']
 ['path_tpc']['grades'][49]['res_coeff']
-['path_tpc']['grades'][49]['res_net']
-['path_tpc']['grades'][50]['offset']
+['path_tpc']['grades'][49]['res_net_meters']
+['path_tpc']['grades'][50]['offset_meters']
 ['path_tpc']['grades'][50]['res_coeff']
-['path_tpc']['grades'][50]['res_net']
-['path_tpc']['grades'][51]['offset']
+['path_tpc']['grades'][50]['res_net_meters']
+['path_tpc']['grades'][51]['offset_meters']
 ['path_tpc']['grades'][51]['res_coeff']
-['path_tpc']['grades'][51]['res_net']
-['path_tpc']['grades'][52]['offset']
+['path_tpc']['grades'][51]['res_net_meters']
+['path_tpc']['grades'][52]['offset_meters']
 ['path_tpc']['grades'][52]['res_coeff']
-['path_tpc']['grades'][52]['res_net']
-['path_tpc']['grades'][53]['offset']
+['path_tpc']['grades'][52]['res_net_meters']
+['path_tpc']['grades'][53]['offset_meters']
 ['path_tpc']['grades'][53]['res_coeff']
-['path_tpc']['grades'][53]['res_net']
-['path_tpc']['grades'][54]['offset']
+['path_tpc']['grades'][53]['res_net_meters']
+['path_tpc']['grades'][54]['offset_meters']
 ['path_tpc']['grades'][54]['res_coeff']
-['path_tpc']['grades'][54]['res_net']
-['path_tpc']['grades'][55]['offset']
+['path_tpc']['grades'][54]['res_net_meters']
+['path_tpc']['grades'][55]['offset_meters']
 ['path_tpc']['grades'][55]['res_coeff']
-['path_tpc']['grades'][55]['res_net']
-['path_tpc']['grades'][56]['offset']
+['path_tpc']['grades'][55]['res_net_meters']
+['path_tpc']['grades'][56]['offset_meters']
 ['path_tpc']['grades'][56]['res_coeff']
-['path_tpc']['grades'][56]['res_net']
-['path_tpc']['grades'][57]['offset']
+['path_tpc']['grades'][56]['res_net_meters']
+['path_tpc']['grades'][57]['offset_meters']
 ['path_tpc']['grades'][57]['res_coeff']
-['path_tpc']['grades'][57]['res_net']
-['path_tpc']['grades'][58]['offset']
+['path_tpc']['grades'][57]['res_net_meters']
+['path_tpc']['grades'][58]['offset_meters']
 ['path_tpc']['grades'][58]['res_coeff']
-['path_tpc']['grades'][58]['res_net']
-['path_tpc']['grades'][59]['offset']
+['path_tpc']['grades'][58]['res_net_meters']
+['path_tpc']['grades'][59]['offset_meters']
 ['path_tpc']['grades'][59]['res_coeff']
-['path_tpc']['grades'][59]['res_net']
-['path_tpc']['grades'][60]['offset']
+['path_tpc']['grades'][59]['res_net_meters']
+['path_tpc']['grades'][60]['offset_meters']
 ['path_tpc']['grades'][60]['res_coeff']
-['path_tpc']['grades'][60]['res_net']
-['path_tpc']['grades'][61]['offset']
+['path_tpc']['grades'][60]['res_net_meters']
+['path_tpc']['grades'][61]['offset_meters']
 ['path_tpc']['grades'][61]['res_coeff']
-['path_tpc']['grades'][61]['res_net']
-['path_tpc']['grades'][62]['offset']
+['path_tpc']['grades'][61]['res_net_meters']
+['path_tpc']['grades'][62]['offset_meters']
 ['path_tpc']['grades'][62]['res_coeff']
-['path_tpc']['grades'][62]['res_net']
-['path_tpc']['grades'][63]['offset']
+['path_tpc']['grades'][62]['res_net_meters']
+['path_tpc']['grades'][63]['offset_meters']
 ['path_tpc']['grades'][63]['res_coeff']
-['path_tpc']['grades'][63]['res_net']
-['path_tpc']['grades'][64]['offset']
+['path_tpc']['grades'][63]['res_net_meters']
+['path_tpc']['grades'][64]['offset_meters']
 ['path_tpc']['grades'][64]['res_coeff']
-['path_tpc']['grades'][64]['res_net']
-['path_tpc']['grades'][65]['offset']
+['path_tpc']['grades'][64]['res_net_meters']
+['path_tpc']['grades'][65]['offset_meters']
 ['path_tpc']['grades'][65]['res_coeff']
-['path_tpc']['grades'][65]['res_net']
-['path_tpc']['grades'][66]['offset']
+['path_tpc']['grades'][65]['res_net_meters']
+['path_tpc']['grades'][66]['offset_meters']
 ['path_tpc']['grades'][66]['res_coeff']
-['path_tpc']['grades'][66]['res_net']
-['path_tpc']['grades'][67]['offset']
+['path_tpc']['grades'][66]['res_net_meters']
+['path_tpc']['grades'][67]['offset_meters']
 ['path_tpc']['grades'][67]['res_coeff']
-['path_tpc']['grades'][67]['res_net']
-['path_tpc']['grades'][68]['offset']
+['path_tpc']['grades'][67]['res_net_meters']
+['path_tpc']['grades'][68]['offset_meters']
 ['path_tpc']['grades'][68]['res_coeff']
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 ['path_tpc']['grades'][674]['res_coeff']
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 ['path_tpc']['grades'][675]['res_coeff']
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 ['path_tpc']['grades'][677]['res_coeff']
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 ['path_tpc']['grades'][678]['res_coeff']
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 ['path_tpc']['grades'][707]['res_coeff']
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 ['path_tpc']['grades'][708]['res_coeff']
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 ['path_tpc']['grades'][709]['res_coeff']
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 ['path_tpc']['grades'][710]['res_coeff']
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 ['path_tpc']['grades'][711]['res_coeff']
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 ['path_tpc']['grades'][712]['res_coeff']
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 ['path_tpc']['grades'][713]['res_coeff']
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 ['path_tpc']['grades'][714]['res_coeff']
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 ['path_tpc']['grades'][715]['res_coeff']
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 ['path_tpc']['grades'][716]['res_coeff']
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 ['path_tpc']['grades'][717]['res_coeff']
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 ['path_tpc']['grades'][718]['res_coeff']
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 ['path_tpc']['grades'][719]['res_coeff']
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 ['path_tpc']['grades'][720]['res_coeff']
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 ['path_tpc']['grades'][721]['res_coeff']
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 ['path_tpc']['grades'][722]['res_coeff']
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 ['path_tpc']['grades'][723]['res_coeff']
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 ['path_tpc']['grades'][724]['res_coeff']
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 ['path_tpc']['grades'][725]['res_coeff']
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 ['path_tpc']['grades'][726]['res_coeff']
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 ['path_tpc']['grades'][727]['res_coeff']
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 ['path_tpc']['grades'][729]['res_coeff']
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 ['path_tpc']['grades'][730]['res_coeff']
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 ['path_tpc']['grades'][731]['res_coeff']
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 ['path_tpc']['grades'][732]['res_coeff']
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 ['path_tpc']['grades'][737]['res_coeff']
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 ['path_tpc']['grades'][740]['res_coeff']
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 ['path_tpc']['grades'][743]['res_coeff']
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 ['path_tpc']['grades'][744]['res_coeff']
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 ['path_tpc']['grades'][749]['res_coeff']
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 ['path_tpc']['grades'][750]['res_coeff']
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 ['path_tpc']['grades'][751]['res_coeff']
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 ['path_tpc']['grades'][752]['res_coeff']
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 ['path_tpc']['grades'][753]['res_coeff']
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 ['path_tpc']['grades'][754]['res_coeff']
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 ['path_tpc']['grades'][755]['res_coeff']
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 ['path_tpc']['grades'][756]['res_coeff']
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 ['path_tpc']['grades'][757]['res_coeff']
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 ['path_tpc']['grades'][758]['res_coeff']
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 ['path_tpc']['grades'][759]['res_coeff']
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 ['path_tpc']['grades'][760]['res_coeff']
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 ['path_tpc']['grades'][761]['res_coeff']
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 ['path_tpc']['grades'][762]['res_coeff']
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 ['path_tpc']['grades'][763]['res_coeff']
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 ['path_tpc']['grades'][764]['res_coeff']
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 ['path_tpc']['grades'][765]['res_coeff']
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 ['path_tpc']['grades'][766]['res_coeff']
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 ['path_tpc']['grades'][767]['res_coeff']
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 ['path_tpc']['grades'][768]['res_coeff']
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 ['path_tpc']['grades'][769]['res_coeff']
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 ['path_tpc']['grades'][770]['res_coeff']
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 ['path_tpc']['grades'][771]['res_coeff']
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 ['path_tpc']['grades'][772]['res_coeff']
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 ['path_tpc']['grades'][773]['res_coeff']
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 ['path_tpc']['grades'][774]['res_coeff']
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+['path_tpc']['grades'][775]['offset_meters']
 ['path_tpc']['grades'][775]['res_coeff']
-['path_tpc']['grades'][775]['res_net']
-['path_tpc']['grades'][776]['offset']
+['path_tpc']['grades'][775]['res_net_meters']
+['path_tpc']['grades'][776]['offset_meters']
 ['path_tpc']['grades'][776]['res_coeff']
-['path_tpc']['grades'][776]['res_net']
-['path_tpc']['grades'][777]['offset']
+['path_tpc']['grades'][776]['res_net_meters']
+['path_tpc']['grades'][777]['offset_meters']
 ['path_tpc']['grades'][777]['res_coeff']
-['path_tpc']['grades'][777]['res_net']
-['path_tpc']['grades'][778]['offset']
+['path_tpc']['grades'][777]['res_net_meters']
+['path_tpc']['grades'][778]['offset_meters']
 ['path_tpc']['grades'][778]['res_coeff']
-['path_tpc']['grades'][778]['res_net']
-['path_tpc']['grades'][779]['offset']
+['path_tpc']['grades'][778]['res_net_meters']
+['path_tpc']['grades'][779]['offset_meters']
 ['path_tpc']['grades'][779]['res_coeff']
-['path_tpc']['grades'][779]['res_net']
-['path_tpc']['grades'][780]['offset']
+['path_tpc']['grades'][779]['res_net_meters']
+['path_tpc']['grades'][780]['offset_meters']
 ['path_tpc']['grades'][780]['res_coeff']
-['path_tpc']['grades'][780]['res_net']
-['path_tpc']['grades'][781]['offset']
+['path_tpc']['grades'][780]['res_net_meters']
+['path_tpc']['grades'][781]['offset_meters']
 ['path_tpc']['grades'][781]['res_coeff']
-['path_tpc']['grades'][781]['res_net']
-['path_tpc']['grades'][782]['offset']
+['path_tpc']['grades'][781]['res_net_meters']
+['path_tpc']['grades'][782]['offset_meters']
 ['path_tpc']['grades'][782]['res_coeff']
-['path_tpc']['grades'][782]['res_net']
-['path_tpc']['grades'][783]['offset']
+['path_tpc']['grades'][782]['res_net_meters']
+['path_tpc']['grades'][783]['offset_meters']
 ['path_tpc']['grades'][783]['res_coeff']
-['path_tpc']['grades'][783]['res_net']
-['path_tpc']['grades'][784]['offset']
+['path_tpc']['grades'][783]['res_net_meters']
+['path_tpc']['grades'][784]['offset_meters']
 ['path_tpc']['grades'][784]['res_coeff']
-['path_tpc']['grades'][784]['res_net']
-['path_tpc']['grades'][785]['offset']
+['path_tpc']['grades'][784]['res_net_meters']
+['path_tpc']['grades'][785]['offset_meters']
 ['path_tpc']['grades'][785]['res_coeff']
-['path_tpc']['grades'][785]['res_net']
-['path_tpc']['grades'][786]['offset']
+['path_tpc']['grades'][785]['res_net_meters']
+['path_tpc']['grades'][786]['offset_meters']
 ['path_tpc']['grades'][786]['res_coeff']
-['path_tpc']['grades'][786]['res_net']
-['path_tpc']['curves'][0]['offset']
+['path_tpc']['grades'][786]['res_net_meters']
+['path_tpc']['curves'][0]['offset_meters']
 ['path_tpc']['curves'][0]['res_coeff']
-['path_tpc']['curves'][0]['res_net']
-['path_tpc']['curves'][1]['offset']
+['path_tpc']['curves'][0]['res_net_meters']
+['path_tpc']['curves'][1]['offset_meters']
 ['path_tpc']['curves'][1]['res_coeff']
-['path_tpc']['curves'][1]['res_net']
-['path_tpc']['curves'][2]['offset']
+['path_tpc']['curves'][1]['res_net_meters']
+['path_tpc']['curves'][2]['offset_meters']
 ['path_tpc']['curves'][2]['res_coeff']
-['path_tpc']['curves'][2]['res_net']
-['path_tpc']['curves'][3]['offset']
+['path_tpc']['curves'][2]['res_net_meters']
+['path_tpc']['curves'][3]['offset_meters']
 ['path_tpc']['curves'][3]['res_coeff']
-['path_tpc']['curves'][3]['res_net']
-['path_tpc']['curves'][4]['offset']
+['path_tpc']['curves'][3]['res_net_meters']
+['path_tpc']['curves'][4]['offset_meters']
 ['path_tpc']['curves'][4]['res_coeff']
-['path_tpc']['curves'][4]['res_net']
-['path_tpc']['curves'][5]['offset']
+['path_tpc']['curves'][4]['res_net_meters']
+['path_tpc']['curves'][5]['offset_meters']
 ['path_tpc']['curves'][5]['res_coeff']
-['path_tpc']['curves'][5]['res_net']
-['path_tpc']['curves'][6]['offset']
+['path_tpc']['curves'][5]['res_net_meters']
+['path_tpc']['curves'][6]['offset_meters']
 ['path_tpc']['curves'][6]['res_coeff']
-['path_tpc']['curves'][6]['res_net']
-['path_tpc']['curves'][7]['offset']
+['path_tpc']['curves'][6]['res_net_meters']
+['path_tpc']['curves'][7]['offset_meters']
 ['path_tpc']['curves'][7]['res_coeff']
-['path_tpc']['curves'][7]['res_net']
-['path_tpc']['curves'][8]['offset']
+['path_tpc']['curves'][7]['res_net_meters']
+['path_tpc']['curves'][8]['offset_meters']
 ['path_tpc']['curves'][8]['res_coeff']
-['path_tpc']['curves'][8]['res_net']
-['path_tpc']['curves'][9]['offset']
+['path_tpc']['curves'][8]['res_net_meters']
+['path_tpc']['curves'][9]['offset_meters']
 ['path_tpc']['curves'][9]['res_coeff']
-['path_tpc']['curves'][9]['res_net']
-['path_tpc']['curves'][10]['offset']
+['path_tpc']['curves'][9]['res_net_meters']
+['path_tpc']['curves'][10]['offset_meters']
 ['path_tpc']['curves'][10]['res_coeff']
-['path_tpc']['curves'][10]['res_net']
-['path_tpc']['curves'][11]['offset']
+['path_tpc']['curves'][10]['res_net_meters']
+['path_tpc']['curves'][11]['offset_meters']
 ['path_tpc']['curves'][11]['res_coeff']
-['path_tpc']['curves'][11]['res_net']
-['path_tpc']['curves'][12]['offset']
+['path_tpc']['curves'][11]['res_net_meters']
+['path_tpc']['curves'][12]['offset_meters']
 ['path_tpc']['curves'][12]['res_coeff']
-['path_tpc']['curves'][12]['res_net']
-['path_tpc']['curves'][13]['offset']
+['path_tpc']['curves'][12]['res_net_meters']
+['path_tpc']['curves'][13]['offset_meters']
 ['path_tpc']['curves'][13]['res_coeff']
-['path_tpc']['curves'][13]['res_net']
-['path_tpc']['curves'][14]['offset']
+['path_tpc']['curves'][13]['res_net_meters']
+['path_tpc']['curves'][14]['offset_meters']
 ['path_tpc']['curves'][14]['res_coeff']
-['path_tpc']['curves'][14]['res_net']
-['path_tpc']['curves'][15]['offset']
+['path_tpc']['curves'][14]['res_net_meters']
+['path_tpc']['curves'][15]['offset_meters']
 ['path_tpc']['curves'][15]['res_coeff']
-['path_tpc']['curves'][15]['res_net']
-['path_tpc']['curves'][16]['offset']
+['path_tpc']['curves'][15]['res_net_meters']
+['path_tpc']['curves'][16]['offset_meters']
 ['path_tpc']['curves'][16]['res_coeff']
-['path_tpc']['curves'][16]['res_net']
-['path_tpc']['curves'][17]['offset']
+['path_tpc']['curves'][16]['res_net_meters']
+['path_tpc']['curves'][17]['offset_meters']
 ['path_tpc']['curves'][17]['res_coeff']
-['path_tpc']['curves'][17]['res_net']
-['path_tpc']['curves'][18]['offset']
+['path_tpc']['curves'][17]['res_net_meters']
+['path_tpc']['curves'][18]['offset_meters']
 ['path_tpc']['curves'][18]['res_coeff']
-['path_tpc']['curves'][18]['res_net']
-['path_tpc']['curves'][19]['offset']
+['path_tpc']['curves'][18]['res_net_meters']
+['path_tpc']['curves'][19]['offset_meters']
 ['path_tpc']['curves'][19]['res_coeff']
-['path_tpc']['curves'][19]['res_net']
-['path_tpc']['curves'][20]['offset']
+['path_tpc']['curves'][19]['res_net_meters']
+['path_tpc']['curves'][20]['offset_meters']
 ['path_tpc']['curves'][20]['res_coeff']
-['path_tpc']['curves'][20]['res_net']
-['path_tpc']['curves'][21]['offset']
+['path_tpc']['curves'][20]['res_net_meters']
+['path_tpc']['curves'][21]['offset_meters']
 ['path_tpc']['curves'][21]['res_coeff']
-['path_tpc']['curves'][21]['res_net']
-['path_tpc']['curves'][22]['offset']
+['path_tpc']['curves'][21]['res_net_meters']
+['path_tpc']['curves'][22]['offset_meters']
 ['path_tpc']['curves'][22]['res_coeff']
-['path_tpc']['curves'][22]['res_net']
-['path_tpc']['curves'][23]['offset']
+['path_tpc']['curves'][22]['res_net_meters']
+['path_tpc']['curves'][23]['offset_meters']
 ['path_tpc']['curves'][23]['res_coeff']
-['path_tpc']['curves'][23]['res_net']
-['path_tpc']['curves'][24]['offset']
+['path_tpc']['curves'][23]['res_net_meters']
+['path_tpc']['curves'][24]['offset_meters']
 ['path_tpc']['curves'][24]['res_coeff']
-['path_tpc']['curves'][24]['res_net']
-['path_tpc']['curves'][25]['offset']
+['path_tpc']['curves'][24]['res_net_meters']
+['path_tpc']['curves'][25]['offset_meters']
 ['path_tpc']['curves'][25]['res_coeff']
-['path_tpc']['curves'][25]['res_net']
-['path_tpc']['curves'][26]['offset']
+['path_tpc']['curves'][25]['res_net_meters']
+['path_tpc']['curves'][26]['offset_meters']
 ['path_tpc']['curves'][26]['res_coeff']
-['path_tpc']['curves'][26]['res_net']
-['path_tpc']['curves'][27]['offset']
+['path_tpc']['curves'][26]['res_net_meters']
+['path_tpc']['curves'][27]['offset_meters']
 ['path_tpc']['curves'][27]['res_coeff']
-['path_tpc']['curves'][27]['res_net']
-['path_tpc']['curves'][28]['offset']
+['path_tpc']['curves'][27]['res_net_meters']
+['path_tpc']['curves'][28]['offset_meters']
 ['path_tpc']['curves'][28]['res_coeff']
-['path_tpc']['curves'][28]['res_net']
-['path_tpc']['curves'][29]['offset']
+['path_tpc']['curves'][28]['res_net_meters']
+['path_tpc']['curves'][29]['offset_meters']
 ['path_tpc']['curves'][29]['res_coeff']
-['path_tpc']['curves'][29]['res_net']
-['path_tpc']['curves'][30]['offset']
+['path_tpc']['curves'][29]['res_net_meters']
+['path_tpc']['curves'][30]['offset_meters']
 ['path_tpc']['curves'][30]['res_coeff']
-['path_tpc']['curves'][30]['res_net']
-['path_tpc']['curves'][31]['offset']
+['path_tpc']['curves'][30]['res_net_meters']
+['path_tpc']['curves'][31]['offset_meters']
 ['path_tpc']['curves'][31]['res_coeff']
-['path_tpc']['curves'][31]['res_net']
-['path_tpc']['curves'][32]['offset']
+['path_tpc']['curves'][31]['res_net_meters']
+['path_tpc']['curves'][32]['offset_meters']
 ['path_tpc']['curves'][32]['res_coeff']
-['path_tpc']['curves'][32]['res_net']
-['path_tpc']['curves'][33]['offset']
+['path_tpc']['curves'][32]['res_net_meters']
+['path_tpc']['curves'][33]['offset_meters']
 ['path_tpc']['curves'][33]['res_coeff']
-['path_tpc']['curves'][33]['res_net']
-['path_tpc']['curves'][34]['offset']
+['path_tpc']['curves'][33]['res_net_meters']
+['path_tpc']['curves'][34]['offset_meters']
 ['path_tpc']['curves'][34]['res_coeff']
-['path_tpc']['curves'][34]['res_net']
-['path_tpc']['curves'][35]['offset']
+['path_tpc']['curves'][34]['res_net_meters']
+['path_tpc']['curves'][35]['offset_meters']
 ['path_tpc']['curves'][35]['res_coeff']
-['path_tpc']['curves'][35]['res_net']
-['path_tpc']['curves'][36]['offset']
+['path_tpc']['curves'][35]['res_net_meters']
+['path_tpc']['curves'][36]['offset_meters']
 ['path_tpc']['curves'][36]['res_coeff']
-['path_tpc']['curves'][36]['res_net']
-['path_tpc']['curves'][37]['offset']
+['path_tpc']['curves'][36]['res_net_meters']
+['path_tpc']['curves'][37]['offset_meters']
 ['path_tpc']['curves'][37]['res_coeff']
-['path_tpc']['curves'][37]['res_net']
-['path_tpc']['curves'][38]['offset']
+['path_tpc']['curves'][37]['res_net_meters']
+['path_tpc']['curves'][38]['offset_meters']
 ['path_tpc']['curves'][38]['res_coeff']
-['path_tpc']['curves'][38]['res_net']
-['path_tpc']['curves'][39]['offset']
+['path_tpc']['curves'][38]['res_net_meters']
+['path_tpc']['curves'][39]['offset_meters']
 ['path_tpc']['curves'][39]['res_coeff']
-['path_tpc']['curves'][39]['res_net']
-['path_tpc']['curves'][40]['offset']
+['path_tpc']['curves'][39]['res_net_meters']
+['path_tpc']['curves'][40]['offset_meters']
 ['path_tpc']['curves'][40]['res_coeff']
-['path_tpc']['curves'][40]['res_net']
-['path_tpc']['curves'][41]['offset']
+['path_tpc']['curves'][40]['res_net_meters']
+['path_tpc']['curves'][41]['offset_meters']
 ['path_tpc']['curves'][41]['res_coeff']
-['path_tpc']['curves'][41]['res_net']
-['path_tpc']['curves'][42]['offset']
+['path_tpc']['curves'][41]['res_net_meters']
+['path_tpc']['curves'][42]['offset_meters']
 ['path_tpc']['curves'][42]['res_coeff']
-['path_tpc']['curves'][42]['res_net']
-['path_tpc']['curves'][43]['offset']
+['path_tpc']['curves'][42]['res_net_meters']
+['path_tpc']['curves'][43]['offset_meters']
 ['path_tpc']['curves'][43]['res_coeff']
-['path_tpc']['curves'][43]['res_net']
-['path_tpc']['curves'][44]['offset']
+['path_tpc']['curves'][43]['res_net_meters']
+['path_tpc']['curves'][44]['offset_meters']
 ['path_tpc']['curves'][44]['res_coeff']
-['path_tpc']['curves'][44]['res_net']
-['path_tpc']['curves'][45]['offset']
+['path_tpc']['curves'][44]['res_net_meters']
+['path_tpc']['curves'][45]['offset_meters']
 ['path_tpc']['curves'][45]['res_coeff']
-['path_tpc']['curves'][45]['res_net']
-['path_tpc']['curves'][46]['offset']
+['path_tpc']['curves'][45]['res_net_meters']
+['path_tpc']['curves'][46]['offset_meters']
 ['path_tpc']['curves'][46]['res_coeff']
-['path_tpc']['curves'][46]['res_net']
-['path_tpc']['curves'][47]['offset']
+['path_tpc']['curves'][46]['res_net_meters']
+['path_tpc']['curves'][47]['offset_meters']
 ['path_tpc']['curves'][47]['res_coeff']
-['path_tpc']['curves'][47]['res_net']
-['path_tpc']['curves'][48]['offset']
+['path_tpc']['curves'][47]['res_net_meters']
+['path_tpc']['curves'][48]['offset_meters']
 ['path_tpc']['curves'][48]['res_coeff']
-['path_tpc']['curves'][48]['res_net']
-['path_tpc']['curves'][49]['offset']
+['path_tpc']['curves'][48]['res_net_meters']
+['path_tpc']['curves'][49]['offset_meters']
 ['path_tpc']['curves'][49]['res_coeff']
-['path_tpc']['curves'][49]['res_net']
-['path_tpc']['curves'][50]['offset']
+['path_tpc']['curves'][49]['res_net_meters']
+['path_tpc']['curves'][50]['offset_meters']
 ['path_tpc']['curves'][50]['res_coeff']
-['path_tpc']['curves'][50]['res_net']
-['path_tpc']['curves'][51]['offset']
+['path_tpc']['curves'][50]['res_net_meters']
+['path_tpc']['curves'][51]['offset_meters']
 ['path_tpc']['curves'][51]['res_coeff']
-['path_tpc']['curves'][51]['res_net']
-['path_tpc']['curves'][52]['offset']
+['path_tpc']['curves'][51]['res_net_meters']
+['path_tpc']['curves'][52]['offset_meters']
 ['path_tpc']['curves'][52]['res_coeff']
-['path_tpc']['curves'][52]['res_net']
-['path_tpc']['curves'][53]['offset']
+['path_tpc']['curves'][52]['res_net_meters']
+['path_tpc']['curves'][53]['offset_meters']
 ['path_tpc']['curves'][53]['res_coeff']
-['path_tpc']['curves'][53]['res_net']
-['path_tpc']['curves'][54]['offset']
+['path_tpc']['curves'][53]['res_net_meters']
+['path_tpc']['curves'][54]['offset_meters']
 ['path_tpc']['curves'][54]['res_coeff']
-['path_tpc']['curves'][54]['res_net']
-['path_tpc']['curves'][55]['offset']
+['path_tpc']['curves'][54]['res_net_meters']
+['path_tpc']['curves'][55]['offset_meters']
 ['path_tpc']['curves'][55]['res_coeff']
-['path_tpc']['curves'][55]['res_net']
-['path_tpc']['curves'][56]['offset']
+['path_tpc']['curves'][55]['res_net_meters']
+['path_tpc']['curves'][56]['offset_meters']
 ['path_tpc']['curves'][56]['res_coeff']
-['path_tpc']['curves'][56]['res_net']
-['path_tpc']['curves'][57]['offset']
+['path_tpc']['curves'][56]['res_net_meters']
+['path_tpc']['curves'][57]['offset_meters']
 ['path_tpc']['curves'][57]['res_coeff']
-['path_tpc']['curves'][57]['res_net']
-['path_tpc']['curves'][58]['offset']
+['path_tpc']['curves'][57]['res_net_meters']
+['path_tpc']['curves'][58]['offset_meters']
 ['path_tpc']['curves'][58]['res_coeff']
-['path_tpc']['curves'][58]['res_net']
-['path_tpc']['curves'][59]['offset']
+['path_tpc']['curves'][58]['res_net_meters']
+['path_tpc']['curves'][59]['offset_meters']
 ['path_tpc']['curves'][59]['res_coeff']
-['path_tpc']['curves'][59]['res_net']
-['path_tpc']['curves'][60]['offset']
+['path_tpc']['curves'][59]['res_net_meters']
+['path_tpc']['curves'][60]['offset_meters']
 ['path_tpc']['curves'][60]['res_coeff']
-['path_tpc']['curves'][60]['res_net']
-['path_tpc']['curves'][61]['offset']
+['path_tpc']['curves'][60]['res_net_meters']
+['path_tpc']['curves'][61]['offset_meters']
 ['path_tpc']['curves'][61]['res_coeff']
-['path_tpc']['curves'][61]['res_net']
-['path_tpc']['curves'][62]['offset']
+['path_tpc']['curves'][61]['res_net_meters']
+['path_tpc']['curves'][62]['offset_meters']
 ['path_tpc']['curves'][62]['res_coeff']
-['path_tpc']['curves'][62]['res_net']
-['path_tpc']['curves'][63]['offset']
+['path_tpc']['curves'][62]['res_net_meters']
+['path_tpc']['curves'][63]['offset_meters']
 ['path_tpc']['curves'][63]['res_coeff']
-['path_tpc']['curves'][63]['res_net']
-['path_tpc']['curves'][64]['offset']
+['path_tpc']['curves'][63]['res_net_meters']
+['path_tpc']['curves'][64]['offset_meters']
 ['path_tpc']['curves'][64]['res_coeff']
-['path_tpc']['curves'][64]['res_net']
-['path_tpc']['curves'][65]['offset']
+['path_tpc']['curves'][64]['res_net_meters']
+['path_tpc']['curves'][65]['offset_meters']
 ['path_tpc']['curves'][65]['res_coeff']
-['path_tpc']['curves'][65]['res_net']
-['path_tpc']['curves'][66]['offset']
+['path_tpc']['curves'][65]['res_net_meters']
+['path_tpc']['curves'][66]['offset_meters']
 ['path_tpc']['curves'][66]['res_coeff']
-['path_tpc']['curves'][66]['res_net']
-['path_tpc']['curves'][67]['offset']
+['path_tpc']['curves'][66]['res_net_meters']
+['path_tpc']['curves'][67]['offset_meters']
 ['path_tpc']['curves'][67]['res_coeff']
-['path_tpc']['curves'][67]['res_net']
-['path_tpc']['curves'][68]['offset']
+['path_tpc']['curves'][67]['res_net_meters']
+['path_tpc']['curves'][68]['offset_meters']
 ['path_tpc']['curves'][68]['res_coeff']
-['path_tpc']['curves'][68]['res_net']
-['path_tpc']['curves'][69]['offset']
+['path_tpc']['curves'][68]['res_net_meters']
+['path_tpc']['curves'][69]['offset_meters']
 ['path_tpc']['curves'][69]['res_coeff']
-['path_tpc']['curves'][69]['res_net']
-['path_tpc']['curves'][70]['offset']
+['path_tpc']['curves'][69]['res_net_meters']
+['path_tpc']['curves'][70]['offset_meters']
 ['path_tpc']['curves'][70]['res_coeff']
-['path_tpc']['curves'][70]['res_net']
-['path_tpc']['curves'][71]['offset']
+['path_tpc']['curves'][70]['res_net_meters']
+['path_tpc']['curves'][71]['offset_meters']
 ['path_tpc']['curves'][71]['res_coeff']
-['path_tpc']['curves'][71]['res_net']
-['path_tpc']['curves'][72]['offset']
+['path_tpc']['curves'][71]['res_net_meters']
+['path_tpc']['curves'][72]['offset_meters']
 ['path_tpc']['curves'][72]['res_coeff']
-['path_tpc']['curves'][72]['res_net']
-['path_tpc']['curves'][73]['offset']
+['path_tpc']['curves'][72]['res_net_meters']
+['path_tpc']['curves'][73]['offset_meters']
 ['path_tpc']['curves'][73]['res_coeff']
-['path_tpc']['curves'][73]['res_net']
-['path_tpc']['curves'][74]['offset']
+['path_tpc']['curves'][73]['res_net_meters']
+['path_tpc']['curves'][74]['offset_meters']
 ['path_tpc']['curves'][74]['res_coeff']
-['path_tpc']['curves'][74]['res_net']
-['path_tpc']['curves'][75]['offset']
+['path_tpc']['curves'][74]['res_net_meters']
+['path_tpc']['curves'][75]['offset_meters']
 ['path_tpc']['curves'][75]['res_coeff']
-['path_tpc']['curves'][75]['res_net']
-['path_tpc']['curves'][76]['offset']
+['path_tpc']['curves'][75]['res_net_meters']
+['path_tpc']['curves'][76]['offset_meters']
 ['path_tpc']['curves'][76]['res_coeff']
-['path_tpc']['curves'][76]['res_net']
-['path_tpc']['curves'][77]['offset']
+['path_tpc']['curves'][76]['res_net_meters']
+['path_tpc']['curves'][77]['offset_meters']
 ['path_tpc']['curves'][77]['res_coeff']
-['path_tpc']['curves'][77]['res_net']
-['path_tpc']['curves'][78]['offset']
+['path_tpc']['curves'][77]['res_net_meters']
+['path_tpc']['curves'][78]['offset_meters']
 ['path_tpc']['curves'][78]['res_coeff']
-['path_tpc']['curves'][78]['res_net']
-['path_tpc']['curves'][79]['offset']
+['path_tpc']['curves'][78]['res_net_meters']
+['path_tpc']['curves'][79]['offset_meters']
 ['path_tpc']['curves'][79]['res_coeff']
-['path_tpc']['curves'][79]['res_net']
-['path_tpc']['curves'][80]['offset']
+['path_tpc']['curves'][79]['res_net_meters']
+['path_tpc']['curves'][80]['offset_meters']
 ['path_tpc']['curves'][80]['res_coeff']
-['path_tpc']['curves'][80]['res_net']
-['path_tpc']['curves'][81]['offset']
+['path_tpc']['curves'][80]['res_net_meters']
+['path_tpc']['curves'][81]['offset_meters']
 ['path_tpc']['curves'][81]['res_coeff']
-['path_tpc']['curves'][81]['res_net']
-['path_tpc']['curves'][82]['offset']
+['path_tpc']['curves'][81]['res_net_meters']
+['path_tpc']['curves'][82]['offset_meters']
 ['path_tpc']['curves'][82]['res_coeff']
-['path_tpc']['curves'][82]['res_net']
-['path_tpc']['curves'][83]['offset']
+['path_tpc']['curves'][82]['res_net_meters']
+['path_tpc']['curves'][83]['offset_meters']
 ['path_tpc']['curves'][83]['res_coeff']
-['path_tpc']['curves'][83]['res_net']
-['path_tpc']['curves'][84]['offset']
+['path_tpc']['curves'][83]['res_net_meters']
+['path_tpc']['curves'][84]['offset_meters']
 ['path_tpc']['curves'][84]['res_coeff']
-['path_tpc']['curves'][84]['res_net']
-['path_tpc']['curves'][85]['offset']
+['path_tpc']['curves'][84]['res_net_meters']
+['path_tpc']['curves'][85]['offset_meters']
 ['path_tpc']['curves'][85]['res_coeff']
-['path_tpc']['curves'][85]['res_net']
-['path_tpc']['curves'][86]['offset']
+['path_tpc']['curves'][85]['res_net_meters']
+['path_tpc']['curves'][86]['offset_meters']
 ['path_tpc']['curves'][86]['res_coeff']
-['path_tpc']['curves'][86]['res_net']
-['path_tpc']['curves'][87]['offset']
+['path_tpc']['curves'][86]['res_net_meters']
+['path_tpc']['curves'][87]['offset_meters']
 ['path_tpc']['curves'][87]['res_coeff']
-['path_tpc']['curves'][87]['res_net']
-['path_tpc']['curves'][88]['offset']
+['path_tpc']['curves'][87]['res_net_meters']
+['path_tpc']['curves'][88]['offset_meters']
 ['path_tpc']['curves'][88]['res_coeff']
-['path_tpc']['curves'][88]['res_net']
-['path_tpc']['curves'][89]['offset']
+['path_tpc']['curves'][88]['res_net_meters']
+['path_tpc']['curves'][89]['offset_meters']
 ['path_tpc']['curves'][89]['res_coeff']
-['path_tpc']['curves'][89]['res_net']
-['path_tpc']['curves'][90]['offset']
+['path_tpc']['curves'][89]['res_net_meters']
+['path_tpc']['curves'][90]['offset_meters']
 ['path_tpc']['curves'][90]['res_coeff']
-['path_tpc']['curves'][90]['res_net']
-['path_tpc']['curves'][91]['offset']
+['path_tpc']['curves'][90]['res_net_meters']
+['path_tpc']['curves'][91]['offset_meters']
 ['path_tpc']['curves'][91]['res_coeff']
-['path_tpc']['curves'][91]['res_net']
-['path_tpc']['curves'][92]['offset']
+['path_tpc']['curves'][91]['res_net_meters']
+['path_tpc']['curves'][92]['offset_meters']
 ['path_tpc']['curves'][92]['res_coeff']
-['path_tpc']['curves'][92]['res_net']
-['path_tpc']['curves'][93]['offset']
+['path_tpc']['curves'][92]['res_net_meters']
+['path_tpc']['curves'][93]['offset_meters']
 ['path_tpc']['curves'][93]['res_coeff']
-['path_tpc']['curves'][93]['res_net']
-['path_tpc']['curves'][94]['offset']
+['path_tpc']['curves'][93]['res_net_meters']
+['path_tpc']['curves'][94]['offset_meters']
 ['path_tpc']['curves'][94]['res_coeff']
-['path_tpc']['curves'][94]['res_net']
-['path_tpc']['curves'][95]['offset']
+['path_tpc']['curves'][94]['res_net_meters']
+['path_tpc']['curves'][95]['offset_meters']
 ['path_tpc']['curves'][95]['res_coeff']
-['path_tpc']['curves'][95]['res_net']
-['path_tpc']['curves'][96]['offset']
+['path_tpc']['curves'][95]['res_net_meters']
+['path_tpc']['curves'][96]['offset_meters']
 ['path_tpc']['curves'][96]['res_coeff']
-['path_tpc']['curves'][96]['res_net']
-['path_tpc']['curves'][97]['offset']
+['path_tpc']['curves'][96]['res_net_meters']
+['path_tpc']['curves'][97]['offset_meters']
 ['path_tpc']['curves'][97]['res_coeff']
-['path_tpc']['curves'][97]['res_net']
-['path_tpc']['curves'][98]['offset']
+['path_tpc']['curves'][97]['res_net_meters']
+['path_tpc']['curves'][98]['offset_meters']
 ['path_tpc']['curves'][98]['res_coeff']
-['path_tpc']['curves'][98]['res_net']
-['path_tpc']['curves'][99]['offset']
+['path_tpc']['curves'][98]['res_net_meters']
+['path_tpc']['curves'][99]['offset_meters']
 ['path_tpc']['curves'][99]['res_coeff']
-['path_tpc']['curves'][99]['res_net']
-['path_tpc']['curves'][100]['offset']
+['path_tpc']['curves'][99]['res_net_meters']
+['path_tpc']['curves'][100]['offset_meters']
 ['path_tpc']['curves'][100]['res_coeff']
-['path_tpc']['curves'][100]['res_net']
-['path_tpc']['curves'][101]['offset']
+['path_tpc']['curves'][100]['res_net_meters']
+['path_tpc']['curves'][101]['offset_meters']
 ['path_tpc']['curves'][101]['res_coeff']
-['path_tpc']['curves'][101]['res_net']
-['path_tpc']['curves'][102]['offset']
+['path_tpc']['curves'][101]['res_net_meters']
+['path_tpc']['curves'][102]['offset_meters']
 ['path_tpc']['curves'][102]['res_coeff']
-['path_tpc']['curves'][102]['res_net']
-['path_tpc']['curves'][103]['offset']
+['path_tpc']['curves'][102]['res_net_meters']
+['path_tpc']['curves'][103]['offset_meters']
 ['path_tpc']['curves'][103]['res_coeff']
-['path_tpc']['curves'][103]['res_net']
-['path_tpc']['curves'][104]['offset']
+['path_tpc']['curves'][103]['res_net_meters']
+['path_tpc']['curves'][104]['offset_meters']
 ['path_tpc']['curves'][104]['res_coeff']
-['path_tpc']['curves'][104]['res_net']
-['path_tpc']['curves'][105]['offset']
+['path_tpc']['curves'][104]['res_net_meters']
+['path_tpc']['curves'][105]['offset_meters']
 ['path_tpc']['curves'][105]['res_coeff']
-['path_tpc']['curves'][105]['res_net']
-['path_tpc']['curves'][106]['offset']
+['path_tpc']['curves'][105]['res_net_meters']
+['path_tpc']['curves'][106]['offset_meters']
 ['path_tpc']['curves'][106]['res_coeff']
-['path_tpc']['curves'][106]['res_net']
-['path_tpc']['curves'][107]['offset']
+['path_tpc']['curves'][106]['res_net_meters']
+['path_tpc']['curves'][107]['offset_meters']
 ['path_tpc']['curves'][107]['res_coeff']
-['path_tpc']['curves'][107]['res_net']
-['path_tpc']['curves'][108]['offset']
+['path_tpc']['curves'][107]['res_net_meters']
+['path_tpc']['curves'][108]['offset_meters']
 ['path_tpc']['curves'][108]['res_coeff']
-['path_tpc']['curves'][108]['res_net']
-['path_tpc']['curves'][109]['offset']
+['path_tpc']['curves'][108]['res_net_meters']
+['path_tpc']['curves'][109]['offset_meters']
 ['path_tpc']['curves'][109]['res_coeff']
-['path_tpc']['curves'][109]['res_net']
-['path_tpc']['curves'][110]['offset']
+['path_tpc']['curves'][109]['res_net_meters']
+['path_tpc']['curves'][110]['offset_meters']
 ['path_tpc']['curves'][110]['res_coeff']
-['path_tpc']['curves'][110]['res_net']
-['path_tpc']['curves'][111]['offset']
+['path_tpc']['curves'][110]['res_net_meters']
+['path_tpc']['curves'][111]['offset_meters']
 ['path_tpc']['curves'][111]['res_coeff']
-['path_tpc']['curves'][111]['res_net']
-['path_tpc']['curves'][112]['offset']
+['path_tpc']['curves'][111]['res_net_meters']
+['path_tpc']['curves'][112]['offset_meters']
 ['path_tpc']['curves'][112]['res_coeff']
-['path_tpc']['curves'][112]['res_net']
-['path_tpc']['curves'][113]['offset']
+['path_tpc']['curves'][112]['res_net_meters']
+['path_tpc']['curves'][113]['offset_meters']
 ['path_tpc']['curves'][113]['res_coeff']
-['path_tpc']['curves'][113]['res_net']
-['path_tpc']['curves'][114]['offset']
+['path_tpc']['curves'][113]['res_net_meters']
+['path_tpc']['curves'][114]['offset_meters']
 ['path_tpc']['curves'][114]['res_coeff']
-['path_tpc']['curves'][114]['res_net']
-['path_tpc']['curves'][115]['offset']
+['path_tpc']['curves'][114]['res_net_meters']
+['path_tpc']['curves'][115]['offset_meters']
 ['path_tpc']['curves'][115]['res_coeff']
-['path_tpc']['curves'][115]['res_net']
-['path_tpc']['curves'][116]['offset']
+['path_tpc']['curves'][115]['res_net_meters']
+['path_tpc']['curves'][116]['offset_meters']
 ['path_tpc']['curves'][116]['res_coeff']
-['path_tpc']['curves'][116]['res_net']
-['path_tpc']['curves'][117]['offset']
+['path_tpc']['curves'][116]['res_net_meters']
+['path_tpc']['curves'][117]['offset_meters']
 ['path_tpc']['curves'][117]['res_coeff']
-['path_tpc']['curves'][117]['res_net']
-['path_tpc']['curves'][118]['offset']
+['path_tpc']['curves'][117]['res_net_meters']
+['path_tpc']['curves'][118]['offset_meters']
 ['path_tpc']['curves'][118]['res_coeff']
-['path_tpc']['curves'][118]['res_net']
-['path_tpc']['curves'][119]['offset']
+['path_tpc']['curves'][118]['res_net_meters']
+['path_tpc']['curves'][119]['offset_meters']
 ['path_tpc']['curves'][119]['res_coeff']
-['path_tpc']['curves'][119]['res_net']
-['path_tpc']['curves'][120]['offset']
+['path_tpc']['curves'][119]['res_net_meters']
+['path_tpc']['curves'][120]['offset_meters']
 ['path_tpc']['curves'][120]['res_coeff']
-['path_tpc']['curves'][120]['res_net']
-['path_tpc']['curves'][121]['offset']
+['path_tpc']['curves'][120]['res_net_meters']
+['path_tpc']['curves'][121]['offset_meters']
 ['path_tpc']['curves'][121]['res_coeff']
-['path_tpc']['curves'][121]['res_net']
-['path_tpc']['curves'][122]['offset']
+['path_tpc']['curves'][121]['res_net_meters']
+['path_tpc']['curves'][122]['offset_meters']
 ['path_tpc']['curves'][122]['res_coeff']
-['path_tpc']['curves'][122]['res_net']
-['path_tpc']['curves'][123]['offset']
+['path_tpc']['curves'][122]['res_net_meters']
+['path_tpc']['curves'][123]['offset_meters']
 ['path_tpc']['curves'][123]['res_coeff']
-['path_tpc']['curves'][123]['res_net']
-['path_tpc']['curves'][124]['offset']
+['path_tpc']['curves'][123]['res_net_meters']
+['path_tpc']['curves'][124]['offset_meters']
 ['path_tpc']['curves'][124]['res_coeff']
-['path_tpc']['curves'][124]['res_net']
-['path_tpc']['curves'][125]['offset']
+['path_tpc']['curves'][124]['res_net_meters']
+['path_tpc']['curves'][125]['offset_meters']
 ['path_tpc']['curves'][125]['res_coeff']
-['path_tpc']['curves'][125]['res_net']
-['path_tpc']['curves'][126]['offset']
+['path_tpc']['curves'][125]['res_net_meters']
+['path_tpc']['curves'][126]['offset_meters']
 ['path_tpc']['curves'][126]['res_coeff']
-['path_tpc']['curves'][126]['res_net']
-['path_tpc']['curves'][127]['offset']
+['path_tpc']['curves'][126]['res_net_meters']
+['path_tpc']['curves'][127]['offset_meters']
 ['path_tpc']['curves'][127]['res_coeff']
-['path_tpc']['curves'][127]['res_net']
-['path_tpc']['curves'][128]['offset']
+['path_tpc']['curves'][127]['res_net_meters']
+['path_tpc']['curves'][128]['offset_meters']
 ['path_tpc']['curves'][128]['res_coeff']
-['path_tpc']['curves'][128]['res_net']
-['path_tpc']['curves'][129]['offset']
+['path_tpc']['curves'][128]['res_net_meters']
+['path_tpc']['curves'][129]['offset_meters']
 ['path_tpc']['curves'][129]['res_coeff']
-['path_tpc']['curves'][129]['res_net']
-['path_tpc']['curves'][130]['offset']
+['path_tpc']['curves'][129]['res_net_meters']
+['path_tpc']['curves'][130]['offset_meters']
 ['path_tpc']['curves'][130]['res_coeff']
-['path_tpc']['curves'][130]['res_net']
-['path_tpc']['curves'][131]['offset']
+['path_tpc']['curves'][130]['res_net_meters']
+['path_tpc']['curves'][131]['offset_meters']
 ['path_tpc']['curves'][131]['res_coeff']
-['path_tpc']['curves'][131]['res_net']
-['path_tpc']['curves'][132]['offset']
+['path_tpc']['curves'][131]['res_net_meters']
+['path_tpc']['curves'][132]['offset_meters']
 ['path_tpc']['curves'][132]['res_coeff']
-['path_tpc']['curves'][132]['res_net']
-['path_tpc']['curves'][133]['offset']
+['path_tpc']['curves'][132]['res_net_meters']
+['path_tpc']['curves'][133]['offset_meters']
 ['path_tpc']['curves'][133]['res_coeff']
-['path_tpc']['curves'][133]['res_net']
-['path_tpc']['curves'][134]['offset']
+['path_tpc']['curves'][133]['res_net_meters']
+['path_tpc']['curves'][134]['offset_meters']
 ['path_tpc']['curves'][134]['res_coeff']
-['path_tpc']['curves'][134]['res_net']
-['path_tpc']['curves'][135]['offset']
+['path_tpc']['curves'][134]['res_net_meters']
+['path_tpc']['curves'][135]['offset_meters']
 ['path_tpc']['curves'][135]['res_coeff']
-['path_tpc']['curves'][135]['res_net']
-['path_tpc']['curves'][136]['offset']
+['path_tpc']['curves'][135]['res_net_meters']
+['path_tpc']['curves'][136]['offset_meters']
 ['path_tpc']['curves'][136]['res_coeff']
-['path_tpc']['curves'][136]['res_net']
-['path_tpc']['curves'][137]['offset']
+['path_tpc']['curves'][136]['res_net_meters']
+['path_tpc']['curves'][137]['offset_meters']
 ['path_tpc']['curves'][137]['res_coeff']
-['path_tpc']['curves'][137]['res_net']
-['path_tpc']['curves'][138]['offset']
+['path_tpc']['curves'][137]['res_net_meters']
+['path_tpc']['curves'][138]['offset_meters']
 ['path_tpc']['curves'][138]['res_coeff']
-['path_tpc']['curves'][138]['res_net']
-['path_tpc']['curves'][139]['offset']
+['path_tpc']['curves'][138]['res_net_meters']
+['path_tpc']['curves'][139]['offset_meters']
 ['path_tpc']['curves'][139]['res_coeff']
-['path_tpc']['curves'][139]['res_net']
-['path_tpc']['curves'][140]['offset']
+['path_tpc']['curves'][139]['res_net_meters']
+['path_tpc']['curves'][140]['offset_meters']
 ['path_tpc']['curves'][140]['res_coeff']
-['path_tpc']['curves'][140]['res_net']
-['path_tpc']['curves'][141]['offset']
+['path_tpc']['curves'][140]['res_net_meters']
+['path_tpc']['curves'][141]['offset_meters']
 ['path_tpc']['curves'][141]['res_coeff']
-['path_tpc']['curves'][141]['res_net']
-['path_tpc']['curves'][142]['offset']
+['path_tpc']['curves'][141]['res_net_meters']
+['path_tpc']['curves'][142]['offset_meters']
 ['path_tpc']['curves'][142]['res_coeff']
-['path_tpc']['curves'][142]['res_net']
-['path_tpc']['curves'][143]['offset']
+['path_tpc']['curves'][142]['res_net_meters']
+['path_tpc']['curves'][143]['offset_meters']
 ['path_tpc']['curves'][143]['res_coeff']
-['path_tpc']['curves'][143]['res_net']
-['path_tpc']['curves'][144]['offset']
+['path_tpc']['curves'][143]['res_net_meters']
+['path_tpc']['curves'][144]['offset_meters']
 ['path_tpc']['curves'][144]['res_coeff']
-['path_tpc']['curves'][144]['res_net']
-['path_tpc']['curves'][145]['offset']
+['path_tpc']['curves'][144]['res_net_meters']
+['path_tpc']['curves'][145]['offset_meters']
 ['path_tpc']['curves'][145]['res_coeff']
-['path_tpc']['curves'][145]['res_net']
-['path_tpc']['curves'][146]['offset']
+['path_tpc']['curves'][145]['res_net_meters']
+['path_tpc']['curves'][146]['offset_meters']
 ['path_tpc']['curves'][146]['res_coeff']
-['path_tpc']['curves'][146]['res_net']
-['path_tpc']['curves'][147]['offset']
+['path_tpc']['curves'][146]['res_net_meters']
+['path_tpc']['curves'][147]['offset_meters']
 ['path_tpc']['curves'][147]['res_coeff']
-['path_tpc']['curves'][147]['res_net']
-['path_tpc']['curves'][148]['offset']
+['path_tpc']['curves'][147]['res_net_meters']
+['path_tpc']['curves'][148]['offset_meters']
 ['path_tpc']['curves'][148]['res_coeff']
-['path_tpc']['curves'][148]['res_net']
-['path_tpc']['curves'][149]['offset']
+['path_tpc']['curves'][148]['res_net_meters']
+['path_tpc']['curves'][149]['offset_meters']
 ['path_tpc']['curves'][149]['res_coeff']
-['path_tpc']['curves'][149]['res_net']
-['path_tpc']['curves'][150]['offset']
+['path_tpc']['curves'][149]['res_net_meters']
+['path_tpc']['curves'][150]['offset_meters']
 ['path_tpc']['curves'][150]['res_coeff']
-['path_tpc']['curves'][150]['res_net']
-['path_tpc']['curves'][151]['offset']
+['path_tpc']['curves'][150]['res_net_meters']
+['path_tpc']['curves'][151]['offset_meters']
 ['path_tpc']['curves'][151]['res_coeff']
-['path_tpc']['curves'][151]['res_net']
-['path_tpc']['curves'][152]['offset']
+['path_tpc']['curves'][151]['res_net_meters']
+['path_tpc']['curves'][152]['offset_meters']
 ['path_tpc']['curves'][152]['res_coeff']
-['path_tpc']['curves'][152]['res_net']
-['path_tpc']['curves'][153]['offset']
+['path_tpc']['curves'][152]['res_net_meters']
+['path_tpc']['curves'][153]['offset_meters']
 ['path_tpc']['curves'][153]['res_coeff']
-['path_tpc']['curves'][153]['res_net']
-['path_tpc']['curves'][154]['offset']
+['path_tpc']['curves'][153]['res_net_meters']
+['path_tpc']['curves'][154]['offset_meters']
 ['path_tpc']['curves'][154]['res_coeff']
-['path_tpc']['curves'][154]['res_net']
-['path_tpc']['curves'][155]['offset']
+['path_tpc']['curves'][154]['res_net_meters']
+['path_tpc']['curves'][155]['offset_meters']
 ['path_tpc']['curves'][155]['res_coeff']
-['path_tpc']['curves'][155]['res_net']
-['path_tpc']['curves'][156]['offset']
+['path_tpc']['curves'][155]['res_net_meters']
+['path_tpc']['curves'][156]['offset_meters']
 ['path_tpc']['curves'][156]['res_coeff']
-['path_tpc']['curves'][156]['res_net']
-['path_tpc']['curves'][157]['offset']
+['path_tpc']['curves'][156]['res_net_meters']
+['path_tpc']['curves'][157]['offset_meters']
 ['path_tpc']['curves'][157]['res_coeff']
-['path_tpc']['curves'][157]['res_net']
-['path_tpc']['curves'][158]['offset']
+['path_tpc']['curves'][157]['res_net_meters']
+['path_tpc']['curves'][158]['offset_meters']
 ['path_tpc']['curves'][158]['res_coeff']
-['path_tpc']['curves'][158]['res_net']
-['path_tpc']['curves'][159]['offset']
+['path_tpc']['curves'][158]['res_net_meters']
+['path_tpc']['curves'][159]['offset_meters']
 ['path_tpc']['curves'][159]['res_coeff']
-['path_tpc']['curves'][159]['res_net']
-['path_tpc']['curves'][160]['offset']
+['path_tpc']['curves'][159]['res_net_meters']
+['path_tpc']['curves'][160]['offset_meters']
 ['path_tpc']['curves'][160]['res_coeff']
-['path_tpc']['curves'][160]['res_net']
-['path_tpc']['curves'][161]['offset']
+['path_tpc']['curves'][160]['res_net_meters']
+['path_tpc']['curves'][161]['offset_meters']
 ['path_tpc']['curves'][161]['res_coeff']
-['path_tpc']['curves'][161]['res_net']
-['path_tpc']['curves'][162]['offset']
+['path_tpc']['curves'][161]['res_net_meters']
+['path_tpc']['curves'][162]['offset_meters']
 ['path_tpc']['curves'][162]['res_coeff']
-['path_tpc']['curves'][162]['res_net']
-['path_tpc']['curves'][163]['offset']
+['path_tpc']['curves'][162]['res_net_meters']
+['path_tpc']['curves'][163]['offset_meters']
 ['path_tpc']['curves'][163]['res_coeff']
-['path_tpc']['curves'][163]['res_net']
-['path_tpc']['curves'][164]['offset']
+['path_tpc']['curves'][163]['res_net_meters']
+['path_tpc']['curves'][164]['offset_meters']
 ['path_tpc']['curves'][164]['res_coeff']
-['path_tpc']['curves'][164]['res_net']
-['path_tpc']['curves'][165]['offset']
+['path_tpc']['curves'][164]['res_net_meters']
+['path_tpc']['curves'][165]['offset_meters']
 ['path_tpc']['curves'][165]['res_coeff']
-['path_tpc']['curves'][165]['res_net']
-['path_tpc']['curves'][166]['offset']
+['path_tpc']['curves'][165]['res_net_meters']
+['path_tpc']['curves'][166]['offset_meters']
 ['path_tpc']['curves'][166]['res_coeff']
-['path_tpc']['curves'][166]['res_net']
-['path_tpc']['curves'][167]['offset']
+['path_tpc']['curves'][166]['res_net_meters']
+['path_tpc']['curves'][167]['offset_meters']
 ['path_tpc']['curves'][167]['res_coeff']
-['path_tpc']['curves'][167]['res_net']
-['path_tpc']['curves'][168]['offset']
+['path_tpc']['curves'][167]['res_net_meters']
+['path_tpc']['curves'][168]['offset_meters']
 ['path_tpc']['curves'][168]['res_coeff']
-['path_tpc']['curves'][168]['res_net']
-['path_tpc']['curves'][169]['offset']
+['path_tpc']['curves'][168]['res_net_meters']
+['path_tpc']['curves'][169]['offset_meters']
 ['path_tpc']['curves'][169]['res_coeff']
-['path_tpc']['curves'][169]['res_net']
-['path_tpc']['curves'][170]['offset']
+['path_tpc']['curves'][169]['res_net_meters']
+['path_tpc']['curves'][170]['offset_meters']
 ['path_tpc']['curves'][170]['res_coeff']
-['path_tpc']['curves'][170]['res_net']
-['path_tpc']['curves'][171]['offset']
+['path_tpc']['curves'][170]['res_net_meters']
+['path_tpc']['curves'][171]['offset_meters']
 ['path_tpc']['curves'][171]['res_coeff']
-['path_tpc']['curves'][171]['res_net']
-['path_tpc']['curves'][172]['offset']
+['path_tpc']['curves'][171]['res_net_meters']
+['path_tpc']['curves'][172]['offset_meters']
 ['path_tpc']['curves'][172]['res_coeff']
-['path_tpc']['curves'][172]['res_net']
-['path_tpc']['curves'][173]['offset']
+['path_tpc']['curves'][172]['res_net_meters']
+['path_tpc']['curves'][173]['offset_meters']
 ['path_tpc']['curves'][173]['res_coeff']
-['path_tpc']['curves'][173]['res_net']
-['path_tpc']['curves'][174]['offset']
+['path_tpc']['curves'][173]['res_net_meters']
+['path_tpc']['curves'][174]['offset_meters']
 ['path_tpc']['curves'][174]['res_coeff']
-['path_tpc']['curves'][174]['res_net']
-['path_tpc']['curves'][175]['offset']
+['path_tpc']['curves'][174]['res_net_meters']
+['path_tpc']['curves'][175]['offset_meters']
 ['path_tpc']['curves'][175]['res_coeff']
-['path_tpc']['curves'][175]['res_net']
-['path_tpc']['curves'][176]['offset']
+['path_tpc']['curves'][175]['res_net_meters']
+['path_tpc']['curves'][176]['offset_meters']
 ['path_tpc']['curves'][176]['res_coeff']
-['path_tpc']['curves'][176]['res_net']
-['path_tpc']['curves'][177]['offset']
+['path_tpc']['curves'][176]['res_net_meters']
+['path_tpc']['curves'][177]['offset_meters']
 ['path_tpc']['curves'][177]['res_coeff']
-['path_tpc']['curves'][177]['res_net']
-['path_tpc']['curves'][178]['offset']
+['path_tpc']['curves'][177]['res_net_meters']
+['path_tpc']['curves'][178]['offset_meters']
 ['path_tpc']['curves'][178]['res_coeff']
-['path_tpc']['curves'][178]['res_net']
-['path_tpc']['curves'][179]['offset']
+['path_tpc']['curves'][178]['res_net_meters']
+['path_tpc']['curves'][179]['offset_meters']
 ['path_tpc']['curves'][179]['res_coeff']
-['path_tpc']['curves'][179]['res_net']
-['path_tpc']['curves'][180]['offset']
+['path_tpc']['curves'][179]['res_net_meters']
+['path_tpc']['curves'][180]['offset_meters']
 ['path_tpc']['curves'][180]['res_coeff']
-['path_tpc']['curves'][180]['res_net']
-['path_tpc']['curves'][181]['offset']
+['path_tpc']['curves'][180]['res_net_meters']
+['path_tpc']['curves'][181]['offset_meters']
 ['path_tpc']['curves'][181]['res_coeff']
-['path_tpc']['curves'][181]['res_net']
-['path_tpc']['curves'][182]['offset']
+['path_tpc']['curves'][181]['res_net_meters']
+['path_tpc']['curves'][182]['offset_meters']
 ['path_tpc']['curves'][182]['res_coeff']
-['path_tpc']['curves'][182]['res_net']
-['path_tpc']['curves'][183]['offset']
+['path_tpc']['curves'][182]['res_net_meters']
+['path_tpc']['curves'][183]['offset_meters']
 ['path_tpc']['curves'][183]['res_coeff']
-['path_tpc']['curves'][183]['res_net']
-['path_tpc']['curves'][184]['offset']
+['path_tpc']['curves'][183]['res_net_meters']
+['path_tpc']['curves'][184]['offset_meters']
 ['path_tpc']['curves'][184]['res_coeff']
-['path_tpc']['curves'][184]['res_net']
-['path_tpc']['curves'][185]['offset']
+['path_tpc']['curves'][184]['res_net_meters']
+['path_tpc']['curves'][185]['offset_meters']
 ['path_tpc']['curves'][185]['res_coeff']
-['path_tpc']['curves'][185]['res_net']
-['path_tpc']['curves'][186]['offset']
+['path_tpc']['curves'][185]['res_net_meters']
+['path_tpc']['curves'][186]['offset_meters']
 ['path_tpc']['curves'][186]['res_coeff']
-['path_tpc']['curves'][186]['res_net']
-['path_tpc']['curves'][187]['offset']
+['path_tpc']['curves'][186]['res_net_meters']
+['path_tpc']['curves'][187]['offset_meters']
 ['path_tpc']['curves'][187]['res_coeff']
-['path_tpc']['curves'][187]['res_net']
-['path_tpc']['curves'][188]['offset']
+['path_tpc']['curves'][187]['res_net_meters']
+['path_tpc']['curves'][188]['offset_meters']
 ['path_tpc']['curves'][188]['res_coeff']
-['path_tpc']['curves'][188]['res_net']
-['path_tpc']['curves'][189]['offset']
+['path_tpc']['curves'][188]['res_net_meters']
+['path_tpc']['curves'][189]['offset_meters']
 ['path_tpc']['curves'][189]['res_coeff']
-['path_tpc']['curves'][189]['res_net']
-['path_tpc']['curves'][190]['offset']
+['path_tpc']['curves'][189]['res_net_meters']
+['path_tpc']['curves'][190]['offset_meters']
 ['path_tpc']['curves'][190]['res_coeff']
-['path_tpc']['curves'][190]['res_net']
-['path_tpc']['curves'][191]['offset']
+['path_tpc']['curves'][190]['res_net_meters']
+['path_tpc']['curves'][191]['offset_meters']
 ['path_tpc']['curves'][191]['res_coeff']
-['path_tpc']['curves'][191]['res_net']
-['path_tpc']['curves'][192]['offset']
+['path_tpc']['curves'][191]['res_net_meters']
+['path_tpc']['curves'][192]['offset_meters']
 ['path_tpc']['curves'][192]['res_coeff']
-['path_tpc']['curves'][192]['res_net']
-['path_tpc']['curves'][193]['offset']
+['path_tpc']['curves'][192]['res_net_meters']
+['path_tpc']['curves'][193]['offset_meters']
 ['path_tpc']['curves'][193]['res_coeff']
-['path_tpc']['curves'][193]['res_net']
-['path_tpc']['curves'][194]['offset']
+['path_tpc']['curves'][193]['res_net_meters']
+['path_tpc']['curves'][194]['offset_meters']
 ['path_tpc']['curves'][194]['res_coeff']
-['path_tpc']['curves'][194]['res_net']
-['path_tpc']['curves'][195]['offset']
+['path_tpc']['curves'][194]['res_net_meters']
+['path_tpc']['curves'][195]['offset_meters']
 ['path_tpc']['curves'][195]['res_coeff']
-['path_tpc']['curves'][195]['res_net']
-['path_tpc']['curves'][196]['offset']
+['path_tpc']['curves'][195]['res_net_meters']
+['path_tpc']['curves'][196]['offset_meters']
 ['path_tpc']['curves'][196]['res_coeff']
-['path_tpc']['curves'][196]['res_net']
-['path_tpc']['curves'][197]['offset']
+['path_tpc']['curves'][196]['res_net_meters']
+['path_tpc']['curves'][197]['offset_meters']
 ['path_tpc']['curves'][197]['res_coeff']
-['path_tpc']['curves'][197]['res_net']
-['path_tpc']['curves'][198]['offset']
+['path_tpc']['curves'][197]['res_net_meters']
+['path_tpc']['curves'][198]['offset_meters']
 ['path_tpc']['curves'][198]['res_coeff']
-['path_tpc']['curves'][198]['res_net']
-['path_tpc']['curves'][199]['offset']
+['path_tpc']['curves'][198]['res_net_meters']
+['path_tpc']['curves'][199]['offset_meters']
 ['path_tpc']['curves'][199]['res_coeff']
-['path_tpc']['curves'][199]['res_net']
-['path_tpc']['curves'][200]['offset']
+['path_tpc']['curves'][199]['res_net_meters']
+['path_tpc']['curves'][200]['offset_meters']
 ['path_tpc']['curves'][200]['res_coeff']
-['path_tpc']['curves'][200]['res_net']
-['path_tpc']['curves'][201]['offset']
+['path_tpc']['curves'][200]['res_net_meters']
+['path_tpc']['curves'][201]['offset_meters']
 ['path_tpc']['curves'][201]['res_coeff']
-['path_tpc']['curves'][201]['res_net']
-['path_tpc']['curves'][202]['offset']
+['path_tpc']['curves'][201]['res_net_meters']
+['path_tpc']['curves'][202]['offset_meters']
 ['path_tpc']['curves'][202]['res_coeff']
-['path_tpc']['curves'][202]['res_net']
-['path_tpc']['curves'][203]['offset']
+['path_tpc']['curves'][202]['res_net_meters']
+['path_tpc']['curves'][203]['offset_meters']
 ['path_tpc']['curves'][203]['res_coeff']
-['path_tpc']['curves'][203]['res_net']
-['path_tpc']['curves'][204]['offset']
+['path_tpc']['curves'][203]['res_net_meters']
+['path_tpc']['curves'][204]['offset_meters']
 ['path_tpc']['curves'][204]['res_coeff']
-['path_tpc']['curves'][204]['res_net']
-['path_tpc']['curves'][205]['offset']
+['path_tpc']['curves'][204]['res_net_meters']
+['path_tpc']['curves'][205]['offset_meters']
 ['path_tpc']['curves'][205]['res_coeff']
-['path_tpc']['curves'][205]['res_net']
-['path_tpc']['curves'][206]['offset']
+['path_tpc']['curves'][205]['res_net_meters']
+['path_tpc']['curves'][206]['offset_meters']
 ['path_tpc']['curves'][206]['res_coeff']
-['path_tpc']['curves'][206]['res_net']
-['path_tpc']['curves'][207]['offset']
+['path_tpc']['curves'][206]['res_net_meters']
+['path_tpc']['curves'][207]['offset_meters']
 ['path_tpc']['curves'][207]['res_coeff']
-['path_tpc']['curves'][207]['res_net']
-['path_tpc']['curves'][208]['offset']
+['path_tpc']['curves'][207]['res_net_meters']
+['path_tpc']['curves'][208]['offset_meters']
 ['path_tpc']['curves'][208]['res_coeff']
-['path_tpc']['curves'][208]['res_net']
-['path_tpc']['curves'][209]['offset']
+['path_tpc']['curves'][208]['res_net_meters']
+['path_tpc']['curves'][209]['offset_meters']
 ['path_tpc']['curves'][209]['res_coeff']
-['path_tpc']['curves'][209]['res_net']
-['path_tpc']['curves'][210]['offset']
+['path_tpc']['curves'][209]['res_net_meters']
+['path_tpc']['curves'][210]['offset_meters']
 ['path_tpc']['curves'][210]['res_coeff']
-['path_tpc']['curves'][210]['res_net']
-['path_tpc']['curves'][211]['offset']
+['path_tpc']['curves'][210]['res_net_meters']
+['path_tpc']['curves'][211]['offset_meters']
 ['path_tpc']['curves'][211]['res_coeff']
-['path_tpc']['curves'][211]['res_net']
-['path_tpc']['curves'][212]['offset']
+['path_tpc']['curves'][211]['res_net_meters']
+['path_tpc']['curves'][212]['offset_meters']
 ['path_tpc']['curves'][212]['res_coeff']
-['path_tpc']['curves'][212]['res_net']
-['path_tpc']['curves'][213]['offset']
+['path_tpc']['curves'][212]['res_net_meters']
+['path_tpc']['curves'][213]['offset_meters']
 ['path_tpc']['curves'][213]['res_coeff']
-['path_tpc']['curves'][213]['res_net']
-['path_tpc']['curves'][214]['offset']
+['path_tpc']['curves'][213]['res_net_meters']
+['path_tpc']['curves'][214]['offset_meters']
 ['path_tpc']['curves'][214]['res_coeff']
-['path_tpc']['curves'][214]['res_net']
-['path_tpc']['curves'][215]['offset']
+['path_tpc']['curves'][214]['res_net_meters']
+['path_tpc']['curves'][215]['offset_meters']
 ['path_tpc']['curves'][215]['res_coeff']
-['path_tpc']['curves'][215]['res_net']
-['path_tpc']['curves'][216]['offset']
+['path_tpc']['curves'][215]['res_net_meters']
+['path_tpc']['curves'][216]['offset_meters']
 ['path_tpc']['curves'][216]['res_coeff']
-['path_tpc']['curves'][216]['res_net']
-['path_tpc']['curves'][217]['offset']
+['path_tpc']['curves'][216]['res_net_meters']
+['path_tpc']['curves'][217]['offset_meters']
 ['path_tpc']['curves'][217]['res_coeff']
-['path_tpc']['curves'][217]['res_net']
-['path_tpc']['curves'][218]['offset']
+['path_tpc']['curves'][217]['res_net_meters']
+['path_tpc']['curves'][218]['offset_meters']
 ['path_tpc']['curves'][218]['res_coeff']
-['path_tpc']['curves'][218]['res_net']
-['path_tpc']['curves'][219]['offset']
+['path_tpc']['curves'][218]['res_net_meters']
+['path_tpc']['curves'][219]['offset_meters']
 ['path_tpc']['curves'][219]['res_coeff']
-['path_tpc']['curves'][219]['res_net']
-['path_tpc']['curves'][220]['offset']
+['path_tpc']['curves'][219]['res_net_meters']
+['path_tpc']['curves'][220]['offset_meters']
 ['path_tpc']['curves'][220]['res_coeff']
-['path_tpc']['curves'][220]['res_net']
-['path_tpc']['curves'][221]['offset']
+['path_tpc']['curves'][220]['res_net_meters']
+['path_tpc']['curves'][221]['offset_meters']
 ['path_tpc']['curves'][221]['res_coeff']
-['path_tpc']['curves'][221]['res_net']
-['path_tpc']['curves'][222]['offset']
+['path_tpc']['curves'][221]['res_net_meters']
+['path_tpc']['curves'][222]['offset_meters']
 ['path_tpc']['curves'][222]['res_coeff']
-['path_tpc']['curves'][222]['res_net']
-['path_tpc']['curves'][223]['offset']
+['path_tpc']['curves'][222]['res_net_meters']
+['path_tpc']['curves'][223]['offset_meters']
 ['path_tpc']['curves'][223]['res_coeff']
-['path_tpc']['curves'][223]['res_net']
-['path_tpc']['curves'][224]['offset']
+['path_tpc']['curves'][223]['res_net_meters']
+['path_tpc']['curves'][224]['offset_meters']
 ['path_tpc']['curves'][224]['res_coeff']
-['path_tpc']['curves'][224]['res_net']
-['path_tpc']['curves'][225]['offset']
+['path_tpc']['curves'][224]['res_net_meters']
+['path_tpc']['curves'][225]['offset_meters']
 ['path_tpc']['curves'][225]['res_coeff']
-['path_tpc']['curves'][225]['res_net']
-['path_tpc']['curves'][226]['offset']
+['path_tpc']['curves'][225]['res_net_meters']
+['path_tpc']['curves'][226]['offset_meters']
 ['path_tpc']['curves'][226]['res_coeff']
-['path_tpc']['curves'][226]['res_net']
-['path_tpc']['curves'][227]['offset']
+['path_tpc']['curves'][226]['res_net_meters']
+['path_tpc']['curves'][227]['offset_meters']
 ['path_tpc']['curves'][227]['res_coeff']
-['path_tpc']['curves'][227]['res_net']
-['path_tpc']['curves'][228]['offset']
+['path_tpc']['curves'][227]['res_net_meters']
+['path_tpc']['curves'][228]['offset_meters']
 ['path_tpc']['curves'][228]['res_coeff']
-['path_tpc']['curves'][228]['res_net']
-['path_tpc']['curves'][229]['offset']
+['path_tpc']['curves'][228]['res_net_meters']
+['path_tpc']['curves'][229]['offset_meters']
 ['path_tpc']['curves'][229]['res_coeff']
-['path_tpc']['curves'][229]['res_net']
-['path_tpc']['curves'][230]['offset']
+['path_tpc']['curves'][229]['res_net_meters']
+['path_tpc']['curves'][230]['offset_meters']
 ['path_tpc']['curves'][230]['res_coeff']
-['path_tpc']['curves'][230]['res_net']
-['path_tpc']['curves'][231]['offset']
+['path_tpc']['curves'][230]['res_net_meters']
+['path_tpc']['curves'][231]['offset_meters']
 ['path_tpc']['curves'][231]['res_coeff']
-['path_tpc']['curves'][231]['res_net']
-['path_tpc']['curves'][232]['offset']
+['path_tpc']['curves'][231]['res_net_meters']
+['path_tpc']['curves'][232]['offset_meters']
 ['path_tpc']['curves'][232]['res_coeff']
-['path_tpc']['curves'][232]['res_net']
-['path_tpc']['curves'][233]['offset']
+['path_tpc']['curves'][232]['res_net_meters']
+['path_tpc']['curves'][233]['offset_meters']
 ['path_tpc']['curves'][233]['res_coeff']
-['path_tpc']['curves'][233]['res_net']
-['path_tpc']['curves'][234]['offset']
+['path_tpc']['curves'][233]['res_net_meters']
+['path_tpc']['curves'][234]['offset_meters']
 ['path_tpc']['curves'][234]['res_coeff']
-['path_tpc']['curves'][234]['res_net']
-['path_tpc']['curves'][235]['offset']
+['path_tpc']['curves'][234]['res_net_meters']
+['path_tpc']['curves'][235]['offset_meters']
 ['path_tpc']['curves'][235]['res_coeff']
-['path_tpc']['curves'][235]['res_net']
-['path_tpc']['curves'][236]['offset']
+['path_tpc']['curves'][235]['res_net_meters']
+['path_tpc']['curves'][236]['offset_meters']
 ['path_tpc']['curves'][236]['res_coeff']
-['path_tpc']['curves'][236]['res_net']
-['path_tpc']['curves'][237]['offset']
+['path_tpc']['curves'][236]['res_net_meters']
+['path_tpc']['curves'][237]['offset_meters']
 ['path_tpc']['curves'][237]['res_coeff']
-['path_tpc']['curves'][237]['res_net']
-['path_tpc']['curves'][238]['offset']
+['path_tpc']['curves'][237]['res_net_meters']
+['path_tpc']['curves'][238]['offset_meters']
 ['path_tpc']['curves'][238]['res_coeff']
-['path_tpc']['curves'][238]['res_net']
-['path_tpc']['curves'][239]['offset']
+['path_tpc']['curves'][238]['res_net_meters']
+['path_tpc']['curves'][239]['offset_meters']
 ['path_tpc']['curves'][239]['res_coeff']
-['path_tpc']['curves'][239]['res_net']
-['path_tpc']['curves'][240]['offset']
+['path_tpc']['curves'][239]['res_net_meters']
+['path_tpc']['curves'][240]['offset_meters']
 ['path_tpc']['curves'][240]['res_coeff']
-['path_tpc']['curves'][240]['res_net']
-['path_tpc']['curves'][241]['offset']
+['path_tpc']['curves'][240]['res_net_meters']
+['path_tpc']['curves'][241]['offset_meters']
 ['path_tpc']['curves'][241]['res_coeff']
-['path_tpc']['curves'][241]['res_net']
-['path_tpc']['curves'][242]['offset']
+['path_tpc']['curves'][241]['res_net_meters']
+['path_tpc']['curves'][242]['offset_meters']
 ['path_tpc']['curves'][242]['res_coeff']
-['path_tpc']['curves'][242]['res_net']
-['path_tpc']['curves'][243]['offset']
+['path_tpc']['curves'][242]['res_net_meters']
+['path_tpc']['curves'][243]['offset_meters']
 ['path_tpc']['curves'][243]['res_coeff']
-['path_tpc']['curves'][243]['res_net']
-['path_tpc']['curves'][244]['offset']
+['path_tpc']['curves'][243]['res_net_meters']
+['path_tpc']['curves'][244]['offset_meters']
 ['path_tpc']['curves'][244]['res_coeff']
-['path_tpc']['curves'][244]['res_net']
-['path_tpc']['curves'][245]['offset']
+['path_tpc']['curves'][244]['res_net_meters']
+['path_tpc']['curves'][245]['offset_meters']
 ['path_tpc']['curves'][245]['res_coeff']
-['path_tpc']['curves'][245]['res_net']
-['path_tpc']['curves'][246]['offset']
+['path_tpc']['curves'][245]['res_net_meters']
+['path_tpc']['curves'][246]['offset_meters']
 ['path_tpc']['curves'][246]['res_coeff']
-['path_tpc']['curves'][246]['res_net']
-['path_tpc']['curves'][247]['offset']
+['path_tpc']['curves'][246]['res_net_meters']
+['path_tpc']['curves'][247]['offset_meters']
 ['path_tpc']['curves'][247]['res_coeff']
-['path_tpc']['curves'][247]['res_net']
-['path_tpc']['curves'][248]['offset']
+['path_tpc']['curves'][247]['res_net_meters']
+['path_tpc']['curves'][248]['offset_meters']
 ['path_tpc']['curves'][248]['res_coeff']
-['path_tpc']['curves'][248]['res_net']
-['path_tpc']['curves'][249]['offset']
+['path_tpc']['curves'][248]['res_net_meters']
+['path_tpc']['curves'][249]['offset_meters']
 ['path_tpc']['curves'][249]['res_coeff']
-['path_tpc']['curves'][249]['res_net']
-['path_tpc']['curves'][250]['offset']
+['path_tpc']['curves'][249]['res_net_meters']
+['path_tpc']['curves'][250]['offset_meters']
 ['path_tpc']['curves'][250]['res_coeff']
-['path_tpc']['curves'][250]['res_net']
-['path_tpc']['curves'][251]['offset']
+['path_tpc']['curves'][250]['res_net_meters']
+['path_tpc']['curves'][251]['offset_meters']
 ['path_tpc']['curves'][251]['res_coeff']
-['path_tpc']['curves'][251]['res_net']
-['path_tpc']['curves'][252]['offset']
+['path_tpc']['curves'][251]['res_net_meters']
+['path_tpc']['curves'][252]['offset_meters']
 ['path_tpc']['curves'][252]['res_coeff']
-['path_tpc']['curves'][252]['res_net']
-['path_tpc']['curves'][253]['offset']
+['path_tpc']['curves'][252]['res_net_meters']
+['path_tpc']['curves'][253]['offset_meters']
 ['path_tpc']['curves'][253]['res_coeff']
-['path_tpc']['curves'][253]['res_net']
-['path_tpc']['curves'][254]['offset']
+['path_tpc']['curves'][253]['res_net_meters']
+['path_tpc']['curves'][254]['offset_meters']
 ['path_tpc']['curves'][254]['res_coeff']
-['path_tpc']['curves'][254]['res_net']
-['path_tpc']['curves'][255]['offset']
+['path_tpc']['curves'][254]['res_net_meters']
+['path_tpc']['curves'][255]['offset_meters']
 ['path_tpc']['curves'][255]['res_coeff']
-['path_tpc']['curves'][255]['res_net']
-['path_tpc']['curves'][256]['offset']
+['path_tpc']['curves'][255]['res_net_meters']
+['path_tpc']['curves'][256]['offset_meters']
 ['path_tpc']['curves'][256]['res_coeff']
-['path_tpc']['curves'][256]['res_net']
-['path_tpc']['curves'][257]['offset']
+['path_tpc']['curves'][256]['res_net_meters']
+['path_tpc']['curves'][257]['offset_meters']
 ['path_tpc']['curves'][257]['res_coeff']
-['path_tpc']['curves'][257]['res_net']
-['path_tpc']['curves'][258]['offset']
+['path_tpc']['curves'][257]['res_net_meters']
+['path_tpc']['curves'][258]['offset_meters']
 ['path_tpc']['curves'][258]['res_coeff']
-['path_tpc']['curves'][258]['res_net']
-['path_tpc']['curves'][259]['offset']
+['path_tpc']['curves'][258]['res_net_meters']
+['path_tpc']['curves'][259]['offset_meters']
 ['path_tpc']['curves'][259]['res_coeff']
-['path_tpc']['curves'][259]['res_net']
-['path_tpc']['curves'][260]['offset']
+['path_tpc']['curves'][259]['res_net_meters']
+['path_tpc']['curves'][260]['offset_meters']
 ['path_tpc']['curves'][260]['res_coeff']
-['path_tpc']['curves'][260]['res_net']
-['path_tpc']['curves'][261]['offset']
+['path_tpc']['curves'][260]['res_net_meters']
+['path_tpc']['curves'][261]['offset_meters']
 ['path_tpc']['curves'][261]['res_coeff']
-['path_tpc']['curves'][261]['res_net']
-['path_tpc']['curves'][262]['offset']
+['path_tpc']['curves'][261]['res_net_meters']
+['path_tpc']['curves'][262]['offset_meters']
 ['path_tpc']['curves'][262]['res_coeff']
-['path_tpc']['curves'][262]['res_net']
-['path_tpc']['curves'][263]['offset']
+['path_tpc']['curves'][262]['res_net_meters']
+['path_tpc']['curves'][263]['offset_meters']
 ['path_tpc']['curves'][263]['res_coeff']
-['path_tpc']['curves'][263]['res_net']
-['path_tpc']['curves'][264]['offset']
+['path_tpc']['curves'][263]['res_net_meters']
+['path_tpc']['curves'][264]['offset_meters']
 ['path_tpc']['curves'][264]['res_coeff']
-['path_tpc']['curves'][264]['res_net']
-['path_tpc']['curves'][265]['offset']
+['path_tpc']['curves'][264]['res_net_meters']
+['path_tpc']['curves'][265]['offset_meters']
 ['path_tpc']['curves'][265]['res_coeff']
-['path_tpc']['curves'][265]['res_net']
-['path_tpc']['curves'][266]['offset']
+['path_tpc']['curves'][265]['res_net_meters']
+['path_tpc']['curves'][266]['offset_meters']
 ['path_tpc']['curves'][266]['res_coeff']
-['path_tpc']['curves'][266]['res_net']
-['path_tpc']['curves'][267]['offset']
+['path_tpc']['curves'][266]['res_net_meters']
+['path_tpc']['curves'][267]['offset_meters']
 ['path_tpc']['curves'][267]['res_coeff']
-['path_tpc']['curves'][267]['res_net']
-['path_tpc']['curves'][268]['offset']
+['path_tpc']['curves'][267]['res_net_meters']
+['path_tpc']['curves'][268]['offset_meters']
 ['path_tpc']['curves'][268]['res_coeff']
-['path_tpc']['curves'][268]['res_net']
-['path_tpc']['curves'][269]['offset']
+['path_tpc']['curves'][268]['res_net_meters']
+['path_tpc']['curves'][269]['offset_meters']
 ['path_tpc']['curves'][269]['res_coeff']
-['path_tpc']['curves'][269]['res_net']
-['path_tpc']['curves'][270]['offset']
+['path_tpc']['curves'][269]['res_net_meters']
+['path_tpc']['curves'][270]['offset_meters']
 ['path_tpc']['curves'][270]['res_coeff']
-['path_tpc']['curves'][270]['res_net']
-['path_tpc']['curves'][271]['offset']
+['path_tpc']['curves'][270]['res_net_meters']
+['path_tpc']['curves'][271]['offset_meters']
 ['path_tpc']['curves'][271]['res_coeff']
-['path_tpc']['curves'][271]['res_net']
-['path_tpc']['curves'][272]['offset']
+['path_tpc']['curves'][271]['res_net_meters']
+['path_tpc']['curves'][272]['offset_meters']
 ['path_tpc']['curves'][272]['res_coeff']
-['path_tpc']['curves'][272]['res_net']
-['path_tpc']['curves'][273]['offset']
+['path_tpc']['curves'][272]['res_net_meters']
+['path_tpc']['curves'][273]['offset_meters']
 ['path_tpc']['curves'][273]['res_coeff']
-['path_tpc']['curves'][273]['res_net']
-['path_tpc']['curves'][274]['offset']
+['path_tpc']['curves'][273]['res_net_meters']
+['path_tpc']['curves'][274]['offset_meters']
 ['path_tpc']['curves'][274]['res_coeff']
-['path_tpc']['curves'][274]['res_net']
-['path_tpc']['curves'][275]['offset']
+['path_tpc']['curves'][274]['res_net_meters']
+['path_tpc']['curves'][275]['offset_meters']
 ['path_tpc']['curves'][275]['res_coeff']
-['path_tpc']['curves'][275]['res_net']
-['path_tpc']['curves'][276]['offset']
+['path_tpc']['curves'][275]['res_net_meters']
+['path_tpc']['curves'][276]['offset_meters']
 ['path_tpc']['curves'][276]['res_coeff']
-['path_tpc']['curves'][276]['res_net']
-['path_tpc']['curves'][277]['offset']
+['path_tpc']['curves'][276]['res_net_meters']
+['path_tpc']['curves'][277]['offset_meters']
 ['path_tpc']['curves'][277]['res_coeff']
-['path_tpc']['curves'][277]['res_net']
-['path_tpc']['curves'][278]['offset']
+['path_tpc']['curves'][277]['res_net_meters']
+['path_tpc']['curves'][278]['offset_meters']
 ['path_tpc']['curves'][278]['res_coeff']
-['path_tpc']['curves'][278]['res_net']
-['path_tpc']['curves'][279]['offset']
+['path_tpc']['curves'][278]['res_net_meters']
+['path_tpc']['curves'][279]['offset_meters']
 ['path_tpc']['curves'][279]['res_coeff']
-['path_tpc']['curves'][279]['res_net']
-['path_tpc']['curves'][280]['offset']
+['path_tpc']['curves'][279]['res_net_meters']
+['path_tpc']['curves'][280]['offset_meters']
 ['path_tpc']['curves'][280]['res_coeff']
-['path_tpc']['curves'][280]['res_net']
-['path_tpc']['curves'][281]['offset']
+['path_tpc']['curves'][280]['res_net_meters']
+['path_tpc']['curves'][281]['offset_meters']
 ['path_tpc']['curves'][281]['res_coeff']
-['path_tpc']['curves'][281]['res_net']
-['path_tpc']['curves'][282]['offset']
+['path_tpc']['curves'][281]['res_net_meters']
+['path_tpc']['curves'][282]['offset_meters']
 ['path_tpc']['curves'][282]['res_coeff']
-['path_tpc']['curves'][282]['res_net']
-['path_tpc']['curves'][283]['offset']
+['path_tpc']['curves'][282]['res_net_meters']
+['path_tpc']['curves'][283]['offset_meters']
 ['path_tpc']['curves'][283]['res_coeff']
-['path_tpc']['curves'][283]['res_net']
-['path_tpc']['curves'][284]['offset']
+['path_tpc']['curves'][283]['res_net_meters']
+['path_tpc']['curves'][284]['offset_meters']
 ['path_tpc']['curves'][284]['res_coeff']
-['path_tpc']['curves'][284]['res_net']
-['path_tpc']['curves'][285]['offset']
+['path_tpc']['curves'][284]['res_net_meters']
+['path_tpc']['curves'][285]['offset_meters']
 ['path_tpc']['curves'][285]['res_coeff']
-['path_tpc']['curves'][285]['res_net']
-['path_tpc']['curves'][286]['offset']
+['path_tpc']['curves'][285]['res_net_meters']
+['path_tpc']['curves'][286]['offset_meters']
 ['path_tpc']['curves'][286]['res_coeff']
-['path_tpc']['curves'][286]['res_net']
-['path_tpc']['curves'][287]['offset']
+['path_tpc']['curves'][286]['res_net_meters']
+['path_tpc']['curves'][287]['offset_meters']
 ['path_tpc']['curves'][287]['res_coeff']
-['path_tpc']['curves'][287]['res_net']
-['path_tpc']['curves'][288]['offset']
+['path_tpc']['curves'][287]['res_net_meters']
+['path_tpc']['curves'][288]['offset_meters']
 ['path_tpc']['curves'][288]['res_coeff']
-['path_tpc']['curves'][288]['res_net']
-['path_tpc']['curves'][289]['offset']
+['path_tpc']['curves'][288]['res_net_meters']
+['path_tpc']['curves'][289]['offset_meters']
 ['path_tpc']['curves'][289]['res_coeff']
-['path_tpc']['curves'][289]['res_net']
-['path_tpc']['curves'][290]['offset']
+['path_tpc']['curves'][289]['res_net_meters']
+['path_tpc']['curves'][290]['offset_meters']
 ['path_tpc']['curves'][290]['res_coeff']
-['path_tpc']['curves'][290]['res_net']
-['path_tpc']['curves'][291]['offset']
+['path_tpc']['curves'][290]['res_net_meters']
+['path_tpc']['curves'][291]['offset_meters']
 ['path_tpc']['curves'][291]['res_coeff']
-['path_tpc']['curves'][291]['res_net']
-['path_tpc']['curves'][292]['offset']
+['path_tpc']['curves'][291]['res_net_meters']
+['path_tpc']['curves'][292]['offset_meters']
 ['path_tpc']['curves'][292]['res_coeff']
-['path_tpc']['curves'][292]['res_net']
-['path_tpc']['curves'][293]['offset']
+['path_tpc']['curves'][292]['res_net_meters']
+['path_tpc']['curves'][293]['offset_meters']
 ['path_tpc']['curves'][293]['res_coeff']
-['path_tpc']['curves'][293]['res_net']
-['path_tpc']['curves'][294]['offset']
+['path_tpc']['curves'][293]['res_net_meters']
+['path_tpc']['curves'][294]['offset_meters']
 ['path_tpc']['curves'][294]['res_coeff']
-['path_tpc']['curves'][294]['res_net']
-['path_tpc']['curves'][295]['offset']
+['path_tpc']['curves'][294]['res_net_meters']
+['path_tpc']['curves'][295]['offset_meters']
 ['path_tpc']['curves'][295]['res_coeff']
-['path_tpc']['curves'][295]['res_net']
-['path_tpc']['curves'][296]['offset']
+['path_tpc']['curves'][295]['res_net_meters']
+['path_tpc']['curves'][296]['offset_meters']
 ['path_tpc']['curves'][296]['res_coeff']
-['path_tpc']['curves'][296]['res_net']
-['path_tpc']['curves'][297]['offset']
+['path_tpc']['curves'][296]['res_net_meters']
+['path_tpc']['curves'][297]['offset_meters']
 ['path_tpc']['curves'][297]['res_coeff']
-['path_tpc']['curves'][297]['res_net']
-['path_tpc']['curves'][298]['offset']
+['path_tpc']['curves'][297]['res_net_meters']
+['path_tpc']['curves'][298]['offset_meters']
 ['path_tpc']['curves'][298]['res_coeff']
-['path_tpc']['curves'][298]['res_net']
-['path_tpc']['curves'][299]['offset']
+['path_tpc']['curves'][298]['res_net_meters']
+['path_tpc']['curves'][299]['offset_meters']
 ['path_tpc']['curves'][299]['res_coeff']
-['path_tpc']['curves'][299]['res_net']
-['path_tpc']['curves'][300]['offset']
+['path_tpc']['curves'][299]['res_net_meters']
+['path_tpc']['curves'][300]['offset_meters']
 ['path_tpc']['curves'][300]['res_coeff']
-['path_tpc']['curves'][300]['res_net']
-['path_tpc']['curves'][301]['offset']
+['path_tpc']['curves'][300]['res_net_meters']
+['path_tpc']['curves'][301]['offset_meters']
 ['path_tpc']['curves'][301]['res_coeff']
-['path_tpc']['curves'][301]['res_net']
-['path_tpc']['curves'][302]['offset']
+['path_tpc']['curves'][301]['res_net_meters']
+['path_tpc']['curves'][302]['offset_meters']
 ['path_tpc']['curves'][302]['res_coeff']
-['path_tpc']['curves'][302]['res_net']
-['path_tpc']['curves'][303]['offset']
+['path_tpc']['curves'][302]['res_net_meters']
+['path_tpc']['curves'][303]['offset_meters']
 ['path_tpc']['curves'][303]['res_coeff']
-['path_tpc']['curves'][303]['res_net']
-['path_tpc']['curves'][304]['offset']
+['path_tpc']['curves'][303]['res_net_meters']
+['path_tpc']['curves'][304]['offset_meters']
 ['path_tpc']['curves'][304]['res_coeff']
-['path_tpc']['curves'][304]['res_net']
-['path_tpc']['curves'][305]['offset']
+['path_tpc']['curves'][304]['res_net_meters']
+['path_tpc']['curves'][305]['offset_meters']
 ['path_tpc']['curves'][305]['res_coeff']
-['path_tpc']['curves'][305]['res_net']
-['path_tpc']['curves'][306]['offset']
+['path_tpc']['curves'][305]['res_net_meters']
+['path_tpc']['curves'][306]['offset_meters']
 ['path_tpc']['curves'][306]['res_coeff']
-['path_tpc']['curves'][306]['res_net']
-['path_tpc']['curves'][307]['offset']
+['path_tpc']['curves'][306]['res_net_meters']
+['path_tpc']['curves'][307]['offset_meters']
 ['path_tpc']['curves'][307]['res_coeff']
-['path_tpc']['curves'][307]['res_net']
-['path_tpc']['curves'][308]['offset']
+['path_tpc']['curves'][307]['res_net_meters']
+['path_tpc']['curves'][308]['offset_meters']
 ['path_tpc']['curves'][308]['res_coeff']
-['path_tpc']['curves'][308]['res_net']
-['path_tpc']['curves'][309]['offset']
+['path_tpc']['curves'][308]['res_net_meters']
+['path_tpc']['curves'][309]['offset_meters']
 ['path_tpc']['curves'][309]['res_coeff']
-['path_tpc']['curves'][309]['res_net']
-['path_tpc']['curves'][310]['offset']
+['path_tpc']['curves'][309]['res_net_meters']
+['path_tpc']['curves'][310]['offset_meters']
 ['path_tpc']['curves'][310]['res_coeff']
-['path_tpc']['curves'][310]['res_net']
-['path_tpc']['curves'][311]['offset']
+['path_tpc']['curves'][310]['res_net_meters']
+['path_tpc']['curves'][311]['offset_meters']
 ['path_tpc']['curves'][311]['res_coeff']
-['path_tpc']['curves'][311]['res_net']
-['path_tpc']['curves'][312]['offset']
+['path_tpc']['curves'][311]['res_net_meters']
+['path_tpc']['curves'][312]['offset_meters']
 ['path_tpc']['curves'][312]['res_coeff']
-['path_tpc']['curves'][312]['res_net']
-['path_tpc']['curves'][313]['offset']
+['path_tpc']['curves'][312]['res_net_meters']
+['path_tpc']['curves'][313]['offset_meters']
 ['path_tpc']['curves'][313]['res_coeff']
-['path_tpc']['curves'][313]['res_net']
-['path_tpc']['curves'][314]['offset']
+['path_tpc']['curves'][313]['res_net_meters']
+['path_tpc']['curves'][314]['offset_meters']
 ['path_tpc']['curves'][314]['res_coeff']
-['path_tpc']['curves'][314]['res_net']
-['path_tpc']['curves'][315]['offset']
+['path_tpc']['curves'][314]['res_net_meters']
+['path_tpc']['curves'][315]['offset_meters']
 ['path_tpc']['curves'][315]['res_coeff']
-['path_tpc']['curves'][315]['res_net']
-['path_tpc']['curves'][316]['offset']
+['path_tpc']['curves'][315]['res_net_meters']
+['path_tpc']['curves'][316]['offset_meters']
 ['path_tpc']['curves'][316]['res_coeff']
-['path_tpc']['curves'][316]['res_net']
-['path_tpc']['curves'][317]['offset']
+['path_tpc']['curves'][316]['res_net_meters']
+['path_tpc']['curves'][317]['offset_meters']
 ['path_tpc']['curves'][317]['res_coeff']
-['path_tpc']['curves'][317]['res_net']
-['path_tpc']['curves'][318]['offset']
+['path_tpc']['curves'][317]['res_net_meters']
+['path_tpc']['curves'][318]['offset_meters']
 ['path_tpc']['curves'][318]['res_coeff']
-['path_tpc']['curves'][318]['res_net']
-['path_tpc']['curves'][319]['offset']
+['path_tpc']['curves'][318]['res_net_meters']
+['path_tpc']['curves'][319]['offset_meters']
 ['path_tpc']['curves'][319]['res_coeff']
-['path_tpc']['curves'][319]['res_net']
-['path_tpc']['curves'][320]['offset']
+['path_tpc']['curves'][319]['res_net_meters']
+['path_tpc']['curves'][320]['offset_meters']
 ['path_tpc']['curves'][320]['res_coeff']
-['path_tpc']['curves'][320]['res_net']
-['path_tpc']['curves'][321]['offset']
+['path_tpc']['curves'][320]['res_net_meters']
+['path_tpc']['curves'][321]['offset_meters']
 ['path_tpc']['curves'][321]['res_coeff']
-['path_tpc']['curves'][321]['res_net']
-['path_tpc']['curves'][322]['offset']
+['path_tpc']['curves'][321]['res_net_meters']
+['path_tpc']['curves'][322]['offset_meters']
 ['path_tpc']['curves'][322]['res_coeff']
-['path_tpc']['curves'][322]['res_net']
-['path_tpc']['curves'][323]['offset']
+['path_tpc']['curves'][322]['res_net_meters']
+['path_tpc']['curves'][323]['offset_meters']
 ['path_tpc']['curves'][323]['res_coeff']
-['path_tpc']['curves'][323]['res_net']
-['path_tpc']['curves'][324]['offset']
+['path_tpc']['curves'][323]['res_net_meters']
+['path_tpc']['curves'][324]['offset_meters']
 ['path_tpc']['curves'][324]['res_coeff']
-['path_tpc']['curves'][324]['res_net']
-['path_tpc']['curves'][325]['offset']
+['path_tpc']['curves'][324]['res_net_meters']
+['path_tpc']['curves'][325]['offset_meters']
 ['path_tpc']['curves'][325]['res_coeff']
-['path_tpc']['curves'][325]['res_net']
-['path_tpc']['curves'][326]['offset']
+['path_tpc']['curves'][325]['res_net_meters']
+['path_tpc']['curves'][326]['offset_meters']
 ['path_tpc']['curves'][326]['res_coeff']
-['path_tpc']['curves'][326]['res_net']
-['path_tpc']['curves'][327]['offset']
+['path_tpc']['curves'][326]['res_net_meters']
+['path_tpc']['curves'][327]['offset_meters']
 ['path_tpc']['curves'][327]['res_coeff']
-['path_tpc']['curves'][327]['res_net']
-['path_tpc']['curves'][328]['offset']
+['path_tpc']['curves'][327]['res_net_meters']
+['path_tpc']['curves'][328]['offset_meters']
 ['path_tpc']['curves'][328]['res_coeff']
-['path_tpc']['curves'][328]['res_net']
-['path_tpc']['curves'][329]['offset']
+['path_tpc']['curves'][328]['res_net_meters']
+['path_tpc']['curves'][329]['offset_meters']
 ['path_tpc']['curves'][329]['res_coeff']
-['path_tpc']['curves'][329]['res_net']
-['path_tpc']['curves'][330]['offset']
+['path_tpc']['curves'][329]['res_net_meters']
+['path_tpc']['curves'][330]['offset_meters']
 ['path_tpc']['curves'][330]['res_coeff']
-['path_tpc']['curves'][330]['res_net']
-['path_tpc']['curves'][331]['offset']
+['path_tpc']['curves'][330]['res_net_meters']
+['path_tpc']['curves'][331]['offset_meters']
 ['path_tpc']['curves'][331]['res_coeff']
-['path_tpc']['curves'][331]['res_net']
-['path_tpc']['curves'][332]['offset']
+['path_tpc']['curves'][331]['res_net_meters']
+['path_tpc']['curves'][332]['offset_meters']
 ['path_tpc']['curves'][332]['res_coeff']
-['path_tpc']['curves'][332]['res_net']
-['path_tpc']['curves'][333]['offset']
+['path_tpc']['curves'][332]['res_net_meters']
+['path_tpc']['curves'][333]['offset_meters']
 ['path_tpc']['curves'][333]['res_coeff']
-['path_tpc']['curves'][333]['res_net']
-['path_tpc']['curves'][334]['offset']
+['path_tpc']['curves'][333]['res_net_meters']
+['path_tpc']['curves'][334]['offset_meters']
 ['path_tpc']['curves'][334]['res_coeff']
-['path_tpc']['curves'][334]['res_net']
-['path_tpc']['curves'][335]['offset']
+['path_tpc']['curves'][334]['res_net_meters']
+['path_tpc']['curves'][335]['offset_meters']
 ['path_tpc']['curves'][335]['res_coeff']
-['path_tpc']['curves'][335]['res_net']
-['path_tpc']['curves'][336]['offset']
+['path_tpc']['curves'][335]['res_net_meters']
+['path_tpc']['curves'][336]['offset_meters']
 ['path_tpc']['curves'][336]['res_coeff']
-['path_tpc']['curves'][336]['res_net']
-['path_tpc']['curves'][337]['offset']
+['path_tpc']['curves'][336]['res_net_meters']
+['path_tpc']['curves'][337]['offset_meters']
 ['path_tpc']['curves'][337]['res_coeff']
-['path_tpc']['curves'][337]['res_net']
-['path_tpc']['curves'][338]['offset']
+['path_tpc']['curves'][337]['res_net_meters']
+['path_tpc']['curves'][338]['offset_meters']
 ['path_tpc']['curves'][338]['res_coeff']
-['path_tpc']['curves'][338]['res_net']
-['path_tpc']['curves'][339]['offset']
+['path_tpc']['curves'][338]['res_net_meters']
+['path_tpc']['curves'][339]['offset_meters']
 ['path_tpc']['curves'][339]['res_coeff']
-['path_tpc']['curves'][339]['res_net']
-['path_tpc']['curves'][340]['offset']
+['path_tpc']['curves'][339]['res_net_meters']
+['path_tpc']['curves'][340]['offset_meters']
 ['path_tpc']['curves'][340]['res_coeff']
-['path_tpc']['curves'][340]['res_net']
-['path_tpc']['curves'][341]['offset']
+['path_tpc']['curves'][340]['res_net_meters']
+['path_tpc']['curves'][341]['offset_meters']
 ['path_tpc']['curves'][341]['res_coeff']
-['path_tpc']['curves'][341]['res_net']
-['path_tpc']['curves'][342]['offset']
+['path_tpc']['curves'][341]['res_net_meters']
+['path_tpc']['curves'][342]['offset_meters']
 ['path_tpc']['curves'][342]['res_coeff']
-['path_tpc']['curves'][342]['res_net']
-['path_tpc']['curves'][343]['offset']
+['path_tpc']['curves'][342]['res_net_meters']
+['path_tpc']['curves'][343]['offset_meters']
 ['path_tpc']['curves'][343]['res_coeff']
-['path_tpc']['curves'][343]['res_net']
-['path_tpc']['curves'][344]['offset']
+['path_tpc']['curves'][343]['res_net_meters']
+['path_tpc']['curves'][344]['offset_meters']
 ['path_tpc']['curves'][344]['res_coeff']
-['path_tpc']['curves'][344]['res_net']
-['path_tpc']['curves'][345]['offset']
+['path_tpc']['curves'][344]['res_net_meters']
+['path_tpc']['curves'][345]['offset_meters']
 ['path_tpc']['curves'][345]['res_coeff']
-['path_tpc']['curves'][345]['res_net']
-['path_tpc']['curves'][346]['offset']
+['path_tpc']['curves'][345]['res_net_meters']
+['path_tpc']['curves'][346]['offset_meters']
 ['path_tpc']['curves'][346]['res_coeff']
-['path_tpc']['curves'][346]['res_net']
-['path_tpc']['curves'][347]['offset']
+['path_tpc']['curves'][346]['res_net_meters']
+['path_tpc']['curves'][347]['offset_meters']
 ['path_tpc']['curves'][347]['res_coeff']
-['path_tpc']['curves'][347]['res_net']
-['path_tpc']['curves'][348]['offset']
+['path_tpc']['curves'][347]['res_net_meters']
+['path_tpc']['curves'][348]['offset_meters']
 ['path_tpc']['curves'][348]['res_coeff']
-['path_tpc']['curves'][348]['res_net']
-['path_tpc']['curves'][349]['offset']
+['path_tpc']['curves'][348]['res_net_meters']
+['path_tpc']['curves'][349]['offset_meters']
 ['path_tpc']['curves'][349]['res_coeff']
-['path_tpc']['curves'][349]['res_net']
-['path_tpc']['curves'][350]['offset']
+['path_tpc']['curves'][349]['res_net_meters']
+['path_tpc']['curves'][350]['offset_meters']
 ['path_tpc']['curves'][350]['res_coeff']
-['path_tpc']['curves'][350]['res_net']
-['path_tpc']['curves'][351]['offset']
+['path_tpc']['curves'][350]['res_net_meters']
+['path_tpc']['curves'][351]['offset_meters']
 ['path_tpc']['curves'][351]['res_coeff']
-['path_tpc']['curves'][351]['res_net']
-['path_tpc']['curves'][352]['offset']
+['path_tpc']['curves'][351]['res_net_meters']
+['path_tpc']['curves'][352]['offset_meters']
 ['path_tpc']['curves'][352]['res_coeff']
-['path_tpc']['curves'][352]['res_net']
-['path_tpc']['curves'][353]['offset']
+['path_tpc']['curves'][352]['res_net_meters']
+['path_tpc']['curves'][353]['offset_meters']
 ['path_tpc']['curves'][353]['res_coeff']
-['path_tpc']['curves'][353]['res_net']
-['path_tpc']['curves'][354]['offset']
+['path_tpc']['curves'][353]['res_net_meters']
+['path_tpc']['curves'][354]['offset_meters']
 ['path_tpc']['curves'][354]['res_coeff']
-['path_tpc']['curves'][354]['res_net']
-['path_tpc']['curves'][355]['offset']
+['path_tpc']['curves'][354]['res_net_meters']
+['path_tpc']['curves'][355]['offset_meters']
 ['path_tpc']['curves'][355]['res_coeff']
-['path_tpc']['curves'][355]['res_net']
-['path_tpc']['curves'][356]['offset']
+['path_tpc']['curves'][355]['res_net_meters']
+['path_tpc']['curves'][356]['offset_meters']
 ['path_tpc']['curves'][356]['res_coeff']
-['path_tpc']['curves'][356]['res_net']
-['path_tpc']['curves'][357]['offset']
+['path_tpc']['curves'][356]['res_net_meters']
+['path_tpc']['curves'][357]['offset_meters']
 ['path_tpc']['curves'][357]['res_coeff']
-['path_tpc']['curves'][357]['res_net']
-['path_tpc']['curves'][358]['offset']
+['path_tpc']['curves'][357]['res_net_meters']
+['path_tpc']['curves'][358]['offset_meters']
 ['path_tpc']['curves'][358]['res_coeff']
-['path_tpc']['curves'][358]['res_net']
-['path_tpc']['curves'][359]['offset']
+['path_tpc']['curves'][358]['res_net_meters']
+['path_tpc']['curves'][359]['offset_meters']
 ['path_tpc']['curves'][359]['res_coeff']
-['path_tpc']['curves'][359]['res_net']
-['path_tpc']['curves'][360]['offset']
+['path_tpc']['curves'][359]['res_net_meters']
+['path_tpc']['curves'][360]['offset_meters']
 ['path_tpc']['curves'][360]['res_coeff']
-['path_tpc']['curves'][360]['res_net']
-['path_tpc']['curves'][361]['offset']
+['path_tpc']['curves'][360]['res_net_meters']
+['path_tpc']['curves'][361]['offset_meters']
 ['path_tpc']['curves'][361]['res_coeff']
-['path_tpc']['curves'][361]['res_net']
-['path_tpc']['curves'][362]['offset']
+['path_tpc']['curves'][361]['res_net_meters']
+['path_tpc']['curves'][362]['offset_meters']
 ['path_tpc']['curves'][362]['res_coeff']
-['path_tpc']['curves'][362]['res_net']
-['path_tpc']['curves'][363]['offset']
+['path_tpc']['curves'][362]['res_net_meters']
+['path_tpc']['curves'][363]['offset_meters']
 ['path_tpc']['curves'][363]['res_coeff']
-['path_tpc']['curves'][363]['res_net']
-['path_tpc']['curves'][364]['offset']
+['path_tpc']['curves'][363]['res_net_meters']
+['path_tpc']['curves'][364]['offset_meters']
 ['path_tpc']['curves'][364]['res_coeff']
-['path_tpc']['curves'][364]['res_net']
-['path_tpc']['curves'][365]['offset']
+['path_tpc']['curves'][364]['res_net_meters']
+['path_tpc']['curves'][365]['offset_meters']
 ['path_tpc']['curves'][365]['res_coeff']
-['path_tpc']['curves'][365]['res_net']
-['path_tpc']['curves'][366]['offset']
+['path_tpc']['curves'][365]['res_net_meters']
+['path_tpc']['curves'][366]['offset_meters']
 ['path_tpc']['curves'][366]['res_coeff']
-['path_tpc']['curves'][366]['res_net']
-['path_tpc']['curves'][367]['offset']
+['path_tpc']['curves'][366]['res_net_meters']
+['path_tpc']['curves'][367]['offset_meters']
 ['path_tpc']['curves'][367]['res_coeff']
-['path_tpc']['curves'][367]['res_net']
-['path_tpc']['curves'][368]['offset']
+['path_tpc']['curves'][367]['res_net_meters']
+['path_tpc']['curves'][368]['offset_meters']
 ['path_tpc']['curves'][368]['res_coeff']
-['path_tpc']['curves'][368]['res_net']
-['path_tpc']['curves'][369]['offset']
+['path_tpc']['curves'][368]['res_net_meters']
+['path_tpc']['curves'][369]['offset_meters']
 ['path_tpc']['curves'][369]['res_coeff']
-['path_tpc']['curves'][369]['res_net']
-['path_tpc']['curves'][370]['offset']
+['path_tpc']['curves'][369]['res_net_meters']
+['path_tpc']['curves'][370]['offset_meters']
 ['path_tpc']['curves'][370]['res_coeff']
-['path_tpc']['curves'][370]['res_net']
-['path_tpc']['curves'][371]['offset']
+['path_tpc']['curves'][370]['res_net_meters']
+['path_tpc']['curves'][371]['offset_meters']
 ['path_tpc']['curves'][371]['res_coeff']
-['path_tpc']['curves'][371]['res_net']
-['path_tpc']['curves'][372]['offset']
+['path_tpc']['curves'][371]['res_net_meters']
+['path_tpc']['curves'][372]['offset_meters']
 ['path_tpc']['curves'][372]['res_coeff']
-['path_tpc']['curves'][372]['res_net']
-['path_tpc']['curves'][373]['offset']
+['path_tpc']['curves'][372]['res_net_meters']
+['path_tpc']['curves'][373]['offset_meters']
 ['path_tpc']['curves'][373]['res_coeff']
-['path_tpc']['curves'][373]['res_net']
-['path_tpc']['curves'][374]['offset']
+['path_tpc']['curves'][373]['res_net_meters']
+['path_tpc']['curves'][374]['offset_meters']
 ['path_tpc']['curves'][374]['res_coeff']
-['path_tpc']['curves'][374]['res_net']
-['path_tpc']['curves'][375]['offset']
+['path_tpc']['curves'][374]['res_net_meters']
+['path_tpc']['curves'][375]['offset_meters']
 ['path_tpc']['curves'][375]['res_coeff']
-['path_tpc']['curves'][375]['res_net']
-['path_tpc']['curves'][376]['offset']
+['path_tpc']['curves'][375]['res_net_meters']
+['path_tpc']['curves'][376]['offset_meters']
 ['path_tpc']['curves'][376]['res_coeff']
-['path_tpc']['curves'][376]['res_net']
-['path_tpc']['curves'][377]['offset']
+['path_tpc']['curves'][376]['res_net_meters']
+['path_tpc']['curves'][377]['offset_meters']
 ['path_tpc']['curves'][377]['res_coeff']
-['path_tpc']['curves'][377]['res_net']
-['path_tpc']['curves'][378]['offset']
+['path_tpc']['curves'][377]['res_net_meters']
+['path_tpc']['curves'][378]['offset_meters']
 ['path_tpc']['curves'][378]['res_coeff']
-['path_tpc']['curves'][378]['res_net']
-['path_tpc']['curves'][379]['offset']
+['path_tpc']['curves'][378]['res_net_meters']
+['path_tpc']['curves'][379]['offset_meters']
 ['path_tpc']['curves'][379]['res_coeff']
-['path_tpc']['curves'][379]['res_net']
-['path_tpc']['curves'][380]['offset']
+['path_tpc']['curves'][379]['res_net_meters']
+['path_tpc']['curves'][380]['offset_meters']
 ['path_tpc']['curves'][380]['res_coeff']
-['path_tpc']['curves'][380]['res_net']
-['path_tpc']['curves'][381]['offset']
+['path_tpc']['curves'][380]['res_net_meters']
+['path_tpc']['curves'][381]['offset_meters']
 ['path_tpc']['curves'][381]['res_coeff']
-['path_tpc']['curves'][381]['res_net']
-['path_tpc']['curves'][382]['offset']
+['path_tpc']['curves'][381]['res_net_meters']
+['path_tpc']['curves'][382]['offset_meters']
 ['path_tpc']['curves'][382]['res_coeff']
-['path_tpc']['curves'][382]['res_net']
-['path_tpc']['curves'][383]['offset']
+['path_tpc']['curves'][382]['res_net_meters']
+['path_tpc']['curves'][383]['offset_meters']
 ['path_tpc']['curves'][383]['res_coeff']
-['path_tpc']['curves'][383]['res_net']
-['path_tpc']['curves'][384]['offset']
+['path_tpc']['curves'][383]['res_net_meters']
+['path_tpc']['curves'][384]['offset_meters']
 ['path_tpc']['curves'][384]['res_coeff']
-['path_tpc']['curves'][384]['res_net']
-['path_tpc']['curves'][385]['offset']
+['path_tpc']['curves'][384]['res_net_meters']
+['path_tpc']['curves'][385]['offset_meters']
 ['path_tpc']['curves'][385]['res_coeff']
-['path_tpc']['curves'][385]['res_net']
-['path_tpc']['curves'][386]['offset']
+['path_tpc']['curves'][385]['res_net_meters']
+['path_tpc']['curves'][386]['offset_meters']
 ['path_tpc']['curves'][386]['res_coeff']
-['path_tpc']['curves'][386]['res_net']
-['path_tpc']['curves'][387]['offset']
+['path_tpc']['curves'][386]['res_net_meters']
+['path_tpc']['curves'][387]['offset_meters']
 ['path_tpc']['curves'][387]['res_coeff']
-['path_tpc']['curves'][387]['res_net']
-['path_tpc']['curves'][388]['offset']
+['path_tpc']['curves'][387]['res_net_meters']
+['path_tpc']['curves'][388]['offset_meters']
 ['path_tpc']['curves'][388]['res_coeff']
-['path_tpc']['curves'][388]['res_net']
-['path_tpc']['curves'][389]['offset']
+['path_tpc']['curves'][388]['res_net_meters']
+['path_tpc']['curves'][389]['offset_meters']
 ['path_tpc']['curves'][389]['res_coeff']
-['path_tpc']['curves'][389]['res_net']
-['path_tpc']['curves'][390]['offset']
+['path_tpc']['curves'][389]['res_net_meters']
+['path_tpc']['curves'][390]['offset_meters']
 ['path_tpc']['curves'][390]['res_coeff']
-['path_tpc']['curves'][390]['res_net']
-['path_tpc']['curves'][391]['offset']
+['path_tpc']['curves'][390]['res_net_meters']
+['path_tpc']['curves'][391]['offset_meters']
 ['path_tpc']['curves'][391]['res_coeff']
-['path_tpc']['curves'][391]['res_net']
-['path_tpc']['curves'][392]['offset']
+['path_tpc']['curves'][391]['res_net_meters']
+['path_tpc']['curves'][392]['offset_meters']
 ['path_tpc']['curves'][392]['res_coeff']
-['path_tpc']['curves'][392]['res_net']
-['path_tpc']['curves'][393]['offset']
+['path_tpc']['curves'][392]['res_net_meters']
+['path_tpc']['curves'][393]['offset_meters']
 ['path_tpc']['curves'][393]['res_coeff']
-['path_tpc']['curves'][393]['res_net']
-['path_tpc']['curves'][394]['offset']
+['path_tpc']['curves'][393]['res_net_meters']
+['path_tpc']['curves'][394]['offset_meters']
 ['path_tpc']['curves'][394]['res_coeff']
-['path_tpc']['curves'][394]['res_net']
-['path_tpc']['curves'][395]['offset']
+['path_tpc']['curves'][394]['res_net_meters']
+['path_tpc']['curves'][395]['offset_meters']
 ['path_tpc']['curves'][395]['res_coeff']
-['path_tpc']['curves'][395]['res_net']
-['path_tpc']['curves'][396]['offset']
+['path_tpc']['curves'][395]['res_net_meters']
+['path_tpc']['curves'][396]['offset_meters']
 ['path_tpc']['curves'][396]['res_coeff']
-['path_tpc']['curves'][396]['res_net']
-['path_tpc']['curves'][397]['offset']
+['path_tpc']['curves'][396]['res_net_meters']
+['path_tpc']['curves'][397]['offset_meters']
 ['path_tpc']['curves'][397]['res_coeff']
-['path_tpc']['curves'][397]['res_net']
-['path_tpc']['curves'][398]['offset']
+['path_tpc']['curves'][397]['res_net_meters']
+['path_tpc']['curves'][398]['offset_meters']
 ['path_tpc']['curves'][398]['res_coeff']
-['path_tpc']['curves'][398]['res_net']
-['path_tpc']['curves'][399]['offset']
+['path_tpc']['curves'][398]['res_net_meters']
+['path_tpc']['curves'][399]['offset_meters']
 ['path_tpc']['curves'][399]['res_coeff']
-['path_tpc']['curves'][399]['res_net']
-['path_tpc']['curves'][400]['offset']
+['path_tpc']['curves'][399]['res_net_meters']
+['path_tpc']['curves'][400]['offset_meters']
 ['path_tpc']['curves'][400]['res_coeff']
-['path_tpc']['curves'][400]['res_net']
-['path_tpc']['curves'][401]['offset']
+['path_tpc']['curves'][400]['res_net_meters']
+['path_tpc']['curves'][401]['offset_meters']
 ['path_tpc']['curves'][401]['res_coeff']
-['path_tpc']['curves'][401]['res_net']
-['path_tpc']['curves'][402]['offset']
+['path_tpc']['curves'][401]['res_net_meters']
+['path_tpc']['curves'][402]['offset_meters']
 ['path_tpc']['curves'][402]['res_coeff']
-['path_tpc']['curves'][402]['res_net']
-['path_tpc']['curves'][403]['offset']
+['path_tpc']['curves'][402]['res_net_meters']
+['path_tpc']['curves'][403]['offset_meters']
 ['path_tpc']['curves'][403]['res_coeff']
-['path_tpc']['curves'][403]['res_net']
-['path_tpc']['curves'][404]['offset']
+['path_tpc']['curves'][403]['res_net_meters']
+['path_tpc']['curves'][404]['offset_meters']
 ['path_tpc']['curves'][404]['res_coeff']
-['path_tpc']['curves'][404]['res_net']
-['path_tpc']['curves'][405]['offset']
+['path_tpc']['curves'][404]['res_net_meters']
+['path_tpc']['curves'][405]['offset_meters']
 ['path_tpc']['curves'][405]['res_coeff']
-['path_tpc']['curves'][405]['res_net']
-['path_tpc']['curves'][406]['offset']
+['path_tpc']['curves'][405]['res_net_meters']
+['path_tpc']['curves'][406]['offset_meters']
 ['path_tpc']['curves'][406]['res_coeff']
-['path_tpc']['curves'][406]['res_net']
-['path_tpc']['curves'][407]['offset']
+['path_tpc']['curves'][406]['res_net_meters']
+['path_tpc']['curves'][407]['offset_meters']
 ['path_tpc']['curves'][407]['res_coeff']
-['path_tpc']['curves'][407]['res_net']
-['path_tpc']['curves'][408]['offset']
+['path_tpc']['curves'][407]['res_net_meters']
+['path_tpc']['curves'][408]['offset_meters']
 ['path_tpc']['curves'][408]['res_coeff']
-['path_tpc']['curves'][408]['res_net']
-['path_tpc']['curves'][409]['offset']
+['path_tpc']['curves'][408]['res_net_meters']
+['path_tpc']['curves'][409]['offset_meters']
 ['path_tpc']['curves'][409]['res_coeff']
-['path_tpc']['curves'][409]['res_net']
-['path_tpc']['curves'][410]['offset']
+['path_tpc']['curves'][409]['res_net_meters']
+['path_tpc']['curves'][410]['offset_meters']
 ['path_tpc']['curves'][410]['res_coeff']
-['path_tpc']['curves'][410]['res_net']
-['path_tpc']['curves'][411]['offset']
+['path_tpc']['curves'][410]['res_net_meters']
+['path_tpc']['curves'][411]['offset_meters']
 ['path_tpc']['curves'][411]['res_coeff']
-['path_tpc']['curves'][411]['res_net']
-['path_tpc']['curves'][412]['offset']
+['path_tpc']['curves'][411]['res_net_meters']
+['path_tpc']['curves'][412]['offset_meters']
 ['path_tpc']['curves'][412]['res_coeff']
-['path_tpc']['curves'][412]['res_net']
-['path_tpc']['curves'][413]['offset']
+['path_tpc']['curves'][412]['res_net_meters']
+['path_tpc']['curves'][413]['offset_meters']
 ['path_tpc']['curves'][413]['res_coeff']
-['path_tpc']['curves'][413]['res_net']
-['path_tpc']['curves'][414]['offset']
+['path_tpc']['curves'][413]['res_net_meters']
+['path_tpc']['curves'][414]['offset_meters']
 ['path_tpc']['curves'][414]['res_coeff']
-['path_tpc']['curves'][414]['res_net']
-['path_tpc']['curves'][415]['offset']
+['path_tpc']['curves'][414]['res_net_meters']
+['path_tpc']['curves'][415]['offset_meters']
 ['path_tpc']['curves'][415]['res_coeff']
-['path_tpc']['curves'][415]['res_net']
-['path_tpc']['curves'][416]['offset']
+['path_tpc']['curves'][415]['res_net_meters']
+['path_tpc']['curves'][416]['offset_meters']
 ['path_tpc']['curves'][416]['res_coeff']
-['path_tpc']['curves'][416]['res_net']
-['path_tpc']['curves'][417]['offset']
+['path_tpc']['curves'][416]['res_net_meters']
+['path_tpc']['curves'][417]['offset_meters']
 ['path_tpc']['curves'][417]['res_coeff']
-['path_tpc']['curves'][417]['res_net']
-['path_tpc']['curves'][418]['offset']
+['path_tpc']['curves'][417]['res_net_meters']
+['path_tpc']['curves'][418]['offset_meters']
 ['path_tpc']['curves'][418]['res_coeff']
-['path_tpc']['curves'][418]['res_net']
-['path_tpc']['curves'][419]['offset']
+['path_tpc']['curves'][418]['res_net_meters']
+['path_tpc']['curves'][419]['offset_meters']
 ['path_tpc']['curves'][419]['res_coeff']
-['path_tpc']['curves'][419]['res_net']
-['path_tpc']['curves'][420]['offset']
+['path_tpc']['curves'][419]['res_net_meters']
+['path_tpc']['curves'][420]['offset_meters']
 ['path_tpc']['curves'][420]['res_coeff']
-['path_tpc']['curves'][420]['res_net']
-['path_tpc']['curves'][421]['offset']
+['path_tpc']['curves'][420]['res_net_meters']
+['path_tpc']['curves'][421]['offset_meters']
 ['path_tpc']['curves'][421]['res_coeff']
-['path_tpc']['curves'][421]['res_net']
-['path_tpc']['curves'][422]['offset']
+['path_tpc']['curves'][421]['res_net_meters']
+['path_tpc']['curves'][422]['offset_meters']
 ['path_tpc']['curves'][422]['res_coeff']
-['path_tpc']['curves'][422]['res_net']
-['path_tpc']['curves'][423]['offset']
+['path_tpc']['curves'][422]['res_net_meters']
+['path_tpc']['curves'][423]['offset_meters']
 ['path_tpc']['curves'][423]['res_coeff']
-['path_tpc']['curves'][423]['res_net']
-['path_tpc']['curves'][424]['offset']
+['path_tpc']['curves'][423]['res_net_meters']
+['path_tpc']['curves'][424]['offset_meters']
 ['path_tpc']['curves'][424]['res_coeff']
-['path_tpc']['curves'][424]['res_net']
-['path_tpc']['curves'][425]['offset']
+['path_tpc']['curves'][424]['res_net_meters']
+['path_tpc']['curves'][425]['offset_meters']
 ['path_tpc']['curves'][425]['res_coeff']
-['path_tpc']['curves'][425]['res_net']
-['path_tpc']['curves'][426]['offset']
+['path_tpc']['curves'][425]['res_net_meters']
+['path_tpc']['curves'][426]['offset_meters']
 ['path_tpc']['curves'][426]['res_coeff']
-['path_tpc']['curves'][426]['res_net']
-['path_tpc']['curves'][427]['offset']
+['path_tpc']['curves'][426]['res_net_meters']
+['path_tpc']['curves'][427]['offset_meters']
 ['path_tpc']['curves'][427]['res_coeff']
-['path_tpc']['curves'][427]['res_net']
-['path_tpc']['curves'][428]['offset']
+['path_tpc']['curves'][427]['res_net_meters']
+['path_tpc']['curves'][428]['offset_meters']
 ['path_tpc']['curves'][428]['res_coeff']
-['path_tpc']['curves'][428]['res_net']
-['path_tpc']['curves'][429]['offset']
+['path_tpc']['curves'][428]['res_net_meters']
+['path_tpc']['curves'][429]['offset_meters']
 ['path_tpc']['curves'][429]['res_coeff']
-['path_tpc']['curves'][429]['res_net']
-['path_tpc']['curves'][430]['offset']
+['path_tpc']['curves'][429]['res_net_meters']
+['path_tpc']['curves'][430]['offset_meters']
 ['path_tpc']['curves'][430]['res_coeff']
-['path_tpc']['curves'][430]['res_net']
-['path_tpc']['curves'][431]['offset']
+['path_tpc']['curves'][430]['res_net_meters']
+['path_tpc']['curves'][431]['offset_meters']
 ['path_tpc']['curves'][431]['res_coeff']
-['path_tpc']['curves'][431]['res_net']
-['path_tpc']['curves'][432]['offset']
+['path_tpc']['curves'][431]['res_net_meters']
+['path_tpc']['curves'][432]['offset_meters']
 ['path_tpc']['curves'][432]['res_coeff']
-['path_tpc']['curves'][432]['res_net']
-['path_tpc']['curves'][433]['offset']
+['path_tpc']['curves'][432]['res_net_meters']
+['path_tpc']['curves'][433]['offset_meters']
 ['path_tpc']['curves'][433]['res_coeff']
-['path_tpc']['curves'][433]['res_net']
-['path_tpc']['curves'][434]['offset']
+['path_tpc']['curves'][433]['res_net_meters']
+['path_tpc']['curves'][434]['offset_meters']
 ['path_tpc']['curves'][434]['res_coeff']
-['path_tpc']['curves'][434]['res_net']
-['path_tpc']['curves'][435]['offset']
+['path_tpc']['curves'][434]['res_net_meters']
+['path_tpc']['curves'][435]['offset_meters']
 ['path_tpc']['curves'][435]['res_coeff']
-['path_tpc']['curves'][435]['res_net']
-['path_tpc']['curves'][436]['offset']
+['path_tpc']['curves'][435]['res_net_meters']
+['path_tpc']['curves'][436]['offset_meters']
 ['path_tpc']['curves'][436]['res_coeff']
-['path_tpc']['curves'][436]['res_net']
-['path_tpc']['curves'][437]['offset']
+['path_tpc']['curves'][436]['res_net_meters']
+['path_tpc']['curves'][437]['offset_meters']
 ['path_tpc']['curves'][437]['res_coeff']
-['path_tpc']['curves'][437]['res_net']
-['path_tpc']['curves'][438]['offset']
+['path_tpc']['curves'][437]['res_net_meters']
+['path_tpc']['curves'][438]['offset_meters']
 ['path_tpc']['curves'][438]['res_coeff']
-['path_tpc']['curves'][438]['res_net']
-['path_tpc']['curves'][439]['offset']
+['path_tpc']['curves'][438]['res_net_meters']
+['path_tpc']['curves'][439]['offset_meters']
 ['path_tpc']['curves'][439]['res_coeff']
-['path_tpc']['curves'][439]['res_net']
-['path_tpc']['curves'][440]['offset']
+['path_tpc']['curves'][439]['res_net_meters']
+['path_tpc']['curves'][440]['offset_meters']
 ['path_tpc']['curves'][440]['res_coeff']
-['path_tpc']['curves'][440]['res_net']
-['path_tpc']['curves'][441]['offset']
+['path_tpc']['curves'][440]['res_net_meters']
+['path_tpc']['curves'][441]['offset_meters']
 ['path_tpc']['curves'][441]['res_coeff']
-['path_tpc']['curves'][441]['res_net']
-['path_tpc']['curves'][442]['offset']
+['path_tpc']['curves'][441]['res_net_meters']
+['path_tpc']['curves'][442]['offset_meters']
 ['path_tpc']['curves'][442]['res_coeff']
-['path_tpc']['curves'][442]['res_net']
-['path_tpc']['curves'][443]['offset']
+['path_tpc']['curves'][442]['res_net_meters']
+['path_tpc']['curves'][443]['offset_meters']
 ['path_tpc']['curves'][443]['res_coeff']
-['path_tpc']['curves'][443]['res_net']
-['path_tpc']['curves'][444]['offset']
+['path_tpc']['curves'][443]['res_net_meters']
+['path_tpc']['curves'][444]['offset_meters']
 ['path_tpc']['curves'][444]['res_coeff']
-['path_tpc']['curves'][444]['res_net']
-['path_tpc']['curves'][445]['offset']
+['path_tpc']['curves'][444]['res_net_meters']
+['path_tpc']['curves'][445]['offset_meters']
 ['path_tpc']['curves'][445]['res_coeff']
-['path_tpc']['curves'][445]['res_net']
-['path_tpc']['curves'][446]['offset']
+['path_tpc']['curves'][445]['res_net_meters']
+['path_tpc']['curves'][446]['offset_meters']
 ['path_tpc']['curves'][446]['res_coeff']
-['path_tpc']['curves'][446]['res_net']
-['path_tpc']['curves'][447]['offset']
+['path_tpc']['curves'][446]['res_net_meters']
+['path_tpc']['curves'][447]['offset_meters']
 ['path_tpc']['curves'][447]['res_coeff']
-['path_tpc']['curves'][447]['res_net']
-['path_tpc']['curves'][448]['offset']
+['path_tpc']['curves'][447]['res_net_meters']
+['path_tpc']['curves'][448]['offset_meters']
 ['path_tpc']['curves'][448]['res_coeff']
-['path_tpc']['curves'][448]['res_net']
-['path_tpc']['curves'][449]['offset']
+['path_tpc']['curves'][448]['res_net_meters']
+['path_tpc']['curves'][449]['offset_meters']
 ['path_tpc']['curves'][449]['res_coeff']
-['path_tpc']['curves'][449]['res_net']
-['path_tpc']['curves'][450]['offset']
+['path_tpc']['curves'][449]['res_net_meters']
+['path_tpc']['curves'][450]['offset_meters']
 ['path_tpc']['curves'][450]['res_coeff']
-['path_tpc']['curves'][450]['res_net']
-['path_tpc']['curves'][451]['offset']
+['path_tpc']['curves'][450]['res_net_meters']
+['path_tpc']['curves'][451]['offset_meters']
 ['path_tpc']['curves'][451]['res_coeff']
-['path_tpc']['curves'][451]['res_net']
-['path_tpc']['curves'][452]['offset']
+['path_tpc']['curves'][451]['res_net_meters']
+['path_tpc']['curves'][452]['offset_meters']
 ['path_tpc']['curves'][452]['res_coeff']
-['path_tpc']['curves'][452]['res_net']
-['path_tpc']['curves'][453]['offset']
+['path_tpc']['curves'][452]['res_net_meters']
+['path_tpc']['curves'][453]['offset_meters']
 ['path_tpc']['curves'][453]['res_coeff']
-['path_tpc']['curves'][453]['res_net']
-['path_tpc']['curves'][454]['offset']
+['path_tpc']['curves'][453]['res_net_meters']
+['path_tpc']['curves'][454]['offset_meters']
 ['path_tpc']['curves'][454]['res_coeff']
-['path_tpc']['curves'][454]['res_net']
-['path_tpc']['curves'][455]['offset']
+['path_tpc']['curves'][454]['res_net_meters']
+['path_tpc']['curves'][455]['offset_meters']
 ['path_tpc']['curves'][455]['res_coeff']
-['path_tpc']['curves'][455]['res_net']
-['path_tpc']['curves'][456]['offset']
+['path_tpc']['curves'][455]['res_net_meters']
+['path_tpc']['curves'][456]['offset_meters']
 ['path_tpc']['curves'][456]['res_coeff']
-['path_tpc']['curves'][456]['res_net']
-['path_tpc']['curves'][457]['offset']
+['path_tpc']['curves'][456]['res_net_meters']
+['path_tpc']['curves'][457]['offset_meters']
 ['path_tpc']['curves'][457]['res_coeff']
-['path_tpc']['curves'][457]['res_net']
-['path_tpc']['curves'][458]['offset']
+['path_tpc']['curves'][457]['res_net_meters']
+['path_tpc']['curves'][458]['offset_meters']
 ['path_tpc']['curves'][458]['res_coeff']
-['path_tpc']['curves'][458]['res_net']
-['path_tpc']['curves'][459]['offset']
+['path_tpc']['curves'][458]['res_net_meters']
+['path_tpc']['curves'][459]['offset_meters']
 ['path_tpc']['curves'][459]['res_coeff']
-['path_tpc']['curves'][459]['res_net']
-['path_tpc']['curves'][460]['offset']
+['path_tpc']['curves'][459]['res_net_meters']
+['path_tpc']['curves'][460]['offset_meters']
 ['path_tpc']['curves'][460]['res_coeff']
-['path_tpc']['curves'][460]['res_net']
-['path_tpc']['curves'][461]['offset']
+['path_tpc']['curves'][460]['res_net_meters']
+['path_tpc']['curves'][461]['offset_meters']
 ['path_tpc']['curves'][461]['res_coeff']
-['path_tpc']['curves'][461]['res_net']
-['path_tpc']['curves'][462]['offset']
+['path_tpc']['curves'][461]['res_net_meters']
+['path_tpc']['curves'][462]['offset_meters']
 ['path_tpc']['curves'][462]['res_coeff']
-['path_tpc']['curves'][462]['res_net']
-['path_tpc']['curves'][463]['offset']
+['path_tpc']['curves'][462]['res_net_meters']
+['path_tpc']['curves'][463]['offset_meters']
 ['path_tpc']['curves'][463]['res_coeff']
-['path_tpc']['curves'][463]['res_net']
-['path_tpc']['curves'][464]['offset']
+['path_tpc']['curves'][463]['res_net_meters']
+['path_tpc']['curves'][464]['offset_meters']
 ['path_tpc']['curves'][464]['res_coeff']
-['path_tpc']['curves'][464]['res_net']
-['path_tpc']['curves'][465]['offset']
+['path_tpc']['curves'][464]['res_net_meters']
+['path_tpc']['curves'][465]['offset_meters']
 ['path_tpc']['curves'][465]['res_coeff']
-['path_tpc']['curves'][465]['res_net']
-['path_tpc']['curves'][466]['offset']
+['path_tpc']['curves'][465]['res_net_meters']
+['path_tpc']['curves'][466]['offset_meters']
 ['path_tpc']['curves'][466]['res_coeff']
-['path_tpc']['curves'][466]['res_net']
-['path_tpc']['curves'][467]['offset']
+['path_tpc']['curves'][466]['res_net_meters']
+['path_tpc']['curves'][467]['offset_meters']
 ['path_tpc']['curves'][467]['res_coeff']
-['path_tpc']['curves'][467]['res_net']
-['path_tpc']['curves'][468]['offset']
+['path_tpc']['curves'][467]['res_net_meters']
+['path_tpc']['curves'][468]['offset_meters']
 ['path_tpc']['curves'][468]['res_coeff']
-['path_tpc']['curves'][468]['res_net']
-['path_tpc']['curves'][469]['offset']
+['path_tpc']['curves'][468]['res_net_meters']
+['path_tpc']['curves'][469]['offset_meters']
 ['path_tpc']['curves'][469]['res_coeff']
-['path_tpc']['curves'][469]['res_net']
-['path_tpc']['curves'][470]['offset']
+['path_tpc']['curves'][469]['res_net_meters']
+['path_tpc']['curves'][470]['offset_meters']
 ['path_tpc']['curves'][470]['res_coeff']
-['path_tpc']['curves'][470]['res_net']
-['path_tpc']['curves'][471]['offset']
+['path_tpc']['curves'][470]['res_net_meters']
+['path_tpc']['curves'][471]['offset_meters']
 ['path_tpc']['curves'][471]['res_coeff']
-['path_tpc']['curves'][471]['res_net']
-['path_tpc']['curves'][472]['offset']
+['path_tpc']['curves'][471]['res_net_meters']
+['path_tpc']['curves'][472]['offset_meters']
 ['path_tpc']['curves'][472]['res_coeff']
-['path_tpc']['curves'][472]['res_net']
-['path_tpc']['curves'][473]['offset']
+['path_tpc']['curves'][472]['res_net_meters']
+['path_tpc']['curves'][473]['offset_meters']
 ['path_tpc']['curves'][473]['res_coeff']
-['path_tpc']['curves'][473]['res_net']
-['path_tpc']['curves'][474]['offset']
+['path_tpc']['curves'][473]['res_net_meters']
+['path_tpc']['curves'][474]['offset_meters']
 ['path_tpc']['curves'][474]['res_coeff']
-['path_tpc']['curves'][474]['res_net']
-['path_tpc']['curves'][475]['offset']
+['path_tpc']['curves'][474]['res_net_meters']
+['path_tpc']['curves'][475]['offset_meters']
 ['path_tpc']['curves'][475]['res_coeff']
-['path_tpc']['curves'][475]['res_net']
-['path_tpc']['curves'][476]['offset']
+['path_tpc']['curves'][475]['res_net_meters']
+['path_tpc']['curves'][476]['offset_meters']
 ['path_tpc']['curves'][476]['res_coeff']
-['path_tpc']['curves'][476]['res_net']
-['path_tpc']['curves'][477]['offset']
+['path_tpc']['curves'][476]['res_net_meters']
+['path_tpc']['curves'][477]['offset_meters']
 ['path_tpc']['curves'][477]['res_coeff']
-['path_tpc']['curves'][477]['res_net']
-['path_tpc']['curves'][478]['offset']
+['path_tpc']['curves'][477]['res_net_meters']
+['path_tpc']['curves'][478]['offset_meters']
 ['path_tpc']['curves'][478]['res_coeff']
-['path_tpc']['curves'][478]['res_net']
-['path_tpc']['curves'][479]['offset']
+['path_tpc']['curves'][478]['res_net_meters']
+['path_tpc']['curves'][479]['offset_meters']
 ['path_tpc']['curves'][479]['res_coeff']
-['path_tpc']['curves'][479]['res_net']
-['path_tpc']['curves'][480]['offset']
+['path_tpc']['curves'][479]['res_net_meters']
+['path_tpc']['curves'][480]['offset_meters']
 ['path_tpc']['curves'][480]['res_coeff']
-['path_tpc']['curves'][480]['res_net']
-['path_tpc']['curves'][481]['offset']
+['path_tpc']['curves'][480]['res_net_meters']
+['path_tpc']['curves'][481]['offset_meters']
 ['path_tpc']['curves'][481]['res_coeff']
-['path_tpc']['curves'][481]['res_net']
-['path_tpc']['curves'][482]['offset']
+['path_tpc']['curves'][481]['res_net_meters']
+['path_tpc']['curves'][482]['offset_meters']
 ['path_tpc']['curves'][482]['res_coeff']
-['path_tpc']['curves'][482]['res_net']
-['path_tpc']['curves'][483]['offset']
+['path_tpc']['curves'][482]['res_net_meters']
+['path_tpc']['curves'][483]['offset_meters']
 ['path_tpc']['curves'][483]['res_coeff']
-['path_tpc']['curves'][483]['res_net']
-['path_tpc']['curves'][484]['offset']
+['path_tpc']['curves'][483]['res_net_meters']
+['path_tpc']['curves'][484]['offset_meters']
 ['path_tpc']['curves'][484]['res_coeff']
-['path_tpc']['curves'][484]['res_net']
-['path_tpc']['curves'][485]['offset']
+['path_tpc']['curves'][484]['res_net_meters']
+['path_tpc']['curves'][485]['offset_meters']
 ['path_tpc']['curves'][485]['res_coeff']
-['path_tpc']['curves'][485]['res_net']
-['path_tpc']['curves'][486]['offset']
+['path_tpc']['curves'][485]['res_net_meters']
+['path_tpc']['curves'][486]['offset_meters']
 ['path_tpc']['curves'][486]['res_coeff']
-['path_tpc']['curves'][486]['res_net']
-['path_tpc']['curves'][487]['offset']
+['path_tpc']['curves'][486]['res_net_meters']
+['path_tpc']['curves'][487]['offset_meters']
 ['path_tpc']['curves'][487]['res_coeff']
-['path_tpc']['curves'][487]['res_net']
-['path_tpc']['curves'][488]['offset']
+['path_tpc']['curves'][487]['res_net_meters']
+['path_tpc']['curves'][488]['offset_meters']
 ['path_tpc']['curves'][488]['res_coeff']
-['path_tpc']['curves'][488]['res_net']
-['path_tpc']['curves'][489]['offset']
+['path_tpc']['curves'][488]['res_net_meters']
+['path_tpc']['curves'][489]['offset_meters']
 ['path_tpc']['curves'][489]['res_coeff']
-['path_tpc']['curves'][489]['res_net']
-['path_tpc']['curves'][490]['offset']
+['path_tpc']['curves'][489]['res_net_meters']
+['path_tpc']['curves'][490]['offset_meters']
 ['path_tpc']['curves'][490]['res_coeff']
-['path_tpc']['curves'][490]['res_net']
-['path_tpc']['curves'][491]['offset']
+['path_tpc']['curves'][490]['res_net_meters']
+['path_tpc']['curves'][491]['offset_meters']
 ['path_tpc']['curves'][491]['res_coeff']
-['path_tpc']['curves'][491]['res_net']
-['path_tpc']['curves'][492]['offset']
+['path_tpc']['curves'][491]['res_net_meters']
+['path_tpc']['curves'][492]['offset_meters']
 ['path_tpc']['curves'][492]['res_coeff']
-['path_tpc']['curves'][492]['res_net']
-['path_tpc']['curves'][493]['offset']
+['path_tpc']['curves'][492]['res_net_meters']
+['path_tpc']['curves'][493]['offset_meters']
 ['path_tpc']['curves'][493]['res_coeff']
-['path_tpc']['curves'][493]['res_net']
-['path_tpc']['curves'][494]['offset']
+['path_tpc']['curves'][493]['res_net_meters']
+['path_tpc']['curves'][494]['offset_meters']
 ['path_tpc']['curves'][494]['res_coeff']
-['path_tpc']['curves'][494]['res_net']
-['path_tpc']['curves'][495]['offset']
+['path_tpc']['curves'][494]['res_net_meters']
+['path_tpc']['curves'][495]['offset_meters']
 ['path_tpc']['curves'][495]['res_coeff']
-['path_tpc']['curves'][495]['res_net']
-['path_tpc']['curves'][496]['offset']
+['path_tpc']['curves'][495]['res_net_meters']
+['path_tpc']['curves'][496]['offset_meters']
 ['path_tpc']['curves'][496]['res_coeff']
-['path_tpc']['curves'][496]['res_net']
-['path_tpc']['curves'][497]['offset']
+['path_tpc']['curves'][496]['res_net_meters']
+['path_tpc']['curves'][497]['offset_meters']
 ['path_tpc']['curves'][497]['res_coeff']
-['path_tpc']['curves'][497]['res_net']
-['path_tpc']['curves'][498]['offset']
+['path_tpc']['curves'][497]['res_net_meters']
+['path_tpc']['curves'][498]['offset_meters']
 ['path_tpc']['curves'][498]['res_coeff']
-['path_tpc']['curves'][498]['res_net']
-['path_tpc']['curves'][499]['offset']
+['path_tpc']['curves'][498]['res_net_meters']
+['path_tpc']['curves'][499]['offset_meters']
 ['path_tpc']['curves'][499]['res_coeff']
-['path_tpc']['curves'][499]['res_net']
-['path_tpc']['curves'][500]['offset']
+['path_tpc']['curves'][499]['res_net_meters']
+['path_tpc']['curves'][500]['offset_meters']
 ['path_tpc']['curves'][500]['res_coeff']
-['path_tpc']['curves'][500]['res_net']
-['path_tpc']['curves'][501]['offset']
+['path_tpc']['curves'][500]['res_net_meters']
+['path_tpc']['curves'][501]['offset_meters']
 ['path_tpc']['curves'][501]['res_coeff']
-['path_tpc']['curves'][501]['res_net']
-['path_tpc']['curves'][502]['offset']
+['path_tpc']['curves'][501]['res_net_meters']
+['path_tpc']['curves'][502]['offset_meters']
 ['path_tpc']['curves'][502]['res_coeff']
-['path_tpc']['curves'][502]['res_net']
-['path_tpc']['curves'][503]['offset']
+['path_tpc']['curves'][502]['res_net_meters']
+['path_tpc']['curves'][503]['offset_meters']
 ['path_tpc']['curves'][503]['res_coeff']
-['path_tpc']['curves'][503]['res_net']
-['path_tpc']['curves'][504]['offset']
+['path_tpc']['curves'][503]['res_net_meters']
+['path_tpc']['curves'][504]['offset_meters']
 ['path_tpc']['curves'][504]['res_coeff']
-['path_tpc']['curves'][504]['res_net']
-['path_tpc']['curves'][505]['offset']
+['path_tpc']['curves'][504]['res_net_meters']
+['path_tpc']['curves'][505]['offset_meters']
 ['path_tpc']['curves'][505]['res_coeff']
-['path_tpc']['curves'][505]['res_net']
-['path_tpc']['curves'][506]['offset']
+['path_tpc']['curves'][505]['res_net_meters']
+['path_tpc']['curves'][506]['offset_meters']
 ['path_tpc']['curves'][506]['res_coeff']
-['path_tpc']['curves'][506]['res_net']
-['path_tpc']['curves'][507]['offset']
+['path_tpc']['curves'][506]['res_net_meters']
+['path_tpc']['curves'][507]['offset_meters']
 ['path_tpc']['curves'][507]['res_coeff']
-['path_tpc']['curves'][507]['res_net']
-['path_tpc']['curves'][508]['offset']
+['path_tpc']['curves'][507]['res_net_meters']
+['path_tpc']['curves'][508]['offset_meters']
 ['path_tpc']['curves'][508]['res_coeff']
-['path_tpc']['curves'][508]['res_net']
-['path_tpc']['curves'][509]['offset']
+['path_tpc']['curves'][508]['res_net_meters']
+['path_tpc']['curves'][509]['offset_meters']
 ['path_tpc']['curves'][509]['res_coeff']
-['path_tpc']['curves'][509]['res_net']
-['path_tpc']['curves'][510]['offset']
+['path_tpc']['curves'][509]['res_net_meters']
+['path_tpc']['curves'][510]['offset_meters']
 ['path_tpc']['curves'][510]['res_coeff']
-['path_tpc']['curves'][510]['res_net']
-['path_tpc']['curves'][511]['offset']
+['path_tpc']['curves'][510]['res_net_meters']
+['path_tpc']['curves'][511]['offset_meters']
 ['path_tpc']['curves'][511]['res_coeff']
-['path_tpc']['curves'][511]['res_net']
-['path_tpc']['curves'][512]['offset']
+['path_tpc']['curves'][511]['res_net_meters']
+['path_tpc']['curves'][512]['offset_meters']
 ['path_tpc']['curves'][512]['res_coeff']
-['path_tpc']['curves'][512]['res_net']
-['path_tpc']['curves'][513]['offset']
+['path_tpc']['curves'][512]['res_net_meters']
+['path_tpc']['curves'][513]['offset_meters']
 ['path_tpc']['curves'][513]['res_coeff']
-['path_tpc']['curves'][513]['res_net']
-['path_tpc']['curves'][514]['offset']
+['path_tpc']['curves'][513]['res_net_meters']
+['path_tpc']['curves'][514]['offset_meters']
 ['path_tpc']['curves'][514]['res_coeff']
-['path_tpc']['curves'][514]['res_net']
-['path_tpc']['curves'][515]['offset']
+['path_tpc']['curves'][514]['res_net_meters']
+['path_tpc']['curves'][515]['offset_meters']
 ['path_tpc']['curves'][515]['res_coeff']
-['path_tpc']['curves'][515]['res_net']
-['path_tpc']['curves'][516]['offset']
+['path_tpc']['curves'][515]['res_net_meters']
+['path_tpc']['curves'][516]['offset_meters']
 ['path_tpc']['curves'][516]['res_coeff']
-['path_tpc']['curves'][516]['res_net']
-['path_tpc']['curves'][517]['offset']
+['path_tpc']['curves'][516]['res_net_meters']
+['path_tpc']['curves'][517]['offset_meters']
 ['path_tpc']['curves'][517]['res_coeff']
-['path_tpc']['curves'][517]['res_net']
-['path_tpc']['curves'][518]['offset']
+['path_tpc']['curves'][517]['res_net_meters']
+['path_tpc']['curves'][518]['offset_meters']
 ['path_tpc']['curves'][518]['res_coeff']
-['path_tpc']['curves'][518]['res_net']
-['path_tpc']['curves'][519]['offset']
+['path_tpc']['curves'][518]['res_net_meters']
+['path_tpc']['curves'][519]['offset_meters']
 ['path_tpc']['curves'][519]['res_coeff']
-['path_tpc']['curves'][519]['res_net']
-['path_tpc']['curves'][520]['offset']
+['path_tpc']['curves'][519]['res_net_meters']
+['path_tpc']['curves'][520]['offset_meters']
 ['path_tpc']['curves'][520]['res_coeff']
-['path_tpc']['curves'][520]['res_net']
-['path_tpc']['curves'][521]['offset']
+['path_tpc']['curves'][520]['res_net_meters']
+['path_tpc']['curves'][521]['offset_meters']
 ['path_tpc']['curves'][521]['res_coeff']
-['path_tpc']['curves'][521]['res_net']
-['path_tpc']['curves'][522]['offset']
+['path_tpc']['curves'][521]['res_net_meters']
+['path_tpc']['curves'][522]['offset_meters']
 ['path_tpc']['curves'][522]['res_coeff']
-['path_tpc']['curves'][522]['res_net']
-['path_tpc']['curves'][523]['offset']
+['path_tpc']['curves'][522]['res_net_meters']
+['path_tpc']['curves'][523]['offset_meters']
 ['path_tpc']['curves'][523]['res_coeff']
-['path_tpc']['curves'][523]['res_net']
-['path_tpc']['curves'][524]['offset']
+['path_tpc']['curves'][523]['res_net_meters']
+['path_tpc']['curves'][524]['offset_meters']
 ['path_tpc']['curves'][524]['res_coeff']
-['path_tpc']['curves'][524]['res_net']
-['path_tpc']['curves'][525]['offset']
+['path_tpc']['curves'][524]['res_net_meters']
+['path_tpc']['curves'][525]['offset_meters']
 ['path_tpc']['curves'][525]['res_coeff']
-['path_tpc']['curves'][525]['res_net']
-['path_tpc']['curves'][526]['offset']
+['path_tpc']['curves'][525]['res_net_meters']
+['path_tpc']['curves'][526]['offset_meters']
 ['path_tpc']['curves'][526]['res_coeff']
-['path_tpc']['curves'][526]['res_net']
-['path_tpc']['curves'][527]['offset']
+['path_tpc']['curves'][526]['res_net_meters']
+['path_tpc']['curves'][527]['offset_meters']
 ['path_tpc']['curves'][527]['res_coeff']
-['path_tpc']['curves'][527]['res_net']
-['path_tpc']['curves'][528]['offset']
+['path_tpc']['curves'][527]['res_net_meters']
+['path_tpc']['curves'][528]['offset_meters']
 ['path_tpc']['curves'][528]['res_coeff']
-['path_tpc']['curves'][528]['res_net']
-['path_tpc']['curves'][529]['offset']
+['path_tpc']['curves'][528]['res_net_meters']
+['path_tpc']['curves'][529]['offset_meters']
 ['path_tpc']['curves'][529]['res_coeff']
-['path_tpc']['curves'][529]['res_net']
-['path_tpc']['curves'][530]['offset']
+['path_tpc']['curves'][529]['res_net_meters']
+['path_tpc']['curves'][530]['offset_meters']
 ['path_tpc']['curves'][530]['res_coeff']
-['path_tpc']['curves'][530]['res_net']
-['path_tpc']['curves'][531]['offset']
+['path_tpc']['curves'][530]['res_net_meters']
+['path_tpc']['curves'][531]['offset_meters']
 ['path_tpc']['curves'][531]['res_coeff']
-['path_tpc']['curves'][531]['res_net']
-['path_tpc']['curves'][532]['offset']
+['path_tpc']['curves'][531]['res_net_meters']
+['path_tpc']['curves'][532]['offset_meters']
 ['path_tpc']['curves'][532]['res_coeff']
-['path_tpc']['curves'][532]['res_net']
-['path_tpc']['curves'][533]['offset']
+['path_tpc']['curves'][532]['res_net_meters']
+['path_tpc']['curves'][533]['offset_meters']
 ['path_tpc']['curves'][533]['res_coeff']
-['path_tpc']['curves'][533]['res_net']
-['path_tpc']['curves'][534]['offset']
+['path_tpc']['curves'][533]['res_net_meters']
+['path_tpc']['curves'][534]['offset_meters']
 ['path_tpc']['curves'][534]['res_coeff']
-['path_tpc']['curves'][534]['res_net']
-['path_tpc']['curves'][535]['offset']
+['path_tpc']['curves'][534]['res_net_meters']
+['path_tpc']['curves'][535]['offset_meters']
 ['path_tpc']['curves'][535]['res_coeff']
-['path_tpc']['curves'][535]['res_net']
-['path_tpc']['curves'][536]['offset']
+['path_tpc']['curves'][535]['res_net_meters']
+['path_tpc']['curves'][536]['offset_meters']
 ['path_tpc']['curves'][536]['res_coeff']
-['path_tpc']['curves'][536]['res_net']
-['path_tpc']['curves'][537]['offset']
+['path_tpc']['curves'][536]['res_net_meters']
+['path_tpc']['curves'][537]['offset_meters']
 ['path_tpc']['curves'][537]['res_coeff']
-['path_tpc']['curves'][537]['res_net']
-['path_tpc']['curves'][538]['offset']
+['path_tpc']['curves'][537]['res_net_meters']
+['path_tpc']['curves'][538]['offset_meters']
 ['path_tpc']['curves'][538]['res_coeff']
-['path_tpc']['curves'][538]['res_net']
-['path_tpc']['curves'][539]['offset']
+['path_tpc']['curves'][538]['res_net_meters']
+['path_tpc']['curves'][539]['offset_meters']
 ['path_tpc']['curves'][539]['res_coeff']
-['path_tpc']['curves'][539]['res_net']
-['path_tpc']['curves'][540]['offset']
+['path_tpc']['curves'][539]['res_net_meters']
+['path_tpc']['curves'][540]['offset_meters']
 ['path_tpc']['curves'][540]['res_coeff']
-['path_tpc']['curves'][540]['res_net']
-['path_tpc']['curves'][541]['offset']
+['path_tpc']['curves'][540]['res_net_meters']
+['path_tpc']['curves'][541]['offset_meters']
 ['path_tpc']['curves'][541]['res_coeff']
-['path_tpc']['curves'][541]['res_net']
-['path_tpc']['curves'][542]['offset']
+['path_tpc']['curves'][541]['res_net_meters']
+['path_tpc']['curves'][542]['offset_meters']
 ['path_tpc']['curves'][542]['res_coeff']
-['path_tpc']['curves'][542]['res_net']
-['path_tpc']['curves'][543]['offset']
+['path_tpc']['curves'][542]['res_net_meters']
+['path_tpc']['curves'][543]['offset_meters']
 ['path_tpc']['curves'][543]['res_coeff']
-['path_tpc']['curves'][543]['res_net']
-['path_tpc']['curves'][544]['offset']
+['path_tpc']['curves'][543]['res_net_meters']
+['path_tpc']['curves'][544]['offset_meters']
 ['path_tpc']['curves'][544]['res_coeff']
-['path_tpc']['curves'][544]['res_net']
-['path_tpc']['curves'][545]['offset']
+['path_tpc']['curves'][544]['res_net_meters']
+['path_tpc']['curves'][545]['offset_meters']
 ['path_tpc']['curves'][545]['res_coeff']
-['path_tpc']['curves'][545]['res_net']
-['path_tpc']['curves'][546]['offset']
+['path_tpc']['curves'][545]['res_net_meters']
+['path_tpc']['curves'][546]['offset_meters']
 ['path_tpc']['curves'][546]['res_coeff']
-['path_tpc']['curves'][546]['res_net']
-['path_tpc']['curves'][547]['offset']
+['path_tpc']['curves'][546]['res_net_meters']
+['path_tpc']['curves'][547]['offset_meters']
 ['path_tpc']['curves'][547]['res_coeff']
-['path_tpc']['curves'][547]['res_net']
-['path_tpc']['curves'][548]['offset']
+['path_tpc']['curves'][547]['res_net_meters']
+['path_tpc']['curves'][548]['offset_meters']
 ['path_tpc']['curves'][548]['res_coeff']
-['path_tpc']['curves'][548]['res_net']
-['path_tpc']['curves'][549]['offset']
+['path_tpc']['curves'][548]['res_net_meters']
+['path_tpc']['curves'][549]['offset_meters']
 ['path_tpc']['curves'][549]['res_coeff']
-['path_tpc']['curves'][549]['res_net']
-['path_tpc']['curves'][550]['offset']
+['path_tpc']['curves'][549]['res_net_meters']
+['path_tpc']['curves'][550]['offset_meters']
 ['path_tpc']['curves'][550]['res_coeff']
-['path_tpc']['curves'][550]['res_net']
-['path_tpc']['curves'][551]['offset']
+['path_tpc']['curves'][550]['res_net_meters']
+['path_tpc']['curves'][551]['offset_meters']
 ['path_tpc']['curves'][551]['res_coeff']
-['path_tpc']['curves'][551]['res_net']
-['path_tpc']['curves'][552]['offset']
+['path_tpc']['curves'][551]['res_net_meters']
+['path_tpc']['curves'][552]['offset_meters']
 ['path_tpc']['curves'][552]['res_coeff']
-['path_tpc']['curves'][552]['res_net']
-['path_tpc']['curves'][553]['offset']
+['path_tpc']['curves'][552]['res_net_meters']
+['path_tpc']['curves'][553]['offset_meters']
 ['path_tpc']['curves'][553]['res_coeff']
-['path_tpc']['curves'][553]['res_net']
-['path_tpc']['curves'][554]['offset']
+['path_tpc']['curves'][553]['res_net_meters']
+['path_tpc']['curves'][554]['offset_meters']
 ['path_tpc']['curves'][554]['res_coeff']
-['path_tpc']['curves'][554]['res_net']
-['path_tpc']['curves'][555]['offset']
+['path_tpc']['curves'][554]['res_net_meters']
+['path_tpc']['curves'][555]['offset_meters']
 ['path_tpc']['curves'][555]['res_coeff']
-['path_tpc']['curves'][555]['res_net']
-['path_tpc']['curves'][556]['offset']
+['path_tpc']['curves'][555]['res_net_meters']
+['path_tpc']['curves'][556]['offset_meters']
 ['path_tpc']['curves'][556]['res_coeff']
-['path_tpc']['curves'][556]['res_net']
-['path_tpc']['curves'][557]['offset']
+['path_tpc']['curves'][556]['res_net_meters']
+['path_tpc']['curves'][557]['offset_meters']
 ['path_tpc']['curves'][557]['res_coeff']
-['path_tpc']['curves'][557]['res_net']
-['path_tpc']['curves'][558]['offset']
+['path_tpc']['curves'][557]['res_net_meters']
+['path_tpc']['curves'][558]['offset_meters']
 ['path_tpc']['curves'][558]['res_coeff']
-['path_tpc']['curves'][558]['res_net']
-['path_tpc']['curves'][559]['offset']
+['path_tpc']['curves'][558]['res_net_meters']
+['path_tpc']['curves'][559]['offset_meters']
 ['path_tpc']['curves'][559]['res_coeff']
-['path_tpc']['curves'][559]['res_net']
-['path_tpc']['curves'][560]['offset']
+['path_tpc']['curves'][559]['res_net_meters']
+['path_tpc']['curves'][560]['offset_meters']
 ['path_tpc']['curves'][560]['res_coeff']
-['path_tpc']['curves'][560]['res_net']
-['path_tpc']['curves'][561]['offset']
+['path_tpc']['curves'][560]['res_net_meters']
+['path_tpc']['curves'][561]['offset_meters']
 ['path_tpc']['curves'][561]['res_coeff']
-['path_tpc']['curves'][561]['res_net']
-['path_tpc']['curves'][562]['offset']
+['path_tpc']['curves'][561]['res_net_meters']
+['path_tpc']['curves'][562]['offset_meters']
 ['path_tpc']['curves'][562]['res_coeff']
-['path_tpc']['curves'][562]['res_net']
-['path_tpc']['curves'][563]['offset']
+['path_tpc']['curves'][562]['res_net_meters']
+['path_tpc']['curves'][563]['offset_meters']
 ['path_tpc']['curves'][563]['res_coeff']
-['path_tpc']['curves'][563]['res_net']
-['path_tpc']['curves'][564]['offset']
+['path_tpc']['curves'][563]['res_net_meters']
+['path_tpc']['curves'][564]['offset_meters']
 ['path_tpc']['curves'][564]['res_coeff']
-['path_tpc']['curves'][564]['res_net']
-['path_tpc']['curves'][565]['offset']
+['path_tpc']['curves'][564]['res_net_meters']
+['path_tpc']['curves'][565]['offset_meters']
 ['path_tpc']['curves'][565]['res_coeff']
-['path_tpc']['curves'][565]['res_net']
-['path_tpc']['curves'][566]['offset']
+['path_tpc']['curves'][565]['res_net_meters']
+['path_tpc']['curves'][566]['offset_meters']
 ['path_tpc']['curves'][566]['res_coeff']
-['path_tpc']['curves'][566]['res_net']
-['path_tpc']['curves'][567]['offset']
+['path_tpc']['curves'][566]['res_net_meters']
+['path_tpc']['curves'][567]['offset_meters']
 ['path_tpc']['curves'][567]['res_coeff']
-['path_tpc']['curves'][567]['res_net']
-['path_tpc']['curves'][568]['offset']
+['path_tpc']['curves'][567]['res_net_meters']
+['path_tpc']['curves'][568]['offset_meters']
 ['path_tpc']['curves'][568]['res_coeff']
-['path_tpc']['curves'][568]['res_net']
-['path_tpc']['curves'][569]['offset']
+['path_tpc']['curves'][568]['res_net_meters']
+['path_tpc']['curves'][569]['offset_meters']
 ['path_tpc']['curves'][569]['res_coeff']
-['path_tpc']['curves'][569]['res_net']
-['path_tpc']['curves'][570]['offset']
+['path_tpc']['curves'][569]['res_net_meters']
+['path_tpc']['curves'][570]['offset_meters']
 ['path_tpc']['curves'][570]['res_coeff']
-['path_tpc']['curves'][570]['res_net']
-['path_tpc']['curves'][571]['offset']
+['path_tpc']['curves'][570]['res_net_meters']
+['path_tpc']['curves'][571]['offset_meters']
 ['path_tpc']['curves'][571]['res_coeff']
-['path_tpc']['curves'][571]['res_net']
-['path_tpc']['curves'][572]['offset']
+['path_tpc']['curves'][571]['res_net_meters']
+['path_tpc']['curves'][572]['offset_meters']
 ['path_tpc']['curves'][572]['res_coeff']
-['path_tpc']['curves'][572]['res_net']
-['path_tpc']['curves'][573]['offset']
+['path_tpc']['curves'][572]['res_net_meters']
+['path_tpc']['curves'][573]['offset_meters']
 ['path_tpc']['curves'][573]['res_coeff']
-['path_tpc']['curves'][573]['res_net']
-['path_tpc']['curves'][574]['offset']
+['path_tpc']['curves'][573]['res_net_meters']
+['path_tpc']['curves'][574]['offset_meters']
 ['path_tpc']['curves'][574]['res_coeff']
-['path_tpc']['curves'][574]['res_net']
-['path_tpc']['curves'][575]['offset']
+['path_tpc']['curves'][574]['res_net_meters']
+['path_tpc']['curves'][575]['offset_meters']
 ['path_tpc']['curves'][575]['res_coeff']
-['path_tpc']['curves'][575]['res_net']
-['path_tpc']['curves'][576]['offset']
+['path_tpc']['curves'][575]['res_net_meters']
+['path_tpc']['curves'][576]['offset_meters']
 ['path_tpc']['curves'][576]['res_coeff']
-['path_tpc']['curves'][576]['res_net']
-['path_tpc']['curves'][577]['offset']
+['path_tpc']['curves'][576]['res_net_meters']
+['path_tpc']['curves'][577]['offset_meters']
 ['path_tpc']['curves'][577]['res_coeff']
-['path_tpc']['curves'][577]['res_net']
-['path_tpc']['curves'][578]['offset']
+['path_tpc']['curves'][577]['res_net_meters']
+['path_tpc']['curves'][578]['offset_meters']
 ['path_tpc']['curves'][578]['res_coeff']
-['path_tpc']['curves'][578]['res_net']
-['path_tpc']['curves'][579]['offset']
+['path_tpc']['curves'][578]['res_net_meters']
+['path_tpc']['curves'][579]['offset_meters']
 ['path_tpc']['curves'][579]['res_coeff']
-['path_tpc']['curves'][579]['res_net']
-['path_tpc']['curves'][580]['offset']
+['path_tpc']['curves'][579]['res_net_meters']
+['path_tpc']['curves'][580]['offset_meters']
 ['path_tpc']['curves'][580]['res_coeff']
-['path_tpc']['curves'][580]['res_net']
-['path_tpc']['curves'][581]['offset']
+['path_tpc']['curves'][580]['res_net_meters']
+['path_tpc']['curves'][581]['offset_meters']
 ['path_tpc']['curves'][581]['res_coeff']
-['path_tpc']['curves'][581]['res_net']
-['path_tpc']['curves'][582]['offset']
+['path_tpc']['curves'][581]['res_net_meters']
+['path_tpc']['curves'][582]['offset_meters']
 ['path_tpc']['curves'][582]['res_coeff']
-['path_tpc']['curves'][582]['res_net']
-['path_tpc']['curves'][583]['offset']
+['path_tpc']['curves'][582]['res_net_meters']
+['path_tpc']['curves'][583]['offset_meters']
 ['path_tpc']['curves'][583]['res_coeff']
-['path_tpc']['curves'][583]['res_net']
-['path_tpc']['curves'][584]['offset']
+['path_tpc']['curves'][583]['res_net_meters']
+['path_tpc']['curves'][584]['offset_meters']
 ['path_tpc']['curves'][584]['res_coeff']
-['path_tpc']['curves'][584]['res_net']
-['path_tpc']['curves'][585]['offset']
+['path_tpc']['curves'][584]['res_net_meters']
+['path_tpc']['curves'][585]['offset_meters']
 ['path_tpc']['curves'][585]['res_coeff']
-['path_tpc']['curves'][585]['res_net']
-['path_tpc']['curves'][586]['offset']
+['path_tpc']['curves'][585]['res_net_meters']
+['path_tpc']['curves'][586]['offset_meters']
 ['path_tpc']['curves'][586]['res_coeff']
-['path_tpc']['curves'][586]['res_net']
-['path_tpc']['curves'][587]['offset']
+['path_tpc']['curves'][586]['res_net_meters']
+['path_tpc']['curves'][587]['offset_meters']
 ['path_tpc']['curves'][587]['res_coeff']
-['path_tpc']['curves'][587]['res_net']
-['path_tpc']['curves'][588]['offset']
+['path_tpc']['curves'][587]['res_net_meters']
+['path_tpc']['curves'][588]['offset_meters']
 ['path_tpc']['curves'][588]['res_coeff']
-['path_tpc']['curves'][588]['res_net']
-['path_tpc']['curves'][589]['offset']
+['path_tpc']['curves'][588]['res_net_meters']
+['path_tpc']['curves'][589]['offset_meters']
 ['path_tpc']['curves'][589]['res_coeff']
-['path_tpc']['curves'][589]['res_net']
-['path_tpc']['curves'][590]['offset']
+['path_tpc']['curves'][589]['res_net_meters']
+['path_tpc']['curves'][590]['offset_meters']
 ['path_tpc']['curves'][590]['res_coeff']
-['path_tpc']['curves'][590]['res_net']
-['path_tpc']['curves'][591]['offset']
+['path_tpc']['curves'][590]['res_net_meters']
+['path_tpc']['curves'][591]['offset_meters']
 ['path_tpc']['curves'][591]['res_coeff']
-['path_tpc']['curves'][591]['res_net']
-['path_tpc']['curves'][592]['offset']
+['path_tpc']['curves'][591]['res_net_meters']
+['path_tpc']['curves'][592]['offset_meters']
 ['path_tpc']['curves'][592]['res_coeff']
-['path_tpc']['curves'][592]['res_net']
-['path_tpc']['curves'][593]['offset']
+['path_tpc']['curves'][592]['res_net_meters']
+['path_tpc']['curves'][593]['offset_meters']
 ['path_tpc']['curves'][593]['res_coeff']
-['path_tpc']['curves'][593]['res_net']
-['path_tpc']['curves'][594]['offset']
+['path_tpc']['curves'][593]['res_net_meters']
+['path_tpc']['curves'][594]['offset_meters']
 ['path_tpc']['curves'][594]['res_coeff']
-['path_tpc']['curves'][594]['res_net']
-['path_tpc']['curves'][595]['offset']
+['path_tpc']['curves'][594]['res_net_meters']
+['path_tpc']['curves'][595]['offset_meters']
 ['path_tpc']['curves'][595]['res_coeff']
-['path_tpc']['curves'][595]['res_net']
-['path_tpc']['curves'][596]['offset']
+['path_tpc']['curves'][595]['res_net_meters']
+['path_tpc']['curves'][596]['offset_meters']
 ['path_tpc']['curves'][596]['res_coeff']
-['path_tpc']['curves'][596]['res_net']
-['path_tpc']['curves'][597]['offset']
+['path_tpc']['curves'][596]['res_net_meters']
+['path_tpc']['curves'][597]['offset_meters']
 ['path_tpc']['curves'][597]['res_coeff']
-['path_tpc']['curves'][597]['res_net']
-['path_tpc']['curves'][598]['offset']
+['path_tpc']['curves'][597]['res_net_meters']
+['path_tpc']['curves'][598]['offset_meters']
 ['path_tpc']['curves'][598]['res_coeff']
-['path_tpc']['curves'][598]['res_net']
-['path_tpc']['curves'][599]['offset']
+['path_tpc']['curves'][598]['res_net_meters']
+['path_tpc']['curves'][599]['offset_meters']
 ['path_tpc']['curves'][599]['res_coeff']
-['path_tpc']['curves'][599]['res_net']
-['path_tpc']['curves'][600]['offset']
+['path_tpc']['curves'][599]['res_net_meters']
+['path_tpc']['curves'][600]['offset_meters']
 ['path_tpc']['curves'][600]['res_coeff']
-['path_tpc']['curves'][600]['res_net']
-['path_tpc']['curves'][601]['offset']
+['path_tpc']['curves'][600]['res_net_meters']
+['path_tpc']['curves'][601]['offset_meters']
 ['path_tpc']['curves'][601]['res_coeff']
-['path_tpc']['curves'][601]['res_net']
-['path_tpc']['curves'][602]['offset']
+['path_tpc']['curves'][601]['res_net_meters']
+['path_tpc']['curves'][602]['offset_meters']
 ['path_tpc']['curves'][602]['res_coeff']
-['path_tpc']['curves'][602]['res_net']
-['path_tpc']['curves'][603]['offset']
+['path_tpc']['curves'][602]['res_net_meters']
+['path_tpc']['curves'][603]['offset_meters']
 ['path_tpc']['curves'][603]['res_coeff']
-['path_tpc']['curves'][603]['res_net']
-['path_tpc']['curves'][604]['offset']
+['path_tpc']['curves'][603]['res_net_meters']
+['path_tpc']['curves'][604]['offset_meters']
 ['path_tpc']['curves'][604]['res_coeff']
-['path_tpc']['curves'][604]['res_net']
-['path_tpc']['curves'][605]['offset']
+['path_tpc']['curves'][604]['res_net_meters']
+['path_tpc']['curves'][605]['offset_meters']
 ['path_tpc']['curves'][605]['res_coeff']
-['path_tpc']['curves'][605]['res_net']
-['path_tpc']['curves'][606]['offset']
+['path_tpc']['curves'][605]['res_net_meters']
+['path_tpc']['curves'][606]['offset_meters']
 ['path_tpc']['curves'][606]['res_coeff']
-['path_tpc']['curves'][606]['res_net']
-['path_tpc']['curves'][607]['offset']
+['path_tpc']['curves'][606]['res_net_meters']
+['path_tpc']['curves'][607]['offset_meters']
 ['path_tpc']['curves'][607]['res_coeff']
-['path_tpc']['curves'][607]['res_net']
-['path_tpc']['curves'][608]['offset']
+['path_tpc']['curves'][607]['res_net_meters']
+['path_tpc']['curves'][608]['offset_meters']
 ['path_tpc']['curves'][608]['res_coeff']
-['path_tpc']['curves'][608]['res_net']
-['path_tpc']['curves'][609]['offset']
+['path_tpc']['curves'][608]['res_net_meters']
+['path_tpc']['curves'][609]['offset_meters']
 ['path_tpc']['curves'][609]['res_coeff']
-['path_tpc']['curves'][609]['res_net']
-['path_tpc']['curves'][610]['offset']
+['path_tpc']['curves'][609]['res_net_meters']
+['path_tpc']['curves'][610]['offset_meters']
 ['path_tpc']['curves'][610]['res_coeff']
-['path_tpc']['curves'][610]['res_net']
-['path_tpc']['curves'][611]['offset']
+['path_tpc']['curves'][610]['res_net_meters']
+['path_tpc']['curves'][611]['offset_meters']
 ['path_tpc']['curves'][611]['res_coeff']
-['path_tpc']['curves'][611]['res_net']
-['path_tpc']['curves'][612]['offset']
+['path_tpc']['curves'][611]['res_net_meters']
+['path_tpc']['curves'][612]['offset_meters']
 ['path_tpc']['curves'][612]['res_coeff']
-['path_tpc']['curves'][612]['res_net']
-['path_tpc']['curves'][613]['offset']
+['path_tpc']['curves'][612]['res_net_meters']
+['path_tpc']['curves'][613]['offset_meters']
 ['path_tpc']['curves'][613]['res_coeff']
-['path_tpc']['curves'][613]['res_net']
-['path_tpc']['curves'][614]['offset']
+['path_tpc']['curves'][613]['res_net_meters']
+['path_tpc']['curves'][614]['offset_meters']
 ['path_tpc']['curves'][614]['res_coeff']
-['path_tpc']['curves'][614]['res_net']
-['path_tpc']['curves'][615]['offset']
+['path_tpc']['curves'][614]['res_net_meters']
+['path_tpc']['curves'][615]['offset_meters']
 ['path_tpc']['curves'][615]['res_coeff']
-['path_tpc']['curves'][615]['res_net']
-['path_tpc']['curves'][616]['offset']
+['path_tpc']['curves'][615]['res_net_meters']
+['path_tpc']['curves'][616]['offset_meters']
 ['path_tpc']['curves'][616]['res_coeff']
-['path_tpc']['curves'][616]['res_net']
-['path_tpc']['curves'][617]['offset']
+['path_tpc']['curves'][616]['res_net_meters']
+['path_tpc']['curves'][617]['offset_meters']
 ['path_tpc']['curves'][617]['res_coeff']
-['path_tpc']['curves'][617]['res_net']
-['path_tpc']['curves'][618]['offset']
+['path_tpc']['curves'][617]['res_net_meters']
+['path_tpc']['curves'][618]['offset_meters']
 ['path_tpc']['curves'][618]['res_coeff']
-['path_tpc']['curves'][618]['res_net']
-['path_tpc']['curves'][619]['offset']
+['path_tpc']['curves'][618]['res_net_meters']
+['path_tpc']['curves'][619]['offset_meters']
 ['path_tpc']['curves'][619]['res_coeff']
-['path_tpc']['curves'][619]['res_net']
-['path_tpc']['curves'][620]['offset']
+['path_tpc']['curves'][619]['res_net_meters']
+['path_tpc']['curves'][620]['offset_meters']
 ['path_tpc']['curves'][620]['res_coeff']
-['path_tpc']['curves'][620]['res_net']
-['path_tpc']['curves'][621]['offset']
+['path_tpc']['curves'][620]['res_net_meters']
+['path_tpc']['curves'][621]['offset_meters']
 ['path_tpc']['curves'][621]['res_coeff']
-['path_tpc']['curves'][621]['res_net']
-['path_tpc']['curves'][622]['offset']
+['path_tpc']['curves'][621]['res_net_meters']
+['path_tpc']['curves'][622]['offset_meters']
 ['path_tpc']['curves'][622]['res_coeff']
-['path_tpc']['curves'][622]['res_net']
-['path_tpc']['curves'][623]['offset']
+['path_tpc']['curves'][622]['res_net_meters']
+['path_tpc']['curves'][623]['offset_meters']
 ['path_tpc']['curves'][623]['res_coeff']
-['path_tpc']['curves'][623]['res_net']
-['path_tpc']['curves'][624]['offset']
+['path_tpc']['curves'][623]['res_net_meters']
+['path_tpc']['curves'][624]['offset_meters']
 ['path_tpc']['curves'][624]['res_coeff']
-['path_tpc']['curves'][624]['res_net']
-['path_tpc']['curves'][625]['offset']
+['path_tpc']['curves'][624]['res_net_meters']
+['path_tpc']['curves'][625]['offset_meters']
 ['path_tpc']['curves'][625]['res_coeff']
-['path_tpc']['curves'][625]['res_net']
-['path_tpc']['curves'][626]['offset']
+['path_tpc']['curves'][625]['res_net_meters']
+['path_tpc']['curves'][626]['offset_meters']
 ['path_tpc']['curves'][626]['res_coeff']
-['path_tpc']['curves'][626]['res_net']
-['path_tpc']['curves'][627]['offset']
+['path_tpc']['curves'][626]['res_net_meters']
+['path_tpc']['curves'][627]['offset_meters']
 ['path_tpc']['curves'][627]['res_coeff']
-['path_tpc']['curves'][627]['res_net']
-['path_tpc']['curves'][628]['offset']
+['path_tpc']['curves'][627]['res_net_meters']
+['path_tpc']['curves'][628]['offset_meters']
 ['path_tpc']['curves'][628]['res_coeff']
-['path_tpc']['curves'][628]['res_net']
-['path_tpc']['curves'][629]['offset']
+['path_tpc']['curves'][628]['res_net_meters']
+['path_tpc']['curves'][629]['offset_meters']
 ['path_tpc']['curves'][629]['res_coeff']
-['path_tpc']['curves'][629]['res_net']
-['path_tpc']['curves'][630]['offset']
+['path_tpc']['curves'][629]['res_net_meters']
+['path_tpc']['curves'][630]['offset_meters']
 ['path_tpc']['curves'][630]['res_coeff']
-['path_tpc']['curves'][630]['res_net']
-['path_tpc']['curves'][631]['offset']
+['path_tpc']['curves'][630]['res_net_meters']
+['path_tpc']['curves'][631]['offset_meters']
 ['path_tpc']['curves'][631]['res_coeff']
-['path_tpc']['curves'][631]['res_net']
-['path_tpc']['curves'][632]['offset']
+['path_tpc']['curves'][631]['res_net_meters']
+['path_tpc']['curves'][632]['offset_meters']
 ['path_tpc']['curves'][632]['res_coeff']
-['path_tpc']['curves'][632]['res_net']
-['path_tpc']['curves'][633]['offset']
+['path_tpc']['curves'][632]['res_net_meters']
+['path_tpc']['curves'][633]['offset_meters']
 ['path_tpc']['curves'][633]['res_coeff']
-['path_tpc']['curves'][633]['res_net']
-['path_tpc']['curves'][634]['offset']
+['path_tpc']['curves'][633]['res_net_meters']
+['path_tpc']['curves'][634]['offset_meters']
 ['path_tpc']['curves'][634]['res_coeff']
-['path_tpc']['curves'][634]['res_net']
-['path_tpc']['curves'][635]['offset']
+['path_tpc']['curves'][634]['res_net_meters']
+['path_tpc']['curves'][635]['offset_meters']
 ['path_tpc']['curves'][635]['res_coeff']
-['path_tpc']['curves'][635]['res_net']
-['path_tpc']['curves'][636]['offset']
+['path_tpc']['curves'][635]['res_net_meters']
+['path_tpc']['curves'][636]['offset_meters']
 ['path_tpc']['curves'][636]['res_coeff']
-['path_tpc']['curves'][636]['res_net']
-['path_tpc']['curves'][637]['offset']
+['path_tpc']['curves'][636]['res_net_meters']
+['path_tpc']['curves'][637]['offset_meters']
 ['path_tpc']['curves'][637]['res_coeff']
-['path_tpc']['curves'][637]['res_net']
-['path_tpc']['curves'][638]['offset']
+['path_tpc']['curves'][637]['res_net_meters']
+['path_tpc']['curves'][638]['offset_meters']
 ['path_tpc']['curves'][638]['res_coeff']
-['path_tpc']['curves'][638]['res_net']
-['path_tpc']['curves'][639]['offset']
+['path_tpc']['curves'][638]['res_net_meters']
+['path_tpc']['curves'][639]['offset_meters']
 ['path_tpc']['curves'][639]['res_coeff']
-['path_tpc']['curves'][639]['res_net']
-['path_tpc']['curves'][640]['offset']
+['path_tpc']['curves'][639]['res_net_meters']
+['path_tpc']['curves'][640]['offset_meters']
 ['path_tpc']['curves'][640]['res_coeff']
-['path_tpc']['curves'][640]['res_net']
-['path_tpc']['curves'][641]['offset']
+['path_tpc']['curves'][640]['res_net_meters']
+['path_tpc']['curves'][641]['offset_meters']
 ['path_tpc']['curves'][641]['res_coeff']
-['path_tpc']['curves'][641]['res_net']
-['path_tpc']['curves'][642]['offset']
+['path_tpc']['curves'][641]['res_net_meters']
+['path_tpc']['curves'][642]['offset_meters']
 ['path_tpc']['curves'][642]['res_coeff']
-['path_tpc']['curves'][642]['res_net']
-['path_tpc']['curves'][643]['offset']
+['path_tpc']['curves'][642]['res_net_meters']
+['path_tpc']['curves'][643]['offset_meters']
 ['path_tpc']['curves'][643]['res_coeff']
-['path_tpc']['curves'][643]['res_net']
-['path_tpc']['curves'][644]['offset']
+['path_tpc']['curves'][643]['res_net_meters']
+['path_tpc']['curves'][644]['offset_meters']
 ['path_tpc']['curves'][644]['res_coeff']
-['path_tpc']['curves'][644]['res_net']
-['path_tpc']['curves'][645]['offset']
+['path_tpc']['curves'][644]['res_net_meters']
+['path_tpc']['curves'][645]['offset_meters']
 ['path_tpc']['curves'][645]['res_coeff']
-['path_tpc']['curves'][645]['res_net']
-['path_tpc']['curves'][646]['offset']
+['path_tpc']['curves'][645]['res_net_meters']
+['path_tpc']['curves'][646]['offset_meters']
 ['path_tpc']['curves'][646]['res_coeff']
-['path_tpc']['curves'][646]['res_net']
-['path_tpc']['curves'][647]['offset']
+['path_tpc']['curves'][646]['res_net_meters']
+['path_tpc']['curves'][647]['offset_meters']
 ['path_tpc']['curves'][647]['res_coeff']
-['path_tpc']['curves'][647]['res_net']
-['path_tpc']['curves'][648]['offset']
+['path_tpc']['curves'][647]['res_net_meters']
+['path_tpc']['curves'][648]['offset_meters']
 ['path_tpc']['curves'][648]['res_coeff']
-['path_tpc']['curves'][648]['res_net']
-['path_tpc']['curves'][649]['offset']
+['path_tpc']['curves'][648]['res_net_meters']
+['path_tpc']['curves'][649]['offset_meters']
 ['path_tpc']['curves'][649]['res_coeff']
-['path_tpc']['curves'][649]['res_net']
-['path_tpc']['curves'][650]['offset']
+['path_tpc']['curves'][649]['res_net_meters']
+['path_tpc']['curves'][650]['offset_meters']
 ['path_tpc']['curves'][650]['res_coeff']
-['path_tpc']['curves'][650]['res_net']
-['path_tpc']['curves'][651]['offset']
+['path_tpc']['curves'][650]['res_net_meters']
+['path_tpc']['curves'][651]['offset_meters']
 ['path_tpc']['curves'][651]['res_coeff']
-['path_tpc']['curves'][651]['res_net']
-['path_tpc']['curves'][652]['offset']
+['path_tpc']['curves'][651]['res_net_meters']
+['path_tpc']['curves'][652]['offset_meters']
 ['path_tpc']['curves'][652]['res_coeff']
-['path_tpc']['curves'][652]['res_net']
-['path_tpc']['curves'][653]['offset']
+['path_tpc']['curves'][652]['res_net_meters']
+['path_tpc']['curves'][653]['offset_meters']
 ['path_tpc']['curves'][653]['res_coeff']
-['path_tpc']['curves'][653]['res_net']
-['path_tpc']['curves'][654]['offset']
+['path_tpc']['curves'][653]['res_net_meters']
+['path_tpc']['curves'][654]['offset_meters']
 ['path_tpc']['curves'][654]['res_coeff']
-['path_tpc']['curves'][654]['res_net']
-['path_tpc']['curves'][655]['offset']
+['path_tpc']['curves'][654]['res_net_meters']
+['path_tpc']['curves'][655]['offset_meters']
 ['path_tpc']['curves'][655]['res_coeff']
-['path_tpc']['curves'][655]['res_net']
-['path_tpc']['curves'][656]['offset']
+['path_tpc']['curves'][655]['res_net_meters']
+['path_tpc']['curves'][656]['offset_meters']
 ['path_tpc']['curves'][656]['res_coeff']
-['path_tpc']['curves'][656]['res_net']
-['path_tpc']['curves'][657]['offset']
+['path_tpc']['curves'][656]['res_net_meters']
+['path_tpc']['curves'][657]['offset_meters']
 ['path_tpc']['curves'][657]['res_coeff']
-['path_tpc']['curves'][657]['res_net']
-['path_tpc']['curves'][658]['offset']
+['path_tpc']['curves'][657]['res_net_meters']
+['path_tpc']['curves'][658]['offset_meters']
 ['path_tpc']['curves'][658]['res_coeff']
-['path_tpc']['curves'][658]['res_net']
-['path_tpc']['curves'][659]['offset']
+['path_tpc']['curves'][658]['res_net_meters']
+['path_tpc']['curves'][659]['offset_meters']
 ['path_tpc']['curves'][659]['res_coeff']
-['path_tpc']['curves'][659]['res_net']
-['path_tpc']['curves'][660]['offset']
+['path_tpc']['curves'][659]['res_net_meters']
+['path_tpc']['curves'][660]['offset_meters']
 ['path_tpc']['curves'][660]['res_coeff']
-['path_tpc']['curves'][660]['res_net']
-['path_tpc']['curves'][661]['offset']
+['path_tpc']['curves'][660]['res_net_meters']
+['path_tpc']['curves'][661]['offset_meters']
 ['path_tpc']['curves'][661]['res_coeff']
-['path_tpc']['curves'][661]['res_net']
-['path_tpc']['curves'][662]['offset']
+['path_tpc']['curves'][661]['res_net_meters']
+['path_tpc']['curves'][662]['offset_meters']
 ['path_tpc']['curves'][662]['res_coeff']
-['path_tpc']['curves'][662]['res_net']
-['path_tpc']['curves'][663]['offset']
+['path_tpc']['curves'][662]['res_net_meters']
+['path_tpc']['curves'][663]['offset_meters']
 ['path_tpc']['curves'][663]['res_coeff']
-['path_tpc']['curves'][663]['res_net']
-['path_tpc']['curves'][664]['offset']
+['path_tpc']['curves'][663]['res_net_meters']
+['path_tpc']['curves'][664]['offset_meters']
 ['path_tpc']['curves'][664]['res_coeff']
-['path_tpc']['curves'][664]['res_net']
-['path_tpc']['curves'][665]['offset']
+['path_tpc']['curves'][664]['res_net_meters']
+['path_tpc']['curves'][665]['offset_meters']
 ['path_tpc']['curves'][665]['res_coeff']
-['path_tpc']['curves'][665]['res_net']
-['path_tpc']['curves'][666]['offset']
+['path_tpc']['curves'][665]['res_net_meters']
+['path_tpc']['curves'][666]['offset_meters']
 ['path_tpc']['curves'][666]['res_coeff']
-['path_tpc']['curves'][666]['res_net']
-['path_tpc']['curves'][667]['offset']
+['path_tpc']['curves'][666]['res_net_meters']
+['path_tpc']['curves'][667]['offset_meters']
 ['path_tpc']['curves'][667]['res_coeff']
-['path_tpc']['curves'][667]['res_net']
-['path_tpc']['curves'][668]['offset']
+['path_tpc']['curves'][667]['res_net_meters']
+['path_tpc']['curves'][668]['offset_meters']
 ['path_tpc']['curves'][668]['res_coeff']
-['path_tpc']['curves'][668]['res_net']
-['path_tpc']['curves'][669]['offset']
+['path_tpc']['curves'][668]['res_net_meters']
+['path_tpc']['curves'][669]['offset_meters']
 ['path_tpc']['curves'][669]['res_coeff']
-['path_tpc']['curves'][669]['res_net']
-['path_tpc']['curves'][670]['offset']
+['path_tpc']['curves'][669]['res_net_meters']
+['path_tpc']['curves'][670]['offset_meters']
 ['path_tpc']['curves'][670]['res_coeff']
-['path_tpc']['curves'][670]['res_net']
-['path_tpc']['curves'][671]['offset']
+['path_tpc']['curves'][670]['res_net_meters']
+['path_tpc']['curves'][671]['offset_meters']
 ['path_tpc']['curves'][671]['res_coeff']
-['path_tpc']['curves'][671]['res_net']
-['path_tpc']['curves'][672]['offset']
+['path_tpc']['curves'][671]['res_net_meters']
+['path_tpc']['curves'][672]['offset_meters']
 ['path_tpc']['curves'][672]['res_coeff']
-['path_tpc']['curves'][672]['res_net']
-['path_tpc']['curves'][673]['offset']
+['path_tpc']['curves'][672]['res_net_meters']
+['path_tpc']['curves'][673]['offset_meters']
 ['path_tpc']['curves'][673]['res_coeff']
-['path_tpc']['curves'][673]['res_net']
-['path_tpc']['curves'][674]['offset']
+['path_tpc']['curves'][673]['res_net_meters']
+['path_tpc']['curves'][674]['offset_meters']
 ['path_tpc']['curves'][674]['res_coeff']
-['path_tpc']['curves'][674]['res_net']
-['path_tpc']['curves'][675]['offset']
+['path_tpc']['curves'][674]['res_net_meters']
+['path_tpc']['curves'][675]['offset_meters']
 ['path_tpc']['curves'][675]['res_coeff']
-['path_tpc']['curves'][675]['res_net']
-['path_tpc']['curves'][676]['offset']
+['path_tpc']['curves'][675]['res_net_meters']
+['path_tpc']['curves'][676]['offset_meters']
 ['path_tpc']['curves'][676]['res_coeff']
-['path_tpc']['curves'][676]['res_net']
-['path_tpc']['curves'][677]['offset']
+['path_tpc']['curves'][676]['res_net_meters']
+['path_tpc']['curves'][677]['offset_meters']
 ['path_tpc']['curves'][677]['res_coeff']
-['path_tpc']['curves'][677]['res_net']
-['path_tpc']['curves'][678]['offset']
+['path_tpc']['curves'][677]['res_net_meters']
+['path_tpc']['curves'][678]['offset_meters']
 ['path_tpc']['curves'][678]['res_coeff']
-['path_tpc']['curves'][678]['res_net']
-['path_tpc']['curves'][679]['offset']
+['path_tpc']['curves'][678]['res_net_meters']
+['path_tpc']['curves'][679]['offset_meters']
 ['path_tpc']['curves'][679]['res_coeff']
-['path_tpc']['curves'][679]['res_net']
-['path_tpc']['curves'][680]['offset']
+['path_tpc']['curves'][679]['res_net_meters']
+['path_tpc']['curves'][680]['offset_meters']
 ['path_tpc']['curves'][680]['res_coeff']
-['path_tpc']['curves'][680]['res_net']
-['path_tpc']['curves'][681]['offset']
+['path_tpc']['curves'][680]['res_net_meters']
+['path_tpc']['curves'][681]['offset_meters']
 ['path_tpc']['curves'][681]['res_coeff']
-['path_tpc']['curves'][681]['res_net']
-['path_tpc']['curves'][682]['offset']
+['path_tpc']['curves'][681]['res_net_meters']
+['path_tpc']['curves'][682]['offset_meters']
 ['path_tpc']['curves'][682]['res_coeff']
-['path_tpc']['curves'][682]['res_net']
-['path_tpc']['curves'][683]['offset']
+['path_tpc']['curves'][682]['res_net_meters']
+['path_tpc']['curves'][683]['offset_meters']
 ['path_tpc']['curves'][683]['res_coeff']
-['path_tpc']['curves'][683]['res_net']
-['path_tpc']['curves'][684]['offset']
+['path_tpc']['curves'][683]['res_net_meters']
+['path_tpc']['curves'][684]['offset_meters']
 ['path_tpc']['curves'][684]['res_coeff']
-['path_tpc']['curves'][684]['res_net']
-['path_tpc']['curves'][685]['offset']
+['path_tpc']['curves'][684]['res_net_meters']
+['path_tpc']['curves'][685]['offset_meters']
 ['path_tpc']['curves'][685]['res_coeff']
-['path_tpc']['curves'][685]['res_net']
-['path_tpc']['curves'][686]['offset']
+['path_tpc']['curves'][685]['res_net_meters']
+['path_tpc']['curves'][686]['offset_meters']
 ['path_tpc']['curves'][686]['res_coeff']
-['path_tpc']['curves'][686]['res_net']
-['path_tpc']['curves'][687]['offset']
+['path_tpc']['curves'][686]['res_net_meters']
+['path_tpc']['curves'][687]['offset_meters']
 ['path_tpc']['curves'][687]['res_coeff']
-['path_tpc']['curves'][687]['res_net']
-['path_tpc']['curves'][688]['offset']
+['path_tpc']['curves'][687]['res_net_meters']
+['path_tpc']['curves'][688]['offset_meters']
 ['path_tpc']['curves'][688]['res_coeff']
-['path_tpc']['curves'][688]['res_net']
-['path_tpc']['curves'][689]['offset']
+['path_tpc']['curves'][688]['res_net_meters']
+['path_tpc']['curves'][689]['offset_meters']
 ['path_tpc']['curves'][689]['res_coeff']
-['path_tpc']['curves'][689]['res_net']
-['path_tpc']['curves'][690]['offset']
+['path_tpc']['curves'][689]['res_net_meters']
+['path_tpc']['curves'][690]['offset_meters']
 ['path_tpc']['curves'][690]['res_coeff']
-['path_tpc']['curves'][690]['res_net']
-['path_tpc']['curves'][691]['offset']
+['path_tpc']['curves'][690]['res_net_meters']
+['path_tpc']['curves'][691]['offset_meters']
 ['path_tpc']['curves'][691]['res_coeff']
-['path_tpc']['curves'][691]['res_net']
-['path_tpc']['curves'][692]['offset']
+['path_tpc']['curves'][691]['res_net_meters']
+['path_tpc']['curves'][692]['offset_meters']
 ['path_tpc']['curves'][692]['res_coeff']
-['path_tpc']['curves'][692]['res_net']
-['path_tpc']['curves'][693]['offset']
+['path_tpc']['curves'][692]['res_net_meters']
+['path_tpc']['curves'][693]['offset_meters']
 ['path_tpc']['curves'][693]['res_coeff']
-['path_tpc']['curves'][693]['res_net']
-['path_tpc']['curves'][694]['offset']
+['path_tpc']['curves'][693]['res_net_meters']
+['path_tpc']['curves'][694]['offset_meters']
 ['path_tpc']['curves'][694]['res_coeff']
-['path_tpc']['curves'][694]['res_net']
-['path_tpc']['curves'][695]['offset']
+['path_tpc']['curves'][694]['res_net_meters']
+['path_tpc']['curves'][695]['offset_meters']
 ['path_tpc']['curves'][695]['res_coeff']
-['path_tpc']['curves'][695]['res_net']
-['path_tpc']['curves'][696]['offset']
+['path_tpc']['curves'][695]['res_net_meters']
+['path_tpc']['curves'][696]['offset_meters']
 ['path_tpc']['curves'][696]['res_coeff']
-['path_tpc']['curves'][696]['res_net']
-['path_tpc']['curves'][697]['offset']
+['path_tpc']['curves'][696]['res_net_meters']
+['path_tpc']['curves'][697]['offset_meters']
 ['path_tpc']['curves'][697]['res_coeff']
-['path_tpc']['curves'][697]['res_net']
-['path_tpc']['curves'][698]['offset']
+['path_tpc']['curves'][697]['res_net_meters']
+['path_tpc']['curves'][698]['offset_meters']
 ['path_tpc']['curves'][698]['res_coeff']
-['path_tpc']['curves'][698]['res_net']
-['path_tpc']['curves'][699]['offset']
+['path_tpc']['curves'][698]['res_net_meters']
+['path_tpc']['curves'][699]['offset_meters']
 ['path_tpc']['curves'][699]['res_coeff']
-['path_tpc']['curves'][699]['res_net']
-['path_tpc']['curves'][700]['offset']
+['path_tpc']['curves'][699]['res_net_meters']
+['path_tpc']['curves'][700]['offset_meters']
 ['path_tpc']['curves'][700]['res_coeff']
-['path_tpc']['curves'][700]['res_net']
-['path_tpc']['curves'][701]['offset']
+['path_tpc']['curves'][700]['res_net_meters']
+['path_tpc']['curves'][701]['offset_meters']
 ['path_tpc']['curves'][701]['res_coeff']
-['path_tpc']['curves'][701]['res_net']
-['path_tpc']['curves'][702]['offset']
+['path_tpc']['curves'][701]['res_net_meters']
+['path_tpc']['curves'][702]['offset_meters']
 ['path_tpc']['curves'][702]['res_coeff']
-['path_tpc']['curves'][702]['res_net']
-['path_tpc']['curves'][703]['offset']
+['path_tpc']['curves'][702]['res_net_meters']
+['path_tpc']['curves'][703]['offset_meters']
 ['path_tpc']['curves'][703]['res_coeff']
-['path_tpc']['curves'][703]['res_net']
-['path_tpc']['curves'][704]['offset']
+['path_tpc']['curves'][703]['res_net_meters']
+['path_tpc']['curves'][704]['offset_meters']
 ['path_tpc']['curves'][704]['res_coeff']
-['path_tpc']['curves'][704]['res_net']
-['path_tpc']['curves'][705]['offset']
+['path_tpc']['curves'][704]['res_net_meters']
+['path_tpc']['curves'][705]['offset_meters']
 ['path_tpc']['curves'][705]['res_coeff']
-['path_tpc']['curves'][705]['res_net']
-['path_tpc']['curves'][706]['offset']
+['path_tpc']['curves'][705]['res_net_meters']
+['path_tpc']['curves'][706]['offset_meters']
 ['path_tpc']['curves'][706]['res_coeff']
-['path_tpc']['curves'][706]['res_net']
-['path_tpc']['curves'][707]['offset']
+['path_tpc']['curves'][706]['res_net_meters']
+['path_tpc']['curves'][707]['offset_meters']
 ['path_tpc']['curves'][707]['res_coeff']
-['path_tpc']['curves'][707]['res_net']
-['path_tpc']['curves'][708]['offset']
+['path_tpc']['curves'][707]['res_net_meters']
+['path_tpc']['curves'][708]['offset_meters']
 ['path_tpc']['curves'][708]['res_coeff']
-['path_tpc']['curves'][708]['res_net']
-['path_tpc']['curves'][709]['offset']
+['path_tpc']['curves'][708]['res_net_meters']
+['path_tpc']['curves'][709]['offset_meters']
 ['path_tpc']['curves'][709]['res_coeff']
-['path_tpc']['curves'][709]['res_net']
-['path_tpc']['curves'][710]['offset']
+['path_tpc']['curves'][709]['res_net_meters']
+['path_tpc']['curves'][710]['offset_meters']
 ['path_tpc']['curves'][710]['res_coeff']
-['path_tpc']['curves'][710]['res_net']
-['path_tpc']['curves'][711]['offset']
+['path_tpc']['curves'][710]['res_net_meters']
+['path_tpc']['curves'][711]['offset_meters']
 ['path_tpc']['curves'][711]['res_coeff']
-['path_tpc']['curves'][711]['res_net']
-['path_tpc']['curves'][712]['offset']
+['path_tpc']['curves'][711]['res_net_meters']
+['path_tpc']['curves'][712]['offset_meters']
 ['path_tpc']['curves'][712]['res_coeff']
-['path_tpc']['curves'][712]['res_net']
-['path_tpc']['curves'][713]['offset']
+['path_tpc']['curves'][712]['res_net_meters']
+['path_tpc']['curves'][713]['offset_meters']
 ['path_tpc']['curves'][713]['res_coeff']
-['path_tpc']['curves'][713]['res_net']
-['path_tpc']['curves'][714]['offset']
+['path_tpc']['curves'][713]['res_net_meters']
+['path_tpc']['curves'][714]['offset_meters']
 ['path_tpc']['curves'][714]['res_coeff']
-['path_tpc']['curves'][714]['res_net']
-['path_tpc']['curves'][715]['offset']
+['path_tpc']['curves'][714]['res_net_meters']
+['path_tpc']['curves'][715]['offset_meters']
 ['path_tpc']['curves'][715]['res_coeff']
-['path_tpc']['curves'][715]['res_net']
-['path_tpc']['curves'][716]['offset']
+['path_tpc']['curves'][715]['res_net_meters']
+['path_tpc']['curves'][716]['offset_meters']
 ['path_tpc']['curves'][716]['res_coeff']
-['path_tpc']['curves'][716]['res_net']
-['path_tpc']['curves'][717]['offset']
+['path_tpc']['curves'][716]['res_net_meters']
+['path_tpc']['curves'][717]['offset_meters']
 ['path_tpc']['curves'][717]['res_coeff']
-['path_tpc']['curves'][717]['res_net']
-['path_tpc']['curves'][718]['offset']
+['path_tpc']['curves'][717]['res_net_meters']
+['path_tpc']['curves'][718]['offset_meters']
 ['path_tpc']['curves'][718]['res_coeff']
-['path_tpc']['curves'][718]['res_net']
-['path_tpc']['curves'][719]['offset']
+['path_tpc']['curves'][718]['res_net_meters']
+['path_tpc']['curves'][719]['offset_meters']
 ['path_tpc']['curves'][719]['res_coeff']
-['path_tpc']['curves'][719]['res_net']
-['path_tpc']['curves'][720]['offset']
+['path_tpc']['curves'][719]['res_net_meters']
+['path_tpc']['curves'][720]['offset_meters']
 ['path_tpc']['curves'][720]['res_coeff']
-['path_tpc']['curves'][720]['res_net']
-['path_tpc']['curves'][721]['offset']
+['path_tpc']['curves'][720]['res_net_meters']
+['path_tpc']['curves'][721]['offset_meters']
 ['path_tpc']['curves'][721]['res_coeff']
-['path_tpc']['curves'][721]['res_net']
-['path_tpc']['curves'][722]['offset']
+['path_tpc']['curves'][721]['res_net_meters']
+['path_tpc']['curves'][722]['offset_meters']
 ['path_tpc']['curves'][722]['res_coeff']
-['path_tpc']['curves'][722]['res_net']
-['path_tpc']['curves'][723]['offset']
+['path_tpc']['curves'][722]['res_net_meters']
+['path_tpc']['curves'][723]['offset_meters']
 ['path_tpc']['curves'][723]['res_coeff']
-['path_tpc']['curves'][723]['res_net']
-['path_tpc']['curves'][724]['offset']
+['path_tpc']['curves'][723]['res_net_meters']
+['path_tpc']['curves'][724]['offset_meters']
 ['path_tpc']['curves'][724]['res_coeff']
-['path_tpc']['curves'][724]['res_net']
-['path_tpc']['curves'][725]['offset']
+['path_tpc']['curves'][724]['res_net_meters']
+['path_tpc']['curves'][725]['offset_meters']
 ['path_tpc']['curves'][725]['res_coeff']
-['path_tpc']['curves'][725]['res_net']
-['path_tpc']['curves'][726]['offset']
+['path_tpc']['curves'][725]['res_net_meters']
+['path_tpc']['curves'][726]['offset_meters']
 ['path_tpc']['curves'][726]['res_coeff']
-['path_tpc']['curves'][726]['res_net']
-['path_tpc']['curves'][727]['offset']
+['path_tpc']['curves'][726]['res_net_meters']
+['path_tpc']['curves'][727]['offset_meters']
 ['path_tpc']['curves'][727]['res_coeff']
-['path_tpc']['curves'][727]['res_net']
-['path_tpc']['curves'][728]['offset']
+['path_tpc']['curves'][727]['res_net_meters']
+['path_tpc']['curves'][728]['offset_meters']
 ['path_tpc']['curves'][728]['res_coeff']
-['path_tpc']['curves'][728]['res_net']
-['path_tpc']['curves'][729]['offset']
+['path_tpc']['curves'][728]['res_net_meters']
+['path_tpc']['curves'][729]['offset_meters']
 ['path_tpc']['curves'][729]['res_coeff']
-['path_tpc']['curves'][729]['res_net']
-['path_tpc']['curves'][730]['offset']
+['path_tpc']['curves'][729]['res_net_meters']
+['path_tpc']['curves'][730]['offset_meters']
 ['path_tpc']['curves'][730]['res_coeff']
-['path_tpc']['curves'][730]['res_net']
-['path_tpc']['curves'][731]['offset']
+['path_tpc']['curves'][730]['res_net_meters']
+['path_tpc']['curves'][731]['offset_meters']
 ['path_tpc']['curves'][731]['res_coeff']
-['path_tpc']['curves'][731]['res_net']
-['path_tpc']['curves'][732]['offset']
+['path_tpc']['curves'][731]['res_net_meters']
+['path_tpc']['curves'][732]['offset_meters']
 ['path_tpc']['curves'][732]['res_coeff']
-['path_tpc']['curves'][732]['res_net']
-['path_tpc']['curves'][733]['offset']
+['path_tpc']['curves'][732]['res_net_meters']
+['path_tpc']['curves'][733]['offset_meters']
 ['path_tpc']['curves'][733]['res_coeff']
-['path_tpc']['curves'][733]['res_net']
-['path_tpc']['curves'][734]['offset']
+['path_tpc']['curves'][733]['res_net_meters']
+['path_tpc']['curves'][734]['offset_meters']
 ['path_tpc']['curves'][734]['res_coeff']
-['path_tpc']['curves'][734]['res_net']
-['path_tpc']['curves'][735]['offset']
+['path_tpc']['curves'][734]['res_net_meters']
+['path_tpc']['curves'][735]['offset_meters']
 ['path_tpc']['curves'][735]['res_coeff']
-['path_tpc']['curves'][735]['res_net']
-['path_tpc']['curves'][736]['offset']
+['path_tpc']['curves'][735]['res_net_meters']
+['path_tpc']['curves'][736]['offset_meters']
 ['path_tpc']['curves'][736]['res_coeff']
-['path_tpc']['curves'][736]['res_net']
-['path_tpc']['curves'][737]['offset']
+['path_tpc']['curves'][736]['res_net_meters']
+['path_tpc']['curves'][737]['offset_meters']
 ['path_tpc']['curves'][737]['res_coeff']
-['path_tpc']['curves'][737]['res_net']
-['path_tpc']['curves'][738]['offset']
+['path_tpc']['curves'][737]['res_net_meters']
+['path_tpc']['curves'][738]['offset_meters']
 ['path_tpc']['curves'][738]['res_coeff']
-['path_tpc']['curves'][738]['res_net']
-['path_tpc']['curves'][739]['offset']
+['path_tpc']['curves'][738]['res_net_meters']
+['path_tpc']['curves'][739]['offset_meters']
 ['path_tpc']['curves'][739]['res_coeff']
-['path_tpc']['curves'][739]['res_net']
-['path_tpc']['curves'][740]['offset']
+['path_tpc']['curves'][739]['res_net_meters']
+['path_tpc']['curves'][740]['offset_meters']
 ['path_tpc']['curves'][740]['res_coeff']
-['path_tpc']['curves'][740]['res_net']
-['path_tpc']['curves'][741]['offset']
+['path_tpc']['curves'][740]['res_net_meters']
+['path_tpc']['curves'][741]['offset_meters']
 ['path_tpc']['curves'][741]['res_coeff']
-['path_tpc']['curves'][741]['res_net']
-['path_tpc']['curves'][742]['offset']
+['path_tpc']['curves'][741]['res_net_meters']
+['path_tpc']['curves'][742]['offset_meters']
 ['path_tpc']['curves'][742]['res_coeff']
-['path_tpc']['curves'][742]['res_net']
-['path_tpc']['curves'][743]['offset']
+['path_tpc']['curves'][742]['res_net_meters']
+['path_tpc']['curves'][743]['offset_meters']
 ['path_tpc']['curves'][743]['res_coeff']
-['path_tpc']['curves'][743]['res_net']
-['path_tpc']['curves'][744]['offset']
+['path_tpc']['curves'][743]['res_net_meters']
+['path_tpc']['curves'][744]['offset_meters']
 ['path_tpc']['curves'][744]['res_coeff']
-['path_tpc']['curves'][744]['res_net']
-['path_tpc']['curves'][745]['offset']
+['path_tpc']['curves'][744]['res_net_meters']
+['path_tpc']['curves'][745]['offset_meters']
 ['path_tpc']['curves'][745]['res_coeff']
-['path_tpc']['curves'][745]['res_net']
-['path_tpc']['curves'][746]['offset']
+['path_tpc']['curves'][745]['res_net_meters']
+['path_tpc']['curves'][746]['offset_meters']
 ['path_tpc']['curves'][746]['res_coeff']
-['path_tpc']['curves'][746]['res_net']
-['path_tpc']['curves'][747]['offset']
+['path_tpc']['curves'][746]['res_net_meters']
+['path_tpc']['curves'][747]['offset_meters']
 ['path_tpc']['curves'][747]['res_coeff']
-['path_tpc']['curves'][747]['res_net']
-['path_tpc']['curves'][748]['offset']
+['path_tpc']['curves'][747]['res_net_meters']
+['path_tpc']['curves'][748]['offset_meters']
 ['path_tpc']['curves'][748]['res_coeff']
-['path_tpc']['curves'][748]['res_net']
-['path_tpc']['curves'][749]['offset']
+['path_tpc']['curves'][748]['res_net_meters']
+['path_tpc']['curves'][749]['offset_meters']
 ['path_tpc']['curves'][749]['res_coeff']
-['path_tpc']['curves'][749]['res_net']
-['path_tpc']['curves'][750]['offset']
+['path_tpc']['curves'][749]['res_net_meters']
+['path_tpc']['curves'][750]['offset_meters']
 ['path_tpc']['curves'][750]['res_coeff']
-['path_tpc']['curves'][750]['res_net']
-['path_tpc']['curves'][751]['offset']
+['path_tpc']['curves'][750]['res_net_meters']
+['path_tpc']['curves'][751]['offset_meters']
 ['path_tpc']['curves'][751]['res_coeff']
-['path_tpc']['curves'][751]['res_net']
-['path_tpc']['curves'][752]['offset']
+['path_tpc']['curves'][751]['res_net_meters']
+['path_tpc']['curves'][752]['offset_meters']
 ['path_tpc']['curves'][752]['res_coeff']
-['path_tpc']['curves'][752]['res_net']
-['path_tpc']['curves'][753]['offset']
+['path_tpc']['curves'][752]['res_net_meters']
+['path_tpc']['curves'][753]['offset_meters']
 ['path_tpc']['curves'][753]['res_coeff']
-['path_tpc']['curves'][753]['res_net']
-['path_tpc']['curves'][754]['offset']
+['path_tpc']['curves'][753]['res_net_meters']
+['path_tpc']['curves'][754]['offset_meters']
 ['path_tpc']['curves'][754]['res_coeff']
-['path_tpc']['curves'][754]['res_net']
-['path_tpc']['curves'][755]['offset']
+['path_tpc']['curves'][754]['res_net_meters']
+['path_tpc']['curves'][755]['offset_meters']
 ['path_tpc']['curves'][755]['res_coeff']
-['path_tpc']['curves'][755]['res_net']
-['path_tpc']['curves'][756]['offset']
+['path_tpc']['curves'][755]['res_net_meters']
+['path_tpc']['curves'][756]['offset_meters']
 ['path_tpc']['curves'][756]['res_coeff']
-['path_tpc']['curves'][756]['res_net']
-['path_tpc']['curves'][757]['offset']
+['path_tpc']['curves'][756]['res_net_meters']
+['path_tpc']['curves'][757]['offset_meters']
 ['path_tpc']['curves'][757]['res_coeff']
-['path_tpc']['curves'][757]['res_net']
-['path_tpc']['curves'][758]['offset']
+['path_tpc']['curves'][757]['res_net_meters']
+['path_tpc']['curves'][758]['offset_meters']
 ['path_tpc']['curves'][758]['res_coeff']
-['path_tpc']['curves'][758]['res_net']
-['path_tpc']['curves'][759]['offset']
+['path_tpc']['curves'][758]['res_net_meters']
+['path_tpc']['curves'][759]['offset_meters']
 ['path_tpc']['curves'][759]['res_coeff']
-['path_tpc']['curves'][759]['res_net']
-['path_tpc']['curves'][760]['offset']
+['path_tpc']['curves'][759]['res_net_meters']
+['path_tpc']['curves'][760]['offset_meters']
 ['path_tpc']['curves'][760]['res_coeff']
-['path_tpc']['curves'][760]['res_net']
-['path_tpc']['curves'][761]['offset']
+['path_tpc']['curves'][760]['res_net_meters']
+['path_tpc']['curves'][761]['offset_meters']
 ['path_tpc']['curves'][761]['res_coeff']
-['path_tpc']['curves'][761]['res_net']
-['path_tpc']['curves'][762]['offset']
+['path_tpc']['curves'][761]['res_net_meters']
+['path_tpc']['curves'][762]['offset_meters']
 ['path_tpc']['curves'][762]['res_coeff']
-['path_tpc']['curves'][762]['res_net']
-['path_tpc']['curves'][763]['offset']
+['path_tpc']['curves'][762]['res_net_meters']
+['path_tpc']['curves'][763]['offset_meters']
 ['path_tpc']['curves'][763]['res_coeff']
-['path_tpc']['curves'][763]['res_net']
-['path_tpc']['curves'][764]['offset']
+['path_tpc']['curves'][763]['res_net_meters']
+['path_tpc']['curves'][764]['offset_meters']
 ['path_tpc']['curves'][764]['res_coeff']
-['path_tpc']['curves'][764]['res_net']
-['path_tpc']['curves'][765]['offset']
+['path_tpc']['curves'][764]['res_net_meters']
+['path_tpc']['curves'][765]['offset_meters']
 ['path_tpc']['curves'][765]['res_coeff']
-['path_tpc']['curves'][765]['res_net']
-['path_tpc']['curves'][766]['offset']
+['path_tpc']['curves'][765]['res_net_meters']
+['path_tpc']['curves'][766]['offset_meters']
 ['path_tpc']['curves'][766]['res_coeff']
-['path_tpc']['curves'][766]['res_net']
-['path_tpc']['curves'][767]['offset']
+['path_tpc']['curves'][766]['res_net_meters']
+['path_tpc']['curves'][767]['offset_meters']
 ['path_tpc']['curves'][767]['res_coeff']
-['path_tpc']['curves'][767]['res_net']
-['path_tpc']['curves'][768]['offset']
+['path_tpc']['curves'][767]['res_net_meters']
+['path_tpc']['curves'][768]['offset_meters']
 ['path_tpc']['curves'][768]['res_coeff']
-['path_tpc']['curves'][768]['res_net']
-['path_tpc']['curves'][769]['offset']
+['path_tpc']['curves'][768]['res_net_meters']
+['path_tpc']['curves'][769]['offset_meters']
 ['path_tpc']['curves'][769]['res_coeff']
-['path_tpc']['curves'][769]['res_net']
-['path_tpc']['curves'][770]['offset']
+['path_tpc']['curves'][769]['res_net_meters']
+['path_tpc']['curves'][770]['offset_meters']
 ['path_tpc']['curves'][770]['res_coeff']
-['path_tpc']['curves'][770]['res_net']
-['path_tpc']['curves'][771]['offset']
+['path_tpc']['curves'][770]['res_net_meters']
+['path_tpc']['curves'][771]['offset_meters']
 ['path_tpc']['curves'][771]['res_coeff']
-['path_tpc']['curves'][771]['res_net']
-['path_tpc']['curves'][772]['offset']
+['path_tpc']['curves'][771]['res_net_meters']
+['path_tpc']['curves'][772]['offset_meters']
 ['path_tpc']['curves'][772]['res_coeff']
-['path_tpc']['curves'][772]['res_net']
-['path_tpc']['curves'][773]['offset']
+['path_tpc']['curves'][772]['res_net_meters']
+['path_tpc']['curves'][773]['offset_meters']
 ['path_tpc']['curves'][773]['res_coeff']
-['path_tpc']['curves'][773]['res_net']
-['path_tpc']['curves'][774]['offset']
+['path_tpc']['curves'][773]['res_net_meters']
+['path_tpc']['curves'][774]['offset_meters']
 ['path_tpc']['curves'][774]['res_coeff']
-['path_tpc']['curves'][774]['res_net']
-['path_tpc']['curves'][775]['offset']
+['path_tpc']['curves'][774]['res_net_meters']
+['path_tpc']['curves'][775]['offset_meters']
 ['path_tpc']['curves'][775]['res_coeff']
-['path_tpc']['curves'][775]['res_net']
-['path_tpc']['curves'][776]['offset']
+['path_tpc']['curves'][775]['res_net_meters']
+['path_tpc']['curves'][776]['offset_meters']
 ['path_tpc']['curves'][776]['res_coeff']
-['path_tpc']['curves'][776]['res_net']
-['path_tpc']['curves'][777]['offset']
+['path_tpc']['curves'][776]['res_net_meters']
+['path_tpc']['curves'][777]['offset_meters']
 ['path_tpc']['curves'][777]['res_coeff']
-['path_tpc']['curves'][777]['res_net']
-['path_tpc']['curves'][778]['offset']
+['path_tpc']['curves'][777]['res_net_meters']
+['path_tpc']['curves'][778]['offset_meters']
 ['path_tpc']['curves'][778]['res_coeff']
-['path_tpc']['curves'][778]['res_net']
-['path_tpc']['curves'][779]['offset']
+['path_tpc']['curves'][778]['res_net_meters']
+['path_tpc']['curves'][779]['offset_meters']
 ['path_tpc']['curves'][779]['res_coeff']
-['path_tpc']['curves'][779]['res_net']
-['path_tpc']['curves'][780]['offset']
+['path_tpc']['curves'][779]['res_net_meters']
+['path_tpc']['curves'][780]['offset_meters']
 ['path_tpc']['curves'][780]['res_coeff']
-['path_tpc']['curves'][780]['res_net']
-['path_tpc']['curves'][781]['offset']
+['path_tpc']['curves'][780]['res_net_meters']
+['path_tpc']['curves'][781]['offset_meters']
 ['path_tpc']['curves'][781]['res_coeff']
-['path_tpc']['curves'][781]['res_net']
-['path_tpc']['curves'][782]['offset']
+['path_tpc']['curves'][781]['res_net_meters']
+['path_tpc']['curves'][782]['offset_meters']
 ['path_tpc']['curves'][782]['res_coeff']
-['path_tpc']['curves'][782]['res_net']
-['path_tpc']['curves'][783]['offset']
+['path_tpc']['curves'][782]['res_net_meters']
+['path_tpc']['curves'][783]['offset_meters']
 ['path_tpc']['curves'][783]['res_coeff']
-['path_tpc']['curves'][783]['res_net']
-['path_tpc']['curves'][784]['offset']
+['path_tpc']['curves'][783]['res_net_meters']
+['path_tpc']['curves'][784]['offset_meters']
 ['path_tpc']['curves'][784]['res_coeff']
-['path_tpc']['curves'][784]['res_net']
-['path_tpc']['curves'][785]['offset']
+['path_tpc']['curves'][784]['res_net_meters']
+['path_tpc']['curves'][785]['offset_meters']
 ['path_tpc']['curves'][785]['res_coeff']
-['path_tpc']['curves'][785]['res_net']
-['path_tpc']['curves'][786]['offset']
+['path_tpc']['curves'][785]['res_net_meters']
+['path_tpc']['curves'][786]['offset_meters']
 ['path_tpc']['curves'][786]['res_coeff']
-['path_tpc']['curves'][786]['res_net']
+['path_tpc']['curves'][786]['res_net_meters']
 ['path_tpc']['speed_points'][0]['offset']
 ['path_tpc']['speed_points'][0]['speed_limit']
 ['path_tpc']['speed_points'][1]['offset']
@@ -6466,8 +6466,8 @@
 ['braking_points']['points'][42]['speed_limit']
 ['braking_points']['points'][42]['speed_target']
 ['braking_points']['idx_curr']
-['fric_brake']['force_max']
-['fric_brake']['ramp_up_time']
+['fric_brake']['force_max_newtons']
+['fric_brake']['ramp_up_time_seconds']
 ['fric_brake']['ramp_up_coeff']
 ['fric_brake']['state']['i']
 ['fric_brake']['state']['force']

From 2ebe7568823f2f0ca981b387dc517eb58afe1023 Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Tue, 21 Jan 2025 13:36:22 -0700
Subject: [PATCH 43/50] fixed build errors

---
 pyproject.toml                                     | 2 +-
 rust/altrios-core/src/train/set_speed_train_sim.rs | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 3a3f970e..0996d31c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,7 +47,7 @@ dependencies = [
     "pyarrow",
     "requests",
     "PyYAML==6.0.2",
-    "simpy"
+    "simpy",
     "msgpack==1.1.0",
 ]
 
diff --git a/rust/altrios-core/src/train/set_speed_train_sim.rs b/rust/altrios-core/src/train/set_speed_train_sim.rs
index 4dd415f4..dada9bdd 100644
--- a/rust/altrios-core/src/train/set_speed_train_sim.rs
+++ b/rust/altrios-core/src/train/set_speed_train_sim.rs
@@ -190,6 +190,7 @@ pub struct SpeedTraceElement {
     #[new]
     #[pyo3(signature = (
         loco_con,
+        n_cars_by_type,
         state,
         speed_trace,
         train_res_file=None,

From a5831259f52f6edfeefe5f2c8e2b269e344de065 Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Tue, 21 Jan 2025 13:37:32 -0700
Subject: [PATCH 44/50] regenerated `variable_path_list_expected.txt`

---
 .../variable_path_list_expected.txt           | 6460 ++++++++---------
 1 file changed, 3230 insertions(+), 3230 deletions(-)

diff --git a/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt b/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
index 1c666211..bbee3394 100644
--- a/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
+++ b/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
@@ -34,10 +34,10 @@
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['state']['min_soc']
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['state']['soc_lo_ramp_start']
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['state']['temperature_celsius']
-['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['mass']
-['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['volume']
-['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['specific_energy']
-['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['energy_density']
+['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['mass_kilograms']
+['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['volume_liters']
+['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['specific_energy_joules_per_kilogram']
+['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['energy_density_kilopascals']
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['eta_interp_grid'][0]
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['eta_interp_grid'][1]
 ['loco_con']['loco_vec'][0]['loco_type']['BatteryElectricLoco']['res']['eta_interp_grid'][2]
@@ -160,10 +160,10 @@
 ['loco_con']['loco_vec'][0]['state']['pwr_aux']
 ['loco_con']['loco_vec'][0]['state']['energy_out']
 ['loco_con']['loco_vec'][0]['state']['energy_aux']
-['loco_con']['loco_vec'][0]['mass']
+['loco_con']['loco_vec'][0]['mass_kilograms']
 ['loco_con']['loco_vec'][0]['mu']
-['loco_con']['loco_vec'][0]['ballast_mass']
-['loco_con']['loco_vec'][0]['baseline_mass']
+['loco_con']['loco_vec'][0]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][0]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][0]['save_interval']
 ['loco_con']['loco_vec'][0]['history']['i']
 ['loco_con']['loco_vec'][0]['history']['pwr_out_max']
@@ -174,9 +174,9 @@
 ['loco_con']['loco_vec'][0]['history']['energy_out']
 ['loco_con']['loco_vec'][0]['history']['energy_aux']
 ['loco_con']['loco_vec'][0]['assert_limits']
-['loco_con']['loco_vec'][0]['pwr_aux_offset']
+['loco_con']['loco_vec'][0]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][0]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][0]['force_max']
+['loco_con']['loco_vec'][0]['force_max_newtons']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -189,10 +189,10 @@
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -223,8 +223,8 @@
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][1]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -286,10 +286,10 @@
 ['loco_con']['loco_vec'][1]['state']['pwr_aux']
 ['loco_con']['loco_vec'][1]['state']['energy_out']
 ['loco_con']['loco_vec'][1]['state']['energy_aux']
-['loco_con']['loco_vec'][1]['mass']
+['loco_con']['loco_vec'][1]['mass_kilograms']
 ['loco_con']['loco_vec'][1]['mu']
-['loco_con']['loco_vec'][1]['ballast_mass']
-['loco_con']['loco_vec'][1]['baseline_mass']
+['loco_con']['loco_vec'][1]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][1]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][1]['save_interval']
 ['loco_con']['loco_vec'][1]['history']['i']
 ['loco_con']['loco_vec'][1]['history']['pwr_out_max']
@@ -300,9 +300,9 @@
 ['loco_con']['loco_vec'][1]['history']['energy_out']
 ['loco_con']['loco_vec'][1]['history']['energy_aux']
 ['loco_con']['loco_vec'][1]['assert_limits']
-['loco_con']['loco_vec'][1]['pwr_aux_offset']
+['loco_con']['loco_vec'][1]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][1]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][1]['force_max']
+['loco_con']['loco_vec'][1]['force_max_newtons']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -315,10 +315,10 @@
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -349,8 +349,8 @@
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][2]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -412,10 +412,10 @@
 ['loco_con']['loco_vec'][2]['state']['pwr_aux']
 ['loco_con']['loco_vec'][2]['state']['energy_out']
 ['loco_con']['loco_vec'][2]['state']['energy_aux']
-['loco_con']['loco_vec'][2]['mass']
+['loco_con']['loco_vec'][2]['mass_kilograms']
 ['loco_con']['loco_vec'][2]['mu']
-['loco_con']['loco_vec'][2]['ballast_mass']
-['loco_con']['loco_vec'][2]['baseline_mass']
+['loco_con']['loco_vec'][2]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][2]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][2]['save_interval']
 ['loco_con']['loco_vec'][2]['history']['i']
 ['loco_con']['loco_vec'][2]['history']['pwr_out_max']
@@ -426,9 +426,9 @@
 ['loco_con']['loco_vec'][2]['history']['energy_out']
 ['loco_con']['loco_vec'][2]['history']['energy_aux']
 ['loco_con']['loco_vec'][2]['assert_limits']
-['loco_con']['loco_vec'][2]['pwr_aux_offset']
+['loco_con']['loco_vec'][2]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][2]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][2]['force_max']
+['loco_con']['loco_vec'][2]['force_max_newtons']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -441,10 +441,10 @@
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -475,8 +475,8 @@
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][3]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -538,10 +538,10 @@
 ['loco_con']['loco_vec'][3]['state']['pwr_aux']
 ['loco_con']['loco_vec'][3]['state']['energy_out']
 ['loco_con']['loco_vec'][3]['state']['energy_aux']
-['loco_con']['loco_vec'][3]['mass']
+['loco_con']['loco_vec'][3]['mass_kilograms']
 ['loco_con']['loco_vec'][3]['mu']
-['loco_con']['loco_vec'][3]['ballast_mass']
-['loco_con']['loco_vec'][3]['baseline_mass']
+['loco_con']['loco_vec'][3]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][3]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][3]['save_interval']
 ['loco_con']['loco_vec'][3]['history']['i']
 ['loco_con']['loco_vec'][3]['history']['pwr_out_max']
@@ -552,9 +552,9 @@
 ['loco_con']['loco_vec'][3]['history']['energy_out']
 ['loco_con']['loco_vec'][3]['history']['energy_aux']
 ['loco_con']['loco_vec'][3]['assert_limits']
-['loco_con']['loco_vec'][3]['pwr_aux_offset']
+['loco_con']['loco_vec'][3]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][3]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][3]['force_max']
+['loco_con']['loco_vec'][3]['force_max_newtons']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -567,10 +567,10 @@
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -601,8 +601,8 @@
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][4]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -664,10 +664,10 @@
 ['loco_con']['loco_vec'][4]['state']['pwr_aux']
 ['loco_con']['loco_vec'][4]['state']['energy_out']
 ['loco_con']['loco_vec'][4]['state']['energy_aux']
-['loco_con']['loco_vec'][4]['mass']
+['loco_con']['loco_vec'][4]['mass_kilograms']
 ['loco_con']['loco_vec'][4]['mu']
-['loco_con']['loco_vec'][4]['ballast_mass']
-['loco_con']['loco_vec'][4]['baseline_mass']
+['loco_con']['loco_vec'][4]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][4]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][4]['save_interval']
 ['loco_con']['loco_vec'][4]['history']['i']
 ['loco_con']['loco_vec'][4]['history']['pwr_out_max']
@@ -678,9 +678,9 @@
 ['loco_con']['loco_vec'][4]['history']['energy_out']
 ['loco_con']['loco_vec'][4]['history']['energy_aux']
 ['loco_con']['loco_vec'][4]['assert_limits']
-['loco_con']['loco_vec'][4]['pwr_aux_offset']
+['loco_con']['loco_vec'][4]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][4]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][4]['force_max']
+['loco_con']['loco_vec'][4]['force_max_newtons']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -693,10 +693,10 @@
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -727,8 +727,8 @@
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][5]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -790,10 +790,10 @@
 ['loco_con']['loco_vec'][5]['state']['pwr_aux']
 ['loco_con']['loco_vec'][5]['state']['energy_out']
 ['loco_con']['loco_vec'][5]['state']['energy_aux']
-['loco_con']['loco_vec'][5]['mass']
+['loco_con']['loco_vec'][5]['mass_kilograms']
 ['loco_con']['loco_vec'][5]['mu']
-['loco_con']['loco_vec'][5]['ballast_mass']
-['loco_con']['loco_vec'][5]['baseline_mass']
+['loco_con']['loco_vec'][5]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][5]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][5]['save_interval']
 ['loco_con']['loco_vec'][5]['history']['i']
 ['loco_con']['loco_vec'][5]['history']['pwr_out_max']
@@ -804,9 +804,9 @@
 ['loco_con']['loco_vec'][5]['history']['energy_out']
 ['loco_con']['loco_vec'][5]['history']['energy_aux']
 ['loco_con']['loco_vec'][5]['assert_limits']
-['loco_con']['loco_vec'][5]['pwr_aux_offset']
+['loco_con']['loco_vec'][5]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][5]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][5]['force_max']
+['loco_con']['loco_vec'][5]['force_max_newtons']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -819,10 +819,10 @@
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -853,8 +853,8 @@
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][6]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -916,10 +916,10 @@
 ['loco_con']['loco_vec'][6]['state']['pwr_aux']
 ['loco_con']['loco_vec'][6]['state']['energy_out']
 ['loco_con']['loco_vec'][6]['state']['energy_aux']
-['loco_con']['loco_vec'][6]['mass']
+['loco_con']['loco_vec'][6]['mass_kilograms']
 ['loco_con']['loco_vec'][6]['mu']
-['loco_con']['loco_vec'][6]['ballast_mass']
-['loco_con']['loco_vec'][6]['baseline_mass']
+['loco_con']['loco_vec'][6]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][6]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][6]['save_interval']
 ['loco_con']['loco_vec'][6]['history']['i']
 ['loco_con']['loco_vec'][6]['history']['pwr_out_max']
@@ -930,9 +930,9 @@
 ['loco_con']['loco_vec'][6]['history']['energy_out']
 ['loco_con']['loco_vec'][6]['history']['energy_aux']
 ['loco_con']['loco_vec'][6]['assert_limits']
-['loco_con']['loco_vec'][6]['pwr_aux_offset']
+['loco_con']['loco_vec'][6]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][6]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][6]['force_max']
+['loco_con']['loco_vec'][6]['force_max_newtons']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['i']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['pwr_out_max']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['eta']
@@ -945,10 +945,10 @@
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['energy_loss']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['energy_idle_fuel']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['state']['engine_on']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['mass']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['specific_pwr']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['mass_kilograms']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_watts']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_out_max_init_watts']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_ramp_lag_seconds']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['fc']['eta_interp']
@@ -979,8 +979,8 @@
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_prop_out']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['state']['energy_elec_aux']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['state']['energy_loss']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['mass']
-['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['specific_pwr']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['mass_kilograms']
+['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['specific_pwr_watts_per_kilogram']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['pwr_out_frac_interp']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['eta_interp']
 ['loco_con']['loco_vec'][7]['loco_type']['ConventionalLoco']['gen']['pwr_out_max_watts']
@@ -1042,10 +1042,10 @@
 ['loco_con']['loco_vec'][7]['state']['pwr_aux']
 ['loco_con']['loco_vec'][7]['state']['energy_out']
 ['loco_con']['loco_vec'][7]['state']['energy_aux']
-['loco_con']['loco_vec'][7]['mass']
+['loco_con']['loco_vec'][7]['mass_kilograms']
 ['loco_con']['loco_vec'][7]['mu']
-['loco_con']['loco_vec'][7]['ballast_mass']
-['loco_con']['loco_vec'][7]['baseline_mass']
+['loco_con']['loco_vec'][7]['ballast_mass_kilograms']
+['loco_con']['loco_vec'][7]['baseline_mass_kilograms']
 ['loco_con']['loco_vec'][7]['save_interval']
 ['loco_con']['loco_vec'][7]['history']['i']
 ['loco_con']['loco_vec'][7]['history']['pwr_out_max']
@@ -1056,9 +1056,9 @@
 ['loco_con']['loco_vec'][7]['history']['energy_out']
 ['loco_con']['loco_vec'][7]['history']['energy_aux']
 ['loco_con']['loco_vec'][7]['assert_limits']
-['loco_con']['loco_vec'][7]['pwr_aux_offset']
+['loco_con']['loco_vec'][7]['pwr_aux_offset_watts']
 ['loco_con']['loco_vec'][7]['pwr_aux_traction_coeff']
-['loco_con']['loco_vec'][7]['force_max']
+['loco_con']['loco_vec'][7]['force_max_newtons']
 ['loco_con']['pdct']['RESGreedy']
 ['loco_con']['assert_limits']
 ['loco_con']['state']['i']
@@ -1100,8 +1100,8 @@
 ['loco_con']['history']['energy_res']
 ['loco_con']['history']['energy_fuel']
 ['loco_con']['save_interval']
-['n_cars_by_type']['Manifest_Loaded']
 ['n_cars_by_type']['Manifest_Empty']
+['n_cars_by_type']['Manifest_Loaded']
 ['state']['time']
 ['state']['i']
 ['state']['offset']
@@ -1596,4728 +1596,4728 @@
 ['path_tpc']['link_points'][90]['curve_count']
 ['path_tpc']['link_points'][90]['cat_power_count']
 ['path_tpc']['link_points'][90]['link_idx']
-['path_tpc']['grades'][0]['offset']
+['path_tpc']['grades'][0]['offset_meters']
 ['path_tpc']['grades'][0]['res_coeff']
-['path_tpc']['grades'][0]['res_net']
-['path_tpc']['grades'][1]['offset']
+['path_tpc']['grades'][0]['res_net_meters']
+['path_tpc']['grades'][1]['offset_meters']
 ['path_tpc']['grades'][1]['res_coeff']
-['path_tpc']['grades'][1]['res_net']
-['path_tpc']['grades'][2]['offset']
+['path_tpc']['grades'][1]['res_net_meters']
+['path_tpc']['grades'][2]['offset_meters']
 ['path_tpc']['grades'][2]['res_coeff']
-['path_tpc']['grades'][2]['res_net']
-['path_tpc']['grades'][3]['offset']
+['path_tpc']['grades'][2]['res_net_meters']
+['path_tpc']['grades'][3]['offset_meters']
 ['path_tpc']['grades'][3]['res_coeff']
-['path_tpc']['grades'][3]['res_net']
-['path_tpc']['grades'][4]['offset']
+['path_tpc']['grades'][3]['res_net_meters']
+['path_tpc']['grades'][4]['offset_meters']
 ['path_tpc']['grades'][4]['res_coeff']
-['path_tpc']['grades'][4]['res_net']
-['path_tpc']['grades'][5]['offset']
+['path_tpc']['grades'][4]['res_net_meters']
+['path_tpc']['grades'][5]['offset_meters']
 ['path_tpc']['grades'][5]['res_coeff']
-['path_tpc']['grades'][5]['res_net']
-['path_tpc']['grades'][6]['offset']
+['path_tpc']['grades'][5]['res_net_meters']
+['path_tpc']['grades'][6]['offset_meters']
 ['path_tpc']['grades'][6]['res_coeff']
-['path_tpc']['grades'][6]['res_net']
-['path_tpc']['grades'][7]['offset']
+['path_tpc']['grades'][6]['res_net_meters']
+['path_tpc']['grades'][7]['offset_meters']
 ['path_tpc']['grades'][7]['res_coeff']
-['path_tpc']['grades'][7]['res_net']
-['path_tpc']['grades'][8]['offset']
+['path_tpc']['grades'][7]['res_net_meters']
+['path_tpc']['grades'][8]['offset_meters']
 ['path_tpc']['grades'][8]['res_coeff']
-['path_tpc']['grades'][8]['res_net']
-['path_tpc']['grades'][9]['offset']
+['path_tpc']['grades'][8]['res_net_meters']
+['path_tpc']['grades'][9]['offset_meters']
 ['path_tpc']['grades'][9]['res_coeff']
-['path_tpc']['grades'][9]['res_net']
-['path_tpc']['grades'][10]['offset']
+['path_tpc']['grades'][9]['res_net_meters']
+['path_tpc']['grades'][10]['offset_meters']
 ['path_tpc']['grades'][10]['res_coeff']
-['path_tpc']['grades'][10]['res_net']
-['path_tpc']['grades'][11]['offset']
+['path_tpc']['grades'][10]['res_net_meters']
+['path_tpc']['grades'][11]['offset_meters']
 ['path_tpc']['grades'][11]['res_coeff']
-['path_tpc']['grades'][11]['res_net']
-['path_tpc']['grades'][12]['offset']
+['path_tpc']['grades'][11]['res_net_meters']
+['path_tpc']['grades'][12]['offset_meters']
 ['path_tpc']['grades'][12]['res_coeff']
-['path_tpc']['grades'][12]['res_net']
-['path_tpc']['grades'][13]['offset']
+['path_tpc']['grades'][12]['res_net_meters']
+['path_tpc']['grades'][13]['offset_meters']
 ['path_tpc']['grades'][13]['res_coeff']
-['path_tpc']['grades'][13]['res_net']
-['path_tpc']['grades'][14]['offset']
+['path_tpc']['grades'][13]['res_net_meters']
+['path_tpc']['grades'][14]['offset_meters']
 ['path_tpc']['grades'][14]['res_coeff']
-['path_tpc']['grades'][14]['res_net']
-['path_tpc']['grades'][15]['offset']
+['path_tpc']['grades'][14]['res_net_meters']
+['path_tpc']['grades'][15]['offset_meters']
 ['path_tpc']['grades'][15]['res_coeff']
-['path_tpc']['grades'][15]['res_net']
-['path_tpc']['grades'][16]['offset']
+['path_tpc']['grades'][15]['res_net_meters']
+['path_tpc']['grades'][16]['offset_meters']
 ['path_tpc']['grades'][16]['res_coeff']
-['path_tpc']['grades'][16]['res_net']
-['path_tpc']['grades'][17]['offset']
+['path_tpc']['grades'][16]['res_net_meters']
+['path_tpc']['grades'][17]['offset_meters']
 ['path_tpc']['grades'][17]['res_coeff']
-['path_tpc']['grades'][17]['res_net']
-['path_tpc']['grades'][18]['offset']
+['path_tpc']['grades'][17]['res_net_meters']
+['path_tpc']['grades'][18]['offset_meters']
 ['path_tpc']['grades'][18]['res_coeff']
-['path_tpc']['grades'][18]['res_net']
-['path_tpc']['grades'][19]['offset']
+['path_tpc']['grades'][18]['res_net_meters']
+['path_tpc']['grades'][19]['offset_meters']
 ['path_tpc']['grades'][19]['res_coeff']
-['path_tpc']['grades'][19]['res_net']
-['path_tpc']['grades'][20]['offset']
+['path_tpc']['grades'][19]['res_net_meters']
+['path_tpc']['grades'][20]['offset_meters']
 ['path_tpc']['grades'][20]['res_coeff']
-['path_tpc']['grades'][20]['res_net']
-['path_tpc']['grades'][21]['offset']
+['path_tpc']['grades'][20]['res_net_meters']
+['path_tpc']['grades'][21]['offset_meters']
 ['path_tpc']['grades'][21]['res_coeff']
-['path_tpc']['grades'][21]['res_net']
-['path_tpc']['grades'][22]['offset']
+['path_tpc']['grades'][21]['res_net_meters']
+['path_tpc']['grades'][22]['offset_meters']
 ['path_tpc']['grades'][22]['res_coeff']
-['path_tpc']['grades'][22]['res_net']
-['path_tpc']['grades'][23]['offset']
+['path_tpc']['grades'][22]['res_net_meters']
+['path_tpc']['grades'][23]['offset_meters']
 ['path_tpc']['grades'][23]['res_coeff']
-['path_tpc']['grades'][23]['res_net']
-['path_tpc']['grades'][24]['offset']
+['path_tpc']['grades'][23]['res_net_meters']
+['path_tpc']['grades'][24]['offset_meters']
 ['path_tpc']['grades'][24]['res_coeff']
-['path_tpc']['grades'][24]['res_net']
-['path_tpc']['grades'][25]['offset']
+['path_tpc']['grades'][24]['res_net_meters']
+['path_tpc']['grades'][25]['offset_meters']
 ['path_tpc']['grades'][25]['res_coeff']
-['path_tpc']['grades'][25]['res_net']
-['path_tpc']['grades'][26]['offset']
+['path_tpc']['grades'][25]['res_net_meters']
+['path_tpc']['grades'][26]['offset_meters']
 ['path_tpc']['grades'][26]['res_coeff']
-['path_tpc']['grades'][26]['res_net']
-['path_tpc']['grades'][27]['offset']
+['path_tpc']['grades'][26]['res_net_meters']
+['path_tpc']['grades'][27]['offset_meters']
 ['path_tpc']['grades'][27]['res_coeff']
-['path_tpc']['grades'][27]['res_net']
-['path_tpc']['grades'][28]['offset']
+['path_tpc']['grades'][27]['res_net_meters']
+['path_tpc']['grades'][28]['offset_meters']
 ['path_tpc']['grades'][28]['res_coeff']
-['path_tpc']['grades'][28]['res_net']
-['path_tpc']['grades'][29]['offset']
+['path_tpc']['grades'][28]['res_net_meters']
+['path_tpc']['grades'][29]['offset_meters']
 ['path_tpc']['grades'][29]['res_coeff']
-['path_tpc']['grades'][29]['res_net']
-['path_tpc']['grades'][30]['offset']
+['path_tpc']['grades'][29]['res_net_meters']
+['path_tpc']['grades'][30]['offset_meters']
 ['path_tpc']['grades'][30]['res_coeff']
-['path_tpc']['grades'][30]['res_net']
-['path_tpc']['grades'][31]['offset']
+['path_tpc']['grades'][30]['res_net_meters']
+['path_tpc']['grades'][31]['offset_meters']
 ['path_tpc']['grades'][31]['res_coeff']
-['path_tpc']['grades'][31]['res_net']
-['path_tpc']['grades'][32]['offset']
+['path_tpc']['grades'][31]['res_net_meters']
+['path_tpc']['grades'][32]['offset_meters']
 ['path_tpc']['grades'][32]['res_coeff']
-['path_tpc']['grades'][32]['res_net']
-['path_tpc']['grades'][33]['offset']
+['path_tpc']['grades'][32]['res_net_meters']
+['path_tpc']['grades'][33]['offset_meters']
 ['path_tpc']['grades'][33]['res_coeff']
-['path_tpc']['grades'][33]['res_net']
-['path_tpc']['grades'][34]['offset']
+['path_tpc']['grades'][33]['res_net_meters']
+['path_tpc']['grades'][34]['offset_meters']
 ['path_tpc']['grades'][34]['res_coeff']
-['path_tpc']['grades'][34]['res_net']
-['path_tpc']['grades'][35]['offset']
+['path_tpc']['grades'][34]['res_net_meters']
+['path_tpc']['grades'][35]['offset_meters']
 ['path_tpc']['grades'][35]['res_coeff']
-['path_tpc']['grades'][35]['res_net']
-['path_tpc']['grades'][36]['offset']
+['path_tpc']['grades'][35]['res_net_meters']
+['path_tpc']['grades'][36]['offset_meters']
 ['path_tpc']['grades'][36]['res_coeff']
-['path_tpc']['grades'][36]['res_net']
-['path_tpc']['grades'][37]['offset']
+['path_tpc']['grades'][36]['res_net_meters']
+['path_tpc']['grades'][37]['offset_meters']
 ['path_tpc']['grades'][37]['res_coeff']
-['path_tpc']['grades'][37]['res_net']
-['path_tpc']['grades'][38]['offset']
+['path_tpc']['grades'][37]['res_net_meters']
+['path_tpc']['grades'][38]['offset_meters']
 ['path_tpc']['grades'][38]['res_coeff']
-['path_tpc']['grades'][38]['res_net']
-['path_tpc']['grades'][39]['offset']
+['path_tpc']['grades'][38]['res_net_meters']
+['path_tpc']['grades'][39]['offset_meters']
 ['path_tpc']['grades'][39]['res_coeff']
-['path_tpc']['grades'][39]['res_net']
-['path_tpc']['grades'][40]['offset']
+['path_tpc']['grades'][39]['res_net_meters']
+['path_tpc']['grades'][40]['offset_meters']
 ['path_tpc']['grades'][40]['res_coeff']
-['path_tpc']['grades'][40]['res_net']
-['path_tpc']['grades'][41]['offset']
+['path_tpc']['grades'][40]['res_net_meters']
+['path_tpc']['grades'][41]['offset_meters']
 ['path_tpc']['grades'][41]['res_coeff']
-['path_tpc']['grades'][41]['res_net']
-['path_tpc']['grades'][42]['offset']
+['path_tpc']['grades'][41]['res_net_meters']
+['path_tpc']['grades'][42]['offset_meters']
 ['path_tpc']['grades'][42]['res_coeff']
-['path_tpc']['grades'][42]['res_net']
-['path_tpc']['grades'][43]['offset']
+['path_tpc']['grades'][42]['res_net_meters']
+['path_tpc']['grades'][43]['offset_meters']
 ['path_tpc']['grades'][43]['res_coeff']
-['path_tpc']['grades'][43]['res_net']
-['path_tpc']['grades'][44]['offset']
+['path_tpc']['grades'][43]['res_net_meters']
+['path_tpc']['grades'][44]['offset_meters']
 ['path_tpc']['grades'][44]['res_coeff']
-['path_tpc']['grades'][44]['res_net']
-['path_tpc']['grades'][45]['offset']
+['path_tpc']['grades'][44]['res_net_meters']
+['path_tpc']['grades'][45]['offset_meters']
 ['path_tpc']['grades'][45]['res_coeff']
-['path_tpc']['grades'][45]['res_net']
-['path_tpc']['grades'][46]['offset']
+['path_tpc']['grades'][45]['res_net_meters']
+['path_tpc']['grades'][46]['offset_meters']
 ['path_tpc']['grades'][46]['res_coeff']
-['path_tpc']['grades'][46]['res_net']
-['path_tpc']['grades'][47]['offset']
+['path_tpc']['grades'][46]['res_net_meters']
+['path_tpc']['grades'][47]['offset_meters']
 ['path_tpc']['grades'][47]['res_coeff']
-['path_tpc']['grades'][47]['res_net']
-['path_tpc']['grades'][48]['offset']
+['path_tpc']['grades'][47]['res_net_meters']
+['path_tpc']['grades'][48]['offset_meters']
 ['path_tpc']['grades'][48]['res_coeff']
-['path_tpc']['grades'][48]['res_net']
-['path_tpc']['grades'][49]['offset']
+['path_tpc']['grades'][48]['res_net_meters']
+['path_tpc']['grades'][49]['offset_meters']
 ['path_tpc']['grades'][49]['res_coeff']
-['path_tpc']['grades'][49]['res_net']
-['path_tpc']['grades'][50]['offset']
+['path_tpc']['grades'][49]['res_net_meters']
+['path_tpc']['grades'][50]['offset_meters']
 ['path_tpc']['grades'][50]['res_coeff']
-['path_tpc']['grades'][50]['res_net']
-['path_tpc']['grades'][51]['offset']
+['path_tpc']['grades'][50]['res_net_meters']
+['path_tpc']['grades'][51]['offset_meters']
 ['path_tpc']['grades'][51]['res_coeff']
-['path_tpc']['grades'][51]['res_net']
-['path_tpc']['grades'][52]['offset']
+['path_tpc']['grades'][51]['res_net_meters']
+['path_tpc']['grades'][52]['offset_meters']
 ['path_tpc']['grades'][52]['res_coeff']
-['path_tpc']['grades'][52]['res_net']
-['path_tpc']['grades'][53]['offset']
+['path_tpc']['grades'][52]['res_net_meters']
+['path_tpc']['grades'][53]['offset_meters']
 ['path_tpc']['grades'][53]['res_coeff']
-['path_tpc']['grades'][53]['res_net']
-['path_tpc']['grades'][54]['offset']
+['path_tpc']['grades'][53]['res_net_meters']
+['path_tpc']['grades'][54]['offset_meters']
 ['path_tpc']['grades'][54]['res_coeff']
-['path_tpc']['grades'][54]['res_net']
-['path_tpc']['grades'][55]['offset']
+['path_tpc']['grades'][54]['res_net_meters']
+['path_tpc']['grades'][55]['offset_meters']
 ['path_tpc']['grades'][55]['res_coeff']
-['path_tpc']['grades'][55]['res_net']
-['path_tpc']['grades'][56]['offset']
+['path_tpc']['grades'][55]['res_net_meters']
+['path_tpc']['grades'][56]['offset_meters']
 ['path_tpc']['grades'][56]['res_coeff']
-['path_tpc']['grades'][56]['res_net']
-['path_tpc']['grades'][57]['offset']
+['path_tpc']['grades'][56]['res_net_meters']
+['path_tpc']['grades'][57]['offset_meters']
 ['path_tpc']['grades'][57]['res_coeff']
-['path_tpc']['grades'][57]['res_net']
-['path_tpc']['grades'][58]['offset']
+['path_tpc']['grades'][57]['res_net_meters']
+['path_tpc']['grades'][58]['offset_meters']
 ['path_tpc']['grades'][58]['res_coeff']
-['path_tpc']['grades'][58]['res_net']
-['path_tpc']['grades'][59]['offset']
+['path_tpc']['grades'][58]['res_net_meters']
+['path_tpc']['grades'][59]['offset_meters']
 ['path_tpc']['grades'][59]['res_coeff']
-['path_tpc']['grades'][59]['res_net']
-['path_tpc']['grades'][60]['offset']
+['path_tpc']['grades'][59]['res_net_meters']
+['path_tpc']['grades'][60]['offset_meters']
 ['path_tpc']['grades'][60]['res_coeff']
-['path_tpc']['grades'][60]['res_net']
-['path_tpc']['grades'][61]['offset']
+['path_tpc']['grades'][60]['res_net_meters']
+['path_tpc']['grades'][61]['offset_meters']
 ['path_tpc']['grades'][61]['res_coeff']
-['path_tpc']['grades'][61]['res_net']
-['path_tpc']['grades'][62]['offset']
+['path_tpc']['grades'][61]['res_net_meters']
+['path_tpc']['grades'][62]['offset_meters']
 ['path_tpc']['grades'][62]['res_coeff']
-['path_tpc']['grades'][62]['res_net']
-['path_tpc']['grades'][63]['offset']
+['path_tpc']['grades'][62]['res_net_meters']
+['path_tpc']['grades'][63]['offset_meters']
 ['path_tpc']['grades'][63]['res_coeff']
-['path_tpc']['grades'][63]['res_net']
-['path_tpc']['grades'][64]['offset']
+['path_tpc']['grades'][63]['res_net_meters']
+['path_tpc']['grades'][64]['offset_meters']
 ['path_tpc']['grades'][64]['res_coeff']
-['path_tpc']['grades'][64]['res_net']
-['path_tpc']['grades'][65]['offset']
+['path_tpc']['grades'][64]['res_net_meters']
+['path_tpc']['grades'][65]['offset_meters']
 ['path_tpc']['grades'][65]['res_coeff']
-['path_tpc']['grades'][65]['res_net']
-['path_tpc']['grades'][66]['offset']
+['path_tpc']['grades'][65]['res_net_meters']
+['path_tpc']['grades'][66]['offset_meters']
 ['path_tpc']['grades'][66]['res_coeff']
-['path_tpc']['grades'][66]['res_net']
-['path_tpc']['grades'][67]['offset']
+['path_tpc']['grades'][66]['res_net_meters']
+['path_tpc']['grades'][67]['offset_meters']
 ['path_tpc']['grades'][67]['res_coeff']
-['path_tpc']['grades'][67]['res_net']
-['path_tpc']['grades'][68]['offset']
+['path_tpc']['grades'][67]['res_net_meters']
+['path_tpc']['grades'][68]['offset_meters']
 ['path_tpc']['grades'][68]['res_coeff']
-['path_tpc']['grades'][68]['res_net']
-['path_tpc']['grades'][69]['offset']
+['path_tpc']['grades'][68]['res_net_meters']
+['path_tpc']['grades'][69]['offset_meters']
 ['path_tpc']['grades'][69]['res_coeff']
-['path_tpc']['grades'][69]['res_net']
-['path_tpc']['grades'][70]['offset']
+['path_tpc']['grades'][69]['res_net_meters']
+['path_tpc']['grades'][70]['offset_meters']
 ['path_tpc']['grades'][70]['res_coeff']
-['path_tpc']['grades'][70]['res_net']
-['path_tpc']['grades'][71]['offset']
+['path_tpc']['grades'][70]['res_net_meters']
+['path_tpc']['grades'][71]['offset_meters']
 ['path_tpc']['grades'][71]['res_coeff']
-['path_tpc']['grades'][71]['res_net']
-['path_tpc']['grades'][72]['offset']
+['path_tpc']['grades'][71]['res_net_meters']
+['path_tpc']['grades'][72]['offset_meters']
 ['path_tpc']['grades'][72]['res_coeff']
-['path_tpc']['grades'][72]['res_net']
-['path_tpc']['grades'][73]['offset']
+['path_tpc']['grades'][72]['res_net_meters']
+['path_tpc']['grades'][73]['offset_meters']
 ['path_tpc']['grades'][73]['res_coeff']
-['path_tpc']['grades'][73]['res_net']
-['path_tpc']['grades'][74]['offset']
+['path_tpc']['grades'][73]['res_net_meters']
+['path_tpc']['grades'][74]['offset_meters']
 ['path_tpc']['grades'][74]['res_coeff']
-['path_tpc']['grades'][74]['res_net']
-['path_tpc']['grades'][75]['offset']
+['path_tpc']['grades'][74]['res_net_meters']
+['path_tpc']['grades'][75]['offset_meters']
 ['path_tpc']['grades'][75]['res_coeff']
-['path_tpc']['grades'][75]['res_net']
-['path_tpc']['grades'][76]['offset']
+['path_tpc']['grades'][75]['res_net_meters']
+['path_tpc']['grades'][76]['offset_meters']
 ['path_tpc']['grades'][76]['res_coeff']
-['path_tpc']['grades'][76]['res_net']
-['path_tpc']['grades'][77]['offset']
+['path_tpc']['grades'][76]['res_net_meters']
+['path_tpc']['grades'][77]['offset_meters']
 ['path_tpc']['grades'][77]['res_coeff']
-['path_tpc']['grades'][77]['res_net']
-['path_tpc']['grades'][78]['offset']
+['path_tpc']['grades'][77]['res_net_meters']
+['path_tpc']['grades'][78]['offset_meters']
 ['path_tpc']['grades'][78]['res_coeff']
-['path_tpc']['grades'][78]['res_net']
-['path_tpc']['grades'][79]['offset']
+['path_tpc']['grades'][78]['res_net_meters']
+['path_tpc']['grades'][79]['offset_meters']
 ['path_tpc']['grades'][79]['res_coeff']
-['path_tpc']['grades'][79]['res_net']
-['path_tpc']['grades'][80]['offset']
+['path_tpc']['grades'][79]['res_net_meters']
+['path_tpc']['grades'][80]['offset_meters']
 ['path_tpc']['grades'][80]['res_coeff']
-['path_tpc']['grades'][80]['res_net']
-['path_tpc']['grades'][81]['offset']
+['path_tpc']['grades'][80]['res_net_meters']
+['path_tpc']['grades'][81]['offset_meters']
 ['path_tpc']['grades'][81]['res_coeff']
-['path_tpc']['grades'][81]['res_net']
-['path_tpc']['grades'][82]['offset']
+['path_tpc']['grades'][81]['res_net_meters']
+['path_tpc']['grades'][82]['offset_meters']
 ['path_tpc']['grades'][82]['res_coeff']
-['path_tpc']['grades'][82]['res_net']
-['path_tpc']['grades'][83]['offset']
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 ['path_tpc']['grades'][584]['res_coeff']
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 ['path_tpc']['grades'][585]['res_coeff']
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 ['path_tpc']['grades'][586]['res_coeff']
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 ['path_tpc']['grades'][589]['res_coeff']
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 ['path_tpc']['grades'][590]['res_coeff']
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 ['path_tpc']['grades'][592]['res_coeff']
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 ['path_tpc']['grades'][595]['res_coeff']
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 ['path_tpc']['grades'][596]['res_coeff']
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 ['path_tpc']['grades'][598]['res_coeff']
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 ['path_tpc']['grades'][599]['res_coeff']
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 ['path_tpc']['grades'][600]['res_coeff']
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 ['path_tpc']['grades'][601]['res_coeff']
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 ['path_tpc']['grades'][602]['res_coeff']
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 ['path_tpc']['grades'][603]['res_coeff']
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 ['path_tpc']['grades'][604]['res_coeff']
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 ['path_tpc']['grades'][605]['res_coeff']
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 ['path_tpc']['grades'][606]['res_coeff']
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 ['path_tpc']['grades'][607]['res_coeff']
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 ['path_tpc']['grades'][608]['res_coeff']
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 ['path_tpc']['grades'][609]['res_coeff']
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 ['path_tpc']['grades'][610]['res_coeff']
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 ['path_tpc']['grades'][611]['res_coeff']
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 ['path_tpc']['grades'][612]['res_coeff']
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 ['path_tpc']['grades'][613]['res_coeff']
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 ['path_tpc']['grades'][702]['res_coeff']
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 ['path_tpc']['grades'][703]['res_coeff']
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 ['path_tpc']['grades'][704]['res_coeff']
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 ['path_tpc']['grades'][705]['res_coeff']
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 ['path_tpc']['grades'][706]['res_coeff']
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 ['path_tpc']['grades'][707]['res_coeff']
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 ['path_tpc']['grades'][708]['res_coeff']
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 ['path_tpc']['grades'][709]['res_coeff']
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 ['path_tpc']['grades'][710]['res_coeff']
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 ['path_tpc']['grades'][711]['res_coeff']
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 ['path_tpc']['grades'][712]['res_coeff']
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 ['path_tpc']['grades'][713]['res_coeff']
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 ['path_tpc']['grades'][714]['res_coeff']
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 ['path_tpc']['grades'][715]['res_coeff']
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 ['path_tpc']['grades'][716]['res_coeff']
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 ['path_tpc']['grades'][717]['res_coeff']
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 ['path_tpc']['grades'][718]['res_coeff']
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 ['path_tpc']['grades'][719]['res_coeff']
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 ['path_tpc']['grades'][720]['res_coeff']
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 ['path_tpc']['grades'][721]['res_coeff']
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 ['path_tpc']['grades'][722]['res_coeff']
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 ['path_tpc']['grades'][723]['res_coeff']
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-['path_tpc']['grades'][724]['offset']
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 ['path_tpc']['grades'][724]['res_coeff']
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-['path_tpc']['grades'][725]['offset']
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 ['path_tpc']['grades'][725]['res_coeff']
-['path_tpc']['grades'][725]['res_net']
-['path_tpc']['grades'][726]['offset']
+['path_tpc']['grades'][725]['res_net_meters']
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 ['path_tpc']['grades'][726]['res_coeff']
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-['path_tpc']['grades'][727]['offset']
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+['path_tpc']['grades'][727]['offset_meters']
 ['path_tpc']['grades'][727]['res_coeff']
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-['path_tpc']['grades'][728]['offset']
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 ['path_tpc']['grades'][728]['res_coeff']
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-['path_tpc']['grades'][729]['offset']
+['path_tpc']['grades'][728]['res_net_meters']
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 ['path_tpc']['grades'][729]['res_coeff']
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-['path_tpc']['grades'][730]['offset']
+['path_tpc']['grades'][729]['res_net_meters']
+['path_tpc']['grades'][730]['offset_meters']
 ['path_tpc']['grades'][730]['res_coeff']
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-['path_tpc']['grades'][731]['offset']
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 ['path_tpc']['grades'][731]['res_coeff']
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-['path_tpc']['grades'][732]['offset']
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+['path_tpc']['grades'][732]['offset_meters']
 ['path_tpc']['grades'][732]['res_coeff']
-['path_tpc']['grades'][732]['res_net']
-['path_tpc']['grades'][733]['offset']
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+['path_tpc']['grades'][733]['offset_meters']
 ['path_tpc']['grades'][733]['res_coeff']
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-['path_tpc']['grades'][734]['offset']
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 ['path_tpc']['grades'][734]['res_coeff']
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-['path_tpc']['grades'][735]['offset']
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 ['path_tpc']['grades'][735]['res_coeff']
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-['path_tpc']['grades'][736]['offset']
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 ['path_tpc']['grades'][736]['res_coeff']
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 ['path_tpc']['grades'][737]['res_coeff']
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-['path_tpc']['grades'][738]['offset']
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 ['path_tpc']['grades'][738]['res_coeff']
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 ['path_tpc']['grades'][739]['res_coeff']
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-['path_tpc']['grades'][740]['offset']
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 ['path_tpc']['grades'][740]['res_coeff']
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-['path_tpc']['grades'][741]['offset']
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+['path_tpc']['grades'][741]['offset_meters']
 ['path_tpc']['grades'][741]['res_coeff']
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+['path_tpc']['grades'][742]['offset_meters']
 ['path_tpc']['grades'][742]['res_coeff']
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+['path_tpc']['grades'][743]['offset_meters']
 ['path_tpc']['grades'][743]['res_coeff']
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 ['path_tpc']['grades'][744]['res_coeff']
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 ['path_tpc']['grades'][745]['res_coeff']
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 ['path_tpc']['grades'][746]['res_coeff']
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 ['path_tpc']['grades'][747]['res_coeff']
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 ['path_tpc']['grades'][748]['res_coeff']
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 ['path_tpc']['grades'][749]['res_coeff']
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 ['path_tpc']['grades'][750]['res_coeff']
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 ['path_tpc']['grades'][751]['res_coeff']
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 ['path_tpc']['grades'][752]['res_coeff']
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 ['path_tpc']['grades'][753]['res_coeff']
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 ['path_tpc']['grades'][754]['res_coeff']
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 ['path_tpc']['grades'][755]['res_coeff']
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 ['path_tpc']['grades'][756]['res_coeff']
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 ['path_tpc']['grades'][757]['res_coeff']
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 ['path_tpc']['grades'][758]['res_coeff']
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 ['path_tpc']['grades'][759]['res_coeff']
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 ['path_tpc']['grades'][760]['res_coeff']
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 ['path_tpc']['grades'][761]['res_coeff']
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 ['path_tpc']['grades'][762]['res_coeff']
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 ['path_tpc']['grades'][763]['res_coeff']
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 ['path_tpc']['grades'][764]['res_coeff']
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 ['path_tpc']['grades'][765]['res_coeff']
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 ['path_tpc']['grades'][766]['res_coeff']
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 ['path_tpc']['grades'][767]['res_coeff']
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+['path_tpc']['grades'][767]['res_net_meters']
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 ['path_tpc']['grades'][768]['res_coeff']
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-['path_tpc']['grades'][769]['offset']
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 ['path_tpc']['grades'][769]['res_coeff']
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-['path_tpc']['grades'][770]['offset']
+['path_tpc']['grades'][769]['res_net_meters']
+['path_tpc']['grades'][770]['offset_meters']
 ['path_tpc']['grades'][770]['res_coeff']
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-['path_tpc']['grades'][771]['offset']
+['path_tpc']['grades'][770]['res_net_meters']
+['path_tpc']['grades'][771]['offset_meters']
 ['path_tpc']['grades'][771]['res_coeff']
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+['path_tpc']['grades'][771]['res_net_meters']
+['path_tpc']['grades'][772]['offset_meters']
 ['path_tpc']['grades'][772]['res_coeff']
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-['path_tpc']['grades'][773]['offset']
+['path_tpc']['grades'][772]['res_net_meters']
+['path_tpc']['grades'][773]['offset_meters']
 ['path_tpc']['grades'][773]['res_coeff']
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-['path_tpc']['grades'][774]['offset']
+['path_tpc']['grades'][773]['res_net_meters']
+['path_tpc']['grades'][774]['offset_meters']
 ['path_tpc']['grades'][774]['res_coeff']
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-['path_tpc']['grades'][775]['offset']
+['path_tpc']['grades'][774]['res_net_meters']
+['path_tpc']['grades'][775]['offset_meters']
 ['path_tpc']['grades'][775]['res_coeff']
-['path_tpc']['grades'][775]['res_net']
-['path_tpc']['grades'][776]['offset']
+['path_tpc']['grades'][775]['res_net_meters']
+['path_tpc']['grades'][776]['offset_meters']
 ['path_tpc']['grades'][776]['res_coeff']
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-['path_tpc']['grades'][777]['offset']
+['path_tpc']['grades'][776]['res_net_meters']
+['path_tpc']['grades'][777]['offset_meters']
 ['path_tpc']['grades'][777]['res_coeff']
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-['path_tpc']['grades'][778]['offset']
+['path_tpc']['grades'][777]['res_net_meters']
+['path_tpc']['grades'][778]['offset_meters']
 ['path_tpc']['grades'][778]['res_coeff']
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-['path_tpc']['grades'][779]['offset']
+['path_tpc']['grades'][778]['res_net_meters']
+['path_tpc']['grades'][779]['offset_meters']
 ['path_tpc']['grades'][779]['res_coeff']
-['path_tpc']['grades'][779]['res_net']
-['path_tpc']['grades'][780]['offset']
+['path_tpc']['grades'][779]['res_net_meters']
+['path_tpc']['grades'][780]['offset_meters']
 ['path_tpc']['grades'][780]['res_coeff']
-['path_tpc']['grades'][780]['res_net']
-['path_tpc']['grades'][781]['offset']
+['path_tpc']['grades'][780]['res_net_meters']
+['path_tpc']['grades'][781]['offset_meters']
 ['path_tpc']['grades'][781]['res_coeff']
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+['path_tpc']['grades'][781]['res_net_meters']
+['path_tpc']['grades'][782]['offset_meters']
 ['path_tpc']['grades'][782]['res_coeff']
-['path_tpc']['grades'][782]['res_net']
-['path_tpc']['grades'][783]['offset']
+['path_tpc']['grades'][782]['res_net_meters']
+['path_tpc']['grades'][783]['offset_meters']
 ['path_tpc']['grades'][783]['res_coeff']
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-['path_tpc']['grades'][784]['offset']
+['path_tpc']['grades'][783]['res_net_meters']
+['path_tpc']['grades'][784]['offset_meters']
 ['path_tpc']['grades'][784]['res_coeff']
-['path_tpc']['grades'][784]['res_net']
-['path_tpc']['grades'][785]['offset']
+['path_tpc']['grades'][784]['res_net_meters']
+['path_tpc']['grades'][785]['offset_meters']
 ['path_tpc']['grades'][785]['res_coeff']
-['path_tpc']['grades'][785]['res_net']
-['path_tpc']['grades'][786]['offset']
+['path_tpc']['grades'][785]['res_net_meters']
+['path_tpc']['grades'][786]['offset_meters']
 ['path_tpc']['grades'][786]['res_coeff']
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+['path_tpc']['grades'][786]['res_net_meters']
+['path_tpc']['curves'][0]['offset_meters']
 ['path_tpc']['curves'][0]['res_coeff']
-['path_tpc']['curves'][0]['res_net']
-['path_tpc']['curves'][1]['offset']
+['path_tpc']['curves'][0]['res_net_meters']
+['path_tpc']['curves'][1]['offset_meters']
 ['path_tpc']['curves'][1]['res_coeff']
-['path_tpc']['curves'][1]['res_net']
-['path_tpc']['curves'][2]['offset']
+['path_tpc']['curves'][1]['res_net_meters']
+['path_tpc']['curves'][2]['offset_meters']
 ['path_tpc']['curves'][2]['res_coeff']
-['path_tpc']['curves'][2]['res_net']
-['path_tpc']['curves'][3]['offset']
+['path_tpc']['curves'][2]['res_net_meters']
+['path_tpc']['curves'][3]['offset_meters']
 ['path_tpc']['curves'][3]['res_coeff']
-['path_tpc']['curves'][3]['res_net']
-['path_tpc']['curves'][4]['offset']
+['path_tpc']['curves'][3]['res_net_meters']
+['path_tpc']['curves'][4]['offset_meters']
 ['path_tpc']['curves'][4]['res_coeff']
-['path_tpc']['curves'][4]['res_net']
-['path_tpc']['curves'][5]['offset']
+['path_tpc']['curves'][4]['res_net_meters']
+['path_tpc']['curves'][5]['offset_meters']
 ['path_tpc']['curves'][5]['res_coeff']
-['path_tpc']['curves'][5]['res_net']
-['path_tpc']['curves'][6]['offset']
+['path_tpc']['curves'][5]['res_net_meters']
+['path_tpc']['curves'][6]['offset_meters']
 ['path_tpc']['curves'][6]['res_coeff']
-['path_tpc']['curves'][6]['res_net']
-['path_tpc']['curves'][7]['offset']
+['path_tpc']['curves'][6]['res_net_meters']
+['path_tpc']['curves'][7]['offset_meters']
 ['path_tpc']['curves'][7]['res_coeff']
-['path_tpc']['curves'][7]['res_net']
-['path_tpc']['curves'][8]['offset']
+['path_tpc']['curves'][7]['res_net_meters']
+['path_tpc']['curves'][8]['offset_meters']
 ['path_tpc']['curves'][8]['res_coeff']
-['path_tpc']['curves'][8]['res_net']
-['path_tpc']['curves'][9]['offset']
+['path_tpc']['curves'][8]['res_net_meters']
+['path_tpc']['curves'][9]['offset_meters']
 ['path_tpc']['curves'][9]['res_coeff']
-['path_tpc']['curves'][9]['res_net']
-['path_tpc']['curves'][10]['offset']
+['path_tpc']['curves'][9]['res_net_meters']
+['path_tpc']['curves'][10]['offset_meters']
 ['path_tpc']['curves'][10]['res_coeff']
-['path_tpc']['curves'][10]['res_net']
-['path_tpc']['curves'][11]['offset']
+['path_tpc']['curves'][10]['res_net_meters']
+['path_tpc']['curves'][11]['offset_meters']
 ['path_tpc']['curves'][11]['res_coeff']
-['path_tpc']['curves'][11]['res_net']
-['path_tpc']['curves'][12]['offset']
+['path_tpc']['curves'][11]['res_net_meters']
+['path_tpc']['curves'][12]['offset_meters']
 ['path_tpc']['curves'][12]['res_coeff']
-['path_tpc']['curves'][12]['res_net']
-['path_tpc']['curves'][13]['offset']
+['path_tpc']['curves'][12]['res_net_meters']
+['path_tpc']['curves'][13]['offset_meters']
 ['path_tpc']['curves'][13]['res_coeff']
-['path_tpc']['curves'][13]['res_net']
-['path_tpc']['curves'][14]['offset']
+['path_tpc']['curves'][13]['res_net_meters']
+['path_tpc']['curves'][14]['offset_meters']
 ['path_tpc']['curves'][14]['res_coeff']
-['path_tpc']['curves'][14]['res_net']
-['path_tpc']['curves'][15]['offset']
+['path_tpc']['curves'][14]['res_net_meters']
+['path_tpc']['curves'][15]['offset_meters']
 ['path_tpc']['curves'][15]['res_coeff']
-['path_tpc']['curves'][15]['res_net']
-['path_tpc']['curves'][16]['offset']
+['path_tpc']['curves'][15]['res_net_meters']
+['path_tpc']['curves'][16]['offset_meters']
 ['path_tpc']['curves'][16]['res_coeff']
-['path_tpc']['curves'][16]['res_net']
-['path_tpc']['curves'][17]['offset']
+['path_tpc']['curves'][16]['res_net_meters']
+['path_tpc']['curves'][17]['offset_meters']
 ['path_tpc']['curves'][17]['res_coeff']
-['path_tpc']['curves'][17]['res_net']
-['path_tpc']['curves'][18]['offset']
+['path_tpc']['curves'][17]['res_net_meters']
+['path_tpc']['curves'][18]['offset_meters']
 ['path_tpc']['curves'][18]['res_coeff']
-['path_tpc']['curves'][18]['res_net']
-['path_tpc']['curves'][19]['offset']
+['path_tpc']['curves'][18]['res_net_meters']
+['path_tpc']['curves'][19]['offset_meters']
 ['path_tpc']['curves'][19]['res_coeff']
-['path_tpc']['curves'][19]['res_net']
-['path_tpc']['curves'][20]['offset']
+['path_tpc']['curves'][19]['res_net_meters']
+['path_tpc']['curves'][20]['offset_meters']
 ['path_tpc']['curves'][20]['res_coeff']
-['path_tpc']['curves'][20]['res_net']
-['path_tpc']['curves'][21]['offset']
+['path_tpc']['curves'][20]['res_net_meters']
+['path_tpc']['curves'][21]['offset_meters']
 ['path_tpc']['curves'][21]['res_coeff']
-['path_tpc']['curves'][21]['res_net']
-['path_tpc']['curves'][22]['offset']
+['path_tpc']['curves'][21]['res_net_meters']
+['path_tpc']['curves'][22]['offset_meters']
 ['path_tpc']['curves'][22]['res_coeff']
-['path_tpc']['curves'][22]['res_net']
-['path_tpc']['curves'][23]['offset']
+['path_tpc']['curves'][22]['res_net_meters']
+['path_tpc']['curves'][23]['offset_meters']
 ['path_tpc']['curves'][23]['res_coeff']
-['path_tpc']['curves'][23]['res_net']
-['path_tpc']['curves'][24]['offset']
+['path_tpc']['curves'][23]['res_net_meters']
+['path_tpc']['curves'][24]['offset_meters']
 ['path_tpc']['curves'][24]['res_coeff']
-['path_tpc']['curves'][24]['res_net']
-['path_tpc']['curves'][25]['offset']
+['path_tpc']['curves'][24]['res_net_meters']
+['path_tpc']['curves'][25]['offset_meters']
 ['path_tpc']['curves'][25]['res_coeff']
-['path_tpc']['curves'][25]['res_net']
-['path_tpc']['curves'][26]['offset']
+['path_tpc']['curves'][25]['res_net_meters']
+['path_tpc']['curves'][26]['offset_meters']
 ['path_tpc']['curves'][26]['res_coeff']
-['path_tpc']['curves'][26]['res_net']
-['path_tpc']['curves'][27]['offset']
+['path_tpc']['curves'][26]['res_net_meters']
+['path_tpc']['curves'][27]['offset_meters']
 ['path_tpc']['curves'][27]['res_coeff']
-['path_tpc']['curves'][27]['res_net']
-['path_tpc']['curves'][28]['offset']
+['path_tpc']['curves'][27]['res_net_meters']
+['path_tpc']['curves'][28]['offset_meters']
 ['path_tpc']['curves'][28]['res_coeff']
-['path_tpc']['curves'][28]['res_net']
-['path_tpc']['curves'][29]['offset']
+['path_tpc']['curves'][28]['res_net_meters']
+['path_tpc']['curves'][29]['offset_meters']
 ['path_tpc']['curves'][29]['res_coeff']
-['path_tpc']['curves'][29]['res_net']
-['path_tpc']['curves'][30]['offset']
+['path_tpc']['curves'][29]['res_net_meters']
+['path_tpc']['curves'][30]['offset_meters']
 ['path_tpc']['curves'][30]['res_coeff']
-['path_tpc']['curves'][30]['res_net']
-['path_tpc']['curves'][31]['offset']
+['path_tpc']['curves'][30]['res_net_meters']
+['path_tpc']['curves'][31]['offset_meters']
 ['path_tpc']['curves'][31]['res_coeff']
-['path_tpc']['curves'][31]['res_net']
-['path_tpc']['curves'][32]['offset']
+['path_tpc']['curves'][31]['res_net_meters']
+['path_tpc']['curves'][32]['offset_meters']
 ['path_tpc']['curves'][32]['res_coeff']
-['path_tpc']['curves'][32]['res_net']
-['path_tpc']['curves'][33]['offset']
+['path_tpc']['curves'][32]['res_net_meters']
+['path_tpc']['curves'][33]['offset_meters']
 ['path_tpc']['curves'][33]['res_coeff']
-['path_tpc']['curves'][33]['res_net']
-['path_tpc']['curves'][34]['offset']
+['path_tpc']['curves'][33]['res_net_meters']
+['path_tpc']['curves'][34]['offset_meters']
 ['path_tpc']['curves'][34]['res_coeff']
-['path_tpc']['curves'][34]['res_net']
-['path_tpc']['curves'][35]['offset']
+['path_tpc']['curves'][34]['res_net_meters']
+['path_tpc']['curves'][35]['offset_meters']
 ['path_tpc']['curves'][35]['res_coeff']
-['path_tpc']['curves'][35]['res_net']
-['path_tpc']['curves'][36]['offset']
+['path_tpc']['curves'][35]['res_net_meters']
+['path_tpc']['curves'][36]['offset_meters']
 ['path_tpc']['curves'][36]['res_coeff']
-['path_tpc']['curves'][36]['res_net']
-['path_tpc']['curves'][37]['offset']
+['path_tpc']['curves'][36]['res_net_meters']
+['path_tpc']['curves'][37]['offset_meters']
 ['path_tpc']['curves'][37]['res_coeff']
-['path_tpc']['curves'][37]['res_net']
-['path_tpc']['curves'][38]['offset']
+['path_tpc']['curves'][37]['res_net_meters']
+['path_tpc']['curves'][38]['offset_meters']
 ['path_tpc']['curves'][38]['res_coeff']
-['path_tpc']['curves'][38]['res_net']
-['path_tpc']['curves'][39]['offset']
+['path_tpc']['curves'][38]['res_net_meters']
+['path_tpc']['curves'][39]['offset_meters']
 ['path_tpc']['curves'][39]['res_coeff']
-['path_tpc']['curves'][39]['res_net']
-['path_tpc']['curves'][40]['offset']
+['path_tpc']['curves'][39]['res_net_meters']
+['path_tpc']['curves'][40]['offset_meters']
 ['path_tpc']['curves'][40]['res_coeff']
-['path_tpc']['curves'][40]['res_net']
-['path_tpc']['curves'][41]['offset']
+['path_tpc']['curves'][40]['res_net_meters']
+['path_tpc']['curves'][41]['offset_meters']
 ['path_tpc']['curves'][41]['res_coeff']
-['path_tpc']['curves'][41]['res_net']
-['path_tpc']['curves'][42]['offset']
+['path_tpc']['curves'][41]['res_net_meters']
+['path_tpc']['curves'][42]['offset_meters']
 ['path_tpc']['curves'][42]['res_coeff']
-['path_tpc']['curves'][42]['res_net']
-['path_tpc']['curves'][43]['offset']
+['path_tpc']['curves'][42]['res_net_meters']
+['path_tpc']['curves'][43]['offset_meters']
 ['path_tpc']['curves'][43]['res_coeff']
-['path_tpc']['curves'][43]['res_net']
-['path_tpc']['curves'][44]['offset']
+['path_tpc']['curves'][43]['res_net_meters']
+['path_tpc']['curves'][44]['offset_meters']
 ['path_tpc']['curves'][44]['res_coeff']
-['path_tpc']['curves'][44]['res_net']
-['path_tpc']['curves'][45]['offset']
+['path_tpc']['curves'][44]['res_net_meters']
+['path_tpc']['curves'][45]['offset_meters']
 ['path_tpc']['curves'][45]['res_coeff']
-['path_tpc']['curves'][45]['res_net']
-['path_tpc']['curves'][46]['offset']
+['path_tpc']['curves'][45]['res_net_meters']
+['path_tpc']['curves'][46]['offset_meters']
 ['path_tpc']['curves'][46]['res_coeff']
-['path_tpc']['curves'][46]['res_net']
-['path_tpc']['curves'][47]['offset']
+['path_tpc']['curves'][46]['res_net_meters']
+['path_tpc']['curves'][47]['offset_meters']
 ['path_tpc']['curves'][47]['res_coeff']
-['path_tpc']['curves'][47]['res_net']
-['path_tpc']['curves'][48]['offset']
+['path_tpc']['curves'][47]['res_net_meters']
+['path_tpc']['curves'][48]['offset_meters']
 ['path_tpc']['curves'][48]['res_coeff']
-['path_tpc']['curves'][48]['res_net']
-['path_tpc']['curves'][49]['offset']
+['path_tpc']['curves'][48]['res_net_meters']
+['path_tpc']['curves'][49]['offset_meters']
 ['path_tpc']['curves'][49]['res_coeff']
-['path_tpc']['curves'][49]['res_net']
-['path_tpc']['curves'][50]['offset']
+['path_tpc']['curves'][49]['res_net_meters']
+['path_tpc']['curves'][50]['offset_meters']
 ['path_tpc']['curves'][50]['res_coeff']
-['path_tpc']['curves'][50]['res_net']
-['path_tpc']['curves'][51]['offset']
+['path_tpc']['curves'][50]['res_net_meters']
+['path_tpc']['curves'][51]['offset_meters']
 ['path_tpc']['curves'][51]['res_coeff']
-['path_tpc']['curves'][51]['res_net']
-['path_tpc']['curves'][52]['offset']
+['path_tpc']['curves'][51]['res_net_meters']
+['path_tpc']['curves'][52]['offset_meters']
 ['path_tpc']['curves'][52]['res_coeff']
-['path_tpc']['curves'][52]['res_net']
-['path_tpc']['curves'][53]['offset']
+['path_tpc']['curves'][52]['res_net_meters']
+['path_tpc']['curves'][53]['offset_meters']
 ['path_tpc']['curves'][53]['res_coeff']
-['path_tpc']['curves'][53]['res_net']
-['path_tpc']['curves'][54]['offset']
+['path_tpc']['curves'][53]['res_net_meters']
+['path_tpc']['curves'][54]['offset_meters']
 ['path_tpc']['curves'][54]['res_coeff']
-['path_tpc']['curves'][54]['res_net']
-['path_tpc']['curves'][55]['offset']
+['path_tpc']['curves'][54]['res_net_meters']
+['path_tpc']['curves'][55]['offset_meters']
 ['path_tpc']['curves'][55]['res_coeff']
-['path_tpc']['curves'][55]['res_net']
-['path_tpc']['curves'][56]['offset']
+['path_tpc']['curves'][55]['res_net_meters']
+['path_tpc']['curves'][56]['offset_meters']
 ['path_tpc']['curves'][56]['res_coeff']
-['path_tpc']['curves'][56]['res_net']
-['path_tpc']['curves'][57]['offset']
+['path_tpc']['curves'][56]['res_net_meters']
+['path_tpc']['curves'][57]['offset_meters']
 ['path_tpc']['curves'][57]['res_coeff']
-['path_tpc']['curves'][57]['res_net']
-['path_tpc']['curves'][58]['offset']
+['path_tpc']['curves'][57]['res_net_meters']
+['path_tpc']['curves'][58]['offset_meters']
 ['path_tpc']['curves'][58]['res_coeff']
-['path_tpc']['curves'][58]['res_net']
-['path_tpc']['curves'][59]['offset']
+['path_tpc']['curves'][58]['res_net_meters']
+['path_tpc']['curves'][59]['offset_meters']
 ['path_tpc']['curves'][59]['res_coeff']
-['path_tpc']['curves'][59]['res_net']
-['path_tpc']['curves'][60]['offset']
+['path_tpc']['curves'][59]['res_net_meters']
+['path_tpc']['curves'][60]['offset_meters']
 ['path_tpc']['curves'][60]['res_coeff']
-['path_tpc']['curves'][60]['res_net']
-['path_tpc']['curves'][61]['offset']
+['path_tpc']['curves'][60]['res_net_meters']
+['path_tpc']['curves'][61]['offset_meters']
 ['path_tpc']['curves'][61]['res_coeff']
-['path_tpc']['curves'][61]['res_net']
-['path_tpc']['curves'][62]['offset']
+['path_tpc']['curves'][61]['res_net_meters']
+['path_tpc']['curves'][62]['offset_meters']
 ['path_tpc']['curves'][62]['res_coeff']
-['path_tpc']['curves'][62]['res_net']
-['path_tpc']['curves'][63]['offset']
+['path_tpc']['curves'][62]['res_net_meters']
+['path_tpc']['curves'][63]['offset_meters']
 ['path_tpc']['curves'][63]['res_coeff']
-['path_tpc']['curves'][63]['res_net']
-['path_tpc']['curves'][64]['offset']
+['path_tpc']['curves'][63]['res_net_meters']
+['path_tpc']['curves'][64]['offset_meters']
 ['path_tpc']['curves'][64]['res_coeff']
-['path_tpc']['curves'][64]['res_net']
-['path_tpc']['curves'][65]['offset']
+['path_tpc']['curves'][64]['res_net_meters']
+['path_tpc']['curves'][65]['offset_meters']
 ['path_tpc']['curves'][65]['res_coeff']
-['path_tpc']['curves'][65]['res_net']
-['path_tpc']['curves'][66]['offset']
+['path_tpc']['curves'][65]['res_net_meters']
+['path_tpc']['curves'][66]['offset_meters']
 ['path_tpc']['curves'][66]['res_coeff']
-['path_tpc']['curves'][66]['res_net']
-['path_tpc']['curves'][67]['offset']
+['path_tpc']['curves'][66]['res_net_meters']
+['path_tpc']['curves'][67]['offset_meters']
 ['path_tpc']['curves'][67]['res_coeff']
-['path_tpc']['curves'][67]['res_net']
-['path_tpc']['curves'][68]['offset']
+['path_tpc']['curves'][67]['res_net_meters']
+['path_tpc']['curves'][68]['offset_meters']
 ['path_tpc']['curves'][68]['res_coeff']
-['path_tpc']['curves'][68]['res_net']
-['path_tpc']['curves'][69]['offset']
+['path_tpc']['curves'][68]['res_net_meters']
+['path_tpc']['curves'][69]['offset_meters']
 ['path_tpc']['curves'][69]['res_coeff']
-['path_tpc']['curves'][69]['res_net']
-['path_tpc']['curves'][70]['offset']
+['path_tpc']['curves'][69]['res_net_meters']
+['path_tpc']['curves'][70]['offset_meters']
 ['path_tpc']['curves'][70]['res_coeff']
-['path_tpc']['curves'][70]['res_net']
-['path_tpc']['curves'][71]['offset']
+['path_tpc']['curves'][70]['res_net_meters']
+['path_tpc']['curves'][71]['offset_meters']
 ['path_tpc']['curves'][71]['res_coeff']
-['path_tpc']['curves'][71]['res_net']
-['path_tpc']['curves'][72]['offset']
+['path_tpc']['curves'][71]['res_net_meters']
+['path_tpc']['curves'][72]['offset_meters']
 ['path_tpc']['curves'][72]['res_coeff']
-['path_tpc']['curves'][72]['res_net']
-['path_tpc']['curves'][73]['offset']
+['path_tpc']['curves'][72]['res_net_meters']
+['path_tpc']['curves'][73]['offset_meters']
 ['path_tpc']['curves'][73]['res_coeff']
-['path_tpc']['curves'][73]['res_net']
-['path_tpc']['curves'][74]['offset']
+['path_tpc']['curves'][73]['res_net_meters']
+['path_tpc']['curves'][74]['offset_meters']
 ['path_tpc']['curves'][74]['res_coeff']
-['path_tpc']['curves'][74]['res_net']
-['path_tpc']['curves'][75]['offset']
+['path_tpc']['curves'][74]['res_net_meters']
+['path_tpc']['curves'][75]['offset_meters']
 ['path_tpc']['curves'][75]['res_coeff']
-['path_tpc']['curves'][75]['res_net']
-['path_tpc']['curves'][76]['offset']
+['path_tpc']['curves'][75]['res_net_meters']
+['path_tpc']['curves'][76]['offset_meters']
 ['path_tpc']['curves'][76]['res_coeff']
-['path_tpc']['curves'][76]['res_net']
-['path_tpc']['curves'][77]['offset']
+['path_tpc']['curves'][76]['res_net_meters']
+['path_tpc']['curves'][77]['offset_meters']
 ['path_tpc']['curves'][77]['res_coeff']
-['path_tpc']['curves'][77]['res_net']
-['path_tpc']['curves'][78]['offset']
+['path_tpc']['curves'][77]['res_net_meters']
+['path_tpc']['curves'][78]['offset_meters']
 ['path_tpc']['curves'][78]['res_coeff']
-['path_tpc']['curves'][78]['res_net']
-['path_tpc']['curves'][79]['offset']
+['path_tpc']['curves'][78]['res_net_meters']
+['path_tpc']['curves'][79]['offset_meters']
 ['path_tpc']['curves'][79]['res_coeff']
-['path_tpc']['curves'][79]['res_net']
-['path_tpc']['curves'][80]['offset']
+['path_tpc']['curves'][79]['res_net_meters']
+['path_tpc']['curves'][80]['offset_meters']
 ['path_tpc']['curves'][80]['res_coeff']
-['path_tpc']['curves'][80]['res_net']
-['path_tpc']['curves'][81]['offset']
+['path_tpc']['curves'][80]['res_net_meters']
+['path_tpc']['curves'][81]['offset_meters']
 ['path_tpc']['curves'][81]['res_coeff']
-['path_tpc']['curves'][81]['res_net']
-['path_tpc']['curves'][82]['offset']
+['path_tpc']['curves'][81]['res_net_meters']
+['path_tpc']['curves'][82]['offset_meters']
 ['path_tpc']['curves'][82]['res_coeff']
-['path_tpc']['curves'][82]['res_net']
-['path_tpc']['curves'][83]['offset']
+['path_tpc']['curves'][82]['res_net_meters']
+['path_tpc']['curves'][83]['offset_meters']
 ['path_tpc']['curves'][83]['res_coeff']
-['path_tpc']['curves'][83]['res_net']
-['path_tpc']['curves'][84]['offset']
+['path_tpc']['curves'][83]['res_net_meters']
+['path_tpc']['curves'][84]['offset_meters']
 ['path_tpc']['curves'][84]['res_coeff']
-['path_tpc']['curves'][84]['res_net']
-['path_tpc']['curves'][85]['offset']
+['path_tpc']['curves'][84]['res_net_meters']
+['path_tpc']['curves'][85]['offset_meters']
 ['path_tpc']['curves'][85]['res_coeff']
-['path_tpc']['curves'][85]['res_net']
-['path_tpc']['curves'][86]['offset']
+['path_tpc']['curves'][85]['res_net_meters']
+['path_tpc']['curves'][86]['offset_meters']
 ['path_tpc']['curves'][86]['res_coeff']
-['path_tpc']['curves'][86]['res_net']
-['path_tpc']['curves'][87]['offset']
+['path_tpc']['curves'][86]['res_net_meters']
+['path_tpc']['curves'][87]['offset_meters']
 ['path_tpc']['curves'][87]['res_coeff']
-['path_tpc']['curves'][87]['res_net']
-['path_tpc']['curves'][88]['offset']
+['path_tpc']['curves'][87]['res_net_meters']
+['path_tpc']['curves'][88]['offset_meters']
 ['path_tpc']['curves'][88]['res_coeff']
-['path_tpc']['curves'][88]['res_net']
-['path_tpc']['curves'][89]['offset']
+['path_tpc']['curves'][88]['res_net_meters']
+['path_tpc']['curves'][89]['offset_meters']
 ['path_tpc']['curves'][89]['res_coeff']
-['path_tpc']['curves'][89]['res_net']
-['path_tpc']['curves'][90]['offset']
+['path_tpc']['curves'][89]['res_net_meters']
+['path_tpc']['curves'][90]['offset_meters']
 ['path_tpc']['curves'][90]['res_coeff']
-['path_tpc']['curves'][90]['res_net']
-['path_tpc']['curves'][91]['offset']
+['path_tpc']['curves'][90]['res_net_meters']
+['path_tpc']['curves'][91]['offset_meters']
 ['path_tpc']['curves'][91]['res_coeff']
-['path_tpc']['curves'][91]['res_net']
-['path_tpc']['curves'][92]['offset']
+['path_tpc']['curves'][91]['res_net_meters']
+['path_tpc']['curves'][92]['offset_meters']
 ['path_tpc']['curves'][92]['res_coeff']
-['path_tpc']['curves'][92]['res_net']
-['path_tpc']['curves'][93]['offset']
+['path_tpc']['curves'][92]['res_net_meters']
+['path_tpc']['curves'][93]['offset_meters']
 ['path_tpc']['curves'][93]['res_coeff']
-['path_tpc']['curves'][93]['res_net']
-['path_tpc']['curves'][94]['offset']
+['path_tpc']['curves'][93]['res_net_meters']
+['path_tpc']['curves'][94]['offset_meters']
 ['path_tpc']['curves'][94]['res_coeff']
-['path_tpc']['curves'][94]['res_net']
-['path_tpc']['curves'][95]['offset']
+['path_tpc']['curves'][94]['res_net_meters']
+['path_tpc']['curves'][95]['offset_meters']
 ['path_tpc']['curves'][95]['res_coeff']
-['path_tpc']['curves'][95]['res_net']
-['path_tpc']['curves'][96]['offset']
+['path_tpc']['curves'][95]['res_net_meters']
+['path_tpc']['curves'][96]['offset_meters']
 ['path_tpc']['curves'][96]['res_coeff']
-['path_tpc']['curves'][96]['res_net']
-['path_tpc']['curves'][97]['offset']
+['path_tpc']['curves'][96]['res_net_meters']
+['path_tpc']['curves'][97]['offset_meters']
 ['path_tpc']['curves'][97]['res_coeff']
-['path_tpc']['curves'][97]['res_net']
-['path_tpc']['curves'][98]['offset']
+['path_tpc']['curves'][97]['res_net_meters']
+['path_tpc']['curves'][98]['offset_meters']
 ['path_tpc']['curves'][98]['res_coeff']
-['path_tpc']['curves'][98]['res_net']
-['path_tpc']['curves'][99]['offset']
+['path_tpc']['curves'][98]['res_net_meters']
+['path_tpc']['curves'][99]['offset_meters']
 ['path_tpc']['curves'][99]['res_coeff']
-['path_tpc']['curves'][99]['res_net']
-['path_tpc']['curves'][100]['offset']
+['path_tpc']['curves'][99]['res_net_meters']
+['path_tpc']['curves'][100]['offset_meters']
 ['path_tpc']['curves'][100]['res_coeff']
-['path_tpc']['curves'][100]['res_net']
-['path_tpc']['curves'][101]['offset']
+['path_tpc']['curves'][100]['res_net_meters']
+['path_tpc']['curves'][101]['offset_meters']
 ['path_tpc']['curves'][101]['res_coeff']
-['path_tpc']['curves'][101]['res_net']
-['path_tpc']['curves'][102]['offset']
+['path_tpc']['curves'][101]['res_net_meters']
+['path_tpc']['curves'][102]['offset_meters']
 ['path_tpc']['curves'][102]['res_coeff']
-['path_tpc']['curves'][102]['res_net']
-['path_tpc']['curves'][103]['offset']
+['path_tpc']['curves'][102]['res_net_meters']
+['path_tpc']['curves'][103]['offset_meters']
 ['path_tpc']['curves'][103]['res_coeff']
-['path_tpc']['curves'][103]['res_net']
-['path_tpc']['curves'][104]['offset']
+['path_tpc']['curves'][103]['res_net_meters']
+['path_tpc']['curves'][104]['offset_meters']
 ['path_tpc']['curves'][104]['res_coeff']
-['path_tpc']['curves'][104]['res_net']
-['path_tpc']['curves'][105]['offset']
+['path_tpc']['curves'][104]['res_net_meters']
+['path_tpc']['curves'][105]['offset_meters']
 ['path_tpc']['curves'][105]['res_coeff']
-['path_tpc']['curves'][105]['res_net']
-['path_tpc']['curves'][106]['offset']
+['path_tpc']['curves'][105]['res_net_meters']
+['path_tpc']['curves'][106]['offset_meters']
 ['path_tpc']['curves'][106]['res_coeff']
-['path_tpc']['curves'][106]['res_net']
-['path_tpc']['curves'][107]['offset']
+['path_tpc']['curves'][106]['res_net_meters']
+['path_tpc']['curves'][107]['offset_meters']
 ['path_tpc']['curves'][107]['res_coeff']
-['path_tpc']['curves'][107]['res_net']
-['path_tpc']['curves'][108]['offset']
+['path_tpc']['curves'][107]['res_net_meters']
+['path_tpc']['curves'][108]['offset_meters']
 ['path_tpc']['curves'][108]['res_coeff']
-['path_tpc']['curves'][108]['res_net']
-['path_tpc']['curves'][109]['offset']
+['path_tpc']['curves'][108]['res_net_meters']
+['path_tpc']['curves'][109]['offset_meters']
 ['path_tpc']['curves'][109]['res_coeff']
-['path_tpc']['curves'][109]['res_net']
-['path_tpc']['curves'][110]['offset']
+['path_tpc']['curves'][109]['res_net_meters']
+['path_tpc']['curves'][110]['offset_meters']
 ['path_tpc']['curves'][110]['res_coeff']
-['path_tpc']['curves'][110]['res_net']
-['path_tpc']['curves'][111]['offset']
+['path_tpc']['curves'][110]['res_net_meters']
+['path_tpc']['curves'][111]['offset_meters']
 ['path_tpc']['curves'][111]['res_coeff']
-['path_tpc']['curves'][111]['res_net']
-['path_tpc']['curves'][112]['offset']
+['path_tpc']['curves'][111]['res_net_meters']
+['path_tpc']['curves'][112]['offset_meters']
 ['path_tpc']['curves'][112]['res_coeff']
-['path_tpc']['curves'][112]['res_net']
-['path_tpc']['curves'][113]['offset']
+['path_tpc']['curves'][112]['res_net_meters']
+['path_tpc']['curves'][113]['offset_meters']
 ['path_tpc']['curves'][113]['res_coeff']
-['path_tpc']['curves'][113]['res_net']
-['path_tpc']['curves'][114]['offset']
+['path_tpc']['curves'][113]['res_net_meters']
+['path_tpc']['curves'][114]['offset_meters']
 ['path_tpc']['curves'][114]['res_coeff']
-['path_tpc']['curves'][114]['res_net']
-['path_tpc']['curves'][115]['offset']
+['path_tpc']['curves'][114]['res_net_meters']
+['path_tpc']['curves'][115]['offset_meters']
 ['path_tpc']['curves'][115]['res_coeff']
-['path_tpc']['curves'][115]['res_net']
-['path_tpc']['curves'][116]['offset']
+['path_tpc']['curves'][115]['res_net_meters']
+['path_tpc']['curves'][116]['offset_meters']
 ['path_tpc']['curves'][116]['res_coeff']
-['path_tpc']['curves'][116]['res_net']
-['path_tpc']['curves'][117]['offset']
+['path_tpc']['curves'][116]['res_net_meters']
+['path_tpc']['curves'][117]['offset_meters']
 ['path_tpc']['curves'][117]['res_coeff']
-['path_tpc']['curves'][117]['res_net']
-['path_tpc']['curves'][118]['offset']
+['path_tpc']['curves'][117]['res_net_meters']
+['path_tpc']['curves'][118]['offset_meters']
 ['path_tpc']['curves'][118]['res_coeff']
-['path_tpc']['curves'][118]['res_net']
-['path_tpc']['curves'][119]['offset']
+['path_tpc']['curves'][118]['res_net_meters']
+['path_tpc']['curves'][119]['offset_meters']
 ['path_tpc']['curves'][119]['res_coeff']
-['path_tpc']['curves'][119]['res_net']
-['path_tpc']['curves'][120]['offset']
+['path_tpc']['curves'][119]['res_net_meters']
+['path_tpc']['curves'][120]['offset_meters']
 ['path_tpc']['curves'][120]['res_coeff']
-['path_tpc']['curves'][120]['res_net']
-['path_tpc']['curves'][121]['offset']
+['path_tpc']['curves'][120]['res_net_meters']
+['path_tpc']['curves'][121]['offset_meters']
 ['path_tpc']['curves'][121]['res_coeff']
-['path_tpc']['curves'][121]['res_net']
-['path_tpc']['curves'][122]['offset']
+['path_tpc']['curves'][121]['res_net_meters']
+['path_tpc']['curves'][122]['offset_meters']
 ['path_tpc']['curves'][122]['res_coeff']
-['path_tpc']['curves'][122]['res_net']
-['path_tpc']['curves'][123]['offset']
+['path_tpc']['curves'][122]['res_net_meters']
+['path_tpc']['curves'][123]['offset_meters']
 ['path_tpc']['curves'][123]['res_coeff']
-['path_tpc']['curves'][123]['res_net']
-['path_tpc']['curves'][124]['offset']
+['path_tpc']['curves'][123]['res_net_meters']
+['path_tpc']['curves'][124]['offset_meters']
 ['path_tpc']['curves'][124]['res_coeff']
-['path_tpc']['curves'][124]['res_net']
-['path_tpc']['curves'][125]['offset']
+['path_tpc']['curves'][124]['res_net_meters']
+['path_tpc']['curves'][125]['offset_meters']
 ['path_tpc']['curves'][125]['res_coeff']
-['path_tpc']['curves'][125]['res_net']
-['path_tpc']['curves'][126]['offset']
+['path_tpc']['curves'][125]['res_net_meters']
+['path_tpc']['curves'][126]['offset_meters']
 ['path_tpc']['curves'][126]['res_coeff']
-['path_tpc']['curves'][126]['res_net']
-['path_tpc']['curves'][127]['offset']
+['path_tpc']['curves'][126]['res_net_meters']
+['path_tpc']['curves'][127]['offset_meters']
 ['path_tpc']['curves'][127]['res_coeff']
-['path_tpc']['curves'][127]['res_net']
-['path_tpc']['curves'][128]['offset']
+['path_tpc']['curves'][127]['res_net_meters']
+['path_tpc']['curves'][128]['offset_meters']
 ['path_tpc']['curves'][128]['res_coeff']
-['path_tpc']['curves'][128]['res_net']
-['path_tpc']['curves'][129]['offset']
+['path_tpc']['curves'][128]['res_net_meters']
+['path_tpc']['curves'][129]['offset_meters']
 ['path_tpc']['curves'][129]['res_coeff']
-['path_tpc']['curves'][129]['res_net']
-['path_tpc']['curves'][130]['offset']
+['path_tpc']['curves'][129]['res_net_meters']
+['path_tpc']['curves'][130]['offset_meters']
 ['path_tpc']['curves'][130]['res_coeff']
-['path_tpc']['curves'][130]['res_net']
-['path_tpc']['curves'][131]['offset']
+['path_tpc']['curves'][130]['res_net_meters']
+['path_tpc']['curves'][131]['offset_meters']
 ['path_tpc']['curves'][131]['res_coeff']
-['path_tpc']['curves'][131]['res_net']
-['path_tpc']['curves'][132]['offset']
+['path_tpc']['curves'][131]['res_net_meters']
+['path_tpc']['curves'][132]['offset_meters']
 ['path_tpc']['curves'][132]['res_coeff']
-['path_tpc']['curves'][132]['res_net']
-['path_tpc']['curves'][133]['offset']
+['path_tpc']['curves'][132]['res_net_meters']
+['path_tpc']['curves'][133]['offset_meters']
 ['path_tpc']['curves'][133]['res_coeff']
-['path_tpc']['curves'][133]['res_net']
-['path_tpc']['curves'][134]['offset']
+['path_tpc']['curves'][133]['res_net_meters']
+['path_tpc']['curves'][134]['offset_meters']
 ['path_tpc']['curves'][134]['res_coeff']
-['path_tpc']['curves'][134]['res_net']
-['path_tpc']['curves'][135]['offset']
+['path_tpc']['curves'][134]['res_net_meters']
+['path_tpc']['curves'][135]['offset_meters']
 ['path_tpc']['curves'][135]['res_coeff']
-['path_tpc']['curves'][135]['res_net']
-['path_tpc']['curves'][136]['offset']
+['path_tpc']['curves'][135]['res_net_meters']
+['path_tpc']['curves'][136]['offset_meters']
 ['path_tpc']['curves'][136]['res_coeff']
-['path_tpc']['curves'][136]['res_net']
-['path_tpc']['curves'][137]['offset']
+['path_tpc']['curves'][136]['res_net_meters']
+['path_tpc']['curves'][137]['offset_meters']
 ['path_tpc']['curves'][137]['res_coeff']
-['path_tpc']['curves'][137]['res_net']
-['path_tpc']['curves'][138]['offset']
+['path_tpc']['curves'][137]['res_net_meters']
+['path_tpc']['curves'][138]['offset_meters']
 ['path_tpc']['curves'][138]['res_coeff']
-['path_tpc']['curves'][138]['res_net']
-['path_tpc']['curves'][139]['offset']
+['path_tpc']['curves'][138]['res_net_meters']
+['path_tpc']['curves'][139]['offset_meters']
 ['path_tpc']['curves'][139]['res_coeff']
-['path_tpc']['curves'][139]['res_net']
-['path_tpc']['curves'][140]['offset']
+['path_tpc']['curves'][139]['res_net_meters']
+['path_tpc']['curves'][140]['offset_meters']
 ['path_tpc']['curves'][140]['res_coeff']
-['path_tpc']['curves'][140]['res_net']
-['path_tpc']['curves'][141]['offset']
+['path_tpc']['curves'][140]['res_net_meters']
+['path_tpc']['curves'][141]['offset_meters']
 ['path_tpc']['curves'][141]['res_coeff']
-['path_tpc']['curves'][141]['res_net']
-['path_tpc']['curves'][142]['offset']
+['path_tpc']['curves'][141]['res_net_meters']
+['path_tpc']['curves'][142]['offset_meters']
 ['path_tpc']['curves'][142]['res_coeff']
-['path_tpc']['curves'][142]['res_net']
-['path_tpc']['curves'][143]['offset']
+['path_tpc']['curves'][142]['res_net_meters']
+['path_tpc']['curves'][143]['offset_meters']
 ['path_tpc']['curves'][143]['res_coeff']
-['path_tpc']['curves'][143]['res_net']
-['path_tpc']['curves'][144]['offset']
+['path_tpc']['curves'][143]['res_net_meters']
+['path_tpc']['curves'][144]['offset_meters']
 ['path_tpc']['curves'][144]['res_coeff']
-['path_tpc']['curves'][144]['res_net']
-['path_tpc']['curves'][145]['offset']
+['path_tpc']['curves'][144]['res_net_meters']
+['path_tpc']['curves'][145]['offset_meters']
 ['path_tpc']['curves'][145]['res_coeff']
-['path_tpc']['curves'][145]['res_net']
-['path_tpc']['curves'][146]['offset']
+['path_tpc']['curves'][145]['res_net_meters']
+['path_tpc']['curves'][146]['offset_meters']
 ['path_tpc']['curves'][146]['res_coeff']
-['path_tpc']['curves'][146]['res_net']
-['path_tpc']['curves'][147]['offset']
+['path_tpc']['curves'][146]['res_net_meters']
+['path_tpc']['curves'][147]['offset_meters']
 ['path_tpc']['curves'][147]['res_coeff']
-['path_tpc']['curves'][147]['res_net']
-['path_tpc']['curves'][148]['offset']
+['path_tpc']['curves'][147]['res_net_meters']
+['path_tpc']['curves'][148]['offset_meters']
 ['path_tpc']['curves'][148]['res_coeff']
-['path_tpc']['curves'][148]['res_net']
-['path_tpc']['curves'][149]['offset']
+['path_tpc']['curves'][148]['res_net_meters']
+['path_tpc']['curves'][149]['offset_meters']
 ['path_tpc']['curves'][149]['res_coeff']
-['path_tpc']['curves'][149]['res_net']
-['path_tpc']['curves'][150]['offset']
+['path_tpc']['curves'][149]['res_net_meters']
+['path_tpc']['curves'][150]['offset_meters']
 ['path_tpc']['curves'][150]['res_coeff']
-['path_tpc']['curves'][150]['res_net']
-['path_tpc']['curves'][151]['offset']
+['path_tpc']['curves'][150]['res_net_meters']
+['path_tpc']['curves'][151]['offset_meters']
 ['path_tpc']['curves'][151]['res_coeff']
-['path_tpc']['curves'][151]['res_net']
-['path_tpc']['curves'][152]['offset']
+['path_tpc']['curves'][151]['res_net_meters']
+['path_tpc']['curves'][152]['offset_meters']
 ['path_tpc']['curves'][152]['res_coeff']
-['path_tpc']['curves'][152]['res_net']
-['path_tpc']['curves'][153]['offset']
+['path_tpc']['curves'][152]['res_net_meters']
+['path_tpc']['curves'][153]['offset_meters']
 ['path_tpc']['curves'][153]['res_coeff']
-['path_tpc']['curves'][153]['res_net']
-['path_tpc']['curves'][154]['offset']
+['path_tpc']['curves'][153]['res_net_meters']
+['path_tpc']['curves'][154]['offset_meters']
 ['path_tpc']['curves'][154]['res_coeff']
-['path_tpc']['curves'][154]['res_net']
-['path_tpc']['curves'][155]['offset']
+['path_tpc']['curves'][154]['res_net_meters']
+['path_tpc']['curves'][155]['offset_meters']
 ['path_tpc']['curves'][155]['res_coeff']
-['path_tpc']['curves'][155]['res_net']
-['path_tpc']['curves'][156]['offset']
+['path_tpc']['curves'][155]['res_net_meters']
+['path_tpc']['curves'][156]['offset_meters']
 ['path_tpc']['curves'][156]['res_coeff']
-['path_tpc']['curves'][156]['res_net']
-['path_tpc']['curves'][157]['offset']
+['path_tpc']['curves'][156]['res_net_meters']
+['path_tpc']['curves'][157]['offset_meters']
 ['path_tpc']['curves'][157]['res_coeff']
-['path_tpc']['curves'][157]['res_net']
-['path_tpc']['curves'][158]['offset']
+['path_tpc']['curves'][157]['res_net_meters']
+['path_tpc']['curves'][158]['offset_meters']
 ['path_tpc']['curves'][158]['res_coeff']
-['path_tpc']['curves'][158]['res_net']
-['path_tpc']['curves'][159]['offset']
+['path_tpc']['curves'][158]['res_net_meters']
+['path_tpc']['curves'][159]['offset_meters']
 ['path_tpc']['curves'][159]['res_coeff']
-['path_tpc']['curves'][159]['res_net']
-['path_tpc']['curves'][160]['offset']
+['path_tpc']['curves'][159]['res_net_meters']
+['path_tpc']['curves'][160]['offset_meters']
 ['path_tpc']['curves'][160]['res_coeff']
-['path_tpc']['curves'][160]['res_net']
-['path_tpc']['curves'][161]['offset']
+['path_tpc']['curves'][160]['res_net_meters']
+['path_tpc']['curves'][161]['offset_meters']
 ['path_tpc']['curves'][161]['res_coeff']
-['path_tpc']['curves'][161]['res_net']
-['path_tpc']['curves'][162]['offset']
+['path_tpc']['curves'][161]['res_net_meters']
+['path_tpc']['curves'][162]['offset_meters']
 ['path_tpc']['curves'][162]['res_coeff']
-['path_tpc']['curves'][162]['res_net']
-['path_tpc']['curves'][163]['offset']
+['path_tpc']['curves'][162]['res_net_meters']
+['path_tpc']['curves'][163]['offset_meters']
 ['path_tpc']['curves'][163]['res_coeff']
-['path_tpc']['curves'][163]['res_net']
-['path_tpc']['curves'][164]['offset']
+['path_tpc']['curves'][163]['res_net_meters']
+['path_tpc']['curves'][164]['offset_meters']
 ['path_tpc']['curves'][164]['res_coeff']
-['path_tpc']['curves'][164]['res_net']
-['path_tpc']['curves'][165]['offset']
+['path_tpc']['curves'][164]['res_net_meters']
+['path_tpc']['curves'][165]['offset_meters']
 ['path_tpc']['curves'][165]['res_coeff']
-['path_tpc']['curves'][165]['res_net']
-['path_tpc']['curves'][166]['offset']
+['path_tpc']['curves'][165]['res_net_meters']
+['path_tpc']['curves'][166]['offset_meters']
 ['path_tpc']['curves'][166]['res_coeff']
-['path_tpc']['curves'][166]['res_net']
-['path_tpc']['curves'][167]['offset']
+['path_tpc']['curves'][166]['res_net_meters']
+['path_tpc']['curves'][167]['offset_meters']
 ['path_tpc']['curves'][167]['res_coeff']
-['path_tpc']['curves'][167]['res_net']
-['path_tpc']['curves'][168]['offset']
+['path_tpc']['curves'][167]['res_net_meters']
+['path_tpc']['curves'][168]['offset_meters']
 ['path_tpc']['curves'][168]['res_coeff']
-['path_tpc']['curves'][168]['res_net']
-['path_tpc']['curves'][169]['offset']
+['path_tpc']['curves'][168]['res_net_meters']
+['path_tpc']['curves'][169]['offset_meters']
 ['path_tpc']['curves'][169]['res_coeff']
-['path_tpc']['curves'][169]['res_net']
-['path_tpc']['curves'][170]['offset']
+['path_tpc']['curves'][169]['res_net_meters']
+['path_tpc']['curves'][170]['offset_meters']
 ['path_tpc']['curves'][170]['res_coeff']
-['path_tpc']['curves'][170]['res_net']
-['path_tpc']['curves'][171]['offset']
+['path_tpc']['curves'][170]['res_net_meters']
+['path_tpc']['curves'][171]['offset_meters']
 ['path_tpc']['curves'][171]['res_coeff']
-['path_tpc']['curves'][171]['res_net']
-['path_tpc']['curves'][172]['offset']
+['path_tpc']['curves'][171]['res_net_meters']
+['path_tpc']['curves'][172]['offset_meters']
 ['path_tpc']['curves'][172]['res_coeff']
-['path_tpc']['curves'][172]['res_net']
-['path_tpc']['curves'][173]['offset']
+['path_tpc']['curves'][172]['res_net_meters']
+['path_tpc']['curves'][173]['offset_meters']
 ['path_tpc']['curves'][173]['res_coeff']
-['path_tpc']['curves'][173]['res_net']
-['path_tpc']['curves'][174]['offset']
+['path_tpc']['curves'][173]['res_net_meters']
+['path_tpc']['curves'][174]['offset_meters']
 ['path_tpc']['curves'][174]['res_coeff']
-['path_tpc']['curves'][174]['res_net']
-['path_tpc']['curves'][175]['offset']
+['path_tpc']['curves'][174]['res_net_meters']
+['path_tpc']['curves'][175]['offset_meters']
 ['path_tpc']['curves'][175]['res_coeff']
-['path_tpc']['curves'][175]['res_net']
-['path_tpc']['curves'][176]['offset']
+['path_tpc']['curves'][175]['res_net_meters']
+['path_tpc']['curves'][176]['offset_meters']
 ['path_tpc']['curves'][176]['res_coeff']
-['path_tpc']['curves'][176]['res_net']
-['path_tpc']['curves'][177]['offset']
+['path_tpc']['curves'][176]['res_net_meters']
+['path_tpc']['curves'][177]['offset_meters']
 ['path_tpc']['curves'][177]['res_coeff']
-['path_tpc']['curves'][177]['res_net']
-['path_tpc']['curves'][178]['offset']
+['path_tpc']['curves'][177]['res_net_meters']
+['path_tpc']['curves'][178]['offset_meters']
 ['path_tpc']['curves'][178]['res_coeff']
-['path_tpc']['curves'][178]['res_net']
-['path_tpc']['curves'][179]['offset']
+['path_tpc']['curves'][178]['res_net_meters']
+['path_tpc']['curves'][179]['offset_meters']
 ['path_tpc']['curves'][179]['res_coeff']
-['path_tpc']['curves'][179]['res_net']
-['path_tpc']['curves'][180]['offset']
+['path_tpc']['curves'][179]['res_net_meters']
+['path_tpc']['curves'][180]['offset_meters']
 ['path_tpc']['curves'][180]['res_coeff']
-['path_tpc']['curves'][180]['res_net']
-['path_tpc']['curves'][181]['offset']
+['path_tpc']['curves'][180]['res_net_meters']
+['path_tpc']['curves'][181]['offset_meters']
 ['path_tpc']['curves'][181]['res_coeff']
-['path_tpc']['curves'][181]['res_net']
-['path_tpc']['curves'][182]['offset']
+['path_tpc']['curves'][181]['res_net_meters']
+['path_tpc']['curves'][182]['offset_meters']
 ['path_tpc']['curves'][182]['res_coeff']
-['path_tpc']['curves'][182]['res_net']
-['path_tpc']['curves'][183]['offset']
+['path_tpc']['curves'][182]['res_net_meters']
+['path_tpc']['curves'][183]['offset_meters']
 ['path_tpc']['curves'][183]['res_coeff']
-['path_tpc']['curves'][183]['res_net']
-['path_tpc']['curves'][184]['offset']
+['path_tpc']['curves'][183]['res_net_meters']
+['path_tpc']['curves'][184]['offset_meters']
 ['path_tpc']['curves'][184]['res_coeff']
-['path_tpc']['curves'][184]['res_net']
-['path_tpc']['curves'][185]['offset']
+['path_tpc']['curves'][184]['res_net_meters']
+['path_tpc']['curves'][185]['offset_meters']
 ['path_tpc']['curves'][185]['res_coeff']
-['path_tpc']['curves'][185]['res_net']
-['path_tpc']['curves'][186]['offset']
+['path_tpc']['curves'][185]['res_net_meters']
+['path_tpc']['curves'][186]['offset_meters']
 ['path_tpc']['curves'][186]['res_coeff']
-['path_tpc']['curves'][186]['res_net']
-['path_tpc']['curves'][187]['offset']
+['path_tpc']['curves'][186]['res_net_meters']
+['path_tpc']['curves'][187]['offset_meters']
 ['path_tpc']['curves'][187]['res_coeff']
-['path_tpc']['curves'][187]['res_net']
-['path_tpc']['curves'][188]['offset']
+['path_tpc']['curves'][187]['res_net_meters']
+['path_tpc']['curves'][188]['offset_meters']
 ['path_tpc']['curves'][188]['res_coeff']
-['path_tpc']['curves'][188]['res_net']
-['path_tpc']['curves'][189]['offset']
+['path_tpc']['curves'][188]['res_net_meters']
+['path_tpc']['curves'][189]['offset_meters']
 ['path_tpc']['curves'][189]['res_coeff']
-['path_tpc']['curves'][189]['res_net']
-['path_tpc']['curves'][190]['offset']
+['path_tpc']['curves'][189]['res_net_meters']
+['path_tpc']['curves'][190]['offset_meters']
 ['path_tpc']['curves'][190]['res_coeff']
-['path_tpc']['curves'][190]['res_net']
-['path_tpc']['curves'][191]['offset']
+['path_tpc']['curves'][190]['res_net_meters']
+['path_tpc']['curves'][191]['offset_meters']
 ['path_tpc']['curves'][191]['res_coeff']
-['path_tpc']['curves'][191]['res_net']
-['path_tpc']['curves'][192]['offset']
+['path_tpc']['curves'][191]['res_net_meters']
+['path_tpc']['curves'][192]['offset_meters']
 ['path_tpc']['curves'][192]['res_coeff']
-['path_tpc']['curves'][192]['res_net']
-['path_tpc']['curves'][193]['offset']
+['path_tpc']['curves'][192]['res_net_meters']
+['path_tpc']['curves'][193]['offset_meters']
 ['path_tpc']['curves'][193]['res_coeff']
-['path_tpc']['curves'][193]['res_net']
-['path_tpc']['curves'][194]['offset']
+['path_tpc']['curves'][193]['res_net_meters']
+['path_tpc']['curves'][194]['offset_meters']
 ['path_tpc']['curves'][194]['res_coeff']
-['path_tpc']['curves'][194]['res_net']
-['path_tpc']['curves'][195]['offset']
+['path_tpc']['curves'][194]['res_net_meters']
+['path_tpc']['curves'][195]['offset_meters']
 ['path_tpc']['curves'][195]['res_coeff']
-['path_tpc']['curves'][195]['res_net']
-['path_tpc']['curves'][196]['offset']
+['path_tpc']['curves'][195]['res_net_meters']
+['path_tpc']['curves'][196]['offset_meters']
 ['path_tpc']['curves'][196]['res_coeff']
-['path_tpc']['curves'][196]['res_net']
-['path_tpc']['curves'][197]['offset']
+['path_tpc']['curves'][196]['res_net_meters']
+['path_tpc']['curves'][197]['offset_meters']
 ['path_tpc']['curves'][197]['res_coeff']
-['path_tpc']['curves'][197]['res_net']
-['path_tpc']['curves'][198]['offset']
+['path_tpc']['curves'][197]['res_net_meters']
+['path_tpc']['curves'][198]['offset_meters']
 ['path_tpc']['curves'][198]['res_coeff']
-['path_tpc']['curves'][198]['res_net']
-['path_tpc']['curves'][199]['offset']
+['path_tpc']['curves'][198]['res_net_meters']
+['path_tpc']['curves'][199]['offset_meters']
 ['path_tpc']['curves'][199]['res_coeff']
-['path_tpc']['curves'][199]['res_net']
-['path_tpc']['curves'][200]['offset']
+['path_tpc']['curves'][199]['res_net_meters']
+['path_tpc']['curves'][200]['offset_meters']
 ['path_tpc']['curves'][200]['res_coeff']
-['path_tpc']['curves'][200]['res_net']
-['path_tpc']['curves'][201]['offset']
+['path_tpc']['curves'][200]['res_net_meters']
+['path_tpc']['curves'][201]['offset_meters']
 ['path_tpc']['curves'][201]['res_coeff']
-['path_tpc']['curves'][201]['res_net']
-['path_tpc']['curves'][202]['offset']
+['path_tpc']['curves'][201]['res_net_meters']
+['path_tpc']['curves'][202]['offset_meters']
 ['path_tpc']['curves'][202]['res_coeff']
-['path_tpc']['curves'][202]['res_net']
-['path_tpc']['curves'][203]['offset']
+['path_tpc']['curves'][202]['res_net_meters']
+['path_tpc']['curves'][203]['offset_meters']
 ['path_tpc']['curves'][203]['res_coeff']
-['path_tpc']['curves'][203]['res_net']
-['path_tpc']['curves'][204]['offset']
+['path_tpc']['curves'][203]['res_net_meters']
+['path_tpc']['curves'][204]['offset_meters']
 ['path_tpc']['curves'][204]['res_coeff']
-['path_tpc']['curves'][204]['res_net']
-['path_tpc']['curves'][205]['offset']
+['path_tpc']['curves'][204]['res_net_meters']
+['path_tpc']['curves'][205]['offset_meters']
 ['path_tpc']['curves'][205]['res_coeff']
-['path_tpc']['curves'][205]['res_net']
-['path_tpc']['curves'][206]['offset']
+['path_tpc']['curves'][205]['res_net_meters']
+['path_tpc']['curves'][206]['offset_meters']
 ['path_tpc']['curves'][206]['res_coeff']
-['path_tpc']['curves'][206]['res_net']
-['path_tpc']['curves'][207]['offset']
+['path_tpc']['curves'][206]['res_net_meters']
+['path_tpc']['curves'][207]['offset_meters']
 ['path_tpc']['curves'][207]['res_coeff']
-['path_tpc']['curves'][207]['res_net']
-['path_tpc']['curves'][208]['offset']
+['path_tpc']['curves'][207]['res_net_meters']
+['path_tpc']['curves'][208]['offset_meters']
 ['path_tpc']['curves'][208]['res_coeff']
-['path_tpc']['curves'][208]['res_net']
-['path_tpc']['curves'][209]['offset']
+['path_tpc']['curves'][208]['res_net_meters']
+['path_tpc']['curves'][209]['offset_meters']
 ['path_tpc']['curves'][209]['res_coeff']
-['path_tpc']['curves'][209]['res_net']
-['path_tpc']['curves'][210]['offset']
+['path_tpc']['curves'][209]['res_net_meters']
+['path_tpc']['curves'][210]['offset_meters']
 ['path_tpc']['curves'][210]['res_coeff']
-['path_tpc']['curves'][210]['res_net']
-['path_tpc']['curves'][211]['offset']
+['path_tpc']['curves'][210]['res_net_meters']
+['path_tpc']['curves'][211]['offset_meters']
 ['path_tpc']['curves'][211]['res_coeff']
-['path_tpc']['curves'][211]['res_net']
-['path_tpc']['curves'][212]['offset']
+['path_tpc']['curves'][211]['res_net_meters']
+['path_tpc']['curves'][212]['offset_meters']
 ['path_tpc']['curves'][212]['res_coeff']
-['path_tpc']['curves'][212]['res_net']
-['path_tpc']['curves'][213]['offset']
+['path_tpc']['curves'][212]['res_net_meters']
+['path_tpc']['curves'][213]['offset_meters']
 ['path_tpc']['curves'][213]['res_coeff']
-['path_tpc']['curves'][213]['res_net']
-['path_tpc']['curves'][214]['offset']
+['path_tpc']['curves'][213]['res_net_meters']
+['path_tpc']['curves'][214]['offset_meters']
 ['path_tpc']['curves'][214]['res_coeff']
-['path_tpc']['curves'][214]['res_net']
-['path_tpc']['curves'][215]['offset']
+['path_tpc']['curves'][214]['res_net_meters']
+['path_tpc']['curves'][215]['offset_meters']
 ['path_tpc']['curves'][215]['res_coeff']
-['path_tpc']['curves'][215]['res_net']
-['path_tpc']['curves'][216]['offset']
+['path_tpc']['curves'][215]['res_net_meters']
+['path_tpc']['curves'][216]['offset_meters']
 ['path_tpc']['curves'][216]['res_coeff']
-['path_tpc']['curves'][216]['res_net']
-['path_tpc']['curves'][217]['offset']
+['path_tpc']['curves'][216]['res_net_meters']
+['path_tpc']['curves'][217]['offset_meters']
 ['path_tpc']['curves'][217]['res_coeff']
-['path_tpc']['curves'][217]['res_net']
-['path_tpc']['curves'][218]['offset']
+['path_tpc']['curves'][217]['res_net_meters']
+['path_tpc']['curves'][218]['offset_meters']
 ['path_tpc']['curves'][218]['res_coeff']
-['path_tpc']['curves'][218]['res_net']
-['path_tpc']['curves'][219]['offset']
+['path_tpc']['curves'][218]['res_net_meters']
+['path_tpc']['curves'][219]['offset_meters']
 ['path_tpc']['curves'][219]['res_coeff']
-['path_tpc']['curves'][219]['res_net']
-['path_tpc']['curves'][220]['offset']
+['path_tpc']['curves'][219]['res_net_meters']
+['path_tpc']['curves'][220]['offset_meters']
 ['path_tpc']['curves'][220]['res_coeff']
-['path_tpc']['curves'][220]['res_net']
-['path_tpc']['curves'][221]['offset']
+['path_tpc']['curves'][220]['res_net_meters']
+['path_tpc']['curves'][221]['offset_meters']
 ['path_tpc']['curves'][221]['res_coeff']
-['path_tpc']['curves'][221]['res_net']
-['path_tpc']['curves'][222]['offset']
+['path_tpc']['curves'][221]['res_net_meters']
+['path_tpc']['curves'][222]['offset_meters']
 ['path_tpc']['curves'][222]['res_coeff']
-['path_tpc']['curves'][222]['res_net']
-['path_tpc']['curves'][223]['offset']
+['path_tpc']['curves'][222]['res_net_meters']
+['path_tpc']['curves'][223]['offset_meters']
 ['path_tpc']['curves'][223]['res_coeff']
-['path_tpc']['curves'][223]['res_net']
-['path_tpc']['curves'][224]['offset']
+['path_tpc']['curves'][223]['res_net_meters']
+['path_tpc']['curves'][224]['offset_meters']
 ['path_tpc']['curves'][224]['res_coeff']
-['path_tpc']['curves'][224]['res_net']
-['path_tpc']['curves'][225]['offset']
+['path_tpc']['curves'][224]['res_net_meters']
+['path_tpc']['curves'][225]['offset_meters']
 ['path_tpc']['curves'][225]['res_coeff']
-['path_tpc']['curves'][225]['res_net']
-['path_tpc']['curves'][226]['offset']
+['path_tpc']['curves'][225]['res_net_meters']
+['path_tpc']['curves'][226]['offset_meters']
 ['path_tpc']['curves'][226]['res_coeff']
-['path_tpc']['curves'][226]['res_net']
-['path_tpc']['curves'][227]['offset']
+['path_tpc']['curves'][226]['res_net_meters']
+['path_tpc']['curves'][227]['offset_meters']
 ['path_tpc']['curves'][227]['res_coeff']
-['path_tpc']['curves'][227]['res_net']
-['path_tpc']['curves'][228]['offset']
+['path_tpc']['curves'][227]['res_net_meters']
+['path_tpc']['curves'][228]['offset_meters']
 ['path_tpc']['curves'][228]['res_coeff']
-['path_tpc']['curves'][228]['res_net']
-['path_tpc']['curves'][229]['offset']
+['path_tpc']['curves'][228]['res_net_meters']
+['path_tpc']['curves'][229]['offset_meters']
 ['path_tpc']['curves'][229]['res_coeff']
-['path_tpc']['curves'][229]['res_net']
-['path_tpc']['curves'][230]['offset']
+['path_tpc']['curves'][229]['res_net_meters']
+['path_tpc']['curves'][230]['offset_meters']
 ['path_tpc']['curves'][230]['res_coeff']
-['path_tpc']['curves'][230]['res_net']
-['path_tpc']['curves'][231]['offset']
+['path_tpc']['curves'][230]['res_net_meters']
+['path_tpc']['curves'][231]['offset_meters']
 ['path_tpc']['curves'][231]['res_coeff']
-['path_tpc']['curves'][231]['res_net']
-['path_tpc']['curves'][232]['offset']
+['path_tpc']['curves'][231]['res_net_meters']
+['path_tpc']['curves'][232]['offset_meters']
 ['path_tpc']['curves'][232]['res_coeff']
-['path_tpc']['curves'][232]['res_net']
-['path_tpc']['curves'][233]['offset']
+['path_tpc']['curves'][232]['res_net_meters']
+['path_tpc']['curves'][233]['offset_meters']
 ['path_tpc']['curves'][233]['res_coeff']
-['path_tpc']['curves'][233]['res_net']
-['path_tpc']['curves'][234]['offset']
+['path_tpc']['curves'][233]['res_net_meters']
+['path_tpc']['curves'][234]['offset_meters']
 ['path_tpc']['curves'][234]['res_coeff']
-['path_tpc']['curves'][234]['res_net']
-['path_tpc']['curves'][235]['offset']
+['path_tpc']['curves'][234]['res_net_meters']
+['path_tpc']['curves'][235]['offset_meters']
 ['path_tpc']['curves'][235]['res_coeff']
-['path_tpc']['curves'][235]['res_net']
-['path_tpc']['curves'][236]['offset']
+['path_tpc']['curves'][235]['res_net_meters']
+['path_tpc']['curves'][236]['offset_meters']
 ['path_tpc']['curves'][236]['res_coeff']
-['path_tpc']['curves'][236]['res_net']
-['path_tpc']['curves'][237]['offset']
+['path_tpc']['curves'][236]['res_net_meters']
+['path_tpc']['curves'][237]['offset_meters']
 ['path_tpc']['curves'][237]['res_coeff']
-['path_tpc']['curves'][237]['res_net']
-['path_tpc']['curves'][238]['offset']
+['path_tpc']['curves'][237]['res_net_meters']
+['path_tpc']['curves'][238]['offset_meters']
 ['path_tpc']['curves'][238]['res_coeff']
-['path_tpc']['curves'][238]['res_net']
-['path_tpc']['curves'][239]['offset']
+['path_tpc']['curves'][238]['res_net_meters']
+['path_tpc']['curves'][239]['offset_meters']
 ['path_tpc']['curves'][239]['res_coeff']
-['path_tpc']['curves'][239]['res_net']
-['path_tpc']['curves'][240]['offset']
+['path_tpc']['curves'][239]['res_net_meters']
+['path_tpc']['curves'][240]['offset_meters']
 ['path_tpc']['curves'][240]['res_coeff']
-['path_tpc']['curves'][240]['res_net']
-['path_tpc']['curves'][241]['offset']
+['path_tpc']['curves'][240]['res_net_meters']
+['path_tpc']['curves'][241]['offset_meters']
 ['path_tpc']['curves'][241]['res_coeff']
-['path_tpc']['curves'][241]['res_net']
-['path_tpc']['curves'][242]['offset']
+['path_tpc']['curves'][241]['res_net_meters']
+['path_tpc']['curves'][242]['offset_meters']
 ['path_tpc']['curves'][242]['res_coeff']
-['path_tpc']['curves'][242]['res_net']
-['path_tpc']['curves'][243]['offset']
+['path_tpc']['curves'][242]['res_net_meters']
+['path_tpc']['curves'][243]['offset_meters']
 ['path_tpc']['curves'][243]['res_coeff']
-['path_tpc']['curves'][243]['res_net']
-['path_tpc']['curves'][244]['offset']
+['path_tpc']['curves'][243]['res_net_meters']
+['path_tpc']['curves'][244]['offset_meters']
 ['path_tpc']['curves'][244]['res_coeff']
-['path_tpc']['curves'][244]['res_net']
-['path_tpc']['curves'][245]['offset']
+['path_tpc']['curves'][244]['res_net_meters']
+['path_tpc']['curves'][245]['offset_meters']
 ['path_tpc']['curves'][245]['res_coeff']
-['path_tpc']['curves'][245]['res_net']
-['path_tpc']['curves'][246]['offset']
+['path_tpc']['curves'][245]['res_net_meters']
+['path_tpc']['curves'][246]['offset_meters']
 ['path_tpc']['curves'][246]['res_coeff']
-['path_tpc']['curves'][246]['res_net']
-['path_tpc']['curves'][247]['offset']
+['path_tpc']['curves'][246]['res_net_meters']
+['path_tpc']['curves'][247]['offset_meters']
 ['path_tpc']['curves'][247]['res_coeff']
-['path_tpc']['curves'][247]['res_net']
-['path_tpc']['curves'][248]['offset']
+['path_tpc']['curves'][247]['res_net_meters']
+['path_tpc']['curves'][248]['offset_meters']
 ['path_tpc']['curves'][248]['res_coeff']
-['path_tpc']['curves'][248]['res_net']
-['path_tpc']['curves'][249]['offset']
+['path_tpc']['curves'][248]['res_net_meters']
+['path_tpc']['curves'][249]['offset_meters']
 ['path_tpc']['curves'][249]['res_coeff']
-['path_tpc']['curves'][249]['res_net']
-['path_tpc']['curves'][250]['offset']
+['path_tpc']['curves'][249]['res_net_meters']
+['path_tpc']['curves'][250]['offset_meters']
 ['path_tpc']['curves'][250]['res_coeff']
-['path_tpc']['curves'][250]['res_net']
-['path_tpc']['curves'][251]['offset']
+['path_tpc']['curves'][250]['res_net_meters']
+['path_tpc']['curves'][251]['offset_meters']
 ['path_tpc']['curves'][251]['res_coeff']
-['path_tpc']['curves'][251]['res_net']
-['path_tpc']['curves'][252]['offset']
+['path_tpc']['curves'][251]['res_net_meters']
+['path_tpc']['curves'][252]['offset_meters']
 ['path_tpc']['curves'][252]['res_coeff']
-['path_tpc']['curves'][252]['res_net']
-['path_tpc']['curves'][253]['offset']
+['path_tpc']['curves'][252]['res_net_meters']
+['path_tpc']['curves'][253]['offset_meters']
 ['path_tpc']['curves'][253]['res_coeff']
-['path_tpc']['curves'][253]['res_net']
-['path_tpc']['curves'][254]['offset']
+['path_tpc']['curves'][253]['res_net_meters']
+['path_tpc']['curves'][254]['offset_meters']
 ['path_tpc']['curves'][254]['res_coeff']
-['path_tpc']['curves'][254]['res_net']
-['path_tpc']['curves'][255]['offset']
+['path_tpc']['curves'][254]['res_net_meters']
+['path_tpc']['curves'][255]['offset_meters']
 ['path_tpc']['curves'][255]['res_coeff']
-['path_tpc']['curves'][255]['res_net']
-['path_tpc']['curves'][256]['offset']
+['path_tpc']['curves'][255]['res_net_meters']
+['path_tpc']['curves'][256]['offset_meters']
 ['path_tpc']['curves'][256]['res_coeff']
-['path_tpc']['curves'][256]['res_net']
-['path_tpc']['curves'][257]['offset']
+['path_tpc']['curves'][256]['res_net_meters']
+['path_tpc']['curves'][257]['offset_meters']
 ['path_tpc']['curves'][257]['res_coeff']
-['path_tpc']['curves'][257]['res_net']
-['path_tpc']['curves'][258]['offset']
+['path_tpc']['curves'][257]['res_net_meters']
+['path_tpc']['curves'][258]['offset_meters']
 ['path_tpc']['curves'][258]['res_coeff']
-['path_tpc']['curves'][258]['res_net']
-['path_tpc']['curves'][259]['offset']
+['path_tpc']['curves'][258]['res_net_meters']
+['path_tpc']['curves'][259]['offset_meters']
 ['path_tpc']['curves'][259]['res_coeff']
-['path_tpc']['curves'][259]['res_net']
-['path_tpc']['curves'][260]['offset']
+['path_tpc']['curves'][259]['res_net_meters']
+['path_tpc']['curves'][260]['offset_meters']
 ['path_tpc']['curves'][260]['res_coeff']
-['path_tpc']['curves'][260]['res_net']
-['path_tpc']['curves'][261]['offset']
+['path_tpc']['curves'][260]['res_net_meters']
+['path_tpc']['curves'][261]['offset_meters']
 ['path_tpc']['curves'][261]['res_coeff']
-['path_tpc']['curves'][261]['res_net']
-['path_tpc']['curves'][262]['offset']
+['path_tpc']['curves'][261]['res_net_meters']
+['path_tpc']['curves'][262]['offset_meters']
 ['path_tpc']['curves'][262]['res_coeff']
-['path_tpc']['curves'][262]['res_net']
-['path_tpc']['curves'][263]['offset']
+['path_tpc']['curves'][262]['res_net_meters']
+['path_tpc']['curves'][263]['offset_meters']
 ['path_tpc']['curves'][263]['res_coeff']
-['path_tpc']['curves'][263]['res_net']
-['path_tpc']['curves'][264]['offset']
+['path_tpc']['curves'][263]['res_net_meters']
+['path_tpc']['curves'][264]['offset_meters']
 ['path_tpc']['curves'][264]['res_coeff']
-['path_tpc']['curves'][264]['res_net']
-['path_tpc']['curves'][265]['offset']
+['path_tpc']['curves'][264]['res_net_meters']
+['path_tpc']['curves'][265]['offset_meters']
 ['path_tpc']['curves'][265]['res_coeff']
-['path_tpc']['curves'][265]['res_net']
-['path_tpc']['curves'][266]['offset']
+['path_tpc']['curves'][265]['res_net_meters']
+['path_tpc']['curves'][266]['offset_meters']
 ['path_tpc']['curves'][266]['res_coeff']
-['path_tpc']['curves'][266]['res_net']
-['path_tpc']['curves'][267]['offset']
+['path_tpc']['curves'][266]['res_net_meters']
+['path_tpc']['curves'][267]['offset_meters']
 ['path_tpc']['curves'][267]['res_coeff']
-['path_tpc']['curves'][267]['res_net']
-['path_tpc']['curves'][268]['offset']
+['path_tpc']['curves'][267]['res_net_meters']
+['path_tpc']['curves'][268]['offset_meters']
 ['path_tpc']['curves'][268]['res_coeff']
-['path_tpc']['curves'][268]['res_net']
-['path_tpc']['curves'][269]['offset']
+['path_tpc']['curves'][268]['res_net_meters']
+['path_tpc']['curves'][269]['offset_meters']
 ['path_tpc']['curves'][269]['res_coeff']
-['path_tpc']['curves'][269]['res_net']
-['path_tpc']['curves'][270]['offset']
+['path_tpc']['curves'][269]['res_net_meters']
+['path_tpc']['curves'][270]['offset_meters']
 ['path_tpc']['curves'][270]['res_coeff']
-['path_tpc']['curves'][270]['res_net']
-['path_tpc']['curves'][271]['offset']
+['path_tpc']['curves'][270]['res_net_meters']
+['path_tpc']['curves'][271]['offset_meters']
 ['path_tpc']['curves'][271]['res_coeff']
-['path_tpc']['curves'][271]['res_net']
-['path_tpc']['curves'][272]['offset']
+['path_tpc']['curves'][271]['res_net_meters']
+['path_tpc']['curves'][272]['offset_meters']
 ['path_tpc']['curves'][272]['res_coeff']
-['path_tpc']['curves'][272]['res_net']
-['path_tpc']['curves'][273]['offset']
+['path_tpc']['curves'][272]['res_net_meters']
+['path_tpc']['curves'][273]['offset_meters']
 ['path_tpc']['curves'][273]['res_coeff']
-['path_tpc']['curves'][273]['res_net']
-['path_tpc']['curves'][274]['offset']
+['path_tpc']['curves'][273]['res_net_meters']
+['path_tpc']['curves'][274]['offset_meters']
 ['path_tpc']['curves'][274]['res_coeff']
-['path_tpc']['curves'][274]['res_net']
-['path_tpc']['curves'][275]['offset']
+['path_tpc']['curves'][274]['res_net_meters']
+['path_tpc']['curves'][275]['offset_meters']
 ['path_tpc']['curves'][275]['res_coeff']
-['path_tpc']['curves'][275]['res_net']
-['path_tpc']['curves'][276]['offset']
+['path_tpc']['curves'][275]['res_net_meters']
+['path_tpc']['curves'][276]['offset_meters']
 ['path_tpc']['curves'][276]['res_coeff']
-['path_tpc']['curves'][276]['res_net']
-['path_tpc']['curves'][277]['offset']
+['path_tpc']['curves'][276]['res_net_meters']
+['path_tpc']['curves'][277]['offset_meters']
 ['path_tpc']['curves'][277]['res_coeff']
-['path_tpc']['curves'][277]['res_net']
-['path_tpc']['curves'][278]['offset']
+['path_tpc']['curves'][277]['res_net_meters']
+['path_tpc']['curves'][278]['offset_meters']
 ['path_tpc']['curves'][278]['res_coeff']
-['path_tpc']['curves'][278]['res_net']
-['path_tpc']['curves'][279]['offset']
+['path_tpc']['curves'][278]['res_net_meters']
+['path_tpc']['curves'][279]['offset_meters']
 ['path_tpc']['curves'][279]['res_coeff']
-['path_tpc']['curves'][279]['res_net']
-['path_tpc']['curves'][280]['offset']
+['path_tpc']['curves'][279]['res_net_meters']
+['path_tpc']['curves'][280]['offset_meters']
 ['path_tpc']['curves'][280]['res_coeff']
-['path_tpc']['curves'][280]['res_net']
-['path_tpc']['curves'][281]['offset']
+['path_tpc']['curves'][280]['res_net_meters']
+['path_tpc']['curves'][281]['offset_meters']
 ['path_tpc']['curves'][281]['res_coeff']
-['path_tpc']['curves'][281]['res_net']
-['path_tpc']['curves'][282]['offset']
+['path_tpc']['curves'][281]['res_net_meters']
+['path_tpc']['curves'][282]['offset_meters']
 ['path_tpc']['curves'][282]['res_coeff']
-['path_tpc']['curves'][282]['res_net']
-['path_tpc']['curves'][283]['offset']
+['path_tpc']['curves'][282]['res_net_meters']
+['path_tpc']['curves'][283]['offset_meters']
 ['path_tpc']['curves'][283]['res_coeff']
-['path_tpc']['curves'][283]['res_net']
-['path_tpc']['curves'][284]['offset']
+['path_tpc']['curves'][283]['res_net_meters']
+['path_tpc']['curves'][284]['offset_meters']
 ['path_tpc']['curves'][284]['res_coeff']
-['path_tpc']['curves'][284]['res_net']
-['path_tpc']['curves'][285]['offset']
+['path_tpc']['curves'][284]['res_net_meters']
+['path_tpc']['curves'][285]['offset_meters']
 ['path_tpc']['curves'][285]['res_coeff']
-['path_tpc']['curves'][285]['res_net']
-['path_tpc']['curves'][286]['offset']
+['path_tpc']['curves'][285]['res_net_meters']
+['path_tpc']['curves'][286]['offset_meters']
 ['path_tpc']['curves'][286]['res_coeff']
-['path_tpc']['curves'][286]['res_net']
-['path_tpc']['curves'][287]['offset']
+['path_tpc']['curves'][286]['res_net_meters']
+['path_tpc']['curves'][287]['offset_meters']
 ['path_tpc']['curves'][287]['res_coeff']
-['path_tpc']['curves'][287]['res_net']
-['path_tpc']['curves'][288]['offset']
+['path_tpc']['curves'][287]['res_net_meters']
+['path_tpc']['curves'][288]['offset_meters']
 ['path_tpc']['curves'][288]['res_coeff']
-['path_tpc']['curves'][288]['res_net']
-['path_tpc']['curves'][289]['offset']
+['path_tpc']['curves'][288]['res_net_meters']
+['path_tpc']['curves'][289]['offset_meters']
 ['path_tpc']['curves'][289]['res_coeff']
-['path_tpc']['curves'][289]['res_net']
-['path_tpc']['curves'][290]['offset']
+['path_tpc']['curves'][289]['res_net_meters']
+['path_tpc']['curves'][290]['offset_meters']
 ['path_tpc']['curves'][290]['res_coeff']
-['path_tpc']['curves'][290]['res_net']
-['path_tpc']['curves'][291]['offset']
+['path_tpc']['curves'][290]['res_net_meters']
+['path_tpc']['curves'][291]['offset_meters']
 ['path_tpc']['curves'][291]['res_coeff']
-['path_tpc']['curves'][291]['res_net']
-['path_tpc']['curves'][292]['offset']
+['path_tpc']['curves'][291]['res_net_meters']
+['path_tpc']['curves'][292]['offset_meters']
 ['path_tpc']['curves'][292]['res_coeff']
-['path_tpc']['curves'][292]['res_net']
-['path_tpc']['curves'][293]['offset']
+['path_tpc']['curves'][292]['res_net_meters']
+['path_tpc']['curves'][293]['offset_meters']
 ['path_tpc']['curves'][293]['res_coeff']
-['path_tpc']['curves'][293]['res_net']
-['path_tpc']['curves'][294]['offset']
+['path_tpc']['curves'][293]['res_net_meters']
+['path_tpc']['curves'][294]['offset_meters']
 ['path_tpc']['curves'][294]['res_coeff']
-['path_tpc']['curves'][294]['res_net']
-['path_tpc']['curves'][295]['offset']
+['path_tpc']['curves'][294]['res_net_meters']
+['path_tpc']['curves'][295]['offset_meters']
 ['path_tpc']['curves'][295]['res_coeff']
-['path_tpc']['curves'][295]['res_net']
-['path_tpc']['curves'][296]['offset']
+['path_tpc']['curves'][295]['res_net_meters']
+['path_tpc']['curves'][296]['offset_meters']
 ['path_tpc']['curves'][296]['res_coeff']
-['path_tpc']['curves'][296]['res_net']
-['path_tpc']['curves'][297]['offset']
+['path_tpc']['curves'][296]['res_net_meters']
+['path_tpc']['curves'][297]['offset_meters']
 ['path_tpc']['curves'][297]['res_coeff']
-['path_tpc']['curves'][297]['res_net']
-['path_tpc']['curves'][298]['offset']
+['path_tpc']['curves'][297]['res_net_meters']
+['path_tpc']['curves'][298]['offset_meters']
 ['path_tpc']['curves'][298]['res_coeff']
-['path_tpc']['curves'][298]['res_net']
-['path_tpc']['curves'][299]['offset']
+['path_tpc']['curves'][298]['res_net_meters']
+['path_tpc']['curves'][299]['offset_meters']
 ['path_tpc']['curves'][299]['res_coeff']
-['path_tpc']['curves'][299]['res_net']
-['path_tpc']['curves'][300]['offset']
+['path_tpc']['curves'][299]['res_net_meters']
+['path_tpc']['curves'][300]['offset_meters']
 ['path_tpc']['curves'][300]['res_coeff']
-['path_tpc']['curves'][300]['res_net']
-['path_tpc']['curves'][301]['offset']
+['path_tpc']['curves'][300]['res_net_meters']
+['path_tpc']['curves'][301]['offset_meters']
 ['path_tpc']['curves'][301]['res_coeff']
-['path_tpc']['curves'][301]['res_net']
-['path_tpc']['curves'][302]['offset']
+['path_tpc']['curves'][301]['res_net_meters']
+['path_tpc']['curves'][302]['offset_meters']
 ['path_tpc']['curves'][302]['res_coeff']
-['path_tpc']['curves'][302]['res_net']
-['path_tpc']['curves'][303]['offset']
+['path_tpc']['curves'][302]['res_net_meters']
+['path_tpc']['curves'][303]['offset_meters']
 ['path_tpc']['curves'][303]['res_coeff']
-['path_tpc']['curves'][303]['res_net']
-['path_tpc']['curves'][304]['offset']
+['path_tpc']['curves'][303]['res_net_meters']
+['path_tpc']['curves'][304]['offset_meters']
 ['path_tpc']['curves'][304]['res_coeff']
-['path_tpc']['curves'][304]['res_net']
-['path_tpc']['curves'][305]['offset']
+['path_tpc']['curves'][304]['res_net_meters']
+['path_tpc']['curves'][305]['offset_meters']
 ['path_tpc']['curves'][305]['res_coeff']
-['path_tpc']['curves'][305]['res_net']
-['path_tpc']['curves'][306]['offset']
+['path_tpc']['curves'][305]['res_net_meters']
+['path_tpc']['curves'][306]['offset_meters']
 ['path_tpc']['curves'][306]['res_coeff']
-['path_tpc']['curves'][306]['res_net']
-['path_tpc']['curves'][307]['offset']
+['path_tpc']['curves'][306]['res_net_meters']
+['path_tpc']['curves'][307]['offset_meters']
 ['path_tpc']['curves'][307]['res_coeff']
-['path_tpc']['curves'][307]['res_net']
-['path_tpc']['curves'][308]['offset']
+['path_tpc']['curves'][307]['res_net_meters']
+['path_tpc']['curves'][308]['offset_meters']
 ['path_tpc']['curves'][308]['res_coeff']
-['path_tpc']['curves'][308]['res_net']
-['path_tpc']['curves'][309]['offset']
+['path_tpc']['curves'][308]['res_net_meters']
+['path_tpc']['curves'][309]['offset_meters']
 ['path_tpc']['curves'][309]['res_coeff']
-['path_tpc']['curves'][309]['res_net']
-['path_tpc']['curves'][310]['offset']
+['path_tpc']['curves'][309]['res_net_meters']
+['path_tpc']['curves'][310]['offset_meters']
 ['path_tpc']['curves'][310]['res_coeff']
-['path_tpc']['curves'][310]['res_net']
-['path_tpc']['curves'][311]['offset']
+['path_tpc']['curves'][310]['res_net_meters']
+['path_tpc']['curves'][311]['offset_meters']
 ['path_tpc']['curves'][311]['res_coeff']
-['path_tpc']['curves'][311]['res_net']
-['path_tpc']['curves'][312]['offset']
+['path_tpc']['curves'][311]['res_net_meters']
+['path_tpc']['curves'][312]['offset_meters']
 ['path_tpc']['curves'][312]['res_coeff']
-['path_tpc']['curves'][312]['res_net']
-['path_tpc']['curves'][313]['offset']
+['path_tpc']['curves'][312]['res_net_meters']
+['path_tpc']['curves'][313]['offset_meters']
 ['path_tpc']['curves'][313]['res_coeff']
-['path_tpc']['curves'][313]['res_net']
-['path_tpc']['curves'][314]['offset']
+['path_tpc']['curves'][313]['res_net_meters']
+['path_tpc']['curves'][314]['offset_meters']
 ['path_tpc']['curves'][314]['res_coeff']
-['path_tpc']['curves'][314]['res_net']
-['path_tpc']['curves'][315]['offset']
+['path_tpc']['curves'][314]['res_net_meters']
+['path_tpc']['curves'][315]['offset_meters']
 ['path_tpc']['curves'][315]['res_coeff']
-['path_tpc']['curves'][315]['res_net']
-['path_tpc']['curves'][316]['offset']
+['path_tpc']['curves'][315]['res_net_meters']
+['path_tpc']['curves'][316]['offset_meters']
 ['path_tpc']['curves'][316]['res_coeff']
-['path_tpc']['curves'][316]['res_net']
-['path_tpc']['curves'][317]['offset']
+['path_tpc']['curves'][316]['res_net_meters']
+['path_tpc']['curves'][317]['offset_meters']
 ['path_tpc']['curves'][317]['res_coeff']
-['path_tpc']['curves'][317]['res_net']
-['path_tpc']['curves'][318]['offset']
+['path_tpc']['curves'][317]['res_net_meters']
+['path_tpc']['curves'][318]['offset_meters']
 ['path_tpc']['curves'][318]['res_coeff']
-['path_tpc']['curves'][318]['res_net']
-['path_tpc']['curves'][319]['offset']
+['path_tpc']['curves'][318]['res_net_meters']
+['path_tpc']['curves'][319]['offset_meters']
 ['path_tpc']['curves'][319]['res_coeff']
-['path_tpc']['curves'][319]['res_net']
-['path_tpc']['curves'][320]['offset']
+['path_tpc']['curves'][319]['res_net_meters']
+['path_tpc']['curves'][320]['offset_meters']
 ['path_tpc']['curves'][320]['res_coeff']
-['path_tpc']['curves'][320]['res_net']
-['path_tpc']['curves'][321]['offset']
+['path_tpc']['curves'][320]['res_net_meters']
+['path_tpc']['curves'][321]['offset_meters']
 ['path_tpc']['curves'][321]['res_coeff']
-['path_tpc']['curves'][321]['res_net']
-['path_tpc']['curves'][322]['offset']
+['path_tpc']['curves'][321]['res_net_meters']
+['path_tpc']['curves'][322]['offset_meters']
 ['path_tpc']['curves'][322]['res_coeff']
-['path_tpc']['curves'][322]['res_net']
-['path_tpc']['curves'][323]['offset']
+['path_tpc']['curves'][322]['res_net_meters']
+['path_tpc']['curves'][323]['offset_meters']
 ['path_tpc']['curves'][323]['res_coeff']
-['path_tpc']['curves'][323]['res_net']
-['path_tpc']['curves'][324]['offset']
+['path_tpc']['curves'][323]['res_net_meters']
+['path_tpc']['curves'][324]['offset_meters']
 ['path_tpc']['curves'][324]['res_coeff']
-['path_tpc']['curves'][324]['res_net']
-['path_tpc']['curves'][325]['offset']
+['path_tpc']['curves'][324]['res_net_meters']
+['path_tpc']['curves'][325]['offset_meters']
 ['path_tpc']['curves'][325]['res_coeff']
-['path_tpc']['curves'][325]['res_net']
-['path_tpc']['curves'][326]['offset']
+['path_tpc']['curves'][325]['res_net_meters']
+['path_tpc']['curves'][326]['offset_meters']
 ['path_tpc']['curves'][326]['res_coeff']
-['path_tpc']['curves'][326]['res_net']
-['path_tpc']['curves'][327]['offset']
+['path_tpc']['curves'][326]['res_net_meters']
+['path_tpc']['curves'][327]['offset_meters']
 ['path_tpc']['curves'][327]['res_coeff']
-['path_tpc']['curves'][327]['res_net']
-['path_tpc']['curves'][328]['offset']
+['path_tpc']['curves'][327]['res_net_meters']
+['path_tpc']['curves'][328]['offset_meters']
 ['path_tpc']['curves'][328]['res_coeff']
-['path_tpc']['curves'][328]['res_net']
-['path_tpc']['curves'][329]['offset']
+['path_tpc']['curves'][328]['res_net_meters']
+['path_tpc']['curves'][329]['offset_meters']
 ['path_tpc']['curves'][329]['res_coeff']
-['path_tpc']['curves'][329]['res_net']
-['path_tpc']['curves'][330]['offset']
+['path_tpc']['curves'][329]['res_net_meters']
+['path_tpc']['curves'][330]['offset_meters']
 ['path_tpc']['curves'][330]['res_coeff']
-['path_tpc']['curves'][330]['res_net']
-['path_tpc']['curves'][331]['offset']
+['path_tpc']['curves'][330]['res_net_meters']
+['path_tpc']['curves'][331]['offset_meters']
 ['path_tpc']['curves'][331]['res_coeff']
-['path_tpc']['curves'][331]['res_net']
-['path_tpc']['curves'][332]['offset']
+['path_tpc']['curves'][331]['res_net_meters']
+['path_tpc']['curves'][332]['offset_meters']
 ['path_tpc']['curves'][332]['res_coeff']
-['path_tpc']['curves'][332]['res_net']
-['path_tpc']['curves'][333]['offset']
+['path_tpc']['curves'][332]['res_net_meters']
+['path_tpc']['curves'][333]['offset_meters']
 ['path_tpc']['curves'][333]['res_coeff']
-['path_tpc']['curves'][333]['res_net']
-['path_tpc']['curves'][334]['offset']
+['path_tpc']['curves'][333]['res_net_meters']
+['path_tpc']['curves'][334]['offset_meters']
 ['path_tpc']['curves'][334]['res_coeff']
-['path_tpc']['curves'][334]['res_net']
-['path_tpc']['curves'][335]['offset']
+['path_tpc']['curves'][334]['res_net_meters']
+['path_tpc']['curves'][335]['offset_meters']
 ['path_tpc']['curves'][335]['res_coeff']
-['path_tpc']['curves'][335]['res_net']
-['path_tpc']['curves'][336]['offset']
+['path_tpc']['curves'][335]['res_net_meters']
+['path_tpc']['curves'][336]['offset_meters']
 ['path_tpc']['curves'][336]['res_coeff']
-['path_tpc']['curves'][336]['res_net']
-['path_tpc']['curves'][337]['offset']
+['path_tpc']['curves'][336]['res_net_meters']
+['path_tpc']['curves'][337]['offset_meters']
 ['path_tpc']['curves'][337]['res_coeff']
-['path_tpc']['curves'][337]['res_net']
-['path_tpc']['curves'][338]['offset']
+['path_tpc']['curves'][337]['res_net_meters']
+['path_tpc']['curves'][338]['offset_meters']
 ['path_tpc']['curves'][338]['res_coeff']
-['path_tpc']['curves'][338]['res_net']
-['path_tpc']['curves'][339]['offset']
+['path_tpc']['curves'][338]['res_net_meters']
+['path_tpc']['curves'][339]['offset_meters']
 ['path_tpc']['curves'][339]['res_coeff']
-['path_tpc']['curves'][339]['res_net']
-['path_tpc']['curves'][340]['offset']
+['path_tpc']['curves'][339]['res_net_meters']
+['path_tpc']['curves'][340]['offset_meters']
 ['path_tpc']['curves'][340]['res_coeff']
-['path_tpc']['curves'][340]['res_net']
-['path_tpc']['curves'][341]['offset']
+['path_tpc']['curves'][340]['res_net_meters']
+['path_tpc']['curves'][341]['offset_meters']
 ['path_tpc']['curves'][341]['res_coeff']
-['path_tpc']['curves'][341]['res_net']
-['path_tpc']['curves'][342]['offset']
+['path_tpc']['curves'][341]['res_net_meters']
+['path_tpc']['curves'][342]['offset_meters']
 ['path_tpc']['curves'][342]['res_coeff']
-['path_tpc']['curves'][342]['res_net']
-['path_tpc']['curves'][343]['offset']
+['path_tpc']['curves'][342]['res_net_meters']
+['path_tpc']['curves'][343]['offset_meters']
 ['path_tpc']['curves'][343]['res_coeff']
-['path_tpc']['curves'][343]['res_net']
-['path_tpc']['curves'][344]['offset']
+['path_tpc']['curves'][343]['res_net_meters']
+['path_tpc']['curves'][344]['offset_meters']
 ['path_tpc']['curves'][344]['res_coeff']
-['path_tpc']['curves'][344]['res_net']
-['path_tpc']['curves'][345]['offset']
+['path_tpc']['curves'][344]['res_net_meters']
+['path_tpc']['curves'][345]['offset_meters']
 ['path_tpc']['curves'][345]['res_coeff']
-['path_tpc']['curves'][345]['res_net']
-['path_tpc']['curves'][346]['offset']
+['path_tpc']['curves'][345]['res_net_meters']
+['path_tpc']['curves'][346]['offset_meters']
 ['path_tpc']['curves'][346]['res_coeff']
-['path_tpc']['curves'][346]['res_net']
-['path_tpc']['curves'][347]['offset']
+['path_tpc']['curves'][346]['res_net_meters']
+['path_tpc']['curves'][347]['offset_meters']
 ['path_tpc']['curves'][347]['res_coeff']
-['path_tpc']['curves'][347]['res_net']
-['path_tpc']['curves'][348]['offset']
+['path_tpc']['curves'][347]['res_net_meters']
+['path_tpc']['curves'][348]['offset_meters']
 ['path_tpc']['curves'][348]['res_coeff']
-['path_tpc']['curves'][348]['res_net']
-['path_tpc']['curves'][349]['offset']
+['path_tpc']['curves'][348]['res_net_meters']
+['path_tpc']['curves'][349]['offset_meters']
 ['path_tpc']['curves'][349]['res_coeff']
-['path_tpc']['curves'][349]['res_net']
-['path_tpc']['curves'][350]['offset']
+['path_tpc']['curves'][349]['res_net_meters']
+['path_tpc']['curves'][350]['offset_meters']
 ['path_tpc']['curves'][350]['res_coeff']
-['path_tpc']['curves'][350]['res_net']
-['path_tpc']['curves'][351]['offset']
+['path_tpc']['curves'][350]['res_net_meters']
+['path_tpc']['curves'][351]['offset_meters']
 ['path_tpc']['curves'][351]['res_coeff']
-['path_tpc']['curves'][351]['res_net']
-['path_tpc']['curves'][352]['offset']
+['path_tpc']['curves'][351]['res_net_meters']
+['path_tpc']['curves'][352]['offset_meters']
 ['path_tpc']['curves'][352]['res_coeff']
-['path_tpc']['curves'][352]['res_net']
-['path_tpc']['curves'][353]['offset']
+['path_tpc']['curves'][352]['res_net_meters']
+['path_tpc']['curves'][353]['offset_meters']
 ['path_tpc']['curves'][353]['res_coeff']
-['path_tpc']['curves'][353]['res_net']
-['path_tpc']['curves'][354]['offset']
+['path_tpc']['curves'][353]['res_net_meters']
+['path_tpc']['curves'][354]['offset_meters']
 ['path_tpc']['curves'][354]['res_coeff']
-['path_tpc']['curves'][354]['res_net']
-['path_tpc']['curves'][355]['offset']
+['path_tpc']['curves'][354]['res_net_meters']
+['path_tpc']['curves'][355]['offset_meters']
 ['path_tpc']['curves'][355]['res_coeff']
-['path_tpc']['curves'][355]['res_net']
-['path_tpc']['curves'][356]['offset']
+['path_tpc']['curves'][355]['res_net_meters']
+['path_tpc']['curves'][356]['offset_meters']
 ['path_tpc']['curves'][356]['res_coeff']
-['path_tpc']['curves'][356]['res_net']
-['path_tpc']['curves'][357]['offset']
+['path_tpc']['curves'][356]['res_net_meters']
+['path_tpc']['curves'][357]['offset_meters']
 ['path_tpc']['curves'][357]['res_coeff']
-['path_tpc']['curves'][357]['res_net']
-['path_tpc']['curves'][358]['offset']
+['path_tpc']['curves'][357]['res_net_meters']
+['path_tpc']['curves'][358]['offset_meters']
 ['path_tpc']['curves'][358]['res_coeff']
-['path_tpc']['curves'][358]['res_net']
-['path_tpc']['curves'][359]['offset']
+['path_tpc']['curves'][358]['res_net_meters']
+['path_tpc']['curves'][359]['offset_meters']
 ['path_tpc']['curves'][359]['res_coeff']
-['path_tpc']['curves'][359]['res_net']
-['path_tpc']['curves'][360]['offset']
+['path_tpc']['curves'][359]['res_net_meters']
+['path_tpc']['curves'][360]['offset_meters']
 ['path_tpc']['curves'][360]['res_coeff']
-['path_tpc']['curves'][360]['res_net']
-['path_tpc']['curves'][361]['offset']
+['path_tpc']['curves'][360]['res_net_meters']
+['path_tpc']['curves'][361]['offset_meters']
 ['path_tpc']['curves'][361]['res_coeff']
-['path_tpc']['curves'][361]['res_net']
-['path_tpc']['curves'][362]['offset']
+['path_tpc']['curves'][361]['res_net_meters']
+['path_tpc']['curves'][362]['offset_meters']
 ['path_tpc']['curves'][362]['res_coeff']
-['path_tpc']['curves'][362]['res_net']
-['path_tpc']['curves'][363]['offset']
+['path_tpc']['curves'][362]['res_net_meters']
+['path_tpc']['curves'][363]['offset_meters']
 ['path_tpc']['curves'][363]['res_coeff']
-['path_tpc']['curves'][363]['res_net']
-['path_tpc']['curves'][364]['offset']
+['path_tpc']['curves'][363]['res_net_meters']
+['path_tpc']['curves'][364]['offset_meters']
 ['path_tpc']['curves'][364]['res_coeff']
-['path_tpc']['curves'][364]['res_net']
-['path_tpc']['curves'][365]['offset']
+['path_tpc']['curves'][364]['res_net_meters']
+['path_tpc']['curves'][365]['offset_meters']
 ['path_tpc']['curves'][365]['res_coeff']
-['path_tpc']['curves'][365]['res_net']
-['path_tpc']['curves'][366]['offset']
+['path_tpc']['curves'][365]['res_net_meters']
+['path_tpc']['curves'][366]['offset_meters']
 ['path_tpc']['curves'][366]['res_coeff']
-['path_tpc']['curves'][366]['res_net']
-['path_tpc']['curves'][367]['offset']
+['path_tpc']['curves'][366]['res_net_meters']
+['path_tpc']['curves'][367]['offset_meters']
 ['path_tpc']['curves'][367]['res_coeff']
-['path_tpc']['curves'][367]['res_net']
-['path_tpc']['curves'][368]['offset']
+['path_tpc']['curves'][367]['res_net_meters']
+['path_tpc']['curves'][368]['offset_meters']
 ['path_tpc']['curves'][368]['res_coeff']
-['path_tpc']['curves'][368]['res_net']
-['path_tpc']['curves'][369]['offset']
+['path_tpc']['curves'][368]['res_net_meters']
+['path_tpc']['curves'][369]['offset_meters']
 ['path_tpc']['curves'][369]['res_coeff']
-['path_tpc']['curves'][369]['res_net']
-['path_tpc']['curves'][370]['offset']
+['path_tpc']['curves'][369]['res_net_meters']
+['path_tpc']['curves'][370]['offset_meters']
 ['path_tpc']['curves'][370]['res_coeff']
-['path_tpc']['curves'][370]['res_net']
-['path_tpc']['curves'][371]['offset']
+['path_tpc']['curves'][370]['res_net_meters']
+['path_tpc']['curves'][371]['offset_meters']
 ['path_tpc']['curves'][371]['res_coeff']
-['path_tpc']['curves'][371]['res_net']
-['path_tpc']['curves'][372]['offset']
+['path_tpc']['curves'][371]['res_net_meters']
+['path_tpc']['curves'][372]['offset_meters']
 ['path_tpc']['curves'][372]['res_coeff']
-['path_tpc']['curves'][372]['res_net']
-['path_tpc']['curves'][373]['offset']
+['path_tpc']['curves'][372]['res_net_meters']
+['path_tpc']['curves'][373]['offset_meters']
 ['path_tpc']['curves'][373]['res_coeff']
-['path_tpc']['curves'][373]['res_net']
-['path_tpc']['curves'][374]['offset']
+['path_tpc']['curves'][373]['res_net_meters']
+['path_tpc']['curves'][374]['offset_meters']
 ['path_tpc']['curves'][374]['res_coeff']
-['path_tpc']['curves'][374]['res_net']
-['path_tpc']['curves'][375]['offset']
+['path_tpc']['curves'][374]['res_net_meters']
+['path_tpc']['curves'][375]['offset_meters']
 ['path_tpc']['curves'][375]['res_coeff']
-['path_tpc']['curves'][375]['res_net']
-['path_tpc']['curves'][376]['offset']
+['path_tpc']['curves'][375]['res_net_meters']
+['path_tpc']['curves'][376]['offset_meters']
 ['path_tpc']['curves'][376]['res_coeff']
-['path_tpc']['curves'][376]['res_net']
-['path_tpc']['curves'][377]['offset']
+['path_tpc']['curves'][376]['res_net_meters']
+['path_tpc']['curves'][377]['offset_meters']
 ['path_tpc']['curves'][377]['res_coeff']
-['path_tpc']['curves'][377]['res_net']
-['path_tpc']['curves'][378]['offset']
+['path_tpc']['curves'][377]['res_net_meters']
+['path_tpc']['curves'][378]['offset_meters']
 ['path_tpc']['curves'][378]['res_coeff']
-['path_tpc']['curves'][378]['res_net']
-['path_tpc']['curves'][379]['offset']
+['path_tpc']['curves'][378]['res_net_meters']
+['path_tpc']['curves'][379]['offset_meters']
 ['path_tpc']['curves'][379]['res_coeff']
-['path_tpc']['curves'][379]['res_net']
-['path_tpc']['curves'][380]['offset']
+['path_tpc']['curves'][379]['res_net_meters']
+['path_tpc']['curves'][380]['offset_meters']
 ['path_tpc']['curves'][380]['res_coeff']
-['path_tpc']['curves'][380]['res_net']
-['path_tpc']['curves'][381]['offset']
+['path_tpc']['curves'][380]['res_net_meters']
+['path_tpc']['curves'][381]['offset_meters']
 ['path_tpc']['curves'][381]['res_coeff']
-['path_tpc']['curves'][381]['res_net']
-['path_tpc']['curves'][382]['offset']
+['path_tpc']['curves'][381]['res_net_meters']
+['path_tpc']['curves'][382]['offset_meters']
 ['path_tpc']['curves'][382]['res_coeff']
-['path_tpc']['curves'][382]['res_net']
-['path_tpc']['curves'][383]['offset']
+['path_tpc']['curves'][382]['res_net_meters']
+['path_tpc']['curves'][383]['offset_meters']
 ['path_tpc']['curves'][383]['res_coeff']
-['path_tpc']['curves'][383]['res_net']
-['path_tpc']['curves'][384]['offset']
+['path_tpc']['curves'][383]['res_net_meters']
+['path_tpc']['curves'][384]['offset_meters']
 ['path_tpc']['curves'][384]['res_coeff']
-['path_tpc']['curves'][384]['res_net']
-['path_tpc']['curves'][385]['offset']
+['path_tpc']['curves'][384]['res_net_meters']
+['path_tpc']['curves'][385]['offset_meters']
 ['path_tpc']['curves'][385]['res_coeff']
-['path_tpc']['curves'][385]['res_net']
-['path_tpc']['curves'][386]['offset']
+['path_tpc']['curves'][385]['res_net_meters']
+['path_tpc']['curves'][386]['offset_meters']
 ['path_tpc']['curves'][386]['res_coeff']
-['path_tpc']['curves'][386]['res_net']
-['path_tpc']['curves'][387]['offset']
+['path_tpc']['curves'][386]['res_net_meters']
+['path_tpc']['curves'][387]['offset_meters']
 ['path_tpc']['curves'][387]['res_coeff']
-['path_tpc']['curves'][387]['res_net']
-['path_tpc']['curves'][388]['offset']
+['path_tpc']['curves'][387]['res_net_meters']
+['path_tpc']['curves'][388]['offset_meters']
 ['path_tpc']['curves'][388]['res_coeff']
-['path_tpc']['curves'][388]['res_net']
-['path_tpc']['curves'][389]['offset']
+['path_tpc']['curves'][388]['res_net_meters']
+['path_tpc']['curves'][389]['offset_meters']
 ['path_tpc']['curves'][389]['res_coeff']
-['path_tpc']['curves'][389]['res_net']
-['path_tpc']['curves'][390]['offset']
+['path_tpc']['curves'][389]['res_net_meters']
+['path_tpc']['curves'][390]['offset_meters']
 ['path_tpc']['curves'][390]['res_coeff']
-['path_tpc']['curves'][390]['res_net']
-['path_tpc']['curves'][391]['offset']
+['path_tpc']['curves'][390]['res_net_meters']
+['path_tpc']['curves'][391]['offset_meters']
 ['path_tpc']['curves'][391]['res_coeff']
-['path_tpc']['curves'][391]['res_net']
-['path_tpc']['curves'][392]['offset']
+['path_tpc']['curves'][391]['res_net_meters']
+['path_tpc']['curves'][392]['offset_meters']
 ['path_tpc']['curves'][392]['res_coeff']
-['path_tpc']['curves'][392]['res_net']
-['path_tpc']['curves'][393]['offset']
+['path_tpc']['curves'][392]['res_net_meters']
+['path_tpc']['curves'][393]['offset_meters']
 ['path_tpc']['curves'][393]['res_coeff']
-['path_tpc']['curves'][393]['res_net']
-['path_tpc']['curves'][394]['offset']
+['path_tpc']['curves'][393]['res_net_meters']
+['path_tpc']['curves'][394]['offset_meters']
 ['path_tpc']['curves'][394]['res_coeff']
-['path_tpc']['curves'][394]['res_net']
-['path_tpc']['curves'][395]['offset']
+['path_tpc']['curves'][394]['res_net_meters']
+['path_tpc']['curves'][395]['offset_meters']
 ['path_tpc']['curves'][395]['res_coeff']
-['path_tpc']['curves'][395]['res_net']
-['path_tpc']['curves'][396]['offset']
+['path_tpc']['curves'][395]['res_net_meters']
+['path_tpc']['curves'][396]['offset_meters']
 ['path_tpc']['curves'][396]['res_coeff']
-['path_tpc']['curves'][396]['res_net']
-['path_tpc']['curves'][397]['offset']
+['path_tpc']['curves'][396]['res_net_meters']
+['path_tpc']['curves'][397]['offset_meters']
 ['path_tpc']['curves'][397]['res_coeff']
-['path_tpc']['curves'][397]['res_net']
-['path_tpc']['curves'][398]['offset']
+['path_tpc']['curves'][397]['res_net_meters']
+['path_tpc']['curves'][398]['offset_meters']
 ['path_tpc']['curves'][398]['res_coeff']
-['path_tpc']['curves'][398]['res_net']
-['path_tpc']['curves'][399]['offset']
+['path_tpc']['curves'][398]['res_net_meters']
+['path_tpc']['curves'][399]['offset_meters']
 ['path_tpc']['curves'][399]['res_coeff']
-['path_tpc']['curves'][399]['res_net']
-['path_tpc']['curves'][400]['offset']
+['path_tpc']['curves'][399]['res_net_meters']
+['path_tpc']['curves'][400]['offset_meters']
 ['path_tpc']['curves'][400]['res_coeff']
-['path_tpc']['curves'][400]['res_net']
-['path_tpc']['curves'][401]['offset']
+['path_tpc']['curves'][400]['res_net_meters']
+['path_tpc']['curves'][401]['offset_meters']
 ['path_tpc']['curves'][401]['res_coeff']
-['path_tpc']['curves'][401]['res_net']
-['path_tpc']['curves'][402]['offset']
+['path_tpc']['curves'][401]['res_net_meters']
+['path_tpc']['curves'][402]['offset_meters']
 ['path_tpc']['curves'][402]['res_coeff']
-['path_tpc']['curves'][402]['res_net']
-['path_tpc']['curves'][403]['offset']
+['path_tpc']['curves'][402]['res_net_meters']
+['path_tpc']['curves'][403]['offset_meters']
 ['path_tpc']['curves'][403]['res_coeff']
-['path_tpc']['curves'][403]['res_net']
-['path_tpc']['curves'][404]['offset']
+['path_tpc']['curves'][403]['res_net_meters']
+['path_tpc']['curves'][404]['offset_meters']
 ['path_tpc']['curves'][404]['res_coeff']
-['path_tpc']['curves'][404]['res_net']
-['path_tpc']['curves'][405]['offset']
+['path_tpc']['curves'][404]['res_net_meters']
+['path_tpc']['curves'][405]['offset_meters']
 ['path_tpc']['curves'][405]['res_coeff']
-['path_tpc']['curves'][405]['res_net']
-['path_tpc']['curves'][406]['offset']
+['path_tpc']['curves'][405]['res_net_meters']
+['path_tpc']['curves'][406]['offset_meters']
 ['path_tpc']['curves'][406]['res_coeff']
-['path_tpc']['curves'][406]['res_net']
-['path_tpc']['curves'][407]['offset']
+['path_tpc']['curves'][406]['res_net_meters']
+['path_tpc']['curves'][407]['offset_meters']
 ['path_tpc']['curves'][407]['res_coeff']
-['path_tpc']['curves'][407]['res_net']
-['path_tpc']['curves'][408]['offset']
+['path_tpc']['curves'][407]['res_net_meters']
+['path_tpc']['curves'][408]['offset_meters']
 ['path_tpc']['curves'][408]['res_coeff']
-['path_tpc']['curves'][408]['res_net']
-['path_tpc']['curves'][409]['offset']
+['path_tpc']['curves'][408]['res_net_meters']
+['path_tpc']['curves'][409]['offset_meters']
 ['path_tpc']['curves'][409]['res_coeff']
-['path_tpc']['curves'][409]['res_net']
-['path_tpc']['curves'][410]['offset']
+['path_tpc']['curves'][409]['res_net_meters']
+['path_tpc']['curves'][410]['offset_meters']
 ['path_tpc']['curves'][410]['res_coeff']
-['path_tpc']['curves'][410]['res_net']
-['path_tpc']['curves'][411]['offset']
+['path_tpc']['curves'][410]['res_net_meters']
+['path_tpc']['curves'][411]['offset_meters']
 ['path_tpc']['curves'][411]['res_coeff']
-['path_tpc']['curves'][411]['res_net']
-['path_tpc']['curves'][412]['offset']
+['path_tpc']['curves'][411]['res_net_meters']
+['path_tpc']['curves'][412]['offset_meters']
 ['path_tpc']['curves'][412]['res_coeff']
-['path_tpc']['curves'][412]['res_net']
-['path_tpc']['curves'][413]['offset']
+['path_tpc']['curves'][412]['res_net_meters']
+['path_tpc']['curves'][413]['offset_meters']
 ['path_tpc']['curves'][413]['res_coeff']
-['path_tpc']['curves'][413]['res_net']
-['path_tpc']['curves'][414]['offset']
+['path_tpc']['curves'][413]['res_net_meters']
+['path_tpc']['curves'][414]['offset_meters']
 ['path_tpc']['curves'][414]['res_coeff']
-['path_tpc']['curves'][414]['res_net']
-['path_tpc']['curves'][415]['offset']
+['path_tpc']['curves'][414]['res_net_meters']
+['path_tpc']['curves'][415]['offset_meters']
 ['path_tpc']['curves'][415]['res_coeff']
-['path_tpc']['curves'][415]['res_net']
-['path_tpc']['curves'][416]['offset']
+['path_tpc']['curves'][415]['res_net_meters']
+['path_tpc']['curves'][416]['offset_meters']
 ['path_tpc']['curves'][416]['res_coeff']
-['path_tpc']['curves'][416]['res_net']
-['path_tpc']['curves'][417]['offset']
+['path_tpc']['curves'][416]['res_net_meters']
+['path_tpc']['curves'][417]['offset_meters']
 ['path_tpc']['curves'][417]['res_coeff']
-['path_tpc']['curves'][417]['res_net']
-['path_tpc']['curves'][418]['offset']
+['path_tpc']['curves'][417]['res_net_meters']
+['path_tpc']['curves'][418]['offset_meters']
 ['path_tpc']['curves'][418]['res_coeff']
-['path_tpc']['curves'][418]['res_net']
-['path_tpc']['curves'][419]['offset']
+['path_tpc']['curves'][418]['res_net_meters']
+['path_tpc']['curves'][419]['offset_meters']
 ['path_tpc']['curves'][419]['res_coeff']
-['path_tpc']['curves'][419]['res_net']
-['path_tpc']['curves'][420]['offset']
+['path_tpc']['curves'][419]['res_net_meters']
+['path_tpc']['curves'][420]['offset_meters']
 ['path_tpc']['curves'][420]['res_coeff']
-['path_tpc']['curves'][420]['res_net']
-['path_tpc']['curves'][421]['offset']
+['path_tpc']['curves'][420]['res_net_meters']
+['path_tpc']['curves'][421]['offset_meters']
 ['path_tpc']['curves'][421]['res_coeff']
-['path_tpc']['curves'][421]['res_net']
-['path_tpc']['curves'][422]['offset']
+['path_tpc']['curves'][421]['res_net_meters']
+['path_tpc']['curves'][422]['offset_meters']
 ['path_tpc']['curves'][422]['res_coeff']
-['path_tpc']['curves'][422]['res_net']
-['path_tpc']['curves'][423]['offset']
+['path_tpc']['curves'][422]['res_net_meters']
+['path_tpc']['curves'][423]['offset_meters']
 ['path_tpc']['curves'][423]['res_coeff']
-['path_tpc']['curves'][423]['res_net']
-['path_tpc']['curves'][424]['offset']
+['path_tpc']['curves'][423]['res_net_meters']
+['path_tpc']['curves'][424]['offset_meters']
 ['path_tpc']['curves'][424]['res_coeff']
-['path_tpc']['curves'][424]['res_net']
-['path_tpc']['curves'][425]['offset']
+['path_tpc']['curves'][424]['res_net_meters']
+['path_tpc']['curves'][425]['offset_meters']
 ['path_tpc']['curves'][425]['res_coeff']
-['path_tpc']['curves'][425]['res_net']
-['path_tpc']['curves'][426]['offset']
+['path_tpc']['curves'][425]['res_net_meters']
+['path_tpc']['curves'][426]['offset_meters']
 ['path_tpc']['curves'][426]['res_coeff']
-['path_tpc']['curves'][426]['res_net']
-['path_tpc']['curves'][427]['offset']
+['path_tpc']['curves'][426]['res_net_meters']
+['path_tpc']['curves'][427]['offset_meters']
 ['path_tpc']['curves'][427]['res_coeff']
-['path_tpc']['curves'][427]['res_net']
-['path_tpc']['curves'][428]['offset']
+['path_tpc']['curves'][427]['res_net_meters']
+['path_tpc']['curves'][428]['offset_meters']
 ['path_tpc']['curves'][428]['res_coeff']
-['path_tpc']['curves'][428]['res_net']
-['path_tpc']['curves'][429]['offset']
+['path_tpc']['curves'][428]['res_net_meters']
+['path_tpc']['curves'][429]['offset_meters']
 ['path_tpc']['curves'][429]['res_coeff']
-['path_tpc']['curves'][429]['res_net']
-['path_tpc']['curves'][430]['offset']
+['path_tpc']['curves'][429]['res_net_meters']
+['path_tpc']['curves'][430]['offset_meters']
 ['path_tpc']['curves'][430]['res_coeff']
-['path_tpc']['curves'][430]['res_net']
-['path_tpc']['curves'][431]['offset']
+['path_tpc']['curves'][430]['res_net_meters']
+['path_tpc']['curves'][431]['offset_meters']
 ['path_tpc']['curves'][431]['res_coeff']
-['path_tpc']['curves'][431]['res_net']
-['path_tpc']['curves'][432]['offset']
+['path_tpc']['curves'][431]['res_net_meters']
+['path_tpc']['curves'][432]['offset_meters']
 ['path_tpc']['curves'][432]['res_coeff']
-['path_tpc']['curves'][432]['res_net']
-['path_tpc']['curves'][433]['offset']
+['path_tpc']['curves'][432]['res_net_meters']
+['path_tpc']['curves'][433]['offset_meters']
 ['path_tpc']['curves'][433]['res_coeff']
-['path_tpc']['curves'][433]['res_net']
-['path_tpc']['curves'][434]['offset']
+['path_tpc']['curves'][433]['res_net_meters']
+['path_tpc']['curves'][434]['offset_meters']
 ['path_tpc']['curves'][434]['res_coeff']
-['path_tpc']['curves'][434]['res_net']
-['path_tpc']['curves'][435]['offset']
+['path_tpc']['curves'][434]['res_net_meters']
+['path_tpc']['curves'][435]['offset_meters']
 ['path_tpc']['curves'][435]['res_coeff']
-['path_tpc']['curves'][435]['res_net']
-['path_tpc']['curves'][436]['offset']
+['path_tpc']['curves'][435]['res_net_meters']
+['path_tpc']['curves'][436]['offset_meters']
 ['path_tpc']['curves'][436]['res_coeff']
-['path_tpc']['curves'][436]['res_net']
-['path_tpc']['curves'][437]['offset']
+['path_tpc']['curves'][436]['res_net_meters']
+['path_tpc']['curves'][437]['offset_meters']
 ['path_tpc']['curves'][437]['res_coeff']
-['path_tpc']['curves'][437]['res_net']
-['path_tpc']['curves'][438]['offset']
+['path_tpc']['curves'][437]['res_net_meters']
+['path_tpc']['curves'][438]['offset_meters']
 ['path_tpc']['curves'][438]['res_coeff']
-['path_tpc']['curves'][438]['res_net']
-['path_tpc']['curves'][439]['offset']
+['path_tpc']['curves'][438]['res_net_meters']
+['path_tpc']['curves'][439]['offset_meters']
 ['path_tpc']['curves'][439]['res_coeff']
-['path_tpc']['curves'][439]['res_net']
-['path_tpc']['curves'][440]['offset']
+['path_tpc']['curves'][439]['res_net_meters']
+['path_tpc']['curves'][440]['offset_meters']
 ['path_tpc']['curves'][440]['res_coeff']
-['path_tpc']['curves'][440]['res_net']
-['path_tpc']['curves'][441]['offset']
+['path_tpc']['curves'][440]['res_net_meters']
+['path_tpc']['curves'][441]['offset_meters']
 ['path_tpc']['curves'][441]['res_coeff']
-['path_tpc']['curves'][441]['res_net']
-['path_tpc']['curves'][442]['offset']
+['path_tpc']['curves'][441]['res_net_meters']
+['path_tpc']['curves'][442]['offset_meters']
 ['path_tpc']['curves'][442]['res_coeff']
-['path_tpc']['curves'][442]['res_net']
-['path_tpc']['curves'][443]['offset']
+['path_tpc']['curves'][442]['res_net_meters']
+['path_tpc']['curves'][443]['offset_meters']
 ['path_tpc']['curves'][443]['res_coeff']
-['path_tpc']['curves'][443]['res_net']
-['path_tpc']['curves'][444]['offset']
+['path_tpc']['curves'][443]['res_net_meters']
+['path_tpc']['curves'][444]['offset_meters']
 ['path_tpc']['curves'][444]['res_coeff']
-['path_tpc']['curves'][444]['res_net']
-['path_tpc']['curves'][445]['offset']
+['path_tpc']['curves'][444]['res_net_meters']
+['path_tpc']['curves'][445]['offset_meters']
 ['path_tpc']['curves'][445]['res_coeff']
-['path_tpc']['curves'][445]['res_net']
-['path_tpc']['curves'][446]['offset']
+['path_tpc']['curves'][445]['res_net_meters']
+['path_tpc']['curves'][446]['offset_meters']
 ['path_tpc']['curves'][446]['res_coeff']
-['path_tpc']['curves'][446]['res_net']
-['path_tpc']['curves'][447]['offset']
+['path_tpc']['curves'][446]['res_net_meters']
+['path_tpc']['curves'][447]['offset_meters']
 ['path_tpc']['curves'][447]['res_coeff']
-['path_tpc']['curves'][447]['res_net']
-['path_tpc']['curves'][448]['offset']
+['path_tpc']['curves'][447]['res_net_meters']
+['path_tpc']['curves'][448]['offset_meters']
 ['path_tpc']['curves'][448]['res_coeff']
-['path_tpc']['curves'][448]['res_net']
-['path_tpc']['curves'][449]['offset']
+['path_tpc']['curves'][448]['res_net_meters']
+['path_tpc']['curves'][449]['offset_meters']
 ['path_tpc']['curves'][449]['res_coeff']
-['path_tpc']['curves'][449]['res_net']
-['path_tpc']['curves'][450]['offset']
+['path_tpc']['curves'][449]['res_net_meters']
+['path_tpc']['curves'][450]['offset_meters']
 ['path_tpc']['curves'][450]['res_coeff']
-['path_tpc']['curves'][450]['res_net']
-['path_tpc']['curves'][451]['offset']
+['path_tpc']['curves'][450]['res_net_meters']
+['path_tpc']['curves'][451]['offset_meters']
 ['path_tpc']['curves'][451]['res_coeff']
-['path_tpc']['curves'][451]['res_net']
-['path_tpc']['curves'][452]['offset']
+['path_tpc']['curves'][451]['res_net_meters']
+['path_tpc']['curves'][452]['offset_meters']
 ['path_tpc']['curves'][452]['res_coeff']
-['path_tpc']['curves'][452]['res_net']
-['path_tpc']['curves'][453]['offset']
+['path_tpc']['curves'][452]['res_net_meters']
+['path_tpc']['curves'][453]['offset_meters']
 ['path_tpc']['curves'][453]['res_coeff']
-['path_tpc']['curves'][453]['res_net']
-['path_tpc']['curves'][454]['offset']
+['path_tpc']['curves'][453]['res_net_meters']
+['path_tpc']['curves'][454]['offset_meters']
 ['path_tpc']['curves'][454]['res_coeff']
-['path_tpc']['curves'][454]['res_net']
-['path_tpc']['curves'][455]['offset']
+['path_tpc']['curves'][454]['res_net_meters']
+['path_tpc']['curves'][455]['offset_meters']
 ['path_tpc']['curves'][455]['res_coeff']
-['path_tpc']['curves'][455]['res_net']
-['path_tpc']['curves'][456]['offset']
+['path_tpc']['curves'][455]['res_net_meters']
+['path_tpc']['curves'][456]['offset_meters']
 ['path_tpc']['curves'][456]['res_coeff']
-['path_tpc']['curves'][456]['res_net']
-['path_tpc']['curves'][457]['offset']
+['path_tpc']['curves'][456]['res_net_meters']
+['path_tpc']['curves'][457]['offset_meters']
 ['path_tpc']['curves'][457]['res_coeff']
-['path_tpc']['curves'][457]['res_net']
-['path_tpc']['curves'][458]['offset']
+['path_tpc']['curves'][457]['res_net_meters']
+['path_tpc']['curves'][458]['offset_meters']
 ['path_tpc']['curves'][458]['res_coeff']
-['path_tpc']['curves'][458]['res_net']
-['path_tpc']['curves'][459]['offset']
+['path_tpc']['curves'][458]['res_net_meters']
+['path_tpc']['curves'][459]['offset_meters']
 ['path_tpc']['curves'][459]['res_coeff']
-['path_tpc']['curves'][459]['res_net']
-['path_tpc']['curves'][460]['offset']
+['path_tpc']['curves'][459]['res_net_meters']
+['path_tpc']['curves'][460]['offset_meters']
 ['path_tpc']['curves'][460]['res_coeff']
-['path_tpc']['curves'][460]['res_net']
-['path_tpc']['curves'][461]['offset']
+['path_tpc']['curves'][460]['res_net_meters']
+['path_tpc']['curves'][461]['offset_meters']
 ['path_tpc']['curves'][461]['res_coeff']
-['path_tpc']['curves'][461]['res_net']
-['path_tpc']['curves'][462]['offset']
+['path_tpc']['curves'][461]['res_net_meters']
+['path_tpc']['curves'][462]['offset_meters']
 ['path_tpc']['curves'][462]['res_coeff']
-['path_tpc']['curves'][462]['res_net']
-['path_tpc']['curves'][463]['offset']
+['path_tpc']['curves'][462]['res_net_meters']
+['path_tpc']['curves'][463]['offset_meters']
 ['path_tpc']['curves'][463]['res_coeff']
-['path_tpc']['curves'][463]['res_net']
-['path_tpc']['curves'][464]['offset']
+['path_tpc']['curves'][463]['res_net_meters']
+['path_tpc']['curves'][464]['offset_meters']
 ['path_tpc']['curves'][464]['res_coeff']
-['path_tpc']['curves'][464]['res_net']
-['path_tpc']['curves'][465]['offset']
+['path_tpc']['curves'][464]['res_net_meters']
+['path_tpc']['curves'][465]['offset_meters']
 ['path_tpc']['curves'][465]['res_coeff']
-['path_tpc']['curves'][465]['res_net']
-['path_tpc']['curves'][466]['offset']
+['path_tpc']['curves'][465]['res_net_meters']
+['path_tpc']['curves'][466]['offset_meters']
 ['path_tpc']['curves'][466]['res_coeff']
-['path_tpc']['curves'][466]['res_net']
-['path_tpc']['curves'][467]['offset']
+['path_tpc']['curves'][466]['res_net_meters']
+['path_tpc']['curves'][467]['offset_meters']
 ['path_tpc']['curves'][467]['res_coeff']
-['path_tpc']['curves'][467]['res_net']
-['path_tpc']['curves'][468]['offset']
+['path_tpc']['curves'][467]['res_net_meters']
+['path_tpc']['curves'][468]['offset_meters']
 ['path_tpc']['curves'][468]['res_coeff']
-['path_tpc']['curves'][468]['res_net']
-['path_tpc']['curves'][469]['offset']
+['path_tpc']['curves'][468]['res_net_meters']
+['path_tpc']['curves'][469]['offset_meters']
 ['path_tpc']['curves'][469]['res_coeff']
-['path_tpc']['curves'][469]['res_net']
-['path_tpc']['curves'][470]['offset']
+['path_tpc']['curves'][469]['res_net_meters']
+['path_tpc']['curves'][470]['offset_meters']
 ['path_tpc']['curves'][470]['res_coeff']
-['path_tpc']['curves'][470]['res_net']
-['path_tpc']['curves'][471]['offset']
+['path_tpc']['curves'][470]['res_net_meters']
+['path_tpc']['curves'][471]['offset_meters']
 ['path_tpc']['curves'][471]['res_coeff']
-['path_tpc']['curves'][471]['res_net']
-['path_tpc']['curves'][472]['offset']
+['path_tpc']['curves'][471]['res_net_meters']
+['path_tpc']['curves'][472]['offset_meters']
 ['path_tpc']['curves'][472]['res_coeff']
-['path_tpc']['curves'][472]['res_net']
-['path_tpc']['curves'][473]['offset']
+['path_tpc']['curves'][472]['res_net_meters']
+['path_tpc']['curves'][473]['offset_meters']
 ['path_tpc']['curves'][473]['res_coeff']
-['path_tpc']['curves'][473]['res_net']
-['path_tpc']['curves'][474]['offset']
+['path_tpc']['curves'][473]['res_net_meters']
+['path_tpc']['curves'][474]['offset_meters']
 ['path_tpc']['curves'][474]['res_coeff']
-['path_tpc']['curves'][474]['res_net']
-['path_tpc']['curves'][475]['offset']
+['path_tpc']['curves'][474]['res_net_meters']
+['path_tpc']['curves'][475]['offset_meters']
 ['path_tpc']['curves'][475]['res_coeff']
-['path_tpc']['curves'][475]['res_net']
-['path_tpc']['curves'][476]['offset']
+['path_tpc']['curves'][475]['res_net_meters']
+['path_tpc']['curves'][476]['offset_meters']
 ['path_tpc']['curves'][476]['res_coeff']
-['path_tpc']['curves'][476]['res_net']
-['path_tpc']['curves'][477]['offset']
+['path_tpc']['curves'][476]['res_net_meters']
+['path_tpc']['curves'][477]['offset_meters']
 ['path_tpc']['curves'][477]['res_coeff']
-['path_tpc']['curves'][477]['res_net']
-['path_tpc']['curves'][478]['offset']
+['path_tpc']['curves'][477]['res_net_meters']
+['path_tpc']['curves'][478]['offset_meters']
 ['path_tpc']['curves'][478]['res_coeff']
-['path_tpc']['curves'][478]['res_net']
-['path_tpc']['curves'][479]['offset']
+['path_tpc']['curves'][478]['res_net_meters']
+['path_tpc']['curves'][479]['offset_meters']
 ['path_tpc']['curves'][479]['res_coeff']
-['path_tpc']['curves'][479]['res_net']
-['path_tpc']['curves'][480]['offset']
+['path_tpc']['curves'][479]['res_net_meters']
+['path_tpc']['curves'][480]['offset_meters']
 ['path_tpc']['curves'][480]['res_coeff']
-['path_tpc']['curves'][480]['res_net']
-['path_tpc']['curves'][481]['offset']
+['path_tpc']['curves'][480]['res_net_meters']
+['path_tpc']['curves'][481]['offset_meters']
 ['path_tpc']['curves'][481]['res_coeff']
-['path_tpc']['curves'][481]['res_net']
-['path_tpc']['curves'][482]['offset']
+['path_tpc']['curves'][481]['res_net_meters']
+['path_tpc']['curves'][482]['offset_meters']
 ['path_tpc']['curves'][482]['res_coeff']
-['path_tpc']['curves'][482]['res_net']
-['path_tpc']['curves'][483]['offset']
+['path_tpc']['curves'][482]['res_net_meters']
+['path_tpc']['curves'][483]['offset_meters']
 ['path_tpc']['curves'][483]['res_coeff']
-['path_tpc']['curves'][483]['res_net']
-['path_tpc']['curves'][484]['offset']
+['path_tpc']['curves'][483]['res_net_meters']
+['path_tpc']['curves'][484]['offset_meters']
 ['path_tpc']['curves'][484]['res_coeff']
-['path_tpc']['curves'][484]['res_net']
-['path_tpc']['curves'][485]['offset']
+['path_tpc']['curves'][484]['res_net_meters']
+['path_tpc']['curves'][485]['offset_meters']
 ['path_tpc']['curves'][485]['res_coeff']
-['path_tpc']['curves'][485]['res_net']
-['path_tpc']['curves'][486]['offset']
+['path_tpc']['curves'][485]['res_net_meters']
+['path_tpc']['curves'][486]['offset_meters']
 ['path_tpc']['curves'][486]['res_coeff']
-['path_tpc']['curves'][486]['res_net']
-['path_tpc']['curves'][487]['offset']
+['path_tpc']['curves'][486]['res_net_meters']
+['path_tpc']['curves'][487]['offset_meters']
 ['path_tpc']['curves'][487]['res_coeff']
-['path_tpc']['curves'][487]['res_net']
-['path_tpc']['curves'][488]['offset']
+['path_tpc']['curves'][487]['res_net_meters']
+['path_tpc']['curves'][488]['offset_meters']
 ['path_tpc']['curves'][488]['res_coeff']
-['path_tpc']['curves'][488]['res_net']
-['path_tpc']['curves'][489]['offset']
+['path_tpc']['curves'][488]['res_net_meters']
+['path_tpc']['curves'][489]['offset_meters']
 ['path_tpc']['curves'][489]['res_coeff']
-['path_tpc']['curves'][489]['res_net']
-['path_tpc']['curves'][490]['offset']
+['path_tpc']['curves'][489]['res_net_meters']
+['path_tpc']['curves'][490]['offset_meters']
 ['path_tpc']['curves'][490]['res_coeff']
-['path_tpc']['curves'][490]['res_net']
-['path_tpc']['curves'][491]['offset']
+['path_tpc']['curves'][490]['res_net_meters']
+['path_tpc']['curves'][491]['offset_meters']
 ['path_tpc']['curves'][491]['res_coeff']
-['path_tpc']['curves'][491]['res_net']
-['path_tpc']['curves'][492]['offset']
+['path_tpc']['curves'][491]['res_net_meters']
+['path_tpc']['curves'][492]['offset_meters']
 ['path_tpc']['curves'][492]['res_coeff']
-['path_tpc']['curves'][492]['res_net']
-['path_tpc']['curves'][493]['offset']
+['path_tpc']['curves'][492]['res_net_meters']
+['path_tpc']['curves'][493]['offset_meters']
 ['path_tpc']['curves'][493]['res_coeff']
-['path_tpc']['curves'][493]['res_net']
-['path_tpc']['curves'][494]['offset']
+['path_tpc']['curves'][493]['res_net_meters']
+['path_tpc']['curves'][494]['offset_meters']
 ['path_tpc']['curves'][494]['res_coeff']
-['path_tpc']['curves'][494]['res_net']
-['path_tpc']['curves'][495]['offset']
+['path_tpc']['curves'][494]['res_net_meters']
+['path_tpc']['curves'][495]['offset_meters']
 ['path_tpc']['curves'][495]['res_coeff']
-['path_tpc']['curves'][495]['res_net']
-['path_tpc']['curves'][496]['offset']
+['path_tpc']['curves'][495]['res_net_meters']
+['path_tpc']['curves'][496]['offset_meters']
 ['path_tpc']['curves'][496]['res_coeff']
-['path_tpc']['curves'][496]['res_net']
-['path_tpc']['curves'][497]['offset']
+['path_tpc']['curves'][496]['res_net_meters']
+['path_tpc']['curves'][497]['offset_meters']
 ['path_tpc']['curves'][497]['res_coeff']
-['path_tpc']['curves'][497]['res_net']
-['path_tpc']['curves'][498]['offset']
+['path_tpc']['curves'][497]['res_net_meters']
+['path_tpc']['curves'][498]['offset_meters']
 ['path_tpc']['curves'][498]['res_coeff']
-['path_tpc']['curves'][498]['res_net']
-['path_tpc']['curves'][499]['offset']
+['path_tpc']['curves'][498]['res_net_meters']
+['path_tpc']['curves'][499]['offset_meters']
 ['path_tpc']['curves'][499]['res_coeff']
-['path_tpc']['curves'][499]['res_net']
-['path_tpc']['curves'][500]['offset']
+['path_tpc']['curves'][499]['res_net_meters']
+['path_tpc']['curves'][500]['offset_meters']
 ['path_tpc']['curves'][500]['res_coeff']
-['path_tpc']['curves'][500]['res_net']
-['path_tpc']['curves'][501]['offset']
+['path_tpc']['curves'][500]['res_net_meters']
+['path_tpc']['curves'][501]['offset_meters']
 ['path_tpc']['curves'][501]['res_coeff']
-['path_tpc']['curves'][501]['res_net']
-['path_tpc']['curves'][502]['offset']
+['path_tpc']['curves'][501]['res_net_meters']
+['path_tpc']['curves'][502]['offset_meters']
 ['path_tpc']['curves'][502]['res_coeff']
-['path_tpc']['curves'][502]['res_net']
-['path_tpc']['curves'][503]['offset']
+['path_tpc']['curves'][502]['res_net_meters']
+['path_tpc']['curves'][503]['offset_meters']
 ['path_tpc']['curves'][503]['res_coeff']
-['path_tpc']['curves'][503]['res_net']
-['path_tpc']['curves'][504]['offset']
+['path_tpc']['curves'][503]['res_net_meters']
+['path_tpc']['curves'][504]['offset_meters']
 ['path_tpc']['curves'][504]['res_coeff']
-['path_tpc']['curves'][504]['res_net']
-['path_tpc']['curves'][505]['offset']
+['path_tpc']['curves'][504]['res_net_meters']
+['path_tpc']['curves'][505]['offset_meters']
 ['path_tpc']['curves'][505]['res_coeff']
-['path_tpc']['curves'][505]['res_net']
-['path_tpc']['curves'][506]['offset']
+['path_tpc']['curves'][505]['res_net_meters']
+['path_tpc']['curves'][506]['offset_meters']
 ['path_tpc']['curves'][506]['res_coeff']
-['path_tpc']['curves'][506]['res_net']
-['path_tpc']['curves'][507]['offset']
+['path_tpc']['curves'][506]['res_net_meters']
+['path_tpc']['curves'][507]['offset_meters']
 ['path_tpc']['curves'][507]['res_coeff']
-['path_tpc']['curves'][507]['res_net']
-['path_tpc']['curves'][508]['offset']
+['path_tpc']['curves'][507]['res_net_meters']
+['path_tpc']['curves'][508]['offset_meters']
 ['path_tpc']['curves'][508]['res_coeff']
-['path_tpc']['curves'][508]['res_net']
-['path_tpc']['curves'][509]['offset']
+['path_tpc']['curves'][508]['res_net_meters']
+['path_tpc']['curves'][509]['offset_meters']
 ['path_tpc']['curves'][509]['res_coeff']
-['path_tpc']['curves'][509]['res_net']
-['path_tpc']['curves'][510]['offset']
+['path_tpc']['curves'][509]['res_net_meters']
+['path_tpc']['curves'][510]['offset_meters']
 ['path_tpc']['curves'][510]['res_coeff']
-['path_tpc']['curves'][510]['res_net']
-['path_tpc']['curves'][511]['offset']
+['path_tpc']['curves'][510]['res_net_meters']
+['path_tpc']['curves'][511]['offset_meters']
 ['path_tpc']['curves'][511]['res_coeff']
-['path_tpc']['curves'][511]['res_net']
-['path_tpc']['curves'][512]['offset']
+['path_tpc']['curves'][511]['res_net_meters']
+['path_tpc']['curves'][512]['offset_meters']
 ['path_tpc']['curves'][512]['res_coeff']
-['path_tpc']['curves'][512]['res_net']
-['path_tpc']['curves'][513]['offset']
+['path_tpc']['curves'][512]['res_net_meters']
+['path_tpc']['curves'][513]['offset_meters']
 ['path_tpc']['curves'][513]['res_coeff']
-['path_tpc']['curves'][513]['res_net']
-['path_tpc']['curves'][514]['offset']
+['path_tpc']['curves'][513]['res_net_meters']
+['path_tpc']['curves'][514]['offset_meters']
 ['path_tpc']['curves'][514]['res_coeff']
-['path_tpc']['curves'][514]['res_net']
-['path_tpc']['curves'][515]['offset']
+['path_tpc']['curves'][514]['res_net_meters']
+['path_tpc']['curves'][515]['offset_meters']
 ['path_tpc']['curves'][515]['res_coeff']
-['path_tpc']['curves'][515]['res_net']
-['path_tpc']['curves'][516]['offset']
+['path_tpc']['curves'][515]['res_net_meters']
+['path_tpc']['curves'][516]['offset_meters']
 ['path_tpc']['curves'][516]['res_coeff']
-['path_tpc']['curves'][516]['res_net']
-['path_tpc']['curves'][517]['offset']
+['path_tpc']['curves'][516]['res_net_meters']
+['path_tpc']['curves'][517]['offset_meters']
 ['path_tpc']['curves'][517]['res_coeff']
-['path_tpc']['curves'][517]['res_net']
-['path_tpc']['curves'][518]['offset']
+['path_tpc']['curves'][517]['res_net_meters']
+['path_tpc']['curves'][518]['offset_meters']
 ['path_tpc']['curves'][518]['res_coeff']
-['path_tpc']['curves'][518]['res_net']
-['path_tpc']['curves'][519]['offset']
+['path_tpc']['curves'][518]['res_net_meters']
+['path_tpc']['curves'][519]['offset_meters']
 ['path_tpc']['curves'][519]['res_coeff']
-['path_tpc']['curves'][519]['res_net']
-['path_tpc']['curves'][520]['offset']
+['path_tpc']['curves'][519]['res_net_meters']
+['path_tpc']['curves'][520]['offset_meters']
 ['path_tpc']['curves'][520]['res_coeff']
-['path_tpc']['curves'][520]['res_net']
-['path_tpc']['curves'][521]['offset']
+['path_tpc']['curves'][520]['res_net_meters']
+['path_tpc']['curves'][521]['offset_meters']
 ['path_tpc']['curves'][521]['res_coeff']
-['path_tpc']['curves'][521]['res_net']
-['path_tpc']['curves'][522]['offset']
+['path_tpc']['curves'][521]['res_net_meters']
+['path_tpc']['curves'][522]['offset_meters']
 ['path_tpc']['curves'][522]['res_coeff']
-['path_tpc']['curves'][522]['res_net']
-['path_tpc']['curves'][523]['offset']
+['path_tpc']['curves'][522]['res_net_meters']
+['path_tpc']['curves'][523]['offset_meters']
 ['path_tpc']['curves'][523]['res_coeff']
-['path_tpc']['curves'][523]['res_net']
-['path_tpc']['curves'][524]['offset']
+['path_tpc']['curves'][523]['res_net_meters']
+['path_tpc']['curves'][524]['offset_meters']
 ['path_tpc']['curves'][524]['res_coeff']
-['path_tpc']['curves'][524]['res_net']
-['path_tpc']['curves'][525]['offset']
+['path_tpc']['curves'][524]['res_net_meters']
+['path_tpc']['curves'][525]['offset_meters']
 ['path_tpc']['curves'][525]['res_coeff']
-['path_tpc']['curves'][525]['res_net']
-['path_tpc']['curves'][526]['offset']
+['path_tpc']['curves'][525]['res_net_meters']
+['path_tpc']['curves'][526]['offset_meters']
 ['path_tpc']['curves'][526]['res_coeff']
-['path_tpc']['curves'][526]['res_net']
-['path_tpc']['curves'][527]['offset']
+['path_tpc']['curves'][526]['res_net_meters']
+['path_tpc']['curves'][527]['offset_meters']
 ['path_tpc']['curves'][527]['res_coeff']
-['path_tpc']['curves'][527]['res_net']
-['path_tpc']['curves'][528]['offset']
+['path_tpc']['curves'][527]['res_net_meters']
+['path_tpc']['curves'][528]['offset_meters']
 ['path_tpc']['curves'][528]['res_coeff']
-['path_tpc']['curves'][528]['res_net']
-['path_tpc']['curves'][529]['offset']
+['path_tpc']['curves'][528]['res_net_meters']
+['path_tpc']['curves'][529]['offset_meters']
 ['path_tpc']['curves'][529]['res_coeff']
-['path_tpc']['curves'][529]['res_net']
-['path_tpc']['curves'][530]['offset']
+['path_tpc']['curves'][529]['res_net_meters']
+['path_tpc']['curves'][530]['offset_meters']
 ['path_tpc']['curves'][530]['res_coeff']
-['path_tpc']['curves'][530]['res_net']
-['path_tpc']['curves'][531]['offset']
+['path_tpc']['curves'][530]['res_net_meters']
+['path_tpc']['curves'][531]['offset_meters']
 ['path_tpc']['curves'][531]['res_coeff']
-['path_tpc']['curves'][531]['res_net']
-['path_tpc']['curves'][532]['offset']
+['path_tpc']['curves'][531]['res_net_meters']
+['path_tpc']['curves'][532]['offset_meters']
 ['path_tpc']['curves'][532]['res_coeff']
-['path_tpc']['curves'][532]['res_net']
-['path_tpc']['curves'][533]['offset']
+['path_tpc']['curves'][532]['res_net_meters']
+['path_tpc']['curves'][533]['offset_meters']
 ['path_tpc']['curves'][533]['res_coeff']
-['path_tpc']['curves'][533]['res_net']
-['path_tpc']['curves'][534]['offset']
+['path_tpc']['curves'][533]['res_net_meters']
+['path_tpc']['curves'][534]['offset_meters']
 ['path_tpc']['curves'][534]['res_coeff']
-['path_tpc']['curves'][534]['res_net']
-['path_tpc']['curves'][535]['offset']
+['path_tpc']['curves'][534]['res_net_meters']
+['path_tpc']['curves'][535]['offset_meters']
 ['path_tpc']['curves'][535]['res_coeff']
-['path_tpc']['curves'][535]['res_net']
-['path_tpc']['curves'][536]['offset']
+['path_tpc']['curves'][535]['res_net_meters']
+['path_tpc']['curves'][536]['offset_meters']
 ['path_tpc']['curves'][536]['res_coeff']
-['path_tpc']['curves'][536]['res_net']
-['path_tpc']['curves'][537]['offset']
+['path_tpc']['curves'][536]['res_net_meters']
+['path_tpc']['curves'][537]['offset_meters']
 ['path_tpc']['curves'][537]['res_coeff']
-['path_tpc']['curves'][537]['res_net']
-['path_tpc']['curves'][538]['offset']
+['path_tpc']['curves'][537]['res_net_meters']
+['path_tpc']['curves'][538]['offset_meters']
 ['path_tpc']['curves'][538]['res_coeff']
-['path_tpc']['curves'][538]['res_net']
-['path_tpc']['curves'][539]['offset']
+['path_tpc']['curves'][538]['res_net_meters']
+['path_tpc']['curves'][539]['offset_meters']
 ['path_tpc']['curves'][539]['res_coeff']
-['path_tpc']['curves'][539]['res_net']
-['path_tpc']['curves'][540]['offset']
+['path_tpc']['curves'][539]['res_net_meters']
+['path_tpc']['curves'][540]['offset_meters']
 ['path_tpc']['curves'][540]['res_coeff']
-['path_tpc']['curves'][540]['res_net']
-['path_tpc']['curves'][541]['offset']
+['path_tpc']['curves'][540]['res_net_meters']
+['path_tpc']['curves'][541]['offset_meters']
 ['path_tpc']['curves'][541]['res_coeff']
-['path_tpc']['curves'][541]['res_net']
-['path_tpc']['curves'][542]['offset']
+['path_tpc']['curves'][541]['res_net_meters']
+['path_tpc']['curves'][542]['offset_meters']
 ['path_tpc']['curves'][542]['res_coeff']
-['path_tpc']['curves'][542]['res_net']
-['path_tpc']['curves'][543]['offset']
+['path_tpc']['curves'][542]['res_net_meters']
+['path_tpc']['curves'][543]['offset_meters']
 ['path_tpc']['curves'][543]['res_coeff']
-['path_tpc']['curves'][543]['res_net']
-['path_tpc']['curves'][544]['offset']
+['path_tpc']['curves'][543]['res_net_meters']
+['path_tpc']['curves'][544]['offset_meters']
 ['path_tpc']['curves'][544]['res_coeff']
-['path_tpc']['curves'][544]['res_net']
-['path_tpc']['curves'][545]['offset']
+['path_tpc']['curves'][544]['res_net_meters']
+['path_tpc']['curves'][545]['offset_meters']
 ['path_tpc']['curves'][545]['res_coeff']
-['path_tpc']['curves'][545]['res_net']
-['path_tpc']['curves'][546]['offset']
+['path_tpc']['curves'][545]['res_net_meters']
+['path_tpc']['curves'][546]['offset_meters']
 ['path_tpc']['curves'][546]['res_coeff']
-['path_tpc']['curves'][546]['res_net']
-['path_tpc']['curves'][547]['offset']
+['path_tpc']['curves'][546]['res_net_meters']
+['path_tpc']['curves'][547]['offset_meters']
 ['path_tpc']['curves'][547]['res_coeff']
-['path_tpc']['curves'][547]['res_net']
-['path_tpc']['curves'][548]['offset']
+['path_tpc']['curves'][547]['res_net_meters']
+['path_tpc']['curves'][548]['offset_meters']
 ['path_tpc']['curves'][548]['res_coeff']
-['path_tpc']['curves'][548]['res_net']
-['path_tpc']['curves'][549]['offset']
+['path_tpc']['curves'][548]['res_net_meters']
+['path_tpc']['curves'][549]['offset_meters']
 ['path_tpc']['curves'][549]['res_coeff']
-['path_tpc']['curves'][549]['res_net']
-['path_tpc']['curves'][550]['offset']
+['path_tpc']['curves'][549]['res_net_meters']
+['path_tpc']['curves'][550]['offset_meters']
 ['path_tpc']['curves'][550]['res_coeff']
-['path_tpc']['curves'][550]['res_net']
-['path_tpc']['curves'][551]['offset']
+['path_tpc']['curves'][550]['res_net_meters']
+['path_tpc']['curves'][551]['offset_meters']
 ['path_tpc']['curves'][551]['res_coeff']
-['path_tpc']['curves'][551]['res_net']
-['path_tpc']['curves'][552]['offset']
+['path_tpc']['curves'][551]['res_net_meters']
+['path_tpc']['curves'][552]['offset_meters']
 ['path_tpc']['curves'][552]['res_coeff']
-['path_tpc']['curves'][552]['res_net']
-['path_tpc']['curves'][553]['offset']
+['path_tpc']['curves'][552]['res_net_meters']
+['path_tpc']['curves'][553]['offset_meters']
 ['path_tpc']['curves'][553]['res_coeff']
-['path_tpc']['curves'][553]['res_net']
-['path_tpc']['curves'][554]['offset']
+['path_tpc']['curves'][553]['res_net_meters']
+['path_tpc']['curves'][554]['offset_meters']
 ['path_tpc']['curves'][554]['res_coeff']
-['path_tpc']['curves'][554]['res_net']
-['path_tpc']['curves'][555]['offset']
+['path_tpc']['curves'][554]['res_net_meters']
+['path_tpc']['curves'][555]['offset_meters']
 ['path_tpc']['curves'][555]['res_coeff']
-['path_tpc']['curves'][555]['res_net']
-['path_tpc']['curves'][556]['offset']
+['path_tpc']['curves'][555]['res_net_meters']
+['path_tpc']['curves'][556]['offset_meters']
 ['path_tpc']['curves'][556]['res_coeff']
-['path_tpc']['curves'][556]['res_net']
-['path_tpc']['curves'][557]['offset']
+['path_tpc']['curves'][556]['res_net_meters']
+['path_tpc']['curves'][557]['offset_meters']
 ['path_tpc']['curves'][557]['res_coeff']
-['path_tpc']['curves'][557]['res_net']
-['path_tpc']['curves'][558]['offset']
+['path_tpc']['curves'][557]['res_net_meters']
+['path_tpc']['curves'][558]['offset_meters']
 ['path_tpc']['curves'][558]['res_coeff']
-['path_tpc']['curves'][558]['res_net']
-['path_tpc']['curves'][559]['offset']
+['path_tpc']['curves'][558]['res_net_meters']
+['path_tpc']['curves'][559]['offset_meters']
 ['path_tpc']['curves'][559]['res_coeff']
-['path_tpc']['curves'][559]['res_net']
-['path_tpc']['curves'][560]['offset']
+['path_tpc']['curves'][559]['res_net_meters']
+['path_tpc']['curves'][560]['offset_meters']
 ['path_tpc']['curves'][560]['res_coeff']
-['path_tpc']['curves'][560]['res_net']
-['path_tpc']['curves'][561]['offset']
+['path_tpc']['curves'][560]['res_net_meters']
+['path_tpc']['curves'][561]['offset_meters']
 ['path_tpc']['curves'][561]['res_coeff']
-['path_tpc']['curves'][561]['res_net']
-['path_tpc']['curves'][562]['offset']
+['path_tpc']['curves'][561]['res_net_meters']
+['path_tpc']['curves'][562]['offset_meters']
 ['path_tpc']['curves'][562]['res_coeff']
-['path_tpc']['curves'][562]['res_net']
-['path_tpc']['curves'][563]['offset']
+['path_tpc']['curves'][562]['res_net_meters']
+['path_tpc']['curves'][563]['offset_meters']
 ['path_tpc']['curves'][563]['res_coeff']
-['path_tpc']['curves'][563]['res_net']
-['path_tpc']['curves'][564]['offset']
+['path_tpc']['curves'][563]['res_net_meters']
+['path_tpc']['curves'][564]['offset_meters']
 ['path_tpc']['curves'][564]['res_coeff']
-['path_tpc']['curves'][564]['res_net']
-['path_tpc']['curves'][565]['offset']
+['path_tpc']['curves'][564]['res_net_meters']
+['path_tpc']['curves'][565]['offset_meters']
 ['path_tpc']['curves'][565]['res_coeff']
-['path_tpc']['curves'][565]['res_net']
-['path_tpc']['curves'][566]['offset']
+['path_tpc']['curves'][565]['res_net_meters']
+['path_tpc']['curves'][566]['offset_meters']
 ['path_tpc']['curves'][566]['res_coeff']
-['path_tpc']['curves'][566]['res_net']
-['path_tpc']['curves'][567]['offset']
+['path_tpc']['curves'][566]['res_net_meters']
+['path_tpc']['curves'][567]['offset_meters']
 ['path_tpc']['curves'][567]['res_coeff']
-['path_tpc']['curves'][567]['res_net']
-['path_tpc']['curves'][568]['offset']
+['path_tpc']['curves'][567]['res_net_meters']
+['path_tpc']['curves'][568]['offset_meters']
 ['path_tpc']['curves'][568]['res_coeff']
-['path_tpc']['curves'][568]['res_net']
-['path_tpc']['curves'][569]['offset']
+['path_tpc']['curves'][568]['res_net_meters']
+['path_tpc']['curves'][569]['offset_meters']
 ['path_tpc']['curves'][569]['res_coeff']
-['path_tpc']['curves'][569]['res_net']
-['path_tpc']['curves'][570]['offset']
+['path_tpc']['curves'][569]['res_net_meters']
+['path_tpc']['curves'][570]['offset_meters']
 ['path_tpc']['curves'][570]['res_coeff']
-['path_tpc']['curves'][570]['res_net']
-['path_tpc']['curves'][571]['offset']
+['path_tpc']['curves'][570]['res_net_meters']
+['path_tpc']['curves'][571]['offset_meters']
 ['path_tpc']['curves'][571]['res_coeff']
-['path_tpc']['curves'][571]['res_net']
-['path_tpc']['curves'][572]['offset']
+['path_tpc']['curves'][571]['res_net_meters']
+['path_tpc']['curves'][572]['offset_meters']
 ['path_tpc']['curves'][572]['res_coeff']
-['path_tpc']['curves'][572]['res_net']
-['path_tpc']['curves'][573]['offset']
+['path_tpc']['curves'][572]['res_net_meters']
+['path_tpc']['curves'][573]['offset_meters']
 ['path_tpc']['curves'][573]['res_coeff']
-['path_tpc']['curves'][573]['res_net']
-['path_tpc']['curves'][574]['offset']
+['path_tpc']['curves'][573]['res_net_meters']
+['path_tpc']['curves'][574]['offset_meters']
 ['path_tpc']['curves'][574]['res_coeff']
-['path_tpc']['curves'][574]['res_net']
-['path_tpc']['curves'][575]['offset']
+['path_tpc']['curves'][574]['res_net_meters']
+['path_tpc']['curves'][575]['offset_meters']
 ['path_tpc']['curves'][575]['res_coeff']
-['path_tpc']['curves'][575]['res_net']
-['path_tpc']['curves'][576]['offset']
+['path_tpc']['curves'][575]['res_net_meters']
+['path_tpc']['curves'][576]['offset_meters']
 ['path_tpc']['curves'][576]['res_coeff']
-['path_tpc']['curves'][576]['res_net']
-['path_tpc']['curves'][577]['offset']
+['path_tpc']['curves'][576]['res_net_meters']
+['path_tpc']['curves'][577]['offset_meters']
 ['path_tpc']['curves'][577]['res_coeff']
-['path_tpc']['curves'][577]['res_net']
-['path_tpc']['curves'][578]['offset']
+['path_tpc']['curves'][577]['res_net_meters']
+['path_tpc']['curves'][578]['offset_meters']
 ['path_tpc']['curves'][578]['res_coeff']
-['path_tpc']['curves'][578]['res_net']
-['path_tpc']['curves'][579]['offset']
+['path_tpc']['curves'][578]['res_net_meters']
+['path_tpc']['curves'][579]['offset_meters']
 ['path_tpc']['curves'][579]['res_coeff']
-['path_tpc']['curves'][579]['res_net']
-['path_tpc']['curves'][580]['offset']
+['path_tpc']['curves'][579]['res_net_meters']
+['path_tpc']['curves'][580]['offset_meters']
 ['path_tpc']['curves'][580]['res_coeff']
-['path_tpc']['curves'][580]['res_net']
-['path_tpc']['curves'][581]['offset']
+['path_tpc']['curves'][580]['res_net_meters']
+['path_tpc']['curves'][581]['offset_meters']
 ['path_tpc']['curves'][581]['res_coeff']
-['path_tpc']['curves'][581]['res_net']
-['path_tpc']['curves'][582]['offset']
+['path_tpc']['curves'][581]['res_net_meters']
+['path_tpc']['curves'][582]['offset_meters']
 ['path_tpc']['curves'][582]['res_coeff']
-['path_tpc']['curves'][582]['res_net']
-['path_tpc']['curves'][583]['offset']
+['path_tpc']['curves'][582]['res_net_meters']
+['path_tpc']['curves'][583]['offset_meters']
 ['path_tpc']['curves'][583]['res_coeff']
-['path_tpc']['curves'][583]['res_net']
-['path_tpc']['curves'][584]['offset']
+['path_tpc']['curves'][583]['res_net_meters']
+['path_tpc']['curves'][584]['offset_meters']
 ['path_tpc']['curves'][584]['res_coeff']
-['path_tpc']['curves'][584]['res_net']
-['path_tpc']['curves'][585]['offset']
+['path_tpc']['curves'][584]['res_net_meters']
+['path_tpc']['curves'][585]['offset_meters']
 ['path_tpc']['curves'][585]['res_coeff']
-['path_tpc']['curves'][585]['res_net']
-['path_tpc']['curves'][586]['offset']
+['path_tpc']['curves'][585]['res_net_meters']
+['path_tpc']['curves'][586]['offset_meters']
 ['path_tpc']['curves'][586]['res_coeff']
-['path_tpc']['curves'][586]['res_net']
-['path_tpc']['curves'][587]['offset']
+['path_tpc']['curves'][586]['res_net_meters']
+['path_tpc']['curves'][587]['offset_meters']
 ['path_tpc']['curves'][587]['res_coeff']
-['path_tpc']['curves'][587]['res_net']
-['path_tpc']['curves'][588]['offset']
+['path_tpc']['curves'][587]['res_net_meters']
+['path_tpc']['curves'][588]['offset_meters']
 ['path_tpc']['curves'][588]['res_coeff']
-['path_tpc']['curves'][588]['res_net']
-['path_tpc']['curves'][589]['offset']
+['path_tpc']['curves'][588]['res_net_meters']
+['path_tpc']['curves'][589]['offset_meters']
 ['path_tpc']['curves'][589]['res_coeff']
-['path_tpc']['curves'][589]['res_net']
-['path_tpc']['curves'][590]['offset']
+['path_tpc']['curves'][589]['res_net_meters']
+['path_tpc']['curves'][590]['offset_meters']
 ['path_tpc']['curves'][590]['res_coeff']
-['path_tpc']['curves'][590]['res_net']
-['path_tpc']['curves'][591]['offset']
+['path_tpc']['curves'][590]['res_net_meters']
+['path_tpc']['curves'][591]['offset_meters']
 ['path_tpc']['curves'][591]['res_coeff']
-['path_tpc']['curves'][591]['res_net']
-['path_tpc']['curves'][592]['offset']
+['path_tpc']['curves'][591]['res_net_meters']
+['path_tpc']['curves'][592]['offset_meters']
 ['path_tpc']['curves'][592]['res_coeff']
-['path_tpc']['curves'][592]['res_net']
-['path_tpc']['curves'][593]['offset']
+['path_tpc']['curves'][592]['res_net_meters']
+['path_tpc']['curves'][593]['offset_meters']
 ['path_tpc']['curves'][593]['res_coeff']
-['path_tpc']['curves'][593]['res_net']
-['path_tpc']['curves'][594]['offset']
+['path_tpc']['curves'][593]['res_net_meters']
+['path_tpc']['curves'][594]['offset_meters']
 ['path_tpc']['curves'][594]['res_coeff']
-['path_tpc']['curves'][594]['res_net']
-['path_tpc']['curves'][595]['offset']
+['path_tpc']['curves'][594]['res_net_meters']
+['path_tpc']['curves'][595]['offset_meters']
 ['path_tpc']['curves'][595]['res_coeff']
-['path_tpc']['curves'][595]['res_net']
-['path_tpc']['curves'][596]['offset']
+['path_tpc']['curves'][595]['res_net_meters']
+['path_tpc']['curves'][596]['offset_meters']
 ['path_tpc']['curves'][596]['res_coeff']
-['path_tpc']['curves'][596]['res_net']
-['path_tpc']['curves'][597]['offset']
+['path_tpc']['curves'][596]['res_net_meters']
+['path_tpc']['curves'][597]['offset_meters']
 ['path_tpc']['curves'][597]['res_coeff']
-['path_tpc']['curves'][597]['res_net']
-['path_tpc']['curves'][598]['offset']
+['path_tpc']['curves'][597]['res_net_meters']
+['path_tpc']['curves'][598]['offset_meters']
 ['path_tpc']['curves'][598]['res_coeff']
-['path_tpc']['curves'][598]['res_net']
-['path_tpc']['curves'][599]['offset']
+['path_tpc']['curves'][598]['res_net_meters']
+['path_tpc']['curves'][599]['offset_meters']
 ['path_tpc']['curves'][599]['res_coeff']
-['path_tpc']['curves'][599]['res_net']
-['path_tpc']['curves'][600]['offset']
+['path_tpc']['curves'][599]['res_net_meters']
+['path_tpc']['curves'][600]['offset_meters']
 ['path_tpc']['curves'][600]['res_coeff']
-['path_tpc']['curves'][600]['res_net']
-['path_tpc']['curves'][601]['offset']
+['path_tpc']['curves'][600]['res_net_meters']
+['path_tpc']['curves'][601]['offset_meters']
 ['path_tpc']['curves'][601]['res_coeff']
-['path_tpc']['curves'][601]['res_net']
-['path_tpc']['curves'][602]['offset']
+['path_tpc']['curves'][601]['res_net_meters']
+['path_tpc']['curves'][602]['offset_meters']
 ['path_tpc']['curves'][602]['res_coeff']
-['path_tpc']['curves'][602]['res_net']
-['path_tpc']['curves'][603]['offset']
+['path_tpc']['curves'][602]['res_net_meters']
+['path_tpc']['curves'][603]['offset_meters']
 ['path_tpc']['curves'][603]['res_coeff']
-['path_tpc']['curves'][603]['res_net']
-['path_tpc']['curves'][604]['offset']
+['path_tpc']['curves'][603]['res_net_meters']
+['path_tpc']['curves'][604]['offset_meters']
 ['path_tpc']['curves'][604]['res_coeff']
-['path_tpc']['curves'][604]['res_net']
-['path_tpc']['curves'][605]['offset']
+['path_tpc']['curves'][604]['res_net_meters']
+['path_tpc']['curves'][605]['offset_meters']
 ['path_tpc']['curves'][605]['res_coeff']
-['path_tpc']['curves'][605]['res_net']
-['path_tpc']['curves'][606]['offset']
+['path_tpc']['curves'][605]['res_net_meters']
+['path_tpc']['curves'][606]['offset_meters']
 ['path_tpc']['curves'][606]['res_coeff']
-['path_tpc']['curves'][606]['res_net']
-['path_tpc']['curves'][607]['offset']
+['path_tpc']['curves'][606]['res_net_meters']
+['path_tpc']['curves'][607]['offset_meters']
 ['path_tpc']['curves'][607]['res_coeff']
-['path_tpc']['curves'][607]['res_net']
-['path_tpc']['curves'][608]['offset']
+['path_tpc']['curves'][607]['res_net_meters']
+['path_tpc']['curves'][608]['offset_meters']
 ['path_tpc']['curves'][608]['res_coeff']
-['path_tpc']['curves'][608]['res_net']
-['path_tpc']['curves'][609]['offset']
+['path_tpc']['curves'][608]['res_net_meters']
+['path_tpc']['curves'][609]['offset_meters']
 ['path_tpc']['curves'][609]['res_coeff']
-['path_tpc']['curves'][609]['res_net']
-['path_tpc']['curves'][610]['offset']
+['path_tpc']['curves'][609]['res_net_meters']
+['path_tpc']['curves'][610]['offset_meters']
 ['path_tpc']['curves'][610]['res_coeff']
-['path_tpc']['curves'][610]['res_net']
-['path_tpc']['curves'][611]['offset']
+['path_tpc']['curves'][610]['res_net_meters']
+['path_tpc']['curves'][611]['offset_meters']
 ['path_tpc']['curves'][611]['res_coeff']
-['path_tpc']['curves'][611]['res_net']
-['path_tpc']['curves'][612]['offset']
+['path_tpc']['curves'][611]['res_net_meters']
+['path_tpc']['curves'][612]['offset_meters']
 ['path_tpc']['curves'][612]['res_coeff']
-['path_tpc']['curves'][612]['res_net']
-['path_tpc']['curves'][613]['offset']
+['path_tpc']['curves'][612]['res_net_meters']
+['path_tpc']['curves'][613]['offset_meters']
 ['path_tpc']['curves'][613]['res_coeff']
-['path_tpc']['curves'][613]['res_net']
-['path_tpc']['curves'][614]['offset']
+['path_tpc']['curves'][613]['res_net_meters']
+['path_tpc']['curves'][614]['offset_meters']
 ['path_tpc']['curves'][614]['res_coeff']
-['path_tpc']['curves'][614]['res_net']
-['path_tpc']['curves'][615]['offset']
+['path_tpc']['curves'][614]['res_net_meters']
+['path_tpc']['curves'][615]['offset_meters']
 ['path_tpc']['curves'][615]['res_coeff']
-['path_tpc']['curves'][615]['res_net']
-['path_tpc']['curves'][616]['offset']
+['path_tpc']['curves'][615]['res_net_meters']
+['path_tpc']['curves'][616]['offset_meters']
 ['path_tpc']['curves'][616]['res_coeff']
-['path_tpc']['curves'][616]['res_net']
-['path_tpc']['curves'][617]['offset']
+['path_tpc']['curves'][616]['res_net_meters']
+['path_tpc']['curves'][617]['offset_meters']
 ['path_tpc']['curves'][617]['res_coeff']
-['path_tpc']['curves'][617]['res_net']
-['path_tpc']['curves'][618]['offset']
+['path_tpc']['curves'][617]['res_net_meters']
+['path_tpc']['curves'][618]['offset_meters']
 ['path_tpc']['curves'][618]['res_coeff']
-['path_tpc']['curves'][618]['res_net']
-['path_tpc']['curves'][619]['offset']
+['path_tpc']['curves'][618]['res_net_meters']
+['path_tpc']['curves'][619]['offset_meters']
 ['path_tpc']['curves'][619]['res_coeff']
-['path_tpc']['curves'][619]['res_net']
-['path_tpc']['curves'][620]['offset']
+['path_tpc']['curves'][619]['res_net_meters']
+['path_tpc']['curves'][620]['offset_meters']
 ['path_tpc']['curves'][620]['res_coeff']
-['path_tpc']['curves'][620]['res_net']
-['path_tpc']['curves'][621]['offset']
+['path_tpc']['curves'][620]['res_net_meters']
+['path_tpc']['curves'][621]['offset_meters']
 ['path_tpc']['curves'][621]['res_coeff']
-['path_tpc']['curves'][621]['res_net']
-['path_tpc']['curves'][622]['offset']
+['path_tpc']['curves'][621]['res_net_meters']
+['path_tpc']['curves'][622]['offset_meters']
 ['path_tpc']['curves'][622]['res_coeff']
-['path_tpc']['curves'][622]['res_net']
-['path_tpc']['curves'][623]['offset']
+['path_tpc']['curves'][622]['res_net_meters']
+['path_tpc']['curves'][623]['offset_meters']
 ['path_tpc']['curves'][623]['res_coeff']
-['path_tpc']['curves'][623]['res_net']
-['path_tpc']['curves'][624]['offset']
+['path_tpc']['curves'][623]['res_net_meters']
+['path_tpc']['curves'][624]['offset_meters']
 ['path_tpc']['curves'][624]['res_coeff']
-['path_tpc']['curves'][624]['res_net']
-['path_tpc']['curves'][625]['offset']
+['path_tpc']['curves'][624]['res_net_meters']
+['path_tpc']['curves'][625]['offset_meters']
 ['path_tpc']['curves'][625]['res_coeff']
-['path_tpc']['curves'][625]['res_net']
-['path_tpc']['curves'][626]['offset']
+['path_tpc']['curves'][625]['res_net_meters']
+['path_tpc']['curves'][626]['offset_meters']
 ['path_tpc']['curves'][626]['res_coeff']
-['path_tpc']['curves'][626]['res_net']
-['path_tpc']['curves'][627]['offset']
+['path_tpc']['curves'][626]['res_net_meters']
+['path_tpc']['curves'][627]['offset_meters']
 ['path_tpc']['curves'][627]['res_coeff']
-['path_tpc']['curves'][627]['res_net']
-['path_tpc']['curves'][628]['offset']
+['path_tpc']['curves'][627]['res_net_meters']
+['path_tpc']['curves'][628]['offset_meters']
 ['path_tpc']['curves'][628]['res_coeff']
-['path_tpc']['curves'][628]['res_net']
-['path_tpc']['curves'][629]['offset']
+['path_tpc']['curves'][628]['res_net_meters']
+['path_tpc']['curves'][629]['offset_meters']
 ['path_tpc']['curves'][629]['res_coeff']
-['path_tpc']['curves'][629]['res_net']
-['path_tpc']['curves'][630]['offset']
+['path_tpc']['curves'][629]['res_net_meters']
+['path_tpc']['curves'][630]['offset_meters']
 ['path_tpc']['curves'][630]['res_coeff']
-['path_tpc']['curves'][630]['res_net']
-['path_tpc']['curves'][631]['offset']
+['path_tpc']['curves'][630]['res_net_meters']
+['path_tpc']['curves'][631]['offset_meters']
 ['path_tpc']['curves'][631]['res_coeff']
-['path_tpc']['curves'][631]['res_net']
-['path_tpc']['curves'][632]['offset']
+['path_tpc']['curves'][631]['res_net_meters']
+['path_tpc']['curves'][632]['offset_meters']
 ['path_tpc']['curves'][632]['res_coeff']
-['path_tpc']['curves'][632]['res_net']
-['path_tpc']['curves'][633]['offset']
+['path_tpc']['curves'][632]['res_net_meters']
+['path_tpc']['curves'][633]['offset_meters']
 ['path_tpc']['curves'][633]['res_coeff']
-['path_tpc']['curves'][633]['res_net']
-['path_tpc']['curves'][634]['offset']
+['path_tpc']['curves'][633]['res_net_meters']
+['path_tpc']['curves'][634]['offset_meters']
 ['path_tpc']['curves'][634]['res_coeff']
-['path_tpc']['curves'][634]['res_net']
-['path_tpc']['curves'][635]['offset']
+['path_tpc']['curves'][634]['res_net_meters']
+['path_tpc']['curves'][635]['offset_meters']
 ['path_tpc']['curves'][635]['res_coeff']
-['path_tpc']['curves'][635]['res_net']
-['path_tpc']['curves'][636]['offset']
+['path_tpc']['curves'][635]['res_net_meters']
+['path_tpc']['curves'][636]['offset_meters']
 ['path_tpc']['curves'][636]['res_coeff']
-['path_tpc']['curves'][636]['res_net']
-['path_tpc']['curves'][637]['offset']
+['path_tpc']['curves'][636]['res_net_meters']
+['path_tpc']['curves'][637]['offset_meters']
 ['path_tpc']['curves'][637]['res_coeff']
-['path_tpc']['curves'][637]['res_net']
-['path_tpc']['curves'][638]['offset']
+['path_tpc']['curves'][637]['res_net_meters']
+['path_tpc']['curves'][638]['offset_meters']
 ['path_tpc']['curves'][638]['res_coeff']
-['path_tpc']['curves'][638]['res_net']
-['path_tpc']['curves'][639]['offset']
+['path_tpc']['curves'][638]['res_net_meters']
+['path_tpc']['curves'][639]['offset_meters']
 ['path_tpc']['curves'][639]['res_coeff']
-['path_tpc']['curves'][639]['res_net']
-['path_tpc']['curves'][640]['offset']
+['path_tpc']['curves'][639]['res_net_meters']
+['path_tpc']['curves'][640]['offset_meters']
 ['path_tpc']['curves'][640]['res_coeff']
-['path_tpc']['curves'][640]['res_net']
-['path_tpc']['curves'][641]['offset']
+['path_tpc']['curves'][640]['res_net_meters']
+['path_tpc']['curves'][641]['offset_meters']
 ['path_tpc']['curves'][641]['res_coeff']
-['path_tpc']['curves'][641]['res_net']
-['path_tpc']['curves'][642]['offset']
+['path_tpc']['curves'][641]['res_net_meters']
+['path_tpc']['curves'][642]['offset_meters']
 ['path_tpc']['curves'][642]['res_coeff']
-['path_tpc']['curves'][642]['res_net']
-['path_tpc']['curves'][643]['offset']
+['path_tpc']['curves'][642]['res_net_meters']
+['path_tpc']['curves'][643]['offset_meters']
 ['path_tpc']['curves'][643]['res_coeff']
-['path_tpc']['curves'][643]['res_net']
-['path_tpc']['curves'][644]['offset']
+['path_tpc']['curves'][643]['res_net_meters']
+['path_tpc']['curves'][644]['offset_meters']
 ['path_tpc']['curves'][644]['res_coeff']
-['path_tpc']['curves'][644]['res_net']
-['path_tpc']['curves'][645]['offset']
+['path_tpc']['curves'][644]['res_net_meters']
+['path_tpc']['curves'][645]['offset_meters']
 ['path_tpc']['curves'][645]['res_coeff']
-['path_tpc']['curves'][645]['res_net']
-['path_tpc']['curves'][646]['offset']
+['path_tpc']['curves'][645]['res_net_meters']
+['path_tpc']['curves'][646]['offset_meters']
 ['path_tpc']['curves'][646]['res_coeff']
-['path_tpc']['curves'][646]['res_net']
-['path_tpc']['curves'][647]['offset']
+['path_tpc']['curves'][646]['res_net_meters']
+['path_tpc']['curves'][647]['offset_meters']
 ['path_tpc']['curves'][647]['res_coeff']
-['path_tpc']['curves'][647]['res_net']
-['path_tpc']['curves'][648]['offset']
+['path_tpc']['curves'][647]['res_net_meters']
+['path_tpc']['curves'][648]['offset_meters']
 ['path_tpc']['curves'][648]['res_coeff']
-['path_tpc']['curves'][648]['res_net']
-['path_tpc']['curves'][649]['offset']
+['path_tpc']['curves'][648]['res_net_meters']
+['path_tpc']['curves'][649]['offset_meters']
 ['path_tpc']['curves'][649]['res_coeff']
-['path_tpc']['curves'][649]['res_net']
-['path_tpc']['curves'][650]['offset']
+['path_tpc']['curves'][649]['res_net_meters']
+['path_tpc']['curves'][650]['offset_meters']
 ['path_tpc']['curves'][650]['res_coeff']
-['path_tpc']['curves'][650]['res_net']
-['path_tpc']['curves'][651]['offset']
+['path_tpc']['curves'][650]['res_net_meters']
+['path_tpc']['curves'][651]['offset_meters']
 ['path_tpc']['curves'][651]['res_coeff']
-['path_tpc']['curves'][651]['res_net']
-['path_tpc']['curves'][652]['offset']
+['path_tpc']['curves'][651]['res_net_meters']
+['path_tpc']['curves'][652]['offset_meters']
 ['path_tpc']['curves'][652]['res_coeff']
-['path_tpc']['curves'][652]['res_net']
-['path_tpc']['curves'][653]['offset']
+['path_tpc']['curves'][652]['res_net_meters']
+['path_tpc']['curves'][653]['offset_meters']
 ['path_tpc']['curves'][653]['res_coeff']
-['path_tpc']['curves'][653]['res_net']
-['path_tpc']['curves'][654]['offset']
+['path_tpc']['curves'][653]['res_net_meters']
+['path_tpc']['curves'][654]['offset_meters']
 ['path_tpc']['curves'][654]['res_coeff']
-['path_tpc']['curves'][654]['res_net']
-['path_tpc']['curves'][655]['offset']
+['path_tpc']['curves'][654]['res_net_meters']
+['path_tpc']['curves'][655]['offset_meters']
 ['path_tpc']['curves'][655]['res_coeff']
-['path_tpc']['curves'][655]['res_net']
-['path_tpc']['curves'][656]['offset']
+['path_tpc']['curves'][655]['res_net_meters']
+['path_tpc']['curves'][656]['offset_meters']
 ['path_tpc']['curves'][656]['res_coeff']
-['path_tpc']['curves'][656]['res_net']
-['path_tpc']['curves'][657]['offset']
+['path_tpc']['curves'][656]['res_net_meters']
+['path_tpc']['curves'][657]['offset_meters']
 ['path_tpc']['curves'][657]['res_coeff']
-['path_tpc']['curves'][657]['res_net']
-['path_tpc']['curves'][658]['offset']
+['path_tpc']['curves'][657]['res_net_meters']
+['path_tpc']['curves'][658]['offset_meters']
 ['path_tpc']['curves'][658]['res_coeff']
-['path_tpc']['curves'][658]['res_net']
-['path_tpc']['curves'][659]['offset']
+['path_tpc']['curves'][658]['res_net_meters']
+['path_tpc']['curves'][659]['offset_meters']
 ['path_tpc']['curves'][659]['res_coeff']
-['path_tpc']['curves'][659]['res_net']
-['path_tpc']['curves'][660]['offset']
+['path_tpc']['curves'][659]['res_net_meters']
+['path_tpc']['curves'][660]['offset_meters']
 ['path_tpc']['curves'][660]['res_coeff']
-['path_tpc']['curves'][660]['res_net']
-['path_tpc']['curves'][661]['offset']
+['path_tpc']['curves'][660]['res_net_meters']
+['path_tpc']['curves'][661]['offset_meters']
 ['path_tpc']['curves'][661]['res_coeff']
-['path_tpc']['curves'][661]['res_net']
-['path_tpc']['curves'][662]['offset']
+['path_tpc']['curves'][661]['res_net_meters']
+['path_tpc']['curves'][662]['offset_meters']
 ['path_tpc']['curves'][662]['res_coeff']
-['path_tpc']['curves'][662]['res_net']
-['path_tpc']['curves'][663]['offset']
+['path_tpc']['curves'][662]['res_net_meters']
+['path_tpc']['curves'][663]['offset_meters']
 ['path_tpc']['curves'][663]['res_coeff']
-['path_tpc']['curves'][663]['res_net']
-['path_tpc']['curves'][664]['offset']
+['path_tpc']['curves'][663]['res_net_meters']
+['path_tpc']['curves'][664]['offset_meters']
 ['path_tpc']['curves'][664]['res_coeff']
-['path_tpc']['curves'][664]['res_net']
-['path_tpc']['curves'][665]['offset']
+['path_tpc']['curves'][664]['res_net_meters']
+['path_tpc']['curves'][665]['offset_meters']
 ['path_tpc']['curves'][665]['res_coeff']
-['path_tpc']['curves'][665]['res_net']
-['path_tpc']['curves'][666]['offset']
+['path_tpc']['curves'][665]['res_net_meters']
+['path_tpc']['curves'][666]['offset_meters']
 ['path_tpc']['curves'][666]['res_coeff']
-['path_tpc']['curves'][666]['res_net']
-['path_tpc']['curves'][667]['offset']
+['path_tpc']['curves'][666]['res_net_meters']
+['path_tpc']['curves'][667]['offset_meters']
 ['path_tpc']['curves'][667]['res_coeff']
-['path_tpc']['curves'][667]['res_net']
-['path_tpc']['curves'][668]['offset']
+['path_tpc']['curves'][667]['res_net_meters']
+['path_tpc']['curves'][668]['offset_meters']
 ['path_tpc']['curves'][668]['res_coeff']
-['path_tpc']['curves'][668]['res_net']
-['path_tpc']['curves'][669]['offset']
+['path_tpc']['curves'][668]['res_net_meters']
+['path_tpc']['curves'][669]['offset_meters']
 ['path_tpc']['curves'][669]['res_coeff']
-['path_tpc']['curves'][669]['res_net']
-['path_tpc']['curves'][670]['offset']
+['path_tpc']['curves'][669]['res_net_meters']
+['path_tpc']['curves'][670]['offset_meters']
 ['path_tpc']['curves'][670]['res_coeff']
-['path_tpc']['curves'][670]['res_net']
-['path_tpc']['curves'][671]['offset']
+['path_tpc']['curves'][670]['res_net_meters']
+['path_tpc']['curves'][671]['offset_meters']
 ['path_tpc']['curves'][671]['res_coeff']
-['path_tpc']['curves'][671]['res_net']
-['path_tpc']['curves'][672]['offset']
+['path_tpc']['curves'][671]['res_net_meters']
+['path_tpc']['curves'][672]['offset_meters']
 ['path_tpc']['curves'][672]['res_coeff']
-['path_tpc']['curves'][672]['res_net']
-['path_tpc']['curves'][673]['offset']
+['path_tpc']['curves'][672]['res_net_meters']
+['path_tpc']['curves'][673]['offset_meters']
 ['path_tpc']['curves'][673]['res_coeff']
-['path_tpc']['curves'][673]['res_net']
-['path_tpc']['curves'][674]['offset']
+['path_tpc']['curves'][673]['res_net_meters']
+['path_tpc']['curves'][674]['offset_meters']
 ['path_tpc']['curves'][674]['res_coeff']
-['path_tpc']['curves'][674]['res_net']
-['path_tpc']['curves'][675]['offset']
+['path_tpc']['curves'][674]['res_net_meters']
+['path_tpc']['curves'][675]['offset_meters']
 ['path_tpc']['curves'][675]['res_coeff']
-['path_tpc']['curves'][675]['res_net']
-['path_tpc']['curves'][676]['offset']
+['path_tpc']['curves'][675]['res_net_meters']
+['path_tpc']['curves'][676]['offset_meters']
 ['path_tpc']['curves'][676]['res_coeff']
-['path_tpc']['curves'][676]['res_net']
-['path_tpc']['curves'][677]['offset']
+['path_tpc']['curves'][676]['res_net_meters']
+['path_tpc']['curves'][677]['offset_meters']
 ['path_tpc']['curves'][677]['res_coeff']
-['path_tpc']['curves'][677]['res_net']
-['path_tpc']['curves'][678]['offset']
+['path_tpc']['curves'][677]['res_net_meters']
+['path_tpc']['curves'][678]['offset_meters']
 ['path_tpc']['curves'][678]['res_coeff']
-['path_tpc']['curves'][678]['res_net']
-['path_tpc']['curves'][679]['offset']
+['path_tpc']['curves'][678]['res_net_meters']
+['path_tpc']['curves'][679]['offset_meters']
 ['path_tpc']['curves'][679]['res_coeff']
-['path_tpc']['curves'][679]['res_net']
-['path_tpc']['curves'][680]['offset']
+['path_tpc']['curves'][679]['res_net_meters']
+['path_tpc']['curves'][680]['offset_meters']
 ['path_tpc']['curves'][680]['res_coeff']
-['path_tpc']['curves'][680]['res_net']
-['path_tpc']['curves'][681]['offset']
+['path_tpc']['curves'][680]['res_net_meters']
+['path_tpc']['curves'][681]['offset_meters']
 ['path_tpc']['curves'][681]['res_coeff']
-['path_tpc']['curves'][681]['res_net']
-['path_tpc']['curves'][682]['offset']
+['path_tpc']['curves'][681]['res_net_meters']
+['path_tpc']['curves'][682]['offset_meters']
 ['path_tpc']['curves'][682]['res_coeff']
-['path_tpc']['curves'][682]['res_net']
-['path_tpc']['curves'][683]['offset']
+['path_tpc']['curves'][682]['res_net_meters']
+['path_tpc']['curves'][683]['offset_meters']
 ['path_tpc']['curves'][683]['res_coeff']
-['path_tpc']['curves'][683]['res_net']
-['path_tpc']['curves'][684]['offset']
+['path_tpc']['curves'][683]['res_net_meters']
+['path_tpc']['curves'][684]['offset_meters']
 ['path_tpc']['curves'][684]['res_coeff']
-['path_tpc']['curves'][684]['res_net']
-['path_tpc']['curves'][685]['offset']
+['path_tpc']['curves'][684]['res_net_meters']
+['path_tpc']['curves'][685]['offset_meters']
 ['path_tpc']['curves'][685]['res_coeff']
-['path_tpc']['curves'][685]['res_net']
-['path_tpc']['curves'][686]['offset']
+['path_tpc']['curves'][685]['res_net_meters']
+['path_tpc']['curves'][686]['offset_meters']
 ['path_tpc']['curves'][686]['res_coeff']
-['path_tpc']['curves'][686]['res_net']
-['path_tpc']['curves'][687]['offset']
+['path_tpc']['curves'][686]['res_net_meters']
+['path_tpc']['curves'][687]['offset_meters']
 ['path_tpc']['curves'][687]['res_coeff']
-['path_tpc']['curves'][687]['res_net']
-['path_tpc']['curves'][688]['offset']
+['path_tpc']['curves'][687]['res_net_meters']
+['path_tpc']['curves'][688]['offset_meters']
 ['path_tpc']['curves'][688]['res_coeff']
-['path_tpc']['curves'][688]['res_net']
-['path_tpc']['curves'][689]['offset']
+['path_tpc']['curves'][688]['res_net_meters']
+['path_tpc']['curves'][689]['offset_meters']
 ['path_tpc']['curves'][689]['res_coeff']
-['path_tpc']['curves'][689]['res_net']
-['path_tpc']['curves'][690]['offset']
+['path_tpc']['curves'][689]['res_net_meters']
+['path_tpc']['curves'][690]['offset_meters']
 ['path_tpc']['curves'][690]['res_coeff']
-['path_tpc']['curves'][690]['res_net']
-['path_tpc']['curves'][691]['offset']
+['path_tpc']['curves'][690]['res_net_meters']
+['path_tpc']['curves'][691]['offset_meters']
 ['path_tpc']['curves'][691]['res_coeff']
-['path_tpc']['curves'][691]['res_net']
-['path_tpc']['curves'][692]['offset']
+['path_tpc']['curves'][691]['res_net_meters']
+['path_tpc']['curves'][692]['offset_meters']
 ['path_tpc']['curves'][692]['res_coeff']
-['path_tpc']['curves'][692]['res_net']
-['path_tpc']['curves'][693]['offset']
+['path_tpc']['curves'][692]['res_net_meters']
+['path_tpc']['curves'][693]['offset_meters']
 ['path_tpc']['curves'][693]['res_coeff']
-['path_tpc']['curves'][693]['res_net']
-['path_tpc']['curves'][694]['offset']
+['path_tpc']['curves'][693]['res_net_meters']
+['path_tpc']['curves'][694]['offset_meters']
 ['path_tpc']['curves'][694]['res_coeff']
-['path_tpc']['curves'][694]['res_net']
-['path_tpc']['curves'][695]['offset']
+['path_tpc']['curves'][694]['res_net_meters']
+['path_tpc']['curves'][695]['offset_meters']
 ['path_tpc']['curves'][695]['res_coeff']
-['path_tpc']['curves'][695]['res_net']
-['path_tpc']['curves'][696]['offset']
+['path_tpc']['curves'][695]['res_net_meters']
+['path_tpc']['curves'][696]['offset_meters']
 ['path_tpc']['curves'][696]['res_coeff']
-['path_tpc']['curves'][696]['res_net']
-['path_tpc']['curves'][697]['offset']
+['path_tpc']['curves'][696]['res_net_meters']
+['path_tpc']['curves'][697]['offset_meters']
 ['path_tpc']['curves'][697]['res_coeff']
-['path_tpc']['curves'][697]['res_net']
-['path_tpc']['curves'][698]['offset']
+['path_tpc']['curves'][697]['res_net_meters']
+['path_tpc']['curves'][698]['offset_meters']
 ['path_tpc']['curves'][698]['res_coeff']
-['path_tpc']['curves'][698]['res_net']
-['path_tpc']['curves'][699]['offset']
+['path_tpc']['curves'][698]['res_net_meters']
+['path_tpc']['curves'][699]['offset_meters']
 ['path_tpc']['curves'][699]['res_coeff']
-['path_tpc']['curves'][699]['res_net']
-['path_tpc']['curves'][700]['offset']
+['path_tpc']['curves'][699]['res_net_meters']
+['path_tpc']['curves'][700]['offset_meters']
 ['path_tpc']['curves'][700]['res_coeff']
-['path_tpc']['curves'][700]['res_net']
-['path_tpc']['curves'][701]['offset']
+['path_tpc']['curves'][700]['res_net_meters']
+['path_tpc']['curves'][701]['offset_meters']
 ['path_tpc']['curves'][701]['res_coeff']
-['path_tpc']['curves'][701]['res_net']
-['path_tpc']['curves'][702]['offset']
+['path_tpc']['curves'][701]['res_net_meters']
+['path_tpc']['curves'][702]['offset_meters']
 ['path_tpc']['curves'][702]['res_coeff']
-['path_tpc']['curves'][702]['res_net']
-['path_tpc']['curves'][703]['offset']
+['path_tpc']['curves'][702]['res_net_meters']
+['path_tpc']['curves'][703]['offset_meters']
 ['path_tpc']['curves'][703]['res_coeff']
-['path_tpc']['curves'][703]['res_net']
-['path_tpc']['curves'][704]['offset']
+['path_tpc']['curves'][703]['res_net_meters']
+['path_tpc']['curves'][704]['offset_meters']
 ['path_tpc']['curves'][704]['res_coeff']
-['path_tpc']['curves'][704]['res_net']
-['path_tpc']['curves'][705]['offset']
+['path_tpc']['curves'][704]['res_net_meters']
+['path_tpc']['curves'][705]['offset_meters']
 ['path_tpc']['curves'][705]['res_coeff']
-['path_tpc']['curves'][705]['res_net']
-['path_tpc']['curves'][706]['offset']
+['path_tpc']['curves'][705]['res_net_meters']
+['path_tpc']['curves'][706]['offset_meters']
 ['path_tpc']['curves'][706]['res_coeff']
-['path_tpc']['curves'][706]['res_net']
-['path_tpc']['curves'][707]['offset']
+['path_tpc']['curves'][706]['res_net_meters']
+['path_tpc']['curves'][707]['offset_meters']
 ['path_tpc']['curves'][707]['res_coeff']
-['path_tpc']['curves'][707]['res_net']
-['path_tpc']['curves'][708]['offset']
+['path_tpc']['curves'][707]['res_net_meters']
+['path_tpc']['curves'][708]['offset_meters']
 ['path_tpc']['curves'][708]['res_coeff']
-['path_tpc']['curves'][708]['res_net']
-['path_tpc']['curves'][709]['offset']
+['path_tpc']['curves'][708]['res_net_meters']
+['path_tpc']['curves'][709]['offset_meters']
 ['path_tpc']['curves'][709]['res_coeff']
-['path_tpc']['curves'][709]['res_net']
-['path_tpc']['curves'][710]['offset']
+['path_tpc']['curves'][709]['res_net_meters']
+['path_tpc']['curves'][710]['offset_meters']
 ['path_tpc']['curves'][710]['res_coeff']
-['path_tpc']['curves'][710]['res_net']
-['path_tpc']['curves'][711]['offset']
+['path_tpc']['curves'][710]['res_net_meters']
+['path_tpc']['curves'][711]['offset_meters']
 ['path_tpc']['curves'][711]['res_coeff']
-['path_tpc']['curves'][711]['res_net']
-['path_tpc']['curves'][712]['offset']
+['path_tpc']['curves'][711]['res_net_meters']
+['path_tpc']['curves'][712]['offset_meters']
 ['path_tpc']['curves'][712]['res_coeff']
-['path_tpc']['curves'][712]['res_net']
-['path_tpc']['curves'][713]['offset']
+['path_tpc']['curves'][712]['res_net_meters']
+['path_tpc']['curves'][713]['offset_meters']
 ['path_tpc']['curves'][713]['res_coeff']
-['path_tpc']['curves'][713]['res_net']
-['path_tpc']['curves'][714]['offset']
+['path_tpc']['curves'][713]['res_net_meters']
+['path_tpc']['curves'][714]['offset_meters']
 ['path_tpc']['curves'][714]['res_coeff']
-['path_tpc']['curves'][714]['res_net']
-['path_tpc']['curves'][715]['offset']
+['path_tpc']['curves'][714]['res_net_meters']
+['path_tpc']['curves'][715]['offset_meters']
 ['path_tpc']['curves'][715]['res_coeff']
-['path_tpc']['curves'][715]['res_net']
-['path_tpc']['curves'][716]['offset']
+['path_tpc']['curves'][715]['res_net_meters']
+['path_tpc']['curves'][716]['offset_meters']
 ['path_tpc']['curves'][716]['res_coeff']
-['path_tpc']['curves'][716]['res_net']
-['path_tpc']['curves'][717]['offset']
+['path_tpc']['curves'][716]['res_net_meters']
+['path_tpc']['curves'][717]['offset_meters']
 ['path_tpc']['curves'][717]['res_coeff']
-['path_tpc']['curves'][717]['res_net']
-['path_tpc']['curves'][718]['offset']
+['path_tpc']['curves'][717]['res_net_meters']
+['path_tpc']['curves'][718]['offset_meters']
 ['path_tpc']['curves'][718]['res_coeff']
-['path_tpc']['curves'][718]['res_net']
-['path_tpc']['curves'][719]['offset']
+['path_tpc']['curves'][718]['res_net_meters']
+['path_tpc']['curves'][719]['offset_meters']
 ['path_tpc']['curves'][719]['res_coeff']
-['path_tpc']['curves'][719]['res_net']
-['path_tpc']['curves'][720]['offset']
+['path_tpc']['curves'][719]['res_net_meters']
+['path_tpc']['curves'][720]['offset_meters']
 ['path_tpc']['curves'][720]['res_coeff']
-['path_tpc']['curves'][720]['res_net']
-['path_tpc']['curves'][721]['offset']
+['path_tpc']['curves'][720]['res_net_meters']
+['path_tpc']['curves'][721]['offset_meters']
 ['path_tpc']['curves'][721]['res_coeff']
-['path_tpc']['curves'][721]['res_net']
-['path_tpc']['curves'][722]['offset']
+['path_tpc']['curves'][721]['res_net_meters']
+['path_tpc']['curves'][722]['offset_meters']
 ['path_tpc']['curves'][722]['res_coeff']
-['path_tpc']['curves'][722]['res_net']
-['path_tpc']['curves'][723]['offset']
+['path_tpc']['curves'][722]['res_net_meters']
+['path_tpc']['curves'][723]['offset_meters']
 ['path_tpc']['curves'][723]['res_coeff']
-['path_tpc']['curves'][723]['res_net']
-['path_tpc']['curves'][724]['offset']
+['path_tpc']['curves'][723]['res_net_meters']
+['path_tpc']['curves'][724]['offset_meters']
 ['path_tpc']['curves'][724]['res_coeff']
-['path_tpc']['curves'][724]['res_net']
-['path_tpc']['curves'][725]['offset']
+['path_tpc']['curves'][724]['res_net_meters']
+['path_tpc']['curves'][725]['offset_meters']
 ['path_tpc']['curves'][725]['res_coeff']
-['path_tpc']['curves'][725]['res_net']
-['path_tpc']['curves'][726]['offset']
+['path_tpc']['curves'][725]['res_net_meters']
+['path_tpc']['curves'][726]['offset_meters']
 ['path_tpc']['curves'][726]['res_coeff']
-['path_tpc']['curves'][726]['res_net']
-['path_tpc']['curves'][727]['offset']
+['path_tpc']['curves'][726]['res_net_meters']
+['path_tpc']['curves'][727]['offset_meters']
 ['path_tpc']['curves'][727]['res_coeff']
-['path_tpc']['curves'][727]['res_net']
-['path_tpc']['curves'][728]['offset']
+['path_tpc']['curves'][727]['res_net_meters']
+['path_tpc']['curves'][728]['offset_meters']
 ['path_tpc']['curves'][728]['res_coeff']
-['path_tpc']['curves'][728]['res_net']
-['path_tpc']['curves'][729]['offset']
+['path_tpc']['curves'][728]['res_net_meters']
+['path_tpc']['curves'][729]['offset_meters']
 ['path_tpc']['curves'][729]['res_coeff']
-['path_tpc']['curves'][729]['res_net']
-['path_tpc']['curves'][730]['offset']
+['path_tpc']['curves'][729]['res_net_meters']
+['path_tpc']['curves'][730]['offset_meters']
 ['path_tpc']['curves'][730]['res_coeff']
-['path_tpc']['curves'][730]['res_net']
-['path_tpc']['curves'][731]['offset']
+['path_tpc']['curves'][730]['res_net_meters']
+['path_tpc']['curves'][731]['offset_meters']
 ['path_tpc']['curves'][731]['res_coeff']
-['path_tpc']['curves'][731]['res_net']
-['path_tpc']['curves'][732]['offset']
+['path_tpc']['curves'][731]['res_net_meters']
+['path_tpc']['curves'][732]['offset_meters']
 ['path_tpc']['curves'][732]['res_coeff']
-['path_tpc']['curves'][732]['res_net']
-['path_tpc']['curves'][733]['offset']
+['path_tpc']['curves'][732]['res_net_meters']
+['path_tpc']['curves'][733]['offset_meters']
 ['path_tpc']['curves'][733]['res_coeff']
-['path_tpc']['curves'][733]['res_net']
-['path_tpc']['curves'][734]['offset']
+['path_tpc']['curves'][733]['res_net_meters']
+['path_tpc']['curves'][734]['offset_meters']
 ['path_tpc']['curves'][734]['res_coeff']
-['path_tpc']['curves'][734]['res_net']
-['path_tpc']['curves'][735]['offset']
+['path_tpc']['curves'][734]['res_net_meters']
+['path_tpc']['curves'][735]['offset_meters']
 ['path_tpc']['curves'][735]['res_coeff']
-['path_tpc']['curves'][735]['res_net']
-['path_tpc']['curves'][736]['offset']
+['path_tpc']['curves'][735]['res_net_meters']
+['path_tpc']['curves'][736]['offset_meters']
 ['path_tpc']['curves'][736]['res_coeff']
-['path_tpc']['curves'][736]['res_net']
-['path_tpc']['curves'][737]['offset']
+['path_tpc']['curves'][736]['res_net_meters']
+['path_tpc']['curves'][737]['offset_meters']
 ['path_tpc']['curves'][737]['res_coeff']
-['path_tpc']['curves'][737]['res_net']
-['path_tpc']['curves'][738]['offset']
+['path_tpc']['curves'][737]['res_net_meters']
+['path_tpc']['curves'][738]['offset_meters']
 ['path_tpc']['curves'][738]['res_coeff']
-['path_tpc']['curves'][738]['res_net']
-['path_tpc']['curves'][739]['offset']
+['path_tpc']['curves'][738]['res_net_meters']
+['path_tpc']['curves'][739]['offset_meters']
 ['path_tpc']['curves'][739]['res_coeff']
-['path_tpc']['curves'][739]['res_net']
-['path_tpc']['curves'][740]['offset']
+['path_tpc']['curves'][739]['res_net_meters']
+['path_tpc']['curves'][740]['offset_meters']
 ['path_tpc']['curves'][740]['res_coeff']
-['path_tpc']['curves'][740]['res_net']
-['path_tpc']['curves'][741]['offset']
+['path_tpc']['curves'][740]['res_net_meters']
+['path_tpc']['curves'][741]['offset_meters']
 ['path_tpc']['curves'][741]['res_coeff']
-['path_tpc']['curves'][741]['res_net']
-['path_tpc']['curves'][742]['offset']
+['path_tpc']['curves'][741]['res_net_meters']
+['path_tpc']['curves'][742]['offset_meters']
 ['path_tpc']['curves'][742]['res_coeff']
-['path_tpc']['curves'][742]['res_net']
-['path_tpc']['curves'][743]['offset']
+['path_tpc']['curves'][742]['res_net_meters']
+['path_tpc']['curves'][743]['offset_meters']
 ['path_tpc']['curves'][743]['res_coeff']
-['path_tpc']['curves'][743]['res_net']
-['path_tpc']['curves'][744]['offset']
+['path_tpc']['curves'][743]['res_net_meters']
+['path_tpc']['curves'][744]['offset_meters']
 ['path_tpc']['curves'][744]['res_coeff']
-['path_tpc']['curves'][744]['res_net']
-['path_tpc']['curves'][745]['offset']
+['path_tpc']['curves'][744]['res_net_meters']
+['path_tpc']['curves'][745]['offset_meters']
 ['path_tpc']['curves'][745]['res_coeff']
-['path_tpc']['curves'][745]['res_net']
-['path_tpc']['curves'][746]['offset']
+['path_tpc']['curves'][745]['res_net_meters']
+['path_tpc']['curves'][746]['offset_meters']
 ['path_tpc']['curves'][746]['res_coeff']
-['path_tpc']['curves'][746]['res_net']
-['path_tpc']['curves'][747]['offset']
+['path_tpc']['curves'][746]['res_net_meters']
+['path_tpc']['curves'][747]['offset_meters']
 ['path_tpc']['curves'][747]['res_coeff']
-['path_tpc']['curves'][747]['res_net']
-['path_tpc']['curves'][748]['offset']
+['path_tpc']['curves'][747]['res_net_meters']
+['path_tpc']['curves'][748]['offset_meters']
 ['path_tpc']['curves'][748]['res_coeff']
-['path_tpc']['curves'][748]['res_net']
-['path_tpc']['curves'][749]['offset']
+['path_tpc']['curves'][748]['res_net_meters']
+['path_tpc']['curves'][749]['offset_meters']
 ['path_tpc']['curves'][749]['res_coeff']
-['path_tpc']['curves'][749]['res_net']
-['path_tpc']['curves'][750]['offset']
+['path_tpc']['curves'][749]['res_net_meters']
+['path_tpc']['curves'][750]['offset_meters']
 ['path_tpc']['curves'][750]['res_coeff']
-['path_tpc']['curves'][750]['res_net']
-['path_tpc']['curves'][751]['offset']
+['path_tpc']['curves'][750]['res_net_meters']
+['path_tpc']['curves'][751]['offset_meters']
 ['path_tpc']['curves'][751]['res_coeff']
-['path_tpc']['curves'][751]['res_net']
-['path_tpc']['curves'][752]['offset']
+['path_tpc']['curves'][751]['res_net_meters']
+['path_tpc']['curves'][752]['offset_meters']
 ['path_tpc']['curves'][752]['res_coeff']
-['path_tpc']['curves'][752]['res_net']
-['path_tpc']['curves'][753]['offset']
+['path_tpc']['curves'][752]['res_net_meters']
+['path_tpc']['curves'][753]['offset_meters']
 ['path_tpc']['curves'][753]['res_coeff']
-['path_tpc']['curves'][753]['res_net']
-['path_tpc']['curves'][754]['offset']
+['path_tpc']['curves'][753]['res_net_meters']
+['path_tpc']['curves'][754]['offset_meters']
 ['path_tpc']['curves'][754]['res_coeff']
-['path_tpc']['curves'][754]['res_net']
-['path_tpc']['curves'][755]['offset']
+['path_tpc']['curves'][754]['res_net_meters']
+['path_tpc']['curves'][755]['offset_meters']
 ['path_tpc']['curves'][755]['res_coeff']
-['path_tpc']['curves'][755]['res_net']
-['path_tpc']['curves'][756]['offset']
+['path_tpc']['curves'][755]['res_net_meters']
+['path_tpc']['curves'][756]['offset_meters']
 ['path_tpc']['curves'][756]['res_coeff']
-['path_tpc']['curves'][756]['res_net']
-['path_tpc']['curves'][757]['offset']
+['path_tpc']['curves'][756]['res_net_meters']
+['path_tpc']['curves'][757]['offset_meters']
 ['path_tpc']['curves'][757]['res_coeff']
-['path_tpc']['curves'][757]['res_net']
-['path_tpc']['curves'][758]['offset']
+['path_tpc']['curves'][757]['res_net_meters']
+['path_tpc']['curves'][758]['offset_meters']
 ['path_tpc']['curves'][758]['res_coeff']
-['path_tpc']['curves'][758]['res_net']
-['path_tpc']['curves'][759]['offset']
+['path_tpc']['curves'][758]['res_net_meters']
+['path_tpc']['curves'][759]['offset_meters']
 ['path_tpc']['curves'][759]['res_coeff']
-['path_tpc']['curves'][759]['res_net']
-['path_tpc']['curves'][760]['offset']
+['path_tpc']['curves'][759]['res_net_meters']
+['path_tpc']['curves'][760]['offset_meters']
 ['path_tpc']['curves'][760]['res_coeff']
-['path_tpc']['curves'][760]['res_net']
-['path_tpc']['curves'][761]['offset']
+['path_tpc']['curves'][760]['res_net_meters']
+['path_tpc']['curves'][761]['offset_meters']
 ['path_tpc']['curves'][761]['res_coeff']
-['path_tpc']['curves'][761]['res_net']
-['path_tpc']['curves'][762]['offset']
+['path_tpc']['curves'][761]['res_net_meters']
+['path_tpc']['curves'][762]['offset_meters']
 ['path_tpc']['curves'][762]['res_coeff']
-['path_tpc']['curves'][762]['res_net']
-['path_tpc']['curves'][763]['offset']
+['path_tpc']['curves'][762]['res_net_meters']
+['path_tpc']['curves'][763]['offset_meters']
 ['path_tpc']['curves'][763]['res_coeff']
-['path_tpc']['curves'][763]['res_net']
-['path_tpc']['curves'][764]['offset']
+['path_tpc']['curves'][763]['res_net_meters']
+['path_tpc']['curves'][764]['offset_meters']
 ['path_tpc']['curves'][764]['res_coeff']
-['path_tpc']['curves'][764]['res_net']
-['path_tpc']['curves'][765]['offset']
+['path_tpc']['curves'][764]['res_net_meters']
+['path_tpc']['curves'][765]['offset_meters']
 ['path_tpc']['curves'][765]['res_coeff']
-['path_tpc']['curves'][765]['res_net']
-['path_tpc']['curves'][766]['offset']
+['path_tpc']['curves'][765]['res_net_meters']
+['path_tpc']['curves'][766]['offset_meters']
 ['path_tpc']['curves'][766]['res_coeff']
-['path_tpc']['curves'][766]['res_net']
-['path_tpc']['curves'][767]['offset']
+['path_tpc']['curves'][766]['res_net_meters']
+['path_tpc']['curves'][767]['offset_meters']
 ['path_tpc']['curves'][767]['res_coeff']
-['path_tpc']['curves'][767]['res_net']
-['path_tpc']['curves'][768]['offset']
+['path_tpc']['curves'][767]['res_net_meters']
+['path_tpc']['curves'][768]['offset_meters']
 ['path_tpc']['curves'][768]['res_coeff']
-['path_tpc']['curves'][768]['res_net']
-['path_tpc']['curves'][769]['offset']
+['path_tpc']['curves'][768]['res_net_meters']
+['path_tpc']['curves'][769]['offset_meters']
 ['path_tpc']['curves'][769]['res_coeff']
-['path_tpc']['curves'][769]['res_net']
-['path_tpc']['curves'][770]['offset']
+['path_tpc']['curves'][769]['res_net_meters']
+['path_tpc']['curves'][770]['offset_meters']
 ['path_tpc']['curves'][770]['res_coeff']
-['path_tpc']['curves'][770]['res_net']
-['path_tpc']['curves'][771]['offset']
+['path_tpc']['curves'][770]['res_net_meters']
+['path_tpc']['curves'][771]['offset_meters']
 ['path_tpc']['curves'][771]['res_coeff']
-['path_tpc']['curves'][771]['res_net']
-['path_tpc']['curves'][772]['offset']
+['path_tpc']['curves'][771]['res_net_meters']
+['path_tpc']['curves'][772]['offset_meters']
 ['path_tpc']['curves'][772]['res_coeff']
-['path_tpc']['curves'][772]['res_net']
-['path_tpc']['curves'][773]['offset']
+['path_tpc']['curves'][772]['res_net_meters']
+['path_tpc']['curves'][773]['offset_meters']
 ['path_tpc']['curves'][773]['res_coeff']
-['path_tpc']['curves'][773]['res_net']
-['path_tpc']['curves'][774]['offset']
+['path_tpc']['curves'][773]['res_net_meters']
+['path_tpc']['curves'][774]['offset_meters']
 ['path_tpc']['curves'][774]['res_coeff']
-['path_tpc']['curves'][774]['res_net']
-['path_tpc']['curves'][775]['offset']
+['path_tpc']['curves'][774]['res_net_meters']
+['path_tpc']['curves'][775]['offset_meters']
 ['path_tpc']['curves'][775]['res_coeff']
-['path_tpc']['curves'][775]['res_net']
-['path_tpc']['curves'][776]['offset']
+['path_tpc']['curves'][775]['res_net_meters']
+['path_tpc']['curves'][776]['offset_meters']
 ['path_tpc']['curves'][776]['res_coeff']
-['path_tpc']['curves'][776]['res_net']
-['path_tpc']['curves'][777]['offset']
+['path_tpc']['curves'][776]['res_net_meters']
+['path_tpc']['curves'][777]['offset_meters']
 ['path_tpc']['curves'][777]['res_coeff']
-['path_tpc']['curves'][777]['res_net']
-['path_tpc']['curves'][778]['offset']
+['path_tpc']['curves'][777]['res_net_meters']
+['path_tpc']['curves'][778]['offset_meters']
 ['path_tpc']['curves'][778]['res_coeff']
-['path_tpc']['curves'][778]['res_net']
-['path_tpc']['curves'][779]['offset']
+['path_tpc']['curves'][778]['res_net_meters']
+['path_tpc']['curves'][779]['offset_meters']
 ['path_tpc']['curves'][779]['res_coeff']
-['path_tpc']['curves'][779]['res_net']
-['path_tpc']['curves'][780]['offset']
+['path_tpc']['curves'][779]['res_net_meters']
+['path_tpc']['curves'][780]['offset_meters']
 ['path_tpc']['curves'][780]['res_coeff']
-['path_tpc']['curves'][780]['res_net']
-['path_tpc']['curves'][781]['offset']
+['path_tpc']['curves'][780]['res_net_meters']
+['path_tpc']['curves'][781]['offset_meters']
 ['path_tpc']['curves'][781]['res_coeff']
-['path_tpc']['curves'][781]['res_net']
-['path_tpc']['curves'][782]['offset']
+['path_tpc']['curves'][781]['res_net_meters']
+['path_tpc']['curves'][782]['offset_meters']
 ['path_tpc']['curves'][782]['res_coeff']
-['path_tpc']['curves'][782]['res_net']
-['path_tpc']['curves'][783]['offset']
+['path_tpc']['curves'][782]['res_net_meters']
+['path_tpc']['curves'][783]['offset_meters']
 ['path_tpc']['curves'][783]['res_coeff']
-['path_tpc']['curves'][783]['res_net']
-['path_tpc']['curves'][784]['offset']
+['path_tpc']['curves'][783]['res_net_meters']
+['path_tpc']['curves'][784]['offset_meters']
 ['path_tpc']['curves'][784]['res_coeff']
-['path_tpc']['curves'][784]['res_net']
-['path_tpc']['curves'][785]['offset']
+['path_tpc']['curves'][784]['res_net_meters']
+['path_tpc']['curves'][785]['offset_meters']
 ['path_tpc']['curves'][785]['res_coeff']
-['path_tpc']['curves'][785]['res_net']
-['path_tpc']['curves'][786]['offset']
+['path_tpc']['curves'][785]['res_net_meters']
+['path_tpc']['curves'][786]['offset_meters']
 ['path_tpc']['curves'][786]['res_coeff']
-['path_tpc']['curves'][786]['res_net']
+['path_tpc']['curves'][786]['res_net_meters']
 ['path_tpc']['speed_points'][0]['offset']
 ['path_tpc']['speed_points'][0]['speed_limit']
 ['path_tpc']['speed_points'][1]['offset']
@@ -6468,8 +6468,8 @@
 ['braking_points']['points'][42]['speed_limit']
 ['braking_points']['points'][42]['speed_target']
 ['braking_points']['idx_curr']
-['fric_brake']['force_max']
-['fric_brake']['ramp_up_time']
+['fric_brake']['force_max_newtons']
+['fric_brake']['ramp_up_time_seconds']
 ['fric_brake']['ramp_up_coeff']
 ['fric_brake']['state']['i']
 ['fric_brake']['state']['force']

From 8b7d396b3e8b3457bc4eb40fbbb504b1e7c5938b Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Tue, 21 Jan 2025 13:50:08 -0700
Subject: [PATCH 45/50] not sure why this changed

---
 .../demo_variable_paths/variable_path_list_expected.txt         | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt b/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
index bbee3394..dcf7ad11 100644
--- a/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
+++ b/python/altrios/resources/demo_data/demo_variable_paths/variable_path_list_expected.txt
@@ -1100,8 +1100,8 @@
 ['loco_con']['history']['energy_res']
 ['loco_con']['history']['energy_fuel']
 ['loco_con']['save_interval']
-['n_cars_by_type']['Manifest_Empty']
 ['n_cars_by_type']['Manifest_Loaded']
+['n_cars_by_type']['Manifest_Empty']
 ['state']['time']
 ['state']['i']
 ['state']['offset']

From 52d64c347923dcb97d8155e614fc14f8c07b1cf9 Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Tue, 21 Jan 2025 13:54:50 -0700
Subject: [PATCH 46/50] removed deprecated hard-coded braking parameters

---
 rust/altrios-core/src/train/friction_brakes.rs |  2 ++
 rust/altrios-core/src/train/train_config.rs    | 11 +----------
 2 files changed, 3 insertions(+), 10 deletions(-)

diff --git a/rust/altrios-core/src/train/friction_brakes.rs b/rust/altrios-core/src/train/friction_brakes.rs
index b86aa6f7..d786e3ac 100644
--- a/rust/altrios-core/src/train/friction_brakes.rs
+++ b/rust/altrios-core/src/train/friction_brakes.rs
@@ -26,6 +26,8 @@ use crate::imports::*;
     }
 )]
 #[derive(Debug, Clone, PartialEq, Serialize, Deserialize, HistoryMethods, SerdeAPI)]
+// brake propagation rate is ~800 ft/s (about speed of sound)
+// ramp up duration is ~30 s
 pub struct FricBrake {
     /// max static force achievable
     pub force_max: si::Force,
diff --git a/rust/altrios-core/src/train/train_config.rs b/rust/altrios-core/src/train/train_config.rs
index a5ae5446..0e79dc48 100644
--- a/rust/altrios-core/src/train/train_config.rs
+++ b/rust/altrios-core/src/train/train_config.rs
@@ -542,12 +542,6 @@ impl TrainSimBuilder {
             ))
         };
 
-        // brake propagation rate is 800 ft/s (about speed of sound)
-        // ramp up duration is ~30 s
-        // TODO: make this not hard coded!
-        let ramp_up_time = 0.0 * uc::S;
-        let ramp_up_coeff = 0.6 * uc::R;
-
         let fric_brake = FricBrake::new(
             max_fric_braking,
             ramp_up_time,
@@ -1457,10 +1451,7 @@ impl SpeedLimitTrainSimVec {
     }
 
     pub fn get_cars_moved(&self, annualize: bool) -> f64 {
-        self.0
-            .iter()
-            .map(|sim| sim.get_cars_moved(annualize))
-            .sum()
+        self.0.iter().map(|sim| sim.get_cars_moved(annualize)).sum()
     }
 
     pub fn get_res_kilometers(&mut self, annualize: bool) -> f64 {

From 52e568d3035b95b83638d51f3243c5aef8cd3d94 Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Tue, 21 Jan 2025 14:09:00 -0700
Subject: [PATCH 47/50] fixed errors related to deleting hardcoded parameters

---
 .../altrios-core/src/train/friction_brakes.rs | 19 +++++++++++--------
 rust/altrios-core/src/train/train_config.rs   |  8 +-------
 2 files changed, 12 insertions(+), 15 deletions(-)

diff --git a/rust/altrios-core/src/train/friction_brakes.rs b/rust/altrios-core/src/train/friction_brakes.rs
index d786e3ac..b376b3f3 100644
--- a/rust/altrios-core/src/train/friction_brakes.rs
+++ b/rust/altrios-core/src/train/friction_brakes.rs
@@ -4,22 +4,22 @@ use crate::imports::*;
     #[new]
     #[pyo3(signature = (
         force_max_newtons,
-        ramp_up_time_seconds,
-        ramp_up_coeff,
+        ramp_up_time_seconds=None,
+        ramp_up_coeff=None,
         state=None,
         save_interval=None,
     ))]
     fn __new__(
         force_max_newtons: f64,
-        ramp_up_time_seconds: f64,
-        ramp_up_coeff: f64,
+        ramp_up_time_seconds: Option<f64>,
+        ramp_up_coeff: Option<f64>,
         state: Option<FricBrakeState>,
         save_interval: Option<usize>,
     ) -> Self {
         Self::new(
             force_max_newtons * uc::N,
-            ramp_up_time_seconds * uc::S,
-            ramp_up_coeff * uc::R,
+            ramp_up_time_seconds.map(|ruts| ruts * uc::S),
+            ramp_up_coeff.map(|ruc| ruc * uc::R),
             state,
             save_interval,
         )
@@ -67,14 +67,17 @@ impl Default for FricBrake {
 impl FricBrake {
     pub fn new(
         force_max: si::Force,
-        ramp_up_time: si::Time,
-        ramp_up_coeff: si::Ratio,
+        ramp_up_time: Option<si::Time>,
+        ramp_up_coeff: Option<si::Ratio>,
         // recharge_rate_pa_per_sec: f64,
         state: Option<FricBrakeState>,
         save_interval: Option<usize>,
     ) -> Self {
         let mut state = state.unwrap_or_default();
         state.force_max_curr = force_max;
+        let fric_brake_def: Self = Default::default();
+        let ramp_up_time = ramp_up_time.unwrap_or(fric_brake_def.ramp_up_time);
+        let ramp_up_coeff = ramp_up_coeff.unwrap_or(fric_brake_def.ramp_up_coeff);
         Self {
             force_max,
             ramp_up_time,
diff --git a/rust/altrios-core/src/train/train_config.rs b/rust/altrios-core/src/train/train_config.rs
index 0e79dc48..b044b53c 100644
--- a/rust/altrios-core/src/train/train_config.rs
+++ b/rust/altrios-core/src/train/train_config.rs
@@ -542,13 +542,7 @@ impl TrainSimBuilder {
             ))
         };
 
-        let fric_brake = FricBrake::new(
-            max_fric_braking,
-            ramp_up_time,
-            ramp_up_coeff,
-            None,
-            save_interval,
-        );
+        let fric_brake = FricBrake::new(max_fric_braking, None, None, None, save_interval);
 
         Ok((train_params, state, path_tpc, train_res, fric_brake))
     }

From ff4cd5c7afad0ad8e5e0c7107128ee489399352a Mon Sep 17 00:00:00 2001
From: sakhtar <sakhtar@nrel.gov>
Date: Tue, 21 Jan 2025 14:16:18 -0700
Subject: [PATCH 48/50] Incorporated Geordie's changes into the code

---
 rust/altrios-core/src/meet_pass/train_disp/free_path.rs | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/rust/altrios-core/src/meet_pass/train_disp/free_path.rs b/rust/altrios-core/src/meet_pass/train_disp/free_path.rs
index 0c380c71..2d591f86 100644
--- a/rust/altrios-core/src/meet_pass/train_disp/free_path.rs
+++ b/rust/altrios-core/src/meet_pass/train_disp/free_path.rs
@@ -719,9 +719,9 @@ impl TrainDisp {
 
         // Update front and back dispatch nodes
         let (idx_min, idx_max) = if self.disp_node_idx_back < self.disp_node_idx_front {
-            (&mut self.disp_node_idx_front, &mut self.disp_node_idx_back)
-        } else {
             (&mut self.disp_node_idx_back, &mut self.disp_node_idx_front)
+        } else {
+            (&mut self.disp_node_idx_front, &mut self.disp_node_idx_back)
         };
         assert!(idx_max.idx() < idx_join_base);
 

From ff8d0c0cb4e16560f295bb4da03ea1aff3d472bd Mon Sep 17 00:00:00 2001
From: "Bruchon, Matthew" <Matthew.Bruchon@nrel.gov>
Date: Tue, 21 Jan 2025 19:04:58 -0500
Subject: [PATCH 49/50] Update sim_manager.py

---
 python/altrios/sim_manager.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/altrios/sim_manager.py b/python/altrios/sim_manager.py
index 0163224a..c3da4239 100644
--- a/python/altrios/sim_manager.py
+++ b/python/altrios/sim_manager.py
@@ -137,6 +137,7 @@ def main(
          'Arrival_Time_Actual_Hr': pl.Series([this[len(this)-1].time_hours for this in timed_paths], dtype=pl.Float64)}
     )
 
+    train_consist_plan_untrimmed = train_consist_plan.clone()
     train_consist_plan = (train_consist_plan
         .join(train_times,on=["Train_ID","Origin_ID","Destination_ID"],how="left")
     )
@@ -165,4 +166,5 @@ def main(
         nodal_energy_prices,
         train_sims,
         timed_paths,
+        train_consist_plan_untrimmed
     )

From 972f9a2540af661d76244087056db46f436c8101 Mon Sep 17 00:00:00 2001
From: Chad Baker <chad.baker@nrel.gov>
Date: Wed, 22 Jan 2025 08:23:57 -0700
Subject: [PATCH 50/50] incorporated change from Geordie

---
 rust/altrios-core/src/meet_pass/train_disp/free_path.rs | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/rust/altrios-core/src/meet_pass/train_disp/free_path.rs b/rust/altrios-core/src/meet_pass/train_disp/free_path.rs
index 0c380c71..2d591f86 100644
--- a/rust/altrios-core/src/meet_pass/train_disp/free_path.rs
+++ b/rust/altrios-core/src/meet_pass/train_disp/free_path.rs
@@ -719,9 +719,9 @@ impl TrainDisp {
 
         // Update front and back dispatch nodes
         let (idx_min, idx_max) = if self.disp_node_idx_back < self.disp_node_idx_front {
-            (&mut self.disp_node_idx_front, &mut self.disp_node_idx_back)
-        } else {
             (&mut self.disp_node_idx_back, &mut self.disp_node_idx_front)
+        } else {
+            (&mut self.disp_node_idx_front, &mut self.disp_node_idx_back)
         };
         assert!(idx_max.idx() < idx_join_base);