README Contents

ALTRIOS

Altrios Logo

homepage rust-tests py-tests wheels Python Documentation GitHub

Model Framework Schematic

The Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS) is a unique, fully integrated, open-source software tool to evaluate strategies for deploying advanced locomotive technologies and associated infrastructure for cost-effective decarbonization. ALTRIOS simulates freight-demand driven train scheduling, mainline meet-pass planning, locomotive dynamics, train dynamics, energy conversion efficiencies, and energy storage dynamics of line-haul train operations. Because new locomotives represent a significant long-term capital investment and new technologies must be thoroughly demonstrated before deployment, this tool provides guidance on the risk/reward tradeoffs of different technology rollout strategies. An open, integrated simulation tool is invaluable for identifying future research needs and making decisions on technology development, routes, and train selection. ALTRIOS was developed as part of a collaborative effort by a team comprising The National Renewable Energy Laboratory (NREL), University of Texas (UT), Southwest Research Institute (SwRI), and BNSF Railway.

Much of the core code in ALTRIOS is written in the Rust Programming Language to ensure excellent computational performance and robustness, but we've built ALTRIOS with the intent of users interacting with the code through our feature-rich Python interface.

Installation

If you are an ALTRIOS developer, see Developer Documentation. Otherwise, read on.

Python Setup

  1. Python installation options:
    • Option 1 -- Python: https://www.python.org/downloads/. We recommend Python 3.10. Be sure to check the Add to PATH option during installation.
    • Option 2 -- Anaconda: we recommend https://docs.conda.io/en/latest/miniconda.html.
  2. Setup a python environment. ALTRIOS can work with Python 3.9, or 3.10, but we recommend 3.10 for better performance and user experience. Create a python environment for ALTRIOS with either of two methods:
    • Option 1 -- Python Venv

      1. Navigate to the ALTRIOS folder you just cloned or any folder you'd like for using ALTRIOS. Remember the folder you use!

      2. Assuming you have Python 3.10 installed, run

        • (path to your python3.10 e.g. ~/AppData/Local/Programs/Python/Python310/python.exe) -m venv altrios-venv in Windows
        • python3.10 -m venv altrios-venv in Mac/Unix/Linux in your terminal enviroment (we recommend PowerShell in Windows, which comes pre-installed).

        This tells Python 3.10 to use the venv module to create a virtual environment (which will be ignored by git if named altrios-venv) in the ALTRIOS/altrios-venv/.

      3. Activate the environment you just created to install packages or anytime you're running ALTRIOS:

        • Mac and Linux: source altrios-venv/bin/activate
        • Windows: altrios-venv/Scripts/activate.bat in a windows command prompt or power shell or source altrios-venv/Scripts/activate in git bash terminal
        • When the environment is activated, your terminal session will have a decorator that looks like (altrios-venv).
    • Option 2 -- Anaconda:
      1. Open an Anaconda prompt (in Windows, we recommend Anaconda Powershell Prompt) and run the command conda create -n altrios python=3.10 to create an Anaconda environment named altrios.
      2. Activate the environment to install packages or anytime you're running ALTRIOS: run conda activate altrios.

ALTRIOS Setup

With your Python environment activated, run pip install altrios.

Congratulations, you've completed installation! Whenever you need to use ALTRIOS, be sure to activate your python environment created above.

How to run ALTRIOS

With your activated Python environment with ALTRIOS fully installed, you can download the demo scripts to the current working directory inside of a demos/ folder with:

import altrios as alt  
alt.copy_demo_files()

You can run the Simulation Manager through a multi-week simulation of train operations in by running python sim_manager_demo.py in demos/. This will create a plots/ subfolder in which the plots will be saved. To run interactively, fire up a Python IDE (e.g. VS Code, Spyder), and run the file. If you're in VS Code, you can run the file as a virtual jupyter notebook because of the "cells" that are marked with the # %% annotation. You can click on line 2, for example, and hit <Shift> + <Enter> to run the current cell in an interactive terminal (which will take several seconds to launch) and advance to the next cell. Alternatively, you can hit <Ctrl> + <Shift> + p to enable interactive commands and type "run current cell". There are several other python files in the demos/ folder to demonstrate various capabilities of ALTRIOS.

If you plan to modify the data used in the demo files, copy the data files to your local directory and load them from there, e.g.

res = alt.ReversibleEnergyStorage.from_file(
   alt.resources_root() / "powertrains/reversible_energy_storages/Kokam_NMC_75Ah_flx_drive.yaml"
)

would become

res = alt.ReversibleEnergyStorage.from_file(
   "./custom_battery.yaml"
)

Nearly every code object in ALTRIOS can be read from or written to common data formats. For more details, see the SerdeAPI trait documentation. All of the functions in the SerdeAPI are available through the python interface.

Acknowledgements

  The ALTRIOS Team would like to thank ARPA-E for financially supporting the research through the LOCOMOTIVES program and Dr. Robert Ledoux for his vision and support. We would also like to thank the ARPA-E team for their support and guidance: Dr. Apoorv Agarwal, Mirjana Marden, Alexis Amos, and Catherine Good. We would also like to thank BNSF for their cost share financial support, guidance, and deep understanding of the rail industry’s needs. Additionally, we would like to thank Jinghu Hu for his contributions to the core ALTRIOS code. We would like to thank Chris Hennessy at SwRI for his support. Thank you to Michael Cleveland for his help with developing and kicking off this project.

About this book

This is the overall ALTRIOS documentation. We're working toward making this a fully integrated document that includes both the Python API and Rust core documentation.

Documentation

Python

Rust

Rust API Documentation

ALTRIOS Rust Documentation

Rail Network

The following schematic shows how links in a network are connected:
Conceptual schematic of rail network links in ALTRIOS

In ALTRIOS, each link (path between junctions along the rail with heading, grade, and location) in the rail network has both a direction and a location so each link has a unique link identification (ID) number for each direction. Each link can must be connected to at least one other link and up to four other links, two in each direction, comprising a previous link, an alternate previous link, a next link, and an alternate next link. Based on the above schematic, we can say that the links are interconnected thusly

Link IDFlipped IDID PrevID Prev AltID NextID Next Alt
18N/AN/A4N/A
29N/AN/A3N/A
3102N/A4N/A
4111375
5124N/A6N/A
6135N/AN/AN/A
7144N/AN/AN/A
8111N/AN/AN/A
9210N/AN/AN/A
10311N/A9N/A
1141412810
12513N/A11N/A
136N/AN/A12N/A
147N/AN/A11N/A

Note that for a particular link, the links corresponding to the "Prev" and "Next" IDs are swapped in the reverse direction -- i.e. in the forward direction, link 4 has links 1 and 3 as "ID Prev" and "ID Prev Alt", respectively, and in the reverse direction, link 4 becomes 11 and has links 8 and 10 as "ID Next" and "ID Next Alt", respectively.

The following schematic shows how link lockouts occur:
Conceptual schematic of links that trigger lockouts

Table of Contents

altrios

variable_path_list

def variable_path_list(self, element_as_list: bool = False) -> List[str]

Returns list of key paths to all variables and sub-variables within dict version of self. See example usage in altrios/demos/ demo_variable_paths.py.

Arguments:

  • element_as_list: if True, each element is itself a list of the path elements

variable_path_list_from_py_objs

def variable_path_list_from_py_objs(
        obj: Union[Dict, List],
        pre_path: Optional[str] = None,
        element_as_list: bool = False) -> List[str]

Returns list of key paths to all variables and sub-variables within dict version of class. See example usage in altrios/demos/ demo_variable_paths.py.

Arguments:

  • obj: altrios object in dictionary form from to_pydict()
  • pre_path: This is used to call the method recursively and should not be specified by user. Specifies a path to be added in front of all paths returned by the method.
  • element_as_list: if True, each element is itself a list of the path elements

history_path_list

def history_path_list(self, element_as_list: bool = False) -> List[str]

Returns a list of relative paths to all history variables (all variables that contain history as a subpath). See example usage in altrios/demo_data/demo_variable_paths.py.

Arguments

  • - element_as_list: if True, each element is itself a list of the path elements

to_pydict

def to_pydict(self) -> Dict

Returns self converted to pure python dictionary with no nested Rust objects

from_pydict

@classmethod
def from_pydict(cls, pydict: Dict) -> Self

Instantiates Self from pure python dictionary

to_dataframe

def to_dataframe(
        self,
        pandas: bool = False) -> [pd.DataFrame, pl.DataFrame, pl.LazyFrame]

Returns time series results from altrios object as a Polars or Pandas dataframe.

Arguments

  • - pandas: returns pandas dataframe if True; otherwise, returns polars dataframe by default

altrios.demos.version_migration_demo

Demonstrates how to convert data between previous and current ALTRIOS release compatibility.

altrios.demos.conv_demo

altrios.demos.set_speed_train_sim_demo

altrios.demos.test_demos

altrios.demos.rollout_demo

altrios.demos.demo_logging

altrios.demos.speed_limit_simple_corr_demo

SetSpeedTrainSim over a simple, hypothetical corridor

altrios.demos.speed_limit_train_sim_demo

altrios.demos

Module containing demo files. Be sure to check out https://nrel.github.io/altrios/how-to-run-altrios.

altrios.demos.bel_demo

altrios.demos.demo_variable_paths

Script demonstrating how to use variable_path_list() and history_path_list() demos to find the paths to variables within altrios classes.

altrios.demos.set_speed_simple_corr_demo

SetSpeedTrainSim over a simple, hypothetical corridor

altrios.demos.sim_manager_demo

altrios.metric_calculator

ScenarioInfo Objects

@dataclass
class ScenarioInfo()

Dataclass class maintaining records of scenario parameters that influence metric calculations.

Fields:

  • sims: SpeedLimitTrainSim (single-train sim) or SpeedLimitTrainSimVec (multi-train sim) including simulation results

  • simulation_days: Number of days included in these results (after any warm-start or cool-down days were excluded)

  • annualize: Whether to scale up output metrics to a full year's equivalent.

  • scenario_year: Year that is being considered in this scenario.

  • loco_pool: polars.DataFrame defining the pool of locomotives that were available to potentially be dispatched, each having a Locomotive_ID,Locomotive_Type,Cost_USD,Lifespan_Years.
    Not required for single-train sim.

  • consist_plan: polars.DataFrame defining dispatched train consists, where each row includes a Locomotive_ID and a Train_ID. Not required for single-train sim.

  • refuel_facilities: polars.DataFrame defining refueling facilities, each with a Refueler_Type, Port_Count, and Cost_USD, and Lifespan_Years. Not required for single-train sim.

  • refuel_sessions: polars.DataFrame defining refueling sessions, each with a Locomotive_ID, Locomotive_Type, Fuel_Type, Node, and Refuel_Energy_J. Not required for single-train sim.

  • emissions_factors: polars.DataFrame with unit CO2eq_kg_per_MWh defined for each Node. Not required for single-train sim.

  • nodal_energy_prices: polars.DataFrame with unit Price defined for each Node and Fuel. Not required for single-train sim.

  • count_unused_locomotives: If True, fleet composition is defined using all locomotives in loco_pool; if False, fleet composition is defined using only the locomotives dispatched. Not required for single-train sim.

main

def main(scenario_infos: Union[ScenarioInfo, List[ScenarioInfo]],
         annual_metrics: Union[Tuple[str, str], List[Tuple[str, str]]] = [
             ('Mt-km', 'million tonne-km'), ('GHG', 'tonne CO2-eq'),
             ('Count_Locomotives', 'assets'), ('Count_Refuelers', 'assets'),
             ('Energy_Costs', 'USD')
         ],
         calculate_multiyear_metrics: bool = True) -> pl.DataFrame

Given a set of simulation results and the associated consist plans, computes economic and environmental metrics.

Arguments:

  • scenario_infos - List (with one entry per scenario year) of Scenario Info objects
  • metricsToCalc - List of metrics to calculate, each specified as a tuple consisting of a metric and the desired unit

  • calculate_multiyear_metrics - True if multi-year rollout costs (including levelized cost) are to be computed Outputs:

  • values - DataFrame of output and intermediate metrics (metric name, units, value, and scenario year)

calculate_annual_metric

def calculate_annual_metric(metric_name: str, units: str,
                            info: ScenarioInfo) -> MetricType

Given a years' worth of simulation results and the associated consist plan, computes the requested metric.

Arguments:

  • thisRow - DataFrame containing the requested metric and requested units

  • info - A scenario information object representing parameters and results for a single year Outputs:

  • values - DataFrame of requested output metric + any intermediate metrics (metric name, units, value, and scenario year)

calculate_rollout_lcotkm

def calculate_rollout_lcotkm(values: MetricType) -> MetricType

Given a DataFrame of each year's costs and gross freight deliveries, computes the multi-year levelized cost per gross tonne-km of freight delivered.

Arguments:

  • values - DataFrame containing total costs and gross freight deliveries for each modeled scenario year Outputs:

    DataFrame of LCOTKM result (metric name, units, value, and scenario year)

calculate_energy_cost

def calculate_energy_cost(info: ScenarioInfo, units: str) -> MetricType

Given a years' worth of simulation results, computes a single year energy costs.

Arguments:

  • info - A scenario information object representing parameters and results for a single year

  • units - Requested units Outputs:

    DataFrame of energy costs + intermediate metrics (metric name, units, value, and scenario year)

calculate_diesel_use

def calculate_diesel_use(info: ScenarioInfo, units: str)

Given a years' worth of simulation results, computes a single year diesel fuel use.

Arguments:

  • info - A scenario information object representing parameters and results for a single year

  • units - Requested units. Outputs:

    DataFrame of diesel use (metric name, units, value, and scenario year)

calculate_electricity_use

def calculate_electricity_use(info: ScenarioInfo, units: str) -> MetricType

Given a years' worth of simulation results, computes a single year grid electricity use.

Arguments:

  • info - A scenario information object representing parameters and results for a single year

  • units - Requested units Outputs:

    DataFrame of grid electricity use (metric name, units, value, and scenario year)

calculate_freight

def calculate_freight(info: ScenarioInfo, units: str) -> MetricType

Given a years' worth of simulation results, computes a single year gross million tonne-km of freight delivered

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)

calculate_ghg

def calculate_ghg(info: ScenarioInfo, units: str) -> MetricType

Given a years' worth of simulation results, computes a single year GHG emissions from energy use

Arguments:

  • info - A scenario information object representing parameters and results for a single year

  • units - Requested units Outputs:

    DataFrame of GHG emissions from energy use (metric name, units, value, and scenario year)

calculate_locomotive_counts

def calculate_locomotive_counts(info: ScenarioInfo, _) -> MetricType

Given a single scenario year's locomotive consist plan, computes the year's locomotive fleet composition

Arguments:

  • info - A scenario information object representing parameters and results for a single year Outputs:

    DataFrame of locomotive fleet composition metrics (metric name, units, value, and scenario year)

calculate_refueler_counts

def calculate_refueler_counts(info: ScenarioInfo, _) -> MetricType

Given a single scenario year's results, counts how many refuelers were included in the simulation.

Arguments:

  • info - A scenario information object representing parameters and results for a single year Outputs:

    DataFrame of locomotive fleet composition metrics (metric name, units, value, and scenario year)

calculate_rollout_investments

def calculate_rollout_investments(values: MetricType) -> MetricType

Given multiple scenario years' locomotive fleet compositions, computes additional across-year metrics

Arguments:

  • values - DataFrame with multiple scenario years' locomotive fleet composition metrics Outputs:

    DataFrame of across-year locomotive fleet composition metrics (metric name, units, value, and scenario year)

calculate_rollout_total_costs

def calculate_rollout_total_costs(values: MetricType) -> MetricType

Given multiple scenario years' locomotive fleet compositions, computes total per-year costs

Arguments:

  • values - DataFrame with annual cost metrics Outputs:

    DataFrame of across-year locomotive fleet composition metrics (metric name, units, value, and scenario year)

altrios.objectives

altrios.resources.networks

altrios.resources

altrios.resources.powertrains.reversible_energy_storages

altrios.resources.powertrains

altrios.resources.powertrains.fuel_converters

altrios.resources.rolling_stock

altrios.resources.trains

altrios.defaults

Module for default modeling assumption constants.

LHV_DIESEL_KJ_PER_KG

https://www.engineeringtoolbox.com/fuels-higher-calorific-values-d_169.html

RHO_DIESEL_KG_PER_M3

https://www.engineeringtoolbox.com/fuels-densities-specific-volumes-d_166.html

DIESEL_REFUEL_RATE_J_PER_HR

300 gallons per minute -> joules per hour

BEL_CHARGER_COST_USD

NREL Cost of Charging (Borlaug) showing ~linear trend on kW; ICCT report showing little change through 2030

DIESEL_LOCO_COST_USD

Zenith et al.

altrios.tests.mock_resources

altrios.tests.test_consist_sim

altrios.tests.test_utilities

altrios.tests.test_locomotive

altrios.tests.test_fuel_grid

altrios.tests.test_metric_calculator

altrios.tests

altrios.tests.test_train_planner

TestTrainPlanner Objects

class TestTrainPlanner(unittest.TestCase)

populate_me

def populate_me()

to be populated with an actual test and renamed accordingly

altrios.tests.test_multi_obj_opt

altrios.tests.test_locomotive_simulation

altrios.tests.test_multi_obj_cal_and_val

altrios.tests.test_powertrain_fuel_conv

altrios.tests.test_powertrain_res

altrios.tests.test_powertrain_generator

altrios.tests.test_objectives

altrios.tests.test_consist

altrios.tests.test_powertrain_edrive

altrios.fuel_grid

altrios.user_interface

altrios.train_planner

TrainPlannerConfig Objects

class TrainPlannerConfig()

__init__

def __init__(
    single_train_mode: bool = False,
    min_cars_per_train: int = 60,
    target_cars_per_train: int = 180,
    manifest_empty_return_ratio: float = 0.6,
    cars_per_locomotive: int = 70,
    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:

demand_loader

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

generate_return_demand

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 laoded 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

generate_origin_manifest_demand

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

balance_trains

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

generate_demand_trains

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

calculate_dispatch_times

def calculate_dispatch_times(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

build_locopool

def build_locopool(config: TrainPlannerConfig,
                   demand_file: Union[pl.DataFrame, Path, str],
                   method: str = "tile",
                   shares: List[float] = []) -> 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

build_refuelers

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

dispatch

def dispatch(dispatch_time: int, ton: float, origin: str,
             loco_pool: pl.DataFrame, hp_per_ton: float) -> pl.Series

Update the locomotive pool by identifying the desired locomotive to dispatch and assign to the new location (destination) with corresponding updated ready time

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 Outputs:

  • selected - Indices of selected locomotives

update_refuel_queue

def update_refuel_queue(
        loco_pool: pl.DataFrame, refuelers: pl.DataFrame, current_time: float,
        event_tracker: pl.DataFrame) -> Tuple[pl.DataFrame, pl.DataFrame]

Update the locomotive pool by identifying the desired locomotive to dispatch and assign to the new location (destination) with corresponding updated ready time

Arguments:

  • loco_pool - locomotive pool dataframe containing all locomotives in the network
  • refuelers - refuelers dataframe containing all refueling ports in the network current_time: event_tracker:

  • hp_per_ton - Horsepower per ton required for this train type on this origin-destination corridor Outputs:

  • loco_pool - Locomotive pool with updates made to servicing, refueling, or queued locomotives

run_train_planner

def run_train_planner(
    rail_vehicles: List[alt.RailVehicle],
    location_map: Dict[str, List[alt.Location]],
    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: TrainPlannerConfig = TrainPlannerConfig(),
    demand_file: Union[pl.DataFrame, Path, str] = defaults.DEMAND_FILE,
    network_charging_guidelines: pl.DataFrame = None
) -> Tuple[pl.DataFrame, pl.DataFrame, pl.DataFrame,
           List[alt.SpeedLimitTrainSim], List[alt.EstTimeNet]]

Run the train planner

Arguments:

rail_vehicles: location_map: network: loco_pool: refuelers: simulation_days:

  • config - Object storing train planner configuration paramaters demand_file: Outputs:

altrios.optimization.cal_and_val

Module for running train, locomotive, and/or consist models to calibrate and validate against test data.

get_delta_seconds

def get_delta_seconds(ds: pd.Series) -> pd.Series

Arugments:

  • ds: pandas.Series; data of the current segment previously passed to to_datetime_from_format returns:
  • out: pandas.Series; a pandas.Series data that shows the datetime deltas between rows of the segment. Row i has time elasped between i and row i-1. Row 0 has value 0.

Returns pd.Series of time delta [s]

get_error

def get_error(t: np.array, mod: np.array, exp: np.array)

Return error for model data, mod, w.r.t. experimental data, exp, over time, t

ModelError Objects

@dataclass
class ModelError(object)

Dataclass class for calculating model error of various ALTRIOS objects w.r.t. test data.

Attributes:

  • ser_model_dict: dict variable in which:

  • key: a str representing trip keyword string

  • value: a str converted from Rust locomotive models' serialization method

  • model_type: str that can only be 'ConsistSimulation', 'SetSpeedTrainSim' or 'LocomotiveSimulation'; indicates which model to instantiate during optimization process

  • dfs: a dict variable in which:

  • key: str representing trip keyword; will be the same keyword as in models

  • value: pandas.DataFrame variable with trip detailed data to be compared against. each df should have a time [s] column

  • objectives: a list of 2-element tuples. For each tuple, element 0 is the name of the reference test data signal in dfs; element 1 is a tuple of strings representing a hierarchical path to the corresponding model signal. This field is used for error calculation.

  • params: a tuple whose individual element is a str containing hierarchical paths to parameters to manipulate starting from one of the 3 possible Rust model structs

  • verbose: bool: if True, the verbose of error calculation will be printed

  • debug: bool: if True, prints more stuff

  • allow_partial: whether to allow partial runs, if True, errors out whenever a run can't be completed

get_errors

def get_errors(
    mod_dict,
    return_mods: Optional[bool] = False,
    pyplot: bool = False,
    plotly: bool = False,
    show_pyplot: bool = False,
    plot_save_dir: Optional[Path] = None,
    plot_perc_err: bool = False,
    font_size: float = 16,
    perc_err_target_for_plot: float = 1.5
) -> Tuple[
        Dict[str, Dict[str, float]],  # error dict
        # if return_mods is True, solved models
        Optional[Tuple[Dict[str, Dict[str, float]], Dict[
            str,
            Union[SetSpeedTrainSim, ConsistSimulation, LocomotiveSimulation],
        ]]]]

Calculate model errors w.r.t. test data for each element in dfs/models for each objective. Arugments:

  • mod_dict: the dict whose values are generated Rust ALTRIOS models
  • return_mods: bool; if true, also returns dict of solved models
  • pyplot: if true, plots objectives with matplotlib.pyplot
  • plotly: if true, plots with plotly. plot_save_dir must be provided.
  • show_pyplot: if true, shows pyplot plots
  • plot_save_dir: Path for saving plots.
  • plot_perc_err: Whether to include axes for plotting % error

Returns:

  • errors: dict whose values are dicts containing the errors wrt each objective
  • solved_mods Optional; dict whose values are the Rust locomotive models; only returned when return_mods is True

update_params

def update_params(
    xs: List[Any]
) -> Dict[str, Union[LocomotiveSimulation, SetSpeedTrainSim,
                     ConsistSimulation]]

Updates model parameters based on xs, which must match length of self.params

setup_plots

def setup_plots(
    key: str,
    mod: Any,
    time_seconds: List[float],
    bc: List[float],
    plots_per_key: int,
    pyplot: bool = False,
    plotly: bool = False,
    plot_save_dir: Optional[str] = None
) -> Tuple[Optional[Figure], Optional[plt.Axes], Optional[go.Figure]]

Arguments:

  • plot: ...
  • plotly: make and save plotly plots

CalibrationProblem Objects

class CalibrationProblem(ElementwiseProblem)

Problem for calibrating models to match test data

run_minimize

def run_minimize(problem: CalibrationProblem,
                 algorithm: GeneticAlgorithm,
                 termination: DMOT,
                 save_history: bool = False,
                 copy_algorithm: bool = False,
                 copy_termination: bool = False,
                 save_path: Optional[str] = "pymoo_res",
                 pickle_res_to_file: bool = False)

Arguments:

  • save_path: filename for results -- will save res_df separately by appending

min_error_selection

def min_error_selection(result_df: pd.DataFrame,
                        param_num: int,
                        norm_num: int = 2) -> np.ndarray

Arguments:

  • result_df - pd.DataFrame containing pymoo res.X and res.F concatenated
  • param_num - number of parameters
  • norm_num - norm number -- e.g. 2 would result in RMS error

altrios.optimization

altrios.optimization.multi_obj_opt

Module for multi-objective optimization. This will likely use PyMOO extensively.

altrios.stringline_old

Created on Wed Dec 15 09:59:15 2021

@author: groscoe2

altrios.sim_manager

Module for getting the output of the Train Consist Planner and Meet Pass Planner to run 3 week simulation.

main

def main(
    rail_vehicles: List[alt.RailVehicle],
    location_map: Dict[str, List[alt.Location]],
    network: List[alt.Link],
    simulation_days: int = defaults.SIMULATION_DAYS,
    warm_start_days: int = defaults.WARM_START_DAYS,
    scenario_year: int = defaults.BASE_ANALYSIS_YEAR,
    target_bel_share: float = 0.5,
    debug: bool = False,
    loco_pool: pl.DataFrame = None,
    refuelers: pl.DataFrame = None,
    grid_emissions_factors: pl.DataFrame = None,
    nodal_energy_prices: pl.DataFrame = None,
    train_planner_config: planner.TrainPlannerConfig = planner.
    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
) -> Tuple[
        pl.DataFrame,
        pl.DataFrame,
        pl.DataFrame,
        pl.DataFrame,
        alt.SpeedLimitTrainSimVec,
        List[List[alt.LinkIdxTime]],
]

Return

return (
    train_consist_plan,
    loco_pool,
    refuelers,
    grid_emissions_factors,
    nodal_energy_prices,
    train_sims,
    timed_paths,
)

altrios.loaders.powertrain_components

altrios.loaders

altrios.rollout

altrios.plot

altrios.utilities

Module for general functions, classes, and unit conversion factors.

package_root

def package_root() -> Path

Returns the package root directory.

resources_root

def resources_root() -> Path

Returns the resources root directory.

KWH_PER_MJ

https://www.eia.gov/energyexplained/units-and-calculators/energy-conversion-calculators.php

cumutrapz

def cumutrapz(x, y)

Returns cumulative trapezoidal integral array for:

Arguments:

  • x - array of monotonically increasing values to integrate over
  • y - array of values being integrated

set_param_from_path

def set_param_from_path(
    model: Union[
        SetSpeedTrainSim,
        ConsistSimulation,
        Consist,
        LocomotiveSimulation,
        Locomotive,
        FuelConverter,
        ReversibleEnergyStorage,
        Generator,
        ElectricDrivetrain,
        PowerTrace,
    ], path: str, value: Any
) -> Union[
        SetSpeedTrainSim,
        ConsistSimulation,
        Consist,
        LocomotiveSimulation,
        Locomotive,
        FuelConverter,
        ReversibleEnergyStorage,
        Generator,
        ElectricDrivetrain,
        PowerTrace,
]

Set parameter value on model for path to parameter

Example usage

import altrios as alt
res = alt.ReversibleEnergyStorage.default()
alt.set_param_from_path(res, "state.soc", 1.0)

resample

def resample(df: pd.DataFrame,
             dt_new: Optional[float] = 1.0,
             time_col: Optional[str] = "Time[s]",
             rate_vars: Optional[Tuple[str]] = [],
             hold_vars: Optional[Tuple[str]] = []) -> pd.DataFrame

Resamples dataframe df.

Arguments:

  • df: dataframe to resample
  • dt_new: new time step size, default 1.0 s
  • time_col: column for time in s
  • rate_vars: list of variables that represent rates that need to be time averaged
  • hold_vars: vars that need zero-order hold from previous nearest time step (e.g. quantized variables like current gear)

smoothen

def smoothen(signal: npt.ArrayLike, period: int = 9) -> npt.ArrayLike

Apply smoothing to signal, assuming 1 Hz data collection.

set_log_level

def set_log_level(level: str | int) -> int

Sets logging level for both Python and Rust. The default logging level is WARNING (30). https://docs.python.org/3/library/logging.html#logging-levels

Parameters

level: str | int Logging level to set. str level name or int logging level

=========== ================
Level       Numeric value
=========== ================
CRITICAL    50
ERROR       40
WARNING     30
INFO        20
DEBUG       10
NOTSET      0

Returns

int Previous log level

copy_demo_files

def copy_demo_files(demo_path: Path = Path("demos"))

Copies demo files from package directory into local directory.

Arguments

  • demo_path: path (relative or absolute in )

Warning

Running this function will overwrite existing files so make sure any files with changes you'd like to keep are renamed.

show_plots

def show_plots() -> bool

Returns true if plots should be displayed

altrios.stringline

Created on Wed Dec 15 09:59:15 2021

@author: groscoe2

warmupLength

hours

cooldownLength

hours

numDays

full simulation length

plotName

name of plot file

networkOverride

'C:/Users/MMP-S/Downloads/network.json' if ussing a different network file than one in results directory

checkSubfolders

walk through subfolders looking for results

overwriteExisting

overwrite existing plots

colors

line graph colors, if unspecified, will use default plotly colors

narrowWidth

narrow width of lines

wideWidth

wide width of lines

Developers

Cloning the GitHub Repo

Clone the repository:

  1. Download and install git -- accept all defaults when installing.
  2. Create a parent directory in your preferred location to contain the repo -- e.g. <USER_HOME>/Documents/altrios_project/.
  3. Open git bash, and inside the directory you created, clone the ALTRIOS repository with e.g. git clone https://github.com/NREL/ALTRIOS.git.

Installing the Python Package

Within the ALTRIOS folder, run pip install -e ".[dev]"

Rust Installation

Install Rust: https://www.rust-lang.org/tools/install.

Automated Building and Testing

There is a shortcut for building and running all tests, assuming you've installed the python package with develop mode. In the root of the ALTRIOS/ folder, run the build_and_test.sh script. In Windows bash (e.g. git bash), run sh build_and_test.sh, or in Linux/Unix, run ./build_and_test.sh. This builds all the Rust code, runs Rust tests, builds the Python-exposed Rust code, and runs the Python tests.

Manually Building the Python API

Run maturin develop --release. Note that not including --release will cause a significant runtime computational performance penalty.

Testing

Manually

Whenever updating code, always run cargo test --release inside ALTRIOS/rust/ to ensure that all tests pass. Also, be sure to rebuild the Python API regularly to ensure that it is up to date. Python unit tests run with python -m unittest discover in the root folder of the git repository.

With GitHub Actions

Any time anyone pushes to main or does any pull request, the GitHub Actions test workflows are triggered.

Releasing

To PyPI With GitHub Actions

To release the package with GitHub Actions, you can follow these steps:

  1. Create a new branch in the format v<major>.<minor>.<patch>, for example v0.2.1.
  2. Update the version number in the pyproject.toml file. Commit and push to https://github.com/NREL/altrios.
  3. Open a pull request into the main branch and make sure all checks pass.
  4. Once the pull request is merged into the main branch by a reviewer, create a new GitHub release and create a tag that matches the branch name. Once the release is created, a GitHub action will be launched to build the wheels and publish them to PyPI.

To crates.io

altrios-core

If you've updated altrios-proc-macros, be sure to publish that crate first and then update the Cargo.toml dependency for this crate.

To release this crate, you need to be setup as developer for this crate in crates.io. Follow these steps:

  1. Increment the version number in rust/altrios-core/Cargo.toml:

    version = "0.2.1".

  2. If changes were made in altrios-proc-macros, follow the release process for that crate first, and then update the altrios-proc-macros dependency version to match the new altrios-proc-macros version in rust/altrios-core/Cargo.toml.

  3. Run git tag ac<major>.<minor>.<patch>, where ac<major>.<minor>.<patch> should look like ac0.1.4, reflecting whatever the current version is.

  4. Push the tag with git push public ac<major>.<minor>.<patch>, where public is this remote: git@github.com:NREL/altrios.git.

  5. Run cargo publish --dry-run to make sure everything checks.

  6. Run cargo publish to release the update.

In the future, we may incorporate this into GitHub Actions.

altrios-proc-macros

To release this crate, you need to be setup as developer for this crate in crates.io. Follow these steps:

  1. Increment the version number in rust/Cargo.toml:

    altrios-proc-macros = { path = "./altrios-core/altrios-proc-macros", version = "0.2.0" }.

  2. Run git tag apm<major>.<minor>.<patch>, where apm<major>.<minor>.<patch> should look like apm0.1.4, reflecting whatever the current version is.

  3. Push the tag with git push public apm<major>.<minor>.<patch>, where public is this remote: git@github.com:NREL/altrios.git.

  4. Run cargo publish --dry-run to make sure everything checks.

  5. Run cargo publish to release the update.

In the future, we may incorporate this into GitHub Actions.

Press Releases

National and International Coverage

Magazines

How to Update This Markdown Book

mdBook Documentation

Setup

If not already done, install mdbook

Serving locally

Run th following in the repository root directory:

  1. If any python files were modified,
    1. Install pipx
    2. Install pydoc-markdown
    3. Install mdbook-toc
    4. run pydoc-markdown -I python/ -p altrios --render-toc > docs/src/python_docs.md. Do not modify this file manually.
  2. Run mdbook serve --open docs/

Publishing

  1. Update book.toml or files in docs/src/
  2. Make sure the docs look good locally by running the steps in Serving Locally
  3. Commit files and push to main branch

After that, a GitHub action will build the book and publish it.