Update what_is_routee.md

docs/installation.md

@@ -13,7 +13,7 @@ pip install nrel.routee.powertrain
 To install the package from source, you can clone the repository and install the package using `pip`:
 
 ```bash
-git clone https://github.nrel.gov/MBAP/routee-powertrain.git
+git clone https://github.com/NREL/routee-powertrain.git
 cd routee-powertrain
 pip install .
 ```
@@ -49,7 +49,7 @@ Then, you'll have to build the python rust extension for powertrain:
 ```bash
 pip install maturin
 
-git clone https://github.nrel.gov/MBAP/routee-powertrain.git
+git clone https://github.com/NREL/routee-powertrain.git
 cd routee-powertrain/rust
 maturin develop --release
 ```

docs/intro.md

@@ -1,8 +1,6 @@
 # RouteE Powertrain
 
-> TODO: Add a better description of the project here.
-
-RouteE Powertrain is a Python package that allows users to work with a set of pre-trained energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available.
+RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc.
 
 ## Quickstart
 

docs/what_is_routee.ipynb

@@ -0,0 +1,146 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What is RouteE?\n",
+    "\n",
+    "RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if \"ground truth\" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc. Common applications of RouteE-Powertrain are energy-aware (\"eco\") routing, energy accounting in mesoscopic simulations, and range estimation (especially for EVs). The diagrams below illustrate the logic and data flows for training custom RouteE-Powertrain models and performing prediction with previously trained models.\n",
+    "\n",
+    "## Training\n",
+    "![image](https://github.com/NREL/routee-powertrain/assets/4818940/f5a89f64-241b-494b-9fe6-e13a34595da0)\n",
+    "\n",
+    "Training new RouteE-Powertrain models requires a set of link aggregate driving data with energy consumption on each link in the road network. Often this data comes from high-frequency GPS or telematics data collected by dedicated loggers or from connected vehicles that are always streaming telematics data. The energy consumption can either be vehicle reported/measured or simulated using a powertrain simulation software like [NREL's FASTSim](https://github.com/NREL/fastsim).\n",
+    "\n",
+    "## Prediction\n",
+    "![image](https://github.com/NREL/routee-powertrain/assets/4818940/b3a1d1af-5060-4bf4-a576-b0b11ffc9424)\n",
+    "\n",
+    "In application, trained RouteE-Powertrain models expect link features as inputs and return predicted energy consumption for a particular vehicle over a link with the particular feature set. The RouteE developers maintain a separate repository for previously trained RouteE-Powertrain models, available for prediction \"off the shelf\". To see which models are available you can use the `pt.list_available_models()` function.\n",
+    "\n",
+    "A couple of models are distributed with the package itself and you can list those like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2016_TOYOTA_Camry_4cyl_2WD', '2017_CHEVROLET_Bolt']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import nrel.routee.powertrain as pt\n",
+    "\n",
+    "pt.list_available_models(external=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition, a larger number of models are available for download and can be listed like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['2012_Ford_Focus',\n",
+       " '2012_Ford_Fusion',\n",
+       " '2016_AUDI_A3_4cyl_2WD',\n",
+       " '2016_BMW_328d_4cyl_2WD',\n",
+       " '2016_CHEVROLET_Malibu_4cyl_2WD',\n",
+       " '2016_CHEVROLET_Spark_EV',\n",
+       " '2016_FORD_C-MAX_HEV',\n",
+       " '2016_FORD_Escape_4cyl_2WD',\n",
+       " '2016_FORD_Explorer_4cyl_2WD',\n",
+       " '2016_HYUNDAI_Elantra_4cyl_2WD',\n",
+       " '2016_Hyundai_Tucson_Fuel_Cell',\n",
+       " '2016_KIA_Optima_Hybrid',\n",
+       " '2016_Leaf_24_kWh',\n",
+       " '2016_MITSUBISHI_i-MiEV',\n",
+       " '2016_Nissan_Leaf_30_kWh',\n",
+       " '2016_TESLA_Model_S60_2WD',\n",
+       " '2016_TOYOTA_Camry_4cyl_2WD',\n",
+       " '2016_TOYOTA_Corolla_4cyl_2WD',\n",
+       " '2016_TOYOTA_Highlander_Hybrid',\n",
+       " '2016_Toyota_Prius_Two_FWD',\n",
+       " '2017_CHEVROLET_Bolt',\n",
+       " '2017_Maruti_Dzire_VDI',\n",
+       " '2017_Toyota_Highlander_3',\n",
+       " '2020_Chevrolet_Colorado_2WD_Diesel',\n",
+       " '2020_VW_Golf_1',\n",
+       " '2020_VW_Golf_2',\n",
+       " '2021_Fiat_Panda_Mild_Hybrid',\n",
+       " '2021_Peugot_3008',\n",
+       " '2022_Ford_F-150_Lightning_4WD',\n",
+       " '2022_Renault_Zoe_ZE50_R135',\n",
+       " '2022_Tesla_Model_3_RWD',\n",
+       " '2022_Tesla_Model_Y_RWD',\n",
+       " '2022_Toyota_Yaris_Hybrid_Mid',\n",
+       " '2022_Volvo_XC40_Recharge_twin',\n",
+       " '2023_Mitsubishi_Pajero_Sport']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pt.list_available_models(local=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To predict with any of these models you can use the `pt.load_model()` function. Here's an example of loading both a local model and an external model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "camry = pt.load_model('2016_TOYOTA_Camry_4cyl_2WD')\n",
+    "tesla = pt.load_model('2022_Tesla_Model_Y_RWD')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

nrel/routee/powertrain/io/load.py

@@ -22,7 +22,6 @@ def list_available_models(local: bool = True, external: bool = True) -> List[str
     Args:
         local: include local models?
         external: include external models?
-        print: should we print the results too?
 
     Returns: a list of model keys
     """

pyproject.toml

@@ -8,7 +8,7 @@ version = "1.0.0"
 description = "RouteE-Powertrain is a tool for predicting energy usage over a set of road links."
 readme = "README.md"
 authors = [{ name = "National Renewable Energy Laboratory" }]
-license = { text = "BSD 3-Clause License Copyright (c) 2022, Alliance for Sustainable Energy, LLC" }
+license = { text = "BSD 3-Clause License Copyright (c) 2023, Alliance for Sustainable Energy, LLC" }
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Science/Research",
@@ -42,7 +42,7 @@ dev = [
 ]
 
 [project.urls]
-Homepage = "https://github.nrel.gov/MBAP/routee-powertrain"
+Homepage = "https://github.com/NREL/routee-powertrain"
 
 [tool.setuptools.packages.find]
 where = ["."] # list of folders that contain the packages (["."] by default)