Feature/enable jupyter notebooks

README.md

@@ -3,28 +3,38 @@
 Analysis and visualization of results from the scheduler service. Part of the Innosuisse project
 Rolling Stock Scheduling (RSSched) by SBB and ETH Zurich.
 
-# Usage
-
 To get started with the whole RSSched project, have a look at this [step-by-step instruction](https://github.com/rolling-stock-scheduling/.github/blob/main/getting_started.md).
 
 ## Setup
 
 1. install poetry a package manager for python: https://python-poetry.org/docs/
 
 2. install the dependencies for this project via:
-   
+
    ```sh
    poetry install
    ```
 
-## Plots
+## Usage
+
+Visualize a rolling stock scheduling response from the solver:
+
+```sh
+poetry run rssched-plot rssched/data/small_test_response.json
+```
 
-Visualize scheduler output:
+## Development
+
+Before committing, run the following commands from the project root directory:
 
 ```sh
-poetry run rssched-plot your/schedule.json
+poetry run isort .
+poetry run black .
+poetry run pytest
 ```
 
+Note: If working with jupyter notebooks, also delete the cell outputs before committing.
+
 ---
 
 © 2024 SBB CFF FFS. Licensed under MIT.

notebooks/analyze-depot-vehicles.ipynb

@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Depot Vehicle Analysis\n",
+    "\n",
+    "This notebooks illustrates the usage of the **rssched-analysis** package to read rolling stock scheduling responses from the solver.\n",
+    "\n",
+    "Run `poetry install` first, and set the ipykernel from the workspace `.venv`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rssched.io.reader import import_response\n",
+    "from rssched.io.reader import import_request\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "SOLVER_REQUEST_FILE = \"../rssched/data/small_test_request.json\"\n",
+    "SOLVER_RESPONSE_FILE = \"../rssched/data/small_test_response.json\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "request = import_request(SOLVER_REQUEST_FILE)\n",
+    "response = import_response(SOLVER_RESPONSE_FILE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vehicles per depot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = []\n",
+    "\n",
+    "for depot_load in response.schedule.depot_loads:\n",
+    "    depot = depot_load.depot\n",
+    "    for load in depot_load.load:\n",
+    "        vehicle_type = load.vehicle_type\n",
+    "        spawn_count = load.spawn_count\n",
+    "        data.append({\"location\": depot, \"type\": vehicle_type, \"count\": spawn_count})\n",
+    "\n",
+    "depots = pd.DataFrame(data)\n",
+    "depots.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Total vehicle demand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vehicles = depots.groupby(\"type\")[\"count\"].sum().reset_index()\n",
+    "vehicles.head()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pyproject.toml

@@ -7,7 +7,7 @@ license = "MIT"
 readme = "README.md"
 
 [tool.poetry.dependencies]
-python = "^3.11"
+python = "^3.10"
 numpy = "^1.26.4"
 pandas = "^2.2.1"
 plotly = "^5.20.0"
@@ -16,11 +16,12 @@ pydantic = "^2.6.4"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.1.1"
-black = "^24.3.0"
+black = {extras = ["jupyter"], version = "^24.4.2"}
 isort = "^5.13.2"
 mypy = "^1.9.0"
 flake8 = "^7.0.0"
 pydocstyle = "^6.3.0"
+ipykernel = "^6.29.5"
 
 [tool.poetry.scripts]
 rssched-plot = 'rssched.cli:app'

rssched/cli.py

@@ -1,9 +1,10 @@
+import time
 from pathlib import Path
 from typing import Annotated
 
 import typer
 from typer import Argument, echo
-import time
+
 from rssched.io.reader import import_response
 from rssched.visualization.plot import generate_plots
 

rssched/data/access.py

@@ -6,8 +6,14 @@ class PkgDataAccess:
     def __init__(self) -> None:
         pass
 
+    @staticmethod
+    def locate_request() -> Path:
+        data_folder = resources.files("rssched.data")
+        file_path = data_folder / "small_test_request.json"
+        return file_path
+
     @staticmethod
     def locate_response() -> Path:
         data_folder = resources.files("rssched.data")
-        file_path = data_folder / "output_small_test_input.json"
+        file_path = data_folder / "small_test_response.json"
         return file_path