diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 676fcae..d4f40b5 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -10,45 +10,47 @@ jobs: test: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - - name: Set up python - uses: actions/setup-python@v2 + - name: Install uv + uses: astral-sh/setup-uv@v4 with: - python-version: 3.8 + version: "0.5.10" - - name: Set up env - run: python -m pip install -e .[docs,dev] + - name: Install tkinter for GUI + run: sudo apt install python3-tk -y - - name: Run black - run: black --check . + - name: Set up env + run: uv sync --all-extras - - name: Run isort - run: isort . + - name: Run ruff format + run: uv run ruff format - - name: Run pylint for mdp folder - run: pylint src/behavior_generation_lecture_python/mdp --errors-only + - name: Run ruff linter for mdp folder + run: uv run ruff check src/behavior_generation_lecture_python/mdp - name: Run mypy for mdp folder - run: mypy src/behavior_generation_lecture_python/mdp + run: uv run mypy src/behavior_generation_lecture_python/mdp - name: Test run: | export DISPLAY=:99 Xvfb :99 & - pytest + uv run pytest - name: Check coverage run: | export DISPLAY=:99 Xvfb :99 & - pytest --cov=src --cov-fail-under=85 + uv run pytest --cov=src --cov-fail-under=85 - name: Copy notebooks to docs folder run: cp -r notebooks/* docs/notebooks - name: Build docs - run: mkdocs build + run: | + uv run ipython kernel install --user --name=behavior_generation_lecture + uv run mkdocs build deploy-pages: runs-on: ubuntu-latest @@ -56,19 +58,21 @@ jobs: if: github.ref == 'refs/heads/main' concurrency: deploy-${{ github.ref }} steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - - name: Set up python - uses: actions/setup-python@v2 + - name: Install uv + uses: astral-sh/setup-uv@v4 with: - python-version: 3.8 + version: "0.5.10" - name: Set up env - run: python -m pip install -e .[docs] + run: uv sync --all-extras - name: Copy notebooks to docs folder run: cp -r notebooks/* docs/notebooks - - run: mkdocs gh-deploy --force + - run: | + uv run ipython kernel install --user --name=behavior_generation_lecture + uv run mkdocs gh-deploy --force diff --git a/README.md b/README.md index 7219ed2..9159e3e 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,9 @@ # Python Code for the Lecture "Verhaltensgenerierung für Fahrzeuge" (Behavior Generation for Vehicles) at KIT +[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv) +[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) ![GitHub CI](https://github.com/KIT-MRT/behavior_generation_lecture_python/actions/workflows/ci.yml/badge.svg) ![License](https://img.shields.io/github/license/kit-mrt/behavior_generation_lecture_python) -![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg) This repository contains the python code for the lecture ["Verhaltensgenerierung für Fahrzeuge" (Behavior Generation for Vehicles)](https://www.mrt.kit.edu/lehre_WS_Verhaltensgenerierung_Fahrzeuge.php) at KIT. It is targeted towards both, exemplifying the content of the lecture, and giving a brief introduction to software development. (Please bare with us, the code is largely ported from matlab.) @@ -11,21 +12,25 @@ An API documentation for new parts of the code and exemplary jupyter notebooks c ## Preparing the environment -We encourage the usage of [conda](https://conda.io/) or [virtualenv](https://virtualenv.pypa.io) instead of installing packages to your system directly. +We use [`uv`](https://docs.astral.sh/uv/) as package and project manager. Having `uv` installed, run -Having activated your environment +```sh +# clone this repo +git clone https://github.com/KIT-MRT/behavior_generation_lecture_python.git -- install this package in editable mode: `pip install --editable .` +# change into the repo folder +cd behavior_generation_lecture_python + +# set up a virtual env and install the requirements +uv sync +```
-Making venv kernels available to jupyter? +Making uv kernels available to jupyter?
diff --git a/docs/gen_ref_pages.py b/docs/gen_ref_pages.py index 0228d0c..a4b026e 100644 --- a/docs/gen_ref_pages.py +++ b/docs/gen_ref_pages.py @@ -17,7 +17,7 @@ full_doc_path = Path("reference", doc_path) parts = tuple(module_path.parts) - if not "mdp" in parts and not "graph_search" in parts: + if "mdp" not in parts and "graph_search" not in parts: continue # todo: add other modules here once docstrings added if parts[-1] == "__init__": @@ -36,5 +36,4 @@ mkdocs_gen_files.set_edit_path(full_doc_path, path) with mkdocs_gen_files.open("reference/SUMMARY.md", "w") as nav_file: - nav_file.writelines(nav.build_literate_nav()) diff --git a/notebooks/a_star_notebook.ipynb b/notebooks/a_star_notebook.ipynb index 52316f6..101f7db 100644 --- a/notebooks/a_star_notebook.ipynb +++ b/notebooks/a_star_notebook.ipynb @@ -3,6 +3,7 @@ { "cell_type": "code", "execution_count": null, + "id": "b0c1f089", "metadata": {}, "outputs": [], "source": [ @@ -15,11 +16,11 @@ { "cell_type": "code", "execution_count": null, + "id": "d2311a94", "metadata": {}, "outputs": [], "source": [ "def main():\n", - "\n", " nodes_list = [\n", " [\"HH\", 170, 620, [\"H\", \"B\"]],\n", " [\"H\", 150, 520, [\"B\", \"L\", \"F\", \"HH\"]],\n", @@ -48,6 +49,7 @@ { "cell_type": "code", "execution_count": null, + "id": "ef38e2e0", "metadata": {}, "outputs": [], "source": [ @@ -57,9 +59,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "behavior_generation_lecture", "language": "python", - "name": "python3" + "name": "behavior_generation_lecture" }, "language_info": { "codemirror_mode": { @@ -71,7 +73,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/notebooks/compare_models_notebook.ipynb b/notebooks/compare_models_notebook.ipynb index 19c22ba..43d0402 100644 --- a/notebooks/compare_models_notebook.ipynb +++ b/notebooks/compare_models_notebook.ipynb @@ -3,9 +3,11 @@ { "cell_type": "code", "execution_count": null, + "id": "f5d345f8", "metadata": {}, "outputs": [], "source": [ + "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", @@ -13,13 +15,20 @@ "from behavior_generation_lecture_python.utils.plot_vehicle import plot_vehicle as pv\n", "from behavior_generation_lecture_python.utils.vizard import vizard as vz\n", "\n", - "%matplotlib inline\n", - "# use '%matplotlib notebook' to interact with the vizard" + "\n", + "interactive_widgets = not os.getenv(\"CI\") == \"true\"\n", + "if interactive_widgets:\n", + " # Use widget backend locally, to be able to interact with the plots\n", + " %matplotlib widget\n", + "else:\n", + " # Use inline backend in CI, to render the notebooks for the hosted docs\n", + " %matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, + "id": "ed1fe982", "metadata": {}, "outputs": [], "source": [ @@ -76,16 +85,17 @@ " (point_r_lin,) = ax2.plot([], [], marker=\"o\", color=\"blue\", ms=3)\n", "\n", " def update(i, *fargs):\n", + " slice_ = slice(i + 1, i + 2)\n", " [l.remove() for l in reversed(ax1.lines)]\n", " ax1.plot(x[: i + 1], y[: i + 1], \"b-\", linewidth=0.5)\n", - " point1.set_data(x[i + 1], y[i + 1])\n", + " point1.set_data(x[slice_], y[slice_])\n", " pv.plot_vehicle(ax1, x[i], y[i], psi[i], delta_vals[i])\n", "\n", - " point_delta.set_data(ti[i + 1], delta_vals[i + 1])\n", - " point_beta.set_data(ti[i + 1], beta[i + 1])\n", - " point_r.set_data(ti[i + 1], r[i + 1])\n", - " point_beta_lin.set_data(ti[i + 1], beta_lin[i + 1])\n", - " point_r_lin.set_data(ti[i + 1], r_lin[i + 1])\n", + " point_delta.set_data(ti[slice_], delta_vals[slice_])\n", + " point_beta.set_data(ti[slice_], beta[slice_])\n", + " point_r.set_data(ti[slice_], r[slice_])\n", + " point_beta_lin.set_data(ti[slice_], beta_lin[slice_])\n", + " point_r_lin.set_data(ti[slice_], r_lin[slice_])\n", " for farg in fargs:\n", " print(farg)\n", "\n", @@ -96,6 +106,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d0828333", "metadata": {}, "outputs": [], "source": [ @@ -105,9 +116,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "behavior_generation_lecture", "language": "python", - "name": "python3" + "name": "behavior_generation_lecture" }, "language_info": { "codemirror_mode": { @@ -119,7 +130,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/notebooks/lateral_control_riccati_notebook.ipynb b/notebooks/lateral_control_riccati_notebook.ipynb index 2479d1b..9a96004 100644 --- a/notebooks/lateral_control_riccati_notebook.ipynb +++ b/notebooks/lateral_control_riccati_notebook.ipynb @@ -3,9 +3,11 @@ { "cell_type": "code", "execution_count": null, + "id": "cee10281", "metadata": {}, "outputs": [], "source": [ + "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", @@ -17,13 +19,19 @@ " DEFAULT_VEHICLE_PARAMS,\n", ")\n", "\n", - "%matplotlib inline\n", - "# use '%matplotlib notebook' to interact with the vizard" + "interactive_widgets = not os.getenv(\"CI\") == \"true\"\n", + "if interactive_widgets:\n", + " # Use widget backend locally, to be able to interact with the plots\n", + " %matplotlib widget\n", + "else:\n", + " # Use inline backend in CI, to render the notebooks for the hosted docs\n", + " %matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, + "id": "4813563b", "metadata": {}, "outputs": [], "source": [ @@ -66,7 +74,7 @@ " def update(i, *fargs):\n", " [l.remove() for l in reversed(ax.lines[1:])]\n", " ax.plot(x[: i + 1], y[: i + 1], \"b-\", linewidth=0.5)\n", - " point1.set_data(x[i], y[i])\n", + " point1.set_data(x[i : i + 1], y[i : i + 1])\n", " pv.plot_vehicle(ax, x[i], y[i], psi[i], delta[i])\n", " for farg in fargs:\n", " print(farg)\n", @@ -78,9 +86,8 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "id": "e8e63277", + "metadata": {}, "outputs": [], "source": [ "main()" @@ -89,9 +96,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "behavior_generation_lecture", "language": "python", - "name": "python3" + "name": "behavior_generation_lecture" }, "language_info": { "codemirror_mode": { @@ -103,7 +110,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/notebooks/lateral_control_state_based_notebook.ipynb b/notebooks/lateral_control_state_based_notebook.ipynb index beb10e1..d760899 100644 --- a/notebooks/lateral_control_state_based_notebook.ipynb +++ b/notebooks/lateral_control_state_based_notebook.ipynb @@ -3,9 +3,11 @@ { "cell_type": "code", "execution_count": null, + "id": "14606e50", "metadata": {}, "outputs": [], "source": [ + "import os\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", @@ -14,13 +16,19 @@ "from behavior_generation_lecture_python.utils.plot_vehicle import plot_vehicle as pv\n", "from behavior_generation_lecture_python.utils.vizard import vizard as vz\n", "\n", - "%matplotlib inline\n", - "# use '%matplotlib notebook' to interact with the vizard" + "interactive_widgets = not os.getenv(\"CI\") == \"true\"\n", + "if interactive_widgets:\n", + " # Use widget backend locally, to be able to interact with the plots\n", + " %matplotlib widget\n", + "else:\n", + " # Use inline backend in CI, to render the notebooks for the hosted docs\n", + " %matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, + "id": "08eeef08", "metadata": {}, "outputs": [], "source": [ @@ -50,7 +58,7 @@ " def update(i, *fargs):\n", " [l.remove() for l in reversed(ax.lines[1:])]\n", " ax.plot(x[:i], y[:i], \"b-\", linewidth=0.5)\n", - " point1.set_data(x[i], y[i])\n", + " point1.set_data(x[i : i + 1], y[i : i + 1])\n", " pv.plot_vehicle(ax, x[i], y[i], psi[i], delta[i])\n", " for farg in fargs:\n", " print(farg)\n", @@ -62,6 +70,7 @@ { "cell_type": "code", "execution_count": null, + "id": "d2c31289", "metadata": {}, "outputs": [], "source": [ @@ -74,9 +83,9 @@ "hash": "ea80bdc8d9eecdfb0ad4850befec70bcf98ec6f56b32ef8090165e65e0e9c093" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "behavior_generation_lecture", "language": "python", - "name": "python3" + "name": "behavior_generation_lecture" }, "language_info": { "codemirror_mode": { @@ -88,7 +97,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/notebooks/mdp_policy_gradient.ipynb b/notebooks/mdp_policy_gradient.ipynb index 0759ff9..22dce58 100644 --- a/notebooks/mdp_policy_gradient.ipynb +++ b/notebooks/mdp_policy_gradient.ipynb @@ -6,6 +6,8 @@ "metadata": {}, "outputs": [], "source": [ + "import os\n", + "\n", "from behavior_generation_lecture_python.mdp.policy import CategorialPolicy\n", "from behavior_generation_lecture_python.utils.grid_plotting import (\n", " make_plot_policy_step_function,\n", @@ -94,8 +96,8 @@ "metadata": {}, "outputs": [], "source": [ - "mkdocs_flag = True\n", - "if mkdocs_flag:\n", + "interactive_widgets = not os.getenv(\"CI\") == \"true\" # non-interative in CI\n", + "if interactive_widgets:\n", " import ipywidgets\n", " from IPython.display import display\n", "\n", @@ -103,16 +105,9 @@ " min=0, max=len(model_checkpoints) - 1, step=1, value=0\n", " )\n", " w = ipywidgets.interactive(plot_policy_step_grid_map, iteration=iteration_slider)\n", - " display(w)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_policy_step_grid_map(100)" + " display(w)\n", + "else:\n", + " plot_policy_step_grid_map(100)" ] }, { @@ -200,7 +195,7 @@ "metadata": {}, "outputs": [], "source": [ - "if mkdocs_flag:\n", + "if interactive_widgets:\n", " import ipywidgets\n", " from IPython.display import display\n", "\n", @@ -208,24 +203,17 @@ " min=0, max=len(model_checkpoints) - 1, step=1, value=0\n", " )\n", " w = ipywidgets.interactive(plot_policy_step_grid_map, iteration=iteration_slider)\n", - " display(w)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_policy_step_grid_map(200)" + " display(w)\n", + "else:\n", + " plot_policy_step_grid_map(200)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.11.5 ('kit_vorlesung_tutorial')", + "display_name": "behavior_generation_lecture", "language": "python", - "name": "python3" + "name": "behavior_generation_lecture" }, "language_info": { "codemirror_mode": { @@ -237,7 +225,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.18" + "version": "3.9.6" }, "vscode": { "interpreter": { diff --git a/notebooks/mdp_q_learning.ipynb b/notebooks/mdp_q_learning.ipynb index c73ec74..3d3b08d 100644 --- a/notebooks/mdp_q_learning.ipynb +++ b/notebooks/mdp_q_learning.ipynb @@ -6,10 +6,10 @@ "metadata": {}, "outputs": [], "source": [ + "import os\n", + "\n", "from behavior_generation_lecture_python.mdp.mdp import (\n", - " MDP,\n", " GridMDP,\n", - " expected_utility_of_action,\n", " derive_policy,\n", " q_learning,\n", " GRID_MDP_DICT,\n", @@ -65,13 +65,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ - "mkdocs_flag = False # set to true if you are running the notebook locally\n", - "if mkdocs_flag:\n", + "interactive_widgets = not os.getenv(\"CI\") == \"true\" # non-interative in CI\n", + "if interactive_widgets:\n", " import ipywidgets\n", " from IPython.display import display\n", "\n", @@ -79,16 +77,9 @@ " min=0, max=len(computed_utility_history) - 1, step=1, value=0\n", " )\n", " w = ipywidgets.interactive(plot_grid_step, iteration=iteration_slider)\n", - " display(w)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_grid_step(1000)" + " display(w)\n", + "else:\n", + " plot_grid_step(1000)" ] }, { @@ -146,26 +137,17 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ - "if mkdocs_flag:\n", + "if interactive_widgets:\n", " iteration_slider = ipywidgets.IntSlider(\n", " min=0, max=len(utility_history_highway) - 1, step=1, value=0\n", " )\n", " w = ipywidgets.interactive(plot_grid_step_highway, iteration=iteration_slider)\n", - " display(w)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_grid_step_highway(1000)" + " display(w)\n", + "else:\n", + " plot_grid_step_highway(1000)" ] }, { @@ -196,29 +178,22 @@ "metadata": {}, "outputs": [], "source": [ - "if mkdocs_flag:\n", + "if interactive_widgets:\n", " iteration_slider = ipywidgets.IntSlider(\n", " min=0, max=len(utility_history_highway) - 1, step=1, value=0\n", " )\n", " w = ipywidgets.interactive(plot_policy_step_highway, iteration=iteration_slider)\n", - " display(w)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_policy_step_highway(1000)" + " display(w)\n", + "else:\n", + " plot_policy_step_highway(1000)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "behavior_generation_lecture", "language": "python", - "name": "python3" + "name": "behavior_generation_lecture" }, "language_info": { "codemirror_mode": { @@ -230,7 +205,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/notebooks/mdp_value_iteration.ipynb b/notebooks/mdp_value_iteration.ipynb index fc91284..6d18864 100644 --- a/notebooks/mdp_value_iteration.ipynb +++ b/notebooks/mdp_value_iteration.ipynb @@ -6,10 +6,10 @@ "metadata": {}, "outputs": [], "source": [ + "import os\n", + "\n", "from behavior_generation_lecture_python.mdp.mdp import (\n", - " MDP,\n", " GridMDP,\n", - " expected_utility_of_action,\n", " derive_policy,\n", " value_iteration,\n", " GRID_MDP_DICT,\n", @@ -65,13 +65,11 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ - "mkdocs_flag = False # set to true if you are running the notebook locally\n", - "if mkdocs_flag:\n", + "interactive_widgets = not os.getenv(\"CI\") == \"true\" # non-interative in CI\n", + "if interactive_widgets:\n", " import ipywidgets\n", " from IPython.display import display\n", "\n", @@ -79,16 +77,9 @@ " min=0, max=len(computed_utility_history) - 1, step=1, value=0\n", " )\n", " w = ipywidgets.interactive(plot_grid_step, iteration=iteration_slider)\n", - " display(w)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_grid_step(19)" + " display(w)\n", + "else:\n", + " plot_grid_step(19)" ] }, { @@ -146,26 +137,17 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ - "if mkdocs_flag:\n", + "if interactive_widgets:\n", " iteration_slider = ipywidgets.IntSlider(\n", " min=0, max=len(utility_history_highway) - 1, step=1, value=0\n", " )\n", " w = ipywidgets.interactive(plot_grid_step_highway, iteration=iteration_slider)\n", - " display(w)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_grid_step_highway(10)" + " display(w)\n", + "else:\n", + " plot_grid_step_highway(10)" ] }, { @@ -196,29 +178,22 @@ "metadata": {}, "outputs": [], "source": [ - "if mkdocs_flag:\n", + "if interactive_widgets:\n", " iteration_slider = ipywidgets.IntSlider(\n", " min=0, max=len(utility_history_highway) - 1, step=1, value=0\n", " )\n", " w = ipywidgets.interactive(plot_policy_step_highway, iteration=iteration_slider)\n", - " display(w)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_policy_step_highway(10)" + " display(w)\n", + "else:\n", + " plot_policy_step_highway(10)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "behavior_generation_lecture", "language": "python", - "name": "python3" + "name": "behavior_generation_lecture" }, "language_info": { "codemirror_mode": { @@ -230,7 +205,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/pyproject.toml b/pyproject.toml index fe8169d..e4610d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "behavior_generation_lecture_python" version = "0.0.2" description = "Python code for the respective lecture at KIT" readme = "README.md" -requires-python = ">=3.8" +requires-python = ">=3.9" license = {file = "LICENSE"} authors = [ {name = "Organizers of the lecture 'Verhaltensgenerierung für Fahrzeuge' at KIT" } @@ -18,17 +18,20 @@ dependencies = [ "scipy", "jupyter", "python-statemachine", - "torch" + "torch", + "ruff>=0.8.3", + "ipykernel>=6.29.5", + "ipython>=8.12.3", + "jupyterlab>=4.3.3", + "ipywidgets>=8.1.5", + "ipympl>=0.9.5", ] [project.optional-dependencies] dev = [ - "black[jupyter]==22.3.0", "pytest", "pytest-cov>=3.0.0", - "pylint", "mypy", - "isort" ] docs = [ "mkdocs-material", @@ -41,12 +44,10 @@ docs = [ [project.urls] # Optional "Homepage" = "https://kit-mrt.github.io/behavior_generation_lecture_python/" -"Bug Reports" = "hhttps://github.com/KIT-MRT/behavior_generation_lecture_python/issues" +"Bug Reports" = "https://github.com/KIT-MRT/behavior_generation_lecture_python/issues" "Source" = "https://github.com/KIT-MRT/behavior_generation_lecture_python" [build-system] requires = ["setuptools>=64.0.0", "wheel", "pip>=21.3.0"] build-backend = "setuptools.build_meta" -[tool.isort] -profile = "black" diff --git a/scripts/run_a_star.py b/scripts/run_a_star.py index 82349da..b3b8f39 100644 --- a/scripts/run_a_star.py +++ b/scripts/run_a_star.py @@ -4,7 +4,6 @@ def main(): - nodes_list = [ ["HH", 170, 620, ["H", "B"]], ["H", 150, 520, ["B", "L", "F", "HH"]], diff --git a/scripts/run_compare_dynamic_one_track_models.py b/scripts/run_compare_dynamic_one_track_models.py index 12ef0d6..015d807 100644 --- a/scripts/run_compare_dynamic_one_track_models.py +++ b/scripts/run_compare_dynamic_one_track_models.py @@ -58,16 +58,17 @@ def delta(t): (point_r_lin,) = ax2.plot([], [], marker="o", color="blue", ms=3) def update(i, *fargs): + slice_ = slice(i + 1, i + 2) [l.remove() for l in reversed(ax1.lines)] ax1.plot(x[: i + 1], y[: i + 1], "b-", linewidth=0.5) - point1.set_data(x[i + 1], y[i + 1]) + point1.set_data(x[slice_], y[slice_]) pv.plot_vehicle(ax1, x[i], y[i], psi[i], delta_vals[i]) - point_delta.set_data(timesteps[i + 1], delta_vals[i + 1]) - point_beta.set_data(timesteps[i + 1], beta[i + 1]) - point_r.set_data(timesteps[i + 1], r[i + 1]) - point_beta_lin.set_data(timesteps[i + 1], beta_lin[i + 1]) - point_r_lin.set_data(timesteps[i + 1], r_lin[i + 1]) + point_delta.set_data(timesteps[slice_], delta_vals[slice_]) + point_beta.set_data(timesteps[slice_], beta[slice_]) + point_r.set_data(timesteps[slice_], r[slice_]) + point_beta_lin.set_data(timesteps[slice_], beta_lin[slice_]) + point_r_lin.set_data(timesteps[slice_], r_lin[slice_]) for farg in fargs: print(farg) diff --git a/scripts/run_lateral_control_riccati.py b/scripts/run_lateral_control_riccati.py index f0db173..a83a9b8 100644 --- a/scripts/run_lateral_control_riccati.py +++ b/scripts/run_lateral_control_riccati.py @@ -48,7 +48,7 @@ def main(): def update(i, *fargs): [l.remove() for l in reversed(ax.lines[1:])] ax.plot(x[: i + 1], y[: i + 1], "b-", linewidth=0.5) - point1.set_data(x[i], y[i]) + point1.set_data(x[i : i + 1], y[i : i + 1]) pv.plot_vehicle(ax, x[i], y[i], psi[i], delta[i]) for farg in fargs: print(farg) diff --git a/scripts/run_lateral_control_state_based.py b/scripts/run_lateral_control_state_based.py index d3167e1..2966549 100644 --- a/scripts/run_lateral_control_state_based.py +++ b/scripts/run_lateral_control_state_based.py @@ -33,7 +33,7 @@ def main(): def update(i, *fargs): [l.remove() for l in reversed(ax.lines[1:])] ax.plot(x[:i], y[:i], "b-", linewidth=0.5) - point1.set_data(x[i], y[i]) + point1.set_data(x[i : i + 1], y[i : i + 1]) pv.plot_vehicle(ax, x[i], y[i], psi[i], delta[i]) for farg in fargs: print(farg) diff --git a/src/behavior_generation_lecture_python/finite_state_machine/traffic_light_visualization.py b/src/behavior_generation_lecture_python/finite_state_machine/traffic_light_visualization.py index 8132a69..ca2a254 100644 --- a/src/behavior_generation_lecture_python/finite_state_machine/traffic_light_visualization.py +++ b/src/behavior_generation_lecture_python/finite_state_machine/traffic_light_visualization.py @@ -3,7 +3,6 @@ class VisualizeTrafficLights: def __init__(self): - self.windows = tk.Tk() self.windows.title("Traffic Light FSM") self.canvas = tk.Canvas(self.windows) diff --git a/src/behavior_generation_lecture_python/mdp/mdp.py b/src/behavior_generation_lecture_python/mdp/mdp.py index cc495c3..a79a9d6 100644 --- a/src/behavior_generation_lecture_python/mdp/mdp.py +++ b/src/behavior_generation_lecture_python/mdp/mdp.py @@ -221,9 +221,9 @@ def __init__( next_state_fn=self._next_state_deterministic, ) if transition_probability_list: - transition_probabilities[ - (state, action) - ] = transition_probability_list + transition_probabilities[(state, action)] = ( + transition_probability_list + ) super().__init__( states=states, @@ -458,7 +458,6 @@ def q_learning( np.random.seed(1337) for _ in range(iterations): - # available actions: avail_actions = mdp.get_actions(state) @@ -580,7 +579,6 @@ def policy_gradient( # training loop for i in range(1, iterations + 1): - # a buffer for storing intermediate values buffer = PolicyGradientBuffer() diff --git a/src/behavior_generation_lecture_python/utils/grid_plotting.py b/src/behavior_generation_lecture_python/utils/grid_plotting.py index dc96bb5..c38f396 100644 --- a/src/behavior_generation_lecture_python/utils/grid_plotting.py +++ b/src/behavior_generation_lecture_python/utils/grid_plotting.py @@ -59,7 +59,7 @@ def plot_grid_step(iteration): data = policy_over_time[iteration] for row in range(rows): for col in range(columns): - if not (col, row) in data: + if (col, row) not in data: continue x = col + 0.5 y = row + 0.5 diff --git a/src/behavior_generation_lecture_python/utils/plot_vehicle/plot_vehicle.py b/src/behavior_generation_lecture_python/utils/plot_vehicle/plot_vehicle.py index 8b3c17c..7518a0a 100644 --- a/src/behavior_generation_lecture_python/utils/plot_vehicle/plot_vehicle.py +++ b/src/behavior_generation_lecture_python/utils/plot_vehicle/plot_vehicle.py @@ -28,7 +28,6 @@ def plot_vehicle(ax, x, y, psi, delta=0, length=4.899, width=2.094): value[:, 1] *= width_scale for key, value in geo.items(): - if key == "wheel1" or key == "wheel2": # deltaX = max(value[:, 0]) - min(value[:, 0]) # deltaY = max(value[:, 1]) - min(value[:, 1]) diff --git a/src/behavior_generation_lecture_python/utils/vizard/vizard.py b/src/behavior_generation_lecture_python/utils/vizard/vizard.py index d79bdb4..7f4e1f6 100755 --- a/src/behavior_generation_lecture_python/utils/vizard/vizard.py +++ b/src/behavior_generation_lecture_python/utils/vizard/vizard.py @@ -88,6 +88,7 @@ def register_plot(self, figure, update_func, time_vec, init_func=None, blit=Fals frames=self.frames, event_source=self.event_source, blit=blit, + save_count=self.max, ) # Store plot supplies for future modification @@ -300,16 +301,16 @@ def setup(self, figure): # Create controls controls = self.VizardControls() controls.button_stop = matplotlib.widgets.Button( - ax_buttons[0], label="\U000025A0" + ax_buttons[0], label="\U000025a0" ) controls.button_playpause = matplotlib.widgets.Button( - ax_buttons[1], label="\U000025B6" + ax_buttons[1], label="\U000025b6" ) controls.button_oneback = matplotlib.widgets.Button( - ax_buttons[2], label="\U000029CF" + ax_buttons[2], label="\U000029cf" ) controls.button_oneforward = matplotlib.widgets.Button( - ax_buttons[3], label="\U000029D0" + ax_buttons[3], label="\U000029d0" ) controls.slider = matplotlib.widgets.Slider( ax_slider, @@ -377,9 +378,9 @@ def set_play_pause_label(self, label, controls=None): """ label_text = label if label == "run": - label_text = "\U000025B6" + label_text = "\U000025b6" elif label == "pause": - label_text = "\U0000275A\U0000275A" + label_text = "\U0000275a\U0000275a" if controls: # Set label only for button in given controls class diff --git a/tests/finite_state_machine/test_traffic_light_state_machine_visu.py b/tests/finite_state_machine/test_traffic_light_state_machine_visu.py index 97aee85..9c3e832 100644 --- a/tests/finite_state_machine/test_traffic_light_state_machine_visu.py +++ b/tests/finite_state_machine/test_traffic_light_state_machine_visu.py @@ -1,7 +1,3 @@ -import os - -import pytest - from behavior_generation_lecture_python.finite_state_machine import ( traffic_light_state_machine_with_visu, ) diff --git a/tests/test_lateral_control_riccati.py b/tests/test_lateral_control_riccati.py index 3c09b82..79ccc2c 100644 --- a/tests/test_lateral_control_riccati.py +++ b/tests/test_lateral_control_riccati.py @@ -11,7 +11,6 @@ @pytest.mark.parametrize("test_r,error_factor", [(10000, 1), (10, 0.5)]) def test_lateral_control_riccati(test_r, error_factor): - radius = 500 vars_0 = [0.0, -radius, 0.0, 0.0, 0.0] v_0 = 33.0 diff --git a/tests/test_lateral_control_state_based.py b/tests/test_lateral_control_state_based.py index 3803ed9..92515a1 100644 --- a/tests/test_lateral_control_state_based.py +++ b/tests/test_lateral_control_state_based.py @@ -6,7 +6,6 @@ def test_lateral_control_state_based(): - radius = 20 vars_0 = [0.1, -radius, 0.0] curve = ref.generate_reference_curve( diff --git a/tests/test_mdp_lecture_tex.py b/tests/test_mdp_lecture_tex.py index 5057164..f7a54e3 100644 --- a/tests/test_mdp_lecture_tex.py +++ b/tests/test_mdp_lecture_tex.py @@ -46,23 +46,21 @@ def rewards_to_tex(mdp: MDP, node_property: str = "") -> str: def expected_utilities_to_tex(mdp: MDP, utility: dict) -> str: - exp_util = {} for s in sorted(mdp.get_states()): for a in sorted(mdp.get_actions(s)): if not a: continue - exp_util[ - tuple(np.array(s) + 0.35 * np.array(a)) - ] = expected_utility_of_action( - action=a, state=s, utility_of_states=utility, mdp=mdp + exp_util[tuple(np.array(s) + 0.35 * np.array(a))] = ( + expected_utility_of_action( + action=a, state=s, utility_of_states=utility, mdp=mdp + ) ) return values_to_tex(exp_util, node_property="[scale=0.8]") def best_policy_to_tex_arrows(mdp: MDP, utility: dict): - source_dest_list = [] policy = derive_policy(mdp=mdp, utility_of_states=utility) for s in sorted(mdp.get_states()): @@ -105,7 +103,6 @@ def best_policy_to_tex_arrows(mdp: MDP, utility: dict): def test_latex_value_over_time(): - grid_mdp = GridMDP(**GRID_MDP_DICT) assert GRID_MPD_REWARDS_TEX == rewards_to_tex(grid_mdp) @@ -152,7 +149,6 @@ def test_latex_value_over_time(): def test_latex_expected_utility(): - grid_mdp = GridMDP(**GRID_MDP_DICT) utility = value_iteration(grid_mdp, epsilon=0.0001, max_iterations=30) @@ -347,7 +343,6 @@ def test_latex_policy_as_arrows(): def test_latex_value_over_time_highway(): - highway_mdp = GridMDP(**HIGHWAY_MDP_DICT) rewards_tex = rewards_to_tex(highway_mdp) print(rewards_tex) diff --git a/tests/utils/test_vizard.py b/tests/utils/test_vizard.py index c62b683..f966819 100644 --- a/tests/utils/test_vizard.py +++ b/tests/utils/test_vizard.py @@ 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