notebooks/quads_focusing_3dp_pla_shims_simulation_time_effect.ipynb

{
	"cells": [
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"# Quads design (3dp pla + blue shims) for energy focusing with single target region\n"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"## Imports"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"NOTE: Please download data associated to this repository from https://doi.org/10.5281/zenodo.12823471\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 1,
			"metadata": {},
			"outputs": [
				{
					"name": "stderr",
					"output_type": "stream",
					"text": [
						"No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
					]
				}
			],
			"source": [
				"from difflexmm.utils import save_data, load_data\n",
				"from difflexmm.geometry import QuadGeometry, compute_inertia\n",
				"from difflexmm.plotting import generate_animation, plot_geometry\n",
				"from problems.quads_focusing import ForwardProblem, OptimizationProblem\n",
				"import matplotlib.pyplot as plt\n",
				"from pathlib import Path\n",
				"from typing import Optional, List\n",
				"\n",
				"import jax.numpy as jnp\n",
				"from jax.config import config\n",
				"config.update(\"jax_enable_x64\", True)  # enable float64 type\n",
				"# config.update(\"jax_log_compiles\", 1)\n",
				"\n",
				"plt.style.use([\"science\", \"grid\"])\n",
				"%matplotlib widget\n"
			]
		},
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"## Plotting functions\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 2,
			"metadata": {},
			"outputs": [],
			"source": [
				"def plot_objective_and_constraints_iterations(optimization: OptimizationProblem, optimization_filename: Optional[str] = None, figsize=(10, 7), fontsize=None, grid=True):\n",
				"\n",
				"    fig, axes = plt.subplots(nrows=3, figsize=figsize, sharex=True, constrained_layout=True)\n",
				"    axes[0].set_ylabel(\"Energy focused [mJ]\", fontsize=fontsize)\n",
				"    axes[0].plot(jnp.array(optimization.objective_values) /\n",
				"                 optimization.forward_problem.n_timepoints, lw=3, color=\"#16a085\")\n",
				"    axes[0].axhline(y=0, color=\"black\")\n",
				"    axes[1].set_ylabel(\"Overlap\", fontsize=fontsize)\n",
				"    axes[1].plot(optimization.constraints_violation[\"angles\"], lw=3, color=\"#c0392b\")\n",
				"    axes[1].axhline(y=0, color=\"black\")\n",
				"    axes[2].set_ylabel(\"Edge-length [mm]\", fontsize=fontsize)\n",
				"    axes[2].plot(optimization.constraints_violation[\"edge_lengths\"], lw=3, color=\"#c0392b\")\n",
				"    axes[2].axhline(y=0, color=\"black\")\n",
				"    axes[-1].set_xlabel(r\"Iteration \\#\", fontsize=fontsize)\n",
				"    # Change fontsize for all axes\n",
				"    for ax in axes:\n",
				"        ax.tick_params(labelsize=fontsize)\n",
				"    if not grid:\n",
				"        for ax in axes:\n",
				"            ax.grid(False)\n",
				"\n",
				"    if optimization_filename is not None:\n",
				"        path = Path(\n",
				"            f\"../out/{optimization.name}/{optimization_filename}/objective_and_constraints_iterations.png\")\n",
				"        path.parent.mkdir(parents=True, exist_ok=True)  # Make sure parents directories exist\n",
				"        fig.savefig(str(path), dpi=300)\n",
				"\n",
				"\n",
				"def plot_objective_iterations(optimization: OptimizationProblem, optimization_filename: Optional[str] = None, figsize=(10, 7), fontsize=None, grid=True):\n",
				"\n",
				"    fig, axes = plt.subplots(figsize=figsize, sharex=True, constrained_layout=True)\n",
				"    axes.plot(jnp.array(optimization.objective_values) /\n",
				"              optimization.forward_problem.n_timepoints, lw=3, color=\"#16a085\")\n",
				"    axes.axhline(y=0, color=\"black\")\n",
				"    axes.set_xlabel(r\"Iteration \\#\", fontsize=fontsize)\n",
				"    axes.set_ylabel(\"Energy focused [mJ]\", fontsize=fontsize)\n",
				"    # Change fontsize for all axes\n",
				"    axes.tick_params(labelsize=fontsize)\n",
				"    if not grid:\n",
				"        axes.grid(False)\n",
				"\n",
				"    if optimization_filename is not None:\n",
				"        path = Path(\n",
				"            f\"../out/{optimization.name}/{optimization_filename}/objective_iterations.png\")\n",
				"        path.parent.mkdir(parents=True, exist_ok=True)  # Make sure parents directories exist\n",
				"        fig.savefig(str(path), dpi=300)\n",
				"\n",
				"\n",
				"def energy_profile(velocity, blocks_inertia):\n",
				"    # velocity is (n_time_steps, n_blocks, 3)\n",
				"    # blocks_inertia is (n_blocks, 3)\n",
				"    return 0.5*jnp.sum(velocity**2 * blocks_inertia, axis=-1)\n",
				"\n",
				"\n",
				"def plot_energy_evolution(optimization: OptimizationProblem, optimization_filename: Optional[str] = None, block_ids=None, figsize=(10, 7), fontsize=None, grid=True):\n",
				"\n",
				"    fig, axes = plt.subplots(figsize=figsize, sharex=True, constrained_layout=True)\n",
				"    _block_ids = block_ids if block_ids is not None else jnp.arange(\n",
				"        optimization.forward_problem.solution_data.block_centroids.shape[0])\n",
				"    energy_history = jnp.sum(\n",
				"        energy_profile(\n",
				"            optimization.forward_problem.solution_data.fields[:, 1, _block_ids, :],\n",
				"            compute_inertia(vertices=optimization.forward_problem.solution_data.centroid_node_vectors,\n",
				"                            density=optimization.forward_problem.density)\n",
				"        ),\n",
				"        axis=-1\n",
				"    )\n",
				"    axes.plot(\n",
				"        optimization.forward_problem.solution_data.timepoints*1e3,\n",
				"        energy_history,\n",
				"        lw=3,\n",
				"        color=\"#16a085\"\n",
				"    )\n",
				"    axes.set_xlabel(r\"Time [ms]\", fontsize=fontsize)\n",
				"    axes.set_ylabel(\"Kinetic energy [mJ]\", fontsize=fontsize)\n",
				"    # Change fontsize for all axes\n",
				"    axes.tick_params(labelsize=fontsize)\n",
				"    if not grid:\n",
				"        axes.grid(False)\n",
				"\n",
				"    if optimization_filename is not None:\n",
				"        path = Path(\n",
				"            f\"../out/{optimization.name}/{optimization_filename}/energy_evolution.png\")\n",
				"        path.parent.mkdir(parents=True, exist_ok=True)  # Make sure parents directories exist\n",
				"        fig.savefig(str(path), dpi=300)\n",
				"\n",
				"\n",
				"def plot_energy_evolution_list(optimization_list: List[OptimizationProblem], optimization_filename: Optional[str] = None, block_ids=None, figsize=(10, 7), fontsize=None, grid=True, labels=None):\n",
				"\n",
				"    fig, axes = plt.subplots(figsize=figsize, sharex=True, constrained_layout=True)\n",
				"    _block_ids = block_ids if block_ids is not None else jnp.arange(\n",
				"        optimization_list[0].forward_problem.solution_data.block_centroids.shape[0])\n",
				"    if block_ids == \"target\":\n",
				"        target_size = optimization_list[0].target_size\n",
				"        target_shift = optimization_list[0].target_shift\n",
				"        _block_ids = jnp.array([\n",
				"            j * optimization_list[0].forward_problem.n1_blocks + i\n",
				"            for i in range((optimization_list[0].forward_problem.n1_blocks-target_size[0])//2 + target_shift[0], (optimization_list[0].forward_problem.n1_blocks+target_size[0])//2 + target_shift[0])\n",
				"            for j in range((optimization_list[0].forward_problem.n2_blocks-target_size[1])//2 + target_shift[1], (optimization_list[0].forward_problem.n2_blocks+target_size[1])//2 + target_shift[1])\n",
				"        ])\n",
				"\n",
				"    labels = labels if labels is not None else [None]*len(optimization_list)\n",
				"    for optimization, label in zip(optimization_list, labels):\n",
				"        energy_history = jnp.sum(\n",
				"            energy_profile(\n",
				"                optimization.forward_problem.solution_data.fields[:, 1, _block_ids, :],\n",
				"                compute_inertia(vertices=optimization.forward_problem.solution_data.centroid_node_vectors,\n",
				"                                density=optimization.forward_problem.density)[_block_ids]\n",
				"            ),\n",
				"            axis=-1\n",
				"        )\n",
				"        axes.plot(\n",
				"            optimization.forward_problem.solution_data.timepoints*1e3,\n",
				"            energy_history,\n",
				"            lw=2,\n",
				"            label=label\n",
				"        )\n",
				"    axes.set_xlabel(r\"Time [ms]\", fontsize=fontsize)\n",
				"    axes.set_ylabel(\"Kinetic energy [mJ]\", fontsize=fontsize)\n",
				"    axes.legend()\n",
				"    # Change fontsize for all axes\n",
				"    axes.tick_params(labelsize=fontsize)\n",
				"    if not grid:\n",
				"        axes.grid(False)\n",
				"\n",
				"    if optimization_filename is not None:\n",
				"        path = Path(\n",
				"            f\"../out/{optimization_list[0].name}/{optimization_filename}/energy_evolutions.png\")\n",
				"        path.parent.mkdir(parents=True, exist_ok=True)  # Make sure parents directories exist\n",
				"        fig.savefig(str(path), dpi=300)\n",
				"\n",
				"\n",
				"def plot_energy_ratio_evolution_list(optimization_list: List[OptimizationProblem], optimization_filename: Optional[str] = None, figsize=(10, 7), fontsize=None, grid=True, labels=None):\n",
				"\n",
				"    fig, axes = plt.subplots(figsize=figsize, sharex=True, constrained_layout=True)\n",
				"    target_size = optimization_list[0].target_size\n",
				"    target_shift = optimization_list[0].target_shift\n",
				"    target_block_ids = jnp.array([\n",
				"        j * optimization_list[0].forward_problem.n1_blocks + i\n",
				"        for i in range((optimization_list[0].forward_problem.n1_blocks-target_size[0])//2 + target_shift[0], (optimization_list[0].forward_problem.n1_blocks+target_size[0])//2 + target_shift[0])\n",
				"        for j in range((optimization_list[0].forward_problem.n2_blocks-target_size[1])//2 + target_shift[1], (optimization_list[0].forward_problem.n2_blocks+target_size[1])//2 + target_shift[1])\n",
				"    ])\n",
				"    not_target_block_ids = jnp.setdiff1d(\n",
				"        jnp.arange(optimization_list[0].forward_problem.solution_data.block_centroids.shape[0]),\n",
				"        target_block_ids\n",
				"    )\n",
				"\n",
				"    labels = labels if labels is not None else [None]*len(optimization_list)\n",
				"    for optimization, label in zip(optimization_list, labels):\n",
				"        energy_ratio_history = jnp.sum(\n",
				"            energy_profile(\n",
				"                optimization.forward_problem.solution_data.fields[:, 1, target_block_ids, :],\n",
				"                compute_inertia(vertices=optimization.forward_problem.solution_data.centroid_node_vectors,\n",
				"                                density=optimization.forward_problem.density)[target_block_ids]\n",
				"            ),\n",
				"            axis=-1\n",
				"        )/jnp.sum(\n",
				"            energy_profile(\n",
				"                optimization.forward_problem.solution_data.fields[:, 1, not_target_block_ids, :],\n",
				"                compute_inertia(vertices=optimization.forward_problem.solution_data.centroid_node_vectors,\n",
				"                                density=optimization.forward_problem.density)[not_target_block_ids]\n",
				"            ),\n",
				"            axis=-1\n",
				"        )\n",
				"        axes.plot(\n",
				"            optimization.forward_problem.solution_data.timepoints*1e3,\n",
				"            energy_ratio_history,\n",
				"            lw=2,\n",
				"            label=label\n",
				"        )\n",
				"        # energy_target_history = jnp.sum(\n",
				"        #     energy_profile(\n",
				"        #         optimization.forward_problem.solution_data.fields[:, 1, target_block_ids, :],\n",
				"        #         compute_inertia(vertices=optimization.forward_problem.solution_data.centroid_node_vectors,\n",
				"        #                         density=optimization.forward_problem.density)[target_block_ids]\n",
				"        #     ),\n",
				"        #     axis=-1\n",
				"        # )\n",
				"        # energy_not_target_history = jnp.sum(\n",
				"        #     energy_profile(\n",
				"        #         optimization.forward_problem.solution_data.fields[:, 1, not_target_block_ids, :],\n",
				"        #         compute_inertia(vertices=optimization.forward_problem.solution_data.centroid_node_vectors,\n",
				"        #                         density=optimization.forward_problem.density)[not_target_block_ids]\n",
				"        #     ),\n",
				"        #     axis=-1\n",
				"        # )\n",
				"        # axes.plot(\n",
				"        #     optimization.forward_problem.solution_data.timepoints*1e3,\n",
				"        #     energy_target_history,\n",
				"        #     lw=2,\n",
				"        #     label=label\n",
				"        # )\n",
				"        # axes.plot(\n",
				"        #     optimization.forward_problem.solution_data.timepoints*1e3,\n",
				"        #     energy_not_target_history,\n",
				"        #     lw=2,\n",
				"        #     ls=\"--\",\n",
				"        #     label=label\n",
				"        # )\n",
				"    axes.set_xlabel(r\"Time [ms]\", fontsize=fontsize)\n",
				"    axes.set_ylabel(\"K target / K rest\", fontsize=fontsize)\n",
				"    axes.legend()\n",
				"    # Change fontsize for all axes\n",
				"    axes.tick_params(labelsize=fontsize)\n",
				"    if not grid:\n",
				"        axes.grid(False)\n",
				"\n",
				"    if optimization_filename is not None:\n",
				"        path = Path(\n",
				"            f\"../out/{optimization_list[0].name}/{optimization_filename}/energy_ratio_evolutions.png\")\n",
				"        path.parent.mkdir(parents=True, exist_ok=True)  # Make sure parents directories exist\n",
				"        fig.savefig(str(path), dpi=300)\n",
				"\n",
				"\n",
				"def generate_response_animation(\n",
				"        optimization: OptimizationProblem,\n",
				"        optimization_filename: str,\n",
				"        animation_filename: str = \"response_animation\",\n",
				"        iteration=-1,\n",
				"        field: str = \"v\",\n",
				"        **plot_kwargs):\n",
				"\n",
				"    if not optimization.forward_problem.is_setup:\n",
				"        optimization.forward_problem.setup()\n",
				"\n",
				"    if iteration >= 0:\n",
				"        solution = optimization.forward_problem.solve(\n",
				"            optimization.design_values[iteration]\n",
				"        )\n",
				"    else:\n",
				"        solution = optimization.forward_problem.solution_data if optimization.forward_problem.solution_data is not None else optimization.forward_problem.solve(\n",
				"            optimization.design_values[iteration]\n",
				"        )\n",
				"\n",
				"    xlim, ylim = optimization.forward_problem.geometry.get_xy_limits(\n",
				"        *optimization.design_values[iteration]) + optimization.forward_problem.spacing * jnp.array([[-1.25, 1.25], [-1., 1.]])\n",
				"\n",
				"    iteration_label = iteration if iteration >= 0 else len(optimization.design_values) + iteration\n",
				"    # Drop alien stuff from plot_kwargs\n",
				"    plot_kwargs.pop(\"field\", None)\n",
				"    plot_kwargs.pop(\"animation_filename\", None)\n",
				"    plot_kwargs.pop(\"iteration\", None)\n",
				"    generate_animation(\n",
				"        data=solution,\n",
				"        field=field,\n",
				"        out_filename=f\"../out/{optimization.name}/{optimization_filename}/{animation_filename}_design_{iteration_label:03d}\",\n",
				"        deformed=True,\n",
				"        xlim=xlim,\n",
				"        ylim=ylim,\n",
				"        **plot_kwargs\n",
				"    )\n",
				"\n",
				"\n",
				"def plot_design(optimization: OptimizationProblem, optimization_filename: Optional[str] = None, iteration=-1, figsize=(10, 7), fontsize=None, grid=True, axis=True):\n",
				"\n",
				"    if not optimization.forward_problem.is_setup:\n",
				"        optimization.forward_problem.setup()\n",
				"\n",
				"    xlim, ylim = optimization.forward_problem.geometry.get_xy_limits(\n",
				"        *optimization.design_values[iteration]) + 0.25*optimization.forward_problem.spacing * jnp.array([[-1.5, 1.5], [-1, 1]])\n",
				"\n",
				"    fig, axes = plot_geometry(\n",
				"        block_centroids=optimization.forward_problem.geometry.block_centroids(*optimization.design_values[iteration]),\n",
				"        centroid_node_vectors=optimization.forward_problem.geometry.centroid_node_vectors(\n",
				"            *optimization.design_values[iteration]),\n",
				"        bond_connectivity=optimization.forward_problem.geometry.bond_connectivity(),\n",
				"        figsize=figsize,\n",
				"        color=\"#2980b9\",\n",
				"        xlim=xlim,\n",
				"        ylim=ylim,\n",
				"    )\n",
				"    axes.set_xlabel(r\"$x$ [mm]\", fontsize=fontsize)\n",
				"    axes.set_ylabel(r\"$y$ [mm]\", fontsize=fontsize)\n",
				"    # axes.set_aspect(\"equal\")\n",
				"    axes.tick_params(labelsize=fontsize)\n",
				"    if not grid:\n",
				"        axes.grid(False)\n",
				"    if not axis:\n",
				"        axes.axis(\"off\")\n",
				"\n",
				"    iteration_label = iteration if iteration >= 0 else len(optimization.design_values) + iteration\n",
				"    if optimization_filename is not None:\n",
				"        path = Path(\n",
				"            f\"../out/{optimization.name}/{optimization_filename}/design_{iteration_label:03d}.png\")\n",
				"        path.parent.mkdir(parents=True, exist_ok=True)  # Make sure parents directories exist\n",
				"        fig.savefig(str(path), dpi=300)\n"
			]
		},
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"## Optimal design and response\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 3,
			"metadata": {},
			"outputs": [],
			"source": [
				"# NOTE: Units are mm, N, s\n",
				"\n",
				"# Geometrical params\n",
				"n1_blocks = 24\n",
				"n2_blocks = 16\n",
				"spacing = 15.  # 1.0  # 15 mm\n",
				"hinge_length = 0.15*spacing  # Same as bond length\n",
				"initial_angle = 25*jnp.pi/180\n",
				"horizontal_shifts, vertical_shifts = QuadGeometry(n1_blocks, n2_blocks, spacing=spacing, bond_length=hinge_length).get_design_from_rotated_square(\n",
				"    angle=initial_angle,\n",
				")  # Initial design\n",
				"\n",
				"# Mechanical params\n",
				"k_stretch = 120.  # stretching stiffness 120. N/mm\n",
				"k_shear = 1.19  # shearing stiffness 1.19 N/mm\n",
				"k_rot = 1.50  # rotational stiffness 1.50 Nmm\n",
				"density = 6.18e-9  # Mg/mm^2\n",
				"# NOTE: Damping is assumed to be the same for all blocks as it is small enough that the inertia change during optimization is negligible.\n",
				"# The reference is taken to be zero angle rotated square geometry.\n",
				"# 0.36125, 0.02175026 = mass and inertia of a single square of zero angle and unitary spacing and density and 0.15 bond length.\n",
				"damping = 0.0186 * jnp.array([\n",
				"    2 * (0.36125 * density * spacing**2 * k_shear)**0.5,\n",
				"    2 * (0.36125 * density * spacing**2 * k_shear)**0.5,\n",
				"    2 * (0.02175026 * density * spacing**4 * k_rot)**0.5\n",
				"]) * jnp.ones((n1_blocks*n2_blocks, 3))\n",
				"\n",
				"# Dynamic loading\n",
				"amplitude = 0.5 * spacing\n",
				"loading_rate = 30.  # Hz loading frequency for dynamic input\n",
				"\n",
				"# Forward problem\n",
				"problem = ForwardProblem(\n",
				"    # Geometrical params\n",
				"    n1_blocks=n1_blocks,\n",
				"    n2_blocks=n2_blocks,\n",
				"    spacing=spacing,\n",
				"    bond_length=hinge_length,\n",
				"    # Mechanical params\n",
				"    k_stretch=k_stretch,\n",
				"    k_shear=k_shear,\n",
				"    k_rot=k_rot,\n",
				"    density=density,\n",
				"    damping=damping,\n",
				"    use_contact=True,\n",
				"    k_contact=k_rot,\n",
				"    min_angle=-15*jnp.pi/180,\n",
				"    cutoff_angle=-10*jnp.pi/180,\n",
				"    # Dynamic loading\n",
				"    amplitude=amplitude,\n",
				"    loading_rate=loading_rate,\n",
				"    input_delay=0.1*loading_rate**-1,\n",
				"    n_excited_blocks=2,\n",
				"    loaded_side=\"left\",\n",
				"    input_shift=0,\n",
				"    # Analysis params\n",
				"    simulation_time=2*2*loading_rate**-1,  # 2*loading_rate**-1,  # s\n",
				"    n_timepoints=200,\n",
				"    # Solver tolerances\n",
				"    atol=1e-4,\n",
				"    # Problem name (folder name)\n",
				"    name=\"quads_focusing_3dp_pla_shims\",\n",
				")\n",
				"\n",
				"# Optimization problem\n",
				"target_size = (2, 2)\n",
				"target_shift = (4, 5)\n",
				"optimization = OptimizationProblem(\n",
				"    forward_problem=problem,\n",
				"    target_size=target_size,\n",
				"    target_shift=target_shift,\n",
				"    name=problem.name,\n",
				")\n",
				"\n",
				"# problem_filename = f\"quads{'_linearized_strains' if problem.linearized_strains else ''}_{problem.n1_blocks}x{problem.n2_blocks}_excited_blocks_{problem.n_excited_blocks}_amplitude_{problem.amplitude:.2f}_loading_rate_{problem.loading_rate:.2f}_input_shift_{problem.input_shift}_initial_angle_{initial_angle*180/jnp.pi:.1f}\"\n",
				"# optimization_filename = f\"opt_with_angle_30_and_length_3_constraints_{problem_filename}_target_size_{optimization.target_size[0]}x{optimization.target_size[1]}_target_shift_{optimization.target_shift[0]}x{optimization.target_shift[1]}\"\n",
				"# Explore simulation time\n",
				"problem_filename = f\"quads{'_linearized_strains' if problem.linearized_strains else ''}_{problem.n1_blocks}x{problem.n2_blocks}_excited_blocks_{problem.n_excited_blocks}_amplitude_{problem.amplitude:.2f}_loading_rate_{problem.loading_rate:.2f}_input_shift_{problem.input_shift}_initial_angle_{initial_angle*180/jnp.pi:.1f}_simulation_time_{problem.simulation_time*loading_rate:.0f}\"\n",
				"optimization_filename = f\"opt_with_angle_30_and_length_3_constraints_{problem_filename}_target_size_{optimization.target_size[0]}x{optimization.target_size[1]}_target_shift_{optimization.target_shift[0]}x{optimization.target_shift[1]}\"\n"
			]
		},
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"### Import most recent optimization object\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 4,
			"metadata": {},
			"outputs": [],
			"source": [
				"# NOTE: Data can be downloaded from https://doi.org/10.5281/zenodo.12823471\n",
				"optimization = OptimizationProblem.from_dict(\n",
				"    load_data(\n",
				"        f\"../data/{optimization.name}/{optimization_filename}.pkl\",\n",
				"    )\n",
				")\n"
			]
		},
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"### Run optimization\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"# optimization.run_optimization_nlopt(\n",
				"#     initial_guess=(horizontal_shifts, vertical_shifts),\n",
				"#     # initial_guess=optimization.design_values[-1],\n",
				"#     n_iterations=80,\n",
				"#     min_block_angle=30*jnp.pi/180,\n",
				"#     min_void_angle=0*jnp.pi/180,\n",
				"#     min_edge_length=3.,  # mm\n",
				"#     max_time=2*60*60,  # 8 hour\n",
				"#     # lower_bound=-0.22*optimization.forward_problem.spacing,\n",
				"#     # upper_bound=0.22*optimization.forward_problem.spacing,\n",
				"# )\n",
				"\n",
				"# save_data(\n",
				"#     f\"../data/{optimization.name}/{optimization_filename}.pkl\",\n",
				"#     optimization.to_dict()  # Optimization problem\n",
				"# )\n"
			]
		},
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"## Plots\n"
			]
		},
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"### Objective iterations"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"plot_objective_and_constraints_iterations(\n",
				"    optimization,\n",
				"    optimization_filename=optimization_filename,\n",
				"    figsize=(7, 6),\n",
				"    fontsize=14,\n",
				")\n"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"### Target kinetic energy evolution\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"plot_energy_evolution(\n",
				"    optimization=optimization,\n",
				"    # optimization_filename=optimization_filename,\n",
				"    figsize=(8, 3),\n",
				"    fontsize=14,\n",
				")\n"
			]
		},
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"### Response animation\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"# Initial design\n",
				"generate_response_animation(\n",
				"    optimization,\n",
				"    field=\"v\",  # Velocity field\n",
				"    optimization_filename=optimization_filename,\n",
				"    figsize=(8, 7.35*(optimization.forward_problem.n2_blocks)/optimization.forward_problem.n1_blocks),\n",
				"    fontsize=14,\n",
				"    fps=30,\n",
				"    dpi=300,\n",
				"    legend_label=\"Velocity [mm/s]\",\n",
				"    cmap=\"inferno\",\n",
				"    animation_filename=\"response_animation_no_grid\",\n",
				"    axis=False,\n",
				"    iteration=0,\n",
				")\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"# Best design\n",
				"generate_response_animation(\n",
				"    optimization,\n",
				"    field=\"v\",  # Velocity field\n",
				"    optimization_filename=optimization_filename,\n",
				"    figsize=(8, 7.35*(optimization.forward_problem.n2_blocks)/optimization.forward_problem.n1_blocks),\n",
				"    fontsize=14,\n",
				"    fps=30,\n",
				"    dpi=300,\n",
				"    legend_label=\"Velocity [mm/s]\",\n",
				"    cmap=\"inferno\",\n",
				"    animation_filename=\"response_animation_no_grid\",\n",
				"    axis=False,\n",
				"    iteration=-1,\n",
				")\n"
			]
		},
		{
			"attachments": {},
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"### Plot designs\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"# Initial design\n",
				"plot_design(\n",
				"    optimization,\n",
				"    optimization_filename=optimization_filename,\n",
				"    iteration=0,\n",
				"    figsize=(7, 7*(optimization.forward_problem.n2_blocks)/optimization.forward_problem.n1_blocks),\n",
				"    fontsize=14,\n",
				"    grid=False,\n",
				"    axis=False,\n",
				")\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"# Best design\n",
				"plot_design(\n",
				"    optimization,\n",
				"    optimization_filename=optimization_filename,\n",
				"    iteration=-1,\n",
				"    figsize=(7, 7*(optimization.forward_problem.n2_blocks)/optimization.forward_problem.n1_blocks),\n",
				"    fontsize=14,\n",
				"    grid=False,\n",
				"    axis=False,\n",
				")\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"# Export all designs\n",
				"for i in range(len(optimization.design_values)):\n",
				"    plot_design(\n",
				"        optimization,\n",
				"        optimization_filename=f\"{optimization_filename}/designs\",\n",
				"        iteration=i,\n",
				"        figsize=(7, 7*(optimization.forward_problem.n2_blocks)/optimization.forward_problem.n1_blocks),\n",
				"        fontsize=14,\n",
				"        grid=False,\n",
				"        axis=True,\n",
				"    )\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": null,
			"metadata": {},
			"outputs": [],
			"source": [
				"# Export some snapshot designs\n",
				"for i in [0, 4, 6, 9, 30, -1]:\n",
				"    plot_design(\n",
				"        optimization,\n",
				"        optimization_filename=f\"{optimization_filename}\",\n",
				"        iteration=i,\n",
				"        figsize=(7, 7*(optimization.forward_problem.n2_blocks)/optimization.forward_problem.n1_blocks),\n",
				"        fontsize=14,\n",
				"        grid=False,\n",
				"        axis=False,\n",
				"    )\n"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"### Objective vs size of time window\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 4,
			"metadata": {},
			"outputs": [],
			"source": [
				"# NOTE: Data can be downloaded from https://doi.org/10.5281/zenodo.12823471\n",
				"\n",
				"simulation_times = [2, 4, 8, 16]\n",
				"data_paths = [Path(\n",
				"    f\"../data/{optimization.name}/opt_with_angle_30_and_length_3_constraints_quads_24x16_excited_blocks_2_amplitude_7.50_loading_rate_30.00_input_shift_0_initial_angle_25.0_target_size_2x2_target_shift_4x5.pkl\"\n",
				")]\n",
				"data_paths = data_paths + [\n",
				"    Path(f\"../data/{optimization.name}/opt_with_angle_30_and_length_3_constraints_quads_24x16_excited_blocks_2_amplitude_7.50_loading_rate_30.00_input_shift_0_initial_angle_25.0_simulation_time_{simulation_time}_target_size_2x2_target_shift_4x5.pkl\")\n",
				"    for simulation_time in simulation_times[1:]\n",
				"]\n",
				"optimizations = [OptimizationProblem.from_dict(load_data(path)) for path in data_paths]\n",
				"\n",
				"integrated_energies = []\n",
				"for optimization in optimizations:\n",
				"    # Use the longest simulation time\n",
				"    optimization.forward_problem.simulation_time = optimizations[-1].forward_problem.simulation_time\n",
				"    # optimization.forward_problem.n_timepoints = 400\n",
				"    optimization.setup_objective()\n",
				"    obj = optimization.objective_fn(optimization.design_values[-1])\n",
				"    integrated_energies.append(\n",
				"        obj*optimization.forward_problem.simulation_time/optimization.forward_problem.n_timepoints\n",
				"    )\n",
				"    optimization.compute_best_forward()\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 10,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"application/vnd.jupyter.widget-view+json": {
							"model_id": "34c4abc39c2b4c588efff37e0e7b9214",
							"version_major": 2,
							"version_minor": 0
						},
						"image/png": 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",
						"text/html": [
							"\n",
							"            <div style=\"display: inline-block;\">\n",
							"                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
							"                    Figure\n",
							"                </div>\n",
							"                <img 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' width=600.0/>\n",
							"            </div>\n",
							"        "
						],
						"text/plain": [
							"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
						]
					},
					"metadata": {},
					"output_type": "display_data"
				}
			],
			"source": [
				"fig, ax = plt.subplots(figsize=(6, 3), constrained_layout=True)\n",
				"ax.set_position([0.2, 0.2, 0.7, 0.7])\n",
				"ax.plot(simulation_times, integrated_energies, 'o-', lw=2, ms=8)\n",
				"ax.axhline(0, lw=1, color='black')\n",
				"ax.set_xlabel(r\"Simulation time [\\# $f^{-1}$]\", fontsize=14)\n",
				"ax.set_ylabel(r\"Integrated energy [mJ s]\", fontsize=14)\n",
				"# ax.set_title(\"Integrated energy vs. simulation time\", fontsize=14)\n",
				"ax.tick_params(labelsize=12)\n",
				"ax.set_xticks(simulation_times)\n",
				"fig.savefig(f\"../out/{optimization.name}/opt_with_angle_30_and_length_3_constraints_quads_24x16_excited_blocks_2_amplitude_7.50_loading_rate_30.00_input_shift_0_initial_angle_25.0_target_size_2x2_target_shift_4x5/integrated_energy_vs_simulation_time.png\", dpi=300)\n",
				"fig.savefig(f\"../out/{optimization.name}/opt_with_angle_30_and_length_3_constraints_quads_24x16_excited_blocks_2_amplitude_7.50_loading_rate_30.00_input_shift_0_initial_angle_25.0_target_size_2x2_target_shift_4x5/integrated_energy_vs_simulation_time.pdf\", dpi=300)\n"
			]
		},
		{
			"cell_type": "markdown",
			"metadata": {},
			"source": [
				"### Kinetic energy evolution vs size of time window\n"
			]
		},
		{
			"cell_type": "code",
			"execution_count": 16,
			"metadata": {},
			"outputs": [
				{
					"data": {
						"application/vnd.jupyter.widget-view+json": {
							"model_id": "c25ee4cc03ea4f998803961393f2dc76",
							"version_major": 2,
							"version_minor": 0
						},
						"image/png": 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",
						"text/html": [
							"\n",
							"            <div style=\"display: inline-block;\">\n",
							"                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
							"                    Figure\n",
							"                </div>\n",
							"                <img 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' width=800.0/>\n",
							"            </div>\n",
							"        "
						],
						"text/plain": [
							"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
						]
					},
					"metadata": {},
					"output_type": "display_data"
				}
			],
			"source": [
				"plot_energy_evolution_list(\n",
				"    optimizations,\n",
				"    labels=simulation_times,\n",
				"    figsize=(8, 3),\n",
				"    fontsize=14,\n",
				"    block_ids=\"target\",\n",
				"    optimization_filename=\"opt_with_angle_30_and_length_3_constraints_quads_24x16_excited_blocks_2_amplitude_7.50_loading_rate_30.00_input_shift_0_initial_angle_25.0_target_size_2x2_target_shift_4x5\"\n",
				")\n"
			]
		}
	],
	"metadata": {
		"kernelspec": {
			"display_name": "difflexmm-fbuxbt7l-py3.9",
			"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": "3.9.16"
		},
		"orig_nbformat": 4
	},
	"nbformat": 4,
	"nbformat_minor": 2
}