ResearchProject/FESurrogateModelTutorial/notebooks/00_beam2d_fea_dataset.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "7e3ade70",
   "metadata": {},
   "source": [
    "# 00 Beam2D FEM Dataset\n",
    "\n",
    "BeamExamples ?? ??? ?? ??? ? LHS sample? ???, repository solver? FEM surrogate ??? dataset? ????."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71929240",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from femsurrogate.data.bounds import DEFAULT_PARAMETER_BOUNDS\n",
    "from femsurrogate.data.dataset import build_dataset\n",
    "from femsurrogate.data.sampling import generate_lhs_samples\n",
    "from femsurrogate.data.schema import DEFAULT_RANDOM_SEED, TARGET_COLUMNS\n",
    "from femsurrogate.fea.io import read_beam_example, read_expected_displacements\n",
    "from femsurrogate.fea.solver import solve_linear_static\n",
    "\n",
    "ROOT = Path.cwd().resolve()\n",
    "if not (ROOT / \"pyproject.toml\").exists():\n",
    "    ROOT = ROOT.parent\n",
    "assert (ROOT / \"pyproject.toml\").exists(), ROOT\n",
    "REFERENCE_DIR = ROOT / \"data\" / \"reference\"\n",
    "REFERENCE_DIR.mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df256ebc",
   "metadata": {},
   "source": [
    "## BeamExamples ?? ??\n",
    "\n",
    "Fixture? ?? node? ?? `Ux`, `Uy`, `Rz`? ???? ???? ?? ????? ????."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a8c947c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = read_beam_example(ROOT / \"BeamExamples\" / \"CantileverBeam.txt\")\n",
    "expected = read_expected_displacements(ROOT / \"BeamExamples\" / \"CantileverBeam_Displacements.txt\")\n",
    "actual = solve_linear_static(model)\n",
    "\n",
    "max_abs_error = 0.0\n",
    "for node_id, expected_displacement in expected.items():\n",
    "    actual_displacement = actual[node_id]\n",
    "    actual_values = np.array(\n",
    "        [actual_displacement.ux, actual_displacement.uy, actual_displacement.rz]\n",
    "    )\n",
    "    expected_values = np.array(\n",
    "        [expected_displacement.ux, expected_displacement.uy, expected_displacement.rz]\n",
    "    )\n",
    "    error = float(np.max(np.abs(actual_values - expected_values)))\n",
    "    max_abs_error = max(max_abs_error, error)\n",
    "\n",
    "assert max_abs_error <= 5e-7\n",
    "max_abs_error"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6620d0ee",
   "metadata": {},
   "source": [
    "## LHS sampling? FEM batch ??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9923fd04",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_SAMPLES = 300\n",
    "\n",
    "samples = generate_lhs_samples(DEFAULT_PARAMETER_BOUNDS, n=N_SAMPLES, seed=DEFAULT_RANDOM_SEED)\n",
    "dataset = build_dataset(samples)\n",
    "\n",
    "assert len(dataset) == N_SAMPLES\n",
    "assert set(TARGET_COLUMNS).issubset(dataset.columns)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0efee15",
   "metadata": {},
   "source": [
    "## Dataset ??\n",
    "\n",
    "?? notebook? ?? CSV? metadata? ??? `data/reference/`? ????."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d80890bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = REFERENCE_DIR / \"beam2d_lhs_300.csv\"\n",
    "metadata_path = REFERENCE_DIR / \"beam2d_lhs_300_metadata.json\"\n",
    "\n",
    "dataset.to_csv(dataset_path, index=False)\n",
    "metadata = {\n",
    "    \"dataset_name\": \"beam2d_lhs_300\",\n",
    "    \"sample_count\": N_SAMPLES,\n",
    "    \"random_seed\": DEFAULT_RANDOM_SEED,\n",
    "    \"unit_system\": \"SI\",\n",
    "    \"fea_model\": \"2D Euler-Bernoulli beam/frame, linear static\",\n",
    "    \"target_columns\": list(TARGET_COLUMNS),\n",
    "    \"parameter_bounds\": {\n",
    "        name: {\"lower\": bound.lower, \"upper\": bound.upper}\n",
    "        for name, bound in DEFAULT_PARAMETER_BOUNDS.items()\n",
    "    },\n",
    "    \"notes\": \"Generated by notebooks/00_beam2d_fea_dataset.ipynb using src/femsurrogate.\",\n",
    "}\n",
    "metadata_path.write_text(json.dumps(metadata, indent=2), encoding=\"utf-8\")\n",
    "\n",
    "{\"dataset_path\": str(dataset_path), \"metadata_path\": str(metadata_path)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56bcddb",
   "metadata": {},
   "source": [
    "## ?? sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e560e50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.describe().T.loc[[\"L_m\", \"b_m\", \"h_m\", \"E_pa\", \"P_n\", *TARGET_COLUMNS]]"
   ]
  }
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