stageM2/simulation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b780b7af-aee0-4d92-b830-64e9b9d3c2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib ipympl\n",
    "import numpy as np\n",
    "from numba import jit, vectorize, float64, types, int64\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import trange, tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fbf192f-aebc-4bce-ae38-339b5610fad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coupling formula\n",
    "alpha = 1\n",
    "\n",
    "# Lattive parameters:\n",
    "lattice_x = np.array([1, 0, 0])\n",
    "lattice_y = np.array([0, 1, 0])\n",
    "lattice_z = np.array([0, 0, 1])\n",
    "\n",
    "lattice_s = np.array([\n",
    "    [0,0,0],\n",
    "    [0.2,0,0],\n",
    "    [-0.2,0,0],\n",
    "    [0,0.2,0],\n",
    "    [0,-0.2,0],\n",
    "    [0,0,0.2],\n",
    "    [0,0,-0.2],\n",
    "])\n",
    "\n",
    "site_nb = lattice_s.shape[0]\n",
    "\n",
    "@vectorize([float64(int64, int64, int64, int64, int64)])\n",
    "def compute_coupling(x, y, z, a, b):\n",
    "    vec = x*lattice_x + y*lattice_y + z*lattice_z + lattice_s[a] - lattice_s[b]\n",
    "    norm = np.linalg.norm(vec)\n",
    "    return alpha/norm**3*np.abs(3*vec[2]**2/norm**2 - 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8719ddbb-3348-48d2-b949-3221ca298b09",
   "metadata": {},
   "source": [
    "# State generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "062e190f-1561-4f9f-8ffc-aca8e43db8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_lattice_sites(n, cell_number = 5):\n",
    "    # TODO: Improve using random picking\n",
    "    r = np.unique(np.random.randint([[cell_number, cell_number, cell_number, len(lattice_s)]], size=(n, 4)), axis = 0)\n",
    "    np.random.shuffle(r)\n",
    "    return r\n",
    "\n",
    "def add_noise(couplings, sigma):\n",
    "    return np.max(0, couplings + np.random.normal(scale = sigma, size = couplings.shape))\n",
    "\n",
    "@jit\n",
    "def couplings(sites):\n",
    "    n = sites.shape[0]\n",
    "    couplings = np.full((n , n), np.nan)\n",
    "    for i in range(sites.shape[0]-1):\n",
    "        for j in range(i+1, sites.shape[0]):\n",
    "            x = sites[i][0] - sites[j][0]\n",
    "            y = sites[i][1] - sites[j][1]\n",
    "            z = sites[i][2] - sites[j][2]\n",
    "            couplings[i, j] = compute_coupling(x, y, z, sites[i][3], sites[j][3])\n",
    "    return couplings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3d1d36c3-1fc4-42e4-af71-8eda4fb34d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sites = generate_lattice_sites(10, cell_number=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae4ecda6-14db-4e50-bfd2-affcd6153630",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_couplings = couplings(test_sites)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311c7c64-84ed-45c2-9f49-5993c67bc21e",
   "metadata": {},
   "source": [
    "# State reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "daaa4792-069c-44fd-bdbd-90af6f51858f",
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit\n",
    "def index_to_coord(index, max_distance, site_nb):\n",
    "    center = max_distance // 2\n",
    "    return (\n",
    "        index // (max_distance**2 * site_nb**2) - center,\n",
    "        index // (max_distance * site_nb**2) % max_distance - center,\n",
    "        index // site_nb**2 % max_distance - center,\n",
    "        index // site_nb % site_nb,\n",
    "        index % site_nb\n",
    "    )  \n",
    "\n",
    "@jit\n",
    "def coord_to_index(vec, max_distance, site_nb):\n",
    "    center = max_distance // 2\n",
    "    return site_nb**2 * ((vec[0] + center)*max_distance**2 + (vec[1] + center) * max_distance + (vec[2] + center)) + vec[3]*site_nb + vec[4]\n",
    "\n",
    "@jit #(locals={'current_set': types.Set(types.UniTuple(float64, 3))})\n",
    "def vector_couplings(max_distance, site_nb, tolerance):\n",
    "    print(\"Computing vector -> couplings lookup\")\n",
    "    couplings = np.empty(max_distance**3*site_nb**2)\n",
    "    for i in range(max_distance**3*site_nb**2):\n",
    "        couplings[i] = compute_coupling(\n",
    "                    *index_to_coord(i, max_distance, site_nb)\n",
    "                )\n",
    "    # Revert the map\n",
    "    print(\"Reverting the map\")\n",
    "    coupling_values = [] # OK to append to that list because O(number_spins)\n",
    "    current_set = set(((0,0,0,0,0),))\n",
    "    current_set.pop() # HACK for type inference\n",
    "    vectors = [] # OK to append because O(number_spins)\n",
    "    sorted_couplings = np.argsort(couplings)\n",
    "    # i_max = coupling index st coupling + tolerance < current cursor\n",
    "    # i_min = coupling index st coupling - tolerance < current cursor\n",
    "    i_min, i_max = 0,0\n",
    "    while not np.isnan(couplings[sorted_couplings[i_max]]):\n",
    "        if np.isnan(couplings[sorted_couplings[i_min]]) or couplings[sorted_couplings[i_min]] - tolerance > couplings[sorted_couplings[i_max]] + tolerance:\n",
    "            # Depile du côté des maxima (ie on retire un vecteur au set)\n",
    "            vec = index_to_coord(sorted_couplings[i_max], max_distance, site_nb)\n",
    "            coupling_cursor = couplings[sorted_couplings[i_max]] + tolerance\n",
    "            if len(coupling_values) == 0 or not np.isclose(coupling_values[-1], coupling_cursor, rtol = 1e-9, atol = tolerance * 1e-9):\n",
    "                vectors.append(current_set.copy())\n",
    "                coupling_values.append(coupling_cursor)\n",
    "            current_set.remove(vec)\n",
    "            i_max += 1\n",
    "        else:\n",
    "            # Empile du côté des minima (ie on ajoute un vecteur au set)\n",
    "            vec = index_to_coord(sorted_couplings[i_min], max_distance, site_nb)\n",
    "            coupling_cursor = couplings[sorted_couplings[i_min]] - tolerance\n",
    "            if len(coupling_values) == 0 or not np.isclose(coupling_values[-1], coupling_cursor, rtol = 1e-9, atol = tolerance * 1e-9):\n",
    "                vectors.append(current_set.copy())\n",
    "                coupling_values.append(coupling_cursor)\n",
    "            current_set.add(vec)\n",
    "            i_min += 1\n",
    "    vectors.append(current_set)\n",
    "    print(\"Successfully reverted the map\")\n",
    "    return np.array(coupling_values), vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a78bf20-e87b-4aeb-99f0-2bccc9dc4233",
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit\n",
    "def cost_function(sites, couplings):\n",
    "    couplings_theory = couplings(sites)\n",
    "    cost = 0\n",
    "    for i in range(sites.shape[0]-1):\n",
    "        for j in range(i+1, sites.shape[0]):\n",
    "            if np.isnan(couplings[i,j]):\n",
    "                continue\n",
    "            x = sites[i][0] - sites[j][0]\n",
    "            y = sites[i][1] - sites[j][1]\n",
    "            z = sites[i][2] - sites[j][2]\n",
    "            cost += (couplings[i,j] - compute_coupling(x, y, z, sites[i][3], sites[j][3]))**2\n",
    "    return cost\n",
    "\n",
    "@jit\n",
    "def exchange_columns(couplings, permutation, a, b):\n",
    "    a, b = min(a, b), max(a, b)\n",
    "    print(f\"Exchange {a}-{b}\")\n",
    "    permutation[a], permutation[b] = permutation[b], permutation[a]\n",
    "    for i in range(a):\n",
    "        couplings[i, a], couplings[i, b] = couplings[i, b], couplings[i, a]\n",
    "    for i in range(a+1, b):\n",
    "        couplings[a, i], couplings[i, b] = couplings[i, b], couplings[a, i]\n",
    "    for i in range(b+1, couplings.shape[0]): \n",
    "        couplings[a, i], couplings[b, i] = couplings[b, i], couplings[a, i]\n",
    "\n",
    "@jit\n",
    "def set_placing_order(couplings):\n",
    "    print(couplings.shape)\n",
    "    n_tot = couplings.shape[0]\n",
    "    permutation = np.arange(n_tot)\n",
    "    m = np.nanmax(couplings[0,:])\n",
    "    first = 0\n",
    "    for i in range(1, n_tot):\n",
    "        if np.nanmax(couplings[i,:])> m:\n",
    "            m = np.nanmax(couplings[i,:])\n",
    "            first = i\n",
    "    exchange_columns(couplings, permutation, 0, first)\n",
    "    for i in range(1, n_tot):\n",
    "        next_index = np.argmax(couplings[:i,i:])%(n_tot-i) + i\n",
    "        if next_index != i:\n",
    "            exchange_columns(couplings, permutation, i, next_index)\n",
    "    return couplings, permutation\n",
    "\n",
    "#@jit\n",
    "def compute_sites(couplings, lattice_size, site_nb, tolerance):\n",
    "    print(\"Begin\")\n",
    "    n_placed = 1\n",
    "    n_tot = couplings.shape[0]\n",
    "    couplings, permutation = set_placing_order(couplings)\n",
    "    print(\"ordered spins\")\n",
    "    couplings_vectors_tup = vector_couplings(tolerance = tolerance, max_distance = lattice_size*2, site_nb = site_nb)\n",
    "    print(\"computed_lookup table\")\n",
    "    possible_configurations = []\n",
    "    for i in range(site_nb):\n",
    "        c = np.zeros((n_tot, 4), dtype = np.int64)\n",
    "        c[0][3] = i\n",
    "        possible_configurations.append(c)\n",
    "        \n",
    "    current_couplings = np.empty(n_tot) # stores current coupling values to other spins\n",
    "\n",
    "    print(\"Initialization successful\")\n",
    "    while n_placed < n_tot:\n",
    "        print(f\"Placing {n_placed}. {len(possible_configurations)} cases to process\")\n",
    "        # First place them in order\n",
    "        n_to_place = n_placed\n",
    "\n",
    "        for i in range(n_tot):\n",
    "            current_couplings[i] = np.nanmax([couplings[n_to_place, i], couplings[i, n_to_place]])\n",
    "        # Be careful, position relative to n_to_place-1\n",
    "        candidates = couplings_vectors_tup[1][np.searchsorted(couplings_vectors_tup[0], current_couplings[n_to_place-1])]\n",
    "        new_possible_configurations = []\n",
    "        for config in tqdm(possible_configurations):\n",
    "            for candidate in candidates:\n",
    "                good_candidate = True\n",
    "                # First check the origin lattice site\n",
    "                if candidate[4] != config[n_to_place - 1][3]:\n",
    "                    continue\n",
    "                for i in range(n_placed-1):\n",
    "                    if np.isnan(current_couplings[i]):\n",
    "                        # no data for this already placed spin\n",
    "                        continue\n",
    "                    x = candidate[0] + config[n_to_place-1][0] - config[i][0]\n",
    "                    y = candidate[1] + config[n_to_place-1][1] - config[i][1]\n",
    "                    z = candidate[2] + config[n_to_place-1][2] - config[i][2]\n",
    "                    a = candidate[3]\n",
    "                    b = config[i][3]\n",
    "                    if (x, y, z) == (0,0,0) and a == b:\n",
    "                        good_candidate = False\n",
    "                        break\n",
    "                    coupling_candidate = compute_coupling(\n",
    "                        x, y, z, a, b\n",
    "                    )\n",
    "                    if np.isnan(coupling_candidate) or np.abs(current_couplings[i] - coupling_candidate) > tolerance:\n",
    "                        good_candidate = False\n",
    "                        break\n",
    "                if good_candidate:\n",
    "                    new_config = config.copy()\n",
    "                    new_config[n_to_place] = np.array((candidate[0] + config[n_to_place-1][0], candidate[1] + config[n_to_place-1][1], candidate[2] + config[n_to_place-1][2], candidate[3]))\n",
    "                    new_possible_configurations.append(new_config)\n",
    "        possible_configurations = new_possible_configurations\n",
    "        n_placed+=1\n",
    "    return [i[permutation] for i in possible_configurations], permutation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9d186b15-36cf-4aff-9287-381e31424afb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Begin\n",
      "(10, 10)\n",
      "Exchange 0-0\n",
      "ordered spins\n",
      "Computing vector -> couplings lookup\n",
      "Reverting the map\n",
      "Successfully reverted the map\n",
      "computed_lookup table\n",
      "Initialization successful\n",
      "Placing 1. 7 cases to process\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_44029/1413072332.py:69: RuntimeWarning: All-NaN axis encountered\n",
      "  current_couplings[i] = np.nanmax([couplings[n_to_place, i], couplings[i, n_to_place]])\n"
     ]
    },
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      "text/plain": [
       "  0%|          | 0/7 [00:00<?, ?it/s]"
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    {
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     "text": [
      "Placing 2. 16 cases to process\n"
     ]
    },
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     "text": [
      "Placing 3. 16 cases to process\n"
     ]
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     "text": [
      "Placing 4. 16 cases to process\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Placing 5. 16 cases to process\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Placing 6. 16 cases to process\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Placing 7. 16 cases to process\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Placing 8. 16 cases to process\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Placing 9. 16 cases to process\n"
     ]
    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time 38.674826944000074\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "t0 = time.perf_counter()\n",
    "final_sites, permutation = compute_sites(test_couplings, lattice_size = 5, site_nb=site_nb, tolerance = 0.005)\n",
    "t = time.perf_counter() - t0\n",
    "print(f\"Time {t}\")"
   ]
  },
  {
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   "execution_count": 9,
   "id": "fd1e7f80-1628-4750-bb71-1ebebb69f83e",
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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=640.0/>\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 = plt.figure()\n",
    "ax = fig.add_subplot(projection='3d')\n",
    "\n",
    "positions = lattice_x[None,:]*test_sites[:,0][:,None] + lattice_y[None,:] * test_sites[:,1][:,None] + lattice_z[None,:] * test_sites[:,2][:,None] + lattice_s[test_sites[:,3]]\n",
    "\n",
    "ax.scatter(positions[:,0], positions[:,1], positions[:,2])\n",
    "ax.plot(positions[permutation,0], positions[permutation,1], positions[permutation,2])\n",
    "\n",
    "ax.set_xlabel('X Label')\n",
    "ax.set_ylabel('Y Label')\n",
    "ax.set_zlabel('Z Label')\n",
    "\n",
    "for i in range(test_sites.shape[0]):\n",
    "    ax.text(positions[i,0], positions[i,1], positions[i,2], str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a904af3-06ae-4e1c-b282-9526dea314bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7537e392-4c17-490c-8ec4-c1d147fa3e24",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}