diff --git a/examples/1d_hubbard.ipynb b/examples/1d_hubbard.ipynb
index 5e94be15a5425b45b18dd05485ef0a036f3b052a..6be94375464c31db4f1cbf723855b10189c1297c 100644
--- a/examples/1d_hubbard.ipynb
+++ b/examples/1d_hubbard.ipynb
@@ -11,7 +11,7 @@
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from codes import utils, hf, model\n",
+    "from codes import utils, model, interface\n",
     "from tqdm import tqdm"
    ]
   },
@@ -95,17 +95,40 @@
   {
    "cell_type": "code",
    "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from codes import solvers, hf\n",
+    "from functools import partial\n",
+    "def solver(model, optimizer, cost_function, optimizer_kwargs):\n",
+    "    model.kgrid_evaluation(nk=model.nk)\n",
+    "    initial_mf = model.mf_k\n",
+    "    partial_cost = partial(cost_function, model=model)\n",
+    "    solvers.optimize(initial_mf, partial_cost, optimizer, optimizer_kwargs)\n",
+    "\n",
+    "def cost(mf, model):\n",
+    "    model.rho, model.mf_k = hf.updated_matrices(mf_k=mf, model=model)\n",
+    "    delta_mf = model.mf_k - mf\n",
+    "    return delta_mf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "id": "41bd9f60-8f29-4e7c-a0c4-a0bbf66445b2",
    "metadata": {},
    "outputs": [],
    "source": [
     "def compute_gap(model, nk, nk_dense, filling=2):\n",
     "    # Find groundstate Hamiltonian on the same grid\n",
-    "    mf_model = hf.find_groundstate_ham(\n",
+    "    mf_model = interface.find_groundstate_ham(\n",
     "        model,\n",
     "        filling=filling,\n",
     "        nk=nk,\n",
     "        cutoff_Vk=0,\n",
+    "        optimizer_kwargs={'M':5},\n",
+    "        # cost_function=cost,\n",
+    "        # solver=solver\n",
     "    )\n",
     "    # Generate Hamiltonian on a denser k-point grid\n",
     "    mf_k = utils.kgrid_hamiltonian(\n",
@@ -138,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "id": "32b9e7c5-db12-44f9-930c-21e5494404b8",
    "metadata": {
     "tags": []
@@ -168,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 18,
    "id": "6a8c08a9-7e31-420b-b6b4-709abfb26793",
    "metadata": {
     "tags": []
@@ -178,14 +201,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  5%|▌         | 1/20 [00:00<00:02,  7.73it/s]"
+      "  0%|          | 0/20 [01:40<?, ?it/s]\n"
      ]
     },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 20/20 [00:13<00:00,  1.54it/s]\n"
+     "ename": "OverflowError",
+     "evalue": "(34, 'Result too large')",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOverflowError\u001b[0m                             Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[18], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m Us \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m0.5\u001b[39m, \u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m20\u001b[39m, endpoint\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      3\u001b[0m nk, nk_dense \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m50\u001b[39m, \u001b[38;5;241m100\u001b[39m\n\u001b[0;32m----> 4\u001b[0m gap, vals \u001b[38;5;241m=\u001b[39m \u001b[43mcompute_phase_diagram\u001b[49m\u001b[43m(\u001b[49m\u001b[43mUs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mUs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnk_dense\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnk_dense\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[17], line 12\u001b[0m, in \u001b[0;36mcompute_phase_diagram\u001b[0;34m(Us, nk, nk_dense)\u001b[0m\n\u001b[1;32m     10\u001b[0m int_model \u001b[38;5;241m=\u001b[39m {(\u001b[38;5;241m0\u001b[39m,): U \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mkron(np\u001b[38;5;241m.\u001b[39mones((\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m2\u001b[39m)), np\u001b[38;5;241m.\u001b[39meye(\u001b[38;5;241m2\u001b[39m))}\n\u001b[1;32m     11\u001b[0m full_model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mModel(tb_model\u001b[38;5;241m=\u001b[39mtb_model, int_model\u001b[38;5;241m=\u001b[39mint_model)\n\u001b[0;32m---> 12\u001b[0m _gap, _vals \u001b[38;5;241m=\u001b[39m \u001b[43mcompute_gap\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfull_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnk_dense\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnk_dense\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     17\u001b[0m gap\u001b[38;5;241m.\u001b[39mappend(_gap)\n\u001b[1;32m     18\u001b[0m vals\u001b[38;5;241m.\u001b[39mappend(_vals)\n",
+      "Cell \u001b[0;32mIn[16], line 3\u001b[0m, in \u001b[0;36mcompute_gap\u001b[0;34m(model, nk, nk_dense, filling)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute_gap\u001b[39m(model, nk, nk_dense, filling\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m):\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;66;03m# Find groundstate Hamiltonian on the same grid\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m     mf_model \u001b[38;5;241m=\u001b[39m \u001b[43minterface\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfind_groundstate_ham\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilling\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfilling\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcutoff_Vk\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptimizer_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mM\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# cost_function=cost,\u001b[39;49;00m\n\u001b[1;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;66;43;03m# solver=solver\u001b[39;49;00m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;66;03m# Generate Hamiltonian on a denser k-point grid\u001b[39;00m\n\u001b[1;32m     13\u001b[0m     mf_k \u001b[38;5;241m=\u001b[39m utils\u001b[38;5;241m.\u001b[39mkgrid_hamiltonian(\n\u001b[1;32m     14\u001b[0m         nk\u001b[38;5;241m=\u001b[39mnk_dense, hk\u001b[38;5;241m=\u001b[39mutils\u001b[38;5;241m.\u001b[39mmodel2hk(tb_model\u001b[38;5;241m=\u001b[39mmf_model), dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m     15\u001b[0m     )\n",
+      "File \u001b[0;32m~/Projects/kwant-scf/examples/codes/interface.py:44\u001b[0m, in \u001b[0;36mfind_groundstate_ham\u001b[0;34m(model, cutoff_Vk, filling, nk, solver, cost_function, optimizer, optimizer_kwargs)\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model\u001b[38;5;241m.\u001b[39mguess \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     43\u001b[0m     model\u001b[38;5;241m.\u001b[39mrandom_guess(model\u001b[38;5;241m.\u001b[39mvectors)\n\u001b[0;32m---> 44\u001b[0m \u001b[43msolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcost_function\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m model\u001b[38;5;241m.\u001b[39mvectors\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m*\u001b[39mmodel\u001b[38;5;241m.\u001b[39mvectors, \u001b[38;5;241m*\u001b[39mmodel\u001b[38;5;241m.\u001b[39mtb_model\u001b[38;5;241m.\u001b[39mkeys()]\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m np\u001b[38;5;241m.\u001b[39mallclose((model\u001b[38;5;241m.\u001b[39mmf_k \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39mmoveaxis(model\u001b[38;5;241m.\u001b[39mmf_k, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39mconj())\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m, atol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-15\u001b[39m)\n",
+      "File \u001b[0;32m~/Projects/kwant-scf/examples/codes/solvers.py:97\u001b[0m, in \u001b[0;36mkspace_solver\u001b[0;34m(model, optimizer, cost_function, optimizer_kwargs)\u001b[0m\n\u001b[1;32m     95\u001b[0m initial_mf \u001b[38;5;241m=\u001b[39m utils\u001b[38;5;241m.\u001b[39mmatrix_to_flat(initial_mf)\n\u001b[1;32m     96\u001b[0m partial_cost \u001b[38;5;241m=\u001b[39m partial(cost_function, model\u001b[38;5;241m=\u001b[39mmodel)\n\u001b[0;32m---> 97\u001b[0m \u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43minitial_mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpartial_cost\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Projects/kwant-scf/examples/codes/solvers.py:7\u001b[0m, in \u001b[0;36moptimize\u001b[0;34m(mf, cost_function, optimizer, optimizer_kwargs)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moptimize\u001b[39m(mf, cost_function, optimizer, optimizer_kwargs):\n\u001b[0;32m----> 7\u001b[0m     _ \u001b[38;5;241m=\u001b[39m \u001b[43moptimizer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcost_function\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43moptimizer_kwargs\u001b[49m\n\u001b[1;32m     11\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m<string>:6\u001b[0m, in \u001b[0;36manderson\u001b[0;34m(F, xin, iter, alpha, w0, M, verbose, maxiter, f_tol, f_rtol, x_tol, x_rtol, tol_norm, line_search, callback, **kw)\u001b[0m\n",
+      "File \u001b[0;32m~/micromamba/envs/python3/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:214\u001b[0m, in \u001b[0;36mnonlin_solve\u001b[0;34m(F, x0, jacobian, iter, verbose, maxiter, f_tol, f_rtol, x_tol, x_rtol, tol_norm, line_search, callback, full_output, raise_exception)\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;66;03m# Line search, or Newton step\u001b[39;00m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m line_search:\n\u001b[0;32m--> 214\u001b[0m     s, x, Fx, Fx_norm_new \u001b[38;5;241m=\u001b[39m \u001b[43m_nonlin_line_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mFx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    215\u001b[0m \u001b[43m                                                \u001b[49m\u001b[43mline_search\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    216\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    217\u001b[0m     s \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n",
+      "File \u001b[0;32m~/micromamba/envs/python3/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:293\u001b[0m, in \u001b[0;36m_nonlin_line_search\u001b[0;34m(func, x, Fx, dx, search_type, rdiff, smin)\u001b[0m\n\u001b[1;32m    290\u001b[0m     s, phi1, phi0 \u001b[38;5;241m=\u001b[39m scalar_search_wolfe1(phi, derphi, tmp_phi[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    291\u001b[0m                                          xtol\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1e-2\u001b[39m, amin\u001b[38;5;241m=\u001b[39msmin)\n\u001b[1;32m    292\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m search_type \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124marmijo\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 293\u001b[0m     s, phi1 \u001b[38;5;241m=\u001b[39m \u001b[43mscalar_search_armijo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mphi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtmp_phi\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mtmp_phi\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    294\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mamin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msmin\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m s \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    297\u001b[0m     \u001b[38;5;66;03m# XXX: No suitable step length found. Take the full Newton step,\u001b[39;00m\n\u001b[1;32m    298\u001b[0m     \u001b[38;5;66;03m#      and hope for the best.\u001b[39;00m\n\u001b[1;32m    299\u001b[0m     s \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n",
+      "File \u001b[0;32m~/micromamba/envs/python3/lib/python3.11/site-packages/scipy/optimize/_linesearch.py:718\u001b[0m, in \u001b[0;36mscalar_search_armijo\u001b[0;34m(phi, phi0, derphi0, c1, alpha0, amin)\u001b[0m\n\u001b[1;32m    714\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39malpha0\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m3\u001b[39m \u001b[38;5;241m*\u001b[39m (phi_a1 \u001b[38;5;241m-\u001b[39m phi0 \u001b[38;5;241m-\u001b[39m derphi0\u001b[38;5;241m*\u001b[39malpha1) \u001b[38;5;241m+\u001b[39m \\\n\u001b[1;32m    715\u001b[0m     alpha1\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m3\u001b[39m \u001b[38;5;241m*\u001b[39m (phi_a0 \u001b[38;5;241m-\u001b[39m phi0 \u001b[38;5;241m-\u001b[39m derphi0\u001b[38;5;241m*\u001b[39malpha0)\n\u001b[1;32m    716\u001b[0m b \u001b[38;5;241m=\u001b[39m b \u001b[38;5;241m/\u001b[39m factor\n\u001b[0;32m--> 718\u001b[0m alpha2 \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m-\u001b[39mb \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39msqrt(\u001b[38;5;28mabs\u001b[39m(\u001b[43mb\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m3\u001b[39m \u001b[38;5;241m*\u001b[39m a \u001b[38;5;241m*\u001b[39m derphi0))) \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m3.0\u001b[39m\u001b[38;5;241m*\u001b[39ma)\n\u001b[1;32m    719\u001b[0m phi_a2 \u001b[38;5;241m=\u001b[39m phi(alpha2)\n\u001b[1;32m    721\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (phi_a2 \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m phi0 \u001b[38;5;241m+\u001b[39m c1\u001b[38;5;241m*\u001b[39malpha2\u001b[38;5;241m*\u001b[39mderphi0):\n",
+      "\u001b[0;31mOverflowError\u001b[0m: (34, 'Result too large')"
      ]
     }
    ],
@@ -198,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "e17fc96c-c463-4e1f-8250-c254d761b92a",
    "metadata": {},
    "outputs": [],
@@ -223,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "868cf368-45a0-465e-b042-6182ff8b6998",
    "metadata": {},
    "outputs": [
@@ -262,13 +298,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 31,
    "id": "ac2eb725-f3bd-4d5b-a509-85d0d0071958",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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QABIREcmoRAfu1ePw+3k5fsBmdy3eLjaZ5/q0JrpQXqurk1ugACQiIpIRJ/ZcvbFh/O+4jJ0XXT0xTcfwn3ZVCPS333y+eAUFIBERkVthDHzzLzxfPE2AcfKHKcLT9vH069uDuyuHW12dZJACkIiIyM1c/hP3ytH4/boGO/CFuz4fRT5BTJ8WRIblsbo6uQ0KQCIiIuk59u3VllfCcZKMPzGuPuRvPpJ/t6mIv59aXr5KAUhERCQtHg9sexXPf58lwLj53RPO0wETGf5Id5pXKGp1dXKHFIBERET+KuEM7uXD8Du8ETuw2t2EVSUe5+WH76JYaLDV1UkmUAASERH5X0e+xLlkEAGXT5NoApjhGkBU62G8dXcF/Ow2q6uTTKIAJCIiAuBxY7bMxmx5kQA8HPQUZ3rgY4zt35XGZQtbXZ1kMgUgERERx0lcSwfjH/s1NuBjV0vWl36M13s1pnD+IKurkyygACQiIrnbwQ24lg3FP/FPLpkgprkHU7HNEP7VvCx2tbxyLAUgERHJndxOzH+fxbbtVfyBnz2leCb4cR7vcx/1ShWyujrJYgpAIiKS+1yIxfXxIPxP7ATgHVcbtpefyLwe9SmQN9Di4iQ7KACJiEjusv9TXCsexT/ZgcPk5Un3MOq2H8Abd5XGZlPLK7dQABIRkdzBlYRZ9zS2b9/CH9jrKUdM3id46pH21CxRwOrqJJspAImISM537hCujwfgf+p7AN5y3cePlcfx7wfrEhocYHFxYgUFIBERydl+WIp79Tj8nQn8afIzxTOSFp0e4dVGJdXyysUUgEREJGdKvoz5bDK2Pe/gB3zjqcxL+R9nxiNtqRoVanV1YjEFIBERyXlO/4JrcX/8z/2Cx9iY4+7C0epjWPBAbfIF6aNPFIBERCQnMQb2vId7zST83YmcMWE87hlNx64PM6ZeCbW8JIUCkIiI5AxJF/F8OhH7Dx/jB2x112BOgUk898g9VAgPsbo68TIKQCIi4vtOfn+15XXhMC5j52XXQ5yvM5JF99cgT6Cf1dWJF1IAEhER32UM7PwPns+fxN+TzAlTiMlmHA8+9BBdahe3ujrxYgpAIiLim65cwLNqNPZfPsEOrHfX5T+FJhHzSEvKFs1vdXXi5RSARETE9/yx6+qNDR3HSDZ+zHL1xll/OIs6VSU4QC0vuTm7lX/51q1b6dy5M1FRUdhsNlauXJnq9QEDBmCz2VI9GjdufNP1Llu2jKpVqxIUFETVqlVZsWJFFm2BiIhkK48Hvn4Nz/x2+DuOEespSj+epX6vp3j2gRoKP3LLLA1Aly5dolatWsyZM+eGY9q3b8/JkydTHmvXrk13ndu3b6dnz5707duXffv20bdvX3r06ME333yT2eWLiEh2unQOzwc9YP007MbFp+5GTC4ylxfGDqRjjUirqxMfYzPGGKuLALDZbKxYsYKuXbumLBswYAAXLly47shQenr27InD4eCzzz5LWda+fXsKFizIhx9+eEvrcDgchIWFER8fT2io7hYqImK537/GtWQw/pdOkmQCmOnqR57Gg5ncoQqB/pb+Li9eJCOf317/v2bz5s0UK1aMihUrMnToUE6fPp3u+O3bt9O2bdtUy9q1a8e2bdtuOCcpKQmHw5HqISIiXsDjhi0vYhZ2wv/SSQ55Iulji6F1n8lM61xN4Udum1efBN2hQwceeughSpUqxZEjR5g2bRp333033333HUFBQWnOiYuLIzw8PNWy8PBw4uLibvj3xMTEMHPmzEytXURE7tDFU7iXDcXv9y3YgGXu5iyPmMCrfZpSvEAeq6sTH+fVAahnz54pf65evTr169enVKlSrFmzhm7dut1w3l9vdW6MSff251OnTmXixIkpzx0OB9HR0XdQuYiI3JFDG3EtHYr/lbNcNkFMcw6kWIuBLGxTkQA/HfWRO+fVAeivIiMjKVWqFAcPHrzhmIiIiOuO9pw+ffq6o0L/Kygo6IZHlEREJBu5XbD5ecyXL+OPYb8nmqf9JzG2z320rFjU6uokB/GpGH3u3DmOHTtGZOSNz/Zv0qQJ69evT7Vs3bp1NG3aNKvLExGROxH/B+4FHeHLl7BheM91D88Xn8Mb43sp/Eims/QIUEJCAr/99lvK8yNHjrB3714KFSpEoUKFmDFjBt27dycyMpLff/+dJ598kiJFivDAAw+kzOnXrx/FixcnJiYGgHHjxtGiRQtmz55Nly5dWLVqFRs2bOCrr77K9u0TEZFbdOAz3MtH4Jd0AYfJw5OuoZRr1ZeF91TAz65vcJfMZ2kA2rVrF61bt055fu08nP79+/Pmm2/yww8/8M4773DhwgUiIyNp3bo1ixcvJiTk/77VNzY2Frv9/w5kNW3alI8++oinn36aadOmUa5cORYvXkyjRo2yb8NEROTWuJIxG6Zj2/EGfsD3njL8LXAST/RtT9PyRayuTnIwr7kPkDfRfYBERLLBn4dxfzwQv7i9AMx3deCr0qN4sVdDiuTXeZmScRn5/Papk6BFRCSH+HE57lVj8XNe5ILJxxOuEdS6tzfzW5bDrpaXZAMFIBERyT7OK5jPp2L7bgF+wE5PRZ4LfoynBralQelCVlcnuYgCkIiIZI8zv+L+uD9+Z37GY2y84b6ffeVGsqBHXQrmC7S6OsllFIBERCTr7f0A96cT8XNd4YwJ5XHXKJq178Fbzcqke6NakayiACQiIlknKQGz9jFs+z7CD/jaXY0X8k1kZp97qR1dwOrqJBdTABIRkawR9+PVltefv+E2Nv7pepBDlYbxzkN1CMsTYHV1ksspAImISOYyBna9jefzKfi5k4kzBZnoHkv7Tt14rHEptbzEKygAiYhI5kmMx6wei+3nldiBje7avBoygef6tKZ68TCrqxNJoQAkIiKZ4/h3V29sGH8Up/FjtqsXp6sN5r1uNQkJVstLvIsCkIiI3BljYMcbeNZPx8/j5JinKBM9Y+nepStPNYhWy0u8kgKQiIjcvst/YlY+iu3Xz7EDn7kbMK/ABGb3aU7lCH2VkHgvBSAREbk9sTuutrwSTpBk/HnW1ZfEWgP4sGt18gbq40W8m/6HiohIxng88PU/MRufw8+4OeyJ4HHG07tbZ7rXK2F1dSK3RAFIRERuXcJpPMuHYT+8CRuw0t2URYXG8WKfZpQvlt/q6kRumQKQiIjcmsObcS8dgt/lM1wxgfzNNQD/un358P5qBAf4WV2dSIYoAImISPrcLtgyG7P1RfwwHPCU4AnbBIb06EjnWlFWVydyWxSARETkxhwn8CwdjD12GzbgQ1drlhYbxau9m1K6SD6rqxO5bQpAIiKStl+/wL18OH6J50kwwTzpHEKhxr35oGNlgvzV8hLfpgAkIiKpuZJh4zOw7XX8gB89pZlin8Do3u1pXz3C6upEMoUCkIiI/J/zR/EsHYj9+HcALHC149OIkbzZuyHRhfJaXJxI5lEAEhGRq35ehWflaOzJDuJNXh53Dqd0s5582LYSgf52q6sTyVQKQCIiuZ0zEdY9DTv/jR3Y7SnP034TmdTvXu6uHG51dSJZQgFIRCQ3O/sbniUDsJ/6AYB5rs5sLj6M+b0bEBmWx+LiRLKOApCISG71/cd4PhmP3XmJcyaEic6RVG/ZjffurYi/n1pekrMpAImI5DbJl+CzJ2DPe9iB7e6qzAwcz5N97qZFxaJWVyeSLRSARERyk9P78XzcH/vZA3iMjVdd3dhZcjDvPFyPYqHBVlcnkm0UgEREcgNjYM+7eNY+jt2VyClTgAnOUTS6+wHevbs8fnab1RWKZCsFIBGRnC7Rgfl0ArYfl2IHNrtrERM0jhn9WtGkXGGrqxOxhAKQiEhOdmIvniUDsZ8/jMvYedHVk/1l+vN+r7oUyR9kdXUillEAEhHJiYyBb/+N54unsHuS+cMUYYJrDK3bdGJhi3LY1fKSXE4BSEQkp7lyHrNqNLZfPsUOrHPX46U8Y3luYAvqly5kdXUiXkEBSEQkJzm282rLy3GMJOPP864+/FH+ET7qUZuC+QKtrk7Ea1h6p6utW7fSuXNnoqKisNlsrFy5MuU1p9PJ5MmTqVGjBvny5SMqKop+/fpx4sSJdNe5cOFCbDbbdY/ExMQs3hoREQt5PPDVK5i322N3HON3Tzg9XDOJbj+e/wxooPAj8heWHgG6dOkStWrVYuDAgXTv3j3Va5cvX2b37t1MmzaNWrVqcf78ecaPH8/999/Prl270l1vaGgoBw4cSLUsOFj3txCRHOrSWcyK4dh+24ANWO1uwpx8o3mhTzNqRxewujoRr2RpAOrQoQMdOnRI87WwsDDWr1+fatnrr79Ow4YNiY2NpWTJkjdcr81mIyIi4pbrSEpKIikpKeW5w+G45bkiIpb6/Ss8SwdjT4gj0QQw3TWA+Eq9WPJQLcLyBFhdnYjX8qkve4mPj8dms1GgQIF0xyUkJFCqVClKlChBp06d2LNnT7rjY2JiCAsLS3lER0dnYtUiIlnA44bNszCLOmNPiOOgpzjdXc9TrdNo3uxbT+FH5CZ8JgAlJiYyZcoUevfuTWho6A3HVa5cmYULF7J69Wo+/PBDgoODueuuuzh48OAN50ydOpX4+PiUx7Fjx7JiE0REMofjJOadLrA5Bpvx8LGrJWPyv8TskT3p16Q0NpsucRe5GZ+4CszpdNKrVy88Hg9vvPFGumMbN25M48aNU57fdddd1K1bl9dff53XXnstzTlBQUEEBemGYCLiA37bgGfZcOxXznLJBPGUczDuGj1Y8kB1QoJ11EfkVnl9AHI6nfTo0YMjR46wcePGdI/+pMVut9OgQYN0jwCJiHg9txM2/h2+fgU78LOnFBM84xjYtQ09G0TrqI9IBnl1ALoWfg4ePMimTZsoXDjj31ljjGHv3r3UqFEjCyoUEckGF2IxSwdj++NbAN5xteGDAsN49ZHGVI7I2C+FInKVpQEoISGB3377LeX5kSNH2Lt3L4UKFSIqKooHH3yQ3bt38+mnn+J2u4mLiwOgUKFCBAZevadFv379KF68ODExMQDMnDmTxo0bU6FCBRwOB6+99hp79+5l7ty52b+BIiJ36pc1eFaOxJ54AYfJyxPOYeSr3Y3lXauRN9Crf4cV8WqWvnt27dpF69atU55PnDgRgP79+zNjxgxWr14NQO3atVPN27RpE61atQIgNjYWu/3/zuW+cOECw4YNIy4ujrCwMOrUqcPWrVtp2LBh1m6MiEhmciXB+unwzZvYgb2eckwy4xnR/W4erFfC6upEfJ7NGGOsLsLbOBwOwsLCiI+Pz/A5RyIid+zcIcySgdji9gHwlus+VhUazKuPNKR8sRCLixPxXhn5/NbxUxERb/LDUjyfjMOenMCfJj+POR8lon4XlnWuSnCAn9XVieQYCkAiIt4g+TJ8PgV2L8IOfOOpzFTGMr5na+6vFWV1dSI5jgKQiIjVTv+CWTIA25n9eIyNOe4urC86kPl9GlCmSD6rqxPJkRSARESsYgzsfR/PmknYXVc4Y8IY5xxFhUb3saRjFbW8RLKQApCIiBWSLsKax+D7xdiBre4a/M1vDJMfbkGHGpFWVyeS4ykAiYhkt5PfX73K68/fcBk7L7seZFtkP97tXY/oQnmtrk4kV1AAEhHJLsbAzv9gvngKmzuJE6YQY5NHU/uuDnzcvjKB/j7z/dQiPk8BSEQkO1y5AKvHwP7V2IAN7jo86z+GaX2bcW/VcKurE8l1FIBERLLaH9/hWTIAe3wsycaPWa7e7Cv+MB/2rktUgTxWVyeSKykAiYhkFY8HdszFbJiB3eMi1lOU0c6x3NWyLR+1qUiAn1peIlZRABIRyQqXzsHKR+HgF9iAT92NeCFgJM/0aUqrSsWsrk4k11MAEhHJbEe3YZYOwnbxJEkmgJmufhyKfpAlvesSHhpsdXUiggKQiEjm8bjhy5cxm5/HZjwc8kQyxjWWe1vfyzN3l8dfLS8Rr6EAJCKSGS6eguVD4cgWbMAyd3NeCRzOrH5NuKt8EaurE5G/UAASEblThzZilg3DdvkMl00Q05wDOVW2G8t71qZoSJDV1YlIGhSARERul9sFm5/HfPkyNgz7PdGMdY2ly72teaFVefzsNqsrFJEbUAASEbkd8X9glg3BFrsdG/C+6x7+lWcwLw5oTKOyha2uTkRuQgFIRCSjDnyGWfEotsTzXDR5mOocQkKF+1nxUC0K51fLS8QXKACJiNwqVzJsmAE75mIDvveUYZxrHL3atWBo87LY1fIS8RkKQCIit+LPI1fv7XNiNwDzXR14N99AXurdkHqlClpcnIhklAKQiMjN/LQCs2oMtuSLXDD5mOQcAZU6svKhmhTIG2h1dSJyGxSARERuxJkIXzwJu+ZjA3Z5KjLRPYb+HZsz6K7S2GxqeYn4qtsOQG63mxUrVrB//35sNhuVK1ema9eu+PsrU4lIDnDmV8zSAdhO/QTAXNf9LAnpy+u9G1IruoC1tYnIHbuttPLjjz/SpUsX4uLiqFSpEgC//vorRYsWZfXq1dSoUSNTixQRyVb7PsJ8OgGb8zJnTCgTnSPJX7Utq7rXJCxPgNXViUgmuK0ANGTIEKpVq8auXbsoWPDqyX/nz59nwIABDBs2jO3bt2dqkSIi2SL5Eqx9HPa+jw342l2NJzyjGd65KX0bl1LLSyQHua0AtG/fvlThB6BgwYI899xzNGjQINOKExHJNnE/YpYOxHb2V9zGxiuu7qwJe5h/9WlA9eJhVlcnIpnstgJQpUqVOHXqFNWqVUu1/PTp05QvXz5TChMRyRbGwHcLMZ9PweZKJM4UZFzyaMJr3sPqbjXIH6TzGkVyott6Zz///POMHTuWGTNm0LhxYwB27NjBM888w+zZs3E4HCljQ0NDM6dSEZHMluiAT8bBT8uxAZvctZhqRjHugSb0ahCtlpdIDmYzxpiMTrLb7f+3gv//A+Laav73uc1mw+12Z0ad2crhcBAWFkZ8fLwCnEhOdXz31ZbX+d9xGj9ecPVkU8GHmPNIfSpH6H0v4osy8vl9W0eANm3adFuFiYhYzhj4Zh5m3TRsHid/mCKMTh5L2TotWdWlOvnU8hLJFW7rnd6yZcvMrkNEJOtd/hNWjYIDa7EBn7kbMIMRTOreiIfqR1tdnYhkozv6Vefy5cvExsaSnJycannNmjXvqCgRkUwXuwOzdDA2xx8kGX+edfXl28Jdea9PPSqEh1hdnYhkM/vNh1zvzJkzdOrUiZCQEKpVq0adOnVSPW7V1q1b6dy5M1FRUdhsNlauXJnqdWMMM2bMICoqijx58tCqVSt++umnm6532bJlVK1alaCgIKpWrcqKFSsyuokiklN4PPDly5gFHbE5/uCwJ4Juyc/gqjuIVaObK/yI5FK3FYDGjx/P+fPn2bFjB3ny5OHzzz9n0aJFVKhQgdWrV9/yei5dukStWrWYM2dOmq+/8MILvPzyy8yZM4edO3cSERFBmzZtuHjx4g3XuX37dnr27Enfvn3Zt28fffv2pUePHnzzzTcZ3k4R8XEJp+H97vDfmdiMmxXuu+jJLIb17Mqs7jXJE+hndYUiYpHbugosMjKSVatW0bBhQ0JDQ9m1axcVK1Zk9erVvPDCC3z11VcZL8RmY8WKFXTt2hW4evQnKiqK8ePHM3nyZACSkpIIDw9n9uzZDB8+PM319OzZE4fDwWeffZayrH379hQsWJAPP/wwzTlJSUkkJSWlPHc4HERHR+sqMBFfdngLZvlQbAmnuGIC+ZtrAD8W7czcPnUpWzS/1dWJSBbIyFVgt3UE6NKlSxQrVgyAQoUKcebMGQBq1KjB7t27b2eV1zly5AhxcXG0bds2ZVlQUBAtW7Zk27ZtN5y3ffv2VHMA2rVrl+6cmJgYwsLCUh7R0ToZUsRnedyw6XnMO12wJZzigKcE9yf/naAG/Vgx6i6FHxEBbjMAVapUiQMHDgBQu3Zt/vWvf3H8+HHmzZtHZGRkphQWFxcHQHh4eKrl4eHhKa/daF5G50ydOpX4+PiUx7Fjx+6gchGxjOMELLoftszGhuFDV2v62GIY93Bn/t61BsEBanmJyFW3dRXY+PHjOXnyJADTp0+nXbt2vPfeewQGBrJo0aJMLfCvd2K9doPFzJwTFBREUFDQ7RcpItb7dR1m5Qhsl8+RYIJ50jmEI5EdWNa7DqUK57O6OhHxMrcVgPr06ZPy5zp16vD777/zyy+/ULJkSYoUKZIphUVERABXj+j871Gl06dPX3eE56/z/nq052ZzRMSHuZ3w32dg22vYgB89pRntHEOrJk14sWNlgvx11EdErndbAWjixIlpLrfZbAQHB1O+fHm6dOlCoUKFbruwMmXKEBERwfr161MurU9OTmbLli3Mnj37hvOaNGnC+vXrmTBhQsqydevW0bRp09uuRUS81PmjsHQQHN8FwAJXO+b49+O5PvVpXz3C4uJExJvdVgDas2cPu3fvxu12U6lSJYwxHDx4ED8/PypXrswbb7zBY489xldffUXVqlVvuJ6EhAR+++23lOdHjhxh7969FCpUiJIlSzJ+/Hief/55KlSoQIUKFXj++efJmzcvvXv3TpnTr18/ihcvTkxMDADjxo2jRYsWzJ49my5durBq1So2bNhwW1emiYgX+3k1ZvVobInxxJu8POEcTlzxNqx8uA7RhfJaXZ2IeLnbCkDXju4sWLAg5TIzh8PB4MGDadasGUOHDqV3795MmDCBL7744obr2bVrF61bt055fu3IUv/+/Vm4cCFPPPEEV65cYeTIkZw/f55GjRqxbt06QkL+78ZlsbGxqb6ctWnTpnz00Uc8/fTTTJs2jXLlyrF48WIaNWp0O5sqIt7GmQjrp8G3b2EDdnvKMyZ5DB2bN+T1dpUJ9L+taztEJJe5rfsAFS9enPXr1193dOenn36ibdu2HD9+nN27d9O2bVvOnj2bacVmF30bvIiXOncIlgyAuO8BmOfqzH8CejO7Rz3uqaLz/ERyuyz/Nvj4+HhOnz59XQA6c+YMDocDgAIFClz3HWEiIrft+yWYT8djS07gnAlhonMkl6JbsfrhOkQVyGN1dSLiY267BTZo0CBeeuklGjRogM1m49tvv2XSpEkpd3L+9ttvqVixYmbWKiK5UfJl+OwJ2PMuNmCHpwpjk0fzYKv6TGhTkQA/tbxEJONuqwWWkJDAhAkTeOedd3C5XAD4+/vTv39//vnPf5IvXz727t0LXL1Roq9RC0zES5zef7XldeYXPMbGa+4HeC+wJy/1qkfLikWtrk5EvExGPr9vKwBdk5CQwOHDhzHGUK5cOfLnzxm3mFcAErGYMbDnXczaJ7C5rnDKFGC8cxSeUs157eE6hIcGW12hiHihLD8H6Jr8+fNTs2bNO1mFiEhqSRfh0wnwwxJswBZ3TR5zPUrv1vUYe08F/NXyEpFMcEcBSEQkU53YC0sHwp+HcRk7/3D1YFlwd17pV5dmFTLnLvMiIqAAJCLewBj49t+YdU9hcyfzhynC2OTR5CnXhDU9a1MsRC0vEclcCkAiYq0r52HVaPjlU2zAOnc9JruGM/DeuoxqXR4/e/pffiwicjsUgETEOsd2YpYOxBZ/jGTjz3OuPnyetzNvPlyXxmULW12diORgCkAikv08Htj2Gmbjs9g8Ln73hDPaOYZCFRqxtkctCucPsrpCEcnhFIBEJHtdOgsrhsNvG7ABq91NmOYewvB2tRnRohx2tbxEJBsoAIlI9vn9K8yyIdguniTRBDDdNYCt+dozv3dd6pcuZHV1IpKLKACJSNbzuGHri5gts7EZDwc9xRntHEOJSvVZ+1AtCuYLtLpCEcllFIBEJGtdjINlQ+D3L7EBH7ta8qxnAOM61mZwszLYbGp5iUj2UwASkazz2wbM8uHYLp/lkgniKedgdoa24Z3edahTsqDV1YlILqYAJCKZz+2EjX+Hr1/BBvzsKcVo5xjKV6nD2gdrEZY3wOoKRSSXUwASkcx14RgsHQR/fAvAO642zDaPMKlTLQY0La2Wl4h4BQUgEck8v6zBrByJLfECF00ennAO46cCrfmwdx1qlihgdXUiIikUgETkzrmSYP10+OZNbMBeTzlGO8dQs3pNPu1ek9BgtbxExLsoAInInTl36Oo3uJ/cB8Bbrvt4lYeZ0qUmjzQqqZaXiHglBSARuX0/LMV8Mh5b8kXOm/w85hzBkULN+bh3HapFhVldnYjIDSkAiUjGOa/A51Pgu4XYgG88lRmXPIqGtWrwSbca5A/SjxYR8W76KSUiGXP6l6str9M/48HGHFcX5vEQ07rVpFeDaLW8RMQnKACJyK0xBvZ+gFk7CZvzMmdMGOOdIzlZuDHLetelSmSo1RWKiNwyBSARubmkBFjzGHz/ETbgS3d1JjpH0rxONd7qWp18anmJiI/RTy0RSd/J76+2vM79hhs7LzkfZIG9KzMfrMlD9Uqo5SUiPkkBSETSZgzsmo/5/Els7iROmkKMTR7NhaL1WdWnLhXDQ6yuUETktikAicj1rlyAT8bCz6uwARvcdXjcOZx761VlZpdq5A3Ujw4R8W36KSYiqf3xHSwdABdiceLPLGcvPrB34rkeNehWt4TV1YmIZAoFIBG5yhjYPhezYTo2j4tYU5QxyWNILFaHT/rUpXyx/FZXKCKSaRSARAQu/wkrH4VfP8cGrHE3ZKpzKPc1rMz0ztUIDvCzukIRkUylACSS2x3dBsuGgOM4SQTwjLMvK/3a8nyvmnSpXdzq6kREsoTd6gJupnTp0thstuseo0aNSnP85s2b0xz/yy+/ZHPlIl7O44atL2IW3geO4xzyRNI16Rl2F+vGJ2OaK/yISI7m9UeAdu7cidvtTnn+448/0qZNGx566KF05x04cIDQ0P+7M23RokWzrEYRn3PxFKwYBoc3YwOWuZsxzTmIbo0r8vR9VdXyEpEcz+sD0F+Dy6xZsyhXrhwtW7ZMd16xYsUoUKDALf0dSUlJJCUlpTx3OBwZrlPEZxzaBMuHwqUzXCGIp5MH8kXA3bzQuwadakZZXZ2ISLbw+hbY/0pOTua9995j0KBBN737bJ06dYiMjOSee+5h06ZN6Y6NiYkhLCws5REdHZ2ZZYt4B7cL/vss5t0H4NIZfvFE0ynp7xyI7MSasc0UfkQkV7EZY4zVRdyqjz/+mN69exMbG0tUVNo/rA8cOMDWrVupV68eSUlJvPvuu8ybN4/NmzfTokWLNOekdQQoOjqa+Pj4VG00EZ8V/8fVE51jtwPwvusennH15eGmFZnasTJB/mp5iYjvczgchIWF3dLnt08FoHbt2hEYGMgnn3ySoXmdO3fGZrOxevXqWxqfkX9AEa934HNYOQKunCeBPExJHsKWwOa8+GBN2lePtLo6EZFMk5HPb68/B+iao0ePsmHDBpYvX57huY0bN+a9997LgqpEvJgrGf47E7bPAeB7TxnGOMdQoHgl1jxcl5KF81pcoIiIdXwmAC1YsIBixYpx3333ZXjunj17iIzUb7qSi/x5BJYOghO7AZjv6sBsVy8euasiUzpUJtDfp07/ExHJdD4RgDweDwsWLKB///74+6cueerUqRw/fpx33nkHgFdeeYXSpUtTrVq1lJOmly1bxrJly6woXST7/bQCVo+FJAfx5GNS8nC+CWzMnIdr0bZahNXViYh4BZ8IQBs2bCA2NpZBgwZd99rJkyeJjY1NeZ6cnMykSZM4fvw4efLkoVq1aqxZs4aOHTtmZ8ki2c+ZCF88CbvmA7DLU5GxyaMpFl2etb3rUKKgWl4iItf41EnQ2UUnQYvPOfMrLB0Ip34E4A3X/bzsepCBzSvweDu1vEQkd8iRJ0GLyA3s+wg+nQjOS5wzoUxwPsr3wfX5V59a3FMl3OrqRES8kgKQiK9KvgRrH4e97wOwzV2Vcc5RlCxVlrUP1yGqQB6LCxQR8V4KQCK+KO7Hqy2vs7/ixs6rzm7McXdlaMvyTGpbiQA/tbxERNKjACTiS4yB7xbC51PAlcgpU5CxyaP5NU9N5veoTevKxayuUETEJygAifiKxHj4ZNzVy9yBTe5aPOZ8lHKlS7H24TpEhqnlJSJyqxSARHzB8d1XW17nf8eFHy84e/Afz3082roCE+6tiL9aXiIiGaIAJOLNjIEdb8L6v4HHyXFTlNHJo4nNW42FPWvTomJRqysUEfFJCkAi3uryn7BqFBxYC8AX7vo87hxG1bIlWdurDuGhwRYXKCLiuxSARLxR7A5YOhgcf5CMP3939uFdT1vG3FORcfdUwM9us7pCERGfpgAk4k08Hvj6n7DxOTBujpoIRiaP4VS+yrzbszbNKhSxukIRkRxBAUjEWySchhXD4dBGAFa5m/KkczC1ypVgQa/aFAtRy0tEJLMoAIl4g8NbYPlQSDhFIoH8zdmfJZ5WjLu3ImPuVstLRCSzKQCJWMntgi2zYeuLgOE3U4JHk8dyIX853u9Zm6bl1fISEckKCkAiVnGcgGVD4OjXAHzkasUMV3/qly/OBz1rUzQkyOICRURyLgUgESv8ug5WjoDL57hMHqYkD+JTcxfj21RkVOvyanmJiGQxBSCR7OR2wn+fgW2vAfCzKc3I5DFczl+a93vVoUm5whYXKCKSOygAiWSX80dh6SA4vguAha62xLh607BCFP/sWZsi+dXyEhHJLgpAItnh59WwejQkxpNAPh5LHsp605DH2lXi0ZblsKvlJSKSrRSARLKSMxHWT4Nv3wJgrynP6OQxOENK8GGvOjQqq5aXiIgVFIBEssq5Q7BkAMR9D8A8Vyf+4epB04qR/LNHLQqr5SUiYhkFIJGs8P0S+HQ8JCdwwRbK+KQRfEkdHmtfkREt1PISEbGaApBIZkq+DJ89AXveBeBbU4UxiaOwhUbxUe86NChdyOICRUQEFIBEMs/p/VdbXmd+wYON110P8JrrAVpUiuClHrUplC/Q6gpFROT/UwASuVPGXD3is/YJcF3hnK0go5NG8i3VebxDJYY1L6uWl4iIl1EAErkTSRfh0wnwwxIAvvLUYHzySALCwln8cB3qq+UlIuKVFIBEbteJvbB0IPx5GDd2/uHswTx3J1pXjuClh2pRUC0vERGvpQAkklHGwLf/hnVPgTuZU7YijEwcxT5bZaZ2rMSQZmp5iYh4OwUgkYy4ch5WjYZfPgVgg6cejyUPJ19YERb3rku9UgUtLlBERG6FApDIrTq28+p3ecXH4sKf55wPs8DdnnurhPOPh2pRIK9aXiIivkIBSORmPB7Y/vrVb3H3uDhuC2dE4hj228rx9H2VGdysDDabWl4iIr5EAUgkPZfOwooR8Nt6ANZ6GjM5eQihBQrzce861C2plpeIiC+yW11AembMmIHNZkv1iIiISHfOli1bqFevHsHBwZQtW5Z58+ZlU7WS4/z+FcxrBr+tJ9kWyFTnYEYmj6FRlTKsGdtM4UdExId5/RGgatWqsWHDhpTnfn5+Nxx75MgROnbsyNChQ3nvvff4+uuvGTlyJEWLFqV79+7ZUa7kBB43bP0HbJkFxsNRWwmGJY7mkK2UWl4iIjmE1wcgf3//mx71uWbevHmULFmSV155BYAqVaqwa9cu/vGPfygAya25GAfLhsDvXwKwzNOSp5P7U6hAQZb0rkMdHfUREckRvLoFBnDw4EGioqIoU6YMvXr14vDhwzccu337dtq2bZtqWbt27di1axdOp/OG85KSknA4HKkekgv9tgHevAt+/5JEWzATkh/lseThNKtairVjmyv8iIjkIF4dgBo1asQ777zDF198wb///W/i4uJo2rQp586dS3N8XFwc4eHhqZaFh4fjcrk4e/bsDf+emJgYwsLCUh7R0dGZuh3i5dxO2DAD3usOl8/ym600HRP/zqe2FvytU1Xe6luPsLwBVlcpIiKZyKtbYB06dEj5c40aNWjSpAnlypVj0aJFTJw4Mc05fz03wxiT5vL/NXXq1FTrczgcCkG5xYVjsGwwHPsGgPfd9/KM8xGKFgxjae+61IouYG19IiKSJbw6AP1Vvnz5qFGjBgcPHkzz9YiICOLi4lItO336NP7+/hQuXPiG6w0KCiIoKChTaxUf8MsaWDkSEi9wxZ6PiYlD+MzTiHbVwnnhwVqE5dFRHxGRnMqnAlBSUhL79++nefPmab7epEkTPvnkk1TL1q1bR/369QkI0IeZ/H+uJFg/Hb55E4Cf7eUZfmUUcfYIpneuwoCmpXWVl4hIDufV5wBNmjSJLVu2cOTIEb755hsefPBBHA4H/fv3B662rvr165cyfsSIERw9epSJEyeyf/9+3n77bebPn8+kSZOs2gTxNn8ehvltU8LPfHdHulz+GxQszdIRTRl4ly5xFxHJDbz6CNAff/zBww8/zNmzZylatCiNGzdmx44dlCpVCoCTJ08SGxubMr5MmTKsXbuWCRMmMHfuXKKionjttdd0Cbxc9eMyWD0Oki+SYA9hbOJwNnrq0r5aBLMfrKmWl4hILmIz184SlhQOh4OwsDDi4+MJDQ21uhy5U84r8PkU+G4hAPvsVRhxeSRn/YrwVMcq9FfLS0QkR8jI57dXHwESuWNnDsCSAXD6Zww23nR34aXE7kQVys+y3nWpWaKA1RWKiIgFFIAk59r7Aax5DJyXcfgVYOSVR/nKU4MO1SOY1V0tLxGR3EwBSHKepISrwef7jwDYaa/JyEsjiPcrxMz7q9CvSSm1vEREcjkFIMlZ4n642vI69xse7LzifpA5ifdTolB+3u5dlxolwqyuUEREvIACkOQMxsCu+fD5k+BO4rxfEYZdHslOU5kO1a9e5RUarJaXiIhcpQAkvu/KBfhkLPy8CoCv7fUYfWkol/wK8EynKvRtrJaXiIikpgAkvu2P72DpALgQi8fmzyxXL95K7EDJQvl4Ry0vERG5AQUg8U3GwPa5sGE6eFyc8Y9kyKVH2WfKq+UlIiI3pQAkvufyn7DyUfj1cwA22ZswLmEQiX4hzLxPV3mJiMjNKQCJbzm6DZYNAcdxXPZAnnU+wqLEeyhZKB9z1fISEZFbpAAkvsHjhq9ehk3Pg/EQF1CCQQkj+dmUVstLREQyTAFIvN/FU7BiGBzeDMDnfi2ZeLE/Lr+8anmJiMhtUQAS73ZoEywfCpfO4LQH83TyABYnNlfLS0RE7ogCkHgntws2x8CXLwGG4wFl6J/wKL+ZEmp5iYjIHVMAEu8TfxyWDYbY7QCs8mvLExd7Y/yC1fISEZFMoQAk3uXA57ByBFw5T7JfPp5IGsTKxCZEF8rD3N51qVmigNUViohIDqAAJN7BlQz/nQnb5wDwe2BF+l8cwVETQYfqEczqXpOwPGp5iYhI5lAAEuv9eQSWDoITuwH42K8TTzseAr8gtbxERCRLKACJtX5aCavHQJKDJP9QxicO4bPE+mp5iYhIllIAEms4E+GLJ2HXfAAOBVWlb/wITlBELS8REclyCkCS/c4ehCUD4NSPALzr342Z8V2x+wUyo2Nl+jctrZaXiIhkKQUgyV77PoJPJ4LzElcCCjLyynA2JdZUy0tERLKVApBkj+RLsPZx2Ps+AL8E16bfhaGcpqBaXiIiku0UgCTrnfoJlgyEswcwNjvz/Xrw/IVO+Pn5MfO+qrrKS0REsp0CkGQdY2D3IvhsMrgSuRxYlKGXR/D1lSpqeYmIiKUUgCRrJDrg0/Hw4zIAfsjbkP5/DuJPQmlf7ep3eanlJSIiVlEAksx3Ys/Vltf5IxibP2/69+bFP9vi7+fHjI5VdJWXiIhYTgFIMo8x8M2/YN3T4HGSEBzJwIRH2XmlPNGF8jDn4brUii5gdZUiIiIKQJJJLv8Jq0bDgTUA7MnXjP7n+uEgv1peIiLidRSA5M7FfgPLBkP8MTz2QF7168+r51oR4GdXy0tERLySApDcPo8Htr0K/30WjBtHnmj6Oh5ln7u0Wl4iIuLVFIDk9iScgRXD4dB/AdiZ/24GnO3DJfLQrlo4LzxYSy0vERHxWnarC0hPTEwMDRo0ICQkhGLFitG1a1cOHDiQ7pzNmzdjs9mue/zyyy/ZVHUucORLmNcMDv0Xj18wswNH8dDZwST75WV656rMe6Sewo+IiHg1rz4CtGXLFkaNGkWDBg1wuVw89dRTtG3blp9//pl8+fKlO/fAgQOEhoamPC9atGhWl5vzedyw5QXY+gIYDxfylaX3hUf52V2cEgWv3thQLS8REfEFXh2APv/881TPFyxYQLFixfjuu+9o0aJFunOLFStGgQIFsrC6XMZxEpYPhd+/BODr0I4MPv0QiQSp5SUiIj7HqwPQX8XHxwNQqFChm46tU6cOiYmJVK1alaeffprWrVvfcGxSUhJJSUkpzx0Ox50Xm5Mc3AArhsHlc7gD8vGcbRhvn25AgJ+Nv3WowsC7dJWXiIj4Fp8JQMYYJk6cSLNmzahevfoNx0VGRvLWW29Rr149kpKSePfdd7nnnnvYvHnzDY8axcTEMHPmzKwq3Xe5nbDxWfj6VQDOhVSi158jOOgOp0TBPMzpXZfaanmJiIgPshljjNVF3IpRo0axZs0avvrqK0qUKJGhuZ07d8Zms7F69eo0X0/rCFB0dDTx8fGpziPKVS7EwtJB8MdOADaFdWXEqa4kEUjbquG8+GAtwvKq5SUiIt7D4XAQFhZ2S5/fPnEEaMyYMaxevZqtW7dmOPwANG7cmPfee++GrwcFBREUFHQnJeYs+z+FVSMhMR5XYCjTGcH7p2qr5SUiIjmGVwcgYwxjxoxhxYoVbN68mTJlytzWevbs2UNkZGQmV5cDuZJg3TT49l8AnA6tQY9zQ/ndXUQ3NhQRkRzFqwPQqFGj+OCDD1i1ahUhISHExcUBEBYWRp48eQCYOnUqx48f55133gHglVdeoXTp0lSrVo3k5GTee+89li1bxrJlyyzbDp9w7hAsHQgn9wHwRVgPRp3qhAt/OlSPYFZ3fZeXiIjkHF4dgN58800AWrVqlWr5ggULGDBgAAAnT54kNjY25bXk5GQmTZrE8ePHyZMnD9WqVWPNmjV07Ngxu8r2PT8shU/GQXICzuBCTHGPZNmpqgT62XmmUxX6Ni6llpeIiOQoPnMSdHbKyElUPi35Mnw+GXZfPXp2skBdHjw9mOOegpQqnJe5vetSvXiYxUWKiIjcmhx3ErRkgdP7YclAOLMfg41Pwh5hQlxb3PhxX81IZnWrQUiwWl4iIpIzKQDlNsbAnvdg7ePguoIzT1EmukbzyakKBPrbmdmpKn0alVTLS0REcjQFoNwk6SJ8OhF++BiA2IKNefBUf057wihTJB9zetehWpRaXiIikvMpAOUWJ/ddbXn9eQhj82Np2ACeONkag50utaN47oEa5A/SfwcREckd9ImX0xkDO/8DXzwJ7mSS8kUxOmkU6+PKEORvZ+b91ejZIFotLxERyVUUgHKyKxdg9WjY/wkAhwu3oPuJRzhv8lOuaD7m9qlL5YgcfJWbiIjIDSgA5VR/7Lp6Y8MLsRh7AO+FDmHa8WaAjW51i/Nsl+rkU8tLRERyKX0C5jQeD2yfA/+dCR4XiflLMvzKKLbERRMcYOfZLtV5qH601VWKiIhYSgEoJ7l0DlaOgIPrAPi1SBseOt6TeJOXCsXy80afulQID7G4SBEREespAOUUv38Fy4bAxZMY/2Dm5x/O3/9oCNjoUb8EM++vTp5AP6urFBER8QoKQL7O44YvX4LNMWA8XA4tx6BLI9kRF0neQD/+3rU63eqWsLpKERERr6IA5MsuxsHyoXBkKwA/FL2Pnn9057IJplJ4CHP71KV8sfwWFykiIuJ9FIB81W//hRXD4dIZPAF5eS1oBK8cqw/Aww2jmd65GsEBanmJiIikRQHI17hdsOk5+OplAC6EVKRP/Ah+uhhBWJ4AYrrVoGONSIuLFBER8W4KQL4k/g9YOhiO7QBgS+j9DDvdjSQCaVy2EC/3qE1UgTwWFykiIuL9FIB8xS9rYdVIuHIeV0B+/mZG8MHpuvjbbUxpV4mhzcviZ9fXWYiIiNwKBSBv50qGDdNhxxsAnMhXlV5/DiXWhFO2SD5e7VWHGiX0De4iIiIZoQDkzf48DEsHwYk9AKwIfoAnzj2AE38ebhjNtE5VyRuoXSgiIpJR+vT0Vj8uh0/GQZKDpIAwxicO57MLtSmQN4BZ3WrSvnqE1RWKiIj4LAUgb+O8Ap9Phe8WAHAwuDr9LgznJIW5q3xhXnqoNhFhwRYXKSIi4tsUgLzJmV9hyQA4/RMGGwvs3XjuQlfsfv481a4yg5uVwa4TnUVERO6YApC32PsBrHkMnJe55F+Q4ZdH8JWnBuWKXj3RuXpxnegsIiKSWRSArJaUAGsnwb4PAdjrX4uhCcM5QwH6NCrJ0/dV1ZeYioiIZDIFICvF/Xi15XXuIB7svOp5iNcTOhOWN4i3utekbTWd6CwiIpIVFICsYAzsevvqyc7uJP70K8LwyyPZaSrTvEIRXnqoFsVCdaKziIhIVlEAym6J8bB6LPy8EoAvbfUYe2kol/wKMK1DZQY2La0TnUVERLKYAlB2OrHnasvr/O+4bX48n9yL+e6OVCgWwvu96lA1KtTqCkVERHIFBaDs5ErGXDjGKXsxhl8ZzT5Tnn5NSvFkxyoEB+hEZxERkeyiAJSN1l0sxUrXBL5yViIgX0HmP1iTe6qEW12WiIhIrqMAlI2qFw9jkl9D6pYtyIsP1qJoSJDVJYmIiORKCkDZKKpAHlaNbkbpwnmx2XSis4iIiFUUgLJZmSL5rC5BREQk17NbXcCteOONNyhTpgzBwcHUq1ePL7/8Mt3xW7ZsoV69egQHB1O2bFnmzZuXTZWKiIiIL/D6ALR48WLGjx/PU089xZ49e2jevDkdOnQgNjY2zfFHjhyhY8eONG/enD179vDkk08yduxYli1bls2Vi4iIiLeyGWOM1UWkp1GjRtStW5c333wzZVmVKlXo2rUrMTEx142fPHkyq1evZv/+/SnLRowYwb59+9i+ffst/Z0Oh4OwsDDi4+MJDdW9eURERHxBRj6/vfoIUHJyMt999x1t27ZNtbxt27Zs27YtzTnbt2+/bny7du3YtWsXTqczzTlJSUk4HI5UDxEREcm5vDoAnT17FrfbTXh46nvlhIeHExcXl+acuLi4NMe7XC7Onj2b5pyYmBjCwsJSHtHR0ZmzASIiIuKVvDoAXfPXS8aNMeleRp7W+LSWXzN16lTi4+NTHseOHbvDikVERMSbefVl8EWKFMHPz++6oz2nT5++7ijPNREREWmO9/f3p3DhwmnOCQoKIihINyUUERHJLbz6CFBgYCD16tVj/fr1qZavX7+epk2bpjmnSZMm141ft24d9evXJyAgIMtqFREREd/h1QEIYOLEifznP//h7bffZv/+/UyYMIHY2FhGjBgBXG1f9evXL2X8iBEjOHr0KBMnTmT//v28/fbbzJ8/n0mTJlm1CSIiIuJlvLoFBtCzZ0/OnTvHM888w8mTJ6levTpr166lVKlSAJw8eTLVPYHKlCnD2rVrmTBhAnPnziUqKorXXnuN7t27W7UJIiIi4mW8/j5AVtB9gERERHxPjrkPkIiIiEhWUAASERGRXMfrzwGywrWuoO4ILSIi4juufW7fytk9CkBpuHjxIoDuCC0iIuKDLl68SFhYWLpjdBJ0GjweDydOnCAkJCTdO07D1bQZHR3NsWPHcvQJ09rOnCU3bGdu2EbQduY02s47Y4zh4sWLREVFYbenf5aPjgClwW63U6JEiQzNCQ0NzdH/Wa/RduYsuWE7c8M2grYzp9F23r6bHfm5RidBi4iISK6jACQiIiK5jgLQHQoKCmL69Ok5/stUtZ05S27YztywjaDtzGm0ndlHJ0GLiIhIrqMjQCIiIpLrKACJiIhIrqMAJCIiIrmOApCIiIjkOgpAt+CNN96gTJkyBAcHU69ePb788st0x2/ZsoV69eoRHBxM2bJlmTdvXjZVentiYmJo0KABISEhFCtWjK5du3LgwIF052zevBmbzXbd45dffsmmqjNuxowZ19UbERGR7hxf25cApUuXTnPfjBo1Ks3xvrIvt27dSufOnYmKisJms7Fy5cpUrxtjmDFjBlFRUeTJk4dWrVrx008/3XS9y5Yto2rVqgQFBVG1alVWrFiRRVtwc+lto9PpZPLkydSoUYN8+fIRFRVFv379OHHiRLrrXLhwYZr7NzExMYu35sZuti8HDBhwXb2NGze+6Xq9aV/Czbczrf1is9l48cUXb7hOb9uft/L54a3vTQWgm1i8eDHjx4/nqaeeYs+ePTRv3pwOHToQGxub5vgjR47QsWNHmjdvzp49e3jyyScZO3Ysy5Yty+bKb92WLVsYNWoUO3bsYP369bhcLtq2bculS5duOvfAgQOcPHky5VGhQoVsqPj2VatWLVW9P/zwww3H+uK+BNi5c2eqbVy/fj0ADz30ULrzvH1fXrp0iVq1ajFnzpw0X3/hhRd4+eWXmTNnDjt37iQiIoI2bdqkfLdfWrZv307Pnj3p27cv+/bto2/fvvTo0YNvvvkmqzYjXelt4+XLl9m9ezfTpk1j9+7dLF++nF9//ZX777//pusNDQ1NtW9PnjxJcHBwVmzCLbnZvgRo3759qnrXrl2b7jq9bV/Czbfzr/vk7bffxmaz0b1793TX603781Y+P7z2vWkkXQ0bNjQjRoxItaxy5cpmypQpaY5/4oknTOXKlVMtGz58uGncuHGW1ZjZTp8+bQCzZcuWG47ZtGmTAcz58+ezr7A7NH36dFOrVq1bHp8T9qUxxowbN86UK1fOeDyeNF/3xX0JmBUrVqQ893g8JiIiwsyaNStlWWJiogkLCzPz5s274Xp69Ohh2rdvn2pZu3btTK9evTK95oz66zam5dtvvzWAOXr06A3HLFiwwISFhWVucZkore3s37+/6dKlS4bW48370phb259dunQxd999d7pjvH1//vXzw5vfmzoClI7k5GS+++472rZtm2p527Zt2bZtW5pztm/fft34du3asWvXLpxOZ5bVmpni4+MBKFSo0E3H1qlTh8jISO655x42bdqU1aXdsYMHDxIVFUWZMmXo1asXhw8fvuHYnLAvk5OTee+99xg0aNBNv9jX1/bl/zpy5AhxcXGp9ldQUBAtW7a84XsVbryP05vjTeLj47HZbBQoUCDdcQkJCZQqVYoSJUrQqVMn9uzZkz0F3oHNmzdTrFgxKlasyNChQzl9+nS64319X546dYo1a9YwePDgm4715v35188Pb35vKgCl4+zZs7jdbsLDw1MtDw8PJy4uLs05cXFxaY53uVycPXs2y2rNLMYYJk6cSLNmzahevfoNx0VGRvLWW2+xbNkyli9fTqVKlbjnnnvYunVrNlabMY0aNeKdd97hiy++4N///jdxcXE0bdqUc+fOpTne1/clwMqVK7lw4QIDBgy44Rhf3Jd/de39mJH36rV5GZ3jLRITE5kyZQq9e/dO98skK1euzMKFC1m9ejUffvghwcHB3HXXXRw8eDAbq82YDh068P7777Nx40Zeeukldu7cyd13301SUtIN5/jyvgRYtGgRISEhdOvWLd1x3rw/0/r88Ob3pr4N/hb89TdnY0y6v02nNT6t5d5o9OjRfP/993z11VfpjqtUqRKVKlVKed6kSROOHTvGP/7xD1q0aJHVZd6WDh06pPy5Ro0aNGnShHLlyrFo0SImTpyY5hxf3pcA8+fPp0OHDkRFRd1wjC/uyxvJ6Hv1dudYzel00qtXLzweD2+88Ua6Yxs3bpzqBOK77rqLunXr8vrrr/Paa69ldam3pWfPnil/rl69OvXr16dUqVKsWbMm3YDgi/vymrfffps+ffrc9Fweb96f6X1+eON7U0eA0lGkSBH8/PyuS5ynT5++LpleExERkeZ4f39/ChcunGW1ZoYxY8awevVqNm3aRIkSJTI8v3Hjxl7xW8itypcvHzVq1Lhhzb68LwGOHj3Khg0bGDJkSIbn+tq+vHY1X0beq9fmZXSO1ZxOJz169ODIkSOsX78+3aM/abHb7TRo0MCn9m9kZCSlSpVKt2Zf3JfXfPnllxw4cOC23qvesj9v9Pnhze9NBaB0BAYGUq9evZSraK5Zv349TZs2TXNOkyZNrhu/bt066tevT0BAQJbVeieMMYwePZrly5ezceNGypQpc1vr2bNnD5GRkZlcXdZJSkpi//79N6zZF/fl/1qwYAHFihXjvvvuy/BcX9uXZcqUISIiItX+Sk5OZsuWLTd8r8KN93F6c6x0LfwcPHiQDRs23FYQN8awd+9en9q/586d49ixY+nW7Gv78n/Nnz+fevXqUatWrQzPtXp/3uzzw6vfm5l2OnUO9dFHH5mAgAAzf/588/PPP5vx48ebfPnymd9//90YY8yUKVNM3759U8YfPnzY5M2b10yYMMH8/PPPZv78+SYgIMAsXbrUqk24qUcffdSEhYWZzZs3m5MnT6Y8Ll++nDLmr9v5z3/+06xYscL8+uuv5scffzRTpkwxgFm2bJkVm3BLHnvsMbN582Zz+PBhs2PHDtOpUycTEhKSo/blNW6325QsWdJMnjz5utd8dV9evHjR7Nmzx+zZs8cA5uWXXzZ79uxJuQJq1qxZJiwszCxfvtz88MMP5uGHHzaRkZHG4XCkrKNv376pruD8+uuvjZ+fn5k1a5bZv3+/mTVrlvH39zc7duzI9u0zJv1tdDqd5v777zclSpQwe/fuTfVeTUpKSlnHX7dxxowZ5vPPPzeHDh0ye/bsMQMHDjT+/v7mm2++sWITjTHpb+fFixfNY489ZrZt22aOHDliNm3aZJo0aWKKFy/uU/vSmJv/nzXGmPj4eJM3b17z5ptvprkOb9+ft/L54a3vTQWgWzB37lxTqlQpExgYaOrWrZvq8vD+/fubli1bphq/efNmU6dOHRMYGGhKly59w//Y3gJI87FgwYKUMX/dztmzZ5ty5cqZ4OBgU7BgQdOsWTOzZs2a7C8+A3r27GkiIyNNQECAiYqKMt26dTM//fRTyus5YV9e88UXXxjAHDhw4LrXfHVfXrtc/6+P/v37G2OuXm47ffp0ExERYYKCgkyLFi3MDz/8kGodLVu2TBl/zZIlS0ylSpVMQECAqVy5sqXBL71tPHLkyA3fq5s2bUpZx1+3cfz48aZkyZImMDDQFC1a1LRt29Zs27Yt+zfuf6S3nZcvXzZt27Y1RYsWNQEBAaZkyZKmf//+JjY2NtU6vH1fGnPz/7PGGPOvf/3L5MmTx1y4cCHNdXj7/ryVzw9vfW/a/v8GiIiIiOQaOgdIREREch0FIBEREcl1FIBEREQk11EAEhERkVxHAUhERERyHQUgERERyXUUgERERCTXUQASERGRXEcBSERERHIdBSARyZFatWrF+PHjr1u+cuVKbDZb9hckIl5FAUhERERyHQUgEcm19u3bR+vWrQkJCSE0NJR69eqxa9cuq8sSkWzgb3UBIiJW6dOnD3Xq1OHNN9/Ez8+PvXv3EhAQYHVZIpINFIBEJNeKjY3l8ccfp3LlygBUqFDB4opEJLuoBSYiudbEiRMZMmQI9957L7NmzeLQoUNWlyQi2UQBSERypNDQUOLj469bfuHCBUJDQwGYMWMGP/30E/fddx8bN26katWqrFixIrtLFRELKACJSI5UuXLlNE9o3rlzJ5UqVUp5XrFiRSZMmMC6devo1q0bCxYsyM4yRcQiCkAikiONHDmSQ4cOMWrUKPbt28evv/7K3LlzmT9/Po8//jhXrlxh9OjRbN68maNHj/L111+zc+dOqlSpYnXpIpINbMYYY3URIiJZ4bvvvuOpp55iz549JCYmUrFiRR577DF69epFcnIy/fv35+uvv+bUqVMUKVKEbt268eKLLxIcHGx16SKSxRSAREREJNdRC0xERERyHQUgERERyXUUgERERCTXUQASERGRXEcBSERERHIdBSARERHJdRSAREREJNdRABIREZFcRwFIREREch0FIBEREcl1FIBEREQk1/l/Tgpp5u5m61wAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -321,6 +357,83 @@
    "source": [
     "ds.to_netcdf(\"./data/1d_hubbard_example.nc\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shape = (4, 3, 5, 5)\n",
+    "test_array = np.random.rand(*shape) * np.exp(1j * np.random.rand(*shape))\n",
+    "test_array += np.moveaxis(test_array, -1, -2).conj()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "do_and_undo = utils.flat_to_matrix(\n",
+    "    utils.matrix_to_flat(test_array),\n",
+    "    test_array.shape\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isclose(do_and_undo - test_array, 0).all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(180,)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utils.matrix_to_flat(test_array).shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {