diff --git a/analysis/scipy_optimizer.ipynb b/analysis/scipy_optimizer.ipynb
index 178ea58bf2fc136cbf240b70cbe39c66d8dce9a6..74b47186af5c55129ac45e7b5cda0a7c2a180f47 100644
--- a/analysis/scipy_optimizer.ipynb
+++ b/analysis/scipy_optimizer.ipynb
@@ -12,7 +12,7 @@
     "import kwant\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import utils, hf\n",
+    "from codes import utils, hf\n",
     "from scipy.optimize import anderson\n",
     "from tqdm import tqdm\n",
     "\n",
@@ -63,20 +63,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 203,
    "id": "d1ef154e-70bd-4f28-887f-72362d8533dd",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "Us = np.linspace(1e-6, 5, 10)\n",
-    "Vs = np.linspace(1e-6, 5, 10)"
+    "Us = np.linspace(0, 4, 50)\n",
+    "Vs = np.linspace(0, 1.5, 20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 204,
    "id": "32b9e7c5-db12-44f9-930c-21e5494404b8",
    "metadata": {
     "tags": []
@@ -101,20 +101,25 @@
     "    nk=12,\n",
     "    tol=1e-5,\n",
     "    norbs=norbs,\n",
-    "    nk_dense=60,\n",
+    "    nk_dense=30,\n",
     "    mixing=0.5,\n",
     "    order=1,\n",
+    "    guess=None\n",
     "):\n",
     "    # Generate coarse-grid k-points\n",
     "    ks, dk = np.linspace(0, 2 * np.pi, nk, endpoint=False, retstep=True)\n",
     "    # Generate Hamiltonian on a k-point grid\n",
     "    hamiltonians_0 = utils.syst2hamiltonian(kxs=ks, kys=ks, syst=wrapped_fsyst)\n",
     "    # Generate guess on the same grid\n",
-    "    guess = utils.generate_guess(max_neighbor, norbs, lattice, ks, ks, dummy_syst)\n",
+    "    if guess is None:\n",
+    "        guess = utils.generate_guess(max_neighbor, norbs, lattice, ks, ks, dummy_syst)\n",
+    "    else:\n",
+    "        # guess *= 0.25\n",
+    "        guess += 0.05 * np.max(guess) * utils.generate_guess(max_neighbor, norbs, lattice, ks, ks, dummy_syst)\n",
+    "        # guess = guess\n",
     "    # Find groundstate Hamiltonian on the same grid\n",
     "    hk = hf.find_groundstate_ham(\n",
     "        H_int=H_int,\n",
-    "        nk=nk,\n",
     "        filling=filling,\n",
     "        hamiltonians_0=hamiltonians_0,\n",
     "        tol=tol,\n",
@@ -126,8 +131,6 @@
     "    vals, vecs = np.linalg.eigh(hk)\n",
     "    # Extract coarse-grid Fermi energy\n",
     "    E_F = utils.get_fermi_energy(vals, 2)\n",
-    "    # Compute coarse-grid gap\n",
-    "    gap1 = utils.calc_gap(vals, E_F)\n",
     "    # Generate kwant system with k-grid Hamiltonian\n",
     "    scf_syst = utils.hk2syst(deltas, hk, ks, dk, max_neighbor, norbs, lattice)\n",
     "    # Generate dense-grid k-points\n",
@@ -141,11 +144,15 @@
     "    # Extract dense-grid Fermi energy\n",
     "    E_F = utils.get_fermi_energy(vals, 2)\n",
     "\n",
-    "    gap2 = utils.calc_gap(vals, E_F)\n",
-    "    return gap1, gap2\n",
+    "    gap = utils.calc_gap(vals, E_F)\n",
+    "    return gap, hk\n",
     "\n",
     "\n",
     "def compute_phase_diagram(Us, Vs, nk, tol, mixing, order):\n",
+    "    import qsymm\n",
+    "    import adaptive\n",
+    "    from codes import utils, hf\n",
+    "\n",
     "    ks = np.linspace(0, 2 * np.pi, nk, endpoint=False)\n",
     "\n",
     "    Uk = utils.potential2hamiltonian(\n",
@@ -167,21 +174,21 @@
     "    )\n",
     "    gap = []\n",
     "    for U in tqdm(Us):\n",
+    "        guess = None\n",
     "        gap_U = []\n",
     "        for V in Vs:\n",
     "            H_int = calculate_Hint(U, V, Uk, Vk)\n",
-    "            gap_U.append(\n",
-    "                compute_gap(\n",
-    "                    U=U, V=V, H_int=H_int, nk=nk, tol=tol, mixing=mixing, order=order\n",
-    "                )\n",
+    "            _gap, guess = compute_gap(\n",
+    "                U=U, V=V, H_int=H_int, nk=nk, tol=tol, mixing=mixing, order=order, guess=guess\n",
     "            )\n",
+    "            gap_U.append(_gap)\n",
     "        gap.append(gap_U)\n",
     "    return np.asarray(gap, dtype=float)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 205,
    "id": "6a8c08a9-7e31-420b-b6b4-709abfb26793",
    "metadata": {
     "tags": []
@@ -191,17 +198,130 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 10/10 [02:05<00:00, 12.58s/it]\n"
+      "  0%|          | 0/50 [00:00<?, ?it/s]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=2.50001e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=1.6988e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=4.47968e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=1.40439e-18): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "  2%|▏         | 1/50 [00:58<48:01, 58.81s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=8.73276e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=8.6841e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 12%|█▏        | 6/50 [06:47<1:00:32, 82.57s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=4.91398e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=3.42637e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=4.66412e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 22%|██▏       | 11/50 [11:20<37:26, 57.59s/it] /opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=6.0485e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=1.06243e-16): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 44%|████▍     | 22/50 [22:44<23:29, 50.35s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=2.39166e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=1.33283e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=4.93204e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=5.56129e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=3.71127e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 46%|████▌     | 23/50 [23:30<22:04, 49.06s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=4.10112e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=4.01573e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 48%|████▊     | 24/50 [24:35<23:20, 53.86s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=2.73759e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=2.66533e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=8.2465e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 50%|█████     | 25/50 [26:04<26:49, 64.39s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=8.44437e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=1.68657e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=5.20015e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 54%|█████▍    | 27/50 [30:07<35:47, 93.36s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=3.61553e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 58%|█████▊    | 29/50 [40:58<1:12:24, 206.88s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=2.72304e-18): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=1.25632e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      " 76%|███████▌  | 38/50 [1:09:16<36:56, 184.69s/it]/opt/conda/lib/python3.11/site-packages/scipy/optimize/_nonlin.py:1074: LinAlgWarning: Ill-conditioned matrix (rcond=8.63078e-17): result may not be accurate.\n",
+      "  gamma = solve(self.a, df_f)\n",
+      "100%|██████████| 50/50 [1:29:09<00:00, 107.00s/it]\n"
      ]
     }
    ],
    "source": [
-    "gap = compute_phase_diagram(Us, Vs, nk=3, tol=1e-10, mixing=0.1, order=3)"
+    "gap = compute_phase_diagram(Us, Vs, nk=15, tol=1e-5, mixing=0.01, order=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "id": "fe2837d4-9590-4ea9-8a8e-18ec76c50f0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from dask_quantumtinkerer import Cluster\n",
+    "# !ssh hpc05 -C \"killall dask-quantumtinkerer-server\"\n",
+    "# cluster = Cluster(extra_path=\"Work/kwant-scf/test\", nodes=20)\n",
+    "# cluster.launch_cluster()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "id": "7ae6858b-928e-499a-bda1-f77bc8199ecc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# client = cluster.get_client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "id": "2c7feca5-0cb8-474a-88a9-cce0ea303195",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def wrapped_fun(xy):\n",
+    "#     U, V = xy\n",
+    "#     return compute_phase_diagram(U, V, nk=15, tol=1e-5, mixing=0.1, order=3)\n",
+    "\n",
+    "# Us = np.linspace(0, 3)\n",
+    "# Vs = np.linspace(0, 3)\n",
+    "\n",
+    "# import itertools as it\n",
+    "# values = list([Us, Vs])\n",
+    "# args = np.array(list(it.product(*values)))\n",
+    "# shapes = [len(value) for value in values]\n",
+    "\n",
+    "# result_ungathered = client.map(wrapped_fun, args)\n",
+    "# result = client.gather(result_ungathered)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "id": "d988ce2e-b830-4234-83ed-4e6115ada377",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plt.plot(Us, gap.T)\n",
+    "# plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 211,
    "id": "a04563c8-81a1-4fd2-9bf3-817224fefe48",
    "metadata": {
     "tags": []
@@ -210,16 +330,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f6d85f7a440>"
+       "<matplotlib.colorbar.Colorbar at 0x7feadc629c50>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -229,13 +349,13 @@
     }
    ],
    "source": [
-    "plt.imshow(np.log10(gap[:,:,0]).T, origin='lower', extent=(0, 5, 0, 5), vmin=-2, vmax=1)\n",
+    "plt.imshow(np.log10(gap).T, origin='lower', extent=(Us.min(), Us.max(), Vs.min(), Vs.max()), vmin=-1)\n",
     "plt.colorbar()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 212,
    "id": "46a0fc12-e273-412b-9a90-306163d225ff",
    "metadata": {
     "tags": []
@@ -244,16 +364,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f6d86017fd0>"
+       "<matplotlib.colorbar.Colorbar at 0x7feadc4a9410>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 212,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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z7rjjDnV1dam9vV2/+MUvVFtbq3/84x9X3X/z5s0aGBiIjt7eXkcBAwBwI0u64s3JydHtt98uSVq4cKE6Ojr06quv6rXXXrvi/j6fTz6fz1mUAIAJx6uPBXR8Ha9t2zFzuAAApIRHr+NNKvE+99xzqqmpUWlpqYaGhrRnzx61tLTo4MGD6YoPAABPSSrxnj59Wo8//rhOnTqlQCCgefPm6eDBg1q1alW64gMATFCsapb0pz/9KV1xAAAQz9B2sRMGXrwKAIB38ZAEAICRaDUDAOAmVjUDAOAm6/Jwcrx5mOMFAMBFVLwAADPRagYAwEUeTby0mgEAcBEVLwDATB59LCCJFwBgJK8+nYhWMwAALqLiBQCYyaOLq0i8AAAzeXSOl1YzAAAuouIFABjJsi8NJ8ebiMQLADATc7wAALiIOV4AAOAUFS8AwEy0mgEAcJFHEy+tZgAAXETFCwAwk0crXhIvAMBMrGoGAABOkXgBAEYau3OVk5GstrY2rV69WiUlJbIsS/v374/5+rp162RZVsxYsmRJUucg8QIAzGSnYCRpeHhY8+fPV2Nj41X3uffee3Xq1KnoeO+995I6B3O8AABcVlNTo5qammvu4/P5VFRUNO5zUPECADxtcHAwZoRCIUfv19LSooKCAs2ePVtPPPGE+vv7kzqexAsAMJIlh3O8l9+ntLRUgUAgOoLB4Lhjqqmp0VtvvaVDhw7ppZdeUkdHh1auXJlUMs9Yq9l3arKyfZMzdfo4WRczHUG8rNFMRxDPyM/JyJjMu4DQxJiyRwyMycCfp8CxrzMdQtRoOKTP3DpZii4n6u3tld/vj272+XzjfsuHHnoo+ueKigotXLhQZWVlevfdd7V27dqE3oM5XgCAp/n9/pjEm0rFxcUqKytTd3d3wseQeAEAZroB7lz19ddfq7e3V8XFxQkfQ+IFAJgpA4n33LlzOnnyZPR1T0+Purq6lJ+fr/z8fDU0NOhnP/uZiouL9cUXX+i5557TLbfcogceeCDhc5B4AQC4rLOzUytWrIi+rq+vlyTV1taqqalJx44d065du3T27FkVFxdrxYoV2rt3r/Ly8hI+B4kXAGCk8d596rvHJ6uqqkq2ffUDP/jgg/EHdBmJFwBgphtgjnc8uI4XAAAXUfECAMzk0YqXxAsAMFIm5njdQKsZAAAXUfECAMyUoltGmobECwAwE3O8AAC4hzleAADgGBUvAMBMtJoBAHCRw1azqYmXVjMAAC6i4gUAmIlWMwAALvJo4qXVDACAi6h4AQBG4jpeAADgGIkXAAAX0WoGAJjJo4urSLwAACN5dY6XxAsAMJehydMJ5ngBAHARFS8AwEzM8QIA4B6vzvHSagYAwEVUvAAAM9FqBgDAPbSaAQCAY0kl3mAwqEWLFikvL08FBQVas2aNTpw4ka7YAAATmZ2CYaCkEm9ra6vq6urU3t6u5uZmjY6Oqrq6WsPDw+mKDwAwUXk08SY1x3vw4MGY1zt27FBBQYGOHDmi5cuXX/GYUCikUCgUfT04ODiOMAEA8AZHi6sGBgYkSfn5+VfdJxgMauvWrXHbp31lKzvHnF9Hsi+aE8uYrIuZjiBe9oh5n1P2SCTTIcTJMvDnKTsUznQIcbIMjGlSv3nFgZ2bk+kQ/suy3DsVi6ti2bat+vp6LVu2TBUVFVfdb/PmzRoYGIiO3t7e8Z4SADCR0GqOtX79eh09elSHDx++5n4+n08+n2+8pwEATFRcx/tfGzZs0IEDB9TW1qYZM2akOiYAADwrqcRr27Y2bNigffv2qaWlReXl5emKCwAwwXl1jjepxFtXV6fdu3frnXfeUV5envr6+iRJgUBAU6ZMSUuAAIAJyqOt5qQWVzU1NWlgYEBVVVUqLi6Ojr1796YrPgAAPCXpVjMAAG6g1QwAgJtoNQMAAKeoeAEAZvJoxUviBQAYybo8nBxvIlrNAAC4iIoXAGAmWs0AALiHy4kAAHCTRyte5ngBAHARFS8AwFyGVq1OkHgBAEby6hwvrWYAAFxE4gUAmMlOwUhSW1ubVq9erZKSElmWpf3798eGZNtqaGhQSUmJpkyZoqqqKh0/fjypc5B4AQBGGms1OxnJGh4e1vz589XY2HjFr7/44ot6+eWX1djYqI6ODhUVFWnVqlUaGhpK+BzM8QIAcFlNTY1qamqu+DXbtvXKK6/o+eef19q1ayVJO3fuVGFhoXbv3q0nn3wyoXNQ8QIAzJSiVvPg4GDMCIVC4wqnp6dHfX19qq6ujm7z+Xy6++679fHHHyf8PiReAICRUtVqLi0tVSAQiI5gMDiuePr6+iRJhYWFMdsLCwujX0sErWYAgKf19vbK7/dHX/t8PkfvZ1mxzz2ybTtu27WQeAEAZkrRLSP9fn9M4h2voqIiSZcq3+Li4uj2/v7+uCr4Wmg1AwDMlIHLia6lvLxcRUVFam5ujm4bGRlRa2urli5dmvD7UPECAIyUiTtXnTt3TidPnoy+7unpUVdXl/Lz8zVz5kxt3LhR27dv16xZszRr1ixt375dN910kx599NGEz0HiBQDgss7OTq1YsSL6ur6+XpJUW1urN954Q88++6zOnz+vp59+Wt98840WL16sDz/8UHl5eQmfg8QLADBTBh4LWFVVJdu++oGWZamhoUENDQ3jDovECwAwkmXbsq6RBBM53kQsrgIAwEVUvAAAM2Wg1ewGEi8AwEg8jxcAADhGxQsAMBOt5tTy/zukSZMSv7dlumWFwpkOIU7WyGimQ4iTdcG8mBQayXQEcawL5sVkX7iQ6RDiWLm5mQ4hjp2bk+kQ4iVxH+C0czEWWs0AAMAxWs0AADPRagYAwD1ebTWTeAEAZvJoxcscLwAALqLiBQAYy9R2sRMkXgCAmWz70nByvIFoNQMA4CIqXgCAkVjVDACAm1jVDAAAnKLiBQAYyYpcGk6ONxGJFwBgJlrNAADAKSpeAICRWNUMAICbPHoDDRIvAMBIXq14meMFAMBFVLwAADN5dFUziRcAYCRazQAAwDEqXgCAmVjVDACAe2g1AwAAx6h4AQBmYlUzAADuodUMAAAco+IFAJgpYl8aTo43EIkXAGAm5ngBAHCPJYdzvCmLJLWY4wUAwEVUvAAAM3HnKgAA3MPlRJe1tbVp9erVKikpkWVZ2r9/fxrCAgDAm5JOvMPDw5o/f74aGxvTEQ8AAJfYKRgGSrrVXFNTo5qamoT3D4VCCoVC0deDg4PJnhIAMAFZti3LwTytk2PTKe1zvMFgUFu3bo3bnvPZ/2hSVk66T5+4iyOZjiCOPXIx0yHEiVwczXQIcazJBi5VyJmc6QjiTTbo39sYy8ALPkyMCZ6S9suJNm/erIGBgejo7e1N9ykBAF4QScEwUNpLBZ/PJ5/Pl+7TAAA8xqutZm6gAQCAiwycHAMAQNyrecy5c+d08uTJ6Ouenh51dXUpPz9fM2fOTGlwAIAJjDtXXdLZ2akVK1ZEX9fX10uSamtr9cYbb6QsMADAxObVO1clnXirqqpkG/pbBAAApmOOFwBgJo+2mlnVDAAwkhVxPpLR0NAgy7JiRlFRUcq/LypeAAAumzNnjj766KPo6+zs7JSfg8QLADBTilrN339GwLVu7DRp0qS0VLnfRasZAGCmFD2dqLS0VIFAIDqCweBVT9nd3a2SkhKVl5fr4Ycf1ueff57yb4uKFwDgab29vfL7/dHXV6t2Fy9erF27dmn27Nk6ffq0tm3bpqVLl+r48eOaPn16yuIh8QIAjJSqezX7/f6YxHs1333k7dy5c1VZWanbbrtNO3fujN6zIhVIvAAAM2X4cqKpU6dq7ty56u7udvQ+38ccLwAAVxAKhfTZZ5+puLg4pe9L4gUAmMmWs2fxJlnwbtq0Sa2trerp6dEnn3yiBx98UIODg6qtrU3N93MZrWYAgJHcfh7vV199pUceeURnzpzRrbfeqiVLlqi9vV1lZWXjjuFKSLwAADPZcjjHm9zue/bsGf+5kkCrGQAAF1HxAgDM5NGHJJB4AQBmikiyHB5vIFrNAAC4iIoXAGAkt1c1u4XECwAwk0fneGk1AwDgIipeAICZPFrxkngBAGbyaOKl1QwAgIuoeAEAZvLodbwkXgCAkbicCAAANzHHCwAAnKLiBQCYKWJLloOqNWJmxUviBQCYiVYzAABwKmMVb/jMGVnW5EydPp7lZM16mljm/V5kZWdnOgSMV5aBP+MmxmQg26D/n9yNxWHFKzMrXlrNAAAz0WoGAABOUfECAMwUseWoXcyqZgAAkmBHLg0nxxuIVjMAAC6i4gUAmMmji6tIvAAAMzHHCwCAizxa8TLHCwCAi6h4AQBmsuWw4k1ZJClF4gUAmIlWMwAAcIqKFwBgpkhEkoObYETMvIEGiRcAYCZazQAAwCkqXgCAmTxa8ZJ4AQBm8uidq2g1AwDgIipeAICRbDsi28Gj/Zwcm04kXgCAmWzbWbuYOV4AAJJgO5zjNTTxMscLAICLqHgBAGaKRCTLwTwtc7wAACSBVjMAAHCKihcAYCQ7EpHtoNXM5UQAACSDVjMAAHCKihcAYKaILVneq3hJvAAAM9m2JCeXE5mZeGk1AwDgIipeAICR7Igt20Gr2Ta04iXxAgDMZEfkrNVs5uVE42o1//73v1d5eblyc3O1YMEC/fWvf011XACACc6O2I7HeKQ7xyWdePfu3auNGzfq+eef19///nf95Cc/UU1Njb788suUBgYAgNvcyHGWnWQTfPHixfrRj36kpqam6LYf/vCHWrNmjYLBYNz+oVBIoVAo+npgYEAzZ87UMv1vTdJkB6GnmGVlOoJ4lnlr36zs7EyHEMeabGBMOQb9bI/J8WU6gjhWbk6mQ4hjTzbv7872mfM5jYZDajvxf3X27FkFAoG0nGNwcFCBQMBxnhjVRR3We+rt7ZXf749u9/l88vmu/O8h2Rw3LnYSQqGQnZ2dbb/99tsx23/5y1/ay5cvv+IxW7ZsGbv1CIPBYDA8Mv71r38lkz6Scv78ebuoqCglcU6bNi1u25YtW6543vHkuPFIanHVmTNnFA6HVVhYGLO9sLBQfX19Vzxm8+bNqq+vj74+e/asysrK9OWXX6bttyUvGBwcVGlpadxvavgvPqPE8Dklhs8pMWNdy/z8/LSdIzc3Vz09PRoZGXH8XrZty/peR/Nq1e54ctx4jGtV8/e/iSt9Y2OuVtIHAgF+uBPg9/v5nK6DzygxfE6J4XNKTFZWeqfCcnNzlZubm9ZzXE0yOW48kvrkbrnlFmVnZ8dl/v7+/rjfEAAAuJG4leOSSrw5OTlasGCBmpubY7Y3Nzdr6dKlKQsKAAC3uZXjkm4119fX6/HHH9fChQtVWVmp119/XV9++aWeeuqphI73+XzasmXLVXvsuITP6fr4jBLD55QYPqfEeP1zcprjEpH05UTSpYuLX3zxRZ06dUoVFRX63e9+p+XLl6csKAAAMiXdOW5ciRcAAIyPeXdoAADAw0i8AAC4iMQLAICLSLwAALjI1cTL4wSvr62tTatXr1ZJSYksy9L+/fszHZJxgsGgFi1apLy8PBUUFGjNmjU6ceJEpsMyTlNTk+bNmxe9E1NlZaXef//9TIdlvGAwKMuytHHjxkyHYpSGhgZZlhUzioqKMh3WDcm1xMvjBBMzPDys+fPnq7GxMdOhGKu1tVV1dXVqb29Xc3OzRkdHVV1dreHh4UyHZpQZM2bohRdeUGdnpzo7O7Vy5Urdf//9On78eKZDM1ZHR4def/11zZs3L9OhGGnOnDk6depUdBw7dizTId2YUva4hev48Y9/bD/11FMx2+68807717/+tVsh3HAk2fv27ct0GMbr7++3Jdmtra2ZDsV4N998s/3HP/4x02EYaWhoyJ41a5bd3Nxs33333fYzzzyT6ZCMsmXLFnv+/PmZDsMTXKl4R0ZGdOTIEVVXV8dsr66u1scff+xGCPCwgYEBSUrr01JudOFwWHv27NHw8LAqKyszHY6R6urqdN999+mee+7JdCjG6u7uVklJicrLy/Xwww/r888/z3RIN6RxPZ0oWW49agkTj23bqq+v17Jly1RRUZHpcIxz7NgxVVZW6sKFC5o2bZr27dunu+66K9NhGWfPnj369NNP1dHRkelQjLV48WLt2rVLs2fP1unTp7Vt2zYtXbpUx48f1/Tp0zMd3g3FlcQ7Jt2PWsLEs379eh09elSHDx/OdChGuuOOO9TV1aWzZ8/qL3/5i2pra9Xa2kry/Y7e3l4988wz+vDDDzP2GLobQU1NTfTPc+fOVWVlpW677Tbt3Lkz5pnruD5XEi+PE0Q6bNiwQQcOHFBbW5tmzJiR6XCMlJOTo9tvv12StHDhQnV0dOjVV1/Va6+9luHIzHHkyBH19/drwYIF0W3hcFhtbW1qbGxUKBRSdnZ2BiM009SpUzV37lx1d3dnOpQbjitzvDxOEKlk27bWr1+vt99+W4cOHVJ5eXmmQ7ph2LatUCiU6TCM8tOf/lTHjh1TV1dXdCxcuFCPPfaYurq6SLpXEQqF9Nlnn6m4uDjTodxwXGs1u/GoJS84d+6cTp48GX3d09Ojrq4u5efna+bMmRmMzBx1dXXavXu33nnnHeXl5UU7KYFAQFOmTMlwdOZ47rnnVFNTo9LSUg0NDWnPnj1qaWnRwYMHMx2aUfLy8uLWB0ydOlXTp09n3cB3bNq0SatXr9bMmTPV39+vbdu2aXBwULW1tZkO7YbjWuJ96KGH9PXXX+s3v/lN9FFL7733nsrKytwK4YbQ2dmpFStWRF+PzZ3U1tbqjTfeyFBUZmlqapIkVVVVxWzfsWOH1q1b535Ahjp9+rQef/xxnTp1SoFAQPPmzdPBgwe1atWqTIeGG9BXX32lRx55RGfOnNGtt96qJUuWqL29nf/Dx4HHAgIA4CLu1QwAgItIvAAAuIjECwCAi0i8AAC4iMQLAICLSLwAALiIxAsAgItIvAAAuIjECwCAi0i8AAC4iMQLAICL/j/VEqEep5KYPgAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -263,78 +383,29 @@
     }
    ],
    "source": [
-    "plt.imshow((gap[:,:,0]).T, origin='lower', extent=(0, 5, 0, 5), vmin=0)\n",
+    "plt.imshow(gap.T, origin='lower', extent=(Us.min(), Us.max(), Vs.min(), Vs.max()), vmin=0)\n",
     "plt.colorbar()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "c17644a5-c42e-47ff-8fd3-eb1798bebad7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f6d85ed5ff0>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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qqlJlZaWjIAEAA5DTeeokTdwRf4wbP368mpqatGvXLt15552aP3++3nnnnfPuv2LFCrW3twdHS0uLo4ABABjIIq64U1NT9fWvf12SVFRUpIaGBv3mN7/RunXrbPd3u91yu93OogQADDg81tOe4wkmy7J6zGEDABAVXMdtK6LEfe+996qsrEx5eXk6ceKENm3apPr6em3dujVW8QEAgHNENMd95MgRzZs3T+PHj9esWbP05ptvauvWrbr22mtjFR8AYIBycvMVJyvS16xZo4KCAqWlpamwsFCvv/56r/tv27ZNhYWFSktL0yWXXKK1a9f26bzhiqji3rBhQ6ziAAAgVD+3uzdv3qylS5dqzZo1+uY3v6l169aprKxM77zzjsaMGROy/6FDh3T99ddr4cKFevbZZ/Xf//3fuuuuu3TxxRfrhz/8YUxiNOfiQAAAYqCjo6PH6G1d1uOPP67bb79dd9xxhyZOnKhVq1YpLy9P1dXVtvuvXbtWY8aM0apVqzRx4kTdcccd+td//Vc9+uijsfp2SNwAADNFq1Wel5cnr9cbHFVVVbbn6+7u1u7du1VaWtpje2lpqXbs2GF7zM6dO0P2/+53v6vGxkadOnUqCu9CKPNuWwQAgBS1VeUtLS3yeDzBzee7RPno0aPy+/3KysrqsT0rK0utra22x7S2ttruf/r0aR09elQ5OTkOvgF7JG4AgKFcXw4nxyv4RMuwj3L1PKdlWSHbLrS/3fZooVUOAICkzMxMpaSkhFTXbW1tIVX1WdnZ2bb7Dx48WCNHjoxJnCRuAICZrCiMCKSmpqqwsFB1dXU9ttfV1WnGjBm2xxQXF4fs/8orr6ioqEhDhgyJLIAwkbgBAGbq58QtSeXl5XryySf11FNPaf/+/Vq2bJmam5u1aNEiSWeev3HbbbcF91+0aJE+/PBDlZeXa//+/Xrqqae0YcMGLV++vK/f9QUxxw0AwJfmzJmjY8eO6f7779fhw4c1efJkbdmyRfn5+ZKkw4cPq7m5Obh/QUGBtmzZomXLlumJJ55Qbm6ufvvb38bsGm6JxA0AMFWcHut511136a677rL9u5qampBtV199tfbs2dOnc/UFiRsAYCSeDmaPOW4AABIIFTcAwEw81tMWiRsAYKY4zXGbjlY5AAAJhIobAGAkl3VmODk+GZG4AQBmYo7bFokbAGAm5rhtMccNAEACoeIGAJiJVrktEjcAwEwkblu0ygEASCBU3AAAM1Fx2yJxAwDMxKpyW7TKAQBIIFTcAAAjcec0eyRuAICZmOO2RascAIAEQuIGACCB0CoHABjJJYdz3FGLxCxxS9x/D6TLF0iJ1+lDnLLM+wzjN/DXzsT3qdsy5/foLBPfp1MGvk/dvE9hqfv0sniHEHSqq1vS3v45GZeD2aJVDgBAAjHv4y4AABKrys+DxA0AMBOJ2xatcgAAEggVNwDASNw5zR6JGwBgJlrltmiVAwCQQKi4AQBmouK2ReIGABiJOW57tMoBAEggVNwAADNxy1NbJG4AgJmY47ZF4gYAGIk5bnvMcQMAkECouAEAZqJVbovEDQAwk8NWebImblrlAAAkECpuAICZaJXbInEDAMxE4rZFqxwAgARCxQ0AMBLXcduj4gYAoA8+++wzzZs3T16vV16vV/PmzdPx48d7PWbBggVyuVw9xvTp0yM6LxU3AAB9cOutt+qjjz7S1q1bJUn/9m//pnnz5unPf/5zr8ddd9112rhxY/B1ampqROclcQMAzGTw4rT9+/dr69at2rVrl6ZNmyZJ+t3vfqfi4mIdOHBA48ePP++xbrdb2dnZfT43rXIAgJHOznE7GZLU0dHRY/h8Psex7dy5U16vN5i0JWn69Onyer3asWNHr8fW19dr1KhRGjdunBYuXKi2traIzk3iBgCYy3IwvpSXlxech/Z6vaqqqnIcVmtrq0aNGhWyfdSoUWptbT3vcWVlZXruuef06quv6rHHHlNDQ4OuueaaiD5M0CoHACS1lpYWeTye4Gu3233efSsqKlRZWdnr12toaJAkuVyhz/u2LMt2+1lz5swJ/nny5MkqKipSfn6+XnrpJd188829nvcsEjcAwExRmuP2eDw9EndvlixZorlz5/a6z9ixY/X222/ryJEjIX/36aefKisrK+wQc3JylJ+fr4MHD4Z9DIkbAGCkeFzHnZmZqczMzAvuV1xcrPb2dv3P//yPrrzySknSm2++qfb2ds2YMSPs8x07dkwtLS3KyckJ+xjmuAEAiNDEiRN13XXXaeHChdq1a5d27dqlhQsX6vvf/36PFeUTJkxQbW2tJKmzs1PLly/Xzp079cEHH6i+vl6zZ89WZmambrrpprDPTeIGAJjJycI0p232MDz33HOaMmWKSktLVVpaqqlTp+r3v/99j30OHDig9vZ2SVJKSor27t2rG264QePGjdP8+fM1btw47dy5UxkZGWGfl1Y5AMBIpt/ydMSIEXr22Wd73cey/hFEenq6/vKXvzg+LxU3AAAJJKLEXVVVpW984xvKyMjQqFGjdOONN+rAgQOxig0AMJAZ3iqPl4gS97Zt27R48WLt2rVLdXV1On36tEpLS9XV1RWr+AAAAxWJ21ZEc9xnb6R+1saNGzVq1Cjt3r1bV111le0xPp+vxx1hOjo6+hAmAACQHC5OO7tSbsSIEefdp6qqyvYuNB+fHqH0U+asjeu2zInlrFNWSrxDCOELDIl3CCFMfJ9OGvg+fR6I7AlE/eELv3nv0//rCL2NZbx5Uk/GO4QgV8rp/juX4YvT4qXPi9Msy1J5ebm+9a1vafLkyefdb8WKFWpvbw+OlpaWvp4SADCQ0Cq31ecyc8mSJXr77bf1xhtv9Lqf2+3u9b6wAADYMvixnvHUp8R99913609/+pO2b9+u0aNHRzsmAABwHhElbsuydPfdd6u2tlb19fUqKCiIVVwAgAGOOW57ESXuxYsX6/nnn9cf//hHZWRkBJ856vV6lZ6eHpMAAQADFK1yWxEtTquurlZ7e7tKSkqUk5MTHJs3b45VfAAA4BwRt8oBAOgPtMrtmXfxMgAAEq3y8+AhIwAAJBAqbgCAmai4bZG4AQBGcn05nByfjGiVAwCQQKi4AQBmolVui8QNADASl4PZI3EDAMxExW2LOW4AABIIFTcAwFxJWjU7QeIGABiJOW57tMoBAEggVNwAADOxOM0WiRsAYCRa5fZolQMAkECouAEAZqJVbovEDQAwEq1ye7TKAQBIIFTcAAAz0Sq3ReIGAJiJxG2LxA0AMBJz3PaY4wYAIIFQcQMAzESr3BaJGwBgJJdlyWX1Pfs6OdZktMoBAEggVNwAADPRKrdF4gYAGIlV5fZolQMAkECouAEAZqJVbituifu9L7LkThkSr9OH+DyQGu8QQnSddsc7hBBdp817nzpOpcU7hBAdPvNi+uzz9HiHEOKioV/EO4QQHvfJeIcQYrArEO8Qgqx+jIVWuT1a5QAA9MGDDz6oGTNmaOjQofra174W1jGWZamiokK5ublKT09XSUmJ9u3bF9F5SdwAADNZURgx1N3drVtuuUV33nln2Mc88sgjevzxx7V69Wo1NDQoOztb1157rU6cOBH21yBxAwCMdLZV7mTEUmVlpZYtW6YpU6aEtb9lWVq1apXuu+8+3XzzzZo8ebKefvppff7553r++efDPi+JGwBgpihV3B0dHT2Gz+fr3+/jS4cOHVJra6tKS0uD29xut66++mrt2LEj7K9D4gYAJLW8vDx5vd7gqKqqikscra2tkqSsrKwe27OysoJ/Fw4SNwDAWNFok7e0tKi9vT04VqxYcd7zVVRUyOVy9ToaGxudfU8uV4/XlmWFbOsN13EDAMxkWWeGk+MleTweeTyesA5ZsmSJ5s6d2+s+Y8eO7VM42dnZks5U3jk5OcHtbW1tIVV4b0jcAAB8KTMzU5mZmTH52gUFBcrOzlZdXZ2uuOIKSWdWpm/btk0PP/xw2F+HVjkAwEimrypvbm5WU1OTmpub5ff71dTUpKamJnV2dgb3mTBhgmpra898Py6Xli5dqoceeki1tbX629/+pgULFmjo0KG69dZbwz4vFTcAwEyG3/L0F7/4hZ5++ung67NV9GuvvaaSkhJJ0oEDB9Te3h7c52c/+5m++OIL3XXXXfrss880bdo0vfLKK8rIyAj7vCRuAAD6oKamRjU1Nb3uY31ljt7lcqmiokIVFRV9Pi+JGwBgJFfgzHByfDIicQMAzGR4qzxeWJwGAEACoeIGABiJx3raI3EDAMwUpRuwJBsSNwDASFTc9pjjBgAggVBxAwDMxKpyWyRuAICRaJXbo1UOAEACoeIGAJiJVeW2SNwAACPRKrdHqxwAgARCxQ0AMBOrym2RuAEARqJVbo9WOQAACYSKGwBgpoB1Zjg5PgmRuAEAZmKO2xaJGwBgJJccznFHLRKzMMcNAEACoeIGAJiJO6fZInEDAIzE5WD2Im6Vb9++XbNnz1Zubq5cLpdefPHFGIQFAADsRJy4u7q6dPnll2v16tWxiAcAgDOsKIwkFHGrvKysTGVlZWHv7/P55PP5gq87OjoiPSUAYAByWZZcDuapnRxrspjPcVdVVamysjJk+18/Hq+Uoe5Ynz5snV1p8Q4hxKmO1HiHECL9oyHxDiHEF6NPxTuEEEM83fEOIcTwYSfjHUKIlEGBeIcQYrDLvJgGGTRZa1IsA1XMLwdbsWKF2tvbg6OlpSXWpwQAJINAFEYSinnF7Xa75XabU1kDABIDrXJ73IAFAIAEwnXcAAAzca9yWxEn7s7OTr333nvB14cOHVJTU5NGjBihMWPGRDU4AMAAxp3TbEWcuBsbGzVz5szg6/LycknS/PnzVVNTE7XAAAADG3dOsxdx4i4pKZGVpJ9iAAAwHXPcAAAz0Sq3ReIGABjJFTgznByfjLgcDACABELFDQAwE61yWyRuAICZuI7bFq1yAAASCBU3AMBI3KvcHokbAGAm5rht0SoHACCBkLgBAGay5OxZ3DEuuB988EHNmDFDQ4cO1de+9rWwjlmwYIFcLlePMX369IjOS6scAGAk0+e4u7u7dcstt6i4uFgbNmwI+7jrrrtOGzduDL5OTU2N6LwkbgCAmSw5nOOOWiS2KisrJSniB2y53W5lZ2f3+by0ygEASa2jo6PH8Pl8cY2nvr5eo0aN0rhx47Rw4UK1tbVFdDyJGwBgprOryp0MSXl5efJ6vcFRVVUVt2+prKxMzz33nF599VU99thjamho0DXXXBPRhwla5QAAMwUkuRweL6mlpUUejye42e12n/eQioqKYAv8fBoaGlRUVNSnkObMmRP88+TJk1VUVKT8/Hy99NJLuvnmm8P6GiRuAEBS83g8PRJ3b5YsWaK5c+f2us/YsWOjENUZOTk5ys/P18GDB8M+hsQNADBSPFaVZ2ZmKjMzs8/njNSxY8fU0tKinJycsI9hjhsAYKYozXHHSnNzs5qamtTc3Cy/36+mpiY1NTWps7MzuM+ECRNUW1srSers7NTy5cu1c+dOffDBB6qvr9fs2bOVmZmpm266KezzUnEDANAHv/jFL/T0008HX19xxRWSpNdee00lJSWSpAMHDqi9vV2SlJKSor179+qZZ57R8ePHlZOTo5kzZ2rz5s3KyMgI+7wkbgCAmQy/V3lNTc0Fr+G2zokhPT1df/nLXxyfl8QNADCT4Yk7XpjjBgAggVBxAwDMFKXruJMNiRsAYCTTHzISLyRuAICZmOO2xRw3AAAJhIobAGCmgCW5HFTNgeSsuEncAAAz0Sq3RascAIAEEreKO3POQQ12DYnX6UMMWlgc7xBCdOU6uQ4iNnwjDby+Yoh5n6oHpZj3PqUMMvB9ctIGjZFBLvN+dibF1L+xOL3fuHm/X9FAqxwAYCZa5bZolQMAkECouAEAZgpYctTuZlU5AAD9yAqcGU6OT0K0ygEASCBU3AAAM7E4zRaJGwBgJua4bZG4AQBmouK2xRw3AAAJhIobAGAmSw4r7qhFYhQSNwDATLTKbdEqBwAggVBxAwDMFAhIcnATlUBy3oCFxA0AMBOtclu0ygEASCBU3AAAM1Fx2yJxAwDMxJ3TbNEqBwAggVBxAwCMZFkBWQ4ezenkWJORuAEAZrIsZ+1u5rgBAOhHlsM57iRN3MxxAwCQQKi4AQBmCgQkl4N5aua4AQDoR7TKbdEqBwAggVBxAwCMZAUCshy0yrkcDACA/kSr3BatcgAAEggVNwDATAFLclFxfxWJGwBgJsuS5ORysORM3LTKAQBIIFTcAAAjWQFLloNWuZWkFTeJGwBgJisgZ63y5LwcrE+t8jVr1qigoEBpaWkqLCzU66+/Hu24AAADnBWwHI9Y+eCDD3T77beroKBA6enpuvTSS7Vy5Up1d3f3/j1ZlioqKpSbm6v09HSVlJRo3759EZ074sS9efNmLV26VPfdd5/eeustffvb31ZZWZmam5sj/VIAACSkd999V4FAQOvWrdO+ffv061//WmvXrtW9997b63GPPPKIHn/8ca1evVoNDQ3Kzs7WtddeqxMnToR9bpcV4STAtGnT9M///M+qrq4Obps4caJuvPFGVVVVhezv8/nk8/mCr9vb2zVmzBh9S9drsIZEcuqY+vuCK+MdQoiubFe8Qwhx6iLzWk+BjNPxDiFE6jDfhXfqZxlDe68E4iHDfTLeIYTwpJoX0/Ah5vw+nerqVu0P/o+OHz8ur9cbk3N0dHTI6/U6zhOndUpvaItaWlrk8XiC291ut9xudzRC7eFXv/qVqqur9b//+7+2f29ZlnJzc7V06VL9/Oc/l3QmR2ZlZenhhx/Wv//7v4d3IisCPp/PSklJsf7whz/02P7Tn/7Uuuqqq2yPWbly5dlb3zAYDAYjScb7778fSfqIyBdffGFlZ2dHJc7hw4eHbFu5cmVM4r7vvvuswsLC8/79+++/b0my9uzZ02P7D37wA+u2224L+zwRLU47evSo/H6/srKyemzPyspSa2ur7TErVqxQeXl58PXx48eVn5+v5ubmmH1aSwYdHR3Ky8sL+aSIf+A9Cg/vU3h4n8Jztms6YsSImJ0jLS1Nhw4duuB8cTgsy5LL1bN7GYtq+/3339d//ud/6rHHHjvvPmfzpF0O/fDDD8M+V59WlX/1TbB7Y846X0vC6/XyP0cYPB4P79MF8B6Fh/cpPLxP4Rk0KLa3AUlLS1NaWlpMz2GnoqJClZWVve7T0NCgoqKi4OtPPvlE1113nW655RbdcccdFzxHJDnUTkSJOzMzUykpKSHVdVtbW8gnCAAAEs2SJUs0d+7cXvcZO3Zs8M+ffPKJZs6cqeLiYq1fv77X47KzsyWdqbxzcnKC2yPNoREl7tTUVBUWFqqurk433XRTcHtdXZ1uuOGGSL4UAADGyczMVGZmZlj7fvzxx5o5c6YKCwu1cePGC3YhCgoKlJ2drbq6Ol1xxRWSpO7ubm3btk0PP/xw2DFG3OsoLy/Xk08+qaeeekr79+/XsmXL1NzcrEWLFoV1vNvt1sqVK2Myx5BMeJ8ujPcoPLxP4eF9Cg/v0xmffPKJSkpKlJeXp0cffVSffvqpWltbQzrSEyZMUG1traQzLfKlS5fqoYceUm1trf72t79pwYIFGjp0qG699dawzx3x5WDSmRuwPPLIIzp8+LAmT56sX//617rqqqsi/TIAACSkmpoa/eQnP7H9u3PTqsvl0saNG7VgwYLg31VWVmrdunX67LPPNG3aND3xxBOaPHly2OfuU+IGAADxwdPBAABIICRuAAASCIkbAIAEQuIGACCB9Gvi5nGgF7Z9+3bNnj1bubm5crlcevHFF+MdknGqqqr0jW98QxkZGRo1apRuvPFGHThwIN5hGae6ulpTp04N3gmsuLhYL7/8crzDMl5VVVXwsh38Q0VFhVwuV49x9oYi6F/9lrh5HGh4urq6dPnll2v16tXxDsVY27Zt0+LFi7Vr1y7V1dXp9OnTKi0tVVdXV7xDM8ro0aP1y1/+Uo2NjWpsbNQ111yjG264IeJn/w4kDQ0NWr9+vaZOnRrvUIw0adIkHT58ODj27t0b75AGpkifftJXV155pbVo0aIe2yZMmGD9x3/8R3+FkHAkWbW1tfEOw3htbW2WJGvbtm3xDsV4F110kfXkk0/GOwwjnThxwvqnf/onq66uzrr66qute+65J94hGWXlypXW5ZdfHu8wYFlWv1Tc3d3d2r17t0pLS3tsLy0t1Y4dO/ojBCSx9vZ2SYrp04oSnd/v16ZNm9TV1aXi4uJ4h2OkxYsX63vf+56+853vxDsUYx08eFC5ubkqKCjQ3Llzz/vcacRWn54OFqm+PA4UCIdlWSovL9e3vvWtiO48NFDs3btXxcXFOnnypIYPH67a2lpddtll8Q7LOJs2bdKePXvU0NAQ71CMNW3aND3zzDMaN26cjhw5ogceeEAzZszQvn37NHLkyHiHN6D0S+I+y+mjzICvWrJkid5++2298cYb8Q7FSOPHj1dTU5OOHz+uF154QfPnz9e2bdtI3udoaWnRPffco1deeSUuj5FMFGVlZcE/T5kyRcXFxbr00kv19NNPq7y8PI6RDTz9krh5HChi4e6779af/vQnbd++XaNHj453OEZKTU3V17/+dUlSUVGRGhoa9Jvf/Ebr1q2Lc2Tm2L17t9ra2lRYWBjc5vf7tX37dq1evVo+n08pKSlxjNBMw4YN05QpU3Tw4MF4hzLg9Msc97mPAz1XXV2dZsyY0R8hIIlYlqUlS5boD3/4g1599VUVFBTEO6SEYVmWfD5fvMMwyqxZs7R37141NTUFR1FRkX784x+rqamJpH0ePp9P+/fv7/FcafSPfmuVl5eXa968eSoqKgo+cDySx4EOFJ2dnXrvvfeCrw8dOqSmpiaNGDFCY8aMiWNk5li8eLGef/55/fGPf1RGRkawk+P1epWenh7n6Mxx7733qqysTHl5eTpx4oQ2bdqk+vp6bd26Nd6hGSUjIyNkfcSwYcM0cuRI1k2cY/ny5Zo9e7bGjBmjtrY2PfDAA+ro6ND8+fPjHdqA02+Je86cOTp27Jjuv//+4ONAt2zZovz8/P4KISE0NjZq5syZwddn547mz5+vmpqaOEVllurqaklSSUlJj+3nPjoP0pEjRzRv3jwdPnxYXq9XU6dO1datW3XttdfGOzQkoI8++kg/+tGPdPToUV188cWaPn26du3axb/hccBjPQEASCDcqxwAgARC4gYAIIGQuAEASCAkbgAAEgiJGwCABELiBgAggZC4AQBIICRuAAASCIkbAIAEQuIGACCBkLgBAEgg/x+5S/nbXt8z+AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": 215,
+   "id": "73f2398b-f0b2-4368-8ae2-dcdce9be296e",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "gap = np.asarray(gap, dtype=float)\n",
-    "plt.imshow(np.log10(gap[:,:,1]).T, origin='lower', extent=(0, 5, 0, 5), vmin=-2)\n",
-    "plt.colorbar()"
+    "import xarray as xr\n",
+    "gap_da = xr.DataArray(data=gap, coords=dict(Us=Us, Vs=Vs))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "a472295f-9dfb-4cd8-86be-7efc69f9a1ac",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f6d85dac1f0>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "1e25512d-720b-4a20-a457-9b8a2638094a",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "gap = np.asarray(gap, dtype=float)\n",
-    "plt.imshow((gap[:,:,1]).T, origin='lower', extent=(0, 5, 0, 5), vmin=0)\n",
-    "plt.colorbar()"
+    "gap."
    ]
   }
  ],
@@ -354,7 +425,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.5"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {