diff --git a/codes/scipy_optimizer.ipynb b/codes/scipy_optimizer.ipynb
index 868a95bfc8b7b6ef4641c82a6422e9e831cab258..b33df50a6e103e643ea4ee85af09702d8851d878 100644
--- a/codes/scipy_optimizer.ipynb
+++ b/codes/scipy_optimizer.ipynb
@@ -108,7 +108,7 @@
" # 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(nk, nk, deltas, ndof=4, scale=0.1)\n",
+ " guess = utils.generate_guess(nk, 2, deltas, ndof=4, scale=0.1)\n",
" \n",
" # Find groundstate Hamiltonian on the same grid\n",
" hk = hf.find_groundstate_ham(\n",
@@ -127,14 +127,15 @@
" 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",
- " ks_dense = np.linspace(0, 2 * np.pi, nk_dense, endpoint=False)\n",
- " # Compute groundstate Hamiltonian on a dense grid\n",
- " scf_ham = utils.syst2hamiltonian(\n",
- " kxs=ks_dense, kys=ks_dense, syst=scf_syst, params={}\n",
- " )\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",
+ " # ks_dense = np.linspace(0, 2 * np.pi, nk_dense, endpoint=False)\n",
+ " # # Compute groundstate Hamiltonian on a dense grid\n",
+ " # scf_ham = utils.syst2hamiltonian(\n",
+ " # kxs=ks_dense, kys=ks_dense, syst=scf_syst, params={}\n",
+ " # )\n",
+ " scf_ham = utils.hktohamiltonian(hk, nk_dense, ks, dk, 2, deltas, 4)\n",
" # Diagonalize groundstate Hamiltonian\n",
" vals, vecs = np.linalg.eigh(scf_ham)\n",
" # Extract dense-grid Fermi energy\n",
@@ -190,7 +191,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 10/10 [02:55<00:00, 17.57s/it]\n"
+ "100%|██████████| 10/10 [02:24<00:00, 14.40s/it]\n"
]
}
],
@@ -200,23 +201,55 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 6,
+ "id": "39edbf19",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.colorbar.Colorbar at 0x7f8018fdcd30>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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WuO3RKgcAIIFQcQMAjESr3B6JGwBgJFrl9kjcAAAjWQ4r7mRN3KxxAwCQQKi4AQBGsiQ5eZtGm76Iow2RuAEARgrJJRdPTotAqxwAgARCxQ0AMBK7yu2RuAEARgpZLrm4jzsCrXIAABIIFTcAwEiW5XBXeZJuKydxAwCMxBq3PVrlAAAkECpuAICRqLjtkbgBAEZiV7k9EjcAwEhsTrPHGjcAAAmEihsAYKQLFbeTNe4WDMYgJG4AgJHYnGaPVjkAAAmEihsAYCRLzt6pnaSdchI3AMBMtMrt0SoHACCBUHEDAMxEr9xWTBX3smXLlJWVJY/HI4/HI5/Ppz/84Q+tFRsA4Gp2sVXe3EO0yqVevXrphRdeUHl5ucrLy3XXXXdp4sSJ+uijj1orPgDAVerSk9OcHMkoplb5hAkTwj4///zzWrZsmUpLSzV48OAWDQwAAERq9hp3MBjU7373O50+fVo+n++y1wUCAQUCgcbPDQ0NzZ0SAHAVYVe5vZgT94EDB+Tz+XTmzBl16dJFmzZt0o033njZ64uKirRw4UJHQQIArkJO16mTNHHHfDvYgAEDVFlZqdLSUv34xz/W9OnT9X//93+XvX7u3Lny+/2NR01NjaOAAQC4msVccXfs2FHf+c53JEnDhg1TWVmZXn75ZS1fvtz2erfbLbfb7SxKAMBVh9d62nN8H7dlWWFr2AAAtAju47YVU+J+9tlnlZeXp8zMTJ08eVIbNmzQtm3bVFxc3FrxAQCAr4kpcR89elT5+fmqra2V1+tVVlaWiouLNXbs2NaKDwBwlWJXub2YNqf95je/0V//+lcFAgHV19frj3/8I0kbANB6LAdHKzt+/Ljy8/Pl9Xrl9XqVn5+vEydOXPb6c+fO6Sc/+YmGDh2qzp07KyMjQ9OmTdPf/va3mOblJSMAADTD1KlTVVlZqeLiYhUXF6uyslL5+fmXvf4f//iH9u7dq5/+9Kfau3ev3n77bX388ce67777YpqXl4wAAIxkcqv84MGDKi4uVmlpqUaMGCFJWrlypXw+n6qqqjRgwICIMV6vVyUlJWHnfvWrX2n48OGqrq5W7969o5qbxA0AMFML7Sr/5hM7W+I25d27d8vr9TYmbUkaOXKkvF6vdu3aZZu47fj9frlcLn3rW9+Kem5a5QAAQ7la4JAyMzMb16G9Xq+KioocR1ZXV6e0tLSI82lpaaqrq4vqO86cOaNnnnlGU6dOlcfjiXpuKm4AQFKrqakJS4xNVdsLFiy44mO6y8rKJEkuV2Qr3rIs2/PfdO7cOU2ZMkWhUEhLly694vVfR+IGAJiphVrlHo8n6oq2oKBAU6ZMafKavn37av/+/Tp69GjEP/viiy+Unp7e5Phz587p+9//vg4fPqw//elPMVXbEokbAGCqODw5LTU1VampqVe8zufzye/368MPP9Tw4cMlSXv27JHf71dOTs5lx11K2ocOHdL777+v7t27xxwja9wAAMRo0KBBGjdunGbMmKHS0lKVlpZqxowZuvfee8M2pg0cOFCbNm2SJJ0/f17//u//rvLycq1fv17BYFB1dXWqq6vT2bNno56bxA0AMNOl13o6OVrR+vXrNXToUOXm5io3N1dZWVl64403wq6pqqqS3++XJB05ckSbN2/WkSNHdPPNN6tnz56Nx65du6Kel1Y5AMBIpr8drFu3blq3bt0VYvhnEH379g373FxU3AAAJBAqbgCAmXitpy0SNwDATE7XqXk7GAAAiDcqbgCAkVzWhcPJ+GRE4gYAmIk1blskbgCAmVjjtsUaNwAACYSKGwBgJlrltkjcAAAzkbht0SoHACCBUHEDAMxExW2LxA0AMBO7ym3RKgcAIIFQcQMAjMST0+yRuAEAZmKN2xatcgAAEgiJGwCABEKrHABgJJccrnG3WCRmIXEDAMzE7WC2aJUDAJBAqLgBAGZiV7ktEjcAwEwkblu0ygEASCBU3AAAI/HkNHskbgCAmWiV26JVDgBAAqHiBgCYiYrbFokbAGAk1rjt0SoHACCBUHEDAMzEI09tkbgBAGZijdsWiRsAYCTWuO2xxg0AQAKh4gYAmIlWuS0SNwDATA5b5cmauGmVAwCQQKi4AQBmolVui8QNADATidsWrXIAABIIFTcAwEjcx22PihsAgARC4gYAoBmOHz+u/Px8eb1eeb1e5efn68SJE1GPf/TRR+VyubR48eKY5iVxAwDMZLXA0YqmTp2qyspKFRcXq7i4WJWVlcrPz49q7DvvvKM9e/YoIyMj5nlZ4wYAGMnkNe6DBw+quLhYpaWlGjFihCRp5cqV8vl8qqqq0oABAy479vPPP1dBQYHee+89jR8/Pua5SdwAAHO1QPJtaGgI++x2u+V2ux195+7du+X1ehuTtiSNHDlSXq9Xu3btumziDoVCys/P19NPP63Bgwc3a25a5QCApJaZmdm4Du31elVUVOT4O+vq6pSWlhZxPi0tTXV1dZcd9/Of/1wdOnTQE0880ey5qbgBAGZqoQew1NTUyOPxNJ5uqtpesGCBFi5c2OTXlpWVSZJcLlfklJZle16SKioq9PLLL2vv3r2XvSYaJG4AgJFaao3b4/GEJe6mFBQUaMqUKU1e07dvX+3fv19Hjx6N+GdffPGF0tPTbcft3LlT9fX16t27d+O5YDCo//zP/9TixYv117/+NaoYSdwAAFyUmpqq1NTUK17n8/nk9/v14Ycfavjw4ZKkPXv2yO/3Kycnx3ZMfn6+xowZE3bunnvuUX5+vn74wx9GHSOJGwBgJoOfVT5o0CCNGzdOM2bM0PLlyyVJP/rRj3TvvfeGbUwbOHCgioqKNGnSJHXv3l3du3cP+55rrrlGPXr0aHIX+jexOQ0AYKRLrXInR2tav369hg4dqtzcXOXm5iorK0tvvPFG2DVVVVXy+/0tOi8VNwAAzdCtWzetW7euyWssq+m/HqJd1/66mCruoqIi3XrrreratavS0tJ0//33q6qqKuZJAQC4IsOfnBYvMSXu7du3a9asWSotLVVJSYnOnz+v3NxcnT59urXiAwBcrUjctmJqlRcXF4d9Xr16tdLS0lRRUaHbbrvNdkwgEFAgEGj8/M0n2AAAgOg52px2acG9W7dul72mqKgo7Ik1mZmZTqYEAFwlTN+cFi/NTtyWZamwsFCjRo3SkCFDLnvd3Llz5ff7G4+amprmTgkAuJrQKrfV7F3lBQUF2r9/vz744IMmr2uJh7kDAK5CBt/HHU/NStyPP/64Nm/erB07dqhXr14tHRMAALiMmBK3ZVl6/PHHtWnTJm3btk39+vVrrbgAAFc5k9/HHU8xJe5Zs2bpzTff1LvvvquuXbs2vrrM6/WqU6dOrRIgAOAqRavcVkyb05YtWya/36877rhDPXv2bDw2btzYWvEBAICviblVDgBAW6BVbo9nlQMAzESr3BZvBwMAIIFQcQMAzETFbYvEDQAwkuvi4WR8MqJVDgBAAqHiBgCYiVa5LRI3AMBI3A5mj8QNADATFbct1rgBAEggVNwAAHMladXsBIkbAGAk1rjt0SoHACCBUHEDAMzE5jRbJG4AgJFoldujVQ4AQAKh4gYAmIlWuS0SNwDASLTK7dEqBwAggVBxAwDMRKvcFokbAGAmErctEjcAwEiscdtjjRsAgARCxQ0AMBOtclskbgCAkVyWJZfV/OzrZKzJaJUDAJBAqLgBAGaiVW6LxA0AMBK7yu3RKgcAIIFQcQMAzESr3BaJG2gF/widjXcIEd4/44l3CBHuTGmIdwgRrm3XMd4h4CJa5fZolQMAkEBI3AAAM1ktcLSi48ePKz8/X16vV16vV/n5+Tpx4sQVxx08eFD33XefvF6vunbtqpEjR6q6ujrqeUncAAAjXWqVOzla09SpU1VZWani4mIVFxersrJS+fn5TY759NNPNWrUKA0cOFDbtm3T//zP/+inP/2pUlJSop6XNW4AgJkM3px28OBBFRcXq7S0VCNGjJAkrVy5Uj6fT1VVVRowYIDtuHnz5um73/2uXnzxxcZzN9xwQ0xzU3EDAJJaQ0ND2BEIBBx/5+7du+X1ehuTtiSNHDlSXq9Xu3btsh0TCoX03//93+rfv7/uuecepaWlacSIEXrnnXdimpvEDQAwVku0yTMzMxvXob1er4qKihzHVVdXp7S0tIjzaWlpqqursx1TX1+vU6dO6YUXXtC4ceO0detWTZo0Sd/73ve0ffv2qOemVQ4AMJNlXTicjJdUU1Mjj+eft0O63e7LDlmwYIEWLlzY5NeWlZVJklwul82Ulu156ULFLUkTJ07UnDlzJEk333yzdu3apVdffVW33357k/NeQuIGACQ1j8cTlribUlBQoClTpjR5Td++fbV//34dPXo04p998cUXSk9Ptx2XmpqqDh066MYbbww7P2jQIH3wwQdRxSeRuAEAhorHA1hSU1OVmpp6xet8Pp/8fr8+/PBDDR8+XJK0Z88e+f1+5eTk2I7p2LGjbr31VlVVVYWd//jjj9WnT5+oY2SNGwBgJoPv4x40aJDGjRunGTNmqLS0VKWlpZoxY4buvffesB3lAwcO1KZNmxo/P/3009q4caNWrlypTz75REuWLNHvf/97PfbYY1HPTeIGAKAZ1q9fr6FDhyo3N1e5ubnKysrSG2+8EXZNVVWV/H5/4+dJkybp1Vdf1YsvvqihQ4dq1apVeuuttzRq1Kio56VVDgAwkit04XAyvjV169ZN69ata/Iay2Zz3UMPPaSHHnqo2fOSuAEAZjL4ASzxRKscAIAEQsUNADASr/W0R+IGAJiphR7AkmxI3AAAI1Fx22ONGwCABELFDQAwE7vKbZG4AQBGolVuj1Y5AAAJhIobAGAmdpXbInEDAIxEq9werXIAABIIFTcAwEzsKrdF4gYAGIlWuT1a5QAAJBAqbgCAmULWhcPJ+CRE4gYAmIk1blskbgCAkVxyuMbdYpGYhTVuAAASCBU3AMBMPDnNFokbAGAkbgezF3OrfMeOHZowYYIyMjLkcrn0zjvvtEJYAADATsyJ+/Tp07rpppu0ZMmS1ogHAIALrBY4klDMrfK8vDzl5eVFfX0gEFAgEGj83NDQEOuUAICrkMuy5HKwTu1krMlafY27qKhICxcujDj/5zNS52tae/boFfuz4h1ChD/V9o93CBG++Dg13iFEuL7/sXiHEOGunh/HO4QI47z74x1ChGvbdYx3CEDCafXbwebOnSu/39941NTUtPaUAIBkEGqBIwm1esXtdrvldrtbexoAQJKhVW6PB7AAAJBAuI8bAGAmnlVuK+bEferUKX3yySeNnw8fPqzKykp169ZNvXv3btHgAABXMZ6cZivmxF1eXq4777yz8XNhYaEkafr06VqzZk2LBQYAuLrx5DR7MSfuO+64Q1aS/hUDAIDpWOMGAJiJVrktEjcAwEiu0IXDyfhkxO1gAAAkECpuAICZaJXbInEDAMzEfdy2aJUDAJBAqLgBAEbiWeX2SNwAADOxxm2LVjkAAAmEihsAYCZLzt6pnZwFNxU3AMBMl9a4nRyt6fjx48rPz5fX65XX61V+fr5OnDjR5JhTp06poKBAvXr1UqdOnTRo0CAtW7YspnmpuAEAZrLkcI27xSKxNXXqVB05ckTFxcWSpB/96EfKz8/X73//+8uOmTNnjt5//32tW7dOffv21datW/XYY48pIyNDEydOjGpeEjcAIKk1NDSEfXa73XK73Y6+8+DBgyouLlZpaalGjBghSVq5cqV8Pp+qqqo0YMAA23G7d+/W9OnTdccdd0i6kOyXL1+u8vLyqBM3rXIAgJku7Sp3ckjKzMxsbGd7vV4VFRU5Dm337t3yer2NSVuSRo4cKa/Xq127dl123KhRo7R582Z9/vnnsixL77//vj7++GPdc889Uc9NxQ0AMFNIksvheEk1NTXyeDyNp51W25JUV1entLS0iPNpaWmqq6u77LhXXnlFM2bMUK9evdShQwe1a9dOq1at0qhRo6Kem4obAJDUPB5P2NFU4l6wYIFcLleTR3l5uSTJ5Yr8q8KyLNvzl7zyyisqLS3V5s2bVVFRoV/+8pd67LHH9Mc//jHqn4eKGwBgpHg8Oa2goEBTpkxp8pq+fftq//79Onr0aMQ/++KLL5Senm477quvvtKzzz6rTZs2afz48ZKkrKwsVVZW6he/+IXGjBkTVYwkbgCAmeLw5LTU1FSlpqZe8Tqfzye/368PP/xQw4cPlyTt2bNHfr9fOTk5tmPOnTunc+fOqV278GZ3+/btFQpFf8M6rXIAAGI0aNAgjRs3TjNmzFBpaalKS0s1Y8YM3XvvvWE7ygcOHKhNmzZJutCyv/322/X0009r27ZtOnz4sNasWaO1a9dq0qRJUc9NxQ0AMJPhzypfv369nnjiCeXm5kqS7rvvPi1ZsiTsmqqqKvn9/sbPGzZs0Ny5c/XAAw/o73//u/r06aPnn39eM2fOjHpeEjcAwEyGJ+5u3bpp3bp1VwghPIYePXpo9erVjualVQ4AQAKh4gYAmKmF7uNONiRuAICR4nE7WCIgcQMAzGT4Gne8sMYNAEACoeIGAJgpZEkuB1VzKDkrbhI3AMBMtMpt0SoHACCBxK3i/kXWUHVwXROv6SO0/06/eIcQ4Uxe5Cvj4i00+Hy8Q4jQsX0w3iFEuLb92XiHEKGzy7yYpI7xDgBGc1hxKzkrblrlAAAz0Sq3RascAIAEQsUNADBTyJKjdje7ygEAaENW6MLhZHwSolUOAEACoeIGAJiJzWm2SNwAADOxxm2LxA0AMBMVty3WuAEASCBU3AAAM1lyWHG3WCRGIXEDAMxEq9wWrXIAABIIFTcAwEyhkCQHD1EJJecDWEjcAAAz0Sq3RascAIAEQsUNADATFbctEjcAwEw8Oc0WrXIAABIIFTcAwEiWFZLl4NWcTsaajMQNADCTZTlrd7PGDQBAG7IcrnEnaeJmjRsAgARCxQ0AMFMoJLkcrFOzxg0AQBuiVW6LVjkAAAmEihsAYCQrFJLloFXO7WAAALQlWuW2aJUDAJBAqLgBAGYKWZKLivubSNwAADNZliQnt4MlZ+KmVQ4AQAKh4gYAGMkKWbIctMqtJK24SdwAADNZITlrlSfn7WDNapUvXbpU/fr1U0pKirKzs7Vz586WjgsAcJWzQpbjozU9//zzysnJ0bXXXqtvfetb0f1MlqUFCxYoIyNDnTp10h133KGPPvoopnljTtwbN27U7NmzNW/ePO3bt0+jR49WXl6eqqurY/0qAAAS1tmzZ/Uf//Ef+vGPfxz1mBdffFEvvfSSlixZorKyMvXo0UNjx47VyZMno/6OmFvlL730kh5++GE98sgjkqTFixfrvffe07Jly1RUVBRxfSAQUCAQaPzs9/slSed1ztF99S3NCgaufFEbCwbOxDuECKGvzsc7hAjnT5v37+7MqXPxDiHCKSctx1bScNa8mNC0hlMX/p21xfrxeSvgqN19Xhf+d9jQ0BB23u12y+12O4pNkhYuXChJWrNmTVTXW5alxYsXa968efre974nSXr99deVnp6uN998U48++mh0E1sxCAQCVvv27a2333477PwTTzxh3XbbbbZj5s+ff+nRNxwcHBwcSXJ8+umnsaSPmHz11VdWjx49WiTOLl26RJybP39+i8a7evVqy+v1XvG6Tz/91JJk7d27N+z8fffdZ02bNi3q+WKquI8dO6ZgMKj09PSw8+np6aqrq7MdM3fuXBUWFjZ+PnHihPr06aPq6mp5vd5Ypr+qNDQ0KDMzUzU1NfJ4PPEOx0j8jqLD7yk6/J6i4/f71bt3b3Xr1q3V5khJSdHhw4d19uxZx99lWZZcLlfYuZaotpvjUp60y6GfffZZ1N/TrF3l3/wl2P1iLrlcS8Lr9fI/jih4PB5+T1fA7yg6/J6iw+8pOu3ate5jQFJSUpSSktKqc9hZsGBBYwv8csrKyjRs2LBmzxFLDrUTU+JOTU1V+/btI6rr+vr6iL8gAABINAUFBZoyZUqT1/Tt27dZ392jRw9JFyrvnj17Np6PNYfGlLg7duyo7OxslZSUaNKkSY3nS0pKNHHixFi+CgAA46Smpio1NbVVvrtfv37q0aOHSkpK9K//+q+SLuxM3759u37+859H/T0x9zoKCwu1atUqvfbaazp48KDmzJmj6upqzZw5M6rxbrdb8+fPj9saQ6Lg93Rl/I6iw+8pOvyeosPv6Z+qq6tVWVmp6upqBYNBVVZWqrKyUqdOnWq8ZuDAgdq0aZOkCy3y2bNn62c/+5k2bdqk//3f/9WDDz6oa6+9VlOnTo1+4pi2zl3061//2urTp4/VsWNH65ZbbrG2b9/enK8BACBhTZ8+3XYn+/vvv994jSRr9erVjZ9DoZA1f/58q0ePHpbb7bZuu+0268CBAzHN67r4xQAAIAHwdjAAABIIiRsAgARC4gYAIIGQuAEASCBtmrh5HeiV7dixQxMmTFBGRoZcLpfeeeedeIdknKKiIt16663q2rWr0tLSdP/996uqqireYRln2bJlysrKanwSmM/n0x/+8Id4h2W8oqKixtt28E8LFiyQy+UKOy49UARtq80SN68Djc7p06d10003acmSJfEOxVjbt2/XrFmzVFpaqpKSEp0/f165ubk6ffp0vEMzSq9evfTCCy+ovLxc5eXluuuuuzRx4sSY3/17NSkrK9OKFSuUlZUV71CMNHjwYNXW1jYeBw4ciHdIV6cWuZktCsOHD7dmzpwZdm7gwIHWM88801YhJBxJ1qZNm+IdhvHq6+stSTxPIArXXXedtWrVqniHYaSTJ09a//Iv/2KVlJRYt99+u/Xkk0/GOySjzJ8/37rpppviHQYsy2qTivvs2bOqqKhQbm5u2Pnc3Fzt2rWrLUJAErv0jvfWfFtRogsGg9qwYYNOnz4tn88X73CMNGvWLI0fP15jxoyJdyjGOnTokDIyMtSvXz9NmTJFf/nLX+Id0lWpWW8Hi1VzXgcKRMOyLBUWFmrUqFEaMmRIvMMxzoEDB+Tz+XTmzBl16dJFmzZt0o033hjvsIyzYcMG7d27V2VlZfEOxVgjRozQ2rVr1b9/fx09elSLFi1STk6OPvroI3Xv3j3e4V1V2iRxX+L0VWbANxUUFGj//v364IMP4h2KkQYMGKDKykqdOHFCb731lqZPn67t27eTvL+mpqZGTz75pLZu3RqX10gmiry8vMb/PnToUPl8Pn3729/W66+/rsLCwjhGdvVpk8TN60DRGh5//HFt3rxZO3bsUK9eveIdjpE6duyo73znO5KkYcOGqaysTC+//LKWL18e58jMUVFRofr6emVnZzeeCwaD2rFjh5YsWaJAIKD27dvHMUIzde7cWUOHDtWhQ4fiHcpVp03WuL/+OtCvKykpUU5OTluEgCRiWZYKCgr09ttv609/+pP69esX75AShmVZCgQC8Q7DKHfffbcOHDjQ+GanyspKDRs2TA888IAqKytJ2pcRCAR08ODBsPdKo220Wau8sLBQ+fn5GjZsmHw+n1asWBHT60CvFqdOndInn3zS+Pnw4cOqrKxUt27d1Lt37zhGZo5Zs2bpzTff1LvvvquuXbs2dnK8Xq86deoU5+jM8eyzzyovL0+ZmZk6efKkNmzYoG3btqm4uDjeoRmla9euEfsjOnfurO7du7Nv4mueeuopTZgwQb1791Z9fb0WLVqkhoYGTZ8+Pd6hXXXaLHFPnjxZX375pZ577jnV1tZqyJAh2rJli/r06dNWISSE8vJy3XnnnY2fL60dTZ8+XWvWrIlTVGZZtmyZJOmOO+4IO7969Wo9+OCDbR+QoY4ePar8/HzV1tbK6/UqKytLxcXFGjt2bLxDQwI6cuSIfvCDH+jYsWO6/vrrNXLkSJWWlvL/4XHAaz0BAEggPKscAIAEQuIGACCBkLgBAEggJG4AABIIiRsAgARC4gYAIIGQuAEASCAkbgAAEgiJGwCABELiBgAggZC4AQBIIP8f0UglyY8lSHkAAAAASUVORK5CYII=",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(np.log10(gap[:,:,0]).T, origin='lower', extent=(0,5, 0, 5), vmin=-1, vmax=0.4)\n",
+ "plt.colorbar()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
"id": "377a1507",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.colorbar.Colorbar at 0x7f9ce813c610>"
+ "<matplotlib.colorbar.Colorbar at 0x7f8e51b10c10>"
]
},
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
@@ -226,29 +259,29 @@
}
],
"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[:,:,0]).T, origin='lower', extent=(0,5, 0, 5), vmin=-1, vmax=0.4)\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 19,
"id": "27f9d0d8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.colorbar.Colorbar at 0x7f9d394981f0>"
+ "<matplotlib.colorbar.Colorbar at 0x7f8e517b6730>"
]
},
- "execution_count": 18,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
@@ -258,29 +291,29 @@
}
],
"source": [
- "plt.imshow((gap[:,:,0]).T, origin='lower', extent=(0, 5, 0, 5), vmin=0)\n",
+ "plt.imshow((gap[:,:,0]).T, origin='lower', extent=(0, 5, 0, 5), vmin=0, vmax=0.4)\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 15,
"id": "be874035",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.colorbar.Colorbar at 0x7f9d085dd5e0>"
+ "<matplotlib.colorbar.Colorbar at 0x7f8e3077ff70>"
]
},
- "execution_count": 12,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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VlVauXOkoSADAAOR0njpJE3fEH+PGjRunxsZG7d69W3fddZfmzZund99997z7L1++XG1tbcHR3NzsKGAAAAayiCvu1NRUff3rX5ckFRcXq76+Xr/+9a+1bt062/3dbrfcbrezKAEAAw6P9bTneILJsqwec9gAAEQF13Hbiihx33fffSovL1deXp5OnjypzZs3q66uTtu2bYtVfAAA4BwRzXEfPXpUc+fO1bhx4zRz5ky99dZb2rZtm6677rpYxQcAGKCc3HzFyYr0NWvWqLCwUGlpaSoqKtIbb7zR6/7bt29XUVGR0tLSdMkll2jt2rV9Om+4Iqq4N2zYEKs4AAAI1c/t7i1btmjJkiVas2aNvvnNb2rdunUqLy/Xu+++q/z8/JD9Dx8+rBtuuEELFizQc889p//+7//W3XffrYsvvlg/+MEPYhKjORcHAgAQA+3t7T1Gb+uynnjiCd1xxx268847NWHCBK1atUp5eXmqrq623X/t2rXKz8/XqlWrNGHCBN15553613/9Vz322GOx+nZI3AAAM0WrVZ6Xlyev1xscVVVVtufr6urSnj17VFZW1mN7WVmZdu7caXvMrl27Qvb/zne+o4aGBp0+fToK70Io825bBACAFLVV5c3NzfJ4PMHN57tE+dixY/L7/crKyuqxPSsrSy0tLbbHtLS02O5/5swZHTt2TDk5OQ6+AXskbgCAoVxfDifHK/hEy7CPcvU8p2VZIdsutL/d9mihVQ4AgKTMzEylpKSEVNetra0hVXW37Oxs2/0HDx6skSNHxiROEjcAwExWFEYEUlNTVVRUpNra2h7ba2trNX36dNtjSkpKQvZ/9dVXVVxcrCFDhkQWQJhI3AAAM/Vz4pakiooKPfXUU3r66ad14MABLV26VE1NTVq4cKGks8/fuP3224P7L1y4UB999JEqKip04MABPf3009qwYYOWLVvW1+/6gpjjBgDgS7Nnz9bx48f1wAMP6MiRI5o0aZK2bt2qgoICSdKRI0fU1NQU3L+wsFBbt27V0qVL9eSTTyo3N1e/+c1vYnYNt0TiBgCYKk6P9bz77rt199132/7dpk2bQrZdc8012rt3b5/O1RckbgCAkXg6mD3muAEASCBU3AAAM/FYT1skbgCAmeI0x206WuUAACQQKm4AgJFc1tnh5PhkROIGAJiJOW5bJG4AgJmY47bFHDcAAAmEihsAYCZa5bZI3AAAM5G4bdEqBwAggVBxAwDMRMVti8QNADATq8pt0SoHACCBUHEDAIzEndPskbgBAGZijtsWrXIAABIIiRsAgARCqxwAYCSXHM5xRy0Ss8Qtcf89kC5fICVepw9x2jLvM4zfwF87E9+nLsuc36NuJr5Ppw18n7p4n8LiCwyJdwhBp7rO9N/JuBzMFq1yAAASiHkfdwEAkFhVfh4kbgCAmUjctmiVAwCQQKi4AQBG4s5p9kjcAAAz0Sq3RascAIAEQsUNADATFbctEjcAwEjMcdujVQ4AQAKh4gYAmIlbntoicQMAzMQcty0SNwDASMxx22OOGwCABELFDQAwE61yWyRuAICZHLbKkzVx0yoHACCBUHEDAMxEq9wWiRsAYCYSty1a5QAAJBAqbgCAkbiO2x4VNwAAffDZZ59p7ty58nq98nq9mjt3rk6cONHrMfPnz5fL5eoxpk2bFtF5qbgBAOiD2267TR9//LG2bdsmSfq3f/s3zZ07V3/60596Pe7666/Xxo0bg69TU1MjOi+JGwBgJoMXpx04cEDbtm3T7t27NXXqVEnSb3/7W5WUlOjgwYMaN27ceY91u93Kzs7u87lplQMAjNQ9x+1kSFJ7e3uP4fP5HMe2a9cueb3eYNKWpGnTpsnr9Wrnzp29HltXV6dRo0Zp7NixWrBggVpbWyM6N4kbAGAuy8H4Ul5eXnAe2uv1qqqqynFYLS0tGjVqVMj2UaNGqaWl5bzHlZeX6/nnn9drr72mxx9/XPX19br22msj+jBBqxwAkNSam5vl8XiCr91u93n3rays1MqVK3v9evX19ZIklyv0ed+WZdlu7zZ79uzgnydNmqTi4mIVFBTo5Zdf1i233NLrebuRuAEAZorSHLfH4+mRuHuzePFizZkzp9d9xowZo3feeUdHjx4N+bu//e1vysrKCjvEnJwcFRQU6NChQ2EfQ+IGABgpHtdxZ2ZmKjMz84L7lZSUqK2tTf/zP/+jK6+8UpL01ltvqa2tTdOnTw/7fMePH1dzc7NycnLCPoY5bgAAIjRhwgRdf/31WrBggXbv3q3du3drwYIF+t73vtdjRfn48eNVU1MjSero6NCyZcu0a9cuffjhh6qrq9OsWbOUmZmpm2++Oexzk7gBAGZysjDNaZs9DM8//7wmT56ssrIylZWVacqUKfrd737XY5+DBw+qra1NkpSSkqJ9+/bpxhtv1NixYzVv3jyNHTtWu3btUkZGRtjnpVUOADCS6bc8HTFihJ577rle97GsfwSRnp6uP//5z47PS8UNAEACiShxV1VV6Rvf+IYyMjI0atQo3XTTTTp48GCsYgMADGSGt8rjJaLEvX37di1atEi7d+9WbW2tzpw5o7KyMnV2dsYqPgDAQEXithXRHHf3jdS7bdy4UaNGjdKePXt09dVX2x7j8/l63BGmvb29D2ECAADJ4eK07pVyI0aMOO8+VVVVtneh+eTMCKWfNmdtXJdlTizdTlsp8Q4hhC8wJN4hhDDxfTpl4Pv0eSCyJxD1hy/85r1P/6899DaW8eZJPRXvEIJOd3b127lMX5wWL31enGZZlioqKnTVVVdp0qRJ591v+fLlamtrC47m5ua+nhIAMJDQKrfV5zJz8eLFeuedd/Tmm2/2up/b7e71vrAAANgy+LGe8dSnxH3PPffoj3/8o3bs2KHRo0dHOyYAAHAeESVuy7J0zz33qKamRnV1dSosLIxVXACAAY45bnsRJe5FixbphRde0B/+8AdlZGQEnznq9XqVnp4ekwABAAMUrXJbES1Oq66uVltbm0pLS5WTkxMcW7ZsiVV8AADgHBG3ygEA6A+0yu2Zd/EyAAASrfLz4CEjAAAkECpuAICZqLhtkbgBAEZyfTmcHJ+MaJUDAJBAqLgBAGaiVW6LxA0AMBKXg9kjcQMAzETFbYs5bgAAEggVNwDAXElaNTtB4gYAGIk5bnu0ygEASCBU3AAAM7E4zRaJGwBgJFrl9miVAwCQQKi4AQBmolVui8QNADASrXJ7tMoBAEggVNwAADPRKrdF4gYAmInEbYvEDQAwEnPc9pjjBgAggVBxAwDMRKvcFokbAGAkl2XJZfU9+zo51mS0ygEASCBU3AAAM9Eqt0XiBgAYiVXl9miVAwCQQKi4AQBmolVuK26J+/0vsuROGRKv04f4PJAa7xBCdJ5xxzuEEJ1nzHuf2k+nxTuEEO0+82L67PP0eIcQ4qKhX8Q7hBAe96l4hxBisCsQ7xCCrH6MhVa5PVrlAAD0wUMPPaTp06dr6NCh+trXvhbWMZZlqbKyUrm5uUpPT1dpaan2798f0XlJ3AAAM1lRGDHU1dWlW2+9VXfddVfYxzz66KN64okntHr1atXX1ys7O1vXXXedTp48GfbXIHEDAIzU3Sp3MmJp5cqVWrp0qSZPnhzW/pZladWqVbr//vt1yy23aNKkSXrmmWf0+eef64UXXgj7vCRuAICZolRxt7e39xg+n69/v48vHT58WC0tLSorKwtuc7vduuaaa7Rz586wvw6JGwCQ1PLy8uT1eoOjqqoqLnG0tLRIkrKysnpsz8rKCv5dOEjcAABjRaNN3tzcrLa2tuBYvnz5ec9XWVkpl8vV62hoaHD2PblcPV5blhWyrTdcxw0AMJNlnR1Ojpfk8Xjk8XjCOmTx4sWaM2dOr/uMGTOmT+FkZ2dLOlt55+TkBLe3traGVOG9IXEDAPClzMxMZWZmxuRrFxYWKjs7W7W1tbriiisknV2Zvn37dj3yyCNhfx1a5QAAI5m+qrypqUmNjY1qamqS3+9XY2OjGhsb1dHREdxn/PjxqqmpOfv9uFxasmSJHn74YdXU1Oivf/2r5s+fr6FDh+q2224L+7xU3AAAMxl+y9Of//zneuaZZ4Kvu6vo119/XaWlpZKkgwcPqq2tLbjPT3/6U33xxRe6++679dlnn2nq1Kl69dVXlZGREfZ5SdwAAPTBpk2btGnTpl73sb4yR+9yuVRZWanKyso+n5fEDQAwkitwdjg5PhmRuAEAZjK8VR4vLE4DACCBUHEDAIzEYz3tkbgBAGaK0g1Ykg2JGwBgJCpue8xxAwCQQKi4AQBmYlW5LRI3AMBItMrt0SoHACCBUHEDAMzEqnJbJG4AgJFoldujVQ4AQAKh4gYAmIlV5bZI3AAAI9Eqt0erHACABELFDQAwU8A6O5wcn4RI3AAAMzHHbYvEDQAwkksO57ijFolZmOMGACCBUHEDAMzEndNskbgBAEbicjB7EbfKd+zYoVmzZik3N1cul0svvfRSDMICAAB2Ik7cnZ2duvzyy7V69epYxAMAwFlWFEYSirhVXl5ervLy8rD39/l88vl8wdft7e2RnhIAMAC5LEsuB/PUTo41WcznuKuqqrRy5cqQ7X/5ZJxShrpjffqwdXSmxTuEEKfbU+MdQoj0j4fEO4QQX4w+He8QQgzxdMU7hBDDh52KdwghUgYF4h1CiMEu82IaZNBkrUmxDFQxvxxs+fLlamtrC47m5uZYnxIAkAwCURhJKOYVt9vtltttTmUNAEgMtMrtcQMWAAASCNdxAwDMxL3KbUWcuDs6OvT+++8HXx8+fFiNjY0aMWKE8vPzoxocAGAA485ptiJO3A0NDZoxY0bwdUVFhSRp3rx52rRpU9QCAwAMbNw5zV7Eibu0tFRWkn6KAQDAdMxxAwDMRKvcFokbAGAkV+DscHJ8MuJyMAAAEggVNwDATLTKbZG4AQBm4jpuW7TKAQBIIFTcAAAjca9yeyRuAICZmOO2RascAIAEQuIGAJjJkrNncce44H7ooYc0ffp0DR06VF/72tfCOmb+/PlyuVw9xrRp0yI6L61yAICRTJ/j7urq0q233qqSkhJt2LAh7OOuv/56bdy4Mfg6NTU1ovOSuAEAZrLkcI47apHYWrlypSRF/IAtt9ut7OzsPp+XVjkAIKm1t7f3GD6fL67x1NXVadSoURo7dqwWLFig1tbWiI4ncQMAzNS9qtzJkJSXlyev1xscVVVVcfuWysvL9fzzz+u1117T448/rvr6el177bURfZigVQ4AMFNAksvh8ZKam5vl8XiCm91u93kPqaysDLbAz6e+vl7FxcV9Cmn27NnBP0+aNEnFxcUqKCjQyy+/rFtuuSWsr0HiBgAkNY/H0yNx92bx4sWaM2dOr/uMGTMmClGdlZOTo4KCAh06dCjsY0jcAAAjxWNVeWZmpjIzM/t8zkgdP35czc3NysnJCfsY5rgBAGaK0hx3rDQ1NamxsVFNTU3y+/1qbGxUY2OjOjo6gvuMHz9eNTU1kqSOjg4tW7ZMu3bt0ocffqi6ujrNmjVLmZmZuvnmm8M+LxU3AAB98POf/1zPPPNM8PUVV1whSXr99ddVWloqSTp48KDa2tokSSkpKdq3b5+effZZnThxQjk5OZoxY4a2bNmijIyMsM9L4gYAmMnwe5Vv2rTpgtdwW+fEkJ6erj//+c+Oz0viBgCYyfDEHS/McQMAkECouAEAZorSddzJhsQNADCS6Q8ZiRcSNwDATMxx22KOGwCABELFDQAwU8CSXA6q5kByVtwkbgCAmWiV26JVDgBAAolbxZ05+5AGu4bE6/QhBi0oiXcIITpznVwHERu+kQZeXzHEvE/Vg1LMe59SBhn4Pjlpg8bIIJd5PzuTYurfWJzeb9y8369ooFUOADATrXJbtMoBAEggVNwAADMFLDlqd7OqHACAfmQFzg4nxychWuUAACQQKm4AgJlYnGaLxA0AMBNz3LZI3AAAM1Fx22KOGwCABELFDQAwkyWHFXfUIjEKiRsAYCZa5bZolQMAkECouAEAZgoEJDm4iUogOW/AQuIGAJiJVrktWuUAACQQKm4AgJmouG2RuAEAZuLOabZolQMAkECouAEARrKsgCwHj+Z0cqzJSNwAADNZlrN2N3PcAAD0I8vhHHeSJm7muAEASCBU3AAAMwUCksvBPDVz3AAA9CNa5bZolQMAkECouAEARrICAVkOWuVcDgYAQH+iVW6LVjkAAAmEihsAYKaAJbmouL+KxA0AMJNlSXJyOVhyJm5a5QAAJBAqbgCAkayAJctBq9xK0oqbxA0AMJMVkLNWeXJeDtanVvmaNWtUWFiotLQ0FRUV6Y033oh2XACAAc4KWI5HrHz44Ye64447VFhYqPT0dF166aVasWKFurq6ev+eLEuVlZXKzc1Venq6SktLtX///ojOHXHi3rJli5YsWaL7779fb7/9tr71rW+pvLxcTU1NkX4pAAAS0nvvvadAIKB169Zp//79+tWvfqW1a9fqvvvu6/W4Rx99VE888YRWr16t+vp6ZWdn67rrrtPJkyfDPrfLinASYOrUqfrnf/5nVVdXB7dNmDBBN910k6qqqkL29/l88vl8wddtbW3Kz8/XVbpBgzUkklPH1N/nXxnvEEJ0ZrviHUKI0xeZ13oKZJyJdwghUof5LrxTP8sY2nslEA8Z7lPxDiGEJ9W8mIYPMef36XRnl2q+/3904sQJeb3emJyjvb1dXq/XcZ44o9N6U1vV3Nwsj8cT3O52u+V2u6MRag+//OUvVV1drf/93/+1/XvLspSbm6slS5boZz/7maSzOTIrK0uPPPKI/v3f/z28E1kR8Pl8VkpKivX73/++x/af/OQn1tVXX217zIoVK7pvfcNgMBiMJBkffPBBJOkjIl988YWVnZ0dlTiHDx8esm3FihUxifv++++3ioqKzvv3H3zwgSXJ2rt3b4/t3//+963bb7897PNEtDjt2LFj8vv9ysrK6rE9KytLLS0ttscsX75cFRUVwdcnTpxQQUGBmpqaYvZpLRm0t7crLy8v5JMi/oH3KDy8T+HhfQpPd9d0xIgRMTtHWlqaDh8+fMH54nBYliWXq2f3MhbV9gcffKD//M//1OOPP37efbrzpF0O/eijj8I+V59WlX/1TbB7Y7qdryXh9Xr5nyMMHo+H9+kCeI/Cw/sUHt6n8AwaFNvbgKSlpSktLS2m57BTWVmplStX9rpPfX29iouLg68//fRTXX/99br11lt15513XvAckeRQOxEl7szMTKWkpIRU162trSGfIAAASDSLFy/WnDlzet1nzJgxwT9/+umnmjFjhkpKSrR+/fpej8vOzpZ0tvLOyckJbo80h0aUuFNTU1VUVKTa2lrdfPPNwe21tbW68cYbI/lSAAAYJzMzU5mZmWHt+8knn2jGjBkqKirSxo0bL9iFKCwsVHZ2tmpra3XFFVdIkrq6urR9+3Y98sgjYccYca+joqJCTz31lJ5++mkdOHBAS5cuVVNTkxYuXBjW8W63WytWrIjJHEMy4X26MN6j8PA+hYf3KTy8T2d9+umnKi0tVV5enh577DH97W9/U0tLS0hHevz48aqpqZF0tkW+ZMkSPfzww6qpqdFf//pXzZ8/X0OHDtVtt90W9rkjvhxMOnsDlkcffVRHjhzRpEmT9Ktf/UpXX311pF8GAICEtGnTJv34xz+2/btz06rL5dLGjRs1f/784N+tXLlS69at02effaapU6fqySef1KRJk8I+d58SNwAAiA+eDgYAQAIhcQMAkEBI3AAAJBASNwAACaRfEzePA72wHTt2aNasWcrNzZXL5dJLL70U75CMU1VVpW984xvKyMjQqFGjdNNNN+ngwYPxDss41dXVmjJlSvBOYCUlJXrllVfiHZbxqqqqgpft4B8qKyvlcrl6jO4biqB/9Vvi5nGg4ens7NTll1+u1atXxzsUY23fvl2LFi3S7t27VVtbqzNnzqisrEydnZ3xDs0oo0eP1i9+8Qs1NDSooaFB1157rW688caIn/07kNTX12v9+vWaMmVKvEMx0sSJE3XkyJHg2LdvX7xDGpgiffpJX1155ZXWwoULe2wbP3689R//8R/9FULCkWTV1NTEOwzjtba2WpKs7du3xzsU41100UXWU089Fe8wjHTy5Enrn/7pn6za2lrrmmuuse699954h2SUFStWWJdffnm8w4BlWf1ScXd1dWnPnj0qKyvrsb2srEw7d+7sjxCQxNra2iQppk8rSnR+v1+bN29WZ2enSkpK4h2OkRYtWqTvfve7+va3vx3vUIx16NAh5ebmqrCwUHPmzDnvc6cRW316Olik+vI4UCAclmWpoqJCV111VUR3Hhoo9u3bp5KSEp06dUrDhw9XTU2NLrvssniHZZzNmzdr7969qq+vj3coxpo6daqeffZZjR07VkePHtWDDz6o6dOna//+/Ro5cmS8wxtQ+iVxd3P6KDPgqxYvXqx33nlHb775ZrxDMdK4cePU2NioEydO6MUXX9S8efO0fft2kvc5mpubde+99+rVV1+Ny2MkE0V5eXnwz5MnT1ZJSYkuvfRSPfPMM6qoqIhjZANPvyRuHgeKWLjnnnv0xz/+UTt27NDo0aPjHY6RUlNT9fWvf12SVFxcrPr6ev3617/WunXr4hyZOfbs2aPW1lYVFRUFt/n9fu3YsUOrV6+Wz+dTSkpKHCM007BhwzR58mQdOnQo3qEMOP0yx33u40DPVVtbq+nTp/dHCEgilmVp8eLF+v3vf6/XXntNhYWF8Q4pYViWJZ/PF+8wjDJz5kzt27dPjY2NwVFcXKwf/ehHamxsJGmfh8/n04EDB3o8Vxr9o99a5RUVFZo7d66Ki4uDDxyP5HGgA0VHR4fef//94OvDhw+rsbFRI0aMUH5+fhwjM8eiRYv0wgsv6A9/+IMyMjKCnRyv16v09PQ4R2eO++67T+Xl5crLy9PJkye1efNm1dXVadu2bfEOzSgZGRkh6yOGDRumkSNHsm7iHMuWLdOsWbOUn5+v1tZWPfjgg2pvb9e8efPiHdqA02+Je/bs2Tp+/LgeeOCB4ONAt27dqoKCgv4KISE0NDRoxowZwdfdc0fz5s3Tpk2b4hSVWaqrqyVJpaWlPbaf++g8SEePHtXcuXN15MgReb1eTZkyRdu2bdN1110X79CQgD7++GP98Ic/1LFjx3TxxRdr2rRp2r17N/+GxwGP9QQAIIFwr3IAABIIiRsAgARC4gYAIIGQuAEASCAkbgAAEgiJGwCABELiBgAggZC4AQBIICRuAAASCIkbAIAEQuIGACCB/H+sx/si0vBttgAAAABJRU5ErkJggg==",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
@@ -297,23 +330,23 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 23,
"id": "f2ddc9a6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.colorbar.Colorbar at 0x7f9d18911700>"
+ "<matplotlib.colorbar.Colorbar at 0x7f8e307d0fa0>"
]
},
- "execution_count": 11,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
@@ -324,7 +357,7 @@
],
"source": [
"gap = np.asarray(gap, dtype=float) \n",
- "plt.imshow((gap[:,:,1]).T, origin='lower', extent=(0, 5, 0, 5), vmin=0)\n",
+ "plt.imshow((gap[:,:,1]).T, origin='lower', extent=(0, 5, 0, 5), vmin=0, vmax=3)\n",
"plt.colorbar()"
]
},
diff --git a/codes/utils.py b/codes/utils.py
index 7576703f38036f2179f210a96a4154a592b8d96a..376218538135bbdf56d2deb12e8540b19f476f1b 100644
--- a/codes/utils.py
+++ b/codes/utils.py
@@ -32,6 +32,19 @@ def potential2hamiltonian(
wrapped_V = kwant.wraparound.wraparound(V).finalized()
return syst2hamiltonian(kxs=ks, kys=ks, syst=wrapped_V, params=params)
+def assign_kdependence(nk, dim, ndof, hopping_vecs, content): #goal and content are bad names, suggestions welcome
+
+ klenlist = [nk for i in range(dim)]
+ goal = np.zeros((klenlist+[ndof, ndof]), dtype=complex)
+ reshape_order = [1 for i in range(dim)] #could use a better name
+ kgrid = np.asarray(np.meshgrid(*[np.linspace(-np.pi, np.pi, nk) for i in range(dim)])).reshape(dim, -1).T
+
+ for hop, hop2 in zip(hopping_vecs, content):
+ k_dependence = np.exp(1j * np.dot(kgrid, hop)).reshape(klenlist+[1,1])
+ goal += hop2.reshape(reshape_order+[ndof, ndof]) * k_dependence
+
+ return goal
+
def generate_guess(nk, dim, hopping_vecs, ndof, scale=0.1):
"""
nk : int
@@ -40,8 +53,8 @@ def generate_guess(nk, dim, hopping_vecs, ndof, scale=0.1):
dimension of the system
hopping_vecs : np.array
hopping vectors as obtained from extract_hopping_vectors
- norb_list : np.array
- number of orbitals in each atom
+ ndof : int
+ number of degrees of freedom
scale : float
scale of the guess. If scale=1 then the guess is random around 0.5
Smaller values of the guess significantly slows down convergence but
@@ -52,29 +65,17 @@ def generate_guess(nk, dim, hopping_vecs, ndof, scale=0.1):
Assumes that the desired max nearest neighbour distance is included in the hopping_vecs information.
Creates a square grid by definition, might still want to change that
"""
- dim = 2
- klenlist = [nk for i in range(dim)]
- guess = np.zeros((klenlist+[ndof, ndof]), dtype=complex)
- kgrid = np.asarray(np.meshgrid(*[np.linspace(-np.pi, np.pi, nk) for i in range(dim)])).reshape(dim, -1).T
-
- #always include onsite/internal hopping
- amplitude = np.random.rand(ndof, ndof)
- phase = 2 * np.pi * np.random.rand(ndof, ndof)
- rand_hermitian = amplitude * np.exp(1j * phase)
- rand_hermitian += rand_hermitian.T.conj()
- rand_hermitian /= 2
- reshape_order = [1 for i in range(dim)] #could use a better name
- guess += rand_hermitian.reshape(reshape_order+[ndof, ndof]) #no k-dependence for onsite
-
- for hop in hopping_vecs:
- k_dependence = np.exp(1j * np.dot(kgrid, hop)).reshape(klenlist+[1,1])
+ all_rand_hermitians = []
+ for n in hopping_vecs:
amplitude = np.random.rand(ndof, ndof)
phase = 2 * np.pi * np.random.rand(ndof, ndof)
rand_hermitian = amplitude * np.exp(1j * phase)
rand_hermitian += rand_hermitian.T.conj()
rand_hermitian /= 2
- guess += rand_hermitian.reshape(reshape_order+[ndof, ndof]) * k_dependence
-
+ all_rand_hermitians.append(rand_hermitian)
+ all_rand_hermitians = np.asarray(all_rand_hermitians)
+
+ guess = assign_kdependence(nk, dim, ndof, hopping_vecs, all_rand_hermitians)
return guess*scale
def extract_hopping_vectors(builder):
@@ -126,6 +127,11 @@ def hk2hop(hk, deltas, ks, dk):
)
return hopps
+def hktohamiltonian(hk, nk, ks, dk, dim, hopping_vecs, ndof):
+ """function is basically tiny so maybe don't separapetly create it"""
+ hops = hk2hop(hk, hopping_vecs, ks, dk)
+ hamil = assign_kdependence(nk, dim, ndof, hopping_vecs, hops)
+ return hamil
def hk2syst(deltas, hk, ks, dk, max_neighbor, norbs, lattice):
hopps = hk2hop(hk, deltas, ks, dk)