diff --git a/Tests/.DS_Store b/Tests/.DS_Store
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diff --git a/Tests/.ipynb_checkpoints/test1-checkpoint.ipynb b/Tests/.ipynb_checkpoints/test1-checkpoint.ipynb
new file mode 100644
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+++ b/Tests/.ipynb_checkpoints/test1-checkpoint.ipynb
@@ -0,0 +1,2312 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d6682c69",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'block_diagonalize'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/_c/qqbkxzc571n6hcyyj207lmhw0000gn/T/ipykernel_18540/1893085187.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mspr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mblock_diagonalize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'block_diagonalize'"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from qutip import * \n",
+    "\n",
+    "import scipy.sparse as spr\n",
+    "import block_diagonalize\n",
+    "import time\n",
+    "import os\n",
+    "import pickle\n",
+    "import matplotlib.image as mpimg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f7f68665",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.colors import LinearSegmentedColormap \n",
+    "import matplotlib.colors as colors\n",
+    "\n",
+    "greiner = { 'red': ((0., 1, 1,), (.2, 0, 0), (.48, 0, 0), (.728, 1, 1), (0.912, 1, 1), (1, .5, .5)),\n",
+    "            'green': ((0., 1, 1), (.2, 0, 0), (.3, 0, 0), (.5, 1, 1), (.712, 1, 1), (.928, 0, 0), (1, 0, 0)),\n",
+    "            'blue':  ((0., 1, 1), (.2, .5, .5), (.288, 1, 1), (.472, 1, 1), (.72, 0, 0), (1, 0, 0)) }\n",
+    "greiner = LinearSegmentedColormap('greiner', greiner)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "107f7025",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0886a3e9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba6d2e59",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5e80c9bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20.10335111618042\n"
+     ]
+    }
+   ],
+   "source": [
+    "a=destroy(28)\n",
+    "\n",
+    "parity=1.j*np.pi*a.dag()*a\n",
+    "parity=parity.expm()\n",
+    "\n",
+    "Delta = 10\n",
+    "U     = 0.1\n",
+    "eta   = 0.1\n",
+    "\n",
+    "Grange = np.geomspace(5e-2, 3e3, 31)\n",
+    "c_ops = [np.sqrt(eta)*a**2]#, np.sqrt(beta)*a.dag()*a]\n",
+    "\n",
+    "num_even     = np.zeros(Grange.size)\n",
+    "num_odd      = np.zeros(Grange.size)\n",
+    "gap_even     = np.zeros(Grange.size, dtype='complex')\n",
+    "gap_odd      = np.zeros(Grange.size, dtype='complex')\n",
+    "gap_even_odd = np.zeros(Grange.size, dtype='complex')\n",
+    "gap_odd_even = np.zeros(Grange.size, dtype='complex')\n",
+    "evals_list   = []\n",
+    "\n",
+    "t1 = time.time()\n",
+    "for g in range(Grange.size):\n",
+    "    H = -Delta*a.dag()*a + Grange[g]/2 *(a.dag()**2 +a**2) +U/2 *a.dag()**2*a**2\n",
+    "    LL = liouvillian(H, c_ops)\n",
+    "    \n",
+    "    P, block_bfs, block_sizes = block_diagonalize.PermMat(LL)\n",
+    "    bd_L = np.dot(P, np.dot(LL.data, np.transpose(P)))\n",
+    "    num_blocks, blocks_list, bl_indices = block_diagonalize.get_blocks(bd_L)   \n",
+    "    \n",
+    "    num_keep = 15\n",
+    "    evals_keep = np.zeros((num_blocks, num_keep), dtype='complex')\n",
+    "    \n",
+    "    done=False\n",
+    "    for i in range(int(len(blocks_list))):\n",
+    "        block = blocks_list[i]\n",
+    "        evals, evecs = Qobj(block).eigenstates()    \n",
+    "        evals_keep[i,:] = evals[-num_keep:]\n",
+    "        \n",
+    "        ss_block_form=evecs[-1]\n",
+    "        evec2 = spr.dok_matrix((LL.shape[0], 1), dtype=np.complex)\n",
+    "        evec2[bl_indices[i]:bl_indices[i]+blocks_list[i].shape[0]] = ss_block_form.data\n",
+    "        evec2 = evec2.tocsr()\n",
+    "        evec2=np.dot(np.transpose(P),evec2)\n",
+    "        ss= Qobj(evec2, dims=[LL.dims[0], [1]])\n",
+    "        ss=vector_to_operator(ss)\n",
+    "\n",
+    "        if ss.tr()>0.05:\n",
+    "            ss=ss+ss.dag()\n",
+    "            ss/=(ss.tr())       \n",
+    "            if expect(parity, ss)>0.5:\n",
+    "                num_even[g] = expect(ss, a.dag()*a)\n",
+    "                gap_even[g] = evals[-2]\n",
+    "            else:\n",
+    "                num_odd[g] = expect(ss, a.dag()*a)                \n",
+    "                gap_odd[g] = evals[-2]\n",
+    "        elif done==False:\n",
+    "            gap_even_odd[g] = np.real(evals[-1])+1.j*np.abs(np.imag(evals[-1]))\n",
+    "            gap_odd_even[g] = np.real(evals[-1])-1.j*np.abs(np.imag(evals[-1]))       \n",
+    "            done=True\n",
+    "    evals_list.append(evals_keep)\n",
+    "    \n",
+    "print(time.time()-t1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dc957fcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd7f50e3820>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "style_list=['ro', 'g^', 'yx', 'cs']\n",
+    "parity_list=['+,+', '+,--', '--,+', '--,--']\n",
+    "\n",
+    "plt.xscale(\"log\")\n",
+    "for i in range(num_blocks):\n",
+    "    plt.plot(np.abs(np.real(evals_list[0][i,:])), np.imag(evals_list[0][i,:]), style_list[i], label=parity_list[i])\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "cf16363e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "AAAAAAEAAAAAAAAAAAAAAAAAAEAAAAABsAAAASAAAAAAACRlZHRzAAAAHGVsc3QAAAAAAAAAAQAA\n",
+       "GDgAAAAAAAEAAAAAAjBtZGlhAAAAIG1kaGQAAAAAAAAAAAAAAAAAACgAAAD4AFXEAAAAAAAtaGRs\n",
+       "cgAAAAAAAAAAdmlkZQAAAAAAAAAAAAAAAFZpZGVvSGFuZGxlcgAAAAHbbWluZgAAABR2bWhkAAAA\n",
+       "AQAAAAAAAAAAAAAAJGRpbmYAAAAcZHJlZgAAAAAAAAABAAAADHVybCAAAAABAAABm3N0YmwAAACn\n",
+       "c3RzZAAAAAAAAAABAAAAl2F2YzEAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAABsAEgAEgAAABIAAAA\n",
+       "AAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAY//8AAAAlYXZjQwFCwBX/4QAO\n",
+       "Z0LAFYyNQNhKQDwiEagBAARozjyAAAAAHHV1aWRraEDyXyRPxbo5pRvPAyPzAAAAAAAAABhzdHRz\n",
+       "AAAAAAAAAAEAAAAfAAAIAAAAABRzdHNzAAAAAAAAAAEAAAABAAAAHHN0c2MAAAAAAAAAAQAAAAEA\n",
+       "AAAfAAAAAQAAAJBzdHN6AAAAAAAAAAAAAAAfAAAZ0wAAA3cAAAIqAAACxAAAAbIAAAK6AAAEegAA\n",
+       "A3AAAASkAAAJ+QAADKgAAAreAAAMdAAAEHgAAA9DAAALUwAADScAAAxsAAAMFQAABnQAAAW2AAAE\n",
+       "8gAABB0AAAJ8AAABTgAAAUEAAAFMAAAA5AAAAiUAAAD2AAAB3gAAABRzdGNvAAAAAAAAAAEAAAAw\n",
+       "AAAAYnVkdGEAAABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAAAAAAAAAAAAAt\n",
+       "aWxzdAAAACWpdG9vAAAAHWRhdGEAAAABAAAAAExhdmY1OC40NS4xMDA=\n",
+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from celluloid import Camera # getting the camera\n",
+    "from IPython.display import HTML # to show the animation in Jupyter\n",
+    "\n",
+    "style_list=['ro', 'g^', 'yx', 'cs']\n",
+    "parity_list=['+,+', '+,--', '--,+', '--,--']\n",
+    "fig, ax = plt.subplots(); # creating my fig\n",
+    "ax.set_xscale(\"log\")\n",
+    "ax.set_ylim(-60,60)\n",
+    "\n",
+    "camera = Camera(fig)# the camera gets the fig we'll plot\n",
+    "for i in range(Grange.size):\n",
+    "    for j in range(num_blocks):\n",
+    "        ax.plot(np.abs(np.real(evals_list[i][j,:])), np.imag(evals_list[i][j,:]), style_list[j], label=parity_list[j])\n",
+    "    if i==0: ax.legend()\n",
+    "    camera.snap(); # the camera takes a snapshot of the plot\n",
+    "animation = camera.animate(); # animation ready\n",
+    "HTML(animation.to_html5_video()) # displaying the animation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a788023",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "734325af",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<video width=\"432\" height=\"288\" controls autoplay loop>\n",
+       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAIGZ0eXBNNFYgAAACAE00ViBpc29taXNvMmF2YzEAAAAIZnJlZQAApkxtZGF0AAAAUmW4AAQA\n",
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+       "\">\n",
+       "  Your browser does not support the video tag.\n",
+       "</video>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import animation\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "style_list=['ro', 'g^', 'yx', 'cs']\n",
+    "parity_list=['+,+', '+,--', '--,+', '--,--']\n",
+    "\n",
+    "# First set up the figure, the axis, and the plot element we want to animate\n",
+    "fig = plt.figure();\n",
+    "ax = plt.axes(xlim=(1e-14, 10), ylim=(-60, 60));\n",
+    "ax.set_xscale(\"log\");\n",
+    "lines = []\n",
+    "\n",
+    "fig = plt.figure();\n",
+    "ax = plt.axes(xlim=(1e-14, 5), ylim=(-60, 60));\n",
+    "ax.set_xscale(\"log\");\n",
+    "for j in range(num_blocks):\n",
+    "    temp_line, = ax.plot([], [], style_list[j], label=parity_list[j]);\n",
+    "    lines.append(temp_line);\n",
+    "\n",
+    "# Initialization function: plot the background of each frame\n",
+    "def init():\n",
+    "    for j in range(len(lines)):\n",
+    "        lines[j].set_data([], []);\n",
+    "    return lines\n",
+    "\n",
+    "# Animation function which updates figure data.  This is called sequentially\n",
+    "def animate(i):\n",
+    "    for j in range(num_blocks):\n",
+    "        lines[j].set_data(np.abs(np.real(evals_list[i][j,:])), np.imag(evals_list[i][j,:]));\n",
+    "    return lines\n",
+    "\n",
+    "# Call the animator.  blit=True means only re-draw the parts that have changed.\n",
+    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
+    "                               frames=31, interval=100, blit=True);\n",
+    "\n",
+    "plt.close(anim._fig);\n",
+    "\n",
+    "# Call function to display the animation\n",
+    "HTML(anim.to_html5_video())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4c494cd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "827580b8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a606aa37",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30c48fa7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5af81bcd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3e7dce67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xscale(\"log\")\n",
+    "plt.plot(Grange, num_even, 'bo-', ms=5, label=\"even\")\n",
+    "plt.plot(Grange, num_odd, 'rs-', ms=5, label=\"odd\")\n",
+    "\n",
+    "plt.xlabel(r\"$G$\")\n",
+    "plt.ylabel(r\"$\\langle a^{\\dagger} a\\rangle$\")\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50f826f2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "22984485",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xscale(\"log\")\n",
+    "plt.yscale(\"log\")\n",
+    "plt.plot(Grange, -np.real(gap_even), 'bo-', ms=5, label=\"even\")\n",
+    "plt.plot(Grange, -np.real(gap_odd), 'rs-', ms=5, label=\"odd\")\n",
+    "\n",
+    "plt.axhline(1, c='k', ls='--')\n",
+    "\n",
+    "plt.ylabel(r\"$-\\Re(\\lambda_1)$\")\n",
+    "plt.xlabel(r\"$G/U$\")\n",
+    "plt.legend();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "2ae076ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.xscale(\"log\")\n",
+    "plt.plot(Grange, -np.real(gap_even_odd), 'g^-')\n",
+    "plt.plot(Grange, -np.real(gap_odd_even), 'yh-')\n",
+    "\n",
+    "plt.yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "277b6ed2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2217ee9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bdefffd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "136fadfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"GoverDelta_scan.pckl\", \"rb\") as fp:\n",
+    "    Deltarange, results = pickle.load(fp)\n",
+    "\n",
+    "Grange, num_even, num_odd, gap_even, gap_odd, gap_even_odd, gap_odd_even = list(list(zip(*results)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7f9574e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.yscale(\"log\")\n",
+    "plt.xscale(\"log\")\n",
+    "plt.pcolormesh(Grange[0]/Deltarange[0], Deltarange, num_even, shading='auto', cmap='viridis', vmin=0, vmax=11)\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36413363",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Tests/.ipynb_checkpoints/test2-checkpoint.ipynb b/Tests/.ipynb_checkpoints/test2-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e8441906e0757ee53330de43fe30c49055e71f75
--- /dev/null
+++ b/Tests/.ipynb_checkpoints/test2-checkpoint.ipynb
@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "466e6e45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(1, '/Users/lucagravina/dptqm/Strucutral codes/Liouvillian block diagonalization')\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from qutip import * \n",
+    "\n",
+    "import scipy.sparse as spr\n",
+    "import block_diagonalize\n",
+    "import time\n",
+    "import os\n",
+    "import pickle\n",
+    "import matplotlib.image as mpimg\n",
+    "\n",
+    "from collections import deque\n",
+    "\n",
+    "\n",
+    "from matplotlib.colors import LinearSegmentedColormap \n",
+    "import matplotlib.colors as colors\n",
+    "greiner = { 'red': ((0., 1, 1,), (.2, 0, 0), (.48, 0, 0), (.728, 1, 1), (0.912, 1, 1), (1, .5, .5)),\n",
+    "            'green': ((0., 1, 1), (.2, 0, 0), (.3, 0, 0), (.5, 1, 1), (.712, 1, 1), (.928, 0, 0), (1, 0, 0)),\n",
+    "            'blue':  ((0., 1, 1), (.2, .5, .5), (.288, 1, 1), (.472, 1, 1), (.72, 0, 0), (1, 0, 0)) }\n",
+    "greiner = LinearSegmentedColormap('greiner', greiner)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a967f6f9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4b11dd09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Parameters():\n",
+    "    def __init__(self, Δ, N, Grat, ηtilde, Utilde):\n",
+    "        self.Δ = Δ\n",
+    "        self.N = N \n",
+    "        self.ηtilde = ηtilde \n",
+    "        self.Utilde = Utilde\n",
+    "        self.U = self.Utilde/self.N\n",
+    "        self.η = self.ηtilde/self.N\n",
+    "        \n",
+    "        self.Gc = np.abs(self.η*self.Δ/np.sqrt(self.η**2+self.U**2))\n",
+    "        self.G = Grat*self.Gc\n",
+    "        self.n_sc = lambda g : self.U*self.Δ/(self.U**2+self.η**2)*(1+np.sqrt((g**2-1)*np.heaviside(g-1,1)))*np.heaviside(g-1,1)\n",
+    "\n",
+    "class Kerr_2γ():\n",
+    "    def __init__(self, Nfock, p):\n",
+    "        self.Nfock = Nfock\n",
+    "        self.a = destroy(Nfock)\n",
+    "        self.parity = 1.j*np.pi*self.a.dag()*self.a\n",
+    "        self.parity = self.parity.expm()\n",
+    "        \n",
+    "        self.p = p\n",
+    "        self.c_ops = [np.sqrt(p.η)*self.a**2]\n",
+    "        self.H = -p.Δ*self.a.dag()*self.a + p.G/2 *(self.a.dag()**2 +self.a**2) + p.U/2 *self.a.dag()**2*self.a**2\n",
+    "        self.LL = liouvillian(self.H, self.c_ops)\n",
+    "    \n",
+    "    def bd(self):\n",
+    "        P, block_bfs, block_sizes = block_diagonalize.PermMat(self.LL)\n",
+    "        self.bd_L = np.dot(P, np.dot(self.LL.data, np.transpose(P)))\n",
+    "        self.num_blocks, self.blocks_list, self.bl_indices = block_diagonalize.get_blocks(self.bd_L)   \n",
+    "\n",
+    "        done=False\n",
+    "        for i in range(int(len(self.blocks_list))):\n",
+    "            block = self.blocks_list[i]\n",
+    "            evals, evecs = Qobj(block).eigenstates()    \n",
+    "\n",
+    "            ss_block_form = evecs[-1]\n",
+    "            evec2 = spr.dok_matrix((self.LL.shape[0], 1), dtype='complex')\n",
+    "            evec2[self.bl_indices[i]:self.bl_indices[i]+self.blocks_list[i].shape[0]] = ss_block_form.data\n",
+    "            evec2 = evec2.tocsr()\n",
+    "            evec2 = np.dot(np.transpose(P),evec2)\n",
+    "            ss= Qobj(evec2, dims=[self.LL.dims[0], [1]])\n",
+    "            ss=vector_to_operator(ss)\n",
+    "\n",
+    "            if np.real(ss.tr())>0.05:\n",
+    "                ss=ss+ss.dag()\n",
+    "                ss/=(ss.tr())       \n",
+    "                if expect(self.parity, ss)>0.5:\n",
+    "                    num_even = expect(ss, self.a.dag()*self.a)\n",
+    "                    gap_even = evals[-2]\n",
+    "                else:\n",
+    "                    num_odd = expect(ss, self.a.dag()*self.a)\n",
+    "                    gap_odd = evals[-2]\n",
+    "            elif done==False:\n",
+    "                gap_even_odd = np.real(evals[-1])+1.j*np.abs(np.imag(evals[-1]))\n",
+    "                gap_odd_even = np.real(evals[-1])-1.j*np.abs(np.imag(evals[-1]))       \n",
+    "                done=True\n",
+    "        return num_even, num_odd, gap_even, gap_odd, gap_even_odd, gap_odd_even\n",
+    "    \n",
+    "    \n",
+    "def get_cutoff(p, c0=5, cmax=100, step=5, precision=0.005):\n",
+    "    metric = lambda x1,x2 : ((np.abs(x1-x2)/np.abs(np.min([x1,x2])))<precision)\n",
+    "    k = Kerr_2γ(Nfock=c0,p=p)\n",
+    "    nprev = k.bd()[0]\n",
+    "    c = c0+step\n",
+    "    while c<cmax:\n",
+    "        k = Kerr_2γ(Nfock=c,p=p)\n",
+    "        n = k.bd()[0]\n",
+    "        if metric(n,nprev):\n",
+    "            c -= step\n",
+    "            while 1:\n",
+    "                step = np.max([step//2,1])\n",
+    "                k = Kerr_2γ(Nfock=c-step,p=p)\n",
+    "                if metric(k.bd()[0],n): \n",
+    "                    c -= step\n",
+    "                elif step==1: return c \n",
+    "        else:\n",
+    "            nprev = n\n",
+    "            c += step\n",
+    "    return np.inf\n",
+    "\n",
+    "def map_cutoff(Utilderange, Δrange, Grange):\n",
+    "    C = np.zeros((Utilderange.size, Δrange.size, Grange.size),dtype='float')\n",
+    "    for i in range(Utilderange.size):\n",
+    "        Utilde=Utilderange[i]\n",
+    "        for j in range(Δrange.size):\n",
+    "            Δ=Δrange[j]\n",
+    "            for k in range(Grange.size):\n",
+    "                Grat=Grange[k]\n",
+    "                p = Parameters(Δ=Δ, N=10, Grat=Grat, ηtilde=1, Utilde=Utilde)\n",
+    "                C[i,j,k] = get_cutoff(p, c0=5, cmax=100, step=5, precision=0.005)\n",
+    "    return C"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d73b1b75",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f886292",
+   "metadata": {},
+   "source": [
+    "# Symmetry-preserving 1st order PT\n",
+    "\n",
+    "In this case we look at the model \n",
+    "\n",
+    "$$\n",
+    "\\hat{H}=-\\Delta \\hat{a}^{\\dagger} \\hat{a}+\\frac{U}{2}\\hat{a}^{\\dagger^2} \\hat{a}^2 + \\frac{G}{2} \\left[\\hat{a}^{{\\dagger}^2} + \\hat{a}^{\\dagger^2}\\right]\n",
+    "$$\n",
+    "\n",
+    "with\n",
+    "\n",
+    "$$\n",
+    "U=\\tilde{U} / N, \\quad \\eta=\\tilde{\\eta} / N\n",
+    "$$\n",
+    "\n",
+    "and two-photon dissipation $\\eta\\mathrm{D}\\left[a^2\\right]$.\n",
+    "\n",
+    "Here we have a strong-parity symmetry which is preserved in the 1st order PT. In particular we have a 1st order PT for each of the two NESS with opposite parity: within each parity sector we have a level touching with subsequent PT.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "375b8dd0",
+   "metadata": {},
+   "source": [
+    "### General example\n",
+    "\n",
+    "We are interested in the PT in $G$. In the following we observe for $\\tilde\\eta = 1$, $\\tilde U/\\tilde\\eta = \\Delta/\\tilde\\eta = 10$ and $N=10$ a typical 1st order PT in both NESS.\n",
+    "\n",
+    "The level touching is observable in both symmetry sectors. \n",
+    "\n",
+    "The question is: how does the hysteresys area change in the remaining parameters ($\\Delta$, $U$)?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3004e4b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Completed: 20/20"
+     ]
+    }
+   ],
+   "source": [
+    "Grange = np.linspace(0,2,20)\n",
+    "\n",
+    "num_even     = np.zeros(Grange.size)\n",
+    "num_odd      = np.zeros(Grange.size)\n",
+    "gap_even     = np.zeros(Grange.size, dtype='complex')\n",
+    "gap_odd      = np.zeros(Grange.size, dtype='complex')\n",
+    "gap_even_odd = np.zeros(Grange.size, dtype='complex')\n",
+    "gap_odd_even = np.zeros(Grange.size, dtype='complex')\n",
+    "\n",
+    "\n",
+    "for i in range(Grange.size):\n",
+    "    Grat = Grange[i]\n",
+    "    p = Parameters(Δ=1.5, N=10, Grat=Grat, ηtilde=1, Utilde=1)\n",
+    "    k = Kerr_2γ(Nfock=40, p=p) \n",
+    "    num_even[i], num_odd[i], gap_even[i], gap_odd[i], gap_even_odd[i], gap_odd_even[i] = k.bd()\n",
+    "    print(\"\\rCompleted: %d/%d\"%(i+1,Grange.size),end='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bfe6d8e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2160x648 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,3, figsize=(30,9))\n",
+    "\n",
+    "g=np.linspace(Grange.min(),Grange.max(),1000)\n",
+    "\n",
+    "ax[0].plot(Grange, num_even/p.N, 'bo-', ms=5, label=\"even\")\n",
+    "ax[0].plot(g, p.n_sc(g)/p.N, 'g-', lw=3, label=\"SC\")\n",
+    "ax[0].plot(Grange, num_odd/p.N, 'rs-', ms=5, label=\"odd\")\n",
+    "ax[0].axvline(1,c='grey',alpha=0.5, ls='--', lw=3)\n",
+    "\n",
+    "#ax[0].set_xscale(\"log\")\n",
+    "ax[0].set_xlabel(r\"$G\\,/\\,G_c$\")\n",
+    "ax[0].set_ylabel(r\"$\\langle a^{\\dagger} a\\rangle\\,/\\,N$\")\n",
+    "ax[0].legend()\n",
+    "\n",
+    "ax[1].plot(Grange, -np.real(gap_even), 'bo-', ms=5, label=\"even\")\n",
+    "ax[1].plot(Grange, -np.real(gap_odd), 'rs-', ms=5, label=\"odd\")\n",
+    "ax[1].axhline(-np.real(gap_even)[0], c='k', ls='--')\n",
+    "ax[1].axvline(1,c='grey',alpha=0.5, ls='--', lw=3)\n",
+    "\n",
+    "#ax[1].set_xscale(\"log\")\n",
+    "ax[1].set_yscale(\"log\")\n",
+    "ax[1].set_ylabel(r\"$-\\Re(\\lambda_1)\\,/\\,\\tilde\\eta$\")\n",
+    "ax[1].set_xlabel(r\"$G\\,/\\,G_c$\")\n",
+    "ax[1].legend()\n",
+    "\n",
+    "\n",
+    "ax[2].plot(Grange, -np.real(gap_even_odd), 'g^-')\n",
+    "ax[2].plot(Grange, -np.real(gap_odd_even), 'yh-')\n",
+    "ax[2].set_xlabel(r\"$G\\,/\\,G_c$\")\n",
+    "ax[2].axvline(1,c='grey',alpha=0.5, ls='--', lw=3)\n",
+    "\n",
+    "#ax[2].set_xscale(\"log\")\n",
+    "ax[2].set_yscale('log')\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "#fig.savefig(\"PL_Delta=Utilde=10_N=10.png\", dpi=500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d909281",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a199be0e",
+   "metadata": {},
+   "source": [
+    "## Estimating convergence of Hilbert space truncation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "971b6bfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33.93870401382446"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "step = 1\n",
+    "cuts = np.arange(10,50,step)\n",
+    "\n",
+    "num_even     = np.zeros(cuts.size)\n",
+    "num_odd      = np.zeros(cuts.size)\n",
+    "gap_even     = np.zeros(cuts.size, dtype='complex')\n",
+    "gap_odd      = np.zeros(cuts.size, dtype='complex')\n",
+    "gap_even_odd = np.zeros(cuts.size, dtype='complex')\n",
+    "gap_odd_even = np.zeros(cuts.size, dtype='complex')\n",
+    "\n",
+    "Grat=1.8\n",
+    "p = Parameters(Δ=1.5, N=10, Grat=Grat, ηtilde=1, Utilde=1)\n",
+    "\n",
+    "t1=time.time()\n",
+    "for i in range(cuts.size):\n",
+    "    k = Kerr_2γ(Nfock=cuts[i], p=p) \n",
+    "    num_even[i], num_odd[i], gap_even[i], gap_odd[i], gap_even_odd[i], gap_odd_even[i] = k.bd()\n",
+    "time.time()-t1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1b5e0401",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c= 35  time= 4.905393362045288\n"
+     ]
+    }
+   ],
+   "source": [
+    "t1 = time.time()\n",
+    "c = get_cutoff(p,c0=36,cmax=60,step=6,precision=0.01)\n",
+    "print(\"c=\",c,\" time=\",time.time()-t1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6876c0dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,ax = plt.subplots(1,3,figsize=(18,5))\n",
+    "\n",
+    "ax[0].plot(cuts,num_even,'o-')\n",
+    "ax[0].plot(cuts,num_odd,'o-')\n",
+    "ax[0].axvline(c,c='r',ls='--',lw=2)\n",
+    "ax[0].set_xlabel(r\"$N_{cut}$\")\n",
+    "ax[0].set_ylabel(r\"$n\\,(\\,N_{cut}\\,)$\")\n",
+    "\n",
+    "ax[1].set_yscale(\"log\")\n",
+    "ax[1].plot(cuts[1:],(np.gradient(num_even,cuts)*100)[1:],'o-')\n",
+    "ax[1].plot(cuts[1:],(np.gradient(num_odd,cuts)*100)[1:],'o-')\n",
+    "ax[1].axvline(c,c='r',ls='--',lw=2)\n",
+    "ax[1].set_xlabel(r\"$N_{cut}$\")\n",
+    "ax[1].set_ylabel(r\"$\\partial_{N_cut}\\,n\\,(\\,N_{cut}\\,)$\")\n",
+    "\n",
+    "ax[2].plot(cuts[2:],(np.gradient(np.gradient(num_odd,cuts),cuts)*100)[2:],'o-')\n",
+    "ax[2].axvline(c,c='r',ls='--',lw=2)\n",
+    "ax[2].set_xlabel(r\"$N_{cut}$\")\n",
+    "ax[2].set_ylabel(r\"$\\partial^2_{N_cut}\\,n\\,(\\,N_{cut}\\,)$\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7aa3b8c2",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2edda78b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbccad86",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "76680780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Δrange = np.linspace(0.,3.5,11)\n",
+    "Utilderange = np.linspace(1,1,1)\n",
+    "Grange = np.linspace(0.1,2,10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b123bbf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b041c2e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b45cdb9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44386e9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22ab118b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d0839e6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93df735b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7171447",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da108646",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "53d5cb37",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_cutoff_MK1(cuts, p, precision=0.01):\n",
+    "    metric = lambda x1,x2 : ((np.abs(x1-x2)/np.abs(np.min([x1,x2])))<precision)\n",
+    "    q = deque()\n",
+    "    \n",
+    "    k = Kerr_2γ(Nfock=cuts[0], p=p)\n",
+    "    q.append(0)\n",
+    "    q.append(k.bd()[0])\n",
+    "    \n",
+    "    for i in range(1,cuts.size):\n",
+    "        k = Kerr_2γ(Nfock=cuts[i], p=p)\n",
+    "        ni = k.bd()[0]\n",
+    "        μi = metric(ni,q.pop())\n",
+    "        q.append(μi)\n",
+    "        print(q[0],q[1],i)\n",
+    "        if (q.popleft())*(q[0])==True : \n",
+    "            return i-1\n",
+    "        q.append(ni)\n",
+    "    \n",
+    "    if q[0]==True:\n",
+    "        print(\"Carefull: Convergence on border\")\n",
+    "        return i\n",
+    "    else: return np.inf\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "def get_cutoff_MK2(p, c0=5, cmax=100, step=5, precision=0.005):\n",
+    "    metric = lambda x1,x2 : ((np.abs(x1-x2)/np.abs(np.min([x1,x2])))<precision)\n",
+    "    k = Kerr_2γ(Nfock=c0,p=p)\n",
+    "    nprev = k.bd()[0]\n",
+    "    c = c0+step\n",
+    "    while c<cmax:\n",
+    "        k = Kerr_2γ(Nfock=c,p=p)\n",
+    "        n = k.bd()[0]\n",
+    "        if metric(n,nprev):\n",
+    "            c -= step\n",
+    "            while 1:\n",
+    "                k = Kerr_2γ(Nfock=c-1,p=p)\n",
+    "                if metric(k.bd()[0],n): c -= 1\n",
+    "                else: return c\n",
+    "        else:\n",
+    "            nprev = n\n",
+    "            c += step\n",
+    "    return np.inf\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5385b39",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Tests/Ramp_G/.DS_Store b/Tests/Ramp_G/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..1eefd3ca3ea01eea95e8188f65466a20c5f74b6a
Binary files /dev/null and b/Tests/Ramp_G/.DS_Store differ
diff --git a/Tests/test1.ipynb b/Tests/test1.ipynb
index b49813ba05675298af5528976aebb19bb3bb3a9b..43c5e77f455e3236026fb999aa2b0a377b5c77b8 100644
--- a/Tests/test1.ipynb
+++ b/Tests/test1.ipynb
@@ -2,11 +2,14 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "id": "d6682c69",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import sys\n",
+    "sys.path.insert(1, '/Users/lucagravina/dptqm/Strucutral codes/Liouvillian block diagonalization')\n",
+    "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from qutip import * \n",
@@ -16,9 +19,16 @@
     "import time\n",
     "import os\n",
     "import pickle\n",
-    "import matplotlib.image as mpimg\n",
-    "\n",
-    "\n",
+    "import matplotlib.image as mpimg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f7f68665",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "from matplotlib.colors import LinearSegmentedColormap \n",
     "import matplotlib.colors as colors\n",
     "\n",
@@ -31,14 +41,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f7f68665",
+   "id": "bfaa372d",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "5e80c9bb",
    "metadata": {},
    "outputs": [
@@ -46,19 +56,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "20.10335111618042\n"
+      "1.5653400421142578\n"
      ]
     }
    ],
    "source": [
-    "a=destroy(28)\n",
+    "a=destroy(10)\n",
     "\n",
     "parity=1.j*np.pi*a.dag()*a\n",
     "parity=parity.expm()\n",
     "\n",
     "Delta = 10\n",
     "U     = 0.1\n",
-    "eta   = 0.1\n",
+    "eta   = 1\n",
     "\n",
     "Grange = np.geomspace(5e-2, 3e3, 31)\n",
     "c_ops = [np.sqrt(eta)*a**2]#, np.sqrt(beta)*a.dag()*a]\n",
@@ -90,7 +100,7 @@
     "        evals_keep[i,:] = evals[-num_keep:]\n",
     "        \n",
     "        ss_block_form=evecs[-1]\n",
-    "        evec2 = spr.dok_matrix((LL.shape[0], 1), dtype=np.complex)\n",
+    "        evec2 = spr.dok_matrix((LL.shape[0], 1), dtype='complex')\n",
     "        evec2[bl_indices[i]:bl_indices[i]+blocks_list[i].shape[0]] = ss_block_form.data\n",
     "        evec2 = evec2.tocsr()\n",
     "        evec2=np.dot(np.transpose(P),evec2)\n",
@@ -117,23 +127,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "dc957fcc",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7fd7f50e3820>"
+       "<matplotlib.legend.Legend at 0x7fa12a218c40>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -156,1994 +166,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "cf16363e",
+   "execution_count": 12,
+   "id": "af20938c",
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "Z0LAFYyNQNhKQDwiEagBAARozjyAAAAAHHV1aWRraEDyXyRPxbo5pRvPAyPzAAAAAAAAABhzdHRz\n",
-       "AAAAAAAAAAEAAAAfAAAIAAAAABRzdHNzAAAAAAAAAAEAAAABAAAAHHN0c2MAAAAAAAAAAQAAAAEA\n",
-       "AAAfAAAAAQAAAJBzdHN6AAAAAAAAAAAAAAAfAAAZ0wAAA3cAAAIqAAACxAAAAbIAAAK6AAAEegAA\n",
-       "A3AAAASkAAAJ+QAADKgAAAreAAAMdAAAEHgAAA9DAAALUwAADScAAAxsAAAMFQAABnQAAAW2AAAE\n",
-       "8gAABB0AAAJ8AAABTgAAAUEAAAFMAAAA5AAAAiUAAAD2AAAB3gAAABRzdGNvAAAAAAAAAAEAAAAw\n",
-       "AAAAYnVkdGEAAABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAAAAAAAAAAAAAt\n",
-       "aWxzdAAAACWpdG9vAAAAHWRhdGEAAAABAAAAAExhdmY1OC40NS4xMDA=\n",
-       "\">\n",
-       "  Your browser does not support the video tag.\n",
-       "</video>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from celluloid import Camera # getting the camera\n",
-    "from IPython.display import HTML # to show the animation in Jupyter\n",
-    "\n",
-    "style_list=['ro', 'g^', 'yx', 'cs']\n",
-    "parity_list=['+,+', '+,--', '--,+', '--,--']\n",
-    "fig, ax = plt.subplots(); # creating my fig\n",
-    "ax.set_xscale(\"log\")\n",
-    "ax.set_ylim(-60,60)\n",
-    "\n",
-    "camera = Camera(fig)# the camera gets the fig we'll plot\n",
-    "for i in range(Grange.size):\n",
-    "    for j in range(num_blocks):\n",
-    "        ax.plot(np.abs(np.real(evals_list[i][j,:])), np.imag(evals_list[i][j,:]), style_list[j], label=parity_list[j])\n",
-    "    if i==0: ax.legend()\n",
-    "    camera.snap(); # the camera takes a snapshot of the plot\n",
-    "animation = camera.animate(); # animation ready\n",
-    "HTML(animation.to_html5_video()) # displaying the animation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0a788023",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "734325af",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAIGZ0eXBNNFYgAAACAE00ViBpc29taXNvMmF2YzEAAAAIZnJlZQAApkxtZGF0AAAAUmW4AAQA\n",
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-      ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "[<matplotlib.lines.Line2D at 0x7fa12bb72430>]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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/B392t88vpwJKAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib import animation\n",
-    "from IPython.display import HTML\n",
-    "\n",
-    "style_list=['ro', 'g^', 'yx', 'cs']\n",
-    "parity_list=['+,+', '+,--', '--,+', '--,--']\n",
-    "\n",
-    "# First set up the figure, the axis, and the plot element we want to animate\n",
-    "fig = plt.figure();\n",
-    "ax = plt.axes(xlim=(1e-14, 10), ylim=(-60, 60));\n",
-    "ax.set_xscale(\"log\");\n",
-    "lines = []\n",
-    "\n",
-    "fig = plt.figure();\n",
-    "ax = plt.axes(xlim=(1e-14, 5), ylim=(-60, 60));\n",
-    "ax.set_xscale(\"log\");\n",
-    "for j in range(num_blocks):\n",
-    "    temp_line, = ax.plot([], [], style_list[j], label=parity_list[j]);\n",
-    "    lines.append(temp_line);\n",
-    "\n",
-    "# Initialization function: plot the background of each frame\n",
-    "def init():\n",
-    "    for j in range(len(lines)):\n",
-    "        lines[j].set_data([], []);\n",
-    "    return lines\n",
-    "\n",
-    "# Animation function which updates figure data.  This is called sequentially\n",
-    "def animate(i):\n",
-    "    for j in range(num_blocks):\n",
-    "        lines[j].set_data(np.abs(np.real(evals_list[i][j,:])), np.imag(evals_list[i][j,:]));\n",
-    "    return lines\n",
-    "\n",
-    "# Call the animator.  blit=True means only re-draw the parts that have changed.\n",
-    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
-    "                               frames=31, interval=100, blit=True);\n",
-    "\n",
-    "plt.close(anim._fig);\n",
-    "\n",
-    "# Call function to display the animation\n",
-    "HTML(anim.to_html5_video())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4c494cd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "827580b8",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a606aa37",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30c48fa7",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5af81bcd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3e7dce67",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.xscale(\"log\")\n",
-    "plt.plot(Grange, num_even, 'bo-', ms=5, label=\"even\")\n",
-    "plt.plot(Grange, num_odd, 'rs-', ms=5, label=\"odd\")\n",
-    "\n",
-    "plt.xlabel(r\"$G$\")\n",
-    "plt.ylabel(r\"$\\langle a^{\\dagger} a\\rangle$\")\n",
-    "plt.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "50f826f2",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "22984485",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.xscale(\"log\")\n",
-    "plt.yscale(\"log\")\n",
-    "plt.plot(Grange, -np.real(gap_even), 'bo-', ms=5, label=\"even\")\n",
-    "plt.plot(Grange, -np.real(gap_odd), 'rs-', ms=5, label=\"odd\")\n",
-    "\n",
-    "plt.axhline(1, c='k', ls='--')\n",
-    "\n",
-    "plt.ylabel(r\"$-\\Re(\\lambda_1)$\")\n",
-    "plt.xlabel(r\"$G/U$\")\n",
-    "plt.legend();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2ae076ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
+      "image/png": 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1jIh9ETERERPj456U1zqnVA5O5h3oli6XHOgRMRUR+WT5ADAkaeySKzNro5P5AuXAT1q0VLnkQJd0lZKHSUvanRzz5KUe16ydjk/5tn9LnyVHuUj6GnATMCbpGPAnwBBAROwFPgHcI6kInAXujIhoW8VmLbAw9dwmn6FbiiwZ6BHxySX230dlWKNZzzh/27/P0C09fKeo9aVscsllzHeJWoo40K0vZadnuXLNMMOD/hGw9PC/ZutLHoNuaeRAt76UnS4w7kC3lHGgW1/KTc36tn9LHQe69Z1yOSqXXPxgLksZB7r1nddn5iiWg02+5GIp40C3vnNuDLpvKrKUcaBb3/FNRZZWDnTrO+fnEvUZuqWLA936zvlLLj5Dt3RxoFvfyU0XGF01yKqhgU6XYtZSDnTrO9npWV8/t1RyoFvfOT5V8GNzLZUc6NZ3fIZuaeVAt74SEWSnCh6Dbqm0ZKBLekBSVtLzi+yXpHslTUp6TtL7Wl+mWWtMzRYpFMs+Q7dUauQM/UHglovsvxXYlbz2APdfellm7ZFLpp7zkxYtjZYM9Ij4AXDqIk3uAB6KiieA9ZI2t6pAs1bKnpsc2pdcLH1acQ19C3C0av1Ysu0CkvZIOijpYC6Xa8GnNmuObyqyNGtFoKvOtqjXMCL2RcREREyMj4+34FObNed4ctu/hy1aGrUi0I8B26rWtwKvtuC4Zi2XnS6weniAtSODnS7FrOVaEej7gU8no11uAE5HxGstOK5Zy3kuUUuzJU9TJH0NuAkYk3QM+BNgCCAi9gIHgNuASWAGuKtdxZpdqqynnrMUWzLQI+KTS+wP4DMtq8isjXLTBd7xlnWdLsOsLXynqPUVX3KxNHOgW9+YmSuSLxR9ycVSy4FufeP8TUU+Q7d0cqBb3/AYdEs7B7r1Dd8lamnnQLe+cS7QfcnFUsqBbn0jOz3L8GCGyy8b6nQpZm3hQLe+kZsqML52BKne44fMep8D3fpGdrrg6+eWag506xueS9TSzoFufSM7XfCQRUs1B7r1hdn5Em/MzPsM3VLNgW59ITftqecs/Rzo1hcWxqCP+4+ilmIOdOsLTx45CcCODWs6XIlZ+zQU6JJukfSPkiYl/UGd/TdJOi3pmeT1+daXarY8s/MlHnj8p/zzXxpn55gD3dKrkRmLBoAvAr9GZf7QpyTtj4gXa5o+FhG3t6FGs0vyrUPHOJEvcM+Hrul0KWZt1cgZ+m5gMiJejog54OvAHe0ty6w1iqUyX/r+y1y/bT03XH1lp8sxa6tGAn0LcLRq/ViyrdaNkp6V9Iikd9Y7kKQ9kg5KOpjL5ZZRrllzHnn+F/z81Az3fOga3/JvqddIoNf7KYia9UPAWyPieuALwMP1DhQR+yJiIiImxsfHmyrUrFkRwf2PHubq8TX8+nWbOl2OWds1EujHgG1V61uBV6sbRMRUROST5QPAkKSxllVptgw/+MkJXnxtirs/dA2ZjM/OLf0aCfSngF2SdkoaBu4E9lc3kHSVkv/PStqdHPdkq4s1a8b9j05y1bpVfPw99a4QmqXPkqNcIqIo6bPAd4EB4IGIeEHS3cn+vcAngHskFYGzwJ0RUXtZxmzF/MPPX+eJl0/xxx99B8ODvt3C+sOSgQ7nLqMcqNm2t2r5PuC+1pZmtnx7v3+Yyy8b4pO7t3e6FLMV41MXS53J7DTffeE4v3njW1kz0tA5i1kqONAtdb70/ZdZNZThN39lR6dLMVtRDnRLlVffOMvDz7zCnb+8nQ1r/SAu6y8OdEuVrzx+hHLA73xwZ6dLMVtxDnRLjdfPzPG1H/6cO65/C1uvWN3pcsxWnAPdUuOhv/8ZM3Ml/r0fwmV9yoFuqTAzV+TBvzvCze/YyNuuGu10OWYd4UC3VPifTx3l9Zl57vbZufUxB7r1vPlSmS8/doRf3nEFEzv8iFzrXw5063l//eyrvPLGWe65yWfn1t8c6NbTyuVg7/cP87ZNo3z4bRs7XY5ZRznQrWedOjPHn/3vH/NPx/Pcc5MnsDDzgy6s5xzO5fnK40f41tPHKBTL3Pquq/jouzd3uiyzjnOgW0+ICJ48coovP3aEv/nxcYYyGf7Fe7fwOx/cya5NHqZoBg5063LFUpkDz/+CLz/2Ms8dO80Vq4f43Q9fy6du3MH4qJ/VYlbNgW5dp1QOXnn9LP/nxV/w3//fT3nljbPsHFvDf/74u/iN923lsuGBTpdo1pUaCnRJtwB/QWXGoi9HxH+t2a9k/23ADPBbEXGoxbVaikQEx6cKHDlxJnnlOXJihiMn8hw9dZa5UhmA3Tuv5D997J386ts3el5QsyUsGeiSBoAvAr9GZcLopyTtj4gXq5rdCuxKXu8H7k/eLYUigvlSMF8qM1csM5e8n5krMj1bZHp2nunZIlNVy+ffi7x2epafnTzDzFzp3DGHBzPs2LCaa8bXcvN1m9i5YQ3v3rqe696yroM9NestjZyh7wYmI+JlAElfB+4AqgP9DuChZB7RJyStl7Q5Il5rdcHPHH2Dh/7upy09ZqOTnzYzTWrjx7yw/cLnedMxAiLZElH9cVFZf9PxgnJUjlNO9kVU2pWr3ssRlMpBKSrjuUvl6m1R2RbBfDHOhfbCezOGBsToqiFGVw0yumqQq9aNcOPVG9g5tpqdY2vZMbaat1x+mc/AzS5RI4G+BThatX6MC8++67XZArwp0CXtAfYAbN++vLkeXz8zx1M/O7Wsj70Y0ViYNDPUudGmC+On39ReFx5DOl+ldL7mhZoWjpNRZVsmaS/pTeuZygczmMkwMigyGTEgGMiIjFR5z4iBZHloQAwPZhgeGGBoUIwMZBgezDCUvC8srxkePBfao6uGWHfZIOtWDTEymPEYcbMV0Eig1/tJrD0BbaQNEbEP2AcwMTHR+OlulQ+/fSOPvf0jy/lQM7NUa+RO0WPAtqr1rcCry2hjZmZt1EigPwXskrRT0jBwJ7C/ps1+4NOquAE43Y7r52ZmtrglL7lERFHSZ4HvUhm2+EBEvCDp7mT/XuAAlSGLk1SGLd7VvpLNzKyehsahR8QBKqFdvW1v1XIAn2ltaWZm1gw/bdHMLCUc6GZmKeFANzNLCQe6mVlKqJnb2Vv6iaUc8AZw+iLNLl9kf73tS22r3V+9PgacWLLoxixW83LaXkr/L7beDX1vpH2a+7/cvi+2r1/6f6k/++3q+2J1LLftxfq/PiLG635U5RkfnXkB+5azv972pbbV7q/Zd3Cl+rRS/V+ivx3ve7/3f7l97/f+X+rPfrv63sn+V786fcnlr5e5v972pbbV7l/qcy9XM8dtZ/8vtt4NfW+kfZr7v9y+L7avX/p/qT/77ep7s8duZf/P6dgll24i6WBETHS6jk7o576D+9/P/U9j3zt9ht4t9nW6gA7q576D+9/P/U9d332GbmaWEj5DNzNLCQe6mVlKONDNzFLCgX4Rkq6W9BVJ3+x0LStF0hpJX5X0l5L+TafrWWn9+D1fIOnjyff9f0n69U7Xs9IkvUPSXknflHRPp+tZjtQGuqQHJGUlPV+z/RZJ/yhpUtIfXOwYEfFyRPx2eyttvya/Fv8S+GZE/DvgYytebBs00/+0fM8XNNn3h5Pv+28B/7oD5bZck/1/KSLuBv4V0JPDGVMb6MCDwC3VGyQNAF8EbgWuAz4p6TpJ/0zSd2peG1e+5LZ5kAa/FlSmD1yY8Lu0gjW204M03v+0eZDm+/7Hyf40eJAm+i/pY8DjwN+sbJmtkdpAj4gfAKdqNu8GJpOzsDng68AdEfGjiLi95pVd8aLbpJmvBZX5YbcmbVLx76PJ/qdKM31PppD8M+CRiDi00rW2Q7Pf+4jYHxG/AvTk5cZU/MA2YQvnzz6hEl5bFmssaYOkvcB7Jf1hu4tbYYt9Lb4N/Iak+2nvbdKdVrf/Kf+eL1jse/+7wM3AJxammEypxb73N0m6V9KXqJmhrVc0NAVdiqjOtkXvrIqIk0Ba/2HX/VpExBn6Y07Yxfqf5u/5gsX6fi9w70oX0wGL9f9R4NGVLaW1+u0M/RiwrWp9K/Bqh2rptH7/WvRz//u575Di/vdboD8F7JK0U9IwcCewv8M1dUq/fy36uf/93HdIcf9TG+iSvgb8PfA2Scck/XZEFIHPAt8FXgK+EREvdLLOldDvX4t+7n8/9x36r/9+OJeZWUqk9gzdzKzfONDNzFLCgW5mlhIOdDOzlHCgm5mlhAPdzCwlHOhmZinhQDczSwkHuplZSvx/lFmriE3ZxmYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2156,16 +195,13 @@
    ],
    "source": [
     "plt.xscale(\"log\")\n",
-    "plt.plot(Grange, -np.real(gap_even_odd), 'g^-')\n",
-    "plt.plot(Grange, -np.real(gap_odd_even), 'yh-')\n",
-    "\n",
-    "plt.yscale('log')"
+    "plt.plot(Grange, num_even)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "277b6ed2",
+   "id": "40dc25c3",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2173,63 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c2217ee9",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3bdefffd",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "136fadfe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(\"GoverDelta_scan.pckl\", \"rb\") as fp:\n",
-    "    Deltarange, results = pickle.load(fp)\n",
-    "\n",
-    "Grange, num_even, num_odd, gap_even, gap_odd, gap_even_odd, gap_odd_even = list(list(zip(*results)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7f9574e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.yscale(\"log\")\n",
-    "plt.xscale(\"log\")\n",
-    "plt.pcolormesh(Grange[0]/Deltarange[0], Deltarange, num_even, shading='auto', cmap='viridis', vmin=0, vmax=11)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36413363",
+   "id": "5fec5b88",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2237,7 +217,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2251,7 +231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.7"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/Tests/test2.ipynb b/Tests/test2.ipynb
index f947a10356b6f9295732c4f8ce8c0d8b85a997ba..e8441906e0757ee53330de43fe30c49055e71f75 100644
--- a/Tests/test2.ipynb
+++ b/Tests/test2.ipynb
@@ -7,6 +7,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import sys\n",
+    "sys.path.insert(1, '/Users/lucagravina/dptqm/Strucutral codes/Liouvillian block diagonalization')\n",
+    "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from qutip import * \n",
@@ -18,6 +21,8 @@
     "import pickle\n",
     "import matplotlib.image as mpimg\n",
     "\n",
+    "from collections import deque\n",
+    "\n",
     "\n",
     "from matplotlib.colors import LinearSegmentedColormap \n",
     "import matplotlib.colors as colors\n",
@@ -27,122 +32,118 @@
     "greiner = LinearSegmentedColormap('greiner', greiner)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "25e250bd",
-   "metadata": {},
-   "source": [
-    "# First order PT with symmetry breaking \n",
-    "\n",
-    "We take: \n",
-    "\n",
-    "$$\n",
-    "U=\\tilde{U} / N, \\quad \\eta=\\tilde{\\eta} / N\n",
-    "$$\n",
-    "\n",
-    "within the Hamiltonian \n",
-    "\n",
-    "$$\n",
-    "\\hat{H}=-\\Delta \\hat{a}^{\\dagger} \\hat{a}+\\frac{U}{2}\\hat{a}^{\\dagger^2} \\hat{a}^2 + \\frac{G}{2} \\left[\\hat{a}^{{\\dagger}^2} + \\hat{a}^{\\dagger^2}\\right]\n",
-    "$$\n",
-    "\n",
-    "We conider both one- and two-photon decay. For $\\Delta > 0$ there is a first-order phase transition associated to a symmetry broken phase, example which seems in contrast with our general theory. The symmetry is broken because of the one-photon dissipation."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "12b14f7f",
+   "id": "a967f6f9",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "bb1fd10c",
+   "execution_count": 29,
+   "id": "4b11dd09",
    "metadata": {},
    "outputs": [],
    "source": [
-    "a=destroy(30)\n",
-    "\n",
-    "γ = 1\n",
-    "ηtilde = 1\n",
-    "Utilde = 10\n",
-    "Δ = 10\n",
-    "\n",
-    "Grange = np.linspace(0,15,15)*γ\n",
-    "\n",
-    "gap = np.zeros(Grange.size)\n",
-    "n   = np.zeros(Grange.size)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2b01d874",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Nvals = np.array([1,5,10,20])\n",
-    "\n",
-    "fig,ax = plt.subplots(1,2, figsize=(15,5))\n",
-    "ax[0].set_xlabel(r\"$G/\\eta$\")\n",
-    "ax[0].set_ylabel(r\"$\\langle{a^{\\dagger} a}\\rangle/N$\")\n",
-    "ax[1].set_yscale(\"log\")\n",
-    "ax[1].set_xlabel(r\"$G/\\eta$\")\n",
-    "ax[1].set_ylabel(r\"$-\\Re(\\lambda_1/\\eta)$\");\n",
-    "\n",
-    "for j in range(Nvals.size):\n",
-    "    N = Nvals[j]\n",
-    "    η = ηtilde/N\n",
-    "    U = Utilde/N\n",
-    "    c_ops=[np.sqrt(η)*a**2, np.sqrt(γ)*a]\n",
+    "class Parameters():\n",
+    "    def __init__(self, Δ, N, Grat, ηtilde, Utilde):\n",
+    "        self.Δ = Δ\n",
+    "        self.N = N \n",
+    "        self.ηtilde = ηtilde \n",
+    "        self.Utilde = Utilde\n",
+    "        self.U = self.Utilde/self.N\n",
+    "        self.η = self.ηtilde/self.N\n",
+    "        \n",
+    "        self.Gc = np.abs(self.η*self.Δ/np.sqrt(self.η**2+self.U**2))\n",
+    "        self.G = Grat*self.Gc\n",
+    "        self.n_sc = lambda g : self.U*self.Δ/(self.U**2+self.η**2)*(1+np.sqrt((g**2-1)*np.heaviside(g-1,1)))*np.heaviside(g-1,1)\n",
+    "\n",
+    "class Kerr_2γ():\n",
+    "    def __init__(self, Nfock, p):\n",
+    "        self.Nfock = Nfock\n",
+    "        self.a = destroy(Nfock)\n",
+    "        self.parity = 1.j*np.pi*self.a.dag()*self.a\n",
+    "        self.parity = self.parity.expm()\n",
+    "        \n",
+    "        self.p = p\n",
+    "        self.c_ops = [np.sqrt(p.η)*self.a**2]\n",
+    "        self.H = -p.Δ*self.a.dag()*self.a + p.G/2 *(self.a.dag()**2 +self.a**2) + p.U/2 *self.a.dag()**2*self.a**2\n",
+    "        self.LL = liouvillian(self.H, self.c_ops)\n",
+    "    \n",
+    "    def bd(self):\n",
+    "        P, block_bfs, block_sizes = block_diagonalize.PermMat(self.LL)\n",
+    "        self.bd_L = np.dot(P, np.dot(self.LL.data, np.transpose(P)))\n",
+    "        self.num_blocks, self.blocks_list, self.bl_indices = block_diagonalize.get_blocks(self.bd_L)   \n",
     "\n",
-    "    for i in range(Grange.size):\n",
-    "        G = Grange[i]\n",
-    "        H = -Δ*a.dag()*a + G/2*(a.dag()**2 + a**2) + U/2*(a.dag()**2 * a**2)\n",
-    "        LL=liouvillian(H, c_ops)\n",
+    "        done=False\n",
+    "        for i in range(int(len(self.blocks_list))):\n",
+    "            block = self.blocks_list[i]\n",
+    "            evals, evecs = Qobj(block).eigenstates()    \n",
     "\n",
-    "        evals, evecs = LL.eigenstates()\n",
+    "            ss_block_form = evecs[-1]\n",
+    "            evec2 = spr.dok_matrix((self.LL.shape[0], 1), dtype='complex')\n",
+    "            evec2[self.bl_indices[i]:self.bl_indices[i]+self.blocks_list[i].shape[0]] = ss_block_form.data\n",
+    "            evec2 = evec2.tocsr()\n",
+    "            evec2 = np.dot(np.transpose(P),evec2)\n",
+    "            ss= Qobj(evec2, dims=[self.LL.dims[0], [1]])\n",
+    "            ss=vector_to_operator(ss)\n",
     "\n",
-    "        gap[i] = -np.real(evals[-2])#-np.abs(evals[-1])\n",
-    "        n[i] = np.abs((vector_to_operator(evecs[-1]) * (a.dag()*a)).tr())\n",
-    "        print(\"\\rCompleted %d/%d\"%(i+1,Grange.size),end=\"\")\n",
-    "        \n",
-    "    ax[0].plot(Grange/γ,n/N, '.-')\n",
-    "    ax[1].plot(Grange/γ,gap/η, '.-')\n",
-    "    print(\"\\rCompleted %d/%d\"%(j+1,Nvals.size),end=\"\")"
+    "            if np.real(ss.tr())>0.05:\n",
+    "                ss=ss+ss.dag()\n",
+    "                ss/=(ss.tr())       \n",
+    "                if expect(self.parity, ss)>0.5:\n",
+    "                    num_even = expect(ss, self.a.dag()*self.a)\n",
+    "                    gap_even = evals[-2]\n",
+    "                else:\n",
+    "                    num_odd = expect(ss, self.a.dag()*self.a)\n",
+    "                    gap_odd = evals[-2]\n",
+    "            elif done==False:\n",
+    "                gap_even_odd = np.real(evals[-1])+1.j*np.abs(np.imag(evals[-1]))\n",
+    "                gap_odd_even = np.real(evals[-1])-1.j*np.abs(np.imag(evals[-1]))       \n",
+    "                done=True\n",
+    "        return num_even, num_odd, gap_even, gap_odd, gap_even_odd, gap_odd_even\n",
+    "    \n",
+    "    \n",
+    "def get_cutoff(p, c0=5, cmax=100, step=5, precision=0.005):\n",
+    "    metric = lambda x1,x2 : ((np.abs(x1-x2)/np.abs(np.min([x1,x2])))<precision)\n",
+    "    k = Kerr_2γ(Nfock=c0,p=p)\n",
+    "    nprev = k.bd()[0]\n",
+    "    c = c0+step\n",
+    "    while c<cmax:\n",
+    "        k = Kerr_2γ(Nfock=c,p=p)\n",
+    "        n = k.bd()[0]\n",
+    "        if metric(n,nprev):\n",
+    "            c -= step\n",
+    "            while 1:\n",
+    "                step = np.max([step//2,1])\n",
+    "                k = Kerr_2γ(Nfock=c-step,p=p)\n",
+    "                if metric(k.bd()[0],n): \n",
+    "                    c -= step\n",
+    "                elif step==1: return c \n",
+    "        else:\n",
+    "            nprev = n\n",
+    "            c += step\n",
+    "    return np.inf\n",
+    "\n",
+    "def map_cutoff(Utilderange, Δrange, Grange):\n",
+    "    C = np.zeros((Utilderange.size, Δrange.size, Grange.size),dtype='float')\n",
+    "    for i in range(Utilderange.size):\n",
+    "        Utilde=Utilderange[i]\n",
+    "        for j in range(Δrange.size):\n",
+    "            Δ=Δrange[j]\n",
+    "            for k in range(Grange.size):\n",
+    "                Grat=Grange[k]\n",
+    "                p = Parameters(Δ=Δ, N=10, Grat=Grat, ηtilde=1, Utilde=Utilde)\n",
+    "                C[i,j,k] = get_cutoff(p, c0=5, cmax=100, step=5, precision=0.005)\n",
+    "    return C"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bd572225",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c1548313",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c10aab12",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a967f6f9",
+   "id": "d73b1b75",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -186,54 +187,22 @@
     "The question is: how does the hysteresys area change in the remaining parameters ($\\Delta$, $U$)?"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fc227a61",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "30b74167",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a=destroy(30)\n",
-    "\n",
-    "ηtilde = 1\n",
-    "Utilde = 10\n",
-    "Δ = 10\n",
-    "\n",
-    "Grange = np.linspace(0.1,8,35)\n",
-    "\n",
-    "N = 10\n",
-    "η = ηtilde/N\n",
-    "U = Utilde/N\n",
-    "\n",
-    "c_ops=[np.sqrt(η)*a**2]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "28ebc4a6",
+   "id": "3004e4b2",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Completed 35/35\n",
-      "Time: 33.25240898132324\n"
+      "Completed: 20/20"
      ]
     }
    ],
    "source": [
-    "parity=1.j*np.pi*a.dag()*a\n",
-    "parity=parity.expm()\n",
+    "Grange = np.linspace(0,2,20)\n",
     "\n",
     "num_even     = np.zeros(Grange.size)\n",
     "num_odd      = np.zeros(Grange.size)\n",
@@ -241,61 +210,25 @@
     "gap_odd      = np.zeros(Grange.size, dtype='complex')\n",
     "gap_even_odd = np.zeros(Grange.size, dtype='complex')\n",
     "gap_odd_even = np.zeros(Grange.size, dtype='complex')\n",
-    "evals_list   = []\n",
     "\n",
-    "t1 = time.time()\n",
-    "for g in range(Grange.size):\n",
-    "    H = -Δ*a.dag()*a + Grange[g]/2 *(a.dag()**2 +a**2) + U/2 *a.dag()**2*a**2\n",
-    "    LL = liouvillian(H, c_ops)\n",
-    "    \n",
-    "    P, block_bfs, block_sizes = block_diagonalize.PermMat(LL)\n",
-    "    bd_L = np.dot(P, np.dot(LL.data, np.transpose(P)))\n",
-    "    num_blocks, blocks_list, bl_indices = block_diagonalize.get_blocks(bd_L)   \n",
-    "    \n",
-    "    num_keep = 15\n",
-    "    evals_keep = np.zeros((num_blocks, num_keep), dtype='complex')\n",
-    "    \n",
-    "    done=False\n",
-    "    for i in range(int(len(blocks_list))):\n",
-    "        block = blocks_list[i]\n",
-    "        evals, evecs = Qobj(block).eigenstates()    \n",
-    "        evals_keep[i,:] = evals[-num_keep:]\n",
-    "        \n",
-    "        ss_block_form = evecs[-1]\n",
-    "        evec2 = spr.dok_matrix((LL.shape[0], 1), dtype=np.complex)\n",
-    "        evec2[bl_indices[i]:bl_indices[i]+blocks_list[i].shape[0]] = ss_block_form.data\n",
-    "        evec2 = evec2.tocsr()\n",
-    "        evec2 = np.dot(np.transpose(P),evec2)\n",
-    "        ss= Qobj(evec2, dims=[LL.dims[0], [1]])\n",
-    "        ss=vector_to_operator(ss)\n",
     "\n",
-    "        if np.real(ss.tr())>0.05:\n",
-    "            ss=ss+ss.dag()\n",
-    "            ss/=(ss.tr())       \n",
-    "            if expect(parity, ss)>0.5:\n",
-    "                num_even[g] = expect(ss, a.dag()*a)\n",
-    "                gap_even[g] = evals[-2]\n",
-    "            else:\n",
-    "                num_odd[g] = expect(ss, a.dag()*a)                \n",
-    "                gap_odd[g] = evals[-2]\n",
-    "        elif done==False:\n",
-    "            gap_even_odd[g] = np.real(evals[-1])+1.j*np.abs(np.imag(evals[-1]))\n",
-    "            gap_odd_even[g] = np.real(evals[-1])-1.j*np.abs(np.imag(evals[-1]))       \n",
-    "            done=True\n",
-    "    evals_list.append(evals_keep)\n",
-    "    print(\"\\rCompleted %d/%d\"%(g+1,Grange.size),end=\"\")\n",
-    "print(\"\\nTime:\",time.time()-t1)"
+    "for i in range(Grange.size):\n",
+    "    Grat = Grange[i]\n",
+    "    p = Parameters(Δ=1.5, N=10, Grat=Grat, ηtilde=1, Utilde=1)\n",
+    "    k = Kerr_2γ(Nfock=40, p=p) \n",
+    "    num_even[i], num_odd[i], gap_even[i], gap_odd[i], gap_even_odd[i], gap_odd_even[i] = k.bd()\n",
+    "    print(\"\\rCompleted: %d/%d\"%(i+1,Grange.size),end='')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "bfe6d8e7",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 2160x648 with 3 Axes>"
       ]
@@ -309,29 +242,34 @@
    "source": [
     "fig, ax = plt.subplots(1,3, figsize=(30,9))\n",
     "\n",
-    "ax[0].plot(Grange, num_even/N, 'bo-', ms=5, label=\"even\")\n",
-    "ax[0].plot(Grange, num_odd/N, 'rs-', ms=5, label=\"odd\")\n",
+    "g=np.linspace(Grange.min(),Grange.max(),1000)\n",
+    "\n",
+    "ax[0].plot(Grange, num_even/p.N, 'bo-', ms=5, label=\"even\")\n",
+    "ax[0].plot(g, p.n_sc(g)/p.N, 'g-', lw=3, label=\"SC\")\n",
+    "ax[0].plot(Grange, num_odd/p.N, 'rs-', ms=5, label=\"odd\")\n",
+    "ax[0].axvline(1,c='grey',alpha=0.5, ls='--', lw=3)\n",
     "\n",
     "#ax[0].set_xscale(\"log\")\n",
-    "ax[0].set_xlabel(r\"$G\\,/\\,\\tilde{\\eta}$\")\n",
+    "ax[0].set_xlabel(r\"$G\\,/\\,G_c$\")\n",
     "ax[0].set_ylabel(r\"$\\langle a^{\\dagger} a\\rangle\\,/\\,N$\")\n",
     "ax[0].legend()\n",
     "\n",
-    "\n",
     "ax[1].plot(Grange, -np.real(gap_even), 'bo-', ms=5, label=\"even\")\n",
     "ax[1].plot(Grange, -np.real(gap_odd), 'rs-', ms=5, label=\"odd\")\n",
-    "ax[1].axhline(1, c='k', ls='--')\n",
+    "ax[1].axhline(-np.real(gap_even)[0], c='k', ls='--')\n",
+    "ax[1].axvline(1,c='grey',alpha=0.5, ls='--', lw=3)\n",
     "\n",
     "#ax[1].set_xscale(\"log\")\n",
     "ax[1].set_yscale(\"log\")\n",
     "ax[1].set_ylabel(r\"$-\\Re(\\lambda_1)\\,/\\,\\tilde\\eta$\")\n",
-    "ax[1].set_xlabel(r\"$G\\,/\\,\\tilde\\eta$\")\n",
+    "ax[1].set_xlabel(r\"$G\\,/\\,G_c$\")\n",
     "ax[1].legend()\n",
     "\n",
     "\n",
     "ax[2].plot(Grange, -np.real(gap_even_odd), 'g^-')\n",
     "ax[2].plot(Grange, -np.real(gap_odd_even), 'yh-')\n",
-    "ax[2].set_xlabel(r\"$G\\,/\\,\\tilde\\eta$\")\n",
+    "ax[2].set_xlabel(r\"$G\\,/\\,G_c$\")\n",
+    "ax[2].axvline(1,c='grey',alpha=0.5, ls='--', lw=3)\n",
     "\n",
     "#ax[2].set_xscale(\"log\")\n",
     "ax[2].set_yscale('log')\n",
@@ -343,1924 +281,132 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "75c91a3d",
+   "id": "2d909281",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "269ac485",
+   "cell_type": "markdown",
+   "id": "a199be0e",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Estimating convergence of Hilbert space truncation"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "f5b4e87e",
+   "execution_count": 5,
+   "id": "971b6bfa",
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "AQAAAAAAAAAAAAAAAAAAAAABsAEgAEgAAABIAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
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-       "aWRraEDyXyRPxbo5pRvPAyPzAAAAAAAAABhzdHRzAAAAAAAAAAEAAAAjAAAIAAAAABRzdHNzAAAA\n",
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-       "AAAfqwAABk4AAAbtAAAI5gAACz4AAAxFAAAO2gAADF8AAAzXAAAM/gAAC+QAAAnYAAAJSgAAC04A\n",
-       "AAu+AAAJMgAACiwAAAn9AAAIrAAACqAAAAkkAAAI7gAACeIAAAmXAAAKBQAACiQAAAmQAAAKJgAA\n",
-       "CKYAAAjtAAAIhwAACHMAAAfdAAAI6AAACI4AAAAUc3RjbwAAAAAAAAABAAAAMAAAAGJ1ZHRhAAAA\n",
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-       "bwAAAB1kYXRhAAAAAQAAAABMYXZmNTguNDUuMTAw\n",
-       "\">\n",
-       "  Your browser does not support the video tag.\n",
-       "</video>"
-      ],
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "33.93870401382446"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from matplotlib import animation\n",
-    "from IPython.display import HTML\n",
-    "\n",
-    "style_list=['ro', 'g^', 'yx', 'cs']\n",
-    "parity_list=['+,+', '+,--', '--,+', '--,--']\n",
-    "\n",
-    "# First set up the figure, the axis, and the plot element we want to animate\n",
-    "lines = []\n",
-    "\n",
-    "fig = plt.figure();\n",
-    "ax = plt.axes(xlim=(5e-15, 25), ylim=(-40, 40));\n",
-    "ax.set_xscale(\"log\")\n",
-    "ax.set_xlabel(r\"$-\\Re(\\lambda_1)$ [$\\tilde\\eta$]\")\n",
-    "ax.set_ylabel(r\"$\\Im(\\lambda_1)$ [$\\tilde\\eta$]\")\n",
-    "\n",
-    "for j in range(num_blocks):\n",
-    "    temp_line, = ax.plot([], [], style_list[j], label=parity_list[j]);\n",
-    "    lines.append(temp_line);\n",
-    "\n",
-    "# Initialization function: plot the background of each frame\n",
-    "def init():\n",
-    "    for j in range(len(lines)):\n",
-    "        lines[j].set_data([], []);\n",
-    "    return lines\n",
-    "\n",
-    "# Animation function which updates figure data.  This is called sequentially\n",
-    "def animate(i):\n",
-    "    for j in range(num_blocks):\n",
-    "        lines[j].set_data(np.abs(np.real(evals_list[i][j,:])), np.imag(evals_list[i][j,:]));\n",
-    "    return lines\n",
-    "\n",
-    "# Call the animator.  blit=True means only re-draw the parts that have changed.\n",
-    "anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
-    "                               frames=Grange.size, interval=200, blit=True);\n",
-    "\n",
-    "ax.legend()\n",
-    "plt.close(anim._fig);\n",
-    "\n",
-    "# Call function to display the animation\n",
-    "#anim.save('spectrum.gif',dpi=300);\n",
-    "HTML(anim.to_html5_video())"
+    "step = 1\n",
+    "cuts = np.arange(10,50,step)\n",
+    "\n",
+    "num_even     = np.zeros(cuts.size)\n",
+    "num_odd      = np.zeros(cuts.size)\n",
+    "gap_even     = np.zeros(cuts.size, dtype='complex')\n",
+    "gap_odd      = np.zeros(cuts.size, dtype='complex')\n",
+    "gap_even_odd = np.zeros(cuts.size, dtype='complex')\n",
+    "gap_odd_even = np.zeros(cuts.size, dtype='complex')\n",
+    "\n",
+    "Grat=1.8\n",
+    "p = Parameters(Δ=1.5, N=10, Grat=Grat, ηtilde=1, Utilde=1)\n",
+    "\n",
+    "t1=time.time()\n",
+    "for i in range(cuts.size):\n",
+    "    k = Kerr_2γ(Nfock=cuts[i], p=p) \n",
+    "    num_even[i], num_odd[i], gap_even[i], gap_odd[i], gap_even_odd[i], gap_odd_even[i] = k.bd()\n",
+    "time.time()-t1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e6242fc5",
+   "execution_count": 30,
+   "id": "1b5e0401",
    "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "63fa2557",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "540f7b9f",
-   "metadata": {},
-   "source": [
-    "### Phase diagrams \n",
-    "\n",
-    "We explore the dependence of the \"Dicke\" phase on $\\Delta$ and $U$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "18ee4130",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a=destroy(30)\n",
-    "\n",
-    "ηtilde = 1\n",
-    "Δrange = np.linspace(0,20,31)\n",
-    "Utilderange = np.linspace(0,20,30)\n",
-    "G = 3.5*ηtilde\n",
-    "\n",
-    "N = 10\n",
-    "η = ηtilde/N\n",
-    "\n",
-    "c_ops=[np.sqrt(η)*a**2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bdf0cc84",
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c= 35  time= 4.905393362045288\n"
+     ]
+    }
+   ],
    "source": [
-    "parity=1.j*np.pi*a.dag()*a\n",
-    "parity=parity.expm()\n",
-    "\n",
-    "num_even     = np.zeros((Utilderange.size, Δrange.size))\n",
-    "num_odd      = np.zeros((Utilderange.size, Δrange.size))\n",
-    "gap_even     = np.zeros((Utilderange.size, Δrange.size), dtype='complex')\n",
-    "gap_odd      = np.zeros((Utilderange.size, Δrange.size), dtype='complex')\n",
-    "gap_even_odd = np.zeros((Utilderange.size, Δrange.size), dtype='complex')\n",
-    "gap_odd_even = np.zeros((Utilderange.size, Δrange.size), dtype='complex')\n",
-    "\n",
     "t1 = time.time()\n",
-    "for k in range(Utilderange.size):\n",
-    "    Utilde = Utilderange[k]\n",
-    "    U = Utilde/N\n",
-    "\n",
-    "    for j in range(Δrange.size):\n",
-    "        Δ = Δrange[j]\n",
-    "\n",
-    "        H = -Δ*a.dag()*a + G/2 *(a.dag()**2 +a**2) + U/2 *a.dag()**2*a**2\n",
-    "        LL = liouvillian(H, c_ops)\n",
-    "\n",
-    "        P, block_bfs, block_sizes = block_diagonalize.PermMat(LL)\n",
-    "        bd_L = np.dot(P, np.dot(LL.data, np.transpose(P)))\n",
-    "        num_blocks, blocks_list, bl_indices = block_diagonalize.get_blocks(bd_L)   \n",
-    "        \n",
-    "        done=False\n",
-    "        for i in range(int(len(blocks_list))):\n",
-    "            block = blocks_list[i]\n",
-    "            evals, evecs = Qobj(block).eigenstates()    \n",
-    "\n",
-    "            ss_block_form = evecs[-1]\n",
-    "            evec2 = spr.dok_matrix((LL.shape[0], 1), dtype=np.complex)\n",
-    "            evec2[bl_indices[i]:bl_indices[i]+blocks_list[i].shape[0]] = ss_block_form.data\n",
-    "            evec2 = evec2.tocsr()\n",
-    "            evec2 = np.dot(np.transpose(P),evec2)\n",
-    "            ss= Qobj(evec2, dims=[LL.dims[0], [1]])\n",
-    "            ss=vector_to_operator(ss)\n",
-    "\n",
-    "            if np.real(ss.tr())>0.05:\n",
-    "                ss=ss+ss.dag()\n",
-    "                ss/=(ss.tr())       \n",
-    "                if expect(parity, ss)>0.5:\n",
-    "                    num_even[k,j] = expect(ss, a.dag()*a)\n",
-    "                    gap_even[k,j] = evals[-2]\n",
-    "                else:\n",
-    "                    num_odd[k,j] = expect(ss, a.dag()*a)                \n",
-    "                    gap_odd[k,j] = evals[-2]\n",
-    "            elif done==False:\n",
-    "                gap_even_odd[k,j] = np.real(evals[-1])+1.j*np.abs(np.imag(evals[-1]))\n",
-    "                gap_odd_even[k,j] = np.real(evals[-1])-1.j*np.abs(np.imag(evals[-1]))       \n",
-    "                done=True\n",
-    "\n",
-    "        print(\"\\rCompleted (%d/%d) -> %d/%d \"%(k,Utilderange.size, j+1,Δrange.size),end=\"\")\n",
-    "print(\"\\nTime:\",time.time()-t1)"
+    "c = get_cutoff(p,c0=36,cmax=60,step=6,precision=0.01)\n",
+    "print(\"c=\",c,\" time=\",time.time()-t1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e2e2b274",
+   "execution_count": 41,
+   "id": "6876c0dd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1296x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "fig, ax = plt.subplots(1,2, figsize=(15,5))\n",
-    "\n",
-    "im0 = ax[0].pcolormesh(Δrange, Utilderange, num_even/N, shading='gouraud', cmap=greiner)\n",
-    "#ax[0].plot(Δrange, Δrange**(2)/N)\n",
-    "ax[0].plot(Δrange, Δrange**(1))\n",
-    "fig.colorbar(im0, ax=ax[0], label=r\"$\\langle a^{\\dagger} a\\rangle\\,/\\,N$\")\n",
-    "\n",
-    "ax[0].set_xlabel(r\"$\\Delta$ [$\\tilde\\eta$]\")\n",
-    "ax[0].set_ylabel(r\"$\\tilde U$ [$\\tilde\\eta$]\")\n",
-    "ax[0].set_ylim(0,20)\n",
-    "ax[0].set_xlim(0,20)\n",
-    "\n",
-    "\n",
-    "im1 = ax[1].pcolormesh(Δrange, Utilderange, np.gradient(num_even, axis=1)/N, shading='gouraud', cmap=greiner)\n",
-    "ax[1].plot(Δrange, Δrange**(2)/N)\n",
-    "ax[1].plot(Δrange, Δrange**(1))\n",
-    "fig.colorbar(im1, ax=ax[1],label=r\"$\\partial_{\\tilde U}\\,\\langle a^{\\dagger} a\\rangle\\,/\\,N$\")\n",
+    "fig,ax = plt.subplots(1,3,figsize=(18,5))\n",
     "\n",
-    "ax[1].set_xlabel(r\"$\\Delta$ [$\\tilde\\eta$]\")\n",
-    "ax[1].set_ylabel(r\"$\\tilde U$ [$\\tilde\\eta$]\")\n",
-    "ax[1].set_ylim(0,20)\n",
-    "ax[1].set_xlim(0,20);\n",
+    "ax[0].plot(cuts,num_even,'o-')\n",
+    "ax[0].plot(cuts,num_odd,'o-')\n",
+    "ax[0].axvline(c,c='r',ls='--',lw=2)\n",
+    "ax[0].set_xlabel(r\"$N_{cut}$\")\n",
+    "ax[0].set_ylabel(r\"$n\\,(\\,N_{cut}\\,)$\")\n",
     "\n",
-    "\n",
-    "#plt.contour(Δrange, Utilderange, gaussian_filter(num_even/N,1), colors='k', alpha=0.3)#, levels=contours)\n",
-    "#plt.contour(Δrange, Utilderange, num_even/N, colors='k', alpha=0.25)#, levels=contours)\n",
-    "#plt.savefig(\"Utilde_Delta_PD_v1.pdf\")"
+    "ax[1].set_yscale(\"log\")\n",
+    "ax[1].plot(cuts[1:],(np.gradient(num_even,cuts)*100)[1:],'o-')\n",
+    "ax[1].plot(cuts[1:],(np.gradient(num_odd,cuts)*100)[1:],'o-')\n",
+    "ax[1].axvline(c,c='r',ls='--',lw=2)\n",
+    "ax[1].set_xlabel(r\"$N_{cut}$\")\n",
+    "ax[1].set_ylabel(r\"$\\partial_{N_cut}\\,n\\,(\\,N_{cut}\\,)$\")\n",
+    "\n",
+    "ax[2].plot(cuts[2:],(np.gradient(np.gradient(num_odd,cuts),cuts)*100)[2:],'o-')\n",
+    "ax[2].axvline(c,c='r',ls='--',lw=2)\n",
+    "ax[2].set_xlabel(r\"$N_{cut}$\")\n",
+    "ax[2].set_ylabel(r\"$\\partial^2_{N_cut}\\,n\\,(\\,N_{cut}\\,)$\");"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36d60d3c",
-   "metadata": {},
+   "id": "7aa3b8c2",
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "93c8b23c",
+   "id": "2edda78b",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2268,119 +414,27 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2c48075b",
+   "id": "fbccad86",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "689ba054",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a=destroy(30)\n",
-    "\n",
-    "ηtilde = 1\n",
-    "\n",
-    "Δrange = np.geomspace(1e-2, 1e2, 31)\n",
-    "Grange = np.geomspace(1e-2,1e2,30)\n",
-    "\n",
-    "N = 10\n",
-    "η = ηtilde/N\n",
-    "\n",
-    "c_ops=[np.sqrt(η)*a**2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b7ed719b",
+   "execution_count": 85,
+   "id": "76680780",
    "metadata": {},
    "outputs": [],
    "source": [
-    "parity=1.j*np.pi*a.dag()*a\n",
-    "parity=parity.expm()\n",
-    "\n",
-    "num_even     = np.zeros((Δrange.size, Grange.size))\n",
-    "num_odd      = np.zeros((Δrange.size, Grange.size))\n",
-    "gap_even     = np.zeros((Δrange.size, Grange.size), dtype='complex')\n",
-    "gap_odd      = np.zeros((Δrange.size, Grange.size), dtype='complex')\n",
-    "gap_even_odd = np.zeros((Δrange.size, Grange.size), dtype='complex')\n",
-    "gap_odd_even = np.zeros((Δrange.size, Grange.size), dtype='complex')\n",
-    "\n",
-    "t1 = time.time()\n",
-    "for k in range(Δrange.size):\n",
-    "    Δ = Δrange[k]\n",
-    "    Utilde = Δ\n",
-    "    U = Utilde/N\n",
-    "\n",
-    "    for j in range(Grange.size):\n",
-    "        G = Grange[j]*Δ\n",
-    "        \n",
-    "        H = -Δ*a.dag()*a + G/2 *(a.dag()**2 +a**2) + U/2 *a.dag()**2*a**2\n",
-    "        LL = liouvillian(H, c_ops)\n",
-    "\n",
-    "        P, block_bfs, block_sizes = block_diagonalize.PermMat(LL)\n",
-    "        bd_L = np.dot(P, np.dot(LL.data, np.transpose(P)))\n",
-    "        num_blocks, blocks_list, bl_indices = block_diagonalize.get_blocks(bd_L)   \n",
-    "        \n",
-    "        done=False\n",
-    "        for i in range(int(len(blocks_list))):\n",
-    "            block = blocks_list[i]\n",
-    "            evals, evecs = Qobj(block).eigenstates()    \n",
-    "\n",
-    "            ss_block_form = evecs[-1]\n",
-    "            evec2 = spr.dok_matrix((LL.shape[0], 1), dtype=np.complex)\n",
-    "            evec2[bl_indices[i]:bl_indices[i]+blocks_list[i].shape[0]] = ss_block_form.data\n",
-    "            evec2 = evec2.tocsr()\n",
-    "            evec2 = np.dot(np.transpose(P),evec2)\n",
-    "            ss= Qobj(evec2, dims=[LL.dims[0], [1]])\n",
-    "            ss=vector_to_operator(ss)\n",
-    "\n",
-    "            if np.real(ss.tr())>0.05:\n",
-    "                ss=ss+ss.dag()\n",
-    "                ss/=(ss.tr())       \n",
-    "                if expect(parity, ss)>0.5:\n",
-    "                    num_even[k,j] = expect(ss, a.dag()*a)\n",
-    "                    gap_even[k,j] = evals[-2]\n",
-    "                else:\n",
-    "                    num_odd[k,j] = expect(ss, a.dag()*a)                \n",
-    "                    gap_odd[k,j] = evals[-2]\n",
-    "            elif done==False:\n",
-    "                gap_even_odd[k,j] = np.real(evals[-1])+1.j*np.abs(np.imag(evals[-1]))\n",
-    "                gap_odd_even[k,j] = np.real(evals[-1])-1.j*np.abs(np.imag(evals[-1]))       \n",
-    "                done=True\n",
-    "\n",
-    "        print(\"\\rCompleted (%d/%d) -> %d/%d \"%(k,Δrange.size, j+1,Utilderange.size),end=\"\")\n",
-    "print(\"\\nTime:\",time.time()-t1)"
+    "Δrange = np.linspace(0.,3.5,11)\n",
+    "Utilderange = np.linspace(1,1,1)\n",
+    "Grange = np.linspace(0.1,2,10)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65ea2c81",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.xscale(\"log\")\n",
-    "plt.yscale(\"log\")\n",
-    "\n",
-    "plt.pcolormesh(Grange, Δrange, (num_even/N), shading='gouraud', vmax=1.4, cmap='viridis')\n",
-    "plt.xlabel(r\"$G\\,/\\,\\Delta$\")\n",
-    "plt.ylabel(r\"$\\Delta\\,=\\,\\tilde U$ [$\\tilde\\eta$]\")\n",
-    "plt.colorbar(label=r\"$\\langle a^{\\dagger} a\\rangle\\,/\\,N$\")\n",
-    "\n",
-    "#plt.contour(Grange, Δrange, gaussian_filter(num_even/N,2), colors='k', alpha=0.3)#, levels=contours)\n",
-    "#plt.contour(Grange, Δrange, num_even/N, 4, colors='k', alpha=0.25)#, levels=contours)\n",
-    "#plt.savefig(\"G_Delta=Utilde_PD_v2.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dfe2e1ed",
+   "id": "2b123bbf",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2388,7 +442,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0ef04a03",
+   "id": "9b041c2e",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2396,7 +450,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ded378b7",
+   "id": "4b45cdb9",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2404,7 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c9f86334",
+   "id": "44386e9c",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2412,172 +466,23 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "a3946cf1",
+   "id": "22ab118b",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "a=destroy(30)\n",
-    "\n",
-    "ηtilde = 1\n",
-    "Utilde = 5\n",
-    "Δ = 10\n",
-    "\n",
-    "Grange, dG = np.linspace(0.1,14,75, retstep=True)*ηtilde\n",
-    "\n",
-    "N = 10\n",
-    "η = ηtilde/N\n",
-    "U = Utilde/N\n",
-    "\n",
-    "c_ops=[np.sqrt(η)*a**2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a9decc16",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "parity=1.j*np.pi*a.dag()*a\n",
-    "parity=parity.expm()\n",
-    "\n",
-    "num_even     = np.zeros(Grange.size)\n",
-    "num_odd      = np.zeros(Grange.size)\n",
-    "gap_even     = np.zeros(Grange.size, dtype='complex')\n",
-    "gap_odd      = np.zeros(Grange.size, dtype='complex')\n",
-    "gap_even_odd = np.zeros(Grange.size, dtype='complex')\n",
-    "gap_odd_even = np.zeros(Grange.size, dtype='complex')\n",
-    "evals_list   = []\n",
-    "\n",
-    "t1 = time.time()\n",
-    "for g in range(Grange.size):\n",
-    "    H = -Δ*a.dag()*a + Grange[g]/2 *(a.dag()**2 +a**2) + U/2 *a.dag()**2*a**2\n",
-    "    LL = liouvillian(H, c_ops)\n",
-    "    \n",
-    "    P, block_bfs, block_sizes = block_diagonalize.PermMat(LL)\n",
-    "    bd_L = np.dot(P, np.dot(LL.data, np.transpose(P)))\n",
-    "    num_blocks, blocks_list, bl_indices = block_diagonalize.get_blocks(bd_L)   \n",
-    "    \n",
-    "    num_keep = 15\n",
-    "    evals_keep = np.zeros((num_blocks, num_keep), dtype='complex')\n",
-    "    \n",
-    "    done=False\n",
-    "    for i in range(int(len(blocks_list))):\n",
-    "        block = blocks_list[i]\n",
-    "        evals, evecs = Qobj(block).eigenstates()    \n",
-    "        evals_keep[i,:] = evals[-num_keep:]\n",
-    "        \n",
-    "        ss_block_form = evecs[-1]\n",
-    "        evec2 = spr.dok_matrix((LL.shape[0], 1), dtype=np.complex)\n",
-    "        evec2[bl_indices[i]:bl_indices[i]+blocks_list[i].shape[0]] = ss_block_form.data\n",
-    "        evec2 = evec2.tocsr()\n",
-    "        evec2 = np.dot(np.transpose(P),evec2)\n",
-    "        ss= Qobj(evec2, dims=[LL.dims[0], [1]])\n",
-    "        ss=vector_to_operator(ss)\n",
-    "\n",
-    "        if np.real(ss.tr())>0.05:\n",
-    "            ss=ss+ss.dag()\n",
-    "            ss/=(ss.tr())       \n",
-    "            if expect(parity, ss)>0.5:\n",
-    "                num_even[g] = expect(ss, a.dag()*a)\n",
-    "                gap_even[g] = evals[-2]\n",
-    "            else:\n",
-    "                num_odd[g] = expect(ss, a.dag()*a)                \n",
-    "                gap_odd[g] = evals[-2]\n",
-    "        elif done==False:\n",
-    "            gap_even_odd[g] = np.real(evals[-1])+1.j*np.abs(np.imag(evals[-1]))\n",
-    "            gap_odd_even[g] = np.real(evals[-1])-1.j*np.abs(np.imag(evals[-1]))       \n",
-    "            done=True\n",
-    "    evals_list.append(evals_keep)\n",
-    "    print(\"\\rCompleted %d/%d\"%(g+1,Grange.size),end=\"\")\n",
-    "print(\"\\nTime:\",time.time()-t1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "89a3ea87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(1,3, figsize=(30,9))\n",
-    "\n",
-    "ax[0].plot(Grange, num_even/N, 'bo-', ms=5, label=\"even\")\n",
-    "ax[0].plot(Grange, num_odd/N, 'rs-', ms=5, label=\"odd\")\n",
-    "\n",
-    "#ax[0].set_xscale(\"log\")\n",
-    "ax[0].set_xlabel(r\"$G\\,/\\,\\tilde{\\eta}$\")\n",
-    "ax[0].set_ylabel(r\"$\\langle a^{\\dagger} a\\rangle\\,/\\,N$\")\n",
-    "ax[0].legend()\n",
-    "\n",
-    "\n",
-    "ax[1].plot(Grange, -np.real(gap_even), 'bo-', ms=5, label=\"even\")\n",
-    "ax[1].plot(Grange, -np.real(gap_odd), 'rs-', ms=5, label=\"odd\")\n",
-    "ax[1].axhline(1, c='k', ls='--')\n",
-    "\n",
-    "#ax[1].set_xscale(\"log\")\n",
-    "ax[1].set_yscale(\"log\")\n",
-    "ax[1].set_ylabel(r\"$-\\Re(\\lambda_1)\\,/\\,\\tilde\\eta$\")\n",
-    "ax[1].set_xlabel(r\"$G\\,/\\,\\tilde\\eta$\")\n",
-    "ax[1].legend()\n",
-    "\n",
-    "\n",
-    "ax[2].plot(Grange, -np.real(gap_even_odd), 'g^-')\n",
-    "ax[2].plot(Grange, -np.real(gap_odd_even), 'yh-')\n",
-    "ax[2].set_xlabel(r\"$G\\,/\\,\\tilde\\eta$\")\n",
-    "\n",
-    "#ax[2].set_xscale(\"log\")\n",
-    "ax[2].set_yscale('log')\n",
-    "\n",
-    "fig.tight_layout()\n",
-    "#fig.savefig(\"PL_Delta=Utilde=10_N=10.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6394fce7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dGnum_even = np.gradient(num_even,dG)/N\n",
-    "dGnum_odd = np.gradient(num_odd,dG)/N\n",
-    "\n",
-    "plt.plot(Grange, dGnum_even, '.-')\n",
-    "plt.plot(Grange, dGnum_odd, '.-')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9f9de67b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "overlap = (dGnum_even*dGnum_odd).sum()*dG/np.sqrt((dGnum_even**2).sum()*dG)/np.sqrt((dGnum_odd**2).sum()*dG)\n",
-    "print(overlap)\n",
-    "\n",
-    "M = Grange[np.argmax(dGnum_even)] - Grange[np.argmax(dGnum_odd)]\n",
-    "m = 0.5*(Grange[np.argmax(dGnum_even)] + Grange[np.argmax(dGnum_odd)])\n",
-    "print(M,m,M/m)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5f12a50d",
+   "id": "4d0839e6",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "plt.plot(Grange, num_even/N, 'bo-', ms=5, label=\"even\")\n",
-    "plt.plot(Grange+M, num_odd/N-1/N, 'rs-', ms=5, label=\"odd\")\n",
-    "plt.axvline(m)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1c932396",
+   "id": "93df735b",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2585,7 +490,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9a8214ad",
+   "id": "b7171447",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2593,23 +498,70 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2ed104db",
+   "id": "da108646",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "371fe39b",
+   "execution_count": 17,
+   "id": "53d5cb37",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "def get_cutoff_MK1(cuts, p, precision=0.01):\n",
+    "    metric = lambda x1,x2 : ((np.abs(x1-x2)/np.abs(np.min([x1,x2])))<precision)\n",
+    "    q = deque()\n",
+    "    \n",
+    "    k = Kerr_2γ(Nfock=cuts[0], p=p)\n",
+    "    q.append(0)\n",
+    "    q.append(k.bd()[0])\n",
+    "    \n",
+    "    for i in range(1,cuts.size):\n",
+    "        k = Kerr_2γ(Nfock=cuts[i], p=p)\n",
+    "        ni = k.bd()[0]\n",
+    "        μi = metric(ni,q.pop())\n",
+    "        q.append(μi)\n",
+    "        print(q[0],q[1],i)\n",
+    "        if (q.popleft())*(q[0])==True : \n",
+    "            return i-1\n",
+    "        q.append(ni)\n",
+    "    \n",
+    "    if q[0]==True:\n",
+    "        print(\"Carefull: Convergence on border\")\n",
+    "        return i\n",
+    "    else: return np.inf\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "def get_cutoff_MK2(p, c0=5, cmax=100, step=5, precision=0.005):\n",
+    "    metric = lambda x1,x2 : ((np.abs(x1-x2)/np.abs(np.min([x1,x2])))<precision)\n",
+    "    k = Kerr_2γ(Nfock=c0,p=p)\n",
+    "    nprev = k.bd()[0]\n",
+    "    c = c0+step\n",
+    "    while c<cmax:\n",
+    "        k = Kerr_2γ(Nfock=c,p=p)\n",
+    "        n = k.bd()[0]\n",
+    "        if metric(n,nprev):\n",
+    "            c -= step\n",
+    "            while 1:\n",
+    "                k = Kerr_2γ(Nfock=c-1,p=p)\n",
+    "                if metric(k.bd()[0],n): c -= 1\n",
+    "                else: return c\n",
+    "        else:\n",
+    "            nprev = n\n",
+    "            c += step\n",
+    "    return np.inf\n",
+    "        \n",
+    "    "
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "455a526c",
+   "id": "b5385b39",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -2617,7 +569,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2631,7 +583,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,