gamma-variation-v0.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Gamma Variation Heuristic**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## import essential modules \n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import qumcmc \n",
    "from qumcmc.basic_utils import *\n",
    "# from qumcmc.energy_models import IsingEnergyFunction\n",
    "from qumcmc.energy_models import IsingEnergyFunction, Exact_Sampling\n",
    "\n",
    "from qumcmc.classical_mcmc_routines import classical_mcmc\n",
    "from qumcmc.quantum_mcmc_routines import quantum_enhanced_mcmc     #for Qulacs Simulator backend (** Faster )\n",
    "\n",
    "from qumcmc.trajectory_processing import calculate_running_js_divergence, calculate_running_kl_divergence, calculate_runnning_magnetisation, get_trajectory_statistics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **BAS Dataset** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gridsize = 3\n",
    "bas = bas_dataset(grid_size= gridsize); \n",
    "data_samples = bas.dataset\n",
    "\n",
    "wts = hebbing_learning(data_samples)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the model\n",
    "np.random.seed(610358)# should always be in the same cell!  ## best seed -> 610358\n",
    "n_spins = gridsize*gridsize\n",
    "\n",
    "## construct problem Hamiltonian ##\n",
    "shape_of_J=(n_spins,n_spins)\n",
    "\n",
    "#evaluating J for BAS dataset\n",
    "J=-5*wts   #np.array([[0,0,0,4],[0,0,4,0],[0,4,0,0],[4,0,0,0]])\n",
    "\n",
    "# defining h\n",
    "h=np.zeros(np.shape(J)[0])\n",
    "\n",
    "# instantiate the model\n",
    "model=IsingEnergyFunction(J,h,name=f\"BAS data of gridsize {gridsize}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============================================\n",
      "            MODEL : BAS data of gridsize 3\n",
      "=============================================\n",
      "Non-zero Interactions (J) : 36 / 36\n",
      "Non-zero Bias (h) : 0 / 9\n",
      "---------------------------------------------\n",
      "Average Interaction Strength <|J|> :  17.77777777777778\n",
      "Average Bias Strength <|h|>:  0.0\n",
      "alpha :  0.025\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.model_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============================================\n",
      "     MODEL : BAS data of gridsize 3 |  beta : 1\n",
      "=============================================\n",
      "Num Most Probable States : 12\n",
      "Entropy : 3.584962500721156\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## set current beta\n",
    "beta = 1#1.100209\n",
    "\n",
    "## run exact sampling over all possible configurations \n",
    "exact_sampled_model = Exact_Sampling(model, beta)\n",
    "\n",
    "## get the distribution from model\n",
    "bpd = exact_sampled_model.boltzmann_pd\n",
    "\n",
    "## exact sampling summary\n",
    "exact_sampled_model.sampling_summary(show_threshold=1e-30)\n",
    "plt.yscale(\"log\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['001001001',\n",
       " '010010010',\n",
       " '100100100',\n",
       " '011011011',\n",
       " '101101101',\n",
       " '110110110',\n",
       " '000000111',\n",
       " '000111000',\n",
       " '111000000',\n",
       " '000111111',\n",
       " '111000111',\n",
       " '111111000']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qumcmc.mixers import GenericMixer, CustomMixer, IncoherentMixerSum, CoherentMixerSum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsteps = 10_000\n",
    "\n",
    "gamma = 0.5\n",
    "\n",
    "ws = np.linspace(0, 0.5, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1008.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1004.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1044.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1036.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1002.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  010010010 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1019.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  010010010 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 991.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1005.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  100100100 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1001.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1014.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111000000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1025.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:14<00:00, 681.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:16<00:00, 599.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  001001001 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:16<00:00, 601.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:16<00:00, 599.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111000111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:16<00:00, 602.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111000111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:14<00:00, 697.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:14<00:00, 692.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:12<00:00, 796.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 907.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "20\n",
      "starting with:  010010010 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 850.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  010010010 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 934.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:20<00:00, 497.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 577.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:15<00:00, 648.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:14<00:00, 675.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111000111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 863.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111000111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 996.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 868.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:15<00:00, 645.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:19<00:00, 517.69it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 577.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:19<00:00, 514.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 579.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  100100100 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:14<00:00, 679.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  100100100 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 582.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:19<00:00, 505.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 574.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:19<00:00, 512.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 573.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:19<00:00, 506.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 568.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  001001001 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:19<00:00, 509.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  001001001 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 576.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  000111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:19<00:00, 511.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:14<00:00, 707.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  001001001 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:14<00:00, 686.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  001001001 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 583.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  010010010 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:19<00:00, 505.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  010010010 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:12<00:00, 772.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  011011011 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:16<00:00, 592.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  011011011 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:16<00:00, 621.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 873.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 997.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  011011011 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 879.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  011011011 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 996.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 889.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 988.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:12<00:00, 779.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 964.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n",
      "20\n",
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:14<00:00, 682.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 997.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 870.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 997.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 854.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 971.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 879.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  101101101 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1002.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 881.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 987.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  100100100 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 857.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  100100100 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 998.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 874.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 986.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:12<00:00, 825.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111111000 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 967.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  111000111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 857.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  111000111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 990.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  100100100 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 855.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  100100100 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 966.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:16<00:00, 591.71it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:17<00:00, 586.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  001001001 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:13<00:00, 768.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  001001001 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 952.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  011011011 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 838.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  011011011 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 969.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 866.78it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:10<00:00, 966.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 10000/10000 [00:11<00:00, 869.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000111111 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|█| 10000/10000 [00:09<00:00, 1003.83it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "starting with:  110110110 with energy: -240.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..:  82%|██▍| 8201/10000 [00:09<00:02, 896.64it/s]"
     ]
    }
   ],
   "source": [
    "generic_mixer = GenericMixer.OneBodyMixer(n_spins)\n",
    "bas_mixer_terms = [[0,3,6],[1,4,7],[2,5,8], [0, 1, 2], [3, 4, 5], [6, 7, 8]]\n",
    "bas_mixer = CustomMixer(n_spins, bas_mixer_terms)\n",
    "\n",
    "\n",
    "\n",
    "nreps = 20\n",
    "\n",
    "\n",
    "all_kls_generic = []\n",
    "all_kls_coherent = []\n",
    "all_kls_incoherent = []\n",
    "\n",
    "for rep in range(nreps):\n",
    "    initial_state = np.random.choice(data_samples)\n",
    "    chain = quantum_enhanced_mcmc(\n",
    "        n_hops=nsteps,\n",
    "        model=model,\n",
    "        mixer=generic_mixer,\n",
    "        gamma = gamma,#(0.9,0.45), # to standardise the problem\n",
    "        initial_state=initial_state,\n",
    "        verbose=True\n",
    "    )\n",
    "    all_kls_generic.append(calculate_running_kl_divergence(exact_sampled_model.boltzmann_pd, chain))\n",
    "    \n",
    "\n",
    "for w in ws:\n",
    "    print(w)\n",
    "    kls_coherent = []\n",
    "    kls_incoherent = []\n",
    "    \n",
    "    for rep in range(nreps):\n",
    "        print(rep)\n",
    "        initial_state = np.random.choice(data_samples)\n",
    "        \n",
    "        coherent_mixer = CoherentMixerSum([generic_mixer, bas_mixer], weights=[1-w, w])\n",
    "\n",
    "        incoherent_mixer = IncoherentMixerSum([generic_mixer, bas_mixer], probabilities=[1-w, w])\n",
    "\n",
    "        chain = quantum_enhanced_mcmc(\n",
    "            n_hops=nsteps,\n",
    "            model=model,\n",
    "            mixer=coherent_mixer,\n",
    "            gamma = gamma,#(0.9,0.45), # to standardise the problem\n",
    "            initial_state=initial_state,\n",
    "            verbose=True\n",
    "        )\n",
    "        kls_coherent.append(calculate_running_kl_divergence(exact_sampled_model.boltzmann_pd, chain))\n",
    "        \n",
    "        chain = quantum_enhanced_mcmc(\n",
    "            n_hops=nsteps,\n",
    "            model=model,\n",
    "            mixer=incoherent_mixer,\n",
    "            gamma = gamma,#(0.9,0.45), # to standardise the problem\n",
    "            initial_state=initial_state,\n",
    "            verbose=True\n",
    "        )\n",
    "        kls_incoherent.append(calculate_running_kl_divergence(exact_sampled_model.boltzmann_pd, chain))\n",
    "\n",
    "    all_kls_coherent.append(kls_coherent)\n",
    "    all_kls_incoherent.append(kls_incoherent)\n",
    "\n",
    "\n",
    "chain_generic = quantum_enhanced_mcmc(\n",
    "                                n_hops=nsteps,\n",
    "                                model=model,\n",
    "                                mixer=generic_mixer,\n",
    "                                gamma = 0.5,#(0.9,0.45), # to standardise the problem\n",
    "                                initial_state=initial_state,\n",
    "                                verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ii, w in enumerate(ws):\n",
    "    if ii > len(all_kls_coherent)-1:\n",
    "        continue\n",
    "    plt.plot(np.median(all_kls_coherent[ii], axis=0), color=plt.get_cmap(\"Blues\", 10)(ii+1), label=f\"{w}\")\n",
    "    plt.plot(np.median(all_kls_incoherent[ii], axis=0), color=plt.get_cmap(\"Reds\", 10)(ii+1), label=f\"{w}\")\n",
    "    \n",
    "plt.plot(np.median(all_kls_generic, axis=0), color=\"black\")\n",
    "\n",
    "plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **DECOY MODEL**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============================================\n",
      "            MODEL : zero-interaction-model\n",
      "=============================================\n",
      "Non-zero Interactions (J) : 36 / 36\n",
      "Non-zero Bias (h) : 0 / 9\n",
      "---------------------------------------------\n",
      "Average Interaction Strength <|J|> :  13.333333333333334\n",
      "Average Bias Strength <|h|>:  0.0\n",
      "alpha :  0.028867513459481287\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###\n",
    "J_zero = np.zeros((n_spins, n_spins))\n",
    "\n",
    "model_zero = IsingEnergyFunction(J, h, name= 'zero-interaction-model', circuit_J=J_zero)\n",
    "\n",
    "model_zero.model_summary();\n",
    "model_zero_exact = Exact_Sampling(model_zero, beta);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=============================================\n",
      "            MODEL : zero-interaction-model\n",
      "=============================================\n",
      "Non-zero Interactions (J) : 36 / 36\n",
      "Non-zero Bias (h) : 0 / 9\n",
      "---------------------------------------------\n",
      "Average Interaction Strength <|J|> :  13.333333333333334\n",
      "Average Bias Strength <|h|>:  0.0\n",
      "alpha :  0.028867513459481287\n",
      "---------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###\n",
    "np.random.seed(42)\n",
    "\n",
    "J_miss = J + np.random.randn(*J.shape)*8\n",
    "\n",
    "model_miss = IsingEnergyFunction(J, h, name= 'zero-interaction-model', circuit_J=J_miss)\n",
    "\n",
    "model_miss.model_summary();\n",
    "model_miss_exact = Exact_Sampling(model_miss, beta);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = model_zero; exact_sampled_model = model_zero_exact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **quMCMC for gamma variation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_state = bas.dataset[0]\n",
    "steps= 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'quantum_enhanced_mcmc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m qmcmc_bmix_pertgamma \u001b[38;5;241m=\u001b[39m quantum_enhanced_mcmc(\n\u001b[1;32m      2\u001b[0m     n_hops\u001b[38;5;241m=\u001b[39msteps,\n\u001b[1;32m      3\u001b[0m     initial_state\u001b[38;5;241m=\u001b[39minitial_state,\n\u001b[1;32m      4\u001b[0m     model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[1;32m      5\u001b[0m     temperature\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mbeta,\n\u001b[1;32m      6\u001b[0m     name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbasic_mixer_pertgamma\u001b[39m\u001b[38;5;124m'\u001b[39m, gamma_range\u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0.001\u001b[39m, \u001b[38;5;241m0.1\u001b[39m)\n\u001b[1;32m      7\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'quantum_enhanced_mcmc' is not defined"
     ]
    }
   ],
   "source": [
    "qmcmc_bmix_pertgamma = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    name = 'basic_mixer_pertgamma', gamma_range= (0.001, 0.1)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "qmcmc_bmix_lowgamma = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    name = 'basic_mixer_lowgamma', gamma_range=(0.1,0.25)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "qmcmc_bmix_medgamma = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    name = 'basic_mixer_medgamma', gamma_range=(0.3,0.65)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "qmcmc_bmix_higamm = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    name = 'basic_mixer_higamma', gamma_range=(0.75, 1.00)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0110', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1000', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1110', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0010', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1000', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1110', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1110', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0010', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1000', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0010', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='1011', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1000', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1110', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1000', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0010', accepted=False),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='1000', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0100', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1001', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1100', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1000', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1001', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1011', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1011', accepted=False),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='1010', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0111', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='1101', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0001', accepted=False),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0101', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='1011', accepted=False),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " MCMCState(bitstring='0011', accepted=True),\n",
       " ...]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qmcmc_bmix_lowgamma.states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "quantum_enhanced_mcmc() got an unexpected keyword argument 'pauli_index_list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[32], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m## Perturbative Gamma range : (0.001 -> 0.09)\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m qmcmc_advmix_pertgamma \u001b[38;5;241m=\u001b[39m quantum_enhanced_mcmc(\n\u001b[1;32m      3\u001b[0m     n_hops\u001b[38;5;241m=\u001b[39msteps,\n\u001b[1;32m      4\u001b[0m     initial_state\u001b[38;5;241m=\u001b[39minitial_state,\n\u001b[1;32m      5\u001b[0m     model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[1;32m      6\u001b[0m     temperature\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39mbeta,\n\u001b[1;32m      7\u001b[0m     gamma_range\u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0.001\u001b[39m, \u001b[38;5;241m0.09\u001b[39m),\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;66;03m# single_qubit_mixer= False, \u001b[39;00m\n\u001b[1;32m      9\u001b[0m     pauli_index_list\u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m*\u001b[39mgridsize,\n\u001b[1;32m     10\u001b[0m     name\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124madvmix_pertgamma\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     11\u001b[0m )\n",
      "\u001b[0;31mTypeError\u001b[0m: quantum_enhanced_mcmc() got an unexpected keyword argument 'pauli_index_list'"
     ]
    }
   ],
   "source": [
    "## Perturbative Gamma range : (0.001 -> 0.09)\n",
    "qmcmc_advmix_pertgamma = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.001, 0.09),\n",
    "    # single_qubit_mixer= False, \n",
    "    pauli_index_list= [1]*gridsize,\n",
    "    name= 'advmix_pertgamma'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Low Gamma range : (0.1 -> 0.25)\n",
    "qmcmc_advmix_lowgamma = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.1, 0.25),\n",
    "    single_qubit_mixer= False, pauli_index_list= [1]*gridsize,\n",
    "    name= 'advmic_lowgamma'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Medium Gamma range : (0.25 -> 0.75)\n",
    "qmcmc_advmix_medgamma = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.25, 0.75),\n",
    "    single_qubit_mixer= False, pauli_index_list= [1]*gridsize,\n",
    "    name= 'advmix_medgamma'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## High Gamma range : (0.75 -> 0.9)\n",
    "qmcmc_advmix_higamma = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.75, 0.9),\n",
    "    single_qubit_mixer= False, pauli_index_list= [1]*gridsize,\n",
    "    name= 'advmix_higamma'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Extreme Gamma range : (0.9 -> 0.9999)\n",
    "qmcmc_advmix_extgamma = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.9, 0.9999),\n",
    "    single_qubit_mixer= False, pauli_index_list= [1]*gridsize,\n",
    "    name= 'advmix_extgamma'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Extra trial ##\n",
    "\n",
    "qmcmc_extra = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.5,0.6),\n",
    "    single_qubit_mixer= False, pauli_index_list= [1]*gridsize,\n",
    "    name= 'advmix_extra'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "qchains = [qmcmc_bmix_pertgamma, qmcmc_bmix_lowgamma, qmcmc_bmix_medgamma, qmcmc_bmix_higamm] #, qmcmc_advmix_pertgamma, qmcmc_advmix_lowgamma, qmcmc_advmix_medgamma, qmcmc_advmix_higamma, qmcmc_advmix_extgamma, qmcmc_extra]\n",
    "\n",
    "\n",
    "# with open('SavedData/GammaVaraitionData/gv_qchains_v0.pickle', 'wb') as f:\n",
    "\n",
    "#     pickle.dump( qchains ,f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('SavedData/GammaVaraitionData/gv_qchains_v0.pickle', 'rb') as f:\n",
    "\n",
    "#     qchains = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "kldivs = {}\n",
    "for chain in qchains: kldivs[chain.name] = calculate_running_kl_divergence(exact_sampled_model.boltzmann_pd, chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "plt.plot(kldivs['basic_mixer_lowgamma'], label='advmix_extra')\n",
    "plt.plot(kldivs['basic_mixer_medgamma'], label='advmix_pertgamma')\n",
    "plt.plot(kldivs['basic_mixer_higamma'], label='advmix_extgamma')\n",
    "\n",
    "plt.xlabel(\"iterations \")\n",
    "plt.ylabel(\"KL divergence\")\n",
    "plt.yscale('log')\n",
    "# plt.xscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "for items in list(kldivs.items()):\n",
    "    plt.plot(items[1], label= items[0])\n",
    "\n",
    "plt.xlabel(\"iterations \")\n",
    "plt.ylabel(\"KL divergence\")\n",
    "plt.yscale('log')\n",
    "# plt.xscale(\"log\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shapes (9,9) and (4,) not aligned: 9 (dim 1) != 4 (dim 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[82], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m trajectory_stat \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chain \u001b[38;5;129;01min\u001b[39;00m qchains : trajectory_stat[chain\u001b[38;5;241m.\u001b[39mname]  \u001b[38;5;241m=\u001b[39m get_trajectory_statistics(chain, exact_sampled_model)\n",
      "File \u001b[0;32m~/Desktop/my_Github/quMCMC/qumcmc/trajectory_processing.py:188\u001b[0m, in \u001b[0;36mget_trajectory_statistics\u001b[0;34m(mcmc_chain, model, to_observe, verbose)\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose : \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrans: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(trajectory[current_state_index]\u001b[38;5;241m.\u001b[39mbitstring) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m -> \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(trajectory[proposed_state_index]\u001b[38;5;241m.\u001b[39mbitstring)\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m status: \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mstr\u001b[39m(trajectory[proposed_state_index]\u001b[38;5;241m.\u001b[39maccepted)  )\n\u001b[1;32m    187\u001b[0m \u001b[38;5;66;03m# if 'acceptance_prob' in to_observe: acceptance_statistic.append( acceptance_prob(trajectory[current_state_index], trajectory[proposed_state_index] ) )\u001b[39;00m\n\u001b[0;32m--> 188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124menergy\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m to_observe: energy_statistic\u001b[38;5;241m.\u001b[39mappend( energy_diff(trajectory[current_state_index], trajectory[proposed_state_index] ) )\n\u001b[1;32m    189\u001b[0m \u001b[38;5;66;03m#if 'hamming' in to_observe: hamming_statistic.append( hamming_diff(trajectory[current_state_index], trajectory[proposed_state_index] ) )\u001b[39;00m\n\u001b[1;32m    192\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trajectory[proposed_state_index]\u001b[38;5;241m.\u001b[39maccepted :\n",
      "File \u001b[0;32m~/Desktop/my_Github/quMCMC/qumcmc/trajectory_processing.py:176\u001b[0m, in \u001b[0;36mget_trajectory_statistics.<locals>.<lambda>\u001b[0;34m(si, sf)\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[38;5;66;03m# acceptance_prob = lambda si, sf: min(1, model.get_boltzmann_factor(sf.bitstring) / model.get_boltzmann_factor(si.bitstring) )\u001b[39;00m\n\u001b[1;32m    175\u001b[0m hamming_diff \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m si, sf: hamming_dist(si\u001b[38;5;241m.\u001b[39mbitstring, sf\u001b[38;5;241m.\u001b[39mbitstring)\n\u001b[0;32m--> 176\u001b[0m energy_diff \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m si, sf: model\u001b[38;5;241m.\u001b[39mget_energy(sf\u001b[38;5;241m.\u001b[39mbitstring) \u001b[38;5;241m-\u001b[39m model\u001b[38;5;241m.\u001b[39mget_energy(si\u001b[38;5;241m.\u001b[39mbitstring)\n\u001b[1;32m    178\u001b[0m acceptance_statistic \u001b[38;5;241m=\u001b[39m [];hamming_statistic \u001b[38;5;241m=\u001b[39m [];energy_statistic \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    180\u001b[0m current_state_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m; proposed_state_index \u001b[38;5;241m=\u001b[39m current_state_index \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/Desktop/my_Github/quMCMC/qumcmc/energy_models.py:130\u001b[0m, in \u001b[0;36mIsingEnergyFunction.get_energy\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m    128\u001b[0m     state \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m elem \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m elem \u001b[38;5;129;01min\u001b[39;00m state])\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;66;03m# state = np.array( [int(list(state)[i]) for i in range(len(state))])\u001b[39;00m\n\u001b[0;32m--> 130\u001b[0m     energy \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mdot(state\u001b[38;5;241m.\u001b[39mtranspose(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mJ\u001b[38;5;241m.\u001b[39mdot(state)) \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39mdot(\n\u001b[1;32m    131\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mh\u001b[38;5;241m.\u001b[39mtranspose(), state\n\u001b[1;32m    132\u001b[0m     )\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m energy\n\u001b[1;32m    134\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mValueError\u001b[0m: shapes (9,9) and (4,) not aligned: 9 (dim 1) != 4 (dim 0)"
     ]
    }
   ],
   "source": [
    "trajectory_stat = {}\n",
    "for chain in qchains : \n",
    "    trajectory_stat[chain.name]  = get_trajectory_statistics(chain, exact_sampled_model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "object of type 'int' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[44], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;241m1\u001b[39m)\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mixers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'000000111'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_state = data_samples[0]\n",
    "initial_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = 50_00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qumcmc.mixers import GenericMixer, CustomMixer, CoherentMixerSum, IncoherentMixerSum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "generic_mixer = GenericMixer.OneBodyMixer(n_spins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -360.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|███| 5000/5000 [00:04<00:00, 1002.31it/s]\n"
     ]
    }
   ],
   "source": [
    "res = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    model=model,\n",
    "    mixer=generic_mixer,\n",
    "    gamma=0.5,\n",
    "    initial_state=initial_state,\n",
    "    temperature=1/beta,\n",
    "    name= 'basic_lowgamma',\n",
    "    verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acceptance_prob': array([0.5       , 0.16666667, 0.25      , ..., 0.33333333, 0.5       ,\n",
       "        0.5       ]),\n",
       " 'energy': array([  0., 320., 240., ..., 120.,   0., 120.]),\n",
       " 'hamming': {0: {'accepted': 554, 'rejected': 0, 'total': 554},\n",
       "  1: {'accepted': 0, 'rejected': 445, 'total': 445},\n",
       "  2: {'accepted': 0, 'rejected': 812, 'total': 812},\n",
       "  3: {'accepted': 838, 'rejected': 456, 'total': 1294},\n",
       "  4: {'accepted': 0, 'rejected': 586, 'total': 586},\n",
       "  5: {'accepted': 0, 'rejected': 327, 'total': 327},\n",
       "  6: {'accepted': 229, 'rejected': 154, 'total': 383},\n",
       "  7: {'accepted': 0, 'rejected': 245, 'total': 245},\n",
       "  8: {'accepted': 0, 'rejected': 155, 'total': 155},\n",
       "  9: {'accepted': 199, 'rejected': 0, 'total': 199}},\n",
       " 'kldiv': array([1.35588078e+01, 1.07197434e+01, 1.07393739e+01, ...,\n",
       "        6.22215167e-03, 6.25424555e-03, 6.28646845e-03])}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_trajectory_statistics(res, exact_sampled_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_mixer = CustomMixer(n_spins, [[0, 1, 2], [3, 4, 5], [6, 7, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -360.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|███| 5000/5000 [00:04<00:00, 1159.52it/s]\n"
     ]
    }
   ],
   "source": [
    "res = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    model=model,\n",
    "    mixer=custom_mixer,\n",
    "    gamma = 0.5, \n",
    "    initial_state=initial_state,\n",
    "    temperature=1/beta,\n",
    "    name= 'basic_lowgamma',\n",
    "    verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acceptance_prob': array([0.33333333, 1.        , 1.        , ..., 1.        , 1.        ,\n",
       "        1.        ]),\n",
       " 'energy': array([0., 0., 0., ..., 0., 0., 0.]),\n",
       " 'hamming': {0: {'accepted': 1026, 'rejected': 0, 'total': 1026},\n",
       "  1: {'accepted': 0, 'rejected': 0, 'total': 0},\n",
       "  2: {'accepted': 0, 'rejected': 0, 'total': 0},\n",
       "  3: {'accepted': 1201, 'rejected': 285, 'total': 1486},\n",
       "  4: {'accepted': 0, 'rejected': 0, 'total': 0},\n",
       "  5: {'accepted': 0, 'rejected': 0, 'total': 0},\n",
       "  6: {'accepted': 859, 'rejected': 174, 'total': 1033},\n",
       "  7: {'accepted': 0, 'rejected': 0, 'total': 0},\n",
       "  8: {'accepted': 0, 'rejected': 0, 'total': 0},\n",
       "  9: {'accepted': 1455, 'rejected': 0, 'total': 1455}},\n",
       " 'kldiv': array([1.35588078e+01, 1.35588078e+01, 1.07393739e+01, ...,\n",
       "        4.19070758e-04, 4.19609388e-04, 4.27022452e-04])}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_trajectory_statistics(res, exact_sampled_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "coherent_mixer = CoherentMixerSum([generic_mixer, CustomMixer(n_spins, [[0, 1, 2], [3, 4, 5]])], weights=[0.5, 0.5]) # partial knowledge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -360.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|████| 5000/5000 [00:05<00:00, 958.72it/s]\n"
     ]
    }
   ],
   "source": [
    "res = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    model=model,\n",
    "    mixer=coherent_mixer,\n",
    "    gamma = 0.5, \n",
    "    initial_state=initial_state,\n",
    "    temperature=1/beta,\n",
    "    name= 'basic_lowgamma',\n",
    "    verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acceptance_prob': array([0.33333333, 1.        , 0.25      , ..., 0.09090909, 1.        ,\n",
       "        1.        ]),\n",
       " 'energy': array([240.,   0.,   0., ..., 120.,   0.,   0.]),\n",
       " 'hamming': {0: {'accepted': 1041, 'rejected': 0, 'total': 1041},\n",
       "  1: {'accepted': 0, 'rejected': 484, 'total': 484},\n",
       "  2: {'accepted': 0, 'rejected': 481, 'total': 481},\n",
       "  3: {'accepted': 1000, 'rejected': 141, 'total': 1141},\n",
       "  4: {'accepted': 0, 'rejected': 210, 'total': 210},\n",
       "  5: {'accepted': 0, 'rejected': 211, 'total': 211},\n",
       "  6: {'accepted': 1153, 'rejected': 66, 'total': 1219},\n",
       "  7: {'accepted': 0, 'rejected': 141, 'total': 141},\n",
       "  8: {'accepted': 0, 'rejected': 28, 'total': 28},\n",
       "  9: {'accepted': 44, 'rejected': 0, 'total': 44}},\n",
       " 'kldiv': array([1.35588078e+01, 1.35588078e+01, 1.07393739e+01, ...,\n",
       "        1.49627089e-03, 1.48572022e-03, 1.47645140e-03])}"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_trajectory_statistics(res, exact_sampled_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "incoherent_mixer = IncoherentMixerSum([generic_mixer, CustomMixer(n_spins, [[0, 1, 2], [3, 4, 5]])], probabilities=[0.5, 0.5]) # patial knowledge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -360.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|███| 5000/5000 [00:04<00:00, 1037.95it/s]\n"
     ]
    }
   ],
   "source": [
    "res = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    model=model,\n",
    "    mixer=incoherent_mixer,\n",
    "    gamma = 0.5, \n",
    "    initial_state=initial_state,\n",
    "    temperature=1/beta,\n",
    "    name= 'basic_lowgamma',\n",
    "    verbose=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'acceptance_prob': array([0.33333333, 1.        , 0.5       , ..., 1.        , 0.25      ,\n",
       "        1.        ]),\n",
       " 'energy': array([240.,   0.,   0., ..., 120., 400.,   0.]),\n",
       " 'hamming': {0: {'accepted': 1330, 'rejected': 0, 'total': 1330},\n",
       "  1: {'accepted': 0, 'rejected': 184, 'total': 184},\n",
       "  2: {'accepted': 0, 'rejected': 437, 'total': 437},\n",
       "  3: {'accepted': 1106, 'rejected': 339, 'total': 1445},\n",
       "  4: {'accepted': 0, 'rejected': 310, 'total': 310},\n",
       "  5: {'accepted': 0, 'rejected': 165, 'total': 165},\n",
       "  6: {'accepted': 725, 'rejected': 129, 'total': 854},\n",
       "  7: {'accepted': 0, 'rejected': 106, 'total': 106},\n",
       "  8: {'accepted': 0, 'rejected': 79, 'total': 79},\n",
       "  9: {'accepted': 90, 'rejected': 0, 'total': 90}},\n",
       " 'kldiv': array([1.35588078e+01, 1.35588078e+01, 1.07393739e+01, ...,\n",
       "        1.55399560e-03, 1.55162740e-03, 1.56145716e-03])}"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_trajectory_statistics(res, exact_sampled_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'000000111'"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_state = data_samples[0]\n",
    "initial_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = 50_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -360.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 50000/50000 [02:07<00:00, 391.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -360.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 50000/50000 [02:06<00:00, 394.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -360.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|██| 50000/50000 [02:35<00:00, 321.01it/s]\n"
     ]
    }
   ],
   "source": [
    "res = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.35,0.35),\n",
    "    name= 'basic_lowgamma',\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "\n",
    "res_miss = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model_miss,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.35, 0.35),\n",
    "    name= 'basic_lowgamma_miss',\n",
    "    verbose=True\n",
    ")\n",
    "\n",
    "res_zero = quantum_enhanced_mcmc(\n",
    "    n_hops=steps,\n",
    "    initial_state=initial_state,\n",
    "    model=model_zero,\n",
    "    temperature=1/beta,\n",
    "    gamma_range= (0.05, 0.05),\n",
    "    name= 'basic_lowgamma_zero',\n",
    "    verbose=True\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'accepted': 26338, 'rejected': 0, 'total': 26338},\n",
       " 1: {'accepted': 0, 'rejected': 10323, 'total': 10323},\n",
       " 2: {'accepted': 0, 'rejected': 5566, 'total': 5566},\n",
       " 3: {'accepted': 3862, 'rejected': 1017, 'total': 4879},\n",
       " 4: {'accepted': 0, 'rejected': 1521, 'total': 1521},\n",
       " 5: {'accepted': 0, 'rejected': 687, 'total': 687},\n",
       " 6: {'accepted': 122, 'rejected': 314, 'total': 436},\n",
       " 7: {'accepted': 0, 'rejected': 198, 'total': 198},\n",
       " 8: {'accepted': 0, 'rejected': 48, 'total': 48},\n",
       " 9: {'accepted': 3, 'rejected': 0, 'total': 3}}"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = get_trajectory_statistics(res, exact_sampled_model)\n",
    "hams = r[\"hamming\"]\n",
    "hams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'accepted': 20406, 'rejected': 0, 'total': 20406},\n",
       " 1: {'accepted': 0, 'rejected': 12962, 'total': 12962},\n",
       " 2: {'accepted': 0, 'rejected': 7540, 'total': 7540},\n",
       " 3: {'accepted': 3894, 'rejected': 2070, 'total': 5964},\n",
       " 4: {'accepted': 0, 'rejected': 1780, 'total': 1780},\n",
       " 5: {'accepted': 0, 'rejected': 782, 'total': 782},\n",
       " 6: {'accepted': 40, 'rejected': 356, 'total': 396},\n",
       " 7: {'accepted': 0, 'rejected': 135, 'total': 135},\n",
       " 8: {'accepted': 0, 'rejected': 31, 'total': 31},\n",
       " 9: {'accepted': 3, 'rejected': 0, 'total': 3}}"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rm = get_trajectory_statistics(res_miss, model_miss_exact)\n",
    "hams_miss = rm[\"hamming\"]\n",
    "hams_miss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'accepted': 23018, 'rejected': 0, 'total': 23018},\n",
       " 1: {'accepted': 0, 'rejected': 13415, 'total': 13415},\n",
       " 2: {'accepted': 0, 'rejected': 7810, 'total': 7810},\n",
       " 3: {'accepted': 100, 'rejected': 3749, 'total': 3849},\n",
       " 4: {'accepted': 0, 'rejected': 1471, 'total': 1471},\n",
       " 5: {'accepted': 0, 'rejected': 362, 'total': 362},\n",
       " 6: {'accepted': 3, 'rejected': 64, 'total': 67},\n",
       " 7: {'accepted': 0, 'rejected': 7, 'total': 7},\n",
       " 8: {'accepted': 0, 'rejected': 1, 'total': 1},\n",
       " 9: {'accepted': 0, 'rejected': 0, 'total': 0}}"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rz = get_trajectory_statistics(res_zero, model_zero_exact)\n",
    "hams_zero = rz[\"hamming\"]\n",
    "hams_zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5e13c73410>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(15,4))\n",
    "(ax1, ax2, ax3) = axes\n",
    "\n",
    "ax1.plot(np.arange(gridsize**2+1), [r[\"total\"]/steps for r in r[\"hamming\"].values()], marker=\"o\", label=\"With System H\")\n",
    "ax1.plot(np.arange(gridsize**2+1), [r[\"total\"]/steps for r in rm[\"hamming\"].values()], marker=\"o\", label=\"Mismatched H\")\n",
    "ax1.plot(np.arange(gridsize**2+1), [r[\"total\"]/steps for r in rz[\"hamming\"].values()], marker=\"o\", label=\"Without System H\", color=\"grey\")\n",
    "ax1.set_yscale(\"log\")\n",
    "ax1.set_ylabel(\"Proposal Probability\")\n",
    "ax1.set_xlabel(\"Hamming Distance\")\n",
    "ax1.legend()\n",
    "\n",
    "ax2.bar(np.arange(gridsize**2+1)-0.8, [r[\"accepted\"]/r[\"total\"] if r[\"total\"] >0 else 0 for r in r[\"hamming\"].values() ], label=\"With System H\")\n",
    "ax2.bar(np.arange(gridsize**2+1)-0.0, [r[\"accepted\"]/r[\"total\"] if r[\"total\"] >0 else 0 for r in rm[\"hamming\"].values() ], label=\"Mismatched H\")\n",
    "ax2.bar(np.arange(gridsize**2+1)+0.8, [r[\"accepted\"]/r[\"total\"] if r[\"total\"] >0 else 0 for r in rz[\"hamming\"].values() ], label=\"Without System H\", color=\"grey\")\n",
    "# plt.yscale(\"log\")\n",
    "ax2.set_ylabel(\"Aceptance Probability\")\n",
    "ax2.set_xlabel(\"Hamming Distance\")\n",
    "ax2.set_xticks([0, 3, 6, 9])\n",
    "# ax2.legend()\n",
    "\n",
    "ax3.plot(r[\"kldiv\"], label=\"With System H\")\n",
    "ax3.plot(rm[\"kldiv\"], label=\"Mismatched H\")\n",
    "ax3.plot(rz[\"kldiv\"], label=\"Without System H\", color=\"grey\")\n",
    "plt.yscale(\"log\")\n",
    "ax3.set_ylabel(\"KL Divergence\")\n",
    "ax3.set_xlabel(\"MCMC Iteration\")\n",
    "ax3.legend(loc=\"upper right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from typing import Union\n",
    "\n",
    "isinstance(1, Union[float, int])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting with:  000000111 with energy: -360.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "runnning quantum MCMC steps . ..: 100%|███| 5000/5000 [00:04<00:00, 1015.71it/s]\n"
     ]
    }
   ],
   "source": [
    "generic_mixer = GenericMixer.OneBodyMixer(n_spins)\n",
    "custom_mixer_stripes = CustomMixer(n_spins,[[0,3,6],[1,4,7],[2,5,8]])\n",
    "custom_mixer_bars =  CustomMixer(n_spins,[[0, 1, 2], [3, 4, 5], [6, 7, 8]] )\n",
    "\n",
    "coherent_mixer_only_bars = CoherentMixerSum([generic_mixer, custom_mixer_bars], \n",
    "                                            weights=[0.75, 0.25])\n",
    "coherent_mixer_with_BAS_info = CoherentMixerSum([generic_mixer, custom_mixer_bars, custom_mixer_stripes],\n",
    "                                                weights=[0.7,0.15,0.15])\n",
    "\n",
    "\n",
    "incoherent_mixer_only_bars = IncoherentMixerSum([generic_mixer, custom_mixer_bars],\n",
    "                                                probabilities=[0.75, 0.25])\n",
    "incoherent_mixer_with_BAS_info = IncoherentMixerSum([generic_mixer, custom_mixer_bars, \n",
    "                                                    custom_mixer_stripes],\n",
    "                                                probabilities=[0.7,0.15,0.15])\n",
    "\n",
    "\n",
    "mcmc_std = quantum_enhanced_mcmc(\n",
    "                                    n_hops=steps,\n",
    "                                    model=model,\n",
    "                                    mixer=incoherent_mixer_with_BAS_info,\n",
    "                                    gamma = 0.5,#(0.9,0.45), # to standardise the problem\n",
    "                                    initial_state=initial_state,\n",
    "                                    temperature=1/beta, \n",
    "                                    verbose=True\n",
    "                                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13.55880782, 13.55880782, 13.55880782, ...,  0.03261017,\n",
       "        0.03266633,  0.03272257])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "hamming_diff = lambda si, sf: hamming_dist(si.bitstring, sf.bitstring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indataset = 0\n",
    "tot = 0\n",
    "trajectory = res_zero.states\n",
    "\n",
    "current_bs_idx=0\n",
    "for proposed_bs_idx in range(1,len(trajectory)):\n",
    "    hamming_dist_val=hamming_diff(si=trajectory[current_bs_idx],\n",
    "                                    sf=trajectory[proposed_bs_idx])\n",
    "    if trajectory[proposed_bs_idx].accepted:\n",
    "        # dict_hamming_distance_statistics[hamming_dist_val]['accepted']+=1\n",
    "        current_bs_idx=proposed_bs_idx\n",
    "    else:\n",
    "        # dict_hamming_distance_statistics[hamming_dist_val]['rejected']+=1\n",
    "        pass\n",
    "    \n",
    "    if hamming_dist_val == 3:\n",
    "        indataset += trajectory[proposed_bs_idx].accepted\n",
    "        tot += 1\n",
    "        \n",
    "    \n",
    "indataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 351)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indataset, tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MCMCState(bitstring='001001001', accepted=True)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trajectory' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[35], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m current_bs_idx\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m proposed_bs_idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m,\u001b[38;5;28mlen\u001b[39m(trajectory)):\n\u001b[1;32m      3\u001b[0m     hamming_dist_val\u001b[38;5;241m=\u001b[39mhamming_diff(si\u001b[38;5;241m=\u001b[39mtrajectory[current_bs_idx],\n\u001b[1;32m      4\u001b[0m                                     sf\u001b[38;5;241m=\u001b[39mtrajectory[proposed_bs_idx])\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m trajectory[proposed_bs_idx]\u001b[38;5;241m.\u001b[39maccepted:\n",
      "\u001b[0;31mNameError\u001b[0m: name 'trajectory' is not defined"
     ]
    }
   ],
   "source": [
    "current_bs_idx=0\n",
    "for proposed_bs_idx in range(1,len(trajectory)):\n",
    "    hamming_dist_val=hamming_diff(si=trajectory[current_bs_idx],\n",
    "                                    sf=trajectory[proposed_bs_idx])\n",
    "    if trajectory[proposed_bs_idx].accepted:\n",
    "        dict_hamming_distance_statistics[hamming_dist_val]['accepted']+=1\n",
    "        current_bs_idx=proposed_bs_idx\n",
    "    else:\n",
    "        dict_hamming_distance_statistics[hamming_dist_val]['rejected']+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.0000e+00, 1.3000e+01, 2.4700e+02, 5.5700e+02, 1.7580e+03,\n",
       "        2.0060e+03, 0.0000e+00, 3.4200e+03, 0.0000e+00, 6.0150e+03,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0556e+04,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 1.8407e+04]),\n",
       " array([0.        , 0.03448276, 0.06896552, 0.10344828, 0.13793103,\n",
       "        0.17241379, 0.20689655, 0.24137931, 0.27586207, 0.31034483,\n",
       "        0.34482759, 0.37931034, 0.4137931 , 0.44827586, 0.48275862,\n",
       "        0.51724138, 0.55172414, 0.5862069 , 0.62068966, 0.65517241,\n",
       "        0.68965517, 0.72413793, 0.75862069, 0.79310345, 0.82758621,\n",
       "        0.86206897, 0.89655172, 0.93103448, 0.96551724, 1.        ]),\n",
       " <BarContainer object of 29 artists>)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAGgCAYAAABMn6ZGAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3tElEQVR4nO3de3RU9b3//9fec8kNBhIMCRaRGEhYlHKTIJxVqCdV6/dX2wp4POdosCJQ1CoFRajKt9wELVBItYsici2X47EFEa0Xqj21pZVIaHu0QrhVbkISLoEQSGaSmf37g2+mTIOQmcxMJtvnY60szN6fz2c++80M83Lvz+wxLMuyBAAAYFNma08AAAAglgg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1gg7AADA1pytPYFEYVmWAoHo31/RNI2YjIumqHV8UOf4oM7xQZ3jI1Z1Nk1DhmFctR1h5/8JBCydPn0+qmM6nabS09NUXX1BDQ2BqI6NUNQ6PqhzfFDn+KDO8RHLOmdkpMnhuHrY4TIWAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNWdrTwAAALQNhtHaM4gMYQcAAFyVS/WyfLVh93OYhvx1rZuSCDsAAOCKDEOyfLXynTgiK+APq6/D5VJKapKk5NhMrhkIOwAAoFmsgF+WP7ywY5mtvzy49WcAAAAQQ4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABgay0KOy+++KJGjx4d/H306NHKz8+/7M/mzZslSX6/X3379m2y/4UXXgiOc/ToUU2YMEEDBw7UV7/6VRUXF8v/T3dsXL9+vb7+9a+rb9++uueee7Rr166WHAoAALCpiL8uYv369SouLtagQYOC21544QXV19cHf7csS5MnT9bZs2d16623SpIOHjwor9er1157TZ06dQq2TU1NlSTV19dr7Nix6t69u15++WUdPnxYTz/9tEzT1MSJEyVJr776qubPn685c+aod+/eWrZsmcaMGaO33npLGRkZkR4SAACwobDDTkVFhWbMmKGSkhJ17949ZF/Hjh1Dfl+3bp0++ugjvfbaa0pLS5Mk7dmzR+3atVOvXr0uO/4777yjY8eO6ZVXXlGHDh2Ul5enU6dOaf78+XrwwQfldru1dOlSFRUV6dvf/rYkad68ebrlllv0y1/+UhMmTAj3kAAAgI2FfRnrk08+kcvl0pYtW9SvX7/PbXf69GkVFxfroYce0g033BDcvmfPHuXm5n5uv9LSUn35y19Whw4dgtuGDBmimpoa7d69W6dOndLBgwc1dOjQ4H6n06lBgwZpx44d4R4OAACwubDP7BQWFqqwsPCq7V566SUlJydr7NixIdv37t2rhoYGjR07VmVlZcrKytJ3v/tdfec735EklZeXKzs7O6RP586dJUnHjx+X03lxyl26dGnSpqysLNzDCeF0Rne9tsNhhvyJ2KHW8UGd44M6xwd1Do/DNGSapixZYfUzTeNi/1asc8Rrdq6kpqZGr7zyih555BElJSWF7Nu3b58CgYAmTpyo7Oxsvf/++3ryySdVX1+vu+66S3V1dfJ4PCF9Gsfwer2qra2VJLnd7iZtvF5vxHM2TUPp6WkR978SjyclJuOiKWodH9Q5PqhzfFDn5qmvrpMjLUkK+K/e+FKmQ1Lr1jkmYefdd9+Vz+fTqFGjmux744035Pf7g2t4evXqpWPHjmnFihW66667lJycLJ/PF9KnMcSkpqYqOTlZki7bJiUl8kIGApaqqy9E3P9yHA5THk+Kqqtr5fcHojo2QlHr+KDO8UGd44M6h8fh9aruvFdWmGHH4XTKLcWkzh5PSrPOGMUs7Hzta19rcoZGUjCsXCovL09btmyRJGVnZ2vv3r0h+ysrKyVJWVlZwctXlZWVIWt/KisrlZWV1aJ5NzTE5snu9wdiNjZCUev4oM7xQZ3jgzpfnWFIClgKBAKywgwshnnxsldr1jkmF9BKS0tDFhA3qq6u1uDBg7Vp06aQ7R9//LF69uwpSSooKNCuXbtUU1MT3L99+3alpaWpV69e6tSpk3JyclRSUhLc39DQoNLSUhUUFMTicAAAQBsW9bBz/PhxVVVVXfaj5R6PR0OGDNHixYv1/vvv6+DBg1q2bJm2bNmiRx99VJJ0yy23KDMzU5MmTVJZWZneffddLVq0SA888EBwnc4DDzygVatW6dVXX9X+/fv11FNPqa6uTnfddVe0DwcAALRxUb+MdeLECUlN77nTaN68eXrhhRc0Y8YMnTp1Srm5uXr++ec1bNgwSRcXGi9fvlyzZs3S3XffrQ4dOuiee+7Rww8/HBzj7rvv1rlz51RcXKwzZ86oT58+WrVqFTcUBAAATRiWZYX3GTKb8vsDOn36fFTHdDpNpaenqarqPNeDY4xaxwd1jg/qHB/UufkMQ3L4quWtOCjLH+YCZZdL6Tl5qvEnR73OGRlpzVqgzM0FAACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArbUo7Lz44osaPXp0yLbp06crPz8/5KewsDC4PxAI6Pnnn9ewYcPUv39/jR8/XkeOHAkZY/fu3SoqKlL//v1VWFioX/ziFyH7mzMGAACA1IKws379ehUXFzfZvmfPHj344IPatm1b8OdXv/pVcP+SJUu0YcMGzZkzRy+//LICgYDGjRsnn88nSaqqqtKYMWPUrVs3bdy4Ud///ve1cOFCbdy4sdljAAAANAo77FRUVOjBBx/UwoUL1b1795B9lmVp//796tOnjzIzM4M/GRkZkiSfz6eVK1dq4sSJuvnmm9WrVy8tXrxY5eXl2rp1qyTplVdekcvl0uzZs5Wbm6tRo0bp/vvv17Jly5o9BgAAQKOww84nn3wil8ulLVu2qF+/fiH7Dh8+rAsXLuiGG264bN+ysjKdP39eQ4cODW7zeDzq3bu3duzYIUkqLS3V4MGD5XQ6g22GDBmigwcP6uTJk80aAwAAoJHz6k1CFRYWhqzBudTevXslSWvXrtXvf/97maap4cOHa/LkyWrfvr3Ky8slSV26dAnp17lz5+C+8vJy5eXlNdkvScePH2/WGJFyOqO7XtvhMEP+ROxQ6/igzvFBneODOofHYRoyTVOWrLD6maZxsX8r1jnssHMle/fulWma6ty5s5YuXarDhw9r/vz52rdvn9asWaPa2lpJktvtDumXlJSks2fPSpLq6uouu1+SvF5vs8aIhGkaSk9Pi7j/lXg8KTEZF01R6/igzvFBneODOjdPfXWdHGlJUsAfXkfTIal16xzVsPPQQw/pnnvuUXp6uiQpLy9PmZmZuvvuu/Xxxx8rOTlZ0sV1N43/LV0MMSkpF4uQnJzcZKGx1+uVJKWmpjZrjEgEApaqqy9E3P9yHA5THk+Kqqtr5fcHojo2QlHr+KDO8UGd44M6h8fh9aruvFdWmGHH4XTKLcWkzh5PSrPOGEU17JimGQw6jXr27Cnp4uWpxktPlZWV6tatW7BNZWWl8vPzJUnZ2dmqrKwMGaPx96ysLDU0NFx1jEg1NMTmye73B2I2NkJR6/igzvFBneODOl+dYUgKWAoEArLCDCyGefGyV2vWOaoX0KZOnar7778/ZNvHH38sSerRo4d69eqldu3aqaSkJLi/urpau3btUkFBgSSpoKBAO3fulN//j+S4fft25eTkqFOnTs0aAwAAoFFUw843vvENffDBB/rZz36mw4cP6/3339dTTz2lO+64Q7m5uXK73SoqKtLChQv13nvvqaysTJMnT1Z2drZuu+02SdKoUaNUU1Ojp59+Wvv379emTZu0evVqTZgwQZKaNQYAAECjqF7G+vrXv67i4mItW7ZML730ktq3b69vfetbmjRpUrDNxIkT1dDQoOnTp6uurk4FBQVasWKFXC6XJKlTp05avny55s6dqxEjRigzM1NTp07ViBEjmj0GAABAI8OyrPA+Q2ZTfn9Ap0+fj+qYTqep9PQ0VVWd53pwjFHr+KDO8UGd44M6N59hSA5ftbwVB2X5w1yg7HIpPSdPNf7kqNc5IyOtWQuUubkAAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwNcIOAACwtRaFnRdffFGjR48O2fbb3/5Wo0aN0oABA1RYWKgf//jHqqurC+7fuXOn8vPzm/yUlJQE23zwwQcaOXKk+vXrp9tvv12//vWvQx7D6/Vq1qxZGjp0qAYMGKDHH39cp0+fbsmhAAAAm4o47Kxfv17FxcUh20pLS/XII4/o1ltv1auvvqoZM2bozTff1KxZs4Jt9uzZo27dumnbtm0hPwMGDJAkHThwQBMmTNCwYcO0adMm/du//ZumTp2qDz74IDjGzJkztW3bNr3wwgtas2aN/v73v2vixImRHgoAALAxZ7gdKioqNGPGDJWUlKh79+4h+15++WXddNNNevDBByVJ3bt31+TJkzV9+nTNmjVLbrdbe/fuVY8ePZSZmXnZ8desWaP8/HxNnjxZkpSbm6tdu3Zp+fLlGjp0qCoqKrR582YtXbpUgwYNkiQtWrRIt99+u/7yl78EQxMAAIAUwZmdTz75RC6XS1u2bFG/fv1C9j3wwAOaNm1a6AOYpurr61VTUyPp4pmd3Nzczx2/tLRUQ4cODdk2ZMgQ7dy5U5ZlaefOncFtjXJycpSVlaUdO3aEezgAAMDmwj6zU1hYqMLCwsvu6927d8jv9fX1Wr16tfr06aOMjAxJ0r59+5Senq6RI0eqoqJCeXl5mjx5svr27StJKi8vV3Z2dsg4nTt3Vm1traqqqlRRUaH09HQlJSU1aVNeXh7u4YRwOqO7XtvhMEP+ROxQ6/igzvFBneODOofHYRoyTVOWrLD6maZxsX8r1jnssNNcDQ0Nmjp1qvbt26f169dLko4fP65z587pwoULmj59uhwOh9atW6eioiJt2rRJPXr0UF1dndxud8hYjb/7fD7V1tY22S9JSUlJ8nq9Ec/XNA2lp6dF3P9KPJ6UmIyLpqh1fFDn+KDO8UGdm6e+uk6OtCQp4A+vo+mQ1Lp1jknYqamp0aRJk/Thhx/qZz/7WfCsTZcuXbRjxw6lpKTI5XJJkr7yla9o165dWrt2rWbNmqWkpCT5fL6Q8Rp/T0lJUXJycpP90sVPaKWkRF7IQMBSdfWFiPtfjsNhyuNJUXV1rfz+QFTHRihqHR/UOT6oc3xQ5/A4vF7VnffKCjPsOJxOuaWY1NnjSWnWGaOoh53KykqNHz9en332mVasWKGCgoJ/mpgn5HfTNJWbm6uKigpJFwNRZWVlkzFTU1PVvn17ZWdn68yZM/L5fCFneCorK5WVldWiuTc0xObJ7vcHYjY2QlHr+KDO8UGd44M6X51hSApYCgQCssIMLIZ58bJXa9Y5qhfQzp49q+9+97s6ffq01q9f3yTo/P73v9eAAQN05MiR4LaGhgaVlZWpR48ekqRBgwbpww8/DOm3fft2DRw4UKZp6sYbb1QgEAguVJakTz/9VBUVFU0eDwAAIKph59lnn9WRI0e0YMECZWRk6MSJE8Efv9+vgQMHKj09XdOmTdPf/vY37dmzR9OmTdOZM2d0//33S5JGjx6tjz76SAsXLtSBAwe0cuVKvf322xo3bpwkKSsrS9/85jc1ffp0lZSU6KOPPtJjjz2mwYMHq3///tE8HAAAYANRu4zl9/v15ptvqr6+Xt/97neb7H/vvffUtWtXrV69WgsXLtTYsWPl9Xp14403at26dbrmmmskST179tSSJUu0YMECrVmzRl27dtWCBQtCPo4+Z84czZs3T4888ogkafjw4Zo+fXq0DgUAANiIYVlWeJ8hsym/P6DTp89HdUyn01R6epqqqs5zPTjGqHV8UOf4oM7xQZ2bzzAkh69a3oqDsvxhLlB2uZSek6caf3LU65yRkdasBcrcXAAAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANias7UnAADRYBiR97Ws6M0DQOIh7ABo81yql+Wrjbi/4UpRvVxRnBGARELYAdCmGYZk+WrlO3FEVsAffn/TIXfmdTLcLs7wADZF2AFgC1bAL8sfftgBYH8sUAYAALZG2AEAALZG2AEAALbWorDz4osvavTo0SHbdu/eraKiIvXv31+FhYX6xS9+EbI/EAjo+eef17Bhw9S/f3+NHz9eR44cifoYAAAAUgvCzvr161VcXByyraqqSmPGjFG3bt20ceNGff/739fChQu1cePGYJslS5Zow4YNmjNnjl5++WUFAgGNGzdOPp8vamMAAAA0CvvTWBUVFZoxY4ZKSkrUvXv3kH2vvPKKXC6XZs+eLafTqdzcXB06dEjLli3TqFGj5PP5tHLlSk2ZMkU333yzJGnx4sUaNmyYtm7dqjvuuCMqYwAAADQK+8zOJ598IpfLpS1btqhfv34h+0pLSzV48GA5nf/IUEOGDNHBgwd18uRJlZWV6fz58xo6dGhwv8fjUe/evbVjx46ojQEAANAo7DM7hYWFKiwsvOy+8vJy5eXlhWzr3LmzJOn48eMqLy+XJHXp0qVJm8Z90RgjUk5ndNdrOxxmyJ+IHWodH4laZ4dpyDRNWQr/roCGacphGlICHVOi1tluqHN4In2dmebF73JpzTpH9aaCdXV1crvdIduSkpIkSV6vV7W1F2/nfrk2Z8+ejdoYkTBNQ+npaRH3vxKPJyUm46Ipah0fiVbn+uo6OdKSpAjuoCzTIXdqklye2Lz+WyLR6mxX1Ll5In6dmQ5JrVvnqIad5OTkJouEvV6vJCk1NVXJycmSJJ/PF/zvxjYpKSlRGyMSgYCl6uoLEfe/HIfDlMeTourqWvn9gaiOjVDUOj4Stc4Or1d1570Rf12E/4JXNf7zMZhZZBK1znZDncMT6evM4XTKLcWkzh5PSrPOGEU17GRnZ6uysjJkW+PvWVlZamhoCG7r1q1bSJv8/PyojRGphobYPNn9/kDMxkYoah0fiVRnw5AUsBQIBGRF8A+pIUP+gCW/P5Bw342VSHW2M+p8dS15nRnmxRdWa9Y5qhfQCgoKtHPnTvkv+X6a7du3KycnR506dVKvXr3Url07lZSUBPdXV1dr165dKigoiNoYAAAAjaIadkaNGqWamho9/fTT2r9/vzZt2qTVq1drwoQJki6usykqKtLChQv13nvvqaysTJMnT1Z2drZuu+22qI0BAADQKKqXsTp16qTly5dr7ty5GjFihDIzMzV16lSNGDEi2GbixIlqaGjQ9OnTVVdXp4KCAq1YsUIulytqYwAAADQyLCvRrlK3Dr8/oNOno7tA0ek0lZ6epqqq81wPjjFqHR+JWGfDkBy+ankrDsryR7BA2eFQUlZ3+d2ehFmzk4h1tiPq3HwteZ05XC6l5+Spxp8c9TpnZKQ1a4EyNxcAAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC2RtgBAAC25ozmYCUlJbrvvvsuu69r165677339POf/1zFxcVN9u/Zsyf43+vXr9fKlSt14sQJ9enTR9OnT1fv3r2D+48ePao5c+Zox44dSk1N1V133aVHH31UDocjmocDAABsIKphZ8CAAdq2bVvItr/+9a969NFH9fDDD0u6GGq+853v6IknnrjsGK+++qrmz5+vOXPmqHfv3lq2bJnGjBmjt956SxkZGaqvr9fYsWPVvXt3vfzyyzp8+LCefvppmaapiRMnRvNwAACADUT1Mpbb7VZmZmbwJy0tTc8++6xGjBihUaNGSZL27t2r3r17h7TLzMwMjrF06VIVFRXp29/+tnr06KF58+YpJSVFv/zlLyVJ77zzjo4dO6b58+crLy9Pt9xyix577DGtWbNGPp8vmocDAABsIKpndv7Z0qVLVVtbq2nTpkmSfD6fDh48qBtuuOGy7U+dOqWDBw9q6NCh/5ig06lBgwZpx44dmjBhgkpLS/XlL39ZHTp0CLYZMmSIampqtHv3bvXr1y/i+Tqd0V3C5HCYIX8idqh1fCRqnR2mIdM0ZckKu69hmnKYhpRAx5SodbYb6hyeSF9npmlc7N+KdY5Z2Dl9+rRWr16txx9/XB07dpQk7d+/X36/X++8847mzp0rr9ergoICPfHEE+rcubPKy8slSV26dAkZq3PnziorK5MklZeXKzs7u8l+STp+/HjEYcc0DaWnp0XU92o8npSYjIumqHV8JFqd66vr5EhLkgL+8DubDrlTk+TyxOb13xKJVme7os7NE/HrzLy4nrY16xyzsLNhwwa1b99e//7v/x7ctnfvXklSSkqKfvrTn+rUqVNatGiR7rvvPm3evFm1tbWSLl4Ou1RSUpK8Xq8kqa6uTh6Pp8l+ScE2kQgELFVXX4i4/+U4HKY8nhRVV9fK7w9EdWyEotbxkah1dni9qjvvlRVB2DFMh/wXvKrxn4/BzCKTqHW2G+ocnkhfZw6nU24pJnX2eFKadcYoZmFn8+bNuvPOO5WcnBzcduedd2r48OHKyMgIbuvZs6eGDx+u3/72t+rWrZskNVl74/V6lZJyMREmJydfdr8kpaamtmjODQ2xebL7/YGYjY1Q1Do+EqnOhiEpYCkQCMiK4B9SQ4b8AUt+f0BW+FfBYiqR6mxn1PnqDEMyWvj6aM06xyTslJWV6ciRI/rWt77VZN+lQUe6eAmqY8eOKi8v10033SRJqqysVG5ubrBNZWWlsrKyJEnZ2dnBM0SX7pcUbAMAAKLHNA2lprjkivQylmXJMIzYTK4ZYhJ2SktL1alTJ/Xq1Stk++LFi/X222/r7bffDh700aNHVVVVpR49eqhTp07KyclRSUlJcJFyQ0ODSktLdc8990iSCgoKtHnzZtXU1Khdu3aSpO3btystLa3J4wEAgJYzDEOGaeqNbZ+q/ER1WH2zMz0aOfJ6mXYLO7t27VJ+fn6T7bfeeqtWrFihmTNn6v7779fJkyc1b948DRw4UMOGDZMkPfDAA5o7d66uv/56feUrX9GyZctUV1enu+66S5J0yy23qLi4WJMmTdKUKVN09OhRLVq0SA888ECTtT4AACB6Tp+t1fFT4a1vc7tdMZpN88Uk7Jw4cSL4CaxL9enTRy+99JJ++tOfauTIkXK73fr617+uadOmBc/03H333Tp37pyKi4t15swZ9enTR6tWrQpe/kpKStLy5cs1a9Ys3X333erQoYPuueee4E0LAQAALmVYVqItyWsdfn9Ap09H99MYTqep9PQ0VVWdZ/FbjFHr+EjEOhuG5PBVy1txUJY/gk9jORxKyuouv9uTMAuUE7HOdkSdm8/pNNXOUaflq97SoeNnwup7fZeOGjfm/6hWqfJ6G6I6r4yMtGZ9Gos7KQEAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFsj7AAAAFtztvYEAIQyjMj7Wlb05gEAdkHYARKIS/WyfLUR9zdcKaqXK4ozAoC2j7ADJAjDkCxfrXwnjsgK+MPvbzrkzrxOhtvFGR4AuARhB0gwVsAvyx9+2AEAXB4LlAEAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK3xdREA2jzDMGSapiyF/6VghmnKaMlXzQNIeFE/s1NRUaH8/PwmP5s2bZIk7d69W0VFRerfv78KCwv1i1/8IqR/IBDQ888/r2HDhql///4aP368jhw5EtLmamMA+OIwTUOpKS6lpSWpXQQ/aWlJSk1xyTQJPIBdRf3MTllZmZKSkvTuu++G/N9S+/btVVVVpTFjxqiwsFCzZs3SX//6V82aNUtpaWkaNWqUJGnJkiXasGGDnnvuOWVnZ2vBggUaN26cXn/9dbnd7maNAeCLwzAMGaapN7Z9qvIT1WH3z870aOTI62VYhhTBmSEAiS/qYWfv3r3q3r27Onfu3GTfmjVr5HK5NHv2bDmdTuXm5urQoUNatmyZRo0aJZ/Pp5UrV2rKlCm6+eabJUmLFy/WsGHDtHXrVt1xxx165ZVXrjgGgC+m02drdfzU+bD7ud2uGMwGQCKJ+mWsPXv2KDc397L7SktLNXjwYDmd/8hYQ4YM0cGDB3Xy5EmVlZXp/PnzGjp0aHC/x+NR7969tWPHjmaNAQAAcKmYnNlJT0/Xvffeq08//VTXX3+9HnroIQ0fPlzl5eXKy8sLad94Buj48eMqLy+XJHXp0qVJm8Z9VxvjmmuuiXjuTmd0s5/DYYb8idixS60dZssW2jpMQ4phDRKxztGaSyIeUyLNyY6oc/NFo0amaUT9fba5ohp2Ghoa9Pe//109evTQD3/4Q7Vr106//vWv9b3vfU+rVq1SXV2d3G53SJ+kpCRJktfrVW1trSRdts3Zs2cl6apjRMo0DaWnp0Xc/0o8npSYjIum2nqt66vr5EhLkgL+8DubDrlTk+TyxOZ5fKlEq3N99YUWj5FoxyQl5pzsiDo3T0tfZ+3aJUdpJuGLathxOp0qKSmRw+FQcvLFg+rTp4/27dunFStWKDk5WT6fL6RPY0BJTU0N9vH5fMH/bmyTknLxyXi1MSIVCFiqjsI/mJdyOEx5PCmqrq6V3x+I6tgIZZdaO7xe1Z33yoog7BimQ/4LXtX4w1+30lyJWGeHw1Q03qoS7ZgSrc52RJ2bLxqvs5qaOtXXR/A/clfg8aQ066xT1C9jpaU1/b/Knj17atu2bcrOzlZlZWXIvsbfs7Ky1NDQENzWrVu3kDb5+fmSdNUxWqKhITZPdr8/ELOxEaot19owJAUsBQIBWRH8w2vIkD9gye8PyIrxh4oSrs6Olg+RcMekxJyTHVHnZmrh6ywQsFqtzlG9eLZv3z4NHDhQJSUlIdv/9re/qUePHiooKNDOnTvl9/8j2W3fvl05OTnq1KmTevXqpXbt2oX0r66u1q5du1RQUCBJVx0DAADgUlENO7m5ubrhhhs0e/ZslZaW6sCBA3r22Wf117/+VQ899JBGjRqlmpoaPf3009q/f782bdqk1atXa8KECZIurtUpKirSwoUL9d5776msrEyTJ09Wdna2brvtNkm66hgAAACXiuplLNM0tXTpUv3kJz/RpEmTVF1drd69e2vVqlXBT1AtX75cc+fO1YgRI5SZmampU6dqxIgRwTEmTpyohoYGTZ8+XXV1dSooKNCKFSvkcl28F0anTp2uOgYAAECjqK/Zueaaa/Tss89+7v6+ffvqv//7vz93v8Ph0BNPPKEnnngi4jEAAAAacXMBAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga1H/IlAAkTMMQ6ZpypIVfl/TlGEYMZgVALRthB0gQZimodQUl1xpSVLAH8EADrlSXKoJGPL7ww9LAGBXhB0gQRiGIcM09ca2T1V+ojrs/tmZHo0ceb0My5AiODMEAHZF2AESzOmztTp+6nzY/dxuVwxmAwBtHwuUAQCArRF2AACArRF2AACArRF2AACArRF2AACArRF2AACArfHRc+AyWnIjYotb3ABAQiHsAP/EpXpZvtqI+xuuFNWLe94AQKIg7ACXMAzJ8tXKd+KIrAi+ssEwHXJnXifD7eIMDwAkCMIOcBlWwC/LH8H3UwEAEg4LlAEAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK0RdgAAgK1xU0HgnxiGIdM0ZSn8WyAbpimjJV+sBQCIOsIOcAnTNJSa4pIrLUmK4OsiZDrkSnGpJmDI7+f7IgAgEUQ97Jw5c0aLFi3S7373O9XU1Cg/P1+PP/64Bg0aJEkaM2aM/vSnP4X0GTx4sNauXStJ8nq9eu655/T222+rrq5OhYWFevrpp5WRkRFs/8EHH2jBggU6cOCAunTpokcffVTf/OY3o30o+AIyDEOGaeqNbZ+q/ER12P2zMz0aOfJ6GZYhRXBmCAAQfVEPO4899phOnDihRYsWqVOnTlq7dq3Gjh2rV199VTfccIP27NmjmTNn6pZbbgn2cbn+8Q3RM2fOVGlpqV544QW53W7NmDFDEydO1Lp16yRJBw4c0IQJEzRmzBgtWLBAv/vd7zR16lRlZGRo6NCh0T4cfEGdPlur46fOh93P7ebbzgEg0UQ17Bw6dEh//OMftWHDBt14442SpP/7f/+v/vCHP+j1119XUVGRTp06pX79+ikzM7NJ/4qKCm3evFlLly4NnglatGiRbr/9dv3lL3/RgAEDtGbNGuXn52vy5MmSpNzcXO3atUvLly8n7CQY0zRkmpGtXwkELAUCnBkBALRcVMNOenq6li1bpq985SvBbYZhyDAMVVdXa8+ePTIMQzk5OZftv3PnTknSkCFDgttycnKUlZWlHTt2aMCAASotLQ05K9TYfu7cubIsi8WhCcI0DXXsmCqHI7IP/Pn9AZ05c4HAAwBosaiGHY/Ho6997Wsh29555x0dOnRITz31lPbu3av27dtr9uzZ+uMf/6jU1FTdfvvtevjhh+V2u1VRUaH09HQlJSWFjNG5c2eVl5dLksrLy5Wdnd1kf21traqqqkLW9oTL6YzuJ/Eb3+gjfcNvyxwOUw6HqYXrd+poxbmw+nbNaq8p994ol8shvz/Q7Me79M9IRevvKpJxWvOxwx07kZ7TbaFu4UrEOtsRdW6+aNTINI2ov882V0w/jfXnP/9ZTz75pG677TbdfPPNeuqpp+T1etW3b1+NGTNGu3fv1vz583Xs2DHNnz9ftbW1crvdTcZJSkqS1+uVJNXV1TVp0/i7z+eLeK6maSg9PS3i/lfi8aTEZNy24GjFOR347GxEfSOpWzRqXV99ocVjRDqP1nzsRHuMcLSVuoUrEedkR9S5eVr6OmvXLjlKMwlfzMLOu+++qylTpmjgwIFauHChJGn27NmaNm2aOnToIEnKy8uTy+XS5MmTNXXqVCUnJ182sHi9XqWkXHwyJiUlNWnT+Htjm0gEApaqo/AP5qUcDlMeT4qqq2ubfYbCLhqPvSXCqVu0au1wmIrGP3uRzKM1H7u5EvE53RbqFq5ErLMdUefmi8brrKamTvX1EdzS4wo8npRmnXWKSdhZt26d5s6dq9tvv10//vGPg2denE5nMOg06tmzp6R/XJ46c+aMfD5fyNmbyspKZWVlSZK6dOmiysrKkDEqKyuVmpqq9u3bt2jeDQ2xebL7/YGYjW1nkdQtKrV2tKx7i+bRmo+dYI8RljZSt3Al4pzsiDo3UwtfZ4GA1Wp1jvrFsw0bNmjOnDm69957tWjRopDQMnr0aD355JMh7T/++GO5XC51795dN954owKBQHChsiR9+umnqqioUEFBgSRp0KBB+vDDD0PG2L59uwYOHCjT5LorAAAIFdV08Omnn2revHm69dZbNWHCBJ08eVInTpzQiRMndO7cOX3jG9/Qa6+9pv/6r//SkSNH9Oabb2r+/PkaO3as2rVrp6ysLH3zm9/U9OnTVVJSoo8++kiPPfaYBg8erP79+0u6GJg++ugjLVy4UAcOHNDKlSv19ttva9y4cdE8FAAAYBNRvYz1zjvvqL6+Xr/5zW/0m9/8JmTfiBEj9Nxzz8kwDK1du1bz5s1TZmam7r//fn3ve98LtpszZ47mzZunRx55RJI0fPhwTZ8+Pbi/Z8+eWrJkiRYsWKA1a9aoa9euWrBgAffYAQAAlxXVsPPggw/qwQcfvGKbe++9V/fee+/n7k9NTdUzzzyjZ5555nPbDB8+XMOHD494ngAA4IuDRS4AAMDW+NZzJKxwbmL1zzcH4+smAACNCDtIONd0TFFD7QWlqDasjzrWV1+4eB8Ih6TkZFVfEIEHAEDYQeLxpLll1ddqy+vbVHkyvK+akKTO17TXt7/1VZlmMmEHAEDYQeKqPHlOh46fae1pAADaOBYoAwAAW+PMDmIq2e1QanJ4TzO3iwwOAIgewg5ixl93QX2vdera1PC+Pi433ZJV75PDYcRoZgCALxLCDmIm4KvVhYrDOlV+Nqx+F9KuleVPl2kQdgAALUfYQUzV++rl9frC6uOvb4jRbAAAX0QsjgAAALZG2AEAALZG2AEAALbGmh18LtM0ZJqRLRIO53utAACIJcIOLss0DXXsmNqi0FJffSGKMwIAIDKEHRtr6ZkZh8PUwvU7dbQi/O+nuqlPtu4akhnRYwMAEE2EHZuKxpkZSTpacU4HPgvvPjmSdMOXOrTocQEAiBbCjk2ZptGiMzMDe3XWff9f7xjMDACA+CLs2FykZ2a6dm4Xg9kAABB/fGQGAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGmEHAADYGl8XgStKdjuUmhz+08TtIkcDABIDYQeX5XQY8tddUN9rnbo2NSXs/rnplqx6nxwOIwazAwCg+Qg7uCzTNBXw1epCxWGdKg//i0QvpF0ry58u0yDsAABaF2EHV1Tvq5fX6wu7n7++IQazAQAgfCysAAAAtsaZnQRnmoZMM/xLQQ4HORYAAImwk9BM01DHjqktCi58mgoA8EXXZsNOIBDQz372M/3yl7/UuXPnVFBQoB/96Ee67rrrWntqUWOahhwOUwvX79TRinNh9S3onaX/uPl6Pk0FAPjCa7NhZ8mSJdqwYYOee+45ZWdna8GCBRo3bpxef/11ud3u1p5eVB2tOKcDn4X3iagbvtSBT1MBAKA2GnZ8Pp9WrlypKVOm6Oabb5YkLV68WMOGDdPWrVt1xx13tO4EEwifpgIAfNG1yYUZZWVlOn/+vIYOHRrc5vF41Lt3b+3YsaMVZwYAABKNYVmW1dqTCNfWrVv16KOP6n//93+VnJwc3P6DH/xAdXV1evHFF8Me07IsBQLRLYVh/L+b8wUCEY9hmqbO1njV4A9vjCS3Q2lJDl04Xyd/BI/vcjqUnOzShVqf/GE+dkv7t/SxHaap1LRkWYrsEpwhK+K6teXHbq6WPqdjoS3ULVyJWGc7os7NF+nr7NLXWLQTh2kaMpqx3KJNXsaqra2VpCZrc5KSknT2bPjrUyTJMIyYLcY1zZadQOvQLinivmnt01r02GntXK3Wv6WP3ZK/zZbWra0+dnO19DkdC22hbuFKxDrbEXVuvpa8zlrzNdYm/4Ybz+b4fKFrUbxer1JSwv/kEQAAsK82GXa6dOkiSaqsrAzZXllZqaysrNaYEgAASFBtMuz06tVL7dq1U0lJSXBbdXW1du3apYKCglacGQAASDRtcs2O2+1WUVGRFi5cqIyMDH3pS1/SggULlJ2drdtuu621pwcAABJImww7kjRx4kQ1NDRo+vTpqqurU0FBgVasWCGXq2WLWgEAgL20yY+eAwAANFebXLMDAADQXIQdAABga4QdAABga4QdAABga4QdAABga4QdAABga4QdAABga4SdFggEAnr++ec1bNgw9e/fX+PHj9eRI0c+t31VVZUef/xxFRQUaPDgwZo1a1bwG9xxZeHWet++ffre976nm266SUOHDtXEiRN17NixOM64bQq3zpfasmWL8vPzdfTo0RjPsu0Lt8719fX6yU9+EmxfVFSk3bt3x3HGbVO4dT516pQef/xxDRkyRDfddJMmT56sioqKOM647XvxxRc1evToK7ZpjfdCwk4LLFmyRBs2bNCcOXP08ssvKxAIaNy4cU2+jb3RxIkTdejQIa1evVo//elP9f7772vmzJnxnXQbFU6tq6qqNGbMGCUnJ2vt2rV66aWXdPr0aY0bN05er7cVZt92hPucbvTZZ59p9uzZcZpl2xdunWfOnKlNmzZp3rx52rhxozIyMjR+/HidO3cuzjNvW8Kt86RJk3Ts2DGtWrVKq1at0rFjx/T9738/zrNuu9avX6/i4uKrtmuV90ILEfF6vdaAAQOs9evXB7edPXvW6tu3r/X66683af/nP//ZysvLs/bv3x/c9oc//MHKz8+3ysvL4zLntircWr/yyivWgAEDrNra2uC2Y8eOWXl5edaf/vSnuMy5LQq3zo38fr/1n//5n9Z9991n5eXlWUeOHInHdNuscOt8+PBhKz8/3/qf//mfkPb/+q//yvP5CsKt89mzZ628vDzrvffeC2579913rby8PKuqqioeU26zysvLrQkTJlj9+/e3br/9dquoqOhz27bWeyFndiJUVlam8+fPa+jQocFtHo9HvXv31o4dO5q0Ly0tVWZmpnJzc4PbBg8eLMMwtHPnzrjMua0Kt9ZDhw7VkiVLlJycHNxmmhef6tXV1bGfcBsVbp0bLV26VPX19ZowYUI8ptnmhVvnP/7xj2rfvr2GDx8e0v63v/1tyBgIFW6dk5OTlZaWps2bN6umpkY1NTV67bXXlJOTI4/HE8+ptzmffPKJXC6XtmzZon79+l2xbWu9F7bZLwJtbeXl5ZKkLl26hGzv3LlzcN+lKioqmrR1u93q2LGjjh8/HruJ2kC4te7atau6du0asm3ZsmVKTk5WQUFB7CbaxoVbZ0n66KOPtHLlSv3qV79ibUMzhVvnTz/9VNddd522bt2qZcuWqaKiQr1799YPf/jDkDcMhAq3zm63W88995x+9KMfadCgQTIMQ507d9a6deuC/7OEyyssLFRhYWGz2rbWeyF/gxFqXEzldrtDticlJV12XUhtbW2Ttldqj38It9b/bO3atVq3bp2mTJmijIyMmMzRDsKt84ULFzRlyhRNmTJF3bt3j8cUbSHcOtfU1OjQoUNasmSJHnvsMf385z+X0+nUPffco1OnTsVlzm1RuHW2LEu7d+/WgAEDtH79eq1Zs0bXXnutHn74YdXU1MRlzl8ErfVeSNiJUOMlkn9e6Ob1epWSknLZ9pdbFOf1epWamhqbSdpEuLVuZFmWiouL9cwzz+ihhx666icEvujCrfMzzzyjnJwc/cd//Edc5mcX4dbZ6XSqpqZGixcv1le/+lX17dtXixcvliS9+uqrsZ9wGxVund966y2tW7dOCxYs0I033qjBgwdr6dKl+uyzz/SrX/0qLnP+Imit90LCToQaT8NVVlaGbK+srFRWVlaT9tnZ2U3a+nw+nTlzRp07d47dRG0g3FpLFz+q+8QTT2jp0qV68sknNWnSpFhPs80Lt84bN27Un/70Jw0YMEADBgzQ+PHjJUl33HGHli5dGvsJt1GR/NvhdDpDLlklJyfruuuu42P+VxBunUtLS5WTk6N27doFt3Xo0EE5OTk6dOhQbCf7BdJa74WEnQj16tVL7dq1U0lJSXBbdXW1du3addl1IQUFBSovLw950Xz44YeSpBtvvDH2E27Dwq21JE2dOlVvv/22fvKTn+j++++P00zbtnDrvHXrVr3xxhvavHmzNm/erGeeeUbSxfVRnO35fJH829HQ0KCPP/44uK2urk5HjhzR9ddfH5c5t0Xh1jk7O1uHDh0KuZRy4cIFHT16lMu0UdRa74UsUI6Q2+1WUVGRFi5cqIyMDH3pS1/SggULlJ2drdtuu01+v1+nT59W+/btlZycrH79+mngwIGaPHmyZs6cqQsXLuhHP/qR7rzzzs89O4GLwq31pk2b9Oabb2rq1KkaPHiwTpw4ERyrsQ2aCrfO//xG27jo89prr1XHjh1b4QjahnDrPGjQIP3Lv/yLpk2bptmzZ6tjx456/vnn5XA49J3vfKe1DydhhVvnO++8UytWrNCkSZP0gx/8QJJUXFyspKQkjRw5spWPpu1KmPfCmH2o/QugoaHBmj9/vjVkyBCrf//+1vjx44P3GDly5IiVl5dnbdy4Mdj+5MmT1qOPPmr179/fuummm6wZM2ZYdXV1rTX9NiWcWo8ZM8bKy8u77M+lfx9oKtzn9KW2b9/OfXaaKdw6nzt3zpoxY4Z10003Wf369bPGjBlj7du3r7Wm32aEW+f9+/dbEyZMsAYPHmwNGTLEeuSRR3g+h2natGkh99lJlPdCw7IsK3ZRCgAAoHWxZgcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANgaYQcAANja/w/4CqxGDfByCgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(r[\"acceptance_prob\"], bins=np.linspace(0, 1, 30))\n",
    "plt.hist(rz[\"acceptance_prob\"], bins=np.linspace(0, 1, 30), alpha=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "the dtypes of parameters x (object) and width (float64) are incompatible",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/qumcmc/lib/python3.11/site-packages/matplotlib/axes/_axes.py:2458\u001b[0m, in \u001b[0;36mAxes.bar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2457\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2458\u001b[0m     left \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m width \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m   2459\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'dict' and 'float'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m plot_hamming_distance_statistics(\u001b[38;5;28mlist\u001b[39m(trajectory_stat\u001b[38;5;241m.\u001b[39mvalues()), nspin\u001b[38;5;241m=\u001b[39m n_spins,labels\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(trajectory_stat\u001b[38;5;241m.\u001b[39mkeys()))\n",
      "File \u001b[0;32m~/Desktop/my_Github/quMCMC/qumcmc/basic_utils.py:465\u001b[0m, in \u001b[0;36mplot_hamming_distance_statistics\u001b[0;34m(trajectory_stat_list, nspin, labels, figsize)\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(trajectory_stat_list, labels):\n\u001b[1;32m    464\u001b[0m     alpha \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0.7\u001b[39m) \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mlen\u001b[39m(trajectory_stat_list)\n\u001b[0;32m--> 465\u001b[0m     plt\u001b[38;5;241m.\u001b[39mbar(\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39munique(item[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhamming\u001b[39m\u001b[38;5;124m'\u001b[39m], return_counts\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),label\u001b[38;5;241m=\u001b[39m item[\u001b[38;5;241m1\u001b[39m] ,alpha\u001b[38;5;241m=\u001b[39m alpha)\n\u001b[1;32m    467\u001b[0m \u001b[38;5;66;03m# plt.xscale(\"log\")\u001b[39;00m\n\u001b[1;32m    469\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHamming-Distance Statistics\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/qumcmc/lib/python3.11/site-packages/matplotlib/pyplot.py:2439\u001b[0m, in \u001b[0;36mbar\u001b[0;34m(x, height, width, bottom, align, data, **kwargs)\u001b[0m\n\u001b[1;32m   2435\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mbar)\n\u001b[1;32m   2436\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbar\u001b[39m(\n\u001b[1;32m   2437\u001b[0m         x, height, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m, bottom\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   2438\u001b[0m         data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2439\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m gca()\u001b[38;5;241m.\u001b[39mbar(\n\u001b[1;32m   2440\u001b[0m         x, height, width\u001b[38;5;241m=\u001b[39mwidth, bottom\u001b[38;5;241m=\u001b[39mbottom, align\u001b[38;5;241m=\u001b[39malign,\n\u001b[1;32m   2441\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: data} \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/qumcmc/lib/python3.11/site-packages/matplotlib/__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1439\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1441\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1442\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1444\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1445\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1446\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m~/anaconda3/envs/qumcmc/lib/python3.11/site-packages/matplotlib/axes/_axes.py:2460\u001b[0m, in \u001b[0;36mAxes.bar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2458\u001b[0m         left \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m width \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m   2459\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 2460\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe dtypes of parameters x (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   2461\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mand width (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwidth\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   2462\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mare incompatible\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   2463\u001b[0m     bottom \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m   2464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# horizontal\u001b[39;00m\n",
      "\u001b[0;31mTypeError\u001b[0m: the dtypes of parameters x (object) and width (float64) are incompatible"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hamming_distance_statistics(list(trajectory_stat.values()), nspin= n_spins,labels= list(trajectory_stat.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "the dtypes of parameters x (object) and width (float64) are incompatible",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/qumcmc/lib/python3.11/site-packages/matplotlib/axes/_axes.py:2458\u001b[0m, in \u001b[0;36mAxes.bar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2457\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2458\u001b[0m     left \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m width \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m   2459\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'dict' and 'float'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[31], line 7\u001b[0m\n\u001b[1;32m      3\u001b[0m bins \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m0\u001b[39m, n_spins\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m item \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(trajectory_stat\u001b[38;5;241m.\u001b[39mitems())[:\u001b[38;5;241m4\u001b[39m]:\n\u001b[0;32m----> 7\u001b[0m     plt\u001b[38;5;241m.\u001b[39mbar(\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39munique(item[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhamming\u001b[39m\u001b[38;5;124m'\u001b[39m], return_counts\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),label\u001b[38;5;241m=\u001b[39m item[\u001b[38;5;241m0\u001b[39m] ,alpha\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.3\u001b[39m)\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# plt.xscale(\"log\")\u001b[39;00m\n\u001b[1;32m     11\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHamming-Distance Statistics\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/qumcmc/lib/python3.11/site-packages/matplotlib/pyplot.py:2439\u001b[0m, in \u001b[0;36mbar\u001b[0;34m(x, height, width, bottom, align, data, **kwargs)\u001b[0m\n\u001b[1;32m   2435\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mbar)\n\u001b[1;32m   2436\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbar\u001b[39m(\n\u001b[1;32m   2437\u001b[0m         x, height, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m, bottom\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m   2438\u001b[0m         data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2439\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m gca()\u001b[38;5;241m.\u001b[39mbar(\n\u001b[1;32m   2440\u001b[0m         x, height, width\u001b[38;5;241m=\u001b[39mwidth, bottom\u001b[38;5;241m=\u001b[39mbottom, align\u001b[38;5;241m=\u001b[39malign,\n\u001b[1;32m   2441\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m: data} \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/qumcmc/lib/python3.11/site-packages/matplotlib/__init__.py:1442\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1439\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1440\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1441\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1442\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m func(ax, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mmap\u001b[39m(sanitize_sequence, args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1444\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1445\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1446\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m~/anaconda3/envs/qumcmc/lib/python3.11/site-packages/matplotlib/axes/_axes.py:2460\u001b[0m, in \u001b[0;36mAxes.bar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2458\u001b[0m         left \u001b[38;5;241m=\u001b[39m x \u001b[38;5;241m-\u001b[39m width \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m   2459\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 2460\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthe dtypes of parameters x (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   2461\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mand width (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwidth\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   2462\u001b[0m                         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mare incompatible\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   2463\u001b[0m     bottom \u001b[38;5;241m=\u001b[39m y\n\u001b[1;32m   2464\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# horizontal\u001b[39;00m\n",
      "\u001b[0;31mTypeError\u001b[0m: the dtypes of parameters x (object) and width (float64) are incompatible"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "bins = np.arange(0, n_spins+1)\n",
    "\n",
    "for item in list(trajectory_stat.items())[:4]:\n",
    "\n",
    "    plt.bar(*np.unique(item[1]['hamming'], return_counts=True),label= item[0] ,alpha= 0.3)\n",
    "\n",
    "# plt.xscale(\"log\")\n",
    "\n",
    "plt.xlabel(\"Hamming-Distance Statistics\")\n",
    "# plt.ylabel(\"Hamming Distance\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'advmic_lowgamma'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[32], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m,\u001b[38;5;241m10\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m bins \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(np\u001b[38;5;241m.\u001b[39mlog10(np\u001b[38;5;241m.\u001b[39mmin([np\u001b[38;5;241m.\u001b[39mmin(trajectory_stat[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madvmic_lowgamma\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124macceptance_prob\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m      4\u001b[0m                                     np\u001b[38;5;241m.\u001b[39mmin(trajectory_stat[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madvmix_extra\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124macceptance_prob\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m      5\u001b[0m                                     np\u001b[38;5;241m.\u001b[39mmin(trajectory_stat[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madvmix_extgamma\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124macceptance_prob\u001b[39m\u001b[38;5;124m'\u001b[39m])]))\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m0\u001b[39m, num\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m30\u001b[39m)\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mhist(np\u001b[38;5;241m.\u001b[39mlog10(trajectory_stat[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madvmic_lowgamma\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124macceptance_prob\u001b[39m\u001b[38;5;124m'\u001b[39m]),label\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124madvmic_lowgamma\u001b[39m\u001b[38;5;124m'\u001b[39m , alpha\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.8\u001b[39m, bins\u001b[38;5;241m=\u001b[39m bins, density\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m      8\u001b[0m plt\u001b[38;5;241m.\u001b[39mhist(np\u001b[38;5;241m.\u001b[39mlog10(trajectory_stat[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madvmix_extra\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124macceptance_prob\u001b[39m\u001b[38;5;124m'\u001b[39m]),label\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124madvmix_extra\u001b[39m\u001b[38;5;124m'\u001b[39m , alpha\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.6\u001b[39m, bins\u001b[38;5;241m=\u001b[39m bins, density\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'advmic_lowgamma'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "bins = np.linspace(np.log10(np.min([np.min(trajectory_stat['advmic_lowgamma']['acceptance_prob']),\n",
    "                                    np.min(trajectory_stat['advmix_extra']['acceptance_prob']),\n",
    "                                    np.min(trajectory_stat['advmix_extgamma']['acceptance_prob'])]))-0.1, 0, num=30)\n",
    "\n",
    "plt.hist(np.log10(trajectory_stat['advmic_lowgamma']['acceptance_prob']),label= 'advmic_lowgamma' , alpha= 0.8, bins= bins, density=True)\n",
    "plt.hist(np.log10(trajectory_stat['advmix_extra']['acceptance_prob']),label= 'advmix_extra' , alpha= 0.6, bins= bins, density=True)\n",
    "plt.hist(np.log10(trajectory_stat['advmix_higamma']['acceptance_prob']),label= 'advmix_higamma' ,alpha= 0.4, bins= bins, density=True)\n",
    "\n",
    "\n",
    "# plt.xscale(\"log\")\n",
    "\n",
    "plt.xlabel(\"Acceptance Probabilities | scale: log10\")\n",
    "plt.ylabel(\"Normalized Counts\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acceptance_prob_statistics(list(trajectory_stat.values()), labels= list(trajectory_stat.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ORQVIZ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import orqviz\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Callable, Optional, Tuple\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from orqviz.aliases import (\n",
    "    ArrayOfParameterVectors,\n",
    "    FullGradientFunction,\n",
    "    LossFunction,\n",
    "    ParameterVector,\n",
    ")\n",
    "from orqviz.gradients import calculate_full_gradient\n",
    "\n",
    "\n",
    "def gradient_descent_optimizer(\n",
    "    init_params: ParameterVector,\n",
    "    loss_function: LossFunction,\n",
    "    n_iters: int,\n",
    "    learning_rate: float = 0.1,\n",
    "    full_gradient_function: FullGradientFunction = None,\n",
    "    eval_loss_during_training: bool = True,\n",
    ") -> Tuple[ArrayOfParameterVectors, np.ndarray]:\n",
    "    \"\"\"Function perform gradient descent optimization on a loss function.\n",
    "\n",
    "    Args:\n",
    "        init_params: Initial parameter vector from which to start the optimization.\n",
    "        loss_function: Function with respect to which the gradient is calculated.\n",
    "            It must receive only a numpy.ndarray of parameters, and return\n",
    "            a real number. If your function requires more arguments, consider using the\n",
    "            'LossFunctionWrapper' class from 'orqviz.loss_function'.\n",
    "        n_iters: Number of iterations to optimize.\n",
    "        learning_rate: Learning rate for gradient descent. The calculated gradient\n",
    "            is multiplied with this value and then updates the parameter vector.\n",
    "        full_gradient_function: Gradient function to calculate the partial derivatives\n",
    "            of the loss function with respect to each parameter vector entry.\n",
    "            If None, a simple numerical gradient is computed. Defaults to None.\n",
    "        eval_loss_during_training: Flag to indicate whether to evaluate the loss\n",
    "            at every iteration during training. Doing so adds 'n_iters-1'\n",
    "            loss function calls. If False, only the initial and final losses\n",
    "            are calculated and returned. Defaults to False.\n",
    "    \"\"\"\n",
    "    if full_gradient_function is None:\n",
    "\n",
    "        def _full_gradient_function(params):\n",
    "            return calculate_full_gradient(params, loss_function=loss_function)\n",
    "\n",
    "        full_gradient_function = _full_gradient_function\n",
    "\n",
    "    params = init_params\n",
    "    all_costs = []\n",
    "    all_params = [init_params]\n",
    "    for _ in range(n_iters):\n",
    "        if eval_loss_during_training:\n",
    "            all_costs.append(loss_function(params))\n",
    "        grad_val = full_gradient_function(params)\n",
    "        params = params - learning_rate * grad_val\n",
    "        all_params.append(params)\n",
    "\n",
    "    all_costs.append(loss_function(params))\n",
    "\n",
    "    return np.array(all_params), np.array(all_costs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_function(x):\n",
    "    global params_shape\n",
    "    assert (x.shape == np.atleast_1d(params_shape)).all()\n",
    "    return np.sum(np.cos(x * x[::-1]))**2 / np.prod(params_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_shape = 10\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "params1 = np.random.randn(params_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gradient_function = lambda pars: calculate_full_gradient(pars, loss_function, eps=1e-5, stochastic=False)\n",
    "\n",
    "parameter_trajectory, costs = gradient_descent_optimizer(init_params=params1, loss_function=loss_function, \n",
    "                                                         n_iters=50, learning_rate=2e-1,\n",
    "                                                         full_gradient_function=gradient_function)\n",
    "    \n",
    "final_params = parameter_trajectory[-1]\n",
    "plt.plot(costs, color=\"royalblue\", linewidth=3)\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.xlabel(\"Training Iterations\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from orqviz.pca import get_pca, perform_2D_pca_scan, plot_pca_landscape, plot_optimization_trajectory_on_pca, plot_scatter_points_on_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = get_pca(parameter_trajectory, components_ids=(0,1))\n",
    "pca_scan_result = perform_2D_pca_scan(pca, loss_function, n_steps_x=200, offset=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "\n",
    "\n",
    "plot_pca_landscape(pca_scan_result, pca, ax=ax)\n",
    "plot_optimization_trajectory_on_pca(parameter_trajectory, pca, ax=ax)\n",
    "plot_scatter_points_on_pca([final_params], pca, color=\"red\", zorder=3, linewidth=4, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "qumcmc",
   "language": "python",
   "name": "qumcmc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}