{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we'll learn how to analyze EIS data in batch mode. Normally, you have a single set of EIS data, i.e., set of impedance measurements at various frequencies, plus a common circuit model that you want to fit to the data. This is what we call single circuit, single dataset or SCSD in short. However, there are two other modes of analysis that you might encounter in practice:\n",
    "\n",
    "- Single circuit, multiple datasets (SCMD): You have multiple datasets, each with its own impedance measurements, but you want to fit the same circuit model to all of them. A good example of this is when you have EIS data for multiple samples which you want to compare, or a single sample under different conditions, e.g., EIS data at different cycles during battery cycling.\n",
    "\n",
    "- Multiple circuits, single dataset (MCSD): You have a single dataset, but you want to fit different circuit models to it. This is useful when you want to compare different models to see which one fits the data best, which is by the way the classic use case of AutoEIS itself!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:47:09.893297Z",
     "iopub.status.busy": "2024-09-16T20:47:09.892740Z",
     "iopub.status.idle": "2024-09-16T20:47:14.344989Z",
     "shell.execute_reply": "2024-09-16T20:47:14.344222Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "import autoeis as ae\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ae.visualization.set_plot_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single circuit, multiple datsets (SCMD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test this, we can use a toy dataset that ships with the package. This dataset contains EIS data for a coin cell battery measured at discharged state at various cycles. Let's load the dataset and see what it looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:47:14.357671Z",
     "iopub.status.busy": "2024-09-16T20:47:14.356758Z",
     "iopub.status.idle": "2024-09-16T20:47:14.371092Z",
     "shell.execute_reply": "2024-09-16T20:47:14.370474Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cycles: 130\n"
     ]
    }
   ],
   "source": [
    "datasets = ae.io.load_battery_dataset()\n",
    "print(f\"Number of cycles: {len(datasets)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save time searching for the optimal circuit by calling the `generate_equivalent_circuits` function, we will use the circuit that we know fits the data well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:47:14.373752Z",
     "iopub.status.busy": "2024-09-16T20:47:14.373565Z",
     "iopub.status.idle": "2024-09-16T20:47:14.382475Z",
     "shell.execute_reply": "2024-09-16T20:47:14.381649Z"
    }
   },
   "outputs": [],
   "source": [
    "circuit = \"R1-P2-[R3,P4]-[R5,P6]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's run Bayesian inference on the entire dataset using the given circuit. For convenience, the API for SCSD, SCMD, and MCSD is the same, so we just need to call `perform_bayesian_inference` with the appropriate arguments: the circuit string, list of frequencies, and list of impedance measurements. Since the loaded dataset is in the form of a list of tuples (frequency, impedance), we can easily extract the frequencies and impedances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:47:14.385129Z",
     "iopub.status.busy": "2024-09-16T20:47:14.384944Z",
     "iopub.status.idle": "2024-09-16T20:47:14.387340Z",
     "shell.execute_reply": "2024-09-16T20:47:14.386871Z"
    }
   },
   "outputs": [],
   "source": [
    "freq, Z = zip(*datasets)\n",
    "# If you don't understand the above syntax, you can use the following code instead\n",
    "# freq, Z = [], []\n",
    "# for dataset in datasets:\n",
    "#     freq.append(dataset[0])\n",
    "#     Z.append(dataset[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "Note\n",
    "\n",
    "`perform_bayesian_inference` can handle all three modes of analysis: SCSD, MCSD, and SCMD. You only need to pass the appropriate arguments. The main three arguments are: `circuit`, `freq`, and `Z`. If any of these arguments is a list, then the function will automatically switch to the corresponding mode of analysis. Of course, you need to make sure the arguments are consistent, e.g., for SCMD, the length of `freq` and `Z` must be the same, etc.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, `freq` and `Z` are lists of frequencies and impedances, respectively, each associated with a different cycle. We can now call `perform_bayesian_inference` with these lists to get the posterior distributions for the circuit parameters for each cycle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:47:14.389856Z",
     "iopub.status.busy": "2024-09-16T20:47:14.389672Z",
     "iopub.status.idle": "2024-09-16T20:58:41.867141Z",
     "shell.execute_reply": "2024-09-16T20:58:41.864043Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " "
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fea8eff61b5646dc93c82ce14c86137d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Refining Initial Guess:   0%|          | 0/130 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b18d0b542d241ee820999e95cb68c3f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Performing Bayesian Inference:   0%|          | 0/130 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = ae.perform_bayesian_inference(circuit, freq, Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`results` is a list of `InferenceResult` objects. Each object contains the posterior distributions for the circuit parameters for a single cycle with other useful information. Let's take a look, e.g., let's see how many of the infereces converged:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:58:41.878476Z",
     "iopub.status.busy": "2024-09-16T20:58:41.877718Z",
     "iopub.status.idle": "2024-09-16T20:58:41.894446Z",
     "shell.execute_reply": "2024-09-16T20:58:41.893349Z"
    }
   },
   "outputs": [],
   "source": [
    "for i, result in enumerate(results):\n",
    "    if not result.converged:\n",
    "        print(f\"Inference for cycle {i+1:3}/{len(results)} did not converge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's inspect a sample inference result randomly picked from the list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-16T20:58:41.899602Z",
     "iopub.status.busy": "2024-09-16T20:58:41.899180Z",
     "iopub.status.idle": "2024-09-16T20:58:42.347503Z",
     "shell.execute_reply": "2024-09-16T20:58:42.346210Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of InferenceResult attributes/methods:\n",
      "  >> Z\n",
      "  >> circuit\n",
      "  >> converged\n",
      "  >> freq\n",
      "  >> mcmc\n",
      "  >> num_divergences\n",
      "  >> print_summary\n",
      "  >> samples\n",
      "  >> variables\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA74AAALmCAYAAABl1EgvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAB7CAAAewgFu0HU+AAB0pklEQVR4nO3dd3gU1eL/8c8mIQUioQQIEAhVpSm9qRQRFCxYaIoFRWxgxSsqIqJY4X7FwkUFBQsqeAUVUZCOoFJVqqhAKGmUUEJ6Ob8/8svcXZNs6iabyfv1PPsw7Jwzc2bO7mY/OzNnHMYYIwAAAAAAbMqnvBsAAAAAAIAnEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwDgcWvXrpXD4ZDD4VD37t3Luzke89xzz1nbOW/ePJd58+bNs+aNGDGifBpYRDntdTgcSklJcZnXpEkTa15FUhH7oTykpaXp1VdfVefOnRUSEiIfHx+NGTOmyMvp06ePy+vI3cPf318NGjRQ+/btNW7cOK1YsaJI69q9e7fmzJmjF154QRMnTtT06dO1dOlSnT59usjtBmA/fuXdAABA6Vm5cqU2bNggKfsLZ58+fcq3QShTc+bM0dGjRyVJo0aNUpMmTcq3QWUsIyNDU6dOtf7/3HPPlV9jKrh7770314836enpHl1nenq6YmJiFBMTo99//10zZ87UFVdcoblz5yo8PDzfelu2bNE999yj3377Lc/5AQEBGjlypF599VWFhoZ6qPUAvB3BFwBsZOXKlXr11Vet/xN8K5c5c+Zo06ZNkrL7vjIG3ylTplj/J/gWT0ZGhj755BPr/71791bHjh3VpUuXEi23R48e6tmzp9v1njhxQhs3blRkZKSk7M+0q666Sj/99JOqV6+eq87XX3+toUOHug3lqamp+uCDD7RixQqtX7++0r0vAGQj+AIAPK5Pnz4yxpR3M8rVqFGjNGrUqPJuRpHYsc8qYj+Utbi4OGVkZEiS6tatqzVr1pTKKe0DBgwo9I8R7733nu677z4ZY7R7927NmDFDzz77rEuZI0eOaNSoUVbo7d+/vx577DF17txZVatW1f79+/XBBx9o1qxZSk1N1ZEjR3T77bdr/fr1Jd4WABUP1/gCAADA4nz0NCIiolyu477nnntcrin+8MMPc5V5+eWXret3R40apR9++EFXXXWVQkNDVbVqVbVr106vv/66li1bJh+f7K+8P/74o7Zv314m2wDAuxB8AQAAkKegoKByW/fw4cOt6YMHDyotLc36vzFGX3zxhSTJz89P06ZNy3c5ffr00bXXXmv9f+PGjR5oLQBvR/AFUOlERkZao4hef/31kqTExES9/fbb6tGjh+rWratq1aqpdevW+te//qXo6OhCLTcjI0Pz5s3Ttddeq0aNGikwMFC1atVSp06d9NRTT+nw4cOFWs5vv/2m++67T61bt9Z5552nwMBANWzYUAMHDtSsWbNcvvzlyBlh1/n63ilTpljbuXbt2jzXdezYMU2dOlXdu3dXnTp1FBgYqObNm2vYsGH69ttvlZWVlW87nUdqzhlQaOfOnRo+fLjq1Knjst6ijOr8xx9/6F//+pcuuugi1apVS4GBgWrcuLGGDBmiBQsWuG2Tc9/efffdkqRDhw5p9OjRatCggXx8fHIN2FNY6enpevfdd9W3b1+FhYUpMDBQzZo10/Dhw/Xjjz8WWL+wowkvWbJEQ4cOVUREhIKCghQcHKxmzZpp5MiR+vbbb91uc871vZLUt2/fPEddznnuiiuukCSdPHlSjzzyiJo0aSI/Pz+XU1Hdjer8T2lpaXr77bd12WWXKTQ0VEFBQWratKnuuOMOt0HDuf1hYWFu1+HcJufrNHP27T9DWk5Z52vdizKq8+bNmzV27Fi1atVKISEhqlq1qpo1a6bbbrtNy5Ytc1vXeT0518vGxMRo0qRJat++vWrUqKFq1aqpRYsWuvfee7Vr164Ct72w4uPj9eqrr+rSSy9VWFiY/P39FRYWpssvv1yvv/66zp07l2e9UaNGyeFwqGnTptZz69evt7ajrE8Rdx7Qyhij+Ph46/+RkZE6ceKEJKlr164FDlrVunVra/r48eOl3FIAFYIBgErm4MGDRpKRZAYPHmx+//1307RpU+u5fz6Cg4PNwoUL3S7z999/N61atcp3GZKMv7+/ef75501WVla+y5k4caLx8fFxu5wWLVqYv/76y6VeRESE2zpr1qzJta65c+eakJAQt/V69Ohh9u7dm2db16xZY5V74YUXzOeff278/f3zXK9z2W7duuW5vKysLPPUU08ZPz8/t2266KKLzB9//JHnMpz7dvTo0Wbt2rW5tnHu3Ln57v/8HDx40LRv395tuyZNmmQmT56c73rmzp1rzRs+fHiudSQkJJiBAwe6XYckM2jQIJOYmJjnNuf3cJbzXL9+/czOnTtNeHi4S9nJkyfnKivJJCcnuyzH+TW3f//+Al//Y8aMMenp6W77rF69egX2RU7ZiIiIPPdtXo/evXsXuh+MMSYlJcXcddddxuFwuF1unz59THR0dJ7LcF7Pxx9/bD777DO377fAwEDzxRdfFLj9BVmwYIGpVauW23aHhoaab775JlfdO+64w229O+64o8jt6d27d56vrcLYvn27VdfHx8fl9fPTTz9Z82666aYCl3XPPfdY5adNm1bUzQBgAwxuBaBSO3z4sPr3769jx44pJCRE/fr1U/369XX06FGtWLFCSUlJOnfunEaMGCFfX1/deOONuZbx22+/6fLLL9epU6ckSdWqVdMVV1yhRo0a6fTp0/rll1/0999/Ky0tTc8++6wOHDiguXPn5lrO9OnT9eKLL1r/v+yyy3ThhRfK399fhw8f1tq1a5WQkKC///5bl19+uXbs2KEaNWpIyr4eLj4+Xj/++KM2b94syXUE1UaNGrmsa8aMGXr00Uet/7du3Vo9e/aUv7+/Dhw4oDVr1ig1NVU///yzLr30Uv300086//zz892P69at0+TJk5WVlaVmzZqpV69eqlGjhurXr1/InpDuuusul6Ox7dq1U+fOnRUYGKiDBw9q9erVSktL044dO9S1a1f9+OOPuuiii/Jd3r59+zRo0CAlJSWpQYMG6tu3r+rUqaMLLrig0G2SpNOnT2vgwIH6448/JGUfRezRo4cuuugipaWlac2aNTp48KBeeOEFNWvWrEjLdjZixAh9//33krJvvzJgwAA1btxYGRkZ2rt3rzZu3KjMzEx99913GjFihL755htJUkhIiMaPHy9J+vTTTxUTEyNJGjZsWK5+d3bixAldccUViouLU61atXTFFVeofv36xRq5t2/fvjp8+LB8fHx0ySWXqF27dkpOTtbq1at16NAhSdLs2bOVmpqa57WaJdW2bVuNHz9emZmZmjFjhvV8zn5p3rx5oZeVkZGha6+91uUest26dVO7du3k6+urffv2af369crKytLatWvVpUsXbdq0SQ0bNsx3mZ9//rm+//57ZWVlqWHDhurXr59q1KihyMhILVu2TGlpaUpJSdFdd92lHj16uF2WO3PnztXdd99tnRVRv3599e3bV7Vq1VJsbKxWr16t+Ph4nThxQoMHD9b777+vO++806o/cOBAhYaG6siRI1q4cKEkqU2bNrrqqqskqcSjOhdVzmeZJF144YXy8/vf19YuXbpYn7n+/v5ul5OcnOxytoS7kaUB2Fh5J28AKGt5HSG78cYbzalTp1zKHT9+3AwYMMAqU7NmTXPs2DGXMomJieaCCy6wygwePNjEx8fnWuf8+fNN1apVrXLvvPNOruUEBwcbKfvI8MqVK3Mt49SpU+aqq66yljFx4sRcZSZMmFDg0ZWNGzcaX19fI8lUr17d/Pe//81VJi4uzlx55ZUuR1n/eaTa+SiuJONwOMyMGTPyPKJd0BHfOXPmWPPPO+888/XXX+fZJuf+aNq0aa6jkHn17RNPPGFSU1Pz3BeF8cADD1jLatCggfnpp59c5mdmZpqXXnop13qLcsR33bp1Lkcyjxw5kqsde/fudTkzYcWKFbnKdOvWzZqf11F+Y0yudt52220mISGhwLLujvhKMueff77Zvn17rn0zceJEl3KLFy92KVMaR3xzJCcnu6wrLwUd8X3mmWes+fXr1zcbNmzIVWb//v2mY8eOVrkePXq4XU/O44UXXsj1Wty+fbupVq2aVebFF18scB/kZdeuXSYwMNBazpQpU0xGRoZLmZSUFPPEE09YZfz9/c3vv/+ea1nO79fiHOV1VtwjvidPnjTNmze36r7++uvFWv/x48fNNddcYy3n0ksvLdZyAFR8BF8Alc4/w1G7du1MWlpanmWTk5Ndwsbzzz/vMn/69Oku4TAlJSXf9c6bN88lQDmX/eGHH6x5/fr1y3cZ0dHR1qnQ7dq1yzW/MMG3U6dOVlDNKzzl+GeoX7Rokcv8fwbfp59+Ot9luQu+ycnJJjQ01Jr/2Wef5bucpKQkl1Nq33zzTZf5/+zbW265Jd9lFUZkZKT1I4G/v7/ZuXNnvmWd931Rg+/TTz/tEo7y8/nnn1vlHnzwwVzzixp8L730UpOZmZnv+gobfOvWrWtiYmLyXc6QIUOssj179nSZ503BNzo62lSpUsV6f2zcuDHfdsTFxZm6detay/rnqcP/DL7//Oxw9q9//csqd/XVV7vZ+vw5h7t7773XbdlRo0ZZZW+88cZc88sr+J4+fdrs2LHDvPrqq6ZBgwZWvV69ehX6x6tvvvnGjB8/3tx7771mwIABLpdfdOrUye3rFIC9MbgVgEpv4sSJqlKlSp7zAgMD9fTTT1v/X7Bggcv8//znP9b0Cy+8oICAgHzXc8cdd1gD8kRHR2vVqlXWvJxBWiRZt+fIS/369TVr1ixNmzZN9913X77l8vPTTz9p27ZtkqRBgwZZAxzlpWrVqi6nQy9ZsiTfssHBwXryySeL3B5JWrhwobX9HTt2dDvgUFBQkJ544gnr/x9//LHbZb/wwgvFalOOefPmKTMzU5J09913q23btvmWnTRpkoKDg4u1Huf+zzl9My9XXXWVpk2bpmnTpql///7FWpez559/3rrNS0k88cQTbgemev75563pn376SXFxcSVepyfMnj3bupXP9ddf7/aU2Lp16+r++++3/u/utVivXj099dRT+c53fh8WdhA8Z5GRkfruu+8kZX9mOe/vvEyePNma/vrrr5WQkFDkdRaH84B7eT1q1Kihiy66SBMmTLAGFRw6dKi+/vrrAk9nzrFp0yb9+9//1rvvvqsffvjBGgwwPDxc77zzTqEGUANgTwRfAJWar6+vBg4c6LbM1VdfbU3v2bNHZ8+elSTt379fBw4ckCTVqlVLgwYNKnB9ztcIO48E7Hxt6LZt2/Twww9b6/mne+65R48//rgeeOCBAtf3T84j0V555ZUFlu/Xr5817W5k3h49eui8884rcnskuVxLOXLkyALLX3/99fL19ZUk/frrr0pMTMyzXMuWLUt0za0kLV++3JouaATgatWqacCAAcVaj3M73377bb3//vtW4HYWEhKixx9/XI8//rjL7VmKIygoSL169SrRMnLccMMNbue3atXKZVRd52s3vYnza/HWW28tsPxNN91kTbsb2fvyyy93uT71n+rVq2dN5/d6dmfVqlXWdb39+/dX3bp13ZZv0qSJOnbsKEnKzMzUTz/9VOR1loUqVaqodevWhQ697hw9elRdunTRgw8+KGNMKbQOQEXD4FYAKrVWrVqpevXqbsvUr19f9evXV0xMjIwxioqKUvXq1bVlyxarTNeuXd1+sc3RsmVLazoyMtKa7tatmy699FJt2LBBkvTmm2/q/fffV79+/dS3b1/17t1b7du3z3VrmqJyDhzLly+3Bh7Kj/MXRHdlS3IUxXk/XnLJJQWWr1GjhkJDQxUXF6eMjAwdPXo0zwGrSnpkxxij3377TVL2gFYdOnQosM5FF12kRYsWFXldd9xxh6ZPn64TJ04oLS1Nd999t5555hldddVV6t27t/r06eNy+57SEBoaav2AUBI5t1sqSIcOHbRnzx5J0pEjR0q83tJmjNHWrVut/xfmtej8fo6NjVVqamqeZ30UNFiVc528fvAoSFHfQ1J227dv3y7J9bPIk5wH3MtLZmamTp48qW3btmnPnj1KT0/XlClT9MMPP2jlypWqWrVqgeuYOnWqpk6dqqSkJB05ckQ//fST3nnnHeuz7+2331b16tVdBhIEUDkQfAFUagUdGclRu3Zta7TcnFNRjx07Zs0v7JFF5yM7zveklKSvvvpKt912mzWyb2Jior755htr9N7atWvrqquu0ogRIzRo0KBinaLqfIrp0qVLtXTp0kLXTU1NzfeLfUlOly3ufszZln/ux9JokySdOXNGycnJkrKP5hbmNOZatWoVa11hYWFasWKFhg8frj///FNSdpCaN2+eNdJ18+bNdc011+i2225Tp06dirUeZ6VxirMka2TxgtSpU8eaPnPmTKmsuzQlJCRY9yquWrWqy3s1P4GBgapevbp1dkZ8fHyeI5n/8/7Cpa20P4s8ZcCAAS73iXbn/ffft+7F/fPPP2vOnDl66KGHCr2uqlWr6oILLtAFF1ygUaNG6YEHHtA777wjKXsE/UceecTlNQnA/jjVGUClVtgv7c7XAAcGBkqSy3VxhTkS4W6ZUnaw/e6777R+/XqNHj061xfokydPav78+br22mt10UUXad++fUVe57lz54pcx1nO9XKlqbT3Y2lxPuU0p88LUpij/vlp3769du3apc8//1yDBw/OFbT379+vN954Q507d9ZNN91UZtdlFqSwp406n61Q3P2Uc/2tJ5T0dSjl/1osrR8Z8uOt76GSGD16tK677jrr/+4utSiIw+HQ66+/bn3ep6WlaeXKlSVtIoAKhuALoFLLOaJXEOcjVDlH9apVq2Y9V9jr8pyPzNSsWTPPMpdddpnmzJmj6Oho7dmzR//5z380bNgwl/K7d+/W1VdfbR2hKizn63CXLVsmkz26f6Efxb2O1x1P7ceSct7Ws2fPFirguRuYrDCqVKmi4cOH66uvvtKpU6f0008/6eWXX1b//v1djrQvWrSoWIObeYK7wbjyK1fcPsvvuvfSUJzXYXJyshU6cwZnKg/e+h4qqT59+ljTJ0+edJn3448/6ttvv9W3335bqNdFYGCgy2nWBV3mAcB+CL4AKrXY2NgCy6SkpFhfknx9fa3To51Pk/7rr78Ktb4dO3ZY061atSqwfKtWrXT//fdrwYIFOnbsmD799FPrtMn9+/e7DFZVGM7XvXrLyLpF3Y/Hjx+3+q1q1aqKiIjwSLuqV69uBYq0tLRCXQdZnKPw+fHz81OPHj305JNP6ocfftCxY8dcRgZesGBBrjBQHpKSklxGpc6P877J65rswvDktaghISHWjwvJycmKiooqsM6uXbusH0RatGhRoiP+JVEWn0XlwTmQZ2RkuMybOHGirr32Wl177bWFHizN+YeJ0hgwC0DFQvAFUKnt2LGjwKN069evtwacad++vXUqYZcuXawyv/zyi5KSkgpcn3NQ7dGjhzX91FNPaciQIRo6dGi+R3H9/Px0880365FHHrGeK2rQ6t69uzW9adOmAssfOHBAt956q2699Va98sorRVpXYTnvx9WrVxdYPucaaEnq3LmzR8NG165dremCTo00xmjt2rVFXkdaWpqGDBmiIUOGaMKECfmWq169ul566SXrdZOZmam///67yOvzhHXr1rmdf/bsWes2WlWqVLFGFJZcB3Yq6D30888/l6CV7jkcDpdrp4v6WnR+P5e1or6HDh8+bA00FhAQUKiB27yN8w9eziHeHeejvI0aNSr1NgHwbgRfAJVaZmZmgfeCfeutt6xp5/tttmzZ0hrV9dy5c/roo4/cLmfFihXW7Y/Cw8NdbiWzc+dOffnll/rvf//rMkJrXpyP2hZ10JxrrrnGml64cGGB1/z++9//1vz58zV//vwin1ZdWM63gZozZ45SU1Pdln/33Xet6VtuucUjbcrhfI3hrFmz3J7uvGTJkmKdPunv769ly5bpyy+/1MyZMwvczyXpf0+ZM2eO2/lz5861rg+/5pprXE7NDQ0Nta7/TUhIyPdMhKysLL333nul1OK8Ob8WZ86c6bZsRkaGPvjgA+v/nn4tunPllVdaI3SvXbtWu3fvdlve+T10ww03eM3rqCh69+5tTS9evLjA8vv27dMvv/wiKftHDufTqAFUDgRfAJXec889l++R03feeUfffvutpOwjI2PHjnWZ7zzK6MSJE/NdzpkzZ1zKPvTQQy63knEOwVOnTs11Wl+O1NRUl4D9z1uDOA9sk9epmh07dlTfvn0lSSdOnNDDDz+c53ok6YsvvtCsWbMkZV9DeO+99+ZbtiSGDh1qDeR1+PBhjR8/Pt+y7777rnXP0Xr16nk8bNx5550KCQmRlH3P4PyOekdFRWncuHHFXk9O/ycmJmratGn5louMjLTuNVujRo1cp6gW1P+esmzZMs2dOzfPeQcOHNDzzz9v/f+fr7kqVaq43Kpp9uzZeS5n6tSp2rlzp9t2+Pv7u5wBUNR9MGbMGCsEbtq0SdOnT8+37KRJk6wfOtq2bav+/fsXaV2lqXHjxrr++uslZf9AcOedd+Z79Pz333/X//3f/0nKDoDOZ5B4s38ObDZ06FBrALgNGza4/fHl+PHjGj58uHXmzrBhwxjRGaiMDABUMgcPHjSSXB61a9c206dPN3v37jUnTpwwW7duNXfffbdxOBxWmSeffDLXstLT0023bt2sMiEhIeaVV14xu3fvNidPnjT79+83c+fONS1atLDKtGvXzqSlpbks59ixYyY4ONgq06NHD7Nw4UITGRlp4uPjzR9//GHee+8906ZNG6tM7969c7Xno48+sub7+vqaW265xTz00ENm3759Vpk///zTVK9e3So3YMAA8/3335vo6Ghz/Phxs3HjRjNmzBjj4+NjlXnjjTdyrWvNmjXW/DvuuMPtPncu261bt1zzv/nmG5f+uPLKK813331noqKizLFjx8zPP/9s7r33Xpf++Pzzz932bV77pzjmzZvn0rabb77ZrFu3zsTFxZkjR46YDz74wNSvX99IMhEREVa5uXPnuixn7ty51rzhw4e7zPvuu+9c1nHrrbealStXmujoaHPixAnz66+/milTppjQ0FCrzOTJk3O19a677nJ5TY8ZM8aMGzfOpUzO/IiIiAK33blNycnJLvNytrVKlSpGknE4HGbMmDHm559/NseOHTMHDx4077zzjqlTp47LvsvLv/71L5fX7cSJE82OHTvMgQMHzA8//GBuuukmI8kEBgaa2rVru21/s2bNrGW1atXKjB071syYMaNQ/WCMMW+++Wau/l6zZo2JjY01MTExZvXq1Wbo0KEu7d2wYUOu5TivJ6++crZ3794i9UteDh8+bGrUqGEt54ILLjAffvihOXDggDl58qTZtWuXefnll10+Z+677748l1WU93ZBevfuXej98E8ffvihVbdFixa55k+bNs2a73A4zO23325WrVploqKiTHx8vNmxY4d57bXXTFhYmFWuTp065tChQyXaJgAVE8EXQKXjHI66dOli+vfvnysI//MxfPhwk5mZmefyoqKiXAKpu0ezZs3M0aNH81zOJ5984hLs3D0aNWpkjhw5kmsZZ86ccQm1OY81a9a4lFu3bp3Ll2R3jwkTJuTZ3tIMvsYY8/rrrxd6+19++eU8l+GJ4GuMMRMnTiywTZ07dzaffvppsYKvMcbcc889hdp2SaZ///65fjwxxnU/Oz+clXbwbd68uXn22WcLbHO3bt3MuXPn8lzPsWPHrB8P8ns4HA7zwQcfWO+1/No/efLkXHWdXwsF9YMxxjz66KOF6gcfHx/zySef5LmMsg6+xmS/r/N6/+f1uOaaa/J8DRnjPcH3iy++cGnzjh07XOZnZmaaW265pdDvm7CwMLNp06YSbQ+AiotTnQFUav7+/vrmm290//335znKZ82aNfXaa6/p008/zfdenA0aNNDGjRv1yCOP5Hu/12rVqunhhx/W9u3b1bBhwzzLjBw5Ut9++63atGmTb3urVKmikSNHauvWrQoPD881v3r16vrqq6/UrVs3BQYGyt/fX02aNMl1u5JevXpp27ZtuuGGG1zur+qsU6dOWrp0qccGtfqnRx55RD/88IMuuuiifMtcfPHF+uGHH/Tkk0+WSZtyTJ06VfPnz1e9evVyzatSpYruu+8+rVu3rkTXSr7zzjuaNm2a21Mwa9asqWeffVZLly7N896rffr00dtvv62WLVvKz89PQUFBateuXbHbVFhTpkzRG2+8Yd3qy1lAQIAeeeQRrV692uXaXmd16tTR2rVrXQYTcxYREaGvv/5ad955Z4FtefLJJzV27FiFhYXJ19dXISEhRR5F+v/+7//0+eefq2nTpnnOdzgc6tWrlzZt2qSRI0cWadme1KtXL23dulXXXXddvu/r+vXr6+2339ZXX33llffvdda1a1eXS0Kuvvpql8tAfHx89Mknn+jtt99WgwYN8l1O1apV9cADD+i3337L9zUGwP4cxhTyzvMAYBORkZHWF9pLLrlEGzZskJR9e5+1a9fq8OHD8vHxUatWrdSnTx+X6yYLkpiYqNWrV+vgwYM6d+6cateurWbNmqlXr14uo9cWZM+ePdq6dauOHz+ulJQUnXfeeWrevLl69OiRZ7goiZiYGK1evVpRUVHKyspSWFiYunfvrgsvvLBU11MUe/bs0S+//KJjx47J399f9erVU48ePdSsWbNya5OUfZ3hqlWrtHfvXmVmZqpRo0a64oorVLt27VJbR1pamrZt26adO3fq1KlTysrKUo0aNdSmTRt17do13x9XvEFiYqJWrlypAwcOyBijxo0bq3///tZ10oWxY8cObd68WcePH1dQUJDat2+vyy67zCUAlRVjjLZt26bt27fr5MmTqlq1qsLCwnTZZZe5DVreIDY2VmvWrFF0dLQyMjJUp04dtWvXTp07d843FFdkWVlZ+vXXX/X777/rxIkTysrKUs2aNdWqVSuvf98AKBsEXwCVTn7BFwAAAPbEqc4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFtjVGcAAAAAgK1xxBcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC25lfeDbCblJQU7dy5U5JUp04d+fmxiwEAAACgMDIyMnT8+HFJUrt27RQYGFgqyyWVlbKdO3eqa9eu5d0MAAAAAKjQNm/erC5dupTKsjjVGQAAAABgaxzxLWV16tSxpjdv3qz69euXY2tcZWVlKTU1VZIUEBAgHx9+9/AW9I13o3+8F33jvegb70b/eC/6xnvRN2UjJibGOoPWOVuVFMG3lDlf01u/fn2Fh4eXY2tcZWVlKSUlRZIUGBjIm9WL0Dfejf7xXvSN96JvvBv9473oG+9F35S90hwvid4CAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC25lcWK1myZIk+/PBDbdu2TbGxsapSpYoaNWqkfv366dFHH1XTpk1z1YmPj9fZs2cLXHZISIhq1qzptszp06eVmpqqOnXqyMeHrA8AAAAAlYlHU2B6erqGDx+u6667Tl9++aUiIyOVkpKihIQE7dmzR2+99ZZatWqlRYsW5ao7duxYNW3atMDHq6++mue6U1JSNHXqVDVp0kQ1a9ZUWFiYAgMD1bdvXy1evNiTmw0AAAAA8CIeDb4TJkzQwoULJUm9e/fW4sWLtWfPHm3dulXPPfecAgMDlZqaqpEjR2r37t0udf/6669irzchIUG9e/fWpEmTdOjQIev59PR0rV27VjfeeKMefvjhYi8fAAAAAFBxeCz4Hjt2TG+99ZYkaeDAgVq1apWuv/56tWrVSp06ddLkyZO1ZMkSSdlHZydOnOhSPyf4bt26VcaYfB+vvPJKrnXfd9992rx5syTpgQce0F9//aXk5GTt2LFD119/vSTpzTff1OzZsz21+QAAAAAAL+Gx4LtgwQJlZGRIkl5++WX5+vrmKnPFFVeoX79+kqSlS5cqISFBUnZoPnv2rBwOhy688MIirXfbtm369NNPJUn333+/Zs6cqRYtWigwMFDt2rXTl19+qR49ekiSnn76aaWkpBR7GwEAAAAA3s9jwXfXrl2SpNDQUF188cX5luvUqZMkKSMjQ3/++aek/x3tbdSokapVq1ak9c6dO1eSVKVKFU2ZMiXXfB8fHz3++OOSpBMnTuibb74p0vIBAAAAABWLx4JvbGysJCksLMxtudTUVGs6KytL0v+Cb6tWrYq83tWrV0vKvqa4Tp06eZYZMGCANbrzihUrirwOAAAAAEDF4bHg+/777+vIkSNWEM1LZmamvvvuu+yG+PioefPmkv4XfJs3b64ZM2aoZ8+eqlGjhgIDA9W8eXPde++9+uOPP3It7/jx49q7d68kqXv37vmuNzg4WC1btpQk7dy5s3gbCAAAAACoEDx2H9/Q0NACyzz11FNWyL3iiitUq1YtSf8LvrNnz1Z6erpLnQMHDui9997TBx98oBkzZmjs2LHWvIMHD1rTzZo1c7vuiIgI7du3T5GRkYXanhxHjx51Oz8mJsaazsrKso5iewNjjMu0N7WtsqNvvBv9473oG+9F33g3+sd70Tfei74pG57arx4Lvu6cOXNGY8eO1fz58yVJgYGBeu2116z5OcE3PT1d/fv319ixY3X++ecrMTFRS5cu1fTp03Xu3DmNGzdO1atX12233SZJio+Pt5aR32nOOWrUqCFJ1oBahdWoUaNCl01NTfXawbOcTzGHd6FvvBv9473om8K79NJLFRcXV97NqJDq1aunDRs2lHczShXvHe9F33gv+sZzPLVvyzz4LliwQI899piio6MlSUFBQfrss89cBsD6+++/JUnjx4/X9OnTXep37txZN954o3r16qXTp0/rkUce0XXXXaeQkBCX4BsUFOS2HQEBAZJ40QIAKp+4uDjr7zAAAJVBmQXfvXv3aty4cS7X/Hbo0EEfffSR2rZt61L2+PHjkrKPBOelXbt2mjx5sh599FHFx8dryZIluvXWW60BqwojJ/Dmt478HDlyxO38mJgYde3aVVJ2uC7q8j3JGGNtd0BAgBwORzm3CDnoG+9G/3gv+qZ4cvaTj6TQwIDybUwFcSIlVVnK3nfe9Le9uHjveC/6xnvRN2Uj5wBlafN48M3KytLLL7+s559/XmlpaZKkkJAQTZkyRePGjcvz/r6F+YNy/fXX69FHH5Uk/frrr7r11lsVHBxszU9MTHRbPykpSdL/TnkurPDw8EKX9fHxKVIY9zTn8+UdDodXta2yo2+8G/3jveibkgkNDNCK/j3LuxkVQv8VP+lYSvYXXju8znjveC/6xnvRN2XDU/vVo8E3PT1dw4YN01dffSUp+wUyevRovfTSSwVeg1uQhg0bWtPnzp2T5Hr9bUGncEVFRUmSmjZtWqJ2AAAAAAC8m0eD79ixY63Q26RJE82fP189e7r/ZTkuLk7JyckKCAhQ/fr18y13+vRpazpnBOnzzz9ffn5+ysjI0K5du/Ktm5mZqf3790uS2rdvX7iNAQAAAABUSB47Pr9t2zbNnj1bUvb9eDdv3lxg6JWyB7Rq2rSp2rVr57bc2rVrrenOnTtLyh7QKuf+vcuXL8+37tatW3X27FlJ0uWXX15gmwAAAAAAFZfHgm/OrYp8fX21ZMmSQp/a3K1bN0nSyZMntXTp0jzLZGVlWbc/qlmzpq688kpr3s033ywpe2ToJUuW5Fn/9ddflyTVqlVLgwYNKlS7AAAAAAAVk8eCb07o7N27t4KCghQZGVngIyMjQyNGjFD16tUlSaNHj9amTZtclpuUlKTRo0dr69atkqQnn3xSVatWtebfeeed1rW+Y8aMcTnl2RijqVOnasGCBZKkKVOmeGzUMAAAAACAd/DINb7GGEVGRkqSVq9eXegBpA4ePKgmTZrovffe08iRIxUXF6fu3burU6dOatmypRITE7VhwwadOnVKkjRo0CCNHz/eZRlBQUH69NNP1a9fP8XFxalDhw7q3bu3ateurS1btujgwYOSpCuvvFIPPPBA6W00AAAAAMAreST4njx5UhkZGcWuP3z4cNWtW1cPPfSQdu3apW3btmnbtm3W/KCgID3yyCN67rnn8rwd0qWXXqq1a9fq9ttv199//61Vq1ZZ8/z9/XX33Xfr9ddfZwhyAAAAAKgEPBJ8Q0NDZYwp0TL69u2rnTt3asuWLdqyZYtOnTql6tWrq0WLFurVq5eqVavmtn6PHj30559/av369dq9e7fOnTunxo0bq0+fPgoLCytR2wAAAAAAFYdHb2dUGrp06aIuXboUq67D4VDv3r3Vu3fvUm4VAAAAAKCi4FxfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2JpfeTcAAIDi6tq1q2JiYiRJDoejnFtTceTsMwAAKguCLwCgwoqNjVV0dHR5NwMAAHg5gi8AoMLzkRQaGFDezagwjqWklncTAAAoUwRfAECFFxoYoBX9e5Z3MyqMDkvWKKu8GwEAQBlicCsAAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK0RfAEAAAAAtkbwBQAAAADYGsEXAAAAAGBrBF8AAAAAgK2VSfBdsmSJhgwZoqZNmyooKEjVq1dXmzZt9NBDD+ngwYMF1s/MzFRsbKzOnj1brPWfPn1acXFxysrKKlZ9AAAAAEDF5dHgm56eruHDh+u6667Tl19+qcjISKWkpCghIUF79uzRW2+9pVatWmnRokV51t+3b5+GDx+ukJAQ1a9fXyEhIapVq5buueceHThwwO26U1JSNHXqVDVp0kQ1a9ZUWFiYAgMD1bdvXy1evNgTmwsAAAAA8EIeDb4TJkzQwoULJUm9e/fW4sWLtWfPHm3dulXPPfecAgMDlZqaqpEjR2r37t0udVevXq1OnTpp4cKFSkxMtJ4/deqUZs+erU6dOmn16tV5rjchIUG9e/fWpEmTdOjQIev59PR0rV27VjfeeKMefvhhD2wxAAAAAMDbeCz4Hjt2TG+99ZYkaeDAgVq1apWuv/56tWrVSp06ddLkyZO1ZMkSSdlHZydOnGjVjYuL05AhQ5SYmKhatWrpo48+Unx8vE6fPq2vvvpK4eHhOn36tIYPH66YmJhc677vvvu0efNmSdIDDzygv/76S8nJydqxY4euv/56SdKbb76p2bNne2rzAQAAAABewmPBd8GCBcrIyJAkvfzyy/L19c1V5oorrlC/fv0kSUuXLlVCQoIk6cUXX9SpU6fk4+Ojb7/9Vrfddptq1qypkJAQDR48WN9//718fX114sQJvfjiiy7L3LZtmz799FNJ0v3336+ZM2eqRYsWCgwMVLt27fTll1+qR48ekqSnn35aKSkpntoFAAAAAAAv4LHgu2vXLklSaGioLr744nzLderUSZKUkZGhP//8U5mZmfroo48kSdddd50VUp21bdtWgwYNkiR9/PHHVsCWpLlz50qSqlSpoilTpuSq6+Pjo8cff1ySdOLECX3zzTfF2TwAAAAAQAXhseAbGxsrSQoLC3NbLjU11ZrOysrStm3bdObMGUnSkCFD8q03cOBASdLZs2e1adMm6/mc63579+6tOnXq5Fl3wIAB8vHJ3vQVK1YUtCkAAAAAgArMz1MLfv/995WSkqKAgIB8y2RmZuq7776TlH0ktnnz5nr//fet+d27d8+3bocOHazpnTt36pJLLtHx48e1d+/eAusGBwerZcuW2rdvn3bu3FnobZKko0ePup3vfM1xVlaWV91CyRjjMu1Nbavs6BvvRv8AcGaHzwA+17wXfeO96Juy4an96rHgGxoaWmCZp556Sn/99Zek7Ot9a9WqZd3X19fXVxEREfnWdZ4XGRkpSS73BG7WrJnbdUdERGjfvn1W3cJq1KhRocumpqZ67TXEzkfa4V3oG+9G/3gX5y8hQFkwxnjt3/bi4nPNe9E33ou+8RxP7VuPBV93zpw5o7Fjx2r+/PmSpMDAQL322muSpPj4eElSjRo15OeXf/Nq1KhhTecMipVTV1K+pzn/s35OXQAAAACAPZV58F2wYIEee+wxRUdHS5KCgoL02WefWQNg5YTXoKAgt8txPoU651cB5+Bb2PpF/UXhyJEjbufHxMSoa9eu1joCAwOLtHxPMsZY2xsQECCHw1HOLUIO+sa70T/ei75AWXM4HF71t724+FzzXvSN96Jvyoa7S2VLosyC7969ezVu3Dhr8Ckp+zrdjz76SG3btrWeyxl0qiDOgTXnD1Bh6zrXL+ofr/Dw8EKX9fHxKVKbPM35fHmHw+FVbavs6BvvRv8AcGaHzwA+17wXfeO96Juy4an96vHeysrK0osvvqj27dtboTckJEQzZszQli1bXEKvlD3wlCQlJia6XW5SUpI1nXPack7dotR3PmUaAAAAAGA/Hj3im56ermHDhumrr76SlP3LyOjRo/XSSy/lew1uzuBRp06dUnJycr6nLEdFRVnTTZs2dakryTqVOj859XPqAgAAAADsyaNHfMeOHWuF3iZNmmjDhg2aPXu224GnWrdubU3v3r0733L79u2zptu3by9JOv/8860BsXbt2pVv3czMTO3fv9+lLgAAAADAnjwWfLdt26bZs2dLkpo3b67NmzerZ8+eBda7/PLLrelly5blWy7ntOlatWpZ4TUoKMi6f+/y5cvzrbt161adPXs21/oAAAAAAPbjseCbc6siX19fLVmypMDbC+Vo3ry5unTpIkmaNWtWnvfKO378uLX8m2++Wb6+vta8m2++WZL0999/a8mSJXmu4/XXX5eUHZoHDRpUyC0CAAAAAFREHgu+OaGzd+/eCgoKUmRkZIGPjIwMSdKzzz4rKfs63dtvv91lIKu4uDgNHjxYCQkJql69up5++mmX9d55553Wtb5jxoxxOeXZGKOpU6dqwYIFkqQpU6Z4bLhsAAAAAIB38MjgVsYYRUZGSso+JbmwA0gdPHhQTZo00TXXXKP7779fs2bN0hdffKEff/xRPXr0UFpamtasWaOkpCT5+Pjo7bffVoMGDVyWERQUpE8//VT9+vVTXFycOnTooN69e6t27drasmWLDh48KEm68sor9cADD5TqdgMAAAAAvI9Hgu/Jkyeto7fFNXPmTNWvX1+vvPKKYmNjtXjxYmteo0aN9MYbb+iGG27Is+6ll16qtWvX6vbbb9fff/+tVatWWfP8/f1199136/XXX+feWwAAAABQCXgk+IaGhsoYU6JlOBwOTZo0SQ899JCWL1+uw4cPKzAwUC1btlS/fv2s0Zvz06NHD/35559av369du/erXPnzqlx48bq06ePwsLCStQ2AAAAAEDF4dH7+JaGkJAQDRs2rFh1HQ6Hevfurd69e5dyqwAAAAAAFQXn+gIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbM2vvBsAAADg7eJT0yRJMTExCg8PL+fWlA5jjCTJ4XB4fF1hYWHaunWrx9cDAPkh+AIAABQg6/+HxKysLEVFRZVzawAARUXwBQAAKCQfSaGBAeXdjArjREqqssq7EQAggi8AAEChhQYGaEX/nuXdjAqj/4qfdCwltbybAQAMbgUAAAAAsDeCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABb8yvvBgAAsnXu3FmxsbHl3YwKJSYmprybAAAAKgCCLwB4idjYWEVFRZV3MwAAAGyH4AsAXsZHUmhgQHk3o0I4lpJa3k0AAAAVAMEXALxMaGCAVvTvWd7NqBA6LFmjrPJuBAAA8HoMbgUAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABb8yvvBuTn7Nmzio+PL7BctWrVVKdOHbdlEhISdO7cOdWtW1e+vr6l1UQAAAAAQAVQ5kd8t2zZIofDoSZNmrgt9/LLL6tp06YFPh588ME862dmZmrmzJlq1aqVqlevrgYNGiggIEBdu3bVBx984IEtAwAAAAB4ozI/4jtv3rxClfvrr7+KvY6MjAzdeOONWrJkicvzmZmZ2rJli7Zs2aKlS5dqwYIF8vPz2oPeAAAAAIBSUKZHfBcuXKh33323UGVzgu9///tfGWPyfXz++ee56k6ePNkKvUOGDNHOnTuVnJysP//8U2PGjJEkLVq0SJMnTy6lLQMAAAAAeCuPHu48fPiwPv/8c+3du1fr16/XgQMHClXPGKO///5bktSqVasirTMmJkbTp0+XJA0aNEgLFy6Uw+GQJLVs2VLvvfeezpw5o4ULF2ratGkaN26c6tevX6R1AAAAAAAqDo8e8d2+fbsmTJigefPmFTr0SlJ0dLSSkpLk5+enli1bFmmd8+fPV1pamiTppZdeskKvswkTJkiS0tPT9fHHHxdp+QAAAACAisWjR3xbt26tF154weW5999/X5GRkW7r5Zzm3Lx5c1WpUqVI61y9erUkqUWLFrr44ovzLNOxY0fVq1dPcXFxWrFihZ544okirQMAAAAAUHF4NPief/75euaZZ1yeW7lyZaGD7wUXXKC5c+fq448/1u+//27dkqh37956+OGH1aVLF5d6WVlZ2rBhgySpe/fubtfRvn17LV++XDt37iziVgEAAAAAKhKvHNI4J/h+//33+uabb1zmHT16VPPnz9f8+fP11FNP6aWXXrLmnThxQgkJCZKkZs2auV1HRESEJCkuLk4pKSkKDAwsVNuOHj3qdn5MTIw1nZWVpaysrEIttywYY1ymvaltlR19493oHwAoOT47C4e/Od6LvikbntqvXh1809PT1aVLFz322GNq166dMjIytHr1ar3yyis6duyYXn75ZQUFBWnSpEmSpPj4eGsZderUcbuOGjVqWNMJCQmFDr6NGjUq9HakpqYqJSWl0OXLUmpqank3Afmgb7ybJ/vH+Q8qANiJMcZrvxN5M74TeC/6xnM8tW+9OvgOGzZM8+fPd7nX7sUXX6yhQ4fq0ksv1aFDh/TCCy9o5MiRatasmUvwDQoKcruOgIAAa5oXLgAAAADYl1cG323btskYo4CAgDxHZQ4PD9eMGTN0ww03KD09XZ9++qmeeeYZ+fgUfpBq57Bb2KO9knTkyBG382NiYtS1a1dJ2eG6KMv2NGOMtd357VuUD/rGu5VV/9DvAOzK4XB41Xcib8Z3Au9F35QN5wOUpckrg29hNnbgwIHy9/dXWlqafv31V0lScHCwNT8xMdFt/aSkJEmSj4+PqlevXui2hYeHF7qsj49PkcK4pzmfL+9wOLyqbZUdfePd6B8AKDk+OwuHvznei74pG57arxW2twICAhQaGipJOnfunCTX62+jo6Pd1o+KipIkNWjQQP7+/h5qJQAAAACgvHld8D158qQiIyMLPKXYGKOzZ89KkhWAQ0JC1KBBA0nSrl273Nbft2+fpOzbGgEAAAAA7Mvrgu/rr7+upk2bKiIiQmfOnMm33NatW60jvZ07d7aev/zyyyVJ69atU3Jycp51Y2NjtXfvXpfyAAAAAAB78rrg261bN0nZR3Q/+eSTfMu98sorkiQ/Pz8NGTLEev7mm2+WlH3685w5c/KsO2PGDBlj5OfnZ5UHAAAAANiT1wXfK6+8UhEREZKkCRMmaOnSpS7zMzIyNHHiRC1atEiSNGbMGJdrewcOHKguXbpIkp566imtW7fOpf68efM0bdo0SdLDDz+ssLAwj20LAAAAAKD8ed2ozv7+/vroo480cOBAJSYm6pprrlHr1q3Vtm1bpaen6+eff1ZsbKwkqVOnTnr11Vdd6jscDs2fP19dunTRmTNn1LdvX/Xs2VPh4eHauXOn9uzZIyn72t4pU6aU+fYBAAAAAMqW1x3xlaRevXpp48aN6tmzpyRpz549WrhwoRYvXqzY2Fj5+fnp3nvv1apVq3Teeeflqt+yZUtt2rRJnTp1kjFGGzdu1IIFC7Rnzx75+Pho+PDhWrdunapVq1bWmwYAAAAAKGNlfsR37dq1hSrXvn17bdy4Ubt379bGjRt1/PhxBQcHKyIiQn369FGNGjXc1r/gggu0detWbdmyRdu3b9fp06fVsGFD9ezZU82aNSv5hgAAAAAAKgSvO9X5n9q0aaM2bdoUu36XLl2sa34BAAAAAJWPV57qDAAAAABAaSH4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALA1gi8AAAAAwNYIvgAAAAAAWyP4AgAAAABsjeALAAAAALC1ChF8jTE6duyY4uPji1U/ISFBMTExyszMLOWWAQAAAAC8XZkH3y1btsjhcKhJkyYFlo2OjtaYMWNUq1Yt1atXT7Vr11ZwcLBuvvlm/fbbb27rZmZmaubMmWrVqpWqV6+uBg0aKCAgQF27dtUHH3xQOhsDAAAAAPB6ZR58582bV6hyO3fuVIcOHTRnzhydPn3aej4xMVGff/65unfvrs8++yzPuhkZGbrhhhs0btw4/fHHH9bzmZmZ2rJli0aPHq2bbrpJGRkZJdkUAAAAAEAFUKbBd+HChXr33XcLLJeSkqIbbrhBx44dU2BgoN544w3FxcXp3LlzWrlypdq0aaPU1FTddddd2rlzZ676kydP1pIlSyRJQ4YM0c6dO5WcnKw///xTY8aMkSQtWrRIkydPLt0NBAAAAAB4HY8G38OHD+u1117TnXfeqebNm2v48OGFus72P//5j/bv3y9J+vDDD/XQQw+pbt26qlatmvr166dVq1bpvPPOU0pKip566imXujExMZo+fbokadCgQVq4cKHatm2rwMBAtWzZUu+9956GDRsmSZo2bZpiYmJKeasBAAAAAN7Eo8F3+/btmjBhgubNm6cDBw4Uul7ONbjt27e3QqqzevXqadSoUZKk7777TrGxsda8+fPnKy0tTZL00ksvyeFw5Ko/YcIESVJ6ero+/vjjQrcLAAAAAFDxeDT4tm7dWi+88ILLo6BBrY4dO6bdu3dLyj5NOT8DBw6UlD3i86pVq6znV69eLUlq0aKFLr744jzrduzYUfXq1ZMkrVixotDbAwAAAACoePw8ufDzzz9fzzzzjMtzK1euVGRkZL511q1bZ013794933IdOnSwpnOu883KytKGDRsKrCtlH01evnx5ntcIAwAAAADsw6PBtzgOHjxoTTdr1izfcvXq1VNAQIBSU1OtIH3ixAklJCQUWFeSIiIiJElxcXFKSUlRYGBgodp39OhRt/OdrxnOyspSVlZWoZZbFowxLtPe1LbKjr7xbvQPAJQcn52Fw98c70XflA1P7VevC77x8fHWdJ06dfIt53A4FBISomPHjllht7B1JalGjRrWdEJCQqGDb6NGjQpVTpJSU1OVkpJS6PJlKTU1tbybgHzQN97Nk/3j/AcVAOzEGOO134m8Gd8JvBd94zme2rdlfh/fgjiH16CgILdlAwICJP1v5xSnrnN9AAAAAID9eN0RXx+fwmfxnMCac7S2OHWd6xfGkSNH3M6PiYlR165dJWWH66Is29OMMdZ2BwQE5DniNcoHfePdyqp/6HcAduVwOLzqO5E34zuB96JvyobzAcrS5HXBNzg42JpOTExU9erV8y2blJQk6X+nLf+zrjs5dX18fNyu45/Cw8MLXdbHx6dIYdzTnM+XdzgcXtW2yo6+8W70DwCUHJ+dhcPfHO9F35QNT+1Xr+st52too6Oj8y139uxZnTt3TpLUtGnTItWVpKioKElSgwYN5O/vX+z2AgAAAAC8m9cF39atW1vTu3btyrfcvn37rOn27dtLkkJCQtSgQYMC6zrXz6kLAAAAALAnrwu+PXv2tM7rXrZsWb7lVq9eLUny9fVVr169rOcvv/xySdn3A05OTs6zbmxsrPbu3etSHgAAAABgT14XfKtVq6brrrtOkvTpp58qLi4uV5mUlBTNmjVLknTVVVe53Lro5ptvliSdO3dOc+bMyXMdM2bMkDFGfn5+VnkAAAAAgD15XfCVpKefflp+fn5KTk7W0KFDXW5TlJCQoBEjRujQoUPy9fXV1KlTXeoOHDhQXbp0kSQ99dRTWrduncv8efPmadq0aZKkhx9+WGFhYR7eGgAAAABAefK6UZ2l7OtuX3zxRU2YMEE//vijmjdvrssuu0y+vr5au3atTp8+LUmaMmVKrmt0HQ6H5s+fry5duujMmTPq27evevbsqfDwcO3cuVN79uyx1jFlypQy3jIAAAAAQFnzyuArSU888YRq1qypJ554QqdPn9aSJUusebVr19bUqVN133335Vm3ZcuW2rRpk0aOHKlt27Zp48aN1jwfHx8NHTpU7733nqpVq+bx7QAAAAAAlK8yD75r164tdNkxY8bo1ltv1Q8//KADBw7I4XCoWbNm6t+/v4KCgtzWveCCC7R161Zt2bJF27dv1+nTp9WwYUP17NlTzZo1K+FWAAAAAAAqCq894psjKChIgwcPLnb9Ll26WNf8AgAAAAAqH68c3AoAAAAAgNJC8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICt+ZV3AwAAAGBP8alpkqSYmBiFh4eXc2sqDmOM6tWrp61bt5Z3UwDbIPgCAADAI7KMyf43K0tRUVHl3BoAlRnBFwAAAB7lIyk0MKC8m1EhnEhJVVZ5NwKwIYIvgFLXuXNnxcbGlnczSpX5/0ctHA6Hx9YRExPjsWUDQHkKDQzQiv49y7sZFUL/FT/pWEpqeTcDsB2CL4BSFxsbyyltAAAA8BoEXwAew6ltRcMv/AAAAJ5B8AXgMZzaVjQdlqzhui4AAAAP4D6+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABbI/gCAAAAAGyN4AsAAAAAsDWCLwAAAADA1gi+AAAAAABb8yvvBrhz+PBhZWVlFViuYcOGqlKlSp7zMjMzdfz4cVWtWlXVq1cv7SYCAAAAALyc1wbf5ORkNWnSRMaYAsvu3btXF154octz+/bt07PPPqulS5cqMTFRklSzZk0NGTJETz75pJo1a+aRdgMAAAAAvIvXnuq8f//+QoXevKxevVqdOnXSwoULrdArSadOndLs2bPVqVMnrV69urSaCgAAAADwYl4bfP/66y9JUtu2bWWMcftwPtobFxenIUOGKDExUbVq1dJHH32k+Ph4nT59Wl999ZXCw8N1+vRpDR8+XDExMeW1eQAAAACAMuL1wbdVq1ZFqvfiiy/q1KlT8vHx0bfffqvbbrtNNWvWVEhIiAYPHqzvv/9evr6+OnHihF588UVPNB0AAAAA4EVsFXwzMzP10UcfSZKuu+469ejRI1eZtm3batCgQZKkjz/+WBkZGaXQWgAAAACAt7JV8N22bZvOnDkjSRoyZEi+5QYOHChJOnv2rDZt2lSCVgIAAAAAvJ3XjuqcE3z9/f31wAMP6IcfflBUVJT8/f3VqlUrDRkyRA888ICqVq1q1Vm3bp013b1793yX3aFDB2t6586duuSSSwrdrqNHj7qd73zdcFZWVqFux1RWnAcLM8Z4VdsqO/oGAAD8E98JvAvf18qGp/arVwbfxMRERUdHS5JGjBih9PR0a15KSoo2bdqkTZs26Z133tE333yj1q1bS5IOHjwoSfL19VVERES+y3eeFxkZWaS2NWrUqNBlU1NTlZKSUqTll5XU1NTybgLyYYe+Ke6I7AAA4H/s8J3Arugbz/HUvvXKU53//vtva9rHx0dPPPGENm7cqH379mnZsmW6/vrrJWXf8mjAgAGKjY2VJMXHx0uSatSoIT+//DN9jRo1rOmEhITS3wAAAAAAgNfwyiO+Oac5V61aVatWrXI5bfn888/XlVdeqalTp2rSpEmKiorSxIkT9f7771vBNygoyO3yAwICrOmi/qJw5MgRt/NjYmLUtWtXaz2BgYFFWr4nGWOs7Q0ICJDD4SjnFiGH3fqmorcfAABvYIfvBHZit+9r3so5q5Umrwy+N9xwg5KTk+Xr66sqVarkWeaZZ57Rl19+qd9++00LFizQf/7zH/n4FO4AtnPYLWowDQ8PL3RZHx+fQrepLDifL+9wOLyqbZUdfQMAAP6J7wTehe9rZcNT+9Ure8vX11eBgYH5ht4cgwcPlpR9TfCff/6p4OBg6//uJCUlWdPOpz0DAAAAAOzHK4NvYTVs2NCaPnfunDXw1KlTp5ScnJxvvaioKGu6adOmnmsgAAAAAKDceV3wzczMVGRkpCIjIwsceOr06dPWdGhoqDW6syTt3r0733r79u2zptu3b1/stgIAAAAAvJ/XBd+MjAw1b95cTZs21XPPPee27Nq1ayVln67cvHlzXX755da8ZcuW5Vtv9erVkqRatWoRfAEAAADA5rwu+AYEBFhhdOHChfneB/e3336zwu3w4cPl4+Oj5s2bq0uXLpKkWbNm5Vn3+PHjmj9/viTp5ptvlq+vrwe2AgAAAADgLbwu+ErSvffeK0k6evSoRowY4XJKs5R9GvONN96orKwsVa1aVRMmTLDmPfvss5Kk6Oho3X777S4DWcXFxWnw4MFKSEhQ9erV9fTTT3t+YwAAAAAA5corb2d09913a+nSpfrmm2/09ddfKzw8XJdeeqlq1aqlw4cP65dfflFmZqZ8fHz07rvvugxQdc011+j+++/XrFmz9MUXX+jHH39Ujx49lJaWpjVr1igpKUk+Pj56++231aBBg3LcSgAAAABAWfDK4Ovj46P//ve/euWVVzR9+nSdPXtWy5cvdylz/vnna8aMGRo4cGCu+jNnzlT9+vX1yiuvKDY2VosXL7bmNWrUSG+88YZuuOEGj28HAAAAAKD8eWXwlaQqVapo0qRJevTRR7VmzRrt27dPGRkZCg0NVefOnXXxxRfL4XDkWdfhcGjSpEl66KGHtHz5ch0+fFiBgYFq2bKl+vXrJz8/r91sAAAAAEAp8/oEGBwcrGuvvVbXXnttkeuGhIRo2LBhHmgVAAAAAKCi8MrBrQAAAAAAKC0EXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGsEXwAAAACArRF8AQAAAAC2RvAFAAAAANgawRcAAAAAYGt+5d0AAAAAANniU9MkSbGxsWrcuHE5t6biCQsL09atW8u7GfBCBF8AAADAS2QZk/1vVpaioqLKuTWAfRB8AQAAAC/jIyk0MKC8m1FhnEhJVVZ5NwJejeALAAAAeJnQwACt6N+zvJtRYfRf8ZOOpaSWdzPgxRjcCgAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALbGfXyBAnTu3FmxsbEeX48xRpLkcDg8vi5Pi4mJKe8mAAAAABaCL1CA2NhYRUVFlXczAAAAABQTwRcoJB9JoYEB5d2MCuFYSmp5NwEAAACwEHyBQgoNDNCK/j3LuxkVQocla5RV3o0AAAAA/j8GtwIAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALZG8AUAAAAA2BrBFwAAAABgawRfAAAAAICtEXwBAAAAALbmV94NAAAAAICSiE9NkyTFxMQoPDzcY+sxxkiSHA6Hx9ZR1sLCwrR169bybobHEXwBAAAAVGhZ/z+QZmVlKSoqqpxbA29E8K1EunbtqpiYGEn2+pXK03L2GQAAALybj6TQwIDybkaFcCIlVVnl3YgyVGmCb1JSkk6fPq06deqoSpUq5d2cchEbG6vo6OjybgYAAADgEaGBAVrRv2d5N6NC6L/iJx1LSS3vZpQZ2wffzz77TNOnT9evv/4qY4wcDodatWqlu+66Sw8//LD8/Gy/C3Lhl7CiqUwfCAAAAIAd2Tr1PfDAA5o1a5bLc8YY7dmzR48//rgWL16s77//Xuedd145tbB88EtY0XRYsqZSnQYCAAAA2I1tb2c0e/ZsK/T27dtXmzZtUlJSkg4fPqyJEyfK4XBo48aNuu+++8q5pQAAAAAAT7Jl8E1JSdHEiRMlSRdffLGWLVumrl27KigoSI0aNdLUqVP1r3/9S5L06aefatu2beXZXAAAAACAB9ky+H777bc6fvy4JGnKlCny9/fPVebxxx+Xr6+vJOn9998v0/YBAAAAAMqOLYPv6tWrJUlVq1bVoEGD8ixTp04dderUSZK0YsWKMmsbAAAAAKBs2TL4rlu3TpLUsWNHt7cu6tChgyRp//79Sk5OLpO2AQAAAADKli1HdT548KAkqVmzZm7LRURESMoe6fnQoUO68MILC1z20aNH3c4/cuSINR0VFaWsLO8ZDzgzMzP7X2MUl5xSzq2pOMz//5f9Vnjss+JhvxUd+6x42G9Fxz4rHvZb0bHPiof9VnSZJnuvZWZm6vDhw+Xcmv+JiYmxpjMyMkptuQ5jjCm4WMWRkpKioKAgSdL48eM1ffr0fMvOmjVLDzzwgCRp8+bN6tKlS4HLdzgcpdNQAAAAAEC+CpvRCsN2pzrHx8db0zkBOD8BAQHWdGpqqsfaBAAAAAAoP7Y71dnHp/BZ3jnsBgYGFqqO86nMeUlJSdEff/yhevXqqU6dOvLz855dHBMTo65du0rK/vWkfv365dwi5KBvvBv9473oG+9F33g3+sd70Tfei74pGxkZGdYdetq1a1dqy/WeVFZKgoODrenExES3ZZOSkqzpGjVqFGr54eHhBZZp0aJFoZZVnurXr1+obUHZo2+8G/3jvegb70XfeDf6x3vRN96LvvGsJk2alPoybXeqc3BwsBVio6Oj3ZaNioqSlH2UuHHjxp5uGgAAAACgHNgu+EpS69atJUm7du1yW27fvn1WeX9/f4+3CwAAAABQ9mwZfC+//HJJ0u7du/O9JjctLU0bNmxwKQ8AAAAAsB9bBt8RI0ZYtx2aMWNGnmU++OADnT17VpJ02223lVXTAAAAAABlzJbBt02bNrrpppskSW+88YYWLFjgMn/ZsmUaP368JOmmm25S586dy7yNAAAAAICyYcvgK0mzZs1So0aNlJmZqREjRqhTp04aMWKEunTpooEDByopKUkRERF68803y7upAAAAAAAPsm3wDQ0N1aZNm3TFFVdIkrZv364FCxZo69atkqT+/fvrp59+UoMGDcqzmQAAAAAAD3MYY0x5N8LTdu/erV9++UXHjx9XWFiYunTpojZt2pR3swAAAAAAZaBSBF8AAAAAQOVl21OdAQAAAACQCL4AAAAAAJsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYJvBZCUlKTo6Gilp6eXyvIWLlyoQYMGqV69egoMDFTTpk113XXXafny5QXWjY6O1mOPPabWrVsrODhYNWvWVIcOHTR58mQdP368VNoHKT09XTExMUpMTCxW/ZMnT9IfHlLSvoFnFad/zpw5o2nTpqlnz56qV6+e/P39VbduXfXt21dvvfWWUlNTPdjiyqM4fbN3716NHTtW559/vqpVq6Zq1aqpZcuWuuOOO/Tzzz97sLWVS2l9rh04cEDVq1eXw+HQvHnzSqdxKHL/JCUlKTIyssBHTEyMh1tuf8V97+zatUt33nmnGjduLH9/f9WsWVNdu3bVyy+/rKSkJA+1FjLwWp9++qnp2LGjcTgcRpJxOBymdevWZvr06SY9Pb3Iy0tNTTU33HCDkZTvY/To0SYzMzPP+qtXrzbnnXdevnVr165ttm7dWtLNrnA2b95sJJmIiIgSL2vTpk1m4MCBJiAgwNqv9evXN48++qiJi4tzW/fUqVNm/Pjxpm7dulbdgIAAc80115g1a9aUuG0Vkbf0zT898MADRpKZPHlyidtVkXlD//z222+mSZMmbj8X27VrZ2JiYkrcxorEG/pmwYIFJigoKN9+cTgc5sknnyxx+yoab+ibvGRkZJiePXtay5k7d26J21cReUP/vPPOO24/03Ie3bp1K3EbKxJv6BtjjJkzZ46pUqVKvv1y8cUXm9OnT5e4jciN4Oul7r//frcfVpdccok5e/ZskZb50EMPWfWvvvpqs3z5crNv3z6zZs0ac+utt1rzJkyYkKvuoUOHrNBbs2ZN8+9//9v8/vvvZvfu3eadd94xDRs2NJJMSEiIiY2NLa3dUCHkhJiSfpB+8sknxs/PL98+b9iwodmxY0eedY8ePWpatGiRb10fHx8zbdq0ErWvIvKGvvmnxMREU6dOHYKvKf/+OX36tGnatKmRZPz8/Mz48ePNxo0bzb59+8zy5cvN1VdfbS2jV69eJisrq0TtrEjKu2/27Nljhd569eqZN9980/z666/mt99+M2+//bapV6+etYwPP/ywRG2saMq7b/IzdepUl/qVNfh6Q/+MHz+e4JsHb+ibFStWGB8fHyPJNG7c2HzwwQdm9+7dZsOGDea2226zlnHfffeVqI3IG8HXC7333nvWC79v375m06ZNJikpyRw+fNhMnDjROgJ8yy23FHqZf/zxh1Xv1ltvzfML3LPPPmskGX9/f7N//36XebfccouRZIKCgszvv/+eq250dLQJCwszkszdd99d9I2uoBYsWGB8fX1L/EG6c+dO4+/vby3n66+/NmfPnjUnT5408+bNMyEhIUaSOf/88825c+dc6mZlZZlLLrnECrjPPvusOXLkiElKSjI///yzueyyy6yjI8uXLy/hFlcc3tA3/5SQkGBuvvlm6/1dmYOvN/TPv//9b6svPv300zyX7/xFZPHixcVuZ0XiDX0zatQo68fUgwcP5lp2dHS0FX7btWtX7DZWNN7QN3nZunVrriNYlTH4ekv/XHfddUaSmT59egm2xl68oW/S09NNy5YtjSQTHh6e55lEOd+3/f39i3zWBQpG8PUyycnJ1tGgiy++2KSmpuYq88QTT1h/WAp7avGECROsN9KxY8fyLJOSkmJq1aplJJnx48dbz589e9b6g/bII4/ku45p06YZSaZKlSomPj6+UO2qaA4dOmReffVVM2rUKNOsWTOXP/Il+SC99tprjSRTrVq1XD86GGPMd999Z63nn0duv/zyS2veq6++mqtuUlKSiYiIMJJM27Zti91Gb+eNfWOMMcuXLzfPPPOMueaaa0xwcLBLuypT8PXG/unYsaORZNq3b5/v8g8fPmzVHz58eLHb6c28rW/S0tKs98qDDz6Y7/Kfeuopq35hwllF5G19k5ekpCTTqlUrI8mMGTOmUgVfb+2fnP5YunRpsdtQ0Xlj3yxatMia9+WXX+a5/F27dhX4gyyKj+DrZb744gvrBf/VV1/lWebYsWPWr1b3339/oZZ76aWXGkmmR48ebssNHDjQSDKtWrWynlu5cqXVJndHDDdt2mSVW7BgQaHaVdEsXrzY5cOzND5I4+LirNNe3P2w0K5du1x9Y4yxTsesU6eOSUtLy7Ou85GtTZs2Faud3s4b+8YYYwYPHpxvuypT8PXG/sm5Nuvxxx93u56c6+bt+sORt/XNwYMHrfV/9tln+dadOXOmVc6ul9h4W9/kZdy4cdb3i7///rtSBV9v7J/MzEzrs+3AgQPFaoMdeGPfDB8+3EjZR3vzG0/HmOwzXoYPH26WLFlSrHYif36CV1m9erUkqWrVqho0aFCeZerUqaNOnTpp8+bNWrFiRaGWGxcXJ0mqX7++23K1atWSJP3xxx9KTExUtWrVrLoF1c+pK0nbtm3TsGHDCtW2iqR169Z64YUXXJ57//33FRkZWexlrl27VllZWZKkIUOG5Ftu4MCB2rlzp/bu3auoqCg1bNhQmZmZWr9+vSRp8ODBqlKlSr51x48fL0lasWKFunbtWuz2eitv65scI0eOVOfOna3/nz59Wv/+97+L3aaKytv659SpU9ZozWFhYW7Xk5aWJknWuuzG2/omNTVVV155pSSpZcuW+dbNaV9gYKDq1q1b7LZ6M2/rm39avny5Zs6cqapVq+qjjz6Sr69vsdtVEXlj/xw+fFipqakKCgpSREREsdtR0Xlj36xZs0aSNGDAAPn45H9jnblz5xa7jXCP4Otl1q1bJ0nq2LFjviFGkjp06KDNmzdr//79Sk5OVlBQkNvlBgYGSlKBt7g5c+aMJMkYoyNHjujCCy+06hZUP6euJB06dMjteiqq888/X88884zLcytXrizRB2lOn/v6+roEpH/q0KGDNb1z5041bNhQ27dvV0JCgiSpe/fu+da94IILFBQUpOTkZO3cubPYbfVm3tY3OYYOHepSNjIyslIGX2/rn+rVq+vIkSOSpJo1a+Zb98cff9Tp06clZW+DHXlb31xwwQVatmyZ2+WfPHlSn3zyiaTsL5EOh6PYbfVm3tY3zuLj43XXXXfJGKNp06apRYsWJWpXReSN/fPXX39Jyv67/80332j27NnasmWLTp8+rdq1a6tHjx66//771b9//2K3sSLwtr6JiYnRsWPHJEnt2rWz5htjdOLECfn6+rocQIJncB9fL3Pw4EFJUrNmzdyWy/kVzxhTqJCZ84Xtjz/+yPd+wFlZWdq2bZv1/5wg6/xlb8eOHfmuY8uWLbnqomA5fd6wYUMFBATkW875l9ucD+6cupL714yPj48aNWrkUhcFK0nfwPNK0j++vr4KDw9XeHi4qlWrlme9kydP6p577rH+b8ezWDyltN87p0+f1sGDB/Xbb79p9uzZ6tatm2JiYhQSEqKXX3651NpdGZRW39x7772Kjo5W//79df/995d6OyurkvZPTvDdvXu3brjhBn333Xc6fvy40tPTFRsbq8WLF2vAgAEaNWqUdTYLCqe0vq81btxYhw4d0m233aaQkBDVrVtXtWvXVlhYmB588EHur+xBBF8vkpKSouTkZEnZpzO7U6NGDWs654ifO9dee62k7CO277//fp5l3nvvPZc3W05AbtOmjZo2bSpJmjFjhnV6oLOzZ8/q//7v/3LVRcHi4+MlFa/Pc+oWpX5hXi/IVpK+ged5sn82b96sbt266Y8//pAkXXLJJRo+fHjxGloJlXbffPXVV2rWrJk6dOige+65R/v371etWrW0Zs0atW7dulTaXFmURt989NFH+u9//6saNWpo7ty5tj3iXh5K2j85wTc9PV0XXHCB5syZo99++027du3Se++9Z32f+/DDD/nBoohK0jc5R3ul7INQ7dq10yeffOLSd3FxcXr77bfVrl07bdy4sRRbjhwEXy/iHGIKOnXZ+ZemvILoP91888264IILJEmPPvqoXn/9dUVFRSk1NVW7du3S/fffr7Fjx7pcn5NzirPD4dBzzz0nKfsU5gEDBmjDhg1KTk7W8ePH9cUXX6hTp07666+/rPrOp0fDvZx+L06fF+c1U5jXC7KVpG/geZ7onzNnzujhhx9Wz549tX//fknZp60tWrTI7TVZcFUW7534+Hj179+/0GNdIFtJ++bQoUN68MEHJUlvv/12ntf+ovhK2j85wffSSy/V9u3bNXr0aF188cVq06aNxowZo19//VUdO3aUJH3wwQf68ccfS3sTbKskfZOYmGg9N2nSJKWlpen5559XZGSk0tLSdOjQIb322msKCAjQyZMnde211+rEiRMe2IrKjb/iXqQoX6qcP+QKEzL9/f21aNEiNWjQQCkpKXrssccUHh6uwMBAtWvXTu+8846qVKmiRx991KpTu3Zta/r222/Xww8/LElav369LrvsMlWtWlV169bVsGHD9Pfff+vaa69V27Ztc9WFe4Xt97z6vDivGX6UKLyS9A08r7T75+OPP9YFF1ygN998U5mZmXI4HBo3bpw2btxo28GTPKW0+2bUqFHKzMxUTEyMNm7cqHvuuUdVqlTRyZMndd111+nvv/8ucZsri5L0TVZWlm6//XadPXtWQ4YM0ciRIz3SxsqspO+dRYsWKTk5WevWrVPVqlVz1QsJCXE58+/DDz8sQWsrl5L0jTHGei4rK0tfffWVJk2apIiICFWpUkWNGzfWv/71L02fPl2SdOrUKc2cObMUWw+J4OtVgoODrWnnX4bykpSUZE07n1LhTuvWrbV9+3aNGzdO9erVs54PDAzU4MGDtX37dmt005zr35zNmDFDX375pS655BKXI8MtW7bUtGnTtGjRIh04cECSKvVIgkWV0+/F6fPivGYK+3pByfoGnlda/XP06FH1799ft99+uzWKfadOnfTTTz/prbfeKvDXfeTmifeOj4+PwsLC1LNnT7377rvW4FYpKSmaMWNGidpbmZSkb9566y2tX79eYWFheueddzzWxsqspO8df39/BQYGug1p7du3t76n/frrryVobeVSkr4577zzrOd69+6tq666Ks+6Y8aMsU6lXrlyZUmaizwQfL1IcHCw9QaJjo52WzYqKkpS9heBxo0bF3od9erV01tvvaXY2FjFx8crJiZG586d01dffaXWrVtbg1u1bt06z1Glb7zxRm3YsEGJiYmKiopSQkKC/vzzTz3++OM6cOCAda1C+/btC92myi5n0KnC9rkk6xqdnLpFqZ9TFwUrSd/A80qjf3bs2KGuXbtaXzDq1KmjOXPmaPPmzW5HSod7ZfHeGTZsmBo0aCBJLgMzwr2S9E1OSIqNjVVoaKgcDofLw7kP77zzTuv5tWvXlvJW2FdZ/d3JOUX93LlzRa5bWZWkb5zvHtClS5d86wYEBFinojsvB6WD4Otlcgbp2LVrl9ty+/bts8r7+/sXa101a9ZUWFiYy9HbDRs2SJIuu+wyt3UDAgLUoEEDlyOOOdeJOBwOXXLJJcVqU2WU0+dxcXFubxeV0+eSdPHFF7vUldy/Zo4fP65Tp05J4keJoihJ38DzSto/x48f18CBA61B/UaMGKE//vhDo0eP5nreEipJ39x4440KDg4u1Jf5nKNWOZ9vKBifa96tJP1z5swZRUZGKjIy0uXU2rzk3KYtNDS0hC2uPErSN61atbKe8/NzfzfZnLOMKtt9scsCf9m9zOWXXy4pexj6nHtM/lNaWpoVUHPKF+TBBx8s8IvExo0brXXecMMN1vMxMTEKDg5WcHCwZs+enW/9zz//XJLUs2dPl1Op4Z5zHy5fvjzfcqtXr5YkXXTRRdYfqpYtW1qnpLu772VO3X+uD+6VpG/geSXtn+eff9765f6hhx7SZ599xn0US0lJ+iY0NFSJiYmKjIx0GQk1L7GxsZLEAEtFUJK+mT59ug4ePJjvw3mgpGnTplnPc/ZE4ZWkf7744gs1bdpUTZs2dftjeFxcnDVivbv70cJVSfqmTp06atKkiaTse/u6s3fvXkn2vXd8eSL4epkRI0ZYtwXI75qlDz74QGfPnpUk3XbbbYVabuPGja0vEjkjlTozxmjy5MmSpBYtWri8uevWrStfX18lJiZq/fr1eS5//fr11qmCzve9RMF69eplfWmbMWNGnr/S7t69Wz/88IOk3H1+8803S8r+oM3rPsvGGL3xxhuSsm9N5XxjdbhX0r6BZ5WkfzIzM60f6y699FK9/vrrZdDiyqMkfdO3b19r+osvvsh3HStXrrTujXnFFVeUSrsrg5L0TWhoqJo0aZLvw3lsEOeyDPpXeCXpn27dulnTH330Ub7reO2115SVlSXpf98hULCSfie45ZZbJGWHZuejws5Wrlxpzbv++utLq+nIYeB1hgwZYiQZX19f8/nnn7vM+/77703VqlWNJHPTTTe5zFu3bp0JCAgwAQEB5u6773aZt3//fuPr62skmT59+phTp05Z85KSkszo0aONJCMp1zqNMebOO++02vTll1+6zNuwYYOpU6eOkWTatm1r0tPTS7gHKpbevXsbSSYiIiLfMpGRkVbf9O/fP9f8t99+29r/jzzyiMs+/Ouvv8z5559vJJnGjRubc+fOudSNiooy1atXN5LMhRdeaA4fPmzNS0tLM/fdd5+17CVLlpR8gyuQ8u6bvBw8eNBa3uTJk4uzWbZRnv2zYcMGq95//vMfc/DgwQIfMTExpbr93qw8+yY1NdWEh4cbSSY4ONgsX74817KXL19uateubSSZWrVqmfj4+JJtcAXijZ9rOZw/3+bOnVuUzbKN8u6fHj16WN/X5syZk2vZM2fONA6Hw0gyAwcOLP6GVkDl3TcxMTGmVq1a1ve1ffv2ucz/66+/TIsWLYwk06xZM5OSklKyDUYuBF8vdPz4cdOoUSPrjdWxY0czfPhw07lzZ+u5iIgIExUV5VJvzZo11vw77rgj13Kffvppa3716tXN4MGDzY033mhCQ0Ot5++6664823T06FEr3Oa0acSIEaZ79+7WB2hISIjZuXOnJ3aJVyvMB6nzl4HevXvnmp+VlWUGDRpklWnSpIkZMmSIueKKK0yVKlWMJBMYGGhWrlyZ5/I///xzqx+CgoLMVVddZYYMGWLCwsKsZf7zx5DKwBv6xt36CL7l1z8fffSRVaewj7zWb1fl/d5ZsWKFVUaSad26tRk6dKgZPny4adWqlfV8Ud57dlHefeMOwbf8+2fPnj0u39eaNGlibrrpJjNkyBDTtGlT6/mmTZuao0ePluKWe7/y7htjjPnuu+9MYGCgkWT8/PxMr169zC233GIuv/xy4+/vbySZ8847z2zevLmUthrOCL5eKjo62lxxxRV5fvnq379/rtBrTMHBNysry7zwwgvWEWPnR0BAgJk0aZLJzMzMt0179+51Cd/Ojw4dOpgdO3aU5i6oMErjg9SY7KMc48aNc/myl/O48MILzfr169224+uvvzb169fPVTc4ONg899xzJisrqwRbWTF5S9/ktz6Cb/n1z7Rp0wi+bnjDe2fVqlXmwgsvzLc/evXqZX777bcSbmnF4w19U5j1Enwj8i3j6f45cOCAufrqq60fxJ0fDofDDB06NM/vkXbnDX1jjDGbNm0y7du3z/NzrVu3bpXyc62sOIwpYNg3lKvdu3frl19+0fHjxxUWFqYuXbqoTZs2JVrmqVOntHr1ah04cEAZGRmKiIjQgAEDCj0oz/bt27Vp0ybFx8crODhYXbt2VY8ePUrUJvxPXFycVq5cqaNHjyokJEStWrVSr169rGu/3cnIyNDKlSv1559/Kj09XU2aNFG/fv24t2wpKUnfwPPoH+9V3L7JysrSr7/+qm3btunkyZOqUqWK6tevrx49eqhZs2Zl1Hp7433j3UrSPwcOHND69esVHR2twMBANWzYUH369GEA0lJSkr4xxmjz5s3asmWLEhISVLt2bfXo0UPt2rUrg5ZXXgRfAAAAAICtMaozAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbI3gCwAAAACwNYIvAAAAAMDWCL4AAAAAAFsj+AIAAAAAbO3/AX+l0kUgGlbzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 550x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 371,
       "width": 479
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "while True:\n",
    "    result = random.choice(results)\n",
    "    if result.converged:\n",
    "        break\n",
    "\n",
    "# Randomly select a parameter and plot its posterior distribution\n",
    "param = random.choice(result.variables)\n",
    "fig, ax = plt.subplots(figsize=(5.5, 4))\n",
    "ax.hist(result.samples[param])\n",
    "ax.set_title(f\"posterior distribution of {param}\")\n",
    "\n",
    "# Let's list InferenceResult attributes/methods\n",
    "print(\n",
    "    \"List of InferenceResult attributes/methods:\\n  >>\",\n",
    "    \"\\n  >> \".join(attr for attr in dir(result) if not attr.startswith(\"_\")),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was just a quick overview, but you can do all sorts of analyses with the results, e.g., plotting the evolution of posterior distributions as a function of cycle number in form of violin plots, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single circuit, single dataset (SCSD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've already covered how to use `perform_bayesian_inference` for SCMD in the previous section. For SCSD, you just need to pass a single impedance dataset to the function, i.e., a NumPy array instead of a list of arrays. The rest of the process is the same!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple circuits, single dataset (MCSD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, you can use `perform_bayesian_inference` for MCSD by passing a list of circuit strings instead of a single string. Alternatively, you can pass a dataframe, but it needs to be formatted with columns named `circuitstring`, and `Parameters` with the circuit strings and initial guesses for the parameters, respectively. This unusual format is for legacy reasons and might be changed in the future."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple circuits, multiple datasets (MCMD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might ask, what about MCMD? Well, we can easily extend the API to support this mode of analysis, but we couldn't find an actual use case for it, so it's not implemented to keep the codebase sane! If you really need this feature, you can easily implement it yourself by calling `perform_bayesian_inference` in a loop over the datasets!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "03a50827c4b9416e8f730f900f7b59e9": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "danger",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_e88c805bdf4a4d2f975320dc0f08d166",
       "max": 130.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_875057af4b3045278c1ace157ac52e95",
       "tabbable": null,
       "tooltip": null,
       "value": 0.0
      }
     },
     "1520b40c729d4ca48fd033ab00342d79": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "17e01462eeda4ae99145ce5622e180cf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_896b0c9439b044029682ff5ecc780f25",
       "placeholder": "​",
       "style": "IPY_MODEL_65df640deb7e407696aed720f375adeb",
       "tabbable": null,
       "tooltip": null,
       "value": " 0/130 [03:11&lt;?, ?it/s]"
      }
     },
     "24da1f016cb24868b04df86c1f439ac6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "258cf4327e80407eab1c01a0ec7e8094": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "26375096176e4851a472ef5696c90573": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_9fd75e7866b5421ab071e836fbd637fb",
        "IPY_MODEL_03a50827c4b9416e8f730f900f7b59e9",
        "IPY_MODEL_17e01462eeda4ae99145ce5622e180cf"
       ],
       "layout": "IPY_MODEL_54c5f5d7b33942e79ba9da7eb474eae3",
       "tabbable": null,
       "tooltip": null
      }
     },
     "29e8fd80075d43ac9d727bf3f3421a10": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "2eb8546111c749f0b7d54057ac1e9690": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_58216c00f15d4f78a2e9ac3654e0a94c",
       "placeholder": "​",
       "style": "IPY_MODEL_5ba31b18016543b3b43f5c62d81390e2",
       "tabbable": null,
       "tooltip": null,
       "value": " 130/130 [08:11&lt;00:00,  3.67s/it]"
      }
     },
     "37e3cfdc715a435c8b41e46e616c5047": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": "2",
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "3ffed67d8a0d4225a38bebd560b0e40f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "41eff6f5068e46808fec1088aa052ea2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "45301d7e5fdc4723ba237d003d0039a2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": "2",
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "4551a32996c7486bb02d8b9bf02b681e": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": "inline-flex",
       "flex": null,
       "flex_flow": "row wrap",
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": "100%"
      }
     },
     "54c5f5d7b33942e79ba9da7eb474eae3": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": "inline-flex",
       "flex": null,
       "flex_flow": "row wrap",
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": "100%"
      }
     },
     "5734067854d44edeadb8cc24973d61a6": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "58216c00f15d4f78a2e9ac3654e0a94c": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "5ba31b18016543b3b43f5c62d81390e2": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "65df640deb7e407696aed720f375adeb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "73c8a12ac6ef44519499e24e467e8827": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": "inline-flex",
       "flex": null,
       "flex_flow": "row wrap",
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": "100%"
      }
     },
     "74fbec8fb7d94634be5c883115f47df8": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "85a36b4b9b7f45eea8b888103362e4d8": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "875057af4b3045278c1ace157ac52e95": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "ProgressStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "ProgressStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "bar_color": null,
       "description_width": ""
      }
     },
     "896b0c9439b044029682ff5ecc780f25": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "89798ae573584f16a3c3f0a73763ebdd": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_41eff6f5068e46808fec1088aa052ea2",
       "placeholder": "​",
       "style": "IPY_MODEL_1520b40c729d4ca48fd033ab00342d79",
       "tabbable": null,
       "tooltip": null,
       "value": "Refining Initial Guess: 100%"
      }
     },
     "8b18d0b542d241ee820999e95cb68c3f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_f6be7e21313f4c598e9bc2213c0c7ecf",
        "IPY_MODEL_b5501e45d92f4950a856c57fa8546ba3",
        "IPY_MODEL_2eb8546111c749f0b7d54057ac1e9690"
       ],
       "layout": "IPY_MODEL_907b70a0c504485ca761de7b759ca5ed",
       "tabbable": null,
       "tooltip": null
      }
     },
     "907b70a0c504485ca761de7b759ca5ed": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": "inline-flex",
       "flex": null,
       "flex_flow": "row wrap",
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": "100%"
      }
     },
     "97c333a163fa4f82bd75f5915adb67c1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "danger",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_e0b4d5eb9bcc4d889975b741561fa989",
       "max": 130.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_258cf4327e80407eab1c01a0ec7e8094",
       "tabbable": null,
       "tooltip": null,
       "value": 0.0
      }
     },
     "9907b102498540cc8e77445d191cc56c": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "9bc3ffd26ab7493a833e16a13800636f": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLStyleModel",
      "state": {
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLStyleModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "StyleView",
       "background": null,
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "9fd75e7866b5421ab071e836fbd637fb": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_74fbec8fb7d94634be5c883115f47df8",
       "placeholder": "​",
       "style": "IPY_MODEL_29e8fd80075d43ac9d727bf3f3421a10",
       "tabbable": null,
       "tooltip": null,
       "value": "Refining Initial Guess:   0%"
      }
     },
     "a1a6e32e74e5432cac949d34530658e4": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_d35cbd23be4b4410a1c911aa187eb937",
        "IPY_MODEL_97c333a163fa4f82bd75f5915adb67c1",
        "IPY_MODEL_ea8d78eaffe04e789b6de56f58542b99"
       ],
       "layout": "IPY_MODEL_4551a32996c7486bb02d8b9bf02b681e",
       "tabbable": null,
       "tooltip": null
      }
     },
     "a68861dad1b54ab9a194a812fdba2892": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_dcbce407c8e841babd15d8bd6428484f",
       "placeholder": "​",
       "style": "IPY_MODEL_85a36b4b9b7f45eea8b888103362e4d8",
       "tabbable": null,
       "tooltip": null,
       "value": " 130/130 [03:10&lt;00:00,  2.85s/it]"
      }
     },
     "b5501e45d92f4950a856c57fa8546ba3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_37e3cfdc715a435c8b41e46e616c5047",
       "max": 130.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_3ffed67d8a0d4225a38bebd560b0e40f",
       "tabbable": null,
       "tooltip": null,
       "value": 130.0
      }
     },
     "bbabbe8b1db8431d960dbcf73be2efce": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "d35cbd23be4b4410a1c911aa187eb937": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_bbabbe8b1db8431d960dbcf73be2efce",
       "placeholder": "​",
       "style": "IPY_MODEL_9bc3ffd26ab7493a833e16a13800636f",
       "tabbable": null,
       "tooltip": null,
       "value": "Performing Bayesian Inference:   0%"
      }
     },
     "da1d83b622c944c6b2f3d3687bfbe291": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "dcbce407c8e841babd15d8bd6428484f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "e0b4d5eb9bcc4d889975b741561fa989": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": "2",
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "e88c805bdf4a4d2f975320dc0f08d166": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": "2",
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "ea8d78eaffe04e789b6de56f58542b99": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_f0969450745145b595ead5082bc73ecc",
       "placeholder": "​",
       "style": "IPY_MODEL_9907b102498540cc8e77445d191cc56c",
       "tabbable": null,
       "tooltip": null,
       "value": " 0/130 [00:36&lt;?, ?it/s]"
      }
     },
     "efc58d3c001343b790b8aff9e0c9ff12": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "FloatProgressModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "FloatProgressModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "ProgressView",
       "bar_style": "success",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_45301d7e5fdc4723ba237d003d0039a2",
       "max": 130.0,
       "min": 0.0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_24da1f016cb24868b04df86c1f439ac6",
       "tabbable": null,
       "tooltip": null,
       "value": 130.0
      }
     },
     "f0969450745145b595ead5082bc73ecc": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "_model_module": "@jupyter-widgets/base",
       "_model_module_version": "2.0.0",
       "_model_name": "LayoutModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/base",
       "_view_module_version": "2.0.0",
       "_view_name": "LayoutView",
       "align_content": null,
       "align_items": null,
       "align_self": null,
       "border_bottom": null,
       "border_left": null,
       "border_right": null,
       "border_top": null,
       "bottom": null,
       "display": null,
       "flex": null,
       "flex_flow": null,
       "grid_area": null,
       "grid_auto_columns": null,
       "grid_auto_flow": null,
       "grid_auto_rows": null,
       "grid_column": null,
       "grid_gap": null,
       "grid_row": null,
       "grid_template_areas": null,
       "grid_template_columns": null,
       "grid_template_rows": null,
       "height": null,
       "justify_content": null,
       "justify_items": null,
       "left": null,
       "margin": null,
       "max_height": null,
       "max_width": null,
       "min_height": null,
       "min_width": null,
       "object_fit": null,
       "object_position": null,
       "order": null,
       "overflow": null,
       "padding": null,
       "right": null,
       "top": null,
       "visibility": null,
       "width": null
      }
     },
     "f6be7e21313f4c598e9bc2213c0c7ecf": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HTMLModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HTMLModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HTMLView",
       "description": "",
       "description_allow_html": false,
       "layout": "IPY_MODEL_da1d83b622c944c6b2f3d3687bfbe291",
       "placeholder": "​",
       "style": "IPY_MODEL_5734067854d44edeadb8cc24973d61a6",
       "tabbable": null,
       "tooltip": null,
       "value": "Performing Bayesian Inference: 100%"
      }
     },
     "fea8eff61b5646dc93c82ce14c86137d": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "HBoxModel",
      "state": {
       "_dom_classes": [],
       "_model_module": "@jupyter-widgets/controls",
       "_model_module_version": "2.0.0",
       "_model_name": "HBoxModel",
       "_view_count": null,
       "_view_module": "@jupyter-widgets/controls",
       "_view_module_version": "2.0.0",
       "_view_name": "HBoxView",
       "box_style": "",
       "children": [
        "IPY_MODEL_89798ae573584f16a3c3f0a73763ebdd",
        "IPY_MODEL_efc58d3c001343b790b8aff9e0c9ff12",
        "IPY_MODEL_a68861dad1b54ab9a194a812fdba2892"
       ],
       "layout": "IPY_MODEL_73c8a12ac6ef44519499e24e467e8827",
       "tabbable": null,
       "tooltip": null
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}