{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial:RNA+ADT (well-labeled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will show how to cluster RNA+ADT(well-labeled) data using scMAGCA. As an example, we use a paired human peripheral blood sample dataset '10X1kpbmc' containing 713 cells with two omics. Among them, ADT has 17 features, while RNA contains 33,538 features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import scanpy as sc\n",
    "import random\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scMAGCA.preprocess import read_dataset, preprocess_dataset\n",
    "from scMAGCA.utils import *\n",
    "from scMAGCA.scMAGCA import scMultiCluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set seed\n",
    "random.seed(3407)\n",
    "np.random.seed(3407)\n",
    "torch.manual_seed(3407)\n",
    "torch.backends.cudnn.deterministic = True\n",
    "torch.backends.cudnn.enabled = False\n",
    "torch.backends.cudnn.benchmark = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The required input files include: \n",
    "1) x1: protein abundance matrix (data format is h5ad file) : 10X1kpbmc_adt.h5ad; \\\n",
    "2) x2: Gene expression matrix (data format is h5ad file) : 10X1kpbmc_rna.h5ad; \\\n",
    "3) Real label (stored as csv file) : 10X1kpbmc_label.csv.\n",
    "\n",
    "To ensure reproducibility of the results, please read the above data as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = np.array(sc.read_h5ad('../datasets/10X1kpbmc/10X1kpbmc_adt.h5ad').to_df()).astype('float32')\n",
    "x2 = np.array(sc.read_h5ad('../datasets/10X1kpbmc/10X1kpbmc_rna.h5ad').to_df()).astype('float32')\n",
    "y = np.array(pd.read_csv('../datasets/10X1kpbmc/10X1kpbmc_label.csv')['Cluster']).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[3.000e+00, 6.000e+00, 2.000e+00, ..., 3.000e+00, 4.000e+00,\n",
       "         1.000e+00],\n",
       "        [3.444e+03, 3.995e+03, 4.900e+01, ..., 6.000e+00, 8.000e+00,\n",
       "         2.000e+00],\n",
       "        [1.733e+03, 3.401e+03, 2.300e+01, ..., 1.000e+00, 6.000e+00,\n",
       "         0.000e+00],\n",
       "        ...,\n",
       "        [3.000e+00, 1.190e+02, 1.800e+01, ..., 1.000e+00, 1.000e+00,\n",
       "         2.000e+00],\n",
       "        [1.700e+01, 8.190e+02, 1.100e+01, ..., 3.000e+00, 2.000e+00,\n",
       "         2.000e+00],\n",
       "        [9.000e+00, 1.300e+01, 1.000e+01, ..., 4.000e+00, 1.100e+01,\n",
       "         5.000e+00]], dtype=float32),\n",
       " array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]], dtype=float32),\n",
       " array([5., 1., 1., 1., 1., 3., 3., 3., 1., 1., 3., 1., 1., 1., 5., 3., 3.,\n",
       "        3., 3., 2., 3., 1., 2., 1., 3., 4., 2., 5., 1., 1., 2., 5., 1., 4.,\n",
       "        2., 1., 3., 5., 2., 4., 1., 1., 3., 1., 1., 5., 1., 1., 1., 5., 5.,\n",
       "        2., 4., 1., 3., 3., 1., 4., 2., 5., 2., 1., 1., 1., 5., 1., 3., 2.,\n",
       "        3., 4., 2., 1., 2., 4., 3., 1., 3., 1., 1., 1., 1., 3., 4., 4., 2.,\n",
       "        4., 2., 3., 5., 5., 2., 4., 3., 4., 1., 1., 5., 2., 1., 1., 2., 1.,\n",
       "        1., 3., 1., 4., 1., 4., 1., 3., 4., 2., 5., 1., 1., 4., 1., 4., 2.,\n",
       "        5., 4., 4., 4., 2., 1., 5., 2., 1., 1., 3., 2., 2., 1., 1., 4., 3.,\n",
       "        4., 1., 4., 3., 1., 2., 1., 1., 5., 1., 3., 3., 3., 3., 5., 4., 3.,\n",
       "        4., 1., 1., 1., 2., 3., 3., 3., 2., 2., 1., 4., 3., 3., 4., 4., 4.,\n",
       "        1., 4., 1., 3., 5., 3., 5., 2., 3., 5., 1., 1., 2., 1., 3., 1., 2.,\n",
       "        1., 1., 5., 4., 4., 4., 2., 2., 5., 1., 2., 2., 2., 5., 2., 2., 1.,\n",
       "        2., 4., 3., 4., 1., 4., 3., 4., 1., 4., 3., 3., 2., 1., 2., 3., 4.,\n",
       "        3., 1., 2., 2., 2., 3., 4., 3., 5., 1., 2., 2., 3., 1., 2., 1., 4.,\n",
       "        5., 4., 2., 5., 1., 5., 2., 2., 3., 1., 1., 1., 1., 4., 5., 3., 4.,\n",
       "        3., 3., 4., 4., 1., 1., 1., 1., 4., 2., 1., 1., 1., 1., 3., 2., 2.,\n",
       "        1., 3., 2., 1., 4., 4., 4., 4., 2., 4., 3., 2., 1., 1., 5., 4., 1.,\n",
       "        2., 1., 2., 2., 1., 4., 1., 4., 1., 1., 3., 1., 4., 3., 1., 5., 2.,\n",
       "        3., 2., 1., 2., 2., 1., 4., 4., 1., 2., 2., 5., 1., 2., 1., 1., 3.,\n",
       "        2., 1., 1., 1., 4., 2., 2., 4., 4., 4., 4., 4., 4., 1., 2., 4., 3.,\n",
       "        4., 4., 2., 3., 4., 4., 1., 2., 1., 1., 1., 1., 5., 3., 2., 3., 1.,\n",
       "        1., 5., 3., 4., 1., 1., 3., 5., 2., 1., 5., 2., 1., 2., 3., 1., 1.,\n",
       "        1., 4., 3., 3., 2., 1., 3., 4., 2., 3., 1., 3., 1., 2., 2., 4., 1.,\n",
       "        3., 3., 4., 1., 1., 1., 1., 1., 5., 2., 1., 5., 5., 1., 3., 2., 1.,\n",
       "        1., 4., 4., 4., 3., 2., 5., 1., 3., 1., 1., 2., 2., 1., 1., 4., 3.,\n",
       "        1., 5., 5., 1., 4., 4., 1., 2., 1., 2., 2., 3., 1., 1., 2., 1., 1.,\n",
       "        5., 4., 5., 3., 4., 5., 1., 2., 1., 2., 5., 4., 3., 3., 1., 1., 3.,\n",
       "        1., 2., 5., 1., 3., 2., 5., 1., 3., 2., 1., 2., 5., 2., 1., 2., 1.,\n",
       "        1., 2., 3., 1., 2., 3., 2., 2., 2., 5., 1., 4., 5., 1., 3., 4., 5.,\n",
       "        4., 2., 1., 1., 1., 2., 5., 2., 3., 4., 4., 5., 1., 2., 1., 2., 1.,\n",
       "        4., 5., 5., 1., 3., 5., 3., 1., 2., 1., 4., 1., 1., 5., 5., 2., 4.,\n",
       "        2., 1., 4., 2., 3., 4., 3., 4., 3., 2., 5., 4., 1., 3., 3., 3., 2.,\n",
       "        2., 3., 1., 1., 1., 1., 2., 1., 4., 2., 3., 3., 1., 2., 4., 2., 4.,\n",
       "        2., 1., 4., 4., 1., 1., 2., 5., 3., 1., 1., 3., 2., 4., 1., 4., 2.,\n",
       "        1., 4., 4., 2., 4., 1., 4., 4., 1., 5., 4., 2., 3., 1., 5., 1., 4.,\n",
       "        2., 1., 1., 1., 3., 5., 3., 2., 3., 1., 1., 2., 5., 4., 1., 3., 4.,\n",
       "        4., 1., 2., 4., 2., 5., 1., 3., 3., 3., 3., 2., 1., 4., 2., 2., 3.,\n",
       "        5., 3., 1., 1., 3., 5., 2., 5., 4., 3., 4., 1., 2., 1., 2., 5., 2.,\n",
       "        5., 3., 1., 3., 1., 1., 1., 2., 4., 2., 1., 4., 2., 2., 3., 1., 3.,\n",
       "        1., 3., 2., 1., 3., 5., 5., 4., 2., 3., 2., 5., 3., 1., 2., 4., 1.,\n",
       "        3., 2., 3., 1., 4., 1., 4., 2., 2., 2., 1., 1., 2., 5., 1., 1., 2.,\n",
       "        2., 1., 5., 5., 5., 1., 3., 2., 1., 1., 4., 1., 2., 4., 3., 5.],\n",
       "       dtype=float32))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1,x2,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the small number of features in ADT omics data and the large gap between the feature dimensions of RNA omics, for RNA+ADT data, we only select high-expression features for RNA omics (the default number of chosen genes is 2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen offset: 0.21\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importantGenes = geneSelection(x2, n=2000)\n",
    "x2 = x2[:, importantGenes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 17 features and 713 cells.\n"
     ]
    }
   ],
   "source": [
    "adata1 = sc.AnnData(x1)\n",
    "adata1 = read_dataset(adata1, copy=True)\n",
    "adata1 = preprocess_dataset(adata1, normalize_input=True, logtrans_input=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 713 × 17\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Autoencoder: Successfully preprocessed 1999 features and 713 cells.\n"
     ]
    }
   ],
   "source": [
    "adata2 = sc.AnnData(x2)\n",
    "adata2 = read_dataset(adata2, copy=True)\n",
    "adata2 = preprocess_dataset(adata2, normalize_input=True, logtrans_input=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 713 × 1999\n",
       "    obs: 'DCA_split', 'size_factors'\n",
       "    var: 'mean', 'std'\n",
       "    uns: 'log1p'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model training is divided into two stages: pre-training stage and formal training stage. The number of epochs in the pre-training phase is set to 400, while the number in the formal training is 2000. In formal training, if the change in training results reaches a set threshold, it will end early. <br>\n",
    "\n",
    "After the training, the potential representations and predicted label values obtained by scMAGCA are saved to the corresponding folder of the data set and can be used for subsequent downstream analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = scMultiCluster(input_dim1=adata1.n_vars,input_dim2=adata2.n_vars,\n",
    "                       alpha=0.5,beta=1,gama=0.1,device='cuda').to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scMultiCluster(\n",
       "  (encoder): Encoder(\n",
       "    (stacked_gnn): ModuleList(\n",
       "      (0): GCNConv(2016, 1024)\n",
       "      (1): GCNConv(1024, 256)\n",
       "      (2): GCNConv(256, 64)\n",
       "      (3): GCNConv(64, 32)\n",
       "    )\n",
       "    (stacked_bns): ModuleList(\n",
       "      (0): BatchNorm1d(1024, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (1): BatchNorm1d(256, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (2): BatchNorm1d(64, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "      (3): BatchNorm1d(32, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (stacked_prelus): ModuleList(\n",
       "      (0-3): 4 x PReLU(num_parameters=1)\n",
       "    )\n",
       "  )\n",
       "  (decoder): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=512, bias=True)\n",
       "    (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): PReLU(num_parameters=1)\n",
       "    (3): Linear(in_features=512, out_features=1024, bias=True)\n",
       "    (4): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (5): PReLU(num_parameters=1)\n",
       "    (6): Linear(in_features=1024, out_features=2016, bias=True)\n",
       "  )\n",
       "  (dec_mean): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=2016, bias=True)\n",
       "    (3): MeanAct()\n",
       "  )\n",
       "  (dec_disp): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=2016, bias=True)\n",
       "    (3): DispAct()\n",
       "  )\n",
       "  (dec_pi): Sequential(\n",
       "    (0): Linear(in_features=32, out_features=256, bias=True)\n",
       "    (1): Linear(in_features=256, out_features=512, bias=True)\n",
       "    (2): Linear(in_features=512, out_features=2016, bias=True)\n",
       "    (3): Sigmoid()\n",
       "  )\n",
       "  (zinb_loss): ZINBLoss()\n",
       ")"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretraining stage\n",
      "Pretrain epoch 1, recon_loss:1.179670, zinb_loss:3.310244, adversial_loss:1.358601\n",
      "Pretrain epoch 2, recon_loss:1.053315, zinb_loss:2.773893, adversial_loss:1.373735\n",
      "Pretrain epoch 3, recon_loss:0.862565, zinb_loss:2.396230, adversial_loss:1.360926\n",
      "Pretrain epoch 4, recon_loss:0.777968, zinb_loss:2.083947, adversial_loss:1.360451\n",
      "Pretrain epoch 5, recon_loss:0.754381, zinb_loss:1.846060, adversial_loss:1.360216\n",
      "Pretrain epoch 6, recon_loss:0.733581, zinb_loss:1.646314, adversial_loss:1.355564\n",
      "Pretrain epoch 7, recon_loss:0.721840, zinb_loss:1.484166, adversial_loss:1.350406\n",
      "Pretrain epoch 8, recon_loss:0.717826, zinb_loss:1.361452, adversial_loss:1.347699\n",
      "Pretrain epoch 9, recon_loss:0.708908, zinb_loss:1.277065, adversial_loss:1.346102\n",
      "Pretrain epoch 10, recon_loss:0.694630, zinb_loss:1.224692, adversial_loss:1.344238\n",
      "Pretrain epoch 11, recon_loss:0.684225, zinb_loss:1.194159, adversial_loss:1.342163\n",
      "Pretrain epoch 12, recon_loss:0.680018, zinb_loss:1.175642, adversial_loss:1.340560\n",
      "Pretrain epoch 13, recon_loss:0.677840, zinb_loss:1.162826, adversial_loss:1.339098\n",
      "Pretrain epoch 14, recon_loss:0.675129, zinb_loss:1.152876, adversial_loss:1.337435\n",
      "Pretrain epoch 15, recon_loss:0.672355, zinb_loss:1.144369, adversial_loss:1.335139\n",
      "Pretrain epoch 16, recon_loss:0.670228, zinb_loss:1.136601, adversial_loss:1.332293\n",
      "Pretrain epoch 17, recon_loss:0.667790, zinb_loss:1.129140, adversial_loss:1.329263\n",
      "Pretrain epoch 18, recon_loss:0.664680, zinb_loss:1.121876, adversial_loss:1.326783\n",
      "Pretrain epoch 19, recon_loss:0.662127, zinb_loss:1.115053, adversial_loss:1.324961\n",
      "Pretrain epoch 20, recon_loss:0.660377, zinb_loss:1.108180, adversial_loss:1.323852\n",
      "Pretrain epoch 21, recon_loss:0.659001, zinb_loss:1.100873, adversial_loss:1.323825\n",
      "Pretrain epoch 22, recon_loss:0.656459, zinb_loss:1.093486, adversial_loss:1.322779\n",
      "Pretrain epoch 23, recon_loss:0.654088, zinb_loss:1.086390, adversial_loss:1.321610\n",
      "Pretrain epoch 24, recon_loss:0.652294, zinb_loss:1.079503, adversial_loss:1.320213\n",
      "Pretrain epoch 25, recon_loss:0.650671, zinb_loss:1.072774, adversial_loss:1.318725\n",
      "Pretrain epoch 26, recon_loss:0.648966, zinb_loss:1.066303, adversial_loss:1.317523\n",
      "Pretrain epoch 27, recon_loss:0.646991, zinb_loss:1.060190, adversial_loss:1.316257\n",
      "Pretrain epoch 28, recon_loss:0.645398, zinb_loss:1.054652, adversial_loss:1.314740\n",
      "Pretrain epoch 29, recon_loss:0.643983, zinb_loss:1.049268, adversial_loss:1.312217\n",
      "Pretrain epoch 30, recon_loss:0.643025, zinb_loss:1.043892, adversial_loss:1.308721\n",
      "Pretrain epoch 31, recon_loss:0.642111, zinb_loss:1.039183, adversial_loss:1.306067\n",
      "Pretrain epoch 32, recon_loss:0.640455, zinb_loss:1.034951, adversial_loss:1.304514\n",
      "Pretrain epoch 33, recon_loss:0.639247, zinb_loss:1.031011, adversial_loss:1.303380\n",
      "Pretrain epoch 34, recon_loss:0.637743, zinb_loss:1.027282, adversial_loss:1.301915\n",
      "Pretrain epoch 35, recon_loss:0.636664, zinb_loss:1.023664, adversial_loss:1.300623\n",
      "Pretrain epoch 36, recon_loss:0.635682, zinb_loss:1.020120, adversial_loss:1.299578\n",
      "Pretrain epoch 37, recon_loss:0.634332, zinb_loss:1.016628, adversial_loss:1.298586\n",
      "Pretrain epoch 38, recon_loss:0.633481, zinb_loss:1.013218, adversial_loss:1.298119\n",
      "Pretrain epoch 39, recon_loss:0.632329, zinb_loss:1.010160, adversial_loss:1.296602\n",
      "Pretrain epoch 40, recon_loss:0.631456, zinb_loss:1.007311, adversial_loss:1.295990\n",
      "Pretrain epoch 41, recon_loss:0.630563, zinb_loss:1.004686, adversial_loss:1.294802\n",
      "Pretrain epoch 42, recon_loss:0.629735, zinb_loss:1.002095, adversial_loss:1.295299\n",
      "Pretrain epoch 43, recon_loss:0.629020, zinb_loss:1.000048, adversial_loss:1.293201\n",
      "Pretrain epoch 44, recon_loss:0.628373, zinb_loss:0.997680, adversial_loss:1.293849\n",
      "Pretrain epoch 45, recon_loss:0.627905, zinb_loss:0.995719, adversial_loss:1.290645\n",
      "Pretrain epoch 46, recon_loss:0.626810, zinb_loss:0.993385, adversial_loss:1.290701\n",
      "Pretrain epoch 47, recon_loss:0.626094, zinb_loss:0.991341, adversial_loss:1.289930\n",
      "Pretrain epoch 48, recon_loss:0.625388, zinb_loss:0.989542, adversial_loss:1.288692\n",
      "Pretrain epoch 49, recon_loss:0.624706, zinb_loss:0.987548, adversial_loss:1.288829\n",
      "Pretrain epoch 50, recon_loss:0.624136, zinb_loss:0.986056, adversial_loss:1.287461\n",
      "Pretrain epoch 51, recon_loss:0.623274, zinb_loss:0.984164, adversial_loss:1.287607\n",
      "Pretrain epoch 52, recon_loss:0.622424, zinb_loss:0.982464, adversial_loss:1.286898\n",
      "Pretrain epoch 53, recon_loss:0.621879, zinb_loss:0.981260, adversial_loss:1.285860\n",
      "Pretrain epoch 54, recon_loss:0.621108, zinb_loss:0.979639, adversial_loss:1.285778\n",
      "Pretrain epoch 55, recon_loss:0.620530, zinb_loss:0.978340, adversial_loss:1.284826\n",
      "Pretrain epoch 56, recon_loss:0.619800, zinb_loss:0.977073, adversial_loss:1.284936\n",
      "Pretrain epoch 57, recon_loss:0.619347, zinb_loss:0.975783, adversial_loss:1.283673\n",
      "Pretrain epoch 58, recon_loss:0.618823, zinb_loss:0.974526, adversial_loss:1.284123\n",
      "Pretrain epoch 59, recon_loss:0.618686, zinb_loss:0.973556, adversial_loss:1.282676\n",
      "Pretrain epoch 60, recon_loss:0.617441, zinb_loss:0.972172, adversial_loss:1.283096\n",
      "Pretrain epoch 61, recon_loss:0.616835, zinb_loss:0.970980, adversial_loss:1.281648\n",
      "Pretrain epoch 62, recon_loss:0.615886, zinb_loss:0.969648, adversial_loss:1.281288\n",
      "Pretrain epoch 63, recon_loss:0.615350, zinb_loss:0.968580, adversial_loss:1.281277\n",
      "Pretrain epoch 64, recon_loss:0.614922, zinb_loss:0.967744, adversial_loss:1.280465\n",
      "Pretrain epoch 65, recon_loss:0.614932, zinb_loss:0.967338, adversial_loss:1.280521\n",
      "Pretrain epoch 66, recon_loss:0.614976, zinb_loss:0.966357, adversial_loss:1.280103\n",
      "Pretrain epoch 67, recon_loss:0.613583, zinb_loss:0.965226, adversial_loss:1.280383\n",
      "Pretrain epoch 68, recon_loss:0.613921, zinb_loss:0.965229, adversial_loss:1.278780\n",
      "Pretrain epoch 69, recon_loss:0.612545, zinb_loss:0.963646, adversial_loss:1.279052\n",
      "Pretrain epoch 70, recon_loss:0.612793, zinb_loss:0.963037, adversial_loss:1.279510\n",
      "Pretrain epoch 71, recon_loss:0.611815, zinb_loss:0.961470, adversial_loss:1.278643\n",
      "Pretrain epoch 72, recon_loss:0.611546, zinb_loss:0.960857, adversial_loss:1.278122\n",
      "Pretrain epoch 73, recon_loss:0.610706, zinb_loss:0.959825, adversial_loss:1.278297\n",
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      "Pretrain epoch 77, recon_loss:0.608211, zinb_loss:0.957331, adversial_loss:1.277107\n",
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      "Pretrain epoch 79, recon_loss:0.607406, zinb_loss:0.956075, adversial_loss:1.276365\n",
      "Pretrain epoch 80, recon_loss:0.607084, zinb_loss:0.955484, adversial_loss:1.276402\n",
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      "Pretrain epoch 82, recon_loss:0.605686, zinb_loss:0.954194, adversial_loss:1.275973\n",
      "Pretrain epoch 83, recon_loss:0.605507, zinb_loss:0.954741, adversial_loss:1.275807\n",
      "Pretrain epoch 84, recon_loss:0.606017, zinb_loss:0.955828, adversial_loss:1.275457\n",
      "Pretrain epoch 85, recon_loss:0.606689, zinb_loss:0.955475, adversial_loss:1.274885\n",
      "Pretrain epoch 86, recon_loss:0.603597, zinb_loss:0.952265, adversial_loss:1.275342\n",
      "Pretrain epoch 87, recon_loss:0.603961, zinb_loss:0.952641, adversial_loss:1.275476\n",
      "Pretrain epoch 88, recon_loss:0.603907, zinb_loss:0.952613, adversial_loss:1.274445\n",
      "Pretrain epoch 89, recon_loss:0.602533, zinb_loss:0.950886, adversial_loss:1.273964\n",
      "Pretrain epoch 90, recon_loss:0.601846, zinb_loss:0.951025, adversial_loss:1.274571\n",
      "Pretrain epoch 91, recon_loss:0.601666, zinb_loss:0.950418, adversial_loss:1.274107\n",
      "Pretrain epoch 92, recon_loss:0.600451, zinb_loss:0.950003, adversial_loss:1.272773\n",
      "Pretrain epoch 93, recon_loss:0.600263, zinb_loss:0.949291, adversial_loss:1.273564\n",
      "Pretrain epoch 94, recon_loss:0.599480, zinb_loss:0.948868, adversial_loss:1.273637\n",
      "Pretrain epoch 95, recon_loss:0.599118, zinb_loss:0.948717, adversial_loss:1.272256\n",
      "Pretrain epoch 96, recon_loss:0.598330, zinb_loss:0.947870, adversial_loss:1.272520\n",
      "Pretrain epoch 97, recon_loss:0.598428, zinb_loss:0.948256, adversial_loss:1.272994\n",
      "Pretrain epoch 98, recon_loss:0.599205, zinb_loss:0.948368, adversial_loss:1.272520\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrain epoch 99, recon_loss:0.600360, zinb_loss:0.950071, adversial_loss:1.271988\n",
      "Pretrain epoch 100, recon_loss:0.597603, zinb_loss:0.946913, adversial_loss:1.272954\n",
      "Pretrain epoch 101, recon_loss:0.598265, zinb_loss:0.948654, adversial_loss:1.273010\n",
      "Pretrain epoch 102, recon_loss:0.598125, zinb_loss:0.946708, adversial_loss:1.272140\n",
      "Pretrain epoch 103, recon_loss:0.596699, zinb_loss:0.946644, adversial_loss:1.271546\n",
      "Pretrain epoch 104, recon_loss:0.596948, zinb_loss:0.945447, adversial_loss:1.271937\n",
      "Pretrain epoch 105, recon_loss:0.594884, zinb_loss:0.945366, adversial_loss:1.271829\n",
      "Pretrain epoch 106, recon_loss:0.595728, zinb_loss:0.944949, adversial_loss:1.270860\n",
      "Pretrain epoch 107, recon_loss:0.593774, zinb_loss:0.944627, adversial_loss:1.270213\n",
      "Pretrain epoch 108, recon_loss:0.593907, zinb_loss:0.944016, adversial_loss:1.270924\n",
      "Pretrain epoch 109, recon_loss:0.592436, zinb_loss:0.943547, adversial_loss:1.270654\n",
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      "Pretrain epoch 364, recon_loss:0.454625, zinb_loss:0.917606, adversial_loss:1.245090\n",
      "Pretrain epoch 365, recon_loss:0.446788, zinb_loss:0.917397, adversial_loss:1.244783\n",
      "Pretrain epoch 366, recon_loss:0.449447, zinb_loss:0.917623, adversial_loss:1.244616\n",
      "Pretrain epoch 367, recon_loss:0.449955, zinb_loss:0.917494, adversial_loss:1.244825\n",
      "Pretrain epoch 368, recon_loss:0.448117, zinb_loss:0.917450, adversial_loss:1.244515\n",
      "Pretrain epoch 369, recon_loss:0.449342, zinb_loss:0.917591, adversial_loss:1.244497\n",
      "Pretrain epoch 370, recon_loss:0.448283, zinb_loss:0.917546, adversial_loss:1.244743\n",
      "Pretrain epoch 371, recon_loss:0.445914, zinb_loss:0.917302, adversial_loss:1.244316\n",
      "Pretrain epoch 372, recon_loss:0.443342, zinb_loss:0.917304, adversial_loss:1.244421\n",
      "Pretrain epoch 373, recon_loss:0.443727, zinb_loss:0.917965, adversial_loss:1.244472\n",
      "Pretrain epoch 374, recon_loss:0.450613, zinb_loss:0.920401, adversial_loss:1.244195\n",
      "Pretrain epoch 375, recon_loss:0.458434, zinb_loss:0.923473, adversial_loss:1.244600\n",
      "Pretrain epoch 376, recon_loss:0.466628, zinb_loss:0.923974, adversial_loss:1.244055\n",
      "Pretrain epoch 377, recon_loss:0.448002, zinb_loss:0.918008, adversial_loss:1.244360\n",
      "Pretrain epoch 378, recon_loss:0.453877, zinb_loss:0.919228, adversial_loss:1.244228\n",
      "Pretrain epoch 379, recon_loss:0.454729, zinb_loss:0.920082, adversial_loss:1.244211\n",
      "Pretrain epoch 380, recon_loss:0.445237, zinb_loss:0.917129, adversial_loss:1.244172\n",
      "Pretrain epoch 381, recon_loss:0.450492, zinb_loss:0.917859, adversial_loss:1.243852\n",
      "Pretrain epoch 382, recon_loss:0.442563, zinb_loss:0.917322, adversial_loss:1.244273\n",
      "Pretrain epoch 383, recon_loss:0.441668, zinb_loss:0.916980, adversial_loss:1.244167\n",
      "Pretrain epoch 384, recon_loss:0.439400, zinb_loss:0.916999, adversial_loss:1.243751\n",
      "Pretrain epoch 385, recon_loss:0.436498, zinb_loss:0.916621, adversial_loss:1.243968\n",
      "Pretrain epoch 386, recon_loss:0.434738, zinb_loss:0.916643, adversial_loss:1.243971\n",
      "Pretrain epoch 387, recon_loss:0.433600, zinb_loss:0.916510, adversial_loss:1.243769\n",
      "Pretrain epoch 388, recon_loss:0.432909, zinb_loss:0.916476, adversial_loss:1.243826\n",
      "Pretrain epoch 389, recon_loss:0.433671, zinb_loss:0.916366, adversial_loss:1.243977\n",
      "Pretrain epoch 390, recon_loss:0.440959, zinb_loss:0.916790, adversial_loss:1.243476\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretrain epoch 391, recon_loss:0.441706, zinb_loss:0.916533, adversial_loss:1.244172\n",
      "Pretrain epoch 392, recon_loss:0.437726, zinb_loss:0.916482, adversial_loss:1.243456\n",
      "Pretrain epoch 393, recon_loss:0.427984, zinb_loss:0.916146, adversial_loss:1.243696\n",
      "Pretrain epoch 394, recon_loss:0.428263, zinb_loss:0.916296, adversial_loss:1.243844\n",
      "Pretrain epoch 395, recon_loss:0.431406, zinb_loss:0.916571, adversial_loss:1.243382\n",
      "Pretrain epoch 396, recon_loss:0.424839, zinb_loss:0.916067, adversial_loss:1.243597\n",
      "Pretrain epoch 397, recon_loss:0.426324, zinb_loss:0.916186, adversial_loss:1.243752\n",
      "Pretrain epoch 398, recon_loss:0.432253, zinb_loss:0.916296, adversial_loss:1.243196\n",
      "Pretrain epoch 399, recon_loss:0.433320, zinb_loss:0.916364, adversial_loss:1.243449\n",
      "Pretrain epoch 400, recon_loss:0.431458, zinb_loss:0.916175, adversial_loss:1.243666\n"
     ]
    }
   ],
   "source": [
    "pretrain_latent = model.pretrain_autoencoder(\n",
    "                        X1=adata1.X, X2=adata2.X, X1_raw=adata1.raw.X, X2_raw=adata2.raw.X, \n",
    "                        epochs=400, file='10X1kpbmc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clustering stage\n",
      "Initializing cluster centers with kmeans.\n",
      "Initializing k-means: AMI= 0.8205, NMI= 0.8218, ARI= 0.8344, ACC= 0.9201\n",
      "Training epoch 1, recon_loss:0.430522, zinb_loss:0.916023, cluster_loss:0.154342\n",
      "Clustering   1: AMI= 0.8205, NMI= 0.8218, ARI= 0.8344, ACC= 0.9201\n",
      "0.0\n",
      "Training epoch 2, recon_loss:0.846306, zinb_loss:1.077583, cluster_loss:0.171737\n",
      "Clustering   2: AMI= 0.8006, NMI= 0.8021, ARI= 0.8141, ACC= 0.9088\n",
      "0.014025245441795231\n",
      "Training epoch 3, recon_loss:0.778181, zinb_loss:1.139828, cluster_loss:0.164231\n",
      "Clustering   3: AMI= 0.8304, NMI= 0.8316, ARI= 0.8507, ACC= 0.9341\n",
      "0.03225806451612903\n",
      "Training epoch 4, recon_loss:0.784476, zinb_loss:1.364775, cluster_loss:0.169950\n",
      "Clustering   4: AMI= 0.8110, NMI= 0.8124, ARI= 0.8221, ACC= 0.9159\n",
      "0.03927068723702665\n",
      "Training epoch 5, recon_loss:0.830803, zinb_loss:1.707875, cluster_loss:0.176492\n",
      "Clustering   5: AMI= 0.8033, NMI= 0.8047, ARI= 0.7921, ACC= 0.8780\n",
      "0.06591865357643759\n",
      "Training epoch 6, recon_loss:0.802670, zinb_loss:1.205563, cluster_loss:0.177979\n",
      "Clustering   6: AMI= 0.8130, NMI= 0.8144, ARI= 0.8361, ACC= 0.9229\n",
      "0.08134642356241234\n",
      "Training epoch 7, recon_loss:0.773048, zinb_loss:1.173137, cluster_loss:0.170710\n",
      "Clustering   7: AMI= 0.8174, NMI= 0.8187, ARI= 0.8373, ACC= 0.9243\n",
      "0.005610098176718092\n",
      "Training epoch 8, recon_loss:0.715895, zinb_loss:1.001829, cluster_loss:0.163364\n",
      "Clustering   8: AMI= 0.8115, NMI= 0.8128, ARI= 0.8306, ACC= 0.9186\n",
      "0.008415147265077139\n",
      "Training epoch 9, recon_loss:0.707180, zinb_loss:0.976355, cluster_loss:0.150441\n",
      "Clustering   9: AMI= 0.8095, NMI= 0.8109, ARI= 0.8083, ACC= 0.9018\n",
      "0.037868162692847124\n",
      "Training epoch 10, recon_loss:0.661050, zinb_loss:0.958861, cluster_loss:0.151975\n",
      "Clustering   10: AMI= 0.8213, NMI= 0.8226, ARI= 0.8308, ACC= 0.9186\n",
      "0.019635343618513323\n",
      "Training epoch 11, recon_loss:0.652488, zinb_loss:0.952982, cluster_loss:0.149777\n",
      "Clustering   11: AMI= 0.8103, NMI= 0.8116, ARI= 0.8189, ACC= 0.9102\n",
      "0.011220196353436185\n",
      "Training epoch 12, recon_loss:0.642588, zinb_loss:0.953146, cluster_loss:0.151356\n",
      "Clustering   12: AMI= 0.8215, NMI= 0.8228, ARI= 0.8335, ACC= 0.9201\n",
      "0.009817671809256662\n",
      "Training epoch 13, recon_loss:0.638285, zinb_loss:0.949473, cluster_loss:0.149443\n",
      "Clustering   13: AMI= 0.8164, NMI= 0.8177, ARI= 0.8254, ACC= 0.9159\n",
      "0.0070126227208976155\n",
      "Training epoch 14, recon_loss:0.641145, zinb_loss:0.953004, cluster_loss:0.147145\n",
      "Clustering   14: AMI= 0.8178, NMI= 0.8192, ARI= 0.8276, ACC= 0.9159\n",
      "0.002805049088359046\n",
      "Training epoch 15, recon_loss:0.637767, zinb_loss:0.952127, cluster_loss:0.148271\n",
      "Clustering   15: AMI= 0.8203, NMI= 0.8216, ARI= 0.8315, ACC= 0.9186\n",
      "0.002805049088359046\n",
      "Training epoch 16, recon_loss:0.625828, zinb_loss:0.957454, cluster_loss:0.145889\n",
      "Clustering   16: AMI= 0.8140, NMI= 0.8154, ARI= 0.8209, ACC= 0.9116\n",
      "0.009817671809256662\n",
      "Training epoch 17, recon_loss:0.611839, zinb_loss:0.962478, cluster_loss:0.148376\n",
      "Clustering   17: AMI= 0.8260, NMI= 0.8273, ARI= 0.8380, ACC= 0.9229\n",
      "0.015427769985974754\n",
      "Training epoch 18, recon_loss:0.621567, zinb_loss:0.977118, cluster_loss:0.145853\n",
      "Clustering   18: AMI= 0.8140, NMI= 0.8154, ARI= 0.8209, ACC= 0.9116\n",
      "0.015427769985974754\n",
      "Training epoch 19, recon_loss:0.607203, zinb_loss:0.988093, cluster_loss:0.149063\n",
      "Clustering   19: AMI= 0.8275, NMI= 0.8288, ARI= 0.8401, ACC= 0.9257\n",
      "0.021037868162692847\n",
      "Training epoch 20, recon_loss:0.615731, zinb_loss:0.995491, cluster_loss:0.145251\n",
      "Clustering   20: AMI= 0.8140, NMI= 0.8154, ARI= 0.8209, ACC= 0.9116\n",
      "0.021037868162692847\n",
      "Training epoch 21, recon_loss:0.609800, zinb_loss:0.997153, cluster_loss:0.147204\n",
      "Clustering   21: AMI= 0.8261, NMI= 0.8274, ARI= 0.8380, ACC= 0.9243\n",
      "0.019635343618513323\n",
      "Training epoch 22, recon_loss:0.621488, zinb_loss:0.992326, cluster_loss:0.146616\n",
      "Clustering   22: AMI= 0.8182, NMI= 0.8195, ARI= 0.8240, ACC= 0.9130\n",
      "0.0182328190743338\n",
      "Training epoch 23, recon_loss:0.611237, zinb_loss:0.988998, cluster_loss:0.145771\n",
      "Clustering   23: AMI= 0.8203, NMI= 0.8216, ARI= 0.8315, ACC= 0.9186\n",
      "0.008415147265077139\n",
      "Training epoch 24, recon_loss:0.600035, zinb_loss:0.983440, cluster_loss:0.144918\n",
      "Clustering   24: AMI= 0.8253, NMI= 0.8265, ARI= 0.8355, ACC= 0.9215\n",
      "0.002805049088359046\n",
      "Training epoch 25, recon_loss:0.596902, zinb_loss:0.978649, cluster_loss:0.143721\n",
      "Clustering   25: AMI= 0.8182, NMI= 0.8195, ARI= 0.8240, ACC= 0.9130\n",
      "0.011220196353436185\n",
      "Training epoch 26, recon_loss:0.606475, zinb_loss:0.980079, cluster_loss:0.145489\n",
      "Clustering   26: AMI= 0.8304, NMI= 0.8316, ARI= 0.8440, ACC= 0.9285\n",
      "0.025245441795231416\n",
      "Training epoch 27, recon_loss:0.620056, zinb_loss:0.975876, cluster_loss:0.146040\n",
      "Clustering   27: AMI= 0.8160, NMI= 0.8173, ARI= 0.8204, ACC= 0.9102\n",
      "0.028050490883590462\n",
      "Training epoch 28, recon_loss:0.627696, zinb_loss:0.977644, cluster_loss:0.149002\n",
      "Clustering   28: AMI= 0.8264, NMI= 0.8277, ARI= 0.8437, ACC= 0.9285\n",
      "0.0364656381486676\n",
      "Training epoch 29, recon_loss:0.607567, zinb_loss:0.963646, cluster_loss:0.145189\n",
      "Clustering   29: AMI= 0.8171, NMI= 0.8184, ARI= 0.8222, ACC= 0.9116\n",
      "0.03506311360448808\n",
      "Training epoch 30, recon_loss:0.596643, zinb_loss:0.960702, cluster_loss:0.144895\n",
      "Clustering   30: AMI= 0.8280, NMI= 0.8292, ARI= 0.8395, ACC= 0.9257\n",
      "0.019635343618513323\n",
      "Training epoch 31, recon_loss:0.586386, zinb_loss:0.957384, cluster_loss:0.143485\n",
      "Clustering   31: AMI= 0.8190, NMI= 0.8204, ARI= 0.8296, ACC= 0.9173\n",
      "0.011220196353436185\n",
      "Training epoch 32, recon_loss:0.587456, zinb_loss:0.956871, cluster_loss:0.142214\n",
      "Clustering   32: AMI= 0.8189, NMI= 0.8202, ARI= 0.8294, ACC= 0.9186\n",
      "0.004207573632538569\n",
      "Training epoch 33, recon_loss:0.586300, zinb_loss:0.957902, cluster_loss:0.142721\n",
      "Clustering   33: AMI= 0.8203, NMI= 0.8216, ARI= 0.8315, ACC= 0.9186\n",
      "0.002805049088359046\n",
      "Training epoch 34, recon_loss:0.597041, zinb_loss:0.956997, cluster_loss:0.141408\n",
      "Clustering   34: AMI= 0.8152, NMI= 0.8166, ARI= 0.8235, ACC= 0.9144\n",
      "0.004207573632538569\n",
      "Training epoch 35, recon_loss:0.582191, zinb_loss:0.959904, cluster_loss:0.142487\n",
      "Clustering   35: AMI= 0.8228, NMI= 0.8241, ARI= 0.8355, ACC= 0.9215\n",
      "0.0070126227208976155\n",
      "Training epoch 36, recon_loss:0.579865, zinb_loss:0.960015, cluster_loss:0.141827\n",
      "Clustering   36: AMI= 0.8166, NMI= 0.8180, ARI= 0.8217, ACC= 0.9130\n",
      "0.011220196353436185\n",
      "Training epoch 37, recon_loss:0.579633, zinb_loss:0.963965, cluster_loss:0.142911\n",
      "Clustering   37: AMI= 0.8275, NMI= 0.8288, ARI= 0.8401, ACC= 0.9257\n",
      "0.016830294530154277\n",
      "Training epoch 38, recon_loss:0.591656, zinb_loss:0.964938, cluster_loss:0.143914\n",
      "Clustering   38: AMI= 0.8103, NMI= 0.8117, ARI= 0.8149, ACC= 0.9088\n",
      "0.021037868162692847\n",
      "Training epoch 39, recon_loss:0.598165, zinb_loss:0.971153, cluster_loss:0.144402\n",
      "Clustering   39: AMI= 0.8260, NMI= 0.8273, ARI= 0.8394, ACC= 0.9257\n",
      "0.02244039270687237\n",
      "Training epoch 40, recon_loss:0.586254, zinb_loss:0.966595, cluster_loss:0.142439\n",
      "Clustering   40: AMI= 0.8155, NMI= 0.8169, ARI= 0.8199, ACC= 0.9116\n",
      "0.019635343618513323\n",
      "Training epoch 41, recon_loss:0.576397, zinb_loss:0.964681, cluster_loss:0.141448\n",
      "Clustering   41: AMI= 0.8202, NMI= 0.8215, ARI= 0.8314, ACC= 0.9201\n",
      "0.011220196353436185\n",
      "Training epoch 42, recon_loss:0.580564, zinb_loss:0.965172, cluster_loss:0.142515\n",
      "Clustering   42: AMI= 0.8164, NMI= 0.8177, ARI= 0.8254, ACC= 0.9159\n",
      "0.004207573632538569\n",
      "Training epoch 43, recon_loss:0.581782, zinb_loss:0.966008, cluster_loss:0.141712\n",
      "Clustering   43: AMI= 0.8178, NMI= 0.8192, ARI= 0.8276, ACC= 0.9159\n",
      "0.002805049088359046\n",
      "Training epoch 44, recon_loss:0.578958, zinb_loss:0.961043, cluster_loss:0.142084\n",
      "Clustering   44: AMI= 0.8202, NMI= 0.8215, ARI= 0.8314, ACC= 0.9201\n",
      "0.0070126227208976155\n",
      "Training epoch 45, recon_loss:0.578888, zinb_loss:0.958669, cluster_loss:0.141906\n",
      "Clustering   45: AMI= 0.8182, NMI= 0.8195, ARI= 0.8240, ACC= 0.9130\n",
      "0.012622720897615708\n",
      "Training epoch 46, recon_loss:0.572623, zinb_loss:0.953206, cluster_loss:0.141412\n",
      "Clustering   46: AMI= 0.8215, NMI= 0.8228, ARI= 0.8334, ACC= 0.9215\n",
      "0.014025245441795231\n",
      "Training epoch 47, recon_loss:0.573183, zinb_loss:0.951851, cluster_loss:0.141826\n",
      "Clustering   47: AMI= 0.8171, NMI= 0.8184, ARI= 0.8222, ACC= 0.9116\n",
      "0.015427769985974754\n",
      "Training epoch 48, recon_loss:0.582879, zinb_loss:0.955231, cluster_loss:0.141006\n",
      "Clustering   48: AMI= 0.8252, NMI= 0.8265, ARI= 0.8354, ACC= 0.9229\n",
      "0.016830294530154277\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch 49, recon_loss:0.577148, zinb_loss:0.950773, cluster_loss:0.141801\n",
      "Clustering   49: AMI= 0.8182, NMI= 0.8195, ARI= 0.8240, ACC= 0.9130\n",
      "0.015427769985974754\n",
      "Training epoch 50, recon_loss:0.582462, zinb_loss:0.950802, cluster_loss:0.140044\n",
      "Clustering   50: AMI= 0.8189, NMI= 0.8202, ARI= 0.8294, ACC= 0.9186\n",
      "0.011220196353436185\n",
      "Training epoch 51, recon_loss:0.578021, zinb_loss:0.947385, cluster_loss:0.141623\n",
      "Clustering   51: AMI= 0.8176, NMI= 0.8190, ARI= 0.8274, ACC= 0.9173\n",
      "0.001402524544179523\n",
      "Training epoch 52, recon_loss:0.593130, zinb_loss:0.945707, cluster_loss:0.141130\n",
      "Clustering   52: AMI= 0.8155, NMI= 0.8169, ARI= 0.8199, ACC= 0.9116\n",
      "0.008415147265077139\n",
      "Training epoch 53, recon_loss:0.584805, zinb_loss:0.945096, cluster_loss:0.142527\n",
      "Clustering   53: AMI= 0.8202, NMI= 0.8215, ARI= 0.8314, ACC= 0.9201\n",
      "0.011220196353436185\n",
      "Training epoch 54, recon_loss:0.585467, zinb_loss:0.943358, cluster_loss:0.140558\n",
      "Clustering   54: AMI= 0.8144, NMI= 0.8158, ARI= 0.8180, ACC= 0.9102\n",
      "0.012622720897615708\n",
      "Training epoch 55, recon_loss:0.568052, zinb_loss:0.941190, cluster_loss:0.141217\n",
      "Clustering   55: AMI= 0.8202, NMI= 0.8215, ARI= 0.8314, ACC= 0.9201\n",
      "0.012622720897615708\n",
      "Training epoch 56, recon_loss:0.577989, zinb_loss:0.941180, cluster_loss:0.139027\n",
      "Clustering   56: AMI= 0.8117, NMI= 0.8131, ARI= 0.8154, ACC= 0.9088\n",
      "0.014025245441795231\n",
      "Training epoch 57, recon_loss:0.572676, zinb_loss:0.940440, cluster_loss:0.141063\n",
      "Clustering   57: AMI= 0.8242, NMI= 0.8255, ARI= 0.8375, ACC= 0.9243\n",
      "0.0182328190743338\n",
      "Training epoch 58, recon_loss:0.580458, zinb_loss:0.941732, cluster_loss:0.139396\n",
      "Clustering   58: AMI= 0.8117, NMI= 0.8131, ARI= 0.8154, ACC= 0.9088\n",
      "0.0182328190743338\n",
      "Training epoch 59, recon_loss:0.564884, zinb_loss:0.940472, cluster_loss:0.141210\n",
      "Clustering   59: AMI= 0.8215, NMI= 0.8228, ARI= 0.8334, ACC= 0.9215\n",
      "0.015427769985974754\n",
      "Training epoch 60, recon_loss:0.566060, zinb_loss:0.942304, cluster_loss:0.139360\n",
      "Clustering   60: AMI= 0.8144, NMI= 0.8158, ARI= 0.8180, ACC= 0.9102\n",
      "0.014025245441795231\n",
      "Training epoch 61, recon_loss:0.561706, zinb_loss:0.942914, cluster_loss:0.141014\n",
      "Clustering   61: AMI= 0.8202, NMI= 0.8215, ARI= 0.8314, ACC= 0.9201\n",
      "0.012622720897615708\n",
      "Training epoch 62, recon_loss:0.577613, zinb_loss:0.950187, cluster_loss:0.139490\n",
      "Clustering   62: AMI= 0.8155, NMI= 0.8169, ARI= 0.8199, ACC= 0.9116\n",
      "0.011220196353436185\n",
      "Training epoch 63, recon_loss:0.569885, zinb_loss:0.951495, cluster_loss:0.140008\n",
      "Clustering   63: AMI= 0.8176, NMI= 0.8190, ARI= 0.8274, ACC= 0.9173\n",
      "0.008415147265077139\n",
      "Training epoch 64, recon_loss:0.569891, zinb_loss:0.955939, cluster_loss:0.139665\n",
      "Clustering   64: AMI= 0.8176, NMI= 0.8190, ARI= 0.8274, ACC= 0.9173\n",
      "0.0\n",
      "delta_label  0.0 < tol  0.001\n",
      "Reach tolerance threshold. Stopping training.\n",
      "Final Result : AMI= 0.8176, NMI= 0.8190, ARI= 0.8274, ACC= 0.9173\n"
     ]
    }
   ],
   "source": [
    "y_pred, final_latent = model.fit(y=y, n_clusters=5, file='10X1kpbmc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "y_pred_ = best_map(y, y_pred).astype(int)\n",
    "np.savetxt(\"../results/10X1kpbmc_pred.csv\", y_pred, delimiter=\",\")\n",
    "np.savetxt(\"../results/10X1kpbmc_embedding.csv\", final_latent.cpu().detach().numpy(), delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(umap)\n",
    "library(ggplot2)\n",
    "library(scattermore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent <- read.csv(file = \"../results/10X1kpbmc_embedding.csv\", sep = \",\", header = FALSE)\n",
    "y_pred <- read.csv(file = \"../results/10X1kpbmc_pred.csv\", sep = \",\", header = FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "z.umap<-umap(latent)\n",
    "y_pred$V1<-as.factor(y_pred$V1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "bm_table_num <- data.frame(UMAP1=z.umap$layout[,1], UMAP2=z.umap$layout[,2],Cluster = y_pred$V1)\n",
    "bm_plot1 <- ggplot(bm_table_num, aes(x = UMAP1, y = UMAP2, color = Cluster)) +\n",
    "  geom_point(size = 0.1) +\n",
    "  ggtitle('scMAGCA') +\n",
    "  theme_bw(base_line_size = 1,base_rect_size = 1)+\n",
    "  scale_color_manual(values = c(\"1\"=\"#e0bc58\", \"2\"=\"#64abc0\", \"3\"=\"#dc8e97\", \"4\"=\"#74a893\", \"5\"=\"#ac9141\"))+\n",
    "  labs(x = \"\", y = \"\", color = \"Label\")+  # Set x and y axis labels\n",
    "  theme(\n",
    "    plot.title = element_text(size = 20, hjust = 0.5, face = 'bold'),  # Set title to center\n",
    "    panel.border = element_blank(),\n",
    "    axis.ticks.length=unit(0, \"lines\"),  # Remove axis ticks but keep axis labels\n",
    "    axis.text = element_blank(),\n",
    "    panel.grid.minor = element_blank(),\n",
    "    panel.grid.major = element_line(color = NA),\n",
    "    axis.title.x = element_text(size = 15, hjust = 0.5, vjust = 0.5, color = \"black\"),\n",
    "    axis.title.y = element_text(size = 15, hjust = 0.5, vjust = 0.5, color = \"black\"),\n",
    "    legend.title = element_blank(),\n",
    "    legend.text = element_text(size = 15),\n",
    "    legend.position = \"none\")+\n",
    "  guides(color = guide_legend(override.aes = list(size = 5), nrow=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pYce5pGoxVCFKXnPnr9XUNdgFGnj91Cw+HuEEJ0eN2BUCjSmdfm7nh+x3q71faT\nW79s1PVrcsPOs0ccro79509Oyx1f2VJ/sPL0FUXT8v/6lGGSJeHhpas7fZ45BSUaSRJC6LW6\ne6+8fvDvDQAGG8EOQL9oJOlbN97tCwaGc2Zo9+285hdO0UiarKSUaBlnG2vqnY56p6Pd4+rn\naskrpl916ELZNZPnCCHWH9lV2VLf4Gz9+rVrog16Wf1ux5kjW47vXTH9ykUTZ1zKLQHAUCLY\nAQNwovZ8Wf2FJSWz0mz2eNcSH8OZ6p7Z/s65provX71qYmZe9KBOq11QNFUIUdPWlGxJtBpN\nM/KLLzgaUqyJGf1IdafrL+i12lnjJs4aNzFypDSvqLGjdXrehH5WdbLuvC8YOFZTQbADMAIR\n7IA++IKB53a8W9XaZDWYgnLIG/AH5VB0liuGSCgsX2hpCMqhypb62GAXcbjq7J93bzYbjP98\ny5dMesMX5i/vzzWf3f7u6foLQojv3XRfdHh3ScnsqTnj+5/UV85cuO/cyQVFfU8EBoDhR7DD\nWNThdZ+uvzA1Z3yf+0TJ4fA7h3ecb66PnJWakKiRNFN7euIeg0un0a69asUFR8OiiT2sSxyS\nQ0KIcDgc7vfeOe2ezkiq02q0sf2O7366Y8eZI1cUTe1nOsxLzsib28OafwAwEhDsMIYooVDt\nurfCwdC2fMvRlurpeRMeWHRT76fsLD+y/9wpIYRJb8hLSb9p+sJxqZnDUixESfa4kuyen3Wb\nUzjZZramJdj7PzRst9iWTZlT7Wi6ofSKRLM1etzpdQshnF7X5RcMAHFHsMMY0r7/sL/JIYSY\nZJKOSsJuSbjgaDhec+6KoqnpFxmJO1hZJoTQa3U/ve0hjaQ5VlOhCKWfa2pgKNS0NlmN5mSr\nrcv8hq0n9nsDvptnLuxlR9dZ4yZ9dOpQ1fbGH656ILow3u1zlk7OHleSVTC0dQPAsCDYYcCU\ncFi6pH3c40IJyZJWIyRJCBF0dgghhJBmLr92gsWQlpD035tebnA6mjravnz1yh5PTzRZ60TL\nlJxCnUZ7rKbiT7s26bTaH6560GayDONN4DNnG6t///Hbeq3un1benxTT61bvdGw5vlcIMT49\np5dpEL6gXwgRCAUDoWA02CWYzPMKpzQ4WxNM5lG63CAARBHsMDBNmz50l5/LvPl6S9Eo6OHw\nVlbXv71Ra7WM+8oXJY0mae6MDldne6o1LyUpTWcQQpRkj3O4nN2fzY+676obq36eKjQAACAA\nSURBVByNRek5QojI+J1Zb2RTqXhRFEUIoQhFfP7Rugxb8vi0bE/QH/mTupii9NyvLl1t0huT\nrZ9bHnn9kV3byj4tyRr38NLVQ1E2AAwbgh0GxnOhOhwMeatrR0Wwcx4+poTDoU6X7HLrEm0i\nOek5UR9sCrWfSbpu6nwhxKqZi26ecVUv/TQmvWFSVn7kdUFq1o9Xf9moNxgIdnEyMWvc169d\nk2CyRLYmi9JqNP8QswpdLyb1tECdO+ATQngCvkEpEgDiiGCHgVCUtKUL/S2t9tk9TFQcgRLn\nzPDWNphys3SJNiGEQafPTEppaHdkJ6VF2wxo9C32oXsMP0mIwrTsQb/s6lmLJ2XmF1+84xYA\nRgtJ6fdiAUD9m+s9F6qtEwrTr79GazZ1bxDq6Kx7411doi3njlWRx9oGn6K0HzoqaTRJs0ov\n4SsURQnIwX5uPAUAwOhCjx0GIDL5wF1RqbMdSFu2uHsDb219sL0j2N4RdHbo7UlDUYO3ps6x\nfbcQwpCaYh6XO9DTJUki1QEA1IopYBiAzFU3CI1GCKHIco8NrMVFSbOnpy5dOFipLhwMes5X\nhf2B6BFDWoouwapPsulTxuimXgAAXAw9dhgAf2OzpNEo4bAhPa3HBhq9Lu2aRYP4jS0ffdJ5\nosycl5Pzhc+mK2rN5oKH1wohhmqoF+pV5Wh89/Ans8dNWtjTbhYAoAIEO/RGCYVatu3SWiwp\nV81reHezu/y8ECKhZELi1JLhKUCKLDar/XzXMpEOl2T/+ZOVLfXNne0EOwBqRbBDb9znqzqO\nnhRCWIsKIqlOCOEqqzCkpiQvmCuEcOzY462qybhxuSEtZSgKSFu2KGHKRFNm+lBcHGPNgqJp\nzZ3OWeOK410IAAwVgh16Y87NNqanaS1mY0Za2pKrnEdPhv1+2evTJViFEIosOz89psiy60x5\nStoVQ1GApNWacwd/eQuMTXkpGf9n2W3xrgIAhhDLnaAviqLIsqT77HcAJSSH3B59ki3y1vnp\nMW91XerShdEjgAp8cubIB6cO3Dxj4fzxU+JdCwAMAD126I0ih6tfWCd7PFkrb5BMRlNWhqTT\nxma4pNnTk0bJYsVA/x2pLnf5vIerzhDsAIwu9NihN237P239ZK8QQqPThUOhzJuvSyjh+SSM\nSooQu84e1Wm1VxRN63P2TWVL/b5zpxZOLM1LzhiO4gBgkNBjh4vyNTRFUl3i1Emu8sp4lwNc\nlnNNtX85tF0IkZaQNCGjj93DCtOyh2LvMgAYagQ7XJTBnqS1WoSi2K+cZ79yvuzxmLIz410U\ncIkyEpOtRrNWo0lL6Hlp65f3bHW4nPcvuimJHYEBjFoMxaI3SjgshIgsSiy7PTpbQrwrAi6d\nHJYlSdJIGiFEKCwHQyGzwRj5qN3j+td3/yCEWDPvmisnlMaxSAC4HPTYoTeSJDW9v03I4ZCz\nw1vXkHHDNbZpk+NdFHCJtJH1roUIyfIvNrzo8nm/unT1hIxcIYTdknDt1HkOl3NG/mdPkVY0\n1dS0NS8ommbSs7kwgFGDYIfeNH/4Sefx00IIrcUshAg6O+JdETAIPAGf0+MSQrS4nJFgJ4RY\nMf3KaIOQLP/hkw2+YCAky1NyCrOTUiX2OwEwGhDsVKttzwFvTX3a0oWG9NRLvkjY5xVCCI0m\ndclViiwnTGJKLNQg0Wz96tLVTq9rTsHneqCDcui5He9JQnz56lXZ9rRqR+Pp+gubju25ckLp\nmnnXxKlYABgAgp06KSG5dfcBIUTnqTOp6Vdd8nVSlyz01NSFPT7PuQuZK68fvAKBOJuUNa77\nwSpHY3ljjRCisqX+75ffEQgFnt+xXgjhDfiGuz4AuCTaxx9/PN41YPBJGo0kSUKSbJMmtO0/\nJCTJkNLzTMDeaYwGSVGCbU77/NmXdgVgFLFbElw+b35KxsKJMzSSpNNoS7IL8lMyspLSvEG/\n3cL2KgBGOmbFqlzz+9s7jp3UWi2Ff/fAQM/1VtW2HzicNLvUMr5gKGoDRr6QLH9cdmjzsb1C\niO+vfCA1ITHeFQFAbzTxLgCXLujslN2e3ttYi8frbAmJpZeyLVL7gcOeC9Wtuw9eUnWAGmwr\n+zSS6ow6g0mvj3c5ANAHnrEbrQItrdUvvirpdAVf+aLWarlYM0thfsHDay/tKxJnTJU9Xvsc\ntoLF2BVZ6C7ZmvjoDXdZDaZ4lwMAfSDYjVbhQEAIoYRC4VBI22tLV1lFx7GTyQvmmPM/W9Yh\n4GhVgiFjVh+bYFqLx1uLxw9OucDotLB4emFadrrNrtfqtpcd3n/+1IrpV07L5e8FgBGKYDea\nKKGQr67RlJPpuVDTunOvfe7MhEkT9El9PPTTtv9QoNkh6XTm/FyhKCG3p/qFV4UQOXesMhf0\nsWMmgBx7WuTFrvKjDlfHq/venz9+6pScwpf2bBmXmnX/whUalrgDMGIQ7EaTpq3bXKfPJkye\nqASDAUebEJJ9/mzP+Spzfq6ku2i3XdLsGZ1HTyTNLnWVlTdt+diUlxU5Hg4GhqtwQA1WTL9q\n8/G9LZ3t28o+PVx1tsPrPl5T4fJ5EtlbFsCIQbAbhRTFPm+WoiiJ06c2vLPJV9dgLS5MnD7V\nUtjDulwN7272XqjJuuVGc0Fe8/vblFAo0NQqhNDodKbcnGEvHRjFZo2bmJeS8ZdD2wpSs8ob\na5xe16SscaQ6ACMKy52MJuFgyF/XYMrNknSfJfK619/1VtcKjSTCSvbtKy2F+bHtlXC48qk/\nhv3+5AVzUhZeEerodB4+bplQqNHptGaTLpFFuYBLFJRDDU5HXnIGW40BGFEIdqNbOBj01TY0\nrt8aDgRy773D1G0+hKeyylfXkDR7htbMhD5gyHkCvprWpqKMXJ2m90lNADAkCHZqIHt94UCg\nh1kUiiLoTgCG0W8+eKOypf6q4tI75l4T71oAjEUsUNwTRQm53PEuYgC0ZlP3VNd+8Mj53zzn\n/PRYXEoCxqbIyCzjswDihckTPah97R1fbX3a8quTZk6Ldy2XznO+KhwMuisqk2azwjAwTL5y\n9aq69ubCtOzI23c+3XHoQtk9C66fnM2+fACGA8GuG0UJOTuEEMF2Z7xL6a+wz9+46QOtxZx+\n7RJJqxVC+JtbLEUFhtRkW+nkeFcHjCEmvaEoPTf69kh1udvvO1ZzbmJmnpan7gAMPZ6x64G/\nucVf35QwuVhjMMS7ln5xlZU3bnhfCJF37x3GrIyw33/h6RfDwVD6dUsTp1/KLrEABsWpusqj\n1eUnas9rNJrv3XSf1cgcJgBDix67HhjT07Rms+z2SBpNZKeH6PIiI5OlMN9SVKA1mw3paa27\n9nnOVwmtVgRDzIQF4mtKTqHZYDxQeVoI8dahbYsnzoiO0gLAUKDHrgeyx1v59IsiHDbl5/iq\n64xZGbl33yZpRsFEEyUcrvztc+FgKLF0sn3+HL29j93GAAyDvedO7Ks4WdXamJmY8t2bvhjv\ncgComfbxxx+Pdw0jixIKte456K9rEELIbo9QFNnlNqTYDWmpkQbemjrH9t2SkALNLRqTaUQN\n10qSpDEYJI2UfMVcQ4q998ayz9fw9kZPZZV1fOGoiK3AKJWXnKHVas82Vs8rnBwIhbQarcVg\njHdRANSJHru/UcJhd1m5u7LadfqsJCSdPTEyf0KSpJy7bzNlZ0aa1b3xnreqRmMyhX0+U05W\nxvXX6JOTBnG5uI7jp3119ckL5umTLndnCNfZc60799rnzEycMbWHT8sqGjdsFUL0uLIxgMGl\nKMqOM0fePfyJWW/86W1fYS4FgKEwoh8dG2au0+VNmz8UQkhCGNJT8u6703nkRKjDlThzauwq\ncYnTSkIdnXp7kqeySnZ7qv74im3a5IwbrhmUGhRZbvlohxKStSZj6pKFl3QJpWXbLkWWU5cs\ndJ0sC7Y5nUdP9BjsLOPzEyYWaSxmY0ba5dYNoC+SJJn0BiGEQa//9MKZeqfj2qnzLAYehAUw\nmAh2f6OzfbaZt2Q05t5zh5CkpFml3ZslTJ6YMHmiECLQ0tq690DQ2SH3czVjRVFkWdLpnEeO\nB1vbUxbO1xi7DsdIWq1tyiRvda2lqLDHK/TZNehraIosSmzOz7VfMUdotYmlPU+M1RgMmatu\n6FflAAbDFUVT81IyrEbT/33vBTkcloR088yFGlYzBjB4xuJQrBIOB5odzsPHg23tlsJ8f1Oz\nOS/XNq1EYzSGOl3BNqchLUVrMffnUrLX6zlXZSnM11otfXxpSK5+4RXZ68u6/ea6dW8LIdKW\nLe4xOF5MwNFa9/q7+uSk3C/c2ku8U2S5/q0NSljOWnlDn1UBiIvnd6wvb6oJhIKluUUPLr45\n3uUAUI+x2GPXvOXjzlNnIq999Y1CCHfFBV99Y+bK63W2BJ0tof+X0prNtmkl/WkpezxBZ6cQ\nQnZ5LEUFgZZWS0HegMr21TfJHq/s8YbcHl2C9WLNJK02585bBnRlAMPsy1evfOPAx3sqjnf6\nPI3tjhZ3x7Tc8fEuCoAajMVgFw4EhBDaBKvBnqhPsrvKK8L+gNbcRxed7PZIOp3GePE5sIri\nrarVp9h7jIZaiyVl0QKNXmctHp8wacIllJ1QUhzq6NDb7b2kOgCjxc0zrypMy86yp/735lcU\nocwcN/HeBddrmZ8O4PKMxeVO9GkpQg77aupCHa7MldclzpwmwmFDeqohLVXS9DzEGWhxVP9x\nnfPwMdvUkoutb9Jx7FTjhq3uMxVJs6Z3v07je1s6jpzQJ9ut4/u1ZWSoo7PLE3iSVmPOzzWm\np/bndAAjnF6ry7GnKUp4e9lhIUSjs9UT9E3JLox3XQBGtzHXYyd7vLUvvamEQpKk0ZgNGrOp\ndt1fgq3tQggRVhJnTuvxrJDbo4TDSiAc9vvFRTrMJK1GCCE0GtFTOAyHQkIIJRTquSxFUcKK\npNU0f7DdV9dozEzvPHE6cfrU9OuWDPwWAYwaSeaEh5fc8vanO5o721tdHU99/PbsvOIFxT3/\nQwQAfRpzwU4JhSLpSlHCtqklgWaHRm8UQkiSpLv4unGWgvysW27UmE2G1JSLtbFNm6xPSdbb\nk3pc7Dfz5ut8tQ3mcbndPwoHQ9UvrAsHAtm3r+w4elIIEfb7hRBBp3Pg93dRgRZHx7FTCZMn\nRhfkAzASlGQXfCM1q7Kl/p1Pd7S4nBWN1W3ezhXTr4x3XQBGpTE3FKsxGq1F43x1DbLXJ4TU\ntveQPjEh85YbUxZeYUzvbTk3Q0qyPrGPFYN1tgSNvuesrNHpDCl2SdvDkqQhZ0fbvkNKSLaM\ny/XXNoSDQUOqPXnBXPu8Wb090jdATVu3uU6d9dc3JV2kVxJAvOi1unSbvaa9ub69RQhxvrlO\np9GMT8+Jd10ARp8x12MnhDBmZuR9cU2grb3z2GlfXYMQIr77LuhT7Bk3Lgv7/NaJRZJG03H8\ntH3eTHN+D317l8M6vsBXXWcpHNefxfAADL97rrjOpNPvKj+hKOFzLXXL410PgNFoLK5jF6WE\nZF9dvTErY0Tt99o7f2OTxmDQJ/exD2yPOk+cbtr6sUarL/i7td3XRgYwEtS1NR+pLp83fnK6\nLTnetQAYfcZij12UpNOaxw1sMbn48tU11K77i6TTjfvyvf1f9KTz1Bl/Y3Py/NmeC9VCEeFQ\nMOjsMGakD2mpAC5NTnJ6TvLf/noG5dCB86czk5KL0ge5Fx+AKo3pYDdiucoqwgF/YumUrmOm\nf30r9XssNRwMNW/dpsiyRq9LXbJI9vpNGWmkOmC0OFh5+s2DHxt0+n9e/SWzno52AH0g2I04\nwbb2xg1bhRAao7HLUsam7Mycu2/319bJXm8/twvT6HWWogJfXYN5XJ4uwZKzZtWQFA1gaKTb\nkjWSJtlqM+r08a4FwCgwpp+xG5nCgWDVH15WgsGcu27tPlG3bd+h1p37tFZLwcNre1xXBYDK\neAI+k96gkfj7DqBv9NiNOBqDvuChtUIJS7oe/nQiS67obAmRVBf2BzwXqs15OVrL57ZEa9ry\nsa+2LnPlDcaM3tZwATDyWQymeJcAYNQg2I1EklYjRM+/nSdMnmjKzY6Owzp27O44dsqcl5Pz\nhdXRNkoo5Dp9VpFlz7lKgh2gPh+dOrT//Knb5iyZlJUf71oAjCz07Y8+0e46IURk1ZKuu8rq\ndGnLF9umTU6cPjUO9QEYYrsrjjd3th2sPB3vQgCMODxjN8opir/ZYUhN+WynWgBjwNHq8sNV\nZ6+bNj/HTpc8gM8h2AEAAKgE3TwAAAAqQbADAABQCYIdAACAShDsAAAAVIJgpx7+xuaGtzd2\nnjwT70IAAEB8EOzUw/npMfe5C47tu+NdCAAAiA92nlAP29RJ/qaWhMkT410IgJGizd3xyt73\nMxKT75h7jSRJ8S4HwJBjHTsAUK0dZ4688+kOIcQPVz2YbLXFuxwAQ44eOwBQrZn5xeWNNRmJ\nyXarTQjR7nElma103QEqRo8dAIwJu84ee+vQtsnZBQ8tuSXetQAYKkyeAIAxweF2CiEcLme8\nCwEwhOixU7Nge4fGoNdazPEuBED8eQP+I9VnJ2TkptuSX9i10eP3P3T1Kr2OB3IAVSHYqZav\nvrH2lbc0ev24h+/TmkzxLgfASLHv/MnX9n0ohCjOyPvqNbdqeOQOUBGGYtWpafOHda++LYRQ\nFEUJhuJdDoARpCgtJxLlLjjqf/j677ae2B/nggAMHnrs1KnyqT/IXp8h2Z5x07XGzPR4lwNg\nZHF63afqzr9x4OPI24LUrEeuuzOuFQEYHPTYqVMkzClyiFQHoLsks/XKCaV2y2cr211wNGw+\nvje+JQEYFAQ7tQn7/a4zFbLHa8rOTLtuabzLATByTcjIFUJEHrFzetzNne1yWI5zTQAuD0Ox\nqtJ56kzTpg+1RqPs9wshjJkZeV+8I95FARihQmG5prXJbDBWORo9fv97Rz4pTMv++rVr4l0X\ngEtHj52qBJodQgghCUmvE0L4G5vCPn+cawIwUuk02sK07MzElPnjp/hDfiFEi8v5p92bG5yO\neJcG4BKxgpGq2OfP0lrM5vxcRQk3bfrIWpivMRnjXRSAUeCayXNSbfa3D24/UnVWr9XdfcW1\n8a4IwKVgKBYA8JmNx/YcrS5fOXNhbWuz3ZqwoGhavCsCMDAEOwDA53x64cxLe7YIIb6/8v7U\nhKR4lwNgABiKBQB8zrjUzESz1W5JSLba4l0LgIGhxw4A0JWiKBJbjQGjEMEOAABAJVjuBAAA\nQCUIdgAAACpBsAMAAFAJgh0AAIBKEOzUiAkxAACMSaxjpyohl7v2pTdkr0+fnJx33x2SVhvv\nigAAwPChx05VfHUNIbdHCYcDDkew3RnvcgAAwLAi2KmHp7K6adOH0bfus+cDjtY41gMAAIYZ\nwU49fLV1iixHXmuNhtbd+xve3RLfkgAAwHAi2KlH0qxSh/58SPKFdWH7/DlCCENqcryLAgAA\nw4ctxVTl/In1dRU7py54MDmzRPZ4tRZzvCsCAADDh2AHAACgEgzFAgAAqATBDgAAQCUIdgAA\nACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpB\nsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMA\nAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJ\ngh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0A\nAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBK\nEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwA\nAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABU\ngmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAH\nAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACg\nEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7\nAAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHjG7eziYlLMe7\nCgDAiKCLdwEALt3ejU/UnN2WnDll+d2/iXctAID4o8cOGK3K9v2p5uw2IYSrvZpOOwCAoMcO\nGI3amso+fu3RsBwQQkhCzLvhB5JGG++iAADxR7ADRpnWxtPbXv9mWA4JITQa7dVrnkzLLo13\nUQCAEYFgB4wm1WUfHNjyi7AiCyE0Wt3yu36blF4c76IAACMFwQ4YNQK+jgPv/3sk1Wn15lUP\nv6HTm+JdFABgBCHYAaOG3mhLTCnsbKsumXt3yfwHNBomPwEAPkdSFCXeNQDoP0UOBbQ6Y7zL\nAACMRAQ7AAAAlWAoBwAAQCUIdsDI5ag/vnv9Txoq98S7EADA6MDkCWDkKjv4Sv25XW5nXVbh\nlfGuBQAwChDsgJGrYMqNrraaCTNuj3chAIDRgckTAAAAKsEzdgAAACpBsAMAAFAJgh0wUsgh\nf8WRt5prDse7EADAaEWwA0aKypMbD2/79a53fxQMuONdCwBgVCLYASNFYkqBRqNLsOfq9OZ4\n1wIAGJWYFQuMIEG/S2ewSBK/cQEALgXBDgAAQCXoGAAAAFAJgh0AAIBKEOwAAABUgmAHAACg\nEgQ7YFgFfJ1b//zQR+v+IeDriHctAAC1IdgBw6q18VSH43xr42lnS0W8awEAqI0u3gUAY0tG\n/pyJs7+g05vScmbEuxYAgNqwjh0AAIBKMBQLAACgEgQ7YDgoYfnj1x7Z+Py9HY7KeNcCAFAt\ngh0wtEIBj6KEPZ2NjvqTns5GR/3xeFcEAFAtJk8AQ8LrajGa7e3NZ7a/8W2rPee6e5+eueTr\nPndrfsm18S4NAKBaBDtg8NWf27XrvX+2JGaVzPuiLAdc7bUBf2fxrDXxrgsAoHIEO2BwKEo4\nLAe1OqMQwtPZJITwdDQ46o/PWvqNBHue0WyPd4EAAPXjGTtgEMhyYPMf71//zJrOtiohRGHp\nzbaUfCGEVmuYMPP2zIL58S4QADAm0GMHDAK/u9XdUS+E6HCctyWP02oNy+76bXvT2dTsUld7\njcWWqdHq410jAED9WKAYGBw1Zz7yeduKpq/WaP72+1LlyU0H3/8Pe/rEa+99SggpjuUBAMYC\neuyAwZE3aVn3g35vmxDC721XwmFJoxVCBP2ufZv+xWBKnHPdd7Vaw3BXCQBQNYIdVKLyxMa2\nloqJM29NsOfHt5Jzx949c+iVknlfHD9tZfHMOywJGfaMSZFUJ4Roqj7YcGGfEKJoxq2p2dPi\nWikAQG0IdlCDmrMfH/zgP4UQF06sn3v9PyanT0qw58armAsnN7qd9VWntoyftlKrM0YXrnPU\nH688sSFv0vKcokUGky05Y5IQSntzeYI9X6c3xataAICaEOygBgGf668vw/s2/txkTb3pSy9d\n/nwFn9shhDBZU4UQshyoPLHRlpyfkT+n97OmLXzo/PH1E2bc1uX48Z1Pt9Qd62i9sOyu30SO\nnP30taM7fpdgz7vxgRcus1QAAATBDupQNH1Vh+Oc192SkTfn8Lb/0enNknS5S/l4Ohs3v/CA\nEGL53b9LSiuqPv3+4Y//n1ZruPmhVw2mxF5OzMifm5E/N/K6uebwvs3/mle8dObSR/ImLets\nr8mftDzaMhTwCCFCQa8QClMrAACXj2AHlZh1zTcjL9LyZlhtWdFn2i5Z0O8Oy0EhRDDgFkIk\nJOdrtDpLUrbOYOn/Reor9/jcjgunNs1Y8vUJM27r0o03ad69SWkTkjNLSHUAgEHBcidQOUUJ\nB3ydoYBbqzNGBlWFEIqi7HrnBwG/a/Ftv9AbEi52bnPNYSFEet6syNug36UzWAbUF+jpbDy9\n/0+Z4+bnFi+JHJHlQGv9yZSsKZE9KgAAGEQEO4xKzsq3wiGvveguSdN7r7Pywctfc7acU5Sw\nVmtYcueTR7f/zmRNzSm+ev+mfxFCFE1fPXvZt3r/LiUs+9wOsy1jUCo/sPUXF05tySpcsGj1\n/x2UCwIAEMWWYhh9Aq6q9opXOy6862051HtLORRwO+sVJSyEkOVAc81hR/3x2vJtZmuqTmeS\nJKn69PsdjsreL7Ltjcc2PH/PhVObB6X4yK9S/EIFABgKPGOHUaOu4hOv2zF+2kq9JduYWBwO\neYz2yb2fotUZF936C2dLhTkhw2RNSUjKaW8uD/ndu979UWHpyvLDbwSDntbG04mphRe7gqIo\nHleTEMLtrB+Uu5i97FvjSq5LzSkdlKsBABCLoViMDt7Opg3P3yOEmHvd9wqn3nQ5l9q3+V+r\nyz6wpRRMmb/W3VE/cc5dve8A4XSca2s4nTdpmU5v7udXhIK+vRuf0OpMC1b8c5dpHOFwyFF3\n3J4xST+QSRgAAPQHPXYYHQwWuyUxK+BttyUXXOalplzxgMFky5u4LC1nen/aJ6UWJaUWDegr\nWuqONlTuFUK0Nq5Jzf5c59zJPc+XHXg5LXfG0jW/GtA1AQDoE8EOo4NWa1jxwIuyHLz8TRps\nyfmzln6z+/FgwF1XsTM9b6bFltn9U0UJVxx5S2+0FUy5oc+vSM+dmTfxGq3OlJI1tdt1FCGE\nEg5fUu0AAPSGoVjgM4e3/briyFv2jInzrvtHt7M+e/xVsaOo9ed373r3R0KI5Xf/Ljmz5BKu\n7+6oP7Ltf1OzpyVnlNgzJxmMtkErHQAAIQSzYoEoszVVCGE02be9/ujuIsKA7AAAIABJREFU\n9T+pKvsg9tPE1EK9McFsy7Ak9tCf110o6Kst3+51tUSPVJd9UH9+9/Fdz6RkTSHVAQCGAkOx\nwGdK5n0xZ8LVFlvm5hfWBgMeozkp9lNrYvbKh1/XaHT9XKD41N4/njm0zp4+8dp7/7/IkdwJ\nV9ef35OWO2NAe1cAANB/BDvgb2zJ+UKI69c+7/e0Jdjzunza++TZLvTGBCFE7NRXW0rBsrv+\ndzDKBACgZzxjB1yWYMBTeWJ9ckZJctbkoN9lsqT89ROlvbncllIwoDgIAMDlINgBl6Xs4MvH\ndz6tN1r1Bouns3nJmifTc2fGuygAwBjF5AngsiSlFmk0OltKgd/bIYTwdDT080RH/Ymasx9H\ntjsDAGBQ0GMHXK5gwK03WBz1J9zO+rxJyzSaPh5ddbXXavXGzX9YK8uBudf9Y+HUFcNTJwBA\n9Zg8AVwuvcEqhEjNLu2yyUSPKo68dXjbr5PSinUGi+wNmK0pfZ4CAEA/EeyA4eDpbKo+80HO\n+IVet0MI4fe0rnjwT8GAx5yQFu/SAADqQbADBsznaT29/89pOdPzJl7Tz1OOffK7mrPbqk9/\ncM0X/seWnJ+aXaozWFjQDgAwuJg8AQzYwff/o+LIWwe2/kIO+ft5SlJasRDCnjFJZ7AUTLkx\nwZ47lAUCAMYoeuyAgfF72xsq9wkhbMkFWp2xz/bNNZ+e2P18wZQbbvna2+wkBgAYUgQ7YGCM\nZntu8dIOx7m51363P+0rjr7tqD/ucTWNL1011LUBAMY4ljsBhlZzzeGTe54fN/mG8aUr410L\nAEDlCHZAHAQD7p1vf1+nNy+85d80WjrOAQCDg/+jAMNMabxwoLOtylF/QgjR1nQmNXtqvEsC\nAKgEwQ4YJvs2/Utrw6mSefce+vCXQoi8SdeYLKmp2VPiXRcAQD0IdsBwCAY81Wc+FEK0t1QI\nIbQ644zFf29OSI93XQAAVeEZO2CQKYpy5tArkqSdOPsLkiRFj58/vr696czUq74c9Lv1RqvR\nbI9jkQAAVSLYAYOsufbI9jceE0IsvvXfMwvmx7scAMAYwlAsMMiSUosstkxJo0lMHR/vWgAA\nYws9dsDgUxRFkoQQUp8tAQAYRAQ7AAAAldDEuwAAAAAMDoIdMIT8XqervTbeVQAAxgqCHTBU\nQgHP5j+u3fzC/Q2Ve+NdCwBgTCDYAUNFlgPhcEgIEfS74l0LAGBMYPIEMITam8/6Pe2ZBfOi\nM2T9XqcQCqsTAwCGAsEOGCaKEvZ72jb9ca2ihJff/duktAnxrggAoDYsUAwMh6OfPHXu6NsT\nZ39BDvmFEH5Pe7wrAgCoEMEOGA4NlXvlkN/ZWrn4tv9QwnLGuLnxrggAoEJMngCGw5zlj1kS\n0purDrY2nMgqXOD3tm9741t7N/1cCJ6FAAAMGoIdMByM1lSPqzkU9J7c88fWhpNN1f9/e/cd\nX9dd33/8c+7ekq72tiVbdrzteGWbDDKAsKGsskpbSil7/dgUCqXQlhZIyyqlFCh7JWRBSEji\nFe9tybb2vFe6e531+0PBMR6SYku60tHr+Ycfuveee76fq+Rhv+937o30Hew9+Ugi2lXs0gAA\n1sFQLDAbdt33SRFRFJvd6fYFq4PhRY1tN3sCFaHyRcUuDQBgHayKBWZcItr52+//pWFoTnfg\nrjf90OH0FLsiAIA1MRQLzLhDT/zn+E7FS9e/jFQHAJg5BDtgBhmGloh2phMD4w/PHLmvuPUA\nAKyNOXbADNpx78cHzmwvqWgZf1jVuKG49QAArI0eO2AG5bMxEfH4K0SktKJ1463vK3ZFAAAr\nY/EEMIOyqZGRvgO1i681Dc3pDiqKUuyKAABWRrADAACwCIZiAQAALIJgBwAAYBEEOwAAAIsg\n2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEA\nAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgE\nwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4A\nAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAi\nCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYA\nAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAW\nQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbAD\nAACwCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACw\nCIIdAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIId\nAACARRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACA\nRRDsAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDs\nAAAALIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAA\nLIJgBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJg\nBwAAYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAA\nYBEEOwAAAIsg2AEAAFgEwQ4AAMAiCHYAAAAWQbADAACwCIIdAACARRDsAAAALIJgBwAAYBEE\nOwAAAIsg2E1VbuxYz6Nv7t/xXtPUTVMvdjkAAADnW+jBLh8/mU90XPi8aeq5sSNadkRN948/\nkej8maFl1HRf9MhXe37/xmTP/bNcKgAAwMQcxS6gmAqprsGnPi4iNZs+7Q61nvtSovPnsdM/\nFsUupu7016uZAW/VVpsj4AotKqS6TUPNjR0JNt5RpMIBAAAuYoEGOy03MnLwX+zukvGHis05\n/oOeHy2kuu2eyszIbhER0xQRNTMopqHGjjfe9HURycfbMyO7g/U3F6d0AACAS1BM0yx2DTNO\ny42Yhur01ZmGmh54NNHzgDPQlBl6UkRKFr3IU3pVeuhJX/VWb3ht3/Z3adkhV6i1kDil2BzV\nGz6iF5L5VFdm4NGy1lf5qq+ZSnOGlkkNPOoOtrpL22b4kwEAADzDsj122ei+2KkfBhtu85St\n7nvy70SkdsvnIoe/rKZ7RBQxDVdoSSHRkej6VWZoh5odzI4earj+y4rNISKuQJNRiHsrNrhL\nlomIr3Jj2eKXTr3pZM/9sdM/ElEqVr1Dy/T7qjY7/fUz9DEBAADOsmywGz3xbS07HD32dZvd\nM/6MoeX0QkxEcfobSltfKWJEDn/Z7g6r2UER8ZQuE1FqNn5KzfS7Q60iymU37fBWiYiIGT16\nj2nks6MHaq7+xJV/IgAAgIlZNtjZXWVadlhEDD1X0vJSU8uOtX+ntOVlit3rr75GsTkjR75q\nGqqI6S5ZahpaePlbRMTm8LlDS66waX/N9an+3+XGjokYIqKmehJdvwo1v+DKPxQAAMAELLvd\nSbDhNhGxe8KlLa8oaX5hZnh3IXE6Fz1kFGLZ6AER8VVutLtCgdobazZ+qnbzP9gc3mlsvXLt\n+ypW/d14t5+hZRLsjQIAAGaeZXvsfFWbq10fcZe2KTannh8ztJRid9q9VWMd3xeRhuu/4qva\n7KvarGWHkr0P+Kq22F2l09i6ze71V19jGmr89I91NRmov2Uabw4AAHBRlu2xG3zqY0P7Pp0Z\n2p5PnEr2PWhoWVNXXaHFit3t9DfYnIHxyyJH7hk98e3osa/PRA3+qi1absTUc6aeExHT1AvJ\nM2IaM9EWAACARXvsTF0vJEREzQ5FT3zL1PP+mus9pVcFam7wV25W7K6zayOcvtp8/ITTVzcT\nVSh2d7jtDfn4yWD9rSISPfLV9NCTwYbbw8veMBPNAQCABc6iwU6xV6//UCHZ6a1Yn+r/na7n\nA3XbPGUrRUSxu8+9sHzFX5W2vtzuDs9QIcHG24ONt4//bGgZETG09Ay1BQAAFjjrb1Bs6FlD\nTTs8FcUuRPRCIh876gmvsTl8xa4FAABYkPWDHQAAwAJh2cUTAAAACw3BDgAAwCIIdgAAABZB\nsAMAALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMA\nALAIgh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAI\ngh0AAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0A\nAIBFEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBF\nEOwAAAAsgmAHAABgEQQ7AAAAiyDYAQAAWATBDgAAwCIIdgAAABZBsAMAALAIgh0AAIBFEOwA\nAAAswlHsAgAAmEQknftdR19vIr28ouSuq5qLXQ4wdxHsAABz3U8OnX6qd0REjg2NramraCjx\nF7siYI5iKBYAMNe1lIfO/rx/IFrESoA5TjFNs9g1AAAwiWgm9+H7d+mG+doNS0Nul9NhW1FV\nZlOUYtcFzC0EOwDA/NAdS8Wy+d+fHjg0EBWRl65uuWNZY7GLAuYWhmIBAPOAYZqmaa6sDht/\n7I948GSPqhvFrQqYa+ixAwDMA9/b1/7Iqf5NjVWv3bD0N8e77z/RIyJr68pfuHJRY0mg2NUB\ncwU9dgCAeSCn6SKSyqsFzXjxqsUvWbW41OM+0B/99dEuVTcMOikAEaHHDgAwL2RU7UB/9P/2\nn0qr6rtvXHNVVdkDJ3p+29H3vKuafnrojNth/8gtG0IeV7HLBIqMfewAAPOAz+lYUV2WVlUR\niWULInL7ssbblzUeGhzNqFpG1aKZPMEOoMcOADBvdETi8VxhfX3F2Y1ODNN85FS/z+m4prm6\nuLUBcwHBDgAAwCJYPAEAAGARBDsAAACLINgBAABYBMEOAGBlvzra9cXHDvTG08UuBJgNBDsA\ngGWpuvHLo53Hh2PbuwaLXQswG9jHDgBgWU677e4Vi06OxLayGQoWBrY7AQAAsAiGYgEAACyC\nYAcAAGARBDsAQNEcHRp73707vrv3ZLELASyCYAcAKJoDA9FYNv9k19BcmO7dEYn/11MnTkUT\nxS4EuHysigUAFM2tS+rzmr6yOqwUuxIR+cmhMx3R+FAy88HnrC92LcBlItgBAGZbQTdM0+xP\npEXkDRuXFbucp21qrBxOZzc1VBW7EODysd0JAGBWJfPqh+/fZRhGXjdE5BO3bawv8Re7KMAi\n6LEDAMyqEyPxrKqdfei0M9sbmDYEOwDArPrl0TMiYlOUz9yxWUQq/J5iVwRYB9+TAACzqjbg\nE5ESj6vC7yHVAdOLHjsAwOwxTfG5Hatryv9664pi1wJYED12AIDZ0xVLPn5m8NBg9Cs7Dj/c\n3lvsciahGcavjnb9/lR/sQsBpooeOwDAbDg+EvvOnpMbGyo31FcMJDJHB8eODo6tr68o983d\n0diDA9FfHu0UkcXhYHNZsNjlAJMj2AEAZsO+vshIKvvY6f5/ufu6oWTm3584XBv0lXrcxa7r\nGYZp/vJop9tuv3N5UzKvfn9/e5nHXeJx+V3OyoC32NUBU0KwAwDMhoKmi0hdyK+I1AR940ti\n55SjQ2P3HusWkauqyzpHk7t7RkTkH+/aEp7DfYrAeQh2AIDZoJumiDhsT8/tzqraN3YdD7id\nb9i4bC6cJyYizWXBqoDX7bDXh/xBl3NRWbC+xE+qw/zCyRMAgNmQLmgHB6IrqstKPC4R2ds3\ncs/2oyLy0VuvbioNFLs6wCLosQMAzAa/y3FNc/XZhyuqw2vryoNuZ+OcTHWxbKE9EltVE/Y6\nHSKSKqiPdPS3lAdXVocneNfvOvp0w7xlab1NmSO9kFhw2O4EAFAEHof9rVtXtoRDRwZHi13L\nRXx959Gv7Tz2o4Onxx8+0tH/y6Od//7EYVU3LvWWzrHk9/d3/PDgqQMD0dkqEzgfwQ4AUBz7\n+iPf2XPyS48fGkpmi1tJfyJzXg0Bt1NEIpnc4cFREVkcDjrtttZwyHHpk22rA96wz13qdTWE\n/DNdMHApzLEDABRHdyz12d/t87scn3zuJr+raFOD+hOZjz+4W0Q+e+eWs0ec6YZ534nuXx7p\ndNhsn3/e1qDbqeqG89Kpbpxpiikm47AoIubYAQCKo6k08MUXXOOy2x222UtC/Yl0Mq+2VZae\nbfJsB4dxTk+H3aasqg7/5nh3ld87HjonTXUioiiiCKkOxUSPHQBgBo2kcw+c6FlZU7a+rqLY\ntUiqoH7wvp15TX/H9atX1TyzDKJzLOmw2RpKzh9CzWm6225XFBnL5vvi6auqyux/mkF/faxr\nX1/k5Wtbl1eWzsYHACbDHDsAwAx66GTPo6f7v7nreLELERGxK0+Pkp43VW5RWdBlt/38SGdP\nLHXu8x6HXVHEMM3P/37/lx4/dO/xrnNfNUUeau/tjqV2dA3NeOnA1DAUCwCYQatry3f3jsyF\n7joR8Todf3/75rSq1l+wvuHHh07v64vs74t84rkbRUQzTN0w3A67iCiKMj4O67TZ4rnCnt6R\n1TXhyoBXEXnZ6pYD/dGbl9TP/mcBLoqhWABA8UUzua6x1Oqa8FSmss2Eh072/vDgqee01r16\n/dK8pn/4/l05Tf/oLVdXB70iklG1Pb0jPzt8xm5TYtlCQ4n/47dtLEqdwMTosQMAFJkp8s+P\nHRxOZW9va3zZmpYJrvz1sa7Hzwy+ev2SNbXl01vDbW0N21rrxmNlPFeI5woiMpjKjAc7n9MR\nyxaSeXV8aUR10De9rQPThWAHACgyRcTjsIuI12mf+MonOwejmdzunpFpD3ZyzrrXqoD3rdes\nSBe0NTXlIqLqRnskvrmxKlVQW8tDSytKyrzuy2uiP5H52eEzK6vLtrXWTVvdwDkYigUAFF9e\n04fT2caSSY4XOzAQfapn5M7lTXWh6ewzSxe03T3DyypLay922//Ze/Kx0wNtFSXv27buChv6\nwf6O33b02W3KPS++kd3uMBPosQMAFJ/bYZ801YnI2trytTPQV/frY10Pt/fWBH1/f/um8176\nzfHu7V1DIqJMRxDb3FR1ciS+qjZMqsMMIdgBAGZbQTe+tuOo0257y5ar5sI5DdUB79k/z/NU\n74iqG02lwbdft+rKG2oJhz5229VXfh/gUgh2AIDZ1h6JHRiIishzWuva5sDWvtta69bVlZd4\n3MOpbFbVmsuCZ1965drWHd3D21rrxrc+AeY45tgBAGabqhtfePTA6dFESzj0wZvXF7/LTkRE\nknn1A/ftUHXjA9vWLakomeXW9/ZGfnH0zM2t9TexrgJXgJMn5iAzH2/XC4lilwEAMyKraj86\neMrrdIhINJM7r39BM4rZ3VDEiPm9/e39icz393cUrwRYAUOxc4up50cOfykb2efwVNRd+6+K\nQs8/AKvZ0xt55FS/iLxo5eK1deXnzrG773j3L4923r1i0V3Lm2atnvtP9CTyhRevXBx0Oz9+\n28acpjeVTrSM41Q0satn+KaWuuldmbuqJvxk52BzODj5pfNf51jyq08eWRQOvvWalXOkv9Yy\nCHZzS2ZkVzayT0RMUxPTLOaXRwCYGcsqSyoD3gqf57ltDeedM3FiJKYb5rHhsVkLdoPJzE8O\nnRaRRWXBzY1VVRdbP3GeH+zv6BxLjmbyb7t25TRW8oaNy167oc1hs+zf+z2xlG6aLrt9V89w\nQdPHsvlYXz6raj4nUWQ68ducW1yhpXZ3qc0RqF73AcXGfx0AVpDT9PZIfGlFicdhH0nn4vnC\nZ+7YfNH88sq1rdu7hq5prpm12qoC3qUVJYl8oa1iqms41tSWDyQza2rDz7atnKbnNb3E47rU\nBRZOdcOp7Kce3iMiJV5XPFtYVllyU2utx27/3r72W5c2LCpbEP2Us4PFE3OPacocWPwPANPl\n6zuP7eoZ3thQ+fqNy95/7/asqr9p0/JrmquLXdflu4y/pwu68YH7dmRV7QPb1i9eGOOt54pm\nch+8b+fZh3ab4rTbK/2enlhqVU34HdevLmJtFsPiibmHVAfAKvoTmf39kfFZdHaboogooojI\npHvX3Xe8+wP37TjQHxURwzTzmj4L1V7UkaGxd//qyfPWNJwt/7HTAx9/cPfO7uFJ75NVtVRe\n1Q0zls1PevH4SbXWsKN76HOP7OuJpT5zx+alFSVBt3NjQ6VumDlVW1QWCPvc8zriz0EM9gEA\nJqfqRjxXqPB7RGQsm3/09MDqmnBreehS13fHUoPJ7P/uO5kpaC9b3fK+besWlwWddtvHb9sY\nzxUm7bLa3jU0msnv7BlaWVP20Qd2J/Pqh25eXx/yT/OnmoIjg6PJvLqje+jP1i25MI0+erq/\nP5F5/MzAlqaqie9T4nG9f9u6eK6wrq5i4isfPNn7o4Onyryu129cvrK67ApqnxMePNHbE0/9\ntr3vPTetff+2daYp0UyuI5Io9br+/Oplxa7Oguyf+MQnil0DAGBOM0U+8dBTvzjSGfZ6msoC\nPzl05qH23uMjsVuXNlz0+oyqfeKhp3b1DNsVRTPMLc3V6+rK7TZFRLxOR5nXPWmLlQGviNy1\nvNkU+cWRTt0w2ypK60v+JNiZIqejCcM0fa4Z7KSoCfk0w7y9rbEmeJE1sGVed17X71jeNB55\nJ1bmdSuKEvQ4J+6ufKJzsDuWymn6UCp7w+LagUTmvuPdfpdzKr+3capuGKZpnxsz9nwuR7Kg\nPbetoTroExFFkW/sOtYdS925vGlx+JJfDHDZ6LEDAExCN4x0QRWRWC4vIksqQk90DlxVdcnO\nJJfd5rTb8pqe0/S/uXbl+sn6qC60uia8uubp1Ql/c83KWK6wof78mxwaiP77E4e9Tvvn7to6\ncysry32e16xfeqlX19SWr5ny2bVf23lsT+/Ic1rrXn3pG4rIi1ct1k2zIxJ7TmudiPz8SOfe\nvpGTI7GP3jqls8hSefUjD+wyTPnUczeVei+5UGPWbGqs2tT4THemKdKXSItIfyI99Zvcd7w7\nniu8dHWLy84UskkQ7AAAl2SKHByI7u4daassXVNTvqmxUkS2NlVvbKh02C75T6zDZnv/tnWf\neniPItJUMtGecFOx/oJIN258op5SjInJkXTu4EB0fX3FxL1ohmkeGhwt87rHN8ZL5Aoiksir\nE9886Ha+ceMzY5SrasoOD0YnHcA9azSbTxc0ERlJZ+dCsDuPIvI316w8ORK/ZtFUp9YNJbM/\nO3xGRFrCoUmHvMGq2HnGNHV2LQYwa35xtPPXR7vGf37rNSs21FdO/b3JvCoiQbdzRioTEZEz\no8kSjyvsm+oY5XT558cOHhseW1Fd9q4b1kxw2b6+yFe3H3HZba9et/T0WOLapurhdG5tbfn4\n2PGpaKI9Er9hca1/sqFkwzQnXW5yrp3dw4ZpztyihLFs/r+fOtFQEnjZmpYZauJchmn+6x8O\nxXP5d92w9tyo2htPG6Y58W7SCxA9dvOFmY93aNn+6PFve8OrKte8p9j1ALC+4VT28MDo+M+N\npYHllc9uIv/ZSNcRiR8YiN7YUlc5hYloz8rZRRgPnOg5GYn/2drWyinsMHzlqoPeY8NjF511\nd67xxOZy2H946FSmoInI6za0jb9kmnLP9iPxXCGjai9ZtfjC93aOJXd0D92wuLY+5H9WqU5E\nZrpba19f5MjQ2JGhsdvaGibYlm+62BTl3TeeH6AHEplPPvSUiHxg2zpTpCeWum5Rjdsxgx0f\n3bFUmdd90S8q7ZF451jy2uaaSTP6LCh+BZiK3OjhoX3/oCg20zSykf3Jvod95Rvs7jL2RgEw\nE06MxL791PFIOl/u87SGQze01F636PI3Df72nhNDyWwknfurrSumsUgR+enhM0PJzMvWtPz4\n0GkRaSz1v2jlRULStHvN+qXPW9486UBnW2XpZ+7YHHA5f3bkzL6+yLmz8RRFmsuCR4ZGL9Xh\n9H/7T3VE40PJ7Ozs8fasdubbUF9xYCDaWBKYhVR3USPp3Gcf2Tv+s2bKV588lFV13TRvW9og\nIjlNN02zcyy5tKJkggkDz8revpF7th8t8bg+e+eWc49LOTkS+9quo4mcappy37HuLzz/mqKv\nWSHYzRfK2T9NUxs9/s1R+aa3fG3Vug8Wt6xZkOx9KDd2tGzJqxzeKhER0zSNgmKf7ZEXYIHI\nqNqJ4djOnuFIOi8ieV3/wHPWX95XyGgm99Unj1QHfSurw6OZgWWVUz3aYYpi2fxvjneLSJnP\nU+Jx2W2Kx+F876+3397WeFvbxZfrTqMpTl8bP6PsNeuXXrgC4+3Xrcqqutd58U6mNbXhnnhq\n7ZRXZlyJ3T3D/73n5I0tta9Y0zrxlT/Y39EVS71p0/KJx6BnWm88lVV1EXn7dauWVZZU+r29\n8XRt0Ccij5zq/96+9pDHlcgVrltU84aN07OjiqqbIqIbpmGaInJ4aPRbu46vrC4byxbi2acn\nTaqmoRqG3Vbk6VIEu/nBE15Vu+nThqkPP/UJU0wRRcRU0/0iImKahqbYZnAWyywrJDvTg4/7\na29wBZpNUx9r/65pFJy+am/lxviZn6qpHl1NhhqeqxXiZa1/ZnfP+02egDnl0w/vHUlnG0oD\nyypLKwOeO5c1XfbAwNGhse5YqjuW+txdW16xpnXaezJKve7rF9UbIfI/AAAgAElEQVQMpbI5\nTYvnCk67bUfXYDxX2N41NAvBblqcTXWaYRwaGG0sDZzdNuXO5U13XMEv/1k5OjyW1/R9fZGJ\ng11G1X7b0Scie3pH7ljWOBuV/ZGqG2PZ/NmTfNfUlL9sTUvI7RrvBP3wLRuyqj4+DNo5lhSR\nrKpNbwGbG6uCbmdlwOt22LOq/t297cm8uuOPG1O77PZtrXVbm6s8MzkWPEUEu3nDFWoVkUDD\nbamB39scfj0/5nCHB/f+vZ4d0tV0zdUfcwWnfwDCUJOKzTXL3WNj7d/JjR3Lx9trNn5SUezB\n+lvSI7tcoaWRw/+mZUfGr0l032uahtNbVbL4pbNZG2Bt6YI2ks6KiGGa771p7RXe7er6yo5I\nvCrgK/dN89S6s16/cZmIDCQyiax6MhLrS6RLvW6nw9YXT5+36d0c93B7308OnQ64nF98wTVn\nZ9TN2lyb5y1v9jgcl1p9fJbP6bh7xaKuWHLr7C5NNUU+/ds9/YnMa9Yv3dZaJyJ2m3J72zPJ\n0qYoZye3vWjloiq/d1lliWoYSytKpqWARK7wi6OdTaXBFdVlIvI/e09E0zkRURQJez0ZVfvw\nLRuqZ2Vy51QQ7OaZ8LI3htv+vH/nB/X8WC5+4uzz+fipaQ92arp3YOcHFYe3/tov2RyTzBGe\nRp7w2nz8lLf86X9UFIdPz0XH2r/j8tVq2RFFcYjdabN7bHa3r3LzrFUFWM9AIvPTw6fGslpN\n0FvQ9JuX1i+vLH3BiuaTI7E3bbrqyu/vczneuGn5ld9nUrUh399dv+qfHj3QEYkn84VYNv/Y\nmYFXrVsyC01PF6/TISJup12RIszQqvB7Xrl2kkHYcS9Y0TzTxVzIMMzxDVwS+fNPWkvkCicj\n8RXVZWc3Mizzup93VdO0tDuayYd97niucN/x7sdOD4gMXF1fEXA7x+ftVfi9L129eEN9hW6Y\nzrm0ux7Bbv7JRPaKKKGm59ns3mTvg7qaEBE9HzW0jGlodtclN/I29XxmeKcrtMTpr5tKQ1ou\napq6qaYMNTWbwa5k0QtLml9g6LlE933u0ja7u1RE7K6SyrUfzMWODu//R1PL6lrWdAb0Qtyu\nhW2O+fS9HJg7fneqb3//qIh0jSVEJF1Ql29bd/eKRUUu63K996a1WVV78GTv0aHRWe5SunI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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm_plot1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.4.2"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}