diff --git a/QuickDraw在线交互识别系统/.keep b/4. QuickDraw(你画我猜)在线交互识别系统/.keep
similarity index 100%
rename from QuickDraw在线交互识别系统/.keep
rename to 4. QuickDraw(你画我猜)在线交互识别系统/.keep
diff --git a/QuickDraw在线交互识别系统/homeworks/.keep b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/.keep
similarity index 100%
rename from QuickDraw在线交互识别系统/homeworks/.keep
rename to 4. QuickDraw(你画我猜)在线交互识别系统/homeworks/.keep
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/Fashion-MNIST分类.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/Fashion-MNIST分类.ipynb
new file mode 100644
index 0000000..1c9cace
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/Fashion-MNIST分类.ipynb	
@@ -0,0 +1,611 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f2b64e25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset,DataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2e469261",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "batch_size=256\n",
+    "#num_workers代表并行数，windows必须填0，否则会卡死\n",
+    "num_workers=0\n",
+    "lr=1e-4\n",
+    "epochs=20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5002933c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torchvision import transforms\n",
+    "\n",
+    "image_size=28\n",
+    "data_transform=transforms.Compose([\n",
+    "    transforms.ToPILImage(),\n",
+    "    transforms.Resize(image_size),\n",
+    "    transforms.ToTensor()\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aa4ccfcc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#读取数据\n",
+    "class FMDataset(Dataset):\n",
+    "    def __init__(self,df,transform=None):\n",
+    "        self.df=df\n",
+    "        self.transform=transform\n",
+    "        self.images=df.iloc[:,1:].values.astype(np.uint8)\n",
+    "        self.labels=df.iloc[:,0].values\n",
+    "    \n",
+    "    def __len__(self):\n",
+    "        return len(self.images)\n",
+    "    \n",
+    "    def __getitem__(self,idx):\n",
+    "        image=self.images[idx].reshape(28,28,1)\n",
+    "        label=int(self.labels[idx])\n",
+    "        if self.transform is not None:\n",
+    "            image=self.transform(image)\n",
+    "        else:\n",
+    "            image=torch.tensor(image/255.,dtype=torch.float)\n",
+    "            label=torch.tensor(label,dtype=torch.long)\n",
+    "        return image,label\n",
+    "\n",
+    "train_df=pd.read_csv(\"./FashionMNIST/fashion-mnist_train.csv\")\n",
+    "test_df=pd.read_csv(\"./FashionMNIST/fashion-mnist_test.csv\")\n",
+    "train_data=FMDataset(train_df,data_transform)\n",
+    "test_data=FMDataset(test_df,data_transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0b8040bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_loader=DataLoader(train_data,batch_size=batch_size,shuffle=True,num_workers=num_workers,drop_last=True)\n",
+    "test_loader=DataLoader(test_data,batch_size=batch_size,shuffle=False,num_workers=num_workers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d182f098",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([256, 1, 28, 28]) torch.Size([256])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1940fa52be0>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#数据可视化\n",
+    "import matplotlib.pyplot as plt\n",
+    "image,label=next(iter(train_loader))\n",
+    "print(image.shape,label.shape)\n",
+    "plt.imshow(image[0][0],cmap=\"gray\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "62ee436d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(Net,self).__init__()\n",
+    "        self.conv=nn.Sequential(\n",
+    "            nn.Conv2d(1,32,5),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool2d(2,stride=2),\n",
+    "            nn.Dropout(0.3),\n",
+    "            nn.Conv2d(32,64,5),\n",
+    "            nn.ReLU(),\n",
+    "            nn.MaxPool2d(2,stride=2),\n",
+    "            nn.Dropout(0.3) \n",
+    "        )\n",
+    "        self.fc=nn.Sequential(\n",
+    "            nn.Linear(64*4*4,512),\n",
+    "            nn.ReLU(),\n",
+    "            nn.Linear(512,10)\n",
+    "        )\n",
+    "        \n",
+    "    def forward(self,x):\n",
+    "        x=self.conv(x)\n",
+    "        x=x.view(-1,64*4*4)\n",
+    "        x=self.fc(x)\n",
+    "        return x\n",
+    "    \n",
+    "model=Net()\n",
+    "model=model.cuda()\n",
+    "criterion=nn.CrossEntropyLoss()\n",
+    "optimizer=optim.Adam(model.parameters(),lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a2bdc7b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(epoch):\n",
+    "    model.train()\n",
+    "    train_loss=0\n",
+    "    for data,label in train_loader:\n",
+    "        data,label=data.cuda(),label.cuda()\n",
+    "        optimizer.zero_grad()\n",
+    "        output=model(data)\n",
+    "        loss=criterion(output,label)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        train_loss+=loss.item()*data.size(0)\n",
+    "    train_loss=train_loss/len(train_loader.dataset)\n",
+    "    print('Epoch:{} Training Loss:{:.6f}'.format(epoch,train_loss))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c0d891f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def val(epoch):\n",
+    "    model.eval()\n",
+    "    val_loss=0\n",
+    "    gt_labels=[]\n",
+    "    pred_labels=[]\n",
+    "    with torch.no_grad():\n",
+    "        for data,label in test_loader:\n",
+    "            data,label=data.cuda(),label.cuda()\n",
+    "            output=model(data)\n",
+    "            preds=torch.argmax(output,1)\n",
+    "            gt_labels.append(label.cpu().data.numpy())\n",
+    "            pred_labels.append(preds.cpu().data.numpy())\n",
+    "            loss=criterion(output,label)\n",
+    "            val_loss+=loss.item()*data.size(0)\n",
+    "    val_loss=val_loss/len(test_loader.dataset)\n",
+    "    ge_labels,pred_labels=np.concatenate(gt_labels),np.concatenate(pred_labels)\n",
+    "    acc=np.sum(ge_labels==pred_labels)/len(pred_labels)\n",
+    "    print('Epoch:{} Validation Loss:{:.6f},Accuracy :{:6f}'.format(epoch,val_loss,acc))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c026db76",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  0%|                                                                                           | 0/20 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:1 Training Loss:0.673626\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  5%|████▏                                                                              | 1/20 [00:22<07:06, 22.45s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:1 Validation Loss:0.451019,Accuracy :0.836600\n",
+      "Epoch:2 Training Loss:0.435428\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 10%|████████▎                                                                          | 2/20 [00:39<05:45, 19.18s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:2 Validation Loss:0.362046,Accuracy :0.877400\n",
+      "Epoch:3 Training Loss:0.373162\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 15%|████████████▍                                                                      | 3/20 [00:55<05:04, 17.92s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:3 Validation Loss:0.316949,Accuracy :0.882800\n",
+      "Epoch:4 Training Loss:0.335349\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 20%|████████████████▌                                                                  | 4/20 [01:13<04:47, 17.95s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:4 Validation Loss:0.296466,Accuracy :0.894400\n",
+      "Epoch:5 Training Loss:0.313721\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 25%|████████████████████▊                                                              | 5/20 [01:30<04:25, 17.67s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:5 Validation Loss:0.273686,Accuracy :0.898200\n",
+      "Epoch:6 Training Loss:0.290939\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 30%|████████████████████████▉                                                          | 6/20 [01:48<04:07, 17.65s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:6 Validation Loss:0.262852,Accuracy :0.903900\n",
+      "Epoch:7 Training Loss:0.277406\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 35%|█████████████████████████████                                                      | 7/20 [02:06<03:52, 17.88s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:7 Validation Loss:0.249661,Accuracy :0.909200\n",
+      "Epoch:8 Training Loss:0.263334\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 40%|█████████████████████████████████▏                                                 | 8/20 [02:24<03:32, 17.73s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:8 Validation Loss:0.241978,Accuracy :0.910800\n",
+      "Epoch:9 Training Loss:0.252032\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 45%|█████████████████████████████████████▎                                             | 9/20 [02:43<03:19, 18.12s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:9 Validation Loss:0.234983,Accuracy :0.915500\n",
+      "Epoch:10 Training Loss:0.242020\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 50%|█████████████████████████████████████████                                         | 10/20 [03:01<03:01, 18.18s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:10 Validation Loss:0.231416,Accuracy :0.914600\n",
+      "Epoch:11 Training Loss:0.235512\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 55%|█████████████████████████████████████████████                                     | 11/20 [03:20<02:44, 18.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:11 Validation Loss:0.233941,Accuracy :0.914200\n",
+      "Epoch:12 Training Loss:0.224529\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 60%|█████████████████████████████████████████████████▏                                | 12/20 [03:38<02:26, 18.36s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:12 Validation Loss:0.223657,Accuracy :0.917500\n",
+      "Epoch:13 Training Loss:0.217922\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 65%|█████████████████████████████████████████████████████▎                            | 13/20 [03:56<02:07, 18.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:13 Validation Loss:0.213363,Accuracy :0.922000\n",
+      "Epoch:14 Training Loss:0.211144\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 70%|█████████████████████████████████████████████████████████▍                        | 14/20 [04:14<01:48, 18.08s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:14 Validation Loss:0.212327,Accuracy :0.920900\n",
+      "Epoch:15 Training Loss:0.203549\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 75%|█████████████████████████████████████████████████████████████▌                    | 15/20 [04:30<01:26, 17.38s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:15 Validation Loss:0.216248,Accuracy :0.920100\n",
+      "Epoch:16 Training Loss:0.201786\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 80%|█████████████████████████████████████████████████████████████████▌                | 16/20 [04:44<01:06, 16.63s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:16 Validation Loss:0.209490,Accuracy :0.922300\n",
+      "Epoch:17 Training Loss:0.193132\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 85%|█████████████████████████████████████████████████████████████████████▋            | 17/20 [05:00<00:48, 16.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:17 Validation Loss:0.204227,Accuracy :0.924700\n",
+      "Epoch:18 Training Loss:0.185849\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 90%|█████████████████████████████████████████████████████████████████████████▊        | 18/20 [05:16<00:32, 16.12s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:18 Validation Loss:0.206971,Accuracy :0.921800\n",
+      "Epoch:19 Training Loss:0.183673\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 95%|█████████████████████████████████████████████████████████████████████████████▉    | 19/20 [05:32<00:16, 16.09s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:19 Validation Loss:0.215200,Accuracy :0.922500\n",
+      "Epoch:20 Training Loss:0.175587\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [05:53<00:00, 17.67s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch:20 Validation Loss:0.200811,Accuracy :0.928500\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm import tqdm\n",
+    "for epoch in tqdm(range(1,epochs+1)):\n",
+    "    train(epoch)\n",
+    "    val(epoch)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b93eb45",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/readme.md b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/readme.md
new file mode 100644
index 0000000..c7ca1ad
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/readme.md	
@@ -0,0 +1,13 @@
+# 全部实习作业
+
+1. 在线垃圾邮件识别
+2. Fashion-MNIST分类
+3. tips.csv数据可视化
+4. 国民经济核算数据分析
+5. 水质检测
+6. 泰坦尼克号数据集分析
+7. 灰度图像人脸识别
+8. 离职数据分析
+9. 菜谱订单数据分析
+10. 鸢尾花数据集的回归与聚类
+
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/tips.csv数据可视化.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/tips.csv数据可视化.ipynb
new file mode 100644
index 0000000..0d91ba9
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/tips.csv数据可视化.ipynb	
@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f1381646",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_bill   tip     sex smoker  day    time  size\n",
+       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
+       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
+       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
+       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
+       "4       24.59  3.61  Female     No  Sun  Dinner     4"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "plt.rcParams['font.sans-serif']=['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus']=False\n",
+    "tips=pd.read_csv('tips.csv',encoding='gbk')\n",
+    "tips.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8b6310ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='total_bill', ylabel='tip'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(x='total_bill',y='tip',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a7966c46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='total_bill', ylabel='tip'>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(x='total_bill',y='tip',hue='day',style='time',size='size',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d4ccb9db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x212994cf4c0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 785.875x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.relplot(x='total_bill',y='tip',hue='time',size='size',palette=['b','r'],col='time',row='sex',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5275f1eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#分类散点图 stripplot\n",
+    "sns.stripplot(x='day',y='total_bill',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f8e006f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.stripplot(x='day',y='total_bill',hue='smoker',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "37d54e2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#分类散点图 swarmplot\n",
+    "sns.swarmplot(x='day',y='total_bill',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7b75c3e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#箱线图，boxplot\n",
+    "sns.boxplot(x='day',y='total_bill',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c7fd5ef8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='day', ylabel='total_bill'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#箱线图，boxplot\n",
+    "sns.boxplot(x='day',y='total_bill',hue='time',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3c7a60cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Count'>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6N2EttGzW0nYcr9SrXS8OHj9I3lHdsavOn5a/alJ/Xv5nxxfL7ObwZsmRyQT4BZC1P8t2FOXBtPxVg8TFx517x22o8Mq3r1C1pgqO2k7svZoFNKNbRDc2Fm7UpZ7VedMdvqpB8nPzTx5yWJ8vd37J8tzl/OL+X/DKvFdclMw3pUWnsf7AerYe2uq44pdSjaQjf9UkyqvKWb13NcmRybRt0dZ2HK+X0CaB0KBQsvdn246iPNR5lb+IXNHUQZRnS89Pp6K6givi9aPhCn7iR2pUKtuKtlFaUWo7jvJADSp/EfnitE3POiGL8lCV1ZWszFtJUngSMaExtuP4jLSYNGpMDRsObLAdRXmgs875i0gqcAkQKyLjazeHAOXODqY8R9b+LMqqyhjQYYDtKD4lKiSKmNAYsvZn0S+un+04ysOca+Qvdfx5CLjpfN5MRF4Vketrv54hIitERK/m7cGMMaTnpxMTGqNr+FiQFp1GwbECCksLbUdRHuas5W+MyTLGvA38yxgzyxjztjFmjjHmcGPfSEQGAjHGmE9EZDTgb4zpDySKSNL5xVe27Ty8k8LjhfSL7adr9FiQEpWCIKw/sN52FOVhGrrD9y8icrOIjP/+1pg3EZFA4A1gt4j8FBgCzKm9exFwxl5CEblPRNaIyJrCQh3VuKtV+asICQwhJSrFdhSfFBoUSkKbBDYc2KDLPahGaWj5fwZ0xjHt8/2tMcYDm4Dngb7AQ0B+7X1FQPTpTzDGTDPG9DHG9ImMjGzk2ylXOHT8ENuKttG7fW8C/PSUEVtSolI4XH6Y/JL8cz9YqVoN/RtbYox56gLe5xJgmjFmn4i8A1wOBNfeF4qeb+CR0vem4yd+XNr+UttRfFq3iG58uvVTPepHNUpDS3epiLwvIteKyCARGdTI99kOJNZ+3QfoxA9TPWnA7ka+nrLsRNUJ1u1bR0pUCqFBobbj+LTmAc1JapvExsKNjf83ufJZDR35VwLfAZfi+HgZYEkj3mcGMFNEbgYCccz5zxOR9sC1wGWNeC3lBtYfWE9FdQV92/e1HUUBPaJ68N3B7xzDKqUaoKHlvxtH4X9f/I1ijCkBbjx1m4gMAYYDzxtjihv7msqutQVriQ6Jpn1Ye9tRFJAUnkSQfxAVPSpsR1EeojFz7YJjnn400NhpnzMYYw7XHja670JfS7lWQUkBBccK6NWulx7e6SYC/QPpFtENujum5JQ6lwaVf+3x/W8bY14zxowCdHjhwzIKMgjwCyA1OtV2FHWKlKgUaA4Lty+0HUV5gIau7TPolNtYQNeQ9VEV1RWsP7Ce5Mhkmgc0tx1HnSKhdQKUwvsb3rcdRXmAhs75D+WHuf4KHMfpKx+08cBGKqor6NWul+0o6jT+fv6wEea1mkfJiRLCmoXZjqTcWEPn/J8B9gPhwEFgi9MSKbe2tmAtES0i6NCyg+0oqi7rHddW+HjLx7aTKDfX0PKfieMs3IVALPCm0xIpt3Wg9AB5JXm6o9ed5UF8q3id+lHn1NDy72CM+T9jzOfGmMmALt/og9btW3fyIiLKTRm4JeUWFu1YxMHjB22nUW6soeW/V0QeF5ErReQJfliXR/kKP8eJXUnhSYQEhdhOo87ilpRbqKqpYu6mubajKDd21vIXkRYi8ijwAXAUmF/75/3Oj6bcSiIcqzhGWkya7STqHFKjU+ke2Z331r9nO4pyY+ca+c/GcdWubGPM33Asy9CsdrvyJWkQHBBMUrheesHdiQi3pNzC0pyl5Bbn2o6j3NS5yj+m9sSu3QDGmHRjzBRA11j2IcXlxdDVcRKRLt3sGW5OuRmADzZ8YDmJclfnKv+vRORrEfm1iEwQkUdE5BNgjSvCKfcwZ+McCHRcMlB5hs7hnekb21eP+lH1OtdlHP8APIFjzf1LcRzm+YYx5r9dkE25iVnZs6AQXcTNw9yScguZ+zIdq30qdZpzHu1jjFlhjHnSGPOgMeY3xph5rgim3MOOoh0sy1kGWeix/R5mXPI4BOH99Tr6V2fSK2ips5qdPRtBINt2EtVY7cLaMTRhKO9teE+v76vOoOWv6mWM4Z3sd7gy4UrHAb7K49zW4za2F21n9d7VtqMoN6Plr+q1Im8FOw7v4PbU221HUedpTLcxNPNvxjvZ79iOotyMlr+q1+ys2QQHBDO622jbUVRDiWPfzPe31sGtOZF9gqmLpyL+8qP74uLjbKdVFulB26pOJ6pO8OHGD/lZt5/p0sCexMCkxZN+tGnLwS18sPEDbp1zK0ltfzhJb/LQya5Op9yIjvxVnRZsW8Dh8sM65eMFOod3JjggmPUH1tuOotyIlr+q0+zs2USHRDMscZjtKOoC+fv50z2yO5sPbtbr+6qTtPzVGYrKipi/dT639rhVl3PwEqnRqVTVVPHdIT3hSzlo+aszzNk4h8qaSp3y8SIdWnagdfPWrN+vUz/KQctfnWF29mySI5PpGdPTdhTVRESEHlE92Hl4J8cqjtmOo9yAlr/6kR1FO/g291tuT71dl3PwMqnRqRgMGw5ssB1FuQEtf/Uj72S/gyDclnqb7SiqiUW0iKBdaDuy9+taHUrLX53CGMPs7NkMTRhKXEs9AcgbpUanUnCsgMLSQttRlGVa/uqklXkrdTkHL5cSlYIgOvpXWv7qB7OzHcs5jOk2xnYU5SShQaF0Du9M1v4s0F06Pk3LXwFQUV3Bhxs/ZFTXUbqcg5frGdOTkooSSLSdRNnksvIXkWgRyaz9eoaIrBCRia56f3V2n279lKKyIp3y8QEXt72Y4IBguMR2EmWTK0f+U4BgERkN+Btj+gOJIpJ0jucpF5i5bibtQtsx/KLhtqMoJwvwC6BHdA/oCofLDtuOoyxxSfmLyJVAKbAPGALMqb1rEXBFPc+5T0TWiMiawkI9MsGZ8o/ms2DbAu7qeZcu5+Ajekb3hAD0Au8+zOnlLyJBwO+B39ZuCgHya78uAqLrep4xZpoxpo8xpk9kZKSzY/q0t7PepsbUMOGSCbajKBdpF9YO9sGb6960HUVZ4oph3m+BV40xR2rPGD0GBNfeF4rudLYmLj6O/Lx8eBgohs5tO9uOpFxpHayJWcOGAxtIiUqxnUa5mCvKfxhwpYg8BPQE4oFcYCWQBmxxQQZVh/zcfMZ/NJ5Z2bMYffloetzZo97H6oU/vFA2BF4XyIy1M3jpmpdsp1Eu5vRRtzFmkDFmiDFmCLAOxy+A20XkReAm4FNnZ1D1y9yXSfOA5nSN6Go7inK14zCq6yhmZc+ivKrcdhrlYi6dcqn9JXAUx07flcBQY0yxKzOoUzSHTYWb6BHVg0D/QNtplAX3976forIi5m6aazuKcjEr8+3GmMPGmDnGmH023l/VSoNqU02vdr1sJ1GWDE0YSufwzrye8brtKMrFdGerj6oxNdAX4sLiiAmNsR1HWeInftzX6z6W5Sxj44GNtuMoF9Ly91GLdiyCttA3rq/tKMqyO3veSZB/ENMyptmOolxIy99HvbzqZSiB7hHdbUdRlkWGRDK622hmZc+irLLMdhzlIlr+Pmjroa0s3L4QVoO/n7/tOMoN3N/7fo6UH2HOxjnnfrDyClr+PuiV9FcI8g+CDNtJlLsY3HEwXSO68srqVzDG2I6jXEDL38ccPXGUN9e9ybjkcY7VlpTCcYH3h/s+zJq9a1iRt8J2HOUCWv4+ZmbmTI5VHOPhvg/bjqLczPi08bRq1sqxP0h5PS1/H3Ki6gRTvp3CoI6DuDT2UttxlJsJDQrlnl73MHfTXPKO5tmOo5xM1+/1IW+ue5P8knzeGvWW7SjKHYhjuudHWgOPQIfRHeCrH98V2yGWvBz9peAttPx9RGV1Jc8te45+sf24KuEq23GUOzAwafGkMzZ/uPFD9gzdwy8f/+WPlv3Qxf28i077+IjZ2bPZU7yH3w/6/ZmjPaVO0S+2H2VVZaw/sN52FOVEWv4+oKqmimeXPcslMZdwXdJ1tuMoN9exVUdiQmNYkbdCD/v0Ylr+PuDDDR+yvWg7EwdN1FG/OicRYUCHARw8fpDvDn5nO45yEi1/L1deVc7ExRNJjU5lVNdRtuMoD9E9sjvhweEsy12mo38vpeXv5aaumsruI7t54eoX8BP9360axk/8uLzD5ewt2cuuI7tsx1FOoG3gxQpLC3lq6VOMTBrJsMRhtuMoD5MWnUZYUBjLcpbZjqKcQMvfiz35zZOUVpTy5+F/th1FeaAAvwD6x/Vn15FdetKXF9Ly91KbCzfzesbrPNDnAbpFdrMdR3mo3u17ExwQzNI9S21HUU1My98LGWN45LNHCAkKYdLgM0/iUaqhgvyD6B/Xn61FWyHOdhrVlLT8vUhcfBwigl8vP77c+SVH/3mUqNAoRKTOm1IN0S+uHyGBIXCl7SSqKenyDl4kPzefX33+K/62+m9EtYjizufvPGvJ6+n6qiGC/IMYGD+Qzyo/46udX3FVoi4P4g105O9lFm5fSGV1Jdd3uV5H96rJ9G7fG4rhd1//To/79xJa/t6kC2wq3MSgjoOIaBFhO43yIgF+AfANpOenM2/LPNtxVBPQ8vcS+4/th+shOiSaAR0G2I6jvFEWdGnbhd9+9Vsqqyttp1EXSMvfC9SYGu78+E5oBqO7jdaLsivnqIEpV0/hu4PfMTV9qu006gJp+XuBl1e9zGfbP4PPISokynYc5cV+cvFPuC7pOp785kn2HdtnO466AFr+Hi6zIJPffPkbbuhyA6yxnUb5gpdGvER5VTmPf/W47SjqAmj5e7Di8mLGzR1HRIsIZtwww3Yc5SMubnsxj/V/jLfWvcXKvJW246jzpOXvoWpMDXf8+w52HdnFB2M+0KN7lEtNHDSR9mHteWjBQ7rz10O5pPxFpJWILBSRRSLyLxEJEpEZIrJCRCa6IoO3eXbps3y85WOmDJ/CwI4DbcdRPiY0KJSXr3mZtQVreX7587bjqPPgqpH/bcCLxpirgX3AzYC/MaY/kCgiSS7K4RU+3/45v1/8e27tcSuP9HvEdhzlo8Z0H8NNyTcx+T+TWb9fr/fraVxS/saYV40xX9R+Gwn8HJhT+/0i4IrTnyMi94nIGhFZU1hY6IqYHmHX4V3c+tGtpESlMO0n0/QsXmXVK9e+Quvmrbnz4zt1+sfDuHTOX0T6A22AXCC/dnMREH36Y40x04wxfYwxfSIjI12Y0n2VVZYxZs4Yqmuq+WjcR4QEhdiOpHxcZEgkfx/5d9YWrOVPy/9kO45qBJeVv4iEA1OBCcAxILj2rlBX5vBUxhjunnc3mfsyeWf0O3QO72w7klKAY/rn5pSbefKbJ1mes9x2HNVArtrhGwT8A3jcGLMHyOCHqZ40YLcrcniyZ5c9y/sb3ueZK5/hJxf/xHYcpX7ktZGv0al1J8bNHUdhqU7TegJXjbjvBnoBT4jIN4AAt4vIi8BNwKcuyuFRvl+fX7oJT3z9BGTD7wb9TtfnV26nVfNWzL1pLgePH+Tn//o51TXVtiOpc3DJev7GmL8Dfz91m4jMA4YDzxtjil2Rw9Pk5+Zz/yf3MzNzJlEhUdzxX3cQ+HBgvY/X9fmVTT1jejL12qncN/8+nl76NH8Y/AfbkdRZWLuYizHmMD8c8aPqEgIfbPiA5gHNGZc8jkD/+otfKXdwT697WJa7jEnfTCI5Mpkx3cfYjqTqoTta3dSJqhMwDkorS7k55WbCmoXZjqR8nVDvlOP3Nz8/P2bdOAtyYOz7Y3X5Bzeml3F0Q8YYHvz0QYiHUV1G0T6sve1ISoGBSYsnNeihpRWlTPl0Cje8fwMr71lJYptEJ4dTjaUjfzf00sqXeHPdm/ANJEcl246jVKOFBIXAu1BVU8V1717HgdIDtiOp02j5u5kF2xbwP1/8D2O7j4X/2E6j1AU4BB/f/DE5xTkMmzWMQ8cP2U6kTqHl70ay92dz89ybSYtO462fvgV6nWzl4QZ2HMi8W+ax9dBWRrwzgiPlR2xHUrW0/N1E3tE8rnv3Olo2a8m8W+bp0g3KawxLHMY/b/on2fuzufbda/UXgJvQ8ncDR08cZeR7Izl64igLbltAXMs425GUalIjLx7JnBvnkLE3gyFvDdFLQLoBLX/LKqsrGTtnLJsKNzH3prmkRqfajqSUU4zqOor5t85nW9E2rph5BbsO77IdyafpoZ4uFhcfR35u/g8bfgpcAvwbRvxhhKVUSjlB7XkBZ4iDHbftIPHpRHgPKHBsju0QS15Onksj+jItfxfLz80/eaz04t2LWbJnCYM7DmbIS0POeKwu16A82lnOCzhQeoD31r9H6YOljOoyiuSoZP28u5hO+1jybe63LNmzhJ4xPRnccbDtOEq5VFRIFPf2upd2oe2Yu3kui3cvdiz3qFxGy9+CjL0ZfLHzC5Ijk7n+4ut1RU7lk0KCQhifNp6e0T1ZsmcJ/BzdEexCWv6ulgrzt80nKTyJn3X9GX6i/wuU7wrwC+CGLjc4rlERD2mvpbFoxyLbsXyCNo8LTcuYBj+DhNYJ3Nj9Rvz9/G1HUso6EaF3u94wDSJbRDLinRE8MP8Bist1pXdn0vJ3kRe+fYH7598P2+CWlFt0eWalTlcI6fem89hlj/HG2jfo9rdufLT5I4zRU92dQcvfyWpMDRO/nsivv/g1N3a/ET5Ei1+perQIbMELI14g/Z50YkJjGDNnDFfNuorV+attR/M6Wv5OdKziGGPnjOXppU9zzyX38P6Y90GvbqfUOfVu35v0e9OZeu1UNhzYQN/pfbnxHzeSvT/bdjSvoeXvJLuP7GbAzAF8vOVjXhrxEtOun6Zz/Eo1QoBfAL/o+wt2PLKDSYMn8dn2z0h7LY2rZ1/N59s/1+mgC6Tl38SMMczKmkXP13qSU5zDwtsW8uhlj+rhnEqdp7BmYTw55ElyHs3h2aueZcOBDVzz7jX4PeqHDBKk1dmvLiYixMXrelmn0zN8m9D+Y/t54NMH+Pd3/2Zg/EDeGvWWXsFIqYaqbzmI0/kDydDprk7sDt+NXCV0bN2R7hHd6RrRtc5LnurZw2fS8m8CZZVlvLzqZZ5d9izlVeVMGT6FRy97VKd5lGqMRlwmcvLQydzR8w6KyorI2p/FpsJNLNi+gAXbFxDfKp5uEd24uO3FhAeHOzm059LyvwBllWW8u/5dJv9nMnlH8/jJxT/hz8P/TNeIrrajKeUTwoPDGdppKEM7DaWwtJBNhZvYdHATn+/4nM93fE54cDhJ4UlwEZRXldM8oLntyG5Dy/887Dmyh9fWvMYba9/gUNkhLm1/Ke/87B0Gd9I1epSyJTIkksEhgxncaTBFZUVsL9rOtqJtZBRkwO3Q9vm2XJVwFdclXceIi0aQ0CbBdmSrtPwbKKc4h482f8ScjXNYkbcCP/Hjp11+ytIXlrJ69WqGMMR2RKVUrfDgcPrG9qVvbF8qqyt55sFnuOsvd7Fg2wI+2foJAB1bdWRIpyGOfzkkDCW+Vbzl1K6l5V+PgpICVuat5KtdX/Hlzi/ZcmgLAGnRaTw19CluS72NTq07ITdLg+cpQXc8KeVqgf6BsA1eue4VjDFsObSFr3Z+xeLdi5m/dT5vZ70NQGKbRAZ1HETf9n25NPZSUqNTCfIPspzeebT8cczdZ+7LZGXeSia+NpGy8DJoXXtnBbAH2AlshaxDWWSRxUQmWsurlDo/IkLXiK50jejKQ30fosbUsOHABhbvWszi3Yv5dOunvLXuLQCC/IPoGdOT1KhUukd2P3mLaxnnFYdu+1z5Hy47TNb+LDILMlm3fx2ZBZlsPriZqpoqxwPCITkpmdiWscSFxdE+rP1Zj9rRkbxSnstP/EiNTiU1OpX/vuy/McaQU5xDen466fnprClYw8dbPmZ65vSTzwkLCqNbZDc6h3cmsXUiCW0SSGidQGKbROJaxnnMUX5eXf6Hjh/i4uEXUxRYBDE4bm1OeUAJjkvI7QPya2/HYOzisa4Pq5RynoaeQ1DLL9CPmsqaHza0ACIdt5LIEtIj00lvkw6t+PGpstVAMXAEOFp7K/nxn+3btCc/55RLuVri1eX/xc4vKLq6CIC2wW2JCY350S00KPSM5+hIXikv1IhzCMDRAw15fHVNNU+NfYrbZ9zO4fLDHCk74viz/AglFSWUnCjB8ONlKPZW7yX2xVgiWkT8cAuOoG2Ltie/b9O8Da2bt6ZNcBtiQmNo3bx1Y/+Lz8lq+YvIDKA78Kkx5qmmfv3hicNhBjz+5uNeveNGKWWHv58/HKbeM/lrTA2lFaWUVJRw9MRRSk6UsODNBewN2cveFnsd/6L4/hZMnZeyDM0OpeSfJU2e3Vr5i8howN8Y019EZopIkjFmW1O+R9sWbSEXLX6llBV+4kdYszDCmoXRPqw9AAu+WlDnvypqTA3lVeUcrzxOeVU55VXllFWV8dHrHzklm9haGU9EXgY+M8YsEJGbgWBjzJun3H8fcF/tt12ALU6MEwEcdOLrNzVPyqtZnUOzOoe3Ze1ojIms6w6b0z4hOHaxAhQBvU690xgzDZjmiiAissYY08cV79UUPCmvZnUOzeocvpTV5pLOx3DMcgGEWs6ilFI+xWbhZgBX1H6dBuy2F0UppXyLzWmffwNLRaQ9cC1wmcUsLpleakKelFezOodmdQ6fyWpthy+AiLQBhgNLjDH7rAVRSikfY7X8lVJK2aE7WWuJSLiIDBeRCNtZlFKu52sdoOXPyemn+UBfYLGI1HlcrDsQkVYislBEFonIv0TErc9gE5FoEVlqO8e5iMgMEVkhIm6/XKsH/Uw95rPqSR3wvdrPQeb5Pl/L3yEVeMwY8zTwOaedc+BmbgNeNMZcjWNJumss56lX7V+ot3Gc0+G2Tj3bHEgUkSTbmerjKT/TWh7zWcWzOuB7U/jhcPlG0/IHjDH/McasFJFBOH7zr7CdqT7GmFeNMV/UfhsJHLCZ5xyqgXE41jR0Z0OAObVfL+KHQ5Ddkaf8TD3qs+pJHQAgIlcCpTh+qZ4Xr17Vsz4i8jqOJSO+9zXwRxx/qQ4DlTZy1aWurMaY/xOR/kAbY8xKS9HOcJastiI11FnPNncnxpij0LjliW1zx89qXcTxQ3W7Djhd7fTZ74Gf4Thk/rz4ZPkbY+6v566HROSPwA3Ahy6MVK+6sopIODAVGOP6RPU7y8/V3enZ5k7irp/VuhjHoY9u1wF1+C3wqjHmyIUMAvRDDojIb0RkfO23rXFcisEt1f7W/wfwuDFmj+08XkLPNncCT/qselIHAMNw/JL6BugpItPP8fg66XH+nNyJNgdoBmwAHjJu+oMRkQeBZ4Cs2k1/N8a46wgFABH5xhgzxHaO+ohIS2Ap8BW1Z5sbY4rtpjo7d/+Zgmd9Vj2pA051IZ8DLX+l0LPNle/R8ldKKR+kc/5KKeWDtPyVUsoHafkrpZQP0vJXSikfpOWvlFI+6P8B9NQoZJTWNCEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#直方图\n",
+    "np.random.seed(100)\n",
+    "x=np.random.randn(1000)\n",
+    "sns.histplot(x,kde=True,color='g')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "992415d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.JointGrid at 0x212bf7bc7c0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#联合分布图\n",
+    "sns.jointplot(x='total_bill',y='tip',data=tips,color='r')    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d649be56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x212bf7bc310>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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z82ZKbr4FT35+SOfRq/XcOuNWdCpdR1uiJZF5cfMGu8vCKDKUyeYPA0ZJki4F1LIsL5Yk6d+SJGXLsjyuh1iayzbRqDcS38O03pYqPfUuNTdkSqyryOdsVqKSJG6ZruPeNS4un6QjN/KYfZ7C05WtYmZeNYy/gTCehBk0GLVqXL6u3/bDTdqQz+UtKsJfWQmA/Ok6rrGsZNmyO2hOspEanU1uRO7Q7lPmqFY2Zi5YA4mzIX0pRKQN3fWEIRFu0ndrS44wYtaPzPonX2Uljvff79Imu914jhxBn9XzzEJvZkXP4n/n/Y+8xjwMagO5kbk9jlIJ48eQjEhJkrQcaAOqgKXAy+1PfQws6eWY2yRJ2ipJ0tba2tqh6Naw8VbtQbL1/A/npQMWlqY4CTdYUQE1TuV3teolLszS8rtNx30ji8xWAilh2I2XezIt0szPL5yKJIHNqEWtkrjttAyyYsNCPpfK2HXaTnprNQnf+yvnamYxN3ZulxWqg87vhrV/gldvgu1Pwzvfhddvg9aaobvmKDJe7keAqQlhnDUltuOxRiXxy4umEWnpHmANB5VWi8piUf5stYJGCehUutADO0mSyInI4YLMC1iZtlIEURPAoIf/kiTpgJ8BlwBvAmbgaPZpAzCnp+NkWX4CeAJg3rx5A8+CHQVMLZUYM07p1l7SoqHMoeHaKUpSZZwljqKWYmJMMQCsSNXw/c/d7K0LMC2q/Vt9ZKZS3DMYUAp0CsNmvNyTkiRxSY6BC66VkKp3IEdkokoxYxjAt39dRjq2Sy+h+fU3OtrCv349urS0QexxLxoKYMuTXdvKNkHtIWVrkHFuvNyPAFFhBn5z6XS+vjiVJpePjCgzOXHWEeuP1hwk8Wd3ogo2ovWXE9RG4HZY0MeKmmhC34ZiHPXHwD9kWW5qL6/fChjbn7MwzhPcg84G1AEv0ZHdK+J+XGRkdqwHdfvfQKwxjsLmAhbEzQdAq5Y4O13DP3d6eGxF+zd/nRlMkUrCeUzucP0awnji96Hb8k90ax/ubMteBZc83rEXZH+pLRZivv99wlasxFtUiC4zE+OMGagMw/CBEwyAHOyh3Tf01xYGXZRFz5Ls6JHuhkIOYA4rQ1rzu44mXex0JMO0EeyUMFYMRSC1AlguSdJ3gFlAClAKbARmAoeG4JqjRmXxWnw6PWadpUu7LMMXJSaundLS0RZjimZT1SaCchCVpERXy1I0fO8zFzVtQWLM7RFXZHvCuQikhIFoPAJf/blr2+EPofYgpHYfOa1pcbO9pJEDlS1MjrcyJzWcmLDOQEkTFUXY8mXAsiHu+HHC02DSKsj7sLPNlqzsACBMWNUtbrYXN3KwqoWpCTZmp9iJDgsxsJdB2vj3Lk1S9R5wt/RygCB0GvRASpbl04/+WZKkL4ALgbWSJCUA5wCLBvuao0llyVqMBhvHT8LlN2lRSTLx5s6EX51aj1lrprqtmnhLPABmrcSiBA0vHvRy19z2N4PwNCjfLhLOhYHxuXuuReZt7dbU6vHzx48O8cq2so62r81N4sELp2IZoUTgDvowWPU7SJgDB96ClFNh3k1gSxrZfgkjxuH28dv3D/DmzoqOtmsWpHD/+ZMxhZLfFPSDt4dyIAHvIPRSGO+GdJpNluWlsiy3oCScbwSWybLcPJTXHGnO6t34zd23fllfbmBypJfjNxOPNkVT2lrWpW1ZioYXDvgIHl0fHJkFFaHXNBEEQJkeTpzbtc0cDcbu03oFta1dgiiAV7aVUVDbPegaERHpsPReuGU1nPN7iJ0y0j0SRtCR2rYuQRTA/zaXUFgbYo00rQlyzu3apjNPiNw74eQNS76SLMuNsiy/LMty1XBcbyRpG4pQ2buv0thUoSc3ovu3myhDFKUtpV3a0m0qjFqJ9eXtowgRWVC9T1Q4FwbG44Dss5Q9G8PiIGsFLLkbXN2/03j8PeQgnaB9xOjMoBrX6ZZCP3h8Pb8nuv0hvld6WyEiA2ZfD9YESFsCy38G7nH9vV8YJOKdaBD5gj6inI0Y7Bld2pvcKirbNKRau+8nFWWKoqK1HOi6CGdJoppXDrUHXnoLGMOhPrTicIIAKFNfu16EmgNKEOVthc9+BeHdA/60SBOZ0V3z+7JiLKRFimrlwuiTHmXudm9OjgsjLTLEMhzWeGXUv/gryFgGKg189FMlB08Q+iACqUFU0HiEJL8fOazrdgA7avRk2n0dq/WOZdYo/+CbPV2TGhcnavi02I/Td+z03s6h6LYw3lli4GtPKx8OO56Hlkq4+kWlRtlxosMMPH7tHK6cl0SCzcCV85L4xzVzQk/eFYRhEGM18H/Xz+Pyucr9evWCZB69enbo9aj0YXDuw8po1M7/Kl86rnhWTB0L/TLC2aPjS1HlVpIkNUFd129IO6t1pNt6X6IdaYyisq0Sm76zErpdLzEpQs3qYj8XZWmVCucV22HmlUPWf2EcS5gFN7wDrdXK6OYJcj8mxYXxq4un0+L2YTVo0Wp6+AbgdoBaC1oRYAkjKycujN9ePAVPWzN6ix2tZoAfazGTlS8YLZVKYGWNH9yOCuOWGJEaRPXlm3EYu28Ls6tWGZHqTYTBTkVrRbf2hfFq3shrn96LzFJW7gnCQBntEJ3TrwRarUZFpEXfPYhqrYYtT8FTK+CFq6HoKwiOsvwpYWKpOYj24x9jeXYl2tX3Q23ewM+lD4PoSSKIEkIiRqQGkbtmPz5zZJe2epcKh1dFrLn35McIQySHm7pvPzgvTs2z+7w0e2RskZlQvVf50BJJtsJI2fMafHSf8ufag1D0Je6vf0i+dhKxVr2YAhSGV2s1vPx1qGsvT7jpcSjZANe9Dse9F/dHQ5uHiiY3YQYNqaHmWQkTlvhEHkTaphIkS9f8qH11yrSeSurlICDCEE6Ns4bgcVWbTVqJaVFqVhf5lG9KRrtIOBdGTmsNrH+0a1vQT8PhjVz2+Hp++/5e8mrG9h5wwhhTl98ZRB1VuRMajoR8qn0VzXz/5Z18dKCIP68+wGvbynpdFSgIxxKB1CBp9jST4HEiWRO6tO+p1ZHSw2q9Y+nUegxqA03uxm7PzY9T8+6R9mnByGyo2DFofRYEgOL6NrYXN/DZwWo2HKmj1uHp+YUqrRLQH8evNvKXr0fii3qeH224lb9te4r9NSVD3GthrPP4AhyobGFTQT0VTa6BnUTTS1K5WhfSaZpdPkqbK/jaYg0mUyOLJ/uJi2zhcI1jYP0SJhQxtTdI8pvySQ+A77hinPvqdJyV7uzz+HBDOFXOGiKMXYejZ8eq+fceLy0eGatIOBcG2Zd5tWwrbuCjfdUcrFI+NKbEh/HYNXPIOK4MAqZwOPMBeOm6zjZzFMHEqTy45Q5cfuXD8EjTX6h11nG7/g7ibP2fHmlyN1HUUoQkSaRZ07osvhDGF4fLx1NfFfLop4cJyhBt0fOvG+YyMzk8tBNFTYIpF8P+NzvbZl0LUVkhnabF6cTpC/CzV2tp8waANq5eFOTyeSbAHlqfBpHD66CouQh/0E+qNZWIHoroCiNPBFKD5EjTEc73uik3dQZCbr9EWauWJMuJR6QA7Ho7VW2VTImc3KXdpJWYEqnm81I/F0Vlwf63Br3vwsRU3uTkoXf2sSQ7uiOIAthf6eCDvVV8Z1kPH0ZZK5TVfwVf4DFE8al7Ms3++o4g6qj3il7movTLibNl9qsvJS0l3P/V/eyoUUZcF8Uv4sHFD5IUJrZ/GY/2V7bwl08680JrWz088NY+nr1lITajtv8nMlhh1W9g8gVKeZjEOZCyGI7b67Qvzd42/vxhdXsQpXhhYyOnZcfACN2Cla2V/G7L7/is5DMAciNy+cNpfyDdnj4yHRJ6Jab2Bkl5zV40QEDX+Q08v1FLvNmP9viN93pgN4RT1Vbd43Nz4tS8f8QnKpwLg6rO4SXMoO1x+uKr/LqeD9IaIf10OPMBgvNvo1CVDHRPADRoDNBtx8nerS5e3RFEAWys3MjasrX9Pl4YW8p7mMrbVdZMk3MAe9tZE2H65XD2r2DapQNacefxqShtcHdrb2wbuRWpm6s2dwRRAAcbDvJm/pvIsnyCo4SRIAKpQdJaswenMZxjN9M71KAlKazv0SiAcL2dWlcNx1c4B5gbq+arcj9utRlMEVB3Est7BaFddJiehjYvUxO6T6GdPTW2z+ONOg03nZrG1MjJxJu75gZenf0tUm1xvRzZlSzLfFn2Zbf2DRUb+nW8MPYk2o3d2mYm2bCbQsttGizpEeFMjus+ipUeaR2B3iiO/WJx1LqKdd1Gf4WRJwKpwdJYhN9yXH5Uva7fgZRRY0RChcPbfXNYq14izabiq3K/knAu6klNHN62Qa3T5PEHKK5vo7rFTYLdyC8vnkYgILM0J7rjNedMi2N5bt+BFIBJp2FGXAZ/XfoPvjf7x1yaeS2/WvQXzkk7h6h+lkKQJImlyUu7tS9OXNyv44WxZ0q8le+emc2sRAvnTbaRFW3hoYumhjatN4giLHoeunhKR4Cn16h48MIcZqd034B+uMyJndOt7fTE0zFqugehwsg6YY6UJEmf032IRAJkWZaXD1mvxhinz0m4swnCZ3RpP9yg5ZSE7sPFvQk32Klx1hKm674yanaMmg8L/ZwZnQHlW2H2tSfbbWE0ayyC3a/A/jeUnI95N0Ps1JM6ZXF9G39ZncdbuyoIN+l48IIpnDU1jtRIEw1tHm5fmolJpyE9yoxZ37/0SafPSX5TPrWuWpYkzed62xVo1aF/GK5IWcGXZV+ytXorAKcknMLpiaeHfB5hbDDo1NyU3sh3Gp9F13AIx/yr8WlSgRCTzQfR/LRo3rj9FMqbXFiNWtIizahPVLdmiC2IW8DK1JWsLl4NwNTIqVyUdRGSNHJ9Enp2wndLWZaXDVdHxrLilmJyZA3+YxLNmz0qWn0qIo39z2ey6WzUOKvJPG7TY4C5cWoeWu8hmJuNatf/BqXfwijlbYOP7oeD7yiPq/fBoQ/glo8GvImqLxDkiS8LeGOnUkG/vs3LXS/u5NVvLWZeWsSAig+6/C6e2fcM/9j1DwBUkorfLPkN52Wc1+Prq5rd7C5roq7VQ2a0helJNkw65S0o2ZrMX5b9haLmzlV7Vv3ITasIQ8tRuoeIVy4Dj5KfF1a1B0dLBZ6zHkSvG5npPZfXT1G9k/waB1EWPSadmnjbyI3+xJnj+MUpv+DmaTd3rNoLN4xcoCn0TqzaGwRFLUVk+IN4j6mke7hRyY8K5QuNzWCn2lnT43OxZhUWHewKZDC7Lg/8nt5rqAhjW0NRZxB1VEu5svXFAAOpOoeHN3eUd2s/XNPKvLSBLak+0nSkI4gCCMpBfrnxl8yInkFyWNd+1jo8/PCVXaw9Jon995dO58oFKR2PbXobM2NmDqgvwtiiqj3QEUQdFbbzSZrm3Ig+oftm2sPhvT2V3PPK7o7HizIiePSq2cRYR65af5gujGlR00bs+kL/iEBqEBQ1F3GK10XdMSNShxu0JJj7lx91lF1vJ6/xUK/Pz45Rs7pMxWxbkrJdTOLcAfdZGMXUalCpu6/OVA38n6tJryYl0syBypau7br+r6wrqG3lYJUDlSQxJT6Mxh4KyLb52mj2NHcLpA5WtXQJogB+/f4BTs2OIim86ybfwvgnqXu4lzUG5P7fjoOqosnFcxuKueesHNz+ADq1isPVDg5UtYxoICWMDSLZfBCUNOZj8brxGe0dbXmNWhL6mWh+lFVnpcXTQqCX8gZzYtWsLhYJ5+NeeAYs/FbXtsS5yu70A2Qz6vjJOblo1Z1DpNMSrRysbKGque9VQPsqmrnkH+u5/b/b+dbz27jiiQ1YyEKn6joNE2+OJ9bcPVG91d3930KL24/bJzY8noi8UVMJWrsG222n/hidfYBFm1oqoHI3OKoG1h9/gLOnxvGn1Yd47LN8HlmdR5PL10NhD0HoTiSbD4K2uoO4DVZlFKFdfqOWUxNDW6aqllSE6cKod9cTY4rp9nxWuIpaZ5Ay8xSSSjfDgltPuu/CKKTRwanfhaT5ULAG4mdCxlKwdL8nQmE3a7nrzGx8gSBqlYrqZjePryngtEnRxPWRC/Li5hKaXb6Ox1XNHtbntfLY6b/nvo2/ot5dT4olid8u+hnRxuhux2fGWNBrVHj8nYHT8txoEuzi2/5EtKExjMQzniLbsQldYwGOlGX8syCKG9tkzKFmLBz5HN74prKBsTURLn0C0paEdAq1SsW/vyrk2BJNaw/XccPitBA7I0xEItl8EKibSruUPnB4JRxeFZHG0L9t2/TKyr2eAimVJDErRs3n7hyur37jpPosjHKWWJh6ifIzSDQqFY+szuP4en56zYkHpv2BIPsquhftPFTVwndK/spLCYtoMtqJbion4rU74Kb3IaxrDalJsWE8d8sCfv3+AQ5Xt3LBjHi+1b5KUJh4DBoNl71Qh9WYQ7hpOqWbnViNbXzjrBDHgOqPwMvXd+ZbtZQrWxh980uwp5z42GN4/UHqWrsXA3X7RfFjoW/iXewkNXuaifd5CJo6CxIeadKSGGKi+VE2XRi1zhqg56XuM2PUfFRl5XpHNbgawShWcUxU9a0eNhbU89nBGibHW1mWG0Pm8fvjHSM9ysQ3Tk1nU1EDpQ1OGp0+Vk6JITPmxNtpaNQqvjYvke0lXXOiVk2ywgcbiC36ki6TeS0VEBbHoSoHBypbUEkSUxOsLEiP5PmbF9Lq8RNl0aPtI4ATxq8pCVYyo8wcqWujxaVM+967Kjf0VXLNZd2S1nE1Ku0hBFJxNgOnZUWxuaiBSbFhVLe4qW/zkhE1gNWsPj/bi5t4b08lESYdZ02NZUaSPeTzCGPHkARSkiRFAHOBHbIs97LXxPhQ3FJMtqzBd0xAc6RRS7x5YN9kbHo7JY6SXp+fEa3miV1enElTMZVvU/Y+EyacQFDm413FpPryuSOiHIffzkcbE7j4tDkk9FA1GsDgrOLe2M1ILR/SOmUhTckr0MVkYTf2vdx8eW4sdyxz8eTaQtQqibvOzGZRfBACPmRbCmUzbsdnn0Rk025sxnB2lzVxzZObaPUoH5J2k5YXbl3E5HgrYSNUdFEYPZLCTTx143w2FdZT1uhiQXoEs1MG8KXQHAXZZynT4H4PaA1QvAFC3NzXrNfw8zOjMJYdIqLoGVozpuLMOg+jJfSp56/y6/nGM1s7Hj+1rpBXv724xx0EhPFh0AMpSZLCgXeB94BHJElaDvwOmAK8J8vyrwb7miOpxFFCmj+Iz9T5DzevUUtciCv2jrLp7dTW9J5IbtJKZIerWK9dyIrSzSKQmqAqmlws8X1F8hd3d7RlpJ1Nef3voIc6ZHhbYfWDaPa+CoAt7wNsCW/DNS8BPQdeDa4GtlRv4bOSz5gUPonLF67kyvlnIEnKFh+Sz0XrBU/wmWcKJW4DH3xVhU69gG/FGTlQUdMRRAE0OX18sLeSyfGiNpSgSIsykzaAEZ8ubMkQngaf/7qzbcn3IDw1pNPUNLWRkPdfjBv+BIDhyAfIh16g+rLXwZrb7/O0efw89ll+lzaXL8BX+XUhB1KyLLOnbg8fF31Mq6+Vc9PPZWb0TPSi7M2oc8KxdUmSHmn/7+eSJH3W/vO5JEmfneCwGcD3ZVn+NfARsBxQy7K8GMiQJGlkioQMkZKWEuK9bnzH1JAqaNKSYBlYIGXWmvD6PbgDvVdEnxGt5hNXDpRsHNA1hLEvzFNF8qZfdGmzFH1EjCu/5wPqC6E9iOpQsb3XfRuDcpBX8l7hnjX38H7h+/xl+1/45qe3otI2khRuUqor60zsti2lxG3k4Y/z2FfRwo7SJr753LYek8gLa9sG9LsKQq8aCmDzE13bvvor1BeEdBqDsxzjlse6tEkt5Zibei9H0xNZlvH4us9GOD2hfx7srdvLjR/eyDP7n+G1w69xy8e3dFT+F0aXvpLNv9/+334nncuyvAZAkqTTgQVABPBy+9MfA0uAw8cfJ0nSbcBtACkp/Z/bHmlFTYVEuFspaq8h5Q1AVZuGmAFO7UlI2PR26l11JFp6Xgo8K0bNHwrDkVt2IAWDoBK5JkNhNN+TYWqvkgtyHFOwt2Cllx3j5Z4XRJS3lvPknie7tFW0VpDXlEdiWGJHW31bgDV53YvIbituIiPKTEGd0p9Tkg08OMcF+98BW5JSykErVuyFYjTfjwMh+3x4iosJOhxok5LQRndf7dknjwMS50DOueB3g8YA+98GT0vfxx4jGJR7/LcQCIS2YEgGLpiVwIEPOwMwjUoiK7b7tl99+aLsC3xBX5e2/+z9D/Pj5qNTj0z1d6FnQ/IJLCmbAV0JNKLcW0dLKjcAPe6GKsvyE7Isz5NleV70QP5BjZCGpgIkSSKoVaZHilu0xJgCaE/ib9amt1Lr7D21LMEioVKpOKjJ7nVEQTh5o/meVNsSCWYc9/1GrUMfm9Pj6931AQKpZ3Zpk2OmQFTPr5dlmYDc/ctA8LgNlMNNWsw9rLwLN2v5xmnp2IxalmaE8eTknUS9eC68fB08uRR2v9i94KhwQqP5fgxVoK2Nhmefo+iKKym7/TsUX3UVrn37Qz+RPQ1ST4XPfgVfPqz8d9LZynRfKP2xJtM447hyMuZo3BH9n9YDUKskqpvd/OCsScxNDWfppGjuOzcXh9vX98HH8QW6H+ML+pB7+1IkjJh+fdxLigslSfqeJEmXSJJ0wuNkxXeA3cApdCZhWPp7zTGjsUjZGqZ9I8nCJs2A86OOsums7Sv3eia1l0H4THM6lG46qWsJY5Q+DFb9nkDOBSBJBCKzqb3wear06bj9bjZXbebxnY/zat6rFFQdoOSO79PsnI9v7g8g9RR8024jsOoxCOvxew3xlniuyb2mS5tdbyc7vOvMfEa0hVXT4rps7mrQqjh3WjzXLEzlo7tP55/nWDGv/WXXC3zwI2Xp+lEBHwRFcc6Jwn3gAOU0suuP1/PhQ6sovfcqKt94mYCje5mNE/I0w4a/d2376q9KewgirSbqp91C8ZI/EExZQsPMb7N/5XMYYjJDOo9Jp2F5bgz/+CKf3DgLZr2Gv6w+zKzk0BPpl6UsQ3XcR+3Xp34dvVrkSI02/U02/y/QAmwHzgSuBq7o6YWSJN0LVMqy/CxgR0k0XwJsBGYCoU06j2Jtvjai3K0EjqnkfKRJS+wAp/WOsult5Df1kuvSbma0mo/rcvhOyRqYe8NJXU8Ym3a7Y/iz41ZWLfomh5vgf6+2cvvSUnIyCrnny3s6XhdniuP5X9yBpWE/6tbD+NMvxlkhoyptJayHvHQArUrLDVNvIMWawjtH3mFq5FQuzb6UFGvXKaUEu5HluTE8dcM8NhU0YNKpWZoTw/QkJbE2zmaAuka6Fa/ye8DVAG11kP8JbP03hKcrRWaT5g3mX5MwCtVoXdxv+5TCstKOtvuXfJOk5mbUYf2fBvO01KI/fkou4MXTUoc+xEG79PRM8s0xbEq/DINWTXqUGbsp9Cm0U1IMbLtSQrvnTwQsEQRvuBJTXOhTe9OipvHUWU/x3wP/pdXXyjW517AwfmHI5xGGXn8DqQRZlju+nkqS9MUJXvsE8LIkSd8A9gJvAl9KkpQAnAMsGlhXR58yRxk5kqFL6YP8Ji2L4ntPFO8Pm95Onav+hK+ZEqXib84wmop2YT+pqwlj1YFKB2sKWllzTF5trbOB97Y90uV1WZZEImreQ7vvNQBUh97GGpGDf9G/T3j+GFMMV+ZcyWVZl6HpaW+0o6+zGoixGlia00vldXsy6CzKysGjzNFKrtTe15TRKVBGVw+8BbeshrjpJ+ybMLYd0TVT2Fbape1vVS9y+owLiQ/hPF5DFHq9tWtOlCkSjz6SUMdt1CqJnLiTX1WqLVyD9pXrlD8D7Pkv3PwRJMwO7TwqLfPi5jEndg5BOYjmJPbaFIZWX6v2Tm9PGm+WJOl+SZJWSpL0Y6DX5TeyLDfKsrxSluXTZVm+XZblZmApyojUsvbH40Kpo5SMAB2lD2QZipu1xA9wxd5RRo2RoBykzdf7KiedWmJqlJo1LbHKt3phwrEYNESYdazKsZMTqywjjwpT4/B2nR65NHJWRxB1lNRwCM0JSrx5a6px7NuDt6b6hEFUv0RmwVX/BWt70drwNLjyeVDrYe2fur7W54KKnSd3PWHU87aXEpscPokzE07FqrPS5msjEGJyqeRthWX3KVvDANhT4fQfKu0D1OxuxuP3DOxgTyusfbhrm98DBV8OuD8qSSWCqFGur/93jmazbgfUKPlOAJtDuYgsy410rtwbN0odpZzm83QEUvVuFZIkE6Y7+WRAu8FOnasOs7b3OivTY9R80nYaF5VshMnnn/Q1hbHl9FgPHyzYTWzRW7QkTmPn3MvRJqShtV/H47se73id1MvWq5LUc3vD+rXU/+QB/FVVaOLiiPzNQ0ScclqPr23xtpDXkEedq45ESyLZ4dkYND2sxstYCrd+rgT9lhjlp60Oelp9JD40xr1J4ZN4esZ3mXR4DWGVByjLPov8mEk9bnh9IgatCj7/DUy7HMyR0FoDnz6E6YZ3Q+5TuaOct/Lf4p3Cd8i2T+LW6d9gevRARkZ7ev8fWP5fZWsleY15+IN+Mu2ZpNnSBnQeYWj1Vf7gFyd6XpKkN2RZHrzNwMaYEkcJUR4n9e2lDwqbtCRYBmclkk1npc5VR6q198Jyc2LV/GR/JoGir1CLQGpi8XuxbXkU2zZles5auYvTiz4kcPMnZMRfhlFj5OW8l0kwJ5CauhR52uVIx9aRiszCGwiirslDrdUphQ1ValqK8qm9+x6CLcpUib+qitq770Hz8n+xpmV16UKbr40ndz/J0/ue7mj7xSm/4JKsS3oO0sLiuu7BZ46CZT+FN7/V2WYMh8TQpkCEsSetsQnNhz/v2N4lqXIncfNvR8q4EEz9r3yviZ5EMHEeqm3/6WgL5l6AOmpSSP3xBrw8vuufvHXkTUBJ29hctYkXzn2BdHt6/0+kt8CS78Mrx+StqnWQfkZI/QEoai7ijk/voNhRDIBVZ+XJs55kSuSUkM8lDK2T/epnH4xOjFXlzcWEuVupbs+RKmzWEmsanEDKqrdRc4KVewCRRhVRRtieV8T8cwblssJo1VQClbuVD56YXNCaYcczXV/jrEddd4DY3PO4adpNXJp1KXqNXhkhWvEggbhZqPM/hOhciJ2GruAT2PY0BLyw6Nuw8Ns4y4o7gqijgi0tOMuKuwVSBU0FXYIogN9t/h1zY+ee8AtAF7nnwTUvw/63wJYCk89T+ieMa1Lt/m575Gl2PEVgxnVg6nmf0R6ZwlEtvQ8S50LNfoibgSp7JRhCS+4uaynnnYK3u7S1+drYX58XWiAFkHUmXP0SbP0PWKJgzg0h50cBrK9Y3xFEgTL6++LBF3lw8YOoVeqQzycMnZMNpCZ0QQtPYwE+vQW5PYckv1FD7EmWPjjKrrOxr77vuiozY/WsLolkvqdV+TYkjD+NxfDiNVC9V3msUsPX3wGVtnstJlXnt3mboXNLijZDPN62NsKDfjj0oTIytOGYSs5f/RXsqagj5oJGA/5j7mONBpXd3r1bns6CoHq1nvMTTydSY8HtD2GxhcGq1P2ZdHb/jxHGPk0Po05qfejFcZpK4MWrlPIZtmQoXAPbn4FbP+vMyesHj1/CoDaQaI5jZdRsjjir+aRqA8HAAD4i9WGQs0r5OQlHmo50a9tfvx9vwItRFeLmzsKQEskIAxQIBjC0VOG3dK6xLWzWMitmgEmKx7HqbdS761Fi1Z5zWQDmxGv5T/ECflK2BTL7XYBeGEsqtncGUaAET+sfU/YU++K3ne2R2RDb87C/Rq3CI2mUbYWic6BqT/cX7XyesGsuw/GrB6l0G6lRGYkJuog3uFClZFLT4iY6TN8xbZccloxBbSDVksDvE1eRtuUZ1M4GAnIYzLoOrKGsvxImFHsKclgCkqOioym4+G7UhhD33mssAmeD8uea9i+e3jbly0cIgZRNG8O/5v6c5INvYl//DN6IDL45/4c4TKHVkRpMpyaeyst5XVOLL8y8EKNWBFGjzckGUr1/wo9z1c5qstDga8+P8gVPbmuY4+nVejSShhaPA6u+9yW5GXYVjVgo3r+FVBFIjU/O7lvBULIezvqVstXK4U+UACprpVJSoAd6rRpn5jLY9hdoq4Xjq6IDxE5HrbPweeRsfvneQcAFwE/OmUXYjhLWFraSlRDJVQuS8QVkvL5I/rLsUWzV+8h8556OWlHqz36ljIwtuXtwfn9h3FFLXoJnPoTcVIzUWICcsgRJRcjbXQV0VtSS1LVOmUpNUG8NaXArwSwRu/sl1PvfAEDnbCC7eh/umz4NqT+DaU7MHO6eczf/3PVPfEEfl0+6nJWpK0esP0LvTiqQCmUPvvGmzFFGVlCD3xTe/lhDpPHktoY5nt1gp9ZVd8JASiVJzI308fHBem69YPCuLYwiPY0yzb4OItIhKhOmXNSv01jT59B81duoi9ZgiEpFfeAdpJb23ZsMdjyzb+SrIw20eQPMSQlne4kSwP3hozzeO8fJRapX2G++lf9uCvLp/hoanF5ePydIfFN194Kbm59Q+miOOpnfXBivwhJQeZ1QuQ28bUg1e2HSyl4r7femVJ2MedZdRO/4a0db9fwf4VYl0s8sPQD8jaVoDrzZtdHngtpDkBBCztYgshvs3DTtJs5OO5tAMECCJQGtuv+J+MLwEVN7A1TWWkaa34/PqJQ+KGzSnvTWMMdTVu7Vkmnvpfx0u9lJYXywI4FbfW6xEex4FD8brngOPrpPWd4990ZYcBuoVASDMo1OL2a9BoP2xAmoapWELX0OpM9RGpLmQNVeggEfDuskLnmploK6LQBcNieRcLOWTw/U4A/KNHogp+BD5pV/RdXC/9KWGcVsUw1JH31d6cvxLLGgUUoiBuUgBU0FlDhKsOqsZIdnY9Pbuh8jTByuBnj9G8pGwwCFa5C9LUiJczvum/4ob5V5pmYZt69YSEywjmpVNI/uM/Dt3EBIgZTDpyZcbwV31zKHbsnISL6jqiQVSWE9jzILo8cJAylJkj6ne0K5hLKd3vIh69UYUOooZZHXreyzBxQ2a4gZpBV7R1n1Nqr7WLkHMC3WwONyMrWHNxM95fRB7YMwCmj1MOVCSF0MPjeExYNaQ3F9Gx/vrSA6UIPNqCU2JZspif3f06tRl8DnLg3PbSgm0uLi2kUp/N+aAmocHl7bXs4PzprEpweUcycG2rcs8jjIVZfzidNGkqlGqSgtoeSjtLTnu0gqWP4zJekW2FixkTs+u6NjJ/uLsy7mB3N/gN1gH8S/JGEs8dfno9EalVFLox3KtiHtfhEWfgvipvX7PEnhRqZnJPFCtYu95VZmJtmZnWUg0W4KqT+16mg8C39K3JofdbS54+ZRZ86e2EvThX7pq47UhJ2660tJSwkRbgel7cU4jzRpmRblHdRr2PV2jjQV9Pk6nVpipqWJ1VtruUYEUuOXuXNhg9vnZ8+hfK4Ly8PYfBi8Wpryt1CrupDo+GRaXD6qmt1YDBqiwtTkNeZR0FyAXW9ncsRkok3RvLenkvvf7Exi/zKvlrtXZPOHjw61XyNIUrieh5eoSF73547XhVmsfLSvikuWt48qffUonHKnsprQ74WsFZCyGIB6Vz0PbXioI4gCeDP/Tc7LOI9F8eNmtyghRF5TOJol34cNf4PWGuT0ZUjLH8Ct0YY0AmTWa3h/bxUHKpWSHfsqWpibaufri0MZj1Lu9UcKJ/PLr72N1VWKzxDB6tpwfA06skYu31wYI8TU3gDVNxagCQYJ6JSSA8XNWlakOgf1Gja9lWZPIwE5gFo68bTN3DgN7xZ6uOaErxLGi8omNyvDitC/9wNlOkJSYZ//DZx1OzmEjR+/vocdpU2kRZr43sUefrb+R8jtg8uL4xdz75zf8PgXXZdXewNBWtw+9BoVHn+QmUk2bkyuIvrNq5WVUEAwbiaHSMblK+NfB41MnX0HUTseU7Z6UWngon9A2hJoX9nX6mulvK28W//r+9hLUhjndGHwyQMduXVS4ecEdEZap18SUiBVUNPSEUQdta24icJaB7NTI/t9Ho1G4vvT3ES/cyu4m9BLEqfM/QGliV8PoTfCRNXvQEqSpGjg6LrLRFmWNwxNl8YGdXMJHnMUSBKtXolWn0S4YWDbAPR6DUmDSWOm0d1IlPHESbuz06J46rCZxuYWwm0nv/GmMLpFqVrQffZAZ06HHITNT2CMzuXfXxWyo7QJgNNy9fx1xy87giiADZUbyKsvQ6fpvjJC1R4A3XpaOgvSI7CrZsEVz0LpZghPR5W6mBn6BF66LYkWtx9f5DSYfQE4qiE8FaIndwRRAD63nbunPEazv5R3S/9DrUuZqk4JSxmavxhhbGis6LZAQZ33AWFnPgQhvH1pAq6OP6skCLafUn1Me39E0EjMxvvA3aQ0yDKxWx9Gm3kahJRtJUxE/QqkJEl6CkgHwgEnSt7UkiHs16jW5msjyt1KwKzsdl/UoiXeHEA1BMUg7AY7Nc7aPgMpg8HITEMBH6zbzDXnrRj8jgijiiXQjNRY1K1dDvj58nDnZsThFqhr6L45cVFLAV+bN4M/fHioo81q0HB6Apx7eTi1+nDe3V1JcriRGUmnE57VeU/ZgYUZx37bj+neD1nmkwM13P3iDtq8AUy6KL579q95v+Z3fGPGLeRE5ADQ7PSxvqCO17aVkRJh4pI5iUxPtIf61yGMMV6tnW5ZTNYkHEEj/U81h0x9C3eenozOYMTlDWDSaQh4nWTomoH+J2mHyQ2oGvK7tetdFT28WhC66u+IVBawEvgvcDXw2ZD1aAwoc5SRg75js+Ki5sGraH48q85GrbMaIif3+dpFEW5e313HNecNSVeEUUQyRyPHz0JKPQV0JiXBe//beCLTuOZUE3/+wE1Qhs2HAyxOXcr6qs87jlVLauaaI9FXFPDvKyazOq+aOJPMSlsF6Tsf4OGwH/HUpu0dr79lSTr3nDUJo67/mQDF9c6OIArA6Q3w5w8beeXbTzA9oTPwen9vJfe93lkc9OWtZbz27cXkxIlR1fGs0jQJ3aybMdmildFUj4PSqNPwqm2EUjBDYw6nwV3Df7/M62i77dRkNJb+T+sBaMISCEZPRlV7oEu7bBUjp0Lf+lv1yAmcCaiBr6GMTE1Y5a3lZARkfO01pAqbtIO+Yu8oez9X7gHMSraR59BS3hTasLYwBpmjkM58AHa/BF8+DGsfITj1Eh6r/JJPG/7A9UuUe3NjQQvnJt7M2amrCNOFkWRJ4uHJ92G/+9eY/vwb5ue9xy+zy7g9uohss4Ej0+/hqU3VXS71768KKaht67UrHl+AWocHX6Bzarva4e4Ioo5y+4I4XJ1vOfWtHv76yeEur2n1+NlT1nUJujD+1LlUBIyR8OUflZ99b9Akm5FDzI441Kbnv5u7jho9ub6Uw226kM5T5Q1j95xfKWU7ANRaKhc/wAFZTOsJfevvV8zLgXjge8AtwO1D1qMxoMxRxqleL16T8t2poFnDksQQ9hcLgd0QzvbaHf16rTYylYXSJl7fnMOdZ/V/CbEwBrXVwQc/Amd70nbQj+qL37LkvF/zbPPb3HG6n4Upc4gw68g1VLKgJYkH485Dip5Bw59eoa2ikvQ/3oVh631wUFlRJ4dn03bmK0Btl0vJMrR6ex5x3VvezKOfHGZHaRMrp8Rwy2kZZEZbiLboMWrVuHydwZReoyImrHPi5vganh3tA/9bEcaIGeoiwjb8sbOhtZrJO35JW+rTQP83HK53tnZrk+Wj7REh9emmj4N8c85T5BoacUgW/r4bvp8kCmAKfevXiJQsy22yLOfLslwsy/IDTPD3uhJHCdHuVnzmKGQZilsGvxjnURatGX/Ah8vXjxWBai2n22p5aXMxcm+fUsL40FYL9d1zOiLcysiRTgPnTo9nUUQb1je+QfzHDxC25g9YXr2OuMtnYLtwFfri/ymbvUZmQnQOUuNhUgLlJIV33csryW4kTaqB0k1dChaWNji54d+b+fhANbWtHv63uZR7X91Ns8tHepSZh782A317QrtOreL3l80gPapzY+2oMD13nZnV5VpmnZoZSaJY53gX5izp1qap3IbZE9pqzigrxFq7ZlWlRBiIDAvt/S/RbuSqhcn87qsWbvxUzZ2fuGhwB8mN639QJ0xc/U02Xy3L8rGb/PwWOG1oujT6VTYXYfa6qDDaqXOp0EgyFt3QBS52Qzg1rlpStX0PM2fG2tEUuthwpJ5TssT2HOOWKQrC06GxsEtzo8FMjDGGTHt78ZvKPd3yPnT7H8d62q8JFm9hx4qXebvMjDcAF890M6PpAA+efwH/Xl/EzpImFqWH88PJjcQ+fwb4PTDzGlj5C7DEUFDbSn1b19ppW4sbKW1wMi3RxjnT4smJs1Ld4iYmTE9GtAX1cSsyzpuegN2k45WtZaRFmbh0dqLIj5oAJGMPwXLcdKQQt0BJDbfyg/NNvL5Rx65SJ3PSzFyyQEOyPbRgXKNWceW8FGLDDKzeX01OXBjnz4gnJTLETZSFCamvyuYzgNlAoiRJRwtqmIGhmccaI3z1R/AY7aBSU9isJd4yNPlRR9n1dqqdNaRa+w6kpKhMlpXu4un1aSKQGs8s0XDxP+DFa8DVCJKKhiV3Uagz8vjKx0kMS1Re19NIprsZGYkd8x/hlndqOW2SCbVa4puftvH3S8/k9he2sygtgscuz2GhvBPL1r8rQRTArv/BtEsg+yyMuu61zTQqCX37hpMqlURWjIWsGEu31x1lM2k5d3o8506PP+m/EmHskCUN0tybYPvTylycOQpmX4dPpeHEFfO6ijBGMD8lnjBTCUF/FGpNK5OjU0Kumh8Iyry5o5wn1haQG2flo33VfLSvipe+uZik8NCqpAsTT18jUlIP/60HrhiyHo1yQTmIsaUSv0WpMl3UrCV2iBLNj7LrbVS1VvbvxdYkTgv+j7uPnEp5k4tEu7HvY4SxKfUUuO0LaCrBrbWT745C1xzA2aDG27IJnSEMoieBWgeBzpEjf9aluA3hbGuxcOvpVt7YUU4gGOSGU9JYX+ZlcXoE9830kF7zDIbybWCKhDMfgM9+qXzotSj3YnZMGNctTCbSYiAgy8gyhOnVpIlv8UIfvOYYDNX74Ix7IRgAbxty3RHkzNCWHAeCMuW1Ft7ZZGZLcROnZIRjnW8mxSojSf2vR1PW6OTxNUfQa1XYTBItLon8WhcHKh0DCqRqWtwU1LWh06jIirZgNYpcq/Gsry1idgG7JEnKkWX52WHq06hW66wlIyjhb9/V/kiThhjT0ORHHRVuiOBwU/d8mB6pVBgjkzld8vDU2gIeuGBkdi4Xhkl4GtUBK997I4/1R5RFCZIE/1xl4+yN58JZv8F35Yto1v4BqakIefIlELMI1fptWBfmcN8bnVvEPPppPj85N5c7lxv4ovYrHvIe4vTcUzk3ciape9+BnHPh4HsQngaASadmUpyVX7yzn0BQxqBV8djVs9Gq+7sYWJioGoN23LN+SOKOR9C1FFKX9TVac67AFgwSyle//BoHP359N2WNyiTJW7uq2FfZwj+vnUdWbGj5TT9aGcGqiHL09dvwT42myjyN+lCXEQJ5VQ5ue24rRfXKaPA50+N48PypxNnEhvLjVX9X7T0oSdJtwGRgH/C0LMtDGz2MUmWtZeQEJXymo5sVa5k+yHvsHc+ms+LwNOMN+ND1J4cgIoNVrTv4yTYDdy7PJtwc2lJgYYxoKYddL7M/OIv1Rzqn8GQZHvjKzexpN+JqK+cNTzk3Tb8UW+UepMMfoNn8OPb0s5Bd53Y7pdPfwl/2/ZHd9Uptp711e1kXsZ2/51yLvfYQJM6D+NkA5Ne08vO393VUk3b7gtzz6m7evdMqpkOEE9rfouW21/2cn/szJuWoeGm/C+++Bt64IbR0hOr6po4g6qj8Gic1jU0hBVIxBi+XaTdjf+3+jjZ7whyaz/sryoL1/vEHgjy9vqgjiAL4YE8V502L5/yZCf0+jzC29Per479R7qYPgUTgP729UJIkmyRJH0iS9LEkSW9IkqSTJOkpSZI2SJJ0f2/HjRVljjJSfX685kj8QahsHbpinEepJBU2vZ1aV3XfLwaIyCaydjML0iJ4Ym3fmx4LY5As497/MVuDWZSpu1dwrnF4aI2ewT9cRWRJamwf/RR2PAft1dDVhR9zirW223ExkY6OIOqo3Q0H2KJTUZm7Ck65C4xKMnh1i7sjiDqqyemjvnVov1gIY1+9N8C3VlhJyT5CtXUd1y71kB6jxhVShhRYNN3TKiQJLOrQ0i18zUXYv/xjlzZDxXYMDYd6OaJnrR4/6/K77ySwr0LURhvP+htIJcuy/AtZlj+SZfkXwInKvV4LPCLL8llAFXAVoJZleTGQIUlS9sl1eWSVOkqJ8zjxmaMoc2gINwToIed20IUbwqlq62cgZVZGyy7MhP9uLKbW4RnCngkjwlHNu205XP6Bimav1G013LmTrOh0rXxYuU7ZiiPg63aKaKkJ8zE3r06tIsnec37T4dZSfr7rMZp8nXV7EuzGbteNsui61IoShJ6kxnv5tOE3PJ33R94ofJbHDvyIM+aUYdCHVkw429jGZVO6jjxdP9NKlqn3ArI9UQN4Wrq1q/yhrasKM2hZntt9y6TpSfaQziOMLf0NpCokSbpPkqTlkiT9BOi+nXs7WZb/Icvy6vaH0cB1wMvtjz9mjO/RV9ZUgNXjxGuKoKhZQ7x5aBPNjwrXR1De2s99nyQJorKJbtrDadnRPLI6tG9VwuhT66xlXfk6Pi76mMONhylz63honQOAl7eW8uNzckmOMKKS4KIpVu5JzcdYu5doYzTrneX44mcqJzqagGuKQBts5dUVbfzi3Ay+eXoGd6/Ixkgspyd0/Sc6P24+hxoOsb5yA0eaj3S0Z8ZYePjyzlpRNqOWv141m3ixwEHoQ42ngPK2UgCk9rVMzx16HC+hjWaG+eu41/Q2T5xl4IenWPnX2Qbukl7B5A9tBMhjzsCTc1yiu8aALzw3pPOoVRLXLUplemJnCY+r5iczL21CbwYy7vU3R+pG4FbgMpQcqcf7OkCSpMUoW8kU0Rl4NQBzenn9bcBtACkpo3d/I2/9YdxGK6g0FDRpiRniab2jIo0RHG7K6/uFHQdkQvlWLj7tPH746i5uOCWNXFGfJySj5Z4sc5SxpmwNZY4yPij8AIfXwT/OeJ0Wl3LvlTQ4+fPqPM6bHs8lF8cyV1+OXj0D5Kn8tHEq39v5Z5af/lOkgJcyZw0xBjt2YwxSYxHTq9dQFJ/C7zbU4/IFmBIfxt+//mNOrVzH1uptTIuahsvv4p+7/wmAJ9A5uqlVq7hoViIzk+3Ut3mItxlFbtQQGi3342Dwy36uzLmSKGMU3oAXk9bEx0Uf45FD3Pk9PJ3wtjzipy5Gwk+8rMZWUgD20LZ2afKqOZj5HeYYIzEcfBt/RBb1p9zLHmcKoW4BnxVj4ZmbF1JU34ZeoyI9yowphH0qhbGnv//vvndsQU5JktZygoKckiRFAH9DCby+Dx0LMSz0Mgomy/ITwBMA8+bNG7VlubWNJXjNSumDgmYtkyOHJx/EprPR6m3FHXBjUPdj9UdEJux7HYs2yKVzE/npG3t59VuLQ1oSPNGNhnvyUMMhHlj/APvr92PT27hhyg28U/AOn5a/yunZS/jysJKP4fQGeHtXBd+I3of+i29D4lw4+zcsiV/MU5G5rK/ZzBO7n+g475kpZ3JT9pUcsS7mvpfrMGhVzE8J46dLwthVu5XHdv6dOHMcGys3ct2U60gKS8If8JNhzejSP5VKIiPaQkZ077WihMExGu7HwZJoSeKFgy9yoKGzWOxPFvwEbX/e247hC4vj1QVX8vstvyQgB9BIGu5f9FMuCYvr93QLQJzNwK/3a/ld86VcNfda9tQFeOPFZt68fWCjqxFmHRFikc+EMegFOSVJ0gGvAPfJslwsSdI2lOm8jcBMYMzOMzl9TuLcrQStkwGlhtTylH5s3TIIJEkiwhhJZWsV6ba0vg/QmZQNOGv2c2bOTNbm1fHy1lKunD+2v8lOJA6vg19u/CX76/cD0Oxp5m87/sYds+/grcNv8afzbiRlo4mGNi8qZK5Na2XShruVg8u3we6X0DYWYznrQf6zt+v6kE9LPmVm1EyszOC6hcmo5CBLEwKonVt4aP/f8QV9OJqUqcN/7fkXP1v0M6ZETiHOEjecfwXCOFXtrO4SRAE8secJ5sXOD+k8B+r28octDxOQlRQLv+znt1t+R649i6mxs/p9HpNOw31nRPPYWh8Pfl5PRqSBp67IJnGIawQK40NfQftACnLegjJ991NJkr5oP+Z6SZIeaT/uvQH3doSVOkrJDarxmaNo9Uo4vBLhhtDrjAxUhCGCyv7mSQFEZkP5dlQqiZtOTed3HxwUiedjSK2zll21u7q0pVgSOV8by4vW2WTlPcdt2a1UNbvRSDJhOJG8x2zi6nNCxjLc7mZ8we7J5lZtND99uYbHPi/g0S+KuOLFMorNSd1e6wv6iNZHkBORMyS/pzDxeAPdR/Ib3A2EOl5e42rAf1wlHk/AQ627MaTztNWVkfnZt/m9+xd8cUY+LyW/yalvn4ahdneIPRImokEvyCnL8uMcl0MlSdLbwErgD7Isj9l1oKWOUjL9fryWaIqatSRYAqiGcaYsyhBJaWtZ/w+IzIJD78G8m0mPMnNadjQPvr2Xf1w7d+g6KQyaMF0YMaYYapw1gJKU+8e0S0l45SalGjSQojXxvdOe4/r3m/j8sJY3T32Q8Kqv8Mz5BvbDr+P0OzFKNiZHTO4yAmDVWcEXh9vXuRLUH5Spa4vApDHh9HeOtJo0JkwBH7gdYBCbuAonL8OUhEbSdAmCzks5C2NIE3Jg0yRg1Bhx+TtX+4VpwwjThLblkM5ZiVSyHj2QWL6xo13deAQ4I6RzCRNPv+5aWZZ/cjIXkWW5UZbll2VZrjqZ84y0EkcJ8W4nXksMBc1DXz/qeFHGKCpbK5DpZ3qELRmcDeBU8mgunZPIjpImPjvYzzIKwohodfs4UNlCm9PIA4seRCMp33fmRM8k68CHHUEUAD4nuU1riQnT0+zykaedzJepd2Ld8zR7J1/H7+qX8ptPXVyTcS+nxC3DoDYwPXI2P5r1R7bmd/8e9fl+iYdn3oVNr2z6atPbuH3W7Vjr8qC5ZFh+f2H8S2+s5x/T72CSLQOjxsiVKWfxbU0sRneI76neCL6Z+6DyxQBlX9Jbcx9E7QttlZxKZwRL97IFkikitP4IE5JYShCCqro8DAEffoOVI41Dv8fe8QwaA3q1gTpXHdHG6L4PUKnap/e2QfbZ6DVqbjwljfvf2MsnP4gUK0lGocK6Nn7+9j7W5NWiVUvcu2oSz5/zApXOMlJMcWje/kG3YzTe5o4NhIPWZKgvpDZzCc/t9PLm9ga+e2Y233v+MPPTLufitKsoqA7y483N/PFrybywqWslk1NytExqCvDIvEdpwIVOZcLtrCF5zyOQcfaw/B0I45/kbmHxR/fxnykX4IyZTuShL9DWHqI+7YKQzhOph3c32Tkv6w9YzV6aHDre3yyx4oLQUi7UWgss/xm8810lt9TdBOlnIJuiQp5uFCYe8UkaAl/dftrMkSCpKGjWcuYwJZofK9oYRVlLWf8CKYCobCjdDNnKh+CMJDvp0Rb+9lk+964KrUaKMLSCQZnnNxazJk+pOO4LyPzqvUP8J3o+K3Lb/79a9C0o3djluJLYMyne6mRSrBmztRF9lA6nZzpXZXvIjY/F55WQZdhc6GBvuZpvrbAwbVIdNfLn/N+tmbz4pURVi4/z5uhAX8BG8xQ+LXqJvQ1bmBoxjxVxX8M595dIrRq0EaFtBisIPQlE5YKkxrr7VY4WZfEt+BYt+nAiQzhPTXMbNyyJpJUCKtqKyUhMJzcujaoWF8mJIZzIFk9zwMj2izayqaSN9AgDC8PqSLP0831WmNBEIBUCXWMJPksMARmKWzTEWYZ/RUe0KZoiRzGzY2f374CobCVPKugHlfJ/9zULUrjv9d1cNT+Z1MieK1kLw6/R6eWDPZXd2neVNbHsaLXkjOVw2VPIX/0VWWOiZf6dfFGVyg9WGsiNs4D/AOHbi5Eef57w1laWXXUBrpXnsCMnmiSNj69lSmz0rOPfBW/Q6mtFo9Lw4JIH+bx4Pc+WfMFfljzLr7beQ2lrMQBflH/Akeb9pCT+GONNlxJzzz3YLr4ItUnUixIGrsxgxHjR0+i2PYnaUYQv40JaJq0EuR97iR4jzCTzXtnLRLgDTJIjyW9dj8e8i2n2y0I6j6zW8aprFr98rbNWX06smf/clILYIU/oiwik+sntdxPb1ogcm01FqxqrTsaoGf5SLjHmGHbX7gZk6M+gsz4MTFFQvRfiZwFKjZNzp8fzq/cO8OTX5w1ld4UQWAwaZibbqWjumkqYEWXG7QtQWNeG0xsgJe18pLjTeXtvHb9/pYjUyAZqHR6MWjUvLvTS9rM/dB78xH8JM1t5aP4ZWA7vRtdiILUtklNjvscd9Y9T667j/cL3seqt3DDpbtoCDR1B1FGlrcU0GNpIcDqpfugh9NlZmOeHtkxdEI5lK6+i6MZ7CFu+FH3GApoe/ghpTj3qH92FUse5f3x6B1/zTSa1uhq15GKZHEtBcgw+fWhbxJQ1uvjTJ0e6tB2qbuNgZQsJolK/0IfQlkhMYGWOMqYEwGeJpbBJS7xleBPNjzJrzGhUampd3TfG7FXUJCjd0qXpnGnx7CxtYntJaMuEhaGj16i5fVkW4abOb+WnZEYyPdHGnz/J49xH13LZ4xv42j83UOBQ8fy2Sn5wgZHlCw9y3cpK7jw7gsC2nd3Oq/pkLfH6GuyRZZhqXsFqPEROtJ6fJN4EKMvOL0y9kcywKehUPRdEDHN1ls3wFhYO7i8uTDjqsiqS7v8mcUtURNo3kPLdszGrZUzNoQVAES2Q463GVvIbLId/ha30t0zx1GNr7fvYY/mDMh5/97wqb2D4ytsIY5cYkeqnYkcx07w+2sJiyS/UEjfMK/aOFWOOo7i5uP95UtG5sPdVWHBrR5NOo+LiWYn8/oODvPTNxUPUUyFU0xNtvPWdUzlS24ZBq2JSbBh7y5v5vzUFHa8pqnfyr3WFfGuVit9s/15HMcJESyKnx17RrVpu9DevRb3naTj4rtJQ8AXqqM9ZfNbDcAQuTD+PJ/b9nqvSf8gzX7ZyftrFvFv0ZsfxF6VegP0/b3J0IlsTLfJGhJMTkRmL6YP7wFkPgL7oM6IX30GbMbTyGtGBNvRbfwlHNxf2ONBv+xVRSS+FdJ4Eu4HL5yby0pbO8jJheg3ZMaLch9A3EUj1U2njEc7wumg0R5HfpGVa1PBsDdOTOFMsR5qPMC+un9Ny1gTljaa5DGxJHc2nT4ri3d0VbCqoZ2FGKCmewlBKiTSTckzuWlF990UNMn4+KHuuI4gCKG8txz0zC3VsDIHq9tpTWi3GFCuse7frCery0LtquC/3ek6NmE6gbQ53PleKLMNFxpX8evESyp1FZJmSSXh5HYEvNwBgWbECw9RpQ/BbCxOJ2l/ZEUQdpdn/H4ILrg7pPNpAc2cQdZTHgU4ObUhKr1Fz5/JskuwmXt9RzuT4ML55eiaZMWLrI6FvIpDqp9bKnbQaOjcrPitt+FfsHRVrjmNz1WZ8AR9adT+SMyVJGZUq3dQlkNKoVFwwM4G/fnqY/4lAatRKjuieozEtQcdnrfXd2j8I7uL6R3+Ft7gO2ePBNHkqqHuugatSq7motJjK5DQumBpGRngCDW0eMmIsTLH50HoywBiO59opeE9ZgcpsRj9pEppwsZO9cHLcsh/98Y2yjERoU2mqmAxQ6+DYSulaE+qYjN4P6kVSuIk7z8zm66ekYtSq0WnUIZ9DmJhEjlQ/SbUHcIXF0OhW4Q1K2PUjN3euU2mJMERQ4ijt/0HRuVD8Vbfm07KiOFztYE/ZmC04P+7NTLJz5bzOADjWquesaB8XZVzZ7bWzw6dxUB/g7bhIXk2MY5PRT4sxCnnyRV1eJ0cp2728ZbmCRz4vxmbUckZONJfMSWKmfAjtM+fBY/PgP+ei11QTtnw55oULRRAlDAp/9GQ4rtilY+E3MepDK4CpislBvuBvSjAFoNEjX/QPpKisAffNZtSJIEoIiRiR6idrYxnBiCkcadKSZPEz0qV04s3x5DUeItPez29eEZmw52VwNYCx881Ko1Zx9rQ4Hv8in39cJ7aOGY0iLXruXJ7NpVPCCFbvJ9W5lRb9cvbsSeamnO+zp+lLfIEAy+KuxOPN5OHPjrCjRNlwONri5/dXprB0/rcgfiZSwReQNA85cwVl6hQe+HIPWTES3kAQrUYFTaXwwtWd0y51h5THt60Be/LI/SUI48o2RyzTL3kGy6E3MDUcoSnnPHbr5pDsUxFSCKRSI02/HBJmQWsVhCUgRWYx4m/QwoQiAql+cHgdpLvbkG0pHG7QEj+CieZHJYUl8WnxpwTTgqikfgwsqjUQlQMlGyHn3C5PLcuJ4e6XdlLe5CJRLPUdlZIiTER5TDir9HjUuYTJYfgCHsyaSBLMaSQY02hqtlMvw6EqD/ctjWN6gokmv574oAuXpxbzhkeVe2DX/1BteAzHRR/w6JIIJvsaYfsW/JMmoXEUd8tdwVkPTcUikBIGTVNbkFqNDWvqtfgzJdR+NV8U+PhazAA+ktQaiMlVfgRhBIhAqh+KmouY5PPTao0jr1BLqnXkAymL1oJBa6TMUUaKNaV/B8VMgcK13QIpk07D6dnRPLO+iJ+cO3kIeisMiM+tFFFVa/AWF1N+55148w4DoI6I4I4//5xnXBtIsCTg9New2/0Zq7J/xifnthK3/SFUETcRPPQBqpL1BJIW4rr4cYxv3aEERpKK2KYWND/7Ee62NkoA06KFJNz/fbSSCuRjpq5VajCKKT1h8CyL8GKr2It219+QWkpRp53DT6bfQLlGfCQJY4/IkeqH0rr92AJ+vOYojjRpSQwb+UAKINmSzP76/f0/IHoS1B8Gd/d8qBWTY3l5Sylu3/BXaxeO42yE/E9g9YPw6s1QspnWr9Z3BFEAgYYGjO98jsvr5IndT2DxOvlT8rnktOwl4cNbUCUvgA1/R3XkU/C5UBd+gfHd79F4/iPK8SnLaP33swTbOuv2ODduwpVfAWf+vGt/lj+g7NkoCIPE3lqM7ovvIYUnw7RL0dRtQ7P7X0R7mka6a4IQMhFI9UNr6UYajXYcPjUtHhVRxtERbKTZUjnUeIhAsJ/9UeuUqZ0eks7jbAYyoy28s6tikHsphKSpBNY+DG/eDuWbIXURfPYQ7v37ur00sPcAKp+f72Zezk371xDz+e/QFLX/f2uOgsbjCmc6qvD62kBrxDHpWryH8rqd01dRAfNvgZs/hsv+rfx3/jdAoxuK31aYoCR3Naz8hZKzmfcRpC1BHZWIwScKBAtjjwik+qN6L66wGA43KqNRqlGSx2jSmAjXh3O46XDfLz4qbjrkf9bjU8tzY3h6fdHgdE4IXcAPG/4OGx6D1moo3w6rH4DMZZhnd59ydS2fx86WA6zUJxKMvoy6wGU01mXhnv1TUGuhh9w5j85E5aVv0Ji+hLBVq7o9b8jNBb0FUhbC9MuU/+pFLR1hcKnt0cq9XbZVude3PwuOaiST2PtTGHtEINUPtoZiArYkDjfoSByBjYpPJMOezvbqHf0/IGoSNJcqK1yOMyvZTq3Dw95yUQphRDgqYdt/urYFfOB3ow+XCb/lZiStFiQJy0UXsHO6GU/Ag60+guL7/k7t//2Xqt/+hZI/vI0nGA2zr+tyqqYZV+CLzCR+8mLSE2KJuOVmEh78AfaLVqEKDyf2vvswTJ8+jL+wMFHJrdVdaz8B7H8T5OPr8gvC6Ccy+/rgDXhJb2tEFZXNwQNa0my+ke5SF0mWZHZU76TOVU+UsR9FNdWa9lGpz2FW1yrCKpXE0pxonttYzO8vmzFEPRZ6pdErSd2O44JcrRltWBiRmWsJ//03cNmSOWCX0Gq8nOadT8O/XgC5cwPtQH09zjIZ95SlqFLOxN9SjctqxhmdTWpU+8hWSyX6ghfRl/8f1pQIYp+8HynnLCRttzKJgjDoZLmHqWKDDV9AJz6UhDFHjEj1obghj1SfD581icONOpL6uVlxoK0N1569tK5dS+u6dbgP5SF7PH0fGCKVpCLTnsmWqs39PyhhNuSv7royq93SnBje311Jq2d0JNRPKJYYOPu3XdsiswkmzkP91s1od7+IfvPPsK++kcyyTYTpwrg45TyCjU3dTtXq8LLsPStL3jKxJfYMTLnnk5W4EK2qvRL+7pfgs19Caw1S7UFUr12PVLl96H9HQQBl26ronC5N8qnfw9tTgCUIo5wI/vtQU/wlVr2Zer8Bp18i0thHRXNZxrV7N+7DeWhj41BbrciyjL+iHNeeXRinTlPyUAaxYFx2eDbvFbzHksQlhOn6scmmLUlZ0l61G+JndXkq3KRjSoKVt3dWcM3CfpZVEAZPzjlw0/tKfpQlDpLmEyj4AtWSu5WcJ0kNrTXE7HqZEosRW8oyTFdfTvMf/9J5DknCP3UKN6slkmyQ1lyKTTep8/m2OtjyZNfryjKUboEUsYG1MAyaCmD+bSAHwFkHEVlI+9/EmLZkpHsmCCETgVQfPCUbaDJHcqheR6r1xBXN5UCAtrVfEvR4MC9YhKQ/ZpokPgGd04nrwH781VWYl5yGNEg1U/RqPRn2TL6qWM+qtLP7PkCSIGk+HHi3WyAFsDQnmuc3FotAaiRojZB6qvLTXAElG5DCYuGTBzrLVsRMRl5wG1cnLSFQtgPX9Bgif3AnLS++jhQRjuPr5/Gq6jNm2qcx468f4y8qJvDqAjQ2m3K8Rq8Eac1lXa9tCm17DkEYKMkWC5ufgMIvQGMAvwfO+jWB/uwd2oOiulbqWr3EhOm7bPgtCMNBTO31wVi1B7ctiYMNWhJPNK0ny7StXYscDGKcMbNrENVOMpkwzZ4NkoTjk08GdapvSkQuhxoO0uDqvpFtj+JnKSNSrTXdnpqRaKe+zSP23xtJ7mb46i/grEPVUACLvg3h6cpzNQeQ9GHUeVvY5QmjuKiKIykm1H+7n7A/3svNTY+yuWYLKftqca1dh3rGTOoCx+wdpg+D5fd3XdVnTYTkRcP6KwpjU5O7ie3V29lUuYkaZ/f3j/6QAl7wu+CMe2HR7XDKncg7nyeoCT1H77MD1Vz/781c/s8N3PCfLXyZVzugPgnCQA3ZiJQkSbHAq7Isn9b++ClgCvCeLMu/GqrrDrbEpkoa05exb5+O+XG9ryhxbt9O0OPGOGOmMm3WG5Uafe5kPIfzcHz+OWErVgzKyJROrSc3YjKflHzKFTlfA/qYOtQaIHEu7H8LFtzatYsqiTMmRfP8pmJ+nySSzkdEQwEYbfDRfaiCAaWcwRn3wrZnoLmUYHM5wbZ4Yr/3J/ApCyDcV5yP9PVL+HPSd4nZXgz//gea6GiOLL2QLRtLuXdVLqqjtTvSToNbVkPFTjCEKfdCZOcuZ75AEIfbR5hBi1Ytvm8JiorWCh746gE2VW0CIM2axl+W/YVMe2ZI5/EHZbT2FPj8N0qDwYa09D60nrYTHne8feXN3P3yTlpcypfcwro2vv/yTv77jYXkxFlDOpcgDNSQvENKkhQOPAOY2x9fCqhlWV4MZEiSNCbKJLe2VBDt86CKmER+k5aUXraG8ZaV4SspwTB12omDqA4S+uxJqIwGWr9cA8E+8q76KSd8Eo3uRg42HOrfASmLlaRzT0u3p5bmxPD+HpF0PmI8rbD2EThabDXgg7V/ghlXKA8T5iL/8q8dQRQAL7+LuqIJo30JlYnzqbnvt3xx1+/4zqZWXthcQo3jmBFQtQaS5sGCb8CMK7sEUXnVDu5/cy/nP7qO+9/YS161Yzh+Y2EM2Fi5sSOIAihqKeLVvFcJ9rBw5URUALte7GxwNyNvfwa/KrRk8+IGZ0cQdVRdq5eSBmdI5xGEkzFUXzUDwJXA0U/opcDL7X/+GBgTGYWVh96mwmCh2GHArg9i0srdXiO7XDg3bUI/dSqSNpQ3AQn9pFwIBGjbtKnvl/eDSlIxP24+nxSvps3XjzcSox1ip8He17o9FW7SMS3Byhs7ygelb0KIfG4IHhfE+lxKsvmi2/EFrfirq7sdpmlyUqcxcvPOIDfsVfHrbY14/EFirQYM2r7/udc5PNz+3+28tKWUimY3L20t5fb/bqfOMfgrToWxZ2/d3m5tm6s24/aHVv9Jbuk+/SbVHEDlCe08URZdtwLJWrVEpFms/hOGz5AEUrIst8iyfGyCjRk4+oncAMQef4wkSbdJkrRVkqSttbWjY47bXfgFDZZo9tfrSLX2XD+qbfNmNPHxqG320C+gUqGfOh1/fT3ufd23ABmIKGMUabZ03i14F5nugV83GUvh0AfKypnjLM+N5Zn1RchyP84zDo3oPRmRpiSeH8tgw5t6Co0aPc0qJ7opx1U7lyRMaZlMT7STHdNZjVwlwU/Om4zd1PeHS2F9G/k1rV3a8mtaKawPbcpFGHyj4T1ybuxc5Qtb1ByWxCxCp9KxLHkZJq0ppPP4w5K6tQUT5tGijwrpPFPjrdy+NKtL290rJjElXkzrCcNnuJIfWoGjnwqWnq4ry/ITsizPk2V5XnR09DB168QsFbtwRqSwu1ZPag/Tet6yMgJNTejT0wd8DUmtxjh9Bu5Dh/CWlJ5MdztMi5yGy9fGV+Xr+n6x0Q7JC2DLU92emppgxesPsKmwYVD6NdaM6D0ZkYnzwn+CoX2lnTEc50V/5+cVq7m8cS3/rliH/Y5vo8vIAEAVFob9179GSsshOcLEv2+cz9+vmc1vL53Oa98+hSVZ/fuA0mt6fkvorV0YPqPhPXJh9DxeT/gFP3ldxT3PtvKC+btcnnheyOc5pMvCt+R+Zf9PQLan4Tj157SqQltxZzZouWVJGs/cNJ/fXjqd525ewHULUzDoxIJ0YfgM1922DWU6byMwE+hnEs8I8nuIb6miZPpl7D2o45QEV5enZb8f15Yt6CdP7mdeVO8kgwHj9Om0bd6E2mxGHXlyy9BVkopTEk5ldcknRBqjmBzRfZ+2LtKXwvpHoXybknR8tF+SxIrJsTy1rpBFGf2omi4MGoe/jT879pBzxnfI0UcStETxo11/5arcq3D73VyyP5Laf/4A67nnEnbWWcg+H3sMcYTVuVhoNZEcofyEKiPawuVzE3l1W+eU7uVzE8mIFvvtCWDMK6P2zp90VtLfsRPTo3FwVmhfJlVhRu6rXcq3z12EKdDGbnck9W0RnJ8e+n0WbtZzRk5MyMcJwmAZrkDqTWCtJEkJwDnAqF9n7SleT5lGg1tKQ5IgwtA1mdK9dw8qmw11+ODU3lFZbRhycnGs+QLr2WejMp9cLRSDxsBpiUtYXbQag9pIui2t9xdrdDD1Elj3Z7jwMWWUqt1p2dF896UdlDY4B/TBLAxMYXMhrxxW0gp/PP9eolvK+VvqpdR4faRZF6P5318J+Hw0v/VWxzExP4ilNCn1pK5r0Wv44dm5nJkby4HKFibHW5mbGo5FL77hC9D62eddtiMCqP/P01hOPx2VwdDv81T7DnLqVDd6jQ3JEyTDDrsa3qPafT5WU8Yg91oQhtaQvjvKsry0/b8tkiQtBVYCfzguf2pUatz/OuUmG/vrjWTYfF0KcQYcDtz5+ZgXLBzUa2piYpA9bhyff4Z15Vk91qIKhV1v59TEJbxz5G0uyLzwxMFUZKaydcwXv4Gzfq0stwcMWjVLJ8Xw1LoCfn7htJPqj9B/Hr8yAhpviucinxrLh78Av4fJKg2tFzxDpa57vpMlTE/qIBQjjLUaOGd6POdMjw/pOJ8/iCzJ6NQnN0IrjF4qY/dgSW00giq0qV+z38dpFWvRb3wcgDhjONdf9Cj1AVcfRwrC6DNsiQ+yLDfKsvyyLMtVfb965GkLvqAhPJXt1TrSj9uo2LV9G/rkFCR9/7+B9fu6ySmo7RE4vvgC2X/ypQeijVGcmngq7xx5m0N9lUXIPFOper72j53L7oGzp8bx2vZyGtq8JzhYGBSOatj6NKlH1pFsSeJnuddjee8epfIzQNCPZfWdhH+na+0vtd1OUYKHSFtoq54Gg9cfYN3hWr7x7Fau/9dmPt5fRZsomzEuWc44vesXPEki4uabUPUQ2J/INL/cEUQB4Gok5vPfk6a1DVJPBWH4iAzSnjgbsDRXEIjOZVeNnqzwzkDKV1lJoKERbcrQbZ+iz8pCpdfT+sUa5ECg7wP6EG2M5ozkM1hd/DFbqrZCb6v5VCqYfgW01SsjUwHlwzvCrGNhegRPrSs86b4IJyDLsP0ZePe7xHz+W/6SfD5ZmjDwdl1Fh7MB3ZQoXH+4B9Ul5xL81jUc+dWN/Lj0bxQ05w97t3eUNHH9vzezJq+WTYUN3PbsNtYf6WeFfWFMMdg8pP7qNiK/fhnhXzuP1N/ehSlqAKNILRXdmtTVewm6JubCFmFsE4FUT/I/5ZDBgI8c9BqZ8KP5UbKMc/s2dFlZJ51gfkKShD53MqgkWgdpZCpcH86ZqSvYUbOdDwo/JBDsJUBTa2HWtcoIyHv3QKsygHjBjASe31hMoxiVGjot5cq2MAABH5Pe/wk6tQ6M4V1fp9bhNhu4qfFRfrDgELfHf8KPyh/D5Xfh8g//1Mj7eyqPT5vhP18V4AsMTqFZYfSQDr6Ncdt9xOhfJS5qNaad96Ja90el7lkIXJZuFXDwJs6hRS3yMIWxRwRSPXDtfYU9egN5DdHkHDMa5T6cj6TWoIkZhhUikoRhylQktRrHp58Oyr58Zo2ZM1NW0Ohu4n8H/0fr8SMdR6k1MP1rEDMF3vke5H9KjFXPgvQI/v758I94TBiSumM5+FGGPa9RuepXncGUzkzl2Q8hRU7irLSzKG4ppsGtfIs3aUxk2kLbqmMwGHTdv1QYtZq+NikSxqKjtc0cldBYpPxZZ+q6b2M/1JjTqV1yF6ja03RtyVSd9kNUurjB66sgDBMRSB3P50ZT+CUNUVlsqtSTHa6MwMgeD+49u9FnZdPnPnaDpX1kSmWx0PLRxwQcJ79Vh1al4dTEU4gyRvH0/meoaK3s9dqknQpzb4TdL8LqB7hkko6Xt5ZSUi+2XxgS1nhY/kCXJlPhOkicz6GvPcnuCx/m0BVPEcg5n2hLLHfNuYsbp95IrCmWJYlLePKsJ0m3D7ym2UCtmhqH7pj9+CQJbl6Sjkbs0Tf+TFoFmmNyQyUJTr1LWfkbAps5ja9iLmT3pf9iz4WPsOmcR9kbyCLBLspsCGOPNBqrVs+bN0/eunXryFz8wLuUvf89Psw6hz9tWclPFzWgVYNz82aCbjf6nNwR6ZavvAxvYSHmU05BGx/aaqrelLeWs7lqMytSVjA1amrvLwwGoGgtFH3FWxE3UW3M4t83LUCSxs2YQ5+/yHDdk+5DuyF/Ddqm7QQNcQSiF6CZsQyN3d7j64NykCZ3EyatCYNm8Bc/9Icsy+wqa+bjfVW4fQFWTYtjVnI4OlHE82Sc8J4csfdIWYaK7XDwPfC2weQLIGlByIFUoLmZA7sOs8OtpUnWkCW3MSclnNgpY2Ib1olq3LzhDzZRHOY48u6X2KhTUeuaTJbdh1YNgaYmvCUlmBaNXPkrbWISKpOJtg0b0GdmYpwxA04ykEm0JLI8eRlflq2h3t3A6UlL6PHfikqtbCUTO51zD7zHT4/IvPdpG+evWH5S1xe6CjgcVD7wa9x79qJLTSXQeJBA05sk//spLKec0uMxKklFhHFwapkNlCRJzEq2MyvZPqL9EIaBJClFe48p3DsQ7gMHUN92EwvtdtTh4XhLSmjLzsb/9NNo7GLlnjC2iK+Mx3I1Iud/wlajgR3VMUyO9IIs07ZpE7rMzBA3JR586vAITPPm46uqxPHxagKtveQ4hcCmt3NmykryG/N5O//t3pPQAcyRaOdez22TnDzwaS2Vz90G9UdOug+CItDSgnv3HggG8RYWEmhqAsBf1X1zYkEYy/zVNUD7l9TCQggE8Bw8SLBl1JcYFIRuRCB1rF0vUR2egj1sMntqdUyO9OLOz0f2B9DGJ4x07wCQ9HqMM2ejjgin5cMP8Rw+fNLnNGj0LEtZSpvPxQsHX8TlO8HKL0kiKyubsydZ+WbRGbifOAteuxUqdnSreCyERh0e3uOopzYpcQR6IwhDR9PDPW2cMwd1xMiOrgrCQIipvaOCQdj8BJ+FmXF45pNp92HwtdG8axem2XNOehptUEkS2pRU1BGRuA/u75h2VJ/EtjJqSc0piYvZXbubZ/c/y+WTLifS2Pv+ehdk6ylpjeQO1aM8rv4Y7QtXKyt6Ms+E+JkQFq88loPgc4HPCUG/sirNEgvROV22ohFAbTIRe++PKLvru/hKSkCjIfquOzFM7mOvREEYYwy5ucT86EfU/PnP4POhTUoi9v6foraIZHNh7BHJ5kcd+pDAR/dxh8FLcfNDzI71krHnIySTEV36KN77KRjEV1KMp7QE4/QZGCZNOumgr6C5gN21uzkr7SxyI3pPrvcFZP623YtJA4+tMBDmOAJVe5R6SM4GCHiVvqj1oNErS50DXnA1Kkuno7KVmlWzrgX9iL6BjppkcwB/XR3esnJUFjP61FQkrXZYriuMKqMz2XwQyT4fnuJigo5WtElJaKOjRrpLwomNotGE0UWMSIEyJfXlHzicMI1wZ5B1ZTou0+0k4GrDNHlkVun1m0qFNi0dTXQ0roMH8RYVYV60CLVt4AmbGbYM7Ho7n5d8Tl5DHstTlmPRdQ90tGqJu+bqeG6fj3Nfc/KHpRksnjapfxcJ+pWg68A78OUfYfnPYM7XR9fI3wjRREWhiRIfKsL4Jmm1GLKyRrobgnDSRI4UQN5H4KznLX8DZS2nMdvejG/3ToyTpwxtBfNBJJktmObMRRMZRcvqj3HtUZKWByrCEMGqtLORJImn9j7FJ8WfUOOs4fjtZTQqiZum6/hajpbvfuriunfb+LzEhz/Yx0inSqNsknzGvbDsp7DxH/DsheAYE1sxDi1HFZRshKp94Dv5QqyCMKg8DqjYCaVblJFnQZjgxNSe3wt/X0DT1Au5t+BD1pXcz40NrxKVHDlqEsxDJbtdePIOEfR6MS9cdNKjG06/k/ymfEpbSvAHA8SZ44g0RhKuDyfSGEmMOQaD2oAvILOuPMCaUj9VbUFOS9JwRrKGJYka4i19xOzBgFL488hncMWzkDKspSZGz9Re5S548RpoLlOqRZ/+I1h0OxjFkvAJZnRO7bWUw8cPwN5XlceJ8+GSf0KUGFmaAMR0QS/E1N7aP0FYHB/662nzXESmt4QIu27MBlEAksGIYcZM/NXVtK5Zgy4pCeOsWV13bQ+BSWNiRtQMZkTNoM3fRpO7iWZPCwXNheyq3UmDp4lwvZ3JkVNYGD+dZSkm6l1BdtYEeT3Pxy/Xu4kxqViVruHibC1Z4T2M8qnUSq5U1CR44So469cw+9qT/JsYY9wO+OgnShAFSqL+mt9B2hJIP21k+yYIAEXrOoMogPItsOt/ytS8mJYXJqiJHUiVboHNT+Bc9Ws+3fQ3tpcv50b/avS546G6roQmNg51RCTegiM0v/suhhkzlJyEk3jDM2vMmC1mEi2dy5eDcpA6Vx1FzUVsrNjAjOgZnJJ4CmemGjgzVUNQlslvDLKlKsCVbztJtam4baaOs9I0qI7vS9J8OOs38PkvlRpVy+8H1QSZgXY1QPFX3dubSoa/L4LQk+L13dvyPoQl3x/pBSOCMGImyCdUD1oq4OXrYNF3eL9yE6UV5zLVV0D81LRx9cEtabXoc3IxzpyJJz+f5nffxVtSMqg1n1SSihhTDAviF3BO+jk0epr41+4n2V9/AJBRSRKTItRcO0XHX880cFqSmoc3ezj7lTY+LfbRbXrZngyr/qC8Qb96k1I+YSIwhkPigu7ttqTh74sg9CSph/szcznoBl56RRDGuvETMYSitQaeuRAmnUO1LYmX9hVS4ZrEykkBJPXYSC4PlSrMimnOHPQZGbj27Kb5nXdw5+Uhe72Deh2jxsj82HmcmrCEdeVree3wG7T52jqeV6skFiVoeGiJnguzNPxivZsr33Gyv/64iupGO5z1S/C0wFMroal0UPs5KhmscM7vwBzd2XbKnRA3Y+T6JAjHSj8dJp3T+ThmKsy+XkzrCRPaxEs2r82D/34N0k7FFbmEn679Ox867uLKxBoyLe6hueaoIxNobMRXUY6/th5tTDSa+AS00dGo7LZBCyYDcpB9dXspbC5kZdpKciNyOD5fMRCU+azEzxt5Ps5K03LPAj3RpmPie1mG/W8qPxc+BrnnDkrfjjN6ks1BCRobC0FvVWptiW/7E9HoTDYHcDVB3WEI+iAyCywxI9MPYbiJaLkXEyeQCgZh+zPw6S8ITr+G5kNu/lG9gRfU32FppItFEU2De70xQvb7CNTXE2hsJNDcQtDZhqTTIekNqHRaJLUG1JKSDC4BKjUqjQZJr0fS61EZjagtFlRhYUi6nvcirHfVs7lqM5HGSFamrsCmt3d7TatX5q3DPr4s83PDNB23ztBj1R/z77bmAKz7M6SeCqt+M9hv3qMrkBKE0RxICROVCKR6Mf4DqWBAybX5/DfIfh+t/pnUrNnCo7Nz+dBzCcujmjk1QmyU2SEYRPZ6CHq94A8gBwNKECrLIMvIwSAEAsh+P7LPh+z1EnS7CLa2IWm1qO12NBERqCMi0URGoGrftiYgBznUcIBDjXlMi5zGgvgFhOnCul2+ui3Im4d97KgJcGWOjuum6kixto9Q+VxKiYT8T2H+rbDoW2AelMKVIpASRhsRSAmjjQikejE+Ayl3M5RtQT74IfLeNwgGtDhq7JTmt/HWrAW8pZmFOxDFZXG1ZFtEwcPBISO73QQcrQQcLcitDgItDmRZRhMRjjo8Ao3djjfMQJ6vnKLWEpLCkpgSOYVUaxpmranL2WqcQVYX+llb7ifDpubsdKUeVW6kCk1bNex9TVmKnX46TLlQ+a91wCUrRCAljDYikBJGGxFI9WLsBVIFX+Bf/Rd8pcUge5FkLyrcqFVu1BolcbpKDucPvquo8EdQrgqjTorAJXcuzc3WFjBDW4paGnjlb6GfAgFkvw/ZHwCfn6OV0QNAmwHajF1rpesCKrSyCjUSallCJ+tokiexR0qgztu555xOFSRW7yNCbsTqrcOICz0+zlNvZFVYoTL1Z4wAnQW0BoibDqd+D9Q9VvwQgZQw2ohAShhtRCDVi1EZSEmSVAsU9/Tcvy4wJN8yR9djgowsg69VzVrPNG6x3NvlOSNOUqUyDIgRqNFEAmRJ+Qn2uPJHxuKCVlkOBGVkj2SQqjVxKofK2uM/6gukdfxN/49u7a1eOZD5aOuemjY50MNhdbIsrzphP09wT4YoCqgbhPOMNuP194KR+d1OeE+e5P042v6/Gm39gdHXp9HQnz7fJyeqURlIDTVJkrbKsjxvpPvRG9G/kzOa+zea+3YyxuvvBePvdxttv89o6w+Mvj6Ntv4IXU3MOlKCIAiCIAiDQARSgiAIgiAIAzRRA6knRroDfRD9OzmjuX+juW8nY7z+XjD+frfR9vuMtv7A6OvTaOuPcIwJmSMlCIIgCIIwGCbqiJQgCIIgCMJJE4GUIAiCIAjCAI3KQGrVqlUySp1G8SN+huOnT+KeFD/D/HNC4n4UPyPwI/RiVAZSdXUjXXdMELoS96Qwmoj7URBGj1EZSAmCIAiCIIwFIpASBEEQBEEYoB53cBWEYdVUArV5oNZCzGRlw2FBEIT+8Lmh9iC0VIAtCaJzQaMb6V4JE8iQBVKSJMUCr8qyfFr746eAKcB7siz/aqiuK4wxVXvh+UuhtVp5nLQQLnsSwlNHtl+CIIx+fh/seA4++CHIMkgSXPA3mHUNqNQj3TthghiSqT1JksKBZwBz++NLAbUsy4uBDEmSsofiusIYE/DDpn92BlEAZZug8MuR65MgCGNH/WH48F4liALlv+//AOrzR7ZfwoQyVDlSAeBKoKX98VLg5fY/fwwsOf4ASZJukyRpqyRJW2tra4eoW8Ko4nNC6abu7dX7hr8vPRjT96TfAyUboU2s7hovxvT9OFTaaiEY6Nrm94CzfmT6I0xIQxJIybLcIsty8zFNZqC8/c8NQGwPxzwhy/I8WZbnRUdHD0W3hNFGHwZTLunennrK8PelB2P2nmypgH8shre+A4/OhgPvjnSPhEEwZu/HoWRLAp2la5sxHKyJI9MfYUIarlV7rYCx/c+WYbyuMJpJEsy6GnLOUx6rNHDq3ZCyeES7NaYFA/DC1ZC2BM7/C6x8SAmo6o+MdM8EYfBFZsIVz4G5PbAMi1ceixxLYRgN16q9bSjTeRuBmcChYbquMNpFpCvJ5Y1FSiAVngEa7Uj3auza+T8I+mHa5crjyCyYdhl8+GO49pWR7ZsgDIWs5XDbGmWazxIL1viR7pEwwQxXIPUmsFaSpATgHGDRMF1XGAt0ZoidOtK9GPuCAVj7MCz4pjLad1TuefDGN6FqD8RNH7n+CcJQsSUqP4IwAoZ0ik2W5aXt/21BSTjfCCw7Ln9KEITBUPA5qHUQO61ru1oH2WfD1n+PTL8EQRDGsWHLVZJluVGW5ZdlWa4armsKwoSy47+QsazraNRRWWfCnteUFU2CIAjCoBFJ34IwHvhccPhjSD+95+fN0UoCbsEXw9otQRCE8U4EUoIwHhSsUVYwGWy9vyZ5Aex7c9i6JAiCMBGIQEoQxoND70HivBO/Jmk+HPmkswq0IAiCcNJEICUI48GRzyFh9olfE5aglJio2T88fRIEQZgARCAlCGNdQ6Gy3Y69jyKEkgTxs5SgSxAEQRgUIpAShLGuaB3Ezeh5td7xYqdC0dqh75MgCMIEMVwFOQVB0VgMtQdBpYaYKWBNGOkejX3F6yE6t3+vjZkKm5+EYBBU4nuUIHQIBpT3poYCMEUq709G+0j3ShgDRCAlDJ/qffDcpdDaXkosejJc9byyjYkwcCUbYMn3+vdac5RSSb7+METnDG2/BGEsOfIZvHCVssUSwJwbYcXPwRQ+kr0SxgDxlVQYHrIM257pDKIAag9A/icj16fxwNkArTV950cdKzoHyrYOXZ8EYaxxVME7d3UGUQDbn4bqvSPWJWHsEIGUMDz8Hijb3L29Ytfw92U8Kd+OHJVNgzeEXZcis6B009D1SRDGGncLtFR0b2+rGf6+CGOOCKSE4aE1wPTLu7dnrxz+vown5dvYHXRyz5of8kb+G/07JioHyrYMbb8EYSwJi4OkBV3bJAkiMkamP8KYIgIpYfhMvghmXau8Qam1cOp3IW3JSPdqTGstWsO2QDO3zbiN1cWf0OBu6PugiHQlodbnHvoOCsJYYLDC+X+G+JnKY2M4XPqUsjhDEPogks2F4WNPhvMeUQIoSa3s/abWjnSvxjS5chfmrMXY9TamRE7my7K1XJx10YkPUuvAlqQU5kycMzwdFYTRLm4afP1taC5TtlqyJ490j4QxQoxICcNLa1CSnaOyRBB1strqUftcxMUq36JzInLZWtXPKbuIDKjcOXR9E4SxyGhXAioRRAkhEIGUIIxRjcVfUqLVEmOOAyDRkkiDp5FGT1PfB4enQcXOoeyeIAjChCACKUEYo6oLPqXFHMHReuYqJFLDUjlQ34+99MLToWrPkPZPEARhIhCBlCCMUYGKnXjD4rq0JVgSyGvM6/vg8HS8NYdxe3xD1DtBEISJQQRSgjBGhTWVIIWndWlLCkvicOPhPo/d3mRgcdvDzPnVJ3y4t3KIeigIgjD+iUBKEMaiYIAYVwv6yEldmqNN0dS56vAEPL0e2ugO8s2PXNwcvpv75wW497U91LX2/npBEAShdyKQEoQxqLl6Ny0qNWZTVJd2jaQm2hhNiaOk12Mf2eJhbpyaOVEB0gPFLEiP4Nn1RUPcY0EQhPFJBFKCMAbVFq+l3mDuSDQ/Vow5luKWngOpqrYgb+b7uDhbC5YYaCrizNwYXt5ahizLQ9tpQRCEcUgEUoIwBrkqd9JmtPf4XLQxmpJeAqmndns4LUmDTS+BJRYai0mJMKFRS+wuC2G/PkEQBAEYpkBKkqRwSZLelyRpqyRJ/zcc1xSE8Uxdl4fbHNnjczGmaIodxd3anT6Zlw/5WJXevqGBJQYclUhykJlJdtbk1Q5llwVBEMal4RqRuh74ryzL84AwSZLmDdN1BWFcCmsqJ2hN6vG5KGM0Va1VBORgl/bXD/vIjVATbWr/Z6/WKlthOKqYlmjjSxFICYIghGy4Aql6YJokSXYgGSgdpusKwvgjy0S5mlGHp/T4tF6tw6w1UeuqPeYQmf/s8bIi9bjtNc0x0FzKpFgL+ypa8PgDQ9lzQRCEcWe4Aql1QCpwF3AA6LZFvSRJt7VP/W2trRXfjIWRN1rvSdlRhRcZsyW+19dEmaIpd5R3PF5fHsAXlJkaddw/eXMUNJdg0mlIsBvYV9EyVN0WTtJovR8FYaIbrkDqQeBbsiw/BBwEbjr+BbIsPyHL8jxZludFR0cPU7cEoXej9Z50VO6gSqvFoNb3+poIfQQVbRUdj5/Y7eGsNC2SdNw6P3M0NCqJ6ZnRFnaXNg1Fl4VBMFrvR0GY6IYrkAoHpkuSpAYWAmKdtSAMkKNyB8168wlfE2mMoMxRBsChhgC7a4OclqTu/kJzNDQrgVRapJntJU2D3V1BEIRxbbgCqd8CTwDNQATwwjBdVxDGHW/NXpx62wlfE2mIoqJVGZF6ZIuHc9I16NQ9VJ0yR4GjEpBJizKzX0ztCYIghETT90tOnizLm4Gpw3EtYYiUb4cDb0NbLUy7DJIXgc400r2akLQNhbjM4Sd8TaQxkhpnDduq/WytDvDwUkPPL9SZARW4mkgKt1Ha6MTtC2DQ9jB6JQjjWVstFK6FQ+9B7HTIOQeic0a6V8IYMCyBlDDGVeyEp88Fn0t5vON5uOp/kHveiHZrojK1VOGPXHjC1+jVOvRqMz/6opUrcw0YND3VQG9niYHmMrRx4cTbDOTXtPL/7J13eFvV+cc/V3tLtry3HWfH2WSQAGEECHsVaAtllEJbdid00xZKW7pZZZVR+AFl703Y2XsPx3sP2dZe9/fHdewo8lI8Yifn8zx+HB2de+5RdC197znv+32nZfe94iUQHFFEo7D6MVh+l/J48wuw5jG48g1w9JwdKxDsRzibC/qn7LNuEbWfT/4EAffhmc/RTDSC1deGZO09Y28/e1vPQa8Osji7n9UlkxPalHiq3GQTu+o7hmKmAsHYwVUOn/81vq1+y+GZj2BMIYSUoH8i4R7aQiBqs408bZW41RpMxuQ+u72510RF+wROzCuPz9Q7mAOEVJbDyPZaIaQERxmyDHIPHmrRaHybQHAQQkgJ+qfwOFAdtAu8+FYwWA/PfI5mWkqp06ix6Xv/v/+sysCTW62cVrgTd7ih/zFNKV1CKtthZKdYkRIcbThy4ZhrY9tMTkifcnjmIxhTiBgpQf9kzYYr34SVD4G3EeZ9BwqWHO5ZHZVEmnbToJJI1fZsf7CjWcs/1zq4uqQNlaRjjyu+5l4cZieUKhl+2Q4jz60WhQcERxlqLRx7AziLYeMzkDUHZl8OyUWHe2aCMYAQUoL+UakgbwHkzFOWwNUio+tw4W3YSofORDrx23X+sMRdK5I4r9hNliWCN2yjxd/a/6Amp5KxFI2QbjPQ6A6IzD3B0YctC465WhFQau3hno1gDCG29gQDR6USIuowE2nahbuXLdXnd1jItoSZlhoEwKgxEowECEaCfQ+q1oLeBu461CqJDJuBsmbPUE9dIBgbCBElSBAhpASCMYTGVUHAGO8h5QlJvLbHzNICb1ebhIRVb6PFH1faMh6zs9OYEzLtBkobhZASCASCgSCElEAwVpBlDO4GgmZn3FMflhsZlxTEaYzNMrJprQMTUkYntClxUhk2A3sbhbWFQCAQDAQhpASCsYK7gZBKja6HFan39pmYmx6IazfrLLT4m/sf2+SEdiVzL73TlFMgEAgE/SOElEAwVmgtw6UzYtFaYprrPGrqvRrGJYXiDrHp7DT5Bri112mBkGkzUNYktvYEAoFgIAghJRCMFVzlNKk1mA+yPlhZY2CyM0BPNYltOistAxFSxpSuGKkMu4GyZm8/BwgEAoEAhJASjDSBDqjfCs17INqDk7Cgd1r20aAibkVqVa2eYkf8ahSAVWfDFWgF+nGhNyWBrxUiIexGLaFIlDZvz2MKBEcs3hao3QytZYd7JoIxhPCROooJRoJsbtrM59WfY9aaWZS9iMnJk4fvhE174M0fwr7loNHDkp/B3KvAIArkDoiWUmoIU6LrFlKRKGxr1nHmuJ634rQqDVqVlo6gG6uuDyd6lRoMDnDXI9lzyLQrFggzTI6hfQ0CwWilbjO8dB00bFXsQM68ByafB1r9YZvStuZtfFH9Bb6wj+NyjmNayjS0KmHPMNoQQuooZnXdar73wfeQO1crHt70ME+c/gSTnJOG/mSREHx1ryKiAMIB+ODXkDkDxp049Oc7Aom07KFBrUKv7v5g3+PSkqSPYtb2vuK03wKhTyEFYE6B9hqw53Ru73mYkesYotkLBKMYXxu8fosiogAC7fDStXDtBMiadVimtLVpK1e9exW+sFIw/tEtj/LQ0oeYnzn/sMxH0Dtia+8oxR/28/Dmh7tEFIA37GVF7YrhOaG3Gba/Ft8uqqsPGNlVgc9gj/E0396sI8/e9xacdcBxUsnQoVggpFn0IuBccPTgrofqNfHtLaUjP5dOPq36tEtEAUTlKE9sfYJQRGy5jzbEitQYIBCKsL22nbJmLykWHVOybCSbe1huDvkVYdJSCuZUyJiuZGP1QESO0BGML07rCQ3Tl6feCpkzYe+Hse2OvOE535FGOIjK20IkIyemeVuTllxruM9DbVoLTb6m/s9hSoa2agBSbQZKhZASjAX87VC3Sbl2HbmQUaJ83iSCwU4wfwk7Cr9FaSiJZLWfqc3v4bSkD8+cB4A7FG9B0h5sJyJH0CK290YTQkiNAd7aXMutz2/senzBrGx+dfYUHCZdbMdtr8DL13U/nn4pnH63Ekh8EGatmSunXsnPPv9ZV5uExLFZxw719BV0Zjjp58pdn79NaRt3CuQcMzznO9JoqyRosGHUxQaa72rVMTcjXhAfiFVvo7ytrP9zmJxQvQ5QTDlXlQ5gFUsgOJyEfPDlv+DTP3W3nXIHLLgeNAmIDWs6H8z4K9f/bweyrKwCLZv0NX5vm0LPt6LDz4m5J/LUtqdidg0um3wZBo3hMM1I0Btia2+UU9ni5devbY1pe2l9NTvqDvrybC2Ht38S27bpWWjY1uvYx+cczx+O+wNTkqcwP2M+Dy19iGkp04Zq6vFkz4HvfAxffw6ueB3Of1ApFCron9Yy3AYrpgOsDzwhiVa/ilRT39mPNt0Ay8SYnOBWLBDSbQbKW8SKlGCU07QLPvtzbNtHv4XmXQkNU+vy8au3SpEPCDV8e4eLHc2HL7N4eup0Hlz6IPMy5jEleQp/Ov5PLMpedNjmI+gdsSI1yvEEwrT747du2nwHFaINurtXeg7E19rr2Ha9nbOKzuKk3JNQq9QxQczDhnOc8iNIDFc57Ro9Jo2pq6nUpSXLEkHVg3/UgZi1ZrxhH8FICF1fBVmN3RYIDpMWbzCCOxDGohcfE4JRir+NGPUDiq2Kvz2hYbzBCE3u+OLerQd/zo4gOrWOY7OOZU76HKLRKEat8bDNRdA3YkVqlJPpMDLroMwpnVpFgTN2iwdbDmTNiW1T6wYkWkxa08iIKMGh01pOi1odY8ZZ6tKSYe47Pgo6ixfrrLT2tyqlUoMhCdx1qCSJDLuBCmHMKRjNJBUosX0HYs2EpPyEhsmwGziuOCWmTa2SKEox93LEyKFX64WIOgBJkn4jSdKSwz2PAxFCapRjN2q5+8ISFnf+keclG3n0yrmMTztISBntcO69ULhEeZxUAN94HlKHwcpAMPK07KNeBWZt94rUXpeG9H629fZj19loHsj23n4LBJQ4qfJmsb0nGMU48pRQgfTOkITMWXDpMwmHDJj1Gn51zhROnJgKQJbdwCPfmsvEDNtQz1hwBCLW7McAEzNs/PvyOTR2BLAYNKRYelk9Sp8Cl/4X3A2KoZwldWQnKhg+XOVUqyKMP8DVfF+blpPzBrZiZNFZafEPIHPPmNxVKibVqhelYgSjn9x5cMUb4GtR4vyMjkMaZnyalfu+OZuG9gBmvZpUqwjqHi1IkpQE/A9QAxKwRpKkdwAzsEeW5askSboD2C7L8rOSJP0G2CHL8rMjMT+xIjVGMOs1FKSYexdR+9Fble08IaKOLNoqqZKDXVt7sgxVHRrSzANbkbLpbDT6GvvvaEruKl6cZtWzT1ggCMYCpiTlc+8QRVTXMDrlc1aIqFHHtcAbsiyfCISATOBfwClAgSRJ6cCTwDc6+58GvDpSkxtRISVJ0v2SJJ09kucUxFLR4uWdLbW8vrGGXfV9p80LRglBL3LATW3U37W11+RToVXJfTqaH4hNb6d5IKacJmeXl1S6zcC+pngvG4HgSCQYjrCh0sXL66r4ZGcDjR3+wz0lQTeFwH4PoDUoYuoa4GkgGTDKsrwXsHbGT22R9/tYjAAjtrUnSdJxQIYsy6+P1DmPWqJRaNmrZGDZc8GWCcDeBjdXPLaSKpfyAWHWqXn6OwuYKcqAjG5cFUQtqeg0GtSSGoDKDg3pA1yNArDprLQF2ojKUVRSH/dPppSurb10m4GKFrG1JxgDtJSBpwEs6QkHmu/nvW313Ph/67uSAJdOSefuC0pw9rcLIBgJKoCpwMfATGAx8CDwPPDJAf2eBR4DrhjJyY3IipQkSVrgYaBMkqRzR+KcRzKekIdSVyl1nrr4J0N+2PA0PLgYHl0KD58IlasB+Gx3Y5eIAvAEIzzyWSnhSHSkpi44FFwVBI1JMRl7VR0aUo0DF1JqSY1JY8Ll790OA1AsEPwuiARJsehp8QTxhw6fl47gyKbOU0epq/TQKyrIMux4C/59nPJ599AJsPuDhIepbfPx61e3xjgpvL+tnu21idkoCIaNh4ALJUlaDtiA94HbgY86n8/u/P0CIAOfj+TkRmpr71vANuBPwDxJkm48uIMkSddKkrRGkqQ1jY0DiOU4Sil1lXLLR7dw7qvncvHrF/Nu2buxtZcatsNrN0C4UzB11MIr3wVPE+U9BA7vrncTCAsh1ROj5pp0lePVW7AcIKQq2rQkJyCkQPENa/I1991JpeoMOK9DrZJIsxqoFKtSo4JRcz0OAaFIiHf2vcPFr1/Mua+ey60f30qp6xDq2jXvgRevVooMg7IK/8JV0LIvoWG8gQjNnnjPKJdP1LUbDciy3CTL8omyLC+RZXmRLMu/lWV5mizLx8myfKwsy19IkjQVeBu4S5YPNhcbXkZKSM0CHpJluQ74L3DiwR1kWX5IluW5sizPTU0VgdI94Qv7+Muav7CiTiks3Bpo5cef/JgdLTu6O7VVxh/YvAfc9Rw3ISXuqUuOycUsDBd7ZNRck63luHWGGFfzSndiK1KgFC8eWM09Z7cFgt0gAs5HCaPmehwCdrTs4Mef/pjWgLJC+lXtV/xt7d9iivQOiPYapUzMgQTau67fgZJhN3D8+Nj/U80o8ZESDAxZlrfKsjxPluVHR/rcIyWk9gBFnf+eC5SP0HmPKBo62lETa54pI1PefsB/pzUj/kBbFhidzM1P4nfnTsVm1KDXqPjuCUWcUZI5AjMXDIrWfbRqdJg03aZ8NR1qUgboIbUfu95Bw0Ay98xOaFcCztOs+h5XMgWCwVDeXo6ExNy0hSzLu4h0UzrLq5YPTOgfiCUNDnbr1xqV9gQw6zX86uwpnDolHUmC3GQjj15xjPCREgyIAS1FSJKkAs4FClBE0RsJLp09CjwmSdKlgBa4KMF5HtW0+4J8tKOR+5fvQas+i+/MvYDlzQ+ypUUpMJtkOKAocdoUOPEXsPxOJX5AayJ41r1UBy1kmdRcvrCAUyanE5FlMu1G1P3VFxEcflwVNDqzMXVm7AUj0BpQk2RIbEvWrrezq3Vn/x2NTnApK5vpNgN7G0XmnmBoSTOl8YOp9/LueokVrWFOnLqUpLzdmDUJrgA5x8OZf4c3blJKw6i1cPa/IDnxMlTFaRbuvqCEG9uKseg1FKZY+j9IIGDgWXvPAg3AJuAM4Ot0+zX0iyzLHcDXEp6dAIAv9jRzy3Mbuh5vfQ1+dt41bG25npPyTmJS8gHu5XoLHHsDjF9KqKOJvWEnV7/YTF37J3xtbg43nTSe7CRT/EkEo5e2SupSM7q29uo9GpINEdQJamCbzkZboI1INIJape69o9mpFINFcTf/aGfDoc5cIOgRQ7SIu19djT+k3Aw88ZmXyxbMwDbXkdhAag3MuASyZyvxoLYsRVypEt9s2VnXzs9f3sKa8lacZh2/P38ap0xOR6sWdouCvhnoFZImy/INnXv030MxwxKMAJFIlCdXxO+Ebq8w8dQZT/Grhb/CaXTGPqk1QtZMlkemcfpTNdS0BYjK8NzqKl7bWDtCMxcMCQE3hHzUhf1dWXs1bjVOY+IJAmpJhUVrodnfT8C5KRU6lBiTTLsoEyMYesoaA10iaj/Pra6htu0QvJvUWqWqQ/HJkDZZEVcJ4vaH+NWrW1lTrsRsNXuCXP/0OnbWCa89Qf8MVEh5JUm6TZKkpZIk/RxokyTp+OGcmEBBkiRSzLq49gyrmRmpM0g2JPdwlMIXe+K/MF9eX4XbLzJRxgxtlWBJpz3kxqxRVhJrO1ekDgWHwUFTf3FSBisEvRD0kGLR0+oN4Q32XxxZIBgoBm38iqhZrzlsqz/17QFW7os1rI3KiESLMYokSWZJkl6WJOkTSZKekiRpWGNYBnrVrgT0wLEo24HrgSXDNCfBAahUElccW4D2gH0co1bNspIegsoPIq6wMTAty46+hw8xwSjFVQHWdDqC7V1be9UdapIOUUjZdDbqPf0IKUmlBOt21KBSSWTaDZQ1iYBzwdAxJcvGuNTYeKjbT59Ehv3wlGaxGDSkWeONN5093MQKhpaC2978RsFtb5YV3PZmtPP3gMOG+uBy4CtZlk8AAihJbsPGgNZAZVm+YzgnIeib2XlJvPDdY/mqtBmtWmJBkZOpWfZ+j1tUnMKEdAu76pVgYbtRy9WLC8We/1jCVYFsSsHdXtlVHqbGrWFaSrznzUBw6B1UtA8gaXZ/qRjneLLsRvY2upmSJTKYBENDTpKJR684hlX7WqhyeZlXkMzMvKT+Dxwm0m0G7jq/hOv+u5ZIVMmjumBWNpMyrYdtTkcDnaLpYWB/4G4+8HDBbW9SdveZzwxi6GrgCkmSXpZl+RpJkn4jSZJZluXlkiRduf/0KMlvx6GYfJ7eadGUMMJAaAygUknMyHUwI8FSLgUpZh6/ah476zoIRaKMT7dSKHxRxhatZQQMNvQefVd5mDqPhuNzD62MVJIhiTV1a/rvaHJ2FS/OsOnZ2yAy9wRDS0GKmYJR9Hm0ZGIqr9+wiMaOACadmuI0K0liRWq4uYtuEbUfU2f7IQspWZZflyTJCLwkSdLHQFsvXYtlWT5ekqRfAScd6jn7FFKSJP1VluUfdE5kv92BpMxTPulQTjiWafEE0WtUo8fAUpaVNHU5AvaceD8VIMthJMth7OFghUAowrb6ekpdZWjVaoodhUzOSGWYt5QFA6W1DI8toyvQPCpDg1d9yDFSJo2JqBzFE/LElJyJ75iibCsCmQ4jO0WBa8Ew4AtG8AbDo6KeXZs/hCraRK6qjqhkod1XKITU8JOXYPuAkCRpPPAO8CKKCfjZwIedTxuB/XeiT3b+rgAO+c3uUxHIsvyDzt9xTuRHE/Xtfl7bUM0TX5WTYdNz69KJzC9MRnM4t8i8rbD+KfjkbogEYe41cOyNYFdKDgXDUdZVtPK/NZWoJImL5uYwKzcJnSZ2zhvq9vKvTXexsWktAIszT+a73MyMzMIRf0mCHnBV0J5S0FUeptWvwqCJohtEmFuSMYl6Tz1FjqLeO1lSoWoVoIjx97fVH/oJBYIeWFPWwt8/2MW+Ji8Xzcnh4rk5h82aJRqViTSso3D579CXfwHWTFpO+TU16lPJSnL2P8ABeAJhVpW18MKaKpLNOi6cnc2MXIe4Oe2ZCpTtvJ7aB8M1wDZZlp+QJGkLsAPYb11/OvBy57+HJJvgkJSAJEmLh+LkY4UX11Zx51s7qGr1sabcxbceW8XWmsNczLLiS3j/lxD0QCQEKx+A7a91Pb2uvJWvP7yCF9dV87+1VVz60ArWlsdmpQRCEZZXvdclogA+r/2QdQ2rR+xlCPrBVUGzWoOxMz6qzqM5JOuDA3Hok3oueH0gphRorwU5SrbDSHmLVxS3FgwZO2rb+eYjK/l8TzPVLh//+HA3//60lNBhusaaWmtI2i+iADpqSX71enSt2xIe67PdjVz1n9W8ubmWp1aUc8lDK9hS3dvO0lHPz4CDM1m8ne2D4R/AlZ1FjuehCKcbJUl6EOjH/yVxBups/r4sy0sPaPoDSoDWEU9jh5//fFEW0xaJymyubks4Zmkg+EMRtta0sa/Rg8OkJcWiZ0+jG5tBy7Rsu7JNJ8uw6534gzc8w95xx5HlGMd/V5bHVDKXZXhudQWztXvZpo5S7q0nz5rP+ub4ItmbW1YDF/c+x7Cf7S3bKW8rJ9mYTKG9kOqOamo9tWSaM5nsnIxdHx8M7wmE2VrTRnmzlxSrnmlZNlKthydLZ0wQ6ICwn2Y5gkmzX0ipSdIf2rbefpINDmr7E1Jag/LjacRgSSfZrKO8xcu4VOH2LBg8u+o74oql/9+qCr5zXCG5yQnGTXlboXaDUtbInguZM8DoSGgIva8e7X4RtZ9oBIOrKqFxPIEwT68o54enTiAUiaKWJLzBCF+VNlOSk9icALbU1rO1pp1wRGZihoW5eVkJjzGaKbv7zGcKbnsTlJioPJSVqJ8NMtAcWZZriK/p26tlkyzLjw/mfP3FSE1HKTicLUnStzqbzcAhuKaNTXRqFXaTlkZ3IKbdNJi9lT54d2sdNz+7oevxMQVJ5CSZeHl9NTNy7Dxw2RyyOjaBPj6DqiO5kNu/+jVXl3yHSDS+qGkgFGVzKMBVH18PQKY5kxNyTmBr89aYftOSZ1HZ4iU3uedl9vfL3+dnnys3DNNSpjHNOY1ndz7b9fzVU6/mezO/h0HTLZJkWeaVDdX8/OUtXW1nTc/k9+dNw2EScQg94qoEawZtwXZMWiXOrc6txpFgaZiDSdYns6lxc/8dLelKwLklnbwkIzvrOoSQEgwJainel8yi16BVJ/i5GvTCZ/fAV/d2t53wU1j8Q9AOPO4qqklSLD/csS7+EUPimYRnTM/iN69t7RKK2Q4jPz9zUj9HxbO+qpZrn9hCY4eSoWvUqnnkKplFRdkJjzWa6RRNgxJOh5v+tvakHn4309dyxRGG3aTjp6fH/hFk2PXMHIbVqFqXj9+8FitqVpe1dmXabaxqo765BT76vbJa4Dhga9ngYHvxcWxv3cWvvvgV585KjxlHkmB+kZM3qld0n89Ti8PgYIJjQlfbnLR5tLcWsaHS1fMc3bXcverursfHZR/Hczufi+nzn63/oay9LKatosXLnW9uj2l7Y1OtcA7uC1c5WNJxBV2YOmuQ1Q7BipRFZyUYCeIJ9RMeYEpRDEGB7CQTO2oP83a24IjBaW2nMCV2NfqqE5KQIwnG4jXtihVRAJ/+GZr3JDRMvUpDxXG3KB+UnXSMX0q5sX+bmQORCfPW5tqY1bZql4/atsSzbD/d1dQlogB8oQhPfllJICwMlUcb/QWbbwQ2SpI0UZblJ/vqeyRz/PgU/nfdAlaXtZJs1nFMYTJFw3Bn7gtFaPXG/5EcGDcghf3QWg7lX8L86+hInUCNr5FdWg137nhUGSfiw2n38IszJ/NVaTMSsKDIiVEd4KPKN2LGfmjTQ9y54F80ucMgS6zdo+XvnzRzxzk9G376wj7ag91fqFE5ikxs/WoZGXcwNl3eG4zgDcYLgHa/cMzuldZyMKfRFmgj26Is6dd7NOTbBr8g7DQ6qfXUUuwo7r2TJQValZjP3CQTW2pEnIdgaGjwbeWMY/2oA+Np80pkOgN81fwQZ4VuSWygQA83YnIUAomJ/rDs56bqt/nhGb8jw+/BozXwuqeMqcEWpicwjicYpLo1XjTVtCV+w1jVGv93XtUSwh8Oo9fEZ2gLDh8DNeQcbODXmEavVXNMoZNjChPL3kiUDLuBkyel8eGO7uVlrVrqyg6UJNBanDD7Cvjot/DVfTSffDuXV7yEL9z9xzvFOYVxznQC/jC76920+YIYPNUcZ9jC+9Z8VgZau/pqJA1ELPzm+dgafJN7MaJLDRk4NvNYvqz9EoCOYAfppnTqvd13kqnGVHKtuTHHZTuMzMlLYm1F97mNWjVFo8hHZtTRWgbmFNrbqrtWpOoHYX1wIMmGZGrc/QgpcxqUKXEjBSkmnl9TOejzCgQAhbZ8frX2e6gkFUaNkfamds7IPYl0W27/Bx9IUmH8lpw9F5ITyzrOtmWSYcnn+9se6mrTqDScMeU7CY2TbrVx7mwnf3svdrV3XqEjoXEATpjg5H+rY7caz5nlxG7o3c5GcHgQFtejCJNOw8/PnMw5M7LQqCSK08z84YLpvLCmgtxkIw9/ay4TMmz4Jl+Ef/6NoDVSsPkV7j/2D0xJnoxKUrEkZwl3Lr6TZKODxcUp/PqcKfzpzFwu031C1ud38LP0xZyaexJqSc14x3geOOUBZmVM4upFBVw5y84zy9SsujKZWWnxGtu7fj11F32d612zWZp2HGpJzYbGDdy5+E6OST8GlaRiTvoc7j3pXtLNsVuLNqOWuy8s4dQp6ahVElMybTxx9TGM66GMjaCT1n1gUcrDmLVmIjK0+AcfIwXgNKZQ1dFPIK0lrXNrTybdZqDDH6LFc2iO6gLBgUxMn80/F91NpikDb8jLOXmncu3UazAbE3TPd+TAN1+E0+6G438Mp/8Rvv4cWPsvoXUgSUYLt074DqdnnIhaUlNkK+Rfc/5AiT1xG5gzSzK5enEWRq2aVIue350/nmMK4mNW+2N+YSq/PHscyWYdZp2a7y7JYemUtITHEQw/kizL/fcaYebOnSuvWTMA9+UjlEA4QmNHALNeg0WvoaHdj1GrJrnTtO651RU89OleLpuspTUAz2z18dhV00ixyyQbkmOCvLuIRpR0do2OgMFOs78Zs9bclV0XbdqD9OoNSJVfKf0nnQWn3w0O5Q4x3NRE2aVfJ1SlfPlqjplF6IRjSD3lDFILJuIJeWgLtGHX2/s0evSHIjS7A1gMGuzGURNk3q/By2G5Ju9bQHT+tVy79o/cOudWWnxabvwgldsXtPZ/bD8EIwFeL32Dm2ffjErq5X5KluHjO+H8f4Mxibve2s6PTpvICRMS/1IQJEyf1+SR8hlZ31qJP+QlzZaN0XAIN1XRKOx4HV7+LoS8oLPABQ/DpDMSGsbf0krVVVcjWwyElsxFXVGH/Nr7pD76H5xzZyU8rXAkQllLK1q1ivzk3gvLD4Sy5hbCskxRchIq1WFd+xgzRliSJJlRjDiTUTIBvyX3IHYkSZoJIMvyhsGcb5RYdAsORK9Rk3OAMd2BJnUdvhAPf7aPvY1e7jig9uzH2zu4+ZQJ9IpKrdy9oVSfzrLEptGqtr4E+0UUwI43YMLpMPtyQBFS+0UUQHj1eqTV6zEUlEDBRMxac99O2Z0YtOrDZro35mivwqMzo1Mr5WHqPRqShmA1CkCn1mPSmGj0NsatHnYhSWDJgLYKMCZRmGJiQ0WrEFKCISM9KcGtvINp2QMvXQvhzniioBtevhau/RScfRjOHoSnrpHQzh0ASGs3sP+vzF9RCYcgpDRqNcWpKQkf1xMFzsEJsVHPb+zf4CD7A37TNtgsvv1Fi/8kSdIjKEWLezJInNn5e8NgTiaE1BhDo1GRZIoPNLQY+g4+DIQibKlpY2+D4k9Vkm0nc3/pmHAAdr4df9C+T7qElNrhQJ2SQqSpKXY+qeJLdVjwtUI0ShsRLFrlTr3eqyZpCOKj9pNiSqXKXdW7kALF4by1AjJmUJxqZVVZS+99BYKRpr2mW0TtJ9AB7rqEhBRWG+qTl1K35EwqNFYccoj8rSshRXy+DSuKiIorWsxv7AxSTB1ctNgiSdI7KPZNe2RZvkqSpD8A5wNIknS5LMsnH+rJRIzUGMOoVXPjScUHZuliM2hYNK7vQPgPttdz4QNf8ZMXN3HtU2u56dn11LV3fgBp9FC8NP6ggm4De21GBll/uAtJ3+nNIkmk3noLuvHjB/uSBD3RWga2LNqC7V3lYeo9auz6oXN+TjWmUtFe3ncnc6oSqwVMzLCyodJFJDr6wgEERynWDFAfFCKgMyvxfQmQnJPOxm/exLfWhvnFylZuWOXm7pRjCRVPHMLJCnqgr6LFh4wsy68Df0MpWvxPIBv4F3AKUCBJUrosy7cDdwN3D0ZEgViRGpMsKErhf9ctZF+TB7tJS2GKifFpvQdpNrT7+c1rsaUOVpe1sq2mjQxbZzzV9Ith97uKQzDAuFNgXGxdavPixRS+8jKhqirUTif6oiJUBuFMPiy0loNFsT4wdQqpWrcaxxAKqTRTKhsa1iMjI/UW/mDNUFYmURIGks06tta0Mf0QXJoFgiHHOR7OvQ9evV6pOaoxwHkPQPK4hIapbfNz5welMW1flLdR4Y5QMITTFcQxUkWLL0XZxrsKJW5qSFMfhZAaYUL19QT2liKp1eiLx6Fx9rGS1LRHWZkwOiB1EuiVLR6dRkWm1oMc3EVlq5pndjv5xoICxqf3bFngC0Vo9gTi2jt8B3g4pRTDZS9C024lniplPBhjXX0lSUJfWIi+UBQ0HnZcipBqD7Rj7iwPU+/VkDsEHlL7MWlMaFU6Gr2NpJl6uYO3ZCgO68iAxNQsG5/vbhJCSjBoIq5G5NqdyIEOsOeiLZyW+CAqNUy7UCkL01EH1kxwFscYaw4EfyiCqwcPv3a/ML8cZkaqaPENwI+A54FPDujnA5wAkiRJPQWkDwSxtTeCBPbupfyKK6m8+moqrriCyutvIFjZizdP2Rfw0PHw9IXwyMlK9pTPBYCvcgMZL1/AMe9dwAWrLuU7pk94fPlWXN6eU9Mz7AbOnhEbXK7YKxyUJWNOgfyFkDsvTkQJRpiWfWBOU1zNdcqKVIN3aFekADIsaZS1lfXeQWdStk46fXpKsu0xPmcCwaEQbqhEXvMkmpe+hvbNq9C8cRWRnZ8d2mAqNaROhKITIHUCHEJmm9Osi0ui0KlV3Sv2guFipIoWXwLcDnzU+fz+OjvvAxdIkvQFg6gfLFakRghZlnG99DKhsrKuNv+GDXg+/xzd17+OLMtsb9nO5sbN6FQaprc3Me7A7ZYV98OkMyFzBrr3f4a6aZfSHvKR9cXPOXPJ/1HZ4u2qW+cP+1lXv47XSl/DrrNz5eLLMWhVvLK+hrxkE78+ewqTMhP0bBGMHK37oOB4WlvWkWpMISpDs29og80B0o0ZlLaVMi9zXu+drJmdW43pTM2yc9/He2h2B3BaBl7LTCA4EKluC3W0svnU22iK+JiksTFl27Pok/JRpw1qV+eQaPYEmZ3nwKBVsXxnI/lOE5cek4frMPqmReUomxo38VbpW7hDbs4edzaz0mb1bG8zVvlN2zP8xg5DnLXXS9HiuCVPWZZbUOKmBoUQUiOEHAziXbkirt23YSNJX7uIDc1b+PZ73yYUVZaS7Xo7j531Rya89sPurJT2WrBmoa74Im6cjEgtsq777VxRu4IbP7qx6/ELu17g6TOe5aaTl2DWqUWh4NFOaxmUXEJbrYtCWwGtfhUGjcxQ18pON2ewsm4lwUgInbqXzE9LGrSUQu48dBoVs/OTeHNzLd9aWDC0kxEcNTToJW5xb2FH9d6utj9MvZZl/sNThkivUfPYF2UUOE1ccWwBNS4fd7+9g4evmHNY5gOwpWkLV717FeGoEoLxeunrPHDyAyzOWdzPkWMMRTQd0UWLBUOESq/HuvTUAxqU/3pzSSHhd3/G45sf6xJRAO2Bdj4PtcKyP0FxZ0JBUgEYHcgpB/hFdcYCGJOzyXcqsTT+sJ9HNj0Sc/5gNMin1R+T7TAKETXaiUagvVoJNg+2Y9aZh6w0zMFoVRpSjCl9b+9ZM2OKwC4al8IzKysYjWa+grHBDvzsaFNE1P5Ehz/vfYEm0+FZbclOMvKLMyezsaqNRz4v5Y1NtczOdzA54/Ct2i+vXN4lovbz+NbHCUZEdYHRxoiuSEmSlA68I8ty4g5no4xWb5DNVW1UtnjJTTYyLdtOsrmHrY66zcqPWkvSsnl02H/Crimp1EZcpBkzkcw51EXGMcm1m8mpU0nWJ+MKuvCFfSSbM/GG1ASmXc3maXdSWW0k1xeh5OzHMb3/I7ZMuJ5dPjtJNgsTbTbqnv8vapMJ7ZyZRIj/0o1EE/sibuoIsKm6jbo2H/lOMyXZdmxGUSxz2GmvAYMDNHrag+2YtRa2etRDZsZ5MFnmLHa5djIhuRcrC2tmV+YeQEmOnf+uLOer0maOHTc0poOCowufFOXSiZfiNDoJRoKYtCbe2/ceIV3i28U7G2rZ3baDBm8tGeZsJtgnUZzahzdaL5w4ycx/vm+hrH0fSfpkJialkHYYY6Qicvzn9cHCSjA6GOmtvXsY4rTDw4E/FOb+j/fw8Gf7utquXlTIj06bgOmA7TUqV8ETZ3dtzfkmn83jOcW8tP6vANh0Nm6YeSN3rboTgGWFy1jtW82qulXd5yr5HvX1i/jbJ92pudcsLmTRwke46qkNQDvQzqyMVn7ra0LzxMOo8/O48t6b+FHTbV3HaCQNi7IXDfg1tvlC3PXWdl5aX93V9qNTJ3DdCePQqsVC5rDSWgbWLELRMIFwAIPGQINXg10/9CtSANnWbN4te5dINIJa1cPeoTkVvC1KCQ6tCZUkcdb0LP74zk5e/p4TlWrMVI4QjBKybOP4z87/Y0fLjq622+f9jIxwYjcLVS4XT+18hFdLn+9qu3TCFXzH8F3SrImVnHm/6l3uWtltXzQjdQZ3LrqTfHtPSWXDz4m5J/LE1idiBNUVU69Ad7BvluCwM2LfiJIknQR4gLqROudwsbfRwyOf74tpe+yLfZQ2HlDxOxKGr+6Lcd0tLV7CS3tf7Hq8rHAZ/970YNfjIntRjIgC+Nv2J/Br3DFtj36xj+0Nvpi29XU+KsYrC32R8gqmlEf510n/YnH2Ys4sOpPHTnuMaSkDTy/e09ARI6IA/vHhbsqaPL0cIRgyWvd1eUhZtGZUSEPuIXUgJo0Ju85GWW/mnCoV2LKUTMJOjitOwRcM8/TKfgw9BYIeqGhviBFRAA9vfojSYGLbxfva98WIKIDndz/FXte+Xo7omT2te7h3/b0xbRsbN7K9ZXtC4wwl01Km8ehpj7KsYBnHZx/PfSffx/zM+YdtPoLeGZEVKUmSdMAvUezYX+mlz7XAtQB5eSOftZEInkCEnsJDPMEDll2jIWVl4cDnD7pzt+qstAa6C9D2tGzrDXvR62K/QGUZ/OEIapUU4zLtlbrfTmlnKUtOuZkTck5AStBTBcAdiF/9CEVkfKHhWRUZjRy2a7K5FKzpuIIuLDrFG6zOo2ZGWrwX2FCRa8tja9MWxjl6KauxP04qfSoAKpXEdceP47dvbGNGrkP4So0AY+kzsj88ofgbslZ/KwE5sa2rjoA7ri0qR3scvy/8YT8dwY64dk/48N04alQa5qTPYU76HGRZPqTP8aMVSZJuAC4C5gMrgSTgZlmWlw/H+UZqReo24H5Zll29dZBl+SFZlufKsjw3dZTXb8t3mshJit2hzLQbyEs+wOlea4S5V8ce11qF09BtwLmydiUn53Y700tIGDWx487LmEdVQ+xSbrZDicm67vgifnLaRKZl29CqJfL83XXQDLOU1alD/eMrcJpwHFTTb3KmLaaY8pHOYbsmW/aCJYM2f1tXeZgGr2bYVqQA8qx5lLaV4o/0YvhpzYT9lhudZDmMfHtxId9+fA1VrQdbwQiGmrH0Gdkf+dYCNFLsffwpuctI0ydWoDffmhdnJptjziHHklgx5FxbLsfnHB/TplPpKLIlUK9vGDmSRVTJEyXfKHmipKzkiZJo5+9vDHZMWZbvlWV5CVDd+fvlwY7ZFyMlpE4Bru80x5rZWY15zJJuM/DQ5XM4cWIqRq2aEyak8sgVc8m0HxT+NXEZLP0dmJxgzyHdOZm7j/0bc9PmY9QYsWiS+Pr4y7ik8CxMGhNralfyl+P/zPSU6Rg1RpbmnsEcy7eZnJnKwiInRq2aJRNTufnkYm58Zj33L9/Ln97dyVklmfzn/GKSn3sMbU4Ozr/cTdKsYwb0WgKhCBsrXby+sYaVpc20+5SMkHynmSeuOoYFhckYtWrOmJbBPy6dSbJZ7M8POy2lYM3EFWjFpDUjy9DoVZM8TMHmAHq1ngxzBtuatvXcwZ4Dzbvjmo8pSOb0aRlc/fhqvEERCCsYGJMNyfxp8T8ptk/ApDFxTtFFfGvilRhNiWXJTc3I586Ff+OYtIUYNUYWZBzHHQv/zKS07P4PPgC71sqlRd/lrILzMGqMTEqawm/n/51sKbFxBInRKZoeRnE3lzp/PzwUYqoHlkqS9KkkSRskScqQJOk3kiQtAZAk6UpJkq7s/PdySZL+LEnSuwMdeES29mRZ7pL6kiQtl2X5mpE473AyJcvO/d+cQ6s3iMOkjQ0y348lDRbdpNSxU6l5ZUeAH92/kbNnXsMDix3k1q4i7ekLmGXL5priUzC112Kr38v0kx9kc209/3ivlpf2tWLRt/Pwt44h32misrGdSx9bE7O1+NBn+3j9xmMx3f83tDojlrTMAb0GWZZ5fVMNP/rfpq627xxXyM2nTMCi1zAjN4lHrziGNn8Ip1mHXjvEJkaCeGRZKQ9jzcTl2opZY8IVUKFTy+g1w2s3MM5RzLqGdcxOnwUH196zpIGnqSvg/ECWTcugvMXDHa9v448XTh/WOQqODGyN61n6zu1MuuAJPBoT+eUfY3ziZPj+V2AY+GpSfbufP7zo4o5zf4lKFyYa1HDHqxU88q3EDGNrGhvZVmlk66aTOWf8GdS0RHh1pYGUeQHSEqt/LEiMvooWD7W3VLEsy8dLkvQr4KQ++i0A/inL8o8HOvCIG3J2LrMdERh1aoy6ASQhWjPo8IV48JMvkWV4bX0zZ+aqmfPF7yDoRtO4g4z6LUrfrLnYDVaOzbeQeX4GTe4A6TYD+Ul62PUua2sscfFZLm+QUBiSchJbhi5v9vKrV7fGtD382T7OKMlkVp5SIsZs0GA2CN/WEcPXGTOnt9Lqb8Gqs1HvUZNsHP7YtHRTGlE5yr62cgrtBbFPqtRKwHnzHsiIFUuSJHHFwgJue2kzK0ubmV/UR/1IgQBQy1E6UmfRUFZJW9SATjZQpLdDNLGbhapWL3+a28rE/10N4SDoTNy58C9UtnoTElLtITUPf7aPxo4Am6r3x0W1cU5J4jYKgoQYlqLFvfBk5+8K4OCtFSNK3T2ALbIsv5TIwOIbcpiIeH2EysqIBvzo8vPRWuykWPXsbXRz44nFWLMl3j33cXx+J00umTxVKzOb38bknMbaXQ3UuHx4AhFyk4xoAh04q/eh90eZ4IBbThlPOCrjC0Z4bnUlJ01KJdORuN9Juz+ENxj/Bd0yBGURtte2s6W6DQkoyXEwMaPngsqCg2jeC7ZskCRa/a1kWrKobFOTNIzxUQcyMWkCK2pWxAspUObVuDNOSAGYdBoumZvLb9/Yxhs3Lj6iYzoEg6c1ZS5Ppowjt8WFMejjbc1U5pz+OgvsOQmNUyDV02JN5+WTP6ba5Vc8/dhHutSEEl88MCKShsaO+GQOf2hk/u6OYoaraHFPHJw5EAT2BxueTnccVXwGQz8IITXUREKE2zpo/Oe/cD37LAC6iRPJ+cs93HBiMZMyrMweJ/PUznvJjn6TR5Z3l0g4dfJZnOvP4devbaTJrYgZnVrFbcsmUR7UcXrbx3yZdC33fbyHUEQmxaLjjxdMY5YziL5pu7L9Yhn4OrRRqybfaaK8uTtQWK9R4TAOLg5qU6WLSx9e0SXSrHoN/3ftAqZl2wc17lFBy15l5QdwBVxYtBbqPBocw+Bq3hP5tgK2NG+j2l1NtuWg+BB7LjRsBb7W47ELxzl5Y1MNy3c2cuIksR8i6J3SlghLt36M/tMP0aQkM6GugZZbfk5rfg5JCcRhBtRm/rMnRKpUxniLn+2lBtZrkrg+PTFjT6NOwzH5Sawu786iVkmQ7jh6kmsOEz9DiZE68D96KIoWD4TXgPslSToZaB7MQEJIDRUtpbD1Fdj5JqrcEzDmWXFJEsgywZ07aX7ySeb9/BcYDe1sbt7CRNtCHnijNWaI97Y3ccqUrC4RBRCMRPlwRz16tZasGZdzx/Pdwisqw0T/RrKe/5FSUsRRAOc/CPkLBzRltz/MFQsLeH5NJTvqOsiyG7h6cSHB8OC+tJ9bUxmz0tURCPP6xhohpAZC8x6wKNsJrmAbFp2FWs/IrUipJBWTkyfxWdVnXDrp0tgnHXmw801AJi6GClBJEmdOz+Lfn+4VQkrQJ86GalKL9Ohz0tF4ywimLMG25ys6JuWTZB74qtQ+r4HLMiqYsuIn0F7NGfYCts7/I3u8c0k0r/H0kgwissy6ChepFj3fXlxIOEGDUEFibL5i8zMlT5TAQUWLN1+xeUjio2RZLu78/ZsD2h4/oMvxBx1ySOFHwqJ6KPC54LWb4MM7oGoNqq/+gs37AsmXnN3VxfPpZwTbm/mo+k38YR8q2UCghz9STw+ZT00dQbQaFbX+WDuCHx+jo/ij6xQRBeAqg+cvA1flgKadbjfw0Kd7GZ9m4foTizlhYir3L9876LIIFS3xqfDlPbQJeqBpN1izCEZDBCIBTBoTdW4NSSO0IgWKMWyrv5Xygw06DXaQ1NBR2+uxCwqT2VnXQWljwqvjgqOINKsP8+4/oNn5PFSuQrf+TzjSa7GEE/ucyKGBKZ98r+szUNVWRsnn3yeTpoTndNdb23Fa9NxwUjHLSjJ48NO9eHoIfRAMLZuv2PzM5is2F2y+YrOq8/eYK2AshNRQ0LwXyj6LaVI1bcU8sfuu3DB/PmWhdra0rGVyah6pjhA/PiMdvab7LbAZNGQ54oPXT5qURrI2RI7UGNOer2mGQHtsZ08TtA1MSGU5jPz90lmsKmvhvo/38M6WOv580XQKU8wDOr43Lpkbn3Vz/iyRRjwgmpWtPZe/FZvOigRKwWLjyN0ZqyQVJanT+LjyY2QOCP6VJEjKh/peLBIAjVrF4uIUnl8zsGtQcHSijdSC3xXbtvNpzIbELDSc4fr4z0Bvs9KeAJl2I6dOyeD9bfXc+9EenvyqHF8wwvh0Edsp6B8hpIYCVW//jcr2hzo3ly2LzqKqzUuJczI3Lv8ud636Pa82/Iw/fSMJm0HDvEI7t59npdhax73nF5KXbCLFouM7xxWSaY7yXedGJpc9w4+X5LHfIN2ncYB00LnVOsW3qgfKmz28u7WOd7fWUdGsxN0tKHLy6vWLeO2GRbxx43GcPDl90LXTFo9P4c7zp5Fu05NpN/DHC0tYWJSY0d5RiSxDaynYsmj1t2LV2YjI0ORTj+iKFECuNY9INBLvK2XPVYpw98Hi8am8tK6aaIIZWIKjCH0PMUwqDUFtYtEmJqOhx89AsyHRGCk1Pz19Et+cn4fNoGFWnoOnvj1PJMkIBoSIkRoKkoth0lmw442uJrngeLRzlxK6ZxZvu/U4VGEmhrfx+Pb/dvWp9dTySsW/+fWll/B+xf+4e9Nyfj/te5z95b0cu/AW3NknEi2rIrmjHFWHB19FFqe8chezf/ArvmhTkVzgAO0d8P4vu8aMnvIbVPYDVn+CHvA2U+vXccljW6lr93POjCz2NLiZmG5haradTLuRjIPNRAeBw6Tjm/PzOW1KBpJEQmnIRzXuelBpQW+lpaUVi9ZCk1eNRRtFO8K3PBISM9Nm8UnVJ0xImoBW3bmtnFQIm5/v89i8ZBMmnZq1Fa0cUyAEtCAel72IFEsGkru79Kr/2B/iQ0Mi0qXGaEW76EbSP/9HV1vdcbcQNdnISnBOmXYD1yzM4vpZekJqIybH4FbmBUcPQkgNBQYrnH43jD8VSpdDwWKk4lOoCCXzudmIzS5xafhl3vWpcegd+EI+AlEl1XZ9w3pmpM5gVf1nSEi0BdvB10LyJz/Hesxt7Pv181S7XDGns6xbwX9qMzl7+iICmXPQn/QLCPlBa0C18Vl8SZMwTjoZ6rbC+7+C0o9Iy5jBgyf+jHdcRaza18JrG2sAyE0y8sgVc5mYkZij8EBIsQoBlRBNu8GhbIu2+ls7M/bUOEfAQ6onUo0pOI3JrKxbxeLsRUqjNR0CHeBtAlNKr8ceU5jM6xtrhJAS9MjuQBo1Jz5ORu3HWP1V1KQspiF5Ltlhbf8HH0BVoIV/+3ZxzRm/xxmJ0KzR8EDtcm4NnpiwkGou30rqynuwlH9AxJpD7bF30DrxRJIsg4sZFRz5CCE1VDhyYc4Vyg/KNtr6TRtZqt5LisGAKWrlXK2FczPOgqQimrVObtn5IOn2PGakHMO3p+mQiTIrbS6hUzOR3M1ETAWk/+7XtD72JL7165XzqFR488bx6GlTmZBuIfTC47C1c4Vg3Mkw9Tz0deuIylGkj36J1KhUL1fXrmdm6zVIy17lwU9cXdOubPXx3xUV/OacqagP3tJzVUDDNgh6lbgrWxae1Nl81mSixuVnfLqFGTkObMbEPvwEvdC8R/FqApp9TVi0FmrcmmEtDdMf01Nn8F7Ze0xPKcGmtynbKMnjoHYzjDux1+PmFSRzz3s7ueOcqcJTShBHsxd0DgNrTMW0+J2Ms6ezqzpAenJimb1pKj2XpJ/MTNmAzl1FriOfS9JPISXBr7aW1lbSdj2NJm8GpOah1lnIqf+YJkcaWOYkNBbRCNRuhJr1Ss3VnLmQMiGxMQSDQpKk3wCXAPuD5V6QZfneXvr+XZblWwZzPiGkhglfSxXn7v4Z+vr1cMJtyOVfoir7tOv5lMU/5B9532NdMtz6yfVEZGXV4UXjizyatYyi5XeikSSk+X/EesF5yMEAcnoqrTd8Da85Qr69DZkkIqrOVZ/UiZBSDB/+Vgl8O/7H0CmiuvC3YfGWA7HCZ0VpM/5QGLP+gPb2Wnj1BihaomQjdqJPmUxV/h/5/RdKVtbtyybxneOKBh1XJUApCmzJAKDZ30yhrZBNDWqSRzg+6kDMGjPFjmI+qvyY84rPVRqTC6BmbZ9CKtthRKNSsbWmXdheCOKYkNrBL9f8lu1te7ra7pr3C3SaxJJS8rCQv28V6u3KzaQWOGPGVZATl9XeJ8ZAExqDFT78bXdj1iySipcmNA4AFV/Bk+dCtDNw3pIGV7wOqZMSH+soYPukyd/gIPuDyTu2D0Xm3p2yLP+3v06DFVEggs2HjZSOXYqIArBlIx0gogCklQ9gsGp4avvjXSIKoMnXxFqNpNzJlFyMzuQmSfMFObedQ8etZ3PNmh9x0/Kb+NrrX+Ojio8IzrhcKd8x6WxY858DziArgecHoTHFu/2ePi0jvlZg/WZILoTVD8ce37SdRZaarsd/fX+XsDYYKhq2K8HcQEtneZhqt2ZEysP0xRTnZKrd1VS0d5oNO8dDzQag92BySZKYlefg3a11vfYRHL1UefbEiCiAv2x6gCiNvRzRM1JbfZeI2o960+PQ0ZDQOLpwB6x6KLaxZj0qf2vPB/RGyAef/rlbRAG4G2DfZ70fcxTTKaLiihZ3tg8pvRUjliRp+WDHFkJqmDDhV4q7ShJEeyi5EvISUsk0++M/OFojXihcAuEA0ke/RbXxcbRv3sT4r/7NWVnHARCMBvn55z+nKTmNhgtfJugogsgB59n2Gsz7Tsy48uwr0aZP5Jvz89i/27K4OIULZ+fEb78E3GBwKHYKB2GIdgunQDiKPyS8VoaEpl3gUMwIW/0u7HobtW4NzsO4tQegljTMTJ3Be+XvKaLf5FS2+FrL+zxuVq6DD7cnloYuODrwReJvvlwBFxE5lNA4cvDgqh+ALCMHE7u5i8rheBsFQA7Hl43pk0gQ2mvi292JCcSjiL6KFg+Wn3eKp/s7Hy8AvpJl+bQhGDsGIaSGgY5gB/tSc9h7wf1UXfoUAVs26CyxnQqOQ++Fpdnnxx0/W58GWTNg2ysx7abST1hm7S5M7A176Qi3kTb1BHxFpyLnLuju3LQLmnYTOv3fhE+4E/myF5FO/iVZGRn86qwpvH3Tcbxx42IeuGw2BT35RqVOgtKPYep5se0qDWWq7nqSC4uSyU0auoy/o5agRxGtlgyCkSD+iB+DxkTtYQw2P5Bcay56tZ41dWuUm4OU8VC1ts9jJmRYqWjx0dDhH6FZCsYKxZYctKrYEIPz8k4lQ5dY0ouUOh7ZFuuELiePQ0otTmicNlMK8sQzYhu1RgKOBGvnGuxxN7AAFCW21XgUMZxFi++UZXmJLMvf73yccDHigSKE1BATlaNUVXxB/hf/YtzzV5HzwrX467cQvfhJ5LxjwZiEPO0iIgt/yPIGK031U/j2lBtIMaZQaC/knoW/ZcreL5U0+B7QRLtXJ2w6G6lGpRCCPSkV6Zx/Ic/4BhiTiOYtxpt8Jq61LUQnXoBUfAqYlSwrvVbNpEwb07LtWA29BIqnTVYyEdOmwuxvKasQWbPwXfI/XqlNxm7UcsncXH5/XgmW3sYQDJym3WDPAZWaZn8zNp2NjoAatQQm7ejwY5qTNpsVtStoC7ggZSJUruizv0alYnqOneU7xd24IJYCTwf/PvZOSpKn4NA7uKL4Qq5JW4Ac7mGFqQ/C6RPxnfkQkcKlYEwiMu4MvKffTzQtMSGlMSdTM+dyojO/qXxGZ8/Fd+HDlNvSExoHgMnnwtLfKaWenOPhkv9CdoIB60cPvRUnHo6ixcNWbkEEmw+SDl+I8hYvWrWKghQTTd5a0na+i2XHm0qHkBf7R7/HffETGM/+N4TcbG5S8ZM368hJDpPlsLNqw3TuOOkkVre8xm/X3sMp6fM4OSmDhVmz0Nas7zpXNHkcm2RlydqqtfLH4/9ItvWA4MzUCUhn/xN8zchRLdpglJTTUw4ta0qSIP9YyCiBkBeW/Ax0FowGK3cVhnH7wySbdWjVQosPCY07u+Kjmn0t2HV2qt0aUkfBatR+LDorE5Mm8fa+d7h0/Pmw6TllO0Tf+ypCSbadj7Y3cHEPbveCoxdJZ+SYF7/NQ/Ovw5d7Ds7NLyA5WmjKnEki7k17Gz2c9biL6+b9lBMmwntVMk890cSbN3mYkIArucPgoNKRw0PJDs4560/s9NcjyW7Oc+Qn/uKs6bDoJphxqXJD3ENcqqCLw1m0eMgQQmoQ7Gvy8POXN/Pl3mZUElxzXBFXzQXn7g/j+mqrN6Gech6Vre3c8u4amj1BdjV0330dU2jkmNwiHg+281LlB7xc+SH3zLmOk3Lno9n7IRSeQHjWVcyIavlbxilkWdKZklYYd55KXx1bm7biDXmZkDyByXIyakl96C9Sb1V+DsCs02A+ODhdMDjqt3Z5SDX7m7DprFR3qEkxDV5IRb0+QtVVRFxtoFGjzcpCm34Id9rA5ORJfFDxPuubtjErpRiq1sC4k3rtPyPXwTOrKghHomiE6BZ0Em6vRX/8j7Ds+RDLlhcV65aQF52vDRLQHa0dbqIyPLCygQcOaHd1eCDB8i4lqSWkmdKocdcwXz+HfFs+GtUgPucsonB3f0zesf2Z7ZMmwxBn7R1YpPiAtiW99O2xPRHEt+EhEo3KPLe6gi/3NpNk0vL7Ej0FjRvwhecQTpuKxhUbiNthzKOhuR5T6V6eTN5FJNuAp3giN33qo7TJxzinink6J49Ov5nPOvaRrHdQgBbNtK/Bkp/RFNTy45e28HHnNolF38aT33YwO6/7U6eivYLvfvBdKjuUOmdqSc2DpzzIgqwFjAjRiOKdUr1WCbTPnafYMgj6p34L5CnvU5OvGavOyo5mDc5BWB9E3G58mzYRqqlGm5KCymxFDvjxrlyBymzBsmgRkiExs0FJkpifsZAPKz9gfNJMLGWf9Smkkkw6Uq161lW4mFcozDkFClGdDV79nvIZkTIRtryArDURXfCDhMbJNfixGTW0+7qz5JxmHTl6X+KTCnSQ3rCbdHedEtMqayFpKEJ1BH3RKZrGXKHiAxFC6hDpCIR4d6uSkfTXmXoyf30rwbt+xP+2tHPjrKvRlH/elQUip0/FlzQR7abNNFx/M3TGOWnTUrn/r3/kzTYLk9mJtrGdec4i5nm9sPtTSCqArAVgtLO2tK5LRAG4A2H+9PYOHr3iGMwG5W3c2LixS0QBROQI/1j3D0pSSjDrRqDcQfmX8NS5iqACJa7qyjeVeCtB3zTugOmXANDgrSfVlEZlu5YiRw8Zn/0gh8P4Nm8msHcPupxczAuPRdJ0x7Hp8gsJ7Cul/b13sS09FcmYWLKATW+lJKWEl1t3cXlTjbL1qz048aab6dl2lu9sEEJK0IVkyiJSsBR12ftKg1pL5KQ/0q4yJrIgRZ7OzUNnpfLjD1qobA1Q5DTyp1McZOkSi7UCoPQT5Saw4ivF+qW9BiYuA1tm4mMJjiqEkDpETDoN8wqSkGWZzJUfI/t8tGSPp2GTH131P2H+dUqckaRG6qjDHmim8YEnu0QUQLShEfX2jVy8cBxJq96kbfKFJG9/A9Y82n2ibS/Dtz+gti0+zmlbbTsdgVCXkHIFXHF96r31+CN+zAlFHhwCIR98dk+3iALwNsO+T4SQ6g9/u/J/ZVG22xq9TRQnjaeyQ8OCrMTurIPl5fjWrUPtcGCetwCpx+KwKvTjigmqVLQvX47ttNOQei283TPFjmIafU3U69tIr1wJRb2bc87IcfDsmkp+crowJBQohLfto2FbFvZ5v0GlChL0GWj67ZMk/3UCOBPYEgv7WPDOebw09WpaLcU427fjfOtJuPKtxCbUVgU734YNnf6NFV/B7vcVR3IhpAT9IITUIaJVq7hqUSGN7gDaPQ2EZ5Ug6004dH40TWXwaawBpzr9BOTm5viB2jswqW28n3kdp+o64ODAcE8T1G9lfFr89tzp0zJwmru/KKemTI3r87WJX8NpdB7Sa0yISAg6auPbe/ChEhxE/VZILlKMVVFipCwaBw1eNSkDDDaPeDx4V64g6vainzIVtcPR7zG6wkKibje+deswzZ2b8LSPSZ/LltZKdFteIKkPITU+3UqNy0d9u590m6hbJgDZ58f12vu4XjugUaNBHUrQMy0SgpCX1A33knpweyJ4mmDzcwe1NUJbObA4sbEERx0i+nMQTMq08fvzSrB+7Wtsvu4E6iI7cavMeBb/QtmW248ksTtqQ3/J12IHkCT0M0p4ZJOKYr0L8wsXQ0spu855jbcXv8Anxz9L/cybQFIxI8fOL8+ajEGrvGWLip1cd/w4tJrut3Cacxr/OPEf5FnzsGqtXFtyLecXx/tUDQsGG8y7Lr698ISROf9Ypn4LdGYH+SMB/GE/HSE7dn0U7QDyBAJ799L+9luoLVZMx8wdkIhSkNBPmkSwooJwQ2JO0AAalYaMwpMwuCqobtzcaz+1Sura3hMIANRFhUjaWNsUxznLICPB1R97ble2axfO8ZCo/5PWqJjMxk00vjqEQHAwYkVqkGQ5jNTMGcff3vkFlweX8SN3K41/+QB3yQXYF6SjL3+Wlrm38FaNlUuWHI8lGsL/fy9Ckh3d967kK3sTl2lbyf7gB4DEmnE3cNnLXvydd2Zzsk/id/PGUVn7Efm5al66aRLqsJPsZCMWfewHkU6t46S8k5idPptAOECaKW1kC8ZOPltx9v3qXiUl/qRfKAU7BX1TswGSFCHV4G3AYUiisl1Luinc93GyjHf1akJ1tZhmzUFlsfTdvwckrQ79+PF4VqzAftZZkOAWn9XoxOXIZvNXfyf1zPvRqXvYSgSm5zh4b1s9lxwjgncF0JadSc69/6DxgX8TqqrGtux07Ocuo1kKkFCunb+djrP/zg5vLXWBFrIMTiYaM7H42hLbkpM0MPcaWHFfd5stB8wi807QP0JIDQEhtcwkSxFLP2yh6dWnAfCt30D7+xkU/PoKrOog15s+5H+NYV5OW8F1f/k2QbXMQ3sfILw9zFLTNAh58RSfxT0bNPhD3U7Qa6u9fFxaxUOlPyEqR8kwZfDvpf/Goi/qbTo49A7o+ftseLGkwYLvQcnXQK1VXH4F/VO7EWZfDkCjt4EkvYPyNg2pfVkfyDLuzz8n6vVgnDM3Jpg8UTRp6YTravFv345havz2cH9I2cewcOdbPLvjWb419Yoe+8zMc/DkijL8oQiGgSyzCY5o7NU1VN/2c0zTp2MYPwH3p5+h0kbRXHkFJGBu7g95eLz2Ex4qfaWr7ebxl3KFYSkJ/UVoDeBvg5N+CdVrlK12gwPk0WGGKxjdiK29ISDdlM71qV8j+No7Me3h2joCta3o6jdi3/kmc1JKmJ0+m6dqXuOfOx+h1lPLGbkn4ajeAIDHVszu5vgsLZcHUowpqCU1heZsaCkFV8/Gr25/iDVlLby9pZat1W0Ew4ehTps5RYiogRIOQvNuSFKEcb23AbvOTlmbhrQ+hJRn5SqiXi/G6TMGJaL2oy+egG/7NqK+xNPGg5Y0DFoLnvLP2d6yrcc+NoOW/GQzX5X2ECcoOOqIllUSbW3FvW4d3vXrCZWX0/LMS1hcbQmNs48gD5e+ikalIducjQoV9+75H2UJ1uzDng3FJ8OnfwKjUwk0r1kH6YnfWAiOPkZkRUqSJDvwLKAGPMAlsiwnntd9GKl2V7OxYSNV7mqKrJNob8ukWCWRXr2VwJ495BWOw79sGR1vxWeLuLPnsG7yeTy29T6q3FWclnc2M5OWYpSS2dvg5omiZcya0YYuKZvTwgGeWdVtYXDGeBPfyWjgO8zFXjQZrcGBase7YN9K0JJNsPBkLDo1VK0hWrMevy6TtQ1Z/OFLDz+cb6agoJlIyx7cyVPpSJ5OdlY2erEiMHpo2AbWTOWOGKh115BkTGJfm5aZaT0XTPXv2kW4oR7jnDldAeqDRTKZ0GZl41u/HvOxxyZ4sIQnYxoXtezlni1P8PvFv0Orio8tmZXn4J0tdZw4UWyXHPVodBT/7z7Unt1IHXXIGd/GtbYWtTqxm4I2JO6a8VMWhDXoWvYSLBrPJ3hoT3CLGqAp4zjU5z2NpnYd0XmLCGbOJdWS2v+BBxGORNlc3cba8lbMOjVzC5IZn6A5qGBsMVJbe98E/irL8vuSJD0AnA681s8xo4YGTwM//uTHbG7azARLIVdbQ8zV+1G9+T4tr70OKOrQdt65GOfOxbdmDQDagjzk7CS26LXsbttEtaeaem89T+54BE+xm00bj2ddheI1pVFJ3H6GFodJ5sSJaSzf1cCUDAt/LlqD+eXfdk8me7ZSUPj9X6Fb/APa9VmYWzcgvXs7KiAFuCJjLoVn3cmS0r+ge/U9AIwAs29mbeD7HDshY8T+7wT9ULsBnOO6H3prmW2ZT51HQ5o5fkUq4nLh27QJ8yC383pCl5+Pd8UKIi0tqJMT83zypI4nq3I1451pvL3vHc4Zd05cn7n5yfzuzW3cdb6MWjWCsXuCISXc0kKwtJRoKIS+aBza9MSFsXV6Hqo3vovUtBsACUg6/U8EMrL7PvAgsnS5TN/1JKZtz3e1nT7jSppzejeJ7Q399hexfvjTrsfB9Fk0nvMfUrPjK0j0xap9LVz26EqinbuCSSYtz127kAkZQkwdqYzI1p4sy/fLstzpvEYqMKbSd3a17mJz02bGWQr4bfvJFN1yH8Ytu+joFFH7aX/lVWxXXoZx6UnYb/4u2t/fzI86PuZHa+6mPdDOaQWndfV9tfQFFkzo1rHhqMzynY2s2teCNxjm+hOLefgMG+Yv/hQ7mep1XRlerH4Eu9qP9NHvYroY6taw0FiFrvS9mPbUDffhrdtFi2dMLQYe2VSuAmd3gdV6TwPuYAZOYwTtwX+d0SjuL75EP24ckql3A8xDRdJo0RUV4l27JuFjZZWGjoxpnOcJ8F75u7gCrXF9MuwG7EYta8pahmK6gsNAsKqKqltvpfyyy6m86mrKL78c/+49CY+jatnRJaL2I316N5rWvQmNY26riRFRAOaNj2Nuq0tonKaq3Vg//31Mm65+PerGrQmN4w9G+OdHu7tEFECrN8SXpcIG5khmRGOkJElaCCTJshxXNl6SpGslSVojSdKaxsbRVS3eF1HiRr5tOx3VXx5RTDUjPcceNWkD/GhpPZckvcBZW3/Kl03r8YQ8aFQa9AdkNNl1dtw+MOvUGDu32lzeEFaDlpX7Wrj3oz34PG4I++NPIneuVATdyJIKwvFxLapID7Eu0TCqSIBgePQUwh3NjMg1WbVaWWEE2oMdROQwtW4b2Zb4jD3/jh1InbXyhgttZjZRf4BgReLF190ZU0lq2stiSxEv7365xz7zCpJ5fWPNYKd5VDIaPiM9X32Fb+Wqrsehigpan38OOZpYLKa8/3NNrQVjp5e5vx0i/WSqHoQq1HNMn6qHz8S+kCJBCMa7oUu9jN8bwUiUJnf8jWqrJ8GYLcGYYsSElCRJycC/gKt7el6W5YdkWZ4ry/Lc1NTE96WHk3H2cRg1RiwdYQgrf+jhlha0OTkx/dRFBcjZGZS3l8e4jJ9ecDqhaIjVdau72m6Z+RPOTPLw/rFbeP+4XTxympFl0zJYtU8JxpUkkCUJJpweOxm9rcsdXZ54JntDKciTD9pG0Vup1xcpQd8HEMheQNiWL0wRB8iwX5P+dmir7PIcq3XXkGJMYU+rlvSDtvWiPh++bVsxjJ+AshEyTEgS+vHj8a1dixxO7EstqtHTkTmd01sbWdewjlp3vGBaUOTkrc21hHu5ERH0zmj4jHRv3hjX5lu5iqi/hxu+vnBOgMW3wqJbYNqFcPKvkY/7MYHkxLbRtKmFRB0FMW2RlEnoUhIbR+fMwzfpwoMazcipibnx24xarlpUENe+qHgETJEFh42RCjbXAf8Dbpdluby//qONIkcRj5z6CK5dW5H0euRAANeLL5J28814163Dv2UzzJuF/uvnIlmN/On4P/Hktiep7Khkaf5SpjqnoQqnk6LPZ376cZjkQhYEVWR8fHGXA2+OzsL0C1/iGbOO4hQNP1iUTF66BnLuguRxsPUlyJiGPOUCpC/+Rnje96gf9zUMzgKkU34D9jzY8gKkTyN43E+IGCYR+voLqD67B3XNGtrzT6N+8pWU5OSOrLeUoHeqViklKDorzFe7a3AanKys1nFCTuydsG/DBrRZ2Ujm4a+ZqE5KRmW349u8GdOsWQkd25FZQtaGZzl93Hxe3PMSN8y8Ieb5DLuBVKuBz/Y0iaDzMUho+gSI3UkjctxcQjoViaQ9BMwZGHa+hdS4s6tNPvNvBDVSQsWsLOlFuM/7D5oV/8JQ/SW+vCVE5n0PS2pifmVWi5WmhT8mYk7HsvNFAs6JBBb9lOSCkoTGAThtagbhiMzDn5ViN2r5wdIJzMhxJDyOYOwgySPgkyFJ0veAu4D9tzMPyLL8XG/9586dK69Zk3icxnDjC3oJfPoF9bf/nGhHB2qHA909v0aX4USljaIlymutm2gMtHHOuHPRhWSiwTDtWjVGlYni9Y/izZ5JR8YCsj6+DdX2V2NPcMJttM7/AVqVCovhgEDiaESpxaa3giwT8nfQobJjM+rQqDsXFaNR8DaBzgq6A4rQhvwEPC58GhsOy9DH1Rwh9Kssh+Wa/OC30F4Nsy4D4PFtTyCh5a4vz+BnC1oxaJS/zYjLRccHH2BauHDIA8x7Qw4E8Kxaie2UUxJwSlcwN+zA1LiT2+1GfjD3h+TZ8mOef29bHfVtfu6/bM4QzviIo89r8nB9Rn6+8XVS/+9joq++A7KMesFcaq47k0XzLkSbQMZdx6Znsb50UCUEUzKuK97AkYDlQDAc4ZmVFQR8bo7NUvFRRYS0JAeXHJOL6hASGoLBEO0t9ehMVmy2wVm4tHiCaNUSVsPI/M2OAOIOvBdGZEVKluUHgAdG4lzDiVFnwnjKUjTFRaze/iEuc5SF8hYy/tsZ7K3ScMbSX/M71178TSvQPf0OUqsL/QWnsGaiisLGjdg3P4U9azZyoCP+BO4Gkkw9FZlVK2aXnWh1JuJyqlSqmD7dnQ3oHRmHxZ9T0A/ln8PEM7seVrZXkmleQpIh2iWiQFmN0uXnj5iIApD0evTjxuH+6ivsp58eXwOyDzypEzE37uJy7Lyw+0V+MOcHMc8fOy6FW5/bQIsnSLJZlOAYSzhzivnHca9xxknfRxWFz+U9nF84LiERBaAJ92DtEeiAaGLbyVuq2/n9m9sJHxDdrddUMzXbxvRDWAXS6bSkZOT033EAiGv76EEYciZIqauUj8Pb+MhWidngJ+Pju6D4FDjnX3Dho6T72vmD+Xz0P/ojgXUbCO4rQ/WXR1i0x8Dakjv4x4QneTXzFsIl34gbu7FwIR9XfEyzLwHTwvptsPpR+PwfSgZYgsGagsNE0AN1myBtMgBRZKrd1TR4MsmzdQemRpqaCLfGx+ONBNqsLCSVCt+WLYkdKEm0FB3PzLrthFr2sscVm9Vl0WuYk5/E82sqexlAMFqZ7JzMTQt/SEu2jaosHZfMuYpZaYlt/wJozAWgiY3VjE65BLSJWQTUtvljRBRAIBylvj3BmC2BYBCIEjEDpKmtAlXVGtLKviDVms6c3JNobtqpBExWrobXbgSNHuZcidxaE1daIPrsK7ylmc7T2xTfqJ3zx3Pr2fei/fJvRNUGymd/nd/te4nVLVv5+qSv84M5P8Cg6ScovH4bPH4G+DpTzSUVXP4KFIlCwaOeiq+U4qpaZRu23lOHWWtiZ4uZXGu3GPZu3IiuoHDIjDcTQ8IwaTLe1avQZmaiSUnp/5BOwkYHbXkL+H71Wh7e/n/8YOEvOHBn4JTJ6TywfA/fOa5IeEqNMSYlT2JScmJB2AfjawijPudBVGv+DW2VyBPOIJx2AlqXVzHDGyDZDgNGrRpfqDs5w2bQkCkSagQjiBBSA8DldxHZ8SYpb9/W1Wa1pGG65Cmoehz2faI0hgOw8t+YTr0vfhCbnbZQt7i6f6WLYyafyoRLZ7PLtZcdnmoW5J9EUK3h2R3PcuH4C5mYPLHvie1b3i2iAOQofHoP5MyLjZMSjD72fAQZ3YGsZe1lpBnTWV6m56oSpUxGqKGBaEc7hilTDtcskQwG9JMm4/nsM2xnnIGkH/gmsTttEg53I2dXbGZz0TpK0rtjoorTLNhNWt7ZUseZ0xMoLis4IjBZ/ahevhqKlkDBcUi730FTu5XIaf9IaJzJmTb+8fWplLWX4o22YFI5GecoYnKWKFElGDmEkBoAblc5OV8dFOLlbkDvaYHyL8CcqgSDy0pKt6pjN9q8PEL7vXgkiY5Lr+S99bFxUb5QlFWuXfzmq98Q7Tz20omXIssygUjP5UFi8Lri2zwNCccZCA4Du96Bhd0ZbXta96BXFxGWIcWoXAv+jRvQHrbVqG40qalE2tpwf/451pNOSiheylW4iORtLTR9cg+R8x9Bre3OyTqjJJN/fbSbM0oyRCbpUYYqUK98XpZ/oSTReJpQuSrQSj3EjvY1jipKI5/yr513AyAh8csFv0LmPMTXm2CkEDFSA0COBiHojmtXqbXI866FSWfCktth6vlKu95Izg8vIvWmG0i+5tvkPvUk7+tzCB7gnWPSqclOVvHH1X/sElEAz+58lmVFy8izDiB9d9yS+LYF3weDKEUwqmkpBb8rpjTMHtcemn0TKHaEkCQI1dYS9frQpo+Ocj76oiLkcBjv2rWJHSip8Ew5C7Uk4Xn1+0qWYiez85IIhKO8t61+iGcrGPUYbYqH1MIbYPI5io/U1AsIaxJLi9nXvo8/r/lz12MZmT+suovy9jHnsiMYwwghNQD0jnyaZh0UHD75XLSrH0F67xew9nH4+E5Q62DmN4mYUogEtaA3knz55VjmzuXri4q54cRxZDuMnDgxlaevmY/O0IInFO+mm25Mx2Fw9D+xrDnwjf9B1mzlS/msvyuirj/aa2Dvx7DvU3CPqWo9RwbbXle2XyXlz88X9lHnqWdnSzrFSUqguXfDBnSFhUo25mhApcIwdRqhqmr8u3f33/9AJDX+SWexniDyG7fChqch6EYlSVw0O4c/vrNDGHQeZUSSCqCjTokrNTkh5IHC4/FaEjPSbPW3EpFjzWtD0VCMIbJAMNyItc8BkGZKo3HG12k1OEja/AIhWxbMuATtsweJq83PE/3Gi0Rt4zBmNGFWAVYlLTg32cQPT53I1YsLMek0GLRqGr1hMswZ1Hm660JpVVrGOcYxILR6mHAq5C9UsvVMSf0f07gDnvk6tJYqj7Nmw0WPQnLRwM4pGDxbXoDpF3c93NW6m3RTDp9V6DmtwKuUZ4lE0KSlH8ZJxiNptRhnzMC7bi1qszmhUjU2vY2q7Jm87m7gnMYd8MKrkLeAWfmLeUdr5umV5VxxbGJfooKxS8TbiDYShI/vUhp0ZqRTfoPavQ8sA/eRyjRnYtaaY25IHXoHmWYRdycYOUbJ7e7oJzVtKruKjuOZRVfzq+xCNrTuiO8ky6jaKtF+9WdUz38DHjsNnv4a7K9wLkkkm/UYOmvrJRtSuOeEe8ix5HQ+TuZvS/5Ggb0gscnprQMTUQAbnukWUQA162D3B4mdT3DoNO5StrfSuwPNtzZtIRSZQ4Y5jEUTwbdhPfpx4xKKRRopJJMJQ0kJ7i+/JNKSWPHhyclTaJGifJ6cBcfeDBoD0vr/clnzP/jbG+tofO8vULW2qwSS4MhFG3Qr1Rr2E/TAqofQk9h7n2fL4+9L/k6KUUn1Szel87clfyPLMnz1KAWCgxErUqB8sZWvgIatkDULcucT1djwbdqMd9UqdEV5WIuMpEl+bt76GJ6Qh/HFlzDHlo3qgJgPMmeCIw/cdTD+VLBnQ2s5rHsCTvlt1zbN1po2nl9dyfa6di45Jo8HT/4PvogLu8E+vHdSkSCUfRbfXrUS5l87fOcVdLP+KSg6MSaAfGPTJirarmNqShD/rl2oDEbUyaO3Npfa7sAwcSIdy5djPfVU1BbLgI6TJImFWcfyQcWH2HV2SgoWQ8Fi8sJ+jt/o4hdrLTy47WqkoBsmnwXTL4G8haNSUB7VlH8B+75QiqUXHgd5i5TV8QSQAvExpzTtRicl/pW0IGsBz571LK2+VpxGJ6mm0VWrVXDkI4SUzwVv/hh2vtndtuAGPOolVN14E5LJROGdV6J69hcUGhw8fOovebhxJS83rOTks+8hZ/PLqMu/gvxjYeY3lXipA++0Ji5TvggCHWC0U9ro5hsPr6TNp8TCrNrXyi2njOemkyYeUkmDhFDrYMp5UL0utr34lOE9r0Ah5IP1/4XT/tDVVOOuxROMsrEhiZun1eJbsQXz7NmHcZIDQ5OWjhwM4v7oQ2ynnoZkGJhvj1Fj5Pjs41le+TGhaJDZ6bNAY+DCmen84jM7L0/+KxdkNClf1q98D5DgmG/DrMvB6BjW1yQYAGVfwDMXdyfffPkPuOQZmHh638cdhGTt4YYx71jQHJptS7opnXTT6NoKFxw9iK29xp2xIgoItzbQ8Ne/AeA47QR0ux9XnvC7KHnth/y5ppqnZtxKXvEy1OfcD9d8AKffDQZbrIgC2Pk25C8GvXLXvr22vUtE7efBT/ZS0xZbpHbYmHKekiUDisCbcyUULhmZcx/tbHwWUsYrK5WdrKxbQThyMkWOEJpNKxU3cfPAVngON9qcXDRpabR/+BFyKNT/AZ3Y9TZOzj+Z1XWreW3Pa3hCHnRqietn6fntl372RNKh5Gtw9r+ULNS9H8HfS+CNHygZj4LDx+73YjOYoxFY+W8IeBMaRg6HFDPj/abDKeNh6nmE/SP0OSgQDCFCSIXjSwnIagtRt/JhobKakIKx3ib68i9wNO9TvG80WrBmgClZMeTsCWt611aOqodtCrUkjZyPTlI+nP8gfPcL+O6XcPofwSYCM4edcBA+uwemXdTVFJGjfFb1BRsbZjJHXU6kpVXJ1BtD6AqLUNusdHz0EXJ44P5lFq2FUwuWAioe2fwIn1Qsx2n0cvEkHde846MtICtCP20yLP6BUoIp6IaHToRnLoWyz+OqBwhGAJ8rvs3vAnngQhog6m6Bra8oQvn4H0P2XPjgN8jhSL/HCgSjDSGkUsaDPdazSRuuIelblwPQ9u7nhIouij1GpVFiqQ4meRykTj5o/ImQMb3r4eQsG6mW2HiCG08eT5Z9BEsa6MyQMQ3Sp4BWlFIYEVbcD7acrtp6AJubNtHknY5aVpG55SP0U6YgHWbzzcSR0I+fgMqgx/1xYmJKLWmYmTaDUwtOpSXo4pHNj+AKvk6OzcO173rxhw8QSiansr13wSOQXACvfB/umw9f3Q/uxqF/WYKemXBqfNvsy8GQmJN4NHmiEkv6+V/h0z/Dxv8jMuVitJnFQzRRgWDkkORReFc3d+5cec2aNSN3wvpt8NW9yl3uhNNh3jWE5STa33mX1qefJunCZTgmgmrj42DJgiU/VWKievrSa9gBq/4Nez6EcSfB/O9CWmxdqp117byzpY5d9W7OKMlg4TgnyebEgjUFQ0q/y4GDuiab98LDJ8GyP4JN2daTkfnlF3fy1u5rOLX1cyamRtHmDsCEdbQiywR27SDi8WI76aSESsnsJxgNUd5exu7WvexsXky62ckzZ6dh1mh7PB91m2Hvh1C5EtKnKQkeufMgdRKYU2KD1MMBxbeorQraKsFVAa5KcNcrZZZCPqUigFqrZMFa0iGpANKmKDdNzhHPouzzZCP+GbkfXzvsfR++/BeEvDD3Gph4BjgSL6od3v4RqhX/RNVeRmTC+YSnXow+b3L/BwoOFyLroxeEkNpPJKQEhBvsMQIp0t6OpNOhMhiUZW21vv86dpEwBNpBbwO1iOcfAwyfkAp64JGlULgYJp3d1fxFzZfc8bkfbZOTC7Qb0E+YMJBpjHJkgntLCTc2YDnhBNQOxyGPVN1Ry/922glFo/z++A6W5i9ALfWyWhcOQO1GRVg17wFXmdKmsyimpyGvkrFqcoIlDcxpyr9NTjAmKfGLGgNIakVMhbyKuHLXK6KrcZfSXrREMbwdv1QRW8PL6BRS+/E0K/8n1sEFeEd9Hci+DtTJwq5gDDDWP6CGDSGkBILhElL+NiWex2CDBdd3rWjUemq59p33qWxcwjWqz3FMKDyiUvzDdbX4d+/GWFKCYcKEQ35tURle2CWxs0XNgqyXuLpkHvMz5/cuqGImEVAEkSwr7tla0+D+jzvqlGzX6jXQsA0KT4CSi2DCacpW+dAzuoWU4GjkyPmQGmLEcolAMBzs+xRevREySuCYa7q+xHfW7+b6j7ZT37aEK02bcOQXcqR9PmkyMjFbbfh27iCwdy/G6dPRZWcnLGRUElw8UWZjg4pX91xORfs2xifdyTnFC1iUdSwWXR/ZjRq98jNUWDNg0hnKT8ANFV/CV/fBazcqK1WTz4Hik5UtRYFAcFQhVqQEgqFYkQoHoGkXlH8Jm56D9lqYcyVy/kI66uv5YMtWXqrxsto/gyy5lYszXVhtR3pcnEy4oYFgRTkEQ2hzc9FkZKBJTkZlNCYkrDwhieUVJlbVackwV2PRraYkJcLinHzGJxWSZcnCoh2WlaG+8bcpMVrVa5XtRXsO5C1QzHlTJylxVpa0nuMp+0asSAlGG0fWHd8QMiqFlCRJjcBwlu9OAZqGcfzBIuY3OBKdX5Msy306CvZ2TTqNkrrpJ9aZB7a1RU3MDDyELMUnxTpoI0eqSWBqRw59fQrLQFT51Sd+9JTKRb0OtUz3PleZnlEetIaCcnOwF0+SgROOotWo6De/X6OSpKIklSHJODB77pe3h5oveN5X1svTfV6Tg/yMHG1/v6NtPjD65jQa5tPv5+TRyqgUUsONJElrZFmee7jn0RtifoNjNM9vNM9tMByprwuOvNc22l7PaJsPjL45jbb5CGIRPlICgUAgEAgEh4gQUgKBQCAQCASHyNEqpB463BPoBzG/wTGa5zea5zYYjtTXBUfeaxttr2e0zQdG35xG23wEB3BUxkgJBAKBQCAQDAVH64qUQCAQCAQCwaARQkogEAgEAoHgEBFCSiAQCAQCgeAQGZVC6vTTT5dRzPnEj/gZiZ9+Edek+Bnhnz4R16P4OQw/gl4YlUKqqelwG7gKBLGIa1IwmhDXo0AwehiVQkogEAgEAoFgLCCElEAgEAgEAsEhMqDimkOFJEn3A2/Lsvz6SJ73SGdf2z7eL3+ftfVrWZq/lMXZi7HpbKytX8tLu1/CoXdwbvG5TE+djuqAQrrekDeuz4zUGUjS8BT5rmvzs3xnA29vqWVufjLLSjIpTrMMy7kEg6fJ28SXtV/y9r63meKcwrKCZRQnFcf02VRdx3vbGlhf7uH4iXZOnJjKhLTUhM8VjATZ0LCBl3a/hEpSccH4C5iRNgOtSjtUL0cgEAiGhREz5JQk6TjgVlmWL+iv79y5c+U1a9aMwKzGPvWeeq5971pK20u72s4vPp8zCs/gO+9/p6tNo9Lw5LInKUkp6Wr7sOJDbvn4lq7HWpWWJ5Y9EdNnqAiEI9zx+jaeWVnR1VbgNPHMdxaQ5TAO+fkSpF/leLRdk5FohPs23MfDmx/uaks1pvLEsifIteYCsK+5he88sYE9Db6uPstKnNx9wXTsRlNC51tRsyLmepWQeOz0x5ibftTWae3zmjzarkfBqGB47rCPAEZka0+SJC3wMFAmSdK5I3HOo4W9rr0xIgrglT2vsMe1J6YtHA3zVc1XXY+9IS+PbH4kpk8oGorpM5RUNHt5dlVFTFtZs5fdDe5hOZ9gcNS4a3hi6xMxbY2+Rna37u56vKPOFSOiAN7e3MyuhtaEz/fczudiHsvIvLH3jYTHEQgEgpFmpGKkvgVsA/4EzJMk6caDO0iSdK0kSWskSVrT2Ng4QtM6AkjgHmFUlgMahVPaz9F+Tcr9vDnDtQW8n6gcHdbxxxpH5PUoy/DeL2HLy4d7JgLBITNSQmoW8JAsy3XAf4ETD+4gy/JDsizPlWV5bmpq4jEWRyvj7OMotBXGtJ1bfC7F9thYFo1Kw7FZx3Y9NmlNXDPtmrg+C7MWDss885wmLj0mL6Yt32lifProjZE6mq/JLEsWV0y5IqYt1ZjK+KTxXY8nptsZlxa7LXt6iZMJaUkJn++SiZfEPJaQOGfcOQmPcyRzRF6Pez6EVf+GN26GgFidFoxNRirYfA9Q1PnvuUD5CJ33iCfdnM7fT/w775e/z5r6NZyafyrH5RyHTWfj/pPv54VdL5BkSOL84vOZmjI15tgFWQu47+T7eHHXi919nFN7OdPg0GvU3HhyMdNz7LyxqZZjCpI4c3rWaIiPEvSAWqXmm5O/SYG9gLdK32JqylSWFS7rio8CKHQm87dLpvLu1jrWl3s4YZKDJRNTEo6PApidPptHTn2EF3a9gFql5qLxFzEjdcZQviTBaGTryzD7CqheC2Wfw8TTD/eMBIKEGZFgc0mSrMBjQDqgBS6SZbm6t/4ikFIwwohgc8Fo4+gINv/nbFh0M1StAoMDTrvzcM9I0Dsi2LwXRmRFSpblDuBrI3EugUAgEIwBfC7oqAFHPgTaYdtrh3tGAsEhIQw5BQKBQDDyNO0CRx6o1IqYatqpBJ8LBGMMIaQEAoFAMPI07gRbZ8ydwaH89hwh2YiCowohpAQCgUAw8jTuBFuW8m9JUlalGncc3jkJBIeAEFICgUAgGHla9oIts/uxNQNayw7bdASCQ0UIKYFAIBCMPK5KMKd1PzanQqtwxhGMPYSQEggEAsHI014FlgOElCUNWvcdvvkIBIeIEFICgUAgGFmCHgh6u4PMASzpYmtPMCYRQkogEAgEI0tb52rUgfUazWnQXnP45iQQHCJCSAkEAoFgZOmoBXNKbJsxSbE/iEYOz5wEgkNECCmBQCAQjCwddWBMjm1Ta8FgF15SgjGHEFICgUAgGFk6asHoiG83pUB7r2VYBYJRiRBSAoFAIBhZ2mvAkBTfbnaKOCnBmEMIKYFAIBCMLO01YEqObzcmK9t+AsEYQggpgUAgEIws7noluPxgDHblOYFgDCGElEAgEAhGFk9jzzFSBodYkRKMOYSQEggEAsHI4m2ONePcjzFZCUQXCMYQQkgJBAKBYOQI+SHkA50l/jmjA9wNIz4lgWAwCCElEAgEgpHD06isPB3oar4fY5KIkRKMOYSQEggEAsHI4WnsOdAclBUpbzPI8ohOSSAYDEJICQQCgWDk8DQp2Xk9odaBxgD+tpGdk0AwCDSHewKCeALhCE0dAUw6DUlmXWIHB73gbQK9DYwO6tp8+ENRtGqJNJsBrXpotHMgHKDZ34xJY8LRU9CoYEQIhCI0ugOY9RqSTP1cK0GPcrevt4Oxly+yQ6C+o502XwiDWodeqyHdZhyysQVHIN5mMNh6f97oUMRWT1l9AsEoZESElCRJGqC08wfgRlmWN4/Euccaexvd/OOD3by5uZYCp4k7zpnGseOcqFQ9xBMcTP02+PAO2P0eckYJrcf/nndacnn4s300dgT42twcvnvCOHKTTYOa4762fdy34T4+KP+AAnsBt8+7nXkZ85B6inkQDBt7Gzr46/u7eGdrPUUpZu44dyoLi5w9vw/1W+H9X8PeDyFjBpzxR8idP+g5fLanirvfKmV7bQfzC52cOjUdrUbi7OlZ2I0J3gQIjg68TaCz9v68obN4cUrxyM1JIBgEI7W1Nx34P1mWl3T+CBHVA95gmDvf3MZrG2uIRGX2Nnq46vFV7KzvGMDBLfDydbDrHZCjSLUbSX7565jd5VS7fAQjUZ5eWcF/vthHJHro8QeekIe7V93Nu2XvEpEj7HXt5XsffI/drt2HPKYgcTyBMHe8vo03N9cRicrsbnBz5WOre75WPM3w4ndgz/sgR6F2Pfz3ImjeO6g5bKmt5/tPbWVrTQdRGb4qbeaZlRWsLXOxqrRlUGMLjmA8TaDvS0jZROFiwZhipITUAuAsSZJWSZL0aOcKleAg6tr8fLQj9gMkFJHZ2+ju/2BXJdRtim0Luhmnjs2A+d+aKho6/Ic8x3pPPV/WfBk7x2iIsrayQx5TkDi1bT4+3d0U0xaMRClt9MR3dlVAw9bYtkA7tJTG902A0sYOOgLhmLbdDW5ykoy8u01kXgl6wdPY99aewQEeYYEgGDuMlJBaDZwiy/I8QAuccXAHSZKulSRpjSRJaxobj867EaNWTZJJG9duN8S3xaEzK0GaB+FTmWMeZzkMmLTqQ5+jxohNF/8h2FPbWGc0X5NGrQa7Mf66sBl7uEfRW0Cjj2/v68tsANh62LrTa1REZSh0Dm77WBDPaL4eE8LTGcPZG3qr0kcgGCOMlJDaJMvyfrvaNcD4gzvIsvyQLMtzZVmem5qaOkLTGl1kOoz8+uypMW0njE9hcuYAvvCSi+CUO2KafFMv4VNXStdjtUri52dOwd5fUHJfc7Rk8tN5P41pOz77eMYnxb2lY57RfE1mJxn59dlTYtpOmpTK5IwerpXkIjjpV7Ftsy6H1EmDmsPkDAfnzEyJafvWwgK+2tvIKVPSBzW2IJ7RfD0mhLcfIWWwCVNOwZhipLbYnpIk6U5gC3AecNcInXfMsWxaBnnJx1La5CbZrGNqlp0Uaw+rCQejUsGsyyBzurJlY82kwzKRRe16xudlE47KTM2yMSVr8Nlap+afSp41j7K2MpKNyUxOnozT6Bz0uILEOKMkk3ynidJGD06LnmlZNpyWHq4VlRrmXAHZs6BlH1gzIXNG7ynoAyTdauPHp01g2bQ0GjuiOM0GDFo1X5+XS1FqD67VAgGAr7Xv1VC9HZp2jdx8BIJBMlJC6rfAM4AEvCbL8gcjdN4xh16rZnZ+ErPzezGs6/NgC+Qfq/wAaUBaxtDOD8CgMTAzbSYz02YO/eCCAWPQqpmTn8yc/OT+O+utkL9I+RlCcpOSyE06hGtVcPTia+0n2NwO3jG8dSk46hgRISXL8haUzD2BQCAQHK1Eo+BvH0DWXvPIzUkgGCTC2VwgEAgEI0OgDbRGUPVxD2+wg0/YZwjGDkJICQQCgWBk8Lb0ny2qtynbf6LenmCMIISUQCAQCEYGX2vfGXsAaq2otycYUwghJRAIBIKRYSArUqCYcnpFnJRgbCCElEAgEAhGBl9r33X29mOwCSElGDMIISUQCASCkcHXqlRh6A8hpARjCCGkBAKBQDAyeJsHJqT0QkgJxg5CSAkEAoFgZPC19O0htR+dRdTbE4wZhJASCAQCwcjgbRlYjJTeCh7hbi4YGwghJRAIBIKRYaArUmJrTzCGEEJKIBAIBCNDf3X29mOwi609wZhBCCmBQCAQjAw+l1JcvT9E1p5gDCGElEAgEAhGBr9LCSTvD71N1NsTjBmEkBIIBALB8BONQsA9cCHlFUJKMDYQQkogEAgEw0+gDbRGUKn776szQ9ADkfDwz0sgGCRCSAkEAoFg+PG5BlZnDxSxpbeK7T3BmEAIKYFAIBAMPwOts7cfg11s7wnGBEJICQQCgWD4Gaj1wX4MDvAKCwTB6EcIKYFAIBAMPz1k7PkjAZ7f+T/e3vc2UeTY/garsEAQjAk0h3sCAoFAIDgK8LXGFSx+cOMDhKIh2gMd+CMBzi8+r/tJnRBSgrGBEFIjTCAcYW+Dh3Z/iDwrZISqwVWBrDFSZy/BF46QEtgDwTYC1gIqgxlkJ5kJRWVaO9zkRStRB1y067NxYSYnXIVWitBmSWOHr5lUi4OJyePQqXV4/CF89XuQOmpQWVPpMOVT2RZCJUkYdSpyk0w4LXq21TZT1tKGzagmzWIix56ESa+BUABa9ijZM/420JoIhU0EGzyoU9LQFxQg6XRE5Sjl7eU0ehtJNaWSb8tHJfWy2NmyDxp3gkYH6VPBkj6yb8ARRlPlLmRXBSqzE31qMRZ/jfKF5WkEYxKkjAdLOo2eRizNe9G5G5B0FnyGdGTnOCKo2NPgJhKVCYTCBENhcuwagrpG6n11pJvSKbSOp6w5QDgSxRuKoFdLpIU9qKsqMORlUWEKIgfd5Id8qMNBgvZcHKnFuP1hSpvcqCSJolQzRq2a0iYPrZ4gOUkmcpNNANS3+ylr9mDWqCjSNWPy1YEjHxy5Pb/o5lJorwZzCjjHgzqBjzF/OzTthmgIkseBJXUI3gXBgPC1gs7U9XBHyw4qO6q4atpVeEMeHt/6BCflnYhdZ1c6GKzgEUJKMPoZUSElSVI68I4sy7NG8ryjhQ5fiEe/2Mc/P9xNVIZUi56HT9Mx8+0r2HniQ6yq3MeFnmcwrXscgKg9l+hZ9/PRjny+2FbGrzO+Imn1H0GOYjM5yVz6e9Rv3gLhALr0aWyYezGXffYY353+fc4t+AaqvcvJePe7EHSDWodv8V38bl0Ru5r8fGNeHmqVxKLxDn7w3FbcgTBatcTNp2YyM7eD6clJ2Db9B3RG+OLv0FEHkoQ05UpaP+qg47NVpN92G7aLLuCjus/4+ec/xx/xo1fruWvxXZySf0q8mKpcA2/eAnWblcfjTobT/wCpE0fwXThyaN/5KSmvfEv5glKp6Vj8C1qKlpL8zIWd77kWTriNHROWUtS0B93rN0OgA9RazItuJeCcxF27izAbDawtb2VdRStZdj3XneHl3s2/wx/xY1AbuG3ez2hvmM6L62vYUt0OwAl5Fn5sqmSraTflnm1c19yEfctLAESTimg99z7uWmXkxXU1AFyxII9Mh4k/v7eTSFTGYdLy8LfmYtZruPbJNVS1+gC4cpaVm6JPk1z1AVz6f5A7L/ZF7/kQ/ndF1+vgjL/CjEsVYd4fbdXwzu2w/VXlcXoJXPQYpE4YmjdE0Dfe1pitvY8qPmJO2mw0khqbzsbEpAl8Xv0FZxaeoXTQ2UThYsGYYKRjpO4BjCN8zlHD1tp2/v6BIqIAGt0BfvVlhOb5P+HBfWmc5qzuElEAqrZKHCv/joZWLi/ykL7qDyBHlSe9zai/+CtMOQ8Abf0W5tfvJcuUyYOb7sfXVE7GhzcpX6gAkSBZn/2UW2dBVIb/rqxgcXEKt7+4HXdA8WoJRWT++k4N7mCASMM2qFoJm/+niCgAWUaz9T8kL50O4TD1v/895fU7u0QUQCAS4Oef/5zy9vLYFx8OwJb/dYsogL0fQunyIfwfPnporq/C8u7NiogCiEawfnqHsnLY9Z6HaG/eiba9Ct1bP1bER2c7n92D3rWXk1NdRGWZdRXKOBctMHSJKAB/xM/dq+4iJbmjS0QBfFLhpnbuOP6w4x+ca8jqElEAqtZSzKse5NwSR1dbqs3I3e/sINJ58bu8IZ5eUcZf3t3ZJaIAHl/fwabMC5U6a6/dHJu15aqEl6+NfR1v3KyscA6E8s+7RRRA/WZY/6RiFCkYfg4oWOyL+NnctJnJzsldT09JmcoX1V909zfYhZASjAlGTEhJknQS4AHqenn+WkmS1kiStKax8cj846l1+eLaNtV6aUo7lpU1QQwHiw/AWLmaQouf5HB9/IBNu2O2P5yVa5nmGIdVZyXa3hCfOhyN4Ix2Z8H4wxGaPcHYLjLUtHWgCbiUrbeadXGnVau9Xf9u8NR3fel2jRvx0+g96D30tkDFivjXULkqvm2UMJqvyainGVVLaVy71F4T89jlHIct4ImPNZGjEAmQKbWyvbZbIEma9h7fz7oevtCqgy2YNCaS2uP/pA0VX5Fu8HSPEY7E9XGY9KzcF5/eXuEzgUoDjdti5+1pjC9kK0ehI/Y190pP19ru9yHkiW8fhYzm63FA+LpXpLY1byPLko1R031fnW3Jxh100+BtUBoMYkVKMDYYESElSZIO+CVwW299ZFl+SJblubIsz01NPTLjFjId8Ytx0zNNpNZ/wYIsLX5rftzzvtxjKHUbaFanxQ+YMh5cFV0Pm3PnsMW1l45gByprGpiSY/ur1DSrUroeGjRqnObYLRGVBFk2C2G9A+q3QvacuNNGIt1xDqnmtJgPQwCD2kCq6aD30JQMeQviX0Pu/Pi2UcJoviYls5NoclFcu2zPinnsaNpDu94EJudBA6hAradWTmJyZrdJYjRsw6A2xHQ1qA1kHPx+Atm6JLxhL622jLjn/HkLqfN3XycGTbybtcsbYH5hclx7nsED0TCkTY2dtyVNiYs6+HVYY19zr/R0rY1fClpzfPsoZDRfjwPiACG1sWEjBbaCmKdVSBTaC9nU1LlqbbCLYHPBmGCkVqRuA+6XZdk1QucblUzNtHHrKeNRScrjVIue3x2rJnnVn7musIF3W7Lxzbmqq3/UkUfr/FuIkMRT+6zUzbu9u7yCyUlk0Q9gm7JVEcooYUX6OGq8tXxv+vUY0/KpO/lf3b4tah3Vx/+Jv65XxNJlC/L5fE8Tf7hwMla9EiqnU6v44bIsLHo96vQpkLsApl0E1kxlDEkiPPVqWt7fBBoN6b/6JYVpk7hr8V1dX74GtYE7F99Jvu0gUajRQ8nXIHNmd1vxKVB4wpD+Hx8tpKTn4D7tn91iWaWm44Q7kAz2mPfcljqZkD2X4Bl/jmnn+B/jdxTzUaMdSYI5+UkAvLDCxw0lv4x5P2+f93OaWi2UZNu7zr8kz0rW6r3cPulmXvXX0FZyYddz0eRxeOZ9j1c3tXW1NXb4uW3ZJNSdF3+SSctlCwr44akTyUnqFuJXz7Yxve5FRTCd/Y/YmwF7Dpz/sFKHbf/rOPufA4+xy18EU8/vfpwxHWZ/C1TCBWZE8LlArwip7c3bKLQXxHUpsBewubFTSOltQkgJxgSSLMv99xrsSSTpU2B/IMJM4AVZlq/prf/cuXPlNWvWDPu8DgeBUITSJg/tvhB5Von0UCW0VSGrjdTbp+ENR0kJ7oFAOwFbPlWBDLKSTYQjMq0dHeRGq1EHXbTrlKy97HAVus6svZ3+JlLNSUxILlKy9oJhfHW7kTpqUZtTabfkU+UKolJJmLQqsg/I2itvacdmVJFmMZHtSMKk25+1t1eJufG3g9ZIKGwm2OhGnZKBviAfSatFlmXK28tp8jWRYkwhz5bXR9ZeGTTtVL4E06eNlqwpqb8Oo/WabKrajdxagWRKxpAxHov3oKw953iwptHgacDaUobeXQ9aMz5DOjiLO7P2OpSsvXCEUChMlk1NSNdIg7+BNFMahZZiyloChMJRfKEIeo2KtJAbVXUlhtxMKs0hooEO8kP+zqy9HJJSi+kIhNnX6EElQWGqBZNWzd4mNy5viJwkIzlJ3Vl75c1ezFqJIk0LRn8dOPJ6z9pr2dedtZdcnGDWXgc071biq5zj4le4Rg99XpOj9Xrsk3smwGl/oFWr4xef/4LrZ12P6qCX6Q65+c/Wx/nXSf9CFQ7Cs5fCLxpB6vdPVDD8iDehF0ZESMWcUJKWy7K8pK8+Y/JDQjCWGbNCSnDEcuQJqd9nwMVPsKp5cTJ70QAAOblJREFUCx9VfMR5xechy/DuPhNbm7V8Y7KbTEuEhzc/wg/m3Eq2JRueuRh+uHPgNfoEw4kQUr0w4mva/YkogUAgEBxhhAOKd5fGyB7XXjLMSlzdZ1UGnt5uIRKVuO1TJ/6wRLYlm12tu5XjjA5RJkYw6hHBAQKBQCAYXnwuJeZJktjXVkqGOZOIDI9ssnHBeDenFXrJMId5dbeZDHM6pa7OjFSDIz5TUyAYZQxaSEmSNE2SpNMkSZosSZKl/yMEAoFAcFTRWbA4Ikep7Kgiw5zBmjo9Ro1MkUPxsVuS6+OVPWbSjJnsa9svpOxCSAlGPYMSUpIk/Qu4A/gDUAQ8MxSTEggEAsERhN8Feit1njosWgsGtZ7lFUZmpAa6umRZIli1UWrcuTT6GglEAp1CquHwzVsgGACDXZEqkWX5QsAly/KbgL2/AwQCgUBwlOFrBb2Fyo5K0kypRGRYXWtgakqsIXBJWpDPq8w4jSlUuauV7UBhyikY5QxWSDVKkvQrIEmSpCvoxbVcIBAIBEcxPhdoLVS0V5BiTGVvqxarLorDEFueZ0pygJW1Bv6/vfsOj6w6Dz/+PdOLpqm3lbSr7WwvwIKBpRcbG1wA2zHEiUsc23Hs/OIax8SJSwpOQhJjA06MsXEBjGleOgtLWbaybG8qq1WXRhqNNKOp5/fHaFcraVRWddC+n+fRszP3nnvve+/cvfPOueeek2fL40TXiVSNVLfUSInMNtFE6nYgALxJqjbqkyMXF0IIcc4Jd4DFSW2wljxHHrubrVR6Y0OK5TqS2IyaWHI+tcHaVGNzSaREhptoIuXRWv+n1vovtdZ3A0PHExFCCHFuC3eAxUF98CT5jjz2tVko9wxNpADm+2I0dVdwIlALdqmREplvoolUjVLqX5Q63e3sP0w0ICGEELNM2E/EZKU3EcFl9nDIb6HMHU9btNIb42hHLo09jWibV9pIiYw30UTqTeAw8KxSqmgS4hFCCDHbhPx06Bi59lyaeoyYDBqvNZm26FxPjKOdNkwGG+0KeWpPZLyzGKQqPa31z5RSO4FHgZKJhySEEGJWCftpMWWTa8vhWIeZUlf62igAh1mTY0tg4DxORjvIjYVSPaObrNMYsBBjN9Eaqe8CaK3fBq4D7ploQEIIIWaZcAcNsSDZ9mwO+80UO4dPpADKPXG6IvOp72kAe7a0kxIZbUKJlNb65TNed2mtfzjxkIQQQswq4U7qIp3k2HM51mGhOGvkRKrMFae5p5S64Emw+ySREhlNxtoTQggxtXo7qY36ybHlUNNloigrMWLxCk+MuqCP+mB9XyIlXRSKzDWuNlJKqR9prb+ilHoZ0KcmA1prfcWkRSeEEOLdLZlER7ppiztJaC/xpMJtSd/Q/BSvNYkBAzWBGDq7ABVsnKZghTh740qktNZf6fv38skNRwghxKwSCZA0WfE5cqgNWChyxjndYc4wlEq1kwrFFxAyJXB2SSIlMtdEBy02KKXcSimTUupypZRrsgITQggxC4T8RM02sq3Z1AZMFDhHvq13SqkrTji2AL/BAF31UxykEOM30TZSDwOXAf8OfAp4bMIRCSGEmD3CHYSMJrw2HzUBE3mOsSVSZa4Y7aFSmnVcEimR0SaaSOVorZ8EFmitPw7YJyEmIYQQs0W4gy4F2bZsarrMFDhGfmLvlBJXHH+vl6poL0gbKZHBJppIBZVSfwB2KqVuAIITD0kIIcSsEfLTqeP4rNnUBU3kj7FGymKEXHuEt7ptIG2kRAabaM/mHwGWaq13KaVWArcOV1AplU1qUOPdWuu2CW5XCCHEu4AOteNPRjGpXAxAlkWPuswpZe4kBwIutE6gegNg80xdoEKM04QSKa11L7Cr7/We4coppXzAU8DTwI+UUldorWfFSJS7ajs42NiFwaBYWuRi5Rxf+oKxMInqt9CtR0gas+jOKqPa5SIayyFIPW2RE7itTpY58tABI/uDTqLaQEWeG2dWjKMtYdqCSUrcDpwqwcmuOPleFz5DDxXRozgjzZhy5xOPx0lGghii3RDtweguRCViYLZBIgY6AUUrwVcxNMbmA9C8HwwGKFxBu72MfSe7aAn2MsfnYFmJmyybefiDoTU074PWIxy1nseBoA1lNLO02MP8/Kzhl2vaBy37wWiBwpWQM++sPoPZrDfeyyH/IaoD1WTbsvEaKzhwUpFIai6dY8ETPIKho4q4PZtWTzFNsQaWOgrJba8harBwwLycY51JVmQbmRs+hKHrBElHHi3eQpIqQm6gmVhM0+FaRKu1gnLVSLM1m7rQcVrDzeTZC1joXURlbkEqoGQCmt6BlkNgcULuQgicTPXz46tInVvWNM+chNqhcQ90NYCnLFXO7kmtr/EdaD0IFhcUrwLvnOk8xMNrPZw6N9FQsAzyF890RO9KPV0niRhNtIccFIzSo/lgFR7NK3UVJF15GAMnJZESGWnCY+2N0QrgK1rrrX1J1Rrg2Wna9pTZWtXOpx7YQXckdXHwOczc+4m1rJ+bM6Ss3vsYxic+d/q9p3gDxSu+wK68Jr679e8JxUMArMpbzWLD57hvc6oqe32Fj/OKs/j5G3Wnl71l3RyONAdxGU9yb8km7Dt/Ap5SWHkbpvaq1JdW9Sv9G7/sq7D7l7DsQ3B4U6rnrz95GLLPSFjqd8EDN0K0O/XemUvjVb/kjt/6Txf5xvWL+fP3zMVkHOaOcN02+MWN7Lvsfj76Qi3BvuPidZh56FMXsLQ4zUWwbltqu/He1HtXMdz+B8hblH4b55gXal/gG6994/T7NXkXkhO6g5ZOEzepI2Rt+uLpebaKqzi48mZ+UP00fxc2sDP7T/j0psNcMd/HB8q2YH79+wAYgcJFH4S8JRhf+x52wG1x0rrxQernLOPp2gf5zZEHTq/3toV38GnbX5CflQU1r8EvPwjJvi/EnPmw6Hp4479S76/5Hlz4OTAY+3ci0g2b/xm2/bR/2sZvwnu+DLWvwa8+nEqoAPKXwkd/A77yyTyMZ6/xHfjF+yHckXpv88AdT6YSQHFWegInwOqmrstErn1st/VOKXPF6IqUE8rNwhU4CQXnTVGUQozftPRsrrV+pS+JuhQ4H3hzOrY71R7eUXc6iQLoCMV49kDz0IKBetQL3xowydjwJt7WWprq93NV+VWnp7/duhuvt7+y7j3zc3ngzboByz68s47LF+XxpwujqSQK4LwPwtafpH41n5lEQWr6sg/Btnth6U3gPwZ12/vnJ5Ow/Wf9SRRATxu59S+Rl9U/UOhdzx2hpj2U/mDEI7DlLvCU8ki973QSBdAZivHUO2naOMSj8Np/9idRAMEGqNqcfhvnmMaeRn64feCoS7tatzK/tIt/usxJ1svfHjDPXvMC5xtcPNf0JicW3cJ33oiiNfzdao35rbsGlDUe/j34zkikoz0sOvEbIrqN3x75xYCyvz3yC475j0NvEF64sz+JAmg/lqqZUn2Xkpe+C/6qgTvSdmRgEgXw6j9Dy0F47tv9SRRAywFo2DXqsZlyex/pT6IAegPw9m9mLp53sWiwAYPNzYkuEzn2kTviHCzHniShLRzHB50npihCISZm2oaIUUopUm2oOoBYmvmfUUrtUErtaG3N/Lt+iaTmhH9oUlGXLtGIhSHkHzJZxXtwJs3k2AbWYCUIn34dT2r0oCYFWqem25M9/RNN1lQilBhyaCHSBSZbKtkx9H3kPWeMXaXj0HZ4yGLO7hq8jv5bedFEkp7IMFXz8V7oqAJnHke7hp5Wx1u6hy6TiEDH8aHTM+SCOdPnZG+8l0AkMGR6TIewE4XeziHzTJHUOdGrHDR3pRJUa6In9dkPoqM9A967gsfRyQiagSecRhOMdkMsBIE0n000lLotC6ntRAZ91pE0z6AkExAJpG4LDhbqGDpturUdSTPt0PTHcYaZPh/HK9nTisHmoy5oIu8sa6SUggJnkNcipUMTdCEyxLQlUjrl88A7wPvTzL9Xa71Oa70uLy9vusIaN6NBcePK4iHTrz6vYGhhdzF6wXWDVmAm5p6D36l5o+GN/snKiClZePp9sDdOgds6YNFCt42u3jgHI7mQlZ+a2HIAStelbqmYbAO3VXYhNO1N3TbpqElNK1lzRiwWWPunQ8JuLL6aqrb+L9tFhS7mZA/Tw4XNA2v+FBr3cEvl0GTrptUlQ5exumDtJ4dOr8yMUYZm+pwsdBRyacmlA6aZDWaMiQJ2+O1EKwYdJ6OFLnc+NqONgtAxPrg01VZpTzgXnbdkYFmbB2VzD5hUO/dWTAYfBY6B53CBo4A57jmpc23Nnw4N1JHdX6uYv2zobbnsueAYdLvbNxdyFsLq2wdOVyozbt+sTPPczKqPT38cZ5jp83G8jL1dWJ251AfH3ofUmUpdMbaFC6E9zY8uITLAtCRSSqmvKaVOXTG9QOd0bHeqbVyYx5euXIDbZsLnMPO16xZxUeXQ9lFYHHDNP5JccjMYTOicBfRecTdHc3wszb2EUucCTAYT5a5y7rrkXynXDvKyrDgsRrw2xb98ZCFryl0YDYrz5/r40pXzeXRnHW8HnDTe8ACxkgvg6HPo1XegO+rgqn9A5y9JbWvhdbDg2lRN1aqPw4mt8OGfQ/HagTEuuAau/A5Y3akvvff+O6a5F7OixIPRoLhiUR5337aKbKd16P6dsuyDsOELXNLxGN+61IfbbsLrMHPnjUvZkO64ACx5P1z2tdTtoax8+MCPYc754/5MZhO72c7frv9bbph7AyZlotI7n+9t+E+e3JHkm8/UEbr8H+hddBMYTCTyluD/4C/4ccMm7tnwj1S89WM+X3SY21bn8rUXW+nY+COS5ZeDwYguWEXv+++jLdKY+qytLlrP/xrHvRfjShj5pw13sSp3LUZlZFXuWv5pw10szS9LJTlr74ALP59K1j2lcPO90LQ/lcAvuBY+dF8qsTqTtww+/jDMuTBVbt4VcOuD4C6E8z8F5382VaPqmQO3PAhFq2bicA8091K47p9TA+bavKm2X/M2znRU70q2aAiTvZD2XiPZtrNPpOZ74WC4FDqqJz84ISaB0oPvG03FRlINzH8HWIF9wOf1CBtet26d3rFjx5THNVmOt3SjFMzLG+HJNIB4hHhrNb0x6LTbMCQtGAxmYspIWLfjsVgpsPmgx09jzEqvtlCc48ZiMtLQ1U4oqii2mYlHwwS1HZ/LTmcohscYISsZBHs2GExEAs3EUNiUxmTLglhPal5vZ6r26VQtVjpd9aCM4ErVigXDMQK9MXKcVuwW4/DLnZJMpjrPM1lpimelKhjctpGX0Tr1NJfBBK40NXpTb5SRv2b2nIwmorSF23CanHhsHqpau9Ea5uU5iYWDhDubiZsdhM1x3IkIbrMrlbDEwsQMVlpiNux2O554F3Q2gNWJ3wLaZCUnGqMjCn6VQ7kzSkfSgS/LRlt3Fx2RAD6rhxLvoCdRk4lUvz5mKzjzIBxI3aZz5KZ+NAwnEky1O3LkpBLnUxJxCDalkqmsDKtpOdV/kbtourc84jn5brlGxpNx4v+Yy8sbvsd3tpbyN+s7z3od0bjmzjc87HH8FVnfqu1vniCm26jXyXPVtDy1p7XuAK6ejm3NhMqRHu0/k8mKqWgxWcDQJc74FW91MfiyXeLJPeOdm1PPvzmtZsAB9H/ZWXPLGVhv1FcbZB1DnO6Bt+BcdjMu+whdHgxmMIAntY7CUYqeptTpZcRQFqOF4qz+28hnJuwWhxuLw51uMQDMQP+RzQNP3qlXp+X2/UH/Z1bi9VHCoATqFIMRvKX97+2e1N9orK70XSMYTQPXl0mmP4GaVZo6qsgHmnpdZ/3E3ikWk8Jl8bPTcB6XBU6k77pFiBkkqb0QQogp0dC2n7DRTH332T+xd6Z8RxevsiLVt5cQGUYSKSGEEFOirf0wEbONuqCJnHG0jzql1N3D69H5qS4zhMgwkkgJIYSYEp3+o8QsTk4GTeSM89YeQKVXUxXNp7tu7yRGJ8TkkERKCCHElOjpPIG2umjoHl/XB6fk2F14LS28VZumTzIhZpgkUkIIIaZEvLuJqCWX7qgBj3X8baTcZhcW8wle7imDnvZJjFCIiZNESgghxKSLJqJYertoVHPIsScwTODheaPBRL6zmReTa9FVr4y+gBDTSBIpIYQQk64uWEcJZmqT+ePu+uBMRVmamMHE4T2vD5yhNRx5Fnb8b2pMRCGmmSRSQgghJl1NVw0FGDgRz57QE3unuC0uKrwB/ngk1D9+Y7QHHroFnv0m7HsM7t2YdlxTIaaSJFJCCCEmXU2ghuykpibiIXsCfUid4ra4yXae5AkuQb/wXfBXw8/fm5p5w7/BZV9NjSf67LcmvC0hzoYkUkIIISZddaAaTyzKiQn0an4mj9WDpoqo2c2eo9Xw00ugcCVs+GJqeCmA1Z+Aw39MJVlCTBNJpIQQQky6qkAVzliYhrB1UhIpt9VDZ6SDS+aY+aX7M3Dbr2H5h1NDTJ1iccL8q2D7/RPenhBjJYmUEEKISdfUWUM4YSYUN+AerusDrYnV15MIjt4/lNlgwmqysaYgxDPVMdrDw6xz/lWw5zepwbCFmAaSSAkhhJhUgUiArFiY46Z55NmTw3Z9ENq1i9DOHQSffZZEYPQn7rxWD5FEKxcWG7n/nWj6Qp5ScGRD7evp5wsxySSREkIIMamqA9UstmRTZSwb9rZeIhAgWl2Nfc06LBUVhHftGnW9LrOb1lAr759v5lcHojT3DFMrVbYBDjw+kV0QYswkkRJCCDGpqgPVlBsdVOkSsodJpCKHD2MuKUGZzZhLSoi1t496i89r89ISbiXPYeDKchN//1ovWuuhBUvPh8ObUn1MCTHFJJESQggxqaoD1ZRi4niyMP1gxVoTOXECc1Fx6r3BiLmggGhNzYjr9Vo9tIZbALhpgZmD/gQPH46lKVgGaGg5MLEdEWIMJJESQggxqY51HqM4qaiJ5ZCXJpGKtbZisNtQdvvpaaa8PKInToy4XrfFTVeki3gyjsWo+OIaK9/fGuGthkENy5WCkjVw9IWzirs1GKG+M5y+lkuIYUgiJYQQYlJVB6rJicepi7nTtpGK1ddjys4ZMM3o8ZIMhdDh8LDrNSgDboubtnAbAKUuA59fbeGzz4XZ0TQomSpaDceeH1O8df4Qf3L/W1xx12bee/cWbv7xG9T5Q2NaVghJpIQQQkyaSCJCc6gZHdKAwmkeWrsTa2zE6MseONFgwOTzEWtqGnH9XquXllDL6ffL8ox8bpWFTz0T5sXaM27zFa6A+p0QGz4xA9h9ooMP/M/rlGU7+PHH1vA/H1vDshI3t/z0TVqDkVH3VwhJpIQQQkya2q5a8h351HebKbBFB/SXCaBjMZLBIEaPe8iyRq+XWHPziOv3Wj009QxMtlbkG/mb9Rb+dnMvD+7vS34sDsiphNo3hl3X23WdfPLn2/nzi+dy48piTEYDBqV47/JiLpibw/97eI/c5hOjkkRKCCHEpKnqrKLYWcyJHju5jqG39eItLRg9HjAYh8wzen3EW0ZJpGzZNIeGlpnvM/L3F1n56Z4od74eJp7UqVqp4y+lXc+Bhi4++X/b+PR75rGm3Ddk/ofWllDd1sPzB0aORwjTdGxEKeUBfgMYgR7gVq31ML2pZYZEIEDP1rfofPQR9OVXE75oMbmt2/EdeJyYr4LIsg+TjNlw1zyHoeFNmHMBuEvBXQQNu1OdwZWsRXvnkrQVE3X5MBx5AmvD28QLLyVkmoc930xv69vsKKjk902v47PlsLZwLe+07Gep+xIqYk5WmU5gbD8MNa8TL15LvPwSLDvvJ+nIhflXoprfwVh5Ncm6XRiOPUO8eCXHytZwz8mXuD7rfFarcnIXrySZ5WJHbQd7a5q4Nf8kzr0PkjDaiK26nWcD5Tz6diPvWZDHDcuLmJvrHPa4HGvp5sk9DWyv8XPD8iKuWJxPsdc+bHlxdiKNNRhPbMYY64BEDKpfhYJlUH4RdDWhu5qg4DzU/kfAmgWL3wsHngRnNnrBjbQ8tY2s9efT9dSTuM5fiiOnC0OWhyZvMZu7jvNC8zbWZy9jY8nllLXWYN/7ENqaQ6TkGhoNFSR8JkJZZrY0v8Gu1jdYU7AWj9VNKHCSq92LKDcXYFx48el4jza+zVtNO7Co1bx6IEk8AR+7oIL1c320dkV57kATLx5s5oJKFzk5dbTGD/K+yvex0LdwBo+ymErHOo9R5MinOhokO39oT5zxtrZUIpWGISuLZDiCjkRQVmvaMj6bl9ZwG0mdxKAG1gUUOA185yIb/707wp88FeK/V68hd++9cO33BpTb01cTdfuGirRJFIDJYOC29XP44TOHuGpJAYbhehUV5zw1HdWWSqm/BI5qrZ9XSt0DbNJaPzFc+XXr1ukdO3ZMeVwj6Xz4ERq//W2MXi9NP/9fFjc/Qf7L3z89P3HNXRh2/x+qdV//Qpd9Hapegbo3+6eVrIU1d6Bf/VdUoC41zWBC33AX6rlvsumab/LV/T89XdxisPC5VZ+jqnkP343aMIX9sPeR/vV55sCSG2Hrj8FkS416fuINePuh00WS7mJ+s+EOfnDkV3yn/LNc2ZTLwfVX8YmfbWPbrZDz2Ef716cM7LryIT74VKpju+XFHv7vk+vJdQ29iDV0hvnYfVupae9vhHnr+lK++/5lWM1Df12+i4x6hZyuczLx/A8xnnwFHDlw8Mn+GdnzYO0nU8nTU1/un24wwpV3wvPfRrsKSX7gAY7e/BncV11CwdJqjJUbCGu4k1b+ePLl04tVuiq4b96t5P3h86kJJhvdi++kKm81d/X8lF2t/fu6Mm8lhc5CqvxH+KlrFb6KGzHNX09923G+sOXLvK/0y3zv9yGSZ1xKfvfZC7n7xaO8dqz99LQVcxwsXvoqb7W8xIPXP0iZu2yyD99sMuI5mQnXyOF88cUvssZRzEtPG6lYNIdluQN/Mweffx5zcTHG3Ly0y4ff3o3tvGVYSkuG3cbTVU/zkUW3kGvPSTs/kdQ8eiTGq3VxvqYe4OYv/SdmbxGxRJKH3qrlR88f5VOXzGVd+aB2WuFOOPpsqmf0yivQGPjOE/v5m2sWcd2ywrM6DrOQZJLDmJZbe1rrH2utTz0+kQe0jFR+psX9flrvuQeAxHXvw4af/B3/O6CMNjkGJlGpqQOTKEg1djRZ+pMogDkXoPY9QnDB1dx3cuDjudFkFH+vn+u9SzDZ3LD/sYHrC9SB1dUXaC9Jiwve+d2AIoauBlZqMwA/8z9NR6idh7bWctWiHDx7Bg3mqZPMa36O8hwHAHsbAhxv7U57XI61dA9IogAe3nGSE/J0y6SI1h/BuON/oOI9cOipgTP9VWD3wr5HB05PJqD1EHjLUMEmDF3H0bEYrnWVGOtfB2XkROnKAUkUwPFgDVVWW/+EeC8WYzPek8fY1/7OgLJ7WvcwzzOPo13VVGX5oPUwANWdR+lNxjhQax2QRAEcbAwOSKIA3qkLMce2Dn+vnyMdR876+Ih3h+OB48xVFo4ni8kffGtPa+L+DgzD1EgBGF0uEu1tI24j254zpJ3UgHUYFLcstvDX66w8qK9n7V07eO/dW1j7j8/z+131fPu9S4cmUd0t8NSXUuf3gcfh1R+hFLx3eRH3vnp81P0W565pubV3ilJqA+DTWm9NM+8zwGcAyspm+peqOt1AUmmNQjGxZHxwvqpBqTPWPWTro4Sn0r8euIXT61Ia1OlyQ8trlHQAnMa0n5OKYT/Pfml++yg1Yg/OYz5zlQINmlHW1Rejoq9smg2MthujnuNiiMy6RqbXG++luaeZ7Ci0Jl3k2DoHzE8EAhisFpTZMuw6jB7PqE/u+axeGrvrWZZ73ojl5vuMfHNpOx3th2hb+TlysqxkO9NsWyfhlX+GknUw7zJIRGHrT6D2DdZVXMSv3qplf0OA84qHTwDFuWvaGpsrpbKB/wL+LN18rfW9Wut1Wut1eXnpq3yniynbR+7nU7c81DNP0UsOLesHhq1i3ej8FUMXLrto4Ps550MsjPZW9E+rewt93kfIOvo8ny69akBxq9GKz+bj6Y79xHq7YdmHB67PW56qfgYwOzBEumDlbQOKJDylvK1SjwH/efZ78bry+NgFZbxwuI3Ayk8NXJ/BSFXBNadrlVaWepifn5X2uCzIz6JyUPup29aXUdZXmzXbTPc5aSleSHzdF1Ltopa8f+DMnEoIdcCymwdON5ggdxEE6tDuYhLuSpTFQte2oyRKL4Vkgjl1u7lxzsDzbKF7HvN6z6hJNNuJxvPpnDOfFbmrB5RdlbeK453HWeSZx7xgB+QtBqDCuwCnwcrSsl6Mg9qPLCl0c9nC3AHTVpc5qQ2/Ra4tV9pIjUMmXSOHczxwnKKsIlrbwuSbezAO+oZJ+P0Y3UOf1juTwe0m3uEfsUyOPYf6nsaxBZW7EF/bdhbkDpNEARx9HhK9MPeS1HujBRZeDzt/jlEl2bgon19urR3b9sQ5Z7raSFmATcAPz7jFN6xMuP+f6OoitG07nY//Af2ejYQ2LCSvdRfuw0+T8JYRWXIzyZgVd90LGBq2pcZ2chdCVhE0vQN1W6FoFdpdTtJaRMTtw3Dsj9ga9xDPv5geUzmOfCuR1t3sLFjAE81b8dh8rMpfxYG2Qyx0XUxZ1M5qcx1G/zGofZN48RripRuw7H6AhDMPNfc9qJYDGOdtJFm/D8PxZ4gVraSqdCX31m/muqz1rDKUkb1wOcksFztrO9lb08RHChtw7Ps1SZON+PKP8kJgDo/vbeGi+Tlcs7SQihEamx9v6eaZfY1sr+ng+uWFXLYwn0KPbdjy7xIZ00Yq0lSLqW4Lhkhbqpap9jXIX5I6v7qa0F0tUHge6uDjYHXCgmtStwGd+eh519L69Dac6y8g+OwmstYswJHbg8HpptFTyOvBWl5q2c7a7KW8p/Ayyv112Pb/hqQ5h0jR5TQZykn4DISzrLzRuo3drVtZnb+aLIuTaLCRy7PmU2YuwLiw/8fC0cY97GjZjZnlvHFYE08obl1fzrpyHy3BXl482MLmw62sm5uFN7uOzuRRrq24lgW+BVN+LN/l3pVtpB47+hjP1jzLxdV5/KGlglvWDrzpEdq2DZTCXFY+4np63ngN11VXY3S50s5P6ASPHf09f7X6rzAZzaMHtu0+WPOJ1P+jwWJh+P2nYdXHwFPaP11r2HoPrP9z2r0r+MZje9n2zauwW97V7UEnQqqRhzFdidTngO8De/om3aO1/u1w5TP1IiFmrYxJpITo865MpH7w1g+IJWOoLUkajEVsXDrwR1bXpk1Y5s4d2hnnIL1792JdMB9L+fAJ1/O1z3NtxbWUukqHLXNa7RvQ2wWXfXXovD2/hqZ9sPLWofNO7oDACbjyTv712UPcvqGCm1YP3wh+lpNEahjT1dj8Hq21T2u9se9v2CRKCCHEu9Mh/yHmuOZwvMdFXtagH+nJJImuAAbXyLf2AAyuLOL+9hHL5NhzORk8ObbACldA/Q6IDnqQpjcA+/8A869Mv1zBslSSFeni4vm5/G5HXfpy4pwmHXIKIYSYsKROcrjjMGXuMo5Hc8h3D7ytlwgEMNjtKNPozzgZs1wk2kZOpPLtedQGRx7k+DRrFuQsgKPPDZy++5dQtAKcuemXM9sgdyGc2Mq68mz21gdoCvSObZvinCGJlBBCiAmrD9ZjN9mxx4w0ay/ZrkGJlN+PcQy1UXCqwXnniE+j5jryaOxuIKmTYwtw7qWp7kMiXan3TXtTbRArh6mNOiV/CVRvwWIysL7Cxx/erh/b9sQ5QxIpIYQQE3bAf4BydzlNdS2UGDswGgc2qYm3t2PISt94fDBlsWAwG0l0p+/TDsBmtOIwO2juGeMQLu4iKFgOL/0THHkWNn8fzvtQaky+keQugpYDEAtzcWUuv981xtuJ4pwhiZQQQogJ29+2nzlZc6hvDFJsCQ6ZH2tvxzBK1wdnMrg9JPwjd4NQ4Cigpqtm7EEuvC71ZF71K7DiVsgbQzccZhv4yqFhF4uL3HSEYhxs7Br7NsWsJ4mUEEKICdvTuoe5nrlUtcYptA1qR5RMkuwKYMhK30ddOoasLOKjtJMqcBZSFagae5AGA8zbmOp7L3ve2JfLWQB12zAoxcWVOTwqtVLiDJJICSGEmJBEMsEh/yHmeuZyrNNCsTM+cH5nJwa7Y0wNzU8xulwk/CMPFVPgyKM51EJvYoobgOctSg33hebi+bn8YXc98cQY22aJWU8SKSGEEBNSFajCY/XgMDs5HvZQ6B2YMMXbR+/RfDCD203c3zlig3OjMlHgyKc6UD2esMfOmZsaRcBfTanPQbbTwpZjIyd54twhiZQQQogJ2d2ym0pvJU2dCbIIYXUPbFQeb287q9t6AMpswWC1kOgauT1SsbOYQ/7DZx3zWctdmOqLCnjP/Fx+t136lBIpkkgJIYSYkJ3NO6n0VFJ9IkCloYmEyT5gfry9DYPn7Af8NbrdJNpHbidV6iqlJlBNNBE76/Wfldz5ULcdgIsqc9lytI327sjUblO8K0giJYQQYkJ2texivm8+1Sc6KLV2g+rv+kDH4yS7uzGeZY0U9N3ea20dsYzVaCXPnsexzqNnvf6zkj0P/FUQ7cFpNbGu3McjO6XRuZBESgghxATUd9cTjocpdhZzuAXKXANraeKnuj0wnP1gvwa3h1jbyIkUQIWngj0t75z1+s+K0QK+CmhKDRl7xeJ8fvFmLcnk1I9XKzKbJFJCCCHGbVvjNpZmLyWWgOpuByXegV8r8dbWMfdoPpjRlUWyuxsdj49YriSrlNZwC/7wyP1OTVjOfDixDYD5+VnYLUZeOtQytdsUGU8SKSGEEOP2esPrLPQtpKolRqmhDdwDx62Lt7RgHEf7KAAMRgxuz6i394zKQKW3ku3NO8a3nbHKW9TX4FyjlOK68wr5ySvHp3abIuNJIiWEEGJcEskEbza8yfK85RyqDrKQOmJ2X38BrYm3tWP0+oZfySiMHg/xltFrfRZ4F3Cw/QDd0eGHlZkwZ27qFl/7MQAunJdDXUeIHTVTXBMmMpokUkIIIcZlX/s+vFYv2bZsDlR3MS+rZ0BD80RHBwabBWWxjHsbJq+XeNPo4+nZTDbmeSvZUr9l3Nsak7zFULsVAKNB8YGVJfzLs4fRI/R3JWY3SaSEEEKMy4u1L7IibwWxhOaw30JF9sD5scamCdVGARi8PuKd/lHbSQEszVnKsc5j1HfXT2ibI8pfArWvn3576cI8GjvD0lbqHCaJlBBCiLOmtea52udYW7CWIw1RilU7puyCAWVijY0YfdnDrGFslNGI0eMl3jx6rZTFYGZt/lqePPYk4fgUDRvjnQORLgikOuQ0GhQfu6Cc7zyxn95YYmq2KTKaJFJCCCHO2gH/AeLJOGWuMvYeauM8Qy1RZ39Dcx2PE29vw5g9sRopAKPPR6yhYUxlS12lFGcV8djRR4lNRSedygCFy6HqldOTVs3xUpHj5Ad/PDj52xMZTxIpIYQQZ+3xY49zQdEFKKXYfjzC4pzogPZRscZGjF4vymSe8LZMuXnETo6988uV+auwGKw8dOghOiOdE97+EEUr4fhLQH+7qDs2VPDM/iY27W2c/O2JjDb2obiFEEIIoDfey9NVT/PNC75JU3svwYgmvzSfM1sxRWtrMeXkDruOs2HIcoJSJNr9GHNGv1WoUJxfdD6H/Yd5YP8DLM1ewtLcpRTYCzAZU4ldLBGjO9ZDVzRAT6yH3ngvGo3NaMNr81Jgzz9ddgh3SapmqmlfqnYKyLKZ+NKVC/n67/eS67KyfnCDMTFrSSJ1SjgAvZ3gyAFrmqEMEjEINoLJRkLbSXR2YvT56DEnCUaDeG1enGbntIctZqFID3TWgtkB2RVpi8SSMTq627B1hLA7PZhzJ+cLS4ix2FS9iXmeeeQ78vnjS2+z1tZJ3Jl/er6Ox4k1NOLccOEkbVFhys8nUl2FYwyJ1CmLshdR5i7jWOcxNlU/Q2ckgAGFRqMBp8mO3ezEbrJjMZoARSwRIxjtIhANUpY1h1X5K6n0zUfRX9uGUjBnHRx88nQiBTA318lfbqzk07/Ywd23rebShXmTtP8ik01rIqWUKgAe0VpfMp3bHVXddtj0VWjYBRWXwLU/gKL+/xx01MLrd8P+Rwmt/C7NP3uS3r37sK1bi/+zN/HZ2h+yMm8lf7v+b1mcvXjm9kO8+zW8Da/9Bxx6AlzFcNV3YNH7wNI/CGx9sJ7aw9vJeXQLySefw5STQ/43vo7riiswTOAxcyHGIpFMcP/e+7ll0S0YeoO8WGXm5vnWAWVitbWYfF6UxTrMWs6eubCI0K6d2NesQRnG3irFbrKzPHc5y3OXo9HEkwkMCoxq5K+/aDJGffAkr5x8lZdPbuaSkktYnL0ITiVURWtgy13Q3QRZhaeXW1Hq5UtXLOBLv9nNpy+Zx19cVonBoNJvRMwK09ZGSinlAx4AMqvapqMGHvpIKokCqNkCv/sEBPseZU0mYfv9sON+ogvv4OQ//ITevfsA6N2xE8+d93BH4QfY1rSNr2z+Cu3hkUcqF2JYkR5442448BgkE6mngn7/aTj51ukiSZ3khZrnyHniTZKP/RHiceLNzTT89Zfp3bdvBoMX54pNNZuwGq0s8S2iY9OvSRgtFBUO7Lk8fOgQ5uLiSd2ucjoxuFzEamrGvw4UZoNp1CQKUk8AzvXM5eryq1mZu4It9a/x4IFf0tzT9/Sg2QZlF8DbDw1ZdnGRm+9+YBlP7W3kwz95g6PNwXHHLDLfdDY2TwC3Al3TuM3R+ash3DFwWkd16tYKQE8LvP1LAGK9WSQ6BpaN1TewKOwFoC5YN7X9l4jZrbMWDjw+cJrW0Hrk9Nv2cDu6vQP++NKQxSNHj011hOIcF4qF+I+d/8GH59/E3M138VDzQtaXqTPbmBM9eRKSSYyT1D7qTJaycsL79qZ+4E6jQmcR15RfTWlWKb89/Dueq3ku1b1C+cVwcju0HRmyTG6WlW9dv4QVpV4+/JM3ufOJfXT0RKc1bjE9pi2R0lp3aa0Dw81XSn1GKbVDKbWjdZRxlSaVLc1gmgZTfzspswO85anJtjSjlxuN9NpSVxGTwSTtpGaRaT8nLc5UI9bBzjhHbSYbYZPGUFw0pJjRO87xzMS7woxdI8/wo50/4mJbAe/f/N+cbI2wPbGINUX9yYGOxwnv3Im1snLAE3yTxZjtw2Cz03tw+rsZUCgqvZXcMPc6emLd3PfOfez0HyS54FrY8m8QCw9ZxmBQXHteIf/8oRU0dPZy+b9t5r9ePEp3ZOTORUOxEHtb9/JszbM8euRRHjv6GJvrNlMTqCGppzeJFKNT092tvVJqs9Z640hl1q1bp3fsmOLBJ0+JBOGlf4K3ftI/7ao7YcMX4NQTG1Wvwq8+SKJgHW31K/D/9sn+sp/5GHfOfZsDXUf48tov84mln8BsmPjjvmJajXrFn7Zzct+j8Oin4NTFsngN3HQP5Pe3vdvWuA3L7kNYv/qv0Nfbs/W8pZTefTeWkjSJmHg3GvGcnNZrZJ/njj1BwzNf5eNdXfgXXs0XTt5IviPJpXP6Egit6d6yBXQS6+KlUxaH7g0T2r4d58UXYy4a+oNiunREOnmndQ/d0SAfiVvINjkxXPX3YLIPu0xjZ5jH3q5n78kAt28o5/aLKsjNshKKhdjWtI3X619nW9M26rvrKc4qJs+eh81kQ2tNMBqkobuB3kQvG4o3cP3c67m05FLMwz1ZOPmkodcwJJECCHVA0x7oakjVPhWtAKurf34yCc17oeUQCUsB4TZFvK0DQ1E+9UVWjsROUpJVwuLsxWRZ0jzxJzJd5iRS0TCc3Ja6VWDzQOFKyF80oEhSJ6lqP4q5phFLbRNWt4+sZSuwzOCXiph0GZVI7XvrbnwvfBeLby6B5R/hZX8R9+9x81drOzEZIBEIENq+HZJJbMuXgyFN7f0kSnR0EN63F/vSpVgXLkSZZu4B9NZwG0faD7K85TilmGhd+RGKK6/FOcJ3QXV7J4/urmLvyV483pNEHVtYVAJLcxeyOHsx5e5yTIb0++Tv9bOndQ/bm7bT3NPMzQtu5qOLP0qhszBt+UkkidQwJJESIpMSKSFSMiKRSnaeoO7hT2Bv3k/DkhuwVVxCbQf8v8153OY9THHoBLGWNnQ4hKWsDPOcMjiLJ+omQodCRI4dJd7RiTkvF2N2NkavF6PHg9HjGd+txWSSZCQCGpTVgjKOPSEMx0JE63dQ1nqMiE5wyOGi3VVApzufsDWLWDJGd6wbf6+f7mgPhc4Ccq1ziHXPpdnvpiWgWVhkYkmJmbl5ZoqzjeS5jJiMw+9HY08jm+s282bDm1xUfBG3n3c7K/NWnv1+j40kUsOY9kRqLORLS0wzSaREppnRREqHOmh8/hu433mEtzw52OZ+AFttO0eOd/JD44e4NLaHVbYWDE4nBrcHo9s15bVQw8YajZLo7CAR7EaHQySD3SQjvZhysjEVFGLKz8eUkzOk1konEiQ7O4m1thJvbSXR7ifZG06VUwodi2Gw2TDm5mEpLsJcUoKyjqE7B60xd7dg9Fdj7m7G2eMnaVD43YV05lQSKTwPm6cUw6Amyj2RJLVtceo7ErR2JWjtShIMJ/E4DOS5jRR4jBT7Un8l2SaKvP1JVigW4rX613i57mW8Ni+3LLyF6+Zeh9uSpg3w+EkiNQxJpISQREpknmlPpKLxCNWH/kBk5/8yr3Y7++xZ1JvmkXeoh+5eeKHoep5Tq7khv5EVvp5J3fZk07EYyUAn8c5OEoEAyWAXymxBWW2pIxuNkuztxZCVhdHtwuj2YnS7UHZHf42a1uhQD/FAgER7O/F2P6ZsH+Y5czCXlGB0uUaMoT8YjSkSxNbVgDVQj62zjoTVTXfBEnryFxHOLkcb0ydo8aQmEErS2ZOkM5TE353E35OktStBIJSkyGdkbp6ZefkmyvNMzMkxUNN9gNcbXmd/237W5K/h8rLLWV+4ngp3BWpiDwBIIjUMSaSEkERKZJ5xJ1JJnaSqs4pYMkZSJ4klY8SSMSKJCOFYCFPLQSLNVcS6mkj0NGMNtVLQ62dZb4ioMnBYOWhsNeEM2zHm5WMpLOSr/pswqyRX+mrxmiJTssNTSoOOREgmEqnbdiYjBrMFRrhtNkRCE+8KkOjsRAdSD6ArjxtDlguDw4nRagGzGWUyoowGlM2RtoNcpZNYQn7sXQ04ulow93YSs7kIZ+UTc+YQtbmJWRwkjXaSJjNJg4mEyUbMObBH91gcWoNJWgKali5Na5emPagxKMhzK3JcGm1qpzvZQEfsBHEVpCjLR7E7l0JnDtkOLx6rkxynk/IcJzajjcXZi3GYHcMdAUmkhpGRiZRSqhWoncJN5AJtU7j+iZL4JuZs42vTWl83UoFJPCcz/diN12zdL5iZfRvxnBzpfHSvd7vLPl+2IN28klicZ042DLvRoFKk+0a4o/fbvKMX4KB3lLDF2Up/xIcKKgN6tFRGK9DDPzU4HOf872Mwd9H8WHNd6+OtLcMUG/U6ea7KyERqqimldmit1810HMOR+CYmk+PL5NgmYrbuF8y+fcu0/cm0eCDzYsq0eMRA09mzuRBCCCHErCKJlBBCCCHEOJ2ridS9Mx3AKCS+icnk+DI5tomYrfsFs2/fMm1/Mi0eyLyYMi0ecYZzso2UEEIIIcRkOFdrpIQQQgghJkwSKSGEEEKIcTrnEiml1M+UUm8qpf5upmM5RSnlUUptUko9p5R6TCllybQ4lVIFSqndfa8zKrZTlFI/Vkrd2Pc642LMxJgmSillUkqdUEpt7vtbPtMxTYa+833LGe9nxWeXSfuR7ro30zHBwGtdpjjz2iYyzzmVSCmlPggYtdYbgHlKqbSd1s2AjwM/0lpfAzQBt5F5cf4bYM/UY6iUugQo1Fo/mYkxZmJMk2QF8Gut9ca+v70zHdBEKaV8wAOAs+/9rPjsMnA/Bl/3MqWzx38Dzr5Xyyly5rVtpmMR6Z1TiRSwEfhd3+vngPfMXCj9tNY/1lo/3/c2D/gTMihOpdQVQA+pi91GMig2AKWUGbgPqFFKfYAMjJHMjGkyXAi8Tym1ra+2wzTqEpkvAdwKdPW938js+Ow2kkH7kea6N1yP2tNm0LVuxqW5tokMdK4lUk6gvu+1HyiYwViGUEptAHxAHRkSZ191+7eBr/dNysRjeDtwAPgX4Hzg82RejJl43CbDduAqrfX5gBm4YYbjmTCtdZfWOnDGpNny2WXkfpy67mmtt85wHIOvdZlgwLVNKfXFGY5HpHGuJVLd9FfZZpFB+6+Uygb+C/gzMivOrwM/1lp39r3PpNhOWQ3cq7VuAn4JvErmxZiJx20yvKO1bux7vQOY6dtFU2G2fHYZtx+DrnszbfC1LhMMvrZdPsPxiDRm/D/SNNtJf3X2SqBm5kLp1/dL6GHgG1rrWjIrzquAzyulNgOrgBvJnNhOOQbM63u9Dqgg82LMpM90Mj2olFqplDICNwF7ZjieqTBbPruM2o80172ZNuBap5S6f4bjgaHXtkw4TmKQc6pDTqWUG9gCvAhcD1w4qAp/RiilPgd8n/4vof8DvkLmxbkZeD8ZdgyVUi7gf0ndqjCTaqz/BJkVY0aeexOllFoGPAQo4Amt9bdmOKRJo5TarLXeOFs+u0zbjzTXvXu01r+dqXjOdOqzz4A4Bl/bPqy1rh95KTHdzqlECk4/kXM18GpfdWlGyuQ4Mzm2UzIxxkyMSYzNbPnsZst+CJFJzrlESgghhBBispxrbaSEEEIIISaNJFJCCCGEEOMkiZQQQgghxDhJIjWNlFKrlFKrJlrmjLKbR5l/p1Jq4zDz/mMs6xttG0JA+vM23TkmxHST81BMNUmkpteqvr+JlpkwrfVfT/U2xDllFYPOWznHRCaQ81BMNUmkpolS6gekes79ulLqRaWUVSn1a6XUK0qpXymlLIPL9C2XpZR6Rim1RSn1f+PY9Ff6tvGbvk4TT8WzeTL2S4h0523f9M1nvL5TKbWp71x8ZJaMyScyiFLKrpR6Sin1qlLqsVPn2KDz8Aql1Oa+v4hSqlgp5eg7J19VSv3PjO2AeNeSRGqaaK2/AfwQ+KHW+krg08A+rfVlwFHgz9KUASgiNYTCVUCFUupsx8fa0beNAKleyYWYVMOct+ls6TsXmwEZgFVMtqVAUmt9KalOjbMGF9Bav9TX0eYvgf/WWjcAnyF1Lb4UKFJKrZjGmMUsIInUzFkKvNX3eiuwZJhyMeBTwK+AbPrHyhqrU9vYBVSe5bJCTKadff++Q2oYHyEm0y5gn1LqOeBaIJSuUF9bvtuAr/VNWgTc3FdzNQ8omfJIxawiidT0CgOOvtf7gQv7Xl/Y935AGaWUAv4ceAT4KNAzjm2u7ft3Be/eMcJE5ht83qZzft+/q0mNISbEZFoJvK61vgbwAZcMLqCU8gD3AHdoreN9kw8D/9FXU/V3wInpCVfMFpJITa/ngQ8qpV4H9gHnKaVeBRYAP09T5pK+998AXuqbf7a/li5RSr1CaqymxycWvhDDGnzeprO+71e/F3hqmuIS544a4K+UUm8AhcCONGX+EigFftXXTuoy4D7g+r5r8V8AddMUr5glZIgYIcSUU0rdCWzWWm+e4VCEEGJSSSI1S6R5Ci+gtZYGvUIIIcQUkkRKCCGEEGKcpI2UEEIIIcQ4SSIlhBBCCDFOkkgJIYQQQoyTJFJCCCGEEOMkiZQQQgghxDj9f6r20anKYu4uAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 596x540 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#变量关系图\n",
+    "sns.pairplot(tips,hue='day')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b39dbe5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='total_bill', ylabel='tip'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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H+7hxW2dDOUTmVu0hn4eA12ioC08hK27GKqWe1FofLPj+Ga31+6s1oUbejC2sZCzMknj47utE7Nc5tfjZ1vv354N/9DSnx+YwlUIp0Bpsrbmmt4Vv/frtRa/VWhNLWcwkMqSt6oRntNaMjM3x5NAoR4YjTMTm2+6ZhuLmHd0c3BPm1v6eqvZTrQSlCmPtjbVqv6zNWKXUDbg+8luUUr+UfbgFSC71nmZno1UybiRq8bPNjWE7mjPjc6RtB1MpvvCt4Zr8/pyZiGMoMLIrYqVAO5ozE/MFWrWIv49GkxwZinBkOMKZ8WKHyD1XtHNwT5j9u8N0hBpjU9VrGnlhD3rNhl21L8dyoRtV4usE8POlX978bJRKxnpQbkhjLUMfhccam02xub24SrLwZ7sW456bimMquDiTQil31eo4mpORGEeHI/njlTPWWszHdjRpWwOajx56jl+8eTs3bOvEqUL8PZa0sm33RnnlfHHbva1dQQ4MhrlrsI8Lkwkee/Ecj714jivag9xz0zZu7u9e9tgvjEzy2IvnuBhNlP2e5VBKEfAahLwegj5zxRTN9RDOLSd083ta6/+7RvNp6NDNvY88v6iSMZ62CLcFePS+W+o4s/VNuSGNtQx9LDzW6bEYlq3Z2hXMp+flfra5wqbVjnvvI8/z8tkpNOSLdxxHowx417YuHr3vlrLO8XI/hw998RinIjFMQ6F1TuTBZyr62gNYjuaTd+5alUgWkrYcfnBmkiNDozw3MlHkENkV8nLHQJgDg2Gu7XMdIl8YmeRLT53CY7hCm8w4K87pct5Tistdtdc7HFfIcqGbFQNMtRT5Ruf+ff1kbE087WYdxNPWuq9kbAQKwyZKuV+9puLQsZHLet3ljNnX5qbnXZpJLvrZrtW49+/rJ+M4aK3RWuM4GgdNX5s/f+dQzliXO58HPzhAV8iLgrzIGwo2tfoJek08huKxF89VdE4LcdvuTfOHT57kI//lOf79E69x7JRrAxzwGBwYDPP7P3s9f3f/rfwfd1zDwOb2vKg+9uI5PNl8c4Uqa06X8x4g/7n1tPrZ2hViW3eITa3+/GdaLmv5O1lNpGCqAtZjmf16oNyQ2FqGzhYeqz3oBTSXoilmEpmin+1nv/HjNRl3/0CYXb2tvDkZx3Y0PtNgU2sAj6kIZy805Zzj5X4O+3b38rl/fh1fe/YMr5yfxu8x6Ar5aM2W3ge8BpeiiWWPsRRvTsxx+IS7qToaLW67t/eqLu4adB0ig76lN1UvRhO0B4olaaU5VfKeasTa10s4V4S+QtZzJWOjUq6511qagJU6lsc0ePf2rkVhuLUc9zMfGix5q5+7KyxnrErnk7GdvP/M4JXt/Oefv5FP/e0rTMylivqdJjMOm9uDZZ/LeCzFd4cjPDkU4XQkVvTctX1t3DUY5s6BMN0t5bXdu6I9WPGclntPLTJk1osxXePkBgkblnJDYmsZOqvkWGs57v6BMA/ffR3htgAziQzhtkBRPLecscqdTzJjE4kmOTcZZyaRKdpkveembViOJpGx0bhfLUdzz03blp1/PG3x7dcu8Rt//wr3PPI8X316JC/yV3QE+Ngt2/mLf3MTX/3Yu/nIe7aWLfKXO6eF70laNo6GX93fz46eEJs7AnQEvVVLg1wv4VwxNRMagnLNvdbSBKySY9XSfKycsZZ6TS7/PZq0VvSfyWWrXIom2LxMtoplOxx/a4onT4zy/TcmSBXk1bcHPNx+bS8HB/u47sr2ssMhS2XKlDunHEopXn5rikdfOMvFmQTbultqHk5tFGO65TZjReiFDct6SIsrl9XaAy8U3o/u3Upb0MuTQ6McfX2MmUSxQ+T7rt7EgcEwN+/srni1vNpMGdNQeXOwkNfM1wVsdMS9UhAW0Cz9Wteie1OR8HoMRsZjfPaJ14o8ZhRw47ZODg6Gef/u3vwG7uVQmCkD5PcqHnvx3JJC7/MYrrCvoa3vRkKEXtiQrPcq57msPcFaNNf+q+ffIpG2SWbsosYdAFf3tnBg0HWI7G1bm7Z75WTKFBqEhbxm0zXrrjUi9MKGZL2kxRXiOJrZpEU0mSFjr86eIJmx+d7pCQ4PjfLaAvtfj6FoC3gwFPzpL5WMBKyKpTJlrugI0hH0NrxB2HpEhF7YkKyXtLijwxG++vQbnJ2M09ce4J69l1/ebzual89OcXgowjOnxkkU3A0ooC3goT3gIeg1SVoOPS3VaZx9z03b+NJTp0haNkGvmd/c/eRdu+jZIM26a40IvbAhWQ/9Wr/9o4v8h/91AlMpWv0mE7EUX3rqFJ+k/PJ+rTWnIjEOD43y1PAYk3PzDpEeQ/He/m52dLdwZHgUr2kUbY6ulGp5Ofg8Bj95/Wa6Wrz8+ffe5MJ0QgoPa4AIvbAhadQqZ601symLmXiGrz49gqkq27TMcXHGbbt3ZCjCW5PF4ah3bGnnwGAft+/uzVYEwzu2dFSU1lguS8XaP3D9FXzg+itWfXyhPETohQ1LI1U5W/Z896ZctkullgDRRCbvEPmjC8Vx96u6QxzYE+augT42dyxuu3dzf/eamZkVZsj4PRJrbwRE6IWSNEqOebN3fUpmcumRi7s3lWMJkLYcnh+Z4MmhUX4wMonlFDtE3jkQ5uCePnaFW6smuIZSrn9M1kNGMmQaDymYEhbRKNar1Z7H0eEIX/jWMCcjMbymoq/Nj8c0qn6uufBMdIXuTUsVFv3aHdcQ8JkcPjHK06fGmEvNb6oGvAb7dvVy12CYd2/vqlrbPVm1Nx5SMCVURKPkmFdzHrmLSCSaxFSgHXh7JsmVHcG8zexan2uhuVg5zT1u7u/mk+zKx847gj42twf4o8OnGIstcIjc0c3BwTDvu2ZT0R3AWmEa8wZhsmpff4jQC4uoZo55JWGSas4j39ZPa0xDoVDguI6MOze1rGk+fTxtEU1YxNNWxd2Qdva2cHN/N4eHRnl9dJbXR2fzzw1sbuPAYB93DPTSFSrfPKxc/F53AzUo1ajrHhF6YRHVyjGv1HagmrnuuYuIzzSwHI1Sbg/VtO2syRiOMx+eyRU3FYZi2gMeJuZKp0vGUhbPnBrnyROjvHJumsK1/5WdAQ4M9nFgMLzmOf+Fq/aQz1O1sI9Qe0TohUVUkmNeyQp9pVDMwmPd2t/N4y9dqEque+4i0tvm5+3pJA5u1yfTUKsaY7nwzHIeL++6qpMXzkxyZCjC90cmimL3HUEvd1zby8E9fQxsblvTeLis2jcGshkrlKRcq9xKNktv+8JTdAa9RUIVTaS5FE3RFvAwm7ToCnnZ1OrPH+sj797CcyOTa57rXjh3y3YYjabIOA67elv5zIcGKx6jMDyzFPf+6fO0BzxumAh3UzaRsZmYS+M1DaLJ+ff6PAY/cXUPB/f0sfeqrjWLicuqvXlpCJtipVQf8C2t9buWe10zC3290vhWM24uM2VkfA6AnT2hvBAWNkuPJjKMx1KkLAevqdjR08Jsyioab2Fz9dlkhvNTCRQaW4Oj3VL8Ta0+NncEy268frnnV3gxa/G5reUWznnh6z//zSHOTMTzn8Wv3bmLG7Z1luU9k+vqZCpFNOmu+jMF6ZCGgndt7+LAYJjbrtlEyyocIguptvNjo6TibnQaRej/CrhJaz2w3OuaVejrlbK4mnGPDkf4jcdfYSqeIbfwczR0hrz8wUduzPdSnU1avD2TwEBhawfLccvrt3QGitIVgaK5nI7ESFsOylCLLHG3d4doC3iYSWR45sE7q3J+lRzj6HCETz/+CtPxDCobNXe022v2wQ8MrFhsNDmX5i++9ybfPnGJjF38N7elM8DdN17JHQNhNq2B10thC70WX3UzZBolFVdYXuhrkiOllLoTmAMu1WK8RqRe3eJXM+6hYyPMJi1MQ2EahvtPKWIpN66+rStEImMzHkthoDAMhZ1dlZuGYjyWLhpvYRs9DXg9Bh6lMJT7PgVo3OyXiTm3UfdtX3iKex95nqPDkTU9v0qO4X4WGQzAMIzsP8VcyuKxF8+VPG4ibfPkiVEe/P9e5ecPPcc//OhiXuQNBeE2P79+1y7+6lfey8/t3bYqkfcYBm0BL5s7AkUt9KqdBlmv32uhMqq+GauU8gG/DfwM8PUlXnMfcB/A9u3bqz2lulAvW9zVjHtuKo7lOEVioZTrgnh+Ks7vfPh6HnriNVKWg2m4K1ytwWuofAbLwvEKbQfufeR5XnxzEo+h8BhGPvyhIO+N3tvqWzZDZy0+15WOMZeyODMRw3Y0RsH+glJg27rIksB2NMffmuTwiQjfOz1e5O/e6vdw++5eDu4Jc/2WjqJjVUp+1e41CfgM/J76bKSuR7vnjUgtsm4+A3xFaz29VLaA1voR4BFwQzc1mFPNqZct7mrG3dYVYnw2hdauqIEr5Kah2NoVyhuDPfDYy8ylLQIeA+Ux0FnB92UvEEuNd/++fl46O5XPZfegsGzthomUorfVR2+b68uyVLHUWnyupY4RT1tsbg9wbjJOxnbY3BZkci6NdhZ/Fn1tAYYvRTl8IsJ3X48wFS9uu3dLfw8HBvt4785ufJ7LX2H7s8Ie9JoN49e+XuyeNzq1CN0cAD6hlDoKvFMp9bUajNlw1Ktb/GrGvX9fP20BD7ajsR3H/ac1rX5P/v37B8J8+Z530ZKNzyYth5TtkLYcNrX6lh1v/0CYT+y/GkMpMraD11Bs7vCzpStEZ9CzKJRRaqV4Oed3dDjCvY88nw8J3drfnT+G7ThEkxkSGYd/8e6t+buMe27aRovPg6M1juPgOA62rTEMxYWZBP/ur1/mv798IS/yN27t4P86uJvH/+2t/Ie7r+P9uzZVLPKmoWgNeAi3B7iqp4UtnUG6W3wEsxvHjUC9fq+FyqhpeqVS6qjWev9yr2nWzVioX7f4yxk3956To1ESaYeM7WAYqijrJseXD5/ki0dO4Sz4VWoPeLjuyo4Vxys1v0PHRkqusktl4VRyfkttHn74xiv43hsTvD29tE3vCyOTfPXoac5NJ9y7lgXH3tET4uAet+1eX/tih8hyWI957fX6vRaKaYism3JpZqFfL1SaSXHD575NImPjMeZXrJbjEPSavPq5D9RkDuVSmOKptcbREEtl6A75+cOP3ljyPamMzfffmODwUIQX3pwsyhDqafVxYCDMgcE++ntbKl5pewwj79ce9JpVzWuXNMjmRkzNhIqo1ExsLm2zMCphKPfxy6VajUHOTcVpD3jI2I5buarB71ns8W47mlfOTfPk0CjPnBonXnAuIZ/Jvl29HNgT5satnRWJs1KuE2XI6yHoM1cVs6+ESu0nhOZChF5YRKWZFC0+d8VdqHeOdh9fDattDLJwBbv3qk6m42nenk7g9xh0t/ho8XnyHu9aa0bG5nhyaJQjwxEmYvNt90xD8d6d3RwYDHNrfw/+CsIquVV7KLtqN+pQjdoojqRCfRChFxZRaSbFx2/byZeeOo3lOBjKFXlHu4/Xi9wK1mNAm9/D6bFZfnBmghafiaFcT5rRmSRdLV6UMtjc7ufj/+2HnMlWAOe47kq37d7+3b10hLxLjLaYRou1n5uKYyoYGYuRth18psGmVp+kQW4QROibiLWKwZYyNZtJZPCZBu/5ne8QT9tY2Zzy/k0tPPjBAe6+YTNPvOpWfZqG4u4bNvPAgd0VzW2h3UJvi5e2oG+RLcGXD5/ka8+eYS5t0+Iz+fhtO4vGAvjq029gKPCYBrajmZ5L42iYTdl4TYV2XNuFibkMjoZvnRjNv3drV5CDg33cORhmS2eQcjANlY+zN6KHTJvfw6lILFv8prAczYXpJLvCrfWeWt3YSHsWIvRNwlrGYBfGx1t8JgqYiqeZimfy2TUeQ3MqEuOBR1/C7zXZ0RPKXxh+eHaGo8ORvCvlSnNbaLfgaM35mRTGTIpt3cH8e97zT+d54tVLrogb7p3Gl546DcADB3bnzcXenJhz+61q1/Y3U2BFY9k6nzGTy0XoCnm5YyDMXQPhshwiC2Pt9SxYKpd80kX+xBc8vsHYaHsWIvQNTrmrjrWOwebi40eHIzzw2MvE0zaO1nlhVLjhGa+piKVt0rZmc0ew5NjlzK3QbsFQCstyNz+1gvFYmv7eVuJpq0Dk3U1MNwxj86fPjPAzBXnvuX6rAY/BeEE3JihOi+wKennwQwO856rFbfcWNgn5xfdu547BcD7W3ii57OUQS9ts6QwwHkvnQzebW/2r2jBfz2y0PQvpB9bA5NvdzSaLVh2lPF/OTcUXtZBbbSl6bvy5tDVvcZB7UpGvmHW0m0651Ngrze3ocISXzk6RtNx8fduZv6BoXWyl4NoQ5J5zPeRzGT6FDpJ3XNvLZDzNyESc9AITMaXAzHrrPPjBAW7e2V1S5L/01Cmm4im6Qz6iyTR//N3T/Pj8TN7XZT2xrSuExzTo721lYHM7/b2teExjw1awVuPvpZERoW9gKjGMyhmMFbLaUvTc+AGPCaiirBqyIq81RSvsUmMvN7fcxURlhVdrigRbqWIrBTPrdKm1G37RuBeaoNdkIpbi74+f42Nf+wF/dOQUcyk7n/OugJDPIOBRmMqNqe/c1LKoKCpnDvbfX75A0GvQHvThMQ1a/N51bdYlFazFVOPvpZERoW9gKll1VOMPOTd+b5s/L+g5cmvklOXgZMX59UtRhi7OcGp0lmgikx97ubnlLiZ9bQFMQ+XFO3d8pV1/+rlUhmTG4a5rN+XvIGzbIWW5tsgZ2+HnDz3PV58e4e2ZJEC+ZV97wENnyEtPi59t3SGu6AjS0+rnvve78wt4TbpbfGzpCrK9J+R2nZpJFGUdLffZrwcWOoeG2wIb2kp4o134JEbfwFSS5lhugVElmQatPpPTY65ro6ncBtp2VoKN7MraZyo6gh4mYhkyWe8XVHEcfKm5Abx0dgpHa3ymQVfI9bZPWQ4a2Nrhp8XvZTZl0RXy520Jrmg/w9+8eI5MwUZiusD+N+gz6Q75XJM1pUhkbLyGoj3o41I0weaOIP/q1qs4sKdvyQyZZjTrWm1dQjNRrYK8RkUsEBqYtbYBWOl4hReBVp/JxWiSuZRdsulIoRfNyFgMK2tE7zFUfuN0OV+ak6NRYik7n/WhUDhoruwIYhrQ3eLnjz76znwYR2vN0MVZnhwa5ejrY8wk5h0iFdDi9xDwGEwn0uzc1JJv1weg0cSSFt/4tdto9XvK2khtpIYaGykNULh8xAJhnbLWq47lMg2AonSz01nx7mnxEUtZpG0Hj6HobfWzfyCc7y4F7mapmQ2yl/Kgz1EonrGkRTqb5ug2KtEo4FI0QXeLn49kM2jOT8U5PBTh8NAob08ni44X9Bq0B7y0+j3ZsI9mJumGeHJibihIWg47NrUSbivfaKzws3/t7Rnm0jaOo3ngsZdL5u1Xi5XSAOUiIJSDCH2Ds5rb7YUicHI0yhUdxQVAOUFeeBHIZbfEUhb9va3MJjNEoklORmLc+8jztGZtDyxbYzuajNYoBf5lPOhzY1i2JlWQCaMBy3EF36PgV35iJ+enE/zl37zE0MXZomNc3dvCgcE+njk5zmwqU7SHkcw4XNUdIplxsByHkM9DImNjO1xW7HX/QJhXz0/zwpuT7oazRy3K279c1iJtFthQueDC5SNC36SUWgnGUjbnp+JkbJ3PpW4LeNi5qXWRv43PdDs+pW2H2WSGt6eTaDQBj0FkNkk0kSGZsUlknHyQJJcxMx5L4jXNReKaG+PMTLHNALgCr5V74fm9bw4VWR73tvq5azDMgcEw/b1uJefOnha+9NQpkpZN0GuSznZy+uw/2wOs3V3Q1549syhv33IcvvbsmcsW+kqKdZbzHdpoueDC5SNC36QcOjZC2rKZiFl5UfcaMJ2wXAsArYmnbebSbs9Xy4HzU/MOjjnx9hgQiboi72iwbCfvB+PkMnEKRNnWcHEmRU/Iwxe+Ncxnv/FjtnWFuLW/m4nZVNEYhVjZVJto0pqfg4LNbX5+7c5d3HJ1T/5xn8fgA9dv5u3pOH/x3FtcSCfzVgg5gVsroSvHmXOp1flSj1ci0MttCjdCG79S5wiUHU6S0FNtkPTKJuXkaJSJuTSW7WbMWLZmLu2uvg3cUElOny1n8fvnC6MUiYyDodyLg+3ovGkZZCtkS4w/Ebc4OTqLqeDM+Cx/dPgUiVIDLYFHuf8isyn+03de5+W3puhu8bGtO8TWrhCvnJvm669cpLfNz+DmNnrb/Dz+0oWSxWSrocVnLmqoUujMuVRR25cPn1yy2G2t0mbrnQte6tw//fgr/Mbjr5RV5FdJQaCwOkTom5SMPZ8GqZTCyOaoq6zRl980KJFVWITfY7C9O0RbwINS4DWMvKjn3movk7Rla1eoZxLWom5MhSjcVbK34LfRNA1Mw8BUinja4vGXLvBPZ6f5pT97gdu+8BQPPPYyactetphsYcvAyxGQj9+2M5+372gn+3XemXOporavPXtmyWK3SgR6ufz3eueClzr3WMpiNmmVVeRXSUGgsDokdNOk+DwGiaw/Ta6CNUcuS2alzFpba4JetznGXNrGVBStbj1ZF8TlyBVULYcGvAuO5fYHVxiGO49To9GiuPbFmQSJjI3fY9KeDV8stFVYi43KXBx+KbfMpcInc2mb7Uus2n/nw9cvcgddTqCX2pCvdy54qXPPVS0XstTdSiOEnjYKIvRNyq5wG29OxIgm5mP0LSEvc2nb9adxdNZzoPT7Fe6GbCJjsyvcxnQ8zZnxOVT2DV7TQClXhJe7YCwUeVNli61snb878Hvci07eME2Rz3PX2rUrSNuajoK4dsBjkrYdxmOpvNAXrorXcqPygQO7l9x4XSqGnmvGUiq2vpYCXc8iqFLnbhrZXfUClrpbacaitEZFhL5JWLipdWt/NxemE2zu8BStGv/1+3bwjz+6yKmxGB7lboKW0mmloC3gKVppPvTEa0QTaWayFw9w/WPi6aVj7wrweRQpa75yFa2L4vopS2dX8FmjNA121iTN0dDpn1+x5+ht83NhKuFW0Wq9aFVcq9ViKe/+jK35+G07efylC0uu2puhSrXUubf6PSgo625lqc+uWW0I6okIfROQC1OkLZvZpMWlmSQvnZ3ip67v41I0vWjV+MCB3fkLw6nRKHNpm3Q2xGIYCp+paPGZ7NzUWrTS/Mj5ab781KmiC0Mi7eD3GKQWbLT6TEVnyAdoruwMcdNVnTx9cpw3J+ZQSmFq9/U590snm3UT9Brs6GnJNx+5ZlOIz3xosKgSF6At4GVTm81cym2KsnBVXKvV4nKr8xu2djZ1iX2pc//tCtJb6x162kiIBUITcO8jz3NmPMbEXBoDlQ+pGEpx6GPvWfUfjmU7xFIW/+Ir33dTK7Mr74W/OQOb2zgw2McdA730tPhd33Zf6Y5LN3zu2yQydpHrpeW4Fa2vfu4Di+ZQqSVBI1kYCEItEAuEBmWtcojPTcWZTVpuwZJ25n3i0ZddPOM4mljaYi5lMTab4pmTY4yMz7niXmJt8OmDu7n7nVsI+U1afB4CXmNZPxmvqUhk3HEKN4t9Zun3VLr6k9WiIMwjQl8n1rKV2bauEG9PJ8jtr4IrnA5wKjK73FsXEU9bxJIW04kMPxiZ4PBQhO+/MZ5P11yKx46f4/otHWXPfXdfO2fGY8wmrYIqXS87Ny3dw7TSuHat4+BS/CM0KiL0dWI1WSGlNl6fPzPhPlmQSWMa5K0BliNl2cSSrsC/cn6aw0MRjr4eKapS9XsMPIZyUzYXvN9ruM9XcveQ24hbuFm8lhtxtRTejdaDVFhfiNDXiZWyQpYrq3/oidfI2HbeUuC5kYn8MXIhEFORr2b90BeP5Tc3d/a4m5vvu2aTu3pPWZwejXF4eJQjQxEuzhQ7ROZo9xv8zLu38egP3mJ2QZZNzhrhVGSWex95vixhrXZopdbCK74zjYncZbnIZmyduPeR5xdlheQ83HOr3VIbiYeOjfDmRIxINFWyKjW3oM+1zkvbOt8dSmvXvqA96OXf3X4108kMh09EeH20OLzT0+JjYi696NhtfpOOgIe3o6m8z42Zq7rVGqUUW7uCl7X5udZ/kMt9vgs98teC277wFJ1Bb9G+hNaamUSGZx68c83HE1Zmo23IL7cZKxYIdaKc9nqlSsPPTcWZiWfmrQgW7l0qCHgNPKZiLu2gs3bDSqm8CE0nMvzuN4f5k+++kRf5cJufX7h5G//1X+8lVVCe7xYvuf+fS9uMzWXY1hXEayq8huH6wGtN2tZ0hbyXVc5eDc+TWjd/rrfvjLAYsViYR4S+TiznYbKcSG3rCpGynSWrUXU2jOLJNtt2c9R11nJYYxdUoLb6Pfz0DVfwxY/eyGP33cKvH7yW9129KW8+tvAikqty9ZgGV3YEUcq1OMi18fMvsHksV1ir8QdZa+Gtt++MsJhaX+wbGYnR15GlskIKi32iiQzjsRQpyyHkM/m593S7fVYdp2SqY9BrsLUzyEzSIjOXxnI0zoLdU6Xgqu4Q/+Vj76E14KE96KXV53H7veI6M0az6ZpF7wP6N7Uwl7bJ2Da2o/Hk0iG15sJ0EpX10Cmc89HhyLK3ytWoYq111aWkczYeYrEwjwh9A5ITqbHZZD5W7vZFNXn8pQt86Low//PVS+7qvOB9Ctdo7MxEfJHZWM4hUmvoCHr51IHd7NjUQmDBiufocIR2v1mUcZNDAx+6fjM3bO3kgcdeRuN2lNrU6gfgwnSCC1Px/K1Abs4rbYJu6wqVSLX0LJtquRL1EN5msDVoJsRiYR4R+gYkJ1IPPPYyjtYEPCabWn04GkajCf7xxwmu6mlhMpZkOumGJ3KbsLOp+XDFjp4QA5vbOfH2DBejSRSwqdVHm9/Df/zO6zz64rmibJ7Pf3OIU2MxvIaBQbHPvEdBb7uf50YmeeDAbtqDXrZ3hxYURWnOTiUwcOfc2+anLeBdMfvk1v7ufLs+I9t3diyW5hdu7l715yjCu3GRu6x5ROgblP0DYdoCHrZ0BtFANJEhMuuKte3A2GyySNRz6/f2gIcPXr+Zg4N99Pe24POYtAU8tPo9PHtqnIeeeA1LU7Tp+ZHz0zz+0gUis0nXvhhX5L2Gm1HjMRT9va1orfPhlFK3xR7TwGca7Aq3Fl0AVgrDPDcySbjNV+S02R70uBeVZT4jSZ0TVkIu9i4i9A2E1ppkxiGetoinbXpbA0zMpQh6TSbnUmgNNotX7oZyN1b9HneT9Ff3X0OL36Q94C0KzSyV6/21Z8/Q2+bHdtxuVG6GDliOxm+qvFNlYXxzqdvi/k0tS9rzLsW5qTg9LX42tQaKPovlLg5SoCQI5SNCX2cs2yGesUmk7XyjkBwf3buV/3z4JNPxDElrcZpNztq3f1MLRnYFPRZLsb07tMhEDIo3PWeTGcZmU6QsG8uBrpAXn2lg2a73jEdBRruNJLyGWpRFstRtMVBxXPRyNs0atUBJ7jKERkSEvg4ks8Iez9hFOes5RqNJjgxFODIcYTxWXLjkMxWdQR/RZDpfDPXmxBw+06Aj5GVHT2tJkYd5QbUdzdvTSVS2ehY0ZycTectgFkwp5PfkC7kKRWup2+Jy46I5UTw5GiWWsulu8dLT4i/r4lBJpk6txFfuMoRGpSZCr5TqAB4DTGAO+KjWenHp5TpiJfEofH5rZ5B/9b4d7N3RTSJtYzkOL4xM8tiL5zgVmSWRsYsNyRaM5fcYbOkMcHYyTsbWRGKpouctx7X4jc+kuDiTYsdn/le+H6yT7dBk4ObQFybjFDaY0lAyNz9jayZjaaZbS/+4lvoclhK2vA9+ZJbZpEVXyMsVHUHGYykm5zJkbM2ucNuKYtzqMzk9FsN2NL5s5o/HVEV3AUeHI3zhW8OcjMQwsw1PLs4keOnsFJ/Yf/WSXaOWYqWfebl3GYXHafN70FoTS9tyByBUjVoVTP0i8Ida658ELgEfrNG4VWGlSs4vHz7JfX91nBfOTDARS3E6MsvD/3CCIydG8yL/padOMTI2y1zazotvtvdGESbgNxVvTsTxGGqpzn+LcApE3XY0GWdxW7+cVcKKxwJevzTLbzz+SlG1aqUVrYWvj6csHK2ZmEszm7TobQuwtSvIrnAbj953y7Jid3Q4wsRc2g0zARnb4cJ0gplEJn8XkBsr1/4wbWvSjvsL72jNnxx9o6LK23LOtZwCncLjmApORWKcHpvDVKxJRbAglKImQq+1/orW+snst73Auv5NLlXJ6THgK0ff4OsvXeD/+e5pHK0xDYVla6YTGTKWzWMvnsPRmq89e4ZoIkM0tThsU4gCDFMxm7IxlNtyz28ai8SkEpYS9pUEXyuYTVpF1aqVVrQWvj7j6OydhmI8e4dSSSVte9DL1q4gXtNA49YP9Lb68xeI3Fh21t8nd362dv15bEdXVHlbzrmWU41beJzxWBrTUJhKMR5Lb+gSfaG61NQCQSl1K9CltX5+weP3KaWOK6WOj42N1XJKl0Vu5eZojWU7pC0H01CcnZzjL77/JpajMQw3e8UwFArFbMriVGSWX/jTH3B6LMZcenmRB3fFbSj3q5ldzS/Ty+OyKPd4WrvhoUIhrrTEvPD1PtPIN0gpldWzHLnjtAW89Pe2MrC5nWvCrcRS1qLX+EzDvZOZL+BFazccVknlbTnnWo4NQuFx0raT9xLKfQYbtURfqC41E3qlVDfwx8AvL3xOa/2I1nqv1npvb29vraZUMbajiaUs+toCRJMZMpaD7eh8WuTm9iAXowl8psoKisZy3H8pSzOXtonMplYeKIsbZ1cY2U3SXGWrvTAGs0pyVbPLvkaBxzCKhLhSP5nC1/e2+d1z0W6MvRJvmHLGzb2mt83vanzBR+agaQt4KiqFL2fM5fyLSh0nd7HT2v1/qWMKwlpQE6FXSvmAvwd+U2v9Vi3GXCuSGZupuTQXphO8NTFHJJrk596zlYytSWRsNO5Xy9Hcc9M2wq0BvNk0xbSt3QtB9ljdIS93Xhum3MiLo13TMKVccW8PeLC0k1/9XQ65uwSAzqCHPVe009fux2MahHxLT0xpaAt4ioT41v5uzk8lGLoYZWQsxthsclmxLlzxtvo99LR6MZQi6DVKiuJSlLNyzr3GNBS9rb78z8BnKnpafPg8ZkWl8OWalu0fCPPofbfwzIN3ltxrKDzOplYftqOxtWZTq0+M0ISqURM/eqXUrwK/B7ySfeirWuu/LfXaevvRW7ZDIpfXnrGXXD3nsmYuRRP0tQV457ZOzk7Fefb04rZ7hoIPXtfH+6/p5cvfPc1o1o6glJ98MNtrNZ628/bCriWwm1FyKjLrxpyV+5jWmlSJA5XKunFwX9/q93DXQC+XoulFKZBfPnySrz79BonM/MXEY8CucBsPfnAgL1yFDVBm4hlStoPHMFbMZsllnKy2JL2c4xS+pjWb3TKXti973GrMfS3mJQiwvB/9hm88UliNmsjYZbXey71v6OIsTw6NcvT1MWYSmfxzHkPR6vcAmu3dLfzCzdu5ub+bT/3tK0zMpRiPpcjYDqWG+pl3XsGlaHrJphm5/PFSDS5+58PXV5wvfrk55rVu7CEIwvIsJ/QbsmAqbTn5FXsiY1PJxe7cZDxfzHRhOpF/XAHv3N7JgcE+3r9rU1boi7k0m6Ar6MNQcHYyseh5gP/xTxcJeg2u7i12brRsh5fOTgGuBXBfW4D2bMFQImPT4jMrLtZZTYFPNayFBUGoDhtC6B3HjaPHszYD1kKD9hWYiqf57vAYh4dGGb5U3Hbv6t4WDgz2cedAmN42/6L3KqVo8Zm0B73s7Gl1jcnU8vnwiYzDeCxFb5vr/RJNZLgwncRjKPra/VyYTmYvMhqPaZCx3Q3NtGUzESu2+l3OEuDQsRG392ys2EysHBsB8foWhPVD0wp9xnaIp23iaYtkxqlo1Q6uaH3/9ASHh0Z58c3JomKj3lY/dw2GOTAYpr9g5Z2L21+MJriyI8gv/8QOPvSOK/BkMyru39fPbzz+ClPxzMLhFjEaTbl51grX6gA38+XSTBInmxv+1mSC9oCHj9+2k7987k2iSQsDNy/bsnW2qCi65BinIrPMxDNuwxGtiWds5tI247H0is1CyvH6Xs++L+t57oKwkKYS+rTlEEtZxNNW2bH2QmxH89LZKQ4PRXjm1BjJgg3JFp/J7bt7ObCnjxu2duRNxHLkql0txyGWcA3DPv34q5wZn8tvTu4fCNPT4mM2ZaFZPk1S496J5DLDDVi0yQvzzUhym6e5LlFKue9Pl9rxzZK2HMi+ruBUsWy9YghnJa/v9ez7sp7nLgilaCqhjyYzRBMrr5YL0VpzKhLj8NAoTw2PMTk37+niMRTv7e/m4GAft/T34CvoifrCyCSPHHuDc9k4vUcp/F5FPK1RSuHzuNWXf3L0DW7Y2plv7nFmIo7tOAQ8birjcoVTDq6LpA356s6ijlIKogmLzR0eMtkLm6Nd90md9VPweZbOoPWaikSGfDeq3KXLNFS+QnM5YVvO06ZR3SXLYT3PXRBK0VRCXwkXZxIcGYpweCjC2cniDcR3bGnnwGAft+/uzW94FvLCyCRf+PYw0UQmn8aYsDUJy01F9BquiJtZT/dcSftDT7yWd4y0HNd5MuQ1iGcW333kRD3XAKTUytyTragMek0MQ9Hd4i1u3tHiulkuxe6+ds6MxxiNukVcubn5PcaqNlaPDkfcvrZ63nCsPehdN5u1stEsNBsbSuijiQxPn3Q3VX90oTh2fVV3iAN7wtw10MfmjsASR3D5ux+eI562MJXCzMbfLcd2hblAjwtL7XOrxDa/63EyX0al8BiUTLUkezy3EUiB8xnF3Z8SGZudPSHiGYfNHZ6yfeBzcXa/18BxdL6xd2+b/7I3VnNhD4V7sbJszdsz2bueBe6SjYpsNAvNRtMLfdpyeH5kgidPjPKDM5NFTbO7W3zcNeBuql6zoP3dQpRyc+Pbgx4is6l8IVIOr+muup2s9YHWuVJ7L1u7QpybimMqmE5kMJW7Une0u9H6M++8gn/88agbdmF+89XMWh/Yjqa3xcdkPIPOXSKysfX2Fi8ZW/Pb/2wPUFl/zFycPd8rVik2d/gxDXXZFZq5C9rmjgBvTydBuVW1o7PJvKd9oyNNpYVmoymF3tGaV8/PcPjEKE+fGmOuwCUy4DV4/65eDg6Gedf2riWbdOTwZtMU2wLe/Gu3dYUYj6XQzrwpmFIK09A4DiQtBwXZlborEIeOjfDyuSkMFIbHwLIdV7Q1fPu1CK0+g4m4u/XqMdwQiq01W9r9tAV9xFIWu8J+tNaMx1KkbY3PY7Cjp7VI0CuNIefi7IXVmqWajJRLYUHXlZ1ku1g5KK3KtjioN9JUWmg2mkroT0difP3lCzw1HCkyDzMU3LSjmwODfbzvmp6ybH6DPrfnakuJwqf79/Xz6cdfYTqeQWfvEBzt9m3V2UwXy3EwDSO/wXn/vn5+5b8dd+P2tpPPcjEVxDM2aVvR2+plNuWuHq/ubS2yHKg2a9VEuTDs0Rbw0hbw5itm15NQSlNpoZloCqHXWvPRQ8/zwpuTRY8PbG7j4J4+9l/bS1fIt+JxDKXyq/flslX2D4T5g4/cyOe/OcSZCXeDbldvC1prMo5eZAtw6NgIj953C7vDrZwZnyOVbZjhNQ0yjrv6N5UinnbYFW4jnrboDPnWpdBI2KN2SK6/UC5NIfRKKbb3hHjhzUm2dAY5MBjmrsFw2Ztn3my/1Ta/Z9k4fSGlVny3feGpZbM1HvzgAA898RpvTyfI7uFmLWrVZXuSN9ofu4Q9aoPk+guV0BRCD+5K8qdvuIJtXcGyxTroM+kIeotW4KthpWyNnAg+8NjLzKUtAh4D5THy/Vor9SRv1D92CXtUH8n1FyqhKYQ+t6p9c2KOvrYA99y0jZv7u0u+VilFi98VeL/HLHmc5VbHy72mnLDF/oEwX77nXXmBtmyHC9NJADa3+sv2JD86HOGBx14mnrbxe+Zz1av1x95odw4bHcn1Fyqhpq0Eq0Fhs+WOgIeJuRRfeuoUL4wUx+tNQ9EZ8rGtK0i4LVBS5Fdq/rzSa8rpMLTwdY6GXeFWrultwdGU1YAjN4+5tIVpzOeqRxOZqvyxV9oEXKg+lXb3EjY2635FX3gLa2WrRBMZtxH3zf3dZcffy7kVLuc15YYtVhPeyM0j4DHz/WlxXPviahQlSZig8ZBNb6ES1v2KvlTT5oDXYHQ2yRUdQbZ1h2gPeFeM25fT/LnSZtjVIjePXN9VR2tQmpTlVOWPvVHOW5in3LtHQYAmWNEv3AA1DUXKctjR00JwmR6oKx0HSjecboTS+Nw82gLefFFS0nJo8Xmq8sfeKOctFCOb3kK5rPsVfWGzZdNQpLMt+ipd1VbScHqlBtHVZmGT7c0dAbZ0hvjyPe+qyh9+o5y3IAiXR1P0jK1Xw+l65ojXeh6Nct6CIJRGmoMLgiA0OcsJ/boP3QiCIAjLI0IvCILQ5IjQC4IgNDki9IIgCE2OCL0gCEKTI0IvCILQ5IjQC4IgNDkNl0evlBoD3qr3PGrIJmC83pOoExv53EHOX85/bc//Kq11b6knGk7oNxpKqeNLFTk0Oxv53EHOX86/ducvoRtBEIQmR4ReEAShyRGhrz+P1HsCdWQjnzvI+cv51wiJ0QuCIDQ5sqIXBEFockToBaFGKKW6lVIHlVKb6j0XYWMhQl9jlFJ9SqlnCr7/M6XUc0qpz9ZzXrVAKdWhlPqmUuo7Sqn/oZTybZTzV0p1Af8A3Ax8VynVu1HOvZDs7//L2f9vmPNXSnmUUmeVUkez/95Ry/MXoa8h2T/2vwRast//LGBqrW8F+pVSu+o5vxrwi8Afaq1/ErgE3MPGOf8bgE9prX8X+DZwJxvn3Av5AyC4AX/3bwAe1Vrv11rvB3ZRw/Nf983B1xk28FHgG9nv9wN/l/3/d4DbgFO1n1Zt0Fp/peDbXuBjwBez3zf1+WutnwZQSu3DXdV3s4F+9gBKqTuBOdyL/H421vnfAvy0UuoO4EdAihqev6zoa4jWOqq1nil4qAW4kP3/JNBX+1nVHqXUrUAXcI4NdP5KKYV7oZ8CNBvr3H3AbwOfyT600X73XwQOaK1vBrzAh6jh+YvQ15cYEMz+v5UN8PNQSnUDfwz8Mhvs/LXLJ4BXgfexgc4dV+C/orWezn6/oX72wKta64vZ/x/H9bmp2fk3+4fb6PwQ95YN4EbgzfpNpfpkV3V/D/ym1votNtD5K6UeVEr9UvbbTuDzbJBzz3IA+IRS6ijwTuCfs7HO/6+UUjcqpUzgfwM+QQ3PX2L09eXrwDNKqStxb+Vuqe90qs6vAO8Gfksp9VvAnwP/coOc/yPA3ymlPg78GPdnf2yDnDta6325/2fF/m421u/+w8DfAAp4ghr/7UtlbJ3JZuIcBI5prS/Vez61ZiOf/0Y+d5Dzr+X5i9ALgiA0ORKjFwRBaHJE6AVBEJocEXpBEIQmR4ReEAShyRGhF5oKpdQ7lVLvXO1rCl57dIXnP6eU2r/Ec18s53grjSEIq0WEXmg23pn9t9rXrBqt9f9Z7TEEoRxE6IWmQSn1+7il9p9RSh1RSvmVUo8qpZ5WSv111ha56DXZ97Uqpb6llHpGKfXnlzH0p7JjPJatfMzN5+hanJcgrBYReqFp0Fr/Jq61wOe11ncB/zvwY6317bjOgL9c4jUAV+D67xwAdiilKjWYOp4dYwa3tF8QGgoReqGZ2QP8IPv/54HBJV6XAT4O/DWufXBwidctRW6Ml4CrK3yvIFQdEXqh2UgAoez/X2PeQ+SW7PdFr8laB/8K8DhwL65feqW8J/v1BprfnEtYh4jQC83Gk8DPKqW+h2sedp1S6hhuR5+/KPGa92e//03gqezzWyoc8/1KqadxPcW/sdKLBaHWiNeNIAhCkyM2xYJQJiWyaGa01h+ux1wEoRJkRS8IgtDkSIxeEAShyRGhFwRBaHJE6AVBEJocEXpBEIQm5/8HMYE9DbcNGLsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##回归图\n",
+    "sns.regplot(x='total_bill',y='tip',data=tips)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "467fb7de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x212c0ee61f0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='total_bill',y='tip',hue='day',data=tips,col_wrap=2,col='day')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3f1d7622",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#热力图\n",
+    "x=np.random.randn(10,12)\n",
+    "sns.heatmap(x,annot=True,fmt='.1f')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba897e63",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/国民经济核算数据分析.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/国民经济核算数据分析.ipynb
new file mode 100644
index 0000000..55900fe
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/国民经济核算数据分析.ipynb	
@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "375dc0f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['columns', 'values']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "data=np.load('./国民经济核算季度数据.npz',allow_pickle=True)\n",
+    "data.files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6a0b74f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['序号', '时间', '国内生产总值_当季值(亿元)', '第一产业增加值_当季值(亿元)', '第二产业增加值_当季值(亿元)',\n",
+       "       '第三产业增加值_当季值(亿元)', '农林牧渔业增加值_当季值(亿元)', '工业增加值_当季值(亿元)',\n",
+       "       '建筑业增加值_当季值(亿元)', '批发和零售业增加值_当季值(亿元)', '交通运输、仓储和邮政业增加值_当季值(亿元)',\n",
+       "       '住宿和餐饮业增加值_当季值(亿元)', '金融业增加值_当季值(亿元)', '房地产业增加值_当季值(亿元)',\n",
+       "       '其他行业增加值_当季值(亿元)'], dtype=object)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['columns']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "24dfbbdf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, '2000年第一季度', 21329.9, ..., 1235.9, 933.7, 3586.1],\n",
+       "       [2, '2000年第二季度', 24043.4, ..., 1124.0, 904.7, 3464.9],\n",
+       "       [3, '2000年第三季度', 25712.5, ..., 1170.4, 1070.9, 3518.2],\n",
+       "       ...,\n",
+       "       [67, '2016年第三季度', 190529.5, ..., 15472.5, 12164.1, 37964.1],\n",
+       "       [68, '2016年第四季度', 211281.3, ..., 15548.7, 13214.9, 39848.4],\n",
+       "       [69, '2017年第一季度', 180682.7, ..., 17213.5, 12393.4, 42443.1]],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['values']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "858e1482",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#第一、二、三产业增长值\n",
+    "for i in [3,4,5]:\n",
+    "    plt.scatter(range(69),data['values'][:,i])\n",
+    "plt.legend(['1','2','3'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "34358f54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#生产总值\n",
+    "plt.plot(range(69),data['values'][:,2])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c560b8bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#c为点的颜色，marker为点的类型，alpha为点的透明度，值为0-1，越小点越透明\n",
+    "l=['r','g','b']  #点的颜色\n",
+    "m=['o','*','D']  #点的类型\n",
+    "#j代表位置，i代表值，比如j=0时，i=3；j=1时，i=4；j=2时，i=5\n",
+    "for j,i in enumerate([3,4,5]):\n",
+    "    plt.plot(range(69),data['values'][:,i],c=l[j],marker=m[j],alpha=0.3)\n",
+    "plt.legend(['1','2','3'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "957f7f36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(4,4))\n",
+    "num=data['values'][68,3:6]\n",
+    "plt.pie(num,autopct='%2.f %%',explode=[0.1,0,0],labels=['1','2','3'],labeldistance=1.2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "48b0ea5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD4CAYAAADy46FuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAQM0lEQVR4nO3df6ie5X3H8fdnSWttO60/jpIlYcdh6KbC1hlSt0IZTanZHIt/KJxCaxiBgLjNjsGI+0e2EVAYdROmEKozulINacHQ4roQW0rBxR6tm42peKhOz8zM6WKtDrSL++6Pc53x5PTkSnKeJE9i3i94uO/7e1/Xle/D88cn930/55xUFZIkHckvjLoBSdLpzaCQJHUZFJKkLoNCktRlUEiSupaOuoET7eKLL67x8fFRtyFJZ5Snnnrqx1U1ttC591xQjI+PMzk5Oeo2JOmMkuTfj3TOW0+SpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqSu99xPZks6vY1v/saoW3jPeumO607Kuke9okhyf5IDSX4wULswya4kL7TtBQPnbksyleT5JNcO1K9O8mw7d3eStPo5SR5p9T1JxgfmbGj/xgtJNpywdy1JOmbHcuvpAWDdvNpmYHdVrQJ2t2OSXAFMAFe2OfckWdLm3AtsAla119yaG4HXq+py4C7gzrbWhcDtwMeBNcDtg4EkSTo1jhoUVfUd4OC88npgW9vfBlw/UH+4qt6pqheBKWBNkmXAeVX1RM3+ke4H582ZW2sHsLZdbVwL7Kqqg1X1OrCLnw8sSdJJttiH2ZdW1X6Atr2k1ZcDrwyMm2615W1/fv2wOVV1CHgDuKiz1s9JsinJZJLJmZmZRb4lSdJCTvS3nrJArTr1xc45vFi1tapWV9XqsbEFf526JGmRFhsUr7XbSbTtgVafBlYOjFsBvNrqKxaoHzYnyVLgfGZvdR1pLUnSKbTYoNgJzH0LaQPw6EB9on2T6TJmH1o/2W5PvZnkmvb84aZ5c+bWugF4vD3H+CbwmSQXtIfYn2k1SdIpdNSfo0jyFeB3gIuTTDP7TaQ7gO1JNgIvAzcCVNXeJNuB54BDwC1V9W5b6mZmv0F1LvBYewHcBzyUZIrZK4mJttbBJH8NfK+N+6uqmv9QXZJ0kh01KKrqs0c4tfYI47cAWxaoTwJXLVB/mxY0C5y7H7j/aD1Kkk4ef4WHJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqMigkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqGiookvxpkr1JfpDkK0k+kOTCJLuSvNC2FwyMvy3JVJLnk1w7UL86ybPt3N1J0urnJHmk1fckGR+mX0nS8Vt0UCRZDvwJsLqqrgKWABPAZmB3Va0CdrdjklzRzl8JrAPuSbKkLXcvsAlY1V7rWn0j8HpVXQ7cBdy52H4lSYsz7K2npcC5SZYCHwReBdYD29r5bcD1bX898HBVvVNVLwJTwJoky4DzquqJqirgwXlz5tbaAaydu9qQJJ0aiw6KqvoP4G+Al4H9wBtV9c/ApVW1v43ZD1zSpiwHXhlYYrrVlrf9+fXD5lTVIeAN4KL5vSTZlGQyyeTMzMxi35IkaQHD3Hq6gNn/8V8G/BLwoSSf601ZoFadem/O4YWqrVW1uqpWj42N9RuXJB2XYW49fRp4sapmqup/gK8Bvw281m4n0bYH2vhpYOXA/BXM3qqabvvz64fNabe3zgcODtGzJOk4DRMULwPXJPlge26wFtgH7AQ2tDEbgEfb/k5gon2T6TJmH1o/2W5PvZnkmrbOTfPmzK11A/B4e44hSTpFli52YlXtSbIDeBo4BHwf2Ap8GNieZCOzYXJjG783yXbguTb+lqp6ty13M/AAcC7wWHsB3Ac8lGSK2SuJicX2K0lanEUHBUBV3Q7cPq/8DrNXFwuN3wJsWaA+CVy1QP1tWtBIkkbDn8yWJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXUP9PQpp1MY3f2PULbxnvXTHdaNuQacJrygkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoaKiiSfCTJjiQ/TLIvyW8luTDJriQvtO0FA+NvSzKV5Pkk1w7Ur07ybDt3d5K0+jlJHmn1PUnGh+lXknT8hr2i+Dvgn6rqV4FfB/YBm4HdVbUK2N2OSXIFMAFcCawD7kmypK1zL7AJWNVe61p9I/B6VV0O3AXcOWS/kqTjtOigSHIe8EngPoCq+llV/QRYD2xrw7YB17f99cDDVfVOVb0ITAFrkiwDzquqJ6qqgAfnzZlbawewdu5qQ5J0agxzRfErwAzwD0m+n+RLST4EXFpV+wHa9pI2fjnwysD86VZb3vbn1w+bU1WHgDeAi+Y3kmRTkskkkzMzM0O8JUnSfMMExVLgN4F7q+pjwH/TbjMdwUJXAtWp9+YcXqjaWlWrq2r12NhYv2tJ0nEZJiimgemq2tOOdzAbHK+120m07YGB8SsH5q8AXm31FQvUD5uTZClwPnBwiJ4lScdp0UFRVf8JvJLko620FngO2AlsaLUNwKNtfycw0b7JdBmzD62fbLen3kxyTXv+cNO8OXNr3QA83p5jSJJOkaVDzv9j4MtJ3g/8CPhDZsNne5KNwMvAjQBVtTfJdmbD5BBwS1W929a5GXgAOBd4rL1g9kH5Q0mmmL2SmBiyX0nScRoqKKrqGWD1AqfWHmH8FmDLAvVJ4KoF6m/TgkaSNBr+ZLYkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqMigkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoaOiiSLEny/SRfb8cXJtmV5IW2vWBg7G1JppI8n+TagfrVSZ5t5+5OklY/J8kjrb4nyfiw/UqSjs+JuKK4Fdg3cLwZ2F1Vq4Dd7ZgkVwATwJXAOuCeJEvanHuBTcCq9lrX6huB16vqcuAu4M4T0K8k6TgMFRRJVgDXAV8aKK8HtrX9bcD1A/WHq+qdqnoRmALWJFkGnFdVT1RVAQ/OmzO31g5g7dzVhiTp1Bj2iuJvgT8H/negdmlV7Qdo20tafTnwysC46VZb3vbn1w+bU1WHgDeAi4bsWZJ0HBYdFEl+HzhQVU8d65QFatWp9+bM72VTkskkkzMzM8fYjiTpWAxzRfEJ4A+SvAQ8DHwqyT8Cr7XbSbTtgTZ+Glg5MH8F8Gqrr1igfticJEuB84GD8xupqq1VtbqqVo+NjQ3xliRJ8y06KKrqtqpaUVXjzD6kfryqPgfsBDa0YRuAR9v+TmCifZPpMmYfWj/Zbk+9meSa9vzhpnlz5ta6of0bP3dFIUk6eZaehDXvALYn2Qi8DNwIUFV7k2wHngMOAbdU1bttzs3AA8C5wGPtBXAf8FCSKWavJCZOQr+SpI4TEhRV9W3g223/v4C1Rxi3BdiyQH0SuGqB+tu0oJEkjYY/mS1J6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqMigkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpC6DQpLUZVBIkroMCklS16KDIsnKJN9Ksi/J3iS3tvqFSXYleaFtLxiYc1uSqSTPJ7l2oH51kmfbubuTpNXPSfJIq+9JMj7Ee5UkLcIwVxSHgD+rql8DrgFuSXIFsBnYXVWrgN3tmHZuArgSWAfck2RJW+teYBOwqr3WtfpG4PWquhy4C7hziH4lSYuw6KCoqv1V9XTbfxPYBywH1gPb2rBtwPVtfz3wcFW9U1UvAlPAmiTLgPOq6omqKuDBeXPm1toBrJ272pAknRon5BlFuyX0MWAPcGlV7YfZMAEuacOWA68MTJtuteVtf379sDlVdQh4A7hogX9/U5LJJJMzMzMn4i1JkpqhgyLJh4GvAl+oqp/2hi5Qq069N+fwQtXWqlpdVavHxsaO1rIk6TgMFRRJ3sdsSHy5qr7Wyq+120m07YFWnwZWDkxfAbza6isWqB82J8lS4Hzg4DA9S5KOzzDfegpwH7Cvqr44cGonsKHtbwAeHahPtG8yXcbsQ+sn2+2pN5Nc09a8ad6cubVuAB5vzzEkSafI0iHmfgL4PPBskmda7S+AO4DtSTYCLwM3AlTV3iTbgeeY/cbULVX1bpt3M/AAcC7wWHvBbBA9lGSK2SuJiSH6lSQtwqKDoqq+y8LPEADWHmHOFmDLAvVJ4KoF6m/TgkaSNBr+ZLYkqcugkCR1GRSSpC6DQpLUZVBIkroMCklSl0EhSeoyKCRJXQaFJKnLoJAkdRkUkqQug0KS1GVQSJK6DApJUpdBIUnqMigkSV0GhSSpa5g/hfqeNL75G6Nu4T3rpTuuG3ULkhbBKwpJUpdBIUnqMigkSV0GhSSpy6CQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEldBoUkqcugkCR1GRSSpK4zIiiSrEvyfJKpJJtH3Y8knU1O+6BIsgT4e+B3gSuAzya5YrRdSdLZ47QPCmANMFVVP6qqnwEPA+tH3JMknTXOhL9wtxx4ZeB4Gvj44IAkm4BN7fCtJM+fot5G7WLgx6Nu4ljlzlF3cFo4Yz4zP6//d7Z8Zr98pBNnQlBkgVoddlC1Fdh6ato5fSSZrKrVo+5Dx87P7MzjZ3Zm3HqaBlYOHK8AXh1RL5J01jkTguJ7wKoklyV5PzAB7BxxT5J01jjtbz1V1aEkfwR8E1gC3F9Ve0fc1unirLvd9h7gZ3bmOes/s1TV0UdJks5aZ8KtJ0nSCBkUkqQug+IMlOT+JAeS/GDUvejokqxM8q0k+5LsTXLrqHtSX5IPJHkyyb+2z+wvR93TKPmM4gyU5JPAW8CDVXXVqPtRX5JlwLKqejrJLwJPAddX1XMjbk1HkCTAh6rqrSTvA74L3FpV/zLi1kbCK4ozUFV9Bzg46j50bKpqf1U93fbfBPYx+xsHdJqqWW+1w/e111n7v2qDQjqFkowDHwP2jLgVHUWSJUmeAQ4Au6rqrP3MDArpFEnyYeCrwBeq6qej7kd9VfVuVf0Gs78NYk2Ss/Y2r0EhnQLtPvdXgS9X1ddG3Y+OXVX9BPg2sG60nYyOQSGdZO3B6H3Avqr64qj70dElGUvykbZ/LvBp4IcjbWqEDIozUJKvAE8AH00ynWTjqHtS1yeAzwOfSvJMe/3eqJtS1zLgW0n+jdnfN7erqr4+4p5Gxq/HSpK6vKKQJHUZFJKkLoNCktRlUEiSugwKSVKXQSFJ6jIoJEld/wfbnVbspFlQ7QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(range(len(num)),num)\n",
+    "plt.xticks(range(len(num)),['1','2','3'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a37fa88b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#图上的横线表示上边缘，下边缘，上4分位数，下4分位数，中位数，边缘之外的为异常值\n",
+    "num=(list(data['values'][:,3]),list(data['values'][:,4]),list(data['values'][:,5]))\n",
+    "plt.boxplot(num)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9f351764",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from scipy import stats\n",
+    "#产生100个正态分布的随机数\n",
+    "x=stats.uniform.rvs(size=100)\n",
+    "#直方图\n",
+    "plt.hist(x,bins=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b8bee42",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/.keep b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/.keep
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diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/__pycache__/gnb_model.cpython-311.pyc b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/__pycache__/gnb_model.cpython-311.pyc
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diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/app.py b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/app.py
new file mode 100644
index 0000000..f03cf15
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/app.py	
@@ -0,0 +1,22 @@
+# Importing Necessary Modules
+from flask import Flask, request, render_template
+from gnb_model import make_predict
+app = Flask(__name__)
+
+result = []
+@app.route("/", methods=['GET', 'POST'])
+def input():
+	global result
+	if request.method == 'POST':
+		print(request.form)
+		if "clean" in request.form.keys():
+			result = []
+		sentence = request.form['sentence']
+		if len(sentence) != 0:
+			result.append((sentence, make_predict(sentence)))
+			# result.append((sentence, "不是"))
+	return render_template("input.html", result=result)
+
+# main route to start with
+if __name__ == '__main__':
+	app.run(debug=True, host='0.0.0.0')
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/gnb_model.py b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/gnb_model.py
new file mode 100644
index 0000000..9a2a717
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/gnb_model.py	
@@ -0,0 +1,77 @@
+import numpy as np
+import pandas as pd
+import jieba
+from pathlib import Path
+PATH_DATASET = Path.cwd().joinpath("../../dataset")
+DELETE_STOPWORDS = False
+
+df = pd.read_csv(PATH_DATASET.joinpath("message80W1.csv"), header=None)
+
+N_pos, N_neg = 10000, 10000
+df_positive = df[df[1]==0]
+df_negative = df[df[1]==1]
+np.random.seed(42)
+def sample_df(df, N):
+    indexs = np.random.choice(np.arange(len(df)), N)
+    return df.iloc[indexs,2]
+corpus_pos = sample_df(df_positive, N_pos)
+corpus_neg = sample_df(df_negative, N_neg)
+# corpus_pos = df_positive.sample(n=N_pos, random_state=42).iloc[:,2]
+# corpus_neg = df_negative.sample(n=N_neg, random_state=42).iloc[:,2]
+corpus = np.concatenate([corpus_pos, corpus_neg]).reshape(-1,1)
+y = np.concatenate([np.full(N_pos, 1), np.full(N_neg, 0)])
+
+corpus_cut = np.apply_along_axis(lambda x: ' '.join(jieba.cut(x[0])), axis=1, arr=corpus)
+
+from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
+
+with open(PATH_DATASET.joinpath("stopword.txt"), encoding='gbk') as file:
+    stopwords = file.read().split()
+
+# 频率向量化，token_pattern不取单个词作为特征的问题: https://blog.csdn.net/xxzhix/article/details/82685372
+if not DELETE_STOPWORDS:
+    vectorizer = CountVectorizer(token_pattern='[\u4e00-\u9fa5_a-zA-Z0-9]{1,}')
+else:
+    vectorizer_stopwords = CountVectorizer(token_pattern='[\u4e00-\u9fa5_a-zA-Z0-9]{1,}', stop_words=stopwords)
+X = vectorizer.fit_transform(corpus_cut)
+tfidf = TfidfTransformer()
+X = tfidf.fit_transform(X)
+
+def to_vector(X, stopwords=False):
+    X = np.array(X).reshape(-1, 1)
+    cut = np.apply_along_axis(lambda x: ' '.join(jieba.cut(x[0])), axis=1, arr=X)
+    vector = vectorizer.transform(cut)
+    return tfidf.transform(vector)
+
+from sklearn.model_selection import train_test_split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=42)
+
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score
+gnb = GaussianNB()
+print("拟合中......")
+gnb.fit(X_train.toarray(), y_train)
+
+print("预测中......")
+pred_train = gnb.predict(X_train.toarray())
+pred_test = gnb.predict(X_test.toarray())
+acc_train = accuracy_score(y_train, pred_train)
+acc_test = accuracy_score(y_test, pred_test)
+print(f"准确率 train/test: {acc_train:.4f}/{acc_test:.4f}")
+
+def make_predict(string:str):
+    string = [string]
+    vector = to_vector(string).toarray()
+    ret = "是垃圾" if gnb.predict(vector) == 0 else "不是垃圾"
+    return ret
+
+if __name__ == '__main__':
+    print(make_predict("尊敬的客上，感谢您一直的支持，亿美亿康美容部特在本月的x、x、x三天举办秒杀活动，现场更是优惠多多，开抢倒计时还有两天，欲抢从速！xx号艳艳"))
+    print(make_predict("CSC喜欢打游戏"))
+    print(make_predict("一刀999"))
+    print(make_predict("你好"))
+    print(make_predict("尊敬的客上，感谢您一直的支持，亿美亿康美容部特在本月的x、x、x三天举办秒杀活动，现场更是优惠多多，开抢倒计时还有两天，欲抢从速！xx号艳艳"))
+    print(make_predict("秒杀价格8848，8848你值得拥有"))
+    print(make_predict("有博主做过同类防晒霜的对比"))
+    print(make_predict("csc每天打游戏"))
+    print(make_predict("今天电脑爆炸了"))
\ No newline at end of file
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/spam_online.png b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/spam_online.png
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index 0000000..a538a39
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diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/Temp.html b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/Temp.html
new file mode 100644
index 0000000..bd3dfef
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/Temp.html	
@@ -0,0 +1,26 @@
+<!DOCTYPE html>
+<html>
+    <head>
+        <meta charset="utf-8">
+        <title>进入</title>
+        <style>
+            body {
+                text-align: center;
+                background-color: green;
+            }
+            form {
+                display: inline-block;
+            }
+        </style>
+    </head>
+    <body>
+        <h3>There is a form.</h3>
+        <form action="/passing" method="post">
+            <p>Name <input type="text", name="name"></p>
+            <p>Email <input type="email", name="email"></p>
+            <p>Phon nume <input type="text", name="phone"></p>
+            <p><input type="submit" value="Submit!!!"></p>
+        </form>
+        <h4>HA!HA!HA!HA!</h4>
+    </body>
+</html>
\ No newline at end of file
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/hello.html b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/hello.html
new file mode 100644
index 0000000..d1294b7
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/hello.html	
@@ -0,0 +1,14 @@
+<!DOCTYPE html>
+<html>
+    <head>
+        <meta charset="utf-8">
+        <title>Hello from Flask</title>
+    </head>
+    <body>
+        {% if name %}
+        <h1>Hello {{ name }}!</h1>
+        {% else %}
+        <h1>Hello, World!</h1>
+        {% endif %}
+    </body>
+</html>
\ No newline at end of file
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/input.html b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/input.html
new file mode 100644
index 0000000..89ce287
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/input.html	
@@ -0,0 +1,39 @@
+<!DOCTYPE html>
+<html>
+    <header>
+        <style>
+            body {
+                text-align: center;
+            }
+        </style>
+        <meta charset="utf-8">
+        <title>CSC识别器</title>
+    </header>
+    <body>
+        <form action="/" method="post">
+            <p>输入待识别文本：<input type="text" name="sentence" minlength="1"></p>
+            <p><button>提交</button></p>
+            <p><button name="clean">清空历史信息</button></p>
+            <!-- <p><input type="submit" name="提交"></p> -->
+        </form>
+        {% if result|length >= 1 %}
+        <table align="center" border="1">
+            <thead><tr><th colspan="2">
+                <strong>历史信息</strong>
+            </th></tr></thead>
+            <tbody>
+                <tr>
+                    <td>文本</td>
+                    <td>是/否为垃圾</td>
+                </tr>
+                {% for sentence, predict in result %}
+                <tr>
+                    <td>{{ sentence }}</td>
+                    <td>{{ predict }}</td>
+                </tr>
+                {% endfor %}
+            </tbody>
+        </table>
+        {% endif %}
+    </body>
+</html>
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/result_data.html b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/result_data.html
new file mode 100644
index 0000000..8d217d7
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/在线垃圾邮件识别/templates/result_data.html	
@@ -0,0 +1,26 @@
+<!DOCTYPE html>
+<html>
+    <head>
+        <style>
+            body {
+                text-align: center;
+                background-color: orange;
+            }
+            table {
+                display: inline-block;
+                border-collapse: collapse;
+            }
+        </style>
+    </head>
+    <body>
+        <p><strong>Your Details</strong></p>
+        <table border=1>
+            {% for key, value in result.items() %}
+            <tr>
+                <th>{{ key }}</th>
+                <th>{{ value }}</th>
+            </tr>
+            {% endfor %}
+        </table>
+    </body>
+</html>
\ No newline at end of file
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/水质检测.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/水质检测.ipynb
new file mode 100644
index 0000000..06637cc
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/水质检测.ipynb	
@@ -0,0 +1,564 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "78c67687",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['images/1_1.jpg',\n",
+       " 'images/1_10.jpg',\n",
+       " 'images/1_11.jpg',\n",
+       " 'images/1_12.jpg',\n",
+       " 'images/1_13.jpg',\n",
+       " 'images/1_14.jpg',\n",
+       " 'images/1_15.jpg',\n",
+       " 'images/1_16.jpg',\n",
+       " 'images/1_17.jpg',\n",
+       " 'images/1_18.jpg',\n",
+       " 'images/1_19.jpg',\n",
+       " 'images/1_2.jpg',\n",
+       " 'images/1_20.jpg',\n",
+       " 'images/1_21.jpg',\n",
+       " 'images/1_22.jpg',\n",
+       " 'images/1_23.jpg',\n",
+       " 'images/1_24.jpg',\n",
+       " 'images/1_25.jpg',\n",
+       " 'images/1_26.jpg',\n",
+       " 'images/1_27.jpg',\n",
+       " 'images/1_28.jpg',\n",
+       " 'images/1_3.jpg',\n",
+       " 'images/1_30.jpg',\n",
+       " 'images/1_31.jpg',\n",
+       " 'images/1_32.jpg',\n",
+       " 'images/1_33.jpg',\n",
+       " 'images/1_34.jpg',\n",
+       " 'images/1_35.jpg',\n",
+       " 'images/1_36.jpg',\n",
+       " 'images/1_37.jpg',\n",
+       " 'images/1_38.jpg',\n",
+       " 'images/1_39.jpg',\n",
+       " 'images/1_4.jpg',\n",
+       " 'images/1_40.jpg',\n",
+       " 'images/1_41.jpg',\n",
+       " 'images/1_42.jpg',\n",
+       " 'images/1_43.jpg',\n",
+       " 'images/1_44.jpg',\n",
+       " 'images/1_45.jpg',\n",
+       " 'images/1_46.jpg',\n",
+       " 'images/1_47.jpg',\n",
+       " 'images/1_48.jpg',\n",
+       " 'images/1_49.jpg',\n",
+       " 'images/1_5.jpg',\n",
+       " 'images/1_51.jpg',\n",
+       " 'images/1_6.jpg',\n",
+       " 'images/1_7.jpg',\n",
+       " 'images/1_8.jpg',\n",
+       " 'images/1_9.jpg',\n",
+       " 'images/2_1.jpg',\n",
+       " 'images/2_10.jpg',\n",
+       " 'images/2_12.jpg',\n",
+       " 'images/2_13.jpg',\n",
+       " 'images/2_14.jpg',\n",
+       " 'images/2_15.jpg',\n",
+       " 'images/2_17.jpg',\n",
+       " 'images/2_18.jpg',\n",
+       " 'images/2_19.jpg',\n",
+       " 'images/2_2.jpg',\n",
+       " 'images/2_20.jpg',\n",
+       " 'images/2_21.jpg',\n",
+       " 'images/2_22.jpg',\n",
+       " 'images/2_23.jpg',\n",
+       " 'images/2_24.jpg',\n",
+       " 'images/2_26.jpg',\n",
+       " 'images/2_27.jpg',\n",
+       " 'images/2_28.jpg',\n",
+       " 'images/2_29.jpg',\n",
+       " 'images/2_3.jpg',\n",
+       " 'images/2_30.jpg',\n",
+       " 'images/2_31.jpg',\n",
+       " 'images/2_32.jpg',\n",
+       " 'images/2_33.jpg',\n",
+       " 'images/2_34.jpg',\n",
+       " 'images/2_35.jpg',\n",
+       " 'images/2_36.jpg',\n",
+       " 'images/2_37.jpg',\n",
+       " 'images/2_38.jpg',\n",
+       " 'images/2_39.jpg',\n",
+       " 'images/2_4.jpg',\n",
+       " 'images/2_40.jpg',\n",
+       " 'images/2_41.jpg',\n",
+       " 'images/2_42.jpg',\n",
+       " 'images/2_43.jpg',\n",
+       " 'images/2_44.jpg',\n",
+       " 'images/2_5.jpg',\n",
+       " 'images/2_6.jpg',\n",
+       " 'images/2_7.jpg',\n",
+       " 'images/2_8.jpg',\n",
+       " 'images/2_9.jpg',\n",
+       " 'images/3_10.jpg',\n",
+       " 'images/3_11.jpg',\n",
+       " 'images/3_12.jpg',\n",
+       " 'images/3_13.jpg',\n",
+       " 'images/3_14.jpg',\n",
+       " 'images/3_15.jpg',\n",
+       " 'images/3_16.jpg',\n",
+       " 'images/3_17.jpg',\n",
+       " 'images/3_18.jpg',\n",
+       " 'images/3_19.jpg',\n",
+       " 'images/3_20.jpg',\n",
+       " 'images/3_21.jpg',\n",
+       " 'images/3_22.jpg',\n",
+       " 'images/3_23.jpg',\n",
+       " 'images/3_25.jpg',\n",
+       " 'images/3_26.jpg',\n",
+       " 'images/3_27.jpg',\n",
+       " 'images/3_28.jpg',\n",
+       " 'images/3_29.jpg',\n",
+       " 'images/3_3.jpg',\n",
+       " 'images/3_30.jpg',\n",
+       " 'images/3_31.jpg',\n",
+       " 'images/3_32.jpg',\n",
+       " 'images/3_34.jpg',\n",
+       " 'images/3_38.jpg',\n",
+       " 'images/3_40.jpg',\n",
+       " 'images/3_42.jpg',\n",
+       " 'images/3_43.jpg',\n",
+       " 'images/3_44.jpg',\n",
+       " 'images/3_45.jpg',\n",
+       " 'images/3_46.jpg',\n",
+       " 'images/3_47.jpg',\n",
+       " 'images/3_48.jpg',\n",
+       " 'images/3_49.jpg',\n",
+       " 'images/3_5.jpg',\n",
+       " 'images/3_50.jpg',\n",
+       " 'images/3_51.jpg',\n",
+       " 'images/3_52.jpg',\n",
+       " 'images/3_53.jpg',\n",
+       " 'images/3_55.jpg',\n",
+       " 'images/3_56.jpg',\n",
+       " 'images/3_57.jpg',\n",
+       " 'images/3_58.jpg',\n",
+       " 'images/3_59.jpg',\n",
+       " 'images/3_65.jpg',\n",
+       " 'images/3_66.jpg',\n",
+       " 'images/3_7.jpg',\n",
+       " 'images/3_71.jpg',\n",
+       " 'images/3_72.jpg',\n",
+       " 'images/3_73.jpg',\n",
+       " 'images/3_74.jpg',\n",
+       " 'images/3_75.jpg',\n",
+       " 'images/3_76.jpg',\n",
+       " 'images/3_77.jpg',\n",
+       " 'images/3_78.jpg',\n",
+       " 'images/3_8.jpg',\n",
+       " 'images/3_9.jpg',\n",
+       " 'images/4_10.jpg',\n",
+       " 'images/4_11.jpg',\n",
+       " 'images/4_13.jpg',\n",
+       " 'images/4_14.jpg',\n",
+       " 'images/4_21.jpg',\n",
+       " 'images/4_6.jpg',\n",
+       " 'images/4_7.jpg',\n",
+       " 'images/4_9.jpg',\n",
+       " 'images/5_1.jpg',\n",
+       " 'images/5_2.jpg',\n",
+       " 'images/5_3.jpg',\n",
+       " 'images/5_4.jpg',\n",
+       " 'images/5_5.jpg',\n",
+       " 'images/5_6.jpg']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import re\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "\n",
+    "def getimgnames(path=None):\n",
+    "    \"\"\"\n",
+    "    获取指定文件夹中的JPG图片名称（含路径）\n",
+    "    :param path: 指定文件夹\n",
+    "    :return: path中的所有JPG图片名称（含路径，例如：./path/image1.jpg）\n",
+    "    \"\"\"\n",
+    "    imgnames = []\n",
+    "    filenames = os.listdir(path)  # 获取path中的所有文件名\n",
+    "    for i in filenames:\n",
+    "        if re.findall('\\.jpg$', i) != []:  # 在所有文件名中找出JPG图片名称\n",
+    "            imgnames.append(os.path.join(path, i))  # 将图片名称和路径合并、保存\n",
+    "    return imgnames\n",
+    "\n",
+    "imglist = getimgnames('images/')\n",
+    "imglist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "356b558c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\ndef cut_image(img, imgFile):\\n    cx = int(np.size(img, 1))/2\\n    cy = int(np.size(img, 0))/2\\n    \\n    plt.figure(figsize=(8,8))\\n    plt.imshow(img)\\n\\n    plt.plot([cx-50, cx+50], [cy+50, cy+50], \\'r\\', linewidth=2)\\n    plt.plot([cx+50, cx+50], [cy-50, cy+50], \\'r\\', linewidth=2)\\n    plt.plot([cx-50, cx+50], [cy-50, cy-50], \\'r\\', linewidth=2)\\n    plt.plot([cx-50, cx-50], [cy-50, cy+50], \\'r\\', linewidth=2)\\n    plt.annotate(\\'选取的水样窗口\\', xy=(cx+50,cy-50), xytext=(cx+300, cy-300),\\n            arrowprops=dict(facecolor=\\'black\\', shrink=0.1))\\n\\n    plt.title(\\'水色样本 \\'+imgFile+\\' 分辨率为\\'+str(img.size)+\" 类别标签 \"+str(imgFile[9]))\\n    plt.show()\\n    \\nfor i in range(len(imglist)):\\n    img = cv2.imread(imglist[i])\\n    cut_image(img, imglist[i])\\n'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "def cut_image(img, imgFile):\n",
+    "    cx = int(np.size(img, 1))/2\n",
+    "    cy = int(np.size(img, 0))/2\n",
+    "    \n",
+    "    plt.figure(figsize=(8,8))\n",
+    "    plt.imshow(img)\n",
+    "\n",
+    "    plt.plot([cx-50, cx+50], [cy+50, cy+50], 'r', linewidth=2)\n",
+    "    plt.plot([cx+50, cx+50], [cy-50, cy+50], 'r', linewidth=2)\n",
+    "    plt.plot([cx-50, cx+50], [cy-50, cy-50], 'r', linewidth=2)\n",
+    "    plt.plot([cx-50, cx-50], [cy-50, cy+50], 'r', linewidth=2)\n",
+    "    plt.annotate('选取的水样窗口', xy=(cx+50,cy-50), xytext=(cx+300, cy-300),\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.1))\n",
+    "\n",
+    "    plt.title('水色样本 '+imgFile+' 分辨率为'+str(img.size)+\" 类别标签 \"+str(imgFile[9]))\n",
+    "    plt.show()\n",
+    "    \n",
+    "for i in range(len(imglist)):\n",
+    "    img = cv2.imread(imglist[i])\n",
+    "    cut_image(img, imglist[i])\n",
+    "\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "85e7fe5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加载图像统计信息模块（注：也可以直接通过颜色通道来计算）\n",
+    "from PIL import ImageStat,Image\n",
+    "\n",
+    "# 遍历全体图像进行快速检查\n",
+    "size = 100\n",
+    "imgPath = './images'\n",
+    "imgWidth = []       # 图像宽度\n",
+    "imgHeight = []      # 图像高度\n",
+    "imgRrange = []      # 图像红色通道极差\n",
+    "imgGrange = []      # 图像绿色通道极差\n",
+    "imgBrange = []      # 图像蓝色通道极差\n",
+    "\n",
+    "newImgs = []        # 获得选取后的图像作为模型训练和验证数据\n",
+    "\n",
+    "imgFiles = os.listdir(imgPath)\n",
+    "for imgFile in imgFiles:\n",
+    "    img = Image.open(os.path.join(imgPath,imgFile))\n",
+    "    imgWidth.append(img.size[0])\n",
+    "    imgHeight.append(img.size[1])\n",
+    "    \n",
+    "    # 获得图像中心区域大小为size的图像块\n",
+    "    cx, cy = (int(i/2) for i in img.size)\n",
+    "    box = (cx-50, cy-50, cx+50, cy+50)\n",
+    "    region = img.crop(box)\n",
+    "    \n",
+    "    # 计算选取图像块的标准差 分为红绿蓝三种\n",
+    "    stat = ImageStat.Stat(region)\n",
+    "    imgRrange.append(stat.extrema[0][1]-stat.extrema[0][0])\n",
+    "    imgGrange.append(stat.extrema[1][1]-stat.extrema[1][0])\n",
+    "    imgBrange.append(stat.extrema[2][1]-stat.extrema[2][0])\n",
+    "    \n",
+    "    newImgs.append(region)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b5ad4f91",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 161 entries, 0 to 160\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   0       161 non-null    float64\n",
+      " 1   1       161 non-null    float64\n",
+      " 2   2       161 non-null    float64\n",
+      " 3   3       161 non-null    float64\n",
+      " 4   4       161 non-null    float64\n",
+      " 5   5       161 non-null    float64\n",
+      " 6   6       161 non-null    float64\n",
+      " 7   7       161 non-null    float64\n",
+      " 8   8       161 non-null    float64\n",
+      "dtypes: float64(9)\n",
+      "memory usage: 11.4 KB\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>148.6041</td>\n",
+       "      <td>138.6381</td>\n",
+       "      <td>64.3694</td>\n",
+       "      <td>3.625902</td>\n",
+       "      <td>4.125497</td>\n",
+       "      <td>10.485931</td>\n",
+       "      <td>4.242425</td>\n",
+       "      <td>4.798916</td>\n",
+       "      <td>12.044228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>163.6788</td>\n",
+       "      <td>145.5487</td>\n",
+       "      <td>54.4581</td>\n",
+       "      <td>3.923370</td>\n",
+       "      <td>2.835001</td>\n",
+       "      <td>3.478972</td>\n",
+       "      <td>4.439465</td>\n",
+       "      <td>3.239916</td>\n",
+       "      <td>4.039823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>153.9485</td>\n",
+       "      <td>147.0810</td>\n",
+       "      <td>71.9576</td>\n",
+       "      <td>2.208766</td>\n",
+       "      <td>1.803397</td>\n",
+       "      <td>3.115478</td>\n",
+       "      <td>2.623796</td>\n",
+       "      <td>2.139289</td>\n",
+       "      <td>3.621357</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>150.3755</td>\n",
+       "      <td>151.3985</td>\n",
+       "      <td>64.3118</td>\n",
+       "      <td>2.015167</td>\n",
+       "      <td>1.514034</td>\n",
+       "      <td>2.698922</td>\n",
+       "      <td>2.344658</td>\n",
+       "      <td>1.809065</td>\n",
+       "      <td>3.136727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>150.7380</td>\n",
+       "      <td>150.9738</td>\n",
+       "      <td>64.6246</td>\n",
+       "      <td>1.902934</td>\n",
+       "      <td>1.658045</td>\n",
+       "      <td>3.098044</td>\n",
+       "      <td>2.242270</td>\n",
+       "      <td>1.952067</td>\n",
+       "      <td>3.593836</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0         1        2         3         4          5         6  \\\n",
+       "0  148.6041  138.6381  64.3694  3.625902  4.125497  10.485931  4.242425   \n",
+       "1  163.6788  145.5487  54.4581  3.923370  2.835001   3.478972  4.439465   \n",
+       "2  153.9485  147.0810  71.9576  2.208766  1.803397   3.115478  2.623796   \n",
+       "3  150.3755  151.3985  64.3118  2.015167  1.514034   2.698922  2.344658   \n",
+       "4  150.7380  150.9738  64.6246  1.902934  1.658045   3.098044  2.242270   \n",
+       "\n",
+       "          7          8  \n",
+       "0  4.798916  12.044228  \n",
+       "1  3.239916   4.039823  \n",
+       "2  2.139289   3.621357  \n",
+       "3  1.809065   3.136727  \n",
+       "4  1.952067   3.593836  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "# 构建训练数据集和分类标签\n",
+    "data = []\n",
+    "dy = []\n",
+    "for i, img in enumerate(newImgs):\n",
+    "    r, g, b = np.split(np.array(img), 3, axis = 2)\n",
+    "    \n",
+    "    #计算一阶矩\n",
+    "    r_m1 = np.mean(r)\n",
+    "    g_m1 = np.mean(g)\n",
+    "    b_m1 = np.mean(b)\n",
+    "    \n",
+    "    #二阶矩\n",
+    "    r_m2 = np.std(r)\n",
+    "    g_m2 = np.std(g)\n",
+    "    b_m2 = np.std(b)\n",
+    "    \n",
+    "    #三阶矩\n",
+    "    r_m3 = np.mean(abs(r - r.mean())**3)**(1/3)\n",
+    "    g_m3 = np.mean(abs(g - g.mean())**3)**(1/3)\n",
+    "    b_m3 = np.mean(abs(b - b.mean())**3)**(1/3)\n",
+    "    \n",
+    "    # 构造新数据集\n",
+    "    df = np.array([r_m1,g_m1,b_m1,r_m2,g_m2,b_m2,r_m3,g_m3,b_m3])\n",
+    "    data.append(df)\n",
+    "    \n",
+    "    # 保存对应的分类标签\n",
+    "    dy.append(int(imgFiles[i][0]))\n",
+    "\n",
+    "dy = np.array(dy)\n",
+    "data = pd.DataFrame(np.array(data))\n",
+    "data.info()\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "da56ab61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "((128, 9), (33, 9), (128,))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test=train_test_split(data,dy,test_size=0.2,random_state=0)\n",
+    "x_train.shape,x_test.shape,y_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8c6be264",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7575757575757576\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "mlp=MLPClassifier()\n",
+    "mlp.fit(x_train,y_train)\n",
+    "y_pred=mlp.predict(x_test)\n",
+    "print(accuracy_score(y_pred,y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7c33fc8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/泰坦尼克号数据集分析.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/泰坦尼克号数据集分析.ipynb
new file mode 100644
index 0000000..8b01e95
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/泰坦尼克号数据集分析.ipynb	
@@ -0,0 +1,662 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d10c64c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 相关性分析，nan填充，标签转数值"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "92457571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.rcParams['font.family'] = ['serif', 'SimSun']\n",
+    "plt.rcParams['mathtext.fontset'] = 'cm'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cbf9bd80",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_base = pd.read_csv(\"train.csv\")\n",
+    "df = df_base.copy()\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "6dd49ea7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>886</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>887</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>888</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23.4500</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>889</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>890</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>891 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
+       "0         3    1  22.0      1      0   7.2500         2\n",
+       "1         1    0  38.0      1      0  71.2833         0\n",
+       "2         3    0  26.0      0      0   7.9250         2\n",
+       "3         1    0  35.0      1      0  53.1000         2\n",
+       "4         3    1  35.0      0      0   8.0500         2\n",
+       "..      ...  ...   ...    ...    ...      ...       ...\n",
+       "886       2    1  27.0      0      0  13.0000         2\n",
+       "887       1    0  19.0      0      0  30.0000         2\n",
+       "888       3    0  29.0      1      2  23.4500         2\n",
+       "889       1    1  26.0      0      0  30.0000         0\n",
+       "890       3    1  32.0      0      0   7.7500         1\n",
+       "\n",
+       "[891 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
+    "df = df_base[cols]\n",
+    "y = df_base['Survived']\n",
+    "\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "label_encoders = {}\n",
+    "for col in cols:\n",
+    "    if df[col].dtype == 'object':\n",
+    "        encoder = LabelEncoder()\n",
+    "        df[col] = encoder.fit_transform(df[col])\n",
+    "        label_encoders[col] = encoder\n",
+    "df['Age'] = df['Age'].fillna(int(df['Age'].mean()))\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "f1dbe0a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "std_scaler = StandardScaler()\n",
+    "X = std_scaler.fit_transform(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "7e8d23f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test = train_test_split(X, test_size=0.3, random_state=42)\n",
+    "y_train, y_test = train_test_split(y, test_size=0.3, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "6751f651",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'SGD': 0.7576387096774194, 'tree': 0.7704903225806452, 'kneighbor': 0.792916129032258, 'forest': 0.7945935483870968, 'svc': 0.8234193548387096, 'log': 0.7993161290322581}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "models = {}\n",
+    "fold_scores = {}\n",
+    "sgd = SGDClassifier(); models['sgd'] = sgd\n",
+    "fold_scores['SGD'] = np.mean(cross_val_score(sgd, X_train, y_train))\n",
+    "tree = DecisionTreeClassifier(); models['tree'] = tree\n",
+    "fold_scores['tree'] = np.mean(cross_val_score(tree, X_train, y_train))\n",
+    "kneighbor = KNeighborsClassifier(); models['kneighbor'] = kneighbor\n",
+    "fold_scores['kneighbor'] = np.mean(cross_val_score(kneighbor, X_train, y_train))\n",
+    "forest = RandomForestClassifier(); models['forest'] = forest\n",
+    "fold_scores['forest'] = np.mean(cross_val_score(forest, X_train, y_train))\n",
+    "svc = SVC(); models['svc'] = svc\n",
+    "fold_scores['svc'] = np.mean(cross_val_score(svc, X_train, y_train))\n",
+    "logistic = LogisticRegression(); models['log'] = logistic\n",
+    "fold_scores['log'] = np.mean(cross_val_score(logistic, X_train, y_train))\n",
+    "print(fold_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "d63dbdc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 600x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "forest.fit(X_train, y_train)\n",
+    "importances = np.asarray(sorted(zip(forest.feature_importances_, cols), key=lambda x: x[0], reverse=True))\n",
+    "plt.figure(figsize=(6,3))\n",
+    "plt.bar(importances[:,1], importances[:,0].astype('float32'), label='重要度')\n",
+    "plt.title(\"参数重要度\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "23fd3798",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sgd训练集准确率： 0.68212, 测试集准确率： 0.76667\n",
+      "tree训练集准确率： 0.99539, 测试集准确率： 0.70000\n",
+      "kneighbor训练集准确率： 0.83902, 测试集准确率： 0.79348\n",
+      "forest训练集准确率： 0.97778, 测试集准确率： 0.74038\n",
+      "svc训练集准确率： 0.86486, 测试集准确率： 0.84444\n",
+      "log训练集准确率： 0.74882, 测试集准确率： 0.80198\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "for name, model in models.items():\n",
+    "    model.fit(X_train, y_train)\n",
+    "    train_pred = model.predict(X_train)\n",
+    "    print(name+\"训练集准确率：\", f\"{precision_score(y_train, train_pred):.5f}\", end=', ')\n",
+    "    test_pred = model.predict(X_test)\n",
+    "    print(\"测试集准确率：\", f\"{precision_score(y_test, test_pred):.5f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "c8922fad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "plt.figure(figsize=(10,4))\n",
+    "plt.subplot(121)\n",
+    "train_pred = svc.predict(X_train)\n",
+    "confuse_mat = confusion_matrix(y_train, train_pred)\n",
+    "sns.heatmap(confuse_mat, annot=True, fmt=\".0f\")\n",
+    "plt.ylabel('True')\n",
+    "plt.xlabel('Predict')\n",
+    "plt.title(\"Train Dataset\")\n",
+    "plt.subplot(122)\n",
+    "test_pred = svc.predict(X_test)\n",
+    "confuse_mat = confusion_matrix(y_test, test_pred)\n",
+    "sns.heatmap(confuse_mat, annot=True, fmt=\".0f\")\n",
+    "plt.ylabel('True')\n",
+    "plt.xlabel('Predict')\n",
+    "plt.title(\"Test Dataset\")\n",
+    "plt.suptitle(\"SVC\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "7206fd63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f0bd3d4f8d0>]"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_train, train_pred)\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.plot([0,1],[0,1],'k--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "91554394",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Embarked</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.553571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>0.389610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>0.336957</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Survived\n",
+       "Embarked          \n",
+       "C         0.553571\n",
+       "Q         0.389610\n",
+       "S         0.336957"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1 = df_base.copy()\n",
+    "df_base[['Embarked', 'Survived']].groupby(['Embarked']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7debb78",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/灰度图像人脸识别.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/灰度图像人脸识别.ipynb
new file mode 100644
index 0000000..05baa33
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/灰度图像人脸识别.ipynb	
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4b3ff29d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "keras = tf.keras\n",
+    "layers = keras.layers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f6e155f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 1611 files belonging to 6 classes.\n",
+      "Using 1289 files for training.\n",
+      "Found 1611 files belonging to 6 classes.\n",
+      "Using 322 files for validation.\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_train = keras.utils.image_dataset_from_directory(\n",
+    "    r\"D:\\data\\gray_face_images\",\n",
+    "    color_mode=\"grayscale\",\n",
+    "    validation_split=0.2,\n",
+    "    subset=\"training\",\n",
+    "    seed=42,\n",
+    ")\n",
+    "data_val = keras.utils.image_dataset_from_directory(\n",
+    "    r\"D:\\data\\gray_face_images\",\n",
+    "    color_mode=\"grayscale\",\n",
+    "    validation_split=0.2,\n",
+    "    subset=\"validation\",\n",
+    "    seed=42,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ceac26df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def data_convert(x, y):\n",
+    "    return x / 255., tf.one_hot(y, depth=6)\n",
+    "data_train = data_train.map(data_convert)\n",
+    "data_val = data_val.map(data_convert)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d6c8a2d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(32, 256, 256, 1) (32, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for x, y in data_train:\n",
+    "    print(x.shape, y.shape)\n",
+    "    break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d9026c53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = layers.Input(shape=(256,256,1), name='Input')\n",
+    "x = layers.Conv2D(filters=32, kernel_size=3,  activation='relu', name='Cov1')(inputs)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool1')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout1')(x)\n",
+    "\n",
+    "x = layers.Conv2D(filters=64, kernel_size=3, activation='relu', name='Cov2')(x)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool2')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout2')(x)\n",
+    "\n",
+    "x = layers.Conv2D(filters=64, kernel_size=3, activation='relu', name='Cov3')(x)\n",
+    "x = layers.MaxPool2D(pool_size=2, name='Pool3')(x)\n",
+    "x = layers.Dropout(rate=0.5, name='Dropout3')(x)\n",
+    "\n",
+    "x = layers.Flatten(name=\"Flatten\")(x)\n",
+    "x = layers.Dropout(rate=0.75, name='Dropout4')(x)\n",
+    "x = layers.Dense(units=512, activation='relu', name=\"Dense1\")(x)\n",
+    "outputs = layers.Dense(units=6, activation='softmax', name=\"Output\")(x)\n",
+    "model = keras.Model(inputs, outputs)\n",
+    "keras.utils.plot_model(model, show_shapes=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ddf4e697",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " Input (InputLayer)          [(None, 256, 256, 1)]     0         \n",
+      "                                                                 \n",
+      " Cov1 (Conv2D)               (None, 254, 254, 32)      320       \n",
+      "                                                                 \n",
+      " Pool1 (MaxPooling2D)        (None, 127, 127, 32)      0         \n",
+      "                                                                 \n",
+      " Dropout1 (Dropout)          (None, 127, 127, 32)      0         \n",
+      "                                                                 \n",
+      " Cov2 (Conv2D)               (None, 125, 125, 64)      18496     \n",
+      "                                                                 \n",
+      " Pool2 (MaxPooling2D)        (None, 62, 62, 64)        0         \n",
+      "                                                                 \n",
+      " Dropout2 (Dropout)          (None, 62, 62, 64)        0         \n",
+      "                                                                 \n",
+      " Cov3 (Conv2D)               (None, 60, 60, 64)        36928     \n",
+      "                                                                 \n",
+      " Pool3 (MaxPooling2D)        (None, 30, 30, 64)        0         \n",
+      "                                                                 \n",
+      " Dropout3 (Dropout)          (None, 30, 30, 64)        0         \n",
+      "                                                                 \n",
+      " Flatten (Flatten)           (None, 57600)             0         \n",
+      "                                                                 \n",
+      " Dropout4 (Dropout)          (None, 57600)             0         \n",
+      "                                                                 \n",
+      " Dense1 (Dense)              (None, 512)               29491712  \n",
+      "                                                                 \n",
+      " Output (Dense)              (None, 6)                 3078      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 29,550,534\n",
+      "Trainable params: 29,550,534\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "4af04e8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "41/41 [==============================] - 8s 75ms/step - loss: 2.1733 - categorical_accuracy: 0.2234 - val_loss: 1.7901 - val_categorical_accuracy: 0.1460\n",
+      "Epoch 2/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 1.3984 - categorical_accuracy: 0.4259 - val_loss: 0.7413 - val_categorical_accuracy: 0.8851\n",
+      "Epoch 3/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.5248 - categorical_accuracy: 0.8348 - val_loss: 0.2736 - val_categorical_accuracy: 0.9658\n",
+      "Epoch 4/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.2337 - categorical_accuracy: 0.9348 - val_loss: 0.1263 - val_categorical_accuracy: 0.9814\n",
+      "Epoch 5/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.1423 - categorical_accuracy: 0.9558 - val_loss: 0.0671 - val_categorical_accuracy: 0.9907\n",
+      "Epoch 6/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0916 - categorical_accuracy: 0.9744 - val_loss: 0.0493 - val_categorical_accuracy: 0.9907\n",
+      "Epoch 7/10\n",
+      "41/41 [==============================] - 3s 64ms/step - loss: 0.0835 - categorical_accuracy: 0.9744 - val_loss: 0.0723 - val_categorical_accuracy: 0.9752\n",
+      "Epoch 8/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0830 - categorical_accuracy: 0.9752 - val_loss: 0.0447 - val_categorical_accuracy: 0.9938\n",
+      "Epoch 9/10\n",
+      "41/41 [==============================] - 3s 63ms/step - loss: 0.0490 - categorical_accuracy: 0.9829 - val_loss: 0.1132 - val_categorical_accuracy: 0.9627\n",
+      "Epoch 10/10\n",
+      "41/41 [==============================] - 3s 64ms/step - loss: 0.0558 - categorical_accuracy: 0.9829 - val_loss: 0.0259 - val_categorical_accuracy: 0.9907\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x17bc55b5430>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
+    "              loss=keras.losses.CategoricalCrossentropy(from_logits=False),\n",
+    "              metrics=[keras.metrics.CategoricalAccuracy()])\n",
+    "model.fit(data_train, validation_data=data_val, epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6933885f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/离职数据分析.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/离职数据分析.ipynb
new file mode 100644
index 0000000..bf1b7e5
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/离职数据分析.ipynb	
@@ -0,0 +1,1388 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e091bec4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\anaconda\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import sklearn\n",
+    "from sklearn.preprocessing import LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6205e765",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Attrition</th>\n",
+       "      <th>BusinessTravel</th>\n",
+       "      <th>Department</th>\n",
+       "      <th>DistanceFromHome</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>EducationField</th>\n",
+       "      <th>EmployeeCount</th>\n",
+       "      <th>EmployeeID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>NumCompaniesWorked</th>\n",
+       "      <th>Over18</th>\n",
+       "      <th>PercentSalaryHike</th>\n",
+       "      <th>StandardHours</th>\n",
+       "      <th>StockOptionLevel</th>\n",
+       "      <th>TotalWorkingYears</th>\n",
+       "      <th>TrainingTimesLastYear</th>\n",
+       "      <th>YearsAtCompany</th>\n",
+       "      <th>YearsSinceLastPromotion</th>\n",
+       "      <th>YearsWithCurrManager</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>51</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Rarely</td>\n",
+       "      <td>Sales</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Life Sciences</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Travel_Frequently</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Life Sciences</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>23</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Frequently</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>15</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Non-Travel</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Life Sciences</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>32</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Travel_Rarely</td>\n",
+       "      <td>Research &amp; Development</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Medical</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age Attrition     BusinessTravel              Department  DistanceFromHome  \\\n",
+       "0   51        No      Travel_Rarely                   Sales                 6   \n",
+       "1   31       Yes  Travel_Frequently  Research & Development                10   \n",
+       "2   32        No  Travel_Frequently  Research & Development                17   \n",
+       "3   38        No         Non-Travel  Research & Development                 2   \n",
+       "4   32        No      Travel_Rarely  Research & Development                10   \n",
+       "\n",
+       "   Education EducationField  EmployeeCount  EmployeeID  Gender  ...  \\\n",
+       "0          2  Life Sciences              1           1  Female  ...   \n",
+       "1          1  Life Sciences              1           2  Female  ...   \n",
+       "2          4          Other              1           3    Male  ...   \n",
+       "3          5  Life Sciences              1           4    Male  ...   \n",
+       "4          1        Medical              1           5    Male  ...   \n",
+       "\n",
+       "   NumCompaniesWorked Over18 PercentSalaryHike  StandardHours  \\\n",
+       "0                 1.0      Y                11              8   \n",
+       "1                 0.0      Y                23              8   \n",
+       "2                 1.0      Y                15              8   \n",
+       "3                 3.0      Y                11              8   \n",
+       "4                 4.0      Y                12              8   \n",
+       "\n",
+       "   StockOptionLevel TotalWorkingYears  TrainingTimesLastYear  YearsAtCompany  \\\n",
+       "0                 0               1.0                      6               1   \n",
+       "1                 1               6.0                      3               5   \n",
+       "2                 3               5.0                      2               5   \n",
+       "3                 3              13.0                      5               8   \n",
+       "4                 2               9.0                      2               6   \n",
+       "\n",
+       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
+       "0                        0                     0  \n",
+       "1                        1                     4  \n",
+       "2                        0                     3  \n",
+       "3                        7                     5  \n",
+       "4                        0                     4  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=pd.read_csv('quit_dataset.csv',encoding='gbk')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ff728823",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4410 entries, 0 to 4409\n",
+      "Data columns (total 24 columns):\n",
+      " #   Column                   Non-Null Count  Dtype  \n",
+      "---  ------                   --------------  -----  \n",
+      " 0   Age                      4410 non-null   int64  \n",
+      " 1   Attrition                4410 non-null   object \n",
+      " 2   BusinessTravel           4410 non-null   object \n",
+      " 3   Department               4410 non-null   object \n",
+      " 4   DistanceFromHome         4410 non-null   int64  \n",
+      " 5   Education                4410 non-null   int64  \n",
+      " 6   EducationField           4410 non-null   object \n",
+      " 7   EmployeeCount            4410 non-null   int64  \n",
+      " 8   EmployeeID               4410 non-null   int64  \n",
+      " 9   Gender                   4410 non-null   object \n",
+      " 10  JobLevel                 4410 non-null   int64  \n",
+      " 11  JobRole                  4410 non-null   object \n",
+      " 12  MaritalStatus            4410 non-null   object \n",
+      " 13  MonthlyIncome            4410 non-null   int64  \n",
+      " 14  NumCompaniesWorked       4391 non-null   float64\n",
+      " 15  Over18                   4410 non-null   object \n",
+      " 16  PercentSalaryHike        4410 non-null   int64  \n",
+      " 17  StandardHours            4410 non-null   int64  \n",
+      " 18  StockOptionLevel         4410 non-null   int64  \n",
+      " 19  TotalWorkingYears        4401 non-null   float64\n",
+      " 20  TrainingTimesLastYear    4410 non-null   int64  \n",
+      " 21  YearsAtCompany           4410 non-null   int64  \n",
+      " 22  YearsSinceLastPromotion  4410 non-null   int64  \n",
+      " 23  YearsWithCurrManager     4410 non-null   int64  \n",
+      "dtypes: float64(2), int64(14), object(8)\n",
+      "memory usage: 827.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8e2de397",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Attrition</th>\n",
+       "      <th>BusinessTravel</th>\n",
+       "      <th>Department</th>\n",
+       "      <th>DistanceFromHome</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>EducationField</th>\n",
+       "      <th>EmployeeCount</th>\n",
+       "      <th>EmployeeID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>NumCompaniesWorked</th>\n",
+       "      <th>Over18</th>\n",
+       "      <th>PercentSalaryHike</th>\n",
+       "      <th>StandardHours</th>\n",
+       "      <th>StockOptionLevel</th>\n",
+       "      <th>TotalWorkingYears</th>\n",
+       "      <th>TrainingTimesLastYear</th>\n",
+       "      <th>YearsAtCompany</th>\n",
+       "      <th>YearsSinceLastPromotion</th>\n",
+       "      <th>YearsWithCurrManager</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>51</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age  Attrition  BusinessTravel  Department  DistanceFromHome  Education  \\\n",
+       "0   51          0               2           2                 6          2   \n",
+       "1   31          1               1           1                10          1   \n",
+       "2   32          0               1           1                17          4   \n",
+       "3   38          0               0           1                 2          5   \n",
+       "4   32          0               2           1                10          1   \n",
+       "\n",
+       "   EducationField  EmployeeCount  EmployeeID  Gender  ...  NumCompaniesWorked  \\\n",
+       "0               1              1           1       0  ...                 1.0   \n",
+       "1               1              1           2       0  ...                 0.0   \n",
+       "2               4              1           3       1  ...                 1.0   \n",
+       "3               1              1           4       1  ...                 3.0   \n",
+       "4               3              1           5       1  ...                 4.0   \n",
+       "\n",
+       "   Over18  PercentSalaryHike  StandardHours  StockOptionLevel  \\\n",
+       "0       0                 11              8                 0   \n",
+       "1       0                 23              8                 1   \n",
+       "2       0                 15              8                 3   \n",
+       "3       0                 11              8                 3   \n",
+       "4       0                 12              8                 2   \n",
+       "\n",
+       "   TotalWorkingYears  TrainingTimesLastYear  YearsAtCompany  \\\n",
+       "0                1.0                      6               1   \n",
+       "1                6.0                      3               5   \n",
+       "2                5.0                      2               5   \n",
+       "3               13.0                      5               8   \n",
+       "4                9.0                      2               6   \n",
+       "\n",
+       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
+       "0                        0                     0  \n",
+       "1                        1                     4  \n",
+       "2                        0                     3  \n",
+       "3                        7                     5  \n",
+       "4                        0                     4  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#unique函数是去重\n",
+    "maps={}\n",
+    "for col in df:\n",
+    "    if df[col].dtype=='object':\n",
+    "        unique=df[col].unique()\n",
+    "        #将每一类编码\n",
+    "        le=LabelEncoder()\n",
+    "        le=le.fit(unique)\n",
+    "        #每列标签保存进字典\n",
+    "        maps[col]=le\n",
+    "        df[col]=le.transform(df[col])\n",
+    "        \n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "29a3dfbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#使用热力图来绘制变量间相关性\n",
+    "plt.figure(figsize=(20,10))\n",
+    "sns.heatmap(df.corr(),annot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7070cea3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Attrition</th>\n",
+       "      <th>BusinessTravel</th>\n",
+       "      <th>Department</th>\n",
+       "      <th>DistanceFromHome</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>EducationField</th>\n",
+       "      <th>EmployeeCount</th>\n",
+       "      <th>EmployeeID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>NumCompaniesWorked</th>\n",
+       "      <th>Over18</th>\n",
+       "      <th>PercentSalaryHike</th>\n",
+       "      <th>StandardHours</th>\n",
+       "      <th>StockOptionLevel</th>\n",
+       "      <th>TotalWorkingYears</th>\n",
+       "      <th>TrainingTimesLastYear</th>\n",
+       "      <th>YearsAtCompany</th>\n",
+       "      <th>YearsSinceLastPromotion</th>\n",
+       "      <th>YearsWithCurrManager</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.159205</td>\n",
+       "      <td>0.024751</td>\n",
+       "      <td>-0.010846</td>\n",
+       "      <td>0.006963</td>\n",
+       "      <td>-0.035706</td>\n",
+       "      <td>0.043194</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.008649</td>\n",
+       "      <td>-0.039352</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.299243</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.033137</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.031753</td>\n",
+       "      <td>0.680661</td>\n",
+       "      <td>-0.027308</td>\n",
+       "      <td>0.311309</td>\n",
+       "      <td>0.216513</td>\n",
+       "      <td>0.202089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Attrition</th>\n",
+       "      <td>-0.159205</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000074</td>\n",
+       "      <td>-0.048206</td>\n",
+       "      <td>-0.009730</td>\n",
+       "      <td>-0.015111</td>\n",
+       "      <td>-0.057940</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.004729</td>\n",
+       "      <td>0.018125</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.042345</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.032533</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.006839</td>\n",
+       "      <td>-0.170338</td>\n",
+       "      <td>-0.049431</td>\n",
+       "      <td>-0.134392</td>\n",
+       "      <td>-0.033019</td>\n",
+       "      <td>-0.156199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BusinessTravel</th>\n",
+       "      <td>0.024751</td>\n",
+       "      <td>0.000074</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.010982</td>\n",
+       "      <td>0.024616</td>\n",
+       "      <td>0.002755</td>\n",
+       "      <td>-0.015462</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.002590</td>\n",
+       "      <td>-0.039243</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.022401</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.031054</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.003683</td>\n",
+       "      <td>0.033997</td>\n",
+       "      <td>-0.041888</td>\n",
+       "      <td>-0.014575</td>\n",
+       "      <td>-0.032591</td>\n",
+       "      <td>-0.022636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Department</th>\n",
+       "      <td>-0.010846</td>\n",
+       "      <td>-0.048206</td>\n",
+       "      <td>-0.010982</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.012134</td>\n",
+       "      <td>-0.004597</td>\n",
+       "      <td>0.013720</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.003455</td>\n",
+       "      <td>0.003158</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.003954</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.007840</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.010512</td>\n",
+       "      <td>-0.019839</td>\n",
+       "      <td>0.012866</td>\n",
+       "      <td>0.010078</td>\n",
+       "      <td>0.014845</td>\n",
+       "      <td>0.018016</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DistanceFromHome</th>\n",
+       "      <td>0.006963</td>\n",
+       "      <td>-0.009730</td>\n",
+       "      <td>0.024616</td>\n",
+       "      <td>0.012134</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.008638</td>\n",
+       "      <td>-0.024665</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.001097</td>\n",
+       "      <td>-0.044173</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.013843</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.038125</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.011169</td>\n",
+       "      <td>0.009374</td>\n",
+       "      <td>-0.009001</td>\n",
+       "      <td>0.031684</td>\n",
+       "      <td>0.002290</td>\n",
+       "      <td>0.021584</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Age  Attrition  BusinessTravel  Department  \\\n",
+       "Age               1.000000  -0.159205        0.024751   -0.010846   \n",
+       "Attrition        -0.159205   1.000000        0.000074   -0.048206   \n",
+       "BusinessTravel    0.024751   0.000074        1.000000   -0.010982   \n",
+       "Department       -0.010846  -0.048206       -0.010982    1.000000   \n",
+       "DistanceFromHome  0.006963  -0.009730        0.024616    0.012134   \n",
+       "\n",
+       "                  DistanceFromHome  Education  EducationField  EmployeeCount  \\\n",
+       "Age                       0.006963  -0.035706        0.043194            NaN   \n",
+       "Attrition                -0.009730  -0.015111       -0.057940            NaN   \n",
+       "BusinessTravel            0.024616   0.002755       -0.015462            NaN   \n",
+       "Department                0.012134  -0.004597        0.013720            NaN   \n",
+       "DistanceFromHome          1.000000  -0.008638       -0.024665            NaN   \n",
+       "\n",
+       "                  EmployeeID    Gender  ...  NumCompaniesWorked  Over18  \\\n",
+       "Age                 0.008649 -0.039352  ...            0.299243     NaN   \n",
+       "Attrition          -0.004729  0.018125  ...            0.042345     NaN   \n",
+       "BusinessTravel     -0.002590 -0.039243  ...            0.022401     NaN   \n",
+       "Department         -0.003455  0.003158  ...           -0.003954     NaN   \n",
+       "DistanceFromHome   -0.001097 -0.044173  ...           -0.013843     NaN   \n",
+       "\n",
+       "                  PercentSalaryHike  StandardHours  StockOptionLevel  \\\n",
+       "Age                       -0.033137            NaN         -0.031753   \n",
+       "Attrition                  0.032533            NaN         -0.006839   \n",
+       "BusinessTravel            -0.031054            NaN          0.003683   \n",
+       "Department                -0.007840            NaN          0.010512   \n",
+       "DistanceFromHome           0.038125            NaN          0.011169   \n",
+       "\n",
+       "                  TotalWorkingYears  TrainingTimesLastYear  YearsAtCompany  \\\n",
+       "Age                        0.680661              -0.027308        0.311309   \n",
+       "Attrition                 -0.170338              -0.049431       -0.134392   \n",
+       "BusinessTravel             0.033997              -0.041888       -0.014575   \n",
+       "Department                -0.019839               0.012866        0.010078   \n",
+       "DistanceFromHome           0.009374              -0.009001        0.031684   \n",
+       "\n",
+       "                  YearsSinceLastPromotion  YearsWithCurrManager  \n",
+       "Age                              0.216513              0.202089  \n",
+       "Attrition                       -0.033019             -0.156199  \n",
+       "BusinessTravel                  -0.032591             -0.022636  \n",
+       "Department                       0.014845              0.018016  \n",
+       "DistanceFromHome                 0.002290              0.021584  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr=df.corr()\n",
+    "corr.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d705e0fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Attrition</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Attrition</th>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MaritalStatus</th>\n",
+       "      <td>0.162070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NumCompaniesWorked</th>\n",
+       "      <td>0.042345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PercentSalaryHike</th>\n",
+       "      <td>0.032533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>JobRole</th>\n",
+       "      <td>0.025809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <td>0.018125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BusinessTravel</th>\n",
+       "      <td>0.000074</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>StandardHours</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EmployeeCount</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Over18</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EmployeeID</th>\n",
+       "      <td>-0.004729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>StockOptionLevel</th>\n",
+       "      <td>-0.006839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DistanceFromHome</th>\n",
+       "      <td>-0.009730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>JobLevel</th>\n",
+       "      <td>-0.010290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Education</th>\n",
+       "      <td>-0.015111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MonthlyIncome</th>\n",
+       "      <td>-0.031176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YearsSinceLastPromotion</th>\n",
+       "      <td>-0.033019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Department</th>\n",
+       "      <td>-0.048206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TrainingTimesLastYear</th>\n",
+       "      <td>-0.049431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EducationField</th>\n",
+       "      <td>-0.057940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YearsAtCompany</th>\n",
+       "      <td>-0.134392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YearsWithCurrManager</th>\n",
+       "      <td>-0.156199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age</th>\n",
+       "      <td>-0.159205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalWorkingYears</th>\n",
+       "      <td>-0.170338</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Attrition\n",
+       "Attrition                 1.000000\n",
+       "MaritalStatus             0.162070\n",
+       "NumCompaniesWorked        0.042345\n",
+       "PercentSalaryHike         0.032533\n",
+       "JobRole                   0.025809\n",
+       "Gender                    0.018125\n",
+       "BusinessTravel            0.000074\n",
+       "StandardHours             0.000000\n",
+       "EmployeeCount             0.000000\n",
+       "Over18                    0.000000\n",
+       "EmployeeID               -0.004729\n",
+       "StockOptionLevel         -0.006839\n",
+       "DistanceFromHome         -0.009730\n",
+       "JobLevel                 -0.010290\n",
+       "Education                -0.015111\n",
+       "MonthlyIncome            -0.031176\n",
+       "YearsSinceLastPromotion  -0.033019\n",
+       "Department               -0.048206\n",
+       "TrainingTimesLastYear    -0.049431\n",
+       "EducationField           -0.057940\n",
+       "YearsAtCompany           -0.134392\n",
+       "YearsWithCurrManager     -0.156199\n",
+       "Age                      -0.159205\n",
+       "TotalWorkingYears        -0.170338"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr=corr.fillna(0)\n",
+    "show=corr[['Attrition']].sort_values(by='Attrition',ascending=False)\n",
+    "show.head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "57eb90b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\99319\\AppData\\Local\\Temp\\ipykernel_3624\\2027094151.py:16: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  show=show.iloc[1:11].append(show.iloc[-last_index:])\n",
+      "C:\\Users\\99319\\AppData\\Local\\Temp\\ipykernel_3624\\2027094151.py:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  show=show.iloc[0:5].append(show.iloc[-5:])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#柱状图\n",
+    "#使用sort_values去求前5和后5的柱状图\n",
+    "# 绘制值最高的前5个\n",
+    "plt.figure(figsize=(20,8))\n",
+    "#1行2列第1个（布局）\n",
+    "plt.subplot(1,2,1)\n",
+    "plt.bar(show.index[:5],show.Attrition[:5])\n",
+    "# 绘制值最低的前5个\n",
+    "#1行2列第2个（布局）\n",
+    "plt.subplot(1,2,2)\n",
+    "plt.bar(show.index[-5:],show.Attrition[-5:])\n",
+    "plt.title(\"The correlation of attrition with other variables\")\n",
+    "plt.show()\n",
+    "#\n",
+    "last_index=len(show[show.Attrition<0])\n",
+    "show=show.iloc[1:11].append(show.iloc[-last_index:])\n",
+    "show=show.iloc[0:5].append(show.iloc[-5:])\n",
+    "index_name=[\"Over Time\",\"Marita Status\",\"Distance From Home\",\"Job Role\",\"Department\",\"Age\",\"Monthly Income\",\"Years In Current Role\",\"Job Level\",\"Total Working Years\"]\n",
+    "show.index=index_name\n",
+    "plt.figure(figsize=(20,8))\n",
+    "plt.bar(show.iloc[0:6].index,show.iloc[0:6].Attrition)\n",
+    "plt.bar(show.iloc[5:].index,show.iloc[5:].Attrition)\n",
+    "plt.title(\"The correlation of attrition with other variables\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "41a084fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1        5\n",
+       "6        0\n",
+       "13      10\n",
+       "28       1\n",
+       "30       3\n",
+       "        ..\n",
+       "4381     4\n",
+       "4386     5\n",
+       "4388     9\n",
+       "4391     3\n",
+       "4402     1\n",
+       "Name: YearsAtCompany, Length: 711, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#离职和非离职\n",
+    "quit_df=df[df['Attrition']==1]\n",
+    "not_quit_df=df[df['Attrition']==0]\n",
+    "quit_df['YearsAtCompany']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "03c071fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>YearsAtCompany</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4381</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4386</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4388</th>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4391</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4402</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>711 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      YearsAtCompany\n",
+       "1                  5\n",
+       "6                  0\n",
+       "13                10\n",
+       "28                 1\n",
+       "30                 3\n",
+       "...              ...\n",
+       "4381               4\n",
+       "4386               5\n",
+       "4388               9\n",
+       "4391               3\n",
+       "4402               1\n",
+       "\n",
+       "[711 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quit_df[['YearsAtCompany']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e2c5d9dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3648"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quit_df['YearsAtCompany'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9ad2576f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27258"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not_quit_df['YearsAtCompany'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f6b53941",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SJEkNYnImSZLUICZnkiSpJyKC4447buH4aaedxsknnzxsy58/fz7jxo3jS1/60sJpf/3rX/niF7+4cHzOnDl885vf7HcZ9913H2984xsBmD17NjNnzlw478orr+TUU08dtng75QUBkiSNAg9N3nRYlzdh7t2DlllllVX49re/zYknnsh66603rO0DXHbZZey4447MmDGDd7zjHcCi5Oxd73oXsCg5O/jgg5eov2DBAp773Ody+eWXAyU5mzVrFnvvvTcA06dPZ/r06cMe92DsOZMkST0xduxYjjzySD772c8uMe/3v/89u+++O1tvvTW77747f/jDHwA4/PDDOeaYY9hpp514wQtesDBxamfGjBmcfvrpzJs3jz/+8Y8AnHDCCfz2t79l22235fjjj+eEE07guuuuY9ttt+Wzn/0sF154Ifvvvz+vfe1refWrX82cOXPYcsstefLJJznppJO45JJLFj694MILL+Too48etng7ZXImSZJ65t3vfjcXXXQRDz/88GLTjz76aA499FBuu+023vzmN3PMMccsnHf//fdz/fXX873vfY8TTjih7XLnzp3LAw88wA477MABBxzAJZdcAsCpp57KxhtvzOzZs/n0pz/Nqaeeyq677srs2bM59thjAbjhhhv46le/ytVXX71weSuvvDKnnHIKBx54ILNnz+bAAw8c1ni7YXImSZJ6Zq211uLQQw/lzDPPXGz6DTfcsPBQ4yGHHML111+/cN7rXvc6VlppJaZOncqf/vSntsu9+OKLOeCAA4DyzM0ZM2Z0HNMee+zBhAkTulqPpY23G55zJkmSeup973sf22+/PUcccUS/ZSJi4etVVlll4eu+Z4B/+MMf5vvf/z5Qzg2bMWMGf/rTn7jooouAcmL/3Xffzbhx4waNZ/XVVx/SenQT79Kw50ySJPXUhAkTOOCAAzjvvPMWTttpp524+OKLAbjooovYZZddBlzGJz/5SWbPns3s2bO56667eOyxx/jjH//InDlzmDNnDieeeCIXX3wxa665Jo8++ujCeq3jAxmobLfxLg2TM0mS1HPHHXccf/nLXxaOn3nmmVxwwQVsvfXWfP3rX+eMM87oeFkzZsxgv/32W2zaG97wBmbMmMG6667LzjvvzJZbbsnxxx/P1ltvzdixY9lmm23aXphQ94pXvII77rhj4QUBdUsTb7diOLrfmmLatGk5a9askQ5jMT5bU5I0Eu6880622GKLkQ5DlXb7IyJuzsxprWXtOZMkSWoQkzNJkqQGMTmTJElqkJ4mZxGxZ0TcFRH3RMQSd2WLiM0j4oaIeCIi3t8yb52IuDwifhMRd0bEy3oZqyRJK5oV6bzy5Vm3+6FnyVlEjAHOAvYCpgJvioipLcUeAo4BTmuziDOAH2Tm5sA2wJ29ilWSpBXN+PHjefDBB03QRlhm8uCDDzJ+/PiO6/TyJrQ7APdk5r0AEXExsC9wR1+BzPwz8OeIeE29YkSsBfwTcHhV7kngyR7GKknSCmXSpEnMmzeP+fPnj3Qoo9748eOZNGlSx+V7mZxtCMytjc8DXtph3RcA84ELImIb4GbgvZn52PCGKEnSimncuHFstNFGIx2GhqCX55xFm2md9q2OBbYHzs7M7YDHgLZPEo2IIyNiVkTM8teBJEla3vUyOZsHTK6NTwLu66LuvMz8RTV+OSVZW0JmnpuZ0zJz2sSJE4ccrCRJUhP0Mjm7Cdg0IjaKiJWBg4ArO6mYmQ8AcyNis2rS7tTOVZMkSVpR9eycs8xcEBFHAz8ExgDnZ+btEXFUNf+ciFgfmAWsBTwTEe8DpmbmI8B7gIuqxO5eoP9H2UuSJK0genlBAJk5E5jZMu2c2usHKIc729WdDSzxvClJkqQVmU8IkCRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqkLEjHYC0vHlo8qaLjU+Ye/cIRSJJWhHZcyZJktQgJmeSJEkNYnImSZLUICZnkiRJDWJyJkmS1CA9Tc4iYs+IuCsi7omIE9rM3zwiboiIJyLi/W3mj4mIWyPie72MU5IkqSl6lpxFxBjgLGAvYCrwpoiY2lLsIeAY4LR+FvNe4M5exShJktQ0vew52wG4JzPvzcwngYuBfesFMvPPmXkT8FRr5YiYBLwG+EoPY5QkSWqUXiZnGwJza+Pzqmmd+hzwAeCZgQpFxJERMSsiZs2fP7/rICVJkpqkl8lZtJmWHVWM2Af4c2bePFjZzDw3M6dl5rSJEyd2G6MkSVKj9DI5mwdMro1PAu7rsO7OwPSImEM5HPrKiPjG8IYnSZLUPL1Mzm4CNo2IjSJiZeAg4MpOKmbmiZk5KTOnVPWuzsy39C5USZKkZujZg88zc0FEHA38EBgDnJ+Zt0fEUdX8cyJifWAWsBbwTES8D5iamY/0Ki5JkqQm61lyBpCZM4GZLdPOqb1+gHK4c6BlXANc04PwJEmSGscnBEiSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg/Q0OYuIPSPiroi4JyJOaDN/84i4ISKeiIj316ZPjoifRsSdEXF7RLy3l3FKkiQ1xdheLTgixgBnAXsA84CbIuLKzLyjVuwh4BjgdS3VFwDHZeYtEbEmcHNE/LilriRJ0gqnlz1nOwD3ZOa9mfkkcDGwb71AZv45M28CnmqZfn9m3lK9fhS4E9iwh7FKkiQ1Qi+Tsw2BubXxeQwhwYqIKcB2wC/6mX9kRMyKiFnz588fSpySJEmN0cvkLNpMy64WELEG8C3gfZn5SLsymXluZk7LzGkTJ04cQpiSJEnN0cvkbB4wuTY+Cbiv08oRMY6SmF2Umd8e5tgkSZIaqZfJ2U3AphGxUUSsDBwEXNlJxYgI4Dzgzsz8TA9jlCRJapSeXa2ZmQsi4mjgh8AY4PzMvD0ijqrmnxMR6wOzgLWAZyLifcBUYGvgEOBXETG7WuSHMnNmr+KVJElqgp4lZwBVMjWzZdo5tdcPUA53trqe9uesSZIkrdB8QoAkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNYjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJkiQ1iMmZJElSg5icSZIkNUhHyVlEPD8iXlW9XjUi1uxtWJIkSaPT2MEKRMTbgSOBCcDGwCTgHGD33oYmNUMcd9Vi4w+OUBySpNGhk56zdwM7A48AZObdwLN7GZQkSdJo1Uly9kRmPtk3EhFjgexdSJIkSaNXJ8nZzyLiQ8CqEbEHcBnw3d6GJUmSNDp1kpydAMwHfgW8A5gJfKSXQUmSJI1Wg14QkJnPAF+uBkmSJPVQJ1dr/oolzzF7GJgFfCIzvXhNkiRpmAyanAH/BTwNfLMaP6j6+whwIfDa4Q9LkiRpdOokOds5M3eujf8qIn6emTtHxFt6FZgkSdJo1MkFAWtExEv7RiJiB2CNanRBT6KSJEkapTrpOXsbcH5ErAEE5XDm2yJideDfexmcJEnSaDNoz1lm3pSZWwHbAttm5taZ+cvMfCwzLx2obkTsGRF3RcQ9EXFCm/mbR8QNEfFERLy/m7qSJEkrok56zoiI1wAvAsZHBACZecogdcYAZwF7APOAmyLiysy8o1bsIeAY4HVDqCtJkrTCGbTnLCLOAQ4E3kM5rLk/8PwOlr0DcE9m3ls9/uliYN96gcz8c2beBDzVbV1JkqQVUScXBOyUmYcC/5eZHwNeBkzuoN6GwNza+LxqWieWpq4kSdJyq5Pk7B/V379HxHMpvVwbdVAv2kzr9IHpHdeNiCMjYlZEzJo/f36Hi5ckSWqmTpKz70XEOsCngVuAOZTDjIOZx+I9bJOA+zqMq+O6mXluZk7LzGkTJ07scPGSJEnN1MkFAZ/KzCeAb0XE94DxwOMd1LsJ2DQiNgL+SHmywMEdxrU0dSVJkpZbnSRnNwDbA1RJ2hMRcUvftP5k5oKIOBr4ITAGOD8zb4+Io6r550TE+pRndK4FPBMR7wOmZuYj7eoOaQ0lSZKWI/0mZ1XitCGwakRsx6LzwNYCVutk4Zk5E5jZMu2c2usHKIcsO6orSZK0ohuo5+z/AYdTkqfP1KY/CnyohzFJkiSNWv0mZ5n5VeCrEfGGzPzWMoxJkiRp1OrknLPvRcTBwJR6+cGeECBJkqTudZKc/SfwMHAz8ERvw5EkSRrdOknOJmXmnj2PRJIkSR3dhPa/I2KrnkciSZKkjnrOdgEOj4jfUQ5rBpCZuXVPI5MkSRqFOknO9up5FJIkSQI6OKyZmb+nPOfyldXrv3dST5IkSd0bNMmKiI8CHwROrCaNA77Ry6AkSZJGq056wPYDpgOPAWTmfcCavQxKkiRptOokOXsyMxNIgIhYvbchSZIkjV6dXBBwaUR8CVgnIt4OvBX4cm/D0oogjrtqsfE8ffcRikSSpOXHoMlZZp4WEXsAjwCbASdl5o97HpkkSdIoNGhyFhEbAdf1JWQRsWpETMnMOb0OTpIkabTp5Jyzy4BnauNPV9MkSZI0zDpJzsZm5pN9I9XrlXsXkiRJ0ujVSXI2PyKm941ExL7AX3oXkiRJ0ujVydWaRwEXRcQXqvF5wCG9C0mSJGn0GjA5i4gxwFGZuWNErAFEZj66bEKTJEkafQZMzjLz6Yh4cfX6b8smJEmSpNGrk8Oat0bElZQrNB/rm5iZ3+5ZVJIkSaNUJ8nZBOBB4JW1aQmYnEmSJA2zTp4QcMSyCESSJEkd3EojIl4YEVdFxK+r8a0j4iO9D02SJGn06eQ+Z18GTgSeAsjM24CDehmUJEnSaNVJcrZaZv6yZdqCXgQjSZI02nWSnP0lIjamXARARLwRuL+nUUmSJI1SnVyt+W7gXGDziPgj8DvgzT2NSpIkaZTq5GrNe4FXRcTqwEo+IUCSJKl3Orlac92IOBO4DrgmIs6IiHV7H5okSdLo08k5ZxcD84E3AG+sXl/Sy6AkSZJGq46eEJCZH6+NfyIiXtejeCRJkka1TnrOfhoRB0XEStVwAPD9XgcmSZI0GnWSnL0D+CbwRDVcDPxLRDwaEY/0MjhJkqTRppOrNddcFoFoxffQ5E0XG58w9+4RikSSpObqpOdMkiRJy4jJmSRJUoOYnEmSJDVIR8lZROwSEUdUrydGxEa9DUuSJGl06uQJAR8FPgicWE0aB3yjl0FJkiSNVp30nO0HTAceA8jM+4COruCMiD0j4q6IuCciTmgzPyLizGr+bRGxfW3esRFxe0T8OiJmRMT4zlZJkiRp+dVJcvZkZiaQANUD0AcVEWOAs4C9gKnAmyJiakuxvYBNq+FI4Oyq7obAMcC0zNwSGAMc1Em7kiRJy7NOkrNLI+JLwDoR8XbgJ8CXO6i3A3BPZt6bmU9Sbl67b0uZfYGvZXFj1cYG1byxwKoRMRZYDbivgzYlSZKWa53chPa0iNgDeATYDDgpM3/cwbI3BObWxucBL+2gzIaZOSsiTgP+APwD+FFm/qhdIxFxJKXXjec973kdhCVJktRcHV2tmZk/zszjM/P9HSZmANFuUZ2UiYhnUXrVNgKeC6weEW/pJ7ZzM3NaZk6bOHFih6FJkiQ1U789ZxHxKEsmU1ASqszMtQZZ9jxgcm18EksemuyvzKuA32Xm/CqWbwM74VWikiRpBddvz1lmrpmZa7UZ1uwgMQO4Cdg0IjaKiJUpJ/Rf2VLmSuDQ6qrNHYGHM/N+yuHMHSNitYgIYHfgziGtoSRJ0nJk0HPOAKpbXOxC6Um7PjNvHaxOZi6IiKOBH1Kutjw/M2+PiKOq+ecAM4G9gXuAvwNHVPN+ERGXA7cAC4BbgXO7XDdJkqTlzqDJWUScBOwPfLuadGFEXJaZnxisbmbOpCRg9Wnn1F4n8O5+6n4U+OhgbUiSJK1IOuk5exOwXWY+DhARp1J6tAZNziRJktSdTq7WnAPU786/CvDbnkQjSZI0ynXSc/YEcHtE/JhyztkewPURcSZAZh7Tw/gkSZJGlU6Ss+9UQ59rehOKRrs47qolpuXpu49AJJIkjZxOnhDw1epWGC+sJt2VmU/1NixJkqTRqZOrNXcDvko59yyAyRFxWGZe29PIJEmSRqFODmueDrw6M+8CiIgXAjOAF/cyMEmSpNGok6s1x/UlZgCZ+b/AuN6FJEmSNHp10nM2KyLOA75ejb8ZuLl3IUmSJI1enSRn76Tcxf8Yyjln1wJf7GVQkiRJo1UnV2s+ERFnA9+vH96UJEnS8Bv0nLOImA7MBn5QjW8bEVf2OC5JkqRRqZMLAj4K7AD8FSAzZwNTehaRJEnSKNZJcrYgMx/ueSSSJEnq6IKAX0fEwcCYiNiUcmHAf/c2LEmSpNGpk56z9wAvojwA/ZvAw8D7ehiTJEnSqNVvz1lEjAeOAjYBfgW8LDMXLKvAJEmSRqOBes6+CkyjJGZ7Aactk4gkSZJGsYHOOZuamVsBVE8I+OWyCUkauocmb7rY+IS5d49QJJIkDc1APWdP9b3wcKYkSdKyMVDP2TYR8Uj1OoBVq/EAMjPX6nl0Uo+19rSBvW2SpJHVb3KWmWOWZSCSJEnq7FYakiRJWkZMziRJkhqkkycESMRxVy0xLU/ffQQikSRpxWbPmSRJUoOYnEmSJDWIyZkkSVKDeM6Zlmut58I9OEJxSJI0XOw5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqkJ4mZxGxZ0TcFRH3RMQJbeZHRJxZzb8tIravzVsnIi6PiN9ExJ0R8bJexipJktQEPUvOImIMcBawFzAVeFNETG0pthewaTUcCZxdm3cG8IPM3BzYBrizV7FKkiQ1RS97znYA7snMezPzSeBiYN+WMvsCX8viRmCdiNggItYC/gk4DyAzn8zMv/YwVkmSpEboZXK2ITC3Nj6vmtZJmRcA84ELIuLWiPhKRKzerpGIODIiZkXErPnz5w9f9JIkSSOgl8lZtJmWHZYZC2wPnJ2Z2wGPAUucswaQmedm5rTMnDZx4sSliVeSJGnE9TI5mwdMro1PAu7rsMw8YF5m/qKafjklWZMkSVqh9TI5uwnYNCI2ioiVgYOAK1vKXAkcWl21uSPwcGben5kPAHMjYrOq3O7AHT2MVZIkqRHG9mrBmbkgIo4GfgiMAc7PzNsj4qhq/jnATGBv4B7g78ARtUW8B7ioSuzubZmnBnho8qaLjU+Ye/cIRSJJ0oqjZ8kZQGbOpCRg9Wnn1F4n8O5+6s4GpvUyPkmSpKbxCQGSJEkNYnImSZLUICZnkiRJDWJyJkmS1CAmZ5IkSQ1iciZJktQgJmeSJEkNYnImSZLUICZnkiRJDWJyJkmS1CAmZ5IkSQ1iciZJktQgJmeSJEkNYnImSZLUICZnkiRJDWJyJkmS1CBjRzoAaSAPTd50sfEJc+8eoUgkSVo2TM5GqTjuqsXG8/TdRygSSZJU52FNSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJapCxIx2AtCzFcVctNv7gCMUhSVJ/7DmTJElqkJ72nEXEnsAZwBjgK5l5asv8qObvDfwdODwzb6nNHwPMAv6Ymfv0MtbR7qHJmy4xbcLcu0cgEkmSRree9ZxVidVZwF7AVOBNETG1pdhewKbVcCRwdsv89wJ39ipGSZKkpunlYc0dgHsy897MfBK4GNi3pcy+wNeyuBFYJyI2AIiIScBrgK/0MEZJkqRG6WVytiEwtzY+r5rWaZnPAR8AnulRfJIkSY3Ty+Qs2kzLTspExD7AnzPz5kEbiTgyImZFxKz58+cPJU5JkqTG6GVyNg+YXBufBNzXYZmdgekRMYdyOPSVEfGNdo1k5rmZOS0zp02cOHG4YpckSRoRvUzObgI2jYiNImJl4CDgypYyVwKHRrEj8HBm3p+ZJ2bmpMycUtW7OjPf0sNYJUmSGqFnt9LIzAURcTTwQ8qtNM7PzNsj4qhq/jnATMptNO6h3ErjiF7FI0mStDzo6X3OMnMmJQGrTzun9jqBdw+yjGuAa3oQXmO13sU+T9+9q/Kd1JEkSc3kEwIkSZIaxORMkiSpQUzOJEmSGsTkTJIkqUFMziRJkhrE5EySJKlBTM4kSZIapKf3OZM0NA9N3nSx8Qlz7x6hSCRJy5o9Z5IkSQ1iciZJktQgJmeSJEkN4jlnI8DziSRJUn/sOZMkSWoQkzNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQG8Sa00jCL465abDxP332EIpEkLY/sOZMkSWoQkzNJkqQG8bCmpI60PhMWfC6sJPWCPWeSJEkNYs+ZpJ5p7W2zp02SBmfPmSRJUoPYc7aCssdidPF8sGbx8ydpadhzJkmS1CAmZ5IkSQ1iciZJktQgJmeSJEkNYnImSZLUIF6tKakxvOpUkuw5kyRJahSTM0mSpAYxOZMkSWoQzzmTeszzqCRJ3TA5k6RB+DgmScuSyZk0SplwSFIzec6ZJElSg/S05ywi9gTOAMYAX8nMU1vmRzV/b+DvwOGZeUtETAa+BqwPPAOcm5ln9DLWTsVxVy02nqfvPkKRaEXS+r56cITiqGtiTJI0GvSs5ywixgBnAXsBU4E3RcTUlmJ7AZtWw5HA2dX0BcBxmbkFsCPw7jZ1JUmSVji9PKy5A3BPZt6bmU8CFwP7tpTZF/haFjcC60TEBpl5f2beApCZjwJ3Ahv2MFZJkqRG6GVytiEwtzY+jyUTrEHLRMQUYDvgF+0aiYgjI2JWRMyaP3/+0sYsSZI0onqZnEWbadlNmYhYA/gW8L7MfKRdI5l5bmZOy8xpEydOHHKwkiRJTdDL5GweMLk2Pgm4r9MyETGOkphdlJnf7mGckiRJjdHLqzVvAjaNiI2APwIHAQe3lLkSODoiLgZeCjycmfdXV3GeB9yZmZ/pYYySlnPer03SiqZnyVlmLoiIo4EfUm6lcX5m3h4RR1XzzwFmUm6jcQ/lVhpHVNV3Bg4BfhURs6tpH8rMmb2KV5IkqQl6ep+zKpma2TLtnNrrBN7dpt71tD8fbVTy2YySJI0ePiFAkiSpQUzOJEmSGsQHn0ujQOujmGD5fBzTirIekjQQkzNJWg55laq04vKwpiRJUoPYcyZpVLHHSVLTmZxJUgOYNErq42FNSZKkBjE5kyRJahCTM0mSpAYxOZMkSWoQkzNJkqQG8WpNaTnUeqf8Jtwlfyh3718W69HEbbUstK53nr77CEUiqVsmZ5I0CrTeqgO8XYfUVB7WlCRJahCTM0mSpAYxOZMkSWoQzzmTJLXV7SOllsV5bZ47p9HAnjNJkqQGMTmTJElqEJMzSZKkBjE5kyRJahCTM0mSpAbxak1JqhnKY6iWRRuj9TFUw6Hbq061Ymv3+Wva483sOZMkSWoQe84kSVrGvF+bBmLPmSRJUoPYcyZJ0lLyvDYNJ3vOJEmSGsSeM0nSiGlij5Png2mk2XMmSZLUIPacLSV/YUmSloXloZexCTGtCOw5kyRJahB7ziRpKa0od+/vdj2WxXo3cdsuL0+RaNpd79U5e84kSZIaxJ4zSZJWQE08H8zztDtjz5kkSVKD2HMmSZKAZva2jUb2nEmSJDWIyZkkSVKD9DQ5i4g9I+KuiLgnIk5oMz8i4sxq/m0RsX2ndSVJklZEPTvnLCLGAGcBewDzgJsi4srMvKNWbC9g02p4KXA28NIO60qSpBXcaDwPrpc9ZzsA92TmvZn5JHAxsG9LmX2Br2VxI7BORGzQYV1JkqQVTi+v1twQmFsbn0fpHRuszIYd1gUgIo4EjqxG/xYRdy1FzF1bd9HL9YC/VEF1V6fb8itKG4OUXxZtrLv4qG10Ux58Hy5HbTT1fbjUMcGg75GljqmDNoayz3vdxlLENJQ6TW2jI/GZljaWnee3m9jL5Kzd1ssOy3RSt0zMPBc4t7vQhl9EzMrMab2sYxu20YQ2mhiTbYy+NpoYk200r41uLYs2OtHL5GweMLk2Pgm4r8MyK3dQV5IkaYXTy3PObgI2jYiNImJl4CDgypYyVwKHVldt7gg8nJn3d1hXkiRphdOznrPMXBARRwM/BMYA52fm7RFxVDX/HGAmsDdwD/B34IiB6vYq1mEylEOr3daxDdtoQhtNjMk2Rl8bTYzJNprXRrdG/DQpgMhseyqXJEmSRoBPCJAkSWoQkzNJkqQGMTmTJElqEJOzYRQRPd+e3bbR6/LLqk5T10OStGKK6MHdbjtt2wsCRlZERLoTVghD2Zfd1hmtbTQhpuqZvy8BnqDcKPvuzHx0pMrbhm30oo0mxjTUOt0YaPkj8T1tcraUIuJgYCvg5cB5lNt/3J+ZTw9Sb73M7OgREd220evyy6pOU9ejqrcDsDkwByAzrx2o/FDqjNY2mhhTVedzwCbAWsAsyq2IfpiZ3x+J8rZhG71oo4kxDbVON3q9/K7jMTkbuohYG7gDeAswAXgr8Cjwzcxse9PciFgH+BzwbGAccAbwy8z8c7vsvNs2el1+WdVp6npU9bYCvgHcVpV/pvp7aWbeOhx1RmsbTYypqvNc4MbMfF5ErAJsD2wHbAFcl5mXLsvytmEbvg8HrtONXi9/SDLTYYgD8Brguy3TDgFuBb5Alfy2zP8S8HnKI6reBfwXcDaw+XC00evyy6pOU9ejKnMG8MHq9YbAK4EPAacBk4ajzmhto4kxVeXWBq4AXlKbti7l6SVXAVssy/K2YRu+Dweu083Q6+UPKaZl3eCKNACrA18DDgOeVZs+AbgE2Kyl/KrARcD+tWlrAp8Afgu8bhja6Gn5ZVWnqetRzT8I+BawZm3apsAXKYn2SktbZ7S20cSYamXeDVwNvANYvTb9k8Dxy7q8bdiG78OB63Qz9Hr5XcezrBtcUQYWHRKeDnwTeCflePW61fQfAYe0qff/gAuBbYFVatP3Bf59adrodfmlqLNSl3F1W36ZrEc9Psr5aV8H9m6Z92Ngo6WtM1rbaGJMLfNfS+l5+xJwOKUH/CbgsJEobxu24ftw4DrdDL1efjeD55wNg4jYjZJtPw78FViP0uuyQ5uyawMfBCYB3wH+JzPvjYhdgS9k5jZL28ayKL+s6jRtPfrOC4yI9YHXUc5NeBr4AfBc4J2ZufXS1BmtbTQxplYRsTKwJeUikjdTzlf7TWa+byTKL+9t1M+zXZ7XY0Vro1cxLc3+HmqdbvR6+V3FYnI2fCJib8oVHqsCv8rMOwYoezjly+EPwBRKQnBGZl4yXG10Uj4ixmbmgqEuf7A6EbF6Zj7WaZ2ImJqZd0TEuMx8qlfrPRx1IuJZlBNGtwMOpZybcGlmzh6uOstrG/V/wh2WH5O1K2Sbst61ZG4MkJn5TMv0dYH/65s+QGwDlq+W/0zti2vQ5bfZZm3rtO6LTmMaynp0Wr6/mDppo9P1bpnX0f4brv3d6XrQ8H0+XOu9NPu7m/Xu1nDu7+FmctaliHghMBl4gLL9ft1hvZWqN+lKtTfAWsCLgccoh/JurKZvBGxESdzuB/4OjMlaEtWy7K0pv/rvA34NZLW8p/sp/1rg75l5VTUewMqZ+cQA8R8N3JyZN9SmDXZfqOOBh4CLMvPx/srVyv8LsFtmTq/FNdB6vJpyQvffgSuBxweKp6qzCbA+ML9a9p0dxNWaaIxtty/6mz7Issdm5oI2bYxpt94DtVF/bw1Up4P9NuT1GCiOAcq3XddOYxpsWw11/0W5KfF6wF8z88lq2jjKF+rT0fIDYoDY+z77S5SP0oP3YC7+Q2Sg8uOA5wFzazGNAWgXU0Q8KzP/r3pd77UYqI0dgbv66nXQxj8Dt2XmTS1xPj1AG6dS/l99vmXftI0rIiZSEulf9v0viYixJaT+90VV7+FO9t9w7e8Otm+j9nm3+7ua39U+73Z/V/OGtM87NZz7uydyGR9HXZ4HSiJwI+V8pPMpJxCfAEyp5o+jnMM0tlZnpfprFiXEY/ppYwPgWuAa4HvAwS3zx1F63MbWyt9IOcF5NrB1NX18Xzv1mCj3cJlHuY/LWcC21fS++ePbrMNzgN/0rWffZ53qpMnWNmp1fgc8vxpfHXgBsHGtzL61dp8D3A38Cvgu8Ly+dqq/K7esxwaUqyrPolzxum1V5jm1+BYuv5r2XOBnlJP9z6j+vqfW1iqt61GLfW3gxS3rv0r1ep0u3kNj2tWh9NatDWzXMn2l/tqgXEyyAbBLy/SV+2ljfWrnOQ4WUzXtecA/1dd7kDY2B/6V2gm1g5TfhvL+3a5lfQeKaV1gB+CNLdPb7o9q/60BbN/p/gO2Br4PnAv8EjiuZf6zgf9k0efsuZQvknH97Pf1WspPBS6jnH/6NWDLlu27WPlq2laU/wnnUf7/tL5PW2PagnJY5l397Pd2bWwP/Lxvf7Wp09rGS4CbgfX7Kf+cftbjj8BXqf0/GGBbbVfFdGm1/uM6aGNL4HLgU8B/A0e31JnY0kZX+3tF2efd7u+h7PNu9/dQ93k3w1D297IeRqTR5XWgfKH/e/V6M8oX+SeB/6jeYDsBB7bU+Tzl5MKJtWljah/IvYBtavMuBD5Uvd4buIvaVYOUm6XuVxs/D/hI9frD1Zvty5SkcStgl76Yam2+F/gIcCQlETqRkqisRfnSa12H84APVK83B06mXHb8Yco/nF3a1DkSuKB6/ZKq/EXVB+0oYDfgoFr586n+gVbb7NiW5e3K4le5fhE4sXp9bLXci6ttvQ/wT/XlV+W+wKJbKGxNSbLPA06qpu1Ub6NW79Iq9iuBW1jyJPKTWPKCg9dTkvn+bsfxURa/qOG8Kv7Lqn2zCosn9h9t08aMan/PBKa3aeOkljZ+TEmAX9NPTO3W44fAWwb4TLS28VPKeVt942u2lG9d7+9Qvjw/0EVM36/283XAAR1s229U+/4i4BfAKztc7/dSEtqdqnp31usCm9Re/7qa/8+UhDlq8/oSzhfWpv0AOI7yeTup733ZEsOmLePfA/4FeBHlM/5q4BgWT57rMW0B3EO5l9//UH7UrUT5nxPVsEmbNt5avd4M2BP4twHaOB84snq9I3B8ta0OaFe+Gr8CeB/l/8ed1H709NPGd4F3Va/PpJwLdDG1z2GbNn4AHE1Jyg8B/kb5QbprP210tb9XlH3e7f4eyj7vdn8PdZ93Mwxlfy/rYcQaXh4Hys1Kz26ZtjXlC/Lj1Xj9C3UHymHJc6oPyvEtddelJDF9v+A3pHy5Ta6V+TyLkpBJlKs9x9bGfw6sVo3/hvLF9HrgM8Bnqumt9wSbVrXz0ir+X1B+2ezfWr568z4EnF6NX0H5Z/EW4PTa9NY2JgPnVq/PoPwDmwzsXm2P1VmULG5cxdC3XrtT/rmc0rLMvn94Y4FTgH+rxm+n/NPbmXJi/1nUeimrMmOqOifVpn2GksR+E3hv6/6rxl9HSR7GUxKmQ4C5lH8eG1RlxrfUOYTy6/VrlKt/1m7zXqq/T94E/ITSS7ULJVHeuqX8mJbxA6o6a1FuBfIF4Ajg033vHxb/JTqm2m99vZM/qt4/b2NR72brehwA/Kw2fiAlMf8C1f3AWPLX7rW18ZMpX0g/ZFEPbf0K5TdU8zeh9IJ+vXVb0fLrH3gjcHX1eueqzumUL5kXtGljf8p7fbVqH36b8kV9CbBeP+v9LMqv5h1aph9G6dHeuWX6dpQEeX/K5+PSar/3Xfm7cd/r2vu7vl23AW6gutch5XPSuh1eRblbed/4PMqX5AcoN9NdrBexVu71lC/QPSi9HX8CftBP2V2A31N+CI1hURL88arufi3lV6Jc3HRKNT6L8h785+p9dmqbNl4H/KQ2/iHgK1Q/Xlny8zeJ0jO+djX+O8oP4mMoh8mObdPGc6s69c//Jyg9upfQci+7bvf3crDP39DJPu92f7fs877vuwH3ObBfN/t7qPu8m6G2v1/SMr3f/T0Sw4gHsDwNlPtf9b2Bd6hNX4vSK/G8lvI7ACdUr19evSH+u+9NT7mvSuuX8JbAGrXxF1PO2YLSc/O2lvJrVX/XpEriajH9gH5uC1B9mF5D+cK6l/LL5j+B/9em7PMp/3weAq6vTV+namNKS/molnsBpadpZl+c1fyfAa+ojY9lUaLTl7C9qIrn0H7i35xyGPe/gJ/Xpo8Brgde2qbO1pTDvxdSkthbq+lTKL9KlzjUTPky+UrfsmvTT6MkKe3qnEo5wfxQSmLwBUoiPK6a//qW8lcAe9TG/5XajwBaenqqaZf1bUNKT9uvKQnzaZT36Jpt6oyl/Fpck3LrkLmUJHLT1rK1Ni6o3kvHV8t9V9XGf1J7n1blJ1GSvhdQDhd/h/JF+RHKj5SpLeWvpfqFTjnM9BXgiNr8dv+43031I6faTr+k9Mx+ipKkT24pfxTw/tr4ftU6fAp4zwCf9bdS7nnWmri9i6qnuuVzsFNt/B2U+yV9nvJ5Xqy3lfLe/ue+fVL9PYtFPcffpdbDU02byqJDv3sBZ9XmHUrtR0dLve2Bq6rXmwF/pnzezwdWbSm7MSWJOanaj5e3bI9/bbP8jSk/vqZT/Ris7c8L27SxW209xlCSkourePq7v9xZlCT868BNLev2RdocVqS8b79YxXEocE01/Yu072395073dzV9zS73+eZU/7u72Oeb9XKfU/6vd7W/a/v8zE72OSUB7Gp/D3WfdzNU63dBp/t7JIYRD2B5Gyjd1x+gHIb6BOXQ4b6Uy23bla+f8zSOkp1fRelC/W2b8tFSfq3qzfwJ4EddxLk75XEU/c3fvHqT3wx8rpq2xPkALXV2AV7eaRtVmTdTutl/SUmO3jhIXMGi840Oovy6XKIbvFb+OZTHYf07pZdvV8oJpP2V35ZyuONYqnOQKL94b+qn/LOr7f++lumrU5KX1vPQVqrW89nVtAmUwwTXVB/8U6l+DVflx1F+fW/J4onpTdXrd1IdHm6ps1s1Pp7yj7KvvVWqf2jTWuLt6539JIsOzV9N+bHwJxY/xByURO4NlF/RZ1F+ta9fa/Miaudw1eoeRUnGPgzsXpv+H1S/9FmUvPf9SOlLWvejfC7e1rrc2nJeWr1nz6rifllt3jksecj5ZZQbPB9DSeJuofRIbEL5wfHslvIvoPyQ2p5yztKfqJ2vRJV01sanUHrwNmPxHyDPBt5PufDkqpby0yiHn+pfYntX+2Z/lnxqxRRKT8gmlB7AVamdJ0d5P19UG9+I8tSDjWufoyOq9e07HPWhNm1Mo7x331y9p7ZqWe9vtLSxe7Uen6T0xt5KSb5WpyQ7P2lp4/nVPngBi/8AnUD5gXIGMKGljZdV634S5bN9am3+O9psq746O1P+z95CSZheVc0/AfiPWvmtKT1Mu1I+N3+iOpzWbn9X07aqtu921JIEyv+iD7TZ51tSDp29uGU5r6H832q3z7eknJqxNeXzsiqLv79a9/nWlEOSW9X2+WGU/1FHte7zavk7V9vq9dX+flF/+7vWxt6UDoN/rfb5zcArKMnqYvuc8n9s56qt+g/bdSkdDYvt71obu1Xb9/hqv/zbQPu8m4GSUO5GSRQ/W+3vYwba3yM1jHgAy+NA+efzCuBjlK7cc/s+/B3WX5PyLL89Oyz/GUoPx24dll+Nchh1wOVXH8orWHSydtvzo/qpu3L14RysjZUov5Y+TOkx+xi1BK+Ddj4y2LatPuyfovQe/YQ2vX8D1F2Lcohgr9a4a693oHzx/Kr6YI+jfPneRe2xHi3/gFZuWcamlHPEnqI6x7C1fEv7X6D8Wr6RWu9qS52+JHbtlrq3Up2nyJKHiVajJIgnsSgBfBnVIcE2bTyPcuj9yE7aoPyqvoSSkH+VklBsQjlMvW3r8qvx+g+YPSi/4velzTk81euXUr7sjqDqBaF8ec1m0ZdTvfzulMPmZ7D4P+JZLH4KQd/FONdSEvKXUw4/3UJJrj/fsh4bUM57+xnlsOqb27T9WEv566vlf7evfDXvOdX76w9UJ4q31Olr4y0t2269ar23aVmHayg91gdT3uO3AHf08xmox/UtqvMXWZQ0P4vy/6S1jZ9REtg9KInXjGrff65qv916XEPpgT24vq1Y1Ks9sWXbXlfti77E/nzK5+6E6u/WLW301bmI8lmtH+KeUG3fF9fK30jp4b2eRRea3FnFeAalN3abljZ+QTk8PptFF2DV7yj/aMs+/2XVxq21bTiOcj7c/1AOLb5ogDb6lrVKbT1urbVdvyjstmpbTqxe39rP/v5FFdMvKMnyWBYdhVm7HmtLG9+u/m4AvLBaxvmUUwt+yqKLHPrW+1vVOi62nVh0wcZ6beK6otoffYd8Z1Dev8dTTt3ZunWdOhlYdDHcrZT/gRMpvf19F6Gd1bq/R3IY8QCW94GSfHTVxUr5ZfrtLspvDnyyw7JjqvLHdFh+7erv2C7iCUrC9e5ut1U3bXS57LUoXxBTuqw3hjbnblC+iM+luvqzmnZM9c/hYsovv8/1U6d+8cfKLEqiTgVmtJT/MrXeGxZ9Ib6VkpD/W5s2vtzSRj2Z+gzwxZbyrRekHAY8SJuLCFraqK97/Vf7af20UV+PN1CSgzMpPRInD7IO41j0Rf1Rljx831dn/dq0HYE5lCThMhY/FNyufP38uM+yZM/AhSy6GGc6JcFcpxp/OeWLe+N+yre7eGcr4PAuyn+DJc9p7bcO5XP4YWpPFmlT/n8pPcobsOiq5NaEvV5nH8oPzvoJ3e+lOseoTfnXVttpYm2dn19/73Sy7tX0TQZo43+BDavxt1F6JXcaoI3p1XrUT8jfh8UvVqlfTHUSi46GHEVJBF5MyzOPGfgCrBdTDj8f2UH5EymJ/1epTpvosI0dq7+nD1D+y5Se6mOpLv5h8fd+vfy/Uk4n+FK13GmUXrn/GCCmvm31H5TTDN5GSXImd7gOfYc5W5+52RrXlymf03dU9Y+lww6KNv/TWi+Geyfl/9Nbq3U/gPLebfuM65EYRjyA0ThQEoK1uqzTcWJTle8quXFYbNu1XsjReghoN8qvyzED1Gm9+ONZlMN2q3VYfjtKIrhmF21sTTl0tWY/5T9QK9vfA75b65zQMn8zyhdKf22c2FL+RdX7faUO16Hvn+jYAWL6YEud4yiHy1bup3zr/ns+5fBFvbej3cU4X2DRlbzrAq8epHz94p3JlCvrVuqw/LMph6XGd9HGc6o6Kw9S/sPV6+dVMUUXbWzQQRtfoDo/qYpp4XbqsI0pHW7bvjZWG0Ibz6VcTNX3Q6m/i6kOoPQCLfFjeIA6fRdgfbbL8qdW01fuos5/VNM7WY/PUG5s3k1Mn6ovv8M2WhO5wdo4s4tt27c/Ptzu/1W3A0teDHcz5WK4fYZj+cM5jHgADg5NG2h/IccNLLqa9ViWvOR9sIs/3svihy76K/+6atphLHk10WBtvI3F7+U1WBv/wpK/Xgdr4+3U7sU2wLbqOwz1/vq26mD5H+xw297AokOa72LxHqhOLsTZqt5GNX2gi3H+kyUP7Q5W/u1dlF94S4Mu6nyX6sKCQcp/s3p9RWtMw9jGRbU2juyyjSXqDFL+yiG00c3FVGtTLjRa4lYKA9RZq6rzgi7a+BFtevoHqfNDlnxW7EDlZ9JyLvEg67DE8oeyrQZpY2aX23YdyoVnA54T3enAkhfDHUv5vO41HMsfrmHEA3BwaOJA/xdy/Aa4p8s6/V38MVAbvxtCXPd2UP5wyoUAdwxxPTppo15+iTYGKD+UmNruj273RVWuq4txel3eNnrbRn8DHVzotLR1mthGE2Maap1Bltf1xXAjMYx4AA4Oy8tAlxdyDKXOaG2jiTFVdbq9GKen5W2j5210dDHV0tRpYhtNjGmodTpc7pAvhltWw1gkdWo68OPM/EEP64zWNpoYE5QTkf+Rmdc0pLxt9KiN6nmSzwPO6/Q90m2dJrbRxJiGWqdTmfntiLgqM58cyrOElwUffC51qPpnsXpmPtKrOqO1jSbGVKs36IPcl2V52+h5GwsfFt6rOk1so4kxDbXOisDkTJIkqUFWGukAJEmStIjJmSRJUoOYnEmSJDWIyZkkSVKDmJxJ0lKorgKVpGFjciZp1IiIj0fEe2vjn4yIYyLi+Ii4KSJui4iP1eZfERE3R8TtEXFkbfrfIuKUiPgF8LKIODUi7qjqn7aMV0vSCsZbaUgaNSJiCvDtzNw+IlYC7gY+RHlEzDuAoDyH8VOZeW1ETMjMhyJiVeAm4OWZ+WBEJHBgZl4aERMoz/rcPDMzItbJzL+OwOpJWkH4hABJo0ZmzomIByNiO+A5wK3AS4BXV68B1gA2Ba4FjomI/arpk6vpDwJPA9+qpj8CPA58JSK+T3mIuSQNmcmZpNHmK5QHwK8PnE/pNfv3zPxSvVBE7Aa8CnhZZv49Iq4BxlezH8/MpwEyc0FE7FAt5yDgaOCVPV8LSSsskzNJo813gFOAccDBwALg4xFxUWb+LSI2BJ4C1gb+r0rMNgd2bLewiFgDWC0zZ0bEjcA9y2QtJK2wTM4kjSrVw45/Cvy16v36UURsAdwQEQB/A94C/AA4KiJuA+4CbuxnkWsC/xkR4ynnrB3b63WQtGLzggBJo0p1IcAtwP6ZefdIxyNJrbyVhqRRIyKmUg47XmViJqmp7DmTJElqEHvOJEmSGsTkTJIkqUFMziRJkhrE5EySJKlBTM4kSZIaxORMkiSpQf4/nZnEgkG/PNgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "quit_level=quit_df.TotalWorkingYears.value_counts().sort_index()\n",
+    "not_quit_level=not_quit_df.TotalWorkingYears.value_counts().sort_index()\n",
+    "newdf=pd.DataFrame({ \"quit\":quit_level, \"not_quit\":not_quit_level})\n",
+    "#每个元素除以总数得到占比\n",
+    "newdf[\"quit\"]/=newdf.quit.sum()\n",
+    "newdf[\"not_quit\"]/=newdf.not_quit.sum()\n",
+    "#填充\n",
+    "newdf=newdf.fillna(0)\n",
+    "#index代表索引\n",
+    "index=newdf.index\n",
+    "plt.figure(figsize=(10,8))\n",
+    "#柱状图宽度\n",
+    "bar_width=0.35\n",
+    "#分别画离职和非离职的图\n",
+    "rects1=plt.bar(index,newdf.quit.values,bar_width,color='#0072BC',label=\"Attrition\")\n",
+    "rects2=plt.bar(index+bar_width,newdf.not_quit,bar_width,color='#ED1C24',label=\"Non-Attrition\")\n",
+    "#横坐标和纵坐标的含义\n",
+    "plt.xlabel('years')\n",
+    "plt.ylabel(\"People percentage\")\n",
+    "plt.xticks(index+bar_width,index,rotation=60)\n",
+    "plt.title(\"Attrition and Non-Attrition in TotalWorkingYears\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2545cc9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    2019\n",
+       "2    1410\n",
+       "0     981\n",
+       "Name: MaritalStatus, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['MaritalStatus'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2f99b3d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dS7Bq1S5mg4h0aibBymvNGx/Ibyh2HzAq9FTBstYPOulMMVd9E5FyF+yj8yCwtekosgmN7MTDBU46k/h9qFR2uuLaFQQbLh5gOopIJ1YAh+JmF3Rw/EbgMyHm2dgI4E4nnakxmEFEkiJ4bb4VrYgaVd0pOxrZMa8C+LuTzow2HaSUVHa69gfgKNMhRDqRA47Dzb5W6KCTzlwAfDPcSAXtAVxuOoSIJMKlBKs+SjRpgYL4GEywYEFilwFX2emMa58PnGo6hkgXfoGbva/QASedORq4IOQ8nTnVSWdKueS1iCSda38f+KnpGNIpTWOLl70ITiAkkspOR1z7RMJZnlekLybiZn9b6ICTzuwO3ER4K6911/VOOqNVk0Sk51z7S8CfTMeQLmkaW/ycnj9BmjgqO4W49h7ANaZjiHThGaDgTshOOjOKYEGC+lATdU8jcJeTzvQzHUREYsS1twfuBKpMR5EuaRpbPN3gpDNjTYcoNpWdjQU7MN9FsOmSSFTNAL6Om23Z+ICTzjQQFJ2RYYfqgU8C15kOISIxEbw2ZwDbdBTpFk1ji6f+JHDkVGVnfa5dCdwOJHpVCom95QQrry3c+ICTzljARGC30FP13LFOOhOFhRNEJMrWbf+wpeEk0n3aZye+DnHSmSNNhygmlZ0NjUdLTEu05YBjcbP/6+D4hUCcnqT+nN/sVESkIxOAvU2HkB7pdGQnf2JuUEhZpOf+4KQziRlFVdlZy7WPAs4yHUOkCz/HzT5Y6EB+lbPzQ87TV5sBfzUdQkQiyrV/BBxrOob0WFfT2Aai96BRNgK4xHSIYtEPGoBr70Bw5kgkyibgZn9f6ICTzuxBfH+GD3bSmR+YDiEiEePa2wEFn/Mk0lbgZlu7uI+u14m+U5x0Zk/TIYpBZce1+wF3A1oZSqLsSeCHhQ7kdz6+D6gLNVFxXeakM1ubDiEiEeHa1cCtRHNFSemcFidIBgv4SxI2G1XZgauA7UyHEOnEe8A3Olh5rRF4ABgeeqriaiS+I1MiUnwXEI+FVmRTWpwgOZqAn5kO0VflXXZc+2vAiaZjmLCmzWeP61ew87Ur2PHPK7jgiTUfH3MnrWHU5cvZ5doV7HLtCh56Z9PR6LcWtbPbX4LPf25WGwBtOZ8v/W0lq1r90L6PMrCMYOW1RRsfyF/geTOwS9ihSuTzTjrzHdMhRMQw194HSJuOIb2mkZ1k+aWTzmxlOkRflO/GXK49DLjedAxTaivh8VQj/WosWtt9PnfjSg7eto09Rwc/EmfuWcNZe9d2+PnXvdzK+C/W4Qy0SD/WzF1jqrjmxVaO36mahmorrG8j6dqBY3Czb3Rw/DfA10PME4ZLnXTm/hnjxy01HUREDHDt/gQncSpNR5FeU9lJlnrgWuDLpoP0VjmP7FwPDDEdwhTLsuhXE5SS1hy0tgeTM7urugJWt/msag0+XrrG54G3W/nOzrGf2hkl/4ebfbjQASedOQ44J+Q8YRgKXGw6hIgYcxXaTyfuNI0teQ7Mr/gaS+VZdlz7OOBQ0zFMa8/57HLtCoZeupwDt6ris6PXDfRdPaWFna5ZwYn3rWbJ6k2npZ26Rw2XP9fCKZk1/GLfWn41uZlz963FsjSqUyTX42avKHQgvzrKDSHnCdMPnHRmd9MhRCRkrn0EcILpGNJnGtlJpoucdCaWM8LKr+y49nCCM0dlr7LCYuop/Zj90/5MmdvO/xa0A/DD3Wt49yf9mHpKIyP6Wfzs32s2+dyxdgWTvtvIcyc10lBtMXd5jk8OruD4e1bzzTtX8fZH7WF/O0kyCTi10AEnnRlLsJN4x3MM468CuMZJZ8rv+UmkXLn2COAvpmNIUXSn7GhkJ362Bo43HaI3yvHNxDXol2wDA+ss9t+iikemBwsNDOtXQWWFRYVl8b3dapgyp/Picu7ja7jogFr+8EIL326q5sL9a7lwcnMY0ZNoOsHKa5usCuGkM/0IVl4bFnqq8O0OnFysB7Ms6yDLst6yLGu6ZVm68Fkkeiags/1J0Z1pbPp/HU/nx3F0p7zKTjBEfrjpGFGwcGWOpWuC6WmrW33+834bnxwc/Dh8uDz38f3umdbKp4Z2/GMyeUYbo/pXsO3mlaxqhQoLKi1Y1dV2YlJIlmDltU1eKPKjHLcAO4WeyhzXSWca+voglmVVAn8CDgZ2AL5lWdYOfX1cESkS1z4GOMh0DCkaTWNLri2B75oO0VOxa2e95tp1wGWmY0TFhyt8Uveuoj0HOR+O3rGaQz4RLC5w9n+amTqvHQtwBlZw3SGF96r0fZ9fP9XMP48M3o9+f7dqvn33atpycM24OO9vaUQ78E3c7JsdHL8E+FqIeaJgBHAGfV+wYA9guu/77wFYlvUPgv+WHa1yJyJhCV6bf2s6hhSVFihItvOcdGbijPHjYnNau3zKDvwf4JgOERU7Davk1R/0K3js5q93b8Nqy7J49PjGj/++/ZBKXungMaVLP8XN/qvQASedSQFnh5wnKs520plrZ4wf150Xz46MAmat9/fZwGf7FktEiuQsYKzpEFJUGtlJti0IFhKJzTV25TGNzbVHow3KJLquxc3+odABJ53Zhxg9oZSADfyij49RaIlA7XwrYlqwKIFem5On07KTv+ZjQEhZeq1t2ULm3XYOc64/hbk3/IhlL923yX2yL9zNB789hPZV2U2OtX40mw9vOp25E35M85xpAPi5dub/41xyrZsu/BQz5zrpTI3pEN1VHmUHfgf0ee6/SAk8BpxW6ICTzmwB3APE5gmlRH6cX4Wut2YDY9b7+2hgbt8iiUgRXAw0dnkviRMfWNLFfeIxha2ikkEHnMSo713L8ON/z/JXMrQsmvnx4bZlC1kz41UqBxTesnH51IcZuN93GXL4OSybck/wb68+ROOOX6CiOvZT/ccCJ5kO0V3JLzuu/TkgthshSaK9AxyFm23b+ICTzvQnWHmtbDe+XU8t4Pbh818EtrUsa0vLsmqAY4D7ixFMRHrJtXcFUqZjSNEtxc3murhPLKawVfXbjNrh2wBQUdtA9eZjaF++btBqyWPXM+iAE+hoS3arsgq/rQW/rRkqKsmtWcHq6VNo/NQXwogfhl846UwstsFIdtlx7Qqg4PQgEcOWAofgZjc5A5Zfee3vQFPYoSLseCed6dWu6r7vtwE/Bv4FTAP+6fv+68UMJyI9diUdvUuUOEvkHjtt2fm0zH+P2pHbAbDqnReo7L85NUO36vBz+u86jmUv3sNH//oT9l5Hs/SZ27D3OjpJm6+PpohbRJRSsstOMMT2adMhRDbSRjCi83YHx38HHBJinjioog+LNPi+/5Dv+5/wfX9r3/d/U8RcItJTrn0UsK/pGFISidtjJ9eymoX3XMxmX/weFbUN5FrXkH3udgbue1ynn1c1YCjDjx3PiOMvw6qupX3FYqo3H82iBy9j4X2/pXXxnJC+g5I6Jw777iS37Lh2PfAr0zFECjgDN/ufQgecdOZE4Gch54mLE5x0ZqTpECLSB65dS3BCR5IpUSux+e1tLLznYhp32J+G7fYGoG3pPNqy85k74TRmX3Mi7csX8eFNZ9C+ouNLlZY+eTMD9z2OZS8/QOMO+zPwc8ey9Jm/h/VtlNIo4FDTIboS+TbWB98HhpsOIbKRP+Nm/1TogJPOfB64JuQ8cVILnA783HQQEem1n6JtIJIsMXvs+L7PRw9fRfXmYxiwx9c//veaIQ5jTrv147/PvuZERqSuoLLBLvg4a2Z6VPbbjOrNRuG3NoNlgVURfJwM3yNYTCmykjmyE5w5Ktd9SSS6HiV4s76J/PUod6GV17ryg/ziDSISN669OXCO6RhSUokZ2Wme8wYrX3+CNTNfY+6NpzH3xtNY/e6LPXoM3/fJPns79j7BOln9dz6IpZMnsvDeSxiwxxGliG3CV5x0ZkzXdzMnqSM7JwOa7iJR8hZwdAcrrw0gWHltcOip4scmGLW9zHQQEemxHwE6WZFsiVmgoG70jmzx8we7vN/oH07o8JhlWQw75tcf/7168BhGfPeqouSLkArgROBC00E6kryRHdeuQdNcJFoWE6y8tnTjA046Uwn8A9gx7FAx9pP8inUiEhfBjIsfm44hJZe4BQqkW06M8utyZIP1wQlsuIGgiEltwJG42ekdHP89cHCIeZJgLPpvJhI33wGGmg4hJZeYaWzSI2OBL5sO0ZFklR3XrkbzgSVafoybfaLQASed+R5wRrhxEuP7pgOISDe5tkWwMIEkX2KmsUmPfc90gI4kq+wEZ462MB1CJO+PuNnrCh1w0pn9gYKrskm3jHPSmdGmQ4hItxwCfNJ0CAmFprGVr0OddGaY6RCFJKfsBGeOtAKbRMW/gDMLHXDSma0JVl6rDjVRslQSbBosItGnvcPKh6axla9qIGU6RCHJKTvwBeATpkOIANOAb+Jm2zc+4KQzNsHKaxrG77uT8ws8iEhUufbuwH6mY0hoOh3ZcdKZOqA+pCwSvpNNBygkSWXnh6YDiBCc1ToUN5vd+ED+jfntwPahp0qm0cBXTYcQkU6dZTqAhKat0GvfRjSqk2zbOulM5E5uJKPsuPYI4GumY0jZawW+gZt9t4PjVwBfCTFPOTjOdAAR6YBrbwEcaTqGhKY71+toVkPyfcN0gI0lo+wEw2ZJ3SBV4uNHuNnJhQ446cwpwGkh5ykHhzjpTKPpECJS0BkE19dJedDiBAIRnHER/7Lj2pVEeLk7KRtX4mZvKHTASWe+CPwx5DzlogE41HQIEdmIa/cnovP3pWS0OIEAbO2kM5G6hj7+ZQfGoU1ExayH6GBeupPObAvcgUYeS+mbpgOIyCYOB/qZDiGh0h47slakRneSUHZOMR1AytrrwLc6WHltIMHKa4PCDlVmDnbSmQGmQ4jIBo4xHUBCp2lsspbKTtEEFz/qgm8xZRHBymvLNj7gpDNVBCM624WeqvzUogVKRKLDtTcDDjQdQ0KnaWyy1uejdD1tvMtOMH0l7t+DxFMLcARu9v0Ojl8FfCnEPOXuaNMBRORj30CbJpcjrcYma9UCXzQdYq24FwUtaSmmnIKbfarQASedORX4Uch5yt0XnHSm1nQIEQE0ha1caWRH1heZqWzxLTvBFLbPmI4hZeky3OyNhQ446cyBwJVhhln00JXM+uO3mfvXwv0q+8LdfPDbQ2hfteleb60fzebDm05n7oQf0zxnGgB+rp35/ziXXOuakuYusgbg86ZDiJQ91x4O7G86hhihBQpkfQebDrBWfMuORnXEjAeBswsdcNKZ7YB/EvLKa/2avsTQoy4seKxt2ULWzHiVygFDCh5fPvVhBu73XYYcfg7LptwT/NurD9G44xeoqK4rWeYS0fV7IuYdRbzfW0jvaYECWd9YJ535lOkQEO8nJJUdCZsHHIubzW18wElnNiMoQgPDDlU35lNU1vcveGzJY9cz6IATAKvgcauyCr+tBb+tGSoqya1ZwerpU2j81BdKmLhkDjIdQEQ0ha2MaRqbbCwSU9niWXZcezTwWdMxpKwsIFh5bfnGB/Irr90JbBN6qk6seucFKvtvTs3QrTq8T/9dx7HsxXv46F9/wt7raJY+cxv2XkdjWYXLUcTt6KQzo02HEClbrj0W2Mt0DDFGCxTIxr5sOgDEd6PDI+noVLVI8TUTrLz2QQfHrwYOCDFPl3Kta8g+dzvDvnlRp/erGjCU4ceOB6B1yVzaVyymevPRLHrwMvz2NgbuexzVm40KI3KxHATcYDqESJn6JnptLmedjuw46Ux/tEpfudnNdACI68iOprBJuL6Pm32m0AEnnfkJ8IOQ83Spbek82rLzmTvhNGZfcyLtyxfx4U1n0L5iSYefs/TJmxm473Ese/kBGnfYn4GfO5alz/w9xNRFob09RMzRFLbytQY3u6qL+2gKW/kZ6KQzW5oOEb+RHdceDOxtOoaUjd/hZv9W6ICTznwFuDzkPN1SM8RhzGm3fvz32decyIjUFVQ22AXvv2amR2W/zajebBR+azNYFlgVwcfxoucGERNcexSwq+kYYoymsElHdgE62pMwFHEc2dkfDZNLOO4Dzil0wElntgduBypDTVTAwvt/x7ybz6J18Rxm/ynF8v/+u0ef7/s+2Wdvx97nWwD03/kglk6eyMJ7L2HAHkeUInIpjdZ1OyJG7Gc6gBilxQmkI582HSB+IzsRuzZCEuu/wHEdrLy2OfAAUHiYJGRDDiu4EvYGRv9wQofHLMti2DG//vjv1YPHMOK7VxUlmyF7AXeYDiFSZvY1HUCM0h470hHjZSeOIzsqO1Jq84HDcLMrNj7gpDPVwF3A1qGnku7SalAi4dOmvuVNe+xIR1R2esS1hwHbm44hidYMHI6bndnB8T+j6RpRp7IjEqbgWlq9Npc3TWOTjoxy0pnBJgPEq+wE1+uIlNJJuNnnCx1w0pkzgZNDziM9t6uTztSaDiFSRvZF19KWOy1QIJ0xOroTt7KjKWxSShfjZm8tdMBJZ74KXBpyHumdGmBn0yFEyoimsIlGdqQzKjs9oLIjpXIPcF6hA046syNwGxFYeU26bUfTAUTKiMqOqOxIZ1R2usW1RwKfMB1DEulV4HjcrL/xgfw80weAAaGnkr5Q2REJg2sPQCOpomls0rldTH7x+JQd2NN0AEmkeQQrr63c+ICTztQAdwPGd/+VHtvBdACRMrEPGvUWjexI5z5h8lraOJUdnTmSYlsDfA03O7uD49eivSPiSiM7IuHQFDYB7bMjnasARpj84nGxk+kAkjgn4GanFDrgpDNnASeEnEeKZ4yTzvQzHUKkDOiEkEAX09icdKYCGBRSFomm4aa+cJzKjkZ2pJguws3+o9ABJ505BPhtyHmkuCy074dIQZZljbEs6wnLsqZZlvW6ZVmn9+qBXLsC2LW46SSmuhrZGUi83nNK8ansdMq1+wOO6RiSGHcCFxQ64KQzTcDficvvhnTmk6YDiERUG/Az3/e3J7ge9lTLsnpznZsD1BczmMTSCtxsaxf30RQ2Udnpwk5owzIpjpeBVAcrrw0lWHmtf+ippBTGmg4gEkW+73/o+/4r+Y+XA9OAUb14KJ1QENDiBNI9umanC7peR4phLsGCBKs2PpBfJeRuYIvQU0mpjDYdQCTqLMtyCPbAeKEXn66pogJanEC6RyM7XdD1OtJXqwmKzpwOjv+FYAlVSQ6VHZFOWJbVD7gLOMP3/WW9eAiVHYHu7bGjkR1R2emCRnakL3yCqWsvFTropDM/B74TbiQJgcqOSAcsy6omKDq3+r5/dy8fRmVHQNPYpHtUdrqwjekAEmsX4mbvKHTASWe+Blwcch4Jh8qOSAGWZVnAX4Fpvu9f3oeH+kSRIkm8aRqbdIeu2emQa9cCQ0zHkNi6HfhVoQNOOrMzcAtx+D2Q3hhscsdmkQjbBzge+IJlWVPzt6/26BFcux8wuBThJHY0jU26Y5ipLxyHN3k6Oyu99SLBxqGFVl4bBtwPaOPJZBtpOoBI1Pi+/7Tv+5bv+zv5vr9L/vZQDx9my5KEkzjSNDbpjhonnTEywqeyI0k1h2BBgtUbH8if7b8XLU1cDgaaDiCSUI7pAN3RnvP59HUrOOTv6xbhnDqvnT1vWMku165g97+sYMqc9k0+761F7ez2lxXsfO0KnpvVBkBbzudLf1vJqtZNzp+Vu+6M7Ggam4Ch0R2VHUmiVcBhuNkPOzj+V4KN9CT5BpgOIJJQsRjZueqFFrYfvOFbnbMfXcMF+9Uw9ZR+/OqAWs5+dM0mn3fdy62M/2Iddx5Vz++fawHgmhdbOX6nahqqte3fRjSyI91lZBNilR1JGh/4Dm72lUIHnXTmXODb4UYSg1R2REoj8mVn9rIcmXfaOHnXmg3+3bJgWXPwcXYNjOy/aXmproDVbT6rWoOPl67xeeDtVr6zc3UY0eNGCxRId1WVzRftoTGmA0is/BI3e1ehA046cwRwUch5xCyVHZHSGGU6QFfOeGQNv/tSHctbNpx2duVX6vjKLas469E15Hx49sTGTT731D1q+M49q2luh+sOqeNXk5s5d99agoXsZCNaoEC6y0jv0MiOJMnfcbO/LnTASWc+DfwN0CtVeVHZESmNSP9uPfh2K0MbLXYbWbnJsWteauWKr9Qx68z+XPGVOk66f5NLOxlrVzDpu408d1IjDdUWc5fn+OTgCo6/ZzXfvHMVb3+06XU+ZazTkR0nnaki4j8vEhqVnQ5E/uyRRMILwEmFDjjpzHCCldc2PX0nSacXWJHSiPRKls/MbOf+t9pwrlzOMXeu5vH32zju7qDUTPxvC0dsH7znOmqHqoILFKzv3MfXcNEBtfzhhRa+3VTNhfvXcuHk5pJ/DzGRA5Z0cR9NYZO1NI2tA9pjR7oyCzgcN7vJVaZOOlMH3IdGCMtVf9MBRBIq0r9bl3ypjku+VAfApBlt/P7ZFm45Irg2emT/CiZ/0M7+ThWPv9/Otpt3fN538ow2RvWvYNvNK1nV2kqFBZUWrGoN5duIgyxuNtfFfRqBaWGEkcjbdDWQEMSh7ET6CVWMW0mw8tq8Do7fCOwRYh6Jljg8x4nEUaRHdjpz/aF1nP7IGtpyUFcFfzmk8AJRvu/z66ea+eeRDQB8f7dqvn33atpycM24ujAjR1mXixPMGD/ufWCHELKIFBSHNwIqO9IRHzgONzu10EEnnfklcEyoiSRqNp2wLyLFEJuys79Txf7Ourc7nxtbxcvf7zq+ZVk8evy62c/bD6nklR/E5tsOS3cWJxAxKtrX7Lh2HaB1HqUj5+Jm7y10wElnjgLcUNNIFKnsiJSGTkQKdG/ZaRGjIj2yc/mggQ0rKqzJ/XN+Rb9crqJfLlfZz89V9cv51f1yuerGXK62MefXNvi5uvqcX9fg+w0VUS9wUiw342Yv6eS4B+wWVhiJrAWmA4gkjmtXYGhzQIkclR2JvEiXnRsHDqgH9uvRJ/l+swWrLVhTCWsqfb+52qelBr+1xvfbanN+W73v5xpyufYG36cxl6Mx59PPz1n9g0JV1T/nVzbmctX9crnqfjm/pjGXq2vw/br64M+GSp0tNu054Hud3WHG+HFvhpRFRKTcaC6XrKVpbBJ5kS47QE3Xd9mIZdX6UOsTrIfYalnFX/rB91uBVRWwuhKaK32/uQpaany/pcb32+pyfnud77fX+7lcY87PNeZyVqPv0y+Xs4IRKr+yfy5X2S+XqwlGqPyaRj8Ypar3c/UNOb+hKvr/b0yZSbDymtb9FBExQ1PYZC2N7EjkRf0Ndc/LThgsqxqwc8GN1lLsqOz7beQLVUW+UFVDS7Xvt9b4fmtdvlTlR6n8fJny82UqmPIX3Kr75fyqfn6utjGXq23I+bUNvl9fn8vV10T1v2/HVgCH4mY1NUlExByN7MhaGtmRyFPZiSrLqgIG5IIbbZZF0YcygkK1ugLWVART/tZUrVeoav1ghKoh57evLVT5aX9W/1zOyo9OVfXz/arGXK66fy5X05jzaxvWTvvzc/W1PrVFSpsDjsXNvlakxxMRkd5R2ZG1NLIjkRf1sqOV2EopKFT9c8GtVIUqB6yyPi5UNFfhN1f7fkut77fV+n5bne+31+X8XKPv54IyFRSqfn7O6pfLVfbP5SoXV1a+dPWgga1MbDqo2BElVj7yUt6LpkOIlLkG0wEkGv400B58rV6XBRZ5Ke8l0yE6EvWyU74jO0lhWRVAPx/6tQPtFrTQq2l/nwVOLWo2iaNHgS+bDiFS5ozsgi7Rc93AAecDQ0znEOMeAQ42HaIjUV+muc10ABGJFD0niJi30nQAMW9mVdUc37JUdASCSw0iK+plJ2s6gIhEisqOiHkrTAcQ8yY31H9gOoNERrvpAJ2JetlZajqAiESKyo6IeSo7wuSG+lbTGSQyIv2zEPWyo5EdEVmfyo6IeZrGJrxRU7O56QwSGctNB+hMpMuOl/Ja0IWQIrJOpM8eiZSFYFNn/S6WsVZoXV5hbWM6h0TGMtMBOhPpspOn0R0RWWuV6QAiAmh0p6z9t672HSyrznQOiQyVnT5aajqAiETGh6YDiAig63bK2hMN9YtMZ5BIifTARBzKTqT/A4pIqOaaDiAigEZ2ytqz9XWVpjNIpGhkp49UdkRkLZUdkWjQyE4Z+6C6epTpDBIpKjt9tNR0ABGJDE1jE4kGlZ0ytbSiYkmrZTmmc0ikLDUdoDNxKDsa2RGRtTSyIxINkV5qVkrn6fq6d01nkMiJ9IlIlR0RiYscMN90CBEBYLbpAGLG440NGtWTjc0yHaAzcSg7S00HEJFIWOilPG0qKhIN000HEDOm1tb0M51BImW1l/I+Mh2iM3EoOzp7JCIQ8WFykTKjqUxlyAd/UWXl1qZzSKRE/n16HMrOG6YDiEgk6HodkejQyE4Zere6eoZvWYNM55BIifQUNohH2XnTdAARiQSVHZHoeBfwTYeQcD3RUK/nYdmYyk5feSlvGTDHdA4RMU7T2ESiws2uRicgys5TDXXtpjNI5KjsFMk00wFExDi9sRKJFk1lKzNv1dQMMZ1BIucd0wG6orIjInGhN1Yi0aLfyTLSbLFmlWVtYzqHRM7/TAfoisqOiMTFK6YDiMgGVHbKyEt1de9gWdWmc0ik5IjBe3SVHRGJg/e9lLfYdAgR2YDKThl5vKFez8Gysfe9lLfadIiuqOyISBy8ZDqAiGxCZaeMvFBfV2M6g0RO5KewQUzKjpfy5gNLTOcQEWNUdkSiR2WnjMyuqhpjOoNEzuumA3RHLMpOnkZ3RMrXy6YDiMhG3OwK4C3TMaT0FlRWLmi3rNGmc0jkqOwUmcqOSPlS2RGJpqdMB5DSe7K+7n3TGSSSYrFwUJzKzhumA4iIEe96KW+p6RAiUtDTpgNI6U1qrI/8RegSuo+IychunMqOnlBFypOu1xGJLo3slIHXamtt0xkkcp7zUp5vOkR3xKnsvAwsNR1CREKnsiMSVW72PWCu6RhSOjnILamo0GaisrFnTQfortiUHS/ltQOTTOcQkdDpeh2RaNPMiwSbVlPzLpbV33QOiRyVnRL5j+kAIhIqH5UdkajTVLYEe6Khfp7pDBI5bcAU0yG6K25l5zHTAUQkVO94KW+Z6RAi0imN7CTY0w11piNI9Ez1Ul5sFq2IVdnxUt6bwGzTOUQkNP82HUBEuvQakDUdQkpjenX1cNMZJHImmQ7QE7EqO3ka3REpH/ebDiAiXXCzOWI0f1+6b6VlrWi2rK1N55DIech0gJ5Q2RGRqMoSs7NHImVMU9kS6Pn6uulYVhzfK0rpLCdmv+9x/AHWIgUi5eFhL+W1mg4hIt0y2XQAKb7HG+qXms4gkfNY3F6bY1d2vJT3ITDNdA4RKTlNYROJj+eB+aZDSHG9VFdXbzqDRM7DpgP0VOzKTp5Gd0SSrZWYzQkWKWtuth24y3QMKa55VZWO6QwSOSo7IVHZEUm2yV7K0+pOIvFyu+kAUjyzqyrn5ixrmOkcEin/81LeLNMheiquZecJYI3pECJSMprCJhI/TwNzTYeQ4pjcUP+B6QwSOfeZDtAbsSw7XspbDjxoOoeIlEwsn1BFylqwBPWdpmNIcUxuqG8xnUEi5zbTAXojlmUn71bTAUSkJKZ6KW+m6RAi0iv/NB1AiuP1mprNTGeQSPG8lPe66RC9Eeey8xCwxHQIESk6TWETia9ngdmmQ0jftEHbsoqKbUznkEiJ5agOxLjseCmvBQ2XiySRprCJxJWb9YE7TMeQvnmttuYdLEvLTsv6/mE6QG/FtuzkaSqbSLJM91LeK6ZDiEifaFW2mHu8sWGh6QwSKc97Ke990yF6K+5l50lAq4WIJMf1pgOISB+52RfQa3OsPVtfF/f3h1JcsZ3CBjEvO17K84EbTecQkaJoQb/PIkmhhQpibEZ19SjTGSQymoG/mw7RF7EuO3kTgJzpECLSZ/d6KU9TJ0SSYaLpANI72Qor2wqO6RwSGXd4KW+R6RB9Efuyk9/J9RHTOUSkz64zHUBEisTNvg48ajqG9Nwz9fXTsSzLdA6JjGtMB+ir2JedvBtMBxCRPnkHeMJ0CBEpqstNB5Cee6KhfrnpDBIZ//VS3rOmQ/RVUsrOA8A80yFEpNeuy1+DJyJJ4WYfAWK5CWE5e6WuttF0BomM2I/qQELKjpfy2tDojkhcrUC/vyJJdaXpANIzCysrtzadQSJhOQnZ4iURZSfvKmCl6RAi0mMTvJSXNR1CREriFmCB6RDSPe9VV33gW9ZmpnNIJPzNS3krTIcohsSUnfxKEYkYbhMpIzngD6ZDiEiJuNk16LU5NiY11M82nUEioR24wnSIYklM2cm7FFhtOoSIdNsDXsp713QIESmpPwNrTIeQrk1uqG83nUEi4c4kvTYnqux4KW8BWr5WJE4Sc+ZIRDrgZheQkLn/SfdWTc1g0xkkEn5rOkAxVZkOUAK/A04B6kwHEZFOPeelvMmmQ4hIKC4HTjIdQjrWAs0rLWsb0zn6quWjFuZcP4e2bBtYMGj/QQz+ctDhVn+wmrkT5+K3+lAJI78zkoatGjb4/OYPm5l17SzIwcjUSBq2acBv95lx2Qy2OH0LKmoTNU5QyCNeynvVdIhiStz/MS/lfYhWdhKJg7NNBxCRkLjZN4B/mY4hHXulrvYdLKvGdI6+siothh8znG0v2Zatzt+KxY8tZs2cYBblvH/OY+jhQ9nmom0Y9vVhzLt9011LFk9azPCjhjPm1DEsenhR8G+PL2bg3gPLoegA/Mp0gGJL6v+18UCz6RAi0qH7vZT3tOkQIhKqS0wHkI491tiw2HSGYqgeWE29Uw9AZX0ltSNraVvSBoBlWeRW5wBoX91O9aDqTT7fqrTItebIteSwKi3aV7azbOoyBu4zMLTvwaDHvJT3nOkQxZbIsuOlvDnAjaZziEhB7UDadAgRCZmbnUywCbhE0PN1dYm7tKFlYQtrPlhD/dZB+Rl+7HDm3T6PN3/6JvP+MY9hRw7b5HM2++JmLHpkEXMnzmXIoUNYcP8Chh46FMuywo5vgms6QCkk7gd7PZcQzA/etLaLiEk3eilvmukQImLEWcBB6LU5cmZXV40xnaGY2te0M/PqmQw/djiV9ZVAMB1t+LeGY3/GJjsly5wJc9jy7C03+LyazWvY6pytAGie30zbkjZqR9Qy67pZ+O0+w44YRu3w2tC/nxDcm9QZF4kc2QHwUt5MYKLpHCKygVXABaZDiIghbvZttO9O5CyqrFjYZlmJKTt+m8+sq2cxcK+B2LvbH//70meWMmD3AQAM+MwAVr/X+W4l8++az9AjhvLRox8xcK+BDDt8GAvuTeQeuW0keMZFYstO3sUE/wNFJBqu9FLeXNMhRMSoC4ElpkPIOk/V179vOkOx+L7PnAlzqB1Ry+CDNlxJu3pgNSvfXAnAymkrqRnW8XoMK99cSfWgamqH15JryQXvmCsIPk6e672U95bpEKWS6LLjpbz3CTYzExHzPiJha/eLSC+42cXARaZjyDpPNNSvMp2hWFa9s4qlzy5lxbQVTD9/OtPPn87y/y4HYOQJI5n3j3lMP3868++cz6gTRhV8DN/3WfDAAoYeNhQIlq+ef8d8Zl49k8EHJ24rohUk9FqdtSzf901nKKmmiU39gWlA4Z9oEQnLmV7Ku9J0CBGJANeuBl4HtjUdRWC/saNeXVxZ+WnTOcSIX3opL9EnHxI9sgPgpbzlwOmmc4iUOY2yisg6brYV7bUVCTnILa6o2Np0DjFiDnCZ6RCllviyA+ClvLuAB03nEClj53kpr8V0CBGJEDd7LzDJcIqy91ZN9ftY1gDTOcSIM7yUl5gpjB0pi7KT92OClaBEJFwvAbeZDiEikfRTIJFXfMfFpIb6D01nECMyXsq703SIMJRN2fFS3gck/AIskQhqBk7wUl6yLw4Ukd5xs68CfzMdo5w9WV+v5+fyswo41XSIsJRN2cm7AnjNdAiRMnKBl/L+ZzqEiERammC1RjFgek31UNMZJHRufhCgLJRV2fFSXhvwAzRkLhKG54BLTYcQkYhzs/MJpppLyFZZ1so1lrWN6RwSqtcITv6XjbIqOwBeynse+IvpHCIJtwpIeSlPJxZEpGtu9h9AWVw/ECVT6uqmY1mVpnNIaHLAD/In/8tG2ZWdvDQwz3QIkQQ7x0t575gOISKx8iNgoekQ5eTxxvolpjNIqH6fP+lfVsqy7HgpLwucaTqHSEI9AfzRdAgRiRk3u5Cg8EhIXqyrqzOdQULzKnC+6RAmWL5fvotwNE1suh042nSOcvPWz96ior4Cy7KgErZxg+nC2SlZFty7gOYPm9n6l1tTv2X9Jp/b/GEzs66dBTkYmRpJwzYN+O0+My6bwRanb0FFbVn29yhZDjSV04WPIlJkrn0zcJzpGOVgZ2fMvJxlDTedQ0puNbCbl/KmmQ5iQpXpAIadBOwEfNJ0kHKz5c+3pKr/hj9+taNrGXvaWObcNKfDz1s8aTHDjxpO9eBq5t8xn7GnjWXx44sZuPdAFZ1o+KmKjoj00anA3sBWpoMk2dyqyg9zljXCdA4JxdnlWnSgTKexreWlvBXAN4CVprMI1I2so3ZEbaf3sSotcq05ci05rEqL9pXtLJu6jIH7DAwnpHTmIS/l3WA6hIjEnJtdBnwbKKuLqMP2ZH29TkyVh4e9lHe16RAmlXXZAfBS3hvA903nKCsWzPj9DKZfMJ3Fkxb36FM3++JmLHpkEXMnzmXIoUNYcP8Chh46NJgSJyYtBk42HUJEEsLNPo82Ai+pSQ31a0xnkJJbCJxoOoRp5T6NDQAv5f29aWLTPujCyFBsde5WVA+qpm1ZGzMunUHtiFoat2vs1ufWbF7DVucEMxua5zfTtqSN2hG1zLpuFn67z7AjhlE7vPPRISk6H/iel/I+NB1ERBLlEuBAYD/TQZLof7U1m5nOICXVDnzTS3llv/pw2Y/srOdMYIrpEOWgelA1AFUDqui/a39Wv7e6V48z/675DD1iKB89+hED9xrIsMOHseDeBcWMKt1zgZfy7jYdQkQSxs3mCKazzTUdJWnaoC1bUaHNRJMt7aW8J0yHiAKVnTwv5bUARwEfmc6SZLnmHO2r2z/+eMXrK6gd1fORmJVvrqR6UDW1w2vJteSCn+QKgo8lTLd5Ke8i0yFEJKHc7BzgMIKNiqVIXq+teRfLajCdQ0rmn17K+73pEFGhsrMeL+XNJFjuUu+YS6Qt28b7F7/P9POn8+6F79J/p/7036k/AMteXsabZ77J6ndXM+OKGcz4/YyCj+H7PgseWMDQw4YCMGj/Qcy/Yz4zr57J4IMHh/WtCLyA5gKLSKm52ZeB4wmmzEoRPN7QoGkQyfU/9Nq8gbLeZ6cjTRObLgR+aTqHSITNAvbQXGARCY1rpwmu45E+OnLk8Kffqq35nOkcUnRZYHcv5U03HSRKNLJT2IXAv02HEImolcBhKjoiEio3Ox64yXSMJHi/ulr76yRPO3Csis6mVHYK8FLe2osi9QMjsiEfON5LeVNNBxGRsvQD4EnTIeJsWYWVbbG0YWsC/chLeQ+ZDhFFKjsd8FLeIuArgM5ei6xznpfy7jEdQkTKlJttAY5AJyN77dn6+unanC5xfuOlvL+YDhFVKjud8FLee8BBBHMgRcrdLV7Ku9h0CBEpc272I+AQYInpKHH0REP9ctMZpKgmeinvPNMhokxlpwteyvsv8DVAOw1LOXsOONl0CBERANzsW8CRQJvpKHHzcl2tlpxOjkeB75kOEXUqO93gpbzJwLEEF3+JlJs3gcO9lNdsOoiIyMfc7OME1/BoWdkeWFBZqet1kmEq8A0v5bWaDhJ1KjvdlL9O4WT0pCrl5W3gC17K054MIhI9bnYCKjzdNqOqaqZvWdqQLv5eB77spTxNSewGlZ0e8FLeTcAp6ElVysM7wAFeyvvQdBARkQ652euBk9CG4F2a1FA/23QG6bM3CE5CLjQdJC5Udnoov9rFT0znECmxdwmKzlzTQUREuuRmbwS+g6abd2pyQ72ucYq3aWi2RY+p7PSCl/KuBn5mOodIibxPUHTmmA4iItJtbvZWgj3y9Ia+A9NqazY3nUF67S2CojPfdJC4UdnpJS/lXQ6cYzqHSJG9BXzeS3mzTAcREekxN3s7cAygi7Y30gItKy1rG9M5pFfeJjgJqb0fe0Flpw+8lDce+D46iyTJ8F+CoqM53SISX272LoJlqVtMR4mSV+tq38Gyak3nkB6bCuyn62d7T2Wnj7yUdz1wKLDCdBaRPnie4KyR5gGLSPy52fuBrwNaMj/v8YaGRaYzSI89QVB0NKLTByo7ReClvEeAfQFd4yBx9ARwoJfytBu5iCSHm30IOAxYbTpKFDxfX1dlOoP0yB3AwV7KW2Y6SNyp7BSJl/KmAnsCnuEoIj1xB/BVL+VpZFJEksfN/hv4ElD2F3XPrK4abTqDdNufgGO0mXdxWL6vLWOKqWli0wDgTuBA01lEOpEDzs1fdyYikmyuPRq4F9jNcBIjFldUfLTfFqO1Els8nO+lvF+bDpEkGtkpsvxw41eBCaaziHRgCTBORUdEyoabnQ18DrjFdBQTnmqof890BunSGuBYFZ3i08hOCTVNbDoPuMh0DpH1/A843Et575oOIiJihGv/DPgtUGk6SljOGDp40mONDfubziEdmgt8zUt5L5kOkkQa2SmhfDs/Di1/KdFwF7CXio6IlDU3exnBDIyyWZRlam3tANMZpEPPAbur6JSOyk6JeSnvVoKLI7V3iZiSA84DjtJCBCIirF244DPA66ajlJoP/keVFVubziEFXQPsrz10SkvT2ELSNLFpEHAtcLTpLFJWsgRzgB8yHUREJHJcux9wM3C44SQl81Z19XtHjh6xlekcsoFVwKleyrvJdJByoJGdkHgpb4mX8r4JfBdYbjiOlIc3gM+o6IiIdMDNrgCOAC4kGAVPnCca6zVqEC2vALuq6IRHZSdkXsqbCOxCMEdTpFRuBfb0Ut47poOIiESam/Vxsy6wH5C458yn6uvbTWcQAHzgMoJrZ98yHaacaBqbIU0TmyqB8wmupSibFWGk5GYDP/RS3oOmg4iIxI5r1wMXAz8hISeE99hi9JurKyo+aTpHmZsHfMdLeY+aDlKOVHYMa5rYtBfBuv+aTyt94QPXA/+X3+tJRER6y7U/R7Bf3ramo/TFGsta/ZktRldjWVWms5SxB4CTvJS3sK8PZFnWQcBVBCfJb/B9X/vldYPKTgQ0TWzqD/wRSJnOIrH0LnCyl/ImGc4hIpIcCRjlebK+7rVThw/dyXSOMrUAON1Lef8oxoNZllUJvA0cSDCL40XgW77vv1GMx0+yWP7yJo2X8pZ7Ke+7BCu1LTYcR+IjRzD/t0lFR0SkyNzsatzsmcT4Wp7HGxrKZi+hiLkR2L5YRSdvD2C67/vv+b7fAvwD+FoRHz+xVHYixEt5dxAMmf8BaDMcR6LtdYKLHM/yUt5q02FERBLLzT4N7AxcScxWbJtSX1tjOkOZmQ580Ut5J3opr9gnr0cBs9b7++z8v0kXVHYixkt5i72UdzrQBGRM55HIaSVYInVXL+VNMR1GRKQsrBvl+TzgmY7TXXOrqhzTGcpEC3AJwUyLx0v0NawC/6ZrUbpB1+xEXNPEpi8DlwM7ms4ixk0CfuKlvNi80IqIJI5rVwDHAb8CtjCcpkPzKivnHTh21HDTOcrAncDPvZT3Xim/iGVZewGu7/tfyf/9HADf9y8p5ddNApWdGMgvU/19gifWwYbjSPieB87zUt5jpoOIiEiea9cCpwLnApsZTrOJf/bv9/xFgzfb03SOBJsC/NRLec+E8cWsYEW9t4EvAnMIFig41vf918P4+nGmshMjTRObbIJ9eX4CaB5u8k0FzteeOSIiEebaNpAGTgfqDaf52A+HDZn8dEP9fqZzJNBM4BzgNi/lhfom2rKsrxJcO1YJTPB9/zdhfv24UtmJoaaJTVsDvwcONxxFSmMacAFwZ9hPpCIi0kuuPQpwgROIwGbhnxs7amq2snIX0zkSZB5wKfBnL+WtMR1Guk9lJ8aaJjbtS3B24WDTWaQo3iN4obzVS3mxWvFHRETyXPuTBBerH24qQju07+KMWYNlNZrKkCBzgN8C16vkxJPKTgI0TWzaCTgb+CagXZLjZxbwa2CCl/K05LiISBK49l7AWQR7oYQ60uPV1Lx97KjhnwjzaybQB8B44EYv5TWbDiO9p7KTIE0Tm7YAfgqcDDQYjiNdewf4I/AXPZGKiCSUa48Bfgh8j5AWGbpqkP3UDQPtfcP4Wgn0GnAVcLOX8lpNh5G+U9lJoKaJTYOAEwlWidnScBzZUDvB/kl/Ah7VNTkiImUiWL3tGOA0YLdSfqmjRw5/alptjcpO97UBdwNXeynvKdNhpLhUdhKsaWJTBfBVgifWAym8IZWEYyFwA3Cdl/I+MB1GREQMcu09CV6bj6QEq6vuvsXo6c0VFdsU+3ETaD7wF+BaL+XNNR1GSkNlp0w0TWzajmCvnqOAMYbjlIs24BHgRuBBL+W1GM4jIiJR4trDCV6bfwCMLMZDLresZXtvMbofllVRjMdLoFbgX8DNwL16bU4+lZ0y0zSxyQL2IDibdCTgGA2UTG8QFJxbvJQ3z3QYERGJONeuBr4EfJ1gQYOhvX2oRxvqX/npsCG7FitagjwP3ALc7qW8RabDSHhUdspc08Sm3VlXfLY2HCeufOAV4GHgfi/lvWg4j4iIxJVrVwD7EBSfr9PDk5LnDNl80oP9GvcvfrBYeh24g+Dk47umw4gZKjvysaaJTZ9mXfHRkpWdWwL8m6DgPOKlvPmG84iISBK59qdZV3w+1dXdDxwzcsq8qqo9Sp4rmpqBScCDBNPHZxhNI5GgsiMFNU1sagK+AewHfAYo943J1h+9eRh4wUt57WYjiYhIWXHtbQlKz+EEr82b7K23kzNmoW9ZQ0JOZtJsgmtwHiRY5XSl4TwSMSo70qWmiU2VwE7AXuvdymHK2zxgMhq9ERGRqHHtRmBPgilvnwP2nFlVlR03ZuRos8FK7l3gGYLX50leynvPcB6JOJUd6ZWmiU1DCJ5k15afOI/+tBAsKvBa/vZf4DUv5S0wmkpERKS7XLvyrn6NO7hDNt8b2J3gdXlHCoz+xMgs1r02TwGe1Wuz9JTKjhTFeqM/uxFcTDkW2CJ/G0V0nmznsl6hyd/e9FJem9FUIiIiRdY0sakG2BbYHvjken9uR3ROUOaAD4EZbHji8TUv5S01F0uSQmVHSi5fhEayrvxswYZlaDjQANT24uHbgcUEm3YuKvDn+h/P1HKTIiJS7vLbUIwCRhO8Pm98GwHYQL/8rboXX2YlwWI+698WAzOBDwjKzQfALC/ltfb+uxHpnMqOREbTxKYKoI6g+NTnb5WAlb+Ltd6thaDELPFSnn6IRURESqRpYlM1QelpzP9ZRXCyMZf/c/2Pm4GlKjASFSo7IiIiIiKSSBWmA4iIiIiIiJSCyo6IiIiIiCSSyo6IiIiIiCSSyo6IiIiIiCSSyo6IiIiIiCSSyo6IiIiIiCSSyo6IiIiIiCSSyo5EgmVZYyzLesKyrGmWZb1uWdbppjOJiIiISLxpU1GJBMuyRgAjfN9/xbKs/sDLwOG+779hOJqIiIiIxJRGdiQSfN//0Pf9V/IfLwemAaPMphIRERGROFPZkcixLMsBPg28YDiKiIiIiMSYyo5EimVZ/YC7gDN8319mOo+IiIiIxJfKjkSGZVnVBEXnVt/37zadR0RERETiTQsUSCRYlmUBE4HFvu+fYTiOiIiIiCSAyo5EgmVZnwOeAjwgl//nX/i+/5C5VCIiIiISZyo7IiIiIiKSSLpmR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEkllR0REREREEun/AVpWMXzdUNZnAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#饼图，可以自动算百分比\n",
+    "quit_level=quit_df.MaritalStatus.value_counts().sort_index()\n",
+    "not_quit_level=not_quit_df.MaritalStatus.value_counts().sort_index()\n",
+    "plt.figure(figsize=(16,6))\n",
+    "plt.subplot(1,2,1)\n",
+    "rects1=plt.pie(quit_level,autopct='%2.f %%',explode=[0.1,0,0],labels=quit_level.index)\n",
+    "plt.subplot(1,2,2)\n",
+    "rects2=plt.pie(not_quit_level,autopct='%2.f %%',explode=[0.1,0,0],labels=['1','2','0'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e9477c3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0S8xsRV8b7C1sl/8EvBZuZ0av+18k6K2/mKDNOh44PmznR9N3Cdqr1wkOWF4z3A2F5+4cQ3COzyKCYXE/IOPg2SC28RbwfoIh88sJ2tCvMojvsEP93uWca3fO3eX6Pj/4ZwTniK0gKEb+Odj3EOrrb6bfv7sBrAMOAB4xs/VhnmcI/g8M+bX6+pzCc7/+QjDL4H/ZfNABgs/+ywS/01UEB4o/N8jsBcOcG8lILxERERERkeKiniQREREREZEMKpJEREREREQyqEgSERERERHJoCJJREREREQkg4okERERERGRDCqSREREREREMqhIEhERERERyaAiSUREREREJIOKJBERERERkQwqkkRERERERDKoSBIREREREcmgIklERERERCSDiiQREREREZEMKpJEREREREQyqEgSERERERHJoCJJREREREQkg4okERERERGRDCqSREREREREMqhIEhERERERyaAiSUREREREJIOKJBkUM0uZ2VNm9qyZPW1m55pZWXjfvmb2izzIGDOzr/jOISIiItljZt8Mv3/MD7+LHGBml5vZ24a5vYiZPZPtnFJcKnwHkILR7pzbC8DMpgB/BBqB7zjnHgceH+kLmFmFc65npNsRERGR4mBmBwHHAXs75zrNbBIwxjl3hudoUuTUkyRD5pxbBvwv8HkLHGFmt5pZmZm1mtm4jY81s1fMbKqZzTGzf4VHgf5lZrPD+680s5+Y2T3AD8xsBzO7K+ytesLMtg8f91Uzeyx8/ncztv9NM3vRzO4Cdh7VD0JERERybTqwwjnXCeCcW+GcW2Rm95rZvgBm1mZm3w+/OzxsZlPD27cPrz9mZuebWVvvjZtZuZn9MOM7xmdG9d1J3lKRJMPinHuN4O9nSsZtaeBm4AQAMzsAaHXOLQUuAa52zu0B/AHIHJ63E3CUc+7L4X2/dM7tCRwMLDazY4Adgf2BvYB9zOwwM9sHOAl4B/BBYL/cvWMRERHx4A5gOzN7ycx+ZWaH9/GYWuDh8LvD/cCnw9t/DvzcObcfsKif7X8KSIaP2Q/4tJnNze5bkEKkIklGwvq47S/AR8OfTwqvAxxEMEQP4BrgnRnPud45lzKzemCmc+4mAOdch3NuA3BMeHkSeALYhaBoOhS4yTm3wTm3Frgla+9MREREvHPOtQH7EIxgWQ78xcxO7fWwLuDW8Of/ApHw54OA68Of/0jfjgFOMbOngEeAiQTfMaTE6ZwkGRYzmwekgGXArhl3PQTsYGaTgQ8A3+tnEy7j5/UbN9vfywEXOucu65XhnF7bERERkSLjnEsB9wL3mlkCaO71kG7n3MbvAymG9v3WgC84524fcVApKupJkiELC6BfA5dk7JQACK/fBPwEeN45tzK860GCniWATwD/6b3dsDdogZl9IHydKjMbC9wOnG5mdeHtM8PJI+4HTjCzmrAX6vjsvlMRERHxycx2NrPMnp29gDcG+fSHgQ+FP5/Uz2NuBz5rZpXh6+1kZrXDySrFRT1JMlg1YVd0JdBDMGTuJ/089i/AY8CpGbedDfzezL5K0F1+Wj/PPRm4zMzOB7qBDzvn7jCzXYGHzAygDfikc+4JM/sL8BTBDvPfw353IiIiko/qgIvDSaF6gFcIht7dMIjnngNca2ZfBuJAso/HXE4wPO8JC75kLCcYCSMlznp1BIiIiIiIFLxwNEq7c86Z2UnAx5xz7/edSwqDepJEREREpBjtA1wS9hCtAU73G0cKiXqSREREREREMmjiBhERERERkQwqkkRERERERDKoSBIREREREcmgIklERERERCSDiiQREREREZEMKpJEREREREQyaJ0kKRiRaHwsMAeYHf47FagFasLL2IyfywELn2oZl05gRcZleR//rmptadLc+CIiIgOIRON1bG6bZwMTgDHhpSrj597XN/4MsA5Ym3FZR7Cu0XJgWcZF7bOMGq2TJHkjEo1PZnMBNKePnyeNUpQUsJrNhVMrkADmA0+3tjQtGaUcIiIi3kSicQOmsWURNKfXv+NHMVIPsBR4EXgWeC7899nWlqZVo5hDSoCKJPEiEo1PBQ7KuLwDqPMaavCWkVE0hf8+19rS1Ok1lYiIyAiEbfMBGZf9gAavoQZvKVsWTs8RFE8rvaaSgqUiSXIuEo1XAHuyZVE012uo7OshOLK1sXC6D3i0taUp7TWViIhIHyLReDWwN5sLogMJeoeKzRLgfuBfwJ2tLU2ve84jBUJFkmRdJBqfCLyTzQXRvgTnC5WalcDtwG3A7a0tTcs95xERkRIVicZrgfcARxIURXsAlV5D+fE6QcF0F/Cv1pamFZ7zSJ5SkSRZEXbRfxD4MHAYwcQJslka+C9BwXQb6mUSEZEci0Tjk4D3AScARwHVfhPlHUcwAuSu8PLv1pam9X4jSb5QkSTDFonGZwAfAk4k6DnSlPKDtwK4A/UyiYhIFkWi8TkERdEJwCHooOVQdAH/Af4IXN/a0rTWcx7xSEWSDEkkGp9FUBSdCBzM5mm2ZfjSwEPAVcBftFMWEZGhiETju7O5MNrLb5qi0Q7cDFwN3NHa0pTynEdGmYokGVB4VOpEgqF0+6PCKJc2ADcBVwB3az0IERHpS3j+7xnhZQfPcYrdEoLepataW5rm+w4jo0NFkvQpXBvhvcDnCU70VGE0+t4ArgR+09rStMhzFhERyQORaHwfgrb5JHSOkQ/zCXqX/qB1E4ubiiTZQiQabwROA85CR6byRQ9Bl/+vWlua7vYdRkRERlckGh8DfISgODrAcxwJpAjOLf5Fa0vTP32HkexTkSQARKLxHYBzgVOAWs9xpH/PA5cSdPnr3CURkSIWngf8WYIhdVM8x5H+PQH8H3CjhskXDxVJJS4Sje8LfJ1g+m7NTlc41gA/An7e2tLU5jmLiIhkUSQafxdBr9H70ex0heR54AcEQ/F6fIeRkVGRVKIi0fi7ga8RLConhWs50EIwFK/DdxgRERm+SDT+ToJ9+iG+s8iIvAH8EPid2ubCpSKpxESi8cOBHwP7+M4iWbUI+B5weWtLU7fvMCIiMniRaHw34ELgON9ZJKuWAj8BLm1taVrnO4wMjYqkEhGJxucRDM86wXcWyalW4LvANVrTQUQkv0Wi8dnA+cDJaMh7MVsNXAz8WOcTFw4VSUUuEo03AN8GzgbGeI4jo+dFIEawOK3+k4uI5JFwjaNvAp8DqjzHkdGzBPhqa0vTtb6DyMBUJBWpSDReTjAbzvloRpxSNh/4dmtL0y2+g4iIlLpIND6WYCbZrwINnuOIP/8GPq+FafObiqQiFInGjwJ+CuzmO4vkjduAz7S2NL3lO4iISKmJRONlwKcJevin+U0jeSIF/BI4r7WlKek7jGxNRVIRiUTjOxFMyqATP6Uv64AowQmk+o8vIjIKItH4jsAVaMY66dtSgqVYrlbbnF9UJBWBSDReC1xAsKZCpec4kv/uB85obWl62XcQEZFiFfYenUMw82iN3zRSAB4EzmptaXrKdxAJqEgqcOFisH8EdvSdRQpKB8Gwjx9pFjwRkeyKROM7E/QeHeQ7ixSUFHAZ8K3WlqbVvsOUOhVJBSo8QhUl+KKr3iMZrv8Cp+vkURGRkQsnTTqXYNKkas9xpHAtAD7R2tJ0v+8gpUxFUgGKRONzgGuAQ31nkaLQDfwAuKC1panLdxgRkUIUicZ3Jeg9OsB3FikKKYKhmhdoxIcfKpIKTCQa/zjwK6DRdxYpOs8Bn2ptaXrYdxARkUIR9h59lWBkh9Y8kmy7n6BXaYHvIKVGRVKBiETjjQTF0cd9Z5GilgK+3trS9GPfQURE8l0kGt+e4Lzg/X1nkaK2kmBovNY8HEUqkgpAJBo/lGB43RzfWaRk/ImgV6nddxARkXwUicaPI2ibx3mOIqXjYuCrrS1Nnb6DlAIVSXksEo1XEnTfR4Eyv2mkBD0JnNDa0vSG7yAiIvkinDgpBnwLML9ppAQ9BXy0taXpJd9Bip2KpDwVicYnAH9DkzOIXysIdsZ3+w4iIuJbJBofD/wBeK/vLFLS1gOfb21putJ3kGKmIikPRaLxecBtwE6+s4gQnKf01daWpp/6DiIi4kskGt8NuAWY6zuLSOgyggVoNftdDqhIyjORaPwA4O/AZN9ZRHq5Fvh0a0tTh+8gIiKjKRKNNxGcq1nvO4tIL38HTmptadrgO0ix0XkueSQSjZ8A3IMKJMlPnwT+E4nGt/MdRERktESi8S8T9CCpQJJ8dDxwdyQan+Q7SLFRT1KeiETj5wA/RoWr5L/lwIdbW5ru8x1ERCRXwsmTfg2c7juLyCC8DLyntaXpNd9BioWKJM/CWXJ+CpztO4vIEPQAp7S2NP3JdxARkWyLROO1BL1HR/rOIjIEy4Cm1pamx30HKQYqkjyKRONjCRahe7/vLCLDkAbObG1p+q3vICIi2RKJxuuBfwDv9J1FZBjWAx9pbWn6h+8ghU5DuzyJRONTgXtRgSSFqwz4TSQaP9d3EBGRbIhE4w3A7ahAksJVC9wcicY/5TtIoVOR5EEkGt8ReAjYz3cWkSz4cSQa/47vECIiIxGJxscBdwEHeY4iMlIVwOWRaDzmO0gh03C7URaugXQfMMt3FpEs+1FrS9NXfYcQERmqcAH3u4B3+M4ikmW/am1pOst3iEKknqRRFInGZwN3owJJitNXItH4Rb5DiIgMRSQan0yw/IYKJClGn4tE4z/xHaIQqUgaJZFofCbBTniO7ywiOfTVSDR+oe8QIiKDEZ4ffA+wh+8sIjn0pUg0/j3fIQqNiqRREInGpxH0IM3znUVkFEQj0fgFvkOIiGxLJBqfTjCB0ts9RxEZDd+MROPf8B2ikOicpBwLxznfj3bCUnq+29rSFPMdQkSkt4zRHTv6ziIyyr7U2tL0M98hCoGKpBwK10HSTDlSys5pbWn6ue8QIiIbhesgPQDs7juLiAeOYDH4a30HyXcqknIkEo1XADcDx/rOIuJRCji+taXpNt9BREQi0Xg5Qdvc5DuLiEfdBG3z7b6D5DOdk5QDkWjcgN+jAkmkHPhzJBp/m+8gIiLAj1GBJFIJ/DUSje/vO0g+U5GUGz8ETvYdQiRPNAB/j0TjE30HEZHSFYnGzwS+6DuHSJ6oBeKRaHwn30HylYbbZVkkGv808BvfOUTy0P3AUa0tTd2+g4hIaYlE40cD/wAqfGcRyTOvA/u2tjSt8h0k36gnKYsi0fjewMW+c4jkqcOAX/sOISKlJRKN7wpcjwokkb7MBa4NTxWRDOpJypJIND4e+C/BH5tk0YJLT6dsTA2UlWFl5Uxv/hmp9nWsuPkH9KxdSkXDVCZ9IEp5dd1Wz013tLHytl/QteJNACYd+0WqZu7K6nuvoP21/zJmylwmHfdlANqeuZt0xzoa9n3/qL6/EvSV1pamH/sOISLFLxKNTwIeQesUjiqXTrH4qi9RUT+RKSd+h+U3/4DuVQsASHesp6y6lhmnbX1Mee1jf6Pt6TvAoHJyhEnHnoNVjFGbPTrOa21p0hqHGdSTlAVh9X01KpByZurH/o8Zp13M9OafAbD24eupjuzJzP/9LdWRPVn78PV9Pm/Vv35D9bx9mPnpXzPj9IupnLgd6c71dC58nhmnX4JzabqWt5Lu7mT9M3dR/w6dzzsKLopE4/qgRSSnItH4GOAmVCCNunWP30LlxO02XZ/8/q8z47SLmXHaxYzd+WDG7nTwVs/pWbeCtf/9O9Oaf8qMT/0K0mnWP3+/2uzREwuHpUpIRVJ2RIHjfIcoJRteeYTa3f4HgNrd/ocNLz+81WPSnRvoeOtZ6vY4BgArr6Ssug4wXKoH5xyupwsrK2ftozdSv8/7sHKNxhgFZcCfItH4br6DiEhRuxx4p+8QpaZn7QraX3uMuj2P2eo+5xwbXvgPtbse1veT0ylcTxcuncL1dFJeNwG12aOmDPhjJBqf5TtIvlCRNEKRaPxIQN2TuWTGsuvOY/GVX2TdU/8EILV+DRV1EwCoqJtAev2arZ7Ws2YJ5WMbWPmPn7HoirNZedsvSHd1UFY1lrE7H8ziK8+monEqVlVL1+KXGLvjgaP5rkpdPXBLJBqf7DuIiBSfSDR+Nppl1ovV//oN4444HbOtT3HpXPAs5bXjqJwwc6v7Kuon0bD/CSy89DQWXHIyVjWWmrl7q80eXZOA6yLReKXvIPlAJfgIRKLxGcCfCNaCkRyZ9omLqKifSGr9Gpb+5VtUThzcQQ6XTtG15FUmHHUmVTN2ZtVdl7H24esZd9jJNB5wIo0HnAjAytt+wbhDP8m6p2+n4/UnqZwSYdzBJ+XyLUlgLvCHSDT+7taWJp0cKSJZEU7U8APfOUrRhlcepax2HFXTdqDjzflb3b/+ufv67UVKdbSx4eVHmHnm7yirqmX5zS20PXsPdW9/l9rs0XUQwVI253jO4Z16koYpEo1XANcBU3xnKXYV9cHyOuW14xi700F0LnqJ8tpx9LQFs1X2tK2irHZcH8+bRHn9JKpm7AzA2J0PoWvpq1s8ZuP1ivEzWf/M3Uz+QJTu5W/QvWphDt+RZDgaONN3CBEpDuER8D8A1b6zlKLOhc/R/vIjLLj0dJbfchEdb8xnxd9/BAQHLje89BBjd+m7SOpofYqKxqmUj23EyiuC9n7h81s8Rm32qPliJBr/iO8QvqlIGr6LgEN8hyh26a4O0p0bNv3c8fqTjJk8h7E7HMD6Z/4FwPpn/sXYHQ7Y6rnldeOpaJhE98pgRp2ON56mctLsLR6z5t/X0vjOT0C6B1w6uNHKcD2dOXxX0ssPI9G4TqwWkWz4LvAO3yFK1fjDT2XWWVcx67O/Z/L7vkb1nD2YdPxXgKAIqpw4i4qGSX0+t6JhMl2LXiTd3YFzLmizMyZ/ALXZo+zySDS+s+8QPqlIGoZINH4i8CXfOUpBasMalvzhayz6/edZcvW51Gy/HzXz9qHhwBPpaH2Shb/5NB2tT9Jw4IcB6Fm3kqXXf2fT8yccdSYrbv0Ri37/ebqWvU7DQZsPjGx46SHGTNuRivqJlFXXUTVjFxb97iwwGDNF39lHUS1wZSQa1/5IRIYtEo0fAnzNdw7p2/rn799qqF1mm101Y2fG7nwIi688h8W/Pwuco37P92x6rNrsUVcP/DUSjdf6DuKL1kkaokg0PhtIAA2+s4gUGa2fJCLDEonG64Gn0VIcItn2q9aWprN8h/BBR26H7mJUIInkwvfCE65FRIbqZ6hAEsmFMyPReElOJaiepCGIROMfIFiYTkRy4zHg4NaWph7fQUSkMESi8fcDf/OdQ6SIJYB9Wluaun0HGU3qSRqkSDReR9CLJCK5sx/B4swiIgOKRONTgd/6ziFS5HYHvuI7xGhTkTR4FwBahVgk986LRON7+g4hIgXhckCLUovk3nmRaHx73yFGk4qkQYhE43sDX/CdQ6REVAJXR6LxMb6DiEj+ikTjHweO851DpERUA7/2HWI0qUgaQDgt8WVAue8sIiVkD+A7Az5KREpSJBqvAVp85xApMUdFovFP+g4xWlQkDewsYF/fIURK0FdLrWtfRAbtK8B2Az5KRLLtJ5FofKLvEKNBRdI2RKLxmcD3fOcQKVGVwPd9hxCR/BKJxmcAX/edQ6RETQZ+5DvEaFCRtG0/R2siifj0kUg0rp5cEcn0faDWdwiREnZqJBp/l+8QuaYiqR+RaPw44EO+c4iUOAN+4DuEiOSHcCKlZt85RIRLI9F4he8QuaQiqQ+RaLwS+IXvHCICwJGRaPw9vkOISF74KcHBExHxa2egqCdxUJHUt2Zgru8QIrLJD8KZJkWkREWi8Q8Ch/nOISKbfLuYe5P0paOX8Jf9/3znEJEt7EGRH7ESkf6F66Zd5DuHiGxhHnCq7xC5oiJpa58k+KWLSH65IBKNV/kOISJenA1oSQCR/POt8DSVoqMiKUMkGi8HvuE7h4j0aTbwBd8hRGR0RaLxScC3fOcQkT7NAU73HSIXVCRt6WPAjr5DiEi/vhGJxsf7DiEio+rzQKPvECLSr2+GQ2KLioqkUHhS+Dd95xCRbRqPzhkUKRmRaLwa+JzvHCKyTdsBZ/gOkW0qkjb7CLCL7xAiMqAvRKLxKb5DiMioOAWY7DuEiAzoG8V23rCKJCASjRsa7yxSKKqBz/oOISK5FbbN5/rOISKDMhP4X98hsklFUuBDwNt9hxCRQftssR2xEpGtNBEsWCkiheH/hUNki0LJF0nqRRIpSFMJJloRkeL1Zd8BRGRIpgOn+Q6RLSVfJAHvA/b0HUJEhuwc3wFEJDci0fjewBG+c4jIkBXNkDsVSfAl3wFEZFj2jETj7/IdQkRyQr1IIoVpr0g0vo/vENlQ0kVSJBrfETjcdw4RGTYtLitSZCLR+HYEM86KSGH6tO8A2VDSRRJFOKe7SIk5PhKNT/cdQkSy6mygwncIERm2j0ei8VrfIUaqZIukSDReCZzqO4eIjEgF8CnfIUQkOyLReD1FchRapITVAx/1HWKkSrZIIpiwQQtSihS+MyLReCnvy0SKyYeARt8hRGTECn60Vil/sSj4X56IADAHeLfvECKSFSf5DiAiWXFQJBov6DVIS7JIikTjM4BjfOcQkawpmilHRUpVJBqfBPyP7xwikjUFPXS2JIskgiNVpfreRYrRcZFofKLvECIyIh9EEzaIFJOTI9F4le8Qw1WqhcInfAcQkayqAJp8hxCRESn4E71FZAsTCA5+FKSSK5Ii0fguwN6+c4hI1r3fdwARGZ5IND4VrVsoUowKdgbakiuSUC+SSLE6ppC79UVK3IeBct8hRCTrDo9E4+N8hxiOUiySPuY7gIjkRB066VukUGmonUhxqqBAJ0srqSIpEo3vCWzvO4eI5IyG3IkUmEg0Pgs4xHcOEcmZY30HGI6SKpKAo30HEJGcOj4SjZvvECIyJB8G9P9WpHi9pxDb5lIrko7yHUBEcmo6sJ/vECIyJFpAVqS4TQX28R1iqEqmSIpE42OAQ33nEJGc05A7kQIRDrXb33cOEcm5ghtyVzJFEnAQMNZ3CBHJuff5DiAig3aE7wAiMioKbi3DUiqSNNROpDTsFonG5/kOISKDohEeIqVh30g0Ptl3iKEopSJJUwOLlA4NuRMpDCqSREpDGfAe3yGGoiSKpEg03oBO5hYpJcf5DiAi2xYeVd7Vdw4RGTUFdV5SSRRJBGOeK3yHEJFRc0AkGi+V/ZtIoXqn7wAiMqreHYnGy32HGKxBfYkwsy+aWYMFfmdmT5hZIa2eq6F2IqWlFtjFdwiRXCqCtllD7URKy3hgb98hBmuwR1pPd86tBY4BJgOnAS05S5V9mrRBpPTs6zuASI4VetusIkmk9BRdkbRxldxjgSucc09TIKtjR6Lx6cDbfOcQkVFXcAvXiQxRIbfNdcA7fOcQkVFXMP/vB1sk/dfM7iDYEd9uZvVAOnexsupdvgOIiBfqSZJiV8ht88FAwZybICJZU3RF0qeAKLCfc24DMIagW78Q7OU7gIh4sVchnSAqMgyF3DZrqJ1Iadq9UNrmwc74tnEGmj3MCqInP5OmFxUpTWMJ/v8/4zuISI4Uctt8mO8AIuJFDcHESs/6DjKQwRZJX834uRrYH/gvcGTWE2WfiiSR0rUvKpKkeBVk2xweRd7fdw4R8WYviqVIcs4dn3ndzLYDLspJoiyKROPVwFzfOUTEm32AK32HEMmFQm2bCdrlat8hRMSbdwB/8B1iIMNdbHEBsFs2g+TITpTOgrkisjVN3iClpFDaZq1hJlLaCmLyhkH1JJnZxYALr5YRdJM9naNM2aShdiKlbc9INF7R2tLU4zuISLYVcNusIkmktBVPkQQ8nvFzD/An59wDOciTbSqSREpbDcE6afN9BxHJgUJtm1UkiZS28ZFofE5rS9MbvoNsy2CLpHHOuZ9n3mBmX+x9Wx5SkSQie6AiSYpTobbNKpJEZC8gr4ukwZ6v09zHbadmMUeuqEgSkZm+A4jkSEG2zd+r+N2aj5Tf8+jbrPXVMXR3+s4jIl7k/fmT2+xJMrOPAR8H5prZLRl31QMrcxlspMIpRnfynUNEvJvuO4BINhVy20ysseGTFTTBvwBwjnSKskVJape95aaseyE9Oz3fza1+Jj13wktu1qwOqmo8JxaR3Mj7tnmg4XYPAouBScCPM25fR/4PX5kLVPkOISLezfAdQCTLCrltnpd5xYyyCtIzJrJuxkRbx15lr3IS9wDgHC5N2ZK1jF260E1a96LbLjU/Pa9qfnre+JfcrJnrqanz8g5EJBum+Q4wkG0WSc65NwjGCx40OnGySkPtRAQK4GiVyFAUeNs8b+CHBMywctLTxtM2bby1sRutfKj835vuTzlb3sbYJQvdxLUvuVk9ifS8MYn03HHPu9kz1lHbmJP0IpIted82D3YK8AOBiwkKjzFAObDeOdeQw2wjtaPvACKSF9STJEWpQNvmWdnaULm5yY2sn9xo63kbb/KB8gc33Zd2trKN6sWL3cTky25WTyI9t3K+m9f4Qnq7GatpGJ+tDCIybIXdk5ThEuAk4HqCxRlPAXbIVagsmeg7gIjkhbw/WiUyTIXYNk8ZjRcpMzexgfaJDbaAnVnAceUPb7ov7VizgepFS9yENS+7md3PpOdWzHfzGp9Pz562gnGTRiOfiBRPkYRz7hUzK3fOpYArzOzBAZ/kl7raRQSgJhKNj2ttaVrjO4hIthVg2zwqRdK2lBnj6ugYt4MtYgcW8d7yxzbd5xxrN1C1aJkbv/oVN6MzkZ5bkXDzGp5Lz5m2lAnes4sUkbGRaLyhtaVpre8g/RlskbTBzMYAT5nZRQQnjNbmLlZWjPMdQETyxgxgje8QIllWiG1zXhcaZjTU0tkw15YwlyUcXf7EpvucY30HYxYuc+NWveamdz7rImVPp7evfy49Z9pCJk0FM4/RRQrRNKDgi6STCdZU+jzwJWA74IO5CpUl6kkSkY2mA8/5DiGSZYXYNk/2HWC4zKitoWunObaMOSzjXTy96T7naO+kcuEKGle9lp7e/qyLlM1Pz6t7zs2Z8qabMt1RNth1KUVKyTTgJd8h+jPYIukD4QreHcB3IVjVG8jnVb1VJInIRpq8QYpRIbbNed2TNFxm1FTTvcMsVjCrfAWHkdh0n3N0dlGxcCWNK1vTUzc86yI2Pz2v9lkXmdzqps1MU1buMbqIT3l9zvBgi6Rmtt7pntrHbflERZKIbJTXO2KRYSrEtrlge5KGy4yqKnrmzWDlvBnlKzk4o1PbObq7KV+wioblb7ipG55Lz2F+et7YZ11k0mtu+sweKio9RhfJtbyevGGbRdI2VvVuIN9X9dY5SSKymXqSpGgUbNscayxHBzC3YEblGFJzprF6zjRbzQFlL2y6zzlSPZS/tYa65W+6KW3Ppee4+W7e2GfScye+6mbM7KKyymN0kWwo3CKJwl7VWztiEdlISwJIMSnUtrnad4BCYkZ5JantJpPcbrIl2afs5U33OUc6RdnCNdQtW+Amt72Qnp2e7+ZWJ9LzJrzsZs7qoKrGY3SRwcrr4bfbLJJ6r+ptZhOBw4A251xP7uMNTyQaN6Dedw4RyRsasiJFo1DbZlQkZY0ZZRWkZ05i7cxJtpa9yl7lJO4BwDlcmrIlScYuXegmr3shvV0q4eZVJdJzJ7zkZs1YT02d5/giG+V1b+hAw+1uBaLOuWfMbDrwBPA4sL2Z/cY597NRyDgcDQQz/oiIwBDWhBPJdwXcNuf1F6JiYYaVk542gbZpE6yN3cte58Pcv+n+lLNl6xi7ZKGbtPYlNyuVSM8bk0jPHf+8mz2jjbENHqNL6cnrtnmgcHOdc8+EP58G3OmcO8XM6oEHgJ/lMtwIaKidiGTK6x2xyBAVatusIikPlJubMo71U8bZet7OG5xQ/sCm+9LOVrZRvXixm5h80W3X80x6buV8N3fc8+k5M5LUjfOXWopUXrfNA4Xrzvj5f4DfAjjn1plZOmepRk5FkohkyusdscgQFWrbrOF2ea7M3MQG2ic22AJ2ZgHvK39o031px5oNVC9a4iasednN7E6k51XMd/Man0/Pnr6SRp33KcOR10PhB/ri8JaZfQFYAOwN/BPAzGrI7zeW7yuOi8joUpEkxaRQ22b1JBWwMmNcHR3jdrBF7MAi3lv+2Kb7nCO5garFS9341a+6mV2JdKQ84eY1PJeeM20pE/L65HzxKq/b5oHCfQo4HzgK+Khzbk14+4HAFTnMNVIp3wFk9JSRTo2lY8NYOjpqrbOjlvbOOmvvqqe967mq8q6llaaF+kqdK1sCTb5TiGRLobbNY3wHkNwwo7GWzsZ5toR5LOHo8v9uus852toZs2i5G7fqVTej8xkXKb96zI5j1lSobS55rjyv2+aBZrdbBpzZx+33QDiNSn7q8h1AAuWkeoICprOj1jo66mjvrLf2rjrau+ttQ3cd7T0NbEjV24Z0Pe2uztqppZ1a67CxdFoNnRXV1lVeRXflGHoqK+iprCBVXY6rMtI1BjVmjCGYzXCrGQ1fq6h44wMzpjc4s/Gj/+4lj6yB/+c7g0hWFHDbnM8z70mOmFE3lq6d5tgy5rCMI3mK+IwZj3RWVuzjO5t4twyivjP0a6DZ7f4OuP7ud869L+uJskNF0iBU0tNdQ0d7LZ0baq29s46OzrqMAiYsXlJ1tG8qYOpod7XWUVZDZ1kNneXV1l1RRVfFGHrGVJAaU06qqpx0VRmuhqCAqSSYbdDLjDnzunvm/GzZiqe+OGVSHWb5PAxFcku9y1I0Crht7vQdQPJDYzrdtch3CMkHeX3gZKDhdj8alRTZV/BF0hi6O8fS0V5LR0etdXTU0tG1cQhZva3vqae9J+x9SdexIV1v7VZLB7XWYTV0ltfQWVZtXRVV9FRWBj0wYypIV5WRru5VwGwsYorWkRva9zojufbfl49rPNR3FvEmn09mFxmqQm2bVSQJABNTKR24EijkIsk5d99oBcmy7oEfMnzVdHbU0Nkenv/SUUtHZ71t6K4PemA2FjCpeja4emt3tbQTFjBlG4eQVVl3xZhNQ8hSY8pJV5eTrjZcNUEBU4VOcs2aL65OHvp0VdX9j9VUH+Y7i3iR032CyGgq4LZZRZIAMCmVMt8ZJC8UbpG0kZntCFwIvI2MKTydc/NylGtExtLRPtHWLqyjvbOO9q6MIWQ99bT3NNiGdF1GAVMXFjA1dJZV01VebV0VY4IemDGVwRCy6jJclW3ugalGU5kWnN8uWXbI0dvNeHx5RcW+vrPIqFvnO4BIthVa20wRjPKQ7JjSkyrznUHyQuEXSQSz5XwH+CnwLoLF6/L2KMBz1ad3ADN955D8Ug7lNy9YvNMRs2e92lVm2/vOI6Nqre8AIjlQUG0z6kmS0JRUSjMdCuR5kTTYSr7GOfcvwJxzbzjnYsCRuYs1Ym1s46RWKV31zjX8deHiSnNule8sMqpUJEkxKrS2WUWSADA1ldLpBAKwxneAbRlskdRhZmXAy2b2eTM7AcjfxcFiSQes9x1D8lOkp2f2xUuXv4VzOk+ldKhIkmJUaG1zOxpyJ8DknlSt7wySFxb7DrAtgy2SzgHGAmcD+wCfBE7JUaZs0TkI0q/D2zv2PHPN2od955BRoyJJitE5FF7bvNx3APFvYipV1LPqyqAt8R1gWwZbJEWcc23OuQXOudOccx8CZucyWBas8R1A8ttZa5KHHtjeXqizRMnQqEiSYlSIbbOKJGFCKjXOdwbJC0VRJPW1VH2+L1+vdcpkQJctWX7o1J6ex3znkJxb7TuASA4UYtu8zHcA8W8MVOGcTouQvC6Stjm7nZm9FzgWmGlmv8i4q4E8n5ECWOA7gOS/Mij724LFuxwxe+bLnWVlO/rOIzmz0HcAkWwp8LZZPUkCQDkkU6Bzk0pbQZ+TtAh4HOgA/ptxuQV4d26jjZiKJBmUOufqb1y4pMacW+E7i+SM9gdSTAq5bVaRJABUOqdzx0tbmjzfH2yzJ8k59zTwtJn9wTmX70enetOXIhm02T09s365dPn8z02d3ICZ1m8oLl1oiI8UkQJvm/V/UQCocW5Dh+8Q4tPyRHMi5TvEtmyzJ8nMrgt/fNLM5ve+jEK+kXjLdwApLIe2d+zx+TXJR33nkKxblGhOaN00KRoF3jZr6KsAUJ9Oq0YqbXl9PhIM0JMEfDH897hcB8kB9STJkH1mzdp3PllVdd8DY2sO951FskYHTKTYFHLb/KrvAJIfxqXS3W9W+k4hHuX1+Ugw8HC7xWZWDvzOOXfUKGXKFhVJMiy/Wrr80PdsN+PRxRUV+/vOIlmhfYEUlQJvm1/xHUDyw4RUSj38pS3ve5IGnALcOZcCNphZ4yjkyZ5YciXQ7juGFJ4yKLtpweK3VafTL/nOIlnxpu8AItlWwG3zUqDNdwzxb3Iqr09HkdzL+yJpoOF2G3UACTO7E9g0r71z7uycpMqeN4GdfYeQwlPrXN1NCxfXHjtrxnJnNtl3HhmRF3wHEMmRQm2bXwX29B1C/JqaSpX7ziBeveY7wEAGWyTFw0uheQYVSTJMs3pSMy9dujxxZjDjXZXvPDJsz/oOIJIjhdo2v4KKpJI3pSeldrW0Pek7wEAGVSQ5567KdZAcmQ98yHcIKVyHtHfsfvbq5AO/mDDuEN9ZZFgc8LzvECK5UMBts85LEqakUtW+M4g3PUDCd4iBDHhOEoCZ7WhmN5jZc2b22sZLrsNlwdO+A0jh+3Ry7SGHbWi/z3cOGZa3Es0Jnf8gRamA2+YXfQcQ/yb3pOp8ZxBvnk80Jzp9hxjIoIok4ArgUoLK713A1cA1uQqVRfm+XoQUiEuWLj9sZnfPw75zyJBpqJ0Us0Jtm/N+mI3k3sRUqt53BvHmKd8BBmOwRVKNc+5fgDnn3nDOxYAjcxcra1qBtb5DSOEzsBsXLt69Op3WEdDC8pzvACI5VKht87NA3h9Fltwal06PxzlNA16aCuJAyWCLpA4zKwNeNrPPm9kJwJQc5sqOWNJRAGMepTCMda72bwsX15c5t8x3Fhm0Z3wHEMmhQm2bu1HbXPLKoRxY5zuHeFFURdI5wFjgbGAf4GSgOUeZsk3nJUnWzOxJzbhsybJlONfhO4sMyiO+A4jk0DkUbtv8hO8A4l8FrPGdQbx4yneAwRjs7HaPhT+2AaflLk5O6LwkyaoDOzp3O3f1mgd+MmG8ZrzLb6vRGklSxAq8bVaRJFQ519Zj5juGjK7WRHNije8Qg7HNIsnMbtnW/c6592U3Tk5oRyxZd1py3SFPVlXdd0/t2MN9Z5F+PZJoTmi8uxQdtc1SLGrSrn39YMc0SbEoiKF2MHBP0kHAW8CfCIatFGK5/yTBmFfNoiJZ9fNlKw47dtb0hxdUVh7oK8OC3y1g3VPrqGioYMfv7wjA0r8uZe2TazEzyhvKmXXGLCrHV2713BV3rGD1favBwfjDxzPp3ZMAWHLdEtbNX0fN7Bpm/e8sAFY/sJrU+hSTjpk0em9u5B7yHUAkR4qhbZ5PMHmDFhQtYQ3pdMcKyn3HkNFVMEXSQPX7NOAbwG7Az4GjgRXOufucc4Wxbkws2QP823cMKT7BjHdL9qhJp70tVjr+neOJfDmyxW2Tjp3Ejt/bkR0u2IGGvRpYdvPW80x0LOhg9X2r2f687dnhgh1Y9/Q6Opd0ktqQYsMrG9jxezvi0o6OtzpId6VZ8581TDxy4ii9q6zRlO1SrIqhbe5E/0dL3rh0KuU7g4y64iiSnHMp59w/nXPNwIEEq2Tfa2ZfGJV02XOP7wBSnGqcG3vzgsXjypxb6uP1a3eupbx2y6Nw5TWbr6c701gf4707F3UydvuxlFWVYeVG7c61rH1iLRi4HodzDtftsHJjxW0rmHj0RKyioA5WOzRpgxQptc1SLCam0mnfGWRUpSmgUR4DjgQ1syoz+yBwLXAW8AvgxlwHyzLtiCVnpqdS0y9fsmwlzrX7zrLR0huW8sK5L7DmoTVMOWHrGYGrZlWx/sX19LT1kO5Ms27+OrpXdlNeU07Dvg28et6rVE6qpGxsGe2vtdOwd4OHdzEizySaE0nfIURyRW2zFIMpPT0FdfRNRuzRRHNipe8QgzXQxA1XEXTn3wZ81zlXqGuOPEkwzeQ4vzGkWO3X0fm2r61a8+BFE8cf7DsLwNQTpzL1xKksv3U5K/+1kqknTN3i/uoZ1Uw6dhKtP2ylrKqM6u2qsfKgrZp87GQmHzsZgIW/X8iUD05h1X2raHumjertqpnyvvxfhgW4w3cAkVwporb5YaAdqPEdRPyYkkptfcKsFLN/+A4wFAP1JJ0M7AR8EXjQzNaGl3Vmtjb38bIklkwD9/uOIcXt5LXrDj56/Ya8Oh+g8cBG1j7e93/VCYdPYIfv7sC8b8yjvK6cMVPHbHF/+xtBx1jVtCrWPLCG2WfNpnNBJ51LOnOeOwtu9x1AJIeKpW3uAh7wHUP8mdqTGjPwo6SIFE+R5Jwrc87Vh5eGjEu9c67Qxt+oW19y7sfLVhw2p7vb63jbzCJm3ZPrqJre9+RRPWt7AOha2cXax9cy7sBxW9y/7MZlTDlhCq7HBaOIAcog3ZX3Q8g3oIMiUsTUNkuxmJxKjfWdQUbNEgps6v9BLSZbJLQjlpwzsBsWLtnr8Nkzn9tQVva2XL/eW5e+xfoXgnOLXvjSC0z5wBTa5rcFhZLBmIljmHHqDAC6V3ez8IqFRM6NAPDmJW+Sakth5caMU2ZsMQHE2v+upWZuzaapw2t2qOHlb71M9axqambn/ciYexPNiYLo7hIR7gK+7zuE+DE5lar1nUFGzT8Lbe1Cc66g8g5frNGAhcB031Gk+C0pL1/y7u1muLSZ/t5G39mJ5sTFvkOIyCAEbfNiYOpAD5Xikyyz5DvnbNfoO4eMio8kmhPX+w4xFKWzznEs6YCbfMeQ0jAtlZr2+8XL1uDcBt9ZSpDORxIpFEHbfKvvGOJHQ9o14JzWSip+PRTghEqlUyQFbvAdQErHPp2du0ZXrX6akumuzQuvJJoTL/kOISJDcrPvAOKHgVkw+7AUtwcLcVmOUiuS7geW+Q4hpeMTa9sOes/6DZpEYPT8xXcAERmyO4E23yHEjwoonBkZZbgKala7jUqrSIolU2jInYyyHy5fefjcru4HfecoESqSRApNLNlBgX6JkpGrTrv1vjNIzhXk/+/SKpICBXXSmBSH6xct3rs2nX7Wd44i93yiOZHwHUJEhuWvvgOIH2NdWufuFrc3C7VtLsUi6V5ghe8QUlqqHNW3LFg8ucy5xb6zFDH1IokUrjigHoUS1JBOd/vOIDl1re8Aw1V6RVIw5O5vvmNI6ZmSSk25avHSpGa8yxkVSSKFKpZcj3qTStKEVLrHdwbJGQf8zneI4Sq9IimgIXfixV6dXbt8a+Xq+ZrxLuvmJ5oTL/gOISIjcoXvADL6JqVSag+L192J5sRrvkMMV6kWSXcBb/oOIaXpo+vaDmzSjHfZdrXvACIyYvcBr/sOIaNrck+q3HcGyZnf+g4wEqVZJMWSaQr8FyeFrWX5ysO37+p+wHeOItEJXOk7hIiMULCw7JW+Y8jomppKVfjOIDmxggKfUbo0i6TA5YBOFhRvrlu4eN+6dPoZ3zmKwF8TzYmVvkOISFZcRXAeg5SIqT09Vb4zSE5ck2hOdPkOMRKlWyTFkkvQKt/i0RioumXBoqnlzi30naXAXeY7gIhkSSz5BnCP7xgyeianUrW+M0hOFPyIrVLv4rwUONF3CCldk1PpyVcvXvriJ6ZPXY+ZGoqhez7RnND5XSLF5TfAkb5DjMRbyTSn/K2dJW2OMoP/3buSLx5YxVNLUpx5awcdPY6KMvhVUw37z+z7lJxU2rHvb9czs76MWz8+FoCv39nBba/0sNe0cq4+oQaAa57uYlW744sHFmaHzKRUqs53hv50rexi4W8X0pPsAYPxR4xn0jGTWPrXpax9ci1mRnlDObPOmEXl+Mqtnr/i9hWsvm81GFTPqmbmp2ZSNqaMJdctYd38ddTMrmHW/84CYPUDq0mtTzHpmEmj/TZz4YFEc+J53yFGqnR7kgBiybuBF33HkNK2R2fXzt9ZseoZzXg3LL/xHUBEsu6vwFu+Q4xERRn8+Jhqnj+rjoc/VcsvH+vmueUpvnZnB985fAxPnVnH+e+q4mt3dvS7jZ8/0sWukzZ/TUt2OB5ckGL+Z+tIOUdiaYr2bseVT3fzuf3GjMbbyonxqfQ43xn6Y+XGtJOmseOFOzLv2/NY9a9VdCzsYNKxk9jxezuywwU70LBXA8tuXrbVc7tXd7PyzpVsH9ueHb+/Iy7tSD6SJLUhxYZXNrDj94LbOt7qIN2VZs1/1jDxyIke3mVOXO47QDaUdpEU0FAd8e7EtvUHvL9tvXpEhqYdzWonUnxiyR7gYt8xRmJ6fRl7Tw96iOqrjF0nl7FwrcMM1nYGj0l2wIx66/P5C9amib/cwxl7by5+ygy6Ug7nHO3dUFkOP3ywi7P3H0Nled/bKQS1ztXiXF6eu1I5rpKaSNBjV15TTtWMKnpW91Bes7n3L92Zxqzvz9+lHemuNC7lcF2OivEVYOB6gt+j63ZYubHithVMPHoiVlG4v8cMSeA63yGyQUVSMJNO/4dyREbJ91asOnzHrq7/+M5RQK5KNCdW+Q4hIjnxG6DNd4hsaF2T5snFKQ6YVc7P3l3NV+/sYLufruMrd3Zw4f9U9/mcc/7ZwUVHVVOW8Z25vsr40K6VvOOy9cwdV0ZjlfHYohTv32XrYV6FpgxW+84wkK7lXXS80UHN9kHRtPSGpbxw7guseWgNU06YstXjK8dXMuk9k3jpyy/xwjkvUFZTRv1u9ZTXlNOwbwOvnvcqlZMqKRtbRvtr7TTs3TDabylX/phoTmzwHSIbVCTFkqvR0WjJE39euGT/+lR6vu8cBSAN/MR3CBHJkVgySREsLtvW5fjQdRv42XuqaagyLn28m5++u5q3vlTPT99dzaduad/qObe+1M2UWmOfGVufq/S1Q6p46sw6fvzuar59TyfnH1HF5U908ZHrN/C9+ztH4y3lRKVz63xn2JZUR4o3L3mTaR+ftqkXaeqJU9nlJ7sw7qBxrPzX1hOsptanWPfkOnb64U7s8tNdSHemWfPgGgAmHzuZHS7Ygekfm86yG5cx5YNTWHXfKt785Zssu2XroXsFJA38wneIbFGRFLgQ6PEdQmQMjPn7gkUzyp1b4DtLnrs50Zx42XcIEcmpnxF86SpI3amgQPrE7pV8cNegt+eqp7v44K7BnFkfflsFjy5MbfW8B95MccuLPUR+to6Tbmjn7td7+OSNWxZTTy4OnrfTxDKufrqb6z48lmeWpXh55dbbKwTVzq33naE/rsfx1iVvMe6gcTTu27jV/Y0HNrL28bVb3d72bBuVkyqpaKjAKoyGfRvY8MqWHSztbwS/16ppVax5YA2zz5pN54JOOpcUbMH7p0Rz4gXfIbJFRRJALNkKXOM7hgjAxHR60rWLlrbjXFEMNcmRFt8BRCTHYsnXKNClOpxzfOqWDnadVM65B22edW5GfRn3vREUMne/nmLHiVt/DbvwqGoWnFtP6zn1/PnEGo6cW8G1H6zZ4jHfvqeT899VRXcaUuGUP2UGGwp09ce6tNu6Sy0POOdY+PuFVE2vYtJ7Ns86l1nErHtyHVXTt55ZsHJiJe2vtpPuTOOcY/1z67d63LIblzHlhCm4Hrf5cEAZpLsK8thACjjfd4hsKvUpwDN9HzgF6HsuTpFRtFtX147nr1j16HmTJuyLmQ5mbOlfiebEo0N9kpk54Frn3Mnh9QpgMfCIc+644YYxsxnAL5xzg15OwMyuBG51zt0w3NcVKRHfB07wHWKoHngrxTXzu9l9Shl7/To43vV//1PFb4+v5ov/7KAnDdUV8JvjguJn0bo0Z9zSwT8+MXbAbf/thW72m1HOjPqgaThoVjm7X9rGHlPL2HNaYX6FaUynehbm4VfSDS9vYM2Da6iaVcUr334FCIbZrb5/dVAoGYyZOIYZp84AghntFl6xkMi5EcZuP5aG/Rp45TuvYOVG9exqxh8xftO21/53LTVzazZNHV6zQw0vf+tlqmdVUzO7Zusw+e8PiebES75DZJNp1uEMscargZN9xxDZ6DuTJtx3Y33d4b5z5JkjE82JIS82aWZtwMvAwc65djN7L8FQ2wWDLZLMrMI519Pf9SFkuRIVSSKDE2u8CfiA7xiSO5+dOvne/4ytOcJ3Dhm2FLBLojnxiu8g2aQj1Fv6PgU8/lmKz3dXrDp8l07NeJfh38MpkDLcBjSFP38M+NPGO8xsfzN70MyeDP/dObz9VDO73sz+DtzRx/WImT0TPrbczH5oZo+Z2Xwz+0x4u5nZJWb2nJnFga2nQhKR/pwH6IhuEZuUShXF3Ncl7JpiK5BARdKWYskXKZK53aV4/GHRkv0bUqmnfefIE18f4fP/DJxkZtXAHsAjGfe9ABzmnHsHwZey/8u47yCg2Tl3ZD/XN/oUkHTO7QfsB3zazOYSDBfaGdgd+DRw8Ajfh0jpiCUTwPW+Y0juTOlJ6fto4eoBLvAdIhf0R7m1C9ARK8kjwYx3i2dVOFfQK9Bnwc2J5sRDI9mAc24+ECHoRfpHr7sbgevDXqGfAm/PuO9O59yqbVzf6BjgFDN7iqAAmwjsCBwG/Mk5l3LOLQLuHsn7EClBMTTSo2hNTaUKf7Gn0nVVojnxmu8QuaAiqbdY8jkyhuCI5IMJ6fTEPyxa0kmeryWRQyng/2VpW7cAP2Lr/+cXAPc453YDjgcyV3nsPT1tf9PVGvAF59xe4WWuc+6O8D4dfBEZrljyedQ2F60pPamCnKlA6Aa+5ztErqhI6tvXgaJYLViKx9u6unf4/oqVL+BcKR5NvSrRnHg+S9v6PXC+cy7R6/ZGYGH486nD3PbtwGfNrBLAzHYys1rgfoJhfuVmNh141zC3L1LKvgN0+Q4h2Tc5lRp4Wj/JR1cmmhOtvkPkioqkvsSSC9A6LJKH3te2Yb+PrGv7t+8co6yD4MtRVjjnFjjnft7HXRcBF5rZAwx/KYDLgeeAJ8Jhe5cRLLVwE8HMegngUuC+YW5fpHTFkq8SLDArRWZiKtXgO4MMWRfBhGdFS1OA9yfWWA08T3D+gkhe+eiMqf9+rqrqUN85RslFiebESCdsEJFiEGusB14EpvuOItnTBZ37zJ299Yqsks/+L9Gc+KbvELmknqT+xJIdwFd8xxDpy7WLlh7YmEo95TvHKFhAka3gLSIjEEuuA6K+Y0h2jYEqnOvvXE/JP69QpDPaZVKRtC2x5F+BkazJIpITlVD59wWLZ1c494bvLDl2TqI5oYZTRDJdAzzsO4RkVzkkfWeQQTsz0Zzo8B0i11QkDeyLBDNrlayOHsf+v21jz1+38fZftfGde4L/F7F7O5j5k3Xs9es29vp1G/94ubvfbaTSjndc1sZxf9w8H8bX7+xgj0vbOOWm9k23XfN0Fz9/uDN3b6aIjE+nJ/xp0ZIenCvWhuW2RHPir75DiEieiSUdcDaaMbKoVJbu7K2F5tpEc+JfvkOMBhVJAwkWsbvMdwyfqsrh7uZanj6zjqc+U8s/X+3h4QU9AHzpwDE8dWYdT51Zx7E79r/Mwc8f6WLXSZv/3JIdjgcXpJj/2TpSzpFYmqK923Hl0918br8xOX9PxWKXru7tW5avfBnniq2Q7wC+4DuEiOSpWPIxgpkqpUiM1XC7QrASONd3iNGiImlwvgUs8R3CFzOjbowB0J2G7lSwGMxgLVibJv5yD2fsvbn4KTPoSjmcc7R3Q2U5/PDBLs7efwyV5UPZujSt37Dvx9a2PeA7R5a1JJoTr/oOISJ57avAYt8hJDvq0mkNI8l/X000J5b7DjFaVCQNRiy5Gvis7xg+pdKOvX7dxpQfruPoeRUcMKsCgEse7WKPS9s4/eZ2Vrf3PfLhnH92cNFR1ZRl1D71VcaHdq3kHZetZ+64MhqrjMcWpXj/Llp0ezi+sWr1Ybt3dN7vO0eWvIym4BeRgQRt85m+Y0h2jEul+x+zL/ng3kRz4grfIUaTiqTBiiX/BvzZdwxfysuMp86sY8G59Ty6KMUzy1J8dt8xvHp2HU+dWcv0OuPLd2x9Dt+tL3UzpdbYZ8bWy8587ZAqnjqzjh+/u5pv39PJ+UdUcfkTXXzk+g18734dUBqqqxcvPXh8KvWk7xwjlAJOSTQn9AcgIgOLJW8B/ug7hozchFRK55jlr07gM75DjDYVSUPzeWCZ7xA+jas2jphTwT9f6WFqXRnlZUaZGZ/eZwyPLtz6tJgH3kxxy4s9RH62jpNuaOfu13v45I3tWzzmycXB83aaWMbVT3dz3YfH8syyFC+vLLbTbHKrAipuWbA4Uulcq+8sI3BRojmhWatEZCi+ACz1HUJGZnJKbX4euzDRnHjJd4jRpiJpKGLJlcD/+o4x2pavT7OmIzjA097tuOv1HnaZVMbidelNj7np+W52m7L1n9OFR1Wz4Nx6Ws+p588n1nDk3Aqu/WDNFo/59j2dnP+uKrrTsPE4UpnBBnW8D9m4dHr8nxcucQU6491TwHd8hxCRAhNLrgI+5zuGjMzUVGrrISeSD14ALvQdwgcVSUMVS95Mic2os7jN8a6r1rPHpW3s99v1HD2vguN2quRrd3Wy+6Vt7HFpG/e0pvjpu6sBWLQuzbF/2DDAVgN/e6Gb/WaUM6O+jHHVxkGzytn90jbMYM9p2l8Ox07d3XN/uHzlKzjX4zvLEHQSDLNTaSwiQxdL3gj8xXcMGb4pPakq3xlkK93AaYnmRJfvID6YcxoCOmSxxjrgaWCe7ygi/blowrj7r2lsOMx3jkH6eqI5cZHvECJSwGKNjcCTwFzfUWToHqipTpw5bcruvnPIFs5JNCd+7juEL+pJGo5Ysg04hRJfZFby29dWrTlsr8KY8e5+4Ee+Q4hIgYslk8BHCY5+S4GZ3JOq851BtnB9KRdIoCJp+GLJB4D/5zuGyLZcsXjpwRNSqSd859iGJcBJieZEesBHiogMJFhk9uu+Y8jQTUylGnxnkE1eBD7lO4RvKpJGIpb8IXCD7xgi/QlmvFu0faVzr/vO0ocUQYGkxSBFJHtiyZ8Ct/iOIUMzLp0eh84ByQcbgBMTzYl1voP4piJp5E4DnvcdQqQ/jWnXeP3CxWbOrfGdpZdvJJoT9/kOISJF6VTgTd8hZPDKoRwo+S/meeDMRHPiGd8h8oGKpJEKzk86AVjrO4pIf7bv7on8eNmK1/JoxrubNVGDiORMLLma4PykkpyVq1BVwBrfGUrcbxLNiWt8h8gXKpKyIZZ8keColbqJJW8dvaF979OS6x70nQN4FWj2HUJEilws+TAluLZhIatyrs13hhL2X+Bs3yHyiYqkbIklbwJ+4DuGyLacu3rNYXt3dPic8W4t8IFEc6IQF7sVkUITS14F/NB3DBmcmrRr952hRK0mOA+p03eQfKIiKbu+BdzpO4TItvx+8bJDJvWkHvfw0j3AhzXWWURGWRT4u+8QMrCGdLrDd4YS5AgWc2/1HSTfqEjKplgyBXwESPiOItKfcii/eeGiHcc499oov/TnEs2JO0b5NUWk1MWSaeDjqG3Oe+NTqXw5b7aUfD3RnLjVd4h8pCIp22LJNcB7gDc8JxHpV0Mw4125Obd6lF7yokRz4rej9FoiIlsKJlk6HljmO4r0b2JaS+aNsp8mmhMajtoPFUm5EEsuAt4NrPAdRaQ/87p75vxs2Yo3cC7Xq9PfQDDcRUTEn1jyDYJCab3vKNK3yT3qSBpFfwK+7DtEPlORlCvBjHdNaGcseezIDe17nZFc+3AOX+IB4OREc0IzP4qIf7Hko8AH0dTgeWlKKlXpO0OJuAs4VW3ztqlIyqVgZ/whINdH6kWG7Yurk4fu156TGe8eB45NNCd0Iq6I5I9Y8g7gFEBju/LM1J7UGN8ZSsB/gQ8mmhM6UDAAFUm5FkveDpyO1lCSPPbbJcsOmdzTk80Z7xLAuxPNCS2yLCL5J5b8C/AZ1DbnlSmp1FjfGYpcAjgm0ZxY5ztIIVCRNBpiyWuBc33HEOlPOZTfvGDxTmPS7tUsbO5F4KhEc2JVFrYlIpIbseTlqG3OK5NSqTrfGYrYi8DRapsHT0XSaIklfwZ8ER21kjxV71zDXxcurjTnRrIDfQ34n0RzQjNIiUj+C9rmb/iOIYEJqVSj7wxF6nWCg5dLfQcpJCqSRlMs+QuC7n2Ng5a8FOnpmX3x0uVvDXPGuzcJCqSF2c4lIpIzseSFwJd8xxBoSLsGnEv5zlFkFhC0zQt8Byk0KpJGWyz5W6AZ0E5A8tLh7R17nrlmyDPevQAcohW7RYqTmaXM7Ckze9bMnjazc82sLLxvXzP7RR5kjJnZV4b15KBHSQcxPTMwgzW+cxSRl4DDEs2J14fzZDNzZnZNxvUKM1tuZiNafNbMZpjZDUN8zpVmduJIXneoVCT5EJyjdBKa9U7y1Flrkoce2N5+3yAf/gTBTlhHqUSKV7tzbi/n3NuBo4Fjge8AOOced86dPdIXMLOKkW5jRGLJ36CDmN5VgCb8yY6HgYOHWyCF1gO7mVlNeP1oYEijRXr/vzazCufcIufcqBY8w6EiyZdY8gbgBKDTdxSRvly2ZPmhU3t6HhvgYfcD70o0J5aPRiYR8c85twz4X+DzFjjCzG41szIzazWzcRsfa2avmNlUM5tjZv8ys/nhv7PD+680s5+Y2T3AD8xsBzO7K+ytesLMtg8f91Uzeyx8/ncztv9NM3vRzO4Cdh7xmwsOYn4EraPkTXXaaX3JkbsFODLRnFiZhW3dRrDuJ8DHCBahBcDM9jezB83syfDfncPbTzWz683s78AdfVyPmNkz4WPLzeyHGf+/PxPebmZ2iZk9Z2ZxYEoW3suQqEjyKZaMA8cBG3xHEemtDMr+tmDxLlXp9Mv9PCQOvEfTfIuUHufcawTfIaZk3JYGbiY4AIiZHQC0OueWApcAVzvn9gD+AGQOz9sJOMo59+Xwvl865/YEDgYWm9kxwI7A/sBewD5mdpiZ7UMwKuMdBAvE7peVNxdL3gi8H2jLyvZkSMa6tL4TjcxvCNZBas/S9v4MnGRm1cAewCMZ970AHOacewdwHvB/GfcdBDQ7547s5/pGnwKSzrn9CP4Pf9rM5hLsR3YGdgc+TbA/GFUqknyLJe8CDmWI3Zcio6HOufobFy6pMedW9LrrWuCELO6ERaTwWB+3/QX4aPjzSeF1CL4g/TH8+RrgnRnPud45lzKzemCmc+4mAOdch3NuA3BMeHmSYHjvLgRF06HATc65Dc65tQRHz7Mjlvwnapu9aEyl1Ys3fOclmhOfSTQnsjZk1Dk3H4gQ9CL9o9fdjcD1Ya/QT4G3Z9x3p9tyttze1zc6BjjFzJ4iKMAmEvz/Pgz4k3Mu5ZxbBNydhbczJCqS8kEs+QRB9TzQ0CaRUTe7p2fWL5cuX4RzGxuuWKI5cXKiOaFz6kRKlJnNIzh3p/d0/w8BO5jZZOADwI39bCJzOYyNw6v6Kro23n5heE7UXs65HZxzv+tjO9kVSz4FHEBQnMkoGZ9O65ywoesBTk80Jy7I0fZvAX5ExlC70AXAPc653YDjgeqM+3oPm+xvGKUBX8j4/z3XOXdHeJ/XZXNUJOWLWHIxcDibj7qJ5I1D2zv2OGtN8kHgE4nmxHcHfIKIFK2wAPo1cIlzbosvMeH1m4CfAM875zaeE/EgQc8SwCeA//TebtgbtMDMPhC+TpWZjQVuB043s7rw9plmNoXgnMgTzKwm7IU6PrvvFIglFxL0KI1oNi8ZvEmplNaTHJr1wPsSzYkrcvgavwfOd84let3eyObe1lOHue3bgc+aWSWAme1kZrUE/79PCs9Zmg68a5jbHzYVSfkklmwnljwJiKFFZyW/LD5zzdpoojnxx4EfKiJFqGbjFODAXcAdQH8HTP4CfJItD/qdDZxmZvOBkwkWV+/LycDZ4eMeBKaFR5X/CDxkZgngBqDeOfdE+BpPAX8F/j2C99e/WHI9wTlKP8/J9mULk3tS5b4zFJBlwBGJ5sRtuXwR59wC51xff/8XARea2QPAcH9vlwPPAU+Ew/YuAyoIDra8DCSAS4HBzribNdbrIJDki1jjh4GrgJqBHiqSY48CJxBLLvIdRETEq1jjGcDFbDmsSLLo2ob6h34wcfxBvnMUgEeAk7Q+Ye6oJylfxZLXE5y0prVnxKergMNUIImIALHk5QSzbL3mO0qxmtrTU+U7Q55zwA+BQ1Ug5ZZ6kvJdrHEC8DuCE2BFRss64CxiyWsGfKSISKmJNTYCVxBOdy7Z81TVmBdPnjFt5GteFadlwCmJ5sTtvoOUAhVJhSLWeCbBibAafie59hjwMWLJV30HERHJa7HGLwMtBOdQSBYsqChf+N7tZs70nSMP3QWcnGhOLPEdpFRouF2hiCV/TTBNeO+ZRUSyxQE/AA5RgSQiMgix5I+BI4A3PScpGhNS6XG+M+SZHuAbwLtVII0u9SQVmlhjNcFc9Wf5jiJFZTFwSri4sYiIDEWssYFgMc3TfUcpBrtHtuvETOcmwRvAxxPNiQd9BylFKpIKVazxfQTz1k/0HUUK3q3AacSSK3wHEREpaLHGJuC3wHTfUQrZnpHtlqbNpvrO4dmNwKcSzYk1voOUKhVJhSzWOJXgyNXHfEeRgrQS+Aqx5JW+g4iIFI1gwqVLUNs8bPvOmfVKZ1nZDr5zeLIK+HqiOXG57yClTkVSMYg1Hk2w0Nb2vqNIwbgC+Cqx5ErfQUREilKs8UPAL4FS7xEZsnfOnvl0srx8T985RpkjmM34/yWaExrZkQdUJBWL4FylbwFfBcZ4TiP56wXgTGLJUV+5WkSk5ARThZ9PcB5xuec0BeM9s2Y8vLCy4kDfOUbRf4GzEs2JR3wHkc1UJBWbWOOuwGXAob6jSF7pAL4PXEQs2eU7jIhISYk17kkwBO+dvqMUgo/OmPqf56qqSuGzWgV8E/hNojmR9h1GtqQiqRjFGg04jWA650me04h/dwKfI5Z8xXcQEZGSFbTNJwMXoSF42/TZqZPv/c/YmiN858ihjUProonmhIa95ykVScUsmJL0K8CXgDrPaWT0PQF8g1hSK3OLiOSLYAjeecDngGrPafLSeZMm3HtTfd0RvnPkyOMEQ+se9R1Etk2LyRazWHItseR5BBM6XAxomFVpeAn4KLCvCiQRkTwTSyaJJb8M7AhcDqQ8J8o7k3tSFb4z5MBi4EzgABVIhUE9SaUk1hgBvgt8EhXIxWghwe/3CmLJHt9hRERkEGKNOxFM7vARwDynyQvX1dc9fMGkCcUycUMrwRDL3yeaE52es8gQqEgqRbHGtxOcxP9+31EkK1YCLcAlxJIdvsOIiMgwxBrfAXwPONZ3FN/ural5+gvTJhf6FOAvABcCf0w0J3TgsgCpSCplscZ9CKYMPxFNTVqIWoFfAJcTS67znEVERLIhmAnvK8BJQDEOOxvQs2PGvHzSzGk7+s4xTE8C/wfcqBnrCpuKJIFY41zgXOB0YKznNDKwh4CfADcRS2osu4hIMYo1bkcw8dKnKbHJl5aWly89avbMQpsB8AHg+4nmxG2+g0h2qEiSzWKNE4AzCGbcmeM5jWwpBfwV+Cmx5MO+w4iIyCiJNY4jOOH/C8AMv2FGRxd07jN3dpXvHIN0J0FxpEXai4yKJNlarLEceB/weeBd6ERSn1YBVwK/IJZ8w3MWERHxJdZYARwPfAY4hiJvm3ePbLces1rfOfrxOnANcHWiOfGq7zCSGyqSZNtijXOATxDMiLer5zSlogu4lWAH/A9iSU3dLiIimwXD5E8DmoHZntPkxF6R7RamzGb6zpFhLXADcBXw70RzQl+gi5yKJBm8YKKHk4GPAVM8pylG/wGuBa4jllztO4yIiOS5WGMZ8D8EBzLfB4zzmieL9p8z68X2srKdPcdIA3cRFEY3JZoT7Z7zyChSkSRDF3T5H0NQMB0P5Gt3eCF4EfgDcC2x5Ou+w4iISIGKNY4BjiKYsfb9wAS/gUbm8Nkzn1hVXr63p5d/jqAwujbRnFjkKYN4piJJRibWWAUcBrw3vOziN1DeawfuBW4jGEqnscwiIpJdwcHMIwkKpuOA6X4DDV3TrOkPvVlZedAovdx64H6CXqM7E82JxCi9ruQxFUmSXbHGCJsLpiNRLxPAK2wsiuA+Ykl114uIyOgJFpE/mqCn6XAKYErxT0yfev/86qrDcrT5FPAYQVF0F/BQojmh839lCyqS8oCZfRP4OMF/2jTBzDWfBn7inHtuGNuLALc653bLZs4hC3qZDg0v+wP7ARO9Zsq9NMEq248CjwB3EUu+4jeSiIhIKNZYCRxIUDQdCuwD1HvN1Iezp0y6957asUdkcZMvsrkouifRnEhmcdtShFQkeWZmBxEsDHqEc67TzCYBY5xzwx4DmzdFUl9ijdsDBxAUTfsD7wCqvWYamYUEBdHGouhxYsl1fiOJiIgMUjD5wy5sPpi5P7AHMMZnrAsmjr/vuob6w4fx1B6CERzPAM+Gl4cTzYm3splPil+F7wDCdGCFc64TwDm3AsDM7gW+4px73MzagJ8TjCtuB97vnFtqZtsTnPRfTjCc61zn3BZd6GZWDrQARwBVwC+dc5eNxhvrU3AOzqvAH4PrjZXAbgTTi+8AbB/+uwP5NYPeYoKjUJmXp4gldUKniIgUrlgyTTBRwXME6/JtnATibcDO4WWnjJ9HpddpSipVPsBD0gTfJzKLoWeBFzV0TrJBRZJ/dwDnmdlLBF3Af3HO9V61uRZ42Dn3TTO7iGAo3vcICqefO+f+ZGZn9rP9TwFJ59x+ZlYFPGBmdzjn8mMmtViyG3gyvPS6r7GezQXTPIKiaUI/l+Ec8VpLsFjr6vDfVRnXFxAsFvc60KrziEREpGQE6/M9FV563dc4neCA5vTwMi28bPx5KsE5T2MJDuIOVTewZmZ3z2rgQWBpeFmW8fOrwAuJ5kTHMLYvMigabpcHwt6eQ4F3EZyPFAVOZXNPUidQ7ZxzZvZR4Gjn3BlmthKY6pzrMbMGYJFzri5zuJ2Z3UDQbb4hfLlG4DPOuTtG9U3mWqyxFhgPVA7i0euANcSSPbkNJSIiUsKCHqmxGZea8J6ejEt3xs/txJLrPSQV2Yp6kvKAcy5FMC30vWaWIFhBO1O321zNphja782ALzjnbh9x0HwW7FS1YxUREckXQY9UF7DGcxKRISvzHaDUmdnOZrZjxk17AW8M8ukPAx8Kfz6pn8fcDnzWzCrD19vJzDQtt4iIiIhIP1Qk+VcHXGVmz5nZfIITJWODfO45wLlm9ijBWOC+prO8nOBkzCfM7BngMtSDKCIiIiLSL52TVMDMbCzQHp6rdBLwMefc+33nEhEREREpZOpRKGz7AJeYmRGM9z3dbxwRERERkcKnniQREREREZEMOidJREREREQkg4okERERERGRDCqSREREREREMqhIEhERERERyaAiSUREREREJIOKJBERERERkQwqkkRERERERDKoSBIREREREcmgIklERERERCSDiiQREREREZEMKpJEREREREQyqEgSERERERHJoCJJREREREQkg4okERERERGRDCqSREREREREMqhIEhERERERyaAiSUREREREJIOKJBERERERkQwqkkRERERERDKoSBIREREREcmgIklERERERCSDiiQREREREZEMKpJEREREREQyqEgSERERERHJoCJJREREREQkg4okERERERGRDCqSREREREREMqhIEhERERERyaAiSUREREREJIOKJBERERERkQwqkkRERERERDKoSBIREREREcmgIklERERERCTD/wfCiBFD3VGrZwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1152x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#饼图，可以自动算百分比\n",
+    "quit_level=quit_df.MaritalStatus.value_counts().sort_index()\n",
+    "not_quit_level=not_quit_df.MaritalStatus.value_counts().sort_index()\n",
+    "unique=df['MaritalStatus'].unique()\n",
+    "unique=maps['MaritalStatus'].inverse_transform(unique)\n",
+    "marital_dict=dict(zip(unique,[i for i in range(len(unique))]))\n",
+    "plt.figure(figsize=(16,6))\n",
+    "plt.subplot(1,2,1)\n",
+    "quit_pie=pd.DataFrame(quit_df[\"MaritalStatus\"].value_counts())\n",
+    "quit_pie.index=quit_pie.index.map(dict(zip(marital_dict.values(),marital_dict.keys())))\n",
+    "quit_pie[\"MaritalStatus\"].plot(kind=\"pie\",autopct='%.1f%%',title=\"Attrition in differrent Marital Status\")\n",
+    "plt.subplot(1,2,2)\n",
+    "not_quit_pie=pd.DataFrame(not_quit_df[\"MaritalStatus\"].value_counts())\n",
+    "not_quit_pie.index=not_quit_pie.index.map(dict(zip(marital_dict.values(),marital_dict.keys())))\n",
+    "not_quit_pie[\"MaritalStatus\"].plot(kind=\"pie\",autopct='%.1f%%',title=\"Not-attrition in differrent Marital Status\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6869219b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/菜谱订单数据分析.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/菜谱订单数据分析.ipynb
new file mode 100644
index 0000000..7c78141
--- /dev/null
+++ b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/菜谱订单数据分析.ipynb	
@@ -0,0 +1,2521 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "03fb4961",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>detail_id</th>\n",
+       "      <th>order_id</th>\n",
+       "      <th>dishes_id</th>\n",
+       "      <th>logicprn_name</th>\n",
+       "      <th>parent_class_name</th>\n",
+       "      <th>dishes_name</th>\n",
+       "      <th>itemis_add</th>\n",
+       "      <th>counts</th>\n",
+       "      <th>amounts</th>\n",
+       "      <th>cost</th>\n",
+       "      <th>place_order_time</th>\n",
+       "      <th>discount_amt</th>\n",
+       "      <th>discount_reason</th>\n",
+       "      <th>kick_back</th>\n",
+       "      <th>add_inprice</th>\n",
+       "      <th>add_info</th>\n",
+       "      <th>bar_code</th>\n",
+       "      <th>picture_file</th>\n",
+       "      <th>emp_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2956</td>\n",
+       "      <td>417</td>\n",
+       "      <td>610062</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>蒜蓉生蚝</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>49</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016/8/111:05:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>caipu/104001.jpg</td>\n",
+       "      <td>1442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2958</td>\n",
+       "      <td>417</td>\n",
+       "      <td>609957</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>蒙古烤羊腿</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>48</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016/8/111:07:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>caipu/202003.jpg</td>\n",
+       "      <td>1442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2961</td>\n",
+       "      <td>417</td>\n",
+       "      <td>609950</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>大蒜苋菜</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016/8/111:07:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>caipu/303001.jpg</td>\n",
+       "      <td>1442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2966</td>\n",
+       "      <td>417</td>\n",
+       "      <td>610038</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>芝麻烤紫菜</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016/8/111:11:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>caipu/105002.jpg</td>\n",
+       "      <td>1442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2968</td>\n",
+       "      <td>417</td>\n",
+       "      <td>610003</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>蒜香包</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2016/8/111:11:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>caipu/503002.jpg</td>\n",
+       "      <td>1442</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   detail_id  order_id  dishes_id  logicprn_name  parent_class_name  \\\n",
+       "0       2956       417     610062            NaN                NaN   \n",
+       "1       2958       417     609957            NaN                NaN   \n",
+       "2       2961       417     609950            NaN                NaN   \n",
+       "3       2966       417     610038            NaN                NaN   \n",
+       "4       2968       417     610003            NaN                NaN   \n",
+       "\n",
+       "  dishes_name  itemis_add  counts  amounts  cost  place_order_time  \\\n",
+       "0        蒜蓉生蚝           0       1       49   NaN  2016/8/111:05:00   \n",
+       "1       蒙古烤羊腿           0       1       48   NaN  2016/8/111:07:00   \n",
+       "2        大蒜苋菜           0       1       30   NaN  2016/8/111:07:00   \n",
+       "3       芝麻烤紫菜           0       1       25   NaN  2016/8/111:11:00   \n",
+       "4         蒜香包           0       1       13   NaN  2016/8/111:11:00   \n",
+       "\n",
+       "   discount_amt  discount_reason  kick_back  add_inprice  add_info  bar_code  \\\n",
+       "0           NaN              NaN        NaN            0       NaN       NaN   \n",
+       "1           NaN              NaN        NaN            0       NaN       NaN   \n",
+       "2           NaN              NaN        NaN            0       NaN       NaN   \n",
+       "3           NaN              NaN        NaN            0       NaN       NaN   \n",
+       "4           NaN              NaN        NaN            0       NaN       NaN   \n",
+       "\n",
+       "       picture_file  emp_id  \n",
+       "0  caipu/104001.jpg    1442  \n",
+       "1  caipu/202003.jpg    1442  \n",
+       "2  caipu/303001.jpg    1442  \n",
+       "3  caipu/105002.jpg    1442  \n",
+       "4  caipu/503002.jpg    1442  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "filepath='detail.csv'\n",
+    "#注意读取时的编码问题\n",
+    "df=pd.read_csv(filepath,encoding='gbk')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "218f0753",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10037 entries, 0 to 10036\n",
+      "Data columns (total 19 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   detail_id          10037 non-null  int64  \n",
+      " 1   order_id           10037 non-null  int64  \n",
+      " 2   dishes_id          10037 non-null  int64  \n",
+      " 3   logicprn_name      0 non-null      float64\n",
+      " 4   parent_class_name  0 non-null      float64\n",
+      " 5   dishes_name        10037 non-null  object \n",
+      " 6   itemis_add         10037 non-null  int64  \n",
+      " 7   counts             10037 non-null  int64  \n",
+      " 8   amounts            10037 non-null  int64  \n",
+      " 9   cost               0 non-null      float64\n",
+      " 10  place_order_time   10037 non-null  object \n",
+      " 11  discount_amt       0 non-null      float64\n",
+      " 12  discount_reason    0 non-null      float64\n",
+      " 13  kick_back          0 non-null      float64\n",
+      " 14  add_inprice        10037 non-null  int64  \n",
+      " 15  add_info           0 non-null      float64\n",
+      " 16  bar_code           0 non-null      float64\n",
+      " 17  picture_file       10037 non-null  object \n",
+      " 18  emp_id             10037 non-null  int64  \n",
+      "dtypes: float64(8), int64(8), object(3)\n",
+      "memory usage: 1.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c649b2cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>detail_id</th>\n",
+       "      <th>order_id</th>\n",
+       "      <th>dishes_id</th>\n",
+       "      <th>logicprn_name</th>\n",
+       "      <th>parent_class_name</th>\n",
+       "      <th>itemis_add</th>\n",
+       "      <th>counts</th>\n",
+       "      <th>amounts</th>\n",
+       "      <th>cost</th>\n",
+       "      <th>discount_amt</th>\n",
+       "      <th>discount_reason</th>\n",
+       "      <th>kick_back</th>\n",
+       "      <th>add_inprice</th>\n",
+       "      <th>add_info</th>\n",
+       "      <th>bar_code</th>\n",
+       "      <th>emp_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10037.000000</td>\n",
+       "      <td>10037.000000</td>\n",
+       "      <td>10037.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10037.0</td>\n",
+       "      <td>10037.000000</td>\n",
+       "      <td>10037.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10037.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10037.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4712.339344</td>\n",
+       "      <td>802.775630</td>\n",
+       "      <td>609985.155026</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.108499</td>\n",
+       "      <td>44.821361</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1207.549766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1747.410959</td>\n",
+       "      <td>320.209032</td>\n",
+       "      <td>118.412398</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.611016</td>\n",
+       "      <td>35.815435</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>166.800691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>753.000000</td>\n",
+       "      <td>137.000000</td>\n",
+       "      <td>606000.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>982.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3369.000000</td>\n",
+       "      <td>542.000000</td>\n",
+       "      <td>609952.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1097.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4666.000000</td>\n",
+       "      <td>780.000000</td>\n",
+       "      <td>609983.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1147.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5971.000000</td>\n",
+       "      <td>1110.000000</td>\n",
+       "      <td>610021.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>56.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1293.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>8246.000000</td>\n",
+       "      <td>1324.000000</td>\n",
+       "      <td>610072.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1610.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          detail_id      order_id      dishes_id  logicprn_name  \\\n",
+       "count  10037.000000  10037.000000   10037.000000            0.0   \n",
+       "mean    4712.339344    802.775630  609985.155026            NaN   \n",
+       "std     1747.410959    320.209032     118.412398            NaN   \n",
+       "min      753.000000    137.000000  606000.000000            NaN   \n",
+       "25%     3369.000000    542.000000  609952.000000            NaN   \n",
+       "50%     4666.000000    780.000000  609983.000000            NaN   \n",
+       "75%     5971.000000   1110.000000  610021.000000            NaN   \n",
+       "max     8246.000000   1324.000000  610072.000000            NaN   \n",
+       "\n",
+       "       parent_class_name  itemis_add        counts       amounts  cost  \\\n",
+       "count                0.0     10037.0  10037.000000  10037.000000   0.0   \n",
+       "mean                 NaN         0.0      1.108499     44.821361   NaN   \n",
+       "std                  NaN         0.0      0.611016     35.815435   NaN   \n",
+       "min                  NaN         0.0      1.000000      1.000000   NaN   \n",
+       "25%                  NaN         0.0      1.000000     25.000000   NaN   \n",
+       "50%                  NaN         0.0      1.000000     35.000000   NaN   \n",
+       "75%                  NaN         0.0      1.000000     56.000000   NaN   \n",
+       "max                  NaN         0.0     10.000000    178.000000   NaN   \n",
+       "\n",
+       "       discount_amt  discount_reason  kick_back  add_inprice  add_info  \\\n",
+       "count           0.0              0.0        0.0      10037.0       0.0   \n",
+       "mean            NaN              NaN        NaN          0.0       NaN   \n",
+       "std             NaN              NaN        NaN          0.0       NaN   \n",
+       "min             NaN              NaN        NaN          0.0       NaN   \n",
+       "25%             NaN              NaN        NaN          0.0       NaN   \n",
+       "50%             NaN              NaN        NaN          0.0       NaN   \n",
+       "75%             NaN              NaN        NaN          0.0       NaN   \n",
+       "max             NaN              NaN        NaN          0.0       NaN   \n",
+       "\n",
+       "       bar_code        emp_id  \n",
+       "count       0.0  10037.000000  \n",
+       "mean        NaN   1207.549766  \n",
+       "std         NaN    166.800691  \n",
+       "min         NaN    982.000000  \n",
+       "25%         NaN   1097.000000  \n",
+       "50%         NaN   1147.000000  \n",
+       "75%         NaN   1293.000000  \n",
+       "max         NaN   1610.000000  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c68b394d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2956, 2958, 2961, ..., 5379, 5380, 5688], dtype=int64)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['detail_id'].values  #这是一个numpy，如果取df['detail_id'],则其为dataframe的series，也就是dataframe的一列"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "06ff220e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Index(['detail_id', 'order_id', 'dishes_id', 'logicprn_name',\n",
+       "        'parent_class_name', 'dishes_name', 'itemis_add', 'counts', 'amounts',\n",
+       "        'cost', 'place_order_time', 'discount_amt', 'discount_reason',\n",
+       "        'kick_back', 'add_inprice', 'add_info', 'bar_code', 'picture_file',\n",
+       "        'emp_id'],\n",
+       "       dtype='object'),\n",
+       " (10037, 19),\n",
+       " 190703)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns,df.shape,df.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "116c7fd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>order_id</th>\n",
+       "      <th>dishes_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>458</td>\n",
+       "      <td>蒜香辣花甲</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>458</td>\n",
+       "      <td>剁椒鱼头</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>458</td>\n",
+       "      <td>凉拌蒜蓉西兰花</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>458</td>\n",
+       "      <td>木须豌豆</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>458</td>\n",
+       "      <td>辣炒鱿鱼</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>458</td>\n",
+       "      <td>酸辣藕丁</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>458</td>\n",
+       "      <td>炝炒大白菜</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>458</td>\n",
+       "      <td>香菇鸡肉粥</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153</th>\n",
+       "      <td>458</td>\n",
+       "      <td>干锅田鸡</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>458</td>\n",
+       "      <td>桂圆枸杞鸽子汤</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>155</th>\n",
+       "      <td>458</td>\n",
+       "      <td>五香酱驴肉</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>156</th>\n",
+       "      <td>458</td>\n",
+       "      <td>路易拉菲红酒干红</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>458</td>\n",
+       "      <td>避风塘炒蟹</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>158</th>\n",
+       "      <td>458</td>\n",
+       "      <td>白饭/大碗</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     order_id dishes_name\n",
+       "145       458       蒜香辣花甲\n",
+       "146       458        剁椒鱼头\n",
+       "147       458     凉拌蒜蓉西兰花\n",
+       "148       458        木须豌豆\n",
+       "149       458        辣炒鱿鱼\n",
+       "150       458        酸辣藕丁\n",
+       "151       458       炝炒大白菜\n",
+       "152       458       香菇鸡肉粥\n",
+       "153       458        干锅田鸡\n",
+       "154       458     桂圆枸杞鸽子汤\n",
+       "155       458       五香酱驴肉\n",
+       "156       458    路易拉菲红酒干红\n",
+       "157       458       避风塘炒蟹\n",
+       "158       458       白饭/大碗"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#提取order_id=458的数据，再取第1列和第5列\n",
+    "detail=df\n",
+    "detail.iloc[(detail['order_id']==458).values,[1,5]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c72626fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "白饭/大碗         323\n",
+       "凉拌菠菜          269\n",
+       "谷稻小庄          238\n",
+       "麻辣小龙虾         216\n",
+       "辣炒鱿鱼          189\n",
+       "             ... \n",
+       "特醇嘉士伯啤酒罐装      13\n",
+       "鸡蛋、肉末肠粉        12\n",
+       "三丝鳝鱼           10\n",
+       "百里香奶油烤紅酒牛肉      5\n",
+       "铁板牛肉            3\n",
+       "Name: dishes_name, Length: 145, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dishes=df['dishes_name'].value_counts()\n",
+    "dishes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6a3d2cee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>info_id</th>\n",
+       "      <th>emp_id</th>\n",
+       "      <th>number_consumers</th>\n",
+       "      <th>mode</th>\n",
+       "      <th>dining_table_id</th>\n",
+       "      <th>dining_table_name</th>\n",
+       "      <th>expenditure</th>\n",
+       "      <th>dishes_count</th>\n",
+       "      <th>accounts_payable</th>\n",
+       "      <th>use_start_time</th>\n",
+       "      <th>...</th>\n",
+       "      <th>lock_time</th>\n",
+       "      <th>cashier_id</th>\n",
+       "      <th>pc_id</th>\n",
+       "      <th>order_number</th>\n",
+       "      <th>org_id</th>\n",
+       "      <th>print_doc_bill_num</th>\n",
+       "      <th>lock_table_info</th>\n",
+       "      <th>order_status</th>\n",
+       "      <th>phone</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>417</td>\n",
+       "      <td>1442</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1501</td>\n",
+       "      <td>1022</td>\n",
+       "      <td>165</td>\n",
+       "      <td>5</td>\n",
+       "      <td>165</td>\n",
+       "      <td>2016/8/1 11:05:36</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/1 11:11:46</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880641</td>\n",
+       "      <td>苗宇怡</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>301</td>\n",
+       "      <td>1095</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1430</td>\n",
+       "      <td>1031</td>\n",
+       "      <td>321</td>\n",
+       "      <td>6</td>\n",
+       "      <td>321</td>\n",
+       "      <td>2016/8/1 11:15:57</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/1 11:31:55</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880174</td>\n",
+       "      <td>赵颖</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>413</td>\n",
+       "      <td>1147</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1488</td>\n",
+       "      <td>1009</td>\n",
+       "      <td>854</td>\n",
+       "      <td>15</td>\n",
+       "      <td>854</td>\n",
+       "      <td>2016/8/1 12:42:52</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/1 12:54:37</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880276</td>\n",
+       "      <td>徐毅凡</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>415</td>\n",
+       "      <td>1166</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1502</td>\n",
+       "      <td>1023</td>\n",
+       "      <td>466</td>\n",
+       "      <td>10</td>\n",
+       "      <td>466</td>\n",
+       "      <td>2016/8/1 12:51:38</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/1 13:08:20</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880231</td>\n",
+       "      <td>张大鹏</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>392</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1499</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>704</td>\n",
+       "      <td>24</td>\n",
+       "      <td>704</td>\n",
+       "      <td>2016/8/1 12:58:44</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/1 13:07:16</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880173</td>\n",
+       "      <td>孙熙凯</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>940</th>\n",
+       "      <td>641</td>\n",
+       "      <td>1095</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1492</td>\n",
+       "      <td>1013</td>\n",
+       "      <td>679</td>\n",
+       "      <td>12</td>\n",
+       "      <td>679</td>\n",
+       "      <td>2016/8/31 21:23:48</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/31 21:31:48</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880307</td>\n",
+       "      <td>李靖</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>941</th>\n",
+       "      <td>672</td>\n",
+       "      <td>1089</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1489</td>\n",
+       "      <td>1010</td>\n",
+       "      <td>800</td>\n",
+       "      <td>24</td>\n",
+       "      <td>800</td>\n",
+       "      <td>2016/8/31 21:24:12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/31 21:56:12</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880305</td>\n",
+       "      <td>莫言</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>942</th>\n",
+       "      <td>692</td>\n",
+       "      <td>1155</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1492</td>\n",
+       "      <td>1013</td>\n",
+       "      <td>735</td>\n",
+       "      <td>10</td>\n",
+       "      <td>735</td>\n",
+       "      <td>2016/8/31 21:25:18</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/31 21:33:34</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880327</td>\n",
+       "      <td>习一冰</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>943</th>\n",
+       "      <td>647</td>\n",
+       "      <td>1094</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1485</td>\n",
+       "      <td>1006</td>\n",
+       "      <td>262</td>\n",
+       "      <td>9</td>\n",
+       "      <td>262</td>\n",
+       "      <td>2016/8/31 21:37:39</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/31 21:55:39</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880207</td>\n",
+       "      <td>章春华</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>944</th>\n",
+       "      <td>570</td>\n",
+       "      <td>1113</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1517</td>\n",
+       "      <td>1038</td>\n",
+       "      <td>589</td>\n",
+       "      <td>13</td>\n",
+       "      <td>589</td>\n",
+       "      <td>2016/8/31 21:41:56</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2016/8/31 21:32:56</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>330</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>18688880313</td>\n",
+       "      <td>唐雅嘉</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>945 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     info_id  emp_id  number_consumers  mode  dining_table_id  \\\n",
+       "0        417    1442                 4   NaN             1501   \n",
+       "1        301    1095                 3   NaN             1430   \n",
+       "2        413    1147                 6   NaN             1488   \n",
+       "3        415    1166                 4   NaN             1502   \n",
+       "4        392    1094                10   NaN             1499   \n",
+       "..       ...     ...               ...   ...              ...   \n",
+       "940      641    1095                 8   NaN             1492   \n",
+       "941      672    1089                 6   NaN             1489   \n",
+       "942      692    1155                 8   NaN             1492   \n",
+       "943      647    1094                 4   NaN             1485   \n",
+       "944      570    1113                 8   NaN             1517   \n",
+       "\n",
+       "     dining_table_name  expenditure  dishes_count  accounts_payable  \\\n",
+       "0                 1022          165             5               165   \n",
+       "1                 1031          321             6               321   \n",
+       "2                 1009          854            15               854   \n",
+       "3                 1023          466            10               466   \n",
+       "4                 1020          704            24               704   \n",
+       "..                 ...          ...           ...               ...   \n",
+       "940               1013          679            12               679   \n",
+       "941               1010          800            24               800   \n",
+       "942               1013          735            10               735   \n",
+       "943               1006          262             9               262   \n",
+       "944               1038          589            13               589   \n",
+       "\n",
+       "         use_start_time  ...           lock_time cashier_id  pc_id  \\\n",
+       "0     2016/8/1 11:05:36  ...   2016/8/1 11:11:46        NaN    NaN   \n",
+       "1     2016/8/1 11:15:57  ...   2016/8/1 11:31:55        NaN    NaN   \n",
+       "2     2016/8/1 12:42:52  ...   2016/8/1 12:54:37        NaN    NaN   \n",
+       "3     2016/8/1 12:51:38  ...   2016/8/1 13:08:20        NaN    NaN   \n",
+       "4     2016/8/1 12:58:44  ...   2016/8/1 13:07:16        NaN    NaN   \n",
+       "..                  ...  ...                 ...        ...    ...   \n",
+       "940  2016/8/31 21:23:48  ...  2016/8/31 21:31:48        NaN    NaN   \n",
+       "941  2016/8/31 21:24:12  ...  2016/8/31 21:56:12        NaN    NaN   \n",
+       "942  2016/8/31 21:25:18  ...  2016/8/31 21:33:34        NaN    NaN   \n",
+       "943  2016/8/31 21:37:39  ...  2016/8/31 21:55:39        NaN    NaN   \n",
+       "944  2016/8/31 21:41:56  ...  2016/8/31 21:32:56        NaN    NaN   \n",
+       "\n",
+       "     order_number  org_id  print_doc_bill_num  lock_table_info  order_status  \\\n",
+       "0             NaN     330                 NaN              NaN             1   \n",
+       "1             NaN     328                 NaN              NaN             1   \n",
+       "2             NaN     330                 NaN              NaN             1   \n",
+       "3             NaN     330                 NaN              NaN             1   \n",
+       "4             NaN     330                 NaN              NaN             1   \n",
+       "..            ...     ...                 ...              ...           ...   \n",
+       "940           NaN     330                 NaN              NaN             1   \n",
+       "941           NaN     330                 NaN              NaN             1   \n",
+       "942           NaN     330                 NaN              NaN             1   \n",
+       "943           NaN     330                 NaN              NaN             1   \n",
+       "944           NaN     330                 NaN              NaN             1   \n",
+       "\n",
+       "           phone  name  \n",
+       "0    18688880641   苗宇怡  \n",
+       "1    18688880174    赵颖  \n",
+       "2    18688880276   徐毅凡  \n",
+       "3    18688880231   张大鹏  \n",
+       "4    18688880173   孙熙凯  \n",
+       "..           ...   ...  \n",
+       "940  18688880307    李靖  \n",
+       "941  18688880305    莫言  \n",
+       "942  18688880327   习一冰  \n",
+       "943  18688880207   章春华  \n",
+       "944  18688880313   唐雅嘉  \n",
+       "\n",
+       "[945 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data=pd.read_csv('meal_order_info.csv',encoding='gbk')\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d7236136",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        1\n",
+       "1        1\n",
+       "2        1\n",
+       "3        1\n",
+       "4        1\n",
+       "        ..\n",
+       "10032    1\n",
+       "10033    1\n",
+       "10034    1\n",
+       "10035    1\n",
+       "10036    1\n",
+       "Name: counts, Length: 10037, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['counts']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "74ded866",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_gb=df[['order_id','counts','amounts']].groupby(by='order_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "086605b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>counts</th>\n",
+       "      <th>amounts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sum</th>\n",
+       "      <td>11126.0</td>\n",
+       "      <td>449872.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>44.821361</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       counts        amounts\n",
+       "sum   11126.0  449872.000000\n",
+       "mean      NaN      44.821361"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "df[['counts','amounts']].agg(np.sum)\n",
+    "df[['counts','amounts']].agg([np.sum,np.mean])\n",
+    "#分别对counts做求和，对amounts做求和以及求均值\n",
+    "df[['counts','amounts']].agg({'counts':np.sum,'amounts':[np.sum,np.mean]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a3b81087",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>counts</th>\n",
+       "      <th>amounts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2304</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>169</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   counts  amounts\n",
+       "0       1     2401\n",
+       "1       1     2304\n",
+       "2       1      900\n",
+       "3       1      625\n",
+       "4       1      169"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['counts','amounts']].transform(lambda x:x**2).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "654c1732",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>counts</th>\n",
+       "      <th>amounts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10032</th>\n",
+       "      <td>0</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10033</th>\n",
+       "      <td>0</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10034</th>\n",
+       "      <td>0</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10035</th>\n",
+       "      <td>0</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10036</th>\n",
+       "      <td>0</td>\n",
+       "      <td>102</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10037 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       counts  amounts\n",
+       "0           0       36\n",
+       "1           0       36\n",
+       "2           0       36\n",
+       "3           0       36\n",
+       "4           0       36\n",
+       "...       ...      ...\n",
+       "10032       0      102\n",
+       "10033       0      102\n",
+       "10034       0       59\n",
+       "10035       0       59\n",
+       "10036       0      102\n",
+       "\n",
+       "[10037 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_gb.transform(lambda x: x.max()-x.min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "778574f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>amounts</th>\n",
+       "      <th>counts</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>194</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>1032</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>182</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>953</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>241</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1320</th>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1321</th>\n",
+       "      <td>458</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1322</th>\n",
+       "      <td>547</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1323</th>\n",
+       "      <td>764</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1324</th>\n",
+       "      <td>438</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>942 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          amounts  counts\n",
+       "order_id                 \n",
+       "137           194       9\n",
+       "162          1032      18\n",
+       "163           182      10\n",
+       "165           953      21\n",
+       "166           241       7\n",
+       "...           ...     ...\n",
+       "1320           78       1\n",
+       "1321          458       7\n",
+       "1322          547      13\n",
+       "1323          764      15\n",
+       "1324          438      13\n",
+       "\n",
+       "[942 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(df[['order_id','counts','amounts']],index='order_id',aggfunc=np.sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "902ea4c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>amounts</th>\n",
+       "      <th>counts</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id</th>\n",
+       "      <th>dishes_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">137</th>\n",
+       "      <th>农夫山泉NFC果汁100%橙汁</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>凉拌菠菜</th>\n",
+       "      <td>27</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>番茄炖牛腩</th>\n",
+       "      <td>35</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>白饭/小碗</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>西瓜胡萝卜沙拉</th>\n",
+       "      <td>26</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">1324</th>\n",
+       "      <th>花蛤蒸蛋</th>\n",
+       "      <td>37</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>葱油凉拌藕片</th>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>香烤牛排</th>\n",
+       "      <td>55</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>香菇鹌鹑蛋</th>\n",
+       "      <td>39</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>黑米恋上葡萄</th>\n",
+       "      <td>33</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10036 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          amounts  counts\n",
+       "order_id dishes_name                     \n",
+       "137      农夫山泉NFC果汁100%橙汁        6       1\n",
+       "         凉拌菠菜                  27       1\n",
+       "         番茄炖牛腩                 35       1\n",
+       "         白饭/小碗                  1       4\n",
+       "         西瓜胡萝卜沙拉               26       1\n",
+       "...                           ...     ...\n",
+       "1324     花蛤蒸蛋                  37       1\n",
+       "         葱油凉拌藕片                30       1\n",
+       "         香烤牛排                  55       1\n",
+       "         香菇鹌鹑蛋                 39       1\n",
+       "         黑米恋上葡萄                33       1\n",
+       "\n",
+       "[10036 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(df[['order_id','counts','amounts','dishes_name']],index=['order_id','dishes_name'],aggfunc=np.sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cc5ff0fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "  <thead>\n",
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+       "      <th>...</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dishes_name</th>\n",
+       "      <th>38度剑南春</th>\n",
+       "      <th>42度海之蓝</th>\n",
+       "      <th>50度古井贡酒</th>\n",
+       "      <th>52度泸州老窖</th>\n",
+       "      <th>53度茅台</th>\n",
+       "      <th>一品香酥藕</th>\n",
+       "      <th>三丝鳝鱼</th>\n",
+       "      <th>三色凉拌手撕兔</th>\n",
+       "      <th>不加一滴油的酸奶蛋糕</th>\n",
+       "      <th>五彩藕苗</th>\n",
+       "      <th>...</th>\n",
+       "      <th>香酥两吃大虾</th>\n",
+       "      <th>鱼香肉丝拌面</th>\n",
+       "      <th>鲜美鳝鱼</th>\n",
+       "      <th>鸡蛋、肉末肠粉</th>\n",
+       "      <th>麻辣小龙虾</th>\n",
+       "      <th>黄尾袋鼠西拉子红葡萄酒</th>\n",
+       "      <th>黄油曲奇饼干</th>\n",
+       "      <th>黄花菜炒木耳</th>\n",
+       "      <th>黑米恋上葡萄</th>\n",
+       "      <th>All</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "      <th></th>\n",
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+       "  </thead>\n",
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+       "      <td>9</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>10</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1321</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1322</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1323</th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1324</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <td>1920.0</td>\n",
+       "      <td>1683.0</td>\n",
+       "      <td>1890.0</td>\n",
+       "      <td>3498.0</td>\n",
+       "      <td>4608.0</td>\n",
+       "      <td>480.0</td>\n",
+       "      <td>550.0</td>\n",
+       "      <td>1452.0</td>\n",
+       "      <td>252.0</td>\n",
+       "      <td>1435.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>179.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>11126</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>943 rows × 292 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            amounts                                                        \\\n",
+       "dishes_name  38度剑南春  42度海之蓝 50度古井贡酒 52度泸州老窖   53度茅台  一品香酥藕   三丝鳝鱼 三色凉拌手撕兔   \n",
+       "order_id                                                                    \n",
+       "137             NaN     NaN     NaN     NaN     NaN    NaN    NaN     NaN   \n",
+       "162             NaN     NaN     NaN     NaN   128.0    NaN    NaN     NaN   \n",
+       "163             NaN     NaN     NaN     NaN     NaN    NaN    NaN     NaN   \n",
+       "165            80.0     NaN     NaN     NaN     NaN   10.0    NaN     NaN   \n",
+       "166             NaN     NaN     NaN     NaN     NaN    NaN    NaN     NaN   \n",
+       "...             ...     ...     ...     ...     ...    ...    ...     ...   \n",
+       "1321            NaN     NaN     NaN     NaN     NaN    NaN    NaN     NaN   \n",
+       "1322            NaN     NaN     NaN     NaN     NaN    NaN    NaN     NaN   \n",
+       "1323           80.0     NaN     NaN     NaN     NaN    NaN    NaN     NaN   \n",
+       "1324            NaN     NaN     NaN     NaN     NaN    NaN    NaN     NaN   \n",
+       "All          1920.0  1683.0  1890.0  3498.0  4608.0  480.0  550.0  1452.0   \n",
+       "\n",
+       "                                ... counts                              \\\n",
+       "dishes_name 不加一滴油的酸奶蛋糕    五彩藕苗  ... 香酥两吃大虾 鱼香肉丝拌面  鲜美鳝鱼 鸡蛋、肉末肠粉  麻辣小龙虾   \n",
+       "order_id                        ...                                      \n",
+       "137                NaN     NaN  ...    NaN    NaN   NaN     NaN    1.0   \n",
+       "162                NaN     NaN  ...    NaN    NaN   NaN     NaN    1.0   \n",
+       "163                NaN     NaN  ...    NaN    NaN   NaN     NaN    1.0   \n",
+       "165                NaN     NaN  ...    NaN    NaN   NaN     NaN    NaN   \n",
+       "166                NaN     NaN  ...    NaN    NaN   NaN     NaN    NaN   \n",
+       "...                ...     ...  ...    ...    ...   ...     ...    ...   \n",
+       "1321               NaN     NaN  ...    NaN    NaN   NaN     NaN    NaN   \n",
+       "1322               NaN     NaN  ...    NaN    NaN   NaN     NaN    NaN   \n",
+       "1323               NaN     NaN  ...    NaN    NaN   NaN     NaN    NaN   \n",
+       "1324               7.0     NaN  ...    NaN    NaN   NaN     NaN    NaN   \n",
+       "All              252.0  1435.0  ...  179.0   40.0  22.0    14.0  219.0   \n",
+       "\n",
+       "                                                     \n",
+       "dishes_name 黄尾袋鼠西拉子红葡萄酒 黄油曲奇饼干 黄花菜炒木耳 黑米恋上葡萄    All  \n",
+       "order_id                                             \n",
+       "137                 NaN    NaN    NaN    NaN      9  \n",
+       "162                 NaN    2.0    NaN    NaN     18  \n",
+       "163                 NaN    NaN    NaN    NaN     10  \n",
+       "165                 NaN    NaN    1.0    NaN     21  \n",
+       "166                 NaN    NaN    NaN    NaN      7  \n",
+       "...                 ...    ...    ...    ...    ...  \n",
+       "1321                NaN    NaN    NaN    NaN      7  \n",
+       "1322                NaN    NaN    NaN    NaN     13  \n",
+       "1323                NaN    NaN    NaN    NaN     15  \n",
+       "1324                NaN    NaN    NaN    1.0     13  \n",
+       "All                53.0   28.0   62.0   58.0  11126  \n",
+       "\n",
+       "[943 rows x 292 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(df[['order_id', 'counts', 'amounts', 'dishes_name']], index='order_id',\n",
+    "               columns='dishes_name', margins=True, aggfunc=np.sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e9a3ca67",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>dishes_name</th>\n",
+       "      <th>38度剑南春</th>\n",
+       "      <th>42度海之蓝</th>\n",
+       "      <th>50度古井贡酒</th>\n",
+       "      <th>52度泸州老窖</th>\n",
+       "      <th>53度茅台</th>\n",
+       "      <th>一品香酥藕</th>\n",
+       "      <th>三丝鳝鱼</th>\n",
+       "      <th>三色凉拌手撕兔</th>\n",
+       "      <th>不加一滴油的酸奶蛋糕</th>\n",
+       "      <th>五彩藕苗</th>\n",
+       "      <th>...</th>\n",
+       "      <th>香酥两吃大虾</th>\n",
+       "      <th>鱼香肉丝拌面</th>\n",
+       "      <th>鲜美鳝鱼</th>\n",
+       "      <th>鸡蛋、肉末肠粉</th>\n",
+       "      <th>麻辣小龙虾</th>\n",
+       "      <th>黄尾袋鼠西拉子红葡萄酒</th>\n",
+       "      <th>黄油曲奇饼干</th>\n",
+       "      <th>黄花菜炒木耳</th>\n",
+       "      <th>黑米恋上葡萄</th>\n",
+       "      <th>All</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>order_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>165</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1321</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1322</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1323</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1324</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>51.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>179.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>219.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>11126</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>943 rows × 146 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "dishes_name  38度剑南春  42度海之蓝  50度古井贡酒  52度泸州老窖  53度茅台  一品香酥藕  三丝鳝鱼  三色凉拌手撕兔  \\\n",
+       "order_id                                                                     \n",
+       "137             0.0     0.0      0.0      0.0    0.0    0.0   0.0      0.0   \n",
+       "162             0.0     0.0      0.0      0.0    1.0    0.0   0.0      0.0   \n",
+       "163             0.0     0.0      0.0      0.0    0.0    0.0   0.0      0.0   \n",
+       "165             1.0     0.0      0.0      0.0    0.0    1.0   0.0      0.0   \n",
+       "166             0.0     0.0      0.0      0.0    0.0    0.0   0.0      0.0   \n",
+       "...             ...     ...      ...      ...    ...    ...   ...      ...   \n",
+       "1321            0.0     0.0      0.0      0.0    0.0    0.0   0.0      0.0   \n",
+       "1322            0.0     0.0      0.0      0.0    0.0    0.0   0.0      0.0   \n",
+       "1323            1.0     0.0      0.0      0.0    0.0    0.0   0.0      0.0   \n",
+       "1324            0.0     0.0      0.0      0.0    0.0    0.0   0.0      0.0   \n",
+       "All            24.0    25.0     21.0     22.0   39.0   51.0  10.0     22.0   \n",
+       "\n",
+       "dishes_name  不加一滴油的酸奶蛋糕  五彩藕苗  ...  香酥两吃大虾  鱼香肉丝拌面  鲜美鳝鱼  鸡蛋、肉末肠粉  麻辣小龙虾  \\\n",
+       "order_id                       ...                                         \n",
+       "137                 0.0   0.0  ...     0.0     0.0   0.0      0.0    1.0   \n",
+       "162                 0.0   0.0  ...     0.0     0.0   0.0      0.0    1.0   \n",
+       "163                 0.0   0.0  ...     0.0     0.0   0.0      0.0    1.0   \n",
+       "165                 0.0   0.0  ...     0.0     0.0   0.0      0.0    0.0   \n",
+       "166                 0.0   0.0  ...     0.0     0.0   0.0      0.0    0.0   \n",
+       "...                 ...   ...  ...     ...     ...   ...      ...    ...   \n",
+       "1321                0.0   0.0  ...     0.0     0.0   0.0      0.0    0.0   \n",
+       "1322                0.0   0.0  ...     0.0     0.0   0.0      0.0    0.0   \n",
+       "1323                0.0   0.0  ...     0.0     0.0   0.0      0.0    0.0   \n",
+       "1324                1.0   0.0  ...     0.0     0.0   0.0      0.0    0.0   \n",
+       "All                38.0  41.0  ...   179.0    40.0  22.0     14.0  219.0   \n",
+       "\n",
+       "dishes_name  黄尾袋鼠西拉子红葡萄酒  黄油曲奇饼干  黄花菜炒木耳  黑米恋上葡萄    All  \n",
+       "order_id                                                 \n",
+       "137                  0.0     0.0     0.0     0.0      9  \n",
+       "162                  0.0     2.0     0.0     0.0     18  \n",
+       "163                  0.0     0.0     0.0     0.0     10  \n",
+       "165                  0.0     0.0     1.0     0.0     21  \n",
+       "166                  0.0     0.0     0.0     0.0      7  \n",
+       "...                  ...     ...     ...     ...    ...  \n",
+       "1321                 0.0     0.0     0.0     0.0      7  \n",
+       "1322                 0.0     0.0     0.0     0.0     13  \n",
+       "1323                 0.0     0.0     0.0     0.0     15  \n",
+       "1324                 0.0     0.0     0.0     1.0     13  \n",
+       "All                 53.0    28.0    62.0    58.0  11126  \n",
+       "\n",
+       "[943 rows x 146 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(index=df['order_id'], columns=df['dishes_name'], values=df['counts'], dropna=True,\n",
+    "            margins=True, aggfunc=np.sum).fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4036fc1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/QuickDraw在线交互识别系统/homeworks/iris.ipynb b/4. QuickDraw(你画我猜)在线交互识别系统/homeworks/鸢尾花数据集的回归与聚类.ipynb
similarity index 100%
rename from QuickDraw在线交互识别系统/homeworks/iris.ipynb
rename to 4. QuickDraw(你画我猜)在线交互识别系统/homeworks/鸢尾花数据集的回归与聚类.ipynb
diff --git a/QuickDraw在线交互识别系统/readme.md b/4. QuickDraw(你画我猜)在线交互识别系统/readme.md
similarity index 100%
rename from QuickDraw在线交互识别系统/readme.md
rename to 4. QuickDraw(你画我猜)在线交互识别系统/readme.md
diff --git a/QuickDraw在线交互识别系统/小组成员.md b/4. QuickDraw(你画我猜)在线交互识别系统/第一组小组成员.md
similarity index 100%
rename from QuickDraw在线交互识别系统/小组成员.md
rename to 4. QuickDraw(你画我猜)在线交互识别系统/第一组小组成员.md
diff --git a/天池风险交易识别数据集/小组成员 b/天池风险交易识别数据集/第十组
similarity index 100%
rename from 天池风险交易识别数据集/小组成员
rename to 天池风险交易识别数据集/第十组