diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..36dce4d
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+*.npy filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..a335e05
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+logs/
+modelo/modelo.old.py
diff --git a/README.md b/README.md
index 179bb95..6c347d7 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,9 @@
 # Smartcare Machine Learning
-## Modelo
-Local onde se encontrará a parte mais técnica referente a deep learning, a construção e treinamento das redes neurais artificiais recorrentes (RNN). Confira [clicando aqui](https://github.com/Unilasalle-SmartCare/smartcare-machinelearning/tree/master/modelo)
-
-## Servidor
-Local onde são carregadas as informações do modelo já treinado, será como uma api que ao receber requisições com dados novos retornará a previsão se o idoso está estressado ou estará estressado. Confira [clicando aqui](https://github.com/Unilasalle-SmartCare/smartcare-machinelearning/tree/master/server)
-
-## Lançamentos
-Visando uma maior facilidade em juntar as features e fixes desenvolvidas no projeto, você pode ver os lançamentos (releases)  [clicando aqui](https://github.com/Unilasalle-SmartCare/smartcare-machinelearning/releases).
+## Model
+Where the technical part of the deep learning is, the build and train of the convolutional neural network (CNN). Check it out by [clicking here](https://github.com/Unilasalle-SmartCare/smartcare-machinelearning/tree/master/modelo)
 
+## Server
+It is planned in the future to have an api server with the model running (Flask). Check it out by [clicking here](https://github.com/Unilasalle-SmartCare/smartcare-machinelearning/tree/master/server)
 
+## Releases
+In order to make it easier to join the features and fixes developed in the project, it was grouped with releases, you can see them by [clicking here](https://github.com/Unilasalle-SmartCare/smartcare-machinelearning/releases).
diff --git a/modelo/DataProcessing.py b/modelo/DataProcessing.py
new file mode 100644
index 0000000..642c7f0
--- /dev/null
+++ b/modelo/DataProcessing.py
@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# Abrir python 3.5
+# In[2]:
+
+
+import pandas as pd
+import json
+import joblib
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# In[4]:
+
+
+path = "dados/eval.json" # Caminho do dataset vindo do gerador
+# Carregamento Dataset Pandas
+with open(path , 'r') as f:
+    data = json.load(f)
+    data = json.loads(data)
+
+
+# In[5]:
+
+
+df = pd.DataFrame.from_dict(pd.io.json.json_normalize(data), orient='columns')
+##
+df["stress"] = df["stress"].astype(int) # True e False para 1 e 0
+
+
+# In[7]:
+
+
+
+# https://stackoverflow.com/questions/31638651/how-can-i-draw-lines-into-numpy-arrays
+def naive_line(r0, c0, r1, c1):
+    # The algorithm below works fine if c1 >= c0 and c1-c0 >= abs(r1-r0).
+    # If either of these cases are violated, do some switches.
+    if abs(c1-c0) < abs(r1-r0):
+        # Switch x and y, and switch again when returning.
+        xx, yy, val = naive_line(c0, r0, c1, r1)
+        return (yy, xx, val)
+
+    # At this point we know that the distance in columns (x) is greater
+    # than that in rows (y). Possibly one more switch if c0 > c1.
+    if c0 > c1:
+        return naive_line(r1, c1, r0, c0)
+
+    # We write y as a function of x, because the slope is always <= 1
+    # (in absolute value)
+    x = np.arange(c0, c1+1, dtype=float)
+    y = x * (r1-r0) / (c1-c0) + (c1*r0-c0*r1) / (c1-c0)
+
+    valbot = np.floor(y)-y+1
+    valtop = y-np.floor(y)
+
+    return (np.concatenate((np.floor(y), np.floor(y)+1)).astype(int), np.concatenate((x,x)).astype(int),
+            np.concatenate((valbot, valtop)))
+
+
+# In[50]:
+
+
+# split the array into daily paths
+x = np.split(np.array(df)[:, 1:4].astype("int"), np.cumsum(np.unique(np.array(df)[:, 0], return_counts=True)[1])[:-1])
+# create a list of 2D arrays of 602x369
+pathImgs = np.zeros((len(x),602,369))
+
+y = df.groupby(by=["date"]).first()["stress"].values
+
+# In[51]:
+
+
+# For each 2D array, paint the path that was made in that day.
+for pathIdx,currPath in enumerate(x):
+    prevRow =  currPath[0]
+    for i,row in enumerate(currPath):
+        pathImgs[pathIdx,row[1],row[2]] = 1
+        if i > 0:
+            #draw a line between the previous xy point and the current xy point
+            rr, cc, _ = naive_line(prevRow[1],prevRow[2], row[1], row[2])
+            ## rr and cc are a lists of indexes that defines where in the x,y axis we our line is drawn
+            pathImgs[pathIdx,rr, cc] = 1
+        prevRow = row
+
+
+# In[59]:
+np.save("dados/x-validacaoo.npy", pathImgs)
+np.save("dados/y-validacaoo.npy", y)
+
+for i,img in enumerate(pathImgs):
+    plt.title("") 
+    plt.imshow(img.T, cmap="gray")
+    plt.show()
+
diff --git a/modelo/Model.ipynb b/modelo/Model.ipynb
new file mode 100644
index 0000000..ed405e4
--- /dev/null
+++ b/modelo/Model.ipynb
@@ -0,0 +1,1220 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Wandering CNN.ipynb",
+      "provenance": [],
+      "collapsed_sections": [
+        "psBEeMq5P7s4",
+        "TXSosetyUiJ6",
+        "Z1z5pawSUaMG",
+        "WQdp5wpSVjo8",
+        "oYPD4faURiFg",
+        "8p2twi2WQkJ_",
+        "bherqonKQsBJ",
+        "2TfHXiMPa8Gk"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NA4-Mz5NP6de"
+      },
+      "source": [
+        "# Google Drive Setup\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hcOfzbZzbf5i"
+      },
+      "source": [
+        "Google drive setup to get and save data with the model running on Google Colab. If running locally, the drive_home variable is not needed on the paths."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BDYq4GJ6e4nt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3225c50f-f653-4726-f903-c1203b0cb10e"
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.flush_and_unmount()\n",
+        "drive.mount('/content/drive')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Drive not mounted, so nothing to flush and unmount.\n",
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DHBzddr4fRIK"
+      },
+      "source": [
+        "drive_home = \"drive/My Drive/Smartcare/\""
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "psBEeMq5P7s4"
+      },
+      "source": [
+        "# Imports and Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "B7Y2zzNMfD69"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import json\n",
+        "import joblib\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from PIL import Image, ImageChops\n",
+        "import keras\n",
+        "from keras.datasets import mnist\n",
+        "from keras.models import Sequential\n",
+        "from keras.utils import np_utils\n",
+        "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, BatchNormalization\n",
+        "from keras.preprocessing.image import ImageDataGenerator\n",
+        "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
+        "from sklearn.metrics import confusion_matrix,f1_score, precision_score, recall_score\n",
+        "#https://datascience.stackexchange.com/questions/45165/how-to-get-accuracy-f1-precision-and-recall-for-a-keras-model\n",
+        "\n",
+        "from skimage.transform import resize\n",
+        "import tensorflow as tf\n",
+        "import time\n",
+        "import numpy as np\n",
+        "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split\n",
+        "from numpy.random import seed\n",
+        "\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZuPylQCXU9MN"
+      },
+      "source": [
+        "To avoid some caching problems seen in Google Colab with keras, this function is used to clear the sessions on every run"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oq_pUF1rPIKe"
+      },
+      "source": [
+        "def reset_keras():\n",
+        "    sess = tf.compat.v1.keras.backend.get_session()\n",
+        "    tf.compat.v1.keras.backend.clear_session()\n",
+        "    sess.close()\n",
+        "    sess = tf.compat.v1.keras.backend.get_session()\n",
+        "\n",
+        "    try:\n",
+        "        del classifier # this is from global space - change this as you need\n",
+        "    except:\n",
+        "        pass\n",
+        "\n",
+        "    # use the same config as you used to create the session\n",
+        "    config = tf.compat.v1.ConfigProto()\n",
+        "    config.gpu_options.per_process_gpu_memory_fraction = 1\n",
+        "    config.gpu_options.visible_device_list = \"0\"\n",
+        "    tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=config))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "omaJJfqoVMJy"
+      },
+      "source": [
+        "Given the X's, Y's and an unique identifier (for saving). The images from the dataset are saved into a folder by using this function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wBy4KyCFL2mF"
+      },
+      "source": [
+        "def salvaImagens(previsores, classe, nome):\n",
+        "  # Salva o dataset em imagens\n",
+        "  stress_path = drive_home+\"dataset-imagens/stress/\"+nome+\"/\"\n",
+        "  normal_path = drive_home+\"dataset-imagens/normal/\"+nome+\"/\"\n",
+        "\n",
+        "  for i, img in enumerate(previsores):\n",
+        "    if classe[i] == 1:\n",
+        "      path = stress_path\n",
+        "    else:\n",
+        "      path = normal_path\n",
+        "    fi = plt.imshow(img.reshape(128,128))\n",
+        "    plt.axis('off')\n",
+        "    plt.savefig(path+str(i)+\".png\")\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bLel-8EBRvd9"
+      },
+      "source": [
+        "Id for Logging\n",
+        "\n",
+        "Timestamp is captured to distinguish every train experiment in the logs\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8jtftREAdLt8"
+      },
+      "source": [
+        "id = int(time.time())\n",
+        "arquivo = \"modelCNN-%d\"%(id)\n",
+        "print(arquivo)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HSXUxFAiSsAb"
+      },
+      "source": [
+        "In order to try to make the experiments the most reproductable we could, the numpy and tensorflow seeds were defined manually. Even though variations still exist due gpu running and the data augmentation. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "s6RwUAVGS44f"
+      },
+      "source": [
+        "seed(1)\n",
+        "tensorflow.random.set_seed(2)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TXSosetyUiJ6"
+      },
+      "source": [
+        "# Data Processing"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HwLSFsj4TlcY"
+      },
+      "source": [
+        "Image datasets are loaded and rescaled to 128x128."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "F-l7tmM9fGwJ"
+      },
+      "source": [
+        "\n",
+        "x = np.load(drive_home+\"dados/2DpathData.npy\")\n",
+        "x = x.reshape(x.shape[0], x.shape[1], x.shape[2], 1)\n",
+        "\n",
+        "x_resize = []\n",
+        "for image in x:\n",
+        "    x_resize.append(resize(image ,(128,128)))\n",
+        "x_resize = np.array(x_resize)\n",
+        "x = x_resize\n",
+        "\n",
+        "y = np.load(drive_home+\"dados/2DpathData-y.npy\")\n",
+        "\n",
+        "\n",
+        "x, x_teste, y, y_teste = train_test_split(x, y, shuffle=False)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d8_PTIvcT_rR"
+      },
+      "source": [
+        "Length of the train image dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jCWJ-wcEQ6nu",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5a36b284-0d40-4a9f-e439-92bda189326e"
+      },
+      "source": [
+        "len(x)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "150"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 176
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zMNxU6t9UIFF"
+      },
+      "source": [
+        "Length of the test image dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VajLJ9_lQ9IP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1ffcf435-6bdd-4b45-b836-0307eb5c5673"
+      },
+      "source": [
+        "len(x_teste)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "50"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 177
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0TONZPd2UQLO"
+      },
+      "source": [
+        "The validation data used to evaluate the model is also loaded and resized to 128x128"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8CzSmMkRO0I7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "80f9df21-af32-41bf-82e6-3b1226d9979b"
+      },
+      "source": [
+        "x_resize = []\n",
+        "x_val = np.load(drive_home+\"dados/x-validacao.npy\")\n",
+        "for image in x_val:\n",
+        "    x_resize.append(resize(image ,(128,128)))\n",
+        "x_val = np.array(x_resize).reshape(x_val.shape[0], 128, 128, 1)\n",
+        "print(x_val.shape)\n",
+        "\n",
+        "y_val = np.load(drive_home+\"dados/y-validacao.npy\")"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(20, 128, 128, 1)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z1z5pawSUaMG"
+      },
+      "source": [
+        "# Convolutional Neural Network"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1InihLtvWKM8"
+      },
+      "source": [
+        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)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fQ9Hx2HdXdgl"
+      },
+      "source": [
+        "The paremeters of the model like filters, activation functions on the layers, and the train loss and optimizer algorithm were choosen empirically "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KOadpSh8fIo5"
+      },
+      "source": [
+        "model = Sequential()\n",
+        "model.add(Conv2D(filters=32, activation='relu', input_shape=(x.shape[1], x.shape[2], 1), kernel_size=(3,3)))\n",
+        "model.add(BatchNormalization())\n",
+        "model.add(Flatten())\n",
+        "\n",
+        "model.add(Dense(units=32, activation='relu'))\n",
+        "model.add(Dropout(0.25))\n",
+        "model.add(Dense(units=32, activation='relu'))\n",
+        "\n",
+        "model.add(Dense(units=1, activation='sigmoid'))\n",
+        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WQdp5wpSVjo8"
+      },
+      "source": [
+        "## Data Augmentation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WCuvQap9Vre1"
+      },
+      "source": [
+        "Data is generated by rotating the images at a maximum of 3 degrees and flipping horizontally and vertically. The data generator parameters and batch size were empirically choosen."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jNU0W3P9G0-J"
+      },
+      "source": [
+        "            gerador_treinamento = ImageDataGenerator(    rotation_range = 3,\n",
+        "                                                         horizontal_flip = True,\n",
+        "                                                         vertical_flip = True,\n",
+        "                                                     )\n",
+        "            \n",
+        "            gerador_teste = ImageDataGenerator(          rotation_range = 3,\n",
+        "                                                         horizontal_flip = True,\n",
+        "                                                         vertical_flip = True)\n",
+        "                \n",
+        "            base_treinamento = gerador_treinamento.flow(x=x, y=y, batch_size = 64)\n",
+        "            base_teste = gerador_teste.flow(x=x_teste, y=y_teste, batch_size = 64)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CzsUtKa3fOCg"
+      },
+      "source": [
+        "#salvaImagens(x_teste, y_teste, 'teste')"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oYPD4faURiFg"
+      },
+      "source": [
+        "# Data Analysis"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D6TL-0faPg75"
+      },
+      "source": [
+        "Plots samples of data generated by data augmentation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tEIEfABsLlbE"
+      },
+      "source": [
+        "try:\n",
+        "\t#plt.subplots()\n",
+        "\tfig, ax = plt.subplots(nrows=2, ncols=2)\n",
+        "\tfor row in ax:\n",
+        "\t\tfor col in row:\n",
+        "\t\t\tbatch = base_treinamento.next()\n",
+        "\t# convert to unsigned integers for viewing\n",
+        "\t\t\timage = batch[0][0]\n",
+        "\t\t\tcol.imshow(image.reshape(128,128))\n",
+        "\n",
+        "\tplt.show()\n",
+        "except:\n",
+        "\tprint(\"Erro\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IvZnL4tPPlGD"
+      },
+      "source": [
+        "Plots samples of the train dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZwnbCBorNEoJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "outputId": "cfa695a4-ded8-4774-8e61-a929603367c1"
+      },
+      "source": [
+        "for i in range(9):\n",
+        "  plt.subplot(330 + 1 + i)\n",
+        "  image = x[i]\n",
+        "  plt.imshow(image.reshape(128,128))\n",
+        "  plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=.2, hspace=.5)\n",
+        "# show the figure\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hIuYbWe0Poq-"
+      },
+      "source": [
+        "Plots samples of the validation dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "M7LSRbqxfRFb",
+        "outputId": "e62651e1-18bb-479e-c3eb-354f3107094b"
+      },
+      "source": [
+        "for i in range(len(x_val)):\n",
+        "  image = x_val[i]\n",
+        "  plt.title(\"Stress ? \"+str(y_val[i])+\" Indice:\"+str(i))\n",
+        "  plt.imshow(image.reshape(128,128))\n",
+        "  plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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HgDtN7sKN1DcMcPBNXdT6oNpvECSdcnHesHLMR1779+xYt57vBVvQ8SN8G2aQoDZZIChGFMt1NvW/xGBhjrVRnj3jF2P1BhZ37lCdNepEExHrv3WE6voe9v/TEl0Hiqw9NoGNTyxOh+V5zkPAnSYa7GV8S4XJS2KigQY9A9OEMoIgJh/GlHJJ73wuiIl7GigfE+RjNg+OIhn7C72U8g1Wleb40aGdDOYm+f70Rp4uX9z5F3N8XMLIEYpmKB6kURLRRUPkXhTxSDXz4wc8BNzJgpDDP9yDvXeUN/aN0luYoyus0ZWrMpCbpm4h440ydz9zJXOHy/TsyTGzISZeXWfvyBriSNh4gZmGGIsgv/4Jriy9SGQBX+uOUKmIavWTxgZ0QnR0lGB6hk0PlJi8pItnP9jFhQ9sovjtKeKZmUwHgYeAO01UEKsrs2zte5HxqMzd265C1YCgAf2vG2VVaY7coz0MHjK6h6tUh/PUugvEuWQIb242PZ4fiGfevo6ry88yZ3kUCeoNOtMhcLq4Vid3YIxKJU95pMTs6hzBVa8jv3038dRUZoPAQ8CdJs5Df3GGt3XvYvvMJWz6C6i8NE14eJw9/24DRzdWed03xtBLR4iOHCGvIBnF10SlIkF3F7tuXsux/gpTUQk1lHyhp4N9Aie/sIjG8H6KgehdfQHT60KmNpW4cPeqs49sXOH8EKE7o5CYuoWUDlXRbJ24u0I4JzRWIBidJJ6YaBqoUz/ph3oda0REcUDEMjq7kBnx4aP0PrKfyqEINWDyhzdgWy/L7EAibwm4eQUyQozIAsKZZMx9o69EUIPcpJJPzlrTWPxTmtJmhpqa/ZEtn8+beGaGeK5KaXSIuf6AmTUhFpTpLpewajVzLYLl85dxy5fE3IZuRq6qUBo1Vj9p2Ozc+b0PHUcUvvM06/78BeYGxdhrQxpXXEq4Yf1SV9Zx3hJwZxUqxiTq3SGza43yYaiMrIwTdcSzyRiBnhdj6l1ifEuZvniA3Ng40dR0Zr5s5C0Bd265gLn+AF08TWEqprhn5MRpvc5ncUQ8PU3/N55hYMcMR7bGjL2+AusGCUrFpa6uYzwE3AkSyueIC7CmOE2EqFtIoytPnAdM5GZjbGJyyQ7ztUM8NU1+31HW/a0wwYEfG0KbLiCoZOPKWB4C7oT0UF+ch4H8NHXLMRfniUphcrqvKCCsRitucI1Vq8RHR+l7/AhBBJMXGfXBboLeVZm4boKHgHuZwpCgdxVzayM+2Pco22Zew/bRC5nckEMNo/JEmcKh6RXRH3CqeHaW+Pl9rH58nKFHYg5fUWHkhs2EPT3tOQ/CMuIh4E4IBGGI5Y2+oMFMXCCygNm1ybn8KgcMzazQL9yYYdUqwdEJuobnkEG9Iti0nmD1wFJX11YeAm5eeZJj+z35OepvnKbRBasfO4odHVvq0tqqMbyfYPtOul+KyM0aB//pAHM/uGlFDyRqKQQk9Um6R9LTknZKequkAUkPStqd/u5frGJdZ/XmZugtzJEvNCAGjY4nFyFdycywRp3uZ8bp310lrCXXQwhKxRUbBK22BD4H/KWZvR74IWAncBvwkJldCjyU3nfnod5wloH8NLkwJoggGjt28ijBlcoM27WXwlPDyXUVY0PF4mnfj1gpFtzjIakXeDvwUQAzqwE1STcC70gXu4PkGoUfb6VI11mqi5EozzXlZwmJ+bOn3sLQC1ESACvoqMDZWL1GfHSU/m8Jm5sjnpo+cS3EFaaVlsBm4DDwe5K+J+mLkrqAITM7kC5zEBia78GSbpW0XdL2Oiu8iXmeCWrixUY/+fSUQZWDojiavYt3HD9xanRsPDkiskJffyshkAOuBD5vZlcA05zS9DczA+bdcmZ2u5ltNbOtebIzOmvZkyiMBvzhoat5dO5CHpu6mLXbpynuGVnqylybtBICw8CwmW1L799DEgojktYDpL8PneHxbplRoUDc301UMXpyVfbMDbFrfC25sRlsemapy3NtsuAQMLODwD5Jr0snXQs8BdwH3JxOuxm4t6UKXceokKe6rpv6qpih4gS7pobYd7gfDo8S+0U9V6xWh0L9LHCnpAKwF/gJkmC5W9ItwAvAB1tch+sQlUpMr8tj+YgXZ/t55IWLCJ8rY9Xaiu0Ucy2GgJk9DmydZ9a1rTyvWyK5kHoFCI1anCMeLVI5Kqi33ikYvnxech+fttz4X8SdTBBWGryue4TCaEDXgRiLWvvGYIjRG84QF+IVfbz9fOUh4E4IAuK8KFeqXF4exgTBIuwF5NVgU+EoqkRoVTfKr+wv5JxvPATcyywXUu+CzQOj3FA5TFxYnOctqc5bikfpXz1JY6gPVcqL88RuUXgIuIRE3NfF3OWz9BVmeGB2gGARRwiHiEAk31R0y4qHgEsooNGV5wc2HqQc1nlk6hKC+uK8YUOMQEIyLAMn6Tjf+M6ZA0CBQFDJ1dh1bIjDk12ENYjD5EIiAFicXDhkvlOLrdAhtVngIeBeZhKBjGLYoJhvMLEmRnFA1w9fSlCNCOoRQa2BIjvpMYoiaERorgZxGhCFPHFXmUp+gkAr53yEK5GHgDvNm1e/wMb1ozy7cS1Hat3sfucgk3NFpmeKRBMVVE33ImUQQDgdkJsV5YNGUE9mWS65puG7+56mL6jhe57Ll4eAA8BiI5xrsOPQOobLffSVZpmolphr5JiaLSIZ5XKNmVjE+RDqAeRjij1VarN5GnMh9e4wueioICrHWCWiFNTZWx/gWDzFbC1PbyEAHyewrHgIuJeFM3Wmh3uYKnSxvxBDrOQ7oJEIuxv0rpqmXghpCKJYhKWI9f0TTFWKVBsh0Zrk016CwZ4pLu4ZJcB4am4DveEMc7MF4lyAfNTgsuIh4BJxhPYO89ovb4JQ2PFDeYGIiyFxLiAq9NKdExZClBdxrshU+QLqXcKKSYPfQmhUYLi4ihfK6/lWTwMMCgfyDOyC0o69RGPHlvSlupN5CLiXRZOT6MndQHq0gOQ05GGlDAogEKqUsXwOqxSxXIDlAmq9BRpdISaIc6LWLeI8xPmAencBxbDq+ZieF2aIjo4lVy12y4aHgDvBDKufPELIAGZnT15OQdNNUVRAsWkQkMIw2ScIAhQGEBvWaGD1xmnP75aeh4A7t1PHANiJLxTMO2SgeUDQ8cCw2McSLFMeAm7xNb/Zzc9DsNx5N61zGech4FzGeQg4l3EeAs5lnIeAcxnnIeBcxnkIOJdxHgLOZZyHgHMZ5yHgXMa1FAKSfkHSDklPSrpLUknSZknbJO2R9EfpJcqcc8vUgkNA0gbg54CtZvYGIARuAj4NfMbMtgBjwC2LUahzrj1a3R3IAWVJOaACHADeSXKZcoA7gPe3uA7nXBu1cmny/cBvAi+SvPnHgceAY2bWSBcbBjbM93hJt0raLml7nepCy3DOtaiV3YF+4EZgM3AB0AVc90ofb2a3m9lWM9uap7jQMpxzLWpld+BHgefM7LCZ1YGvAdcAfenuAcBGYH+LNTrn2qiVEHgRuFpSRZKAa4GngIeBD6TL3Azc21qJzrl2aqVPYBtJB+B3ge+nz3U78HHgFyXtAVYDX1qEOp1zbdLS6cXM7FPAp06ZvBe4qpXndc51jo8YdC7jPAScyzgPAecyzkPAuYzzEHAu4zwEnMs4DwHnMs5DwLmM8xBwLuM8BJzLOA8B5zLOQ8C5jPMQcC7jPAScyzgPAecyzkPAuYzzEHAu4zwEnMs4DwHnMs5DwLmM8xBwLuM8BJzLOA8B5zLOQ8C5jPMQcC7jzhkCkr4s6ZCkJ5umDUh6UNLu9Hd/Ol2SflvSHklPSLqyncU751r3SloCX+H0S47fBjxkZpcCD6X3Ad4DXJr+3Ap8fnHKdM61yzlDwMz+Bhg9ZfKNwB3p7TuA9zdN/31LfIfkMuXrF6tY59ziW2ifwJCZHUhvHwSG0tsbgH1Nyw2n004j6VZJ2yVtr1NdYBnOuVa13DFoZgbYAh53u5ltNbOteYqtluGcW6CFhsDI8WZ++vtQOn0/sKlpuY3pNOfcMrXQELgPuDm9fTNwb9P0j6RHCa4Gxpt2G5xzy1DuXAtIugt4B7BG0jDwKeDXgbsl3QK8AHwwXfzrwPXAHmAG+Ik21OycW0TnDAEz+9AZZl07z7IG/EyrRTnnOsdHDDqXcR4CzmWch4BzGech4FzGeQg4l3EeAs5lnIeAcxnnIeBcxnkIOJdxHgLOZZyHgHMZ5yHgXMZ5CDiXcR4CzmWch4BzGech4FzGeQg4l3EeAs5lnIeAcxnnIeBcxnkIOJdxHgLOZZyHgHMZ5yHgXMZ5CDiXcecMAUlflnRI0pNN0/67pKclPSHpTyT1Nc37hKQ9knZJene7CnfOLY5X0hL4CnDdKdMeBN5gZm8EngE+ASDpMuAm4PL0Mf9LUrho1TrnFt05Q8DM/gYYPWXaA2bWSO9+h+QS5AA3Al81s6qZPUdyYdKrFrFe59wiW4w+gZ8E/iK9vQHY1zRvOJ12Gkm3StouaXud6iKU4ZxbiJZCQNIngQZw56t9rJndbmZbzWxrnmIrZTjnWnDOS5OfiaSPAjcA16aXJAfYD2xqWmxjOs05t0wtqCUg6Trgl4H3mdlM06z7gJskFSVtBi4FHmm9TOdcu5yzJSDpLuAdwBpJw8CnSI4GFIEHJQF8x8x+2sx2SLobeIpkN+FnzCxqV/HOudbpREt+6azSgL1F1y51Gc6taN+0ex4zs62nTvcRg85lnIeAcxnnIeBcxnkIOJdxHgLOZZyHgHMZ5yHgXMYti3ECkg4D08CRpa4FWIPX0czrONn5XMdFZjZ46sRlEQIAkrbPN5DB6/A6vI721uG7A85lnIeAcxm3nELg9qUuIOV1nMzrONmKq2PZ9Ak455bGcmoJOOeWgIeAcxm3LEJA0nXpdQr2SLqtQ+vcJOlhSU9J2iHp59PpA5IelLQ7/d3foXpCSd+TdH96f7Okbek2+SNJhQ7U0CfpnvSaEjslvXUptoekX0j/Jk9KuktSqVPb4wzX2Zh3Gyjx22lNT0i6ss11tOd6H2a2pD9ACDwLXAIUgH8ELuvAetcDV6a3e0iun3AZ8BvAben024BPd2g7/CLwh8D96f27gZvS218A/n0HargD+Kn0dgHo6/T2IDk79XNAuWk7fLRT2wN4O3Al8GTTtHm3AXA9yZm2BVwNbGtzHT8G5NLbn26q47L0fVMENqfvp/AVr6vd/1iv4MW+FfhG0/1PAJ9YgjruBd4F7ALWp9PWA7s6sO6NwEPAO4H703+qI01/8JO2UZtq6E3ffDpleke3BydOWz9Acvq7+4F3d3J7ABef8uabdxsA/xv40HzLtaOOU+b9C+DO9PZJ7xngG8BbX+l6lsPuwCu+VkG7SLoYuALYBgyZ2YF01kFgqAMlfJbkxK1xen81cMxOXOClE9tkM3AY+L10t+SLkrro8PYws/3AbwIvAgeAceAxOr89mp1pGyzl/+6Crvcxn+UQAktKUjfwx8DHzGyieZ4lsdrWY6iSbgAOmdlj7VzPK5AjaX5+3syuIPkux0n9Mx3aHv0kV7LaDFwAdHH6ZfCWTCe2wbm0cr2P+SyHEFiyaxVIypMEwJ1m9rV08oik9en89cChNpdxDfA+Sc8DXyXZJfgc0Cfp+NmgO7FNhoFhM9uW3r+HJBQ6vT1+FHjOzA6bWR34Gsk26vT2aHambdDx/92m6318OA2klutYDiHwKHBp2vtbILmg6X3tXqmSc6V/CdhpZr/VNOs+4Ob09s0kfQVtY2afMLONZnYxyWv/KzP7MPAw8IEO1nEQ2Cfpdemka0lOHd/R7UGyG3C1pEr6NzpeR0e3xynOtA3uAz6SHiW4Ghhv2m1YdG273kc7O3leRQfI9SS9888Cn+zQOt9G0qx7Ang8/bmeZH/8IWA38E1goIPb4R2cODpwSfqH3AP8P6DYgfW/CdiebpM/BfqXYnsAvwo8DTwJ/AFJr3dHtgdwF0lfRJ2kdXTLmbYBSQfu76b/t98Htra5jj0k+/7H/1+/0LT8J9M6dgHveTXr8mHDzmXcctgdcM4tIQ8B5zLOQ8C5jPMQcC7jPAScyzgPAecyzkPAuYz7/4akTJ9yfmq2AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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1ZvYlBK1JwoRDmJi5+aECQSaJCuip3Q8ip02rtAjHr6wyuT1B6nXb6Xh0AP/AoTnXuBiSEwGpcYdse56p9nT0flZRf4Y5XUOGgH/4KO6xYbYWt1Pc2MLgqxOoRNuzrUcDskdLeLuPoMUSYbEUbWvH/8j1+Hd2RE7fFxt3OjrJRNQZOZ3rIp6HtLe+EBoiVNd1MnaNQ6VPGL0wQeuBNji4tB/CRM4nkfPozhYYy3Qv2XJN/TRkCBAGhOUQ59l9ZPclOfepDoLudip9GcZekmTsghb8619CyxFY+/1xZPA44eg4Wq3Up57ZPqQabe+H5fD0xwDEQaamZuyh8I6Psf0z59L/ugTveOd3+drA1fQd6CIYHbNvY7NgjRkCAKqEpRKUSpDL4xZKpCvdpPq6UMchTApBEoqbWkm2JHHXduMcHY5aB4XC0n2ozrQcDU7uDDgh1JDk3iGSl2ylwytQ7hR0/RpkMle/ADMNr3FDYLowIBgehpEROp9PIMkEsn4NhQt6GXy1h9/iopJhy9fbSB+dwtlzCK1Uol13K4j6Pn7/UdoOb+Lu/a+ktEY5dnUX6/pbCMYsBMzCNEcInKCK+lXQEDk+TnaPy1rpptLu4KeF3HqPwpouvAs7yQxVSA7l0MMDaLG4cgJBlZZDOca/20t1u8/YqwPWfbMTKRSWZE+BBCESQMINwLVNkEbQXCEAcRD4BGNjMDZGerdLS18PdHdw+Of6KHcqfqvStj9N+4EE7VMFwjBEg2BumwjTDz4S5+QRihrqjA7IWjgHBtj01YA9t6V5x0uf4Inul+OOpAmWIgT8ECdQ0m4V9TTqs9BTD4Awq0nzhcCpwoBwdByZyrH5yz7akqbanaHYJxT6HMZv2oJT3ULP0xXS/VPo3oPR9fYdJzr0OJlAshnUcyHhUVnTSph0CD2h1OVS7nDAAbekdD1XIjE4QbBnf01hEE5MIpUqcuhiHu7eTumyVjrbzsN78NFFXDGz8w4P05bxeEvvU+xe14fTkl1ZLSUzbxYCgFYrUcfa3gM4qRSp3h5gLdVMCj8DZKDQ5wFtpMPN4DioK6jjoAmXIO2hnhC6Qrk7QehB6AnlDqHaFh0D4FSF/MYULU4HydxawsmpaPdkOP9vUfV9NJcjc0w4OtBFcjM41SQ9nlf3D6PmC7i5CpsTx+nIlBDPQ+dzjoVZcSwEpov3KIT9R/GODdOT8Ohb20fQ08bIpW0MvCZJ6Z2d0bawKFQdpOLg5hwcP/qgZwcUtxIddty5zyc5XkUFqu0Jjl2RYOwlGbxXb2f99wp4z/cTHB9dUBCgysavDtO5u4uNH36Wneduoe++dsJcvq59A+r7iB+SdqpRv4Aj0Y9ZtSwEZqMatQ78KnJ8DLfq07E/RSKfoDCVODmb+OBUlUQhPtY/UNIjVdxqiPiKmyvjTBZABG88TVd7J3462jWZ25wm0b2Nlr1dyEQOf+DY/PsMRifIZJPsm+ghDB3Kl2wjvXcI/+DhOqyU0zk2OnFDsBB4MaoEk5MwOYnXf5SOZJKubDY6cefEqcNhvMdh2nNOCOOfE9qf9XA3rqd07hoGXpui3Au9nT20HW4lMTY+792SwdAwru8ztPd8NBkycFWCDWEf7hKFgGkMFgJzpYpW/ai5HcTN93me2qtBQDh8nHS5wubcGirdafLrPMbPTxFeeAndz5VJ7RsmGByaW5NeFc0X2PCvMHpBggvftJuDQ+fR971kvCvUvqnN2VmPznyEQdSBGAbRj+r8PmiqhIUC/uAxnCd3k33iEE5V8TNCbivkNibx13bitLfjtLTM6fJdYaVK+zNjpEeVn1/7GMVewWltmd8FURagqi5+6IDjInaZsVXNWgLLJCyVCIeqdH+liLS34W/o5vgrWjl0XRvpkTYywyGd33wezReiw5/P+EIBeuAIbds7+droy6i2Kfmrzqfle7ujYyEWmQYBTqnKv+XPZ7KUIr0hi/SH0eHZZlWyEFhOYUAwOYnj+3hAy5oMQdJDwqh1UL5kG8mRPN7QGEHcZzBbyyMslUmNlnl493lIWhm+1KP1qTaoQwgQBBAEPJtbR7GcpNqdJHXcxh9YzWxzYAUICwX8I/1kvvoY6+78MV3PV0Bh3w0uR97YTeHSLbjdXYh7+jUToxcI8HYfZesXXMKWgHfc8F2qG7tnvcZirdT3kXyRR/s3U5xMM7k1hXa0LvpyzNKxlsAKokEA5TLpvUMkR9tIFNoIUiFTGz1K3eeQyG+hbeeR6ECjqamZz83nyew9jjuxhlyQorA+Rfv2LQR7D9Slg9D6HBuHtQRWkvi8Bv/gYfSp5+l4aA/ZwSqVDmHsAofjL/MINvTgdHee9i0fFovo4aMkJxyGSm0U+hwqm7rq3kFoA5CsftYSWKHU9wlGx0nv3MPmZ1upbu4lvynNoQ8p4TOb2P75BDo4/EKLQJWwXGbdIxV+UnophauKjL80yQU/TBEWi/bVbc7IQmAlCwOC8QmYmCQhQia9hrClyLGeForn9pAtlmduFqiSOpqjs7WDzDU58pkk0taKBMHiHUosAo6D48SHTguo7SJc1WxzYDVQxT/ST2pXPxMPr8UpCgduUEovWXfasQS6ez/t3z/AprZxXrnhMMHWtbi9PYtWingJSHikEj5uKiBMADb+wKpWU0tARH4N+CWi63P+hGgYsvXA3UAP8CjwXlW1y97USpUwX6DvSZ+hyzwu3NHPyIatdPf2zjgJSas+Yb7AE4+/DOkpk3l9hr4n0iSPDsxvk0AESSaRZBKnox1ty+J3Zin1pij0uoiMEpQ8vAJI1a4nsJotuCUgIhuBXwF2qOrFgAvcCNwOfFxVzwPGgFsWo1ADYS5Hy3efIzUG79/wPXIbHVjTPXPXYRigxSIbvqNkn8zQec0gIxcn5t5BKBIdBeglcFpbcDo7qG7uZerCboZ2tNB/jcPIT1UJQgcpuCRzIZw6UpJZVWrtE/CAjIhUgSwwALweeHf8+J3Ax4BP1rgcA1FrIJenc2+V//mDG3B6lUPX97DlsyMEI8dfmC0I6HhsEHXWcdOWH/En51yHt2Uj4eDQaUcfiuchnofT14u2t1Dc1EZhTYJST3wtBIHkFHh5JTsU0rk3wC0pTpBmfS6H2z9CODl1aqVmFVlwCKhqv4j8MXAIKAJfJ2r+j6vqiVPhjgAbZ3u+iNwK3AqQZh6D/jU59X3SwyUyz7ZR2OSTb1ektQUZn3jhDERVwmPDpI/3UlUXTQdUNnWTyBcR30dSKSThQSqFpFNoMkG1tw2/xaPY51HsE8rdip9VJASv5OBUIT3mkz4wBmOThOMTaBDgL+RaCGZFWXAIiEgX8DZgGzAO/APw5rk+X1XvAO4AaJdu2381D/LMPs4Z7mHX7/Xyzpc/xs5zX0k6CPEPHzk5T1gokN43zKfvuQ63LWTPexy2fWkLmb1Z8heuodjrkdsklHtCgtYQb8IlMSW0HVK6d1VJDxdxhyfQUhmKpehqRlWfIAgW7VqJZmWoZXPgDcB+VR0GEJEvA1cBnSLixa2BTUB/7WWa6bRcRkfHcY5t5LGNm5nYlkSCNbjTQgBAc3m6d4WMvFzofukYx1/eR1vnOoKkoC6kxhWv6KCuQ3JCSeRDWgYqJEYLOKNThGPjaKVqYxo0uFpC4BDwGhHJEm0OXAvsBB4CbiDaQ3AzcF+tRZqZ1PcJJifpfBb2uRvRS0OqbWnWfcedcamyYHScjgefZ2Lbhbz3nB/ymZ9+Lf2DbbTvcsmMhPTuLuEdm4CxCcLJ3MlrEJx6MRTT2GrpE3hERO4FHgN84HGi5v0/AXeLyP+Op312MQo1p+t9bJLUVBvuLUMcTvewaf1awvEJwnw+mkFDwlyejQ9N8bnx62gfCumZ8kkNTyGFMkzm0EIxvqKRXYSkWdW0d0BVPwp89JTJ+4AranldMzfO/iO0V9fT136cqb4UYV9nNJryyRBQtFxGfvw8a3fHlwYPQkJr3ptp7LDhVSyYzOEODvP9711EkA0p/LzLpm9n8B4cnjGflssElfiDb9/25hQWAqtZGKDFEm37HAobhPDcIlN7MvStW4uWo4uhhuVydFHUuY6gZJqOnTuwyoWFAhvuP0jHHvjYK7/CyBUhuVdtpXrxOYTnb8bt7cFpa0OSyej0YzvZx5zCWgKrnSrhxCSdu4v89jdvIH3UJb9Oya9xQdKo247E3f3JnOKVQtJDZdxCBWcijxZLUC5HoyFZi6EpWQg0gHBqCm/PUbb801by6xxKPUKQhiCtVPr8eMQkcEc9EjmP1oMuqck02cEU3ngRKZRw3Cmo+tHmQ6hxGMzvkupmdbIQaBDh8VGy/1alJZ2CZCIaL9Fz0UwSTbiESY8gowRph1KXS7HHYWJ7Fr8lS5BWVMDxITkhpI4rmbGQ7JECbq4Mw2NooUBYKFggNCALgQZxcrj16UQQ10WSSbxUikRXB2FrGqQNURc/I5CJn+8poQh+Gpw2QUIHCTK45TRsbic9mMfZfZCwVF7Y2IlmxbIQaGTxNQvV96FQgPFxANLPuKTFocOR6CxC10WyGcikCbpbKfdmKHV7jF3gUVgX8h9+5vvc/f0rueDTW3APHI2GZjMNw0KgmcRN+enjHWqlAuLgVCpIoYhbqZIpVEiNpklNpnGLCXrfmEMzAWE6gZuwf5lGY3/RZqcKGhCWgmgUocnJk6d8pURYt+NinF8OcZIBQTaBW4exDMzysuMEzJmp4kwW+dTTP0U4lWD4sjT0di53VWaRWQiYFyXlCtX+FpyyQ2GtEmaTy12SWWQWAuZFabFE+24HtyjIOXn8ttRyl2QWmYWAeXGVKtnhABTOWzuC3+LWZYxDs3wsBMyL0mKR9ucnkRA+sPlblLrc6PqEpmFYCJgXpUGIMzoFChclRvBTcubRkc2qZCFgXpQGAZrLI6Gw1k0SJkCSCTsbsYFYCJgXFwaEhQLJCfiXQi/VViHctikajsw0BAsBc1Za9XErcLTahXoQtCTAsZZAo7AQMGcXBrhl5UCph9CDakcCsc2BhmEhYOYkOxzwj89cQrVdOX5RAknb8QKNwkLAzElmsET2yQx+e0Du/Cok7MjBRmEhYObEyVVIjyikAlKdJcSz3YSNwkLAzImUK6QnQtxUQG97HjzPdhM2CAsBMyc6OEz7YwNs6Jng1nO+g7a34KSsX6ARnDUEROSvRWRIRJ6aNq1bRL4hIrvj313xdBGRPxeRPSLypIhcXs/izRIKAiiVcZ2QdreEugKOfYc0grn8FT/H6UOO3wY8qKrnAw/G9wHeApwf/9wKfHJxyjTLTYMgGtAEyEoZdV2ww4cbwllDQFW/A4yeMvltwJ3x7TuBt0+b/rca+QHRMOXrF6tYs3y06qPFIoeHu/jG5MUUN7UgG9Yud1lmESy0PbdWVQfi24PAif+GjcDhafMdiaedRkRuFZGdIrKzSnmBZZglo2F0ReOpBAcL3ZTbHYKOzHJXZRZBzRt1qqrAvC9Gr6p3qOoOVd2RwDqYVjyNBiRxcy5Hcx1U2gS/zY4VaAQLDYFjJ5r58e+heHo/sHnafJs4edlKs+qpkhpxOHqsk+I6obDWTiJqBAsNgfuBm+PbNwP3TZv+C/FegtcAE9M2G0wD6HuyStvjadKXjXL8YrFjBRrAWS8RIyJfBK4BekXkCPBR4A+Be0TkFuAg8K549n8GrgP2AAXg/XWo2SwjLx+QyHt0tBSYzNiVhxvBWUNAVW86w0PXzjKvAh+otSizcrmFKl4hSU86z75MiLiujWS8ytnRHmZe3IPHaDtY4vreH9O9cRwnm7ULjKxyFgJmXrRQxC1W6XQLZBJ+PAKy9QusZhYCZn6qVZySz3iQRQFpa0WStqtwNbMQMPOivg/lCg+Nv5TxfIby1h6c9rblLsvUwELAzIv6PlIs86OjWyhOpZnanEI7Wpe7LFMDCwEzb+r75I9n0ZJLYZ0QtNoRn6uZhYCZvyDAG/WQilDq1ejqw2bVshAw86a5PBseDkgPu7zl2p1MbEvZkYOrmIWAmTcNQtLDJZwKXN56kGqL2LECq5iFgJk3DQK8/lG8Irw2s59qKziZtLUGVikLATN/GqITkyQnlSfKGwiTwOb1drzAKmUhYOZPlbBYIlFQdhU3EiaUypoWCxkDi44AAAdYSURBVIFVykLALIhWK2QHytz5+JWESRi8Io3T2rLcZZkFsBAwC+ZNlUkdSBFkQvLbfMikl7skswAWAmbBZGCEDd8p43RX+E+v/VeCrhbrHFyFLATMgmmhSGpgEkS5KNNPkE3agCSrkIWAWTAtlWHoOKiw2RvFz7pIxq5AvNpYCJgFU79KOJnDOZTm4wNvJLcxgf/SLbZJsMpYCJiFiy9DnhoTnh5eR7lLKK61zsHVxkLA1CYM6NwdUHq0m6mXVBm5xEVseLJVxULA1CwzWKLtkLJuyyjlbSXEhi1fVc56tWFjziZxeIRO6eWqDbv4SesG8okEVKqgwXKXZubAQsDULBwbx0snSUhAV7JIsbsbwpAwn1/u0swc2OaAqZmWy0i+yFC1jWKQIOxsRbLZ5S7LzJG1BEzNNAgIc3nue+RySId0vTZB74+TyPDwcpdm5uCsLQER+WsRGRKRp6ZN+z8i8qyIPCki/09EOqc99mER2SMiz4nIm+pVuFlBVCEIyB72cMc8JrdjuwpXkblsDnwOePMp074BXKyqrwCeBz4MICIXATcCL4uf839FxPYXNQGtVOh9yicz6HDRFfuZ3OraHoJV4qwhoKrfAUZPmfZ1VfXjuz8gGoIc4G3A3apaVtX9RAOTXrGI9ZoVSkMle3CS1LjyixseprhOcTIZC4JVYDE6Bn8R+Jf49kbg8LTHjsTTTiMit4rIThHZWaW8CGWYZaUhcmwUr6D8dHqIanuApFJ24NAqUFMIiMhHAB+4a77PVdU7VHWHqu5IYGeerXqqhKPjpCZCDgcOeIpuXIvYWYUr3oJDQETeB1wPvCcekhygH9g8bbZN8TTTBDQISOR9vjTxSgiE4pY2xC40suItKARE5M3AbwJvVdXCtIfuB24UkZSIbAPOB35Ye5lmVQgDkkcn+cLXXoc36TLwWg96upa7KnMWZz1OQES+CFwD9IrIEeCjRHsDUsA3JOr4+YGq/rKqPi0i9wDPEG0mfEDVjh1tJjKZo+vpPiodQrUFCMPlLsmcxVlDQFVvmmXyZ19k/t8Hfr+WoszqFU5O0f3kJPltreQ2uIgfRHsITm4xmpXGjhg0iyoslnCeP0DbkSzt2TTB4JAFwApnIWAWVxhEJw7l89YCWCXsBCJTPxYAq4KFgDFNzkLAmCZnIWBMk7MQMKbJia6AzhsRGQbywMhy1wL0YnVMZ3XMtJrr2KqqfadOXBEhACAiO1V1h9VhdVgdS1uHbQ4Y0+QsBIxpcispBO5Y7gJiVsdMVsdMDVfHiukTMMYsj5XUEjDGLAMLAWOa3IoIARF5czxOwR4RuW2JlrlZRB4SkWdE5GkR+WA8vVtEviEiu+PfS3JpHBFxReRxEXkgvr9NRB6J18nfi0hyCWroFJF74zEldonIlcuxPkTk1+K/yVMi8kURSS/V+jjDOBuzrgOJ/Hlc05Micnmd66jPeB+quqw/gAvsBbYDSeDHwEVLsNz1wOXx7Tai8RMuAv4IuC2efhtw+xKth18HvgA8EN+/B7gxvv0p4L8sQQ13Ar8U304CnUu9PoiuTr0fyExbD+9bqvUBvA64HHhq2rRZ1wFwHdGVtgV4DfBInet4I+DFt2+fVsdF8ecmBWyLP0/unJdV73+sObzZK4GvTbv/YeDDy1DHfcDPAs8B6+Np64HnlmDZm4AHgdcDD8T/VCPT/uAz1lGdauiIP3xyyvQlXR+8cNn6bqLrXTwAvGkp1wdwzikfvlnXAfBp4KbZ5qtHHac89u+Bu+LbMz4zwNeAK+e6nJWwOTDnsQrqRUTOAS4DHgHWqupA/NAgsHYJSvgzogu3nrggXw8wri8M8LIU62QbMAz8TbxZ8hkRaWGJ14eq9gN/DBwCBoAJ4FGWfn1Md6Z1sJz/uwsa72M2KyEElpWItAJfAn5VVSenP6ZRrNZ1H6qIXA8Mqeqj9VzOHHhEzc9PquplROdyzOifWaL10UU0ktU2YAPQwunD4C2bpVgHZ1PLeB+zWQkhsGxjFYhIgigA7lLVL8eTj4nI+vjx9cBQncu4CniriBwA7ibaJPgE0CkiJy7/thTr5AhwRFUfie/fSxQKS70+3gDsV9VhVa0CXyZaR0u9PqY70zpY8v/deoz3sRJC4EfA+XHvb5JoQNP7671Qia6V/llgl6r+6bSH7gdujm/fTNRXUDeq+mFV3aSq5xC992+p6nuAh4AblrCOQeCwiFwQT7qW6NLxS7o+iDYDXiMi2fhvdKKOJV0fpzjTOrgf+IV4L8FrgIlpmw2Lrm7jfdSzk2ceHSDXEfXO7wU+skTLvJqoWfck8ET8cx3R9viDwG7gm0D3Eq6Ha3hh78D2+A+5B/gHILUEy78U2Bmvk38EupZjfQC/CzwLPAV8nqjXe0nWB/BFor6IKlHr6JYzrQOiDty/jP9vfwLsqHMde4i2/U/8v35q2vwfiet4DnjLfJZlhw0b0+RWwuaAMWYZWQgY0+QsBIxpchYCxjQ5CwFjmpyFgDFNzkLAmCb3/wGaQRHS45DwDgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PaGy8/p/IZmSODtH1eIaBy/Lk20tMXbiZhgOthE8/W991u9TwEACI4v779MFeOAiNuzK09qwn6m7n6Ms7meqGqQ0hDYdzdOxpo7M8TRBGRGGIhWFd++jThw6T6R8g87MXQzsM7ijQFXSSe0Y+NuBqwkNgNlFIeKwfjY6xYWQCa2qgsraZchsUuzIc+Oluomw3XbtDGvvK5J7qxSYniYpTtT+ox4yoVGLrF6cY2dZM/tqj9PasZ/vjPUQDg0RTcxyt6Nw8eQjMwSplrFImGh9H2Ry54+1kNq0jLLQwnstQaTGKXQGQJ1NcTzBRIjtVxsbGoVyJ90bUqpVgRn7PEdq1gUJDkUOdIeWt68iHIdFRDwFXHQ+BUzGLjzfo74eBQVqeyNL2jTbU2szIpesZ78lw+CeawZrRtOh8QjT3hbQ81IuNjcdhUINBvOm+Y+TMOPjFbWTPMJ77Vdj492fScNeADxK6qngIzJdZvEuvFBKNjhJUyrTsbyU72UCmnCXKCgtAoVFqDwiv3ExmyshMhTQcGkVjk0SDQ1hlGpuunH4LwQwbn6DrqQpRPkfDiyeZ6FlD0/atRHv2Y5VyfV63W/U8BBbASiXCUgnuf4yCREM2R9DVAa3NjF68jokzMvRdWAETqmRZe383LYfKNDxl2MQE0Xi0oK5CNDFB4Z5Hae24hObGIge3QLa4hs5DfYQeAm6BPASqZYZNV4iGR9D4BO3TIW1NDax5vIWwkCEswFQHDG/LU37JFoIS5MeM9n0l8scm4PAxrFQiKhbnFQpWLtP+xDB9/7SJ0sUh/dvKdD60jqBc9kFCtyAeArVghpVK8Zt5YgIkss/kyLe3otYWKlf0UOoMmNgYEVSgNCky5TxNuYCGKCKYnELjE9hUCStXXri7YEZwbIg1uxoYvjLgJWcdYLipB+Vy4CHgFsBDoB5ODCYODKLhETr6B+nM51BTE9baRKWriZFtjRy/KM/kNWtQKPIjouOZiNZ9E2T292HjE0STk88PA4lw8zoOv7IJRkPue2wbO0aOEZa9O+AWxkOgnpLvK9jYGEhoZJRgvJX8VDvNLTmC6QwEARZAUIbpRjG5qYlcx5lkiiG5wUk0UYz3MoxPIIlgbTejm5oo9oQUBjLkh8AmJiH0k464hfEQWCwzv8A0NER+337yCuhsKBC0tRJu7GboRW0MXJChuNkgCmg6uIbWgxFte4vk9sfnEhi+YiODOzKcdcEhSp/soeMruwmHh/3oQbdgHgJLJdnlGE3F3z8IzOiqhLTtb2SqK0+YF1E2IsqKwQuamN65hXIbtL3sGJXhFg7fu4Eze4uYdwNclTwElloUxn3/yUnoO0Yg0dLYSNDWSvm8DYye1cDkBlE6r8iWMwb41Lmf5VeefRsjXzyT7PAUyuehOAXm3QG3MB4Cy40ZUTH+hM9OTNL9XAedXW08d2HA2sZxXv2F3yQzKTLniyOvbAe1c8GfHSY8dMSPHHQL4iGwHJ0YUBwfJ+juotLZQLloPD2wlq5H4yMTy20Q7iiyrmOcqLUZFQoeAm5BPASWK4mgqYm+qzdgbxqg4d5u8s/l6bxjF4Qhamzg6Z1n8p83P8i/bHgtjSOd8TEKzp2mai5IiqR3S3pc0i5Jt0pqkLRV0n2S9kj6x+QSZe50SGS6u5m4+kUUu8XwcDPteyM6do9jxSLRVIlofIJosMBDY2dS7M4Sru9Y6qrdCrXgEJC0Efg1YKeZvZj4pLvXAjcCHzKzbcAQcH0tCk0TZTKwtpPDrwwodUcERxroeHwYe+jJuMkfhVipROF4hkf6NjK5PqDY07TUZbsVqtruQBZolFQBmoAjwGuAdyTzbwZ+F/holeupneQ04bOd4bdmqwgUj9rrBc40PJsgQJmA4uXbGT0rR7BpgsJDLWy8ewQ9d/h5JyzpfixksNzF1KVFyu2NnPmljF+pyJ22BYeAmR2S9OfAAaAIfBV4EBg2sxMjVL3AxtkeL+kG4AaABhbhUyzIoEyGoKsjPs7+5HP913RdAZbLxiEw28VF5iJh2YDRLTmK68R0KUv7cYNde2Y9LLjx6BQtLU2sedUwBydyKBBmftoxd3oWHAKSOoE3A1uBYeCfgGvm+3gzuwm4CaBNXXX/r810dcD6bg7+1BomeyIsX59Vmiy+2lAm+X26Vz8VZBuLRH0NnH/jOBw/GH9teRa5fX2023p+vOdJ7gwzBC3NRBNFP7eAOy3VdAeuBvaZ2XEASV8AXg50SMomrYFNwKHqy6xCMspeueBMjl/SiAkajgUEc1wMuOarP40MsKTRYNk8Df0Gff1E43OP+FuxSGasxMh0Y7yurk6CyPzcAu60VBMCB4ArJDURdweuAh4A7gHeCnwOuA64vdoiq6FsjqCrkyMva+Tyn3mUhz5zEWu/P0mudwCm69R/DkOiE1/qOfmS5PNgYQQWEZ5iv380PkFmZJxnx7spVbKUN7aSL1dgdHShlbsUqmZM4D5JtwEPAdPAw8TN+y8Bn5P0h8m0T9ai0IVQoUCwZTN7376OsGB88xsXsuWxIrlnjxCNjdev7xxF8cE+0QKff67LEp28WBhiUyUe2bcZi4RelGddsRN6l7bx5VaWqvYOmNnvAL9z0uS9wGXVPG+tBG1tlHvayFw0QuXZNrofMfLPHY+v57camEGlTPZIgemmiInNUN5TILfUdbkVpY5D5EsoGQc49I7tHPjJAsUDrXR/3+j68m7CI0eXurqaiopTbPiPaZoOZ3jZVbsY3r6AXZMu1VZlCGS6OtHGMyiuN6abjda9AS2HyoTDw6vv+PowpKGvSHYSLm/bS6VlgccouNRalSEQbt9E/8vXUzmjggw23PIU2W/tWpX7zy0MCfYdJj9qvK55N+UOI2hpruvBUG51WZVfIMoMTtB6MIfuyZOfiLCJyfjknauRGVacojAa8S9jF2EBRGdvJHii5F8ocvOyKkOAoVEKZhR2jUOpRLjKz8IbTZXIjU7z9f7zQDCxuYnWvQXwEHDzsCpDIBoaiveVh+HCd9OtJFFIob/IUw+eheWNo5cHtD3SBgODS12ZWwFWZcfRpqfj6wAk37hLA01M0XQowLIGm4tYY2GpS3IrxKoMgVTqH+KM705gTSH/85KvUulqgiCz1FW5FcBDYLUIhOUDMDESNq7KPSGuPjwEVotslkpLFgwOlToJpqN5H37s0s1DYJWwnm56X5MhM5bhS197KZn+MW8NuHnxEFgNggzT7QWazhlBFdH+NGh8cqmrciuEh8AqEDQ3Mb6hwIcu/DwyWPeNo4RDw0tdllshPARWuiBDsKaTSrOYiApkioLhUb9AqZu3VXmwUJook6G0pZtyu/jexDnkRyAcHE7N8RGuet4SWOHUUKDvsgaK64xb7r+CtgOh7xVwp8VbAiuc8jnGz6lAAG2P52g8OuF7Bdxp8ZbACqZCAbW1cvXFT7B2wzA93xolt291nTTF1Z+HwAoWtDQTtTfTli1iJjLHR+ITnDp3GjwEVjDbuI7xs1vZP9nFwGALUd9xovHxpS7LrTA+JrCCjW1vZ+i8DIf2nUlhb0P8rUkfD3CnyVsCK9joWRmK501R2NtA27OG+bEBbgE8BFaiIEPQ0MDYudNcs+MJOp+M6HpsxFsBbkE8BFYg5bKotZWgucKmwhCN/RWC/pGlLsutUD4msAJlOjsob9+AAnh2ci2Fo+OEfceXuiy3Qp2yJSDp7yQdk7RrxrQuSXdJeib53ZlMl6S/krRH0qOSLq1n8WllazoY3NFAOJHl2we2oskpHw9wCzaf7sCnef4lx98H3G1m24G7k/sArwe2Jz83AB+tTZlupqmeFgZfMk1mNEPw/VZsbNy/K+AW7JQhYGbfBE4+be2bgZuT2zcDb5kx/TMWu5f4MuU9tSrWgXJ5Jjbkef0lj5EbDVj3cAUrru5Tqrv6WujA4HozO5LcPgqsT25vBA7OWK43mfY8km6Q9ICkByqUFlhGykiooUC5VVzV/gTZKWjaP4qVV+mFVdyiqHrvgJkZcNr7pszsJjPbaWY7c/jpsedD+TycvYlyGwyHTRQGDXuud/VeXcktioWGQN+JZn7y+1gy/RCwecZym5JprgaCQoHxs9sIG+ArAy+iMGpxK8CPD3BVWGgI3AFcl9y+Drh9xvRfTPYSXAGMzOg2uCqptYWjlwdUWiMe/ta5NB8sYpXyUpflVrj57CK8FfgucJ6kXknXA38CvFbSM8DVyX2ALwN7gT3Ax4FfrUvVKZTtOYPy1nW07hjEcsbG/5gm1zuw1GW5VeCUBwuZ2dvnmHXVLMsa8K5qi3LPF63vYmJjA5es28M9z3XQdO+zhKP+jUFXPT9icLkLMgSNDey+vpWWzSM89JmL2Lq7RDg04scGuJrwEFjmMl0dsL4bdZRpyldoerpE4cAQoQeAqxEPgeVMonTRFo5cWcCGQwYOraXr4d1xK8C5GvFvES5TymbJrl/H8PY8wUtGaOrNsO4hwyYmvRvgaspDYJlSoUDY083INvi1C+6hfW9I+91PE5X86EpXWx4Cy5ByeezcLTzznjyWM/76U2+h/dEBopFRPzDI1ZyHwHIjkVnTydQZTVx45mFUEeseLEH/YHwOQedqzAcGlxnl8xy/5mzGN4tDh3roeBry336c0LsBrk48BJaRoKmJoLOD4fNhujWk6dEm2vaX43EA7wa4OvEQWEaCrk6mN61h60sPMjTVyNq/qGD9g4QeAK6OPASWAwllcwz8xGaOvwT0cBONRwOsfxfRpF9RyNWXDwwuA8rmCNpaGN0ScMaOY7TuC+jeVSEcn/DBQFd33hJYBoKzNnLs1WcwtS5iYLSZLd8bI9i93w8NdovCWwJLLGhqYnp9O8PnGUEFoj0tZPpHCccnlro0lxIeAkspyKAN6xm8oJG3XfUdGvoDtn2qj/DwUT802C0a7w4sFYlMexuH39BDcZ1x64OXseHZEPqHsIqPA7jF4yGwRJTNobYWxl5aJCpl6HwwR+ueUcKhoaUuzaWMh8ASiS7bwdA5jTQ2jzJ1rJWefz1ENDB0+qdtdq5KHgKLLTkmYGRLA6NbxdRgI83HAsIjfVjZTxrqFp+HwCILmpoIurvou7rClefu5cgfbKNxzxH/boBbMr53YDFJcM5m+l67CU1mufeZs2noHYOB4aWuzKWYh8BiUsD4Oe2MvXaC3HBAx3151HvEBwPdkvLuwGKRCPI5ZEZlKssZjxodjw4Q+UFBbol5S2AxBQGZYkTQn6eld4po7wH/boBbct4SWCxmRJOTNHxjF+c+2Ew0No75YKBbBuZzGbK/k3RM0q4Z0/5M0lOSHpX0L5I6Zsx7v6Q9knZLel29Cl+poqkpwoFBDwC3bMynO/Bp4JqTpt0FvNjMLgKeBt4PIGkHcC3wouQxfyMpU7NqVws/SYhbRk4ZAmb2TWDwpGlfNbMTndl7iS9BDvBm4HNmVjKzfcQXJr2shvU652qsFgOD/wX41+T2RuDgjHm9ybTnkXSDpAckPVDBm8bOLZWqQkDSB4Fp4JbTfayZ3WRmO81sZ45CNWU456qw4L0Dkt4JvBG4KrkkOcAhYPOMxTYl05xzy9SCWgKSrgF+C3iTmc08E+YdwLWSCpK2AtuB71VfpnOuXk7ZEpB0K/AqoFtSL/A7xHsDCsBdkgDuNbNfMbPHJX0eeIK4m/AuM/NT5Di3jMmWwe6qNnXZ5bpqqctwblX7mt32oJntPHm6HzbsXMp5CDiXch4CzqWch4BzKech4FzKeQg4l3IeAs6l3LI4TkDScWAC6F/qWoBuvI6ZvI4ftZLrOMvM1p48cVmEAICkB2Y7kMHr8Dq8jvrW4d0B51LOQ8C5lFtOIXDTUheQ8Dp+lNfxo1ZdHctmTMA5tzSWU0vAObcEPAScS7llEQKSrkmuU7BH0vsWaZ2bJd0j6QlJj0v69WR6l6S7JD2T/O5cpHoykh6WdGdyf6uk+5Jt8o+S8otQQ4ek25JrSjwp6cql2B6S3p38TXZJulVSw2JtjzmuszHrNlDsr5KaHpV0aZ3rqM/1PsxsSX+ADPAscDaQB74P7FiE9fYAlya3W4mvn7AD+FPgfcn09wE3LtJ2eA/wWeDO5P7ngWuT2x8D/tsi1HAz8MvJ7TzQsdjbg/js1JAZA+wAAALQSURBVPuAxhnb4Z2LtT2AHwcuBXbNmDbrNgDeQHymbQFXAPfVuY6fBLLJ7Rtn1LEjed8UgK3J+ykz73XV+x9rHi/2SuArM+6/H3j/EtRxO/BaYDfQk0zrAXYvwro3AXcDrwHuTP6p+mf8wX9kG9WphvbkzaeTpi/q9uCHp63vIj793Z3A6xZzewBbTnrzzboNgL8F3j7bcvWo46R5PwPcktz+kfcM8BXgyvmuZzl0B+Z9rYJ6kbQFuAS4D1hvZkeSWUeB9YtQwoeJT9waJffXAMP2wwu8LMY22QocBz6VdEs+IamZRd4eZnYI+HPgAHAEGAEeZPG3x0xzbYOl/N9d0PU+ZrMcQmBJSWoB/hn4DTMbnTnP4lit6z5USW8EjpnZg/VczzxkiZufHzWzS4i/y/Ej4zOLtD06ia9ktRXYADTz/MvgLZnF2AanUs31PmazHEJgya5VIClHHAC3mNkXksl9knqS+T3AsTqX8XLgTZKeAz5H3CX4CNAh6cTZoBdjm/QCvWZ2X3L/NuJQWOztcTWwz8yOm1kF+ALxNlrs7THTXNtg0f93Z1zv4+eSQKq6juUQAvcD25PR3zzxBU3vqPdKFZ8r/ZPAk2b2lzNm3QFcl9y+jnisoG7M7P1mtsnMthC/9q+b2c8B9wBvXcQ6jgIHJZ2XTLqK+NTxi7o9iLsBV0hqSv5GJ+pY1O1xkrm2wR3ALyZ7Ca4ARmZ0G2qubtf7qOcgz2kMgLyBeHT+WeCDi7TOVxA36x4FHkl+3kDcH78beAb4GtC1iNvhVfxw78DZyR9yD/BPQGER1n8x8ECyTb4IdC7F9gB+D3gK2AX8PfGo96JsD+BW4rGICnHr6Pq5tgHxAO7/Tf5vHwN21rmOPcR9/xP/rx+bsfwHkzp2A68/nXX5YcPOpdxy6A4455aQh4BzKech4FzKeQg4l3IeAs6lnIeAcynnIeBcyv1/ULfiY87qqKcAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OjFcF8YdP01s",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cc172354-5b36-4c07-de76-ce7ac0f38cba"
+      },
+      "source": [
+        "for i in range(9):\n",
+        "  plt.subplot(330 + 1 + i)\n",
+        "  image = x_val[i]\n",
+        "  plt.imshow(image.reshape(128,128))\n",
+        "# show the figure\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8p2twi2WQkJ_"
+      },
+      "source": [
+        "# Model Training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3kgdlmWrJaCw"
+      },
+      "source": [
+        "h = model.fit(base_treinamento, steps_per_epoch= len(base_treinamento),\n",
+        "                                    epochs = 150, validation_data = base_teste,\n",
+        "                                    validation_steps = len(base_teste),\n",
+        "                                    verbose=1, shuffle=False)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Aq2sM26lYheB"
+      },
+      "source": [
+        "The charts shown below were essential to empiracally choose the parameters."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T1Bi6yxDYYpC"
+      },
+      "source": [
+        "Accuracy per epoch chart"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vJxD9BO94NyJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "531dda87-37ba-4f6a-a180-7a4da82adf01"
+      },
+      "source": [
+        "    plt.plot(h.history['accuracy'])\n",
+        "    plt.plot(h.history['val_accuracy'])\n",
+        "    plt.title('Acurácia do modelo')\n",
+        "    plt.ylabel('Acurácia')\n",
+        "    plt.xlabel('Época')\n",
+        "    plt.legend(['Treino', 'Teste'], loc='best')\n",
+        "    plt_accuracy = plt.gcf()\n",
+        "    plt_accuracy.savefig(\"%sgraficos/%s-accuracy.png\"%(drive_home, arquivo))\n",
+        "    plt.show() "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cEzHhSAHYemb"
+      },
+      "source": [
+        "Learning curve chart"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "O_3PHJbQmjvL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6e6fbee0-6350-4517-ec97-0204c7559804"
+      },
+      "source": [
+        "    plt.plot(h.history['loss'])\n",
+        "    plt.plot(h.history['val_loss'])\n",
+        "    plt.title('Perda do modelo')\n",
+        "    plt.ylabel('Perda')\n",
+        "    plt.xlabel('Época')\n",
+        "    plt.legend(['Treino', 'Teste'], loc='best')\n",
+        "    plt_accuracy = plt.gcf()\n",
+        "    plt.savefig(\"%sgraficos/%s-loss.png\"%(drive_home, arquivo))\n",
+        "    plt.show() "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bherqonKQsBJ"
+      },
+      "source": [
+        "# Model Evaluation and Results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XiB2GmTWOwMr"
+      },
+      "source": [
+        "#keras.models.load_model(drive_home+\"dados/modelCNN-1606236872.h5\")\n",
+        "print(drive_home+\"dados/modelCNN-1606236872.h5\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IfIaFm4oYz2y"
+      },
+      "source": [
+        "To be sure about the model performance, by predicting the validation dataset using the tranining mode, metrics were calculated (revoke, precision, f1 score)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CoGlcBkK5zWu"
+      },
+      "source": [
+        "model.evaluate(x_val, y_val, verbose=0)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "74uR9p0W57ul"
+      },
+      "source": [
+        "y_pred = np.round(model.predict(x_val),0)\n",
+        "print(y_pred)\n",
+        "y_true = np.array(y_val)\n",
+        "y_true = y_true.reshape(y_true.shape[0], 1)\n",
+        "diff = y_pred - y_true\n",
+        "plt.plot(diff)\n",
+        "plt.show()\n",
+        "\n",
+        "plt.imshow(x_val[17].reshape(128, 128))\n",
+        "plt.show()\n",
+        "print(y_pred[17] == y[17])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "72Yx2IxKZ4d8"
+      },
+      "source": [
+        "Scikit learn was used to calculate the f1 score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lm2FrW366uWS"
+      },
+      "source": [
+        "fscore = f1_score(y_true, y_pred)\n",
+        "print(fscore)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-TGrLxdbZ_of"
+      },
+      "source": [
+        "How many samples marked as wandering really were ? (Recall)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-X71VAfNzhlD"
+      },
+      "source": [
+        "recall = recall_score(y_true, y_pred)\n",
+        "print(recall)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U6t0k3IeaagQ"
+      },
+      "source": [
+        "From all the data, how much were correctly marked as wandering ? (Precision)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1D3MUMQ82J5p"
+      },
+      "source": [
+        "precision = precision_score(y_true, y_pred)\n",
+        "print(precision)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2TfHXiMPa8Gk"
+      },
+      "source": [
+        "# Logs and Resets"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Yeq_WsFUbAHz"
+      },
+      "source": [
+        "Adds into the log the metrics of the experiment"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XmSzWX0_bM2G"
+      },
+      "source": [
+        "with open(drive_home+'experiments.txt', 'a') as f:\n",
+        "  f.write(\"\\n f1:%f - recall:%f - precision:%f - %s.h5\"%(fscore, recall, precision, arquivo))\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1YwjZdrwbK-T"
+      },
+      "source": [
+        "Saves the experiment trained model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IsWunW747DY9"
+      },
+      "source": [
+        "model.save(drive_home+'dados/modelCNN-%d.h5'%(id))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_DPkWnRYbUDl"
+      },
+      "source": [
+        "Resets keras sessions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Q_Lp-t5ePUgL"
+      },
+      "source": [
+        "reset_keras()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/modelo/README.md b/modelo/README.md
index 0a126a3..8e15429 100644
--- a/modelo/README.md
+++ b/modelo/README.md
@@ -1,2 +1,18 @@
-# Smartcare Machine Learning - Modelo
-Aqui no modelo se encontrará a parte mais técnica referente a deep learning, a construção e treinamento das redes neurais artificiais recorrentes (RNN)
+# Smartcare Machine Learning - Model
+## Development Flow
+### [Data processing](DataProcessing.py)
+The dataset is processed to images and normalized to fit the machine learn model, [Pandas](https://pandas.pydata.org) and [Numpy](https://numpy.org) libraries were used.
+
+### [Convolutional Neural Network Model](Model.ipynb)
+![Convolutional neural network model developed](representacao/2x/modelo.png)
+<br>
+With [Keras](https://keras.io) and Scikit Learn(https://scikit-learn.org) libraries, a CNN model was created to infer by the alzheimer elderly's movimentation whether it is a wandering movement or not.
+
+### [Train analysis](Model.ipynb)
+![Gráficos do modelo com o tensorboard](https://i.imgur.com/fqVUBJ3.jpg)
+
+Initially data was analyzed with [Tensorboard](https://www.tensorflow.org/tensorboard?hl=pt-br) and [Matplotlib](https://matplotlib.org) afterwards migration to google colab notebook. By analyzing the train metrics, the model parameters could be tunned empirically.
+
+### [Evaluation and results](Model.ipynb)
+
+With the validation dataset, our trained model was evaluated and revoke, precision and f1 score metrics were calculated to give an overview of the model prediction quality.
diff --git a/modelo/checkpoints/README.md b/modelo/checkpoints/README.md
deleted file mode 100644
index 00c81fc..0000000
--- a/modelo/checkpoints/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Smartcare Machine Learning - Modelo - Checkpoints
-Neste diretório são armazenados os checkpoints do keras gerados durante o treinamento
-
-## Por que usar checkpoints ?
-Muitas vezes passamos horas treinando o modelo e por algum motivo ele tem que ser encerrado antes do término. Com os checkpoints, o keras armazena o modelo e os melhores pesos (A definição de melhores pesos é configurável, pode ser um menor loss ou maior accuracy) a cada época. Portanto caso o processo de treino seja abruptamente encerrado, é possível carregar um checkpoint e treinar a partir dos melhores pesos salvos como checkpoint anteriormente.
diff --git a/modelo/dados/2DpathData-y.npy b/modelo/dados/2DpathData-y.npy
new file mode 100644
index 0000000..12874ef
--- /dev/null
+++ b/modelo/dados/2DpathData-y.npy
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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd13a0e147aca1b7a366eaba97601a28206099b5e31fd8c84a82a627bff7c458
+size 928
diff --git a/modelo/dados/2DpathData.npy b/modelo/dados/2DpathData.npy
new file mode 100644
index 0000000..8073548
--- /dev/null
+++ b/modelo/dados/2DpathData.npy
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+version https://git-lfs.github.com/spec/v1
+oid sha256:12d86b3863544e04754e0fe8aa2109dbe147836750e5926ef2c86674e8f5345c
+size 355420928
diff --git a/modelo/dados/dataset-validacao.json b/modelo/dados/dataset-validacao.json
new file mode 100644
index 0000000..162a79e
--- /dev/null
+++ b/modelo/dados/dataset-validacao.json
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diff --git a/modelo/dados/dataset.df b/modelo/dados/dataset.df
deleted file mode 100644
index 4885c9a..0000000
Binary files a/modelo/dados/dataset.df and /dev/null differ
diff --git a/modelo/dados/dataset.json b/modelo/dados/dataset.json
index 924e801..bb0313d 100644
--- a/modelo/dados/dataset.json
+++ b/modelo/dados/dataset.json
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diff --git a/modelo/dados/eval.json b/modelo/dados/eval.json
new file mode 100644
index 0000000..692855e
--- /dev/null
+++ b/modelo/dados/eval.json
@@ -0,0 +1 @@
+"[{\"date\":\"18/11/2020 09:00\",\"x\":36,\"y\":66,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":30,\"y\":121,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":79,\"y\":141,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":159,\"y\":171,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":242,\"y\":175,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":374,\"y\":172,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":421,\"y\":208,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":245,\"y\":210,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":235,\"y\":177,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":377,\"y\":168,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":417,\"y\":203,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":257,\"y\":212,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":225,\"y\":181,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":365,\"y\":174,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":409,\"y\":204,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":249,\"y\":204,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":243,\"y\":180,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":352,\"y\":168,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":412,\"y\":202,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":259,\"y\":207,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":240,\"y\":178,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":352,\"y\":171,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":401,\"y\":203,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":260,\"y\":212,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":236,\"y\":175,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":373,\"y\":175,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":415,\"y\":175,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":437,\"y\":155,\"stress\":true},{\"date\":\"18/11/2020 09:00\",\"x\":482,\"y\":119,\"stress\":true}]"
diff --git a/modelo/dados/modelCNN-1605022224.h5 b/modelo/dados/modelCNN-1605022224.h5
new file mode 100644
index 0000000..32e94a9
Binary files /dev/null and b/modelo/dados/modelCNN-1605022224.h5 differ
diff --git a/modelo/dados/normalizador.save b/modelo/dados/normalizador.save
new file mode 100644
index 0000000..b21e959
Binary files /dev/null and b/modelo/dados/normalizador.save differ
diff --git a/modelo/manipulacaoDados.py b/modelo/manipulacaoDados.py
deleted file mode 100644
index 2e36bb9..0000000
--- a/modelo/manipulacaoDados.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import pandas as pd
-import json
-from pandas.io.json import json_normalize
-from sklearn.preprocessing import MinMaxScaler
-
-
-
-path = "dados/dataset.json"
-with open(path , 'r') as f:
-    data = json.load(f)
-    data = json.loads(data)
-    
-df = pd.DataFrame.from_dict(json_normalize(data), orient='columns')
-df["stress"] = df["stress"].astype(int) # True e False para 1 e 0
-
-df_treino = df.iloc[:, 1:3].values
-
-previsores = []
-stress = []
-
-
-
-normalizador = MinMaxScaler(feature_range=(0,1))
-df_treino = normalizador.fit_transform(df_treino)
-
-df.to_pickle("dados/dataset.df")
-
diff --git a/modelo/modulos/__pycache__/analise_dados.cpython-38.pyc b/modelo/modulos/__pycache__/analise_dados.cpython-38.pyc
new file mode 100644
index 0000000..41bc08d
Binary files /dev/null and b/modelo/modulos/__pycache__/analise_dados.cpython-38.pyc differ
diff --git a/modelo/modulos/__pycache__/machine_learning.cpython-38.pyc b/modelo/modulos/__pycache__/machine_learning.cpython-38.pyc
new file mode 100644
index 0000000..ff774fb
Binary files /dev/null and b/modelo/modulos/__pycache__/machine_learning.cpython-38.pyc differ
diff --git a/modelo/modulos/analise_dados.py b/modelo/modulos/analise_dados.py
new file mode 100644
index 0000000..f84134e
--- /dev/null
+++ b/modelo/modulos/analise_dados.py
@@ -0,0 +1,175 @@
+from time import time
+from contextlib import redirect_stdout
+from sklearn.utils import shuffle
+from numpy import mean, std, array, rint
+import matplotlib.pyplot as plt
+from difflib import SequenceMatcher
+from modulos.machine_learning import kfold
+from sklearn.metrics import confusion_matrix
+from mlxtend.plotting import plot_confusion_matrix
+import os
+
+
+
+def kfold_log_batches(model_func, x, y, num_folds=10, checks_per_batch=5, batches=[]):
+    if not batches:
+        batches = [10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130]
+    model = model_func()
+    histories = []
+    identificador = int(time())
+    f = open("logs/batch_size/batch-size_%s.log"%(identificador), "a")
+    
+    with redirect_stdout(f):
+        model.summary()
+        
+    f.write('\n'+str(model.optimizer.get_config()))
+    f.close()
+    
+    
+    for batch in batches:
+        medias = []
+        desvios_padrao = []
+        f = open("logs/batch_size/batch-size_%s.log"%(identificador), "a")
+        print("Batch Size: %d:"%(batch))
+        f.write("\nBatch Size %d: "%(batch))
+        f.close()
+        
+        for i in range(0, checks_per_batch):
+            cvscores = []
+            
+            f = open("logs/batch_size/batch-size_%s.log"%(identificador), "a")
+            h, cvscores = kfold(x, y, model, batch_size=batch, verbose=0)
+            f.write(" %.2f%% (+/- %.2f%%); " % (mean(cvscores), std(cvscores)))
+            histories.append(h)
+            medias.append(mean(cvscores))
+            desvios_padrao.append(std(cvscores))
+            f.close()
+            print("Batch %s - Check %d: %.2f%% (+/- %.2f%%)" % (batch, i+1, mean(cvscores), std(cvscores))) # Resultado Final
+            ##
+        f = open("logs/batch_size/batch-size_%s.log"%(identificador), "a")
+        f.write(" Média: %.2f%% (+/- %.2f%%)"%(mean(medias), mean(desvios_padrao)))
+        f.close()
+    return histories
+
+# Função que plota um gráfico comparativo entre as classes reais e previstas
+def plotarComparacoes(model, x_teste, y_teste, estatisticas=1):
+    previsao = model.predict(x_teste)
+    previsao = array([previsao > 0.5]).astype('int32')
+    previsao = previsao.reshape(previsao.shape[1])
+    
+    if estatisticas:
+        sm = SequenceMatcher(None,previsao, y_teste)
+        print("\nEstatísticas")
+        print("\tMédia das previsões: %.2f"%(previsao.mean()))
+        print("\tMédia real: %.2f"%(y_teste.mean()))
+        print("\tDesvio Padrão das previsões: %.2f"%(previsao.std()))
+        print("\tDesvio Padrão: %.2f"%(y_teste.std()))
+        print("\tPorcentagem de similaridade: %.2f %%"%(sm.ratio()*100))
+    
+    plt.plot(previsao, color = 'blue', label = 'Previsão', alpha=0.5)
+    plt.plot(y_teste, color = 'red', label = 'Real', alpha=0.5)
+    plt.title('Previsão de stress de idoso com base na movimentação')
+    plt.xlabel('Hora de Teste')
+    plt.ylabel('Stress')
+    plt.legend()
+    plt_comparacoes = plt.gcf()
+    plt.show()
+    return plt_comparacoes
+    
+def plotar(h):
+    if not isinstance(h, list):
+        plt.plot(h.history['accuracy'])
+        if 'val_accuracy' in h.history:
+            plt.plot(h.history['val_accuracy'])
+        plt.title('Acurácia do modelo')
+        plt.ylabel('acurácia')
+        plt.xlabel('época')
+        plt.legend(['Treino', 'Teste'], loc='best')
+        plt_accuracy = plt.gcf()
+        plt.show()  
+        
+        plt.plot(h.history['loss'])
+        if 'val_loss' in h.history:
+            plt.plot(h.history['val_loss'])
+        plt.title('Perda do modelo')
+        plt.ylabel('perda')
+        plt.xlabel('época')
+        plt.legend(['train', 'test'], loc='best')
+        plt_loss = plt.gcf()
+        plt.show()
+        return plt_loss, plt_accuracy
+
+    for historico in range(h):
+        plt.plot(historico.history['accuracy'])
+        if 'val_accuracy' in historico.history:
+            plt.plot(historico.history['val_accuracy'])
+        plt.title('Acurácia do modelo')
+        plt.ylabel('acurácia')
+        plt.xlabel('época')
+        plt.legend(['Treino', 'Teste'], loc='best')
+        plt.show()  
+        plt.plot(historico.history['loss'])
+        if 'val_loss' in historico.history:
+            plt.plot(historico.history['val_loss'])
+        plt.title('Perda do modelo')
+        plt.ylabel('perda')
+        plt.xlabel('época')
+        plt.legend(['train', 'test'], loc='best')
+        plt.show()
+    return 1
+
+def plotar_log(lista, tags, nfolds=10):
+    path = "logs/plots/%d"%(int(time()))
+    os.mkdir(path)
+    print("Total Lista: %d | %d lista[0]"%(len(lista), len(lista[0])))  
+    i=0
+    for grupo in lista:
+        j=0
+        fold=0
+        print(fold)
+        for h in grupo:
+            
+            if i%5 == 0:
+                tag = tags[j]
+                j = j + 1
+            if not os.path.exists("%s/%d"%(path, tag)):
+                os.mkdir("%s/%d"%(path, tag))
+            plot_accuracy, plot_loss = plotar(h)
+
+            print(i%nfolds)    
+            if i%(nfolds/len(tags)) == 0:
+                if not os.path.exists("%s/%d/%d"%(path, tag, fold)):
+                    os.mkdir("%s/%d/%d"%(path, tag, fold))
+                    fold = fold + 1
+            
+            print("%s/%d/%d/%d_accuracy.png"%(path, tag, fold-1, i))
+            plot_accuracy.savefig("%s/%d/%d/%d_accuracy.png"%(path, tag, fold-1, i))
+            plot_loss.savefig("%s/%d/%d/%d_loss.png"%(path, tag, fold-1, i))
+            i = i+1
+
+def matriz_confusao(y_teste, y_previsto):
+     cm = confusion_matrix(y_teste, y_previsto)  
+     plot_confusion_matrix(cm, class_names=["Estressado", "Não Estressado"])
+     plt_matriz_confusao = plt.gcf()
+     plt.show()
+     return plt_matriz_confusao
+     
+def plotar_matriz_confusao(model, x_teste, y_teste):
+    y_pred = rint(array(model.predict(x_teste)))
+    return matriz_confusao(y_teste, y_pred)
+    
+def log_previsoes(model, x_teste, y_teste):
+    log_path = "logs/previsoes/previsao-%d"%(int(time()))
+    os.mkdir(log_path)
+    matriz = plotar_matriz_confusao(model, x_teste, y_teste)
+    comparacoes = plotarComparacoes(model, x_teste, y_teste, estatisticas=0)
+    
+    matriz.savefig("%s/matriz_confusao.png"%(log_path))
+    comparacoes.savefig("%s/grafico_comparacoes.png"%(log_path))
+    
+    f = open("%s/model.log"%(log_path), "a")
+    with redirect_stdout(f):
+        model.summary()
+    f.write('\n'+str(model.optimizer.get_config()))
+    f.close()        
+    
\ No newline at end of file
diff --git a/modelo/modulos/machine_learning.py b/modelo/modulos/machine_learning.py
new file mode 100644
index 0000000..89b9f60
--- /dev/null
+++ b/modelo/modulos/machine_learning.py
@@ -0,0 +1,29 @@
+import numpy as np
+from sklearn.utils import shuffle
+from sklearn.model_selection import StratifiedKFold, train_test_split
+
+def carrega_dados():
+    # Dados de treino
+    x = np.load("dados/x_treino.npy")
+    y = np.load("dados/y_treino.npy")
+    ##
+    x, x_teste, y, y_teste = train_test_split(x, y)
+    
+    x,y = shuffle(x,y)
+    x_teste, y_teste = shuffle(x_teste,y_teste)
+    
+    return x, y, x_teste, y_teste
+
+def kfold(x, y, model, kfold_=StratifiedKFold, n=10, epochs=500, batch_size=75, verbose=1):
+    x, y = shuffle(x , y)
+    kf = kfold_(n_splits=n, shuffle=True)
+    cvscores = []
+    histories = []
+    for treino, teste in kf.split(x, y):
+        history = model.fit(x[treino], y[treino], epochs=epochs, batch_size=batch_size, verbose=0)
+        scores = model.evaluate(x[teste], y[teste], verbose=0)
+        cvscores.append(scores[1] * 100)
+        histories.append(history)
+    if(verbose):
+        print("%.2f%% (+/- %.2f%%)" % (np.mean(cvscores), np.std(cvscores))) # Resultado Final
+    return histories, cvscores
diff --git a/modelo/representacao/2x/modelo.png b/modelo/representacao/2x/modelo.png
new file mode 100644
index 0000000..4c0716b
Binary files /dev/null and b/modelo/representacao/2x/modelo.png differ
diff --git a/modelo/representacao/SVG/modelo.svg b/modelo/representacao/SVG/modelo.svg
new file mode 100644
index 0000000..fdcad29
--- /dev/null
+++ b/modelo/representacao/SVG/modelo.svg
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diff --git a/modelo/requirements.txt b/modelo/requirements.txt
new file mode 100644
index 0000000..b6288c1
--- /dev/null
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+python-dateutil==2.8.1
+python-jsonrpc-server @ file:///tmp/build/80754af9/python-jsonrpc-server_1594397536060/work
+python-language-server @ file:///C:/ci/python-language-server_1594162130238/work
+pytz==2020.1
+PyWavelets==1.1.1
+pywin32==227
+pywin32-ctypes==0.2.0
+pywinpty @ file:///D:/bld/pywinpty_1602377663724/work
+PyYAML==5.3.1
+pyzmq==19.0.1
+QDarkStyle==2.8.1
+QtAwesome==0.7.2
+qtconsole @ file:///tmp/build/80754af9/qtconsole_1598374667791/work
+QtPy==1.9.0
+requests @ file:///tmp/build/80754af9/requests_1592841827918/work
+requests-oauthlib==1.3.0
+rope==0.17.0
+rsa==4.6
+Rtree==0.9.4
+scikit-image==0.17.2
+scikit-learn==0.23.2
+scipy==1.4.1
+Send2Trash==1.5.0
+sip==4.19.13
+six==1.15.0
+snowballstemmer==2.0.0
+sortedcontainers==2.2.2
+Sphinx @ file:///tmp/build/80754af9/sphinx_1597428793432/work
+sphinxcontrib-applehelp==1.0.2
+sphinxcontrib-devhelp==1.0.2
+sphinxcontrib-htmlhelp==1.0.3
+sphinxcontrib-jsmath==1.0.1
+sphinxcontrib-qthelp==1.0.3
+sphinxcontrib-serializinghtml==1.1.4
+spyder @ file:///C:/ci/spyder_1594830825244/work
+spyder-kernels @ file:///C:/ci/spyder-kernels_1598375198994/work
+tabulate==0.8.7
+tensorboard==2.3.0
+tensorboard-plugin-wit==1.7.0
+tensorflow==2.3.0
+tensorflow-estimator==2.3.0
+termcolor==1.1.0
+terminado @ file:///D:/bld/terminado_1602679807967/work
+terminaltables==3.1.0
+testpath==0.4.4
+threadpoolctl==2.1.0
+tifffile==2020.10.1
+toml @ file:///tmp/build/80754af9/toml_1592853716807/work
+tornado==6.0.4
+tqdm==4.48.2
+traitlets==4.3.3
+ujson==1.35
+urllib3 @ file:///tmp/build/80754af9/urllib3_1597086586889/work
+watchdog @ file:///C:/ci/watchdog_1593447437088/work
+wcwidth @ file:///tmp/build/80754af9/wcwidth_1593447189090/work
+webencodings==0.5.1
+Werkzeug==1.0.1
+win-inet-pton==1.1.0
+wincertstore==0.2
+wrapt==1.11.2
+yapf @ file:///tmp/build/80754af9/yapf_1593528177422/work
+zipp==3.1.0
diff --git a/modelo/rodar-tensorboard.bat b/modelo/rodar-tensorboard.bat
new file mode 100644
index 0000000..d5f2922
--- /dev/null
+++ b/modelo/rodar-tensorboard.bat
@@ -0,0 +1,2 @@
+tensorboard --logdir=logs/tensorboard/
+pause
\ No newline at end of file
diff --git a/server/README.md b/server/README.md
index a263979..cff3fb5 100644
--- a/server/README.md
+++ b/server/README.md
@@ -1,2 +1,2 @@
 # Smartcare Machine Learning - Server
-Aqui no servidor são carregadas as informações do modelo já treinado, será como uma api que ao receber requisições com dados novos irá mandar de volta a previsão se o idoso está estressado ou estará estressado.
+It is planned in the future to have an api server with the machine learning model running (Flask)