Image_prediction.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Copy of Image_prediction.ipynb","provenance":[{"file_id":"15Dq71cHVgM4CsOjxLAwB2wAYkbBDvU2n","timestamp":1595606274248}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"1Le00Nit6sp0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":364},"executionInfo":{"status":"ok","timestamp":1596446337196,"user_tz":240,"elapsed":3276,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"96c3238f-fc2e-4b71-fa4c-5bc28809b067"},"source":["\n","import numpy as np\n","from scipy import io\n","\n","# Loading Data\n","!wget https://github.com/AmbarCRoAl/TREND-2020/blob/master/imagedata.mat?raw=true\n","!mv imagedata.mat\\?raw\\=true imagedata.mat\n","data = io.loadmat('imagedata.mat')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["--2020-08-03 09:18:55--  https://github.com/AmbarCRoAl/TREND-2020/blob/master/imagedata.mat?raw=true\n","Resolving github.com (github.com)... 140.82.114.4\n","Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://github.com/AmbarCRoAl/TREND-2020/raw/master/imagedata.mat [following]\n","--2020-08-03 09:18:55--  https://github.com/AmbarCRoAl/TREND-2020/raw/master/imagedata.mat\n","Reusing existing connection to github.com:443.\n","HTTP request sent, awaiting response... 302 Found\n","Location: https://raw.githubusercontent.com/AmbarCRoAl/TREND-2020/master/imagedata.mat [following]\n","--2020-08-03 09:18:56--  https://raw.githubusercontent.com/AmbarCRoAl/TREND-2020/master/imagedata.mat\n","Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n","Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 44624 (44K) [application/octet-stream]\n","Saving to: ‘imagedata.mat?raw=true’\n","\n","\rimagedata.mat?raw=t   0%[                    ]       0  --.-KB/s               \rimagedata.mat?raw=t 100%[===================>]  43.58K  --.-KB/s    in 0.02s   \n","\n","2020-08-03 09:18:56 (1.71 MB/s) - ‘imagedata.mat?raw=true’ saved [44624/44624]\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"0noFhRNKNDHX","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":537},"executionInfo":{"status":"ok","timestamp":1596446360359,"user_tz":240,"elapsed":525,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"85ab8844-5494-4703-fe58-fbbf3be679d2"},"source":["images_arrays = data['images']\n","images_arrays[0][7]"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  0,   0,   0,   0,   0,   0,   0,   0,   1,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   1,   1,   1,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   1,   2,   6,   7,   4,   4,   3,   2,   0,   0,\n","          0,   0],\n","       [  0,   0,   1,   6,  11,  15,  27,  37,  19,  10,   5,   3,   0,\n","          0,   0],\n","       [  0,   1,   2,  17,  33,  66,  84, 106,  68,  39,  14,   4,   1,\n","          0,   0],\n","       [  0,   1,   7,  41, 116, 181, 263, 253, 190,  97,  28,  10,   4,\n","          1,   0],\n","       [  0,   7,  24,  69, 150, 328, 437, 431, 340, 144,  63,  22,   4,\n","          0,   0],\n","       [  0,   5,  23,  89, 178, 351, 486, 543, 346, 168,  97,  26,   1,\n","          0,   0],\n","       [  1,   3,  14,  66, 153, 329, 435, 414, 324, 189,  66,  14,  11,\n","          1,   0],\n","       [  0,   1,  12,  38, 114, 193, 237, 243, 184, 101,  24,  20,   3,\n","          4,   0],\n","       [  1,   1,   6,  21,  42,  78, 122, 108,  68,  40,  19,   4,   1,\n","          1,   0],\n","       [  0,   0,   2,   6,  10,  19,  30,  35,  20,   9,   3,   3,   0,\n","          0,   0],\n","       [  0,   0,   0,   2,   3,   3,   6,   4,   4,   4,   1,   0,   1,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   1,   2,   0,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0]], dtype=uint16)"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"id":"kujSeDOKOrzi","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":483},"executionInfo":{"status":"ok","timestamp":1596142584835,"user_tz":240,"elapsed":1439,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"160b5d09-6182-4b55-a130-40ce386835b0"},"source":["import matplotlib.pyplot as plt\n","from mpl_toolkits.axes_grid1 import ImageGrid\n","import numpy as np\n","\n","# Looking at the data\n","\n","fig = plt.figure(figsize=(8., 8.))\n","grid = ImageGrid(fig, 111,  # similar to subplot(111)\n","                 nrows_ncols=(2, 2),  # creates 2x2 grid of axes\n","                 axes_pad=0.1,  # pad between axes in inch.\n","                 )\n","\n","for ax, im in zip(grid, images_arrays[0][0:4]):\n","    # Iterating over the grid returns the Axes.\n","    ax.imshow(im)\n","\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x576 with 8 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ClIMXiYkl0sK","colab_type":"text"},"source":["## One array: multi-points prediction"]},{"cell_type":"code","metadata":{"id":"sgPPuxizR-y6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"status":"ok","timestamp":1596142587547,"user_tz":240,"elapsed":728,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"5e3f66ad-8138-4cb3-bf09-c4a25194060c"},"source":["# Formating data\n","\n","# Adjusting the data, originally is grid and we want array\n","# Given alldata_array[0][i], i->253, i are the images(15x15) in each step of the turn\n","# Predictions will be made as if each pixel is an attribute \n","\n","def convert2D_to1D(data, pixels,steps):\n","  #data is an array of the form data[steps][pixels][pixels]\n","  array = np.zeros((steps,pixels**2), float)\n","  for i in range(steps):\n","    array[i] = data[i].reshape(pixels**2)                                                                                                                                                                                            \n","  return array\n","\n","def convert1D_to2D(data, pixels, steps = False):\n","  # steps = True = data is an array of the form data[rows][pixels * pixels]\n","  if steps:\n","    array = np.zeros((data.shape[0],pixels,pixels), float)\n","    for i in range(data.shape[0]):\n","      array[i] = data[i].reshape(pixels,pixels)\n","    return array\n","  return data.reshape(pixels,pixels)\n","\n","\n","copy_data =  images_arrays[0]\n","pixels = 15\n","steps = 253\n","alldata_matrix = convert2D_to1D(copy_data, pixels, steps)\n","\n","plt.plot(alldata_matrix[0])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f6fcd080e10>]"]},"metadata":{"tags":[]},"execution_count":5},{"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":"0ilpTWGCd6lU","colab_type":"code","colab":{}},"source":["# Functions to prepare the data\n","\n","# Convert series to supervised learning\n","def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n","  if type(data) is list:  \n","\t  n_vars = 1 ; \n","  else:\n","    n_vars = data.shape[1]\n","  print(n_vars)\n","  df = DataFrame(data)\n","  cols, names = list(), list()\n","\t# Input sequence (t-n, ... t-1)\n","  for i in range(n_in, 0, -1):\n","    cols.append(df.shift(i))\n","    names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n","  # Forecast sequence (t, t+1, ... t+n)\n","  for i in range(0, n_out):\n","    cols.append(df.shift(-i))\n","    if i == 0:\n","      names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n","    else:\n","      names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n","\t# Put it all together\n","  agg = concat(cols, axis=1)\n","  agg.columns = names\n","\t# Drop rows with NaN values\n","  if dropnan:\n","    agg.dropna(inplace=True)\n","  return agg\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"TszmnHTi1kkD","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":267},"executionInfo":{"status":"ok","timestamp":1596142591775,"user_tz":240,"elapsed":1080,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"b1c98c23-0f6a-412f-cfaf-23821deb1cb4"},"source":["\n","# Prepare data for LSTM\n","\n","import pandas as pd\n","from pandas import DataFrame\n","from pandas import concat\n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# normalize features\n","scaler = MinMaxScaler(feature_range=(0, 1))\n","scaled = scaler.fit_transform(alldata_matrix)\n","\n","input, predict = 3, 3 \n","supervised_data = series_to_supervised(scaled,input, predict)\n"," \n","supervised_data.head\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["225\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<bound method NDFrame.head of      var1(t-3)  var2(t-3)  var3(t-3)  ...  var223(t+2)  var224(t+2)  var225(t+2)\n","3          0.0        0.0        0.0  ...          0.0          0.0          0.0\n","4          0.0        0.0        0.0  ...          0.0          0.0          0.0\n","5          0.0        0.0        0.0  ...          0.0          0.0          0.0\n","6          0.0        0.0        0.0  ...          0.0          0.0          0.0\n","7          0.0        0.0        0.0  ...          0.0          0.0          0.0\n","..         ...        ...        ...  ...          ...          ...          ...\n","246        0.0        0.0        0.0  ...          0.0          0.0          0.0\n","247        0.0        0.0        0.0  ...          0.0          0.0          0.0\n","248        0.0        0.0        0.0  ...          0.0          0.0          0.0\n","249        0.0        0.0        0.0  ...          0.0          0.0          0.0\n","250        0.0        0.0        0.0  ...          0.0          0.0          0.0\n","\n","[248 rows x 1350 columns]>"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"YgLKBFu04LJu","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":50},"executionInfo":{"status":"ok","timestamp":1596142595155,"user_tz":240,"elapsed":2301,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"544ee054-8a9e-4f99-e823-79b56154cacb"},"source":["\n","#Creating a model\n","\n","from math import sqrt\n","from numpy import concatenate\n","from sklearn.metrics import mean_squared_error\n","from keras.models import Sequential\n","from keras.layers import Dense\n","from keras.layers import LSTM\n"," \n","# split into train and test sets\n","n_train = 195\n","attributes = pixels**2\n","train = supervised_data.iloc[:n_train,:]\n","test = supervised_data.iloc[n_train:,:]\n","\n","# split into input and outputs\n","train_X, train_y = train.iloc[:, 0:input*attributes], train.iloc[:, -predict*attributes:]\n","test_X, test_y = test.iloc[:, 0:input*attributes], test.iloc[:, -predict*attributes:]\n","\n","# transform from pandas to numpy array\n","train_X = train_X.to_numpy()\n","test_X = test_X.to_numpy()\n","train_y = train_y.to_numpy()\n","test_y = test_y.to_numpy()\n","\n","# reshape input to be 3D [samples, timesteps, features]\n","train_X = train_X.reshape((train_X.shape[0], input, attributes))\n","test_X = test_X.reshape((test_X.shape[0], input, attributes))\n","print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"stream","text":["(195, 3, 225) (195, 675) (53, 3, 225) (53, 675)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"f_tuHuPlNmJE","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1596142605602,"user_tz":240,"elapsed":9803,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"9a8681d5-ca04-42d5-d742-6b8db9d0a4b7"},"source":["import matplotlib.pyplot as pyplot\n","\n","# design network\n","model = Sequential()\n","model.add(LSTM(25, input_shape=(train_X.shape[1], train_X.shape[2])))\n","model.add(Dense(input*attributes))\n","model.compile(loss='mae', optimizer='adam')\n","# fit network\n","history = model.fit(train_X, train_y, epochs=100, batch_size=10, validation_data=(test_X, test_y), verbose=2, shuffle=False)\n","# plot history\n","pyplot.plot(history.history['loss'], label='train')\n","pyplot.plot(history.history['val_loss'], label='test')\n","pyplot.legend()\n","pyplot.show()\n","\n","# Mean Absolute Error (MAE) loss function and the efficient Adam version of stochastic gradient descent"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Train on 195 samples, validate on 53 samples\n","Epoch 1/100\n"," - 1s - loss: 0.1443 - val_loss: 0.1298\n","Epoch 2/100\n"," - 0s - loss: 0.1096 - val_loss: 0.0881\n","Epoch 3/100\n"," - 0s - loss: 0.0764 - val_loss: 0.0695\n","Epoch 4/100\n"," - 0s - loss: 0.0666 - val_loss: 0.0666\n","Epoch 5/100\n"," - 0s - loss: 0.0645 - val_loss: 0.0651\n","Epoch 6/100\n"," - 0s - loss: 0.0610 - val_loss: 0.0602\n","Epoch 7/100\n"," - 0s - loss: 0.0546 - val_loss: 0.0552\n","Epoch 8/100\n"," - 0s - loss: 0.0496 - val_loss: 0.0530\n","Epoch 9/100\n"," - 0s - loss: 0.0475 - val_loss: 0.0519\n","Epoch 10/100\n"," - 0s - loss: 0.0463 - val_loss: 0.0514\n","Epoch 11/100\n"," - 0s - loss: 0.0457 - val_loss: 0.0510\n","Epoch 12/100\n"," - 0s - loss: 0.0453 - val_loss: 0.0509\n","Epoch 13/100\n"," - 0s - loss: 0.0449 - val_loss: 0.0507\n","Epoch 14/100\n"," - 0s - loss: 0.0446 - val_loss: 0.0506\n","Epoch 15/100\n"," - 0s - loss: 0.0444 - val_loss: 0.0504\n","Epoch 16/100\n"," - 0s - loss: 0.0441 - val_loss: 0.0504\n","Epoch 17/100\n"," - 0s - loss: 0.0439 - val_loss: 0.0503\n","Epoch 18/100\n"," - 0s - loss: 0.0438 - val_loss: 0.0503\n","Epoch 19/100\n"," - 0s - loss: 0.0436 - val_loss: 0.0503\n","Epoch 20/100\n"," - 0s - loss: 0.0435 - val_loss: 0.0504\n","Epoch 21/100\n"," - 0s - loss: 0.0433 - val_loss: 0.0503\n","Epoch 22/100\n"," - 0s - loss: 0.0431 - val_loss: 0.0505\n","Epoch 23/100\n"," - 0s - loss: 0.0429 - val_loss: 0.0503\n","Epoch 24/100\n"," - 0s - loss: 0.0425 - val_loss: 0.0501\n","Epoch 25/100\n"," - 0s - loss: 0.0420 - val_loss: 0.0499\n","Epoch 26/100\n"," - 0s - loss: 0.0416 - val_loss: 0.0498\n","Epoch 27/100\n"," - 0s - loss: 0.0413 - val_loss: 0.0498\n","Epoch 28/100\n"," - 0s - loss: 0.0411 - val_loss: 0.0497\n","Epoch 29/100\n"," - 0s - loss: 0.0409 - val_loss: 0.0496\n","Epoch 30/100\n"," - 0s - loss: 0.0408 - val_loss: 0.0496\n","Epoch 31/100\n"," - 0s - loss: 0.0406 - val_loss: 0.0495\n","Epoch 32/100\n"," - 0s - loss: 0.0405 - val_loss: 0.0495\n","Epoch 33/100\n"," - 0s - loss: 0.0404 - val_loss: 0.0494\n","Epoch 34/100\n"," - 0s - loss: 0.0403 - val_loss: 0.0494\n","Epoch 35/100\n"," - 0s - loss: 0.0402 - val_loss: 0.0493\n","Epoch 36/100\n"," - 0s - loss: 0.0401 - val_loss: 0.0491\n","Epoch 37/100\n"," - 0s - loss: 0.0399 - val_loss: 0.0493\n","Epoch 38/100\n"," - 0s - loss: 0.0397 - val_loss: 0.0493\n","Epoch 39/100\n"," - 0s - loss: 0.0396 - val_loss: 0.0493\n","Epoch 40/100\n"," - 0s - loss: 0.0395 - val_loss: 0.0491\n","Epoch 41/100\n"," - 0s - loss: 0.0394 - val_loss: 0.0490\n","Epoch 42/100\n"," - 0s - loss: 0.0393 - val_loss: 0.0490\n","Epoch 43/100\n"," - 0s - loss: 0.0392 - val_loss: 0.0491\n","Epoch 44/100\n"," - 0s - loss: 0.0392 - val_loss: 0.0491\n","Epoch 45/100\n"," - 0s - loss: 0.0392 - val_loss: 0.0494\n","Epoch 46/100\n"," - 0s - loss: 0.0393 - val_loss: 0.0492\n","Epoch 47/100\n"," - 0s - loss: 0.0393 - val_loss: 0.0490\n","Epoch 48/100\n"," - 0s - loss: 0.0393 - val_loss: 0.0486\n","Epoch 49/100\n"," - 0s - loss: 0.0390 - val_loss: 0.0485\n","Epoch 50/100\n"," - 0s - loss: 0.0388 - val_loss: 0.0485\n","Epoch 51/100\n"," - 0s - loss: 0.0386 - val_loss: 0.0482\n","Epoch 52/100\n"," - 0s - loss: 0.0384 - val_loss: 0.0478\n","Epoch 53/100\n"," - 0s - loss: 0.0382 - val_loss: 0.0475\n","Epoch 54/100\n"," - 0s - loss: 0.0381 - val_loss: 0.0475\n","Epoch 55/100\n"," - 0s - loss: 0.0380 - val_loss: 0.0478\n","Epoch 56/100\n"," - 0s - loss: 0.0376 - val_loss: 0.0480\n","Epoch 57/100\n"," - 0s - loss: 0.0375 - val_loss: 0.0477\n","Epoch 58/100\n"," - 0s - loss: 0.0373 - val_loss: 0.0473\n","Epoch 59/100\n"," - 0s - loss: 0.0372 - val_loss: 0.0471\n","Epoch 60/100\n"," - 0s - loss: 0.0373 - val_loss: 0.0475\n","Epoch 61/100\n"," - 0s - loss: 0.0371 - val_loss: 0.0482\n","Epoch 62/100\n"," - 0s - loss: 0.0369 - val_loss: 0.0478\n","Epoch 63/100\n"," - 0s - loss: 0.0369 - val_loss: 0.0468\n","Epoch 64/100\n"," - 0s - loss: 0.0365 - val_loss: 0.0464\n","Epoch 65/100\n"," - 0s - loss: 0.0368 - val_loss: 0.0466\n","Epoch 66/100\n"," - 0s - loss: 0.0366 - val_loss: 0.0479\n","Epoch 67/100\n"," - 0s - loss: 0.0364 - val_loss: 0.0472\n","Epoch 68/100\n"," - 0s - loss: 0.0363 - val_loss: 0.0461\n","Epoch 69/100\n"," - 0s - loss: 0.0358 - val_loss: 0.0459\n","Epoch 70/100\n"," - 0s - loss: 0.0361 - val_loss: 0.0467\n","Epoch 71/100\n"," - 0s - loss: 0.0358 - val_loss: 0.0474\n","Epoch 72/100\n"," - 0s - loss: 0.0358 - val_loss: 0.0466\n","Epoch 73/100\n"," - 0s - loss: 0.0356 - val_loss: 0.0458\n","Epoch 74/100\n"," - 0s - loss: 0.0354 - val_loss: 0.0459\n","Epoch 75/100\n"," - 0s - loss: 0.0355 - val_loss: 0.0466\n","Epoch 76/100\n"," - 0s - loss: 0.0353 - val_loss: 0.0467\n","Epoch 77/100\n"," - 0s - loss: 0.0353 - val_loss: 0.0456\n","Epoch 78/100\n"," - 0s - loss: 0.0348 - val_loss: 0.0453\n","Epoch 79/100\n"," - 0s - loss: 0.0349 - val_loss: 0.0455\n","Epoch 80/100\n"," - 0s - loss: 0.0349 - val_loss: 0.0463\n","Epoch 81/100\n"," - 0s - loss: 0.0348 - val_loss: 0.0459\n","Epoch 82/100\n"," - 0s - loss: 0.0346 - val_loss: 0.0452\n","Epoch 83/100\n"," - 0s - loss: 0.0344 - val_loss: 0.0451\n","Epoch 84/100\n"," - 0s - loss: 0.0345 - val_loss: 0.0453\n","Epoch 85/100\n"," - 0s - loss: 0.0343 - val_loss: 0.0460\n","Epoch 86/100\n"," - 0s - loss: 0.0343 - val_loss: 0.0455\n","Epoch 87/100\n"," - 0s - loss: 0.0342 - val_loss: 0.0452\n","Epoch 88/100\n"," - 0s - loss: 0.0340 - val_loss: 0.0452\n","Epoch 89/100\n"," - 0s - loss: 0.0341 - val_loss: 0.0457\n","Epoch 90/100\n"," - 0s - loss: 0.0340 - val_loss: 0.0459\n","Epoch 91/100\n"," - 0s - loss: 0.0340 - val_loss: 0.0455\n","Epoch 92/100\n"," - 0s - loss: 0.0339 - val_loss: 0.0453\n","Epoch 93/100\n"," - 0s - loss: 0.0338 - val_loss: 0.0451\n","Epoch 94/100\n"," - 0s - loss: 0.0337 - val_loss: 0.0451\n","Epoch 95/100\n"," - 0s - loss: 0.0337 - val_loss: 0.0448\n","Epoch 96/100\n"," - 0s - loss: 0.0335 - val_loss: 0.0445\n","Epoch 97/100\n"," - 0s - loss: 0.0334 - val_loss: 0.0447\n","Epoch 98/100\n"," - 0s - loss: 0.0334 - val_loss: 0.0452\n","Epoch 99/100\n"," - 0s - loss: 0.0336 - val_loss: 0.0454\n","Epoch 100/100\n"," - 0s - loss: 0.0337 - val_loss: 0.0450\n"],"name":"stdout"},{"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":"Mz_C6S3HUDiq","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":639},"executionInfo":{"status":"ok","timestamp":1596142607866,"user_tz":240,"elapsed":8395,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"23f60c59-54cd-4916-fc20-005ad68c8ecf"},"source":["# make a prediction\n","prediction = model.predict(test_X)\n","\n","# prediction has shape (53, 675), for 53 steps 3 images predictions\n","# first we compare images in 1D, then 3D, and finally compare numerically\n","\n","examples = np.zeros((4,3, pixels*pixels), float)\n","for i in range(3):\n","  start = i*(pixels**2)\n","  end = start + pixels**2\n","  examples[0][i] = prediction[0][start:end]\n","\n","examples[1][0:3] = scaled[0:3]\n","\n","for i in range(3):\n","  start = i*(pixels**2)\n","  end = start + pixels**2\n","  examples[2][i] = prediction[1][start:end]\n","\n","examples[3][0:3] = scaled[1:4]\n","\n","fig = plt.figure(figsize=(16., 12.))\n","index = 431\n","count = 1\n","for i in range(4):\n","  for j in range(3):\n","    if (count >= 10):\n","      plt.subplot(4, 3, int(count))\n","      plt.plot(examples[i][j])\n","      plt.grid(True)\n","      count+=1\n","    else:\n","      plt.subplot(int(index))\n","      plt.plot(examples[i][j])\n","      plt.grid(True)\n","      index+=1\n","      count+=1\n","      "],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x864 with 12 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"QwWWYIq3iuZi","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":700},"executionInfo":{"status":"ok","timestamp":1596142609859,"user_tz":240,"elapsed":6660,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"f2bec653-2524-4ed5-dcd9-aad08becb06e"},"source":["# make a prediction\n","prediction = model.predict(test_X)\n","\n","# prediction has shape (53, 675), for 53 steps 3 images predictions\n","# first we compare images in 1D, then 3D, and finally compare numerically\n","\n","examples = np.zeros((12, pixels, pixels), float)\n","for i in range(3):\n","  start = i*(pixels**2)\n","  end = start + pixels**2\n","  examples[0:3] = convert1D_to2D(prediction[0][start:end], pixels)\n","\n","examples[3:6] = convert1D_to2D(scaled[0:3], pixels, steps=True) \n","\n","for i in range(3):\n","  start = i*(pixels**2)\n","  end = start + pixels**2\n","  examples[6:9] = convert1D_to2D(prediction[1][start:end], pixels)\n","\n","\n","examples[9:12] = convert1D_to2D(scaled[1:4], pixels, steps=True) \n","\n","fig = plt.figure(figsize=(16., 12.))\n","grid = ImageGrid(fig, 111, nrows_ncols=(4, 3), axes_pad=0.1)\n","\n","for ax, im in zip(grid, examples):\n","    # Iterating over the grid returns the Axes.\n","    ax.imshow(im)\n","\n","plt.show()\n","      "],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1152x864 with 24 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"3-zMtjpUItgV","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":446},"executionInfo":{"status":"ok","timestamp":1596142610033,"user_tz":240,"elapsed":3854,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"d261b4c9-ca20-4409-cf4c-d0f2e07c9b69"},"source":["# Saving images for the gif\n","from google.colab import files\n","\n","fig = plt.figure(figsize=(6., 6.))\n","grid = ImageGrid(fig, 111, nrows_ncols=(1, 1))\n","gif_array = np.zeros((predict, prediction.shape[0], pixels, pixels), float)\n","\n","# files.download has a limit of 10 dowloads per run of a cell\n","# the code below has to be modified by hand to go 10 by 10\n","'''\n","for i in range(predict):\n","  for j in range(prediction.shape[0]):\n","      start = i*(pixels**2)\n","      end = start + pixels**2\n","      gif_array[i][j] = convert1D_to2D(prediction[j][start:end], pixels)\n","      filename = 'imagen'+str(i)+'_'+str(j)+'.png'\n","      for ax, im in zip(grid, gif_array[i][j:j+1]):\n","          ax.imshow(im)\n","      plt.savefig(filename)\n","      files.download(filename)\n","\n","\n","'''"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["\"\\nfor i in range(predict):\\n  for j in range(prediction.shape[0]):\\n      start = i*(pixels**2)\\n      end = start + pixels**2\\n      gif_array[i][j] = convert1D_to2D(prediction[j][start:end], pixels)\\n      filename = 'imagen'+str(i)+'_'+str(j)+'.png'\\n      for ax, im in zip(grid, gif_array[i][j:j+1]):\\n          ax.imshow(im)\\n      plt.savefig(filename)\\n      files.download(filename)\\n\\n\\n\""]},"metadata":{"tags":[]},"execution_count":12},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXMAAAFpCAYAAABu98hvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAPHElEQVR4nO3cX6jkd3nH8c9jtqnU+qc0K0j+mEjX6mIL2kNqEapFW5JcJBdtJQGxluCibaSgFFIsVuKVLbUgpNUtFaugMXohC66k1EYEMTYr0WgSImu0ZqM065/mRjSGPr2YsRxPdvfMbuacEx9fLzgwv5nvmXn8Oued2flX3R0AfrY9Za8HAOCJE3OAAcQcYAAxBxhAzAEGEHOAAbaNeVW9r6oerqqvnObyqqp3V9Xxqrq7ql6y/jEBOJNVHpm/P8kVZ7j8yiQHlj+HkvzTEx8LgLOxbcy7+zNJvneGJdck+UAv3JHkWVX1nHUNCMD21vGc+YVJHtx0fGJ5HgC7ZN9u3lhVHcriqZg87WlP+60XvOAFu3nzAE9qX/jCF77T3fvP5XfXEfOHkly86fii5XmP092HkxxOko2NjT527Ngabh5ghqr6r3P93XU8zXIkyWuX72p5aZJHuvvba7heAFa07SPzqvpwklckuaCqTiT5myS/kCTd/Z4kR5NcleR4kh8k+dOdGhaAU9s25t193TaXd5I/X9tEAJw1nwAFGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxgADEHGEDMAQYQc4ABxBxggJViXlVXVNX9VXW8qm48xeWXVNXtVXVXVd1dVVetf1QATmfbmFfVeUluTnJlkoNJrquqg1uW/XWSW7v7xUmuTfKP6x4UgNNb5ZH55UmOd/cD3f1okluSXLNlTSd5xvL0M5N8a30jArCdfSusuTDJg5uOTyT57S1r3p7k36rqTUmeluRVa5kOgJWs6wXQ65K8v7svSnJVkg9W1eOuu6oOVdWxqjp28uTJNd00AKvE/KEkF286vmh53mbXJ7k1Sbr7c0memuSCrVfU3Ye7e6O7N/bv339uEwPwOKvE/M4kB6rqsqo6P4sXOI9sWfPNJK9Mkqp6YRYx99AbYJdsG/PufizJDUluS3JfFu9auaeqbqqqq5fL3pLk9VX1pSQfTvK67u6dGhqAn7bKC6Dp7qNJjm45722bTt+b5GXrHQ2AVfkEKMAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAK8W8qq6oqvur6nhV3XiaNa+uqnur6p6q+tB6xwTgTPZtt6Cqzktyc5LfT3IiyZ1VdaS779205kCSv0rysu7+flU9e6cGBuDxVnlkfnmS4939QHc/muSWJNdsWfP6JDd39/eTpLsfXu+YAJzJKjG/MMmDm45PLM/b7PlJnl9Vn62qO6rqilNdUVUdqqpjVXXs5MmT5zYxAI+zrhdA9yU5kOQVSa5L8s9V9ayti7r7cHdvdPfG/v3713TTAKwS84eSXLzp+KLleZudSHKku3/c3V9P8tUs4g7ALlgl5ncmOVBVl1XV+UmuTXJky5qPZ/GoPFV1QRZPuzywxjkBOINtY97djyW5IcltSe5Lcmt331NVN1XV1ctltyX5blXdm+T2JH/Z3d/dqaEB+GnV3XtywxsbG33s2LE9uW2AJ6Oq+kJ3b5zL7/oEKMAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOsFPOquqKq7q+q41V14xnW/WFVdVVtrG9EALazbcyr6rwkNye5MsnBJNdV1cFTrHt6kr9I8vl1DwnAma3yyPzyJMe7+4HufjTJLUmuOcW6dyR5Z5IfrnE+AFawSswvTPLgpuMTy/P+X1W9JMnF3f2JM11RVR2qqmNVdezkyZNnPSwAp/aEXwCtqqckeVeSt2y3trsPd/dGd2/s37//id40AEurxPyhJBdvOr5oed5PPD3Ji5J8uqq+keSlSY54ERRg96wS8zuTHKiqy6rq/CTXJjnykwu7+5HuvqC7L+3uS5PckeTq7j62IxMD8Djbxry7H0tyQ5LbktyX5Nbuvqeqbqqqq3d6QAC2t2+VRd19NMnRLee97TRrX/HExwLgbPgEKMAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOIOcAAYg4wgJgDDCDmAAOsFPOquqKq7q+q41V14ykuf3NV3VtVd1fVp6rquesfFYDT2TbmVXVekpuTXJnkYJLrqurglmV3Jdno7t9M8rEkf7vuQQE4vVUemV+e5Hh3P9Ddjya5Jck1mxd09+3d/YPl4R1JLlrvmACcySoxvzDJg5uOTyzPO53rk3zyiQwFwNnZt84rq6rXJNlI8vLTXH4oyaEkueSSS9Z50wA/11Z5ZP5Qkos3HV+0PO+nVNWrkrw1ydXd/aNTXVF3H+7uje7e2L9//7nMC8AprBLzO5McqKrLqur8JNcmObJ5QVW9OMl7swj5w+sfE4Az2Tbm3f1YkhuS3JbkviS3dvc9VXVTVV29XPZ3SX45yUer6otVdeQ0VwfADljpOfPuPprk6Jbz3rbp9KvWPBcAZ8EnQAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhAzAEGEHOAAcQcYAAxBxhgpZhX1RVVdX9VHa+qG09x+S9W1UeWl3++qi5d96AAnN62Ma+q85LcnOTKJAeTXFdVB7csuz7J97v715L8Q5J3rntQAE5vlUfmlyc53t0PdPejSW5Jcs2WNdck+dfl6Y8leWVV1frGBOBMVon5hUke3HR8YnneKdd092NJHknyq+sYEIDt7dvNG6uqQ0kOLQ9/VFVf2c3bfxK6IMl39nqIJwH7YA8Se5Akv36uv7hKzB9KcvGm44uW551qzYmq2pfkmUm+u/WKuvtwksNJUlXHunvjXIaewh4s2Ad7kNiDZLEH5/q7qzzNcmeSA1V1WVWdn+TaJEe2rDmS5E+Wp/8oyX90d5/rUACcnW0fmXf3Y1V1Q5LbkpyX5H3dfU9V3ZTkWHcfSfIvST5YVceTfC+L4AOwS1Z6zry7jyY5uuW8t206/cMkf3yWt334LNdPZA8W7IM9SOxB8gT2oDwbAvCzz8f5AQbY8Zj7KoCV9uDNVXVvVd1dVZ+qqufuxZw7abs92LTuD6uqq2rcuxpW2YOqevXyvnBPVX1ot2fcDSv8PVxSVbdX1V3Lv4mr9mLOnVJV76uqh0/31uxaePdyf+6uqpesdMXdvWM/Wbxg+rUkz0tyfpIvJTm4Zc2fJXnP8vS1ST6ykzPt9s+Ke/B7SX5pefqNP497sFz39CSfSXJHko29nnsP7gcHktyV5FeWx8/e67n3aB8OJ3nj8vTBJN/Y67nXvAe/m+QlSb5ymsuvSvLJJJXkpUk+v8r17vQjc18FsMIedPft3f2D5eEdWbyXf5JV7gdJ8o4svtfnh7s53C5ZZQ9en+Tm7v5+knT3w7s8425YZR86yTOWp5+Z5Fu7ON+O6+7PZPGuv9O5JskHeuGOJM+qqudsd707HXNfBbDaHmx2fRb/VZ5k2z1Y/lPy4u7+xG4OtotWuR88P8nzq+qzVXVHVV2xa9PtnlX24e1JXlNVJ7J4F92bdme0J42zbUaSXf44P2dWVa9JspHk5Xs9y26qqqckeVeS1+3xKHttXxZPtbwii3+dfaaqfqO7/2dPp9p91yV5f3f/fVX9ThafYXlRd//vXg/2ZLbTj8zP5qsAcqavAvgZtsoepKpeleStSa7u7h/t0my7Zbs9eHqSFyX5dFV9I4vnCY8MexF0lfvBiSRHuvvH3f31JF/NIu6TrLIP1ye5NUm6+3NJnprF97b8vFipGVvtdMx9FcAKe1BVL07y3ixCPvF50jPuQXc/0t0XdPel3X1pFq8bXN3d5/w9FU9Cq/wtfDyLR+WpqguyeNrlgd0cchessg/fTPLKJKmqF2YR85O7OuXeOpLktct3tbw0ySPd/e1tf2sXXrm9KotHGF9L8tbleTdl8ceaLP6P+miS40n+M8nz9vrV5j3Yg39P8t9Jvrj8ObLXM+/2HmxZ++kMezfLiveDyuLppnuTfDnJtXs98x7tw8Ekn83inS5fTPIHez3zmv/3fzjJt5P8OIt/jV2f5A1J3rDpfnDzcn++vOrfgk+AAgzgE6AAA4g5wABiDjCAmAMMIOYAA4g5wABiDjCAmAMM8H9s85ob2sbYxQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x432 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Tp9AH75u6vVC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":412},"executionInfo":{"status":"ok","timestamp":1596142610866,"user_tz":240,"elapsed":494,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"89b85727-8465-40ba-eb0e-420ac8ec97a3"},"source":["\n","gif_array = convert1D_to2D(scaled, pixels, True) \n","fig = plt.figure(figsize=(6., 6.))\n","grid = ImageGrid(fig, 111, nrows_ncols=(1, 1))\n","\n","# files.download has a limit of 10 dowloads per run of a cell\n","# the code below has to be modified by hand to go 10 by 10\n","'''\n","for j in range(50,53):\n","  filename = 'images_'+str(j)+'.png'\n","  for ax, im in zip(grid, gif_array[j:j+1]):\n","      ax.imshow(im)\n","  plt.savefig(filename)\n","  files.download(filename)\n","'''"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["\"\\nfor j in range(50,53):\\n  filename = 'images_'+str(j)+'.png'\\n  for ax, im in zip(grid, gif_array[j:j+1]):\\n      ax.imshow(im)\\n  plt.savefig(filename)\\n  files.download(filename)\\n\""]},"metadata":{"tags":[]},"execution_count":13},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x432 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Pz5AIn3lm2EE","colab_type":"text"},"source":["## Grid input: multi-step and multi-variate"]},{"cell_type":"code","metadata":{"id":"CWv2mt4-19ap","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":517},"executionInfo":{"status":"ok","timestamp":1595957907828,"user_tz":240,"elapsed":1989,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"f74d1a88-b3dd-4254-ba42-e057544aaf5e"},"source":["# This part was not finish\n","\n","copy_data =  images_arrays\n","copy_data[0][0]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  0,   0,   0,   0,   0,   0,   1,   0,   1,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   1,   0,   0,   4,   3,   1,   0,   0,   1,   0,\n","          0,   0],\n","       [  0,   0,   1,   3,   7,  14,  23,  20,  13,  13,   3,   4,   0,\n","          0,   0],\n","       [  1,   0,   4,  15,  27,  59,  76,  82,  55,  27,  15,   5,   1,\n","          1,   0],\n","       [  0,   0,   7,  36,  81, 134, 187, 195, 155,  79,  22,  11,   3,\n","          2,   0],\n","       [  1,   2,  17,  55, 144, 266, 384, 367, 282, 167,  76,  21,   7,\n","          3,   0],\n","       [  0,   5,  16,  92, 172, 337, 452, 514, 355, 205,  94,  29,   8,\n","          2,   0],\n","       [  2,   5,  23,  62, 184, 333, 451, 489, 353, 175,  90,  28,   5,\n","          0,   0],\n","       [  0,   1,   8,  44, 118, 228, 301, 328, 254, 117,  46,  16,   6,\n","          1,   0],\n","       [  0,   0,   5,  21,  49,  87, 128, 148,  95,  55,  21,   6,   0,\n","          0,   0],\n","       [  0,   1,   2,   8,  16,  19,  43,  38,  34,  24,   9,   3,   2,\n","          0,   0],\n","       [  0,   0,   0,   1,   4,   6,   6,  12,   9,   5,   2,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   1,   1,   1,   2,   0,   1,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   1,   0,   0,   0,   0,\n","          0,   0],\n","       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n","          0,   0]], dtype=uint16)"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"code","metadata":{"id":"VHrMmUjnng0-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":452},"executionInfo":{"status":"error","timestamp":1595957759868,"user_tz":240,"elapsed":3199,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"9d68e165-7899-4878-d326-e22b57c5b3ab"},"source":["# Take 15 input and 15 attribute, predict 15 points and 15 attributes\n","# Must have run all previous cells\n","# We also change the scaling to -1->1 instead of 0->1; predicts one-one\n","copy_data =  images_arrays\n","\n","# Prepare data for LSTM\n","# normalize features\n","scaler = MinMaxScaler(feature_range=(-1, 1))\n","scaled = scaler.fit_transform(copy_data)\n","\n","input, predict = pixels, pixels\n","supervised_data = series_to_supervised(scaled[0][0],input, predict)\n","for i in range(steps-1):\n","  values = series_to_supervised(scaled[0][i+1],input, predict)\n","  supervised_data = supervised_data.append(values, ignore_index=True)\n"," \n","supervised_data.head\n"],"execution_count":null,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-30-b6f6c0598ac0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# normalize features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mscaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpixels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpixels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    569\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m             \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 571\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    572\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m             \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    337\u001b[0m         \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 339\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    529\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    530\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."]}]},{"cell_type":"code","metadata":{"id":"2pvxBbBNlqYp","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"O7V_EuTElqbj","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"K_EmRSbTlwwH","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"uIrmE1Nllwy1","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kO_ENFqklqec","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":33},"executionInfo":{"status":"ok","timestamp":1596142667060,"user_tz":240,"elapsed":368,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"0838b307-b5a5-4d5a-dac4-6ebc2e34f5b3"},"source":["test_X[:,:,:].shape"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(53, 3, 225)"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"UC5Ep0uhlqhP","colab_type":"code","colab":{}},"source":["test_X[:,:,:].shape\n","test_X2=np.zeros((1,3,attributes))\n","test_X2[0,:,:] = test_X[0,:,:] # grab the first 3 input images into a new variable\n","\n","iter_pred = 30 # number of feedbacks to do\n","y_pred_total = [] # predictions\n","y_real_total = [] # real values\n","for i in range(iter_pred):\n","  #print('running iter '+str(i))\n","  y_pred = model.predict(test_X2) # input first 3 images and predict\n","\n","  # append prediction of 3 images to array\n","  y_pred_total.append(y_pred[0,0:attributes])\n","  y_pred_total.append(y_pred[0,attributes:attributes*2])\n","  y_pred_total.append(y_pred[0,attributes*2:attributes*3])\n","\n","  # append the 3 real images to array\n","  y_real_total.append(test_X[i,0,:])\n","  y_real_total.append(test_X[i,1,:])\n","  y_real_total.append(test_X[i,2,:])\n","\n","  # the 3 predictions are now set as the 3 inputs again\n","  test_X2[0,0,:] = (y_pred[0,0:attributes])\n","  test_X2[0,1,:] = y_pred[0,attributes:attributes*2]\n","  test_X2[0,2,:] = y_pred[0,attributes*2:attributes*3]\n","\n","# lets make this into a numpy array\n","y_pred_total = np.array(y_pred_total)\n","y_real_total = np.array(y_real_total)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8P6cEqmoltbe","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":610},"executionInfo":{"status":"ok","timestamp":1596142803411,"user_tz":240,"elapsed":9613,"user":{"displayName":"Ambar C Rodriguez Alicea","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjL6czV2_4Ff3P-7pxTiR7jpH6a5fr3UnZvL0cIqA=s64","userId":"15506484599280598998"}},"outputId":"2d72f9f3-af55-4d6a-8709-c6c33a01e630"},"source":["from matplotlib import animation\n","from IPython.display import HTML, display\n","\n","fig,ax = plt.subplots(1,2)\n","\n","def updatefig(image_selected):\n","    # input is essentially the frame to plot, here frame for us is the same as the image index to select, e.g. image # 0,1,2,3,4,5,....\n","    image_pred = y_pred_total[image_selected]\n","    image_real = y_real_total[image_selected]\n","\n","    # 2 subplots, in the first one we plot the real image, second one is the prediction image\n","    # you can change this to add more subplots, etc... if you wanted to plot 3 real and 3 predic\n","    plt.subplot(1,2,1)\n","    im = plt.imshow(convert1D_to2D(image_real,15),interpolation='nearest')\n","    plt.title('Iteration '+str(image_selected+1)+'\\n Real value')\n","    plt.subplot(1,2,2)\n","    im = plt.imshow(convert1D_to2D(image_pred,15),interpolation='nearest')\n","    plt.title('Iteration '+str(image_selected+1)+'\\n Prediction value')\n","    return plt.gcf(),\n","\n","# you can look up FuncAnimation to see what the inputs odo\n","# frames is the total number of frames\n","# interval is the delay in ms for each frame\n","# updatefig is the function to call each frame\n","ani = animation.FuncAnimation(fig, updatefig, interval=250, frames=30, blit=True)\n","\n","HTML(ani.to_html5_video()) # this is a jupyter notebook / collab function to make an animated gif from the matplotlib animation\n","\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n","  del sys.path[0]\n","/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:16: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n","  app.launch_new_instance()\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/html":["<video width=\"432\" height=\"288\" controls autoplay loop>\n","  <source type=\"video/mp4\" 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538v3R6brJxbduZaw8Y3pKbeLhrfm4y46GFuQmmu+5/KBU3vGtXKq6dms3t2rye3jePwZe+cMxtTr/z56l9G5jXl4oD2PS8XOyeZ+baG5yVGw6DyA1D0LsrP1d7z+7cseybf5AxAWp05UZAYNH396Xieo5M/hKB4eR7aviu+9Nt1tN0oZc0G/h74L0RMbpK3gEsjoidkl4N/CNwUr12IuJy4HKAOVqQ+wuZTaJW5HZtXvfPWeS8trZo6q4bSb0Ub4SvRcQ/jF4fEdsjYmf5+DqgV1LuVNisjZzbViXN3HUj4MvAvRHxFw1ijirjkHR6ub1NE92m2aHg3LaqaebSzUuBtwB3SVpdLvsI8CyAiLgMeAPwLkmDwB7gwojwx1frdM5tq5QJF/qIuAU46DcoEXEpcOlEt2HWDs5tqxr3jDUzqzgXejOzinOhNzOrOBd6M7OKm9KTgw/v2JGK65qVnHgb0MwZqbjdpyxMxU3fuDsVt/3kXC/f6VtzcQB9j+ZiB+bkehr2bsz1Gh4eHEzFqSeffpFss5N17R2g7571Y8YN7831wByPZAdVep+f6019bH+uh/Ypcx9PxfX35CeUX7XpWam4J3fOSsV1X5/rGd41kJwQfTjZaxjQrvxxN8Nn9GZmFedCb2ZWcS70ZmYV50JvZlZxLvRmZhXnQm9mVnEu9GZmFedCb2ZWcS70ZmYV15E9Y9XTQ/cRiZ6ne1rfqyzmzE7FDfXluhpu/PXcfLU7Tsj1pgvl/2SzH16Qi3s0t+05W3O/b03LzX87vCvXa7gyurog0fNau/ekmpv2aK6nMsC046en4p7clusZ/psn/zAV94b+Nam4feMYyf+8OXNSce+8/c2puBndufdy9/Zcj2V1jeP8eVfub81wrlduIz6jNzOruKYLvaSHJN0labWkVXXWS9JfSVor6SeSXtzsNs0mm/PaqqRVl25eERFPNlh3HnBS+bMU+JvyX7NO57y2SjgUl27OB74ShVuBeZKOPgTbNZtMzmubMlpR6AO4UdLtkpbVWX8ssK7m+fpy2X4kLZO0StKqgeHkFxRmk6f1eT10mH35bB2jFZduXhYRGyQtBFZIui8ibhpvIxFxOXA5wNzeheP4Dt5sUrQ+r/uOcl5bWzR9Rh8RG8p/HweWA6ePCtkAHFfzfFG5zKxjOa+tSpoq9JJmSeofeQycA4y+cfZa4K3lXQq/BmyLiEeb2a7ZZHJeW9U0e+nmSGC5pJG2ro6I6yW9EyAiLgOuA14NrAV2A29rcptmk815bZXSVKGPiAeBF9RZflnN4wD+w7jaHRxkaOPYc0129eXmOlV/rrcrwN5n5eaP3Hl0dyrujIvvSMU9uOMZqbite3M9FwEWviQ3r+faG09IxU3fmvs99j2R28duJScyBTjyiFTY0AMP5ttsYLLymoGnGH7ksbHjkj0ru57Kz6M7MCf5u1bua4TnTs9dpXoq+a3Es3vz79Eucj2CB/b2puK6cx1t0b6BVFwM5OIAGMr1eE3Nr3yQdHDPWDOzinOhNzOrOBd6M7OKc6E3M6s4F3ozs4pzoTczqzgXejOzinOhNzOrOBd6M7OK69w5Yxc8c8y42J0b9jXm5nvdbTw9N7fmnlNz86fO6s7NM3n9qd9Nxf3RYy9KxQEcPW1bKm7NMYtTcduPy/U0nHF3Lq2GxjHnb7Sgx2vb9XTTNW/sOYRjMNfjNXrzb9++Tbl5gbftybW5O3Lvkxt2nZKK6+t6KhUHcHxv7vw09uZ6r89en+y+G8m44fwgpbE3Vx8i0+ZBQnxGb2ZWcS70ZmYV50JvZlZxLvRmZhXnQm9mVnEu9GZmFTfhQi/pFEmra362S3rvqJgzJW2rifl487tsNrmc21Y1E76PPiJ+CrwQQFI3xcTIy+uE3hwRr5nodswONee2VU2rLt2cBfwsIh5uUXtmncK5bVNeqwr9hcA1DdadIelOSd+TdFqLtmd2qDi3bcpreggESdOA1wIfrrP6DmBxROyU9GrgH4GTGrSzDFgG0Nfdj6aN3d1ePf25ndyRGyoBYPa6XPflN1/4z6m4R/blJhv/8rajUnFnzbknFQdw/bZfTcVpIDdx9OxHkpNRJyf9Vnf+PKNr3thDYgAMPfFEus2xtCK398trzWJ4VyIXh3PDFWhzbogLgDk/n5WK27MwF/fBJ9+SitPJuQnqp08bx0Tnq+en4mbvyrU3d20uUIO5ibyHk0NYAGhubmZybdqc2HDjVa04oz8PuCMiNo5eERHbI2Jn+fg6oFfSEfUaiYjLI2JJRCyZ1jWjBbtl1rSmc3u/vFbf5O+xWR2tKPRvosFHW0lHScXpnaTTy+1tasE2zQ4F57ZVQlOXbiTNAl4JvKNm2TsBIuIy4A3AuyQNAnuACyOyQ8CZtY9z26qkqUIfEbuAZ4xadlnN40uBS5vZhlk7OLetStwz1sys4lzozcwqzoXezKziXOjNzCrOhd7MrOI6cnLwgQXTWHfB8WPGHfetX+TaW1y3j1ZdC+7ckor72zVnpOKGNuUmUe759Vyvu6u3nJ6KA5jeneuhN+eEram4LScvSMXNWpPrGTu8Nz85OOOJ7WRDib9zsmfxuCRv/Oz/RS4Pn3FXLre2rp+dihucmT/mBety+7h3fnIS8Z5k3M5cD9rshN8AsWdPLi6TN54c3Mzs8OVCb2ZWcS70ZmYV50JvZlZxLvRmZhXnQm9mVnEu9GZmFedCb2ZWcS70ZmYV15E9YzUMvbvG7so3vDnXi7Vnbq53HsAjZ+d6f05bk2uvO9lJ7v8MLknF9e7I9yDUUC62d0euvWm7c90rh46Ym4rrSv79AAaWnpqK6/n+7ek2DzkJTR+7p/Tw7twcx13jmHO3Z2uuzWwH1Z5tuZ7Kc7tz8zr3bn8qt2Gga2+uV25/ssdrdo7eOCY3bzE7cvPkApDr5AtNzmnjM3ozs4pLFXpJV0h6XNKammULJK2Q9ED5b92p2SVdVMY8IOmiVu24WbOc13a4yJ7RXwmcO2rZh4CVEXESsLJ8vh9JC4BPAEuB04FPNHrjmLXBlTiv7TCQKvQRcROwedTi84GrysdXAa+r89JXASsiYnNEbAFWcOAby6wtnNd2uGjmGv2REfFo+fgx4Mg6MccC62qery+XHUDSMkmrJK0a3JMbDtRsEkxaXg9ERYZatimnJV/GRkSQHvG6YRuXR8SSiFjSM2NWK3bLrCmtzutp6mvRnpmNTzOFfqOkowHKfx+vE7MBOK7m+aJymVmncl5b5TRT6K8FRu42uAj4dp2YG4BzJM0vv6w6p1xm1qmc11Y52dsrrwF+BJwiab2kS4DPAK+U9ABwdvkcSUskfQkgIjYDfwr8uPz5ZLnMrO2c13a4UDTZ42oyzNGCWKqzWtbejgt+LR077456n9QPNDx7RipOyd/v0MzeVFzP1twck0C6N110d6filJ0LszfX4Xro3gdy7bXYbbGS7bF5EiZmPbg5WhBLu84eO1C5D9pdM/LX/JWNTW47vd1pubyO8cwJPJzLa82amdv2jNy8zvHIxlTc8J78sagrl4aROObbhm5smNfuGWtmVnEu9GZmFedCb2ZWcS70ZmYV50JvZlZxLvRmZhXnQm9mVnEu9GZmFedCb2ZWcR05Z2yrzf/xY+nY4UdysV3TpuXaS/aS607O/zmefszDA7l5OLuSPQiZnjvmoSc35dqzupTsWZydWxZA+3K9mjM9MCG/j9lerCR7iI6nTe1J9iLvTfbeTf4OidwctEVo8lx7HG3W4zN6M7OKc6E3M6s4F3ozs4pzoTczqzgXejOzinOhNzOruDELvaQrJD0uaU3Nsv8h6T5JP5G0XNK8Bq99SNJdklZLWtXKHTdrlnPbDheZM/orgXNHLVsBPC8ing/cD3z4IK9/RUS8MCKWTGwXzSbNlTi37TAwZqGPiJuAzaOW3RgRg+XTW4FFk7BvZpPKuW2Hi1Zco3878L0G6wK4UdLtkpa1YFtmh5Jz2yqhqSEQJH0UGAS+1iDkZRGxQdJCYIWk+8qzqHptLQOWAfSR7JKfNPjgQy1tD4DkZMbqSXZn3zuQ225XbiLvotGhXNiOHbn2kmFV0KrcPiCvE5Nvp7vat1N2aIOkGEjmP9A1Mznpd3IIkBhIDiXR5DAETWly0vYJv1rSxcBrgDdHRN2/ekRsKP99HFgOnN6ovYi4PCKWRMSSXnKzsptNhlbmtvPaOsGECr2kc4E/Bl4bEXX/O5Q0S1L/yGPgHGBNvVizTuHctirK3F55DfAj4BRJ6yVdAlwK9FN8ZF0t6bIy9hhJ15UvPRK4RdKdwL8A342I6yflKMwmwLlthws1+GTaVnO0IJbqrHbvRktkr9HH4ODYQTAp1+gPN7fFSrbH5nGMi9sac7QglnafM3bgJPzd0nmYHQK4exx5mNnuYO56OkzCNfqh5O97Mq7RN3ntvdZtQzc2zGv3jDUzqzgXejOzinOhNzOrOBd6M7OKc6E3M6u4w2Jy8HZK302T5TtpprbEnRutvkNmvLGp9lp9p8o47j4Zz6Tord52uygzefpBftWdf4RmZtYUF3ozs4pzoTczqzgXejOzinOhNzOrOBd6M7OKc6E3M6s4F3ozs4pzoTczq7iOHI9e0hPAw6MWHwE82YbdabWqHAdM3WNZHBHPPNQbrXheg4+l3RrmdUcW+nokrYqIJe3ej2ZV5TigWsfSLlX6HfpYOpcv3ZiZVZwLvZlZxU2lQn95u3egRapyHFCtY2mXKv0OfSwdaspcozczs4mZSmf0ZmY2AR1f6CWdK+mnktZK+lC796cZkh6SdJek1ZJWtXt/xkPSFZIel7SmZtkCSSskPVD+O7+d+zjVOLc7w+GQ2x1d6CV1A58HzgOeC7xJ0nPbu1dNe0VEvHAK3rp1JXDuqGUfAlZGxEnAyvK5JTi3O8qVVDy3O7rQA6cDayPiwYgYAL4OnN/mfTosRcRNwOZRi88HriofXwW87pDu1NTm3O4Qh0Nud3qhPxZYV/N8fblsqgrgRkm3S1rW7p1pgSMj4tHy8WPAke3cmSnGud3ZKpXbnhz80HpZRGyQtBBYIem+8mxiyouIkORbuA5fzu0O1uln9BuA42qeLyqXTUkRsaH893FgOcXH96lso6SjAcp/H2/z/kwlzu3OVqnc7vRC/2PgJEm/ImkacCFwbZv3aUIkzZLUP/IYOAdYc/BXdbxrgYvKxxcB327jvkw1zu3OVqnc7uhLNxExKOk9wA1AN3BFRNzd5t2aqCOB5ZKg+L1fHRHXt3eX8iRdA5wJHCFpPfAJ4DPANyVdQjEq4xvbt4dTi3O7cxwOue2esWZmFdfpl27MzKxJLvRmZhXnQm9mVnEu9GZmFedCb2ZWcS70ZmYV50JvZlZxLvRmZhX3/wGFAVJyAYQ5GQAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"DCiTnS48mBYD","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}