deep_dynamics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results reproducing S. Scher \"Toward Data‐Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning\" using ERA-Interim data\n",
    "\n",
    "https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018GL080704\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.- Load geopotential for ERA-Interim data (6 hours/0.75 deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 241, lev: 3, lon: 480, time: 7304)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1986-01-01 ... 1990-12-31T18:00:00\n",
       "  * lon      (lon) float64 -180.0 -179.2 -178.5 -177.8 ... 177.8 178.5 179.2\n",
       "  * lat      (lat) float64 90.0 89.25 88.5 87.75 ... -87.75 -88.5 -89.25 -90.0\n",
       "  * lev      (lev) float64 8.5e+04 7e+04 5e+04\n",
       "Data variables:\n",
       "    z        (time, lev, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    CDI:          Climate Data Interface version 1.9.8 (https://mpimet.mpg.de...\n",
       "    history:      Wed Jul 08 10:37:05 2020: cdo mergetime erai_z_1986.nc erai...\n",
       "    source:       Original grib files obtained from http://apps.ecmwf.int/dat...\n",
       "    institution:  ARCCSS ARC Centre of Excellence for Climate System Science ...\n",
       "    Conventions:  CF-1.4\n",
       "    title:        ERA-Interim Geopotential [m**2 s**-2] analysis on pressure ...\n",
       "    references:   Please acknowledge both ECMWF for original files and the AR...\n",
       "    NCO:          4.3.8\n",
       "    CDO:          Climate Data Operators version 1.9.8 (https://mpimet.mpg.de..."
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "ds = xr.open_dataset(\"/data/ERA-Int/erai_z.nc\")\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.- Create train and test data to forecast one time step ahead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000, 241, 480, 1), (200, 241, 480, 1))"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = ds.z.isel(time=slice(0,1000)).values[:,2,:,:]\n",
    "x_train = np.moveaxis(x_train, 1, -1)\n",
    "x_train = np.swapaxes(x_train, 1, 2)[:,:,:,None]\n",
    "\n",
    "x_test = ds.z.isel(time=slice(1000,1200)).values[:,2,:,:]\n",
    "x_test = np.moveaxis(x_test, 1, -1)\n",
    "x_test = np.swapaxes(x_test, 1, 2)[:,:,:,None]\n",
    "\n",
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4ac2dbd438>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(ds.z.isel(time=100).values[0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((241, 480), -1.7248787237282068, 2.555202092636598e-17, 0.9999999999999999)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lats = np.repeat(np.arange(241)[:,None], 480, axis=1)\n",
    "\n",
    "lats = (lats-lats.mean())/lats.std()\n",
    "lats.shape, lats.min(), lats.mean(), lats.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.- Add topography information (ERA5 subsampled and retiled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4ac2d952b0>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = xr.open_dataset(\"land_sea_topo_era5.nc\")\n",
    "topo5 = ds.z.values[0,::3,::3]\n",
    "topo = np.ones_like(topo5)\n",
    "topo[:,:240] = topo5[:,240:]\n",
    "topo[:,240:] = topo5[:,:240]\n",
    "\n",
    "topo = (topo-topo.mean())/topo.std()\n",
    "\n",
    "plt.imshow(topo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.- Normalise and aggregate the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = x_train.mean()\n",
    "std = x_train.std()\n",
    "\n",
    "x_train = (x_train-mean)/std\n",
    "x_test = (x_test-mean)/std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = x_train[1:,:]#[1:,:229,:468,:]\n",
    "x_train = x_train[:-1,:]\n",
    "\n",
    "y_test = x_test[1:,:]#[1:,:229,:468,:]\n",
    "x_test = x_test[:-1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.10187, 1.4743066, 7.955709e-05, 1.0000985)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.min(), y_train.max(), y_train.mean(), y_train.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4ac2d676a0>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_test[10,:,:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.concatenate((x_train,np.repeat(topo[None,:,:,None], x_train.shape[0], axis=0)), axis=3)\n",
    "x_test = np.concatenate((x_test,np.repeat(topo[None,:,:,None], x_test.shape[0], axis=0)), axis=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((999, 241, 480, 2), (199, 241, 480, 2))"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.- Define and train the model (code adapted from original paper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model(channels):\n",
    "\n",
    "    conv_depth = 32\n",
    "    kernel_size = 6\n",
    "    batch_size = 32\n",
    "    num_epochs = 10\n",
    "    pool_size = 2\n",
    "    drop_prob=0\n",
    "    conv_activation='relu'\n",
    "    n_hidden_layers = 0\n",
    "\n",
    "    model = tf.keras.Sequential([\n",
    "                ## Convolution with dimensionality reduction (similar to Encoder in an autoencoder)\n",
    "                layers.Conv2D(conv_depth, kernel_size, padding='same', activation=conv_activation, input_shape=(240,480,channels)),\n",
    "                layers.MaxPooling2D(pool_size=pool_size),\n",
    "                #layers.Dropout(drop_prob),\n",
    "                layers.Conv2D(conv_depth, kernel_size, padding='same', activation=conv_activation),\n",
    "                layers.MaxPooling2D(pool_size=pool_size),\n",
    "                # end \"encoder\"\n",
    "\n",
    "\n",
    "                # dense layers (flattening and reshaping happens automatically)\n",
    "\n",
    "                ] + [layers.Dense(hidden_size, activation='sigmoid') for i in range(n_hidden_layers)] +\n",
    "\n",
    "                [\n",
    "\n",
    "                # start \"Decoder\" (mirror of the encoder above)\n",
    "                layers.Conv2D(conv_depth, kernel_size, padding='same', activation=conv_activation),\n",
    "                layers.UpSampling2D(size=pool_size),\n",
    "                layers.Conv2D(conv_depth, kernel_size, padding='same', activation=conv_activation),\n",
    "                layers.UpSampling2D(size=pool_size),\n",
    "                layers.Convolution2D(3, kernel_size, padding='same', activation=None)\n",
    "                ])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = get_model(2)\n",
    "model.compile(loss='mse', optimizer=Adam(lr=0.0001))\n",
    "print(model.summary())\n",
    "model.fit(x_train[:,:240,:], y_train[:,:240,:], validation_data=(x_test[:,:240,:], y_test[:,:240,:]), shuffle=True, epochs=100, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.- Generate output dataset from trained model and create video comparing output for 20 temporal steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-f100e6b16610>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdelta\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0minput_z\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdelta\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mdelta\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m240\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\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[1;32m      6\u001b[0m     \u001b[0minput_z\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_z\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'x_test' is not defined"
     ]
    }
   ],
   "source": [
    "out = []\n",
    "\n",
    "delta = 0\n",
    "input_z = x_test[delta:delta+1,:240, :]\n",
    "for _ in range(20):\n",
    "    input_z = model.predict(input_z)\n",
    "    input_z = np.concatenate((input_z, topo[None,:240,:,None]), axis=3)\n",
    "    out.append(input_z)\n",
    "    \n",
    "y_hat = np.concatenate(out, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (480, 481) to (480, 496) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "\n",
    "writer = imageio.get_writer(f'forecast{delta:03d}.mp4', fps=1)\n",
    "\n",
    "for i in range(20):\n",
    "    writer.append_data(plt.cm.viridis(np.concatenate((y_hat[i,:,:,0], y_test[delta+i,:,:,0]), axis=0)))\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((998, 241, 480, 6),\n",
       " (198, 241, 480, 6),\n",
       " (998, 241, 480, 3),\n",
       " (198, 241, 480, 3))"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = ds.z.isel(time=slice(0,1000)).values[:,:,:,:]\n",
    "x_train = np.moveaxis(x_train, 1, -1)\n",
    "\n",
    "x_test = ds.z.isel(time=slice(1000,1200)).values[:,:,:,:]\n",
    "x_test = np.moveaxis(x_test, 1, -1)\n",
    "\n",
    "for i in range(3):\n",
    "    mean = x_train[:,:,:,i].mean()\n",
    "    std = x_train[:,:,:,i].std()\n",
    "\n",
    "    x_train[:,:,:,i] = (x_train[:,:,:,i]-mean)/std\n",
    "    x_test[:,:,:,i] = (x_test[:,:,:,i]-mean)/std\n",
    "    \n",
    "    \n",
    "y_train = x_train[2:,:]\n",
    "x_train = np.concatenate((x_train[:-2,:],x_train[1:-1,:]), axis=3)\n",
    "\n",
    "y_test = x_test[2:,:]\n",
    "x_test = np.concatenate((x_test[:-2,:],x_test[1:-1,:]), axis=3)\n",
    "\n",
    "x_train.shape, x_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((998, 241, 480, 7), (198, 241, 480, 7))"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = np.concatenate((x_train,np.repeat(topo[None,:,:,None], x_train.shape[0], axis=0)), axis=3)\n",
    "x_test = np.concatenate((x_test,np.repeat(topo[None,:,:,None], x_test.shape[0], axis=0)), axis=3)\n",
    "\n",
    "x_train.shape, x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_90 (Conv2D)           (None, 240, 480, 32)      8096      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_34 (MaxPooling (None, 120, 240, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_91 (Conv2D)           (None, 120, 240, 32)      36896     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_35 (MaxPooling (None, 60, 120, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_92 (Conv2D)           (None, 60, 120, 32)       36896     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_32 (UpSampling (None, 120, 240, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_93 (Conv2D)           (None, 120, 240, 32)      36896     \n",
      "_________________________________________________________________\n",
      "up_sampling2d_33 (UpSampling (None, 240, 480, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_94 (Conv2D)           (None, 240, 480, 3)       3459      \n",
      "=================================================================\n",
      "Total params: 122,243\n",
      "Trainable params: 122,243\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 998 samples, validate on 198 samples\n",
      "Epoch 1/100\n",
      "998/998 [==============================] - 19s 19ms/sample - loss: 0.3653 - val_loss: 0.1045\n",
      "Epoch 2/100\n",
      "998/998 [==============================] - 7s 7ms/sample - loss: 0.0615 - val_loss: 0.0482\n",
      "Epoch 3/100\n",
      "998/998 [==============================] - 7s 7ms/sample - loss: 0.0348 - val_loss: 0.0316\n",
      "Epoch 4/100\n",
      "998/998 [==============================] - 7s 7ms/sample - loss: 0.0247 - val_loss: 0.0249\n",
      "Epoch 5/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0193 - val_loss: 0.0184\n",
      "Epoch 6/100\n",
      "998/998 [==============================] - 7s 7ms/sample - loss: 0.0160 - val_loss: 0.0154\n",
      "Epoch 7/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0139 - val_loss: 0.0135\n",
      "Epoch 8/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0124 - val_loss: 0.0125\n",
      "Epoch 9/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0115 - val_loss: 0.0114\n",
      "Epoch 10/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0106 - val_loss: 0.0105\n",
      "Epoch 11/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0099 - val_loss: 0.0102\n",
      "Epoch 12/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0094 - val_loss: 0.0095\n",
      "Epoch 13/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0088 - val_loss: 0.0088\n",
      "Epoch 14/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0084 - val_loss: 0.0094\n",
      "Epoch 15/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0081 - val_loss: 0.0080\n",
      "Epoch 16/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0077 - val_loss: 0.0084\n",
      "Epoch 17/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0076 - val_loss: 0.0074\n",
      "Epoch 18/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0073 - val_loss: 0.0073\n",
      "Epoch 19/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0071 - val_loss: 0.0071\n",
      "Epoch 20/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0070 - val_loss: 0.0075\n",
      "Epoch 21/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0069 - val_loss: 0.0068\n",
      "Epoch 22/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0066 - val_loss: 0.0066\n",
      "Epoch 23/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0066 - val_loss: 0.0068\n",
      "Epoch 24/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0064 - val_loss: 0.0063\n",
      "Epoch 25/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0063 - val_loss: 0.0062\n",
      "Epoch 26/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0062 - val_loss: 0.0065\n",
      "Epoch 27/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0061 - val_loss: 0.0064\n",
      "Epoch 28/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0061 - val_loss: 0.0068\n",
      "Epoch 29/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0060 - val_loss: 0.0065\n",
      "Epoch 30/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0061 - val_loss: 0.0058\n",
      "Epoch 31/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0058 - val_loss: 0.0062\n",
      "Epoch 32/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0058 - val_loss: 0.0057\n",
      "Epoch 33/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0058 - val_loss: 0.0060\n",
      "Epoch 34/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0058 - val_loss: 0.0058\n",
      "Epoch 35/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0056 - val_loss: 0.0058\n",
      "Epoch 36/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0056 - val_loss: 0.0054\n",
      "Epoch 37/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0054 - val_loss: 0.0056\n",
      "Epoch 38/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0054 - val_loss: 0.0060\n",
      "Epoch 39/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0055 - val_loss: 0.0059\n",
      "Epoch 40/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0055 - val_loss: 0.0053\n",
      "Epoch 41/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0053 - val_loss: 0.0053\n",
      "Epoch 42/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0052 - val_loss: 0.0053\n",
      "Epoch 43/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0051 - val_loss: 0.0051\n",
      "Epoch 44/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0052 - val_loss: 0.0053\n",
      "Epoch 45/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0051 - val_loss: 0.0051\n",
      "Epoch 46/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0051 - val_loss: 0.0055\n",
      "Epoch 47/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0051 - val_loss: 0.0050\n",
      "Epoch 48/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0050 - val_loss: 0.0051\n",
      "Epoch 49/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0049 - val_loss: 0.0050\n",
      "Epoch 50/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0049 - val_loss: 0.0050\n",
      "Epoch 51/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0050 - val_loss: 0.0051\n",
      "Epoch 52/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0049 - val_loss: 0.0051\n",
      "Epoch 53/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0049 - val_loss: 0.0051\n",
      "Epoch 54/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0051 - val_loss: 0.0049\n",
      "Epoch 55/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0048 - val_loss: 0.0054\n",
      "Epoch 56/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0050 - val_loss: 0.0049\n",
      "Epoch 57/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0047 - val_loss: 0.0053\n",
      "Epoch 58/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0048 - val_loss: 0.0047\n",
      "Epoch 59/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0047 - val_loss: 0.0050\n",
      "Epoch 60/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0047 - val_loss: 0.0049\n",
      "Epoch 61/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0049 - val_loss: 0.0049\n",
      "Epoch 62/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0048 - val_loss: 0.0047\n",
      "Epoch 63/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0046 - val_loss: 0.0046\n",
      "Epoch 64/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0046 - val_loss: 0.0046\n",
      "Epoch 65/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0045 - val_loss: 0.0045\n",
      "Epoch 66/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0045 - val_loss: 0.0046\n",
      "Epoch 67/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0045 - val_loss: 0.0045\n",
      "Epoch 68/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0046 - val_loss: 0.0047\n",
      "Epoch 69/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0046 - val_loss: 0.0053\n",
      "Epoch 70/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0045 - val_loss: 0.0053\n",
      "Epoch 71/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0046 - val_loss: 0.0046\n",
      "Epoch 72/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0045 - val_loss: 0.0045\n",
      "Epoch 73/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0044 - val_loss: 0.0046\n",
      "Epoch 74/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0044 - val_loss: 0.0044\n",
      "Epoch 75/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0044 - val_loss: 0.0044\n",
      "Epoch 76/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0043 - val_loss: 0.0049\n",
      "Epoch 77/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0046 - val_loss: 0.0046\n",
      "Epoch 78/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0043 - val_loss: 0.0043\n",
      "Epoch 79/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0043 - val_loss: 0.0044\n",
      "Epoch 80/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0043 - val_loss: 0.0045\n",
      "Epoch 81/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0043 - val_loss: 0.0044\n",
      "Epoch 82/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0042 - val_loss: 0.0043\n",
      "Epoch 83/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0043 - val_loss: 0.0054\n",
      "Epoch 84/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0045 - val_loss: 0.0042\n",
      "Epoch 85/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0042 - val_loss: 0.0041\n",
      "Epoch 86/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 87/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0042 - val_loss: 0.0042\n",
      "Epoch 88/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0043 - val_loss: 0.0047\n",
      "Epoch 89/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0042 - val_loss: 0.0041\n",
      "Epoch 90/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0042 - val_loss: 0.0053\n",
      "Epoch 91/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0043 - val_loss: 0.0043\n",
      "Epoch 92/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 93/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0041 - val_loss: 0.0045\n",
      "Epoch 94/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0041 - val_loss: 0.0043\n",
      "Epoch 95/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0041 - val_loss: 0.0041\n",
      "Epoch 96/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 97/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0041 - val_loss: 0.0042\n",
      "Epoch 98/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0042 - val_loss: 0.0043\n",
      "Epoch 99/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0041 - val_loss: 0.0057\n",
      "Epoch 100/100\n",
      "998/998 [==============================] - 8s 8ms/sample - loss: 0.0042 - val_loss: 0.0042\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f48f80be4a8>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = get_model(x_train.shape[-1])\n",
    "model.compile(loss='mse', optimizer=Adam(lr=0.0001))\n",
    "print(model.summary())\n",
    "model.fit(x_train[:,:240,:], y_train[:,:240,:], validation_data=(x_test[:,:240,:], y_test[:,:240,:]), shuffle=True, epochs=100, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = []\n",
    "\n",
    "delta = 0\n",
    "steps = 100\n",
    "input_z = x_test[delta:delta+1,:240, :]\n",
    "delta += 1\n",
    "for i in range(delta,delta+steps):\n",
    "    input_z = model.predict(input_z)\n",
    "    input_z = np.concatenate((input_z, topo[None,:240,:,None]), axis=3)\n",
    "    out.append(input_z)\n",
    "    \n",
    "y_hat = np.concatenate(out, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (480, 481) to (480, 496) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n",
      "Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning.\n"
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "\n",
    "writer = imageio.get_writer(f'forecast{delta:03d}.mp4', fps=1)\n",
    "\n",
    "for i in range(steps):\n",
    "    writer.append_data(plt.cm.viridis(np.concatenate((y_hat[i,:,:,2], y_test[delta+i,:,:,2]), axis=0)))\n",
    "\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}