addition_CNN_trial.ipynb

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   "execution_count": 1,
   "id": "44071c5c-757f-4b06-b8a3-e274d0c68554",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgcm import Grid\n",
    "import pop_tools\n",
    "import gcsfs\n",
    "import fsspec as fs\n",
    "import numpy as np\n",
    "import xesmf as xe\n",
    "import xarray as xr\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from xgcm import Grid\n",
    "import importlib\n",
    "import preprocessing\n",
    "import os\n",
    "import xrft\n",
    "import gcm_filters\n",
    "import random\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "importlib.reload(preprocessing)\n",
    "from preprocessing import preprocess_data\n",
    "\n",
    "from gcm_filtering import filter_inputs_dataset\n",
    "from gcm_filtering import filter_inputs\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "\n",
    "from matplotlib.lines import Line2D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e354beee-78d0-485a-ae1c-ba53d39e526a",
   "metadata": {},
   "source": [
    "# Prep training data"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "5e25b991-92a6-419f-ac54-df127325cbaa",
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   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 217MB\n",
       "Dimensions:   (time: 185, yt_ocean: 163, xt_ocean: 150)\n",
       "Coordinates:\n",
       "  * time      (time) object 1kB 0181-01-01 12:00:00 ... 0185-12-26 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 1kB -24.95 -24.85 -24.75 ... -10.15 -10.05\n",
       "  * yt_ocean  (yt_ocean) float64 1kB -29.98 -29.89 -29.81 ... -15.17 -15.07\n",
       "Data variables:\n",
       "    q_ref     (time, yt_ocean, xt_ocean) float64 36MB dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;\n",
       "    slp       (time, yt_ocean, xt_ocean) float64 36MB dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;\n",
       "    sst       (time, yt_ocean, xt_ocean) float64 36MB dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;\n",
       "    t_ref     (time, yt_ocean, xt_ocean) float64 36MB dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;\n",
       "    u_rel     (time, yt_ocean, xt_ocean) float64 36MB dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;\n",
       "    v_rel     (time, yt_ocean, xt_ocean) float64 36MB dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d2d9e457-93da-440a-9f7e-2f98ce5d1e12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d2d9e457-93da-440a-9f7e-2f98ce5d1e12' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 185</li><li><span class='xr-has-index'>yt_ocean</span>: 163</li><li><span class='xr-has-index'>xt_ocean</span>: 150</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6498a0ab-e4b3-4147-9c3e-a0e53cece335' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6498a0ab-e4b3-4147-9c3e-a0e53cece335' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0181-01-01 12:00:00 ... 0185-12-...</div><input id='attrs-19fd4db2-e46f-450a-be8b-a7ffa26a8c20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19fd4db2-e46f-450a-be8b-a7ffa26a8c20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7508ea0a-715e-402f-b397-46377f36c595' class='xr-var-data-in' type='checkbox'><label for='data-7508ea0a-715e-402f-b397-46377f36c595' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeJulian(181, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 1, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 2, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 2, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 3, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 3, 12, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 3, 22, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 4, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 4, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 4, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 5, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 6, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 6, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 6, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 7, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 7, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 7, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 8, 9, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 8, 19, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 8, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 9, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 9, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 9, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 10, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 10, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 10, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 11, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 11, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 11, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(181, 12, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 1, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 1, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 1, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 2, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 2, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 3, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 3, 12, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 3, 22, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 4, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 4, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 4, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 5, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 5, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 5, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 5, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 6, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 6, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 6, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 7, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 7, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 7, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 8, 9, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 8, 19, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 8, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 9, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 9, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 9, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 10, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 10, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 10, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 11, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 11, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 11, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 12, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 12, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(182, 12, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 1, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 1, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 1, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 2, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 2, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 3, 2, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 3, 12, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 3, 22, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 4, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 4, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 4, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 5, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 5, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 5, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 5, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 6, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 6, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 6, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 7, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 7, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 7, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 8, 9, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 8, 19, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 8, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 9, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 9, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 9, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 10, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 10, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 10, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 11, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 11, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 11, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 12, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 12, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(183, 12, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 1, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 1, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 1, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 1, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 2, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 2, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 3, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 3, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 3, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 3, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 4, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 4, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 4, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 5, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 5, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 5, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 6, 9, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 6, 19, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 6, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 7, 9, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 7, 19, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 7, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 8, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 8, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 8, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 9, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 9, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 9, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 10, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 10, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 10, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 11, 6, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 11, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 11, 26, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 12, 6, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 12, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 12, 26, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(184, 12, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 1, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 1, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 1, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 2, 9, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 2, 19, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 3, 1, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 3, 11, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 3, 21, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 3, 31, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 4, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 4, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 4, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 5, 10, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 5, 20, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 5, 30, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 6, 9, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 6, 19, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 6, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 7, 9, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 7, 19, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 7, 29, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 8, 8, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 8, 18, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 8, 28, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 9, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 9, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 9, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 10, 7, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 10, 17, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 10, 27, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 11, 6, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 11, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 11, 26, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 12, 6, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 12, 16, 12, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeJulian(185, 12, 26, 12, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>xt_ocean</span></div><div class='xr-var-dims'>(xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-24.95 -24.85 ... -10.15 -10.05</div><input id='attrs-a835ab38-d8fe-48bf-9b9e-85422f4ca3eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a835ab38-d8fe-48bf-9b9e-85422f4ca3eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0600780a-41bb-4e79-8315-9068cdc40781' class='xr-var-data-in' type='checkbox'><label for='data-0600780a-41bb-4e79-8315-9068cdc40781' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-24.95, -24.85, -24.75, -24.65, -24.55, -24.45, -24.35, -24.25, -24.15,\n",
       "       -24.05, -23.95, -23.85, -23.75, -23.65, -23.55, -23.45, -23.35, -23.25,\n",
       "       -23.15, -23.05, -22.95, -22.85, -22.75, -22.65, -22.55, -22.45, -22.35,\n",
       "       -22.25, -22.15, -22.05, -21.95, -21.85, -21.75, -21.65, -21.55, -21.45,\n",
       "       -21.35, -21.25, -21.15, -21.05, -20.95, -20.85, -20.75, -20.65, -20.55,\n",
       "       -20.45, -20.35, -20.25, -20.15, -20.05, -19.95, -19.85, -19.75, -19.65,\n",
       "       -19.55, -19.45, -19.35, -19.25, -19.15, -19.05, -18.95, -18.85, -18.75,\n",
       "       -18.65, -18.55, -18.45, -18.35, -18.25, -18.15, -18.05, -17.95, -17.85,\n",
       "       -17.75, -17.65, -17.55, -17.45, -17.35, -17.25, -17.15, -17.05, -16.95,\n",
       "       -16.85, -16.75, -16.65, -16.55, -16.45, -16.35, -16.25, -16.15, -16.05,\n",
       "       -15.95, -15.85, -15.75, -15.65, -15.55, -15.45, -15.35, -15.25, -15.15,\n",
       "       -15.05, -14.95, -14.85, -14.75, -14.65, -14.55, -14.45, -14.35, -14.25,\n",
       "       -14.15, -14.05, -13.95, -13.85, -13.75, -13.65, -13.55, -13.45, -13.35,\n",
       "       -13.25, -13.15, -13.05, -12.95, -12.85, -12.75, -12.65, -12.55, -12.45,\n",
       "       -12.35, -12.25, -12.15, -12.05, -11.95, -11.85, -11.75, -11.65, -11.55,\n",
       "       -11.45, -11.35, -11.25, -11.15, -11.05, -10.95, -10.85, -10.75, -10.65,\n",
       "       -10.55, -10.45, -10.35, -10.25, -10.15, -10.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>yt_ocean</span></div><div class='xr-var-dims'>(yt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-29.98 -29.89 ... -15.17 -15.07</div><input id='attrs-d1425152-88d2-4ac8-bb68-b62c404ad092' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1425152-88d2-4ac8-bb68-b62c404ad092' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a267ab44-92b3-4387-863b-1e7cc3b7d1f1' class='xr-var-data-in' type='checkbox'><label for='data-a267ab44-92b3-4387-863b-1e7cc3b7d1f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-29.980157, -29.893499, -29.806766, -29.719958, -29.633074, -29.546116,\n",
       "       -29.459083, -29.371975, -29.284792, -29.197535, -29.110203, -29.022798,\n",
       "       -28.935318, -28.847764, -28.760137, -28.672436, -28.584661, -28.496814,\n",
       "       -28.408893, -28.320899, -28.232832, -28.144692, -28.05648 , -27.968195,\n",
       "       -27.879838, -27.791409, -27.702908, -27.614335, -27.525691, -27.436975,\n",
       "       -27.348187, -27.259328, -27.170399, -27.081398, -26.992326, -26.903184,\n",
       "       -26.813972, -26.724689, -26.635336, -26.545914, -26.456421, -26.366859,\n",
       "       -26.277227, -26.187526, -26.097756, -26.007918, -25.91801 , -25.828033,\n",
       "       -25.737989, -25.647876, -25.557694, -25.467445, -25.377128, -25.286744,\n",
       "       -25.196292, -25.105773, -25.015187, -24.924534, -24.833814, -24.743028,\n",
       "       -24.652175, -24.561257, -24.470272, -24.379221, -24.288105, -24.196924,\n",
       "       -24.105677, -24.014365, -23.922988, -23.831547, -23.740041, -23.64847 ,\n",
       "       -23.556836, -23.465138, -23.373375, -23.28155 , -23.189661, -23.097708,\n",
       "       -23.005693, -22.913615, -22.821475, -22.729272, -22.637006, -22.544679,\n",
       "       -22.45229 , -22.35984 , -22.267328, -22.174755, -22.08212 , -21.989425,\n",
       "       -21.89667 , -21.803854, -21.710978, -21.618041, -21.525045, -21.43199 ,\n",
       "       -21.338875, -21.245701, -21.152468, -21.059176, -20.965826, -20.872418,\n",
       "       -20.778951, -20.685426, -20.591844, -20.498204, -20.404507, -20.310754,\n",
       "       -20.216943, -20.123075, -20.029152, -19.935172, -19.841136, -19.747044,\n",
       "       -19.652897, -19.558695, -19.464437, -19.370125, -19.275758, -19.181337,\n",
       "       -19.086861, -18.992332, -18.897749, -18.803112, -18.708423, -18.61368 ,\n",
       "       -18.518884, -18.424036, -18.329135, -18.234183, -18.139178, -18.044122,\n",
       "       -17.949015, -17.853856, -17.758646, -17.663386, -17.568075, -17.472714,\n",
       "       -17.377303, -17.281843, -17.186332, -17.090773, -16.995164, -16.899507,\n",
       "       -16.803801, -16.708047, -16.612245, -16.516395, -16.420497, -16.324552,\n",
       "       -16.22856 , -16.132521, -16.036436, -15.940304, -15.844126, -15.747902,\n",
       "       -15.651633, -15.555318, -15.458958, -15.362554, -15.266105, -15.169611,\n",
       "       -15.073074])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-734132d0-7c93-48b8-9e37-f85f2d98a147' class='xr-section-summary-in' type='checkbox'  checked><label for='section-734132d0-7c93-48b8-9e37-f85f2d98a147' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q_ref</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;</div><input id='attrs-f1294bce-b6e6-42ef-bd97-dd3d19765bf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f1294bce-b6e6-42ef-bd97-dd3d19765bf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28f28977-5847-4ec4-9a49-f88f946a2047' class='xr-var-data-in' type='checkbox'><label for='data-28f28977-5847-4ec4-9a49-f88f946a2047' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 34.51 MiB </td>\n",
       "                        <td> 480.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (185, 163, 150) </td>\n",
       "                        <td> (10, 82, 75) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 80 chunks in 11 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slp</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;</div><input id='attrs-82270ef3-7c3c-4d2a-97d3-63000331668b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82270ef3-7c3c-4d2a-97d3-63000331668b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f7c3917-e113-4c71-9d56-859442ba4b21' class='xr-var-data-in' type='checkbox'><label for='data-2f7c3917-e113-4c71-9d56-859442ba4b21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;</div><input id='attrs-f8198bf4-71db-44bd-9f3f-3bacb03c7c84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8198bf4-71db-44bd-9f3f-3bacb03c7c84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b07e78fb-524d-45a8-8431-7eb29184f82e' class='xr-var-data-in' type='checkbox'><label for='data-b07e78fb-524d-45a8-8431-7eb29184f82e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 34.51 MiB </td>\n",
       "                        <td> 480.47 kiB </td>\n",
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       "                        <td> (185, 163, 150) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                        <th> Bytes </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_rel</span></div><div class='xr-var-dims'>(time, yt_ocean, xt_ocean)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10, 82, 75), meta=np.ndarray&gt;</div><input id='attrs-7f76c2d1-c2f2-451a-a077-8fa1fe3ff239' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f76c2d1-c2f2-451a-a077-8fa1fe3ff239' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11e6a6cb-6a29-4b37-9ca5-892feb50a8f7' class='xr-var-data-in' type='checkbox'><label for='data-11e6a6cb-6a29-4b37-9ca5-892feb50a8f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 34.51 MiB </td>\n",
       "                        <td> 480.47 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (185, 163, 150) </td>\n",
       "                        <td> (10, 82, 75) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 80 chunks in 11 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5dd9cfed-2cb0-4387-b7ce-5a3195c99283' class='xr-section-summary-in' type='checkbox'  ><label for='section-5dd9cfed-2cb0-4387-b7ce-5a3195c99283' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-11105288-6174-4c17-9794-d304d62e84fb' class='xr-index-data-in' type='checkbox'/><label for='index-11105288-6174-4c17-9794-d304d62e84fb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([0181-01-01 12:00:00, 0181-01-11 12:00:00, 0181-01-21 12:00:00,\n",
       "             0181-01-31 12:00:00, 0181-02-10 12:00:00, 0181-02-20 12:00:00,\n",
       "             0181-03-02 12:00:00, 0181-03-12 12:00:00, 0181-03-22 12:00:00,\n",
       "             0181-04-01 12:00:00,\n",
       "             ...\n",
       "             0185-09-27 12:00:00, 0185-10-07 12:00:00, 0185-10-17 12:00:00,\n",
       "             0185-10-27 12:00:00, 0185-11-06 12:00:00, 0185-11-16 12:00:00,\n",
       "             0185-11-26 12:00:00, 0185-12-06 12:00:00, 0185-12-16 12:00:00,\n",
       "             0185-12-26 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=185, calendar=&#x27;julian&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>xt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-128d2887-1859-43c5-8ec2-bed52b36f8e0' class='xr-index-data-in' type='checkbox'/><label for='index-128d2887-1859-43c5-8ec2-bed52b36f8e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-24.950000000004948,  -24.85000000000491, -24.750000000004945,\n",
       "        -24.65000000000491, -24.550000000004943, -24.450000000004906,\n",
       "        -24.35000000000494, -24.250000000004903, -24.150000000004937,\n",
       "         -24.0500000000049,\n",
       "       ...\n",
       "       -10.950000000004838, -10.850000000004805, -10.750000000004839,\n",
       "       -10.650000000004805,  -10.55000000000484, -10.450000000004806,\n",
       "        -10.35000000000484, -10.250000000004807, -10.150000000004841,\n",
       "       -10.050000000004808],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;xt_ocean&#x27;, length=150))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>yt_ocean</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5174d3ce-5e3d-4ca9-ba13-a049e8a38057' class='xr-index-data-in' type='checkbox'/><label for='index-5174d3ce-5e3d-4ca9-ba13-a049e8a38057' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -29.98015681018598, -29.893499230101426, -29.806766149847146,\n",
       "       -29.719957878327065,  -29.63307437419011,  -29.54611594764974,\n",
       "        -29.45908255927211,  -29.37197452058372,  -29.28479179406711,\n",
       "       -29.197534692565167,\n",
       "       ...\n",
       "       -15.940303812710189,  -15.84412589220787, -15.747902209967636,\n",
       "       -15.651632857377699, -15.555318242957538, -15.458958459508088,\n",
       "        -15.36255391665811,  -15.26610470860825,  -15.16961124608732,\n",
       "       -15.073073624682506],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;yt_ocean&#x27;, length=163))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a435c74c-086b-447a-bc11-194e398f245b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a435c74c-086b-447a-bc11-194e398f245b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 217MB\n",
       "Dimensions:   (time: 185, yt_ocean: 163, xt_ocean: 150)\n",
       "Coordinates:\n",
       "  * time      (time) object 1kB 0181-01-01 12:00:00 ... 0185-12-26 12:00:00\n",
       "  * xt_ocean  (xt_ocean) float64 1kB -24.95 -24.85 -24.75 ... -10.15 -10.05\n",
       "  * yt_ocean  (yt_ocean) float64 1kB -29.98 -29.89 -29.81 ... -15.17 -15.07\n",
       "Data variables:\n",
       "    q_ref     (time, yt_ocean, xt_ocean) float64 36MB dask.array<chunksize=(10, 82, 75), meta=np.ndarray>\n",
       "    slp       (time, yt_ocean, xt_ocean) float64 36MB dask.array<chunksize=(10, 82, 75), meta=np.ndarray>\n",
       "    sst       (time, yt_ocean, xt_ocean) float64 36MB dask.array<chunksize=(10, 82, 75), meta=np.ndarray>\n",
       "    t_ref     (time, yt_ocean, xt_ocean) float64 36MB dask.array<chunksize=(10, 82, 75), meta=np.ndarray>\n",
       "    u_rel     (time, yt_ocean, xt_ocean) float64 36MB dask.array<chunksize=(10, 82, 75), meta=np.ndarray>\n",
       "    v_rel     (time, yt_ocean, xt_ocean) float64 36MB dask.array<chunksize=(10, 82, 75), meta=np.ndarray>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_train1 = xr.open_dataset(\n",
    "    \"gs://leap-persistent/panalluri/ds_trainingData_southAtlantic_every10TimeSteps_yr1.zarr\", engine=\"zarr\", chunks={}\n",
    ") \n",
    "ds_train2 = xr.open_dataset(\n",
    "    \"gs://leap-persistent/panalluri/ds_trainingData_southAtlantic_every10TimeSteps_yr2.zarr\", engine=\"zarr\", chunks={}\n",
    ") \n",
    "ds_train3 = xr.open_dataset(\n",
    "    \"gs://leap-persistent/panalluri/ds_trainingData_southAtlantic_every10TimeSteps_yr3.zarr\", engine=\"zarr\", chunks={}\n",
    ") \n",
    "ds_train4 = xr.open_dataset(\n",
    "    \"gs://leap-persistent/panalluri/ds_trainingData_southAtlantic_every10TimeSteps_yr4.zarr\", engine=\"zarr\", chunks={}\n",
    ") \n",
    "ds_train5 = xr.open_dataset(\n",
    "    \"gs://leap-persistent/panalluri/ds_trainingData_southAtlantic_every10TimeSteps_yr5.zarr\", engine=\"zarr\", chunks={}\n",
    ") \n",
    "datasets = [ds_train1, ds_train2, ds_train3, ds_train4, ds_train5]\n",
    "\n",
    "# Concatenate along the 'time' dimension\n",
    "combined_dataset = xr.concat(datasets, dim='time')\n",
    "combined_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91aa805f-646a-47f5-a2e7-1b1372a84388",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 129\n",
      "Validation set size: 27\n",
      "Testing set size: 29\n"
     ]
    }
   ],
   "source": [
    "# Total number of samples\n",
    "n_samples = len(combined_dataset.time)  # Assuming 'time' is the dimension\n",
    "\n",
    "# Generate random indices\n",
    "indices = np.arange(n_samples)\n",
    "np.random.shuffle(indices)\n",
    "\n",
    "# Split indices for training, validation, and testing\n",
    "train_size = int(0.7 * n_samples)\n",
    "val_size = int(0.15 * n_samples)\n",
    "\n",
    "train_indices = indices[:train_size]\n",
    "val_indices = indices[train_size:train_size + val_size]\n",
    "test_indices = indices[train_size + val_size:]\n",
    "\n",
    "# Select data using the indices\n",
    "train_data = combined_dataset.isel(time=train_indices)\n",
    "val_data = combined_dataset.isel(time=val_indices)\n",
    "test_data = combined_dataset.isel(time=test_indices)\n",
    "\n",
    "print(\"Training set size:\", len(train_data.time))\n",
    "print(\"Validation set size:\", len(val_data.time))\n",
    "print(\"Testing set size:\", len(test_data.time))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6c72ef39-7560-49e6-b988-ce287e1c779a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[-5.8738,  6.7661,  0.8923],\n",
       "          [-5.8305,  6.8215,  0.9910],\n",
       "          [-5.7866,  6.8759,  1.0893],\n",
       "          ...,\n",
       "          [-1.9038,  7.9455,  6.0416],\n",
       "          [-1.8977,  7.9531,  6.0553],\n",
       "          [-1.8920,  7.9603,  6.0683]],\n",
       "\n",
       "         [[-5.9470,  6.7755,  0.8285],\n",
       "          [-5.9038,  6.8305,  0.9266],\n",
       "          [-5.8602,  6.8845,  1.0243],\n",
       "          ...,\n",
       "          [-1.9608,  7.9692,  6.0084],\n",
       "          [-1.9542,  7.9766,  6.0224],\n",
       "          [-1.9478,  7.9836,  6.0357]],\n",
       "\n",
       "         [[-6.0186,  6.7848,  0.7662],\n",
       "          [-5.9756,  6.8393,  0.8637],\n",
       "          [-5.9322,  6.8929,  0.9607],\n",
       "          ...,\n",
       "          [-2.0181,  7.9929,  5.9748],\n",
       "          [-2.0109,  8.0001,  5.9892],\n",
       "          [-2.0040,  8.0067,  6.0028]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-8.9905,  7.9828, -1.0077],\n",
       "          [-8.9600,  7.9875, -0.9725],\n",
       "          [-8.9290,  7.9922, -0.9368],\n",
       "          ...,\n",
       "          [-2.6016,  4.4607,  1.8591],\n",
       "          [-2.5791,  4.4380,  1.8589],\n",
       "          [-2.5570,  4.4151,  1.8581]],\n",
       "\n",
       "         [[-8.9589,  7.9526, -1.0063],\n",
       "          [-8.9285,  7.9569, -0.9716],\n",
       "          [-8.8976,  7.9612, -0.9364],\n",
       "          ...,\n",
       "          [-2.6500,  4.4713,  1.8213],\n",
       "          [-2.6277,  4.4490,  1.8212],\n",
       "          [-2.6059,  4.4264,  1.8206]],\n",
       "\n",
       "         [[-8.9274,  7.9220, -1.0054],\n",
       "          [-8.8971,  7.9259, -0.9712],\n",
       "          [-8.8663,  7.9298, -0.9365],\n",
       "          ...,\n",
       "          [-2.6977,  4.4818,  1.7840],\n",
       "          [-2.6757,  4.4597,  1.7840],\n",
       "          [-2.6540,  4.4375,  1.7834]]],\n",
       "\n",
       "\n",
       "        [[[ 2.4515, -3.6553, -1.2038],\n",
       "          [ 2.4069, -3.6599, -1.2530],\n",
       "          [ 2.3617, -3.6623, -1.3006],\n",
       "          ...,\n",
       "          [-4.4989,  1.6176, -2.8812],\n",
       "          [-4.5267,  1.6533, -2.8734],\n",
       "          [-4.5545,  1.6889, -2.8656]],\n",
       "\n",
       "         [[ 2.3544, -3.5911, -1.2367],\n",
       "          [ 2.3092, -3.5933, -1.2840],\n",
       "          [ 2.2635, -3.5933, -1.3299],\n",
       "          ...,\n",
       "          [-4.5446,  1.6638, -2.8807],\n",
       "          [-4.5717,  1.6994, -2.8723],\n",
       "          [-4.5987,  1.7349, -2.8638]],\n",
       "\n",
       "         [[ 2.2544, -3.5237, -1.2693],\n",
       "          [ 2.2087, -3.5236, -1.3149],\n",
       "          [ 2.1624, -3.5213, -1.3590],\n",
       "          ...,\n",
       "          [-4.5890,  1.7094, -2.8796],\n",
       "          [-4.6154,  1.7449, -2.8705],\n",
       "          [-4.6418,  1.7803, -2.8615]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-5.5854,  1.0457, -4.5397],\n",
       "          [-5.5852,  1.0636, -4.5216],\n",
       "          [-5.5850,  1.0820, -4.5030],\n",
       "          ...,\n",
       "          [-4.1391,  2.5349, -1.6042],\n",
       "          [-4.1328,  2.5281, -1.6047],\n",
       "          [-4.1267,  2.5215, -1.6052]],\n",
       "\n",
       "         [[-5.5856,  1.0698, -4.5157],\n",
       "          [-5.5847,  1.0874, -4.4973],\n",
       "          [-5.5838,  1.1055, -4.4783],\n",
       "          ...,\n",
       "          [-4.1516,  2.5304, -1.6212],\n",
       "          [-4.1448,  2.5241, -1.6207],\n",
       "          [-4.1383,  2.5180, -1.6202]],\n",
       "\n",
       "         [[-5.5866,  1.0960, -4.4906],\n",
       "          [-5.5850,  1.1132, -4.4718],\n",
       "          [-5.5834,  1.1310, -4.4524],\n",
       "          ...,\n",
       "          [-4.1625,  2.5273, -1.6352],\n",
       "          [-4.1553,  2.5215, -1.6338],\n",
       "          [-4.1484,  2.5160, -1.6324]]],\n",
       "\n",
       "\n",
       "        [[[ 3.4817, -3.3448,  0.1369],\n",
       "          [ 3.4984, -3.3656,  0.1328],\n",
       "          [ 3.5138, -3.3864,  0.1274],\n",
       "          ...,\n",
       "          [ 0.8870, -3.3995, -2.5125],\n",
       "          [ 0.8586, -3.3545, -2.4959],\n",
       "          [ 0.8299, -3.3095, -2.4797]],\n",
       "\n",
       "         [[ 3.4371, -3.3955,  0.0416],\n",
       "          [ 3.4527, -3.4140,  0.0387],\n",
       "          [ 3.4668, -3.4325,  0.0343],\n",
       "          ...,\n",
       "          [ 0.7360, -3.3293, -2.5932],\n",
       "          [ 0.7073, -3.2843, -2.5770],\n",
       "          [ 0.6783, -3.2394, -2.5611]],\n",
       "\n",
       "         [[ 3.3887, -3.4431, -0.0544],\n",
       "          [ 3.4031, -3.4593, -0.0562],\n",
       "          [ 3.4160, -3.4753, -0.0594],\n",
       "          ...,\n",
       "          [ 0.5846, -3.2590, -2.6744],\n",
       "          [ 0.5557, -3.2142, -2.6585],\n",
       "          [ 0.5264, -3.1693, -2.6428]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-7.5884,  1.0906, -6.4978],\n",
       "          [-7.5875,  1.1048, -6.4828],\n",
       "          [-7.5867,  1.1186, -6.4680],\n",
       "          ...,\n",
       "          [-7.8302,  3.1582, -4.6720],\n",
       "          [-7.8311,  3.1779, -4.6532],\n",
       "          [-7.8317,  3.1974, -4.6343]],\n",
       "\n",
       "         [[-7.6127,  1.1291, -6.4836],\n",
       "          [-7.6123,  1.1433, -6.4690],\n",
       "          [-7.6118,  1.1571, -6.4547],\n",
       "          ...,\n",
       "          [-7.7908,  3.1467, -4.6441],\n",
       "          [-7.7919,  3.1663, -4.6257],\n",
       "          [-7.7928,  3.1857, -4.6071]],\n",
       "\n",
       "         [[-7.6352,  1.1659, -6.4694],\n",
       "          [-7.6353,  1.1801, -6.4552],\n",
       "          [-7.6353,  1.1939, -6.4414],\n",
       "          ...,\n",
       "          [-7.7503,  3.1342, -4.6161],\n",
       "          [-7.7517,  3.1537, -4.5979],\n",
       "          [-7.7527,  3.1731, -4.5796]]],\n",
       "\n",
       "\n",
       "        ...,\n",
       "\n",
       "\n",
       "        [[[ 1.1668, -7.7670, -6.6002],\n",
       "          [ 1.1282, -7.7335, -6.6053],\n",
       "          [ 1.0919, -7.6990, -6.6071],\n",
       "          ...,\n",
       "          [-3.0478, -0.8913, -3.9391],\n",
       "          [-3.0849, -0.8906, -3.9755],\n",
       "          [-3.1219, -0.8886, -4.0105]],\n",
       "\n",
       "         [[ 1.0682, -7.6905, -6.6223],\n",
       "          [ 1.0287, -7.6570, -6.6283],\n",
       "          [ 0.9914, -7.6225, -6.6312],\n",
       "          ...,\n",
       "          [-3.1320, -0.8590, -3.9909],\n",
       "          [-3.1680, -0.8585, -4.0264],\n",
       "          [-3.2040, -0.8565, -4.0605]],\n",
       "\n",
       "         [[ 0.9692, -7.6135, -6.6443],\n",
       "          [ 0.9286, -7.5801, -6.6514],\n",
       "          [ 0.8903, -7.5456, -6.6553],\n",
       "          ...,\n",
       "          [-3.2160, -0.8276, -4.0436],\n",
       "          [-3.2510, -0.8272, -4.0782],\n",
       "          [-3.2860, -0.8254, -4.1114]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-8.1878,  2.7074, -5.4804],\n",
       "          [-8.1990,  2.7129, -5.4860],\n",
       "          [-8.2099,  2.7182, -5.4918],\n",
       "          ...,\n",
       "          [-8.5069,  4.8175, -3.6895],\n",
       "          [-8.4949,  4.8294, -3.6656],\n",
       "          [-8.4828,  4.8406, -3.6422]],\n",
       "\n",
       "         [[-8.1769,  2.7456, -5.4314],\n",
       "          [-8.1882,  2.7509, -5.4374],\n",
       "          [-8.1993,  2.7558, -5.4434],\n",
       "          ...,\n",
       "          [-8.4683,  4.8182, -3.6501],\n",
       "          [-8.4560,  4.8293, -3.6268],\n",
       "          [-8.4437,  4.8398, -3.6039]],\n",
       "\n",
       "         [[-8.1653,  2.7829, -5.3824],\n",
       "          [-8.1767,  2.7880, -5.3888],\n",
       "          [-8.1879,  2.7927, -5.3952],\n",
       "          ...,\n",
       "          [-8.4284,  4.8167, -3.6117],\n",
       "          [-8.4159,  4.8270, -3.5889],\n",
       "          [-8.4032,  4.8368, -3.5664]]],\n",
       "\n",
       "\n",
       "        [[[-5.1184,  3.5139, -1.6045],\n",
       "          [-5.0806,  3.5477, -1.5329],\n",
       "          [-5.0434,  3.5812, -1.4622],\n",
       "          ...,\n",
       "          [-1.2128,  5.7322,  4.5194],\n",
       "          [-1.1763,  5.7352,  4.5589],\n",
       "          [-1.1398,  5.7382,  4.5984]],\n",
       "\n",
       "         [[-5.1759,  3.5228, -1.6531],\n",
       "          [-5.1373,  3.5569, -1.5804],\n",
       "          [-5.0993,  3.5907, -1.5086],\n",
       "          ...,\n",
       "          [-1.2987,  5.7108,  4.4121],\n",
       "          [-1.2625,  5.7143,  4.4518],\n",
       "          [-1.2261,  5.7177,  4.4916]],\n",
       "\n",
       "         [[-5.2332,  3.5307, -1.7025],\n",
       "          [-5.1939,  3.5651, -1.6288],\n",
       "          [-5.1551,  3.5992, -1.5559],\n",
       "          ...,\n",
       "          [-1.3833,  5.6882,  4.3049],\n",
       "          [-1.3473,  5.6921,  4.3448],\n",
       "          [-1.3112,  5.6960,  4.3848]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-6.1078,  0.7607, -5.3472],\n",
       "          [-6.1053,  0.7749, -5.3303],\n",
       "          [-6.1027,  0.7898, -5.3129],\n",
       "          ...,\n",
       "          [-5.1337,  2.9304, -2.2032],\n",
       "          [-5.1245,  2.9428, -2.1817],\n",
       "          [-5.1154,  2.9554, -2.1600]],\n",
       "\n",
       "         [[-6.0742,  0.7489, -5.3253],\n",
       "          [-6.0717,  0.7635, -5.3082],\n",
       "          [-6.0691,  0.7786, -5.2905],\n",
       "          ...,\n",
       "          [-5.1042,  2.9103, -2.1939],\n",
       "          [-5.0954,  2.9231, -2.1724],\n",
       "          [-5.0866,  2.9360, -2.1506]],\n",
       "\n",
       "         [[-6.0406,  0.7371, -5.3035],\n",
       "          [-6.0381,  0.7520, -5.2861],\n",
       "          [-6.0356,  0.7675, -5.2681],\n",
       "          ...,\n",
       "          [-5.0741,  2.8898, -2.1843],\n",
       "          [-5.0656,  2.9029, -2.1627],\n",
       "          [-5.0571,  2.9163, -2.1408]]],\n",
       "\n",
       "\n",
       "        [[[-3.2852,  6.1588,  2.8736],\n",
       "          [-3.2240,  6.2211,  2.9971],\n",
       "          [-3.1622,  6.2827,  3.1205],\n",
       "          ...,\n",
       "          [ 8.6751,  6.9672, 15.6422],\n",
       "          [ 8.7177,  6.8738, 15.5914],\n",
       "          [ 8.7583,  6.7794, 15.5377]],\n",
       "\n",
       "         [[-3.3300,  6.1481,  2.8181],\n",
       "          [-3.2684,  6.2106,  2.9422],\n",
       "          [-3.2063,  6.2726,  3.0663],\n",
       "          ...,\n",
       "          [ 8.6127,  6.9597, 15.5724],\n",
       "          [ 8.6552,  6.8677, 15.5229],\n",
       "          [ 8.6957,  6.7748, 15.4705]],\n",
       "\n",
       "         [[-3.3745,  6.1374,  2.7629],\n",
       "          [-3.3126,  6.2002,  2.8875],\n",
       "          [-3.2502,  6.2623,  3.0122],\n",
       "          ...,\n",
       "          [ 8.5488,  6.9533, 15.5021],\n",
       "          [ 8.5911,  6.8627, 15.4539],\n",
       "          [ 8.6315,  6.7712, 15.4027]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-5.3563,  4.4935, -0.8628],\n",
       "          [-5.3354,  4.5162, -0.8192],\n",
       "          [-5.3141,  4.5390, -0.7752],\n",
       "          ...,\n",
       "          [-1.1594,  5.0198,  3.8604],\n",
       "          [-1.1463,  5.0309,  3.8846],\n",
       "          [-1.1338,  5.0415,  3.9077]],\n",
       "\n",
       "         [[-5.3452,  4.4736, -0.8716],\n",
       "          [-5.3244,  4.4964, -0.8280],\n",
       "          [-5.3032,  4.5192, -0.7840],\n",
       "          ...,\n",
       "          [-1.2444,  5.0586,  3.8142],\n",
       "          [-1.2316,  5.0695,  3.8379],\n",
       "          [-1.2194,  5.0800,  3.8606]],\n",
       "\n",
       "         [[-5.3366,  4.4556, -0.8810],\n",
       "          [-5.3159,  4.4785, -0.8373],\n",
       "          [-5.2947,  4.5014, -0.7933],\n",
       "          ...,\n",
       "          [-1.3283,  5.0963,  3.7680],\n",
       "          [-1.3158,  5.1069,  3.7911],\n",
       "          [-1.3038,  5.1171,  3.8133]]]], dtype=torch.float64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract TRAINING data\n",
    "u_rel = train_data['u_rel'].values\n",
    "v_rel = train_data['v_rel'].values\n",
    "uv_sum = u_rel + v_rel\n",
    "\n",
    "# Stack these arrays into a 4D tensor\n",
    "# Shape will be (time, yt_ocean, xt_ocean, num_variables)\n",
    "training_NN_inputs = torch.tensor(np.stack([u_rel, v_rel, uv_sum], axis=3))\n",
    "\n",
    "# Now data_array is a 4D tensor\n",
    "training_NN_inputs.shape\n",
    "\n",
    "# Extract VALIDATION data\n",
    "u_rel = val_data['u_rel'].values\n",
    "v_rel = val_data['v_rel'].values\n",
    "uv_sum = u_rel + v_rel\n",
    "\n",
    "# Stack these arrays into a 4D tensor\n",
    "# Shape will be (time, yt_ocean, xt_ocean, num_variables)\n",
    "val_NN_inputs = torch.tensor(np.stack([u_rel, v_rel, uv_sum], axis=3))\n",
    "\n",
    "# Now data_array is a 4D tensor\n",
    "val_NN_inputs.shape\n",
    "\n",
    "# Extract TESTING data\n",
    "u_rel = test_data['u_rel'].values\n",
    "v_rel = test_data['v_rel'].values\n",
    "uv_sum = u_rel + v_rel\n",
    "\n",
    "# Stack these arrays into a 4D tensor\n",
    "# Shape will be (time, yt_ocean, xt_ocean, num_variables)\n",
    "testing_NN_inputs = torch.tensor(np.stack([u_rel, v_rel, uv_sum], axis=3))\n",
    "\n",
    "# Now data_array is a 4D tensor\n",
    "testing_NN_inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efca6dfd-f48c-4891-89d3-a7145e596744",
   "metadata": {},
   "source": [
    "# Set up NN architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "073c159e-abff-4a01-b37c-5426047a4c8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SimpleANN(\n",
      "  (fc1): Linear(in_features=2, out_features=64, bias=True)\n",
      "  (fc2): Linear(in_features=64, out_features=64, bias=True)\n",
      "  (fc3): Linear(in_features=64, out_features=1, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class SimpleANN(nn.Module):\n",
    "    def __init__(self, input_size, hidden_units, output_size):\n",
    "        super(SimpleANN, self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size, hidden_units)  # First fully connected layer\n",
    "        self.fc2 = nn.Linear(hidden_units, hidden_units)  # Hidden layer\n",
    "        self.fc3 = nn.Linear(hidden_units, output_size)  # Output layer\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))  # Apply ReLU activation\n",
    "        x = torch.relu(self.fc2(x))  # Apply ReLU activation\n",
    "        x = self.fc3(x)  # Final layer (no activation if used for regression)\n",
    "        return x\n",
    "\n",
    "# Example setup\n",
    "input_size = 2  # Number of input features\n",
    "hidden_units = 64  # Number of neurons in the hidden layers\n",
    "output_size = 1  # Single target output\n",
    "\n",
    "model = SimpleANN(input_size, hidden_units, output_size)\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d86bb386-9c82-4763-a625-ce3aa114d85b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TensorDataset(Dataset):\n",
    "    def __init__(self, tensor_data):\n",
    "        self.tensor_data = tensor_data  # The 3D or 4D tensor dataset\n",
    "        self.time_steps = tensor_data.shape[0]  # Total number of time steps\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.time_steps  # Total number of samples (time steps)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        # Access the data at the specified index (time step)\n",
    "        data = self.tensor_data[idx]  # Get data for the specified time step\n",
    "\n",
    "        # Assuming the shape is (x, y, vars), extract variables as inputs and target\n",
    "        # Flatten the spatial dimensions if they exist\n",
    "        inputs = data.reshape(-1)[:2]  # Flatten inputs to a 1D vector (6 input features)\n",
    "        \n",
    "        # Extract the target, assuming it's the next variable (var 7)\n",
    "        target = data.reshape(-1)[2]  # Replace 6 with the appropriate index for your target variable\n",
    "\n",
    "        return inputs, target  # Return input (6 features) and target\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cd78882-52ed-4dd1-bf79-e01796bdc983",
   "metadata": {},
   "source": [
    "# Training NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4224543b-cde8-4736-b296-05644cae7a74",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = TensorDataset(training_NN_inputs)\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "36cdf18d-04f2-4616-86fd-9231d151081b",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dataset = TensorDataset(val_NN_inputs)\n",
    "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=True, num_workers=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48a72449-5bf6-40e1-8a59-9af67d237e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()  # Use appropriate loss for your task\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a97103a1-ca57-4c2d-9526-eff3070088a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/500], Training Loss: 26.4882, Validation Loss: 28.7432\n",
      "Epoch [2/500], Training Loss: 24.8828, Validation Loss: 24.6328\n",
      "Epoch [3/500], Training Loss: 16.9720, Validation Loss: 20.7550\n",
      "Epoch [4/500], Training Loss: 15.4764, Validation Loss: 16.8497\n",
      "Epoch [5/500], Training Loss: 17.9786, Validation Loss: 13.0362\n",
      "Epoch [6/500], Training Loss: 10.1010, Validation Loss: 9.5276\n",
      "Epoch [7/500], Training Loss: 6.7106, Validation Loss: 6.4641\n",
      "Epoch [8/500], Training Loss: 4.2992, Validation Loss: 4.0662\n",
      "Epoch [9/500], Training Loss: 3.6795, Validation Loss: 2.3075\n",
      "Epoch [10/500], Training Loss: 1.5834, Validation Loss: 1.1628\n",
      "Epoch [11/500], Training Loss: 0.8237, Validation Loss: 0.6447\n",
      "Epoch [12/500], Training Loss: 0.5091, Validation Loss: 0.4768\n",
      "Epoch [13/500], Training Loss: 0.4511, Validation Loss: 0.3938\n",
      "Epoch [14/500], Training Loss: 0.4040, Validation Loss: 0.3421\n",
      "Epoch [15/500], Training Loss: 0.3863, Validation Loss: 0.3083\n",
      "Epoch [16/500], Training Loss: 0.4311, Validation Loss: 0.2848\n",
      "Epoch [17/500], Training Loss: 0.2574, Validation Loss: 0.2610\n",
      "Epoch [18/500], Training Loss: 0.2183, Validation Loss: 0.2106\n",
      "Epoch [19/500], Training Loss: 0.1819, Validation Loss: 0.1581\n",
      "Epoch [20/500], Training Loss: 0.1565, Validation Loss: 0.1388\n",
      "Epoch [21/500], Training Loss: 0.1716, Validation Loss: 0.1338\n",
      "Epoch [22/500], Training Loss: 0.1441, Validation Loss: 0.1320\n",
      "Epoch [23/500], Training Loss: 0.2032, Validation Loss: 0.1241\n",
      "Epoch [24/500], Training Loss: 0.1217, Validation Loss: 0.1210\n",
      "Epoch [25/500], Training Loss: 0.1205, Validation Loss: 0.1069\n",
      "Epoch [26/500], Training Loss: 0.1037, Validation Loss: 0.0911\n",
      "Epoch [27/500], Training Loss: 0.0958, Validation Loss: 0.0852\n",
      "Epoch [28/500], Training Loss: 0.0803, Validation Loss: 0.0868\n",
      "Epoch [29/500], Training Loss: 0.1346, Validation Loss: 0.0823\n",
      "Epoch [30/500], Training Loss: 0.0643, Validation Loss: 0.0762\n",
      "Epoch [31/500], Training Loss: 0.0665, Validation Loss: 0.0707\n",
      "Epoch [32/500], Training Loss: 0.0834, Validation Loss: 0.0552\n",
      "Epoch [33/500], Training Loss: 0.0436, Validation Loss: 0.0508\n",
      "Epoch [34/500], Training Loss: 0.0492, Validation Loss: 0.0576\n",
      "Epoch [35/500], Training Loss: 0.0527, Validation Loss: 0.0501\n",
      "Epoch [36/500], Training Loss: 0.0428, Validation Loss: 0.0410\n",
      "Epoch [37/500], Training Loss: 0.0364, Validation Loss: 0.0347\n",
      "Epoch [38/500], Training Loss: 0.0300, Validation Loss: 0.0313\n",
      "Epoch [39/500], Training Loss: 0.0320, Validation Loss: 0.0303\n",
      "Epoch [40/500], Training Loss: 0.0254, Validation Loss: 0.0286\n",
      "Epoch [41/500], Training Loss: 0.0253, Validation Loss: 0.0271\n",
      "Epoch [42/500], Training Loss: 0.0299, Validation Loss: 0.0259\n",
      "Epoch [43/500], Training Loss: 0.0231, Validation Loss: 0.0239\n",
      "Epoch [44/500], Training Loss: 0.0222, Validation Loss: 0.0236\n",
      "Epoch [45/500], Training Loss: 0.0202, Validation Loss: 0.0239\n",
      "Epoch [46/500], Training Loss: 0.0240, Validation Loss: 0.0227\n",
      "Epoch [47/500], Training Loss: 0.0231, Validation Loss: 0.0217\n",
      "Epoch [48/500], Training Loss: 0.0397, Validation Loss: 0.0256\n",
      "Epoch [49/500], Training Loss: 0.0270, Validation Loss: 0.0475\n",
      "Epoch [50/500], Training Loss: 0.0347, Validation Loss: 0.0376\n",
      "Epoch [51/500], Training Loss: 0.0291, Validation Loss: 0.0232\n",
      "Epoch [52/500], Training Loss: 0.0194, Validation Loss: 0.0179\n",
      "Epoch [53/500], Training Loss: 0.0163, Validation Loss: 0.0187\n",
      "Epoch [54/500], Training Loss: 0.0163, Validation Loss: 0.0203\n",
      "Epoch [55/500], Training Loss: 0.0184, Validation Loss: 0.0203\n",
      "Epoch [56/500], Training Loss: 0.0157, Validation Loss: 0.0191\n",
      "Epoch [57/500], Training Loss: 0.0129, Validation Loss: 0.0168\n",
      "Epoch [58/500], Training Loss: 0.0142, Validation Loss: 0.0161\n",
      "Epoch [59/500], Training Loss: 0.0139, Validation Loss: 0.0162\n",
      "Epoch [60/500], Training Loss: 0.0163, Validation Loss: 0.0170\n",
      "Epoch [61/500], Training Loss: 0.0133, Validation Loss: 0.0163\n",
      "Epoch [62/500], Training Loss: 0.0197, Validation Loss: 0.0146\n",
      "Epoch [63/500], Training Loss: 0.0110, Validation Loss: 0.0199\n",
      "Epoch [64/500], Training Loss: 0.0137, Validation Loss: 0.0192\n",
      "Epoch [65/500], Training Loss: 0.0136, Validation Loss: 0.0151\n",
      "Epoch [66/500], Training Loss: 0.0144, Validation Loss: 0.0133\n",
      "Epoch [67/500], Training Loss: 0.0149, Validation Loss: 0.0133\n",
      "Epoch [68/500], Training Loss: 0.0116, Validation Loss: 0.0148\n",
      "Epoch [69/500], Training Loss: 0.0116, Validation Loss: 0.0194\n",
      "Epoch [70/500], Training Loss: 0.0122, Validation Loss: 0.0156\n",
      "Epoch [71/500], Training Loss: 0.0093, Validation Loss: 0.0124\n",
      "Epoch [72/500], Training Loss: 0.0097, Validation Loss: 0.0119\n",
      "Epoch [73/500], Training Loss: 0.0388, Validation Loss: 0.0150\n",
      "Epoch [74/500], Training Loss: 0.0447, Validation Loss: 0.0366\n",
      "Epoch [75/500], Training Loss: 0.0185, Validation Loss: 0.0141\n",
      "Epoch [76/500], Training Loss: 0.0156, Validation Loss: 0.0137\n",
      "Epoch [77/500], Training Loss: 0.0131, Validation Loss: 0.0127\n",
      "Epoch [78/500], Training Loss: 0.0121, Validation Loss: 0.0134\n",
      "Epoch [79/500], Training Loss: 0.0085, Validation Loss: 0.0157\n",
      "Epoch [80/500], Training Loss: 0.0084, Validation Loss: 0.0122\n",
      "Epoch [81/500], Training Loss: 0.0072, Validation Loss: 0.0100\n",
      "Epoch [82/500], Training Loss: 0.0075, Validation Loss: 0.0099\n",
      "Epoch [83/500], Training Loss: 0.0080, Validation Loss: 0.0106\n",
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      "Epoch [85/500], Training Loss: 0.0073, Validation Loss: 0.0113\n",
      "Epoch [86/500], Training Loss: 0.0066, Validation Loss: 0.0112\n",
      "Epoch [87/500], Training Loss: 0.0076, Validation Loss: 0.0096\n",
      "Epoch [88/500], Training Loss: 0.0062, Validation Loss: 0.0089\n",
      "Epoch [89/500], Training Loss: 0.0058, Validation Loss: 0.0087\n",
      "Epoch [90/500], Training Loss: 0.0069, Validation Loss: 0.0086\n",
      "Epoch [91/500], Training Loss: 0.0054, Validation Loss: 0.0088\n",
      "Epoch [92/500], Training Loss: 0.0056, Validation Loss: 0.0091\n",
      "Epoch [93/500], Training Loss: 0.0075, Validation Loss: 0.0094\n",
      "Epoch [94/500], Training Loss: 0.0057, Validation Loss: 0.0097\n",
      "Epoch [95/500], Training Loss: 0.0063, Validation Loss: 0.0083\n",
      "Epoch [96/500], Training Loss: 0.0056, Validation Loss: 0.0077\n",
      "Epoch [97/500], Training Loss: 0.0085, Validation Loss: 0.0086\n",
      "Epoch [98/500], Training Loss: 0.0099, Validation Loss: 0.0133\n",
      "Epoch [99/500], Training Loss: 0.0079, Validation Loss: 0.0103\n",
      "Epoch [100/500], Training Loss: 0.0075, Validation Loss: 0.0078\n",
      "Epoch [101/500], Training Loss: 0.0068, Validation Loss: 0.0080\n",
      "Epoch [102/500], Training Loss: 0.0053, Validation Loss: 0.0099\n",
      "Epoch [103/500], Training Loss: 0.0051, Validation Loss: 0.0082\n",
      "Epoch [104/500], Training Loss: 0.0060, Validation Loss: 0.0068\n",
      "Epoch [105/500], Training Loss: 0.0054, Validation Loss: 0.0066\n",
      "Epoch [106/500], Training Loss: 0.0083, Validation Loss: 0.0069\n",
      "Epoch [107/500], Training Loss: 0.0051, Validation Loss: 0.0073\n",
      "Epoch [108/500], Training Loss: 0.0041, Validation Loss: 0.0081\n",
      "Epoch [109/500], Training Loss: 0.0051, Validation Loss: 0.0076\n",
      "Epoch [110/500], Training Loss: 0.0041, Validation Loss: 0.0061\n",
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      "Epoch [122/500], Training Loss: 0.0039, Validation Loss: 0.0052\n",
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      "Epoch [124/500], Training Loss: 0.0039, Validation Loss: 0.0049\n",
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      "Epoch [186/500], Training Loss: 0.0016, Validation Loss: 0.0028\n",
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      "Epoch [190/500], Training Loss: 0.0016, Validation Loss: 0.0029\n",
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      "Epoch [205/500], Training Loss: 0.0013, Validation Loss: 0.0022\n",
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      "Epoch [207/500], Training Loss: 0.0008, Validation Loss: 0.0021\n",
      "Epoch [208/500], Training Loss: 0.0007, Validation Loss: 0.0021\n",
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      "Epoch [213/500], Training Loss: 0.0007, Validation Loss: 0.0018\n",
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      "Epoch [221/500], Training Loss: 0.0012, Validation Loss: 0.0018\n",
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      "Epoch [224/500], Training Loss: 0.0009, Validation Loss: 0.0021\n",
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      "Epoch [227/500], Training Loss: 0.0009, Validation Loss: 0.0017\n",
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      "Epoch [230/500], Training Loss: 0.0008, Validation Loss: 0.0022\n",
      "Epoch [231/500], Training Loss: 0.0007, Validation Loss: 0.0017\n",
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      "Epoch [233/500], Training Loss: 0.0015, Validation Loss: 0.0018\n",
      "Epoch [234/500], Training Loss: 0.0007, Validation Loss: 0.0018\n",
      "Epoch [235/500], Training Loss: 0.0007, Validation Loss: 0.0019\n",
      "Epoch [236/500], Training Loss: 0.0008, Validation Loss: 0.0019\n",
      "Epoch [237/500], Training Loss: 0.0012, Validation Loss: 0.0017\n",
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      "Epoch [251/500], Training Loss: 0.0053, Validation Loss: 0.0095\n",
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      "Epoch [291/500], Training Loss: 0.0010, Validation Loss: 0.0017\n",
      "Epoch [292/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [293/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [294/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [295/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [296/500], Training Loss: 0.0005, Validation Loss: 0.0014\n",
      "Epoch [297/500], Training Loss: 0.0009, Validation Loss: 0.0015\n",
      "Epoch [298/500], Training Loss: 0.0003, Validation Loss: 0.0016\n",
      "Epoch [299/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [300/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [301/500], Training Loss: 0.0003, Validation Loss: 0.0017\n",
      "Epoch [302/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [303/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [304/500], Training Loss: 0.0008, Validation Loss: 0.0021\n",
      "Epoch [305/500], Training Loss: 0.0032, Validation Loss: 0.0023\n",
      "Epoch [306/500], Training Loss: 0.0018, Validation Loss: 0.0033\n",
      "Epoch [307/500], Training Loss: 0.0041, Validation Loss: 0.0019\n",
      "Epoch [308/500], Training Loss: 0.0009, Validation Loss: 0.0057\n",
      "Epoch [309/500], Training Loss: 0.0038, Validation Loss: 0.0020\n",
      "Epoch [310/500], Training Loss: 0.0031, Validation Loss: 0.0035\n",
      "Epoch [311/500], Training Loss: 0.0062, Validation Loss: 0.0088\n",
      "Epoch [312/500], Training Loss: 0.0052, Validation Loss: 0.0043\n",
      "Epoch [313/500], Training Loss: 0.0021, Validation Loss: 0.0042\n",
      "Epoch [314/500], Training Loss: 0.0031, Validation Loss: 0.0019\n",
      "Epoch [315/500], Training Loss: 0.0011, Validation Loss: 0.0024\n",
      "Epoch [316/500], Training Loss: 0.0012, Validation Loss: 0.0027\n",
      "Epoch [317/500], Training Loss: 0.0007, Validation Loss: 0.0012\n",
      "Epoch [318/500], Training Loss: 0.0008, Validation Loss: 0.0013\n",
      "Epoch [319/500], Training Loss: 0.0008, Validation Loss: 0.0028\n",
      "Epoch [320/500], Training Loss: 0.0008, Validation Loss: 0.0021\n",
      "Epoch [321/500], Training Loss: 0.0008, Validation Loss: 0.0016\n",
      "Epoch [322/500], Training Loss: 0.0006, Validation Loss: 0.0013\n",
      "Epoch [323/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [324/500], Training Loss: 0.0004, Validation Loss: 0.0017\n",
      "Epoch [325/500], Training Loss: 0.0004, Validation Loss: 0.0013\n",
      "Epoch [326/500], Training Loss: 0.0004, Validation Loss: 0.0012\n",
      "Epoch [327/500], Training Loss: 0.0003, Validation Loss: 0.0017\n",
      "Epoch [328/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [329/500], Training Loss: 0.0005, Validation Loss: 0.0016\n",
      "Epoch [330/500], Training Loss: 0.0003, Validation Loss: 0.0019\n",
      "Epoch [331/500], Training Loss: 0.0003, Validation Loss: 0.0016\n",
      "Epoch [332/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [333/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [334/500], Training Loss: 0.0005, Validation Loss: 0.0014\n",
      "Epoch [335/500], Training Loss: 0.0004, Validation Loss: 0.0016\n",
      "Epoch [336/500], Training Loss: 0.0005, Validation Loss: 0.0015\n",
      "Epoch [337/500], Training Loss: 0.0004, Validation Loss: 0.0012\n",
      "Epoch [338/500], Training Loss: 0.0005, Validation Loss: 0.0014\n",
      "Epoch [339/500], Training Loss: 0.0003, Validation Loss: 0.0019\n",
      "Epoch [340/500], Training Loss: 0.0003, Validation Loss: 0.0013\n",
      "Epoch [341/500], Training Loss: 0.0006, Validation Loss: 0.0013\n",
      "Epoch [342/500], Training Loss: 0.0004, Validation Loss: 0.0019\n",
      "Epoch [343/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [344/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [345/500], Training Loss: 0.0003, Validation Loss: 0.0016\n",
      "Epoch [346/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [347/500], Training Loss: 0.0003, Validation Loss: 0.0018\n",
      "Epoch [348/500], Training Loss: 0.0004, Validation Loss: 0.0016\n",
      "Epoch [349/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [350/500], Training Loss: 0.0003, Validation Loss: 0.0018\n",
      "Epoch [351/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [352/500], Training Loss: 0.0003, Validation Loss: 0.0013\n",
      "Epoch [353/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [354/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [355/500], Training Loss: 0.0004, Validation Loss: 0.0018\n",
      "Epoch [356/500], Training Loss: 0.0007, Validation Loss: 0.0015\n",
      "Epoch [357/500], Training Loss: 0.0003, Validation Loss: 0.0017\n",
      "Epoch [358/500], Training Loss: 0.0003, Validation Loss: 0.0012\n",
      "Epoch [359/500], Training Loss: 0.0009, Validation Loss: 0.0010\n",
      "Epoch [360/500], Training Loss: 0.0004, Validation Loss: 0.0016\n",
      "Epoch [361/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [362/500], Training Loss: 0.0004, Validation Loss: 0.0013\n",
      "Epoch [363/500], Training Loss: 0.0003, Validation Loss: 0.0017\n",
      "Epoch [364/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [365/500], Training Loss: 0.0006, Validation Loss: 0.0021\n",
      "Epoch [366/500], Training Loss: 0.0020, Validation Loss: 0.0020\n",
      "Epoch [367/500], Training Loss: 0.0010, Validation Loss: 0.0016\n",
      "Epoch [368/500], Training Loss: 0.0014, Validation Loss: 0.0020\n",
      "Epoch [369/500], Training Loss: 0.0008, Validation Loss: 0.0019\n",
      "Epoch [370/500], Training Loss: 0.0006, Validation Loss: 0.0014\n",
      "Epoch [371/500], Training Loss: 0.0004, Validation Loss: 0.0020\n",
      "Epoch [372/500], Training Loss: 0.0004, Validation Loss: 0.0018\n",
      "Epoch [373/500], Training Loss: 0.0005, Validation Loss: 0.0016\n",
      "Epoch [374/500], Training Loss: 0.0004, Validation Loss: 0.0017\n",
      "Epoch [375/500], Training Loss: 0.0003, Validation Loss: 0.0019\n",
      "Epoch [376/500], Training Loss: 0.0003, Validation Loss: 0.0016\n",
      "Epoch [377/500], Training Loss: 0.0003, Validation Loss: 0.0016\n",
      "Epoch [378/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [379/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [380/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [381/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [382/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [383/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [384/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [385/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [386/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [387/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [388/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [389/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [390/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [391/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [392/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [393/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [394/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Epoch [395/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [396/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [397/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [398/500], Training Loss: 0.0004, Validation Loss: 0.0015\n",
      "Epoch [399/500], Training Loss: 0.0005, Validation Loss: 0.0013\n",
      "Epoch [400/500], Training Loss: 0.0005, Validation Loss: 0.0015\n",
      "Epoch [401/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [402/500], Training Loss: 0.0004, Validation Loss: 0.0021\n",
      "Epoch [403/500], Training Loss: 0.0004, Validation Loss: 0.0016\n",
      "Epoch [404/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [405/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [406/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [407/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [408/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [409/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [410/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [411/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [412/500], Training Loss: 0.0003, Validation Loss: 0.0016\n",
      "Epoch [413/500], Training Loss: 0.0003, Validation Loss: 0.0016\n",
      "Epoch [414/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [415/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Epoch [416/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [417/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [418/500], Training Loss: 0.0003, Validation Loss: 0.0012\n",
      "Epoch [419/500], Training Loss: 0.0003, Validation Loss: 0.0013\n",
      "Epoch [420/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [421/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Epoch [422/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [423/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [424/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [425/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [426/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [427/500], Training Loss: 0.0003, Validation Loss: 0.0013\n",
      "Epoch [428/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [429/500], Training Loss: 0.0003, Validation Loss: 0.0018\n",
      "Epoch [430/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [431/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [432/500], Training Loss: 0.0004, Validation Loss: 0.0016\n",
      "Epoch [433/500], Training Loss: 0.0003, Validation Loss: 0.0012\n",
      "Epoch [434/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [435/500], Training Loss: 0.0003, Validation Loss: 0.0017\n",
      "Epoch [436/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [437/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Epoch [438/500], Training Loss: 0.0003, Validation Loss: 0.0012\n",
      "Epoch [439/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [440/500], Training Loss: 0.0003, Validation Loss: 0.0012\n",
      "Epoch [441/500], Training Loss: 0.0003, Validation Loss: 0.0012\n",
      "Epoch [442/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [443/500], Training Loss: 0.0003, Validation Loss: 0.0013\n",
      "Epoch [444/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [445/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [446/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [447/500], Training Loss: 0.0003, Validation Loss: 0.0018\n",
      "Epoch [448/500], Training Loss: 0.0003, Validation Loss: 0.0013\n",
      "Epoch [449/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [450/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [451/500], Training Loss: 0.0002, Validation Loss: 0.0011\n",
      "Epoch [452/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [453/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [454/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [455/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Epoch [456/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [457/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [458/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [459/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [460/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [461/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [462/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [463/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [464/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [465/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [466/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [467/500], Training Loss: 0.0007, Validation Loss: 0.0020\n",
      "Epoch [468/500], Training Loss: 0.0004, Validation Loss: 0.0012\n",
      "Epoch [469/500], Training Loss: 0.0004, Validation Loss: 0.0011\n",
      "Epoch [470/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [471/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [472/500], Training Loss: 0.0005, Validation Loss: 0.0018\n",
      "Epoch [473/500], Training Loss: 0.0006, Validation Loss: 0.0015\n",
      "Epoch [474/500], Training Loss: 0.0005, Validation Loss: 0.0013\n",
      "Epoch [475/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [476/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [477/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [478/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [479/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [480/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [481/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [482/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [483/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [484/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [485/500], Training Loss: 0.0001, Validation Loss: 0.0013\n",
      "Epoch [486/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [487/500], Training Loss: 0.0003, Validation Loss: 0.0014\n",
      "Epoch [488/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [489/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Epoch [490/500], Training Loss: 0.0006, Validation Loss: 0.0019\n",
      "Epoch [491/500], Training Loss: 0.0009, Validation Loss: 0.0019\n",
      "Epoch [492/500], Training Loss: 0.0004, Validation Loss: 0.0014\n",
      "Epoch [493/500], Training Loss: 0.0003, Validation Loss: 0.0015\n",
      "Epoch [494/500], Training Loss: 0.0002, Validation Loss: 0.0013\n",
      "Epoch [495/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Epoch [496/500], Training Loss: 0.0002, Validation Loss: 0.0015\n",
      "Epoch [497/500], Training Loss: 0.0002, Validation Loss: 0.0014\n",
      "Epoch [498/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Epoch [499/500], Training Loss: 0.0002, Validation Loss: 0.0016\n",
      "Epoch [500/500], Training Loss: 0.0002, Validation Loss: 0.0012\n",
      "Training complete!\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 500  # Number of epochs to train\n",
    "loss_values = []\n",
    "val_loss_values = []\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  # Set model to training mode\n",
    "    running_loss = 0.0\n",
    "\n",
    "    for inputs, targets in train_loader:\n",
    "        # inputs shape: [batch_size, 2]\n",
    "        # targets shape: [batch_size]\n",
    "\n",
    "        # Convert inputs and targets to float32 (if necessary)\n",
    "        inputs = inputs.float()\n",
    "        targets = targets.float()\n",
    "\n",
    "        # Zero/reset the parameter gradients before the next iteration\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # Forward pass\n",
    "        outputs = model(inputs)  # Shape: [batch_size, 1]\n",
    "\n",
    "        # Compute loss\n",
    "        loss = criterion(outputs.squeeze(), targets)  # Squeeze to match target shape if necessary\n",
    "\n",
    "        # Backward pass and optimization step\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Accumulate running loss\n",
    "        running_loss += loss.item()\n",
    "\n",
    "    # Calculate average loss for the epoch\n",
    "    average_loss = running_loss / len(train_loader)\n",
    "\n",
    "    # Store average loss in the list\n",
    "    loss_values.append(average_loss)\n",
    "\n",
    "    # Validation phase (no gradient calculation)\n",
    "    model.eval()  # Set model to evaluation mode\n",
    "    val_running_loss = 0.0\n",
    "\n",
    "    with torch.no_grad():  # Disable gradient calculation for validation\n",
    "        for inputs, targets in val_loader:\n",
    "            # Convert inputs and targets to float32 (if necessary)\n",
    "            inputs = inputs.float()\n",
    "            targets = targets.float()\n",
    "\n",
    "            # Forward pass\n",
    "            outputs = model(inputs)  # Shape: [batch_size, 1]\n",
    "\n",
    "            # Compute validation loss\n",
    "            val_loss = criterion(outputs.squeeze(), targets)  # Squeeze to match target shape if necessary\n",
    "            val_running_loss += val_loss.item()\n",
    "\n",
    "    # Calculate average validation loss for the epoch\n",
    "    average_val_loss = val_running_loss / len(val_loader)\n",
    "    val_loss_values.append(average_val_loss)\n",
    "\n",
    "    # Print epoch statistics\n",
    "    print(f\"Epoch [{epoch + 1}/{num_epochs}], \"\n",
    "          f\"Training Loss: {average_loss:.4f}, Validation Loss: {average_val_loss:.4f}\")\n",
    "\n",
    "print(\"Training complete!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ab3ff5b-3cd9-4047-a0fd-3994416cf583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Izc3FiRMnHhiHv78/evfujSVLluDXX38tMM14vwoVKuDpp5/GmDFjcOPGDauLJDrL/d+XH3/8EXl5eZYL7BX3c23Xrh38/Pzw1VdfFbrQ2B4lzTs5F68zQy7Xrl07+Pv746WXXsK0adOgVquxdOnSAmfZyGnIkCH47LPPMHDgQMyYMQO1a9fGxo0bsXnzZgDms4Me5PHHH8f777+PadOmoWPHjjhz5gzee+891KhRA3l5eSWOR6FQ4P3338fIkSPRp08fjBo1Crdu3UJcXFyJppkA81TTl19+iU8//RSRkZFWa24iIyNRq1YtvPXWWxBCoGLFivjll19sHkaPiYnBo48+ikmTJiEzMxNRUVH4448/8N133xVo+/jjj2PRokWIjIxE48aNcejQIXz88ccFpghq1aoFT09PLF26FPXq1YO3tzdCQ0MRGhpaoM+6devihRdewL/+9S8oFArExsYiOTkZU6dORVhYGF577TWb3ldRUlNT8dNPPxXYXr16dURHR6Nbt2548803kZGRgfbt2+Po0aOYNm0amjVrZjll+quvvsK2bdvQs2dPVKtWDTk5OZbLDnTt2hUAMGrUKHh6eqJ9+/YICQlBamoqZs2aBT8/P8up6w8yfPhwrFixAmPHjkXVqlUt/ebr1asXGjZsiKioKFSuXBkpKSmYO3cuwsPDERERAcC8rqxLly5499138e677z70mLdu3Sr0cgNarRbNmjWz2rZ69WqoVCpER0fjxIkTmDp1Kpo0aWKZDi3u5+rt7Y05c+Zg5MiR6Nq1K0aNGoWgoCD89ddfOHLkCObNm/fQuO9lb97JieRcfUzlR1FnMzVo0KDQ9nv27BFt27YVOp1OVK5cWYwcOVIcPny4wFkqRZ3NVNhZIx07dhQdO3a0PC/qbKb74yzqOOfPnxd9+/YV3t7ewsfHRzz11FNiw4YNBc7qKYxerxcTJ04UVapUER4eHqJ58+Zi7dq1Bc72yT+b6eOPPy7QBwo52+ebb74RERERQqPRiDp16ogFCxYU6LM4mjVrVuiZNUIIcfLkSREdHS18fHyEv7+/eOaZZ8T58+cLxFOcs5mEEOLWrVti+PDhokKFCkKn04no6Ghx+vTpAv3dvHlTjBgxQgQGBgqdTic6dOggdu/eXeBzFUKIH374QURGRgq1Wm3VT2Gfo9FoFB9++KGoU6eOUKvVolKlSmLgwIHiwoULVu2K+r4WN78POmNnyJAhQgjzWXdvvvmmCA8PF2q1WoSEhIiXX35Z3Lx509LP3r17RZ8+fUR4eLjQarUiICBAdOzYUaxbt87SZvHixaJz584iKChIaDQaERoaKp599llx9OjRh8aZn5OwsDABQEyZMqXA/jlz5oh27dqJSpUqCY1GI6pVqyZGjBghkpOTLW3yf78edEZacXJTpUoVS7v8z+/QoUOiV69elt+9fv36iStXrhR4D8X5XIUQYsOGDaJjx47Cy8tL6HQ6Ub9+ffHhhx9a9hf33wV7807OIwnh4LE3onJs5syZeOedd3D+/HleaZbIweLi4jB9+nRcvXrVpeuZqOzjNBNREfKHoCMjI2EwGLBt2zZ88cUXGDhwIAsZIqJShMUMURF0Oh0+++wzJCcnQ6/Xo1q1anjzzTfxzjvvyB0aERHdg9NMREREVKbx1GwiIiIq01jMEBERUZkmazEzf/58NG7cGL6+vvD19UXbtm2tLlImhEBcXBxCQ0Ph6emJTp068cJEREREZEXWNTO//PILlEolateuDQBYvHgxPv74Y/z5559o0KABPvzwQ3zwwQdYtGgR6tSpgxkzZmDXrl04c+ZMsS99bzKZcOnSJfj4+Dz0RmNERERUOgghcPv2bYSGhj70QqWl7qJ5/v7+4ptvvhEmk0kEBweL2bNnW/bl5OQIPz8/8dVXXxW7vwsXLhR5sSY++OCDDz744KN0Pwq7EOL9Ss2p2UajEStXrkRmZibatm2LpKQkpKamIiYmxtJGq9WiY8eO2LNnD1588cVC+9Hr9dDr9Zbn4u7AU1JSkkNvZGgwGLB9+3Z07twZarXaYf2SNebZNZhn12GuXYN5dg1n5vn27duoUaNGsf52y17MHDt2DG3btkVOTg68vb2xZs0a1K9f33LzvKCgIKv2QUFBD7w76axZszB9+vQC2/fu3QudTufQ2HU6Hfbv3+/QPqkg5tk1mGfXYa5dg3l2DWflOf8O7cVZIiJ7MVO3bl0kJibi1q1bWLVqFYYMGYKdO3da9t//JoQQD3xjkydPxoQJEyzPMzIyEBYWhpiYGPj6+josboPBgPj4eERHR7PqdyLm2TWYZ9dhrl2DeXYNZ+Y5IyOj2G1lL2Y0Go1lAXBUVBQSEhLw+eef48033wRgvgttSEiIpX1aWlqB0Zp7abVaaLXaAtvVarVTvtDO6pesMc+uwTy7DnPtGsyzazgjzyXpr9RdZ0YIAb1ejxo1aiA4OBjx8fGWfbm5udi5cyfatWsnY4RERERUmsg6MvP2228jNjYWYWFhuH37NpYvX44dO3Zg06ZNkCQJ48ePx8yZMxEREYGIiAjMnDkTOp0O/fv3lzNsIiK3YjKZkJubK3cYJWIwGKBSqZCTkwOj0Sh3OOWWPXlWq9VQKpUOiUPWYubKlSsYNGgQLl++DD8/PzRu3BibNm1CdHQ0AGDSpEnIzs7G6NGjcfPmTbRu3Rpbtmxx6FlJRERUtNzcXCQlJcFkMskdSokIIRAcHIwLFy7wGmNOZG+eK1SogODgYLs/I1mLmW+//faB+yVJQlxcHOLi4lwTEBERWQghcPnyZSiVSoSFhT38wmWliMlkwp07d+Dt7V2m4i5rbM2zEAJZWVlIS0sDAKu1sbaQfQEwERGVTnl5ecjKykJoaKjDL23hbPlTYx4eHixmnMiePHt6egIwn9gTGBho15QTP2EiIipU/hoIjUYjcyRUXuUXyQaDwa5+WMwQEdEDcc0JOYujvlssZoiIiKhMYzFDRET0EJ06dcL48eOL3T45ORmSJCExMdFpMdE/WMwQEVG5IUkSJEmCUqmEv78/lEqlZZskSRg6dKhN/a5evRrvv/9+sduHhYXh8uXLaNiwoU3HKy4WTWY8m8kOwemHoYj/A6jZCYjsIXc4RERu7/LlywDMZ9ksWbIEs2bNwpkzZyz788+gyWcwGIp12fyKFSuWKA6lUong4OASvYZsx5EZG0mn1qH1ublQJHwDLO8HnN4gd0hERG4vODjY8vD19YUkSZbnOTk5qFChAn788Ud06tQJHh4e+P7773H9+nX069cPVatWhU6nQ6NGjfDDDz9Y9Xv/NFP16tUxc+ZMDB8+HD4+PqhWrRq+/vpry/77R0x27NgBSZLw22+/ISoqCjqdDu3atbMqtABgxowZCAwMhI+PD0aOHIm33noLTZs2tTkfer0er7zyCgIDA+Hh4YEOHTogISHBsv/mzZsYMGAAKleuDE9PT0RERGDhwoUAzBdMHDt2LEJCQuDh4YHq1atj1qxZNsfiTCxmbKRI+D8AgCSMgKQEkn+XOSIiIucSQiArN8+mx69HL+Hdn4/j16OXbHq9EMJh7+PNN9/EK6+8glOnTqFbt27IyclBixYt8Ouvv+L48eN44YUXMGjQIOzfv/+B/cyZMwdRUVH4888/MXr0aLz88ss4ffr0A18zZcoUzJkzBwcPHoRKpcLw4cMt+5YuXYoPPvgAH374IQ4dOoRq1aph/vz5dr3XSZMmYdWqVVi8eDEOHz6M2rVro1u3brhx4wYAYOrUqTh58iQ2btyIU6dOYf78+ahUqRIA4IsvvsC6devw448/4syZM/j+++9RvXp1u+JxFk4z2UhUrAVc2A8ByVzQVO8gd0hERE6VbTCi/rub7epjyd4Um1538r1u0Gkc8ydr/Pjx6Nu3r9W2iRMnWn4eN24cNm3ahJUrV6J169ZF9tOjRw+MHj0agLlA+uyzz7Bjxw5ERkYW+ZoPPvgAHTt2BAC89dZb6NmzJ3JycuDh4YF//etfGDFiBIYNGwYAePfdd7FlyxbcuXPHpveZmZmJ+fPnY9GiRYiNjQUA/Oc//0F8fDy+/fZbvPHGGzh//jyaNWuGqKgoALAqVs6fP4+IiAh06NABkiQhPDzcpjhcgSMztgpqbP5vxZrA8z9wzQwRURmR/4c7n9FoxAcffIDGjRsjICAA3t7e2LJlC86fP//Afho3bmz5OX86K//y/MV5Tf4l/PNfc+bMGbRq1cqq/f3PS+Lvv/+GwWBA+/btLdvUajVatWqFU6dOAQBefvllLF++HE2bNsWkSZOwZ88eS9uhQ4ciMTERdevWxSuvvIItW7bYHIuzcWTGVkpz6kSlupBYyBCRG/BUK3HyvW4lft2202kYu+xPKCUJRiEwr38zPBYZWOJjO4qXl5fV8zlz5uCzzz7D3Llz0ahRI3h5eWH8+PEPvVP4/QuHJUl66A05731N/gXj7n3N/ReRs2d6Lf+1hfWZvy02NhYpKSlYv349tm7dii5dumDMmDH45JNP0Lx5cyQlJWHjxo3YunUrnn32WXTt2hU//fSTzTE5C4sZGwnF3S+kyb5LMBMRlRWSJNk01fN441BoVUrsO3cdbWoGILp+kBOis93u3bvx5JNPYuDAgQDMxcXZs2dRr149l8ZRt25dHDhwAIMGDbJsO3jwoM391a5dGxqNBr///jv69+8PwHz21sGDB60WM1euXBlDhw7F0KFD8cgjj+CNN97AJ598AgDw9fXFc889h+eeew5PP/00unfvjhs3bpT47C5nYzFjK2V+MZMnbxxERGVAdP2gUlfE5KtduzZWrVqFPXv2wN/fH59++ilSU1NdXsyMGzcOo0aNQlRUFNq1a4cVK1bg6NGjqFmz5kNfe/9ZUQBQv359vPzyy3jjjTdQsWJFVKtWDR999BGysrIwYsQIAOZ1OS1atECDBg2g1+vx66+/Wt73Z599hpCQEDRt2hQKhQIrV65EcHAwKlSo4ND37QgsZmyluJs6I0dmiIjKsqlTpyIpKQndunWDTqfDCy+8gN69eyM9Pd2lcQwYMADnzp3DxIkTkZOTg2effRZDhw7FgQMHHvra559/vsC2pKQkzJ49GyaTCYMGDcLt27cRFRWFzZs3w9/fH4D5JqKTJ09GcnIyPD098cgjj2D58uUAAG9vb3z44Yc4e/YslEolWrZsiQ0bNpTKu5BLwpHnu5VCGRkZ8PPzQ3p6Onx9fR3Wb17yflz+eTpCmz4GZceJD38B2cRgMGDDhg3o0aNHsS5sRbZhnl2nLOU6JycHSUlJqFGjBjw8POQOp0RMJhMyMjLg6+tbKv/4Fld0dDSCg4Px3XffyR1KoezN84O+YyX5+82RGRuJKs1xuPqLCG7XA45blkZERO4qKysLX331Fbp16walUokffvgBW7duRXx8vNyhlXosZoiIiEoBSZKwYcMGzJgxA3q9HnXr1sWqVavQtWtXuUMr9VjM2EqYoDTpgdw7gNpf7miIiKiM8/T0xNatW+UOo0wquxOJMpPO78XjR0ZBtSBa7lCIiIjcGosZW/HUbCIiolKBxYyteGo2ERFRqcBixka8AjAREVHpwGLGVpxmIiIiKhVYzNiK00xERESlAosZW3Fkhoio3OrUqZPVzRirV6+OuXPnPvA1kiRh7dq1dh/bUf24ExYzttJ445JfFETdHnJHQkREd/Xq1avIi8zt3bsXkiTh8OHDJe43ISEBL7zwgr3hWYmLi0PTpk0LbL98+TJiY2Mdeqz7LVq0qFTeMNJWLGZspQtAQs1XYHzyK7kjISKiu0aMGIFt27YhJSWlwL4FCxagadOmaN68eYn7rVy5MnQ6nSNCfKjg4GBotVqXHKu8YDFDRETlxuOPP47AwEAsXrzYantWVhZWrFiBESNG4Pr16+jXrx+qVq0KnU6HRo0a4Ycffnhgv/dPM509exaPPvooPDw8UL9+/ULvn/Tmm2+iTp060Ol0qFmzJqZOnQqDwbzOctGiRZg+fTqOHDkCSZIgSRIWLVoEoOA007Fjx/DYY4/B09MTAQEBeOGFF3Dnzh3L/qFDh6J379745JNPEBISgoCAAIwZM8ZyLFucP38eTz75JLy9veHr64tnn30WV65csew/cuQIOnfuDD8/P1SrVg0tW7bEwYMHAQApKSno1asX/P394eXlhQYNGmDDhg02x1IcvJ2BvUxGQKgASZI7EiIi18jNLHqfpATUHgXb/nczkLIHCG8H1Ol2t60CUHs+vF+NV7FDU6lUGDx4MBYvXoxXX33Vsn3lypXIzc3FgAEDkJWVhRYtWuDNN9+Er68v1q9fj0GDBqFmzZpo3br1Q49hMpnQt29fVKpUCfv27UNGRobV+pp8Pj4+WLRoEUJDQ3Hs2DGMGjUKPj4+mDRpEp577jkcP34cmzZtstzCwM/Pr0AfWVlZ6N69O9q0aYOEhASkpaVh5MiRGDt2rKX4AYDt27cjJCQE27dvx19//YXnnnsOTZs2xahRo4qdu3xCCPTu3RteXl7YuXMn8vLyMHr0aDz33HPYsWMHAGDAgAFo1qwZvvzyS2RnZ+Ovv/6y3AV+zJgxyM3Nxa5du+Dl5YWTJ0/C29u7xHGUBIsZW+Xl4Ik/h0D6UwBvnQc8Cn4JiYjKpZmhRe+LiAEGrPzn+ce1AUPWP88T/vPPz+EdgGHr/3k+txGQdb1gn3HpJQpv+PDh+Pjjj/H777+jZ8+eAMxTTH379oW/vz/8/f0xceJES/tx48Zh06ZNWLlyZbGKma1bt+LUqVNITk5G1apVAQAzZ84ssM7lnXfesfxcvXp1vP7661ixYgUmTZoET09PeHt7Q6VSITg4uMhjLV26FNnZ2ViyZAm8vMxF3bx589CrVy98+OGHCAoKAgD4+/tj3rx5UCqViIyMRM+ePfHbb7/ZVMxs3boVR48eRVJSEsLCwgAA3333HRo0aICEhAS0bNkS58+fxxtvvIHIyEhkZGSgWbNmUCjMkz3nz5/HU089hUaNGgEAatasWeIYSorTTLZSqCFBmH82GeWNhYiILCIjI9GuXTt8//33AIC///4bu3fvxvDhwwEARqMRH3zwARo3boyAgAB4e3tjy5YtOH/+fLH6P3XqFKpVq2YpZACgbdu2Bdr99NNP6NChA4KDg+Ht7Y2pU6cW+xj3HqtJkyaWQgYA2rdvD5PJhDNnzli2NWjQAEql0vI8JCQEaWlpJTrWvccMCwuzFDIAUL9+fVSoUAGnTp0CAEyYMAEjR45ETEwMPvvsM/z999+Wtq+88gpmzJiB9u3bY9q0aTh69KhNcZQER2ZspVBCQIIEgZ2nLqFji4pyR0RE5BpvXyp6n6S0fv7GX+Yppp+GmfcJI/D0QvNUk3Tf/0+PP+awEIcNG4ZXXnkFGRkZWLhwIcLDw9GlSxcAwJw5c/DZZ59h7ty5aNSoEby8vDB+/Hjk5uYWq28hRIFt0n1LDfbt24fnn38e06dPR7du3eDn54fly5djzpw5JXofQogCfRd2zPwpnnv3mUymEh3rYce8d3tcXBz69++PX3/9Fb/++itmz56N5cuXo0+fPhg5ciS6deuG9evXY8uWLZg1axbmzJmDcePG2RRPcXBkxkYfbDiNXGH+pX1r5WHEn7zykFcQEZUTGq+iH/eul8lv27Av8PwPQOuXzP9t2PduW8/i9WuDZ599FkqlEsuWLcPixYsxbNgwyx/i3bt348knn8TAgQPRpEkT1KxZE2fPni123/Xr18f58+dx6dI/Rd3evXut2vzxxx8IDw/HlClTEBUVhYiIiAJnWGk0GhiNDx7Zr1+/PhITE5GZ+c96oj/++AMKhQJ16tQpdswlkf/+Lly4YNl28uRJpKeno169epZtderUwfjx47F69Wr06dMHCxcutOwLCwvDSy+9hNWrV+P111/Hf/7zHzgTixkbHftfBvJgLma0khH7zhUyz0tERGaRPYDuM83/dQFvb2/06dMH77zzDi5duoShQ4da9tWuXRvx8fHYs2cPTp06hRdffBGpqanF7rtr166oW7cuBg8ejCNHjmD37t2YMmWKVZvatWvj/PnzWL58Of7++2988cUXWLNmjVWb6tWrIykpCYmJibh27Rr0en2BYw0YMAAeHh4YMmQIjh8/ju3bt2PcuHEYNGiQZb2MrYxGIxITE60eJ0+eRNeuXdG4cWMMGDAAhw8fxoEDBzB48GB07NgRUVFRyM7OxtixY7Fjxw6kpKRg3759OHjwoKXQGT9+PDZv3oykpCQcPnwY27ZtsyqCnIHFjI0iAr0sxYwCeWhTM0DmiIiI6F4DBw7EzZs30bVrV1SrVs2yferUqWjevDm6deuGTp06ITg4GL179y52vwqFAmvWrIFer0erVq0wcuRIfPDBB1ZtnnzySbz22msYO3YsmjZtij179mDq1KlWbZ566il0794dnTt3RuXKlQs9PVyn02Hz5s24ceMGWrZsiaeffhpdunTBvHnzSpaMQty5cwfNmjWzevTo0cNyari/vz8effRRdO3aFTVr1sSKFSsAAEqlEtevX8fgwYMRGRmJ4cOHo3v37pg+fToAc5E0ZswY1KtXD927d0fdunXx73//2+54H0QShU3+lSMZGRnw8/NDeno6fH19HdbvyoQUdP61AypJGdjT7Ve0a/uIw/qmfxgMBmzYsAE9evQoMCdMjsM8u05ZynVOTg6SkpJQo0YNeHh4PPwFpYjJZEJGRgZ8fX0tZ9mQ49mb5wd9x0ry95sLgG2k0yjxh6khqnga0K5u1Ye/gIiIiJyCxYyNdBoVhhnGIrKiNzZVrCF3OERERG6LY2828tKa18vcyeU1ZoiIiOTEYsZGXhpzMZOVmydzJERERO6NxYyNdBoVVmmmYW9ef+Dv7XKHQ0TkNOX8PBGSkaO+WyxmbKTTKKGBAVrJAKOh4LUBiIjKuvzL4xf3yrhEJZWVZb5vl71n9nEBsI28tCrk3U2fPlcPnczxEBE5mkqlgk6nw9WrV6FWq8vUKc4mkwm5ubnIyckpU3GXNbbmWQiBrKwspKWloUKFClb3lbIFixkbaZSS5aJ5en0uixkiKnckSUJISAiSkpIKXIq/tBNCIDs7G56enkXe24jsZ2+eK1So8MC7hhcXixkbSZIEY34xk8tpJiIqnzQaDSIiIsrcVJPBYMCuXbvw6KOPlvqLE5Zl9uRZrVbbPSKTj8WMHYySOX25LGaIqBxTKBRl7grASqUSeXl58PDwYDHjRKUlz7JOJM6aNQstW7aEj48PAgMD0bt3b5w5c8aqzdChQyFJktWjTZs2MkVszXT3VveGQm4ORkRERK4hazGzc+dOjBkzBvv27UN8fDzy8vIQExNjdatzAOjevTsuX75seWzYsEGmiK0lSdWw11gft1UV5Q6FiIjIbck6zbRp0yar5wsXLkRgYCAOHTqERx991LJdq9U6ZIGQo32nfgZnsxX4vFJTNJU7GCIiIjdVqs5XS09PBwBUrGg90rFjxw4EBgaiTp06GDVqFNLS0uQIr4C7dzRApp63NCAiIpJLqVkALITAhAkT0KFDBzRs2NCyPTY2Fs888wzCw8ORlJSEqVOn4rHHHsOhQ4eg1WoL9KPX66G/Zw1LRkYGAPOKa4PB4LB4DQYDsu/eyeBwynU80zzEYX3TP/I/M0d+dlQQ8+w6zLVrMM+u4cw8l6RPSZSS61SPGTMG69evx++//46qVasW2e7y5csIDw/H8uXL0bdv3wL74+LiMH369ALbly1bBp3OcVeDOXZDQuW/l+NZ5U7Mz+sFU+0eaFSxVKSSiIiozMvKykL//v2Rnp4OX1/fB7YtFSMz48aNw7p167Br164HFjIAEBISgvDwcJw9e7bQ/ZMnT8aECRMszzMyMhAWFoaYmJiHJqMkDv16Ep7Qw1+6Ay9Jj1sBNdEjtq7D+iczg8GA+Ph4REdH8/RKJ2KeXYe5dg3m2TWcmef8mZXikLWYEUJg3LhxWLNmDXbs2IEaNWo89DXXr1/HhQsXEBJS+LSOVqstdPpJrVY7NNHtalWC8vANAEBt6SI0tSvzF8aJHP35UeGYZ9dhrl2DeXYNZ+S5JP3JugB4zJgx+P7777Fs2TL4+PggNTUVqampyM7OBgDcuXMHEydOxN69e5GcnIwdO3agV69eqFSpEvr06SNn6OiqPIRo5WEAQE/lAUQrDskaDxERkbuStZiZP38+0tPT0alTJ4SEhFgeK1asAGC+suCxY8fw5JNPok6dOhgyZAjq1KmDvXv3wsfHR87QIaX8DhPM96EQkIDk32WNh4iIyF3JPs30IJ6enti8ebOLoikZEd4BygP/BwCQIIDqHWSOiIiIyD2VquvMlCWiTiz2adoBAP7yaQVE9pA5IiIiIvdUKs5mKqtOqxvCIycVV72borbcwRAREbkpjszYYZ/nI+idOwM7AgfLHQoREZHbYjFjB4V5/S9MpeO6g0RERG6JxYwd7tYyMJlkDYOIiMitcc2MHZrr92OydjkuX4wCsELucIiIiNwSixk7aIQBVaVruGO4IXcoREREbovTTHYwSUoAgELkyRwJERGR+2IxYwehYDFDREQkNxYzdjCCxQwREZHcWMzY4+40k5LFDBERkWxYzNiBa2aIiIjkx2LGDnqFJ/5rqoKrqlC5QyEiInJbPDXbDhc1tRGT+zG6BAbiW7mDISIiclMcmbGDxNsZEBERyY7FjB3yk2dkLUNERCQbTjPZIcB4BfGazyCl+gDYL3c4REREbonFjB2UwoQIxf+QafCWOxQiIiK3xWkmOwiFOX1K8NRsIiIiubCYsYOwXDTPKHMkRERE7ovFjB2EZJ6lU4DFDBERkVxYzNjBMjIDE2AyyRwNERGRe2IxY4f8YgYAYDLIFwgREZEb49lMdjBJKlwUlaBQqhFq4lQTERGRHFjM2MGk0KCD/gs0CPXFeo1O7nCIiIjcEqeZ7JB/OwOjiZcAJiIikguLGTvkFzO8NRMREZF8OM1kBwWAFZr3EHBbD6SvB/yqyB0SERGR22ExYwdJEqgnpcDXlA0YsuUOh4iIyC1xmskOCgB5uHt6Nk/NJiIikgWLGTtIEpCXP7hlZDFDREQkBxYzdlAAMHBkhoiISFYsZuwgSUCeuFvMGHnnbCIiIjmwmLGDQuKaGSIiIrmxmLGDBOAa/HANFYB779NERERELsNTs+0gScBzue8iwEuDQ+Ft5Q6HiIjILXFkxg75yTPxEsBERESyYTFjB96biYiISH6cZrKDBGCq6js0F+eAv9VArcfkDomIiMjtcGTGDgoJiJAuopl0BrhzVe5wiIiI3BKLGTtIuOfUbGOurLEQERG5KxYzdlBIgD9um59c+lPeYIiIiNwUixk7VLt9GM2Uf5ufHPwWOL1B3oCIiIjcEIsZO4RknoLlRCZJApJ/lzUeIiIid8Rixg5XvOtBcff0bAgBVO8gazxERETuiMWMHS75Nsd2YxPkCiVMTQYAkT3kDomIiMjt8DozdpAkYJjhTQDA2SdiWRkSERHJgH9/7XBv8ngVYCIiInnIWszMmjULLVu2hI+PDwIDA9G7d2+cOXPGqo0QAnFxcQgNDYWnpyc6deqEEydOyBSxtfzbGQDmJTNERETkerIWMzt37sSYMWOwb98+xMfHIy8vDzExMcjMzLS0+eijj/Dpp59i3rx5SEhIQHBwMKKjo3H79m0ZIzeTADyt3Ikl6lmQDn4jdzhERERuSdY1M5s2bbJ6vnDhQgQGBuLQoUN49NFHIYTA3LlzMWXKFPTt2xcAsHjxYgQFBWHZsmV48cUX5QjbQiEB1aQreFR5DLnX/itrLERERO6qVC0ATk9PBwBUrFgRAJCUlITU1FTExMRY2mi1WnTs2BF79uwptJjR6/XQ6/WW5xkZGQAAg8EAg8HgsFgNBgMkCcgT5hQaDXqH9k9m+Tllbp2LeXYd5to1mGfXcGaeS9JnqSlmhBCYMGECOnTogIYNGwIAUlNTAQBBQUFWbYOCgpCSklJoP7NmzcL06dMLbN+yZQt0Op1DY7733kz/u5CCExt4BWBniY+PlzsEt8A8uw5z7RrMs2s4I89ZWVnFbltqipmxY8fi6NGj+P33glfRle5daQtz4XP/tnyTJ0/GhAkTLM8zMjIQFhaGmJgY+Pr6Oixeg8GA+Ph4GO4WM6HBlRHeg9eZcbT8PEdHR0OtVssdTrnFPLsOc+0azLNrODPP+TMrxVEqiplx48Zh3bp12LVrF6pWrWrZHhwcDMA8QhMSEmLZnpaWVmC0Jp9Wq4VWqy2wXa1WO+ULbZTMxYxCGPkL40TO+vzIGvPsOsy1azDPruGMPJekP1nPZhJCYOzYsVi9ejW2bduGGjVqWO2vUaMGgoODrYavcnNzsXPnTrRr187V4RbKKJnrQWHkvCwREZEcZB2ZGTNmDJYtW4aff/4ZPj4+ljUyfn5+8PT0hCRJGD9+PGbOnImIiAhERERg5syZ0Ol06N+/v5yhWxihglEUPuVFREREzidrMTN//nwAQKdOnay2L1y4EEOHDgUATJo0CdnZ2Rg9ejRu3ryJ1q1bY8uWLfDx8XFxtIVbhcfwvb4zdvfojDC5gyEiInJDshYzohiXzZUkCXFxcYiLi3N+QDZQSAoARph4CWAiIiJZ8N5MdlIozFNMvDcTERGRPFjM2KkR/sJX6s/g/8cMuUMhIiJyS6Xi1OyyrJKUge7KBORcypU7FCIiIrfEkRk7me6emg1jnryBEBERuSkWM3ayFDMmXmeGiIhIDixm7JR/BWDJxJEZIiIiObCYsRNHZoiIiOTFYsZO+cUMR2aIiIjkwWLGTkZFfjHDkRkiIiI5sJixU4oiHHVzFiHxmX1yh0JEROSWWMzYSVIooYcGRqaSiIhIFvwLbKe7dzPgvZmIiIhkwmLGTr5SJj5V/xu1d42XOxQiIiK3xNsZ2EkNE/oqfwdSAJhMgIL1IRERkSvxL6+dhEL5zxOe0URERORyLGbsZFKoLT9P/ukQ4k9ekTEaIiIi98Nixk5C+membn3iRYxacpAFDRERkQuxmLGTuGdkRgUjJAnYd+66jBERERG5FxYzdlIoJOQJcxpVMEIIoE3NAJmjIiIich8sZuykkCTkwbwIWI08DGxTDdH1g2SOioiIyH3w1Gw7KSSgjX4e8qBEJjzQuGoFuUMiIiJyKyxm7KRQSLgFH8tztVKSMRoiIiL3w2kmOykk6+JFrWRKiYiIXIkjM3ZSShLeUC1HJWTgX8beUPEKwERERC7Fv7x2kiTgCcVePKfagcpIt9x4koiIiFyDxYydlAoJhrtnM6lghIk3zyYiInIpFjN2UkgSDHdn61SSEUKwmiEiInIlFjN2unZHb3WdGSOLGSIiIpdiMWOHYzck/HkhHZ7IAQBESWc4zURERORiLGbscDZDQrTiEGoqzDeWfEW9FoH/+03mqIiIiNwLixk7RPgKtFGcRP7MklFICLh2QN6giIiI3AyLGTs0qiiQW7UdJAnIEwooJYErFVvKHRYREZFb4UXz7GSq0x0jz99CG8VJ7DPVR0xQZ7lDIiIicissZuzkpVVhq6kFtppaAAC68GwmIiIil2IxYydvrQp9FbvQSJGEzaaWMImGcodERETkVljM2Mlbo8SjyqPordyDi4ZKMPHcbCIiIpfiAmA7eWlVyLtbE6p5OwMiIiKXYzFjJy+tErnin3szGVnNEBERuRSLGTt5ae4ZmZHyYOICYCIiIpdiMWMnL63ScqNJ8zQTixkiIiJXYjFjJ2+tCgb8M83EWSYiIiLXYjFjJ53m3pGZPK6ZISIicjGemm0nSZKwMK87VhkfwS3hjZGcZiIiInIpFjMOcAO+uCF8AQBGk8zBEBERuRlOMzkYFwATERG5Fkdm7PTbqTQ0k84iWnkIZ0xV8VdaiNwhERERuRWOzNhpX9INNFAkY7RqHbopD+LizSy5QyIiInIrNhUzFy5cwMWLFy3PDxw4gPHjx+Prr78uUT+7du1Cr169EBoaCkmSsHbtWqv9Q4cOhSRJVo82bdrYErLTtKlR0epsphA/T5kjIiIici82FTP9+/fH9u3bAQCpqamIjo7GgQMH8Pbbb+O9994rdj+ZmZlo0qQJ5s2bV2Sb7t274/Lly5bHhg0bbAnZabrUC8Sg9rUBmC+aFx6gkzkiIiIi92LTmpnjx4+jVatWAIAff/wRDRs2xB9//IEtW7bgpZdewrvvvlusfmJjYxEbG/vANlqtFsHBwbaE6TINwyoBB3idGSIiIjnYNDJjMBig1WoBAFu3bsUTTzwBAIiMjMTly5cdFx2AHTt2IDAwEHXq1MGoUaOQlpbm0P4dQmGuCVUSrwBMRETkajaNzDRo0ABfffUVevbsifj4eLz//vsAgEuXLiEgIMBhwcXGxuKZZ55BeHg4kpKSMHXqVDz22GM4dOiQpZi6n16vh16vtzzPyMgAYC7ADAaDw2LL78tgMECCAioAGuTBYDQ69Dju7t48k/Mwz67DXLsG8+wazsxzSfqUhCj5hVF27NiBPn36ICMjA0OGDMGCBQsAAG+//TZOnz6N1atXl7RLSJKENWvWoHfv3kW2uXz5MsLDw7F8+XL07du30DZxcXGYPn16ge3Lli2DTuec9SyB6UfQ9twcHDNVx0f+M/B0TV45j4iIyB5ZWVno378/0tPT4evr+8C2NhUzAGA0GpGRkQF/f3/LtuTkZOh0OgQGBpa4v+IUMwAQERGBkSNH4s033yx0f2EjM2FhYbh27dpDk1ESBoMB8fHxiI6OhtqUg+Vbfsc3CVfRLioK7z1R32HHcXdWeVar5Q6n3GKeXYe5dg3m2TWcmeeMjAxUqlSpWMWMTdNM2dnZEEJYCpmUlBSsWbMG9erVQ7du3WzpsliuX7+OCxcuICSk6AvTabXaQqeg1Gq1U77Q5n51uOlXDylCiXaSgr84TuCsz4+sMc+uw1y7BvPsGs7Ic0n6s2kB8JNPPoklS5YAAG7duoXWrVtjzpw56N27N+bPn1/sfu7cuYPExEQkJiYCAJKSkpCYmIjz58/jzp07mDhxIvbu3Yvk5GTs2LEDvXr1QqVKldCnTx9bwnYqpUICAJi4ApiIiMilbCpmDh8+jEceeQQA8NNPPyEoKAgpKSlYsmQJvvjii2L3c/DgQTRr1gzNmjUDAEyYMAHNmjXDu+++C6VSiWPHjuHJJ59EnTp1MGTIENSpUwd79+6Fj4+PLWE7z500RKV8g5HK9bw3ExERkYvZNM2UlZVlKSi2bNmCvn37QqFQoE2bNkhJSSl2P506dcKDluxs3rzZlvBc704aWifPR02VH2aJF+SOhoiIyK3YNDJTu3ZtrF27FhcuXMDmzZsRExMDAEhLS3PoItsyQ6kBYL5oHgdmiIiIXMumYubdd9/FxIkTUb16dbRq1Qpt27YFYB6lyZ8ycivKuxfNg5FXACYiInIxm6aZnn76aXTo0AGXL19GkyZNLNu7dOlSKhfnOp3CvOJajTwYOTRDRETkUjYVMwAQHByM4OBgXLx4EZIkoUqVKpb7NbkdyzSTEcLEC+YRERG5kk3TTCaTCe+99x78/PwQHh6OatWqoUKFCnj//fdhcsc/5krzyIxCEhAmo8zBEBERuRebRmamTJmCb7/9FrNnz0b79u0hhMAff/yBuLg45OTk4IMPPnB0nKWb4p80SqY8GQMhIiJyPzYVM4sXL8Y333xjuVs2ADRp0gRVqlTB6NGj3a+Y0Xhha5sl+NfOZFQWSrmjISIicis2TTPduHEDkZGRBbZHRkbixo0bdgdV5iiUuFGpOY6I2jDallIiIiKykU1/eZs0aYJ58+YV2D5v3jw0btzY7qDKIqVkvp2BkSczERERuZRN00wfffQRevbsia1bt6Jt27aQJAl79uzBhQsXsGHDBkfHWCbUTl6KF5VJSDI+K3coREREbsWmkZmOHTviv//9L/r06YNbt27hxo0b6Nu3L06cOIGFCxc6OsYyocGpuZis/gFeeTflDoWIiMit2HydmdDQ0AILfY8cOYLFixdjwYIFdgdW1pgk8+nZPJuJiIjItbha1UHE3asAK1nMEBERuRSLGQcx3S1mFMIgcyRERETuhcWMg4i7F86TBEdmiIiIXKlEa2b69u37wP23bt2yJ5YyTWHKBQDU1x+VORIiIiL3UqJixs/P76H7Bw8ebFdAZdLpDfDIvgIAGJy1GDjdHYjsIXNQRERE7qFExYy7nnb9UMm7IaCABBOMUECZ/DuLGSIiIhfhmhlHqP4IJJiQJxRQwgRU7yB3RERERG7D5uvM0D0ie+BUx//DH7+txQXfFpjOURkiIiKXYTHjKEoVkkUwLkqhckdCRETkVjjN5CAhp5dghnohIo1n5A6FiIjIrbCYcRSleZCLVwAmIiJyLRYzDiIUGgCAklcAJiIicikWM46izL+dAUdmiIiIXInFjIOIu8WMisUMERGRS7GYcZT8G02CxQwREZErsZhxFI7MEBERyYLXmXGQjAaDMDExCDc8wvGC3MEQERG5ERYzDmKoVB/bTNfgD7XcoRAREbkVTjM5iEIhAQCMJiFzJERERO6FxYyDaK+fRl/FLjQRp+UOhYiIyK2wmHEQ7+Qt+FTzFZ7ADrlDISIicissZhyFZzMRERHJgsWMg0j5xQyvM0NERORSLGYcRFKZ782kglHmSIiIiNwLixlHyb9rNqeZiIiIXIrFjIMo7t41W408CMHTs4mIiFyFxYyD3DvNxEvNEBERuQ6vAOwgxrA2GJs7DmmiAtoIASUkuUMiIiJyCyxmHESqEIZfTW0BmK8CrFbKHBAREZGb4DSTgygV/4zEcMkMERGR63BkxkEUWdfRXXEAOdDAKLrJHQ4REZHbYDHjIMprZ/CVZi7OmqrAJCbJHQ4REZHb4DSTg0gqc12oRh5MPJ2JiIjIZVjMOIhSpQUAqCSemk1ERORKLGYcJP/eTGrkwchqhoiIyGVYzDjKPcUMrwBMRETkOrIWM7t27UKvXr0QGhoKSZKwdu1aq/1CCMTFxSE0NBSenp7o1KkTTpw4IU+wD6P85wrARhYzRERELiNrMZOZmYkmTZpg3rx5he7/6KOP8Omnn2LevHlISEhAcHAwoqOjcfv2bRdHWgyK/AXARuz871WZgyEiInIfsp6aHRsbi9jY2EL3CSEwd+5cTJkyBX379gUALF68GEFBQVi2bBlefPFFV4b6UL9dMGGzYRRyhRprVx1DgJcW0fWD5A6LiIio3Cu115lJSkpCamoqYmJiLNu0Wi06duyIPXv2FFnM6PV66PV6y/OMjAwAgMFggMFgcFh8+X3l/3d30h38aOwMAFBIwJ6/rqJTREWHHc9d3Z9ncg7m2XWYa9dgnl3DmXkuSZ+ltphJTU0FAAQFWY9uBAUFISUlpcjXzZo1C9OnTy+wfcuWLdDpdI4NEkB8fDwAQHVDAmC+IZNJAMrr57Bhw98OP567ys8zORfz7DrMtWswz67hjDxnZWUVu22pLWbySZL13aeFEAW23Wvy5MmYMGGC5XlGRgbCwsIQExMDX19fh8VlMBgQHx+P6OhoqNVq9DAZ8b9ZnyM3V482XZ/B0EdqO+xY7uz+PJNzMM+uw1y7BvPsGs7Mc/7MSnGU2mImODgYgHmEJiQkxLI9LS2twGjNvbRaLbRabYHtarXaKV9oS79GYD5mAhrgaNhg/vI4mLM+P7LGPLsOc+0azLNrOCPPJemv1F5npkaNGggODrYausrNzcXOnTvRrl07GSMrguKfujDPoH9AQyIiInIkWUdm7ty5g7/++svyPCkpCYmJiahYsSKqVauG8ePHY+bMmYiIiEBERARmzpwJnU6H/v37yxh1ESQJeVBABRN8zm8D6teVOyIiIiK3IGsxc/DgQXTu3NnyPH+ty5AhQ7Bo0SJMmjQJ2dnZGD16NG7evInWrVtjy5Yt8PHxkSvkop3eABVMAICIfW8B1cOByB4yB0VERFT+yVrMdOrU6YGX/pckCXFxcYiLi3NdULZK3g0BQAIgJAWk5N9ZzBAREblAqV0zU+ZUfwT551hJwgRU7yBrOERERO6CxYyjRPZAhsIPAHC8wRsclSEiInKRUntqdln0o/8oJKdeR6vQnmgodzBERERugsWMA+33i0X8/66gvipA7lCIiIjcBqeZHEijNKczz2SSORIiIiL3wWLGgcINf6Ot4gSUmVflDoWIiMhtsJhxoKfS/oUfNB+g0o1DcodCRETkNljMOJBJcfc+EsZceQMhIiJyIyxmHMik0Jh/yOO9mYiIiFyFxYwDibsjM4IjM0RERC7DYsaBTErzyIzEYoaIiMhlWMw4kMifZmIxQ0RE5DIsZhyJIzNEREQuxysAO1BSYBesveAJb88WiJE7GCIiIjfBYsaBrgR2wH+MFfGENlTuUIiIiNwGp5kcSKOUAAAGI29nQERE5CosZhzIx3AdTaW/4J9zUe5QiIiI3AaLGQeqfWkd1mrfRbeby+QOhYiIyG2wmHEgSWU+m0lhMsgcCRERkftgMeNAktJ8BWCl4KnZRERErsJixoEklRYAR2aIiIhcicWMA0lqczGjZDFDRETkMixmHEhxd82MSrCYISIichUWMw6kuDvNpBR5MkdCRETkPngFYAfKC6iDuXl9kelRBY3lDoaIiMhNcGTGgYwBdTA372n8LHWSOxQiIiK3wWLGgTRKczp5OwMiIiLX4TSTA2mEHhHSReiMSrlDISIichssZhzIM/0vxGsn4bKoCGCk3OEQERG5BU4zOZBSYz6bSY08xJ+8InM0RERE7oHFjAMdvpgJANAgD6OWHGRBQ0RE5AIsZhzoyOUcAIAGBiglCfvOXZc5IiIiovKPxYwDtahRGYC5mOksHUSbmgEyR0RERFT+sZhxoM6a0wAAhQR8o5mDaMUhmSMiIiIq/1jMONLFA5YfhaQEkn+XMRgiIiL3wGLGkWp2BgCYhARJGIHqHWQOiIiIqPzjdWYcqd7jeEP1FurmHEF0j6cQHtlD7oiIiIjKPRYzDpagbYOVdxqjWZW2CJc7GCIiIjfAYsbBqihuQiFdR25OttyhEBERuQUWMw42N/MNVNZeQ8L1mgCqyB0OERFRuccFwA5mkMy3NDDqOTJDRETkCixmHCxPoQEAGHNZzBAREbkCixkHM0p3ixlDjsyREBERuQcWMw6WpzRPM5lyWcwQERG5AosZBzMqzMWMMGTJHAkREZF7YDHjYCZlfjGjlzkSIiIi98BTsx3srwrtse+GNzy01eUOhYiIyC2U6pGZuLg4SJJk9QgODpY7rAc6EdIH7+UNRpKuodyhEBERuYVSPzLToEEDbN261fJcqVTKGM3DaVXm+HIMJpkjISIicg+lvphRqVSlfjTmXl6SHoG4CUWOr9yhEBERuYVSX8ycPXsWoaGh0Gq1aN26NWbOnImaNWsW2V6v10Ov/2fxbUZGBgDAYDDAYDA4LK78vu7vs/X5/+BFj8XYlvo0DIY2Djueuyoqz+RYzLPrMNeuwTy7hjPzXJI+JSGEcHgEDrJx40ZkZWWhTp06uHLlCmbMmIHTp0/jxIkTCAgIKPQ1cXFxmD59eoHty5Ytg06nc3bI8Dy7BjF31mCDqgsMjYY4/XhERETlUVZWFvr374/09HT4+j54tqNUFzP3y8zMRK1atTBp0iRMmDCh0DaFjcyEhYXh2rVrD01GSRgMBsTHxyM6Ohpqtdqy/cTK99H0v59jt1cM2oxf5rDjuaui8kyOxTy7DnPtGsyzazgzzxkZGahUqVKxiplSP810Ly8vLzRq1Ahnz54tso1Wq4VWqy2wXa1WO+ULfX+/Sq159EdpyuUvkAM56/Mja8yz6zDXrsE8u4Yz8lyS/kr1qdn30+v1OHXqFEJCQuQOpUhKtYf5v0ZeNI+IiMgVSnUxM3HiROzcuRNJSUnYv38/nn76aWRkZGDIkNK7FkWhuTsyI3JljoSIiMg9lOppposXL6Jfv364du0aKleujDZt2mDfvn0IDw+XO7QiKTTmkRm1iSMzRERErlCqi5nly5fLHUKJmSrUwPK8TriuDkcTuYMhIiJyA6W6mCmLREgTvJX3AjxzlKhz8gqi6wfJHRIREVG5VqrXzJRFB5NvAACyDUaMWnIQ8SevyBwRERFR+cZixsH+m5qOxxV7MEP1DWKUh7Dv3HW5QyIiIirXOM3kYP0V8ainmQchgIHSNiRqawOoL3dYRERE5RZHZhysnvgbACBJgAlKNDUelzkiIiKi8o3FjKOFdwAACAEoYASqd5A5ICIiovKNxYyj1esFwDwys6rWDCCyh8wBERERlW8sZhxN42X5ccnlajybiYiIyMlYzDiaQgmDZL7R5bUbN3l6NhERkZOxmHEC/d1iRifpoZQknp5NRETkRDw12wkuhMTgeEoasoQWRgi0qRkgd0hERETlFkdmnEDb+3O8kfcS0pSB+M/gKN7SgIiIyIlYzDiBh1pp+ZmFDBERkXOxmHECT5UET+RAMubCaBJyh0NERFSusZhxAt+1g3DKYzieVP6BHINR7nCIiIjKNRYzTqC4e60ZL+Qgm8UMERGRU7GYcQLpbjGjg54jM0RERE7GYsYZ7hYznhKLGSIiImdjMeMMGh0A8zRTjsEkczBERETlG4sZZ1DfHZmBnmtmiIiInIzFjDPcHZnRcZqJiIjI6VjMOEOlOtirbYcjplrIzmUxQ0RE5Ey8N5MzRETjiwAf7E2/jqZ5XDNDRETkTCxmnKRt3n50Ve1FxQvXgSaD5Q6HiIio3GIx4wynN+CVtHeRp1RAdXAjULsSENlD7qiIiIjKJa6ZcYbjqyEAqCQTTFACyb/LHREREVG5xWLGGcLbQQIgBKCAEajeQe6IiIiIyi0WM87QoDcAQJKA1RGzOcVERETkRCxmnMHDz/Lj3x4NZQyEiIio/GMx4wwKJfRKb/OP+nSZgyEiIirfWMw4Sa7aFwCg0N+SNxAiIqJyjsWMk+RqzMXM5dTLiD95ReZoiIiIyi8WM05yxrMZNhpb4myGGqOWHGRBQ0RE5CS8aJ6TfOM5AtsMaQAApSRh37nriK4fJHNURERE5Q9HZpykZXV/y89GIdCmZoCM0RAREZVfLGac5PHGoQAEPKQ8fD2oBUdliIiInITTTE4SfHoR/tJOQZIIQWjebABPyB0SERFRucSRGSdR3/gbKsmEWtL/4LVmEHB6g9whERERlUssZpwl4yIAQCEBQuLNJomIiJyFxYyz1OwMwHyzSUnwZpNERETOwmLGWVoMAWC+2eTnvq8j3tRC5oCIiIjKJxYzzqL2xHVVIABg91VvXjiPiIjISVjMONElZRUAwCuqVYhRHsK+c9dljoiIiKj84anZTuRRsSpw+U+0V5zAo8rjSNTWBlBf7rCIiIjKFY7MOFHtalWRJxRQSgImAKFJK+UOiYiIqNxhMeNEUo1HoZJMAMyJDry0DZNnzsawhQe4foaIiMhBWMw4U2QPnFHWsdr0Ws6XUJ7dxAXBREREDsJixsmyNNY3mAxUZOAbzRzsUL8Kz58GIDF+mUyRERERlQ9lopj597//jRo1asDDwwMtWrTA7t275Q6p2KTmgwGYL553r+rKq+hgSkDTP17G+bhInI+LxIW4COz+v1es2sWfvIL3fjnJURwiIqIilPqzmVasWIHx48fj3//+N9q3b4//+7//Q2xsLE6ePIlq1arJHd5DNY3uj3PXj6Dm6a+KbFMNly0/h11ejFvTVuK6ohLyhBKVjZL5FpUHgBRlNryQhSzokK30wS2FPw7490Rkp+cfeFfu+JNXcP3QWjyWtQmBPlqg+WAgsodD3l/8ySs4vWM5InMSUblRNJpG93dIv0RERMUlCXH/mEHp0rp1azRv3hzz58+3bKtXrx569+6NWbNmPfT1GRkZ8PPzQ3p6Onx9fR0Wl8FgwIYNG9CjRw+o1eqHv+D0BmDXJ8ClQw6LId91kxcMCi0kAAISNMiFQphwS/LFbXjBx5iO6sqrVq9Jhw6SENBLWmRIvsiVNAAAhTBBJ7IhYL56sVbkQAs9cqCFXvKEAWrkSub3K0wmBBivI0iZbr5tgwRcgT/0kicAwCCpkQtzW2/TbfjgDvR3+wGAe794eZIKudDAy3QHOmTCCBVyJE8IACYhoJD+GUTUCD3UyEU2dMhSeMED+gI50Zqy4Ak9MuGJDEUFAIAEAS1yrdp5mLLggRzkwAOZCm/k4Z/PUnO3Xw9TFjyRg2x4IEehAwCYoLBqq0YuPE2Z97TzsuwTkGC421ZnugNv3EEOtFZt7m2rNunhiSxkQweDQlOgTT5PU9Y9efCGGob7soq7MemRDQ9LHszx5gEwWdp4QI9soUGO0hsAYMA/x1UhDxJMlnY50CL7ntjvbettSocO2QXa3N9WZ7oDL9yBHh7IvpvT+6lNuZY86BWeUMBYSA4yoUM2MuCDLIUPAEAJY4G2/8TugdsKPwhIBdp63vNdyI8pD2qrtl6m2wXa5Lu3rbcpAzpkFdoOAHJNCnghB57Igh4e0Cs8rPZLEFDc/SyVplx4IAfZ0CFHoYMSeYXkwRx7JnTIVPje7cME1T1t739/Rqhgujs4f2/bwvJg3VbAx5ReZB6MUMIEJXSmO9AhC7nQFPkZm6CE1pR993P2hEGhLbSdpykLWuiRCS9kKbwhQUAFQ5F5yIIn7ij8YDQZoVIorNre//5MUMII5d29AmoYCs1Bfrz/tAV8TbfuaWf9fTdBghEq6Ex34IksGKAp9HcCMP97pYX+vt/lwvKQCS1ykAlvZCnMv6vm309h1SY/piyFN/LuGbO4t+297bIVXhCQCrTVme4U+f7u/TdNIfJwLjgWj778r0LjtlVJ/n6X6mImNzcXOp0OK1euRJ8+fSzbX331VSQmJmLnzp0FXqPX66HX//PHLSMjA2FhYbh27ZrDi5n4+HhER0cXr5i5S/rvRlz/fQEMV86gqumSw+IhIiKS056QIWg5fI7D+svIyEClSpWKVcyU6mmma9euwWg0IijIegolKCgIqamphb5m1qxZmD59eoHtW7ZsgU5X+P8d2CM+Pr7kLwoeBgQDf1z8E+HXd8LXdAsBpusIUqQ7PD4iIiJnEwKoeHknNmzY4LA+s7Kyit22VBcz+SRJsnouhCiwLd/kyZMxYcIEy/P8kZmYmJhSMTJjrQeAKZZnh35bDsWRpdDl/nPbA5VSgqdaiWyDEercDMuwu0IhcWSHiIhKBUkCboR0RI8ejlmPCZj/fhdXqS5mKlWqBKVSWWAUJi0trcBoTT6tVguttuC8q1qttqPoKJoj+23RfRDQfVDxX3B6A9J2/Qe3r/0PecZ750xvw1OYi55spQ9USgVMXoHYpO6KjGwDuuZsQUjeeas2Rbm/r/uplIq7xVaeVQwl7edB7YzGPCiVqge2ceTx5O5LruNlCQ/kqPycfjxnxF7Wjmc05sEb2Zb/OSlLsZfW4xXW7v5/O0pr7KUhVza3Md6GJIy4EBqLdi9+UeTxbFGSv62lupjRaDRo0aIF4uPjrdbMxMfH48knn5QxslIisgcCI3sgsJjNIyw/TXROPE5Q4oXWZBODwYA9zLNL8DvtGsyza9ybZzmV6mIGACZMmIBBgwYhKioKbdu2xddff43z58/jpZdekjs0IiIiKgVKfTHz3HPP4fr163jvvfdw+fJlNGzYEBs2bEB4eLjcoREREVEpUOqLGQAYPXo0Ro8eLXcYREREVAqVidsZEBERERWFxQwRERGVaSxmiIiIqExjMUNERERlGosZIiIiKtNYzBAREVGZxmKGiIiIyjQWM0RERFSmsZghIiKiMq1MXAHYHkKY7+RckluJF4fBYEBWVhYyMjJ4EzMnYp5dg3l2HebaNZhn13BmnvP/buf/HX+Qcl/M3L59GwAQFhYmcyRERERUUrdv34afn98D20iiOCVPGWYymXDp0iX4+PhAkiSH9ZuRkYGwsDBcuHABvr6+DuuXrDHPrsE8uw5z7RrMs2s4M89CCNy+fRuhoaFQKB68Kqbcj8woFApUrVrVaf37+vryF8UFmGfXYJ5dh7l2DebZNZyV54eNyOTjAmAiIiIq01jMEBERUZnGYsZGWq0W06ZNg1arlTuUco15dg3m2XWYa9dgnl2jtOS53C8AJiIiovKNIzNERERUprGYISIiojKNxQwRERGVaSxmiIiIqExjMWODf//736hRowY8PDzQokUL7N69W+6QypRdu3ahV69eCA0NhSRJWLt2rdV+IQTi4uIQGhoKT09PdOrUCSdOnLBqo9frMW7cOFSqVAleXl544okncPHiRRe+i9Jv1qxZaNmyJXx8fBAYGIjevXvjzJkzVm2Ya8eYP38+GjdubLlwWNu2bbFx40bLfubZ8WbNmgVJkjB+/HjLNubZMeLi4iBJktUjODjYsr9U5llQiSxfvlyo1Wrxn//8R5w8eVK8+uqrwsvLS6SkpMgdWpmxYcMGMWXKFLFq1SoBQKxZs8Zq/+zZs4WPj49YtWqVOHbsmHjuuedESEiIyMjIsLR56aWXRJUqVUR8fLw4fPiw6Ny5s2jSpInIy8tz8bspvbp16yYWLlwojh8/LhITE0XPnj1FtWrVxJ07dyxtmGvHWLdunVi/fr04c+aMOHPmjHj77beFWq0Wx48fF0Iwz4524MABUb16ddG4cWPx6quvWrYzz44xbdo00aBBA3H58mXLIy0tzbK/NOaZxUwJtWrVSrz00ktW2yIjI8Vbb70lU0Rl2/3FjMlkEsHBwWL27NmWbTk5OcLPz0989dVXQgghbt26JdRqtVi+fLmlzf/+9z+hUCjEpk2bXBZ7WZOWliYAiJ07dwohmGtn8/f3F9988w3z7GC3b98WERERIj4+XnTs2NFSzDDPjjNt2jTRpEmTQveV1jxzmqkEcnNzcejQIcTExFhtj4mJwZ49e2SKqnxJSkpCamqqVY61Wi06duxoyfGhQ4dgMBis2oSGhqJhw4b8HB4gPT0dAFCxYkUAzLWzGI1GLF++HJmZmWjbti3z7GBjxoxBz5490bVrV6vtzLNjnT17FqGhoahRowaef/55nDt3DkDpzXO5v9GkI127dg1GoxFBQUFW24OCgpCamipTVOVLfh4Ly3FKSoqljUajgb+/f4E2/BwKJ4TAhAkT0KFDBzRs2BAAc+1ox44dQ9u2bZGTkwNvb2+sWbMG9evXt/zjzTzbb/ny5Th8+DASEhIK7OP32XFat26NJUuWoE6dOrhy5QpmzJiBdu3a4cSJE6U2zyxmbCBJktVzIUSBbWQfW3LMz6FoY8eOxdGjR/H7778X2MdcO0bdunWRmJiIW7duYdWqVRgyZAh27txp2c882+fChQt49dVXsWXLFnh4eBTZjnm2X2xsrOXnRo0aoW3btqhVqxYWL16MNm3aACh9eeY0UwlUqlQJSqWyQGWZlpZWoEol2+SvmH9QjoODg5Gbm4ubN28W2Yb+MW7cOKxbtw7bt29H1apVLduZa8fSaDSoXbs2oqKiMGvWLDRp0gSff/458+wghw4dQlpaGlq0aAGVSgWVSoWdO3fiiy++gEqlsuSJeXY8Ly8vNGrUCGfPni2132cWMyWg0WjQokULxMfHW22Pj49Hu3btZIqqfKlRowaCg4Otcpybm4udO3dactyiRQuo1WqrNpcvX8bx48f5OdxDCIGxY8di9erV2LZtG2rUqGG1n7l2LiEE9Ho98+wgXbp0wbFjx5CYmGh5REVFYcCAAUhMTETNmjWZZyfR6/U4deoUQkJCSu/32SnLisux/FOzv/32W3Hy5Ekxfvx44eXlJZKTk+UOrcy4ffu2+PPPP8Wff/4pAIhPP/1U/Pnnn5bT22fPni38/PzE6tWrxbFjx0S/fv0KPe2vatWqYuvWreLw4cPiscce4+mV93n55ZeFn5+f2LFjh9UplllZWZY2zLVjTJ48WezatUskJSWJo0ePirffflsoFAqxZcsWIQTz7Cz3ns0kBPPsKK+//rrYsWOHOHfunNi3b594/PHHhY+Pj+XvXGnMM4sZG3z55ZciPDxcaDQa0bx5c8uprlQ827dvFwAKPIYMGSKEMJ/6N23aNBEcHCy0Wq149NFHxbFjx6z6yM7OFmPHjhUVK1YUnp6e4vHHHxfnz5+X4d2UXoXlGIBYuHChpQ1z7RjDhw+3/JtQuXJl0aVLF0shIwTz7Cz3FzPMs2PkXzdGrVaL0NBQ0bdvX3HixAnL/tKYZ0kIIZwz5kNERETkfFwzQ0RERGUaixkiIiIq01jMEBERUZnGYoaIiIjKNBYzREREVKaxmCEiIqIyjcUMERERlWksZojILUiShLVr18odBhE5AYsZInK6oUOHQpKkAo/u3bvLHRoRlQMquQMgIvfQvXt3LFy40GqbVquVKRoiKk84MkNELqHVahEcHGz18Pf3B2CeApo/fz5iY2Ph6emJGjVqYOXKlVavP3bsGB577DF4enoiICAAL7zwAu7cuWPVZsGCBWjQoAG0Wi1CQkIwduxYq/3Xrl1Dnz59oNPpEBERgXXr1ln23bx5EwMGDEDlypXh6emJiIiIAsUXEZVOLGaIqFSYOnUqnnrqKRw5cgQDBw5Ev379cOrUKQBAVlYWunfvDn9/fyQkJGDlypXYunWrVbEyf/58jBkzBi+88AKOHTuGdevWoXbt2lbHmD59Op599lkcPXoUPXr0wIABA3Djxg3L8U+ePImNGzfi1KlTmD9/PipVquS6BBCR7Zx2C0sioruGDBkilEql8PLysnq89957QgjzHb5feuklq9e0bt1avPzyy0IIIb7++mvh7+8v7ty5Y9m/fv16oVAoRGpqqhBCiNDQUDFlypQiYwAg3nnnHcvzO3fuCEmSxMaNG4UQQvTq1UsMGzbMMW+YiFyKa2aIyCU6d+6M+fPnW22rWLGi5ee2bdta7Wvbti0SExMBAKdOnUKTJk3g5eVl2d++fXuYTCacOXMGkiTh0qVL6NKlywNjaNy4seVnLy8v+Pj4IC0tDQDw8ssv46mnnsLhw4cRExOD3r17o127dja9VyJyLRYzROQSXl5eBaZ9HkaSJACAEMLyc2FtPD09i9WfWq0u8FqTyQQAiI2NRUpKCtavX4+tW7eiS5cuGDNmDD755JMSxUxErsc1M0RUKuzbt6/A88jISABA/fr1kZiYiMzMTMv+P/74AwqFAnXq1IGPjw+qV6+O3377za4YKleujKFDh+L777/H3Llz8fXXX9vVHxG5BkdmiMgl9Ho9UlNTrbapVCrLItuVK1ciKioKHTp0wNKlS3HgwAF8++23AIABAwZg2rRpGDJkCOLi4nD16lWMGzcOgwYNQlBQEAAgLi4OL730EgIDAxEbG4vbt2/jjz/+wLhx44oV37vvvosWLVqgQYMG0Ov1+PXXX1GvXj0HZoCInIXFDBG5xKZNmxASEmK1rW7dujh9+jQA85lGy5cvx+jRoxEcHIylS5eifv36AACdTofNmzfj1VdfRcuWLaHT6fDUU0/h008/tfQ1ZMgQ5OTk4LPPPsPEiRNRqVIlPP3008WOT6PRYPLkyUhOToanpyceeeQRLF++3AHvnIicTRJCCLmDICL3JkkS1qxZg969e8sdChGVQVwzQ0RERGUaixkiIiIq07hmhohkx9luIrIHR2aIiIioTGMxQ0RERGUaixkiIiIq01jMEBERUZnGYoaIiIjKNBYzREREVKaxmCEiIqIyjcUMERERlWksZoiIiKhM+3+w2kxBtjWBxwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a list for epochs\n",
    "epochs_plot = list(range(1, num_epochs + 1))\n",
    "\n",
    "# Plot training losses\n",
    "plt.plot(epochs_plot, loss_values, marker='o', markersize=2, label='Training Loss')\n",
    "\n",
    "# Plot validation losses\n",
    "plt.plot(epochs_plot, val_loss_values, marker='o', markersize=2, label='Validation Loss', linestyle='--')\n",
    "\n",
    "# Add labels, title, and legend\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('Training and Validation Loss vs. Epochs')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42368f27-b648-4dab-9f0c-22406784538b",
   "metadata": {},
   "source": [
    "# Testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe164336-6a01-4c13-b79d-61f1ebac4736",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Flatten the input tensor to shape [num_samples, 3]\n",
    "num_samples = testing_NN_inputs.shape[0] * testing_NN_inputs.shape[1] * testing_NN_inputs.shape[2]\n",
    "testing_NN_inputs_flat = testing_NN_inputs.view(num_samples, 3)\n",
    "\n",
    "# Create the test dataset and loader\n",
    "test_dataset = TensorDataset(testing_NN_inputs_flat)  # Flattened tensor\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)  # Set shuffle=False for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e2070eb-4375-4f45-9b8c-2634a3260908",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(model, test_loader, criterion):\n",
    "    model.eval()  # Set the model to evaluation mode\n",
    "    total_loss = 0.0\n",
    "    all_predictions = []\n",
    "    all_targets = []\n",
    "\n",
    "    with torch.no_grad():  # Disable gradient calculation for evaluation\n",
    "        for inputs, targets in test_loader:\n",
    "            # Ensure inputs and targets are float tensors\n",
    "            inputs = inputs.float()  # Shape: [batch_size, 7]\n",
    "            targets = targets.float()  # Shape: [batch_size]\n",
    "\n",
    "            # Forward pass\n",
    "            outputs = model(inputs)  # Shape: [batch_size, 1]\n",
    "\n",
    "            # Calculate loss\n",
    "            loss = criterion(outputs.squeeze(), targets)  # Match output and target shapes\n",
    "\n",
    "            # Accumulate loss\n",
    "            total_loss += loss.item()\n",
    "\n",
    "            # Store predictions and targets (ensure they are 1-dimensional)\n",
    "            all_predictions.append(outputs.squeeze(dim=-1))  # Ensure outputs are 1D\n",
    "            all_targets.append(targets)  # Already 1D for ANN\n",
    "\n",
    "    # Calculate average loss\n",
    "    average_loss = total_loss / len(test_loader)\n",
    "\n",
    "    # Concatenate lists into tensors\n",
    "    all_predictions = torch.cat(all_predictions, dim=0)  # Shape: [num_test_samples]\n",
    "    all_targets = torch.cat(all_targets, dim=0)  # Shape: [num_test_samples]\n",
    "\n",
    "    return average_loss, all_predictions, all_targets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "367ed619-402e-4fbd-ae6a-36288a2e29ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your criterion (loss function)\n",
    "criterion = nn.MSELoss()  # or whatever loss function you're using\n",
    "\n",
    "# Evaluate the model on the test dataset\n",
    "test_loss, predictions, targets = evaluate_model(model, test_loader, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fe87c25a-51ac-4a45-863a-b38c85024194",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions_vs_targets(predictions, targets, num_samples=9, marker_size=20, legend_marker_size=10):\n",
    "    # Ensure we're only plotting a limited number of samples\n",
    "    num_samples = min(num_samples, predictions.shape[0])\n",
    "\n",
    "    # Create a figure with a single subplot\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Scatter plot predictions and targets with the specified marker sizes\n",
    "    plt.scatter(range(num_samples), predictions[:num_samples].numpy(), label='Predictions', color='blue', marker='o', s=marker_size)\n",
    "    plt.scatter(range(num_samples), targets[:num_samples].numpy(), label='Targets', color='red', marker='x', s=marker_size)\n",
    "\n",
    "    # Create custom legend markers with no lines (only points)\n",
    "    legend_handles = [\n",
    "        Line2D([0], [0], marker='o', color='blue', markerfacecolor='blue', markersize=legend_marker_size, label='Predictions', linestyle=''),\n",
    "        Line2D([0], [0], marker='o', color='red', markerfacecolor='red', markersize=legend_marker_size, label='Targets', linestyle='')\n",
    "    ]\n",
    "\n",
    "    # Add labels, title, and legend with custom markers\n",
    "    plt.xlabel('Sample Index')\n",
    "    plt.ylabel('Value')\n",
    "    plt.title('Predictions vs Targets')\n",
    "\n",
    "    # Add the legend with custom handles (markers only)\n",
    "    plt.legend(handles=legend_handles)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Assuming predictions and targets are your outputs from the evaluation function\n",
    "plot_predictions_vs_targets(predictions, targets, num_samples=num_samples, marker_size=0.0001, legend_marker_size=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "61254f44-0a35-482e-8c27-5c1d2a0e4f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Test Loss: 0.0005, % of Accurate Predictions (within 0.001 margin): 99.76%\n"
     ]
    }
   ],
   "source": [
    "# Define a threshold for acceptable error margin (for regression)\n",
    "threshold = 0.001  # You can change this based on your needs\n",
    "\n",
    "# Function to evaluate the model on test data\n",
    "def evaluate_model_on_test_data(model, test_loader, criterion, threshold=0.1):\n",
    "    model.eval()  # Set model to evaluation mode\n",
    "    total_loss = 0.0\n",
    "    correct_predictions_count = 0  # To count predictions within the threshold\n",
    "\n",
    "    with torch.no_grad():  # Disable gradient calculation for evaluation\n",
    "        for inputs, targets in test_loader:\n",
    "            inputs = inputs.view(inputs.size(0), -1).float()  # Flatten inputs to shape [batch_size, 48900]\n",
    "            targets = targets.float()  # Shape: [batch_size]\n",
    "\n",
    "            # Forward pass: Get model predictions\n",
    "            outputs = model(inputs)  # Shape: [batch_size, 1]\n",
    "\n",
    "            # Compute loss (using MSELoss)\n",
    "            loss = criterion(outputs.squeeze(), targets)  # Squeeze outputs to remove extra dimensions\n",
    "            total_loss += loss.item()\n",
    "\n",
    "            # Calculate \"accuracy\" based on the threshold\n",
    "            correct_predictions = torch.abs(outputs.squeeze() - targets) <= threshold\n",
    "            correct_predictions_count += correct_predictions.sum().item()\n",
    "\n",
    "    # Calculate average loss for the test set\n",
    "    average_loss = total_loss / len(test_loader)\n",
    "\n",
    "    # Calculate accuracy as the ratio of correct predictions\n",
    "    accuracy = correct_predictions_count / len(test_loader.dataset)\n",
    "\n",
    "    return average_loss, accuracy\n",
    "\n",
    "# After training, evaluate on the test set\n",
    "test_loss, test_accuracy = evaluate_model_on_test_data(model, test_loader, criterion, threshold=0.1)\n",
    "\n",
    "# Print the test results\n",
    "print(f\"Average Test Loss: {test_loss:.4f}, % of Accurate Predictions (within {threshold} margin): {test_accuracy * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e711f00-525c-4b31-91a2-fc3d45398422",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define function to calculate RMSE and plot it\n",
    "def calculate_and_plot_rmse(model, test_loader):\n",
    "    model.eval()  # Set the model to evaluation mode\n",
    "    rmse_values = []  # To store RMSE values for each sample\n",
    "\n",
    "    with torch.no_grad():  # Disable gradient calculation for evaluation\n",
    "        for inputs, targets in test_loader:\n",
    "            inputs = inputs.view(inputs.size(0), -1).float()  # Flatten inputs if needed\n",
    "            targets = targets.float()  # Make sure targets are in float\n",
    "\n",
    "            # Forward pass: Get model predictions\n",
    "            outputs = model(inputs)  # Shape: [batch_size, 1]\n",
    "\n",
    "            # Calculate squared differences between predictions and targets\n",
    "            squared_diff = (outputs.squeeze() - targets) ** 2  # Squared differences\n",
    "\n",
    "            # Calculate RMSE for the current batch\n",
    "            rmse = torch.sqrt(squared_diff.mean())  # Square root of the mean squared differences\n",
    "            rmse_values.append(rmse.item())  # Convert to Python scalar and store it\n",
    "\n",
    "    # Plot RMSE values\n",
    "    epochs_plot = list(range(1, len(rmse_values) + 1))\n",
    "    plt.plot(epochs_plot, rmse_values, marker='o', markersize=0.001, label='RMSE')\n",
    "\n",
    "    # Add labels and title\n",
    "    plt.xlabel('Sample')\n",
    "    plt.ylabel('RMSE')\n",
    "    plt.title('RMSE for Each Test Sample')\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "    # Return the RMSE values for further analysis if needed\n",
    "    return rmse_values\n",
    "\n",
    "# Assuming your model and test_loader are already defined, you can now call the function\n",
    "rmse_values = calculate_and_plot_rmse(model, test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07218c58-e80a-414f-a0fd-22d968b1e012",
   "metadata": {},
   "source": [
    "## Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f9264e-3ee6-4bb0-919d-5bd9df0edd1a",
   "metadata": {},
   "source": [
    "I switched this to an ANN and it worked way better. The ANN only has linear layers. The former CNN was good for non-linear cases, but was unneccesarily complex for this linear relation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33c0a5fd-c58f-4f15-a1e0-680419b24d2f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}