3a.cnn-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# NCI WeatherBench-3a: Train a CNN\n",
    "\n",
    "In this notebook we will go through all the steps required to train a fully convolutional neural network. Because this takes a while and uses a lot of GPU RAM a separate command line script (`train_nn.py`) is also provided in the `src` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-26 21:03:38.591466: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "os.environ[\"TF_ENABLE_ONEDNN_OPTS\"] = \"0\"\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras.layers import *\n",
    "import tensorflow.keras.backend as K\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from score import *\n",
    "from collections import OrderedDict\n",
    "from dask.diagnostics import ProgressBar\n",
    "from datetime import datetime\n",
    "from dask.distributed import Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:03:40]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.transition-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.scheduler.events-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/mlenv/lib/python3.9/site-packages/dask/config.py:742: FutureWarning: Dask configuration key 'distributed.comm.recent-messages-log-length' has been deprecated; please use 'distributed.admin.low-level-log-length' instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-1f42da30-eb58-11ee-a12c-0000032ffe80</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
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       "\n",
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       "\n",
       "        \n",
       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/proxy/8787/status\" }'>\n",
       "                Launch dashboard in JupyterLab\n",
       "            </button>\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">640e233f</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 12\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 12\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 191.00 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-54ae255c-bbce-47b2-97a0-cb686c8509ad</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:38091\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 12\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"/proxy/8787/status\" target=\"_blank\">/proxy/8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 12\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 191.00 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:37387\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/41997/status\" target=\"_blank\">/proxy/41997/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43765\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-l52wgpvc\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:44993\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/40587/status\" target=\"_blank\">/proxy/40587/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43241\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-idhq4dbd\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:36239\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/43063/status\" target=\"_blank\">/proxy/43063/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:33753\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-e_w_omch\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:44757\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/37885/status\" target=\"_blank\">/proxy/37885/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41907\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-ov5ertpq\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:34625\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/45959/status\" target=\"_blank\">/proxy/45959/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:44897\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-mjfz8sf2\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:44421\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/35707/status\" target=\"_blank\">/proxy/35707/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43291\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-zbhjvqzf\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:39139\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/46865/status\" target=\"_blank\">/proxy/46865/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:41021\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-7p3kz_7r\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:33943\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/36163/status\" target=\"_blank\">/proxy/36163/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:40751\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-s5jcc4r9\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 8</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:34781\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/38559/status\" target=\"_blank\">/proxy/38559/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37107\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-g5ak5oud\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 9</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:40589\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/37993/status\" target=\"_blank\">/proxy/37993/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:37059\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-jm65042u\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 10</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:36289\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/39537/status\" target=\"_blank\">/proxy/39537/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:35907\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-4i5ouyph\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 11</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:36407\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 1\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"/proxy/34767/status\" target=\"_blank\">/proxy/34767/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 15.92 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:43199\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> /jobfs/111789589.gadi-pbs/dask-scratch-space/worker-_w803zlt\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU: </strong>Tesla V100-SXM2-32GB\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>GPU memory: </strong> 32.00 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:38091' processes=12 threads=12, memory=191.00 GiB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "client = Client(n_workers=12, threads_per_worker=1)  \n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.13.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-26 21:03:42.937450: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 31141 MB memory:  -> device: 0, name: Tesla V100-SXM2-32GB, pci bus id: 0000:3e:00.0, compute capability: 7.0\n"
     ]
    }
   ],
   "source": [
    "def limit_mem():\n",
    "    \"\"\"By default TF uses all available GPU memory. This function prevents this.\"\"\"\n",
    "    config = tf.compat.v1.ConfigProto()\n",
    "    config.gpu_options.allow_growth = True\n",
    "    tf.compat.v1.Session(config=config)\n",
    "    \n",
    "limit_mem()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "sns.set_style('darkgrid')\n",
    "sns.set_context('notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:03:43]\n",
      "res: 5.625\n",
      "DATADIR: /g/data/wb00/NCI-Weatherbench/5.625deg\n",
      "PREDDIR: /scratch/vp91/mah900/NCI-Weatherbench/pred_dir\n"
     ]
    }
   ],
   "source": [
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "res = '5.625'\n",
    "print ('res:', res)\n",
    "DATADIR = f'/g/data/wb00/NCI-Weatherbench/{res}deg' \n",
    "print('DATADIR:', DATADIR)\n",
    "PREDDIR = f\"/scratch/vp91/{os.environ['USER']}/NCI-Weatherbench/pred_dir\"\n",
    "print('PREDDIR:', PREDDIR)\n",
    "! mkdir -p {PREDDIR} \n",
    "! mkdir -p {PREDDIR+\"/saved_models\"} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Create data generator\n",
    "\n",
    "First up, we want to write our own Keras data generator. The key advantage to just feeding in numpy arrays is that we don't have to load the data twice because our intputs and outputs are the same data just offset by the lead time. Since the dataset is quite large and we might run out of CPU RAM this is important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:03:43]\n",
      "[2021, 2022]\n",
      "load_test_data, var: z\n",
      "load_test_data, var: t\n",
      "CPU times: user 6.27 s, sys: 755 ms, total: 7.02 s\n",
      "Wall time: 8.25 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "valid_years = list(range( 2021, 2022+1))\n",
    "print (valid_years)\n",
    "z500_valid_files = [ file for year in valid_years for file in glob.glob (fr'{DATADIR}/geopotential/*{year}*') ] \n",
    "t850_valid_files = [ file for year in valid_years for file in glob.glob (fr'{DATADIR}/temperature/*{year}*')  ] \n",
    "\n",
    "z500_valid = load_test_data(z500_valid_files, 'z', slice('2021', '2022')).load()  \n",
    "t850_valid = load_test_data(t850_valid_files, 't', slice('2021', '2022')).load() \n",
    "valid = xr.merge([z500_valid, t850_valid]).compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:03:51]\n",
      "[1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022]\n",
      "CPU times: user 50 s, sys: 4.83 s, total: 54.8 s\n",
      "Wall time: 56.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "years = list(range(1999, 2022+1))\n",
    "print (years)\n",
    "z_files = [ file for year in years for file in glob.glob (fr'{DATADIR}/geopotential/*{year}*')  ] \n",
    "t_files = [ file for year in years for file in glob.glob (fr'{DATADIR}/temperature/*{year}*')    ] \n",
    "\n",
    "z = xr.open_mfdataset(z_files, combine='by_coords', parallel=True, chunks={'time': 10}).z.sel(level=[500]).load()    \n",
    "t = xr.open_mfdataset(t_files, combine='by_coords', parallel=True, chunks={'time': 10}).t.sel(level=[850]).drop('level').load()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:04:48]\n",
      "CPU times: user 4.06 ms, sys: 677 µs, total: 4.74 ms\n",
      "Wall time: 3.63 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "datasets = [z, t]\n",
    "ds = xr.merge(datasets).compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:04:48]\n",
      "CPU times: user 11.6 ms, sys: 589 µs, total: 12.2 ms\n",
      "Wall time: 9.37 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "ds_train = ds.sel(time=slice( '1999', '2020' ))   \n",
    "ds_test  = ds.sel(time=slice( '2021', '2022' ))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "class DataGenerator(keras.utils.Sequence):\n",
    "    def __init__(self, ds, var_dict, lead_time, batch_size=32, shuffle=True, load=True, mean=None, std=None):\n",
    "        \"\"\"\n",
    "        Data generator for WeatherBench data.\n",
    "        Args:\n",
    "            ds: Dataset containing all variables\n",
    "            var_dict: Dictionary of the form {'var': level}. Use None for level if data is of single level\n",
    "            lead_time: Lead time in hours\n",
    "            batch_size: Batch size\n",
    "            shuffle: bool. If True, data is shuffled.\n",
    "            load: bool. If True, datadet is loaded into RAM.\n",
    "            mean: If None, compute mean from data.\n",
    "            std: If None, compute standard deviation from data.\n",
    "        \"\"\"\n",
    "        self.ds = ds\n",
    "        self.var_dict = var_dict\n",
    "        self.batch_size = batch_size\n",
    "        self.shuffle = shuffle\n",
    "        self.lead_time = lead_time\n",
    "       \n",
    "        data = []\n",
    "        generic_level = xr.DataArray([1], coords={'level': [1]}, dims=['level'])\n",
    "        for var, levels in var_dict.items():\n",
    "            print (f\"({var}, {levels})\")\n",
    "            try:\n",
    "                data.append(ds[var].sel(level=levels))               \n",
    "            except ValueError:\n",
    "                data.append(ds[var].expand_dims({'level': generic_level}, 1))\n",
    "            #except KeyError:  \n",
    "            #    data.append(ds[var].expand_dims({'level': generic_level}, 1))\n",
    "\n",
    "        self.data = xr.concat(data, 'level').transpose('time', 'lat', 'lon', 'level')\n",
    "        self.mean = self.data.mean(('time', 'lat', 'lon')).compute() if mean is None else mean\n",
    "        self.std = self.data.std('time').mean(('lat', 'lon')).compute() if std is None else std\n",
    "        # Normalize\n",
    "        self.data = (self.data - self.mean) / self.std\n",
    "        self.n_samples = self.data.isel(time=slice(0, -lead_time)).shape[0]\n",
    "        self.init_time = self.data.isel(time=slice(None, -lead_time)).time\n",
    "        self.valid_time = self.data.isel(time=slice(lead_time, None)).time\n",
    "\n",
    "        self.on_epoch_end()\n",
    "\n",
    "        if load: print('Loading data into RAM'); self.data.load()\n",
    "\n",
    "    def __len__(self):\n",
    "        'Denotes the number of batches per epoch'\n",
    "        return int(np.ceil(self.n_samples / self.batch_size))\n",
    "\n",
    "    def __getitem__(self, i):\n",
    "        'Generate one batch of data'\n",
    "        idxs = self.idxs[i * self.batch_size:(i + 1) * self.batch_size]\n",
    "        X = self.data.isel(time=idxs).values\n",
    "        y = self.data.isel(time=idxs + self.lead_time).values\n",
    "        return X, y\n",
    "\n",
    "    def on_epoch_end(self):\n",
    "        'Updates indexes after each epoch'\n",
    "        self.idxs = np.arange(self.n_samples)\n",
    "        if self.shuffle == True:\n",
    "            np.random.shuffle(self.idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "dic = OrderedDict({'z': 500, 't': 500})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "# Batch zise and lead time\n",
    "bs=32\n",
    "lead_time=6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (level: 1, time: 192864, lat: 32, lon: 64)\n",
       "Coordinates:\n",
       "  * level    (level) int32 500\n",
       "  * time     (time) datetime64[ns] 1999-01-01 ... 2020-12-31T23:00:00\n",
       "  * lat      (lat) float64 -87.19 -81.56 -75.94 -70.31 ... 75.94 81.56 87.19\n",
       "  * lon      (lon) float64 0.0 5.625 11.25 16.88 ... 337.5 343.1 348.8 354.4\n",
       "Data variables:\n",
       "    z        (time, level, lat, lon) float32 5.016e+04 5.017e+04 ... 5.151e+04\n",
       "    t        (time, level, lat, lon) float32 254.1 254.5 254.7 ... 261.8 261.4</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-e0c177ec-18d3-413c-bdcb-d8fb1b642a78' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e0c177ec-18d3-413c-bdcb-d8fb1b642a78' 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'>level</span>: 1</li><li><span class='xr-has-index'>time</span>: 192864</li><li><span class='xr-has-index'>lat</span>: 32</li><li><span class='xr-has-index'>lon</span>: 64</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-44ff8c0f-5c20-4ec7-8c08-7231289eebe5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44ff8c0f-5c20-4ec7-8c08-7231289eebe5' class='xr-section-summary' >Coordinates: <span>(4)</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'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>500</div><input id='attrs-2d93dac8-a81d-4e29-b193-1fd8962d9018' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d93dac8-a81d-4e29-b193-1fd8962d9018' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75f277af-cedd-4685-a2bc-4e04df40c074' class='xr-var-data-in' type='checkbox'><label for='data-75f277af-cedd-4685-a2bc-4e04df40c074' 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([500], dtype=int32)</pre></div></li><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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1999-01-01 ... 2020-12-31T23:00:00</div><input id='attrs-1cabb2fd-f73c-476b-b15d-d5c445c193d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cabb2fd-f73c-476b-b15d-d5c445c193d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a222773-b76b-4905-aed1-9b1c387f3abd' class='xr-var-data-in' type='checkbox'><label for='data-8a222773-b76b-4905-aed1-9b1c387f3abd' 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([&#x27;1999-01-01T00:00:00.000000000&#x27;, &#x27;1999-01-01T01:00:00.000000000&#x27;,\n",
       "       &#x27;1999-01-01T02:00:00.000000000&#x27;, ..., &#x27;2020-12-31T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T22:00:00.000000000&#x27;, &#x27;2020-12-31T23:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.19 -81.56 ... 81.56 87.19</div><input id='attrs-9a7eec0a-38f9-4af9-9546-a96131f0223c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a7eec0a-38f9-4af9-9546-a96131f0223c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7804c1cf-b32b-4a6e-b742-c42e3b39ded0' class='xr-var-data-in' type='checkbox'><label for='data-7804c1cf-b32b-4a6e-b742-c42e3b39ded0' 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([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
       "       -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,\n",
       "        -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,\n",
       "        30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,\n",
       "        70.3125,  75.9375,  81.5625,  87.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.625 11.25 ... 348.8 354.4</div><input id='attrs-cb51b6c6-944e-406e-b180-a18e98139171' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb51b6c6-944e-406e-b180-a18e98139171' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3a8b80d-82a5-42bd-ac82-88370fe32001' class='xr-var-data-in' type='checkbox'><label for='data-f3a8b80d-82a5-42bd-ac82-88370fe32001' 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([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
       "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
       "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
       "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
       "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
       "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
       "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1561b075-5c70-4eec-b5f1-a21d171a8741' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1561b075-5c70-4eec-b5f1-a21d171a8741' class='xr-section-summary' >Data variables: <span>(2)</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>z</span></div><div class='xr-var-dims'>(time, level, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.016e+04 5.017e+04 ... 5.151e+04</div><input id='attrs-680add56-27cc-4fe2-9901-73222fd792e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-680add56-27cc-4fe2-9901-73222fd792e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eae43aa-b6e3-4e5a-a9a2-8c60b42f7e1f' class='xr-var-data-in' type='checkbox'><label for='data-5eae43aa-b6e3-4e5a-a9a2-8c60b42f7e1f' 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([[[[50163.348, 50174.004, 50182.16 , ..., 50121.94 , 50137.348,\n",
       "          50151.004],\n",
       "         [50514.848, 50540.598, 50560.41 , ..., 50383.16 , 50437.098,\n",
       "          50483.38 ],\n",
       "         [50851.535, 50945.754, 51019.035, ..., 50663.38 , 50668.098,\n",
       "          50744.88 ],\n",
       "         ...,\n",
       "         [50995.973, 50823.633, 50679.035, ..., 51122.734, 51129.535,\n",
       "          51078.984],\n",
       "         [49666.723, 49735.883, 49778.723, ..., 49564.32 , 49577.848,\n",
       "          49603.16 ],\n",
       "         [48736.973, 48751.316, 48766.535, ..., 48692.754, 48707.785,\n",
       "          48722.848]]],\n",
       "\n",
       "\n",
       "       [[[50170.934, 50180.934, 50188.31 , ..., 50130.277, 50145.496,\n",
       "          50158.934],\n",
       "         [50507.62 , 50539.59 , 50563.31 , ..., 50365.496, 50420.996,\n",
       "          50469.59 ],\n",
       "         [50843.934, 50937.777, 51013.31 , ..., 50650.34 , 50658.934,\n",
       "...\n",
       "          52268.28 ],\n",
       "         [51856.625, 52038.066, 52256.062, ..., 51139.94 , 51260.812,\n",
       "          51557.754],\n",
       "         [51536.75 , 51601.344, 51663.188, ..., 51330.906, 51399.938,\n",
       "          51469.188]]],\n",
       "\n",
       "\n",
       "       [[[48644.336, 48643.9  , 48645.086, ..., 48654.96 , 48650.46 ,\n",
       "          48646.9  ],\n",
       "         [48525.4  , 48459.65 , 48365.773, ..., 48598.242, 48576.21 ,\n",
       "          48549.492],\n",
       "         [49026.273, 48958.965, 48910.15 , ..., 48984.31 , 49048.9  ,\n",
       "          49064.87 ],\n",
       "         ...,\n",
       "         [52519.21 , 52745.742, 52923.15 , ..., 52460.934, 52265.9  ,\n",
       "          52299.867],\n",
       "         [51851.836, 52052.31 , 52271.086, ..., 51225.562, 51297.21 ,\n",
       "          51535.434],\n",
       "         [51579.023, 51649.96 , 51718.273, ..., 51355.805, 51430.523,\n",
       "          51505.336]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(time, level, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>254.1 254.5 254.7 ... 261.8 261.4</div><input id='attrs-0d9983b8-1500-4670-a686-259c9d2e1382' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d9983b8-1500-4670-a686-259c9d2e1382' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91503962-c011-43cd-8dae-a1557bfd86c7' class='xr-var-data-in' type='checkbox'><label for='data-91503962-c011-43cd-8dae-a1557bfd86c7' 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([[[[254.07285, 254.51042, 254.65247, ..., 254.2952 , 254.0958 ,\n",
       "          253.933  ],\n",
       "         [258.65573, 259.50037, 258.948  , ..., 258.28427, 258.45306,\n",
       "          258.22748],\n",
       "         [263.36703, 262.35583, 262.20615, ..., 264.5411 , 266.0761 ,\n",
       "          264.49844],\n",
       "         ...,\n",
       "         [260.0783 , 256.217  , 256.08813, ..., 258.40662, 259.65274,\n",
       "          259.46072],\n",
       "         [250.77592, 251.01799, 251.17854, ..., 250.33617, 250.31978,\n",
       "          250.46594],\n",
       "         [244.27553, 244.35747, 244.49568, ..., 243.98708, 244.09798,\n",
       "          244.18839]]],\n",
       "\n",
       "\n",
       "       [[[253.89203, 254.34189, 254.48529, ..., 254.15506, 253.94392,\n",
       "          253.768  ],\n",
       "         [258.6874 , 259.5184 , 258.82016, ..., 258.26788, 258.50494,\n",
       "          258.1685 ],\n",
       "         [263.12012, 262.07587, 261.8609 , ..., 264.26825, 265.9204 ,\n",
       "...\n",
       "          266.6551 ],\n",
       "         [263.81113, 264.94858, 265.4533 , ..., 258.8377 , 258.89777,\n",
       "          261.5298 ],\n",
       "         [260.6675 , 260.49304, 260.99466, ..., 261.21832, 261.35846,\n",
       "          261.09192]]],\n",
       "\n",
       "\n",
       "       [[[253.94891, 254.05788, 253.97351, ..., 254.22232, 254.09763,\n",
       "          253.97466],\n",
       "         [256.38385, 255.77272, 255.4378 , ..., 256.637  , 256.34842,\n",
       "          256.2263 ],\n",
       "         [258.5706 , 257.33484, 256.64926, ..., 260.6506 , 260.96494,\n",
       "          260.29773],\n",
       "         ...,\n",
       "         [268.44537, 269.3243 , 269.5236 , ..., 264.38052, 265.52368,\n",
       "          266.3625 ],\n",
       "         [264.28644, 265.35983, 265.7473 , ..., 259.3491 , 259.59613,\n",
       "          261.39505],\n",
       "         [260.81506, 260.2897 , 260.43927, ..., 261.95706, 261.8023 ,\n",
       "          261.40338]]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb5806c2-3c28-41e6-a056-d8301462c674' class='xr-section-summary-in' type='checkbox'  ><label for='section-fb5806c2-3c28-41e6-a056-d8301462c674' class='xr-section-summary' >Indexes: <span>(4)</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>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-23a7cb5f-7202-4db3-97c6-6f85161af490' class='xr-index-data-in' type='checkbox'/><label for='index-23a7cb5f-7202-4db3-97c6-6f85161af490' 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([500], dtype=&#x27;int32&#x27;, name=&#x27;level&#x27;))</pre></div></li><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-c8821634-5c6a-4af2-ab9f-6c205b102342' class='xr-index-data-in' type='checkbox'/><label for='index-c8821634-5c6a-4af2-ab9f-6c205b102342' 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(DatetimeIndex([&#x27;1999-01-01 00:00:00&#x27;, &#x27;1999-01-01 01:00:00&#x27;,\n",
       "               &#x27;1999-01-01 02:00:00&#x27;, &#x27;1999-01-01 03:00:00&#x27;,\n",
       "               &#x27;1999-01-01 04:00:00&#x27;, &#x27;1999-01-01 05:00:00&#x27;,\n",
       "               &#x27;1999-01-01 06:00:00&#x27;, &#x27;1999-01-01 07:00:00&#x27;,\n",
       "               &#x27;1999-01-01 08:00:00&#x27;, &#x27;1999-01-01 09:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2020-12-31 14:00:00&#x27;, &#x27;2020-12-31 15:00:00&#x27;,\n",
       "               &#x27;2020-12-31 16:00:00&#x27;, &#x27;2020-12-31 17:00:00&#x27;,\n",
       "               &#x27;2020-12-31 18:00:00&#x27;, &#x27;2020-12-31 19:00:00&#x27;,\n",
       "               &#x27;2020-12-31 20:00:00&#x27;, &#x27;2020-12-31 21:00:00&#x27;,\n",
       "               &#x27;2020-12-31 22:00:00&#x27;, &#x27;2020-12-31 23:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=192864, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1d1cebb6-d147-4bfb-a306-1ea940a87231' class='xr-index-data-in' type='checkbox'/><label for='index-1d1cebb6-d147-4bfb-a306-1ea940a87231' 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([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
       "       -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,\n",
       "        -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,\n",
       "        30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,\n",
       "        70.3125,  75.9375,  81.5625,  87.1875],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-980ec08b-5c4b-4c2a-a1a0-e64ad5eb649d' class='xr-index-data-in' type='checkbox'/><label for='index-980ec08b-5c4b-4c2a-a1a0-e64ad5eb649d' 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([    0.0,   5.625,   11.25,  16.875,    22.5,  28.125,   33.75,  39.375,\n",
       "          45.0,  50.625,   56.25,  61.875,    67.5,  73.125,   78.75,  84.375,\n",
       "          90.0,  95.625,  101.25, 106.875,   112.5, 118.125,  123.75, 129.375,\n",
       "         135.0, 140.625,  146.25, 151.875,   157.5, 163.125,  168.75, 174.375,\n",
       "         180.0, 185.625,  191.25, 196.875,   202.5, 208.125,  213.75, 219.375,\n",
       "         225.0, 230.625,  236.25, 241.875,   247.5, 253.125,  258.75, 264.375,\n",
       "         270.0, 275.625,  281.25, 286.875,   292.5, 298.125,  303.75, 309.375,\n",
       "         315.0, 320.625,  326.25, 331.875,   337.5, 343.125,  348.75, 354.375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-378c17b0-bcb1-4bc6-9d5a-398061ca133a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-378c17b0-bcb1-4bc6-9d5a-398061ca133a' 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>\n",
       "Dimensions:  (level: 1, time: 192864, lat: 32, lon: 64)\n",
       "Coordinates:\n",
       "  * level    (level) int32 500\n",
       "  * time     (time) datetime64[ns] 1999-01-01 ... 2020-12-31T23:00:00\n",
       "  * lat      (lat) float64 -87.19 -81.56 -75.94 -70.31 ... 75.94 81.56 87.19\n",
       "  * lon      (lon) float64 0.0 5.625 11.25 16.88 ... 337.5 343.1 348.8 354.4\n",
       "Data variables:\n",
       "    z        (time, level, lat, lon) float32 5.016e+04 5.017e+04 ... 5.151e+04\n",
       "    t        (time, level, lat, lon) float32 254.1 254.5 254.7 ... 261.8 261.4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:04:48]\n",
      "Start year: 1999\n",
      "(z, 500)\n",
      "(t, 500)\n",
      "Loading data into RAM\n",
      "(z, 500)\n",
      "(t, 500)\n",
      "Loading data into RAM\n",
      "CPU times: user 19 s, sys: 3.08 s, total: 22.1 s\n",
      "Wall time: 17.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "print ('Start year: 1999')\n",
    "# Create a training and validation data generator. Use the train mean and std for validation as well.\n",
    "dg_train = DataGenerator(\n",
    "    ds_train.sel(time=slice('1999', '2015')), dic, lead_time, batch_size=bs, load=True)\n",
    "dg_valid = DataGenerator(\n",
    "    ds_train.sel(time=slice('2016', '2020')), dic, lead_time, batch_size=bs, mean=dg_train.mean, std=dg_train.std, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<xarray.DataArray 'z' (level: 2)>\n",
       " array([54140.027  ,   274.65103], dtype=float32)\n",
       " Coordinates:\n",
       "   * level    (level) int32 500 500,\n",
       " <xarray.DataArray 'z' (level: 2)>\n",
       " array([1100.1848  ,    5.642098], dtype=float32)\n",
       " Coordinates:\n",
       "   * level    (level) int32 500 500)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dg_train.mean, dg_train.std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:05:06]\n",
      "(z, 500)\n",
      "(t, 500)\n",
      "Loading data into RAM\n",
      "CPU times: user 201 ms, sys: 160 ms, total: 360 ms\n",
      "Wall time: 272 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "dg_test = DataGenerator(ds_test, dic, lead_time, batch_size=bs, mean=dg_train.mean, std=dg_train.std, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Create and train model\n",
    "\n",
    "Next up, we need to create the model architecture. \n",
    "- Here we will use a fully connected convolutional network. Because the Earth is periodic in longitude, we want to use a periodic convolution in the lon-direction. \n",
    "- This is not implemented in Keras, so we will do it manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "class PeriodicPadding2D(tf.keras.layers.Layer):\n",
    "    def __init__(self, pad_width, **kwargs):\n",
    "        super().__init__(**kwargs)\n",
    "        self.pad_width = pad_width\n",
    "\n",
    "    def call(self, inputs, **kwargs):\n",
    "        if self.pad_width == 0:\n",
    "            return inputs\n",
    "        inputs_padded = tf.concat(\n",
    "            [inputs[:, :, -self.pad_width:, :], inputs, inputs[:, :, :self.pad_width, :]], axis=2)\n",
    "        # Zero padding in the lat direction\n",
    "        inputs_padded = tf.pad(inputs_padded, [[0, 0], [self.pad_width, self.pad_width], [0, 0], [0, 0]])\n",
    "        return inputs_padded\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super().get_config()\n",
    "        config.update({'pad_width': self.pad_width})\n",
    "        return config\n",
    "\n",
    "\n",
    "class PeriodicConv2D(tf.keras.layers.Layer):\n",
    "    def __init__(self, filters,\n",
    "                 kernel_size,\n",
    "                 conv_kwargs={},\n",
    "                 **kwargs, ):\n",
    "        super().__init__(**kwargs)\n",
    "        self.filters = filters\n",
    "        self.kernel_size = kernel_size\n",
    "        self.conv_kwargs = conv_kwargs\n",
    "        if type(kernel_size) is not int:\n",
    "            assert kernel_size[0] == kernel_size[1], 'PeriodicConv2D only works for square kernels'\n",
    "            kernel_size = kernel_size[0]\n",
    "        pad_width = (kernel_size - 1) // 2\n",
    "        self.padding = PeriodicPadding2D(pad_width)\n",
    "        self.conv = Conv2D(\n",
    "            filters, kernel_size, padding='valid', **conv_kwargs\n",
    "        )\n",
    "\n",
    "    def call(self, inputs):\n",
    "        return self.conv(self.padding(inputs))\n",
    "\n",
    "    def get_config(self):\n",
    "        config = super().get_config()\n",
    "        config.update({'filters': self.filters, 'kernel_size': self.kernel_size, 'conv_kwargs': self.conv_kwargs})\n",
    "        return config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def build_cnn(filters, kernels, input_shape, dr=0):\n",
    "    \"\"\"Fully convolutional network\"\"\"\n",
    "    x = input = Input(shape=input_shape)\n",
    "    for f, k in zip(filters[:-1], kernels[:-1]):\n",
    "        x = PeriodicConv2D(f, k)(x)\n",
    "        x = LeakyReLU()(x)\n",
    "        if dr > 0: x = Dropout(dr)(x)\n",
    "    output = PeriodicConv2D(filters[-1], kernels[-1])(x)\n",
    "    return keras.models.Model(input, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow\n",
      "GPU name:  [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "print(K.backend())\n",
    "print('GPU name: ', tf.config.experimental.list_physical_devices('GPU'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:05:06]\n",
      "CPU times: user 216 ms, sys: 25 ms, total: 241 ms\n",
      "Wall time: 207 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-26 21:05:06.948772: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 31141 MB memory:  -> device: 0, name: Tesla V100-SXM2-32GB, pci bus id: 0000:3e:00.0, compute capability: 7.0\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "cnn = build_cnn([64, 64, 64, 64, 2], [5, 5, 5, 5, 5], (32, 64, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:05:07]\n",
      "CPU times: user 10.8 ms, sys: 1.64 ms, total: 12.4 ms\n",
      "Wall time: 10.3 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "cnn.compile(keras.optimizers.Adam(1e-4), 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 32, 64, 2)]       0         \n",
      "                                                                 \n",
      " periodic_conv2d (PeriodicC  (None, 32, 64, 64)        3264      \n",
      " onv2D)                                                          \n",
      "                                                                 \n",
      " leaky_re_lu (LeakyReLU)     (None, 32, 64, 64)        0         \n",
      "                                                                 \n",
      " periodic_conv2d_1 (Periodi  (None, 32, 64, 64)        102464    \n",
      " cConv2D)                                                        \n",
      "                                                                 \n",
      " leaky_re_lu_1 (LeakyReLU)   (None, 32, 64, 64)        0         \n",
      "                                                                 \n",
      " periodic_conv2d_2 (Periodi  (None, 32, 64, 64)        102464    \n",
      " cConv2D)                                                        \n",
      "                                                                 \n",
      " leaky_re_lu_2 (LeakyReLU)   (None, 32, 64, 64)        0         \n",
      "                                                                 \n",
      " periodic_conv2d_3 (Periodi  (None, 32, 64, 64)        102464    \n",
      " cConv2D)                                                        \n",
      "                                                                 \n",
      " leaky_re_lu_3 (LeakyReLU)   (None, 32, 64, 64)        0         \n",
      "                                                                 \n",
      " periodic_conv2d_4 (Periodi  (None, 32, 64, 2)         3202      \n",
      " cConv2D)                                                        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 313858 (1.20 MB)\n",
      "Trainable params: 313858 (1.20 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:05:07]\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-26 21:05:08.369470: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8800\n",
      "2024-03-26 21:05:08.794041: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55acf5795540 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2024-03-26 21:05:08.794056: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla V100-SXM2-32GB, Compute Capability 7.0\n",
      "2024-03-26 21:05:08.798164: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-03-26 21:05:08.960991: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4657/4657 [==============================] - 75s 16ms/step - loss: 0.0709 - val_loss: 0.0407\n",
      "Epoch 2/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0380 - val_loss: 0.0367\n",
      "Epoch 3/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0353 - val_loss: 0.0349\n",
      "Epoch 4/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0337 - val_loss: 0.0344\n",
      "Epoch 5/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0326 - val_loss: 0.0328\n",
      "Epoch 6/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0319 - val_loss: 0.0332\n",
      "Epoch 7/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0312 - val_loss: 0.0314\n",
      "Epoch 8/100\n",
      "2653/4657 [================>.............] - ETA: 26s - loss: 0.0308"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub message rate exceeded.\n",
      "The Jupyter server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--ServerApp.iopub_msg_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
      "ServerApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0303 - val_loss: 0.0305\n",
      "Epoch 10/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0299 - val_loss: 0.0302\n",
      "Epoch 11/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0295 - val_loss: 0.0301\n",
      "Epoch 12/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0293 - val_loss: 0.0295\n",
      "Epoch 13/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0290 - val_loss: 0.0293\n",
      "Epoch 14/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0287 - val_loss: 0.0296\n",
      "Epoch 15/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0285 - val_loss: 0.0293\n",
      "Epoch 16/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0282 - val_loss: 0.0296\n",
      "Epoch 17/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0280 - val_loss: 0.0288\n",
      "Epoch 18/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0278 - val_loss: 0.0285\n",
      "Epoch 19/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0276 - val_loss: 0.0283\n",
      "Epoch 20/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0275 - val_loss: 0.0278\n",
      "Epoch 21/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0273 - val_loss: 0.0277\n",
      "Epoch 22/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0271 - val_loss: 0.0275\n",
      "Epoch 23/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0270 - val_loss: 0.0276\n",
      "Epoch 24/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0269 - val_loss: 0.0276\n",
      "Epoch 25/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0267 - val_loss: 0.0275\n",
      "Epoch 26/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0266 - val_loss: 0.0273\n",
      "Epoch 27/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0265 - val_loss: 0.0275\n",
      "Epoch 28/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0264 - val_loss: 0.0269\n",
      "Epoch 29/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0263 - val_loss: 0.0268\n",
      "Epoch 30/100\n",
      "4657/4657 [==============================] - 72s 15ms/step - loss: 0.0262 - val_loss: 0.0271\n",
      "Epoch 31/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0261 - val_loss: 0.0267\n",
      "Epoch 32/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0260 - val_loss: 0.0266\n",
      "Epoch 33/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0259 - val_loss: 0.0267\n",
      "Epoch 34/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0258 - val_loss: 0.0264\n",
      "Epoch 35/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0257 - val_loss: 0.0264\n",
      "Epoch 36/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0256 - val_loss: 0.0262\n",
      "Epoch 37/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0256 - val_loss: 0.0264\n",
      "Epoch 38/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0255 - val_loss: 0.0267\n",
      "Epoch 39/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0254 - val_loss: 0.0260\n",
      "Epoch 40/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0253 - val_loss: 0.0260\n",
      "Epoch 41/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0253 - val_loss: 0.0262\n",
      "Epoch 42/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0252 - val_loss: 0.0268\n",
      "Epoch 43/100\n",
      "4657/4657 [==============================] - 71s 15ms/step - loss: 0.0252 - val_loss: 0.0264\n",
      "Epoch 43: early stopping\n",
      "CPU times: user 1h 15min 22s, sys: 5min 53s, total: 1h 21min 15s\n",
      "Wall time: 51min 17s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x153489242bb0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "\n",
    "# Since we didn't load the full data, this is only for demonstration.\n",
    "cnn.fit(dg_train, epochs=100, validation_data=dg_valid, \n",
    "          callbacks=[tf.keras.callbacks.EarlyStopping(\n",
    "                        monitor='val_loss',\n",
    "                        min_delta=0,\n",
    "                        patience=3,\n",
    "                        verbose=1, \n",
    "                        mode='auto'\n",
    "                    )]\n",
    "         )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "## Create predictions\n",
    "\n",
    "Now that we have our model we need to create a prediction NetCDF file. This function does this. \n",
    "\n",
    "We can either directly predict the target lead time (e.g. 5 days) or create an iterative forecast by chaining together many e.g. 6h forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def create_predictions(model, dg):\n",
    "    \"\"\"Create predictions for non-iterative model\"\"\"\n",
    "    preds = model.predict_generator(dg)\n",
    "    # Unnormalize\n",
    "    preds = preds * dg.std.values + dg.mean.values\n",
    "    fcs = []\n",
    "    lev_idx = 0\n",
    "    for var, levels in dg.var_dict.items():\n",
    "        if levels is None or isinstance(levels, int):\n",
    "            fcs.append(xr.DataArray(\n",
    "                preds[:, :, :, lev_idx],\n",
    "                dims=['time', 'lat', 'lon'],\n",
    "                coords={'time': dg.valid_time, 'lat': dg.ds.lat, 'lon': dg.ds.lon},\n",
    "                name=var\n",
    "            ))\n",
    "            lev_idx += 1\n",
    "        else:\n",
    "            nlevs = len(levels)\n",
    "            fcs.append(xr.DataArray(\n",
    "                preds[:, :, :, lev_idx:lev_idx+nlevs],\n",
    "                dims=['time', 'lat', 'lon', 'level'],\n",
    "                coords={'time': dg.valid_time, 'lat': dg.ds.lat, 'lon': dg.ds.lon, 'level': levels},\n",
    "                name=var\n",
    "            ))\n",
    "            lev_idx += nlevs\n",
    "    return xr.merge(fcs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:56:25]\n",
      "CPU times: user 6.83 s, sys: 961 ms, total: 7.79 s\n",
      "Wall time: 4.54 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "fc = create_predictions(cnn, dg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'time' (time: 17520)>\n",
      "array(['2021-01-01T00:00:00.000000000', '2021-01-01T01:00:00.000000000',\n",
      "       '2021-01-01T02:00:00.000000000', ..., '2022-12-31T21:00:00.000000000',\n",
      "       '2022-12-31T22:00:00.000000000', '2022-12-31T23:00:00.000000000'],\n",
      "      dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 2021-01-01 ... 2022-12-31T23:00:00\n",
      "<xarray.DataArray 'time' (time: 17514)>\n",
      "array(['2021-01-01T06:00:00.000000000', '2021-01-01T07:00:00.000000000',\n",
      "       '2021-01-01T08:00:00.000000000', ..., '2022-12-31T21:00:00.000000000',\n",
      "       '2022-12-31T22:00:00.000000000', '2022-12-31T23:00:00.000000000'],\n",
      "      dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 2021-01-01T06:00:00 ... 2022-12-31T23:00:00\n"
     ]
    }
   ],
   "source": [
    "print( valid['time'])\n",
    "print(    fc['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:  ()\n",
      "Data variables:\n",
      "    z        float64 103.3\n",
      "    t        float64 1.114\n",
      "CPU times: user 707 ms, sys: 446 ms, total: 1.15 s\n",
      "Wall time: 927 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rmse_1 = compute_weighted_rmse(fc, valid).compute()\n",
    "print (rmse_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Iterative predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [],
   "source": [
    "def create_iterative_predictions(model, dg, max_lead_time=5*24):\n",
    "    state = dg.data[:dg.n_samples]\n",
    "    preds = []\n",
    "    for _ in range(max_lead_time // dg.lead_time):\n",
    "        state = model.predict(state)\n",
    "        p = state * dg.std.values + dg.mean.values\n",
    "        preds.append(p)\n",
    "    preds = np.array(preds)\n",
    "    \n",
    "    lead_time = np.arange(dg.lead_time, max_lead_time + dg.lead_time, dg.lead_time)\n",
    "    das = []; lev_idx = 0\n",
    "    for var, levels in dg.var_dict.items():\n",
    "        if levels is None or isinstance(levels, int):\n",
    "            das.append(xr.DataArray(\n",
    "                preds[:, :, :, :, lev_idx],\n",
    "                dims=['lead_time', 'time', 'lat', 'lon'],\n",
    "                coords={'lead_time': lead_time, 'time': dg.init_time, 'lat': dg.ds.lat, 'lon': dg.ds.lon},\n",
    "                name=var\n",
    "            ))\n",
    "            lev_idx += 1\n",
    "        else:\n",
    "            nlevs = len(levels)\n",
    "            das.append(xr.DataArray(\n",
    "                preds[:, :, :, :, lev_idx:lev_idx+nlevs],\n",
    "                dims=['lead_time', 'time', 'lat', 'lon', 'level'],\n",
    "                coords={'lead_time': lead_time, 'time': dg.init_time, 'lat': dg.ds.lat, 'lon': dg.ds.lon, 'level': levels},\n",
    "                name=var\n",
    "            ))\n",
    "            lev_idx += nlevs\n",
    "    return xr.merge(das)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:56:31]\n",
      "WARNING:tensorflow:Keras is training/fitting/evaluating on array-like data. Keras may not be optimized for this format, so if your input data format is supported by TensorFlow I/O (https://github.com/tensorflow/io) we recommend using that to load a Dataset instead.\n",
      "548/548 [==============================] - 3s 6ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "548/548 [==============================] - 3s 5ms/step\n",
      "CPU times: user 1min 41s, sys: 18.8 s, total: 2min\n",
      "Wall time: 1min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "fc_iter = create_iterative_predictions(cnn, dg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'time' (time: 17514)>\n",
      "array(['2021-01-01T00:00:00.000000000', '2021-01-01T01:00:00.000000000',\n",
      "       '2021-01-01T02:00:00.000000000', ..., '2022-12-31T15:00:00.000000000',\n",
      "       '2022-12-31T16:00:00.000000000', '2022-12-31T17:00:00.000000000'],\n",
      "      dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 2021-01-01 ... 2022-12-31T17:00:00\n",
      "<xarray.DataArray 'time' (time: 17520)>\n",
      "array(['2021-01-01T00:00:00.000000000', '2021-01-01T01:00:00.000000000',\n",
      "       '2021-01-01T02:00:00.000000000', ..., '2022-12-31T21:00:00.000000000',\n",
      "       '2022-12-31T22:00:00.000000000', '2022-12-31T23:00:00.000000000'],\n",
      "      dtype='datetime64[ns]')\n",
      "Coordinates:\n",
      "  * time     (time) datetime64[ns] 2021-01-01 ... 2022-12-31T23:00:00\n"
     ]
    }
   ],
   "source": [
    "print (fc_iter['time'])\n",
    "print (  valid['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:58:03]\n",
      "Merging ...\n",
      "CPU times: user 15.3 s, sys: 9.83 s, total: 25.1 s\n",
      "Wall time: 20.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "rmse_z_t = evaluate_iterative_forecast(fc_iter, valid, compute_weighted_rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "Collapsed": "false"
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[2024-03-26 21:58:23]\n",
      "CPU times: user 265 µs, sys: 37 µs, total: 302 µs\n",
      "Wall time: 262 µs\n"
     ]
    },
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lead_time: 20)\n",
       "Coordinates:\n",
       "  * lead_time  (lead_time) int64 6 12 18 24 30 36 42 ... 90 96 102 108 114 120\n",
       "Data variables:\n",
       "    z          (lead_time) float64 103.3 159.2 219.6 ... 1.223e+03 1.261e+03\n",
       "    t          (lead_time) float64 1.114 1.473 1.692 1.869 ... 4.711 4.86 4.998</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-d7ee744f-ff8c-42f1-b58f-cbad0486da54' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d7ee744f-ff8c-42f1-b58f-cbad0486da54' 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'>lead_time</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7732aff2-e45a-4474-b3d3-2520820e47d5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7732aff2-e45a-4474-b3d3-2520820e47d5' class='xr-section-summary' >Coordinates: <span>(1)</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'>lead_time</span></div><div class='xr-var-dims'>(lead_time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>6 12 18 24 30 ... 102 108 114 120</div><input id='attrs-6d6d820e-6262-497e-8856-002db9395ab9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d6d820e-6262-497e-8856-002db9395ab9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f60b90bf-cdd8-4013-b28e-66fd986025b6' class='xr-var-data-in' type='checkbox'><label for='data-f60b90bf-cdd8-4013-b28e-66fd986025b6' 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([  6,  12,  18,  24,  30,  36,  42,  48,  54,  60,  66,  72,  78,  84,\n",
       "        90,  96, 102, 108, 114, 120])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e5ccf4f-ab70-46fe-a31c-d95ccaa7d727' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e5ccf4f-ab70-46fe-a31c-d95ccaa7d727' class='xr-section-summary' >Data variables: <span>(2)</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>z</span></div><div class='xr-var-dims'>(lead_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>103.3 159.2 ... 1.223e+03 1.261e+03</div><input id='attrs-ced39cc9-7004-42bc-a35f-58ebfafbad26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ced39cc9-7004-42bc-a35f-58ebfafbad26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-412b0cca-d75d-4293-916e-2c89e8719fd4' class='xr-var-data-in' type='checkbox'><label for='data-412b0cca-d75d-4293-916e-2c89e8719fd4' 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([ 103.32353055,  159.22988739,  219.56934713,  282.26764144,\n",
       "        355.19004502,  430.58957152,  504.48899937,  576.23207482,\n",
       "        646.77531452,  716.16596688,  783.76990018,  849.51979172,\n",
       "        912.98440652,  973.88257676, 1031.25667789, 1085.00913778,\n",
       "       1134.82452896, 1180.77932558, 1222.69100612, 1260.7921137 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(lead_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.114 1.473 1.692 ... 4.86 4.998</div><input id='attrs-65868a5b-17f5-411a-9b1c-20387a66f743' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65868a5b-17f5-411a-9b1c-20387a66f743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f77bdb32-ba05-4669-9a7d-f29f5f597ad7' class='xr-var-data-in' type='checkbox'><label for='data-f77bdb32-ba05-4669-9a7d-f29f5f597ad7' 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([1.1140808 , 1.47299663, 1.69222505, 1.86885393, 2.07753673,\n",
       "       2.28971011, 2.49614993, 2.7027682 , 2.92665044, 3.1540068 ,\n",
       "       3.37526302, 3.58871227, 3.80092177, 4.00549278, 4.19773246,\n",
       "       4.37801287, 4.54994904, 4.71091808, 4.86009435, 4.99806555])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-864635b6-7cde-4d54-8d7a-c5e36e6eed0d' class='xr-section-summary-in' type='checkbox'  ><label for='section-864635b6-7cde-4d54-8d7a-c5e36e6eed0d' class='xr-section-summary' >Indexes: <span>(1)</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>lead_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e62dabc3-c279-4d26-a149-c0b64c48594d' class='xr-index-data-in' type='checkbox'/><label for='index-e62dabc3-c279-4d26-a149-c0b64c48594d' 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([  6,  12,  18,  24,  30,  36,  42,  48,  54,  60,  66,  72,  78,  84,\n",
       "        90,  96, 102, 108, 114, 120],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;lead_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86c9d4a4-4a95-46f6-bc67-e6af11745391' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-86c9d4a4-4a95-46f6-bc67-e6af11745391' 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>\n",
       "Dimensions:    (lead_time: 20)\n",
       "Coordinates:\n",
       "  * lead_time  (lead_time) int64 6 12 18 24 30 36 42 ... 90 96 102 108 114 120\n",
       "Data variables:\n",
       "    z          (lead_time) float64 103.3 159.2 219.6 ... 1.223e+03 1.261e+03\n",
       "    t          (lead_time) float64 1.114 1.473 1.692 1.869 ... 4.711 4.86 4.998"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "rmse_z_t.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:58:23]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15348c89bfa0>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "rmse_z_t.z.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-03-26 21:58:24]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15348bd66100>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print( f'[{datetime.now().replace(microsecond=0)}]' )\n",
    "rmse_z_t.t.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "Collapsed": "false"
   },
   "source": [
    "# The end"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}