01_data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "403e4bfd-7925-43e2-977b-2baca78c2eaf",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 1. Data Exploration\n",
    "\n",
    "## Overview \n",
    "In the first section we will look at techniques to load and explore the data to be used for machine learning. The purpose of this exploration to gain an understanding of the data and how it relates to the problem we are trying to solve. This will help us make sensible choices for differents elements of our machine learning pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40489368-10b7-4757-b486-3af6db2a2a2b",
   "metadata": {},
   "source": [
    "### Prerequisities \n",
    "\n",
    "To be able to sucessfully work through this notebook, you will need some understanding of the following \n",
    "* The python programming language\n",
    "* Simple data handling in python\n",
    "* Basic plotting using matplotlib\n",
    "* Environment set up using pip or conda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b82b8cb-681d-44f8-bc23-e218e7653def",
   "metadata": {},
   "source": [
    "### Learning Outcomes \n",
    "\n",
    "* Loading tabular and gridded datasets using common python libraries\n",
    "* typical techniques for *exploratory data analysis* on different\n",
    "* key characteristics of datasets to use in choosing components for a machine learning pipeline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cceadd3-46b6-42dc-b702-943d09365ca6",
   "metadata": {},
   "source": [
    "## Tutorial - Exploratory Data Analysis\n",
    "\n",
    "The first step in a machine learning problem is to understand the data you have available for training a machine learning algorithm. In any data driven technique, the quality of the results is only going to be as good as the quality of the data. This can only be achieved by matching the appropriate techniques to the data that you have, so understanding the particular dataset to be used is the foundation of a successful outcome.\n",
    "\n",
    "To achieve this, one typically performs an *Exploratory Data Analysis*, where one produce a series of summary statistics and plots that highlight the most salient characteristics of the dataset for the problem at hand. Typical traits to be considered include:\n",
    "* The range of values different input feature can take e.g. min and max temperature, start and end dates.\n",
    "* Important subsets of the data e.g. different seasons or time of the day.\n",
    "* Distributions of different features, both as a whole and important subsets\n",
    "* Correlations between different features, especially input and target features.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de71ed31-7104-4b61-8c9e-a2b6b7b70ddf",
   "metadata": {},
   "source": [
    "### Best Practices & Values\n",
    "\n",
    "Performing a through data exploration implements the [Met Office Machine Learning best practices](https://metoffice.sharepoint.com/:w:/r/sites/MetOfficeDataScienceCommunity/_layouts/15/Doc.aspx?sourcedoc=%7BAE255421-7D03-42F2-A9C8-CF8906BFBE38%7D&file=MachineLearningBestPracticesAndValues) in the following ways\n",
    "* Ethics - Ensure we can justify our choices from understanding of the data and how it relates to the potential impacts of this work.\n",
    "* Data - Ensure that we understand biases or other issues with the data\n",
    "* ML Pitfall -  Avoid pitfalls through making informed choices based on analysis of data\n",
    "* ML Lifecycle - Ensure we store and proces the data so we can reproduce the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d421494-3e87-4e51-8efd-b35ad50764e2",
   "metadata": {},
   "source": [
    "## Excercise 1: Load and explore tabular dataset - Falkland Islands Airfield Rotors\n",
    "\n",
    "The first dataset we will explore comes from a challenge by Operational Meteorologist Steve Ramsdale.  The aim of challenge is to forecast rotors, a type of localised topographically-driven turbulent wind gusts, at the Mount Pleasant Airfield in the Falkland Islands. \n",
    "\n",
    "![Image of the topography of the Falkland Islands](images/falklands_mtpleasant.png) \n",
    "\n",
    "These occur through northerly winds interacting with mountains to the north of the airfield.  The aim is to predict whether or not these will occur, using data from global, coarse-resolution models which are not able to to explicitly resolve this phenomenon. Where these occur at an airfield they make conditions sufficiently hazardous so as to cause operations to be suspended. Rotors therefore impact the aviation industry, in partticular where flights have to be diverted for considerable distances from isolated airfields.  \n",
    "\n",
    "What is needed is a prediction of whether such rotors are likely in a given time window, so that flight can be postponed or cancelled rather than risking the flight and turning back wasting fuel. As these are small scale features, they are not explicitly resolved in coarse resolution global models. Operational Meteorologists (OpMets) are able to look at this model data and determine whether rotors are likely, but this is error-prone and time consuming.\n",
    "\n",
    "\n",
    "![Depiction of how turbulent lee wind gusts form](images/rotors_diagram.png)\n",
    "\n",
    "[Met Office Youtube video explaining rotors](https://www.youtube.com/watch?v=jgSZG9SqN_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d6c251-f968-4401-b1e0-ff23d85fe2b1",
   "metadata": {},
   "source": [
    "### Dataset 1 - Predicting impactful turbulent wind gusts\n",
    "The target is a human-created dataset produced by Operational Meterologists at the airfield, as to whether a rotor occured in any given three hour period.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb443a63-d2b5-4577-ad20-0f34c1e23e10",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "677db567-6260-41fa-bdb9-13850c81c9dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pathlib\n",
    "import datetime\n",
    "import os\n",
    "import functools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9b4ad3d-958d-4b04-ac0e-5555800898bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0096b3e9-f17e-4c6b-a4cc-12947c6f7192",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e4c1340-753c-44f8-ac43-7d00fbd045f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "404790fe-fdc9-4890-ae53-497e255b3d48",
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris\n",
    "import iris.quickplot\n",
    "import iris.coord_categorisation\n",
    "import cartopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64fdc2e3-7dfa-44b7-96dd-79ba9994aa72",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b0237d-990c-4261-a461-6dd579984be2",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71a8c8e2-98df-4ffe-9d0d-9e613a29e618",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/stephen.haddad/data/ml_weather_tutorial\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    falklands_data_dir = os.environ['ML_TUTORIAL_DIR']\n",
    "except KeyError:\n",
    "    falklands_data_dir = '/project/informatics_lab/data_science_cop/ml_tutorial/'\n",
    "falklands_data_dir = pathlib.Path(falklands_data_dir)\n",
    "print(falklands_data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0349b603-8f6d-4858-a42e-0b65363688b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "falklands_data_fname = '2021_met_office_aviation_rotors.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5652d845-14b4-4b87-b249-66d85135fcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/Users/stephen.haddad/data/ml_weather_tutorial/2021_met_office_aviation_rotors.csv')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_data_path = falklands_data_dir / falklands_data_fname\n",
    "falklands_data_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8fc58fb-295a-4345-88e5-9dadc58f83b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_data_path.is_file()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c279ac65-86c2-4199-a270-5fd05147feb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "falklands_df = pandas.read_csv(falklands_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a7d75cf1-8827-4a97-bef6-a3496062319d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>...</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>rotors_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>01/01/2015 00:00</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>341.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>330.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>01/01/2015 03:00</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>344.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>01/01/2015 06:00</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>01/01/2015 09:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>354.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>20101</td>\n",
       "      <td>31/12/2020 06:00</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>...</td>\n",
       "      <td>12.1</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>20102</td>\n",
       "      <td>31/12/2020 09:00</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>227.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>20103</td>\n",
       "      <td>31/12/2020 12:00</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>20104</td>\n",
       "      <td>31/12/2020 15:00</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>20105</td>\n",
       "      <td>01/01/2021 00:00</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>227.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20106 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0               DTG  air_temp_obs  dewpoint_obs  \\\n",
       "0               0               NaN           NaN           NaN   \n",
       "1               1  01/01/2015 00:00         283.9         280.7   \n",
       "2               2  01/01/2015 03:00         280.7         279.7   \n",
       "3               3  01/01/2015 06:00         279.8         278.1   \n",
       "4               4  01/01/2015 09:00         279.9         277.0   \n",
       "...           ...               ...           ...           ...   \n",
       "20101       20101  31/12/2020 06:00         276.7         275.5   \n",
       "20102       20102  31/12/2020 09:00         277.9         276.9   \n",
       "20103       20103  31/12/2020 12:00         283.5         277.1   \n",
       "20104       20104  31/12/2020 15:00         286.1         276.9   \n",
       "20105       20105  01/01/2021 00:00         285.1         279.3   \n",
       "\n",
       "       wind_direction_obs  wind_speed_obs  wind_gust_obs  air_temp_1  \\\n",
       "0                     NaN             NaN            NaN         NaN   \n",
       "1                   110.0             4.1     -9999999.0     284.000   \n",
       "2                    90.0             7.7     -9999999.0     281.500   \n",
       "3                   100.0             7.7     -9999999.0     279.875   \n",
       "4                   120.0             7.2     -9999999.0     279.625   \n",
       "...                   ...             ...            ...         ...   \n",
       "20101               270.0             3.6     -9999999.0     277.875   \n",
       "20102               270.0             3.1     -9999999.0     277.875   \n",
       "20103               220.0             3.6     -9999999.0     281.125   \n",
       "20104               250.0             3.6     -9999999.0     284.625   \n",
       "20105               300.0             6.2     -9999999.0     284.250   \n",
       "\n",
       "       air_temp_2  air_temp_3  ...  windspd_18  winddir_19  windspd_19  \\\n",
       "0             NaN         NaN  ...         NaN         NaN         NaN   \n",
       "1         283.625     283.250  ...         5.8       341.0         6.0   \n",
       "2         281.250     280.750  ...         6.8       344.0         5.3   \n",
       "3         279.625     279.125  ...         6.0       345.0         5.5   \n",
       "4         279.250     278.875  ...         3.1       338.0         3.5   \n",
       "...           ...         ...  ...         ...         ...         ...   \n",
       "20101     277.750     277.625  ...        12.1       223.0        11.8   \n",
       "20102     277.625     277.875  ...        10.2       230.0        10.8   \n",
       "20103     280.625     280.125  ...        10.3       218.0        11.9   \n",
       "20104     284.125     283.625  ...         9.4       218.0         8.6   \n",
       "20105     284.000     283.750  ...         8.6       241.0        10.2   \n",
       "\n",
       "       winddir_20  windspd_20  winddir_21  windspd_21  winddir_22  windspd_22  \\\n",
       "0             NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1           334.0         6.1       330.0         6.0       329.0         5.8   \n",
       "2           348.0         3.8       360.0         3.2        12.0         3.5   \n",
       "3           358.0         5.0        10.0         4.2        38.0         4.0   \n",
       "4           354.0         3.9         9.0         4.4        22.0         4.6   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "20101       221.0        11.4       219.0        11.3       215.0        11.4   \n",
       "20102       230.0        11.6       227.0        12.3       222.0        12.0   \n",
       "20103       221.0        12.8       222.0        11.9       225.0        10.6   \n",
       "20104       212.0         8.3       218.0         8.7       226.0        10.1   \n",
       "20105       236.0        10.5       232.0        10.5       227.0        11.3   \n",
       "\n",
       "       rotors_present  \n",
       "0                 0.0  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "...               ...  \n",
       "20101             0.0  \n",
       "20102             0.0  \n",
       "20103             0.0  \n",
       "20104             0.0  \n",
       "20105             0.0  \n",
       "\n",
       "[20106 rows x 96 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2643e469-3c3a-468d-95b1-40df8aedb431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'DTG', 'air_temp_obs', 'dewpoint_obs',\n",
       "       'wind_direction_obs', 'wind_speed_obs', 'wind_gust_obs', 'air_temp_1',\n",
       "       'air_temp_2', 'air_temp_3', 'air_temp_4', 'air_temp_5', 'air_temp_6',\n",
       "       'air_temp_7', 'air_temp_8', 'air_temp_9', 'air_temp_10', 'air_temp_11',\n",
       "       'air_temp_12', 'air_temp_13', 'air_temp_14', 'air_temp_15',\n",
       "       'air_temp_16', 'air_temp_17', 'air_temp_18', 'air_temp_19',\n",
       "       'air_temp_20', 'air_temp_21', 'air_temp_22', 'sh_1', 'sh_2', 'sh_3',\n",
       "       'sh_4', 'sh_5', 'sh_6', 'sh_7', 'sh_8', 'sh_9', 'sh_10', 'sh_11',\n",
       "       'sh_12', 'sh_13', 'sh_14', 'sh_15', 'sh_16', 'sh_17', 'sh_18', 'sh_19',\n",
       "       'sh_20', 'sh_21', 'sh_22', 'winddir_1', 'windspd_1', 'winddir_2',\n",
       "       'windspd_2', 'winddir_3', 'windspd_3', 'winddir_4', 'windspd_4',\n",
       "       'winddir_5', 'windspd_5', 'winddir_6', 'windspd_6', 'winddir_7',\n",
       "       'windspd_7', 'winddir_8', 'windspd_8', 'winddir_9', 'windspd_9',\n",
       "       'winddir_10', 'windspd_10', 'winddir_11', 'windspd_11', 'winddir_12',\n",
       "       'windspd_12', 'winddir_13', 'windspd_13', 'winddir_14', 'windspd_14',\n",
       "       'winddir_15', 'windspd_15', 'winddir_16', 'windspd_16', 'winddir_17',\n",
       "       'windspd_17', 'winddir_18', 'windspd_18', 'winddir_19', 'windspd_19',\n",
       "       'winddir_20', 'windspd_20', 'winddir_21', 'windspd_21', 'winddir_22',\n",
       "       'windspd_22', 'rotors_present'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6699d1-6b34-4f2d-a7f6-1fb99699b0b2",
   "metadata": {},
   "source": [
    "### Clean data\n",
    "We perform some initial cleaning of the dataset to make it easier to work with. Ideally this could be done once and saved out for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2fb3d1b3-6cd7-4ddb-b92b-2256e13cdea5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# rename target variable\n",
    "falklands_df = falklands_df.rename({'Rotors 1 is true': 'rotors_present'},axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0ba6c22a-6f95-4e2f-b0fa-b547d4902428",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the dataset, NaNs are used to represent that no rotors was observed. This is not good practice, so we are replacing the NaNs with 0's\n",
    "falklands_df.loc[falklands_df[falklands_df['rotors_present'].isna()].index, 'rotors_present'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "47f3032b-008c-4664-815e-67c0c5e31cb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Create date and times using the pandas datetime functionality\n",
    "falklands_df['DTG'] = pandas.to_datetime(falklands_df['DTG'],dayfirst=True)\n",
    "\n",
    "# There are some duplicate dates and times, so we drop those\n",
    "falklands_df = falklands_df.drop_duplicates(subset=['DTG'])\n",
    "\n",
    "# get rid of data points without a date and time\n",
    "falklands_df = falklands_df[~falklands_df['DTG'].isnull()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "92f3c2e2-cdaf-4122-aa15-9239b08e8fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  subset observation so that wind speed, air temp, wind direction and dewpoints\n",
    "# are all greater than 0  (temperatures are in Kelvin, which must be greater than 0)\n",
    "falklands_df = falklands_df[(falklands_df['wind_speed_obs'] >= 0.0) &\n",
    "                            (falklands_df['air_temp_obs'] >= 0.0) &\n",
    "                            (falklands_df['wind_direction_obs'] >= 0.0) &\n",
    "                            (falklands_df['dewpoint_obs'] >= 0.0) \n",
    "                           ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "64ddac97-18ff-4e33-aef9-cb76b2a4debe",
   "metadata": {},
   "outputs": [],
   "source": [
    "falklands_df = falklands_df.drop([c1 for c1 in falklands_df.columns if 'unnamed' in c1.lower()] , axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9cb695ec-36a3-4d34-b540-ed9a59b17b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DTG</th>\n",
       "      <th>air_temp_obs</th>\n",
       "      <th>dewpoint_obs</th>\n",
       "      <th>wind_direction_obs</th>\n",
       "      <th>wind_speed_obs</th>\n",
       "      <th>wind_gust_obs</th>\n",
       "      <th>air_temp_1</th>\n",
       "      <th>air_temp_2</th>\n",
       "      <th>air_temp_3</th>\n",
       "      <th>air_temp_4</th>\n",
       "      <th>...</th>\n",
       "      <th>windspd_18</th>\n",
       "      <th>winddir_19</th>\n",
       "      <th>windspd_19</th>\n",
       "      <th>winddir_20</th>\n",
       "      <th>windspd_20</th>\n",
       "      <th>winddir_21</th>\n",
       "      <th>windspd_21</th>\n",
       "      <th>winddir_22</th>\n",
       "      <th>windspd_22</th>\n",
       "      <th>rotors_present</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>283.9</td>\n",
       "      <td>280.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.250</td>\n",
       "      <td>282.625</td>\n",
       "      <td>...</td>\n",
       "      <td>5.8</td>\n",
       "      <td>341.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>330.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>280.7</td>\n",
       "      <td>279.7</td>\n",
       "      <td>90.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.500</td>\n",
       "      <td>281.250</td>\n",
       "      <td>280.750</td>\n",
       "      <td>280.250</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>344.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>348.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-01 06:00:00</td>\n",
       "      <td>279.8</td>\n",
       "      <td>278.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.875</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.125</td>\n",
       "      <td>278.625</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>358.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-01 09:00:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.625</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.250</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>338.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>354.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-01 12:00:00</td>\n",
       "      <td>279.9</td>\n",
       "      <td>277.4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>279.250</td>\n",
       "      <td>278.875</td>\n",
       "      <td>278.375</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>273.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>329.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>338.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20101</th>\n",
       "      <td>2020-12-31 06:00:00</td>\n",
       "      <td>276.7</td>\n",
       "      <td>275.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.750</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.500</td>\n",
       "      <td>...</td>\n",
       "      <td>12.1</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.8</td>\n",
       "      <td>221.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>219.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>215.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20102</th>\n",
       "      <td>2020-12-31 09:00:00</td>\n",
       "      <td>277.9</td>\n",
       "      <td>276.9</td>\n",
       "      <td>270.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.625</td>\n",
       "      <td>277.875</td>\n",
       "      <td>277.875</td>\n",
       "      <td>...</td>\n",
       "      <td>10.2</td>\n",
       "      <td>230.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>227.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>222.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20103</th>\n",
       "      <td>2020-12-31 12:00:00</td>\n",
       "      <td>283.5</td>\n",
       "      <td>277.1</td>\n",
       "      <td>220.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>281.125</td>\n",
       "      <td>280.625</td>\n",
       "      <td>280.125</td>\n",
       "      <td>279.625</td>\n",
       "      <td>...</td>\n",
       "      <td>10.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>221.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>225.0</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20104</th>\n",
       "      <td>2020-12-31 15:00:00</td>\n",
       "      <td>286.1</td>\n",
       "      <td>276.9</td>\n",
       "      <td>250.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.625</td>\n",
       "      <td>284.125</td>\n",
       "      <td>283.625</td>\n",
       "      <td>283.000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>212.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>218.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20105</th>\n",
       "      <td>2021-01-01 00:00:00</td>\n",
       "      <td>285.1</td>\n",
       "      <td>279.3</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>-9999999.0</td>\n",
       "      <td>284.250</td>\n",
       "      <td>284.000</td>\n",
       "      <td>283.750</td>\n",
       "      <td>283.250</td>\n",
       "      <td>...</td>\n",
       "      <td>8.6</td>\n",
       "      <td>241.0</td>\n",
       "      <td>10.2</td>\n",
       "      <td>236.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>227.0</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17486 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      DTG  air_temp_obs  dewpoint_obs  wind_direction_obs  \\\n",
       "1     2015-01-01 00:00:00         283.9         280.7               110.0   \n",
       "2     2015-01-01 03:00:00         280.7         279.7                90.0   \n",
       "3     2015-01-01 06:00:00         279.8         278.1               100.0   \n",
       "4     2015-01-01 09:00:00         279.9         277.0               120.0   \n",
       "5     2015-01-01 12:00:00         279.9         277.4               120.0   \n",
       "...                   ...           ...           ...                 ...   \n",
       "20101 2020-12-31 06:00:00         276.7         275.5               270.0   \n",
       "20102 2020-12-31 09:00:00         277.9         276.9               270.0   \n",
       "20103 2020-12-31 12:00:00         283.5         277.1               220.0   \n",
       "20104 2020-12-31 15:00:00         286.1         276.9               250.0   \n",
       "20105 2021-01-01 00:00:00         285.1         279.3               300.0   \n",
       "\n",
       "       wind_speed_obs  wind_gust_obs  air_temp_1  air_temp_2  air_temp_3  \\\n",
       "1                 4.1     -9999999.0     284.000     283.625     283.250   \n",
       "2                 7.7     -9999999.0     281.500     281.250     280.750   \n",
       "3                 7.7     -9999999.0     279.875     279.625     279.125   \n",
       "4                 7.2     -9999999.0     279.625     279.250     278.875   \n",
       "5                 8.7     -9999999.0     279.250     278.875     278.375   \n",
       "...               ...            ...         ...         ...         ...   \n",
       "20101             3.6     -9999999.0     277.875     277.750     277.625   \n",
       "20102             3.1     -9999999.0     277.875     277.625     277.875   \n",
       "20103             3.6     -9999999.0     281.125     280.625     280.125   \n",
       "20104             3.6     -9999999.0     284.625     284.125     283.625   \n",
       "20105             6.2     -9999999.0     284.250     284.000     283.750   \n",
       "\n",
       "       air_temp_4  ...  windspd_18  winddir_19  windspd_19  winddir_20  \\\n",
       "1         282.625  ...         5.8       341.0         6.0       334.0   \n",
       "2         280.250  ...         6.8       344.0         5.3       348.0   \n",
       "3         278.625  ...         6.0       345.0         5.5       358.0   \n",
       "4         278.250  ...         3.1       338.0         3.5       354.0   \n",
       "5         277.875  ...         1.6       273.0         2.0       303.0   \n",
       "...           ...  ...         ...         ...         ...         ...   \n",
       "20101     277.500  ...        12.1       223.0        11.8       221.0   \n",
       "20102     277.875  ...        10.2       230.0        10.8       230.0   \n",
       "20103     279.625  ...        10.3       218.0        11.9       221.0   \n",
       "20104     283.000  ...         9.4       218.0         8.6       212.0   \n",
       "20105     283.250  ...         8.6       241.0        10.2       236.0   \n",
       "\n",
       "       windspd_20  winddir_21  windspd_21  winddir_22  windspd_22  \\\n",
       "1             6.1       330.0         6.0       329.0         5.8   \n",
       "2             3.8       360.0         3.2        12.0         3.5   \n",
       "3             5.0        10.0         4.2        38.0         4.0   \n",
       "4             3.9         9.0         4.4        22.0         4.6   \n",
       "5             2.3       329.0         2.5       338.0         2.4   \n",
       "...           ...         ...         ...         ...         ...   \n",
       "20101        11.4       219.0        11.3       215.0        11.4   \n",
       "20102        11.6       227.0        12.3       222.0        12.0   \n",
       "20103        12.8       222.0        11.9       225.0        10.6   \n",
       "20104         8.3       218.0         8.7       226.0        10.1   \n",
       "20105        10.5       232.0        10.5       227.0        11.3   \n",
       "\n",
       "       rotors_present  \n",
       "1                 0.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  \n",
       "5                 0.0  \n",
       "...               ...  \n",
       "20101             0.0  \n",
       "20102             0.0  \n",
       "20103             0.0  \n",
       "20104             0.0  \n",
       "20105             0.0  \n",
       "\n",
       "[17486 rows x 95 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce97b9a9-4a05-43be-bf43-7349ae6897b5",
   "metadata": {},
   "source": [
    "## Exploring key characteristics of the dataset\n",
    "Now that we have loaded our dataset and done some intial cleaning, we can take a look at the key characteristics that will influence the performance of machine learning algorithms for which it is used as a training dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4012374d-291e-47fd-a56d-ed3d6125d939",
   "metadata": {},
   "source": [
    "### Exploring class imbalance\n",
    "\n",
    "In classification problems, like this one, the term class imbalance is defined as follows:\n",
    "\n",
    "*An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed*\n",
    "\n",
    "(taken from article [*A Gentle Introduction to Imbalanced Classification* by Jason Brownlee](https://machinelearningmastery.com/what-is-imbalanced-classification/) )\n",
    "\n",
    "Lets look at the distribution of our target variable, in this case it is the *rotors* column of the data. This variables labels whether or not one or more rotors occured in a given 3 hour period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8872aba2-5442-40dc-8513-1bbf8baa7e42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rotors_present\n",
       "0.0    17037\n",
       "1.0      449\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df['rotors_present'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d121cb54-cad1-4213-b68a-ccce047b82a4",
   "metadata": {},
   "source": [
    "We see that this is an *imbalanced* classification problem. As a result we will have to consider carefully how to take this into account in our ML pipeline. This is because if we don't  the classifier will get a good score by never predicting rotors,  in this particular problem an accuracy of greater than 97% is achieved by this approach.  This is of course not a useful result for this problem based on what we want to achieve. Some of the ways we can take the class imbalance into account include: \n",
    "\n",
    "* *train/test split* - Ensure the distribution between classes in the train, validation and test sets is close to the distribution for the whole dataset, and also that this distribution is representative of the real-world problem.\n",
    "* *possible resampling strategies* - One can resample/undersample/oversample training data so the algorithm is presented with balanced training data\n",
    "* *classification metrics* - Ensure that the metric represents the business need and actual costs of making one prediction or another. In this, failing to predict a rotor event that occurs is very costly and should be penalised by the metric we use, where as predicting a rotor event that does not occur (false positive) is still ndesriable, but less costly so should be less penalised by our algorithm.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7a9355b9-3143-43a7-8de5-bdcd8b4a3953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DTG', 'air_temp_obs', 'dewpoint_obs', 'wind_direction_obs',\n",
       "       'wind_speed_obs', 'wind_gust_obs', 'air_temp_1', 'air_temp_2',\n",
       "       'air_temp_3', 'air_temp_4', 'air_temp_5', 'air_temp_6', 'air_temp_7',\n",
       "       'air_temp_8', 'air_temp_9', 'air_temp_10', 'air_temp_11', 'air_temp_12',\n",
       "       'air_temp_13', 'air_temp_14', 'air_temp_15', 'air_temp_16',\n",
       "       'air_temp_17', 'air_temp_18', 'air_temp_19', 'air_temp_20',\n",
       "       'air_temp_21', 'air_temp_22', 'sh_1', 'sh_2', 'sh_3', 'sh_4', 'sh_5',\n",
       "       'sh_6', 'sh_7', 'sh_8', 'sh_9', 'sh_10', 'sh_11', 'sh_12', 'sh_13',\n",
       "       'sh_14', 'sh_15', 'sh_16', 'sh_17', 'sh_18', 'sh_19', 'sh_20', 'sh_21',\n",
       "       'sh_22', 'winddir_1', 'windspd_1', 'winddir_2', 'windspd_2',\n",
       "       'winddir_3', 'windspd_3', 'winddir_4', 'windspd_4', 'winddir_5',\n",
       "       'windspd_5', 'winddir_6', 'windspd_6', 'winddir_7', 'windspd_7',\n",
       "       'winddir_8', 'windspd_8', 'winddir_9', 'windspd_9', 'winddir_10',\n",
       "       'windspd_10', 'winddir_11', 'windspd_11', 'winddir_12', 'windspd_12',\n",
       "       'winddir_13', 'windspd_13', 'winddir_14', 'windspd_14', 'winddir_15',\n",
       "       'windspd_15', 'winddir_16', 'windspd_16', 'winddir_17', 'windspd_17',\n",
       "       'winddir_18', 'windspd_18', 'winddir_19', 'windspd_19', 'winddir_20',\n",
       "       'windspd_20', 'winddir_21', 'windspd_21', 'winddir_22', 'windspd_22',\n",
       "       'rotors_present'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "falklands_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32ecb5ed-b32e-4500-a4d9-597788f232b0",
   "metadata": {},
   "source": [
    "Above we see the forecast variables from the Met Office Global Forecast model, which we want to use an input to our algorithm. We see there are 4 variables, each on 22 height levels, including\n",
    "* temperature\n",
    "* specific humidity\n",
    "* wind speed\n",
    "* wind direction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b01037-db7e-4965-be42-026fe3e1af55",
   "metadata": {},
   "source": [
    "Next we split into two groups, to see how the distribution of input variables differs for times with and without rotors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5245c7a1-f6b8-4cb0-b7ec-9dbf64f9d7f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a rotors and no rotors dataframe\n",
    "no_rotors_df = falklands_df[falklands_df['rotors_present'] ==False]\n",
    "rotors_present_df = falklands_df[falklands_df['rotors_present'] == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8a380eb0-4467-4aec-bd94-4fff8093f157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17037, 95)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_rotors_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9c7a73b9-0067-45c4-b6ea-1bb6c35c0a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(449, 95)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotors_present_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ff0f5aa9-03c9-4b39-a390-ad9ba591c27f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 17037 events where no rotors are presents, and 449 where rotors are present.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {no_rotors_df.shape[0]} events where no rotors are presents, and {rotors_present_df.shape[0]} where rotors are present.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d6a3bc-7fb1-47bb-9f49-c52eca161c94",
   "metadata": {},
   "source": [
    "### Explore the distributions of meteorological variables\n",
    "\n",
    "We should also explore the distributions of key input or predictor variables more generally. This will tell us about the range of values that are typical for each variable. We also want to know though how that range might be different for our two classes: *rotor's present* and *rotors absent*.\n",
    "\n",
    "We will start by doing this for our *surface observation* variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "87ce6744-b5f5-471a-843c-ff1f786eef65",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/w0/2x361bn95wj7lfgl33vksx1w0000gn/T/ipykernel_17092/1275007709.py:17: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig1.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABSwAAAK7CAYAAAD4L8GRAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAABx8ElEQVR4nOzdeZhWZf0/8M/IMowsI9vMgCJQAkK4JYqYCogsKuJWmhaikkuuJOaaid9vCWpu5Te1UnEhscWtMBJUUEMUUHJDNBPFBHGBAREB4f794Y8nH4bdYeYwvF7XNdfFc879nHOf+zln+Mz7OUtBSikFAAAAAEAGbFPdHQAAAAAAWEVgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJlWDYsGFRUFCQN61NmzZx4oknbtRyJk2aFMOGDYsFCxZs1PtWX9eECROioKAg/vSnP23Uctbl008/jWHDhsWECRMqzBs5cmQUFBTErFmzKm19m8NPfvKT2HHHHaN27dqx3XbbfeXlrfrcP/zww6/euS3MpuzfG2pz7E+rjok17b81wYknnhht2rSp7m4AUMnUmGrMrc3WVmNW1jFeGV599dUYNmzYGsdHrUl1qF3dHYCa6oEHHohGjRpt1HsmTZoUV1xxRZx44okbVexsyro21qeffhpXXHFFRET06NEjb96hhx4azzzzTLRo0WKz9uGreOihh+LnP/95XHrppXHwwQdHYWFhdXeJtdgS9icAqC5qzGxRY245toT9KaJqjrs1efXVV+OKK66IHj16VAgnL7vssjj33HOrvE9s3QSWsJnssccem30dS5YsiaKioipZ17o0b948mjdvXq19WJ+XX345IiLOOeecKCkpqebeVL5V+0JNsCXsTwBQXdSY2aLG3HJsCftTxIYd48uXL4+CgoKoXbtqIp2vf/3rVbIe+DKXhMNGGjNmTOy+++5RWFgYbdu2jV/84hdrbLf6qfwrV66Mn/3sZ9GhQ4coKiqK7bbbLnbddde48cYbI+KLywF+/OMfR0RE27Zto6CgIO/ygjZt2kT//v3j/vvvjz322CPq1auX+zZ6bZcNfPbZZ3HeeedFWVlZFBUVRffu3eOFF17Ia9OjR48K32ZH5J/2P2vWrNx/7ldccUWub6vWubbLK26//fbYbbfdol69etGkSZM48sgjY8aMGRXW06BBg/jXv/4VhxxySDRo0CBatWoVQ4cOjaVLl65xbL9s5cqVcfXVV8fOO+8chYWFUVJSEieccEK8++67uTZt2rSJn/zkJxERUVpaGgUFBTFs2LB1Lvfhhx+Obt26xbbbbhsNGzaM3r17xzPPPLPGtrNnz46jjjoqGjVqFMXFxfH9738/Pvjgg7w2jz/+ePTo0SOaNm0aRUVFseOOO8bRRx8dn376aa7NsmXL4mc/+1luW5o3bx4nnXRShWWtbV/YY489Yv/996/QvxUrVsT2228fRx111Eava/ny5XHBBRdEWVlZbLvttrHffvvFc889t86xW2WvvfaKQw89NG/aLrvsEgUFBTFlypTctPvvvz8KCgripZdeiog17089evSIzp07x5QpU2L//fePbbfdNr72ta/FiBEjYuXKlXnreO2116Jfv36x7bbbRrNmzeL000+PRYsWbVCfP/jggzj11FOjVatWuXH51re+FePHj6/Ql6eeeir22WefKCoqiu233z4uu+yyWLFiRd7yNnScIyLuu+++6NatW9SvXz8aNGgQffv2rXC8rhqfDh06RGFhYXTs2DHuuuuuNW7LzTffHLvttls0aNAgGjZsGDvvvHNccsklGzQOAFQ9NaYac3VqzDXbEmvMiE0/xldddn733XfH0KFDY/vtt4/CwsL417/+FRER48ePj169ekWjRo1i2223jW9961vx2GOPVVjua6+9Fscdd1yUlpZGYWFh7LjjjnHCCSfE0qVLY+TIkfGd73wnIiJ69uyZOxZHjhwZEWu+JPyzzz6Liy++ONq2bRt169aN7bffPs4888wKt55YtV+NHTs2vvnNb0ZRUVHsvPPOcfvtt+e1+/TTT+P888+Ptm3b5o7tLl26xL333rvBY0wNk4ANNn78+FSrVq203377pfvvvz/98Y9/THvttVfacccd0+qHU+vWrdOgQYNyr4cPH55q1aqVLr/88vTYY4+lsWPHphtuuCENGzYspZTS7Nmz09lnn50iIt1///3pmWeeSc8880wqLy/PLa9Fixbpa1/7Wrr99tvTE088kZ577rk1ruuJJ55IEZFatWqVDj/88PSXv/wl3XPPPWmnnXZKjRo1Sm+++Waubffu3VP37t0rbOugQYNS69atU0opffbZZ2ns2LEpItLgwYNzffvXv/6VUkrpjjvuSBGR3nrrrdz7r7zyyhQR6bjjjktjxoxJd911V/ra176WiouL0+uvv563nrp166aOHTumX/ziF2n8+PHppz/9aSooKEhXXHHFej+TU089NUVEOuuss9LYsWPTLbfckpo3b55atWqVPvjgg5RSSs8//3waPHhwiog0duzY9Mwzz6TZs2evdZmjRo1KEZH69OmTHnzwwXTfffelPffcM9WtWzc99dRTuXaXX355iojUunXr9OMf/zj9/e9/T9ddd12qX79+2mOPPdKyZctSSim99dZbqV69eql3797pwQcfTBMmTEijRo1KAwcOTPPnz08ppbRixYrUr1+/VL9+/XTFFVekcePGpd/97ndp++23T506dUqffvppbr1r2xduvPHGFBF545tSSo888kiKiPTwww9v9LoGDRqUCgoK0o9//OP06KOPpuuuuy5tv/32qVGjRnn73JpcdNFFqUGDBrlxmDt3boqIVFRUlH7+85/n2v3whz9MpaWluddr2p+6d++emjZtmtq1a5duueWWNG7cuHTGGWekiEh33nlnrt3cuXNTSUlJ2n777dMdd9yRHnnkkfS9730vd4w+8cQT6+xz3759U/PmzdNvfvObNGHChPTggw+mn/70p2n06NEV+tKyZcv0y1/+Mv39739P55xzToqIdOaZZ+babcw4//znP08FBQXp5JNPTn/961/T/fffn7p165bq16+fXnnllQpjs/px3apVq9zxmlJK9957b4qIdPbZZ6dHH300jR8/Pt1yyy3pnHPOWef2A1A91JhqTDVmza4xv8oxvuq423777dO3v/3t9PDDD6e//vWv6aOPPkp33313KigoSEcccUS6//7701/+8pfUv3//VKtWrTR+/PjcMqZPn54aNGiQ2rRpk2655Zb02GOPpXvuuScdc8wxaeHChWnevHm5Y+v//u//csfivHnzcp/Xl2vNlStXpr59+6batWunyy67LD366KPpF7/4RW4f/eyzz/K2Z4cddkidOnVKd911V/r73/+evvOd76SISBMnTsy1O+2009K2226brrvuuvTEE0+kv/71r2nEiBHpV7/61TrHlppLYAkboWvXrqlly5ZpyZIluWkLFy5MTZo0We9/NP3790+77777Opd/zTXXVPhP9MvLq1WrVpo5c+Ya563pP7VvfvObaeXKlbnps2bNSnXq1Ek/+MEPctM2pJhMKaUPPvggRUS6/PLLK7Rd/T//+fPnp6KionTIIYfktXvnnXdSYWFhOv744/PWExHpD3/4Q17bQw45JHXo0KHCur5sxowZKSLSGWeckTf92WefTRGRLrnkkty0VYXfqgJzbVasWJFatmyZdtlll7RixYrc9EWLFqWSkpK07777Vljmj370o7xlrCpG77nnnpRSSn/6059SRKTp06evdb2rAqY///nPedOnTJmSIiL9+te/zk1b277w4Ycfprp16+Ztd0opHXPMMam0tDQtX758o9a1anzXtn3rKybHjx+fIiI9+eSTKaWU7rnnntSwYcN0xhlnpJ49e+batWvXLm+fWFsxGRHp2WefzVtHp06dUt++fXOvL7zwwlRQUFBhrHv37r1BxWSDBg3SkCFD1tlmVV8eeuihvOmnnHJK2mabbdLbb7+dUtrwcX7nnXdS7dq109lnn53XbtGiRamsrCwdc8wxKaX/7ptrO66/fLyeddZZabvttlvndgCQHWpMNaYas2bXmF/lGF913B1wwAF57RYvXpyaNGmSDjvssLzpK1asSLvttlvae++9c9MOPPDAtN122+UCyDX54x//uNZtWf24XfVFw9VXX53X7r777ksRkX7zm9/kbU+9evVyNXJKKS1ZsiQ1adIknXbaablpnTt3TkccccRa+8fWxyXhsIEWL14cU6ZMiaOOOirq1auXm96wYcM47LDD1vv+vffeO/75z3/GGWecEX//+99j4cKFG92HXXfdNdq3b7/B7Y8//vi8p861bt069t1333jiiSc2et0b45lnnoklS5ZUuISoVatWceCBB1a4RKGgoKDCGO66667x9ttvr3M9q7Zj9fXsvffe0bFjxzVeCrE+M2fOjPfeey8GDhwY22zz31+RDRo0iKOPPjomT56cd4lNRMT3vve9vNfHHHNM1K5dO9e/3XffPerWrRunnnpq3HnnnfHvf/+7wnr/+te/xnbbbReHHXZYfP7557mf3XffPcrKyio8eXBN+0LTpk3jsMMOizvvvDN3Ccv8+fPjoYceihNOOCF3j5sNXdeq/q9t+9bnW9/6VtSrVy93OfW4ceOiR48e0a9fv5g0aVJ8+umnMXv27HjjjTfioIMOWu/yysrKYu+9964wDl/eT5544on4xje+Ebvttlteu+OPP369y4/4Yt8ZOXJk/OxnP4vJkyfH8uXL19iuYcOGMWDAgArrWLlyZTz55JMRseHj/Pe//z0+//zzOOGEE/La1atXL7p3755rt2rfXNtxvfp2LFiwII477rh46KGHtsqnjAJsKdSYG06NqcaM2PJqzK96jK9y9NFH572eNGlSfPzxxzFo0KC88V65cmX069cvpkyZEosXL45PP/00Jk6cGMccc0yl3cPz8ccfj4iKx8h3vvOdqF+/foVjZPfdd48dd9wx97pevXrRvn37vDHee++9429/+1tcdNFFMWHChFiyZEml9JUtl8ASNtD8+fNj5cqVUVZWVmHemqat7uKLL45f/OIXMXny5Dj44IOjadOm0atXr5g6deoG92Fjn2i3tr5+9NFHG7WcjbVq+Wvqb8uWLSusf9ttt837zzsiorCwMD777LNKXc+GWN8yV65cGfPnz8+bvvo4165dO5o2bZpb1te//vUYP358lJSUxJlnnhlf//rX4+tf/3ru3lIREe+//34sWLAg6tatG3Xq1Mn7mTt3boXAaW37wsknnxz/+c9/Yty4cRERce+998bSpUvziokNXdeq/q9t+9anXr16efd/fOyxx6J3797Ro0ePWLFiRTz11FO5fm5IMbmmdRYWFuYVMx999NEmH6MRX9xHctCgQfG73/0uunXrFk2aNIkTTjgh5s6dm9eutLR0retYNW4bOs7vv/9+RHxxP6bV2913333r/TzWNG3gwIFx++23x9tvvx1HH310lJSURNeuXXPjDUB2qDE3nBpTjRmx5dWYX/UYX2X1z2ZVDfntb3+7wnhfddVVkVKKjz/+OObPnx8rVqyIHXbYYYPXtT4fffRR1K5du0IAWlBQsMbfBRsyxr/85S/jwgsvjAcffDB69uwZTZo0iSOOOCLeeOONSus3WxZPCYcN1Lhx4ygoKKgQXETEGqetrnbt2nHeeefFeeedFwsWLIjx48fHJZdcEn379o3Zs2fHtttuu95lfPmb7A2xtr5++T+MevXqRXl5eYV2X+WMrFXLnzNnToV57733XjRr1myTl7229az+H/Cmrmd9fd9mm22icePGedPnzp0b22+/fe71559/Hh999FHeOO+///6x//77x4oVK2Lq1Knxq1/9KoYMGRKlpaXx3e9+N5o1axZNmzaNsWPHrrFfDRs2zHu9tn2hb9++0bJly7jjjjuib9++cccdd0TXrl2jU6dOuTYbuq5V/V/b9m2IXr16xU9/+tN47rnn4t13343evXtHw4YNY6+99opx48bFe++9F+3bt49WrVpt0PLWp2nTppt8jEZ8MTY33HBD3HDDDfHOO+/Eww8/HBdddFHMmzcvb7xWFYhrWseqcdvQcV61n/7pT3+K1q1br3Pb1rYta5p20kknxUknnRSLFy+OJ598Mi6//PLo379/vP766+tcDwBVS4254dSYasxVtqQa86se46us/tms2g9/9atfxT777LPG95SWlsaKFSuiVq1aeQ+M+qqaNm0an3/+eXzwwQd5oWVKKebOnRt77bXXRi+zfv36ccUVV8QVV1wR77//fu5sy8MOOyxee+21Sus7Ww5nWMIGql+/fuy9995x//33530ru2jRovjLX/6yUcvabrvt4tvf/naceeaZ8fHHH+eeVFdYWBgRUWmnv997772RUsq9fvvtt2PSpEl5T2xs06ZNvP7663lPS/zoo49i0qRJecvamL5169YtioqK4p577smb/u6778bjjz8evXr12pTNqeDAAw+MiKiwnilTpsSMGTM2aT0dOnSI7bffPn7/+9/njd3ixYvjz3/+c+6pjl82atSovNd/+MMf4vPPP1/jkzFr1aoVXbt2jf/7v/+LiIjnn38+IiL69+8fH330UaxYsSK6dOlS4adDhw4b1P9atWrFwIED48EHH4ynnnoqpk6dGieffHJemw1d16r+r237NsRBBx0Un3/+eVx22WWxww47xM4775ybPn78+Hj88cc36JvvDdWzZ8945ZVX4p///Gfe9N///vcbvawdd9wxzjrrrOjdu3fuc1pl0aJF8fDDD1dYxzbbbBMHHHBARGz4OPft2zdq164db7755hrbdenSJSK+2DdbtGix1uN6berXrx8HH3xwXHrppbFs2bJ45ZVXNnosANh81JhqTDVm/vZtiC2pxqzMY/zLvvWtb8V2220Xr7766lpryLp160ZRUVF07949/vjHP67zC4ONORZXHQOrHyN//vOfY/HixV/5WCwtLY0TTzwxjjvuuJg5c2aF2yWwdXCGJWyE//3f/41+/fpF7969Y+jQobFixYq46qqron79+vHxxx+v872HHXZYdO7cObp06RLNmzePt99+O2644YZo3bp1tGvXLiIidtlll4iIuPHGG2PQoEFRp06d6NChQ4VvPjfUvHnz4sgjj4xTTjklysvL4/LLL4969erFxRdfnGszcODAuPXWW+P73/9+nHLKKfHRRx/F1VdfHY0aNcpbVsOGDaN169bx0EMPRa9evaJJkybRrFmzaNOmTYX1brfddnHZZZfFJZdcEieccEIcd9xx8dFHH8UVV1wR9erVi8svv3yTtmd1HTp0iFNPPTV+9atfxTbbbBMHH3xwzJo1Ky677LJo1apV/OhHP9roZW6zzTZx9dVXx/e+973o379/nHbaabF06dK45pprYsGCBTFixIgK77n//vujdu3a0bt373jllVfisssui9122y2OOeaYiIi45ZZb4vHHH49DDz00dtxxx/jss8/i9ttvj4j/Xqby3e9+N0aNGhWHHHJInHvuubH33ntHnTp14t13340nnngiDj/88DjyyCM3aBtOPvnkuOqqq+L444+PoqKiOPbYY/Pmb+i6OnbsGN///vfjhhtuiDp16sRBBx0UL7/8cvziF7+osH+szZ577hmNGzeORx99NE466aTc9IMOOij+93//N28MKsOQIUPi9ttvj0MPPTR+9rOfRWlpaYwaNWqDvpUtLy+Pnj17xvHHHx8777xzNGzYMKZMmRJjx46No446Kq9t06ZN44c//GG888470b59+3jkkUfit7/9bfzwhz/M3Z9nQ8e5TZs28T//8z9x6aWXxr///e/o169fNG7cON5///147rnnct82b7PNNvG///u/8YMf/CB3XC9YsCCGDRtW4XKiU045JYqKiuJb3/pWtGjRIubOnRvDhw+P4uLiTfrGG4DNS42pxlRj1twaM+KrHeNr06BBg/jVr34VgwYNio8//ji+/e1vR0lJSXzwwQfxz3/+Mz744IO4+eabIyLiuuuui/322y+6du0aF110Uey0007x/vvvx8MPPxy33nprNGzYMDp37hwREb/5zW+iYcOGUa9evWjbtu0aL+fu3bt39O3bNy688MJYuHBhfOtb34oXX3wxLr/88thjjz1i4MCBG709Xbt2jf79+8euu+4ajRs3jhkzZsTdd9+9xjCfrUQ1PvAHtkgPP/xw2nXXXVPdunXTjjvumEaMGJF7kt+Xrf50t2uvvTbtu+++qVmzZrn3Dh48OM2aNSvvfRdffHFq2bJl2mabbfKe0ta6det06KGHrrFPa3uS3N13353OOeec1Lx581RYWJj233//NHXq1Arvv/POO1PHjh1TvXr1UqdOndJ9991X4UlwKX3xRL499tgjFRYW5j3Bb01P3Esppd/97ne5sSouLk6HH354euWVV/LaDBo0KNWvX79Cn9Y0pmuyYsWKdNVVV6X27dunOnXqpGbNmqXvf//7afbs2Wtc3vqe4LjKgw8+mLp27Zrq1auX6tevn3r16pX+8Y9/rHGZ06ZNS4cddlhq0KBBatiwYTruuOPS+++/n2v3zDPPpCOPPDK1bt06FRYWpqZNm6bu3bunhx9+OG95y5cvT7/4xS/SbrvtlurVq5caNGiQdt5553TaaaelN954I9duXfvCKvvuu2+KiPS9731vjfM3dF1Lly5NQ4cOTSUlJalevXppn332Sc8880yFfW5djjzyyBQRadSoUblpy5YtS/Xr10/bbLNNmj9/fl77tT3B8Rvf+EaFZa9pP3311VdT7969U7169VKTJk3S4MGD00MPPbTeJzh+9tln6fTTT0+77rpratSoUSoqKkodOnRIl19+eVq8eHGFvkyYMCF16dIlFRYWphYtWqRLLrkk95TMVTZ0nFP6Yp/r2bNnatSoUSosLEytW7dO3/72t9P48ePz2v3ud79L7dq1S3Xr1k3t27dPt99+e4VxuPPOO1PPnj1TaWlpqlu3bmrZsmU65phj0osvvrjW7Qegeqkx1ZirL1ONuW5bSo25yqYe46uOuz/+8Y9rXO7EiRPToYcempo0aZLq1KmTtt9++3TooYdWaP/qq6+m73znO6lp06a5Ppx44onps88+y7W54YYbUtu2bVOtWrVSRKQ77rhjreOxZMmSdOGFF6bWrVunOnXqpBYtWqQf/vCHFcZ9bftV9+7dU/fu3XOvL7rootSlS5fUuHHjVFhYmL72ta+lH/3oR+nDDz9cy4hS0xWk9KXz0QGAderRo0d8+OGH8fLLL1d3VwAAAGok97AEAAAAADJDYAkAAAAAZIZLwgEAAACAzKjWMyxvvvnm2HXXXaNRo0bRqFGj6NatW/ztb3/LzT/xxBOjoKAg72efffbJW8bSpUvj7LPPjmbNmkX9+vVjwIAB8e677+a1mT9/fgwcODCKi4ujuLg4Bg4cGAsWLKiKTQQAIMPUowAA2VOtgeUOO+wQI0aMiKlTp8bUqVPjwAMPjMMPPzxeeeWVXJt+/frFnDlzcj+PPPJI3jKGDBkSDzzwQIwePTqefvrp+OSTT6J///6xYsWKXJvjjz8+pk+fHmPHjo2xY8fG9OnTY+DAgVW2nQAAZJN6FAAgezJ3SXiTJk3immuuicGDB8eJJ54YCxYsiAcffHCNbcvLy6N58+Zx9913x7HHHhsREe+99160atUqHnnkkejbt2/MmDEjOnXqFJMnT46uXbtGRMTkyZOjW7du8dprr0WHDh2qatMAANgCqEcBAKpX7eruwCorVqyIP/7xj7F48eLo1q1bbvqECROipKQktttuu+jevXv8/Oc/j5KSkoiImDZtWixfvjz69OmTa9+yZcvo3LlzTJo0Kfr27RvPPPNMFBcX54rDiIh99tkniouLY9KkSWstEJcuXRpLly7NvV65cmV8/PHH0bRp0ygoKKjszQcA2KxSSrFo0aJo2bJlbLON5y6uiXoUAGDz2tCatNoDy5deeim6desWn332WTRo0CAeeOCB6NSpU0REHHzwwfGd73wnWrduHW+99VZcdtllceCBB8a0adOisLAw5s6dG3Xr1o3GjRvnLbO0tDTmzp0bERFz587NFZRfVlJSkmuzJsOHD48rrriiErcUAKD6zZ49O3bYYYfq7kamqEcBAKrW+mrSag8sO3ToENOnT48FCxbEn//85xg0aFBMnDgxOnXqlLusJiKic+fO0aVLl2jdunWMGTMmjjrqqLUuM6WU963zmr6BXr3N6i6++OI477zzcq/Ly8tjxx13jNmzZ0ejRo02djMBAKrVwoULo1WrVtGwYcPq7krmqEcBAKrGhtak1R5Y1q1bN3baaaeIiOjSpUtMmTIlbrzxxrj11lsrtG3RokW0bt063njjjYiIKCsri2XLlsX8+fPzvtWeN29e7Lvvvrk277//foVlffDBB1FaWrrWfhUWFkZhYWGF6aueIAkAsCVyKXFF6lEAgKq1vpo0czcwSinl3avnyz766KOYPXt2tGjRIiIi9txzz6hTp06MGzcu12bOnDnx8ssv5wrEbt26RXl5eTz33HO5Ns8++2yUl5fn2gAAwCrqUQCA6lWtZ1hecsklcfDBB0erVq1i0aJFMXr06JgwYUKMHTs2Pvnkkxg2bFgcffTR0aJFi5g1a1Zccskl0axZszjyyCMjIqK4uDgGDx4cQ4cOjaZNm0aTJk3i/PPPj1122SUOOuigiIjo2LFj9OvXL0455ZTct+Snnnpq9O/f3xMZAQC2cupRAIDsqdbA8v3334+BAwfGnDlzori4OHbdddcYO3Zs9O7dO5YsWRIvvfRS3HXXXbFgwYJo0aJF9OzZM+67776869yvv/76qF27dhxzzDGxZMmS6NWrV4wcOTJq1aqVazNq1Kg455xzck9vHDBgQNx0001Vvr0AAGSLehQAIHsKUkqpujuxJVi4cGEUFxdHeXm5ewYBAFsctcyWz2cIAGzpNrSeydw9LAEAAACArZfAEgAAAADIDIElAAAAAJAZ1frQHQAAAACyoc1FY6pkPbNGHFol62HL5QxLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzqjWwvPnmm2PXXXeNRo0aRaNGjaJbt27xt7/9LTc/pRTDhg2Lli1bRlFRUfTo0SNeeeWVvGUsXbo0zj777GjWrFnUr18/BgwYEO+++25em/nz58fAgQOjuLg4iouLY+DAgbFgwYKq2EQAADJMPQoAkD3VGljusMMOMWLEiJg6dWpMnTo1DjzwwDj88MNzReDVV18d1113Xdx0000xZcqUKCsri969e8eiRYtyyxgyZEg88MADMXr06Hj66afjk08+if79+8eKFStybY4//viYPn16jB07NsaOHRvTp0+PgQMHVvn2AgCQLepRAIDsKUgpperuxJc1adIkrrnmmjj55JOjZcuWMWTIkLjwwgsj4otvr0tLS+Oqq66K0047LcrLy6N58+Zx9913x7HHHhsREe+99160atUqHnnkkejbt2/MmDEjOnXqFJMnT46uXbtGRMTkyZOjW7du8dprr0WHDh02qF8LFy6M4uLiKC8vj0aNGm2ejQcA2EzUMhtOPQrA1qrNRWOqZD2zRhxaJesheza0nsnMPSxXrFgRo0ePjsWLF0e3bt3irbfeirlz50afPn1ybQoLC6N79+4xadKkiIiYNm1aLF++PK9Ny5Yto3Pnzrk2zzzzTBQXF+eKw4iIffbZJ4qLi3Nt1mTp0qWxcOHCvB8AAGou9SgAQDZUe2D50ksvRYMGDaKwsDBOP/30eOCBB6JTp04xd+7ciIgoLS3Na19aWpqbN3fu3Khbt240btx4nW1KSkoqrLekpCTXZk2GDx+eu8dQcXFxtGrV6ittJwAA2aQeBQDIlmoPLDt06BDTp0+PyZMnxw9/+MMYNGhQvPrqq7n5BQUFee1TShWmrW71Nmtqv77lXHzxxVFeXp77mT179oZuEgAAWxD1KABAtlR7YFm3bt3YaaedokuXLjF8+PDYbbfd4sYbb4yysrKIiArfOs+bNy/3LXdZWVksW7Ys5s+fv84277//foX1fvDBBxW+Lf+ywsLC3NMiV/0AAFDzqEcBALKl2gPL1aWUYunSpdG2bdsoKyuLcePG5eYtW7YsJk6cGPvuu29EROy5555Rp06dvDZz5syJl19+OdemW7duUV5eHs8991yuzbPPPhvl5eW5NgAAsIp6FACgetWuzpVfcsklcfDBB0erVq1i0aJFMXr06JgwYUKMHTs2CgoKYsiQIXHllVdGu3btol27dnHllVfGtttuG8cff3xERBQXF8fgwYNj6NCh0bRp02jSpEmcf/75scsuu8RBBx0UEREdO3aMfv36xSmnnBK33nprRESceuqp0b9//w1+IiMAADWTehQAIHuqNbB8//33Y+DAgTFnzpwoLi6OXXfdNcaOHRu9e/eOiIgLLrgglixZEmeccUbMnz8/unbtGo8++mg0bNgwt4zrr78+ateuHcccc0wsWbIkevXqFSNHjoxatWrl2owaNSrOOeec3NMbBwwYEDfddFPVbiwAAJmjHgUAyJ6ClFKq7k5sCRYuXBjFxcVRXl7u/kEAwBZHLbPl8xkCsLm1uWhMlaxn1ohDq2Q9ZM+G1jOZu4clAAAAALD1ElgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGbWruwNA1Wtz0ZgqWc+sEYdWyXoAAACAmsMZlgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMsNDd2ADVcWDajykBgAAANjaOcMSAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyIxqDSyHDx8ee+21VzRs2DBKSkriiCOOiJkzZ+a1OfHEE6OgoCDvZ5999slrs3Tp0jj77LOjWbNmUb9+/RgwYEC8++67eW3mz58fAwcOjOLi4iguLo6BAwfGggULNvcmAgCQYepRAIDsqdbAcuLEiXHmmWfG5MmTY9y4cfH5559Hnz59YvHixXnt+vXrF3PmzMn9PPLII3nzhwwZEg888ECMHj06nn766fjkk0+if//+sWLFilyb448/PqZPnx5jx46NsWPHxvTp02PgwIFVsp0AAGSTehQAIHtqV+fKx44dm/f6jjvuiJKSkpg2bVoccMABuemFhYVRVla2xmWUl5fHbbfdFnfffXccdNBBERFxzz33RKtWrWL8+PHRt2/fmDFjRowdOzYmT54cXbt2jYiI3/72t9GtW7eYOXNmdOjQYTNtIQAAWaYeBQDInkzdw7K8vDwiIpo0aZI3fcKECVFSUhLt27ePU045JebNm5ebN23atFi+fHn06dMnN61ly5bRuXPnmDRpUkREPPPMM1FcXJwrDiMi9tlnnyguLs61Wd3SpUtj4cKFeT8AANRs6lEAgOqXmcAypRTnnXde7LffftG5c+fc9IMPPjhGjRoVjz/+eFx77bUxZcqUOPDAA2Pp0qURETF37tyoW7duNG7cOG95paWlMXfu3FybkpKSCussKSnJtVnd8OHDc/cXKi4ujlatWlXWpgIAkEHqUQCAbKjWS8K/7KyzzooXX3wxnn766bzpxx57bO7fnTt3ji5dukTr1q1jzJgxcdRRR611eSmlKCgoyL3+8r/X1ubLLr744jjvvPNyrxcuXKhIBACowdSjAADZkIkzLM8+++x4+OGH44knnogddthhnW1btGgRrVu3jjfeeCMiIsrKymLZsmUxf/78vHbz5s2L0tLSXJv333+/wrI++OCDXJvVFRYWRqNGjfJ+AAComdSjAADZUa2BZUopzjrrrLj//vvj8ccfj7Zt2673PR999FHMnj07WrRoERERe+65Z9SpUyfGjRuXazNnzpx4+eWXY999942IiG7dukV5eXk899xzuTbPPvtslJeX59oAALD1UY8CAGRPtV4SfuaZZ8bvf//7eOihh6Jhw4a5+/cUFxdHUVFRfPLJJzFs2LA4+uijo0WLFjFr1qy45JJLolmzZnHkkUfm2g4ePDiGDh0aTZs2jSZNmsT5558fu+yyS+4pjR07dox+/frFKaecErfeemtERJx66qnRv39/T2QEANiKqUcBALKnWgPLm2++OSIievTokTf9jjvuiBNPPDFq1aoVL730Utx1112xYMGCaNGiRfTs2TPuu+++aNiwYa799ddfH7Vr145jjjkmlixZEr169YqRI0dGrVq1cm1GjRoV55xzTu7pjQMGDIibbrpp828kAACZpR4FAMiegpRSqu5ObAkWLlwYxcXFUV5e7v5BW6k2F43Z7OuYNeLQzb6OiKrZloiq2x4A1k8ts+XzGQKwuflbkc1tQ+uZTDx0BwAAAAAgQmAJAAAAAGSIwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADKjdnV3AAAAAICtR5uLxmz2dcwacehmXwebjzMsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADKjWgPL4cOHx1577RUNGzaMkpKSOOKII2LmzJl5bVJKMWzYsGjZsmUUFRVFjx494pVXXslrs3Tp0jj77LOjWbNmUb9+/RgwYEC8++67eW3mz58fAwcOjOLi4iguLo6BAwfGggULNvcmAgCQYepRAIDsqdbAcuLEiXHmmWfG5MmTY9y4cfH5559Hnz59YvHixbk2V199dVx33XVx0003xZQpU6KsrCx69+4dixYtyrUZMmRIPPDAAzF69Oh4+umn45NPPon+/fvHihUrcm2OP/74mD59eowdOzbGjh0b06dPj4EDB1bp9gIAkC3qUQCA7ClIKaXq7sQqH3zwQZSUlMTEiRPjgAMOiJRStGzZMoYMGRIXXnhhRHzx7XVpaWlcddVVcdppp0V5eXk0b9487r777jj22GMjIuK9996LVq1axSOPPBJ9+/aNGTNmRKdOnWLy5MnRtWvXiIiYPHlydOvWLV577bXo0KHDevu2cOHCKC4ujvLy8mjUqNHmGwQyq81FYzb7OmaNOHSzryOiarYlouq2B4D1U8tsGPUoAFuzqvpbsSr4ezSbNrSeydQ9LMvLyyMiokmTJhER8dZbb8XcuXOjT58+uTaFhYXRvXv3mDRpUkRETJs2LZYvX57XpmXLltG5c+dcm2eeeSaKi4tzxWFExD777BPFxcW5NqtbunRpLFy4MO8HAICaTT0KAFD9MhNYppTivPPOi/322y86d+4cERFz586NiIjS0tK8tqWlpbl5c+fOjbp160bjxo3X2aakpKTCOktKSnJtVjd8+PDc/YWKi4ujVatWX20DAQDINPUoAEA2ZCawPOuss+LFF1+Me++9t8K8goKCvNcppQrTVrd6mzW1X9dyLr744igvL8/9zJ49e0M2AwCALZR6FAAgG2pXdwciIs4+++x4+OGH48knn4wddtghN72srCwivvhGukWLFrnp8+bNy33LXVZWFsuWLYv58+fnfas9b9682HfffXNt3n///Qrr/eCDDyp8W75KYWFhFBYWfvWNg41Qk+4XAgBbEvUoAEB2VOsZlimlOOuss+L++++Pxx9/PNq2bZs3v23btlFWVhbjxo3LTVu2bFlMnDgxV/ztueeeUadOnbw2c+bMiZdffjnXplu3blFeXh7PPfdcrs2zzz4b5eXluTYAAGx91KMAANlTrWdYnnnmmfH73/8+HnrooWjYsGHu/j3FxcVRVFQUBQUFMWTIkLjyyiujXbt20a5du7jyyitj2223jeOPPz7XdvDgwTF06NBo2rRpNGnSJM4///zYZZdd4qCDDoqIiI4dO0a/fv3ilFNOiVtvvTUiIk499dTo37//Bj2REQCAmkk9CgCQPdUaWN58880REdGjR4+86XfccUeceOKJERFxwQUXxJIlS+KMM86I+fPnR9euXePRRx+Nhg0b5tpff/31Ubt27TjmmGNiyZIl0atXrxg5cmTUqlUr12bUqFFxzjnn5J7eOGDAgLjppps27wYCAJBp6lEAgOwpSCml6u7ElmDhwoVRXFwc5eXl0ahRo+ruDtXA/SU33qwRh1Z3FwD4/9QyWz6fIQCbW036u9ffo9m0ofVMZp4SDgAAAAAgsAQAAAAAMkNgCQAAAABkhsASAAAAAMiMTQos33rrrcruBwAAbBQ1KQBAzbRJgeVOO+0UPXv2jHvuuSc+++yzyu4TAACsl5oUAKBm2qTA8p///GfsscceMXTo0CgrK4vTTjstnnvuucruGwAArJWaFACgZtqkwLJz585x3XXXxX/+85+44447Yu7cubHffvvFN77xjbjuuuvigw8+qOx+AgBAHjUpAEDN9JUeulO7du048sgj4w9/+ENcddVV8eabb8b5558fO+ywQ5xwwgkxZ86cyuonAACskZoUAKBm+UqB5dSpU+OMM86IFi1axHXXXRfnn39+vPnmm/H444/Hf/7znzj88MMrq58AALBGalIAgJql9qa86brrros77rgjZs6cGYccckjcddddccghh8Q223yRf7Zt2zZuvfXW2HnnnSu1swAAsIqaFACgZtqkwPLmm2+Ok08+OU466aQoKytbY5sdd9wxbrvttq/UOQAAWBs1KQBAzbRJgeUbb7yx3jZ169aNQYMGbcriAQBgvdSkAAA10ybdw/KOO+6IP/7xjxWm//GPf4w777zzK3cKAADWR00KAFAzbVJgOWLEiGjWrFmF6SUlJXHllVd+5U4BAMD6qEkBAGqmTQos33777Wjbtm2F6a1bt4533nnnK3cKAADWR00KAFAzbVJgWVJSEi+++GKF6f/85z+jadOmX7lTAACwPmpSAICaaZMeuvPd7343zjnnnGjYsGEccMABERExceLEOPfcc+O73/1upXYQYF3aXDSmStYza8ShVbIeADacmhQAoGbapMDyZz/7Wbz99tvRq1evqF37i0WsXLkyTjjhBPcLAgCgSqhJAQBqpk0KLOvWrRv33Xdf/O///m/885//jKKiothll12idevWld0/AABYIzUpAEDNtEmB5Srt27eP9u3bV1ZfAABgo6lJAQBqlk0KLFesWBEjR46Mxx57LObNmxcrV67Mm//4449XSucAAGBt1KQAADXTJgWW5557bowcOTIOPfTQ6Ny5cxQUFFR2vwAAYJ3UpAAANdMmBZajR4+OP/zhD3HIIYdUdn8AAGCDqEkBAGqmbTblTXXr1o2ddtqpsvsCAAAbTE0KAFAzbVJgOXTo0LjxxhsjpVTZ/QEAgA2iJgUAqJk26ZLwp59+Op544on429/+Ft/4xjeiTp06efPvv//+SukcAACsjZoUAKBm2qTAcrvttosjjzyysvsCAAAbTE0KAFAzbVJgeccdd1R2PwAAYKOoSQEAaqZNuodlRMTnn38e48ePj1tvvTUWLVoUERHvvfdefPLJJ5XWOQAAWBc1KQBAzbNJZ1i+/fbb0a9fv3jnnXdi6dKl0bt372jYsGFcffXV8dlnn8Utt9xS2f0EAIA8alIAgJppk86wPPfcc6NLly4xf/78KCoqyk0/8sgj47HHHqu0zgEAwNqoSQEAaqZNfkr4P/7xj6hbt27e9NatW8d//vOfSukYAACsi5oUAKBm2qQzLFeuXBkrVqyoMP3dd9+Nhg0bfuVOAQDA+qhJAQBqpk0KLHv37h033HBD7nVBQUF88skncfnll8chhxxSWX0DAIC1UpMCANRMm3RJ+PXXXx89e/aMTp06xWeffRbHH398vPHGG9GsWbO49957K7uPAABQgZoUAKBm2qTAsmXLljF9+vS499574/nnn4+VK1fG4MGD43vf+17eDc8BAGBzUZMCANRMmxRYRkQUFRXFySefHCeffHJl9gcAADaYmhQAoObZpMDyrrvuWuf8E044YZM6AwAAG0pNCgBQM21SYHnuuefmvV6+fHl8+umnUbdu3dh2220VhwAAbHZqUgCAmmmTnhI+f/78vJ9PPvkkZs6cGfvtt58bnAMAUCXUpAAANdMmBZZr0q5duxgxYkSFb7oBAKCqqEkBALZ8lRZYRkTUqlUr3nvvvcpcJAAAbBQ1KQDAlm2T7mH58MMP571OKcWcOXPipptuim9961uV0jEAAFgXNSkAQM20SYHlEUcckfe6oKAgmjdvHgceeGBce+21ldEvAABYJzUpAEDNtEmB5cqVKyu7HwAAsFHUpAAANVOl3sMSAAAAAOCr2KQzLM8777wNbnvddddtyioAAGCd1KQAADXTJgWWL7zwQjz//PPx+eefR4cOHSIi4vXXX49atWrFN7/5zVy7goKCyuklAACsRk0KAFAzbVJgedhhh0XDhg3jzjvvjMaNG0dExPz58+Okk06K/fffP4YOHVqpnQQAgNWpSQEAaqZNuofltddeG8OHD88VhhERjRs3jp/97GeeyAgAQJVQkwIA1EybFFguXLgw3n///QrT582bF4sWLfrKnQIAgPVRkwIA1EybFFgeeeSRcdJJJ8Wf/vSnePfdd+Pdd9+NP/3pTzF48OA46qijKruPAABQgZoUAKBm2qR7WN5yyy1x/vnnx/e///1Yvnz5FwuqXTsGDx4c11xzTaV2EAAA1kRNCgBQM21SYLntttvGr3/967jmmmvizTffjJRS7LTTTlG/fv3K7h8AAKyRmhQAoGbapEvCV5kzZ07MmTMn2rdvH/Xr14+UUmX1CwAANoiaFACgZtmkwPKjjz6KXr16Rfv27eOQQw6JOXPmRETED37wgxg6dGildhAAANZETQoAUDNtUmD5ox/9KOrUqRPvvPNObLvttrnpxx57bIwdO7bSOgcAAGujJgUAqJk26R6Wjz76aPz973+PHXbYIW96u3bt4u23366UjgEAwLqoSQHIgjYXjdns65g14tDNvg7Ikk06w3Lx4sV532Kv8uGHH0ZhYeFX7hQAAKyPmhQAoGbapMDygAMOiLvuuiv3uqCgIFauXBnXXHNN9OzZs9I6BwAAa6MmBQComTbpkvBrrrkmevToEVOnTo1ly5bFBRdcEK+88kp8/PHH8Y9//KOy+wgAABWoSQEAaqZNOsOyU6dO8eKLL8bee+8dvXv3jsWLF8dRRx0VL7zwQnz961+v7D4CAEAFalIAgJppo8+wXL58efTp0yduvfXWuOKKKzZHnwAAYJ3UpAAANddGn2FZp06dePnll6OgoGBz9AcAANZLTQoAUHNt0iXhJ5xwQtx2222V3RcAANhgalIAgJppkx66s2zZsvjd734X48aNiy5dukT9+vXz5l933XWV0jkAAFgbNSkAQM20UYHlv//972jTpk28/PLL8c1vfjMiIl5//fW8Ni7LAQBgc1KTAgDUbBsVWLZr1y7mzJkTTzzxREREHHvssfHLX/4ySktLN0vnAABgdWpSAICabaPuYZlSynv9t7/9LRYvXlypHQIAgHVRkwIA1Gyb9NCdVVYvFgEAoKqpSQEAapaNuiS8oKCgwv2A3B8IWJs2F42p7i4AUAOpSQEAaraNCixTSnHiiSdGYWFhRER89tlncfrpp1d4IuP9999feT0EAIAvUZMCANRsGxVYDho0KO/197///UrtDAAArI+aFACgZtuowPKOO+7YXP0AAIANoiYFAKjZvtJDdwAAAAAAKpPAEgAAAADIjGoNLJ988sk47LDDomXLllFQUBAPPvhg3vwTTzwx9xTIVT/77LNPXpulS5fG2WefHc2aNYv69evHgAED4t13381rM3/+/Bg4cGAUFxdHcXFxDBw4MBYsWLCZtw4AgKxTjwIAZM9G3cOysi1evDh22223OOmkk+Loo49eY5t+/frl3aeobt26efOHDBkSf/nLX2L06NHRtGnTGDp0aPTv3z+mTZsWtWrVioiI448/Pt59990YO3ZsRESceuqpMXDgwPjLX/6ymbaMVdpcNKa6uwAAsFbqUQCA7KnWwPLggw+Ogw8+eJ1tCgsLo6ysbI3zysvL47bbbou77747DjrooIiIuOeee6JVq1Yxfvz46Nu3b8yYMSPGjh0bkydPjq5du0ZExG9/+9vo1q1bzJw5Mzp06FC5GwUAwBZDPQoAkD2Zv4flhAkToqSkJNq3bx+nnHJKzJs3Lzdv2rRpsXz58ujTp09uWsuWLaNz584xadKkiIh45plnori4OFccRkTss88+UVxcnGuzJkuXLo2FCxfm/QAAsPVRjwIAVK1MB5YHH3xwjBo1Kh5//PG49tprY8qUKXHggQfG0qVLIyJi7ty5Ubdu3WjcuHHe+0pLS2Pu3Lm5NiUlJRWWXVJSkmuzJsOHD8/dY6i4uDhatWpViVsGAMCWQD0KAFD1qvWS8PU59thjc//u3LlzdOnSJVq3bh1jxoyJo446aq3vSylFQUFB7vWX/722Nqu7+OKL47zzzsu9XrhwoSIRAGArox4FAKh6mT7DcnUtWrSI1q1bxxtvvBEREWVlZbFs2bKYP39+Xrt58+ZFaWlprs37779fYVkffPBBrs2aFBYWRqNGjfJ+AADYuqlHAQA2vy0qsPzoo49i9uzZ0aJFi4iI2HPPPaNOnToxbty4XJs5c+bEyy+/HPvuu29ERHTr1i3Ky8vjueeey7V59tlno7y8PNcGAAA2hHoUAGDzq9ZLwj/55JP417/+lXv91ltvxfTp06NJkybRpEmTGDZsWBx99NHRokWLmDVrVlxyySXRrFmzOPLIIyMiori4OAYPHhxDhw6Npk2bRpMmTeL888+PXXbZJfeUxo4dO0a/fv3ilFNOiVtvvTUiIk499dTo37+/JzICAGzl1KMAANlTrYHl1KlTo2fPnrnXq+7RM2jQoLj55pvjpZdeirvuuisWLFgQLVq0iJ49e8Z9990XDRs2zL3n+uuvj9q1a8cxxxwTS5YsiV69esXIkSOjVq1auTajRo2Kc845J/f0xgEDBsRNN91URVsJAEBWqUcBALKnIKWUqrsTW4KFCxdGcXFxlJeXu3/QRmhz0Zjq7gJUilkjDq3uLgB8JWqZLZ/PECCbquLv3qr6e6Qm/Q3vb7hs2tB6Zou6hyUAAAAAULMJLAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMyoXd0dAAAAAADWrs1FYzb7OmaNOHSzr2NDOcMSAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmVG7ujsAAAAAUNnaXDSmursAbCJnWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMiM2tXdAQAAAADYErW5aEx1d6FGcoYlAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGRG7eruAAAAAABr1+aiMdXdBahSAksAAAAAahQh75bNJeEAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkRrUGlk8++WQcdthh0bJlyygoKIgHH3wwb35KKYYNGxYtW7aMoqKi6NGjR7zyyit5bZYuXRpnn312NGvWLOrXrx8DBgyId999N6/N/PnzY+DAgVFcXBzFxcUxcODAWLBgwWbeOgAAsk49CgCQPdUaWC5evDh22223uOmmm9Y4/+qrr47rrrsubrrpppgyZUqUlZVF7969Y9GiRbk2Q4YMiQceeCBGjx4dTz/9dHzyySfRv3//WLFiRa7N8ccfH9OnT4+xY8fG2LFjY/r06TFw4MDNvn0AAGSbehQAIHsKUkqpujsREVFQUBAPPPBAHHHEERHxxbfZLVu2jCFDhsSFF14YEV98e11aWhpXXXVVnHbaaVFeXh7NmzePu+++O4499tiIiHjvvfeiVatW8cgjj0Tfvn1jxowZ0alTp5g8eXJ07do1IiImT54c3bp1i9deey06dOiwQf1buHBhFBcXR3l5eTRq1KjyB6CGanPRmOruAlSKWSMOre4uAHwlapn1U48C1Cz+HoWNUxV/925oPZPZe1i+9dZbMXfu3OjTp09uWmFhYXTv3j0mTZoUERHTpk2L5cuX57Vp2bJldO7cOdfmmWeeieLi4lxxGBGxzz77RHFxca7NmixdujQWLlyY9wMAwNZDPQoAUD0yG1jOnTs3IiJKS0vzppeWlubmzZ07N+rWrRuNGzdeZ5uSkpIKyy8pKcm1WZPhw4fn7jFUXFwcrVq1+krbAwDAlkU9CgBQPTIbWK5SUFCQ9zqlVGHa6lZvs6b261vOxRdfHOXl5bmf2bNnb2TPAQCoCdSjAABVK7OBZVlZWUREhW+d582bl/uWu6ysLJYtWxbz589fZ5v333+/wvI/+OCDCt+Wf1lhYWE0atQo7wcAgK2HehQAoHpkNrBs27ZtlJWVxbhx43LTli1bFhMnTox99903IiL23HPPqFOnTl6bOXPmxMsvv5xr061btygvL4/nnnsu1+bZZ5+N8vLyXBsAAFidehQAoHrUrs6Vf/LJJ/Gvf/0r9/qtt96K6dOnR5MmTWLHHXeMIUOGxJVXXhnt2rWLdu3axZVXXhnbbrttHH/88RERUVxcHIMHD46hQ4dG06ZNo0mTJnH++efHLrvsEgcddFBERHTs2DH69esXp5xyStx6660REXHqqadG//79N/iJjAAA1EzqUQCA7KnWwHLq1KnRs2fP3OvzzjsvIiIGDRoUI0eOjAsuuCCWLFkSZ5xxRsyfPz+6du0ajz76aDRs2DD3nuuvvz5q164dxxxzTCxZsiR69eoVI0eOjFq1auXajBo1Ks4555zc0xsHDBgQN910UxVtJQAAWaUeBQDInoKUUqruTmwJFi5cGMXFxVFeXu7+QRuhzUVjqrsLUClmjTi0ursA8JWoZbZ8PkOAjePvUdg4VfF374bWM5m9hyUAAAAAsPURWAIAAAAAmVGt97AEAAAAtj4u1wbWxRmWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADKjdnV3AGBL0OaiMVWynlkjDq2S9QAAAEBWOcMSAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBm1K7uDgBQtdpcNKZK1jNrxKFVsh4AAABqFmdYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJlRu7o7AMB/tbloTHV3AQAAAKqVMywBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzald3B6gebS4aU91dAAAAAIAKnGEJAAAAAGSGwBIAAAAAyAyXhAMAAAAR4fZhQDY4wxIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMyHVgOGzYsCgoK8n7Kyspy81NKMWzYsGjZsmUUFRVFjx494pVXXslbxtKlS+Pss8+OZs2aRf369WPAgAHx7rvvVvWmAACwhVKTAgBUrUwHlhER3/jGN2LOnDm5n5deeik37+qrr47rrrsubrrpppgyZUqUlZVF7969Y9GiRbk2Q4YMiQceeCBGjx4dTz/9dHzyySfRv3//WLFiRXVsDgAAWyA1KQBA1ald3R1Yn9q1a+d9g71KSiluuOGGuPTSS+Ooo46KiIg777wzSktL4/e//32cdtppUV5eHrfddlvcfffdcdBBB0VExD333BOtWrWK8ePHR9++fat0WwAA2DKpSQEAqk7mz7B84403omXLltG2bdv47ne/G//+978jIuKtt96KuXPnRp8+fXJtCwsLo3v37jFp0qSIiJg2bVosX748r03Lli2jc+fOuTZrs3Tp0li4cGHeDwAAW6fqqEnVowDA1irTZ1h27do17rrrrmjfvn28//778bOf/Sz23XffeOWVV2Lu3LkREVFaWpr3ntLS0nj77bcjImLu3LlRt27daNy4cYU2q96/NsOHD48rrriiErcGAIAtUXXVpDW9Hm1z0ZgqWc+sEYdWyXoAgMqT6TMsDz744Dj66KNjl112iYMOOijGjPmiqLnzzjtzbQoKCvLek1KqMG11G9Lm4osvjvLy8tzP7NmzN3ErAADYklVXTaoeBQC2VpkOLFdXv3792GWXXeKNN97I3UNo9W+l582bl/uGu6ysLJYtWxbz589fa5u1KSwsjEaNGuX9AABAVdWk6lEAYGu1RQWWS5cujRkzZkSLFi2ibdu2UVZWFuPGjcvNX7ZsWUycODH23XffiIjYc889o06dOnlt5syZEy+//HKuDQAAbAw1KQDA5pXpe1ief/75cdhhh8WOO+4Y8+bNi5/97GexcOHCGDRoUBQUFMSQIUPiyiuvjHbt2kW7du3iyiuvjG233TaOP/74iIgoLi6OwYMHx9ChQ6Np06bRpEmTOP/883OX8wAAwPqoSQEAqlamA8t33303jjvuuPjwww+jefPmsc8++8TkyZOjdevWERFxwQUXxJIlS+KMM86I+fPnR9euXePRRx+Nhg0b5pZx/fXXR+3ateOYY46JJUuWRK9evWLkyJFRq1at6tosAAC2IGpSAICqVZBSStXdiS3BwoULo7i4OMrLy2vE/YOq6qmMwNbLU1khW2paLbM1qmmfoaeEQzb5WxG2XlXxf+aG1jOZPsMSAACoegILAKA6bVEP3QEAAAAAajaBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMyoXd0dAAAA2FzaXDSmStYza8ShVbIeANgaOMMSAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwBAAAAAAyQ2AJAAAAAGSGwBIAAAAAyAyBJQAAAACQGQJLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADKjdnV3AAAAYEvX5qIxm30ds0YcutnXAQBZILAEAACAr0BgDVC5XBIOAAAAAGSGMywBAACoUlVxRmKEsxIBtlQCSwAAAHKqKkwEgLURWAKwWbiXEwAAAJvCPSwBAAAAgMxwhmXGuPwCAACgcvj7CmDLJLAEYIvlhv0AAAA1j0vCAQAAAIDMEFgCAAAAAJkhsAQAAAAAMkNgCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMwSWAAAAAEBm1K7uDgAAAADr1uaiMdXdBYAq4wxLAAAAACAzBJYAAAAAQGYILAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIjNrV3QEAAADWr81FY6q7CwBQJZxhCQAAAABkhsASAAAAAMgMgSUAAAAAkBkCSwAAAAAgMzx0BwDWo6oecjBrxKFVsh4AAIAsc4YlAAAAAJAZAksAAAAAIDMElgAAAABAZggsAQAAAIDM8NAdAMiIqni4jwf7AAAAWecMSwAAAAAgMwSWAAAAAEBmCCwBAAAAgMzYqgLLX//619G2bduoV69e7LnnnvHUU09Vd5cAANiKqEcBANZvqwks77vvvhgyZEhceuml8cILL8T+++8fBx98cLzzzjvV3TUAALYC6lEAgA1TkFJK1d2JqtC1a9f45je/GTfffHNuWseOHeOII46I4cOHr/f9CxcujOLi4igvL49GjRpttn5WxRNiAaCm8NTzDVdVtQxrt6XUoxFqUgDYGlVFbb2h9Uztzd6TDFi2bFlMmzYtLrroorzpffr0iUmTJq3xPUuXLo2lS5fmXpeXl0fEFwO7Oa1c+ulmXT4A1CSb+//liIjOl/99s68jIuLlK/pu1uWvGqut5LvqzNmS6tEINSkAbI2qosbY0Jp0qwgsP/zww1ixYkWUlpbmTS8tLY25c+eu8T3Dhw+PK664osL0Vq1abZY+AgAbr/iG6u5B5amqbVm0aFEUFxdXzcrIUY8CAFlXlbX1+mrSrSKwXKWgoCDvdUqpwrRVLr744jjvvPNyr1euXBkff/xxNG3adK3v+aoWLlwYrVq1itmzZ7tUK0N8Ltnkc8ken0k2+VyyqTo+l5RSLFq0KFq2bFkl62PNsl6PRvi9sS7GZu2MzdoZm7UzNutmfNbO2Kxd1sdmQ2vSrSKwbNasWdSqVavCt9fz5s2r8C33KoWFhVFYWJg3bbvttttcXczTqFGjTO5UWzufSzb5XLLHZ5JNPpdsqurPxZmV1WdLq0cj/N5YF2OzdsZm7YzN2hmbdTM+a2ds1i7LY7MhNelW8ZTwunXrxp577hnjxo3Lmz5u3LjYd999q6lXAABsLdSjAAAbbqs4wzIi4rzzzouBAwdGly5dolu3bvGb3/wm3nnnnTj99NOru2sAAGwF1KMAABtmqwksjz322Pjoo4/if/7nf2LOnDnRuXPneOSRR6J169bV3bWcwsLCuPzyyytc+kP18rlkk88le3wm2eRzySafy9ZpS6hHI+yf62Js1s7YrJ2xWTtjs27GZ+2MzdrVlLEpSOt7jjgAAAAAQBXZKu5hCQAAAABsGQSWAAAAAEBmCCwBAAAAgMwQWAIAAAAAmSGwzJBf//rX0bZt26hXr17sueee8dRTT1V3l7Zqw4YNi4KCgryfsrKy6u7WVuXJJ5+Mww47LFq2bBkFBQXx4IMP5s1PKcWwYcOiZcuWUVRUFD169IhXXnmlejq7FVnf53LiiSdWOHb22Wef6unsVmL48OGx1157RcOGDaOkpCSOOOKImDlzZl4bx0vV25DPxfFC1qhH118Dbk2/TyujFlu6dGmcffbZ0axZs6hfv34MGDAg3n333Srcis2nMmqimjg+lVWXbK1js7XuNxERN998c+y6667RqFGjaNSoUXTr1i3+9re/5eZvrftNxPrHpibuNwLLjLjvvvtiyJAhcemll8YLL7wQ+++/fxx88MHxzjvvVHfXtmrf+MY3Ys6cObmfl156qbq7tFVZvHhx7LbbbnHTTTetcf7VV18d1113Xdx0000xZcqUKCsri969e8eiRYuquKdbl/V9LhER/fr1yzt2HnnkkSrs4dZn4sSJceaZZ8bkyZNj3Lhx8fnnn0efPn1i8eLFuTaOl6q3IZ9LhOOF7FCP/te6asCt6fdpZdRiQ4YMiQceeCBGjx4dTz/9dHzyySfRv3//WLFiRVVtxmZTGTVRTRyfyqpLttaxidg695uIiB122CFGjBgRU6dOjalTp8aBBx4Yhx9+eC6U3Fr3m4j1j01EDdxvEpmw9957p9NPPz1v2s4775wuuuiiauoRl19+edptt92quxv8fxGRHnjggdzrlStXprKysjRixIjctM8++ywVFxenW265pRp6uHVa/XNJKaVBgwalww8/vFr6wxfmzZuXIiJNnDgxpeR4yYrVP5eUHC9ki3r0C+uqAbfm36ebUostWLAg1alTJ40ePTrX5j//+U/aZptt0tixY6us71VhU2qirWV8NqUu2VrHJiX7zeoaN26cfve739lv1mDV2KRUM/cbZ1hmwLJly2LatGnRp0+fvOl9+vSJSZMmVVOviIh44403omXLltG2bdv47ne/G//+97+ru0v8f2+99VbMnTs377gpLCyM7t27O24yYMKECVFSUhLt27ePU045JebNm1fdXdqqlJeXR0REkyZNIsLxkhWrfy6rOF7IAvVovrXVgH6f/teGjMW0adNi+fLleW1atmwZnTt33mrGa12/47eW8dmUumRrHZtV7DcRK1asiNGjR8fixYujW7du9psvWX1sVqlp+43AMgM+/PDDWLFiRZSWluZNLy0tjblz51ZTr+jatWvcdddd8fe//z1++9vfxty5c2PfffeNjz76qLq7RkTu2HDcZM/BBx8co0aNiscffzyuvfbamDJlShx44IGxdOnS6u7aViGlFOedd17st99+0blz54hwvGTBmj6XCMcL2aEe/a911YB+n/7XhozF3Llzo27dutG4ceO1tqnJ1vc7fmsYn02tS7bWsYmw37z00kvRoEGDKCwsjNNPPz0eeOCB6NSpk/0m1j42ETVzv6ld3R3gvwoKCvJep5QqTKPqHHzwwbl/77LLLtGtW7f4+te/HnfeeWecd9551dgzvsxxkz3HHnts7t+dO3eOLl26ROvWrWPMmDFx1FFHVWPPtg5nnXVWvPjii/H0009XmOd4qT5r+1wcL2SN3xPrrgFXPcDAOP3XpozF1jJem/o7viaNT2XXJVvD2Gzt+02HDh1i+vTpsWDBgvjzn/8cgwYNiokTJ+bmb837zdrGplOnTjVyv3GGZQY0a9YsatWqVSHVnjdvXoVvD6g+9evXj1122SXeeOON6u4KEbmndTpusq9FixbRunVrx04VOPvss+Phhx+OJ554InbYYYfcdMdL9Vrb57Imjheqi3p07b5cA/p9+l8bMhZlZWWxbNmymD9//lrbbE1W/x1f08fnq9QlW+vYrMnWtt/UrVs3dtppp+jSpUsMHz48dtttt7jxxhvtN7H2sVmTmrDfCCwzoG7durHnnnvGuHHj8qaPGzcu9t1332rqFatbunRpzJgxI1q0aFHdXSEi2rZtG2VlZXnHzbJly2LixImOm4z56KOPYvbs2Y6dzSilFGeddVbcf//98fjjj0fbtm3z5jteqsf6Ppc1cbxQXdSja/flGtDv0//akLHYc889o06dOnlt5syZEy+//PJWN14RFX/H19TxqYy6ZGsdmzXZWvabtUkpxdKlS7fq/WZtVo3NmtSI/abqnu/DuowePTrVqVMn3XbbbenVV19NQ4YMSfXr10+zZs2q7q5ttYYOHZomTJiQ/v3vf6fJkyen/v37p4YNG/pMqtCiRYvSCy+8kF544YUUEem6665LL7zwQnr77bdTSimNGDEiFRcXp/vvvz+99NJL6bjjjkstWrRICxcurOae12zr+lwWLVqUhg4dmiZNmpTeeuut9MQTT6Ru3bql7bff3ueyGf3whz9MxcXFacKECWnOnDm5n08//TTXxvFS9db3uTheyBr16BfWVwNuTb9PK6MWO/3009MOO+yQxo8fn55//vl04IEHpt122y19/vnn1bVZlaYyaqKaOD6VVZdsjWOzNe83KaV08cUXpyeffDK99dZb6cUXX0yXXHJJ2mabbdKjjz6aUtp695uU1j02NXW/EVhmyP/93/+l1q1bp7p166ZvfvObaeLEidXdpa3asccem1q0aJHq1KmTWrZsmY466qj0yiuvVHe3tipPPPFEiogKP4MGDUoppbRy5cp0+eWXp7KyslRYWJgOOOCA9NJLL1Vvp7cC6/pcPv3009SnT5/UvHnzVKdOnbTjjjumQYMGpXfeeae6u12jrenziIh0xx135No4Xqre+j4XxwtZpB5dfw24Nf0+rYxabMmSJemss85KTZo0SUVFRal///415vdcZdRENXF8Kqsu2RrHZmveb1JK6eSTT879H9S8efPUq1evXFiZ0ta736S07rGpqftNQUopVf55mwAAAAAAG889LAEAAACAzBBYAgAAAACZIbAEAAAAADJDYAkAAAAAZIbAEgAAAADIDIElAAAAAJAZAksAAAAAIDMElgAAAABAZggsgS3WyJEjY7vttvvKy+nRo0cMGTLkKy9nc2vTpk3ccMMN62yzbNmy2GmnneIf//hH1XTqK5g3b140b948/vOf/1R3VwAA1qs6a89hw4bF7rvvnnt94oknxhFHHPGV+7IpqnLdt912W/Tp06fK+/PXv/419thjj1i5cmWlLhfYcAJLYIt17LHHxuuvv17d3ciU3/zmN9G6dev41re+VSnLGzZsWHz3u9/d5L706NEjGjVqFAUFBbFgwYK8+SUlJTFw4MC4/PLLK6GnAACbV5ZqzxtvvDFGjhy5Wdcxa9asKCgoiOnTp1f5uiMili5dGj/96U/jsssu2+zrWl3//v2joKAgfv/731f5uoEvCCyBLVZRUVGUlJRUdzcy5Ve/+lX84Ac/qLTlPfzww3H44Ydv0ns//fTT6NevX1xyySVrbXPSSSfFqFGjYv78+ZvaRQCAKpGl2rO4uHidZ3suW7as2tZdWf785z9HgwYNYv/999/s61qTk046KX71q19Vy7oBgSWQIX/5y19iu+22y116MX369CgoKIgf//jHuTannXZaHHfccRFR8bKcVZfK3H333dGmTZsoLi6O7373u7Fo0aJcm8WLF8cJJ5wQDRo0iBYtWsS1115boR+//vWvo127dlGvXr0oLS2Nb3/727l5PXr0iLPOOivOOuus2G677aJp06bxk5/8JFJKuTbLli2LCy64ILbffvuoX79+dO3aNSZMmJC3jkmTJsUBBxwQRUVF0apVqzjnnHNi8eLFufnz5s2Lww47LIqKiqJt27YxatSo9Y7f888/H//617/i0EMPzU1b9c34H/7wh9h///2jqKgo9tprr3j99ddjypQp0aVLl2jQoEH069cvPvjgg7zlzZ49O15++eU4+OCDc+O74447RmFhYbRs2TLOOeecdfZnyJAhcdFFF8U+++yz1ja77LJLlJWVxQMPPLDe7QMAqExZqT3XZMSIEVFaWhoNGzaMwYMHx2effZY3f/XLoFfVqOedd140a9YsevfuHRERr776ahxyyCHRoEGDKC0tjYEDB8aHH36Ye9/KlSvjqquuip122ikKCwtjxx13jJ///OcREdG2bduIiNhjjz2ioKAgevToscZ1L126NM4555woKSmJevXqxX777RdTpkzJzZ8wYUIUFBTEY489Fl26dIltt9029t1335g5c+Y6x2D06NExYMCAvGkrVqyI8847L1eHX3DBBXl1eERESimuvvrq+NrXvhZFRUWx2267xZ/+9Ke8Ng8//HC0a9cuioqKomfPnnHnnXdWuCJowIAB8dxzz8W///3vdfYT2DwElkBmHHDAAbFo0aJ44YUXIiJi4sSJ0axZs5g4cWKuzYQJE6J79+5rXcabb74ZDz74YPz1r3+Nv/71rzFx4sQYMWJEbv6Pf/zjeOKJJ+KBBx6IRx99NCZMmBDTpk3LzZ86dWqcc8458T//8z8xc+bMGDt2bBxwwAF567jzzjujdu3a8eyzz8Yvf/nLuP766+N3v/tdbv5JJ50U//jHP2L06NHx4osvxne+853o169fvPHGGxER8dJLL0Xfvn3jqKOOihdffDHuu+++ePrpp+Oss87KLePEE0+MWbNmxeOPPx5/+tOf4te//nXMmzdvneP35JNPRvv27aNRo0YV5l1++eXxk5/8JJ5//vmoXbt2HHfccXHBBRfEjTfeGE899VS8+eab8dOf/jTvPQ8//HAccMABsd1228Wf/vSnuP766+PWW2+NN954Ix588MHYZZdd1tmfDbX33nvHU089VSnLAgDYUFmoPdfkD3/4Q1x++eXx85//PKZOnRotWrSIX//61+vdnlU16j/+8Y+49dZbY86cOdG9e/fYfffdY+rUqTF27Nh4//3345hjjsm95+KLL46rrroqLrvssnj11Vfj97//fZSWlkZExHPPPRcREePHj485c+bE/fffv8b1XnDBBfHnP/857rzzznj++edjp512ir59+8bHH3+c1+7SSy+Na6+9NqZOnRq1a9eOk08+eZ3b89RTT0WXLl3ypl177bVx++23x2233RZPP/10fPzxxxW++P7JT34Sd9xxR9x8883xyiuvxI9+9KP4/ve/n/tcZ82aFd/+9rfjiCOOiOnTp8dpp50Wl156aYX1t27dOkpKStSpUF0SQIZ885vfTL/4xS9SSikdccQR6ec//3mqW7duWrhwYZozZ06KiDRjxoyUUkp33HFHKi4uzr338ssvT9tuu21auHBhbtqPf/zj1LVr15RSSosWLUp169ZNo0ePzs3/6KOPUlFRUTr33HNTSin9+c9/To0aNcpbxpd17949dezYMa1cuTI37cILL0wdO3ZMKaX0r3/9KxUUFKT//Oc/ee/r1atXuvjii1NKKQ0cODCdeuqpefOfeuqptM0226QlS5akmTNnpohIkydPzs2fMWNGioh0/fXXr3Xszj333HTggQfmTXvrrbdSRKTf/e53uWn33ntvioj02GOP5aYNHz48dejQIe+9vXv3Tr/85S9TSilde+21qX379mnZsmVrXf/aPPHEEyki0vz589c4/0c/+lHq0aPHRi8XAOCrqu7ac026deuWTj/99LxpXbt2Tbvttlvu9aBBg9Lhhx+ee929e/e0++67573nsssuS3369MmbNnv27BQRaebMmWnhwoWpsLAw/fa3v11jP1bVkS+88ELe9C+v+5NPPkl16tRJo0aNys1ftmxZatmyZbr66qtTSv+tBcePH59rM2bMmBQRacmSJWtc9/z581NEpCeffDJveosWLdKIESNyr5cvX5522GGHvP7Uq1cvTZo0Ke99gwcPTscdd1xK6YvavXPnznnzL7300jXWq3vssUcaNmzYGvsIbF7OsAQypUePHjFhwoRIKcVTTz0Vhx9+eHTu3DmefvrpeOKJJ6K0tDR23nnntb6/TZs20bBhw9zrFi1a5M5MfPPNN2PZsmXRrVu33PwmTZpEhw4dcq979+4drVu3jq997WsxcODAGDVqVHz66ad569hnn32ioKAg97pbt27xxhtvxIoVK+L555+PlFK0b98+GjRokPuZOHFivPnmmxERMW3atBg5cmTe/L59+8bKlSvjrbfeihkzZkTt2rXzvlHeeeed13uvoCVLlkS9evXWOG/XXXfN/XvVt+ZfPkOytLQ07wzOhQsXxsSJE3OX4XznO9+JJUuWxNe+9rU45ZRT4oEHHojPP/88IiKuvPLKvG1555131tnP1RUVFVUYYwCAqlDdteeazJgxI+89EVHh9ZqsfjbitGnT4oknnsir01Zty5tvvhkzZsyIpUuXRq9evda77LV58803Y/ny5XkPfKxTp07svffeMWPGjLy2X65HW7RoERGx1iuIlixZEhGRV9uWl5fHnDlz8sZi9Zr51Vdfjc8++yx69+6dt9133XVXrhafOXNm7LXXXnnr23vvvdfYD3UqVJ/a1d0BgC/r0aNH3HbbbfHPf/4zttlmm+jUqVN07949Jk6cGPPnz1/nJTkRXxRIX1ZQUJC7L1Fa7f42a9KwYcN4/vnnY8KECfHoo4/GT3/60xg2bFhMmTJlg24uvnLlyqhVq1ZMmzYtatWqlTevQYMGuTannXbaGu8BueOOO+bu5/PlUHRDNGvWLF566aU1zvvyuKxa7urTVo1TRMTf/va36NixY7Ru3ToiIlq1ahUzZ86McePGxfjx4+OMM86Ia665JiZOnBinn3563qVFLVu23Kh+f/zxx9G8efONeg8AQGWo7tqzMtWvXz/v9cqVK+Owww6Lq666qkLbFi1aVMq9GVdt4+p1a0qpwrQ11aNfrj+/rGnTplFQULDRD2ZctbwxY8bE9ttvnzevsLBwrX1b22elToXq4wxLIFNW3UvohhtuiO7du0dBQUF07949JkyYsN57CK3PTjvtFHXq1InJkyfnps2fPz9ef/31vHa1a9eOgw46KK6++up48cUXc/eSXOXL71/1ul27dlGrVq3YY489YsWKFTFv3rzYaaed8n7KysoiIuKb3/xmvPLKKxXm77TTTlG3bt3o2LFjfP755zF16tTcOmbOnJl3E/A12WOPPeK1116rlOL4oYceqnCT86KiohgwYED88pe/jAkTJsQzzzwTL730UjRp0iRvG2rX3rjvwl5++eXYY489vnKfAQA2VhZqz9V17NhxjfXmxlpVc7Zp06ZCzVm/fv3cQ2cee+yxNb6/bt26EfHFg27WZlX9+vTTT+emLV++PKZOnRodO3bc6D5/ed2dOnWKV199NTetuLg4WrRokTcWn3/+ed49QTt16hSFhYXxzjvvVNjmVq1aRcQXVy59+aFAEZFXd6/y2WefxZtvvqlOhWoisAQypbi4OHbfffe45557ck8iPOCAA+L555+P119/PTdtUzRo0CAGDx4cP/7xj+Oxxx6Ll19+OU488cTYZpv//ir861//Gr/85S9j+vTp8fbbb8ddd90VK1euzLt0Z/bs2XHeeefFzJkz4957741f/epXce6550ZERPv27eN73/tenHDCCXH//ffHW2+9FVOmTImrrroqHnnkkYiIuPDCC+OZZ56JM888M6ZPnx5vvPFGPPzww3H22WdHRESHDh2iX79+ccopp8Szzz4b06ZNix/84AdRVFS0zu3r2bNnLF68OF555ZVNHqOILwq/v/3tb3H44Yfnpo0cOTJuu+22ePnll+Pf//533H333VFUVJQ7A3NN5s6dG9OnT49//etfEfHFw4amT5+edwP2Tz/9NKZNmxZ9+vT5Sn0GANgU1V17rsm5554bt99+e9x+++3x+uuvx+WXX75J9d2ZZ54ZH3/8cRx33HG5p10/+uijcfLJJ8eKFSuiXr16ceGFF8YFF1yQu2R68uTJcdttt0VERElJSRQVFeUe1lNeXl5hHfXr148f/vCH8eMf/zjGjh0br776apxyyinx6aefxuDBgze6z1/Wt2/fvCA04ouxGTFiRDzwwAPx2muvxRlnnJH3pX7Dhg3j/PPPjx/96Edx5513xptvvhkvvPBC/N///V/ceeedEfHFk99fe+21uPDCC+P111+PP/zhDzFy5MiIyD9TdPLkyVFYWLhBl+MDlU9gCWROz549Y8WKFbkCsXHjxtGpU6do3rz5V/qmNiLimmuuiQMOOCAGDBgQBx10UOy3336x55575uZvt912cf/998eBBx4YHTt2jFtuuSXuvffe+MY3vpFrc8IJJ8SSJUti7733jjPPPDPOPvvsOPXUU3Pz77jjjjjhhBNi6NCh0aFDhxgwYEA8++z/a+/uXVqH4jCOPxecRBEpOBQsdLPQBqMgiBQdhM5WsahUXSq4+EIH6dAsIopdxCFWnEUEEWpBcHDo4CC6dWxBxU3Bv0C5d7gYLHqFVqvx8v2sSc5LpvCck985d1Z1DcNQoVBQqVRSOByWaZpKp9NOLZ/nNtrb29Xf369oNKqZmRm1tbW9OzePx6NoNKrd3d0PvaNCoaCmpqZX72VnZ0d9fX0yDEOnp6fK5/PyeDz/bCebzco0TSUSCUl/P/5N09TR0ZFzTy6Xk8/nUzgc/tCYAQAAavWd355vicVisixLS0tL6u7u1s3NjWZnZ6vu2+v16uzsTE9PT4pEIgoGg5qfn1dLS4sTmqbTaSWTSVmWpUAgoFgs5tSVbGho0Obmpra3t+X1eisWs19aW1vT8PCw4vG4urq6VC6XdXJyotbW1qrH/FIikdDx8XFFUJpMJjU5Oanp6Wn19vaqublZQ0NDFc8tLy/Lsiytrq4qEAgoEokon8/L7/dLkvx+vw4ODnR4eCjDMLS1teWcEv7827gk7e3taWJiQo2NjR+aB4Da/Pr91YU1AOAHGxgYUGdnpzY2Nr57KG8qFosaHBxUuVyuKABfjbm5OT0+Psq27U8e3Ws9PT1aWFjQ+Ph43fsCAADAzzI6OirTNJVKperaz8rKirLZrG5vbyVJ9/f36ujo0OXlpRN0Avha7LAEgP9IKBTS+vq6rq+va24jGAzWtIpfrbu7O42MjGhsbKzufQEAAODnyWQyzsGVn8m2bV1cXDiljjKZjKamppzrV1dXsm2bsBL4RuywBIAquH2HJQAAAID3LS4uan9/Xw8PD/L5fIrH40qlUlUfHgmgfggsAQAAAAAAALgGv4QDAAAAAAAAcA0CSwAAAAAAAACuQWAJAAAAAAAAwDUILAEAAAAAAAC4BoElAAAAAAAAANcgsAQAAAAAAADgGgSWAAAAAAAAAFyDwBIAAAAAAACAa/wBxLYasNQTu0wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figures\n",
    "fig1 = matplotlib.pyplot.figure('distribution_wind_spevalue_countsirection', \n",
    "                                figsize=(16,8))\n",
    "\n",
    "ax1 = fig1.add_subplot(1,2,1,title='distribution of observed wind speeds')\n",
    "\n",
    "# In the first plot create histogram of wind speed with 20 bins\n",
    "falklands_df['wind_speed_obs'].plot.hist(ax=ax1,bins=20)\n",
    "ax1.set_xlabel('windspeed (m/s-1)',)  # x-label\n",
    "ax1.set_ylim((0,3500))      # y-limits\n",
    "\n",
    "# second plot\n",
    "ax1 = fig1.add_subplot(1,2,2,title='distribution of observed wind directions')\n",
    "falklands_df['wind_direction_obs'].plot.hist(ax=ax1,bins=20)\n",
    "ax1.set_xlabel('wind direction (deg)',)\n",
    "ax1.set_ylim((0,3500))\n",
    "fig1.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0a4f1180-69d2-4c0e-856d-ae751f00464f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/w0/2x361bn95wj7lfgl33vksx1w0000gn/T/ipykernel_17092/3924308351.py:10: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  fig1.show()\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure('distribution_temp_dewpoint', figsize=(16,8))\n",
    "ax1 = fig1.add_subplot(1,2,1,title='distribution of observed air temperatures')\n",
    "falklands_df['air_temp_obs'].plot.hist(ax=ax1,bins=20)\n",
    "ax1.set_xlabel('air temperature (K)',)\n",
    "ax1.set_ylim((0,3500))\n",
    "ax1 = fig1.add_subplot(1,2,2,title='distribution of observed dew point temperatures')\n",
    "falklands_df['dewpoint_obs'].plot.hist(ax=ax1,bins=20)\n",
    "ax1.set_xlabel('dew point temperature (K)',)\n",
    "ax1.set_ylim((0,3500))\n",
    "fig1.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "944a2ea8-bfc1-4bd0-9216-e5a4c18ba517",
   "metadata": {},
   "source": [
    "Now we compare the distributions of observations between our two classes in the classification problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9cfc7721-7260-4462-bf4f-27e699a59f02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['air_temp_obs', 'dewpoint_obs', 'wind_direction_obs', 'wind_speed_obs']\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x2400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#List of variables that we want to plot\n",
    "vars_to_plot = ['air_temp_obs', 'dewpoint_obs', 'wind_direction_obs', 'wind_speed_obs',]\n",
    "print(vars_to_plot)\n",
    "\n",
    "\n",
    "# Create plots\n",
    "fig1 = matplotlib.pyplot.figure('comparing rotor events',\n",
    "                                figsize=(16,6*len(vars_to_plot)))\n",
    "for ix1, var_name in enumerate(vars_to_plot):\n",
    "    # no rotors plot\n",
    "    ax1 = fig1.add_subplot(len(vars_to_plot),2,ix1*2+1, \n",
    "                           title=f'{var_name} histogram for no rotors')\n",
    "    bins=np.linspace(min(no_rotors_df[var_name]), max(no_rotors_df[var_name]), 20)\n",
    "    no_rotors_df[var_name].hist(bins=bins,ax=ax1)\n",
    "    ax1.set_ylim((0,3500))\n",
    "    xlim = ax1.get_xlim()\n",
    "\n",
    "    # Rotor plots\n",
    "    ax1 = fig1.add_subplot(len(vars_to_plot),2,ix1*2+2, \n",
    "                           title=f'{var_name} histogram for rotors')\n",
    "    rotors_present_df[var_name].hist(bins=bins,ax=ax1)\n",
    "    ax1.set_ylim((0,150))\n",
    "    ax1.set_xlim(xlim)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79428b80-ba0e-4a42-a76f-acfcb9592ff5",
   "metadata": {},
   "source": [
    "The above diagrams already show some differences for conditions at the surface when a rotor is observed compared to when it is not.  Most obvious is the wind direction , where rotors are found mostly in northerly winds, as is already known. Other surface variables show smaller differences."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b558c455-e830-41e5-a5c9-e5d2c703464e",
   "metadata": {},
   "source": [
    "### Temporal occurence of observations\n",
    "\n",
    "We might also be interested in the link between occurences frequency at different times of day or in different seasons. The plots below show a small peak in the middle of the day (which may due to observation bias), but no other obviously exploitable patterns in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9d02ad7d-33f6-40b6-be26-3607a2f25b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 80.0)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure('time of year',figsize=(16,16))\n",
    "\n",
    "# Monthly plots - no rotors\n",
    "ax1 = fig1.add_subplot(2,2,1, title='number of periods without rotors by month')\n",
    "# lambda function converst data to month - then a count of months is applied\n",
    "no_rotors_df.groupby([ no_rotors_df['DTG'].apply(lambda x: x.month)])['DTG'].count().plot.bar( ax=ax1)\n",
    "ax1.set_xlabel('month of the year')\n",
    "ax1.set_ylim((0,2200))\n",
    "\n",
    "# Monthly plots - rotors\n",
    "ax1 = fig1.add_subplot(2,2,2, title='number of periods with rotors by month') \n",
    "rotors_present_df.groupby([ rotors_present_df['DTG'].apply(lambda x: x.month)])['DTG'].count().plot.bar(ax=ax1)\n",
    "ax1.set_xlabel('month of the year')\n",
    "ax1.set_ylim((0,80))\n",
    "\n",
    "# Hour of the day plots - no rotors\n",
    "ax1 = fig1.add_subplot(2,2,3, title='number of periods without rotors by hour of the day') \n",
    "no_rotors_df.groupby([ no_rotors_df['DTG'].apply(lambda x: x.hour)])['DTG'].count().plot.bar( ax=ax1)\n",
    "ax1.set_xlabel('hour of the day')\n",
    "ax1.set_ylim((0,2200))\n",
    "\n",
    "# Hour of the day plots - with rotors\n",
    "ax1 = fig1.add_subplot(2,2,4, title='number of periods with rotors by hour of the day') \n",
    "rotors_present_df.groupby([ rotors_present_df['DTG'].apply(lambda x: x.hour)])['DTG'].count().plot.bar(ax=ax1)\n",
    "ax1.set_xlabel('hour of the day')\n",
    "ax1.set_ylim((0,80))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f76b29d-8d84-4af9-8d40-0d6f7da348d8",
   "metadata": {},
   "source": [
    "#### Distributions at different height levels\n",
    "\n",
    "Let's explore the meteorological observations at different height levels, and how they vary in the presence of rotors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ae7a2943-7f53-4b9c-90b4-3ca630227312",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Create a dictionary for the model data by variable type\n",
    "features_dict = {\n",
    "    'air_temp': [c1 for c1 in falklands_df.columns if 'air_temp' in c1 and 'obs' not in c1],\n",
    "    'dewpoint': [c1 for c1 in falklands_df.columns if 'sh' in c1 and 'obs' not in c1],\n",
    "    'wind_speed': [c1 for c1 in falklands_df.columns if 'windspd' in c1 and 'obs' not in c1],\n",
    "    'wind_dir': [c1 for c1 in falklands_df.columns if 'winddir' in c1 and 'obs' not in c1],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "94a527e9-d168-4329-a5ee-85019dc6ddde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x3200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How many keys are there in the dictionary\n",
    "num_vars = len(features_dict.keys())\n",
    "num_plots = 3\n",
    "\n",
    "# plots of mean values for no rotors, rotors and the difference\n",
    "fig1 = matplotlib.pyplot.figure(figsize=(16,8*num_vars))\n",
    "for ix1, (feature_name, fl1) in enumerate(features_dict.items()):\n",
    "    ax1 = fig1.add_subplot(num_vars,num_plots,ix1*num_plots+1,\n",
    "        title=f'average {feature_name} with height for no rotors periods')\n",
    "    no_rotors_df[fl1].mean().plot.bar(ax=ax1)\n",
    "    ax1 = fig1.add_subplot(num_vars,num_plots,ix1*num_plots+2, \n",
    "        title=f'average {feature_name} with height for rotors reported')\n",
    "    rotors_present_df[fl1].mean().plot.bar(ax=ax1)\n",
    "    ax1 = fig1.add_subplot(num_vars,num_plots,ix1*num_plots+3, \n",
    "        title=f'difference in {feature_name} with height for rotors reported compared to no rotors')\n",
    "    (rotors_present_df[fl1].mean() - no_rotors_df[fl1].mean() ).plot.bar(ax=ax1)\n",
    "matplotlib.pyplot.tight_layout(w_pad=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0de52c-532e-4bc9-8f01-69c89d019c13",
   "metadata": {},
   "source": [
    "Next up we want to explore the relationship between the forecast data and the observations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70264b7d-2eec-4bca-b35c-e958f4610ee8",
   "metadata": {},
   "source": [
    "### Additional Exercises\n",
    "Try to plot the histogram of model data at the surface level (height level index 1)\n",
    "Plot the model surface data grouped by different times of day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94271903-b87e-4d7d-b67a-206630f23433",
   "metadata": {},
   "source": [
    "### Comparison with human performance\n",
    "\n",
    "This dataset also has some data contyaining the prediction made by human OpMets ahead of time. This is the baseline that we would need to match or improve upon for the algorithm to be useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0df0adb1-5b8e-4cee-bc62-94b1ab7baf6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/Users/stephen.haddad/data/ml_weather_tutorial/rotors_opmet_performance_2016_2021.csv')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opmet_results_path = falklands_data_dir / 'rotors_opmet_performance_2016_2021.csv'\n",
    "opmet_results_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "33fba922-9f72-4a3d-9cd6-14100965946f",
   "metadata": {},
   "outputs": [],
   "source": [
    "opmet_predictions_df = pandas.read_csv(opmet_results_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c95d234-6d50-477e-8859-189cc0ca4b1a",
   "metadata": {},
   "source": [
    "We first need to do a bit of cleaning / preparation with the data before we can expore and visualise it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "56510f49-b9a7-42a7-8724-9c6d382bd6b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/w0/2x361bn95wj7lfgl33vksx1w0000gn/T/ipykernel_17092/1554758510.py:6: UserWarning: Parsing dates in %d/%m/%Y %H:%M format when dayfirst=False (the default) was specified. Pass `dayfirst=True` or specify a format to silence this warning.\n",
      "  opmet_predictions_df['DTG'] = pandas.to_datetime(opmet_predictions_df['DTG'])\n"
     ]
    }
   ],
   "source": [
    "# set missing values to zero\n",
    "opmet_predictions_df.loc[opmet_predictions_df[opmet_predictions_df.observation.isna()].index,'observation'] = 0.0\n",
    "opmet_predictions_df.loc[opmet_predictions_df[opmet_predictions_df['opmet_forecast'].isna()].index,'opmet_forecast'] = 0.0\n",
    "\n",
    "# Create datatime variables\n",
    "opmet_predictions_df['DTG'] = pandas.to_datetime(opmet_predictions_df['DTG'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "455f7079-eab4-4988-ac36-ecafc6d85472",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove invalid values for the opmet forecast field\n",
    "opmet_predictions_df = opmet_predictions_df[(opmet_predictions_df['opmet_forecast'] <= 1.0) & (opmet_predictions_df['opmet_forecast'] >= 0.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "cf845855-a8f0-4477-aa81-140b8c7bbffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DTG</th>\n",
       "      <th>observation</th>\n",
       "      <th>opmet_forecast</th>\n",
       "      <th>Hit</th>\n",
       "      <th>False Positive</th>\n",
       "      <th>True Negative</th>\n",
       "      <th>Miss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-09-19 00:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-09-19 01:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-09-19 02:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-19 03:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-09-19 04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38971</th>\n",
       "      <td>2021-02-28 19:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38972</th>\n",
       "      <td>2021-02-28 20:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38973</th>\n",
       "      <td>2021-02-28 21:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38974</th>\n",
       "      <td>2021-02-28 22:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38975</th>\n",
       "      <td>2021-02-28 23:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38975 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      DTG  observation  opmet_forecast    Hit  False Positive  \\\n",
       "0     2016-09-19 00:00:00          0.0             0.0  False           False   \n",
       "1     2016-09-19 01:00:00          0.0             0.0  False           False   \n",
       "2     2016-09-19 02:00:00          0.0             0.0  False           False   \n",
       "3     2016-09-19 03:00:00          0.0             0.0  False           False   \n",
       "4     2016-09-19 04:00:00          0.0             0.0  False           False   \n",
       "...                   ...          ...             ...    ...             ...   \n",
       "38971 2021-02-28 19:00:00          0.0             1.0  False            True   \n",
       "38972 2021-02-28 20:00:00          0.0             1.0  False            True   \n",
       "38973 2021-02-28 21:00:00          0.0             0.0  False           False   \n",
       "38974 2021-02-28 22:00:00          0.0             0.0  False           False   \n",
       "38975 2021-02-28 23:00:00          0.0             0.0  False           False   \n",
       "\n",
       "       True Negative   Miss  \n",
       "0               True  False  \n",
       "1               True  False  \n",
       "2               True  False  \n",
       "3               True  False  \n",
       "4               True  False  \n",
       "...              ...    ...  \n",
       "38971          False  False  \n",
       "38972          False  False  \n",
       "38973           True  False  \n",
       "38974           True  False  \n",
       "38975           True  False  \n",
       "\n",
       "[38975 rows x 7 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opmet_predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5f44cd2b-8e78-4202-adc3-6ca04de34f80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "observation\n",
       "0.0    37725\n",
       "1.0     1250\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opmet_predictions_df['observation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7b410f2c-9fe8-4e83-a148-93d43fd6fcfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "opmet_forecast\n",
       "0.0    33551\n",
       "1.0     5424\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opmet_predictions_df['opmet_forecast'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6fbd636-69c6-4d1b-a3f0-297635aaf5e8",
   "metadata": {},
   "source": [
    "We can use a [confusion matrix](https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62) to compare the number of true-positive, true-negative, false-positive and false-negative predictions which were made.§"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "488dd1b1-cae8-445b-be00-73b56d224aaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6751be46-56bc-4afe-9f07-577b3133704e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33475,  4250],\n",
       "       [   76,  1174]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = sklearn.metrics.confusion_matrix(opmet_predictions_df['observation'], opmet_predictions_df['opmet_forecast'])\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2e21e76c-9f37-4bd7-88da-dc05c69b94db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x149bce170>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sklearn.metrics.ConfusionMatrixDisplay(confusion_matrix=cm).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "71c24820-2611-4b50-ab93-638cd0c9cda8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[33475,  4250],\n",
       "       [   76,  1174]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "abd91668-6ee8-4647-99dd-65ba3a31b8ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure('comparisonn of hits/isses/etc.')\n",
    "ax1 = fig1.add_subplot(1,3,1,title='rotor events')\n",
    "opmet_predictions_df['observation'].value_counts().plot.pie(figsize=(18,6))\n",
    "ax1 = fig1.add_subplot(1,3,2,title='opmet forecasts')\n",
    "_ = opmet_predictions_df['opmet_forecast'].value_counts().plot.pie(figsize=(18,6))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6a0c4e-c69b-47fc-a345-86de0ad21330",
   "metadata": {},
   "source": [
    "Exploration of this data shows us that OpMets routinely overpredict rotor events (large values of false positives). This is what you would expect, as the cost of missing a rotor event (false negative) is much higher than the cost of predicting an event and it not happening (false positive). **So the challenge for our ML algorithm is to eliminate false negatives while maintaining an acceptably low false positive rate** so as to maintain trust in forecast output. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d02b966-a8df-4428-841a-6ba6c5ae736b",
   "metadata": {},
   "source": [
    "## Exercise 2 - Weather Regimes in ERA5 Reanalsysis Dataset\n",
    "\n",
    "A common tool used by forecasters is to use typical patterns in meteorlogical fields, such as pressure or temperature, to characterise the weather on a given day, with the expectation that the for days belonging to a particular pattern will be broadly similar when considering an area as a whole e.g. the UK, North Atlantic, rather than specific point. In this past these typical patterns were specified manually by experts. We can also divide up our weather data automatically using machine learning, which is what we will explore in this exercise\n",
    "\n",
    "We will use the ERA5 reanalsysis dataset as our representation of the state of the atmosphere every hour. ERA5 is Renanlysis data created by ECMWF. Reanalysis combines observations from many sources. by assimilating these into a forecast model (ECMWF's IFS in this case), to provide a consistent physically valid gridded dataset that is a close to observations as possible. The dataset consists of many different variables representing different physical values (e.g. temperature, wind, pressure). In this case we are going to use just one, **Mean Sea Level Pressure (MSLP)**, which will characterise the large scale *weather pattern*. We can then group similar pattern by consider all the values in the 2D array as a *feature vector* describing the pattern, and clustering those that are close in featue space.\n",
    "\n",
    "* ![Example of ERA5 data](https://datastore.copernicus-climate.eu/c3s/published-forms-v2/c3sprod/reanalysis-era5-single-levels/overview.jpg)\n",
    "\n",
    "Further reading\n",
    "* [Met Office Decider weather regimes](https://www.metoffice.gov.uk/services/business-industry/energy/decider)\n",
    "* [More info on ERA5 dataset](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5caf666-d707-4f6a-a180-f32f70ff4ba8",
   "metadata": {},
   "source": [
    "## Load and explore gridded model data - ERA5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0dff5256-4704-4f44-9ee1-b7b985fcdd4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/stephen.haddad/data/ml_weather_tutorial\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    era5_root = os.environ['ML_TUTORIAL_DIR']\n",
    "except KeyError:\n",
    "    era5_root = '/project/informatics_lab/data_science_cop/ml_tutorial/'\n",
    "era5_data_dir = pathlib.Path(era5_root) \n",
    "print(era5_data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b1b1eef-17d0-48bb-9a55-84c73980fe81",
   "metadata": {},
   "source": [
    "### Prepare the raw data\n",
    "\n",
    "For this example, you need to either download the summary file as described in the set up page on the wiki (recommended), or load the \"raw\" data files from an AWS S3 nucket. The bucket is publicly available, but you need to be signed in with an AWS ID to be able to access it.\n",
    "* Data on google drive https://drive.google.com/file/d/10HhCp0unrTBc-ynnu348umZ5ndbfRXyX/view?usp=sharing\n",
    "* AWS S3 bucket - original data files https://registry.opendata.aws/ecmwf-era5/\n",
    "\n",
    "**The next few cells show how to prepare the summary file from the raw data for interest, you can rather  ignore and move on to loading the file as downloading the raw data can be time consuming and not especially important for the purposes of this tutorial.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b828d889-e871-49ee-a7be-54a97f473a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "era5_raw_dir = era5_data_dir / 'era5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27660aaa-8eb6-4169-8f95-649b02615f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "nc_files = sorted([str(i1) for i1 in era5_raw_dir.iterdir() if '.nc' in str(i1) and 'air_pressure' in str(i1)])\n",
    "nc_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bdb10c5-5b5a-4a27-9dc5-e526df762bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "era5_cubeList = iris.load(nc_files)\n",
    "iris.util.equalise_attributes(era5_cubeList)\n",
    "mslp_era5_cube = iris.cube.CubeList.concatenate_cube(era5_cubeList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9f1b72a-b937-4a74-a399-206eec11b28a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mslp_era5_cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca639606-1c36-49fe-bb0d-165df7576787",
   "metadata": {},
   "outputs": [],
   "source": [
    "uk_na_bounds = {'latitude': (40,65), 'longitude': (-10,10)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0622f1-8aaa-45c4-90ae-32a49edc0012",
   "metadata": {},
   "outputs": [],
   "source": [
    "mslp_era5_uk_cube = mslp_era5_cube.intersection(latitude=uk_na_bounds['latitude'], \n",
    "                                                longitude=uk_na_bounds['longitude'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4758bb56-36cc-4fa3-ab67-850145f5f71e",
   "metadata": {},
   "source": [
    "**Continue here if you are not looking at raw data**\n",
    "\n",
    "### Load summary cube\n",
    "Having demonstrated how to prepare the file of interest from the original ERA5 files, we now load the cube directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "65b09d83-519a-48a3-aea8-a9200cfb7e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PosixPath('/Users/stephen.haddad/data/ml_weather_tutorial/era5_mslp_UK_2017_2020.nc')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era5_fname = 'era5_mslp_UK_2017_2020.nc'\n",
    "era5_path = era5_data_dir / era5_fname \n",
    "print(era5_path.is_file())\n",
    "era5_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f2624a51-db1c-412b-93ff-264a030b561d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mslp_era5_uk_cube = iris.load_cube(str(era5_path))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7997ab6-f8b6-4c06-bee4-77e76f63623e",
   "metadata": {},
   "source": [
    "### Explore ERA5 data to use for creating weather pattern clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b35b2f0c-07b1-4117-8a66-5cc7a157e4cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  a.iris {\n",
       "      text-decoration: none !important;\n",
       "  }\n",
       "  table.iris {\n",
       "      white-space: pre;\n",
       "      border: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-family: monaco, monospace;\n",
       "  }\n",
       "  th.iris {\n",
       "      background: #303f3f;\n",
       "      color: #e0e0e0;\n",
       "      border-left: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-size: 1.05em;\n",
       "      min-width: 50px;\n",
       "      max-width: 125px;\n",
       "  }\n",
       "  tr.iris :first-child {\n",
       "      border-right: 1px solid #9c9c9c !important;\n",
       "  }\n",
       "  td.iris-title {\n",
       "      background: #d5dcdf;\n",
       "      border-top: 1px solid #9c9c9c;\n",
       "      font-weight: bold;\n",
       "  }\n",
       "  .iris-word-cell {\n",
       "      text-align: left !important;\n",
       "      white-space: pre;\n",
       "  }\n",
       "  .iris-subheading-cell {\n",
       "      padding-left: 2em !important;\n",
       "  }\n",
       "  .iris-inclusion-cell {\n",
       "      padding-right: 1em !important;\n",
       "  }\n",
       "  .iris-panel-body {\n",
       "      padding-top: 0px;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      padding-left: 3em;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      margin-top: 7px;\n",
       "  }\n",
       "</style>\n",
       "<table class=\"iris\" id=\"5560056560\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Air Pressure At Mean Sea Level (Pa)</th>\n",
       "<th class=\"iris iris-word-cell\">time</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">35064</td>\n",
       "<td class=\"iris iris-inclusion-cell\">101</td>\n",
       "<td class=\"iris iris-inclusion-cell\">81</td>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CF-1.7&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tinstitution</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;ECMWF&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tnameCDM</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Mean_sea_level_pressure_surface&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tnameECMWF</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Mean sea level pressure&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tproduct_type</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;analysis&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tshortNameECMWF</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;msl&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Reanalysis&#x27;</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of air_pressure_at_mean_sea_level / (Pa) (time: 35064; latitude: 101; longitude: 81)>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mslp_era5_uk_cube"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fa8009-5a1b-4681-8b94-347dab6029e6",
   "metadata": {},
   "source": [
    "When we cluster the data, typically one normalises by season, to account for differences in seasonal means, so that cluster apply across all seasons rather than being specific to 1 season. To do this, we need to add a coordinate to our cube representing the season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "682e953a-ff0d-42ef-a887-79002e9bfbe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris.iris.coord_categorisation.add_season_number(mslp_era5_uk_cube,'time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "333ac1b6-b18b-4add-b5bf-64e9f87c4c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# functools.reduce(lambda x,y: x*y, mslp_2020_cube.shape), functools.reduce(lambda x,y: x*y, mslp_uk_2020_cube.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "51056a2f-d335-4958-9ed0-e40bf0bb3d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  a.iris {\n",
       "      text-decoration: none !important;\n",
       "  }\n",
       "  table.iris {\n",
       "      white-space: pre;\n",
       "      border: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-family: monaco, monospace;\n",
       "  }\n",
       "  th.iris {\n",
       "      background: #303f3f;\n",
       "      color: #e0e0e0;\n",
       "      border-left: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-size: 1.05em;\n",
       "      min-width: 50px;\n",
       "      max-width: 125px;\n",
       "  }\n",
       "  tr.iris :first-child {\n",
       "      border-right: 1px solid #9c9c9c !important;\n",
       "  }\n",
       "  td.iris-title {\n",
       "      background: #d5dcdf;\n",
       "      border-top: 1px solid #9c9c9c;\n",
       "      font-weight: bold;\n",
       "  }\n",
       "  .iris-word-cell {\n",
       "      text-align: left !important;\n",
       "      white-space: pre;\n",
       "  }\n",
       "  .iris-subheading-cell {\n",
       "      padding-left: 2em !important;\n",
       "  }\n",
       "  .iris-inclusion-cell {\n",
       "      padding-right: 1em !important;\n",
       "  }\n",
       "  .iris-panel-body {\n",
       "      padding-top: 0px;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      padding-left: 3em;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      margin-top: 7px;\n",
       "  }\n",
       "</style>\n",
       "<table class=\"iris\" id=\"5560056560\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Air Pressure At Mean Sea Level (Pa)</th>\n",
       "<th class=\"iris iris-word-cell\">time</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">35064</td>\n",
       "<td class=\"iris iris-inclusion-cell\">101</td>\n",
       "<td class=\"iris iris-inclusion-cell\">81</td>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Auxiliary coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tseason_number</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CF-1.7&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tinstitution</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;ECMWF&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tnameCDM</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Mean_sea_level_pressure_surface&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tnameECMWF</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Mean sea level pressure&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tproduct_type</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;analysis&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tshortNameECMWF</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;msl&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Reanalysis&#x27;</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of air_pressure_at_mean_sea_level / (Pa) (time: 35064; latitude: 101; longitude: 81)>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mslp_era5_uk_cube"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1411a391-f964-42d4-bc2a-a905797246f5",
   "metadata": {},
   "source": [
    "We can use the metadata in the iris cube data strucuture to review the area covered by the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ac3cfcc2-a840-4482-aee6-edd355f93493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40.0, 65.0)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(mslp_era5_uk_cube.coord('latitude').points), max(mslp_era5_uk_cube.coord('latitude').points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "92e7cea1-5a85-460e-909f-727f73a28a24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 10.0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(mslp_era5_uk_cube.coord('longitude').points), max(mslp_era5_uk_cube.coord('longitude').points)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9ae893-1897-4079-9982-02fa5626d721",
   "metadata": {},
   "source": [
    "The next thing to explore is the seasonal means of our data. We can use the `aggregated_by` function. The next cell will execute suprisingly quickly. This is because the calculation won't actuallty be done until it is needed, for example to create a visualisation, or by forcing the calculation to happen by calling `compute()`. In this cell, none of the calculations described will actually be executed, due to the lazy loading paradigm employed in Iris."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "be1c6551-9fda-4619-a936-7fd0f7c21046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  a.iris {\n",
       "      text-decoration: none !important;\n",
       "  }\n",
       "  table.iris {\n",
       "      white-space: pre;\n",
       "      border: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-family: monaco, monospace;\n",
       "  }\n",
       "  th.iris {\n",
       "      background: #303f3f;\n",
       "      color: #e0e0e0;\n",
       "      border-left: 1px solid;\n",
       "      border-color: #9c9c9c;\n",
       "      font-size: 1.05em;\n",
       "      min-width: 50px;\n",
       "      max-width: 125px;\n",
       "  }\n",
       "  tr.iris :first-child {\n",
       "      border-right: 1px solid #9c9c9c !important;\n",
       "  }\n",
       "  td.iris-title {\n",
       "      background: #d5dcdf;\n",
       "      border-top: 1px solid #9c9c9c;\n",
       "      font-weight: bold;\n",
       "  }\n",
       "  .iris-word-cell {\n",
       "      text-align: left !important;\n",
       "      white-space: pre;\n",
       "  }\n",
       "  .iris-subheading-cell {\n",
       "      padding-left: 2em !important;\n",
       "  }\n",
       "  .iris-inclusion-cell {\n",
       "      padding-right: 1em !important;\n",
       "  }\n",
       "  .iris-panel-body {\n",
       "      padding-top: 0px;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      padding-left: 3em;\n",
       "  }\n",
       "  .iris-panel-title {\n",
       "      margin-top: 7px;\n",
       "  }\n",
       "</style>\n",
       "<table class=\"iris\" id=\"5570067376\">\n",
       "    <tr class=\"iris\">\n",
       "<th class=\"iris iris-word-cell\">Air Pressure At Mean Sea Level (Pa)</th>\n",
       "<th class=\"iris iris-word-cell\">--</th>\n",
       "<th class=\"iris iris-word-cell\">latitude</th>\n",
       "<th class=\"iris iris-word-cell\">longitude</th>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "<td class=\"iris-word-cell iris-subheading-cell\">Shape</td>\n",
       "<td class=\"iris iris-inclusion-cell\">4</td>\n",
       "<td class=\"iris iris-inclusion-cell\">101</td>\n",
       "<td class=\"iris iris-inclusion-cell\">81</td>\n",
       "</tr>\n",
       "    <tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Dimension coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlatitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tlongitude</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Auxiliary coordinates</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tseason_number</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\ttime</td>\n",
       "    <td class=\"iris-inclusion-cell\">x</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "    <td class=\"iris-inclusion-cell\">-</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Cell methods</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\t0</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">season_number: mean</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-title iris-word-cell\">Attributes</td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "    <td class=\"iris-title\"></td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tConventions</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;CF-1.7&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tinstitution</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;ECMWF&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tnameCDM</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Mean_sea_level_pressure_surface&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tnameECMWF</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Mean sea level pressure&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tproduct_type</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;analysis&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tshortNameECMWF</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;msl&#x27;</td>\n",
       "</tr>\n",
       "<tr class=\"iris\">\n",
       "    <td class=\"iris-word-cell iris-subheading-cell\">\tsource</td>\n",
       "    <td class=\"iris-word-cell\" colspan=\"3\">&#x27;Reanalysis&#x27;</td>\n",
       "</tr>\n",
       "</table>\n",
       "        "
      ],
      "text/plain": [
       "<iris 'Cube' of air_pressure_at_mean_sea_level / (Pa) (-- : 4; latitude: 101; longitude: 81)>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mslp_uk_seasonal_mean = mslp_era5_uk_cube.aggregated_by(['season_number'],iris.analysis.MEAN)\n",
    "mslp_uk_seasonal_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e74ea056-3a7c-403e-a242-0f519957974a",
   "metadata": {},
   "source": [
    "Next we will plot the seasonal averages as a way of exploring the data. Before that, we will force the calculation of the means to happen by calling the `data` attribute. As a result this cell will take a while to execute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "a0002d45-6ca9-4686-bb7a-c9741f80b6ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.16 s, sys: 1.67 s, total: 2.83 s\n",
      "Wall time: 1.57 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "masked_array(\n",
       "  data=[[[ 99885.15 ,  99890.6  ,  99896.14 , ..., 100256.875,\n",
       "          100266.67 , 100276.34 ],\n",
       "         [ 99885.945,  99892.2  ,  99898.62 , ..., 100277.84 ,\n",
       "          100287.5  , 100296.75 ],\n",
       "         [ 99888.766,  99894.39 ,  99900.01 , ..., 100298.016,\n",
       "          100306.555, 100315.08 ],\n",
       "         ...,\n",
       "         [102295.9  , 102294.266, 102295.14 , ..., 101818.875,\n",
       "          101783.97 , 101777.8  ],\n",
       "         [102303.516, 102301.15 , 102300.86 , ..., 101833.78 ,\n",
       "          101784.79 , 101784.11 ],\n",
       "         [102309.73 , 102307.516, 102305.92 , ..., 101840.87 ,\n",
       "          101784.87 , 101790.22 ]],\n",
       "\n",
       "        [[101109.43 , 101113.98 , 101118.45 , ..., 101193.27 ,\n",
       "          101194.62 , 101196.06 ],\n",
       "         [101105.32 , 101110.23 , 101115.305, ..., 101202.78 ,\n",
       "          101204.44 , 101206.19 ],\n",
       "         [101102.24 , 101106.625, 101111.016, ..., 101211.65 ,\n",
       "          101213.57 , 101215.555],\n",
       "         ...,\n",
       "         [101668.48 , 101661.27 , 101656.71 , ..., 101485.15 ,\n",
       "          101468.24 , 101464.05 ],\n",
       "         [101672.81 , 101665.24 , 101660.1  , ..., 101498.9  ,\n",
       "          101469.44 , 101466.39 ],\n",
       "         [101676.42 , 101668.96 , 101662.97 , ..., 101508.555,\n",
       "          101472.07 , 101470.234]],\n",
       "\n",
       "        [[100982.05 , 100984.59 , 100987.086, ..., 101122.71 ,\n",
       "          101123.1  , 101123.46 ],\n",
       "         [100981.2  , 100983.53 , 100985.87 , ..., 101127.24 ,\n",
       "          101128.   , 101128.65 ],\n",
       "         [100980.97 , 100982.78 , 100984.73 , ..., 101131.1  ,\n",
       "          101131.65 , 101132.234],\n",
       "         ...,\n",
       "         [101776.05 , 101758.42 , 101744.586, ..., 101460.05 ,\n",
       "          101417.43 , 101400.125],\n",
       "         [101775.29 , 101758.24 , 101744.73 , ..., 101490.45 ,\n",
       "          101421.15 , 101400.05 ],\n",
       "         [101774.75 , 101758.164, 101744.03 , ..., 101524.23 ,\n",
       "          101436.9  , 101404.15 ]],\n",
       "\n",
       "        [[100487.12 , 100489.445, 100491.836, ..., 100723.016,\n",
       "          100729.57 , 100736.12 ],\n",
       "         [100487.234, 100489.88 , 100492.77 , ..., 100735.77 ,\n",
       "          100742.52 , 100749.09 ],\n",
       "         [100488.67 , 100490.914, 100493.19 , ..., 100747.516,\n",
       "          100753.74 , 100759.81 ],\n",
       "         ...,\n",
       "         [101811.75 , 101806.375, 101804.26 , ..., 101626.54 ,\n",
       "          101590.99 , 101579.25 ],\n",
       "         [101814.48 , 101808.87 , 101806.125, ..., 101645.03 ,\n",
       "          101590.55 , 101579.46 ],\n",
       "         [101816.586, 101811.516, 101807.65 , ..., 101661.07 ,\n",
       "          101594.445, 101581.516]]],\n",
       "  mask=False,\n",
       "  fill_value=1e+20,\n",
       "  dtype=float32)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "mslp_uk_seasonal_mean.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5acd692c-807d-41eb-93ac-1b86d59c2837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = matplotlib.pyplot.figure(figsize=(16,8))\n",
    "for ix1 in range(mslp_uk_seasonal_mean.shape[0]):\n",
    "    ax1 = fig1.add_subplot(1,4,ix1+1,projection=cartopy.crs.PlateCarree())\n",
    "    iris.quickplot.contourf(mslp_uk_seasonal_mean[ix1],axes=ax1)\n",
    "    ax1.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88bcd1b7-1f3f-4448-b263-c2feb1184f22",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "There are further example notebooks looking at loading and exploring data in python in the following locations:\n",
    "* [Introduction to Data Analsyis in Python](https://github.com/informatics-lab/intro_python_data_analysis/)\n",
    "* [Pangeo Lectures](https://github.com/informatics-lab/PangeoLectures)\n",
    "* [Using Climate Data](https://github.com/Informatics-lab/UsingClimateData)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e70e46b-e202-47b9-8d6d-a479a11e2b25",
   "metadata": {},
   "source": [
    "## Dataset Info\n",
    "\n",
    "### Falklands Rotors Challenge Dataset\n",
    "Crown Copyright 2021 - This dataset was created by Met Office Chief Operational Meterologist Steve Ramsdale from Met Office forecast and observation data.\n",
    "* Model Data - Met Office Global 10km resolution model\n",
    "* Observations - made by meteorologists at Mount Pleasant airfield in the Falkland Islands.\n",
    "\n",
    "### ERA5\n",
    "ERA5 is Renanlysis data created by ECMWF. Reanalysis combines observations from many sources. by assimilating these into a forecast model (ECMWF's IFS in this case), to provide a consistent physically valid gridded dataset that is a close to observations as possible\n",
    "https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260219d6-401b-4a45-b0ac-5daf6e4b03d1",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "The format of this notebook is based on the [template for tutorial notebooks](https://github.com/geo-yrao/notebook-dev/blob/main/templates/NCAI_Training_Notebook_template%20-%20Distribution%20Copy.ipynb) developed by NOAA, available on GitHub."
   ]
  }
 ],
 "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}