05_bootstrap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-09T04:07:29.886482Z",
     "start_time": "2020-04-09T04:07:23.399559Z"
    }
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import cartopy.crs as ccrs\n",
    "from constants import NWABounds\n",
    "import constants\n",
    "import calculation_functions\n",
    "import warnings\n",
    "import matplotlib as mpl\n",
    "warnings.filterwarnings('ignore')\n",
    "import matplotlib.gridspec as gridspec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-09T04:07:30.572450Z",
     "start_time": "2020-04-09T04:07:29.889285Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "from importlib import reload\n",
    "sys.path.append('')\n",
    "import load_dataset as load\n",
    "import phase_calc_functions as phase_calc\n",
    "import map_trend_functions\n",
    "import miscellaneous\n",
    "import plotting_functions as pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/g/data/w40/ab2313/images/mjo_and_rainfall_trend_images'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constants.IMAGE_SAVE_DIR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.rc('xtick', labelsize=14)\n",
    "matplotlib.rc('ytick', labelsize=14)\n",
    "\n",
    "import matplotlib.pylab as pylab\n",
    "\n",
    "params = {'axes.labelsize' : 16,\n",
    "         'legend.fontsize': 'x-large',\n",
    "          'axes.titlesize': 'xx-large',\n",
    "         }\n",
    "\n",
    "pylab.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_dir = '/g/data/w40/ab2313/images/mjo_and_rainfall_trend_images/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New datasets are now of lenght d1 = 5578 and d2 = 5578\n"
     ]
    }
   ],
   "source": [
    "# Loading awap\n",
    "awap = load.load_awap()\n",
    "awap_ws = phase_calc.wet_season_year(awap)\n",
    "\n",
    "# Loading RMM\n",
    "rmm = load.load_rmm()\n",
    "rmm_ws = phase_calc.wet_season_year(rmm)\n",
    "\n",
    "awap_ws, rmm_ws = calculation_functions.match_dataset_time(awap_ws, rmm_ws)\n",
    "\n",
    "awap_ws = miscellaneous.apply_masks(awap_ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f607a1c5e50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "awap_ws.sum(dim = 'time').precip.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase Splitting and Wet Season Calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting into different MJO phases (1-8) and then moving the data from Jan-March back one year. The moving of the data back one year means that all of the data for an individual wet season is now with in the one year. This makes resampling much easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-09T04:08:27.780872Z",
     "start_time": "2020-04-09T04:08:22.669752Z"
    }
   },
   "outputs": [],
   "source": [
    "# Splitting AWAP into the the different phases of the Wheeler and Hendon MJO\n",
    "awap_split_ws = phase_calc.split_into_1to8(awap_ws, rmm_ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enhanced phase definition being used [5, 6]\n"
     ]
    },
    {
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       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (phase: 4, year: 46)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1975 1976 1977 1978 1979 ... 2016 2017 2018 2019 2020\n",
       "  * phase    (phase) object &#x27;enhanced&#x27; &#x27;suppressed&#x27; &#x27;transition&#x27; &#x27;inactive&#x27;\n",
       "Data variables:\n",
       "    number   (phase, year) int64 12 25 22 9 12 19 11 19 ... 43 29 18 56 15 23 46</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-06a450c1-cf30-4799-8b16-9a3d54584ed6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-06a450c1-cf30-4799-8b16-9a3d54584ed6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>phase</span>: 4</li><li><span class='xr-has-index'>year</span>: 46</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-03eff796-4d22-4746-a0b3-2d1a1f7c7bea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03eff796-4d22-4746-a0b3-2d1a1f7c7bea' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1975 1976 1977 ... 2018 2019 2020</div><input id='attrs-19f24351-77c5-4ec9-83d5-931598dfb77e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19f24351-77c5-4ec9-83d5-931598dfb77e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d3d7bbc-f524-4f4d-854d-ee3ad52ab186' class='xr-var-data-in' type='checkbox'><label for='data-7d3d7bbc-f524-4f4d-854d-ee3ad52ab186' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986,\n",
       "       1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998,\n",
       "       1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,\n",
       "       2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>phase</span></div><div class='xr-var-dims'>(phase)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;enhanced&#x27; ... &#x27;inactive&#x27;</div><input id='attrs-ae0f895c-4758-4dd8-8858-528ece9089de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae0f895c-4758-4dd8-8858-528ece9089de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-419bb756-af66-40ad-b029-8103edfe39ba' class='xr-var-data-in' type='checkbox'><label for='data-419bb756-af66-40ad-b029-8103edfe39ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;enhanced&#x27;, &#x27;suppressed&#x27;, &#x27;transition&#x27;, &#x27;inactive&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6632e41f-e9e4-453f-9079-fc3d6a534b1f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6632e41f-e9e4-453f-9079-fc3d6a534b1f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>(phase, year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>12 25 22 9 12 19 ... 18 56 15 23 46</div><input id='attrs-4b020480-1bea-40ed-9faf-2e7c839dba7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b020480-1bea-40ed-9faf-2e7c839dba7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cfeafa4-bac3-4c14-8ace-4f00d346acdf' class='xr-var-data-in' type='checkbox'><label for='data-5cfeafa4-bac3-4c14-8ace-4f00d346acdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[12, 25, 22,  9, 12, 19, 11, 19,  7,  0, 24, 15, 10, 25, 11, 25,\n",
       "        22, 13, 12, 15, 31,  5, 30,  9, 17, 12, 26, 21, 20, 40, 21, 18,\n",
       "        37, 20, 26,  7, 26, 45, 22, 24, 33, 28, 13, 20, 32, 35],\n",
       "       [48,  7, 27, 25, 20,  6, 18, 13, 14, 51, 25, 40, 45, 41, 34, 43,\n",
       "        26, 39, 27, 22, 24, 28, 29, 44, 41, 11, 32, 33, 33, 30, 24, 16,\n",
       "        24, 52,  9, 29, 10, 22, 25, 23, 14, 18, 32, 40, 22, 15],\n",
       "       [38, 38, 30, 35, 31, 24, 40, 27, 31, 21, 40, 25, 26, 35, 34, 48,\n",
       "        23, 48, 31, 26, 35, 37, 44, 12, 23, 29, 19, 39, 17, 40, 36, 30,\n",
       "         9, 35, 36, 41, 31, 32, 41, 31, 45, 58, 20, 46, 44, 26],\n",
       "       [23, 52, 42, 37, 27, 73, 52, 62, 69, 50, 32, 41, 40, 21, 42,  5,\n",
       "        50, 22, 51, 58, 31, 52, 18, 56, 40, 70, 44, 28, 51, 12, 40, 57,\n",
       "        51, 15, 50, 44, 54, 23, 33, 43, 29, 18, 56, 15, 23, 46]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-daa1d0ef-6365-422a-af9b-136c4de65feb' class='xr-section-summary-in' type='checkbox'  ><label for='section-daa1d0ef-6365-422a-af9b-136c4de65feb' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d4f01512-107e-4366-8388-b576a29d1d6f' class='xr-index-data-in' type='checkbox'/><label for='index-d4f01512-107e-4366-8388-b576a29d1d6f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "            1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "            1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
       "            2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,\n",
       "            2019, 2020],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>phase</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9380dea4-5cb9-40a5-a3cd-b58be8320fdc' class='xr-index-data-in' type='checkbox'/><label for='index-9380dea4-5cb9-40a5-a3cd-b58be8320fdc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;enhanced&#x27;, &#x27;suppressed&#x27;, &#x27;transition&#x27;, &#x27;inactive&#x27;], dtype=&#x27;object&#x27;, name=&#x27;phase&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14dd26e6-0971-4654-83f9-da5c04be96e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-14dd26e6-0971-4654-83f9-da5c04be96e6' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (phase: 4, year: 46)\n",
       "Coordinates:\n",
       "  * year     (year) int64 1975 1976 1977 1978 1979 ... 2016 2017 2018 2019 2020\n",
       "  * phase    (phase) object 'enhanced' 'suppressed' 'transition' 'inactive'\n",
       "Data variables:\n",
       "    number   (phase, year) int64 12 25 22 9 12 19 11 19 ... 43 29 18 56 15 23 46"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmm_count = phase_calc.count_in_rmm_phase(rmm_ws)\n",
    "rmm_count = calculation_functions.convert_time_to_year(rmm_count)\n",
    "rmm_count_sub = phase_calc.resample_phase_to_subphase(rmm_count)\n",
    "rmm_count_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bootstrap Calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trend_line(y, use = [0][0]):\n",
    "    '''\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: the y values of our trend\n",
    "    use: \n",
    "    [0][0] will just return the gradient\n",
    "    [0,1] will return the gradient and y-intercept.\n",
    "    Previosly: _grid_trend\n",
    "    '''\n",
    "#     if ~isinstance(y, np.ndarray) or ~isinstance(y, list):\n",
    "#         return np.nan\n",
    "\n",
    "    if all(~np.isfinite(y)):\n",
    "        return np.nan\n",
    "\n",
    "\n",
    "    # Getting the gradient of a linear interpolation\n",
    "    x = np.arange(len(y))\n",
    "    idx = np.isfinite(y) #checking where the nans.\n",
    "    x = x[idx]\n",
    "    y = y[idx]\n",
    "\n",
    "    if len(x) < 3:\n",
    "        return np.nan\n",
    "\n",
    "    poly = np.polyfit(x,y,1)\n",
    "\n",
    "    return poly[use].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nancount_nonzero(array, axis):\n",
    "    '''\n",
    "    Counts all the non-zero and non-nan values in an array. Like the .count function for \n",
    "    xarray. Like count_nonzero for numpy but replaces nans with 0 first.\n",
    "    '''\n",
    "    return np.count_nonzero(np.where(np.isfinite(np.array(array)), array, 0), axis=axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def generate_bootstrapped_data(awap_ws_array: np.ndarray, awap_time:np.ndarray, \n",
    "                               func1d, number_bootstrapping:int=10, verbose:bool=False):\n",
    "    '''\n",
    "    awap_ws_array: numpy.ndarray\n",
    "        Array of data converted from xarray to numpy array. Time has to be on axis 0.\n",
    "    awap_time: numpy.ndarray\n",
    "        Array of all the time values\n",
    "    func1d: func1d\n",
    "        Function that is applied for the trend. In this instnace should be either of np.nansum\n",
    "        or nancount_nonzero.\n",
    "    number_bootstrapping: int\n",
    "        The number of itterations the bootstrapping should go through\n",
    "    \n",
    "    '''\n",
    "    # Creating an empty array to store the output of each butstrap\n",
    "    boostrap_itterations = []#np.empty(number_bootstrapping, dtype=np.ndarray)\n",
    "    \n",
    "    unique_years = np.unique(awap_time.year)\n",
    "    total_number_time_points = len(awap_time)\n",
    "    \n",
    "    successful_bootstraps = 0\n",
    "    for num_bootstrap in range(number_bootstrapping):\n",
    "        print(f'{num_bootstrap}, ', end='')\n",
    "        # This is a list of empty lists. The data for each monsoon will be stored \n",
    "        # in one of the contained empty lists. They will be stored in teh list based upon \n",
    "        # which monsoon. E.g. 2020 will be in the last list.\n",
    "        random_monsoon_list = [[] for i in range(len(unique_years))]\n",
    "\n",
    "        # Create array of random data. \n",
    "        for random_number in np.random.randint(0,total_number_time_points, total_number_time_points):\n",
    "            corrsepoding_time = awap_time[random_number]\n",
    "            if corrsepoding_time in enhanced_phase_times:\n",
    "                single_time_array = awap_ws_array[random_number]\n",
    "                corrsepoding_time = awap_time[random_number]\n",
    "                time_arg = int(np.argwhere(corrsepoding_time.year == unique_years))\n",
    "                random_monsoon_list[time_arg].append(single_time_array)\n",
    "\n",
    "        # Sum or count the data\n",
    "        monsoon_list = []\n",
    "        for single_bootstrap in random_monsoon_list:\n",
    "            monsoon_yearly_stat = func1d(np.array(single_bootstrap), axis=0)\n",
    "            monsoon_list.append(monsoon_yearly_stat)\n",
    "   \n",
    "        # For some reason, for certain monsoon lists, a single array is not passed in,\n",
    "        # but rather and array of arrays. Happens very infrequently, and is hard to \n",
    "        # debug, but better to ignore.\n",
    "        try:\n",
    "            successful_bootstraps+=1\n",
    "            # Get the trend along the axis\n",
    "            grad_trend = np.apply_along_axis(trend_line, 0, monsoon_list)\n",
    "            # Get the sign of the trend\n",
    "            sign_grad_trend = np.where(np.sign(grad_trend) > 0, 1, 0)\n",
    "\n",
    "            # Store the sign of the trend.\n",
    "            boostrap_itterations.append(sign_grad_trend) # [num_bootstrap] =\n",
    "        except TypeError as e:\n",
    "            successful_bootstraps-=1\n",
    "            print('Array error occured. Breaking loop')\n",
    "        \n",
    "    number_positive_trends = np.sum(np.array(boostrap_itterations), axis=0)\n",
    "    \n",
    "    print(f'Function completed {successful_bootstraps=}')\n",
    "    percent_trends_positive = number_positive_trends * 100 /successful_bootstraps\n",
    "    \n",
    "    if verbose:\n",
    "        return boostrap_itterations, number_positive_trends, percent_trends_positive\n",
    "        \n",
    "    return percent_trends_positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1975-01-08T00:00:00.000000000', '1975-01-09T00:00:00.000000000',\n",
       "       '1975-01-10T00:00:00.000000000', ...,\n",
       "       '2020-12-19T00:00:00.000000000', '2020-12-20T00:00:00.000000000',\n",
       "       '2020-12-21T00:00:00.000000000'], dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enhanced_phase_times = (rmm_ws\n",
    "                        .where(rmm.amplitude > 1, drop=True)\n",
    "                        .where(rmm.phase.isin([4,5,6]), drop=True)\n",
    "                        .time.values\n",
    "                       )\n",
    "enhanced_phase_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5578, 80, 161)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "awap_ws_array = awap_ws.precip.values\n",
    "awap_ws_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5578,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "awap_time = pd.to_datetime(awap_ws.time.values)\n",
    "awap_time.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985,\n",
       "       1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,\n",
       "       1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
       "       2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018,\n",
       "       2019, 2020])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(awap_time.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5578"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_number_time_points = len(awap_time)\n",
    "total_number_time_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_bootstrapping = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, Function completed successful_bootstraps=1000\n",
      "CPU times: user 58min 3s, sys: 21.2 s, total: 58min 24s\n",
      "Wall time: 59min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "percent_trends_positive_sum  = generate_bootstrapped_data(awap_ws_array, awap_time, np.nansum,\n",
    "                                                       number_bootstrapping=number_bootstrapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, Array error occured. Breaking loop\n",
      "201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, Function completed successful_bootstraps=1001\n"
     ]
    }
   ],
   "source": [
    "boostrap_itterations_count, number_positive_trends_count, percent_trends_positive_count=\\\n",
    "        generate_bootstrapped_data(awap_ws_array, awap_time, nancount_nonzero,\n",
    "                                   number_bootstrapping=number_bootstrapping, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# percent_trends_positive_count = generate_bootstrapped_data(awap_ws_array, awap_time, nancount_nonzero,\n",
    "#                                                        number_bootstrapping=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div style=\"vertical-align: middle;\"><strong>from_list</strong> </div><div class=\"cmap\"><img alt=\"from_list colormap\" title=\"from_list\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#d3d3d3ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #d3d3d3ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#1b5f27ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #1b5f27ff;\"></div></div>"
      ],
      "text/plain": [
       "<matplotlib.colors.ListedColormap at 0x7f5fb337fd90>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_array = ['lightgrey', '#D6EFD1', '#3EA95B', '#ADDEA7', '#1B5F27']\n",
    "bootstrap_cmap = mpl.colors.ListedColormap(color_array)\n",
    "bootstrap_cmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels = (0, 90, 95, 99, 100)\n",
    "norm = mpl.colors.BoundaryNorm(levels, bootstrap_cmap.N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
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       "\n",
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       "  grid-column: 1;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 80, lon: 161)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -30.0 -29.75 -29.5 -29.25 ... -10.5 -10.25 -10.0\n",
       "  * lon      (lon) float64 113.5 113.8 114.0 114.2 ... 152.8 153.0 153.2 153.5\n",
       "Data variables:\n",
       "    percent  (lat, lon) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    Information:  Only contains the wet season [10,11,12,1,2,3],rainfall &gt;= 1...\n",
       "    History:      AGCD Regrid on the 11th of June 2021 from /g/data/rr5 by ab...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8dcbf08f-2c9e-4f2a-89bb-55cbb78e900f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8dcbf08f-2c9e-4f2a-89bb-55cbb78e900f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 80</li><li><span class='xr-has-index'>lon</span>: 161</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1febc09f-02a4-42c1-bf97-6f559dc60c0e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1febc09f-02a4-42c1-bf97-6f559dc60c0e' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-30.0 -29.75 -29.5 ... -10.25 -10.0</div><input id='attrs-ef29a752-4048-4cfa-942d-14f648fec56a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef29a752-4048-4cfa-942d-14f648fec56a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6521dc95-3b7f-40dd-9a00-d640801960bf' class='xr-var-data-in' type='checkbox'><label for='data-6521dc95-3b7f-40dd-9a00-d640801960bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-30.  , -29.75, -29.5 , -29.25, -29.  , -28.75, -28.5 , -28.25, -28.  ,\n",
       "       -27.75, -27.5 , -27.25, -27.  , -26.75, -26.5 , -26.25, -26.  , -25.75,\n",
       "       -25.5 , -25.25, -25.  , -24.75, -24.5 , -24.25, -24.  , -23.75, -23.5 ,\n",
       "       -23.25, -23.  , -22.75, -22.5 , -22.25, -22.  , -21.75, -21.5 , -21.25,\n",
       "       -21.  , -20.75, -20.5 , -20.25, -20.  , -19.75, -19.5 , -19.25, -19.  ,\n",
       "       -18.75, -18.5 , -18.25, -18.  , -17.75, -17.5 , -17.25, -17.  , -16.75,\n",
       "       -16.5 , -16.25, -16.  , -15.75, -15.5 , -15.25, -15.  , -14.75, -14.5 ,\n",
       "       -14.25, -14.  , -13.75, -13.5 , -13.25, -13.  , -12.75, -12.5 , -12.25,\n",
       "       -12.  , -11.75, -11.5 , -11.25, -11.  , -10.5 , -10.25, -10.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>113.5 113.8 114.0 ... 153.2 153.5</div><input id='attrs-0fadcb19-6f9d-486b-a73b-6f0857cf72c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fadcb19-6f9d-486b-a73b-6f0857cf72c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41b688d5-c7dc-45df-a89a-5d25175a43ab' class='xr-var-data-in' type='checkbox'><label for='data-41b688d5-c7dc-45df-a89a-5d25175a43ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([113.5 , 113.75, 114.  , 114.25, 114.5 , 114.75, 115.  , 115.25, 115.5 ,\n",
       "       115.75, 116.  , 116.25, 116.5 , 116.75, 117.  , 117.25, 117.5 , 117.75,\n",
       "       118.  , 118.25, 118.5 , 118.75, 119.  , 119.25, 119.5 , 119.75, 120.  ,\n",
       "       120.25, 120.5 , 120.75, 121.  , 121.25, 121.5 , 121.75, 122.  , 122.25,\n",
       "       122.5 , 122.75, 123.  , 123.25, 123.5 , 123.75, 124.  , 124.25, 124.5 ,\n",
       "       124.75, 125.  , 125.25, 125.5 , 125.75, 126.  , 126.25, 126.5 , 126.75,\n",
       "       127.  , 127.25, 127.5 , 127.75, 128.  , 128.25, 128.5 , 128.75, 129.  ,\n",
       "       129.25, 129.5 , 129.75, 130.  , 130.25, 130.5 , 130.75, 131.  , 131.25,\n",
       "       131.5 , 131.75, 132.  , 132.25, 132.5 , 132.75, 133.  , 133.25, 133.5 ,\n",
       "       133.75, 134.  , 134.25, 134.5 , 134.75, 135.  , 135.25, 135.5 , 135.75,\n",
       "       136.  , 136.25, 136.5 , 136.75, 137.  , 137.25, 137.5 , 137.75, 138.  ,\n",
       "       138.25, 138.5 , 138.75, 139.  , 139.25, 139.5 , 139.75, 140.  , 140.25,\n",
       "       140.5 , 140.75, 141.  , 141.25, 141.5 , 141.75, 142.  , 142.25, 142.5 ,\n",
       "       142.75, 143.  , 143.25, 143.5 , 143.75, 144.  , 144.25, 144.5 , 144.75,\n",
       "       145.  , 145.25, 145.5 , 145.75, 146.  , 146.25, 146.5 , 146.75, 147.  ,\n",
       "       147.25, 147.5 , 147.75, 148.  , 148.25, 148.5 , 148.75, 149.  , 149.25,\n",
       "       149.5 , 149.75, 150.  , 150.25, 150.5 , 150.75, 151.  , 151.25, 151.5 ,\n",
       "       151.75, 152.  , 152.25, 152.5 , 152.75, 153.  , 153.25, 153.5 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b812c988-a8b0-43db-bde3-8920f44f2694' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b812c988-a8b0-43db-bde3-8920f44f2694' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>percent</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-699560f2-f4cf-4f6a-8c73-1731f3e5b61e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-699560f2-f4cf-4f6a-8c73-1731f3e5b61e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-149979b4-190c-40fc-8a79-588d1f845684' class='xr-var-data-in' type='checkbox'><label for='data-149979b4-190c-40fc-8a79-588d1f845684' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0. ,  0. ,  0. , ..., 99.8,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ..., 99.8, 99.7,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ..., 99.7, 98.7,  0. ],\n",
       "       ...,\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a621a17-c30d-4a7e-9ce8-9dc1e2d74546' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a621a17-c30d-4a7e-9ce8-9dc1e2d74546' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51f6a026-1c58-40b8-97f0-98613e4dce8b' class='xr-index-data-in' type='checkbox'/><label for='index-51f6a026-1c58-40b8-97f0-98613e4dce8b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ -30.0, -29.75,  -29.5, -29.25,  -29.0, -28.75,  -28.5, -28.25,\n",
       "               -28.0, -27.75,  -27.5, -27.25,  -27.0, -26.75,  -26.5, -26.25,\n",
       "               -26.0, -25.75,  -25.5, -25.25,  -25.0, -24.75,  -24.5, -24.25,\n",
       "               -24.0, -23.75,  -23.5, -23.25,  -23.0, -22.75,  -22.5, -22.25,\n",
       "               -22.0, -21.75,  -21.5, -21.25,  -21.0, -20.75,  -20.5, -20.25,\n",
       "               -20.0, -19.75,  -19.5, -19.25,  -19.0, -18.75,  -18.5, -18.25,\n",
       "               -18.0, -17.75,  -17.5, -17.25,  -17.0, -16.75,  -16.5, -16.25,\n",
       "               -16.0, -15.75,  -15.5, -15.25,  -15.0, -14.75,  -14.5, -14.25,\n",
       "               -14.0, -13.75,  -13.5, -13.25,  -13.0, -12.75,  -12.5, -12.25,\n",
       "               -12.0, -11.75,  -11.5, -11.25,  -11.0,  -10.5, -10.25,  -10.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d53362da-034b-4c44-9ead-6912f04dfb2a' class='xr-index-data-in' type='checkbox'/><label for='index-d53362da-034b-4c44-9ead-6912f04dfb2a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 113.5, 113.75,  114.0, 114.25,  114.5, 114.75,  115.0, 115.25,\n",
       "               115.5, 115.75,\n",
       "              ...\n",
       "              151.25,  151.5, 151.75,  152.0, 152.25,  152.5, 152.75,  153.0,\n",
       "              153.25,  153.5],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=161))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4da1986-7808-4678-b563-c211dc30ef5a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4da1986-7808-4678-b563-c211dc30ef5a' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Information :</span></dt><dd>Only contains the wet season [10,11,12,1,2,3],rainfall &gt;= 1mm and the North of Australia</dd><dt><span>History :</span></dt><dd>AGCD Regrid on the 11th of June 2021 from /g/data/rr5 by ab2313</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 80, lon: 161)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -30.0 -29.75 -29.5 -29.25 ... -10.5 -10.25 -10.0\n",
       "  * lon      (lon) float64 113.5 113.8 114.0 114.2 ... 152.8 153.0 153.2 153.5\n",
       "Data variables:\n",
       "    percent  (lat, lon) float64 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    Information:  Only contains the wet season [10,11,12,1,2,3],rainfall >= 1...\n",
       "    History:      AGCD Regrid on the 11th of June 2021 from /g/data/rr5 by ab..."
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_ds = awap_ws.isel(time=0).drop('time').rename({'precip': 'percent'})\n",
    "                                                   \n",
    "boostrap_sum_ds = xr.zeros_like(base_ds) + percent_trends_positive_sum\n",
    "boostrap_count_ds = xr.zeros_like(base_ds) + percent_trends_positive_count\n",
    "\n",
    "boostrap_sum_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_value=100\n",
      "levels=(0, 90, 95, 99, 100)\n",
      "max_value=100\n",
      "levels=(0, 90, 95, 99, 100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, gs = pf.fig_formatter(height_ratios = [1,1], width_ratios = [1, 0.05], hspace = 0.0, wspace = 0.1)\n",
    "\n",
    "ax1 = fig.add_subplot(gs[0, 0], projection=ccrs.PlateCarree())\n",
    "c = pf.nwa_map_plot(boostrap_count_ds.percent, ax1, cmap=bootstrap_cmap, levels=levels, #norm=norm,\n",
    "                   title='Number of Rain Days', debug=True)\n",
    "pf.add_figure_label(ax1, 'a)')\n",
    "\n",
    "ax2 = fig.add_subplot(gs[1, 0], projection=ccrs.PlateCarree())\n",
    "c = pf.nwa_map_plot(boostrap_sum_ds.percent, ax2, cmap=bootstrap_cmap, levels=levels,\n",
    "                    title='Rainfall', debug=True)\n",
    "pf.add_figure_label(ax2, 'b)')\n",
    "\n",
    "\n",
    "cax = plt.subplot(gs[:,1])\n",
    "ticks = [str(l) + '%' for l in levels]\n",
    "pf.create_colorbar(c, cax, levels,  ticks=ticks, cbar_title =  \"Confidence\\nLevel\",\n",
    "                   orientation = 'vertical', rotation = 0,  labelpad=45)\n",
    "\n",
    "fig.savefig(os.path.join(constants.IMAGE_SAVE_DIR, '05_boostrapping_awap_count_sum.png'),\n",
    "            bbox_inches='tight', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}