AR_notrack_6.calc_comp_corr.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate atmospheric river anomaly regime composite pattern correlations between ERA5 and Metis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculates the AR anomaly correlations for each regime in ERA5 and Metis. Borrows pattern correlation method from pattern_correlation_matrix.ipynb\n",
    "\n",
    "* ERA5 Input: `/glade/work/mkorendy/metisregimes/era5.AR.10%.PNA.months.k#.composite.nc`\n",
    "\n",
    "* Metis Input: `/glade/work/mkorendy/metisregimes/resolution/data.AR.10%.PNA.months.k#.composite.nc`\n",
    "\n",
    "* Correlation output: `data.AR.10%.PNA.months.k#.correlation.png`\n",
    "\n",
    "* Summary output: `data.AR.10%.PNA.months.k#.correlation_summary.png`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pattern Correlation Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PattCorr(ds_met, ds_comp, ds_coslat):    \n",
    "    \n",
    "    #Numerator\n",
    "    ds_num = ds_met * ds_comp * ds_coslat\n",
    "    ds_num_sum = ds_num.sum(dim=['lat','lon'])\n",
    "\n",
    "    #Denominator\n",
    "    ds_met_sq = ds_met**2\n",
    "    ds_met_sq_cos = ds_met_sq * ds_coslat\n",
    "    ds_met_sum = ds_met_sq_cos.sum(dim=['lat','lon'])\n",
    "    ds_comp_sq = ds_comp**2\n",
    "    ds_comp_sq_cos = ds_comp_sq * ds_coslat\n",
    "    ds_comp_sum = ds_comp_sq_cos.sum(dim=['lat','lon'])\n",
    "    ds_den = ds_met_sum * ds_comp_sum\n",
    "    ds_den_sqrt = ds_den**(1/2)\n",
    "\n",
    "    #Correlation\n",
    "    ds_corr = ds_num_sum/ds_den_sqrt\n",
    "    \n",
    "    return(ds_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = '1279'\n",
    "era5_comp = False\n",
    "\n",
    "threshold = '10%'\n",
    "\n",
    "months = 'ND'\n",
    "era_months = 'ND'\n",
    "\n",
    "k = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = '/glade/work/mkorendy/metisregimes/'\n",
    "\n",
    "# ERA5 input\n",
    "f_era = path + 'era5.AR.' + threshold + '.PNA.' + months + '.k' + str(k) + '.composite.nc'\n",
    "\n",
    "if era5_comp == False and res != '199':\n",
    "    path = path + 'tco' + res + '/'\n",
    "\n",
    "# Metis input\n",
    "if era5_comp == False:\n",
    "    f_met = path + 'metis' + res + '.AR.' + threshold + '.PNA.' + months + '.k' + str(k) + '.composite.nc'\n",
    "else:\n",
    "    f_met = path + 'era5.AR.' + threshold + '.PNA.' + era_months + '.k' + str(k) + '.composite.nc'\n",
    "\n",
    "# Picture output\n",
    "if era5_comp == False:\n",
    "    f_corr = 'metis' + res + '.AR.' + threshold + '.PNA.' + months + '.k' + str(k) + '.correlation.png'\n",
    "    f_sum = 'metis' + res + '.AR.' + threshold + '.PNA.' + months + '.k' + str(k) + '.correlation_summary.png'\n",
    "else:\n",
    "    f_corr = 'era5.AR.' + threshold + '.PNA.' + months + '.vs.' + era_months + '.k' + str(k) + '.correlation.png'\n",
    "    f_sum = 'era5.AR.' + threshold + '.PNA.' + months + '.vs.' + era_months + '.k' + str(k) + '.correlation_summary.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/glade/work/mkorendy/metisregimes/era5.AR.10%.PNA.ND.k4.composite.nc\n",
      "/glade/work/mkorendy/metisregimes/tco1279/metis1279.AR.10%.PNA.ND.k4.composite.nc\n",
      "metis1279.AR.10%.PNA.ND.k4.correlation.png\n",
      "metis1279.AR.10%.PNA.ND.k4.correlation_summary.png\n"
     ]
    }
   ],
   "source": [
    "print(f_era)\n",
    "print(f_met)\n",
    "print(f_corr)\n",
    "print(f_sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_era = xr.open_dataset(f_era)\n",
    "ds_met = xr.open_dataset(f_met)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select river anoms\n",
    "da_era = ds_era.river_anoms\n",
    "da_met = ds_met.river_anoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Account for cosine latitude weighting in era5 data\n",
    "coslat = np.cos(np.deg2rad(ds_era['lat'].values))\n",
    "ds_coslat = xr.DataArray(coslat,coords={'lat':ds_era['lat'].values},dims=['lat'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Empty list for pattern correlations\n",
    "ls_corr = []\n",
    "\n",
    "#For loop to compute pattern correlation between each metis cluster and all five ERA5 clusters\n",
    "for cluster_k in np.arange(0,k):\n",
    "    ls_corr.append(PattCorr(da_met[cluster_k],da_era,ds_coslat).rename('metis cluster ' + str(cluster_k)))\n",
    "    \n",
    "#Merge list of DataArrays into a Dataset\n",
    "ds_pattmetis = xr.merge(ls_corr)\n",
    "    \n",
    "#Convert Dataset to DataArray\n",
    "da_pattmetis = ds_pattmetis.to_array()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide data repr</title>\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<title>Show/Hide attributes</title>\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  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-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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, 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",
       "  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><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>variable</span>: 4</li><li><span class='xr-has-index'>cluster</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e4c3229f-284e-4389-bfa8-26221cfb17fc' class='xr-array-in' type='checkbox' ><label for='section-e4c3229f-284e-4389-bfa8-26221cfb17fc' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.4193 -0.06357 -0.4575 0.1109 ... 0.3503 0.1612 -0.6061 0.3584</span></div><pre class='xr-array-data'>array([[ 0.41931328, -0.06356869, -0.45753183,  0.11089006],\n",
       "       [-0.45837228,  0.20158325,  0.62743001, -0.44646016],\n",
       "       [-0.34171108, -0.31053133,  0.46822419, -0.01764169],\n",
       "       [ 0.35031712,  0.16123968, -0.60614093,  0.35835936]])</pre></div></li><li class='xr-section-item'><input id='section-e19fba11-c1be-48b7-a8b4-74a1a7f4f553' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e19fba11-c1be-48b7-a8b4-74a1a7f4f553' 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'>cluster</span></div><div class='xr-var-dims'>(cluster)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-ff9b46ae-1571-4c76-84a9-b5d39632e757' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff9b46ae-1571-4c76-84a9-b5d39632e757' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbeb3ae9-1ae8-41d0-9271-ceaff5e93fee' class='xr-var-data-in' type='checkbox'><label for='data-bbeb3ae9-1ae8-41d0-9271-ceaff5e93fee' 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><pre class='xr-var-data'>array([0, 1, 2, 3])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>variable</span></div><div class='xr-var-dims'>(variable)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;metis cluster 0&#x27; ... &#x27;metis cluster 3&#x27;</div><input id='attrs-d03b0421-4052-440e-9fec-0002cb805acb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d03b0421-4052-440e-9fec-0002cb805acb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db52b113-8b3b-4c74-8716-43bc6c852a84' class='xr-var-data-in' type='checkbox'><label for='data-db52b113-8b3b-4c74-8716-43bc6c852a84' 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><pre class='xr-var-data'>array([&#x27;metis cluster 0&#x27;, &#x27;metis cluster 1&#x27;, &#x27;metis cluster 2&#x27;,\n",
       "       &#x27;metis cluster 3&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-2ff8efa9-beb7-45cd-80ca-5d77897f34bb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2ff8efa9-beb7-45cd-80ca-5d77897f34bb' 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.DataArray (variable: 4, cluster: 4)>\n",
       "array([[ 0.41931328, -0.06356869, -0.45753183,  0.11089006],\n",
       "       [-0.45837228,  0.20158325,  0.62743001, -0.44646016],\n",
       "       [-0.34171108, -0.31053133,  0.46822419, -0.01764169],\n",
       "       [ 0.35031712,  0.16123968, -0.60614093,  0.35835936]])\n",
       "Coordinates:\n",
       "  * cluster   (cluster) int64 0 1 2 3\n",
       "  * variable  (variable) <U15 'metis cluster 0' ... 'metis cluster 3'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_pattmetis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the pattern correlation matrix image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "['Pacific Wavetrain', 'Alaskan Ridge', 'Arctic Low', 'Pacific Trough']\n"
     ]
    }
   ],
   "source": [
    "#Set ERA5 regime names\n",
    "if k == 5:\n",
    "    cluster_names = ['Pacific Wavetrain', 'Arctic Low', 'Pacific Trough', 'Arctic High', 'Alaskan Ridge']\n",
    "    print(k)\n",
    "    if months == 'ND':\n",
    "        cluster_names = ['Alaskan Ridge', 'Arctic Low','Pacific Trough', 'Pacific Wavetrain','Arctic High']\n",
    "elif k == 4:\n",
    "    cluster_names = ['Arctic Low','Pacific Trough','Alaskan Ridge', 'Pacific Wavetrain']\n",
    "    if months == 'ND':\n",
    "        cluster_names = ['Pacific Wavetrain', 'Alaskan Ridge', 'Arctic Low', 'Pacific Trough']\n",
    "    print(cluster_k)\n",
    "else:\n",
    "    cluster_names = []\n",
    "print(cluster_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set Metis regime names\n",
    "if k == 5:\n",
    "    if era5_comp == False:\n",
    "        if months == 'NDJFM':\n",
    "            if res == '199':\n",
    "                titles = ['Pacific Trough', 'Pacific Wavetrain', 'Arctic High', 'Arctic Low', 'Alaskan Ridge']\n",
    "            elif res == '639':\n",
    "                titles = ['Pacific Trough', 'Arctic High', 'Pacific Wavetrain', 'Arctic Low', 'Alaskan Ridge']\n",
    "        elif months == 'ND':\n",
    "            if res == '199':\n",
    "                titles = ['Hybrid Pacific Wavetrain/Pacific Trough', 'Alaskan Ridge/Arctic Low', \n",
    "                      'Hybrid Pacific Wavetrain/Alaskan Ridge', 'Arctic High', 'Pacific Trough']\n",
    "            elif res == '639':\n",
    "                titles = ['Pacific Trough', 'Alaskan Ridge', 'Pacific Wavetrain', 'Arctic High', 'Arctic Low']\n",
    "            elif res == '1279':\n",
    "                titles = ['Pacific Trough', 'Hybrid Pacific Trough/Arctic Low',\n",
    "                         'Hybrid Pacific Wavetrain/Alaskan Ridge', 'Arctic High', 'Hybrid Alaskan Ridge/Arctic Low']\n",
    "    else:\n",
    "        if era_months == months:\n",
    "            titles = cluster_names\n",
    "        else:\n",
    "            titles = ['Alaskan Ridge', 'Arctic Low','Pacific Trough', 'Pacific Wavetrain','Arctic High']\n",
    "elif k == 4:\n",
    "    if era5_comp == False:\n",
    "        if months == 'NDJFM':\n",
    "            if res == '199':\n",
    "                titles = ['Hybrid Alaskan Ridge/Arctic Low', 'Hybrid Pacific Wavetrain/Pacific Trough',\n",
    "                     'Hybrid Pacific Trough/Arctic Low', 'Hybrid Alaskan Ridge/Pacific Wavetrain']\n",
    "            elif res == '639':\n",
    "                titles = ['Hybrid Arctic Low/Pacific Trough', 'Hybrid Pacific Trough/Pacific Wavetrain',\n",
    "                         'Alaskan Ridge', 'Hybrid Pacific Wavetrain/Alaskan Ridge']\n",
    "        elif months == 'ND':\n",
    "            if res == '199':\n",
    "                titles = ['Hybrid Arctic Low/Pacific Trough', 'Hybrid Pacific Wavetrain/Alaskan Ridge',\n",
    "                             'Hybrid Alaskan Ridge/Arctic Low', 'Pacific Trough']\n",
    "            elif res == '639':\n",
    "                titles = ['Arctic Low', 'Pacific Trough', 'Pacific Wavetrain', 'Alaskan Ridge']\n",
    "            elif res == '1279':\n",
    "                titles = ['Hybrid Pacific Wavetrain/Alaskan Ridge', 'Hybrid Alaskan Ridge/Arctic Low', \n",
    "                              'Hybrid Pacific Trough/Arctic Low', 'Pacific Trough']\n",
    "    else:\n",
    "        if era_months == months:\n",
    "            titles = cluster_names\n",
    "        else:\n",
    "            titles = ['Pacific Wavetrain', 'Alaskan Ridge', 'Arctic Low', 'Pacific Trough']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hybrid Pacific Wavetrain/Alaskan Ridge', 'Hybrid Alaskan Ridge/Arctic Low', 'Hybrid Pacific Trough/Arctic Low', 'Pacific Trough']\n"
     ]
    }
   ],
   "source": [
    "print(titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the matrix\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "#Color the corresponding box accordingly\n",
    "im = ax.imshow(da_pattmetis, cmap='bwr', vmax = 1, vmin = -1)\n",
    "\n",
    "#Label ERA5 ticks with regime names\n",
    "ax.set_xticklabels(cluster_names)\n",
    "ax.set_yticklabels(titles)\n",
    "\n",
    "# Rotate the x tick labels and set their alignment.\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "#Show the ticks\n",
    "ax.set_xticks(np.arange(k))\n",
    "ax.set_yticks(np.arange(k))\n",
    "\n",
    "#Label the x and y axes\n",
    "plt.xlabel('ERA5 Regimes')\n",
    "\n",
    "if era5_comp == False:\n",
    "    plt.ylabel('Metis' + res + ' Regimes')\n",
    "else:\n",
    "    plt.xlabel('ERA5 ' + months + ' Regimes')\n",
    "    plt.ylabel('ERA5 ' + era_months + ' Regimes')\n",
    "\n",
    "#Print the correlation out to 3rd decimal place as text\n",
    "for i in np.arange(0,k):\n",
    "    for j in np.arange(0,k):\n",
    "        text = ax.text(j, i, round(da_pattmetis.values[i, j],3),\n",
    "                       ha=\"center\", va=\"center\", color='k')\n",
    "\n",
    "# Set the title\n",
    "if era5_comp == False:\n",
    "    ax.set_title(\"Correlation of \" + months + \" Metis\" + res + \", ERA5 Atmospheric River Anomaly Composites, k=\" \n",
    "             + str(k) + \" Clusters\")\n",
    "else:\n",
    "    ax.set_title(\"Correlation of \" + era_months + \" ERA5, \" + months + \" ERA5 Atmospheric River Anomaly Composites, k=\" \n",
    "             + str(k) + \" Clusters\")\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig(f_corr, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line plot of model correlation summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For each ERA5 regime:  model correlation of the most highest correlated model regime\n",
    "ch = da_pattmetis.max('variable').values\n",
    "\n",
    "# For each ERA5 regime: model correlation of the second most highest correlated model regime C2\n",
    "c2 = []\n",
    "\n",
    "# For each ERA5 regime: average all but the highest correlation to get CL\n",
    "cl = []\n",
    "\n",
    "# For each ERA5 regime: \n",
    "for cluster_k in np.arange(0,len(da_pattmetis.cluster)):\n",
    "    c2.append((np.sort(da_pattmetis.values[:,cluster_k]))[-2])\n",
    "    cl.append(np.mean(np.sort(da_pattmetis.values[:,cluster_k])[:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.35031712086497674,\n",
       " 0.1612396831217317,\n",
       " 0.468224186813281,\n",
       " 0.11089006101339528]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot correlation measurements\n",
    "plt.scatter(np.arange(0,len(da_pattmetis.cluster)), ch)\n",
    "plt.scatter(np.arange(0,len(da_pattmetis.cluster)), c2)\n",
    "plt.scatter(np.arange(0,len(da_pattmetis.cluster)), cl)\n",
    "\n",
    "#Set y limits to [-1,1]\n",
    "plt.ylim([-1,1])\n",
    "\n",
    "#Put a legend on the plot\n",
    "plt.legend(['Max Correlation', '2nd Highest Correlation', 'Average of Correlations (Excluding Maximum)'],\n",
    "          bbox_to_anchor=(1,.65))\n",
    "\n",
    "#Put a horizontal line at zero\n",
    "plt.axhline(c='gray')\n",
    "\n",
    "#Set number of x-ticks to the number of cluster\n",
    "plt.xticks(da_pattmetis.cluster, labels = cluster_names, rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "# Rotate xtick labels\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "         rotation_mode=\"anchor\")\n",
    "\n",
    "#Set x label\n",
    "plt.xlabel('ERA5 ' + months + ' Regimes')\n",
    "\n",
    "#Set y label\n",
    "plt.ylabel('Correlation Value')\n",
    "\n",
    "#Put a title on the plot\n",
    "if era5_comp == False:\n",
    "    plt.title(\"Correlation Summary of \" + months + ' Metis' + res + \n",
    "          \", ERA5 Atmospheric River Anomaly Composites, k=\" + str(k) + \" Clusters\")\n",
    "else:\n",
    "    plt.title(\"Correlation Summary of \" + era_months + ' ERA5, ' + months + \n",
    "          \" ERA5 Atmospheric River Anomaly Composites, k=\" + str(k) + \" Clusters\")\n",
    "plt.savefig(f_sum, bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (wxregimes)",
   "language": "python",
   "name": "wxregimes"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}