code/ROC_values.ipynb

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    "import rank_histograms as rh\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "import xskillscore as xs\n",
    "import numpy as np\n",
    "from string import ascii_lowercase, ascii_uppercase\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import bias_spread as bs\n"
   ]
  },
  {
   "cell_type": "code",
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    "filename =  'CanESM5': 'CMIP.CCCma.CanESM5.historical.day.gn_moments@10and50and100hPa.nc'"
   ]
  },
  {
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   "source": [
    "plt.text()"
   ]
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    "\n",
    "\n",
    "def plot_roc(ax,roc,model,event_type='split'):\n",
    "    false_positive=roc.sel(metric='false positive rate')\n",
    "    true_positive=roc.sel(metric='true positive rate')\n",
    "    #roc.to_dataset(dim='metric').plot.line(y='true positive rate', x='false positive rate')\n",
    "    #roc.to_dataset(dim='metric').plot.plot(y='true positive rate', x='false positive rate')\n",
    "    ax.plot(false_positive, true_positive,'o-')\n",
    "    ax.fill_between(false_positive, true_positive, alpha = 0.3)\n",
    "    \n",
    "    #ax.set_xlabel('false positive rate')\n",
    "    if event_type == 'split':\n",
    "        ax.set_ylabel('')\n",
    "    else:\n",
    "        ax.set_ylabel('true positive rate')\n",
    "    area=roc.sel(metric='area under curve').values[0]\n",
    "    ax.plot([0, 1], [0, 1], 'k:')\n",
    "    #ax.text(0.5,1.08,'ROC curve '+event_type+' events '+model,fontsize=12,ha='center',transform=ax.transAxes)\n",
    "    ax.text(0.67,0.2,'AUC='+str(round(area,2)),ha='center', color = 'C0',fontweight='bold')#,transform=ax.transAxes)\n",
    "    ax.set_title(event_type,fontsize=13)\n",
    "\n",
    "#estimate uncertainty with perfect model range\n",
    "def freq_test(ds,threshold,lower=True):\n",
    "    nmem=len(ds['member'])\n",
    "    freq_stats=np.empty((nmem))\n",
    "    for mem in range(nmem):\n",
    "        observations=ds.isel(member=mem)\n",
    "        if lower:\n",
    "            roc_areas=roc=xs.roc(observations<threshold, (ds<threshold).mean('member'),\n",
    "                   return_results='all_as_metric_dim')\n",
    "        if not lower:\n",
    "            roc_areas=roc=xs.roc(observations>threshold, (ds>threshold).mean('member'),\n",
    "                   return_results='all_as_metric_dim')    \n",
    "        area=roc.sel(metric='area under curve').values[0]\n",
    "        freq_stats[mem]=area\n",
    "    mean=np.mean(freq_stats[:])\n",
    "    perc5=np.percentile(freq_stats[:]-mean,5)\n",
    "    perc95=np.percentile(freq_stats[:]-mean,95)\n",
    "    return perc5,perc95\n",
    "\n",
    "def label_bars(roc, ax):\n",
    "        \"\"\"Attach a text label on top of each bar.\"\"\"\n",
    "        for i, pb in enumerate(roc.probability_bin):\n",
    "            y = roc.sel(metric = 'true positive rate')[i]\n",
    "            x = roc.sel(metric = 'false positive rate')[i]\n",
    "            if (y > 0.2) & (y < 0.9) & (x > 0.2) & (x < 0.9): \n",
    "                ax.annotate(f'{pb.values.round(2)}',\n",
    "                            xy=(x, y),\n",
    "                            xytext=(0, 4),  # 4 points vertical offset.\n",
    "                            textcoords='offset points',\n",
    "                            color = 'C0',\n",
    "                            alpha = 0.6,\n",
    "                            ha='center', va='bottom')\n",
    "\n",
    "\n",
    "obs=rh.get_dataset(rh.path_to_data+'ERA5_moments@10and50and100hPa_NHonly.nc')\n",
    "obs_cl=obs['centroid_latitude'].loc['1979-01-01':'2014-12-28',1e3]\n",
    "obs_ar=obs['aspect_ratio'].loc['1979-01-01':'2014-12-28',1e3]\n"
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    "area_displacements=[]\n",
    "area_splits=[]\n",
    "models = []"
   ]
  },
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   "source": [
    "import warnings\n"
   ]
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    "warnings.filterwarnings(\"ignore\")"
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       "  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:             (member: 35, time: 13300, plev: 3)\n",
       "Coordinates:\n",
       "  * time                (time) object &#x27;1920-01-01&#x27; &#x27;1920-01-02&#x27; ... &#x27;2014-12-28&#x27;\n",
       "  * plev                (plev) float64 1e+04 5e+03 1e+03\n",
       "  * member              (member) &lt;U9 &#x27;r10i1p1f1&#x27; &#x27;r10i1p2f1&#x27; ... &#x27;r9i1p2f1&#x27;\n",
       "Data variables:\n",
       "    angle               (member, time, plev) float64 -6.53 10.81 ... -13.8\n",
       "    aspect_ratio        (member, time, plev) float64 2.073 2.098 ... 1.422 1.206\n",
       "    objective_area      (member, time, plev) float64 7.786e+10 ... 3.643e+11\n",
       "    kurtosis            (member, time, plev) float64 0.1251 -0.05142 ... 0.3044\n",
       "    centroid_latitude   (member, time, plev) float64 88.46 83.7 ... 84.2 82.32\n",
       "    centroid_longitude  (member, time, plev) float64 -12.93 44.22 ... 87.99</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-2c5ee309-976f-440c-bfd0-44a95d059a85' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2c5ee309-976f-440c-bfd0-44a95d059a85' 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'>member</span>: 35</li><li><span class='xr-has-index'>time</span>: 13300</li><li><span class='xr-has-index'>plev</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e11223f9-d5ff-419b-a983-a0ecad4a90ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e11223f9-d5ff-419b-a983-a0ecad4a90ba' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;1920-01-01&#x27; ... &#x27;2014-12-28&#x27;</div><input id='attrs-b1f13458-413f-4287-b045-a62f3d436bb8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1f13458-413f-4287-b045-a62f3d436bb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a65821a5-af29-42ae-843f-49cb58f51096' class='xr-var-data-in' type='checkbox'><label for='data-a65821a5-af29-42ae-843f-49cb58f51096' 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;1920-01-01&#x27;, &#x27;1920-01-02&#x27;, &#x27;1920-01-03&#x27;, ..., &#x27;2014-12-26&#x27;,\n",
       "       &#x27;2014-12-27&#x27;, &#x27;2014-12-28&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+04 5e+03 1e+03</div><input id='attrs-8abe7bdd-84a9-4d50-8d1c-aa6cc3bb58ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8abe7bdd-84a9-4d50-8d1c-aa6cc3bb58ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80493c4a-c354-4023-bbed-dd07c4e89b54' class='xr-var-data-in' type='checkbox'><label for='data-80493c4a-c354-4023-bbed-dd07c4e89b54' 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([10000.,  5000.,  1000.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member</span></div><div class='xr-var-dims'>(member)</div><div class='xr-var-dtype'>&lt;U9</div><div class='xr-var-preview xr-preview'>&#x27;r10i1p1f1&#x27; ... &#x27;r9i1p2f1&#x27;</div><input id='attrs-47efd93b-11d0-48a4-a45c-71bac576c41c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47efd93b-11d0-48a4-a45c-71bac576c41c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6f953f9-16bf-4aab-abc1-ec7b8c76a2e2' class='xr-var-data-in' type='checkbox'><label for='data-c6f953f9-16bf-4aab-abc1-ec7b8c76a2e2' 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;r10i1p1f1&#x27;, &#x27;r10i1p2f1&#x27;, &#x27;r11i1p1f1&#x27;, &#x27;r12i1p1f1&#x27;, &#x27;r13i1p1f1&#x27;,\n",
       "       &#x27;r14i1p1f1&#x27;, &#x27;r15i1p1f1&#x27;, &#x27;r16i1p1f1&#x27;, &#x27;r17i1p1f1&#x27;, &#x27;r18i1p1f1&#x27;,\n",
       "       &#x27;r19i1p1f1&#x27;, &#x27;r1i1p1f1&#x27;, &#x27;r1i1p2f1&#x27;, &#x27;r20i1p1f1&#x27;, &#x27;r21i1p1f1&#x27;,\n",
       "       &#x27;r22i1p1f1&#x27;, &#x27;r23i1p1f1&#x27;, &#x27;r24i1p1f1&#x27;, &#x27;r25i1p1f1&#x27;, &#x27;r2i1p1f1&#x27;,\n",
       "       &#x27;r2i1p2f1&#x27;, &#x27;r3i1p1f1&#x27;, &#x27;r3i1p2f1&#x27;, &#x27;r4i1p1f1&#x27;, &#x27;r4i1p2f1&#x27;, &#x27;r5i1p1f1&#x27;,\n",
       "       &#x27;r5i1p2f1&#x27;, &#x27;r6i1p1f1&#x27;, &#x27;r6i1p2f1&#x27;, &#x27;r7i1p1f1&#x27;, &#x27;r7i1p2f1&#x27;, &#x27;r8i1p1f1&#x27;,\n",
       "       &#x27;r8i1p2f1&#x27;, &#x27;r9i1p1f1&#x27;, &#x27;r9i1p2f1&#x27;], dtype=&#x27;&lt;U9&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6a2fdd5-af98-4a89-a46b-8327e6e23af5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6a2fdd5-af98-4a89-a46b-8327e6e23af5' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>angle</span></div><div class='xr-var-dims'>(member, time, plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.53 10.81 20.33 ... -25.64 -13.8</div><input id='attrs-80a4a64b-9ec5-4042-b2c1-47418951d53f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80a4a64b-9ec5-4042-b2c1-47418951d53f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bae6a16b-1eb4-4011-98d2-a96dac7b3b06' class='xr-var-data-in' type='checkbox'><label for='data-bae6a16b-1eb4-4011-98d2-a96dac7b3b06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ -6.52970569,  10.80571465,  20.32963252],\n",
       "        [ -8.72410862,  10.28072753,  19.07920226],\n",
       "        [ -9.8972418 ,   8.43290153,  16.19986051],\n",
       "        ...,\n",
       "        [-36.10076487, -33.28328593, -30.92264709],\n",
       "        [-40.93046211, -39.3384934 , -37.10951434],\n",
       "        [-44.27445302, -44.47819   , -43.34924436]],\n",
       "\n",
       "       [[ 22.8158512 ,  20.81151899,  21.9595379 ],\n",
       "        [ 17.92777617,  15.45346194,  16.79882348],\n",
       "        [ 13.64235097,  13.53575522,  16.57015606],\n",
       "        ...,\n",
       "        [-22.0885158 , -24.94670378, -34.6008603 ],\n",
       "        [-33.30015579, -33.21786287, -39.57635225],\n",
       "        [-32.43050442, -31.78619488, -35.2427149 ]],\n",
       "\n",
       "       [[-33.49227062, -29.54688133, -25.88269704],\n",
       "        [-42.77103488, -39.37227885, -33.72693901],\n",
       "        [ 36.73955188,  40.54250761,  39.4608321 ],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 14.64529524,  13.36976706,  24.91929073],\n",
       "        [  7.89834621,   8.97124756,  30.24917396],\n",
       "        [  3.17907331,   7.14625514,  28.75422289]],\n",
       "\n",
       "       [[-39.73392367, -31.84441887, -17.82430138],\n",
       "        [-36.09282454, -30.02843483, -16.86032465],\n",
       "        [-35.38127574, -30.30351697, -19.30868436],\n",
       "        ...,\n",
       "        [ -6.95273924, -12.01089143,  -8.40775222],\n",
       "        [ -3.25039974,  -1.36382288,   9.95523842],\n",
       "        [  6.10558695,  10.3540377 ,  29.51414545]],\n",
       "\n",
       "       [[ 28.17649209,  25.81252364,  18.52984281],\n",
       "        [ 25.99624805,  24.33128144,  14.15297941],\n",
       "        [ 24.03945896,  23.72053581,  10.95308936],\n",
       "        ...,\n",
       "        [-18.74048443, -16.97578248, -14.93765971],\n",
       "        [-25.92130156, -26.17691382, -15.22174344],\n",
       "        [-26.10208294, -25.64270805, -13.80420467]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>aspect_ratio</span></div><div class='xr-var-dims'>(member, time, plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.073 2.098 1.831 ... 1.422 1.206</div><input id='attrs-adc31e14-f475-4c26-bd97-4d3698e91952' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-adc31e14-f475-4c26-bd97-4d3698e91952' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f3324c3-ee0b-4f43-bec3-fd629649a7c7' class='xr-var-data-in' type='checkbox'><label for='data-9f3324c3-ee0b-4f43-bec3-fd629649a7c7' 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([[[2.07250209, 2.09758893, 1.83117384],\n",
       "        [2.26086599, 2.34819128, 2.07399313],\n",
       "        [2.20918664, 2.47342573, 2.23485952],\n",
       "        ...,\n",
       "        [1.74133295, 1.59892668, 1.30952693],\n",
       "        [1.91630165, 1.77176664, 1.44916471],\n",
       "        [1.93407688, 1.80699324, 1.44870266]],\n",
       "\n",
       "       [[1.81876078, 1.88060855, 1.71144392],\n",
       "        [1.681936  , 1.62753809, 1.4436899 ],\n",
       "        [1.51112258, 1.4093257 , 1.2751812 ],\n",
       "        ...,\n",
       "        [1.23062514, 1.24719608, 1.17836952],\n",
       "        [1.1929111 , 1.19010735, 1.11702301],\n",
       "        [1.08165605, 1.10572684, 1.09666529]],\n",
       "\n",
       "       [[1.54802978, 1.46610693, 1.25782034],\n",
       "        [1.66382631, 1.54656668, 1.32414568],\n",
       "        [1.4991308 , 1.39728228, 1.18401503],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [2.07982996, 1.86459723, 1.39146744],\n",
       "        [1.82605868, 1.57691796, 1.30779136],\n",
       "        [1.6801704 , 1.44921571, 1.33982443]],\n",
       "\n",
       "       [[1.43250911, 1.40925439, 1.2899414 ],\n",
       "        [1.51645242, 1.49176556, 1.32115607],\n",
       "        [1.57785368, 1.5485936 , 1.37042075],\n",
       "        ...,\n",
       "        [1.63146974, 1.41231308, 1.19846585],\n",
       "        [1.48206876, 1.27674552, 1.11156873],\n",
       "        [1.32853022, 1.28758988, 1.13048702]],\n",
       "\n",
       "       [[1.7539328 , 1.62281762, 1.4690345 ],\n",
       "        [1.78382062, 1.64794942, 1.38845908],\n",
       "        [1.70988554, 1.58050885, 1.22127802],\n",
       "        ...,\n",
       "        [1.491465  , 1.41645915, 1.12245243],\n",
       "        [1.53143527, 1.42879188, 1.13629328],\n",
       "        [1.5826327 , 1.42227275, 1.20603582]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>objective_area</span></div><div class='xr-var-dims'>(member, time, plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.786e+10 1.05e+11 ... 3.643e+11</div><input id='attrs-7c194cef-2189-49fc-ad1b-24068947af80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c194cef-2189-49fc-ad1b-24068947af80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39ff4b9b-d7db-4035-9cad-e24d33fb12b3' class='xr-var-data-in' type='checkbox'><label for='data-39ff4b9b-d7db-4035-9cad-e24d33fb12b3' 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([[[7.78590177e+10, 1.05011262e+11, 1.44610044e+11],\n",
       "        [8.57360257e+10, 1.07444230e+11, 1.42084578e+11],\n",
       "        [9.95547687e+10, 1.16518294e+11, 1.39884347e+11],\n",
       "        ...,\n",
       "        [2.12088843e+11, 2.68767857e+11, 3.09168282e+11],\n",
       "        [2.22584786e+11, 2.77300598e+11, 3.16530293e+11],\n",
       "        [2.25171992e+11, 2.79962838e+11, 3.19293367e+11]],\n",
       "\n",
       "       [[2.59301461e+11, 2.76966774e+11, 3.16734241e+11],\n",
       "        [2.60076324e+11, 2.81444058e+11, 3.32594827e+11],\n",
       "        [2.57472382e+11, 2.85113979e+11, 3.48757286e+11],\n",
       "        ...,\n",
       "        [2.57672430e+11, 3.29368763e+11, 4.51430340e+11],\n",
       "        [2.71273880e+11, 3.40243444e+11, 4.64202813e+11],\n",
       "        [2.74142007e+11, 3.44734128e+11, 4.75773754e+11]],\n",
       "\n",
       "       [[3.34613397e+11, 3.59046665e+11, 3.11992140e+11],\n",
       "        [3.41725835e+11, 3.62024453e+11, 3.11358503e+11],\n",
       "        [3.57476817e+11, 3.74660391e+11, 3.27446298e+11],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [1.69546008e+11, 2.10642098e+11, 2.57101884e+11],\n",
       "        [1.73177506e+11, 2.18013924e+11, 2.67866449e+11],\n",
       "        [1.68179422e+11, 2.21331442e+11, 2.76013517e+11]],\n",
       "\n",
       "       [[3.19729683e+11, 3.81362921e+11, 5.03276770e+11],\n",
       "        [3.15533857e+11, 3.78456545e+11, 4.97762022e+11],\n",
       "        [3.22740370e+11, 3.82105495e+11, 4.97781085e+11],\n",
       "        ...,\n",
       "        [1.31637378e+11, 1.57900313e+11, 2.07613755e+11],\n",
       "        [1.28074340e+11, 1.59423790e+11, 2.14568131e+11],\n",
       "        [1.20572724e+11, 1.54177590e+11, 2.16694098e+11]],\n",
       "\n",
       "       [[2.40914477e+11, 2.74996916e+11, 2.39902419e+11],\n",
       "        [2.42204591e+11, 2.65169553e+11, 2.22690408e+11],\n",
       "        [2.35285185e+11, 2.48818019e+11, 2.11756172e+11],\n",
       "        ...,\n",
       "        [2.28727597e+11, 2.82259114e+11, 3.57338384e+11],\n",
       "        [2.27215864e+11, 2.81528591e+11, 3.55863416e+11],\n",
       "        [2.41295208e+11, 2.91954441e+11, 3.64321118e+11]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kurtosis</span></div><div class='xr-var-dims'>(member, time, plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1251 -0.05142 ... 0.1799 0.3044</div><input id='attrs-49be302a-635a-416b-b4f5-743821e72129' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49be302a-635a-416b-b4f5-743821e72129' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e536d6d-3a73-4430-99f3-e477e4859c3e' class='xr-var-data-in' type='checkbox'><label for='data-7e536d6d-3a73-4430-99f3-e477e4859c3e' 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([[[ 0.1250913 , -0.05142205,  0.16932111],\n",
       "        [ 0.49650026, -0.04569602,  0.13142875],\n",
       "        [ 0.56487688,  0.0357231 ,  0.19182247],\n",
       "        ...,\n",
       "        [ 0.05827501,  0.12179762,  0.27347953],\n",
       "        [ 0.03518445,  0.10741412,  0.27812835],\n",
       "        [ 0.05727731,  0.11636075,  0.2880959 ]],\n",
       "\n",
       "       [[ 0.12063254,  0.14832096,  0.28659901],\n",
       "        [ 0.19367021,  0.23781702,  0.33314433],\n",
       "        [ 0.23805871,  0.24607163,  0.29368625],\n",
       "        ...,\n",
       "        [ 0.15892824,  0.15877955,  0.25658245],\n",
       "        [ 0.15703986,  0.16304513,  0.25835927],\n",
       "        [ 0.16386464,  0.16136888,  0.25812409]],\n",
       "\n",
       "       [[ 0.18625844,  0.20630938,  0.31780915],\n",
       "        [ 0.15841916,  0.19551026,  0.31217808],\n",
       "        [ 0.1860072 ,  0.21579788,  0.3046893 ],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 0.07546243,  0.31965647,  0.41858994],\n",
       "        [ 0.113355  ,  0.32518402,  0.32326213],\n",
       "        [ 0.16952577,  0.25869542,  0.28639222]],\n",
       "\n",
       "       [[ 0.19977908,  0.20424589,  0.29703909],\n",
       "        [ 0.16202399,  0.17699491,  0.28304707],\n",
       "        [ 0.15400178,  0.16083085,  0.27170234],\n",
       "        ...,\n",
       "        [ 0.21368782,  0.16954515,  0.2686619 ],\n",
       "        [ 0.28344519,  0.19312501,  0.26766078],\n",
       "        [ 0.40298383,  0.22304763,  0.26427271]],\n",
       "\n",
       "       [[ 0.11503715,  0.1384632 ,  0.28837736],\n",
       "        [ 0.07423368,  0.12455863,  0.2786337 ],\n",
       "        [ 0.06885776,  0.14198974,  0.27463993],\n",
       "        ...,\n",
       "        [ 0.17134706,  0.17469504,  0.28184035],\n",
       "        [ 0.1410203 ,  0.16562294,  0.28319542],\n",
       "        [ 0.13590024,  0.17993378,  0.30438798]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>centroid_latitude</span></div><div class='xr-var-dims'>(member, time, plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>88.46 83.7 80.24 ... 84.2 82.32</div><input id='attrs-5c666c77-a11e-4d04-81b2-f47e4d57e88a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c666c77-a11e-4d04-81b2-f47e4d57e88a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1859ccc-dfde-458d-ab2f-a02ef93929b7' class='xr-var-data-in' type='checkbox'><label for='data-d1859ccc-dfde-458d-ab2f-a02ef93929b7' 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([[[88.46161987, 83.7009932 , 80.2409244 ],\n",
       "        [86.07659785, 86.57135547, 81.65075482],\n",
       "        [86.07556783, 87.37003845, 82.5088414 ],\n",
       "        ...,\n",
       "        [79.17291146, 77.29145792, 75.1742426 ],\n",
       "        [78.3461625 , 77.06306257, 75.85138556],\n",
       "        [78.31924884, 77.37677226, 76.48891622]],\n",
       "\n",
       "       [[86.58803461, 85.65187643, 79.75768687],\n",
       "        [87.02544616, 84.54832015, 77.54756528],\n",
       "        [86.17782541, 82.8993995 , 76.04085074],\n",
       "        ...,\n",
       "        [88.99362646, 89.42145671, 88.229759  ],\n",
       "        [88.64058365, 89.4860329 , 88.88320912],\n",
       "        [89.13494235, 89.58964935, 89.81274645]],\n",
       "\n",
       "       [[87.52440621, 89.58672787, 85.37683519],\n",
       "        [86.13071779, 88.24332776, 86.3216873 ],\n",
       "        [85.14417622, 86.04072164, 83.90114672],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [82.76745985, 77.81814043, 68.7736998 ],\n",
       "        [80.71626177, 75.70418243, 68.02836885],\n",
       "        [79.08574045, 74.16549771, 67.36634701]],\n",
       "\n",
       "       [[84.1897478 , 82.31142281, 79.57711843],\n",
       "        [84.44998056, 82.48974636, 79.29455803],\n",
       "        [84.35889693, 82.58790779, 79.17800745],\n",
       "        ...,\n",
       "        [77.60284194, 75.25194148, 72.99781737],\n",
       "        [77.59047606, 75.52743693, 73.62691892],\n",
       "        [78.26557295, 75.99937106, 73.56906751]],\n",
       "\n",
       "       [[75.09465206, 73.37371035, 70.59586599],\n",
       "        [74.97806534, 73.85785808, 71.01319364],\n",
       "        [76.2521926 , 75.12519695, 71.46483031],\n",
       "        ...,\n",
       "        [82.3774284 , 80.34276433, 77.92056654],\n",
       "        [83.88914534, 82.10990182, 79.82844825],\n",
       "        [86.29324388, 84.19607649, 82.32257193]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>centroid_longitude</span></div><div class='xr-var-dims'>(member, time, plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-12.93 44.22 6.391 ... 86.93 87.99</div><input id='attrs-bca9cb9a-7fdf-46a3-8df5-d99d4d6279b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bca9cb9a-7fdf-46a3-8df5-d99d4d6279b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f5087ac-f33b-4a6e-914f-a0dd10cf85dd' class='xr-var-data-in' type='checkbox'><label for='data-0f5087ac-f33b-4a6e-914f-a0dd10cf85dd' 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([[[-12.92639876,  44.21915744,   6.39099432],\n",
       "        [-82.33552651,  12.97281326,  -9.48287181],\n",
       "        [ 88.26053615, -16.69583006, -35.08093899],\n",
       "        ...,\n",
       "        [ 78.58151452,  76.03733094,  70.93778877],\n",
       "        [ 85.55450043,  79.62715979,  67.60094978],\n",
       "        [ 87.50215751,  79.38009185,  58.96062667]],\n",
       "\n",
       "       [[-69.70208559,  61.39035295,  24.45981705],\n",
       "        [ 81.77284705,  42.24788386,  19.19824176],\n",
       "        [ 47.77477083,  31.51731758,  19.32058234],\n",
       "        ...,\n",
       "        [-86.77771897, -28.04748814,  81.79953342],\n",
       "        [-78.00556047,  50.89383154, -73.80380231],\n",
       "        [-43.27534761, -63.40676637,   6.04516349]],\n",
       "\n",
       "       [[-86.46997469,  20.66476448,  17.7345094 ],\n",
       "        [ 66.33485657,  14.04655082,  55.60848264],\n",
       "        [ 46.35127604,   2.43825704, -68.69622976],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 65.16569958,  44.19984818,  29.20567121],\n",
       "        [ 62.90190862,  47.25756737,  34.90319799],\n",
       "        [ 62.80115302,  49.92677278,  38.19948069]],\n",
       "\n",
       "       [[ 37.94693193,  17.07885745,  -5.06038664],\n",
       "        [ 50.91058594,  24.91188984,   1.04370437],\n",
       "        [ 64.88884362,  35.76630651,   7.96905359],\n",
       "        ...,\n",
       "        [ 49.43563929,  43.11083991,  41.39836012],\n",
       "        [ 53.7892345 ,  45.53436167,  36.93590061],\n",
       "        [ 57.4442844 ,  47.59138565,  33.69096619]],\n",
       "\n",
       "       [[-81.59753947,  81.70541034,  43.84175348],\n",
       "        [-75.41428626,  87.25782238,  47.26661295],\n",
       "        [-73.92664748,  87.08519305,  46.69828002],\n",
       "        ...,\n",
       "        [ 89.52233575,  89.759706  ,  79.70460262],\n",
       "        [ 85.2685178 ,  88.04851196,  82.20200178],\n",
       "        [ 78.47411315,  86.9273761 ,  87.98772917]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0f8884b-b2f1-4f07-98a6-1c9fb9644e3b' class='xr-section-summary-in' type='checkbox'  ><label for='section-f0f8884b-b2f1-4f07-98a6-1c9fb9644e3b' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-460358c9-c63e-48cd-8bb8-6178f0e2be9e' class='xr-index-data-in' type='checkbox'/><label for='index-460358c9-c63e-48cd-8bb8-6178f0e2be9e' 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;1920-01-01&#x27;, &#x27;1920-01-02&#x27;, &#x27;1920-01-03&#x27;, &#x27;1920-01-04&#x27;, &#x27;1920-01-05&#x27;,\n",
       "       &#x27;1920-01-06&#x27;, &#x27;1920-01-07&#x27;, &#x27;1920-01-08&#x27;, &#x27;1920-01-09&#x27;, &#x27;1920-01-10&#x27;,\n",
       "       ...\n",
       "       &#x27;2014-12-19&#x27;, &#x27;2014-12-20&#x27;, &#x27;2014-12-21&#x27;, &#x27;2014-12-22&#x27;, &#x27;2014-12-23&#x27;,\n",
       "       &#x27;2014-12-24&#x27;, &#x27;2014-12-25&#x27;, &#x27;2014-12-26&#x27;, &#x27;2014-12-27&#x27;, &#x27;2014-12-28&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;time&#x27;, length=13300))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f3ceb752-34f8-4883-8343-debcc8fb4a28' class='xr-index-data-in' type='checkbox'/><label for='index-f3ceb752-34f8-4883-8343-debcc8fb4a28' 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([10000.0, 5000.0, 1000.0], dtype=&#x27;float64&#x27;, name=&#x27;plev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>member</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fff23021-ad8e-44ca-889e-82dcd1338129' class='xr-index-data-in' type='checkbox'/><label for='index-fff23021-ad8e-44ca-889e-82dcd1338129' 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;r10i1p1f1&#x27;, &#x27;r10i1p2f1&#x27;, &#x27;r11i1p1f1&#x27;, &#x27;r12i1p1f1&#x27;, &#x27;r13i1p1f1&#x27;,\n",
       "       &#x27;r14i1p1f1&#x27;, &#x27;r15i1p1f1&#x27;, &#x27;r16i1p1f1&#x27;, &#x27;r17i1p1f1&#x27;, &#x27;r18i1p1f1&#x27;,\n",
       "       &#x27;r19i1p1f1&#x27;, &#x27;r1i1p1f1&#x27;, &#x27;r1i1p2f1&#x27;, &#x27;r20i1p1f1&#x27;, &#x27;r21i1p1f1&#x27;,\n",
       "       &#x27;r22i1p1f1&#x27;, &#x27;r23i1p1f1&#x27;, &#x27;r24i1p1f1&#x27;, &#x27;r25i1p1f1&#x27;, &#x27;r2i1p1f1&#x27;,\n",
       "       &#x27;r2i1p2f1&#x27;, &#x27;r3i1p1f1&#x27;, &#x27;r3i1p2f1&#x27;, &#x27;r4i1p1f1&#x27;, &#x27;r4i1p2f1&#x27;, &#x27;r5i1p1f1&#x27;,\n",
       "       &#x27;r5i1p2f1&#x27;, &#x27;r6i1p1f1&#x27;, &#x27;r6i1p2f1&#x27;, &#x27;r7i1p1f1&#x27;, &#x27;r7i1p2f1&#x27;, &#x27;r8i1p1f1&#x27;,\n",
       "       &#x27;r8i1p2f1&#x27;, &#x27;r9i1p1f1&#x27;, &#x27;r9i1p2f1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83ebdec0-fc97-4a77-b1ff-36af3be0c551' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83ebdec0-fc97-4a77-b1ff-36af3be0c551' 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:             (member: 35, time: 13300, plev: 3)\n",
       "Coordinates:\n",
       "  * time                (time) object '1920-01-01' '1920-01-02' ... '2014-12-28'\n",
       "  * plev                (plev) float64 1e+04 5e+03 1e+03\n",
       "  * member              (member) <U9 'r10i1p1f1' 'r10i1p2f1' ... 'r9i1p2f1'\n",
       "Data variables:\n",
       "    angle               (member, time, plev) float64 -6.53 10.81 ... -13.8\n",
       "    aspect_ratio        (member, time, plev) float64 2.073 2.098 ... 1.422 1.206\n",
       "    objective_area      (member, time, plev) float64 7.786e+10 ... 3.643e+11\n",
       "    kurtosis            (member, time, plev) float64 0.1251 -0.05142 ... 0.3044\n",
       "    centroid_latitude   (member, time, plev) float64 88.46 83.7 ... 84.2 82.32\n",
       "    centroid_longitude  (member, time, plev) float64 -12.93 44.22 ... 87.99"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "974a399f-7acf-4140-aa95-8bff4d204416",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1718ef0b-ed71-4c57-a6ed-87b619dccd64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-19T15:12:30.614486Z",
     "iopub.status.busy": "2024-01-19T15:12:30.614040Z",
     "iopub.status.idle": "2024-01-19T15:13:01.335682Z",
     "shell.execute_reply": "2024-01-19T15:13:01.334649Z",
     "shell.execute_reply.started": "2024-01-19T15:12:30.614444Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "displacement CanESM5\n",
      "split CanESM5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = 'CanESM5' # 'CanESM2'\n",
    "forecast=rh.get_dataset(rh.path_to_data+rh.file_name[model])\n",
    "#define bin edges\n",
    "#edges=np.arange(0,1.02,0.02)\n",
    "fig, axes = plt.subplots(1,2, sharey = True)\n",
    "if any(forecast['plev'].isin(1e3)):\n",
    "    #roc curve for displacement\n",
    "    forecast_cl=forecast['centroid_latitude'].loc[:,'1979-01-01':'2014-12-28',1e3]\n",
    "    roc_d=xs.roc(obs_cl<66, (forecast_cl<66).mean('member'),return_results='all_as_metric_dim') #thresholds as in Seviour et al. 2013\n",
    "    area_d=roc_d.sel(metric='area under curve').values[0]\n",
    "    #use rank frequency test to produce error bars\n",
    "    err_5,err_95=freq_test(forecast_cl,threshold=66,lower=True)\n",
    "    area_displacements.append('{model}&{area:.3f}&{perc5:.3f}&{perc95:.3f}'.format(\n",
    "        model=model,area=area,perc5=err_5,perc95=err_95))\n",
    "    plt.rcParams.update({'font.size': 18})\n",
    "\n",
    "    plot_roc(axes[0],roc_d,model,event_type='displacement')\n",
    "    #axes[0].fill_between()\n",
    "    # roc.to_dataset(dim='metric').plot.scatter(y='true positive rate', x='false positive rate')\n",
    "    #plt.plot([0, 1], [0, 1], 'k:')\n",
    "    #plt.title('ROC curve displacement events '+model)\n",
    "    #plt.savefig('../plots/displacement_'+model+'.pdf', bbox_inches = 'tight')\n",
    "    #plt.close()\n",
    "    print('displacement',model)\n",
    "\n",
    "    #roc curve for split\n",
    "    forecast_ar=forecast['aspect_ratio'].loc[:,'1979-01-01':'2014-12-28',1e3]\n",
    "    roc_s=xs.roc(obs_ar>2.4, (forecast_ar>2.4).mean('member'),return_results='all_as_metric_dim')\n",
    "    area_s=roc_s.sel(metric='area under curve').values[0]\n",
    "    err_5,err_95=freq_test(forecast_ar,threshold=2.4,lower=False)\n",
    "    area_splits.append('{model}&{area:.3f}&{perc5:.3f}&{perc95:.3f}'.format(\n",
    "        model=model,area=area,perc5=err_5,perc95=err_95))\n",
    "    plot_roc(axes[1],roc_s,model)\n",
    "    #plt.plot([0,1], [0, 1], 'k:')\n",
    "    #plt.title('ROC curver ')\n",
    "    #plt.savefig('../plots/split_'+model+'.pdf', bbox_inches = 'tight')\n",
    "    #plt.close()\n",
    "    models.append(model)\n",
    "    print('split',model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3c53c5c5-cf6e-4f14-9069-6ee6535e11ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-19T14:54:55.909228Z",
     "iopub.status.busy": "2024-01-19T14:54:55.908772Z",
     "iopub.status.idle": "2024-01-19T14:54:56.058858Z",
     "shell.execute_reply": "2024-01-19T14:54:56.057809Z",
     "shell.execute_reply.started": "2024-01-19T14:54:55.909187Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-inf   0.  inf]\n",
      "[-inf 0.02  inf]\n",
      "[-inf 0.04  inf]\n",
      "[-inf 0.06  inf]\n",
      "[-inf 0.08  inf]\n",
      "[-inf  0.1  inf]\n",
      "[-inf 0.12  inf]\n",
      "[-inf 0.14  inf]\n",
      "[-inf 0.16  inf]\n",
      "[-inf 0.18  inf]\n",
      "[-inf 1.18  inf]\n"
     ]
    }
   ],
   "source": [
    "observations = (obs_ar>2.4)\n",
    "forecasts = (forecast_ar>2.4).mean('member').reset_coords(drop = True)\n",
    "dim = list(forecasts.dims)\n",
    "\n",
    "f_bin = forecasts.stack(ndim=forecasts.dims)\n",
    "f_bin = f_bin.sortby(-f_bin)\n",
    "bin_edges = np.append(f_bin[0] + 1, f_bin)\n",
    "bin_edges = np.unique(bin_edges)\n",
    "\n",
    "tpr, fpr = [], []\n",
    "for i in bin_edges:\n",
    "    \n",
    "    dichotomous_category_edges = np.array(\n",
    "        [-np.inf, i, np.inf]\n",
    "    )  # \"dichotomous\" means two-category\n",
    "    print(dichotomous_category_edges)\n",
    "    dichotomous_contingency = xs.Contingency(\n",
    "        observations,\n",
    "        forecasts,\n",
    "        dichotomous_category_edges,\n",
    "        dichotomous_category_edges,\n",
    "        dim=dim,\n",
    "    )\n",
    "    fpr.append(dichotomous_contingency.false_alarm_rate())\n",
    "    tpr.append(dichotomous_contingency.hit_rate())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "7aec8220-9b41-4fbf-8775-979a610a947e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-19T14:57:26.474192Z",
     "iopub.status.busy": "2024-01-19T14:57:26.473575Z",
     "iopub.status.idle": "2024-01-19T14:57:26.505704Z",
     "shell.execute_reply": "2024-01-19T14:57:26.503891Z",
     "shell.execute_reply.started": "2024-01-19T14:57:26.474132Z"
    }
   },
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;histogram_observations_forecasts&#x27; (probability_bin: 11)&gt;\n",
       "array([1.        , 0.88297872, 0.67375887, 0.35460993, 0.16312057,\n",
       "       0.04609929, 0.0070922 , 0.        , 0.        , 0.        ,\n",
       "              nan])\n",
       "Dimensions without coordinates: probability_bin</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'histogram_observations_forecasts'</div><ul class='xr-dim-list'><li><span>probability_bin</span>: 11</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f9ee9b67-1020-4f05-b8a1-144b82d607e7' class='xr-array-in' type='checkbox' checked><label for='section-f9ee9b67-1020-4f05-b8a1-144b82d607e7' 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>1.0 0.883 0.6738 0.3546 0.1631 0.0461 0.007092 0.0 0.0 0.0 nan</span></div><div class='xr-array-data'><pre>array([1.        , 0.88297872, 0.67375887, 0.35460993, 0.16312057,\n",
       "       0.04609929, 0.0070922 , 0.        , 0.        , 0.        ,\n",
       "              nan])</pre></div></div></li><li class='xr-section-item'><input id='section-8f2713d7-d6e5-44ba-938a-d378a9361db9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f2713d7-d6e5-44ba-938a-d378a9361db9' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-246316f8-4a32-4f39-bfef-55646480c154' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-246316f8-4a32-4f39-bfef-55646480c154' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-c203c135-cd7f-4696-be83-bad2bb226d71' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c203c135-cd7f-4696-be83-bad2bb226d71' 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 'histogram_observations_forecasts' (probability_bin: 11)>\n",
       "array([1.        , 0.88297872, 0.67375887, 0.35460993, 0.16312057,\n",
       "       0.04609929, 0.0070922 , 0.        , 0.        , 0.        ,\n",
       "              nan])\n",
       "Dimensions without coordinates: probability_bin"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpr = xr.concat(tpr, \"probability_bin\")\n",
    "fpr = xr.concat(fpr, \"probability_bin\")\n",
    "tpr"
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     "shell.execute_reply": "2024-01-19T14:58:50.006128Z",
     "shell.execute_reply.started": "2024-01-19T14:58:49.974758Z"
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       "  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-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.DataArray &#x27;histogram_observations_forecasts&#x27; (metric: 3,\n",
       "                                                      probability_bin: 11)&gt;\n",
       "array([[0.00000000e+00, 4.20344683e-04, 1.26103405e-03, 4.41361917e-03,\n",
       "        1.76544767e-02, 5.69567045e-02, 1.38503573e-01, 3.00546448e-01,\n",
       "        5.66834805e-01, 8.46574191e-01, 1.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        7.09219858e-03, 4.60992908e-02, 1.63120567e-01, 3.54609929e-01,\n",
       "        6.73758865e-01, 8.82978723e-01, 1.00000000e+00],\n",
       "       [5.50680601e-01, 5.50680601e-01, 5.50680601e-01, 5.50680601e-01,\n",
       "        5.50680601e-01, 5.50680601e-01, 5.50680601e-01, 5.50680601e-01,\n",
       "        5.50680601e-01, 5.50680601e-01, 5.50680601e-01]])\n",
       "Coordinates:\n",
       "  * probability_bin  (probability_bin) float64 1.18 0.18 0.16 ... 0.04 0.02 0.0\n",
       "    plev             float64 1e+03\n",
       "  * metric           (metric) &lt;U19 &#x27;false positive rate&#x27; ... &#x27;area under curve&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'histogram_observations_forecasts'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>metric</span>: 3</li><li><span class='xr-has-index'>probability_bin</span>: 11</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3aee599c-9f03-49dd-81a3-2b582c4064c4' class='xr-array-in' type='checkbox' checked><label for='section-3aee599c-9f03-49dd-81a3-2b582c4064c4' 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>0.0 0.0004203 0.001261 0.004414 ... 0.5507 0.5507 0.5507 0.5507</span></div><div class='xr-array-data'><pre>array([[0.00000000e+00, 4.20344683e-04, 1.26103405e-03, 4.41361917e-03,\n",
       "        1.76544767e-02, 5.69567045e-02, 1.38503573e-01, 3.00546448e-01,\n",
       "        5.66834805e-01, 8.46574191e-01, 1.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        7.09219858e-03, 4.60992908e-02, 1.63120567e-01, 3.54609929e-01,\n",
       "        6.73758865e-01, 8.82978723e-01, 1.00000000e+00],\n",
       "       [5.50680601e-01, 5.50680601e-01, 5.50680601e-01, 5.50680601e-01,\n",
       "        5.50680601e-01, 5.50680601e-01, 5.50680601e-01, 5.50680601e-01,\n",
       "        5.50680601e-01, 5.50680601e-01, 5.50680601e-01]])</pre></div></div></li><li class='xr-section-item'><input id='section-411bde34-1467-41b0-878e-1118389a9939' class='xr-section-summary-in' type='checkbox'  checked><label for='section-411bde34-1467-41b0-878e-1118389a9939' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>probability_bin</span></div><div class='xr-var-dims'>(probability_bin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.18 0.18 0.16 ... 0.04 0.02 0.0</div><input id='attrs-cf8ffef1-a94b-44e7-a6a4-c1284f93a985' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf8ffef1-a94b-44e7-a6a4-c1284f93a985' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44019a02-2c2f-4509-89c0-d9296be6db37' class='xr-var-data-in' type='checkbox'><label for='data-44019a02-2c2f-4509-89c0-d9296be6db37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1.18, 0.18, 0.16, 0.14, 0.12, 0.1 , 0.08, 0.06, 0.04, 0.02, 0.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-ab0e850a-bfba-4065-a28d-ede6f15bd3a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab0e850a-bfba-4065-a28d-ede6f15bd3a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c64a43ea-8fde-48a6-a4cc-152b2a622a4e' class='xr-var-data-in' type='checkbox'><label for='data-c64a43ea-8fde-48a6-a4cc-152b2a622a4e' 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(1000.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>metric</span></div><div class='xr-var-dims'>(metric)</div><div class='xr-var-dtype'>&lt;U19</div><div class='xr-var-preview xr-preview'>&#x27;false positive rate&#x27; ... &#x27;area ...</div><input id='attrs-2f98f4c0-10b4-42eb-84de-ec229c89c476' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f98f4c0-10b4-42eb-84de-ec229c89c476' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1612158-df08-4787-9ae8-b87c755ef3fc' class='xr-var-data-in' type='checkbox'><label for='data-e1612158-df08-4787-9ae8-b87c755ef3fc' 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;false positive rate&#x27;, &#x27;true positive rate&#x27;, &#x27;area under curve&#x27;],\n",
       "      dtype=&#x27;&lt;U19&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1dcb477-f696-45eb-8b76-c794aea91e11' class='xr-section-summary-in' type='checkbox'  ><label for='section-d1dcb477-f696-45eb-8b76-c794aea91e11' 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>probability_bin</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-84d46cd1-cbb3-4ea9-9ae9-92050102fcc3' class='xr-index-data-in' type='checkbox'/><label for='index-84d46cd1-cbb3-4ea9-9ae9-92050102fcc3' 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([1.18, 0.18, 0.16, 0.14, 0.12, 0.1, 0.08, 0.06, 0.04, 0.02, 0.0], dtype=&#x27;float64&#x27;, name=&#x27;probability_bin&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>metric</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a34ecbc-4164-4661-bf24-6826ee1d5ca0' class='xr-index-data-in' type='checkbox'/><label for='index-1a34ecbc-4164-4661-bf24-6826ee1d5ca0' 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;false positive rate&#x27;, &#x27;true positive rate&#x27;, &#x27;area under curve&#x27;], dtype=&#x27;object&#x27;, name=&#x27;metric&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-08c0b7b6-6142-433b-ad0a-290bfa027c5c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-08c0b7b6-6142-433b-ad0a-290bfa027c5c' 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 'histogram_observations_forecasts' (metric: 3,\n",
       "                                                      probability_bin: 11)>\n",
       "array([[0.00000000e+00, 4.20344683e-04, 1.26103405e-03, 4.41361917e-03,\n",
       "        1.76544767e-02, 5.69567045e-02, 1.38503573e-01, 3.00546448e-01,\n",
       "        5.66834805e-01, 8.46574191e-01, 1.00000000e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        7.09219858e-03, 4.60992908e-02, 1.63120567e-01, 3.54609929e-01,\n",
       "        6.73758865e-01, 8.82978723e-01, 1.00000000e+00],\n",
       "       [5.50680601e-01, 5.50680601e-01, 5.50680601e-01, 5.50680601e-01,\n",
       "        5.50680601e-01, 5.50680601e-01, 5.50680601e-01, 5.50680601e-01,\n",
       "        5.50680601e-01, 5.50680601e-01, 5.50680601e-01]])\n",
       "Coordinates:\n",
       "  * probability_bin  (probability_bin) float64 1.18 0.18 0.16 ... 0.04 0.02 0.0\n",
       "    plev             float64 1e+03\n",
       "  * metric           (metric) <U19 'false positive rate' ... 'area under curve'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_s#.sel(metric = 'true positive rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6736bd64-a375-499b-bc4d-5c875e993fee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-19T14:40:37.348848Z",
     "iopub.status.busy": "2024-01-19T14:40:37.348387Z",
     "iopub.status.idle": "2024-01-19T14:40:37.604421Z",
     "shell.execute_reply": "2024-01-19T14:40:37.603322Z",
     "shell.execute_reply.started": "2024-01-19T14:40:37.348806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe95de54430>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp = (obs_ar>2.4)*1.\n",
    "plt.plot(temp)\n",
    "plt.plot())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "5fe17c7d-b52a-42e1-b7a7-66ff44ec4a36",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-19T12:58:51.612112Z",
     "iopub.status.busy": "2024-01-19T12:58:51.611455Z",
     "iopub.status.idle": "2024-01-19T12:58:51.909550Z",
     "shell.execute_reply": "2024-01-19T12:58:51.908463Z",
     "shell.execute_reply.started": "2024-01-19T12:58:51.612050Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([13798., 31377., 34258., 28515., 20987., 14352.,  9580.,  6469.,\n",
       "         4220.,  2896.,  2084.,  1515.,  1138.,   816.,   619.,   508.,\n",
       "          466.,   330.,   310.,   253.]),\n",
       " array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2,\n",
       "        2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. ]),\n",
       " <BarContainer object of 20 artists>)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast_ar.plot.hist(bins = np.linspace(1,3,21))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1558db54-3bb9-44c6-8935-5bfe4b73bb50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-19T14:51:56.495547Z",
     "iopub.status.busy": "2024-01-19T14:51:56.495099Z",
     "iopub.status.idle": "2024-01-19T14:51:56.706048Z",
     "shell.execute_reply": "2024-01-19T14:51:56.705043Z",
     "shell.execute_reply.started": "2024-01-19T14:51:56.495506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 763., 1390., 1357.,  825.,  421.,  198.,   65.,   15.,    4.,\n",
       "           2.]),\n",
       " array([0.  , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 1.18]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "(forecast_ar>2.4).mean('member').plot.hist(bins = bin_edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ba1214b4-af34-429e-99e4-99eae5f9e8d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-19T12:57:58.956682Z",
     "iopub.status.busy": "2024-01-19T12:57:58.956047Z",
     "iopub.status.idle": "2024-01-19T12:57:59.217822Z",
     "shell.execute_reply": "2024-01-19T12:57:59.216755Z",
     "shell.execute_reply.started": "2024-01-19T12:57:58.956622Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.266e+03, 1.857e+03, 0.000e+00, 7.030e+02, 0.000e+00, 1.780e+02,\n",
       "        2.700e+01, 0.000e+00, 8.000e+00, 1.000e+00]),\n",
       " array([0.        , 0.01714286, 0.03428571, 0.05142857, 0.06857143,\n",
       "        0.08571429, 0.10285714, 0.12      , 0.13714286, 0.15428571,\n",
       "        0.17142857]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(forecast_ar>2.4).mean('member').plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "f08db637-2caf-455e-b8e7-97e50da6759d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-19T15:04:36.400394Z",
     "iopub.status.busy": "2024-01-19T15:04:36.399971Z",
     "iopub.status.idle": "2024-01-19T15:04:36.413164Z",
     "shell.execute_reply": "2024-01-19T15:04:36.411847Z",
     "shell.execute_reply.started": "2024-01-19T15:04:36.400354Z"
    }
   },
   "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",
       "<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",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;histogram_observations_forecasts&#x27; ()&gt;\n",
       "array(0.)\n",
       "Coordinates:\n",
       "    probability_bin  float64 1.18\n",
       "    plev             float64 1e+03\n",
       "    metric           &lt;U19 &#x27;true positive rate&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'histogram_observations_forecasts'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2e1bdb0c-a1cf-4fa2-9e65-c4feb65a5ca6' class='xr-array-in' type='checkbox' checked><label for='section-2e1bdb0c-a1cf-4fa2-9e65-c4feb65a5ca6' 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>0.0</span></div><div class='xr-array-data'><pre>array(0.)</pre></div></div></li><li class='xr-section-item'><input id='section-11b94ffc-885f-456e-8f31-f9c7e4549f6d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11b94ffc-885f-456e-8f31-f9c7e4549f6d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>probability_bin</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.18</div><input id='attrs-749408e7-0774-4f94-98bf-f22f4d4bbed4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-749408e7-0774-4f94-98bf-f22f4d4bbed4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd84422e-45aa-42dd-be74-8a874b548072' class='xr-var-data-in' type='checkbox'><label for='data-cd84422e-45aa-42dd-be74-8a874b548072' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.18)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-022ec723-8206-4246-9c2b-b27db1c738a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-022ec723-8206-4246-9c2b-b27db1c738a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e105079-a85e-4bea-8501-0ee52c207701' class='xr-var-data-in' type='checkbox'><label for='data-6e105079-a85e-4bea-8501-0ee52c207701' 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(1000.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>metric</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U19</div><div class='xr-var-preview xr-preview'>&#x27;true positive rate&#x27;</div><input id='attrs-2fc99fa6-4d20-47db-b9d8-e9a1c580028c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2fc99fa6-4d20-47db-b9d8-e9a1c580028c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acaeb090-cfbf-4e63-9155-a9eca373528f' class='xr-var-data-in' type='checkbox'><label for='data-acaeb090-cfbf-4e63-9155-a9eca373528f' 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;true positive rate&#x27;, dtype=&#x27;&lt;U19&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b6a86f32-8787-4350-88a9-fa71cb5a9263' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b6a86f32-8787-4350-88a9-fa71cb5a9263' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-4e87e93a-bea6-4ec5-bf34-75a2b9bf3c8d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4e87e93a-bea6-4ec5-bf34-75a2b9bf3c8d' 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 'histogram_observations_forecasts' ()>\n",
       "array(0.)\n",
       "Coordinates:\n",
       "    probability_bin  float64 1.18\n",
       "    plev             float64 1e+03\n",
       "    metric           <U19 'true positive rate'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_s.sel(metric = 'true positive rate')[0]"
   ]
  },
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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2,sharey=True)\n",
    "plot_roc(axes[0],roc_d,model,event_type='displacement')\n",
    "plot_roc(axes[1],roc_s,model,event_type='split')\n",
    "axes[0].text(0.05, 1.075, ascii_uppercase[0], transform=axes[0].transAxes,\n",
    "                      fontsize=10, fontweight='bold', va='top', bbox={'alpha': 0.7, 'facecolor': 'white'})\n",
    "axes[1].text(0.05, 1.075, ascii_uppercase[1], transform=axes[1].transAxes,\n",
    "                      fontsize=10, fontweight='bold', va='top', bbox={'alpha': 0.7, 'facecolor': 'white'})\n",
    "fig.supxlabel('false positive rate', y = -0.02,verticalalignment='bottom')\n",
    "fig.suptitle(model, y = 1.03)\n",
    "\n",
    "\n",
    "label_bars(roc_d, axes[0])\n",
    "label_bars(roc_s, axes[1])\n",
    "\n",
    "plt.savefig('ROC_example_'+model+'.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "16c5cda7-1e98-4144-8639-95c73ecebbde",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-23T21:29:37.679533Z",
     "iopub.status.busy": "2024-01-23T21:29:37.679155Z",
     "iopub.status.idle": "2024-01-23T21:29:39.599937Z",
     "shell.execute_reply": "2024-01-23T21:29:39.598788Z",
     "shell.execute_reply.started": "2024-01-23T21:29:37.679493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['angle',\n",
       " 'aspect_ratio',\n",
       " 'objective_area',\n",
       " 'kurtosis',\n",
       " 'centroid_latitude',\n",
       " 'centroid_longitude']"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era_reanalysis=rh.get_dataset(rh.path_to_data+'ERA5_moments@10and50and100hPa_NHonly.nc')\n",
    "vars=list(era_reanalysis.keys())\n",
    "vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "4146ac12-a0cd-4ef6-b2eb-a6fb4c8201da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-23T23:10:22.843036Z",
     "iopub.status.busy": "2024-01-23T23:10:22.841874Z",
     "iopub.status.idle": "2024-01-23T23:10:22.863421Z",
     "shell.execute_reply": "2024-01-23T23:10:22.862130Z",
     "shell.execute_reply.started": "2024-01-23T23:10:22.842976Z"
    }
   },
   "outputs": [
    {
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       ".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.DataArray &#x27;aspect_ratio&#x27; (time: 5040)&gt;\n",
       "array([1.06396639, 1.08336642, 1.15513537, ..., 1.17706752, 1.16612916,\n",
       "       1.13073937])\n",
       "Coordinates:\n",
       "  * time     (time) object &#x27;1979-01-01&#x27; &#x27;1979-01-02&#x27; ... &#x27;2014-12-28&#x27;\n",
       "    plev     float64 1e+03</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'aspect_ratio'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5040</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fe9fa35d-fcca-4ccf-ab04-8d9ebaf7d882' class='xr-array-in' type='checkbox' checked><label for='section-fe9fa35d-fcca-4ccf-ab04-8d9ebaf7d882' 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>1.064 1.083 1.155 1.226 1.289 1.256 ... 1.174 1.179 1.177 1.166 1.131</span></div><div class='xr-array-data'><pre>array([1.06396639, 1.08336642, 1.15513537, ..., 1.17706752, 1.16612916,\n",
       "       1.13073937])</pre></div></div></li><li class='xr-section-item'><input id='section-1a1a9864-cf6b-47d5-a91f-91a8415141ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1a1a9864-cf6b-47d5-a91f-91a8415141ae' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;1979-01-01&#x27; ... &#x27;2014-12-28&#x27;</div><input id='attrs-5603fcab-5ded-4372-8df8-8de35cc9ac5b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5603fcab-5ded-4372-8df8-8de35cc9ac5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-260cba0e-ac87-4189-a1e7-1ad90a0de430' class='xr-var-data-in' type='checkbox'><label for='data-260cba0e-ac87-4189-a1e7-1ad90a0de430' 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;1979-01-01&#x27;, &#x27;1979-01-02&#x27;, &#x27;1979-01-03&#x27;, ..., &#x27;2014-12-26&#x27;,\n",
       "       &#x27;2014-12-27&#x27;, &#x27;2014-12-28&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+03</div><input id='attrs-9b997b2f-0729-413d-8b65-5cd589c12704' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b997b2f-0729-413d-8b65-5cd589c12704' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-827f96c1-3644-4fc5-a4ba-2c57665a893d' class='xr-var-data-in' type='checkbox'><label for='data-827f96c1-3644-4fc5-a4ba-2c57665a893d' 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(1000.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b4e63fd-65a8-4a93-b810-a6c250d96cdd' class='xr-section-summary-in' type='checkbox'  ><label for='section-5b4e63fd-65a8-4a93-b810-a6c250d96cdd' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ce72e7cb-b71e-4cc0-a9c0-3f5ce0bc915f' class='xr-index-data-in' type='checkbox'/><label for='index-ce72e7cb-b71e-4cc0-a9c0-3f5ce0bc915f' 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;1979-01-01&#x27;, &#x27;1979-01-02&#x27;, &#x27;1979-01-03&#x27;, &#x27;1979-01-04&#x27;, &#x27;1979-01-05&#x27;,\n",
       "       &#x27;1979-01-06&#x27;, &#x27;1979-01-07&#x27;, &#x27;1979-01-08&#x27;, &#x27;1979-01-09&#x27;, &#x27;1979-01-10&#x27;,\n",
       "       ...\n",
       "       &#x27;2014-12-19&#x27;, &#x27;2014-12-20&#x27;, &#x27;2014-12-21&#x27;, &#x27;2014-12-22&#x27;, &#x27;2014-12-23&#x27;,\n",
       "       &#x27;2014-12-24&#x27;, &#x27;2014-12-25&#x27;, &#x27;2014-12-26&#x27;, &#x27;2014-12-27&#x27;, &#x27;2014-12-28&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;time&#x27;, length=5040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1ac9354-347a-4b40-acde-01cebd10b7b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d1ac9354-347a-4b40-acde-01cebd10b7b9' 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 'aspect_ratio' (time: 5040)>\n",
       "array([1.06396639, 1.08336642, 1.15513537, ..., 1.17706752, 1.16612916,\n",
       "       1.13073937])\n",
       "Coordinates:\n",
       "  * time     (time) object '1979-01-01' '1979-01-02' ... '2014-12-28'\n",
       "    plev     float64 1e+03"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
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    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " var in ['aspect_ratio'] and plev == 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "2f608a37-8cbd-44ef-8488-8dfc61c4c5e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-23T23:48:20.580484Z",
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     "iopub.status.idle": "2024-01-23T23:48:20.589586Z",
     "shell.execute_reply": "2024-01-23T23:48:20.588144Z",
     "shell.execute_reply.started": "2024-01-23T23:48:20.580426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['angle',\n",
       " 'aspect_ratio',\n",
       " 'objective_area',\n",
       " 'kurtosis',\n",
       " 'centroid_latitude',\n",
       " 'centroid_longitude']"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "3cf99d53-0b49-4381-b8b8-d503a4e8b614",
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     "iopub.execute_input": "2024-01-24T01:00:38.584302Z",
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     "shell.execute_reply": "2024-01-24T01:04:45.665520Z",
     "shell.execute_reply.started": "2024-01-24T01:00:38.584247Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CanESM2\n",
      "CanESM5\n",
      "CESM2\n",
      "CNRM-CM6-1\n",
      "INM-CM5-0\n",
      "IPSL-CM6A-LR\n",
      "MIROC6\n",
      "UKESM1-0-LL\n",
      "MPI-ESM1-2-HR\n",
      "MPI-ESM1-2-LR\n",
      "GFDL-CM3\n"
     ]
    }
   ],
   "source": [
    "fce = lambda x: str(int(x))+\" hPa\"\n",
    "\n",
    "df3_ls = []\n",
    "for model in rh.file_name:\n",
    "    print(model)\n",
    "    forecast=rh.get_dataset(rh.path_to_data+rh.file_name[model])\n",
    "    plev_str_ls = list(map(fce,forecast['plev'].values/100))\n",
    "    #sys.exit()\n",
    "    #plot and save rank histogram for every variable and pressure level\n",
    "    df2_ls = []\n",
    "    for n in range(len(forecast['plev'])):\n",
    "        plev=float(forecast['plev'][n])\n",
    "        df_ls = []\n",
    "        for var in vars[1:]:\n",
    "            rank_hist=rh.get_rank_stats(era_reanalysis,forecast,\n",
    "                                var,'1979-01-01','2014-12-31',forecast['plev'][n],dim='time',member_dim='member')\n",
    "            \"\"\"plt.step(rank_hist['rank'],rank_hist,'r',where='mid')\n",
    "            expected=np.empty(len(rank_hist))\n",
    "            expected[:]=np.sum(rank_hist)/len(rank_hist)\n",
    "            plt.step(rank_hist['rank'],expected,'b',where='mid')\n",
    "            plt.xlim([1,len(rank_hist)])\n",
    "            plt.ylim(bottom=0)\n",
    "            \"\"\"#calculate statistics and add them to the plot\n",
    "            bias=bs.bias(rank_hist)\n",
    "            spread=bs.spread(rank_hist)\n",
    "\n",
    "            \n",
    "            if var in ['aspect_ratio'] and plev == 1000:\n",
    "                obs_ar = era_reanalysis['aspect_ratio'].loc['1979-01-01':'2014-12-28',1e3]\n",
    "                forecast_ar=forecast['aspect_ratio'].loc[:,'1979-01-01':'2014-12-28',1e3]\n",
    "                \n",
    "                \n",
    "                roc=xs.roc(obs_ar>2.4, (forecast_ar>2.4).mean('member'),return_results='all_as_metric_dim')\n",
    "                area=roc.sel(metric='area under curve').values[0]      \n",
    "                \n",
    "                df = pd.DataFrame(\n",
    "                    {'bias': [bias], 'spread': [spread], 'AUC': [area]}, \n",
    "                    index = pd.MultiIndex.from_product([[model], [var.replace('_', ' ')]])\n",
    "                )\n",
    "            elif var in ['centroid_latitude'] and plev == 1000:\n",
    "                obs_cl=obs['centroid_latitude'].loc['1979-01-01':'2014-12-28',1e3]\n",
    "                forecast_cl=forecast['centroid_latitude'].loc[:,'1979-01-01':'2014-12-28',1e3]\n",
    "                \n",
    "                roc=xs.roc(obs_cl<66, (forecast_cl<66).mean('member'),return_results='all_as_metric_dim') \n",
    "                area=roc.sel(metric='area under curve').values[0]\n",
    "                \n",
    "                df = pd.DataFrame(\n",
    "                    {'bias': [bias], 'spread': [spread], 'AUC': [area]}, \n",
    "                    index = pd.MultiIndex.from_product([[model], [var.replace('_', ' ')]])\n",
    "                )\n",
    "            else:\n",
    "                df = pd.DataFrame(\n",
    "                    {'bias': [bias], 'spread': [spread],'AUC': [np.nan]}, \n",
    "                    index = pd.MultiIndex.from_product([[model], [var.replace('_', ' ')]])\n",
    "                )\n",
    "                \n",
    "            df_ls.append(df)\n",
    "\n",
    "        df2_ls.append(pd.concat(df_ls))\n",
    "\n",
    "    temp = pd.concat(df2_ls, axis = 1)\n",
    "    temp.columns=pd.MultiIndex.from_product([plev_str_ls, ['bias','spread','AUC']])\n",
    "\n",
    "    df3_ls.append(temp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abf2bacd-63d3-4119-8c02-c749353324f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "a7a4bd18-71ba-4148-824a-236137714697",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-24T01:04:45.668669Z",
     "iopub.status.busy": "2024-01-24T01:04:45.668413Z",
     "iopub.status.idle": "2024-01-24T01:04:45.701649Z",
     "shell.execute_reply": "2024-01-24T01:04:45.700854Z",
     "shell.execute_reply.started": "2024-01-24T01:04:45.668643Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">100 hPa</th>\n",
       "      <th colspan=\"3\" halign=\"left\">50 hPa</th>\n",
       "      <th colspan=\"3\" halign=\"left\">10 hPa</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>bias</th>\n",
       "      <th>spread</th>\n",
       "      <th>AUC</th>\n",
       "      <th>bias</th>\n",
       "      <th>spread</th>\n",
       "      <th>AUC</th>\n",
       "      <th>bias</th>\n",
       "      <th>spread</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CanESM2</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>590.991956</td>\n",
       "      <td>213.265864</td>\n",
       "      <td></td>\n",
       "      <td>210.741987</td>\n",
       "      <td>54.055497</td>\n",
       "      <td></td>\n",
       "      <td>148.607151</td>\n",
       "      <td>7.097994</td>\n",
       "      <td>0.550681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>343.125869</td>\n",
       "      <td>122.475559</td>\n",
       "      <td></td>\n",
       "      <td>15.733539</td>\n",
       "      <td>77.342659</td>\n",
       "      <td></td>\n",
       "      <td>197.265869</td>\n",
       "      <td>128.908475</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>11.782572</td>\n",
       "      <td>226.711966</td>\n",
       "      <td></td>\n",
       "      <td>591.698546</td>\n",
       "      <td>240.177249</td>\n",
       "      <td></td>\n",
       "      <td>5525.352506</td>\n",
       "      <td>3790.655943</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>418.515462</td>\n",
       "      <td>79.544978</td>\n",
       "      <td></td>\n",
       "      <td>336.711752</td>\n",
       "      <td>53.662129</td>\n",
       "      <td></td>\n",
       "      <td>781.144467</td>\n",
       "      <td>47.831216</td>\n",
       "      <td>0.663545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>118.844015</td>\n",
       "      <td>66.857357</td>\n",
       "      <td></td>\n",
       "      <td>3.128968</td>\n",
       "      <td>141.428238</td>\n",
       "      <td></td>\n",
       "      <td>224.014342</td>\n",
       "      <td>13.948153</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CanESM5</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>184.777762</td>\n",
       "      <td>48.573559</td>\n",
       "      <td></td>\n",
       "      <td>266.946247</td>\n",
       "      <td>59.4233</td>\n",
       "      <td></td>\n",
       "      <td>772.060094</td>\n",
       "      <td>140.73827</td>\n",
       "      <td>0.449699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>894.648798</td>\n",
       "      <td>1.512671</td>\n",
       "      <td></td>\n",
       "      <td>1520.33009</td>\n",
       "      <td>23.750737</td>\n",
       "      <td></td>\n",
       "      <td>425.063477</td>\n",
       "      <td>11.870566</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>118.641458</td>\n",
       "      <td>410.998739</td>\n",
       "      <td></td>\n",
       "      <td>16.795183</td>\n",
       "      <td>353.753936</td>\n",
       "      <td></td>\n",
       "      <td>4577.709221</td>\n",
       "      <td>4368.734203</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>289.995369</td>\n",
       "      <td>53.737284</td>\n",
       "      <td></td>\n",
       "      <td>276.068901</td>\n",
       "      <td>65.125488</td>\n",
       "      <td></td>\n",
       "      <td>19.299857</td>\n",
       "      <td>6.202124</td>\n",
       "      <td>0.591273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>44.198215</td>\n",
       "      <td>47.258027</td>\n",
       "      <td></td>\n",
       "      <td>1.48499</td>\n",
       "      <td>15.651676</td>\n",
       "      <td></td>\n",
       "      <td>120.331407</td>\n",
       "      <td>224.546663</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CESM2</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>104.882014</td>\n",
       "      <td>10.337744</td>\n",
       "      <td></td>\n",
       "      <td>271.809642</td>\n",
       "      <td>32.920941</td>\n",
       "      <td></td>\n",
       "      <td>576.896076</td>\n",
       "      <td>127.07166</td>\n",
       "      <td>0.590714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>334.062792</td>\n",
       "      <td>91.538477</td>\n",
       "      <td></td>\n",
       "      <td>476.735609</td>\n",
       "      <td>166.813736</td>\n",
       "      <td></td>\n",
       "      <td>1587.794479</td>\n",
       "      <td>344.50551</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>6.817608</td>\n",
       "      <td>217.615629</td>\n",
       "      <td></td>\n",
       "      <td>61.088983</td>\n",
       "      <td>378.661627</td>\n",
       "      <td></td>\n",
       "      <td>2239.477373</td>\n",
       "      <td>2203.391792</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>252.673665</td>\n",
       "      <td>88.903880</td>\n",
       "      <td></td>\n",
       "      <td>366.220988</td>\n",
       "      <td>115.060417</td>\n",
       "      <td></td>\n",
       "      <td>233.955691</td>\n",
       "      <td>21.506311</td>\n",
       "      <td>0.608055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>26.310548</td>\n",
       "      <td>50.333578</td>\n",
       "      <td></td>\n",
       "      <td>0.005806</td>\n",
       "      <td>33.145656</td>\n",
       "      <td></td>\n",
       "      <td>67.852019</td>\n",
       "      <td>56.456555</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">CNRM-CM6-1</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>6.744165</td>\n",
       "      <td>0.717461</td>\n",
       "      <td></td>\n",
       "      <td>14.666975</td>\n",
       "      <td>0.033401</td>\n",
       "      <td></td>\n",
       "      <td>108.766339</td>\n",
       "      <td>0.281049</td>\n",
       "      <td>0.636289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>3.001583</td>\n",
       "      <td>5.459485</td>\n",
       "      <td></td>\n",
       "      <td>51.572609</td>\n",
       "      <td>14.758802</td>\n",
       "      <td></td>\n",
       "      <td>847.545801</td>\n",
       "      <td>78.800788</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>227.266143</td>\n",
       "      <td>10.947593</td>\n",
       "      <td></td>\n",
       "      <td>866.917085</td>\n",
       "      <td>176.199253</td>\n",
       "      <td></td>\n",
       "      <td>4018.979266</td>\n",
       "      <td>2329.240919</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>0.064103</td>\n",
       "      <td>2.240237</td>\n",
       "      <td></td>\n",
       "      <td>22.69243</td>\n",
       "      <td>0.348441</td>\n",
       "      <td></td>\n",
       "      <td>15.463355</td>\n",
       "      <td>8.788739</td>\n",
       "      <td>0.709564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>0.393802</td>\n",
       "      <td>4.149227</td>\n",
       "      <td></td>\n",
       "      <td>15.796974</td>\n",
       "      <td>2.720467</td>\n",
       "      <td></td>\n",
       "      <td>0.902987</td>\n",
       "      <td>2.94447</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">INM-CM5-0</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>257.000020</td>\n",
       "      <td>69.240144</td>\n",
       "      <td></td>\n",
       "      <td>119.676391</td>\n",
       "      <td>51.189866</td>\n",
       "      <td></td>\n",
       "      <td>0.64664</td>\n",
       "      <td>1.804775</td>\n",
       "      <td>0.534863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>26.652401</td>\n",
       "      <td>71.305700</td>\n",
       "      <td></td>\n",
       "      <td>71.702401</td>\n",
       "      <td>80.818081</td>\n",
       "      <td></td>\n",
       "      <td>165.945714</td>\n",
       "      <td>436.670763</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>52.264464</td>\n",
       "      <td>221.071573</td>\n",
       "      <td></td>\n",
       "      <td>114.181419</td>\n",
       "      <td>133.799047</td>\n",
       "      <td></td>\n",
       "      <td>3.006983</td>\n",
       "      <td>25.446765</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>304.266984</td>\n",
       "      <td>182.845369</td>\n",
       "      <td></td>\n",
       "      <td>391.941196</td>\n",
       "      <td>148.441644</td>\n",
       "      <td></td>\n",
       "      <td>90.276993</td>\n",
       "      <td>7.539083</td>\n",
       "      <td>0.668542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>55.004464</td>\n",
       "      <td>53.036744</td>\n",
       "      <td></td>\n",
       "      <td>0.033406</td>\n",
       "      <td>75.008647</td>\n",
       "      <td></td>\n",
       "      <td>0.51665</td>\n",
       "      <td>7.358513</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">IPSL-CM6A-LR</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>41.733826</td>\n",
       "      <td>4.267439</td>\n",
       "      <td></td>\n",
       "      <td>115.304717</td>\n",
       "      <td>15.538755</td>\n",
       "      <td></td>\n",
       "      <td>450.035528</td>\n",
       "      <td>2.54894</td>\n",
       "      <td>0.629325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>507.800002</td>\n",
       "      <td>75.514577</td>\n",
       "      <td></td>\n",
       "      <td>137.372919</td>\n",
       "      <td>71.862643</td>\n",
       "      <td></td>\n",
       "      <td>570.494827</td>\n",
       "      <td>730.880701</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>1.260681</td>\n",
       "      <td>94.650421</td>\n",
       "      <td></td>\n",
       "      <td>10.647187</td>\n",
       "      <td>132.024779</td>\n",
       "      <td></td>\n",
       "      <td>45.859473</td>\n",
       "      <td>69.424723</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>44.413132</td>\n",
       "      <td>18.485090</td>\n",
       "      <td></td>\n",
       "      <td>64.222584</td>\n",
       "      <td>46.136514</td>\n",
       "      <td></td>\n",
       "      <td>156.462848</td>\n",
       "      <td>7.659616</td>\n",
       "      <td>0.627866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>77.083826</td>\n",
       "      <td>41.454719</td>\n",
       "      <td></td>\n",
       "      <td>3.294524</td>\n",
       "      <td>403.830562</td>\n",
       "      <td></td>\n",
       "      <td>289.330744</td>\n",
       "      <td>6.172841</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">MIROC6</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>8.382937</td>\n",
       "      <td>7.858433</td>\n",
       "      <td></td>\n",
       "      <td>6.055326</td>\n",
       "      <td>13.837451</td>\n",
       "      <td></td>\n",
       "      <td>19.067286</td>\n",
       "      <td>6.550297</td>\n",
       "      <td>0.594052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>841.650179</td>\n",
       "      <td>420.276898</td>\n",
       "      <td></td>\n",
       "      <td>900.004464</td>\n",
       "      <td>790.830006</td>\n",
       "      <td></td>\n",
       "      <td>624.891746</td>\n",
       "      <td>1015.175548</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>62.653353</td>\n",
       "      <td>0.384425</td>\n",
       "      <td></td>\n",
       "      <td>19.477345</td>\n",
       "      <td>15.542961</td>\n",
       "      <td></td>\n",
       "      <td>40.579697</td>\n",
       "      <td>19.195274</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>0.164306</td>\n",
       "      <td>19.326053</td>\n",
       "      <td></td>\n",
       "      <td>97.236566</td>\n",
       "      <td>43.674365</td>\n",
       "      <td></td>\n",
       "      <td>332.993824</td>\n",
       "      <td>19.251876</td>\n",
       "      <td>0.617909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>101.610496</td>\n",
       "      <td>9.900467</td>\n",
       "      <td></td>\n",
       "      <td>26.327524</td>\n",
       "      <td>199.907868</td>\n",
       "      <td></td>\n",
       "      <td>209.121312</td>\n",
       "      <td>4.14409</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">UKESM1-0-LL</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>99.946503</td>\n",
       "      <td>19.765452</td>\n",
       "      <td></td>\n",
       "      <td>91.159281</td>\n",
       "      <td>17.088247</td>\n",
       "      <td></td>\n",
       "      <td>0.333005</td>\n",
       "      <td>0.274966</td>\n",
       "      <td>0.581171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>0.150670</td>\n",
       "      <td>127.420459</td>\n",
       "      <td></td>\n",
       "      <td>161.292072</td>\n",
       "      <td>91.838299</td>\n",
       "      <td></td>\n",
       "      <td>67.103183</td>\n",
       "      <td>14.829112</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>54.179894</td>\n",
       "      <td>107.000689</td>\n",
       "      <td></td>\n",
       "      <td>17.966092</td>\n",
       "      <td>51.795695</td>\n",
       "      <td></td>\n",
       "      <td>421.632703</td>\n",
       "      <td>99.533475</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>1.936640</td>\n",
       "      <td>0.189629</td>\n",
       "      <td></td>\n",
       "      <td>18.805056</td>\n",
       "      <td>1.026998</td>\n",
       "      <td></td>\n",
       "      <td>16.884368</td>\n",
       "      <td>27.500173</td>\n",
       "      <td>0.663228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>0.327389</td>\n",
       "      <td>5.424667</td>\n",
       "      <td></td>\n",
       "      <td>1.189396</td>\n",
       "      <td>3.252702</td>\n",
       "      <td></td>\n",
       "      <td>156.714506</td>\n",
       "      <td>29.470719</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">MPI-ESM1-2-HR</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>1.809603</td>\n",
       "      <td>2.838733</td>\n",
       "      <td></td>\n",
       "      <td>11.328001</td>\n",
       "      <td>1.055239</td>\n",
       "      <td></td>\n",
       "      <td>116.149364</td>\n",
       "      <td>0.012578</td>\n",
       "      <td>0.555245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>535.270556</td>\n",
       "      <td>72.349862</td>\n",
       "      <td></td>\n",
       "      <td>484.982857</td>\n",
       "      <td>196.415978</td>\n",
       "      <td></td>\n",
       "      <td>1046.073829</td>\n",
       "      <td>824.845239</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>3.566984</td>\n",
       "      <td>8.819089</td>\n",
       "      <td></td>\n",
       "      <td>27.343581</td>\n",
       "      <td>78.913678</td>\n",
       "      <td></td>\n",
       "      <td>453.432896</td>\n",
       "      <td>40.794932</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>151.910496</td>\n",
       "      <td>10.348465</td>\n",
       "      <td></td>\n",
       "      <td>89.400656</td>\n",
       "      <td>7.42372</td>\n",
       "      <td></td>\n",
       "      <td>123.32561</td>\n",
       "      <td>0.912637</td>\n",
       "      <td>0.589232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>16.502857</td>\n",
       "      <td>9.087189</td>\n",
       "      <td></td>\n",
       "      <td>1.262003</td>\n",
       "      <td>13.318224</td>\n",
       "      <td></td>\n",
       "      <td>4.534329</td>\n",
       "      <td>80.936493</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">MPI-ESM1-2-LR</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>3.766286</td>\n",
       "      <td>0.560200</td>\n",
       "      <td></td>\n",
       "      <td>11.933272</td>\n",
       "      <td>0.552632</td>\n",
       "      <td></td>\n",
       "      <td>242.945742</td>\n",
       "      <td>7.781022</td>\n",
       "      <td>0.651371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>objective area</th>\n",
       "      <td>1030.157143</td>\n",
       "      <td>372.841344</td>\n",
       "      <td></td>\n",
       "      <td>776.320015</td>\n",
       "      <td>568.096372</td>\n",
       "      <td></td>\n",
       "      <td>1118.400008</td>\n",
       "      <td>1266.024971</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurtosis</th>\n",
       "      <td>1.909465</td>\n",
       "      <td>13.731812</td>\n",
       "      <td></td>\n",
       "      <td>0.044393</td>\n",
       "      <td>71.312618</td>\n",
       "      <td></td>\n",
       "      <td>324.226018</td>\n",
       "      <td>102.144244</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid latitude</th>\n",
       "      <td>298.717693</td>\n",
       "      <td>32.050428</td>\n",
       "      <td></td>\n",
       "      <td>154.317147</td>\n",
       "      <td>12.510477</td>\n",
       "      <td></td>\n",
       "      <td>199.475857</td>\n",
       "      <td>21.32837</td>\n",
       "      <td>0.656467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid longitude</th>\n",
       "      <td>0.650777</td>\n",
       "      <td>57.889802</td>\n",
       "      <td></td>\n",
       "      <td>26.557871</td>\n",
       "      <td>2.037587</td>\n",
       "      <td></td>\n",
       "      <td>39.196928</td>\n",
       "      <td>214.106712</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GFDL-CM3</th>\n",
       "      <th>aspect ratio</th>\n",
       "      <td>271.246753</td>\n",
       "      <td>116.714681</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>objective area</th>\n",
       "      <td>6615.536932</td>\n",
       "      <td>2364.374026</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>kurtosis</th>\n",
       "      <td>42.212386</td>\n",
       "      <td>423.245648</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>centroid latitude</th>\n",
       "      <td>471.851970</td>\n",
       "      <td>282.039889</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>centroid longitude</th>\n",
       "      <td>69.896299</td>\n",
       "      <td>52.722641</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      100 hPa                       50 hPa  \\\n",
       "                                         bias       spread AUC        bias   \n",
       "CanESM2       aspect ratio         590.991956   213.265864      210.741987   \n",
       "              objective area       343.125869   122.475559       15.733539   \n",
       "              kurtosis              11.782572   226.711966      591.698546   \n",
       "              centroid latitude    418.515462    79.544978      336.711752   \n",
       "              centroid longitude   118.844015    66.857357        3.128968   \n",
       "CanESM5       aspect ratio         184.777762    48.573559      266.946247   \n",
       "              objective area       894.648798     1.512671      1520.33009   \n",
       "              kurtosis             118.641458   410.998739       16.795183   \n",
       "              centroid latitude    289.995369    53.737284      276.068901   \n",
       "              centroid longitude    44.198215    47.258027         1.48499   \n",
       "CESM2         aspect ratio         104.882014    10.337744      271.809642   \n",
       "              objective area       334.062792    91.538477      476.735609   \n",
       "              kurtosis               6.817608   217.615629       61.088983   \n",
       "              centroid latitude    252.673665    88.903880      366.220988   \n",
       "              centroid longitude    26.310548    50.333578        0.005806   \n",
       "CNRM-CM6-1    aspect ratio           6.744165     0.717461       14.666975   \n",
       "              objective area         3.001583     5.459485       51.572609   \n",
       "              kurtosis             227.266143    10.947593      866.917085   \n",
       "              centroid latitude      0.064103     2.240237        22.69243   \n",
       "              centroid longitude     0.393802     4.149227       15.796974   \n",
       "INM-CM5-0     aspect ratio         257.000020    69.240144      119.676391   \n",
       "              objective area        26.652401    71.305700       71.702401   \n",
       "              kurtosis              52.264464   221.071573      114.181419   \n",
       "              centroid latitude    304.266984   182.845369      391.941196   \n",
       "              centroid longitude    55.004464    53.036744        0.033406   \n",
       "IPSL-CM6A-LR  aspect ratio          41.733826     4.267439      115.304717   \n",
       "              objective area       507.800002    75.514577      137.372919   \n",
       "              kurtosis               1.260681    94.650421       10.647187   \n",
       "              centroid latitude     44.413132    18.485090       64.222584   \n",
       "              centroid longitude    77.083826    41.454719        3.294524   \n",
       "MIROC6        aspect ratio           8.382937     7.858433        6.055326   \n",
       "              objective area       841.650179   420.276898      900.004464   \n",
       "              kurtosis              62.653353     0.384425       19.477345   \n",
       "              centroid latitude      0.164306    19.326053       97.236566   \n",
       "              centroid longitude   101.610496     9.900467       26.327524   \n",
       "UKESM1-0-LL   aspect ratio          99.946503    19.765452       91.159281   \n",
       "              objective area         0.150670   127.420459      161.292072   \n",
       "              kurtosis              54.179894   107.000689       17.966092   \n",
       "              centroid latitude      1.936640     0.189629       18.805056   \n",
       "              centroid longitude     0.327389     5.424667        1.189396   \n",
       "MPI-ESM1-2-HR aspect ratio           1.809603     2.838733       11.328001   \n",
       "              objective area       535.270556    72.349862      484.982857   \n",
       "              kurtosis               3.566984     8.819089       27.343581   \n",
       "              centroid latitude    151.910496    10.348465       89.400656   \n",
       "              centroid longitude    16.502857     9.087189        1.262003   \n",
       "MPI-ESM1-2-LR aspect ratio           3.766286     0.560200       11.933272   \n",
       "              objective area      1030.157143   372.841344      776.320015   \n",
       "              kurtosis               1.909465    13.731812        0.044393   \n",
       "              centroid latitude    298.717693    32.050428      154.317147   \n",
       "              centroid longitude     0.650777    57.889802       26.557871   \n",
       "GFDL-CM3      aspect ratio         271.246753   116.714681                   \n",
       "              objective area      6615.536932  2364.374026                   \n",
       "              kurtosis              42.212386   423.245648                   \n",
       "              centroid latitude    471.851970   282.039889                   \n",
       "              centroid longitude    69.896299    52.722641                   \n",
       "\n",
       "                                                       10 hPa               \\\n",
       "                                      spread AUC         bias       spread   \n",
       "CanESM2       aspect ratio         54.055497       148.607151     7.097994   \n",
       "              objective area       77.342659       197.265869   128.908475   \n",
       "              kurtosis            240.177249      5525.352506  3790.655943   \n",
       "              centroid latitude    53.662129       781.144467    47.831216   \n",
       "              centroid longitude  141.428238       224.014342    13.948153   \n",
       "CanESM5       aspect ratio           59.4233       772.060094    140.73827   \n",
       "              objective area       23.750737       425.063477    11.870566   \n",
       "              kurtosis            353.753936      4577.709221  4368.734203   \n",
       "              centroid latitude    65.125488        19.299857     6.202124   \n",
       "              centroid longitude   15.651676       120.331407   224.546663   \n",
       "CESM2         aspect ratio         32.920941       576.896076    127.07166   \n",
       "              objective area      166.813736      1587.794479    344.50551   \n",
       "              kurtosis            378.661627      2239.477373  2203.391792   \n",
       "              centroid latitude   115.060417       233.955691    21.506311   \n",
       "              centroid longitude   33.145656        67.852019    56.456555   \n",
       "CNRM-CM6-1    aspect ratio          0.033401       108.766339     0.281049   \n",
       "              objective area       14.758802       847.545801    78.800788   \n",
       "              kurtosis            176.199253      4018.979266  2329.240919   \n",
       "              centroid latitude     0.348441        15.463355     8.788739   \n",
       "              centroid longitude    2.720467         0.902987      2.94447   \n",
       "INM-CM5-0     aspect ratio         51.189866          0.64664     1.804775   \n",
       "              objective area       80.818081       165.945714   436.670763   \n",
       "              kurtosis            133.799047         3.006983    25.446765   \n",
       "              centroid latitude   148.441644        90.276993     7.539083   \n",
       "              centroid longitude   75.008647          0.51665     7.358513   \n",
       "IPSL-CM6A-LR  aspect ratio         15.538755       450.035528      2.54894   \n",
       "              objective area       71.862643       570.494827   730.880701   \n",
       "              kurtosis            132.024779        45.859473    69.424723   \n",
       "              centroid latitude    46.136514       156.462848     7.659616   \n",
       "              centroid longitude  403.830562       289.330744     6.172841   \n",
       "MIROC6        aspect ratio         13.837451        19.067286     6.550297   \n",
       "              objective area      790.830006       624.891746  1015.175548   \n",
       "              kurtosis             15.542961        40.579697    19.195274   \n",
       "              centroid latitude    43.674365       332.993824    19.251876   \n",
       "              centroid longitude  199.907868       209.121312      4.14409   \n",
       "UKESM1-0-LL   aspect ratio         17.088247         0.333005     0.274966   \n",
       "              objective area       91.838299        67.103183    14.829112   \n",
       "              kurtosis             51.795695       421.632703    99.533475   \n",
       "              centroid latitude     1.026998        16.884368    27.500173   \n",
       "              centroid longitude    3.252702       156.714506    29.470719   \n",
       "MPI-ESM1-2-HR aspect ratio          1.055239       116.149364     0.012578   \n",
       "              objective area      196.415978      1046.073829   824.845239   \n",
       "              kurtosis             78.913678       453.432896    40.794932   \n",
       "              centroid latitude      7.42372        123.32561     0.912637   \n",
       "              centroid longitude   13.318224         4.534329    80.936493   \n",
       "MPI-ESM1-2-LR aspect ratio          0.552632       242.945742     7.781022   \n",
       "              objective area      568.096372      1118.400008  1266.024971   \n",
       "              kurtosis             71.312618       324.226018   102.144244   \n",
       "              centroid latitude    12.510477       199.475857     21.32837   \n",
       "              centroid longitude    2.037587        39.196928   214.106712   \n",
       "GFDL-CM3      aspect ratio                                                   \n",
       "              objective area                                                 \n",
       "              kurtosis                                                       \n",
       "              centroid latitude                                              \n",
       "              centroid longitude                                             \n",
       "\n",
       "                                            \n",
       "                                       AUC  \n",
       "CanESM2       aspect ratio        0.550681  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.663545  \n",
       "              centroid longitude            \n",
       "CanESM5       aspect ratio        0.449699  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.591273  \n",
       "              centroid longitude            \n",
       "CESM2         aspect ratio        0.590714  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.608055  \n",
       "              centroid longitude            \n",
       "CNRM-CM6-1    aspect ratio        0.636289  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.709564  \n",
       "              centroid longitude            \n",
       "INM-CM5-0     aspect ratio        0.534863  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.668542  \n",
       "              centroid longitude            \n",
       "IPSL-CM6A-LR  aspect ratio        0.629325  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.627866  \n",
       "              centroid longitude            \n",
       "MIROC6        aspect ratio        0.594052  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.617909  \n",
       "              centroid longitude            \n",
       "UKESM1-0-LL   aspect ratio        0.581171  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.663228  \n",
       "              centroid longitude            \n",
       "MPI-ESM1-2-HR aspect ratio        0.555245  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.589232  \n",
       "              centroid longitude            \n",
       "MPI-ESM1-2-LR aspect ratio        0.651371  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude   0.656467  \n",
       "              centroid longitude            \n",
       "GFDL-CM3      aspect ratio                  \n",
       "              objective area                \n",
       "              kurtosis                      \n",
       "              centroid latitude             \n",
       "              centroid longitude            "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.concat(df3_ls)#.reset_index()\n",
    "df_all = df_all.replace(np.nan, '')\n",
    "#df_all = df_all.replace('100.0', '100\\,hPa')\n",
    "df_all#.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "b363040f-e33f-4d11-baa2-da421b2ef7eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-24T01:04:45.725709Z",
     "iopub.status.busy": "2024-01-24T01:04:45.725474Z",
     "iopub.status.idle": "2024-01-24T01:04:45.744882Z",
     "shell.execute_reply": "2024-01-24T01:04:45.743982Z",
     "shell.execute_reply.started": "2024-01-24T01:04:45.725685Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{longtable}{llrrlllllll}\n",
      "\\caption{Summary table including metrics: bias, spread and AOC. AOC related to displacement and split events is in the table associated with centroid latitude and aspect ratio, respectively.} \\label{tab:summary} \\\\\n",
      "\\toprule\n",
      " &  & \\multicolumn{3}{r}{100 hPa} & \\multicolumn{3}{r}{50 hPa} & \\multicolumn{3}{r}{10 hPa} \\\\\n",
      " &  & bias & spread & AUC & bias & spread & AUC & bias & spread & AUC \\\\\n",
      "\\midrule\n",
      "\\endfirsthead\n",
      "\\caption[]{Summary table including metrics: bias, spread and AOC. AOC related to displacement and split events is in the table associated with centroid latitude and aspect ratio, respectively.} \\\\\n",
      "\\toprule\n",
      " &  & \\multicolumn{3}{r}{100 hPa} & \\multicolumn{3}{r}{50 hPa} & \\multicolumn{3}{r}{10 hPa} \\\\\n",
      " &  & bias & spread & AUC & bias & spread & AUC & bias & spread & AUC \\\\\n",
      "\\midrule\n",
      "\\endhead\n",
      "\\midrule\n",
      "\\multicolumn{11}{r}{Continued on next page} \\\\\n",
      "\\midrule\n",
      "\\endfoot\n",
      "\\bottomrule\n",
      "\\endlastfoot\n",
      "\\multirow[t]{5}{*}{CanESM2} & aspect ratio & 590.99 & 213.27 &  & 210.74 & 54.06 &  & 148.61 & 7.10 & 0.55 \\\\\n",
      " & objective area & 343.13 & 122.48 &  & 15.73 & 77.34 &  & 197.27 & 128.91 &  \\\\\n",
      " & kurtosis & 11.78 & 226.71 &  & 591.70 & 240.18 &  & 5525.35 & 3790.66 &  \\\\\n",
      " & centroid latitude & 418.52 & 79.54 &  & 336.71 & 53.66 &  & 781.14 & 47.83 & 0.66 \\\\\n",
      " & centroid longitude & 118.84 & 66.86 &  & 3.13 & 141.43 &  & 224.01 & 13.95 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{CanESM5} & aspect ratio & 184.78 & 48.57 &  & 266.95 & 59.42 &  & 772.06 & 140.74 & 0.45 \\\\\n",
      " & objective area & 894.65 & 1.51 &  & 1520.33 & 23.75 &  & 425.06 & 11.87 &  \\\\\n",
      " & kurtosis & 118.64 & 411.00 &  & 16.80 & 353.75 &  & 4577.71 & 4368.73 &  \\\\\n",
      " & centroid latitude & 290.00 & 53.74 &  & 276.07 & 65.13 &  & 19.30 & 6.20 & 0.59 \\\\\n",
      " & centroid longitude & 44.20 & 47.26 &  & 1.48 & 15.65 &  & 120.33 & 224.55 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{CESM2} & aspect ratio & 104.88 & 10.34 &  & 271.81 & 32.92 &  & 576.90 & 127.07 & 0.59 \\\\\n",
      " & objective area & 334.06 & 91.54 &  & 476.74 & 166.81 &  & 1587.79 & 344.51 &  \\\\\n",
      " & kurtosis & 6.82 & 217.62 &  & 61.09 & 378.66 &  & 2239.48 & 2203.39 &  \\\\\n",
      " & centroid latitude & 252.67 & 88.90 &  & 366.22 & 115.06 &  & 233.96 & 21.51 & 0.61 \\\\\n",
      " & centroid longitude & 26.31 & 50.33 &  & 0.01 & 33.15 &  & 67.85 & 56.46 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{CNRM-CM6-1} & aspect ratio & 6.74 & 0.72 &  & 14.67 & 0.03 &  & 108.77 & 0.28 & 0.64 \\\\\n",
      " & objective area & 3.00 & 5.46 &  & 51.57 & 14.76 &  & 847.55 & 78.80 &  \\\\\n",
      " & kurtosis & 227.27 & 10.95 &  & 866.92 & 176.20 &  & 4018.98 & 2329.24 &  \\\\\n",
      " & centroid latitude & 0.06 & 2.24 &  & 22.69 & 0.35 &  & 15.46 & 8.79 & 0.71 \\\\\n",
      " & centroid longitude & 0.39 & 4.15 &  & 15.80 & 2.72 &  & 0.90 & 2.94 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{INM-CM5-0} & aspect ratio & 257.00 & 69.24 &  & 119.68 & 51.19 &  & 0.65 & 1.80 & 0.53 \\\\\n",
      " & objective area & 26.65 & 71.31 &  & 71.70 & 80.82 &  & 165.95 & 436.67 &  \\\\\n",
      " & kurtosis & 52.26 & 221.07 &  & 114.18 & 133.80 &  & 3.01 & 25.45 &  \\\\\n",
      " & centroid latitude & 304.27 & 182.85 &  & 391.94 & 148.44 &  & 90.28 & 7.54 & 0.67 \\\\\n",
      " & centroid longitude & 55.00 & 53.04 &  & 0.03 & 75.01 &  & 0.52 & 7.36 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{IPSL-CM6A-LR} & aspect ratio & 41.73 & 4.27 &  & 115.30 & 15.54 &  & 450.04 & 2.55 & 0.63 \\\\\n",
      " & objective area & 507.80 & 75.51 &  & 137.37 & 71.86 &  & 570.49 & 730.88 &  \\\\\n",
      " & kurtosis & 1.26 & 94.65 &  & 10.65 & 132.02 &  & 45.86 & 69.42 &  \\\\\n",
      " & centroid latitude & 44.41 & 18.49 &  & 64.22 & 46.14 &  & 156.46 & 7.66 & 0.63 \\\\\n",
      " & centroid longitude & 77.08 & 41.45 &  & 3.29 & 403.83 &  & 289.33 & 6.17 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{MIROC6} & aspect ratio & 8.38 & 7.86 &  & 6.06 & 13.84 &  & 19.07 & 6.55 & 0.59 \\\\\n",
      " & objective area & 841.65 & 420.28 &  & 900.00 & 790.83 &  & 624.89 & 1015.18 &  \\\\\n",
      " & kurtosis & 62.65 & 0.38 &  & 19.48 & 15.54 &  & 40.58 & 19.20 &  \\\\\n",
      " & centroid latitude & 0.16 & 19.33 &  & 97.24 & 43.67 &  & 332.99 & 19.25 & 0.62 \\\\\n",
      " & centroid longitude & 101.61 & 9.90 &  & 26.33 & 199.91 &  & 209.12 & 4.14 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{UKESM1-0-LL} & aspect ratio & 99.95 & 19.77 &  & 91.16 & 17.09 &  & 0.33 & 0.27 & 0.58 \\\\\n",
      " & objective area & 0.15 & 127.42 &  & 161.29 & 91.84 &  & 67.10 & 14.83 &  \\\\\n",
      " & kurtosis & 54.18 & 107.00 &  & 17.97 & 51.80 &  & 421.63 & 99.53 &  \\\\\n",
      " & centroid latitude & 1.94 & 0.19 &  & 18.81 & 1.03 &  & 16.88 & 27.50 & 0.66 \\\\\n",
      " & centroid longitude & 0.33 & 5.42 &  & 1.19 & 3.25 &  & 156.71 & 29.47 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{MPI-ESM1-2-HR} & aspect ratio & 1.81 & 2.84 &  & 11.33 & 1.06 &  & 116.15 & 0.01 & 0.56 \\\\\n",
      " & objective area & 535.27 & 72.35 &  & 484.98 & 196.42 &  & 1046.07 & 824.85 &  \\\\\n",
      " & kurtosis & 3.57 & 8.82 &  & 27.34 & 78.91 &  & 453.43 & 40.79 &  \\\\\n",
      " & centroid latitude & 151.91 & 10.35 &  & 89.40 & 7.42 &  & 123.33 & 0.91 & 0.59 \\\\\n",
      " & centroid longitude & 16.50 & 9.09 &  & 1.26 & 13.32 &  & 4.53 & 80.94 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{MPI-ESM1-2-LR} & aspect ratio & 3.77 & 0.56 &  & 11.93 & 0.55 &  & 242.95 & 7.78 & 0.65 \\\\\n",
      " & objective area & 1030.16 & 372.84 &  & 776.32 & 568.10 &  & 1118.40 & 1266.02 &  \\\\\n",
      " & kurtosis & 1.91 & 13.73 &  & 0.04 & 71.31 &  & 324.23 & 102.14 &  \\\\\n",
      " & centroid latitude & 298.72 & 32.05 &  & 154.32 & 12.51 &  & 199.48 & 21.33 & 0.66 \\\\\n",
      " & centroid longitude & 0.65 & 57.89 &  & 26.56 & 2.04 &  & 39.20 & 214.11 &  \\\\\n",
      "\\cline{1-11}\n",
      "\\multirow[t]{5}{*}{GFDL-CM3} & aspect ratio & 271.25 & 116.71 &  &  &  &  &  &  &  \\\\\n",
      " & objective area & 6615.54 & 2364.37 &  &  &  &  &  &  &  \\\\\n",
      " & kurtosis & 42.21 & 423.25 &  &  &  &  &  &  &  \\\\\n",
      " & centroid latitude & 471.85 & 282.04 &  &  &  &  &  &  &  \\\\\n",
      " & centroid longitude & 69.90 & 52.72 &  &  &  &  &  &  &  \\\\\n",
      "\\cline{1-11}\n",
      "\\end{longtable}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "latex_table = df_all.to_latex(\n",
    "    float_format=\"{:.2f}\".format,\n",
    "    caption = 'Summary table including metrics: bias, spread and AOC. AOC related to displacement and split events is in the table associated with centroid latitude and aspect ratio, respectively.',\n",
    "    label = 'tab:summary',\n",
    "    #formatters={\"index\": lambda x: x.split('_')},\n",
    "    longtable=True\n",
    ")\n",
    "print(latex_table)"
   ]
  }
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