Case2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee98e3db-0f34-456e-a8a0-35b12832747b",
   "metadata": {},
   "source": [
    "# Simplified Direct Sampling aka Direct Sampling Best Candidate (DSBC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94a51c8d-f616-4e63-98ef-3d3ef3129fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from geone import img\n",
    "from geone import imgplot\n",
    "from geone import deesseinterface\n",
    "\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d67f327-d94e-44d7-912d-851a6fbbc408",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "%aimport ensemble, simulator, scoring, tsanfleuron, mpstool"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a984e66-46c8-4bd0-9613-5211dc134597",
   "metadata": {},
   "source": [
    "## Test 2. Continuous without pyramides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "560a74dc-1255-47ee-a45f-edb5c3b8b18e",
   "metadata": {},
   "outputs": [],
   "source": [
    "bedmap = np.loadtxt('clip_ti_2011.txt')\n",
    "ti = img.Img(nx=bedmap.shape[1], ny=bedmap.shape[0], nz=1, nv=1, sx=1, sy=1, sz=1, ox=0, val=bedmap[np.newaxis, np.newaxis])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f9d07092-ecd6-4167-a50f-1a85ca87f706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2126.084229"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmin(ti.val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "11e804dc-734c-4a73-a926-44b1f4ed38ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 510.236x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2*3.54330709,3.54330709))\n",
    "imgplot.drawImage2D(ti,\n",
    "                categ=False,\n",
    "                cmap='cividis',\n",
    "                cticks=[2126, 2969],\n",
    "                cticklabels=['2126 m', '2969 m'],\n",
    "                vmin=2126,\n",
    "                vmax=2969,\n",
    "                cticklabels_fontsize=14,\n",
    "                xticklabels_fontsize=14,\n",
    "                yticklabels_fontsize=14,\n",
    "                xticks=[0,ti.nx],\n",
    "                yticks=[0,ti.ny],\n",
    "                #title='TI',\n",
    "                title_fontsize=14,\n",
    "               )\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'figures/tsanfleuron-ti.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7b3d46fe-825f-4a51-87c2-f02b0a92f84b",
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator_ds = tsanfleuron.TsanfleuronDS('test/test', nneighboringNode=8,\n",
    "                                  distanceThreshold=0.002,\n",
    "                                  maxScanFraction=0.005,\n",
    "                                 ox=1200, oy=500, nx=200, ny=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9a6fb5d0-6e1c-4d3f-8e36-65e26023a8c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "simulator_dsbc = tsanfleuron.TsanfleuronDSBC('test/testdsbc', nneighboringNode=16,\n",
    "                                  distanceThreshold=0,\n",
    "                                  maxScanFraction=0.0005,\n",
    "                                 ox=1200, oy=500, nx=200, ny=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "49f09535-2c57-4435-9be5-4c6bd8fef217",
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.figure(figsize=(15,15))\n",
    "#imgplot.drawImage2D(simulator.cut_ti(ti), cmap='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0b5af89e-ca84-4208-b3d7-80b4727c3e15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deesse running... [VERSION 3.2 / BUILD NUMBER 20210112 / OpenMP 40 thread(s)]\n",
      "Deesse run complete\n",
      "\n",
      "Warnings encountered (1 times in all):\n",
      "#  1: WARNING 00010: a variable in a training image is not exhaustively informed\n"
     ]
    }
   ],
   "source": [
    "deesse_input_ds = simulator_ds.deesse_input(ti, 40, 40, 444)\n",
    "output_ds = deesseinterface.deesseRun(deesse_input_ds, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "81e66e15-c935-4386-af44-8f0a4cea6585",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deesse running... [VERSION 3.2 / BUILD NUMBER 20210112 / OpenMP 40 thread(s)]\n",
      "Deesse run complete\n",
      "\n",
      "Warnings encountered (1 times in all):\n",
      "#  1: WARNING 00010: a variable in a training image is not exhaustively informed\n"
     ]
    }
   ],
   "source": [
    "deesse_input_dsbc = simulator_dsbc.deesse_input(ti, 40, 40, 444)\n",
    "output_dsbc = deesseinterface.deesseRun(deesse_input_dsbc, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "685ebbf4-a601-44ed-807f-5460ba6aa9e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def restrict(image):\n",
    "    new_image = copy.deepcopy(image)\n",
    "    new_image.resize(ix0=100, ix1=300, iy0=100, iy1=300)\n",
    "    return new_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "44756dae-9794-4ca2-a732-d0fad1663b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_tsanfleuron_example(image, title, vmin, vmax, cmap='cividis'):\n",
    "    plt.figure(figsize=(3.54330709,3.54330709))\n",
    "    imgplot.drawImage2D(restrict(image),\n",
    "                    cmap=cmap,\n",
    "                    cticks=[vmin,vmax],\n",
    "                    cticklabels=[f'{vmin} m', f'{vmax} m'],\n",
    "                    vmin=vmin, vmax=vmax,\n",
    "                    cticklabels_fontsize=14,\n",
    "                    xaxis=False,\n",
    "                    yaxis=False,\n",
    "                    title=title,\n",
    "                    title_fontsize=14\n",
    "                   )\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f'figures/tsanfleuron-{title}.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "73fca134-0e5d-433d-ad7e-e901354c684f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tsanfleuron_example(simulator_ds.im_ref_with_sim(ti), 'A', 2550, 2640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "e5f6d249-e6e6-4052-8fd0-ee08afa94c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tsanfleuron_example(simulator_ds.sim_grid(ti), 'B', 2550, 2640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3692253e-a95f-4af7-a161-d834af97fc32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tsanfleuron_example(output_ds['sim'][0], 'C', 2550, 2640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "d927c4b0-0085-4f44-b934-c9ac24a45633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tsanfleuron_example(output_dsbc['sim'][0], 'D', 2550, 2640)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "d3559bee-65eb-4d50-a9f8-a2d4c2e50254",
   "metadata": {},
   "outputs": [],
   "source": [
    "import properscoring\n",
    "def crps_map(true_ref, list_sim):\n",
    "    e = np.stack([list_sim[x].val[0,0] for x in range(40)], axis=-1)\n",
    "    t = true_ref.val[0,0]\n",
    "    return properscoring.crps_ensemble(t,e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "5e08a1dd-ebf7-4b3a-b2bf-5f927dbbf2e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff_ds = copy.deepcopy(output_ds['sim'][0])\n",
    "diff_ds.val[0,0] = crps_map(simulator_ds.im_ref_with_sim(ti),\n",
    "                       output_ds['sim'])\n",
    "plot_tsanfleuron_example(diff_ds, 'E', 0, 11, cmap='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d10e84f8-eef9-4d52-aa3a-e1f294fbedfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff_dsbc = copy.deepcopy(output_dsbc['sim'][0])\n",
    "diff_dsbc.val[0,0] = crps_map(simulator_dsbc.im_ref_with_sim(ti),\n",
    "                       output_dsbc['sim'])\n",
    "plot_tsanfleuron_example(diff_dsbc, 'F', 0, 11, cmap='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5d83b12-973f-4449-87ad-93443c12ca25",
   "metadata": {},
   "outputs": [],
   "source": [
    "locations = [(1200, 500),\n",
    "             (800, 500),\n",
    "             (1700, 600),\n",
    "             (2600, 800),\n",
    "             (1900, 900),\n",
    "             (2200, 700),\n",
    "             (1000, 900),\n",
    "             (500,  800),\n",
    "             (2700, 1200),\n",
    "             (1500, 1000)\n",
    "             ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4857a78c-4116-4033-93d7-d4adef5c593b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ds_simulation_parameters(ox, oy):\n",
    "    parameter_sets = [ [n, i] for n in [8, 16, 32, 64] for i in [0.001, 0.002, 0.005, 0.01]]\n",
    "\n",
    "    df = pd.DataFrame(parameter_sets, columns = ['nneighboringNode', 'distanceThreshold'])\n",
    "    df['maxScanFraction'] = 0.005\n",
    "    df['ox'] = ox\n",
    "    df['oy'] = oy\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cac4c364-6068-4561-b3b2-a7b85e471efc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_df(locations, parameter_function):\n",
    "    return pd.concat([parameter_function(ox, oy)\n",
    "           for ox, oy in locations],\n",
    "          ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9486326-1e52-4975-91a7-e306b85d4ccd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ds = generate_df(locations, ds_simulation_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "943c8f23-e3b8-4b01-a1a8-3c0b86b5a60c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MVE(ensemble, true):\n",
    "    return scoring.crps_pointwise(ensemble, true.val[0,0], lambda x:np.mean(x))\n",
    "\n",
    "def MPE(ensemble, true):\n",
    "    return np.mean(scoring.crps_pointwise(ensemble, true.val[0,0], lambda x:x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c47fd3b2-203a-4715-b51e-b052becf2938",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_ensembles(row, simulator_class):\n",
    "    ox = int(row['ox'])\n",
    "    oy = int(row['oy'])\n",
    "    simulator_obj = simulator_class(\n",
    "                 nneighboringNode=row['nneighboringNode'],\n",
    "                 distanceThreshold=row['distanceThreshold'],\n",
    "                 maxScanFraction=row['maxScanFraction'],\n",
    "                 root_dir=f'case2B/ds-{ox}-{oy}/',\n",
    "                 ox=ox,\n",
    "                 oy=oy)\n",
    "    \n",
    "    ensemble, timing = simulator_obj.get_ensemble(ti,\n",
    "                                                  ensemble_size=40,\n",
    "                                                  nthreads=40,\n",
    "                                                  seed=444,\n",
    "                                                  overwrite=True,\n",
    "                                                  dtype='float')\n",
    "    true = simulator_obj.im_ref(ti)\n",
    "    \n",
    "    \n",
    "    \n",
    "    row['timing'] = timing\n",
    "    row['ensemble_size'] = len(ensemble)\n",
    "    for function in (MPE, MVE):\n",
    "        row[f'crps_{function.__name__}'] = function(ensemble, true)\n",
    "    \n",
    "    \n",
    "    \n",
    "    return row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "55065ac4-f193-4923-b913-b0c02dd8eb9b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>nneighboringNode</th>\n",
       "      <th>distanceThreshold</th>\n",
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       "      <th>ox</th>\n",
       "      <th>oy</th>\n",
       "      <th>timing</th>\n",
       "      <th>ensemble_size</th>\n",
       "      <th>crps_MPE</th>\n",
       "      <th>crps_MVE</th>\n",
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       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>49.737684</td>\n",
       "      <td>40.0</td>\n",
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       "      <td>45.249641</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.988947</td>\n",
       "      <td>0.655207</td>\n",
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       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>43.274896</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.984827</td>\n",
       "      <td>0.776107</td>\n",
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       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>42.593153</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.863662</td>\n",
       "      <td>0.690317</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.001</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>71.658427</td>\n",
       "      <td>40.0</td>\n",
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       "      <th>155</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>54.878859</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.254213</td>\n",
       "      <td>1.646975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>303.166465</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.076583</td>\n",
       "      <td>1.026815</td>\n",
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       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>175.007496</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.259521</td>\n",
       "      <td>1.403330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>89.401636</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.428452</td>\n",
       "      <td>1.646657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>54.526952</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.716858</td>\n",
       "      <td>2.204789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     nneighboringNode  distanceThreshold  maxScanFraction      ox      oy  \\\n",
       "0                 8.0              0.001            0.005  1200.0   500.0   \n",
       "1                 8.0              0.002            0.005  1200.0   500.0   \n",
       "2                 8.0              0.005            0.005  1200.0   500.0   \n",
       "3                 8.0              0.010            0.005  1200.0   500.0   \n",
       "4                16.0              0.001            0.005  1200.0   500.0   \n",
       "..                ...                ...              ...     ...     ...   \n",
       "155              32.0              0.010            0.005  1500.0  1000.0   \n",
       "156              64.0              0.001            0.005  1500.0  1000.0   \n",
       "157              64.0              0.002            0.005  1500.0  1000.0   \n",
       "158              64.0              0.005            0.005  1500.0  1000.0   \n",
       "159              64.0              0.010            0.005  1500.0  1000.0   \n",
       "\n",
       "         timing  ensemble_size  crps_MPE  crps_MVE  \n",
       "0     49.737684           40.0  2.043068  0.790142  \n",
       "1     45.249641           40.0  1.988947  0.655207  \n",
       "2     43.274896           40.0  1.984827  0.776107  \n",
       "3     42.593153           40.0  1.863662  0.690317  \n",
       "4     71.658427           40.0  1.866128  0.599168  \n",
       "..          ...            ...       ...       ...  \n",
       "155   54.878859           40.0  2.254213  1.646975  \n",
       "156  303.166465           40.0  2.076583  1.026815  \n",
       "157  175.007496           40.0  2.259521  1.403330  \n",
       "158   89.401636           40.0  2.428452  1.646657  \n",
       "159   54.526952           40.0  2.716858  2.204789  \n",
       "\n",
       "[160 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ds = df_ds.apply(lambda row: load_ensembles(row, tsanfleuron.TsanfleuronDS), axis=1)\n",
    "df_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "11f720f9-2db9-4275-9651-f4307c94df75",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dsbc_simulation_parameters(ox, oy):\n",
    "    parameter_sets = [ [n, f] for n in [8, 16, 32, 64] \n",
    "                      for f in [0.001, 0.0005, 0.0002, 0.0001]]\n",
    "\n",
    "    df = pd.DataFrame(parameter_sets, columns=['nneighboringNode','maxScanFraction'])\n",
    "    df['distanceThreshold'] = 0\n",
    "    df['ox'] = ox\n",
    "    df['oy'] = oy\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e31afbd1-12d5-4c7a-b0d8-140d0be18863",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dsbc = generate_df(locations, dsbc_simulation_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "7a1b95d8-7ca5-401e-a98f-1acc94382ac1",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>nneighboringNode</th>\n",
       "      <th>maxScanFraction</th>\n",
       "      <th>distanceThreshold</th>\n",
       "      <th>ox</th>\n",
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       "      <th>timing</th>\n",
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       "      <th>crps_MPE</th>\n",
       "      <th>crps_MVE</th>\n",
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       "      <td>500.0</td>\n",
       "      <td>100.608428</td>\n",
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       "      <td>1.758052</td>\n",
       "      <td>0.722566</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>72.171379</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.734936</td>\n",
       "      <td>0.638669</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>85.104975</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.773520</td>\n",
       "      <td>0.575735</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>87.013911</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.785664</td>\n",
       "      <td>0.554003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>108.771865</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1.595565</td>\n",
       "      <td>0.582820</td>\n",
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       "      <td>32.0</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>115.334787</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.080058</td>\n",
       "      <td>1.306113</td>\n",
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       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>193.897180</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.098856</td>\n",
       "      <td>1.128810</td>\n",
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       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>127.156422</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.157514</td>\n",
       "      <td>1.172743</td>\n",
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       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>103.413357</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.161242</td>\n",
       "      <td>1.159584</td>\n",
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       "      <td>64.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>120.775131</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.231446</td>\n",
       "      <td>1.318232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     nneighboringNode  maxScanFraction  distanceThreshold      ox      oy  \\\n",
       "0                 8.0           0.0010                0.0  1200.0   500.0   \n",
       "1                 8.0           0.0005                0.0  1200.0   500.0   \n",
       "2                 8.0           0.0002                0.0  1200.0   500.0   \n",
       "3                 8.0           0.0001                0.0  1200.0   500.0   \n",
       "4                16.0           0.0010                0.0  1200.0   500.0   \n",
       "..                ...              ...                ...     ...     ...   \n",
       "155              32.0           0.0001                0.0  1500.0  1000.0   \n",
       "156              64.0           0.0010                0.0  1500.0  1000.0   \n",
       "157              64.0           0.0005                0.0  1500.0  1000.0   \n",
       "158              64.0           0.0002                0.0  1500.0  1000.0   \n",
       "159              64.0           0.0001                0.0  1500.0  1000.0   \n",
       "\n",
       "         timing  ensemble_size  crps_MPE  crps_MVE  \n",
       "0    100.608428           40.0  1.758052  0.722566  \n",
       "1     72.171379           40.0  1.734936  0.638669  \n",
       "2     85.104975           40.0  1.773520  0.575735  \n",
       "3     87.013911           40.0  1.785664  0.554003  \n",
       "4    108.771865           40.0  1.595565  0.582820  \n",
       "..          ...            ...       ...       ...  \n",
       "155  115.334787           40.0  2.080058  1.306113  \n",
       "156  193.897180           40.0  2.098856  1.128810  \n",
       "157  127.156422           40.0  2.157514  1.172743  \n",
       "158  103.413357           40.0  2.161242  1.159584  \n",
       "159  120.775131           40.0  2.231446  1.318232  \n",
       "\n",
       "[160 rows x 9 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dsbc = df_dsbc.apply(lambda row:\n",
    "                            load_ensembles(row,\n",
    "                                tsanfleuron.TsanfleuronDSBC),\n",
    "                        axis=1)\n",
    "df_dsbc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "be2a0d22-4bfd-4768-a067-0581715e4f34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nneighboringNode</th>\n",
       "      <th>maxScanFraction</th>\n",
       "      <th>distanceThreshold</th>\n",
       "      <th>crps_MPE</th>\n",
       "      <th>crps_MVE</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.794823</td>\n",
       "      <td>0.694145</td>\n",
       "      <td>52.519002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1.763774</td>\n",
       "      <td>0.643601</td>\n",
       "      <td>47.609415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.801827</td>\n",
       "      <td>0.666880</td>\n",
       "      <td>45.081211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.773325</td>\n",
       "      <td>0.652152</td>\n",
       "      <td>44.297453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.718144</td>\n",
       "      <td>0.683572</td>\n",
       "      <td>72.342257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nneighboringNode  maxScanFraction  distanceThreshold  crps_MPE  crps_MVE  \\\n",
       "0               8.0            0.005              0.001  1.794823  0.694145   \n",
       "1               8.0            0.005              0.002  1.763774  0.643601   \n",
       "2               8.0            0.005              0.005  1.801827  0.666880   \n",
       "3               8.0            0.005              0.010  1.773325  0.652152   \n",
       "4              16.0            0.005              0.001  1.718144  0.683572   \n",
       "\n",
       "      timing  \n",
       "0  52.519002  \n",
       "1  47.609415  \n",
       "2  45.081211  \n",
       "3  44.297453  \n",
       "4  72.342257  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# global comparison without std\n",
    "df_compare_ds = df_ds.groupby(['nneighboringNode','maxScanFraction', 'distanceThreshold']).agg(\n",
    "    {\n",
    "        'crps_MPE' : np.mean,\n",
    "        'crps_MVE' : np.mean,\n",
    "        'timing' : np.mean,\n",
    "                                                                                       }).reset_index()\n",
    "df_compare_ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "f7e8d64d-1587-4bef-ac84-7e5213a630fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nneighboringNode</th>\n",
       "      <th>maxScanFraction</th>\n",
       "      <th>distanceThreshold</th>\n",
       "      <th>crps_MPE</th>\n",
       "      <th>crps_MVE</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1.763774</td>\n",
       "      <td>0.643601</td>\n",
       "      <td>47.609415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.010</td>\n",
       "      <td>1.773325</td>\n",
       "      <td>0.652152</td>\n",
       "      <td>44.297453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.801827</td>\n",
       "      <td>0.666880</td>\n",
       "      <td>45.081211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1.695638</td>\n",
       "      <td>0.673086</td>\n",
       "      <td>115.810322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.005</td>\n",
       "      <td>1.771346</td>\n",
       "      <td>0.675036</td>\n",
       "      <td>47.911644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nneighboringNode  maxScanFraction  distanceThreshold  crps_MPE  crps_MVE  \\\n",
       "1               8.0            0.005              0.002  1.763774  0.643601   \n",
       "3               8.0            0.005              0.010  1.773325  0.652152   \n",
       "2               8.0            0.005              0.005  1.801827  0.666880   \n",
       "8              32.0            0.005              0.001  1.695638  0.673086   \n",
       "6              16.0            0.005              0.005  1.771346  0.675036   \n",
       "\n",
       "       timing  \n",
       "1   47.609415  \n",
       "3   44.297453  \n",
       "2   45.081211  \n",
       "8  115.810322  \n",
       "6   47.911644  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df_ds = df_compare_ds.sort_values(by='crps_MVE').copy()\n",
    "plot_df_ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "444e0c3b-1207-422a-b40c-f26e2edad16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df_ds['timing'] = np.round(plot_df_ds['timing']/40, 1)\n",
    "plot_df_ds['crps_MPE'] = np.round(plot_df_ds['crps_MPE'], 2)\n",
    "plot_df_ds['crps_MVE'] = np.round(plot_df_ds['crps_MVE'], 2)\n",
    "plot_df_ds['nneighboringNode'] = plot_df_ds['nneighboringNode'].map(lambda x: int(x))\n",
    "plot_df_ds.to_latex('figures/tsanfleuron-table-ds.tex', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "9f8f29ae-40f9-4178-9b88-1c0a49bef4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nneighboringNode</th>\n",
       "      <th>maxScanFraction</th>\n",
       "      <th>distanceThreshold</th>\n",
       "      <th>crps_MPE</th>\n",
       "      <th>crps_MVE</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.584470</td>\n",
       "      <td>0.579769</td>\n",
       "      <td>88.758901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.591174</td>\n",
       "      <td>0.583380</td>\n",
       "      <td>87.238723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.581502</td>\n",
       "      <td>0.591247</td>\n",
       "      <td>117.618481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.605726</td>\n",
       "      <td>0.605138</td>\n",
       "      <td>127.796988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.658981</td>\n",
       "      <td>0.612094</td>\n",
       "      <td>105.828847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nneighboringNode  maxScanFraction  distanceThreshold  crps_MPE  crps_MVE  \\\n",
       "6               16.0           0.0005                0.0  1.584470  0.579769   \n",
       "5               16.0           0.0002                0.0  1.591174  0.583380   \n",
       "7               16.0           0.0010                0.0  1.581502  0.591247   \n",
       "11              32.0           0.0010                0.0  1.605726  0.605138   \n",
       "3                8.0           0.0010                0.0  1.658981  0.612094   \n",
       "\n",
       "        timing  \n",
       "6    88.758901  \n",
       "5    87.238723  \n",
       "7   117.618481  \n",
       "11  127.796988  \n",
       "3   105.828847  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# global comparison without std\n",
    "df_compare_dsbc = df_dsbc.groupby(['nneighboringNode','maxScanFraction', 'distanceThreshold']).agg({'crps_MPE' : np.mean,\n",
    "                                                                                        'crps_MVE' : np.mean,\n",
    "                                                                                                    \n",
    "                                                                                                'timing' : np.mean\n",
    "                                                                                       }).reset_index()\n",
    "plot_df_dsbc = df_compare_dsbc.sort_values(by='crps_MVE').copy()\n",
    "plot_df_dsbc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "109be880-aa09-4c81-9064-ac9faed1074f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df_dsbc['timing'] = np.round(plot_df_dsbc['timing']/40,1)\n",
    "plot_df_dsbc['crps_MPE'] = np.round(plot_df_dsbc['crps_MPE'], 2)\n",
    "plot_df_dsbc['crps_MVE'] = np.round(plot_df_dsbc['crps_MVE'], 2)\n",
    "plot_df_dsbc['nneighboringNode'] = plot_df_dsbc['nneighboringNode'].map(lambda x: int(x))\n",
    "plot_df_dsbc.drop(columns=['distanceThreshold'], inplace=True)\n",
    "plot_df_dsbc.to_latex('figures/tsanfleuron-table-dsbc.tex', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "c4d90801-a9bd-48f1-b8cf-657ff74878bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd223b91820>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df_compare_ds['timing'], y=df_compare_ds['crps_MPE']+df_compare_ds['crps_MVE'])\n",
    "plt.scatter(x=df_compare_dsbc['timing'], y=df_compare_dsbc['crps_MPE']+df_compare_dsbc['crps_MVE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "937ec261-a101-4765-94a1-cd5682f5bc7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3.54330709,3.54330709))\n",
    "ax.scatter(x=df_compare_ds['timing']/40, y=df_compare_ds['crps_MPE']+df_compare_ds['crps_MVE'])\n",
    "ax.scatter(x=df_compare_dsbc['timing']/40, y=df_compare_dsbc['crps_MPE']+df_compare_dsbc['crps_MVE'], marker='x')\n",
    "ax.set(xlim=[0,6], ylim=[2,3.2])\n",
    "ax.set_xticks([0,6])\n",
    "ax.set_yticks([2,3.2])\n",
    "ax.set_xticklabels(['0 s','6 s'], fontsize=14)\n",
    "ax.set_yticklabels(['2.0 m','3.2 m'], fontsize=14)\n",
    "ax.legend(['DS', 'DSBC'], fontsize=14)\n",
    "ax.set_title('A', fontsize=14)\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'figures/tsanfleuron-scatter.pdf', dpi=300)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "cb4347ec-3722-49ff-987b-877372410b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Nf6WZ2/R1nqSVmc0HbgAmhBDeb3TXidd4a1//s4BS4D9Ivn3faGa3Nlnsa6/1EELT25p97bcpjNCGA0ZCCB+FEBaEEK7lq88SIu2KmT3OV2FvbXL3DpIRlzdavjMwjjO//v8SQngihDAJWAj8KNKJE82BI60+YCT1B/sDsA3oDlwJbI5gLiKRMbMngZuBvwUOmNmJz9iHQgiHQgjBzB4D7jezrSRfzz8juTV7yTc8ZgHwKPA7YCfJtfxYkt86RarNcYcQfmtmvUg+qRMHjPwghPBBM7+WR3IDwQUkvzaoIfldoUh7ckfq35omtz/AV1uq55L8+utJklu1/wRcEUKo/4bHPJ5abjHJXj4m+bXWtKgmfYIOHBFxShujRJxS3CJOKW4RpxS3iFOKW8QpxS3ilOIWcUpxizj1/xc7dje3poHZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 255.118x255.118 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3.54330709,3.54330709))\n",
    "ax.hist((df_compare_ds['crps_MPE']+df_compare_ds['crps_MVE'])*df_compare_ds['timing']/40, bins=np.linspace(0,25,10), alpha=0.5)\n",
    "ax.hist((df_compare_dsbc['crps_MPE']+df_compare_dsbc['crps_MVE'])*df_compare_dsbc['timing']/40, bins=np.linspace(0,25,10), alpha=0.5)\n",
    "ax.set(xlim=[0,20], ylim=[0, 12])\n",
    "ax.set_xticks([0,20])\n",
    "ax.set_yticks([0, 12])\n",
    "ax.set_xticklabels(['0 sm','20 sm'], fontsize=14)\n",
    "ax.set_yticklabels(['0','12'], fontsize=14)\n",
    "ax.legend(['DS', 'DSBC'], fontsize=14)\n",
    "ax.set_title('B', fontsize=14)\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'figures/tsanfleuron-histogram.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2b87065-e168-4dd9-a782-23c058a7b8eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}