analysis_scn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-22T17:15:12.988706Z",
     "iopub.status.busy": "2020-12-22T17:15:12.988298Z",
     "iopub.status.idle": "2020-12-22T17:15:13.230048Z",
     "shell.execute_reply": "2020-12-22T17:15:13.228271Z",
     "shell.execute_reply.started": "2020-12-22T17:15:12.988665Z"
    }
   },
   "source": [
    "# Comparison of scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T08:00:40.902109Z",
     "iopub.status.busy": "2021-02-25T08:00:40.901877Z",
     "iopub.status.idle": "2021-02-25T08:00:42.168962Z",
     "shell.execute_reply": "2021-02-25T08:00:42.168390Z",
     "shell.execute_reply.started": "2021-02-25T08:00:40.902083Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded Italy Setup with 107 nodes.\n",
      "Loaded Italy Setup with 107 nodes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['U-r3-t479700-id0', 'L-r3-t479700-id1', 'U-r15-t125000-id2', 'L-r15-t125000-id3', 'U-r15-t250000-id4', 'L-r15-t250000-id5', 'U-r15-t479700-id6', 'L-r15-t479700-id7', 'U-r15-t1000000-id8', 'L-r15-t1000000-id9', 'U-r15-t1500000-id10', 'L-r15-t1500000-id11', 'U-r15-t2000000-id12', 'L-r15-t2000000-id13', 'U-r150-t479700-id14', 'L-r150-t479700-id15'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import copy\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx\n",
    "from ItalySetupProvinces import ItalySetupProvinces\n",
    "from covidOCP import COVIDVaccinationOCP, COVIDParametersOCP\n",
    "from main import pick_scenario, build_scenario\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "import pickle\n",
    "import os\n",
    "\n",
    "nx = 9\n",
    "states_names = ['S', 'E', 'P', 'I', 'A', 'Q', 'H', 'R', 'V']\n",
    "when = 'future'\n",
    "file_prefix = f'week'\n",
    "outdir = '2021-01-19-107_90/'\n",
    "\n",
    "n_int_steps = 6\n",
    "nnodes = 107  # nodes\n",
    "ndays_ocp = 90\n",
    "ndays = 90\n",
    "\n",
    "setup = ItalySetupProvinces(nnodes, ndays, when)\n",
    "setup_ocp = ItalySetupProvinces(nnodes, ndays_ocp, when)\n",
    "M = setup.nnodes\n",
    "N = len(setup.model_days) - 1\n",
    "\n",
    "with open(f'italy-data/parameters_{nnodes}_{when}.pkl', 'rb') as inp:\n",
    "    p = pickle.load(inp)\n",
    "    \n",
    "scenarios = {pick_scenario(setup, i)['name']:pick_scenario(setup, i) for i in np.arange(16)}\n",
    "scenarios.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-26T11:02:00.523591Z",
     "iopub.status.busy": "2021-02-26T11:02:00.523356Z",
     "iopub.status.idle": "2021-02-26T11:02:00.586657Z",
     "shell.execute_reply": "2021-02-26T11:02:00.586041Z",
     "shell.execute_reply.started": "2021-02-26T11:02:00.523564Z"
    }
   },
   "outputs": [],
   "source": [
    "summary = pd.read_csv('model_output/2021-02-19/all_summary.csv', index_col=0)\n",
    "summary['method_str'] = summary['method']\n",
    "summary['method'].replace('opi', 'Optimal', inplace=True)\n",
    "summary['method'].replace('pinc', 'Incidence', inplace=True)\n",
    "summary['method'].replace('ppop', 'Population', inplace=True)\n",
    "summary['method'].replace('psus', 'Susceptibility', inplace=True)\n",
    "summary['method'].replace('finc', 'Focused Incidence', inplace=True)\n",
    "summary['method'].replace('fpop', 'Focused Population', inplace=True)\n",
    "summary['method'].replace('fsus', 'Focused Susceptibility', inplace=True)\n",
    "\n",
    "summary['method'].replace('fincpp', 'Focused Proportional Incidence', inplace=True)\n",
    "summary['method'].replace('fsuspp', 'Focused Proportional Susceptibility', inplace=True)\n",
    "\n",
    "summary['method'].replace('pincpp', 'Proportional Incidence', inplace=True)\n",
    "summary['method'].replace('psuspp', 'Proportional Susceptibility', inplace=True)\n",
    "summary.replace('U', 'Pessimistic', inplace=True)\n",
    "summary.replace('L', 'Optimistic', inplace=True)\n",
    "summary.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-26T11:02:04.509423Z",
     "iopub.status.busy": "2021-02-26T11:02:04.509201Z",
     "iopub.status.idle": "2021-02-26T11:02:34.640153Z",
     "shell.execute_reply": "2021-02-26T11:02:34.639010Z",
     "shell.execute_reply.started": "2021-02-26T11:02:04.509397Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17952it [00:30, 596.06it/s]\n"
     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "summary['averted-rel'] = 0\n",
    "summary_opt = summary[summary['method']=='Optimal']\n",
    "for i, row in tqdm.tqdm(summary.iterrows()):\n",
    "    summary.loc[i,'averted-rel'] = summary.loc[i,'averted']/float(summary_opt[(summary_opt['post_sample']== summary.loc[i,'post_sample']) & \n",
    "            (summary_opt['scenario-beta']== summary.loc[i,'scenario-beta'])& \n",
    "            (summary_opt['scenario-rate']== summary.loc[i,'scenario-rate'])\n",
    "           & (summary_opt['scenario-tot']== summary.loc[i,'scenario-tot'])]['averted'].values)\n",
    "summary['averted-rel'] = summary['averted-rel']*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-26T11:02:34.642359Z",
     "iopub.status.busy": "2021-02-26T11:02:34.641953Z",
     "iopub.status.idle": "2021-02-26T11:02:56.576558Z",
     "shell.execute_reply": "2021-02-26T11:02:56.575834Z",
     "shell.execute_reply.started": "2021-02-26T11:02:34.642286Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "17952it [00:21, 818.89it/s]\n"
     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "summary['averted-rel2'] = 0\n",
    "summary_opt = summary[(summary['method']=='Optimal') & (summary['post_sample']== 101)]\n",
    "for i, row in tqdm.tqdm(summary.iterrows()):\n",
    "    summary.loc[i,'averted-rel2'] = summary.loc[i,'averted']/float(summary_opt[\n",
    "            (summary_opt['scenario-beta']== summary.loc[i,'scenario-beta'])& \n",
    "            (summary_opt['scenario-rate']== summary.loc[i,'scenario-rate'])\n",
    "           & (summary_opt['scenario-tot']== summary.loc[i,'scenario-tot'])]['averted'].values)\n",
    "summary['averted-rel2'] = summary['averted-rel2']*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-26T11:03:37.695729Z",
     "iopub.status.busy": "2021-02-26T11:03:37.695563Z",
     "iopub.status.idle": "2021-02-26T11:03:37.936912Z",
     "shell.execute_reply": "2021-02-26T11:03:37.936262Z",
     "shell.execute_reply.started": "2021-02-26T11:03:37.695710Z"
    }
   },
   "outputs": [],
   "source": [
    "summary.to_csv('model_output/2021-02-19/all_summary_full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-26T11:25:12.177448Z",
     "iopub.status.busy": "2021-02-26T11:25:12.177198Z",
     "iopub.status.idle": "2021-02-26T11:25:17.558282Z",
     "shell.execute_reply": "2021-02-26T11:25:17.557228Z",
     "shell.execute_reply.started": "2021-02-26T11:25:12.177417Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chadi/opt/miniconda3/envs/ocp-covid/lib/python3.7/site-packages/pandas/core/generic.py:5168: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n",
      "/Users/chadi/opt/miniconda3/envs/ocp-covid/lib/python3.7/site-packages/pandas/core/frame.py:4385: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 902.757x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = ['opi', 'finc', 'pinc', 'fsus', 'psus','fincpp', 'pincpp', 'fsuspp', 'psuspp', 'ppop', 'fpop']\n",
    "#methods = ['opi', 'finc','fincpp', 'pincpp']\n",
    "#methods = ['opi', 'pinc','ppop', 'psus']\n",
    "\n",
    "toplot = \"averted-rel2\"\n",
    "#var_x = \"scenario-rate\"  \n",
    "var_x = \"newdoseperweek\" \n",
    "\n",
    "dpw = summary['newdoseperweek'].unique()\n",
    "dpw = [479700, 1000000, 1500000, 2000000]\n",
    "#dpw = [ 479700, 1000000, 1500000, 2000000]\n",
    "\n",
    "\n",
    "#smry = summary[(summary['scenario-tot']=='t479700') & (summary['method'].isin(methods))]\n",
    "\n",
    "\n",
    "smry = summary[(summary['scenario-rate']=='r15') & (summary['method_str'].isin(methods)) & (summary['newdoseperweek'].isin(dpw))]\n",
    "smry.newdoseperweek = smry.newdoseperweek.astype(str)\n",
    "smry.replace('479700', \"479'700\", inplace=True)\n",
    "smry.replace('1000000', \"1M\", inplace=True)\n",
    "smry.replace('1500000', \"1.5M\", inplace=True)\n",
    "smry.replace('2000000', \"2M\", inplace=True)\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "#sns.set_context(\"talk\")\n",
    "mpl.rc('text', usetex=True)\n",
    "#mpl.rc('font', size=25)\n",
    "#mpl.rc('legend', fontsize=24)\n",
    "\n",
    "\n",
    "grid = sns.FacetGrid(smry, col=\"scenario-beta\",  palette=\"mako\", height=3.5, aspect = 1.5, col_wrap=1, sharey=True,sharex=True, legend_out=True)\n",
    "def quantile_plot(index, reduction, method, scn_beta, **kwargs):\n",
    "\n",
    "    g = sns.boxenplot(\n",
    "            #data=smry,,\n",
    "            x=index, y=reduction,\n",
    "            hue=method,\n",
    "            palette=\"mako\",\n",
    "            dodge=9,\n",
    "            #size=2,\n",
    "            #alpha = .4,\n",
    "            #inner=\"stick\"\n",
    "            showfliers=False,\n",
    "        saturation=1,\n",
    "        #linewidth=0\n",
    "        #width=.\n",
    "        \n",
    "        )\n",
    "    ax = plt.gca()\n",
    "    #ax.set_ylim(0)\n",
    "    plt.setp(ax.lines, zorder=100)\n",
    "    plt.legend(loc = 'upper left')\n",
    "    plt.setp(ax.collections, zorder=100, label=\"\")\n",
    "    \n",
    "    \n",
    "    for line in ax.get_lines():\n",
    "        line.set_color('white')\n",
    "        line.set_linewidth(2.5)\n",
    "        line.set_alpha(10)\n",
    "\n",
    "\n",
    "\n",
    "# Draw a line plot to show the trajectory of each random walk\n",
    "#grid.map(sns.violinplot, 'index', \"reduction\" )#, marker=\"o\")\n",
    "grid.map(quantile_plot, var_x, toplot, \"method\", \"scenario-beta\")#, marker=\"o\")\n",
    "g = grid\n",
    "\n",
    "grid.add_legend(title='Allocation strategy', loc='center right',bbox_to_anchor=(1,0.57),frameon=True, framealpha=1)\n",
    "\n",
    "#for ax in g.axes.flat:\n",
    "#    box = ax.get_position()\n",
    "#    ax.set_position([box.x0,box.y0,box.width*0.85,box.height])\n",
    "\n",
    "#grid.add_legend(title='Allocation strategy', loc='center left',bbox_to_anchor=(1,0.5))\n",
    "\n",
    "\n",
    "def quantile_plot(index, reduction, method, scn_beta, **kwargs):\n",
    "    g2 = sns.stripplot(\n",
    "        x=var_x,\n",
    "        y=toplot,\n",
    "        data = smry[(smry['post_sample']==101) & (smry['scenario-beta']==scn_beta.unique()[0])], \n",
    "        dodge=.63, \n",
    "        hue=\"method\",\n",
    "        jitter=False,\n",
    "        #ax = ax,\n",
    "        color = 'k',\n",
    "        #palette=\"husl\",\n",
    "        marker=\"D\",   # passed to https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter\n",
    "        s=10, \n",
    "        linewidths = 10,\n",
    "        edgecolors = 'k',\n",
    "        zorder=100000,\n",
    "    )\n",
    "grid.map(quantile_plot, \"newdoseperweek\", toplot, \"method\", \"scenario-beta\")#, marker=\"o\")\n",
    "\n",
    "\n",
    "for ax in grid.axes.flat:\n",
    "    for label in ax.get_xticklabels():\n",
    "        label.set_rotation(0)\n",
    "        \n",
    "# Adjust the arrangement of the plots\n",
    "#grid.fig.suptitle(run_name)\n",
    "grid.despine(left=True)\n",
    "grid.despine(bottom=True)\n",
    "\n",
    "#grid.set_axis_labels(\"Stockpile delivery per week\",\"Averted infections after 90 days\",)\n",
    "grid.set_axis_labels(\"Stockpile delivery per week\",\"\\% of Averted infections  w.r.t  \\n the optimal control solution\",)\n",
    "grid.set_titles(col_template=\"{col_name} Scenario\", row_template=\"{row_name}\", fontsize=40, fontweight='bold')\n",
    "grid.fig.tight_layout(w_pad=1)\n",
    "grid.fig.savefig(f'figures/scenarios_perturb_all.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T08:44:52.335557Z",
     "iopub.status.busy": "2021-02-25T08:44:52.335337Z",
     "iopub.status.idle": "2021-02-25T08:44:52.344758Z",
     "shell.execute_reply": "2021-02-25T08:44:52.344042Z",
     "shell.execute_reply.started": "2021-02-25T08:44:52.335532Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "scenario-beta &    Optimistic &   Pessimistic \\\\\n",
      "newdoseperweek &               &               \\\\\n",
      "\\midrule\n",
      "479'700        &  2.010679e+06 &  8.538973e+06 \\\\\n",
      "1M             &  3.897599e+06 &  1.690910e+07 \\\\\n",
      "1.5M           &  5.519258e+06 &  2.411142e+07 \\\\\n",
      "2M             &  6.976918e+06 &  3.055589e+07 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = smry[(smry['method'] == 'Optimal')&(smry['post_sample']==101)].pivot(index = 'newdoseperweek', columns = 'scenario-beta', values = 'averted').sort_values('Optimistic')\n",
    "print(a.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-26T10:42:52.594641Z",
     "iopub.status.busy": "2021-02-26T10:42:52.594416Z",
     "iopub.status.idle": "2021-02-26T10:42:52.612667Z",
     "shell.execute_reply": "2021-02-26T10:42:52.611849Z",
     "shell.execute_reply.started": "2021-02-26T10:42:52.594615Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      "        & {} & \\multicolumn{2}{l}{averted} & \\multicolumn{2}{l}{avertedpervacc} \\\\\n",
      "        & scenario-beta & Optimistic & Pessimistic &     Optimistic & Pessimistic \\\\\n",
      "newdoseperweek & method &            &             &                &             \\\\\n",
      "\\midrule\n",
      "2M & Optimal &   6.98M &    3.06e+07 &          0.268 &        1.18 \\\\\n",
      "        & Incidence &   6.32M &    2.81e+07 &          0.243 &        1.08 \\\\\n",
      "        & Population &   6.02M &    2.68e+07 &          0.231 &        1.03 \\\\\n",
      "        & Susceptibility &   5.97M &    2.67e+07 &          0.229 &        1.02 \\\\\n",
      "1.5M & Optimal &   5.52M &    2.41e+07 &          0.283 &        1.24 \\\\\n",
      "        & Incidence &   4.89M &    2.17e+07 &           0.25 &        1.11 \\\\\n",
      "        & Population &   4.57M &    2.04e+07 &          0.234 &        1.05 \\\\\n",
      "        & Susceptibility &   4.51M &    2.03e+07 &          0.231 &        1.04 \\\\\n",
      "1M & Optimal &    3.9M &    1.69e+07 &            0.3 &         1.3 \\\\\n",
      "        & Incidence &   3.41M &    1.51e+07 &          0.262 &        1.16 \\\\\n",
      "        & Population &   3.08M &    1.38e+07 &          0.237 &        1.06 \\\\\n",
      "        & Susceptibility &   3.02M &    1.37e+07 &          0.232 &        1.05 \\\\\n",
      "479'700 & Optimal &   2.01M &    8.54M &          0.322 &        1.37 \\\\\n",
      "        & Incidence &   1.73M &    7.57M &          0.278 &        1.21 \\\\\n",
      "        & Population &   1.49M &    6.74M &          0.239 &        1.08 \\\\\n",
      "        & Susceptibility &   1.43M &    6.54M &          0.229 &        1.05 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "a = smry[(smry['post_sample']==101)].pivot_table(columns= ['scenario-beta'], index = ['newdoseperweek', 'method'], values = ['averted', 'avertedpervacc']).sort_values(by=('averted', 'Optimistic'), ascending=False)\n",
    "print(a.to_latex(float_format=\"{:0.3g}\".format).replace('e+06','M'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-26T11:12:21.006925Z",
     "iopub.status.busy": "2021-02-26T11:12:21.006699Z",
     "iopub.status.idle": "2021-02-26T11:12:21.061674Z",
     "shell.execute_reply": "2021-02-26T11:12:21.060908Z",
     "shell.execute_reply.started": "2021-02-26T11:12:21.006899Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llrrrr}\n",
      "\\toprule\n",
      "        & {} & \\multicolumn{2}{l}{averted} & \\multicolumn{2}{l}{avertedpervacc} \\\\\n",
      "        & scenario-beta & Optimistic & Pessimistic &     Optimistic & Pessimistic \\\\\n",
      "newdoseperweek & method &            &             &                &             \\\\\n",
      "\\midrule\n",
      "2M & Optimal &   6.98e+06 &    3.06e+07 &          0.268 &        1.18 \\\\\n",
      "        & Incidence &   6.32e+06 &    2.81e+07 &          0.243 &        1.08 \\\\\n",
      "        & Proportional Incidence &   6.23e+06 &    2.75e+07 &          0.239 &        1.06 \\\\\n",
      "        & Focused Susceptibility &   6.03e+06 &    2.69e+07 &          0.232 &        1.03 \\\\\n",
      "        & Focused Proportional Susceptibility &   6.03e+06 &    2.69e+07 &          0.232 &        1.03 \\\\\n",
      "        & Focused Proportional Incidence &   6.03e+06 &    2.69e+07 &          0.232 &        1.03 \\\\\n",
      "        & Focused Population &   6.03e+06 &    2.69e+07 &          0.232 &        1.03 \\\\\n",
      "        & Focused Incidence &   6.03e+06 &    2.69e+07 &          0.232 &        1.03 \\\\\n",
      "        & Population &   6.02e+06 &    2.68e+07 &          0.231 &        1.03 \\\\\n",
      "        & Susceptibility &   5.97e+06 &    2.67e+07 &          0.229 &        1.02 \\\\\n",
      "        & Proportional Susceptibility &    5.6e+06 &    2.53e+07 &          0.215 &       0.971 \\\\\n",
      "1.5M & Optimal &   5.52e+06 &    2.41e+07 &          0.283 &        1.24 \\\\\n",
      "        & Incidence &   4.89e+06 &    2.17e+07 &           0.25 &        1.11 \\\\\n",
      "        & Proportional Incidence &   4.82e+06 &    2.13e+07 &          0.246 &        1.09 \\\\\n",
      "        & Focused Population &   4.58e+06 &    2.05e+07 &          0.235 &        1.05 \\\\\n",
      "        & Focused Incidence &   4.58e+06 &    2.05e+07 &          0.235 &        1.05 \\\\\n",
      "        & Focused Proportional Incidence &   4.58e+06 &    2.05e+07 &          0.235 &        1.05 \\\\\n",
      "        & Focused Proportional Susceptibility &   4.58e+06 &    2.05e+07 &          0.235 &        1.05 \\\\\n",
      "        & Focused Susceptibility &   4.58e+06 &    2.05e+07 &          0.235 &        1.05 \\\\\n",
      "        & Population &   4.57e+06 &    2.04e+07 &          0.234 &        1.05 \\\\\n",
      "        & Susceptibility &   4.51e+06 &    2.03e+07 &          0.231 &        1.04 \\\\\n",
      "        & Proportional Susceptibility &   4.18e+06 &     1.9e+07 &          0.214 &       0.975 \\\\\n",
      "1M & Optimal &    3.9e+06 &    1.69e+07 &            0.3 &         1.3 \\\\\n",
      "        & Incidence &   3.41e+06 &    1.51e+07 &          0.262 &        1.16 \\\\\n",
      "        & Proportional Incidence &   3.34e+06 &    1.47e+07 &          0.257 &        1.13 \\\\\n",
      "        & Focused Population &   3.09e+06 &    1.39e+07 &          0.238 &        1.07 \\\\\n",
      "        & Focused Susceptibility &   3.09e+06 &    1.39e+07 &          0.238 &        1.07 \\\\\n",
      "        & Focused Proportional Susceptibility &   3.09e+06 &    1.39e+07 &          0.238 &        1.07 \\\\\n",
      "        & Focused Incidence &   3.09e+06 &    1.39e+07 &          0.238 &        1.07 \\\\\n",
      "        & Focused Proportional Incidence &   3.09e+06 &    1.39e+07 &          0.238 &        1.07 \\\\\n",
      "        & Population &   3.08e+06 &    1.38e+07 &          0.237 &        1.06 \\\\\n",
      "        & Susceptibility &   3.02e+06 &    1.37e+07 &          0.232 &        1.05 \\\\\n",
      "        & Proportional Susceptibility &   2.75e+06 &    1.26e+07 &          0.211 &       0.972 \\\\\n",
      "479'700 & Optimal &   1.96e+06 &    8.39e+06 &          0.314 &        1.34 \\\\\n",
      "        & Focused Proportional Incidence &   1.95e+06 &    7.74e+06 &          0.312 &        1.24 \\\\\n",
      "        & Proportional Incidence &   1.69e+06 &    7.32e+06 &          0.271 &        1.17 \\\\\n",
      "        & Incidence &   1.63e+06 &    7.21e+06 &          0.262 &        1.15 \\\\\n",
      "        & Focused Incidence &   1.59e+06 &    6.64e+06 &          0.254 &        1.06 \\\\\n",
      "        & Focused Population &   1.57e+06 &    6.85e+06 &          0.251 &        1.09 \\\\\n",
      "        & Population &   1.45e+06 &    6.57e+06 &          0.233 &        1.05 \\\\\n",
      "        & Focused Susceptibility &   1.45e+06 &    6.53e+06 &          0.232 &        1.04 \\\\\n",
      "        & Susceptibility &   1.41e+06 &    6.43e+06 &          0.225 &        1.03 \\\\\n",
      "        & Focused Proportional Susceptibility &   1.28e+06 &    6.09e+06 &          0.204 &       0.973 \\\\\n",
      "        & Proportional Susceptibility &   1.26e+06 &    5.89e+06 &          0.202 &       0.944 \\\\\n",
      "250'000 & Optimal &   1.11e+06 &    4.55e+06 &          0.342 &         1.4 \\\\\n",
      "        & Focused Proportional Incidence &   1.06e+06 &    4.54e+06 &          0.325 &         1.4 \\\\\n",
      "        & Focused Incidence &   9.69e+05 &    4.24e+06 &          0.298 &         1.3 \\\\\n",
      "        & Proportional Incidence &   9.51e+05 &    4.08e+06 &          0.293 &        1.25 \\\\\n",
      "        & Incidence &   9.02e+05 &    3.93e+06 &          0.278 &        1.21 \\\\\n",
      "        & Focused Population &      8e+05 &    3.58e+06 &          0.246 &         1.1 \\\\\n",
      "        & Population &   7.74e+05 &     3.5e+06 &          0.238 &        1.08 \\\\\n",
      "        & Focused Susceptibility &   7.61e+05 &    3.46e+06 &          0.234 &        1.06 \\\\\n",
      "        & Susceptibility &   7.39e+05 &    3.39e+06 &          0.227 &        1.04 \\\\\n",
      "        & Proportional Susceptibility &    6.4e+05 &    3.03e+06 &          0.197 &       0.932 \\\\\n",
      "        & Focused Proportional Susceptibility &   5.03e+05 &    2.63e+06 &          0.155 &        0.81 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chadi/opt/miniconda3/envs/ocp-covid/lib/python3.7/site-packages/pandas/core/generic.py:5168: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n",
      "/Users/chadi/opt/miniconda3/envs/ocp-covid/lib/python3.7/site-packages/pandas/core/frame.py:4385: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  method=method,\n"
     ]
    }
   ],
   "source": [
    "dpw = [250000,479700, 1000000, 1500000, 2000000]\n",
    "\n",
    "a = summary[summary['newdoseperweek'].isin(dpw)]\n",
    "a.newdoseperweek = a.newdoseperweek.astype(str)\n",
    "a.replace('250000', \"250'000\", inplace=True)\n",
    "a.replace('479700', \"479'700\", inplace=True)\n",
    "a.replace('1000000', \"1M\", inplace=True)\n",
    "a.replace('1500000', \"1.5M\", inplace=True)\n",
    "a.replace('2000000', \"2M\", inplace=True)\n",
    "a = a[(a['post_sample']==101)].pivot_table(columns= ['scenario-beta'], index = ['newdoseperweek', 'method'], values = ['averted', 'avertedpervacc']).sort_values(by=('averted', 'Optimistic'), ascending=False)\n",
    "print(a.to_latex(float_format=\"{:0.3g}\".format))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-22T17:04:02.635353Z",
     "iopub.status.busy": "2021-02-22T17:04:02.635130Z",
     "iopub.status.idle": "2021-02-22T17:04:03.457779Z",
     "shell.execute_reply": "2021-02-22T17:04:03.457248Z",
     "shell.execute_reply.started": "2021-02-22T17:04:02.635327Z"
    }
   },
   "outputs": [],
   "source": [
    "methods = ['opi', 'finc', 'pinc', 'fsus', 'psus','fincpp', 'pincpp', 'fsuspp', 'psuspp', 'ppop', 'fpop']\n",
    "\n",
    "dpw = summary['newdoseperweek'].unique()\n",
    "dpw = [ 479700, 1000000, 1500000, 2000000]\n",
    "smry = summary[(summary['scenario-rate']=='r15') & (summary['method_str'].isin(methods)) & (summary['newdoseperweek'].isin(dpw))]\n",
    "\n",
    "sm = smry[smry['scenario-beta']=='Pessimistic']\n",
    "for sample in sm['post_sample'].unique():\n",
    "    for ndpw in  sm['newdoseperweek'].unique():\n",
    "        a = sm[(sm['post_sample'] == sample) & (sm['newdoseperweek'] == ndpw)]\n",
    "        a = a.sort_values('averted', ascending=False)\n",
    "        if (a.iloc[0]['method'] is not 'Optimal'):\n",
    "            print(sample, ndpw, a.iloc[0]['method'] )\n",
    "sm = smry[smry['scenario-beta']=='Optimistic']\n",
    "for sample in sm['post_sample'].unique():\n",
    "    for ndpw in  sm['newdoseperweek'].unique():\n",
    "        a = sm[(sm['post_sample'] == sample) & (sm['newdoseperweek'] == ndpw)]\n",
    "        a = a.sort_values('averted', ascending=False)\n",
    "        if (a.iloc[0]['method'] is not 'Optimal'):\n",
    "            print(sample, ndpw, a.iloc[0]['method'] )\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-18T13:27:26.824545Z",
     "iopub.status.busy": "2021-02-18T13:27:26.824331Z",
     "iopub.status.idle": "2021-02-18T13:27:27.131455Z",
     "shell.execute_reply": "2021-02-18T13:27:27.130983Z",
     "shell.execute_reply.started": "2021-02-18T13:27:26.824507Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "245109.2340726376"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'model_output/scenarios_102_90/L-r15-t479700-id7-opi-107_90.csv'\n",
    "#filename = 'helvetios-runs/2021-02-15-107_90/week-L-r150-t479700-id11-opt-107_90.csv'\n",
    "mydata = pd.read_csv(filename, index_col= 'date', parse_dates=True)\n",
    "maxvaccrate_regional, stockpile_national, stockpile_national_constraint, control_initial = build_scenario(setup, pick_scenario(setup, int(filename.split('-')[-3][2:])))\n",
    "mydata[mydata['comp']=='yell'].sum()['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-18T13:27:28.265039Z",
     "iopub.status.busy": "2021-02-18T13:27:28.264814Z",
     "iopub.status.idle": "2021-02-18T13:27:28.572653Z",
     "shell.execute_reply": "2021-02-18T13:27:28.571747Z",
     "shell.execute_reply.started": "2021-02-18T13:27:28.265013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "254720.32989864826"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'model_output/scenarios_102_90/L-r15-t479700-id7-fincpp-107_90.csv'\n",
    "mydata = pd.read_csv(filename, index_col= 'date', parse_dates=True)\n",
    "maxvaccrate_regional, stockpile_national, stockpile_national_constraint, control_initial = build_scenario(setup, pick_scenario(setup, int(filename.split('-')[-3][2:])))\n",
    "mydata[mydata['comp']=='yell'].sum()['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-22T13:37:15.911075Z",
     "iopub.status.busy": "2021-02-22T13:37:15.910896Z",
     "iopub.status.idle": "2021-02-22T13:37:15.928878Z",
     "shell.execute_reply": "2021-02-22T13:37:15.928301Z",
     "shell.execute_reply.started": "2021-02-22T13:37:15.911056Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>newdoseperweek</th>\n",
       "      <th>method</th>\n",
       "      <th>infected</th>\n",
       "      <th>post_sample</th>\n",
       "      <th>doses</th>\n",
       "      <th>scenario-beta</th>\n",
       "      <th>scenario-rate</th>\n",
       "      <th>scenario-tot</th>\n",
       "      <th>scenario</th>\n",
       "      <th>bl</th>\n",
       "      <th>averted</th>\n",
       "      <th>avertedpervacc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>opi</td>\n",
       "      <td>8.616819e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.236100e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>2.010679e+06</td>\n",
       "      <td>0.322426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>pinc</td>\n",
       "      <td>8.644766e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.238414e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.731203e+06</td>\n",
       "      <td>0.277507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>pincpp</td>\n",
       "      <td>8.645119e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.239378e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.727672e+06</td>\n",
       "      <td>0.276898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>fincpp</td>\n",
       "      <td>8.664006e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.236100e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.538807e+06</td>\n",
       "      <td>0.246758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>finc</td>\n",
       "      <td>8.664182e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.236100e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.537047e+06</td>\n",
       "      <td>0.246476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>ppop</td>\n",
       "      <td>8.668635e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.238393e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.492516e+06</td>\n",
       "      <td>0.239247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>fpop</td>\n",
       "      <td>8.668716e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.236100e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.491708e+06</td>\n",
       "      <td>0.239205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>fsus</td>\n",
       "      <td>8.669935e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.236100e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.479510e+06</td>\n",
       "      <td>0.237249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>fsuspp</td>\n",
       "      <td>8.671517e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.236100e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.463693e+06</td>\n",
       "      <td>0.234713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>psus</td>\n",
       "      <td>8.675124e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.238393e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.427623e+06</td>\n",
       "      <td>0.228845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>479700</td>\n",
       "      <td>psuspp</td>\n",
       "      <td>8.690874e+07</td>\n",
       "      <td>101.0</td>\n",
       "      <td>6.238902e+06</td>\n",
       "      <td>Optimistic</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>L-r15-t479700-id7</td>\n",
       "      <td>8.817886e+07</td>\n",
       "      <td>1.270121e+06</td>\n",
       "      <td>0.203581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     newdoseperweek  method      infected  post_sample         doses  \\\n",
       "101          479700     opi  8.616819e+07        101.0  6.236100e+06   \n",
       "101          479700    pinc  8.644766e+07        101.0  6.238414e+06   \n",
       "101          479700  pincpp  8.645119e+07        101.0  6.239378e+06   \n",
       "101          479700  fincpp  8.664006e+07        101.0  6.236100e+06   \n",
       "101          479700    finc  8.664182e+07        101.0  6.236100e+06   \n",
       "101          479700    ppop  8.668635e+07        101.0  6.238393e+06   \n",
       "101          479700    fpop  8.668716e+07        101.0  6.236100e+06   \n",
       "101          479700    fsus  8.669935e+07        101.0  6.236100e+06   \n",
       "101          479700  fsuspp  8.671517e+07        101.0  6.236100e+06   \n",
       "101          479700    psus  8.675124e+07        101.0  6.238393e+06   \n",
       "101          479700  psuspp  8.690874e+07        101.0  6.238902e+06   \n",
       "\n",
       "    scenario-beta scenario-rate scenario-tot           scenario            bl  \\\n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "101    Optimistic           r15      t479700  L-r15-t479700-id7  8.817886e+07   \n",
       "\n",
       "          averted  avertedpervacc  \n",
       "101  2.010679e+06        0.322426  \n",
       "101  1.731203e+06        0.277507  \n",
       "101  1.727672e+06        0.276898  \n",
       "101  1.538807e+06        0.246758  \n",
       "101  1.537047e+06        0.246476  \n",
       "101  1.492516e+06        0.239247  \n",
       "101  1.491708e+06        0.239205  \n",
       "101  1.479510e+06        0.237249  \n",
       "101  1.463693e+06        0.234713  \n",
       "101  1.427623e+06        0.228845  \n",
       "101  1.270121e+06        0.203581  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary[(summary['post_sample']==101) & (summary['scenario-rate']=='r15') & (summary['newdoseperweek']==479700) & (summary['scenario-beta']=='Optimistic')].sort_values('averted', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "smry = summary[(summary['scenario-rate']=='r15')]\n",
    "\n",
    "grid = sns.FacetGrid(smry, col=\"scenario-beta\",  palette=\"mako\", height=8.5, aspect = 1.5, col_wrap=2, sharey=False,sharex=True)\n",
    "def quantile_plot(index, reduction, method, scn_beta, **kwargs):\n",
    "    g = sns.boxenplot(\n",
    "        #data=smry,,\n",
    "        x=index, y=reduction,\n",
    "        hue=method,\n",
    "        palette=\"husl\",\n",
    "        dodge=9,\n",
    "        #size=2,\n",
    "        #alpha = .4,\n",
    "        #inner=\"stick\"\n",
    "        showfliers=False\n",
    "    )\n",
    "    ax = plt.gca()\n",
    "    ax.set_ylim(0)\n",
    "    g.legend(True)\n",
    "\n",
    "    g2 = sns.pointplot(x=\"newdoseperweek\", y=\"avertedpervacc\",\n",
    "          data = smry[(smry['post_sample']==64) & (smry['scenario-beta']==scn_beta.unique()[0])], \n",
    "          dodge=.60, \n",
    "          join=False, \n",
    "          #palette=\"cool\",\n",
    "          hue=\"method\",\n",
    "          #palette=\"husl\",\n",
    "                       color = 'black',\n",
    "          markers=\"D\", \n",
    "          scale=1, \n",
    "          ci=None,\n",
    "          zorder=100000,\n",
    "                       edgecolor='black',\n",
    "                       hatch = \"|\",\n",
    "                        linewidth=3,\n",
    "                       markeredgewidth=4, #markeredgecolor=(1, 1, 1, 0)\n",
    "          #legend=True\n",
    "         )\n",
    "    \n",
    "    #sns.violinplot(x=index, y=reduction, #hue=\"geoid\",\n",
    "    #               dodge=True,  zorder=1)#Ç, scale='width')#, alpha=.25,capsize=.8, size = .2, errwidth=.75);\n",
    "    #sns.pointplot(x=\"index\", y=\"reduction\", data = npi_truth[npi_truth['geoid']==kwargs['label']], dodge=.532, join=False, #palette=\"dark\",\n",
    "    #          markers=\"d\", scale=1.75, ci=None,zorder=102, color=\"black\")\n",
    "    #plt.scatter((1,1),  **kwargs)\n",
    "\n",
    "# Draw a line plot to show the trajectory of each random walk\n",
    "#grid.map(sns.violinplot, 'index', \"reduction\" )#, marker=\"o\")\n",
    "grid.map(quantile_plot, \"newdoseperweek\", \"avertedpervacc\", \"method\", \"scenario-beta\") #, marker=\"o\")\n",
    "\n",
    "\n",
    "for ax in grid.axes.flat:\n",
    "    for label in ax.get_xticklabels():\n",
    "        label.set_rotation(0)\n",
    "        \n",
    "# Adjust the arrangement of the plots\n",
    "#grid.fig.suptitle(run_name)\n",
    "grid.despine(left=True)\n",
    "\n",
    "grid.set_axis_labels(\"Averted infection per dose\", \"New doses available, per week\")\n",
    "grid.set_titles(col_template=\"Scenario: {col_name}\", row_template=\"{row_name}\")\n",
    "#grid.add_legend()\n",
    "grid.fig.tight_layout(w_pad=1)\n",
    "grid.fig.savefig(f'figures/scenarios_perturb_all.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-28T10:26:01.026225Z",
     "iopub.status.busy": "2021-01-28T10:26:01.026018Z",
     "iopub.status.idle": "2021-01-28T10:26:01.033977Z",
     "shell.execute_reply": "2021-01-28T10:26:01.033383Z",
     "shell.execute_reply.started": "2021-01-28T10:26:01.026202Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newdoseperweek</th>\n",
       "      <th>method</th>\n",
       "      <th>infected</th>\n",
       "      <th>post_sample</th>\n",
       "      <th>doses</th>\n",
       "      <th>scenario-beta</th>\n",
       "      <th>scenario-rate</th>\n",
       "      <th>scenario-tot</th>\n",
       "      <th>scenario</th>\n",
       "      <th>bl</th>\n",
       "      <th>averted</th>\n",
       "      <th>avertedpervacc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [newdoseperweek, method, infected, post_sample, doses, scenario-beta, scenario-rate, scenario-tot, scenario, bl, averted, avertedpervacc]\n",
       "Index: []"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smry[smry['avertedpervacc'] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-29T15:51:05.808679Z",
     "iopub.status.busy": "2021-01-29T15:51:05.808440Z",
     "iopub.status.idle": "2021-01-29T15:51:05.817737Z",
     "shell.execute_reply": "2021-01-29T15:51:05.816686Z",
     "shell.execute_reply.started": "2021-01-29T15:51:05.808653Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-213-e6d5899bd2ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'sn' is not defined"
     ]
    }
   ],
   "source": [
    "sn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-28T10:04:21.821951Z",
     "iopub.status.busy": "2021-01-28T10:04:21.821750Z",
     "iopub.status.idle": "2021-01-28T10:04:21.840364Z",
     "shell.execute_reply": "2021-01-28T10:04:21.839718Z",
     "shell.execute_reply.started": "2021-01-28T10:04:21.821929Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>newdoseperweek</th>\n",
       "      <th>method</th>\n",
       "      <th>infected</th>\n",
       "      <th>post_sample</th>\n",
       "      <th>doses</th>\n",
       "      <th>scenario-beta</th>\n",
       "      <th>scenario-rate</th>\n",
       "      <th>scenario-tot</th>\n",
       "      <th>scenario</th>\n",
       "      <th>bl</th>\n",
       "      <th>averted</th>\n",
       "      <th>avertedpervacc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>125000</td>\n",
       "      <td>opi</td>\n",
       "      <td>9.801593e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.625000e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t125000</td>\n",
       "      <td>U-r15-t125000-id4</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>3.318549e+07</td>\n",
       "      <td>20.421839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>250000</td>\n",
       "      <td>opi</td>\n",
       "      <td>9.527705e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.250000e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t250000</td>\n",
       "      <td>U-r15-t250000-id10</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>6.057431e+07</td>\n",
       "      <td>18.638251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>479700</td>\n",
       "      <td>opi</td>\n",
       "      <td>9.106863e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6.236100e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>U-r15-t479700-id16</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>1.026585e+08</td>\n",
       "      <td>16.461971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1000000</td>\n",
       "      <td>opi</td>\n",
       "      <td>8.382537e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.297121e+07</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t1000000</td>\n",
       "      <td>U-r15-t1000000-id22</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>1.750910e+08</td>\n",
       "      <td>13.498432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2000000</td>\n",
       "      <td>opi</td>\n",
       "      <td>7.420616e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.491384e+07</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t2000000</td>\n",
       "      <td>U-r15-t2000000-id28</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>2.712832e+08</td>\n",
       "      <td>10.888853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>125000</td>\n",
       "      <td>cas</td>\n",
       "      <td>9.926378e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.608347e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t125000</td>\n",
       "      <td>U-r15-t125000-id4</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>2.070701e+07</td>\n",
       "      <td>12.874710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>250000</td>\n",
       "      <td>cas</td>\n",
       "      <td>9.719664e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.216392e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t250000</td>\n",
       "      <td>U-r15-t250000-id10</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>4.137836e+07</td>\n",
       "      <td>12.864836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>479700</td>\n",
       "      <td>cas</td>\n",
       "      <td>9.367470e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6.159978e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>U-r15-t479700-id16</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>7.659775e+07</td>\n",
       "      <td>12.434743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1000000</td>\n",
       "      <td>cas</td>\n",
       "      <td>8.678225e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.267881e+07</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t1000000</td>\n",
       "      <td>U-r15-t1000000-id22</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>1.455223e+08</td>\n",
       "      <td>11.477598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2000000</td>\n",
       "      <td>cas</td>\n",
       "      <td>7.611833e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.486851e+07</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t2000000</td>\n",
       "      <td>U-r15-t2000000-id28</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>2.521615e+08</td>\n",
       "      <td>10.139789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>125000</td>\n",
       "      <td>pop</td>\n",
       "      <td>9.993053e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.607675e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t125000</td>\n",
       "      <td>U-r15-t125000-id4</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>1.403951e+07</td>\n",
       "      <td>8.732800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>250000</td>\n",
       "      <td>pop</td>\n",
       "      <td>9.847032e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.210368e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t250000</td>\n",
       "      <td>U-r15-t250000-id10</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>2.864160e+07</td>\n",
       "      <td>8.921594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>479700</td>\n",
       "      <td>pop</td>\n",
       "      <td>9.573247e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6.134800e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>U-r15-t479700-id16</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>5.602009e+07</td>\n",
       "      <td>9.131527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1000000</td>\n",
       "      <td>pop</td>\n",
       "      <td>8.988667e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.263973e+07</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t1000000</td>\n",
       "      <td>U-r15-t1000000-id22</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>1.144781e+08</td>\n",
       "      <td>9.057006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2000000</td>\n",
       "      <td>pop</td>\n",
       "      <td>7.936755e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.476997e+07</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t2000000</td>\n",
       "      <td>U-r15-t2000000-id28</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>2.196693e+08</td>\n",
       "      <td>8.868371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>125000</td>\n",
       "      <td>sus</td>\n",
       "      <td>9.998784e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.608419e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t125000</td>\n",
       "      <td>U-r15-t125000-id4</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>1.346638e+07</td>\n",
       "      <td>8.372434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>250000</td>\n",
       "      <td>sus</td>\n",
       "      <td>9.864523e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>3.216837e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t250000</td>\n",
       "      <td>U-r15-t250000-id10</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>2.689246e+07</td>\n",
       "      <td>8.359908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>479700</td>\n",
       "      <td>sus</td>\n",
       "      <td>9.619018e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>6.172467e+06</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t479700</td>\n",
       "      <td>U-r15-t479700-id16</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>5.144300e+07</td>\n",
       "      <td>8.334269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1000000</td>\n",
       "      <td>sus</td>\n",
       "      <td>9.018208e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.281750e+07</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t1000000</td>\n",
       "      <td>U-r15-t1000000-id22</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>1.115239e+08</td>\n",
       "      <td>8.700910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2000000</td>\n",
       "      <td>sus</td>\n",
       "      <td>7.934502e+08</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2.499106e+07</td>\n",
       "      <td>U</td>\n",
       "      <td>r15</td>\n",
       "      <td>t2000000</td>\n",
       "      <td>U-r15-t2000000-id28</td>\n",
       "      <td>1.013345e+09</td>\n",
       "      <td>2.198945e+08</td>\n",
       "      <td>8.798929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    newdoseperweek method      infected  post_sample         doses  \\\n",
       "64          125000    opi  9.801593e+08         64.0  1.625000e+06   \n",
       "64          250000    opi  9.527705e+08         64.0  3.250000e+06   \n",
       "64          479700    opi  9.106863e+08         64.0  6.236100e+06   \n",
       "64         1000000    opi  8.382537e+08         64.0  1.297121e+07   \n",
       "64         2000000    opi  7.420616e+08         64.0  2.491384e+07   \n",
       "64          125000    cas  9.926378e+08         64.0  1.608347e+06   \n",
       "64          250000    cas  9.719664e+08         64.0  3.216392e+06   \n",
       "64          479700    cas  9.367470e+08         64.0  6.159978e+06   \n",
       "64         1000000    cas  8.678225e+08         64.0  1.267881e+07   \n",
       "64         2000000    cas  7.611833e+08         64.0  2.486851e+07   \n",
       "64          125000    pop  9.993053e+08         64.0  1.607675e+06   \n",
       "64          250000    pop  9.847032e+08         64.0  3.210368e+06   \n",
       "64          479700    pop  9.573247e+08         64.0  6.134800e+06   \n",
       "64         1000000    pop  8.988667e+08         64.0  1.263973e+07   \n",
       "64         2000000    pop  7.936755e+08         64.0  2.476997e+07   \n",
       "64          125000    sus  9.998784e+08         64.0  1.608419e+06   \n",
       "64          250000    sus  9.864523e+08         64.0  3.216837e+06   \n",
       "64          479700    sus  9.619018e+08         64.0  6.172467e+06   \n",
       "64         1000000    sus  9.018208e+08         64.0  1.281750e+07   \n",
       "64         2000000    sus  7.934502e+08         64.0  2.499106e+07   \n",
       "\n",
       "   scenario-beta scenario-rate scenario-tot             scenario  \\\n",
       "64             U           r15      t125000    U-r15-t125000-id4   \n",
       "64             U           r15      t250000   U-r15-t250000-id10   \n",
       "64             U           r15      t479700   U-r15-t479700-id16   \n",
       "64             U           r15     t1000000  U-r15-t1000000-id22   \n",
       "64             U           r15     t2000000  U-r15-t2000000-id28   \n",
       "64             U           r15      t125000    U-r15-t125000-id4   \n",
       "64             U           r15      t250000   U-r15-t250000-id10   \n",
       "64             U           r15      t479700   U-r15-t479700-id16   \n",
       "64             U           r15     t1000000  U-r15-t1000000-id22   \n",
       "64             U           r15     t2000000  U-r15-t2000000-id28   \n",
       "64             U           r15      t125000    U-r15-t125000-id4   \n",
       "64             U           r15      t250000   U-r15-t250000-id10   \n",
       "64             U           r15      t479700   U-r15-t479700-id16   \n",
       "64             U           r15     t1000000  U-r15-t1000000-id22   \n",
       "64             U           r15     t2000000  U-r15-t2000000-id28   \n",
       "64             U           r15      t125000    U-r15-t125000-id4   \n",
       "64             U           r15      t250000   U-r15-t250000-id10   \n",
       "64             U           r15      t479700   U-r15-t479700-id16   \n",
       "64             U           r15     t1000000  U-r15-t1000000-id22   \n",
       "64             U           r15     t2000000  U-r15-t2000000-id28   \n",
       "\n",
       "              bl       averted  avertedpervacc  \n",
       "64  1.013345e+09  3.318549e+07       20.421839  \n",
       "64  1.013345e+09  6.057431e+07       18.638251  \n",
       "64  1.013345e+09  1.026585e+08       16.461971  \n",
       "64  1.013345e+09  1.750910e+08       13.498432  \n",
       "64  1.013345e+09  2.712832e+08       10.888853  \n",
       "64  1.013345e+09  2.070701e+07       12.874710  \n",
       "64  1.013345e+09  4.137836e+07       12.864836  \n",
       "64  1.013345e+09  7.659775e+07       12.434743  \n",
       "64  1.013345e+09  1.455223e+08       11.477598  \n",
       "64  1.013345e+09  2.521615e+08       10.139789  \n",
       "64  1.013345e+09  1.403951e+07        8.732800  \n",
       "64  1.013345e+09  2.864160e+07        8.921594  \n",
       "64  1.013345e+09  5.602009e+07        9.131527  \n",
       "64  1.013345e+09  1.144781e+08        9.057006  \n",
       "64  1.013345e+09  2.196693e+08        8.868371  \n",
       "64  1.013345e+09  1.346638e+07        8.372434  \n",
       "64  1.013345e+09  2.689246e+07        8.359908  \n",
       "64  1.013345e+09  5.144300e+07        8.334269  \n",
       "64  1.013345e+09  1.115239e+08        8.700910  \n",
       "64  1.013345e+09  2.198945e+08        8.798929  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scn = 'L'\n",
    "smry = summary[(summary['scenario-rate']=='r15') & (summary['scenario-beta']==scn)]\n",
    "\n",
    "sns.set(rc={'figure.figsize':(10,5), 'figure.dpi':100})\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "g = sns.catplot(\n",
    "    data=smry,\n",
    "    kind=\"boxen\",\n",
    "    x=\"newdoseperweek\",\n",
    "    y=\"avertedpervacc\", \n",
    "    hue=\"method\",\n",
    "    palette=\"rocket\",\n",
    "    dodge=.59,\n",
    "    height=8,\n",
    "    #alpha = .4,\n",
    "    #inner=\"stick\"\n",
    "    legend=False,\n",
    "    showfliers=False\n",
    ")\n",
    "g2 = sns.pointplot(x=\"newdoseperweek\",\n",
    "              y=\"avertedpervacc\", \n",
    "              data = smry[smry['post_sample']==64], \n",
    "              dodge=.59, \n",
    "              join=False, \n",
    "              #palette=\"cool\",\n",
    "              hue=\"method\",\n",
    "              palette=\"mako_r\",\n",
    "              markers=\"d\", \n",
    "              scale=1.25, \n",
    "              ci=None,\n",
    "              zorder=100000,\n",
    "              #legend=False\n",
    "             )\n",
    "\n",
    "plt.setp(g.ax.lines, zorder=100)\n",
    "plt.setp(g.ax.collections, zorder=100, label=\"\")\n",
    "g.despine(left=True)\n",
    "g.set_axis_labels(\"Dose per week\", \"Averted Cases per dose\")\n",
    "#g2.legend.set_title(\"Allocation\")\n",
    "\n",
    "\n",
    "g.fig.savefig(f'figures/scenarios_perturb{scn}.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-25T07:25:09.879524Z",
     "iopub.status.busy": "2021-02-25T07:25:09.871662Z",
     "iopub.status.idle": "2021-02-25T07:25:10.047855Z",
     "shell.execute_reply": "2021-02-25T07:25:10.046356Z",
     "shell.execute_reply.started": "2021-02-25T07:25:09.877150Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'smry' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-1629ebebd45d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msmry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'smry' is not defined"
     ]
    }
   ],
   "source": [
    "smry"
   ]
  }
 ],
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