{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pandas as pnd\n",
    "import seaborn as sns\n",
    "import time\n",
    "import datetime\n",
    "from matplotlib.colors import ListedColormap\n",
    "current_palette = sns.color_palette(\"muted\", n_colors=5)\n",
    "cmap = ListedColormap(sns.color_palette(current_palette).as_hex())\n",
    "import matplotlib.ticker as mtick\n",
    "from palettable.colorbrewer.qualitative import Dark2_8\n",
    "from palettable.colorbrewer.qualitative import Paired_6\n",
    "from palettable.colorbrewer.qualitative import Set3_10\n",
    "from palettable.colorbrewer.qualitative import Set3_9\n",
    "from palettable.colorbrewer.diverging import RdBu_11\n",
    "from palettable.colorbrewer.diverging import BrBG_11\n",
    "from palettable.colorbrewer.diverging import RdYlBu_11\n",
    "import xarray as xr\n",
    "from matplotlib.legend import Legend\n",
    "import scipy\n",
    "from palettable.colorbrewer.qualitative import Dark2_5\n",
    "import scipy.signal\n",
    "from docx import Document\n",
    "from docx.shared import Inches\n",
    "from sklearn.linear_model import LinearRegression\n",
    "deg = '\\u00b0'\n",
    "pm = \"\\u00B1\"\n",
    "\n",
    "color_gtp_shaded = Paired_6.mpl_colors[0]\n",
    "color_gwp_shaded = Paired_6.mpl_colors[2]\n",
    "color_igtp_shaded = Paired_6.mpl_colors[4]\n",
    "color_gtp = Paired_6.mpl_colors[1]\n",
    "color_gwp = Paired_6.mpl_colors[3]\n",
    "color_igtp = Paired_6.mpl_colors[5]\n",
    "\n",
    "# colormap for different models\n",
    "cmap = Set3_10.mpl_colors\n",
    "\n",
    "linestyle_20 = ':'\n",
    "linestyle_100 = '--'\n",
    "linestyle_temporal = '-'\n",
    "\n",
    "styles = [linestyle_20, linestyle_100, linestyle_temporal]\n",
    "\n",
    "linewidth_temporal = 4\n",
    "linewidth_other = 3\n",
    "\n",
    "SMALL_SIZE = 10.5\n",
    "MEDIUM_SIZE = 12.5\n",
    "BIGGER_SIZE = 14\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "plt.rc('axes', titlesize=BIGGER_SIZE)\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "H = np.linspace(0,300,301) # H is time horizon, 200 years\n",
    "tau_ch4 = 12.4 # years\n",
    "A_ch4 = 1.65 * 3.63*10**(-4) * (28.96/16.04)*(10**9/(5.15*10**18)) # this is in ppb, so we need to convert to mass...\n",
    "A_co2 =  1.7517 * 10**(-15) # from IPCC 8.SM.11.3.1\n",
    "tau_n2o = 121 # years\n",
    "A_n2o = 3 * 10**(-3) * (28.96/44.01)*(10**9/(5.15*10**18))\n",
    "\n",
    "#from Joos 2013, used in IPCC AR5:\n",
    "a0 = 0.2173\n",
    "a1 = 0.224\n",
    "a2 = 0.2824\n",
    "a3 = 0.2763\n",
    "tau1 = 394.4\n",
    "tau2 = 36.54\n",
    "tau3 = 4.304\n",
    "\n",
    "# IPCC table 8.SM.9 for temp impact of pulse RF for non-CO2 gas - updated by Gasser 2017?\n",
    "c1 = 0.631\n",
    "c2 = 0.429\n",
    "d1 = 8.4\n",
    "d2 = 409.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-d6d3c865ff84>:9: RuntimeWarning: invalid value encountered in true_divide\n",
      "  GWP_ch4 = AGWP_ch4 / AGWP_co2\n",
      "<ipython-input-3-d6d3c865ff84>:10: RuntimeWarning: invalid value encountered in true_divide\n",
      "  GWP_n2o = AGWP_n2o / AGWP_co2\n",
      "<ipython-input-3-d6d3c865ff84>:23: RuntimeWarning: invalid value encountered in true_divide\n",
      "  GTP_ch4 = AGTP_ch4 / AGTP_co2\n",
      "<ipython-input-3-d6d3c865ff84>:24: RuntimeWarning: invalid value encountered in true_divide\n",
      "  GTP_n2o = AGTP_n2o / AGTP_co2\n",
      "<ipython-input-3-d6d3c865ff84>:28: RuntimeWarning: invalid value encountered in true_divide\n",
      "  iGTP_ch4 = iAGTP_ch4 / iAGTP_co2\n",
      "<ipython-input-3-d6d3c865ff84>:30: RuntimeWarning: invalid value encountered in true_divide\n",
      "  CGTP_ch4 = iAGTP_ch4 / AGTP_co2\n"
     ]
    }
   ],
   "source": [
    "# calculate emission metrics based on IPCC AR5\n",
    "# The integrated Global Temperature 2 change Potential (iGTP) and relationships 3 between emission metrics SI\n",
    "# peters2011 points to fuglestvedt 2010\n",
    "\n",
    "AGWP_ch4 = A_ch4 * tau_ch4 * (1.0 - np.exp(-H/tau_ch4))\n",
    "AGWP_co2 = A_co2 * (a0 * H + a1*tau1*(1.0-np.exp(-H/tau1)) + a2*tau2*(1.0-np.exp(-H/tau2)) + a3*tau3*(1.0-np.exp(-H/tau3)))\n",
    "AGWP_n2o = A_n2o * tau_n2o * (1.0 - np.exp(-H/tau_n2o)) * (1.0-0.36*1.65*(3.63*10**(-4))/(3*10**(-3))) # correcting as in section 8.SM.11.3.3\n",
    "\n",
    "GWP_ch4 = AGWP_ch4 / AGWP_co2\n",
    "GWP_n2o = AGWP_n2o / AGWP_co2\n",
    "\n",
    "AGTP_ch4 = A_ch4 * ((tau_ch4 * c1)/(tau_ch4 - d1)*(np.exp(-H/tau_ch4) - np.exp(-H/d1))+(tau_ch4 * c2)/(tau_ch4 - d2)*(np.exp(-H/tau_ch4) - np.exp(-H/d2)))\n",
    "AGTP_n2o = A_n2o * ((tau_n2o * c1)/(tau_n2o - d1)*(np.exp(-H/tau_n2o) - np.exp(-H/d1))+(tau_n2o * c2)/(tau_n2o - d2)*(np.exp(-H/tau_n2o) - np.exp(-H/d2))) * (1.0-0.36*1.65*(3.63*10**(-4))/(3*10**(-3)))\n",
    "AGTP_co2 = A_co2 * (a0*c1*(1.0-np.exp(-H/d1))+\n",
    "                           a0*c2*(1.0-np.exp(-H/d2))+\n",
    "                           (np.exp(-H/tau1)-np.exp(-H/d1))*(a1*tau1*c1)/(tau1-d1) + #j=1, i=1\n",
    "                           (np.exp(-H/tau2)-np.exp(-H/d1))*(a2*tau2*c1)/(tau2-d1) + #j=1, i=2\n",
    "                           (np.exp(-H/tau3)-np.exp(-H/d1))*(a3*tau3*c1)/(tau3-d1) + #j=1, i=3\n",
    "                           (np.exp(-H/tau1)-np.exp(-H/d2))*(a1*tau1*c2)/(tau1-d2) + #j=2, i=1\n",
    "                           (np.exp(-H/tau2)-np.exp(-H/d2))*(a2*tau2*c2)/(tau2-d2) + #j=2, i=2\n",
    "                           (np.exp(-H/tau3)-np.exp(-H/d2))*(a3*tau3*c2)/(tau3-d2)) #j=2, i=3\n",
    "                          \n",
    "GTP_ch4 = AGTP_ch4 / AGTP_co2\n",
    "GTP_n2o = AGTP_n2o / AGTP_co2\n",
    "\n",
    "iAGTP_ch4 = np.array([sum(AGTP_ch4[0:x+1]) for x in range(0, len(AGTP_ch4))])\n",
    "iAGTP_co2 = np.array([sum(AGTP_co2[0:x+1]) for x in range(0, len(AGTP_co2))])\n",
    "iGTP_ch4 = iAGTP_ch4 / iAGTP_co2\n",
    "\n",
    "CGTP_ch4 = iAGTP_ch4 / AGTP_co2\n",
    "\n",
    "# iAGTP_n2o = np.array([sum(AGTP_n2o[0:x+1]) for x in range(0, len(AGTP_n2o))])\n",
    "# iGTP_n2o = iAGTP_n2o / iAGTP_co2\n",
    "\n",
    "# AGWP_ch4[20] # should be 2.1e-12 AND IT IS\n",
    "# AGTP_ch4[20] # should be 4.6e-14 AND IT IS\n",
    "# AGWP_co2[20] # should be 2.5e-14 AND IT IS\n",
    "# AGTP_co2[20] # should be 6.89e-16 AND IT IS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# these all don't take into account cc feedback! if they did, gtp100 is 11 and gwp100 is 34 in AR5 (revised down after)\n",
    "# top left: DO CO2\n",
    "# bottom right: do CH4\n",
    "# uncertainty bars?\n",
    "# change y axis?\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "lines = []\n",
    "lines += ax.plot(H[::-1], iGTP_ch4[::-1], label='iGTP', color=color_igtp, linestyle=linestyle_temporal, linewidth=linewidth_temporal)\n",
    "lines += ax.plot(H[::-1], GWP_ch4[::-1], label='GWP', color=color_gwp, linestyle=linestyle_temporal, linewidth=linewidth_temporal)\n",
    "lines += ax.plot(H[::-1], GTP_ch4[::-1], label='GTP', color=color_gtp, linestyle=linestyle_temporal, linewidth=linewidth_temporal)\n",
    "\n",
    "# fake lines for linestyles\n",
    "lines += ax.plot(H[::-1], H[::-1]+1000, linestyle=linestyle_temporal,color='black')\n",
    "lines += ax.plot(H[::-1], H[::-1]+1000, linestyle=linestyle_20,color='black')\n",
    "lines += ax.plot(H[::-1], H[::-1]+1000, linestyle=linestyle_100,color='black')\n",
    "\n",
    "# ax.annotate('1.5$^\\circ$C (SSP1-1.9)', xy=(29, 0),xytext=(29,-15),                     #draws an arrow from one set of coordinates to the other\n",
    "#             arrowprops={'arrowstyle':'simple', 'facecolor':'black'}, ha='center',   #sets style of arrow and colour\n",
    "#             annotation_clip=False)\n",
    "# ax.annotate('2$^\\circ$C (SSP1-2.6)', xy=(43, 0),xytext=(43,-25),                     #draws an arrow from one set of coordinates to the other\n",
    "#             arrowprops={'arrowstyle':'simple', 'facecolor':'black'}, ha='center',   #sets style of arrow and colour\n",
    "#             annotation_clip=False)\n",
    "\n",
    "ax.axhline(GWP_ch4[20], label = 'GWP 20', color=color_gwp, linestyle=linestyle_20, linewidth=linewidth_other)\n",
    "ax.axhline(GWP_ch4[100], label = 'GWP 100', color=color_gwp, linestyle=linestyle_100, linewidth=linewidth_other)\n",
    "ax.axhline(GTP_ch4[20], label = 'GTP 20', color=color_gtp, linestyle=linestyle_20, linewidth=linewidth_other)\n",
    "ax.axhline(GTP_ch4[100], label = 'GTP 100', color=color_gtp, linestyle=linestyle_100, linewidth=linewidth_other)\n",
    "ax.axhline(iGTP_ch4[20], label = 'iGTP 20', color=color_igtp, linestyle=linestyle_20, linewidth=linewidth_other)\n",
    "ax.axhline(iGTP_ch4[100], label = 'iGTP 100', color=color_igtp, linestyle=linestyle_100, linewidth=linewidth_other)\n",
    "\n",
    "#plt.axhline(50, label = 'SCM 3% discount', color=color_scm) # 3% discount rate\n",
    "#plt.axhline(100, label = 'SCM 5% discount', color=color_scm) # 5% discount rate\n",
    "\n",
    "# plt.xlabel('Years before peak temperature', horizontalalignment='left', x=0)\n",
    "plt.xlabel('Years before peak temperature')\n",
    "\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position(\"right\")\n",
    "plt.ylabel('CH$_4$ emission metric')\n",
    "#plt.ylabel('Relative cost of removal (CH$_4$ / CO$_2$)')\n",
    "sns.despine(ax=ax, left=True, right=False)\n",
    "\n",
    "ax.legend(lines[:3], ['iGTP', 'GWP', 'GTP'], loc='upper left', bbox_to_anchor=(0,1), frameon=False)\n",
    "#ax.legend(lines[:3], ['iGTP', 'GTP'], loc='upper left', bbox_to_anchor=(0,1), frameon=False)\n",
    "plt.ylim(0,120)\n",
    "plt.xlim(100,0)\n",
    "#leg = Legend(ax, lines[2:5], ['Time-dependent', '20 years', '100 years'], loc='upper left', bbox_to_anchor=(0.2,1), frameon=False)\n",
    "leg = Legend(ax, lines[3:6], ['Time-dependent', '20 years', '100 years'], loc='upper left', bbox_to_anchor=(0.2,1), frameon=False)\n",
    "ax.add_artist(leg)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('myfile.png', bbox_inches = \"tight\")\n",
    "#plt.savefig('/Users/samabernethy/Documents/Methane/framework/figures/ch4.png', format='png', dpi=600, bbox_inches='tight')\n",
    "plt.savefig('figures/ch4.png', format='png', dpi=600, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading in peak temp data from SR1.5 Scenario Database\n",
    "raw_sr15 = pnd.read_csv('data/sr15.csv')\n",
    "#raw_sr15 = pnd.read_csv('/Users/samabernethy/Documents/Methane/framework/data/sr15.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#temperatures_raw_sr15 = raw_sr15[raw_sr15.Variable == 'AR5 climate diagnostics|Temperature|Global Mean|FAIR|MED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "temperatures_raw_sr15 = raw_sr15[raw_sr15.Variable == 'AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/samabernethy/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py:4163: 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",
      "  return super().drop(\n",
      "<ipython-input-8-68fedfb77c38>:6: 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",
      "  temperatures_raw_sr15['MODEL'] = temperatures_raw_sr15['Model']\n"
     ]
    }
   ],
   "source": [
    "#temperatures_raw_sr15 = raw_sr15[raw_sr15.Variable == 'AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED']\n",
    "temperatures_raw_sr15.drop('Variable',axis=1,inplace=True)\n",
    "temperatures_raw_sr15.drop('Unit',axis=1,inplace=True)\n",
    "temperatures_raw_sr15.drop('Region',axis=1,inplace=True)\n",
    "temperatures_raw_sr15.drop('Scenario',axis=1,inplace=True)\n",
    "temperatures_raw_sr15['MODEL'] = temperatures_raw_sr15['Model']\n",
    "temperatures_raw_sr15.drop('Model',axis=1,inplace=True)\n",
    "\n",
    "# dropping 2000-2004 to align with ar5 scenario format\n",
    "temperatures_raw_sr15.drop('2000',axis=1,inplace=True)\n",
    "temperatures_raw_sr15.drop('2001',axis=1,inplace=True)\n",
    "temperatures_raw_sr15.drop('2002',axis=1,inplace=True)\n",
    "temperatures_raw_sr15.drop('2003',axis=1,inplace=True)\n",
    "temperatures_raw_sr15.drop('2004',axis=1,inplace=True)\n",
    "\n",
    "temps_df = temperatures_raw_sr15.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determine which scenarios have a peak before 2100\n",
    "temps_df['peak year'] = np.NaN\n",
    "temps_df['peak temp'] = np.NaN\n",
    "for i in temps_df.index:\n",
    "    if scipy.signal.find_peaks(temps_df.loc[i][0:96].values)[0].size > 0:\n",
    "        peak_index = scipy.signal.find_peaks(temps_df.loc[i][0:96].values)[0][0]\n",
    "        peak_year = peak_index + 2005 # as index 0 is year 2005\n",
    "        peak_temp = temps_df.loc[i][0:96].values[peak_index]\n",
    "        temps_df.at[i,'peak year']=peak_year\n",
    "        temps_df.at[i,'peak temp']=peak_temp\n",
    "        \n",
    "# keep only scenarios with peaks\n",
    "peaks_df = temps_df.dropna().copy()\n",
    "peaks_df['color']=0\n",
    "peaks_df['general model']='none'\n",
    "\n",
    "temps_df.index = np.arange(0, len(temps_df))\n",
    "peaks_df.index = np.arange(0, len(peaks_df))\n",
    "# 411 scenarios, 242 scenarios with peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# organize by IAM: AIM, C-ROADS, GCAM, IEA, IMAGE, MERGE, MESSAGE, POLES, REMIND, WITCH\n",
    "# 11'GCAM', 33'IMAGE', 2'MERGE', 39'MESSAGE', 71'REMIND', 17'WITCH', 28'POLES', 1'IEA', 5'C-ROADS',  5'AIM'\n",
    "\n",
    "for i in peaks_df.index:\n",
    "    if peaks_df.at[i,'MODEL'] == 'GCAM 4.2':\n",
    "        peaks_df.at[i,'color'] = 0\n",
    "        peaks_df.at[i,'general model']='GCAM'\n",
    "    if peaks_df.at[i,'MODEL'] == 'IMAGE 3.0.1' or peaks_df.at[i,'MODEL'] == 'IMAGE 3.0.2':\n",
    "        peaks_df.at[i,'color'] = 1\n",
    "        peaks_df.at[i,'general model']='IMAGE'\n",
    "    if peaks_df.at[i,'MODEL'] == 'MERGE-ETL 6.0':\n",
    "        peaks_df.at[i,'color'] = 2\n",
    "        peaks_df.at[i,'general model']='MERGE'\n",
    "    if peaks_df.at[i,'MODEL'] == 'MESSAGE V.3' or peaks_df.at[i,'MODEL'] == 'MESSAGE-GLOBIOM 1.0' or peaks_df.at[i,'MODEL'] == 'MESSAGEix-GLOBIOM 1.0':\n",
    "        peaks_df.at[i,'color'] = 3\n",
    "        peaks_df.at[i,'general model']='MESSAGE'\n",
    "    if peaks_df.at[i,'MODEL'] == 'REMIND 1.5' or peaks_df.at[i,'MODEL'] == 'REMIND 1.7' or peaks_df.at[i,'MODEL'] == 'REMIND-MAgPIE 1.5' or peaks_df.at[i,'MODEL'] == 'REMIND-MAgPIE 1.7-3.0':\n",
    "        peaks_df.at[i,'color'] = 4\n",
    "        peaks_df.at[i,'general model']='REMIND'\n",
    "    if peaks_df.at[i,'MODEL'] == 'WITCH-GLOBIOM 3.1' or peaks_df.at[i,'MODEL'] == 'WITCH-GLOBIOM 4.2' or peaks_df.at[i,'MODEL'] == 'WITCH-GLOBIOM 4.4':\n",
    "        peaks_df.at[i,'color'] = 5\n",
    "        peaks_df.at[i,'general model']='WITCH'\n",
    "    if peaks_df.at[i,'MODEL'] == 'POLES ADVANCE' or peaks_df.at[i,'MODEL'] == 'POLES CD-LINKS' or peaks_df.at[i,'MODEL'] == 'POLES EMF33':\n",
    "        peaks_df.at[i,'color'] = 6\n",
    "        peaks_df.at[i,'general model']='POLES'\n",
    "    if peaks_df.at[i,'MODEL'] == 'C-ROADS-5.005':\n",
    "        peaks_df.at[i,'color'] = 7\n",
    "        peaks_df.at[i,'general model']='C-ROADS'\n",
    "    if peaks_df.at[i,'MODEL'] == 'AIM/CGE 2.0' or peaks_df.at[i,'MODEL'] == 'AIM/CGE 2.1':\n",
    "        peaks_df.at[i,'color'] = 8\n",
    "        peaks_df.at[i,'general model']='AIM'\n",
    "    if peaks_df.at[i,'MODEL'] == 'IEA World Energy Model 2017':\n",
    "        peaks_df.at[i,'color'] = 9\n",
    "        peaks_df.at[i,'general model']='IEA'\n",
    "    \n",
    "# len(peaks_df[peaks_df['general model']=='AIM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3x3 subplots of each IAM's simulations\n",
    "# SWITCH COLORS TO JUST BLACKS? maybe not since it helps for fig 2\n",
    "cmap = Set3_10.mpl_colors\n",
    "cmap = 'black'\n",
    "fig, axs = plt.subplots(2, 5, sharex=True, sharey=True, figsize=(8,6))\n",
    "for i in peaks_df[peaks_df['general model']=='GCAM'].index:\n",
    "    axs[0, 0].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[0, 0].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[0], zorder=1)\n",
    "axs[0, 0].set_title('GCAM')\n",
    "axs[0, 0].annotate('n='+str(len(peaks_df[peaks_df['general model']=='GCAM'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "#axs[0, 0].legend(loc='best', frameon=False)\n",
    "for i in peaks_df[peaks_df['general model']=='IMAGE'].index:\n",
    "    axs[0, 1].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[0, 1].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[1], zorder=1)\n",
    "axs[0, 1].set_title('IMAGE')\n",
    "axs[0, 1].annotate('n='+str(len(peaks_df[peaks_df['general model']=='IMAGE'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "for i in peaks_df[peaks_df['general model']=='MERGE'].index:\n",
    "    axs[0, 2].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[0, 2].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[2], zorder=1)\n",
    "axs[0, 2].set_title('MERGE')\n",
    "axs[0, 2].annotate('n='+str(len(peaks_df[peaks_df['general model']=='MERGE'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "for i in peaks_df[peaks_df['general model']=='MESSAGE'].index:\n",
    "    axs[0, 3].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[0, 3].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[3], zorder=1)\n",
    "axs[0, 3].set_title('MESSAGE')\n",
    "axs[0, 3].annotate('n='+str(len(peaks_df[peaks_df['general model']=='MESSAGE'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "for i in peaks_df[peaks_df['general model']=='REMIND'].index:\n",
    "    axs[0, 4].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[0, 4].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[4], zorder=1)\n",
    "axs[0, 4].set_title('REMIND')\n",
    "axs[0, 4].annotate('n='+str(len(peaks_df[peaks_df['general model']=='REMIND'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "for i in peaks_df[peaks_df['general model']=='WITCH'].index:\n",
    "    axs[1, 0].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[1, 0].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[5], zorder=1)\n",
    "axs[1, 0].set_title('WITCH')\n",
    "axs[1, 0].annotate('n='+str(len(peaks_df[peaks_df['general model']=='WITCH'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "for i in peaks_df[peaks_df['general model']=='POLES'].index:\n",
    "    axs[1, 1].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[1, 1].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[6], zorder=1)\n",
    "axs[1, 1].set_title('POLES')\n",
    "axs[1, 1].annotate('n='+str(len(peaks_df[peaks_df['general model']=='POLES'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "# for i in peaks_df[peaks_df['general model']=='IEA'].index:\n",
    "#     axs[2, 1].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5)\n",
    "for i in peaks_df[peaks_df['general model']=='C-ROADS'].index:\n",
    "    axs[1, 2].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[1, 2].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[7], zorder=1)\n",
    "axs[1, 2].set_title('C-ROADS')\n",
    "axs[1, 2].annotate('n='+str(len(peaks_df[peaks_df['general model']=='C-ROADS'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "for i in peaks_df[peaks_df['general model']=='AIM'].index:\n",
    "    axs[1, 3].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[1, 3].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[8], zorder=1)\n",
    "axs[1, 3].set_title('AIM')\n",
    "axs[1, 3].annotate('n='+str(len(peaks_df[peaks_df['general model']=='AIM'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "for i in peaks_df[peaks_df['general model']=='IEA'].index:\n",
    "    axs[1, 4].plot(np.arange(2005,2101), peaks_df.loc[i][0:96], color='black', alpha=0.5, zorder=0)\n",
    "    axs[1, 4].scatter(peaks_df.at[i,'peak year'], peaks_df.at[i,'peak temp'], color=Set3_10.mpl_colors[9], zorder=1)\n",
    "axs[1, 4].set_title('IEA')\n",
    "axs[1, 4].annotate('n='+str(len(peaks_df[peaks_df['general model']=='IEA'])), xy=(0.1,0.9),xycoords='axes fraction',fontsize=11)\n",
    "\n",
    "plt.ylim(1, 2.4)\n",
    "plt.xlim(2020, 2100)\n",
    "\n",
    "fig.add_subplot(111, frameon=False)\n",
    "# hide tick and tick label of the big axes\n",
    "plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n",
    "plt.grid(False)\n",
    "plt.ylabel('Temperature above preindustrial ('+deg+'C)')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('/Users/samabernethy/Documents/Methane/framework/figures/allIAMs.png', format='png', dpi=600, bbox_inches=\"tight\")\n",
    "plt.savefig('figures/allIAMs.png', format='png', dpi=600, bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'AR5 climate diagnostics|Temperature|Global Mean|FAIR|MED'\n",
    "# 'AR5 climate diagnostics|Temperature|Global Mean|MAGICC6|MED' # but note that expected value is now an option!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # reading in peak temp data from AR5 Scenario Database\n",
    "# # sensitivity to interpolation scheme?\n",
    "# raw = pnd.read_csv('data/ar5.csv')\n",
    "# raw = pnd.read_csv('/Users/samabernethy/Documents/Methane/framework/data/ar5.csv')\n",
    "# temperatures_raw=raw[raw.VARIABLE == 'Temperature|Global Mean|MAGICC6|MED']\n",
    "# temps_df = temperatures_raw.copy()[['2005','2010','2015','2020','2025','2030','2035','2040','2045','2050','2055','2060','2065','2070','2075','2080','2085','2090','2095','2100']]\n",
    "# # 12 models, 524 scenarios, 254 with peak before 2100\n",
    "\n",
    "# # interpolate between years\n",
    "# year_range = (i for i in range(1,96) if i % 5 != 0) \n",
    "# for i in year_range: \n",
    "#     temps_df.insert(i,str(2005+i), np.NaN)\n",
    "\n",
    "# oldcolumns = temps_df.columns\n",
    "# temps_df.columns = np.arange(2005,2101)\n",
    "# temps_df.index = np.arange(0,len(temps_df.index))\n",
    "# temps_df.interpolate(axis=1, method='polynomial', order=2, inplace=True)\n",
    "# temps_df.columns = oldcolumns\n",
    "# temps_df['MODEL'] = temperatures_raw['MODEL'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # determine which scenarios have a peak before 2100\n",
    "# temps_df['peak year'] = np.NaN\n",
    "# temps_df['peak temp'] = np.NaN\n",
    "# for i in temps_df.index:\n",
    "#     if scipy.signal.find_peaks(temps_df.loc[i][0:96].values)[0].size > 0:\n",
    "#         peak_index = scipy.signal.find_peaks(temps_df.loc[i][0:96].values)[0][0]\n",
    "#         peak_year = peak_index + 2005 # as index 0 is year 2005\n",
    "#         peak_temp = temps_df.loc[i][0:96].values[peak_index]\n",
    "#         temps_df.at[i,'peak year']=peak_year\n",
    "#         temps_df.at[i,'peak temp']=peak_temp\n",
    "        \n",
    "# # keep only scenarios with peaks\n",
    "# peaks_df = temps_df.dropna().copy()\n",
    "# peaks_df['color']=0\n",
    "# peaks_df['general model']='none'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # associate models with categories\n",
    "# # 11 models with peak scenarios, 5 categories\n",
    "# # GCAM x3, MERGE x2, MESSAGE x3, REMIND x2, IMAGE x1\n",
    "# for i in peaks_df.index:\n",
    "#     if peaks_df.at[i,'MODEL'] == 'GCAM 2.0' or peaks_df.at[i,'MODEL'] == 'GCAM 3.0' or peaks_df.at[i,'MODEL'] == 'GCAM 3.1':\n",
    "#         peaks_df.at[i,'color'] = 0\n",
    "#         peaks_df.at[i,'general model']='GCAM'\n",
    "#     if peaks_df.at[i,'MODEL'] == 'IMAGE 2.4':\n",
    "#         peaks_df.at[i,'color'] = 1\n",
    "#         peaks_df.at[i,'general model']='IMAGE'\n",
    "#     if peaks_df.at[i,'MODEL'] == 'MERGE-ETL_2011' or peaks_df.at[i,'MODEL'] == 'MERGE_EMF27':\n",
    "#         peaks_df.at[i,'color'] = 2\n",
    "#         peaks_df.at[i,'general model']='MERGE'\n",
    "#     if peaks_df.at[i,'MODEL'] == 'MESSAGE V.2' or peaks_df.at[i,'MODEL'] == 'MESSAGE V.3' or peaks_df.at[i,'MODEL'] == 'MESSAGE V.4':\n",
    "#         peaks_df.at[i,'color'] = 3\n",
    "#         peaks_df.at[i,'general model']='MESSAGE'\n",
    "#     if peaks_df.at[i,'MODEL'] == 'REMIND 1.4' or peaks_df.at[i,'MODEL'] == 'REMIND 1.5':\n",
    "#         peaks_df.at[i,'color'] = 4\n",
    "#         peaks_df.at[i,'general model']='REMIND'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "tempgoals = np.arange(1.3,3.01,0.01)\n",
    "n_bootstrap = 10000\n",
    "times = np.zeros((n_bootstrap, len(tempgoals))) # times is n_bootstraps X # tempgoals\n",
    "# 242 scenarios\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics_from_df(timings):\n",
    "    AGTP_ch4_tempgoals = A_ch4 * ((tau_ch4 * c1)/(tau_ch4 - d1)*(np.exp(-timings/tau_ch4) - np.exp(-timings/d1))+(tau_ch4 * c2)/(tau_ch4 - d2)*(np.exp(-timings/tau_ch4) - np.exp(-timings/d2)))\n",
    "    AGTP_co2_tempgoals = A_co2 * (a0*c1*(1.0-np.exp(-timings/d1))+\n",
    "                               a0*c2*(1.0-np.exp(-timings/d2))+\n",
    "                               (np.exp(-timings/tau1)-np.exp(-timings/d1))*(a1*tau1*c1)/(tau1-d1) + #j=1, i=1\n",
    "                               (np.exp(-timings/tau2)-np.exp(-timings/d1))*(a2*tau2*c1)/(tau2-d1) + #j=1, i=2\n",
    "                               (np.exp(-timings/tau3)-np.exp(-timings/d1))*(a3*tau3*c1)/(tau3-d1) + #j=1, i=3\n",
    "                               (np.exp(-timings/tau1)-np.exp(-timings/d2))*(a1*tau1*c2)/(tau1-d2) + #j=2, i=1\n",
    "                               (np.exp(-timings/tau2)-np.exp(-timings/d2))*(a2*tau2*c2)/(tau2-d2) + #j=2, i=2\n",
    "                               (np.exp(-timings/tau3)-np.exp(-timings/d2))*(a3*tau3*c2)/(tau3-d2)) #j=2, i=3\n",
    "\n",
    "    GTP_ch4_tempgoals = AGTP_ch4_tempgoals / AGTP_co2_tempgoals\n",
    "\n",
    "    AGWP_ch4_tempgoals = A_ch4 * tau_ch4 * (1.0 - np.exp(-timings/tau_ch4))\n",
    "    AGWP_co2_tempgoals = A_co2 * (a0 * timings + a1*tau1*(1.0-np.exp(-timings/tau1)) + a2*tau2*(1.0-np.exp(-timings/tau2)) + a3*tau3*(1.0-np.exp(-timings/tau3)))\n",
    "\n",
    "    GWP_ch4_tempgoals = AGWP_ch4_tempgoals / AGWP_co2_tempgoals\n",
    "  \n",
    "    iAGTP_ch4_tempgoals = np.array([iAGTP_ch4[int(np.floor(timings[x]))] + AGTP_ch4_tempgoals[x] * np.remainder(timings[x],1) for x in range(0, len(timings))])\n",
    "    iAGTP_co2_tempgoals = np.array([iAGTP_co2[int(np.floor(timings[x]))] + AGTP_co2_tempgoals[x] * np.remainder(timings[x],1) for x in range(0, len(timings))])\n",
    "    # might be off by index in timings[x]?\n",
    "\n",
    "    iGTP_ch4_tempgoals = iAGTP_ch4_tempgoals / iAGTP_co2_tempgoals\n",
    "\n",
    "    CGTP_ch4_tempgoals = iAGTP_ch4_tempgoals / AGTP_co2_tempgoals\n",
    "    \n",
    "    return GWP_ch4_tempgoals, GTP_ch4_tempgoals, iGTP_ch4_tempgoals, CGTP_ch4_tempgoals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# full bootstrapping the whole way through:\n",
    "# for each temperature goal, get a distribution of the associated time of peak temp\n",
    "# then for each of these peak temp times, calculate what the metric should be!\n",
    "# score is R2 if that matters\n",
    "\n",
    "def temp_time(peaks_df, n_bootstrap, tempgoals, name):\n",
    "    x=peaks_df['peak temp'].values\n",
    "    y=peaks_df['peak year'].values\n",
    "    X = np.vstack([x, np.ones(len(x))]).T\n",
    "\n",
    "    slopes = np.zeros(n_bootstrap)\n",
    "    intercepts = np.zeros(n_bootstrap)\n",
    "    time15s = np.zeros(n_bootstrap)\n",
    "    time2s = np.zeros(n_bootstrap)\n",
    "    time25s = np.zeros(n_bootstrap)\n",
    "    plt.close()\n",
    "    fig, ax = plt.subplots()\n",
    "    #fig, (ax, ax2) = plt.subplots(2,1,gridspec_kw={'height_ratios': [3, 1]})\n",
    "\n",
    "    for i in range(0, n_bootstrap):\n",
    "        sample_index = np.random.choice(range(0, len(y)), len(y))\n",
    "        X_samples = X[sample_index]\n",
    "        y_samples = y[sample_index]    \n",
    "\n",
    "        lr = LinearRegression()\n",
    "        lr.fit(X_samples, y_samples)\n",
    "\n",
    "        for j in range(0,len(tempgoals)):\n",
    "            tempgoal = tempgoals[j]\n",
    "            times[i,j] = lr.predict(np.array([tempgoal, 1]).reshape(1, -1))[0]\n",
    "\n",
    "        slopes[i] = lr.coef_[0]\n",
    "        intercepts[i] = lr.intercept_\n",
    "        #time15s[i] = lr.predict(np.array([1.5, 1]).reshape(1, -1))[0]   \n",
    "        #time2s[i] = lr.predict(np.array([2, 1]).reshape(1, -1))[0]\n",
    "        #time25s[i] = lr.predict(np.array([2.5, 1]).reshape(1, -1))[0]\n",
    "        ax.plot(x, lr.predict(X), color='gray', alpha=10.0/n_bootstrap, zorder=0)\n",
    "\n",
    "    horizons = times - 2021 \n",
    "\n",
    "    #ax.scatter(x, y, facecolor='white', edgecolor='black', zorder=4)\n",
    "\n",
    "    ax.scatter(peaks_df['peak temp'], peaks_df['peak year'], c=peaks_df['color'], cmap=Set3_10.mpl_colormap, zorder=4)\n",
    "\n",
    "    sns.despine(ax=ax, right=True, left=False)\n",
    "\n",
    "    lr = LinearRegression()\n",
    "    lr.fit(X, y)\n",
    "    ax.plot(x, lr.predict(X), color='black', zorder=1, linewidth=linewidth_other, label='R$^2$='+str(round(lr.score(X, y),2)))\n",
    "    ax.set_ylim(2030,2105)\n",
    "    ax.set_xlim(1.4,2.35)\n",
    "    ax.set_xlabel('Peak temperature above preindustrial ('+deg+'C)')\n",
    "    ax.set_ylabel('Time of peak temperature')\n",
    "\n",
    "#     ax2.set_xlabel('Peak temperature above preindustrial ('+deg+'C)')\n",
    "#     ax2.set_ylabel('Scenarios')\n",
    "#     ax2.hist(peaks_df['peak temp'], np.arange(1.4,2.85,0.025), edgecolor='black', facecolor='white', label=['n='+str(len(peaks_df))])\n",
    "#     ax2.set_xlim(1.4,2.35)\n",
    "#     ax2.legend(loc='upper right',frameon=False)\n",
    "    if name != 'peaks_df':\n",
    "        ax.set_title(name)\n",
    "    plt.legend(loc='best', frameon=False)\n",
    "    plt.tight_layout()\n",
    "#    plt.savefig('/Users/samabernethy/Documents/Methane/framework/figures/temp-timing/temp-timing-bootstrap'+name+'.png', format='png', dpi=600, bbox_inches=\"tight\")\n",
    "    plt.savefig('figures/temp-timing/temp-timing-bootstrap'+name+'.png', format='png', dpi=600, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "    \n",
    "    for i in range(0, n_bootstrap):\n",
    "        sample_index = np.random.choice(range(0, len(y)), len(y))\n",
    "        X_samples = X[sample_index]\n",
    "        y_samples = y[sample_index]    \n",
    "\n",
    "        lr = LinearRegression()\n",
    "        lr.fit(X_samples, y_samples)\n",
    "\n",
    "        for j in range(0,len(tempgoals)):\n",
    "            tempgoal = tempgoals[j]\n",
    "            times[i,j] = lr.predict(np.array([tempgoal, 1]).reshape(1, -1))[0]\n",
    "\n",
    "    GWPs = np.zeros((n_bootstrap, len(tempgoals)))\n",
    "    GTPs = np.zeros((n_bootstrap, len(tempgoals)))\n",
    "    iGTPs = np.zeros((n_bootstrap, len(tempgoals)))\n",
    "    CGTPs = np.zeros((n_bootstrap, len(tempgoals)))\n",
    "\n",
    "    for i in range(0, len(tempgoals)):\n",
    "        timings = horizons[:,i] # one tempgoal's timing for all bootsraps?\n",
    "\n",
    "        GWP_ch4_tempgoals, GTP_ch4_tempgoals, iGTP_ch4_tempgoals, CGTP_ch4_tempgoals = metrics_from_df(timings)\n",
    "\n",
    "        GWPs[:,i] = GWP_ch4_tempgoals\n",
    "        GTPs[:,i] = GTP_ch4_tempgoals\n",
    "        iGTPs[:,i] = iGTP_ch4_tempgoals\n",
    "        CGTPs[:,i] = CGTP_ch4_tempgoals\n",
    "\n",
    "    # general baseline one\n",
    "    #tempgoals = np.arange(1.3,3.5,0.01)\n",
    "    p, cov = np.polyfit(peaks_df['peak temp'], peaks_df['peak year'], 1,cov=True)\n",
    "    timings = p[0] * tempgoals + p[1] - 2021 # present day, 2021\n",
    "    GWP_ch4_tempgoals, GTP_ch4_tempgoals, iGTP_ch4_tempgoals, CGTP_ch4_tempgoals = metrics_from_df(timings)\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    for i in range(0, n_bootstrap):\n",
    "        plt.plot(tempgoals, GWPs[i,:], color=color_gwp_shaded, alpha=10/n_bootstrap)\n",
    "        plt.plot(tempgoals, GTPs[i,:], color=color_gtp_shaded, alpha=10/n_bootstrap)\n",
    "        plt.plot(tempgoals, iGTPs[i,:], color=color_igtp_shaded, alpha=10/n_bootstrap)\n",
    "\n",
    "    plt.plot(tempgoals, iGTP_ch4_tempgoals, label='iGTP', color=color_igtp, linewidth=linewidth_other)\n",
    "    plt.plot(tempgoals, GWP_ch4_tempgoals, label='GWP', color=color_gwp, linewidth=linewidth_other)\n",
    "    plt.plot(tempgoals, GTP_ch4_tempgoals, label='GTP', color=color_gtp, linewidth=linewidth_other)\n",
    "\n",
    "    plt.xlabel('Temperature goal ('+deg+'C above preindustrial)')\n",
    "    plt.ylabel('CH$_4$ emission metric')\n",
    "    sns.despine(ax=ax, right=True, left=False)\n",
    "\n",
    "    #plt.axvline(2,linestyle=':',color='black',label='1.5 $^\\circ$C')\n",
    "    #plt.axvline(1.5,linestyle='--',color='black',label='1.5 $^\\circ$C')\n",
    "    plt.legend(loc='upper right', frameon=False)\n",
    "    if name != 'peaks_df':\n",
    "        plt.title(name)\n",
    "    plt.ylim(0,100)\n",
    "    plt.xlim(1.4,3)\n",
    "#    plt.savefig('/Users/samabernethy/Documents/Methane/framework/figures/temp-metrics/temp-metrics_'+name+'.png', format='png', dpi=600, bbox_inches=\"tight\")\n",
    "    plt.savefig('figures/temp-metrics/temp-metrics_'+name+'.png', format='png', dpi=600, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "    \n",
    "    # things to return: \n",
    "    # model by model slopes & intercepts\n",
    "    # model by model times for 1.5, 2, 2.5, 3\n",
    "    # model by model gwps, gtps, igtps for 1.5, 2, 2.5, 3\n",
    "    return name, str(round(np.mean(slopes)))+' '+pm+' '+str(round(np.std(slopes))), str(round(np.mean(intercepts)))+' '+pm+' '+str(round(np.std(intercepts))), str(round(np.mean(times[:,20])))+' '+pm+' '+str(round(np.std(times[:,20]))), str(round(np.mean(times[:,70])))+' '+pm+' '+str(round(np.std(times[:,70]))), str(round(np.mean(times[:,120])))+' '+pm+' '+str(round(np.std(times[:,120]))), str(round(np.mean(times[:,170])))+' '+pm+' '+str(round(np.std(times[:,170]))), str(round(np.mean(GWPs[:,20])))+' '+pm+' '+str(round(np.std(GWPs[:,20]))), str(round(np.mean(GWPs[:,70])))+' '+pm+' '+str(round(np.std(GWPs[:,70]))), str(round(np.mean(GWPs[:,120])))+' '+pm+' '+str(round(np.std(GWPs[:,120]))), str(round(np.mean(GWPs[:,170])))+' '+pm+' '+str(round(np.std(GWPs[:,170]))), str(round(np.mean(GTPs[:,20])))+' '+pm+' '+str(round(np.std(GTPs[:,20]))), str(round(np.mean(GTPs[:,70])))+' '+pm+' '+str(round(np.std(GTPs[:,70]))), str(round(np.mean(GTPs[:,120])))+' '+pm+' '+str(round(np.std(GTPs[:,120]))), str(round(np.mean(GTPs[:,170])))+' '+pm+' '+str(round(np.std(GTPs[:,170]))), str(round(np.mean(iGTPs[:,20]),-1))+' '+pm+' '+str(round(np.std(iGTPs[:,20]),-1)), str(round(np.mean(iGTPs[:,70]),-1))+' '+pm+' '+str(round(np.std(iGTPs[:,70]),-1)), str(round(np.mean(iGTPs[:,120]),-1))+' '+pm+' '+str(round(np.std(iGTPs[:,120]),-1)), str(round(np.mean(iGTPs[:,170]),-1))+' '+pm+' '+str(round(np.std(iGTPs[:,170]),-1)), str(round(np.mean(CGTPs[:,20]),-1))+' '+pm+' '+str(round(np.std(CGTPs[:,20]),-1)), str(round(np.mean(CGTPs[:,70]),-1))+' '+pm+' '+str(round(np.std(CGTPs[:,70]),-1)), str(round(np.mean(CGTPs[:,120]),-1))+' '+pm+' '+str(round(np.std(CGTPs[:,120]),-1)), str(round(np.mean(CGTPs[:,170]),-1))+' '+pm+' '+str(round(np.std(CGTPs[:,170]),-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = temp_time(peaks_df, n_bootstrap, tempgoals, 'peaks_df')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THE BIG BOOTSTRAPPING EXTRAVAGANZA\n",
    "document = Document()\n",
    "document.add_heading('Aligning emission metrics with temperature goals', 0)\n",
    "\n",
    "pp = document.add_paragraph('The good stuff')\n",
    "\n",
    "table = document.add_table(rows=1, cols=24)\n",
    "hdr_cells = table.rows[0].cells\n",
    "\n",
    "hdr_cells[0].text = 'IAM'\n",
    "\n",
    "hdr_cells[1].text = 'Slope'\n",
    "hdr_cells[2].text = 'Intercept'\n",
    "\n",
    "hdr_cells[3].text = 'Time for 1.5',deg,'C'\n",
    "hdr_cells[4].text = 'Time for 2',deg,'C'\n",
    "hdr_cells[5].text = 'Time for 2.5',deg,'C'\n",
    "hdr_cells[6].text = 'Time for 3',deg,'C'\n",
    "\n",
    "hdr_cells[7].text = 'GWP for 1.5',deg,'C'\n",
    "hdr_cells[8].text = 'GWP for 2',deg,'C'\n",
    "hdr_cells[9].text = 'GWP for 2.5',deg,'C'\n",
    "hdr_cells[10].text = 'GWP for 3',deg,'C'\n",
    "\n",
    "hdr_cells[11].text = 'GTP for 1.5',deg,'C'\n",
    "hdr_cells[12].text = 'GTP for 2',deg,'C'\n",
    "hdr_cells[13].text = 'GTP for 2.5',deg,'C'\n",
    "hdr_cells[14].text = 'GTP for 3',deg,'C'\n",
    "\n",
    "hdr_cells[15].text = 'iGTP for 1.5',deg,'C'\n",
    "hdr_cells[16].text = 'iGTP for 2',deg,'C'\n",
    "hdr_cells[17].text = 'iGTP for 2.5',deg,'C'\n",
    "hdr_cells[18].text = 'iGTP for 3',deg,'C'\n",
    "\n",
    "hdr_cells[19].text = 'CGTP for 1.5',deg,'C'\n",
    "hdr_cells[20].text = 'CGTP for 2',deg,'C'\n",
    "hdr_cells[21].text = 'CGTP for 2.5',deg,'C'\n",
    "hdr_cells[22].text = 'CGTP for 3',deg,'C'\n",
    "\n",
    "df = peaks_df\n",
    "outputs = temp_time(df, n_bootstrap, tempgoals, 'peaks_df')\n",
    "row_cells = table.add_row().cells\n",
    "row_cells[0].text = 'All'\n",
    "\n",
    "for j in range(1,23):\n",
    "    row_cells[j].text = outputs[j]\n",
    "        \n",
    "for model in peaks_df['general model'].unique():\n",
    "    if model != 'IEA' and model != 'MERGE':\n",
    "        df = peaks_df[peaks_df['general model'] == model]\n",
    "        \n",
    "        outputs = temp_time(df, n_bootstrap, tempgoals, model)\n",
    "        row_cells = table.add_row().cells\n",
    "        #row_cells[0].text = df['general model'].unique()[0]        \n",
    "        for j in range(0,23):\n",
    "            row_cells[j].text = outputs[j]\n",
    "            \n",
    "table.style = 'LightShading-Accent1'\n",
    "\n",
    "#document.save('/Users/samabernethy/Documents/Methane/framework/figures/tables.docx')\n",
    "document.save('figures/tables.docx')\n",
    "        # outputs[0] = name\n",
    "        \n",
    "        # outputs[1] = slope\n",
    "        # outputs[2] = intercepts\n",
    "        \n",
    "        # outputs[3] = time for 1.5\n",
    "        # outputs[4] = time for 2\n",
    "        # outputs[5] = time for 2.5\n",
    "        # outputs[6] = time for 3\n",
    "        \n",
    "        # outputs[7] = GWP for 1.5\n",
    "        # outputs[8] = GWP for 2\n",
    "        # outputs[9] = GWP for 2.5\n",
    "        # outputs[10] = GWP for 3\n",
    "        \n",
    "        # outputs[11] = GTP for 1.5\n",
    "        # outputs[12] = GTP for 2\n",
    "        # outputs[13] = GTP for 2.5\n",
    "        # outputs[14] = GTP for 3\n",
    "        \n",
    "        # outputs[15] = iGTP for 1.5\n",
    "        # outputs[16] = iGTP for 2\n",
    "        # outputs[17] = iGTP for 2.5\n",
    "        # outputs[18] = iGTP for 3"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}