{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments of Opportunity Jupyter Notebook\n",
    "## For creating Figure 10 and Figure 4S in Christensen et al. (2022), ACP\n",
    "**Cloud properties**\n",
    "1. cloud droplet effective radius\n",
    "2. cloud liquid water path\n",
    "3. droplet concentration\n",
    "\n",
    "**Composites**  \n",
    "1. ship tracks (in situ, satellite, models)\n",
    "2. shipping corridors\n",
    "3. fire tracks\n",
    "4. industrial tracks\n",
    "5. volcano tracks\n",
    "6. effusive volcanic eruptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Python libraries\n",
    "import numpy as np\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "csvFile = './Natural_Laboratories_Data.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Read csv file\n",
    "with open(csvFile, newline='') as f:\n",
    "    reader = csv.reader(f)\n",
    "    data = list(reader)\n",
    "data = np.asarray(data)\n",
    "sz = np.shape(data)\n",
    "rows = sz[0]\n",
    "\n",
    "#Extract top two lines\n",
    "header = np.asarray( data[0] )\n",
    "names = np.asarray( data[1] )\n",
    "\n",
    "#Indices for data extraction\n",
    "pID, = ( np.where(names == 'Author/Year') )[0]\n",
    "mID, = ( np.where(names == 'Methods') )[0]\n",
    "lID, = ( np.where(names == 'Laboratory') )[0]\n",
    "rID, = ( np.where(names == 'Regime/Type') )[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Collect data for each variable\n",
    "vNames = ['ΔRe/Re','ΔLWP/LWP','ΔNd/Nd','ΔlnNd','ΔlnRe/ΔlnNd','ΔlnLWP/ΔlnNd']\n",
    "composites = ['volcano tracks Satellite','industry tracks Satellite','fire tracks Satellite',\n",
    "             'ship tracks Satellite','ship tracks LES','ship tracks CRM','ship tracks in situ',\n",
    "             'shipping corridor Sc Satellite','shipping corridor Cu Satellite','effusive volcanic eruption Sat.','global shipping Model']\n",
    "composites = composites[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Functions for extracting and averaging data\n",
    "\n",
    "#Program to make number a string for use in LaTEX tables\n",
    "def num_dimf(num):\n",
    "    tstr = ''\n",
    "    if (num >= 1.) or (num <= 1.):\n",
    "        if num >= 1.:\n",
    "            if num < 10.:\n",
    "                tstr = \"{:4.2f}\".format(num)\n",
    "            if (num < 100.) & (num >= 10.):\n",
    "                tstr = \"{:4.1f}\".format(num)\n",
    "            if (num < 1000.) & (num >= 100.):\n",
    "                tstr = '{:3d}'.format(int(num))\n",
    "            if (num < 10000.) & (num >= 1000.):\n",
    "                tstr = '{:4d}'.format(int(num))\n",
    "            if num >= 10000.:\n",
    "                tstr = '{:.2e}'.format(num)\n",
    "    if num <= 1.:\n",
    "        if num > -10.:\n",
    "            tstr = \"{:4.1f}\".format(num)\n",
    "        if (num > -100.) and (num < -10.):\n",
    "            tstr = \"{:5.1f}\".format(num)\n",
    "        if (num > -1000.) and (num < -100.):\n",
    "            tstr = \"{:4d}\".format(int(num))\n",
    "        if num <= 10000.:\n",
    "            tstr = '{:.2e}'.format(num)\n",
    "\n",
    "    if num == 0.: tstr = '{:1d}'.format(int(num))\n",
    "    if (num < 1.) and (num > -1.):\n",
    "        if num > 0.:#positive numbers\n",
    "            if num >= 0.1:\n",
    "                tstr = \"{:4.2f}\".format(num)\n",
    "            if (num < 0.1) and (num >= 0.01):\n",
    "                tstr = \"{:4.2f}\".format(num)\n",
    "            if (num < 0.01) and (num >= 0.001):\n",
    "                tstr = \"{:5.3f}\".format(num)\n",
    "            if (num < 0.001):\n",
    "                tstr = '{:.2e}'.format(num)\n",
    "    if num < 0.:\n",
    "        if num <= -0.1:\n",
    "            tstr = \"{:5.2f}\".format(num)\n",
    "        if (num > -0.1) and (num <= -0.01):\n",
    "            tstr = \"{:5.2f}\".format(num)\n",
    "        if (num > -0.01) and (num <= -0.001):\n",
    "            tstr = \"{:6.3f}\".format(num)\n",
    "        if num > -0.001:\n",
    "            tstr = '{:.2e}'.format(num)\n",
    "    return tstr\n",
    "\n",
    "#Find all of the papers with valid data for the given variable\n",
    "def extract_indices(data,names,vlab):\n",
    "    vID, = ( np.where(names == vlab) )[0]\n",
    "    sID = [ ]\n",
    "    #Find all studies with valid data\n",
    "    for i in range(2,rows-2):\n",
    "        if len( data[i][vID] ) > 0:\n",
    "            sID.append(i)\n",
    "    return vID,sID\n",
    "\n",
    "def average_composite(val,composites,lab,meth,regime):\n",
    "    #Average table-entered cloud property data into each composite\n",
    "    stats = np.zeros((3,100))\n",
    "    for iC in range(len(composites)):\n",
    "        tID = -999\n",
    "        if composites[iC] == 'volcano tracks Satellite':\n",
    "            tID, = np.where( (lab == 'volcano tracks') & (meth == 'Satellite') )\n",
    "        if composites[iC] == 'industry tracks Satellite':\n",
    "            tID, = np.where( (lab == 'industry tracks') & (meth == 'Satellite') )\n",
    "        if composites[iC] == 'fire tracks Satellite':\n",
    "            tID, = np.where( (lab == 'fire tracks') & (meth == 'Satellite') )\n",
    "        if composites[iC] == 'ship tracks Satellite':\n",
    "            tID, = np.where( (lab == 'ship tracks') & (meth == 'Satellite') )\n",
    "        if composites[iC] == 'ship tracks LES': \n",
    "            tID, = np.where( (lab == 'ship tracks') & (meth == 'LES') )\n",
    "        if composites[iC] == 'ship tracks CRM': \n",
    "            tID, = np.where( (lab == 'ship tracks') & (meth == 'CRM') )\n",
    "        if composites[iC] == 'ship tracks in situ':\n",
    "            tID, = np.where( (lab == 'ship tracks') & (meth == 'in situ') )\n",
    "        if composites[iC] == 'shipping corridor Sc Satellite':\n",
    "            tID, = np.where( (lab == 'shipping corridor') & (meth == 'Satellite') & (regime == 'Sc') )\n",
    "        if composites[iC] == 'shipping corridor Cu Satellite':\n",
    "            tID, = np.where( (lab == 'shipping corridor') & (meth == 'Satellite') & (regime == 'Cu') )\n",
    "        if composites[iC] == 'effusive volcanic eruption Sat.':\n",
    "            tID, = np.where( (lab == 'effusive volcanic eruption') & (meth == 'Satellite') )\n",
    "        if composites[iC] == 'global shipping Model':\n",
    "            tID, = np.where( (lab == 'global shipping') & (meth == 'Model') )\n",
    "        if len(tID) > 0:\n",
    "            stats[0,iC] = len(tID)\n",
    "            stats[1,iC] = np.mean(val[tID])\n",
    "            stats[2,iC] = np.std(val[tID])\n",
    "    stats = stats[:,0:len(composites)]\n",
    "    return stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write out table of values from all studies used here (Table S1)\n",
    "#LaTEX table format\n",
    "Nid = np.where( names == 'ΔNd/Nd')[0][0]\n",
    "Rid = np.where( names == 'ΔRe/Re')[0][0]\n",
    "Lid = np.where( names == 'ΔLWP/LWP')[0][0]\n",
    "f = open(\"./natural_labs_table.txt\", \"w\")\n",
    "f.write('\\\\begin{longtable}{ |p{4.3cm}||p{1.5cm}|p{1.25cm}|p{1cm}|p{1.75cm}|p{1.1cm}|p{1.35cm}|p{1.6cm}| } \\n')\n",
    "f.write('\\\\caption{List of experiments of opportunity}\\\\\\ \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('Author & Laboratory & Data & Regime & Location & ΔNd/Nd & ΔRe/Re & ΔLWP/LWP \\\\\\ \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('\\\\endfirsthead \\n')\n",
    "f.write('\\\\multicolumn{8}{c}% \\n')\n",
    "f.write('{\\\\tablename\\ \\\\thetable\\ -- \\\\textit{Continued from previous page}} \\\\\\ \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('Author & Laboratory & Data & Regime & Location & ΔNd/Nd & ΔRe/Re & ΔLWP/LWP \\\\\\ \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('\\\\endhead \\n')\n",
    "f.write('\\\\hline \\multicolumn{8}{r}{\\\\textit{Continued on next page}} \\\\\\ \\n')\n",
    "f.write('\\\\endfoot \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('\\\\endlastfoot \\n')\n",
    "for iVal in range(2, np.shape(data)[0]):\n",
    "    if (len(data[iVal][Rid]) > 0) | (len(data[iVal][Lid]) > 0) | (len(data[iVal][Nid]) > 0):\n",
    "        ReVal = ''\n",
    "        if (len(data[iVal][Rid]) > 0): ReVal = num_dimf( float(data[iVal][Rid]) )\n",
    "        NdVal = ''\n",
    "        if (len(data[iVal][Nid]) > 0): NdVal = num_dimf( float(data[iVal][Nid]) )\n",
    "        LWPVal = ''\n",
    "        if (len(data[iVal][Lid]) > 0): LWPVal = num_dimf( float(data[iVal][Lid]) )\n",
    "        labVal = data[iVal][1]\n",
    "        if labVal == 'ship tracks': labVal = 'sTracks'\n",
    "        if labVal == 'fire tracks': labVal = 'fTracks'\n",
    "        if labVal == 'volcano tracks': labVal = 'vTracks'\n",
    "        if labVal == 'industry tracks': labVal = 'iTracks'\n",
    "        if labVal == 'shipping corridor': labVal = 'sCorridor'\n",
    "        if labVal == 'global shipping': labVal = 'globe ship'\n",
    "        if labVal == 'effusive volcanic eruption': labVal = 'vEruption'\n",
    "        noteStr = data[iVal][2]\n",
    "        if noteStr == 'NE Pacific': noteStr = 'NEPAC'\n",
    "        if noteStr == 'California': noteStr = 'NEPAC'\n",
    "        if noteStr == 'West coast of US': noteStr = 'NEPAC'\n",
    "        if noteStr == 'Multiple basins': noteStr = 'multi'\n",
    "        if noteStr == 'Multiple Basins': noteStr = 'multi'\n",
    "        if noteStr == 'East Pacific': noteStr = 'NEPAC'\n",
    "        if noteStr == 'SE Pacific': noteStr = 'SEPAC'\n",
    "        if noteStr == 'Piton de la Fournaise': noteStr = 'Piton'\n",
    "        if noteStr == 'Kazakhstan, Kazakhoyl Alibekmola oil production facilities in the Aktobe region': noteStr = 'Kazakhstan'\n",
    "        if noteStr == 'Russia, Moscow': noteStr = 'Moscow'\n",
    "        if noteStr == 'Australia, Kalgoorlie-Boulder Super Pit gold mine and smelting facilities': noteStr='Kalgoorlie'\n",
    "        if noteStr == 'Canada, Labrador city iron ore facility': noteStr = 'Labrador'\n",
    "        if noteStr == 'Canada, Newfoundland crude oil refinery near Arnold’s Cove': noteStr='Newfoundland'\n",
    "        if noteStr == 'Russia, oil refineries in the Nenets region in the Timan-Pechora Basin': noteStr='Nenets'\n",
    "        if noteStr == 'Canada, nickel smelting and refining industries in Thompson, Manitoba': noteStr='Manitoba'\n",
    "        if noteStr == 'Australia, brown coal-fired thermal Loy Yang power station in Traralgon, Victoria': noteStr='Traralgon'\n",
    "        if noteStr == 'Ambrym, Vanuatu': noteStr='Ambrym'\n",
    "        if noteStr == 'Kuril Islands volcanoes': noteStr='Kuril'\n",
    "        if noteStr == 'South Sandwich Islands volcanoes': noteStr='Sandwich'\n",
    "        if noteStr == 'Russia, Norilsk Nickel smelting facilities': noteStr='Norilsk'\n",
    "        if (noteStr == 'South East Atlantic (subtropical)') & (data[iVal][5] == 'Morning climatology'): noteStr = 'SEATL(sub)T'\n",
    "        if (noteStr == 'South East Atlantic (subtropical)') & (data[iVal][5] == 'Afternoon climatology'): noteStr = 'SEATL(sub)A'\n",
    "        if (noteStr == 'South East Atlantic (subtropical)') & (data[iVal][5] == 'Daily climatology'): noteStr = 'SEATL(sub)D'\n",
    "        if (noteStr == 'South East Atlantic (tropical)') & (data[iVal][5] == 'Morning climatology'): noteStr = 'SEATL(trp)T'\n",
    "        if (noteStr == 'South East Atlantic (tropical)') & (data[iVal][5] == 'Afternoon climatology'): noteStr = 'SEATL(trp)A'\n",
    "        if (noteStr == 'South East Atlantic (tropical)') & (data[iVal][5] == 'Daily climatology'): noteStr = 'SEATL(trp)D'\n",
    "        if (data[iVal][6] == 'SENSPERP Fig 15 hour 10'): noteStr = 'NEPAC(perp)'\n",
    "        if (data[iVal][6] == 'BASETRACK Fig 15 hour 10'): noteStr = 'NEPAC(base)'\n",
    "        if (data[iVal][6] == 'SENSHiAER Fig 15 hour 10'): noteStr = 'NEPAC(iAer)'\n",
    "        if (data[iVal][6] == 'Table 2 ctrl & ship; ships may sometimes have a substantial radiative effect on marine clouds and albedo, even when ship tracks are not readily visible.'): noteStr = 'SEPAC(ctrl)'\n",
    "        if (data[iVal][6] == 'Table 2 detrained; ships may sometimes have a substantial radiative effect on marine clouds and albedo, even when ship tracks are not readily visible.'): noteStr = 'SEPAC(detr)'\n",
    "        if (data[iVal][6] == 'Table 2 wall; ships may sometimes have a substantial radiative effect on marine clouds and albedo, even when ship tracks are not readily visible.'): noteStr = 'SEPAC(wall)'\n",
    "        f.write( '\\\\cite{'+data[iVal][28]+'}'+' & '+labVal+' & '+data[iVal][3]+' & '+data[iVal][4]+' & '+noteStr+' & '+NdVal+' & '+ReVal+' & '+LWPVal + ' \\\\\\ \\n' )\n",
    "f.write('\\hline \\n')\n",
    "#f.write('\\caption{List of experiments of opportunity from an expert solicitation of peer-reviewed articles used in Figure 10. Ship tracks (sTracks), Industry tracks (iTracks), Fire tracks (fTracks), Volcano tracks (vTracks), vEruption (effusive volcanic eruption) are renamed for brevity. Liquid cloud types include Stratus (St), Stratocumulus (Sc), Cumulus (Cu), all top heights greater than 500 hPa (Liq) and mixed (MIX) phase cloud. Northeast Pacific (NEPAC), South East Atlantic (SEATL), and multiple basins (multi) have shorter names. Diamond et al. (2020) separate subtropical (sub) from tropical (trp) for retrievals from Terra (T), Aqua (A) and combined to for a daily average (D). The full list can be found on google docs}')\n",
    "#f.write('\\end{longtable}')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing: ΔRe/Re\n",
      "processing: ΔLWP/LWP\n",
      "processing: ΔNd/Nd\n",
      "processing: ΔlnNd\n",
      "processing: ΔlnRe/ΔlnNd\n",
      "processing: ΔlnLWP/ΔlnNd\n"
     ]
    }
   ],
   "source": [
    "#Fetch multiple cloud property changes on single plot\n",
    "allStats = []\n",
    "vlabs = []\n",
    "for i in range(len(vNames)):\n",
    "    #Variable to extract\n",
    "    vlab = vNames[i]\n",
    "    print('processing: '+vlab)\n",
    "\n",
    "    #Fetch Data\n",
    "    vID,sID = extract_indices(data,names,vlab)\n",
    "    paper = data[sID,pID]\n",
    "    lab = data[sID,lID]\n",
    "    val = data[sID,vID]\n",
    "    meth = data[sID,mID]\n",
    "    regime = data[sID,rID]\n",
    "    val = np.asarray( [ float(val[i]) for i in range(len(val))] )\n",
    "    stats = average_composite(val,composites,lab,meth,regime)\n",
    "    allStats.append(stats)\n",
    "    vlabs.append(vlab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['global shipping Model', 'effusive volcanic eruption Sat.', 'shipping corridor Cu Satellite', 'shipping corridor Sc Satellite', 'ship tracks in situ', 'ship tracks CRM', 'ship tracks LES', 'ship tracks Satellite', 'fire tracks Satellite', 'industry tracks Satellite', 'volcano tracks Satellite']\n",
      "['global shipping Model', 'effusive volcanic eruption Sat.', 'shipping corridor Cu Satellite', 'shipping corridor Sc Satellite', 'ship tracks in situ', 'ship tracks CRM', 'ship tracks LES', 'ship tracks Satellite', 'fire tracks Satellite', 'industry tracks Satellite', 'volcano tracks Satellite']\n",
      "['global shipping Model', 'effusive volcanic eruption Sat.', 'shipping corridor Cu Satellite', 'shipping corridor Sc Satellite', 'ship tracks in situ', 'ship tracks CRM', 'ship tracks LES', 'ship tracks Satellite', 'fire tracks Satellite', 'industry tracks Satellite', 'volcano tracks Satellite']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Figure S4\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "fig = plt.figure(figsize=((20,8)))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "lbl=['A) ','B) ','C) ']\n",
    "method_cls=['red','blue','green']\n",
    "\n",
    "for iv in range(3):\n",
    "    ax = fig.add_subplot(1, 3, iv+1 )\n",
    "    x = composites\n",
    "    n = np.asarray( (allStats[iv])[0,:] )\n",
    "    y = np.asarray( (allStats[iv])[1,:] )\n",
    "    variance = np.asarray( (allStats[iv])[2,:] )\n",
    "    x_pos = [i for i, _ in enumerate(x)]\n",
    "    ax.barh(x_pos, y, color=method_cls[iv], xerr=variance)\n",
    "    ax.plot( y,x_pos,'.', color='black')\n",
    "    ax.set_xlabel(vlabs[iv])\n",
    "    #ax.set_title(lbl[iv]+vNames[iv])\n",
    "    ax.set_title(lbl[iv],loc='left')\n",
    "    print(x)\n",
    "    if iv == 0: plt.yticks(x_pos, x)\n",
    "    if iv > 0: ax.axes.get_yaxis().set_visible(False)\n",
    "    #print('Statistics for each category: ',vlabs[iv],n[::-1],y[::-1],variance[::-1])\n",
    "plt.tight_layout()\n",
    "plt.savefig('./BarChart_fractional.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write out table of fractional values (Table S3)\n",
    "#LaTEX table format\n",
    "f = open(\"./cloud_properties_fractional_changes.txt\", \"w\")\n",
    "f.write('\\\\begin{table}[ht] \\n')\n",
    "f.write('\\\\small \\n')\n",
    "f.write('\\\\begin{tabular} \\n')\n",
    "f.write('{ |p{5cm}||p{2.5cm}|p{2.5cm}|p{2.5cm}|} \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('Laboratory & '+vlabs[0]+' & '+vlabs[1]+' & '+vlabs[2]+' \\\\\\ \\n')\n",
    "f.write('\\hline \\n')\n",
    "for iC in range(len(composites)-1,-1,-1):\n",
    "    f.write(composites[iC]+' & '+num_dimf(((allStats[0])[1,:] )[iC]) +' ('+num_dimf(((allStats[0])[2,:] )[iC])+')' +' & ' + num_dimf(((allStats[1])[1,:] )[iC]) +' ('+num_dimf(((allStats[1])[2,:] )[iC])+')' + ' & ' + num_dimf(((allStats[2])[1,:] )[iC]) +' ('+num_dimf(((allStats[2])[2,:] )[iC])+') \\\\\\ \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('\\end{tabular}')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot Figure 10\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "fig = plt.figure(figsize=((20,8)))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n",
    "lbl=['A) ','B) ','C) ']\n",
    "method_cls=['green','green','green','green','red','blue','green','green']\n",
    "\n",
    "pID = [3,4,5]\n",
    "for iv in range(len(pID)):\n",
    "    ax = fig.add_subplot(1, 3, iv+1 )\n",
    "    x = composites\n",
    "    n = np.asarray( (allStats[pID[iv]])[0,:] )\n",
    "    y = np.asarray( (allStats[pID[iv]])[1,:] )\n",
    "    variance = np.asarray( (allStats[pID[iv]])[2,:] )\n",
    "    #print(vNames[pID[iv]],n,y,variance)\n",
    "    x_pos = [i for i, _ in enumerate(x)]\n",
    "    ax.barh(x_pos, y, color=method_cls[pID[iv]], xerr=variance)\n",
    "    ax.plot( y,x_pos,'.', color='black')\n",
    "    if iv == 0:\n",
    "        for ijk in range(len(x)): x[ijk] = x[ijk]\n",
    "        plt.yticks(x_pos, x)\n",
    "        ax.set_xscale('log')\n",
    "        xname = 'Δln($N_d$)'\n",
    "    if iv == 1: xname = 'Δln($R_e$)/Δln($N_d$)'\n",
    "    if iv == 2: xname = 'Δln($LWP$)/Δln($N_d$)'\n",
    "    if iv > 0: ax.axes.get_yaxis().set_visible(False)\n",
    "    ax.set_xlabel(xname)\n",
    "    ax.set_title(lbl[iv],loc='left')\n",
    "    #print('number of studies for each category: ',vlabs[iv],n)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./BarChart.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write out table of cloud properties scaled by delta ln(Nd) (Table S4)\n",
    "#LaTEX table format\n",
    "f = open(\"./cloud_property_changes.txt\", \"w\")\n",
    "f.write('\\\\begin{table}[ht] \\n')\n",
    "f.write('\\\\small \\n')\n",
    "f.write('\\\\begin{tabular} \\n')\n",
    "f.write('{ |p{5cm}||p{2.5cm}|p{2.5cm}|p{2.5cm}|} \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('Laboratory & '+vlabs[3]+' & '+vlabs[4]+' & '+vlabs[5]+' \\\\\\ \\n')\n",
    "f.write('\\hline \\n')\n",
    "for iC in range(len(composites)-1,-1,-1):\n",
    "    f.write(composites[iC]+' & '+num_dimf(((allStats[3])[1,:] )[iC]) +' ('+num_dimf(((allStats[3])[2,:] )[iC])+')' +' & ' + num_dimf(((allStats[4])[1,:] )[iC]) +' ('+num_dimf(((allStats[4])[2,:] )[iC])+')' + ' & ' + num_dimf(((allStats[5])[1,:] )[iC]) +' ('+num_dimf(((allStats[5])[2,:] )[iC])+') \\\\\\ \\n')\n",
    "f.write('\\hline \\n')\n",
    "f.write('\\end{tabular}')\n",
    "f.close()"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}