amm_prediction_calculator.ipynb

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    "## AMM Predicted Emission Calculator v1.2, 7/27/20\n",
    "\n",
    "### Introduction\n",
    "This program estimates future US AMM emissions from mines closed in a given set of years. The methodology is adapted from Kholod et al., 2020 (full citation in RefWorks), and uses gross coal production rather than individual mine data. To use, click the button that says \"Run\" above (it might take more than one click to start) and follow the prompts.\n",
    "\n",
    "### Methodology\n",
    "The model has a few underlying assumptions. First, it expects active coal mine methane emissions to equal approximately the product of total coal mined nationally (tonnes) and the average gas content of that coal (m^3/tonne; also known as the emission factor). This is the same approach taken by the EPA and the IPCC in forecasting CMM emissions.\n",
    "\n",
    "Next, it assumes that some fraction of coal produced annually is abandoned, and therefore should be moved into the AMM-producing category. This fraction is the \"coal abandonment rate.\" \n",
    "\n",
    "Once coal becomes abandoned, its emissions are predicted by industry-standard decline curves. Because flooded and dry mines emit at different rates, we need here another initial parameter, the \"flooding rate\" (the fraction of abandoned coal that floods annually).  \n",
    "\n",
    "Kholod et al. use a 5% coal abandonment rate and a 50% flooding rate globally; I think these may be a bit high, but I've kept them as defaults.\n",
    "\n",
    "Finally, because abandoned mine methane is only recoverable from underground mines, I've included the percentage of coal mined underground (35%) as a fixed coefficient. \n",
    "\n",
    "Putting it all together, then,\n",
    "AMM emissions for a given year  = (total coal production that year) x (emission factor) x (abandonment rate) x (percent underground) + (emissions from dry mines closed in prior years) + (emissions from flooded mines closed in prior years,\n",
    "where (emissions from dry mines closed in prior years) and (emissions from flooded mines closed in prior years) are just sums of the various decline curves. \n",
    "\n",
    "One important note: *this method only calculates emissions from mines closed in a future range of years that you choose.* It does not include methane from already abandoned mines, and although it forecasts out to 2050, it will only \"close\" mines for the number of years you tell it to (this may make more sense if you try it).\n",
    "\n",
    "### Parameters\n",
    "**Fixed:**\n",
    "\n",
    "Initial decline rate for dry mines = 0.302 (Source: Kholod et. al)\n",
    "\n",
    "Hyperbolic rate of change for dry decline rate = 2.017 (Source: Kholod et. al)\n",
    "\n",
    "Initial decline rate for flooded mines = 0.672 (Source: Kholod et. al)\n",
    "\n",
    "Fraction of coal mined underground = 0.35 (Source: EIA)\n",
    "\n",
    "**Adjustable:**\n",
    "\n",
    "Coal mine closure time range: Number of years for which you want to take into account coal production. For example, if you input '2' you will get back estimations for how coal mines closed in 2020 and 2021 will emit out to 2050. If you input '31' you will get back estimations for how all future mine closures will emit out to 2050.\n",
    "\n",
    "Coal production forecast: Prediction of number of tonnes coal that will be mined in the US annually, from now until 2050. Right now the only option is 'EIA', but I'm looking to add more scenarios soon.\n",
    "\n",
    "Abandonment rate: The percentage of coal \"that would have been produced but is not produced because of mine closure\" (Kholod et al., 2020). The default is '5'.\n",
    "\n",
    "Emission factors: Roughly, the proportionality constants betweeen coal mined at a certain depth/rank and gas produced. Options are 'IPCC' (the standard factors used in the IPCC's 2006 GHG inventory methodology) and 'M2CM' (the proprietary factors developed by Kholod et al.).\n",
    "\n",
    "Flood rate: Percentage of coal that floods annually. Default is '50'.\n",
    "\n",
    "### Updates\n",
    "**v1.2**\n",
    "\n",
    "— Added histogram of cumulative emissions across a range of abandonment/flooding rates.\n",
    "\n",
    "— Visualizations of low/mid/high estimates from an imported .csv. \n",
    "\n",
    "**v1.1**\n",
    "\n",
    "— Cumulative emission graph\n",
    "\n",
    "— DataFrame with annual emissions in mmcf and MtCO2e, as well as cumulative MtCO2e\n",
    "\n",
    "— Fixed recursion bug\n",
    "\n",
    "### Next steps\n",
    "\n",
    "— Quantify/include uncertainty\n",
    "\n",
    "— Obtain more precise range of values for abandonment rate\n",
    "\n",
    "— Add basin-specific granularity wherever possible"
   ]
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I want to consider coal mine closures in the next __ years.\n",
      "    (Input a number, or hit return for default.) 1\n",
      "Which coal production forecast?\n",
      "    Input 'info' to see options, or hit return for default. \n",
      "Default = EIA.\n",
      "What abandonment rate?\n",
      "    (Input a percentage, or hit return for the default value of 5.) \n",
      "Default = 5%.\n",
      "Which set of emission factors?\n",
      "    Input 'info' to see options, or hit return for default. \n",
      "Default = IPCC.\n",
      "What abandoned mine flood rate would you like to use? \n",
      "    (Enter as a percentage, or hit return for the default value of 50.)\n",
      "Default = 50%.\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AMM emissions from mines abandoned in the next year: \n",
      " Year    mmcf/year  MtCO2e/year  MtCO2e (cumulative)\n",
      " 2020  4926.446583     1.992079             1.992079\n",
      " 2021  3203.644919     1.295439             3.287518\n",
      " 2022  2301.824294     0.930775             4.218293\n",
      " 2023  1799.406939     0.727616             4.945909\n",
      " 2024  1503.221335     0.607849             5.553758\n",
      " 2025  1317.435359     0.532724             6.086481\n",
      " 2026  1192.820901     0.482334             6.568815\n",
      " 2027  1103.469690     0.446204             7.015019\n",
      " 2028  1035.422229     0.418688             7.433707\n",
      " 2029   980.950907     0.396661             7.830368\n",
      " 2030   935.634977     0.378337             8.208705\n",
      " 2031   896.843334     0.362651             8.571357\n",
      " 2032   862.936921     0.348941             8.920298\n",
      " 2033   832.843096     0.336772             9.257070\n",
      " 2034   805.824296     0.325846             9.582916\n",
      " 2035   781.349454     0.315950             9.898866\n",
      " 2036   759.020497     0.306921            10.205786\n",
      " 2037   738.528947     0.298635            10.504421\n",
      " 2038   719.629284     0.290992            10.795413\n",
      " 2039   702.121950     0.283913            11.079326\n",
      " 2040   685.842028     0.277330            11.356656\n",
      " 2041   670.651409     0.271187            11.627844\n",
      " 2042   656.433185     0.265438            11.893282\n",
      " 2043   643.087491     0.260042            12.153323\n",
      " 2044   630.528366     0.254963            12.408286\n",
      " 2045   618.681316     0.250173            12.658459\n",
      " 2046   607.481396     0.245644            12.904103\n",
      " 2047   596.871681     0.241354            13.145456\n",
      " 2048   586.802030     0.237282            13.382738\n",
      " 2049   577.228076     0.233410            13.616148\n",
      " 2050   568.110397     0.229723            13.845872\n"
     ]
    }
   ],
   "source": [
    "# This cell runs an interactive calculator, which prompts the user to input parameters and returns predicted\n",
    "# AMM estimations.\n",
    "\n",
    "## Import standard libraries.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## Some initial parameters.\n",
    "years = [2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, \n",
    "         2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050]\n",
    "eia_estimate = [442.861272, 428.98104, 435.525624, 415.021608, 411.54426, 394.532784, 409.4955, 408.471768,\n",
    "            405.04014, 401.961816, 401.42394, 399.451068, 398.7099, 400.991436, 398.13984, 394.628148,\n",
    "            392.405976, 391.904352, 388.400364, 386.64108, 385.63866, 384.313716, 384.122988,\n",
    "            383.962608, 383.930532, 382.910616, 385.213068, 384.607296, 384.273828, 383.38416, 384.307416]\n",
    "b = 2.017\n",
    "Di_dry = 0.302\n",
    "Di_flooded = 0.672\n",
    "underground = 0.35\n",
    "\n",
    "## Define inputs as functions.\n",
    "def ask1():\n",
    "    production_years = 0\n",
    "    q1 = input(\"\"\"I want to consider coal mine closures in the next __ years.\n",
    "    (Input a number, or hit return for default.) \"\"\")\n",
    "    if q1.isdigit():\n",
    "        production_years = int(q1)\n",
    "        return production_years\n",
    "    elif q1.lower() == \"\":\n",
    "        production_years = 3\n",
    "        print(\"Default = 3 years.\")\n",
    "        return production_years\n",
    "    else:\n",
    "        print(\"Not a valid input.\")\n",
    "        return ask1()\n",
    "\n",
    "def ask2():\n",
    "    coal_production = []\n",
    "    q2 = input(\"\"\"Which coal production forecast?\n",
    "    Input 'info' to see options, or hit return for default. \"\"\")\n",
    "    if q2.lower() == \"info\":\n",
    "        print(\"\"\"'EIA': Energy Information Association's Annual Energy Outlook 2020 Coal Production Forecasts.\"\"\")\n",
    "        return ask2()\n",
    "    elif q2.lower() == \"eia\":\n",
    "        coal_production = eia_estimate\n",
    "        return coal_production\n",
    "    elif q2 == \"\":\n",
    "        coal_production = eia_estimate\n",
    "        print(\"Default = EIA.\")\n",
    "        return coal_production\n",
    "    else:\n",
    "        print(\"Not a valid input.\")\n",
    "        return ask2()\n",
    "\n",
    "def ask3():\n",
    "    abandonment_rate = 0.0\n",
    "    q3 = input(\"\"\"What abandonment rate?\n",
    "    (Input a percentage, or hit return for the default value of 5.) \"\"\")\n",
    "    if q3 == \"\":\n",
    "        abandonment_rate = .05\n",
    "        print(\"Default = 5%.\")\n",
    "        return abandonment_rate\n",
    "    else:\n",
    "        try:\n",
    "            if float(q3) >= 0 and float(q3) <= 100:\n",
    "                    abandonment_rate = float(q3)/100\n",
    "                    return abandonment_rate\n",
    "            else:\n",
    "                print(\"Not a valid input.\")\n",
    "                return ask3()\n",
    "        except ValueError:\n",
    "            print(\"Not a valid input.\")\n",
    "            return ask3()\n",
    "    \n",
    "def ask4():\n",
    "    ef_leq200 = 0\n",
    "    ef_200to400 = 0\n",
    "    ef_geq400 = 0\n",
    "    q4 = input(\"\"\"Which set of emission factors?\n",
    "    Input 'info' to see options, or hit return for default. \"\"\")\n",
    "    if q4.lower() == \"info\":\n",
    "        print(\"\"\"\n",
    "        'IPCC': IPCC's Tier 1 GHG inventory emission factors (2006).\n",
    "        'M2CM': standard Languir isotherms, adjusted using a 1.7 emission factor coefficient\n",
    "        (Kholod et al., 2020).\n",
    "        'Manual': manually input emission factors for <200m, 200-400m, and >400m.\"\"\")\n",
    "        return ask4()\n",
    "    elif q4.lower() == \"ipcc\":\n",
    "        ef_leq200 = 10\n",
    "        ef_200to400 = 18\n",
    "        ef_geq400 = 25\n",
    "        return (ef_leq200, ef_200to400, ef_geq400)\n",
    "    elif q4 == \"\":\n",
    "        print(\"Default = IPCC.\")\n",
    "        ef_leq200 = 10\n",
    "        ef_200to400 = 18\n",
    "        ef_geq400 = 25\n",
    "        return (ef_leq200, ef_200to400, ef_geq400)\n",
    "    elif q4.lower() == \"manual\":\n",
    "        try:\n",
    "            ef_leq200 = float(input(\"<200m emission factor: \"))\n",
    "            ef_200to400 = float(input(\"200m to 400m emission factor: \"))\n",
    "            ef_geq400 = float(input(\">400m emission factor: \"))\n",
    "            return (ef_leq200, ef_200to400, ef_geq400)\n",
    "        except ValueError:\n",
    "            print(\"Not a valid input.\")\n",
    "            return ask4()\n",
    "    elif q4.lower() == \"m2cm\":\n",
    "        ef_leq200 = 14.9\n",
    "        ef_200to400 = 18.9\n",
    "        ef_geq400 = 21.6\n",
    "        return (ef_leq200, ef_200to400, ef_geq400)\n",
    "    else:\n",
    "        print(\"Not a valid input.\")\n",
    "        return ask4()\n",
    "    \n",
    "def ask5():\n",
    "    flood_rate = 0.0\n",
    "    q5 = input(\"\"\"What abandoned mine flood rate would you like to use? \n",
    "    (Enter as a percentage, or hit return for the default value of 50.)\"\"\")\n",
    "    if q5 == \"\":\n",
    "        flood_rate = .5\n",
    "        print(\"Default = 50%.\")\n",
    "        return flood_rate\n",
    "    else:\n",
    "        try:\n",
    "            if float(q5) >= 0 and float(q5) <= 100:\n",
    "                flood_rate = float(q5)/100\n",
    "                return flood_rate\n",
    "            else:\n",
    "                print(\"Not a valid input.\")\n",
    "                return ask5()\n",
    "        except ValueError:\n",
    "            print(\"Not a valid input.\")\n",
    "            return ask5()\n",
    "            \n",
    "def cmm_to_amm():\n",
    "    try:\n",
    "        py = int(ask1())\n",
    "        cp = ask2()[0:py]\n",
    "        ar = float(ask3())\n",
    "        efs = ask4()\n",
    "        new_amm = [efs[1] * ar * x * 10 ** 6 for x in cp]\n",
    "        fr = ask5()\n",
    "        return (new_amm, py, fr)\n",
    "    except TypeError:\n",
    "        print(\"Whoops! Something went wrong. Let's start over.\")\n",
    "        return cmm_to_amm()\n",
    "        \n",
    "## Run calculator. Output is a MtCO2e vs year graph.\n",
    "\n",
    "inputs = cmm_to_amm()\n",
    "new_amm_cm = inputs[0]\n",
    "closing_years = inputs[1]\n",
    "fr = inputs[2]\n",
    "i = 0\n",
    "total_amm = []\n",
    "while i < len(years):\n",
    "    total_amm.append(0)\n",
    "    i += 1\n",
    "i = 0\n",
    "while i < len(new_amm_cm):\n",
    "    t = 0\n",
    "    while t < len(years):\n",
    "        if t < i:\n",
    "            total_amm[t] += 0\n",
    "        else:\n",
    "            total_amm[t] += (1-fr) * underground * new_amm_cm[i]*((1+b*Di_dry*(t-i)) ** (-1/b))\n",
    "            total_amm[t] += fr * underground * new_amm_cm[i]*np.exp(-t * Di_flooded)\n",
    "        t+=1\n",
    "    i+=1\n",
    "total_amm_tco2e = [0.01428 * x for x in total_amm]\n",
    "total_amm_Mtco2e = [x/(10**6) for x in total_amm_tco2e]\n",
    "\n",
    "plt.scatter(x=years, y=total_amm_Mtco2e)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Million tCO2e\")\n",
    "if int(closing_years) == 1:\n",
    "    plt.title(\"AMM emitted annually from mines abandoned in the next year\")\n",
    "elif int(closing_years) >= len(years):\n",
    "    plt.title(\"AMM emitted annually from future mine closures, 2020-2050\")\n",
    "else:\n",
    "    plt.title(\"AMM emitted annually from mines abandoned in the next %s years\" % str(closing_years))\n",
    "plt.show()\n",
    "\n",
    "## Additional cumulative graph.\n",
    "\n",
    "cumulative_amm_Mtco2e = []\n",
    "i = 0\n",
    "while i < len(total_amm_Mtco2e):\n",
    "    cumulative_amm_Mtco2e.append(total_amm_Mtco2e[i])\n",
    "    i+=1 \n",
    "i = 0\n",
    "while i < len(cumulative_amm_Mtco2e):\n",
    "    t=0\n",
    "    while t < i:\n",
    "        cumulative_amm_Mtco2e[i]+=total_amm_Mtco2e[t]\n",
    "        t+=1\n",
    "    i+=1\n",
    "    \n",
    "plt.scatter(x=years, y=cumulative_amm_Mtco2e)\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Million tCO2e\")\n",
    "if int(closing_years) == 1:\n",
    "    plt.title(\"Cumulative AMM emitted from mines abandoned in the next year\")\n",
    "elif int(closing_years) >= len(years):\n",
    "    plt.title(\"Cumulative AMM emitted from future mine closures, 2020-2050\")\n",
    "else:\n",
    "    plt.title(\"Cumulative AMM emitted from mines abandoned in the next %s years\" % str(closing_years))\n",
    "plt.show()\n",
    "\n",
    "total_amm_mmcf = [(x * 35.3147) / (10 ** 6) for x in total_amm]\n",
    "total_amm_mmcf\n",
    "df = pd.DataFrame()\n",
    "df['Year']=pd.Series(years)\n",
    "df['mmcf/year']=pd.Series(total_amm_mmcf)\n",
    "df['MtCO2e/year']=pd.Series(total_amm_Mtco2e)\n",
    "df['MtCO2e (cumulative)']=pd.Series(cumulative_amm_Mtco2e)\n",
    "if int(closing_years) == 1:\n",
    "    print(\"AMM emissions from mines abandoned in the next year: \")\n",
    "elif int(closing_years) >= len(years):\n",
    "    print(\"AMM emissions from future mine closures, 2020-2050: \")\n",
    "else:\n",
    "    print(\"AMM emissions from mines abandoned in the next %s years: \" % str(closing_years))\n",
    "print(df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building off the calculator above, this cell returns a DataFrame of the most likely cumulative AMM\n",
    "# emission totals (adjustable parameters are abandonment rate and flooding rate).\n",
    "\n",
    "## Import standard libraries.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## Some initial parameters.\n",
    "years = [2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, \n",
    "         2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050]\n",
    "eia_estimate = [442.861272, 428.98104, 435.525624, 415.021608, 411.54426, 394.532784, 409.4955, 408.471768,\n",
    "            405.04014, 401.961816, 401.42394, 399.451068, 398.7099, 400.991436, 398.13984, 394.628148,\n",
    "            392.405976, 391.904352, 388.400364, 386.64108, 385.63866, 384.313716, 384.122988,\n",
    "            383.962608, 383.930532, 382.910616, 385.213068, 384.607296, 384.273828, 383.38416, 384.307416]\n",
    "b = 2.017\n",
    "Di_dry = 0.302\n",
    "Di_flooded = 0.672\n",
    "underground = 0.35\n",
    "\n",
    "def cmm_to_amm_hist(ar_hist, fr_hist):\n",
    "    py = len(eia_estimate)\n",
    "    cp = eia_estimate\n",
    "    ar = ar_hist\n",
    "    efs = 18\n",
    "    new_amm = [efs * ar * x * 10 ** 6 for x in cp]\n",
    "    fr = fr_hist\n",
    "    i = 0\n",
    "    total_amm = []\n",
    "    while i < len(new_amm):\n",
    "        total_amm.append(0)\n",
    "        i += 1\n",
    "    i = 0\n",
    "    while i < len(new_amm):\n",
    "        t = 0\n",
    "        while t < len(new_amm):\n",
    "            if t < i:\n",
    "                total_amm[t] += 0\n",
    "            else:\n",
    "                total_amm[t] += (1-fr) * underground * new_amm[i]*((1+b*Di_dry*(t-i)) ** (-1/b))\n",
    "                total_amm[t] += fr * underground * new_amm[i]*np.exp(-t * Di_flooded)\n",
    "            t+=1\n",
    "        i+=1\n",
    "    total_amm_tco2e = [0.01428 * x for x in total_amm]\n",
    "    total_amm_Mtco2e = [x/(10**6) for x in total_amm_tco2e]\n",
    "    cumulative_amm_Mtco2e = []\n",
    "    i = 0\n",
    "    while i < len(total_amm_Mtco2e):\n",
    "        cumulative_amm_Mtco2e.append(total_amm_Mtco2e[i])\n",
    "        i+=1 \n",
    "    i = 0\n",
    "    while i < len(cumulative_amm_Mtco2e):\n",
    "        t=0\n",
    "        while t < i:\n",
    "            cumulative_amm_Mtco2e[i]+=total_amm_Mtco2e[t]\n",
    "            t+=1\n",
    "        i+=1\n",
    "    return ([round(ar*100, 2), int(round(fr*100)), cumulative_amm_Mtco2e[30]])\n",
    "\n",
    "ar_fr_cumulative = []\n",
    "ar_hist = 4\n",
    "while ar_hist <= 6:\n",
    "    fr_hist = 40\n",
    "    while fr_hist <= 60:\n",
    "        new_amm_hist=cmm_to_amm_hist(ar_hist/100, fr_hist/100)\n",
    "        ar_fr_cumulative.append(new_amm_hist)\n",
    "        fr_hist += 1\n",
    "    ar_hist += 0.05\n",
    "    \n",
    "hist_df = pd.DataFrame(ar_fr_cumulative, columns = ['Abandonment Rate', 'Flooding Rate', 'Cumulative MtCO2e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finally, this cell builds visualizations of the DataFrame created above, as well as a Low/Mid/High \n",
    "# estimate .csv.\n",
    "\n",
    "## Low estimate: 4% abandonment, 60% flooding.\n",
    "## Mid estimate: 5% abandonment, 50% flooding.\n",
    "## High estimate: 6% abandonment, 40% flooding.\n",
    "\n",
    "## Import standard libraries.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## Pull DataFrame and .csv. Name columns.\n",
    "hist_data = hist_df\n",
    "lowmidhigh_data = pd.read_csv('amm_lowmidhigh.csv')\n",
    "\n",
    "year = lowmidhigh_data['Year']\n",
    "mmcf_low = lowmidhigh_data['mmcf/year (low)']\n",
    "mmcf_mid = lowmidhigh_data['mmcf/year (mid)']\n",
    "mmcf_high = lowmidhigh_data['mmcf/year (high)']\n",
    "Mtco2e_low = lowmidhigh_data['MtCO2e/year (low)']\n",
    "Mtco2e_mid = lowmidhigh_data['MtCO2e/year (mid)']\n",
    "Mtco2e_high = lowmidhigh_data['MtCO2e/year (high)']\n",
    "cMtco2e_low = lowmidhigh_data['Cumulative MtCO2e (low)']\n",
    "cMtco2e_mid = lowmidhigh_data['Cumulative MtCO2e (mid)'],\n",
    "cMtco2e_high = lowmidhigh_data['Cumulative MtCO2e (high)']\n",
    "cMtco2e_hist = hist_data['Cumulative MtCO2e']\n",
    "\n",
    "## Create graphs and histogram.\n",
    "\n",
    "plt.scatter(x=year, y=mmcf_low, color='blue')\n",
    "plt.plot(year, mmcf_low, color='blue', label='Low abandonment, high flooding')\n",
    "plt.scatter(x=year, y=mmcf_mid, color='purple')\n",
    "plt.plot(year, mmcf_mid, color='purple', label='Medium abandonment, medium flooding')\n",
    "plt.scatter(x=year, y=mmcf_high, color='red')\n",
    "plt.plot(year, mmcf_high, color='red', label='High abandonment, low flooding')\n",
    "plt.legend()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('mmcf/year')\n",
    "plt.title('Predicted AMM emissions, 2020-2050 (mmcf/year)')\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 6)\n",
    "plt.savefig('mmcf_lowmidhigh.pdf')\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(x=year, y=Mtco2e_low, color='blue')\n",
    "plt.plot(year, Mtco2e_low, color='blue', label='Low abandonment, high flooding')\n",
    "plt.scatter(x=year, y=Mtco2e_mid, color='purple')\n",
    "plt.plot(year, Mtco2e_mid, color='purple', label='Medium abandonment, medium flooding')\n",
    "plt.scatter(x=year, y=Mtco2e_high, color='red')\n",
    "plt.plot(year, Mtco2e_high, color='red', label='High abandonment, low flooding')\n",
    "plt.legend()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('MtCO2e/year')\n",
    "plt.title('Predicted AMM emissions, 2020-2050 (MtCO2e/year)')\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 6)\n",
    "plt.savefig('Mtco2e_lowmidhigh.pdf')\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(x=year, y=cMtco2e_low, color='blue')\n",
    "plt.plot(year, cMtco2e_low, color='blue', label='Low abandonment, high flooding')\n",
    "plt.scatter(x=year, y=cMtco2e_mid, color='purple')\n",
    "plt.plot(year, lowmidhigh_data['Cumulative MtCO2e (mid)'], color='purple', \n",
    "         label='Medium abandonment, medium flooding')\n",
    "plt.scatter(x=year, y=cMtco2e_high, color='red')\n",
    "plt.plot(year, cMtco2e_high, color='red', label='High abandonment, low flooding')\n",
    "plt.legend()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('MtCO2e')\n",
    "plt.title('Predicted AMM emissions, 2020-2050 (Cumulative MtCO2e)')\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 6)\n",
    "plt.savefig('cMtco2e_lowmidhigh.pdf')\n",
    "plt.show()\n",
    "\n",
    "bins_no = 30\n",
    "plt.hist(cMtco2e_hist, bins = bins_no, color='blue', label = 'Number of bins = %s' % (bins_no))\n",
    "plt.hist(cMtco2e_hist, bins = bins_no, color='blue', label = 'Average = %s MtCO2e' \n",
    "         % round(np.mean(cMtco2e_hist)))\n",
    "plt.xlabel('Cumulative MtCO2e')\n",
    "plt.ylabel('Counts')\n",
    "plt.legend()\n",
    "plt.title('Histogram of Cumulative MtCO2e for %s most likely scenarios' % len(cMtco2e_hist))\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 6)\n",
    "plt.savefig('cMtco2e_hist.pdf')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}