{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Harmonization with Carbon Budget Conservation\n",
    "\n",
    "We seek an emissions trajectory consistent with a provided historical emissions timeseries that closely matches a modeled result and maintains the overall carbon budget consistent with that model result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, model and history data are read in. The model is then harmonized. Finally, output is analyzed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pyomo.environ as pyomo\n",
    "\n",
    "import aneris\n",
    "from aneris.tutorial import load_data\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `driver` is used to execute the harmonization. It will handle the data formatting needed to execute the harmonizaiton operation and stores the harmonized results until they are needed.\n",
    "\n",
    "Some logging output is provided. It can be suppressed with \n",
    "\n",
    "```\n",
    "aneris.logger().setLevel('WARN')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model, hist, driver = load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the default function which chooses, which method to use does not apply the `budget` method, we specify overrides to use budget for all the variables in the model data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Model Scenario   Region                            Variable      Unit  \\\n0  model     sspn  regionc  prefix|Emissions|BC|sector1|suffix  Mt BC/yr   \n1  model     sspn  regionc  prefix|Emissions|BC|sector2|suffix  Mt BC/yr   \n2  model     sspn  regionc          prefix|Emissions|BC|suffix  Mt BC/yr   \n3  model     sspn    World  prefix|Emissions|BC|sector1|suffix  Mt BC/yr   \n4  model     sspn    World  prefix|Emissions|BC|sector2|suffix  Mt BC/yr   \n\n   Method  \n0  budget  \n1  budget  \n2  budget  \n3  budget  \n4  budget  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Model</th>\n      <th>Scenario</th>\n      <th>Region</th>\n      <th>Variable</th>\n      <th>Unit</th>\n      <th>Method</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>model</td>\n      <td>sspn</td>\n      <td>regionc</td>\n      <td>prefix|Emissions|BC|sector1|suffix</td>\n      <td>Mt BC/yr</td>\n      <td>budget</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>model</td>\n      <td>sspn</td>\n      <td>regionc</td>\n      <td>prefix|Emissions|BC|sector2|suffix</td>\n      <td>Mt BC/yr</td>\n      <td>budget</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>model</td>\n      <td>sspn</td>\n      <td>regionc</td>\n      <td>prefix|Emissions|BC|suffix</td>\n      <td>Mt BC/yr</td>\n      <td>budget</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>model</td>\n      <td>sspn</td>\n      <td>World</td>\n      <td>prefix|Emissions|BC|sector1|suffix</td>\n      <td>Mt BC/yr</td>\n      <td>budget</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>model</td>\n      <td>sspn</td>\n      <td>World</td>\n      <td>prefix|Emissions|BC|sector2|suffix</td>\n      <td>Mt BC/yr</td>\n      <td>budget</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "driver.overrides = model[[\"Model\", \"Scenario\", \"Region\", \"Variable\", \"Unit\"]].assign(Method=\"budget\")\n",
    "driver.overrides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "INFO:root:Downselecting prefix|suffix variables\nINFO:root:Translating to standard format\nINFO:root:Aggregating historical values to native regions\nINFO:root:Harmonizing (with example methods):\nWARNING:root:Removing override methods not in processed model output:\nregion  gas  sector                 units\nWorld   BC   prefix|sector1|suffix  kt       budget\n             prefix|sector2|suffix  kt       budget\n             prefix|suffix          kt       budget\nName: method, dtype: object\nINFO:root:                                         method            default override\nregion  gas sector                units                                    \nregionc BC  prefix|sector1|suffix kt     budget  reduce_ratio_2050   budget\n            prefix|sector2|suffix kt     budget  reduce_ratio_2050   budget\n            prefix|suffix         kt     budget  reduce_ratio_2050   budget\nINFO:root:and override methods:\nINFO:root:region   gas  sector                 units\nWorld    BC   prefix|sector1|suffix  kt       budget\n              prefix|sector2|suffix  kt       budget\n              prefix|suffix          kt       budget\nregionc  BC   prefix|sector1|suffix  kt       budget\n              prefix|sector2|suffix  kt       budget\nName: method, dtype: object\nWARNING:root:Removing override methods not in processed model output:\nregion  gas  sector                 units\nWorld   BC   prefix|sector1|suffix  kt       budget\n             prefix|sector2|suffix  kt       budget\n             prefix|suffix          kt       budget\nName: method, dtype: object\nINFO:root:Harmonizing with budget\nWARNING:root:Removing sector aggregates. Recalculating with harmonized totals.\nINFO:root:Translating to IAMC template\n"
    }
   ],
   "source": [
    "for scenario in driver.scenarios():\n",
    "    driver.harmonize(scenario)\n",
    "harmonized, metadata, diagnostics = driver.harmonized_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data of interest is combined in order to easily view it. We will specifically investigate output for the `World` in this example. A few operations are performed in order to get the data into a plotting-friendly format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([hist, model, harmonized], sort=True)\n",
    "df = data[data.Region == 'World']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.melt(df, id_vars=aneris.iamc_idx, value_vars=aneris.numcols(df), \n",
    "             var_name='Year', value_name='Emissions')\n",
    "df['Label'] = df['Model'] + ' ' + df['Variable']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     Model Scenario Region                            Variable  Year  \\\n0  History     scen  World  prefix|Emissions|BC|sector1|suffix  2000   \n1  History     scen  World  prefix|Emissions|BC|sector2|suffix  2000   \n2  History     scen  World          prefix|Emissions|BC|suffix  2000   \n3    model     sspn  World  prefix|Emissions|BC|sector1|suffix  2000   \n4    model     sspn  World  prefix|Emissions|BC|sector2|suffix  2000   \n\n   Emissions                                       Label  \n0        4.0  History prefix|Emissions|BC|sector1|suffix  \n1        6.0  History prefix|Emissions|BC|sector2|suffix  \n2       10.0          History prefix|Emissions|BC|suffix  \n3        NaN    model prefix|Emissions|BC|sector1|suffix  \n4        NaN    model prefix|Emissions|BC|sector2|suffix  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Model</th>\n      <th>Scenario</th>\n      <th>Region</th>\n      <th>Variable</th>\n      <th>Year</th>\n      <th>Emissions</th>\n      <th>Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>History</td>\n      <td>scen</td>\n      <td>World</td>\n      <td>prefix|Emissions|BC|sector1|suffix</td>\n      <td>2000</td>\n      <td>4.0</td>\n      <td>History prefix|Emissions|BC|sector1|suffix</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>History</td>\n      <td>scen</td>\n      <td>World</td>\n      <td>prefix|Emissions|BC|sector2|suffix</td>\n      <td>2000</td>\n      <td>6.0</td>\n      <td>History prefix|Emissions|BC|sector2|suffix</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>History</td>\n      <td>scen</td>\n      <td>World</td>\n      <td>prefix|Emissions|BC|suffix</td>\n      <td>2000</td>\n      <td>10.0</td>\n      <td>History prefix|Emissions|BC|suffix</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>model</td>\n      <td>sspn</td>\n      <td>World</td>\n      <td>prefix|Emissions|BC|sector1|suffix</td>\n      <td>2000</td>\n      <td>NaN</td>\n      <td>model prefix|Emissions|BC|sector1|suffix</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>model</td>\n      <td>sspn</td>\n      <td>World</td>\n      <td>prefix|Emissions|BC|sector2|suffix</td>\n      <td>2000</td>\n      <td>NaN</td>\n      <td>model prefix|Emissions|BC|sector2|suffix</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
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     "metadata": {},
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     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sns.lineplot(x='Year', y='Emissions', hue='Label', data=df.assign(Year=df.Year.astype(int)))\n",
    "plt.legend(bbox_to_anchor=(1.05, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculation details\n",
    "\n",
    "\n",
    "In the following we explain the method in more detail by harmonizing manually only the total `prefix|Emissions|BC|suffix` in the `World` region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms = df.loc[(df.Variable == 'prefix|Emissions|BC|suffix') & (df.Model == 'model')].set_index('Year')['Emissions'].dropna()\n",
    "hs = df.loc[(df.Variable == 'prefix|Emissions|BC|suffix') & (df.Model == 'History')].set_index('Year')['Emissions'].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We calculate the carbon budget from the model and historical data by estimating the integral between discrete data points using the Riemann trapezoidal sum. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "2.99"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "def calc_budget(data):\n",
    "    # trapezoid rule reimann sum\n",
    "    dx = data.index.to_series().astype(int).diff()\n",
    "    y1 = data\n",
    "    dy = data.diff()\n",
    "    budget = (dx * (y1 - .5 * dy)).iloc[1:].sum()\n",
    "    return budget\n",
    "\n",
    "calc_budget(ms) / 1e3 # in Gt BC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Harmonization via Optimization\n",
    "\n",
    "We create a model that matches as close as possible the dynamic rates of change of the modeled emission trajectory but adhering to the original carbon budget of the model.\n",
    "\n",
    "This takes in as data:\n",
    "- the year values of the harmonized data\n",
    "- the original model values (unharmonized)\n",
    "- the historical value to be harmonized to (assumed to occur in the first year)\n",
    "- the total carbon budget to match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = ms.index[ms.index.astype(int) >= int(hs.index[-1])]\n",
    "model_vals = ms.loc[years]\n",
    "hist_val = hs.iloc[-1]\n",
    "budget = calc_budget(model_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model itself minimizes the $L_2$ norm of the rates of change:\n",
    "\n",
    "$$\n",
    "\\min_{x_i} \\sum_{i \\in |I - 1|} \\big( \\frac{m_{i+1} - m_i}{y_{i + 1} - y_{i}} - \\frac{x_{i+1} - x_i}{y_{i + 1} - y_{i}} \\big)^2\n",
    "$$\n",
    "\n",
    "Given model results, $m_i$, years, $y_i$, and harmonized results, $x_i$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pyomo.ConcreteModel()\n",
    "model.x = pyomo.Var(list(years), initialize=0, domain=pyomo.Reals)\n",
    "x = pd.Series([model.x[y] for y in years], years)\n",
    "\n",
    "delta_years = years.to_series().astype(int).diff()\n",
    "delta_x = x.diff()\n",
    "delta_m = model_vals.diff()\n",
    "\n",
    "def l2_norm():\n",
    "    return pyomo.quicksum(((delta_m / delta_years - delta_x / delta_years) ** 2).dropna())\n",
    "\n",
    "model.obj = pyomo.Objective(expr=l2_norm(), sense=pyomo.minimize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The historical value must match\n",
    "\n",
    "$$\n",
    "x_0 = h\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.hist_val = pyomo.Constraint(expr=model.x[years[0]] == hist_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the carbon budget must be maintained, using a trapezoidal rule Reimann sum,\n",
    "\n",
    "$$\n",
    "\\sum_{i \\in |I - 1|} (y_{i + 1} - y_{i}) \\big( x_i + 0.5 (x_{i+1} - x_i) \\big) = B\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.budget = pyomo.Constraint(expr=calc_budget(x) == budget)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is solved with IPOPT and compared with the original trajectory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "{'Problem': [{'Lower bound': -inf, 'Upper bound': inf, 'Number of objectives': 1, 'Number of constraints': 2, 'Number of variables': 11, 'Sense': 'unknown'}], 'Solver': [{'Status': 'ok', 'Message': 'Ipopt 3.13.2\\\\x3a Optimal Solution Found', 'Termination condition': 'optimal', 'Id': 0, 'Error rc': 0, 'Time': 0.0256350040435791}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "solver = pyomo.SolverFactory('ipopt')\n",
    "solver.solve(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fb3050a57c0>"
     },
     "metadata": {},
     "execution_count": 16
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data = pd.concat(\n",
    "    dict(\n",
    "        model=ms,\n",
    "        history=hs,\n",
    "        model_harmonized=pd.Series([pyomo.value(model.x[y]) for y in years], years)\n",
    "    ),\n",
    "    axis=1,\n",
    "    sort=True\n",
    ")\n",
    "data.index = data.index.astype(int)\n",
    "data.plot.line()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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