Notebook_11b_visualise_differences_in_emissions.ipynb

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    "## Visualisation of differences between estimated GHG emissions of hydroelectric reservoirs according to emission factors of Soued et al. and via explicit calculation with G-res\n",
    "\n",
    "#### Author: Tomasz Janus\n",
    "#### Date: 06/11/24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9eaf8dc8-fe87-4bd4-8a98-8a3b8dd71815",
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   "source": [
    "# Import required libraries\n",
    "from typing import List, Set, Any, Optional\n",
    "import pathlib\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import HTML\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager\n",
    "from matplotlib.font_manager import FontProperties\n",
    "from pypalettes import load_cmap\n",
    "# Set the required constants and paths\n",
    "font_path = pathlib.Path('custom_fonts/Urbanist-main/fonts/ttf/Urbanist-Thin.ttf')\n",
    "bold_font_path = pathlib.Path('custom_fonts/Urbanist-main/fonts/ttf/Urbanist-Medium.ttf')\n",
    "from matplotlib.colors import Normalize"
   ]
  },
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   "id": "e019bc9e-5d2b-48b0-9839-9f89590ce159",
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    "fpath = os.path.join(matplotlib.get_data_path(), font_path.absolute())\n",
    "fpath_bold = os.path.join(matplotlib.get_data_path(), bold_font_path.absolute())\n",
    "prop = FontProperties(fname=fpath)\n",
    "prop_bold = FontProperties(fname=fpath_bold)\n",
    "plot_fonts = False\n",
    "prop_legend = FontProperties(fname=fpath, size=13)\n",
    "prop_title = FontProperties(fname=fpath_bold, size= 16)"
   ]
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   "cell_type": "code",
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   "id": "5f1d1c53-8171-499b-a688-341440571170",
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    "def plot_installed_fonts() -> HTML:\n",
    "    def make_html(fontname):\n",
    "        return \"<p>{font}: <span style='font-family:{font}; font-size: 24px;'>{font}</p>\".format(font=fontname)\n",
    "    code = \"\\n\".join([make_html(font) for font in sorted(set([f.name for f in font_manager.fontManager.ttflist]))])\n",
    "    return HTML(\"<div style='column-count: 2;'>{}</div>\".format(code))\n",
    "if plot_fonts:\n",
    "    installed_plots = plot_installed_fonts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55b5a15b-a3ff-4afc-9a46-8ae2596c73ea",
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   "source": [
    "dam_data_filename = pathlib.Path(\"outputs/moo/all_hp.csv\")\n",
    "dam_data: pd.DataFrame = (\n",
    "    pd.read_csv(dam_data_filename).\n",
    "    drop(columns=['Unnamed: 0']).\n",
    "    assign(tot_em = lambda x: x['tot_em'] / 1_000).\n",
    "    assign(tot_em_soued = lambda x: x['tot_em_soued'] / 1_000)\n",
    ")\n",
    "dam_data['em_diff'] = dam_data.tot_em_soued - dam_data.tot_em\n",
    "# Restrict our analysis only to storage dams, i.e. exclude RoR for which emissions are equal in\n",
    "# Soued's and ReEmission's estimates.\n",
    "dam_data_sto = dam_data.query(\"hp_type_reem == 'sto'\") \t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a62ae32",
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   "outputs": [],
   "source": [
    "# Check data classification - hp_type_reem != sto should coincide with tot_em == tot_em_soued\n",
    "ror_units = dam_data.query(\"hp_type_reem != 'sto'\") \n",
    "assert (any(ror_units.tot_em - ror_units.tot_em_soued) is False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db6beab2-f58d-4029-baef-624180fe8833",
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    "font=font_path\n",
    "bold_font = bold_font_path\n",
    "def make_stem_plot(df: pd.DataFrame) -> None:\n",
    "    cmap = load_cmap('Coconut', cmap_type='continuous', reverse=True)\n",
    "    fig, ax = plt.subplots(figsize=(10,15), dpi=300)\n",
    "    df_sorted = df.sort_values(by=['tot_em'], ascending=True)\n",
    "    names = df_sorted['name']\n",
    "    em_diff = df_sorted['em_diff']\n",
    "    hp_factor = 0.7\n",
    "    hp = df_sorted['HP_mean'] * hp_factor\n",
    "    tot_em = df_sorted['tot_em']\n",
    "    min_col = tot_em.min()\n",
    "    max_col = tot_em.max()\n",
    "    norm = Normalize(vmin=min_col, vmax=max_col)\n",
    "    scatter = ax.scatter(\n",
    "        marker='o', y=names, x = em_diff, c = tot_em, cmap=cmap, \n",
    "        edgecolor='k', s = hp, linewidth=0.4, alpha=0.8)\n",
    "    cbar = plt.colorbar(\n",
    "        scatter, shrink=0.7, label=\"GHG emissions, ktCO$_{2e}$/year\", \n",
    "        pad=0.03, aspect=30)\n",
    "    cbar.outline.set_linewidth(0.5)\n",
    "    cbar.set_ticklabels(cbar.ax.get_yticklabels(), font=font)\n",
    "    cbar.set_label(\"GHG emissions, ktCO$_{2e}$/year\", fontsize=14, font=font)\n",
    "    # Add stems\n",
    "    for _, row in df_sorted.iterrows():\n",
    "        name = row['name']\n",
    "        em_diff = row['em_diff']\n",
    "        color = cmap(row['tot_em'])\n",
    "        normalized_col = norm(row['tot_em'])\n",
    "        stems = ax.plot(\n",
    "            [0, em_diff], [name, name], \n",
    "            c=cmap(normalized_col), alpha=0.8, linewidth=0.8)\n",
    "    \n",
    "    v_lines = [-200, 0, 200, 400, 600]\n",
    "    ax.vlines(\n",
    "        x=v_lines, ymin=0, ymax=len(df_sorted)-1, colors=[cmap(val) for val in v_lines], \n",
    "        linewidth=1.2, zorder=-1, alpha=0.5)\n",
    "    ax.spines[['left', 'right', 'top', 'bottom']].set_visible(False)\n",
    "    ax.tick_params(axis=u'both', which=u'both',length=0)\n",
    "    plt.setp(ax.get_xticklabels(), visible=False)\n",
    "    ax.yaxis.set_tick_params(labelsize=4)\n",
    "    for value in v_lines:\n",
    "       ax.text(\n",
    "           y=-1, x=value, s=f'{value}', color=cmap(value), \n",
    "           size=14, va='top', ha='center', font=font)\n",
    "\n",
    "    for hp_value in [1, 100, 500, 2000, 4000]:  # Adjust these values as necessary for your range\n",
    "        plt.scatter(\n",
    "            [], [], marker = \"o\", s=hp_factor * hp_value, color='grey', \n",
    "            alpha=0.45, label=f'{hp_value} MW')\n",
    "\n",
    "    leg = plt.legend(\n",
    "        scatterpoints=1, frameon=False, \n",
    "        labelspacing=1.5, title=\"Mean Power Output\", \n",
    "        fontsize=15, prop=prop_legend, loc=\"right\")\n",
    "    leg.set_title(\"Mean Power Output\", prop={'size': 14})\n",
    "    title = plt.title(\n",
    "        'Difference in Net Anthropogenic Emissions: Emission Factors vs. Explicit Calculation', \n",
    "        fontsize=15, \n",
    "        fontproperties=prop_title)\n",
    "    plt.xlabel(\n",
    "        'Calculated Emission Difference (ktCO$_{2e}$/year): Emission Factors - Explicit Calculation', \n",
    "        fontsize=15, font=font)\n",
    "\n",
    "    for tick in ax.get_xticklabels():\n",
    "        tick.set_fontproperties(prop)\n",
    "        \n",
    "    for tick in ax.get_yticklabels():\n",
    "        tick_label = tick.get_text()\n",
    "        status = df[df['name'] == tick_label]['status'].iloc[0]\n",
    "        tick.set_fontproperties(prop)\n",
    "        tick.set_visible(True)\n",
    "        if status == \"Future\":\n",
    "            tick.set_color('black')  # Set font weight to bold\n",
    "        else:\n",
    "            tick.set_color('tab:blue')  # Set font weight to bold\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "    plt.savefig(\n",
    "        \"figures/emission_comparison/hp_total_emission_comparison.pdf\", \n",
    "        format=\"pdf\", bbox_inches=\"tight\")\n",
    "    plt.savefig(\n",
    "        \"figures/emission_comparison/hp_total_emission_comparison.svg\", \n",
    "        format=\"svg\", bbox_inches=\"tight\")\n",
    "    plt.savefig(\n",
    "        \"figures/emission_comparison/hp_total_emission_comparison.png\", \n",
    "        format=\"png\", bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "make_stem_plot(dam_data_sto)"
   ]
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