Cost calculation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cae4bafa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scienceplots as sp\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "850e2838",
   "metadata": {},
   "outputs": [],
   "source": [
    "EM=pd.read_csv(\"Emission trajectories.csv\")#排放量文件\n",
    "PR=pd.read_excel(\"pyplot.xlsx\",sheet_name=\"Price\")#碳价文件\n",
    "CP2025=pd.read_csv(\"New Capacity2025-pork.csv\")#补贴增加的capacity文件\n",
    "CP2030=pd.read_csv(\"New Capacity2030-pork.csv\")\n",
    "CP2035=pd.read_csv(\"New Capacity2035-pork.csv\")\n",
    "CP2040=pd.read_csv(\"New Capacity2040-pork.csv\")\n",
    "CP2045=pd.read_csv(\"New Capacity2045-pork.csv\")\n",
    "CP2050=pd.read_csv(\"New Capacity2050-pork.csv\")\n",
    "\n",
    "SD1=pd.read_excel(\"cost_reduction_v4.xlsx\", sheet_name=\"wind and solar\")#补贴量文件\n",
    "SD2=pd.read_excel(\"cost_reduction_v4.xlsx\", sheet_name=\"other REs\")#补贴量文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e08716a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "Year=EM[\"Year\"]\n",
    "EmB=EM[\"Baseline Emissions\"]\n",
    "EmPO=EM[\"Pork Emissions\"]\n",
    "EmPaS=EM[\"Pork-and-stick emissions\"]\n",
    "Price=PR[\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cb84dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ED=[]#emission difference\n",
    "Spending1=[]\n",
    "for i in range(4,10):#2025~2050\n",
    "    EDi=EmB[i]-EmPO[i]\n",
    "    ED.append(EDi)\n",
    "    spending=(EDi*Price[i-4])*12/44  #1990$/tC\n",
    "    Spending1.append(spending)\n",
    "    #print(Year[i])\n",
    "#print(len(ED))\n",
    "#print(\"Unit: million 1990$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "969d8114",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Carbon pricing cost, million 1990$')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Year2=Year[4:10]\n",
    "plt.plot(Year2, Spending1)\n",
    "plt.title(\"Carbon pricing cost, million 1990$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a0ce643f",
   "metadata": {},
   "outputs": [],
   "source": [
    "NewCost2=SD2[\"New cost\"]\n",
    "OldCost2=SD2[\"Originalcost\"]\n",
    "CA2025=CP2025[\"Capacity added,kW\"]\n",
    "CA2030=CP2030[\"Capacity added,kW\"]\n",
    "CA2035=CP2035[\"Capacity added,kW\"]\n",
    "CA2040=CP2040[\"Capacity added,kW\"]\n",
    "CA2045=CP2045[\"Capacity added,kW\"]\n",
    "CA2050=CP2050[\"Capacity added,kW\"]\n",
    "CA=[CA2025,CA2030,CA2035,CA2040,CA2045,CA2050]\n",
    "Time=SD2[\"Year\"]\n",
    "#CA.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c44e4a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# #technology-去掉\"year=2025\"\n",
    "# Files=[CP2025, CP2030,CP2035,CP2040,CP2045,CP2050]\n",
    "# for file in Files:\n",
    "#     Tech=file[\"Technology\"]\n",
    "\n",
    "#     print(Tech[1:5])\n",
    "#     for i in range(len(Tech)):\n",
    "#         Tech[i] = Tech[i].replace(',year=2025','')\n",
    "#         Tech[i] = Tech[i].replace(',year=2030','')\n",
    "#         Tech[i] = Tech[i].replace(',year=2035','')\n",
    "#         Tech[i] = Tech[i].replace(',year=2040','')\n",
    "#         Tech[i] = Tech[i].replace(',year=2045','')\n",
    "#         Tech[i] = Tech[i].replace(',year=2050','')\n",
    "#     #print(Tech[1:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f78740be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name=gas (CC) (cooling pond)\n",
      "0.0\n",
      "name=biomass (conv) (seawater)\n",
      "410.79999999999995\n",
      "name=refined liquids (CC) (dry cooling)\n",
      "0.0\n",
      "name=biomass (conv) (cooling pond)\n",
      "412.0\n",
      "name=gas (CC) (dry cooling)\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# Tech=CP2025[\"Technology\"]\n",
    "# Cost_diff=[]\n",
    "\n",
    "# # for i in range(len(Tech)):\n",
    "# for i in range(5):\n",
    "#     tech_name = \"name=\" + Tech[i].replace(',year=2025','')\n",
    "#     print(tech_name)\n",
    "#     Total_cost_diff=0\n",
    "#     for type in [\"name=capital\", \"name=cooling system\"]:\n",
    "       \n",
    "#         DF = SD2[(SD2[\"Technology\"] == tech_name) & (SD2[\"Year\"] == 2025) & (SD2[\"Cost type\"] == type)]\n",
    "# #         print(DF[\"Originalcost\"].tolist()[0])\n",
    "#         original_cost = DF[\"Originalcost\"].tolist()[0]\n",
    "#         new_cost = DF[\"New cost\"].tolist()[0]\n",
    "#         cost_diff = original_cost - new_cost\n",
    "#         Total_cost_diff=Total_cost_diff+cost_diff\n",
    "#     Cost_diff.append(Total_cost_diff)\n",
    "#     print(Cost_diff[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e63b0ce",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot concatenate object of type '<class 'list'>'; only Series and DataFrame objs are valid",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     42\u001b[0m     SUB2\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTechnology\u001b[39m\u001b[38;5;124m'\u001b[39m: Tech,\n\u001b[0;32m     43\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSubsidy\u001b[39m\u001b[38;5;124m'\u001b[39m: SUB}\n\u001b[0;32m     44\u001b[0m     SUBT\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mDataFrame(SUB2)\n\u001b[1;32m---> 46\u001b[0m     SUBSIDY\u001b[38;5;241m=\u001b[39m\u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mSUBSIDY\u001b[49m\u001b[43m,\u001b[49m\u001b[43mSUB2\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mouter\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;66;03m#     SUBSIDY['Tech']=Tech\u001b[39;00m\n\u001b[0;32m     49\u001b[0m \u001b[38;5;66;03m#     SUBSIDY['Subsidy']=SUB\u001b[39;00m\n\u001b[0;32m     50\u001b[0m \u001b[38;5;66;03m#     SUBSIDY['{}'.format(TECHS[j])]=Tech\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     53\u001b[0m \n\u001b[0;32m     54\u001b[0m \u001b[38;5;66;03m# SUBSIDYDF=pd.DataFrame(SUBSIDY)\u001b[39;00m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28mtype\u001b[39m(SUBSIDY)\n",
      "File \u001b[1;32mE:\\Anacoda\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:285\u001b[0m, in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconcat\u001b[39m(\n\u001b[0;32m     83\u001b[0m     objs: Union[Iterable[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNDFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m], Mapping[Label, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNDFrame\u001b[39m\u001b[38;5;124m\"\u001b[39m]],\n\u001b[0;32m     84\u001b[0m     axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     92\u001b[0m     copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m     93\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m FrameOrSeriesUnion:\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     95\u001b[0m \u001b[38;5;124;03m    Concatenate pandas objects along a particular axis with optional set logic\u001b[39;00m\n\u001b[0;32m     96\u001b[0m \u001b[38;5;124;03m    along the other axes.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    283\u001b[0m \u001b[38;5;124;03m    ValueError: Indexes have overlapping values: ['a']\u001b[39;00m\n\u001b[0;32m    284\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 285\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    286\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    287\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    288\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    289\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    290\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    291\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    292\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    293\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    294\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    295\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    296\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    298\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
      "File \u001b[1;32mE:\\Anacoda\\lib\\site-packages\\pandas\\core\\reshape\\concat.py:370\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[1;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[0;32m    365\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, (ABCSeries, ABCDataFrame)):\n\u001b[0;32m    366\u001b[0m     msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    367\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot concatenate object of type \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(obj)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m; \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    368\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124monly Series and DataFrame objs are valid\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    369\u001b[0m     )\n\u001b[1;32m--> 370\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m    372\u001b[0m \u001b[38;5;66;03m# consolidate\u001b[39;00m\n\u001b[0;32m    373\u001b[0m obj\u001b[38;5;241m.\u001b[39m_consolidate_inplace()\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot concatenate object of type '<class 'list'>'; only Series and DataFrame objs are valid"
     ]
    }
   ],
   "source": [
    "Files=[CP2025, CP2030,CP2035,CP2040,CP2045,CP2050]\n",
    "SUBSIDY=[]\n",
    "Years=[2025,2030,2035,2040,2045,2050]\n",
    "NAMES=[\"SUB2025\",\"SUB2030\",\"SUB2035\",\"SUB2040\",\"SUB2045\",\"SUB2050\"]\n",
    "TECHS=[\"tech2025\",\"tech2030\",\"tech2035\",\"tech2040\",\"tech2045\",\"tech2050\"]\n",
    "for j in range(len(Files)):\n",
    "    file=Files[j]\n",
    "\n",
    "    Tech=file[\"Technology\"]\n",
    "    #print(Tech)\n",
    "    Cost_diff=[]\n",
    "\n",
    "    for i in range(len(Tech)):\n",
    "    #for i in range(5):\n",
    "        tech_name = \"name=\" + Tech[i].replace(',year={}'.format(Years[j]),'')\n",
    "        \n",
    "        #print(tech_name)\n",
    "        Total_cost_diff=0\n",
    "        #print(len(SD2))\n",
    "        for tp in [\"name=capital\", \"name=cooling system\"]:\n",
    "            DF = SD2[(SD2[\"Technology\"] == tech_name) & (SD2[\"Year\"] == Years[j]) & (SD2[\"Cost type\"] == tp)]\n",
    "            #print(len(DF))\n",
    "            if len(DF) == 0:\n",
    "                continue\n",
    "#                 print(\"wrong df\")\n",
    "#                 print(file, type, tech_name, DF)\n",
    "#         print(DF[\"Originalcost\"].tolist()[0])\n",
    "            original_cost = DF[\"Originalcost\"].tolist()[0]\n",
    "            new_cost = DF[\"New cost\"].tolist()[0]\n",
    "            cost_diff = original_cost - new_cost\n",
    "            Total_cost_diff=Total_cost_diff+cost_diff\n",
    "        Cost_diff.append(Total_cost_diff)\n",
    "#         print(Cost_diff[i])\n",
    "\n",
    "    \n",
    "    SUB=[]#subsidy*capacity\n",
    "    for i in range(len(CA[j])):\n",
    "        SUB.append(CA[j][i]*(OldCost2[i]-NewCost2[i]))\n",
    "    #print(CA[i])\n",
    "    #print(len(Tech))\n",
    "    #print(len(SUB))\n",
    "    SUB2={'Technology': Tech,\n",
    "                     'Subsidy': SUB}\n",
    "    SUBT=pd.DataFrame(SUB2)\n",
    "\n",
    "    SUBSIDY=pd.concat([SUBSIDY,SUB2],join='outer',axis=1)\n",
    "    \n",
    "#     SUBSIDY['Tech']=Tech\n",
    "#     SUBSIDY['Subsidy']=SUB\n",
    "#     SUBSIDY['{}'.format(TECHS[j])]=Tech\n",
    "#     SUBSIDY['{}'.format(NAMES[j])]=SUB\n",
    "#     SUBT.to_csv(\"{}.csv\".format(NAMES[j]))\n",
    "\n",
    "# SUBSIDYDF=pd.DataFrame(SUBSIDY)\n",
    "type(SUBSIDY)\n",
    "#print(SUBSIDY)\n",
    "# SUBSIDYDF.to_csv(\"Subsidy_total.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "25d39c8b",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'str' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [11]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m wuyijia\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m]\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mwuyijia\u001b[49m\u001b[43m)\u001b[49m)\n",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object is not callable"
     ]
    }
   ],
   "source": [
    "wuyijia=[1,2]\n",
    "print(type(wuyijia))\n",
    "# type(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0c71ffc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bb5f036",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subsidy=[]\n",
    "# for i in range(len(OldCost2)):\n",
    "#     if Time[i]=2025:\n",
    "#         if \n",
    "#         Subsidy.append(NewCost2*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b14b228",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SUB=[]#subsidy*capacity\n",
    "# for i in range(len(CA)):\n",
    "#     SUB.append(CA[i]*(OldCost2[i]-NewCost2[i]))\n",
    "#     #print(CA[i])\n",
    "# print(len(SUB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0643c47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Years=[2025,2030,2035,2040,2045,2050]\n",
    "plt.figure(dpi=800, figsize=(20,14))\n",
    "plt.bar(Year2, Spending1)\n",
    "plt.bar(Year2, SUB, bottom=Spending1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85fe0cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GEN_all_years = np.array([F2025, F2030, F2035, F2040, F2045, F2050])\n",
    "# print(GEN_all_years[:, 0])\n",
    "# # len(GenTechClean)\n",
    "# Year=[\"2025\",\"2030\",\"2035\",\"2040\",\"2045\",\"2050\"]\n",
    "# #Year=[\"1990\",\"2005\",\"2010\",\"2015\",\"2020\",\"2025\",\"2030\",\"2035\",\"2040\",\"2045\",\"2050\"]\n",
    "# # 绘图\n",
    "\n",
    "\n",
    "# # plt.style.use(['science', 'no-latex'])\n",
    "# #with plt.style.use('science'):\n",
    "#     # 图形和轴\n",
    "# colormap = mpl.cm.Set3.colors\n",
    "# fig, ax = plt.subplots(1, figsize=(12, 10))\n",
    "# bottom = len(Year) * [0]\n",
    "# for i in range(len(Fuels)):\n",
    "#     plt.bar(Year, GEN_all_years[:, i], bottom = bottom, label = Fuels[i], color=colormap[i])\n",
    "#     bottom += GEN_all_years[:, i]\n",
    "# # for idx, name in enumerate(fields):\n",
    "# #     plt.barh(df_grouped.index, df_grouped[name], left = left, color=colors[idx])\n",
    "# #     left = left + df_grouped[name]\n",
    "\n",
    "# # 标题、图例、标签\n",
    "# plt.title('Total generation by sector, EJ (Original)', loc='left')\n",
    "# # plt.legend(labels, bbox_to_anchor=([0.55, 1, 0, 0]), ncol=4, frameon=False)\n",
    "# plt.ylabel('Generation, EJ')\n",
    "# plt.xlabel(\"Year\")\n",
    "# plt.legend()"
   ]
  }
 ],
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