TEMBA_results_0307_binder.ipynb

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    "import pandas as pd\n",
    "import requests\n",
    "import csv\n",
    "import pickle\n",
    "import os, sys\n",
    "import numpy as np\n",
    "from IPython.display import HTML\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "import matplotlib as plt\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "#importing plotly and cufflinks in offline mode\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import cufflinks\n",
    "import plotly.offline as pyo\n",
    "from plotly.offline import plot, iplot, init_notebook_mode\n",
    "pyo.init_notebook_mode()\n",
    "cufflinks.go_offline()\n",
    "cufflinks.set_config_file(world_readable=True, theme='white')\n",
    "#import tkinter for prompting the user to choose a particular file\n",
    "#from tkinter import filedialog\n",
    "#from tkinter import *\n",
    "#print(py.__version__)\n",
    "#print(cufflinks.__version__)\n",
    "print('TEMBA visualization: Base code written by \\033[95mAbhishek Shivakumar\\033[0m.\\n\\t\\t     Adapted and modified by\\033[95m Vignesh Sridharan and Will Usher\\033[0m.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choice of power pools\n",
    "pools=['EAPP','NAPP','SAPP','CAPP','WAPP','TEMBA','TEMBA15','TEMBA20']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "# Function to choose the corresponding pickle file absed on the selected power pool\n",
    "def result_select(Power_pools):\n",
    "    if Power_pools=='NAPP':\n",
    "        picklefile='./napp.pickle'\n",
    "    elif Power_pools=='EAPP':\n",
    "        picklefile='./eapp.pickle'\n",
    "    elif Power_pools=='SAPP':\n",
    "        picklefile='./sapp.pickle'\n",
    "    elif Power_pools=='WAPP':\n",
    "        picklefile='./wapp.pickle'      \n",
    "    elif Power_pools=='CAPP':\n",
    "        picklefile='./capp.pickle'\n",
    "    elif Power_pools=='TEMBA':\n",
    "        picklefile='./temba.pickle'\n",
    "    elif Power_pools=='TEMBA15':\n",
    "        picklefile='./temba15.pickle'\n",
    "    elif Power_pools=='TEMBA20':\n",
    "        picklefile='./temba20.pickle'\n",
    "    return picklefile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Please select the power-pool or TEMBA results that you want to visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
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   "outputs": [],
   "source": [
    "# Interactive widget for choosing the results to visualise\n",
    "Powerpool_Selector = interactive(result_select,Power_pools=pools)\n",
    "display(Powerpool_Selector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The pickle file for the corressponding powerpool/TEMBA is unbundled in the following steps\n",
    "picklefile=Powerpool_Selector.result\n",
    "pkl_file = open(picklefile, 'rb')\n",
    "# The pickle file is loaded onto the all_params dictionary\n",
    "all_params = pickle.load(pkl_file)"
   ]
  },
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    "#Fundamental dictionaries that govern naming and colour coding\n",
    "url1='./agg_col.csv'\n",
    "url2='./agg_pow_col.csv'\n",
    "url3='./countrycode.csv'\n",
    "url4='./power_tech.csv'\n",
    "url5='./techcodes.csv'\n",
    "colorcode=pd.read_csv(url5,sep=',',encoding = \"ISO-8859-1\")\n",
    "colorcode1=colorcode.drop('colour',axis=1)\n",
    "colorcode2=colorcode.drop('tech_code',axis=1)\n",
    "det_col=dict([(a,b) for a,b in zip(colorcode1.tech_code,colorcode1.tech_name)])\n",
    "color_dict=dict([(a,b) for a,b in zip(colorcode2.tech_name,colorcode2.colour)])\n",
    "agg1=pd.read_csv(url1,sep=',',encoding = \"ISO-8859-1\")\n",
    "agg2=pd.read_csv(url2,sep=',',encoding = \"ISO-8859-1\")\n",
    "agg_col=agg1.to_dict('list')\n",
    "agg_pow_col=agg2.to_dict('list')\n",
    "power_tech=pd.read_csv(url4,sep=',',encoding = \"ISO-8859-1\")\n",
    "t_include = list(power_tech['power_tech'])\n",
    "#Country code list\n",
    "country_code=pd.read_csv(url3,sep=',',encoding = \"ISO-8859-1\")"
   ]
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   "source": [
    "# time period definition\n",
    "years = pd.Series(range(2015,2071))"
   ]
  },
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    "#base function used for many different variables (mainly cost)\n",
    "def df_filter(df,lb,ub,t_exclude):\n",
    "    df['t'] = df['t'].str[lb:ub]\n",
    "    df['value'] = df['value'].astype('float64')\n",
    "    df = df[~df['t'].isin(t_exclude)].pivot_table(index='y', \n",
    "                                          columns='t',\n",
    "                                          values='value', \n",
    "                                          aggfunc='sum').reset_index().fillna(0)\n",
    "    df = df.reindex(sorted(df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    df['y'] = years\n",
    "    df=df[df['y']>2018]\n",
    "    return df\n",
    "#### PLotting function for all graphs except Gas (as it needs relative charts)\n",
    "def df_plot(df,y_title,p_title):\n",
    "    if len(df.columns)==1:\n",
    "        print('There are no values for the result variable that you want to plot')\n",
    "    else:\n",
    "         return df.iplot(x='y',\n",
    "             kind='bar', \n",
    "             barmode='stack',\n",
    "             xTitle='Year',\n",
    "             yTitle=y_title,\n",
    "             color=[color_dict[x] for x in df.columns if x != 'y'],\n",
    "             title=p_title,showlegend=True\n",
    "                   )\n",
    "#### Emissions#####\n",
    "def df_filter_emission_tech(df,lb,ub):\n",
    "    df['t'] = df['t'].str[lb:ub]\n",
    "    df['e'] = df['e'].str[2:5]\n",
    "    df['value'] = df['value'].astype('float64')\n",
    "    df = df.pivot_table(index='y',columns='t',\n",
    "                        values='value',\n",
    "                        aggfunc='sum').reset_index().fillna(0)\n",
    "    df = df.reindex(sorted(df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    df['y'] = years\n",
    "    df=df[df['y']>2018]\n",
    "    return df\n",
    "### Annual Emissions\n",
    "def df_filter_emission_tot(df):\n",
    "    df['e'] = df['e'].str[2:5]\n",
    "    df['value'] = df['value'].astype('float64')\n",
    "    df = df.pivot_table(index='y',columns='e',\n",
    "                        values='value',\n",
    "                        aggfunc='sum').reset_index().fillna(0)\n",
    "    df = df.reindex(sorted(df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    df['y'] = years\n",
    "    df=df[df['y']>2018]\n",
    "    return df\n"
   ]
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   "source": [
    "def power_chart(Country):\n",
    "    cc=country_code[country_code['Country Name']==Country]['Country code'].tolist()[0]\n",
    "    # Power capacity (detailed)\n",
    "    #cap_df = all_params['TotalCapacityAnnual'][all_params['TotalCapacityAnnual'].t.str.startswith('PWR')].drop('r', axis=1)\n",
    "    cap_df = all_params['TotalCapacityAnnual']\n",
    "    cap_df=cap_df[cap_df['t'].str[:2]==cc].copy()\n",
    "    cap_df['t'] = cap_df['t'].str[2:10]\n",
    "    cap_df['value'] = cap_df['value'].astype('float64')\n",
    "    cap_df = cap_df[cap_df['t'].isin(t_include)].pivot_table(index='y', \n",
    "                                               columns='t',\n",
    "                                               values='value', \n",
    "                                               aggfunc='sum').reset_index().fillna(0)\n",
    "    cap_df = cap_df.reindex(sorted(cap_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #cap_df['y'] = years\n",
    "    #cap_df=cap_df[cap_df['y']>2018]\n",
    "    # The following code can be unhashed to get a detailed power capcity graph.\n",
    "    #df_plot(cap_df,'Gigawatts (GW)',cc+\"-\"+'Power Generation Capacity (Detail)')\n",
    "    #***********************************************\n",
    "    # Power capacity (Aggregated)\n",
    "    cap_agg_df = pd.DataFrame(columns=agg_pow_col)\n",
    "    cap_agg_df.insert(0,'y',cap_df['y'])\n",
    "    cap_agg_df  = cap_agg_df.fillna(0.00)\n",
    "    #\n",
    "    for each in agg_pow_col:\n",
    "        for tech_exists in agg_pow_col[each]:\n",
    "            if tech_exists in cap_df.columns:\n",
    "                cap_agg_df[each] = cap_agg_df[each] + cap_df[tech_exists]\n",
    "                cap_agg_df[each] = cap_agg_df[each].round(3)\n",
    "    #\n",
    "    df_plot(cap_agg_df,'Gigawatts (GW)',cc+\"-\"+'Power Generation Capacity (Aggregate)')\n",
    "    ## Power generation (Detailed)\n",
    "    gen_df = all_params['ProductionByTechnologyAnnual'].copy()\n",
    "    gen_df_export=gen_df[(gen_df['t'].str[0:4]==cc+'EL')&(gen_df['t'].str[6:10]=='BP00')&(gen_df['f'].str[0:6]!=cc+'EL01')].copy()\n",
    "    gen_df_export['value'] = gen_df_export['value'].astype(float)*-1\n",
    "    #gen_df=gen_df[gen_df['t'].str[:2]==cc].copy()\n",
    "    #gen_df['t'] = gen_df['t'].str[2:10]\n",
    "    gen_df=gen_df[(gen_df['f'].str[:2]==cc)].copy()\n",
    "    gen_df=gen_df[(gen_df['f'].str[2:6]=='EL01')|(gen_df['f'].str[2:6]=='EL03')].copy()\n",
    "    gen_df=gen_df[(gen_df['t'].str[2:10]!='EL00T00X')&(gen_df['t'].str[2:10]!='EL00TDTX')].copy()\n",
    "    gen_df['value'] = gen_df['value'].astype('float64')\n",
    "    gen_df=pd.concat([gen_df,gen_df_export])\n",
    "    gen_df = gen_df.pivot_table(index='y', \n",
    "                                           columns='t',\n",
    "                                           values='value', \n",
    "                                           aggfunc='sum').reset_index().fillna(0)\n",
    "    for each in gen_df.columns:\n",
    "        if len(each)!=1:\n",
    "            if (each[2:4]=='EL') & (each[6:10]=='BP00'):\n",
    "                pass\n",
    "            else:\n",
    "                gen_df.rename(columns={each:each[2:10]},inplace=True)\n",
    "        else:\n",
    "            pass\n",
    "    gen_df = gen_df.reindex(sorted(gen_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #gen_df['y'] = years\n",
    "    #gen_df=gen_df[gen_df['y']>2018]\n",
    "    #df_plot(gen_df,'Petajoules (PJ)',cc+\"-\"+'Power Generation (Detail)')\n",
    "    #gen_df.iplot(x='y',\n",
    "    #                 kind='bar', \n",
    "    #                 barmode='relative',\n",
    "    #                 xTitle='Year',\n",
    "    #                 yTitle=\"Petajoules (PJ)\",\n",
    "    #                 color=[color_dict[x] for x in gen_df.columns if x != 'y'],\n",
    "    #                 title=cc+\"-\"+'Power Generation (Detail)')\n",
    "    # Power generation (Aggregated)\n",
    "    gen_agg_df = pd.DataFrame(columns=agg_pow_col)\n",
    "    gen_agg_df.insert(0,'y',gen_df['y'])\n",
    "    gen_agg_df = gen_agg_df.fillna(0.00)\n",
    "    for each in agg_pow_col:\n",
    "        for tech_exists in agg_pow_col[each]:\n",
    "            if tech_exists in gen_df.columns:\n",
    "                gen_agg_df[each] = gen_agg_df[each] + gen_df[tech_exists]\n",
    "                gen_agg_df[each] = gen_agg_df[each].round(2)\n",
    "    gen_agg_df.iplot(x='y',\n",
    "                     kind='bar', \n",
    "                     barmode='relative',\n",
    "                     xTitle='Year',\n",
    "                     yTitle=\"Petajoules (PJ)\",\n",
    "                     color=[color_dict[x] for x in gen_agg_df.columns if x != 'y'],\n",
    "                     title=cc+\"-\"+'Power Generation (Aggregate)')\n",
    "    # New capacity (detailed)\n",
    "    cap_new_df = all_params['NewCapacity']\n",
    "    cap_new_df=cap_new_df[cap_new_df['t'].str[:2]==cc].copy()\n",
    "    cap_new_df['t'] = cap_new_df['t'].str[2:10]\n",
    "    cap_new_df['value'] = cap_new_df['value'].astype('float64')\n",
    "    cap_new_df = cap_new_df[cap_new_df['t'].isin(t_include)].pivot_table(index='y', \n",
    "                                               columns='t',\n",
    "                                               values='value', \n",
    "                                               aggfunc='sum').reset_index().fillna(0)\n",
    "    cap_new_df = cap_new_df.reindex(sorted(cap_new_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #cap_new_df['y'] = years\n",
    "    #cap_new_df=cap_new_df[cap_new_df['y']>2018]\n",
    "    # The following code can be unhashed to get a detailed power capacity graph.\n",
    "    #df_plot(cap_new_df,'Gigawatts (GW)','New Power Generation Capacity (Detail)')\n",
    "    #***********************************************\n",
    "    # Power capacity (Aggregated)\n",
    "    cap_new_agg_df = pd.DataFrame(columns=agg_pow_col)\n",
    "    cap_new_agg_df.insert(0,'y',cap_new_df['y'])\n",
    "    cap_new_agg_df  = cap_new_agg_df.fillna(0.00)\n",
    "    #\n",
    "    for each in agg_pow_col:\n",
    "        for tech_exists in agg_pow_col[each]:\n",
    "            if tech_exists in cap_new_df.columns:\n",
    "                cap_new_agg_df[each] = cap_new_agg_df[each] + cap_new_df[tech_exists]\n",
    "                cap_new_agg_df[each] = cap_new_agg_df[each].round(3)\n",
    "                ##\n",
    "    df_plot(cap_new_agg_df,'Gigawatts (GW)',cc+\"-\"+ 'New power generation capacity (Aggregate)')\n",
    "    #######\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "def water_chart (Country):\n",
    "    cc=country_code[country_code['Country Name']==Country]['Country code'].tolist()[0]\n",
    "    #print('The country code is:'+cc)\n",
    "    #water withdrawal detailed\n",
    "    wat_w_df = all_params['UseByTechnologyAnnual']\n",
    "    wat_w_df=wat_w_df[wat_w_df['f'].str[:6]==cc+'WAT1'].copy()\n",
    "\n",
    "    wat_w_df['t'] = wat_w_df['t'].str[2:10]\n",
    "    wat_w_df['value'] = wat_w_df['value'].astype('float64')\n",
    "    wat_w_df = wat_w_df.pivot_table(index='y', \n",
    "                                  columns='t',\n",
    "                                  values='value', \n",
    "                                  aggfunc='sum').reset_index().fillna(0)\n",
    "    wat_w_df = wat_w_df.reindex(sorted(wat_w_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #wat_w_df['y'] = years\n",
    "    #wat_w_df=wat_w_df[wat_w_df['y']>2018]\n",
    "    #df_plot(wat_w_df,'Million cubic metres (Mm^3)',cc+\"-\"+'Water Withdrawal')\n",
    "    ###\n",
    "    #Water Withdrawal (Aggregated)\n",
    "    watw_agg_df = pd.DataFrame(columns=agg_col)\n",
    "    watw_agg_df.insert(0,'y',wat_w_df['y'])\n",
    "    watw_agg_df  = watw_agg_df.fillna(0.00)\n",
    "    for each in agg_col:\n",
    "        for tech_exists in agg_col[each]:\n",
    "            if tech_exists in wat_w_df.columns:\n",
    "                watw_agg_df[each] = watw_agg_df[each] + wat_w_df[tech_exists]\n",
    "                watw_agg_df[each] = watw_agg_df[each].round(2)\n",
    "\n",
    "    df_plot(watw_agg_df,'Million cubic metres (Mm^3)',cc+\"-\"+'Water Withdrawal')\n",
    "    ##\n",
    "    #water output detailed\n",
    "    wat_o_df = all_params['ProductionByTechnologyAnnual']\n",
    "    wat_o_df=wat_o_df[wat_o_df['f'].str[:6]==cc+'WAT2'].copy()\n",
    "    wat_o_df['t'] = wat_o_df['t'].str[2:10].copy()\n",
    "    wat_o_df['value'] = wat_o_df['value'].astype('float64')\n",
    "    wat_o_df = wat_o_df.pivot_table(index='y', \n",
    "                                 columns='t',\n",
    "                                 values='value', \n",
    "                                 aggfunc='sum').reset_index().fillna(0)\n",
    "    wat_o_df = wat_o_df.reindex(sorted(wat_o_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #wat_o_df['y'] = years\n",
    "    #wat_o_df=wat_o_df[wat_o_df['y']>2018]\n",
    "    #df_plot(wat_o_df,'Million cubic metres (Mm^3)',cc+\"-\"+'Water output')\n",
    "    ###\n",
    "    #Water consumption missing row additions\n",
    "    for wd in wat_w_df.columns:\n",
    "        for wc in wat_o_df.columns:\n",
    "            if wd in wat_o_df.columns:\n",
    "                pass\n",
    "            else:\n",
    "                wat_o_df[wd]=0\n",
    "    #####\n",
    "    ####Water consumption (Detailed)\n",
    "    wat_c_df=wat_w_df.set_index('y')-wat_o_df.set_index('y')\n",
    "    wat_c_df=wat_c_df.fillna(0.00)\n",
    "    wat_c_df.reset_index(inplace=True)\n",
    "    #wat_c_df['y']=years\n",
    "    #df_plot(wat_c_df,'Million cubic metres (Mm^3)',cc+\"-\"+'Water consumption')\n",
    "    #Water consumption (Aggregate)\n",
    "    watc_agg_df = pd.DataFrame(columns=agg_col)\n",
    "    watc_agg_df.insert(0,'y',wat_c_df['y'])\n",
    "    watc_agg_df  = watc_agg_df.fillna(0.00)\n",
    "    for each in agg_col:\n",
    "        for tech_exists in agg_col[each]:\n",
    "            if tech_exists in wat_c_df.columns:\n",
    "                watc_agg_df[each] = watc_agg_df[each] + wat_c_df[tech_exists]\n",
    "                watc_agg_df[each] = watc_agg_df[each].round(2)\n",
    "    df_plot(watc_agg_df,'Million cubic metres (Mm^3)',cc+'-'+'Water consumption aggregated')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "def emissions_chart(Country):\n",
    "    cc=country_code[country_code['Country Name']==Country]['Country code'].tolist()[0]\n",
    "#     #CO2-Emission detailed\n",
    "#     co2_df = all_params['AnnualTechnologyEmission']\n",
    "#     co2_df=co2_df[co2_df['e'].str[:6]==cc+'CO2'].copy()\n",
    "\n",
    "#     co2_df['value'] = co2_df['value'].astype('float64')\n",
    "#     co2_df = co2_df.pivot_table(index='y',columns='t',\n",
    "#                             values='value',\n",
    "#                             aggfunc='sum').reset_index().fillna(0)\n",
    "#     for each in co2_df.columns:\n",
    "#         if len(each)!=1:\n",
    "#             if (each[2:4]=='NG') & (each[6:10]=='BP00'):\n",
    "#                 pass\n",
    "#             else:\n",
    "#                 co2_df.rename(columns={each:each[2:10]},inplace=True)\n",
    "#         else:\n",
    "#             pass\n",
    "#     co2_df = co2_df.reindex(sorted(co2_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #co2_df['y'] = years\n",
    "    #co2_df=co2_df[co2_df['y']>2018]\n",
    "    #df_plot(co2_df,'Million Tonnes (Mt)',cc+'-''Emissions (CO2)-by technology')\n",
    "#Total emissions by type- This graph shows the total emissions in the country by emissiontype\n",
    "    emis_df = all_params['AnnualEmissions']\n",
    "    emis_df=emis_df[emis_df['e'].str[:5]==cc+'CO2'].copy()\n",
    "    emis_df = df_filter_emission_tot(emis_df)\n",
    "    df_plot(emis_df,'Million Tonnes of CO2 (Mt)',cc+'-''Annual Emissions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gas_chart(Country):\n",
    "    cc=country_code[country_code['Country Name']==Country]['Country code'].tolist()[0]\n",
    "    #GAS Production (Detailed)\n",
    "    gas_df = all_params['ProductionByTechnologyAnnual']\n",
    "    gas_df_export1=gas_df[(gas_df['t'].str[0:4]==cc+'NG')&(gas_df['t'].str[6:10]=='BP00')].copy()\n",
    "    gas_df_export1['value'] = gas_df_export1['value'].astype(float)*-1\n",
    "    gas_df_import1=gas_df[(gas_df['t'].str[2:10]=='NG'+cc+'BP00')].copy()\n",
    "    gas_df=gas_df[(gas_df['t'].str[:2]==cc)&(gas_df['t'].str[2:4]=='NG')&(gas_df['t'].str[6:7]!='P')].copy()\n",
    "    gas_df= gas_df[(gas_df['t'].str[6:10]=='ELGX')|(gas_df['t'].str[6:10]=='ILGX')|(gas_df['t'].str[6:10]=='X00X')].copy()\n",
    "    #gas_df = df_filter_gas(gas_df,2,10,gas_df_export1,gas_df_import1)\n",
    "    gas_df['t'] = gas_df['t'].str[2:10]\n",
    "    gas_df['value'] = gas_df['value'].astype('float64')\n",
    "    gas_df['t'] = gas_df['t'].astype(str)\n",
    "    gas_df=pd.concat([gas_df,gas_df_export1,gas_df_import1])\n",
    "    gas_df = gas_df.pivot_table(index='y',columns='t',\n",
    "                                      values='value', \n",
    "                                      aggfunc='sum').reset_index().fillna(0)\n",
    "    gas_df = gas_df.reindex(sorted(gas_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #gas_df['y'] = years\n",
    "    #gas_df=gas_df[gas_df['y']>2018]\n",
    "    for each in gas_df.columns:\n",
    "        if each=='Natural gas exports (Liquification terminal)':\n",
    "            gas_df[each] =gas_df[each].astype(float)*-1\n",
    "        else:\n",
    "            pass\n",
    "    if len(gas_df.columns)==1:\n",
    "            print('There are no values for the result variable that you want to plot')\n",
    "    else:\n",
    "        gas_df.iplot(x='y',\n",
    "                 kind='bar', \n",
    "                 barmode='relative',\n",
    "                 xTitle='Year',\n",
    "                 yTitle=\"Petajoules (PJ)\",\n",
    "                 color=[color_dict[x] for x in gas_df.columns if x != 'y'],\n",
    "                 showlegend=True,   \n",
    "                 title=cc+\"-\"+\"Gas extraction, imports and exports\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def crude_chart(Country):\n",
    "    cc=country_code[country_code['Country Name']==Country]['Country code'].tolist()[0]\n",
    "    #Crude oil refined in the country\n",
    "    cru_r_df = all_params['ProductionByTechnologyAnnual']\n",
    "    cru_r_df=cru_r_df[cru_r_df['f'].str[:6]==cc+'CRU2'].copy()\n",
    "    cru_r_df['t'] = cru_r_df['t'].str[2:10]\n",
    "    cru_r_df['value'] = cru_r_df['value'].astype('float64')\n",
    "    cru_r_df = cru_r_df.pivot_table(index='y',columns='t',\n",
    "                                      values='value', \n",
    "                                      aggfunc='sum').reset_index().fillna(0)\n",
    "    cru_r_df = cru_r_df.reindex(sorted(cru_r_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #cru_r_df['y'] = years\n",
    "    #cru_r_df=cru_r_df[cru_r_df['y']>2018]\n",
    "    df_plot(cru_r_df,'Petajoules (PJ)',cc+'-'+'Crude oil refined in the country')\n",
    "    #Crude oil production/imports/exports (Detailed)\n",
    "    cru_df = all_params['ProductionByTechnologyAnnual']\n",
    "    cru_df=cru_df[(cru_df['f'].str[:6]==cc+'CRU1')].copy()\n",
    "    cru_df['t'] = cru_df['t'].str[2:10]\n",
    "    cru_df['value'] = cru_df['value'].astype('float64')\n",
    "    cru_df['t'] = cru_df['t'].astype(str)\n",
    "    cru_df = cru_df.pivot_table(index='y',columns='t',\n",
    "                                      values='value', \n",
    "                                      aggfunc='sum').reset_index().fillna(0)\n",
    "    cru_df = cru_df.reindex(sorted(cru_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #cru_df['y'] = years\n",
    "    #cru_df=cru_df[cru_df['y']>2018]\n",
    "    cru_df.iplot(x='y',\n",
    "                  kind='bar', \n",
    "                  barmode='relative',\n",
    "                  xTitle='Year',\n",
    "                  yTitle=\"Petajoules (PJ)\",\n",
    "                  color=[color_dict[x] for x in cru_df.columns if x != 'y'],\n",
    "                  title=cc+\"-\"+\"Crude oil extraction, imports and exports\",showlegend=True)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coal_biomass_chart(Country):\n",
    "    cc=country_code[country_code['Country Name']==Country]['Country code'].tolist()[0]\n",
    "    #Coal overview\n",
    "    coal_df = all_params['ProductionByTechnologyAnnual']\n",
    "    coal_df=coal_df[coal_df['f'].str[:6]==cc+'COAL'].copy()\n",
    "    coal_df['t'] = coal_df['t'].str[2:10]\n",
    "    coal_df['value'] = coal_df['value'].astype('float64')\n",
    "    coal_df = coal_df.pivot_table(index='y',columns='t',\n",
    "                                      values='value', \n",
    "                                      aggfunc='sum').reset_index().fillna(0)\n",
    "    coal_df = coal_df.reindex(sorted(coal_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #coal_df['y'] = years\n",
    "    #coal_df=coal_df[coal_df['y']>2018]\n",
    "    df_plot(coal_df,'Petajoules (PJ)',cc+'-'+'Coal production by technology')\n",
    "    #Biomass overview\n",
    "    biom_df = all_params['ProductionByTechnologyAnnual']\n",
    "    biom_df=biom_df[biom_df['f'].str[:6]==cc+'BIOM'].copy()\n",
    "    biom_df['t'] = biom_df['t'].str[2:10]\n",
    "    biom_df['value'] = biom_df['value'].astype('float64')\n",
    "    biom_df = biom_df.pivot_table(index='y',columns='t',\n",
    "                                      values='value', \n",
    "                                      aggfunc='sum').reset_index().fillna(0)\n",
    "    biom_df = biom_df.reindex(sorted(biom_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #biom_df['y'] = years\n",
    "    #biom_df=biom_df[biom_df['y']>2018]\n",
    "    df_plot(biom_df,'Petajoules (PJ)',cc+'-'+'Biomass production by technology')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hfo_lfo_chart(Country):\n",
    "    cc=country_code[country_code['Country Name']==Country]['Country code'].tolist()[0]\n",
    "    #Heavy Fuel Oil overview\n",
    "    hfo_df = all_params['ProductionByTechnologyAnnual']\n",
    "    hfo_df=hfo_df[hfo_df['f'].str[:6]==cc+'HFOI'].copy()\n",
    "    hfo_df['t'] = hfo_df['t'].str[2:10]\n",
    "    hfo_df['value'] = hfo_df['value'].astype('float64')\n",
    "    hfo_df = hfo_df.pivot_table(index='y',columns='t',\n",
    "                                      values='value', \n",
    "                                      aggfunc='sum').reset_index().fillna(0)\n",
    "    hfo_df = hfo_df.reindex(sorted(hfo_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #hfo_df['y'] = years\n",
    "    #hfo_df=hfo_df[hfo_df['y']>2018]\n",
    "    df_plot(hfo_df,'Petajoules (PJ)',cc+'-'+'HFO production by technology')\n",
    "    #Light Fuel Oil overview\n",
    "    lfo_df = all_params['ProductionByTechnologyAnnual']\n",
    "    lfo_df=lfo_df[lfo_df['f'].str[:6]==cc+'LFOI'].copy()\n",
    "    lfo_df['t'] = lfo_df['t'].str[2:10]\n",
    "    lfo_df['value'] = lfo_df['value'].astype('float64')\n",
    "    lfo_df = lfo_df.pivot_table(index='y',columns='t',\n",
    "                                      values='value', \n",
    "                                      aggfunc='sum').reset_index().fillna(0)\n",
    "    lfo_df = lfo_df.reindex(sorted(lfo_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "    #lfo_df['y'] = years\n",
    "    #lfo_df=lfo_df[lfo_df['y']>2018]\n",
    "    df_plot(lfo_df,'Petajoules (PJ)',cc+'-'+'LFO production by technology')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CHOOSE THE COUNTRY TO VISUALIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {},
       "report_default": {}
      }
     }
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "country = widgets.Dropdown(options=country_code['Country Name'])\n",
    "display(country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "power_chart(country.value)\n",
    "water_chart(country.value)\n",
    "emissions_chart(country.value)\n",
    "gas_chart(country.value)\n",
    "crude_chart(country.value)\n",
    "coal_biomass_chart(country.value)\n",
    "hfo_lfo_chart(country.value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The following blocks are to visualize aggregated results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Please provide the name of the scenario you want to visualize (this is just for naming of figures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenario=input(\"Please specify the scenario name:\") \n",
    "scenario=str(scenario)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In the follwoing block, the capacity and generation graphs for all the powerpools and TEMBA will be plotted for the chosen scenario (ref/1.5/2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary for the powerpool classifications and countries\n",
    "pp_def={'CAPP':['CM','CF','TD','CG','CD','GQ','GA'],\n",
    "'EAPP':['BI','DJ','ER','ET','KE','RW','SO','SD','TZ','UG'],\n",
    "'NAPP':['DZ','EG','LY','MR','MA','TN'],\n",
    "'SAPP':['AO','BW','LS','MW','MZ','NM','ZA','SZ','ZM','ZW'],\n",
    "'WAPP':['BJ','BF','CI','GM','GH','GN','GW','LR','ML','NE','NG','SN','SL','TG'],     \n",
    "'TEMBA':['DZ','EG','LY','MR','MA','TN','BI','DJ','ER','ET','KE','RW','SO','SD','TZ','UG',\n",
    "                    'AO','BW','LS','MW','MZ','NM','ZA','SZ','ZM','ZW',\n",
    "                    'BJ','BF','CI','GM','GH','GN','GW','LR','ML','NE','NG','SN','SL','TG','CM','CF','TD','CG','CD','GQ','GA']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# first for loop to loop over the major dictionary keys \n",
    "for tk in pp_def.keys():\n",
    "    # The following lines are used for creating dummy \n",
    "    #(empty) dataframes to print aggregated (powerpool/TEMBA) results as csv files\n",
    "    total_gen_df=pd.DataFrame(np.zeros(shape=(56,14)),columns=['y','Coal','Oil','Gas','Hydro','Nuclear','Solar CSP','Solar PV',\n",
    "                'Wind','Biomass','Geothermal','Backstop','power_trade','gas_trade'],dtype='float64')\n",
    "    total_gen_df['y']=years\n",
    "    total_cap_df=pd.DataFrame(np.zeros(shape=(56,14)),columns=['y','Coal','Oil','Gas','Hydro','Nuclear','Solar CSP','Solar PV',\n",
    "                'Wind','Biomass','Geothermal','Backstop','power_trade','gas_trade'],dtype='float64')\n",
    "    total_cap_df['y'] = total_cap_df['y'].astype('float64')\n",
    "\n",
    "    total_cap_df['y']=years\n",
    "    #for loop for each country inside a powerpool/TEMBA starts here\n",
    "    for cc in pp_def[tk]:\n",
    "        cap_df = all_params['TotalCapacityAnnual']\n",
    "        cap_df=cap_df[cap_df['t'].str[:2]==cc].copy()\n",
    "        cap_df['t'] = cap_df['t'].str[2:10]\n",
    "        cap_df['value'] = cap_df['value'].astype('float64')\n",
    "        cap_df = cap_df[cap_df['t'].isin(t_include)].pivot_table(index='y', \n",
    "                                                   columns='t',\n",
    "                                                   values='value', \n",
    "                                                   aggfunc='sum').reset_index().fillna(0)\n",
    "        cap_df = cap_df.reindex(sorted(cap_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "        #***********************************************\n",
    "        # Power capacity (Aggregated)\n",
    "        cap_agg_df = pd.DataFrame(columns=agg_pow_col)\n",
    "        cap_agg_df.insert(0,'y',cap_df['y'])\n",
    "        cap_agg_df  = cap_agg_df.fillna(0.00)\n",
    "        #\n",
    "        for each in agg_pow_col:\n",
    "            for tech_exists in agg_pow_col[each]:\n",
    "                if tech_exists in cap_df.columns:\n",
    "                    cap_agg_df[each] = cap_agg_df[each] + cap_df[tech_exists]\n",
    "                    cap_agg_df[each] = cap_agg_df[each].round(3)\n",
    "        #df_plot(cap_agg_df,'Gigawatts (GW)',cc+\"-\"+'Power Generation Capacity (Aggregate)')\n",
    "        #total_cap_df=cap_agg_df+total_cap_df\n",
    "        total_cap_df= total_cap_df.set_index('y').add(cap_agg_df.set_index('y'), fill_value=0).reset_index()\n",
    "        #Power generation\n",
    "        gen_df = all_params['ProductionByTechnologyAnnual'].copy()\n",
    "        gen_df_export=gen_df[(gen_df['f'].str[2:6]=='EL01')&(gen_df['f'].str[0:2]!=cc)].copy()\n",
    "        gen_df_export=gen_df_export[gen_df_export['t'].str[6:10]=='BP00'].copy()\n",
    "        gen_df_export=gen_df_export[(gen_df_export['t'].str[0:2]==cc)|(gen_df_export['t'].str[4:6]==cc)]\n",
    "        gen_df_export['value'] = gen_df_export['value'].astype(float)*-1\n",
    "        gen_df=gen_df[(gen_df['f'].str[:2]==cc)].copy()\n",
    "        gen_df=gen_df[(gen_df['f'].str[2:6]=='EL01')|(gen_df['f'].str[2:6]=='EL03')].copy()\n",
    "        gen_df=gen_df[(gen_df['t'].str[2:10]!='EL00T00X')&(gen_df['t'].str[2:10]!='EL00TDTX')].copy()\n",
    "        gen_df=pd.concat([gen_df,gen_df_export])\n",
    "        gen_df['value'] = gen_df['value'].astype('float64')\n",
    "        gen_df = gen_df.pivot_table(index='y', \n",
    "                                               columns='t',\n",
    "                                               values='value', \n",
    "                                               aggfunc='sum').reset_index().fillna(0)\n",
    "        for each in gen_df.columns:\n",
    "            if len(each)!=1:\n",
    "                if (each[2:4]=='EL') & (each[6:10]=='BP00'):\n",
    "                    pass\n",
    "                else:\n",
    "                    gen_df.rename(columns={each:each[2:10]},inplace=True)\n",
    "            else:\n",
    "                pass\n",
    "        gen_df = gen_df.reindex(sorted(gen_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "        #df_plot(gen_df,'Petajoules (PJ)',cc+\"-\"+'Power Generation (Detail)')\n",
    "        #####\n",
    "        # Power generation (Aggregated)\n",
    "        gen_agg_df = pd.DataFrame(columns=agg_pow_col)\n",
    "        gen_agg_df.insert(0,'y',gen_df['y'])\n",
    "        gen_agg_df  = gen_agg_df.fillna(0.00)\n",
    "        for each in agg_pow_col:\n",
    "            for tech_exists in agg_pow_col[each]:\n",
    "                if tech_exists in gen_df.columns:\n",
    "                    gen_agg_df[each] = gen_agg_df[each] + gen_df[tech_exists]\n",
    "                    gen_agg_df[each] = gen_agg_df[each].round(2)\n",
    "        total_gen_df=gen_agg_df+total_gen_df\n",
    "    total_gen_df['y']=years\n",
    "    total_gen_df['y']=total_gen_df['y'].astype('float64')\n",
    "    total_cap_df['y']=total_cap_df['y'].astype('float64')\n",
    "    total_gen_df=total_gen_df.drop('gas_trade',axis=1)\n",
    "    total_gen_df=total_gen_df[total_gen_df['y']<=2065]\n",
    "    total_cap_df=total_cap_df[total_cap_df['y']<=2065]\n",
    "    total_gen_df.iplot(x='y',\n",
    "                          kind='bar', \n",
    "                          barmode='relative',\n",
    "                          xTitle='Year',\n",
    "                          yTitle=\"Petajoules (PJ)\",\n",
    "                          color=[color_dict[x] for x in gen_agg_df.columns if x != 'y'],\n",
    "                          title=tk +\"-\"+scenario +\"-\"+\"Power Generation (Aggregate)\")\n",
    "    #total_cap_df['y']=years\n",
    "    total_cap_df=total_cap_df.drop('gas_trade',axis=1)\n",
    "    df_plot(total_cap_df,'Gigawatts (GW)',tk + \"-\" + scenario +\"-\"+'Power Generation Capacity (Aggregate)')\n",
    "   #total_gen_df.to_csv(os.path.join(homedir,tk +\"-\"+ scenario + \"-generation.csv\"))\n",
    "   # total_cap_df.to_csv(os.path.join(homedir,tk +\"-\"+ scenario + \"-capacity.csv\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In the follwoing block, the water consumption and withdrawal graphs for all the powerpools and TEMBA will be plotted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for tk in pp_def.keys():\n",
    "    # The following lines are used for creating dummy \n",
    "    #(empty) dataframes to print aggregated (powerpool/TEMBA) results as csv files\n",
    "    total_watc_df=pd.DataFrame(np.zeros(shape=(56,19)),columns=['y','Coal','Oil','Gas','Hydro','Nuclear','Solar CSP','Solar PV',\n",
    "            'Wind','Geothermal','Biomass','Coal Production','Crude Oil production','Crude oil Refinery',\n",
    "            'Natural gas extraction','Uranium extraction','Transmission & Distribution','Backstop',\n",
    "                                                            'Biofuel and Biomass production'],dtype='float64')\n",
    "    total_watc_df['y']=years\n",
    "    total_watw_df=pd.DataFrame(np.zeros(shape=(56,19)),columns=['y','Coal','Oil','Gas','Hydro','Nuclear','Solar CSP','Solar PV',\n",
    "            'Wind','Geothermal','Biomass','Coal Production','Crude Oil production','Crude oil Refinery',\n",
    "            'Natural gas extraction','Uranium extraction','Transmission & Distribution','Backstop',\n",
    "                                                            'Biofuel and Biomass production'],dtype='float64')\n",
    "    total_watw_df['y']=years\n",
    "    ######\n",
    "    for cc in pp_def[tk]:\n",
    "        wat_w_df = all_params['UseByTechnologyAnnual']\n",
    "        wat_w_df=wat_w_df[wat_w_df['f'].str[:6]==cc+'WAT1'].copy()\n",
    "\n",
    "        wat_w_df['t'] = wat_w_df['t'].str[2:10]\n",
    "        wat_w_df['value'] = wat_w_df['value'].astype('float64')\n",
    "        wat_w_df = wat_w_df.pivot_table(index='y', \n",
    "                                      columns='t',\n",
    "                                      values='value', \n",
    "                                      aggfunc='sum').reset_index().fillna(0)\n",
    "        wat_w_df = wat_w_df.reindex(sorted(wat_w_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "        #wat_w_df['y'] = years\n",
    "        #wat_w_df=wat_w_df[wat_w_df['y']>2018]\n",
    "        #df_plot(wat_w_df,'Million cubic metres (Mm^3)',cc+\"-\"+'Water Withdrawal')\n",
    "        ###\n",
    "        #Water Withdrawal (Aggregated)\n",
    "        watw_agg_df = pd.DataFrame(columns=agg_col)\n",
    "        watw_agg_df.insert(0,'y',wat_w_df['y'])\n",
    "        watw_agg_df  = watw_agg_df.fillna(0.00)\n",
    "        for each in agg_col:\n",
    "            for tech_exists in agg_col[each]:\n",
    "                if tech_exists in wat_w_df.columns:\n",
    "                    watw_agg_df[each] = watw_agg_df[each] + wat_w_df[tech_exists]\n",
    "                    watw_agg_df[each] = watw_agg_df[each].round(2)\n",
    "        total_watw_df= total_watw_df.set_index('y').add(watw_agg_df.set_index('y'), fill_value=0).reset_index()\n",
    "        ##\n",
    "        #water output detailed\n",
    "        wat_o_df = all_params['ProductionByTechnologyAnnual']\n",
    "        wat_o_df=wat_o_df[wat_o_df['f'].str[:6]==cc+'WAT2'].copy()\n",
    "        wat_o_df['t'] = wat_o_df['t'].str[2:10].copy()\n",
    "        wat_o_df['value'] = wat_o_df['value'].astype('float64')\n",
    "        wat_o_df = wat_o_df.pivot_table(index='y', \n",
    "                                     columns='t',\n",
    "                                     values='value', \n",
    "                                     aggfunc='sum').reset_index().fillna(0)\n",
    "        wat_o_df = wat_o_df.reindex(sorted(wat_o_df.columns), axis=1).set_index('y').reset_index().rename(columns=det_col)\n",
    "        #wat_o_df['y'] = years\n",
    "        #wat_o_df=wat_o_df[wat_o_df['y']>2018]\n",
    "        #df_plot(wat_o_df,'Million cubic metres (Mm^3)',cc+\"-\"+'Water output')\n",
    "        ###\n",
    "        #Water consumption missing row additions\n",
    "        for wd in wat_w_df.columns:\n",
    "            for wc in wat_o_df.columns:\n",
    "                if wd in wat_o_df.columns:\n",
    "                    pass\n",
    "                else:\n",
    "                    wat_o_df[wd]=0\n",
    "        #####\n",
    "        ####Water consumption (Detailed)\n",
    "        wat_c_df=wat_w_df.set_index('y')-wat_o_df.set_index('y')\n",
    "        wat_c_df=wat_c_df.fillna(0.00)\n",
    "        wat_c_df.reset_index(inplace=True)\n",
    "        #wat_c_df['y']=years\n",
    "        #df_plot(wat_c_df,'Million cubic metres (Mm^3)',cc+\"-\"+'Water consumption')\n",
    "        #Water consumption (Aggregate)\n",
    "        watc_agg_df = pd.DataFrame(columns=agg_col)\n",
    "        watc_agg_df.insert(0,'y',wat_c_df['y'])\n",
    "        watc_agg_df  = watc_agg_df.fillna(0.00)\n",
    "        for each in agg_col:\n",
    "            for tech_exists in agg_col[each]:\n",
    "                if tech_exists in wat_c_df.columns:\n",
    "                    watc_agg_df[each] = watc_agg_df[each] + wat_c_df[tech_exists]\n",
    "                    watc_agg_df[each] = watc_agg_df[each].round(2)\n",
    "        total_watc_df= total_watc_df.set_index('y').add(watc_agg_df.set_index('y'), fill_value=0).reset_index()\n",
    "    total_watw_df['y']=years\n",
    "    total_watc_df['y']=years\n",
    "    total_watc_df['y']=total_watc_df['y'].astype('float64')\n",
    "    total_watw_df['y']=total_watw_df['y'].astype('float64')\n",
    "    total_watw_df=total_watw_df[total_watw_df['y']<=2065]\n",
    "    total_watc_df=total_watc_df[total_watc_df['y']<=2065]\n",
    "    df_plot(total_watw_df,'Million cubic metres (Mm^3)',tk+ \"-\" +scenario +\"-\"+'Water Withdrawal')\n",
    "    df_plot(total_watc_df,'Million cubic metres (Mm^3)',tk+ \"-\" +scenario +\"-\"+'Water Consumption')\n",
    "    #total_watc_df.to_csv(os.path.join(homedir,tk +\"-\"+ scenario + \"-wat consumption.csv\"))\n",
    "    #total_watw_df.to_csv(os.path.join(homedir,tk +\"-\"+ scenario + \"-wat withdrawal.csv\"))"
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