Gill-Wiehl_et_al_Figure_3a_all_factors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns\n",
    "df = pd.read_excel('9_27_23_python_inputs_charc_firewood_baseline_adjusted_gillwiehl_et_al.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "VERs_all=pd.read_excel('VERS.xlsx')\n",
    "CDM_I_E_VERs=pd.read_excel('CDM_I_E_VERs.xlsx')\n",
    "CDM_II_G_VERs=pd.read_excel('CDM_II_G_VERs.xlsx')\n",
    "GS_VERs=pd.read_excel('GS_VERs.xlsx')\n",
    "GS_Meter_VERs=pd.read_excel('GS_Meter_VERs.xlsx')\n",
    "\n",
    "\n",
    "bangladesh_VERs=df.loc[df['country']=='Bangladesh','verified_credits_per_stove'].sum()\n",
    "burkinafaso_VERs=df.loc[df['country']=='Burkina Faso','verified_credits_per_stove'].sum()\n",
    "china_VERs=df.loc[df['country']=='China','verified_credits_per_stove'].sum()\n",
    "cameroon_VERs=df.loc[df['country']=='Cameroon','verified_credits_per_stove'].sum()\n",
    "ethiopia_VERs=df.loc[df['country']=='Ethiopia','verified_credits_per_stove'].sum()\n",
    "ghana_VERs=df.loc[df['country']=='Ghana','verified_credits_per_stove'].sum()\n",
    "guatemala_VERs=df.loc[df['country']=='Guatemala','verified_credits_per_stove'].sum()\n",
    "haiti_VERs=df.loc[df['country']=='Haiti','verified_credits_per_stove'].sum()\n",
    "honduras_VERs=df.loc[df['country']=='Honduras','verified_credits_per_stove'].sum()\n",
    "india_VERs=df.loc[df['country']=='India','verified_credits_per_stove'].sum()\n",
    "kenya_VERs=df.loc[df['country']=='Kenya','verified_credits_per_stove'].sum()\n",
    "lesotho_VERs=df.loc[df['country']=='Lesotho','verified_credits_per_stove'].sum()\n",
    "malawi_VERs=df.loc[df['country']=='Malawi','verified_credits_per_stove'].sum()\n",
    "mali_VERs=df.loc[df['country']=='Mali','verified_credits_per_stove'].sum()\n",
    "mexico_VERs=df.loc[df['country']=='Mexico','verified_credits_per_stove'].sum()\n",
    "mozambique_VERs=df.loc[df['country']=='Mozambique','verified_credits_per_stove'].sum()\n",
    "myanmar_VERs=df.loc[df['country']=='Myanmar','verified_credits_per_stove'].sum()\n",
    "nepal_VERs=df.loc[df['country']=='Nepal','verified_credits_per_stove'].sum()\n",
    "nigeria_VERs=df.loc[df['country']=='Nigeria','verified_credits_per_stove'].sum()\n",
    "peru_VERs=df.loc[df['country']=='Peru','verified_credits_per_stove'].sum()\n",
    "rwanda_VERs=df.loc[df['country']=='Rwanda','verified_credits_per_stove'].sum()\n",
    "sudan_VERs=df.loc[df['country']=='Sudan','verified_credits_per_stove'].sum()\n",
    "uganda_VERs=df.loc[df['country']=='Uganda','verified_credits_per_stove'].sum()\n",
    "zambia_VERs=df.loc[df['country']=='Zambia','verified_credits_per_stove'].sum()\n",
    "\n",
    "firewood_VERs=df.loc[df['type']=='firewood','verified_credits_per_stove'].sum()\n",
    "charcoal_VERs=df.loc[df['type']=='charcoal','verified_credits_per_stove'].sum()\n",
    "biomass_VERs=df.loc[df['type']=='biomass','verified_credits_per_stove'].sum()\n",
    "switch_VERs=df.loc[df['type']=='switch','verified_credits_per_stove'].sum()\n",
    "\n",
    "gs_simple_firewood_VERs=df.loc[df['stove']=='gs_simple_firewood','verified_credits_per_stove'].sum()\n",
    "gs_lpg_VERs=df.loc[df['stove']=='gs_lpg','verified_credits_per_stove'].sum()\n",
    "gs_imp_firewood_VERs=df.loc[df['stove']=='gs_imp_firewood','verified_credits_per_stove'].sum()\n",
    "gs_imp_charcoal_VERs=df.loc[df['stove']=='gs_imp_charcoal','verified_credits_per_stove'].sum()\n",
    "cdm_imp_firewood_VERs=df.loc[df['stove']=='cdm_imp_firewood','verified_credits_per_stove'].sum()\n",
    "cdm_imp_charcoal_VERs=df.loc[df['stove']=='cdm_imp_charcoal','verified_credits_per_stove'].sum()\n",
    "\n",
    "CDM_II_G_verified_total=df.loc[df['protocol_type'] == 'CDM AMS II G', 'verified_credits_per_stove'].sum()\n",
    "CDM_I_E_verified_total=df.loc[df['protocol_type'] == 'CDM AMS I E', 'verified_credits_per_stove'].sum()\n",
    "GS_verified_total=df.loc[df['protocol_type'] == 'GS TPDDTEC', 'verified_credits_per_stove'].sum()\n",
    "GS_metered_verified_total=df.loc[df['protocol_type'] == 'GS Metered', 'verified_credits_per_stove'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bangladesh_total=df.loc[df['country'] == 'Bangladesh', 'verified_credits'].sum()\n",
    "burkinafaso_total=df.loc[df['country'] == 'Burkina Faso', 'verified_credits'].sum()\n",
    "cameroon_total=df.loc[df['country'] == 'Cameroon', 'verified_credits'].sum()\n",
    "china_total=df.loc[df['country'] == 'China', 'verified_credits'].sum()\n",
    "ethiopia_total=df.loc[df['country'] == 'Ethiopia', 'verified_credits'].sum()\n",
    "ghana_total=df.loc[df['country'] == 'Ghana', 'verified_credits'].sum()\n",
    "guatemala_total=df.loc[df['country'] == 'Guatemala', 'verified_credits'].sum()\n",
    "haiti_total=df.loc[df['country'] == 'Haiti', 'verified_credits'].sum()\n",
    "honduras_total=df.loc[df['country'] == 'Honduras', 'verified_credits'].sum()\n",
    "india_total=df.loc[df['country'] == 'India', 'verified_credits'].sum()\n",
    "kenya_total=df.loc[df['country'] == 'Kenya', 'verified_credits'].sum()\n",
    "lesotho_total=df.loc[df['country'] == 'Lesotho', 'verified_credits'].sum()\n",
    "malawi_total=df.loc[df['country'] == 'Malawi', 'verified_credits'].sum()\n",
    "mali_total=df.loc[df['country'] == 'Mali', 'verified_credits'].sum()\n",
    "mexico_total=df.loc[df['country'] == 'Mexico', 'verified_credits'].sum()\n",
    "mozambique_total=df.loc[df['country'] == 'Mozambique', 'verified_credits'].sum()\n",
    "myanmar_total=df.loc[df['country'] == 'Myanmar', 'verified_credits'].sum()\n",
    "nepal_total=df.loc[df['country'] == 'Nepal', 'verified_credits'].sum()\n",
    "nigeria_total=df.loc[df['country'] == 'Nigeria', 'verified_credits'].sum()\n",
    "peru_total=df.loc[df['country'] == 'Peru', 'verified_credits'].sum()\n",
    "rwanda_total=df.loc[df['country'] == 'Rwanda', 'verified_credits'].sum()\n",
    "uganda_total=df.loc[df['country'] == 'Uganda', 'verified_credits'].sum()\n",
    "sudan_total=df.loc[df['country'] == 'Sudan', 'verified_credits'].sum()\n",
    "zambia_total=df.loc[df['country'] == 'Zambia', 'verified_credits'].sum()\n",
    "\n",
    "firewood_total=df.loc[df['type'] == 'firewood', 'verified_credits'].sum()\n",
    "charcoal_total=df.loc[df['type'] == 'charcoal', 'verified_credits'].sum()\n",
    "biomass_total=df.loc[df['type'] == 'biomass', 'verified_credits'].sum()\n",
    "switch_total=df.loc[df['type'] == 'switch', 'verified_credits'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_analysis_project_mean_compilation=[]\n",
    "ratio_project_analysis_mean_compilation=[]\n",
    "ratio_analysis_project_lower_compilation=[]\n",
    "ratio_analysis_project_upper_compilation=[]\n",
    "ratio_project_analysis_lower_compilation=[]\n",
    "ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# For protocol wide over-estimation\n",
    "GS_Meter_N=0\n",
    "GS_N=0\n",
    "CDM_II_G_N=0\n",
    "CDM_I_E_N=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bangladesh_N=0\n",
    "burkinafaso_N=0\n",
    "cameroon_N=0\n",
    "china_N=0\n",
    "ethiopia_N=0\n",
    "ghana_N=0\n",
    "guatemala_N=0\n",
    "haiti_N=0\n",
    "honduras_N=0\n",
    "india_N=0\n",
    "kenya_N=0\n",
    "lesotho_N=0\n",
    "malawi_N=0\n",
    "mali_N=0\n",
    "mexico_N=0\n",
    "mozambique_N=0\n",
    "myanmar_N=0\n",
    "nepal_N=0\n",
    "nigeria_N=0\n",
    "peru_N=0\n",
    "rwanda_N=0\n",
    "sudan_N=0\n",
    "uganda_N=0\n",
    "zambia_N=0\n",
    "\n",
    "firewood_N=0\n",
    "charcoal_N=0\n",
    "biomass_N=0\n",
    "switch_N=0\n",
    "\n",
    "ALL_N=0\n",
    "\n",
    "gs_simple_firewood_N=0\n",
    "gs_lpg_N=0\n",
    "gs_imp_firewood_N=0\n",
    "gs_imp_charcoal_N=0\n",
    "cdm_imp_firewood_N=0\n",
    "cdm_imp_charcoal_N=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bangladesh_VERs_agw=[]\n",
    "burkinafaso_VERs_agw=[]\n",
    "cameroon_VERs_agw=[]\n",
    "china_VERs_agw=[]\n",
    "ethiopia_VERs_agw=[]\n",
    "ghana_VERs_agw=[]\n",
    "guatemala_VERs_agw=[]\n",
    "haiti_VERs_agw=[]\n",
    "honduras_VERs_agw=[]\n",
    "india_VERs_agw=[]\n",
    "kenya_VERs_agw=[]\n",
    "lesotho_VERs_agw=[]\n",
    "malawi_VERs_agw=[]\n",
    "mali_VERs_agw=[]\n",
    "mexico_VERs_agw=[]\n",
    "mozambique_VERs_agw=[]\n",
    "myanmar_VERs_agw=[]\n",
    "nepal_VERs_agw=[]\n",
    "nigeria_VERs_agw=[]\n",
    "peru_VERs_agw=[]\n",
    "rwanda_VERs_agw=[]\n",
    "sudan_VERs_agw=[]\n",
    "uganda_VERs_agw=[]\n",
    "zambia_VERs_agw=[]\n",
    "\n",
    "firewood_VERs_agw=[]\n",
    "charcoal_VERs_agw=[]\n",
    "biomass_VERs_agw=[]\n",
    "switch_VERs_agw=[]\n",
    "\n",
    "gs_simple_firewood_VERs_agw=[]\n",
    "gs_lpg_VERs_agw=[]\n",
    "gs_imp_firewood_VERs_agw=[]\n",
    "gs_imp_charcoal_VERs_agw=[]\n",
    "cdm_imp_firewood_VERs_agw=[]\n",
    "cdm_imp_charcoal_VERs_agw=[]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compiling our results on a protocol basis\n",
    "all_VERS_agw=[]\n",
    "GS_Meter_VERs_agw=[]\n",
    "GS_VERs_agw=[]\n",
    "CDM_II_G_VERs_agw=[]\n",
    "CDM_I_E_VERs_agw=[]\n",
    "\n",
    "\n",
    "# GS \n",
    "GS_Meter_ratio_analysis_project_mean_compilation=[]\n",
    "GS_Meter_ratio_project_analysis_mean_compilation=[]\n",
    "GS_Meter_ratio_analysis_project_lower_compilation=[]\n",
    "GS_Meter_ratio_analysis_project_upper_compilation=[]\n",
    "GS_Meter_ratio_project_analysis_lower_compilation=[]\n",
    "GS_Meter_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# GS \n",
    "GS_ratio_analysis_project_mean_compilation=[]\n",
    "GS_ratio_project_analysis_mean_compilation=[]\n",
    "GS_ratio_analysis_project_lower_compilation=[]\n",
    "GS_ratio_analysis_project_upper_compilation=[]\n",
    "GS_ratio_project_analysis_lower_compilation=[]\n",
    "GS_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "#CDM II G\n",
    "CDM_II_G_ratio_analysis_project_mean_compilation=[]\n",
    "CDM_II_G_ratio_project_analysis_mean_compilation=[]\n",
    "CDM_II_G_ratio_analysis_project_lower_compilation=[]\n",
    "CDM_II_G_ratio_analysis_project_upper_compilation=[]\n",
    "CDM_II_G_ratio_project_analysis_lower_compilation=[]\n",
    "CDM_II_G_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "#CDM I E\n",
    "CDM_I_E_ratio_analysis_project_mean_compilation=[]\n",
    "CDM_I_E_ratio_project_analysis_mean_compilation=[]\n",
    "CDM_I_E_ratio_analysis_project_lower_compilation=[]\n",
    "CDM_I_E_ratio_analysis_project_upper_compilation=[]\n",
    "CDM_I_E_ratio_project_analysis_lower_compilation=[]\n",
    "CDM_I_E_ratio_project_analysis_upper_compilation=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bangladesh \n",
    "bangladesh_ratio_analysis_project_mean_compilation=[]\n",
    "bangladesh_ratio_project_analysis_mean_compilation=[]\n",
    "bangladesh_ratio_analysis_project_lower_compilation=[]\n",
    "bangladesh_ratio_analysis_project_upper_compilation=[]\n",
    "bangladesh_ratio_project_analysis_lower_compilation=[]\n",
    "bangladesh_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# burkinafaso \n",
    "burkinafaso_ratio_analysis_project_mean_compilation=[]\n",
    "burkinafaso_ratio_project_analysis_mean_compilation=[]\n",
    "burkinafaso_ratio_analysis_project_lower_compilation=[]\n",
    "burkinafaso_ratio_analysis_project_upper_compilation=[]\n",
    "burkinafaso_ratio_project_analysis_lower_compilation=[]\n",
    "burkinafaso_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# cameroon \n",
    "cameroon_ratio_analysis_project_mean_compilation=[]\n",
    "cameroon_ratio_project_analysis_mean_compilation=[]\n",
    "cameroon_ratio_analysis_project_lower_compilation=[]\n",
    "cameroon_ratio_analysis_project_upper_compilation=[]\n",
    "cameroon_ratio_project_analysis_lower_compilation=[]\n",
    "cameroon_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "\n",
    "# china \n",
    "china_ratio_analysis_project_mean_compilation=[]\n",
    "china_ratio_project_analysis_mean_compilation=[]\n",
    "china_ratio_analysis_project_lower_compilation=[]\n",
    "china_ratio_analysis_project_upper_compilation=[]\n",
    "china_ratio_project_analysis_lower_compilation=[]\n",
    "china_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# ethiopia \n",
    "ethiopia_ratio_analysis_project_mean_compilation=[]\n",
    "ethiopia_ratio_project_analysis_mean_compilation=[]\n",
    "ethiopia_ratio_analysis_project_lower_compilation=[]\n",
    "ethiopia_ratio_analysis_project_upper_compilation=[]\n",
    "ethiopia_ratio_project_analysis_lower_compilation=[]\n",
    "ethiopia_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# ghana \n",
    "ghana_ratio_analysis_project_mean_compilation=[]\n",
    "ghana_ratio_project_analysis_mean_compilation=[]\n",
    "ghana_ratio_analysis_project_lower_compilation=[]\n",
    "ghana_ratio_analysis_project_upper_compilation=[]\n",
    "ghana_ratio_project_analysis_lower_compilation=[]\n",
    "ghana_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# guatemala \n",
    "guatemala_ratio_analysis_project_mean_compilation=[]\n",
    "guatemala_ratio_project_analysis_mean_compilation=[]\n",
    "guatemala_ratio_analysis_project_lower_compilation=[]\n",
    "guatemala_ratio_analysis_project_upper_compilation=[]\n",
    "guatemala_ratio_project_analysis_lower_compilation=[]\n",
    "guatemala_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# haiti \n",
    "haiti_ratio_analysis_project_mean_compilation=[]\n",
    "haiti_ratio_project_analysis_mean_compilation=[]\n",
    "haiti_ratio_analysis_project_lower_compilation=[]\n",
    "haiti_ratio_analysis_project_upper_compilation=[]\n",
    "haiti_ratio_project_analysis_lower_compilation=[]\n",
    "haiti_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# honduras \n",
    "honduras_ratio_analysis_project_mean_compilation=[]\n",
    "honduras_ratio_project_analysis_mean_compilation=[]\n",
    "honduras_ratio_analysis_project_lower_compilation=[]\n",
    "honduras_ratio_analysis_project_upper_compilation=[]\n",
    "honduras_ratio_project_analysis_lower_compilation=[]\n",
    "honduras_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# india \n",
    "india_ratio_analysis_project_mean_compilation=[]\n",
    "india_ratio_project_analysis_mean_compilation=[]\n",
    "india_ratio_analysis_project_lower_compilation=[]\n",
    "india_ratio_analysis_project_upper_compilation=[]\n",
    "india_ratio_project_analysis_lower_compilation=[]\n",
    "india_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# kenya \n",
    "kenya_ratio_analysis_project_mean_compilation=[]\n",
    "kenya_ratio_project_analysis_mean_compilation=[]\n",
    "kenya_ratio_analysis_project_lower_compilation=[]\n",
    "kenya_ratio_analysis_project_upper_compilation=[]\n",
    "kenya_ratio_project_analysis_lower_compilation=[]\n",
    "kenya_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# lesotho \n",
    "lesotho_ratio_analysis_project_mean_compilation=[]\n",
    "lesotho_ratio_project_analysis_mean_compilation=[]\n",
    "lesotho_ratio_analysis_project_lower_compilation=[]\n",
    "lesotho_ratio_analysis_project_upper_compilation=[]\n",
    "lesotho_ratio_project_analysis_lower_compilation=[]\n",
    "lesotho_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# malawi \n",
    "malawi_ratio_analysis_project_mean_compilation=[]\n",
    "malawi_ratio_project_analysis_mean_compilation=[]\n",
    "malawi_ratio_analysis_project_lower_compilation=[]\n",
    "malawi_ratio_analysis_project_upper_compilation=[]\n",
    "malawi_ratio_project_analysis_lower_compilation=[]\n",
    "malawi_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# mali \n",
    "mali_ratio_analysis_project_mean_compilation=[]\n",
    "mali_ratio_project_analysis_mean_compilation=[]\n",
    "mali_ratio_analysis_project_lower_compilation=[]\n",
    "mali_ratio_analysis_project_upper_compilation=[]\n",
    "mali_ratio_project_analysis_lower_compilation=[]\n",
    "mali_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# mexico \n",
    "mexico_ratio_analysis_project_mean_compilation=[]\n",
    "mexico_ratio_project_analysis_mean_compilation=[]\n",
    "mexico_ratio_analysis_project_lower_compilation=[]\n",
    "mexico_ratio_analysis_project_upper_compilation=[]\n",
    "mexico_ratio_project_analysis_lower_compilation=[]\n",
    "mexico_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# mozambique \n",
    "mozambique_ratio_analysis_project_mean_compilation=[]\n",
    "mozambique_ratio_project_analysis_mean_compilation=[]\n",
    "mozambique_ratio_analysis_project_lower_compilation=[]\n",
    "mozambique_ratio_analysis_project_upper_compilation=[]\n",
    "mozambique_ratio_project_analysis_lower_compilation=[]\n",
    "mozambique_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# myanmar \n",
    "myanmar_ratio_analysis_project_mean_compilation=[]\n",
    "myanmar_ratio_project_analysis_mean_compilation=[]\n",
    "myanmar_ratio_analysis_project_lower_compilation=[]\n",
    "myanmar_ratio_analysis_project_upper_compilation=[]\n",
    "myanmar_ratio_project_analysis_lower_compilation=[]\n",
    "myanmar_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# nepal \n",
    "nepal_ratio_analysis_project_mean_compilation=[]\n",
    "nepal_ratio_project_analysis_mean_compilation=[]\n",
    "nepal_ratio_analysis_project_lower_compilation=[]\n",
    "nepal_ratio_analysis_project_upper_compilation=[]\n",
    "nepal_ratio_project_analysis_lower_compilation=[]\n",
    "nepal_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# nigeria \n",
    "nigeria_ratio_analysis_project_mean_compilation=[]\n",
    "nigeria_ratio_project_analysis_mean_compilation=[]\n",
    "nigeria_ratio_analysis_project_lower_compilation=[]\n",
    "nigeria_ratio_analysis_project_upper_compilation=[]\n",
    "nigeria_ratio_project_analysis_lower_compilation=[]\n",
    "nigeria_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# peru \n",
    "peru_ratio_analysis_project_mean_compilation=[]\n",
    "peru_ratio_project_analysis_mean_compilation=[]\n",
    "peru_ratio_analysis_project_lower_compilation=[]\n",
    "peru_ratio_analysis_project_upper_compilation=[]\n",
    "peru_ratio_project_analysis_lower_compilation=[]\n",
    "peru_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "\n",
    "# rwanda \n",
    "rwanda_ratio_analysis_project_mean_compilation=[]\n",
    "rwanda_ratio_project_analysis_mean_compilation=[]\n",
    "rwanda_ratio_analysis_project_lower_compilation=[]\n",
    "rwanda_ratio_analysis_project_upper_compilation=[]\n",
    "rwanda_ratio_project_analysis_lower_compilation=[]\n",
    "rwanda_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# sudan \n",
    "sudan_ratio_analysis_project_mean_compilation=[]\n",
    "sudan_ratio_project_analysis_mean_compilation=[]\n",
    "sudan_ratio_analysis_project_lower_compilation=[]\n",
    "sudan_ratio_analysis_project_upper_compilation=[]\n",
    "sudan_ratio_project_analysis_lower_compilation=[]\n",
    "sudan_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# zambia \n",
    "zambia_ratio_analysis_project_mean_compilation=[]\n",
    "zambia_ratio_project_analysis_mean_compilation=[]\n",
    "zambia_ratio_analysis_project_lower_compilation=[]\n",
    "zambia_ratio_analysis_project_upper_compilation=[]\n",
    "zambia_ratio_project_analysis_lower_compilation=[]\n",
    "zambia_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# uganda \n",
    "uganda_ratio_analysis_project_mean_compilation=[]\n",
    "uganda_ratio_project_analysis_mean_compilation=[]\n",
    "uganda_ratio_analysis_project_lower_compilation=[]\n",
    "uganda_ratio_analysis_project_upper_compilation=[]\n",
    "uganda_ratio_project_analysis_lower_compilation=[]\n",
    "uganda_ratio_project_analysis_upper_compilation=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# gs_lpg\n",
    "gs_lpg_ratio_analysis_project_mean_compilation=[]\n",
    "gs_lpg_ratio_project_analysis_mean_compilation=[]\n",
    "gs_lpg_ratio_analysis_project_lower_compilation=[]\n",
    "gs_lpg_ratio_analysis_project_upper_compilation=[]\n",
    "gs_lpg_ratio_project_analysis_lower_compilation=[]\n",
    "gs_lpg_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# gs_imp_firewood\n",
    "gs_imp_firewood_ratio_analysis_project_mean_compilation=[]\n",
    "gs_imp_firewood_ratio_project_analysis_mean_compilation=[]\n",
    "gs_imp_firewood_ratio_analysis_project_lower_compilation=[]\n",
    "gs_imp_firewood_ratio_analysis_project_upper_compilation=[]\n",
    "gs_imp_firewood_ratio_project_analysis_lower_compilation=[]\n",
    "gs_imp_firewood_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# gs_imp_charcoal\n",
    "gs_imp_charcoal_ratio_analysis_project_mean_compilation=[]\n",
    "gs_imp_charcoal_ratio_project_analysis_mean_compilation=[]\n",
    "gs_imp_charcoal_ratio_analysis_project_lower_compilation=[]\n",
    "gs_imp_charcoal_ratio_analysis_project_upper_compilation=[]\n",
    "gs_imp_charcoal_ratio_project_analysis_lower_compilation=[]\n",
    "gs_imp_charcoal_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# cdm_imp_firewood\n",
    "cdm_imp_firewood_ratio_analysis_project_mean_compilation=[]\n",
    "cdm_imp_firewood_ratio_project_analysis_mean_compilation=[]\n",
    "cdm_imp_firewood_ratio_analysis_project_lower_compilation=[]\n",
    "cdm_imp_firewood_ratio_analysis_project_upper_compilation=[]\n",
    "cdm_imp_firewood_ratio_project_analysis_lower_compilation=[]\n",
    "cdm_imp_firewood_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# cdm_imp_charcoal\n",
    "cdm_imp_charcoal_ratio_analysis_project_mean_compilation=[]\n",
    "cdm_imp_charcoal_ratio_project_analysis_mean_compilation=[]\n",
    "cdm_imp_charcoal_ratio_analysis_project_lower_compilation=[]\n",
    "cdm_imp_charcoal_ratio_analysis_project_upper_compilation=[]\n",
    "cdm_imp_charcoal_ratio_project_analysis_lower_compilation=[]\n",
    "cdm_imp_charcoal_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# gs_simple_firewood\n",
    "gs_simple_firewood_ratio_analysis_project_mean_compilation=[]\n",
    "gs_simple_firewood_ratio_project_analysis_mean_compilation=[]\n",
    "gs_simple_firewood_ratio_analysis_project_lower_compilation=[]\n",
    "gs_simple_firewood_ratio_analysis_project_upper_compilation=[]\n",
    "gs_simple_firewood_ratio_project_analysis_lower_compilation=[]\n",
    "gs_simple_firewood_ratio_project_analysis_upper_compilation=[]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import relevant packages\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "from scipy import stats as stats\n",
    "from scipy.stats import triang\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#triangular distributions\n",
    "\n",
    "np.random.seed(4)\n",
    "N = 10000                # random numbers to generate\n",
    "\n",
    "    \n",
    "#Adoption \n",
    "min_adoption=.4\n",
    "mean_adoption=.58\n",
    "max_adoption= .92\n",
    "n_adoption=np.random.triangular(left=min_adoption, mode=mean_adoption, right=max_adoption, size=(N))\n",
    "    \n",
    "req_adoption=np.random.uniform(0,mean_adoption,N)      \n",
    "#Usage\n",
    "min_usage=0.16\n",
    "mean_usage=0.52\n",
    "max_usage=0.85\n",
    "n_usage=np.random.triangular(left=min_usage, mode=mean_usage, right=max_usage, size=(N))\n",
    "req_usage=np.random.uniform(0,mean_usage,N)\n",
    "                               \n",
    "#Stacking\n",
    "min_stacking=.193\n",
    "mean_stacking=.673\n",
    "max_stacking=.99\n",
    "n_stacking=np.random.triangular(left=min_stacking, mode=mean_stacking, right=max_stacking, size=(N))\n",
    "req_stacking=np.random.uniform(0,mean_stacking,N)\n",
    "\n",
    "# Renewable GWP from Whitman & Lehman\n",
    "\n",
    "gwp_ch4_renewable=28 #tCO2e/tonne CH4 i.e., instead of typically 25\n",
    "\n",
    "# Whitman & lehmann 2011 also cite a value for CO \n",
    "# but we do not include CO in our analysis\n",
    "    \n",
    "gwp_ch4_agw=30\n",
    "gwp_n2O_agw=298\n",
    "    \n",
    "eff_trad_firewood=0.14\n",
    "eff_improv_firewood=.21\n",
    "eff_trad_charcoal=0.2\n",
    "eff_improv_charcoal=.22\n",
    "eff_kerosene=.47\n",
    "eff_LPG=.5\n",
    "eff_ethanol=.47\n",
    "eff_pellets=.5 #only two pellet projects is the mimi motto\n",
    "\n",
    "ncv_firewood_agw=.016\n",
    "ncv_charcoal_agw=.027\n",
    "ncv_kerosene_agw=.043\n",
    "ncv_LPG_agw=.045\n",
    "ncv_ethanol_agw=.027\n",
    "ncv_pellets_agw=.017\n",
    "\n",
    "kg_per_liter_ethanol_agw=0.783 #kg/liter\n",
    "    \n",
    "ef_combustion_co2_firewood=95.1 #tCO2/TJ \n",
    "ef_combustion_ch4_firewood=0.58 #tCH4/TJ \n",
    "ef_combustion_n2O_firewood=0 #tN2O/TJ\n",
    "\n",
    "ef_combustion_co2_charcoal=114.75 #tCO2/TJ \n",
    "ef_combustion_ch4_charcoal=0.255 #tCH4/TJ \n",
    "ef_combustion_n2O_charcoal=0 #tN2O/TJ \n",
    "\n",
    "\n",
    "ef_total_not_combustion_co2_charcoal=87.725 #tCO2/TJ \n",
    "ef_total_not_combustion_ch4_charcoal=2.06 #tCH4/TJ \n",
    "ef_total_not_combustion_n2O_charcoal=0.0075#tN2O/TJ \n",
    "    \n",
    "ef_total_co2_kerosene=79.9 #tCO2/TJ \n",
    "ef_total_ch4_kerosene=.107 #tCH4/TJ \n",
    "ef_total_n2O_kerosene=0.000141 #tN2O/TJ \n",
    "    \n",
    "ef_total_co2_LPG=82.8#tCO2/TJ \n",
    "ef_total_ch4_LPG=0.117 #tCH4/TJ \n",
    "ef_total_n2O_LPG=0.000188 #tN2O/TJ \n",
    "    \n",
    "ef_total_co2_ethanol=60.45#tCO2/TJ \n",
    "ef_total_ch4_ethanol=0.61 #tCH4/TJ \n",
    "ef_total_n2O_ethanol=0.05 #tN2O/TJ\n",
    "    \n",
    "ef_total_co2_pellets=90.05#tCO2/TJ \n",
    "ef_total_ch4_pellets=0.105#tCH4/TJ \n",
    "ef_total_n2O_pellets=0 #tN2O/TJ \n",
    "    \n",
    "ef_total_zeroed_co2_ethanol=-9.78#tCO2/TJ \n",
    "ef_total_zeroed_ch4_ethanol=0.61 #tCH4/TJ \n",
    "ef_total_zeroed_n2O_ethanol=0.05 #tN2O/TJ \n",
    "    \n",
    "ef_total_zeroed_co2_pellets=0 #tCO2/TJ \n",
    "ef_total_zeroed_ch4_pellets=0.105 #tCH4/TJ \n",
    "ef_total_zeroed_n2O_pellets=0 #tN2O/TJ \n",
    "    \n",
    "sum_VERS_not_per_stove_per_day_agw=0  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.39528718687797e-05\n",
      "We find that GS10974 project reports emissions which are 25.1 [ -135.13  ,  185.33 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.98 [ 3.98  ,  3.98 ] % of the verified credits in the PDD.\n",
      "0.030081576300695405\n",
      "We find that GS10974 project reports emissions which are 1.48 [ 1.48  ,  1.48 ] times overcredited.\n",
      "Our analysis finds emissions which are 67.72 [ 67.71  ,  67.72 ] % of the verified credits in the PDD.\n",
      "7.234485315552362e-05\n",
      "We find that GS10974 project reports emissions which are 24.89 [ -137.95  ,  187.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.02 [ 4.02  ,  4.02 ] % of the verified credits in the PDD.\n",
      "0.029570674125931264\n",
      "We find that GS10974 project reports emissions which are 1.48 [ 1.48  ,  1.48 ] times overcredited.\n",
      "Our analysis finds emissions which are 67.75 [ 67.74  ,  67.75 ] % of the verified credits in the PDD.\n",
      "0.00018886727296005544\n",
      "We find that VCS1216 project reports emissions which are 41.37 [ 40.02  ,  42.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.42 [ 2.42  ,  2.42 ] % of the verified credits in the PDD.\n",
      "0.00018838100288904883\n",
      "We find that VCS1216 project reports emissions which are 41.48 [ 40.06  ,  42.9 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.41 [ 2.41  ,  2.41 ] % of the verified credits in the PDD.\n",
      "0.00019046792621848973\n",
      "We find that VCS1216 project reports emissions which are 41.2 [ 39.8  ,  42.6 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.43 [ 2.43  ,  2.43 ] % of the verified credits in the PDD.\n",
      "0.00019035667247268784\n",
      "We find that VCS1216 project reports emissions which are 41.23 [ 39.86  ,  42.59 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.43 [ 2.43  ,  2.43 ] % of the verified credits in the PDD.\n",
      "0.00018761674835351277\n",
      "We find that VCS1216 project reports emissions which are 41.52 [ 40.13  ,  42.91 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.41 [ 2.41  ,  2.41 ] % of the verified credits in the PDD.\n",
      "0.0001875141313127151\n",
      "We find that VCS1216 project reports emissions which are 41.54 [ 40.17  ,  42.92 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.41 [ 2.41  ,  2.41 ] % of the verified credits in the PDD.\n",
      "0.00019234230824144186\n",
      "We find that VCS1216 project reports emissions which are 40.99 [ 39.59  ,  42.39 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.44 [ 2.44  ,  2.44 ] % of the verified credits in the PDD.\n",
      "0.0001926128415690308\n",
      "We find that VCS1216 project reports emissions which are 40.93 [ 39.57  ,  42.29 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.44 [ 2.44  ,  2.44 ] % of the verified credits in the PDD.\n",
      "0.0001899369322914841\n",
      "We find that VCS1216 project reports emissions which are 41.23 [ 39.85  ,  42.61 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.43 [ 2.43  ,  2.43 ] % of the verified credits in the PDD.\n",
      "0.00018960644067852897\n",
      "We find that VCS1216 project reports emissions which are 41.3 [ 39.93  ,  42.68 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.42 [ 2.42  ,  2.42 ] % of the verified credits in the PDD.\n",
      "0.0001858418020375878\n",
      "We find that VCS1216 project reports emissions which are 41.77 [ 40.41  ,  43.14 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.39 [ 2.39  ,  2.39 ] % of the verified credits in the PDD.\n",
      "0.00018581927627861074\n",
      "We find that VCS1216 project reports emissions which are 41.78 [ 40.4  ,  43.15 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.39 [ 2.39  ,  2.39 ] % of the verified credits in the PDD.\n",
      "0.0001560711807095598\n",
      "We find that VCS1216 project reports emissions which are 25.66 [ 24.78  ,  26.53 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.9 [ 3.9  ,  3.9 ] % of the verified credits in the PDD.\n",
      "0.00015606438738836182\n",
      "We find that VCS1216 project reports emissions which are 25.66 [ 24.8  ,  26.51 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.9 [ 3.9  ,  3.9 ] % of the verified credits in the PDD.\n",
      "0.00015101376574707577\n",
      "We find that VCS1216 project reports emissions which are 26.2 [ 25.32  ,  27.08 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.82 [ 3.82  ,  3.82 ] % of the verified credits in the PDD.\n",
      "0.00015087704781970533\n",
      "We find that VCS1216 project reports emissions which are 26.22 [ 25.35  ,  27.1 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.81 [ 3.81  ,  3.81 ] % of the verified credits in the PDD.\n",
      "0.0001581109268106007\n",
      "We find that VCS1216 project reports emissions which are 25.47 [ 24.58  ,  26.35 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.93 [ 3.93  ,  3.93 ] % of the verified credits in the PDD.\n",
      "0.00015814869744389885\n",
      "We find that VCS1216 project reports emissions which are 25.46 [ 24.62  ,  26.31 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.93 [ 3.93  ,  3.93 ] % of the verified credits in the PDD.\n",
      "0.00015880010953559016\n",
      "We find that VCS1216 project reports emissions which are 25.41 [ 24.57  ,  26.24 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.94 [ 3.94  ,  3.94 ] % of the verified credits in the PDD.\n",
      "0.00015899231049955654\n",
      "We find that VCS1216 project reports emissions which are 25.38 [ 24.52  ,  26.23 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.94 [ 3.94  ,  3.94 ] % of the verified credits in the PDD.\n",
      "0.0001586703225442441\n",
      "We find that VCS1216 project reports emissions which are 25.43 [ 24.56  ,  26.3 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.93 [ 3.93  ,  3.93 ] % of the verified credits in the PDD.\n",
      "0.00015873649515748577\n",
      "We find that VCS1216 project reports emissions which are 25.42 [ 24.56  ,  26.27 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.93 [ 3.93  ,  3.93 ] % of the verified credits in the PDD.\n",
      "0.00017190393147201806\n",
      "We find that VCS1216 project reports emissions which are 24.2 [ 23.39  ,  25.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.13 [ 4.13  ,  4.13 ] % of the verified credits in the PDD.\n",
      "0.00017176844945062655\n",
      "We find that VCS1216 project reports emissions which are 24.21 [ 23.43  ,  25.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.13 [ 4.13  ,  4.13 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 17.83 [ 17.27  ,  18.4 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.61 [ 5.61  ,  5.61 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 17.83 [ 17.27  ,  18.39 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.61 [ 5.61  ,  5.61 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 18.38 [ 17.8  ,  18.96 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.44 [ 5.44  ,  5.44 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 18.39 [ 17.81  ,  18.96 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.44 [ 5.44  ,  5.44 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 18.79 [ 18.19  ,  19.38 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.32 [ 5.32  ,  5.32 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 18.78 [ 18.19  ,  19.37 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.32 [ 5.32  ,  5.33 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 20.27 [ 19.63  ,  20.91 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.93 [ 4.93  ,  4.93 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 20.27 [ 19.63  ,  20.91 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.93 [ 4.93  ,  4.93 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 22.16 [ 21.47  ,  22.86 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.51 [ 4.51  ,  4.51 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 22.16 [ 21.46  ,  22.85 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.51 [ 4.51  ,  4.51 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 27.5 [ 26.64  ,  28.36 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.64 [ 3.64  ,  3.64 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 27.48 [ 26.62  ,  28.34 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.64 [ 3.64  ,  3.64 ] % of the verified credits in the PDD.\n",
      "0.00024869374276589314\n",
      "We find that VCS1719 project reports emissions which are 23.29 [ 22.55  ,  24.02 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.29 [ 4.29  ,  4.29 ] % of the verified credits in the PDD.\n",
      "0.00039173762873671213\n",
      "We find that VCS1721 project reports emissions which are 21.99 [ 21.3  ,  22.68 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.55 [ 4.55  ,  4.55 ] % of the verified credits in the PDD.\n",
      "We find that GS7312 project reports emissions which are 7.81 [ 7.76  ,  7.86 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.81 [ 12.8  ,  12.81 ] % of the verified credits in the PDD.\n",
      "We find that GS7312 project reports emissions which are 7.81 [ 7.76  ,  7.86 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.81 [ 12.81  ,  12.81 ] % of the verified credits in the PDD.\n",
      "We find that GS500 project reports emissions which are 35.02 [ 34.72  ,  35.32 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.86 [ 2.86  ,  2.86 ] % of the verified credits in the PDD.\n",
      "We find that GS10884 project reports emissions which are 3.29 [ 3.26  ,  3.32 ] times overcredited.\n",
      "Our analysis finds emissions which are 30.41 [ 30.41  ,  30.42 ] % of the verified credits in the PDD.\n",
      "We find that GS10884 project reports emissions which are 0.97 [ 0.96  ,  0.97 ] times overcredited.\n",
      "Our analysis finds emissions which are 103.56 [ 103.56  ,  103.56 ] % of the verified credits in the PDD.\n",
      "We find that GS447 project reports emissions which are 7.99 [ 7.94  ,  8.04 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.51 [ 12.51  ,  12.51 ] % of the verified credits in the PDD.\n",
      "We find that GS447 project reports emissions which are 8.13 [ 8.08  ,  8.18 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.3 [ 12.3  ,  12.31 ] % of the verified credits in the PDD.\n",
      "We find that GS2564 project reports emissions which are 11.12 [ 11.05  ,  11.19 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.99 [ 8.99  ,  8.99 ] % of the verified credits in the PDD.\n",
      "We find that GS7438 project reports emissions which are 4.71 [ 4.68  ,  4.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 21.25 [ 21.25  ,  21.25 ] % of the verified credits in the PDD.\n",
      "We find that GS7438 project reports emissions which are 42.46 [ 42.2  ,  42.71 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.36 [ 2.36  ,  2.36 ] % of the verified credits in the PDD.\n",
      "0.0003857385776082566\n",
      "We find that GS913 project reports emissions which are 19.51 [ 18.89  ,  20.14 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.12 [ 5.12  ,  5.12 ] % of the verified credits in the PDD.\n",
      "0.0004840093201385621\n",
      "We find that GS6212 project reports emissions which are 12.06 [ 11.67  ,  12.44 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.3 [ 8.29  ,  8.3 ] % of the verified credits in the PDD.\n",
      "0.0004840869563974565\n",
      "We find that GS6212 project reports emissions which are 13.41 [ 12.98  ,  13.83 ] times overcredited.\n",
      "Our analysis finds emissions which are 7.46 [ 7.46  ,  7.46 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 7.64 [ -0.65  ,  15.93 ] times overcredited.\n",
      "Our analysis finds emissions which are 13.09 [ 13.09  ,  13.1 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 7.67 [ -2.31  ,  17.65 ] times overcredited.\n",
      "Our analysis finds emissions which are 13.04 [ 13.04  ,  13.04 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 7.8 [ -1.21  ,  16.8 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.83 [ 12.82  ,  12.83 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 8.36 [ 0.15  ,  16.57 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.96 [ 11.96  ,  11.96 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 8.45 [ -1.64  ,  18.55 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.83 [ 11.83  ,  11.83 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 8.49 [ -1.99  ,  18.97 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.78 [ 11.78  ,  11.78 ] % of the verified credits in the PDD.\n",
      "We find that GS3112 project reports emissions which are 18.61 [ 18.02  ,  19.2 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.37 [ 5.37  ,  5.37 ] % of the verified credits in the PDD.\n",
      "We find that GS3112 project reports emissions which are 18.17 [ 17.6  ,  18.74 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.5 [ 5.5  ,  5.5 ] % of the verified credits in the PDD.\n",
      "We find that GS3112 project reports emissions which are 18.16 [ 17.59  ,  18.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.51 [ 5.51  ,  5.51 ] % of the verified credits in the PDD.\n",
      "We find that GS3112 project reports emissions which are 17.87 [ 17.3  ,  18.43 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.6 [ 5.6  ,  5.6 ] % of the verified credits in the PDD.\n",
      "We find that GS5642 project reports emissions which are 3.23 [ 3.22  ,  3.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 30.91 [ 30.91  ,  30.92 ] % of the verified credits in the PDD.\n",
      "We find that GS5642 project reports emissions which are 19.58 [ 19.46  ,  19.7 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.11 [ 5.11  ,  5.11 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 19.92 [ 19.3  ,  20.55 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.02 [ 5.02  ,  5.02 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 21.07 [ 20.41  ,  21.74 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.75 [ 4.74  ,  4.75 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 18.12 [ 17.55  ,  18.69 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.52 [ 5.52  ,  5.52 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 19.92 [ 19.3  ,  20.55 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.02 [ 5.02  ,  5.02 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 21.07 [ 20.41  ,  21.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.75 [ 4.75  ,  4.75 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 18.12 [ 17.55  ,  18.69 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.52 [ 5.52  ,  5.52 ] % of the verified credits in the PDD.\n",
      "8.871213498566201e-06\n",
      "We find that GS2744 project reports emissions which are 134.14 [ 129.48  ,  138.8 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.75 [ 0.75  ,  0.75 ] % of the verified credits in the PDD.\n",
      "2.0460430138664044e-05\n",
      "We find that GS2744 project reports emissions which are 64.14 [ 61.99  ,  66.29 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.56 [ 1.56  ,  1.56 ] % of the verified credits in the PDD.\n",
      "0.00019633518206032646\n",
      "We find that GS1094 project reports emissions which are 174.83 [ 169.26  ,  180.39 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.57 [ 0.57  ,  0.57 ] % of the verified credits in the PDD.\n",
      "0.00020229919167245468\n",
      "We find that GS1094 project reports emissions which are 169.24 [ 163.88  ,  174.61 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.59 [ 0.59  ,  0.59 ] % of the verified credits in the PDD.\n",
      "0.00019876322680920712\n",
      "We find that GS1094 project reports emissions which are 181.18 [ 175.45  ,  186.92 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.55 [ 0.55  ,  0.55 ] % of the verified credits in the PDD.\n",
      "0.00017571200799853766\n",
      "We find that GS1094 project reports emissions which are 194.27 [ 188.1  ,  200.44 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.51 [ 0.51  ,  0.51 ] % of the verified credits in the PDD.\n",
      "0.00014333094732354938\n",
      "We find that GS1094 project reports emissions which are 219.64 [ 212.67  ,  226.6 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.46 [ 0.46  ,  0.46 ] % of the verified credits in the PDD.\n",
      "0.00015322053809305818\n",
      "We find that GS1094 project reports emissions which are 210.81 [ 204.16  ,  217.45 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.47 [ 0.47  ,  0.47 ] % of the verified credits in the PDD.\n",
      "0.0001012347528090441\n",
      "We find that GS4291 project reports emissions which are 22.53 [ 21.82  ,  23.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.44 [ 4.44  ,  4.44 ] % of the verified credits in the PDD.\n",
      "0.00010093215751563111\n",
      "We find that GS4291 project reports emissions which are 22.6 [ 21.87  ,  23.33 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.42 [ 4.42  ,  4.43 ] % of the verified credits in the PDD.\n",
      "6.015730221322031e-05\n",
      "We find that GS6604 project reports emissions which are 53.42 [ 51.72  ,  55.11 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.87 [ 1.87  ,  1.87 ] % of the verified credits in the PDD.\n",
      "We find that GS 2896  project reports emissions which are 30.4 [ 29.41  ,  31.38 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.29 [ 3.29  ,  3.29 ] % of the verified credits in the PDD.\n",
      "We find that GS 3422  project reports emissions which are 53.27 [ 51.57  ,  54.97 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.88 [ 1.88  ,  1.88 ] % of the verified credits in the PDD.\n",
      "We find that GS 2456 project reports emissions which are 69.63 [ 67.44  ,  71.83 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.44 [ 1.44  ,  1.44 ] % of the verified credits in the PDD.\n",
      "We find that GS 2456 project reports emissions which are 83.41 [ 80.77  ,  86.04 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.2 [ 1.2  ,  1.2 ] % of the verified credits in the PDD.\n",
      "We find that GS 2456 project reports emissions which are 91.93 [ 89.05  ,  94.82 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.09 [ 1.09  ,  1.09 ] % of the verified credits in the PDD.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We find that GS 2456 project reports emissions which are 99.39 [ 96.25  ,  102.52 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.01 [ 1.01  ,  1.01 ] % of the verified credits in the PDD.\n",
      "We find that GS 2456 project reports emissions which are 84.92 [ 82.25  ,  87.59 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.18 [ 1.18  ,  1.18 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 65.72 [ 63.61  ,  67.82 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.52 [ 1.52  ,  1.52 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 59.0 [ 57.11  ,  60.89 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.69 [ 1.69  ,  1.7 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 58.38 [ 56.47  ,  60.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.71 [ 1.71  ,  1.71 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 98.92 [ 95.75  ,  102.09 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.01 [ 1.01  ,  1.01 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 29.89 [ 28.93  ,  30.85 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.35 [ 3.35  ,  3.35 ] % of the verified credits in the PDD.\n",
      "We find that GS 3018 project reports emissions which are 7.9 [ 7.65  ,  8.15 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.66 [ 12.66  ,  12.66 ] % of the verified credits in the PDD.\n",
      "We find that GS 3018 project reports emissions which are 5.21 [ 5.05  ,  5.38 ] times overcredited.\n",
      "Our analysis finds emissions which are 19.18 [ 19.18  ,  19.18 ] % of the verified credits in the PDD.\n",
      "We find that GS 3018 project reports emissions which are 2.62 [ 2.54  ,  2.7 ] times overcredited.\n",
      "Our analysis finds emissions which are 38.17 [ 38.17  ,  38.18 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 6.67 [ 6.63  ,  6.71 ] times overcredited.\n",
      "Our analysis finds emissions which are 14.99 [ 14.99  ,  14.99 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 6.47 [ 6.43  ,  6.51 ] times overcredited.\n",
      "Our analysis finds emissions which are 15.45 [ 15.45  ,  15.45 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 6.4 [ 6.36  ,  6.44 ] times overcredited.\n",
      "Our analysis finds emissions which are 15.63 [ 15.63  ,  15.63 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 5.95 [ 5.92  ,  5.99 ] times overcredited.\n",
      "Our analysis finds emissions which are 16.8 [ 16.8  ,  16.8 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 5.64 [ 5.6  ,  5.67 ] times overcredited.\n",
      "Our analysis finds emissions which are 17.74 [ 17.74  ,  17.74 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 1.56 [ 1.55  ,  1.57 ] times overcredited.\n",
      "Our analysis finds emissions which are 64.19 [ 64.18  ,  64.19 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 10.13 [ 10.07  ,  10.2 ] times overcredited.\n",
      "Our analysis finds emissions which are 9.87 [ 9.87  ,  9.87 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 10.03 [ 9.97  ,  10.09 ] times overcredited.\n",
      "Our analysis finds emissions which are 9.97 [ 9.97  ,  9.97 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 10.19 [ 10.12  ,  10.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 9.82 [ 9.82  ,  9.82 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 8.23 [ 8.18  ,  8.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.15 [ 12.15  ,  12.15 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 8.11 [ 8.06  ,  8.16 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.33 [ 12.33  ,  12.33 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 8.68 [ 8.62  ,  8.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.52 [ 11.52  ,  11.52 ] % of the verified credits in the PDD.\n",
      "We find that GS2445 project reports emissions which are 6.56 [ 6.51  ,  6.6 ] times overcredited.\n",
      "Our analysis finds emissions which are 15.25 [ 15.25  ,  15.26 ] % of the verified credits in the PDD.\n",
      "We find that GS7578 project reports emissions which are 7.52 [ 7.45  ,  7.59 ] times overcredited.\n",
      "Our analysis finds emissions which are 13.3 [ 13.3  ,  13.3 ] % of the verified credits in the PDD.\n",
      "We find that GS2513 project reports emissions which are 8.71 [ 8.64  ,  8.79 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.48 [ 11.48  ,  11.48 ] % of the verified credits in the PDD.\n",
      "We find that GS4677 project reports emissions which are 3.47 [ 3.44  ,  3.5 ] times overcredited.\n",
      "Our analysis finds emissions which are 28.84 [ 28.83  ,  28.84 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 9.22 [ 9.14  ,  9.31 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.84 [ 10.84  ,  10.84 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 9.45 [ 9.37  ,  9.53 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.58 [ 10.58  ,  10.58 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 441.93 [ 438.56  ,  445.29 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.23 [ 0.23  ,  0.23 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 442.14 [ 438.81  ,  445.47 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.23 [ 0.23  ,  0.23 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 24.09 [ 23.91  ,  24.27 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.15 [ 4.15  ,  4.15 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 24.06 [ 23.88  ,  24.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.16 [ 4.16  ,  4.16 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 24.06 [ 23.88  ,  24.24 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.16 [ 4.16  ,  4.16 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 24.12 [ 23.94  ,  24.3 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.15 [ 4.15  ,  4.15 ] % of the verified credits in the PDD.\n",
      "We find that GS5003 project reports emissions which are 37.67 [ 37.44  ,  37.89 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.65 [ 2.65  ,  2.66 ] % of the verified credits in the PDD.\n",
      "We find that GS5003 project reports emissions which are 4.97 [ 4.94  ,  5.01 ] times overcredited.\n",
      "Our analysis finds emissions which are 20.1 [ 20.1  ,  20.1 ] % of the verified credits in the PDD.\n",
      "We find that GS5003 project reports emissions which are 5.1 [ 5.07  ,  5.13 ] times overcredited.\n",
      "Our analysis finds emissions which are 19.61 [ 19.61  ,  19.61 ] % of the verified credits in the PDD.\n",
      "We find that GS6129 project reports emissions which are 76.24 [ 73.84  ,  78.64 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.31 [ 1.31  ,  1.31 ] % of the verified credits in the PDD.\n",
      "We find that GS6129 project reports emissions which are 67.87 [ 65.74  ,  70.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.47 [ 1.47  ,  1.47 ] % of the verified credits in the PDD.\n",
      "We find that GS6129 project reports emissions which are 68.72 [ 66.56  ,  70.89 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.46 [ 1.45  ,  1.46 ] % of the verified credits in the PDD.\n",
      "We find that GS407 project reports emissions which are 10.06 [ 10.0  ,  10.12 ] times overcredited.\n",
      "Our analysis finds emissions which are 9.94 [ 9.94  ,  9.94 ] % of the verified credits in the PDD.\n",
      "We find that GS407 project reports emissions which are 9.1 [ 9.05  ,  9.16 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.99 [ 10.99  ,  10.99 ] % of the verified credits in the PDD.\n",
      "We find that GS407 project reports emissions which are 0.64 [ 0.64  ,  0.64 ] times overcredited.\n",
      "Our analysis finds emissions which are 156.64 [ 156.64  ,  156.64 ] % of the verified credits in the PDD.\n",
      "We find that GS407 project reports emissions which are 0.64 [ 0.64  ,  0.64 ] times overcredited.\n",
      "Our analysis finds emissions which are 156.65 [ 156.65  ,  156.65 ] % of the verified credits in the PDD.\n",
      "We find that GS11509 project reports emissions which are 1.38 [ 1.38  ,  1.38 ] times overcredited.\n",
      "Our analysis finds emissions which are 72.52 [ 72.51  ,  72.52 ] % of the verified credits in the PDD.\n",
      "We find that GS11352 project reports emissions which are 2.0 [ 2.0  ,  2.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 49.91 [ 49.91  ,  49.91 ] % of the verified credits in the PDD.\n",
      "We find that GS11507 project reports emissions which are 1.46 [ 1.46  ,  1.46 ] times overcredited.\n",
      "Our analysis finds emissions which are 68.48 [ 68.48  ,  68.48 ] % of the verified credits in the PDD.\n",
      "We find that GS1146 project reports emissions which are 12.2 [ 6.48  ,  17.92 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.2 [ 8.2  ,  8.2 ] % of the verified credits in the PDD.\n",
      "We find that GS1146 project reports emissions which are 4.05 [ 4.03  ,  4.08 ] times overcredited.\n",
      "Our analysis finds emissions which are 24.68 [ 24.68  ,  24.68 ] % of the verified credits in the PDD.\n",
      "We find that GS1146 project reports emissions which are 9.11 [ 9.02  ,  9.2 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.98 [ 10.98  ,  10.98 ] % of the verified credits in the PDD.\n",
      "We find that GS1146 project reports emissions which are 7.63 [ 1.03  ,  14.23 ] times overcredited.\n",
      "Our analysis finds emissions which are 13.1 [ 13.1  ,  13.1 ] % of the verified credits in the PDD.\n",
      "We find that GS 11330 project reports emissions which are 2.72 [ 2.7  ,  2.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 36.82 [ 36.81  ,  36.82 ] % of the verified credits in the PDD.\n",
      "We find that GS 3071 project reports emissions which are 3.17 [ 3.15  ,  3.19 ] times overcredited.\n",
      "Our analysis finds emissions which are 31.58 [ 31.58  ,  31.58 ] % of the verified credits in the PDD.\n",
      "We find that GS 10777 project reports emissions which are 11.72 [ 11.65  ,  11.79 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.53 [ 8.53  ,  8.53 ] % of the verified credits in the PDD.\n",
      "We find that GS 10886 project reports emissions which are 11.5 [ 11.43  ,  11.57 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.69 [ 8.69  ,  8.7 ] % of the verified credits in the PDD.\n",
      "We find that GS 5107 project reports emissions which are 18.59 [ 18.48  ,  18.7 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.38 [ 5.38  ,  5.38 ] % of the verified credits in the PDD.\n",
      "0.0005298376248614529\n",
      "We find that GS 11195 project reports emissions which are 18.85 [ 18.26  ,  19.44 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.31 [ 5.3  ,  5.31 ] % of the verified credits in the PDD.\n",
      "0.00017734978501746386\n",
      "We find that GS 2077 project reports emissions which are 48.27 [ 46.76  ,  49.79 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.07 [ 2.07  ,  2.07 ] % of the verified credits in the PDD.\n",
      "0.0004166809150947603\n",
      "We find that GS 411 project reports emissions which are 16.19 [ 15.68  ,  16.7 ] times overcredited.\n",
      "Our analysis finds emissions which are 6.18 [ 6.18  ,  6.18 ] % of the verified credits in the PDD.\n",
      "We find that GS 10914 project reports emissions which are 8.62 [ 8.35  ,  8.89 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.6 [ 11.6  ,  11.6 ] % of the verified credits in the PDD.\n",
      "We find that GS 10781 project reports emissions which are 52.45 [ 50.76  ,  54.13 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.91 [ 1.91  ,  1.91 ] % of the verified credits in the PDD.\n",
      "We find that GS 5660 project reports emissions which are 68.62 [ 66.46  ,  70.78 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.46 [ 1.46  ,  1.46 ] % of the verified credits in the PDD.\n"
     ]
    }
   ],
   "source": [
    "i=1 # The row you want to evaluate\n",
    "while i<152:\n",
    "    # first row is units\n",
    "    VERs=VERs_all.iloc[i,1]\n",
    "    protocol_type=df['protocol_type'][i]\n",
    "\n",
    "    protocol_id=df['protocol_id'][i]\n",
    "\n",
    "    monitoring_period=df['monitoring_period'][i]\n",
    "\n",
    "    verified_credits=df['verified_credits'][i]\n",
    "\n",
    "    num_countries=df['num_countries'][i]\n",
    "\n",
    "    prov_project=df['prov_project'][i]\n",
    "\n",
    "    fNRB_project=df['fNRB_project'][i]\n",
    "\n",
    "    mfNRB_bailis=df['mfNRB_bailis'][i]\n",
    "\n",
    "    expfNRB_bailis=df['expfNRB_bailis'][i]\n",
    "\n",
    "    n_devices_domestic=df['n_devices_domestic'][i]\n",
    "\n",
    "    n_devices_commercial=df['n_devices_commercial'][i]\n",
    "\n",
    "    project_days=df['project_days'][i]\n",
    "    \n",
    "    project_days_commercial=df['project_days_commercial'][i]\n",
    "\n",
    "    av_hhsize=df['av_hhsize'][i]\n",
    "\n",
    "    \n",
    "    stove_year_cohort=df['stove_year_cohort'][i]\n",
    "\n",
    "    # Baseline Fuel % What are the baseline fuels in this project location? [put 0% if no use in this location]\n",
    "    prc_firewood_baseline=df['prc_firewood_baseline'][i]\n",
    "\n",
    "    prc_charcoal_baseline=df['prc_charcoal_baseline'][i]\n",
    "\n",
    "    prc_kerosene_baseline=df['prc_kerosene_baseline'][i]\n",
    "\n",
    "    prc_lpg_baseline=df['prc_lpg_baseline'][i]\n",
    "\n",
    "    prc_ethanol_baseline=df['prc_ethanol_baseline'][i]\n",
    "\n",
    "    prc_electric_baseline=df['prc_electric_baseline'][i]\n",
    "\n",
    "    prc_pellets_baseline=df['prc_pellets_baseline'][i]\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "    # Baseline Fuel % What are the baseline commercial fuels in this project location? [put 0% if no use in this location]\n",
    "    prc_firewood_baseline_commercial=df['prc_firewood_baseline_commercial'][i]\n",
    "\n",
    "    prc_charcoal_baseline_commercial=df['prc_charcoal_baseline_commercial'][i]\n",
    "\n",
    "    prc_kerosene_baseline_commercial=df['prc_kerosene_baseline_commercial'][i]\n",
    "\n",
    "    prc_lpg_baseline_commercial=df['prc_lpg_baseline_commercial'][i]\n",
    "\n",
    "    prc_ethanol_baseline_commercial=df['prc_ethanol_baseline_commercial'][i]\n",
    "\n",
    "    prc_electric_baseline_commercial=df['prc_electric_baseline_commercial'][i]\n",
    "\n",
    "    prc_pellets_baseline_commercial=df['prc_pellets_baseline_commercial'][i]\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # Baseline Consumption: What is the baseline fuel consumption of each baseline fuel in this project location [put 0 if no use in this location]\n",
    "    kg_firewood_baseline=df['kg_firewood_baseline'][i]\n",
    "\n",
    "    kg_charcoal_baseline=df['kg_charcoal_baseline'][i]\n",
    "\n",
    "    kg_kerosene_baseline=df['kg_kerosene_baseline'][i]\n",
    "\n",
    "    kg_lpg_baseline=df['kg_lpg_baseline'][i]\n",
    "\n",
    "    l_ethanol_baseline=df['l_ethanol_baseline'][i]\n",
    "\n",
    "    kg_per_liter_ethanol_baseline=df['kg_per_liter_ethanol_baseline'][i]\n",
    "\n",
    "    kWh_electric_baseline=df['kWh_electric_baseline'][i]\n",
    "\n",
    "    kg_pellets_baseline=df['kg_pellets_baseline'][i]\n",
    "\n",
    "\n",
    "    kg_firewood_baseline_commercial=df['kg_firewood_baseline_commercial'][i]\n",
    "\n",
    "    kg_charcoal_baseline_commercial=df['kg_charcoal_baseline_commercial'][i]\n",
    "\n",
    "    kg_kerosene_baseline_commercial=df['kg_kerosene_baseline_commercial'][i]\n",
    "\n",
    "    kg_lpg_baseline_commercial=df['kg_lpg_baseline_commercial'][i]\n",
    "\n",
    "    l_ethanol_baseline_commercial=df['l_ethanol_baseline_commercial'][i]\n",
    "\n",
    "    kWh_electric_baseline_commercial=df['kWh_electric_baseline_commercial'][i]\n",
    "\n",
    "    kg_pellets_baseline_commercial=df['kg_pellets_baseline_commercial'][i]\n",
    "\n",
    "\n",
    "    # Stove Efficiencies at Baseline\n",
    "\n",
    "\n",
    "    eff_firewood_baseline=df['eff_firewood_baseline'][i]\n",
    "\n",
    "    eff_charcoal_baseline=df['eff_charcoal_baseline'][i]\n",
    "\n",
    "    eff_kerosene_baseline=df['eff_kerosene_baseline'][i]\n",
    "\n",
    "    eff_LPG_baseline=df['eff_LPG_baseline'][i]\n",
    "\n",
    "    eff_ethanol_baseline=df['eff_ethanol_baseline'][i]\n",
    "\n",
    "    eff_electric_baseline=df['eff_electric_baseline'][i]\n",
    "\n",
    "    eff_pellets_baseline=df['eff_pellets_baseline'][i]\n",
    "\n",
    "\n",
    "    # Commercial Baseline Stove Efficiences\n",
    "\n",
    "    eff_firewood_baseline_commercial=df['eff_firewood_baseline_commercial'][i]\n",
    "\n",
    "    eff_charcoal_baseline_commercial=df['eff_charcoal_baseline_commercial'][i]\n",
    "\n",
    "    eff_kerosene_baseline_commercial=df['eff_kerosene_baseline_commercial'][i]\n",
    "\n",
    "    eff_LPG_baseline_commercial=df['eff_LPG_baseline_commercial'][i]\n",
    "\n",
    "    eff_ethanol_baseline_commercial=df['eff_ethanol_baseline_commercial'][i]\n",
    "\n",
    "    eff_electric_baseline_commercial=df['eff_electric_baseline_commercial'][i]\n",
    "\n",
    "    eff_pellet_baseline_commercial=df['eff_pellet_baseline_commercial'][i]\n",
    "\n",
    "\n",
    "    # Caloric Values: What are the assumed calorific values  of the baseline fuels? (TJ/ton) [put 0 if not in use in this location]\n",
    "\n",
    "    ncv_firewood_baseline=df['ncv_firewood_baseline'][i]\n",
    "\n",
    "    ncv_charcoal_baseline=df['ncv_charcoal_baseline'][i]\n",
    "\n",
    "    ncv_kerosene_baseline=df['ncv_kerosene_baseline'][i]\n",
    "\n",
    "    ncv_LPG_baseline=df['ncv_LPG_baseline'][i]\n",
    "\n",
    "    ncv_ethanol_baseline=df['ncv_ethanol_baseline'][i]\n",
    "\n",
    "    ncv_pellets_baseline=df['ncv_pellets_baseline'][i]\n",
    "\n",
    "   \n",
    "    # CO2 Emission Factors: What is the assumed carbon dioxide emission factor of the baseline fuels? \n",
    "    #[if CDM write in the assumed EF of the projected fossil fuel] \n",
    "    #[ put 0 if you do not account for CO2 EF at Baseline or this fuel is not applicable for the project]\n",
    "    co2_ef_firewood=df['co2_ef_firewood'][i]\n",
    "    co2_ef_charcoal=df['co2_ef_charcoal'][i]\n",
    "\n",
    "    co2_ef_kerosene=df['co2_ef_kerosene'][i]\n",
    "\n",
    "    co2_ef_LPG=df['co2_ef_LPG'][i]\n",
    "\n",
    "    co2_ef_ethanol=df['co2_ef_ethanol'][i]\n",
    "\n",
    "    co2_ef_electric=df['co2_ef_electric'][i]\n",
    "\n",
    "    co2_ef_pellets=df['co2_ef_pellets'][i]\n",
    "\n",
    "\n",
    "    #Non-CO2 Baseline Emission Factors \n",
    "\n",
    "    nonco2_ef_firewood=df['nonco2_ef_firewood'][i]\n",
    "    nonco2_ef_charcoal=df['nonco2_ef_charcoal'][i]\n",
    "\n",
    "    nonco2_ef_kerosene=df['nonco2_ef_kerosene'][i]\n",
    "\n",
    "    nonco2_ef_LPG=df['nonco2_ef_LPG'][i]\n",
    "\n",
    "    nonco2_ef_ethanol=df['nonco2_ef_ethanol'][i]\n",
    "\n",
    "    nonco2_ef_electric=df['nonco2_ef_electric'][i]\n",
    "\n",
    "    nonco2_ef_pellets=df['nonco2_ef_pellets'][i]\n",
    "\n",
    "\n",
    "    # Emissions from Stove Production\n",
    "\n",
    "    stoveproduction_ef_co2_firewood_baseline= df['stoveproduction_ef_co2_firewood_baseline'][i]\n",
    "    stoveproduction_ef_ch4_firewood_baseline= df['stoveproduction_ef_ch4_firewood_baseline'][i]\n",
    "    stoveproduction_ef_n2O_firewood_baseline= df['stoveproduction_ef_n2O_firewood_baseline'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_charcoal_baseline=df['stoveproduction_ef_co2_charcoal_baseline'][i]\n",
    "    stoveproduction_ef_ch4_charcoal_baseline=df['stoveproduction_ef_ch4_charcoal_baseline'][i]\n",
    "    stoveproduction_ef_n2O_charcoal_baseline=df['stoveproduction_ef_n2O_charcoal_baseline'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_kerosene_baseline=df['stoveproduction_ef_co2_kerosene_baseline'][i]\n",
    "    stoveproduction_ef_ch4_kerosene_baseline=df['stoveproduction_ef_ch4_kerosene_baseline'][i]\n",
    "    stoveproduction_ef_n2O_kerosene_baseline=df['stoveproduction_ef_n2O_kerosene_baseline'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_LPG_baseline=df['stoveproduction_ef_co2_LPG_baseline'][i]\n",
    "    stoveproduction_ef_ch4_LPG_baseline=df['stoveproduction_ef_ch4_LPG_baseline'][i]\n",
    "    stoveproduction_ef_n2O_LPG_baseline=df['stoveproduction_ef_n2O_LPG_baseline'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_ethanol_baseline=df['stoveproduction_ef_co2_ethanol_baseline'][i]\n",
    "    stoveproduction_ef_ch4_ethanol_baseline=df['stoveproduction_ef_ch4_ethanol_baseline'][i]\n",
    "    stoveproduction_ef_n2O_ethanol_baseline=df['stoveproduction_ef_n2O_ethanol_baseline'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_electric_baseline=df['stoveproduction_ef_co2_electric_baseline'][i]\n",
    "    stoveproduction_ef_ch4_electric_baseline=df['stoveproduction_ef_ch4_electric_baseline'][i]\n",
    "    stoveproduction_ef_n2O_electric_baseline=df['stoveproduction_ef_n2O_electric_baseline'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_pellets_baseline=df['stoveproduction_ef_co2_pellets_baseline'][i]\n",
    "    stoveproduction_ef_ch4_pellets_baseline=df['stoveproduction_ef_ch4_pellets_baseline'][i]\n",
    "    stoveproduction_ef_n2O_pellets_baseline=df['stoveproduction_ef_n2O_pellets_baseline'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_firewood_baseline=df['feedstockproduction_ef_co2_firewood_baseline'][i]\n",
    "    feedstockproduction_ef_ch4_firewood_baseline=df['feedstockproduction_ef_ch4_firewood_baseline'][i]\n",
    "    feedstockproduction_ef_n2O_firewood_baseline=df['feedstockproduction_ef_n2O_firewood_baseline'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_charcoal_baseline=df['feedstockproduction_ef_co2_charcoal_baseline'][i]\n",
    "    feedstockproduction_ef_ch4_charcoal_baseline=df['stoveproduction_ef_ch4_charcoal_baseline'][i]\n",
    "    feedstockproduction_ef_n2O_charcoal_baseline=df['feedstockproduction_ef_n2O_charcoal_baseline'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_kerosene_baseline=df['feedstockproduction_ef_co2_kerosene_baseline'][i]\n",
    "    feedstockproduction_ef_ch4_kerosene_baseline=df['feedstockproduction_ef_ch4_kerosene_baseline'][i]\n",
    "    feedstockproduction_ef_n2O_kerosene_baseline=df['feedstockproduction_ef_n2O_kerosene_baseline'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_LPG_baseline=df['feedstockproduction_ef_co2_LPG_baseline'][i]\n",
    "    feedstockproduction_ef_ch4_LPG_baseline=df['feedstockproduction_ef_ch4_LPG_baseline'][i]\n",
    "    feedstockproduction_ef_n2O_LPG_baseline=df['feedstockproduction_ef_n2O_LPG_baseline'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_ethanol_baseline=df['feedstockproduction_ef_co2_firewood_baseline'][i]\n",
    "    feedstockproduction_ef_ch4_ethanol_baseline=df['feedstockproduction_ef_ch4_firewood_baseline'][i]\n",
    "    feedstockproduction_ef_n2O_ethanol_baseline=df['feedstockproduction_ef_n2O_firewood_baseline'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_electric_baseline=df['feedstockproduction_ef_co2_firewood_baseline'][i]\n",
    "    feedstockproduction_ef_ch4_electric_baseline=df['feedstockproduction_ef_ch4_firewood_baseline'][i]\n",
    "    feedstockproduction_ef_n2O_electric_baseline=df['feedstockproduction_ef_n2O_firewood_baseline'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_pellets_baseline=df['feedstockproduction_ef_co2_firewood_baseline'][i]\n",
    "    feedstockproduction_ef_ch4_pellets_baseline=df['feedstockproduction_ef_ch4_firewood_baseline'][i]\n",
    "    feedstockproduction_ef_n2O_pellets_baseline=df['feedstockproduction_ef_n2O_firewood_baseline'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_firewood_baseline=df['distribution_ef_co2_firewood_baseline'][i]\n",
    "    distribution_ef_ch4_firewood_baseline=df['distribution_ef_ch4_firewood_baseline'][i]\n",
    "    distribution_ef_n2O_firewood_baseline=df['distribution_ef_n2O_firewood_baseline'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_charcoal_baseline=df['distribution_ef_co2_charcoal_baseline'][i]\n",
    "    distribution_ef_ch4_charcoal_baseline=df['distribution_ef_ch4_charcoal_baseline'][i]\n",
    "    distribution_ef_n2O_charcoal_baseline=df['distribution_ef_n2O_charcoal_baseline'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_kerosene_baseline=df['distribution_ef_co2_kerosene_baseline'][i]\n",
    "    distribution_ef_ch4_kerosene_baseline=df['distribution_ef_ch4_kerosene_baseline'][i]\n",
    "    distribution_ef_n2O_kerosene_baseline=df['distribution_ef_n2O_kerosene_baseline'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_LPG_baseline=df['distribution_ef_co2_LPG_baseline'][i]\n",
    "    distribution_ef_ch4_LPG_baseline=df['distribution_ef_ch4_LPG_baseline'][i]\n",
    "    distribution_ef_n2O_LPG_baseline=df['distribution_ef_n2O_LPG_baseline'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_ethanol_baseline=df['distribution_ef_co2_ethanol_baseline'][i]\n",
    "    distribution_ef_ch4_ethanol_baseline=df['distribution_ef_ch4_ethanol_baseline'][i]\n",
    "    distribution_ef_n2O_ethanol_baseline=df['distribution_ef_n2O_ethanol_baseline'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_electric_baseline=df['distribution_ef_co2_electric_baseline'][i]\n",
    "    distribution_ef_ch4_electric_baseline=df['distribution_ef_ch4_electric_baseline'][i]\n",
    "    distribution_ef_n2O_electric_baseline=df['distribution_ef_n2O_electric_baseline'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_pellets_baseline=df['distribution_ef_co2_pellets_baseline'][i]\n",
    "    distribution_ef_ch4_pellets_baseline=df['distribution_ef_ch4_pellets_baseline'][i]\n",
    "    distribution_ef_n2O_pellets_baseline=df['distribution_ef_n2O_pellets_baseline'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_firewood_baseline=df['processing_ef_co2_firewood_baseline'][i]\n",
    "    processing_ef_ch4_firewood_baseline=df['processing_ef_ch4_firewood_baseline'][i]\n",
    "    processing_ef_n2O_firewood_baseline=df['processing_ef_n2O_firewood_baseline'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_charcoal_baseline=df['processing_ef_co2_firewood_baseline'][i]\n",
    "    processing_ef_ch4_charcoal_baseline=df['processing_ef_co2_firewood_baseline'][i]\n",
    "    processing_ef_n2O_charcoal_baseline=df['processing_ef_co2_firewood_baseline'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_kerosene_baseline=df['processing_ef_co2_kerosene_baseline'][i]\n",
    "    processing_ef_ch4_kerosene_baseline=df['processing_ef_ch4_kerosene_baseline'][i]\n",
    "    processing_ef_n2O_kerosene_baseline=df['processing_ef_n2O_kerosene_baseline'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_LPG_baseline=df['processing_ef_co2_LPG_baseline'][i]\n",
    "    processing_ef_ch4_LPG_baseline=df['processing_ef_ch4_LPG_baseline'][i]\n",
    "    processing_ef_n2O_LPG_baseline=df['processing_ef_n2O_LPG_baseline'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_ethanol_baseline=df['processing_ef_co2_ethanol_baseline'][i]\n",
    "    processing_ef_ch4_ethanol_baseline=df['processing_ef_ch4_ethanol_baseline'][i]\n",
    "    processing_ef_n2O_ethanol_baseline=df['processing_ef_n2O_ethanol_baseline'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_electric_baseline=df['processing_ef_co2_electric_baseline'][i]\n",
    "    processing_ef_ch4_electric_baseline=df['processing_ef_ch4_electric_baseline'][i]\n",
    "    processing_ef_n2O_electric_baseline=df['processing_ef_n2O_electric_baseline'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_pellets_baseline=df['processing_ef_co2_firewood_baseline'][i]\n",
    "    processing_ef_ch4_pellets_baseline=df['processing_ef_ch4_firewood_baseline'][i]\n",
    "    processing_ef_n2O_pellets_baseline=df['processing_ef_n2O_firewood_baseline'][i]\n",
    "\n",
    "    # Project Fuel % What are the project domestic fuels in this project location? [put 0% if no use in this location]\n",
    "    prc_firewood_project=df['prc_firewood_project'][i]\n",
    "\n",
    "    prc_charcoal_project=df['prc_charcoal_project'][i]\n",
    "\n",
    "    prc_kerosene_project=df['prc_kerosene_project'][i]\n",
    "\n",
    "    prc_lpg_project=df['prc_lpg_project'][i]\n",
    "\n",
    "    prc_ethanol_project=df['prc_ethanol_project'][i]\n",
    "\n",
    "    prc_electric_project=df['prc_electric_project'][i]\n",
    "\n",
    "    prc_pellets_project=df['prc_pellets_project'][i]\n",
    "\n",
    "    \n",
    "\n",
    "    # Project Fuel % What are the project commercial fuels in this project location? [put 0% if no use in this location]\n",
    "    prc_firewood_project_commercial=df['prc_firewood_project_commercial'][i]\n",
    "\n",
    "    prc_charcoal_project_commercial=df['prc_charcoal_project_commercial'][i]\n",
    "\n",
    "    prc_kerosene_project_commercial=df['prc_kerosene_project_commercial'][i]\n",
    "\n",
    "    prc_lpg_project_commercial=df['prc_lpg_project_commercial'][i]\n",
    "\n",
    "    prc_ethanol_project_commercial=df['prc_ethanol_project_commercial'][i]\n",
    "\n",
    "    prc_electric_project_commercial=df['prc_electric_project_commercial'][i]\n",
    "\n",
    "    prc_pellets_project_commercial=df['prc_pellets_project_commercial'][i]\n",
    "\n",
    "    \n",
    "\n",
    "    # Project Fuel Consumption\n",
    "\n",
    "    kg_firewood_project=df['kg_firewood_project'][i]\n",
    "\n",
    "    kg_charcoal_project=df['kg_charcoal_project'][i]\n",
    "\n",
    "    kg_kerosene_project=df['kg_kerosene_project'][i]\n",
    "\n",
    "    kg_lpg_project=df['kg_lpg_project'][i]\n",
    "\n",
    "    l_ethanol_project=df['l_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    kg_per_liter_ethanol_project=df['kg_per_liter_ethanol_project'][i]\n",
    "\n",
    "    kWh_electric_project=df['kWh_electric_project'][i]\n",
    "\n",
    "    kg_pellets_project=df['kg_pellets_project'][i]\n",
    "\n",
    "\n",
    "    # Project Insitutional Consumption\n",
    "\n",
    "\n",
    "    kg_firewood_project_commercial=df['kg_firewood_project_commercial'][i]\n",
    "\n",
    "    kg_charcoal_project_commercial=df['kg_charcoal_project_commercial'][i]\n",
    "\n",
    "    kg_kerosene_project_commercial=df['kg_kerosene_project_commercial'][i]\n",
    "\n",
    "    kg_lpg_project_commercial=df['kg_lpg_project_commercial'][i]\n",
    "\n",
    "    l_ethanol_project_commercial=df['l_ethanol_project_commercial'][i]\n",
    "\n",
    "    kWh_electric_project_commercial=df['kWh_electric_project_commercial'][i]\n",
    "\n",
    "    kg_pellets_project_commercial=df['kg_pellets_project_commercial'][i]\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "    # Project Stove Efficiencies\n",
    "\n",
    "    eff_firewood_project=df['eff_firewood_project'][i]\n",
    "\n",
    "    eff_charcoal_project=df['eff_charcoal_project'][i]\n",
    "\n",
    "    eff_kerosene_project=df['eff_kerosene_project'][i]\n",
    "\n",
    "    eff_LPG_project=df['eff_LPG_project'][i]\n",
    "\n",
    "    eff_ethanol_project=df['eff_ethanol_project'][i]\n",
    "\n",
    "    eff_electric_project=df['eff_electric_project'][i]\n",
    "\n",
    "    eff_pellet_project=df['eff_pellet_project'][i]\n",
    "\n",
    "\n",
    "    # Project Instituitonal Stove Efficiencies\n",
    "\n",
    "    eff_firewood_project_commercial=df['eff_firewood_project_commercial'][i]\n",
    "\n",
    "    eff_charcoal_project_commercial=df['eff_charcoal_project_commercial'][i]\n",
    "\n",
    "    eff_kerosene_project_commercial=df['eff_kerosene_project_commercial'][i]\n",
    "\n",
    "    eff_LPG_project_commercial=df['eff_LPG_project_commercial'][i]\n",
    "\n",
    "    eff_ethanol_project_commercial=df['eff_ethanol_project_commercial'][i]\n",
    "\n",
    "    eff_electric_project_commercial=df['eff_electric_project_commercial'][i]\n",
    "\n",
    "    eff_pellet_project_commercial=df['eff_pellet_project_commercial'][i]\n",
    "\n",
    "\n",
    "    #Assumed Caloric Values in the Project Scenario\n",
    "\n",
    "    ncv_firewood_project=df['ncv_firewood_project'][i]\n",
    "\n",
    "    ncv_charcoal_project=df['ncv_charcoal_project'][i]\n",
    "\n",
    "    ncv_kerosene_project=df['ncv_kerosene_project'][i]\n",
    "\n",
    "    ncv_LPG_project=df['ncv_LPG_project'][i]\n",
    "\n",
    "    ncv_ethanol_project=df['ncv_ethanol_project'][i]\n",
    "\n",
    "    ncv_pellets_project=df['ncv_pellets_project'][i]\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "    # CO2 Emission Factors in the Project\n",
    "\n",
    "    co2_ef_firewood_project=df['co2_ef_firewood_project'][i]\n",
    "\n",
    "    co2_ef_charcoal_project=df['co2_ef_charcoal_project'][i]\n",
    "\n",
    "    co2_ef_kerosene_project=df['co2_ef_kerosene_project'][i]\n",
    "\n",
    "    co2_ef_LPG_project=df['co2_ef_LPG_project'][i]\n",
    "\n",
    "    co2_ef_ethanol_project=df['co2_ef_ethanol_project'][i]\n",
    "\n",
    "    co2_ef_electric_project=df['co2_ef_electric_project'][i]\n",
    "\n",
    "    co2_ef_pellets_project=df['co2_ef_pellets_project'][i]\n",
    "\n",
    "\n",
    "    # Non-CO2 Emission Factors in the Project\n",
    "\n",
    "    nonco2_ef_firewood_project=df['nonco2_ef_firewood_project'][i]\n",
    "\n",
    "    nonco2_ef_charcoal_project=df['nonco2_ef_charcoal_project'][i]\n",
    "\n",
    "    nonco2_ef_kerosene_project=df['nonco2_ef_kerosene_project'][i]\n",
    "\n",
    "    nonco2_ef_LPG_project=df['nonco2_ef_LPG_project'][i]\n",
    "\n",
    "    nonco2_ef_ethanol_project=df['nonco2_ef_ethanol_project'][i]\n",
    "\n",
    "    nonco2_ef_electric_project=df['nonco2_ef_electric_project'][i]\n",
    "\n",
    "    nonco2_ef_pellets_project=df['nonco2_ef_pellets_project'][i]\n",
    "\n",
    "    # Stove Production Emission Factors\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_firewood_project=df['stoveproduction_ef_co2_firewood_project'][i]\n",
    "    stoveproduction_ef_ch4_firewood_project=df['stoveproduction_ef_ch4_firewood_project'][i]\n",
    "    stoveproduction_ef_n2O_firewood_project=df['stoveproduction_ef_n2O_firewood_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_charcoal_project=df['stoveproduction_ef_co2_charcoal_project'][i]\n",
    "    stoveproduction_ef_ch4_charcoal_project=df['stoveproduction_ef_ch4_charcoal_project'][i]\n",
    "    stoveproduction_ef_n2O_charcoal_project=df['stoveproduction_ef_n2O_charcoal_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_kerosene_project=df['stoveproduction_ef_co2_kerosene_project'][i]\n",
    "    stoveproduction_ef_ch4_kerosene_project=df['stoveproduction_ef_ch4_kerosene_project'][i]\n",
    "    stoveproduction_ef_n2O_kerosene_project=df['stoveproduction_ef_n2O_kerosene_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_LPG_project=df['stoveproduction_ef_co2_LPG_project'][i]\n",
    "    stoveproduction_ef_ch4_LPG_project=df['stoveproduction_ef_ch4_LPG_project'][i]\n",
    "    stoveproduction_ef_n2O_LPG_project=df['stoveproduction_ef_n2O_LPG_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_ethanol_project=df['stoveproduction_ef_co2_ethanol_project'][i]\n",
    "    stoveproduction_ef_ch4_ethanol_project=df['stoveproduction_ef_ch4_ethanol_project'][i]\n",
    "    stoveproduction_ef_n2O_ethanol_project=df['stoveproduction_ef_n2O_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_electric_project=df['stoveproduction_ef_co2_electric_project'][i]\n",
    "    stoveproduction_ef_ch4_electric_project=df['stoveproduction_ef_ch4_electric_project'][i]\n",
    "    stoveproduction_ef_n2O_electric_project=df['stoveproduction_ef_n2O_electric_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_pellets_project=df['stoveproduction_ef_co2_pellets_project'][i]\n",
    "    stoveproduction_ef_ch4_pellets_project=df['stoveproduction_ef_ch4_pellets_project'][i]\n",
    "    stoveproduction_ef_n2O_pellets_project=df['stoveproduction_ef_n2O_pellets_project'][i]\n",
    "\n",
    "    # Project Feedstock Emission Factors\n",
    "\n",
    "    feedstockproduction_ef_co2_firewood_project=df['feedstockproduction_ef_co2_firewood_project'][i]\n",
    "    feedstockproduction_ef_ch4_firewood_project=df['feedstockproduction_ef_ch4_firewood_project'][i]\n",
    "    feedstockproduction_ef_n2O_firewood_project=df['feedstockproduction_ef_n2O_firewood_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_charcoal_project=df['feedstockproduction_ef_co2_charcoal_project'][i]\n",
    "    feedstockproduction_ef_ch4_charcoal_project=df['feedstockproduction_ef_ch4_charcoal_project'][i]\n",
    "    feedstockproduction_ef_n2O_charcoal_project=df['feedstockproduction_ef_n2O_charcoal_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_kerosene_project=df['feedstockproduction_ef_co2_kerosene_project'][i]\n",
    "    feedstockproduction_ef_ch4_kerosene_project=df['feedstockproduction_ef_ch4_kerosene_project'][i]\n",
    "    feedstockproduction_ef_n2O_kerosene_project=df['feedstockproduction_ef_n2O_kerosene_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_LPG_project=df['feedstockproduction_ef_co2_LPG_project'][i]\n",
    "    feedstockproduction_ef_ch4_LPG_project=df['feedstockproduction_ef_ch4_LPG_project'][i]\n",
    "    feedstockproduction_ef_n2O_LPG_project=df['feedstockproduction_ef_n2O_LPG_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_ethanol_project=df['feedstockproduction_ef_co2_ethanol_project'][i]\n",
    "    feedstockproduction_ef_ch4_ethanol_project=df['feedstockproduction_ef_ch4_ethanol_project'][i]\n",
    "    feedstockproduction_ef_n2O_ethanol_project=df['feedstockproduction_ef_n2O_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_electric_project=df['feedstockproduction_ef_co2_electric_project'][i]\n",
    "    feedstockproduction_ef_ch4_electric_project=df['feedstockproduction_ef_ch4_electric_project'][i]\n",
    "    feedstockproduction_ef_n2O_electric_project=df['feedstockproduction_ef_n2O_electric_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_pellets_project=df['feedstockproduction_ef_co2_pellets_project'][i]\n",
    "    feedstockproduction_ef_ch4_pellets_project=df['feedstockproduction_ef_ch4_pellets_project'][i]\n",
    "    feedstockproduction_ef_n2O_pellets_project=df['feedstockproduction_ef_n2O_pellets_project'][i]\n",
    "\n",
    "\n",
    "    # Project Distribution Emission Factors\n",
    "    distribution_ef_co2_firewood_project=df['distribution_ef_co2_firewood_project'][i]\n",
    "    distribution_ef_ch4_firewood_project=df['distribution_ef_ch4_firewood_project'][i]\n",
    "    distribution_ef_n2O_firewood_project=df['distribution_ef_n2O_firewood_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_charcoal_project=df['distribution_ef_co2_charcoal_project'][i]\n",
    "    distribution_ef_ch4_charcoal_project=df['distribution_ef_ch4_charcoal_project'][i]\n",
    "    distribution_ef_n2O_charcoal_project=df['distribution_ef_n2O_charcoal_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_kerosene_project=df['distribution_ef_co2_kerosene_project'][i]\n",
    "    distribution_ef_ch4_kerosene_project=df['distribution_ef_ch4_kerosene_project'][i]\n",
    "    distribution_ef_n2O_kerosene_project=df['distribution_ef_n2O_kerosene_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_LPG_project=df['distribution_ef_co2_LPG_project'][i]\n",
    "    distribution_ef_ch4_LPG_project=df['distribution_ef_ch4_LPG_project'][i]\n",
    "    distribution_ef_n2O_LPG_project=df['distribution_ef_n2O_LPG_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_ethanol_project=df['distribution_ef_co2_ethanol_project'][i]\n",
    "    distribution_ef_ch4_ethanol_project=df['distribution_ef_ch4_ethanol_project'][i]\n",
    "    distribution_ef_n2O_ethanol_project=df['distribution_ef_n2O_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_electric_project=df['distribution_ef_co2_electric_project'][i]\n",
    "    distribution_ef_ch4_electric_project=df['distribution_ef_ch4_electric_project'][i]\n",
    "    distribution_ef_n2O_electric_project=df['distribution_ef_n2O_electric_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_pellets_project=df['distribution_ef_co2_pellets_project'][i]\n",
    "    distribution_ef_ch4_pellets_project=df['distribution_ef_ch4_pellets_project'][i]\n",
    "    distribution_ef_n2O_pellets_project=df['distribution_ef_n2O_pellets_project'][i]\n",
    "\n",
    "    # Project Processing Emission Factors\n",
    "    processing_ef_co2_firewood_project=df['processing_ef_co2_firewood_project'][i]\n",
    "    processing_ef_ch4_firewood_project=df['processing_ef_ch4_firewood_project'][i]\n",
    "    processing_ef_n2O_firewood_project=df['processing_ef_n2O_firewood_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_charcoal_project=df['processing_ef_co2_charcoal_project'][i]\n",
    "    processing_ef_ch4_charcoal_project=df['processing_ef_ch4_charcoal_project'][i]\n",
    "    processing_ef_n2O_charcoal_project=df['processing_ef_n2O_charcoal_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_kerosene_project=df['processing_ef_co2_kerosene_project'][i]\n",
    "    processing_ef_ch4_kerosene_project=df['processing_ef_ch4_kerosene_project'][i]\n",
    "    processing_ef_n2O_kerosene_project=df['processing_ef_n2O_kerosene_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_LPG_project=df['processing_ef_co2_LPG_project'][i]\n",
    "    processing_ef_ch4_LPG_project=df['processing_ef_ch4_LPG_project'][i]\n",
    "    processing_ef_n2O_LPG_project=df['processing_ef_n2O_LPG_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_ethanol_project=df['processing_ef_co2_ethanol_project'][i]\n",
    "    processing_ef_ch4_ethanol_project=df['processing_ef_ch4_ethanol_project'][i]\n",
    "    processing_ef_n2O_ethanol_project=df['processing_ef_n2O_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_electric_project=df['processing_ef_co2_electric_project'][i]\n",
    "    processing_ef_ch4_electric_project=df['processing_ef_ch4_electric_project'][i]\n",
    "    processing_ef_n2O_electric_project=df['processing_ef_n2O_electric_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_pellets_project=df['processing_ef_co2_pellets_project'][i]\n",
    "    processing_ef_ch4_pellets_project=df['processing_ef_ch4_pellets_project'][i]\n",
    "    processing_ef_n2O_pellets_project=df['processing_ef_n2O_pellets_project'][i]\n",
    "    \n",
    "    \n",
    "    #fNRB\n",
    "    min_fNRB=df['mfNRB_bailis'][i]\n",
    "\n",
    "    exp_fNRB=df['expfNRB_bailis'][i]\n",
    "    max_fNRB=exp_fNRB*1.1\n",
    "\n",
    "    n_fNRB=np.random.triangular(left=min_fNRB, mode=exp_fNRB, right=max_fNRB, size=(N))\n",
    "    \n",
    "    # Adoption, Usage, and Leakage \n",
    "\n",
    "    #adoption_definition\n",
    "\n",
    "    project_adoption=df['project_adoption'][i]\n",
    "\n",
    "\n",
    "    project_usage=df['project_usage'][i]\n",
    "    \n",
    "    kpt_adj_usage=np.random.uniform(0,0.53,N)\n",
    "    \n",
    "    kpt_usage=project_usage*(1-kpt_adj_usage)\n",
    "\n",
    "\n",
    "    project_adoption_commercial=df['project_adoption_commercial'][i]\n",
    "\n",
    "\n",
    "    project_usage_commercial=df['project_usage_commercial'][i]\n",
    "    \n",
    "    project_stacking=df['project_stacking'][i]\n",
    "    \n",
    "    project_stacking_commercial=df['project_stacking_commercial'][i]\n",
    "    \n",
    "    kpt_adj_stacking=np.random.uniform(0,0.29,N)\n",
    "\n",
    "    kpt_stacking=project_stacking*kpt_adj_stacking\n",
    "\n",
    "    leakage_binary=df['leakage_binary'][i]\n",
    "\n",
    "   \n",
    "\n",
    "    leakage_default= df['leakage_default'][i] # put 1 if not being multiplied\n",
    "\n",
    "   \n",
    "\n",
    "    leakage_tCO2e=df['leakage_tCO2e'][i]\n",
    "\n",
    " \n",
    "\n",
    "    more_than_one_stove_adj=df['more_than_one_stove_adj'][i]\n",
    "    \n",
    "    other_tCO2e_reduction=df['other_tCO2e_reduction'][i]\n",
    "    \n",
    "    #rebound=1\n",
    "    \n",
    "    rebound=df['rebound'][i]\n",
    "\n",
    "\n",
    "    \n",
    "    # Baseline Emissions\n",
    "    # Baseline Firewood \n",
    "\n",
    "    be_firewood_gillwiehl=prc_firewood_baseline*(ncv_firewood_agw*kg_firewood_baseline/1000*((n_fNRB*(ef_combustion_co2_firewood\n",
    "                                                                                                      +ef_combustion_ch4_firewood*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_firewood*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw))))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "        \n",
    "    # Baseline Charcoal\n",
    "\n",
    "    be_charcoal_gillwiehl=prc_charcoal_baseline*(ncv_charcoal_agw*kg_charcoal_baseline/1000*((n_fNRB*(ef_combustion_co2_charcoal\n",
    "                                                                                                      +ef_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_charcoal*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "\n",
    "\n",
    "                                                                                             +ef_total_not_combustion_co2_charcoal  \n",
    "                                                                                             +ef_total_not_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                             +ef_total_not_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "    # Baseline LPG\n",
    "\n",
    "    be_LPG_gillwiehl=prc_lpg_baseline*(ncv_LPG_agw*kg_lpg_baseline/1000*(ef_total_co2_LPG +ef_total_ch4_LPG*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_LPG*gwp_n2O_agw))\n",
    "    # Baseline Kerosene\n",
    "\n",
    "    be_kerosene_gillwiehl=prc_kerosene_baseline*(ncv_kerosene_agw*kg_kerosene_baseline/1000*(ef_total_co2_kerosene +ef_total_ch4_kerosene*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_kerosene*gwp_n2O_agw))\n",
    "    # Baseline Ethanol\n",
    "\n",
    "    be_ethanol_gillwiehl=prc_ethanol_baseline*(ncv_ethanol_agw*l_ethanol_baseline_commercial*kg_per_liter_ethanol_baseline/1000*(ef_total_zeroed_co2_ethanol +ef_total_zeroed_ch4_ethanol*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_ethanol*gwp_n2O_agw))\n",
    "    # Baseline Pellets\n",
    "\n",
    "    be_pellets_gillwiehl=prc_pellets_baseline*(ncv_pellets_agw*kg_pellets_baseline/1000*(ef_total_zeroed_co2_pellets +ef_total_zeroed_ch4_pellets*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_pellets*gwp_n2O_agw))\n",
    "    # Total Baseline\n",
    "    be_total_domestic_gillwiehl = be_firewood_gillwiehl + be_charcoal_gillwiehl + be_LPG_gillwiehl+ be_ethanol_gillwiehl + be_pellets_gillwiehl\n",
    "\n",
    "    be_total_domestic_gillwiehl\n",
    "    \n",
    "   # Baseline Commercial Emissions\n",
    "    # Baseline Commercial Firewood \n",
    "\n",
    "    be_firewood_commercial_gillwiehl=prc_firewood_baseline_commercial*(ncv_firewood_agw*kg_firewood_baseline_commercial/1000*((n_fNRB*(ef_combustion_co2_firewood\n",
    "                                                                                                      +ef_combustion_ch4_firewood*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_firewood*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw))))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "        \n",
    "    # Baseline Charcoal\n",
    "\n",
    "    be_charcoal_commercial_gillwiehl=prc_charcoal_baseline_commercial*(ncv_charcoal_agw*kg_charcoal_baseline_commercial/1000*((n_fNRB*(ef_combustion_co2_charcoal\n",
    "                                                                                                      +ef_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_charcoal*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "\n",
    "\n",
    "                                                                                             +ef_total_not_combustion_co2_charcoal  \n",
    "                                                                                             +ef_total_not_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                             +ef_total_not_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "    # Baseline LPG\n",
    "\n",
    "    be_LPG_commercial_gillwiehl=prc_lpg_baseline_commercial*(ncv_LPG_agw*kg_lpg_baseline_commercial/1000*(ef_total_co2_LPG +ef_total_ch4_LPG*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_LPG*gwp_n2O_agw))\n",
    "    # Baseline Kerosene\n",
    "\n",
    "    be_kerosene_commercial_gillwiehl=prc_kerosene_baseline*(ncv_kerosene_agw*kg_kerosene_baseline_commercial/1000*(ef_total_co2_kerosene +ef_total_ch4_kerosene*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_kerosene*gwp_n2O_agw))\n",
    "    # Baseline Ethanol\n",
    "\n",
    "    be_ethanol_commercial_gillwiehl=prc_ethanol_baseline*(ncv_ethanol_agw*l_ethanol_baseline_commercial*kg_per_liter_ethanol_baseline/1000*(ef_total_zeroed_co2_ethanol +ef_total_zeroed_ch4_ethanol*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_ethanol*gwp_n2O_agw))\n",
    "    # Baseline Ethanol\n",
    "\n",
    "    be_pellets_commercial_gillwiehl=prc_pellets_baseline*(ncv_pellets_agw*kg_pellets_baseline/1000*(ef_total_zeroed_co2_pellets +ef_total_zeroed_ch4_pellets*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_pellets*gwp_n2O_agw))\n",
    "    # Total Baseline\n",
    "    be_total_commercial_gillwiehl = be_firewood_commercial_gillwiehl + be_charcoal_commercial_gillwiehl + be_LPG_commercial_gillwiehl+ be_ethanol_commercial_gillwiehl + be_pellets_commercial_gillwiehl\n",
    "\n",
    "    be_total_commercial_gillwiehl\n",
    "\n",
    "    # Project Emissions\n",
    "    # Project Firewood \n",
    "\n",
    "    pe_firewood_gillwiehl=prc_firewood_project*(ncv_firewood_agw*kg_firewood_project/1000*((n_fNRB*(ef_combustion_co2_firewood\n",
    "                                                                                                      +ef_combustion_ch4_firewood*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_firewood*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw))))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "        \n",
    "    # Project Charcoal\n",
    "\n",
    "    pe_charcoal_gillwiehl=prc_charcoal_project*(ncv_charcoal_agw*kg_charcoal_project/1000*((n_fNRB*(ef_combustion_co2_charcoal\n",
    "                                                                                                      +ef_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_charcoal*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "\n",
    "\n",
    "                                                                                             +ef_total_not_combustion_co2_charcoal  \n",
    "                                                                                             +ef_total_not_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                             +ef_total_not_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "    # Project LPG\n",
    "\n",
    "    pe_LPG_gillwiehl=prc_lpg_project*(ncv_LPG_agw*kg_lpg_project/1000*(ef_total_co2_LPG +ef_total_ch4_LPG*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_LPG*gwp_n2O_agw))\n",
    "    # Project Kerosene\n",
    "\n",
    "    pe_kerosene_gillwiehl=prc_kerosene_project*(ncv_kerosene_agw*kg_kerosene_project/1000*(ef_total_co2_kerosene +ef_total_ch4_kerosene*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_kerosene*gwp_n2O_agw))\n",
    "    # Project Ethanol\n",
    "\n",
    "    pe_ethanol_gillwiehl=prc_ethanol_project*(ncv_ethanol_agw*l_ethanol_project*kg_per_liter_ethanol_project/1000*(ef_total_zeroed_co2_ethanol +ef_total_zeroed_ch4_ethanol*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_ethanol*gwp_n2O_agw))\n",
    "    # Project Pellets\n",
    "\n",
    "    pe_pellets_gillwiehl=prc_pellets_project*(ncv_pellets_agw*kg_pellets_project/1000*(ef_total_zeroed_co2_pellets +ef_total_zeroed_ch4_pellets*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_pellets*gwp_n2O_agw))\n",
    "    # Total project\n",
    "    pe_total_domestic_gillwiehl = pe_firewood_gillwiehl + pe_charcoal_gillwiehl + pe_LPG_gillwiehl+ pe_ethanol_gillwiehl + pe_pellets_gillwiehl\n",
    "\n",
    "    pe_total_domestic_gillwiehl\n",
    "    \n",
    "    # Project Commercial Emissions\n",
    "    # Project Commercial Firewood \n",
    "\n",
    "    pe_firewood_commercial_gillwiehl=prc_firewood_project_commercial*(ncv_firewood_agw*kg_firewood_project_commercial/1000*((n_fNRB*(ef_combustion_co2_firewood\n",
    "                                                                                                      +ef_combustion_ch4_firewood*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_firewood*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw))))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "        \n",
    "    # Project Charcoal\n",
    "\n",
    "    pe_charcoal_commercial_gillwiehl=prc_charcoal_project_commercial*(ncv_charcoal_agw*kg_charcoal_project_commercial/1000*((n_fNRB*(ef_combustion_co2_charcoal\n",
    "                                                                                                      +ef_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_charcoal*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "\n",
    "\n",
    "                                                                                             +ef_total_not_combustion_co2_charcoal  \n",
    "                                                                                             +ef_total_not_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                             +ef_total_not_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "    # Project LPG\n",
    "\n",
    "    pe_LPG_commercial_gillwiehl=prc_lpg_project_commercial*(ncv_LPG_agw*kg_lpg_project_commercial/1000*(ef_total_co2_LPG +ef_total_ch4_LPG*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_LPG*gwp_n2O_agw))\n",
    "    # Project Kerosene\n",
    "\n",
    "    pe_kerosene_commercial_gillwiehl=prc_kerosene_project_commercial*(ncv_kerosene_agw*kg_kerosene_project_commercial/1000*(ef_total_co2_kerosene +ef_total_ch4_kerosene*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_kerosene*gwp_n2O_agw))\n",
    "    # Project Ethanol\n",
    "\n",
    "    pe_ethanol_commercial_gillwiehl=prc_ethanol_project_commercial*(ncv_ethanol_agw*l_ethanol_project_commercial*kg_per_liter_ethanol_project/1000*(ef_total_zeroed_co2_ethanol +ef_total_zeroed_ch4_ethanol*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_ethanol*gwp_n2O_agw))\n",
    "    # Project Ethanol\n",
    "\n",
    "    pe_pellets_commercial_gillwiehl=prc_pellets_project_commercial*(ncv_pellets_agw*kg_pellets_project_commercial/1000*(ef_total_zeroed_co2_pellets +ef_total_zeroed_ch4_pellets*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_pellets*gwp_n2O_agw))\n",
    "    # Total project\n",
    "    pe_total_commercial_gillwiehl = pe_firewood_commercial_gillwiehl + pe_charcoal_commercial_gillwiehl + pe_LPG_commercial_gillwiehl+ pe_ethanol_commercial_gillwiehl + pe_pellets_commercial_gillwiehl\n",
    "\n",
    "    pe_total_commercial_gillwiehl\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    ## Adjusting adoption, usage, and stacking for the different methodologies \n",
    "    \n",
    "\n",
    "        \n",
    "    be_total_agw=be_total_domestic_gillwiehl\n",
    "\n",
    "    be_total_agw=be_total_agw+be_total_commercial_gillwiehl\n",
    "    \n",
    "    pe_total_agw=pe_total_domestic_gillwiehl\n",
    "    pe_total_agw=pe_total_agw+pe_total_commercial_gillwiehl\n",
    "    \n",
    "    #be_total_agw= be_total_agw*n_adoption*n_usage*n_stacking\n",
    "     \n",
    "    #pe_total_agw= be_total_agw*n_adoption*n_usage*n_stacking\n",
    "    \n",
    "    if df.protocol_type[i]=='GS Metered'and df.stove_year_cohort[i]!='commercial':\n",
    "    \n",
    "        be_total_agw= be_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "        \n",
    "    if df.protocol_type[i]=='GS TPDDTEC'and df.protocol_id[i]!='GS2758'and df.stove_year_cohort[i]!='commercial':\n",
    "        \n",
    "        be_total_agw= be_total_agw*n_adoption*kpt_usage*(1-kpt_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*n_adoption*kpt_usage*(1-kpt_stacking)\n",
    "    \n",
    "    if df.protocol_type[i]=='GS TPDDTEC'and df.stove_year_cohort[i]=='commercial':\n",
    "        \n",
    "        be_total_agw= be_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "          \n",
    "                                                             \n",
    "    if df.protocol_type[i]=='GS Simplified'and df.stove_year_cohort[i]!='commercial':\n",
    "        \n",
    "        be_total_agw= be_total_agw*n_adoption*n_usage*(1-n_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*n_adoption*n_usage*(1-n_stacking)\n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS II G'and df.protocol_id[i]!='GS10974'and df.stove_year_cohort[i]!='commercial':\n",
    "        \n",
    "        \n",
    "        be_total_agw= be_total_agw*n_adoption*n_usage*(1-n_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*n_adoption*n_usage*(1-n_stacking)\n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS I E'and df.stove_year_cohort[i]!='commercial': \n",
    "        \n",
    "        be_total_agw= be_total_agw*n_adoption*n_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*n_adoption*n_usage*(1-project_stacking)\n",
    "       \n",
    "    if df.protocol_id[i]=='GS2758': \n",
    "       \n",
    "        be_total_agw= be_total_agw*req_adoption*kpt_usage*(1-kpt_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*req_adoption*kpt_usage*(1-kpt_stacking)\n",
    "        \n",
    "        \n",
    "    if df.protocol_id[i]=='GS10974'and df.stove_year_cohort[i]!='commercial':\n",
    "    \n",
    "        be_total_agw= be_total_agw*req_adoption*req_usage*(1-req_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*req_adoption*req_usage*(1-req_stacking)\n",
    "        \n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS I E'and df.stove_year_cohort[i]=='commercial': \n",
    "        \n",
    "        be_total_agw= be_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "        \n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS II G'and df.stove_year_cohort[i]=='commercial': \n",
    "        \n",
    "        be_total_agw= be_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "        \n",
    "        \n",
    "    # Leakage if not default\n",
    "    le_total_agw=(leakage_tCO2e+other_tCO2e_reduction)/(n_devices_domestic+n_devices_commercial)/(project_days+project_days_commercial) # Project Emission Reductions (PERs) in (tCO2e)\n",
    "    \n",
    "        \n",
    "        \n",
    "    \n",
    "        \n",
    "    # Totally for all\n",
    "    PEs_agw=(pe_total_agw)# Project Emission Reductions (PERs) in (tCO2e)\n",
    "    BEs_agw=(be_total_agw)# Project Emission Reductions (PERs) in (tCO2e)\n",
    "\n",
    "    VERs_agw=rebound*(be_total_agw-pe_total_agw-le_total_agw)*more_than_one_stove_adj*leakage_default\n",
    "    \n",
    "  \n",
    "    # Protocol Analysis: \n",
    "    \n",
    "    \n",
    "    #All \n",
    "    \n",
    "    all_VERS_agw.append(VERs_agw)\n",
    "    \n",
    "    sum_VERS_not_per_stove_per_day_agw=sum_VERS_not_per_stove_per_day_agw+VERs_agw*n_devices_domestic*project_days\n",
    "    \n",
    "   \n",
    "        \n",
    "    #bangladesh\n",
    "    if df.country[i]=='Bangladesh':\n",
    "        bangladesh_N=bangladesh_N+1\n",
    "        bangladesh_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #burkina faso\n",
    "    if df.country[i]=='Burkina Faso':\n",
    "        burkinafaso_N=burkinafaso_N+1\n",
    "        burkinafaso_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #cameroon\n",
    "    if df.country[i]=='Cameroon':\n",
    "        cameroon_N=cameroon_N+1\n",
    "        cameroon_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #china\n",
    "    if df.country[i]=='China':\n",
    "        china_N=china_N+1\n",
    "        china_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #ethiopia\n",
    "    if df.country[i]=='Ethiopia':\n",
    "        ethiopia_N=ethiopia_N+1\n",
    "        ethiopia_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #ghana\n",
    "    if df.country[i]=='Ghana':\n",
    "        ghana_N=ghana_N+1\n",
    "        ghana_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #guatemala\n",
    "    if df.country[i]=='Guatemala':\n",
    "        guatemala_N=guatemala_N+1\n",
    "        guatemala_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #haiti\n",
    "    if df.country[i]=='Haiti':\n",
    "        haiti_N=haiti_N+1\n",
    "        haiti_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #honduras\n",
    "    if df.country[i]=='Honduras':\n",
    "        honduras_N=honduras_N+1\n",
    "        honduras_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #india\n",
    "    if df.country[i]=='India':\n",
    "        india_N=india_N+1\n",
    "        india_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #kenya\n",
    "    if df.country[i]=='Kenya':\n",
    "        kenya_N=kenya_N+1\n",
    "        kenya_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #lesotho\n",
    "    if df.country[i]=='Lesotho':\n",
    "        lesotho_N=lesotho_N+1\n",
    "        lesotho_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #malawi\n",
    "    if df.country[i]=='Malawi':\n",
    "        malawi_N=malawi_N+1\n",
    "        malawi_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #mali\n",
    "    if df.country[i]=='Mali':\n",
    "        mali_N=mali_N+1\n",
    "        mali_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #mexico\n",
    "    if df.country[i]=='Mexico':\n",
    "        mexico_N=mexico_N+1\n",
    "        mexico_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #mozambique\n",
    "    if df.country[i]=='Mozambique':\n",
    "        mozambique_N=mozambique_N+1\n",
    "        mozambique_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #myanmar\n",
    "    if df.country[i]=='Myanmar':\n",
    "        myanmar_N=myanmar_N+1\n",
    "        myanmar_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #nepal\n",
    "    if df.country[i]=='Nepal':\n",
    "        nepal_N=nepal_N+1\n",
    "        nepal_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #nigeria\n",
    "    if df.country[i]=='Nigeria':\n",
    "        nigeria_N=nigeria_N+1\n",
    "        nigeria_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #peru\n",
    "    if df.country[i]=='Peru':\n",
    "        peru_N=peru_N+1\n",
    "        peru_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #rwanda\n",
    "    if df.country[i]=='Rwanda':\n",
    "        rwanda_N=rwanda_N+1\n",
    "        rwanda_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #sudan\n",
    "    if df.country[i]=='Sudan':\n",
    "        sudan_N=sudan_N+1\n",
    "        sudan_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #uganda\n",
    "    if df.country[i]=='Uganda':\n",
    "        uganda_N=uganda_N+1\n",
    "        uganda_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #zambia\n",
    "    if df.country[i]=='Zambia':\n",
    "        zambia_N=zambia_N+1\n",
    "        zambia_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #gs metered pellets\n",
    "    if df.protocol_type[i]=='GS Metered':\n",
    "        GS_Meter_N=GS_Meter_N+1\n",
    "        GS_Meter_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #gs_lpg\n",
    "    if df.stove[i]=='gs_lpg':\n",
    "        gs_lpg_N=gs_lpg_N+1\n",
    "        gs_lpg_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #gs_imp_firewood\n",
    "    if df.stove[i]=='gs_imp_firewood':\n",
    "        gs_imp_firewood_N=gs_imp_firewood_N+1\n",
    "        gs_imp_firewood_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #gs_simple_firewood\n",
    "    if df.stove[i]=='gs_simple_firewood':\n",
    "        gs_simple_firewood_N=gs_simple_firewood_N+1\n",
    "        gs_simple_firewood_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "        \n",
    "    #gs_imp_charcoal\n",
    "    if df.stove[i]=='gs_imp_charcoal':\n",
    "        gs_imp_charcoal_N=gs_imp_charcoal_N+1\n",
    "        gs_imp_charcoal_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #cdm_imp_firewood\n",
    "    if df.stove[i]=='cdm_imp_firewood':\n",
    "        cdm_imp_firewood_N=cdm_imp_firewood_N+1\n",
    "        cdm_imp_firewood_VERs_agw.append(VERs_agw)\n",
    "        print(np.mean(VERs_agw))\n",
    "        \n",
    "        \n",
    "    #cdm_imp_charcoal\n",
    "    if df.stove[i]=='cdm_imp_charcoal':\n",
    "        cdm_imp_charcoal_N=cdm_imp_charcoal_N+1\n",
    "        cdm_imp_charcoal_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    # CDM I E\n",
    "    if df.protocol_type[i]=='CDM AMS I E':\n",
    "        CDM_I_E_N=CDM_I_E_N+1\n",
    "        CDM_I_E_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    \n",
    "    #Times Over Credited \n",
    "    ratio_analysis_project=VERs_agw/VERs\n",
    "    ratio_analysis_project_mean=np.mean(VERs_agw)/VERs*100\n",
    "    ratio_analysis_project_mean \n",
    "    ratio_analysis_project_mean_compilation.append(ratio_analysis_project_mean)\n",
    "\n",
    "    \n",
    "    ratio_project_analysis=VERs/VERs_agw\n",
    "    ratio_project_analysis_mean=VERs/np.mean(VERs_agw)\n",
    "    ratio_project_analysis_mean\n",
    "    ratio_project_analysis_mean_compilation.append(ratio_project_analysis_mean)\n",
    "    z=1.96 \n",
    "\n",
    "    sd_VERs_agw=np.std(VERs_agw)\n",
    "    sd_ratio_analysis_project=np.std(ratio_analysis_project)\n",
    "    sd_ratio_project_analysis=np.std(ratio_project_analysis)\n",
    "    \n",
    "    \n",
    "    ratio_analysis_project_lower=ratio_analysis_project_mean - z*sd_ratio_analysis_project/np.sqrt(N)\n",
    "    ratio_analysis_project_upper=ratio_analysis_project_mean + z*sd_ratio_analysis_project/np.sqrt(N)\n",
    "\n",
    "    ratio_analysis_project_lower_compilation.append(ratio_analysis_project_lower)\n",
    "    ratio_analysis_project_upper_compilation.append(ratio_analysis_project_upper)\n",
    "    \n",
    "    \n",
    "    ratio_project_analysis_lower=ratio_project_analysis_mean - z*sd_ratio_project_analysis/np.sqrt(N)\n",
    "    ratio_project_analysis_upper=ratio_project_analysis_mean + z*sd_ratio_project_analysis/np.sqrt(N)\n",
    "    \n",
    "    #print(sd_ratio_project_analysis)\n",
    "    \n",
    "    ratio_project_analysis_lower_compilation.append(ratio_project_analysis_lower)\n",
    "    ratio_project_analysis_upper_compilation.append(ratio_project_analysis_upper)\n",
    "    \n",
    "    #if ratio_project_analysis_mean<2:\n",
    "    print('We find that', protocol_id,  'project reports emissions which are',round(ratio_project_analysis_mean,2),'[',round(ratio_project_analysis_lower,2),' , ',round(ratio_project_analysis_upper,2),']','times overcredited.')\n",
    "    print('Our analysis finds emissions which are',round(ratio_analysis_project_mean,2),'[',round(ratio_analysis_project_lower,2),' , ',round(ratio_analysis_project_upper,2),']','% of the verified credits in the PDD.')\n",
    "    \n",
    "    i=i+1\n",
    "    ALL_N=ALL_N+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_project_analysis_mean_compilation=pd.DataFrame(ratio_project_analysis_mean_compilation)\n",
    "ratio_project_analysis_mean_compilation.to_excel('ratio_project_analysis_mean_compilation.xlsx')\n",
    "\n",
    "ratio_project_analysis_lower_compilation=pd.DataFrame(ratio_project_analysis_lower_compilation)\n",
    "ratio_project_analysis_lower_compilation.to_excel('ratio_project_analysis_lower_compilation.xlsx')\n",
    "\n",
    "ratio_project_analysis_upper_compilation=pd.DataFrame(ratio_project_analysis_upper_compilation)\n",
    "ratio_project_analysis_upper_compilation.to_excel('ratio_project_analysis_upper_compilation.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_analysis_project_mean_compilation=pd.DataFrame(ratio_analysis_project_mean_compilation)\n",
    "ratio_analysis_project_mean_compilation.to_excel('ratio_analysis_project_mean_compilation.xlsx')\n",
    "\n",
    "ratio_analysis_project_lower_compilation=pd.DataFrame(ratio_analysis_project_lower_compilation)\n",
    "ratio_analysis_project_lower_compilation.to_excel('ratio_analysis_project_lower_compilation.xlsx')\n",
    "\n",
    "ratio_analysis_project_upper_compilation=pd.DataFrame(ratio_analysis_project_upper_compilation)\n",
    "ratio_analysis_project_upper_compilation.to_excel('ratio_analysis_project_upper_compilation.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_average(dataframe, value, weight):\n",
    "    val = dataframe[value]\n",
    "    wt = dataframe[weight]\n",
    "    return (val * wt).sum() / wt.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_average_grouped(dataframe, group_var, value, weight):\n",
    "    dataframe_grouped=dataframe.groupby(group_var)\n",
    "    val = dataframe_grouped[value]\n",
    "    wt = dataframe_grouped[weight]\n",
    "    return (val * wt).sum() / wt.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>protocol_id</th>\n",
       "      <th>stove</th>\n",
       "      <th>country</th>\n",
       "      <th>total_verified_credits</th>\n",
       "      <th>stove_days</th>\n",
       "      <th>lower</th>\n",
       "      <th>mean</th>\n",
       "      <th>upper</th>\n",
       "      <th>stove_days_mean_overcrediting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GS10974</td>\n",
       "      <td>cdm_imp_firewood</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>69556</td>\n",
       "      <td>8762474.7</td>\n",
       "      <td>-135.127988</td>\n",
       "      <td>25.100274</td>\n",
       "      <td>185.328536</td>\n",
       "      <td>219940516.793832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GS10974</td>\n",
       "      <td>cdm_imp_firewood</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>69556</td>\n",
       "      <td>191384.1</td>\n",
       "      <td>1.475837</td>\n",
       "      <td>1.476771</td>\n",
       "      <td>1.477705</td>\n",
       "      <td>282630.537747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GS10974</td>\n",
       "      <td>cdm_imp_firewood</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>69556</td>\n",
       "      <td>16226805</td>\n",
       "      <td>-137.945260</td>\n",
       "      <td>24.893798</td>\n",
       "      <td>187.732856</td>\n",
       "      <td>403946802.208441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GS10974</td>\n",
       "      <td>cdm_imp_firewood</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>69556</td>\n",
       "      <td>191384.1</td>\n",
       "      <td>1.475176</td>\n",
       "      <td>1.476118</td>\n",
       "      <td>1.477060</td>\n",
       "      <td>282505.508581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VCS1216</td>\n",
       "      <td>cdm_imp_firewood</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>414109</td>\n",
       "      <td>662872</td>\n",
       "      <td>40.016694</td>\n",
       "      <td>41.373792</td>\n",
       "      <td>42.730890</td>\n",
       "      <td>27425527.985671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>GS 2077</td>\n",
       "      <td>cdm_imp_firewood</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>975</td>\n",
       "      <td>20805</td>\n",
       "      <td>46.755380</td>\n",
       "      <td>48.272046</td>\n",
       "      <td>49.788713</td>\n",
       "      <td>1004299.926146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>GS 411</td>\n",
       "      <td>cdm_imp_firewood</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>97297</td>\n",
       "      <td>448913.5</td>\n",
       "      <td>15.681577</td>\n",
       "      <td>16.192165</td>\n",
       "      <td>16.702753</td>\n",
       "      <td>7268881.325452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>GS 10914</td>\n",
       "      <td>cdm_imp_charcoal</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>61013</td>\n",
       "      <td>3059482.56</td>\n",
       "      <td>8.346982</td>\n",
       "      <td>8.618211</td>\n",
       "      <td>8.889439</td>\n",
       "      <td>26367265.571491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>GS 10781</td>\n",
       "      <td>gs_simple_firewood</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>2344</td>\n",
       "      <td>200020</td>\n",
       "      <td>50.764903</td>\n",
       "      <td>52.449626</td>\n",
       "      <td>54.134349</td>\n",
       "      <td>10490974.181621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>GS 5660</td>\n",
       "      <td>gs_simple_firewood</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>3397</td>\n",
       "      <td>236885</td>\n",
       "      <td>66.458546</td>\n",
       "      <td>68.617212</td>\n",
       "      <td>70.775877</td>\n",
       "      <td>16254388.22699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    protocol_id               stove       country total_verified_credits  \\\n",
       "0       GS10974    cdm_imp_firewood    Bangladesh                  69556   \n",
       "1       GS10974    cdm_imp_firewood    Bangladesh                  69556   \n",
       "2       GS10974    cdm_imp_firewood    Bangladesh                  69556   \n",
       "3       GS10974    cdm_imp_firewood    Bangladesh                  69556   \n",
       "4       VCS1216    cdm_imp_firewood        Mexico                 414109   \n",
       "..          ...                 ...           ...                    ...   \n",
       "146     GS 2077    cdm_imp_firewood     Guatemala                    975   \n",
       "147      GS 411    cdm_imp_firewood       Nigeria                  97297   \n",
       "148    GS 10914    cdm_imp_charcoal        Uganda                  61013   \n",
       "149    GS 10781  gs_simple_firewood  Burkina Faso                   2344   \n",
       "150     GS 5660  gs_simple_firewood       Myanmar                   3397   \n",
       "\n",
       "     stove_days       lower       mean       upper  \\\n",
       "0     8762474.7 -135.127988  25.100274  185.328536   \n",
       "1      191384.1    1.475837   1.476771    1.477705   \n",
       "2      16226805 -137.945260  24.893798  187.732856   \n",
       "3      191384.1    1.475176   1.476118    1.477060   \n",
       "4        662872   40.016694  41.373792   42.730890   \n",
       "..          ...         ...        ...         ...   \n",
       "146       20805   46.755380  48.272046   49.788713   \n",
       "147    448913.5   15.681577  16.192165   16.702753   \n",
       "148  3059482.56    8.346982   8.618211    8.889439   \n",
       "149      200020   50.764903  52.449626   54.134349   \n",
       "150      236885   66.458546  68.617212   70.775877   \n",
       "\n",
       "    stove_days_mean_overcrediting  \n",
       "0                219940516.793832  \n",
       "1                   282630.537747  \n",
       "2                403946802.208441  \n",
       "3                   282505.508581  \n",
       "4                 27425527.985671  \n",
       "..                            ...  \n",
       "146                1004299.926146  \n",
       "147                7268881.325452  \n",
       "148               26367265.571491  \n",
       "149               10490974.181621  \n",
       "150                16254388.22699  \n",
       "\n",
       "[151 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids=pd.DataFrame(df.iloc[:,2])\n",
    "category=pd.DataFrame(df.iloc[:,10])\n",
    "country=pd.DataFrame(df.iloc[:,13])\n",
    "total_credits=pd.DataFrame(df.iloc[:,5])\n",
    "stove_days=pd.DataFrame(df.iloc[:,23])\n",
    "\n",
    "df_info=ids.join(category)\n",
    "df_info=df_info.join(country)\n",
    "df_info=df_info.join(total_credits)\n",
    "df_info=df_info.join(stove_days)\n",
    "df_info=pd.concat([df_info[1:].reset_index(drop=True),ratio_project_analysis_lower_compilation], axis=1)\n",
    "df_info= pd.concat([df_info.reset_index(drop=True),ratio_project_analysis_mean_compilation], axis=1)\n",
    "df_info= pd.concat([df_info.reset_index(drop=True),ratio_project_analysis_upper_compilation], axis=1)\n",
    "df_info.columns = ['protocol_id', 'stove', 'country','total_verified_credits','stove_days','lower','mean','upper']\n",
    "df_info['stove_days_mean_overcrediting']=df_info['mean']*df_info['stove_days']\n",
    "df_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    27.662739\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_project_weighted_stove_days_numerator=df_info.groupby('protocol_id')['stove_days_mean_overcrediting'].sum()\n",
    "df_project_weighted_stove_days_numerator=pd.DataFrame(df_project_weighted_stove_days_numerator)\n",
    "\n",
    "\n",
    "df_project_weighted_stove_days_denominator=df_info.groupby('protocol_id')['stove_days'].sum()\n",
    "df_project_weighted_stove_days_denominator=pd.DataFrame(df_project_weighted_stove_days_denominator)\n",
    "df_project_weighted_stove_days=df_project_weighted_stove_days_numerator['stove_days_mean_overcrediting']/df_project_weighted_stove_days_denominator['stove_days']\n",
    "df_project_weighted_stove_days=pd.DataFrame(df_project_weighted_stove_days)\n",
    "df_project_weighted_stove_days.mean() #This is the average project over-crediting\n",
    "print(df_project_weighted_stove_days.mean())\n",
    "df_project_weighted_stove_days\n",
    "df_project_weighted_stove_days.to_excel(\"df_project_overcrediting.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total over-crediting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.208567566896663\n",
      "9.208567566896663 [ 6.900244063354325 , 11.516891070439002 ]\n"
     ]
    }
   ],
   "source": [
    "protocol_ids=pd.DataFrame(df_info['protocol_id'].unique())\n",
    "protocol_ids.columns=['protocol_id']\n",
    "\n",
    "df_total=df_project_weighted_stove_days.merge(df_info,left_on='protocol_id', right_on='protocol_id',how='left')\n",
    "df_total.columns=['protocol_id','project_over_crediting','category','country','total_verified_credits','stove_days','lower','mean','upper','stove_days_mean_overcrediting']\n",
    "df_total['over_credited_total']=df_total['total_verified_credits']/df_total['project_over_crediting']\n",
    "total_verified_credits=df_total['total_verified_credits'].unique().sum()\n",
    "total_over_crediting =df_total['over_credited_total'].unique().sum()\n",
    "reported_value=total_verified_credits/total_over_crediting\n",
    "print(total_verified_credits/total_over_crediting)\n",
    "sd=df_total['total_verified_credits'].unique().std()/df_total['over_credited_total'].unique().std()\n",
    "ci=sd*1.96/np.sqrt(51)\n",
    "ci\n",
    "ci_lower=reported_value-ci \n",
    "ci_upper=reported_value+ci\n",
    "print(reported_value,'[',ci_lower,',',ci_upper,']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26774845\n"
     ]
    }
   ],
   "source": [
    "print(total_verified_credits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2907601.5140781845\n"
     ]
    }
   ],
   "source": [
    "print(total_over_crediting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Methodology-stove combination analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Methodology-stove combination analysis\n",
    "my_tuple=(df_total.groupby('category')['total_verified_credits'].unique())\n",
    "combinations=pd.DataFrame(my_tuple.index)\n",
    "\n",
    "df_combination_totals = pd.DataFrame(list(my_tuple))\n",
    "df_combination_totals.index=my_tuple.index\n",
    "\n",
    "df_combination_totals['total']=df_combination_totals.sum(axis=1)\n",
    "df_combination_totals\n",
    "my_tuple2=(df_total.groupby('category')['over_credited_total'].unique())\n",
    "combinations=pd.DataFrame(my_tuple2.index)\n",
    "\n",
    "df_combination_totals_analysis = pd.DataFrame(list(my_tuple2))\n",
    "df_combination_totals_analysis.index=my_tuple2.index\n",
    "\n",
    "df_combination_totals_analysis['total']=df_combination_totals_analysis.sum(axis=1)\n",
    "df_combination_totals_analysis\n",
    "df_combination_final=df_combination_totals/df_combination_totals_analysis\n",
    "df_combination_final\n",
    "df_combination_final['sd']=df_combination_final.std(axis=1)\n",
    "df_combination_final['count']=df_combination_final.iloc[:,0:13].count(axis=1)\n",
    "df_combination_final['lower']=df_combination_final['total']-df_combination_final['sd']*1.96/np.sqrt(df_combination_final['count'])\n",
    "df_combination_final['upper']=df_combination_final['total']+df_combination_final['sd']*1.96/np.sqrt(df_combination_final['count'])\n",
    "df_combination_final.iloc[:,14:18]\n",
    "\n",
    "df_combination_final.iloc[:,14:18].to_excel('all_results_categories.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Country analysis\n",
    "my_tuple=(df_total.groupby('country')['total_verified_credits'].unique())\n",
    "countries=pd.DataFrame(my_tuple.index)\n",
    "df_country_totals = pd.DataFrame(list(my_tuple))\n",
    "df_country_totals.index=my_tuple.index\n",
    "df_country_totals['total']=df_country_totals.sum(axis=1)\n",
    "df_country_totals\n",
    "my_tuple2=(df_total.groupby('country')['over_credited_total'].unique())\n",
    "countries=pd.DataFrame(my_tuple2.index)\n",
    "\n",
    "df_country_totals_analysis = pd.DataFrame(list(my_tuple2))\n",
    "df_country_totals_analysis.index=my_tuple2.index\n",
    "\n",
    "df_country_totals_analysis['total']=df_country_totals_analysis.sum(axis=1)\n",
    "df_country_totals_analysis\n",
    "df_country_final=df_country_totals/df_country_totals_analysis\n",
    "df_country_final\n",
    "df_country_final['sd']=df_country_final.std(axis=1)\n",
    "df_country_final['count']=df_country_final.iloc[:,0:13].count(axis=1)\n",
    "df_country_final['lower']=df_country_final['total']-df_country_final['sd']*1.96/np.sqrt(df_country_final['count'])\n",
    "df_country_final['upper']=df_country_final['total']+df_country_final['sd']*1.96/np.sqrt(df_country_final['count'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis at the lowest level of granularity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "df_gs_lpg_VERs_agw=pd.DataFrame(gs_lpg_VERs_agw).transpose()\n",
    "print(df_gs_lpg_VERs_agw.shape[1])\n",
    "gs_lpg_i=0\n",
    "df_gs_lpg_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while gs_lpg_i<df_gs_lpg_VERs_agw.shape[1]:\n",
    "    df_gs_lpg_VERs_agw_final=df_gs_lpg_VERs_agw_final+df_gs_lpg_VERs_agw[gs_lpg_i]\n",
    "    gs_lpg_i=gs_lpg_i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "df_GS_Meter_VERs_agw=pd.DataFrame(GS_Meter_VERs_agw).transpose()\n",
    "print(df_GS_Meter_VERs_agw.shape[1])\n",
    "gs_meter_i=0\n",
    "df_GS_Meter_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while gs_meter_i<df_GS_Meter_VERs_agw.shape[1]:\n",
    "    df_GS_Meter_VERs_agw_final=df_GS_Meter_VERs_agw_final+df_GS_Meter_VERs_agw[gs_meter_i]\n",
    "    gs_meter_i=gs_meter_i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "df_gs_simple_firewood_VERs_agw=pd.DataFrame(gs_simple_firewood_VERs_agw).transpose()\n",
    "print(df_gs_simple_firewood_VERs_agw.shape[1])\n",
    "gs_simple_firewood_i=0\n",
    "df_gs_simple_firewood_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while gs_simple_firewood_i<df_gs_simple_firewood_VERs_agw.shape[1]:\n",
    "    df_gs_simple_firewood_VERs_agw_final=df_gs_simple_firewood_VERs_agw_final+df_gs_simple_firewood_VERs_agw[gs_simple_firewood_i]\n",
    "    gs_simple_firewood_i=gs_simple_firewood_i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n"
     ]
    }
   ],
   "source": [
    "df_gs_imp_firewood_VERs_agw=pd.DataFrame(gs_imp_firewood_VERs_agw).transpose()\n",
    "print(df_gs_imp_firewood_VERs_agw.shape[1])\n",
    "gs_imp_firewood_i=0\n",
    "df_gs_imp_firewood_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while gs_imp_firewood_i<df_gs_imp_firewood_VERs_agw.shape[1]:\n",
    "    df_gs_imp_firewood_VERs_agw_final=df_gs_imp_firewood_VERs_agw_final+df_gs_imp_firewood_VERs_agw[gs_imp_firewood_i]\n",
    "    gs_imp_firewood_i=gs_imp_firewood_i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "df_gs_imp_charcoal_VERs_agw=pd.DataFrame(gs_imp_charcoal_VERs_agw).transpose()\n",
    "print(df_gs_imp_charcoal_VERs_agw.shape[1])\n",
    "gs_imp_charcoal_i=0\n",
    "df_gs_imp_charcoal_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while gs_imp_charcoal_i<df_gs_imp_charcoal_VERs_agw.shape[1]:\n",
    "    df_gs_imp_charcoal_VERs_agw_final=df_gs_imp_charcoal_VERs_agw_final+df_gs_imp_charcoal_VERs_agw[gs_imp_charcoal_i]\n",
    "    gs_imp_charcoal_i=gs_imp_charcoal_i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "df_cdm_imp_charcoal_VERs_agw=pd.DataFrame(cdm_imp_charcoal_VERs_agw).transpose()\n",
    "print(df_cdm_imp_charcoal_VERs_agw.shape[1])\n",
    "cdm_imp_charcoal_i=0\n",
    "df_cdm_imp_charcoal_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while cdm_imp_charcoal_i<df_cdm_imp_charcoal_VERs_agw.shape[1]:\n",
    "    df_cdm_imp_charcoal_VERs_agw_final=df_cdm_imp_charcoal_VERs_agw_final+df_cdm_imp_charcoal_VERs_agw[cdm_imp_charcoal_i]\n",
    "    cdm_imp_charcoal_i=cdm_imp_charcoal_i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47\n"
     ]
    }
   ],
   "source": [
    "df_cdm_imp_firewood_VERs_agw=pd.DataFrame(cdm_imp_firewood_VERs_agw).transpose()\n",
    "print(df_cdm_imp_firewood_VERs_agw.shape[1])\n",
    "cdm_imp_firewood_i=0\n",
    "df_cdm_imp_firewood_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while cdm_imp_firewood_i<df_cdm_imp_firewood_VERs_agw.shape[1]:\n",
    "    df_cdm_imp_firewood_VERs_agw_final=df_cdm_imp_firewood_VERs_agw_final+df_cdm_imp_firewood_VERs_agw[cdm_imp_firewood_i]\n",
    "    cdm_imp_firewood_i=cdm_imp_firewood_i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "df_CDM_I_E_VERs_agw=pd.DataFrame(CDM_I_E_VERs_agw).transpose()\n",
    "print(df_CDM_I_E_VERs_agw.shape[1])\n",
    "cdm_i_e_i=0\n",
    "df_CDM_I_E_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while cdm_i_e_i<df_CDM_I_E_VERs_agw.shape[1]:\n",
    "    df_CDM_I_E_VERs_agw_final=df_CDM_I_E_VERs_agw_final+df_CDM_I_E_VERs_agw[cdm_i_e_i]\n",
    "    cdm_i_e_i=cdm_i_e_i+1\n",
    "    print(cdm_i_e_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.107556\n",
      "dtype: float64\n",
      "0    0.125849\n",
      "dtype: float64\n",
      "0    0.144142\n",
      "dtype: float64\n",
      "0    9.297502\n",
      "dtype: float64\n",
      "0    7.946034\n",
      "dtype: float64\n",
      "0    6.937597\n",
      "dtype: float64\n",
      "0.13 [ 0.11 , 0.14 ]\n",
      "7.95 [ 6.94 , 9.3 ]\n"
     ]
    }
   ],
   "source": [
    "gs_lpg_ratio_analysis_project=df_gs_lpg_VERs_agw_final/gs_lpg_VERs\n",
    "gs_lpg_ratio_analysis_project_mean=np.mean(df_gs_lpg_VERs_agw_final, axis=1)/gs_lpg_VERs\n",
    "gs_lpg_ratio_project_analysis_mean=gs_lpg_ratio_analysis_project_mean**-1\n",
    "\n",
    "gs_lpg_sd_ratio_analysis_project=np.std(gs_lpg_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "gs_lpg_ratio_analysis_project_lower=gs_lpg_ratio_analysis_project_mean - z*gs_lpg_sd_ratio_analysis_project/np.sqrt(gs_lpg_N)\n",
    "gs_lpg_ratio_analysis_project_upper=gs_lpg_ratio_analysis_project_mean + z*gs_lpg_sd_ratio_analysis_project/np.sqrt(gs_lpg_N)\n",
    "\n",
    "gs_lpg_ratio_project_analysis_lower=gs_lpg_ratio_analysis_project_lower**-1\n",
    "gs_lpg_ratio_project_analysis_upper=gs_lpg_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(gs_lpg_ratio_analysis_project_lower)\n",
    "print(gs_lpg_ratio_analysis_project_mean)\n",
    "print(gs_lpg_ratio_analysis_project_upper)\n",
    "\n",
    "print(gs_lpg_ratio_project_analysis_lower)\n",
    "print(gs_lpg_ratio_project_analysis_mean)\n",
    "print(gs_lpg_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(gs_lpg_ratio_analysis_project_mean[0],2),'[',round(gs_lpg_ratio_analysis_project_lower[0],2),',',round(gs_lpg_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(gs_lpg_ratio_project_analysis_mean[0],2),'[',round(gs_lpg_ratio_project_analysis_upper[0],2),',',round(gs_lpg_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.109951\n",
      "dtype: float64\n",
      "0    0.117147\n",
      "dtype: float64\n",
      "0    0.124343\n",
      "dtype: float64\n",
      "0    9.094926\n",
      "dtype: float64\n",
      "0    8.536272\n",
      "dtype: float64\n",
      "0    8.042276\n",
      "dtype: float64\n",
      "0.12 [ 0.11 , 0.12 ]\n",
      "8.54 [ 8.04 , 9.09 ]\n"
     ]
    }
   ],
   "source": [
    "gs_imp_firewood_ratio_analysis_project=df_gs_imp_firewood_VERs_agw_final/gs_imp_firewood_VERs\n",
    "gs_imp_firewood_ratio_analysis_project_mean=np.mean(df_gs_imp_firewood_VERs_agw_final, axis=1)/gs_imp_firewood_VERs\n",
    "gs_imp_firewood_ratio_project_analysis_mean=gs_imp_firewood_ratio_analysis_project_mean**-1\n",
    "\n",
    "gs_imp_firewood_sd_ratio_analysis_project=np.std(gs_imp_firewood_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "gs_imp_firewood_ratio_analysis_project_lower=gs_imp_firewood_ratio_analysis_project_mean - z*gs_imp_firewood_sd_ratio_analysis_project/np.sqrt(gs_imp_firewood_N)\n",
    "gs_imp_firewood_ratio_analysis_project_upper=gs_imp_firewood_ratio_analysis_project_mean + z*gs_imp_firewood_sd_ratio_analysis_project/np.sqrt(gs_imp_firewood_N)\n",
    "\n",
    "gs_imp_firewood_ratio_project_analysis_lower=gs_imp_firewood_ratio_analysis_project_lower**-1\n",
    "gs_imp_firewood_ratio_project_analysis_upper=gs_imp_firewood_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(gs_imp_firewood_ratio_analysis_project_lower)\n",
    "print(gs_imp_firewood_ratio_analysis_project_mean)\n",
    "print(gs_imp_firewood_ratio_analysis_project_upper)\n",
    "print(gs_imp_firewood_ratio_project_analysis_lower)\n",
    "print(gs_imp_firewood_ratio_project_analysis_mean)\n",
    "print(gs_imp_firewood_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(gs_imp_firewood_ratio_analysis_project_mean[0],2),'[',round(gs_imp_firewood_ratio_analysis_project_lower[0],2),',',round(gs_imp_firewood_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(gs_imp_firewood_ratio_project_analysis_mean[0],2),'[',round(gs_imp_firewood_ratio_project_analysis_upper[0],2),',',round(gs_imp_firewood_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.021493\n",
      "dtype: float64\n",
      "0    0.027738\n",
      "dtype: float64\n",
      "0    0.033983\n",
      "dtype: float64\n",
      "0    46.526962\n",
      "dtype: float64\n",
      "0    36.051958\n",
      "dtype: float64\n",
      "0    29.426846\n",
      "dtype: float64\n",
      "0.03 [ 0.02 , 0.03 ]\n",
      "36.05 [ 29.43 , 46.53 ]\n"
     ]
    }
   ],
   "source": [
    "gs_simple_firewood_ratio_analysis_project=df_gs_simple_firewood_VERs_agw_final/gs_simple_firewood_VERs\n",
    "gs_simple_firewood_ratio_analysis_project_mean=np.mean(df_gs_simple_firewood_VERs_agw_final, axis=1)/gs_simple_firewood_VERs\n",
    "gs_simple_firewood_ratio_project_analysis_mean=gs_simple_firewood_ratio_analysis_project_mean**-1\n",
    "\n",
    "gs_simple_firewood_sd_ratio_analysis_project=np.std(gs_simple_firewood_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "gs_simple_firewood_ratio_analysis_project_lower=gs_simple_firewood_ratio_analysis_project_mean - z*gs_simple_firewood_sd_ratio_analysis_project/np.sqrt(gs_simple_firewood_N)\n",
    "gs_simple_firewood_ratio_analysis_project_upper=gs_simple_firewood_ratio_analysis_project_mean + z*gs_simple_firewood_sd_ratio_analysis_project/np.sqrt(gs_simple_firewood_N)\n",
    "\n",
    "gs_simple_firewood_ratio_project_analysis_lower=gs_simple_firewood_ratio_analysis_project_lower**-1\n",
    "gs_simple_firewood_ratio_project_analysis_upper=gs_simple_firewood_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(gs_simple_firewood_ratio_analysis_project_lower)\n",
    "print(gs_simple_firewood_ratio_analysis_project_mean)\n",
    "print(gs_simple_firewood_ratio_analysis_project_upper)\n",
    "print(gs_simple_firewood_ratio_project_analysis_lower)\n",
    "print(gs_simple_firewood_ratio_project_analysis_mean)\n",
    "print(gs_simple_firewood_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(gs_simple_firewood_ratio_analysis_project_mean[0],2),'[',round(gs_simple_firewood_ratio_analysis_project_lower[0],2),',',round(gs_simple_firewood_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(gs_simple_firewood_ratio_project_analysis_mean[0],2),'[',round(gs_simple_firewood_ratio_project_analysis_upper[0],2),',',round(gs_simple_firewood_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.309823\n",
      "dtype: float64\n",
      "0    0.318498\n",
      "dtype: float64\n",
      "0    0.327173\n",
      "dtype: float64\n",
      "0    3.227647\n",
      "dtype: float64\n",
      "0    3.139737\n",
      "dtype: float64\n",
      "0    3.056488\n",
      "dtype: float64\n",
      "0.32 [ 0.31 , 0.33 ]\n",
      "3.14 [ 3.06 , 3.23 ]\n"
     ]
    }
   ],
   "source": [
    "gs_imp_charcoal_ratio_analysis_project=df_gs_imp_charcoal_VERs_agw_final/gs_imp_charcoal_VERs\n",
    "gs_imp_charcoal_ratio_analysis_project_mean=np.mean(df_gs_imp_charcoal_VERs_agw_final, axis=1)/gs_imp_charcoal_VERs\n",
    "gs_imp_charcoal_ratio_project_analysis_mean=gs_imp_charcoal_ratio_analysis_project_mean**-1\n",
    "\n",
    "gs_imp_charcoal_sd_ratio_analysis_project=np.std(gs_imp_charcoal_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "gs_imp_charcoal_ratio_analysis_project_lower=gs_imp_charcoal_ratio_analysis_project_mean - z*gs_imp_charcoal_sd_ratio_analysis_project/np.sqrt(gs_imp_charcoal_N)\n",
    "gs_imp_charcoal_ratio_analysis_project_upper=gs_imp_charcoal_ratio_analysis_project_mean + z*gs_imp_charcoal_sd_ratio_analysis_project/np.sqrt(gs_imp_charcoal_N)\n",
    "\n",
    "gs_imp_charcoal_ratio_project_analysis_lower=gs_imp_charcoal_ratio_analysis_project_lower**-1\n",
    "gs_imp_charcoal_ratio_project_analysis_upper=gs_imp_charcoal_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(gs_imp_charcoal_ratio_analysis_project_lower)\n",
    "print(gs_imp_charcoal_ratio_analysis_project_mean)\n",
    "print(gs_imp_charcoal_ratio_analysis_project_upper)\n",
    "print(gs_imp_charcoal_ratio_project_analysis_lower)\n",
    "print(gs_imp_charcoal_ratio_project_analysis_mean)\n",
    "print(gs_imp_charcoal_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(gs_imp_charcoal_ratio_analysis_project_mean[0],2),'[',round(gs_imp_charcoal_ratio_analysis_project_lower[0],2),',',round(gs_imp_charcoal_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(gs_imp_charcoal_ratio_project_analysis_mean[0],2),'[',round(gs_imp_charcoal_ratio_project_analysis_upper[0],2),',',round(gs_imp_charcoal_ratio_project_analysis_lower[0],2),']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.039343\n",
      "dtype: float64\n",
      "0    0.052668\n",
      "dtype: float64\n",
      "0    0.065993\n",
      "dtype: float64\n",
      "0    25.417322\n",
      "dtype: float64\n",
      "0    18.986886\n",
      "dtype: float64\n",
      "0    15.153211\n",
      "dtype: float64\n",
      "0.05 [ 0.04 , 0.07 ]\n",
      "18.99 [ 15.15 , 25.42 ]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "cdm_imp_charcoal_ratio_analysis_project=df_cdm_imp_charcoal_VERs_agw_final/cdm_imp_charcoal_VERs\n",
    "cdm_imp_charcoal_ratio_analysis_project_mean=np.mean(df_cdm_imp_charcoal_VERs_agw_final, axis=1)/cdm_imp_charcoal_VERs\n",
    "cdm_imp_charcoal_ratio_project_analysis_mean=cdm_imp_charcoal_ratio_analysis_project_mean**-1\n",
    "\n",
    "cdm_imp_charcoal_sd_ratio_analysis_project=np.std(cdm_imp_charcoal_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "cdm_imp_charcoal_ratio_analysis_project_lower=cdm_imp_charcoal_ratio_analysis_project_mean - z*cdm_imp_charcoal_sd_ratio_analysis_project/np.sqrt(cdm_imp_charcoal_N)\n",
    "cdm_imp_charcoal_ratio_analysis_project_upper=cdm_imp_charcoal_ratio_analysis_project_mean + z*cdm_imp_charcoal_sd_ratio_analysis_project/np.sqrt(cdm_imp_charcoal_N)\n",
    "\n",
    "cdm_imp_charcoal_ratio_project_analysis_lower=cdm_imp_charcoal_ratio_analysis_project_lower**-1\n",
    "cdm_imp_charcoal_ratio_project_analysis_upper=cdm_imp_charcoal_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(cdm_imp_charcoal_ratio_analysis_project_lower)\n",
    "print(cdm_imp_charcoal_ratio_analysis_project_mean)\n",
    "print(cdm_imp_charcoal_ratio_analysis_project_upper)\n",
    "print(cdm_imp_charcoal_ratio_project_analysis_lower)\n",
    "print(cdm_imp_charcoal_ratio_project_analysis_mean)\n",
    "print(cdm_imp_charcoal_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(cdm_imp_charcoal_ratio_analysis_project_mean[0],2),'[',round(cdm_imp_charcoal_ratio_analysis_project_lower[0],2),',',round(cdm_imp_charcoal_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(cdm_imp_charcoal_ratio_project_analysis_mean[0],2),'[',round(cdm_imp_charcoal_ratio_project_analysis_upper[0],2),',',round(cdm_imp_charcoal_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.132342\n",
      "dtype: float64\n",
      "0    0.135198\n",
      "dtype: float64\n",
      "0    0.138054\n",
      "dtype: float64\n",
      "0    7.556207\n",
      "dtype: float64\n",
      "0    7.396571\n",
      "dtype: float64\n",
      "0    7.243541\n",
      "dtype: float64\n",
      "0.14 [ 0.13 , 0.14 ]\n",
      "7.4 [ 7.24 , 7.56 ]\n"
     ]
    }
   ],
   "source": [
    "cdm_imp_firewood_ratio_analysis_project=df_cdm_imp_firewood_VERs_agw_final/cdm_imp_firewood_VERs\n",
    "cdm_imp_firewood_ratio_analysis_project_mean=np.mean(df_cdm_imp_firewood_VERs_agw_final, axis=1)/cdm_imp_firewood_VERs\n",
    "cdm_imp_firewood_ratio_project_analysis_mean=cdm_imp_firewood_ratio_analysis_project_mean**-1\n",
    "\n",
    "cdm_imp_firewood_sd_ratio_analysis_project=np.std(cdm_imp_firewood_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "cdm_imp_firewood_ratio_analysis_project_lower=cdm_imp_firewood_ratio_analysis_project_mean - z*cdm_imp_firewood_sd_ratio_analysis_project/np.sqrt(cdm_imp_firewood_N)\n",
    "cdm_imp_firewood_ratio_analysis_project_upper=cdm_imp_firewood_ratio_analysis_project_mean + z*cdm_imp_firewood_sd_ratio_analysis_project/np.sqrt(cdm_imp_firewood_N)\n",
    "\n",
    "cdm_imp_firewood_ratio_project_analysis_lower=cdm_imp_firewood_ratio_analysis_project_lower**-1\n",
    "cdm_imp_firewood_ratio_project_analysis_upper=cdm_imp_firewood_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(cdm_imp_firewood_ratio_analysis_project_lower)\n",
    "print(cdm_imp_firewood_ratio_analysis_project_mean)\n",
    "print(cdm_imp_firewood_ratio_analysis_project_upper)\n",
    "print(cdm_imp_firewood_ratio_project_analysis_lower)\n",
    "print(cdm_imp_firewood_ratio_project_analysis_mean)\n",
    "print(cdm_imp_firewood_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(cdm_imp_firewood_ratio_analysis_project_mean[0],2),'[',round(cdm_imp_firewood_ratio_analysis_project_lower[0],2),',',round(cdm_imp_firewood_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(cdm_imp_firewood_ratio_project_analysis_mean[0],2),'[',round(cdm_imp_firewood_ratio_project_analysis_upper[0],2),',',round(cdm_imp_firewood_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.638156\n",
      "dtype: float64\n",
      "0    0.648721\n",
      "dtype: float64\n",
      "0    0.659286\n",
      "dtype: float64\n",
      "0    1.567016\n",
      "dtype: float64\n",
      "0    1.541495\n",
      "dtype: float64\n",
      "0    1.516792\n",
      "dtype: float64\n",
      "0.65 [ 0.64 , 0.66 ]\n",
      "1.54 [ 1.52 , 1.57 ]\n"
     ]
    }
   ],
   "source": [
    "GS_Meter_ratio_analysis_project=df_GS_Meter_VERs_agw_final/sum(GS_Meter_VERs.iloc[:,1])\n",
    "GS_Meter_ratio_analysis_project_mean=np.mean(df_GS_Meter_VERs_agw_final, axis=1)/sum(GS_Meter_VERs.iloc[:,1])\n",
    "GS_Meter_ratio_project_analysis_mean=GS_Meter_ratio_analysis_project_mean**-1\n",
    "\n",
    "GS_Meter_sd_ratio_analysis_project=np.std(GS_Meter_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "GS_Meter_ratio_analysis_project_lower=GS_Meter_ratio_analysis_project_mean - z*GS_Meter_sd_ratio_analysis_project/np.sqrt(GS_Meter_N)\n",
    "GS_Meter_ratio_analysis_project_upper=GS_Meter_ratio_analysis_project_mean + z*GS_Meter_sd_ratio_analysis_project/np.sqrt(GS_Meter_N)\n",
    "\n",
    "GS_Meter_ratio_project_analysis_lower=GS_Meter_ratio_analysis_project_lower**-1\n",
    "GS_Meter_ratio_project_analysis_upper=GS_Meter_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(GS_Meter_ratio_analysis_project_lower)\n",
    "print(GS_Meter_ratio_analysis_project_mean)\n",
    "print(GS_Meter_ratio_analysis_project_upper)\n",
    "print(GS_Meter_ratio_project_analysis_lower)\n",
    "print(GS_Meter_ratio_project_analysis_mean)\n",
    "print(GS_Meter_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(GS_Meter_ratio_analysis_project_mean[0],2),'[',round(GS_Meter_ratio_analysis_project_lower[0],2),',',round(GS_Meter_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(GS_Meter_ratio_project_analysis_mean[0],2),'[',round(GS_Meter_ratio_project_analysis_upper[0],2),',',round(GS_Meter_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.389301\n",
      "dtype: float64\n",
      "0    0.431711\n",
      "dtype: float64\n",
      "0    0.474121\n",
      "dtype: float64\n",
      "0    2.568704\n",
      "dtype: float64\n",
      "0    2.316365\n",
      "dtype: float64\n",
      "0    2.109168\n",
      "dtype: float64\n",
      "0.43 [ 0.39 , 0.47 ]\n",
      "2.32 [ 2.11 , 2.57 ]\n"
     ]
    }
   ],
   "source": [
    "CDM_I_E_ratio_analysis_project=df_CDM_I_E_VERs_agw_final/sum(CDM_I_E_VERs.iloc[:,1])\n",
    "CDM_I_E_ratio_analysis_project_mean=np.mean(df_CDM_I_E_VERs_agw_final, axis=1)/sum(CDM_I_E_VERs.iloc[:,1])\n",
    "\n",
    "CDM_I_E_ratio_project_analysis_mean=CDM_I_E_ratio_analysis_project_mean**-1\n",
    "CDM_I_E_sd_ratio_analysis_project=np.std(CDM_I_E_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "CDM_I_E_ratio_analysis_project_lower=CDM_I_E_ratio_analysis_project_mean - z*CDM_I_E_sd_ratio_analysis_project/np.sqrt(CDM_I_E_N)\n",
    "CDM_I_E_ratio_analysis_project_upper=CDM_I_E_ratio_analysis_project_mean + z*CDM_I_E_sd_ratio_analysis_project/np.sqrt(CDM_I_E_N)\n",
    "\n",
    "CDM_I_E_ratio_project_analysis_lower=CDM_I_E_ratio_analysis_project_lower**-1\n",
    "CDM_I_E_ratio_project_analysis_upper=CDM_I_E_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(CDM_I_E_ratio_analysis_project_lower)\n",
    "print(CDM_I_E_ratio_analysis_project_mean)\n",
    "print(CDM_I_E_ratio_analysis_project_upper)\n",
    "print(CDM_I_E_ratio_project_analysis_lower)\n",
    "print(CDM_I_E_ratio_project_analysis_mean)\n",
    "print(CDM_I_E_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(CDM_I_E_ratio_analysis_project_mean[0],2),'[',round(CDM_I_E_ratio_analysis_project_lower[0],2),',',round(CDM_I_E_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(CDM_I_E_ratio_project_analysis_mean[0],2),'[',round(CDM_I_E_ratio_project_analysis_upper[0],2),',',round(CDM_I_E_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pellets: 0.007266593838481848\n",
      "Ethanol: 0.005818617726482989\n",
      "CDM Firewood: 0.0014565217155805563\n",
      "GS Charcoal: 0.002960394714866847\n",
      "GS Simplified Firewood 0.00020045532927121586\n",
      "GS Firewood: 0.00148319333366967\n",
      "GS LPG: 0.0014073675981717028\n",
      "CDM Charcoal: 0.00037994420817094284\n"
     ]
    }
   ],
   "source": [
    "print('Pellets:',df_GS_Meter_VERs_agw_final.iloc[0,:].mean()/3)\n",
    "print('Ethanol:',df_CDM_I_E_VERs_agw_final.iloc[0,:].mean()/5)\n",
    "print('CDM Firewood:',df_cdm_imp_firewood_VERs_agw_final.iloc[0,:].mean()/47)\n",
    "print('GS Charcoal:',df_gs_imp_charcoal_VERs_agw_final.iloc[0,:].mean()/15)\n",
    "print('GS Simplified Firewood',df_gs_simple_firewood_VERs_agw_final.iloc[0,:].mean()/24)\n",
    "print('GS Firewood:',df_gs_imp_firewood_VERs_agw_final.iloc[0,:].mean()/31) ###FIX \n",
    "print('GS LPG:',df_gs_lpg_VERs_agw_final.iloc[0,:].mean()/7)\n",
    "print('CDM Charcoal:',df_cdm_imp_charcoal_VERs_agw_final.iloc[0,:].mean()/19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "0    0.580225\n",
      "dtype: float64\n",
      "0    0.58981\n",
      "dtype: float64\n",
      "0    0.599395\n",
      "dtype: float64\n",
      "0    1.72347\n",
      "dtype: float64\n",
      "0    1.695461\n",
      "dtype: float64\n",
      "0    1.668348\n",
      "dtype: float64\n",
      "0.59 [ 0.58 , 0.6 ]\n",
      "1.7 [ 1.67 , 1.72 ]\n"
     ]
    }
   ],
   "source": [
    "df_bangladesh_VERs_agw=pd.DataFrame(bangladesh_VERs_agw).transpose()\n",
    "print(df_bangladesh_VERs_agw.shape[1])\n",
    "bangladesh_i=0\n",
    "df_bangladesh_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while bangladesh_i<df_bangladesh_VERs_agw.shape[1]:\n",
    "    df_bangladesh_VERs_agw_final=df_bangladesh_VERs_agw_final+df_bangladesh_VERs_agw[bangladesh_i]\n",
    "    bangladesh_i=bangladesh_i+1\n",
    "    \n",
    "\n",
    "bangladesh_ratio_analysis_project=df_bangladesh_VERs_agw_final/bangladesh_VERs\n",
    "bangladesh_ratio_analysis_project_mean=np.mean(df_bangladesh_VERs_agw_final, axis=1)/bangladesh_VERs\n",
    "bangladesh_ratio_project_analysis_mean=bangladesh_ratio_analysis_project_mean**-1\n",
    "\n",
    "bangladesh_sd_ratio_analysis_project=np.std(bangladesh_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "bangladesh_ratio_analysis_project_lower=bangladesh_ratio_analysis_project_mean - z*bangladesh_sd_ratio_analysis_project/np.sqrt(bangladesh_N)\n",
    "bangladesh_ratio_analysis_project_upper=bangladesh_ratio_analysis_project_mean + z*bangladesh_sd_ratio_analysis_project/np.sqrt(bangladesh_N)\n",
    "\n",
    "bangladesh_ratio_project_analysis_lower=bangladesh_ratio_analysis_project_lower**-1\n",
    "bangladesh_ratio_project_analysis_upper=bangladesh_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(bangladesh_ratio_analysis_project_lower)\n",
    "print(bangladesh_ratio_analysis_project_mean)\n",
    "print(bangladesh_ratio_analysis_project_upper)\n",
    "print(bangladesh_ratio_project_analysis_lower)\n",
    "print(bangladesh_ratio_project_analysis_mean)\n",
    "print(bangladesh_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(bangladesh_ratio_analysis_project_mean[0],2),'[',round(bangladesh_ratio_analysis_project_lower[0],2),',',round(bangladesh_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(bangladesh_ratio_project_analysis_mean[0],2),'[',round(bangladesh_ratio_project_analysis_upper[0],2),',',round(bangladesh_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "0    0.056199\n",
      "dtype: float64\n",
      "0    0.06521\n",
      "dtype: float64\n",
      "0    0.07422\n",
      "dtype: float64\n",
      "0    17.793759\n",
      "dtype: float64\n",
      "0    15.335163\n",
      "dtype: float64\n",
      "0    13.473505\n",
      "dtype: float64\n",
      "0.07 [ 0.06 , 0.07 ]\n",
      "15.34 [ 13.47 , 17.79 ]\n"
     ]
    }
   ],
   "source": [
    "df_burkinafaso_VERs_agw=pd.DataFrame(burkinafaso_VERs_agw).transpose()\n",
    "print(df_burkinafaso_VERs_agw.shape[1])\n",
    "burkinafaso_i=0\n",
    "df_burkinafaso_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while burkinafaso_i<df_burkinafaso_VERs_agw.shape[1]:\n",
    "    df_burkinafaso_VERs_agw_final=df_burkinafaso_VERs_agw_final+df_burkinafaso_VERs_agw[burkinafaso_i]\n",
    "    burkinafaso_i=burkinafaso_i+1\n",
    "\n",
    "burkinafaso_ratio_analysis_project=df_burkinafaso_VERs_agw_final/burkinafaso_VERs\n",
    "burkinafaso_ratio_analysis_project_mean=np.mean(df_burkinafaso_VERs_agw_final, axis=1)/burkinafaso_VERs\n",
    "burkinafaso_ratio_project_analysis_mean=burkinafaso_ratio_analysis_project_mean**-1\n",
    "\n",
    "burkinafaso_sd_ratio_analysis_project=np.std(burkinafaso_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "burkinafaso_ratio_analysis_project_lower=burkinafaso_ratio_analysis_project_mean - z*burkinafaso_sd_ratio_analysis_project/np.sqrt(burkinafaso_N)\n",
    "burkinafaso_ratio_analysis_project_upper=burkinafaso_ratio_analysis_project_mean + z*burkinafaso_sd_ratio_analysis_project/np.sqrt(burkinafaso_N)\n",
    "\n",
    "burkinafaso_ratio_project_analysis_lower=burkinafaso_ratio_analysis_project_lower**-1\n",
    "burkinafaso_ratio_project_analysis_upper=burkinafaso_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(burkinafaso_ratio_analysis_project_lower)\n",
    "print(burkinafaso_ratio_analysis_project_mean)\n",
    "print(burkinafaso_ratio_analysis_project_upper)\n",
    "print(burkinafaso_ratio_project_analysis_lower)\n",
    "print(burkinafaso_ratio_project_analysis_mean)\n",
    "print(burkinafaso_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(burkinafaso_ratio_analysis_project_mean[0],2),'[',round(burkinafaso_ratio_analysis_project_lower[0],2),',',round(burkinafaso_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(burkinafaso_ratio_project_analysis_mean[0],2),'[',round(burkinafaso_ratio_project_analysis_upper[0],2),',',round(burkinafaso_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0    0.002901\n",
      "dtype: float64\n",
      "0    0.005283\n",
      "dtype: float64\n",
      "0    0.007664\n",
      "dtype: float64\n",
      "0    344.751603\n",
      "dtype: float64\n",
      "0    189.304013\n",
      "dtype: float64\n",
      "0    130.473745\n",
      "dtype: float64\n",
      "0.01 [ 0.0 , 0.01 ]\n",
      "189.3 [ 130.47 , 344.75 ]\n"
     ]
    }
   ],
   "source": [
    "df_china_VERs_agw=pd.DataFrame(china_VERs_agw).transpose()\n",
    "print(df_china_VERs_agw.shape[1])\n",
    "china_i=0\n",
    "df_china_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while china_i<df_china_VERs_agw.shape[1]:\n",
    "    df_china_VERs_agw_final=df_china_VERs_agw_final+df_china_VERs_agw[china_i]\n",
    "    china_i=china_i+1\n",
    "\n",
    "china_ratio_analysis_project=df_china_VERs_agw_final/china_VERs\n",
    "china_ratio_analysis_project_mean=np.mean(df_china_VERs_agw_final, axis=1)/china_VERs\n",
    "china_ratio_project_analysis_mean=china_ratio_analysis_project_mean**-1\n",
    "\n",
    "china_sd_ratio_analysis_project=np.std(china_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "china_ratio_analysis_project_lower=china_ratio_analysis_project_mean - z*china_sd_ratio_analysis_project/np.sqrt(china_N)\n",
    "china_ratio_analysis_project_upper=china_ratio_analysis_project_mean + z*china_sd_ratio_analysis_project/np.sqrt(china_N)\n",
    "\n",
    "china_ratio_project_analysis_lower=china_ratio_analysis_project_lower**-1\n",
    "china_ratio_project_analysis_upper=china_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(china_ratio_analysis_project_lower)\n",
    "print(china_ratio_analysis_project_mean)\n",
    "print(china_ratio_analysis_project_upper)\n",
    "print(china_ratio_project_analysis_lower)\n",
    "print(china_ratio_project_analysis_mean)\n",
    "print(china_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(china_ratio_analysis_project_mean[0],2),'[',round(china_ratio_analysis_project_lower[0],2),',',round(china_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(china_ratio_project_analysis_mean[0],2),'[',round(china_ratio_project_analysis_upper[0],2),',',round(china_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    0.147485\n",
      "dtype: float64\n",
      "0    0.315818\n",
      "dtype: float64\n",
      "0    0.484152\n",
      "dtype: float64\n",
      "0    6.780342\n",
      "dtype: float64\n",
      "0    3.166376\n",
      "dtype: float64\n",
      "0    2.065468\n",
      "dtype: float64\n",
      "0.32 [ 0.15 , 0.48 ]\n",
      "3.17 [ 2.07 , 6.78 ]\n"
     ]
    }
   ],
   "source": [
    "df_cameroon_VERs_agw=pd.DataFrame(cameroon_VERs_agw).transpose()\n",
    "print(df_cameroon_VERs_agw.shape[1])\n",
    "cameroon_i=0\n",
    "df_cameroon_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while cameroon_i<df_cameroon_VERs_agw.shape[1]:\n",
    "    df_cameroon_VERs_agw_final=df_cameroon_VERs_agw_final+df_cameroon_VERs_agw[cameroon_i]\n",
    "    cameroon_i=cameroon_i+1\n",
    "\n",
    "cameroon_ratio_analysis_project=df_cameroon_VERs_agw_final/cameroon_VERs\n",
    "cameroon_ratio_analysis_project_mean=np.mean(df_cameroon_VERs_agw_final, axis=1)/cameroon_VERs\n",
    "cameroon_ratio_project_analysis_mean=cameroon_ratio_analysis_project_mean**-1\n",
    "\n",
    "cameroon_sd_ratio_analysis_project=np.std(cameroon_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "cameroon_ratio_analysis_project_lower=cameroon_ratio_analysis_project_mean - z*cameroon_sd_ratio_analysis_project/np.sqrt(cameroon_N)\n",
    "cameroon_ratio_analysis_project_upper=cameroon_ratio_analysis_project_mean + z*cameroon_sd_ratio_analysis_project/np.sqrt(cameroon_N)\n",
    "\n",
    "cameroon_ratio_project_analysis_lower=cameroon_ratio_analysis_project_lower**-1\n",
    "cameroon_ratio_project_analysis_upper=cameroon_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(cameroon_ratio_analysis_project_lower)\n",
    "print(cameroon_ratio_analysis_project_mean)\n",
    "print(cameroon_ratio_analysis_project_upper)\n",
    "print(cameroon_ratio_project_analysis_lower)\n",
    "print(cameroon_ratio_project_analysis_mean)\n",
    "print(cameroon_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(cameroon_ratio_analysis_project_mean[0],2),'[',round(cameroon_ratio_analysis_project_lower[0],2),',',round(cameroon_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(cameroon_ratio_project_analysis_mean[0],2),'[',round(cameroon_ratio_project_analysis_upper[0],2),',',round(cameroon_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.11532\n",
      "dtype: float64\n",
      "0    0.215716\n",
      "dtype: float64\n",
      "0    0.316111\n",
      "dtype: float64\n",
      "0    8.671535\n",
      "dtype: float64\n",
      "0    4.635733\n",
      "dtype: float64\n",
      "0    3.163442\n",
      "dtype: float64\n",
      "0.22 [ 0.12 , 0.32 ]\n",
      "4.64 [ 3.16 , 8.67 ]\n"
     ]
    }
   ],
   "source": [
    "df_ethiopia_VERs_agw=pd.DataFrame(ethiopia_VERs_agw).transpose()\n",
    "print(df_ethiopia_VERs_agw.shape[1])\n",
    "ethiopia_i=0\n",
    "df_ethiopia_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while ethiopia_i<df_ethiopia_VERs_agw.shape[1]:\n",
    "    df_ethiopia_VERs_agw_final=df_ethiopia_VERs_agw_final+df_ethiopia_VERs_agw[ethiopia_i]\n",
    "    ethiopia_i=ethiopia_i+1\n",
    "ethiopia_ratio_analysis_project=df_ethiopia_VERs_agw_final/ethiopia_VERs\n",
    "ethiopia_ratio_analysis_project_mean=np.mean(df_ethiopia_VERs_agw_final, axis=1)/ethiopia_VERs\n",
    "ethiopia_ratio_project_analysis_mean=ethiopia_ratio_analysis_project_mean**-1\n",
    "\n",
    "ethiopia_sd_ratio_analysis_project=np.std(ethiopia_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "ethiopia_ratio_analysis_project_lower=ethiopia_ratio_analysis_project_mean - z*ethiopia_sd_ratio_analysis_project/np.sqrt(ethiopia_N)\n",
    "ethiopia_ratio_analysis_project_upper=ethiopia_ratio_analysis_project_mean + z*ethiopia_sd_ratio_analysis_project/np.sqrt(ethiopia_N)\n",
    "\n",
    "ethiopia_ratio_project_analysis_lower=ethiopia_ratio_analysis_project_lower**-1\n",
    "ethiopia_ratio_project_analysis_upper=ethiopia_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(ethiopia_ratio_analysis_project_lower)\n",
    "print(ethiopia_ratio_analysis_project_mean)\n",
    "print(ethiopia_ratio_analysis_project_upper)\n",
    "print(ethiopia_ratio_project_analysis_lower)\n",
    "print(ethiopia_ratio_project_analysis_mean)\n",
    "print(ethiopia_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(ethiopia_ratio_analysis_project_mean[0],2),'[',round(ethiopia_ratio_analysis_project_lower[0],2),',',round(ethiopia_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(ethiopia_ratio_project_analysis_mean[0],2),'[',round(ethiopia_ratio_project_analysis_upper[0],2),',',round(ethiopia_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "0    0.416582\n",
      "dtype: float64\n",
      "0    0.42633\n",
      "dtype: float64\n",
      "0    0.436079\n",
      "dtype: float64\n",
      "0    2.400489\n",
      "dtype: float64\n",
      "0    2.345599\n",
      "dtype: float64\n",
      "0    2.293163\n",
      "dtype: float64\n",
      "0.43 [ 0.42 , 0.44 ]\n",
      "2.35 [ 2.29 , 2.4 ]\n"
     ]
    }
   ],
   "source": [
    "df_ghana_VERs_agw=pd.DataFrame(ghana_VERs_agw).transpose()\n",
    "print(df_ghana_VERs_agw.shape[1])\n",
    "ghana_i=0\n",
    "df_ghana_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while ghana_i<df_ghana_VERs_agw.shape[1]:\n",
    "    df_ghana_VERs_agw_final=df_ghana_VERs_agw_final+df_ghana_VERs_agw[ghana_i]\n",
    "    ghana_i=ghana_i+1\n",
    "\n",
    "ghana_ratio_analysis_project=df_ghana_VERs_agw_final/ghana_VERs\n",
    "ghana_ratio_analysis_project_mean=np.mean(df_ghana_VERs_agw_final, axis=1)/ghana_VERs\n",
    "ghana_ratio_project_analysis_mean=ghana_ratio_analysis_project_mean**-1\n",
    "\n",
    "ghana_sd_ratio_analysis_project=np.std(ghana_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "ghana_ratio_analysis_project_lower=ghana_ratio_analysis_project_mean - z*ghana_sd_ratio_analysis_project/np.sqrt(ghana_N)\n",
    "ghana_ratio_analysis_project_upper=ghana_ratio_analysis_project_mean + z*ghana_sd_ratio_analysis_project/np.sqrt(ghana_N)\n",
    "\n",
    "ghana_ratio_project_analysis_lower=ghana_ratio_analysis_project_lower**-1\n",
    "ghana_ratio_project_analysis_upper=ghana_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(ghana_ratio_analysis_project_lower)\n",
    "print(ghana_ratio_analysis_project_mean)\n",
    "print(ghana_ratio_analysis_project_upper)\n",
    "print(ghana_ratio_project_analysis_lower)\n",
    "print(ghana_ratio_project_analysis_mean)\n",
    "print(ghana_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(ghana_ratio_analysis_project_mean[0],2),'[',round(ghana_ratio_analysis_project_lower[0],2),',',round(ghana_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(ghana_ratio_project_analysis_mean[0],2),'[',round(ghana_ratio_project_analysis_upper[0],2),',',round(ghana_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "0    0.085753\n",
      "dtype: float64\n",
      "0    0.099003\n",
      "dtype: float64\n",
      "0    0.112254\n",
      "dtype: float64\n",
      "0    11.661414\n",
      "dtype: float64\n",
      "0    10.100655\n",
      "dtype: float64\n",
      "0    8.908363\n",
      "dtype: float64\n",
      "0.1 [ 0.09 , 0.11 ]\n",
      "10.1 [ 8.91 , 11.66 ]\n"
     ]
    }
   ],
   "source": [
    "df_guatemala_VERs_agw=pd.DataFrame(guatemala_VERs_agw).transpose()\n",
    "print(df_guatemala_VERs_agw.shape[1])\n",
    "guatemala_i=0\n",
    "df_guatemala_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while guatemala_i<df_guatemala_VERs_agw.shape[1]:\n",
    "    df_guatemala_VERs_agw_final=df_guatemala_VERs_agw_final+df_guatemala_VERs_agw[guatemala_i]\n",
    "    guatemala_i=guatemala_i+1\n",
    "\n",
    "guatemala_ratio_analysis_project=df_guatemala_VERs_agw_final/guatemala_VERs\n",
    "guatemala_ratio_analysis_project_mean=np.mean(df_guatemala_VERs_agw_final, axis=1)/guatemala_VERs\n",
    "guatemala_ratio_project_analysis_mean=guatemala_ratio_analysis_project_mean**-1\n",
    "\n",
    "guatemala_sd_ratio_analysis_project=np.std(guatemala_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "guatemala_ratio_analysis_project_lower=guatemala_ratio_analysis_project_mean - z*guatemala_sd_ratio_analysis_project/np.sqrt(guatemala_N)\n",
    "guatemala_ratio_analysis_project_upper=guatemala_ratio_analysis_project_mean + z*guatemala_sd_ratio_analysis_project/np.sqrt(guatemala_N)\n",
    "\n",
    "guatemala_ratio_project_analysis_lower=guatemala_ratio_analysis_project_lower**-1\n",
    "guatemala_ratio_project_analysis_upper=guatemala_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(guatemala_ratio_analysis_project_lower)\n",
    "print(guatemala_ratio_analysis_project_mean)\n",
    "print(guatemala_ratio_analysis_project_upper)\n",
    "print(guatemala_ratio_project_analysis_lower)\n",
    "print(guatemala_ratio_project_analysis_mean)\n",
    "print(guatemala_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(guatemala_ratio_analysis_project_mean[0],2),'[',round(guatemala_ratio_analysis_project_lower[0],2),',',round(guatemala_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(guatemala_ratio_project_analysis_mean[0],2),'[',round(guatemala_ratio_project_analysis_upper[0],2),',',round(guatemala_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    0.041779\n",
      "dtype: float64\n",
      "0    0.089935\n",
      "dtype: float64\n",
      "0    0.138091\n",
      "dtype: float64\n",
      "0    23.935503\n",
      "dtype: float64\n",
      "0    11.11912\n",
      "dtype: float64\n",
      "0    7.24158\n",
      "dtype: float64\n",
      "0.09 [ 0.04 , 0.14 ]\n",
      "11.12 [ 7.24 , 23.94 ]\n"
     ]
    }
   ],
   "source": [
    "df_haiti_VERs_agw=pd.DataFrame(haiti_VERs_agw).transpose()\n",
    "print(df_haiti_VERs_agw.shape[1])\n",
    "haiti_i=0\n",
    "df_haiti_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while haiti_i<df_haiti_VERs_agw.shape[1]:\n",
    "    df_haiti_VERs_agw_final=df_haiti_VERs_agw_final+df_haiti_VERs_agw[haiti_i]\n",
    "    haiti_i=haiti_i+1\n",
    " \n",
    "haiti_ratio_analysis_project=df_haiti_VERs_agw_final/haiti_VERs\n",
    "haiti_ratio_analysis_project_mean=np.mean(df_haiti_VERs_agw_final, axis=1)/haiti_VERs\n",
    "haiti_ratio_project_analysis_mean=haiti_ratio_analysis_project_mean**-1\n",
    "\n",
    "haiti_sd_ratio_analysis_project=np.std(haiti_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "haiti_ratio_analysis_project_lower=haiti_ratio_analysis_project_mean - z*haiti_sd_ratio_analysis_project/np.sqrt(haiti_N)\n",
    "haiti_ratio_analysis_project_upper=haiti_ratio_analysis_project_mean + z*haiti_sd_ratio_analysis_project/np.sqrt(haiti_N)\n",
    "\n",
    "haiti_ratio_project_analysis_lower=haiti_ratio_analysis_project_lower**-1\n",
    "haiti_ratio_project_analysis_upper=haiti_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(haiti_ratio_analysis_project_lower)\n",
    "print(haiti_ratio_analysis_project_mean)\n",
    "print(haiti_ratio_analysis_project_upper)\n",
    "print(haiti_ratio_project_analysis_lower)\n",
    "print(haiti_ratio_project_analysis_mean)\n",
    "print(haiti_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(haiti_ratio_analysis_project_mean[0],2),'[',round(haiti_ratio_analysis_project_lower[0],2),',',round(haiti_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(haiti_ratio_project_analysis_mean[0],2),'[',round(haiti_ratio_project_analysis_upper[0],2),',',round(haiti_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0    0.065969\n",
      "dtype: float64\n",
      "0    0.12457\n",
      "dtype: float64\n",
      "0    0.183171\n",
      "dtype: float64\n",
      "0    15.158665\n",
      "dtype: float64\n",
      "0    8.027621\n",
      "dtype: float64\n",
      "0    5.459381\n",
      "dtype: float64\n",
      "0.12 [ 0.07 , 0.18 ]\n",
      "8.03 [ 5.46 , 15.16 ]\n"
     ]
    }
   ],
   "source": [
    "df_honduras_VERs_agw=pd.DataFrame(honduras_VERs_agw).transpose()\n",
    "print(df_honduras_VERs_agw.shape[1])\n",
    "honduras_i=0\n",
    "df_honduras_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while honduras_i<df_honduras_VERs_agw.shape[1]:\n",
    "    df_honduras_VERs_agw_final=df_honduras_VERs_agw_final+df_honduras_VERs_agw[honduras_i]\n",
    "    honduras_i=honduras_i+1\n",
    "\n",
    "honduras_ratio_analysis_project=df_honduras_VERs_agw_final/honduras_VERs\n",
    "honduras_ratio_analysis_project_mean=np.mean(df_honduras_VERs_agw_final, axis=1)/honduras_VERs\n",
    "honduras_ratio_project_analysis_mean=honduras_ratio_analysis_project_mean**-1\n",
    "\n",
    "honduras_sd_ratio_analysis_project=np.std(honduras_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "honduras_ratio_analysis_project_lower=honduras_ratio_analysis_project_mean - z*honduras_sd_ratio_analysis_project/np.sqrt(honduras_N)\n",
    "honduras_ratio_analysis_project_upper=honduras_ratio_analysis_project_mean + z*honduras_sd_ratio_analysis_project/np.sqrt(honduras_N)\n",
    "\n",
    "honduras_ratio_project_analysis_lower=honduras_ratio_analysis_project_lower**-1\n",
    "honduras_ratio_project_analysis_upper=honduras_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(honduras_ratio_analysis_project_lower)\n",
    "print(honduras_ratio_analysis_project_mean)\n",
    "print(honduras_ratio_analysis_project_upper)\n",
    "print(honduras_ratio_project_analysis_lower)\n",
    "print(honduras_ratio_project_analysis_mean)\n",
    "print(honduras_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(honduras_ratio_analysis_project_mean[0],2),'[',round(honduras_ratio_analysis_project_lower[0],2),',',round(honduras_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(honduras_ratio_project_analysis_mean[0],2),'[',round(honduras_ratio_project_analysis_upper[0],2),',',round(honduras_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.009416\n",
      "dtype: float64\n",
      "0    0.044315\n",
      "dtype: float64\n",
      "0    0.079214\n",
      "dtype: float64\n",
      "0    106.203424\n",
      "dtype: float64\n",
      "0    22.565781\n",
      "dtype: float64\n",
      "0    12.62405\n",
      "dtype: float64\n",
      "0.04 [ 0.01 , 0.08 ]\n",
      "22.57 [ 12.62 , 106.2 ]\n"
     ]
    }
   ],
   "source": [
    "df_india_VERs_agw=pd.DataFrame(india_VERs_agw).transpose()\n",
    "print(df_india_VERs_agw.shape[1])\n",
    "india_i=0\n",
    "df_india_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while india_i<df_india_VERs_agw.shape[1]:\n",
    "    df_india_VERs_agw_final=df_india_VERs_agw_final+df_india_VERs_agw[india_i]\n",
    "    india_i=india_i+1\n",
    "\n",
    "india_ratio_analysis_project=df_india_VERs_agw_final/india_VERs\n",
    "india_ratio_analysis_project_mean=np.mean(df_india_VERs_agw_final, axis=1)/india_VERs\n",
    "india_ratio_project_analysis_mean=india_ratio_analysis_project_mean**-1\n",
    "\n",
    "india_sd_ratio_analysis_project=np.std(india_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "india_ratio_analysis_project_lower=india_ratio_analysis_project_mean - z*india_sd_ratio_analysis_project/np.sqrt(india_N)\n",
    "india_ratio_analysis_project_upper=india_ratio_analysis_project_mean + z*india_sd_ratio_analysis_project/np.sqrt(india_N)\n",
    "\n",
    "india_ratio_project_analysis_lower=india_ratio_analysis_project_lower**-1\n",
    "india_ratio_project_analysis_upper=india_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(india_ratio_analysis_project_lower)\n",
    "print(india_ratio_analysis_project_mean)\n",
    "print(india_ratio_analysis_project_upper)\n",
    "print(india_ratio_project_analysis_lower)\n",
    "print(india_ratio_project_analysis_mean)\n",
    "print(india_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(india_ratio_analysis_project_mean[0],2),'[',round(india_ratio_analysis_project_lower[0],2),',',round(india_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(india_ratio_project_analysis_mean[0],2),'[',round(india_ratio_project_analysis_upper[0],2),',',round(india_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "0    0.473003\n",
      "dtype: float64\n",
      "0    0.494091\n",
      "dtype: float64\n",
      "0    0.515179\n",
      "dtype: float64\n",
      "0    2.114151\n",
      "dtype: float64\n",
      "0    2.023919\n",
      "dtype: float64\n",
      "0    1.941075\n",
      "dtype: float64\n",
      "0.49 [ 0.47 , 0.52 ]\n",
      "2.02 [ 1.94 , 2.11 ]\n"
     ]
    }
   ],
   "source": [
    "df_kenya_VERs_agw=pd.DataFrame(kenya_VERs_agw).transpose()\n",
    "print(df_kenya_VERs_agw.shape[1])\n",
    "kenya_i=0\n",
    "df_kenya_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while kenya_i<df_kenya_VERs_agw.shape[1]:\n",
    "    df_kenya_VERs_agw_final=df_kenya_VERs_agw_final+df_kenya_VERs_agw[kenya_i]\n",
    "    kenya_i=kenya_i+1\n",
    "kenya_ratio_analysis_project=df_kenya_VERs_agw_final/kenya_VERs\n",
    "kenya_ratio_analysis_project_mean=np.mean(df_kenya_VERs_agw_final, axis=1)/kenya_VERs\n",
    "kenya_ratio_project_analysis_mean=kenya_ratio_analysis_project_mean**-1\n",
    "\n",
    "kenya_sd_ratio_analysis_project=np.std(kenya_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "kenya_ratio_analysis_project_lower=kenya_ratio_analysis_project_mean - z*kenya_sd_ratio_analysis_project/np.sqrt(kenya_N)\n",
    "kenya_ratio_analysis_project_upper=kenya_ratio_analysis_project_mean + z*kenya_sd_ratio_analysis_project/np.sqrt(kenya_N)\n",
    "\n",
    "kenya_ratio_project_analysis_lower=kenya_ratio_analysis_project_lower**-1\n",
    "kenya_ratio_project_analysis_upper=kenya_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(kenya_ratio_analysis_project_lower)\n",
    "print(kenya_ratio_analysis_project_mean)\n",
    "print(kenya_ratio_analysis_project_upper)\n",
    "print(kenya_ratio_project_analysis_lower)\n",
    "print(kenya_ratio_project_analysis_mean)\n",
    "print(kenya_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(kenya_ratio_analysis_project_mean[0],2),'[',round(kenya_ratio_analysis_project_lower[0],2),',',round(kenya_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(kenya_ratio_project_analysis_mean[0],2),'[',round(kenya_ratio_project_analysis_upper[0],2),',',round(kenya_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0   -0.005677\n",
      "dtype: float64\n",
      "0    0.051244\n",
      "dtype: float64\n",
      "0    0.108164\n",
      "dtype: float64\n",
      "0   -176.149372\n",
      "dtype: float64\n",
      "0    19.514625\n",
      "dtype: float64\n",
      "0    9.2452\n",
      "dtype: float64\n",
      "0.05 [ -0.01 , 0.11 ]\n",
      "19.51 [ 9.25 , -176.15 ]\n"
     ]
    }
   ],
   "source": [
    "df_lesotho_VERs_agw=pd.DataFrame(lesotho_VERs_agw).transpose()\n",
    "print(df_lesotho_VERs_agw.shape[1])\n",
    "lesotho_i=0\n",
    "df_lesotho_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while lesotho_i<df_lesotho_VERs_agw.shape[1]:\n",
    "    df_lesotho_VERs_agw_final=df_lesotho_VERs_agw_final+df_lesotho_VERs_agw[lesotho_i]\n",
    "    lesotho_i=lesotho_i+1\n",
    "\n",
    "lesotho_ratio_analysis_project=df_lesotho_VERs_agw_final/lesotho_VERs\n",
    "lesotho_ratio_analysis_project_mean=np.mean(df_lesotho_VERs_agw_final, axis=1)/lesotho_VERs\n",
    "lesotho_ratio_project_analysis_mean=lesotho_ratio_analysis_project_mean**-1\n",
    "\n",
    "lesotho_sd_ratio_analysis_project=np.std(lesotho_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "lesotho_ratio_analysis_project_lower=lesotho_ratio_analysis_project_mean - z*lesotho_sd_ratio_analysis_project/np.sqrt(lesotho_N)\n",
    "lesotho_ratio_analysis_project_upper=lesotho_ratio_analysis_project_mean + z*lesotho_sd_ratio_analysis_project/np.sqrt(lesotho_N)\n",
    "\n",
    "lesotho_ratio_project_analysis_lower=lesotho_ratio_analysis_project_lower**-1\n",
    "lesotho_ratio_project_analysis_upper=lesotho_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(lesotho_ratio_analysis_project_lower)\n",
    "print(lesotho_ratio_analysis_project_mean)\n",
    "print(lesotho_ratio_analysis_project_upper)\n",
    "print(lesotho_ratio_project_analysis_lower)\n",
    "print(lesotho_ratio_project_analysis_mean)\n",
    "print(lesotho_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(lesotho_ratio_analysis_project_mean[0],2),'[',round(lesotho_ratio_analysis_project_lower[0],2),',',round(lesotho_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(lesotho_ratio_project_analysis_mean[0],2),'[',round(lesotho_ratio_project_analysis_upper[0],2),',',round(lesotho_ratio_project_analysis_lower[0],2),']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "0    0.092891\n",
      "dtype: float64\n",
      "0    0.113574\n",
      "dtype: float64\n",
      "0    0.134257\n",
      "dtype: float64\n",
      "0    10.76529\n",
      "dtype: float64\n",
      "0    8.804811\n",
      "dtype: float64\n",
      "0    7.448379\n",
      "dtype: float64\n",
      "0.11 [ 0.09 , 0.13 ]\n",
      "8.8 [ 7.45 , 10.77 ]\n"
     ]
    }
   ],
   "source": [
    "df_malawi_VERs_agw=pd.DataFrame(malawi_VERs_agw).transpose()\n",
    "print(df_malawi_VERs_agw.shape[1])\n",
    "malawi_i=0\n",
    "df_malawi_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while malawi_i<df_malawi_VERs_agw.shape[1]:\n",
    "    df_malawi_VERs_agw_final=df_malawi_VERs_agw_final+df_malawi_VERs_agw[malawi_i]\n",
    "    malawi_i=malawi_i+1\n",
    "\n",
    "malawi_ratio_analysis_project=df_malawi_VERs_agw_final/malawi_VERs\n",
    "malawi_ratio_analysis_project_mean=np.mean(df_malawi_VERs_agw_final, axis=1)/malawi_VERs\n",
    "malawi_ratio_project_analysis_mean=malawi_ratio_analysis_project_mean**-1\n",
    "\n",
    "malawi_sd_ratio_analysis_project=np.std(malawi_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "malawi_ratio_analysis_project_lower=malawi_ratio_analysis_project_mean - z*malawi_sd_ratio_analysis_project/np.sqrt(malawi_N)\n",
    "malawi_ratio_analysis_project_upper=malawi_ratio_analysis_project_mean + z*malawi_sd_ratio_analysis_project/np.sqrt(malawi_N)\n",
    "\n",
    "malawi_ratio_project_analysis_lower=malawi_ratio_analysis_project_lower**-1\n",
    "malawi_ratio_project_analysis_upper=malawi_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(malawi_ratio_analysis_project_lower)\n",
    "print(malawi_ratio_analysis_project_mean)\n",
    "print(malawi_ratio_analysis_project_upper)\n",
    "print(malawi_ratio_project_analysis_lower)\n",
    "print(malawi_ratio_project_analysis_mean)\n",
    "print(malawi_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(malawi_ratio_analysis_project_mean[0],2),'[',round(malawi_ratio_analysis_project_lower[0],2),',',round(malawi_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(malawi_ratio_project_analysis_mean[0],2),'[',round(malawi_ratio_project_analysis_upper[0],2),',',round(malawi_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.002288\n",
      "dtype: float64\n",
      "0    0.011722\n",
      "dtype: float64\n",
      "0    0.021156\n",
      "dtype: float64\n",
      "0    437.08381\n",
      "dtype: float64\n",
      "0    85.310424\n",
      "dtype: float64\n",
      "0    47.268131\n",
      "dtype: float64\n",
      "0.01 [ 0.0 , 0.02 ]\n",
      "85.31 [ 47.27 , 437.08 ]\n"
     ]
    }
   ],
   "source": [
    "df_mali_VERs_agw=pd.DataFrame(mali_VERs_agw).transpose()\n",
    "print(df_mali_VERs_agw.shape[1])\n",
    "mali_i=0\n",
    "df_mali_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while mali_i<df_mali_VERs_agw.shape[1]:\n",
    "    df_mali_VERs_agw_final=df_mali_VERs_agw_final+df_mali_VERs_agw[mali_i]\n",
    "    mali_i=mali_i+1\n",
    "\n",
    "mali_ratio_analysis_project=df_mali_VERs_agw_final/mali_VERs\n",
    "mali_ratio_analysis_project_mean=np.mean(df_mali_VERs_agw_final, axis=1)/mali_VERs\n",
    "mali_ratio_project_analysis_mean=mali_ratio_analysis_project_mean**-1\n",
    "\n",
    "mali_sd_ratio_analysis_project=np.std(mali_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "mali_ratio_analysis_project_lower=mali_ratio_analysis_project_mean - z*mali_sd_ratio_analysis_project/np.sqrt(mali_N)\n",
    "mali_ratio_analysis_project_upper=mali_ratio_analysis_project_mean + z*mali_sd_ratio_analysis_project/np.sqrt(mali_N)\n",
    "\n",
    "mali_ratio_project_analysis_lower=mali_ratio_analysis_project_lower**-1\n",
    "mali_ratio_project_analysis_upper=mali_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(mali_ratio_analysis_project_lower)\n",
    "print(mali_ratio_analysis_project_mean)\n",
    "print(mali_ratio_analysis_project_upper)\n",
    "print(mali_ratio_project_analysis_lower)\n",
    "print(mali_ratio_project_analysis_mean)\n",
    "print(mali_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(mali_ratio_analysis_project_mean[0],2),'[',round(mali_ratio_analysis_project_lower[0],2),',',round(mali_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(mali_ratio_project_analysis_mean[0],2),'[',round(mali_ratio_project_analysis_upper[0],2),',',round(mali_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "0    0.031364\n",
      "dtype: float64\n",
      "0    0.035386\n",
      "dtype: float64\n",
      "0    0.039408\n",
      "dtype: float64\n",
      "0    31.883282\n",
      "dtype: float64\n",
      "0    28.259795\n",
      "dtype: float64\n",
      "0    25.375866\n",
      "dtype: float64\n",
      "0.04 [ 0.03 , 0.04 ]\n",
      "28.26 [ 25.38 , 31.88 ]\n"
     ]
    }
   ],
   "source": [
    "df_mexico_VERs_agw=pd.DataFrame(mexico_VERs_agw).transpose()\n",
    "print(df_mexico_VERs_agw.shape[1])\n",
    "mexico_i=0\n",
    "df_mexico_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while mexico_i<df_mexico_VERs_agw.shape[1]:\n",
    "    df_mexico_VERs_agw_final=df_mexico_VERs_agw_final+df_mexico_VERs_agw[mexico_i]\n",
    "    mexico_i=mexico_i+1\n",
    "\n",
    "mexico_ratio_analysis_project=df_mexico_VERs_agw_final/mexico_VERs\n",
    "mexico_ratio_analysis_project_mean=np.mean(df_mexico_VERs_agw_final, axis=1)/mexico_VERs\n",
    "mexico_ratio_project_analysis_mean=mexico_ratio_analysis_project_mean**-1\n",
    "\n",
    "mexico_sd_ratio_analysis_project=np.std(mexico_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "mexico_ratio_analysis_project_lower=mexico_ratio_analysis_project_mean - z*mexico_sd_ratio_analysis_project/np.sqrt(mexico_N)\n",
    "mexico_ratio_analysis_project_upper=mexico_ratio_analysis_project_mean + z*mexico_sd_ratio_analysis_project/np.sqrt(mexico_N)\n",
    "\n",
    "mexico_ratio_project_analysis_lower=mexico_ratio_analysis_project_lower**-1\n",
    "mexico_ratio_project_analysis_upper=mexico_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(mexico_ratio_analysis_project_lower)\n",
    "print(mexico_ratio_analysis_project_mean)\n",
    "print(mexico_ratio_analysis_project_upper)\n",
    "print(mexico_ratio_project_analysis_lower)\n",
    "print(mexico_ratio_project_analysis_mean)\n",
    "print(mexico_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(mexico_ratio_analysis_project_mean[0],2),'[',round(mexico_ratio_analysis_project_lower[0],2),',',round(mexico_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(mexico_ratio_project_analysis_mean[0],2),'[',round(mexico_ratio_project_analysis_upper[0],2),',',round(mexico_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "0    0.074835\n",
      "dtype: float64\n",
      "0    0.109918\n",
      "dtype: float64\n",
      "0    0.145001\n",
      "dtype: float64\n",
      "0    13.362817\n",
      "dtype: float64\n",
      "0    9.097716\n",
      "dtype: float64\n",
      "0    6.89651\n",
      "dtype: float64\n",
      "0.11 [ 0.07 , 0.15 ]\n",
      "9.1 [ 6.9 , 13.36 ]\n"
     ]
    }
   ],
   "source": [
    "df_mozambique_VERs_agw=pd.DataFrame(mozambique_VERs_agw).transpose()\n",
    "print(df_mozambique_VERs_agw.shape[1])\n",
    "mozambique_i=0\n",
    "df_mozambique_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while mozambique_i<df_mozambique_VERs_agw.shape[1]:\n",
    "    df_mozambique_VERs_agw_final=df_mozambique_VERs_agw_final+df_mozambique_VERs_agw[mozambique_i]\n",
    "    mozambique_i=mozambique_i+1\n",
    "\n",
    "mozambique_ratio_analysis_project=df_mozambique_VERs_agw_final/mozambique_VERs\n",
    "mozambique_ratio_analysis_project_mean=np.mean(df_mozambique_VERs_agw_final, axis=1)/mozambique_VERs\n",
    "mozambique_ratio_project_analysis_mean=mozambique_ratio_analysis_project_mean**-1\n",
    "\n",
    "mozambique_sd_ratio_analysis_project=np.std(mozambique_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "mozambique_ratio_analysis_project_lower=mozambique_ratio_analysis_project_mean - z*mozambique_sd_ratio_analysis_project/np.sqrt(mozambique_N)\n",
    "mozambique_ratio_analysis_project_upper=mozambique_ratio_analysis_project_mean + z*mozambique_sd_ratio_analysis_project/np.sqrt(mozambique_N)\n",
    "\n",
    "mozambique_ratio_project_analysis_lower=mozambique_ratio_analysis_project_lower**-1\n",
    "mozambique_ratio_project_analysis_upper=mozambique_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(mozambique_ratio_analysis_project_lower)\n",
    "print(mozambique_ratio_analysis_project_mean)\n",
    "print(mozambique_ratio_analysis_project_upper)\n",
    "print(mozambique_ratio_project_analysis_lower)\n",
    "print(mozambique_ratio_project_analysis_mean)\n",
    "print(mozambique_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(mozambique_ratio_analysis_project_mean[0],2),'[',round(mozambique_ratio_analysis_project_lower[0],2),',',round(mozambique_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(mozambique_ratio_project_analysis_mean[0],2),'[',round(mozambique_ratio_project_analysis_upper[0],2),',',round(mozambique_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "0    0.006375\n",
      "dtype: float64\n",
      "0    0.01421\n",
      "dtype: float64\n",
      "0    0.022045\n",
      "dtype: float64\n",
      "0    156.871121\n",
      "dtype: float64\n",
      "0    70.374571\n",
      "dtype: float64\n",
      "0    45.362384\n",
      "dtype: float64\n",
      "0.01 [ 0.01 , 0.02 ]\n",
      "70.37 [ 45.36 , 156.87 ]\n"
     ]
    }
   ],
   "source": [
    "df_myanmar_VERs_agw=pd.DataFrame(myanmar_VERs_agw).transpose()\n",
    "print(df_myanmar_VERs_agw.shape[1])\n",
    "myanmar_i=0\n",
    "df_myanmar_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while myanmar_i<df_myanmar_VERs_agw.shape[1]:\n",
    "    df_myanmar_VERs_agw_final=df_myanmar_VERs_agw_final+df_myanmar_VERs_agw[myanmar_i]\n",
    "    myanmar_i=myanmar_i+1\n",
    "\n",
    "myanmar_ratio_analysis_project=df_myanmar_VERs_agw_final/myanmar_VERs\n",
    "myanmar_ratio_analysis_project_mean=np.mean(df_myanmar_VERs_agw_final, axis=1)/myanmar_VERs\n",
    "myanmar_ratio_project_analysis_mean=myanmar_ratio_analysis_project_mean**-1\n",
    "\n",
    "myanmar_sd_ratio_analysis_project=np.std(myanmar_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "myanmar_ratio_analysis_project_lower=myanmar_ratio_analysis_project_mean - z*myanmar_sd_ratio_analysis_project/np.sqrt(myanmar_N)\n",
    "myanmar_ratio_analysis_project_upper=myanmar_ratio_analysis_project_mean + z*myanmar_sd_ratio_analysis_project/np.sqrt(myanmar_N)\n",
    "\n",
    "myanmar_ratio_project_analysis_lower=myanmar_ratio_analysis_project_lower**-1\n",
    "myanmar_ratio_project_analysis_upper=myanmar_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(myanmar_ratio_analysis_project_lower)\n",
    "print(myanmar_ratio_analysis_project_mean)\n",
    "print(myanmar_ratio_analysis_project_upper)\n",
    "print(myanmar_ratio_project_analysis_lower)\n",
    "print(myanmar_ratio_project_analysis_mean)\n",
    "print(myanmar_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(myanmar_ratio_analysis_project_mean[0],2),'[',round(myanmar_ratio_analysis_project_lower[0],2),',',round(myanmar_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(myanmar_ratio_project_analysis_mean[0],2),'[',round(myanmar_ratio_project_analysis_upper[0],2),',',round(myanmar_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "0    0.064289\n",
      "dtype: float64\n",
      "0    0.126985\n",
      "dtype: float64\n",
      "0    0.189681\n",
      "dtype: float64\n",
      "0    15.554722\n",
      "dtype: float64\n",
      "0    7.874947\n",
      "dtype: float64\n",
      "0    5.272015\n",
      "dtype: float64\n",
      "0.13 [ 0.06 , 0.19 ]\n",
      "7.87 [ 5.27 , 15.55 ]\n"
     ]
    }
   ],
   "source": [
    "df_nepal_VERs_agw=pd.DataFrame(nepal_VERs_agw).transpose()\n",
    "print(df_nepal_VERs_agw.shape[1])\n",
    "nepal_i=0\n",
    "df_nepal_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while nepal_i<df_nepal_VERs_agw.shape[1]:\n",
    "    df_nepal_VERs_agw_final=df_nepal_VERs_agw_final+df_nepal_VERs_agw[nepal_i]\n",
    "    nepal_i=nepal_i+1\n",
    "\n",
    "nepal_ratio_analysis_project=df_nepal_VERs_agw_final/nepal_VERs\n",
    "nepal_ratio_analysis_project_mean=np.mean(df_nepal_VERs_agw_final, axis=1)/nepal_VERs\n",
    "nepal_ratio_project_analysis_mean=nepal_ratio_analysis_project_mean**-1\n",
    "\n",
    "nepal_sd_ratio_analysis_project=np.std(nepal_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "nepal_ratio_analysis_project_lower=nepal_ratio_analysis_project_mean - z*nepal_sd_ratio_analysis_project/np.sqrt(nepal_N)\n",
    "nepal_ratio_analysis_project_upper=nepal_ratio_analysis_project_mean + z*nepal_sd_ratio_analysis_project/np.sqrt(nepal_N)\n",
    "\n",
    "nepal_ratio_project_analysis_lower=nepal_ratio_analysis_project_lower**-1\n",
    "nepal_ratio_project_analysis_upper=nepal_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(nepal_ratio_analysis_project_lower)\n",
    "print(nepal_ratio_analysis_project_mean)\n",
    "print(nepal_ratio_analysis_project_upper)\n",
    "print(nepal_ratio_project_analysis_lower)\n",
    "print(nepal_ratio_project_analysis_mean)\n",
    "print(nepal_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(nepal_ratio_analysis_project_mean[0],2),'[',round(nepal_ratio_analysis_project_lower[0],2),',',round(nepal_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(nepal_ratio_project_analysis_mean[0],2),'[',round(nepal_ratio_project_analysis_upper[0],2),',',round(nepal_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "0    0.085328\n",
      "dtype: float64\n",
      "0    0.114397\n",
      "dtype: float64\n",
      "0    0.143466\n",
      "dtype: float64\n",
      "0    11.719519\n",
      "dtype: float64\n",
      "0    8.74149\n",
      "dtype: float64\n",
      "0    6.970283\n",
      "dtype: float64\n",
      "0.11 [ 0.09 , 0.14 ]\n",
      "8.74 [ 6.97 , 11.72 ]\n"
     ]
    }
   ],
   "source": [
    "df_nigeria_VERs_agw=pd.DataFrame(nigeria_VERs_agw).transpose()\n",
    "print(df_nigeria_VERs_agw.shape[1])\n",
    "nigeria_i=0\n",
    "df_nigeria_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while nigeria_i<df_nigeria_VERs_agw.shape[1]:\n",
    "    df_nigeria_VERs_agw_final=df_nigeria_VERs_agw_final+df_nigeria_VERs_agw[nigeria_i]\n",
    "    nigeria_i=nigeria_i+1\n",
    "\n",
    "nigeria_ratio_analysis_project=df_nigeria_VERs_agw_final/nigeria_VERs\n",
    "nigeria_ratio_analysis_project_mean=np.mean(df_nigeria_VERs_agw_final, axis=1)/nigeria_VERs\n",
    "nigeria_ratio_project_analysis_mean=nigeria_ratio_analysis_project_mean**-1\n",
    "\n",
    "nigeria_sd_ratio_analysis_project=np.std(nigeria_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "nigeria_ratio_analysis_project_lower=nigeria_ratio_analysis_project_mean - z*nigeria_sd_ratio_analysis_project/np.sqrt(nigeria_N)\n",
    "nigeria_ratio_analysis_project_upper=nigeria_ratio_analysis_project_mean + z*nigeria_sd_ratio_analysis_project/np.sqrt(nigeria_N)\n",
    "\n",
    "nigeria_ratio_project_analysis_lower=nigeria_ratio_analysis_project_lower**-1\n",
    "nigeria_ratio_project_analysis_upper=nigeria_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(nigeria_ratio_analysis_project_lower)\n",
    "print(nigeria_ratio_analysis_project_mean)\n",
    "print(nigeria_ratio_analysis_project_upper)\n",
    "print(nigeria_ratio_project_analysis_lower)\n",
    "print(nigeria_ratio_project_analysis_mean)\n",
    "print(nigeria_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(nigeria_ratio_analysis_project_mean[0],2),'[',round(nigeria_ratio_analysis_project_lower[0],2),',',round(nigeria_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(nigeria_ratio_project_analysis_mean[0],2),'[',round(nigeria_ratio_project_analysis_upper[0],2),',',round(nigeria_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    0.025025\n",
      "dtype: float64\n",
      "0    0.053799\n",
      "dtype: float64\n",
      "0    0.082572\n",
      "dtype: float64\n",
      "0    39.959392\n",
      "dtype: float64\n",
      "0    18.587846\n",
      "dtype: float64\n",
      "0    12.110675\n",
      "dtype: float64\n",
      "0.05 [ 0.03 , 0.08 ]\n",
      "18.59 [ 12.11 , 39.96 ]\n"
     ]
    }
   ],
   "source": [
    "df_peru_VERs_agw=pd.DataFrame(peru_VERs_agw).transpose()\n",
    "print(df_peru_VERs_agw.shape[1])\n",
    "peru_i=0\n",
    "df_peru_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while peru_i<df_peru_VERs_agw.shape[1]:\n",
    "    df_peru_VERs_agw_final=df_peru_VERs_agw_final+df_peru_VERs_agw[peru_i]\n",
    "    peru_i=peru_i+1\n",
    "\n",
    "peru_ratio_analysis_project=df_peru_VERs_agw_final/peru_VERs\n",
    "peru_ratio_analysis_project_mean=np.mean(df_peru_VERs_agw_final, axis=1)/peru_VERs\n",
    "peru_ratio_project_analysis_mean=peru_ratio_analysis_project_mean**-1\n",
    "\n",
    "peru_sd_ratio_analysis_project=np.std(peru_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "peru_ratio_analysis_project_lower=peru_ratio_analysis_project_mean - z*peru_sd_ratio_analysis_project/np.sqrt(peru_N)\n",
    "peru_ratio_analysis_project_upper=peru_ratio_analysis_project_mean + z*peru_sd_ratio_analysis_project/np.sqrt(peru_N)\n",
    "\n",
    "peru_ratio_project_analysis_lower=peru_ratio_analysis_project_lower**-1\n",
    "peru_ratio_project_analysis_upper=peru_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(peru_ratio_analysis_project_lower)\n",
    "print(peru_ratio_analysis_project_mean)\n",
    "print(peru_ratio_analysis_project_upper)\n",
    "print(peru_ratio_project_analysis_lower)\n",
    "print(peru_ratio_project_analysis_mean)\n",
    "print(peru_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(peru_ratio_analysis_project_mean[0],2),'[',round(peru_ratio_analysis_project_lower[0],2),',',round(peru_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(peru_ratio_project_analysis_mean[0],2),'[',round(peru_ratio_project_analysis_upper[0],2),',',round(peru_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n",
      "0    0.145446\n",
      "dtype: float64\n",
      "0    0.158562\n",
      "dtype: float64\n",
      "0    0.171678\n",
      "dtype: float64\n",
      "0    6.875384\n",
      "dtype: float64\n",
      "0    6.306672\n",
      "dtype: float64\n",
      "0    5.824857\n",
      "dtype: float64\n",
      "0.16 [ 0.15 , 0.17 ]\n",
      "6.31 [ 5.82 , 6.88 ]\n"
     ]
    }
   ],
   "source": [
    "df_rwanda_VERs_agw=pd.DataFrame(rwanda_VERs_agw).transpose()\n",
    "print(df_rwanda_VERs_agw.shape[1])\n",
    "rwanda_i=0\n",
    "df_rwanda_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while rwanda_i<df_rwanda_VERs_agw.shape[1]:\n",
    "    df_rwanda_VERs_agw_final=df_rwanda_VERs_agw_final+df_rwanda_VERs_agw[rwanda_i]\n",
    "    rwanda_i=rwanda_i+1\n",
    "\n",
    "rwanda_ratio_analysis_project=df_rwanda_VERs_agw_final/rwanda_VERs\n",
    "rwanda_ratio_analysis_project_mean=np.mean(df_rwanda_VERs_agw_final, axis=1)/rwanda_VERs\n",
    "rwanda_ratio_project_analysis_mean=rwanda_ratio_analysis_project_mean**-1\n",
    "\n",
    "rwanda_sd_ratio_analysis_project=np.std(rwanda_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "rwanda_ratio_analysis_project_lower=rwanda_ratio_analysis_project_mean - z*rwanda_sd_ratio_analysis_project/np.sqrt(rwanda_N)\n",
    "rwanda_ratio_analysis_project_upper=rwanda_ratio_analysis_project_mean + z*rwanda_sd_ratio_analysis_project/np.sqrt(rwanda_N)\n",
    "\n",
    "rwanda_ratio_project_analysis_lower=rwanda_ratio_analysis_project_lower**-1\n",
    "rwanda_ratio_project_analysis_upper=rwanda_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(rwanda_ratio_analysis_project_lower)\n",
    "print(rwanda_ratio_analysis_project_mean)\n",
    "print(rwanda_ratio_analysis_project_upper)\n",
    "print(rwanda_ratio_project_analysis_lower)\n",
    "print(rwanda_ratio_project_analysis_mean)\n",
    "print(rwanda_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(rwanda_ratio_analysis_project_mean[0],2),'[',round(rwanda_ratio_analysis_project_lower[0],2),',',round(rwanda_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(rwanda_ratio_project_analysis_mean[0],2),'[',round(rwanda_ratio_project_analysis_upper[0],2),',',round(rwanda_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.014436\n",
      "dtype: float64\n",
      "0    0.030891\n",
      "dtype: float64\n",
      "0    0.047345\n",
      "dtype: float64\n",
      "0    69.270494\n",
      "dtype: float64\n",
      "0    32.372128\n",
      "dtype: float64\n",
      "0    21.121385\n",
      "dtype: float64\n",
      "0.03 [ 0.01 , 0.05 ]\n",
      "32.37 [ 21.12 , 69.27 ]\n"
     ]
    }
   ],
   "source": [
    "df_sudan_VERs_agw=pd.DataFrame(sudan_VERs_agw).transpose()\n",
    "print(df_sudan_VERs_agw.shape[1])\n",
    "sudan_i=0\n",
    "df_sudan_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while sudan_i<df_sudan_VERs_agw.shape[1]:\n",
    "    df_sudan_VERs_agw_final=df_sudan_VERs_agw_final+df_sudan_VERs_agw[sudan_i]\n",
    "    sudan_i=sudan_i+1\n",
    "\n",
    "sudan_ratio_analysis_project=df_sudan_VERs_agw_final/sudan_VERs\n",
    "sudan_ratio_analysis_project_mean=np.mean(df_sudan_VERs_agw_final, axis=1)/sudan_VERs\n",
    "sudan_ratio_project_analysis_mean=sudan_ratio_analysis_project_mean**-1\n",
    "\n",
    "sudan_sd_ratio_analysis_project=np.std(sudan_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "sudan_ratio_analysis_project_lower=sudan_ratio_analysis_project_mean - z*sudan_sd_ratio_analysis_project/np.sqrt(sudan_N)\n",
    "sudan_ratio_analysis_project_upper=sudan_ratio_analysis_project_mean + z*sudan_sd_ratio_analysis_project/np.sqrt(sudan_N)\n",
    "\n",
    "sudan_ratio_project_analysis_lower=sudan_ratio_analysis_project_lower**-1\n",
    "sudan_ratio_project_analysis_upper=sudan_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(sudan_ratio_analysis_project_lower)\n",
    "print(sudan_ratio_analysis_project_mean)\n",
    "print(sudan_ratio_analysis_project_upper)\n",
    "print(sudan_ratio_project_analysis_lower)\n",
    "print(sudan_ratio_project_analysis_mean)\n",
    "print(sudan_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(sudan_ratio_analysis_project_mean[0],2),'[',round(sudan_ratio_analysis_project_lower[0],2),',',round(sudan_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(sudan_ratio_project_analysis_mean[0],2),'[',round(sudan_ratio_project_analysis_upper[0],2),',',round(sudan_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0    0.07466\n",
      "dtype: float64\n",
      "0    0.091503\n",
      "dtype: float64\n",
      "0    0.108346\n",
      "dtype: float64\n",
      "0    13.394069\n",
      "dtype: float64\n",
      "0    10.92863\n",
      "dtype: float64\n",
      "0    9.22972\n",
      "dtype: float64\n",
      "0.09 [ 0.07 , 0.11 ]\n",
      "10.93 [ 9.23 , 13.39 ]\n"
     ]
    }
   ],
   "source": [
    "df_uganda_VERs_agw=pd.DataFrame(uganda_VERs_agw).transpose()\n",
    "print(df_uganda_VERs_agw.shape[1])\n",
    "uganda_i=0\n",
    "df_uganda_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while uganda_i<df_uganda_VERs_agw.shape[1]:\n",
    "    df_uganda_VERs_agw_final=df_uganda_VERs_agw_final+df_uganda_VERs_agw[uganda_i]\n",
    "    uganda_i=uganda_i+1\n",
    "\n",
    "uganda_ratio_analysis_project=df_uganda_VERs_agw_final/uganda_VERs\n",
    "uganda_ratio_analysis_project_mean=np.mean(df_uganda_VERs_agw_final, axis=1)/uganda_VERs\n",
    "uganda_ratio_project_analysis_mean=uganda_ratio_analysis_project_mean**-1\n",
    "\n",
    "uganda_sd_ratio_analysis_project=np.std(uganda_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "uganda_ratio_analysis_project_lower=uganda_ratio_analysis_project_mean - z*uganda_sd_ratio_analysis_project/np.sqrt(uganda_N)\n",
    "uganda_ratio_analysis_project_upper=uganda_ratio_analysis_project_mean + z*uganda_sd_ratio_analysis_project/np.sqrt(uganda_N)\n",
    "\n",
    "uganda_ratio_project_analysis_lower=uganda_ratio_analysis_project_lower**-1\n",
    "uganda_ratio_project_analysis_upper=uganda_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(uganda_ratio_analysis_project_lower)\n",
    "print(uganda_ratio_analysis_project_mean)\n",
    "print(uganda_ratio_analysis_project_upper)\n",
    "print(uganda_ratio_project_analysis_lower)\n",
    "print(uganda_ratio_project_analysis_mean)\n",
    "print(uganda_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(uganda_ratio_analysis_project_mean[0],2),'[',round(uganda_ratio_analysis_project_lower[0],2),',',round(uganda_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(uganda_ratio_project_analysis_mean[0],2),'[',round(uganda_ratio_project_analysis_upper[0],2),',',round(uganda_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.274706\n",
      "dtype: float64\n",
      "0    0.299738\n",
      "dtype: float64\n",
      "0    0.324771\n",
      "dtype: float64\n",
      "0    3.640253\n",
      "dtype: float64\n",
      "0    3.336242\n",
      "dtype: float64\n",
      "0    3.079095\n",
      "dtype: float64\n",
      "0.3 [ 0.27 , 0.32 ]\n",
      "3.34 [ 3.08 , 3.64 ]\n"
     ]
    }
   ],
   "source": [
    "df_zambia_VERs_agw=pd.DataFrame(zambia_VERs_agw).transpose()\n",
    "print(df_zambia_VERs_agw.shape[1])\n",
    "zambia_i=0\n",
    "df_zambia_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while zambia_i<df_zambia_VERs_agw.shape[1]:\n",
    "    df_zambia_VERs_agw_final=df_zambia_VERs_agw_final+df_zambia_VERs_agw[zambia_i]\n",
    "    zambia_i=zambia_i+1\n",
    "\n",
    "zambia_ratio_analysis_project=df_zambia_VERs_agw_final/zambia_VERs\n",
    "zambia_ratio_analysis_project_mean=np.mean(df_zambia_VERs_agw_final, axis=1)/zambia_VERs\n",
    "zambia_ratio_project_analysis_mean=zambia_ratio_analysis_project_mean**-1\n",
    "\n",
    "zambia_sd_ratio_analysis_project=np.std(zambia_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "zambia_ratio_analysis_project_lower=zambia_ratio_analysis_project_mean - z*zambia_sd_ratio_analysis_project/np.sqrt(zambia_N)\n",
    "zambia_ratio_analysis_project_upper=zambia_ratio_analysis_project_mean + z*zambia_sd_ratio_analysis_project/np.sqrt(zambia_N)\n",
    "\n",
    "zambia_ratio_project_analysis_lower=zambia_ratio_analysis_project_lower**-1\n",
    "zambia_ratio_project_analysis_upper=zambia_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(zambia_ratio_analysis_project_lower)\n",
    "print(zambia_ratio_analysis_project_mean)\n",
    "print(zambia_ratio_analysis_project_upper)\n",
    "print(zambia_ratio_project_analysis_lower)\n",
    "print(zambia_ratio_project_analysis_mean)\n",
    "print(zambia_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(zambia_ratio_analysis_project_mean[0],2),'[',round(zambia_ratio_analysis_project_lower[0],2),',',round(zambia_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(zambia_ratio_project_analysis_mean[0],2),'[',round(zambia_ratio_project_analysis_upper[0],2),',',round(zambia_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "0    0.149404\n",
      "dtype: float64\n",
      "0    0.15158\n",
      "dtype: float64\n",
      "0    0.153757\n",
      "dtype: float64\n",
      "0    6.693279\n",
      "dtype: float64\n",
      "0    6.59717\n",
      "dtype: float64\n",
      "0    6.503783\n",
      "dtype: float64\n",
      "0.1515801384 [ 0.1494036118 , 0.1537566649 ]\n",
      "6.5971703867 [ 6.5037831083 , 6.6932786153 ]\n"
     ]
    }
   ],
   "source": [
    "df_ALL_VERs_agw=pd.DataFrame(all_VERS_agw).transpose()\n",
    "print(df_ALL_VERs_agw.shape[1])\n",
    "ALL_i=0\n",
    "df_ALL_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while ALL_i<df_ALL_VERs_agw.shape[1]:\n",
    "    df_ALL_VERs_agw_final=df_ALL_VERs_agw_final+df_ALL_VERs_agw[ALL_i]\n",
    "    ALL_i=ALL_i+1\n",
    "    print(ALL_i)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ALL_VERs=VERs_all\n",
    "\n",
    "ALL_ratio_analysis_project=df_ALL_VERs_agw_final/sum(ALL_VERs.iloc[:,1])\n",
    "ALL_ratio_analysis_project_mean=np.mean(df_ALL_VERs_agw_final, axis=1)/sum(ALL_VERs.iloc[:,1])\n",
    "ALL_ratio_project_analysis_mean=ALL_ratio_analysis_project_mean**-1\n",
    "\n",
    "ALL_sd_ratio_analysis_project=np.std(ALL_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "ALL_ratio_analysis_project_lower=ALL_ratio_analysis_project_mean - z*ALL_sd_ratio_analysis_project/np.sqrt(ALL_N)\n",
    "ALL_ratio_analysis_project_upper=ALL_ratio_analysis_project_mean + z*ALL_sd_ratio_analysis_project/np.sqrt(ALL_N)\n",
    "\n",
    "ALL_ratio_project_analysis_lower=ALL_ratio_analysis_project_lower**-1\n",
    "ALL_ratio_project_analysis_upper=ALL_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(ALL_ratio_analysis_project_lower)\n",
    "print(ALL_ratio_analysis_project_mean)\n",
    "print(ALL_ratio_analysis_project_upper)\n",
    "print(ALL_ratio_project_analysis_lower)\n",
    "print(ALL_ratio_project_analysis_mean)\n",
    "print(ALL_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(ALL_ratio_analysis_project_mean[0],10),'[',round(ALL_ratio_analysis_project_lower[0],10),',',round(ALL_ratio_analysis_project_upper[0],10),']')\n",
    "print(round(ALL_ratio_project_analysis_mean[0],10),'[',round(ALL_ratio_project_analysis_upper[0],10),',',round(ALL_ratio_project_analysis_lower[0],10),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_VERS_agw=pd.DataFrame(df_ALL_VERs_agw_final)\n",
    "df_all_VERS_agw.to_excel('df_all_VERs_agw_scenario_all.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gs_lpg_VERs_agw_final=np.array(df_gs_lpg_VERs_agw_final)\n",
    "df_gs_lpg_VERs_agw_final=df_gs_lpg_VERs_agw_final.flatten()\n",
    "\n",
    "df_GS_Meter_VERs_agw_final=np.array(df_GS_Meter_VERs_agw_final)\n",
    "df_GS_Meter_VERs_agw_final=df_GS_Meter_VERs_agw_final.flatten()\n",
    "\n",
    "df_cdm_imp_firewood_VERs_agw_final=np.array(df_cdm_imp_firewood_VERs_agw_final)\n",
    "df_cdm_imp_firewood_VERs_agw_final=df_cdm_imp_firewood_VERs_agw_final.flatten()\n",
    "\n",
    "df_CDM_I_E_VERs_agw_final=np.array(df_CDM_I_E_VERs_agw_final)\n",
    "df_CDM_I_E_VERs_agw_final=df_CDM_I_E_VERs_agw_final.flatten()\n",
    "\n",
    "df_cdm_imp_charcoal_VERs_agw_final=np.array(df_cdm_imp_charcoal_VERs_agw_final)\n",
    "df_cdm_imp_charcoal_VERs_agw_final=df_cdm_imp_charcoal_VERs_agw_final.flatten()\n",
    "\n",
    "df_gs_simple_firewood_VERs_agw_final=np.array(df_gs_simple_firewood_VERs_agw_final)\n",
    "df_gs_simple_firewood_VERs_agw_final=df_gs_simple_firewood_VERs_agw_final.flatten()\n",
    "\n",
    "\n",
    "df_gs_imp_firewood_VERs_agw_final=np.array(df_gs_imp_firewood_VERs_agw_final)\n",
    "df_gs_imp_firewood_VERs_agw_final=df_gs_imp_firewood_VERs_agw_final.flatten()\n",
    "\n",
    "df_gs_imp_charcoal_VERs_agw_final=np.array(df_gs_imp_charcoal_VERs_agw_final)\n",
    "df_gs_imp_charcoal_VERs_agw_final=df_gs_imp_charcoal_VERs_agw_final.flatten()\n",
    "\n",
    "df_ALL_VERs_agw_final=np.array(df_ALL_VERs_agw_final)\n",
    "df_ALL_VERs_agw_final=df_ALL_VERs_agw_final.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure S1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 708x826 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Rectangle\n",
    "xticks=[0,0.005,0.01,0.015]\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "fig = plt.figure(figsize =(7.08, 8.26)) # Set up a figure\n",
    "\n",
    "\n",
    "ax0 = fig.add_subplot(5, 2, 2) # Among the 1 X 2 grids, first subfigure\n",
    "ax0.hist(df_gs_lpg_VERs_agw_final/7,color='#4DBBD5FF') #UPDATE NUMBERS\n",
    "ax0.plot([np.sum(gs_lpg_VERs)/7, np.sum(gs_lpg_VERs)/7], [0, 3000], '#4DBBD5FF', lw=2) #UPDATE\n",
    "\n",
    "ax0.set_ylim(0,3000)\n",
    "ax0.set_xlim(-0.0005,0.015)\n",
    "ax0.tick_params(labelsize=7)\n",
    "ax0.set_xticks(xticks)\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1, color='#4DBBD5FF',ec=\"k\") for c in [low]]\n",
    "labels= [\"Analysis GS-LPG ERs\"]\n",
    "\n",
    "ax0.text(0.0115, 1000, \"GS-LPG\", fontsize=7)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax3 = fig.add_subplot(5, 2, 8) # Among the 1 X 2 grids, first subfigure\n",
    "ax3.hist(df_GS_Meter_VERs_agw_final/3,color='#91D1C2FF')\n",
    "ax3.plot([np.sum(GS_Meter_VERs.iloc[:,1])/3, np.sum(GS_Meter_VERs.iloc[:,1])/3], [0, 3000], '#91D1C2FF', lw=2)\n",
    "ax3.set_xlabel('ERs per stove per day',fontsize=7)\n",
    "\n",
    "ax3.set_ylim(0,3000)\n",
    "ax3.set_xlim(-0.0005,0.015)\n",
    "ax3.set_xticks(xticks)\n",
    "ax3.tick_params(labelsize=7)\n",
    "\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1, color='#91D1C2FF',ec=\"k\") for c in [low]]\n",
    "labels= [\"Analysis GS Metered-Pellets ERs\"]\n",
    "\n",
    "ax3.text(0.0115, 1000, \"GS-Metered\", fontsize=7)\n",
    "ax3.text(0.0115, 750, \"Pellets\", fontsize=7)\n",
    "\n",
    "ax1 = fig.add_subplot(5, 2, 9)\n",
    "\n",
    "ax1.hist(df_cdm_imp_firewood_VERs_agw_final/47, color='#8491B4FF')\n",
    "ax1.plot([np.sum(cdm_imp_firewood_VERs)/47, np.sum(cdm_imp_firewood_VERs)/47], [0, 3000], '#8491B4FF', lw=2)\n",
    "\n",
    "ax1.set_ylabel('Frequency',fontsize=7);\n",
    "ax1.set_ylim(0,3000)\n",
    "ax1.set_xlim(-0.0005,0.015)\n",
    "ax1.set_xticks(xticks)\n",
    "ax1.tick_params(labelsize=7)\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1,color='#8491B4FF',ec=\"k\") for c in [low]]\n",
    "labels= [\"Analysis CDM AMS II G-Firewood ERs\"]\n",
    "\n",
    "ax1.text(0.006, 1200, \"CDM-AMS-II-G\", fontsize=7)\n",
    "ax1.text(0.006, 950, \"Firewood\", fontsize=7)\n",
    "ax1.set_xlabel('ERs per stove per day',fontsize=7)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(5,2,7)\n",
    "ax2.hist(df_CDM_I_E_VERs_agw_final/5, color='#F39B7FFF')\n",
    "ax2.plot([np.sum(CDM_I_E_VERs.iloc[:,1])/5, np.sum(CDM_I_E_VERs.iloc[:,1])/5], [0, 3000], '#F39B7FFF', lw=2)\n",
    "ax2.set_ylabel('Frequency',fontsize=7);\n",
    "ax2.set_ylim(0,3000)\n",
    "ax2.set_xlim(-0.0005,0.015)\n",
    "ax2.set_xticks(xticks)\n",
    "ax2.tick_params(labelsize=7)\n",
    "\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1,color='#F39B7FFF',ec=\"k\") for c in [low]]\n",
    "labels= [\"Analysis AMS I E - Ethanol ERs\"]\n",
    "\n",
    "ax2.text(0.0091, 1000, \"CDM-AMS-I-E\", fontsize=7)\n",
    "ax2.text(0.0091, 750, \"Ethanol\", fontsize=7)\n",
    "\n",
    "ax4 = fig.add_subplot(5, 2, 5)\n",
    "\n",
    "ax4.hist(df_cdm_imp_charcoal_VERs_agw_final/19, color='#7E6148FF')\n",
    "ax4.plot([np.sum(cdm_imp_charcoal_VERs)/19, np.sum(cdm_imp_charcoal_VERs)/19], [0, 3000], '#7E6148FF', lw=2)\n",
    "ax4.set_ylabel('Frequency',fontsize=7);\n",
    "ax4.set_ylim(0,3000)\n",
    "ax4.set_xlim(-0.0005,0.015)\n",
    "ax4.set_xticks(xticks)\n",
    "ax4.tick_params(labelsize=7)\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1,color='#7E6148FF',ec=\"k\") for c in [low]]\n",
    "labels= [\"Analysis AMS-II-G-Charcoal ERs\"]\n",
    "\n",
    "ax4.text(0.008, 1200, \"CDM-AMS-II-G \", fontsize=7)\n",
    "ax4.text(0.008, 950, \"Charcoal\", fontsize=7)\n",
    "\n",
    "ax5 = fig.add_subplot(5, 2, 4) # Among the 1 X 2 grids, first subfigure\n",
    "ax5.hist(df_gs_imp_firewood_VERs_agw_final/31,color='#00A087FF')\n",
    "ax5.plot([np.sum(gs_imp_firewood_VERs)/31, np.sum(gs_imp_firewood_VERs)/31], [0, 3000], '#00A087FF', lw=2)\n",
    "ax5.set_ylim(0,3000)\n",
    "ax5.set_xlim(-0.0005,0.015)\n",
    "ax5.set_xticks(xticks, fontsize=7)\n",
    "ax5.tick_params(labelsize=7)\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1, color='#00A087FF',ec=\"k\") for c in [low]]\n",
    "labels= [\"Analysis GS-Firewood ERs\"]\n",
    "\n",
    "ax5.text(0.007, 1000, \"GS-Firewood\", fontsize=7)\n",
    "\n",
    "ax6 = fig.add_subplot(5, 2, 6) # Among the 1 X 2 grids, first subfigure\n",
    "ax6.hist(df_gs_imp_charcoal_VERs_agw_final/15,color='#3C5488FF')\n",
    "ax6.plot([np.sum(gs_imp_charcoal_VERs)/15, np.sum(gs_imp_charcoal_VERs)/15], [0, 3000], '#3C5488FF', lw=2)\n",
    "ax6.set_ylim(0,3000)\n",
    "ax6.set_xlim(-0.0005,0.015)\n",
    "ax6.tick_params(labelsize=7)\n",
    "ax6.set_xticks(xticks)\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1, color='#3C5488FF',ec=\"k\") for c in [low]]\n",
    "ax6.text(0.010, 1000, \"GS-Charcoal\", fontsize=7)\n",
    "\n",
    "ax7 = fig.add_subplot(5, 2, (1,3)) # Among the 1 X 2 grids, first subfigure\n",
    "ax7.hist(df_ALL_VERs_agw_final/151,color='#DC0000FF')\n",
    "ax7.plot([np.sum(ALL_VERs.iloc[:,1])/151, np.sum(ALL_VERs.iloc[:,1])/151], [0, 3000], '#DC0000FF', lw=2)\n",
    "ax7.set_ylabel('Frequency',fontsize=7);\n",
    "ax7.set_ylim(0,3000)\n",
    "ax7.set_xlim(-0.0005,0.015)\n",
    "ax7.tick_params(labelsize=7)\n",
    "ax7.set_xticks(xticks)\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1, color='#DC0000FF',ec=\"k\") for c in [low]]\n",
    "labels= [\"Analysis All ERs\"]\n",
    "\n",
    "ax7.text(0.0115, 1000, \"All Projects\", fontsize=7)\n",
    "\n",
    "ax8 = fig.add_subplot(5, 2, 10) # Among the 1 X 2 grids, first subfigure\n",
    "ax8.hist(df_gs_simple_firewood_VERs_agw_final/24,color='#B09C85FF')\n",
    "ax8.plot([np.sum(gs_simple_firewood_VERs)/24, np.sum(gs_simple_firewood_VERs)/24], [0, 3000], '#B09C85FF', lw=2)\n",
    "ax8.set_ylim(0,3000)\n",
    "ax8.set_xlim(-0.0005,0.015)\n",
    "ax8.set_xticks(xticks, fontsize=7)\n",
    "ax8.tick_params(labelsize=7)\n",
    "cmap = plt.get_cmap('jet')\n",
    "low = cmap(0.5)\n",
    "high = cmap(0.8)\n",
    "handles = [Rectangle((0,0),1,1, color='#B09C85FF',ec=\"k\") for c in [low]]\n",
    "labels= [\"Analysis GS-Simplified-Firewood ERs\"]\n",
    "#ax5.legend(handles, labels,fontsize=5)\n",
    "ax8.text(0.0075, 1000, \"GS-Simplified-Firewood\", fontsize=7)\n",
    "ax8.set_xlabel('ERs per stove per day',fontsize=7)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('september_11_All_factors_over_under_format.png',dpi=600)\n",
    "fig.savefig('september_11_All_factors_over_under_format.pdf',dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lowest granularity compilation\n",
    "results_category = ['All', 'GS-Metered-Pellets', 'GS-LPG', 'GS-Firewood','GS-Simplified Firewood','GS-Charcoal',\n",
    "                     'CDM-Firewood','CDM-Charcoal','AMS-I-E-Ethanol','Bangladesh',\n",
    "                     'Burkinafaso','Cameroon','China','Ethiopia','Ghana','Guatemala','Haiti','Honduras',\n",
    "                     'India', 'Kenya','Lesotho','Malawi','Mali','Mexico','Mozambique','Myanmar',\n",
    "                     'Nepal','Nigeria','Peru','Rwanda','Sudan','Uganda','Zambia']\n",
    "results_lower = np.array([round(ALL_ratio_project_analysis_upper[0],2), \n",
    "                 round(GS_Meter_ratio_project_analysis_upper[0],2), \n",
    "                 round(gs_lpg_ratio_project_analysis_upper[0],2),\n",
    "                 round(gs_imp_firewood_ratio_project_analysis_upper[0],2),\n",
    "                 round(gs_simple_firewood_ratio_project_analysis_upper[0],2),\n",
    "                 round(gs_imp_charcoal_ratio_project_analysis_upper[0],2),\n",
    "                 round(cdm_imp_firewood_ratio_project_analysis_upper[0],2),\n",
    "                 round(cdm_imp_charcoal_ratio_project_analysis_upper[0],2),\n",
    "                 round(CDM_I_E_ratio_project_analysis_upper[0],2),\n",
    "                 round(bangladesh_ratio_project_analysis_upper[0],2),\n",
    "                 round(burkinafaso_ratio_project_analysis_upper[0],2),\n",
    "                 round(cameroon_ratio_project_analysis_upper[0],2),\n",
    "                 round(china_ratio_project_analysis_upper[0],2),\n",
    "                 round(ethiopia_ratio_project_analysis_upper[0],2),\n",
    "                 round(ghana_ratio_project_analysis_upper[0],2),\n",
    "                 round(guatemala_ratio_project_analysis_upper[0],2),\n",
    "                 round(haiti_ratio_project_analysis_upper[0],2),\n",
    "                 round(honduras_ratio_project_analysis_upper[0],2),\n",
    "                 round(india_ratio_project_analysis_upper[0],2),\n",
    "                 round(kenya_ratio_project_analysis_upper[0],2),\n",
    "                 round(lesotho_ratio_project_analysis_upper[0],2),\n",
    "                 round(malawi_ratio_project_analysis_upper[0],2),\n",
    "                 round(mali_ratio_project_analysis_upper[0],2),       \n",
    "                 round(mexico_ratio_project_analysis_upper[0],2),\n",
    "                 round(mozambique_ratio_project_analysis_upper[0],2),\n",
    "                 round(myanmar_ratio_project_analysis_upper[0],2),\n",
    "                 round(nepal_ratio_project_analysis_upper[0],2),\n",
    "                 round(nigeria_ratio_project_analysis_upper[0],2),\n",
    "                 round(peru_ratio_project_analysis_upper[0],2),\n",
    "                 round(rwanda_ratio_project_analysis_upper[0],2),\n",
    "                 round(sudan_ratio_project_analysis_upper[0],2),\n",
    "                 round(uganda_ratio_project_analysis_upper[0],2),\n",
    "                 round(zambia_ratio_project_analysis_upper[0],2)])\n",
    "\n",
    "results_mean =np.array([round(ALL_ratio_project_analysis_mean[0],2), \n",
    "                 round(GS_Meter_ratio_project_analysis_mean[0],2), \n",
    "                 round(gs_lpg_ratio_project_analysis_mean[0],2),\n",
    "                 round(gs_imp_firewood_ratio_project_analysis_mean[0],2),\n",
    "                 round(gs_simple_firewood_ratio_project_analysis_mean[0],2),\n",
    "                 round(gs_imp_charcoal_ratio_project_analysis_mean[0],2),\n",
    "                 round(cdm_imp_firewood_ratio_project_analysis_mean[0],2),\n",
    "                 round(cdm_imp_charcoal_ratio_project_analysis_mean[0],2),\n",
    "                 round(CDM_I_E_ratio_project_analysis_mean[0],2),\n",
    "                 round(bangladesh_ratio_project_analysis_mean[0],2),\n",
    "                 round(burkinafaso_ratio_project_analysis_mean[0],2),\n",
    "                 round(cameroon_ratio_project_analysis_upper[0],2),\n",
    "                 round(china_ratio_project_analysis_mean[0],2),\n",
    "                 round(ethiopia_ratio_project_analysis_mean[0],2),\n",
    "                 round(ghana_ratio_project_analysis_mean[0],2),\n",
    "                 round(guatemala_ratio_project_analysis_mean[0],2),\n",
    "                 round(haiti_ratio_project_analysis_mean[0],2),\n",
    "                 round(honduras_ratio_project_analysis_mean[0],2),\n",
    "                 round(india_ratio_project_analysis_mean[0],2),\n",
    "                 round(kenya_ratio_project_analysis_mean[0],2),\n",
    "                 round(lesotho_ratio_project_analysis_mean[0],2),\n",
    "                 round(malawi_ratio_project_analysis_mean[0],2),\n",
    "                 round(mali_ratio_project_analysis_mean[0],2),\n",
    "                 round(mexico_ratio_project_analysis_mean[0],2),\n",
    "                 round(mozambique_ratio_project_analysis_mean[0],2),\n",
    "                 round(myanmar_ratio_project_analysis_mean[0],2),\n",
    "                 round(nepal_ratio_project_analysis_mean[0],2),\n",
    "                 round(nigeria_ratio_project_analysis_mean[0],2),\n",
    "                 round(peru_ratio_project_analysis_upper[0],2),\n",
    "                 round(rwanda_ratio_project_analysis_mean[0],2),\n",
    "                 round(sudan_ratio_project_analysis_mean[0],2),\n",
    "                 round(uganda_ratio_project_analysis_mean[0],2),\n",
    "                 round(zambia_ratio_project_analysis_mean[0],2)])\n",
    "                    \n",
    "                    \n",
    "results_high = np.array([round(ALL_ratio_project_analysis_lower[0],2), \n",
    "                 round(GS_Meter_ratio_project_analysis_lower[0],2), \n",
    "                 round(gs_lpg_ratio_project_analysis_lower[0],2),\n",
    "                 round(gs_imp_firewood_ratio_project_analysis_lower[0],2),\n",
    "                 round(gs_simple_firewood_ratio_project_analysis_lower[0],2),\n",
    "                 round(gs_imp_charcoal_ratio_project_analysis_lower[0],2),\n",
    "                 round(cdm_imp_firewood_ratio_project_analysis_lower[0],2),\n",
    "                 round(cdm_imp_charcoal_ratio_project_analysis_lower[0],2),\n",
    "                 round(CDM_I_E_ratio_project_analysis_lower[0],2),\n",
    "                 round(bangladesh_ratio_project_analysis_lower[0],2),\n",
    "                 round(burkinafaso_ratio_project_analysis_lower[0],2),\n",
    "                 round(cameroon_ratio_project_analysis_upper[0],2),\n",
    "                 round(china_ratio_project_analysis_lower[0],2),\n",
    "                 round(ethiopia_ratio_project_analysis_lower[0],2),\n",
    "                 round(ghana_ratio_project_analysis_lower[0],2),\n",
    "                 round(guatemala_ratio_project_analysis_lower[0],2),\n",
    "                 round(haiti_ratio_project_analysis_lower[0],2),\n",
    "                 round(honduras_ratio_project_analysis_lower[0],2),\n",
    "                 round(india_ratio_project_analysis_lower[0],2),\n",
    "                 round(kenya_ratio_project_analysis_lower[0],2),\n",
    "                 round(lesotho_ratio_project_analysis_lower[0],2),\n",
    "                 round(malawi_ratio_project_analysis_lower[0],2),\n",
    "                 round(mali_ratio_project_analysis_lower[0],2),\n",
    "                 round(mexico_ratio_project_analysis_lower[0],2),\n",
    "                 round(mozambique_ratio_project_analysis_lower[0],2),\n",
    "                 round(myanmar_ratio_project_analysis_lower[0],2),\n",
    "                 round(nepal_ratio_project_analysis_lower[0],2),\n",
    "                 round(nigeria_ratio_project_analysis_lower[0],2),\n",
    "                 round(peru_ratio_project_analysis_upper[0],2),\n",
    "                 round(rwanda_ratio_project_analysis_lower[0],2),\n",
    "                 round(sudan_ratio_project_analysis_lower[0],2),\n",
    "                 round(uganda_ratio_project_analysis_lower[0],2),\n",
    "                 round(zambia_ratio_project_analysis_lower[0],2)])\n",
    "\n",
    "# Create DataFrame\n",
    "\n",
    "df_All_results_table= [results_lower,results_mean, results_high]\n",
    "results_category=np.array(results_category)\n",
    "x=pd.DataFrame(df_All_results_table)\n",
    "x=x.transpose()\n",
    "x['Category']=results_category\n",
    "x\n",
    "x.to_excel('df_all_results_table_v1.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Total overcrediting method\n",
    "results_category = ['All', 'GS-Metered-Pellets', 'GS-LPG', 'GS-Firewood','GS-Simplified Firewood','GS-Charcoal',\n",
    "                     'CDM-Firewood','CDM-Charcoal','AMS-I-E-Ethanol','Bangladesh','Brazil',\n",
    "                     'Burkinafaso','Cameroon','China','Ethiopia','Ghana','Guatemala','Haiti','Honduras',\n",
    "                     'India', 'Kenya','Lesotho','Malawi','Mali','Mexico','Mozambique','Myanmar',\n",
    "                     'Nepal','Nigeria','Peru','Rwanda','Sudan','Uganda','Zambia']\n",
    "results_lower = np.array([round(ci_lower,2), \n",
    "                 round(df_combination_final['lower'][7],2), \n",
    "                 round(df_combination_final['lower'][5],2),\n",
    "                 round(df_combination_final['lower'][4],2),\n",
    "                 round(df_combination_final['lower'][6],2),\n",
    "                 round(df_combination_final['lower'][3],2),\n",
    "                 round(df_combination_final['lower'][1],2),\n",
    "                 round(df_combination_final['lower'][0],2),\n",
    "                 round(df_combination_final['lower'][2],2),\n",
    "                 round(df_country_final['lower'][2],2),\n",
    "                 round(df_country_final['lower'][0],2),\n",
    "                 round(df_country_final['lower'][1],2),\n",
    "                 round(df_country_final['lower'][3],2),\n",
    "                 round(df_country_final['lower'][4],2),\n",
    "                 round(df_country_final['lower'][5],2),\n",
    "                 round(df_country_final['lower'][6],2),\n",
    "                 round(df_country_final['lower'][7],2),\n",
    "                 round(df_country_final['lower'][8],2),\n",
    "                 round(df_country_final['lower'][9],2),\n",
    "                 round(df_country_final['lower'][10],2),\n",
    "                 round(df_country_final['lower'][11],2),\n",
    "                 round(df_country_final['lower'][12],2),\n",
    "                 round(df_country_final['lower'][13],2),       \n",
    "                 round(df_country_final['lower'][14],2),\n",
    "                 round(df_country_final['lower'][15],2),\n",
    "                 round(df_country_final['lower'][16],2),\n",
    "                 round(df_country_final['lower'][17],2),\n",
    "                 round(df_country_final['lower'][18],2),\n",
    "                 round(df_country_final['lower'][19],2),\n",
    "                 round(df_country_final['lower'][20],2),\n",
    "                 round(df_country_final['lower'][21],2),\n",
    "                 round(df_country_final['lower'][22],2),\n",
    "                 round(df_country_final['lower'][23],2),\n",
    "                 round(df_country_final['lower'][24],2)])\n",
    "\n",
    "results_mean =np.array([round(reported_value,2), \n",
    "                 round(df_combination_final['total'][7],2), \n",
    "                 round(df_combination_final['total'][5],2),\n",
    "                 round(df_combination_final['total'][4],2),\n",
    "                 round(df_combination_final['total'][6],2),\n",
    "                 round(df_combination_final['total'][3],2),\n",
    "                 round(df_combination_final['total'][1],2),\n",
    "                 round(df_combination_final['total'][0],2),\n",
    "                 round(df_combination_final['total'][2],2),\n",
    "                 round(df_country_final['total'][2],2),\n",
    "                 round(df_country_final['total'][0],2),\n",
    "                 round(df_country_final['total'][1],2),\n",
    "                 round(df_country_final['total'][3],2),\n",
    "                 round(df_country_final['total'][4],2),\n",
    "                 round(df_country_final['total'][5],2),\n",
    "                 round(df_country_final['total'][6],2),\n",
    "                 round(df_country_final['total'][7],2),\n",
    "                 round(df_country_final['total'][8],2),\n",
    "                 round(df_country_final['total'][9],2),\n",
    "                 round(df_country_final['total'][10],2),\n",
    "                 round(df_country_final['total'][11],2),\n",
    "                 round(df_country_final['total'][12],2),\n",
    "                 round(df_country_final['total'][13],2),       \n",
    "                 round(df_country_final['total'][14],2),\n",
    "                 round(df_country_final['total'][15],2),\n",
    "                 round(df_country_final['total'][16],2),\n",
    "                 round(df_country_final['total'][17],2),\n",
    "                 round(df_country_final['total'][18],2),\n",
    "                 round(df_country_final['total'][19],2),\n",
    "                 round(df_country_final['total'][20],2),\n",
    "                 round(df_country_final['total'][21],2),\n",
    "                 round(df_country_final['total'][22],2),\n",
    "                 round(df_country_final['total'][23],2),\n",
    "                 round(df_country_final['total'][24],2)])\n",
    "       \n",
    "                    \n",
    "results_high = np.array([round(ci_upper,2), \n",
    "                 round(df_combination_final['upper'][7],2), \n",
    "                 round(df_combination_final['upper'][5],2),\n",
    "                 round(df_combination_final['upper'][4],2),\n",
    "                 round(df_combination_final['upper'][6],2),\n",
    "                 round(df_combination_final['upper'][3],2),\n",
    "                 round(df_combination_final['upper'][1],2),\n",
    "                 round(df_combination_final['upper'][0],2),\n",
    "                 round(df_combination_final['upper'][2],2),\n",
    "                 round(df_country_final['upper'][2],2),\n",
    "                 round(df_country_final['upper'][0],2),\n",
    "                 round(df_country_final['upper'][1],2),\n",
    "                 round(df_country_final['upper'][3],2),\n",
    "                 round(df_country_final['upper'][4],2),\n",
    "                 round(df_country_final['upper'][5],2),\n",
    "                 round(df_country_final['upper'][6],2),\n",
    "                 round(df_country_final['upper'][7],2),\n",
    "                 round(df_country_final['upper'][8],2),\n",
    "                 round(df_country_final['upper'][9],2),\n",
    "                 round(df_country_final['upper'][10],2),\n",
    "                 round(df_country_final['upper'][11],2),\n",
    "                 round(df_country_final['upper'][12],2),\n",
    "                 round(df_country_final['upper'][13],2),       \n",
    "                 round(df_country_final['upper'][14],2),\n",
    "                 round(df_country_final['upper'][15],2),\n",
    "                 round(df_country_final['upper'][16],2),\n",
    "                 round(df_country_final['upper'][17],2),\n",
    "                 round(df_country_final['upper'][18],2),\n",
    "                 round(df_country_final['upper'][19],2),\n",
    "                 round(df_country_final['upper'][20],2),\n",
    "                 round(df_country_final['upper'][21],2),\n",
    "                 round(df_country_final['upper'][22],2),\n",
    "                 round(df_country_final['upper'][23],2),\n",
    "                 round(df_country_final['upper'][24],2)])\n",
    "\n",
    "# Create DataFrame\n",
    "\n",
    "df_All_results_table= [results_lower,results_mean, results_high]\n",
    "results_category=np.array(results_category)\n",
    "x=pd.DataFrame(df_All_results_table)\n",
    "x=x.transpose()\n",
    "x['Category']=results_category\n",
    "x\n",
    "x.to_excel('df_all_results_table.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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