Gill-Wiehl_et_al_Figure_3g_only_Charcoal_conversion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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_type</th>\n",
       "      <th>protocol_version</th>\n",
       "      <th>protocol_id</th>\n",
       "      <th>monitoring_period</th>\n",
       "      <th>stove_year_cohort</th>\n",
       "      <th>total_verified_credits</th>\n",
       "      <th>type</th>\n",
       "      <th>verified_credits</th>\n",
       "      <th>verified_credits_per_stove_manual</th>\n",
       "      <th>verified_credits_per_stove</th>\n",
       "      <th>...</th>\n",
       "      <th>0.14</th>\n",
       "      <th>0.15</th>\n",
       "      <th>0.16</th>\n",
       "      <th>0.17</th>\n",
       "      <th>0.18</th>\n",
       "      <th>0.19</th>\n",
       "      <th>0.20</th>\n",
       "      <th>0.21</th>\n",
       "      <th>0.22</th>\n",
       "      <th>0.23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Units</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>tCO2e</td>\n",
       "      <td>0</td>\n",
       "      <td>tCO2e</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>tCO2e/stove/day (when changing Efs only)</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CDM AMS II G</td>\n",
       "      <td>AMS-II.G. ver. 3</td>\n",
       "      <td>GS10974</td>\n",
       "      <td>2</td>\n",
       "      <td>domestic</td>\n",
       "      <td>69556</td>\n",
       "      <td>firewood</td>\n",
       "      <td>16258.34792</td>\n",
       "      <td>0.001855</td>\n",
       "      <td>0.001856</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CDM AMS II G</td>\n",
       "      <td>AMS-II.G. ver. 3</td>\n",
       "      <td>GS10974</td>\n",
       "      <td>2</td>\n",
       "      <td>commercial</td>\n",
       "      <td>69556</td>\n",
       "      <td>firewood</td>\n",
       "      <td>8463.65208</td>\n",
       "      <td>0.044223</td>\n",
       "      <td>0.044424</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CDM AMS II G</td>\n",
       "      <td>AMS-II.G. ver. 3</td>\n",
       "      <td>GS10974</td>\n",
       "      <td>3</td>\n",
       "      <td>domestic</td>\n",
       "      <td>69556</td>\n",
       "      <td>firewood</td>\n",
       "      <td>29387.015158</td>\n",
       "      <td>0.001772</td>\n",
       "      <td>0.001801</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CDM AMS II G</td>\n",
       "      <td>AMS-II.G. ver. 3</td>\n",
       "      <td>GS10974</td>\n",
       "      <td>3</td>\n",
       "      <td>commercial</td>\n",
       "      <td>69556</td>\n",
       "      <td>firewood</td>\n",
       "      <td>8294.984842</td>\n",
       "      <td>0.043342</td>\n",
       "      <td>0.04365</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>CDM AMS II G</td>\n",
       "      <td>AMS-II.G. Version 11.1</td>\n",
       "      <td>VCS 2077</td>\n",
       "      <td>MR 2018-2020</td>\n",
       "      <td>2018 vintage</td>\n",
       "      <td>975</td>\n",
       "      <td>firewood</td>\n",
       "      <td>175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008561</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>CDM AMS II G</td>\n",
       "      <td>AMS-II.G. Version 1</td>\n",
       "      <td>GS 411</td>\n",
       "      <td>MR5</td>\n",
       "      <td>0</td>\n",
       "      <td>97297</td>\n",
       "      <td>firewood</td>\n",
       "      <td>3016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006747</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>CDM AMS II G</td>\n",
       "      <td>AMS-II.G. Version 12</td>\n",
       "      <td>GS 10914</td>\n",
       "      <td>MP2</td>\n",
       "      <td>0</td>\n",
       "      <td>61013</td>\n",
       "      <td>charcoal</td>\n",
       "      <td>18752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006303</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>GS Simplified</td>\n",
       "      <td>The Gold Standard Simplified Methodology for E...</td>\n",
       "      <td>GS 10781</td>\n",
       "      <td>0</td>\n",
       "      <td>MP6</td>\n",
       "      <td>2344</td>\n",
       "      <td>firewood</td>\n",
       "      <td>2345</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>GS Simplified</td>\n",
       "      <td>The Gold Standard Simplified Methodology for E...</td>\n",
       "      <td>GS 5660</td>\n",
       "      <td>0</td>\n",
       "      <td>MR1</td>\n",
       "      <td>3397</td>\n",
       "      <td>firewood</td>\n",
       "      <td>2771</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011774</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>152 rows × 355 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     protocol_type                                   protocol_version  \\\n",
       "0            Units                                                  0   \n",
       "1     CDM AMS II G                                   AMS-II.G. ver. 3   \n",
       "2     CDM AMS II G                                   AMS-II.G. ver. 3   \n",
       "3     CDM AMS II G                                   AMS-II.G. ver. 3   \n",
       "4     CDM AMS II G                                   AMS-II.G. ver. 3   \n",
       "..             ...                                                ...   \n",
       "147   CDM AMS II G                             AMS-II.G. Version 11.1   \n",
       "148   CDM AMS II G                                AMS-II.G. Version 1   \n",
       "149   CDM AMS II G                               AMS-II.G. Version 12   \n",
       "150  GS Simplified  The Gold Standard Simplified Methodology for E...   \n",
       "151  GS Simplified  The Gold Standard Simplified Methodology for E...   \n",
       "\n",
       "    protocol_id monitoring_period stove_year_cohort total_verified_credits  \\\n",
       "0             0                 0                 0                  tCO2e   \n",
       "1       GS10974                 2          domestic                  69556   \n",
       "2       GS10974                 2        commercial                  69556   \n",
       "3       GS10974                 3          domestic                  69556   \n",
       "4       GS10974                 3        commercial                  69556   \n",
       "..          ...               ...               ...                    ...   \n",
       "147    VCS 2077     MR 2018-2020       2018 vintage                    975   \n",
       "148      GS 411               MR5                 0                  97297   \n",
       "149    GS 10914               MP2                 0                  61013   \n",
       "150    GS 10781                 0               MP6                   2344   \n",
       "151     GS 5660                 0               MR1                   3397   \n",
       "\n",
       "         type verified_credits  verified_credits_per_stove_manual  \\\n",
       "0           0            tCO2e                           0.000000   \n",
       "1    firewood      16258.34792                           0.001855   \n",
       "2    firewood       8463.65208                           0.044223   \n",
       "3    firewood     29387.015158                           0.001772   \n",
       "4    firewood      8294.984842                           0.043342   \n",
       "..        ...              ...                                ...   \n",
       "147  firewood              175                           0.000000   \n",
       "148  firewood             3016                           0.000000   \n",
       "149  charcoal            18752                           0.000000   \n",
       "150  firewood             2345                           0.000000   \n",
       "151  firewood             2771                           0.000000   \n",
       "\n",
       "                   verified_credits_per_stove  ... 0.14 0.15 0.16 0.17 0.18  \\\n",
       "0    tCO2e/stove/day (when changing Efs only)  ...    0    0    0    0    0   \n",
       "1                                    0.001856  ...    0    0    0    0    0   \n",
       "2                                    0.044424  ...    0    0    0    0    0   \n",
       "3                                    0.001801  ...    0    0    0    0    0   \n",
       "4                                     0.04365  ...    0    0    0    0    0   \n",
       "..                                        ...  ...  ...  ...  ...  ...  ...   \n",
       "147                                  0.008561  ...    0    0    0    0    0   \n",
       "148                                  0.006747  ...    0    0    0    0    0   \n",
       "149                                  0.006303  ...    0    0    0    0    0   \n",
       "150                                    0.0117  ...    0    0    0    0    0   \n",
       "151                                  0.011774  ...    0    0    0    0    0   \n",
       "\n",
       "    0.19 0.20 0.21 0.22 0.23  \n",
       "0      0    0    0    0    0  \n",
       "1      0    0    0    0    0  \n",
       "2      0    0    0    0    0  \n",
       "3      0    0    0    0    0  \n",
       "4      0    0    0    0    0  \n",
       "..   ...  ...  ...  ...  ...  \n",
       "147    0    0    0    0    0  \n",
       "148    0    0    0    0    0  \n",
       "149    0    0    0    0    0  \n",
       "150    0    0    0    0    0  \n",
       "151    0    0    0    0    0  \n",
       "\n",
       "[152 rows x 355 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_excel('9_27_23_python_inputs_charc_firewood_adjusted_gillwiehl_et_al.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "VERs_all=pd.read_excel('df_all_VERs_agw_only_EFs.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",
    "CDM_II_G_verified_total=df.loc[df['protocol_type'] == 'CDM AMS II G', 'verified_credits'].sum()\n",
    "CDM_I_E_verified_total=df.loc[df['protocol_type'] == 'CDM AMS I E', 'verified_credits'].sum()\n",
    "GS_verified_total=df.loc[df['protocol_type'] == 'GS TPDDTEC', 'verified_credits'].sum()\n",
    "GS_metered_verified_total=df.loc[df['protocol_type'] == 'GS Metered', 'verified_credits'].sum()\n",
    "\n",
    "bangladesh_VERs=df.loc[df['country']=='Bangladesh','verified_credits_per_stove'].sum()\n",
    "china_VERs=df.loc[df['country']=='China','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",
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "peru_VERs=df.loc[df['country']=='Peru','verified_credits_per_stove'].sum()\n",
    "cameroon_VERs=df.loc[df['country']=='Cameroon','verified_credits_per_stove'].sum()\n",
    "cameroon_N=0\n",
    "peru_N=0\n",
    "cameroon_VERs_agw=[]\n",
    "peru_VERs_agw=[]\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",
    "# 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "gs_simple_firewood_VERs=df.loc[df['stove']=='gs_simple_firewood','verified_credits_per_stove'].sum()\n",
    "\n",
    "\n",
    "\n",
    "burkinafaso_VERs=df.loc[df['country']=='Burkina Faso','verified_credits_per_stove'].sum()\n",
    "burkinafaso_total=df.loc[df['country'] == 'Burkina Faso', 'verified_credits'].sum()\n",
    "burkinafaso_VERs_agw=[]\n",
    "\n",
    "burkinafaso_N=0\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",
    "\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",
    "\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",
    "# 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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bangladesh_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",
    "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",
    "bangladesh_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",
    "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",
    "all_VERS_agw=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# firewood\n",
    "firewood_ratio_analysis_project_mean_compilation=[]\n",
    "firewood_ratio_project_analysis_mean_compilation=[]\n",
    "firewood_ratio_analysis_project_lower_compilation=[]\n",
    "firewood_ratio_analysis_project_upper_compilation=[]\n",
    "firewood_ratio_project_analysis_lower_compilation=[]\n",
    "firewood_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# charcoal\n",
    "charcoal_ratio_analysis_project_mean_compilation=[]\n",
    "charcoal_ratio_project_analysis_mean_compilation=[]\n",
    "charcoal_ratio_analysis_project_lower_compilation=[]\n",
    "charcoal_ratio_analysis_project_upper_compilation=[]\n",
    "charcoal_ratio_project_analysis_lower_compilation=[]\n",
    "charcoal_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# biomass\n",
    "biomass_ratio_analysis_project_mean_compilation=[]\n",
    "biomass_ratio_project_analysis_mean_compilation=[]\n",
    "biomass_ratio_analysis_project_lower_compilation=[]\n",
    "biomass_ratio_analysis_project_upper_compilation=[]\n",
    "biomass_ratio_project_analysis_lower_compilation=[]\n",
    "biomass_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "\n",
    "# switch\n",
    "switch_ratio_analysis_project_mean_compilation=[]\n",
    "switch_ratio_project_analysis_mean_compilation=[]\n",
    "switch_ratio_analysis_project_lower_compilation=[]\n",
    "switch_ratio_analysis_project_upper_compilation=[]\n",
    "switch_ratio_project_analysis_lower_compilation=[]\n",
    "switch_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# 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",
    "# 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",
    "# 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": 7,
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compiling our results on a protocol basis\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": 9,
   "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",
    "\n",
    "    \n",
    "    \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 updated good \n",
    "ef_combustion_ch4_firewood=0.58 #tCH4/TJ updated good\n",
    "ef_combustion_n2O_firewood=0 #tN2O/TJ\n",
    "\n",
    "ef_combustion_co2_charcoal=114.75 #tCO2/TJ updated good\n",
    "ef_combustion_ch4_charcoal=0.255 #tCH4/TJ updated good\n",
    "ef_combustion_n2O_charcoal=0 #tN2O/TJ good\n",
    "\n",
    "\n",
    "ef_total_not_combustion_co2_charcoal=87.725 #tCO2/TJ updated good\n",
    "ef_total_not_combustion_ch4_charcoal=2.06 #tCH4/TJ updated good\n",
    "ef_total_not_combustion_n2O_charcoal=0.0075#tN2O/TJ good\n",
    "    \n",
    "ef_total_co2_kerosene=79.9 #tCO2/TJ good\n",
    "ef_total_ch4_kerosene=.107 #tCH4/TJ good\n",
    "ef_total_n2O_kerosene=0.000141 #tN2O/TJ good\n",
    "    \n",
    "ef_total_co2_LPG=82.8#tCO2/TJ good\n",
    "ef_total_ch4_LPG=0.117 #tCH4/TJ good\n",
    "ef_total_n2O_LPG=0.000188 #tN2O/TJ good\n",
    "    \n",
    "ef_total_co2_ethanol=60.45#tCO2/TJ good\n",
    "ef_total_ch4_ethanol=0.61 #tCH4/TJ good\n",
    "ef_total_n2O_ethanol=0.05 #tN2O/TJ good \n",
    "    \n",
    "ef_total_co2_pellets=90.05#tCO2/TJ good\n",
    "ef_total_ch4_pellets=0.105#tCH4/TJ good\n",
    "ef_total_n2O_pellets=0 #tN2O/TJ good\n",
    "    \n",
    "ef_total_zeroed_co2_ethanol=-9.78#tCO2/TJ good\n",
    "ef_total_zeroed_ch4_ethanol=0.61 #tCH4/TJ good\n",
    "ef_total_zeroed_n2O_ethanol=0.05 #tN2O/TJ good\n",
    "    \n",
    "ef_total_zeroed_co2_pellets=0 #tCO2/TJ good\n",
    "ef_total_zeroed_ch4_pellets=0.105 #tCH4/TJ good\n",
    "ef_total_zeroed_n2O_pellets=0 #tN2O/TJ good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "GS10974\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "2\n",
      "GS10974\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "3\n",
      "GS10974\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "4\n",
      "GS10974\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "5\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "6\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "7\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "8\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "9\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "10\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "11\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "12\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "13\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "14\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "15\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "16\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "17\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "18\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "19\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "20\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "21\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "22\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "23\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "24\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "25\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "26\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "27\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "28\n",
      "VCS1216\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "29\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "30\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "31\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "32\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "33\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "34\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "35\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "36\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "37\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "38\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "39\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "40\n",
      "GS2094\n",
      "We find that this project reports emissions which are 1.28 [ 1.28  ,  1.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 78.11 [ 78.11  ,  78.11 ] % of the verified credits in the PDD.\n",
      "41\n",
      "VCS1719\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "42\n",
      "VCS1721\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "43\n",
      "GS7312\n",
      "We find that this project reports emissions which are 9.72 [ 9.72  ,  9.72 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.29 [ 10.29  ,  10.29 ] % of the verified credits in the PDD.\n",
      "44\n",
      "GS7312\n",
      "We find that this project reports emissions which are 9.72 [ 9.72  ,  9.72 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.29 [ 10.29  ,  10.29 ] % of the verified credits in the PDD.\n",
      "45\n",
      "GS500\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "46\n",
      "GS10884\n",
      "We find that this project reports emissions which are 1.13 [ 1.13  ,  1.13 ] times overcredited.\n",
      "Our analysis finds emissions which are 88.66 [ 88.66  ,  88.66 ] % of the verified credits in the PDD.\n",
      "47\n",
      "GS10884\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "48\n",
      "GS447\n",
      "We find that this project reports emissions which are 6.0 [ 6.0  ,  6.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 16.67 [ 16.67  ,  16.67 ] % of the verified credits in the PDD.\n",
      "49\n",
      "GS447\n",
      "We find that this project reports emissions which are 6.0 [ 6.0  ,  6.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 16.67 [ 16.67  ,  16.67 ] % of the verified credits in the PDD.\n",
      "50\n",
      "GS2564\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "51\n",
      "GS7438\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "52\n",
      "GS7438\n",
      "We find that this project reports emissions which are 8.0 [ 8.0  ,  8.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.5 [ 12.5  ,  12.5 ] % of the verified credits in the PDD.\n",
      "53\n",
      "GS913\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "54\n",
      "GS6212\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "55\n",
      "GS6212\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "56\n",
      "GS2758\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "57\n",
      "GS2758\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "58\n",
      "GS2758\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "59\n",
      "GS2758\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "60\n",
      "GS2758\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "61\n",
      "GS2758\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "62\n",
      "GS3112\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "63\n",
      "GS3112\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "64\n",
      "GS3112\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "65\n",
      "GS3112\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "66\n",
      "GS5642\n",
      "We find that this project reports emissions which are 0.48 [ 0.48  ,  0.48 ] times overcredited.\n",
      "Our analysis finds emissions which are 207.89 [ 207.89  ,  207.89 ] % of the verified credits in the PDD.\n",
      "67\n",
      "GS5642\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "68\n",
      "GS1060\n",
      "We find that this project reports emissions which are 1.92 [ 1.92  ,  1.92 ] times overcredited.\n",
      "Our analysis finds emissions which are 52.15 [ 52.15  ,  52.15 ] % of the verified credits in the PDD.\n",
      "69\n",
      "GS1060\n",
      "We find that this 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",
      "70\n",
      "GS1060\n",
      "We find that this project reports emissions which are 1.78 [ 1.78  ,  1.78 ] times overcredited.\n",
      "Our analysis finds emissions which are 56.24 [ 56.24  ,  56.24 ] % of the verified credits in the PDD.\n",
      "71\n",
      "GS1060\n",
      "We find that this project reports emissions which are 1.92 [ 1.92  ,  1.92 ] times overcredited.\n",
      "Our analysis finds emissions which are 52.15 [ 52.15  ,  52.15 ] % of the verified credits in the PDD.\n",
      "72\n",
      "GS1060\n",
      "We find that this 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",
      "73\n",
      "GS1060\n",
      "We find that this project reports emissions which are 1.78 [ 1.78  ,  1.78 ] times overcredited.\n",
      "Our analysis finds emissions which are 56.24 [ 56.24  ,  56.24 ] % of the verified credits in the PDD.\n",
      "74\n",
      "GS2744\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "75\n",
      "GS2744\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "76\n",
      "GS1094\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "77\n",
      "GS1094\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "78\n",
      "GS1094\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "79\n",
      "GS1094\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "80\n",
      "GS1094\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "81\n",
      "GS1094\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "82\n",
      "GS4291\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "83\n",
      "GS4291\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "84\n",
      "GS6604\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "85\n",
      "GS 2896 \n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "86\n",
      "GS 3422 \n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "87\n",
      "GS 2456\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "88\n",
      "GS 2456\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "89\n",
      "GS 2456\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "90\n",
      "GS 2456\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "91\n",
      "GS 2456\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "92\n",
      "GS 1028\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "93\n",
      "GS 1028\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "94\n",
      "GS 1028\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "95\n",
      "GS 1028\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "96\n",
      "GS 1028\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "97\n",
      "GS 3018\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "98\n",
      "GS 3018\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "99\n",
      "GS 3018\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "100\n",
      "GS1267\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "101\n",
      "GS1267\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "102\n",
      "GS1267\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "103\n",
      "GS1267\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "104\n",
      "GS1267\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "105\n",
      "GS1267\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "106\n",
      "GS2439\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "107\n",
      "GS2439\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "108\n",
      "GS2439\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "109\n",
      "GS2439\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "110\n",
      "GS2439\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "111\n",
      "GS2439\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "112\n",
      "GS2445\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "113\n",
      "GS7578\n",
      "We find that this project reports emissions which are 1.57 [ 1.57  ,  1.57 ] times overcredited.\n",
      "Our analysis finds emissions which are 63.83 [ 63.83  ,  63.83 ] % of the verified credits in the PDD.\n",
      "114\n",
      "GS2513\n",
      "We find that this project reports emissions which are 1.52 [ 1.52  ,  1.52 ] times overcredited.\n",
      "Our analysis finds emissions which are 65.93 [ 65.93  ,  65.93 ] % of the verified credits in the PDD.\n",
      "115\n",
      "GS4677\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "116\n",
      "GS2441\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "117\n",
      "GS2441\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "118\n",
      "GS2441\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "119\n",
      "GS2441\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "120\n",
      "GS2441\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "121\n",
      "GS2441\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "122\n",
      "GS2441\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "123\n",
      "GS2441\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "124\n",
      "GS5003\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "125\n",
      "GS5003\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "126\n",
      "GS5003\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "127\n",
      "GS6129\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "128\n",
      "GS6129\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "129\n",
      "GS6129\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "130\n",
      "GS407\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "131\n",
      "GS407\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "132\n",
      "GS407\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "133\n",
      "GS407\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "134\n",
      "GS11509\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "135\n",
      "GS11352\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "136\n",
      "GS11507\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "137\n",
      "GS1146\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "138\n",
      "GS1146\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "139\n",
      "GS1146\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "140\n",
      "GS1146\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "141\n",
      "GS 11330\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "142\n",
      "GS 3071\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "143\n",
      "GS 10777\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "144\n",
      "GS 10886\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "145\n",
      "GS 5107\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "146\n",
      "GS 11195\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "147\n",
      "VCS 2077\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "148\n",
      "GS 411\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "149\n",
      "GS 10914\n",
      "We find that this project reports emissions which are 1.07 [ 1.07  ,  1.07 ] times overcredited.\n",
      "Our analysis finds emissions which are 93.61 [ 93.61  ,  93.61 ] % of the verified credits in the PDD.\n",
      "150\n",
      "GS 10781\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % of the verified credits in the PDD.\n",
      "151\n",
      "GS 5660\n",
      "We find that this project reports emissions which are 1.0 [ 1.0  ,  1.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 100.0 [ 100.0  ,  100.0 ] % 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-1,1]\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",
    "    verified_credits=df['verified_credits'][i]\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",
    "\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",
    "\n",
    "\n",
    "\n",
    "    # GWP: What are the assumed global warming potentials of CH4 & N20? If not included put 0 or leave blank\n",
    "    gwp_ch4= 25\n",
    "\n",
    "    gwp_n2O=298\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 in your location]\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",
    "    # Assumed GWP potentials of non-Co2 gases in the project\n",
    "    gwp_ch4_project=25\n",
    "\n",
    "    gwp_n2O_project=298\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",
    "    print(i)\n",
    "    # Adoption, Usage, and Leakage \n",
    "\n",
    "\n",
    "\n",
    "    project_adoption=df['project_adoption'][i]\n",
    "\n",
    "\n",
    "    project_usage=df['project_usage'][i]\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",
    "    \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\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*((fNRB_project*(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-fNRB_project)*(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*((fNRB_project*(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-fNRB_project)*(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_agw/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*((fNRB_project*(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-fNRB_project)*(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*((fNRB_project*(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-fNRB_project)*(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_agw/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_commercial_gillwiehl=prc_pellets_baseline*(ncv_pellets_agw*kg_pellets_baseline_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 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*((fNRB_project*(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-fNRB_project)*(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*((fNRB_project*(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-fNRB_project)*(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_agw/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*((fNRB_project*(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-fNRB_project)*(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*((fNRB_project*(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-fNRB_project)*(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_agw/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",
    "    # Totalling Baseline and Project Emissions\n",
    "    \n",
    "    be_total_agw=be_total_domestic_gillwiehl*project_adoption*project_usage*(1-project_stacking)\n",
    "\n",
    "    be_total_agw=be_total_agw+be_total_commercial_gillwiehl*project_adoption_commercial*project_usage_commercial*(1-project_stacking_commercial)\n",
    "\n",
    "    pe_total_agw=pe_total_domestic_gillwiehl*project_adoption*project_usage*(1-project_stacking)\n",
    "    pe_total_agw=pe_total_agw+pe_total_commercial_gillwiehl*project_adoption_commercial*project_usage*(1-project_stacking_commercial)\n",
    "\n",
    "    PEs_agw=(pe_total_agw)*more_than_one_stove_adj # Project Emission Reductions (PERs) in (tCO2e)\n",
    "    BEs_agw=(be_total_agw)*more_than_one_stove_adj # Project Emission Reductions (PERs) in (tCO2e)\n",
    "\n",
    "    VERs_agw=(be_total_agw-pe_total_agw)*more_than_one_stove_adj*leakage_default-(leakage_tCO2e+other_tCO2e_reduction)/(n_devices_domestic+n_devices_commercial)/(project_days+project_days_commercial) # Project Emission Reductions (PERs) in (tCO2e) # Project Emission Reductions (PERs) in (tCO2e)\n",
    "    \n",
    "    \n",
    "    \n",
    "  \n",
    "    # Protocol Analysis: \n",
    "    \n",
    "    all_VERS_agw.append(VERs_agw)\n",
    "    ALL_N=ALL_N+1\n",
    "    \n",
    "    #GS Metered\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\n",
    "    if df.protocol_type[i]=='GS TPDDTEC':\n",
    "        GS_N=GS_N+1\n",
    "        GS_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "        \n",
    "    # CDM II G    \n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS II G':\n",
    "        CDM_II_G_N=CDM_II_G_N+1\n",
    "        CDM_II_G_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",
    "    #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",
    "    #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",
    "    #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",
    "    #firewood\n",
    "    if df.type[i]=='firewood':\n",
    "        firewood_N=firewood_N+1\n",
    "        firewood_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #charcoal\n",
    "    if df.type[i]=='charcoal':\n",
    "        charcoal_N=charcoal_N+1\n",
    "        charcoal_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #biomass\n",
    "    if df.type[i]=='biomass':\n",
    "        biomass_N=charcoal_N+1\n",
    "        biomass_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #switch\n",
    "    if df.type[i]=='switch':\n",
    "        switch_N=switch_N+1\n",
    "        switch_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_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",
    "        \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",
    "    #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",
    "    #cameroon\n",
    "    if df.country[i]=='Cameroon':\n",
    "        cameroon_N=cameroon_N+1\n",
    "        cameroon_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",
    "    \n",
    "    print(protocol_id)\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",
    "    ratio_project_analysis_lower_compilation.append(ratio_project_analysis_lower)\n",
    "    ratio_project_analysis_upper_compilation.append(ratio_project_analysis_upper)\n",
    "    \n",
    "    print('We find that this 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_VERS_agw=pd.DataFrame(all_VERS_agw)\n",
    "df_all_VERS_agw.to_excel('df_all_VERs_agw_both_EF_charcoal.xlsx')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is the total over-crediting method (main result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.381178\n",
      "dtype: float64\n",
      "1.5410768328867113\n",
      "1.5410768328867113 [ 1.1007053011813253 , 1.9814483645920973 ]\n"
     ]
    }
   ],
   "source": [
    "#This is the total over-crediting method (main result)\n",
    "ratio_project_analysis_mean_compilation=pd.DataFrame(ratio_project_analysis_mean_compilation)\n",
    "ratio_project_analysis_lower_compilation=pd.DataFrame(ratio_project_analysis_lower_compilation)\n",
    "ratio_project_analysis_upper_compilation=pd.DataFrame(ratio_project_analysis_upper_compilation)\n",
    "ratio_analysis_project_mean_compilation=pd.DataFrame(ratio_analysis_project_mean_compilation)\n",
    "ratio_analysis_project_lower_compilation=pd.DataFrame(ratio_analysis_project_lower_compilation)\n",
    "ratio_analysis_project_upper_compilation=pd.DataFrame(ratio_analysis_project_upper_compilation)\n",
    "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",
    "\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.DataFrame(df_info)\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\n",
    "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",
    "\n",
    "df_project_weighted_stove_days\n",
    "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",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "# Country/Methodology-stove combination analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Country analysis\n",
    "my_tuple=(df_total.groupby('country')['total_verified_credits'].unique())\n",
    "countries=pd.DataFrame(my_tuple.index)\n",
    "#list(my_tuple)\n",
    "df_country_totals = pd.DataFrame(list(my_tuple))\n",
    "df_country_totals.index=my_tuple.index\n",
    "#df_country_totals.columns=['country', 'total1', 'total2','total3','total3','total4','total5']\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",
    "#list(my_tuple)\n",
    "df_country_totals_analysis = pd.DataFrame(list(my_tuple2))\n",
    "df_country_totals_analysis.index=my_tuple2.index\n",
    "#df_country_totals.columns=['country', 'total1', 'total2','total3','total3','total4','total5']\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",
    "#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",
    "\n",
    "df_combination_final\n",
    "df_combination_final.iloc[:,14:18].to_excel('all_results_firewood_charcoal_categories.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Highest level of granularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.476027\n",
      "dtype: float64\n",
      "0    0.476027\n",
      "dtype: float64\n",
      "0    0.476027\n",
      "dtype: float64\n",
      "0    2.100721\n",
      "dtype: float64\n",
      "0    2.100721\n",
      "dtype: float64\n",
      "0    2.100721\n",
      "dtype: float64\n",
      "0.48 [ 0.48 , 0.48 ]\n",
      "2.1 [ 2.1 , 2.1 ]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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",
    "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.958692\n",
      "dtype: float64\n",
      "0    0.958692\n",
      "dtype: float64\n",
      "0    0.958692\n",
      "dtype: float64\n",
      "0    1.043088\n",
      "dtype: float64\n",
      "0    1.043088\n",
      "dtype: float64\n",
      "0    1.043088\n",
      "dtype: float64\n",
      "0.96 [ 0.96 , 0.96 ]\n",
      "1.04 [ 1.04 , 1.04 ]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.878206\n",
      "dtype: float64\n",
      "0    0.878206\n",
      "dtype: float64\n",
      "0    0.878206\n",
      "dtype: float64\n",
      "0    1.138684\n",
      "dtype: float64\n",
      "0    1.138684\n",
      "dtype: float64\n",
      "0    1.138684\n",
      "dtype: float64\n",
      "0.88 [ 0.88 , 0.88 ]\n",
      "1.14 [ 1.14 , 1.14 ]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.996555\n",
      "dtype: float64\n",
      "0    0.996555\n",
      "dtype: float64\n",
      "0    0.996555\n",
      "dtype: float64\n",
      "0    1.003457\n",
      "dtype: float64\n",
      "0    1.003457\n",
      "dtype: float64\n",
      "0    1.003457\n",
      "dtype: float64\n",
      "1.0 [ 1.0 , 1.0 ]\n",
      "1.0 [ 1.0 , 1.0 ]\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": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.497853\n",
      "dtype: float64\n",
      "0    1.497853\n",
      "dtype: float64\n",
      "0    1.497853\n",
      "dtype: float64\n",
      "0    0.667622\n",
      "dtype: float64\n",
      "0    0.667622\n",
      "dtype: float64\n",
      "0    0.667622\n",
      "dtype: float64\n",
      "1.5 [ 1.5 , 1.5 ]\n",
      "0.67 [ 0.67 , 0.67 ]\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0    0.925455\n",
      "dtype: float64\n",
      "0    0.925455\n",
      "dtype: float64\n",
      "0    0.925455\n",
      "dtype: float64\n",
      "0    1.080549\n",
      "dtype: float64\n",
      "0    1.080549\n",
      "dtype: float64\n",
      "0    1.080549\n",
      "dtype: float64\n",
      "0.9254552904 [ 0.9254552904 , 0.9254552904 ]\n",
      "1.0805492284 [ 1.0805492284 , 1.0805492284 ]\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": 25,
   "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": 26,
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.801341\n",
      "dtype: float64\n",
      "0    0.801341\n",
      "dtype: float64\n",
      "0    0.801341\n",
      "dtype: float64\n",
      "0    1.247908\n",
      "dtype: float64\n",
      "0    1.247908\n",
      "dtype: float64\n",
      "0    1.247908\n",
      "dtype: float64\n",
      "0.8 [ 0.8 , 0.8 ]\n",
      "1.25 [ 1.25 , 1.25 ]\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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.127097\n",
      "dtype: float64\n",
      "0    1.127097\n",
      "dtype: float64\n",
      "0    1.127097\n",
      "dtype: float64\n",
      "0    0.887235\n",
      "dtype: float64\n",
      "0    0.887235\n",
      "dtype: float64\n",
      "0    0.887235\n",
      "dtype: float64\n",
      "1.13 [ 1.13 , 1.13 ]\n",
      "0.89 [ 0.89 , 0.89 ]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53\n"
     ]
    }
   ],
   "source": [
    "df_GS_VERs_agw=pd.DataFrame(GS_VERs_agw).transpose()\n",
    "print(df_GS_VERs_agw.shape[1])\n",
    "gs_i=0\n",
    "df_GS_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while gs_i<df_GS_VERs_agw.shape[1]:\n",
    "    df_GS_VERs_agw_final=df_GS_VERs_agw_final+df_GS_VERs_agw[gs_i]\n",
    "    gs_i=gs_i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_GS_VERs_agw_final.to_excel('df_GS_VERs_agw_final_Scenario_D.xlsx')\n",
    "df_GS_VERs_agw.to_excel('df_GS_VERs_agw_Scenario_D.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.878381\n",
      "dtype: float64\n",
      "0    0.878381\n",
      "dtype: float64\n",
      "0    0.878381\n",
      "dtype: float64\n",
      "0    1.138458\n",
      "dtype: float64\n",
      "0    1.138458\n",
      "dtype: float64\n",
      "0    1.138458\n",
      "dtype: float64\n",
      "0.88 [ 0.88 , 0.88 ]\n",
      "1.14 [ 1.14 , 1.14 ]\n"
     ]
    }
   ],
   "source": [
    "GS_ratio_analysis_project=df_GS_VERs_agw_final/sum(GS_VERs.iloc[:,1])\n",
    "GS_ratio_analysis_project_mean=np.mean(df_GS_VERs_agw_final, axis=1)/sum(GS_VERs.iloc[:,1])\n",
    "GS_ratio_project_analysis_mean=GS_ratio_analysis_project_mean**-1\n",
    "\n",
    "GS_sd_ratio_analysis_project=np.std(GS_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "GS_ratio_analysis_project_lower=GS_ratio_analysis_project_mean - z*GS_sd_ratio_analysis_project/np.sqrt(GS_N)\n",
    "GS_ratio_analysis_project_upper=GS_ratio_analysis_project_mean + z*GS_sd_ratio_analysis_project/np.sqrt(GS_N)\n",
    "\n",
    "GS_ratio_project_analysis_lower=GS_ratio_analysis_project_lower**-1\n",
    "GS_ratio_project_analysis_upper=GS_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(GS_ratio_analysis_project_lower)\n",
    "print(GS_ratio_analysis_project_mean)\n",
    "print(GS_ratio_analysis_project_upper)\n",
    "print(GS_ratio_project_analysis_lower)\n",
    "print(GS_ratio_project_analysis_mean)\n",
    "print(GS_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(GS_ratio_analysis_project_mean[0],2),'[',round(GS_ratio_analysis_project_lower[0],2),',',round(GS_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(GS_ratio_project_analysis_mean[0],2),'[',round(GS_ratio_project_analysis_upper[0],2),',',round(GS_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "df_CDM_II_G_VERs_agw=pd.DataFrame(CDM_II_G_VERs_agw).transpose()\n",
    "print(df_CDM_II_G_VERs_agw.shape[1])\n",
    "cdm_II_g_i=0\n",
    "df_CDM_II_G_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while cdm_II_g_i<df_CDM_II_G_VERs_agw.shape[1]:\n",
    "    df_CDM_II_G_VERs_agw_final=df_CDM_II_G_VERs_agw_final+df_CDM_II_G_VERs_agw[cdm_II_g_i]\n",
    "    cdm_II_g_i=cdm_II_g_i+1\n",
    "    print(cdm_II_g_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.391062\n",
      "dtype: float64\n",
      "0    1.391062\n",
      "dtype: float64\n",
      "0    1.391062\n",
      "dtype: float64\n",
      "0    0.718875\n",
      "dtype: float64\n",
      "0    0.718875\n",
      "dtype: float64\n",
      "0    0.718875\n",
      "dtype: float64\n",
      "1.39 [ 1.39 , 1.39 ]\n",
      "0.72 [ 0.72 , 0.72 ]\n"
     ]
    }
   ],
   "source": [
    "CDM_II_G_ratio_analysis_project=df_CDM_II_G_VERs_agw_final/sum(CDM_II_G_VERs.iloc[:,1])\n",
    "CDM_II_G_ratio_analysis_project_mean=np.mean(df_CDM_II_G_VERs_agw_final, axis=1)/sum(CDM_II_G_VERs.iloc[:,1])\n",
    "\n",
    "CDM_II_G_ratio_project_analysis_mean=CDM_II_G_ratio_analysis_project_mean**-1\n",
    "CDM_II_G_sd_ratio_analysis_project=np.std(CDM_II_G_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "CDM_II_G_ratio_analysis_project_lower=CDM_II_G_ratio_analysis_project_mean - z*CDM_II_G_sd_ratio_analysis_project/np.sqrt(CDM_II_G_N)\n",
    "CDM_II_G_ratio_analysis_project_upper=CDM_II_G_ratio_analysis_project_mean + z*CDM_II_G_sd_ratio_analysis_project/np.sqrt(CDM_II_G_N)\n",
    "\n",
    "CDM_II_G_ratio_project_analysis_lower=CDM_II_G_ratio_analysis_project_lower**-1\n",
    "CDM_II_G_ratio_project_analysis_upper=CDM_II_G_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(CDM_II_G_ratio_analysis_project_lower)\n",
    "print(CDM_II_G_ratio_analysis_project_mean)\n",
    "print(CDM_II_G_ratio_analysis_project_upper)\n",
    "print(CDM_II_G_ratio_project_analysis_lower)\n",
    "print(CDM_II_G_ratio_project_analysis_mean)\n",
    "print(CDM_II_G_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(CDM_II_G_ratio_analysis_project_mean[0],2),'[',round(CDM_II_G_ratio_analysis_project_lower[0],2),',',round(CDM_II_G_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(CDM_II_G_ratio_project_analysis_mean[0],2),'[',round(CDM_II_G_ratio_project_analysis_upper[0],2),',',round(CDM_II_G_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_CDM_II_G_VERs_agw_final.to_excel('df_CDM_II_G_VERs_agw_final_Scenario_D.xlsx')\n",
    "df_CDM_II_G_VERs_agw.to_excel('df_CDM_II_G_VERs_agw_Scenario_D.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.39573\n",
      "dtype: float64\n",
      "0    1.39573\n",
      "dtype: float64\n",
      "0    1.39573\n",
      "dtype: float64\n",
      "0    0.716471\n",
      "dtype: float64\n",
      "0    0.716471\n",
      "dtype: float64\n",
      "0    0.716471\n",
      "dtype: float64\n",
      "1.4 [ 1.4 , 1.4 ]\n",
      "0.72 [ 0.72 , 0.72 ]\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": [],
   "source": [
    "# Country analysis\n",
    "my_tuple=(df_total.groupby('country')['total_verified_credits'].unique())\n",
    "countries=pd.DataFrame(my_tuple.index)\n",
    "#list(my_tuple)\n",
    "df_country_totals = pd.DataFrame(list(my_tuple))\n",
    "df_country_totals.index=my_tuple.index\n",
    "#df_country_totals.columns=['country', 'total1', 'total2','total3','total3','total4','total5']\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",
    "#list(my_tuple)\n",
    "df_country_totals_analysis = pd.DataFrame(list(my_tuple2))\n",
    "df_country_totals_analysis.index=my_tuple2.index\n",
    "#df_country_totals.columns=['country', 'total1', 'total2','total3','total3','total4','total5']\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",
    "#Methodology-stove combination analysis\n",
    "my_tuple=(df_total.groupby('category')['total_verified_credits'].unique())\n",
    "combinations=pd.DataFrame(my_tuple.index)\n",
    "#list(my_tuple)\n",
    "df_combination_totals = pd.DataFrame(list(my_tuple))\n",
    "df_combination_totals.index=my_tuple.index\n",
    "#df_country_totals.columns=['country', 'total1', 'total2','total3','total3','total4','total5']\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",
    "#list(my_tuple)\n",
    "df_combination_totals_analysis = pd.DataFrame(list(my_tuple2))\n",
    "df_combination_totals_analysis.index=my_tuple2.index\n",
    "#df_country_totals.columns=['country', 'total1', 'total2','total3','total3','total4','total5']\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",
    "\n",
    "df_combination_final\n",
    "df_combination_final.iloc[:,14:18].to_excel('all_results_firewood_charcoal_categories.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "0    1.398425\n",
      "dtype: float64\n",
      "0    1.398425\n",
      "dtype: float64\n",
      "0    1.398425\n",
      "dtype: float64\n",
      "0    0.71509\n",
      "dtype: float64\n",
      "0    0.71509\n",
      "dtype: float64\n",
      "0    0.71509\n",
      "dtype: float64\n",
      "1.4 [ 1.4 , 1.4 ]\n",
      "0.72 [ 0.72 , 0.72 ]\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",
    "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": [
      "5\n",
      "0    0.763813\n",
      "dtype: float64\n",
      "0    0.763813\n",
      "dtype: float64\n",
      "0    0.763813\n",
      "dtype: float64\n",
      "0    1.309221\n",
      "dtype: float64\n",
      "0    1.309221\n",
      "dtype: float64\n",
      "0    1.309221\n",
      "dtype: float64\n",
      "0.76 [ 0.76 , 0.76 ]\n",
      "1.31 [ 1.31 , 1.31 ]\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    1.470588\n",
      "dtype: float64\n",
      "0    1.470588\n",
      "dtype: float64\n",
      "0    1.470588\n",
      "dtype: float64\n",
      "0    0.68\n",
      "dtype: float64\n",
      "0    0.68\n",
      "dtype: float64\n",
      "0    0.68\n",
      "dtype: float64\n",
      "1.47 [ 1.47 , 1.47 ]\n",
      "0.68 [ 0.68 , 0.68 ]\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": [
      "2\n",
      "0    1.298417\n",
      "dtype: float64\n",
      "0    1.298417\n",
      "dtype: float64\n",
      "0    1.298417\n",
      "dtype: float64\n",
      "0    0.770169\n",
      "dtype: float64\n",
      "0    0.770169\n",
      "dtype: float64\n",
      "0    0.770169\n",
      "dtype: float64\n",
      "1.3 [ 1.3 , 1.3 ]\n",
      "0.77 [ 0.77 , 0.77 ]\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",
    "\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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "0    1.591779\n",
      "dtype: float64\n",
      "0    1.591779\n",
      "dtype: float64\n",
      "0    1.591779\n",
      "dtype: float64\n",
      "0    0.628228\n",
      "dtype: float64\n",
      "0    0.628228\n",
      "dtype: float64\n",
      "0    0.628228\n",
      "dtype: float64\n",
      "1.59 [ 1.59 , 1.59 ]\n",
      "0.63 [ 0.63 , 0.63 ]\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "0    1.112482\n",
      "dtype: float64\n",
      "0    1.112482\n",
      "dtype: float64\n",
      "0    1.112482\n",
      "dtype: float64\n",
      "0    0.898891\n",
      "dtype: float64\n",
      "0    0.898891\n",
      "dtype: float64\n",
      "0    0.898891\n",
      "dtype: float64\n",
      "1.11 [ 1.11 , 1.11 ]\n",
      "0.9 [ 0.9 , 0.9 ]\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    0.215298\n",
      "dtype: float64\n",
      "0    0.215298\n",
      "dtype: float64\n",
      "0    0.215298\n",
      "dtype: float64\n",
      "0    4.644725\n",
      "dtype: float64\n",
      "0    4.644725\n",
      "dtype: float64\n",
      "0    4.644725\n",
      "dtype: float64\n",
      "0.22 [ 0.22 , 0.22 ]\n",
      "4.64 [ 4.64 , 4.64 ]\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0    0.824363\n",
      "dtype: float64\n",
      "0    0.824363\n",
      "dtype: float64\n",
      "0    0.824363\n",
      "dtype: float64\n",
      "0    1.213058\n",
      "dtype: float64\n",
      "0    1.213058\n",
      "dtype: float64\n",
      "0    1.213058\n",
      "dtype: float64\n",
      "0.82 [ 0.82 , 0.82 ]\n",
      "1.21 [ 1.21 , 1.21 ]\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",
    "\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": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    1.493653\n",
      "dtype: float64\n",
      "0    1.493653\n",
      "dtype: float64\n",
      "0    1.493653\n",
      "dtype: float64\n",
      "0    0.6695\n",
      "dtype: float64\n",
      "0    0.6695\n",
      "dtype: float64\n",
      "0    0.6695\n",
      "dtype: float64\n",
      "1.49 [ 1.49 , 1.49 ]\n",
      "0.67 [ 0.67 , 0.67 ]\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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "0    1.661274\n",
      "dtype: float64\n",
      "0    1.661274\n",
      "dtype: float64\n",
      "0    1.661274\n",
      "dtype: float64\n",
      "0    0.601948\n",
      "dtype: float64\n",
      "0    0.601948\n",
      "dtype: float64\n",
      "0    0.601948\n",
      "dtype: float64\n",
      "1.66 [ 1.66 , 1.66 ]\n",
      "0.6 [ 0.6 , 0.6 ]\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",
    "\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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    1.474921\n",
      "dtype: float64\n",
      "0    1.474921\n",
      "dtype: float64\n",
      "0    1.474921\n",
      "dtype: float64\n",
      "0    0.678003\n",
      "dtype: float64\n",
      "0    0.678003\n",
      "dtype: float64\n",
      "0    0.678003\n",
      "dtype: float64\n",
      "1.47 [ 1.47 , 1.47 ]\n",
      "0.68 [ 0.68 , 0.68 ]\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",
    "\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": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "0    0.713391\n",
      "dtype: float64\n",
      "0    0.713391\n",
      "dtype: float64\n",
      "0    0.713391\n",
      "dtype: float64\n",
      "0    1.401755\n",
      "dtype: float64\n",
      "0    1.401755\n",
      "dtype: float64\n",
      "0    1.401755\n",
      "dtype: float64\n",
      "0.71 [ 0.71 , 0.71 ]\n",
      "1.4 [ 1.4 , 1.4 ]\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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    1.680044\n",
      "dtype: float64\n",
      "0    1.680044\n",
      "dtype: float64\n",
      "0    1.680044\n",
      "dtype: float64\n",
      "0    0.595223\n",
      "dtype: float64\n",
      "0    0.595223\n",
      "dtype: float64\n",
      "0    0.595223\n",
      "dtype: float64\n",
      "1.68 [ 1.68 , 1.68 ]\n",
      "0.6 [ 0.6 , 0.6 ]\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": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "0    1.292296\n",
      "dtype: float64\n",
      "0    1.292296\n",
      "dtype: float64\n",
      "0    1.292296\n",
      "dtype: float64\n",
      "0    0.773817\n",
      "dtype: float64\n",
      "0    0.773817\n",
      "dtype: float64\n",
      "0    0.773817\n",
      "dtype: float64\n",
      "1.29 [ 1.29 , 1.29 ]\n",
      "0.77 [ 0.77 , 0.77 ]\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "0    0.805201\n",
      "dtype: float64\n",
      "0    0.805201\n",
      "dtype: float64\n",
      "0    0.805201\n",
      "dtype: float64\n",
      "0    1.241926\n",
      "dtype: float64\n",
      "0    1.241926\n",
      "dtype: float64\n",
      "0    1.241926\n",
      "dtype: float64\n",
      "0.81 [ 0.81 , 0.81 ]\n",
      "1.24 [ 1.24 , 1.24 ]\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "0    0.845053\n",
      "dtype: float64\n",
      "0    0.845053\n",
      "dtype: float64\n",
      "0    0.845053\n",
      "dtype: float64\n",
      "0    1.183358\n",
      "dtype: float64\n",
      "0    1.183358\n",
      "dtype: float64\n",
      "0    1.183358\n",
      "dtype: float64\n",
      "0.85 [ 0.85 , 0.85 ]\n",
      "1.18 [ 1.18 , 1.18 ]\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": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "0    1.374543\n",
      "dtype: float64\n",
      "0    1.374543\n",
      "dtype: float64\n",
      "0    1.374543\n",
      "dtype: float64\n",
      "0    0.727515\n",
      "dtype: float64\n",
      "0    0.727515\n",
      "dtype: float64\n",
      "0    0.727515\n",
      "dtype: float64\n",
      "1.37 [ 1.37 , 1.37 ]\n",
      "0.73 [ 0.73 , 0.73 ]\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": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "0    0.471774\n",
      "dtype: float64\n",
      "0    0.471774\n",
      "dtype: float64\n",
      "0    0.471774\n",
      "dtype: float64\n",
      "0    2.119658\n",
      "dtype: float64\n",
      "0    2.119658\n",
      "dtype: float64\n",
      "0    2.119658\n",
      "dtype: float64\n",
      "0.47 [ 0.47 , 0.47 ]\n",
      "2.12 [ 2.12 , 2.12 ]\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": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n",
      "0    0.904206\n",
      "dtype: float64\n",
      "0    0.904206\n",
      "dtype: float64\n",
      "0    0.904206\n",
      "dtype: float64\n",
      "0    1.105942\n",
      "dtype: float64\n",
      "0    1.105942\n",
      "dtype: float64\n",
      "0    1.105942\n",
      "dtype: float64\n",
      "0.9 [ 0.9 , 0.9 ]\n",
      "1.11 [ 1.11 , 1.11 ]\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.556173\n",
      "dtype: float64\n",
      "0    0.556173\n",
      "dtype: float64\n",
      "0    0.556173\n",
      "dtype: float64\n",
      "0    1.798002\n",
      "dtype: float64\n",
      "0    1.798002\n",
      "dtype: float64\n",
      "0    1.798002\n",
      "dtype: float64\n",
      "0.56 [ 0.56 , 0.56 ]\n",
      "1.8 [ 1.8 , 1.8 ]\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": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0    0.565969\n",
      "dtype: float64\n",
      "0    0.565969\n",
      "dtype: float64\n",
      "0    0.565969\n",
      "dtype: float64\n",
      "0    1.766882\n",
      "dtype: float64\n",
      "0    1.766882\n",
      "dtype: float64\n",
      "0    1.766882\n",
      "dtype: float64\n",
      "0.57 [ 0.57 , 0.57 ]\n",
      "1.77 [ 1.77 , 1.77 ]\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.950361\n",
      "dtype: float64\n",
      "0    0.950361\n",
      "dtype: float64\n",
      "0    0.950361\n",
      "dtype: float64\n",
      "0    1.052232\n",
      "dtype: float64\n",
      "0    1.052232\n",
      "dtype: float64\n",
      "0    1.052232\n",
      "dtype: float64\n",
      "0.95 [ 0.95 , 0.95 ]\n",
      "1.05 [ 1.05 , 1.05 ]\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": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    0.971978\n",
      "dtype: float64\n",
      "0    0.971978\n",
      "dtype: float64\n",
      "0    0.971978\n",
      "dtype: float64\n",
      "0    1.02883\n",
      "dtype: float64\n",
      "0    1.02883\n",
      "dtype: float64\n",
      "0    1.02883\n",
      "dtype: float64\n",
      "0.97 [ 0.97 , 0.97 ]\n",
      "1.03 [ 1.03 , 1.03 ]\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": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    1.018913\n",
      "dtype: float64\n",
      "0    1.018913\n",
      "dtype: float64\n",
      "0    1.018913\n",
      "dtype: float64\n",
      "0    0.981438\n",
      "dtype: float64\n",
      "0    0.981438\n",
      "dtype: float64\n",
      "0    0.981438\n",
      "dtype: float64\n",
      "1.02 [ 1.02 , 1.02 ]\n",
      "0.98 [ 0.98 , 0.98 ]\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": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102\n",
      "0    1.187839\n",
      "dtype: float64\n",
      "0    1.187839\n",
      "dtype: float64\n",
      "0    1.187839\n",
      "dtype: float64\n",
      "0    0.841865\n",
      "dtype: float64\n",
      "0    0.841865\n",
      "dtype: float64\n",
      "0    0.841865\n",
      "dtype: float64\n",
      "1.19 [ 1.19 , 1.19 ]\n",
      "0.84 [ 0.84 , 0.84 ]\n"
     ]
    }
   ],
   "source": [
    "df_firewood_VERs_agw=pd.DataFrame(firewood_VERs_agw).transpose()\n",
    "print(df_firewood_VERs_agw.shape[1])\n",
    "firewood_i=0\n",
    "df_firewood_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while firewood_i<df_firewood_VERs_agw.shape[1]:\n",
    "    df_firewood_VERs_agw_final=df_firewood_VERs_agw_final+df_firewood_VERs_agw[firewood_i]\n",
    "    firewood_i=firewood_i+1\n",
    "\n",
    "firewood_ratio_analysis_project=df_firewood_VERs_agw_final/firewood_VERs\n",
    "firewood_ratio_analysis_project_mean=np.mean(df_firewood_VERs_agw_final, axis=1)/firewood_VERs\n",
    "firewood_ratio_project_analysis_mean=firewood_ratio_analysis_project_mean**-1\n",
    "\n",
    "firewood_sd_ratio_analysis_project=np.std(firewood_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "firewood_ratio_analysis_project_lower=firewood_ratio_analysis_project_mean - z*firewood_sd_ratio_analysis_project/np.sqrt(firewood_N)\n",
    "firewood_ratio_analysis_project_upper=firewood_ratio_analysis_project_mean + z*firewood_sd_ratio_analysis_project/np.sqrt(firewood_N)\n",
    "\n",
    "firewood_ratio_project_analysis_lower=firewood_ratio_analysis_project_lower**-1\n",
    "firewood_ratio_project_analysis_upper=firewood_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(firewood_ratio_analysis_project_lower)\n",
    "print(firewood_ratio_analysis_project_mean)\n",
    "print(firewood_ratio_analysis_project_upper)\n",
    "print(firewood_ratio_project_analysis_lower)\n",
    "print(firewood_ratio_project_analysis_mean)\n",
    "print(firewood_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(firewood_ratio_analysis_project_mean[0],2),'[',round(firewood_ratio_analysis_project_lower[0],2),',',round(firewood_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(firewood_ratio_project_analysis_mean[0],2),'[',round(firewood_ratio_project_analysis_upper[0],2),',',round(firewood_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n",
      "0    0.996195\n",
      "dtype: float64\n",
      "0    0.996195\n",
      "dtype: float64\n",
      "0    0.996195\n",
      "dtype: float64\n",
      "0    1.00382\n",
      "dtype: float64\n",
      "0    1.00382\n",
      "dtype: float64\n",
      "0    1.00382\n",
      "dtype: float64\n",
      "1.0 [ 1.0 , 1.0 ]\n",
      "1.0 [ 1.0 , 1.0 ]\n"
     ]
    }
   ],
   "source": [
    "df_charcoal_VERs_agw=pd.DataFrame(charcoal_VERs_agw).transpose()\n",
    "print(df_charcoal_VERs_agw.shape[1])\n",
    "charcoal_i=0\n",
    "df_charcoal_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while charcoal_i<df_charcoal_VERs_agw.shape[1]:\n",
    "    df_charcoal_VERs_agw_final=df_charcoal_VERs_agw_final+df_charcoal_VERs_agw[charcoal_i]\n",
    "    charcoal_i=charcoal_i+1\n",
    "\n",
    "charcoal_ratio_analysis_project=df_charcoal_VERs_agw_final/charcoal_VERs\n",
    "charcoal_ratio_analysis_project_mean=np.mean(df_charcoal_VERs_agw_final, axis=1)/charcoal_VERs\n",
    "charcoal_ratio_project_analysis_mean=charcoal_ratio_analysis_project_mean**-1\n",
    "\n",
    "charcoal_sd_ratio_analysis_project=np.std(charcoal_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "charcoal_ratio_analysis_project_lower=charcoal_ratio_analysis_project_mean - z*charcoal_sd_ratio_analysis_project/np.sqrt(charcoal_N)\n",
    "charcoal_ratio_analysis_project_upper=charcoal_ratio_analysis_project_mean + z*charcoal_sd_ratio_analysis_project/np.sqrt(charcoal_N)\n",
    "\n",
    "charcoal_ratio_project_analysis_lower=charcoal_ratio_analysis_project_lower**-1\n",
    "charcoal_ratio_project_analysis_upper=charcoal_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(charcoal_ratio_analysis_project_lower)\n",
    "print(charcoal_ratio_analysis_project_mean)\n",
    "print(charcoal_ratio_analysis_project_upper)\n",
    "print(charcoal_ratio_project_analysis_lower)\n",
    "print(charcoal_ratio_project_analysis_mean)\n",
    "print(charcoal_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(charcoal_ratio_analysis_project_mean[0],2),'[',round(charcoal_ratio_analysis_project_lower[0],2),',',round(charcoal_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(charcoal_ratio_project_analysis_mean[0],2),'[',round(charcoal_ratio_project_analysis_upper[0],2),',',round(charcoal_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "# \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_only_charcoal_results_table_v1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "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_only_charcoal_results_table.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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