Gill-WIehl_et_al_sensitvity_Usage_Not_survey.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</th>\n",
       "      <th>verified_credits_per_stove_isolating</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>tCO2e/stove/day</td>\n",
       "      <td>0.000000</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>0.001856</td>\n",
       "      <td>0.002730</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>0.044424</td>\n",
       "      <td>0.065329</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>0.001801</td>\n",
       "      <td>0.002648</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>0.04365</td>\n",
       "      <td>0.064191</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>GS 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>0.008561</td>\n",
       "      <td>0.000000</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>0.006747</td>\n",
       "      <td>0.000000</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>0.006303</td>\n",
       "      <td>0.000000</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>0.0117</td>\n",
       "      <td>0.000000</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>0.011774</td>\n",
       "      <td>0.000000</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     GS 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  \\\n",
       "0           0            tCO2e            tCO2e/stove/day   \n",
       "1    firewood      16258.34792                   0.001856   \n",
       "2    firewood       8463.65208                   0.044424   \n",
       "3    firewood     29387.015158                   0.001801   \n",
       "4    firewood      8294.984842                    0.04365   \n",
       "..        ...              ...                        ...   \n",
       "147  firewood              175                   0.008561   \n",
       "148  firewood             3016                   0.006747   \n",
       "149  charcoal            18752                   0.006303   \n",
       "150  firewood             2345                     0.0117   \n",
       "151  firewood             2771                   0.011774   \n",
       "\n",
       "     verified_credits_per_stove_isolating  ... 0.14 0.15 0.16 0.17 0.18 0.19  \\\n",
       "0                                0.000000  ...    0    0    0    0    0    0   \n",
       "1                                0.002730  ...    0    0    0    0    0    0   \n",
       "2                                0.065329  ...    0    0    0    0    0    0   \n",
       "3                                0.002648  ...    0    0    0    0    0    0   \n",
       "4                                0.064191  ...    0    0    0    0    0    0   \n",
       "..                                    ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "147                              0.000000  ...    0    0    0    0    0    0   \n",
       "148                              0.000000  ...    0    0    0    0    0    0   \n",
       "149                              0.000000  ...    0    0    0    0    0    0   \n",
       "150                              0.000000  ...    0    0    0    0    0    0   \n",
       "151                              0.000000  ...    0    0    0    0    0    0   \n",
       "\n",
       "    0.20 0.21 0.22 0.23  \n",
       "0      0    0    0    0  \n",
       "1      0    0    0    0  \n",
       "2      0    0    0    0  \n",
       "3      0    0    0    0  \n",
       "4      0    0    0    0  \n",
       "..   ...  ...  ...  ...  \n",
       "147    0    0    0    0  \n",
       "148    0    0    0    0  \n",
       "149    0    0    0    0  \n",
       "150    0    0    0    0  \n",
       "151    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_baseline_adjusted_gillwiehl_et_al.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "VERs_all=pd.read_excel('VERS.xlsx')\n",
    "CDM_I_E_VERs=pd.read_excel('CDM_I_E_VERs.xlsx')\n",
    "CDM_II_G_VERs=pd.read_excel('CDM_II_G_VERs.xlsx')\n",
    "GS_VERs=pd.read_excel('GS_VERs.xlsx')\n",
    "GS_Meter_VERs=pd.read_excel('GS_Meter_VERs.xlsx')\n",
    "\n",
    "\n",
    "bangladesh_VERs=df.loc[df['country']=='Bangladesh','verified_credits_per_stove'].sum()\n",
    "burkinafaso_VERs=df.loc[df['country']=='Burkina Faso','verified_credits_per_stove'].sum()\n",
    "china_VERs=df.loc[df['country']=='China','verified_credits_per_stove'].sum()\n",
    "cameroon_VERs=df.loc[df['country']=='Cameroon','verified_credits_per_stove'].sum()\n",
    "ethiopia_VERs=df.loc[df['country']=='Ethiopia','verified_credits_per_stove'].sum()\n",
    "ghana_VERs=df.loc[df['country']=='Ghana','verified_credits_per_stove'].sum()\n",
    "guatemala_VERs=df.loc[df['country']=='Guatemala','verified_credits_per_stove'].sum()\n",
    "haiti_VERs=df.loc[df['country']=='Haiti','verified_credits_per_stove'].sum()\n",
    "honduras_VERs=df.loc[df['country']=='Honduras','verified_credits_per_stove'].sum()\n",
    "india_VERs=df.loc[df['country']=='India','verified_credits_per_stove'].sum()\n",
    "kenya_VERs=df.loc[df['country']=='Kenya','verified_credits_per_stove'].sum()\n",
    "lesotho_VERs=df.loc[df['country']=='Lesotho','verified_credits_per_stove'].sum()\n",
    "malawi_VERs=df.loc[df['country']=='Malawi','verified_credits_per_stove'].sum()\n",
    "mali_VERs=df.loc[df['country']=='Mali','verified_credits_per_stove'].sum()\n",
    "mexico_VERs=df.loc[df['country']=='Mexico','verified_credits_per_stove'].sum()\n",
    "mozambique_VERs=df.loc[df['country']=='Mozambique','verified_credits_per_stove'].sum()\n",
    "myanmar_VERs=df.loc[df['country']=='Myanmar','verified_credits_per_stove'].sum()\n",
    "nepal_VERs=df.loc[df['country']=='Nepal','verified_credits_per_stove'].sum()\n",
    "nigeria_VERs=df.loc[df['country']=='Nigeria','verified_credits_per_stove'].sum()\n",
    "peru_VERs=df.loc[df['country']=='Peru','verified_credits_per_stove'].sum()\n",
    "rwanda_VERs=df.loc[df['country']=='Rwanda','verified_credits_per_stove'].sum()\n",
    "sudan_VERs=df.loc[df['country']=='Sudan','verified_credits_per_stove'].sum()\n",
    "uganda_VERs=df.loc[df['country']=='Uganda','verified_credits_per_stove'].sum()\n",
    "zambia_VERs=df.loc[df['country']=='Zambia','verified_credits_per_stove'].sum()\n",
    "\n",
    "firewood_VERs=df.loc[df['type']=='firewood','verified_credits_per_stove'].sum()\n",
    "charcoal_VERs=df.loc[df['type']=='charcoal','verified_credits_per_stove'].sum()\n",
    "biomass_VERs=df.loc[df['type']=='biomass','verified_credits_per_stove'].sum()\n",
    "switch_VERs=df.loc[df['type']=='switch','verified_credits_per_stove'].sum()\n",
    "\n",
    "gs_simple_firewood_VERs=df.loc[df['stove']=='gs_simple_firewood','verified_credits_per_stove'].sum()\n",
    "gs_lpg_VERs=df.loc[df['stove']=='gs_lpg','verified_credits_per_stove'].sum()\n",
    "gs_imp_firewood_VERs=df.loc[df['stove']=='gs_imp_firewood','verified_credits_per_stove'].sum()\n",
    "gs_imp_charcoal_VERs=df.loc[df['stove']=='gs_imp_charcoal','verified_credits_per_stove'].sum()\n",
    "cdm_imp_firewood_VERs=df.loc[df['stove']=='cdm_imp_firewood','verified_credits_per_stove'].sum()\n",
    "cdm_imp_charcoal_VERs=df.loc[df['stove']=='cdm_imp_charcoal','verified_credits_per_stove'].sum()\n",
    "\n",
    "\n",
    "CDM_II_G_verified_total=df.loc[df['protocol_type'] == 'CDM AMS II G', 'verified_credits_per_stove'].sum()\n",
    "CDM_I_E_verified_total=df.loc[df['protocol_type'] == 'CDM AMS I E', 'verified_credits_per_stove'].sum()\n",
    "GS_verified_total=df.loc[df['protocol_type'] == 'GS TPDDTEC', 'verified_credits_per_stove'].sum()\n",
    "GS_metered_verified_total=df.loc[df['protocol_type'] == 'GS Metered', 'verified_credits_per_stove'].sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bangladesh_total=df.loc[df['country'] == 'Bangladesh', 'verified_credits'].sum()\n",
    "burkinafaso_total=df.loc[df['country'] == 'Burkina Faso', 'verified_credits'].sum()\n",
    "cameroon_total=df.loc[df['country'] == 'Cameroon', 'verified_credits'].sum()\n",
    "china_total=df.loc[df['country'] == 'China', 'verified_credits'].sum()\n",
    "ethiopia_total=df.loc[df['country'] == 'Ethiopia', 'verified_credits'].sum()\n",
    "ghana_total=df.loc[df['country'] == 'Ghana', 'verified_credits'].sum()\n",
    "guatemala_total=df.loc[df['country'] == 'Guatemala', 'verified_credits'].sum()\n",
    "haiti_total=df.loc[df['country'] == 'Haiti', 'verified_credits'].sum()\n",
    "honduras_total=df.loc[df['country'] == 'Honduras', 'verified_credits'].sum()\n",
    "india_total=df.loc[df['country'] == 'India', 'verified_credits'].sum()\n",
    "kenya_total=df.loc[df['country'] == 'Kenya', 'verified_credits'].sum()\n",
    "lesotho_total=df.loc[df['country'] == 'Lesotho', 'verified_credits'].sum()\n",
    "malawi_total=df.loc[df['country'] == 'Malawi', 'verified_credits'].sum()\n",
    "mali_total=df.loc[df['country'] == 'Mali', 'verified_credits'].sum()\n",
    "mexico_total=df.loc[df['country'] == 'Mexico', 'verified_credits'].sum()\n",
    "mozambique_total=df.loc[df['country'] == 'Mozambique', 'verified_credits'].sum()\n",
    "myanmar_total=df.loc[df['country'] == 'Myanmar', 'verified_credits'].sum()\n",
    "nepal_total=df.loc[df['country'] == 'Nepal', 'verified_credits'].sum()\n",
    "nigeria_total=df.loc[df['country'] == 'Nigeria', 'verified_credits'].sum()\n",
    "peru_total=df.loc[df['country'] == 'Peru', 'verified_credits'].sum()\n",
    "rwanda_total=df.loc[df['country'] == 'Rwanda', 'verified_credits'].sum()\n",
    "uganda_total=df.loc[df['country'] == 'Uganda', 'verified_credits'].sum()\n",
    "sudan_total=df.loc[df['country'] == 'Sudan', 'verified_credits'].sum()\n",
    "zambia_total=df.loc[df['country'] == 'Zambia', 'verified_credits'].sum()\n",
    "\n",
    "firewood_total=df.loc[df['type'] == 'firewood', 'verified_credits'].sum()\n",
    "charcoal_total=df.loc[df['type'] == 'charcoal', 'verified_credits'].sum()\n",
    "biomass_total=df.loc[df['type'] == 'biomass', 'verified_credits'].sum()\n",
    "switch_total=df.loc[df['type'] == 'switch', 'verified_credits'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_analysis_project_mean_compilation=[]\n",
    "ratio_project_analysis_mean_compilation=[]\n",
    "ratio_analysis_project_lower_compilation=[]\n",
    "ratio_analysis_project_upper_compilation=[]\n",
    "ratio_project_analysis_lower_compilation=[]\n",
    "ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# For protocol wide over-estimation\n",
    "GS_Meter_N=0\n",
    "GS_N=0\n",
    "CDM_II_G_N=0\n",
    "CDM_I_E_N=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "bangladesh_N=0\n",
    "burkinafaso_N=0\n",
    "cameroon_N=0\n",
    "china_N=0\n",
    "ethiopia_N=0\n",
    "ghana_N=0\n",
    "guatemala_N=0\n",
    "haiti_N=0\n",
    "honduras_N=0\n",
    "india_N=0\n",
    "kenya_N=0\n",
    "lesotho_N=0\n",
    "malawi_N=0\n",
    "mali_N=0\n",
    "mexico_N=0\n",
    "mozambique_N=0\n",
    "myanmar_N=0\n",
    "nepal_N=0\n",
    "nigeria_N=0\n",
    "peru_N=0\n",
    "rwanda_N=0\n",
    "sudan_N=0\n",
    "uganda_N=0\n",
    "zambia_N=0\n",
    "\n",
    "firewood_N=0\n",
    "charcoal_N=0\n",
    "biomass_N=0\n",
    "switch_N=0\n",
    "\n",
    "ALL_N=0\n",
    "\n",
    "gs_simple_firewood_N=0\n",
    "gs_lpg_N=0\n",
    "gs_imp_firewood_N=0\n",
    "gs_imp_charcoal_N=0\n",
    "cdm_imp_firewood_N=0\n",
    "cdm_imp_charcoal_N=0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bangladesh_VERs_agw=[]\n",
    "burkinafaso_VERs_agw=[]\n",
    "cameroon_VERs_agw=[]\n",
    "china_VERs_agw=[]\n",
    "ethiopia_VERs_agw=[]\n",
    "ghana_VERs_agw=[]\n",
    "guatemala_VERs_agw=[]\n",
    "haiti_VERs_agw=[]\n",
    "honduras_VERs_agw=[]\n",
    "india_VERs_agw=[]\n",
    "kenya_VERs_agw=[]\n",
    "lesotho_VERs_agw=[]\n",
    "malawi_VERs_agw=[]\n",
    "mali_VERs_agw=[]\n",
    "mexico_VERs_agw=[]\n",
    "mozambique_VERs_agw=[]\n",
    "myanmar_VERs_agw=[]\n",
    "nepal_VERs_agw=[]\n",
    "nigeria_VERs_agw=[]\n",
    "peru_VERs_agw=[]\n",
    "rwanda_VERs_agw=[]\n",
    "sudan_VERs_agw=[]\n",
    "uganda_VERs_agw=[]\n",
    "zambia_VERs_agw=[]\n",
    "\n",
    "firewood_VERs_agw=[]\n",
    "charcoal_VERs_agw=[]\n",
    "biomass_VERs_agw=[]\n",
    "switch_VERs_agw=[]\n",
    "\n",
    "gs_simple_firewood_VERs_agw=[]\n",
    "gs_lpg_VERs_agw=[]\n",
    "gs_imp_firewood_VERs_agw=[]\n",
    "gs_imp_charcoal_VERs_agw=[]\n",
    "cdm_imp_firewood_VERs_agw=[]\n",
    "cdm_imp_charcoal_VERs_agw=[]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compiling our results on a protocol basis\n",
    "all_VERS_agw=[]\n",
    "GS_Meter_VERs_agw=[]\n",
    "GS_VERs_agw=[]\n",
    "CDM_II_G_VERs_agw=[]\n",
    "CDM_I_E_VERs_agw=[]\n",
    "\n",
    "\n",
    "# GS \n",
    "GS_Meter_ratio_analysis_project_mean_compilation=[]\n",
    "GS_Meter_ratio_project_analysis_mean_compilation=[]\n",
    "GS_Meter_ratio_analysis_project_lower_compilation=[]\n",
    "GS_Meter_ratio_analysis_project_upper_compilation=[]\n",
    "GS_Meter_ratio_project_analysis_lower_compilation=[]\n",
    "GS_Meter_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# GS \n",
    "GS_ratio_analysis_project_mean_compilation=[]\n",
    "GS_ratio_project_analysis_mean_compilation=[]\n",
    "GS_ratio_analysis_project_lower_compilation=[]\n",
    "GS_ratio_analysis_project_upper_compilation=[]\n",
    "GS_ratio_project_analysis_lower_compilation=[]\n",
    "GS_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "#CDM II G\n",
    "CDM_II_G_ratio_analysis_project_mean_compilation=[]\n",
    "CDM_II_G_ratio_project_analysis_mean_compilation=[]\n",
    "CDM_II_G_ratio_analysis_project_lower_compilation=[]\n",
    "CDM_II_G_ratio_analysis_project_upper_compilation=[]\n",
    "CDM_II_G_ratio_project_analysis_lower_compilation=[]\n",
    "CDM_II_G_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "#CDM I E\n",
    "CDM_I_E_ratio_analysis_project_mean_compilation=[]\n",
    "CDM_I_E_ratio_project_analysis_mean_compilation=[]\n",
    "CDM_I_E_ratio_analysis_project_lower_compilation=[]\n",
    "CDM_I_E_ratio_analysis_project_upper_compilation=[]\n",
    "CDM_I_E_ratio_project_analysis_lower_compilation=[]\n",
    "CDM_I_E_ratio_project_analysis_upper_compilation=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bangladesh \n",
    "bangladesh_ratio_analysis_project_mean_compilation=[]\n",
    "bangladesh_ratio_project_analysis_mean_compilation=[]\n",
    "bangladesh_ratio_analysis_project_lower_compilation=[]\n",
    "bangladesh_ratio_analysis_project_upper_compilation=[]\n",
    "bangladesh_ratio_project_analysis_lower_compilation=[]\n",
    "bangladesh_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# burkinafaso \n",
    "burkinafaso_ratio_analysis_project_mean_compilation=[]\n",
    "burkinafaso_ratio_project_analysis_mean_compilation=[]\n",
    "burkinafaso_ratio_analysis_project_lower_compilation=[]\n",
    "burkinafaso_ratio_analysis_project_upper_compilation=[]\n",
    "burkinafaso_ratio_project_analysis_lower_compilation=[]\n",
    "burkinafaso_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# cameroon \n",
    "cameroon_ratio_analysis_project_mean_compilation=[]\n",
    "cameroon_ratio_project_analysis_mean_compilation=[]\n",
    "cameroon_ratio_analysis_project_lower_compilation=[]\n",
    "cameroon_ratio_analysis_project_upper_compilation=[]\n",
    "cameroon_ratio_project_analysis_lower_compilation=[]\n",
    "cameroon_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "\n",
    "# china \n",
    "china_ratio_analysis_project_mean_compilation=[]\n",
    "china_ratio_project_analysis_mean_compilation=[]\n",
    "china_ratio_analysis_project_lower_compilation=[]\n",
    "china_ratio_analysis_project_upper_compilation=[]\n",
    "china_ratio_project_analysis_lower_compilation=[]\n",
    "china_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# ethiopia \n",
    "ethiopia_ratio_analysis_project_mean_compilation=[]\n",
    "ethiopia_ratio_project_analysis_mean_compilation=[]\n",
    "ethiopia_ratio_analysis_project_lower_compilation=[]\n",
    "ethiopia_ratio_analysis_project_upper_compilation=[]\n",
    "ethiopia_ratio_project_analysis_lower_compilation=[]\n",
    "ethiopia_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# ghana \n",
    "ghana_ratio_analysis_project_mean_compilation=[]\n",
    "ghana_ratio_project_analysis_mean_compilation=[]\n",
    "ghana_ratio_analysis_project_lower_compilation=[]\n",
    "ghana_ratio_analysis_project_upper_compilation=[]\n",
    "ghana_ratio_project_analysis_lower_compilation=[]\n",
    "ghana_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# guatemala \n",
    "guatemala_ratio_analysis_project_mean_compilation=[]\n",
    "guatemala_ratio_project_analysis_mean_compilation=[]\n",
    "guatemala_ratio_analysis_project_lower_compilation=[]\n",
    "guatemala_ratio_analysis_project_upper_compilation=[]\n",
    "guatemala_ratio_project_analysis_lower_compilation=[]\n",
    "guatemala_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# haiti \n",
    "haiti_ratio_analysis_project_mean_compilation=[]\n",
    "haiti_ratio_project_analysis_mean_compilation=[]\n",
    "haiti_ratio_analysis_project_lower_compilation=[]\n",
    "haiti_ratio_analysis_project_upper_compilation=[]\n",
    "haiti_ratio_project_analysis_lower_compilation=[]\n",
    "haiti_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# honduras \n",
    "honduras_ratio_analysis_project_mean_compilation=[]\n",
    "honduras_ratio_project_analysis_mean_compilation=[]\n",
    "honduras_ratio_analysis_project_lower_compilation=[]\n",
    "honduras_ratio_analysis_project_upper_compilation=[]\n",
    "honduras_ratio_project_analysis_lower_compilation=[]\n",
    "honduras_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# india \n",
    "india_ratio_analysis_project_mean_compilation=[]\n",
    "india_ratio_project_analysis_mean_compilation=[]\n",
    "india_ratio_analysis_project_lower_compilation=[]\n",
    "india_ratio_analysis_project_upper_compilation=[]\n",
    "india_ratio_project_analysis_lower_compilation=[]\n",
    "india_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# kenya \n",
    "kenya_ratio_analysis_project_mean_compilation=[]\n",
    "kenya_ratio_project_analysis_mean_compilation=[]\n",
    "kenya_ratio_analysis_project_lower_compilation=[]\n",
    "kenya_ratio_analysis_project_upper_compilation=[]\n",
    "kenya_ratio_project_analysis_lower_compilation=[]\n",
    "kenya_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# lesotho \n",
    "lesotho_ratio_analysis_project_mean_compilation=[]\n",
    "lesotho_ratio_project_analysis_mean_compilation=[]\n",
    "lesotho_ratio_analysis_project_lower_compilation=[]\n",
    "lesotho_ratio_analysis_project_upper_compilation=[]\n",
    "lesotho_ratio_project_analysis_lower_compilation=[]\n",
    "lesotho_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# malawi \n",
    "malawi_ratio_analysis_project_mean_compilation=[]\n",
    "malawi_ratio_project_analysis_mean_compilation=[]\n",
    "malawi_ratio_analysis_project_lower_compilation=[]\n",
    "malawi_ratio_analysis_project_upper_compilation=[]\n",
    "malawi_ratio_project_analysis_lower_compilation=[]\n",
    "malawi_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# mali \n",
    "mali_ratio_analysis_project_mean_compilation=[]\n",
    "mali_ratio_project_analysis_mean_compilation=[]\n",
    "mali_ratio_analysis_project_lower_compilation=[]\n",
    "mali_ratio_analysis_project_upper_compilation=[]\n",
    "mali_ratio_project_analysis_lower_compilation=[]\n",
    "mali_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# mexico \n",
    "mexico_ratio_analysis_project_mean_compilation=[]\n",
    "mexico_ratio_project_analysis_mean_compilation=[]\n",
    "mexico_ratio_analysis_project_lower_compilation=[]\n",
    "mexico_ratio_analysis_project_upper_compilation=[]\n",
    "mexico_ratio_project_analysis_lower_compilation=[]\n",
    "mexico_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# mozambique \n",
    "mozambique_ratio_analysis_project_mean_compilation=[]\n",
    "mozambique_ratio_project_analysis_mean_compilation=[]\n",
    "mozambique_ratio_analysis_project_lower_compilation=[]\n",
    "mozambique_ratio_analysis_project_upper_compilation=[]\n",
    "mozambique_ratio_project_analysis_lower_compilation=[]\n",
    "mozambique_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# myanmar \n",
    "myanmar_ratio_analysis_project_mean_compilation=[]\n",
    "myanmar_ratio_project_analysis_mean_compilation=[]\n",
    "myanmar_ratio_analysis_project_lower_compilation=[]\n",
    "myanmar_ratio_analysis_project_upper_compilation=[]\n",
    "myanmar_ratio_project_analysis_lower_compilation=[]\n",
    "myanmar_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# nepal \n",
    "nepal_ratio_analysis_project_mean_compilation=[]\n",
    "nepal_ratio_project_analysis_mean_compilation=[]\n",
    "nepal_ratio_analysis_project_lower_compilation=[]\n",
    "nepal_ratio_analysis_project_upper_compilation=[]\n",
    "nepal_ratio_project_analysis_lower_compilation=[]\n",
    "nepal_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# nigeria \n",
    "nigeria_ratio_analysis_project_mean_compilation=[]\n",
    "nigeria_ratio_project_analysis_mean_compilation=[]\n",
    "nigeria_ratio_analysis_project_lower_compilation=[]\n",
    "nigeria_ratio_analysis_project_upper_compilation=[]\n",
    "nigeria_ratio_project_analysis_lower_compilation=[]\n",
    "nigeria_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# peru \n",
    "peru_ratio_analysis_project_mean_compilation=[]\n",
    "peru_ratio_project_analysis_mean_compilation=[]\n",
    "peru_ratio_analysis_project_lower_compilation=[]\n",
    "peru_ratio_analysis_project_upper_compilation=[]\n",
    "peru_ratio_project_analysis_lower_compilation=[]\n",
    "peru_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "\n",
    "# rwanda \n",
    "rwanda_ratio_analysis_project_mean_compilation=[]\n",
    "rwanda_ratio_project_analysis_mean_compilation=[]\n",
    "rwanda_ratio_analysis_project_lower_compilation=[]\n",
    "rwanda_ratio_analysis_project_upper_compilation=[]\n",
    "rwanda_ratio_project_analysis_lower_compilation=[]\n",
    "rwanda_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# sudan \n",
    "sudan_ratio_analysis_project_mean_compilation=[]\n",
    "sudan_ratio_project_analysis_mean_compilation=[]\n",
    "sudan_ratio_analysis_project_lower_compilation=[]\n",
    "sudan_ratio_analysis_project_upper_compilation=[]\n",
    "sudan_ratio_project_analysis_lower_compilation=[]\n",
    "sudan_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# zambia \n",
    "zambia_ratio_analysis_project_mean_compilation=[]\n",
    "zambia_ratio_project_analysis_mean_compilation=[]\n",
    "zambia_ratio_analysis_project_lower_compilation=[]\n",
    "zambia_ratio_analysis_project_upper_compilation=[]\n",
    "zambia_ratio_project_analysis_lower_compilation=[]\n",
    "zambia_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# uganda \n",
    "uganda_ratio_analysis_project_mean_compilation=[]\n",
    "uganda_ratio_project_analysis_mean_compilation=[]\n",
    "uganda_ratio_analysis_project_lower_compilation=[]\n",
    "uganda_ratio_analysis_project_upper_compilation=[]\n",
    "uganda_ratio_project_analysis_lower_compilation=[]\n",
    "uganda_ratio_project_analysis_upper_compilation=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "# gs_lpg\n",
    "gs_lpg_ratio_analysis_project_mean_compilation=[]\n",
    "gs_lpg_ratio_project_analysis_mean_compilation=[]\n",
    "gs_lpg_ratio_analysis_project_lower_compilation=[]\n",
    "gs_lpg_ratio_analysis_project_upper_compilation=[]\n",
    "gs_lpg_ratio_project_analysis_lower_compilation=[]\n",
    "gs_lpg_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# gs_imp_firewood\n",
    "gs_imp_firewood_ratio_analysis_project_mean_compilation=[]\n",
    "gs_imp_firewood_ratio_project_analysis_mean_compilation=[]\n",
    "gs_imp_firewood_ratio_analysis_project_lower_compilation=[]\n",
    "gs_imp_firewood_ratio_analysis_project_upper_compilation=[]\n",
    "gs_imp_firewood_ratio_project_analysis_lower_compilation=[]\n",
    "gs_imp_firewood_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# gs_imp_charcoal\n",
    "gs_imp_charcoal_ratio_analysis_project_mean_compilation=[]\n",
    "gs_imp_charcoal_ratio_project_analysis_mean_compilation=[]\n",
    "gs_imp_charcoal_ratio_analysis_project_lower_compilation=[]\n",
    "gs_imp_charcoal_ratio_analysis_project_upper_compilation=[]\n",
    "gs_imp_charcoal_ratio_project_analysis_lower_compilation=[]\n",
    "gs_imp_charcoal_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# cdm_imp_firewood\n",
    "cdm_imp_firewood_ratio_analysis_project_mean_compilation=[]\n",
    "cdm_imp_firewood_ratio_project_analysis_mean_compilation=[]\n",
    "cdm_imp_firewood_ratio_analysis_project_lower_compilation=[]\n",
    "cdm_imp_firewood_ratio_analysis_project_upper_compilation=[]\n",
    "cdm_imp_firewood_ratio_project_analysis_lower_compilation=[]\n",
    "cdm_imp_firewood_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# cdm_imp_charcoal\n",
    "cdm_imp_charcoal_ratio_analysis_project_mean_compilation=[]\n",
    "cdm_imp_charcoal_ratio_project_analysis_mean_compilation=[]\n",
    "cdm_imp_charcoal_ratio_analysis_project_lower_compilation=[]\n",
    "cdm_imp_charcoal_ratio_analysis_project_upper_compilation=[]\n",
    "cdm_imp_charcoal_ratio_project_analysis_lower_compilation=[]\n",
    "cdm_imp_charcoal_ratio_project_analysis_upper_compilation=[]\n",
    "\n",
    "# gs_simple_firewood\n",
    "gs_simple_firewood_ratio_analysis_project_mean_compilation=[]\n",
    "gs_simple_firewood_ratio_project_analysis_mean_compilation=[]\n",
    "gs_simple_firewood_ratio_analysis_project_lower_compilation=[]\n",
    "gs_simple_firewood_ratio_analysis_project_upper_compilation=[]\n",
    "gs_simple_firewood_ratio_project_analysis_lower_compilation=[]\n",
    "gs_simple_firewood_ratio_project_analysis_upper_compilation=[]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import relevant packages\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "from scipy import stats as stats\n",
    "from scipy.stats import triang\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#triangular distributions\n",
    "\n",
    "np.random.seed(4)\n",
    "N = 10000                # random numbers to generate\n",
    "\n",
    "    \n",
    "#Adoption \n",
    "min_adoption=.4\n",
    "mean_adoption=.58\n",
    "max_adoption= .92\n",
    "n_adoption=np.random.triangular(left=min_adoption, mode=mean_adoption, right=max_adoption, size=(N))\n",
    "    \n",
    "req_adoption=np.random.uniform(0,mean_adoption,N)      \n",
    "#Usage\n",
    "min_usage=0.16\n",
    "mean_usage=0.53\n",
    "max_usage=0.853\n",
    "n_usage=np.random.triangular(left=min_usage, mode=mean_usage, right=max_usage, size=(N))\n",
    "req_usage=np.random.uniform(0,mean_usage,N)\n",
    "                               \n",
    "#Stacking\n",
    "min_stacking=.193\n",
    "mean_stacking=.673\n",
    "max_stacking=.99\n",
    "n_stacking=np.random.triangular(left=min_stacking, mode=mean_stacking, right=max_stacking, size=(N))\n",
    "req_stacking=np.random.uniform(0,mean_stacking,N)\n",
    "\n",
    "# Renewable GWP from Whitman & Lehman\n",
    "\n",
    "gwp_ch4_renewable=28 #tCO2e/tonne CH4 i.e., instead of typically 25\n",
    "\n",
    "# Whitman & lehmann 2011 also cite a value for CO \n",
    "# but we do not include CO in our analysis\n",
    "    \n",
    "gwp_ch4_agw=30\n",
    "gwp_n2O_agw=298\n",
    "    \n",
    "eff_trad_firewood=0.14\n",
    "eff_improv_firewood=.21\n",
    "eff_trad_charcoal=0.2\n",
    "eff_improv_charcoal=.22\n",
    "eff_kerosene=.47\n",
    "eff_LPG=.5\n",
    "eff_ethanol=.47\n",
    "eff_pellets=.5 #only two pellet projects is the mimi motto\n",
    "\n",
    "ncv_firewood_agw=.016\n",
    "ncv_charcoal_agw=.027\n",
    "ncv_kerosene_agw=.043\n",
    "ncv_LPG_agw=.045\n",
    "ncv_ethanol_agw=.027\n",
    "ncv_pellets_agw=.017\n",
    "\n",
    "kg_per_liter_ethanol_agw=0.783 #kg/liter\n",
    "    \n",
    "ef_combustion_co2_firewood=95.1 #tCO2/TJ 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\n",
    "    \n",
    "sum_VERS_not_per_stove_per_day_agw=0  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.537504248164085e-05\n",
      "We find that GS10974 project reports emissions which are 24.63 [ -132.58  ,  181.83 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.06 [ 4.06  ,  4.06 ] % of the verified credits in the PDD.\n",
      "0.030081576300695405\n",
      "We find that GS10974 project reports emissions which are 1.48 [ 1.48  ,  1.48 ] times overcredited.\n",
      "Our analysis finds emissions which are 67.72 [ 67.71  ,  67.72 ] % of the verified credits in the PDD.\n",
      "7.373610033159138e-05\n",
      "We find that GS10974 project reports emissions which are 24.42 [ -135.34  ,  184.19 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.09 [ 4.09  ,  4.1 ] % of the verified credits in the PDD.\n",
      "0.029570674125931264\n",
      "We find that GS10974 project reports emissions which are 1.48 [ 1.48  ,  1.48 ] times overcredited.\n",
      "Our analysis finds emissions which are 67.75 [ 67.74  ,  67.75 ] % of the verified credits in the PDD.\n",
      "0.00019046727290833953\n",
      "We find that VCS1216 project reports emissions which are 41.03 [ 39.68  ,  42.37 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.44 [ 2.44  ,  2.44 ] % of the verified credits in the PDD.\n",
      "0.00018997767773079526\n",
      "We find that VCS1216 project reports emissions which are 41.13 [ 39.73  ,  42.54 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.43 [ 2.43  ,  2.43 ] % of the verified credits in the PDD.\n",
      "0.00019208096509779155\n",
      "We find that VCS1216 project reports emissions which are 40.86 [ 39.47  ,  42.24 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.45 [ 2.45  ,  2.45 ] % of the verified credits in the PDD.\n",
      "0.00019196909152036182\n",
      "We find that VCS1216 project reports emissions which are 40.88 [ 39.53  ,  42.23 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.45 [ 2.45  ,  2.45 ] % of the verified credits in the PDD.\n",
      "0.00018920671180655332\n",
      "We find that VCS1216 project reports emissions which are 41.17 [ 39.79  ,  42.55 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.43 [ 2.43  ,  2.43 ] % of the verified credits in the PDD.\n",
      "0.0001891023861634562\n",
      "We find that VCS1216 project reports emissions which are 41.19 [ 39.83  ,  42.56 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.43 [ 2.43  ,  2.43 ] % of the verified credits in the PDD.\n",
      "0.000193971443970401\n",
      "We find that VCS1216 project reports emissions which are 40.64 [ 39.25  ,  42.03 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.46 [ 2.46  ,  2.46 ] % of the verified credits in the PDD.\n",
      "0.00019424480397050548\n",
      "We find that VCS1216 project reports emissions which are 40.59 [ 39.24  ,  41.93 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.46 [ 2.46  ,  2.46 ] % of the verified credits in the PDD.\n",
      "0.00019154506966179957\n",
      "We find that VCS1216 project reports emissions which are 40.88 [ 39.51  ,  42.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.45 [ 2.45  ,  2.45 ] % of the verified credits in the PDD.\n",
      "0.0001912127352268159\n",
      "We find that VCS1216 project reports emissions which are 40.95 [ 39.59  ,  42.32 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.44 [ 2.44  ,  2.44 ] % of the verified credits in the PDD.\n",
      "0.0001874160689802064\n",
      "We find that VCS1216 project reports emissions which are 41.42 [ 40.07  ,  42.77 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.41 [ 2.41  ,  2.41 ] % of the verified credits in the PDD.\n",
      "0.00018739378152142513\n",
      "We find that VCS1216 project reports emissions which are 41.43 [ 40.06  ,  42.79 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.41 [ 2.41  ,  2.41 ] % of the verified credits in the PDD.\n",
      "0.00015739342678235707\n",
      "We find that VCS1216 project reports emissions which are 25.44 [ 24.57  ,  26.31 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.93 [ 3.93  ,  3.93 ] % of the verified credits in the PDD.\n",
      "0.00015738635257891482\n",
      "We find that VCS1216 project reports emissions which are 25.44 [ 24.6  ,  26.29 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.93 [ 3.93  ,  3.93 ] % of the verified credits in the PDD.\n",
      "0.0001522936855304013\n",
      "We find that VCS1216 project reports emissions which are 25.98 [ 25.1  ,  26.85 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.85 [ 3.85  ,  3.85 ] % of the verified credits in the PDD.\n",
      "0.00015215505792257465\n",
      "We find that VCS1216 project reports emissions which are 26.0 [ 25.13  ,  26.87 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.85 [ 3.85  ,  3.85 ] % of the verified credits in the PDD.\n",
      "0.0001594506286401419\n",
      "We find that VCS1216 project reports emissions which are 25.25 [ 24.37  ,  26.13 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.96 [ 3.96  ,  3.96 ] % of the verified credits in the PDD.\n",
      "0.00015948814018224065\n",
      "We find that VCS1216 project reports emissions which are 25.25 [ 24.41  ,  26.09 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.96 [ 3.96  ,  3.96 ] % of the verified credits in the PDD.\n",
      "0.00016014560682873244\n",
      "We find that VCS1216 project reports emissions which are 25.19 [ 24.37  ,  26.02 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.97 [ 3.97  ,  3.97 ] % of the verified credits in the PDD.\n",
      "0.00016033908135325384\n",
      "We find that VCS1216 project reports emissions which are 25.16 [ 24.31  ,  26.01 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.97 [ 3.97  ,  3.97 ] % of the verified credits in the PDD.\n",
      "0.00016001446463056592\n",
      "We find that VCS1216 project reports emissions which are 25.22 [ 24.36  ,  26.07 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.97 [ 3.97  ,  3.97 ] % of the verified credits in the PDD.\n",
      "0.00016008230823156644\n",
      "We find that VCS1216 project reports emissions which are 25.2 [ 24.35  ,  26.05 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.97 [ 3.97  ,  3.97 ] % of the verified credits in the PDD.\n",
      "0.00017336047402523907\n",
      "We find that VCS1216 project reports emissions which are 23.99 [ 23.19  ,  24.79 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.17 [ 4.17  ,  4.17 ] % of the verified credits in the PDD.\n",
      "0.00017322365593588793\n",
      "We find that VCS1216 project reports emissions which are 24.01 [ 23.23  ,  24.79 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.16 [ 4.16  ,  4.17 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 17.68 [ 17.13  ,  18.24 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.65 [ 5.65  ,  5.66 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 17.68 [ 17.12  ,  18.24 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.66 [ 5.66  ,  5.66 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 18.22 [ 17.65  ,  18.8 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.49 [ 5.49  ,  5.49 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 18.23 [ 17.66  ,  18.8 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.49 [ 5.48  ,  5.49 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 18.63 [ 18.04  ,  19.21 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.37 [ 5.37  ,  5.37 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 18.62 [ 18.04  ,  19.21 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.37 [ 5.37  ,  5.37 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 20.1 [ 19.47  ,  20.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.97 [ 4.97  ,  4.98 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 20.1 [ 19.46  ,  20.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.98 [ 4.98  ,  4.98 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 21.98 [ 21.29  ,  22.67 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.55 [ 4.55  ,  4.55 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 21.97 [ 21.28  ,  22.66 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.55 [ 4.55  ,  4.55 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 27.27 [ 26.41  ,  28.13 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.67 [ 3.67  ,  3.67 ] % of the verified credits in the PDD.\n",
      "We find that GS2094 project reports emissions which are 27.25 [ 26.4  ,  28.11 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.67 [ 3.67  ,  3.67 ] % of the verified credits in the PDD.\n",
      "0.00025080054994196374\n",
      "We find that VCS1719 project reports emissions which are 23.09 [ 22.36  ,  23.82 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.33 [ 4.33  ,  4.33 ] % of the verified credits in the PDD.\n",
      "0.0003950562683656033\n",
      "We find that VCS1721 project reports emissions which are 21.81 [ 21.12  ,  22.49 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.59 [ 4.59  ,  4.59 ] % of the verified credits in the PDD.\n",
      "We find that GS7312 project reports emissions which are 7.81 [ 7.76  ,  7.86 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.81 [ 12.8  ,  12.81 ] % of the verified credits in the PDD.\n",
      "We find that GS7312 project reports emissions which are 7.81 [ 7.76  ,  7.86 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.81 [ 12.81  ,  12.81 ] % of the verified credits in the PDD.\n",
      "We find that GS500 project reports emissions which are 35.02 [ 34.72  ,  35.32 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.86 [ 2.86  ,  2.86 ] % of the verified credits in the PDD.\n",
      "We find that GS10884 project reports emissions which are 3.26 [ 3.23  ,  3.29 ] times overcredited.\n",
      "Our analysis finds emissions which are 30.67 [ 30.67  ,  30.67 ] % of the verified credits in the PDD.\n",
      "We find that GS10884 project reports emissions which are 0.97 [ 0.96  ,  0.97 ] times overcredited.\n",
      "Our analysis finds emissions which are 103.56 [ 103.56  ,  103.56 ] % of the verified credits in the PDD.\n",
      "We find that GS447 project reports emissions which are 7.99 [ 7.94  ,  8.04 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.51 [ 12.51  ,  12.51 ] % of the verified credits in the PDD.\n",
      "We find that GS447 project reports emissions which are 8.13 [ 8.08  ,  8.18 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.3 [ 12.3  ,  12.31 ] % of the verified credits in the PDD.\n",
      "We find that GS2564 project reports emissions which are 11.12 [ 11.05  ,  11.19 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.99 [ 8.99  ,  8.99 ] % of the verified credits in the PDD.\n",
      "We find that GS7438 project reports emissions which are 4.71 [ 4.68  ,  4.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 21.25 [ 21.25  ,  21.25 ] % of the verified credits in the PDD.\n",
      "We find that GS7438 project reports emissions which are 42.46 [ 42.2  ,  42.71 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.36 [ 2.36  ,  2.36 ] % of the verified credits in the PDD.\n",
      "0.00038900624275097574\n",
      "We find that GS913 project reports emissions which are 19.35 [ 18.73  ,  19.97 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.17 [ 5.17  ,  5.17 ] % of the verified credits in the PDD.\n",
      "0.0004881098772744477\n",
      "We find that GS6212 project reports emissions which are 11.95 [ 11.58  ,  12.33 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.37 [ 8.36  ,  8.37 ] % of the verified credits in the PDD.\n",
      "0.00048818845901940546\n",
      "We find that GS6212 project reports emissions which are 13.29 [ 12.87  ,  13.71 ] times overcredited.\n",
      "Our analysis finds emissions which are 7.52 [ 7.52  ,  7.52 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 7.64 [ -0.65  ,  15.93 ] times overcredited.\n",
      "Our analysis finds emissions which are 13.09 [ 13.09  ,  13.1 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 7.67 [ -2.31  ,  17.65 ] times overcredited.\n",
      "Our analysis finds emissions which are 13.04 [ 13.04  ,  13.04 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 7.8 [ -1.21  ,  16.8 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.83 [ 12.82  ,  12.83 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 8.36 [ 0.15  ,  16.57 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.96 [ 11.96  ,  11.96 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 8.45 [ -1.64  ,  18.55 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.83 [ 11.83  ,  11.83 ] % of the verified credits in the PDD.\n",
      "We find that GS2758 project reports emissions which are 8.49 [ -1.99  ,  18.97 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.78 [ 11.78  ,  11.78 ] % of the verified credits in the PDD.\n",
      "We find that GS3112 project reports emissions which are 18.45 [ 17.87  ,  19.04 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.42 [ 5.42  ,  5.42 ] % of the verified credits in the PDD.\n",
      "We find that GS3112 project reports emissions which are 18.02 [ 17.45  ,  18.59 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.55 [ 5.55  ,  5.55 ] % of the verified credits in the PDD.\n",
      "We find that GS3112 project reports emissions which are 18.01 [ 17.44  ,  18.58 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.55 [ 5.55  ,  5.55 ] % of the verified credits in the PDD.\n",
      "We find that GS3112 project reports emissions which are 17.72 [ 17.16  ,  18.27 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.64 [ 5.64  ,  5.65 ] % of the verified credits in the PDD.\n",
      "We find that GS5642 project reports emissions which are 3.23 [ 3.22  ,  3.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 30.91 [ 30.91  ,  30.92 ] % of the verified credits in the PDD.\n",
      "We find that GS5642 project reports emissions which are 19.58 [ 19.46  ,  19.7 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.11 [ 5.11  ,  5.11 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 19.76 [ 19.14  ,  20.38 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.06 [ 5.06  ,  5.06 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 20.9 [ 20.24  ,  21.55 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.79 [ 4.78  ,  4.79 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 17.97 [ 17.4  ,  18.53 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.57 [ 5.57  ,  5.57 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 19.76 [ 19.13  ,  20.38 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.06 [ 5.06  ,  5.06 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 20.89 [ 20.24  ,  21.55 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.79 [ 4.79  ,  4.79 ] % of the verified credits in the PDD.\n",
      "We find that GS1060 project reports emissions which are 17.97 [ 17.4  ,  18.53 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.57 [ 5.57  ,  5.57 ] % of the verified credits in the PDD.\n",
      "8.946363336349036e-06\n",
      "We find that GS2744 project reports emissions which are 133.02 [ 128.39  ,  137.64 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.75 [ 0.75  ,  0.75 ] % of the verified credits in the PDD.\n",
      "2.0633749188035845e-05\n",
      "We find that GS2744 project reports emissions which are 63.6 [ 61.47  ,  65.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.57 [ 1.57  ,  1.57 ] % of the verified credits in the PDD.\n",
      "0.00019799842769634826\n",
      "We find that GS1094 project reports emissions which are 173.36 [ 167.84  ,  178.87 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.58 [ 0.58  ,  0.58 ] % of the verified credits in the PDD.\n",
      "0.00020401283465571826\n",
      "We find that GS1094 project reports emissions which are 167.82 [ 162.5  ,  173.14 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.6 [ 0.6  ,  0.6 ] % of the verified credits in the PDD.\n",
      "0.000200446948691456\n",
      "We find that GS1094 project reports emissions which are 179.66 [ 173.97  ,  185.35 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.56 [ 0.56  ,  0.56 ] % of the verified credits in the PDD.\n",
      "0.00017720073773679553\n",
      "We find that GS1094 project reports emissions which are 192.64 [ 186.51  ,  198.76 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.52 [ 0.52  ,  0.52 ] % of the verified credits in the PDD.\n",
      "0.00014454467716957668\n",
      "We find that GS1094 project reports emissions which are 217.79 [ 210.88  ,  224.7 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.46 [ 0.46  ,  0.46 ] % of the verified credits in the PDD.\n",
      "0.00015451854915178785\n",
      "We find that GS1094 project reports emissions which are 209.04 [ 202.45  ,  215.63 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.48 [ 0.48  ,  0.48 ] % of the verified credits in the PDD.\n",
      "0.00010209199420474438\n",
      "We find that GS4291 project reports emissions which are 22.34 [ 21.63  ,  23.05 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.48 [ 4.48  ,  4.48 ] % of the verified credits in the PDD.\n",
      "0.00010178711965340872\n",
      "We find that GS4291 project reports emissions which are 22.41 [ 21.69  ,  23.13 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.46 [ 4.46  ,  4.46 ] % of the verified credits in the PDD.\n",
      "6.066688152559505e-05\n",
      "We find that GS6604 project reports emissions which are 52.97 [ 51.28  ,  54.65 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.89 [ 1.89  ,  1.89 ] % of the verified credits in the PDD.\n",
      "We find that GS 2896  project reports emissions which are 30.14 [ 29.16  ,  31.12 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.32 [ 3.32  ,  3.32 ] % of the verified credits in the PDD.\n",
      "We find that GS 3422  project reports emissions which are 52.82 [ 51.14  ,  54.51 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.89 [ 1.89  ,  1.89 ] % of the verified credits in the PDD.\n",
      "We find that GS 2456 project reports emissions which are 69.05 [ 66.87  ,  71.22 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.45 [ 1.45  ,  1.45 ] % of the verified credits in the PDD.\n",
      "We find that GS 2456 project reports emissions which are 82.71 [ 80.1  ,  85.32 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.21 [ 1.21  ,  1.21 ] % of the verified credits in the PDD.\n",
      "We find that GS 2456 project reports emissions which are 91.16 [ 88.3  ,  94.02 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.1 [ 1.1  ,  1.1 ] % of the verified credits in the PDD.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We find that GS 2456 project reports emissions which are 98.55 [ 95.44  ,  101.67 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.01 [ 1.01  ,  1.01 ] % of the verified credits in the PDD.\n",
      "We find that GS 2456 project reports emissions which are 84.21 [ 81.56  ,  86.86 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.19 [ 1.19  ,  1.19 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 65.17 [ 63.08  ,  67.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.53 [ 1.53  ,  1.53 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 58.5 [ 56.63  ,  60.37 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.71 [ 1.71  ,  1.71 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 57.89 [ 56.0  ,  59.77 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.73 [ 1.73  ,  1.73 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 98.09 [ 94.94  ,  101.24 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.02 [ 1.02  ,  1.02 ] % of the verified credits in the PDD.\n",
      "We find that GS 1028 project reports emissions which are 29.64 [ 28.68  ,  30.59 ] times overcredited.\n",
      "Our analysis finds emissions which are 3.37 [ 3.37  ,  3.37 ] % of the verified credits in the PDD.\n",
      "We find that GS 3018 project reports emissions which are 7.83 [ 7.58  ,  8.08 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.77 [ 12.77  ,  12.77 ] % of the verified credits in the PDD.\n",
      "We find that GS 3018 project reports emissions which are 5.17 [ 5.01  ,  5.33 ] times overcredited.\n",
      "Our analysis finds emissions which are 19.34 [ 19.34  ,  19.35 ] % of the verified credits in the PDD.\n",
      "We find that GS 3018 project reports emissions which are 2.6 [ 2.52  ,  2.68 ] times overcredited.\n",
      "Our analysis finds emissions which are 38.5 [ 38.49  ,  38.5 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 6.67 [ 6.63  ,  6.71 ] times overcredited.\n",
      "Our analysis finds emissions which are 14.99 [ 14.99  ,  14.99 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 6.47 [ 6.43  ,  6.51 ] times overcredited.\n",
      "Our analysis finds emissions which are 15.45 [ 15.45  ,  15.45 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 6.4 [ 6.36  ,  6.44 ] times overcredited.\n",
      "Our analysis finds emissions which are 15.63 [ 15.63  ,  15.63 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 5.95 [ 5.92  ,  5.99 ] times overcredited.\n",
      "Our analysis finds emissions which are 16.8 [ 16.8  ,  16.8 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 5.64 [ 5.6  ,  5.67 ] times overcredited.\n",
      "Our analysis finds emissions which are 17.74 [ 17.74  ,  17.74 ] % of the verified credits in the PDD.\n",
      "We find that GS1267 project reports emissions which are 1.56 [ 1.55  ,  1.57 ] times overcredited.\n",
      "Our analysis finds emissions which are 64.19 [ 64.18  ,  64.19 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 10.13 [ 10.07  ,  10.2 ] times overcredited.\n",
      "Our analysis finds emissions which are 9.87 [ 9.87  ,  9.87 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 10.03 [ 9.97  ,  10.09 ] times overcredited.\n",
      "Our analysis finds emissions which are 9.97 [ 9.97  ,  9.97 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 10.19 [ 10.12  ,  10.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 9.82 [ 9.82  ,  9.82 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 8.23 [ 8.18  ,  8.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.15 [ 12.15  ,  12.15 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 8.11 [ 8.06  ,  8.16 ] times overcredited.\n",
      "Our analysis finds emissions which are 12.33 [ 12.33  ,  12.33 ] % of the verified credits in the PDD.\n",
      "We find that GS2439 project reports emissions which are 8.68 [ 8.62  ,  8.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.52 [ 11.52  ,  11.52 ] % of the verified credits in the PDD.\n",
      "We find that GS2445 project reports emissions which are 6.56 [ 6.51  ,  6.6 ] times overcredited.\n",
      "Our analysis finds emissions which are 15.25 [ 15.25  ,  15.26 ] % of the verified credits in the PDD.\n",
      "We find that GS7578 project reports emissions which are 7.46 [ 7.39  ,  7.52 ] times overcredited.\n",
      "Our analysis finds emissions which are 13.41 [ 13.41  ,  13.41 ] % of the verified credits in the PDD.\n",
      "We find that GS2513 project reports emissions which are 8.64 [ 8.56  ,  8.72 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.57 [ 11.57  ,  11.58 ] % of the verified credits in the PDD.\n",
      "We find that GS4677 project reports emissions which are 3.44 [ 3.41  ,  3.47 ] times overcredited.\n",
      "Our analysis finds emissions which are 29.08 [ 29.08  ,  29.08 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 9.22 [ 9.14  ,  9.31 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.84 [ 10.84  ,  10.84 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 9.45 [ 9.37  ,  9.53 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.58 [ 10.58  ,  10.58 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 441.93 [ 438.56  ,  445.29 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.23 [ 0.23  ,  0.23 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 442.14 [ 438.81  ,  445.47 ] times overcredited.\n",
      "Our analysis finds emissions which are 0.23 [ 0.23  ,  0.23 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 24.09 [ 23.91  ,  24.27 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.15 [ 4.15  ,  4.15 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 24.06 [ 23.88  ,  24.25 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.16 [ 4.16  ,  4.16 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 24.06 [ 23.88  ,  24.24 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.16 [ 4.16  ,  4.16 ] % of the verified credits in the PDD.\n",
      "We find that GS2441 project reports emissions which are 24.12 [ 23.94  ,  24.3 ] times overcredited.\n",
      "Our analysis finds emissions which are 4.15 [ 4.15  ,  4.15 ] % of the verified credits in the PDD.\n",
      "We find that GS5003 project reports emissions which are 37.67 [ 37.44  ,  37.89 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.65 [ 2.65  ,  2.66 ] % of the verified credits in the PDD.\n",
      "We find that GS5003 project reports emissions which are 4.97 [ 4.94  ,  5.01 ] times overcredited.\n",
      "Our analysis finds emissions which are 20.1 [ 20.1  ,  20.1 ] % of the verified credits in the PDD.\n",
      "We find that GS5003 project reports emissions which are 5.1 [ 5.07  ,  5.13 ] times overcredited.\n",
      "Our analysis finds emissions which are 19.61 [ 19.61  ,  19.61 ] % of the verified credits in the PDD.\n",
      "We find that GS6129 project reports emissions which are 75.6 [ 73.22  ,  77.98 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.32 [ 1.32  ,  1.32 ] % of the verified credits in the PDD.\n",
      "We find that GS6129 project reports emissions which are 67.3 [ 65.19  ,  69.41 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.49 [ 1.49  ,  1.49 ] % of the verified credits in the PDD.\n",
      "We find that GS6129 project reports emissions which are 68.15 [ 66.0  ,  70.29 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.47 [ 1.47  ,  1.47 ] % of the verified credits in the PDD.\n",
      "We find that GS407 project reports emissions which are 10.06 [ 10.0  ,  10.12 ] times overcredited.\n",
      "Our analysis finds emissions which are 9.94 [ 9.94  ,  9.94 ] % of the verified credits in the PDD.\n",
      "We find that GS407 project reports emissions which are 9.1 [ 9.05  ,  9.16 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.99 [ 10.99  ,  10.99 ] % of the verified credits in the PDD.\n",
      "We find that GS407 project reports emissions which are 0.64 [ 0.64  ,  0.64 ] times overcredited.\n",
      "Our analysis finds emissions which are 156.64 [ 156.64  ,  156.64 ] % of the verified credits in the PDD.\n",
      "We find that GS407 project reports emissions which are 0.64 [ 0.64  ,  0.64 ] times overcredited.\n",
      "Our analysis finds emissions which are 156.65 [ 156.65  ,  156.65 ] % of the verified credits in the PDD.\n",
      "We find that GS11509 project reports emissions which are 1.38 [ 1.38  ,  1.38 ] times overcredited.\n",
      "Our analysis finds emissions which are 72.52 [ 72.51  ,  72.52 ] % of the verified credits in the PDD.\n",
      "We find that GS11352 project reports emissions which are 2.0 [ 2.0  ,  2.0 ] times overcredited.\n",
      "Our analysis finds emissions which are 49.91 [ 49.91  ,  49.91 ] % of the verified credits in the PDD.\n",
      "We find that GS11507 project reports emissions which are 1.46 [ 1.46  ,  1.46 ] times overcredited.\n",
      "Our analysis finds emissions which are 68.48 [ 68.48  ,  68.48 ] % of the verified credits in the PDD.\n",
      "We find that GS1146 project reports emissions which are 12.2 [ 6.48  ,  17.92 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.2 [ 8.2  ,  8.2 ] % of the verified credits in the PDD.\n",
      "We find that GS1146 project reports emissions which are 4.05 [ 4.03  ,  4.08 ] times overcredited.\n",
      "Our analysis finds emissions which are 24.68 [ 24.68  ,  24.68 ] % of the verified credits in the PDD.\n",
      "We find that GS1146 project reports emissions which are 9.11 [ 9.02  ,  9.2 ] times overcredited.\n",
      "Our analysis finds emissions which are 10.98 [ 10.98  ,  10.98 ] % of the verified credits in the PDD.\n",
      "We find that GS1146 project reports emissions which are 7.63 [ 1.03  ,  14.23 ] times overcredited.\n",
      "Our analysis finds emissions which are 13.1 [ 13.1  ,  13.1 ] % of the verified credits in the PDD.\n",
      "We find that GS 11330 project reports emissions which are 2.72 [ 2.7  ,  2.73 ] times overcredited.\n",
      "Our analysis finds emissions which are 36.82 [ 36.81  ,  36.82 ] % of the verified credits in the PDD.\n",
      "We find that GS 3071 project reports emissions which are 3.17 [ 3.15  ,  3.19 ] times overcredited.\n",
      "Our analysis finds emissions which are 31.58 [ 31.58  ,  31.58 ] % of the verified credits in the PDD.\n",
      "We find that GS 10777 project reports emissions which are 11.72 [ 11.65  ,  11.79 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.53 [ 8.53  ,  8.53 ] % of the verified credits in the PDD.\n",
      "We find that GS 10886 project reports emissions which are 11.5 [ 11.43  ,  11.57 ] times overcredited.\n",
      "Our analysis finds emissions which are 8.69 [ 8.69  ,  8.7 ] % of the verified credits in the PDD.\n",
      "We find that GS 5107 project reports emissions which are 18.59 [ 18.48  ,  18.7 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.38 [ 5.38  ,  5.38 ] % of the verified credits in the PDD.\n",
      "0.0005343256986584998\n",
      "We find that GS 11195 project reports emissions which are 18.69 [ 18.1  ,  19.28 ] times overcredited.\n",
      "Our analysis finds emissions which are 5.35 [ 5.35  ,  5.35 ] % of the verified credits in the PDD.\n",
      "0.00017885217665375864\n",
      "We find that GS 2077 project reports emissions which are 47.87 [ 46.36  ,  49.37 ] times overcredited.\n",
      "Our analysis finds emissions which are 2.09 [ 2.09  ,  2.09 ] % of the verified credits in the PDD.\n",
      "0.0004202109267298521\n",
      "We find that GS 411 project reports emissions which are 16.06 [ 15.55  ,  16.56 ] times overcredited.\n",
      "Our analysis finds emissions which are 6.23 [ 6.23  ,  6.23 ] % of the verified credits in the PDD.\n",
      "We find that GS 10914 project reports emissions which are 8.55 [ 8.28  ,  8.81 ] times overcredited.\n",
      "Our analysis finds emissions which are 11.7 [ 11.7  ,  11.7 ] % of the verified credits in the PDD.\n",
      "We find that GS 10781 project reports emissions which are 52.15 [ 50.47  ,  53.82 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.92 [ 1.92  ,  1.92 ] % of the verified credits in the PDD.\n",
      "We find that GS 5660 project reports emissions which are 68.04 [ 65.9  ,  70.18 ] times overcredited.\n",
      "Our analysis finds emissions which are 1.47 [ 1.47  ,  1.47 ] % of the verified credits in the PDD.\n"
     ]
    }
   ],
   "source": [
    "i=1 # The row you want to evaluate\n",
    "while i<152:\n",
    "    # first row is units\n",
    "    VERs=VERs_all.iloc[i,1]\n",
    "    protocol_type=df['protocol_type'][i]\n",
    "\n",
    "    protocol_id=df['protocol_id'][i]\n",
    "\n",
    "    monitoring_period=df['monitoring_period'][i]\n",
    "\n",
    "    verified_credits=df['verified_credits'][i]\n",
    "\n",
    "    num_countries=df['num_countries'][i]\n",
    "\n",
    "    prov_project=df['prov_project'][i]\n",
    "\n",
    "    fNRB_project=df['fNRB_project'][i]\n",
    "\n",
    "    mfNRB_bailis=df['mfNRB_bailis'][i]\n",
    "\n",
    "    expfNRB_bailis=df['expfNRB_bailis'][i]\n",
    "\n",
    "    n_devices_domestic=df['n_devices_domestic'][i]\n",
    "\n",
    "    n_devices_commercial=df['n_devices_commercial'][i]\n",
    "\n",
    "    project_days=df['project_days'][i]\n",
    "    \n",
    "    project_days_commercial=df['project_days_commercial'][i]\n",
    "\n",
    "    av_hhsize=df['av_hhsize'][i]\n",
    "\n",
    "    \n",
    "    stove_year_cohort=df['stove_year_cohort'][i]\n",
    "\n",
    "    # Baseline Fuel % What are the baseline fuels in this project location? [put 0% if no use in this location]\n",
    "    prc_firewood_baseline=df['prc_firewood_baseline'][i]\n",
    "\n",
    "    prc_charcoal_baseline=df['prc_charcoal_baseline'][i]\n",
    "\n",
    "    prc_kerosene_baseline=df['prc_kerosene_baseline'][i]\n",
    "\n",
    "    prc_lpg_baseline=df['prc_lpg_baseline'][i]\n",
    "\n",
    "    prc_ethanol_baseline=df['prc_ethanol_baseline'][i]\n",
    "\n",
    "    prc_electric_baseline=df['prc_electric_baseline'][i]\n",
    "\n",
    "    prc_pellets_baseline=df['prc_pellets_baseline'][i]\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "    # Baseline Fuel % What are the baseline commercial fuels in this project location? [put 0% if no use in this location]\n",
    "    prc_firewood_baseline_commercial=df['prc_firewood_baseline_commercial'][i]\n",
    "\n",
    "    prc_charcoal_baseline_commercial=df['prc_charcoal_baseline_commercial'][i]\n",
    "\n",
    "    prc_kerosene_baseline_commercial=df['prc_kerosene_baseline_commercial'][i]\n",
    "\n",
    "    prc_lpg_baseline_commercial=df['prc_lpg_baseline_commercial'][i]\n",
    "\n",
    "    prc_ethanol_baseline_commercial=df['prc_ethanol_baseline_commercial'][i]\n",
    "\n",
    "    prc_electric_baseline_commercial=df['prc_electric_baseline_commercial'][i]\n",
    "\n",
    "    prc_pellets_baseline_commercial=df['prc_pellets_baseline_commercial'][i]\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # Baseline Consumption: What is the baseline fuel consumption of each baseline fuel in this project location [put 0 if no use in this location]\n",
    "    kg_firewood_baseline=df['kg_firewood_baseline'][i]\n",
    "\n",
    "    kg_charcoal_baseline=df['kg_charcoal_baseline'][i]\n",
    "\n",
    "    kg_kerosene_baseline=df['kg_kerosene_baseline'][i]\n",
    "\n",
    "    kg_lpg_baseline=df['kg_lpg_baseline'][i]\n",
    "\n",
    "    l_ethanol_baseline=df['l_ethanol_baseline'][i]\n",
    "\n",
    "    kg_per_liter_ethanol_baseline=df['kg_per_liter_ethanol_baseline'][i]\n",
    "\n",
    "    kWh_electric_baseline=df['kWh_electric_baseline'][i]\n",
    "\n",
    "    kg_pellets_baseline=df['kg_pellets_baseline'][i]\n",
    "\n",
    "\n",
    "    kg_firewood_baseline_commercial=df['kg_firewood_baseline_commercial'][i]\n",
    "\n",
    "    kg_charcoal_baseline_commercial=df['kg_charcoal_baseline_commercial'][i]\n",
    "\n",
    "    kg_kerosene_baseline_commercial=df['kg_kerosene_baseline_commercial'][i]\n",
    "\n",
    "    kg_lpg_baseline_commercial=df['kg_lpg_baseline_commercial'][i]\n",
    "\n",
    "    l_ethanol_baseline_commercial=df['l_ethanol_baseline_commercial'][i]\n",
    "\n",
    "    kWh_electric_baseline_commercial=df['kWh_electric_baseline_commercial'][i]\n",
    "\n",
    "    kg_pellets_baseline_commercial=df['kg_pellets_baseline_commercial'][i]\n",
    "\n",
    "\n",
    "    # Stove Efficiencies at Baseline\n",
    "\n",
    "\n",
    "    eff_firewood_baseline=df['eff_firewood_baseline'][i]\n",
    "\n",
    "    eff_charcoal_baseline=df['eff_charcoal_baseline'][i]\n",
    "\n",
    "    eff_kerosene_baseline=df['eff_kerosene_baseline'][i]\n",
    "\n",
    "    eff_LPG_baseline=df['eff_LPG_baseline'][i]\n",
    "\n",
    "    eff_ethanol_baseline=df['eff_ethanol_baseline'][i]\n",
    "\n",
    "    eff_electric_baseline=df['eff_electric_baseline'][i]\n",
    "\n",
    "    eff_pellets_baseline=df['eff_pellets_baseline'][i]\n",
    "\n",
    "\n",
    "    # Commercial Baseline Stove Efficiences\n",
    "\n",
    "    eff_firewood_baseline_commercial=df['eff_firewood_baseline_commercial'][i]\n",
    "\n",
    "    eff_charcoal_baseline_commercial=df['eff_charcoal_baseline_commercial'][i]\n",
    "\n",
    "    eff_kerosene_baseline_commercial=df['eff_kerosene_baseline_commercial'][i]\n",
    "\n",
    "    eff_LPG_baseline_commercial=df['eff_LPG_baseline_commercial'][i]\n",
    "\n",
    "    eff_ethanol_baseline_commercial=df['eff_ethanol_baseline_commercial'][i]\n",
    "\n",
    "    eff_electric_baseline_commercial=df['eff_electric_baseline_commercial'][i]\n",
    "\n",
    "    eff_pellet_baseline_commercial=df['eff_pellet_baseline_commercial'][i]\n",
    "\n",
    "\n",
    "    # Caloric Values: What are the assumed calorific values  of the baseline fuels? (TJ/ton) [put 0 if not in use in this location]\n",
    "\n",
    "    ncv_firewood_baseline=df['ncv_firewood_baseline'][i]\n",
    "\n",
    "    ncv_charcoal_baseline=df['ncv_charcoal_baseline'][i]\n",
    "\n",
    "    ncv_kerosene_baseline=df['ncv_kerosene_baseline'][i]\n",
    "\n",
    "    ncv_LPG_baseline=df['ncv_LPG_baseline'][i]\n",
    "\n",
    "    ncv_ethanol_baseline=df['ncv_ethanol_baseline'][i]\n",
    "\n",
    "    ncv_pellets_baseline=df['ncv_pellets_baseline'][i]\n",
    "\n",
    "   \n",
    "    # CO2 Emission Factors: What is the assumed carbon dioxide emission factor of the baseline fuels? \n",
    "    #[if CDM write in the assumed EF of the projected fossil fuel] \n",
    "    #[ put 0 if you do not account for CO2 EF at Baseline or this fuel is not applicable 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",
    "    \n",
    "\n",
    "\n",
    "    # CO2 Emission Factors in the Project\n",
    "\n",
    "    co2_ef_firewood_project=df['co2_ef_firewood_project'][i]\n",
    "\n",
    "    co2_ef_charcoal_project=df['co2_ef_charcoal_project'][i]\n",
    "\n",
    "    co2_ef_kerosene_project=df['co2_ef_kerosene_project'][i]\n",
    "\n",
    "    co2_ef_LPG_project=df['co2_ef_LPG_project'][i]\n",
    "\n",
    "    co2_ef_ethanol_project=df['co2_ef_ethanol_project'][i]\n",
    "\n",
    "    co2_ef_electric_project=df['co2_ef_electric_project'][i]\n",
    "\n",
    "    co2_ef_pellets_project=df['co2_ef_pellets_project'][i]\n",
    "\n",
    "\n",
    "    # Non-CO2 Emission Factors in the Project\n",
    "\n",
    "    nonco2_ef_firewood_project=df['nonco2_ef_firewood_project'][i]\n",
    "\n",
    "    nonco2_ef_charcoal_project=df['nonco2_ef_charcoal_project'][i]\n",
    "\n",
    "    nonco2_ef_kerosene_project=df['nonco2_ef_kerosene_project'][i]\n",
    "\n",
    "    nonco2_ef_LPG_project=df['nonco2_ef_LPG_project'][i]\n",
    "\n",
    "    nonco2_ef_ethanol_project=df['nonco2_ef_ethanol_project'][i]\n",
    "\n",
    "    nonco2_ef_electric_project=df['nonco2_ef_electric_project'][i]\n",
    "\n",
    "    nonco2_ef_pellets_project=df['nonco2_ef_pellets_project'][i]\n",
    "\n",
    "    # Stove Production Emission Factors\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_firewood_project=df['stoveproduction_ef_co2_firewood_project'][i]\n",
    "    stoveproduction_ef_ch4_firewood_project=df['stoveproduction_ef_ch4_firewood_project'][i]\n",
    "    stoveproduction_ef_n2O_firewood_project=df['stoveproduction_ef_n2O_firewood_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_charcoal_project=df['stoveproduction_ef_co2_charcoal_project'][i]\n",
    "    stoveproduction_ef_ch4_charcoal_project=df['stoveproduction_ef_ch4_charcoal_project'][i]\n",
    "    stoveproduction_ef_n2O_charcoal_project=df['stoveproduction_ef_n2O_charcoal_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_kerosene_project=df['stoveproduction_ef_co2_kerosene_project'][i]\n",
    "    stoveproduction_ef_ch4_kerosene_project=df['stoveproduction_ef_ch4_kerosene_project'][i]\n",
    "    stoveproduction_ef_n2O_kerosene_project=df['stoveproduction_ef_n2O_kerosene_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_LPG_project=df['stoveproduction_ef_co2_LPG_project'][i]\n",
    "    stoveproduction_ef_ch4_LPG_project=df['stoveproduction_ef_ch4_LPG_project'][i]\n",
    "    stoveproduction_ef_n2O_LPG_project=df['stoveproduction_ef_n2O_LPG_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_ethanol_project=df['stoveproduction_ef_co2_ethanol_project'][i]\n",
    "    stoveproduction_ef_ch4_ethanol_project=df['stoveproduction_ef_ch4_ethanol_project'][i]\n",
    "    stoveproduction_ef_n2O_ethanol_project=df['stoveproduction_ef_n2O_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_electric_project=df['stoveproduction_ef_co2_electric_project'][i]\n",
    "    stoveproduction_ef_ch4_electric_project=df['stoveproduction_ef_ch4_electric_project'][i]\n",
    "    stoveproduction_ef_n2O_electric_project=df['stoveproduction_ef_n2O_electric_project'][i]\n",
    "\n",
    "\n",
    "    stoveproduction_ef_co2_pellets_project=df['stoveproduction_ef_co2_pellets_project'][i]\n",
    "    stoveproduction_ef_ch4_pellets_project=df['stoveproduction_ef_ch4_pellets_project'][i]\n",
    "    stoveproduction_ef_n2O_pellets_project=df['stoveproduction_ef_n2O_pellets_project'][i]\n",
    "\n",
    "    # Project Feedstock Emission Factors\n",
    "\n",
    "    feedstockproduction_ef_co2_firewood_project=df['feedstockproduction_ef_co2_firewood_project'][i]\n",
    "    feedstockproduction_ef_ch4_firewood_project=df['feedstockproduction_ef_ch4_firewood_project'][i]\n",
    "    feedstockproduction_ef_n2O_firewood_project=df['feedstockproduction_ef_n2O_firewood_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_charcoal_project=df['feedstockproduction_ef_co2_charcoal_project'][i]\n",
    "    feedstockproduction_ef_ch4_charcoal_project=df['feedstockproduction_ef_ch4_charcoal_project'][i]\n",
    "    feedstockproduction_ef_n2O_charcoal_project=df['feedstockproduction_ef_n2O_charcoal_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_kerosene_project=df['feedstockproduction_ef_co2_kerosene_project'][i]\n",
    "    feedstockproduction_ef_ch4_kerosene_project=df['feedstockproduction_ef_ch4_kerosene_project'][i]\n",
    "    feedstockproduction_ef_n2O_kerosene_project=df['feedstockproduction_ef_n2O_kerosene_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_LPG_project=df['feedstockproduction_ef_co2_LPG_project'][i]\n",
    "    feedstockproduction_ef_ch4_LPG_project=df['feedstockproduction_ef_ch4_LPG_project'][i]\n",
    "    feedstockproduction_ef_n2O_LPG_project=df['feedstockproduction_ef_n2O_LPG_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_ethanol_project=df['feedstockproduction_ef_co2_ethanol_project'][i]\n",
    "    feedstockproduction_ef_ch4_ethanol_project=df['feedstockproduction_ef_ch4_ethanol_project'][i]\n",
    "    feedstockproduction_ef_n2O_ethanol_project=df['feedstockproduction_ef_n2O_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_electric_project=df['feedstockproduction_ef_co2_electric_project'][i]\n",
    "    feedstockproduction_ef_ch4_electric_project=df['feedstockproduction_ef_ch4_electric_project'][i]\n",
    "    feedstockproduction_ef_n2O_electric_project=df['feedstockproduction_ef_n2O_electric_project'][i]\n",
    "\n",
    "\n",
    "    feedstockproduction_ef_co2_pellets_project=df['feedstockproduction_ef_co2_pellets_project'][i]\n",
    "    feedstockproduction_ef_ch4_pellets_project=df['feedstockproduction_ef_ch4_pellets_project'][i]\n",
    "    feedstockproduction_ef_n2O_pellets_project=df['feedstockproduction_ef_n2O_pellets_project'][i]\n",
    "\n",
    "\n",
    "    # Project Distribution Emission Factors\n",
    "    distribution_ef_co2_firewood_project=df['distribution_ef_co2_firewood_project'][i]\n",
    "    distribution_ef_ch4_firewood_project=df['distribution_ef_ch4_firewood_project'][i]\n",
    "    distribution_ef_n2O_firewood_project=df['distribution_ef_n2O_firewood_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_charcoal_project=df['distribution_ef_co2_charcoal_project'][i]\n",
    "    distribution_ef_ch4_charcoal_project=df['distribution_ef_ch4_charcoal_project'][i]\n",
    "    distribution_ef_n2O_charcoal_project=df['distribution_ef_n2O_charcoal_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_kerosene_project=df['distribution_ef_co2_kerosene_project'][i]\n",
    "    distribution_ef_ch4_kerosene_project=df['distribution_ef_ch4_kerosene_project'][i]\n",
    "    distribution_ef_n2O_kerosene_project=df['distribution_ef_n2O_kerosene_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_LPG_project=df['distribution_ef_co2_LPG_project'][i]\n",
    "    distribution_ef_ch4_LPG_project=df['distribution_ef_ch4_LPG_project'][i]\n",
    "    distribution_ef_n2O_LPG_project=df['distribution_ef_n2O_LPG_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_ethanol_project=df['distribution_ef_co2_ethanol_project'][i]\n",
    "    distribution_ef_ch4_ethanol_project=df['distribution_ef_ch4_ethanol_project'][i]\n",
    "    distribution_ef_n2O_ethanol_project=df['distribution_ef_n2O_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_electric_project=df['distribution_ef_co2_electric_project'][i]\n",
    "    distribution_ef_ch4_electric_project=df['distribution_ef_ch4_electric_project'][i]\n",
    "    distribution_ef_n2O_electric_project=df['distribution_ef_n2O_electric_project'][i]\n",
    "\n",
    "\n",
    "    distribution_ef_co2_pellets_project=df['distribution_ef_co2_pellets_project'][i]\n",
    "    distribution_ef_ch4_pellets_project=df['distribution_ef_ch4_pellets_project'][i]\n",
    "    distribution_ef_n2O_pellets_project=df['distribution_ef_n2O_pellets_project'][i]\n",
    "\n",
    "    # Project Processing Emission Factors\n",
    "    processing_ef_co2_firewood_project=df['processing_ef_co2_firewood_project'][i]\n",
    "    processing_ef_ch4_firewood_project=df['processing_ef_ch4_firewood_project'][i]\n",
    "    processing_ef_n2O_firewood_project=df['processing_ef_n2O_firewood_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_charcoal_project=df['processing_ef_co2_charcoal_project'][i]\n",
    "    processing_ef_ch4_charcoal_project=df['processing_ef_ch4_charcoal_project'][i]\n",
    "    processing_ef_n2O_charcoal_project=df['processing_ef_n2O_charcoal_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_kerosene_project=df['processing_ef_co2_kerosene_project'][i]\n",
    "    processing_ef_ch4_kerosene_project=df['processing_ef_ch4_kerosene_project'][i]\n",
    "    processing_ef_n2O_kerosene_project=df['processing_ef_n2O_kerosene_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_LPG_project=df['processing_ef_co2_LPG_project'][i]\n",
    "    processing_ef_ch4_LPG_project=df['processing_ef_ch4_LPG_project'][i]\n",
    "    processing_ef_n2O_LPG_project=df['processing_ef_n2O_LPG_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_ethanol_project=df['processing_ef_co2_ethanol_project'][i]\n",
    "    processing_ef_ch4_ethanol_project=df['processing_ef_ch4_ethanol_project'][i]\n",
    "    processing_ef_n2O_ethanol_project=df['processing_ef_n2O_ethanol_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_electric_project=df['processing_ef_co2_electric_project'][i]\n",
    "    processing_ef_ch4_electric_project=df['processing_ef_ch4_electric_project'][i]\n",
    "    processing_ef_n2O_electric_project=df['processing_ef_n2O_electric_project'][i]\n",
    "\n",
    "\n",
    "    processing_ef_co2_pellets_project=df['processing_ef_co2_pellets_project'][i]\n",
    "    processing_ef_ch4_pellets_project=df['processing_ef_ch4_pellets_project'][i]\n",
    "    processing_ef_n2O_pellets_project=df['processing_ef_n2O_pellets_project'][i]\n",
    "    \n",
    "    \n",
    "    #fNRB\n",
    "    min_fNRB=df['mfNRB_bailis'][i]\n",
    "\n",
    "    exp_fNRB=df['expfNRB_bailis'][i]\n",
    "    max_fNRB=exp_fNRB*1.1\n",
    "\n",
    "    n_fNRB=np.random.triangular(left=min_fNRB, mode=exp_fNRB, right=max_fNRB, size=(N))\n",
    "    \n",
    "    # Adoption, Usage, and Leakage \n",
    "\n",
    "    #adoption_definition\n",
    "\n",
    "    project_adoption=df['project_adoption'][i]\n",
    "\n",
    "\n",
    "    project_usage=df['project_usage'][i]\n",
    "    \n",
    "    kpt_adj_usage=np.random.uniform(0,0.53,N)\n",
    "    \n",
    "    kpt_usage=project_usage*(1-kpt_adj_usage)\n",
    "\n",
    "\n",
    "    project_adoption_commercial=df['project_adoption_commercial'][i]\n",
    "\n",
    "\n",
    "    project_usage_commercial=df['project_usage_commercial'][i]\n",
    "    \n",
    "    project_stacking=df['project_stacking'][i]\n",
    "    \n",
    "    project_stacking_commercial=df['project_stacking_commercial'][i]\n",
    "    \n",
    "    kpt_adj_stacking=np.random.uniform(0,0.29,N)\n",
    "\n",
    "    kpt_stacking=project_stacking*kpt_adj_stacking\n",
    "\n",
    "    leakage_binary=df['leakage_binary'][i]\n",
    "\n",
    "   \n",
    "\n",
    "    leakage_default= df['leakage_default'][i] # put 1 if not being multiplied\n",
    "\n",
    "   \n",
    "\n",
    "    leakage_tCO2e=df['leakage_tCO2e'][i]\n",
    "\n",
    " \n",
    "\n",
    "    more_than_one_stove_adj=df['more_than_one_stove_adj'][i]\n",
    "    \n",
    "    other_tCO2e_reduction=df['other_tCO2e_reduction'][i]\n",
    "    \n",
    "    #rebound=1\n",
    "    \n",
    "    rebound=df['rebound'][i]\n",
    "\n",
    "\n",
    "    \n",
    "    # Baseline Emissions\n",
    "    # Baseline Firewood \n",
    "\n",
    "    be_firewood_gillwiehl=prc_firewood_baseline*(ncv_firewood_agw*kg_firewood_baseline/1000*((n_fNRB*(ef_combustion_co2_firewood\n",
    "                                                                                                      +ef_combustion_ch4_firewood*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_firewood*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw))))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "        \n",
    "    # Baseline Charcoal\n",
    "\n",
    "    be_charcoal_gillwiehl=prc_charcoal_baseline*(ncv_charcoal_agw*kg_charcoal_baseline/1000*((n_fNRB*(ef_combustion_co2_charcoal\n",
    "                                                                                                      +ef_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_charcoal*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "\n",
    "\n",
    "                                                                                             +ef_total_not_combustion_co2_charcoal  \n",
    "                                                                                             +ef_total_not_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                             +ef_total_not_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "    # Baseline LPG\n",
    "\n",
    "    be_LPG_gillwiehl=prc_lpg_baseline*(ncv_LPG_agw*kg_lpg_baseline/1000*(ef_total_co2_LPG +ef_total_ch4_LPG*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_LPG*gwp_n2O_agw))\n",
    "    # Baseline Kerosene\n",
    "\n",
    "    be_kerosene_gillwiehl=prc_kerosene_baseline*(ncv_kerosene_agw*kg_kerosene_baseline/1000*(ef_total_co2_kerosene +ef_total_ch4_kerosene*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_kerosene*gwp_n2O_agw))\n",
    "    # Baseline Ethanol\n",
    "\n",
    "    be_ethanol_gillwiehl=prc_ethanol_baseline*(ncv_ethanol_agw*l_ethanol_baseline_commercial*kg_per_liter_ethanol_baseline/1000*(ef_total_zeroed_co2_ethanol +ef_total_zeroed_ch4_ethanol*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_ethanol*gwp_n2O_agw))\n",
    "    # Baseline Pellets\n",
    "\n",
    "    be_pellets_gillwiehl=prc_pellets_baseline*(ncv_pellets_agw*kg_pellets_baseline/1000*(ef_total_zeroed_co2_pellets +ef_total_zeroed_ch4_pellets*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_pellets*gwp_n2O_agw))\n",
    "    # Total Baseline\n",
    "    be_total_domestic_gillwiehl = be_firewood_gillwiehl + be_charcoal_gillwiehl + be_LPG_gillwiehl+ be_ethanol_gillwiehl + be_pellets_gillwiehl\n",
    "\n",
    "    be_total_domestic_gillwiehl\n",
    "    \n",
    "   # Baseline Commercial Emissions\n",
    "    # Baseline Commercial Firewood \n",
    "\n",
    "    be_firewood_commercial_gillwiehl=prc_firewood_baseline_commercial*(ncv_firewood_agw*kg_firewood_baseline_commercial/1000*((n_fNRB*(ef_combustion_co2_firewood\n",
    "                                                                                                      +ef_combustion_ch4_firewood*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_firewood*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw))))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "        \n",
    "    # Baseline Charcoal\n",
    "\n",
    "    be_charcoal_commercial_gillwiehl=prc_charcoal_baseline_commercial*(ncv_charcoal_agw*kg_charcoal_baseline_commercial/1000*((n_fNRB*(ef_combustion_co2_charcoal\n",
    "                                                                                                      +ef_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_charcoal*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "\n",
    "\n",
    "                                                                                             +ef_total_not_combustion_co2_charcoal  \n",
    "                                                                                             +ef_total_not_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                             +ef_total_not_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "    # Baseline LPG\n",
    "\n",
    "    be_LPG_commercial_gillwiehl=prc_lpg_baseline_commercial*(ncv_LPG_agw*kg_lpg_baseline_commercial/1000*(ef_total_co2_LPG +ef_total_ch4_LPG*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_LPG*gwp_n2O_agw))\n",
    "    # Baseline Kerosene\n",
    "\n",
    "    be_kerosene_commercial_gillwiehl=prc_kerosene_baseline*(ncv_kerosene_agw*kg_kerosene_baseline_commercial/1000*(ef_total_co2_kerosene +ef_total_ch4_kerosene*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_kerosene*gwp_n2O_agw))\n",
    "    # Baseline Ethanol\n",
    "\n",
    "    be_ethanol_commercial_gillwiehl=prc_ethanol_baseline*(ncv_ethanol_agw*l_ethanol_baseline_commercial*kg_per_liter_ethanol_baseline/1000*(ef_total_zeroed_co2_ethanol +ef_total_zeroed_ch4_ethanol*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_ethanol*gwp_n2O_agw))\n",
    "    # Baseline Ethanol\n",
    "\n",
    "    be_pellets_commercial_gillwiehl=prc_pellets_baseline*(ncv_pellets_agw*kg_pellets_baseline/1000*(ef_total_zeroed_co2_pellets +ef_total_zeroed_ch4_pellets*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_pellets*gwp_n2O_agw))\n",
    "    # Total Baseline\n",
    "    be_total_commercial_gillwiehl = be_firewood_commercial_gillwiehl + be_charcoal_commercial_gillwiehl + be_LPG_commercial_gillwiehl+ be_ethanol_commercial_gillwiehl + be_pellets_commercial_gillwiehl\n",
    "\n",
    "    be_total_commercial_gillwiehl\n",
    "\n",
    "    # Project Emissions\n",
    "    # Project Firewood \n",
    "\n",
    "    pe_firewood_gillwiehl=prc_firewood_project*(ncv_firewood_agw*kg_firewood_project/1000*((n_fNRB*(ef_combustion_co2_firewood\n",
    "                                                                                                      +ef_combustion_ch4_firewood*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_firewood*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw))))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "        \n",
    "    # Project Charcoal\n",
    "\n",
    "    pe_charcoal_gillwiehl=prc_charcoal_project*(ncv_charcoal_agw*kg_charcoal_project/1000*((n_fNRB*(ef_combustion_co2_charcoal\n",
    "                                                                                                      +ef_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_charcoal*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "\n",
    "\n",
    "                                                                                             +ef_total_not_combustion_co2_charcoal  \n",
    "                                                                                             +ef_total_not_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                             +ef_total_not_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "    # Project LPG\n",
    "\n",
    "    pe_LPG_gillwiehl=prc_lpg_project*(ncv_LPG_agw*kg_lpg_project/1000*(ef_total_co2_LPG +ef_total_ch4_LPG*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_LPG*gwp_n2O_agw))\n",
    "    # Project Kerosene\n",
    "\n",
    "    pe_kerosene_gillwiehl=prc_kerosene_project*(ncv_kerosene_agw*kg_kerosene_project/1000*(ef_total_co2_kerosene +ef_total_ch4_kerosene*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_kerosene*gwp_n2O_agw))\n",
    "    # Project Ethanol\n",
    "\n",
    "    pe_ethanol_gillwiehl=prc_ethanol_project*(ncv_ethanol_agw*l_ethanol_project*kg_per_liter_ethanol_project/1000*(ef_total_zeroed_co2_ethanol +ef_total_zeroed_ch4_ethanol*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_ethanol*gwp_n2O_agw))\n",
    "    # Project Pellets\n",
    "\n",
    "    pe_pellets_gillwiehl=prc_pellets_project*(ncv_pellets_agw*kg_pellets_project/1000*(ef_total_zeroed_co2_pellets +ef_total_zeroed_ch4_pellets*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_pellets*gwp_n2O_agw))\n",
    "    # Total project\n",
    "    pe_total_domestic_gillwiehl = pe_firewood_gillwiehl + pe_charcoal_gillwiehl + pe_LPG_gillwiehl+ pe_ethanol_gillwiehl + pe_pellets_gillwiehl\n",
    "\n",
    "    pe_total_domestic_gillwiehl\n",
    "    \n",
    "    # Project Commercial Emissions\n",
    "    # Project Commercial Firewood \n",
    "\n",
    "    pe_firewood_commercial_gillwiehl=prc_firewood_project_commercial*(ncv_firewood_agw*kg_firewood_project_commercial/1000*((n_fNRB*(ef_combustion_co2_firewood\n",
    "                                                                                                      +ef_combustion_ch4_firewood*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_firewood*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_firewood*gwp_n2O_agw))))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "        \n",
    "    # Project Charcoal\n",
    "\n",
    "    pe_charcoal_commercial_gillwiehl=prc_charcoal_project_commercial*(ncv_charcoal_agw*kg_charcoal_project_commercial/1000*((n_fNRB*(ef_combustion_co2_charcoal\n",
    "                                                                                                      +ef_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw) \n",
    "                                                                                              # non-renewable\n",
    "                                                                                              +(1-n_fNRB)*(ef_combustion_ch4_charcoal*gwp_ch4_renewable\n",
    "                                                                                                      + ef_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "                                                                                             # renewable emissions use a different ch4 gwp\n",
    "\n",
    "\n",
    "                                                                                             +ef_total_not_combustion_co2_charcoal  \n",
    "                                                                                             +ef_total_not_combustion_ch4_charcoal*gwp_ch4_agw\n",
    "                                                                                             +ef_total_not_combustion_n2O_charcoal*gwp_n2O_agw))\n",
    "    # Project LPG\n",
    "\n",
    "    pe_LPG_commercial_gillwiehl=prc_lpg_project_commercial*(ncv_LPG_agw*kg_lpg_project_commercial/1000*(ef_total_co2_LPG +ef_total_ch4_LPG*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_LPG*gwp_n2O_agw))\n",
    "    # Project Kerosene\n",
    "\n",
    "    pe_kerosene_commercial_gillwiehl=prc_kerosene_project_commercial*(ncv_kerosene_agw*kg_kerosene_project_commercial/1000*(ef_total_co2_kerosene +ef_total_ch4_kerosene*gwp_ch4_agw\n",
    "                                                                                             +ef_total_n2O_kerosene*gwp_n2O_agw))\n",
    "    # Project Ethanol\n",
    "\n",
    "    pe_ethanol_commercial_gillwiehl=prc_ethanol_project_commercial*(ncv_ethanol_agw*l_ethanol_project_commercial*kg_per_liter_ethanol_project/1000*(ef_total_zeroed_co2_ethanol +ef_total_zeroed_ch4_ethanol*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_ethanol*gwp_n2O_agw))\n",
    "    # Project Ethanol\n",
    "\n",
    "    pe_pellets_commercial_gillwiehl=prc_pellets_project_commercial*(ncv_pellets_agw*kg_pellets_project_commercial/1000*(ef_total_zeroed_co2_pellets +ef_total_zeroed_ch4_pellets*gwp_ch4_agw\n",
    "                                                                                             +ef_total_zeroed_n2O_pellets*gwp_n2O_agw))\n",
    "    # Total project\n",
    "    pe_total_commercial_gillwiehl = pe_firewood_commercial_gillwiehl + pe_charcoal_commercial_gillwiehl + pe_LPG_commercial_gillwiehl+ pe_ethanol_commercial_gillwiehl + pe_pellets_commercial_gillwiehl\n",
    "\n",
    "    pe_total_commercial_gillwiehl\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    ## Adjusting adoption, usage, and stacking for the different methodologies \n",
    "    \n",
    "\n",
    "        \n",
    "    be_total_agw=be_total_domestic_gillwiehl\n",
    "\n",
    "    be_total_agw=be_total_agw+be_total_commercial_gillwiehl\n",
    "    \n",
    "    pe_total_agw=pe_total_domestic_gillwiehl\n",
    "    pe_total_agw=pe_total_agw+pe_total_commercial_gillwiehl\n",
    "    \n",
    "    #be_total_agw= be_total_agw*n_adoption*n_usage*n_stacking\n",
    "     \n",
    "    #pe_total_agw= be_total_agw*n_adoption*n_usage*n_stacking\n",
    "    \n",
    "    if df.protocol_type[i]=='GS Metered'and df.stove_year_cohort[i]!='commercial':\n",
    "    \n",
    "        be_total_agw= be_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "        \n",
    "    if df.protocol_type[i]=='GS TPDDTEC'and df.protocol_id[i]!='GS2758'and df.stove_year_cohort[i]!='commercial':\n",
    "        \n",
    "        be_total_agw= be_total_agw*n_adoption*kpt_usage*(1-kpt_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*n_adoption*kpt_usage*(1-kpt_stacking)\n",
    "    \n",
    "    if df.protocol_type[i]=='GS TPDDTEC'and df.stove_year_cohort[i]=='commercial':\n",
    "        \n",
    "        be_total_agw= be_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "          \n",
    "                                                             \n",
    "    if df.protocol_type[i]=='GS Simplified'and df.stove_year_cohort[i]!='commercial':\n",
    "        \n",
    "        be_total_agw= be_total_agw*n_adoption*n_usage*(1-n_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*n_adoption*n_usage*(1-n_stacking)\n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS II G'and df.protocol_id[i]!='GS10974'and df.stove_year_cohort[i]!='commercial':\n",
    "        \n",
    "        \n",
    "        be_total_agw= be_total_agw*n_adoption*n_usage*(1-n_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*n_adoption*n_usage*(1-n_stacking)\n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS I E'and df.stove_year_cohort[i]!='commercial': \n",
    "        \n",
    "        be_total_agw= be_total_agw*n_adoption*n_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*n_adoption*n_usage*(1-project_stacking)\n",
    "       \n",
    "    if df.protocol_id[i]=='GS2758': \n",
    "       \n",
    "        be_total_agw= be_total_agw*req_adoption*kpt_usage*(1-kpt_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*req_adoption*kpt_usage*(1-kpt_stacking)\n",
    "        \n",
    "        \n",
    "    if df.protocol_id[i]=='GS10974'and df.stove_year_cohort[i]!='commercial':\n",
    "    \n",
    "        be_total_agw= be_total_agw*req_adoption*req_usage*(1-req_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*req_adoption*req_usage*(1-req_stacking)\n",
    "        \n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS I E'and df.stove_year_cohort[i]=='commercial': \n",
    "        \n",
    "        be_total_agw= be_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "        \n",
    "        \n",
    "    if df.protocol_type[i]=='CDM AMS II G'and df.stove_year_cohort[i]=='commercial': \n",
    "        \n",
    "        be_total_agw= be_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "     \n",
    "        pe_total_agw= pe_total_agw*project_adoption*project_usage*(1-project_stacking)\n",
    "        \n",
    "        \n",
    "    # Leakage if not default\n",
    "    le_total_agw=(leakage_tCO2e+other_tCO2e_reduction)/(n_devices_domestic+n_devices_commercial)/(project_days+project_days_commercial) # Project Emission Reductions (PERs) in (tCO2e)\n",
    "    \n",
    "        \n",
    "        \n",
    "    \n",
    "        \n",
    "    # Totally for all\n",
    "    PEs_agw=(pe_total_agw)# Project Emission Reductions (PERs) in (tCO2e)\n",
    "    BEs_agw=(be_total_agw)# Project Emission Reductions (PERs) in (tCO2e)\n",
    "\n",
    "    VERs_agw=rebound*(be_total_agw-pe_total_agw-le_total_agw)*more_than_one_stove_adj*leakage_default\n",
    "    \n",
    "  \n",
    "    # Protocol Analysis: \n",
    "    \n",
    "    \n",
    "    #All \n",
    "    \n",
    "    all_VERS_agw.append(VERs_agw)\n",
    "    \n",
    "    sum_VERS_not_per_stove_per_day_agw=sum_VERS_not_per_stove_per_day_agw+VERs_agw*n_devices_domestic*project_days\n",
    "    \n",
    "   \n",
    "        \n",
    "    #bangladesh\n",
    "    if df.country[i]=='Bangladesh':\n",
    "        bangladesh_N=bangladesh_N+1\n",
    "        bangladesh_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #burkina faso\n",
    "    if df.country[i]=='Burkina Faso':\n",
    "        burkinafaso_N=burkinafaso_N+1\n",
    "        burkinafaso_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #cameroon\n",
    "    if df.country[i]=='Cameroon':\n",
    "        cameroon_N=cameroon_N+1\n",
    "        cameroon_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #china\n",
    "    if df.country[i]=='China':\n",
    "        china_N=china_N+1\n",
    "        china_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #ethiopia\n",
    "    if df.country[i]=='Ethiopia':\n",
    "        ethiopia_N=ethiopia_N+1\n",
    "        ethiopia_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #ghana\n",
    "    if df.country[i]=='Ghana':\n",
    "        ghana_N=ghana_N+1\n",
    "        ghana_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #guatemala\n",
    "    if df.country[i]=='Guatemala':\n",
    "        guatemala_N=guatemala_N+1\n",
    "        guatemala_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #haiti\n",
    "    if df.country[i]=='Haiti':\n",
    "        haiti_N=haiti_N+1\n",
    "        haiti_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #honduras\n",
    "    if df.country[i]=='Honduras':\n",
    "        honduras_N=honduras_N+1\n",
    "        honduras_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #india\n",
    "    if df.country[i]=='India':\n",
    "        india_N=india_N+1\n",
    "        india_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #kenya\n",
    "    if df.country[i]=='Kenya':\n",
    "        kenya_N=kenya_N+1\n",
    "        kenya_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #lesotho\n",
    "    if df.country[i]=='Lesotho':\n",
    "        lesotho_N=lesotho_N+1\n",
    "        lesotho_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #malawi\n",
    "    if df.country[i]=='Malawi':\n",
    "        malawi_N=malawi_N+1\n",
    "        malawi_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #mali\n",
    "    if df.country[i]=='Mali':\n",
    "        mali_N=mali_N+1\n",
    "        mali_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #mexico\n",
    "    if df.country[i]=='Mexico':\n",
    "        mexico_N=mexico_N+1\n",
    "        mexico_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #mozambique\n",
    "    if df.country[i]=='Mozambique':\n",
    "        mozambique_N=mozambique_N+1\n",
    "        mozambique_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #myanmar\n",
    "    if df.country[i]=='Myanmar':\n",
    "        myanmar_N=myanmar_N+1\n",
    "        myanmar_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #nepal\n",
    "    if df.country[i]=='Nepal':\n",
    "        nepal_N=nepal_N+1\n",
    "        nepal_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #nigeria\n",
    "    if df.country[i]=='Nigeria':\n",
    "        nigeria_N=nigeria_N+1\n",
    "        nigeria_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #peru\n",
    "    if df.country[i]=='Peru':\n",
    "        peru_N=peru_N+1\n",
    "        peru_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #rwanda\n",
    "    if df.country[i]=='Rwanda':\n",
    "        rwanda_N=rwanda_N+1\n",
    "        rwanda_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #sudan\n",
    "    if df.country[i]=='Sudan':\n",
    "        sudan_N=sudan_N+1\n",
    "        sudan_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #uganda\n",
    "    if df.country[i]=='Uganda':\n",
    "        uganda_N=uganda_N+1\n",
    "        uganda_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #zambia\n",
    "    if df.country[i]=='Zambia':\n",
    "        zambia_N=zambia_N+1\n",
    "        zambia_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #gs metered pellets\n",
    "    if df.protocol_type[i]=='GS Metered':\n",
    "        GS_Meter_N=GS_Meter_N+1\n",
    "        GS_Meter_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #gs_lpg\n",
    "    if df.stove[i]=='gs_lpg':\n",
    "        gs_lpg_N=gs_lpg_N+1\n",
    "        gs_lpg_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #gs_imp_firewood\n",
    "    if df.stove[i]=='gs_imp_firewood':\n",
    "        gs_imp_firewood_N=gs_imp_firewood_N+1\n",
    "        gs_imp_firewood_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    #gs_simple_firewood\n",
    "    if df.stove[i]=='gs_simple_firewood':\n",
    "        gs_simple_firewood_N=gs_simple_firewood_N+1\n",
    "        gs_simple_firewood_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "        \n",
    "    #gs_imp_charcoal\n",
    "    if df.stove[i]=='gs_imp_charcoal':\n",
    "        gs_imp_charcoal_N=gs_imp_charcoal_N+1\n",
    "        gs_imp_charcoal_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    #cdm_imp_firewood\n",
    "    if df.stove[i]=='cdm_imp_firewood':\n",
    "        cdm_imp_firewood_N=cdm_imp_firewood_N+1\n",
    "        cdm_imp_firewood_VERs_agw.append(VERs_agw)\n",
    "        print(np.mean(VERs_agw))\n",
    "        \n",
    "        \n",
    "    #cdm_imp_charcoal\n",
    "    if df.stove[i]=='cdm_imp_charcoal':\n",
    "        cdm_imp_charcoal_N=cdm_imp_charcoal_N+1\n",
    "        cdm_imp_charcoal_VERs_agw.append(VERs_agw)\n",
    "        \n",
    "    # CDM I E\n",
    "    if df.protocol_type[i]=='CDM AMS I E':\n",
    "        CDM_I_E_N=CDM_I_E_N+1\n",
    "        CDM_I_E_VERs_agw.append(VERs_agw)\n",
    "    \n",
    "    \n",
    "    #Times Over Credited \n",
    "    ratio_analysis_project=VERs_agw/VERs\n",
    "    ratio_analysis_project_mean=np.mean(VERs_agw)/VERs*100\n",
    "    ratio_analysis_project_mean \n",
    "    ratio_analysis_project_mean_compilation.append(ratio_analysis_project_mean)\n",
    "\n",
    "    \n",
    "    ratio_project_analysis=VERs/VERs_agw\n",
    "    ratio_project_analysis_mean=VERs/np.mean(VERs_agw)\n",
    "    ratio_project_analysis_mean\n",
    "    ratio_project_analysis_mean_compilation.append(ratio_project_analysis_mean)\n",
    "    z=1.96 \n",
    "\n",
    "    sd_VERs_agw=np.std(VERs_agw)\n",
    "    sd_ratio_analysis_project=np.std(ratio_analysis_project)\n",
    "    sd_ratio_project_analysis=np.std(ratio_project_analysis)\n",
    "    \n",
    "    \n",
    "    ratio_analysis_project_lower=ratio_analysis_project_mean - z*sd_ratio_analysis_project/np.sqrt(N)\n",
    "    ratio_analysis_project_upper=ratio_analysis_project_mean + z*sd_ratio_analysis_project/np.sqrt(N)\n",
    "\n",
    "    ratio_analysis_project_lower_compilation.append(ratio_analysis_project_lower)\n",
    "    ratio_analysis_project_upper_compilation.append(ratio_analysis_project_upper)\n",
    "    \n",
    "    \n",
    "    ratio_project_analysis_lower=ratio_project_analysis_mean - z*sd_ratio_project_analysis/np.sqrt(N)\n",
    "    ratio_project_analysis_upper=ratio_project_analysis_mean + z*sd_ratio_project_analysis/np.sqrt(N)\n",
    "    \n",
    "    #print(sd_ratio_project_analysis)\n",
    "    \n",
    "    ratio_project_analysis_lower_compilation.append(ratio_project_analysis_lower)\n",
    "    ratio_project_analysis_upper_compilation.append(ratio_project_analysis_upper)\n",
    "    \n",
    "    #if ratio_project_analysis_mean<2:\n",
    "    print('We find that', protocol_id,  'project reports emissions which are',round(ratio_project_analysis_mean,2),'[',round(ratio_project_analysis_lower,2),' , ',round(ratio_project_analysis_upper,2),']','times overcredited.')\n",
    "    print('Our analysis finds emissions which are',round(ratio_analysis_project_mean,2),'[',round(ratio_analysis_project_lower,2),' , ',round(ratio_analysis_project_upper,2),']','% of the verified credits in the PDD.')\n",
    "    \n",
    "    i=i+1\n",
    "    ALL_N=ALL_N+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    27.481159\n",
      "dtype: float64\n",
      "9.199457007667\n",
      "9.2 [ 6.89 , 11.51 ]\n"
     ]
    }
   ],
   "source": [
    "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(round(reported_value,2),'[',round(ci_lower,2),',',round(ci_upper,2),']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "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": 15,
   "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": 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": [
      "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": 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": [
      "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": 20,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.107556\n",
      "dtype: float64\n",
      "0    0.125849\n",
      "dtype: float64\n",
      "0    0.144142\n",
      "dtype: float64\n",
      "0    9.297502\n",
      "dtype: float64\n",
      "0    7.946034\n",
      "dtype: float64\n",
      "0    6.937597\n",
      "dtype: float64\n",
      "0.13 [ 0.11 , 0.14 ]\n",
      "7.95 [ 6.94 , 9.3 ]\n"
     ]
    }
   ],
   "source": [
    "\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",
    "\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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.109951\n",
      "dtype: float64\n",
      "0    0.117147\n",
      "dtype: float64\n",
      "0    0.124343\n",
      "dtype: float64\n",
      "0    9.094926\n",
      "dtype: float64\n",
      "0    8.536272\n",
      "dtype: float64\n",
      "0    8.042276\n",
      "dtype: float64\n",
      "0.12 [ 0.11 , 0.12 ]\n",
      "8.54 [ 8.04 , 9.09 ]\n"
     ]
    }
   ],
   "source": [
    "\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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.021682\n",
      "dtype: float64\n",
      "0    0.027973\n",
      "dtype: float64\n",
      "0    0.034263\n",
      "dtype: float64\n",
      "0    46.120812\n",
      "dtype: float64\n",
      "0    35.749122\n",
      "dtype: float64\n",
      "0    29.185794\n",
      "dtype: float64\n",
      "0.03 [ 0.02 , 0.03 ]\n",
      "35.75 [ 29.19 , 46.12 ]\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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.309823\n",
      "dtype: float64\n",
      "0    0.318498\n",
      "dtype: float64\n",
      "0    0.327173\n",
      "dtype: float64\n",
      "0    3.227647\n",
      "dtype: float64\n",
      "0    3.139737\n",
      "dtype: float64\n",
      "0    3.056488\n",
      "dtype: float64\n",
      "0.32 [ 0.31 , 0.33 ]\n",
      "3.14 [ 3.06 , 3.23 ]\n"
     ]
    }
   ],
   "source": [
    "\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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.039692\n",
      "dtype: float64\n",
      "0    0.053114\n",
      "dtype: float64\n",
      "0    0.066536\n",
      "dtype: float64\n",
      "0    25.194046\n",
      "dtype: float64\n",
      "0    18.827397\n",
      "dtype: float64\n",
      "0    15.0294\n",
      "dtype: float64\n",
      "0.05 [ 0.04 , 0.07 ]\n",
      "18.83 [ 15.03 , 25.19 ]\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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.132472\n",
      "dtype: float64\n",
      "0    0.135348\n",
      "dtype: float64\n",
      "0    0.138224\n",
      "dtype: float64\n",
      "0    7.548737\n",
      "dtype: float64\n",
      "0    7.388351\n",
      "dtype: float64\n",
      "0    7.234639\n",
      "dtype: float64\n",
      "0.14 [ 0.13 , 0.14 ]\n",
      "7.39 [ 7.23 , 7.55 ]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.638156\n",
      "dtype: float64\n",
      "0    0.648721\n",
      "dtype: float64\n",
      "0    0.659286\n",
      "dtype: float64\n",
      "0    1.567016\n",
      "dtype: float64\n",
      "0    1.541495\n",
      "dtype: float64\n",
      "0    1.516792\n",
      "dtype: float64\n",
      "0.65 [ 0.64 , 0.66 ]\n",
      "1.54 [ 1.52 , 1.57 ]\n"
     ]
    }
   ],
   "source": [
    "\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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.390262\n",
      "dtype: float64\n",
      "0    0.432903\n",
      "dtype: float64\n",
      "0    0.475543\n",
      "dtype: float64\n",
      "0    2.562382\n",
      "dtype: float64\n",
      "0    2.309989\n",
      "dtype: float64\n",
      "0    2.102858\n",
      "dtype: float64\n",
      "0.43 [ 0.39 , 0.48 ]\n",
      "2.31 [ 2.1 , 2.56 ]\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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "0    0.580295\n",
      "dtype: float64\n",
      "0    0.589886\n",
      "dtype: float64\n",
      "0    0.599477\n",
      "dtype: float64\n",
      "0    1.723263\n",
      "dtype: float64\n",
      "0    1.695243\n",
      "dtype: float64\n",
      "0    1.66812\n",
      "dtype: float64\n",
      "0.59 [ 0.58 , 0.6 ]\n",
      "1.7 [ 1.67 , 1.72 ]\n"
     ]
    }
   ],
   "source": [
    "df_bangladesh_VERs_agw=pd.DataFrame(bangladesh_VERs_agw).transpose()\n",
    "print(df_bangladesh_VERs_agw.shape[1])\n",
    "bangladesh_i=0\n",
    "df_bangladesh_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while bangladesh_i<df_bangladesh_VERs_agw.shape[1]:\n",
    "    df_bangladesh_VERs_agw_final=df_bangladesh_VERs_agw_final+df_bangladesh_VERs_agw[bangladesh_i]\n",
    "    bangladesh_i=bangladesh_i+1\n",
    "\n",
    "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0   NaN\n",
      "dtype: float64\n",
      "0   NaN\n",
      "dtype: float64\n",
      "0   NaN\n",
      "dtype: float64\n",
      "0   NaN\n",
      "dtype: float64\n",
      "0   NaN\n",
      "dtype: float64\n",
      "0   NaN\n",
      "dtype: float64\n",
      "nan [ nan , nan ]\n",
      "nan [ nan , nan ]\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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0    0.002928\n",
      "dtype: float64\n",
      "0    0.005327\n",
      "dtype: float64\n",
      "0    0.007727\n",
      "dtype: float64\n",
      "0    341.534974\n",
      "dtype: float64\n",
      "0    187.713892\n",
      "dtype: float64\n",
      "0    129.423792\n",
      "dtype: float64\n",
      "0.01 [ 0.0 , 0.01 ]\n",
      "187.71 [ 129.42 , 341.53 ]\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",
    "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": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    0.147485\n",
      "dtype: float64\n",
      "0    0.315818\n",
      "dtype: float64\n",
      "0    0.484152\n",
      "dtype: float64\n",
      "0    6.780342\n",
      "dtype: float64\n",
      "0    3.166376\n",
      "dtype: float64\n",
      "0    2.065468\n",
      "dtype: float64\n",
      "0.32 [ 0.15 , 0.48 ]\n",
      "3.17 [ 2.07 , 6.78 ]\n"
     ]
    }
   ],
   "source": [
    "df_cameroon_VERs_agw=pd.DataFrame(cameroon_VERs_agw).transpose()\n",
    "print(df_cameroon_VERs_agw.shape[1])\n",
    "cameroon_i=0\n",
    "df_cameroon_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while cameroon_i<df_cameroon_VERs_agw.shape[1]:\n",
    "    df_cameroon_VERs_agw_final=df_cameroon_VERs_agw_final+df_cameroon_VERs_agw[cameroon_i]\n",
    "    cameroon_i=cameroon_i+1\n",
    "\n",
    "cameroon_ratio_analysis_project=df_cameroon_VERs_agw_final/cameroon_VERs\n",
    "cameroon_ratio_analysis_project_mean=np.mean(df_cameroon_VERs_agw_final, axis=1)/cameroon_VERs\n",
    "cameroon_ratio_project_analysis_mean=cameroon_ratio_analysis_project_mean**-1\n",
    "\n",
    "cameroon_sd_ratio_analysis_project=np.std(cameroon_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "cameroon_ratio_analysis_project_lower=cameroon_ratio_analysis_project_mean - z*cameroon_sd_ratio_analysis_project/np.sqrt(cameroon_N)\n",
    "cameroon_ratio_analysis_project_upper=cameroon_ratio_analysis_project_mean + z*cameroon_sd_ratio_analysis_project/np.sqrt(cameroon_N)\n",
    "\n",
    "cameroon_ratio_project_analysis_lower=cameroon_ratio_analysis_project_lower**-1\n",
    "cameroon_ratio_project_analysis_upper=cameroon_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(cameroon_ratio_analysis_project_lower)\n",
    "print(cameroon_ratio_analysis_project_mean)\n",
    "print(cameroon_ratio_analysis_project_upper)\n",
    "print(cameroon_ratio_project_analysis_lower)\n",
    "print(cameroon_ratio_project_analysis_mean)\n",
    "print(cameroon_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(cameroon_ratio_analysis_project_mean[0],2),'[',round(cameroon_ratio_analysis_project_lower[0],2),',',round(cameroon_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(cameroon_ratio_project_analysis_mean[0],2),'[',round(cameroon_ratio_project_analysis_upper[0],2),',',round(cameroon_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.116557\n",
      "dtype: float64\n",
      "0    0.217544\n",
      "dtype: float64\n",
      "0    0.31853\n",
      "dtype: float64\n",
      "0    8.579526\n",
      "dtype: float64\n",
      "0    4.596782\n",
      "dtype: float64\n",
      "0    3.139417\n",
      "dtype: float64\n",
      "0.22 [ 0.12 , 0.32 ]\n",
      "4.6 [ 3.14 , 8.58 ]\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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "0    0.416799\n",
      "dtype: float64\n",
      "0    0.426613\n",
      "dtype: float64\n",
      "0    0.436426\n",
      "dtype: float64\n",
      "0    2.399236\n",
      "dtype: float64\n",
      "0    2.344046\n",
      "dtype: float64\n",
      "0    2.291338\n",
      "dtype: float64\n",
      "0.43 [ 0.42 , 0.44 ]\n",
      "2.34 [ 2.29 , 2.4 ]\n"
     ]
    }
   ],
   "source": [
    "df_ghana_VERs_agw=pd.DataFrame(ghana_VERs_agw).transpose()\n",
    "print(df_ghana_VERs_agw.shape[1])\n",
    "ghana_i=0\n",
    "df_ghana_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while ghana_i<df_ghana_VERs_agw.shape[1]:\n",
    "    df_ghana_VERs_agw_final=df_ghana_VERs_agw_final+df_ghana_VERs_agw[ghana_i]\n",
    "    ghana_i=ghana_i+1\n",
    "\n",
    "ghana_ratio_analysis_project=df_ghana_VERs_agw_final/ghana_VERs\n",
    "ghana_ratio_analysis_project_mean=np.mean(df_ghana_VERs_agw_final, axis=1)/ghana_VERs\n",
    "ghana_ratio_project_analysis_mean=ghana_ratio_analysis_project_mean**-1\n",
    "\n",
    "ghana_sd_ratio_analysis_project=np.std(ghana_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "ghana_ratio_analysis_project_lower=ghana_ratio_analysis_project_mean - z*ghana_sd_ratio_analysis_project/np.sqrt(ghana_N)\n",
    "ghana_ratio_analysis_project_upper=ghana_ratio_analysis_project_mean + z*ghana_sd_ratio_analysis_project/np.sqrt(ghana_N)\n",
    "\n",
    "ghana_ratio_project_analysis_lower=ghana_ratio_analysis_project_lower**-1\n",
    "ghana_ratio_project_analysis_upper=ghana_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(ghana_ratio_analysis_project_lower)\n",
    "print(ghana_ratio_analysis_project_mean)\n",
    "print(ghana_ratio_analysis_project_upper)\n",
    "print(ghana_ratio_project_analysis_lower)\n",
    "print(ghana_ratio_project_analysis_mean)\n",
    "print(ghana_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(ghana_ratio_analysis_project_mean[0],2),'[',round(ghana_ratio_analysis_project_lower[0],2),',',round(ghana_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(ghana_ratio_project_analysis_mean[0],2),'[',round(ghana_ratio_project_analysis_upper[0],2),',',round(ghana_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "0    0.085785\n",
      "dtype: float64\n",
      "0    0.099041\n",
      "dtype: float64\n",
      "0    0.112297\n",
      "dtype: float64\n",
      "0    11.65704\n",
      "dtype: float64\n",
      "0    10.09682\n",
      "dtype: float64\n",
      "0    8.904949\n",
      "dtype: float64\n",
      "0.1 [ 0.09 , 0.11 ]\n",
      "10.1 [ 8.9 , 11.66 ]\n"
     ]
    }
   ],
   "source": [
    "df_guatemala_VERs_agw=pd.DataFrame(guatemala_VERs_agw).transpose()\n",
    "print(df_guatemala_VERs_agw.shape[1])\n",
    "guatemala_i=0\n",
    "df_guatemala_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while guatemala_i<df_guatemala_VERs_agw.shape[1]:\n",
    "    df_guatemala_VERs_agw_final=df_guatemala_VERs_agw_final+df_guatemala_VERs_agw[guatemala_i]\n",
    "    guatemala_i=guatemala_i+1\n",
    "\n",
    "guatemala_ratio_analysis_project=df_guatemala_VERs_agw_final/guatemala_VERs\n",
    "guatemala_ratio_analysis_project_mean=np.mean(df_guatemala_VERs_agw_final, axis=1)/guatemala_VERs\n",
    "guatemala_ratio_project_analysis_mean=guatemala_ratio_analysis_project_mean**-1\n",
    "\n",
    "guatemala_sd_ratio_analysis_project=np.std(guatemala_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "guatemala_ratio_analysis_project_lower=guatemala_ratio_analysis_project_mean - z*guatemala_sd_ratio_analysis_project/np.sqrt(guatemala_N)\n",
    "guatemala_ratio_analysis_project_upper=guatemala_ratio_analysis_project_mean + z*guatemala_sd_ratio_analysis_project/np.sqrt(guatemala_N)\n",
    "\n",
    "guatemala_ratio_project_analysis_lower=guatemala_ratio_analysis_project_lower**-1\n",
    "guatemala_ratio_project_analysis_upper=guatemala_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(guatemala_ratio_analysis_project_lower)\n",
    "print(guatemala_ratio_analysis_project_mean)\n",
    "print(guatemala_ratio_analysis_project_upper)\n",
    "print(guatemala_ratio_project_analysis_lower)\n",
    "print(guatemala_ratio_project_analysis_mean)\n",
    "print(guatemala_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(guatemala_ratio_analysis_project_mean[0],2),'[',round(guatemala_ratio_analysis_project_lower[0],2),',',round(guatemala_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(guatemala_ratio_project_analysis_mean[0],2),'[',round(guatemala_ratio_project_analysis_upper[0],2),',',round(guatemala_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    0.041779\n",
      "dtype: float64\n",
      "0    0.089935\n",
      "dtype: float64\n",
      "0    0.138091\n",
      "dtype: float64\n",
      "0    23.935503\n",
      "dtype: float64\n",
      "0    11.11912\n",
      "dtype: float64\n",
      "0    7.24158\n",
      "dtype: float64\n",
      "0.09 [ 0.04 , 0.14 ]\n",
      "11.12 [ 7.24 , 23.94 ]\n"
     ]
    }
   ],
   "source": [
    "df_haiti_VERs_agw=pd.DataFrame(haiti_VERs_agw).transpose()\n",
    "print(df_haiti_VERs_agw.shape[1])\n",
    "haiti_i=0\n",
    "df_haiti_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while haiti_i<df_haiti_VERs_agw.shape[1]:\n",
    "    df_haiti_VERs_agw_final=df_haiti_VERs_agw_final+df_haiti_VERs_agw[haiti_i]\n",
    "    haiti_i=haiti_i+1\n",
    "\n",
    "haiti_ratio_analysis_project=df_haiti_VERs_agw_final/haiti_VERs\n",
    "haiti_ratio_analysis_project_mean=np.mean(df_haiti_VERs_agw_final, axis=1)/haiti_VERs\n",
    "haiti_ratio_project_analysis_mean=haiti_ratio_analysis_project_mean**-1\n",
    "\n",
    "haiti_sd_ratio_analysis_project=np.std(haiti_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "haiti_ratio_analysis_project_lower=haiti_ratio_analysis_project_mean - z*haiti_sd_ratio_analysis_project/np.sqrt(haiti_N)\n",
    "haiti_ratio_analysis_project_upper=haiti_ratio_analysis_project_mean + z*haiti_sd_ratio_analysis_project/np.sqrt(haiti_N)\n",
    "\n",
    "haiti_ratio_project_analysis_lower=haiti_ratio_analysis_project_lower**-1\n",
    "haiti_ratio_project_analysis_upper=haiti_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(haiti_ratio_analysis_project_lower)\n",
    "print(haiti_ratio_analysis_project_mean)\n",
    "print(haiti_ratio_analysis_project_upper)\n",
    "print(haiti_ratio_project_analysis_lower)\n",
    "print(haiti_ratio_project_analysis_mean)\n",
    "print(haiti_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(haiti_ratio_analysis_project_mean[0],2),'[',round(haiti_ratio_analysis_project_lower[0],2),',',round(haiti_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(haiti_ratio_project_analysis_mean[0],2),'[',round(haiti_ratio_project_analysis_upper[0],2),',',round(haiti_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0    0.065969\n",
      "dtype: float64\n",
      "0    0.12457\n",
      "dtype: float64\n",
      "0    0.183171\n",
      "dtype: float64\n",
      "0    15.158665\n",
      "dtype: float64\n",
      "0    8.027621\n",
      "dtype: float64\n",
      "0    5.459381\n",
      "dtype: float64\n",
      "0.12 [ 0.07 , 0.18 ]\n",
      "8.03 [ 5.46 , 15.16 ]\n"
     ]
    }
   ],
   "source": [
    "df_honduras_VERs_agw=pd.DataFrame(honduras_VERs_agw).transpose()\n",
    "print(df_honduras_VERs_agw.shape[1])\n",
    "honduras_i=0\n",
    "df_honduras_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while honduras_i<df_honduras_VERs_agw.shape[1]:\n",
    "    df_honduras_VERs_agw_final=df_honduras_VERs_agw_final+df_honduras_VERs_agw[honduras_i]\n",
    "    honduras_i=honduras_i+1\n",
    "\n",
    "honduras_ratio_analysis_project=df_honduras_VERs_agw_final/honduras_VERs\n",
    "honduras_ratio_analysis_project_mean=np.mean(df_honduras_VERs_agw_final, axis=1)/honduras_VERs\n",
    "honduras_ratio_project_analysis_mean=honduras_ratio_analysis_project_mean**-1\n",
    "\n",
    "honduras_sd_ratio_analysis_project=np.std(honduras_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "honduras_ratio_analysis_project_lower=honduras_ratio_analysis_project_mean - z*honduras_sd_ratio_analysis_project/np.sqrt(honduras_N)\n",
    "honduras_ratio_analysis_project_upper=honduras_ratio_analysis_project_mean + z*honduras_sd_ratio_analysis_project/np.sqrt(honduras_N)\n",
    "\n",
    "honduras_ratio_project_analysis_lower=honduras_ratio_analysis_project_lower**-1\n",
    "honduras_ratio_project_analysis_upper=honduras_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(honduras_ratio_analysis_project_lower)\n",
    "print(honduras_ratio_analysis_project_mean)\n",
    "print(honduras_ratio_analysis_project_upper)\n",
    "print(honduras_ratio_project_analysis_lower)\n",
    "print(honduras_ratio_project_analysis_mean)\n",
    "print(honduras_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(honduras_ratio_analysis_project_mean[0],2),'[',round(honduras_ratio_analysis_project_lower[0],2),',',round(honduras_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(honduras_ratio_project_analysis_mean[0],2),'[',round(honduras_ratio_project_analysis_upper[0],2),',',round(honduras_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.009536\n",
      "dtype: float64\n",
      "0    0.04469\n",
      "dtype: float64\n",
      "0    0.079845\n",
      "dtype: float64\n",
      "0    104.870814\n",
      "dtype: float64\n",
      "0    22.37627\n",
      "dtype: float64\n",
      "0    12.524288\n",
      "dtype: float64\n",
      "0.04 [ 0.01 , 0.08 ]\n",
      "22.38 [ 12.52 , 104.87 ]\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": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n",
      "0    0.473384\n",
      "dtype: float64\n",
      "0    0.494541\n",
      "dtype: float64\n",
      "0    0.515697\n",
      "dtype: float64\n",
      "0    2.112449\n",
      "dtype: float64\n",
      "0    2.022079\n",
      "dtype: float64\n",
      "0    1.939124\n",
      "dtype: float64\n",
      "0.49 [ 0.47 , 0.52 ]\n",
      "2.02 [ 1.94 , 2.11 ]\n"
     ]
    }
   ],
   "source": [
    "df_kenya_VERs_agw=pd.DataFrame(kenya_VERs_agw).transpose()\n",
    "print(df_kenya_VERs_agw.shape[1])\n",
    "kenya_i=0\n",
    "df_kenya_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while kenya_i<df_kenya_VERs_agw.shape[1]:\n",
    "    df_kenya_VERs_agw_final=df_kenya_VERs_agw_final+df_kenya_VERs_agw[kenya_i]\n",
    "    kenya_i=kenya_i+1\n",
    "\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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0   -0.005661\n",
      "dtype: float64\n",
      "0    0.051678\n",
      "dtype: float64\n",
      "0    0.109016\n",
      "dtype: float64\n",
      "0   -176.659864\n",
      "dtype: float64\n",
      "0    19.350701\n",
      "dtype: float64\n",
      "0    9.172964\n",
      "dtype: float64\n",
      "0.05 [ -0.01 , 0.11 ]\n",
      "19.35 [ 9.17 , -176.66 ]\n"
     ]
    }
   ],
   "source": [
    "df_lesotho_VERs_agw=pd.DataFrame(lesotho_VERs_agw).transpose()\n",
    "print(df_lesotho_VERs_agw.shape[1])\n",
    "lesotho_i=0\n",
    "df_lesotho_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while lesotho_i<df_lesotho_VERs_agw.shape[1]:\n",
    "    df_lesotho_VERs_agw_final=df_lesotho_VERs_agw_final+df_lesotho_VERs_agw[lesotho_i]\n",
    "    lesotho_i=lesotho_i+1\n",
    "\n",
    "lesotho_ratio_analysis_project=df_lesotho_VERs_agw_final/lesotho_VERs\n",
    "lesotho_ratio_analysis_project_mean=np.mean(df_lesotho_VERs_agw_final, axis=1)/lesotho_VERs\n",
    "lesotho_ratio_project_analysis_mean=lesotho_ratio_analysis_project_mean**-1\n",
    "\n",
    "lesotho_sd_ratio_analysis_project=np.std(lesotho_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "lesotho_ratio_analysis_project_lower=lesotho_ratio_analysis_project_mean - z*lesotho_sd_ratio_analysis_project/np.sqrt(lesotho_N)\n",
    "lesotho_ratio_analysis_project_upper=lesotho_ratio_analysis_project_mean + z*lesotho_sd_ratio_analysis_project/np.sqrt(lesotho_N)\n",
    "\n",
    "lesotho_ratio_project_analysis_lower=lesotho_ratio_analysis_project_lower**-1\n",
    "lesotho_ratio_project_analysis_upper=lesotho_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(lesotho_ratio_analysis_project_lower)\n",
    "print(lesotho_ratio_analysis_project_mean)\n",
    "print(lesotho_ratio_analysis_project_upper)\n",
    "print(lesotho_ratio_project_analysis_lower)\n",
    "print(lesotho_ratio_project_analysis_mean)\n",
    "print(lesotho_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(lesotho_ratio_analysis_project_mean[0],2),'[',round(lesotho_ratio_analysis_project_lower[0],2),',',round(lesotho_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(lesotho_ratio_project_analysis_mean[0],2),'[',round(lesotho_ratio_project_analysis_upper[0],2),',',round(lesotho_ratio_project_analysis_lower[0],2),']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "0    0.092924\n",
      "dtype: float64\n",
      "0    0.113614\n",
      "dtype: float64\n",
      "0    0.134304\n",
      "dtype: float64\n",
      "0    10.761445\n",
      "dtype: float64\n",
      "0    8.801732\n",
      "dtype: float64\n",
      "0    7.445813\n",
      "dtype: float64\n",
      "0.11 [ 0.09 , 0.13 ]\n",
      "8.8 [ 7.45 , 10.76 ]\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": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.002318\n",
      "dtype: float64\n",
      "0    0.011821\n",
      "dtype: float64\n",
      "0    0.021325\n",
      "dtype: float64\n",
      "0    431.482127\n",
      "dtype: float64\n",
      "0    84.593828\n",
      "dtype: float64\n",
      "0    46.893772\n",
      "dtype: float64\n",
      "0.01 [ 0.0 , 0.02 ]\n",
      "84.59 [ 46.89 , 431.48 ]\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": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "0    0.031491\n",
      "dtype: float64\n",
      "0    0.035537\n",
      "dtype: float64\n",
      "0    0.039583\n",
      "dtype: float64\n",
      "0    31.75501\n",
      "dtype: float64\n",
      "0    28.139498\n",
      "dtype: float64\n",
      "0    25.263128\n",
      "dtype: float64\n",
      "0.04 [ 0.03 , 0.04 ]\n",
      "28.14 [ 25.26 , 31.76 ]\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",
    "mexico_ratio_analysis_project=df_mexico_VERs_agw_final/mexico_VERs\n",
    "mexico_ratio_analysis_project_mean=np.mean(df_mexico_VERs_agw_final, axis=1)/mexico_VERs\n",
    "mexico_ratio_project_analysis_mean=mexico_ratio_analysis_project_mean**-1\n",
    "\n",
    "mexico_sd_ratio_analysis_project=np.std(mexico_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "mexico_ratio_analysis_project_lower=mexico_ratio_analysis_project_mean - z*mexico_sd_ratio_analysis_project/np.sqrt(mexico_N)\n",
    "mexico_ratio_analysis_project_upper=mexico_ratio_analysis_project_mean + z*mexico_sd_ratio_analysis_project/np.sqrt(mexico_N)\n",
    "\n",
    "mexico_ratio_project_analysis_lower=mexico_ratio_analysis_project_lower**-1\n",
    "mexico_ratio_project_analysis_upper=mexico_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(mexico_ratio_analysis_project_lower)\n",
    "print(mexico_ratio_analysis_project_mean)\n",
    "print(mexico_ratio_analysis_project_upper)\n",
    "print(mexico_ratio_project_analysis_lower)\n",
    "print(mexico_ratio_project_analysis_mean)\n",
    "print(mexico_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(mexico_ratio_analysis_project_mean[0],2),'[',round(mexico_ratio_analysis_project_lower[0],2),',',round(mexico_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(mexico_ratio_project_analysis_mean[0],2),'[',round(mexico_ratio_project_analysis_upper[0],2),',',round(mexico_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "0    0.075349\n",
      "dtype: float64\n",
      "0    0.11062\n",
      "dtype: float64\n",
      "0    0.145891\n",
      "dtype: float64\n",
      "0    13.271604\n",
      "dtype: float64\n",
      "0    9.039968\n",
      "dtype: float64\n",
      "0    6.854438\n",
      "dtype: float64\n",
      "0.11 [ 0.08 , 0.15 ]\n",
      "9.04 [ 6.85 , 13.27 ]\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",
    "mozambique_ratio_analysis_project=df_mozambique_VERs_agw_final/mozambique_VERs\n",
    "mozambique_ratio_analysis_project_mean=np.mean(df_mozambique_VERs_agw_final, axis=1)/mozambique_VERs\n",
    "mozambique_ratio_project_analysis_mean=mozambique_ratio_analysis_project_mean**-1\n",
    "\n",
    "mozambique_sd_ratio_analysis_project=np.std(mozambique_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "mozambique_ratio_analysis_project_lower=mozambique_ratio_analysis_project_mean - z*mozambique_sd_ratio_analysis_project/np.sqrt(mozambique_N)\n",
    "mozambique_ratio_analysis_project_upper=mozambique_ratio_analysis_project_mean + z*mozambique_sd_ratio_analysis_project/np.sqrt(mozambique_N)\n",
    "\n",
    "mozambique_ratio_project_analysis_lower=mozambique_ratio_analysis_project_lower**-1\n",
    "mozambique_ratio_project_analysis_upper=mozambique_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(mozambique_ratio_analysis_project_lower)\n",
    "print(mozambique_ratio_analysis_project_mean)\n",
    "print(mozambique_ratio_analysis_project_upper)\n",
    "print(mozambique_ratio_project_analysis_lower)\n",
    "print(mozambique_ratio_project_analysis_mean)\n",
    "print(mozambique_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(mozambique_ratio_analysis_project_mean[0],2),'[',round(mozambique_ratio_analysis_project_lower[0],2),',',round(mozambique_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(mozambique_ratio_project_analysis_mean[0],2),'[',round(mozambique_ratio_project_analysis_upper[0],2),',',round(mozambique_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "0    0.006438\n",
      "dtype: float64\n",
      "0    0.01433\n",
      "dtype: float64\n",
      "0    0.022222\n",
      "dtype: float64\n",
      "0    155.334711\n",
      "dtype: float64\n",
      "0    69.783415\n",
      "dtype: float64\n",
      "0    44.999653\n",
      "dtype: float64\n",
      "0.01 [ 0.01 , 0.02 ]\n",
      "69.78 [ 45.0 , 155.33 ]\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": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "0    0.064906\n",
      "dtype: float64\n",
      "0    0.128061\n",
      "dtype: float64\n",
      "0    0.191215\n",
      "dtype: float64\n",
      "0    15.406863\n",
      "dtype: float64\n",
      "0    7.808797\n",
      "dtype: float64\n",
      "0    5.229708\n",
      "dtype: float64\n",
      "0.13 [ 0.06 , 0.19 ]\n",
      "7.81 [ 5.23 , 15.41 ]\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": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "0    0.085405\n",
      "dtype: float64\n",
      "0    0.114505\n",
      "dtype: float64\n",
      "0    0.143604\n",
      "dtype: float64\n",
      "0    11.708888\n",
      "dtype: float64\n",
      "0    8.733261\n",
      "dtype: float64\n",
      "0    6.963579\n",
      "dtype: float64\n",
      "0.11 [ 0.09 , 0.14 ]\n",
      "8.73 [ 6.96 , 11.71 ]\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",
    "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": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "0    0.025025\n",
      "dtype: float64\n",
      "0    0.053799\n",
      "dtype: float64\n",
      "0    0.082572\n",
      "dtype: float64\n",
      "0    39.959392\n",
      "dtype: float64\n",
      "0    18.587846\n",
      "dtype: float64\n",
      "0    12.110675\n",
      "dtype: float64\n",
      "0.05 [ 0.03 , 0.08 ]\n",
      "18.59 [ 12.11 , 39.96 ]\n"
     ]
    }
   ],
   "source": [
    "df_peru_VERs_agw=pd.DataFrame(peru_VERs_agw).transpose()\n",
    "print(df_peru_VERs_agw.shape[1])\n",
    "peru_i=0\n",
    "df_peru_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while peru_i<df_peru_VERs_agw.shape[1]:\n",
    "    df_peru_VERs_agw_final=df_peru_VERs_agw_final+df_peru_VERs_agw[peru_i]\n",
    "    peru_i=peru_i+1\n",
    "\n",
    "peru_ratio_analysis_project=df_peru_VERs_agw_final/peru_VERs\n",
    "peru_ratio_analysis_project_mean=np.mean(df_peru_VERs_agw_final, axis=1)/peru_VERs\n",
    "peru_ratio_project_analysis_mean=peru_ratio_analysis_project_mean**-1\n",
    "\n",
    "peru_sd_ratio_analysis_project=np.std(peru_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "peru_ratio_analysis_project_lower=peru_ratio_analysis_project_mean - z*peru_sd_ratio_analysis_project/np.sqrt(peru_N)\n",
    "peru_ratio_analysis_project_upper=peru_ratio_analysis_project_mean + z*peru_sd_ratio_analysis_project/np.sqrt(peru_N)\n",
    "\n",
    "peru_ratio_project_analysis_lower=peru_ratio_analysis_project_lower**-1\n",
    "peru_ratio_project_analysis_upper=peru_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(peru_ratio_analysis_project_lower)\n",
    "print(peru_ratio_analysis_project_mean)\n",
    "print(peru_ratio_analysis_project_upper)\n",
    "print(peru_ratio_project_analysis_lower)\n",
    "print(peru_ratio_project_analysis_mean)\n",
    "print(peru_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(peru_ratio_analysis_project_mean[0],2),'[',round(peru_ratio_analysis_project_lower[0],2),',',round(peru_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(peru_ratio_project_analysis_mean[0],2),'[',round(peru_ratio_project_analysis_upper[0],2),',',round(peru_ratio_project_analysis_lower[0],2),']')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n",
      "0    0.145591\n",
      "dtype: float64\n",
      "0    0.158741\n",
      "dtype: float64\n",
      "0    0.171891\n",
      "dtype: float64\n",
      "0    6.868549\n",
      "dtype: float64\n",
      "0    6.299574\n",
      "dtype: float64\n",
      "0    5.817653\n",
      "dtype: float64\n",
      "0.16 [ 0.15 , 0.17 ]\n",
      "6.3 [ 5.82 , 6.87 ]\n"
     ]
    }
   ],
   "source": [
    "df_rwanda_VERs_agw=pd.DataFrame(rwanda_VERs_agw).transpose()\n",
    "print(df_rwanda_VERs_agw.shape[1])\n",
    "rwanda_i=0\n",
    "df_rwanda_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while rwanda_i<df_rwanda_VERs_agw.shape[1]:\n",
    "    df_rwanda_VERs_agw_final=df_rwanda_VERs_agw_final+df_rwanda_VERs_agw[rwanda_i]\n",
    "    rwanda_i=rwanda_i+1\n",
    "\n",
    "rwanda_ratio_analysis_project=df_rwanda_VERs_agw_final/rwanda_VERs\n",
    "rwanda_ratio_analysis_project_mean=np.mean(df_rwanda_VERs_agw_final, axis=1)/rwanda_VERs\n",
    "rwanda_ratio_project_analysis_mean=rwanda_ratio_analysis_project_mean**-1\n",
    "\n",
    "rwanda_sd_ratio_analysis_project=np.std(rwanda_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "rwanda_ratio_analysis_project_lower=rwanda_ratio_analysis_project_mean - z*rwanda_sd_ratio_analysis_project/np.sqrt(rwanda_N)\n",
    "rwanda_ratio_analysis_project_upper=rwanda_ratio_analysis_project_mean + z*rwanda_sd_ratio_analysis_project/np.sqrt(rwanda_N)\n",
    "\n",
    "rwanda_ratio_project_analysis_lower=rwanda_ratio_analysis_project_lower**-1\n",
    "rwanda_ratio_project_analysis_upper=rwanda_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(rwanda_ratio_analysis_project_lower)\n",
    "print(rwanda_ratio_analysis_project_mean)\n",
    "print(rwanda_ratio_analysis_project_upper)\n",
    "print(rwanda_ratio_project_analysis_lower)\n",
    "print(rwanda_ratio_project_analysis_mean)\n",
    "print(rwanda_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(rwanda_ratio_analysis_project_mean[0],2),'[',round(rwanda_ratio_analysis_project_lower[0],2),',',round(rwanda_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(rwanda_ratio_project_analysis_mean[0],2),'[',round(rwanda_ratio_project_analysis_upper[0],2),',',round(rwanda_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.014486\n",
      "dtype: float64\n",
      "0    0.031041\n",
      "dtype: float64\n",
      "0    0.047595\n",
      "dtype: float64\n",
      "0    69.029895\n",
      "dtype: float64\n",
      "0    32.215794\n",
      "dtype: float64\n",
      "0    21.010667\n",
      "dtype: float64\n",
      "0.03 [ 0.01 , 0.05 ]\n",
      "32.22 [ 21.01 , 69.03 ]\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": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "0    0.074778\n",
      "dtype: float64\n",
      "0    0.091668\n",
      "dtype: float64\n",
      "0    0.108557\n",
      "dtype: float64\n",
      "0    13.372894\n",
      "dtype: float64\n",
      "0    10.908957\n",
      "dtype: float64\n",
      "0    9.211711\n",
      "dtype: float64\n",
      "0.09 [ 0.07 , 0.11 ]\n",
      "10.91 [ 9.21 , 13.37 ]\n"
     ]
    }
   ],
   "source": [
    "df_uganda_VERs_agw=pd.DataFrame(uganda_VERs_agw).transpose()\n",
    "print(df_uganda_VERs_agw.shape[1])\n",
    "uganda_i=0\n",
    "df_uganda_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while uganda_i<df_uganda_VERs_agw.shape[1]:\n",
    "    df_uganda_VERs_agw_final=df_uganda_VERs_agw_final+df_uganda_VERs_agw[uganda_i]\n",
    "    uganda_i=uganda_i+1\n",
    "\n",
    "uganda_ratio_analysis_project=df_uganda_VERs_agw_final/uganda_VERs\n",
    "uganda_ratio_analysis_project_mean=np.mean(df_uganda_VERs_agw_final, axis=1)/uganda_VERs\n",
    "uganda_ratio_project_analysis_mean=uganda_ratio_analysis_project_mean**-1\n",
    "\n",
    "uganda_sd_ratio_analysis_project=np.std(uganda_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "uganda_ratio_analysis_project_lower=uganda_ratio_analysis_project_mean - z*uganda_sd_ratio_analysis_project/np.sqrt(uganda_N)\n",
    "uganda_ratio_analysis_project_upper=uganda_ratio_analysis_project_mean + z*uganda_sd_ratio_analysis_project/np.sqrt(uganda_N)\n",
    "\n",
    "uganda_ratio_project_analysis_lower=uganda_ratio_analysis_project_lower**-1\n",
    "uganda_ratio_project_analysis_upper=uganda_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(uganda_ratio_analysis_project_lower)\n",
    "print(uganda_ratio_analysis_project_mean)\n",
    "print(uganda_ratio_analysis_project_upper)\n",
    "print(uganda_ratio_project_analysis_lower)\n",
    "print(uganda_ratio_project_analysis_mean)\n",
    "print(uganda_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(uganda_ratio_analysis_project_mean[0],2),'[',round(uganda_ratio_analysis_project_lower[0],2),',',round(uganda_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(uganda_ratio_project_analysis_mean[0],2),'[',round(uganda_ratio_project_analysis_upper[0],2),',',round(uganda_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "0    0.274706\n",
      "dtype: float64\n",
      "0    0.299738\n",
      "dtype: float64\n",
      "0    0.324771\n",
      "dtype: float64\n",
      "0    3.640253\n",
      "dtype: float64\n",
      "0    3.336242\n",
      "dtype: float64\n",
      "0    3.079095\n",
      "dtype: float64\n",
      "0.3 [ 0.27 , 0.32 ]\n",
      "3.34 [ 3.08 , 3.64 ]\n"
     ]
    }
   ],
   "source": [
    "df_zambia_VERs_agw=pd.DataFrame(zambia_VERs_agw).transpose()\n",
    "print(df_zambia_VERs_agw.shape[1])\n",
    "zambia_i=0\n",
    "df_zambia_VERs_agw_final=pd.DataFrame(np.zeros((1, 10000)))\n",
    "while zambia_i<df_zambia_VERs_agw.shape[1]:\n",
    "    df_zambia_VERs_agw_final=df_zambia_VERs_agw_final+df_zambia_VERs_agw[zambia_i]\n",
    "    zambia_i=zambia_i+1\n",
    "\n",
    "zambia_ratio_analysis_project=df_zambia_VERs_agw_final/zambia_VERs\n",
    "zambia_ratio_analysis_project_mean=np.mean(df_zambia_VERs_agw_final, axis=1)/zambia_VERs\n",
    "zambia_ratio_project_analysis_mean=zambia_ratio_analysis_project_mean**-1\n",
    "\n",
    "zambia_sd_ratio_analysis_project=np.std(zambia_ratio_analysis_project, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "zambia_ratio_analysis_project_lower=zambia_ratio_analysis_project_mean - z*zambia_sd_ratio_analysis_project/np.sqrt(zambia_N)\n",
    "zambia_ratio_analysis_project_upper=zambia_ratio_analysis_project_mean + z*zambia_sd_ratio_analysis_project/np.sqrt(zambia_N)\n",
    "\n",
    "zambia_ratio_project_analysis_lower=zambia_ratio_analysis_project_lower**-1\n",
    "zambia_ratio_project_analysis_upper=zambia_ratio_analysis_project_upper**-1\n",
    "\n",
    "print(zambia_ratio_analysis_project_lower)\n",
    "print(zambia_ratio_analysis_project_mean)\n",
    "print(zambia_ratio_analysis_project_upper)\n",
    "print(zambia_ratio_project_analysis_lower)\n",
    "print(zambia_ratio_project_analysis_mean)\n",
    "print(zambia_ratio_project_analysis_upper)\n",
    "\n",
    "print(round(zambia_ratio_analysis_project_mean[0],2),'[',round(zambia_ratio_analysis_project_lower[0],2),',',round(zambia_ratio_analysis_project_upper[0],2),']')\n",
    "print(round(zambia_ratio_project_analysis_mean[0],2),'[',round(zambia_ratio_project_analysis_upper[0],2),',',round(zambia_ratio_project_analysis_lower[0],2),']')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "0    0.149561\n",
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      "0    0.151746\n",
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      "0    0.153931\n",
      "dtype: float64\n",
      "0    6.686243\n",
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      "0    6.589953\n",
      "dtype: float64\n",
      "0    6.496397\n",
      "dtype: float64\n",
      "0.1517461468 [ 0.1495608247 , 0.1539314689 ]\n",
      "6.5899531618 [ 6.4963974371 , 6.6862428832 ]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "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": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "import seaborn as sns"
   ]
  }
 ],
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