{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce3fd50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from itertools import takewhile\n",
    "Euro_to_Dollar = 1.24"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d18c339",
   "metadata": {},
   "source": [
    "# Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7de6003d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ec9e90ce",
   "metadata": {},
   "source": [
    "#### Tool in short\n",
    "Mathijs Harmsen - 5 June 2022\n",
    "(This tool and the assessment of these Non-CO2 Marginal Abatement Cost (MAC) curves will be described in an upcoming paper, likely Harmsen et al., 2022)\n",
    "\n",
    "This script can be used to generate \"optimistic\", default & \"pessimistic\" agriculture non-CO2 MAC curves with high, medium, low reduction potential, respectively. The MACs describe relative reductions (compared to global average values in 2015) at carbon equivalent prices up to 4000 $/tC (in 201 steps) for the 2020-2100 period. The MACs are built up from underlying paramaters that describe technical applicability of mitigation measures, reduction efficiencies, implementation barriers, level of overlap between measures, technological progress and costs. In a Monte Carlo analysis, the values for these factors are varied randomly resulting in different (1000x in the current setup) MAC profiles for the various emission sources. The final high, medium, low macs are based on the 5th, 50th and 95th percential for each time step. There are 5 agricultural sources (CH4: enteric fermentation, rice, manure. N2O: fertilizer, manure). In addition, the tool also generates variants of the enteric fermentation and fertilizer MACs with additional measures added (seaweed and biochar, respectively). These additional (promising, but not fully proven) technologies are included in the optimistic MACs. Note that the tool is not run automatically with FAIR/IMAGE. That's also not needed, since the datafiles that the tool generates are also used as input in FAIR. However, the tool can be run when you want to change or test the underlying MAC parameters. The MC routine includes a seed, meaning that the random values chosen will be saved (for 3 consecutive runs).\n",
    "The following paragraphs explain the structure of the code below.\n",
    "\n",
    "#### Constants\n",
    "This code starts with defining the constants used to calculate the MAC-curves. These constants are given in a certain order, initially, but this is not the order of implementation. This order of implementation is determined on the basis of the costs: the least costly measures are implemented first. This is done later in the code. \n",
    "The RE-input values are given and written in our initial order. The same goes for the technical applicability. Then, the delta values are given. Then, the implementation potential and technological progress values are given. \n",
    "Next, the order of implementation is calculated. This order is needed to calculate the correction for overlap values since these depend on previously implemented measures.\n",
    "The correction for overlap values are a bit more complicated. First, the values for overlap between each of the measures is given. For example: ni1 = {'bc': 0.50, 'irr': 1}, shows the overlap between nitrification inhibitors and biochar and irrigation practices. This is respectively 50% and 100%. The overlap between nitrification inhibitors and spreader maintenance can be found in the line above: spread_m1 = { 'ni': 0.7, 'bc': 1, 'irr': 1}. That overlap is 70%. As a second, we needed to specify for each measure which measures were already implemented. Note here that as you have different countries with different costs and therefore different order of implementation, you will have to specify this for each country that has different order of implementation. \n",
    "This was written down in an easier way with the order as described on top. This might sound confusing because the order of real implementation is used already to write down which measures were implemented before each measure. However, we use the order on top only so that the order is the same for the different variables and so that we can adjust all of them in an easy way later. \n",
    "The correction for overlap values were calculated by using the product of overlap with previously implemented measures, with a minimum value of 0.2\n",
    "As a last, the marginal costs were calculated. \n",
    "#### Making random values\n",
    "In this part, a definition is written to put the values for the variables in the right order for each country. Then, random values are calculated for each of the variables. \n",
    "Then, definitions are written to specify which RP values belong to which costs. This will be used later. \n",
    "Reduction potentials and costs\n",
    "In this part, definitions are written to calculate the cumulative reduction potentials and the costs. In these definitions the year and the country need to be specified.\n",
    "#### Run a 1000 times\n",
    "Definitions are written to calculate the list of RP values a 1000 times. These are still definitions and the country and year need to be specified. These definitions are used to calculate the list of RP values a 1000 times for each country and for the years 2020, 2050 and 2100. This is defined as step1_2020 etc. \n",
    "Calculate the mean, 95th percentile and 5th percentile\n",
    "The mean, 95th percentile and 5th percentile are calculated. Also, for each country and for each of these percentiles, the 2020, 2050 and 2100 values are put into one list. The values are divided by 100. Then, the time and x values are calculated. These are needed for the excel file.  \n",
    "#### Export to excel\n",
    "Here the data is exported to an excel file. Here the differentiation is made between the different countries. For N2O fertilizer, some countries have the first option and some the second. \n",
    "#### Plotting\n",
    "Here graphs are made. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22fd2059",
   "metadata": {},
   "source": [
    "# CH4 Enteric Fermentation without seaweed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4532c208",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29921f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial order of the mitigation measures in this code:\n",
    "# Nitrate, tannins, improved health, genetic selection, grain processing\n",
    "\n",
    "# countries: Canada\tUSA\tMexico\tCentral America\tBrazil\tRest of South-America;North Africa;West Africa\tEast Africa\tSouth Africa\n",
    "#West Europe\tCentral Europe\tTurkey\tUkraine\tKazachstan\tRussia\tMiddle East\tIndia\tKorea\tChina+\t\n",
    "#South East Asia\tIndonesia\tJapan\tOceania\tRest of South Asia\tRest of South Africa\t\n",
    "\n",
    "#RE input values: (This is three times the same, for each of the different TA groups)\n",
    "RE_EF = [[30,32,16,8,15,16,2,19,40,32,42,21,26],[32,32,10,17,11],[10,20,15,4,16,5,6],[32,32,10,17,11],[38,17,27,10]]*3\n",
    "\n",
    "#Costs (This is three times the same, for each of the different TA groups)\n",
    "costs_EF = [[107,15,0,0,50,214]]*3\n",
    "\n",
    "#Technical applicability:\n",
    "TAr = [20,25,25,25,25,25]; TAo = [50,55,55,55,55,55,55]; TAg = [70,75,75,75,75,75]\n",
    "#There are three different options for TA, based on different regions. List of which regions gets which TA:\n",
    "#TAnew = [TAg, TAg, TAg, TAg, TAg, TAg, TAr, TAr,TAr,TAr,TAg,TAg, TAr,TAr,TAr,TAo,TAr,TAr,TAr,TAo,TAr,TAr,TAg, TAg,TAr,TAr]\n",
    "TAnew = [TAg,TAo,TAr]\n",
    "\n",
    "#Delta values\n",
    "DeltaTA_EF = 40 #Maximum change in TA\n",
    "DeltaOV_EF = 30 #Maximum change in OV_corr\n",
    "DeltaMC_EF = 0.8 #Maximum change in costs\n",
    "DeltaIP_EF = 30 #Maximum change in IP\n",
    "DeltaTP_EF = 10 #Maximum change in TP\n",
    "\n",
    "#Implementation potential and technological progress values:\n",
    "IP_EF = [20,90,100]\n",
    "TP_EF = [100,90,80]\n",
    "\n",
    "#order\n",
    "order_EF = [[x for _, x in sorted(zip(i,range(0,len(costs_EF))))] for i in costs_EF]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b232d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The correction for overlap values change when changing the order of implementation measures. \n",
    "#The order of implementation measures changes when the costs are different for different countries. \n",
    "#With the costs written above, measures are implemented in only one way (ad,sd,sc,ma,rdp,hsb)\n",
    "#We make lists with the overlap values with the previous implemented measures now for this way:\n",
    "\n",
    "#First writing down the overlap values between measures\n",
    "nitrate = {'gs': 0.7, 'impr. h' : 0.7, 'tannins': 0.2, 'grain_processing': 0.2}\n",
    "tannins = {'gs': 0.7, 'impr. h' : 0.7, 'grain_processing': 0.2}\n",
    "improved_health = {'gs': 0.2, 'grain_processing': 0.7, 'impr. h': 1}\n",
    "genetic_selection = { 'grain_processing': 0.2}\n",
    "\n",
    "#Writing down for each measure which measures were already implemented\n",
    "Corr_o_EF = {'':['nitrate','tannins','impr. h' ,'gs','grain_processing'], \n",
    "          'nitrate': [nitrate['impr. h'], nitrate['gs'], nitrate['tannins'], nitrate['gs']], \n",
    "          'tannins': [tannins['impr. h'], tannins['gs']],\n",
    "          'impr. h' : [improved_health['impr. h']], \n",
    "          'genetic_selection': [improved_health['gs']],\n",
    "          'grain_processing': [ tannins['grain_processing'],improved_health['grain_processing'], genetic_selection['grain_processing']]}\n",
    "\n",
    "#Rewriting the lists in an easier way (order as described on top):\n",
    "OV_corr_EF = [Corr_o_EF['nitrate'],Corr_o_EF['tannins'],Corr_o_EF['impr. h'],Corr_o_EF['genetic_selection'],Corr_o_EF['grain_processing']]\n",
    "#Calculating the product of overlap of previously implemented measures:\n",
    "OV_EF = [np.fmax(0.2,np.product(i)) for i in OV_corr_EF]; \n",
    "#Multiply by 100 & having this list three times, because of the three TA groups\n",
    "OVcorr_EF = [[i*100 for i in OV_EF]]*3\n",
    "\n",
    "#Calculate the marginal costs:\n",
    "c_EF  = [[a*100/b for a,b in zip(i, j)] for i,j in zip(costs_EF,OVcorr_EF)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55ffed0",
   "metadata": {},
   "source": [
    "## Making random variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfb9e079",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "def generate_random(): #This function generates values for RE, TA, OV_corr, Marginal Costs, IP, and TP using uniform distributions \n",
    "    #RE values \n",
    "    #random.seed(3)\n",
    "    RE = [[RE_EF[i] for i in j] for j in order_EF]\n",
    "    RE_uniform = [[random.uniform(np.min(i), np.max(i))/100 for i in l] for l in RE] #Generating random value between de minimum and maximum of each of the measures\n",
    "    RE_uniform = {i:j for i,j in zip(range(1,len(costs_EF)+1),RE_uniform)} #Assigning country group to RE_uniform\n",
    "    \n",
    "    #TA values\n",
    "    a= [[[TAnew[q][i] for i in l] for l in order_EF] for q in range(0,len(costs_EF))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_EF))]\n",
    "    \n",
    "    TA_uniform = [[random.uniform(np.max([0,i-DeltaTA_EF]), np.min([100,i+DeltaTA_EF]))/100 for i in l]for l in a] #Generate values between TA-TA*delta and TA+TA*delta\n",
    "    TA_uniform = {i:j for i,j in zip(range(1,len(costs_EF)+1),TA_uniform)} #Assigning country group to TA_uniform\n",
    "\n",
    "    #OVcorr\n",
    "    a= [[[OVcorr_EF[q][i] for i in l] for l in order_EF] for q in range(0,len(costs_EF))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_EF))]\n",
    "    OV_corr_uniform = [[random.uniform(np.max([0,i-DeltaOV_EF]), np.min([100,i+DeltaOV_EF]))/100 for i in l]for l in a]  #Generate values between OV_corr-OV_corr*delta and OV_corr+OV_corr*delta\n",
    "    OV_corr_uniform = {i:j for i,j in zip(range(1,len(costs_EF)+1),OV_corr_uniform)} #Assigning country group to OV_corr_uniform\n",
    "\n",
    "    #costs \n",
    "    Euro_to_Dollar = 1.24\n",
    "    a= [[[c_EF[q][i] for i in l] for l in order_EF] for q in range(0,len(costs_EF))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_EF))]\n",
    "    MC_uniform = [[random.uniform(i-i*DeltaMC_EF,i+i*DeltaMC_EF)/100 for i in l] for l in a] #Generate values between marginal costs-marginal costs*delta and marginal costs+marginal costs*delta\n",
    "    MC_uniform = {i:j for i,j in zip(range(1,len(costs_EF)+1),MC_uniform)} #Assigning country group to costs\n",
    "    \n",
    "    #Implementation potential\n",
    "    IP = {2020: IP_EF[0], 2050: IP_EF[1], 2100:IP_EF[2]} #Values implementation potential\n",
    "    IP_uniform = {year: random.uniform(np.max([0,i-DeltaIP_EF]), np.min([100,i+DeltaIP_EF]))/100 for year,i in IP.items()} #Generate values between IP-IP*delta and IP+IP*delta\n",
    "\n",
    "    #Technological progress\n",
    "    TP_2020,TP_2050,TP_2100 = [TP_EF[0], TP_EF[1], TP_EF[2]]#Values technological progress\n",
    "    TP_uniform = {2020: TP_2020/100, 2050: random.uniform(np.max([0,TP_2050-DeltaTP_EF]), np.min([100,TP_2050+DeltaTP_EF]))/100, 2100: random.uniform(np.max([0,TP_2100-DeltaTP_EF]), np.min([100,TP_2100+DeltaTP_EF]))/100}\n",
    "    return RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4f0dc037",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wat is de index van het eerste getal in de lijst dat groter is dan ...\n",
    "#use these definitions to say, if costs are lower than this, than use this RP \n",
    "#This way you get a list of RPs for each costs in the list (0,4000 $/tCeq)\n",
    "def fa(l, ba): return len([x for x in takewhile(lambda x: x[1] < ba, enumerate(l))]) #<: gives the index of the first number that is smaller or equal to the number you give. <=: gives the first number that is smaller than the number you give\n",
    "\n",
    "def aut(een,twee,drie): #een: long list with costs; twee: corrected marginal costs; drie: RP \n",
    "    z = [fa(twee,i) for i in een]\n",
    "    nu = []\n",
    "    [nu.append(drie[i]) for i in z]\n",
    "    return nu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e58f022",
   "metadata": {},
   "source": [
    "## Reduction potentials and costs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e84258fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# range in dollars in c eq or CO2 eq. C eq goes from 0 to 4000 with steps of 20. \n",
    "USD_tC = [*range(0, 4020, 20)]\n",
    "USD_tC = np.arange(0,4020,20)\n",
    "USD_tCO2eq = [i / 44*12 for i in USD_tC]\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs for 2020.\n",
    "#The country can be specified, year also, but this difinition only works for 2050 adn 2100. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values. \n",
    "\n",
    "def generate_f_tp(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    #calculate AP, which is the initial reduction potential\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    #calculate the inverse\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    #Calculate the cumulative reduction potential \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    #Calculate the cumulative costs\n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    #Make a list of reduction potentials that belong to each cost value in the list of 0 to 4000 ceq.\n",
    "    Average_without_tp = []\n",
    "    #Add the technological progress\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    Average_with_tp= [(Average_without_tp[151]+(j-USD_tC[151])*(((1-(1-RP[-1]/100)*TP_uniform[year])*100-Average_without_tp[151])/(USD_tC[200]-USD_tC[151]))) for j in USD_tC[152:]] #calculate the influence of techonological progress on RP. Is linearly implemented from 824 USD/tCO2 eq.\n",
    "    f = Average_without_tp[:152]+Average_with_tp\n",
    "    return f\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs.\n",
    "#The country can be specified, year also, this definition works for 2020. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values and you can choose the year and country.\n",
    "def generate_f(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    return Average_without_tp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4516c834",
   "metadata": {},
   "source": [
    "## Run a 1000 times "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7ebe08a",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#definitions to generate the list of RP values a 1000 times:\n",
    "def k(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "def ktp(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f_tp(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "#Generate the list of RP values a 1000 times for each country:\n",
    "random.seed(3) \n",
    "step1_2020= [k(2020,i) for i in range(1,len(costs_EF)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2050= [ktp(2050,i) for i in range(1,len(costs_EF)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2100= [ktp(2100,i) for i in range(1,len(costs_EF)+1)] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01076eb1",
   "metadata": {},
   "source": [
    "## Calculate the mean, 95th percentile, 5th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0db02191",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#Calculate the mean of the 1000 runs for each different country for 2020,2050,2100\n",
    "mean_2020 = [step1_2020[i].mean(axis=0) for i in range(0,len(costs_EF))]\n",
    "mean_2050 = [step1_2050[i].mean(axis=0) for i in range(0,len(costs_EF))]\n",
    "mean_2100 = [step1_2100[i].mean(axis=0) for i in range(0,len(costs_EF))]\n",
    "\n",
    "#Calculate the 95th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile95_2020 = [np.percentile(step1_2020[i],95,axis=0) for i in range(0,len(costs_EF))]\n",
    "percentile95_2050 = [np.percentile(step1_2050[i],95,axis=0) for i in range(0,len(costs_EF))]\n",
    "percentile95_2100 = [np.percentile(step1_2100[i],95,axis=0) for i in range(0,len(costs_EF))]\n",
    "\n",
    "#Calculate the 5th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile5_2020 = [np.percentile(step1_2020[i],5,axis=0) for i in range(0,len(costs_EF))]\n",
    "percentile5_2050 = [np.percentile(step1_2050[i],5,axis=0) for i in range(0,len(costs_EF))]\n",
    "percentile5_2100 = [np.percentile(step1_2100[i],5,axis=0) for i in range(0,len(costs_EF))]\n",
    "\n",
    "#For each country, put 2020, 2050, and 2100 in one list\n",
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(costs_EF)) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(costs_EF)) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(costs_EF)) ]\n",
    "\n",
    "#plt.plot(range(0,603), together_mean[0])\n",
    "#plt.plot(range(0,603), together_95[0])\n",
    "#plt.plot(range(0,603), together_5[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95aa26ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "ml1 = together_mean[0] \n",
    "ml95_1 = together_95[0] \n",
    "ml5_1 = together_5[0] \n",
    "#time and x are needed for the excel file. time shows the year for each value in the lists.\n",
    "x = np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist()\n",
    "time = [2020] * 201 + [2050] * 201 + [2100] * 201"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "62d70434",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import xlsxwriter\n",
    "#definition.variable.to_excel(\"test.xlsx\") \n",
    "tm = together_mean\n",
    "t9 = together_95\n",
    "t5 = together_5\n",
    "\n",
    "tm = [[i/100 for i in l]for l in tm]\n",
    "t9 = [[i/100 for i in l]for l in t9]\n",
    "t5 = [[i/100 for i in l]for l in t5] \n",
    "##TAnew = [TAg, TAg, TAg, TAg, TAg, TAg, TAr, TAr,TAr,TAr,TAg,TAg, TAr,TAr,TAr,TAo,TAr,TAr,TAr,TAo,TAr,TAr,TAg, TAg,TAr,TAr]\n",
    "\n",
    "random.seed(3) #Use this to have the same outcome every time !!\n",
    "writer = pd.ExcelWriter('EFminussw_16_5_2022_.xlsx') #Change the date if you want to!\n",
    "df = DataFrame({'t': time, 'DIM_1': x, 'class_1' : tm[0], 'class_2' : tm[0], 'class_3' : tm[0], 'class_4' : tm[0], \n",
    "                'class_5' : tm[0], 'class_6' : tm[0], 'class_7' : tm[2], 'class_8' : tm[2], \n",
    "                'class_9' : tm[2], 'class_10' : tm[2], 'class_11' : tm[0], 'class_12' : tm[0], \n",
    "                'class_13' : tm[2], 'class_14' : tm[2], 'class_15' : tm[2], 'class_16' : tm[1], \n",
    "                'class_17' : tm[2], 'class_18' : tm[2], 'class_19' : tm[2], 'class_20' : tm[1], \n",
    "                'class_21' : tm[2], 'class_22' : tm[2], 'class_23' : tm[0], 'class_24' : tm[0], \n",
    "                'class_25' : tm[2], 'class_26' : tm[2]})\n",
    "df2 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : t9[0], 'class_2' : t9[0], 'class_3' : t9[0], 'class_4' : t9[0], \n",
    "                'class_5' : t9[0], 'class_6' : t9[0], 'class_7' : t9[2], 'class_8' : t9[2], \n",
    "                'class_9' : t9[2], 'class_10' : t9[2], 'class_11' : t9[0], 'class_12' : t9[0], \n",
    "                'class_13' : t9[2], 'class_14' : t9[2], 'class_15' : t9[2], 'class_16' : t9[1], \n",
    "                'class_17' : t9[2], 'class_18' : t9[2], 'class_19' : t9[2], 'class_20' : t9[1], \n",
    "                'class_21' : t9[2], 'class_22' : t9[2], 'class_23' : t9[0], 'class_24' : t9[0], \n",
    "                'class_25' : t9[2], 'class_26' : t9[2]})\n",
    "df3 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : t5[0], 'class_2' : t5[0], 'class_3' : t5[0], 'class_4' : t5[0], \n",
    "                'class_5' : t5[0], 'class_6' : t5[0], 'class_7' : t5[2], 'class_8' : t5[2], \n",
    "                'class_9' : t5[2], 'class_10' : t5[2], 'class_11' : t5[0], 'class_12' : t5[0], \n",
    "                'class_13' : t5[2], 'class_14' : t5[2], 'class_15' : t5[2], 'class_16' : t5[1], \n",
    "                'class_17' : t5[2], 'class_18' : t5[2], 'class_19' : t5[2], 'class_20' : t5[1], \n",
    "                'class_21' : t5[2], 'class_22' : t5[2], 'class_23' : t5[0], 'class_24' : t5[0], \n",
    "                'class_25' : t5[2], 'class_26' : t5[2]})\n",
    "df.to_excel(writer, sheet_name='EFminusSWmean', index=False)\n",
    "df2.to_excel(writer, sheet_name='EFminusSW95', index=False)\n",
    "df3.to_excel(writer, sheet_name='EFminusSW5', index=False)\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f13deb10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "#plt.plot(USD_tCO2eq,mean_2100[0])\n",
    "for i,j,k,l,m,n,o,p,q,r,s,t in zip(mean_2020,mean_2050,mean_2100, np.arange(1,19,3),np.arange(2,20,3),np.arange(3,21,3),percentile95_2020,percentile95_2050,percentile95_2100,percentile5_2020,percentile5_2050,percentile5_2100):\n",
    "    plt.subplot(6,3,l)\n",
    "    plt.title('2020')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,i)\n",
    "    plt.plot(USD_tCO2eq,o)\n",
    "    plt.plot(USD_tCO2eq,r)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,m)\n",
    "    plt.title('2050')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,j)\n",
    "    plt.plot(USD_tCO2eq,p)\n",
    "    plt.plot(USD_tCO2eq,s)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,n)\n",
    "    plt.title('2100')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,k)\n",
    "    plt.plot(USD_tCO2eq,q)\n",
    "    plt.ylim(0,100)\n",
    "    plt.plot(USD_tCO2eq,t)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6e5013",
   "metadata": {},
   "source": [
    "# CH4 Enteric Fermentation including seaweed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d59ad3",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ce72a972",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial order of the mitigation measures in this code:\n",
    "# Nitrate, tannins, improved health, genetic selection, grain processing, seaweed\n",
    "\n",
    "\n",
    "# countries: Canada\tUSA\tMexico\tCentral America\tBrazil\tRest of South-America;North Africa;West Africa\tEast Africa\tSouth Africa\n",
    "#West Europe\tCentral Europe\tTurkey\tUkraine\tKazachstan\tRussia\tMiddle East\tIndia\tKorea\tChina+\t\n",
    "#South East Asia\tIndonesia\tJapan\tOceania\tRest of South Asia\tRest of South Africa\t\n",
    "\n",
    "#RE input values: (This is three times the same, for each of the different TA groups)\n",
    "RE_EF = [[30,32,16,8,15,16,2,19,40,32,42,21,26],[32,32,10,17,11],[10,20,15,4,16,5,6],[32,32,10,17,11],[38,17,27,10],[65,74,50,62,81,85,99,67,98]]*3\n",
    "\n",
    "#Costs (This is three times the same, for each of the different TA groups)\n",
    "costs_EF = [[107,15,0,0,50,214]]*3\n",
    "\n",
    "#Technical applicability:\n",
    "TAr = [20,25,25,25,25,25]; TAo = [50,55,55,55,55,55,55]; TAg = [70,75,75,75,75,75]\n",
    "\n",
    "#There are three different options for TA, based on different regions. List of which regions gets which TA:\n",
    "#TAnew = [TAg, TAg, TAg, TAg, TAg, TAg, TAr, TAr,TAr,TAr,TAg,TAg, TAr,TAr,TAr,TAo,TAr,TAr,TAr,TAo,TAr,TAr,TAg, TAg,TAr,TAr]\n",
    "TAnew = [TAg,TAo,TAr]\n",
    "\n",
    "#Delta values\n",
    "DeltaTA_EF = 40 #Maximum change in TA\n",
    "DeltaOV_EF = 30 #Maximum change in OV_corr\n",
    "DeltaMC_EF = 0.8 #Maximum change in costs\n",
    "DeltaIP_EF = 30 #Maximum change in IP\n",
    "DeltaTP_EF = 10 #Maximum change in TP\n",
    "\n",
    "#Implementation potential and technological progress values:\n",
    "IP_EF = [20,90,100]\n",
    "TP_EF = [100,90,80]\n",
    "\n",
    "#order\n",
    "order_EF = [[x for _, x in sorted(zip(i,range(0,len(costs_EF))))] for i in costs_EF]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fef531cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The correction for overlap values change when changing the order of implementation measures. \n",
    "#The order of implementation measures changes when the costs are different for different countries. \n",
    "#With the costs written above, measures are implemented in only one way (ad,sd,sc,ma,rdp,hsb)\n",
    "#We make lists with the overlap values with the previous implemented measures now for this way:\n",
    "\n",
    "#First writing down the overlap values between measures\n",
    "seaweed = {'gs': 1, 'impr. h' : 1, 'tannins': 0.7, 'grain_processing': 1}\n",
    "nitrate = {'gs': 0.7, 'impr. h' : 0.7, 'tannins': 0.2, 'grain_processing': 0.2, 'seaweed': 0.7}\n",
    "tannins = {'gs': 0.7, 'impr. h' : 0.7, 'grain_processing': 0.2}\n",
    "improved_health = {'gs': 0.2, 'grain_processing': 0.7, 'impr. h': 1}\n",
    "genetic_selection = { 'grain_processing': 0.2}\n",
    "\n",
    "#Writing down for each measure which measures were already implemented\n",
    "Corr_o_EF = {'':['nitrate','tannins','impr. h' ,'gs','grain_processing', 'seaweed'], \n",
    "          'nitrate': [nitrate['impr. h'], nitrate['gs'], nitrate['tannins'], nitrate['gs']], \n",
    "          'tannins': [tannins['impr. h'], tannins['gs']],\n",
    "          'impr. h' : [improved_health['impr. h']], \n",
    "          'genetic_selection': [improved_health['gs']],\n",
    "          'grain_processing': [ tannins['grain_processing'],improved_health['grain_processing'], genetic_selection['grain_processing']],\n",
    "         'seaweed': [nitrate['seaweed'], seaweed['gs'],seaweed['impr. h'], seaweed['tannins'], seaweed['grain_processing']]}\n",
    "\n",
    "#Rewriting the lists in an easier way (order as described on top):\n",
    "OV_corr_EF = [Corr_o_EF['nitrate'],Corr_o_EF['tannins'],Corr_o_EF['impr. h'],Corr_o_EF['genetic_selection'],Corr_o_EF['grain_processing'],Corr_o_EF['seaweed']]\n",
    "#Calculating the product of overlap of previously implemented measures:\n",
    "OV_EF = [np.fmax(0.2,np.product(i)) for i in OV_corr_EF]; \n",
    "#Multiply by 100 & having this list three times, because of the three TA groups\n",
    "OVcorr_EF = [[i*100 for i in OV_EF]]*3\n",
    "\n",
    "#Calculate the marginal costs:\n",
    "c_EF  = [[a*100/b for a,b in zip(i, j)] for i,j in zip(costs_EF,OVcorr_EF)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ccad4ae",
   "metadata": {},
   "source": [
    "## Making random variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "46b68799",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "def generate_random(): #This function generates values for RE, TA, OV_corr, Marginal Costs, IP, and TP using uniform distributions \n",
    "    #RE values \n",
    "    #random.seed(3)\n",
    "    RE = [[RE_EF[i] for i in j] for j in order_EF]\n",
    "    RE_uniform = [[random.uniform(np.min(i), np.max(i))/100 for i in l] for l in RE] #Generating random value between de minimum and maximum of each of the measures\n",
    "    RE_uniform = {i:j for i,j in zip(range(1,len(costs_EF)+1),RE_uniform)} #Assigning country group to RE_uniform\n",
    "    \n",
    "    #TA values\n",
    "    a= [[[TAnew[q][i] for i in l] for l in order_EF] for q in range(0,len(costs_EF))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_EF))]\n",
    "    \n",
    "    TA_uniform = [[random.uniform(np.max([0,i-DeltaTA_EF]), np.min([100,i+DeltaTA_EF]))/100 for i in l]for l in a] #Generate values between TA-TA*delta and TA+TA*delta\n",
    "    TA_uniform = {i:j for i,j in zip(range(1,len(costs_EF)+1),TA_uniform)} #Assigning country group to TA_uniform\n",
    "\n",
    "    #OVcorr\n",
    "    a= [[[OVcorr_EF[q][i] for i in l] for l in order_EF] for q in range(0,len(costs_EF))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_EF))]\n",
    "    OV_corr_uniform = [[random.uniform(np.max([0,i-DeltaOV_EF]), np.min([100,i+DeltaOV_EF]))/100 for i in l]for l in a]  #Generate values between OV_corr-OV_corr*delta and OV_corr+OV_corr*delta\n",
    "    OV_corr_uniform = {i:j for i,j in zip(range(1,len(costs_EF)+1),OV_corr_uniform)} #Assigning country group to OV_corr_uniform\n",
    "\n",
    "    #costs \n",
    "    Euro_to_Dollar = 1.24\n",
    "    a= [[[c_EF[q][i] for i in l] for l in order_EF] for q in range(0,len(costs_EF))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_EF))]\n",
    "    MC_uniform = [[random.uniform(i-i*DeltaMC_EF,i+i*DeltaMC_EF)/100 for i in l] for l in a] #Generate values between marginal costs-marginal costs*delta and marginal costs+marginal costs*delta\n",
    "    MC_uniform = {i:j for i,j in zip(range(1,len(costs_EF)+1),MC_uniform)} #Assigning country group to costs\n",
    "    \n",
    "    #Implementation potential\n",
    "    IP = {2020: IP_EF[0], 2050: IP_EF[1], 2100:IP_EF[2]} #Values implementation potential\n",
    "    IP_uniform = {year: random.uniform(np.max([0,i-DeltaIP_EF]), np.min([100,i+DeltaIP_EF]))/100 for year,i in IP.items()} #Generate values between IP-IP*delta and IP+IP*delta\n",
    "\n",
    "    #Technological progress\n",
    "    TP_2020,TP_2050,TP_2100 = [TP_EF[0], TP_EF[1], TP_EF[2]]#Values technological progress\n",
    "    TP_uniform = {2020: TP_2020/100, 2050: random.uniform(np.max([0,TP_2050-DeltaTP_EF]), np.min([100,TP_2050+DeltaTP_EF]))/100, 2100: random.uniform(np.max([0,TP_2100-DeltaTP_EF]), np.min([100,TP_2100+DeltaTP_EF]))/100}\n",
    "    return RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dc992c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wat is de index van het eerste getal in de lijst dat groter is dan ...\n",
    "#use these definitions to say, if costs are lower than this, than use this RP \n",
    "#This way you get a list of RPs for each costs in the list (0,4000 $/tCeq)\n",
    "def fa(l, ba): return len([x for x in takewhile(lambda x: x[1] < ba, enumerate(l))]) #<: gives the index of the first number that is smaller or equal to the number you give. <=: gives the first number that is smaller than the number you give\n",
    "\n",
    "def aut(een,twee,drie): #een: long list with costs; twee: corrected marginal costs; drie: RP \n",
    "    z = [fa(twee,i) for i in een]\n",
    "    nu = []\n",
    "    [nu.append(drie[i]) for i in z]\n",
    "    return nu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6404d18e",
   "metadata": {},
   "source": [
    "## Reduction potentials and costs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5e20bd03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# range in dollars in c eq or CO2 eq. C eq goes from 0 to 4000 with steps of 20. \n",
    "USD_tC = [*range(0, 4020, 20)]\n",
    "USD_tC = np.arange(0,4020,20)\n",
    "USD_tCO2eq = [i / 44*12 for i in USD_tC]\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs for 2020.\n",
    "#The country can be specified, year also, but this difinition only works for 2050 adn 2100. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values. \n",
    "\n",
    "\n",
    "def generate_f_tp(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    #calculate AP, which is the initial reduction potential\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    #calculate the inverse\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    #Calculate the cumulative reduction potential \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    #Calculate the cumulative costs\n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    #Make a list of reduction potentials that belong to each cost value in the list of 0 to 4000 ceq.\n",
    "    Average_without_tp = []\n",
    "    #Add the technological progress\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    Average_with_tp= [(Average_without_tp[151]+(j-USD_tC[151])*(((1-(1-RP[-1]/100)*TP_uniform[year])*100-Average_without_tp[151])/(USD_tC[200]-USD_tC[151]))) for j in USD_tC[152:]] #calculate the influence of techonological progress on RP. Is linearly implemented from 824 USD/tCO2 eq.\n",
    "    f = Average_without_tp[:152]+Average_with_tp\n",
    "    return f\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs.\n",
    "#The country can be specified, year also, this definition works for 2020. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values and you can choose the year and country.\n",
    "def generate_f(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    return Average_without_tp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7029cbd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#definitions to generate the list of RP values a 1000 times:\n",
    "def k(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "def ktp(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f_tp(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "random.seed(3) \n",
    "step1_2020= [k(2020,i) for i in range(1,len(costs_EF)+1)] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dbba731b",
   "metadata": {},
   "outputs": [],
   "source": [
    "step1_2050= [ktp(2050,i) for i in range(1,len(costs_EF)+1)] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "76f4eab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "step1_2100= [ktp(2100,i) for i in range(1,len(costs_EF)+1)] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61414a64",
   "metadata": {},
   "source": [
    "## Calculate the mean, 95th percentile, 5th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "65ed2171",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#Calculate the mean of the 1000 runs for each different country for 2020,2050,2100\n",
    "mean_2020 = [step1_2020[i].mean(axis=0) for i in range(0,len(costs_EF))]\n",
    "mean_2050 = [step1_2050[i].mean(axis=0) for i in range(0,len(costs_EF))]\n",
    "mean_2100 = [step1_2100[i].mean(axis=0) for i in range(0,len(costs_EF))]\n",
    "\n",
    "#Calculate the 95th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile95_2020 = [np.percentile(step1_2020[i],95,axis=0) for i in range(0,len(costs_EF))]\n",
    "percentile95_2050 = [np.percentile(step1_2050[i],95,axis=0) for i in range(0,len(costs_EF))]\n",
    "percentile95_2100 = [np.percentile(step1_2100[i],95,axis=0) for i in range(0,len(costs_EF))]\n",
    "\n",
    "#Calculate the 5th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile5_2020 = [np.percentile(step1_2020[i],5,axis=0) for i in range(0,len(costs_EF))]\n",
    "percentile5_2050 = [np.percentile(step1_2050[i],5,axis=0) for i in range(0,len(costs_EF))]\n",
    "percentile5_2100 = [np.percentile(step1_2100[i],5,axis=0) for i in range(0,len(costs_EF))]\n",
    "\n",
    "#For each country, put 2020, 2050, and 2100 in one list\n",
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(costs_EF)) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(costs_EF)) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(costs_EF)) ]\n",
    "\n",
    "#plt.plot(range(0,603), together_mean[0])\n",
    "#plt.plot(range(0,603), together_95[0])\n",
    "#plt.plot(range(0,603), together_5[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4fc1e85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#time and x are needed for the excel file. time shows the year for each value in the lists.\n",
    "x = np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist()\n",
    "time = [2020] * 201 + [2050] * 201 + [2100] * 201\n",
    "\n",
    "tm = together_mean\n",
    "t9 = together_95\n",
    "t5 = together_5\n",
    "\n",
    "tm = [[i/100 for i in l]for l in tm]\n",
    "t9 = [[i/100 for i in l]for l in t9]\n",
    "t5 = [[i/100 for i in l]for l in t5] \n",
    "#TAnew = [TAg, TAg, TAg, TAg, TAg, TAg, TAr, TAr,TAr,TAr,TAg,TAg, TAr,TAr,TAr,TAo,TAr,TAr,TAr,TAo,TAr,TAr,TAg, TAg,TAr,TAr]\n",
    "\n",
    "random.seed(3) #Use this to have the same outcome every time !!\n",
    "writer = pd.ExcelWriter('EFplussw_28_3_2022.xlsx')\n",
    "df = DataFrame({'t': time, 'DIM_1': x, 'class_1' : tm[0], 'class_2' : tm[0], 'class_3' : tm[0], 'class_4' : tm[0], \n",
    "                'class_5' : tm[0], 'class_6' : tm[0], 'class_7' : tm[2], 'class_8' : tm[2], \n",
    "                'class_9' : tm[2], 'class_10' : tm[2], 'class_11' : tm[0], 'class_12' : tm[0], \n",
    "                'class_13' : tm[2], 'class_14' : tm[2], 'class_15' : tm[2], 'class_16' : tm[1], \n",
    "                'class_17' : tm[2], 'class_18' : tm[2], 'class_19' : tm[2], 'class_20' : tm[1], \n",
    "                'class_21' : tm[2], 'class_22' : tm[2], 'class_23' : tm[0], 'class_24' : tm[0], \n",
    "                'class_25' : tm[2], 'class_26' : tm[2]})\n",
    "df2 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : t9[0], 'class_2' : t9[0], 'class_3' : t9[0], 'class_4' : t9[0], \n",
    "                'class_5' : t9[0], 'class_6' : t9[0], 'class_7' : t9[2], 'class_8' : t9[2], \n",
    "                'class_9' : t9[2], 'class_10' : t9[2], 'class_11' : t9[0], 'class_12' : t9[0], \n",
    "                'class_13' : t9[2], 'class_14' : t9[2], 'class_15' : t9[2], 'class_16' : t9[1], \n",
    "                'class_17' : t9[2], 'class_18' : t9[2], 'class_19' : t9[2], 'class_20' : t9[1], \n",
    "                'class_21' : t9[2], 'class_22' : t9[2], 'class_23' : t9[0], 'class_24' : t9[0], \n",
    "                'class_25' : t9[2], 'class_26' : t9[2]})\n",
    "df3 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : t5[0], 'class_2' : t5[0], 'class_3' : t5[0], 'class_4' : t5[0], \n",
    "                'class_5' : t5[0], 'class_6' : t5[0], 'class_7' : t5[2], 'class_8' : t5[2], \n",
    "                'class_9' : t5[2], 'class_10' : t5[2], 'class_11' : t5[0], 'class_12' : t5[0], \n",
    "                'class_13' : t5[2], 'class_14' : t5[2], 'class_15' : t5[2], 'class_16' : t5[1], \n",
    "                'class_17' : t5[2], 'class_18' : t5[2], 'class_19' : t5[2], 'class_20' : t5[1], \n",
    "                'class_21' : t5[2], 'class_22' : t5[2], 'class_23' : t5[0], 'class_24' : t5[0], \n",
    "                'class_25' : t5[2], 'class_26' : t5[2]})\n",
    "df.to_excel(writer, sheet_name='EFplusSWmean', index=False)\n",
    "df2.to_excel(writer, sheet_name='EFplusSW95', index=False)\n",
    "df3.to_excel(writer, sheet_name='EFplusSW5', index=False)\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4f48566d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "#plt.plot(USD_tCO2eq,mean_2100[0])\n",
    "for i,j,k,l,m,n,o,p,q,r,s,t in zip(mean_2020,mean_2050,mean_2100, np.arange(1,19,3),np.arange(2,20,3),np.arange(3,21,3),percentile95_2020,percentile95_2050,percentile95_2100,percentile5_2020,percentile5_2050,percentile5_2100):\n",
    "    plt.subplot(6,3,l)\n",
    "    plt.title('2020')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,i)\n",
    "    plt.plot(USD_tCO2eq,o)\n",
    "    plt.plot(USD_tCO2eq,r)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,m)\n",
    "    plt.title('2050')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,j)\n",
    "    plt.plot(USD_tCO2eq,p)\n",
    "    plt.plot(USD_tCO2eq,s)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,n)\n",
    "    plt.title('2100')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,k)\n",
    "    plt.plot(USD_tCO2eq,q)\n",
    "    plt.ylim(0,100)\n",
    "    plt.plot(USD_tCO2eq,t)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1003556",
   "metadata": {},
   "source": [
    "# N2O fertilizer without biochar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c355f62",
   "metadata": {},
   "source": [
    "## Constants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "47e47878",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 6, 2, 4, 0, 5, 3], [1, 6, 2, 0, 4, 5, 3]]\n"
     ]
    }
   ],
   "source": [
    "# Initial order of the mitigation measures in this code:\n",
    "# 1. nitrification inhibitor, 2. improved land manure, 3. improved agronomy\n",
    "# 4. irrigation practices, 5. spreader maintenance, 6. biochar, 7. no tillage\n",
    "\n",
    "#For fertilizer, two regions are used. \n",
    "#1 country group numbers: 1 = East Eur, Japan, USA&Canada, OECD Eur & OC, 2 = East Asia, Africa, South and Central America, South, South east Asia\n",
    "\n",
    "#RE input values:\n",
    "RE_fertilizer = {'nitr_i': [34,30,38,60.10,30,36,42.30,40,40,16.90,44,52,50,50,41,17,39.8,50], 'impr. l.m.': [5,5,27,5,5,50,30.5,7,7,30,35], \n",
    "        'impr. agronomy': [24,14.29,35,54,18.6,20,20,15.13], 'irr.p': [55,67,32,46,52,15], 'spr m': [26,22,42,21.50], 'bioc': [38,27,32,14,35], 'no tillage': [30,48,45,27,25]}\n",
    "#RE values in the order written above:\n",
    "RE_fertilizer = [RE_fertilizer['nitr_i'],RE_fertilizer['impr. l.m.'],RE_fertilizer['impr. agronomy'],RE_fertilizer['irr.p'],RE_fertilizer['spr m'],RE_fertilizer['bioc'],RE_fertilizer['no tillage']]\n",
    "\n",
    "#Costs\n",
    "costs_fertilizer_1 = {'nitr_i': 177, 'impr. l.m.': 0, 'impr. agronomy': 4.2, 'irr.p': 400, 'spr m': 49.2, \n",
    "                    'bioc': 220, 'no tillage': 0}\n",
    "costs_fertilizer_2 = {'nitr_i': 32, 'impr. l.m.': 0, 'impr. agronomy': 4.2, 'irr.p': 400, 'spr m': 49.2, \n",
    "                    'bioc': 220, 'no tillage': 0}\n",
    "\n",
    "costs_fertilizer = [[costs_fertilizer_1['nitr_i'],costs_fertilizer_1['impr. l.m.'],costs_fertilizer_1['impr. agronomy'],\n",
    "                     costs_fertilizer_1['irr.p'],costs_fertilizer_1['spr m'],costs_fertilizer_1['bioc'],\n",
    "                     costs_fertilizer_1['no tillage']],[costs_fertilizer_2['nitr_i'],costs_fertilizer_2['impr. l.m.'],\n",
    "                    costs_fertilizer_2['impr. agronomy'],costs_fertilizer_2['irr.p'],costs_fertilizer_2['spr m'],\n",
    "                    costs_fertilizer_2['bioc'],costs_fertilizer_2['no tillage']]]\n",
    "\n",
    "#Technical applicability:\n",
    "TA_fertilizer = {'nitr_i': 100, 'impr. l.m.': 45, 'impr. agronomy': 45, 'irr.p': 63, 'spr m': 100, 'bioc': 80, 'no tillage': 63}\n",
    "\n",
    "#Technical applicability in the order as written above\n",
    "TA_fertilizer = [TA_fertilizer['nitr_i'],TA_fertilizer['impr. l.m.'],TA_fertilizer['impr. agronomy'],TA_fertilizer['irr.p'],TA_fertilizer['spr m'],TA_fertilizer['bioc'],TA_fertilizer['no tillage']]\n",
    "\n",
    "#Delta values\n",
    "DeltaTA_fertilizer = 40 #Maximum change in TA %.\n",
    "DeltaOV_fertilizer = 30 #Maximum change in OV_corr\n",
    "DeltaMC_fertilizer = 0.8 #Maximum change in costs\n",
    "DeltaIP_fertilizer = 30 #Maximum change in IP\n",
    "DeltaTP_fertilizer = 10 #Maximum change in TP\n",
    "\n",
    "#Implementation potential and technological progress values:\n",
    "IP_fertilizer = [10,70,100]\n",
    "TP_fertilizer = [100,90,80]\n",
    "\n",
    "#Calculate in which order the measures should be implemented compared to our initial order\n",
    "#This is based on the initial costs and is calculated for each region:\n",
    "order_fertilizer = [[x for _, x in sorted(zip(i,range(0,len(costs_fertilizer[0])+1)))] for i in costs_fertilizer]\n",
    "print(order_fertilizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6632c3c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The correction for overlap values change when changing the order of implementation measures. \n",
    "#The order of implementation measures changes when the costs are different for different countries. \n",
    "#With the costs written above, measures are implemented in only one way (ad,sd,sc,ma,rdp,hsb)\n",
    "#We make lists with the overlap values with the previous implemented measures now for this way:\n",
    "\n",
    "#First writing down the overlap values between measures\n",
    "no_tillage1 = {'ilm': 0.7, 'ia': 0.5, 'sm': 1, 'ni': 1, 'bc': 1, 'irr': 0.5, 'nt': 1}\n",
    "impr_landmanure1 = {'ia': 1, 'sm': 1, 'ni': 1, 'bc': 0.7, 'irr': 0.7}\n",
    "impr_agr1 = {'sm': 0.70, 'ni': 0.7, 'bc': 1, 'irr': 0.7}\n",
    "spread_m1 = { 'ni': 0.7, 'bc': 1, 'irr': 1}\n",
    "ni1 = {'bc': 0.50, 'irr': 1}\n",
    "b1c = {'irr': 0.7}\n",
    "\n",
    "#Writing down for each measure which measures were already implemented\n",
    "Corr_o = {'':['nitr_i','impr. l.m.','impr. agronomy','irr.p','spr m','bioc','no tillage'], \n",
    "          'nitr_i': [no_tillage1['ni'], impr_landmanure1['ni'], impr_agr1['ni'], spread_m1['ni']], \n",
    "          'impr. l.m.': [no_tillage1['ilm']],\n",
    "          'impr. agronomy': [no_tillage1['ia'], impr_landmanure1['ia']], \n",
    "          'irr.p': [no_tillage1['irr'], impr_landmanure1['irr'], impr_agr1['irr'], spread_m1['irr'], ni1['irr'], b1c['irr']],\n",
    "          'spr m': [no_tillage1['sm'], impr_landmanure1['sm'],impr_agr1['sm']], \n",
    "          'bioc': [no_tillage1['bc'], impr_landmanure1['bc'], impr_agr1['bc'], spread_m1['bc'], ni1['bc']], \n",
    "          'no tillage': [no_tillage1['nt']]}\n",
    "\n",
    "Corr_o2 = {'':['nitr_i','impr. l.m.','impr. agronomy','irr.p','spr m','bioc','no tillage'], \n",
    "          'nitr_i': [no_tillage1['ni'], impr_landmanure1['ni'], impr_agr1['ni']], \n",
    "          'impr. l.m.': [no_tillage1['ilm']],\n",
    "          'impr. agronomy': [no_tillage1['ia'], impr_landmanure1['ia']], \n",
    "          'irr.p': [no_tillage1['irr'], impr_landmanure1['irr'], impr_agr1['irr'], spread_m1['irr'], ni1['irr'], b1c['irr']],\n",
    "          'spr m': [no_tillage1['sm'], impr_landmanure1['sm'],impr_agr1['sm'], spread_m1['ni']], \n",
    "          'bioc': [no_tillage1['bc'], impr_landmanure1['bc'], impr_agr1['bc'], spread_m1['bc'], ni1['bc']], \n",
    "          'no tillage': [no_tillage1['nt']]}\n",
    "\n",
    "#Rewriting the lists in an easier way (order as described on top):\n",
    "OV_corr = [[Corr_o['nitr_i'],Corr_o['impr. l.m.'],Corr_o['impr. agronomy'],Corr_o['irr.p'],Corr_o['spr m'],Corr_o['bioc'],\n",
    "            Corr_o['no tillage']],[Corr_o2['nitr_i'],Corr_o2['impr. l.m.'],Corr_o2['impr. agronomy'],Corr_o2['irr.p'],\n",
    "                                   Corr_o2['spr m'],Corr_o2['bioc'],Corr_o2['no tillage']]]\n",
    "#Calculating the product of overlap of previously implemented measures:\n",
    "OV_corr = [[np.fmax(0.2,np.product(i)) for i in l] for l in OV_corr]\n",
    "#Multiply by 100:\n",
    "OV_corr_fertilizer = [[i*100 for i in l] for l in OV_corr]\n",
    "\n",
    "#Calculate the marginal costs:\n",
    "c_fertilizer  = [[a*100/b for a,b in zip(i, j)] for i,j in zip(costs_fertilizer,OV_corr_fertilizer)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077ed4ab",
   "metadata": {},
   "source": [
    "## Making random variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "66fcb912",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "def generate_random(): #This function generates values for RE, TA, OV_corr, Marginal Costs, IP, and TP using uniform distributions \n",
    "    #RE values \n",
    "    #random.seed(3)\n",
    "    RE = [[RE_fertilizer[i] for i in j] for j in order_fertilizer]\n",
    "    RE_uniform = [[random.uniform(np.min(i), np.max(i))/100 for i in l] for l in RE] #Generating random value between de minimum and maximum of each of the measures\n",
    "    RE_uniform = {i:j for i,j in zip(range(1,len(costs_fertilizer)+1),RE_uniform)} #Assigning country group to RE_uniform\n",
    "    RE_uniform[1][4] = min(RE_uniform[1][4], 0.70)\n",
    "    RE_uniform[2][2] = min(RE_uniform[2][2], 0.70)\n",
    "    #TA values\n",
    "    TA = [[TA_fertilizer[i] for i in j] for j in order_fertilizer] #TA input values for the measures: storage duration, anaerobic digestion, storage covering, manure acidification, housing systems and beddings, solid liquid seperation\n",
    "    TA_uniform = [[random.uniform(np.max([0,i-DeltaTA_fertilizer]), np.min([100,i+DeltaTA_fertilizer]))/100 for i in l]for l in TA] #Generate values between TA-TA*delta and TA+TA*delta\n",
    "    TA_uniform = {i:j for i,j in zip(range(1,len(costs_fertilizer)+1),TA_uniform)} #Assigning country group to TA_uniform\n",
    "\n",
    "    #OVcorr\n",
    "    a= [[[OV_corr_fertilizer[q][i] for i in l] for l in order_fertilizer] for q in range(0,len(costs_fertilizer))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_fertilizer))]\n",
    "    OV_corr_uniform = [[random.uniform(np.max([0,i-DeltaOV_fertilizer]), np.min([100,i+DeltaOV_fertilizer]))/100 for i in l]for l in a]  #Generate values between OV_corr-OV_corr*delta and OV_corr+OV_corr*delta\n",
    "    OV_corr_uniform = {i:j for i,j in zip(range(1,len(costs_fertilizer)+1),OV_corr_uniform)} #Assigning country group to OV_corr_uniform\n",
    "\n",
    "    #costs \n",
    "    Euro_to_Dollar = 1.24\n",
    "    a= [[[c_fertilizer[q][i] for i in l] for l in order_fertilizer] for q in range(0,len(costs_fertilizer))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_fertilizer))]\n",
    "    MC_uniform = [[random.uniform(i-i*DeltaMC_fertilizer,i+i*DeltaMC_fertilizer)/100 for i in l] for l in a] #Generate values between marginal costs-marginal costs*delta and marginal costs+marginal costs*delta\n",
    "    MC_uniform = {i:j for i,j in zip(range(1,len(costs_fertilizer)+1),MC_uniform)} #Assigning country group to costs\n",
    "    \n",
    "    #Implementation potential\n",
    "    IP = {2020: IP_fertilizer[0], 2050: IP_fertilizer[1], 2100:IP_fertilizer[2]} #Values implementation potential\n",
    "    IP_uniform = {year: random.uniform(np.max([0,i-DeltaIP_fertilizer]), np.min([100,i+DeltaIP_fertilizer]))/100 for year,i in IP.items()} #Generate values between IP-IP*delta and IP+IP*delta\n",
    "\n",
    "    #Technological progress\n",
    "    TP_2020,TP_2050,TP_2100 = TP_fertilizer[0], TP_fertilizer[1], TP_fertilizer[2]#Values technological progress\n",
    "    TP_uniform = {2020: TP_2020/100, 2050: random.uniform(np.max([0,TP_2050-DeltaTP_fertilizer]), np.min([100,TP_2050+DeltaTP_fertilizer]))/100, 2100: random.uniform(np.max([0,TP_2100-DeltaTP_fertilizer]), np.min([100,TP_2100+DeltaTP_fertilizer]))/100}\n",
    "    return RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5cb68896",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wat is de index van het eerste getal in de lijst dat groter is dan ...\n",
    "#use these definitions to say, if costs are lower than this, than use this RP \n",
    "#This way you get a list of RPs for each costs in the list (0,4000 $/tCeq)\n",
    "def fa(l, ba): return len([x for x in takewhile(lambda x: x[1] < ba, enumerate(l))]) #<: gives the index of the first number that is smaller or equal to the number you give. <=: gives the first number that is smaller than the number you give\n",
    "\n",
    "def aut(een,twee,drie): #een: long list with costs; twee: corrected marginal costs; drie: RP \n",
    "    z = [fa(twee,i) for i in een]\n",
    "    nu = []\n",
    "    [nu.append(drie[i]) for i in z]\n",
    "    return nu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1362580",
   "metadata": {},
   "source": [
    "## Reduction potentials and costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2223440",
   "metadata": {},
   "outputs": [],
   "source": [
    "# range in dollars in c eq or CO2 eq. C eq goes from 0 to 4000 with steps of 20. \n",
    "USD_tC = [*range(0, 4020, 20)]\n",
    "USD_tC = np.arange(0,4020,20)\n",
    "USD_tCO2eq = [i / 44*12 for i in USD_tC]\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs for 2020.\n",
    "#The country can be specified, year also, but this difinition only works for 2050 adn 2100. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values. \n",
    "\n",
    "def generate_f_tp(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    #calculate AP, which is the initial reduction potential\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    #calculate the inverse\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    #Calculate the cumulative reduction potential \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    #Calculate the cumulative costs\n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    #Make a list of reduction potentials that belong to each cost value in the list of 0 to 4000 ceq.\n",
    "    Average_without_tp = []\n",
    "    #Add the technological progress\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    Average_with_tp= [(Average_without_tp[151]+(j-USD_tC[151])*(((1-(1-RP[-1]/100)*TP_uniform[year])*100-Average_without_tp[151])/(USD_tC[200]-USD_tC[151]))) for j in USD_tC[152:]] #calculate the influence of techonological progress on RP. Is linearly implemented from 824 USD/tCO2 eq.\n",
    "    f = Average_without_tp[:152]+Average_with_tp\n",
    "    return f\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs.\n",
    "#The country can be specified, year also, this definition works for 2020. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values and you can choose the year and country.\n",
    "def generate_f(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    return Average_without_tp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8222d29b",
   "metadata": {},
   "source": [
    "## Run a 1000 times "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a9c3f64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#definitions to generate the list of RP values a 1000 times:\n",
    "def k(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "def ktp(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f_tp(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "#Generate the list of RP values a 1000 times for each country:\n",
    "random.seed(3) \n",
    "step1_2020= [k(2020,i) for i in range(1,len(costs_fertilizer)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2050= [ktp(2050,i) for i in range(1,len(costs_fertilizer)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2100= [ktp(2100,i) for i in range(1,len(costs_fertilizer)+1)] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b655d9fe",
   "metadata": {},
   "source": [
    "## Calculate the mean, 95th percentile, 5th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e9775ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#Calculate the mean of the 1000 runs for each different country for 2020,2050,2100\n",
    "mean_2020 = [step1_2020[i].mean(axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "mean_2050 = [step1_2050[i].mean(axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "mean_2100 = [step1_2100[i].mean(axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "\n",
    "#Calculate the 95th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile95_2020 = [np.percentile(step1_2020[i],95,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "percentile95_2050 = [np.percentile(step1_2050[i],95,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "percentile95_2100 = [np.percentile(step1_2100[i],95,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "\n",
    "#Calculate the 5th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile5_2020 = [np.percentile(step1_2020[i],5,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "percentile5_2050 = [np.percentile(step1_2050[i],5,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "percentile5_2100 = [np.percentile(step1_2100[i],5,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "\n",
    "#For each country, put 2020, 2050, and 2100 in one list\n",
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "\n",
    "#plt.plot(range(0,603), together_mean[0])\n",
    "#plt.plot(range(0,603), together_95[0])\n",
    "#plt.plot(range(0,603), together_5[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5c479138",
   "metadata": {},
   "outputs": [],
   "source": [
    "together_mean = [[i/100 for i in l]for l in together_mean]\n",
    "together_95 = [[i/100 for i in l]for l in together_95]\n",
    "together_5 = [[i/100 for i in l]for l in together_5]\n",
    "\n",
    "ml1 = together_mean[0] \n",
    "ml2 = together_mean[1] \n",
    "\n",
    "ml95_1 = together_95[0] \n",
    "ml95_2 = together_95[1] \n",
    "\n",
    "ml5_1 = together_5[0] \n",
    "ml5_2 = together_5[1] \n",
    "\n",
    "#time and x are needed for the excel file. time shows the year for each value in the lists.\n",
    "x = np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist()\n",
    "time = [2020] * 201 + [2050] * 201 + [2100] * 201"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d46d9f2c",
   "metadata": {},
   "source": [
    "## Export to excel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e71e5346",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3) #Use this to have the same outcome every time !!\n",
    "writer = pd.ExcelWriter('N2O_fertilizer_withbiochar_28_3_2022.xlsx')\n",
    "df = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml1, 'class_2' : ml1, 'class_3' : ml2, 'class_4' : ml2, \n",
    "                'class_5' : ml2, 'class_6' : ml2, 'class_7' : ml2, 'class_8' : ml2, \n",
    "                'class_9' : ml2, 'class_10' : ml2, 'class_11' : ml1, 'class_12' : ml1, \n",
    "                'class_13' : ml1, 'class_14' : ml1, 'class_15' : ml1, 'class_16' : ml1, \n",
    "                'class_17' : ml1, 'class_18' : ml2, 'class_19' : ml2, 'class_20' : ml2, \n",
    "                'class_21' : ml2, 'class_22' : ml2, 'class_23' : ml1, 'class_24' : ml1, \n",
    "                'class_25' : ml2, 'class_26' : ml2})\n",
    "df2 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml95_1, 'class_2' : ml95_1, 'class_3' : ml95_2, 'class_4' : ml95_2, \n",
    "                'class_5' : ml95_2, 'class_6' : ml95_2, 'class_7' : ml95_2, 'class_8' : ml95_2, \n",
    "                'class_9' : ml95_2, 'class_10' : ml95_2, 'class_11' : ml95_1, 'class_12' : ml95_1, \n",
    "                'class_13' : ml95_1, 'class_14' : ml95_1, 'class_15' : ml95_1, 'class_16' : ml95_1, \n",
    "                'class_17' : ml95_1, 'class_18' : ml95_2, 'class_19' : ml95_2, 'class_20' : ml95_2, \n",
    "                'class_21' : ml95_2, 'class_22' : ml95_2, 'class_23' : ml95_1, 'class_24' : ml95_1, \n",
    "                'class_25' : ml95_2, 'class_26' : ml95_2})\n",
    "df3 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml5_1, 'class_2' : ml5_1, 'class_3' : ml5_2, 'class_4' : ml5_2, \n",
    "                'class_5' : ml5_2, 'class_6' : ml5_2, 'class_7' : ml5_2, 'class_8' : ml5_2, \n",
    "                'class_9' : ml5_2, 'class_10' : ml5_2, 'class_11' : ml5_1, 'class_12' : ml5_1, \n",
    "                'class_13' : ml5_1, 'class_14' : ml5_1, 'class_15' : ml5_1, 'class_16' : ml5_1, \n",
    "                'class_17' : ml5_1, 'class_18' : ml5_2, 'class_19' : ml5_2, 'class_20' : ml5_2, \n",
    "                'class_21' : ml5_2, 'class_22' : ml5_2, 'class_23' : ml5_1, 'class_24' : ml5_1, \n",
    "                'class_25' : ml5_2, 'class_26' : ml5_2})\n",
    "df.to_excel(writer, sheet_name='fertilizer', index=False)\n",
    "df2.to_excel(writer, sheet_name='fertilizer95', index=False)\n",
    "df3.to_excel(writer, sheet_name='fertilizer5', index=False)\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "955dee88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "#plt.plot(USD_tCO2eq,mean_2100[0])\n",
    "for i,j,k,l,m,n,o,p,q,r,s,t in zip(mean_2020,mean_2050,mean_2100, np.arange(1,19,3),np.arange(2,20,3),np.arange(3,21,3),percentile95_2020,percentile95_2050,percentile95_2100,percentile5_2020,percentile5_2050,percentile5_2100):\n",
    "    plt.subplot(6,3,l)\n",
    "    plt.title('2020')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,i)\n",
    "    plt.plot(USD_tCO2eq,o)\n",
    "    plt.plot(USD_tCO2eq,r)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,m)\n",
    "    plt.title('2050')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,j)\n",
    "    plt.plot(USD_tCO2eq,p)\n",
    "    plt.plot(USD_tCO2eq,s)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,n)\n",
    "    plt.title('2100')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,k)\n",
    "    plt.plot(USD_tCO2eq,q)\n",
    "    plt.ylim(0,100)\n",
    "    plt.plot(USD_tCO2eq,t)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f6b0d4c",
   "metadata": {},
   "source": [
    "# N2O fertilizer without biochar "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62695447",
   "metadata": {},
   "source": [
    "## Constants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "12a899de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial order of the mitigation measures in this code:\n",
    "# Nitrification inhibitors, improved land manure, improved agronomy, irrigation practices,\n",
    "# Spreader maintenance, no tillage\n",
    "\n",
    "#For fertilizer, two regions are used. \n",
    "#1 country group numbers: 1 = East Eur, Japan, USA&Canada, OECD Eur & OC, 2 = East Asia, Africa, South and Central America, South, South east Asia\n",
    "\n",
    "#RE input values:\n",
    "RE_fertilizer = {'nitr_i': [34,30,38,60.10,30,36,42.30,40,40,16.90,44,52,50,50,41,17,39.8,50], 'impr. l.m.': [5,5,27,5,5,50,30.5,7,7,30,35], \n",
    "        'impr. agronomy': [24,14.29,35,54,18.6,20,20,15.13], 'irr.p': [55,67,32,46,52,15], 'spr m': [26,22,42,21.50],'no tillage': [30,48,45,27,25]}\n",
    "\n",
    "#RE values in the order written above:\n",
    "RE_fertilizer = [RE_fertilizer['nitr_i'],RE_fertilizer['impr. l.m.'],RE_fertilizer['impr. agronomy'],RE_fertilizer['irr.p'],RE_fertilizer['spr m'],RE_fertilizer['no tillage']]\n",
    "\n",
    "#Costs\n",
    "costs_fertilizer_1 = {'nitr_i': 177, 'impr. l.m.': 0, 'impr. agronomy': 4.2, 'irr.p': 400, 'spr m': 49.2, \n",
    "                    'no tillage': 55}\n",
    "costs_fertilizer_2 = {'nitr_i': 32, 'impr. l.m.': 0, 'impr. agronomy': 4.2, 'irr.p': 400, 'spr m': 49.2, \n",
    "                    'no tillage': 55}\n",
    "\n",
    "costs_fertilizer = [[costs_fertilizer_1['nitr_i'],costs_fertilizer_1['impr. l.m.'],costs_fertilizer_1['impr. agronomy'],costs_fertilizer_1['irr.p'],costs_fertilizer_1['spr m'],costs_fertilizer_1['no tillage']],[costs_fertilizer_2['nitr_i'],costs_fertilizer_2['impr. l.m.'],costs_fertilizer_2['impr. agronomy'],costs_fertilizer_2['irr.p'],costs_fertilizer_2['spr m'],costs_fertilizer_2['no tillage']]]\n",
    "\n",
    "#Technical applicability:\n",
    "TA_fertilizer = {'nitr_i': 100, 'impr. l.m.': 45, 'impr. agronomy': 45, 'irr.p': 63, 'spr m': 100, 'no tillage': 63}\n",
    "#Technical applicability in the order as written above\n",
    "TA_fertilizer = [TA_fertilizer['nitr_i'],TA_fertilizer['impr. l.m.'],TA_fertilizer['impr. agronomy'],TA_fertilizer['irr.p'],TA_fertilizer['spr m'],TA_fertilizer['no tillage']]\n",
    "\n",
    "#Delta values\n",
    "DeltaTA_fertilizer = 40 #Maximum change in TA %.\n",
    "DeltaOV_fertilizer = 30 #Maximum change in OV_corr\n",
    "DeltaMC_fertilizer = 0.8 #Maximum change in costs\n",
    "DeltaIP_fertilizer = 30 #Maximum change in IP\n",
    "DeltaTP_fertilizer = 10 #Maximum change in TP\n",
    "\n",
    "#Implementation potential and technological progress values:\n",
    "IP_fertilizer = [10,70,100]\n",
    "TP_fertilizer = [100,90,80]\n",
    "\n",
    "#Calculate in which order the measures should be implemented compared to our initial order\n",
    "#This is based on the initial costs and is calculated for each region:\n",
    "order_fertilizer = [[x for _, x in sorted(zip(i,range(0,len(costs_fertilizer[0])+1)))] for i in costs_fertilizer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a6434932",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The correction for overlap values change when changing the order of implementation measures. \n",
    "#The order of implementation measures changes when the costs are different for different countries. \n",
    "#With the costs written above, measures are implemented in only one way (ad,sd,sc,ma,rdp,hsb)\n",
    "#We make lists with the overlap values with the previous implemented measures now for this way:\n",
    "\n",
    "#First writing down the overlap values between measures\n",
    "\n",
    "no_tillage1 = {'ilm': 0.7, 'ia': 0.5, 'sm': 1, 'ni': 1, 'bc': 1, 'irr': 0.5, 'nt': 1}\n",
    "impr_landmanure1 = {'ia': 1, 'sm': 1, 'ni': 1, 'bc': 0.7, 'irr': 0.7}\n",
    "impr_agr1 = {'sm': 0.70, 'ni': 0.7, 'bc': 1, 'irr': 0.7}\n",
    "spread_m1 = { 'ni': 0.7, 'bc': 1, 'irr': 1}\n",
    "ni1 = {'bc': 0.50, 'irr': 1}\n",
    "b1c = {'irr': 0.7}\n",
    "\n",
    "#Writing down for each measure which measures were already implemented\n",
    "Corr_o = {'':['nitr_i','impr. l.m.','impr. agronomy','irr.p','spr m','bioc','no tillage'], \n",
    "          'nitr_i': [no_tillage1['ni'], impr_landmanure1['ni'], impr_agr1['ni'], spread_m1['ni']], \n",
    "          'impr. l.m.': [no_tillage1['ilm']],\n",
    "          'impr. agronomy': [no_tillage1['ia'], impr_landmanure1['ia']], \n",
    "          'irr.p': [no_tillage1['irr'], impr_landmanure1['irr'], impr_agr1['irr'], spread_m1['irr'], ni1['irr']],\n",
    "          'spr m': [no_tillage1['sm'], impr_landmanure1['sm'],impr_agr1['sm']], \n",
    "           'no tillage': [no_tillage1['nt']]}\n",
    "\n",
    "Corr_o2 = {'':['nitr_i','impr. l.m.','impr. agronomy','irr.p','spr m','bioc','no tillage'], \n",
    "          'nitr_i': [no_tillage1['ni'], impr_landmanure1['ni'], impr_agr1['ni']], \n",
    "          'impr. l.m.': [no_tillage1['ilm']],\n",
    "          'impr. agronomy': [no_tillage1['ia'], impr_landmanure1['ia']], \n",
    "          'irr.p': [no_tillage1['irr'], impr_landmanure1['irr'], impr_agr1['irr'], spread_m1['irr'], ni1['irr']],\n",
    "          'spr m': [no_tillage1['sm'], impr_landmanure1['sm'],impr_agr1['sm'], spread_m1['ni']], \n",
    "          'no tillage': [no_tillage1['nt']]}\n",
    "\n",
    "#Rewriting the lists in an easier way (order as described on top):\n",
    "OV_corr = [[Corr_o['nitr_i'],Corr_o['impr. l.m.'],Corr_o['impr. agronomy'],Corr_o['irr.p'],Corr_o['spr m'],Corr_o['no tillage']],[Corr_o2['nitr_i'],Corr_o2['impr. l.m.'],Corr_o2['impr. agronomy'],Corr_o2['irr.p'],Corr_o2['spr m'],Corr_o2['no tillage']]]\n",
    "#Calculating the product of overlap of previously implemented measures:\n",
    "OV_corr = [[np.fmax(0.2,np.product(i)) for i in l] for l in OV_corr]\n",
    "#Multiply by 100:\n",
    "OV_corr_fertilizer = [[i*100 for i in l] for l in OV_corr]\n",
    "#Calculate the marginal costs:\n",
    "c_fertilizer  = [[a*100/b for a,b in zip(i, j)] for i,j in zip(costs_fertilizer,OV_corr_fertilizer)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6fb714d",
   "metadata": {},
   "source": [
    "## Making random variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "21b56dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "def generate_random(): #This function generates values for RE, TA, OV_corr, Marginal Costs, IP, and TP using uniform distributions \n",
    "    #RE values \n",
    "    #random.seed(3)\n",
    "    RE = [[RE_fertilizer[i] for i in j] for j in order_fertilizer]\n",
    "    RE_uniform = [[random.uniform(np.min(i), np.max(i))/100 for i in l] for l in RE] #Generating random value between de minimum and maximum of each of the measures\n",
    "    RE_uniform = {i:j for i,j in zip(range(1,len(costs_fertilizer)+1),RE_uniform)} #Assigning country group to RE_uniform\n",
    "    RE_uniform[1][4] = min(RE_uniform[1][4], 0.70)\n",
    "    RE_uniform[2][2] = min(RE_uniform[2][2], 0.70)\n",
    "    #TA values\n",
    "    TA = [[TA_fertilizer[i] for i in j] for j in order_fertilizer] #TA input values for the measures: storage duration, anaerobic digestion, storage covering, manure acidification, housing systems and beddings, solid liquid seperation\n",
    "    TA_uniform = [[random.uniform(np.max([0,i-DeltaTA_fertilizer]), np.min([100,i+DeltaTA_fertilizer]))/100 for i in l]for l in TA] #Generate values between TA-TA*delta and TA+TA*delta\n",
    "    TA_uniform = {i:j for i,j in zip(range(1,len(costs_fertilizer)+1),TA_uniform)} #Assigning country group to TA_uniform\n",
    "\n",
    "    #OVcorr\n",
    "    a= [[[OV_corr_fertilizer[q][i] for i in l] for l in order_fertilizer] for q in range(0,len(costs_fertilizer))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_fertilizer))]\n",
    "    OV_corr_uniform = [[random.uniform(np.max([0,i-DeltaOV_fertilizer]), np.min([100,i+DeltaOV_fertilizer]))/100 for i in l]for l in a]  #Generate values between OV_corr-OV_corr*delta and OV_corr+OV_corr*delta\n",
    "    OV_corr_uniform = {i:j for i,j in zip(range(1,len(costs_fertilizer)+1),OV_corr_uniform)} #Assigning country group to OV_corr_uniform\n",
    "\n",
    "    #costs \n",
    "    Euro_to_Dollar = 1.24\n",
    "    a= [[[c_fertilizer[q][i] for i in l] for l in order_fertilizer] for q in range(0,len(costs_fertilizer))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_fertilizer))]\n",
    "    MC_uniform = [[random.uniform(i-i*DeltaMC_fertilizer,i+i*DeltaMC_fertilizer)/100 for i in l] for l in a] #Generate values between marginal costs-marginal costs*delta and marginal costs+marginal costs*delta\n",
    "    MC_uniform = {i:j for i,j in zip(range(1,len(costs_fertilizer)+1),MC_uniform)} #Assigning country group to costs\n",
    "    \n",
    "    #Implementation potential\n",
    "    IP = {2020: IP_fertilizer[0], 2050: IP_fertilizer[1], 2100:IP_fertilizer[2]} #Values implementation potential\n",
    "    IP_uniform = {year: random.uniform(np.max([0,i-DeltaIP_fertilizer]), np.min([100,i+DeltaIP_fertilizer]))/100 for year,i in IP.items()} #Generate values between IP-IP*delta and IP+IP*delta\n",
    "\n",
    "    #Technological progress\n",
    "    TP_2020,TP_2050,TP_2100 = TP_fertilizer[0], TP_fertilizer[1], TP_fertilizer[2]#Values technological progress\n",
    "    TP_uniform = {2020: TP_2020/100, 2050: random.uniform(np.max([0,TP_2050-DeltaTP_fertilizer]), np.min([100,TP_2050+DeltaTP_fertilizer]))/100, 2100: random.uniform(np.max([0,TP_2100-DeltaTP_fertilizer]), np.min([100,TP_2100+DeltaTP_fertilizer]))/100}\n",
    "    return RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1c15f73f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#What is the first index of the first number in the list that is greater than ...\n",
    "#use these definitions to say, if costs are lower than this, than use this RP \n",
    "#This way you get a list of RPs for each costs in the list (0,4000 $/tCeq)\n",
    "def fa(l, ba): return len([x for x in takewhile(lambda x: x[1] < ba, enumerate(l))]) #<: gives the index of the first number that is smaller or equal to the number you give. <=: gives the first number that is smaller than the number you give\n",
    "\n",
    "def aut(een,twee,drie): #een: long list with costs; twee: corrected marginal costs; drie: RP \n",
    "    z = [fa(twee,i) for i in een]\n",
    "    nu = []\n",
    "    [nu.append(drie[i]) for i in z]\n",
    "    return nu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694f49f6",
   "metadata": {},
   "source": [
    "## Reduction potentials and costs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d0dff84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# range in dollars in c eq or CO2 eq. C eq goes from 0 to 4000 with steps of 20.\n",
    "USD_tC = [*range(0, 4020, 20)]\n",
    "USD_tC = np.arange(0,4020,20)\n",
    "USD_tCO2eq = [i / 44*12 for i in USD_tC]\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs for 2020.\n",
    "#The country can be specified, year also, but this difinition only works for 2050 adn 2100. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values. \n",
    "\n",
    "def generate_f_tp(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    #calculate AP, which is the initial reduction potential\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    #calculate the inverse\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    #Calculate the cumulative reduction potential \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    #Calculate the cumulative costs\n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    #Make a list of reduction potentials that belong to each cost value in the list of 0 to 4000 ceq.\n",
    "    Average_without_tp = []\n",
    "    #Add the technological progress\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    Average_with_tp= [(Average_without_tp[151]+(j-USD_tC[151])*(((1-(1-RP[-1]/100)*TP_uniform[year])*100-Average_without_tp[151])/(USD_tC[200]-USD_tC[151]))) for j in USD_tC[152:]] #calculate the influence of techonological progress on RP. Is linearly implemented from 824 USD/tCO2 eq.\n",
    "    f = Average_without_tp[:152]+Average_with_tp\n",
    "    return f\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs.\n",
    "#The country can be specified, year also, this definition works for 2020. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values and you can choose the year and country.\n",
    "def generate_f(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    return Average_without_tp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99382afd",
   "metadata": {},
   "source": [
    "## Run a 1000 times \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7d197192",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#definitions to generate the list of RP values a 1000 times:\n",
    "def k(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "def ktp(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f_tp(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "#Generate the list of RP values a 1000 times for each country:\n",
    "random.seed(3) \n",
    "step1_2020= [k(2020,i) for i in range(1,len(costs_fertilizer)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2050= [ktp(2050,i) for i in range(1,len(costs_fertilizer)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2100= [ktp(2100,i) for i in range(1,len(costs_fertilizer)+1)] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f585505b",
   "metadata": {},
   "source": [
    "## Calculate the mean, 95th percentile, 5th percentile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2cb65383",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#Calculate the mean of the 1000 runs for each different country for 2020,2050,2100\n",
    "mean_2020 = [step1_2020[i].mean(axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "mean_2050 = [step1_2050[i].mean(axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "mean_2100 = [step1_2100[i].mean(axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "\n",
    "#Calculate the 95th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile95_2020 = [np.percentile(step1_2020[i],95,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "percentile95_2050 = [np.percentile(step1_2050[i],95,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "percentile95_2100 = [np.percentile(step1_2100[i],95,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "\n",
    "#Calculate the 5th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile5_2020 = [np.percentile(step1_2020[i],5,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "percentile5_2050 = [np.percentile(step1_2050[i],5,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "percentile5_2100 = [np.percentile(step1_2100[i],5,axis=0) for i in range(0,len(costs_fertilizer))]\n",
    "\n",
    "#For each country, put 2020, 2050, and 2100 in one list\n",
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "\n",
    "#plt.plot(range(0,603), together_mean[0])\n",
    "#plt.plot(range(0,603), together_95[0])\n",
    "#plt.plot(range(0,603), together_5[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bbd97b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(costs_fertilizer)) ]\n",
    "\n",
    "together_mean = [[i/100 for i in l]for l in together_mean]\n",
    "together_95 = [[i/100 for i in l]for l in together_95]\n",
    "together_5 = [[i/100 for i in l]for l in together_5] \n",
    "\n",
    "ml1 = together_mean[0] \n",
    "ml2 = together_mean[1] \n",
    "\n",
    "ml95_1 = together_95[0] \n",
    "ml95_2 = together_95[1] \n",
    "\n",
    "ml5_1 = together_5[0] \n",
    "ml5_2 = together_5[1] \n",
    "\n",
    "#time and x are needed for the excel file. time shows the year for each value in the lists.\n",
    "x = np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist()\n",
    "time = [2020] * 201 + [2050] * 201 + [2100] * 201"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6aaa22d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Export to excel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "29640b40",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3) #Use this to have the same outcome every time !!\n",
    "writer = pd.ExcelWriter('N2O_fertilizer_16_5_2022_without_biochar.xlsx')\n",
    "df = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml1, 'class_2' : ml1, 'class_3' : ml2, 'class_4' : ml2, \n",
    "                'class_5' : ml2, 'class_6' : ml2, 'class_7' : ml2, 'class_8' : ml2, \n",
    "                'class_9' : ml2, 'class_10' : ml2, 'class_11' : ml1, 'class_12' : ml1, \n",
    "                'class_13' : ml1, 'class_14' : ml1, 'class_15' : ml1, 'class_16' : ml1, \n",
    "                'class_17' : ml1, 'class_18' : ml2, 'class_19' : ml2, 'class_20' : ml2, \n",
    "                'class_21' : ml2, 'class_22' : ml2, 'class_23' : ml1, 'class_24' : ml1, \n",
    "                'class_25' : ml2, 'class_26' : ml2})\n",
    "df2 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml95_1, 'class_2' : ml95_1, 'class_3' : ml95_2, 'class_4' : ml95_2, \n",
    "                'class_5' : ml95_2, 'class_6' : ml95_2, 'class_7' : ml95_2, 'class_8' : ml95_2, \n",
    "                'class_9' : ml95_2, 'class_10' : ml95_2, 'class_11' : ml95_1, 'class_12' : ml95_1, \n",
    "                'class_13' : ml95_1, 'class_14' : ml95_1, 'class_15' : ml95_1, 'class_16' : ml95_1, \n",
    "                'class_17' : ml95_1, 'class_18' : ml95_2, 'class_19' : ml95_2, 'class_20' : ml95_2, \n",
    "                'class_21' : ml95_2, 'class_22' : ml95_2, 'class_23' : ml95_1, 'class_24' : ml95_1, \n",
    "                'class_25' : ml95_2, 'class_26' : ml95_2})\n",
    "df3 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml5_1, 'class_2' : ml5_1, 'class_3' : ml5_2, 'class_4' : ml5_2, \n",
    "                'class_5' : ml5_2, 'class_6' : ml5_2, 'class_7' : ml5_2, 'class_8' : ml5_2, \n",
    "                'class_9' : ml5_2, 'class_10' : ml5_2, 'class_11' : ml5_1, 'class_12' : ml5_1, \n",
    "                'class_13' : ml5_1, 'class_14' : ml5_1, 'class_15' : ml5_1, 'class_16' : ml5_1, \n",
    "                'class_17' : ml5_1, 'class_18' : ml5_2, 'class_19' : ml5_2, 'class_20' : ml5_2, \n",
    "                'class_21' : ml5_2, 'class_22' : ml5_2, 'class_23' : ml5_1, 'class_24' : ml5_1, \n",
    "                'class_25' : ml5_2, 'class_26' : ml5_2})\n",
    "df.to_excel(writer, sheet_name='fertilizer', index=False)\n",
    "df2.to_excel(writer, sheet_name='fertilizer95', index=False)\n",
    "df3.to_excel(writer, sheet_name='fertilizer5', index=False)\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2d4baff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "#plt.plot(USD_tCO2eq,mean_2100[0])\n",
    "for i,j,k,l,m,n,o,p,q,r,s,t in zip(mean_2020,mean_2050,mean_2100, np.arange(1,19,3),np.arange(2,20,3),np.arange(3,21,3),percentile95_2020,percentile95_2050,percentile95_2100,percentile5_2020,percentile5_2050,percentile5_2100):\n",
    "    plt.subplot(6,3,l)\n",
    "    plt.title('2020')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,i)\n",
    "    plt.plot(USD_tCO2eq,o)\n",
    "    plt.plot(USD_tCO2eq,r)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,m)\n",
    "    plt.title('2050')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,j)\n",
    "    plt.plot(USD_tCO2eq,p)\n",
    "    plt.plot(USD_tCO2eq,s)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,n)\n",
    "    plt.title('2100')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,k)\n",
    "    plt.plot(USD_tCO2eq,q)\n",
    "    plt.ylim(0,100)\n",
    "    plt.plot(USD_tCO2eq,t)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b532ad",
   "metadata": {},
   "source": [
    "# N2O Manure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a07aef8",
   "metadata": {},
   "source": [
    "## Constants "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fc545c8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[30, 26, 86, 70, 83, 149]]\n"
     ]
    }
   ],
   "source": [
    "# Initial order of the mitigation measures in this code:\n",
    "# 1. storage duration (sd), 2. anaerobic digestion (ad), 3. reduced dietary protein (rdp)\n",
    "# 4. storage covering (sc), 5. manure acidification (ma), 6. housing system and beddings (hsb)\n",
    "\n",
    "#For N2O only one region is used: ROW \n",
    "\n",
    "#RE input values:\n",
    "RE_N2Omanure = {'rdp':[32,37,25,52,12,16,24,33,23,46,58.5,43], 'an digestion': [34,47,75], \n",
    "        'storage duration': [35], 'storage covering': [75,30], 'hsb': [30.2,35,9,88], 'ma': [90,92,96,21,79,52]}\n",
    "\n",
    "#RE values in the order written above:\n",
    "RE_N2Omanure = [RE_N2Omanure['storage duration'],RE_N2Omanure['an digestion'],RE_N2Omanure['rdp'],RE_N2Omanure['storage covering'],RE_N2Omanure['ma'],RE_N2Omanure['hsb']] \n",
    "\n",
    "#Technical applicability:\n",
    "TA_N2Omanure =  {'rdp':50, 'an  digestion': 90, 'storage duration':100, 'storage covering ': 100, \n",
    "                 'hsb': 100, 'ma': 50}\n",
    "\n",
    "#Technical applicability in the order as written above\n",
    "TA_N2Omanure = [TA_N2Omanure['storage duration'],TA_N2Omanure['an  digestion'],TA_N2Omanure['rdp'],TA_N2Omanure['storage covering '],TA_N2Omanure['ma'],TA_N2Omanure['hsb']]\n",
    "\n",
    "#costs\n",
    "costs_N2Omanure = [[30,26,86,70,83,149]]\n",
    "\n",
    "#delta values\n",
    "DeltaTA_N2Omanure = 40 #Maximum change in TA\n",
    "DeltaOV_N2Omanure = 0.3 #Maximum change in OV_corr\n",
    "DeltaMC_N2Omanure = 0.8 #Maximum change in costs\n",
    "DeltaIP_N2Omanure = 30 #Maximum change in IP\n",
    "DeltaTP_N2Omanure = 10 #Maximum change in TP\n",
    "\n",
    "#Implementation potential and technological progress values:\n",
    "IP_N2Omanure = [20,50,70]\n",
    "TP_N2Omanure = [100,90,80]\n",
    "\n",
    "\n",
    "print(costs_N2Omanure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9f43ca9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The correction for overlap values change when changing the order of implementation measures. \n",
    "#The order of implementation measures changes when the costs are different for different countries. \n",
    "#With the costs written above, measures are implemented in only one way (ad,sd,sc,ma,rdp,hsb)\n",
    "#We make lists with the overlap values with the previous implemented measures now for this way:\n",
    "\n",
    "#First writing down the overlap values between measures\n",
    "storage_duration = {'ad': 0.2, 'sc': 0.7, 'ma': 0.7, 'hsb': 0.7, 'rdp': 0.7, 'sd': 1}\n",
    "an_digestion = {'sc': 0.2, 'ma': 0.2, 'hsb': 0.7, 'rdp': 0.7, 'ad': 1}\n",
    "storage_covering = {'ma': 0.7, 'hsb': 0.7, 'rdp': 0.7}\n",
    "manure_acidif = { 'hsb': 0.7, 'rdp': 0.7}\n",
    "hsb = {'rdp': 0.7}\n",
    "\n",
    "#Writing down for each measure which measures were already implemented\n",
    "Corr_o = {'':['sd','ad','sc','ma','hsb'], \n",
    "          'sd': [storage_duration['ad']], \n",
    "          'ad': [an_digestion['ad']],\n",
    "          'sc': [storage_duration['sc'], an_digestion['sc']], \n",
    "          'ma': [storage_duration['ma'], an_digestion['ma'], storage_covering['ma']],\n",
    "          'hsb': [storage_duration['hsb'], an_digestion['hsb'],storage_covering['hsb'], manure_acidif['hsb'], hsb['rdp']],\n",
    "         'rdp': [storage_duration['rdp'], an_digestion['rdp'],storage_covering['rdp'], manure_acidif['rdp']]}\n",
    "\n",
    "\n",
    "#Rewriting the lists in an easier way (order as described on top):\n",
    "OV_corr_N2Omanure = [Corr_o['sd'],Corr_o['ad'],Corr_o['rdp'],Corr_o['sc'],Corr_o['ma'],Corr_o['hsb']]\n",
    "#Calculating the product of overlap of previously implemented measures:\n",
    "OV_corr_N2Omanure = [np.fmax(0.2,np.product(i)) for i in OV_corr_N2Omanure]\n",
    "#Multiply by 100:\n",
    "OVcorr_N2Omanure = [i*100 for i in OV_corr_N2Omanure]\n",
    "OV_corr_N2Omanuren = [OV_corr_N2Omanure]\n",
    "\n",
    "#Calculate the marginal costs:\n",
    "c_N2Omanure  = [[a*100/b for a,b in zip(i, OVcorr_N2Omanure)] for i in costs_N2Omanure]\n",
    "#Calculate the order of implementation\n",
    "order_N2Omanure = [[x for _, x in sorted(zip(i,range(0,len(OVcorr_N2Omanure)+1)))] for i in costs_N2Omanure]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5820db88",
   "metadata": {},
   "source": [
    "## Making random values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f744b111",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1 country group numbers: 1 = ROW \n",
    "random.seed(3)\n",
    "def generate_random(): #This function generates values for RE, TA, OV_corr, Marginal Costs, IP, and TP using uniform distributions \n",
    "    #RE values \n",
    "    #random.seed(3)\n",
    "    RE = [[RE_N2Omanure[i] for i in j] for j in order_N2Omanure]\n",
    "    RE_uniform = [[random.uniform(np.min(i), np.max(i))/100 for i in l] for l in RE] #Generating random value between de minimum and maximum of each of the measures\n",
    "    RE_uniform = {i:j for i,j in zip(range(1,len(costs_N2Omanure)+1),RE_uniform)} #Assigning country group to RE_uniform\n",
    "    \n",
    "    #TA values\n",
    "    TA = [[TA_N2Omanure[i] for i in j] for j in order_N2Omanure] #TA input values for the measures: storage duration, anaerobic digestion, storage covering, manure acidification, housing systems and beddings, solid liquid seperation\n",
    "    TA_uniform = [[random.uniform(np.max([0,i-DeltaTA_N2Omanure]), np.min([100,i+DeltaTA_N2Omanure]))/100 for i in l]for l in TA] #Generate values between TA-TA*delta and TA+TA*delta\n",
    "    TA_uniform = {i:j for i,j in zip(range(1,len(costs_N2Omanure)+1),TA_uniform)} #Assigning country group to TA_uniformf\n",
    "\n",
    "    #OVcorr\n",
    "    a= [[[OV_corr_N2Omanuren[q][i] for i in l] for l in order_N2Omanure] for q in range(0,len(costs_N2Omanure))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_N2Omanure))]\n",
    "    OV_corr_uniform = [[random.uniform(np.max([0,i-DeltaOV_N2Omanure]), np.min([1,i+DeltaOV_N2Omanure])) for i in l]for l in a]  #Generate values between OV_corr-OV_corr*delta and OV_corr+OV_corr*delta\n",
    "    OV_corr_uniform = {i:j for i,j in zip(range(1,len(costs_N2Omanure)+1),OV_corr_uniform)} #Assigning country group to OV_corr_uniform\n",
    "   \n",
    "    #costs \n",
    "    Euro_to_Dollar = 1.24\n",
    "    a= [[[c_N2Omanure[q][i] for i in l] for l in order_N2Omanure] for q in range(0,len(costs_N2Omanure))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_N2Omanure))]\n",
    "    MC_uniform = [[random.uniform(i-i*DeltaMC_N2Omanure,i+i*DeltaMC_N2Omanure)/100 for i in l] for l in a] #Generate values between marginal costs-marginal costs*delta and marginal costs+marginal costs*delta\n",
    "    MC_uniform = {i:j for i,j in zip(range(1,len(costs_N2Omanure)+1),MC_uniform)} #Assigning country group to costs\n",
    "    \n",
    "    #Implementation potential\n",
    "    IP = {2020: IP_N2Omanure[0], 2050: IP_N2Omanure[1], 2100:IP_N2Omanure[2]} #Values implementation potential\n",
    "    IP_uniform = {year: random.uniform(np.max([0,i-DeltaIP_N2Omanure]), np.min([100,i+DeltaIP_N2Omanure]))/100 for year,i in IP.items()} #Generate values between IP-IP*delta and IP+IP*delta\n",
    "\n",
    "    #Technological progress\n",
    "    TP_2020,TP_2050,TP_2100 = [TP_N2Omanure[0], TP_N2Omanure[1], TP_N2Omanure[2]]#Values technological progress\n",
    "    TP_uniform = {2020: TP_2020/100, 2050: random.uniform(np.max([0,TP_2050-DeltaTP_N2Omanure]), np.min([100,TP_2050+DeltaTP_N2Omanure]))/100, 2100: random.uniform(np.max([0,TP_2100-DeltaTP_N2Omanure]), np.min([100,TP_2100+DeltaTP_N2Omanure]))/100}\n",
    "    return RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "82a566b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wat is de index van het eerste getal in de lijst dat groter is dan ...\n",
    "#use these definitions to say, if costs are lower than this, than use this RP \n",
    "#This way you get a list of RPs for each costs in the list (0,4000 $/tCeq)\n",
    "def fa(l, ba): return len([x for x in takewhile(lambda x: x[1] <= ba, enumerate(l))]) #<: gives the index of the first number that is smaller or equal to the number you give. <=: gives the first number that is smaller than the number you give\n",
    "\n",
    "def aut(een,twee,drie): #een: long list with costs; twee: corrected marginal costs; drie: RP \n",
    "    z = [fa(twee,i) for i in een]\n",
    "    nu = []\n",
    "    [nu.append(drie[i]) for i in z]\n",
    "    return nu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a66b01",
   "metadata": {},
   "source": [
    "## Reduction potentials and costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3ce377aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# range in dollars in c eq or CO2 eq. C eq goes from 0 to 4000 with steps of 20. \n",
    "USD_tC = [*range(0, 4020, 20)]\n",
    "USD_tC = np.arange(0,4020,20)\n",
    "USD_tCO2eq = [i / 44*12 for i in USD_tC]\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs for 2020.\n",
    "#The country can be specified, year also, but this difinition only works for 2050 adn 2100. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values. \n",
    "\n",
    "def generate_f_tp(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    #calculate AP, which is the initial reduction potential\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] \n",
    "    #calculate the inverse\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    #Calculate the cumulative reduction potential \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    #Calculate the cumulative costs\n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    #Make a list of reduction potentials that belong to each cost value in the list of 0 to 4000 ceq.\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    #Add the technological progress\n",
    "    Average_with_tp= [(Average_without_tp[151]+(j-USD_tC[151])*(((1-(1-RP[-1]/100)*TP_uniform[year])*100-Average_without_tp[151])/(USD_tC[200]-USD_tC[151]))) for j in USD_tC[152:]] #calculate the influence of techonological progress on RP. Is linearly implemented from 824 USD/tCO2 eq.\n",
    "    f = Average_without_tp[:152]+Average_with_tp\n",
    "    return f\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs.\n",
    "#The country can be specified, year also, this definition works for 2020. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values and you can choose the year and country.\n",
    "def generate_f(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    return Average_without_tp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d064d4",
   "metadata": {},
   "source": [
    "## Run a 1000 times "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6662a8a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#definitions to generate the list of RP values a 1000 times:\n",
    "def k(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "def ktp(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f_tp(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "#Generate the list of RP values a 1000 times for each country:\n",
    "random.seed(3) \n",
    "step1_2020= [k(2020,i) for i in range(1,len(costs_N2Omanure)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2050= [ktp(2050,i) for i in range(1,len(costs_N2Omanure)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2100= [ktp(2100,i) for i in range(1,len(costs_N2Omanure)+1)] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfaa3c1c",
   "metadata": {},
   "source": [
    "## Calculate the mean, 95th percentile and 5th percentile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "354fdf4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#Calculate the mean of the 1000 runs for each different country for 2020,2050,2100\n",
    "mean_2020 = [step1_2020[i].mean(axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "mean_2050 = [step1_2050[i].mean(axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "mean_2100 = [step1_2100[i].mean(axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "\n",
    "#Calculate the 95th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile95_2020 = [np.percentile(step1_2020[i],95,axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "percentile95_2050 = [np.percentile(step1_2050[i],95,axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "percentile95_2100 = [np.percentile(step1_2100[i],95,axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "\n",
    "#Calculate the 5th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile5_2020 = [np.percentile(step1_2020[i],5,axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "percentile5_2050 = [np.percentile(step1_2050[i],5,axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "percentile5_2100 = [np.percentile(step1_2100[i],5,axis=0) for i in range(0,len(costs_N2Omanure))]\n",
    "\n",
    "#For each country, put 2020, 2050, and 2100 in one list\n",
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(costs_N2Omanure)) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(costs_N2Omanure)) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(costs_N2Omanure)) ]\n",
    "\n",
    "together_mean = [[i/100 for i in l]for l in together_mean]\n",
    "together_95 = [[i/100 for i in l]for l in together_95]\n",
    "together_5 = [[i/100 for i in l]for l in together_5]\n",
    "\n",
    "#plt.plot(range(0,603), together_mean[0])\n",
    "#plt.plot(range(0,603), together_95[0])\n",
    "#plt.plot(range(0,603), together_5[0])\n",
    "\n",
    "#time and x are needed for the excel file. time shows the year for each value in the lists.\n",
    "x = np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist()\n",
    "time = [2020] * 201 + [2050] * 201 + [2100] * 201"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c617d086",
   "metadata": {},
   "source": [
    "## Export to excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "374d35fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "tm = [[i/100 for i in l]for l in together_mean]\n",
    "t9 = [[i/100 for i in l]for l in together_95]\n",
    "t5 = [[i/100 for i in l]for l in together_5] \n",
    "\n",
    "#The first country is the first in the lists above, it is not super necessary to specify this\n",
    "ml1 = tm[0]\n",
    "ml95_1 = t9[0] \n",
    "ml5_1 = t5[0] \n",
    "\n",
    "\n",
    "random.seed(3) #Use this to have the same outcome every time !!\n",
    "writer = pd.ExcelWriter('N2O_manure_16_5_2022.xlsx')\n",
    "df = DataFrame({'t': time, 'DIM_1': x, 'class_1' : tm[0], 'class_2' : tm[0], 'class_3' : tm[0], 'class_4' : tm[0], \n",
    "                'class_5' : tm[0], 'class_6' : tm[0], 'class_7' : ml1, 'class_8' : ml1, \n",
    "                'class_9' : ml1, 'class_10' : ml1, 'class_11' : ml1, 'class_12' : ml1, \n",
    "                'class_13' : ml1, 'class_14' : ml1, 'class_15' : ml1, 'class_16' : ml1, \n",
    "                'class_17' : ml1, 'class_18' : ml1, 'class_19' : ml1, 'class_20' : ml1, \n",
    "                'class_21' : ml1, 'class_22' : ml1, 'class_23' : ml1, 'class_24' : ml1, \n",
    "                'class_25' : ml1, 'class_26' : ml1})\n",
    "df2 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml95_1, 'class_2' : ml95_1, 'class_3' : ml95_1, 'class_4' : ml95_1, \n",
    "                'class_5' : ml95_1, 'class_6' : ml95_1, 'class_7' : ml95_1, 'class_8' : ml95_1, \n",
    "                'class_9' : ml95_1, 'class_10' : ml95_1, 'class_11' : ml95_1, 'class_12' : ml95_1, \n",
    "                'class_13' : ml95_1, 'class_14' : ml95_1, 'class_15' : ml95_1, 'class_16' : ml95_1, \n",
    "                'class_17' : ml95_1, 'class_18' : ml95_1, 'class_19' : ml95_1, 'class_20' : ml95_1, \n",
    "                'class_21' : ml95_1, 'class_22' : ml95_1, 'class_23' : ml95_1, 'class_24' : ml95_1, \n",
    "                'class_25' : ml95_1, 'class_26' : ml95_1})\n",
    "df3 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml5_1, 'class_2' : ml5_1, 'class_3' : ml5_1, 'class_4' : ml95_1, \n",
    "                'class_5' : ml5_1, 'class_6' : ml5_1, 'class_7' : ml5_1, 'class_8' : ml5_1, \n",
    "                'class_9' : ml5_1, 'class_10' : ml5_1, 'class_11' : ml5_1, 'class_12' : ml5_1, \n",
    "                'class_13' : ml5_1, 'class_14' : ml5_1, 'class_15' : ml5_1, 'class_16' : ml5_1, \n",
    "                'class_17' : ml5_1, 'class_18' : ml5_1, 'class_19' : ml5_1, 'class_20' : ml5_1, \n",
    "                'class_21' : ml5_1, 'class_22' : ml5_1, 'class_23' : ml5_1, 'class_24' : ml5_1, \n",
    "                'class_25' : ml5_1, 'class_26' : ml5_1})\n",
    "df.to_excel(writer, sheet_name='N2O manure average', index=False)\n",
    "df2.to_excel(writer, sheet_name='N2O manure 95', index=False)\n",
    "df3.to_excel(writer, sheet_name='N2O manure 5', index=False)\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0a42a085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "#plt.plot(USD_tCO2eq,mean_2100[0])\n",
    "for i,j,k,l,m,n,o,p,q,r,s,t in zip(mean_2020,mean_2050,mean_2100, np.arange(1,19,3),np.arange(2,20,3),np.arange(3,21,3),percentile95_2020,percentile95_2050,percentile95_2100,percentile5_2020,percentile5_2050,percentile5_2100):\n",
    "    plt.subplot(6,3,l)\n",
    "    plt.title('2020')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,i)\n",
    "    plt.plot(USD_tCO2eq,o)\n",
    "    plt.plot(USD_tCO2eq,r)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,m)\n",
    "    plt.title('2050')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,j)\n",
    "    plt.plot(USD_tCO2eq,p)\n",
    "    plt.plot(USD_tCO2eq,s)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,n)\n",
    "    plt.title('2100')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,k)\n",
    "    plt.plot(USD_tCO2eq,q)\n",
    "    plt.ylim(0,100)\n",
    "    plt.plot(USD_tCO2eq,t)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1618f83",
   "metadata": {},
   "source": [
    "# CH4 Rice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "069adaa4",
   "metadata": {},
   "source": [
    "## Constants\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d7d46ff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100.0, 100.0, 100.0, 100.0, 100.0]\n"
     ]
    }
   ],
   "source": [
    "# Initial order of the mitigation measures in this code:\n",
    "# Direct seeding, Replace urea with ammonium sulphate,Straw mitigation, Alternate wetting and drying, Addition of Phosphogypsum\n",
    "\n",
    "#For rice, two regions are used. 1 = ROW, 2 = Korea, China, South East Asia, Indonesia\n",
    "#RE input values:\n",
    "RE_rice = [[19,44,19,16.60,47,19],[14.18,41.67,20,24],[61,27,27,40.50,53.1,49,58],[49,62,44,60,24,74,46,73,25,25,41,24,64.50,35,18.80,25,31,32,25,35,26,41,60,71,71,79,32],[32,28,53,61.1,41,85.5,48.7]] #RE input values found in literature for the measures: Direct seeding, Replace urea with ammonium sulphate,Straw mitigation, Alternate wetting and drying, Addition of Phosphogypsum\n",
    "\n",
    "#Costs\n",
    "costs_rice = [[0,15,142,148,385], [63.28,20,28,148,61.02]]\n",
    "\n",
    "#Technical applicability:\n",
    "TA_rice = [75,75,50,40,75]\n",
    "\n",
    "#The correction for overlap values change when changing the order of implementation measures. \n",
    "#The order of implementation measures changes when the costs are different for different countries. \n",
    "#With the costs written above, measures are implemented in only one way (ad,sd,sc,ma,rdp,hsb)\n",
    "#We make lists with the overlap values with the previous implemented measures now for this way:\n",
    "\n",
    "Corr_ov_rice = {'':['ds','replace urea','straw m','awd','phosphogypsum'],'ds': [1], 'replace urea': [1], \n",
    "        'straw m': [1,1], 'awd': [1,1,1], 'phosphogypsum': [1,1,1,1]}\n",
    "OV_corr_rice = [Corr_ov_rice['ds'],Corr_ov_rice['replace urea'],Corr_ov_rice['straw m'],Corr_ov_rice['awd'],Corr_ov_rice['phosphogypsum']]\n",
    "#Calculating the product of overlap of previously implemented measures:\n",
    "OV_corr_rice = [np.fmax(0.2,np.product(i)) for i in OV_corr_rice]; OV_corr_rice = [i*100 for i in OV_corr_rice] #1.Calculating the product of overlapping values, saying that it should be minimum 20%. 2. Multiplying the values with 100 to be back in percentages and not percentages/100. \n",
    "\n",
    "\n",
    "#Delta values\n",
    "DeltaTA_rice = 40 #Maximum change in TA in %.\n",
    "DeltaOV_rice = 30 #Maximum change in OV_corr in %.\n",
    "DeltaMC_rice = 0.8 #Maximum change in costs\n",
    "DeltaIP_rice = 30 #Maximum change in IP\n",
    "DeltaTP_rice = 10 #Maximum change in TP\n",
    "\n",
    "#Implementation potential and technological progress values:\n",
    "IP_rice = [20,70,100]\n",
    "TP_rice = [100,90,80]\n",
    "print(OV_corr_rice)\n",
    "\n",
    "#Marginal costs:\n",
    "cr  = [[a*100/b for a,b in zip(i, OV_corr_rice)] for i in costs_rice]\n",
    "\n",
    "#Calculate in which order the measures should be implemented compared to our initial order\n",
    "#This is based on the initial costs and is calculated for each region:\n",
    "order_rice = [[x for _, x in sorted(zip(i,range(0,len(OV_corr_rice))))] for i in cr]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ce65e0",
   "metadata": {},
   "source": [
    "## Making random variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e3ae3da4",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "def generate_random(): #This function generates values for RE, TA, OV_corr, Marginal Costs, IP, and TP using uniform distributions \n",
    "    #RE values \n",
    "    #random.seed(3)\n",
    "    RE = [[RE_rice[i] for i in j] for j in order_rice]\n",
    "    RE_uniform = [[random.uniform(np.min(i), np.max(i))/100 for i in l] for l in RE] #Generating random value between de minimum and maximum of each of the measures\n",
    "    RE_uniform = {i:j for i,j in zip(range(1,len(costs_rice)+1),RE_uniform)} #Assigning country group to RE_uniform\n",
    "    \n",
    "    #TA values (%/100)\n",
    "    TA = [[TA_rice[i] for i in j] for j in order_rice] #TA input values for the measures: storage duration, anaerobic digestion, storage covering, manure acidification, housing systems and beddings, solid liquid seperation\n",
    "    TA_uniform = [[random.uniform(np.max([0,i-DeltaTA_rice]), np.min([100,i+DeltaTA_rice]))/100 for i in l]for l in TA] #Generate values between TA-TA*delta and TA+TA*delta\n",
    "    TA_uniform = {i:j for i,j in zip(range(1,len(costs_rice)+1),TA_uniform)} #Assigning country group to TA_uniform\n",
    "\n",
    "    #OVcorr\n",
    "    OV_corr = [[OV_corr_rice[i] for i in j] for j in order_rice]    \n",
    "    OV_corr_uniform = [[random.uniform(np.max([0,i-DeltaOV_rice]), np.min([100,i+DeltaOV_rice]))/100 for i in l]for l in OV_corr]  #Generate values between OV_corr-OV_corr*delta and OV_corr+OV_corr*delta\n",
    "    OV_corr_uniform = {i:j for i,j in zip(range(1,len(costs_rice)+1),OV_corr_uniform)} #Assigning country group to OV_corr_uniform\n",
    "\n",
    "    #costs \n",
    "    Euro_to_Dollar = 1.24\n",
    "    a= [[[cr[q][i] for i in l] for l in order_rice] for q in (0,1)]\n",
    "    a= [a[i][i] for i in (0,len(costs_rice)-1)]\n",
    "    MC_uniform = [[random.uniform(i-i*DeltaMC_rice,i+i*DeltaMC_rice)/100 for i in l] for l in a] #Generate values between marginal costs-marginal costs*delta and marginal costs+marginal costs*delta\n",
    "    MC_uniform = {i:j for i,j in zip(range(1,len(costs_rice)+1),MC_uniform)} #Assigning country group to costs\n",
    "    \n",
    "    #Implementation potential\n",
    "    IP = {2020: IP_rice[0], 2050: IP_rice[1], 2100:IP_rice[2]} #Values implementation potential\n",
    "    IP_uniform = {year: random.uniform(np.max([0,i-DeltaIP_rice]), np.min([100,i+DeltaIP_rice]))/100 for year,i in IP.items()} #Generate values between IP-IP*delta and IP+IP*delta\n",
    "\n",
    "    #Technological progress\n",
    "    TP_2020,TP_2050,TP_2100 = [TP_rice[0], TP_rice[1], TP_rice[2]]#Values technological progress\n",
    "    TP_uniform = {2020: TP_2020/100, 2050: random.uniform(np.max([0,TP_2050-DeltaTP_rice]), np.min([100,TP_2050+DeltaTP_rice]))/100, 2100: random.uniform(np.max([0,TP_2100-DeltaTP_rice]), np.min([100,TP_2100+DeltaTP_rice]))/100}\n",
    "    return RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "820dfb25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wat is de index van het eerste getal in de lijst dat groter is dan ...\n",
    "#use these definitions to say, if costs are lower than this, than use this RP \n",
    "#This way you get a list of RPs for each costs in the list (0,4000 $/tCeq)\n",
    "def fa(l, ba): return len([x for x in takewhile(lambda x: x[1] < ba, enumerate(l))]) #<: gives the index of the first number that is smaller or equal to the number you give. <=: gives the first number that is smaller than the number you give\n",
    "\n",
    "def aut(een,twee,drie): #een: long list with costs; twee: corrected marginal costs; drie: RP \n",
    "    z = [fa(twee,i) for i in een]\n",
    "    nu = []\n",
    "    [nu.append(drie[i]) for i in z]\n",
    "    return nu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a76e2870",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e6bd485a",
   "metadata": {},
   "source": [
    "## Reduction potentials and costs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "85733032",
   "metadata": {},
   "outputs": [],
   "source": [
    "# range in dollars in c eq or CO2 eq. C eq goes from 0 to 4000 with steps of 20. \n",
    "USD_tC = [*range(0, 4020, 20)]\n",
    "USD_tC = np.arange(0,4020,20)\n",
    "USD_tCO2eq = [i / 44*12 for i in USD_tC]\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs for 2020.\n",
    "#The country can be specified, year also, but this difinition only works for 2050 adn 2100. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values. \n",
    "\n",
    "def generate_f_tp(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    #calculate AP, which is the initial reduction potential\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    #calculate the inverse\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    #Calculate the cumulative reduction potentia\n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    #Calculate the cumulative costs\n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    #Make a list of reduction potentials that belong to each cost value in the list of 0 to 4000 ceq.\n",
    "    Average_without_tp = []\n",
    "    #Add the technological progress\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    Average_with_tp= [(Average_without_tp[151]+(j-USD_tC[151])*(((1-(1-RP[-1]/100)*TP_uniform[year])*100-Average_without_tp[151])/(USD_tC[200]-USD_tC[151]))) for j in USD_tC[152:]] #calculate the influence of techonological progress on RP. Is linearly implemented from 824 USD/tCO2 eq.\n",
    "    f = Average_without_tp[:152]+Average_with_tp\n",
    "    return f\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs.\n",
    "#The country can be specified, year also, this definition works for 2020. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values and you can choose the year and country.\n",
    "def generate_f(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    return Average_without_tp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3908a5",
   "metadata": {},
   "source": [
    "## Run a 1000 times "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a3b28cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#definitions to generate the list of RP values a 1000 times:\n",
    "def k(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "def ktp(year,country): #definition to generate the values a 1000 times\n",
    "    k = np.array([generate_f_tp(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "#Generate the list of RP values a 1000 times for each country:\n",
    "random.seed(3) \n",
    "step1_2020= [k(2020,i) for i in range(1,len(costs_rice)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2050= [ktp(2050,i) for i in range(1,len(costs_rice)+1)] # 2020 values; step1[0] is the first land\n",
    "step1_2100= [ktp(2100,i) for i in range(1,len(costs_rice)+1)] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "30f0dddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,5)\n",
    "random.seed(3)\n",
    "#Calculate the mean of the 1000 runs for each different country for 2020,2050,2100\n",
    "mean_2020 = [step1_2020[i].mean(axis=0) for i in range(0,len(costs_rice))]\n",
    "mean_2050 = [step1_2050[i].mean(axis=0) for i in range(0,len(costs_rice))]\n",
    "mean_2100 = [step1_2100[i].mean(axis=0) for i in range(0,len(costs_rice))]\n",
    "\n",
    "#Calculate the 95th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile95_2020 = [np.percentile(step1_2020[i],95,axis=0) for i in range(0,len(costs_rice))]\n",
    "percentile95_2050 = [np.percentile(step1_2050[i],95,axis=0) for i in range(0,len(costs_rice))]\n",
    "percentile95_2100 = [np.percentile(step1_2100[i],95,axis=0) for i in range(0,len(costs_rice))]\n",
    "\n",
    "#Calculate the 5th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile5_2020 = [np.percentile(step1_2020[i],5,axis=0) for i in range(0,len(costs_rice))]\n",
    "percentile5_2050 = [np.percentile(step1_2050[i],5,axis=0) for i in range(0,len(costs_rice))]\n",
    "percentile5_2100 = [np.percentile(step1_2100[i],5,axis=0) for i in range(0,len(costs_rice))]\n",
    "\n",
    "#For each country, put 2020, 2050, and 2100 in one list\n",
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(costs_rice)) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(costs_rice)) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(costs_rice)) ]\n",
    "\n",
    "#plt.plot(range(0,603), together_mean[0])\n",
    "#plt.plot(range(0,603), together_95[0])\n",
    "#plt.plot(range(0,603), together_5[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8636ffa4",
   "metadata": {},
   "source": [
    "## Calculate the mean, 95th percentile, 5th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2dd82208",
   "metadata": {},
   "outputs": [],
   "source": [
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(costs_rice)) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(costs_rice)) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(costs_rice)) ]\n",
    "\n",
    "together_mean = [[i/100 for i in l]for l in together_mean]\n",
    "together_95 = [[i/100 for i in l]for l in together_95]\n",
    "together_5 = [[i/100 for i in l]for l in together_5]\n",
    "\n",
    "ml1 = together_mean[0] \n",
    "ml2 = together_mean[1] \n",
    "\n",
    "ml95_1 = together_95[0] \n",
    "ml95_2 = together_95[1] \n",
    "\n",
    "ml5_1 = together_5[0] \n",
    "ml5_2 = together_5[1] \n",
    "\n",
    "#time and x are needed for the excel file. time shows the year for each value in the lists.\n",
    "x = np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist()\n",
    "time = [2020] * 201 + [2050] * 201 + [2100] * 201"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1491ea6",
   "metadata": {},
   "source": [
    "## Export to excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "863e31d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3) #Use this to have the same outcome every time !!\n",
    "writer = pd.ExcelWriter('Rice_16_5_2022.xlsx')\n",
    "df = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml1, 'class_2' : ml1, 'class_3' : ml1, 'class_4' : ml1, \n",
    "                'class_5' : ml1, 'class_6' : ml1, 'class_7' : ml1, 'class_8' : ml1, \n",
    "                'class_9' : ml1, 'class_10' : ml1, 'class_11' : ml1, 'class_12' : ml1, \n",
    "                'class_13' : ml1, 'class_14' : ml1, 'class_15' : ml1, 'class_16' : ml1, \n",
    "                'class_17' : ml1, 'class_18' : ml1, 'class_19' : ml2, 'class_20' : ml2, \n",
    "                'class_21' : ml2, 'class_22' : ml2, 'class_23' : ml1, 'class_24' : ml1, \n",
    "                'class_25' : ml1, 'class_26' : ml1})\n",
    "df2 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml95_1, 'class_2' : ml95_1, 'class_3' : ml95_1, 'class_4' : ml95_1, \n",
    "                'class_5' : ml95_1, 'class_6' : ml95_1, 'class_7' : ml95_1, 'class_8' : ml95_1, \n",
    "                'class_9' : ml95_1, 'class_10' : ml95_1, 'class_11' : ml95_1, 'class_12' : ml95_1, \n",
    "                'class_13' : ml95_1, 'class_14' : ml95_1, 'class_15' : ml95_1, 'class_16' : ml95_1, \n",
    "                'class_17' : ml95_1, 'class_18' : ml95_1, 'class_19' : ml95_2, 'class_20' : ml95_2, \n",
    "                'class_21' : ml95_2, 'class_22' : ml95_2, 'class_23' : ml95_1, 'class_24' : ml95_1, \n",
    "                'class_25' : ml95_1, 'class_26' : ml95_1})\n",
    "df3 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : ml5_1, 'class_2' : ml5_1, 'class_3' : ml5_1, 'class_4' : ml5_1, \n",
    "                'class_5' : ml5_1, 'class_6' : ml5_1, 'class_7' : ml5_1, 'class_8' : ml5_1, \n",
    "                'class_9' : ml5_1, 'class_10' : ml5_1, 'class_11' : ml5_1, 'class_12' : ml5_1, \n",
    "                'class_13' : ml5_1, 'class_14' : ml5_1, 'class_15' : ml5_1, 'class_16' : ml5_1, \n",
    "                'class_17' : ml5_1, 'class_18' : ml5_1, 'class_19' : ml5_2, 'class_20' : ml5_2, \n",
    "                'class_21' : ml5_2, 'class_22' : ml5_2, 'class_23' : ml5_1, 'class_24' : ml5_1, \n",
    "                'class_25' : ml5_1, 'class_26' : ml5_1})\n",
    "df.to_excel(writer, sheet_name='Rice average', index=False)\n",
    "df2.to_excel(writer, sheet_name='Rice 95', index=False)\n",
    "df3.to_excel(writer, sheet_name='Rice 5', index=False)\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "dd80eb87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "#plt.plot(USD_tCO2eq,mean_2100[0])\n",
    "for i,j,k,l,m,n,o,p,q,r,s,t in zip(mean_2020,mean_2050,mean_2100, np.arange(1,19,3),np.arange(2,20,3),np.arange(3,21,3),percentile95_2020,percentile95_2050,percentile95_2100,percentile5_2020,percentile5_2050,percentile5_2100):\n",
    "    plt.subplot(6,3,l)\n",
    "    plt.title('2020')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,i)\n",
    "    plt.plot(USD_tCO2eq,o)\n",
    "    plt.plot(USD_tCO2eq,r)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,m)\n",
    "    plt.title('2050')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,j)\n",
    "    plt.plot(USD_tCO2eq,p)\n",
    "    plt.plot(USD_tCO2eq,s)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,n)\n",
    "    plt.title('2100')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,k)\n",
    "    plt.plot(USD_tCO2eq,q)\n",
    "    plt.ylim(0,100)\n",
    "    plt.plot(USD_tCO2eq,t)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa534e3e",
   "metadata": {},
   "source": [
    "# CH4 manure "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273137b5",
   "metadata": {},
   "source": [
    "## Constants\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f8c88d79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initial order of the mitigation measures in this code:\n",
    "# 1. storage duration (sd), 2. anaerobic digestion (ad), 3. storage covering (sc), \n",
    "# 4. manure acidification (ma), 5. housing systems and beddings (hsb), 6. solid liquid seperation (sls)\n",
    "\n",
    "\n",
    "# Order of 26 regions: Canada,USA,Mexico,Central America,Brazil,Rest of South-America,North Africa,West Africa,East Africa,\n",
    "#South Africa West Europe,Central Europe,Turkey,Ukraine,Kazachstan,Russia,Middle East,India,Korea,China,\n",
    "#South East Asia,Indonesia,Japan,Oceania,Rest of South Asia,Rest of South Africa\n",
    "\n",
    "#RE input values found in literature. A differentiation is made between warm and cold\n",
    "#RE values are given for the 6 measures, the order is as described above. \n",
    "REw = [[39,76],[75,59,55],[30,90,30,22,0,38,70],[77,89,95,84,68,61,98],[35,96,49,6,44,4,60,49],[81,68,46]] #RE warm countries\n",
    "REc = [[39,76],[50,59,25],[30,90,30,22,0,38,70],[77,89,95,84,68,61,98],[35,96,49,6,44,4,60,49],[81,68,46]] #RE cold countries\n",
    "\n",
    "#Specifying for each region whether it is warm or cold:\n",
    "RE_ch4_manure = [REc,REc,REw,REw,REw,REw,REw,REw,REw,REw,REc,REc,REc,REc,REw,REc,REw,REw,REw,REw,REw,REw,REw,REw,REw,REw,REw]\n",
    "\n",
    "\n",
    "#The costs are different in different regions\n",
    "#for the following regions: 1 = Canada, 2 = USA, 3 = East EU, 4 = Ukr/Kaz/Rus, 5 = India/Indonesie, 6 = ROW:\n",
    "crow = [30,17,70,83,149,122.6*Euro_to_Dollar]; ccan = [30,52,70,83,149,122.6*Euro_to_Dollar]; cin = [30,26,70,83,149,122.6*Euro_to_Dollar]\n",
    "cusa = [30,39.5,70,83,149,122.6*Euro_to_Dollar]; ceeu = [30,26,70,83,149,122.6*Euro_to_Dollar]; cukaru = [30,45,70,83,149,122.6*Euro_to_Dollar]\n",
    "#Specifying which of these costs to use for which region: \n",
    "costs_ch4_manure = [ccan,cusa,crow,crow,crow,crow,crow,crow,crow,crow,crow,crow,ceeu,cukaru,cukaru,cukaru,crow,cin,crow,crow,crow,cin,crow,crow,crow,crow,crow]\n",
    "\n",
    "#Specifying the correction for overlap values: \n",
    "storage_duration = {'ad': 0.2, 'sc': 0.7, 'ma': 0.7, 'hsb': 0.7, 'sls': 0.7, 'sd': 1}\n",
    "an_digestion = {'sc': 0.2, 'ma': 0.2, 'hsb': 0.7, 'sls': 0.2, 'ad': 1}\n",
    "storage_covering = {'ma': 0.7, 'hsb': 0.7, 'sls': 0.7}\n",
    "manure_acidif = { 'hsb': 0.7, 'sls': 0.7}\n",
    "hsb = {'sls': 0.7}\n",
    "\n",
    "#The correction for overlap values change when changing the order of implementation measures. \n",
    "#The order of implementation measures changes because the least costly measure is implemented first. \n",
    "#With the costs written above, measures are implemented in 2 ways (sd,ad,sc,ma,hsb,sls; ad,sd,sc,ma,hsb,sls)\n",
    "#We make lists with the overlap values with the previous implemented measures now for each of the 2 ways:\n",
    "Corr_o = {'':['sd','ad','sc','ma','hsb'], \n",
    "          'sd': [storage_duration['sd']], \n",
    "          'ad': [storage_duration['ad']],\n",
    "          'sc': [storage_duration['sc'], an_digestion['sc']], \n",
    "          'ma': [storage_duration['ma'], an_digestion['ma'], storage_covering['ma']],\n",
    "          'hsb': [storage_duration['hsb'], an_digestion['hsb'],storage_covering['hsb'], manure_acidif['hsb']],\n",
    "         'sls': [storage_duration['hsb'], an_digestion['hsb'],storage_covering['hsb'], manure_acidif['hsb'], hsb['sls']]}\n",
    "\n",
    "Corr_o2 = {'':['sd','ad','sc','ma','hsb'], \n",
    "          'sd': [storage_duration['ad']], \n",
    "          'ad': [an_digestion['ad']],\n",
    "          'sc': [storage_duration['sc'], an_digestion['sc']], \n",
    "          'ma': [storage_duration['ma'], an_digestion['ma'], storage_covering['ma']],\n",
    "          'hsb': [storage_duration['hsb'], an_digestion['hsb'],storage_covering['hsb'], manure_acidif['hsb']],\n",
    "         'sls': [storage_duration['hsb'], an_digestion['hsb'],storage_covering['hsb'], manure_acidif['hsb'], hsb['sls']]}\n",
    "\n",
    "#Rewriting the lists in an easier way (order as described on top):\n",
    "ovc1 = [Corr_o['sd'],Corr_o['ad'],Corr_o['sc'],Corr_o['ma'],Corr_o['hsb'],Corr_o['sls']]\n",
    "ovc2 = [Corr_o2['sd'],Corr_o2['ad'],Corr_o2['sc'],Corr_o2['ma'],Corr_o2['hsb'],Corr_o2['sls']]\n",
    "\n",
    "#Specifying which country has which of the 2 ways:\n",
    "OV_corr_ch4_manure = [ovc1,ovc1,ovc1,ovc1,ovc1,ovc1,ovc1,ovc1,ovc1,ovc1,ovc1,ovc1,ovc2,ovc1,ovc1,ovc1,ovc1,ovc2,ovc1,ovc1,ovc1,ovc2,ovc1,ovc1,ovc1,ovc1,ovc1]\n",
    "\n",
    "#Calculating the product of overlap of previously implemented measures:\n",
    "OV_corr = [[np.fmax(0.2,np.product(i)) for i in l] for l in OV_corr_ch4_manure]\n",
    "#Multiply by 100:\n",
    "OV_corr_ch4_manure = [[i*100 for i in l] for l in OV_corr]\n",
    "\n",
    "#Technical applicability for each of the measures (order as described on top)\n",
    "#Specified for red,orange and green countries based on GAINS data\n",
    "TAg = [90,90,50,50,50,50]; TAo=[50,50,30,30,30,30]; TAr = [30,30,10,10,10,10]\n",
    "#TA specified for each region:\n",
    "TA_ch4_manurenew = [TAg, TAg, TAo, TAo, TAo, TAo, TAr, TAr,TAr,TAr,TAg,TAg, TAr,TAr,TAr,TAo,TAr,TAr,TAr,TAg,TAr,TAr,TAg, TAg,TAr,TAr,TAr]\n",
    "\n",
    "#Delta values:\n",
    "DeltaTA_ch4_manure = 40 #Maximum change in TA\n",
    "DeltaOV_ch4_manure = 30 #Maximum change in OV_corr\n",
    "DeltaMC_ch4_manure = 0.8 #Maximum change in costs\n",
    "DeltaIP_ch4_manure = 30 #Maximum change in IP\n",
    "DeltaTP_ch4_manure = 10 #Maximum change in TP\n",
    "\n",
    "#Implementation potential and technological progress values:\n",
    "IP_ch4_manure = [20,50,70]\n",
    "TP_ch4_manure = [100,90,80]\n",
    "\n",
    "#Calculate the marginal costs:\n",
    "c_ch4_manure  = [[a*100/b for a,b in zip(i, j)] for i,j in zip(costs_ch4_manure,OV_corr_ch4_manure)]\n",
    "\n",
    "#Calculate in which order the measures should be implemented compared to our initial order\n",
    "#This is based on the initial costs and is calculated for each region:\n",
    "order_ch4_manure = [[x for _, x in sorted(zip(i,range(0,len(OV_corr_ch4_manure[0])+1)))] for i in costs_ch4_manure]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "93befd81",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wat is de index van het eerste getal in de lijst dat groter is dan ...\n",
    "#use these definitions to say, if costs are lower than this, than use this RP \n",
    "#This way you get a list of RPs for each costs in the list (0,4000 $/tCeq)\n",
    "def fa(l, ba): return len([x for x in takewhile(lambda x: x[1] < ba, enumerate(l))]) #<: gives the index of the first number that is smaller or equal to the number you give. <=: gives the first number that is smaller than the number you give\n",
    "\n",
    "def aut(een,twee,drie): #een: long list with costs; twee: corrected marginal costs; drie: RP \n",
    "    z = [fa(twee,i) for i in een]\n",
    "    nu = []\n",
    "    [nu.append(drie[i]) for i in z]\n",
    "    return nu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894eba1b",
   "metadata": {},
   "source": [
    "## Making random variables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "73ddbb9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3) #To make sure it has the same outcome\n",
    "#This function generates random values for RE, TA, OV_corr, Marginal Costs, IP, and TP using uniform distributions \n",
    "def generate_random():     \n",
    "    #RE values \n",
    "    #random.seed(3)\n",
    "    a= [[[RE_ch4_manure[q][i] for i in l] for l in order_ch4_manure] for q in range(0,len(RE_ch4_manure))]\n",
    "    a= [a[i][i] for i in range(0,len(RE_ch4_manure))]\n",
    "    RE_uniform = [[random.uniform(np.min(i), np.max(i))/100 for i in l] for l in a]\n",
    " \n",
    "    #TA values\n",
    "    a= [[[TA_ch4_manurenew[q][i] for i in l] for l in order_ch4_manure] for q in range(0,len(costs_ch4_manure))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_ch4_manure))]\n",
    "    TA_uniform = [[random.uniform(np.max([0,i-DeltaTA_ch4_manure]), np.min([100,i+DeltaTA_ch4_manure]))/100 for i in l]for l in a] #Generate values between TA-TA*delta and TA+TA*delta\n",
    "    TA_uniform = {i:j for i,j in zip(range(1,len(costs_ch4_manure)+1),TA_uniform)} #Assigning country group to TA_uniform\n",
    "\n",
    "    #OVcorr\n",
    "    a= [[[OV_corr_ch4_manure[q][i] for i in l] for l in order_ch4_manure] for q in range(0,len(costs_ch4_manure))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_ch4_manure))]\n",
    "    OV_corr_uniform = [[random.uniform(np.max([0,i-DeltaOV_ch4_manure]), np.min([100,i+DeltaOV_ch4_manure]))/100 for i in l]for l in a]  #Generate values between OV_corr-OV_corr*delta and OV_corr+OV_corr*delta\n",
    "    OV_corr_uniform = {i:j for i,j in zip(range(1,len(costs_ch4_manure)+1),OV_corr_uniform)} #Assigning country group to OV_corr_uniform\n",
    "\n",
    "    #costs \n",
    "    Euro_to_Dollar = 1.24\n",
    "    a= [[[c_ch4_manure[q][i] for i in l] for l in order_ch4_manure] for q in range(0,len(costs_ch4_manure))]\n",
    "    a= [a[i][i] for i in range(0,len(costs_ch4_manure))]\n",
    "    MC_uniform = [[random.uniform(i-i*DeltaMC_ch4_manure,i+i*DeltaMC_ch4_manure)/100 for i in l] for l in a] #Generate values between marginal costs-marginal costs*delta and marginal costs+marginal costs*delta\n",
    "    MC_uniform = {i:j for i,j in zip(range(1,len(costs_ch4_manure)+1),MC_uniform)} #Assigning country group to costs\n",
    "       \n",
    "    #Implementation potential\n",
    "    IP = {2020: IP_ch4_manure[0], 2050: IP_ch4_manure[1], 2100:IP_ch4_manure[2]} #Values implementation potential\n",
    "    IP_uniform = {year: random.uniform(np.max([0,i-DeltaIP_ch4_manure]), np.min([100,i+DeltaIP_ch4_manure]))/100 for year,i in IP.items()} #Generate values between IP-IP*delta and IP+IP*delta\n",
    "\n",
    "    #Technological progress\n",
    "    TP_2020,TP_2050,TP_2100 = [TP_ch4_manure[0], TP_ch4_manure[1], TP_ch4_manure[2]]#Values technological progress\n",
    "    TP_uniform = {2020: TP_2020/100, 2050: random.uniform(np.max([0,TP_2050-DeltaTP_ch4_manure]), np.min([100,TP_2050+DeltaTP_ch4_manure]))/100, 2100: random.uniform(np.max([0,TP_2100-DeltaTP_ch4_manure]), np.min([100,TP_2100+DeltaTP_ch4_manure]))/100}\n",
    "    return RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2a41fae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wat is de index van het eerste getal in de lijst dat groter is dan ...\n",
    "#use these definitions to say, if costs are lower than this, than use this RP \n",
    "def fa(l, ba): return len([x for x in takewhile(lambda x: x[1] < ba, enumerate(l))]) #<: gives the index of the first number that is smaller or equal to the number you give. <=: gives the first number that is smaller than the number you give\n",
    "\n",
    "def aut(een,twee,drie): #een: long list with costs; twee: corrected marginal costs; drie: RP \n",
    "    z = [fa(twee,i) for i in een]\n",
    "    nu = []\n",
    "    [nu.append(drie[i]) for i in z]\n",
    "    return nu"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f287150",
   "metadata": {},
   "source": [
    "## Reduction potentials and costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "713c92a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# range in dollars in c eq or CO2 eq. C eq goes from 0 to 4000 with steps of 20. \n",
    "USD_tC = [*range(0, 4020, 20)]\n",
    "USD_tC = np.arange(0,4020,20)\n",
    "USD_tCO2eq = [i / 44*12 for i in USD_tC]\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs for 2020.\n",
    "#The country can be specified, year also, but this difinition only works for 2020. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values. \n",
    "def generate_f_tp(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    Average_with_tp= [(Average_without_tp[151]+(j-USD_tC[151])*(((1-(1-RP[-1]/100)*TP_uniform[year])*100-Average_without_tp[151])/(USD_tC[200]-USD_tC[151]))) for j in USD_tC[152:]] #calculate the influence of techonological progress on RP. Is linearly implemented from 824 USD/tCO2 eq.\n",
    "    f = Average_without_tp[:152]+Average_with_tp\n",
    "    return f\n",
    "\n",
    "#Definition for generating the RP belonging to each costs value in the list of the costs.\n",
    "#The country can be specified, year also, this definition works for 2050 and 2100. \n",
    "#The outcome of this difnition is a list with RP values belonging to the costs values and you can choose the year and country. \n",
    "def generate_f(year,country):\n",
    "    RE_uniform, TA_uniform, OV_corr_uniform, MC_uniform, IP_uniform, TP_uniform = generate_random()\n",
    "    AP = [i*j*k*IP_uniform[year] for i,j,k in zip(RE_uniform[country],TA_uniform[country],OV_corr_uniform[country])] #dependant on IP\n",
    "    inverse = [1-i for i in AP]\n",
    "    \n",
    "    RP = [1-np.prod(inverse[0:i]) for i in range(1,len(inverse)+1)]\n",
    "    RP = [0]+[i*100 for i in RP]\n",
    "    \n",
    "    Costs = [(i)/(k-l)*m*10000 for i,k,l,m in zip(MC_uniform[country], RP[1:], RP, AP)]\n",
    "    \n",
    "    Average_without_tp = []\n",
    "    Average_without_tp = aut(USD_tCO2eq,Costs,RP)\n",
    "    return Average_without_tp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ffbb28",
   "metadata": {},
   "source": [
    "## Run a 1000 times \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb1afc64",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#definitions to generate the list of RP values a 1000 times:\n",
    "def k(year,country): \n",
    "    k = np.array([generate_f(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "def ktp(year,country): \n",
    "    k = np.array([generate_f_tp(year,country) for i in range(1000)])\n",
    "    return k\n",
    "\n",
    "#Generate the list of RP values a 1000 times for each country:\n",
    "random.seed(3) \n",
    "step1_2020= [k(2020,i) for i in range(1,(len(RE_ch4_manure)))] # 2020 values; step1[0] is the first land\n",
    "step1_2050= [ktp(2050,i) for i in range(1,(len(RE_ch4_manure)))] # 2020 values; step1[0] is the first land\n",
    "step1_2100= [ktp(2100,i) for i in range(1,(len(RE_ch4_manure)))] # 2020 values; step1[0] is the first land"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d639aa",
   "metadata": {},
   "source": [
    "## Calculate the mean, 95th percentile, 5th percentile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "581f1033",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.seed(3)\n",
    "#Calculate the mean of the 1000 runs for each different country for 2020,2050,2100\n",
    "mean_2020 = [step1_2020[i].mean(axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "mean_2050 = [step1_2050[i].mean(axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "mean_2100 = [step1_2100[i].mean(axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "\n",
    "#Calculate the 95th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile95_2020 = [np.percentile(step1_2020[i],95,axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "percentile95_2050 = [np.percentile(step1_2050[i],95,axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "percentile95_2100 = [np.percentile(step1_2100[i],95,axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "\n",
    "#Calculate the 5th percentile of the 1000 runs for each different country for 2020,2050,2100\n",
    "percentile5_2020 = [np.percentile(step1_2020[i],5,axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "percentile5_2050 = [np.percentile(step1_2050[i],5,axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "percentile5_2100 = [np.percentile(step1_2100[i],5,axis=0) for i in range(0,len(RE_ch4_manure)-1)]\n",
    "\n",
    "#For each country, put 2020, 2050, and 2100 in one list\n",
    "together_mean = [mean_2020[i].tolist()+mean_2050[i].tolist()+mean_2100[i].tolist() for i in range(0,len(RE_ch4_manure)-1) ]\n",
    "together_95 = [percentile95_2020[i].tolist()+percentile95_2050[i].tolist()+percentile95_2100[i].tolist() for i in range(0,len(RE_ch4_manure)-1) ]\n",
    "together_5 = [percentile5_2020[i].tolist()+percentile5_2050[i].tolist()+percentile5_2100[i].tolist() for i in range(0,len(RE_ch4_manure)-1) ]\n",
    "\n",
    "#plt.plot(range(0,603), together_mean[0])\n",
    "#plt.plot(range(0,603), together_95[0])\n",
    "#plt.plot(range(0,603), together_5[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a648a8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#time and x are needed for the excel file. time shows the year for each value in the lists.\n",
    "x = np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist() + np.arange(1,202,1).tolist()\n",
    "time = [2020] * 201 + [2050] * 201 + [2100] * 201"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e997c72",
   "metadata": {},
   "source": [
    "## Export to excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8753576d",
   "metadata": {},
   "outputs": [],
   "source": [
    "tm = together_mean\n",
    "t9 = together_95\n",
    "t5 = together_5\n",
    "\n",
    "tm = [[i/100 for i in l]for l in tm]\n",
    "t9 = [[i/100 for i in l]for l in t9]\n",
    "t5 = [[i/100 for i in l]for l in t5] \n",
    "random.seed(3) #Use this to have the same outcome every time !!\n",
    "\n",
    "#Write it to an excel file:\n",
    "writer = pd.ExcelWriter('CH4_manure_28_3_2022.xlsx)\n",
    "df = DataFrame({'t': time, 'DIM_1': x, 'class_1' : tm[0], 'class_2' : tm[1], 'class_3' : tm[2], 'class_4' : tm[3], \n",
    "                'class_5' : tm[4], 'class_6' : tm[5], 'class_7' : tm[6], 'class_8' : tm[7], \n",
    "                'class_9' : tm[8], 'class_10' : tm[9], 'class_11' : tm[10], 'class_12' : tm[11], \n",
    "                'class_13' : tm[12], 'class_14' : tm[13], 'class_15' : tm[14], 'class_16' : tm[15], \n",
    "                'class_17' : tm[16], 'class_18' : tm[17], 'class_19' : tm[18], 'class_20' : tm[19], \n",
    "                'class_21' : tm[20], 'class_22' : tm[21], 'class_23' : tm[22], 'class_24' : tm[23], \n",
    "                'class_25' : tm[24], 'class_26' : tm[25]})\n",
    "df2 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : t9[0], 'class_2' : t9[1], 'class_3' : t9[2], 'class_4' : t9[3], \n",
    "                'class_5' : t9[4], 'class_6' : t9[5], 'class_7' : t9[6], 'class_8' : t9[7], \n",
    "                'class_9' : t9[8], 'class_10' : t9[9], 'class_11' : t9[10], 'class_12' : t9[11], \n",
    "                'class_13' : t9[12], 'class_14' : t9[13], 'class_15' : t9[14], 'class_16' : t9[15], \n",
    "                'class_17' : t9[16], 'class_18' : t9[17], 'class_19' : t9[18], 'class_20' : t9[19], \n",
    "                'class_21' : t9[20], 'class_22' : t9[21], 'class_23' : t9[22], 'class_24' : t9[23], \n",
    "                'class_25' : t9[24], 'class_26' : t9[25]})\n",
    "df3 = DataFrame({'t': time, 'DIM_1': x, 'class_1' : t5[0], 'class_2' : t5[1], 'class_3' : t5[2], 'class_4' : t5[3], \n",
    "                'class_5' : t5[4], 'class_6' : t5[5], 'class_7' : t5[6], 'class_8' : t5[7], \n",
    "                'class_9' : t5[8], 'class_10' : t5[9], 'class_11' : t5[10], 'class_12' : t5[11], \n",
    "                'class_13' : t5[12], 'class_14' : t5[13], 'class_15' : t5[14], 'class_16' : t5[15], \n",
    "                'class_17' : t5[16], 'class_18' : t5[17], 'class_19' : t5[18], 'class_20' : t5[19], \n",
    "                'class_21' : t5[20], 'class_22' : t5[21], 'class_23' : t5[22], 'class_24' : t5[23], \n",
    "                'class_25' : t5[24], 'class_26' : t5[25]})\n",
    "df.to_excel(writer, sheet_name='CH4_manure', index=False)\n",
    "df2.to_excel(writer, sheet_name='CH4_manure95', index=False)\n",
    "df3.to_excel(writer, sheet_name='CH4_manure5', index=False)\n",
    "\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d122961",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plotting of the first 6 countries:\n",
    "plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "#plt.plot(USD_tCO2eq,mean_2100[0])\n",
    "for i,j,k,l,m,n,o,p,q,r,s,t in zip(mean_2020,mean_2050,mean_2100, np.arange(1,19,3),np.arange(2,20,3),np.arange(3,21,3),percentile95_2020,percentile95_2050,percentile95_2100,percentile5_2020,percentile5_2050,percentile5_2100):\n",
    "    plt.subplot(6,3,l)\n",
    "    plt.title('2020')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,i)\n",
    "    plt.plot(USD_tCO2eq,o)\n",
    "    plt.plot(USD_tCO2eq,r)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,m)\n",
    "    plt.title('2050')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,j)\n",
    "    plt.plot(USD_tCO2eq,p)\n",
    "    plt.plot(USD_tCO2eq,s)\n",
    "    plt.ylim(0,100)\n",
    "    plt.subplot(6,3,n)\n",
    "    plt.title('2100')\n",
    "    plt.ylabel('reduction')\n",
    "    plt.xlabel('costs')\n",
    "    plt.plot(USD_tCO2eq,k)\n",
    "    plt.plot(USD_tCO2eq,q)\n",
    "    plt.ylim(0,100)\n",
    "    plt.plot(USD_tCO2eq,t)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f06c5a5c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7478a7b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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