FinalModel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mesa import Agent, Model\n",
    "from mesa.time import RandomActivation\n",
    "from mesa.datacollection import DataCollector\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "import os, sys\n",
    "from itertools import product\n",
    "import math\n",
    "import statistics\n",
    "import cmath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#0<gamma_L<gamma_H<1\n",
    "gamma_L = 0.3\n",
    "gamma_H = 0.45\n",
    "fixed_cost = 0.45\n",
    "sigma = 1.5\n",
    "beta = 0.95\n",
    "delta = 0.08\n",
    "theta = 0.8\n",
    "g = 1\n",
    "\n",
    "#global function that calculates the weight of the edge, args: the 2 nodes (agent class objects)\n",
    "def Edge_Weight(node1,node2, b, a):\n",
    "        try:\n",
    "             weight = 1+math.exp(a*((node1.k-node2.k)-b))\n",
    "        except OverflowError:\n",
    "             weight = float('inf')\n",
    "        return 1/weight  \n",
    "    \n",
    "def calculating_k_c(agent, gamma, E_t, time):\n",
    "        a1 = pow(agent.k,gamma) \n",
    "       \n",
    "        #k_t+1 = theta*(alpha*k_t^gamma - C_t + (1-delta)*k_t)\n",
    "        k_new = theta*(agent.alpha*a1-agent.consum + (1-delta)*agent.k)\n",
    "        #print(\"New k = \", k_new)\n",
    "\n",
    "        slope = gamma*agent.alpha*pow(agent.k, gamma -1) + 1 - delta - 1/theta\n",
    "        #print(\"Slope = \", slope)\n",
    "    \n",
    "        #k_t+1^(gamma-1)\n",
    "        a2 = pow(k_new,(gamma-1)) \n",
    "\n",
    "        #beta*E*theta*(alpha*gamma*k_t+1^(gamma-1)+(1-delta))\n",
    "        e1 = pow(beta, time - 1)*E_t*theta*(agent.alpha*gamma*a2 + (1-delta)) \n",
    "\n",
    "        #(beta*E_t*theta*(alpha*gamma_H*a2 + (1-delta)))^(1/sigma)\n",
    "        e2 = pow(e1, (1/sigma))\n",
    "        \n",
    "        #c*sigmathroot(beta*E_t*theta*(alpha*gamma_H*a2 + (1-delta)))^(1/sigma)\n",
    "        con = agent.consum * e2\n",
    "        #print(\"Calculated consumption :\", con)\n",
    "        \n",
    "        return k_new, con, slope\n",
    "    \n",
    "def isocline(agent):\n",
    "        if(agent.tec == 'H'):\n",
    "            con_cond = agent.alpha*pow(agent.k, gamma_H) + (1-delta)*agent.k - agent.k/theta\n",
    "        if(agent.tec == 'L'):\n",
    "            con_cond = agent.alpha*pow(agent.k, gamma_L) + (1-delta)*agent.k - agent.k/theta   \n",
    "        return con_cond   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MoneyAgent(Agent):\n",
    "    \n",
    "    def __init__(self, unique_id, model):\n",
    "        \n",
    "        super().__init__(unique_id, model)\n",
    "        self.k = (capital[unique_id]) #initial stock of wealth\n",
    "        self.lamda = round(random.uniform(0.1,1),1) #saving propensity\n",
    "        while (self.lamda == 1):\n",
    "            self.lamda = round(random.uniform(0.1,1),1)    \n",
    "        self.alpha = alpha[unique_id]#human capital \n",
    "        self.tec = 'NA'\n",
    "        self.income = 0 #initialising income\n",
    "        self.income_generation() #finding income corresponding to the human capital,\n",
    "                                 #needed here to set the initial consumption\n",
    "        self.front = 0 #for micawber frontier\n",
    "        con_cond = isocline(self)\n",
    "        #self.consum = isocline(self)\n",
    "        #if(self.consum < 0):\n",
    "            #self.consum = 0.1\n",
    "        if(self.tec == 'H'):\n",
    "            self.slope = gamma_H*self.alpha*pow(self.k, gamma_H -1) + 1 - delta - 1/theta\n",
    "        else:\n",
    "            self.slope = gamma_L*self.alpha*pow(self.k, gamma_L -1) + 1 - delta - 1/theta\n",
    "    \n",
    "        if(self.slope > 0): #small k_t\n",
    "            #print(\"1st quadrant\")\n",
    "            if(con_cond > 0 and con_cond<self.k):\n",
    "                self.consum = con_cond\n",
    "            else:\n",
    "                con = con_cond - random.random()\n",
    "                while(con>self.k or con < 0):\n",
    "                    con = con_cond - random.random()\n",
    "                self.consum = con\n",
    "        else:\n",
    "            #print(\"4th quadrant\")\n",
    "            if(con_cond > 0 and con_cond <self.k):\n",
    "                self.consum = con_cond\n",
    "            else:\n",
    "                con = con_cond + random.random()\n",
    "                while(con>self.k or con<0):\n",
    "                    con = con_cond + random.random()\n",
    "                self.consum = con\n",
    "    \n",
    "        self.model.agents.append(self)\n",
    "\n",
    "        \n",
    "        \n",
    "    #function that decides income based on the type of technology\n",
    "    def income_generation(self): \n",
    "        b1 = pow(self.k,gamma_H)\n",
    "        H = self.alpha*b1 - fixed_cost\n",
    "        \n",
    "        b2 = pow(self.k,gamma_L)\n",
    "        L = self.alpha*b2\n",
    "        \n",
    "        self.front = H\n",
    "        if(H>=L): #\n",
    "            self.income = H\n",
    "            self.tec = 'H'\n",
    "        else:\n",
    "            self.income = L\n",
    "            self.tec = 'L'\n",
    "            \n",
    "    \n",
    "    #function that updates the capital and consumption for the next time step    \n",
    "    def income_updation(self):\n",
    "        \n",
    "        #finding expected value of income at each time step\n",
    "        e_t = [a.income for a in self.model.agents] #is this k or f(alpha,k)?\n",
    "        E_t = statistics.mean(e_t)\n",
    "        k = self.k\n",
    "        alpha = self.alpha\n",
    "        consum = self.consum\n",
    "        #print(\"Agent:{}  Tec: {}\".format(self.unique_id, self.tec))\n",
    "        #print(\"Old k = {}, alpha = {} \" .format(k, alpha))\n",
    "        #print(\"mean : \", E_t)\n",
    "       \n",
    "        if(self.tec == 'H'):\n",
    "            \n",
    "            k_new, con, slope = calculating_k_c(self, gamma_H, E_t, self.model.time)\n",
    "            self.k = k_new\n",
    "            \n",
    "            c_cond = isocline(self)\n",
    "            #print(\"c_cond = \", c_cond)\n",
    "            \n",
    "            if(slope > 0):\n",
    "                #print(\"1st quadrant\")\n",
    "                if(con <=c_cond and con<self.k):\n",
    "                    self.consum = con\n",
    "                else:\n",
    "                    con = c_cond - random.random()\n",
    "                    while(con>self.k or con < 0):\n",
    "                        con = c_cond - random.random()\n",
    "                    self.consum = con\n",
    "            else:\n",
    "                #print(\"4th quadrant\")\n",
    "                if(con > c_cond and con<self.k):\n",
    "                    self.consum = con\n",
    "                else:\n",
    "                    con = c_cond - random.random()\n",
    "                    while(con>self.k or con < 0):\n",
    "                        con = c_cond - random.random()\n",
    "                    self.consum = con\n",
    "\n",
    "        if(self.tec == 'L'):\n",
    "            \n",
    "            k_new, con, slope = calculating_k_c(self, gamma_L, E_t, self.model.time)\n",
    "            self.k = k_new\n",
    "            \n",
    "            c_cond = isocline(self)\n",
    "            #print(\"c_cond = \", c_cond)\n",
    "\n",
    "            if(slope > 0):\n",
    "                #print(\"1st quadrant\")\n",
    "                if(con <=c_cond and con<self.k):\n",
    "                    self.consum = con\n",
    "                else:\n",
    "                    con = c_cond - random.random()\n",
    "                    while(con>self.k or con < 0):\n",
    "                        con = c_cond - random.random()\n",
    "                    self.consum = con\n",
    "            else:\n",
    "                #print(\"4th quadrant\")\n",
    "                if(con > c_cond and con<self.k):\n",
    "                    self.consum = con\n",
    "                else:\n",
    "                    con = c_cond - random.random()\n",
    "                    while(con>self.k or con<0):\n",
    "                        con = c_cond - random.random()\n",
    "                    self.consum = con  \n",
    "        #print(\"Old C:\", consum)   \n",
    "        #print(\"New Consum :\", self.consum)\n",
    "    \n",
    "    #finding neighbor nodes for the purpose of making an edge/connection\n",
    "    def neighbors(self):\n",
    "        neighbors_nodes = list(nx.all_neighbors(self.model.G,self.unique_id))\n",
    "        neighbors = []\n",
    "        for node in neighbors_nodes:\n",
    "            for agent in self.model.agents:\n",
    "                if(agent.unique_id == node):\n",
    "                    neighbors.append(agent)\n",
    "        return neighbors\n",
    "    \n",
    "     #function used to trade/communicate     \n",
    "    def give_money(self): \n",
    "        b = self.model.b\n",
    "        a = self.model.a\n",
    "        neighbors = self.neighbors()\n",
    "        epsilon = random.random()\n",
    "        if len(neighbors) > 1 :\n",
    "            other = self.random.choice(neighbors)\n",
    "            while(other.unique_id == self.unique_id):\n",
    "                other = self.random.choice(neighbors)  \n",
    "            w = self.model.G[self.unique_id][other.unique_id]['weight'] \n",
    "            if(w >= random.random()): \n",
    "                xi = self.income\n",
    "                xj = other.income\n",
    "                delta_income = (1-self.lamda)*(xi - epsilon*(xi + xj))\n",
    "                xi_new = xi - delta_income\n",
    "                xj_new = xj + delta_income\n",
    "                other.income = xj_new\n",
    "                self.income = xi_new\n",
    "                for neighbor in neighbors:\n",
    "                    self.model.G[self.unique_id][neighbor.unique_id]['weight'] = Edge_Weight(self,neighbor,b, a)\n",
    "                other_neighbors = other.neighbors()\n",
    "                for neighbor in other_neighbors:\n",
    "                    if(neighbor.unique_id != other.unique_id):\n",
    "                        self.model.G[other.unique_id][neighbor.unique_id]['weight'] = Edge_Weight(other,neighbor,b, a)\n",
    "        \n",
    "   \n",
    "    #link addition happening at every time step\n",
    "    def Local_Attachment(self): \n",
    "        b = self.model.b\n",
    "        a = self.model.a\n",
    "        node1 = random.choice(self.model.nodes)\n",
    "        node2 = random.choice(self.model.nodes)\n",
    "        count = 0 #to avoid an infinite loop when all agents have already made links with each other\n",
    "        while(self.model.G.has_edge(node1,node2)==True and count <5):\n",
    "            node2 = random.choice(self.model.nodes)\n",
    "            node1 = random.choice(self.model.nodes)\n",
    "            count +=1\n",
    "        for agent in self.model.agents:\n",
    "            if(agent.unique_id == node1):\n",
    "                node1_a = agent\n",
    "            if(agent.unique_id == node2):\n",
    "                node2_a = agent\n",
    "        self.model.G.add_edge(node1,node2,weight = Edge_Weight(node1_a,node2_a, b, a))\n",
    "    \n",
    "    \n",
    "   #links are deleted randomly at every time step\n",
    "    def Link_Deletion(self):\n",
    "        node1 = random.choice(self.model.nodes)\n",
    "        node2 = random.choice(self.model.nodes)\n",
    "        while(self.model.G.has_edge(node1,node2)==False):\n",
    "            node1 = random.choice(self.model.nodes)\n",
    "            node2 = random.choice(self.model.nodes)\n",
    "        self.model.G.remove_edge(node1,node2)\n",
    "                    \n",
    "    def step(self):\n",
    "        #if(self.k > 0):\n",
    "        self.income_updation()\n",
    "        self.give_money()\n",
    "        self.Local_Attachment()\n",
    "        self.Link_Deletion()\n",
    "        self.income_generation() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BoltzmannWealthModelNetwork(Model):\n",
    "    \"\"\"A model with some number of agents.\"\"\"\n",
    "\n",
    "    def __init__(self,b, a,N=500): #N- number of agents\n",
    "\n",
    "        self.N = N\n",
    "        self.b =b\n",
    "        self.a = a\n",
    "        self.agents = []\n",
    "        self.gini = 0\n",
    "        self.time = 0\n",
    "        self.G = nx.barabasi_albert_graph(n=N, m = 1)\n",
    "        nx.set_edge_attributes(self.G, 1, 'weight') #setting all initial edges with a weight of 1\n",
    "        self.nodes = np.linspace(0,N-1,N, dtype = 'int') #to keep track of the N nodes   \n",
    "        \n",
    "        self.schedule = RandomActivation(self)\n",
    "        self.datacollector = DataCollector(model_reporters = {\"Gini\": 'gini'},agent_reporters={\"k_t\":'k','income':'income',\n",
    "                                           'H':'front', 'consumption':'consum','lamda':'lamda','alpha':'alpha', 'technology':'tec' })       \n",
    "        for i, node in enumerate(self.G.nodes()):\n",
    "            agent = MoneyAgent(i, self)\n",
    "            self.schedule.add(agent)\n",
    "           \n",
    "        self.running = True\n",
    "        self.datacollector.collect(self)\n",
    "        \n",
    "    def Global_Attachment(self):\n",
    "        #print(\"Global Attachment no: {}\".format(self.count))\n",
    "        node1 = random.choice(self.nodes)\n",
    "        node2 = random.choice(self.nodes)\n",
    "        while(self.G.has_edge(node1,node2)==True):\n",
    "            node2 = random.choice(self.nodes)\n",
    "            node1 = random.choice(self.nodes)\n",
    "        #adding the edge node1-node2\n",
    "        for agent in self.agents:\n",
    "            if(agent.unique_id == node1):\n",
    "                node1_a = agent\n",
    "            if(agent.unique_id == node2):\n",
    "                node2_a = agent\n",
    "        self.G.add_edge(node1,node2,weight = Edge_Weight(node1_a,node2_a, self.b, self.a))\n",
    "        \n",
    "    def compute_gini(self):\n",
    "        agent_wealths = [agent.k for agent in self.schedule.agents]\n",
    "        x = sorted(agent_wealths)\n",
    "        B = sum(xi * (self.N - i) for i, xi in enumerate(x)) / (self.N * sum(x))\n",
    "        return 1 + (1 / self.N) - 2 * B\n",
    "    \n",
    "    def step(self):\n",
    "        self.schedule.step()\n",
    "        # collect data\n",
    "        self.datacollector.collect(self)\n",
    "\n",
    "    def run_model(self, n):\n",
    "        for i in tqdm(range(n)):\n",
    "            #print(\"Step:\", i+1)\n",
    "            self.time = i+1\n",
    "            self.step()\n",
    "            self.Global_Attachment()\n",
    "            self.gini = self.compute_gini()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "text": [
      "C:\\Users\\Public\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:53: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
     ]
    },
    {
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     "ename": "KeyboardInterrupt",
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     "output_type": "error",
     "traceback": [
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      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-04713f8720d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mcapital\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlow\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhigh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBoltzmannWealthModelNetwork\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[0mmodel_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatacollector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_model_vars_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0magent_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatacollector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_agent_vars_dataframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-4-97ed642b7040>\u001b[0m in \u001b[0;36mrun_model\u001b[1;34m(self, n)\u001b[0m\n\u001b[0;32m     54\u001b[0m             \u001b[1;31m#print(\"Step:\", i+1)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGlobal_Attachment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgini\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_gini\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-4-97ed642b7040>\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 48\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m         \u001b[1;31m# collect data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdatacollector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Public\\Anaconda3\\lib\\site-packages\\mesa\\time.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    125\u001b[0m         \"\"\"\n\u001b[0;32m    126\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0magent\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magent_buffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshuffled\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 127\u001b[1;33m             \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    128\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msteps\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-6d2a2565878c>\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    199\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\u001b[0m         \u001b[1;31m#if(self.k > 0):\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 201\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mincome_updation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    202\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgive_money\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    203\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLocal_Attachment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-6d2a2565878c>\u001b[0m in \u001b[0;36mincome_updation\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    127\u001b[0m                     \u001b[0mcon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mc_cond\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    128\u001b[0m                     \u001b[1;32mwhile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcon\u001b[0m\u001b[1;33m>\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mcon\u001b[0m\u001b[1;33m<\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 129\u001b[1;33m                         \u001b[0mcon\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mc_cond\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    130\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconsum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcon\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m         \u001b[1;31m#print(\"Old C:\", consum)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "N = 100\n",
    "steps = 200\n",
    "b = 35\n",
    "a = 0.69\n",
    "alpha = np.random.normal(loc = 1.08, scale = 0.074, size = N) \n",
    "capital = np.random.uniform(low = 0.1, high = 10, size = N)\n",
    "model = BoltzmannWealthModelNetwork(b, a,N)\n",
    "model.run_model(steps)\n",
    "model_df = model.datacollector.get_model_vars_dataframe()\n",
    "agent_df = model.datacollector.get_agent_vars_dataframe()\n",
    "agent_df.reset_index(level=1, inplace = True)\n",
    "agent_df.to_csv(\"{}Agents{}Steps.csv\".format(N,steps))\n",
    "#model_df.to_csv(\"Final_Model_100Steps.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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