iteration_analysis.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "folder = '/Users/uknowit/Dissertation/'\n",
    "df_not_nine = pd.read_csv(folder + 'sim_vh_0.9.csv')\n",
    "df_half = pd.read_csv(folder + 'sim_vh_0.5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.6       , 0.86666667, 1.13333333, 1.4       , 1.66666667,\n",
       "       1.93333333, 2.2       , 2.46666667, 2.73333333, 3.        ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_half.head()\n",
    "df_half.n.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_half = df_half.drop(columns = df_half.columns[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n</th>\n",
       "      <th>h_b</th>\n",
       "      <th>h_g</th>\n",
       "      <th>a_b</th>\n",
       "      <th>a_g</th>\n",
       "      <th>e_b</th>\n",
       "      <th>e_g</th>\n",
       "      <th>ebh</th>\n",
       "      <th>egh</th>\n",
       "      <th>iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3888</th>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.659028</td>\n",
       "      <td>0.659028</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7257</th>\n",
       "      <td>1.933333</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.735586</td>\n",
       "      <td>0.735586</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             n  h_b  h_g  a_b  a_g  e_b  e_g       ebh       egh  iteration\n",
       "3888  1.400000  0.5  0.5  0.5  0.5  0.5  0.5  0.659028  0.659028     1000.0\n",
       "7257  1.933333  0.8  0.8  0.8  0.8  0.5  0.5  0.735586  0.735586     1000.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df_half[(df_half.h_b == df_half.h_g) & (df_half.a_b == df_half.a_g) & (df_half.a_b == df_half.h_b)]\n",
    "a[a.iteration == a.iteration.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12960"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_half.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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