characteristic_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0321623-8eb7-440b-8c14-7c58790d7dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a1e05d9-34b0-400b-baba-81e4a4638960",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PL_Index</th>\n",
       "      <th>nu_syn</th>\n",
       "      <th>LP_Index</th>\n",
       "      <th>Pivot_Energy</th>\n",
       "      <th>Frac_Variability</th>\n",
       "      <th>Variability_Index</th>\n",
       "      <th>nuFnu_syn</th>\n",
       "      <th>CLASS</th>\n",
       "      <th>bcu_predictions</th>\n",
       "      <th>Pr(BLL|X)</th>\n",
       "      <th>Pr(FSRQ|X)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.271696</td>\n",
       "      <td>32.246143</td>\n",
       "      <td>2.254081</td>\n",
       "      <td>7.791760</td>\n",
       "      <td>0.675882</td>\n",
       "      <td>3.231356</td>\n",
       "      <td>-27.315010</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.676629</td>\n",
       "      <td>0.323371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.876663</td>\n",
       "      <td>30.498686</td>\n",
       "      <td>1.716620</td>\n",
       "      <td>8.396140</td>\n",
       "      <td>0.902851</td>\n",
       "      <td>3.201036</td>\n",
       "      <td>-26.952175</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.862644</td>\n",
       "      <td>0.137356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.092075</td>\n",
       "      <td>31.972977</td>\n",
       "      <td>2.060727</td>\n",
       "      <td>8.131412</td>\n",
       "      <td>0.096862</td>\n",
       "      <td>2.566042</td>\n",
       "      <td>-27.295017</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.819423</td>\n",
       "      <td>0.180577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.914457</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>1.535265</td>\n",
       "      <td>8.312380</td>\n",
       "      <td>0.571694</td>\n",
       "      <td>2.872791</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.901446</td>\n",
       "      <td>0.098554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.915512</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>1.859399</td>\n",
       "      <td>8.129377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.079245</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.864505</td>\n",
       "      <td>0.135495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>2.785040</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>3.004548</td>\n",
       "      <td>6.639556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.753791</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>FSRQ</td>\n",
       "      <td>0.305747</td>\n",
       "      <td>0.694253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>2.071567</td>\n",
       "      <td>31.160704</td>\n",
       "      <td>2.042366</td>\n",
       "      <td>8.682343</td>\n",
       "      <td>0.328630</td>\n",
       "      <td>2.681185</td>\n",
       "      <td>-26.934721</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.832393</td>\n",
       "      <td>0.167607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>2.226648</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>1.962429</td>\n",
       "      <td>8.043736</td>\n",
       "      <td>1.071415</td>\n",
       "      <td>3.533271</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.753103</td>\n",
       "      <td>0.246897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>2.045913</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>2.019132</td>\n",
       "      <td>8.530411</td>\n",
       "      <td>0.337927</td>\n",
       "      <td>3.107236</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.838974</td>\n",
       "      <td>0.161026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>1.743453</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>1.572163</td>\n",
       "      <td>8.655350</td>\n",
       "      <td>0.175460</td>\n",
       "      <td>2.628590</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>0.935733</td>\n",
       "      <td>0.064267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PL_Index     nu_syn  LP_Index  Pivot_Energy  Frac_Variability  \\\n",
       "0     2.271696  32.246143  2.254081      7.791760          0.675882   \n",
       "1     1.876663  30.498686  1.716620      8.396140          0.902851   \n",
       "2     2.092075  31.972977  2.060727      8.131412          0.096862   \n",
       "3     1.914457  27.631021  1.535265      8.312380          0.571694   \n",
       "4     1.915512  27.631021  1.859399      8.129377          0.000000   \n",
       "...        ...        ...       ...           ...               ...   \n",
       "1110  2.785040  27.631021  3.004548      6.639556          0.000000   \n",
       "1111  2.071567  31.160704  2.042366      8.682343          0.328630   \n",
       "1112  2.226648  27.631021  1.962429      8.043736          1.071415   \n",
       "1113  2.045913  27.631021  2.019132      8.530411          0.337927   \n",
       "1114  1.743453  27.631021  1.572163      8.655350          0.175460   \n",
       "\n",
       "      Variability_Index  nuFnu_syn CLASS bcu_predictions  Pr(BLL|X)  \\\n",
       "0              3.231356 -27.315010   BCU             BLL   0.676629   \n",
       "1              3.201036 -26.952175   BCU             BLL   0.862644   \n",
       "2              2.566042 -27.295017   BCU             BLL   0.819423   \n",
       "3              2.872791 -27.631021   BCU             BLL   0.901446   \n",
       "4              2.079245 -27.631021   BCU             BLL   0.864505   \n",
       "...                 ...        ...   ...             ...        ...   \n",
       "1110           1.753791 -27.631021   BCU            FSRQ   0.305747   \n",
       "1111           2.681185 -26.934721   BCU             BLL   0.832393   \n",
       "1112           3.533271 -27.631021   BCU             BLL   0.753103   \n",
       "1113           3.107236 -27.631021   BCU             BLL   0.838974   \n",
       "1114           2.628590 -27.631021   BCU             BLL   0.935733   \n",
       "\n",
       "      Pr(FSRQ|X)  \n",
       "0       0.323371  \n",
       "1       0.137356  \n",
       "2       0.180577  \n",
       "3       0.098554  \n",
       "4       0.135495  \n",
       "...          ...  \n",
       "1110    0.694253  \n",
       "1111    0.167607  \n",
       "1112    0.246897  \n",
       "1113    0.161026  \n",
       "1114    0.064267  \n",
       "\n",
       "[1115 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"bias_init_soft_voting.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3857f7fe-2d3b-4497-877a-abc69544fabc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PL_Index</th>\n",
       "      <th>nu_syn</th>\n",
       "      <th>LP_Index</th>\n",
       "      <th>Pivot_Energy</th>\n",
       "      <th>Frac_Variability</th>\n",
       "      <th>Variability_Index</th>\n",
       "      <th>nuFnu_syn</th>\n",
       "      <th>CLASS</th>\n",
       "      <th>bcu_predictions</th>\n",
       "      <th>Pr(BLL|X)</th>\n",
       "      <th>Pr(FSRQ|X)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.271696</td>\n",
       "      <td>32.246143</td>\n",
       "      <td>2.254081</td>\n",
       "      <td>7.791760</td>\n",
       "      <td>0.675882</td>\n",
       "      <td>3.231356</td>\n",
       "      <td>-27.315010</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.323371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.876663</td>\n",
       "      <td>30.498686</td>\n",
       "      <td>1.716620</td>\n",
       "      <td>8.396140</td>\n",
       "      <td>0.902851</td>\n",
       "      <td>3.201036</td>\n",
       "      <td>-26.952175</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.092075</td>\n",
       "      <td>31.972977</td>\n",
       "      <td>2.060727</td>\n",
       "      <td>8.131412</td>\n",
       "      <td>0.096862</td>\n",
       "      <td>2.566042</td>\n",
       "      <td>-27.295017</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.180577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.914457</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>1.535265</td>\n",
       "      <td>8.312380</td>\n",
       "      <td>0.571694</td>\n",
       "      <td>2.872791</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.098554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.915512</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>1.859399</td>\n",
       "      <td>8.129377</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.079245</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.135495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>2.785040</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>3.004548</td>\n",
       "      <td>6.639556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.753791</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>FSRQ</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.694253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>2.071567</td>\n",
       "      <td>31.160704</td>\n",
       "      <td>2.042366</td>\n",
       "      <td>8.682343</td>\n",
       "      <td>0.328630</td>\n",
       "      <td>2.681185</td>\n",
       "      <td>-26.934721</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.167607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>2.226648</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>1.962429</td>\n",
       "      <td>8.043736</td>\n",
       "      <td>1.071415</td>\n",
       "      <td>3.533271</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.246897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>2.045913</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>2.019132</td>\n",
       "      <td>8.530411</td>\n",
       "      <td>0.337927</td>\n",
       "      <td>3.107236</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.161026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>1.743453</td>\n",
       "      <td>27.631021</td>\n",
       "      <td>1.572163</td>\n",
       "      <td>8.655350</td>\n",
       "      <td>0.175460</td>\n",
       "      <td>2.628590</td>\n",
       "      <td>-27.631021</td>\n",
       "      <td>BCU</td>\n",
       "      <td>BLL</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.064267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PL_Index     nu_syn  LP_Index  Pivot_Energy  Frac_Variability  \\\n",
       "0     2.271696  32.246143  2.254081      7.791760          0.675882   \n",
       "1     1.876663  30.498686  1.716620      8.396140          0.902851   \n",
       "2     2.092075  31.972977  2.060727      8.131412          0.096862   \n",
       "3     1.914457  27.631021  1.535265      8.312380          0.571694   \n",
       "4     1.915512  27.631021  1.859399      8.129377          0.000000   \n",
       "...        ...        ...       ...           ...               ...   \n",
       "1110  2.785040  27.631021  3.004548      6.639556          0.000000   \n",
       "1111  2.071567  31.160704  2.042366      8.682343          0.328630   \n",
       "1112  2.226648  27.631021  1.962429      8.043736          1.071415   \n",
       "1113  2.045913  27.631021  2.019132      8.530411          0.337927   \n",
       "1114  1.743453  27.631021  1.572163      8.655350          0.175460   \n",
       "\n",
       "      Variability_Index  nuFnu_syn CLASS bcu_predictions  Pr(BLL|X)  \\\n",
       "0              3.231356 -27.315010   BCU             BLL        1.0   \n",
       "1              3.201036 -26.952175   BCU             BLL        1.0   \n",
       "2              2.566042 -27.295017   BCU             BLL        1.0   \n",
       "3              2.872791 -27.631021   BCU             BLL        1.0   \n",
       "4              2.079245 -27.631021   BCU             BLL        1.0   \n",
       "...                 ...        ...   ...             ...        ...   \n",
       "1110           1.753791 -27.631021   BCU            FSRQ        0.0   \n",
       "1111           2.681185 -26.934721   BCU             BLL        1.0   \n",
       "1112           3.533271 -27.631021   BCU             BLL        1.0   \n",
       "1113           3.107236 -27.631021   BCU             BLL        1.0   \n",
       "1114           2.628590 -27.631021   BCU             BLL        1.0   \n",
       "\n",
       "      Pr(FSRQ|X)  \n",
       "0       0.323371  \n",
       "1       0.137356  \n",
       "2       0.180577  \n",
       "3       0.098554  \n",
       "4       0.135495  \n",
       "...          ...  \n",
       "1110    0.694253  \n",
       "1111    0.167607  \n",
       "1112    0.246897  \n",
       "1113    0.161026  \n",
       "1114    0.064267  \n",
       "\n",
       "[1115 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Pr(BLL|X)\"] = np.round(df[\"Pr(BLL|X)\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccaa0a75-ba81-429d-be55-88c75f46aa23",
   "metadata": {},
   "outputs": [],
   "source": [
    "bllac_pl = []\n",
    "fsrq_pl = []\n",
    "bllac_pe = []\n",
    "fsrq_pe = []\n",
    "\n",
    "for i in range(len(df)):\n",
    "    if df[\"Pr(BLL|X)\"][i] == 0:\n",
    "        fsrq_pl.append(df[\"PL_Index\"][i])\n",
    "        fsrq_pe.append(df[\"Pivot_Energy\"][i])\n",
    "    else:\n",
    "        bllac_pl.append(df[\"PL_Index\"][i])\n",
    "        bllac_pe.append(df[\"Pivot_Energy\"][i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9f3b1e0-38b3-40d4-bf83-8ea6c048b458",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_33231/2138504321.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  plt.gca().set_yticklabels(['{:,.2f}'.format(x) for x in current_values])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights_bllac = np.ones_like(bllac_pl)/len(bllac_pl)\n",
    "weights_fsrq = np.ones_like(fsrq_pl)/len(fsrq_pl)\n",
    "\n",
    "plt.hist(bllac_pl, histtype = \"step\", label = \"BL Lac\", color = 'red', bins = 12, weights=weights_bllac)\n",
    "plt.hist(fsrq_pl, histtype = \"step\", label = \"FSRQ\", color = 'darkblue', bins = 12, weights=weights_fsrq)\n",
    "plt.legend(prop={\"size\":16})\n",
    "plt.xlabel(\"Photon Index\",fontsize=16)\n",
    "plt.ylabel(\"Fraction of Sources\",fontsize=16)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "current_values = plt.gca().get_yticks()\n",
    "plt.gca().set_yticklabels(['{:,.2f}'.format(x) for x in current_values])\n",
    "plt.savefig(\"photon_index.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bdeceaad-61c0-4985-a25f-7ed8fcbdd24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(bllac_pe, bllac_pl, label = \"BL Lac\", marker = \">\", c = \"red\", s=10)\n",
    "plt.scatter(fsrq_pe, fsrq_pl, label = \"FSRQ\", marker = \"<\", c = \"darkblue\", s=10)\n",
    "plt.xlabel(\"log(Pivot Energy)\",fontsize=16)\n",
    "plt.ylabel(\"Photon Index\",fontsize=16)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.legend(prop={\"size\":16})\n",
    "plt.savefig(\"scatter.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe0da76e-5f9b-4b64-bf8a-3d6a19d1ba96",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}