docs/examples/rates/ler bns example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LeR complete examples of BNS events (O4 design sensitivity)\n",
    "\n",
    "* I am using Amplitude Spectral Density (asds) included in [bilby](https://github.com/lscsoft/bilby/tree/master/bilby) package; L1:'aLIGO_O4_high_asd.txt', 'H1': 'aLIGO_O4_high_asd.txt', 'V1': 'AdV_asd.txt'\n",
    "\n",
    "* LeR by default set the mass range (detector frame) $M_{tot}$=[2.0, 200.0], for the SNR calculation\n",
    "\n",
    "* I will change it to $M_{tot}$=[1.0, 100.0], as the default source $m_1^{max}=2.3$ and so the maximum detector frame $M_{tot}$ can be $M_{tot}^{max}*(1+z_{max})\\sim 51$. Here $z_{max}=10$.\n",
    "\n",
    "* I will consider both **sup** and **sub** events for the BNS events, where\n",
    "    * sup: super-threshold evevnts, SNR>8\n",
    "    * sub: sub+super-threshold evevnts, SNR>6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models Considered\n",
    "\n",
    "### Mass model: BNS mass distribution: Bimodal Gaussian Model\n",
    "\n",
    "Refer to this sub-section of the LeR documentation for more details: [Default BNS mass model](https://ler.readthedocs.io/en/latest/GW_events.html#BNS-mass-distribution:-Bimodal-Gaussian-Model)\n",
    "\n",
    "### Merger rate density model: BBH (population I/II star) merger-rate density [WIERDA et al. 2021](https://arxiv.org/pdf/2106.06303.pdf)\n",
    "\n",
    "Refer to this sub-section of the LeR documentation for more details: [Default BNS red-shift distribution](https://ler.readthedocs.io/en/latest/GW_events.html#Merger-Rate-Density-Formula)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ler.rates import LeR\n",
    "from ler.utils import get_param_from_json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Un-lensed events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ler = LeR(verbose=False, event_type='BNS', mtot_max=51, z_max=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of detectable events =  1000\n",
      "storing detectable unlensed params in ./ler_data/n_unlensed_detectable_bns.json\n",
      "\n",
      " trmming final result to size=1000\n"
     ]
    }
   ],
   "source": [
    "# snr_cut=8.0\n",
    "ler.selecting_n_unlensed_detectable_events(size=1000, batch_size=100000,snr_threshold=8.0, output_jsonfile='n_unlensed_detectable_bns.json', meta_data_file='n_unlensed_detectable_bns_meta.json', resume=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['events_total', 'detectable_events', 'total_rate'])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's see how rate varies with sampling size\n",
    "meta_data = get_param_from_json('ler_data/n_unlensed_detectable_bns_meta.json')\n",
    "meta_data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the rate vs sampling size\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(meta_data['events_total'], meta_data['total_rate'], 'o-')\n",
    "plt.xlabel('Sampling size')\n",
    "plt.ylabel('Rate (per year)')\n",
    "plt.title('Rate vs Sampling size')\n",
    "plt.grid(alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rate (per year) = 3.0621442247433555\n"
     ]
    }
   ],
   "source": [
    "# select only events after sampling size of 2.3e7\n",
    "idx = np.where(meta_data['events_total'] > 2.3e7)[0]\n",
    "# take average of the rate after 2.3e7\n",
    "rate = np.mean(meta_data['total_rate'][idx])\n",
    "print('Rate (per year) =', rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Conclusion is, you will need at-least 30 million samples to get a good estimate of detection rate for BNS events.\n",
    "\n",
    "* You can get away with lesser sample if you set z_max=5, but that is not a realistic scenario.\n",
    "\n",
    "* Rate obtained from the simulation (super-threshold) is ~3.06 detectable BNS events per year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of detectable events =  1000\n",
      "storing detectable unlensed params in ./ler_data/n_unlensed_detectable_bns_sub.json\n",
      "\n",
      " trmming final result to size=1000\n"
     ]
    }
   ],
   "source": [
    "# snr_cut=6.0\n",
    "ler.selecting_n_unlensed_detectable_events(size=1000, batch_size=100000,snr_threshold=6.0, output_jsonfile='n_unlensed_detectable_bns_sub.json', meta_data_file='n_unlensed_detectable_bns_sub_meta.json', resume=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see how rate varies with sampling size\n",
    "meta_data = get_param_from_json('ler_data/n_unlensed_detectable_bns_sub_meta.json')\n",
    "# plot the rate vs sampling size\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(meta_data['events_total'], meta_data['total_rate'], 'o-')\n",
    "plt.xlabel('Sampling size')\n",
    "plt.ylabel('Rate (per year)')\n",
    "plt.title('Rate vs Sampling size')\n",
    "plt.grid(alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rate (per year) = 7.254233879342404\n"
     ]
    }
   ],
   "source": [
    "# select only events after sampling size of 1e7\n",
    "idx = np.where(meta_data['events_total'] > 1e7)[0]\n",
    "# take average of the rate after 1e7\n",
    "rate = np.mean(meta_data['total_rate'][idx])\n",
    "print('Rate (per year) =', rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Rate obtained from the simulation (sub+super-threshold) is ~7.25 detectable BNS events per year.\n",
    "\n",
    "* This is 2.37 times higher than the rate obtained from the super-threshold events. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lensed events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ler = LeR(verbose=False, event_type='BNS', mtot_max=51, z_max=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of detectable events =  501.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3898.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  503.0\n",
      "total number of events =  16400000\n",
      "total lensed rate (yr^-1): 0.007621251359076047\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3949.18it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00,  1.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  507.0\n",
      "total number of events =  16500000\n",
      "total lensed rate (yr^-1): 0.007635301018187302\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3967.10it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 6/6 [00:03<00:00,  1.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  512.0\n",
      "total number of events =  16600000\n",
      "total lensed rate (yr^-1): 0.007664150448141243\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4004.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  514.0\n",
      "total number of events =  16700000\n",
      "total lensed rate (yr^-1): 0.007648016149387203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3928.84it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.35s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  519.0\n",
      "total number of events =  16800000\n",
      "total lensed rate (yr^-1): 0.007676446448441693\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4018.19it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.47s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  522.0\n",
      "total number of events =  16900000\n",
      "total lensed rate (yr^-1): 0.007675133651908457\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4009.16it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  525.0\n",
      "total number of events =  17000000\n",
      "total lensed rate (yr^-1): 0.007673836300040318\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  532.0\n",
      "total number of events =  17100000\n",
      "total lensed rate (yr^-1): 0.007730679531892469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  534.0\n",
      "total number of events =  17200000\n",
      "total lensed rate (yr^-1): 0.007714627456452493\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  537.0\n",
      "total number of events =  17300000\n",
      "total lensed rate (yr^-1): 0.007713124314128055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  544.0\n",
      "total number of events =  17400000\n",
      "total lensed rate (yr^-1): 0.0077687616970742055\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  548.0\n",
      "total number of events =  17500000\n",
      "total lensed rate (yr^-1): 0.007781165602304828\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  549.0\n",
      "total number of events =  17600000\n",
      "total lensed rate (yr^-1): 0.007751072964099165\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  550.0\n",
      "total number of events =  17700000\n",
      "total lensed rate (yr^-1): 0.0077213203557037376\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  553.0\n",
      "total number of events =  17800000\n",
      "total lensed rate (yr^-1): 0.007719821835920709\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  557.0\n",
      "total number of events =  17900000\n",
      "total lensed rate (yr^-1): 0.007732221965957091\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  561.0\n",
      "total number of events =  18000000\n",
      "total lensed rate (yr^-1): 0.007744484316770848\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  563.0\n",
      "total number of events =  18100000\n",
      "total lensed rate (yr^-1): 0.007729154141298172\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  567.0\n",
      "total number of events =  18200000\n",
      "total lensed rate (yr^-1): 0.007741298597183529\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  569.0\n",
      "total number of events =  18300000\n",
      "total lensed rate (yr^-1): 0.00772615337291595\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  570.0\n",
      "total number of events =  18400000\n",
      "total lensed rate (yr^-1): 0.007697668089791996\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  574.0\n",
      "total number of events =  18500000\n",
      "total lensed rate (yr^-1): 0.007709785803427895\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  579.0\n",
      "total number of events =  18600000\n",
      "total lensed rate (yr^-1): 0.0077351326883048485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  581.0\n",
      "total number of events =  18700000\n",
      "total lensed rate (yr^-1): 0.00772034439882844\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  584.0\n",
      "total number of events =  18800000\n",
      "total lensed rate (yr^-1): 0.007718930778804486\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  588.0\n",
      "total number of events =  18900000\n",
      "total lensed rate (yr^-1): 0.007730679531892468\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
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     "output_type": "stream",
     "text": [
      "collected number of events =  590.0\n",
      "total number of events =  19000000\n",
      "total lensed rate (yr^-1): 0.007716148179388912\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  593.0\n",
      "total number of events =  19100000\n",
      "total lensed rate (yr^-1): 0.007714778732556115\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  595.0\n",
      "total number of events =  19200000\n",
      "total lensed rate (yr^-1): 0.007700481564971013\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  600.0\n",
      "total number of events =  19300000\n",
      "total lensed rate (yr^-1): 0.007724957341269306\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  603.0\n",
      "total number of events =  19400000\n",
      "total lensed rate (yr^-1): 0.007723563663398458\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  606.0\n",
      "total number of events =  19500000\n",
      "total lensed rate (yr^-1): 0.00772218427965962\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  607.0\n",
      "total number of events =  19600000\n",
      "total lensed rate (yr^-1): 0.0076954632439374185\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  609.0\n",
      "total number of events =  19700000\n",
      "total lensed rate (yr^-1): 0.007681626996791627\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  612.0\n",
      "total number of events =  19800000\n",
      "total lensed rate (yr^-1): 0.007680480314152907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  616.0\n",
      "total number of events =  19900000\n",
      "total lensed rate (yr^-1): 0.007691831896053813\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  618.0\n",
      "total number of events =  20000000\n",
      "total lensed rate (yr^-1): 0.007678221349354627\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  621.0\n",
      "total number of events =  20100000\n",
      "total lensed rate (yr^-1): 0.007677108724901743\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  629.0\n",
      "total number of events =  20200000\n",
      "total lensed rate (yr^-1): 0.007737513583812459\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  632.0\n",
      "total number of events =  20300000\n",
      "total lensed rate (yr^-1): 0.007736119841204145\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  634.0\n",
      "total number of events =  20400000\n",
      "total lensed rate (yr^-1): 0.007722559070199304\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  636.0\n",
      "total number of events =  20500000\n",
      "total lensed rate (yr^-1): 0.007709130599399388\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "collected number of events =  639.0\n",
      "total number of events =  20600000\n",
      "total lensed rate (yr^-1): 0.007707894935491261\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "collected number of events =  639.0\n",
      "total number of events =  20700000\n",
      "total lensed rate (yr^-1): 0.007670658728073428\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "collected number of events =  640.0\n",
      "total number of events =  20800000\n",
      "total lensed rate (yr^-1): 0.0076457270095639795\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "collected number of events =  643.0\n",
      "total number of events =  20900000\n",
      "total lensed rate (yr^-1): 0.007644812448916904\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "collected number of events =  645.0\n",
      "total number of events =  21000000\n",
      "total lensed rate (yr^-1): 0.007632073925618331\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "collected number of events =  648.0\n",
      "total number of events =  21100000\n",
      "total lensed rate (yr^-1): 0.007631232740351536\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  653.0\n",
      "total number of events =  21200000\n",
      "total lensed rate (yr^-1): 0.0076538415783241305\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  656.0\n",
      "total number of events =  21300000\n",
      "total lensed rate (yr^-1): 0.007652906095957936\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  661.0\n",
      "total number of events =  21400000\n",
      "total lensed rate (yr^-1): 0.007675202359016739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  664.0\n",
      "total number of events =  21500000\n",
      "total lensed rate (yr^-1): 0.007674176226343752\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  667.0\n",
      "total number of events =  21600000\n",
      "total lensed rate (yr^-1): 0.007673159594899222\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  669.0\n",
      "total number of events =  21700000\n",
      "total lensed rate (yr^-1): 0.007660701359697919\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  671.0\n",
      "total number of events =  21800000\n",
      "total lensed rate (yr^-1): 0.0076483574202324085\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  674.0\n",
      "total number of events =  21900000\n",
      "total lensed rate (yr^-1): 0.0076474726093378525\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  680.0\n",
      "total number of events =  22000000\n",
      "total lensed rate (yr^-1): 0.007680480314152907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  683.0\n",
      "total number of events =  22100000\n",
      "total lensed rate (yr^-1): 0.0076794581581355855\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  685.0\n",
      "total number of events =  22200000\n",
      "total lensed rate (yr^-1): 0.007667252141910726\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  692.0\n",
      "total number of events =  22300000\n",
      "total lensed rate (yr^-1): 0.007710869975116318\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  695.0\n",
      "total number of events =  22400000\n",
      "total lensed rate (yr^-1): 0.007709725840559214\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  697.0\n",
      "total number of events =  22500000\n",
      "total lensed rate (yr^-1): 0.007697548048184358\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  698.0\n",
      "total number of events =  22600000\n",
      "total lensed rate (yr^-1): 0.007674483062476057\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  699.0\n",
      "total number of events =  22700000\n",
      "total lensed rate (yr^-1): 0.007651621292500868\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "collected number of events =  700.0\n",
      "total number of events =  22800000\n",
      "total lensed rate (yr^-1): 0.0076289600643675674\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  702.0\n",
      "total number of events =  22900000\n",
      "total lensed rate (yr^-1): 0.007617347673502279\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  702.0\n",
      "total number of events =  23000000\n",
      "total lensed rate (yr^-1): 0.007584228770574008\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
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     "output_type": "stream",
     "text": [
      "collected number of events =  704.0\n",
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     ]
    },
    {
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     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  709.0\n",
      "total number of events =  23200000\n",
      "total lensed rate (yr^-1): 0.007593821750770603\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  717.0\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  720.0\n",
      "total number of events =  23400000\n",
      "total lensed rate (yr^-1): 0.00764572700956398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  721.0\n",
      "total number of events =  23500000\n",
      "total lensed rate (yr^-1): 0.007623765878791828\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  725.0\n",
      "total number of events =  23600000\n",
      "total lensed rate (yr^-1): 0.007633578078934376\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  727.0\n",
      "total number of events =  23700000\n",
      "total lensed rate (yr^-1): 0.007622338182214048\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  731.0\n",
      "total number of events =  23800000\n",
      "total lensed rate (yr^-1): 0.00763207392561833\n"
     ]
    },
    {
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     "text": [
      "collected number of events =  733.0\n",
      "total number of events =  23900000\n",
      "total lensed rate (yr^-1): 0.007620934380139661\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  735.0\n",
      "total number of events =  24000000\n",
      "total lensed rate (yr^-1): 0.007609887664206649\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  736.0\n",
      "total number of events =  24100000\n",
      "total lensed rate (yr^-1): 0.007588621994554789\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  739.0\n",
      "total number of events =  24200000\n",
      "total lensed rate (yr^-1): 0.007588068117859623\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "collected number of events =  739.0\n",
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      "total lensed rate (yr^-1): 0.007556841500090654\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  744.0\n",
      "total number of events =  24400000\n",
      "total lensed rate (yr^-1): 0.007576790126690863\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  747.0\n",
      "total number of events =  24500000\n",
      "total lensed rate (yr^-1): 0.0075762913254975315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  753.0\n",
      "total number of events =  24600000\n",
      "total lensed rate (yr^-1): 0.007606099765916849\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  755.0\n",
      "total number of events =  24700000\n",
      "total lensed rate (yr^-1): 0.007595426173974743\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  758.0\n",
      "total number of events =  24800000\n",
      "total lensed rate (yr^-1): 0.007594858261314865\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  761.0\n",
      "total number of events =  24900000\n",
      "total lensed rate (yr^-1): 0.007594294910202455\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  764.0\n",
      "total number of events =  25000000\n",
      "total lensed rate (yr^-1): 0.007593736065898945\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  765.0\n",
      "total number of events =  25100000\n",
      "total lensed rate (yr^-1): 0.007573381983079061\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  766.0\n",
      "total number of events =  25200000\n",
      "total lensed rate (yr^-1): 0.007553189440599019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  769.0\n",
      "total number of events =  25300000\n",
      "total lensed rate (yr^-1): 0.007552799695119674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  770.0\n",
      "total number of events =  25400000\n",
      "total lensed rate (yr^-1): 0.007532847181666874\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  773.0\n",
      "total number of events =  25500000\n",
      "total lensed rate (yr^-1): 0.007532540266579258\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  777.0\n",
      "total number of events =  25600000\n",
      "total lensed rate (yr^-1): 0.007541942238633375\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  782.0\n",
      "total number of events =  25700000\n",
      "total lensed rate (yr^-1): 0.007560939764516286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  784.0\n",
      "total number of events =  25800000\n",
      "total lensed rate (yr^-1): 0.007550896286964737\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  786.0\n",
      "total number of events =  25900000\n",
      "total lensed rate (yr^-1): 0.007540930365224396\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  789.0\n",
      "total number of events =  26000000\n",
      "total lensed rate (yr^-1): 0.007540598263182475\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "collected number of events =  795.0\n",
      "total number of events =  26100000\n",
      "total lensed rate (yr^-1): 0.007568830329870089\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  801.0\n",
      "total number of events =  26200000\n",
      "total lensed rate (yr^-1): 0.007596846884598256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "collected number of events =  803.0\n",
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      "total lensed rate (yr^-1): 0.007586857818710872\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "collected number of events =  805.0\n",
      "total number of events =  26400000\n",
      "total lensed rate (yr^-1): 0.007576944427565061\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  807.0\n",
      "total number of events =  26500000\n",
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     ]
    },
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     "text": [
      "collected number of events =  810.0\n",
      "total number of events =  26600000\n",
      "total lensed rate (yr^-1): 0.007566682839352322\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  813.0\n",
      "total number of events =  26700000\n",
      "total lensed rate (yr^-1): 0.007566262992891545\n"
     ]
    },
    {
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    {
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     "text": [
      "collected number of events =  816.0\n",
      "total number of events =  26800000\n",
      "total lensed rate (yr^-1): 0.007565846279613311\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  818.0\n",
      "total number of events =  26900000\n",
      "total lensed rate (yr^-1): 0.007556195262054216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  823.0\n",
      "total number of events =  27000000\n",
      "total lensed rate (yr^-1): 0.007574225303270836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  825.0\n",
      "total number of events =  27100000\n",
      "total lensed rate (yr^-1): 0.007564614592027093\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  828.0\n",
      "total number of events =  27200000\n",
      "total lensed rate (yr^-1): 0.007564210067182599\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  829.0\n",
      "total number of events =  27300000\n",
      "total lensed rate (yr^-1): 0.0075456043939625475\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  833.0\n",
      "total number of events =  27400000\n",
      "total lensed rate (yr^-1): 0.007554341038920469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  836.0\n",
      "total number of events =  27500000\n",
      "total lensed rate (yr^-1): 0.007553978285449212\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  841.0\n",
      "total number of events =  27600000\n",
      "total lensed rate (yr^-1): 0.0075716244017720105\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  844.0\n",
      "total number of events =  27700000\n",
      "total lensed rate (yr^-1): 0.007571201872647652\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  849.0\n",
      "total number of events =  27800000\n",
      "total lensed rate (yr^-1): 0.007588659083143674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  852.0\n",
      "total number of events =  27900000\n",
      "total lensed rate (yr^-1): 0.0075881785266962935\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  854.0\n",
      "total number of events =  28000000\n",
      "total lensed rate (yr^-1): 0.007578826898230295\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  857.0\n",
      "total number of events =  28100000\n",
      "total lensed rate (yr^-1): 0.0075783847520954\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  858.0\n",
      "total number of events =  28200000\n",
      "total lensed rate (yr^-1): 0.007560322612116722\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
      "collected number of events =  861.0\n",
      "total number of events =  28300000\n",
      "total lensed rate (yr^-1): 0.007559948976859508\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  864.0\n",
      "total number of events =  28400000\n",
      "total lensed rate (yr^-1): 0.0075595779728364985\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  866.0\n",
      "total number of events =  28500000\n",
      "total lensed rate (yr^-1): 0.007550490760848358\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  870.0\n",
      "total number of events =  28600000\n",
      "total lensed rate (yr^-1): 0.0075588437480916615\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  871.0\n",
      "total number of events =  28700000\n",
      "total lensed rate (yr^-1): 0.007541164366663149\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
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     "text": [
      "collected number of events =  871.0\n",
      "total number of events =  28800000\n",
      "total lensed rate (yr^-1): 0.00751497976816779\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  875.0\n",
      "total number of events =  28900000\n",
      "total lensed rate (yr^-1): 0.007523368921608155\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  877.0\n",
      "total number of events =  29000000\n",
      "total lensed rate (yr^-1): 0.0075145632444861155\n"
     ]
    },
    {
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     "text": [
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     "output_type": "stream",
     "text": [
      "collected number of events =  881.0\n",
      "total number of events =  29100000\n",
      "total lensed rate (yr^-1): 0.0075228961718673765\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "collected number of events =  883.0\n",
      "total number of events =  29200000\n",
      "total lensed rate (yr^-1): 0.007514152426608299\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
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     "text": [
      "collected number of events =  885.0\n",
      "total number of events =  29300000\n",
      "total lensed rate (yr^-1): 0.0075054683656172\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "collected number of events =  886.0\n",
      "total number of events =  29400000\n",
      "total lensed rate (yr^-1): 0.007488391470761728\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     "text": [
      "collected number of events =  889.0\n",
      "total number of events =  29500000\n",
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     ]
    },
    {
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     "text": [
      "collected number of events =  890.0\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "text": [
      "collected number of events =  893.0\n",
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     ]
    },
    {
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     "text": [
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     "text": [
      "collected number of events =  895.0\n",
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     ]
    },
    {
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      "100%|█████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.63s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  898.0\n",
      "total number of events =  29900000\n",
      "total lensed rate (yr^-1): 0.007462894407161362\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4013.56it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 6/6 [00:03<00:00,  1.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  905.0\n",
      "total number of events =  30000000\n",
      "total lensed rate (yr^-1): 0.007495998188960019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  908.0\n",
      "total number of events =  30100000\n",
      "total lensed rate (yr^-1): 0.007495860599741961\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4020.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  912.0\n",
      "total number of events =  30200000\n",
      "total lensed rate (yr^-1): 0.007503951939187958\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "100%|█████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00,  1.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  913.0\n",
      "total number of events =  30300000\n",
      "total lensed rate (yr^-1): 0.007487387283540831\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4034.40it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.65s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  916.0\n",
      "total number of events =  30400000\n",
      "total lensed rate (yr^-1): 0.007487279377457884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  922.0\n",
      "total number of events =  30500000\n",
      "total lensed rate (yr^-1): 0.007511613437429006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  927.0\n",
      "total number of events =  30600000\n",
      "total lensed rate (yr^-1): 0.007527667989563359\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4010.23it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 1/1 [00:02<00:00,  2.95s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  929.0\n",
      "total number of events =  30700000\n",
      "total lensed rate (yr^-1): 0.007519335919747898\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  931.0\n",
      "total number of events =  30800000\n",
      "total lensed rate (yr^-1): 0.007511057954281887\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4034.27it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  932.0\n",
      "total number of events =  30900000\n",
      "total lensed rate (yr^-1): 0.0074947919456211315\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4050.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  934.0\n",
      "total number of events =  31000000\n",
      "total lensed rate (yr^-1): 0.00748664656049402\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3976.88it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.31s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  937.0\n",
      "total number of events =  31100000\n",
      "total lensed rate (yr^-1): 0.007486543464911482\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3997.56it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 5/5 [00:02<00:00,  1.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  940.0\n",
      "total number of events =  31200000\n",
      "total lensed rate (yr^-1): 0.007486441030198064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4020.63it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  942.0\n",
      "total number of events =  31300000\n",
      "total lensed rate (yr^-1): 0.007478400396095887\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4022.19it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  950.0\n",
      "total number of events =  31400000\n",
      "total lensed rate (yr^-1): 0.007517892401919996\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4037.07it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  955.0\n",
      "total number of events =  31500000\n",
      "total lensed rate (yr^-1): 0.0075334683193441915\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4037.19it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  957.0\n",
      "total number of events =  31600000\n",
      "total lensed rate (yr^-1): 0.007525355199840622\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4037.33it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  959.0\n",
      "total number of events =  31700000\n",
      "total lensed rate (yr^-1): 0.007517293267210894\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4018.28it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.40s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  961.0\n",
      "total number of events =  31800000\n",
      "total lensed rate (yr^-1): 0.0075092820385599685\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3980.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  965.0\n",
      "total number of events =  31900000\n",
      "total lensed rate (yr^-1): 0.0075169001046222665\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3959.56it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  966.0\n",
      "total number of events =  32000000\n",
      "total lensed rate (yr^-1): 0.007501174983289411\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4037.99it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  970.0\n",
      "total number of events =  32100000\n",
      "total lensed rate (yr^-1): 0.0075087708403895476\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4032.79it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  973.0\n",
      "total number of events =  32200000\n",
      "total lensed rate (yr^-1): 0.007508602557762017\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4016.81it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  979.0\n",
      "total number of events =  32300000\n",
      "total lensed rate (yr^-1): 0.007531514524049595\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4029.05it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  983.0\n",
      "total number of events =  32400000\n",
      "total lensed rate (yr^-1): 0.007538946408581643\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4020.17it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  987.0\n",
      "total number of events =  32500000\n",
      "total lensed rate (yr^-1): 0.007546332558439648\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4037.93it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 6/6 [00:02<00:00,  2.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  993.0\n",
      "total number of events =  32600000\n",
      "total lensed rate (yr^-1): 0.007568917942213298\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4023.61it/s]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  996.0\n",
      "total number of events =  32700000\n",
      "total lensed rate (yr^-1): 0.007568568296623426\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:24<00:00, 4020.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  999.0\n",
      "total number of events =  32800000\n",
      "total lensed rate (yr^-1): 0.007568220783018857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████| 100000/100000 [00:25<00:00, 3958.85it/s]\n",
      "100%|█████████████████████████████████████████████████████████████████| 2/2 [00:02<00:00,  1.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of events =  1001.0\n",
      "total number of events =  32900000\n",
      "total lensed rate (yr^-1): 0.007560322612116722\n",
      "storing detectable lensed params in ./ler_data/n_lensed_detectable_bns.json\n",
      "\n",
      " trmming final result to size=1000\n"
     ]
    }
   ],
   "source": [
    "# snr_cut=8.0\n",
    "# time will take long time sample\n",
    "ler.selecting_n_lensed_detectable_events(size=1000, batch_size=100000,snr_threshold=8.0, num_img=2, output_jsonfile='n_lensed_detectable_bns.json', meta_data_file='n_lensed_detectable_bns_meta.json', resume=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see how rate varies with sampling size\n",
    "meta_data = get_param_from_json('ler_data/n_lensed_detectable_bns_meta.json')\n",
    "# plot the rate vs sampling size\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(meta_data['events_total'], meta_data['total_rate'], 'o-')\n",
    "plt.xlabel('Sampling size')\n",
    "plt.ylabel('Rate (per year)')\n",
    "plt.title('Rate vs Sampling size')\n",
    "plt.grid(alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rate (per year) = 0.007576792374189939\n"
     ]
    }
   ],
   "source": [
    "# select only events after sampling size of 1e7\n",
    "idx = np.where(meta_data['events_total'] > 2e7)[0]\n",
    "# take average of the rate after 1e7\n",
    "rate = np.mean(meta_data['total_rate'][idx])\n",
    "print('Rate (per year) =', rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Rate obtained from the simulation (super-threshold) is ~0.0076 detectable BNS events per year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collected number of detectable events =  3575.0\n",
      "storing detectable lensed params in ./ler_data/n_lensed_detectable_bns_sub.json\n",
      "\n",
      " trmming final result to size=3500\n"
     ]
    }
   ],
   "source": [
    "# snr_cut=6.0\n",
    "# time will take long time sample\n",
    "ler.selecting_n_lensed_detectable_events(size=3500, batch_size=100000,snr_threshold=6.0, num_img=2, output_jsonfile='n_lensed_detectable_bns_sub.json', meta_data_file='n_lensed_detectable_bns__sub_meta.json', resume=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see how rate varies with sampling size\n",
    "meta_data = get_param_from_json('ler_data/n_lensed_detectable_bns__sub_meta.json')\n",
    "# plot the rate vs sampling size\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(meta_data['events_total'], meta_data['total_rate'], 'o-')\n",
    "plt.xlabel('Sampling size')\n",
    "plt.ylabel('Rate (per year)')\n",
    "plt.title('Rate vs Sampling size')\n",
    "plt.grid(alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rate (per year) = 0.0213948570740686\n"
     ]
    }
   ],
   "source": [
    "# select only events after sampling size of 1e7\n",
    "idx = np.where(meta_data['events_total'] > 3e7)[0]\n",
    "# take average of the rate after 1e7\n",
    "rate = np.mean(meta_data['total_rate'][idx])\n",
    "print('Rate (per year) =', rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8237354301735107"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.0213948570740686/0.007576792374189939"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Rate obtained from the simulation (sub+super-threshold) is ~0.02 detectable BNS events per year.\n",
    "\n",
    "* This is 2.8 times higher than the rate obtained from the super-threshold events. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of detectable events: 1000\n",
      "\n",
      "Number of events with detectable 2 images or more: 949\n",
      "Total/2_images: 94.89999999999999%\n",
      "\n",
      "Number of events with detectable 3 images or more: 45\n",
      "Total/3_images: 4.5%\n",
      "\n",
      "Number of events with detectable 4 images: 6\n",
      "Total/4_images: 0.6%\n"
     ]
    }
   ],
   "source": [
    "sup_lensed = get_param_from_json('ler_data/n_lensed_detectable_bns.json')\n",
    "\n",
    "# get snrs\n",
    "sup_snrs = sup_lensed['optimal_snr_net']\n",
    "\n",
    "# check each row for 3 and 4 images with snr>8\n",
    "sup_idx_2 = np.where(np.sum(sup_snrs>8, axis=1)==2)[0]\n",
    "sup_idx_3 = np.where(np.sum(sup_snrs>8, axis=1)==3)[0]\n",
    "sup_idx_4 = np.where(np.sum(sup_snrs>8, axis=1)==4)[0]\n",
    "\n",
    "print('Total number of detectable events:', len(sup_snrs))\n",
    "print('\\nNumber of events with detectable 2 images or more:', len(sup_idx_2))\n",
    "print(f'Total/2_images: {len(sup_idx_2)/len(sup_snrs)*100}%')\n",
    "print('\\nNumber of events with detectable 3 images or more:', len(sup_idx_3))\n",
    "print(f'Total/3_images: {len(sup_idx_3)/len(sup_snrs)*100}%')\n",
    "print('\\nNumber of events with detectable 4 images:', len(sup_idx_4))\n",
    "print(f'Total/4_images: {len(sup_idx_4)/len(sup_snrs)*100}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of detectable events: 3500\n",
      "\n",
      "Number of events with detectable 2 images or more: 3313\n",
      "Total/2_images: 94.65714285714286%\n",
      "\n",
      "Number of events with detectable 3 images or more: 170\n",
      "Total/3_images: 4.857142857142857%\n",
      "\n",
      "Number of events with detectable 4 images: 17\n",
      "Total/4_images: 0.48571428571428565%\n"
     ]
    }
   ],
   "source": [
    "sub_lensed = get_param_from_json('ler_data/n_lensed_detectable_bns_sub.json')\n",
    "\n",
    "# get snrs\n",
    "sub_snrs = sub_lensed['optimal_snr_net']\n",
    "\n",
    "# check each row for 3 and 4 images with snr>6\n",
    "sub_idx_2 = np.where(np.sum(sub_snrs>6, axis=1)==2)[0]\n",
    "sub_idx_3 = np.where(np.sum(sub_snrs>6, axis=1)==3)[0]\n",
    "sub_idx_4 = np.where(np.sum(sub_snrs>6, axis=1)==4)[0]\n",
    "\n",
    "print('Total number of detectable events:', len(sub_snrs))\n",
    "print('\\nNumber of events with detectable 2 images or more:', len(sub_idx_2))\n",
    "print(f'Total/2_images: {len(sub_idx_2)/len(sub_snrs)*100}%')\n",
    "print('\\nNumber of events with detectable 3 images or more:', len(sub_idx_3))\n",
    "print(f'Total/3_images: {len(sub_idx_3)/len(sub_snrs)*100}%')\n",
    "print('\\nNumber of events with detectable 4 images:', len(sub_idx_4))\n",
    "print(f'Total/4_images: {len(sub_idx_4)/len(sub_snrs)*100}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time delay distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lensed sub events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting data from json\n",
    "lensed_params_detectable = get_param_from_json(\"ler_data/n_lensed_detectable_bns_sub.json\")\n",
    "\n",
    "# time delays according to image type difference\n",
    "# dn0 for [typeI,typeI] or [typeII,typeII]\n",
    "# dn90 for [typeI,typeII] or [typeI,typeII]\n",
    "img_type = lensed_params_detectable['image_type']\n",
    "dt_eff = lensed_params_detectable['effective_geocent_time']\n",
    "snr_l = lensed_params_detectable['optimal_snr_net']\n",
    "mu = lensed_params_detectable['magnifications']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt0 = []\n",
    "dt90 = []\n",
    "dmu0 = []\n",
    "dmu90 = []\n",
    "\n",
    "list_idx = np.array([[0,1],\n",
    "                  [0,2],\n",
    "                  [0,3],\n",
    "                  [1,2],\n",
    "                  [1,3],\n",
    "                  [2,3],])\n",
    "\n",
    "for j in range(len(img_type)):\n",
    "    for idx in list_idx:\n",
    "        dn = abs(img_type[j][idx[0]]-img_type[j][idx[1]])\n",
    "        snr1 = snr_l[j][idx[0]]\n",
    "        snr2 = snr_l[j][idx[1]]\n",
    "\n",
    "        if dn==0 and snr1>8 and snr2>8:\n",
    "            # tye I-I, II-II\n",
    "            dt0.append(abs(dt_eff[j][idx[0]]-dt_eff[j][idx[1]])/ (24*3600))\n",
    "            dmu0.append(abs(mu[j][idx[0]]/mu[j][idx[1]]))\n",
    "        if dn==1 and snr1>8 and snr2>8:\n",
    "            # tye I-I, II-II\n",
    "            dt90.append(abs(dt_eff[j][idx[0]]-dt_eff[j][idx[1]])/ (24*3600))\n",
    "            dmu90.append(abs(mu[j][idx[0]]/mu[j][idx[1]]))\n",
    "\n",
    "dt0 = np.array(dt0)\n",
    "dt90 = np.array(dt90)\n",
    "dmu0 = np.array(dmu0)\n",
    "dmu90 = np.array(dmu90)\n",
    "\n",
    "log_dt0 = np.log10(dt0)\n",
    "log_dt90 = np.log10(dt90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un-lensed sub events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "unlensed_params_detectable = get_param_from_json(\"ler_data/n_unlensed_detectable_bns_sub.json\")\n",
    "\n",
    "# simulating time delay difference and magnification ratio, for unlensed population\n",
    "size = 1000\n",
    "\n",
    "t = unlensed_params_detectable[\"geocent_time\"]\n",
    "mu = unlensed_params_detectable[\"luminosity_distance\"]\n",
    "\n",
    "len_ = len(t)\n",
    "t_ = []\n",
    "mu_ = []\n",
    "idx1 = np.random.choice(np.arange(0,len_), size, replace=False)\n",
    "idx2 = np.random.choice(np.arange(0,len_), size, replace=False)\n",
    "t_.append(t[idx2] - t[idx1])\n",
    "mu_.append(mu[idx2] / mu[idx1])\n",
    "\n",
    "dt_ul = np.abs(np.array(t_).flatten()) / (60 * 60 * 24)  # in days\n",
    "dmu_ul = np.abs(np.array(mu_).flatten())**2\n",
    "\n",
    "# unlensed\n",
    "log_dt_ul = np.log10(dt_ul)\n",
    "log_dmu_ul = np.log10(dmu_ul)\n",
    "# avoid inf\n",
    "idx_nan = np.isinf(log_dt_ul)\n",
    "log_dt_ul = log_dt_ul[~idx_nan]\n",
    "log_dmu_ul = log_dmu_ul[~idx_nan]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kde for time delays\n",
    "from sklearn.neighbors import KernelDensity\n",
    "\n",
    "kde_log_dt0 = KernelDensity(kernel='gaussian', bandwidth=0.5).fit(np.array(log_dt0).reshape(-1,1))\n",
    "kde_log_dt90 = KernelDensity(kernel='gaussian', bandwidth=0.5).fit(np.array(log_dt90).reshape(-1,1))\n",
    "kde_log_dt_ul = KernelDensity(kernel='gaussian', bandwidth=0.5).fit(np.array(log_dt_ul).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plt.figure(figsize=(6,4))\n",
    "dt_log = np.linspace(-8, 4, 1000)\n",
    "plt.plot(dt_log, np.exp(kde_log_dt0.score_samples(dt_log.reshape(-1,1))), label='dn=0', color='C0', linestyle='-', alpha=0.5)\n",
    "plt.plot(dt_log, np.exp(kde_log_dt90.score_samples(dt_log.reshape(-1,1))), label='dn=90', color='C1', linestyle='-', alpha=0.8)\n",
    "plt.plot(dt_log, np.exp(kde_log_dt_ul.score_samples(dt_log.reshape(-1,1))), label='unlensed', color='C2', linestyle='-', alpha=0.8)\n",
    "\n",
    "plt.xlabel(r'$log_{10}\\Delta$t [days]')\n",
    "plt.ylabel(r'$P(\\Delta t)$')\n",
    "plt.title('Time delays wrt image type')\n",
    "leg = plt.legend(handlelength=4)\n",
    "for line in leg.get_lines():\n",
    "    line.set_linewidth(1.5)\n",
    "plt.grid(alpha=0.4)\n",
    "#plt.xlim(-3, 3)\n",
    "#plt.savefig(\"redshift_distribution_bns.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* time delay distribution for lensed events are very different from the un-lensed events.\n",
    "\n",
    "* for lensed events the time delay distribution is very broad, with a peak around ~15mins-1day. This very unlike BBH events, where the peak is at ~7-10days.\n",
    "\n",
    "* This shows lensed BNS events need higher magnification to be detectable. Higher magnification is associated with smaller time delay.\n",
    "\n",
    "* This can also be simply from the lack of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Magnification distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting data from json\n",
    "lensed_params_detectable = get_param_from_json(\"ler_data/n_lensed_detectable_bns_sub.json\")\n",
    "\n",
    "snr_l = lensed_params_detectable['optimal_snr_net'].flatten()\n",
    "mu = lensed_params_detectable['magnifications'].flatten()\n",
    "mu_arr = abs(mu[np.where(snr_l>6)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make kde\n",
    "from sklearn.neighbors import KernelDensity\n",
    "\n",
    "kde_mu = KernelDensity(kernel='gaussian', bandwidth=20).fit(mu_arr.reshape(-1,1))\n",
    "\n",
    "# plot\n",
    "plt.figure(figsize=(6,4))\n",
    "mu = np.linspace(0, 1000, 1000)\n",
    "plt.plot(mu, np.exp(kde_mu.score_samples(mu.reshape(-1,1))), label='lensed (snr>6)', color='C0', linestyle='-', alpha=0.5)\n",
    "\n",
    "plt.xlabel(r'$\\mu$')\n",
    "plt.ylabel(r'$P(\\mu)$')\n",
    "plt.title('Magnification distribution')\n",
    "leg = plt.legend(handlelength=4)\n",
    "for line in leg.get_lines():\n",
    "    line.set_linewidth(1.5)\n",
    "plt.grid(alpha=0.4)\n",
    "#plt.xlim(-3, 3)\n",
    "#plt.savefig(\"redshift_distribution_bns.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* which is quite high as compared to the BBH events."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ler",
   "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": 2
}