{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21f50d4f-eebc-421c-85cd-fd7bead28314",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:40:35.017413Z",
     "iopub.status.busy": "2025-09-06T01:40:35.017206Z",
     "iopub.status.idle": "2025-09-06T01:40:44.077481Z",
     "shell.execute_reply": "2025-09-06T01:40:44.076860Z",
     "shell.execute_reply.started": "2025-09-06T01:40:35.017393Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "# from simulation_pipeline import SimPipeline \n",
    "import importlib\n",
    "import simulation_pipeline\n",
    "importlib.reload(simulation_pipeline)\n",
    "from simulation_pipeline import SimPipeline "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b122cfce-5c22-4062-8a3c-db73667ff42a",
   "metadata": {},
   "source": [
    "### Create a simulation instance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9289bc2c-6895-4c5e-9901-07e0af6fdc60",
   "metadata": {},
   "source": [
    "From config file path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b7a9a25-a513-4a85-954c-1cd357abc979",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:13:35.498576Z",
     "iopub.status.busy": "2025-09-06T01:13:35.498271Z",
     "iopub.status.idle": "2025-09-06T01:13:35.524599Z",
     "shell.execute_reply": "2025-09-06T01:13:35.523921Z",
     "shell.execute_reply.started": "2025-09-06T01:13:35.498554Z"
    }
   },
   "outputs": [],
   "source": [
    "sim_name = \"new_test\"\n",
    "sim = SimPipeline(from_folder = sim_name, new=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b33b38e-7bbd-4565-ac42-c1ab7f36b92f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:40:44.080039Z",
     "iopub.status.busy": "2025-09-06T01:40:44.079686Z",
     "iopub.status.idle": "2025-09-06T01:40:44.114621Z",
     "shell.execute_reply": "2025-09-06T01:40:44.114025Z",
     "shell.execute_reply.started": "2025-09-06T01:40:44.080018Z"
    }
   },
   "outputs": [],
   "source": [
    "CONFIG_PATH = \"config_file.yaml\"\n",
    "sim = SimPipeline(CONFIG_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a856dee5-6cad-4adc-bae1-38a4e25502f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:40:44.116920Z",
     "iopub.status.busy": "2025-09-06T01:40:44.116724Z",
     "iopub.status.idle": "2025-09-06T01:40:44.122184Z",
     "shell.execute_reply": "2025-09-06T01:40:44.121706Z",
     "shell.execute_reply.started": "2025-09-06T01:40:44.116902Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimPipeline: new_test\n",
       "=====================\n",
       "\n",
       "General configuration:\n",
       " - Number of events: 20\n",
       " - Model: USBL\n",
       " - System type: Planets_systems\n",
       " - Sources catalog: AstroDataLab\n",
       " - Bands: u, g, r, i, z, y\n",
       " - Simulation type: lsst_images\n",
       " - Data Preview: dp0\n",
       "\n",
       "\n",
       "Sky region:\n",
       " - Distribution: circle\n",
       " - Center: (ra=62, dec=-36) deg (icrs)\n",
       " - Radius: 0.05 deg\n",
       " - Blend distance: 0.001 deg\n",
       "\n",
       " - Survey dates (MJD): 60849 to 61944\n",
       " - Peak range (MJD): 61579 to 63769.25\n",
       "\n",
       "Processing:\n",
       " - Event processor: ulens\n",
       " - Photometry processor: synthetic\n",
       "\n",
       "Directories:\n",
       " - Output dir: runs/new_test\n",
       " - Data events: runs/new_test/data-events_1.parquet\n",
       " - Photometry: runs/new_test/photometry_1.parquet\n",
       " - Calexps photometry: runs/new_test/calexps-photometry_1.parquet"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68291be-4e75-492e-a97d-58f1f0380079",
   "metadata": {},
   "source": [
    "### Simulate light curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "19f06bba-0bff-4900-9065-0a461b385796",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:40:47.381775Z",
     "iopub.status.busy": "2025-09-06T01:40:47.381208Z",
     "iopub.status.idle": "2025-09-06T01:41:13.531021Z",
     "shell.execute_reply": "2025-09-06T01:41:13.530346Z",
     "shell.execute_reply.started": "2025-09-06T01:40:47.381746Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting calexps for bands ('u', 'g', 'r', 'i', 'z', 'y')...\n",
      "Found 999 calexps.\n",
      "Loading data from dp02_dc2_catalogs.CcdVisit...\n",
      "Records found: 5256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Chunk 1: 100%|██████████| 2/2 [00:06<00:00,  3.50s/it]:00<?, ?it/s]\n",
      "Chunk 6: 100%|██████████| 2/2 [00:07<00:00,  3.51s/it]\n",
      "Chunk 5: 100%|██████████| 2/2 [00:07<00:00,  3.56s/it]\n",
      "Chunk 0: 100%|██████████| 2/2 [00:07<00:00,  3.65s/it]\n",
      "Chunk 7: 100%|██████████| 2/2 [00:07<00:00,  3.66s/it]:07<01:05,  7.30s/it]\n",
      "Chunk 4: 100%|██████████| 2/2 [00:07<00:00,  3.69s/it]\n",
      "Chunk 3: 100%|██████████| 2/2 [00:07<00:00,  3.71s/it]\n",
      "Chunk 2: 100%|██████████| 2/2 [00:07<00:00,  3.76s/it]\n",
      "Chunk 8: 100%|██████████| 2/2 [00:02<00:00,  1.21s/it]:07<00:13,  1.98s/it]\n",
      "Chunk 9: 100%|██████████| 2/2 [00:02<00:00,  1.22s/it]:09<00:00,  1.40it/s]\n",
      "Running parallel processing: 100%|██████████| 10/10 [00:09<00:00,  1.06it/s]\n"
     ]
    }
   ],
   "source": [
    "results_events = sim.simulate_lightcurves()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef350ef5-8085-45ee-972f-cf53b5da82de",
   "metadata": {},
   "source": [
    "#### Check succes or errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fac9a401-e2f2-40e2-80ab-eae9df0d0724",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-05T20:26:28.582787Z",
     "iopub.status.busy": "2025-09-05T20:26:28.582568Z",
     "iopub.status.idle": "2025-09-05T20:26:28.586288Z",
     "shell.execute_reply": "2025-09-05T20:26:28.585850Z",
     "shell.execute_reply.started": "2025-09-05T20:26:28.582766Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(results_events.loc[5].error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6b1bc29-6585-499f-a95e-1251218efffb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:41:13.538134Z",
     "iopub.status.busy": "2025-09-06T01:41:13.537932Z",
     "iopub.status.idle": "2025-09-06T01:41:13.547684Z",
     "shell.execute_reply": "2025-09-06T01:41:13.547198Z",
     "shell.execute_reply.started": "2025-09-06T01:41:13.538116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>status</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    event_id   status error\n",
       "0          3  success      \n",
       "1          4  success      \n",
       "2         13  success      \n",
       "3         14  success      \n",
       "4         11  success      \n",
       "5         12  success      \n",
       "6          1  success      \n",
       "7          2  success      \n",
       "8         15  success      \n",
       "9         16  success      \n",
       "10         9  success      \n",
       "11        10  success      \n",
       "12         7  success      \n",
       "13         8  success      \n",
       "14         5  success      \n",
       "15         6  success      \n",
       "16        17  success      \n",
       "17        18  success      \n",
       "18        19  success      \n",
       "19        20  success      "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_events"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb3b7982-c123-4f67-9557-f695f9ff2ac0",
   "metadata": {},
   "source": [
    "#### Inspect results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "77663868-e2fd-498b-ad35-7842ef2198cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T22:43:40.457767Z",
     "iopub.status.busy": "2025-09-06T22:43:40.457021Z",
     "iopub.status.idle": "2025-09-06T22:43:40.742870Z",
     "shell.execute_reply": "2025-09-06T22:43:40.742205Z",
     "shell.execute_reply.started": "2025-09-06T22:43:40.457741Z"
    }
   },
   "outputs": [],
   "source": [
    "# from simulation_pipeline import SimPipeline \n",
    "import importlib\n",
    "import light_curves\n",
    "importlib.reload(light_curves)\n",
    "from light_curves import Event\n",
    "event = Event.from_parquet(8, sim.calexps_photometry_file, sim.events_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "394c5fe4-72d7-4b17-993f-0ef6188f0ef1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T22:43:41.664063Z",
     "iopub.status.busy": "2025-09-06T22:43:41.663330Z",
     "iopub.status.idle": "2025-09-06T22:44:17.065910Z",
     "shell.execute_reply": "2025-09-06T22:44:17.065344Z",
     "shell.execute_reply.started": "2025-09-06T22:43:41.664036Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Event 8'}, xlabel='Time (MJD)', ylabel='Magnitude'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "event.plot(show_ideal=False, simulate_ideal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "257c4491-665e-4368-8fec-ccd0d2706914",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T22:55:30.367951Z",
     "iopub.status.busy": "2025-09-06T22:55:30.367140Z",
     "iopub.status.idle": "2025-09-06T22:56:37.050058Z",
     "shell.execute_reply": "2025-09-06T22:56:37.049391Z",
     "shell.execute_reply.started": "2025-09-06T22:55:30.367920Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "075db5a7e7094503a0c17d0a1bcbe217",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Event 8 - Plotting filter light curves:   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from analysis import plot_event_fov\n",
    "plot_event_fov(8, sim.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ecd5ac07-daac-4b71-9267-4891be571d16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-05T21:23:27.206929Z",
     "iopub.status.busy": "2025-09-05T21:23:27.206211Z",
     "iopub.status.idle": "2025-09-05T21:23:27.228123Z",
     "shell.execute_reply": "2025-09-05T21:23:27.227538Z",
     "shell.execute_reply.started": "2025-09-05T21:23:27.206900Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "ev = pd.read_parquet(sim.events_file)\n",
    "# ev.columns\n",
    "# pd.read_csv(\"runs/new_test/AstroDataLab_event_sources_catalog.csv\")\n",
    "ph = pd.read_parquet(sim.photometry_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caaa7b1f-dbdf-4369-997b-2a2a7e4bad4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-05T19:53:45.292358Z",
     "iopub.status.busy": "2025-09-05T19:53:45.291610Z",
     "iopub.status.idle": "2025-09-05T19:53:45.295854Z",
     "shell.execute_reply": "2025-09-05T19:53:45.295400Z",
     "shell.execute_reply.started": "2025-09-05T19:53:45.292324Z"
    }
   },
   "source": [
    "### If simulation type is \"lsst_images\", you can load nearby objects as follows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d490f364-5d30-4413-bc69-c14fe528fe40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:41:13.548860Z",
     "iopub.status.busy": "2025-09-06T01:41:13.548627Z",
     "iopub.status.idle": "2025-09-06T01:41:21.855746Z",
     "shell.execute_reply": "2025-09-06T01:41:21.855195Z",
     "shell.execute_reply.started": "2025-09-06T01:41:13.548835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from dp02_dc2_catalogs.Object...\n",
      "Records found: 6639\n"
     ]
    }
   ],
   "source": [
    "sim.load_nearby_objects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e3a44752-12be-4ea2-9004-d5f2e18f5596",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:41:21.856673Z",
     "iopub.status.busy": "2025-09-06T01:41:21.856456Z",
     "iopub.status.idle": "2025-09-06T01:52:25.751181Z",
     "shell.execute_reply": "2025-09-06T01:52:25.750539Z",
     "shell.execute_reply.started": "2025-09-06T01:41:21.856656Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Chunk 3: 100%|██████████| 125/125 [10:41<00:00,  5.13s/it], ?it/s]\n",
      "Chunk 7: 100%|██████████| 124/124 [10:43<00:00,  5.19s/it]\n",
      "Chunk 1: 100%|██████████| 125/125 [10:44<00:00,  5.16s/it]\n",
      "Chunk 2: 100%|██████████| 125/125 [10:45<00:00,  5.17s/it]\n",
      "Chunk 6: 100%|██████████| 125/125 [10:46<00:00,  5.17s/it]\n",
      "Chunk 4: 100%|██████████| 125/125 [10:47<00:00,  5.18s/it]\n",
      "Chunk 5: 100%|██████████| 125/125 [10:57<00:00,  5.26s/it]\n",
      "Chunk 0: 100%|██████████| 125/125 [10:58<00:00,  5.27s/it]\n",
      "Running parallel processing: 100%|██████████| 8/8 [10:58<00:00, 82.32s/it]   \n"
     ]
    }
   ],
   "source": [
    "results_calexps = sim.process_synthetic_photometry()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95caea46-0f78-4ee9-bae9-278440d3463f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:54:21.440533Z",
     "iopub.status.busy": "2025-09-06T01:54:21.439679Z",
     "iopub.status.idle": "2025-09-06T01:54:21.446303Z",
     "shell.execute_reply": "2025-09-06T01:54:21.445783Z",
     "shell.execute_reply.started": "2025-09-06T01:54:21.440504Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "error\n",
       "                                              779\n",
       "No sources to inject: skipping this calexp    220\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_calexps.error.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9bc37186-f11c-405d-a594-3741ad68e5c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:52:25.813533Z",
     "iopub.status.busy": "2025-09-06T01:52:25.813311Z",
     "iopub.status.idle": "2025-09-06T01:52:25.834764Z",
     "shell.execute_reply": "2025-09-06T01:52:25.834085Z",
     "shell.execute_reply.started": "2025-09-06T01:52:25.813516Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "376    \n",
      "Name: error, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(results_calexps[results_calexps[\"calexp_id\"]==252].error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8658764f-1719-43ff-bc1d-250b06bd079a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T01:52:25.786912Z",
     "iopub.status.busy": "2025-09-06T01:52:25.786689Z",
     "iopub.status.idle": "2025-09-06T01:52:25.812630Z",
     "shell.execute_reply": "2025-09-06T01:52:25.811994Z",
     "shell.execute_reply.started": "2025-09-06T01:52:25.786894Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calexp_id</th>\n",
       "      <th>status</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>376</td>\n",
       "      <td>failed</td>\n",
       "      <td>No sources to inject: skipping this calexp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>378</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>379</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>120</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>121</td>\n",
       "      <td>failed</td>\n",
       "      <td>No sources to inject: skipping this calexp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>122</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>123</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>124</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>999 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     calexp_id   status                                       error\n",
       "0          375  success                                            \n",
       "1          376   failed  No sources to inject: skipping this calexp\n",
       "2          377  success                                            \n",
       "3          378  success                                            \n",
       "4          379  success                                            \n",
       "..         ...      ...                                         ...\n",
       "994        120  success                                            \n",
       "995        121   failed  No sources to inject: skipping this calexp\n",
       "996        122  success                                            \n",
       "997        123  success                                            \n",
       "998        124  success                                            \n",
       "\n",
       "[999 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_calexps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a22d80d-1349-447f-8897-e17c7fc5870c",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2fd42ad8-bc58-4587-9cb6-4a0d6daf7723",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T22:08:24.497264Z",
     "iopub.status.busy": "2025-09-06T22:08:24.496545Z",
     "iopub.status.idle": "2025-09-06T22:08:55.260612Z",
     "shell.execute_reply": "2025-09-06T22:08:55.259947Z",
     "shell.execute_reply.started": "2025-09-06T22:08:24.497240Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data from dp02_dc2_catalogs.Source...\n",
      "Records found: 138380\n"
     ]
    }
   ],
   "source": [
    "import analysis\n",
    "importlib.reload(analysis)\n",
    "from analysis import plot_sky_map, generate_time_log, generate_summary_plot\n",
    "plot_sky_map(sim.name, zoom = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0ddac892-3402-49c7-9900-35ee711408d7",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2025-09-06T22:41:18.149119Z",
     "iopub.status.busy": "2025-09-06T22:41:18.148763Z",
     "iopub.status.idle": "2025-09-06T22:41:20.107238Z",
     "shell.execute_reply": "2025-09-06T22:41:20.106631Z",
     "shell.execute_reply.started": "2025-09-06T22:41:18.149095Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                             item                                        task  \\\n",
       " 4                          Calexp                              Loading calexp   \n",
       " 6                          Calexp                                   Measuring   \n",
       " 3                          Calexp                                   Injecting   \n",
       " 5                          Calexp                Loading events for injection   \n",
       " 8                          Calexp                   Saving photometry results   \n",
       " ..                            ...                                         ...   \n",
       " 203              lsst.measurement  Performing forced measurement on 7 sources   \n",
       " 204              lsst.measurement  Performing forced measurement on 8 sources   \n",
       " 205              lsst.measurement  Performing forced measurement on 9 sources   \n",
       " 206  process_synthetic_photometry                process_synthetic_photometry   \n",
       " 207          simulate_lightcurves                        simulate_lightcurves   \n",
       " \n",
       "      count  total_duration  mean_duration  \n",
       " 4      999        4495.080       0.595217  \n",
       " 6      779         321.609       0.042586  \n",
       " 3      999         298.057       0.039467  \n",
       " 5      999          17.582       0.002328  \n",
       " 8      779           7.229       0.000957  \n",
       " ..     ...             ...            ...  \n",
       " 203     35           0.000       0.000000  \n",
       " 204     27           0.000       0.000000  \n",
       " 205     24           0.000       0.000000  \n",
       " 206      2         663.891     331.945500  \n",
       " 207      2          26.145      13.072500  \n",
       " \n",
       " [208 rows x 5 columns],\n",
       "                            item  count  total_duration\n",
       " 0                        Calexp   7552        5149.976\n",
       " 5  process_synthetic_photometry      2         663.891\n",
       " 1                         Event    100          62.345\n",
       " 6          simulate_lightcurves      2          26.145\n",
       " 2           load_nearby_objects      2           8.292\n",
       " 4              lsst.measurement    779           0.000\n",
       " 3                lsst.injection   6127           0.000)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import analysis\n",
    "importlib.reload(analysis)\n",
    "from analysis import plot_sky_map, generate_time_log, generate_summary_plot\n",
    "generate_time_log(sim.name)\n",
    "generate_summary_plot(sim.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ee477f17-1c56-48df-a7be-f892d14589fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T22:41:28.605131Z",
     "iopub.status.busy": "2025-09-06T22:41:28.604405Z",
     "iopub.status.idle": "2025-09-06T22:41:33.281197Z",
     "shell.execute_reply": "2025-09-06T22:41:33.280523Z",
     "shell.execute_reply.started": "2025-09-06T22:41:28.605105Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Chunk 2: 100%|██████████| 2/2 [00:03<00:00,  1.69s/it]:00<?, ?it/s]\n",
      "Chunk 0: 100%|██████████| 2/2 [00:03<00:00,  1.75s/it]\n",
      "Chunk 3: 100%|██████████| 2/2 [00:03<00:00,  1.78s/it]:03<00:31,  3.50s/it]\n",
      "Chunk 1: 100%|██████████| 2/2 [00:03<00:00,  1.83s/it]\n",
      "Chunk 7: 100%|██████████| 2/2 [00:03<00:00,  1.83s/it]:03<00:12,  1.54s/it]\n",
      "Chunk 5: 100%|██████████| 2/2 [00:03<00:00,  1.88s/it]\n",
      "Chunk 6: 100%|██████████| 2/2 [00:03<00:00,  1.88s/it]\n",
      "Chunk 4: 100%|██████████| 2/2 [00:03<00:00,  1.94s/it]\n",
      "Chunk 8: 100%|██████████| 2/2 [00:00<00:00,  2.35it/s]:03<00:02,  2.03it/s]\n",
      "Chunk 9: 100%|██████████| 2/2 [00:00<00:00,  2.64it/s]:04<00:00,  3.88it/s]\n",
      "Running parallel processing: 100%|██████████| 10/10 [00:04<00:00,  2.35it/s]\n"
     ]
    }
   ],
   "source": [
    "results_df = sim.compute_events_chi2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "be776f11-1af4-4d8a-9e73-8574805e70ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T22:41:40.081294Z",
     "iopub.status.busy": "2025-09-06T22:41:40.080557Z",
     "iopub.status.idle": "2025-09-06T22:41:40.088954Z",
     "shell.execute_reply": "2025-09-06T22:41:40.088378Z",
     "shell.execute_reply.started": "2025-09-06T22:41:40.081268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>status</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
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       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>success</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    event_id   status error\n",
       "0          1  success      \n",
       "1         14  success      \n",
       "2         13  success      \n",
       "3          3  success      \n",
       "4         15  success      \n",
       "5          9  success      \n",
       "6          7  success      \n",
       "7         11  success      \n",
       "8          8  success      \n",
       "9          6  success      \n",
       "10        10  success      \n",
       "11        16  success      \n",
       "12        12  success      \n",
       "13         2  success      \n",
       "14         5  success      \n",
       "15         4  success      \n",
       "16        17  success      \n",
       "17        19  success      \n",
       "18        18  success      \n",
       "19        20  success      "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "227d4d80-d636-4a8e-aa7b-377a7663407c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T22:42:29.810345Z",
     "iopub.status.busy": "2025-09-06T22:42:29.810038Z",
     "iopub.status.idle": "2025-09-06T22:42:29.851989Z",
     "shell.execute_reply": "2025-09-06T22:42:29.851379Z",
     "shell.execute_reply.started": "2025-09-06T22:42:29.810324Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>model</th>\n",
       "      <th>system_type</th>\n",
       "      <th>points</th>\n",
       "      <th>logL</th>\n",
       "      <th>logTe</th>\n",
       "      <th>D_L</th>\n",
       "      <th>D_S</th>\n",
       "      <th>...</th>\n",
       "      <th>p_value_r</th>\n",
       "      <th>p_value_u</th>\n",
       "      <th>p_value_y</th>\n",
       "      <th>p_value_z</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>61.986305</td>\n",
       "      <td>-35.993687</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-2.169</td>\n",
       "      <td>3.5707</td>\n",
       "      <td>5248.0</td>\n",
       "      <td>7889.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.170076e-239</td>\n",
       "      <td>3.245981e-02</td>\n",
       "      <td>5.028333e-03</td>\n",
       "      <td>1.586426e-16</td>\n",
       "      <td>53.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>61.966808</td>\n",
       "      <td>-36.003048</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>1.659</td>\n",
       "      <td>3.7111</td>\n",
       "      <td>6834.0</td>\n",
       "      <td>7359.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>62.009853</td>\n",
       "      <td>-35.979130</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>3.7699</td>\n",
       "      <td>6965.0</td>\n",
       "      <td>7516.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>62.006210</td>\n",
       "      <td>-35.961353</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-1.677</td>\n",
       "      <td>3.6234</td>\n",
       "      <td>7887.0</td>\n",
       "      <td>7989.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.739302e-01</td>\n",
       "      <td>2.113814e-01</td>\n",
       "      <td>6.926081e-02</td>\n",
       "      <td>1.288388e-01</td>\n",
       "      <td>53.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>62.034294</td>\n",
       "      <td>-36.027569</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-2.298</td>\n",
       "      <td>3.5191</td>\n",
       "      <td>4581.0</td>\n",
       "      <td>7715.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.467976e-01</td>\n",
       "      <td>2.781362e-01</td>\n",
       "      <td>7.064363e-01</td>\n",
       "      <td>2.075542e-03</td>\n",
       "      <td>52.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>62.058517</td>\n",
       "      <td>-35.988567</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-1.186</td>\n",
       "      <td>3.6664</td>\n",
       "      <td>6545.0</td>\n",
       "      <td>7158.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>53.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>62.055229</td>\n",
       "      <td>-35.979580</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-1.701</td>\n",
       "      <td>3.5808</td>\n",
       "      <td>3828.0</td>\n",
       "      <td>7901.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.399416e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>61.971760</td>\n",
       "      <td>-36.030972</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-0.968</td>\n",
       "      <td>3.6864</td>\n",
       "      <td>4616.0</td>\n",
       "      <td>7994.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.881908e-02</td>\n",
       "      <td>2.403833e-73</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>52.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>62.034660</td>\n",
       "      <td>-35.981194</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-2.174</td>\n",
       "      <td>3.5934</td>\n",
       "      <td>3573.0</td>\n",
       "      <td>6326.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.760936e-01</td>\n",
       "      <td>1.312552e-01</td>\n",
       "      <td>1.008930e-01</td>\n",
       "      <td>8.158348e-01</td>\n",
       "      <td>52.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>61.978260</td>\n",
       "      <td>-36.024632</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-3.020</td>\n",
       "      <td>3.4340</td>\n",
       "      <td>7332.0</td>\n",
       "      <td>7353.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.736299e-05</td>\n",
       "      <td>5.313865e-01</td>\n",
       "      <td>2.197062e-01</td>\n",
       "      <td>7.087297e-02</td>\n",
       "      <td>52.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>61.987141</td>\n",
       "      <td>-36.002804</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-1.306</td>\n",
       "      <td>3.6458</td>\n",
       "      <td>6905.0</td>\n",
       "      <td>7653.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.862744e-05</td>\n",
       "      <td>8.844074e-01</td>\n",
       "      <td>6.979194e-01</td>\n",
       "      <td>2.215075e-01</td>\n",
       "      <td>51.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>16</td>\n",
       "      <td>62.050762</td>\n",
       "      <td>-35.981724</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>3.7699</td>\n",
       "      <td>5436.0</td>\n",
       "      <td>5547.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>61.995205</td>\n",
       "      <td>-35.979721</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-2.429</td>\n",
       "      <td>3.4835</td>\n",
       "      <td>5872.0</td>\n",
       "      <td>7203.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.292519e-04</td>\n",
       "      <td>8.514533e-01</td>\n",
       "      <td>6.857833e-01</td>\n",
       "      <td>1.172805e-01</td>\n",
       "      <td>52.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>61.992966</td>\n",
       "      <td>-35.982212</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-2.080</td>\n",
       "      <td>3.6163</td>\n",
       "      <td>2758.0</td>\n",
       "      <td>6055.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9.882754e-01</td>\n",
       "      <td>6.260478e-01</td>\n",
       "      <td>6.451013e-02</td>\n",
       "      <td>5.023511e-19</td>\n",
       "      <td>51.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>61.966984</td>\n",
       "      <td>-35.976086</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-1.474</td>\n",
       "      <td>3.6386</td>\n",
       "      <td>1832.0</td>\n",
       "      <td>7772.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.211696e-236</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>48.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>62.045471</td>\n",
       "      <td>-35.975319</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-1.934</td>\n",
       "      <td>3.5520</td>\n",
       "      <td>5568.0</td>\n",
       "      <td>6203.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.394904e-02</td>\n",
       "      <td>9.437494e-01</td>\n",
       "      <td>9.732254e-03</td>\n",
       "      <td>5.873648e-01</td>\n",
       "      <td>54.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>62.032154</td>\n",
       "      <td>-36.009220</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-2.912</td>\n",
       "      <td>3.4496</td>\n",
       "      <td>2817.0</td>\n",
       "      <td>5156.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.214567e-01</td>\n",
       "      <td>3.702088e-01</td>\n",
       "      <td>7.029415e-01</td>\n",
       "      <td>6.609165e-01</td>\n",
       "      <td>50.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>62.021210</td>\n",
       "      <td>-36.037632</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-0.239</td>\n",
       "      <td>3.7338</td>\n",
       "      <td>7642.0</td>\n",
       "      <td>7881.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.145106e-01</td>\n",
       "      <td>3.931846e-01</td>\n",
       "      <td>5.595125e-01</td>\n",
       "      <td>3.526637e-01</td>\n",
       "      <td>52.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>61.997005</td>\n",
       "      <td>-36.015438</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-1.001</td>\n",
       "      <td>3.6500</td>\n",
       "      <td>7377.0</td>\n",
       "      <td>7671.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.386555e-04</td>\n",
       "      <td>1.237014e-311</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>62.039639</td>\n",
       "      <td>-36.008492</td>\n",
       "      <td>USBL</td>\n",
       "      <td>Planets_systems</td>\n",
       "      <td>491</td>\n",
       "      <td>-1.499</td>\n",
       "      <td>3.5880</td>\n",
       "      <td>6743.0</td>\n",
       "      <td>7998.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.357156e-12</td>\n",
       "      <td>2.541213e-01</td>\n",
       "      <td>9.072839e-03</td>\n",
       "      <td>1.124680e-02</td>\n",
       "      <td>50.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    event_id         ra        dec model      system_type  points   logL  \\\n",
       "0         13  61.986305 -35.993687  USBL  Planets_systems     491 -2.169   \n",
       "1          3  61.966808 -36.003048  USBL  Planets_systems     491  1.659   \n",
       "2          7  62.009853 -35.979130  USBL  Planets_systems     491 -0.023   \n",
       "3         11  62.006210 -35.961353  USBL  Planets_systems     491 -1.677   \n",
       "4          1  62.034294 -36.027569  USBL  Planets_systems     491 -2.298   \n",
       "5         14  62.058517 -35.988567  USBL  Planets_systems     491 -1.186   \n",
       "6         15  62.055229 -35.979580  USBL  Planets_systems     491 -1.701   \n",
       "7          9  61.971760 -36.030972  USBL  Planets_systems     491 -0.968   \n",
       "8          5  62.034660 -35.981194  USBL  Planets_systems     491 -2.174   \n",
       "9          4  61.978260 -36.024632  USBL  Planets_systems     491 -3.020   \n",
       "10        10  61.987141 -36.002804  USBL  Planets_systems     491 -1.306   \n",
       "11        16  62.050762 -35.981724  USBL  Planets_systems     491 -0.023   \n",
       "12        12  61.995205 -35.979721  USBL  Planets_systems     491 -2.429   \n",
       "13         2  61.992966 -35.982212  USBL  Planets_systems     491 -2.080   \n",
       "14         8  61.966984 -35.976086  USBL  Planets_systems     491 -1.474   \n",
       "15         6  62.045471 -35.975319  USBL  Planets_systems     491 -1.934   \n",
       "16        17  62.032154 -36.009220  USBL  Planets_systems     491 -2.912   \n",
       "17        19  62.021210 -36.037632  USBL  Planets_systems     491 -0.239   \n",
       "18        18  61.997005 -36.015438  USBL  Planets_systems     491 -1.001   \n",
       "19        20  62.039639 -36.008492  USBL  Planets_systems     491 -1.499   \n",
       "\n",
       "     logTe     D_L     D_S  ...      p_value_r      p_value_u      p_value_y  \\\n",
       "0   3.5707  5248.0  7889.0  ...  6.170076e-239   3.245981e-02   5.028333e-03   \n",
       "1   3.7111  6834.0  7359.0  ...   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "2   3.7699  6965.0  7516.0  ...   0.000000e+00            NaN   0.000000e+00   \n",
       "3   3.6234  7887.0  7989.0  ...   4.739302e-01   2.113814e-01   6.926081e-02   \n",
       "4   3.5191  4581.0  7715.0  ...   3.467976e-01   2.781362e-01   7.064363e-01   \n",
       "5   3.6664  6545.0  7158.0  ...   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "6   3.5808  3828.0  7901.0  ...   0.000000e+00   4.399416e-01   0.000000e+00   \n",
       "7   3.6864  4616.0  7994.0  ...   0.000000e+00   1.881908e-02   2.403833e-73   \n",
       "8   3.5934  3573.0  6326.0  ...   8.760936e-01   1.312552e-01   1.008930e-01   \n",
       "9   3.4340  7332.0  7353.0  ...   1.736299e-05   5.313865e-01   2.197062e-01   \n",
       "10  3.6458  6905.0  7653.0  ...   4.862744e-05   8.844074e-01   6.979194e-01   \n",
       "11  3.7699  5436.0  5547.0  ...   0.000000e+00   0.000000e+00   0.000000e+00   \n",
       "12  3.4835  5872.0  7203.0  ...   6.292519e-04   8.514533e-01   6.857833e-01   \n",
       "13  3.6163  2758.0  6055.0  ...   9.882754e-01   6.260478e-01   6.451013e-02   \n",
       "14  3.6386  1832.0  7772.0  ...   0.000000e+00  2.211696e-236   0.000000e+00   \n",
       "15  3.5520  5568.0  6203.0  ...   2.394904e-02   9.437494e-01   9.732254e-03   \n",
       "16  3.4496  2817.0  5156.0  ...   4.214567e-01   3.702088e-01   7.029415e-01   \n",
       "17  3.7338  7642.0  7881.0  ...   8.145106e-01   3.931846e-01   5.595125e-01   \n",
       "18  3.6500  7377.0  7671.0  ...   0.000000e+00   6.386555e-04  1.237014e-311   \n",
       "19  3.5880  6743.0  7998.0  ...   7.357156e-12   2.541213e-01   9.072839e-03   \n",
       "\n",
       "       p_value_z  dof_g  dof_i  dof_r  dof_u  dof_y  dof_z  \n",
       "0   1.586426e-16   53.0   99.0  108.0   32.0   80.0   50.0  \n",
       "1   0.000000e+00   52.0  101.0  110.0   34.0   78.0   48.0  \n",
       "2   0.000000e+00   52.0   96.0  107.0   36.0   79.0   49.0  \n",
       "3   1.288388e-01   53.0  101.0  103.0   31.0   74.0   50.0  \n",
       "4   2.075542e-03   52.0  102.0  106.0   34.0   79.0   49.0  \n",
       "5   0.000000e+00   53.0  103.0  108.0   34.0   76.0   51.0  \n",
       "6   0.000000e+00   55.0   99.0  105.0   33.0   74.0   48.0  \n",
       "7   0.000000e+00   52.0   95.0  110.0   29.0   76.0   48.0  \n",
       "8   8.158348e-01   52.0  101.0  111.0   34.0   77.0   49.0  \n",
       "9   7.087297e-02   52.0   93.0  110.0   30.0   76.0   48.0  \n",
       "10  2.215075e-01   51.0   93.0  109.0   31.0   81.0   51.0  \n",
       "11  0.000000e+00   54.0   99.0   99.0   32.0   72.0   47.0  \n",
       "12  1.172805e-01   52.0  100.0  107.0   34.0   78.0   50.0  \n",
       "13  5.023511e-19   51.0  102.0  106.0   33.0   77.0   50.0  \n",
       "14  0.000000e+00   48.0   99.0  106.0   30.0   78.0   49.0  \n",
       "15  5.873648e-01   54.0   99.0  103.0   33.0   72.0   50.0  \n",
       "16  6.609165e-01   50.0  100.0  101.0   34.0   74.0   47.0  \n",
       "17  3.526637e-01   52.0   93.0  109.0   32.0   79.0   49.0  \n",
       "18  0.000000e+00   51.0   97.0  105.0   30.0   80.0   46.0  \n",
       "19  1.124680e-02   50.0   99.0  104.0   34.0   74.0   49.0  \n",
       "\n",
       "[20 rows x 105 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_parquet(sim.events_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "88c91440-e291-4172-ac71-3de8b4d379a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-06T23:16:47.683432Z",
     "iopub.status.busy": "2025-09-06T23:16:47.683247Z",
     "iopub.status.idle": "2025-09-06T23:18:04.859557Z",
     "shell.execute_reply": "2025-09-06T23:18:04.858947Z",
     "shell.execute_reply.started": "2025-09-06T23:16:47.683414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "876fe17dcd68477290a7a8dfb29c2f50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Event 8 - Plotting filter light curves:   0%|          | 0/6 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import analysis\n",
    "import importlib\n",
    "importlib.reload(analysis)\n",
    "from analysis import plot_event_fov\n",
    "plot_event_fov(8, \"new_test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abfd17a6-c556-4a8d-9aa0-bca8bc10b01f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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