notebooks uncommented to filter and download data

utils/jwst_downloading.ipynb

@@ -0,0 +1,1099 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07b57859",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "\n",
+    "FULLY UNCLEANED CODE\n",
+    "\n",
+    "Contains the necessary scripts to actually download the FITS files that are in your JWST csv.\n",
+    "\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "240cc56f-e1b6-47f7-93ee-dc1a0918f5af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "from astropy import units as u\n",
+    "from sklearn.cluster import AgglomerativeClustering\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as patches\n",
+    "import os\n",
+    "import numpy as np\n",
+    "from astropy.io import fits\n",
+    "from astropy.wcs import WCS\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "df = pd.read_csv(\"jwst_FINAL.csv\")\n",
+    "\n",
+    "df = df.rename(columns={'sci_data_set_name': 'obs_id'})\n",
+    "\n",
+    "# Effective integration time should be more than 30 seconds\n",
+    "df = df[df['effinttm'] > 30]\n",
+    "\n",
+    "df = df[df['exp_type'] == \"NRC_IMAGE\"]\n",
+    "\n",
+    "\"\"\"\n",
+    "The data downloading process looks like the following:\n",
+    "\n",
+    "1. Use MastMissions to query the list of observations and their metadata, like ra/dec\n",
+    "\n",
+    "2. Filtering process to make sure there are no overlapping observations.\n",
+    "\n",
+    "3. Use Observations to pull the names of the data files associated with each observation.\n",
+    "\n",
+    "4. Pull the data by wget all those file links.\n",
+    "\n",
+    "5. Preprocess.\n",
+    "\n",
+    "Note that the data file names use the first 6 chars of obs_id from this observations array\n",
+    "that we have created. That's why we create the shortened identifier, to match\n",
+    "observations to product file names. This will be used later.\n",
+    "\"\"\"\n",
+    "\n",
+    "df['obs_id_short'] = df['obs_id'].str[:6]\n",
+    "\n",
+    "RA_NAME = 'targ_ra'\n",
+    "DEC_NAME = 'targ_dec'\n",
+    "\n",
+    "assert df[RA_NAME].isna().sum() < 10\n",
+    "assert df[DEC_NAME].isna().sum() < 10\n",
+    "\n",
+    "df = df.dropna(subset=[RA_NAME, DEC_NAME])\n",
+    "\n",
+    "df = df.groupby([RA_NAME, DEC_NAME]).apply(lambda x: x.drop_duplicates(subset='detector', keep='first'))\n",
+    "\n",
+    "multi_index_df = df.index.to_frame().groupby(level=0).first().reset_index(drop=True)\n",
+    "multi_index_df = multi_index_df.drop(columns=[2])\n",
+    "\n",
+    "df = df.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "cd89a849-6ef4-493a-910f-0b385e254eb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████████| 117/117 [13:48<00:00,  7.08s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import csv\n",
+    "\n",
+    "# Function to search datasets using the given endpoint\n",
+    "def search_datasets(dataset_ids):\n",
+    "    # Base URL for the search API\n",
+    "    base_url = 'https://mast.stsci.edu/search/jwst/api/v0.1/list_products'\n",
+    "    \n",
+    "    # List to store search results\n",
+    "    search_results = []\n",
+    "    \n",
+    "    ids_str = ','.join(dataset_ids)\n",
+    "\n",
+    "    # Construct the search URL\n",
+    "    search_url = f\"{base_url}?dataset_ids={ids_str}\"\n",
+    "\n",
+    "    # Make the API request\n",
+    "    response = requests.get(search_url)\n",
+    "\n",
+    "    # Check if the request was successful\n",
+    "    if response.status_code == 200:\n",
+    "        # Parse the JSON response\n",
+    "        data = response.json()\n",
+    "        search_results.append(data)\n",
+    "    else:\n",
+    "        # Handle errors\n",
+    "        print(f\"Error: Unable to fetch data for dataset ID {dataset_id}\")\n",
+    "    \n",
+    "    return search_results\n",
+    "\n",
+    "# Example usage\n",
+    "dataset_ids_csv = list(df['fileSetName'])\n",
+    "\n",
+    "sz_chunk = 10\n",
+    "chunks = [dataset_ids_csv[i:i+sz_chunk] for i in range(0,len(dataset_ids_csv), sz_chunk)]\n",
+    "\n",
+    "all_results = []\n",
+    "\n",
+    "for chunk in tqdm(chunks):\n",
+    "    all_results.append(search_datasets(chunk))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "b6aad3d4-fdd0-4799-a84c-692d1c94463d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████| 117/117 [00:00<00:00, 3803.87it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "new_all_results = []\n",
+    "\n",
+    "for result in tqdm(all_results):\n",
+    "    l = result[0]['products']\n",
+    "    new_all_results.extend(l)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "0c2caafa-d649-4e2a-92a6-24680ee06cb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_all_results_df = pd.DataFrame(new_all_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "e189e901-dbad-4689-8454-ee9e1ccab09a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detectors = ['NRCA1_FULL', 'NRCA2_FULL', 'NRCA3_FULL', 'NRCA4_FULL', 'NRCB1_FULL', 'NRCB2_FULL', 'NRCB3_FULL', 'NRCB4_FULL']\n",
+    "\n",
+    "\n",
+    "resultsdf = new_all_results_df[new_all_results_df['category'] == '1b']\n",
+    "resultsdf = resultsdf[resultsdf['filters'].isin(detectors)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "1f46cbf4-5b53-437d-8e9b-f7d126839ccc",
+   "metadata": {},
+   "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>product_key</th>\n",
+       "      <th>access</th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>instrument_name</th>\n",
+       "      <th>filters</th>\n",
+       "      <th>filename</th>\n",
+       "      <th>uri</th>\n",
+       "      <th>authz_primary_identifier</th>\n",
+       "      <th>authz_secondary_identifier</th>\n",
+       "      <th>file_suffix</th>\n",
+       "      <th>category</th>\n",
+       "      <th>size</th>\n",
+       "      <th>type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>jw02561001002_06101_00001_jw02561001002_06101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02561001002_06101_00001</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCA3_FULL</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca3_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001/jw02561001002_06101_...</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca3_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca3_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>jw02561001002_06101_00001_jw02561001002_06101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02561001002_06101_00001</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCB3_FULL</td>\n",
+       "      <td>jw02561001002_06101_00001_nrcb3_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001/jw02561001002_06101_...</td>\n",
+       "      <td>jw02561001002_06101_00001_nrcb3_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001_nrcb3_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>jw02561001002_06101_00001_jw02561001002_06101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02561001002_06101_00001</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCA2_FULL</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca2_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001/jw02561001002_06101_...</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca2_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca2_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>jw02561001002_06101_00001_jw02561001002_06101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02561001002_06101_00001</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCB2_FULL</td>\n",
+       "      <td>jw02561001002_06101_00001_nrcb2_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001/jw02561001002_06101_...</td>\n",
+       "      <td>jw02561001002_06101_00001_nrcb2_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001_nrcb2_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>jw02561001002_06101_00001_jw02561001002_06101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02561001002_06101_00001</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCA4_FULL</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca4_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001/jw02561001002_06101_...</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca4_uncal.fits</td>\n",
+       "      <td>jw02561001002_06101_00001_nrca4_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216718</th>\n",
+       "      <td>jw02130007001_03101_00002_jw02130007001_03101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02130007001_03101_00002</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCB1_FULL</td>\n",
+       "      <td>jw02130007001_03101_00002_nrcb1_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002/jw02130007001_03101_...</td>\n",
+       "      <td>jw02130007001_03101_00002_nrcb1_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002_nrcb1_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216740</th>\n",
+       "      <td>jw02130007001_03101_00002_jw02130007001_03101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02130007001_03101_00002</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCA4_FULL</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca4_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002/jw02130007001_03101_...</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca4_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca4_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216786</th>\n",
+       "      <td>jw02130007001_03101_00002_jw02130007001_03101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02130007001_03101_00002</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCA1_FULL</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca1_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002/jw02130007001_03101_...</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca1_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca1_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216808</th>\n",
+       "      <td>jw02130007001_03101_00002_jw02130007001_03101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02130007001_03101_00002</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCA3_FULL</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca3_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002/jw02130007001_03101_...</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca3_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca3_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>216830</th>\n",
+       "      <td>jw02130007001_03101_00002_jw02130007001_03101_...</td>\n",
+       "      <td>PUBLIC</td>\n",
+       "      <td>jw02130007001_03101_00002</td>\n",
+       "      <td>NIRCAM</td>\n",
+       "      <td>NRCA2_FULL</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca2_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002/jw02130007001_03101_...</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca2_uncal.fits</td>\n",
+       "      <td>jw02130007001_03101_00002_nrca2_uncal.fits</td>\n",
+       "      <td>_uncal</td>\n",
+       "      <td>1b</td>\n",
+       "      <td>75553920</td>\n",
+       "      <td>science</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7537 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              product_key  access  \\\n",
+       "28      jw02561001002_06101_00001_jw02561001002_06101_...  PUBLIC   \n",
+       "50      jw02561001002_06101_00001_jw02561001002_06101_...  PUBLIC   \n",
+       "72      jw02561001002_06101_00001_jw02561001002_06101_...  PUBLIC   \n",
+       "93      jw02561001002_06101_00001_jw02561001002_06101_...  PUBLIC   \n",
+       "114     jw02561001002_06101_00001_jw02561001002_06101_...  PUBLIC   \n",
+       "...                                                   ...     ...   \n",
+       "216718  jw02130007001_03101_00002_jw02130007001_03101_...  PUBLIC   \n",
+       "216740  jw02130007001_03101_00002_jw02130007001_03101_...  PUBLIC   \n",
+       "216786  jw02130007001_03101_00002_jw02130007001_03101_...  PUBLIC   \n",
+       "216808  jw02130007001_03101_00002_jw02130007001_03101_...  PUBLIC   \n",
+       "216830  jw02130007001_03101_00002_jw02130007001_03101_...  PUBLIC   \n",
+       "\n",
+       "                          dataset instrument_name     filters  \\\n",
+       "28      jw02561001002_06101_00001          NIRCAM  NRCA3_FULL   \n",
+       "50      jw02561001002_06101_00001          NIRCAM  NRCB3_FULL   \n",
+       "72      jw02561001002_06101_00001          NIRCAM  NRCA2_FULL   \n",
+       "93      jw02561001002_06101_00001          NIRCAM  NRCB2_FULL   \n",
+       "114     jw02561001002_06101_00001          NIRCAM  NRCA4_FULL   \n",
+       "...                           ...             ...         ...   \n",
+       "216718  jw02130007001_03101_00002          NIRCAM  NRCB1_FULL   \n",
+       "216740  jw02130007001_03101_00002          NIRCAM  NRCA4_FULL   \n",
+       "216786  jw02130007001_03101_00002          NIRCAM  NRCA1_FULL   \n",
+       "216808  jw02130007001_03101_00002          NIRCAM  NRCA3_FULL   \n",
+       "216830  jw02130007001_03101_00002          NIRCAM  NRCA2_FULL   \n",
+       "\n",
+       "                                          filename  \\\n",
+       "28      jw02561001002_06101_00001_nrca3_uncal.fits   \n",
+       "50      jw02561001002_06101_00001_nrcb3_uncal.fits   \n",
+       "72      jw02561001002_06101_00001_nrca2_uncal.fits   \n",
+       "93      jw02561001002_06101_00001_nrcb2_uncal.fits   \n",
+       "114     jw02561001002_06101_00001_nrca4_uncal.fits   \n",
+       "...                                            ...   \n",
+       "216718  jw02130007001_03101_00002_nrcb1_uncal.fits   \n",
+       "216740  jw02130007001_03101_00002_nrca4_uncal.fits   \n",
+       "216786  jw02130007001_03101_00002_nrca1_uncal.fits   \n",
+       "216808  jw02130007001_03101_00002_nrca3_uncal.fits   \n",
+       "216830  jw02130007001_03101_00002_nrca2_uncal.fits   \n",
+       "\n",
+       "                                                      uri  \\\n",
+       "28      jw02561001002_06101_00001/jw02561001002_06101_...   \n",
+       "50      jw02561001002_06101_00001/jw02561001002_06101_...   \n",
+       "72      jw02561001002_06101_00001/jw02561001002_06101_...   \n",
+       "93      jw02561001002_06101_00001/jw02561001002_06101_...   \n",
+       "114     jw02561001002_06101_00001/jw02561001002_06101_...   \n",
+       "...                                                   ...   \n",
+       "216718  jw02130007001_03101_00002/jw02130007001_03101_...   \n",
+       "216740  jw02130007001_03101_00002/jw02130007001_03101_...   \n",
+       "216786  jw02130007001_03101_00002/jw02130007001_03101_...   \n",
+       "216808  jw02130007001_03101_00002/jw02130007001_03101_...   \n",
+       "216830  jw02130007001_03101_00002/jw02130007001_03101_...   \n",
+       "\n",
+       "                          authz_primary_identifier  \\\n",
+       "28      jw02561001002_06101_00001_nrca3_uncal.fits   \n",
+       "50      jw02561001002_06101_00001_nrcb3_uncal.fits   \n",
+       "72      jw02561001002_06101_00001_nrca2_uncal.fits   \n",
+       "93      jw02561001002_06101_00001_nrcb2_uncal.fits   \n",
+       "114     jw02561001002_06101_00001_nrca4_uncal.fits   \n",
+       "...                                            ...   \n",
+       "216718  jw02130007001_03101_00002_nrcb1_uncal.fits   \n",
+       "216740  jw02130007001_03101_00002_nrca4_uncal.fits   \n",
+       "216786  jw02130007001_03101_00002_nrca1_uncal.fits   \n",
+       "216808  jw02130007001_03101_00002_nrca3_uncal.fits   \n",
+       "216830  jw02130007001_03101_00002_nrca2_uncal.fits   \n",
+       "\n",
+       "                        authz_secondary_identifier file_suffix category  \\\n",
+       "28      jw02561001002_06101_00001_nrca3_uncal.fits      _uncal       1b   \n",
+       "50      jw02561001002_06101_00001_nrcb3_uncal.fits      _uncal       1b   \n",
+       "72      jw02561001002_06101_00001_nrca2_uncal.fits      _uncal       1b   \n",
+       "93      jw02561001002_06101_00001_nrcb2_uncal.fits      _uncal       1b   \n",
+       "114     jw02561001002_06101_00001_nrca4_uncal.fits      _uncal       1b   \n",
+       "...                                            ...         ...      ...   \n",
+       "216718  jw02130007001_03101_00002_nrcb1_uncal.fits      _uncal       1b   \n",
+       "216740  jw02130007001_03101_00002_nrca4_uncal.fits      _uncal       1b   \n",
+       "216786  jw02130007001_03101_00002_nrca1_uncal.fits      _uncal       1b   \n",
+       "216808  jw02130007001_03101_00002_nrca3_uncal.fits      _uncal       1b   \n",
+       "216830  jw02130007001_03101_00002_nrca2_uncal.fits      _uncal       1b   \n",
+       "\n",
+       "            size     type  \n",
+       "28      75553920  science  \n",
+       "50      75553920  science  \n",
+       "72      75553920  science  \n",
+       "93      75553920  science  \n",
+       "114     75553920  science  \n",
+       "...          ...      ...  \n",
+       "216718  75553920  science  \n",
+       "216740  75553920  science  \n",
+       "216786  75553920  science  \n",
+       "216808  75553920  science  \n",
+       "216830  75553920  science  \n",
+       "\n",
+       "[7537 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resultsdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "id": "f086c0f9-9ef6-4945-a53a-f3cf051b4dee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resultsdf.to_csv(\"all_jwst_uris.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "09ca516e-3bbb-4a2b-9c6e-984c9a7e801a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Symmetric?\n",
+      "True\n",
+      "(475, 475)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Array of latitudes and longitudes\n",
+    "# MAKE SURE TO PUT RA=LON, DEC=LAT\n",
+    "latitudes = np.array(multi_index_df.groupby(level=0).first()[DEC_NAME])  # Example latitudes\n",
+    "longitudes = np.array(multi_index_df.groupby(level=0).first()[RA_NAME])  # Example longitudes\n",
+    "\n",
+    "n_points = len(latitudes)\n",
+    "\n",
+    "# Repeat each point n_points times for lat1, lon1\n",
+    "lat1 = np.repeat(latitudes, n_points)\n",
+    "lon1 = np.repeat(longitudes, n_points)\n",
+    "\n",
+    "# Tile the whole array n_points times for lat2, lon2\n",
+    "lat2 = np.tile(latitudes, n_points)\n",
+    "lon2 = np.tile(longitudes, n_points)\n",
+    "\n",
+    "# Calculates angular separation between two spherical coords\n",
+    "# This can be lat/lon or ra/dec\n",
+    "# Taken from astropy\n",
+    "def angular_separation_deg(lon1, lat1, lon2, lat2):\n",
+    "    lon1 = np.deg2rad(lon1)\n",
+    "    lon2 = np.deg2rad(lon2)\n",
+    "    lat1 = np.deg2rad(lat1)\n",
+    "    lat2 = np.deg2rad(lat2)\n",
+    "    \n",
+    "    sdlon = np.sin(lon2 - lon1)\n",
+    "    cdlon = np.cos(lon2 - lon1)\n",
+    "    slat1 = np.sin(lat1)\n",
+    "    slat2 = np.sin(lat2)\n",
+    "    clat1 = np.cos(lat1)\n",
+    "    clat2 = np.cos(lat2)\n",
+    "\n",
+    "    num1 = clat2 * sdlon\n",
+    "    num2 = clat1 * slat2 - slat1 * clat2 * cdlon\n",
+    "    denominator = slat1 * slat2 + clat1 * clat2 * cdlon\n",
+    "\n",
+    "    return np.rad2deg(np.arctan2(np.hypot(num1, num2), denominator))\n",
+    "\n",
+    "# Compute the pairwise angular separations\n",
+    "angular_separations = angular_separation_deg(lon1, lat1, lon2, lat2)\n",
+    "\n",
+    "# Reshape the result into a matrix form\n",
+    "angular_separations_matrix = angular_separations.reshape(n_points, n_points)\n",
+    "\n",
+    "def check_symmetric(a, rtol=1e-05, atol=1e-07):\n",
+    "    return np.allclose(a, a.T, rtol=rtol, atol=atol)\n",
+    "\n",
+    "print(\"Symmetric?\")\n",
+    "print(check_symmetric(angular_separations_matrix))\n",
+    "print(angular_separations_matrix.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "85edd94e-5591-4c7c-8251-b8c976962f72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24     40\n",
+      "12     40\n",
+      "76     36\n",
+      "34     30\n",
+      "7      24\n",
+      "       ..\n",
+      "221     1\n",
+      "166     1\n",
+      "139     1\n",
+      "176     1\n",
+      "20      1\n",
+      "Length: 311, dtype: int64\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████| 311/311 [00:00<00:00, 3456.86it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max subset with minimum distance: 321\n",
+      "1110\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#HUBBLE_FOV = 0.057\n",
+    "JWST_FOV = 0.0366667\n",
+    "\n",
+    "THRESH = JWST_FOV\n",
+    "\n",
+    "clustering = AgglomerativeClustering(n_clusters=None, metric='precomputed', linkage='single', distance_threshold=THRESH)\n",
+    "labels = clustering.fit_predict(angular_separations_matrix)\n",
+    "\n",
+    "multi_index_df['label'] = labels\n",
+    "\n",
+    "print(pd.Series(labels).value_counts())\n",
+    "\n",
+    "def max_subset_with_min_distance(points, min_distance):\n",
+    "    subset = []\n",
+    "    for i, row in points.iterrows():\n",
+    "        if all(angular_separation_deg(row[RA_NAME], row[DEC_NAME], existing_point[RA_NAME], existing_point[DEC_NAME]) >= min_distance for existing_point in subset):\n",
+    "            subset.append(row)\n",
+    "    return subset\n",
+    "\n",
+    "all_subsets = []\n",
+    "\n",
+    "for label in tqdm(np.unique(labels)):\n",
+    "    cds = multi_index_df[multi_index_df['label'] == label]\n",
+    "    subset = max_subset_with_min_distance(cds, THRESH)\n",
+    "    all_subsets.extend(subset)\n",
+    "\n",
+    "print(\"Max subset with minimum distance:\", len(all_subsets))\n",
+    "\n",
+    "locations = pd.DataFrame(all_subsets)\n",
+    "\n",
+    "df = pd.merge(df, locations, on=[RA_NAME, DEC_NAME], how='right')\n",
+    "\n",
+    "print(len(df))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "55a9d951-3210-4a7f-aca8-a0a80628a8ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detectors = ['nrca1', 'nrca2', 'nrca3', 'nrca4', 'nrcb1', 'nrcb2', 'nrcb3', 'nrcb4']\n",
+    "obsids_search = []\n",
+    "\n",
+    "for fileset in df['fileSetName']:\n",
+    "    for detector in detectors:\n",
+    "        obsids_search.append(fileset + \"_\" + detector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "55ae58fc-5c2f-46ec-8fa7-d7229fd61b5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from astroquery.mast import Observations\n",
+    "\n",
+    "# Query for data with the specified obs_id\n",
+    "result = Observations.query_criteria(obs_id=obsids_search)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "5a29aaf9-dd68-4fa2-910a-8d399f4580cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████████| 108/108 [01:07<00:00,  1.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 0 unique files, which are 0.0 GB in size.\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "no values provided to stack.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [120]\u001b[0m, in \u001b[0;36m<cell line: 25>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m files[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobs_id_short\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m files[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobs_id\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr[:\u001b[38;5;241m6\u001b[39m]\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThere are \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(files)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m unique files, which are \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28msum\u001b[39m(files[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m10\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m9\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m GB in size.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 25\u001b[0m manifest \u001b[38;5;241m=\u001b[39m \u001b[43mObservations\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_products\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiles\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mobsID\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcurl_flag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/astroquery/mast/observations.py:715\u001b[0m, in \u001b[0;36mObservationsClass.download_products\u001b[0;34m(self, products, download_dir, cache, curl_flag, mrp_only, cloud_only, **filters)\u001b[0m\n\u001b[1;32m    712\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m oid \u001b[38;5;129;01min\u001b[39;00m products:\n\u001b[1;32m    713\u001b[0m         product_lists\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_product_list(oid))\n\u001b[0;32m--> 715\u001b[0m     products \u001b[38;5;241m=\u001b[39m \u001b[43mvstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproduct_lists\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    717\u001b[0m \u001b[38;5;66;03m# apply filters\u001b[39;00m\n\u001b[1;32m    718\u001b[0m products \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfilter_products(products, mrp_only\u001b[38;5;241m=\u001b[39mmrp_only, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfilters)\n",
+      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/astropy/table/operations.py:677\u001b[0m, in \u001b[0;36mvstack\u001b[0;34m(tables, join_type, metadata_conflicts)\u001b[0m\n\u001b[1;32m    623\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    624\u001b[0m \u001b[38;5;124;03mStack tables vertically (along rows).\u001b[39;00m\n\u001b[1;32m    625\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    673\u001b[0m \u001b[38;5;124;03m    6   8\u001b[39;00m\n\u001b[1;32m    674\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    675\u001b[0m _check_join_type(join_type, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvstack\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 677\u001b[0m tables \u001b[38;5;241m=\u001b[39m \u001b[43m_get_list_of_tables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtables\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# validates input\u001b[39;00m\n\u001b[1;32m    678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(tables) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    679\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m tables[\u001b[38;5;241m0\u001b[39m]  \u001b[38;5;66;03m# no point in stacking a single table\u001b[39;00m\n",
+      "File \u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/astropy/table/operations.py:60\u001b[0m, in \u001b[0;36m_get_list_of_tables\u001b[0;34m(tables)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;66;03m# Make sure there is something to stack\u001b[39;00m\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(tables) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 60\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno values provided to stack.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     62\u001b[0m \u001b[38;5;66;03m# Convert inputs (Table, Row, or anything column-like) to Tables.\u001b[39;00m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;66;03m# Special case that Quantity converts to a QTable.\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ii, val \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(tables):\n",
+      "\u001b[0;31mValueError\u001b[0m: no values provided to stack."
+     ]
+    }
+   ],
+   "source": [
+    "from astropy.table import unique, vstack, Table\n",
+    "\n",
+    "matched_obs = result\n",
+    "\n",
+    "# Split the observations into \"chunks\" of size five\n",
+    "sz_chunk = 5\n",
+    "chunks = [matched_obs[i:i+sz_chunk] for i in range(0,len(matched_obs), sz_chunk)]\n",
+    "\n",
+    "# Get the list of products for each chunk\n",
+    "t = []\n",
+    "for chunk in tqdm(chunks):\n",
+    "    t.append(Observations.get_product_list(chunk))\n",
+    "\n",
+    "files = unique(vstack(t), keys='productFilename')\n",
+    "files = files.to_pandas()\n",
+    "\n",
+    "# Ensure we only keep raw data files\n",
+    "files = files[files['productSubGroupDescription'] == 'UNCAL']\n",
+    "\n",
+    "# Create a shortened identified\n",
+    "files['obs_id_short'] = files['obs_id'].str[:6]\n",
+    "\n",
+    "print(f\"There are {len(files)} unique files, which are {sum(files['size'])/10**9:.1f} GB in size.\")\n",
+    "\n",
+    "manifest = Observations.download_products(files['obsID'], curl_flag=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2307af8-d6ea-4969-8b8a-4d42f21d674f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "files[['obsID']].to_csv('list_of_hubble_filenames.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab5d1fd5-502f-4b94-a8f1-23636043f779",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "manifest = Observations.download_products(files['obsID'], curl_flag=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8b7937f-ca1d-4b4c-abef-1055cdf115ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "556fdab2-517e-466c-a9ee-c7b629df5276",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from astropy.io import fits\n",
+    "from astropy.table import Table\n",
+    "import glob\n",
+    "\n",
+    "def create_combined_hubble_file(short_obs_id):\n",
+    "    \n",
+    "    file_list = list(files[files['obs_id_short'] == short_obs_id]['productFilename'])\n",
+    "    \n",
+    "    ccd1_data = []\n",
+    "    ccd2_data = []\n",
+    "    ccd1_times = []\n",
+    "    ccd2_times = []\n",
+    "    ccd1_headers = []\n",
+    "    ccd2_headers = []\n",
+    "\n",
+    "    for file in file_list:\n",
+    "        with fits.open(file) as hdul:\n",
+    "            # Extract TIME-OBS from the primary header\n",
+    "            time_obs = hdul[0].header['TIME-OBS']\n",
+    "\n",
+    "            # Extract data and headers from HDUs\n",
+    "            for hdu in [hdul[1], hdul[4]]:\n",
+    "                if hdu.header['CCDCHIP'] == 1:\n",
+    "                    ccd1_data.append(hdu.data)\n",
+    "                    ccd1_times.append(time_obs)\n",
+    "                    ccd1_headers.append(hdul[0].header.copy())\n",
+    "                elif hdu.header['CCDCHIP'] == 2:\n",
+    "                    ccd2_data.append(hdu.data)\n",
+    "                    ccd2_times.append(time_obs)\n",
+    "                    ccd2_headers.append(hdul[0].header.copy())\n",
+    "\n",
+    "    # Sort the data based on TIME-OBS\n",
+    "    ccd1_times, ccd1_data, ccd1_headers = zip(*sorted(zip(ccd1_times, ccd1_data, ccd1_headers)))\n",
+    "    ccd2_times, ccd2_data, ccd2_headers = zip(*sorted(zip(ccd2_times, ccd2_data, ccd2_headers)))\n",
+    "\n",
+    "    # Concatenate the data for each CCDCHIP\n",
+    "    ccd1_concat = np.stack(ccd1_data)\n",
+    "    ccd2_concat = np.stack(ccd2_data)\n",
+    "\n",
+    "    # Function to create a new FITS file for a given CCDCHIP\n",
+    "    def create_fits_file(output_file, ccd_data, ccd_headers, ccd_chip):\n",
+    "\n",
+    "        primary_hdu = fits.PrimaryHDU()\n",
+    "        primary_hdu.header['EXTEND'] = True\n",
+    "        primary_hdu.header['CCDCHIP'] = ccd_chip\n",
+    "\n",
+    "        metadata_hdus = [fits.ImageHDU(header=header) for header in ccd_headers]\n",
+    "\n",
+    "        # Create ImageHDU with concatenated data\n",
+    "        image_hdu = fits.ImageHDU(data=ccd_data, header=fits.Header({'CCDCHIP': ccd_chip}))\n",
+    "\n",
+    "        # Create HDUList and write to a new FITS file\n",
+    "        hdulist = fits.HDUList([primary_hdu] + [image_hdu] + metadata_hdus)\n",
+    "        hdulist.writeto(output_file, overwrite=True)\n",
+    "\n",
+    "    # Create FITS files for CCDCHIP 1 and 2\n",
+    "    create_fits_file(f'{short_obs_id}_ccd1.fits', ccd1_concat, ccd1_headers, 1)\n",
+    "    create_fits_file(f'{short_obs_id}_ccd2.fits', ccd2_concat, ccd2_headers, 2)\n",
+    "\n",
+    "    print(\"New FITS files created successfully.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d09be044-ce2e-4cb5-ae1f-9562f9ac6fa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_label(df, LABEL=None):\n",
+    "    \n",
+    "    cds = df\n",
+    "    \n",
+    "    if LABEL is not None:\n",
+    "        cds = cds[labels == LABEL]\n",
+    "\n",
+    "    ras, decs = [], []\n",
+    "    cmap = []\n",
+    "    \n",
+    "    print(len(cds))\n",
+    "\n",
+    "    for i, cd in cds.iterrows():\n",
+    "        ras.append(cd[RA_NAME])\n",
+    "        decs.append(cd[DEC_NAME])\n",
+    "\n",
+    "    if LABEL is None:\n",
+    "        # No gridlines, will be overwhelming\n",
+    "\n",
+    "        # Compute the 2D histogram\n",
+    "        # Number of bins for x and y; adjust these based on your dataset\n",
+    "        bins = (30, 30)\n",
+    "\n",
+    "        # Compute the histogram\n",
+    "        hist, xedges, yedges = np.histogram2d(ras, decs, bins=bins)\n",
+    "        \n",
+    "        N_cutoff = 50\n",
+    "        hist = np.clip(hist, 0, N_cutoff)\n",
+    "\n",
+    "        # Generate a 2D histogram plot\n",
+    "        plt.figure(figsize=(8, 6))\n",
+    "        plt.imshow(hist, interpolation='nearest', origin='lower',\n",
+    "                   extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],\n",
+    "                   cmap='viridis')  # Choose a colormap (e.g., 'viridis', 'plasma', 'inferno')\n",
+    "\n",
+    "        # Add labels and title if necessary\n",
+    "        plt.colorbar(label='Number of points in bin')\n",
+    "        plt.xlabel('RA')\n",
+    "        plt.ylabel('DEC')\n",
+    "        plt.title(f'2D Histogram of Point Density, clipped to {N_cutoff} points')\n",
+    "\n",
+    "        # Show the plot\n",
+    "        plt.show()\n",
+    "\n",
+    "        return\n",
+    "    else:\n",
+    "        fig = plt.figure()\n",
+    "        ax = fig.gca()\n",
+    "        ax.set_xticks(np.arange(np.min(ras), np.max(ras), THRESH))\n",
+    "        ax.set_yticks(np.arange(np.min(decs), np.max(decs), THRESH))\n",
+    "        plt.scatter(ras, decs, alpha=0.1)\n",
+    "        plt.grid()\n",
+    "        plt.show()\n",
+    "\n",
+    "visualize_label(df, 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35a648ab-07cd-409e-be32-125ead927bdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "data = list(range(len(labels)))\n",
+    "\n",
+    "# Perform the train-test split with an 80-20 ratio\n",
+    "train_indices, test_indices = train_test_split(data, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f6a0472-b8fc-4989-a506-39019914c853",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdss_test = all_sdss_data[all_sdss_data['cluster_label'].isin(test_indices)]\n",
+    "sdss_train = all_sdss_data[all_sdss_data['cluster_label'].isin(train_indices)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfbe8686-e580-4285-bd8f-43154d18182b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(sdss_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cacb74dd-0d54-43d7-a301-ea4eb7b3e07e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(sdss_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0fb157d-1307-42d9-ac57-7e6aa3cecd84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdss_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62fb160e-74e5-4f50-96ee-109a6261370f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data = pd.DataFrame(test_data)\n",
+    "train_data = pd.DataFrame(train_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7b1379c-d5a9-4f3a-ac68-e8b5a46f4c8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Code to verify test/train pollution.\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "import json\n",
+    "from math import radians, sin, cos, sqrt, atan2, degrees\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "# Function to load data from file\n",
+    "def load_data(file_path):\n",
+    "    data = []\n",
+    "    with open(file_path, 'r') as f:\n",
+    "        for line in f:\n",
+    "            data.append(json.loads(line))\n",
+    "    return data\n",
+    "\n",
+    "# Load data\n",
+    "train_file_path = '/Users/rithwik/Desktop/full_train.jsonl.txt'\n",
+    "test_file_path = '/Users/rithwik/Desktop/full_test.jsonl.txt'\n",
+    "#train_data = sdss_train\n",
+    "#test_data = sdss_test\n",
+    "\n",
+    "# Define the threshold\n",
+    "threshold = 0.09*3\n",
+    "\n",
+    "# Find test dataset rows with a minimum great circle distance less than the threshold\n",
+    "close_pairs = []\n",
+    "\n",
+    "for i, test_point in test_data.iterrows():\n",
+    "    ra_test, dec_test = test_point['ra'], test_point['dec']\n",
+    "    distances = [(train_point, angular_separation_deg(ra_test, dec_test, train_point['ra'], train_point['dec'])) for i, train_point in train_data.iterrows()]\n",
+    "    closest_train_point, min_distance = min(distances, key=lambda x: x[1])\n",
+    "    if min_distance < threshold:\n",
+    "        close_pairs.append((test_point, closest_train_point, min_distance))\n",
+    "\n",
+    "close_pairs_summary = [\n",
+    "    {\n",
+    "        \"test_image_id\": test_point['image_id'],\n",
+    "        \"test_ra\": test_point['ra'],\n",
+    "        \"test_dec\": test_point['dec'],\n",
+    "        \"train_image_id\": closest_train_point['image_id'],\n",
+    "        \"train_ra\": closest_train_point['ra'],\n",
+    "        \"train_dec\": closest_train_point['dec'],\n",
+    "        \"min_distance_deg\": min_distance\n",
+    "    }\n",
+    "    for test_point, closest_train_point, min_distance in close_pairs\n",
+    "]\n",
+    "\n",
+    "# Print the results\n",
+    "result = \"Success\"\n",
+    "for pair in close_pairs_summary:\n",
+    "    print(pair)\n",
+    "    result = \"FAIL\"\n",
+    "    \n",
+    "print(f\"Done. result is {result}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93cf2978-0799-4d6c-93ec-921f4f41e10c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdss_test.to_json('full_test1.jsonl', orient='records', lines=True)\n",
+    "sdss_train.to_json('full_train1.jsonl', orient='records', lines=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}