{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "09ca516e-3bbb-4a2b-9c6e-984c9a7e801a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Symmetric?\n",
      "True\n",
      "6555\n"
     ]
    }
   ],
   "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(\"~/hst_FINAL.csv\")\n",
    "\n",
    "df = df.rename(columns={'sci_data_set_name': 'obs_id'})\n",
    "\n",
    "# Create a shortened identifier.\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 = 'sci_ra'\n",
    "DEC_NAME = 'sci_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",
    "# Sample data: let's create an example array of latitudes and longitudes\n",
    "# MAKE SURE TO PUT RA=LON, DEC=LAT\n",
    "latitudes = np.array(df[DEC_NAME])  # Example latitudes\n",
    "longitudes = np.array(df[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(len(angular_separations_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "d143cb03-8501-4a02-b77f-8efb6ce99a7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6555\n"
     ]
    }
   ],
   "source": [
    "print(len(angular_separations_matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "85edd94e-5591-4c7c-8251-b8c976962f72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128     369\n",
      "60      284\n",
      "156     198\n",
      "99      163\n",
      "78      118\n",
      "       ... \n",
      "2025      1\n",
      "605       1\n",
      "731       1\n",
      "1129      1\n",
      "561       1\n",
      "Length: 2215, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████| 2215/2215 [00:00<00:00, 3215.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max subset with minimum distance: 2367\n"
     ]
    }
   ],
   "source": [
    "HUBBLE_FOV = 0.057\n",
    "#JWST_FOV = 0.0172222\n",
    "\n",
    "THRESH = 2 * HUBBLE_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",
    "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 = df[labels == 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",
    "df = pd.DataFrame(all_subsets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "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=df[\"obs_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "5a29aaf9-dd68-4fa2-910a-8d399f4580cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 2143/2143 [07:54<00:00,  4.52it/s]\n"
     ]
    }
   ],
   "source": [
    "from astropy.table import unique, vstack, Table\n",
    "\n",
    "matched_obs = result\n",
    "\n",
    "# Split the observations into \"chunks\"\n",
    "sz_chunk = 1\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",
    "    out = Observations.get_product_list(chunk)\n",
    "    out = out[out['productSubGroupDescription'] == 'RAW']\n",
    "    out = out[out['dataRights'] == 'PUBLIC']\n",
    "    out = out.to_pandas()\n",
    "    if len(out) > 0:\n",
    "        t.append(out.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "7ab7dd7d-189f-402a-a796-22d859fe15ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2118 unique files, which are 72.9 GB in size.\n"
     ]
    }
   ],
   "source": [
    "files = pd.DataFrame(t)\n",
    "\n",
    "'''\n",
    "# Ensure we only keep raw data files\n",
    "files = files[files['productSubGroupDescription'] == 'RAW']\n",
    "\n",
    "# Create a shortened identified\n",
    "files['obs_id_short'] = files['obs_id'].str[:6]\n",
    "files = files.drop_duplicates(subset='obs_id_short')\n",
    "'''\n",
    "\n",
    "print(f\"There are {len(files)} unique files, which are {sum(files['size'])/10**9:.1f} GB in size.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "94c7dc65-9002-458f-b53f-1120fd8c7be1",
   "metadata": {},
   "outputs": [
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       "      <td>PUBLIC</td>\n",
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       "      <td>13364</td>\n",
       "      <td>jcdm96g1q_raw.fits</td>\n",
       "      <td>34344000</td>\n",
       "      <td>24839150</td>\n",
       "      <td>PUBLIC</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24035175</td>\n",
       "      <td>HST</td>\n",
       "      <td>image</td>\n",
       "      <td>j94se5qmq</td>\n",
       "      <td>DADS RAW file - Raw exposure COS/NICMOS/STIS/W...</td>\n",
       "      <td>S</td>\n",
       "      <td>mast:HST/product/j94se5qmq_raw.fits</td>\n",
       "      <td>SCIENCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RAW</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CALACS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10189</td>\n",
       "      <td>j94se5qmq_raw.fits</td>\n",
       "      <td>34344000</td>\n",
       "      <td>24035175</td>\n",
       "      <td>PUBLIC</td>\n",
       "      <td>1</td>\n",
       "      <td>F606W</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2118 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       obsID obs_collection dataproduct_type     obs_id  \\\n",
       "0   26113888            HST            image  jdq318n8q   \n",
       "0   24979066            HST            image  jcoz32dvq   \n",
       "0   25874266            HST            image  jdba3nvyq   \n",
       "0   24042703            HST            image  j96l03bcq   \n",
       "0   26270229            HST            image  jdrz3ytfq   \n",
       "..       ...            ...              ...        ...   \n",
       "0   24085175            HST            image  jb2p31fnq   \n",
       "0   68563813            HST            image  jenm44ygq   \n",
       "0   25857452            HST            image  jd9r08r2q   \n",
       "0   24127301            HST            image  jcdm96g1q   \n",
       "0   24035175            HST            image  j94se5qmq   \n",
       "\n",
       "                                          description type  \\\n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "..                                                ...  ...   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "0   DADS RAW file - Raw exposure COS/NICMOS/STIS/W...    S   \n",
       "\n",
       "                                dataURI productType  productGroupDescription  \\\n",
       "0   mast:HST/product/jdq318n8q_raw.fits     SCIENCE                      NaN   \n",
       "0   mast:HST/product/jcoz32dvq_raw.fits     SCIENCE                      NaN   \n",
       "0   mast:HST/product/jdba3nvyq_raw.fits     SCIENCE                      NaN   \n",
       "0   mast:HST/product/j96l03bcq_raw.fits     SCIENCE                      NaN   \n",
       "0   mast:HST/product/jdrz3ytfq_raw.fits     SCIENCE                      NaN   \n",
       "..                                  ...         ...                      ...   \n",
       "0   mast:HST/product/jb2p31fnq_raw.fits     SCIENCE                      NaN   \n",
       "0   mast:HST/product/jenm44ygq_raw.fits     SCIENCE                      NaN   \n",
       "0   mast:HST/product/jd9r08r2q_raw.fits     SCIENCE                      NaN   \n",
       "0   mast:HST/product/jcdm96g1q_raw.fits     SCIENCE                      NaN   \n",
       "0   mast:HST/product/j94se5qmq_raw.fits     SCIENCE                      NaN   \n",
       "\n",
       "   productSubGroupDescription  productDocumentationURL project  prvversion  \\\n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "..                        ...                      ...     ...         ...   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "0                         RAW                      NaN  CALACS         NaN   \n",
       "\n",
       "   proposal_id     productFilename      size parent_obsid dataRights  \\\n",
       "0        15213  jdq318n8q_raw.fits  34344000     26114025     PUBLIC   \n",
       "0        13671  jcoz32dvq_raw.fits  34344000     24981158     PUBLIC   \n",
       "0        14840  jdba3nvyq_raw.fits  34344000     25874435     PUBLIC   \n",
       "0        10438  j96l03bcq_raw.fits  34344000     24825269     PUBLIC   \n",
       "0        15446  jdrz3ytfq_raw.fits  34344000     26270246     PUBLIC   \n",
       "..         ...                 ...       ...          ...        ...   \n",
       "0        11613  jb2p31fnq_raw.fits  34344000     24831290     PUBLIC   \n",
       "0        16711  jenm44ygq_raw.fits  34344000     68565137     PUBLIC   \n",
       "0        14658  jd9r08r2q_raw.fits  34344000     25857751     PUBLIC   \n",
       "0        13364  jcdm96g1q_raw.fits  34344000     24839150     PUBLIC   \n",
       "0        10189  j94se5qmq_raw.fits  34344000     24035175     PUBLIC   \n",
       "\n",
       "    calib_level filters  \n",
       "0             1   F606W  \n",
       "0             1   F606W  \n",
       "0             1   F606W  \n",
       "0             1   F606W  \n",
       "0             1   F606W  \n",
       "..          ...     ...  \n",
       "0             1   F606W  \n",
       "0             1   F606W  \n",
       "0             1   F606W  \n",
       "0             1   F606W  \n",
       "0             1   F606W  \n",
       "\n",
       "[2118 rows x 20 columns]"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
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     "output_type": "stream",
     "text": [
      " 95%|█████████████████████████████████████████  | 83/87 [13:05<00:34,  8.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/bundle.sh to ./mastDownload_20240520161715.sh ... [Done]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████████████████████████████████████▌ | 84/87 [13:13<00:25,  8.44s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/bundle.sh to ./mastDownload_20240520161723.sh ... [Done]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|██████████████████████████████████████████ | 85/87 [13:21<00:16,  8.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/bundle.sh to ./mastDownload_20240520161732.sh ... [Done]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|██████████████████████████████████████████▌| 86/87 [13:30<00:08,  8.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading URL https://mast.stsci.edu/api/v0.1/Download/bundle.sh to ./mastDownload_20240520161740.sh ... [Done]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 87/87 [13:39<00:00,  9.42s/it]\n"
     ]
    }
   ],
   "source": [
    "sz_chunk = 25\n",
    "chunks = [files[i:i+sz_chunk] for i in range(0,len(files), sz_chunk)]\n",
    "\n",
    "# Get the list of products for each chunk\n",
    "t = []\n",
    "for chunk in tqdm(chunks):\n",
    "    manifest = Observations.download_products(chunk['obsID'], curl_flag=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "556fdab2-517e-466c-a9ee-c7b629df5276",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New FITS files created successfully.\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "\n",
    "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.\")\n",
    "    \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "d09be044-ce2e-4cb5-ae1f-9562f9ac6fa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "251\n"
     ]
    },
    {
     "data": {
      "image/png": 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nMZmjFslosvys42hq7WT3gRbaWtvYfaCFptZOlp91XK6bJjJiNMYySN0/dqyOffin048dpVv3OMrap9/k/abUXWFXLzh+TI6vaGJV6YsCyyCFf+w4xZz6xjb92FF6GQ+zA2ti1fw23F8KdClskFI/dpxNScxo7+yiJGb6saOMO5pYNX+Fb0CaUVkyLDcgqccyBN0/dkwk6vnPdYfMhyky5mli1fwV/lIA9FzWf2zLrsi+IKvHIiKDpolV89dI3ICkwCIig7ZkznT2NbdR39hGe3tHz1jjkjnTc900GcBIfClQYBGRQQuPNe7c36qxxjwyEl8KNMYiIkMyXBOrdi+bfOaEJu5Z+yctmxyxkZhtXYFFREaN8Lrz8TKjJdnJjb96ke+ve5XCwgKqy4oVaCIw3LOt61KYiIwa4WWTzQrocOf9/W289cFBjq4o7lkiWOvPj24KLCIyaqQvm/zmnoMUFwIG8XhcSwTnCQUWERk1wuvOA7S1d1JYCCVFH35UVZYUUt+oJYJHMwUWERk1ls07hoaWJLsPtODeRVdXF60dMKu6oqeMlgge/TR4LyKjRnjlxmSnM3vyJPY0t2iJ4DyjHouIjCoLTzqKHy0/g49MnsT/+VodN11YQ2m8kHca2iiNF3LteVoieLRTj0VERjUtEZx/1GMREZFIKbCIiEikFFhERCRSCiwiIhIpDd6LRKx7EsX6xjbNbSXjknosIhHqnkSxJdmpua1k3FJgEYlQeBJFzW0l45UCi0iE0idRBM1tJeOPAotIhNInUQTNbSXjT1aBxcxuMbNXzGyzmT1sZpVB+hVmtin06DKzmgz1a8zsmaDMRjObF6QfaWa/M7MmM7sjrU7CzF4NHXtKkH61mW0J0jaY2cnZvDaRoQhPophMpv5taEmybN4xuW6ayIjJtseyHjjF3ecCrwE3ALj7Pe5e4+41wOeB7e6+KUP9m4FvB+W+FewDtAL/Hbiuj+e9ovv47r47SLvX3ecEx7oZ+OcsX5vIoC086SiuPe9EzW0l41pWtxu7+7rQ7jPA0gzFlgH39XUIoDzYrgB2Bcc9CGwws+MH0ZYDod2JwbFFRpzmtpLxLsrfsVwFPJAh/TLgoj7q/D3wGzO7lVTv6ezDfK6fmFkn8BDwHXd3ADP7CvBNIA4s7Kuyma0EVgJMnTqVRCJxmE/bW1NT05DrysjT+covOl/5y4LP5L4LmD0BTMuQtcrdHwnKrAJqgUs8dEAzOwNY4+5z+jj27cDv3f0hM7sUWOnui0P5VwK17v7VUNrR7v6OmZWRCiw/d/e70477OeA/uvvyfl8cUFtb6xs3bhyoWEaJRIK6uroh1ZWRp/OVX3S+Rjcze97dazPlDdhjCX/Q93Hw5cAFwCI/NEpdTt+XwQCWA98Itn8BrDmM9rwT/NtoZvcC84C704rdD9w50LFERCR62d4Vdj5wPXChuzen5RUAnyX1Id+XXcC5wfZC4PUBnq/IzKqD7RipgLY12D8hVHTJQMcSEZHhke0Yyx1AMbDezACecferg7wFwE533xauYGZrgLvcfSPwJeAHZlZE6k6wlaFy20kN7MfN7GLgE8BbpMZkYkAh8ATwo6DKV81sMdAO7CPVGxIRkRGW7V1hfd615e4J4MwM6StC2xuA0/qoP6uPQ/dV/huZ0kVEZGRpdmORMWzzjr08tmUXexqTTC6Ls2TOdObOPCLXzZIxTlO6iIxRm3fsZc2GbbS2OzMqS2htd9Zs2MbmHXtz3TQZ4xRYRMaox7bsompCMdVlxcRiRVSXFVM1oZjHtuzKddNkjFNgERmj9jQmqUibabmipJA9jckctUjGC42xiIxRk8viNLR2Uh378G3e0NrJ5LJ4DlsVvfcaWti0Yx/7m9upnBCjZmYV0ypKc92scU09FpExasmc6exrbqO+sY329g7qG9vY19zGkjnTc920yLzX0MJvX36P9g5nSlkx7R3Ob19+j/caWnLdtHFNgUVkjJo78whWzJ9NSczYub+VkpixYv7sMXVX2KYd+ygviVNeGqOwoIDy0hjlJXE27diX66aNa7oUJjKGzZ15xJgKJOn2N7czJW0RtYnFhezWip05pR6LiOStygkxDrb1XrHzYFsnlRNiOWqRgAKLiOSxmplVHGhNcqClnc6uLg60tHOgNUnNzKpcN21cU2ARkbw1raKURSdNI1Zk7G5sI1ZkLDppmu4KyzGNsYhIXptWUcr5CiSjinosIiISKQUWERGJlC6FiUhWNIOypFOPRUSGTDMoSyYKLCIyZJpBWTJRYBGRIdMMypKJAouIDFn3DMphY3EGZRkcDd6LSC+DGYxfMmc6azZsA1I9lYbWTvY1t7Fi/uyRbLKMMuqxiEiPwQ7Gj4cZlGXw1GMRkR7hwXigZ5Gwx7bs6jNYjPUZlGXw1GMRkR4ajJcoKLCISA8NxksUFFhEpMd4WM5Yhp8Ci4j00GC8REGD9yLSiwbjJVvqsYiISKQUWEREJFIKLCIiEikFFhERiZQCi4iIREqBRUREIqXAIiIikcoqsJjZLWb2ipltNrOHzawySL/CzDaFHl1mVpOhfo2ZPROU2Whm89LyjzGzJjO7LpR2WfB8L5rZzaH020LP95qZ7c/mtYmIyNBk22NZD5zi7nOB14AbANz9Hnevcfca4PPAdnfflKH+zcC3g3LfCvbDbgMe794xsyOBW4BF7v63wFQzWxQ857Wh5/wX4JdZvjYRERmCrAKLu69z945g9xlgRoZiy4D7+joEUB5sVwA9C2Wb2cXANuDFUPnZwGvuvifYfwL4zCCfU0REhlGUU7pcBTyQIf0y4KI+6vw98Bszu5VUkDsbwMwmAtcD5wHXhcr/FfgbM5sF7AQuBnpNu2pmxwLHAU/21VAzWwmsBJg6dSqJRKK/19WnpqamIdeVkafzlV90vvLXgIHFzJ4ApmXIWuXujwRlVgEdwD1pdc8Amt19ax+Hvwa41t0fMrNLgR8Di4FvA7e5e5OZ9RR2931mdg2pANYFPEWqFxN2OfCgu3fSB3dfDawGqK2t9bq6ur6K9iuRSDDUujLydL7yi85X/howsLj74v7yzWw5cAGpcQ9Py76c/i9JLQe+EWz/AlgTbJ8BLA0G5yuBLjNrdfc73P1R4NHguVcC6QHkcuAr/b4oEREZNlldCjOz80ldsjrX3ZvT8gqAzwIL+jnELuBcIAEsBF4HcPdzQse5EWhy9zuC/SnuvtvMqoD/AlwaKnsiUAU8nc3rEhGRoct2jOUOoBhYH1yyesbdrw7yFgA73X1buIKZrQHucveNwJeAH5hZEdBKMO4xgB+Y2X8Itv+Hu78WylsG3J+h5yQiIiMkq8Di7sf3k5cAzsyQviK0vQE4bYDnuDFtf9nhlhURkZGnX96LiEikFFhERCRSCiwiIhIpBRYREYmUAouIiERKgUVERCKlwCIiIpFSYBERkUhFObuxyLDaXt/EU2/s4YOmJEdOinP2RyYzq3pSrpslImnUY5G8sL2+iYf/spNku3NURQnJdufhv+xke31TrpsmImkUWCQvPPXGHqpK41ROjFNUWEjlxDhVpXGeemPPwJVFZEQpsEhe+KApyaSSwl5pk0oK+aApmaMWiUhfFFgkLxw5KU5Ta++ld5paOzlyUryPGiKSKwoskhfO/shk9rUk2X8wSUdnJ/sPJtnXkuTsj0zOddNEJI0Ci+SFWdWT+PSpM4jHjHcbWonHjE+fOkN3hYmMQrrdWPLGrOpJCiQieUA9FhERiZQCi4iIREqBRUREIqXAIiIikVJgERGRSCmwiIhIpBRYREQkUgosIiISKQUWERGJlAKLiIhESoFFREQipcAiIiKRUmAREZFIKbCIiEikFFhERCRSCiwiIhIpBRYREYmUAouIiEQqq8BiZreY2StmttnMHjazyiD9CjPbFHp0mVlNhvo1ZvZMUGajmc0L0mNmttbMtpjZy2Z2Q4a6vzKzraH9BWb2ZzPrMLOl2bwukXz04HNv8anbE8z/7no+dXuCB597K9dNknEq2x7LeuAUd58LvAbcAODu97h7jbvXAJ8Htrv7pgz1bwa+HZT7VrAP8Fmg2N3nAKcBXzazWd2VzOwSoCntWG8DVwL3ZvmaRPLOg8+9xa3rXqWlvZOpk4poae/k1nWvKrhITmQVWNx9nbt3BLvPADMyFFsG3NfXIYDyYLsC2BVKn2hmRUApkAQOAJjZJOCbwHfS2rLd3TcDXUN7NSL5a+3TbzKppJAp5aUUlxQzpbyUSSWFrH36zVw3TcahogiPdRXwQIb0y4CL+qjz98BvzOxWUkHu7CD9waDOu8AE4Fp33xvk3QR8D2geakPNbCWwEmDq1KkkEokhHaepqWnIdWXkjeXzdcGURooKoKCgtSetq6KLji7y9jWP5fM11g0YWMzsCWBahqxV7v5IUGYV0AHck1b3DKDZ3bdmqA9wDamg8ZCZXQr8GFgMzAM6gelAFfCHoB3lwPHufm340thguftqYDVAbW2t19XVDek4iUSCodaVkTeWz9f3bk/Q0t7JlPLSnrTdB1oojRXy5cvrctewLIzl8zXWDRhY3H1xf/lmthy4AFjk7p6WfTl9XwYDWA58I9j+BbAm2P4c8Gt3bwd2m9kfgVrgSOA0M9setH2KmSXcvW6g1yEyli0/6zhuXfcq0EJFvICGZBdNrZ1cveD4XDdNxqFs7wo7H7geuNDdm9PyCkgNwt/fzyF2AecG2wuB14Ptt4GFljIROBN4xd3vdPfp7j4LmA+8pqAiAktPP5brPnEipbFC3m/qoDRWyHWfOJGlpx+b66bJOJTtGMsdQDGw3swAnnH3q4O8BcBOd98WrmBma4C73H0j8CXgB8EgfSvBuAfwQ+AnwFbAgJ8EA/N9MrPTgYdJXTr7lJl9293/NsvXJ5I3lp5+rAKJjApZBRZ377Of7e4JUj2N9PQVoe0NpG4nTi/TRKq3099zbwdOCe0/R+a70kREZATpl/ciIhIpBRYREYmUAouIiERKgUVERCJlh/70ZHwxsz3AUCdUqgbqI2yODC+dr/yi8zW6HevukzNljPvAkg0z2+jutbluhxwena/8ovOVv3QpTEREIqXAIiIikVJgyc7qXDdABkXnK7/ofOUpjbGIiEik1GMREZFIKbCIiEikxkVgMbPtZrbFzDaZ2cYg7SYz2xykrTOz6X3U/Vcz221mW9PSjzCz9Wb2evBvVZB+RXDM7keXmdUEeacF7firmd1uwZTQZrbAzP5sZh1mtnRY/xh5YDjOVyj/OjNzM6sO9o80s9+ZWZOZ3dFHnV+Fj2dmV5rZntA5XpGp3ngxTO+vB0J/3+1mtilIn2VmLaG8u0J1lgXt2Gxmvw6d42PN7LdBesLMNFntcHP3Mf8AtgPVaWnloe2vk5rKP1PdBcDHgK1p6TcD/xhs/yPwvzLUnQNsC+0/C5xFaimAx4FPBumzgLnA3cDSXP+9cv0YjvMV5M0EfkPqB7HVQdpEUmv7XA3ckaHOJcC94eMBV2YqO14fw3W+QmW+B3wr2J7Vx7ktAnaHzuvNwI3B9i+A5cH2QuBnuf6bjfXHuOixZOLuB0K7E4GMdzG4+/8D9mbIughYG2yvBS7OUGYZwQqaZnYUqTfb0576H353dx133+6p9Wa6Bv1CxokIzhfAbcB/Ddd194OeWr6hNb2wmU0Cvgl8Z4jNHrciOl8EvfpL6X8lWkh9WTNgYlCnnNRCggAnA78Ntn9H6r0rw2i8BBYH1pnZ82bWvZgYZvZPZrYDuAL41iCPOdXd3wUI/p2SocxlfPiGOBrYGcrbGaTJoSI/X2Z2IfCOu78wiGo3kfq23Jwh7zPBpZUHzWzmYNoyBg3H+6vbOcD77v56KO04M/uLmf3ezM4B8NQy5tcAW0gFlJOBHwflXwA+E2x/GigzsyOH2B45DOMlsPydu38M+CTwFTNbAODuq9x9JnAP8NUon9DMzgCa3b372rFlKKZ7vTOL9HyZ2QRgFYP4cAvGxY5394czZD8KzHL3ucATfNhzHa+G8/3V0+sPvAsc4+6nkupN3mtm5WYWIxVYTgWmA5uBG4I61wHnmtlfSC2F/g7QMcT2yGEYF4HF3XcF/+4mtXzxvLQi9/LhN5rD9X5weav7MtfutPzL6f2G2EnvFS5n8GFXXUKG4Xx9BDgOeMHMtpP62//ZzKb1U+cs4LSg/Abgo2aWCNr1gbu3BeV+RIZVUMeTYXp/Yaklyy8BHgg9V5u7fxBsPw+8AXwUqAnS3gguNf8bcHZ3+9z9kiAYrQrSGgbbHjl8Yz6wmNlEMyvr3gY+AWw1sxNCxS4EXhnkoX8FLA+2lwOPhJ6zgNTSyvd3pwWXyxrN7MzgGvAXwnUkZTjOl7tvcfcp7j7L3WeRCvIfc/f3+qlzp7tPD8rPB15z97qgXUelteXlw23LWDOM7y+AxcAr7t5zCdnMJptZYbA9GzgB2EaqF3KymXXPtnsewXkxs+rgPQmpXsy/DqEtMhi5vntguB/AbFLXWF8AXgRWBekPAVtJdZkfBY4O0qcD/x6qfx+p7nc7qQ+kLwbpR5IaEHw9+PeIUJ064JkMbakNnvMN4A4+nPng9ODYB4EPgBdz/Xcba+cr7Tm2E7qLKdjfCzQFdU5OKz+L3neFfTdo2wukBoP/Jtd/t7F4voCfAlenPd9nQn/7PwOfCuVdTSqYdD/nkUH60uB9+hqwBijO9d9trD80pYuIiERqzF8KExGRkaXAIiIikVJgERGRSCmwiIhIpBRYREQkUgosIiISKQUWERGJ1P8H2puh9ORs5XAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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, 4)"
   ]
  },
  {
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23efe470-c037-432a-9f7b-9276b89b94b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import huggingface_hub\n",
    "token = ''\n",
    "huggingface_hub.login(token=token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c208823b-1354-49c4-816d-17d4d1272930",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "dataset = load_dataset(\"AstroCompress/GBI-16-4D\", \"tiny\")\n",
    "ds = dataset.with_format(\"np\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22c589af-08d2-4227-99a5-16240f2cd390",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_jwst = pd.read_csv(\"/Users/rithwik/Desktop/jwst_all.csv\")\n",
    "df_jwst['opticalElementsList'] = df_jwst['opticalElements'].apply(lambda x: x.split(', '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eeef9b8-0327-4452-8dbb-f4f83c7d4b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.Series(sum(df_jwst['opticalElementsList'], [])).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e9eac4d9-db7b-463d-803a-5ec5702ae95b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /Users/rithwik/Desktop/cube_center_run1752_camcol1_f164_82-5-800-800.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     174   (800, 800, 5, 82)   int16 (rescales to uint16)   \n",
      "None\n",
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      "{'JPEG_2K_BYTES': 1.853658536585366, 'JPEG_2K_WRITE_RUNTIME': 0.0001220703125, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.8365853658536586, 'JPEG_2K_WRITE_RUNTIME': 0.00012302398681640625, 'JPEG_2K_READ_RUNTIME': 7.390975952148438e-05}\n",
      "{'JPEG_2K_BYTES': 1.8390243902439025, 'JPEG_2K_WRITE_RUNTIME': 0.00012087821960449219, 'JPEG_2K_READ_RUNTIME': 7.414817810058594e-05}\n",
      "{'JPEG_2K_BYTES': 1.8609756097560977, 'JPEG_2K_WRITE_RUNTIME': 0.0001232624053955078, 'JPEG_2K_READ_RUNTIME': 7.390975952148438e-05}\n",
      "{'JPEG_2K_BYTES': 1.8317073170731708, 'JPEG_2K_WRITE_RUNTIME': 0.00012230873107910156, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.8390243902439025, 'JPEG_2K_WRITE_RUNTIME': 0.00015807151794433594, 'JPEG_2K_READ_RUNTIME': 6.890296936035156e-05}\n",
      "{'JPEG_2K_BYTES': 1.8414634146341464, 'JPEG_2K_WRITE_RUNTIME': 0.00012302398681640625, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.851219512195122, 'JPEG_2K_WRITE_RUNTIME': 0.00012612342834472656, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.8634146341463416, 'JPEG_2K_WRITE_RUNTIME': 8.20159912109375e-05, 'JPEG_2K_READ_RUNTIME': 9.393692016601562e-05}\n",
      "{'JPEG_2K_BYTES': 1.8390243902439025, 'JPEG_2K_WRITE_RUNTIME': 0.00013685226440429688, 'JPEG_2K_READ_RUNTIME': 7.200241088867188e-05}\n",
      "{'JPEG_2K_BYTES': 1.851219512195122, 'JPEG_2K_WRITE_RUNTIME': 0.00012803077697753906, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.8487804878048781, 'JPEG_2K_WRITE_RUNTIME': 0.0001220703125, 'JPEG_2K_READ_RUNTIME': 7.390975952148438e-05}\n",
      "{'JPEG_2K_BYTES': 1.8463414634146342, 'JPEG_2K_WRITE_RUNTIME': 0.00012230873107910156, 'JPEG_2K_READ_RUNTIME': 7.390975952148438e-05}\n",
      "{'JPEG_2K_BYTES': 1.8390243902439025, 'JPEG_2K_WRITE_RUNTIME': 0.00012087821960449219, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.8463414634146342, 'JPEG_2K_WRITE_RUNTIME': 0.00012493133544921875, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.8585365853658538, 'JPEG_2K_WRITE_RUNTIME': 0.00012111663818359375, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.8439024390243903, 'JPEG_2K_WRITE_RUNTIME': 0.0001239776611328125, 'JPEG_2K_READ_RUNTIME': 8.988380432128906e-05}\n",
      "{'JPEG_2K_BYTES': 1.8487804878048781, 'JPEG_2K_WRITE_RUNTIME': 0.000148773193359375, 'JPEG_2K_READ_RUNTIME': 7.009506225585938e-05}\n",
      "{'JPEG_2K_BYTES': 1.8487804878048781, 'JPEG_2K_WRITE_RUNTIME': 0.00015997886657714844, 'JPEG_2K_READ_RUNTIME': 6.985664367675781e-05}\n",
      "{'JPEG_2K_BYTES': 1.8341463414634147, 'JPEG_2K_WRITE_RUNTIME': 0.000125885009765625, 'JPEG_2K_READ_RUNTIME': 7.390975952148438e-05}\n",
      "{'JPEG_2K_BYTES': 1.8341463414634147, 'JPEG_2K_WRITE_RUNTIME': 9.202957153320312e-05, 'JPEG_2K_READ_RUNTIME': 6.914138793945312e-05}\n",
      "{'JPEG_2K_BYTES': 1.826829268292683, 'JPEG_2K_WRITE_RUNTIME': 0.00012302398681640625, 'JPEG_2K_READ_RUNTIME': 9.179115295410156e-05}\n",
      "{'JPEG_2K_BYTES': 1.8439024390243903, 'JPEG_2K_WRITE_RUNTIME': 8.296966552734375e-05, 'JPEG_2K_READ_RUNTIME': 0.00010395050048828125}\n",
      "{'JPEG_2K_BYTES': 1.851219512195122, 'JPEG_2K_WRITE_RUNTIME': 0.0001289844512939453, 'JPEG_2K_READ_RUNTIME': 7.200241088867188e-05}\n",
      "{'JPEG_2K_BYTES': 1.8146341463414635, 'JPEG_2K_WRITE_RUNTIME': 0.00012421607971191406, 'JPEG_2K_READ_RUNTIME': 7.581710815429688e-05}\n",
      "{'JPEG_2K_BYTES': 1.8439024390243903, 'JPEG_2K_WRITE_RUNTIME': 0.00011682510375976562, 'JPEG_2K_READ_RUNTIME': 7.128715515136719e-05}\n",
      "{'JPEG_2K_BYTES': 1.853658536585366, 'JPEG_2K_WRITE_RUNTIME': 0.00011920928955078125, 'JPEG_2K_READ_RUNTIME': 7.486343383789062e-05}\n",
      "{'JPEG_2K_BYTES': 1.8121951219512196, 'JPEG_2K_WRITE_RUNTIME': 8.320808410644531e-05, 'JPEG_2K_READ_RUNTIME': 9.894371032714844e-05}\n",
      "{'JPEG_2K_BYTES': 1.8365853658536586, 'JPEG_2K_WRITE_RUNTIME': 9.775161743164062e-05, 'JPEG_2K_READ_RUNTIME': 8.916854858398438e-05}\n",
      "{'JPEG_2K_BYTES': 1.8439024390243903, 'JPEG_2K_WRITE_RUNTIME': 0.0001270771026611328, 'JPEG_2K_READ_RUNTIME': 7.390975952148438e-05}\n",
      "{'JPEG_2K_BYTES': 1.8414634146341464, 'JPEG_2K_WRITE_RUNTIME': 0.0001220703125, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n",
      "{'JPEG_2K_BYTES': 1.829268292682927, 'JPEG_2K_WRITE_RUNTIME': 8.177757263183594e-05, 'JPEG_2K_READ_RUNTIME': 9.608268737792969e-05}\n",
      "{'JPEG_2K_BYTES': 1.8463414634146342, 'JPEG_2K_WRITE_RUNTIME': 9.083747863769531e-05, 'JPEG_2K_READ_RUNTIME': 9.012222290039062e-05}\n",
      "{'JPEG_2K_BYTES': 1.8365853658536586, 'JPEG_2K_WRITE_RUNTIME': 0.00014019012451171875, 'JPEG_2K_READ_RUNTIME': 6.794929504394531e-05}\n",
      "{'JPEG_2K_BYTES': 1.853658536585366, 'JPEG_2K_WRITE_RUNTIME': 8.177757263183594e-05, 'JPEG_2K_READ_RUNTIME': 7.295608520507812e-05}\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from time import time\n",
    "import pandas as pd\n",
    "\n",
    "from imagecodecs import (\n",
    "    imwrite, \n",
    "    imread, \n",
    "    jpeg2k_encode, \n",
    "    jpeg2k_decode, \n",
    "    jpegls_encode, \n",
    "    jpegls_decode, \n",
    "    jpegxl_encode,\n",
    "    jpegxl_decode,\n",
    ")\n",
    "\n",
    "\n",
    "ALGORITHMS = [\"JPEG_XL\", \"JPEG_2K\", \"JPEG_LS\"]\n",
    "\n",
    "\n",
    "ALGORITHMS = [\"JPEG_2K\"]\n",
    "\n",
    "JPEG_ALGOS = {\n",
    "    \"JPEG_XL\": [jpegxl_encode, jpegxl_decode, 101], \n",
    "    \"JPEG_2K\": [jpeg2k_encode, jpeg2k_decode, 0], \n",
    "    \"JPEG_LS\": [jpegls_encode, jpegls_decode, 0],\n",
    "}\n",
    "\n",
    "def benchmark_imagecodec_compression_algos(arr, compression_type, residual=-1):\n",
    "\n",
    "    if compression_type in JPEG_ALGOS.keys():\n",
    "        encoder, decoder, level = JPEG_ALGOS[compression_type]\n",
    "\n",
    "        write_start_time = time()\n",
    "        encoded = encoder(arr, level=level)\n",
    "        write_time = time() - write_start_time\n",
    "        \n",
    "        read_start_time = time()\n",
    "        decoded = decoder(encoded)\n",
    "        read_time = time() - read_start_time\n",
    "        \n",
    "        assert np.array_equal(arr, decoded)\n",
    "\n",
    "    buflength = len(encoded)\n",
    "    \n",
    "    \n",
    "    return {compression_type + \"_BYTES\": buflength / arr.size,\n",
    "            compression_type + \"_WRITE_RUNTIME\": write_time,\n",
    "            compression_type + \"_READ_RUNTIME\": read_time\n",
    "           }\n",
    "\n",
    "\n",
    "\n",
    "file_paths = [\"/Users/rithwik/Desktop/cube_center_run1752_camcol1_f164_82-5-800-800.fits\"]\n",
    "\n",
    "for path in file_paths:\n",
    "    with fits.open(path) as hdul:  \n",
    "        print(hdul.info())\n",
    "        arr = hdul[0].data\n",
    "    for algo in ALGORITHMS:\n",
    "        for i in range(arr.shape[2]):  # Loop through the 5 sets\n",
    "            for j in range(arr.shape[3]):  # Loop through the 82 images in each set\n",
    "                image = arr[:, :, i, j]  # Extract the 800x800 image\n",
    "        \n",
    "                test_results = benchmark_imagecodec_compression_algos(image, algo, residual=1)\n",
    "                print(test_results)\n",
    "                break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "2dd1aad4-25a0-4a97-a61c-46e4bfa02e9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: /Users/rithwik/Desktop/hubble2im/MAST_2024-05-10T07_48_45.055Z/HST/jw04498038001_0310a_00002_nrcb1_uncal.fits\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     202   ()      \n",
      "  1  SCI           1 ImageHDU        80   (64, 64, 5, 2)   int16 (rescales to uint16)   \n",
      "  2  GROUP         1 BinTableHDU     38   1R x 13C   [I, I, I, J, I, 26A, I, I, I, I, 36A, D, D]   \n",
      "  3  INT_TIMES     1 BinTableHDU     24   2R x 7C   [J, D, D, D, D, D, D]   \n",
      "  4  ASDF          1 BinTableHDU     11   1R x 1C   [8163B]   \n",
      "None\n",
      "SIMPLE  =                    T / conforms to FITS standard                      BITPIX  =                    8 / array data type                                NAXIS   =                    0 / number of array dimensions                     EXTEND  =                    T                                                  DATE    = '2024-05-05T09:46:11.157' / UTC date file created                     ORIGIN  = 'STSCI   '           / Organization responsible for creating file     TIMESYS = 'UTC     '           / principal time system for time-related keywordsTIMEUNIT= 's       '           / Default unit applicable to all time values     FILENAME= 'jw04498038001_0310a_00002_nrcb1_uncal.fits' / Name of the file       SDP_VER = '2023_4a '           / Data processing (DP) Software Version          PRD_VER = 'PRDOPSSOC-065'      / S&OC Project Reference Database (PRD) Version  OSS_VER = '009.002.000.000'    / Observatory Scheduling Software (OSS) Version  GSC_VER = 'GSC30   '           / Guide Star Catalog (GSC) Version               DATAMODL= 'Level1bModel'       / Type of data model                             TELESCOP= 'JWST    '           / Telescope used to acquire the data             HGA_MOVE=                    F / High Gain Antenna moved during data collection PWFSEET =    60434.53219585648 / Previous WFS exposure end time                 NWFSEST =                  0.0 / Next WFS exposure start time                   COMPRESS=                    F / On-board data compression was used (T/F)                                                                                               Program information                                                                                                                                     TITLE   = 'Absolute Flux Calibration (Solar Analogs)' / Proposal title          PI_NAME = 'Gordon, Karl D.'    / Principal investigator name                    CATEGORY= 'CAL     '           / Program category                               SUBCAT  = 'CROSS   '           / Program sub-category                                                                                                                   Observation identifiers                                                                                                                                 DATE-OBS= '2024-05-05'         / [yyyy-mm-dd] UTC date at start of exposure     TIME-OBS= '03:33:39.660'       / [hh:mm:ss.sss] UTC time at start of exposure   DATE-BEG= '2024-05-05T03:33:39.660' / Date-time start of exposure               DATE-END= '2024-05-05T03:33:40.262' / Date-time end of exposure                 OBS_ID  = 'V04498038001P000000000310A' / Programmatic observation identifier    VISIT_ID= '04498038001'        / Visit identifier                               PROGRAM = '04498   '           / Program number                                 OBSERVTN= '038     '           / Observation number                             VISIT   = '001     '           / Visit number                                   VISITGRP= '03      '           / Visit group identifier                         SEQ_ID  = '1       '           / Parallel sequence identifier                   ACT_ID  = '0A      '           / Activity identifier                            EXPOSURE= '2       '           / Exposure request number                        BKGDTARG=                    F / Background target                              TEMPLATE= 'NIRCam Imaging'     / Observation template used                      OBSLABEL= 'NIRCam Imaging Sub64P Module B' / Proposer label for the observation OBSFOLDR= 'P330-E  '           / Name of the APT observation folder                                                                                                     Visit information                                                                                                                                       ENG_QUAL= 'OK      '           / Engineering data quality indicator from EngDB  ENGQLPTG= 'CALCULATED_TRACK_TR_202111' / Quality of pointing information from EnVISITYPE= 'PRIME_TARGETED_FIXED' / Visit type                                   VSTSTART= '2024-05-05 02:43:09.5450000' / UTC visit start time                  VISITSTA= 'SUCCESSFUL'         / Status of a visit                              NEXPOSUR=                   24 / Total number of planned exposures in visit     INTARGET=                    F / At least one exposure in visit is internal     TARGOOPP=                    F / Visit scheduled as target of opportunity       TSOVISIT=                    F / Time Series Observation visit indicator        EXP_ONLY=                    F / Special commanding without SI configuration    CROWDFLD=                    F / Are the FGSes in a crowded field?                                                                                                      Target information                                                                                                                                      TARGPROP= 'P330-E  '           / Proposer's name for the target                 TARGNAME= 'GSPC P330-E'        / Standard astronomical catalog name for target  TARGTYPE= 'FIXED   '           / Type of target (fixed, moving, generic)        TARGCAT = 'Star    '           / Target category from APT                       TARGDESC= 'G dwarfs'           / Target description from APT                    TARG_RA =   247.89081554763152 / Target RA at mid time of exposure              TARG_DEC=   30.145960505058927 / Target Dec at mid time of exposure             TARGURA =                  0.1 / Target RA uncertainty                          TARGUDEC=                  0.1 / Target Dec uncertainty                         MU_RA   =            -0.008882 / Target proper motion in RA                     MU_DEC  = -0.03870499999999999 / Target proper motion in Dec                    MU_EPOCH= '2000-01-01 00:00:00.0000000' / Target proper motion epoch            PROP_RA =   247.89088499999997 / Proposer's target RA                           PROP_DEC=    30.14622222222222 / Proposer's target Dec                                                                                                                  Instrument configuration information                                                                                                                    INSTRUME= 'NIRCAM  '           / Instrument used to acquire the data            DETECTOR= 'NRCB1   '           / Name of detector used to acquire the data      MODULE  = 'B       '           / NIRCam module: A, B, or MULTIPLE               CHANNEL = 'SHORT   '           / Instrument channel                             FILTER  = 'F200W   '           / Name of the filter element used                PUPIL   = 'CLEAR   '           / Name of the pupil element used                 PILIN   =                    F / Pupil imaging lens in the optical path?                                                                                                Exposure parameters                                                                                                                                     EXPCOUNT=                   18 / Running count of exposures in visit            EXPRIPAR= 'PRIME   '           / Prime or parallel exposure                     EXP_TYPE= 'NRC_IMAGE'          / Type of data in the exposure                   EXPSTART=    60435.14837569537 / [d] exposure start time in MJD                 EXPMID  =     60435.1483791787 / [d] exposure mid time in MJD                   EXPEND  =    60435.14838266204 / [d] exposure end time in MJD                   OSF_FILE= '2024126T091026166_001_osf.xml' / Observatory Status File name coverinREADPATT= 'RAPID   '           / Readout pattern                                NOUTPUTS=                    1 / Number of detector outputs used                NINTS   =                    2 / Number of integrations in exposure             NGROUPS =                    5 / Number of groups in integration                NFRAMES =                    1 / Number of frames per group                     FRMDIVSR=                    1 / Divisor applied to frame-averaged groups       GROUPGAP=                    0 / Number of frames dropped between groups        DRPFRMS1=                    0 / Frames dropped prior to first integration      DRPFRMS3=                    0 / Frames dropped between integrations            NSAMPLES=                    1 / Number of A/D samples per pixel                TSAMPLE =                 10.0 / [us] Time between samples                      TFRAME  =              0.05016 / [s] Time between frames                        TGROUP  =              0.05016 / [s] Time between groups                        EFFINTTM=               0.2508 / [s] Effective integration time                 EFFEXPTM=                0.502 / [s] Effective exposure time                    DURATION=                0.607 / [s] Total duration of exposure                 NRSTSTRT=                    1 / Number of resets at start of exposure          NRESETS =                    1 / Number of resets between integrations          ZEROFRAM=                    F / Zero frame was downlinked separately           DATAPROB=                    F / Science telemetry indicated a problem          SCA_NUM =                  486 / Sensor Chip Assembly number                    DATAMODE=                   33 / post-processing method used in FPAP            SCTARATE=                  0.0 / [ms/s] Spacecraft Clock Time Adjust RATE                                                                                               Subarray parameters                                                                                                                                     SUBARRAY= 'SUB64P  '           / Subarray used                                  SUBSTRT1=                 1851 / Starting pixel in axis 1 direction             SUBSTRT2=                 1817 / Starting pixel in axis 2 direction             SUBSIZE1=                   64 / Number of pixels in axis 1 direction           SUBSIZE2=                   64 / Number of pixels in axis 2 direction           FASTAXIS=                    1 / Fast readout axis direction                    SLOWAXIS=                   -2 / Slow readout axis direction                                                                                                            Dither information                                                                                                                                      PATTTYPE= 'SUBARRAY_DITHER'    / Primary dither pattern type                    PRIDTYPE= '4       '           / Primary dither points and packing              PRIDTPTS=                    4 / Number of points in primary dither pattern     PATT_NUM=                    2 / Position number within dither pattern          NUMDTHPT=                    4 / Total number of points in pattern              SUBPXPTS=                    1 / Number of points in subpixel dither pattern    SUBPXPAT= 'STANDARD'           / Subpixel dither pattern type                   XOFFSET =            0.3350007 / x offset from pattern starting position        YOFFSET =            0.3349999 / y offset from pattern starting position                                                                                                Aperture information                                                                                                                                    APERNAME= 'NRCB1_SUB64P'       / PRD science aperture used                      PPS_APER= 'NRCB5_SUB64P'       / original AperName supplied by PPS                                                                                                      Time information                                                                                                                                        BARTDELT=   380.69275945890695 / [s] Barycentric correction at exposure start   BSTRTIME=   60435.152781861565 / [d] Barycentric exposure start time in MJD     BENDTIME=   60435.152788828345 / [d] Barycentric exposure end time in MJD       BMIDTIME=    60435.15278534495 / [d] Barycentric exposure mid time in MJD       HELIDELT=   378.43239214271307 / [s] Heliocentric correction at exposure start  HSTRTIME=   60435.152755699906 / [d] Heliocentric exposure start time in MJD    HENDTIME=    60435.15276266669 / [d] Heliocentric exposure end time in MJD      HMIDTIME=     60435.1527591833 / [d] Heliocentric exposure mid time in MJD                                                                                              NIRCam Focus Adjust Mechanism parameters                                                                                                                FAM_LA1 =                    0 / position in steps of linear actuator 1         FASTEP1 =                    0 / requested focus actuator 1 starting steps      FAUNIT1 =                  0.0 / requested focus actuator 1 starting units      FAPHASE1= 'NULL    '           / requested focus actuator 1 starting phase      FA1VALUE=                    0 / requested focus actuator 1 position value      FAM_LA2 =                    0 / position in steps of linear actuator 2         FASTEP2 =                    0 / requested focus actuator 2 starting steps      FAUNIT2 =                  0.0 / requested focus actuator 2 starting units      FAPHASE2= 'NULL    '           / requested focus actuator 2 starting phase      FA2VALUE=                    0 / requested focus actuator 2 position value      FAM_LA3 =                    0 / position in steps of linear actuator 3         FASTEP3 =                    0 / requested focus actuator 3 starting steps      FAUNIT3 =                  0.0 / requested focus actuator 3 starting units      FAPHASE3= 'NULL    '           / requested focus actuator 3 starting phase      FA3VALUE=                    0 / requested focus actuator 3 position value                                                                                              Guide star information                                                                                                                                  GS_ORDER=                    1 / index of guide star within list of selected guiGSSTRTTM= '2024-05-05 02:55:08.3920000' / UTC time when guide star activity starGSENDTIM= '2024-05-05 02:59:11.0800000' / UTC time when guide star activity compGDSTARID= 'N62G002475'         / guide star identifier                          GS_RA   =     247.887317487359 / guide star right ascension                     GS_DEC  =    30.23942786320166 / guide star declination                         GS_URA  =         0.0381623022 / guide star right ascension uncertainty         GS_UDEC =           0.04689268 / guide star declination uncertainty             GS_MAG  =    15.00238132476807 / guide star magnitude in FGS detector           GS_UMAG =  0.02401826716959476 / guide star magnitude uncertainty               GS_V3_PA=    210.9691490535722 / V3 Position Angle of guide star for science    PCS_MODE= 'FINEGUIDE'          / Pointing Control System mode                   VISITEND= '2024-05-05 03:46:17.3170000' / Observatory UTC time when the visit coNEXTEND =                    3 / Number of file extensions                      VA_RA   =   247.89666010132925                                                  VA_DEC  =    30.14366511305928                                                  COMMENT /Exposure parameters                                                    COMMENT / Program information                                                   COMMENT / Observation identifiers                                               COMMENT / Visit information                                                     COMMENT / Target information                                                    COMMENT / Exposure times                                                        COMMENT / NIRCam Focus Adjust Mechanism                                         END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n",
      "[(1, 60435.14838266, 60435.14838266, 60435.14838266, 60435.15278883, 60435.15278883, 60435.15278883)\n",
      " (2, 60435.14838266, 60435.14838266, 60435.14838266, 60435.15278883, 60435.15278883, 60435.15278883)]\n"
     ]
    }
   ],
   "source": [
    "path = \"~/Desktop/hubble2im/MAST_2024-05-10T07_48_45.055Z/HST/jw04498038001_0310a_00002_nrcb1_uncal.fits\"\n",
    "\n",
    "path2 = \"~/Desktop/hubble2im/MAST_2024-05-10T07_48_45.055Z/HST/jw04498038001_0310a_00002_nrcblong_uncal.fits\"\n",
    "\n",
    "with fits.open(path) as hdul:  \n",
    "    print(hdul.info())\n",
    "    print(hdul[0].header)\n",
    "    arr = hdul[3].data\n",
    "    print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec59e584-248f-4ad3-9f00-09a2a6d69086",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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