added code to do downloading and filtering

utils/hubble_filtering.ipynb

@@ -0,0 +1,730 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6e84dd0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from tqdm import tqdm\n",
+    "import glob\n",
+    "from astropy.io import fits\n",
+    "import os\n",
+    "from astropy.io import fits\n",
+    "from astropy.wcs import WCS\n",
+    "from spherical_geometry.polygon import SphericalPolygon\n",
+    "import os\n",
+    "from astropy.io import fits\n",
+    "from astropy.wcs import WCS\n",
+    "from spherical_geometry.polygon import SphericalPolygon\n",
+    "from sklearn.cluster import AgglomerativeClustering\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from astropy.io import fits\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "def get_all_fits_files(root_dir):\n",
+    "    # Use glob to recursively find all .fits files\n",
+    "    pattern = os.path.join(root_dir, '**', '*.fits')\n",
+    "    fits_files = glob.glob(pattern, recursive=True)\n",
+    "    return fits_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "4f34a245",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████| 14/14 [02:03<00:00,  8.81s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "dirs = [d for d in os.listdir('.') if os.path.isdir(d) and str(d).startswith(\"MAST\")]\n",
+    "\n",
+    "all_fits = []\n",
+    "\n",
+    "for d in tqdm(dirs):\n",
+    "    fits_files = get_all_fits_files(d)\n",
+    "    all_fits.extend(fits_files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "51770e43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 43%|███████████████▋                     | 10175/23915 [08:12<12:36, 18.16it/s]WARNING: File may have been truncated: actual file length (28813816) is smaller than the expected size (33598080) [astropy.io.fits.file]\n",
+      "100%|█████████████████████████████████████| 23915/23915 [20:00<00:00, 19.92it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2149\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct = 0\n",
+    "\n",
+    "valid_fits_paths = []\n",
+    "\n",
+    "for fits_path in tqdm(all_fits):\n",
+    "    with fits.open(fits_path) as hdul:\n",
+    "        try:\n",
+    "            if hdul[1].data.dtype == np.dtype('uint16'):\n",
+    "                #print(hdul.info())\n",
+    "                assert hdul[1].data.shape == hdul[4].data.shape\n",
+    "                ct += 1\n",
+    "                valid_fits_paths.append(fits_path)\n",
+    "        except:\n",
+    "            continue\n",
+    "            \n",
+    "print(ct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "cfad3290",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File paths saved to valid_fits_paths.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "def save_filepaths_to_text(filepaths, output_file):\n",
+    "    try:\n",
+    "        with open(output_file, 'w') as f:\n",
+    "            for filepath in filepaths:\n",
+    "                f.write(filepath + '\\n')\n",
+    "        print(f\"File paths saved to {output_file}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error saving file paths: {e}\")\n",
+    "\n",
+    "save_filepaths_to_text(valid_fits_paths, \"valid_fits_paths.txt\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1a460324",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valid_fits_paths = os.listdir('./data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e68b6a9e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████| 2149/2149 [02:00<00:00, 17.77it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize the list of confirmed FITS paths\n",
+    "confirmed_fits_paths = []\n",
+    "\n",
+    "all_polys = []\n",
+    "\n",
+    "for i in tqdm(range(len(valid_fits_paths))):\n",
+    "\n",
+    "    path1 = os.path.join('data', valid_fits_paths[i])\n",
+    "    try:\n",
+    "        with fits.open(path1) as hdul1:\n",
+    "            wcs1a = WCS(hdul1[1].header)\n",
+    "            shape1a = sorted(tuple(wcs1a.pixel_shape))[:2]\n",
+    "\n",
+    "            wcs1b = WCS(hdul1[4].header)\n",
+    "            shape1b = sorted(tuple(wcs1b.pixel_shape))[:2]\n",
+    "\n",
+    "            # Get the footprints of the two WCS frames\n",
+    "            footprint1a = wcs1a.calc_footprint(axes=shape1a)\n",
+    "            footprint1b = wcs1b.calc_footprint(axes=shape1b)\n",
+    "\n",
+    "\n",
+    "            # Define two polygons\n",
+    "            poly1a = SphericalPolygon.from_radec(footprint1a[:, 0], footprint1a[:, 1])\n",
+    "            poly1b = SphericalPolygon.from_radec(footprint1b[:, 0], footprint1b[:, 1])\n",
+    "\n",
+    "            poly1 = poly1a.union(poly1b)\n",
+    "\n",
+    "            all_polys.append(poly1)\n",
+    "    except:\n",
+    "        continue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "72347e84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████████████████████████████████████| 2148/2148 [00:00<00:00, 77320.99it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "latitudes = []\n",
+    "longitudes = []\n",
+    "\n",
+    "for poly in tqdm(all_polys):\n",
+    "    pts = list(poly.to_radec())[0]\n",
+    "    ra = pts[0][0]\n",
+    "    dec = pts[1][0]\n",
+    "    \n",
+    "    longitudes.append(ra)\n",
+    "    latitudes.append(dec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a396a37f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Symmetric?\n",
+      "True\n",
+      "(2148, 2148)\n"
+     ]
+    }
+   ],
+   "source": [
+    "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": 15,
+   "id": "ae7ed213",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HUBBLE_FOV = 0.057\n",
+    "#JWST_FOV = 0.0366667\n",
+    "\n",
+    "THRESH = HUBBLE_FOV * 3\n",
+    "\n",
+    "clustering = AgglomerativeClustering(n_clusters=None, metric='precomputed', linkage='single', distance_threshold=THRESH)\n",
+    "labels = clustering.fit_predict(angular_separations_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bd5cc1db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                          | 1/1947 [00:00<03:29,  9.28it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL 0.2290158291821388\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  2%|▋                                        | 30/1947 [00:19<05:46,  5.54it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL 0.25478384325067566\n",
+      "FAIL 0.11201573962968173\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  2%|▋                                        | 32/1947 [00:20<04:39,  6.86it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL 0.08182961205102905\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  6%|██▏                                     | 108/1947 [00:47<08:23,  3.65it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL 0.31680112298937957\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 24%|█████████▍                             | 470/1947 [00:51<00:06, 231.53it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAIL 0.08882975311005689\n",
+      "FAIL 0.008033477806590562\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████| 1947/1947 [00:51<00:00, 37.70it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "failed_labels = []\n",
+    "failed_paths = []\n",
+    "\n",
+    "for label in tqdm(np.unique(labels)):\n",
+    "    polys = [(all_polys[i], valid_fits_paths[i]) for i in range(len(labels)) if labels[i] == label]\n",
+    "    if len(polys) > 1:\n",
+    "        total_poly = polys[0][0]\n",
+    "        for i in range(1, len(polys)):\n",
+    "            new_poly = polys[i][0]\n",
+    "            new_path = polys[i][1]\n",
+    "            if total_poly.intersects_poly(new_poly):\n",
+    "                union_over_max = total_poly.intersection(new_poly).area() / new_poly.area()\n",
+    "                print(f\"FAIL {union_over_max}\")\n",
+    "                failed_labels.append(label)\n",
+    "                failed_paths.append(new_path)\n",
+    "                continue\n",
+    "            else:\n",
+    "                total_poly = total_poly.union(new_poly)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7170d0e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['j9l919b6q_raw.fits',\n",
+       " 'je2r07ajq_raw.fits',\n",
+       " 'jcdm56ncq_raw.fits',\n",
+       " 'j9fc0tqaq_raw.fits',\n",
+       " 'jbpk02ioq_raw.fits',\n",
+       " 'jepx44lrq_raw.fits',\n",
+       " 'j9cx01cfq_raw.fits']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "failed_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "5baea239",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22      15\n",
+       "58      12\n",
+       "49       7\n",
+       "55       7\n",
+       "28       6\n",
+       "        ..\n",
+       "1493     1\n",
+       "1264     1\n",
+       "1214     1\n",
+       "1387     1\n",
+       "141      1\n",
+       "Name: count, Length: 1946, dtype: int64"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Series(labels).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cbb7bf27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Function to plot the rectangle\n",
+    "def plot_rectangle(corners):\n",
+    "    # Close the rectangle by repeating the first point at the end\n",
+    "    closed_corners = np.append(corners, [corners[0]], axis=0)\n",
+    "\n",
+    "    # Plot the rectangle\n",
+    "    plt.plot(closed_corners[:, 0], closed_corners[:, 1], 'b-')\n",
+    "    plt.scatter(corners[:, 0], corners[:, 1], color='red')\n",
+    "    \n",
+    "    # Annotate the points\n",
+    "    for i, corner in enumerate(corners):\n",
+    "        plt.annotate(f'P{i+1}', (corner[0], corner[1]), textcoords=\"offset points\", xytext=(5,5), ha='center')\n",
+    "    \n",
+    "    plt.xlabel('Longitude')\n",
+    "    plt.ylabel('Latitude')\n",
+    "    plt.title('Rectangle Plot from Given Corners')\n",
+    "    plt.grid(True)\n",
+    "\n",
+    "# Call the function to plot the rectangle\n",
+    "plot_rectangle(footprint1)\n",
+    "plot_rectangle(footprint2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "37557566",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XTENSION= 'IMAGE   '           / extension type                                 BITPIX  =                   16 / bits per data value                            NAXIS   =                    2 / number of data axes                            NAXIS1  =                 4144 / length of first data axis                      NAXIS2  =                 2068 / length of second data axis                     PCOUNT  =                    0 / number of group parameters                     GCOUNT  =                    1 / number of groups                               INHERIT =                    T / inherit the primary header                     EXTNAME = 'SCI     '           / extension name                                 EXTVER  =                    1 / extension version number                       ROOTNAME= 'jcuh01euq                         ' / rootname of the observation setEXPNAME = 'jcuh01euq                ' / exposure identifier                     DATAMIN =                2205. / the minimum value of the data                  DATAMAX =               51795. / the maximum value of the data                  BUNIT   = 'COUNTS            ' / brightness units                               BSCALE  =                  1.0 / scale factor for array value to physical value BZERO   =              32768.0 / physical value for an array value of zero                                                                                                    / WFC CCD CHIP IDENTIFICATION                                                                                                                     CCDCHIP =                    2 / CCD chip (1 or 2)                                                                                                                            / World Coordinate System and Related Parameters                                                                                                  WCSAXES =                    2 / number of World Coordinate System axes         CRPIX1  =               2124.0 / x-coordinate of reference pixel                CRPIX2  =               1024.0 / y-coordinate of reference pixel                CRVAL1  =   2.980193405890E+02 / first axis value at reference pixel            CRVAL2  =   1.447422452918E+01 / second axis value at reference pixel           CTYPE1  = 'RA---TAN'           / the coordinate type for the first axis         CTYPE2  = 'DEC--TAN'           / the coordinate type for the second axis        CD1_1   =          1.40038E-06 / partial of first axis coordinate w.r.t. x      CD1_2   =          1.39725E-05 / partial of first axis coordinate w.r.t. y      CD2_1   =          1.37888E-05 / partial of second axis coordinate w.r.t. x     CD2_2   =         -4.58499E-07 / partial of second axis coordinate w.r.t. y     LTV1    =                 24.0 / offset in X to subsection start                LTV2    =                  0.0 / offset in Y to subsection start                RAW_LTV1=                 24.0 / original offset in X to subsection start       RAW_LTV2=                  0.0 / original offset in Y to subsection start       LTM1_1  =                  1.0 / reciprocal of sampling rate in X               LTM2_2  =                  1.0 / reciprocal of sampling rate in Y               ORIENTAT=              91.8795 / position angle of image y axis (deg. e of n)   RA_APER =   2.980491666667E+02 / RA of aperture reference position              DEC_APER=   1.447333333333E+01 / Declination of aperture reference position     PA_APER =              91.4584 / Position Angle of reference aperture center (deVAFACTOR=   9.999498853766E-01 / velocity aberration plate scale factor                                                                                                       / READOUT DEFINITION PARAMETERS                                                                                                                   CENTERA1=                 2073 / subarray axis1 center pt in unbinned dect. pix CENTERA2=                 1035 / subarray axis2 center pt in unbinned dect. pix SIZAXIS1=                 4144 / subarray axis1 size in unbinned detector pixelsSIZAXIS2=                 2068 / subarray axis2 size in unbinned detector pixelsBINAXIS1=                    1 / axis1 data bin size in unbinned detector pixelsBINAXIS2=                    1 / axis2 data bin size in unbinned detector pixels                                                                                              / PHOTOMETRY KEYWORDS                                                                                                                             PHOTMODE= '                                                            ' / obserPHOTFLAM=   0.000000000000E+00 / inverse sensitivity, ergs/cm2/Ang/electron     PHOTZPT =             0.000000 / ST magnitude zero point                        PHOTPLAM=             0.000000 / Pivot wavelength (Angstroms)                   PHOTBW  =             0.000000 / RMS bandwidth of filter plus detector                                                                                                        / REPEATED EXPOSURES INFO                                                                                                                         NCOMBINE=                    1 / number of image sets combined during CR rejecti                                                                                              / DATA PACKET INFORMATION                                                                                                                         FILLCNT =                    0 / number of segments containing fill             ERRCNT  =                    0 / number of segments containing errors           PODPSFF =                    F / podps fill present (T/F)                       STDCFFF =                    F / science telemetry fill data present (T=1/F=0)  STDCFFP = '0x5569'             / science telemetry fill pattern (hex)                                                                                                         / ON-BOARD COMPRESSION INFORMATION                                                                                                                WFCMPRSD=                    F / was WFC data compressed? (T/F)                 CBLKSIZ =                    0 / size of compression block in 2-byte words      LOSTPIX =                    0 / #pixels lost due to buffer overflow            COMPTYP = 'None    '           / compression type performed (Partial/Full/None)                                                                                               / IMAGE STATISTICS AND DATA QUALITY FLAGS                                                                                                         NGOODPIX=              8569792 / number of good pixels                          SDQFLAGS=                31743 / serious data quality flags                     GOODMIN =                2205. / minimum value of good pixels                   GOODMAX =               51795. / maximum value of good pixels                   GOODMEAN=     2346.49479940703 / mean value of good pixels                      SOFTERRS=                    0 / number of soft error pixels (DQF=1)            SNRMIN  =             0.000000 / minimum signal to noise of good pixels         SNRMAX  =             0.000000 / maximum signal to noise of good pixels         SNRMEAN =             0.000000 / mean value of signal to noise of good pixels   MEANDARK=             0.000000 / average of the dark values subtracted          MEANBLEV=             0.000000 / average of all bias levels subtracted          MEANFLSH=             0.000000 / Mean number of counts in post flash exposure   END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n"
+     ]
+    }
+   ],
+   "source": [
+    "fitpath = \"./data/jcuh01euq_raw.fits\"\n",
+    "\n",
+    "with fits.open(fitpath) as hdul1:\n",
+    "    print(hdul1[1].header)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "da51818b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_corners_and_metadata(fits_path):\n",
+    "    with fits.open(fits_path) as hdul1:\n",
+    "        wcs1a = WCS(hdul1[1].header)\n",
+    "        shape1a = sorted(tuple(wcs1a.pixel_shape))[:2]\n",
+    "        footprint1a = wcs1a.calc_footprint(axes=shape1a)\n",
+    "        coords = list(footprint1a.flatten())\n",
+    "        inf = hdul1[0].header\n",
+    "        ra_targ = inf['RA_TARG']\n",
+    "        dec_targ = inf['DEC_TARG']\n",
+    "        exp_time = inf['EXPTIME']\n",
+    "        \n",
+    "        return coords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "cac0e38b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████| 2142/2142 [00:26<00:00, 80.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               fits_file         ra1       dec1         ra2       dec2   \n",
+      "0     jcuh01euq_raw.fits  298.001510  14.445419  298.061288  14.443517  \\\n",
+      "1     jbkh05h9q_raw.fits  287.858027 -60.066247  287.752068 -60.042645   \n",
+      "2     jcnu10r9q_raw.fits  201.025443 -43.460893  201.056301 -43.407484   \n",
+      "3     jdba3qokq_raw.fits  141.681252 -24.804074  141.744131 -24.813923   \n",
+      "4     jdrz77m0q_raw.fits  150.936991  40.747275  151.004173  40.719653   \n",
+      "...                  ...         ...        ...         ...        ...   \n",
+      "2136  jbkz29rzq_raw.fits  138.498478  40.943594  138.437903  40.979121   \n",
+      "2137  jdba3bi3q_raw.fits  131.959813  72.952872  131.893400  73.007432   \n",
+      "2138  jbkz90hxq_raw.fits   46.020489 -44.048250   46.094849 -44.070590   \n",
+      "2139  jcb805vtq_raw.fits  182.552746  49.999954  182.561072  49.942283   \n",
+      "2140  jdba8aw2q_raw.fits  224.393934 -19.196982  224.334389 -19.183120   \n",
+      "\n",
+      "             ra3       dec3         ra4       dec4  exposure_time  \n",
+      "0     298.064282  14.472018  298.004497  14.473921          580.0  \n",
+      "1     287.725102 -60.067934  287.831124 -60.091557          500.0  \n",
+      "2     201.021070 -43.394620  200.990187 -43.448019          430.0  \n",
+      "3     141.751603 -24.786090  141.688738 -24.776244          348.0  \n",
+      "4     151.024449  40.743832  150.957250  40.771467          390.0  \n",
+      "...          ...        ...         ...        ...            ...  \n",
+      "2136  138.412650  40.957740  138.473220  40.922227          400.0  \n",
+      "2137  131.799083  72.999647  131.865760  72.945118          348.0  \n",
+      "2138   46.112684 -44.044969   46.038349 -44.022640          400.0  \n",
+      "2139  182.605575  49.942966  182.597302  50.000640          659.0  \n",
+      "2140  224.325127 -19.210400  224.384680 -19.224265          348.0  \n",
+      "\n",
+      "[2141 rows x 10 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Directory containing the FITS files\n",
+    "data_dir = './data'\n",
+    "\n",
+    "# List to hold the data for the DataFrame\n",
+    "data = []\n",
+    "\n",
+    "# Loop through all FITS files in the \"data\" directory\n",
+    "for fits_file in tqdm(os.listdir(data_dir)):\n",
+    "    if fits_file.endswith('.fits'):\n",
+    "        file_path = os.path.join(data_dir, fits_file)\n",
+    "        ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4, exposure_time = get_corners_and_metadata(file_path)\n",
+    "        data.append([fits_file, ra1, dec1, ra2, dec2, ra3, dec3, ra4, dec4, exposure_time])\n",
+    "\n",
+    "# Create a DataFrame\n",
+    "df = pd.DataFrame(data, columns=['fits_file', 'ra1', 'dec1', 'ra2', 'dec2', 'ra3', 'dec3', 'ra4', 'dec4', 'exposure_time'])\n",
+    "\n",
+    "# Display the DataFrame\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "fc086514",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Assuming df is your DataFrame\n",
+    "# df = pd.DataFrame(...) # Your DataFrame should already be defined\n",
+    "\n",
+    "# Perform an 85/15 train-test split\n",
+    "train_df, test_df = train_test_split(df, test_size=0.15, random_state=42)\n",
+    "\n",
+    "# Save the train and test DataFrames to CSV files\n",
+    "train_df.to_csv('train_split.csv', index=False)\n",
+    "test_df.to_csv('test_split.csv', index=False)\n",
+    "\n",
+    "print(\"Train and test datasets have been saved to 'train_data.csv' and 'test_data.csv'.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "ab4a9a6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['README.md',\n",
+       " 'hst_FINAL.csv',\n",
+       " 'hubble_data_filtering.ipynb',\n",
+       " 'data',\n",
+       " 'valid_fits_paths.txt',\n",
+       " 'SBI-16-2D.py',\n",
+       " '.gitattributes',\n",
+       " '.git',\n",
+       " '.ipynb_checkpoints']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.listdir('.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2a77c29e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CSV file has been converted and saved as JSONL at test_split.jsonl\n",
+      "CSV file has been converted and saved as JSONL at train_split.jsonl\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "names = [\"test_split\", \"train_split\"]\n",
+    "\n",
+    "for name in names:\n",
+    "\n",
+    "    # Step 1: Load the CSV file into a DataFrame\n",
+    "    csv_file_path = f'{name}.csv'  # Replace with your actual CSV file path\n",
+    "    df = pd.read_csv(csv_file_path)\n",
+    "\n",
+    "    # Step 2: Save the DataFrame as a JSONL file\n",
+    "    jsonl_file_path = f'{name}.jsonl'  # Replace with your desired output file path\n",
+    "    df.to_json(jsonl_file_path, orient='records', lines=True)\n",
+    "\n",
+    "    print(f\"CSV file has been converted and saved as JSONL at {jsonl_file_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c78322ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}