filtering code

utils/keck_filtering.ipynb

@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "b4c4c986",
+   "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",
+    "import shutil\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, '**', '*LR*.fits')\n",
+    "    fits_files = glob.glob(pattern, recursive=True)\n",
+    "    return fits_files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "ba3bf5f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1014"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "valid_fits_paths = get_all_fits_files('./GBI-16-2D/prelim_data')\n",
+    "len(valid_fits_paths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "a9a90d18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1014\n",
+      "861\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_test = pd.read_json('./GBI-16-2D/splits/full_test.jsonl', lines=True)\n",
+    "df_train = pd.read_json('./GBI-16-2D/splits/full_train.jsonl', lines=True)\n",
+    "\n",
+    "df = pd.concat([df_train, df_test])\n",
+    "\n",
+    "print(len(df))\n",
+    "df = df[df['exposure_time'] >= 30]\n",
+    "print(len(df))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "f965da24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Symmetric?\n",
+      "True\n",
+      "(861, 861)\n"
+     ]
+    }
+   ],
+   "source": [
+    "latitudes = list(df['dec'])\n",
+    "longitudes = list(df['ra'])\n",
+    "\n",
+    "n_points = len(latitudes)\n",
+    "\n",
+    "# Repeat each point n_points times for lat1, lon1\n",
+    "lat1 = np.repeat(latitudes, n_points)\n",
+    "lon1 = np.repeat(longitudes, n_points)\n",
+    "\n",
+    "# Tile the whole array n_points times for lat2, lon2\n",
+    "lat2 = np.tile(latitudes, n_points)\n",
+    "lon2 = np.tile(longitudes, n_points)\n",
+    "\n",
+    "# Calculates angular separation between two spherical coords\n",
+    "# This can be lat/lon or ra/dec\n",
+    "# Taken from astropy\n",
+    "def angular_separation_deg(lon1, lat1, lon2, lat2):\n",
+    "    lon1 = np.deg2rad(lon1)\n",
+    "    lon2 = np.deg2rad(lon2)\n",
+    "    lat1 = np.deg2rad(lat1)\n",
+    "    lat2 = np.deg2rad(lat2)\n",
+    "    \n",
+    "    sdlon = np.sin(lon2 - lon1)\n",
+    "    cdlon = np.cos(lon2 - lon1)\n",
+    "    slat1 = np.sin(lat1)\n",
+    "    slat2 = np.sin(lat2)\n",
+    "    clat1 = np.cos(lat1)\n",
+    "    clat2 = np.cos(lat2)\n",
+    "\n",
+    "    num1 = clat2 * sdlon\n",
+    "    num2 = clat1 * slat2 - slat1 * clat2 * cdlon\n",
+    "    denominator = slat1 * slat2 + clat1 * clat2 * cdlon\n",
+    "\n",
+    "    return np.rad2deg(np.arctan2(np.hypot(num1, num2), denominator))\n",
+    "\n",
+    "# Compute the pairwise angular separations\n",
+    "angular_separations = angular_separation_deg(lon1, lat1, lon2, lat2)\n",
+    "\n",
+    "# Reshape the result into a matrix form\n",
+    "angular_separations_matrix = angular_separations.reshape(n_points, n_points)\n",
+    "\n",
+    "def check_symmetric(a, rtol=1e-05, atol=1e-07):\n",
+    "    return np.allclose(a, a.T, rtol=rtol, atol=atol)\n",
+    "\n",
+    "print(\"Symmetric?\")\n",
+    "print(check_symmetric(angular_separations_matrix))\n",
+    "print(angular_separations_matrix.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "6670e994",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "KECK_DEG_PER_PIXEL = 3.75e-5\n",
+    "KECK_FOV = 3768 * KECK_DEG_PER_PIXEL\n",
+    "THRESH = KECK_FOV * 2\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": 60,
+   "id": "ec592fb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|████���██████████████████████████████████| 137/137 [00:00<00:00, 1211.58it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max subset with minimum distance: 137\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "RA_NAME = 'ra'\n",
+    "DEC_NAME = 'dec'\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",
+    "locations = pd.DataFrame(all_subsets)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "b141c2e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for path in [\"./GBI-16-2D/prelim_data/\" + s.split('/')[-1] for s in locations['image']]:\n",
+    "    shutil.move(path, path.replace(\"prelim_data\", \"data\"))"
+   ]
+  }
+ ],
+ "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
+}