{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "19078572-4d7d-4453-9511-e4aece14efcc", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from astropy.io import fits\n", "import matplotlib.pyplot as plt\n", "from astropy.io import fits\n", "from photutils.detection import DAOStarFinder\n", "from astropy.stats import sigma_clipped_stats" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def extract_source(data):\n", " # Calculate background statistics\n", " mean, median, std = sigma_clipped_stats(data)\n", " signal = data - median\n", "\n", " # Define the star finder parameters\n", " threshold = std*3 # Adjust the threshold as needed\n", " fwhm = 5 # Adjust the FWHM as needed\n", " brightest_stars = 10 # Number of stars to find\n", " daofind = DAOStarFinder(fwhm=fwhm, threshold=threshold, brightest=brightest_stars, sky=mean, exclude_border=True)\n", "\n", " # Find stars in the data\n", " sources = daofind(signal)\n", " return sources, signal" ] }, { "cell_type": "code", "execution_count": 3, "id": "b091ac35", "metadata": {}, "outputs": [], "source": [ "def plot_aperature(data, aperture):\n", " mask = aperture.to_mask(method='center')\n", " roi_data = mask.cutout(data)\n", " plt.imshow(roi_data, cmap='Greys', origin='lower')\n", " plt.colorbar(label='Intensity')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "id": "43f3d7d4", "metadata": {}, "outputs": [], "source": [ "from photutils.aperture import aperture_photometry, CircularAperture, CircularAnnulus\n", "from photutils.centroids import centroid_quadratic\n", "from photutils.profiles import RadialProfile\n", "\n", "APERTURE_R = 10\n", "\n", "def calc_hfd(signal, aperture):\n", " mask = aperture.to_mask(method='center')\n", " roi_data = mask.cutout(signal)\n", " dist_weighted_flux = 0\n", " for (y, x), pix in np.ndenumerate(roi_data):\n", " dist = np.sqrt(y*y+x*x)\n", " if dist < APERTURE_R:\n", " dist_weighted_flux += pix * dist\n", "\n", " # total_flux = aperture_photometry(signal, aperture)['aperture_sum'][0]\n", " total_flux = np.sum(roi_data)\n", " return dist_weighted_flux / total_flux * 2\n", "\n", "def calc_fwhm(signal, aperture):\n", " xycen = aperture.positions\n", " edge_radii = np.arange(25)\n", " rp = RadialProfile(signal, xycen, edge_radii, mask=None)\n", " fwhm_value = rp.gaussian_fwhm\n", " # plot_aperature(signal, rp.apertures[-1])\n", " return fwhm_value" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['focus_test/manual/2023-10-12_22-07-46_L_-15.00_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-07-56_L_-15.00_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-08-04_L_-14.90_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-08-13_L_-14.90_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-08-25_L_-15.00_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-08-41_L_-15.00_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-08-49_L_-14.90_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-08-57_L_-14.90_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-09-06_L_-14.90_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-09-14_L_-14.90_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-09-26_L_-15.00_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-09-33_L_-15.00_2.00s_g60_0000.fits',\n", " 'focus_test/manual/2023-10-12_22-09-40_L_-14.90_2.00s_g60_0000.fits']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from glob import glob\n", "images = glob('focus_test/manual/*.fits')\n", "images.sort()\n", "images" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Focus: 13695 FWHM: 3.5362879023293727 HFD: 0.24995504547183\n", "Focus: 13720 FWHM: 3.721355450354107 HFD: 0.2974685289160995\n", "Focus: 13745 FWHM: 3.908496528441391 HFD: 0.3723565239345575\n", "Focus: 13770 FWHM: 5.362550479999639 HFD: 0.6788237805346665\n", "Focus: 13820 FWHM: 8.882569137436757 HFD: 1.5777035611759163\n", "Focus: 13645 FWHM: 3.3949519482240076 HFD: 0.18952763253357524\n", "Focus: 13620 FWHM: 3.988128913975961 HFD: 0.2021809629894956\n", "Focus: 13595 FWHM: 5.037383743194988 HFD: 0.3591840033880472\n", "Focus: 13570 FWHM: 6.22355643314642 HFD: 0.31517429705087\n", "Focus: 13545 FWHM: 8.458608474403956 HFD: 1.0884813152453303\n", "Focus: 13520 FWHM: 10.158831853608943 HFD: 2.2867862105955803\n", "Focus: 13495 FWHM: 12.665609036281914 HFD: 2.174804405346097\n", "Focus: 13470 FWHM: 14.209474821223722 HFD: 2.533190987822837\n" ] } ], "source": [ "fwhm_curve_dp = []\n", "hfd_curve_dp = []\n", "focuser_positions = []\n", "\n", "for img in images:\n", " hdul = fits.open(img)\n", " data = hdul[0].data\n", " current_focus = hdul[0].header['FOCUSPOS']\n", " sources, signal = extract_source(data)\n", "\n", " x_coords = sources['xcentroid']\n", " y_coords = sources['ycentroid']\n", " x,y = list(zip(x_coords, y_coords))[0]\n", "\n", " # Calculate the FWHM for each detected star\n", " fwhm_values = []\n", " hfd_values = []\n", "\n", " for x, y in zip(x_coords, y_coords):\n", " aperture = CircularAperture((x, y), r=APERTURE_R)\n", " fwhm_value = calc_fwhm(signal, aperture)\n", " fwhm_values.append(fwhm_value)\n", "\n", " # HFD\n", " hfd = calc_hfd(signal, aperture)\n", " hfd_values.append(hfd)\n", " \n", " fwhm_values = np.array(fwhm_values)\n", " hfd_values = np.array(hfd_values)\n", " mean_fwhm = np.mean(fwhm_values)\n", " mean_hfd = np.mean(hfd_values)\n", " fwhm_curve_dp.append(mean_fwhm)\n", " hfd_curve_dp.append(mean_hfd)\n", " focuser_positions.append(current_focus)\n", " print(f'Focus: {current_focus} FWHM: {mean_fwhm} HFD: {mean_hfd}')\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FWHM predicted focuser position is 13678.46\n", "HFD predicted focuser position is 13670.96\n" ] } ], "source": [ "# fit the data with a quadratic\n", "fwhm_fit = np.polyfit(focuser_positions, fwhm_curve_dp, 2)\n", "\n", "# predict the minimum value\n", "fwhm_min_value = -fwhm_fit[1] / (2 * fwhm_fit[0])\n", "\n", "print(f\"FWHM predicted focuser position is {fwhm_min_value:.2f}\")\n", "\n", "# fit the data with a quadratic\n", "hfd_fit = np.polyfit(focuser_positions, hfd_curve_dp, 2)\n", "\n", "# predict the minimum value\n", "hfd_min_value = -hfd_fit[1] / (2 * hfd_fit[0])\n", "\n", "print(f\"HFD predicted focuser position is {hfd_min_value:.2f}\")\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "bb78bad5", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))\n", "\n", "x_fit = np.linspace(min(focuser_positions), max(focuser_positions), 100)\n", "\n", "# plot FWHM curve and fit\n", "ax1.plot(focuser_positions, fwhm_curve_dp, 'o')\n", "y_fit = np.polyval(fwhm_fit, x_fit)\n", "ax1.plot(x_fit, y_fit, label='Fit')\n", "ax1.set_ylabel('FWHM (pixels)')\n", "ax1.set_title('Focus vs FWHM')\n", "\n", "# plot HFD curve and fit\n", "ax2.plot(focuser_positions, hfd_curve_dp, 'o')\n", "y_fit = np.polyval(hfd_fit, x_fit)\n", "ax2.plot(x_fit, y_fit, label='Fit')\n", "ax2.set_xlabel('Focus position')\n", "ax2.set_ylabel('HFD (pixels)')\n", "ax2.set_title('Focus vs HFD')\n", "\n", "# mark the minimum value of the fit\n", "fwhm_min_value = -fwhm_fit[1] / (2 * fwhm_fit[0])\n", "ax1.axvline(fwhm_min_value, color='r', linestyle='--', label=f'Minimum FWHM: {fwhm_min_value:.2f}')\n", "\n", "hfd_min_value = -hfd_fit[1] / (2 * hfd_fit[0])\n", "ax2.axvline(hfd_min_value, color='r', linestyle='--', label=f'Minimum HFD: {hfd_min_value:.2f}')\n", "\n", "ax1.legend()\n", "ax2.legend()\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "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": "3.9.15" } }, "nbformat": 4, "nbformat_minor": 5 }