{ "cells": [ { "cell_type": "code", "execution_count": 65, "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\n", "import sep" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "def extract_source(data):\n", " # Calculate background statistics\n", " data = data.astype(np.float32)\n", "\n", " bkg = sep.Background(data)\n", " signal = data - bkg\n", " sources = sep.extract(signal, 1.5, err=bkg.globalrms, minarea=40)\n", " return sources, signal" ] }, { "cell_type": "code", "execution_count": 67, "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": 68, "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": 69, "metadata": {}, "outputs": [], "source": [ "# from glob import glob\n", "base = \"http://72.233.250.83/data/ecam/20231021/ecam\"\n", "# images = [f\"{base}-00{i}.fits\" for i in range(33,40)]\n", "# # images = glob('focus_test/manual/*.fits')\n", "# # images.sort()\n", "# images" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "dp = {\n", " 33: 3000,\n", " 31: 0,\n", " 32: 1500,\n", " 34: 4500,\n", " 36: 6000,\n", " 37: -1500,\n", " 38: -3000,\n", " 39: -4500,\n", " 40: -6000,\n", "}" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "33\n", "Focus: 3000 FWHM: 5.3984241283893795 HFD: 0.1862868526723464\n", "31\n", "Focus: 0 FWHM: 4.693866276406432 HFD: 0.25530916093950434\n", "32\n", "Focus: 1500 FWHM: 5.001645570361233 HFD: 0.17307995425196937\n", "34\n", "Focus: 4500 FWHM: 6.423743539021113 HFD: 0.5937330969250255\n", "36\n", "Focus: 6000 FWHM: 9.111733089390158 HFD: 0.3100014696564545\n", "37\n", "Focus: -1500 FWHM: 4.301254043956106 HFD: 0.06826273099563598\n", "38\n", "Focus: -3000 FWHM: 3.5162538361290387 HFD: 0.22818754608735933\n", "39\n", "Focus: -4500 FWHM: 3.6657812166098642 HFD: -0.1674028362002335\n", "40\n", "Focus: -6000 FWHM: 3.952133872257408 HFD: 0.49696797433050227\n" ] } ], "source": [ "fwhm_curve_dp = []\n", "hfd_curve_dp = []\n", "focuser_positions = []\n", "\n", "\n", "for (img_id, current_focus) in dp.items():\n", " print(img_id)\n", " img = f\"{base}-00{img_id}.fits\"\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['x']\n", " y_coords = sources['y']\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": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FWHM predicted focuser position is -3409.41\n", "HFD predicted focuser position is -1377.15\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": 73, "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" ] } ], "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 }