diff --git a/stellarphot/notebooks/photometry/broeg2-trial.ipynb b/stellarphot/notebooks/photometry/broeg2-trial.ipynb new file mode 100644 index 00000000..a553442f --- /dev/null +++ b/stellarphot/notebooks/photometry/broeg2-trial.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "7e195b60-aa45-404b-ae1b-180a7089cac4", + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.table import Table, join \n", + "\n", + "import numpy as np\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05672aa4-ce5c-4be2-bf37-aa5b5f47c115", + "metadata": {}, + "outputs": [], + "source": [ + "from broeg_weights import broeg_weights2" + ] + }, + { + "cell_type": "markdown", + "id": "f9596998-0aef-4a47-87da-e54f773c7841", + "metadata": {}, + "source": [ + "## 👇👇 change the file name 👇👇" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc155555-0847-4a50-ac40-46d407a3b1ed", + "metadata": {}, + "outputs": [], + "source": [ + "use_phot = Table.read('../../Combined/TIC-81247877-combined-killer-brooks-ified.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2852b113-821b-48ff-8760-721ff71802aa", + "metadata": {}, + "outputs": [], + "source": [ + "star_ids = np.array(sorted(set(use_phot['star_id'])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be327b49-65e7-4c9e-ab4f-1391d4d09f7c", + "metadata": {}, + "outputs": [], + "source": [ + "mags = []\n", + "errs = []\n", + "good_star_ids = []\n", + "for star_id in star_ids:\n", + " mag = use_phot[use_phot[\"star_id\"] == star_id][\"mag_inst\"]\n", + " err = use_phot[use_phot[\"star_id\"] == star_id][\"mag_error\"]\n", + " if np.isnan(mag).any():\n", + " continue\n", + " good_star_ids.append(star_id)\n", + " mags.append(mag)\n", + " errs.append(err)" + ] + }, + { + "cell_type": "markdown", + "id": "ed648acb-ecb8-4f7f-846d-00940a7e8c30", + "metadata": {}, + "source": [ + "## Number of stars input, number of stars with no NaNs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee43cf36-9fa6-4f4a-a662-fd04a1dd770a", + "metadata": {}, + "outputs": [], + "source": [ + "len(star_ids), len(good_star_ids)" + ] + }, + { + "cell_type": "markdown", + "id": "b5ae488c-a42e-4a12-9dfd-2f79eac5e89b", + "metadata": {}, + "source": [ + "Check for any stars that are missing data..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1cd4ba06-a0f9-4fe7-8c0e-d8eb99427e6c", + "metadata": {}, + "outputs": [], + "source": [ + "n_data = [len(mag) for mag in mags]\n", + "n_err = [len(err) for err in errs]\n", + "\n", + "len(mags), len(errs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb55c400-7740-460b-abe1-1d977bbee278", + "metadata": {}, + "outputs": [], + "source": [ + "n_max = max(n_data)\n", + "\n", + "bad_star_id_index = []\n", + "for i, n in enumerate(n_data):\n", + " if n != n_max:\n", + " print(f\"Bad {i=} with {n=}\")\n", + " bad_star_id_index.append(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03c5bb67-30a6-4ebc-9716-7fd37e2a3eb4", + "metadata": {}, + "outputs": [], + "source": [ + "for idx in bad_star_id_index:\n", + " del good_star_ids[idx]\n", + " del mags[idx]\n", + " del errs[idx]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af9efa44-9bd1-481c-8fd5-d10977a51e6f", + "metadata": {}, + "outputs": [], + "source": [ + "mags_a = np.array(mags)\n", + "errs_a = np.array(errs)\n", + "mags_a.shape, errs_a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d348ff35-c396-42cd-9258-64dad04a5b5d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "weights, lights = broeg_weights2(mags_a, errs_a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8e699bd-4500-43d3-8940-fdf9ca74a163", + "metadata": {}, + "outputs": [], + "source": [ + "len(weights)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6116368-e66d-4f5c-a663-5094d505c26a", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(weights[-1] - weights[-2])\n", + "#plt.ylim(0.002, 0.004)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b6c8a52-ea5f-4bd3-a4ac-3293a8cae483", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(weights[0], label=\"0\")\n", + "plt.plot(weights[-1], label=\"-1\")\n", + "plt.legend()\n", + "plt.grid()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12483d7d-698a-4077-b354-935d250edee1", + "metadata": {}, + "outputs": [], + "source": [ + "lights[-1].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c02a61e2-5bcf-4f82-8c96-24f58ce95289", + "metadata": {}, + "outputs": [], + "source": [ + "good_star_ids[np.argmax(weights[0])]" + ] + }, + { + "cell_type": "markdown", + "id": "4162f569-3855-443d-b328-7016d5f7143e", + "metadata": {}, + "source": [ + "## 👇👇 set which star you want to plot 👇👇" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e8435c8-c3e3-4c49-b933-39cc5c493bf5", + "metadata": {}, + "outputs": [], + "source": [ + "da_star = 303\n", + "da_index = np.where(np.array(good_star_ids) == da_star)[0][0]\n", + "da_index, good_star_ids[da_index]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7b5cfc76-dfdb-4df2-8843-e24c4900b560", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(lights[-1][da_index, :], '.')\n", + "plt.grid()\n", + "plt.ylim(reversed(plt.ylim()))\n", + "plt.xlabel('data point number')\n", + "plt.ylabel('differential magnitude')\n", + "plt.title(f'Star ID: {da_star}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05e25851-200c-455f-8f0b-631d58651e4d", + "metadata": {}, + "outputs": [], + "source": [ + "just_da_star = use_phot[use_phot['star_id'] == da_star]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "671e1989-8c82-44a9-a36e-a9106800d020", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(just_da_star['mag_inst_cal'], '.')\n", + "plt.grid()\n", + "plt.ylim(reversed(plt.ylim()))\n", + "plt.xlabel('data point number')\n", + "plt.ylabel('calibrated magnitude')\n", + "plt.title(f'Star ID: {da_star}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8286191d-9f08-43ab-ae7d-86351f3a3c55", + "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": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}