Draft transform notebook

stellarphot/notebooks/photometry/transform-pared-back.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import numpy as np\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "from astropy.table import Table, vstack\n",
+    "from astropy.coordinates import SkyCoord\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from fit_functions import get_cat, f, opts_to_str, calc_residual\n",
+    "from calib_function import transform_to_catalog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parameters\n",
+    "\n",
+    "The magnitudes in each image are fit using this model:\n",
+    "\n",
+    "$$\n",
+    "r_{p, c} = a r_{p, inst} + b r_{p, inst}^2 + c (B_c - V_c) + d (B_c - V_c)^2 + z\n",
+    "$$\n",
+    "\n",
+    "The parameters in the cell below set the range of values the fit is constrained to. The way to fix a parameter is to give it a very, very small range for the constraint.\n",
+    "\n",
+    "More specifically, each of the fit values is subject to these constraints:\n",
+    "\n",
+    "+ $1 - a_{delta} < a < 1 + a_{delta}$\n",
+    "+ $b_{min} < b < -b_{min}$\n",
+    "+ $c_{min} < c < -c_{min}$\n",
+    "+ $d_{min} < d < -d_{min}$\n",
+    "+ The range for the zero point is $18 < z < 22$.\n",
+    "\n",
+    "\n",
+    "`output_dir` is where the PNG and FITS files generated by this notebook are stored. `run_name` is a descriptive name for the settings you have chosen that gets included in the output file names.\n",
+    "\n",
+    "### *Recommendation:*\n",
+    "\n",
+    "+ Keep $b$ essentially fixed. \n",
+    "+ Fixing $d$ is ok for now too, I think.\n",
+    "\n",
+    "In both cases, setting a min of `1e-6` or something should do the trick.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "a_delta = 0.5\n",
+    "b_min = -0.1\n",
+    "c_min = -0.5\n",
+    "d_min = -1e-6\n",
+    "\n",
+    "our_filters = ['B', 'ip']\n",
+    "\n",
+    "aavso_band_names = dict(B='B', ip='SI')\n",
+    "\n",
+    "cat_color_colums = dict(\n",
+    "    B=('Bmag', 'Vmag'),\n",
+    "    ip=('r_mag', 'i_mag')\n",
+    ")\n",
+    "\n",
+    "cat_filter = dict(B='Bmag', ip='i_mag')\n",
+    "\n",
+    "input_photometry_file = 'combined_photometry.csv'\n",
+    "output_photometry_file = 'some_name.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_mags = Table.read(input_photometry_file)\n",
+    "\n",
+    "# Ensure we have the right table ordering later\n",
+    "all_mags.sort(['filter', 'BJD'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get ready for the transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filter_groups = all_mags.group_by('filter')\n",
+    "\n",
+    "\n",
+    "# Check: do we have any unexpected filters?\n",
+    "\n",
+    "assert set(k[0] for k in filter_groups.groups.keys) == set(our_filters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Do the transforms, one filter at a time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_table = [] \n",
+    "\n",
+    "for k, group in zip(our_filters, filter_groups.groups):\n",
+    "    print(f'Transforming band {k}')\n",
+    "    by_bjd = group.group_by('BJD')\n",
+    "    \n",
+    "    transform_to_catalog(by_bjd, f'mag_inst', aavso_band_names[k], \n",
+    "                     obs_error_column='mag_error', \n",
+    "                     zero_point_range=[12, 25],\n",
+    "                     c_delta=0.5, # b_delta=0.1, \n",
+    "                     cat_filter=cat_filter[k], cat_color=cat_color_colums[k],\n",
+    "                     in_place=True);\n",
+    "    output_table.append(by_bjd.copy())\n",
+    "\n",
+    "output_table = vstack(output_table, join_type='outer')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_table.colnames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_table.write(output_photometry_file)"
+   ]
+  },
+  {
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}