{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Usage\n",
    "\n",
    "This tutorial walks through the basic usage of the `pynrc` package to calculate sensitivities and saturation limits for NIRCam in a variety of modes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the usual libraries\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Enable inline plotting at lower left\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "We assume you have already installed `pynrc` as outlined in the documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import main module\n",
    "import pynrc\n",
    "from pynrc import nrc_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log messages for `pynrc` follow the same the logging functionality included in `webbpsf`. Logging levels include `DEBUG`, `INFO`, `WARN`, and `ERROR`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pyNRC log messages of level INFO and above will be shown.\n",
      "pyNRC log outputs will be directed to the screen.\n"
     ]
    }
   ],
   "source": [
    "pynrc.setup_logging()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get tired of the `INFO` level messages, simply type:\n",
    "```python\n",
    "pynrc.setup_logging('WARN', verbose=False)\n",
    "```    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First NIRCam Observation\n",
    "\n",
    "The basic NIRCam object consists of all the instrument settings one would specify for a JWST observation, including filter, pupil, and coronagraphic mask selections along with detector subarray settings and ramp sampling cadence (i.e., `MULTIACCUM`).\n",
    "\n",
    "The NIRCam class makes use of high order polynomial coefficient maps to quickly generate large numbers of monochromatic PSFs that can be convolved with arbitrary spectra and collapsed into a final broadband PSF (or dispersed with NIRCam's slitless grisms). The PSF coefficients are calculated from a series of WebbPSF monochromatic PSFs and saved to disk. These polynomial coefficients are further modifed based on focal plane position and drift in the wavefront error relative to nominal OPD mao.\n",
    "\n",
    "There are a multitude of posssible keywords one can pass upon initialization, including certain detector settings and PSF generation parameters. If not passed initially, then defaults are assumed. The user can update these parameters at any time by either setting attributes directly (e.g., `filter`, `mask`, `pupil`, etc.) along with using the `update_detectors()` and `update_psf_coeff()` methods.\n",
    "\n",
    "For instance,\n",
    "```python\n",
    "nrc = pynrc.NIRCam('F210M')\n",
    "nrc.module = 'B'\n",
    "nrc.update_detectors(read_mode='DEEP8', nint=10, ngroup=5)\n",
    "```\n",
    "is the same as:\n",
    "```python\n",
    "nrc = pynrc.NIRCam('F210M', module='B', read_mode='DEEP8', nint=10, ngroup=5)\n",
    "```\n",
    "\n",
    "To start, we'll set up a simple observation using the `F430M` filter. Defaults will be populated for unspecified attributes such as `module`, `pupil`, `mask`, etc. \n",
    "\n",
    "**Check the function docstrings for more detailed information**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[     stpsf:INFO] NIRCam aperture name updated to NRCA1_FULL\n",
      "[     stpsf:INFO] NIRCam pixel scale switched to 0.062907 arcsec/pixel for the long wave channel.\n",
      "[     stpsf:INFO] NIRCam aperture name updated to NRCA5_FULL\n",
      "[     pynrc:INFO] RAPID readout mode selected.\n",
      "[     pynrc:INFO] Setting ngroup=1, nf=1, nd1=0, nd2=0, nd3=0.\n",
      "[     pynrc:INFO] Initializing SCA 485/A5\n",
      "[     pynrc:INFO] Suggested SIAF aperture name: NRCA5_FULL\n",
      "[     pynrc:INFO] RAPID readout mode selected.\n",
      "[     pynrc:INFO] Setting ngroup=1, nf=1, nd1=0, nd2=0, nd3=0.\n",
      "[     pynrc:INFO] Initializing SCA 485/A5\n",
      "[webbpsf_ext:INFO] Loading /Users/jarron/NIRCam/webbpsf_ext_data/psf_coeffs/NIRCam/LWA_F430M_CLEAR_NONE_pix33_os4_jsig1_r0.00_th+0.0_OPD-2022-07-30_siwfe_distort_legendre.fits\n",
      "[     pynrc:INFO] Calculating PSF offset to center of array...\n",
      "[webbpsf_ext:INFO] Skipping WFE field dependence: self._psf_coeff_mod['si_field']=None\n",
      "[webbpsf_ext:INFO]   self.include_si_wfe=True and self.include_ote_field_dependence=True \n",
      "[webbpsf_ext.imreg_tools:INFO] Recentered oversampled PSF (0.028, -0.017) pixels using Gaussian Fit algorithm.\n",
      "[     pynrc:INFO] Suggested SIAF aperture name: NRCA5_FULL\n",
      "\n",
      "Filter: F430M;  Pupil Mask: None;  Image Mask: None;  Module: A\n"
     ]
    }
   ],
   "source": [
    "nrc = pynrc.NIRCam(filter='F430M', fov_pix=33, oversample=4)\n",
    "print(f'\\nFilter: {nrc.filter};  Pupil Mask: {nrc.pupil_mask};  Image Mask: {nrc.image_mask};  Module: {nrc.module}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keyword information for detector settings are stored in the `det_info` dictionary. These cannot be modified directly, but instead are updated via the `update_detectors()` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detector Info Keywords:\n",
      "{'wind_mode': 'FULL', 'nout': 4, 'xpix': 2048, 'ypix': 2048, 'x0': 0, 'y0': 0, 'read_mode': 'RAPID', 'nint': 1, 'ngroup': 1, 'nf': 1, 'nd1': 0, 'nd2': 0, 'nd3': 0}\n"
     ]
    }
   ],
   "source": [
    "print('Detector Info Keywords:')\n",
    "print(nrc.det_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PSF settings are stored and modified in the same manner as WebbPSF (https://webbpsf.readthedocs.io/en/stable/usage.html) with a couple minor modifications:\n",
    "  1. The calculated field of view is specified as `nrc.fov_pix` along with the `nrc.oversample` attributes. \n",
    "  2. In addition, you can turn on/off distortions via the `nrc.include_distortions` attribute.\n",
    "  3. The primary method for calculating PSFs has changed to `nrc.calc_psf_from_coeff`.\n",
    "  \n",
    "You can quickly obtain a brief overview of important settings through the `psf_info` dictionary property. These properties can also be updated via the `nrc.update_psf_coeff()` function, which immediately generates a new set of PSF coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PSF Info:\n",
      "{'fov_pix': 33, 'oversample': 4, 'npsf': 7, 'ndeg': 6, 'include_si_wfe': True, 'include_ote_field_dependence': True, 'include_distortions': True, 'jitter': 'gaussian', 'jitter_sigma': 0.001, 'offset_r': 0, 'offset_theta': 0, 'bar_offset': None, 'pupil': '/Users/jarron/NIRCam/stpsf-data/jwst_pupil_RevW_npix1024.fits.gz', 'pupilopd': 'JWST_OTE_OPD_cycle1_example_2022-07-30.fits'}\n"
     ]
    }
   ],
   "source": [
    "print('PSF Info:')\n",
    "print(nrc.psf_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PSF coefficient information is stored in the `psf_coeff` attribute. An associated header file exists in the `psf_coeff_header`, showing all of the parameters used to generate that data. This data is accessed by many of the NIRCam class functions to generate PSFs with arbitrary wavelength weights, such as the `calc_psf_from_coeff()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Demonstrate the color difference of the PSF for different spectral types, same magnitude\n",
    "sp_M0V = pynrc.stellar_spectrum('M0V', 10, 'vegamag', nrc.bandpass)\n",
    "sp_A0V = pynrc.stellar_spectrum('A0V', 10, 'vegamag', nrc.bandpass)\n",
    "\n",
    "# Generate oversampled PSFs (counts/sec)\n",
    "hdul_M0V = nrc.calc_psf_from_coeff(sp=sp_M0V, return_oversample=True)\n",
    "hdul_A0V = nrc.calc_psf_from_coeff(sp=sp_A0V, return_oversample=True)\n",
    "\n",
    "psf_M0V = hdul_M0V[0].data\n",
    "psf_A0V = hdul_A0V[0].data\n",
    "\n",
    "fig, axes = plt.subplots(1,3, figsize=(12,4))\n",
    "\n",
    "axes[0].imshow(psf_M0V**0.5)\n",
    "axes[0].set_title('M0V PSF ({})'.format(nrc.filter))\n",
    "axes[1].imshow(psf_A0V**0.5)\n",
    "axes[1].set_title('A0V PSF ({})'.format(nrc.filter))\n",
    "\n",
    "diff = psf_M0V - psf_A0V\n",
    "\n",
    "minmax = np.abs(diff).max() / 2\n",
    "axes[2].imshow(diff, cmap='RdBu', vmin=-minmax, vmax=minmax)\n",
    "axes[2].set_title('Difference')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bandpass information is stored in the `bandpass` attribute and can be plotted with the convenience function `plot_bandpass()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1)\n",
    "nrc.plot_bandpass(ax=ax, color='C3')\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Saturation Limits\n",
    "\n",
    "One of the most basic functions is to determine the saturation limit of a CDS observation, so let's try this for the current filter selection. Generally, saturation is considered to be 80% of the full well, but can go as high as 95%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Ramp Settings\n",
      "  read_mode  :    RAPID\n",
      "  nf         :        1\n",
      "  nd2        :        0\n",
      "  ngroup     :        2\n",
      "  nint       :        1\n",
      "New Detector Settings\n",
      "  wind_mode  :     FULL\n",
      "  xpix       :     2048\n",
      "  ypix       :     2048\n",
      "  x0         :        0\n",
      "  y0         :        0\n",
      "New Ramp Times\n",
      "  t_group    :   10.737\n",
      "  t_frame    :   10.737\n",
      "  t_int      :   21.474\n",
      "  t_int_tot1 :   21.474\n",
      "  t_int_tot2 :    0.000\n",
      "  t_exp      :   21.474\n",
      "  t_acq      :   21.479\n"
     ]
    }
   ],
   "source": [
    "# Turn off those pesky informational texts\n",
    "pynrc.setup_logging('WARN', verbose=False)\n",
    "\n",
    "# Configure the observation for CDS frames (ngroup=2)\n",
    "# Print out frame and ramp information using verbose=True\n",
    "nrc.update_detectors(ngroup=2, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `sat_limits()` function returns a dictionary of results. There's the option in include a Pysynphot spectrum, but if None is specificed then it defaults to a G2V star."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F430M_ModA Saturation Limit assuming G2V source (point source): 12.24 vegamag\n",
      "F430M_ModA Saturation Limit assuming G2V source (extended): 8.53 vegamag/arcsec^2\n",
      "\n",
      "Dictionary Info: ({'satlim': 12.238364536563166, 'units': 'vegamag', 'bp_lim': 'F430M_ModA', 'Spectrum': 'G2V'}, {'satlim': 8.533744742439737, 'units': 'vegamag/arcsec^2', 'bp_lim': 'F430M_ModA', 'Spectrum': 'G2V'})\n"
     ]
    }
   ],
   "source": [
    "# Set verbose=True to print results in a user-friendly manner\n",
    "sat_lims = nrc.sat_limits(verbose=True)\n",
    "\n",
    "# Dictionary information\n",
    "print(\"\\nDictionary Info:\", sat_lims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the function `sat_limits()` uses a G2V stellar spectrum, but any arbritrary spectrum can be passed via the `sp` keyword. In addition, using the `bp_lim` keyword, you can use spectral information to determine the brightness in some other bandpass that saturates the source within the NIRCam filter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ks-Band Saturation Limit for F430M_ModA assuming M0V source (point source): 12.31 vegamag\n",
      "Ks-Band Saturation Limit for F430M_ModA assuming M0V source (extended): 8.61 vegamag/arcsec^2\n"
     ]
    }
   ],
   "source": [
    "# Spectrum of an M0V star (not normalized)\n",
    "sp_M0V = pynrc.stellar_spectrum('M0V')\n",
    "# 2MASS Ks Bandpass\n",
    "bp_k = pynrc.bp_2mass('K')\n",
    "\n",
    "sat_lims = nrc.sat_limits(sp=sp_M0V, bp_lim=bp_k, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's get the same saturation limit assuming a 128x128 detector subarray (faster frame rate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ks-Band Saturation Limit for F430M_ModA assuming M0V source (point source): 7.89 vegamag\n",
      "Ks-Band Saturation Limit for F430M_ModA assuming M0V source (extended): 4.18 vegamag/arcsec^2\n"
     ]
    }
   ],
   "source": [
    "nrc.update_detectors(wind_mode='WINDOW', xpix=128, ypix=128)\n",
    "sat_lims = nrc.sat_limits(sp=sp_M0V, bp_lim=bp_k, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also use the `saturation_levels()` function to generate an image of a point source indicating the fractional well fill level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Well Fraction: 0.72\n"
     ]
    }
   ],
   "source": [
    "# Spectum of A0V star with Ks = 8 mag\n",
    "sp = pynrc.stellar_spectrum('M0V', 8, 'vegamag', bp_k)\n",
    "sat_levels = nrc.saturation_levels(sp, full_size=False, ngroup=nrc.det_info['ngroup'])\n",
    "\n",
    "print('Max Well Fraction: {:.2f}'.format(sat_levels.max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the well fill levels for each pixel\n",
    "fig, axes = plt.subplots(1,2, figsize=(12,5))\n",
    "\n",
    "for i,ax in enumerate(axes):\n",
    "    extent = 0.5 * nrc.psf_info['fov_pix'] * np.array([-1,1,-1,1])\n",
    "    if i==0:\n",
    "        cax = ax.imshow(sat_levels, extent=extent, vmin=0, vmax=1)\n",
    "    else:\n",
    "        norm = matplotlib.colors.LogNorm(vmin=0.001, vmax=1)\n",
    "        cax = ax.imshow(sat_levels, extent=extent, norm=norm)\n",
    "    ax.set_xlabel('Pixels')\n",
    "    ax.set_ylabel('Pixels')\n",
    "    ax.set_title('Well Fraction in {} of $K_s = 5$ M0V star'.format(nrc.filter))\n",
    "\n",
    "    cbar = fig.colorbar(cax, ax=ax)\n",
    "    cbar.set_label('Well Fill Fraction')\n",
    "\n",
    "    ax.tick_params(axis='both', color='white', which='both')\n",
    "    for k in ax.spines.keys():\n",
    "        ax.spines[k].set_color('white')\n",
    "        \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Information for slitless grism observations show wavelength-dependent results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ks-Band Saturation Limit for F444W_ModA_GRISMR assuming M0V source:\n",
      "   Wave   Sat Limit (vegamag)\n",
      "--------- -------------------\n",
      "     3.90                4.79\n",
      "     4.00                4.78\n",
      "     4.10                4.67\n",
      "     4.20                4.58\n",
      "     4.30                4.43\n",
      "     4.40                4.09\n",
      "     4.50                3.91\n",
      "     4.60                3.75\n",
      "     4.70                3.57\n",
      "     4.80                3.39\n",
      "     4.90                3.17\n",
      "     5.00                2.55\n"
     ]
    }
   ],
   "source": [
    "nrc = pynrc.NIRCam(filter='F444W', pupil_mask='GRISM0', ngroup=2, wind_mode='STRIPE', ypix=128)\n",
    "sat_lims = nrc.sat_limits(sp=sp_M0V, bp_lim=bp_k, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Sensitivity Limits\n",
    "\n",
    "Similarly, we can determine sensitivity limits of point sources (and extended sources) for the defined instrument configuration. By default, the `sensitivity()` function uses a flat spectrum. In this case, let's find the sensitivities NIRCam can reach in a single ~1000sec integration with the F430M filter. Noise values will depend on the exact `MULTIACCUM` settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cc091e5254945beaac991354321da16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Monochromatic PSFs:   0%|          | 0/7 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[webbpsf_ext.imreg_tools:INFO] Recentered oversampled PSF (0.028, -0.017) pixels using Gaussian Fit algorithm.\n",
      "{'t_frame': 10.73677, 't_group': 107.3677, 't_int': 1052.20346, 't_exp': 1052.20346, 't_acq': 1052.2087, 't_int_tot1': 1052.20346, 't_int_tot2': 0.0}\n"
     ]
    }
   ],
   "source": [
    "nrc = pynrc.NIRCam(filter='F430M')\n",
    "nrc.update_detectors(read_mode='MEDIUM8', ngroup=10)\n",
    "\n",
    "# The multiaccum_times attribute describes the various timing information\n",
    "print(nrc.multiaccum_times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point Source Sensitivity (5-sigma): 23.38 vegamag\n",
      "Surface Brightness Sensitivity (5-sigma): 21.25 vegamag/arcsec^2\n"
     ]
    }
   ],
   "source": [
    "sens = nrc.sensitivity(nsig=5, units='vegamag', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sensitivity function also includes a keyword `forwardSNR`, which allows the user to pass a normalized spectrum and estimate the SNR For some extraction aperture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point Source SNR (20.00 vegamag): 79.06 sigma\n",
      "Surface Brightness SNR (20.00 vegamag/arcsec^2): 14.93 sigma\n"
     ]
    }
   ],
   "source": [
    "sp = pynrc.stellar_spectrum('M0V', 20, 'vegamag', nrc.bandpass)\n",
    "snr = nrc.sensitivity(sp=sp, forwardSNR=True, units='vegamag', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Ramp Optimization\n",
    "\n",
    "Armed with these two basic functions, we can attempt to determine the best instrument settings to optimize for SNR and efficiency. In these types of optimizations, we must consider observational constraints such as saturation levels, SNR requirements, and limits on acquisition time.\n",
    "\n",
    "**Note**: The reported acquisition times do not include obsevatory and instrument-level overheads, such as slew times, filter changes, script compilations, etc. It only includes detector readout times (including reset frames and Fast Row Resets).\n",
    "\n",
    "For instance, we want to observe an M-Dwarf (K=18 mag) in the F430M filter. What is the most efficient configuration to obtain an SNR of 100?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[webbpsf_ext.imreg_tools:INFO] Recentered oversampled PSF (0.028, -0.017) pixels using Gaussian Fit algorithm.\n"
     ]
    }
   ],
   "source": [
    "# Setup observation\n",
    "nrc = pynrc.NIRCam(filter='F430M', wind_mode='WINDOW', xpix=160, ypix=160)\n",
    "\n",
    "# Spectrum of an M2V star\n",
    "bp_k = pynrc.bp_2mass('K')\n",
    "sp_M0V = pynrc.stellar_spectrum('M0V', 18, 'vegamag', bp_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRIGHT1\n",
      "BRIGHT2\n",
      "DEEP2\n",
      "DEEP8\n",
      "MEDIUM2\n",
      "MEDIUM8\n",
      "RAPID\n",
      "SHALLOW2\n",
      "SHALLOW4\n",
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "DEEP8         5   10     24.52    245.20    248.04    115.4    0.005    7.325\n",
      "MEDIUM8       7   11     18.95    208.42    211.54    100.8    0.004    6.932\n",
      "MEDIUM8       7   12     18.95    227.37    230.78    105.3    0.004    6.932\n",
      "MEDIUM2       9   10     22.85    228.48    231.32    101.5    0.005    6.673\n",
      "MEDIUM2       9   11     22.85    251.33    254.46    106.5    0.005    6.673\n",
      "SHALLOW4     10   18     13.65    245.76    250.87    100.1    0.003    6.322\n",
      "SHALLOW4     10   19     13.65    259.41    264.81    102.9    0.003    6.322\n",
      "DEEP2         5   11     22.85    251.33    254.46     99.0    0.005    6.206\n",
      "DEEP2         5   12     22.85    274.18    277.59    103.4    0.005    6.206\n",
      "SHALLOW2     10   22     13.10    288.11    294.36     98.6    0.003    5.749\n",
      "...         ...  ...       ...       ...       ...      ...      ...      ...\n",
      "RAPID        10  632      2.79   1761.00   1940.37    101.3    0.001    2.298\n",
      "RAPID        10  633      2.79   1763.79   1943.44    101.3    0.001    2.298\n",
      "RAPID        10  634      2.79   1766.58   1946.51    101.4    0.001    2.298\n",
      "RAPID        10  635      2.79   1769.36   1949.58    101.5    0.001    2.298\n",
      "RAPID        10  636      2.79   1772.15   1952.65    101.6    0.001    2.298\n",
      "RAPID        10  637      2.79   1774.94   1955.72    101.7    0.001    2.298\n",
      "RAPID        10  638      2.79   1777.72   1958.79    101.7    0.001    2.298\n",
      "RAPID        10  639      2.79   1780.51   1961.86    101.8    0.001    2.298\n",
      "RAPID        10  640      2.79   1783.30   1964.93    101.9    0.001    2.298\n",
      "RAPID        10  641      2.79   1786.08   1968.00    102.0    0.001    2.298\n",
      "RAPID        10  642      2.79   1788.87   1971.07    102.1    0.001    2.298\n",
      "Length = 55 rows\n"
     ]
    }
   ],
   "source": [
    "# Run optimizer. Result is a sorted by efficiency.\n",
    "tbl = nrc.ramp_optimize(sp_M0V, snr_goal=100, ng_min=5, nint_min=10, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a slightly more complicated scenario, consider an additional foreground source. In this scenario, the F0V star will saturate much more quickly compared to the fainter M2V, so it limits which ramp settings we may want to use (assuming we want unsaturated frames, which isn't always necessarily true)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRIGHT1\n",
      "BRIGHT2\n",
      "DEEP2\n",
      "DEEP8\n",
      "MEDIUM2\n",
      "MEDIUM8\n",
      "RAPID\n",
      "SHALLOW2\n",
      "SHALLOW4\n",
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "RAPID        10  616      2.79   1716.42   1891.24    100.0    0.827    2.298\n",
      "RAPID        10  617      2.79   1719.21   1894.31    100.1    0.827    2.298\n",
      "RAPID        10  618      2.79   1722.00   1897.38    100.1    0.827    2.298\n",
      "RAPID        10  619      2.79   1724.78   1900.45    100.2    0.827    2.298\n",
      "RAPID        10  620      2.79   1727.57   1903.52    100.3    0.827    2.298\n",
      "RAPID        10  621      2.79   1730.35   1906.59    100.4    0.827    2.298\n",
      "RAPID        10  622      2.79   1733.14   1909.66    100.5    0.827    2.298\n",
      "RAPID        10  623      2.79   1735.93   1912.73    100.5    0.827    2.298\n",
      "RAPID        10  624      2.79   1738.71   1915.80    100.6    0.827    2.298\n",
      "RAPID        10  625      2.79   1741.50   1918.88    100.7    0.827    2.298\n",
      "...         ...  ...       ...       ...       ...      ...      ...      ...\n",
      "BRIGHT1       5 1188      2.51   2979.22   3316.37    101.6    0.744    1.764\n",
      "BRIGHT1       5 1189      2.51   2981.73   3319.16    101.6    0.744    1.764\n",
      "BRIGHT1       5 1190      2.51   2984.23   3321.96    101.7    0.744    1.764\n",
      "BRIGHT1       5 1191      2.51   2986.74   3324.75    101.7    0.744    1.764\n",
      "BRIGHT1       5 1192      2.51   2989.25   3327.54    101.8    0.744    1.764\n",
      "BRIGHT1       5 1193      2.51   2991.76   3330.33    101.8    0.744    1.764\n",
      "BRIGHT1       5 1194      2.51   2994.27   3333.12    101.9    0.744    1.764\n",
      "BRIGHT1       5 1195      2.51   2996.77   3335.91    101.9    0.744    1.764\n",
      "BRIGHT1       5 1196      2.51   2999.28   3338.71    101.9    0.744    1.764\n",
      "BRIGHT1       5 1197      2.51   3001.79   3341.50    102.0    0.744    1.764\n",
      "BRIGHT1       5 1198      2.51   3004.30   3344.29    102.0    0.744    1.764\n",
      "Length = 105 rows\n"
     ]
    }
   ],
   "source": [
    "sp_F0V = pynrc.stellar_spectrum('F0V', 10, 'vegamag', bp_k)\n",
    "tbl = nrc.ramp_optimize(sp_M0V, sp_bright=sp_F0V, snr_goal=100, ng_min=5, nint_min=10, \n",
    "                        well_frac_max=0.9, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there are no objections to saturating the bright source, then we can set the `well_frac_max` parameter to something like 5 times the hard saturation limit. This allows for much more efficient exposure settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRIGHT1\n",
      "BRIGHT2\n",
      "DEEP2\n",
      "DEEP8\n",
      "MEDIUM2\n",
      "MEDIUM8\n",
      "RAPID\n",
      "SHALLOW2\n",
      "SHALLOW4\n",
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "MEDIUM8       6   14     16.16    226.26    230.23     99.9    4.795    6.582\n",
      "MEDIUM8       6   15     16.16    242.42    246.67    103.4    4.795    6.582\n",
      "SHALLOW4     10   18     13.65    245.76    250.87    100.1    4.051    6.322\n",
      "SHALLOW4     10   19     13.65    259.41    264.81    102.9    4.051    6.322\n",
      "MEDIUM8       5   21     13.37    280.87    286.83    103.7    3.969    6.123\n",
      "SHALLOW2     10   22     13.10    288.11    294.36     98.6    3.886    5.749\n",
      "SHALLOW2     10   23     13.10    301.21    307.74    100.9    3.886    5.749\n",
      "SHALLOW2     10   24     13.10    314.31    321.12    103.0    3.886    5.749\n",
      "MEDIUM2       6   22     14.49    318.76    325.01     99.0    4.299    5.493\n",
      "MEDIUM2       6   23     14.49    333.25    339.78    101.3    4.299    5.493\n",
      "...         ...  ...       ...       ...       ...      ...      ...      ...\n",
      "RAPID        10  632      2.79   1761.00   1940.37    101.3    0.827    2.298\n",
      "RAPID        10  633      2.79   1763.79   1943.44    101.3    0.827    2.298\n",
      "RAPID        10  634      2.79   1766.58   1946.51    101.4    0.827    2.298\n",
      "RAPID        10  635      2.79   1769.36   1949.58    101.5    0.827    2.298\n",
      "RAPID        10  636      2.79   1772.15   1952.65    101.6    0.827    2.298\n",
      "RAPID        10  637      2.79   1774.94   1955.72    101.7    0.827    2.298\n",
      "RAPID        10  638      2.79   1777.72   1958.79    101.7    0.827    2.298\n",
      "RAPID        10  639      2.79   1780.51   1961.86    101.8    0.827    2.298\n",
      "RAPID        10  640      2.79   1783.30   1964.93    101.9    0.827    2.298\n",
      "RAPID        10  641      2.79   1786.08   1968.00    102.0    0.827    2.298\n",
      "RAPID        10  642      2.79   1788.87   1971.07    102.1    0.827    2.298\n",
      "Length = 54 rows\n"
     ]
    }
   ],
   "source": [
    "tbl = nrc.ramp_optimize(sp_M0V, sp_bright=sp_F0V, snr_goal=100, ng_min=5, nint_min=10, \n",
    "                        well_frac_max=5, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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