Merge pull request #14 from hover2pi/extraction

Extraction

Author: hover2pi

.gitignore

@@ -104,6 +104,11 @@ venv.bak/
 .mypy_cache/
 
 # Pipeline processed files
-files/*_rate.fits
-files/*_rateints.fits
-files/*_ramp.fits
+specialsoss/files/*_rate.fits
+specialsoss/files/*_ramp.fits
+specialsoss/files/*_cal.fits
+specialsoss/files/*_x1d.fits
+specialsoss/files/*_calints.fits
+specialsoss/files/*_x1dints.fits
+notebooks/*.fits
+

.travis.yml

@@ -3,7 +3,6 @@
 language: python
 python:
   - 3.6
-  - 3.5
 
 # command to install dependencies, e.g. pip install -r requirements.txt --use-mirrors
 install:

README.rst

@@ -26,7 +26,7 @@ SPECtral Image AnaLysis for SOSS
 
 Authors: Joe Filippazzo
 
-This pure Python 3.5+ package performs optimal spectral extraction routines
+This pure Python 3.6+ package performs optimal spectral extraction routines
 for the Single Object Slitless Spectroscopy (SOSS) mode of the
 Near-Infrared Imager and Slitless Spectrograph (NIRISS) instrument
 onboard the James Webb Space Telescope (JWST).

notebooks/SOSS end-to-end.ipynb

@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# End-to-end Test of SOSS Simulations\n",
+    "This notebook will simulate SOSS data using `awesimsoss` and then quantify how well `specialsoss` can extract it.\n",
+    "\n",
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Imports\n",
+    "from astropy.modeling.models import BlackBody1D\n",
+    "from astropy.modeling.blackbody import FLAM\n",
+    "import astropy.units as q\n",
+    "from awesimsoss import TSO\n",
+    "from bokeh.plotting import figure, show\n",
+    "from bokeh.io import output_notebook\n",
+    "import numpy as np\n",
+    "from specialsoss import SossExposure\n",
+    "output_notebook()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "First let's make a 1D blackbody spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Variables\n",
+    "teff = 2000*q.K\n",
+    "\n",
+    "# Generate a blackbody at the given temperature from 0.5 - 3 um\n",
+    "bb = BlackBody1D(temperature=teff)\n",
+    "wave = np.linspace(0.5, 3., 1000)*q.um\n",
+    "flux = bb(wave).to(FLAM, q.spectral_density(wave))*1E-8\n",
+    "\n",
+    "# Plot it\n",
+    "fig = figure(width=800, height=300, x_axis_label='Wavelength [um]', y_axis_label='Flux Density [{}]'.format(flux.unit))\n",
+    "fig.line(wave, flux, legend='Input Spectrum')\n",
+    "show(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets make a SOSS simulation for this star with 2 integrations and 2 groups using `awesimsoss`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the TSO object\n",
+    "sim = TSO(nints=2, ngrps=2, star=[wave, flux])\n",
+    "\n",
+    "# Run the simulation\n",
+    "sim.simulate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run the plot method\n",
+    "sim.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's export the simulation to a pipeline ingestible '_uncal.fits' file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Name the file\n",
+    "filename = 'SOSS_simulation_uncal.fits'\n",
+    "\n",
+    "# Export the data\n",
+    "sim.export(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reduction\n",
+    "Next let's load the \"raw\" data into `specialsoss` by passing a filename to the `SossExposure` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the exposure object with the '_uncal.fits' file\n",
+    "obs = SossExposure(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can calibrate the data using the JWST reduction pipeline with the `calibrate` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run DETECTOR1 and SPEC2 pipelines\n",
+    "obs.calibrate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check out object info\n",
+    "obs.info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the calibrated and uncalibrated data are stored as object properties (`uncal`, `rate`, `rateints`, `ramp`, `calints`, and `x1dints`) corresponding to the JWST pipeline dataproducts for SOSS mode, which can each be plotted and analyzed independently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inspect `rateints` data\n",
+    "# obs.rateints.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extraction\n",
+    "\n",
+    "Now let's perform the spectral extraction on the `rateints` data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run extraction method\n",
+    "obs.extract('sum', 'rateints', name='Extracted Spectrum')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can take a look at the extracted spectra like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot extracted time series spectra\n",
+    "obs.plot_results('Extracted Spectrum')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, let's compare the extracted spectrum for the first integration with the input spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot input spectrum...\n",
+    "fig = figure(width=800, height=300, x_axis_label='Wavelength [um]', y_axis_label='Flux Density [{}]'.format(flux.unit))\n",
+    "fig.line(wave, flux, legend='Input Spectrum')\n",
+    "\n",
+    "# ...and extracted spectrum\n",
+    "wave_ext = obs.results['Extracted Spectrum']['wavelength']\n",
+    "flux_ext = obs.results['Extracted Spectrum']['flux'][0]\n",
+    "fig.line(wave_ext, flux_ext, legend='Extracted Spectrum')\n",
+    "\n",
+    "show(fig)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voila!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "awesimsoss",
+   "language": "python",
+   "name": "awesimsoss"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}