{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": "slide"
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"tags": []
},
"source": [
"# MOS Optimal Spectral Extraction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE:** This is a static version of the notebook for web rendering. The full, interactive version is available [here](https://github.com/spacetelescope/dat_pyinthesky/blob/master/jdat_notebooks/optimal_extraction/Spectral%20Extraction.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"**Use case:** optimal spectral extraction; method by [Horne (1986)](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/abstract). \n",
"**Data:** JWST simulated NIRSpec MOS data; point sources. \n",
"**Tools:** jwst, webbpsf, matplotlib, scipy, custom functions. \n",
"**Cross-intrument:** any spectrograph. \n",
"**Documentation:** This notebook is part of a STScI's larger [post-pipeline Data Analysis Tools Ecosystem](https://jwst-docs.stsci.edu/jwst-post-pipeline-data-analysis). \n",
"\n",
"## Introduction\n",
"\n",
"The JWST pipeline produces 1-D and 2-D rectified spectra from combined exposures for each spectroscopic mode. Currently, the 1D products are produced using aperture extraction, with plans to implement optimal extraction via PSF-weighting or fitting. However, there are many situations in which the output will not necessarily be \"optimal\", and fine-tuning the parameters will be needed to improve the results. This notebook is intended to provide a walkthrough of the optimal extraction procedure with example JWST data.\n",
"\n",
"## Defining terms\n",
"__Optimal extraction:__ a method of aperture extraction first defined in [Horne (1986)](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/). \n",
"__S/N:__ Signal-to-noise ratio, a measure of how noisy a spectrum is. \n",
"__WCS:__ World Coordinate System, used for converting between different reference frames. \n",
"\n",
"## Imports\n",
"We will be using the following libraries to perform optimal spectral extraction.\n",
"- `glob glob` for collecting filenames\n",
"- `numpy` to handle array functions, as well as other various and sundry activities\n",
"- `jwst.datamodels ImageModel, MultiSpecModel` for accessing the datamodels for our example data\n",
"- `astropy.io fits` for low-level FITS file I/O\n",
"- `astropy.modeling models, fitting` for the many fitting tasks\n",
"- `astropy.visualization astropy_mpl_style, simple_norm` for displaying nice images\n",
"- `scipy.interpolate interp1d, RegularGridInterpolator` for all our interpolation needs\n",
"- `matplotlib.pyplot` for plotting data\n",
"- `matplotlib.patches Rectangle` for plotting rectangles on our data\n",
"- `webbpsf NIRSpec` to generate and visualize a PSF from the instrument model (see Appendix B)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:11.760794Z",
"iopub.status.busy": "2023-12-18T20:23:11.760638Z",
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"source": [
"import os\n",
"import tarfile\n",
"import urllib.request\n",
"\n",
"# Set environmental variables\n",
"os.environ[\"WEBBPSF_PATH\"] = \"./webbpsf-data/webbpsf-data\"\n",
"\n",
"# WEBBPSF Data\n",
"boxlink = 'https://stsci.box.com/shared/static/34o0keicz2iujyilg4uz617va46ks6u9.gz' \n",
"boxfile = './webbpsf-data/webbpsf-data-1.0.0.tar.gz'\n",
"\n",
"webbpsf_folder = './webbpsf-data'\n",
"\n",
"# Gather webbpsf files\n",
"psfExist = os.path.exists(webbpsf_folder)\n",
"if not psfExist:\n",
" os.makedirs(webbpsf_folder)\n",
" urllib.request.urlretrieve(boxlink, boxfile)\n",
" gzf = tarfile.open(boxfile)\n",
" gzf.extractall(webbpsf_folder)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:11.769337Z",
"iopub.status.busy": "2023-12-18T20:23:11.769184Z",
"iopub.status.idle": "2023-12-18T20:23:12.864885Z",
"shell.execute_reply": "2023-12-18T20:23:12.864372Z"
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"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from glob import glob\n",
"import numpy as np\n",
"from stdatamodels.jwst.datamodels import ImageModel\n",
"from stdatamodels.jwst.datamodels import MultiSpecModel\n",
"from astropy.io import fits\n",
"from astropy.modeling import models, fitting\n",
"from astropy.visualization import astropy_mpl_style, simple_norm\n",
"from specutils import Spectrum1D\n",
"from scipy.interpolate import interp1d, RegularGridInterpolator\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import Rectangle\n",
"\n",
"plt.style.use(astropy_mpl_style) #use the style we imported for matplotlib displays"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Loading data\n",
"We will be using simulated level 3 MOS data provided by James Muzerolle. These files come from a simulated visit with many point sources, and we will begin with the products of the `resample` step, which have the file extension `s2d.fits`. We will also compare the results of our optimal extraction with the products of the `extract1d` step, with the `x1d.fits` extension. See [the science data products specification](https://jwst-pipeline.readthedocs.io/en/stable/jwst/data_products/product_types.html#stage-3-data-products) and links therein for details on structure and format of these files."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The optimal extraction procedure laid out below can be repeated for each `'SCI'` extension in each `s2d` file. For the purposes of this notebook, we will assume that the `resample` step has produced optimal output, so those are the only extensions we need to access. (Rectifying and combining the input spectra is a complicated process on its own, and is far beyond the scope of this notebook!)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:12.867272Z",
"iopub.status.busy": "2023-12-18T20:23:12.866969Z",
"iopub.status.idle": "2023-12-18T20:23:16.170099Z",
"shell.execute_reply": "2023-12-18T20:23:16.169531Z"
}
},
"outputs": [],
"source": [
"import os\n",
"# If the example dataset has already been downloaded, comment out these lines:\n",
"import zipfile\n",
"import urllib.request\n",
"boxlink = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/optimal_extraction/optimal_extraction.zip'\n",
"boxfile = './optimal_extraction.zip'\n",
"urllib.request.urlretrieve(boxlink, boxfile)\n",
"zf = zipfile.ZipFile(boxfile, 'r')\n",
"zf.extractall()\n",
"# ...to here\n",
"\n",
"example_file = 'F170LP-G235M_MOS_observation-6_mod_correctedWCS_noflat_nooutlierdet_combined_s30263_'\n",
"s2d_file = os.path.join('s2d_files', example_file+'s2d.fits')\n",
"x1d_file = os.path.join('x1d_files', example_file+'x1d.fits')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:16.172470Z",
"iopub.status.busy": "2023-12-18T20:23:16.172296Z",
"iopub.status.idle": "2023-12-18T20:23:17.328340Z",
"shell.execute_reply": "2023-12-18T20:23:17.327775Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(25, 1298)\n"
]
}
],
"source": [
"data_model = ImageModel(s2d_file)\n",
"resampled_2d_image = data_model.data # if multiple SCI extensions, also specify EXTVER\n",
"weights_2d_image = data_model.wht # we will use this to estimate the per-pixel variance later\n",
"\n",
"image_shape = resampled_2d_image.shape\n",
"print(image_shape) #note the swap of x and y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we want to view 2d spectra, we'll generally need to stretch the pixels vertically to get a useful image. We can do this by setting the plot aspect ratio explicitly (we'll try to retain a measure of rectangularity)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:17.355270Z",
"iopub.status.busy": "2023-12-18T20:23:17.354841Z",
"iopub.status.idle": "2023-12-18T20:23:17.564556Z",
"shell.execute_reply": "2023-12-18T20:23:17.563999Z"
}
},
"outputs": [
{
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"norm = simple_norm(resampled_2d_image, stretch='power')\n",
"aspect_ratio = image_shape[1] / (2 * image_shape[0])\n",
"fig1 = plt.figure() # we save these in dummy variables to avoid spurious Jupyter Notebook output\n",
"img1 = plt.imshow(resampled_2d_image, cmap='gray', aspect=aspect_ratio, \n",
" norm=norm, interpolation='none')\n",
"clb1 = plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optimal Extraction algorithm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is an outline of the steps we'll be following:\n",
"1. [Define an extraction region on the 2D image](#Define-an-extraction-region)\n",
"1. [Identify a high S/N cross-dispersion (binned & coadded) slice to use for the initial kernel fit](#Create-kernel-slice)\n",
"3. [Define the extraction kernel](#Define-the-extraction-kernel)\n",
" 1. Single or composite PSF\n",
" 1. Polynomial fit to background\n",
"4. [Fit extraction kernel to initial slice](#Fit-extraction-kernel)\n",
"5. ***Skipped:*** [*Fit geometric distortion*](#Fit-geometric-distortion-(skipped))\n",
" 1. *Determine cross-dispersion bins for trace fitting*\n",
" 1. *First-pass fit of kernel to each bin to find trace center*\n",
" 1. *Polynomial fit of trace centers*\n",
"6. [Combine composite model (kernel | trace) with 2D image to create output 1D spectrum](#Construct-final-1D-spectrum)\n",
"7. Compare output spectrum with catalog photometry for flux calibration (not sure how to do this yet)\n",
"\n",
"Appendices:\n",
"- [Appendix A: Batch Processing](#Appendix-A:-Batch-Processing)\n",
"- [Appendix B: WebbPSF](#Appendix-B:-WebbPSF)"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"remove-cell"
]
},
"source": [
"*Developer Note:*\n",
"\n",
"This sort of functionality is desired by many, and as of yet, no general-purpose optimal extraction Python packages exist. While this notebook can provide optimal extraction for 2D resampled JWST pipeline products, and could be adapted for use with other data, it is a far cry from a widely-applicable, maintained and updated spectral extraction codebase. It would be very nice if such a thing existed...!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define an extraction region"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We begin by identifying the region in the 2D resampled image which contains the spectral trace we want to extract. For a simple case with only a single source, we can theoretically use the entire image. However, we may still want to exclude large systematic fluctuations in the background which might complicate the fit, or part of the trace with essentially no signal which will make fitting the trace centers difficult. In addition, when working with background nod-subtracted data, the images will contain negative traces, which we will want to exclude."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:17.567065Z",
"iopub.status.busy": "2023-12-18T20:23:17.566656Z",
"iopub.status.idle": "2023-12-18T20:23:17.721583Z",
"shell.execute_reply": "2023-12-18T20:23:17.721075Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig2 = plt.figure(figsize=(9,9)) # we want the largest figure that will fit in the notebook\n",
"img2 = plt.imshow(resampled_2d_image, cmap='gray', aspect=aspect_ratio, \n",
" norm=norm, interpolation='none') # reuse norm from earlier\n",
"\n",
"# create region box and slider\n",
"region_x = region_y = 0\n",
"region_h, region_w = image_shape\n",
"region_rectangle = Rectangle((region_x, region_y), region_w, region_h, \n",
" facecolor='none', edgecolor='b', linestyle='--')\n",
"current_axis = plt.gca()\n",
"rect_patch = current_axis.add_patch(region_rectangle)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll set the region coordinates to `x1=51, y1=3, x2=1268, y2=9`, then create a new array containing only our extraction region (so that we don't need to continually index our original array)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:17.723656Z",
"iopub.status.busy": "2023-12-18T20:23:17.723359Z",
"iopub.status.idle": "2023-12-18T20:23:17.906527Z",
"shell.execute_reply": "2023-12-18T20:23:17.906018Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x1, x2 = 51, 1268\n",
"y1, y2 = 3, 9\n",
"\n",
"er_y, er_x = np.mgrid[y1:y2+1, x1:x2+1]\n",
"extraction_region = resampled_2d_image[er_y, er_x]\n",
"weights_region = weights_2d_image[er_y, er_x]\n",
"er_ny, er_nx = extraction_region.shape\n",
"\n",
"aspect_ratio = er_nx / (3. * er_ny)\n",
"\n",
"er_norm = simple_norm(extraction_region, stretch='power')\n",
"fig3 = plt.figure()\n",
"img3 = plt.imshow(extraction_region, cmap='gray', aspect=aspect_ratio, \n",
" norm=er_norm, interpolation='none')\n",
"clb3 = plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create kernel slice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now define a cross-dispersion slice of our extraction region with which to fit our initial extraction kernel. As an initial guess, we'll coadd the 30 columns centered on the middle of the trace."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:17.908403Z",
"iopub.status.busy": "2023-12-18T20:23:17.908237Z",
"iopub.status.idle": "2023-12-18T20:23:17.911817Z",
"shell.execute_reply": "2023-12-18T20:23:17.911311Z"
}
},
"outputs": [],
"source": [
"slice_width = 30\n",
"initial_column = er_nx // 2\n",
"\n",
"def kernel_slice_coadd(width, column_idx):\n",
" \"\"\"\n",
" Coadd a number of columns (= width) of the extraction region,\n",
" centered on column_idx.\n",
" \"\"\"\n",
" \n",
" half_width = width // 2\n",
" to_coadd = np.arange(max(0, column_idx - half_width), \n",
" min(er_nx-1, column_idx + half_width))\n",
" return extraction_region[:, to_coadd].sum(axis=1) / width\n",
"\n",
"slice_0 = kernel_slice_coadd(slice_width, initial_column)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll plot the resulting slice, and see if we need to adjust the width and center of the coadd region."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:17.913760Z",
"iopub.status.busy": "2023-12-18T20:23:17.913478Z",
"iopub.status.idle": "2023-12-18T20:23:18.144667Z",
"shell.execute_reply": "2023-12-18T20:23:18.144124Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig4, (iax4, pax4) = plt.subplots(nrows=2, ncols=1, figsize=(8, 12))\n",
"plt.subplots_adjust(hspace=0.15, top=0.95, bottom=0.05)\n",
"img4 = iax4.imshow(extraction_region, cmap='gray', aspect=aspect_ratio, \n",
" norm=er_norm, interpolation='none')\n",
"\n",
"#create slice box\n",
"def make_slice(width, column_idx):\n",
" sy, sh, sw = 0, er_ny, width\n",
" sx = column_idx - width // 2\n",
" return sx, sy, sw, sh\n",
"\n",
"*sxy, sw, sh = make_slice(slice_width, initial_column)\n",
"slice_rectangle = Rectangle(sxy, sw, sh, facecolor='none', \n",
" edgecolor='b', linestyle='--')\n",
"iax4.add_patch(slice_rectangle)\n",
"\n",
"#plot the coadded slice\n",
"xd_pixels = np.arange(er_ny)\n",
"lin4, = pax4.plot(xd_pixels, slice_0, 'k-')\n",
"xlbl4 = pax4.set_xlabel('Cross-dispersion pixel')\n",
"ylbl4 = pax4.set_ylabel('Coadded signal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A column index of 670 and width 50 seem to work reasonably well for this file, so we can now generate the final slice for kernel fitting."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:18.146698Z",
"iopub.status.busy": "2023-12-18T20:23:18.146311Z",
"iopub.status.idle": "2023-12-18T20:23:18.148896Z",
"shell.execute_reply": "2023-12-18T20:23:18.148466Z"
}
},
"outputs": [],
"source": [
"kernel_slice = kernel_slice_coadd(50, 670)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define the extraction kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will define an extraction kernel which will be used to fit our trace at each pixel in the dispersion direction. This kernel will be made of 2 parts:\n",
"- a PSF template (or a composite of multiple PSFs, for deblending purposes)\n",
"- a polynomial for background fitting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Select a PSF template"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are many options for PSF template that we could consider for our kernel, but a full comparison is outside the scope of this notebook. We will be demonstrating only Gaussian and Moffat profiles.\n",
"\n",
"There are two things to note:\n",
"1. The methods shown here are only applicable to a true point source. Extended sources require a different methodology.\n",
"2. The `WebbPSF` package can be used to directly construct a composite PSF from the instrument model; however, this process is far more arduous than fitting a 1D profile using the `astropy.modeling` tools, and has thus been banished to Appendix B."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by plotting the two profiles against the kernel slice, with a naive normalization so that we can ignore scaling for the time being, centered on the pixel with the kernel's maximum value. (We will perform a true fit later, don't worry!)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:18.150900Z",
"iopub.status.busy": "2023-12-18T20:23:18.150613Z",
"iopub.status.idle": "2023-12-18T20:23:18.298575Z",
"shell.execute_reply": "2023-12-18T20:23:18.298138Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"max_pixel = np.argmax(kernel_slice)\n",
"fwhm = 1.\n",
"\n",
"moffat_profile = models.Moffat1D(amplitude=1, gamma=fwhm, x_0=max_pixel, alpha=1)\n",
"gauss_profile = models.Gaussian1D(amplitude=1, mean=max_pixel, stddev=fwhm)\n",
"\n",
"fig5 = plt.figure()\n",
"kern5 = plt.plot(xd_pixels, kernel_slice / kernel_slice[max_pixel], label='Kernel Slice')\n",
"moff5 = plt.plot(xd_pixels, moffat_profile(xd_pixels), label='Moffat Profile')\n",
"gaus5 = plt.plot(xd_pixels, gauss_profile(xd_pixels), label='Gaussian Profile')\n",
"lgd5 = plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Gaussian profile looks like a better approximation, so that's the profile we'll use for this spectrum. In the cell below, we could add more PSF templates using [model operations](https://docs.astropy.org/en/stable/modeling/compound-models.html); this is left as an exercise for the reader.\n",
"\n",
"We need to de-normalize our amplitude, so we'll set it to the maximum pixel value of the slice."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:18.300476Z",
"iopub.status.busy": "2023-12-18T20:23:18.300169Z",
"iopub.status.idle": "2023-12-18T20:23:18.303369Z",
"shell.execute_reply": "2023-12-18T20:23:18.302896Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Gaussian1D\n",
"Inputs: ('x',)\n",
"Outputs: ('y',)\n",
"Model set size: 1\n",
"Parameters:\n",
" amplitude mean stddev\n",
" ----------------- ---- ------\n",
" 213.2068328857422 3.0 1.0\n"
]
}
],
"source": [
"psf_template = gauss_profile\n",
"psf_template.amplitude = kernel_slice[max_pixel]\n",
"print(psf_template)\n",
"# If deblending multiple sources, add more PSF templates here:\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Polynomial background"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will fit the background with a polynomial. Some experimentation is recommended to find the polynomial degree which best fits the data; for this example, we'll use a 2nd-degree polynomial.\n",
"\n",
"For nod-subtracted data, there may not be enough pixels in the extraction region to accurately fit a residual. In such cases, use a 0th-order polynomial or a `Const1D` model for the background; to avoid fitting the background at all, set the parameter to `fixed = True`."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:18.305311Z",
"iopub.status.busy": "2023-12-18T20:23:18.305010Z",
"iopub.status.idle": "2023-12-18T20:23:18.308074Z",
"shell.execute_reply": "2023-12-18T20:23:18.307605Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Polynomial1D\n",
"Inputs: ('x',)\n",
"Outputs: ('y',)\n",
"Model set size: 1\n",
"Degree: 2\n",
"Parameters:\n",
" c0 c1 c2\n",
" --- --- ---\n",
" 0.0 0.0 0.0\n"
]
}
],
"source": [
"background_poly = models.Polynomial1D(2)\n",
"print(background_poly)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The final step is to combine the PSF(s) and the background to create our compound model."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:18.310051Z",
"iopub.status.busy": "2023-12-18T20:23:18.309641Z",
"iopub.status.idle": "2023-12-18T20:23:18.313504Z",
"shell.execute_reply": "2023-12-18T20:23:18.312990Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: CompoundModel\n",
"Inputs: ('x',)\n",
"Outputs: ('y',)\n",
"Model set size: 1\n",
"Expression: [0] + [1]\n",
"Components: \n",
" [0]: \n",
"\n",
" [1]: \n",
"Parameters:\n",
" amplitude_0 mean_0 stddev_0 c0_1 c1_1 c2_1\n",
" ----------------- ------ -------- ---- ---- ----\n",
" 213.2068328857422 3.0 1.0 0.0 0.0 0.0\n"
]
}
],
"source": [
"extraction_kernel = psf_template + background_poly\n",
"print(extraction_kernel)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit extraction kernel"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have an extraction kernel, we want to fit it to our kernel slice, so as to have the best tool for fitting trace centers in the next step. We also plot the fit components, as well as the fit vs the kernel slice, as visual checks; if they are unacceptable, we can go back to the previous section, tweak parameters, and try again."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:18.315391Z",
"iopub.status.busy": "2023-12-18T20:23:18.315091Z",
"iopub.status.idle": "2023-12-18T20:23:18.648036Z",
"shell.execute_reply": "2023-12-18T20:23:18.647539Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: CompoundModel\n",
"Inputs: ('x',)\n",
"Outputs: ('y',)\n",
"Model set size: 1\n",
"Expression: [0] + [1]\n",
"Components: \n",
" [0]: \n",
"\n",
" [1]: \n",
"Parameters:\n",
" amplitude_0 mean_0 ... c1_1 c2_1 \n",
" ------------------ ---------------- ... ----------------- ------------------\n",
" 206.65933152600502 3.10605299997226 ... 8.417681632304308 -1.363500190321699\n"
]
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fitter = fitting.LevMarLSQFitter()\n",
"fit_extraction_kernel = fitter(extraction_kernel, xd_pixels, kernel_slice)\n",
"print(fit_extraction_kernel)\n",
"\n",
"fit_line = fit_extraction_kernel(xd_pixels)\n",
"\n",
"fig6, (fax6, fln6) = plt.subplots(nrows=2, ncols=1, figsize=(8, 12))\n",
"plt.subplots_adjust(hspace=0.15, top=0.95, bottom=0.05)\n",
"psf6 = fax6.plot(xd_pixels, fit_extraction_kernel[0](xd_pixels), label=\"PSF\")\n",
"poly6 = fax6.plot(xd_pixels, fit_extraction_kernel[1](xd_pixels), label=\"Background\")\n",
"sum6 = fax6.plot(xd_pixels, fit_line, label=\"Composite Kernel\")\n",
"lgd6a = fax6.legend()\n",
"lin6 = fln6.plot(xd_pixels, kernel_slice, label='Kernel Slice')\n",
"fit6 = fln6.plot(xd_pixels, fit_line, 'o', label='Extraction Kernel')\n",
"lgd6b = fln6.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Wavelength-varying FWHM (skipped)\n",
"\n",
"The NIRSpec PSF width changes with wavelength, and so for science data, it may be beneficial to fit multiple locations along the spectral trace. Below is a demonstration of the process; note, however, for this example dataset, the (not-yet-optimized) resampling and combining of the dithered input spectra introduces a width variation artifact, so we will not actually be using the results of this step for the extraction."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we wish to account for a varying FWHM, we can bin the 2D spectrum in the dispersion direction and fit each bin. The kernel we defined above can act as our initial estimate, which can be helpful in very faint regions of the spectrum, since `astropy.modeling` fitting routines can be sensitive to initial estimates.\n",
"\n",
"(Once the binned kernel FWHMs have been calculated and plotted, the next step would be to find an appropriate model and fit the FWHM as a function of bin center. The fit model would then be included in the final 1D extraction below.)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:18.650289Z",
"iopub.status.busy": "2023-12-18T20:23:18.649974Z",
"iopub.status.idle": "2023-12-18T20:23:19.947184Z",
"shell.execute_reply": "2023-12-18T20:23:19.946695Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from astropy.stats import sigma_clip\n",
"\n",
"n_bin = 100\n",
"bin_width = er_nx // n_bin\n",
"bin_centers = np.arange(0, er_nx, bin_width+1, dtype=float) + bin_width // 2\n",
"binned_spectrum = np.hstack([extraction_region[:, i:i+bin_width+1].sum(axis=1)[:, None] \n",
" for i in range(0, er_nx, bin_width+1)])\n",
"bin_fwhms = np.zeros_like(bin_centers, dtype=float)\n",
"\n",
"for y in range(bin_centers.size):\n",
" bin_fit = fitter(fit_extraction_kernel, xd_pixels, binned_spectrum[:, y])\n",
" bin_fwhms[y] = bin_fit.stddev_0.value\n",
" \n",
"bin_ny, bin_nx = binned_spectrum.shape\n",
"bin_ar = bin_nx / (3 * bin_ny)\n",
"\n",
"fig_fwhm, ax_fwhm = plt.subplots(nrows=2, ncols=1, figsize=(6, 10))\n",
"plt.subplots_adjust(hspace=0.05)\n",
"fwhm_img = ax_fwhm[0].imshow(binned_spectrum, aspect=bin_ar, interpolation='none',\n",
" cmap='gray')\n",
"fwhm_plot = ax_fwhm[1].plot(bin_centers, bin_fwhms)\n",
"xlbl_fwhm = ax_fwhm[1].set_xlabel(\"Bin center (px)\")\n",
"ylbl_fwhm = ax_fwhm[1].set_ylabel(\"FWHM (arcsec)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit geometric distortion *(skipped)*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The pipeline `resample` step drizzles all input 2d spectra onto a rectified grid, so this particular step of our optimal extraction process is not typically necessary. A brief discussion of the procedure is included here as a guideline for extracting unrectified spectra (with the suffix `_cal.fits`), where the trace can have significant curvature and the trace dispersion is not column-aligned."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define bins for trace fitting\n",
"\n",
"Depending on how noisy the 2D resampled spectrum is, it may be beneficial to define bins in the dispersion direction. These can be evenly- or unevenly-spaced, and once they're defined, coadd the columns in each bin (possibly using the `WHT` extension in the `s2d` file) and create an array of bin center locations.\n",
"\n",
"If the 2D spectrum has high S/N, this may not be necessary, and each cross-dispersed column can be fit individually in the next step."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Fit each bin with a modified extraction kernel\n",
"\n",
"We want to fit each of the defined bins with our extraction kernel, but we don't want any other artifacts or noise to confuse the trace. So, we copy the extraction kernel, then set each parameter other than the profile center (`mean_0` in the example above) to `fixed = True`. Starting at one end of the trace, iterate over each bin to fit the slice with the extraction kernel, and store the resulting trace centers in an array."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Fit the trace centers with a 1D polynomial\n",
"\n",
"This step is straightforward: create a `Polynomial1D` model, then fit it to the trace centers from the previous step.\n",
"\n",
"Since we won't be fitting, instead we'll create a placeholder trace center model: a 0th-order polynomial."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:19.949176Z",
"iopub.status.busy": "2023-12-18T20:23:19.949026Z",
"iopub.status.idle": "2023-12-18T20:23:19.952743Z",
"shell.execute_reply": "2023-12-18T20:23:19.952323Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: Polynomial1D\n",
"Inputs: ('x',)\n",
"Outputs: ('y',)\n",
"Model set size: 1\n",
"Degree: 0\n",
"Parameters:\n",
" c0 \n",
" ----------------\n",
" 3.10605299997226\n"
]
}
],
"source": [
"trace_center_model = models.Polynomial1D(0) #we use a constant because the spectrum has already been rectified\n",
"trace_center_model.c0 = fit_extraction_kernel.mean_0.value # use the parameter for center of the PSF profile\n",
"print(trace_center_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Construct final 1D spectrum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We calculate the final 1D spectrum as a weighted sum in the cross-dispersion direction the 2D spectrum, using our composite model (the extraction kernel centered on the trace) for the weights. We also need to incorporate the variance for each pixel, which we'll estimate from the `WHT` extension output by the resample step."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create a variance image\n",
"\n",
"Horne's algorithm requires the variance for each pixel. Errors are not currently propagated through the resample step; however, as per the [DrizzlePac Handbook](https://www.stsci.edu/files/live/sites/www/files/home/scientific-community/software/drizzlepac/_documents/drizzlepac-handbook.pdf), we can estimate the variance from the drizzle weights image: $ Var \\approx 1 / (W \\times s^4) $, where $s$ is the pixel scale. Currently, the NIRSpec drizzle parameters are set to `PIXFRAC = 1.0`."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:19.954555Z",
"iopub.status.busy": "2023-12-18T20:23:19.954261Z",
"iopub.status.idle": "2023-12-18T20:23:19.957273Z",
"shell.execute_reply": "2023-12-18T20:23:19.956887Z"
}
},
"outputs": [],
"source": [
"scale = 1.0 # adjust this if and when the NIRSpec PIXFRAC changes\n",
"\n",
"# We want any pixel with 0 weight to be excluded from the calculation\n",
"# in the next step, so we'll use masked array operations.\n",
"bad_pixels = weights_region == 0\n",
"masked_wht = np.ma.array(weights_region, mask=bad_pixels)\n",
"variance_image = np.ma.divide(1., weights_region * scale**4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can display the variance image to see if there are any regions of the extraction region which will not be included in the spectrum (indicated in red below). For this particular example spectrum, every pixel has a nonzero weight."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:19.959097Z",
"iopub.status.busy": "2023-12-18T20:23:19.958798Z",
"iopub.status.idle": "2023-12-18T20:23:20.074622Z",
"shell.execute_reply": "2023-12-18T20:23:20.074158Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from copy import copy\n",
"\n",
"fig_var = plt.figure()\n",
"palette = copy(plt.cm.gray)\n",
"palette.set_bad('r', alpha=0.7)\n",
"var_norm = simple_norm(variance_image, stretch='log', min_cut=0.006, max_cut=0.1)\n",
"img_var = plt.imshow(variance_image, interpolation='none', aspect=aspect_ratio, norm=var_norm, cmap=palette)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate the 1D spectrum\n",
"\n",
"Now, we finally calculate our 1D spectrum, summing over cross-dispersed columns:\n",
"$$S_x = \\frac{1}{G_x}\\sum_{y} \\frac{I_{xy}\\cdot K_y(x)}{V_{xy}}$$\n",
"where $I$ is the pixel value in the 2D resampled image, $K$ is our extraction kernel set to the column's trace center, $V$ is the pixel value in the variance image, and $G$ is the kernel normalization given by:\n",
"$$G_x = \\sum_y \\frac{K_y^2(x)}{V_{xy}}$$"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:20.076938Z",
"iopub.status.busy": "2023-12-18T20:23:20.076766Z",
"iopub.status.idle": "2023-12-18T20:23:21.080917Z",
"shell.execute_reply": "2023-12-18T20:23:21.080339Z"
}
},
"outputs": [],
"source": [
"spectrum = np.zeros(er_nx, dtype=float) #initialize our spectrum with zeros\n",
"column_pixels = np.arange(er_nx)\n",
"trace_centers = trace_center_model(column_pixels) # calculate our trace centers array\n",
"\n",
"# Loop over columns\n",
"for x in column_pixels:\n",
" # create the kernel for this column, using the fit trace centers\n",
" kernel_column = fit_extraction_kernel.copy()\n",
" kernel_column.mean_0 = trace_centers[x]\n",
" # kernel_column.stddev_0 = fwhm_fit(x) # if accounting for a varying FWHM, uncomment this line.\n",
" kernel_values = kernel_column(xd_pixels)\n",
" \n",
" # unit normalize the kernel following Horne1986 eqn 5, P_x = P/sum(P_x). \n",
" kernel_values = np.ma.masked_outside(kernel_values, 0, np.inf) \n",
" kernel_values = kernel_values/np.ma.sum(kernel_values)\n",
" \n",
" # isolate the relevant column in the spectrum and variance images\n",
" variance_column = variance_image[:, x] # remember that numpy arrays are row, column\n",
" image_pixels = extraction_region[:, x]\n",
" \n",
" # calculate the kernal normalization\n",
" g_x = np.ma.sum(kernel_values**2 / variance_column)\n",
" if np.ma.is_masked(g_x): #this column isn't valid, so we'll skip it\n",
" continue\n",
" \n",
" # and now sum the weighted column\n",
" weighted_column = np.ma.divide(image_pixels * kernel_values, variance_column)\n",
" spectrum[x] = np.ma.sum(weighted_column) / g_x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need a wavelength array to display the spectrum, which we can create from the WCS object stored in the data model's metadata."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.083025Z",
"iopub.status.busy": "2023-12-18T20:23:21.082855Z",
"iopub.status.idle": "2023-12-18T20:23:21.090903Z",
"shell.execute_reply": "2023-12-18T20:23:21.090387Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
" [1]: \n",
"\n",
" [2]: \n",
"\n",
" [3]: \n",
"\n",
" [4]: \n",
"\n",
" [5]: ,), lookup_table=[1.64694116 1.64694116 1.64694116 ... 3.17850353 3.17956468 3.17956468])>\n",
"Parameters:\n",
" slope_1 intercept_1 ... lon_pole_4\n",
" ----------------------- ---------------------- ... ----------\n",
" -2.1203814393372336e-05 0.00014842677756392322 ... 180.0)>\n"
]
}
],
"source": [
"wcs = data_model.meta.wcs\n",
"print(wcs.__repr__())\n",
"alpha_C, delta_C, y = wcs(er_x, er_y)\n",
"wavelength = y[0]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.092739Z",
"iopub.status.busy": "2023-12-18T20:23:21.092420Z",
"iopub.status.idle": "2023-12-18T20:23:21.202006Z",
"shell.execute_reply": "2023-12-18T20:23:21.201460Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig7 = plt.figure()\n",
"spec7 = plt.plot(wavelength, spectrum)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.203795Z",
"iopub.status.busy": "2023-12-18T20:23:21.203640Z",
"iopub.status.idle": "2023-12-18T20:23:21.205974Z",
"shell.execute_reply": "2023-12-18T20:23:21.205473Z"
}
},
"outputs": [],
"source": [
"# Write the extracted spectrum out to a file\n",
"# This is left as an exercise for the reader\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also want to compare our optimally-extracted spectrum with the `x1d` pipeline product. We'll normalize the spectra so we can plot them on the same axes.\n",
"\n",
"(Note that the `x1d` spectrum includes negative traces from the background subtraction step, which usually results in a negative flux calculation. We need to correct for that when comparing with our optimally-extracted version.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"remove-cell"
]
},
"source": [
"#### Developer Note: We will not use datamodels here because the latest packages do not support the simulated data previously created for this notebook. The data set will have to be updated after commissioning."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"x1d_model = MultiSpecModel(x1d_file)\n",
"# For a file with multiple spectra, the index to .spec is EXTVAR-1\n",
"x1d_wave = x1d_model.spec[0].spec_table.WAVELENGTH\n",
"x1d_flux = x1d_model.spec[0].spec_table.FLUX "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.208033Z",
"iopub.status.busy": "2023-12-18T20:23:21.207728Z",
"iopub.status.idle": "2023-12-18T20:23:21.341404Z",
"shell.execute_reply": "2023-12-18T20:23:21.340946Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x1d_model = fits.open(x1d_file)\n",
"# For a file with multiple spectra, the index to .spec is EXTVAR\n",
"tmp = x1d_model[1].data\n",
"x1d_wave = tmp['WAVELENGTH']\n",
"x1d_flux = tmp['FLUX']\n",
"\n",
"if x1d_flux.sum() <= 0:\n",
" x1d_flux = -x1d_flux\n",
"fig8 = plt.figure()\n",
"x1d8 = plt.plot(x1d_wave, x1d_flux / x1d_flux.max(), label=\"Pipeline\")\n",
"opt8 = plt.plot(wavelength, spectrum / spectrum.max(), label=\"Optimal\", alpha=0.7)\n",
"lgd8 = plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Appendix A: Batch Processing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When optimal extraction is desired for a large number of spectra, going step-by-step through the process laid out above for each spectrum may not be practical. In such cases, we can initially use those interactive methods on one or two spectra to make decisions about some of our extraction parameters (e.g., what PSF template profile to use, or what degree polynonmial to fit the background with), then use those parameters to process all of the spectra non-interactively. Afterwards, we can examine the output from each extracted spectrum and revisit any which need more individualized handling."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can extract a large number of spectra non-interactively by defining functions for each of the steps above, and a single master function to iterate over all the spectra in a single directory."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define an extraction region\n",
"\n",
"There's no way to perform this step non-interactively, so we'll skip it here. However, there are two good ways (and one bad way) to deal with this for a real dataset:\n",
"1. Define an extraction region for each 2D spectrum before batch processing. You can save the region bounding boxes to a python dictionary (or write them to a file, then read it in during iteration).\n",
"1. Visually examine the 2D spectra, and only batch process those spectra for which a specific extraction region (i.e., smaller than the full 2D spectrum) doesn't need to be defined. The remainder of the spectra can be extracted individually.\n",
"1. Skip this step, and assume that any spectra for which a specific extraction region would need to be defined will need individualized reprocessing anyway. This step is not recommended, but it is the one we will be using here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Kernel Slice"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.343383Z",
"iopub.status.busy": "2023-12-18T20:23:21.343231Z",
"iopub.status.idle": "2023-12-18T20:23:21.346608Z",
"shell.execute_reply": "2023-12-18T20:23:21.346206Z"
}
},
"outputs": [],
"source": [
"def batch_kernel_slice(extraction_region, slice_width=30, column_idx=None):\n",
" \"\"\"\n",
" Create a slice in the cross-dispersion direction out of the \n",
" 2D array `extraction_region`, centered on `column_idx` and \n",
" `slice_width` pixels wide. If `column_idx` is not given, use\n",
" the column with the largest total signal.\n",
" \"\"\"\n",
" \n",
" if column_idx is None:\n",
" column_idx = np.argmax(extraction_region.sum(axis=0))\n",
" \n",
" ny, nx = extraction_region.shape\n",
" half_width = slice_width // 2\n",
" \n",
" #make sure we don't go past the edges of the extraction region\n",
" to_coadd = np.arange(max(0, column_idx - half_width), \n",
" min(nx-1, column_idx + half_width))\n",
" \n",
" return extraction_region[:, to_coadd].sum(axis=1) / slice_width\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create and fit the extraction kernel"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.348372Z",
"iopub.status.busy": "2023-12-18T20:23:21.348221Z",
"iopub.status.idle": "2023-12-18T20:23:21.352664Z",
"shell.execute_reply": "2023-12-18T20:23:21.352249Z"
}
},
"outputs": [],
"source": [
"def batch_fit_extraction_kernel(xd_slice, psf_profile=models.Gaussian1D, \n",
" height_param_name='amplitude', height_param_value=None,\n",
" width_param_name='stddev', width_param_value=1.,\n",
" center_param_name='mean', center_param_value=None,\n",
" other_psf_args=[], other_psf_kw={},\n",
" bg_model=models.Polynomial1D,\n",
" bg_args=[3], bg_kw={}):\n",
" \"\"\"\n",
" Initialize a composite extraction kernel, then fit it to \n",
" the 1D array `xd_slice`, which has been nominally\n",
" generated via the `kernel_slice` function defined above. \n",
" \n",
" To allow for PSF template models with different parameter \n",
" names, we use the `height_param_*`, `width_param_*`, and\n",
" `center_param_*` keyword arguments. We collect any other\n",
" positional or keyword arguments for the PSF model in \n",
" `other_psf_*`. If the height or center values are `None`, \n",
" they will be calculated from the data.\n",
" \n",
" Similarly, any desired positional or keyword arguments to\n",
" the background fit model (default `Polynomial1D`) are\n",
" accepted via `bg_args` and `bg_kw`.\n",
" \n",
" Note that this function can not handle cases which involve\n",
" multiple PSFs for deblending. It is recommended to process\n",
" such spectra individually, using the interactive procedure\n",
" above.\n",
" \"\"\"\n",
" xd_pixels = np.arange(xd_slice.size)\n",
" \n",
" if center_param_value is None:\n",
" center_param_value = np.argmax(xd_slice)\n",
" \n",
" if height_param_value is None:\n",
" # In case of non-integer values passed via center_param_value,\n",
" # we need to interpolate.\n",
" slice_interp = interp1d(xd_pixels, xd_slice)\n",
" height_param_value = slice_interp(center_param_value)\n",
" \n",
" # Create the PSF and the background models\n",
" psf_kw = dict([(height_param_name, height_param_value), \n",
" (width_param_name, width_param_value),\n",
" (center_param_name, center_param_value)])\n",
" psf_kw.update(other_psf_kw)\n",
" psf = psf_profile(*other_psf_args, **psf_kw)\n",
" \n",
" bg = bg_model(*bg_args, **bg_kw)\n",
" \n",
" composite_kernel = psf + bg\n",
" fitter = fitting.LevMarLSQFitter()\n",
" return fitter(composite_kernel, xd_pixels, xd_slice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Account for varying FWHM\n",
"\n",
"This is left as an exercise for the user, as per the process shown [here](#Wavelength-varying-FWHM). Note that `batch_extract_spectrum` and `batch_optimal_extraction` below will also need to be modified to incorporate this function, if desired."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.354530Z",
"iopub.status.busy": "2023-12-18T20:23:21.354250Z",
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"shell.execute_reply": "2023-12-18T20:23:21.356231Z"
}
},
"outputs": [],
"source": [
"def batch_vary_fwhm(extraction_region, kernel):\n",
" pass # implement a function which fits a wavelength-varying FWHM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fit the trace centers\n",
"\n",
"If this is required, replace this with a real function that does the fitting."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.358667Z",
"iopub.status.busy": "2023-12-18T20:23:21.358387Z",
"iopub.status.idle": "2023-12-18T20:23:21.361348Z",
"shell.execute_reply": "2023-12-18T20:23:21.360837Z"
}
},
"outputs": [],
"source": [
"def batch_fit_trace_centers(extraction_region, kernel,\n",
" trace_model=models.Polynomial1D,\n",
" trace_args=[0], trace_kw={}):\n",
" \"\"\"\n",
" Fit the geometric distortion of the trace with\n",
" a model. Currently this is a placeholder function,\n",
" since geometric distortion is typically removed\n",
" during the `resample` step. However, if this\n",
" functionality is necesary, use this function\n",
" signature to remain compatible with the rest of\n",
" this Appendix.\n",
" \"\"\"\n",
" \n",
" trace_centers = trace_model(*trace_args, **trace_kw)\n",
" trace_centers.c0 = kernel.mean_0\n",
" return trace_centers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate the 1D spectrum\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.363246Z",
"iopub.status.busy": "2023-12-18T20:23:21.362970Z",
"iopub.status.idle": "2023-12-18T20:23:21.368733Z",
"shell.execute_reply": "2023-12-18T20:23:21.368199Z"
}
},
"outputs": [],
"source": [
"def batch_extract_spectrum(extraction_region, trace, kernel, \n",
" weights_image, \n",
" trace_center_param='mean',\n",
" scale=1.0):\n",
" \"\"\"\n",
" Optimally extract the 1D spectrum from the extraction \n",
" region.\n",
" \n",
" A variance image is created from `weights_image` (which \n",
" should have the same dimensions as `extraction_region`).\n",
" Then, for each column of the spectrum, we sum the aperture\n",
" as per the equations defined above, masking pixels with\n",
" zero weights. \n",
" \n",
" Note that unlike the interactive, step-by-step method, \n",
" here we will vectorize for speed. This requires using\n",
" a model set for the kernel, but this is allowed since\n",
" we are not fitting anything.\n",
" \n",
" `trace_center_param` is the name of the parameter which \n",
" will defines the trace centers, *without the model number\n",
" subscript* (since we will be dealing with the components\n",
" individually).\n",
" \n",
" `scale` is the size ratio of input to output pixels when\n",
" drizzling, equivalent to PIXFRAC in the drizzle parameters\n",
" from the `resample` step.\n",
" \"\"\"\n",
" \n",
" bad_pixels = weights_image == 0.\n",
" masked_wht = np.ma.array(weights_image, mask=bad_pixels)\n",
" variance_image = np.ma.divide(1., masked_wht * scale**4)\n",
" \n",
" ny, nx = extraction_region.shape\n",
" trace_pixels = np.arange(nx)\n",
" xd_pixels = np.arange(ny)\n",
" trace_centers = trace(trace_pixels) # calculate our trace centers array\n",
" \n",
" # Create kernel image for vectorizing, which requires some gymnastics...\n",
" # ******************************************************************\n",
" # * IMPORTANT: *\n",
" # * ---------- *\n",
" # * Note that because of the way model sets are implemented, it is *\n",
" # * not feasible to alter an existing model instance to use them. *\n",
" # * Instead we'll create a new kernel instance, using the fitted *\n",
" # * parameters from the original kernel. *\n",
" # * *\n",
" # * Caveat: this assumes that the PSF is the first element, and *\n",
" # * the background is the second. If you change that when creating *\n",
" # * your composite kernel, make sure you update this section *\n",
" # * similarly, or it will not work! *\n",
" # ******************************************************************\n",
" psf0, bg0 = kernel\n",
" psf_params = {}\n",
" for pname, pvalue in zip(psf0.param_names, psf0.parameters):\n",
" if pname == trace_center_param:\n",
" psf_params[pname] = trace_centers\n",
" else:\n",
" psf_params[pname] = np.full(nx, pvalue)\n",
" psf_set = psf0.__class__(n_models=nx, **psf_params)\n",
" #if not using Polynomial1D for background model, edit this:\n",
" bg_set = bg0.__class__(len(bg0.param_names)-1, n_models=nx)\n",
" for pname, pvalue in zip(bg0.param_names, bg0.parameters):\n",
" setattr(bg_set, pname, np.full(nx, pvalue))\n",
" kernel_set = psf_set + bg_set\n",
" # We pass model_set_axis=False so that every model in the set \n",
" # uses the same input, and we transpose the result to fix the\n",
" # orientation.\n",
" kernel_image = kernel_set(xd_pixels, model_set_axis=False).T\n",
" \n",
" # Now we perform our weighted sum, using numpy.ma routines\n",
" # to preserve our masks\n",
" \n",
" # unit normalize the kernel following Horne1986 eqn 5, P_x = P/sum(P_x). \n",
" kernel_image = np.ma.masked_outside(kernel_image, 0, np.inf) \n",
" kernel_image = kernel_image/np.ma.sum(kernel_image)\n",
" \n",
" g = np.ma.sum(kernel_image**2 / variance_image, axis=0)\n",
" weighted_spectrum = np.ma.divide(kernel_image * extraction_region, variance_image)\n",
" spectrum1d = np.ma.sum(weighted_spectrum, axis=0) / g\n",
" \n",
" # Any masked values we set to 0.\n",
" return spectrum1d.filled(0.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convenience functions"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.370544Z",
"iopub.status.busy": "2023-12-18T20:23:21.370266Z",
"iopub.status.idle": "2023-12-18T20:23:21.375894Z",
"shell.execute_reply": "2023-12-18T20:23:21.375391Z"
}
},
"outputs": [],
"source": [
"def batch_wavelength_from_wcs(datamodel, pix_x, pix_y):\n",
" \"\"\"\n",
" Convenience function to grab the WCS object from the\n",
" datamodel's metadata, generate world coordinates from\n",
" the given pixel coordinates, and return the 1D \n",
" wavelength.\n",
" \"\"\"\n",
" \n",
" wcs = datamodel.meta.wcs\n",
" aC, dC, y = wcs(pix_x, pix_y)\n",
" return y[0]\n",
"\n",
"def batch_save_extracted_spectrum(filename, wavelength, spectrum):\n",
" \"\"\"\n",
" Quick & dirty fits dump of an extracted spectrum.\n",
" Replace with your preferred output format & function.\n",
" \"\"\"\n",
" \n",
" wcol = fits.Column(name='wavelength', format='E', \n",
" array=wavelength)\n",
" scol = fits.Column(name='spectrum', format='E',\n",
" array=spectrum)\n",
" cols = fits.ColDefs([wcol, scol])\n",
" hdu = fits.BinTableHDU.from_columns(cols)\n",
" hdu.writeto(filename, overwrite=True)\n",
"\n",
"def batch_plot_output(resampled_image, extraction_bbox, \n",
" kernel_slice, kernel_model,\n",
" wavelength, spectrum, filename):\n",
" \"\"\"\n",
" Convenience function for summary output figures,\n",
" allowing visual inspection of the results from \n",
" each file being processed.\n",
" \"\"\"\n",
" \n",
" fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, \n",
" figsize=(8,12))\n",
" fig.suptitle(filename)\n",
" \n",
" ny, nx = resampled_image.shape\n",
" aspect = nx / (2 * ny)\n",
" \n",
" # Subplot 1: Extraction region\n",
" power_norm = simple_norm(resampled_image, 'power')\n",
" er_img = ax1.imshow(resampled_image, interpolation='none',\n",
" aspect=aspect, norm=power_norm, cmap='gray')\n",
" rx, ry, rw, rh = extraction_bbox\n",
" region = Rectangle((rx, ry), rw, rh, facecolor='none', \n",
" edgecolor='b', linestyle='--')\n",
" er_ptch = ax1.add_patch(region)\n",
" \n",
" # Subplot 2: Kernel fit\n",
" xd_pixels = np.arange(kernel_slice.size)\n",
" fit_line = kernel_model(xd_pixels)\n",
" ks_line = ax2.plot(xd_pixels, kernel_slice, label='Kernel Slice')\n",
" kf_line = ax2.plot(xd_pixels, fit_line, 'o', label='Extraction Kernel')\n",
" k_lgd = ax2.legend()\n",
" \n",
" # Subplot 3: Extracted spectrum\n",
" spec_line = ax3.plot(wavelength, spectrum)\n",
" \n",
" fig.savefig(filename, bbox_inches='tight')\n",
" plt.close(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Iterate over the desired files\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.377850Z",
"iopub.status.busy": "2023-12-18T20:23:21.377571Z",
"iopub.status.idle": "2023-12-18T20:23:21.381709Z",
"shell.execute_reply": "2023-12-18T20:23:21.381215Z"
}
},
"outputs": [],
"source": [
"def batch_optimal_extraction(file_list):\n",
" \"\"\"\n",
" Iterate over a list of fits file paths, optimally extract\n",
" the SCI extension in each file, generate an output summary\n",
" image, and then save the resulting spectrum.\n",
" \n",
" Note that in the example dataset, there is only one SCI\n",
" extension in each file. For data with multiple SCI \n",
" extensions, a second loop over those extensions is\n",
" required.\n",
" \"\"\"\n",
" \n",
" # For this example data, we'll just use the default values\n",
" # for all the functions\n",
" for i, fitsfile in enumerate(file_list):\n",
" print(\"Processing file {} of {}: {}\".format(i+1, len(file_list), fitsfile))\n",
" dmodel = ImageModel(fitsfile)\n",
" spec2d = dmodel.data\n",
" wht2d = dmodel.wht\n",
" \n",
" k_slice = batch_kernel_slice(spec2d)\n",
" k_model = batch_fit_extraction_kernel(k_slice)\n",
" trace = batch_fit_trace_centers(spec2d, k_model)\n",
" spectrum = batch_extract_spectrum(spec2d, trace, k_model, wht2d)\n",
" \n",
" ny, nx = spec2d.shape\n",
" y2d, x2d = np.mgrid[:ny, :nx]\n",
" wavelength = batch_wavelength_from_wcs(dmodel, x2d, y2d)\n",
" \n",
" bbox = [0, 0, nx-1, ny-1]\n",
" \n",
" outfile = fitsfile.replace('s2d.fits', 'x1d_optimal')\n",
" \n",
" batch_plot_output(spec2d, bbox, k_slice, k_model,\n",
" wavelength, spectrum, \n",
" outfile+'.png')\n",
" batch_save_extracted_spectrum(outfile+'.fits', wavelength, spectrum)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run on example dataset\n",
"\n",
"Take particular note of any spectrum which produces a warning during fitting - these are likely to be good candidates for interactive reprocessing."
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"remove-cell"
]
},
"source": [
"*Developer Note:*\n",
"\n",
"It would be great if there was a way to do this without spawning invisible plots from the creation of matplotlib figures, so that the `ioff` and `ion` calls could be removed."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:23:21.383553Z",
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"iopub.status.idle": "2023-12-18T20:24:16.815511Z",
"shell.execute_reply": "2023-12-18T20:24:16.815054Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing file 1 of 79: s2d_files/F170LP-G235M_MOS_observation-6_mod_correctedWCS_noflat_nooutlierdet_combined_s05225_s2d.fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n",
"2023-12-18 20:23:21,772 - stpipe - WARNING - The fit may be unsuccessful; check fit_info['message'] for more information.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.ioff() # if we don't turn this off, then matplotlib tries to display an (invisible) plot for each spectrum\n",
"s2d_files = glob(os.path.join('s2d_files', '*s2d.fits'))\n",
"batch_optimal_extraction(s2d_files)\n",
"plt.ion() # now we turn it back on so everything else plots as it should!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Appendix B: WebbPSF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of using a PSF template, we can generate a PSF directly from the instrument model with [WebbPSF](https://webbpsf.readthedocs.io/en/stable/index.html). Currently, only the F110W and F140X imaging filters are supported, but we'll walk through the process anyway for whenever more filters become available.\n",
"\n",
"The primary function of WebbPSF is to produce imaging PSFs; however, it *can* generate a set of monochromatic PSFs, which we can combine."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`webbpsf` is only needed here so we import it at the start of this appendix:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:24:16.817573Z",
"iopub.status.busy": "2023-12-18T20:24:16.817377Z",
"iopub.status.idle": "2023-12-18T20:24:17.549139Z",
"shell.execute_reply": "2023-12-18T20:24:17.548624Z"
}
},
"outputs": [],
"source": [
"from webbpsf import NIRSpec, display_psf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"WebbPSF has a number of data files which are required to run, so we'll begin by verifying that they can be accessed (and downloading them if necessary).\n",
"\n",
"Note that you will see a big red error message if you have not yet downloaded the data files. Don't worry, as long as you see \"Downloading WebbPSF data files.\" everything is still proceeding as expected."
]
},
{
"cell_type": "markdown",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"remove-cell"
]
},
"source": [
"*Developer Note:*\n",
"\n",
"WebbPSF should be updated so that the red error doesn't appear. See https://github.com/spacetelescope/webbpsf/issues/380"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:24:17.551765Z",
"iopub.status.busy": "2023-12-18T20:24:17.551132Z",
"iopub.status.idle": "2023-12-18T20:24:22.278276Z",
"shell.execute_reply": "2023-12-18T20:24:22.277787Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading WebbPSF data files.\n",
"Extracting into ./webbpsf-data ...\n"
]
}
],
"source": [
"try:\n",
" instrument = NIRSpec()\n",
"except OSError:\n",
" # assume that WebbPSF data files have not been downloaded\n",
" import tarfile, sys\n",
" print(\"Downloading WebbPSF data files.\")\n",
" webb_url = \"https://stsci.box.com/shared/static/qxpiaxsjwo15ml6m4pkhtk36c9jgj70k.gz\"\n",
" webb_file = os.path.join('.', \"webbpsf-data-1.2.1.tar.gz\")\n",
" urllib.request.urlretrieve(webb_url, webb_file)\n",
" print(\"Extracting into ./webbpsf-data ...\")\n",
" tar = tarfile.open(webb_file)\n",
" tar.extractall()\n",
" tar.close()\n",
" os.environ[\"WEBBPSF_PATH\"] = os.path.join(\".\",\"webbpsf-data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instrument properties\n",
"See the WebbPSF documentation for a full list of instrument settings."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:24:22.280438Z",
"iopub.status.busy": "2023-12-18T20:24:22.280271Z",
"iopub.status.idle": "2023-12-18T20:24:22.321131Z",
"shell.execute_reply": "2023-12-18T20:24:22.320691Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['F110W', 'F140X', 'IFU']\n"
]
}
],
"source": [
"instrument = NIRSpec()\n",
"print(instrument.filter_list)\n",
"\n",
"# For reference:\n",
"allowed_masks = ('S200A1','S200A2','S400A1','S1600A1','S200B1', \n",
" 'MSA all open', 'Single MSA open shutter', \n",
" 'Three adjacent MSA open shutters')\n",
"\n",
"# Edit these as necessary\n",
"instrument.filter = 'F110W' \n",
"instrument.image_mask = 'Three adjacent MSA open shutters'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monochromatic PSFs\n",
"\n",
"The most rigorous method we could use is to generate a PSF for each wavelength in the 2D spectrum and combine all of them. However, the computation time and memory required for this method is generally very large unless the spectra are quite short (in the dispersion direction). A more reasonable method (which is what we will be doing here) is to create a subset of monochromatic PSFs spaced evenly across the wavelength range, and interpolate between them."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:24:22.322877Z",
"iopub.status.busy": "2023-12-18T20:24:22.322718Z",
"iopub.status.idle": "2023-12-18T20:26:13.256242Z",
"shell.execute_reply": "2023-12-18T20:26:13.255728Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(10, 48, 48)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"psf_wavelengths = np.linspace(wavelength[0], wavelength[-1], num=10) * 1.0e-6 # wavelengths must be in meters\n",
"\n",
"cube_hdul = instrument.calc_datacube(psf_wavelengths) #the output is a HDUList\n",
"psf_cube = cube_hdul[1].data\n",
"psf_cube.shape"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:26:13.258573Z",
"iopub.status.busy": "2023-12-18T20:26:13.258074Z",
"iopub.status.idle": "2023-12-18T20:26:13.563687Z",
"shell.execute_reply": "2023-12-18T20:26:13.563139Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Display the contents of the data cube\n",
"fig9, ax9 = plt.subplots(nrows=5, ncols=2, figsize=(8,12))\n",
"plt.subplots_adjust(hspace=0.15, wspace=0.01, left=0.06, \n",
" right=0.94, bottom=0.05, top=0.95)\n",
"for row in range(5):\n",
" for col in range(2):\n",
" ax = ax9[row, col]\n",
" w = row * 2 + col\n",
" wl = psf_wavelengths[w]\n",
" \n",
" display_psf(cube_hdul, ax=ax, cube_slice=w,\n",
" title=\"$\\lambda$ = {:.3f} $\\mu$m\".format(wl*1e6),\n",
" vmax=.2, vmin=1e-4, ext=1, colorbar=False)\n",
" ax.xaxis.set_visible(False)\n",
" ax.yaxis.set_visible(False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Interpolation Methods\n",
"\n",
"The method of interpolation we choose depends strongly on how the PSF varies with wavelength. For evaluating the different methods, we'll create another monochromatic PSF for comparison."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:26:13.565883Z",
"iopub.status.busy": "2023-12-18T20:26:13.565581Z",
"iopub.status.idle": "2023-12-18T20:26:24.756575Z",
"shell.execute_reply": "2023-12-18T20:26:24.755984Z"
}
},
"outputs": [],
"source": [
"reference_psf_hdul = instrument.calc_psf(monochromatic=3.0e-6)\n",
"reference_psf = reference_psf_hdul[1].data\n",
"ref_norm = simple_norm(reference_psf, stretch='log', min_cut=1e-4, max_cut=0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simplest way is a 3D linear interpolation, so let's see how it does. In the figure below, the top-left image is the reference PSF, the top-right is the linearly-interpolated PSF, the bottom left is a difference image, and the bottom right is a log-log plot of the pixel values in the reference (X) and interpolated (Y) PSFs."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:26:24.758924Z",
"iopub.status.busy": "2023-12-18T20:26:24.758612Z",
"iopub.status.idle": "2023-12-18T20:26:25.089420Z",
"shell.execute_reply": "2023-12-18T20:26:25.089028Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reference: min 1.827e-09, max 1.718e-01\n",
"Linear: min 2.226e-09, max 1.718e-01\n",
"Diff: min -2.237e-05, max 1.871e-05\n",
"Total error: 5.04410e-05\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ref_pix = reference_psf >= 1e-4\n",
"psf_x = psf_y = np.arange(48)\n",
"out_x, out_y = np.meshgrid(psf_x, psf_y, indexing='ij')\n",
"interpolator = RegularGridInterpolator((psf_wavelengths, psf_x, psf_y), psf_cube, method='linear')\n",
"linear_psf = interpolator((3.0e-6, out_x, out_y))\n",
"\n",
"diff_lin_psf = reference_psf - linear_psf\n",
"\n",
"print(\"Reference: min {:.3e}, max {:.3e}\".format(reference_psf.min(), reference_psf.max()))\n",
"print(\"Linear: min {:.3e}, max {:.3e}\".format(linear_psf.min(), linear_psf.max()))\n",
"print(\"Diff: min {:.3e}, max {:.3e}\".format(diff_lin_psf.min(), diff_lin_psf.max()))\n",
"print(\"Total error: {:.5e}\".format(np.sqrt((diff_lin_psf**2).sum())))\n",
"\n",
"figA, axA = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))\n",
"plt.subplots_adjust(wspace=0.01, left=0.05, right=0.95)\n",
"axA[0, 0].imshow(reference_psf, interpolation='none', norm=ref_norm)\n",
"axA[0, 0].xaxis.set_visible(False)\n",
"axA[0, 0].yaxis.set_visible(False)\n",
"axA[0, 1].imshow(linear_psf, interpolation='none', norm=ref_norm)\n",
"axA[0, 1].xaxis.set_visible(False)\n",
"axA[0, 1].yaxis.set_visible(False)\n",
"axA[1, 0].imshow(diff_lin_psf, interpolation='none', vmin=-5e-4, vmax=5e-4)\n",
"axA[1, 0].xaxis.set_visible(False)\n",
"axA[1, 0].yaxis.set_visible(False)\n",
"axA[1, 1].loglog(reference_psf[ref_pix], linear_psf[ref_pix], 'k+')\n",
"axA[1, 1].set_aspect('equal', 'box')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next method is more calculation-intensive, but could be more accurate. We go pixel-by-pixel through the PSF cube and interpolate with a 1D cubic spline along the wavelength axis."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:26:25.091328Z",
"iopub.status.busy": "2023-12-18T20:26:25.091167Z",
"iopub.status.idle": "2023-12-18T20:26:25.692201Z",
"shell.execute_reply": "2023-12-18T20:26:25.691658Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reference: min 1.827e-09, max 1.718e-01\n",
"Cubic: min 2.050e-09, max 1.718e-01\n",
"Diff: min -2.048e-05, max 8.523e-05\n",
"Total error: 1.09058e-04\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cubic_psf = np.zeros_like(psf_cube[0])\n",
"for row in np.arange(48):\n",
" for col in np.arange(48):\n",
" spline = interp1d(psf_wavelengths, psf_cube[:, row, col], kind='cubic')\n",
" cubic_psf[row, col] = spline(3.0e-6)\n",
" \n",
"diff_cub_psf = reference_psf - cubic_psf\n",
"\n",
"print(\"Reference: min {:.3e}, max {:.3e}\".format(reference_psf.min(), reference_psf.max()))\n",
"print(\"Cubic: min {:.3e}, max {:.3e}\".format(cubic_psf.min(), cubic_psf.max()))\n",
"print(\"Diff: min {:.3e}, max {:.3e}\".format(diff_cub_psf.min(), diff_cub_psf.max()))\n",
"print(\"Total error: {:.5e}\".format(np.sqrt((diff_cub_psf**2).sum())))\n",
"\n",
"figB, axB = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))\n",
"plt.subplots_adjust(wspace=0.01, left=0.05, right=0.95)\n",
"axB[0, 0].imshow(reference_psf, interpolation='none', norm=ref_norm)\n",
"axB[0, 0].xaxis.set_visible(False)\n",
"axB[0, 0].yaxis.set_visible(False)\n",
"axB[0, 1].imshow(cubic_psf, interpolation='none', norm=ref_norm)\n",
"axB[0, 1].xaxis.set_visible(False)\n",
"axB[0, 1].yaxis.set_visible(False)\n",
"axB[1, 0].imshow(diff_cub_psf, interpolation='none', vmin=-5e-4, vmax=5e-4)\n",
"axB[1, 0].xaxis.set_visible(False)\n",
"axB[1, 0].yaxis.set_visible(False)\n",
"axB[1, 1].loglog(reference_psf[ref_pix], cubic_psf[ref_pix], 'k+')\n",
"axB[1, 1].set_aspect('equal', 'box')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While the log-log plot looks virtually identical to the linear case, the difference image in the spline case shows slightly larger errors in some of the central pixels. This is consistent with the \"total error\" statistic (the sum of squares of the difference image), which is larger in this second case.\n",
"\n",
"We can see in the plot below that the difference between the two methods is very slight, but the linearly-interpolated PSF is more accurate by about a factor of ~3 in total error."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:26:25.694197Z",
"iopub.status.busy": "2023-12-18T20:26:25.694039Z",
"iopub.status.idle": "2023-12-18T20:26:25.967002Z",
"shell.execute_reply": "2023-12-18T20:26:25.966605Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Cubic interpolation')"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figC = plt.figure()\n",
"plt.loglog(linear_psf[ref_pix], cubic_psf[ref_pix], 'k+')\n",
"plt.xlabel('Linear interpolation')\n",
"plt.ylabel('Cubic interpolation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Full trace PSF\n",
"\n",
"Now we can generate a full PSF for the spectral trace. Note that the PSF at each wavelength is going to be a linear combination of the overlapping adjacent monochromatic PSFs. If geometric distortion is present, it may be beneficial to create this PSF *after* the trace centers have been fit."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:26:25.969014Z",
"iopub.status.busy": "2023-12-18T20:26:25.968696Z",
"iopub.status.idle": "2023-12-18T20:26:26.557190Z",
"shell.execute_reply": "2023-12-18T20:26:26.556671Z"
}
},
"outputs": [],
"source": [
"cube_w, cube_x, cube_y = np.meshgrid(wavelength * 1e-6, psf_x, psf_y, indexing='ij')\n",
"full_psf_cube = interpolator((cube_w, cube_x, cube_y))\n",
"nw, ny, nx = full_psf_cube.shape\n",
"half = ny // 2\n",
"trace = np.zeros((ny, nw), dtype=float)\n",
"\n",
"for wl, psf in enumerate(full_psf_cube):\n",
" lo = wl - half\n",
" lo_w = max(lo, 0)\n",
" lo_x = lo_w - lo\n",
" hi = wl + half\n",
" hi_w = min(hi, nw)\n",
" hi_x = nx - (hi - hi_w)\n",
" trace[:, lo_w:hi_w] += psf[:, lo_x:hi_x]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:26:26.559361Z",
"iopub.status.busy": "2023-12-18T20:26:26.559185Z",
"iopub.status.idle": "2023-12-18T20:26:26.749196Z",
"shell.execute_reply": "2023-12-18T20:26:26.748663Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wpsf_aspect = nw / (2. * ny)\n",
"figD = plt.figure(figsize=(10, 8))\n",
"trace_norm = simple_norm(trace, stretch='log', min_cut=1e-4, max_cut=0.2)\n",
"plt.imshow(trace, interpolation='none', aspect=wpsf_aspect, norm=trace_norm)\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Resampling the trace\n",
"\n",
"Currently, our PSF array is not the same size or position as the trace in the extraction region. While we could shift and trim to the correct size, the spectrum will rarely be centered on a pixel, and is sufficiently under-sampled that fractional pixel shifts in the PSF could cause significant errors in the final extraction. Thus, we will perform a final resampling to the location of the spectrum in the extraction region. To do this, we can use our old friend `RegularGridInterpolator`. We set the center of the WebbPSF trace (originally at row 23) to our fit trace center, and resample appropriately."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-18T20:26:26.751264Z",
"iopub.status.busy": "2023-12-18T20:26:26.750966Z",
"iopub.status.idle": "2023-12-18T20:26:26.954544Z",
"shell.execute_reply": "2023-12-18T20:26:26.954127Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trace_row = np.arange(ny)\n",
"trace_interpolator = RegularGridInterpolator((trace_row, wavelength), trace)\n",
"center_0 = 23\n",
"center_1 = fit_extraction_kernel.mean_0\n",
"\n",
"out_lo = center_0 - center_1\n",
"out_hi = out_lo + er_ny\n",
"\n",
"resample_row = np.linspace(out_lo, out_hi, er_ny)\n",
"resample_y, resample_w = np.meshgrid(resample_row, wavelength, indexing='ij')\n",
"\n",
"resampled_trace = trace_interpolator((resample_y, resample_w))\n",
"\n",
"figE, axE = plt.subplots(nrows=2, ncols=1, figsize=(10, 8))\n",
"plt.subplots_adjust(hspace=0.1)\n",
"trace_renorm = simple_norm(resampled_trace, stretch='log')\n",
"axE[0].imshow(resampled_trace, interpolation='none', aspect=aspect_ratio, norm=trace_renorm)\n",
"axE[1].imshow(extraction_region, cmap='gray', aspect=aspect_ratio, \n",
" norm=er_norm, interpolation='none')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## About this notebook\n",
"**Author:** Graham Kanarek, Staff Scientist, Science Support \n",
"**Updated On:** 2020-07-13\n",
"\n",
"Optimal extraction algorithm adapted from [Horne (1986)](https://ui.adsabs.harvard.edu/abs/1986PASP...98..609H/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Top of Page](#top)\n",
" "
]
},
{
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}