diff --git a/docs/index.rst b/docs/index.rst
index 8795cea0..1f3086bd 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -61,6 +61,7 @@ Contents
    jwst_measured_opds.ipynb
    jwst_detector_effects.ipynb
    jwst_matching_psfs_to_data.ipynb
+   jwst_ifu_datacubes.ipynb
    jwst_large_psf.ipynb
    jwst_optical_budgets.ipynb
    jwst_psf_subtraction.ipynb
diff --git a/docs/jwst_ifu_datacubes.ipynb b/docs/jwst_ifu_datacubes.ipynb
new file mode 100644
index 00000000..8bfd2b0e
--- /dev/null
+++ b/docs/jwst_ifu_datacubes.ipynb
@@ -0,0 +1,579 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "74185321-c269-45be-96c5-6e39e8d317ee",
+   "metadata": {},
+   "source": [
+    "# Simulating IFU mode and Datacubes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54d3884f-b190-43b3-9dff-f169c4833678",
+   "metadata": {},
+   "source": [
+    "This page describes how to simulate IFU mode data and spectral datacubes for JWST's NIRSpec IFU and MIRI MRS.\n",
+    "\n",
+    "<div class=\"alert alert-info\">\n",
+    "IFU support is a relatively recent addition, and efforts are still in progress to refine and improve the fidelity of IFU mode simulations. \n",
+    "</div>\n",
+    "\n",
+    "\n",
+    "## Selecting IFU mode simulations\n",
+    "\n",
+    "These instruments have a `mode` attribute, which can be set to either 'IFU' or 'imaging'. Selecting IFU mode has the following effects:\n",
+    "\n",
+    " - The normal `filter` attribute for selecting spectral bandpass is superceded by a `band` attribute for selected IFU bands, the specific details of which differ for NIRSpec and MIRI. A `get_IFU_wavelengths()` function is added, which allows looking up the wavelength range for each band. \n",
+    " - The PSF simulation gets an added step for adding \"IFU broadening\" effects, which are empirical models for slightly broadening/blurring the simulated PSF to better match the observed PSF FWHMs. Physically this is a simplified model for optical blurring effects due to imperfect wavefront quality in the IFU image slicer optics, for example.\n",
+    " - For NIRSpec IFU simulations only, the PSF output is rotated by an additional 90 degrees to match the orientation of JWST pipeline output dataproducts created with the \"orient='IFUalign'\" option in the Cube Build step."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4eac3034-b525-4576-b3c8-ddfd02d7a7b7",
+   "metadata": {},
+   "source": [
+    "Note there are *three options* for computing PSFs in IFU mode. \n",
+    "\n",
+    "1. If you only need one wavelength, use regular `calc_psf()` with the `monochromatic` keyword to specify a wavelength.\n",
+    "2. If you want a datacube at all wavelengths, use `calc_datacube()` with a list or array of wavelengths. This is the recommended path for many typical use cases. \n",
+    "3. If you want a datacube at all wavelengths, you can also use `calc_datacube_fast()` which achieves a much-faster calculation runtime by making a simplifying assumption in the optical calculation, and currently by not including the detector effects or distortion modeling steps.\n",
+    "  \n",
+    "   *  Specifically, it assumes that the wavefront optical path difference in the IFU exit pupil is independent of wavelength. This assumption is reasonably true for both MIRI and NIRSpec IFU modes within the current level of fidelity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7ce1366f-dcc8-47b7-9e73-f0ebbee3f1ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "**WARNING**: LOCAL JWST PRD VERSION PRDOPSSOC-065 DOESN'T MATCH THE CURRENT ONLINE VERSION PRDOPSSOC-067\n",
+      "Please consider updating pysiaf, e.g. pip install --upgrade pysiaf or conda update pysiaf\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import webbpsf\n",
+    "import astropy.units as u"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e25e70a8-7a8b-456f-9719-e52951207bac",
+   "metadata": {},
+   "source": [
+    "## NIRSpec IFU example\n",
+    "\n",
+    "For NIRSpec, the `band` attribute is derived from the `prism` and `disperser` elements. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b3ec32d8-5ede-46fb-8f51-da8c06039fbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Band is PRISM/CLEAR\n"
+     ]
+    }
+   ],
+   "source": [
+    "nrs = webbpsf.NIRSpec()\n",
+    "nrs.mode = 'IFU'\n",
+    "\n",
+    "nrs.disperser = 'PRISM'\n",
+    "nrs.filter = 'CLEAR'\n",
+    "print(\"Band is\", nrs.band)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d950963b-b4df-42ff-8729-04ad44e17889",
+   "metadata": {},
+   "source": [
+    "The wavelength sampling can be obtained using the `get_IFU_wavelengths()` function.  By default this returns the same wavelength sampling as the pipeline uses. But if desired you can also specify some other number of wavelengths `nlambda`, for instance to reduce simulation runtimes when the full spectral resolution is not needed. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e68eb5e3-229f-4bcf-a8a0-5133788ba767",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PRISM/CLEAR default wavelength sampling uses 940 wavelengths\n"
+     ]
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$[0.6,~0.605,~0.61,~\\dots,~5.285,~5.29,~5.295] \\; \\mathrm{\\mu m}$"
+      ],
+      "text/plain": [
+       "<Quantity [0.6  , 0.605, 0.61 , 0.615, 0.62 , 0.625, 0.63 , 0.635, 0.64 ,\n",
+       "           0.645, 0.65 , 0.655, 0.66 , 0.665, 0.67 , 0.675, 0.68 , 0.685,\n",
+       "           0.69 , 0.695, 0.7  , 0.705, 0.71 , 0.715, 0.72 , 0.725, 0.73 ,\n",
+       "           0.735, 0.74 , 0.745, 0.75 , 0.755, 0.76 , 0.765, 0.77 , 0.775,\n",
+       "           0.78 , 0.785, 0.79 , 0.795, 0.8  , 0.805, 0.81 , 0.815, 0.82 ,\n",
+       "           0.825, 0.83 , 0.835, 0.84 , 0.845, 0.85 , 0.855, 0.86 , 0.865,\n",
+       "           0.87 , 0.875, 0.88 , 0.885, 0.89 , 0.895, 0.9  , 0.905, 0.91 ,\n",
+       "           0.915, 0.92 , 0.925, 0.93 , 0.935, 0.94 , 0.945, 0.95 , 0.955,\n",
+       "           0.96 , 0.965, 0.97 , 0.975, 0.98 , 0.985, 0.99 , 0.995, 1.   ,\n",
+       "           1.005, 1.01 , 1.015, 1.02 , 1.025, 1.03 , 1.035, 1.04 , 1.045,\n",
+       "           1.05 , 1.055, 1.06 , 1.065, 1.07 , 1.075, 1.08 , 1.085, 1.09 ,\n",
+       "           1.095, 1.1  , 1.105, 1.11 , 1.115, 1.12 , 1.125, 1.13 , 1.135,\n",
+       "           1.14 , 1.145, 1.15 , 1.155, 1.16 , 1.165, 1.17 , 1.175, 1.18 ,\n",
+       "           1.185, 1.19 , 1.195, 1.2  , 1.205, 1.21 , 1.215, 1.22 , 1.225,\n",
+       "           1.23 , 1.235, 1.24 , 1.245, 1.25 , 1.255, 1.26 , 1.265, 1.27 ,\n",
+       "           1.275, 1.28 , 1.285, 1.29 , 1.295, 1.3  , 1.305, 1.31 , 1.315,\n",
+       "           1.32 , 1.325, 1.33 , 1.335, 1.34 , 1.345, 1.35 , 1.355, 1.36 ,\n",
+       "           1.365, 1.37 , 1.375, 1.38 , 1.385, 1.39 , 1.395, 1.4  , 1.405,\n",
+       "           1.41 , 1.415, 1.42 , 1.425, 1.43 , 1.435, 1.44 , 1.445, 1.45 ,\n",
+       "           1.455, 1.46 , 1.465, 1.47 , 1.475, 1.48 , 1.485, 1.49 , 1.495,\n",
+       "           1.5  , 1.505, 1.51 , 1.515, 1.52 , 1.525, 1.53 , 1.535, 1.54 ,\n",
+       "           1.545, 1.55 , 1.555, 1.56 , 1.565, 1.57 , 1.575, 1.58 , 1.585,\n",
+       "           1.59 , 1.595, 1.6  , 1.605, 1.61 , 1.615, 1.62 , 1.625, 1.63 ,\n",
+       "           1.635, 1.64 , 1.645, 1.65 , 1.655, 1.66 , 1.665, 1.67 , 1.675,\n",
+       "           1.68 , 1.685, 1.69 , 1.695, 1.7  , 1.705, 1.71 , 1.715, 1.72 ,\n",
+       "           1.725, 1.73 , 1.735, 1.74 , 1.745, 1.75 , 1.755, 1.76 , 1.765,\n",
+       "           1.77 , 1.775, 1.78 , 1.785, 1.79 , 1.795, 1.8  , 1.805, 1.81 ,\n",
+       "           1.815, 1.82 , 1.825, 1.83 , 1.835, 1.84 , 1.845, 1.85 , 1.855,\n",
+       "           1.86 , 1.865, 1.87 , 1.875, 1.88 , 1.885, 1.89 , 1.895, 1.9  ,\n",
+       "           1.905, 1.91 , 1.915, 1.92 , 1.925, 1.93 , 1.935, 1.94 , 1.945,\n",
+       "           1.95 , 1.955, 1.96 , 1.965, 1.97 , 1.975, 1.98 , 1.985, 1.99 ,\n",
+       "           1.995, 2.   , 2.005, 2.01 , 2.015, 2.02 , 2.025, 2.03 , 2.035,\n",
+       "           2.04 , 2.045, 2.05 , 2.055, 2.06 , 2.065, 2.07 , 2.075, 2.08 ,\n",
+       "           2.085, 2.09 , 2.095, 2.1  , 2.105, 2.11 , 2.115, 2.12 , 2.125,\n",
+       "           2.13 , 2.135, 2.14 , 2.145, 2.15 , 2.155, 2.16 , 2.165, 2.17 ,\n",
+       "           2.175, 2.18 , 2.185, 2.19 , 2.195, 2.2  , 2.205, 2.21 , 2.215,\n",
+       "           2.22 , 2.225, 2.23 , 2.235, 2.24 , 2.245, 2.25 , 2.255, 2.26 ,\n",
+       "           2.265, 2.27 , 2.275, 2.28 , 2.285, 2.29 , 2.295, 2.3  , 2.305,\n",
+       "           2.31 , 2.315, 2.32 , 2.325, 2.33 , 2.335, 2.34 , 2.345, 2.35 ,\n",
+       "           2.355, 2.36 , 2.365, 2.37 , 2.375, 2.38 , 2.385, 2.39 , 2.395,\n",
+       "           2.4  , 2.405, 2.41 , 2.415, 2.42 , 2.425, 2.43 , 2.435, 2.44 ,\n",
+       "           2.445, 2.45 , 2.455, 2.46 , 2.465, 2.47 , 2.475, 2.48 , 2.485,\n",
+       "           2.49 , 2.495, 2.5  , 2.505, 2.51 , 2.515, 2.52 , 2.525, 2.53 ,\n",
+       "           2.535, 2.54 , 2.545, 2.55 , 2.555, 2.56 , 2.565, 2.57 , 2.575,\n",
+       "           2.58 , 2.585, 2.59 , 2.595, 2.6  , 2.605, 2.61 , 2.615, 2.62 ,\n",
+       "           2.625, 2.63 , 2.635, 2.64 , 2.645, 2.65 , 2.655, 2.66 , 2.665,\n",
+       "           2.67 , 2.675, 2.68 , 2.685, 2.69 , 2.695, 2.7  , 2.705, 2.71 ,\n",
+       "           2.715, 2.72 , 2.725, 2.73 , 2.735, 2.74 , 2.745, 2.75 , 2.755,\n",
+       "           2.76 , 2.765, 2.77 , 2.775, 2.78 , 2.785, 2.79 , 2.795, 2.8  ,\n",
+       "           2.805, 2.81 , 2.815, 2.82 , 2.825, 2.83 , 2.835, 2.84 , 2.845,\n",
+       "           2.85 , 2.855, 2.86 , 2.865, 2.87 , 2.875, 2.88 , 2.885, 2.89 ,\n",
+       "           2.895, 2.9  , 2.905, 2.91 , 2.915, 2.92 , 2.925, 2.93 , 2.935,\n",
+       "           2.94 , 2.945, 2.95 , 2.955, 2.96 , 2.965, 2.97 , 2.975, 2.98 ,\n",
+       "           2.985, 2.99 , 2.995, 3.   , 3.005, 3.01 , 3.015, 3.02 , 3.025,\n",
+       "           3.03 , 3.035, 3.04 , 3.045, 3.05 , 3.055, 3.06 , 3.065, 3.07 ,\n",
+       "           3.075, 3.08 , 3.085, 3.09 , 3.095, 3.1  , 3.105, 3.11 , 3.115,\n",
+       "           3.12 , 3.125, 3.13 , 3.135, 3.14 , 3.145, 3.15 , 3.155, 3.16 ,\n",
+       "           3.165, 3.17 , 3.175, 3.18 , 3.185, 3.19 , 3.195, 3.2  , 3.205,\n",
+       "           3.21 , 3.215, 3.22 , 3.225, 3.23 , 3.235, 3.24 , 3.245, 3.25 ,\n",
+       "           3.255, 3.26 , 3.265, 3.27 , 3.275, 3.28 , 3.285, 3.29 , 3.295,\n",
+       "           3.3  , 3.305, 3.31 , 3.315, 3.32 , 3.325, 3.33 , 3.335, 3.34 ,\n",
+       "           3.345, 3.35 , 3.355, 3.36 , 3.365, 3.37 , 3.375, 3.38 , 3.385,\n",
+       "           3.39 , 3.395, 3.4  , 3.405, 3.41 , 3.415, 3.42 , 3.425, 3.43 ,\n",
+       "           3.435, 3.44 , 3.445, 3.45 , 3.455, 3.46 , 3.465, 3.47 , 3.475,\n",
+       "           3.48 , 3.485, 3.49 , 3.495, 3.5  , 3.505, 3.51 , 3.515, 3.52 ,\n",
+       "           3.525, 3.53 , 3.535, 3.54 , 3.545, 3.55 , 3.555, 3.56 , 3.565,\n",
+       "           3.57 , 3.575, 3.58 , 3.585, 3.59 , 3.595, 3.6  , 3.605, 3.61 ,\n",
+       "           3.615, 3.62 , 3.625, 3.63 , 3.635, 3.64 , 3.645, 3.65 , 3.655,\n",
+       "           3.66 , 3.665, 3.67 , 3.675, 3.68 , 3.685, 3.69 , 3.695, 3.7  ,\n",
+       "           3.705, 3.71 , 3.715, 3.72 , 3.725, 3.73 , 3.735, 3.74 , 3.745,\n",
+       "           3.75 , 3.755, 3.76 , 3.765, 3.77 , 3.775, 3.78 , 3.785, 3.79 ,\n",
+       "           3.795, 3.8  , 3.805, 3.81 , 3.815, 3.82 , 3.825, 3.83 , 3.835,\n",
+       "           3.84 , 3.845, 3.85 , 3.855, 3.86 , 3.865, 3.87 , 3.875, 3.88 ,\n",
+       "           3.885, 3.89 , 3.895, 3.9  , 3.905, 3.91 , 3.915, 3.92 , 3.925,\n",
+       "           3.93 , 3.935, 3.94 , 3.945, 3.95 , 3.955, 3.96 , 3.965, 3.97 ,\n",
+       "           3.975, 3.98 , 3.985, 3.99 , 3.995, 4.   , 4.005, 4.01 , 4.015,\n",
+       "           4.02 , 4.025, 4.03 , 4.035, 4.04 , 4.045, 4.05 , 4.055, 4.06 ,\n",
+       "           4.065, 4.07 , 4.075, 4.08 , 4.085, 4.09 , 4.095, 4.1  , 4.105,\n",
+       "           4.11 , 4.115, 4.12 , 4.125, 4.13 , 4.135, 4.14 , 4.145, 4.15 ,\n",
+       "           4.155, 4.16 , 4.165, 4.17 , 4.175, 4.18 , 4.185, 4.19 , 4.195,\n",
+       "           4.2  , 4.205, 4.21 , 4.215, 4.22 , 4.225, 4.23 , 4.235, 4.24 ,\n",
+       "           4.245, 4.25 , 4.255, 4.26 , 4.265, 4.27 , 4.275, 4.28 , 4.285,\n",
+       "           4.29 , 4.295, 4.3  , 4.305, 4.31 , 4.315, 4.32 , 4.325, 4.33 ,\n",
+       "           4.335, 4.34 , 4.345, 4.35 , 4.355, 4.36 , 4.365, 4.37 , 4.375,\n",
+       "           4.38 , 4.385, 4.39 , 4.395, 4.4  , 4.405, 4.41 , 4.415, 4.42 ,\n",
+       "           4.425, 4.43 , 4.435, 4.44 , 4.445, 4.45 , 4.455, 4.46 , 4.465,\n",
+       "           4.47 , 4.475, 4.48 , 4.485, 4.49 , 4.495, 4.5  , 4.505, 4.51 ,\n",
+       "           4.515, 4.52 , 4.525, 4.53 , 4.535, 4.54 , 4.545, 4.55 , 4.555,\n",
+       "           4.56 , 4.565, 4.57 , 4.575, 4.58 , 4.585, 4.59 , 4.595, 4.6  ,\n",
+       "           4.605, 4.61 , 4.615, 4.62 , 4.625, 4.63 , 4.635, 4.64 , 4.645,\n",
+       "           4.65 , 4.655, 4.66 , 4.665, 4.67 , 4.675, 4.68 , 4.685, 4.69 ,\n",
+       "           4.695, 4.7  , 4.705, 4.71 , 4.715, 4.72 , 4.725, 4.73 , 4.735,\n",
+       "           4.74 , 4.745, 4.75 , 4.755, 4.76 , 4.765, 4.77 , 4.775, 4.78 ,\n",
+       "           4.785, 4.79 , 4.795, 4.8  , 4.805, 4.81 , 4.815, 4.82 , 4.825,\n",
+       "           4.83 , 4.835, 4.84 , 4.845, 4.85 , 4.855, 4.86 , 4.865, 4.87 ,\n",
+       "           4.875, 4.88 , 4.885, 4.89 , 4.895, 4.9  , 4.905, 4.91 , 4.915,\n",
+       "           4.92 , 4.925, 4.93 , 4.935, 4.94 , 4.945, 4.95 , 4.955, 4.96 ,\n",
+       "           4.965, 4.97 , 4.975, 4.98 , 4.985, 4.99 , 4.995, 5.   , 5.005,\n",
+       "           5.01 , 5.015, 5.02 , 5.025, 5.03 , 5.035, 5.04 , 5.045, 5.05 ,\n",
+       "           5.055, 5.06 , 5.065, 5.07 , 5.075, 5.08 , 5.085, 5.09 , 5.095,\n",
+       "           5.1  , 5.105, 5.11 , 5.115, 5.12 , 5.125, 5.13 , 5.135, 5.14 ,\n",
+       "           5.145, 5.15 , 5.155, 5.16 , 5.165, 5.17 , 5.175, 5.18 , 5.185,\n",
+       "           5.19 , 5.195, 5.2  , 5.205, 5.21 , 5.215, 5.22 , 5.225, 5.23 ,\n",
+       "           5.235, 5.24 , 5.245, 5.25 , 5.255, 5.26 , 5.265, 5.27 , 5.275,\n",
+       "           5.28 , 5.285, 5.29 , 5.295] micron>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "allwaves = nrs.get_IFU_wavelengths()\n",
+    "print(f\"{nrs.band} default wavelength sampling uses {len(allwaves)} wavelengths\")\n",
+    "allwaves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7417534b-6550-4830-9f23-a590aad7357f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's get a subset for a faster PSF sim runtime\n",
+    "waves = nrs.get_IFU_wavelengths(nlambda=50)\n",
+    "\n",
+    "# convert waves from  microns to meters\n",
+    "# (this is a work around for a current issue with the poppy library upstream)\n",
+    "waves = waves.to_value(u.meter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f5180f4-2890-47db-aea7-b89c6d176130",
+   "metadata": {},
+   "source": [
+    "Compute a datacube:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "212dcbf1-4fef-43a3-8675-a7dbad84ace1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cube = nrs.calc_datacube(waves)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8662db59-3a15-4a6d-b3de-341a4189a05a",
+   "metadata": {},
+   "source": [
+    "The output FITS file has the same extensions as a regular PSF calculation, but each extension contains a 3D datacube rather than a 2D image: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "58429d9f-621b-4bcd-96c1-917abf609318",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filename: (No file associated with this HDUList)\n",
+      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
+      "  0  OVERSAMP      1 PrimaryHDU    1288   (192, 192, 50)   float64   \n",
+      "  1  DET_SAMP      1 ImageHDU      1290   (48, 48, 50)   float64   \n",
+      "  2  OVERDIST      1 ImageHDU      1343   (192, 192, 50)   float64   \n",
+      "  3  DET_DIST      1 ImageHDU      1344   (48, 48, 50)   float64   \n"
+     ]
+    }
+   ],
+   "source": [
+    "cube.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd3b4280-6ec4-4f62-b6ea-8108780dfe20",
+   "metadata": {},
+   "source": [
+    "The `calc_datacube_fast` routine does a simplified calculation, much faster: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "228264f6-0f16-489d-99a7-4dadbea39bc7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quickcube = nrs.calc_datacube_fast(waves)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b12112c-d610-4579-8072-f644c01d4a83",
+   "metadata": {},
+   "source": [
+    "Note that in this case, the output FITS file only contains the first oversampled extension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "de797cb5-3b4f-4a4a-8152-59e68f7bc78f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filename: (No file associated with this HDUList)\n",
+      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
+      "  0  OVERSAMP      1 PrimaryHDU     159   (192, 192, 50)   float64   \n"
+     ]
+    }
+   ],
+   "source": [
+    "quickcube.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e550c2b5-20fa-493d-ae39-317f7c5a4cdf",
+   "metadata": {},
+   "source": [
+    "The display_psf function works with datacubes, but you have to specify which slice of the cube to display. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2ffd3c45-4d4c-4a0f-9a41-6d28e7618eac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index = 20\n",
+    "webbpsf.display_psf(cube, ext=3, cube_slice=index, \n",
+    "                    # Note that currently the default plot title isn't very informative for datacube modes\n",
+    "                    # so we can specify a better title directly here:\n",
+    "                    title=f'NRS IFU cube slice {index}, $\\\\lambda$={cube[0].header[\"WAVELN\"+str(index)]*1e6:.4} micron') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03938bc2-f831-4dbd-9188-f9c4ef3dc33c",
+   "metadata": {},
+   "source": [
+    "## MIRI MRS example\n",
+    "\n",
+    "This is mostly the same, except the selection of the IFU bands is done via setting `band` directly. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b9196827-456e-4f09-b40a-831550af07d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Band is 2A\n"
+     ]
+    }
+   ],
+   "source": [
+    "miri = webbpsf.MIRI()\n",
+    "miri.mode = 'IFU'\n",
+    "miri.band= '2A'  \n",
+    "print(\"Band is\", miri.band)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7612fcf-5793-497a-a4f5-38843792d31b",
+   "metadata": {},
+   "source": [
+    "Note that selecting an IFU band automatically configures the simulated pixelscale to match the default scale used in pipeline output datacubes: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2bc6face-54c4-42ad-b4a5-d80de8d120fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pixelscale for band 2A is 0.17 arcsec/pix\n",
+      "Pixelscale for band 3B is 0.2 arcsec/pix\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Pixelscale for band {miri.band} is {miri.pixelscale} arcsec/pix\")\n",
+    "\n",
+    "miri.band = '3B'\n",
+    "print(f\"Pixelscale for band {miri.band} is {miri.pixelscale} arcsec/pix\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4e131678-a7b0-476f-b458-5ad3e731ecf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's get a subset for a faster PSF sim runtime\n",
+    "waves = miri.get_IFU_wavelengths(nlambda=50)\n",
+    "\n",
+    "# convert waves from  microns to meters\n",
+    "# (this is a work around for a current issue with the poppy library upstream)\n",
+    "waves = waves.to_value(u.meter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d747fa7-1851-4176-b1f7-8222f550a866",
+   "metadata": {},
+   "source": [
+    "Compute a datacube:\n",
+    "\n",
+    "(Note, for MIRI MRS you may see a warning message about the valid region of the field dependence model; this is a benign warning and can be mostly ignored.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "e18451d5-5144-4e4f-b643-c811840362e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/mperrin/Dropbox (Personal)/Documents/software/webbpsf/webbpsf/opds.py:1759: UserWarning: For (V2,V3) = [-8.40143167 -5.31995333] arcmin, Field point -8.254199999999999 arcmin, -2.4800466666666674 arcmin not within valid region for field dependence model of OTE WFE for MIRI: -8.254199999999999 arcmin--6.21738 arcmin, -2.557224 arcmin--0.5632056 arcmin.  Clipping to closest available valid location, 0.14723166666666643 arcmin away from the requested coordinates.\n",
+      "  warnings.warn(warning_message)\n"
+     ]
+    }
+   ],
+   "source": [
+    "cube = miri.calc_datacube(waves)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "695ff9ec-44f3-481c-8752-f6a7220cf77d",
+   "metadata": {},
+   "source": [
+    "The output FITS file has the same extensions as a regular PSF calculation, but each extension contains a 3D datacube rather than a 2D image: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5986841b-97c2-443d-9677-ac48adc3fdc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filename: (No file associated with this HDUList)\n",
+      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
+      "  0  OVERSAMP      1 PrimaryHDU     990   (240, 240, 50)   float64   \n",
+      "  1  DET_SAMP      1 ImageHDU       992   (60, 60, 50)   float64   \n",
+      "  2  OVERDIST      1 ImageHDU      1097   (240, 240, 50)   float64   \n",
+      "  3  DET_DIST      1 ImageHDU      1098   (60, 60, 50)   float64   \n"
+     ]
+    }
+   ],
+   "source": [
+    "cube.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6436fd6-e24e-4f01-af0a-c31f74c215fa",
+   "metadata": {},
+   "source": [
+    "The display_psf function works with datacubes, but you have to specify which slice of the cube to display. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c73ffde0-7f85-49fc-9552-d0daabd124f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "index = 20\n",
+    "webbpsf.display_psf(cube, ext=3, cube_slice=index, \n",
+    "                    # Note that currently the default plot title isn't very informative for datacube modes\n",
+    "                    # so we can specify a better title directly here:\n",
+    "                    title=f'MIRI MRS band {miri.band}, cube slice {index}, $\\\\lambda$={cube[0].header[\"WAVELN\"+str(index)]*1e6:.4} micron') "
+   ]
+  }
+ ],
+ "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/jwst_matching_psfs_to_data.ipynb b/docs/jwst_matching_psfs_to_data.ipynb
index a610f1a0..6bf5fc9e 100644
--- a/docs/jwst_matching_psfs_to_data.ipynb
+++ b/docs/jwst_matching_psfs_to_data.ipynb
@@ -27,7 +27,9 @@
    "id": "42ce87d3",
    "metadata": {},
    "source": [
-    "Often one wants to generate PSFs matched to some particular science dataset or file. The convenience function `webbpsf.setup_sim_to_match_data` helps with this, using the file's FITS header to set up a simulated instrument matched to the appropriate instrument setup and date of observation.  "
+    "Often one wants to generate PSFs matched to some particular science dataset or file. The convenience function `webbpsf.setup_sim_to_match_file` helps with this, using the file's FITS header to set up a simulated instrument matched to the appropriate instrument setup and date of observation. \n",
+    "\n",
+    "Let's call that function, providing a filename of a data file already downloaded in the current directory:"
    ]
   },
   {
@@ -91,6 +93,7 @@
    "metadata": {},
    "source": [
     "This function will:\n",
+    "\n",
     " * Create a webbpsf instrument object for the relevant instrument\n",
     " * Configure it to have the correct filter, detector, and other relevant instrument parameters for that science data file (e.g. coronagraph masks and so on). \n",
     " * Load the measured telescope mirror alignment data from the closest-in-time wavefront sensing visit to that science data. "
@@ -160,6 +163,7 @@
    "metadata": {},
    "source": [
     "The difference between the oversampled and detector-sampled output products is readily apparent. The distortion effects are generally more subtle: \n",
+    "\n",
     " * In this example case, note the slightly blurred softer look of the DET_DIST output compared to DET_SAMP, or of OVERDIST compared to OVERSAMP. This aspect of the simulation is a model for charge transfer physics and inter-pixel capacitance within the detector which result in crosstalk between adjacent pixels.\n",
     " * Also included as part of the distortion is a model for optical geometric distortions (including for instance slight differences between X and Y pixel scales, small rotations and skews of the detector pixel axes, the very-slightly-different position angles for each NIRCam detector, etc.). This attempts to forward-model the distortions which the \"drizzle\" pipeline algorithm corrects for, using the same astrometric calibration information for the instruments recorded in the [science instrument aperture file](https://pysiaf.readthedocs.io/en/latest/). \n",
     "\n",
@@ -488,7 +492,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,