From 71ead9fba5fe75a9cca1a6710a19272e2a718112 Mon Sep 17 00:00:00 2001 From: jacobdparker Date: Thu, 21 May 2026 15:21:54 -0600 Subject: [PATCH 1/3] Add FZP telescope tutorial to docs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds docs/tutorials/fzp_focus.ipynb, a new Jupyter tutorial demonstrating how to model a Fresnel Zone Plate (FZP) as a holographic grating using HolographicRulingSpacing. The tutorial covers: - Recording geometry: x1 at 1 AU (sun distance) and x2 at the focal point, producing an ideal on-axis FZP for 171 Å EUV light - Solar disk source (0.5° full diameter) as the object surface - Known limitation with SequentialSystem backward-trace normalization for transmissive gratings, and the workaround (override rayfunction_default with physical coordinates) - Spot diagrams across the ±0.19° field of view - Diffraction-limited performance verification: geometric RMS spot 0.33 pm vs Airy disk radius 0.149 μm (ratio ~2×10⁻⁶) - Chromatic aberration demo: defocused disks at 169 Å (r=0.82 mm) and 174 Å (r=1.23 mm) vs diffraction-limited point at 171 Å Also registers the new tutorial in docs/index.rst. Co-Authored-By: Claude Sonnet 4.6 --- docs/index.rst | 1 + docs/tutorials/fzp_focus.ipynb | 560 +++++++++++++++++++++++++++++++++ 2 files changed, 561 insertions(+) create mode 100644 docs/tutorials/fzp_focus.ipynb diff --git a/docs/index.rst b/docs/index.rst index 37ae8ad3..bdb0e296 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -82,6 +82,7 @@ Jupyter notebook examples on how to use :mod:`optika`. :maxdepth: 1 tutorials/prime_focus + tutorials/fzp_focus API Reference diff --git a/docs/tutorials/fzp_focus.ipynb b/docs/tutorials/fzp_focus.ipynb new file mode 100644 index 00000000..93010769 --- /dev/null +++ b/docs/tutorials/fzp_focus.ipynb @@ -0,0 +1,560 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60001", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "FZP Telescope Tutorial\n======================\n\nA `Fresnel Zone Plate (FZP) `_\nis a diffractive optical element that focuses light by encoding the\ninterference pattern of two spherical waves on a flat surface.\nIn :mod:`optika`, an FZP is modeled as a transmissive surface with\n:class:`~optika.rulings.HolographicRulingSpacing` rulings, where the\ntwo recording-beam origins define the focal geometry.\n\nThis tutorial builds a single-element FZP telescope that focuses\ncollimated 171 Å EUV light onto a detector and examines the resulting\nspot diagrams." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60002", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.386818500Z", + "start_time": "2026-05-21T21:15:11.377958500Z" + } + }, + "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport astropy.units as u\nimport astropy.visualization\nimport named_arrays as na\nimport optika", + "outputs": [], + "execution_count": 44 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60003", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "FZP\n---\n\nWe start by defining the aperture radius of the FZP" + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60004", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.394516800Z", + "start_time": "2026-05-21T21:15:11.387816Z" + } + }, + "source": "radius_aperture = 70 * u.mm", + "outputs": [], + "execution_count": 45 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60005", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "and the focal length" + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60006", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.406927800Z", + "start_time": "2026-05-21T21:15:11.395516900Z" + } + }, + "source": "focal_length = 1000 * u.mm", + "outputs": [], + "execution_count": 46 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60007", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "and the design wavelength" + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60008", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.415787300Z", + "start_time": "2026-05-21T21:15:11.407927600Z" + } + }, + "source": "wavelength = 171 * u.AA", + "outputs": [], + "execution_count": 47 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60009", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "Holographic recording geometry\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nAn FZP is equivalent to a holographic grating whose rulings were\nrecorded by interfering two coherent beams.\nTo focus incoming collimated light (a plane wave from :math:`z = -\\infty`)\nonto a focal point at :math:`z = +\\text{focal\\_length}`, we choose:\n\n- **Recording beam 1** (``x1``): originates very far away on the optical\n axis, representing a plane wave. With ``is_diverging_1=True`` and\n :math:`x_1 \\to -\\infty`, the unit vectors from :math:`x_1` to every\n point on the surface become parallel, so the ruling pattern is\n identical to that produced by a true plane wave. We place ``x1`` at\n 1 AU (the Earth–Sun distance) so that the residual defocus\n :math:`\\delta f = f^2 / |x_1| \\approx 7\\ \\mathrm{pm}` is far below\n the diffraction limit.\n- **Recording beam 2** (``x2``): converges *to* the desired focal point\n at :math:`z = +\\text{focal\\_length}`. ``is_diverging_2=False`` means\n rays are directed toward :math:`x_2`, i.e. the second recording beam\n is a converging spherical wave.\n\nThe :class:`~optika.rulings.HolographicRulingSpacing` class computes\nthe spatially-varying ruling vector :math:`\\mathbf{d}(\\mathbf{r})` from\nthese two source points." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60010", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.428674800Z", + "start_time": "2026-05-21T21:15:11.421780800Z" + } + }, + "source": "spacing = optika.rulings.HolographicRulingSpacing(\n x1=na.Cartesian3dVectorArray(0, 0, -1) * (1 * u.au).to(u.mm),\n x2=na.Cartesian3dVectorArray(\n x=0 * u.mm,\n y=0 * u.mm,\n z=focal_length,\n ),\n wavelength=wavelength,\n is_diverging_1=True,\n is_diverging_2=False,\n)", + "outputs": [], + "execution_count": 48 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60011", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "We wrap the spacing in an ideal :class:`~optika.rulings.Rulings` instance.\n:class:`~optika.rulings.Rulings` assumes perfect diffraction efficiency\nin the specified order, which is appropriate for an ideal FZP." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60012", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.433985600Z", + "start_time": "2026-05-21T21:15:11.429675400Z" + } + }, + "source": "rulings_fzp = optika.rulings.Rulings(\n spacing=spacing,\n diffraction_order=1,\n)", + "outputs": [], + "execution_count": 49 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60013", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "We assemble the FZP as a flat, transmissive :class:`~optika.surfaces.Surface`.\nNo sag profile is needed (the surface is flat) and no mirror material is\nassigned (the default :class:`~optika.materials.Vacuum` material makes the\nsurface transmissive, not reflective).\nSetting ``is_pupil_stop=True`` marks the FZP aperture as the entrance pupil." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60014", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.440393600Z", + "start_time": "2026-05-21T21:15:11.434994100Z" + } + }, + "source": "fzp = optika.surfaces.Surface(\n name=\"FZP\",\n aperture=optika.apertures.CircularAperture(radius_aperture),\n rulings=rulings_fzp,\n is_pupil_stop=True,\n)", + "outputs": [], + "execution_count": 50 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60015", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "Sensor\n------\n\nIf the size of each pixel in the sensor is" + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60016", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.446651700Z", + "start_time": "2026-05-21T21:15:11.441392900Z" + } + }, + "source": "width_pixel = 13 * u.um", + "outputs": [], + "execution_count": 51 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60017", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "the number of pixels along each axis is" + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60018", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.452153Z", + "start_time": "2026-05-21T21:15:11.447652500Z" + } + }, + "source": "num_pixel = na.Cartesian2dVectorArray(512, 512)", + "outputs": [], + "execution_count": 52 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60019", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "The sensor is placed at the focal plane, one focal length downstream\nof the FZP.\nUnlike a reflective system (such as the prime-focus telescope, where the\nreflected beam travels in the :math:`-z` direction and the sensor must\nbe rotated 180°), here the beam continues in the :math:`+z` direction\nafter transmission through the FZP, so no rotation is needed.\nSetting ``is_field_stop=True`` marks the sensor as the field stop." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60020", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.458054700Z", + "start_time": "2026-05-21T21:15:11.453154900Z" + } + }, + "source": "sensor = optika.sensors.ImagingSensor(\n name=\"sensor\",\n width_pixel=width_pixel,\n axis_pixel=na.Cartesian2dVectorArray(\"detector_x\", \"detector_y\"),\n num_pixel=num_pixel,\n transformation=na.transformations.Cartesian3dTranslation(z=focal_length),\n is_field_stop=True,\n)", + "outputs": [], + "execution_count": 53 + }, + { + "cell_type": "code", + "id": "3f2e63a9", + "source": "radius_sun = 0.25 * u.deg # 0.5 deg full angular diameter", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.463542500Z", + "start_time": "2026-05-21T21:15:11.459054900Z" + } + }, + "outputs": [], + "execution_count": 54 + }, + { + "cell_type": "code", + "id": "a6af380e", + "source": "source = optika.surfaces.Surface(\n name=\"solar disk\",\n aperture=optika.apertures.CircularAperture(\n radius=np.cos(radius_sun),\n ),\n transformation=na.transformations.Cartesian3dTranslation(\n z=-200 * u.mm,\n ),\n)", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.468891900Z", + "start_time": "2026-05-21T21:15:11.464617900Z" + } + }, + "outputs": [], + "execution_count": 55 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60021", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "Input rays\n----------\n\nFor a transmissive FZP (object at infinity), we specify the input rays\nusing **physical** coordinates rather than normalized ones.\nThe pupil grid gives physical positions on the FZP aperture in mm." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60022", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.475205700Z", + "start_time": "2026-05-21T21:15:11.469893200Z" + } + }, + "source": "# Use an even number of samples so the grid never lands exactly at the\n# FZP center (0, 0), where the ruling vector degenerates to zero.\npupil = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px\", \"py\"),\n num=10,\n centers=True,\n)", + "outputs": [], + "execution_count": 56 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60023", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "The field grid gives the angular directions of the incoming collimated\nbeams in degrees.\nThe half-field angle is set by the sensor size:\n:math:`\\theta_{1/2} = \\arctan(N_\\mathrm{pix} \\, w_\\mathrm{pix} / 2 \\, f)`." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60024", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.489465200Z", + "start_time": "2026-05-21T21:15:11.475205700Z" + } + }, + "source": "half_field = np.arctan(\n (num_pixel.x * width_pixel / (2 * focal_length)).to(u.dimensionless_unscaled).value\n) * u.rad\nhalf_field = half_field.to(u.deg)\nhalf_field", + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/latex": "$0.19067965\\mathrm{{}^{\\circ}}$" + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 57 + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60025", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.496479600Z", + "start_time": "2026-05-21T21:15:11.490464100Z" + } + }, + "source": "field = na.Cartesian2dVectorLinearSpace(\n start=-half_field,\n stop=half_field,\n axis=na.Cartesian2dVectorArray(\"fx\", \"fy\"),\n num=5,\n centers=True,\n)", + "outputs": [], + "execution_count": 58 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60026", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "We combine the field, pupil, and wavelength into an\n:class:`~optika.vectors.ObjectVectorArray`." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60027", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.501575200Z", + "start_time": "2026-05-21T21:15:11.496479600Z" + } + }, + "source": "grid_input = optika.vectors.ObjectVectorArray(\n wavelength=wavelength,\n field=field,\n pupil=pupil,\n)", + "outputs": [], + "execution_count": 59 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60028", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "Building the system\n-------------------\n\n:class:`~optika.systems.SequentialSystem` assembles the optical surfaces\nand the input ray grid.\nNo explicit ``object`` surface is provided, so the object is treated as\nbeing at infinity (collimated input).\nThe FZP is the pupil stop and the sensor is the field stop." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60029", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.507581800Z", + "start_time": "2026-05-21T21:15:11.502568900Z" + } + }, + "source": "system = optika.systems.SequentialSystem(\n object=source,\n surfaces=[fzp],\n sensor=sensor,\n grid_input=grid_input,\n)", + "outputs": [], + "execution_count": 60 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60030", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "Because the internal normalization algorithm in\n:class:`~optika.systems.SequentialSystem` traces rays *backward* through\nthe system to establish the field-of-view scale, and because this\nbackward trace re-applies the grating equation of a transmissive FZP\n(doubling the deflection angle), we must compute the default ray function\ndirectly with physical coordinates and cache the result manually.\n\n.. note::\n\n This is a known limitation when ``object_is_at_infinity=True`` with\n a transmissive grating as the pupil stop. Reflective systems (e.g.\n the prime-focus telescope) are not affected." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60031", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.542014200Z", + "start_time": "2026-05-21T21:15:11.508582400Z" + } + }, + "source": "rays_default = system.rayfunction(\n field=field,\n pupil=pupil,\n normalized_field=False,\n normalized_pupil=False,\n)\nsystem.__dict__[\"rayfunction_default\"] = rays_default", + "outputs": [], + "execution_count": 61 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60032", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "We can plot the optical layout using\n:meth:`~optika.systems.SequentialSystem.plot`.\nTo avoid the same normalization issue affecting the displayed rays,\nwe plot the surfaces only and overlay the ray paths from a separate\n:meth:`~optika.systems.SequentialSystem.raytrace` call." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60033", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:11.665846200Z", + "start_time": "2026-05-21T21:15:11.543014400Z" + } + }, + "source": "# Use a coarser, on-axis pupil grid for the layout diagram\npupil_layout = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px\", \"py\"),\n num=5,\n centers=True,\n)\n\nraytrace_layout = system.raytrace(\n field=na.Cartesian2dVectorArray(0 * u.deg, 0 * u.deg),\n pupil=pupil_layout,\n normalized_field=False,\n normalized_pupil=False,\n)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True)\n system.plot(\n ax=ax,\n components=(\"z\", \"y\"),\n color=\"black\",\n zorder=10,\n plot_rays=False,\n )\n na.plt.plot(\n raytrace_layout.outputs.position,\n ax=ax,\n axis=system.axis_surface,\n components=(\"z\", \"y\"),\n color=\"tab:blue\",\n )", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 62 + }, + { + "cell_type": "raw", + "id": "a1b2c3d4e5f60034", + "metadata": { + "raw_mimetype": "text/restructuredtext" + }, + "source": "Spot Diagrams\n-------------\n\nWe can plot a spot diagram for each field angle using the\n:meth:`~optika.systems.SequentialSystem.spot_diagram` method.\nThe on-axis field point (center of the grid) should show a tight\nspot, while off-axis points will exhibit the coma and astigmatism\nthat are characteristic of a single on-axis zone plate." + }, + { + "cell_type": "code", + "id": "a1b2c3d4e5f60035", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:13.158025Z", + "start_time": "2026-05-21T21:15:11.667839500Z" + } + }, + "source": "fig, ax = system.spot_diagram()", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 63 + }, + { + "cell_type": "markdown", + "id": "e573f298", + "source": "## Diffraction-limited performance\n\nAn ideal FZP focuses on-axis collimated light to a single geometric point — the only resolution limit is diffraction. The radius of the first dark ring of the Airy disk sets the diffraction limit:\n\n$$r_\\mathrm{Airy} = \\frac{1.22\\,\\lambda\\,f}{D}$$\n\nWe verify this by tracing a dense on-axis pupil grid and comparing the geometric RMS spot radius to $r_\\mathrm{Airy}$.", + "metadata": {} + }, + { + "cell_type": "code", + "id": "296fba5a", + "source": "r_airy = (1.22 * wavelength * focal_length / (2 * radius_aperture)).to(u.um)\nr_airy", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:13.236338100Z", + "start_time": "2026-05-21T21:15:13.208457300Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ], + "text/latex": "$0.14901429 \\; \\mathrm{\\mu m}$" + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 64 + }, + { + "cell_type": "code", + "id": "3563e97c", + "source": "# Dense on-axis pupil grid — use even num to avoid (0, 0)\npupil_dense = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px_dense\", \"py_dense\"),\n num=50,\n centers=True,\n)\n\nrays_onaxis = system.rayfunction(\n field=na.Cartesian2dVectorArray(0 * u.deg, 0 * u.deg),\n pupil=pupil_dense,\n normalized_field=False,\n normalized_pupil=False,\n).outputs\n\nwhere = rays_onaxis.unvignetted.ndarray\npos_x = rays_onaxis.position.x.to(u.um).ndarray.value # plain float array, in um\npos_y = rays_onaxis.position.y.to(u.um).ndarray.value\n\ncentroid_x = np.mean(pos_x[where])\ncentroid_y = np.mean(pos_y[where])\ndx = pos_x[where] - centroid_x\ndy = pos_y[where] - centroid_y\n\nr_geometric = np.sqrt(np.mean(dx**2 + dy**2)) # um\n\nprint(f\"Airy disk radius (first dark ring): {r_airy:.4f}\")\nprint(f\"Geometric RMS spot radius (on-axis): {r_geometric:.4e} um\")\nprint(f\"Ratio (geometric / Airy): {r_geometric / r_airy.value:.2e}\")", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:13.280031100Z", + "start_time": "2026-05-21T21:15:13.236338100Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Airy disk radius (first dark ring): 0.1490 um\n", + "Geometric RMS spot radius (on-axis): 3.3354e-07 um\n", + "Ratio (geometric / Airy): 2.24e-06\n" + ] + } + ], + "execution_count": 65 + }, + { + "cell_type": "code", + "id": "4fe5afa4", + "source": "theta = np.linspace(0, 2 * np.pi, 361)\nx_airy = r_airy.value * np.cos(theta)\ny_airy = r_airy.value * np.sin(theta)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True)\n ax.scatter(dx, dy, s=4, alpha=0.4, color=\"tab:blue\",\n label=f\"ray positions (RMS = {r_geometric:.2e} um)\")\n ax.plot(x_airy, y_airy, color=\"tab:orange\", lw=2,\n label=f\"Airy disk ($r$ = {r_airy:.4f})\")\n ax.set_aspect(\"equal\")\n ax.set_xlabel(f\"$x$ ({u.um:latex_inline})\")\n ax.set_ylabel(f\"$y$ ({u.um:latex_inline})\")\n ax.legend()\n ax.set_title(\"On-axis geometric PSF vs. diffraction limit\")", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:13.442886Z", + "start_time": "2026-05-21T21:15:13.281031900Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 66 + }, + { + "cell_type": "markdown", + "id": "07e5586a", + "source": "## Chromatic aberration\n\nZone plates are notorious for strong chromatic aberration: focal length scales *inversely* with wavelength,\n\n$$f(\\lambda) = f_0 \\frac{\\lambda_0}{\\lambda}$$\n\nwhere $\\lambda_0 = 171$ Å and $f_0 = 1000$ mm are the design values.\nA wavelength offset $\\Delta\\lambda$ produces a defocused disk of radius\n\n$$r_\\mathrm{blur} = R_\\mathrm{pupil}\\left|1 - \\frac{\\lambda}{\\lambda_0}\\right| \\approx R_\\mathrm{pupil} \\frac{|\\Delta\\lambda|}{\\lambda_0}$$\n\nat the design focal plane.\nWe use *asymmetric* offsets (−2 Å and +3 Å from 171 Å) so the three spots\nhave distinct radii (0, 0.82 mm, and 1.23 mm) and are visually separable.", + "metadata": {} + }, + { + "cell_type": "code", + "id": "acd9a4c0", + "source": "# Build the on-axis pupil grid from the 1-D coordinate arrays.\n# Using meshgrid with indexing='ij' guarantees consistent (x, y) pairing\n# and avoids axis-ordering ambiguity in the named_arrays output arrays.\npx_1d = pupil_dense.x.to(u.mm).ndarray.value # (50,) px_dense\npy_1d = pupil_dense.y.to(u.mm).ndarray.value # (50,) py_dense\npx_2d, py_2d = np.meshgrid(px_1d, py_1d, indexing=\"ij\") # (50, 50)\ninside = np.sqrt(px_2d**2 + py_2d**2) <= radius_aperture.to(u.mm).value\n\nwl_design = wavelength.to(u.AA).value # 171\nf0 = focal_length.to(u.mm).value # 1000\n\n# Asymmetric wavelengths so each spot has a distinct radius.\n# Radii: 169 Å → 0.82 mm, 171 Å → 0 (design), 174 Å → 1.23 mm\nwavelengths_plot = [169, 171, 174]\ncolors = [\"tab:blue\", \"tab:green\", \"tab:red\"]\nlabels = [\n f\"169 Å ($f$ = {f0 * wl_design / 169:.0f} mm, $r_\\\\mathrm{{blur}}$ = {70 * abs(1 - 169/wl_design):.2f} mm)\",\n \"171 Å (design)\",\n f\"174 Å ($f$ = {f0 * wl_design / 174:.0f} mm, $r_\\\\mathrm{{blur}}$ = {70 * abs(1 - 174/wl_design):.2f} mm)\",\n]\n\n# Compute max disk radius for axis limits\nr_max = max(70 * abs(1 - wl / wl_design) for wl in wavelengths_plot)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 6))\n for wl_val, color, label in zip(wavelengths_plot, colors, labels):\n f_wl = f0 * wl_design / wl_val # focal length at this wavelength, mm\n scale = 1.0 - f0 / f_wl # defocus magnification (0 at design λ)\n x_det = px_2d[inside] * scale\n y_det = py_2d[inside] * scale\n ax.scatter(x_det, y_det, s=2, alpha=0.5, color=color,\n label=label, rasterized=True)\n margin = 1.15\n ax.set_xlim(-r_max * margin, r_max * margin)\n ax.set_ylim(-r_max * margin, r_max * margin)\n ax.set_aspect(\"equal\")\n ax.legend(markerscale=4, fontsize=9)\n ax.set_xlabel(\"$x$ (mm)\")\n ax.set_ylabel(\"$y$ (mm)\")\n ax.set_title(\"On-axis spot at 171 Å focal plane: chromatic aberration\")", + "metadata": { + "ExecuteTime": { + "end_time": "2026-05-21T21:15:13.650775200Z", + "start_time": "2026-05-21T21:15:13.448793400Z" + } + }, + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 6575839d35e17bd3c8f92bb87ebcc5c5b83f319f Mon Sep 17 00:00:00 2001 From: jacobdparker Date: Fri, 22 May 2026 17:09:07 -0600 Subject: [PATCH 2/3] Address PR review feedback on FZP tutorial MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Expand FZP acronym in notebook title - Fix duplicate "and" in parameter introduction cells - Replace \text{focal_length} with f symbol; add explicit definition - Show full δf calculation inline and compare to depth of focus (~32 μm) - Add Source section heading and explanation before solar disk cells - Soften normalization workaround description: remove speculative mechanism claim, state factual observation that the algorithm does not correctly infer field-of-view extents for transmissive pupil stops Co-Authored-By: Claude Sonnet 4.6 --- docs/tutorials/fzp_focus.ipynb | 178 ++++++++++++++++++--------------- 1 file changed, 99 insertions(+), 79 deletions(-) diff --git a/docs/tutorials/fzp_focus.ipynb b/docs/tutorials/fzp_focus.ipynb index 93010769..fa4fb1a8 100644 --- a/docs/tutorials/fzp_focus.ipynb +++ b/docs/tutorials/fzp_focus.ipynb @@ -6,20 +6,20 @@ "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "FZP Telescope Tutorial\n======================\n\nA `Fresnel Zone Plate (FZP) `_\nis a diffractive optical element that focuses light by encoding the\ninterference pattern of two spherical waves on a flat surface.\nIn :mod:`optika`, an FZP is modeled as a transmissive surface with\n:class:`~optika.rulings.HolographicRulingSpacing` rulings, where the\ntwo recording-beam origins define the focal geometry.\n\nThis tutorial builds a single-element FZP telescope that focuses\ncollimated 171 Å EUV light onto a detector and examines the resulting\nspot diagrams." + "source": "Fresnel Zone Plate (FZP) Telescope Tutorial\n============================================\n\nA `Fresnel Zone Plate (FZP) `_\nis a diffractive optical element that focuses light by encoding the\ninterference pattern of two spherical waves on a flat surface.\nIn :mod:`optika`, an FZP is modeled as a transmissive surface with\n:class:`~optika.rulings.HolographicRulingSpacing` rulings, where the\ntwo recording-beam origins define the focal geometry.\n\nThis tutorial builds a single-element FZP telescope that focuses\ncollimated 171 Å EUV light onto a detector and examines the resulting\nspot diagrams." }, { "cell_type": "code", "id": "a1b2c3d4e5f60002", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.386818500Z", - "start_time": "2026-05-21T21:15:11.377958500Z" + "end_time": "2026-05-21T21:20:07.933944800Z", + "start_time": "2026-05-21T21:20:07.923014300Z" } }, "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport astropy.units as u\nimport astropy.visualization\nimport named_arrays as na\nimport optika", "outputs": [], - "execution_count": 44 + "execution_count": 68 }, { "cell_type": "raw", @@ -34,13 +34,13 @@ "id": "a1b2c3d4e5f60004", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.394516800Z", - "start_time": "2026-05-21T21:15:11.387816Z" + "end_time": "2026-05-21T21:20:07.944487400Z", + "start_time": "2026-05-21T21:20:07.935565600Z" } }, "source": "radius_aperture = 70 * u.mm", "outputs": [], - "execution_count": 45 + "execution_count": 69 }, { "cell_type": "raw", @@ -55,13 +55,13 @@ "id": "a1b2c3d4e5f60006", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.406927800Z", - "start_time": "2026-05-21T21:15:11.395516900Z" + "end_time": "2026-05-21T21:20:07.954534300Z", + "start_time": "2026-05-21T21:20:07.945494400Z" } }, "source": "focal_length = 1000 * u.mm", "outputs": [], - "execution_count": 46 + "execution_count": 70 }, { "cell_type": "raw", @@ -69,20 +69,20 @@ "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "and the design wavelength" + "source": "the design wavelength is" }, { "cell_type": "code", "id": "a1b2c3d4e5f60008", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.415787300Z", - "start_time": "2026-05-21T21:15:11.407927600Z" + "end_time": "2026-05-21T21:20:07.963614100Z", + "start_time": "2026-05-21T21:20:07.958535500Z" } }, "source": "wavelength = 171 * u.AA", "outputs": [], - "execution_count": 47 + "execution_count": 71 }, { "cell_type": "raw", @@ -90,20 +90,20 @@ "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "Holographic recording geometry\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nAn FZP is equivalent to a holographic grating whose rulings were\nrecorded by interfering two coherent beams.\nTo focus incoming collimated light (a plane wave from :math:`z = -\\infty`)\nonto a focal point at :math:`z = +\\text{focal\\_length}`, we choose:\n\n- **Recording beam 1** (``x1``): originates very far away on the optical\n axis, representing a plane wave. With ``is_diverging_1=True`` and\n :math:`x_1 \\to -\\infty`, the unit vectors from :math:`x_1` to every\n point on the surface become parallel, so the ruling pattern is\n identical to that produced by a true plane wave. We place ``x1`` at\n 1 AU (the Earth–Sun distance) so that the residual defocus\n :math:`\\delta f = f^2 / |x_1| \\approx 7\\ \\mathrm{pm}` is far below\n the diffraction limit.\n- **Recording beam 2** (``x2``): converges *to* the desired focal point\n at :math:`z = +\\text{focal\\_length}`. ``is_diverging_2=False`` means\n rays are directed toward :math:`x_2`, i.e. the second recording beam\n is a converging spherical wave.\n\nThe :class:`~optika.rulings.HolographicRulingSpacing` class computes\nthe spatially-varying ruling vector :math:`\\mathbf{d}(\\mathbf{r})` from\nthese two source points." + "source": "Holographic recording geometry\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nAn FZP is equivalent to a holographic grating whose rulings were\nrecorded by interfering two coherent beams.\nLet :math:`f` denote the focal length.\nTo focus incoming collimated light (a plane wave from :math:`z = -\\infty`)\nonto a focal point at :math:`z = +f`, we choose:\n\n- **Recording beam 1** (``x1``): originates very far away on the optical\n axis, representing a plane wave. With ``is_diverging_1=True`` and\n :math:`x_1 \\to -\\infty`, the unit vectors from :math:`x_1` to every\n point on the surface become parallel, so the ruling pattern is\n identical to that produced by a true plane wave. We place ``x1`` at\n 1 AU (the Earth–Sun distance) so that the residual focal-length shift\n :math:`\\delta f = f^2 / d_{x_1} = (1000\\ \\mathrm{mm})^2 /\n (1.496 \\times 10^{14}\\ \\mathrm{mm}) \\approx 6.7\\ \\mathrm{pm}`\n is negligible compared to the diffraction-limited depth of focus\n :math:`\\sim\\lambda (f/D)^2 \\approx 32\\ \\mu\\mathrm{m}`.\n- **Recording beam 2** (``x2``): converges *to* the desired focal point\n at :math:`z = +f`. ``is_diverging_2=False`` means rays are directed\n toward :math:`x_2`, i.e. the second recording beam is a converging\n spherical wave.\n\nThe :class:`~optika.rulings.HolographicRulingSpacing` class computes\nthe spatially-varying ruling vector :math:`\\mathbf{d}(\\mathbf{r})` from\nthese two source points." }, { "cell_type": "code", "id": "a1b2c3d4e5f60010", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.428674800Z", - "start_time": "2026-05-21T21:15:11.421780800Z" + "end_time": "2026-05-21T21:20:07.970067200Z", + "start_time": "2026-05-21T21:20:07.964614100Z" } }, "source": "spacing = optika.rulings.HolographicRulingSpacing(\n x1=na.Cartesian3dVectorArray(0, 0, -1) * (1 * u.au).to(u.mm),\n x2=na.Cartesian3dVectorArray(\n x=0 * u.mm,\n y=0 * u.mm,\n z=focal_length,\n ),\n wavelength=wavelength,\n is_diverging_1=True,\n is_diverging_2=False,\n)", "outputs": [], - "execution_count": 48 + "execution_count": 72 }, { "cell_type": "raw", @@ -118,13 +118,13 @@ "id": "a1b2c3d4e5f60012", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.433985600Z", - "start_time": "2026-05-21T21:15:11.429675400Z" + "end_time": "2026-05-21T21:20:07.975938700Z", + "start_time": "2026-05-21T21:20:07.971067200Z" } }, "source": "rulings_fzp = optika.rulings.Rulings(\n spacing=spacing,\n diffraction_order=1,\n)", "outputs": [], - "execution_count": 49 + "execution_count": 73 }, { "cell_type": "raw", @@ -139,13 +139,13 @@ "id": "a1b2c3d4e5f60014", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.440393600Z", - "start_time": "2026-05-21T21:15:11.434994100Z" + "end_time": "2026-05-21T21:20:07.982426400Z", + "start_time": "2026-05-21T21:20:07.976952100Z" } }, "source": "fzp = optika.surfaces.Surface(\n name=\"FZP\",\n aperture=optika.apertures.CircularAperture(radius_aperture),\n rulings=rulings_fzp,\n is_pupil_stop=True,\n)", "outputs": [], - "execution_count": 50 + "execution_count": 74 }, { "cell_type": "raw", @@ -160,13 +160,13 @@ "id": "a1b2c3d4e5f60016", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.446651700Z", - "start_time": "2026-05-21T21:15:11.441392900Z" + "end_time": "2026-05-21T21:20:07.987621600Z", + "start_time": "2026-05-21T21:20:07.982426400Z" } }, "source": "width_pixel = 13 * u.um", "outputs": [], - "execution_count": 51 + "execution_count": 75 }, { "cell_type": "raw", @@ -181,13 +181,13 @@ "id": "a1b2c3d4e5f60018", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.452153Z", - "start_time": "2026-05-21T21:15:11.447652500Z" + "end_time": "2026-05-21T21:20:07.992860600Z", + "start_time": "2026-05-21T21:20:07.988620700Z" } }, "source": "num_pixel = na.Cartesian2dVectorArray(512, 512)", "outputs": [], - "execution_count": 52 + "execution_count": 76 }, { "cell_type": "raw", @@ -202,13 +202,19 @@ "id": "a1b2c3d4e5f60020", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.458054700Z", - "start_time": "2026-05-21T21:15:11.453154900Z" + "end_time": "2026-05-21T21:20:07.999086500Z", + "start_time": "2026-05-21T21:20:07.993868400Z" } }, "source": "sensor = optika.sensors.ImagingSensor(\n name=\"sensor\",\n width_pixel=width_pixel,\n axis_pixel=na.Cartesian2dVectorArray(\"detector_x\", \"detector_y\"),\n num_pixel=num_pixel,\n transformation=na.transformations.Cartesian3dTranslation(z=focal_length),\n is_field_stop=True,\n)", "outputs": [], - "execution_count": 53 + "execution_count": 77 + }, + { + "cell_type": "markdown", + "id": "7e17d2a2", + "source": "## Source\n\nWe model the source as a solar disk — a circular patch of sky subtending\n0.5° full diameter (0.25° half-angle). The aperture is specified as a\ndimensionless cosine so that the surface is treated as being at infinity\n(a directional source, not a physical aperture at a finite distance).", + "metadata": {} }, { "cell_type": "code", @@ -216,12 +222,12 @@ "source": "radius_sun = 0.25 * u.deg # 0.5 deg full angular diameter", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.463542500Z", - "start_time": "2026-05-21T21:15:11.459054900Z" + "end_time": "2026-05-21T21:20:08.004154500Z", + "start_time": "2026-05-21T21:20:07.999086500Z" } }, "outputs": [], - "execution_count": 54 + "execution_count": 78 }, { "cell_type": "code", @@ -229,12 +235,12 @@ "source": "source = optika.surfaces.Surface(\n name=\"solar disk\",\n aperture=optika.apertures.CircularAperture(\n radius=np.cos(radius_sun),\n ),\n transformation=na.transformations.Cartesian3dTranslation(\n z=-200 * u.mm,\n ),\n)", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.468891900Z", - "start_time": "2026-05-21T21:15:11.464617900Z" + "end_time": "2026-05-21T21:20:08.009536700Z", + "start_time": "2026-05-21T21:20:08.005160300Z" } }, "outputs": [], - "execution_count": 55 + "execution_count": 79 }, { "cell_type": "raw", @@ -249,13 +255,13 @@ "id": "a1b2c3d4e5f60022", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.475205700Z", - "start_time": "2026-05-21T21:15:11.469893200Z" + "end_time": "2026-05-21T21:20:08.014896900Z", + "start_time": "2026-05-21T21:20:08.010535900Z" } }, "source": "# Use an even number of samples so the grid never lands exactly at the\n# FZP center (0, 0), where the ruling vector degenerates to zero.\npupil = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px\", \"py\"),\n num=10,\n centers=True,\n)", "outputs": [], - "execution_count": 56 + "execution_count": 80 }, { "cell_type": "raw", @@ -270,8 +276,8 @@ "id": "a1b2c3d4e5f60024", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.489465200Z", - "start_time": "2026-05-21T21:15:11.475205700Z" + "end_time": "2026-05-21T21:20:08.028842900Z", + "start_time": "2026-05-21T21:20:08.015899300Z" } }, "source": "half_field = np.arctan(\n (num_pixel.x * width_pixel / (2 * focal_length)).to(u.dimensionless_unscaled).value\n) * u.rad\nhalf_field = half_field.to(u.deg)\nhalf_field", @@ -283,25 +289,25 @@ ], "text/latex": "$0.19067965\\mathrm{{}^{\\circ}}$" }, - "execution_count": 57, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 57 + "execution_count": 81 }, { "cell_type": "code", "id": "a1b2c3d4e5f60025", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.496479600Z", - "start_time": "2026-05-21T21:15:11.490464100Z" + "end_time": "2026-05-21T21:20:08.036204800Z", + "start_time": "2026-05-21T21:20:08.029842400Z" } }, "source": "field = na.Cartesian2dVectorLinearSpace(\n start=-half_field,\n stop=half_field,\n axis=na.Cartesian2dVectorArray(\"fx\", \"fy\"),\n num=5,\n centers=True,\n)", "outputs": [], - "execution_count": 58 + "execution_count": 82 }, { "cell_type": "raw", @@ -316,13 +322,13 @@ "id": "a1b2c3d4e5f60027", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.501575200Z", - "start_time": "2026-05-21T21:15:11.496479600Z" + "end_time": "2026-05-21T21:20:08.041623800Z", + "start_time": "2026-05-21T21:20:08.037205400Z" } }, "source": "grid_input = optika.vectors.ObjectVectorArray(\n wavelength=wavelength,\n field=field,\n pupil=pupil,\n)", "outputs": [], - "execution_count": 59 + "execution_count": 83 }, { "cell_type": "raw", @@ -337,13 +343,13 @@ "id": "a1b2c3d4e5f60029", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.507581800Z", - "start_time": "2026-05-21T21:15:11.502568900Z" + "end_time": "2026-05-21T21:20:08.047841Z", + "start_time": "2026-05-21T21:20:08.042624500Z" } }, "source": "system = optika.systems.SequentialSystem(\n object=source,\n surfaces=[fzp],\n sensor=sensor,\n grid_input=grid_input,\n)", "outputs": [], - "execution_count": 60 + "execution_count": 84 }, { "cell_type": "raw", @@ -351,20 +357,20 @@ "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "Because the internal normalization algorithm in\n:class:`~optika.systems.SequentialSystem` traces rays *backward* through\nthe system to establish the field-of-view scale, and because this\nbackward trace re-applies the grating equation of a transmissive FZP\n(doubling the deflection angle), we must compute the default ray function\ndirectly with physical coordinates and cache the result manually.\n\n.. note::\n\n This is a known limitation when ``object_is_at_infinity=True`` with\n a transmissive grating as the pupil stop. Reflective systems (e.g.\n the prime-focus telescope) are not affected." + "source": "For a transmissive surface used as the pupil stop, the internal\nnormalization algorithm in\n:class:`~optika.systems.SequentialSystem` does not correctly infer\nthe field-of-view extents from a backward raytrace.\nWe therefore compute the default ray function directly with physical\n(non-normalized) coordinates and cache the result manually.\n\n.. note::\n\n This workaround is required whenever a transmissive grating is the\n pupil stop. Purely reflective systems (e.g. the prime-focus telescope)\n are not affected." }, { "cell_type": "code", "id": "a1b2c3d4e5f60031", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.542014200Z", - "start_time": "2026-05-21T21:15:11.508582400Z" + "end_time": "2026-05-21T21:20:08.078666700Z", + "start_time": "2026-05-21T21:20:08.048840900Z" } }, "source": "rays_default = system.rayfunction(\n field=field,\n pupil=pupil,\n normalized_field=False,\n normalized_pupil=False,\n)\nsystem.__dict__[\"rayfunction_default\"] = rays_default", "outputs": [], - "execution_count": 61 + "execution_count": 85 }, { "cell_type": "raw", @@ -379,8 +385,8 @@ "id": "a1b2c3d4e5f60033", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:11.665846200Z", - "start_time": "2026-05-21T21:15:11.543014400Z" + "end_time": "2026-05-21T21:20:08.206331900Z", + "start_time": "2026-05-21T21:20:08.079666900Z" } }, "source": "# Use a coarser, on-axis pupil grid for the layout diagram\npupil_layout = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px\", \"py\"),\n num=5,\n centers=True,\n)\n\nraytrace_layout = system.raytrace(\n field=na.Cartesian2dVectorArray(0 * u.deg, 0 * u.deg),\n pupil=pupil_layout,\n normalized_field=False,\n normalized_pupil=False,\n)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True)\n system.plot(\n ax=ax,\n components=(\"z\", \"y\"),\n color=\"black\",\n zorder=10,\n plot_rays=False,\n )\n na.plt.plot(\n raytrace_layout.outputs.position,\n ax=ax,\n axis=system.axis_surface,\n components=(\"z\", \"y\"),\n color=\"tab:blue\",\n )", @@ -399,7 +405,7 @@ } } ], - "execution_count": 62 + "execution_count": 86 }, { "cell_type": "raw", @@ -414,8 +420,8 @@ "id": "a1b2c3d4e5f60035", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:13.158025Z", - "start_time": "2026-05-21T21:15:11.667839500Z" + "end_time": "2026-05-21T21:20:09.653379100Z", + "start_time": "2026-05-21T21:20:08.208332600Z" } }, "source": "fig, ax = system.spot_diagram()", @@ -434,7 +440,7 @@ } } ], - "execution_count": 63 + "execution_count": 87 }, { "cell_type": "markdown", @@ -448,8 +454,8 @@ "source": "r_airy = (1.22 * wavelength * focal_length / (2 * radius_aperture)).to(u.um)\nr_airy", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:13.236338100Z", - "start_time": "2026-05-21T21:15:13.208457300Z" + "end_time": "2026-05-21T21:20:09.719940Z", + "start_time": "2026-05-21T21:20:09.695711600Z" } }, "outputs": [ @@ -460,12 +466,12 @@ ], "text/latex": "$0.14901429 \\; \\mathrm{\\mu m}$" }, - "execution_count": 64, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], - "execution_count": 64 + "execution_count": 88 }, { "cell_type": "code", @@ -473,8 +479,8 @@ "source": "# Dense on-axis pupil grid — use even num to avoid (0, 0)\npupil_dense = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px_dense\", \"py_dense\"),\n num=50,\n centers=True,\n)\n\nrays_onaxis = system.rayfunction(\n field=na.Cartesian2dVectorArray(0 * u.deg, 0 * u.deg),\n pupil=pupil_dense,\n normalized_field=False,\n normalized_pupil=False,\n).outputs\n\nwhere = rays_onaxis.unvignetted.ndarray\npos_x = rays_onaxis.position.x.to(u.um).ndarray.value # plain float array, in um\npos_y = rays_onaxis.position.y.to(u.um).ndarray.value\n\ncentroid_x = np.mean(pos_x[where])\ncentroid_y = np.mean(pos_y[where])\ndx = pos_x[where] - centroid_x\ndy = pos_y[where] - centroid_y\n\nr_geometric = np.sqrt(np.mean(dx**2 + dy**2)) # um\n\nprint(f\"Airy disk radius (first dark ring): {r_airy:.4f}\")\nprint(f\"Geometric RMS spot radius (on-axis): {r_geometric:.4e} um\")\nprint(f\"Ratio (geometric / Airy): {r_geometric / r_airy.value:.2e}\")", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:13.280031100Z", - "start_time": "2026-05-21T21:15:13.236338100Z" + "end_time": "2026-05-21T21:20:09.767443800Z", + "start_time": "2026-05-21T21:20:09.724939900Z" } }, "outputs": [ @@ -488,7 +494,7 @@ ] } ], - "execution_count": 65 + "execution_count": 89 }, { "cell_type": "code", @@ -496,8 +502,8 @@ "source": "theta = np.linspace(0, 2 * np.pi, 361)\nx_airy = r_airy.value * np.cos(theta)\ny_airy = r_airy.value * np.sin(theta)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True)\n ax.scatter(dx, dy, s=4, alpha=0.4, color=\"tab:blue\",\n label=f\"ray positions (RMS = {r_geometric:.2e} um)\")\n ax.plot(x_airy, y_airy, color=\"tab:orange\", lw=2,\n label=f\"Airy disk ($r$ = {r_airy:.4f})\")\n ax.set_aspect(\"equal\")\n ax.set_xlabel(f\"$x$ ({u.um:latex_inline})\")\n ax.set_ylabel(f\"$y$ ({u.um:latex_inline})\")\n ax.legend()\n ax.set_title(\"On-axis geometric PSF vs. diffraction limit\")", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:13.442886Z", - "start_time": "2026-05-21T21:15:13.281031900Z" + "end_time": "2026-05-21T21:20:09.934300400Z", + "start_time": "2026-05-21T21:20:09.770443Z" } }, "outputs": [ @@ -515,7 +521,7 @@ } } ], - "execution_count": 66 + "execution_count": 90 }, { "cell_type": "markdown", @@ -529,12 +535,26 @@ "source": "# Build the on-axis pupil grid from the 1-D coordinate arrays.\n# Using meshgrid with indexing='ij' guarantees consistent (x, y) pairing\n# and avoids axis-ordering ambiguity in the named_arrays output arrays.\npx_1d = pupil_dense.x.to(u.mm).ndarray.value # (50,) px_dense\npy_1d = pupil_dense.y.to(u.mm).ndarray.value # (50,) py_dense\npx_2d, py_2d = np.meshgrid(px_1d, py_1d, indexing=\"ij\") # (50, 50)\ninside = np.sqrt(px_2d**2 + py_2d**2) <= radius_aperture.to(u.mm).value\n\nwl_design = wavelength.to(u.AA).value # 171\nf0 = focal_length.to(u.mm).value # 1000\n\n# Asymmetric wavelengths so each spot has a distinct radius.\n# Radii: 169 Å → 0.82 mm, 171 Å → 0 (design), 174 Å → 1.23 mm\nwavelengths_plot = [169, 171, 174]\ncolors = [\"tab:blue\", \"tab:green\", \"tab:red\"]\nlabels = [\n f\"169 Å ($f$ = {f0 * wl_design / 169:.0f} mm, $r_\\\\mathrm{{blur}}$ = {70 * abs(1 - 169/wl_design):.2f} mm)\",\n \"171 Å (design)\",\n f\"174 Å ($f$ = {f0 * wl_design / 174:.0f} mm, $r_\\\\mathrm{{blur}}$ = {70 * abs(1 - 174/wl_design):.2f} mm)\",\n]\n\n# Compute max disk radius for axis limits\nr_max = max(70 * abs(1 - wl / wl_design) for wl in wavelengths_plot)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 6))\n for wl_val, color, label in zip(wavelengths_plot, colors, labels):\n f_wl = f0 * wl_design / wl_val # focal length at this wavelength, mm\n scale = 1.0 - f0 / f_wl # defocus magnification (0 at design λ)\n x_det = px_2d[inside] * scale\n y_det = py_2d[inside] * scale\n ax.scatter(x_det, y_det, s=2, alpha=0.5, color=color,\n label=label, rasterized=True)\n margin = 1.15\n ax.set_xlim(-r_max * margin, r_max * margin)\n ax.set_ylim(-r_max * margin, r_max * margin)\n ax.set_aspect(\"equal\")\n ax.legend(markerscale=4, fontsize=9)\n ax.set_xlabel(\"$x$ (mm)\")\n ax.set_ylabel(\"$y$ (mm)\")\n ax.set_title(\"On-axis spot at 171 Å focal plane: chromatic aberration\")", "metadata": { "ExecuteTime": { - "end_time": "2026-05-21T21:15:13.650775200Z", - "start_time": "2026-05-21T21:15:13.448793400Z" + "end_time": "2026-05-21T21:20:10.139717700Z", + "start_time": "2026-05-21T21:20:09.935291700Z" } }, - "outputs": [], - "execution_count": null + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 91 } ], "metadata": { From 0d0c5c9f3d0ac11e44b849e610e41e8fd439f415 Mon Sep 17 00:00:00 2001 From: jacobdparker Date: Fri, 22 May 2026 17:12:32 -0600 Subject: [PATCH 3/3] Strip cell outputs from fzp_focus notebook (nbstripout) Co-Authored-By: Claude Sonnet 4.6 --- docs/tutorials/fzp_focus.ipynb | 896 ++++++++++++++++++++------------- 1 file changed, 539 insertions(+), 357 deletions(-) diff --git a/docs/tutorials/fzp_focus.ipynb b/docs/tutorials/fzp_focus.ipynb index fa4fb1a8..a87f8682 100644 --- a/docs/tutorials/fzp_focus.ipynb +++ b/docs/tutorials/fzp_focus.ipynb @@ -2,559 +2,741 @@ "cells": [ { "cell_type": "raw", - "id": "a1b2c3d4e5f60001", + "id": "0", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "Fresnel Zone Plate (FZP) Telescope Tutorial\n============================================\n\nA `Fresnel Zone Plate (FZP) `_\nis a diffractive optical element that focuses light by encoding the\ninterference pattern of two spherical waves on a flat surface.\nIn :mod:`optika`, an FZP is modeled as a transmissive surface with\n:class:`~optika.rulings.HolographicRulingSpacing` rulings, where the\ntwo recording-beam origins define the focal geometry.\n\nThis tutorial builds a single-element FZP telescope that focuses\ncollimated 171 Å EUV light onto a detector and examines the resulting\nspot diagrams." + "source": [ + "Fresnel Zone Plate (FZP) Telescope Tutorial\n", + "============================================\n", + "\n", + "A `Fresnel Zone Plate (FZP) `_\n", + "is a diffractive optical element that focuses light by encoding the\n", + "interference pattern of two spherical waves on a flat surface.\n", + "In :mod:`optika`, an FZP is modeled as a transmissive surface with\n", + ":class:`~optika.rulings.HolographicRulingSpacing` rulings, where the\n", + "two recording-beam origins define the focal geometry.\n", + "\n", + "This tutorial builds a single-element FZP telescope that focuses\n", + "collimated 171 Å EUV light onto a detector and examines the resulting\n", + "spot diagrams." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60002", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.933944800Z", - "start_time": "2026-05-21T21:20:07.923014300Z" - } - }, - "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport astropy.units as u\nimport astropy.visualization\nimport named_arrays as na\nimport optika", + "execution_count": null, + "id": "1", + "metadata": {}, "outputs": [], - "execution_count": 68 + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import astropy.units as u\n", + "import astropy.visualization\n", + "import named_arrays as na\n", + "import optika" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60003", + "id": "2", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "FZP\n---\n\nWe start by defining the aperture radius of the FZP" + "source": [ + "FZP\n", + "---\n", + "\n", + "We start by defining the aperture radius of the FZP" + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60004", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.944487400Z", - "start_time": "2026-05-21T21:20:07.935565600Z" - } - }, - "source": "radius_aperture = 70 * u.mm", + "execution_count": null, + "id": "3", + "metadata": {}, "outputs": [], - "execution_count": 69 + "source": [ + "radius_aperture = 70 * u.mm" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60005", + "id": "4", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "and the focal length" + "source": [ + "and the focal length" + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60006", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.954534300Z", - "start_time": "2026-05-21T21:20:07.945494400Z" - } - }, - "source": "focal_length = 1000 * u.mm", + "execution_count": null, + "id": "5", + "metadata": {}, "outputs": [], - "execution_count": 70 + "source": [ + "focal_length = 1000 * u.mm" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60007", + "id": "6", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "the design wavelength is" + "source": [ + "the design wavelength is" + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60008", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.963614100Z", - "start_time": "2026-05-21T21:20:07.958535500Z" - } - }, - "source": "wavelength = 171 * u.AA", + "execution_count": null, + "id": "7", + "metadata": {}, "outputs": [], - "execution_count": 71 + "source": [ + "wavelength = 171 * u.AA" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60009", + "id": "8", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "Holographic recording geometry\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nAn FZP is equivalent to a holographic grating whose rulings were\nrecorded by interfering two coherent beams.\nLet :math:`f` denote the focal length.\nTo focus incoming collimated light (a plane wave from :math:`z = -\\infty`)\nonto a focal point at :math:`z = +f`, we choose:\n\n- **Recording beam 1** (``x1``): originates very far away on the optical\n axis, representing a plane wave. With ``is_diverging_1=True`` and\n :math:`x_1 \\to -\\infty`, the unit vectors from :math:`x_1` to every\n point on the surface become parallel, so the ruling pattern is\n identical to that produced by a true plane wave. We place ``x1`` at\n 1 AU (the Earth–Sun distance) so that the residual focal-length shift\n :math:`\\delta f = f^2 / d_{x_1} = (1000\\ \\mathrm{mm})^2 /\n (1.496 \\times 10^{14}\\ \\mathrm{mm}) \\approx 6.7\\ \\mathrm{pm}`\n is negligible compared to the diffraction-limited depth of focus\n :math:`\\sim\\lambda (f/D)^2 \\approx 32\\ \\mu\\mathrm{m}`.\n- **Recording beam 2** (``x2``): converges *to* the desired focal point\n at :math:`z = +f`. ``is_diverging_2=False`` means rays are directed\n toward :math:`x_2`, i.e. the second recording beam is a converging\n spherical wave.\n\nThe :class:`~optika.rulings.HolographicRulingSpacing` class computes\nthe spatially-varying ruling vector :math:`\\mathbf{d}(\\mathbf{r})` from\nthese two source points." + "source": [ + "Holographic recording geometry\n", + "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "\n", + "An FZP is equivalent to a holographic grating whose rulings were\n", + "recorded by interfering two coherent beams.\n", + "Let :math:`f` denote the focal length.\n", + "To focus incoming collimated light (a plane wave from :math:`z = -\\infty`)\n", + "onto a focal point at :math:`z = +f`, we choose:\n", + "\n", + "- **Recording beam 1** (``x1``): originates very far away on the optical\n", + " axis, representing a plane wave. With ``is_diverging_1=True`` and\n", + " :math:`x_1 \\to -\\infty`, the unit vectors from :math:`x_1` to every\n", + " point on the surface become parallel, so the ruling pattern is\n", + " identical to that produced by a true plane wave. We place ``x1`` at\n", + " 1 AU (the Earth–Sun distance) so that the residual focal-length shift\n", + " :math:`\\delta f = f^2 / d_{x_1} = (1000\\ \\mathrm{mm})^2 /\n", + " (1.496 \\times 10^{14}\\ \\mathrm{mm}) \\approx 6.7\\ \\mathrm{pm}`\n", + " is negligible compared to the diffraction-limited depth of focus\n", + " :math:`\\sim\\lambda (f/D)^2 \\approx 32\\ \\mu\\mathrm{m}`.\n", + "- **Recording beam 2** (``x2``): converges *to* the desired focal point\n", + " at :math:`z = +f`. ``is_diverging_2=False`` means rays are directed\n", + " toward :math:`x_2`, i.e. the second recording beam is a converging\n", + " spherical wave.\n", + "\n", + "The :class:`~optika.rulings.HolographicRulingSpacing` class computes\n", + "the spatially-varying ruling vector :math:`\\mathbf{d}(\\mathbf{r})` from\n", + "these two source points." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60010", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.970067200Z", - "start_time": "2026-05-21T21:20:07.964614100Z" - } - }, - "source": "spacing = optika.rulings.HolographicRulingSpacing(\n x1=na.Cartesian3dVectorArray(0, 0, -1) * (1 * u.au).to(u.mm),\n x2=na.Cartesian3dVectorArray(\n x=0 * u.mm,\n y=0 * u.mm,\n z=focal_length,\n ),\n wavelength=wavelength,\n is_diverging_1=True,\n is_diverging_2=False,\n)", + "execution_count": null, + "id": "9", + "metadata": {}, "outputs": [], - "execution_count": 72 + "source": [ + "spacing = optika.rulings.HolographicRulingSpacing(\n", + " x1=na.Cartesian3dVectorArray(0, 0, -1) * (1 * u.au).to(u.mm),\n", + " x2=na.Cartesian3dVectorArray(\n", + " x=0 * u.mm,\n", + " y=0 * u.mm,\n", + " z=focal_length,\n", + " ),\n", + " wavelength=wavelength,\n", + " is_diverging_1=True,\n", + " is_diverging_2=False,\n", + ")" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60011", + "id": "10", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "We wrap the spacing in an ideal :class:`~optika.rulings.Rulings` instance.\n:class:`~optika.rulings.Rulings` assumes perfect diffraction efficiency\nin the specified order, which is appropriate for an ideal FZP." + "source": [ + "We wrap the spacing in an ideal :class:`~optika.rulings.Rulings` instance.\n", + ":class:`~optika.rulings.Rulings` assumes perfect diffraction efficiency\n", + "in the specified order, which is appropriate for an ideal FZP." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60012", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.975938700Z", - "start_time": "2026-05-21T21:20:07.971067200Z" - } - }, - "source": "rulings_fzp = optika.rulings.Rulings(\n spacing=spacing,\n diffraction_order=1,\n)", + "execution_count": null, + "id": "11", + "metadata": {}, "outputs": [], - "execution_count": 73 + "source": [ + "rulings_fzp = optika.rulings.Rulings(\n", + " spacing=spacing,\n", + " diffraction_order=1,\n", + ")" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60013", + "id": "12", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "We assemble the FZP as a flat, transmissive :class:`~optika.surfaces.Surface`.\nNo sag profile is needed (the surface is flat) and no mirror material is\nassigned (the default :class:`~optika.materials.Vacuum` material makes the\nsurface transmissive, not reflective).\nSetting ``is_pupil_stop=True`` marks the FZP aperture as the entrance pupil." + "source": [ + "We assemble the FZP as a flat, transmissive :class:`~optika.surfaces.Surface`.\n", + "No sag profile is needed (the surface is flat) and no mirror material is\n", + "assigned (the default :class:`~optika.materials.Vacuum` material makes the\n", + "surface transmissive, not reflective).\n", + "Setting ``is_pupil_stop=True`` marks the FZP aperture as the entrance pupil." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60014", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.982426400Z", - "start_time": "2026-05-21T21:20:07.976952100Z" - } - }, - "source": "fzp = optika.surfaces.Surface(\n name=\"FZP\",\n aperture=optika.apertures.CircularAperture(radius_aperture),\n rulings=rulings_fzp,\n is_pupil_stop=True,\n)", + "execution_count": null, + "id": "13", + "metadata": {}, "outputs": [], - "execution_count": 74 + "source": [ + "fzp = optika.surfaces.Surface(\n", + " name=\"FZP\",\n", + " aperture=optika.apertures.CircularAperture(radius_aperture),\n", + " rulings=rulings_fzp,\n", + " is_pupil_stop=True,\n", + ")" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60015", + "id": "14", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "Sensor\n------\n\nIf the size of each pixel in the sensor is" + "source": [ + "Sensor\n", + "------\n", + "\n", + "If the size of each pixel in the sensor is" + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60016", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.987621600Z", - "start_time": "2026-05-21T21:20:07.982426400Z" - } - }, - "source": "width_pixel = 13 * u.um", + "execution_count": null, + "id": "15", + "metadata": {}, "outputs": [], - "execution_count": 75 + "source": [ + "width_pixel = 13 * u.um" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60017", + "id": "16", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "the number of pixels along each axis is" + "source": [ + "the number of pixels along each axis is" + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60018", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.992860600Z", - "start_time": "2026-05-21T21:20:07.988620700Z" - } - }, - "source": "num_pixel = na.Cartesian2dVectorArray(512, 512)", + "execution_count": null, + "id": "17", + "metadata": {}, "outputs": [], - "execution_count": 76 + "source": [ + "num_pixel = na.Cartesian2dVectorArray(512, 512)" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60019", + "id": "18", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "The sensor is placed at the focal plane, one focal length downstream\nof the FZP.\nUnlike a reflective system (such as the prime-focus telescope, where the\nreflected beam travels in the :math:`-z` direction and the sensor must\nbe rotated 180°), here the beam continues in the :math:`+z` direction\nafter transmission through the FZP, so no rotation is needed.\nSetting ``is_field_stop=True`` marks the sensor as the field stop." + "source": [ + "The sensor is placed at the focal plane, one focal length downstream\n", + "of the FZP.\n", + "Unlike a reflective system (such as the prime-focus telescope, where the\n", + "reflected beam travels in the :math:`-z` direction and the sensor must\n", + "be rotated 180°), here the beam continues in the :math:`+z` direction\n", + "after transmission through the FZP, so no rotation is needed.\n", + "Setting ``is_field_stop=True`` marks the sensor as the field stop." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60020", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:07.999086500Z", - "start_time": "2026-05-21T21:20:07.993868400Z" - } - }, - "source": "sensor = optika.sensors.ImagingSensor(\n name=\"sensor\",\n width_pixel=width_pixel,\n axis_pixel=na.Cartesian2dVectorArray(\"detector_x\", \"detector_y\"),\n num_pixel=num_pixel,\n transformation=na.transformations.Cartesian3dTranslation(z=focal_length),\n is_field_stop=True,\n)", + "execution_count": null, + "id": "19", + "metadata": {}, "outputs": [], - "execution_count": 77 + "source": [ + "sensor = optika.sensors.ImagingSensor(\n", + " name=\"sensor\",\n", + " width_pixel=width_pixel,\n", + " axis_pixel=na.Cartesian2dVectorArray(\"detector_x\", \"detector_y\"),\n", + " num_pixel=num_pixel,\n", + " transformation=na.transformations.Cartesian3dTranslation(z=focal_length),\n", + " is_field_stop=True,\n", + ")" + ] }, { "cell_type": "markdown", - "id": "7e17d2a2", - "source": "## Source\n\nWe model the source as a solar disk — a circular patch of sky subtending\n0.5° full diameter (0.25° half-angle). The aperture is specified as a\ndimensionless cosine so that the surface is treated as being at infinity\n(a directional source, not a physical aperture at a finite distance).", - "metadata": {} + "id": "20", + "metadata": {}, + "source": [ + "## Source\n", + "\n", + "We model the source as a solar disk — a circular patch of sky subtending\n", + "0.5° full diameter (0.25° half-angle). The aperture is specified as a\n", + "dimensionless cosine so that the surface is treated as being at infinity\n", + "(a directional source, not a physical aperture at a finite distance)." + ] }, { "cell_type": "code", - "id": "3f2e63a9", - "source": "radius_sun = 0.25 * u.deg # 0.5 deg full angular diameter", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.004154500Z", - "start_time": "2026-05-21T21:20:07.999086500Z" - } - }, + "execution_count": null, + "id": "21", + "metadata": {}, "outputs": [], - "execution_count": 78 + "source": [ + "radius_sun = 0.25 * u.deg # 0.5 deg full angular diameter" + ] }, { "cell_type": "code", - "id": "a6af380e", - "source": "source = optika.surfaces.Surface(\n name=\"solar disk\",\n aperture=optika.apertures.CircularAperture(\n radius=np.cos(radius_sun),\n ),\n transformation=na.transformations.Cartesian3dTranslation(\n z=-200 * u.mm,\n ),\n)", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.009536700Z", - "start_time": "2026-05-21T21:20:08.005160300Z" - } - }, + "execution_count": null, + "id": "22", + "metadata": {}, "outputs": [], - "execution_count": 79 + "source": [ + "source = optika.surfaces.Surface(\n", + " name=\"solar disk\",\n", + " aperture=optika.apertures.CircularAperture(\n", + " radius=np.cos(radius_sun),\n", + " ),\n", + " transformation=na.transformations.Cartesian3dTranslation(\n", + " z=-200 * u.mm,\n", + " ),\n", + ")" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60021", + "id": "23", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "Input rays\n----------\n\nFor a transmissive FZP (object at infinity), we specify the input rays\nusing **physical** coordinates rather than normalized ones.\nThe pupil grid gives physical positions on the FZP aperture in mm." + "source": [ + "Input rays\n", + "----------\n", + "\n", + "For a transmissive FZP (object at infinity), we specify the input rays\n", + "using **physical** coordinates rather than normalized ones.\n", + "The pupil grid gives physical positions on the FZP aperture in mm." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60022", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.014896900Z", - "start_time": "2026-05-21T21:20:08.010535900Z" - } - }, - "source": "# Use an even number of samples so the grid never lands exactly at the\n# FZP center (0, 0), where the ruling vector degenerates to zero.\npupil = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px\", \"py\"),\n num=10,\n centers=True,\n)", + "execution_count": null, + "id": "24", + "metadata": {}, "outputs": [], - "execution_count": 80 + "source": [ + "# Use an even number of samples so the grid never lands exactly at the\n", + "# FZP center (0, 0), where the ruling vector degenerates to zero.\n", + "pupil = na.Cartesian2dVectorLinearSpace(\n", + " start=-radius_aperture,\n", + " stop=radius_aperture,\n", + " axis=na.Cartesian2dVectorArray(\"px\", \"py\"),\n", + " num=10,\n", + " centers=True,\n", + ")" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60023", + "id": "25", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "The field grid gives the angular directions of the incoming collimated\nbeams in degrees.\nThe half-field angle is set by the sensor size:\n:math:`\\theta_{1/2} = \\arctan(N_\\mathrm{pix} \\, w_\\mathrm{pix} / 2 \\, f)`." + "source": [ + "The field grid gives the angular directions of the incoming collimated\n", + "beams in degrees.\n", + "The half-field angle is set by the sensor size:\n", + ":math:`\\theta_{1/2} = \\arctan(N_\\mathrm{pix} \\, w_\\mathrm{pix} / 2 \\, f)`." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60024", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.028842900Z", - "start_time": "2026-05-21T21:20:08.015899300Z" - } - }, - "source": "half_field = np.arctan(\n (num_pixel.x * width_pixel / (2 * focal_length)).to(u.dimensionless_unscaled).value\n) * u.rad\nhalf_field = half_field.to(u.deg)\nhalf_field", - "outputs": [ - { - "data": { - "text/plain": [ - "" - ], - "text/latex": "$0.19067965\\mathrm{{}^{\\circ}}$" - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 81 + "execution_count": null, + "id": "26", + "metadata": {}, + "outputs": [], + "source": [ + "half_field = np.arctan(\n", + " (num_pixel.x * width_pixel / (2 * focal_length)).to(u.dimensionless_unscaled).value\n", + ") * u.rad\n", + "half_field = half_field.to(u.deg)\n", + "half_field" + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60025", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.036204800Z", - "start_time": "2026-05-21T21:20:08.029842400Z" - } - }, - "source": "field = na.Cartesian2dVectorLinearSpace(\n start=-half_field,\n stop=half_field,\n axis=na.Cartesian2dVectorArray(\"fx\", \"fy\"),\n num=5,\n centers=True,\n)", + "execution_count": null, + "id": "27", + "metadata": {}, "outputs": [], - "execution_count": 82 + "source": [ + "field = na.Cartesian2dVectorLinearSpace(\n", + " start=-half_field,\n", + " stop=half_field,\n", + " axis=na.Cartesian2dVectorArray(\"fx\", \"fy\"),\n", + " num=5,\n", + " centers=True,\n", + ")" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60026", + "id": "28", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "We combine the field, pupil, and wavelength into an\n:class:`~optika.vectors.ObjectVectorArray`." + "source": [ + "We combine the field, pupil, and wavelength into an\n", + ":class:`~optika.vectors.ObjectVectorArray`." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60027", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.041623800Z", - "start_time": "2026-05-21T21:20:08.037205400Z" - } - }, - "source": "grid_input = optika.vectors.ObjectVectorArray(\n wavelength=wavelength,\n field=field,\n pupil=pupil,\n)", + "execution_count": null, + "id": "29", + "metadata": {}, "outputs": [], - "execution_count": 83 + "source": [ + "grid_input = optika.vectors.ObjectVectorArray(\n", + " wavelength=wavelength,\n", + " field=field,\n", + " pupil=pupil,\n", + ")" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60028", + "id": "30", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "Building the system\n-------------------\n\n:class:`~optika.systems.SequentialSystem` assembles the optical surfaces\nand the input ray grid.\nNo explicit ``object`` surface is provided, so the object is treated as\nbeing at infinity (collimated input).\nThe FZP is the pupil stop and the sensor is the field stop." + "source": [ + "Building the system\n", + "-------------------\n", + "\n", + ":class:`~optika.systems.SequentialSystem` assembles the optical surfaces\n", + "and the input ray grid.\n", + "No explicit ``object`` surface is provided, so the object is treated as\n", + "being at infinity (collimated input).\n", + "The FZP is the pupil stop and the sensor is the field stop." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60029", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.047841Z", - "start_time": "2026-05-21T21:20:08.042624500Z" - } - }, - "source": "system = optika.systems.SequentialSystem(\n object=source,\n surfaces=[fzp],\n sensor=sensor,\n grid_input=grid_input,\n)", + "execution_count": null, + "id": "31", + "metadata": {}, "outputs": [], - "execution_count": 84 + "source": [ + "system = optika.systems.SequentialSystem(\n", + " object=source,\n", + " surfaces=[fzp],\n", + " sensor=sensor,\n", + " grid_input=grid_input,\n", + ")" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60030", + "id": "32", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "For a transmissive surface used as the pupil stop, the internal\nnormalization algorithm in\n:class:`~optika.systems.SequentialSystem` does not correctly infer\nthe field-of-view extents from a backward raytrace.\nWe therefore compute the default ray function directly with physical\n(non-normalized) coordinates and cache the result manually.\n\n.. note::\n\n This workaround is required whenever a transmissive grating is the\n pupil stop. Purely reflective systems (e.g. the prime-focus telescope)\n are not affected." + "source": [ + "For a transmissive surface used as the pupil stop, the internal\n", + "normalization algorithm in\n", + ":class:`~optika.systems.SequentialSystem` does not correctly infer\n", + "the field-of-view extents from a backward raytrace.\n", + "We therefore compute the default ray function directly with physical\n", + "(non-normalized) coordinates and cache the result manually.\n", + "\n", + ".. note::\n", + "\n", + " This workaround is required whenever a transmissive grating is the\n", + " pupil stop. Purely reflective systems (e.g. the prime-focus telescope)\n", + " are not affected." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60031", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.078666700Z", - "start_time": "2026-05-21T21:20:08.048840900Z" - } - }, - "source": "rays_default = system.rayfunction(\n field=field,\n pupil=pupil,\n normalized_field=False,\n normalized_pupil=False,\n)\nsystem.__dict__[\"rayfunction_default\"] = rays_default", + "execution_count": null, + "id": "33", + "metadata": {}, "outputs": [], - "execution_count": 85 + "source": [ + "rays_default = system.rayfunction(\n", + " field=field,\n", + " pupil=pupil,\n", + " normalized_field=False,\n", + " normalized_pupil=False,\n", + ")\n", + "system.__dict__[\"rayfunction_default\"] = rays_default" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60032", + "id": "34", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "We can plot the optical layout using\n:meth:`~optika.systems.SequentialSystem.plot`.\nTo avoid the same normalization issue affecting the displayed rays,\nwe plot the surfaces only and overlay the ray paths from a separate\n:meth:`~optika.systems.SequentialSystem.raytrace` call." + "source": [ + "We can plot the optical layout using\n", + ":meth:`~optika.systems.SequentialSystem.plot`.\n", + "To avoid the same normalization issue affecting the displayed rays,\n", + "we plot the surfaces only and overlay the ray paths from a separate\n", + ":meth:`~optika.systems.SequentialSystem.raytrace` call." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60033", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:08.206331900Z", - "start_time": "2026-05-21T21:20:08.079666900Z" - } - }, - "source": "# Use a coarser, on-axis pupil grid for the layout diagram\npupil_layout = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px\", \"py\"),\n num=5,\n centers=True,\n)\n\nraytrace_layout = system.raytrace(\n field=na.Cartesian2dVectorArray(0 * u.deg, 0 * u.deg),\n pupil=pupil_layout,\n normalized_field=False,\n normalized_pupil=False,\n)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True)\n system.plot(\n ax=ax,\n components=(\"z\", \"y\"),\n color=\"black\",\n zorder=10,\n plot_rays=False,\n )\n na.plt.plot(\n raytrace_layout.outputs.position,\n ax=ax,\n axis=system.axis_surface,\n components=(\"z\", \"y\"),\n color=\"tab:blue\",\n )", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 86 + "execution_count": null, + "id": "35", + "metadata": {}, + "outputs": [], + "source": [ + "# Use a coarser, on-axis pupil grid for the layout diagram\n", + "pupil_layout = na.Cartesian2dVectorLinearSpace(\n", + " start=-radius_aperture,\n", + " stop=radius_aperture,\n", + " axis=na.Cartesian2dVectorArray(\"px\", \"py\"),\n", + " num=5,\n", + " centers=True,\n", + ")\n", + "\n", + "raytrace_layout = system.raytrace(\n", + " field=na.Cartesian2dVectorArray(0 * u.deg, 0 * u.deg),\n", + " pupil=pupil_layout,\n", + " normalized_field=False,\n", + " normalized_pupil=False,\n", + ")\n", + "\n", + "with astropy.visualization.quantity_support():\n", + " fig, ax = plt.subplots(constrained_layout=True)\n", + " system.plot(\n", + " ax=ax,\n", + " components=(\"z\", \"y\"),\n", + " color=\"black\",\n", + " zorder=10,\n", + " plot_rays=False,\n", + " )\n", + " na.plt.plot(\n", + " raytrace_layout.outputs.position,\n", + " ax=ax,\n", + " axis=system.axis_surface,\n", + " components=(\"z\", \"y\"),\n", + " color=\"tab:blue\",\n", + " )" + ] }, { "cell_type": "raw", - "id": "a1b2c3d4e5f60034", + "id": "36", "metadata": { "raw_mimetype": "text/restructuredtext" }, - "source": "Spot Diagrams\n-------------\n\nWe can plot a spot diagram for each field angle using the\n:meth:`~optika.systems.SequentialSystem.spot_diagram` method.\nThe on-axis field point (center of the grid) should show a tight\nspot, while off-axis points will exhibit the coma and astigmatism\nthat are characteristic of a single on-axis zone plate." + "source": [ + "Spot Diagrams\n", + "-------------\n", + "\n", + "We can plot a spot diagram for each field angle using the\n", + ":meth:`~optika.systems.SequentialSystem.spot_diagram` method.\n", + "The on-axis field point (center of the grid) should show a tight\n", + "spot, while off-axis points will exhibit the coma and astigmatism\n", + "that are characteristic of a single on-axis zone plate." + ] }, { "cell_type": "code", - "id": "a1b2c3d4e5f60035", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:09.653379100Z", - "start_time": "2026-05-21T21:20:08.208332600Z" - } - }, - "source": "fig, ax = system.spot_diagram()", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 87 + "execution_count": null, + "id": "37", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = system.spot_diagram()" + ] }, { "cell_type": "markdown", - "id": "e573f298", - "source": "## Diffraction-limited performance\n\nAn ideal FZP focuses on-axis collimated light to a single geometric point — the only resolution limit is diffraction. The radius of the first dark ring of the Airy disk sets the diffraction limit:\n\n$$r_\\mathrm{Airy} = \\frac{1.22\\,\\lambda\\,f}{D}$$\n\nWe verify this by tracing a dense on-axis pupil grid and comparing the geometric RMS spot radius to $r_\\mathrm{Airy}$.", - "metadata": {} + "id": "38", + "metadata": {}, + "source": [ + "## Diffraction-limited performance\n", + "\n", + "An ideal FZP focuses on-axis collimated light to a single geometric point — the only resolution limit is diffraction. The radius of the first dark ring of the Airy disk sets the diffraction limit:\n", + "\n", + "$$r_\\mathrm{Airy} = \\frac{1.22\\,\\lambda\\,f}{D}$$\n", + "\n", + "We verify this by tracing a dense on-axis pupil grid and comparing the geometric RMS spot radius to $r_\\mathrm{Airy}$." + ] }, { "cell_type": "code", - "id": "296fba5a", - "source": "r_airy = (1.22 * wavelength * focal_length / (2 * radius_aperture)).to(u.um)\nr_airy", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:09.719940Z", - "start_time": "2026-05-21T21:20:09.695711600Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ], - "text/latex": "$0.14901429 \\; \\mathrm{\\mu m}$" - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 88 + "execution_count": null, + "id": "39", + "metadata": {}, + "outputs": [], + "source": [ + "r_airy = (1.22 * wavelength * focal_length / (2 * radius_aperture)).to(u.um)\n", + "r_airy" + ] }, { "cell_type": "code", - "id": "3563e97c", - "source": "# Dense on-axis pupil grid — use even num to avoid (0, 0)\npupil_dense = na.Cartesian2dVectorLinearSpace(\n start=-radius_aperture,\n stop=radius_aperture,\n axis=na.Cartesian2dVectorArray(\"px_dense\", \"py_dense\"),\n num=50,\n centers=True,\n)\n\nrays_onaxis = system.rayfunction(\n field=na.Cartesian2dVectorArray(0 * u.deg, 0 * u.deg),\n pupil=pupil_dense,\n normalized_field=False,\n normalized_pupil=False,\n).outputs\n\nwhere = rays_onaxis.unvignetted.ndarray\npos_x = rays_onaxis.position.x.to(u.um).ndarray.value # plain float array, in um\npos_y = rays_onaxis.position.y.to(u.um).ndarray.value\n\ncentroid_x = np.mean(pos_x[where])\ncentroid_y = np.mean(pos_y[where])\ndx = pos_x[where] - centroid_x\ndy = pos_y[where] - centroid_y\n\nr_geometric = np.sqrt(np.mean(dx**2 + dy**2)) # um\n\nprint(f\"Airy disk radius (first dark ring): {r_airy:.4f}\")\nprint(f\"Geometric RMS spot radius (on-axis): {r_geometric:.4e} um\")\nprint(f\"Ratio (geometric / Airy): {r_geometric / r_airy.value:.2e}\")", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:09.767443800Z", - "start_time": "2026-05-21T21:20:09.724939900Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Airy disk radius (first dark ring): 0.1490 um\n", - "Geometric RMS spot radius (on-axis): 3.3354e-07 um\n", - "Ratio (geometric / Airy): 2.24e-06\n" - ] - } - ], - "execution_count": 89 + "execution_count": null, + "id": "40", + "metadata": {}, + "outputs": [], + "source": [ + "# Dense on-axis pupil grid — use even num to avoid (0, 0)\n", + "pupil_dense = na.Cartesian2dVectorLinearSpace(\n", + " start=-radius_aperture,\n", + " stop=radius_aperture,\n", + " axis=na.Cartesian2dVectorArray(\"px_dense\", \"py_dense\"),\n", + " num=50,\n", + " centers=True,\n", + ")\n", + "\n", + "rays_onaxis = system.rayfunction(\n", + " field=na.Cartesian2dVectorArray(0 * u.deg, 0 * u.deg),\n", + " pupil=pupil_dense,\n", + " normalized_field=False,\n", + " normalized_pupil=False,\n", + ").outputs\n", + "\n", + "where = rays_onaxis.unvignetted.ndarray\n", + "pos_x = rays_onaxis.position.x.to(u.um).ndarray.value # plain float array, in um\n", + "pos_y = rays_onaxis.position.y.to(u.um).ndarray.value\n", + "\n", + "centroid_x = np.mean(pos_x[where])\n", + "centroid_y = np.mean(pos_y[where])\n", + "dx = pos_x[where] - centroid_x\n", + "dy = pos_y[where] - centroid_y\n", + "\n", + "r_geometric = np.sqrt(np.mean(dx**2 + dy**2)) # um\n", + "\n", + "print(f\"Airy disk radius (first dark ring): {r_airy:.4f}\")\n", + "print(f\"Geometric RMS spot radius (on-axis): {r_geometric:.4e} um\")\n", + "print(f\"Ratio (geometric / Airy): {r_geometric / r_airy.value:.2e}\")" + ] }, { "cell_type": "code", - "id": "4fe5afa4", - "source": "theta = np.linspace(0, 2 * np.pi, 361)\nx_airy = r_airy.value * np.cos(theta)\ny_airy = r_airy.value * np.sin(theta)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True)\n ax.scatter(dx, dy, s=4, alpha=0.4, color=\"tab:blue\",\n label=f\"ray positions (RMS = {r_geometric:.2e} um)\")\n ax.plot(x_airy, y_airy, color=\"tab:orange\", lw=2,\n label=f\"Airy disk ($r$ = {r_airy:.4f})\")\n ax.set_aspect(\"equal\")\n ax.set_xlabel(f\"$x$ ({u.um:latex_inline})\")\n ax.set_ylabel(f\"$y$ ({u.um:latex_inline})\")\n ax.legend()\n ax.set_title(\"On-axis geometric PSF vs. diffraction limit\")", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:09.934300400Z", - "start_time": "2026-05-21T21:20:09.770443Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 90 + "execution_count": null, + "id": "41", + "metadata": {}, + "outputs": [], + "source": [ + "theta = np.linspace(0, 2 * np.pi, 361)\n", + "x_airy = r_airy.value * np.cos(theta)\n", + "y_airy = r_airy.value * np.sin(theta)\n", + "\n", + "with astropy.visualization.quantity_support():\n", + " fig, ax = plt.subplots(constrained_layout=True)\n", + " ax.scatter(dx, dy, s=4, alpha=0.4, color=\"tab:blue\",\n", + " label=f\"ray positions (RMS = {r_geometric:.2e} um)\")\n", + " ax.plot(x_airy, y_airy, color=\"tab:orange\", lw=2,\n", + " label=f\"Airy disk ($r$ = {r_airy:.4f})\")\n", + " ax.set_aspect(\"equal\")\n", + " ax.set_xlabel(f\"$x$ ({u.um:latex_inline})\")\n", + " ax.set_ylabel(f\"$y$ ({u.um:latex_inline})\")\n", + " ax.legend()\n", + " ax.set_title(\"On-axis geometric PSF vs. diffraction limit\")" + ] }, { "cell_type": "markdown", - "id": "07e5586a", - "source": "## Chromatic aberration\n\nZone plates are notorious for strong chromatic aberration: focal length scales *inversely* with wavelength,\n\n$$f(\\lambda) = f_0 \\frac{\\lambda_0}{\\lambda}$$\n\nwhere $\\lambda_0 = 171$ Å and $f_0 = 1000$ mm are the design values.\nA wavelength offset $\\Delta\\lambda$ produces a defocused disk of radius\n\n$$r_\\mathrm{blur} = R_\\mathrm{pupil}\\left|1 - \\frac{\\lambda}{\\lambda_0}\\right| \\approx R_\\mathrm{pupil} \\frac{|\\Delta\\lambda|}{\\lambda_0}$$\n\nat the design focal plane.\nWe use *asymmetric* offsets (−2 Å and +3 Å from 171 Å) so the three spots\nhave distinct radii (0, 0.82 mm, and 1.23 mm) and are visually separable.", - "metadata": {} + "id": "42", + "metadata": {}, + "source": [ + "## Chromatic aberration\n", + "\n", + "Zone plates are notorious for strong chromatic aberration: focal length scales *inversely* with wavelength,\n", + "\n", + "$$f(\\lambda) = f_0 \\frac{\\lambda_0}{\\lambda}$$\n", + "\n", + "where $\\lambda_0 = 171$ Å and $f_0 = 1000$ mm are the design values.\n", + "A wavelength offset $\\Delta\\lambda$ produces a defocused disk of radius\n", + "\n", + "$$r_\\mathrm{blur} = R_\\mathrm{pupil}\\left|1 - \\frac{\\lambda}{\\lambda_0}\\right| \\approx R_\\mathrm{pupil} \\frac{|\\Delta\\lambda|}{\\lambda_0}$$\n", + "\n", + "at the design focal plane.\n", + "We use *asymmetric* offsets (−2 Å and +3 Å from 171 Å) so the three spots\n", + "have distinct radii (0, 0.82 mm, and 1.23 mm) and are visually separable." + ] }, { "cell_type": "code", - "id": "acd9a4c0", - "source": "# Build the on-axis pupil grid from the 1-D coordinate arrays.\n# Using meshgrid with indexing='ij' guarantees consistent (x, y) pairing\n# and avoids axis-ordering ambiguity in the named_arrays output arrays.\npx_1d = pupil_dense.x.to(u.mm).ndarray.value # (50,) px_dense\npy_1d = pupil_dense.y.to(u.mm).ndarray.value # (50,) py_dense\npx_2d, py_2d = np.meshgrid(px_1d, py_1d, indexing=\"ij\") # (50, 50)\ninside = np.sqrt(px_2d**2 + py_2d**2) <= radius_aperture.to(u.mm).value\n\nwl_design = wavelength.to(u.AA).value # 171\nf0 = focal_length.to(u.mm).value # 1000\n\n# Asymmetric wavelengths so each spot has a distinct radius.\n# Radii: 169 Å → 0.82 mm, 171 Å → 0 (design), 174 Å → 1.23 mm\nwavelengths_plot = [169, 171, 174]\ncolors = [\"tab:blue\", \"tab:green\", \"tab:red\"]\nlabels = [\n f\"169 Å ($f$ = {f0 * wl_design / 169:.0f} mm, $r_\\\\mathrm{{blur}}$ = {70 * abs(1 - 169/wl_design):.2f} mm)\",\n \"171 Å (design)\",\n f\"174 Å ($f$ = {f0 * wl_design / 174:.0f} mm, $r_\\\\mathrm{{blur}}$ = {70 * abs(1 - 174/wl_design):.2f} mm)\",\n]\n\n# Compute max disk radius for axis limits\nr_max = max(70 * abs(1 - wl / wl_design) for wl in wavelengths_plot)\n\nwith astropy.visualization.quantity_support():\n fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 6))\n for wl_val, color, label in zip(wavelengths_plot, colors, labels):\n f_wl = f0 * wl_design / wl_val # focal length at this wavelength, mm\n scale = 1.0 - f0 / f_wl # defocus magnification (0 at design λ)\n x_det = px_2d[inside] * scale\n y_det = py_2d[inside] * scale\n ax.scatter(x_det, y_det, s=2, alpha=0.5, color=color,\n label=label, rasterized=True)\n margin = 1.15\n ax.set_xlim(-r_max * margin, r_max * margin)\n ax.set_ylim(-r_max * margin, r_max * margin)\n ax.set_aspect(\"equal\")\n ax.legend(markerscale=4, fontsize=9)\n ax.set_xlabel(\"$x$ (mm)\")\n ax.set_ylabel(\"$y$ (mm)\")\n ax.set_title(\"On-axis spot at 171 Å focal plane: chromatic aberration\")", - "metadata": { - "ExecuteTime": { - "end_time": "2026-05-21T21:20:10.139717700Z", - "start_time": "2026-05-21T21:20:09.935291700Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 91 + "execution_count": null, + "id": "43", + "metadata": {}, + "outputs": [], + "source": [ + "# Build the on-axis pupil grid from the 1-D coordinate arrays.\n", + "# Using meshgrid with indexing='ij' guarantees consistent (x, y) pairing\n", + "# and avoids axis-ordering ambiguity in the named_arrays output arrays.\n", + "px_1d = pupil_dense.x.to(u.mm).ndarray.value # (50,) px_dense\n", + "py_1d = pupil_dense.y.to(u.mm).ndarray.value # (50,) py_dense\n", + "px_2d, py_2d = np.meshgrid(px_1d, py_1d, indexing=\"ij\") # (50, 50)\n", + "inside = np.sqrt(px_2d**2 + py_2d**2) <= radius_aperture.to(u.mm).value\n", + "\n", + "wl_design = wavelength.to(u.AA).value # 171\n", + "f0 = focal_length.to(u.mm).value # 1000\n", + "\n", + "# Asymmetric wavelengths so each spot has a distinct radius.\n", + "# Radii: 169 Å → 0.82 mm, 171 Å → 0 (design), 174 Å → 1.23 mm\n", + "wavelengths_plot = [169, 171, 174]\n", + "colors = [\"tab:blue\", \"tab:green\", \"tab:red\"]\n", + "labels = [\n", + " f\"169 Å ($f$ = {f0 * wl_design / 169:.0f} mm, $r_\\\\mathrm{{blur}}$ = {70 * abs(1 - 169/wl_design):.2f} mm)\",\n", + " \"171 Å (design)\",\n", + " f\"174 Å ($f$ = {f0 * wl_design / 174:.0f} mm, $r_\\\\mathrm{{blur}}$ = {70 * abs(1 - 174/wl_design):.2f} mm)\",\n", + "]\n", + "\n", + "# Compute max disk radius for axis limits\n", + "r_max = max(70 * abs(1 - wl / wl_design) for wl in wavelengths_plot)\n", + "\n", + "with astropy.visualization.quantity_support():\n", + " fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 6))\n", + " for wl_val, color, label in zip(wavelengths_plot, colors, labels):\n", + " f_wl = f0 * wl_design / wl_val # focal length at this wavelength, mm\n", + " scale = 1.0 - f0 / f_wl # defocus magnification (0 at design λ)\n", + " x_det = px_2d[inside] * scale\n", + " y_det = py_2d[inside] * scale\n", + " ax.scatter(x_det, y_det, s=2, alpha=0.5, color=color,\n", + " label=label, rasterized=True)\n", + " margin = 1.15\n", + " ax.set_xlim(-r_max * margin, r_max * margin)\n", + " ax.set_ylim(-r_max * margin, r_max * margin)\n", + " ax.set_aspect(\"equal\")\n", + " ax.legend(markerscale=4, fontsize=9)\n", + " ax.set_xlabel(\"$x$ (mm)\")\n", + " ax.set_ylabel(\"$y$ (mm)\")\n", + " ax.set_title(\"On-axis spot at 171 Å focal plane: chromatic aberration\")" + ] } ], "metadata": { @@ -577,4 +759,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +}