diff --git a/src/tutorials/spectral-cube-reproject.jl b/src/tutorials/spectral-cube-reproject.jl
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+### A Pluto.jl notebook ###
+# v1.0.1
+
+#> [frontmatter]
+#> title = "Cube Reprojection Tutorial"
+#> layout = "layout.jlhtml"
+#> date = "2025-12-31"
+#> description = "TODO"
+#> tags = ["TODO"]
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ ecf46459-76ca-457d-8fd5-43cb66770388
+using Downloads: download
+
+# ╔═╡ 48156d40-df5c-47de-8d23-acf1186d4331
+using FITSFiles
+
+# ╔═╡ e3cc0abc-8bb5-4b60-9fc0-bddb46ca9cdb
+using PlutoUI
+
+# ╔═╡ ea3a159a-6174-11ef-0cb1-b73af72665d4
+md"""
+# Cube Reprojection Tutorial
+"""
+
+# ╔═╡ 7075d9f9-090f-461c-8982-6c7f38453bb0
+md"""
+Original notebook at <https://learn.astropy.org/tutorials/SpectralCubeReprojectExample.html>
+"""
+
+# ╔═╡ b8c5d49b-b8ca-4f8d-b154-b5a034a82c2e
+md"""
+Original authors: Adam Ginsburg, Eric Koch
+"""
+
+# ╔═╡ 987be673-b74a-4fb9-98fd-9bafef61c0e4
+md"""
+## Learning Goals
+
+- reproject a cube spectrally
+- smooth it spectrally
+- reproject it spatially
+"""
+
+# ╔═╡ 3c37443f-c88d-48d9-95b1-b48fac544454
+md"""
+## Keywords
+
+spectral cube, radio astronomy, astroquery, units, dask
+"""
+
+# ╔═╡ 6b5f101b-f246-4a13-8ef5-ce08da03414e
+md"""
+## Summary
+"""
+
+# ╔═╡ 7abaa07b-c97e-4ae1-9eaf-650fb529fe14
+md"""
+Spectroscopic cube observations taken at different wavelength can trace the motion of gas or stars using spectral lines, but often lines at different wavelengths give different information. For example, one might observe a galaxy in the 21cm line of HI and the 115 GHz line of CO, or a protoplanetary disk in a line of N2H+ and a line of CO, or a galactic disk in the Hα and Hβ lines (in absorption or emission). In order to compare these data sets pixel-by-pixel, they must be placed onto a common grid with common resolution.
+"""
+
+# ╔═╡ fd420c28-966c-424b-acc2-5c11988cf253
+md"""
+This tutorial shows how to take two spectral cubes observed toward the same part of the sky, but different frequencies, and put them onto the same grid using [spectral-cube](https://learn.astropy.org/tutorials/spectral-cube.readthedocs.io).
+"""
+
+# ╔═╡ 1042a4e0-2524-4ea9-ba04-b12315096107
+md"""
+It uses [astroquery](https://astroquery.readthedocs.io/) to obtain line frequencies from [splatalogue](https://astroquery.readthedocs.io/en/latest/splatalogue/splatalogue.html); this example uses radio-wavelength data for which Splatalogue's molecular line lists are appropriate. Finally, it shows how to do the reprojection using [dask](https://dask.org/) to enable parallelization.
+"""
+
+# ╔═╡ 36ee5590-9a34-46e6-a621-ac7b05055046
+
+
+# ╔═╡ be71871c-b5e1-44b5-9be0-985dcf6cadbf
+md"""
+In this example, we do spectral smoothing and interpolation (step 4) before spatial smoothing and interpolation (step 5), but if you have a varying-resolution cube (with a different beam size for each channel), you have to do spatial smoothing first. For more information see the spectral-cube documentation.
+"""
+
+# ╔═╡ dd726d8d-5823-4553-8216-3c1e5065219e
+md"""
+## Step 1: Download the data
+"""
+
+# ╔═╡ 733a3cee-4907-4940-9c4e-6dd1f3055e2c
+md"""
+We download two example spectral cubes of a point in the Galactic center from a permalink on the ALMA archives. These are moderately large files, with sizes 18 MB and 337 MB.
+"""
+
+# ╔═╡ b4b92de1-812f-4f10-a818-d3ad3f3f0fb3
+filename1 = download("https://almascience.eso.org/dataPortal/member.uid___A001_X1465_X3a33.BrickMaser_sci.spw71.cube.I.manual.image.pbcor.fits")
+
+# ╔═╡ ff11ad50-129a-45e2-86a9-d74768816a18
+filename2 = download("https://almascience.eso.org/dataPortal/member.uid___A001_X87d_X141.a_sma1_sci.spw27.cube.I.pbcor.fits")
+
+# ╔═╡ 12bffd04-b8e1-4c02-bf1d-e577b3db92a1
+md"""
+## Step 2: Load the cubes
+"""
+
+# ╔═╡ 1534acf5-1b39-4a02-819b-0ba304f70d84
+hdu1 = only(FITSFiles.fits(filename1))
+
+# ╔═╡ 772d38fd-20a2-4043-83ab-344b64d74d64
+hdu1.cards
+
+# ╔═╡ 4b1c3cfb-d001-4e78-b77b-3ed5d7c083e1
+cube1 = dropdims(hdu1.data, dims = 4)
+
+# ╔═╡ 56ff05c6-365b-4cf0-b16f-c0b9288aaf75
+hdu2 = only(FITSFiles.fits(filename2))
+
+# ╔═╡ 5dc48eac-607b-48a9-96fd-5210a2ecae05
+cube2 = dropdims(hdu2.data, dims = 4)
+
+# ╔═╡ 4d5e3fff-3158-4ed5-9070-b190f09d6113
+FITSFiles.typeofhdu(hdu1)
+
+# ╔═╡ 6413f759-8891-4480-a7a9-23ca13bd421d
+hdu2.cards
+
+# ╔═╡ 1536e606-0ae3-4d10-80c9-336d0adc3c3e
+md"""
+## Step 3: Convert cubes from frequency to velocity
+"""
+
+# ╔═╡ ef201298-5424-4661-aae3-a5c7ea30b4e1
+md"""
+To compare the kinematic structure of the target, we need to convert from the observed frequency (which must be in a common reference frame; in this case, it already is) to the doppler velocity.
+"""
+
+# ╔═╡ 6fed040b-fb90-433f-be1b-af4d43f742b8
+md"""
+### Create spatial maps of the peak intensity to quickly explore the cubes
+"""
+
+# ╔═╡ 5738bf09-cd54-493f-a2b3-9ece1c64a3e3
+md"""
+## Step 4: Spectral Interpolation
+"""
+
+# ╔═╡ 619c2904-39a2-40e1-8c15-5211eb0112cf
+md"""
+We can choose to do either the spatial or spectral step first.
+In this case, we choose the spectral step first because the H₂CS cube is narrower in velocity (cube1vel) and this will reduce the number of channels we need to spatially interpolate over in the next step.
+"""
+
+# ╔═╡ 00cd85d9-9af0-4a46-8710-1f8314a68bc8
+md"""
+We need to match resolution to the cube with the largest channel width:
+"""
+
+# ╔═╡ aa4af422-79b5-489f-be59-406aa899f798
+
+
+# ╔═╡ 87429d88-58dd-43c6-a9ec-c53bacef0805
+md"""
+Next, we will reduce `cube2vel` to have the same spectral range as `cube1vel`:
+"""
+
+# ╔═╡ 8e6269ed-306b-4e15-b7ab-4a58e3406325
+
+
+# ╔═╡ 735320ad-dd59-47bb-9208-54e9f410346c
+md"""
+Note that it is important for the to-be-interpolated cube, in this case cube2, to have pixels bounding cube1’s spectral axis, but in this case it does not. If the pixel range doesn’t overlap perfectly, it may blank out one of the edge pixels. So, to fix this, we add a little buffer:
+"""
+
+# ╔═╡ 6cd61a39-b384-4bd4-988a-3f17a8cbce5d
+
+
+# ╔═╡ 4d6f1df3-3300-45d3-8e6d-35928f2e0749
+
+
+# ╔═╡ cfa20b5d-b172-4422-9e0d-81fc8028f6d9
+md"""
+## Step 5: Spatial Smoothing
+"""
+
+# ╔═╡ 590877e7-d2f3-41ab-955f-abcbeed6b7f2
+md"""
+Now that we’ve done spectral smoothing, we also need to follow a similar procedure of smoothing then resampling for the spatial axes.
+"""
+
+# ╔═╡ cb169320-4ca3-43a6-af86-a9432ad8dc02
+md"""
+The `beam` is the resolution element of our cubes:
+"""
+
+# ╔═╡ 6facfe35-2d72-45a4-88a8-351070440ce3
+
+
+# ╔═╡ dba82ba0-60db-45f0-90c6-738ad6692913
+md"""
+`cube1` again hase the larger beam, so we’ll smooth `cube2` to its resolution
+"""
+
+# ╔═╡ 7ea93647-2a92-45cf-91e0-e8504457bf0f
+md"""
+### Aside: mixed beams
+"""
+
+# ╔═╡ 8e6564ce-5869-434c-9034-bf0dddece65f
+md"""
+If cube1 and cube2 had different sized beams, but neither was clearly larger, we would have to convolve _both_ to a [common beam](https://radio-beam.readthedocs.io/en/latest/commonbeam.html#finding-the-smallest-common-beam).
+
+In this case, it’s redundant and we could have just used `cube1`’s beam, but this is the more general approach:
+"""
+
+# ╔═╡ f07f71a0-c5bb-4fdf-bdb2-b2cfee9fd214
+
+
+# ╔═╡ 439357d1-0cad-45c5-8285-0e9e38b7a501
+md"""
+We then convolve:
+"""
+
+# ╔═╡ f2f259a1-2d1f-4ca5-896d-9308619b5ef8
+
+
+# ╔═╡ 780d4bc8-3e27-4fb2-a8c1-bec8df15b6c8
+md"""
+## Step 6: Reprojection
+"""
+
+# ╔═╡ 65cb16b6-9ace-4f9e-9c7c-4aac7877cffd
+md"""
+Now we can do the spatial resampling as the final step for producing two cubes matched to the same spatial and spectral pixel grid:
+"""
+
+# ╔═╡ f2adf9fd-a826-4dc8-80b0-8bf897eb1ac7
+
+
+# ╔═╡ 63c1cc87-1a58-4347-9112-b3edd4ae99cc
+md"""
+These two cubes are now on an identical grid, and can be directly compared:
+"""
+
+# ╔═╡ 32b3f31e-e4de-4ded-b989-582fb39cdbc3
+
+
+# ╔═╡ 0e2daa33-648e-4271-a779-a3143e872d08
+md"""
+These spectra can now be overplotted as they are in the same unit with the same beam.
+"""
+
+# ╔═╡ 0e0c0d60-7f99-442d-956b-868d0dec825d
+
+
+# ╔═╡ a126afc1-14d0-4e6c-a109-dff801e992e7
+
+
+# ╔═╡ 9ce43d1c-16f5-4ee0-ab7c-f15ee48dd630
+md"""
+# Notebook setup 🔧
+"""
+
+# ╔═╡ 9aa6d05f-4a00-412e-ac5d-5bb973ca112d
+TableOfContents()
+
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+[[deps.TranscodingStreams]]
+git-tree-sha1 = "0c45878dcfdcfa8480052b6ab162cdd138781742"
+uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
+version = "0.11.3"
+
+[[deps.Tricks]]
+git-tree-sha1 = "311349fd1c93a31f783f977a71e8b062a57d4101"
+uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
+version = "0.1.13"
+
+[[deps.URIs]]
+git-tree-sha1 = "bef26fb046d031353ef97a82e3fdb6afe7f21b1a"
+uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
+version = "1.6.1"
+
+[[deps.UUIDs]]
+deps = ["Random", "SHA"]
+uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
+version = "1.11.0"
+
+[[deps.Unicode]]
+uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
+version = "1.11.0"
+
+[[deps.Unitful]]
+deps = ["Dates", "LinearAlgebra", "Random"]
+git-tree-sha1 = "57e1b2c9de4bd6f40ecb9de4ac1797b81970d008"
+uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
+version = "1.28.0"
+
+    [deps.Unitful.extensions]
+    ConstructionBaseUnitfulExt = "ConstructionBase"
+    ForwardDiffExt = "ForwardDiff"
+    InverseFunctionsUnitfulExt = "InverseFunctions"
+    LatexifyExt = ["Latexify", "LaTeXStrings"]
+    NaNMathExt = "NaNMath"
+    PrintfExt = "Printf"
+
+    [deps.Unitful.weakdeps]
+    ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
+    ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+    InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
+    LaTeXStrings = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
+    Latexify = "23fbe1c1-3f47-55db-b15f-69d7ec21a316"
+    NaNMath = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3"
+    Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+
+[[deps.UnitfulAngles]]
+deps = ["Dates", "Unitful"]
+git-tree-sha1 = "79875b1f2e4bf918f0702a5980816955066d9ae2"
+uuid = "6fb2a4bd-7999-5318-a3b2-8ad61056cd98"
+version = "0.7.2"
+
+[[deps.UnitfulAstro]]
+deps = ["Unitful", "UnitfulAngles"]
+git-tree-sha1 = "fbe44a0ade62ae5ed0240ad314dfdd5482b90b40"
+uuid = "6112ee07-acf9-5e0f-b108-d242c714bf9f"
+version = "1.2.2"
+
+[[deps.UnitfulAtomic]]
+deps = ["Unitful"]
+git-tree-sha1 = "903be579194534af1c4b4778d1ace676ca042238"
+uuid = "a7773ee8-282e-5fa2-be4e-bd808c38a91a"
+version = "1.0.0"
+
+[[deps.Zlib_jll]]
+deps = ["Libdl"]
+uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
+version = "1.3.1+2"
+
+[[deps.libblastrampoline_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
+version = "5.15.0+0"
+
+[[deps.nghttp2_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
+version = "1.64.0+1"
+
+[[deps.p7zip_jll]]
+deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
+uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
+version = "17.7.0+0"
+"""
+
+# ╔═╡ Cell order:
+# ╟─ea3a159a-6174-11ef-0cb1-b73af72665d4
+# ╟─7075d9f9-090f-461c-8982-6c7f38453bb0
+# ╟─b8c5d49b-b8ca-4f8d-b154-b5a034a82c2e
+# ╟─987be673-b74a-4fb9-98fd-9bafef61c0e4
+# ╟─3c37443f-c88d-48d9-95b1-b48fac544454
+# ╟─6b5f101b-f246-4a13-8ef5-ce08da03414e
+# ╟─7abaa07b-c97e-4ae1-9eaf-650fb529fe14
+# ╟─fd420c28-966c-424b-acc2-5c11988cf253
+# ╟─1042a4e0-2524-4ea9-ba04-b12315096107
+# ╠═36ee5590-9a34-46e6-a621-ac7b05055046
+# ╟─be71871c-b5e1-44b5-9be0-985dcf6cadbf
+# ╟─dd726d8d-5823-4553-8216-3c1e5065219e
+# ╠═ecf46459-76ca-457d-8fd5-43cb66770388
+# ╟─733a3cee-4907-4940-9c4e-6dd1f3055e2c
+# ╠═b4b92de1-812f-4f10-a818-d3ad3f3f0fb3
+# ╠═ff11ad50-129a-45e2-86a9-d74768816a18
+# ╟─12bffd04-b8e1-4c02-bf1d-e577b3db92a1
+# ╠═48156d40-df5c-47de-8d23-acf1186d4331
+# ╠═1534acf5-1b39-4a02-819b-0ba304f70d84
+# ╠═772d38fd-20a2-4043-83ab-344b64d74d64
+# ╠═4b1c3cfb-d001-4e78-b77b-3ed5d7c083e1
+# ╠═56ff05c6-365b-4cf0-b16f-c0b9288aaf75
+# ╠═5dc48eac-607b-48a9-96fd-5210a2ecae05
+# ╠═4d5e3fff-3158-4ed5-9070-b190f09d6113
+# ╠═6413f759-8891-4480-a7a9-23ca13bd421d
+# ╟─1536e606-0ae3-4d10-80c9-336d0adc3c3e
+# ╟─ef201298-5424-4661-aae3-a5c7ea30b4e1
+# ╟─6fed040b-fb90-433f-be1b-af4d43f742b8
+# ╟─5738bf09-cd54-493f-a2b3-9ece1c64a3e3
+# ╟─619c2904-39a2-40e1-8c15-5211eb0112cf
+# ╟─00cd85d9-9af0-4a46-8710-1f8314a68bc8
+# ╠═aa4af422-79b5-489f-be59-406aa899f798
+# ╟─87429d88-58dd-43c6-a9ec-c53bacef0805
+# ╠═8e6269ed-306b-4e15-b7ab-4a58e3406325
+# ╟─735320ad-dd59-47bb-9208-54e9f410346c
+# ╠═6cd61a39-b384-4bd4-988a-3f17a8cbce5d
+# ╠═4d6f1df3-3300-45d3-8e6d-35928f2e0749
+# ╟─cfa20b5d-b172-4422-9e0d-81fc8028f6d9
+# ╟─590877e7-d2f3-41ab-955f-abcbeed6b7f2
+# ╟─cb169320-4ca3-43a6-af86-a9432ad8dc02
+# ╠═6facfe35-2d72-45a4-88a8-351070440ce3
+# ╟─dba82ba0-60db-45f0-90c6-738ad6692913
+# ╟─7ea93647-2a92-45cf-91e0-e8504457bf0f
+# ╟─8e6564ce-5869-434c-9034-bf0dddece65f
+# ╠═f07f71a0-c5bb-4fdf-bdb2-b2cfee9fd214
+# ╟─439357d1-0cad-45c5-8285-0e9e38b7a501
+# ╠═f2f259a1-2d1f-4ca5-896d-9308619b5ef8
+# ╟─780d4bc8-3e27-4fb2-a8c1-bec8df15b6c8
+# ╟─65cb16b6-9ace-4f9e-9c7c-4aac7877cffd
+# ╠═f2adf9fd-a826-4dc8-80b0-8bf897eb1ac7
+# ╟─63c1cc87-1a58-4347-9112-b3edd4ae99cc
+# ╠═32b3f31e-e4de-4ded-b989-582fb39cdbc3
+# ╟─0e2daa33-648e-4271-a779-a3143e872d08
+# ╠═0e0c0d60-7f99-442d-956b-868d0dec825d
+# ╠═a126afc1-14d0-4e6c-a109-dff801e992e7
+# ╟─9ce43d1c-16f5-4ee0-ab7c-f15ee48dd630
+# ╠═e3cc0abc-8bb5-4b60-9fc0-bddb46ca9cdb
+# ╠═9aa6d05f-4a00-412e-ac5d-5bb973ca112d
+# ╟─00000000-0000-0000-0000-000000000001
+# ╟─00000000-0000-0000-0000-000000000002