Tutorials content fixes

changelog/652.doc.rst

@@ -0,0 +1 @@
+Assorted clarifications and improvements to tutorial content.

docs/examples/example_cryo_plots.md

@@ -72,7 +72,7 @@ Next we will need to calculate the pixel size in both Longitude and Latitude, wh
 First get a 2x2 grid of SkyCoord objects:
 
 ```{code-cell} ipython3
-space_2 = sp[:2,:2,0].axis_world_coords()[0] # [0] because we only care about space, not time
+space_2 = sp[:2,:2,0].axis_world_coords("lon", "lat")[0] # [0] because this always returns a tuple
 ```
 
  We calculate pixel size by calculating the step in both lat and lon.
@@ -84,7 +84,7 @@ dlat = np.abs(space_2[0,1].Ty - space_2[0,0].Ty)
 aspect = dlon / dlat
 ```
 
-Finally, we can plot both our mean-subtracted scan at the peak wavelength and the mean values over all all wavelengths.
+Finally, we can plot both our mean-subtracted scan at the peak wavelength and the mean values over all wavelengths.
 
 ```{code-cell} ipython3
 fig = plt.figure(figsize=(7, 5), layout="constrained")
@@ -145,7 +145,7 @@ plt.show()
 Now we can plot the slit position. We do this by taking the first raster step position of the SP dataset and computing the world coordinates of each pixel along the slit.
 
 ```{code-cell} ipython3
-slit_coords = sp[0,:,0].axis_world_coords()[0]  # Again, [0] extracts the spatial coordinates and drops time.
+slit_coords = sp[0,:,0].axis_world_coords("lon", "lat")[0]  # Again, [0] because this always returns a tuple
 
 fig = plt.figure(layout="constrained")
 # Reuse the norm from the first plot
@@ -162,7 +162,7 @@ plt.show()
 Notice how the slit has a larger field of view along the latitude dimension. We can re-plot the image, and crop the extent of the plot back to the extent of the context imager.
 
 ```{code-cell} ipython3
-slit_coords = sp[0,:,0].axis_world_coords()[0]  # Again, [0] extracts the spatial coordinates and drops time.
+slit_coords = sp[0,:,0].axis_world_coords("lon", "lat")[0]  # Again, [0] because this always returns a tuple
 
 fig = plt.figure(layout="constrained")
 # Reuse the norm from the first plot

docs/examples/reproject_vbi_mosaic.md

@@ -38,7 +38,7 @@ from dkist.data.sample import VBI_L1_NZJTB
 
 ## Obtaining some data
 
-In this example we will use the VBI sample dataset [AJQWW](https://dkist.data.nso.edu/datasetview/AJQWW).
+In this example we will use the VBI sample dataset [L1_NZJTB](https://dkist.data.nso.edu/product/L1-NZJTB).
 If you want to replace this dataset with your own dataset, see {ref}`dkist:howto-guide:sample-data`.
 
 Let's load the data with {obj}`dkist.load_dataset`:

docs/examples/vbi_extents.md

@@ -16,6 +16,7 @@ kernelspec:
 # Showing the Field of View of VBI on AIA
 
 ```{code-cell} ipython3
+:tags: [keep-inputs]
 import numpy as np
 import matplotlib.pyplot as plt
 import astropy.units as u

docs/tutorial/1_astropy_and_sunpy.md

@@ -101,7 +101,7 @@ Currently, {obj}`astropy.coordinates` supports:
 
 * Spatial coordinates via {obj}`astropy.coordinates.SkyCoord`
 * Spectral coordinates via {obj}`astropy.coordinates.SpectralCoord`
-* Stokes profiles via {obj}`astropy.coordinates.StokesCoord` (introduced in astropy 7.0)
+* Stokes profiles via {obj}`astropy.coordinates.StokesCoord` (introduced in astropy 5.3)
 
 ### Spatial Coordinates
 
@@ -142,6 +142,17 @@ There are few things to notice about the difference between these two `SkyCoord`
 1. The `obstime` and `rsun` attributes are still present, but the `observer` attribute isn't. This is because heliographic coordinates are not observer dependent.
 1. The `obstime` attribute is still important to transform to other frames, as the heliographic frame needs to know the location of Earth.
 
++++
+
+Sunpy provides more equivalencies that might be useful here, for example if we wish to convert the solar angle values used for this SkyCoord into distance:
+
+```{code-cell} ipython3
+from sunpy.coordinates.utils import solar_angle_equivalency
+
+print((hpc1.Tx).to(u.km, equivalencies=solar_angle_equivalency(observer=hpc1.observer)))
+print((hpc1.Ty).to(u.km, equivalencies=solar_angle_equivalency(observer=hpc1.observer)))
+```
+
 ### Spectral Coordinates
 
 {obj}`astropy.coordinates.SpectralCoord` is a `Quantity`-like object which also holds information about the observer and target coordinates and relative velocities.

docs/tutorial/3_dataset_dimensionality.md

@@ -131,7 +131,7 @@ ds.wcs.pixel_axis_names
 note how these are reversed from one another, we can print them together with:
 
 ```{code-cell} ipython3
-for name, length in zip(ds.wcs.pixel_axis_names[::-1], ds.shape):
+for name, length in zip(ds.wcs.pixel_axis_names, ds.wcs.pixel_shape):
     print(f"{name}: {length}")
 ```