Add assets folder and update docs

docs/src/benchmarks.md

@@ -8,7 +8,7 @@ The Julia implementation of the EHT scattering framework takes advantage of Juli
 
 With Julia's speed improvements, the `ScatteringOptics.jl` scattering kernel loads 100 times faster than the `eht-imaging` kernel. The scattering kernel may be called upon to compute visibilities for a given sample of Fourier space points. In these computations, `ScatteringOptics.jl` also exhibits speed improvements up to 100 times over the three included models (Dipole, Boxcar, and Von Mises; see [Use Non-default Models](@ref) for more info) and varying observing wavelengths. These computation runtimes are compared over a range of sample in the plots below. 
 
-<img src="images/speed_dipole.png" width="300"/> <img src="images/speed_boxcar.png" width="300"/> <img src="images/speed_vonmises.png" width="300"/>
+<img src="assets/speed_dipole.png" width="300"/> <img src="assets/speed_boxcar.png" width="300"/> <img src="assets/speed_vonmises.png" width="300"/>
 
 
 With this significant speed up, the Julia implementation allows for the joint modeling of scattering parameters and reconstructed images in self-consistent way. 
@@ -17,5 +17,5 @@ With this significant speed up, the Julia implementation allows for the joint mo
 
 Our scattering kernel produces visibilities that are consistent with those computed by `eht-imaging` across all 3 kernel models as well as the range of standard observing wavelengths in radio astronomy. Below, we demonstrate this by plotting the Dipole kernel over the Fourier space major and minor axis for both the Julia and Python implementations in one plot per observing wavelength. The lines completely overlap, indicating complete consistency in results. All fractional errors between the two implementations range on the order of $10^{-6}$ and $10^{-7}$ (Figure \ref{kernelerrors}), which is well under scattering kernel uncertainty limits.
 
-<img src="images/kernel87.png" width="400"/> <img src="images/kernel36.png" width="400"/>
+<img src="assets/kernel87.png" width="400"/> <img src="assets/kernel36.png" width="400"/>
 

docs/src/tutorials/diffractive.md

@@ -7,7 +7,7 @@ CurrentModule = ScatteringOptics
 This page describes how to simulate diffractive scattering.
 
 ## Loading your image
-Here, we use an example image in [`eht-imaging`](https://github.com/achael/eht-imaging). Data can be downloaded from [here](data/jason_mad_eofn.fits). This is a general relativistic magnetohydrodynamic (GRMHD) model of the magnetic arrestic disk originally from [Dexter et al. 2020](https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.4168D/abstract).
+Here, we use an example image in [`eht-imaging`](https://github.com/achael/eht-imaging). Data can be downloaded from [here](docs/src/assets/jason_mad_eofn.fits). This is a general relativistic magnetohydrodynamic (GRMHD) model of the magnetic arrestic disk originally from [Dexter et al. 2020](https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.4168D/abstract).
 
 ```@example 1
 using CairoMakie
@@ -17,7 +17,7 @@ using VLBISkyModels
 # using Comrade
 
 # Load a image model from an image FITS file
-im = load_fits("data/jason_mad_eofn.fits", IntensityMap)
+im = load_fits("docs/src/assets/jason_mad_eofn.fits", IntensityMap)
 
 # Plot source image
 imageviz(im, size=(600, 500), colormap=:afmhot)
@@ -124,20 +124,20 @@ The output images may be saved to fits files. Here, we save the images generated
 
 ```@example 1
 # Ensemble average image of provided EHT fits file
-save_fits("data/im_ea.fits", im_ea)
+save_fits("docs/src/assets/im_ea.fits", im_ea)
 # Gaussian model and its scattered ensemble average image
-save_fits("data/im_g.fits", im_g)
-save_fits("data/im_gea.fits", im_gea)
+save_fits("docs/src/assets/im_g.fits", im_g)
+save_fits("docs/src/assets/im_gea.fits", im_gea)
 # Scattering kernel
-save_fits("data/im_skm.fits", im_skm)
+save_fits("docs/src/assets/im_skm.fits", im_skm)
 ```
 
 We also save the kernel visibilities calculated in the tutorial.
 ```@example 1
 using HDF5
 
 # Save the computed kernel data
-h5open("output_data.h5", "w") do file
+h5open("docs/src/assets/kernel.h5", "w") do file
     file["u"] = collect(u)  
     file["vis"] = vis       
 end

docs/src/tutorials/refractive.md

@@ -6,7 +6,7 @@ CurrentModule = ScatteringOptics
 Another feature of `ScatteringOptics.jl` is simulating refractive scattering. This page gives a tutorial to simulate refractive scattering effects. 
 
 ## Loading your image
-Again, we use an example image in [`eht-imaging`](https://github.com/achael/eht-imaging). Data can be downloaded from [here](data/jason_mad_eofn.fits). This is a general relativistic magnetohydrodynamic (GRMHD) model of the magnetic arrestic disk originally from [Dexter et al. 2020](https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.4168D/abstract).
+Again, we use an example image in [`eht-imaging`](https://github.com/achael/eht-imaging). Data can be downloaded from [here](docs/src/assets/jason_mad_eofn.fits). This is a general relativistic magnetohydrodynamic (GRMHD) model of the magnetic arrestic disk originally from [Dexter et al. 2020](https://ui.adsabs.harvard.edu/abs/2020MNRAS.494.4168D/abstract).
 
 ```@example 1
 using CairoMakie
@@ -16,7 +16,7 @@ using VLBISkyModels
 # using Comrade
 
 # Load a image model from an image FITS file
-im = load_fits("data/jason_mad_eofn.fits", IntensityMap)
+im = load_fits("docs/src/assets/jason_mad_eofn.fits", IntensityMap)
 
 # Frequency of the image
 νref = metadata(im).frequency
@@ -112,7 +112,7 @@ The output images may be saved to fits files. Here, we save the images generated
 
 ```@example 1
 # Average image of provided EHT fits file
-save_fits("data/im_a.fits", im_a)
+save_fits("docs/src/assets/im_a.fits", im_a)
 # Scattered average image of Gaussian model
-save_fits("data/im_ga.fits", im_ga)
+save_fits("docs/src/assets/im_ga.fits", im_ga)
 ```