update docs

README.md

@@ -10,7 +10,7 @@ Starlight is a Godot addon that renders 100 000 stars in realtime, with
 low performance cost. It's an alternative to using a skybox, and
 also may be relevant to anyone making a space game.
 
-Check out the demo in your web browser: https://tiffnix.com/starlight-demo/
+Check out the demo in your web browser: https://tiffnix.com/starlight-demo
 
 # Features
 
@@ -86,7 +86,7 @@ The following visual properties are exposed:
   of 3 or 4 is a good balance.
 - `billboard_size_deg` - This controls how much of the screen the PSF
   texture takes up, in degrees. For the default JWST PSF I recommend a
-  value of around 90.
+  value of around 45.
 - `meters_per_lightyear` - This is a scaling setting, you'll need to set
   it depending on how far away you want your stars to be.
 - `luminosity_cap` - This is the maximum brightness a star can have. The
@@ -97,6 +97,9 @@ The following visual properties are exposed:
   is the PSF from the James Webb Space Telescope, because it looks cool.
   There are a few others in the `psf-textures` folder which can be used
   instead.
+- `texture_emission_tint` - A tint value applied to the PSF texture. The
+  provided PSF textures need a tint value to look correct, there is a
+  table in psf-textures/README.md.
 - `clamp_output` - Clamps the output from 0 to 1 when enabled. Can be
   useful depending on how your HDR is setup.
 
@@ -119,7 +122,7 @@ behavior, the shader has these properties:
   max_luminosity until it's just below that point.
 - `scaling_gamma` - Diffraction spikes usually fall in brightness
   according to distance^2 from the center of the texture, which means a
-  value of 0.5 is ideal. You may need to use other values depending on
+  value of ~0.5 is ideal. You may need to use other values depending on
   your PSF texture. For a perfect airy disk in particular, the falloff
   is faster than quadratic.
 - `debug_show_rects` - This can be useful while tweaking any of these
@@ -142,13 +145,13 @@ from the scene.
 
 Code is released under [MIT license](./LICENSE.md).
 
-The default PSF texture, `jwst.exr`, is based on FITS data [obtained
-from here][5]. Code for cropping, downscaling, and converting to OpenEXR
-is located in `docs/fits2exr.py`.
+The default PSF texture, `jwst.png`, is based on FITS data [obtained
+from here][5]. Code for cropping, downscaling, and packing into PNG
+is located in `docs/jwst2png.py`.
 
-The alternative PSF textures `hst.exr`, `hex_aperture.exr`, and
-`airy_disk.exr` were created using [Poppy][6] based on examples in the
-documentation. Code is located in `docs/poppy psfs.ipynb`.
+The alternative PSF textures `hst.png`, `hex_aperture.png`, and
+`airy_disk.png` were created using [Poppy][6] based on examples in the
+documentation. Code is located in `docs/poppy_psfs.ipynb`.
 
 [5]: https://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/simulated-data
 [6]: https://poppy-optics.readthedocs.io/en/latest/

docs/fits2exr.py → docs/jwst2png.py

@@ -1,7 +1,12 @@
 #!/usr/bin/env python3
 
-# Modified by Tiffany for the purposes of creating the JWST EXR file.
-# Original description:
+# Converts the JWST simulated PSF to the packed PNG format expected by
+# the godot-starlight addon. The source PSF images must be obtained in
+# FITS format from here:
+# <https://www.stsci.edu/jwst/science-planning/proposal-planning-toolbox/simulated-data>
+
+# This script was adapted by me (Tiffany) from a FITS-to-EXR conversion
+# script, the original description follows.
 
 # Presented by Min-Su Shin 
 # (2015 - , KASI, Republic of Korea)
@@ -19,10 +24,10 @@
 # check the output EXR files.
 
 from astropy.io import fits
-import OpenEXR
 import numpy
 from img_scale import cbrt
 from skimage.measure import block_reduce
+from PIL import Image
 
 # read FITS images
 # red channel
@@ -64,33 +69,51 @@ def crop_center(img,cropx,cropy):
     starty = y//2 - cropy//2    
     return img[starty:starty+cropy, startx:startx+cropx]
 
-width = 4096
-height = 4096
+width = 2048
+height = 2048
 r_img_data = crop_center(r_img_data, width, height)
 g_img_data = crop_center(g_img_data, width, height)
 b_img_data = crop_center(b_img_data, width, height)
 
-downscale = 4
-
-r_img_data = block_reduce(r_img_data, block_size=(downscale,downscale), func=numpy.sum, cval=numpy.sum(r_img_data))
-g_img_data = block_reduce(g_img_data, block_size=(downscale,downscale), func=numpy.sum, cval=numpy.sum(r_img_data))
-b_img_data = block_reduce(b_img_data, block_size=(downscale,downscale), func=numpy.sum, cval=numpy.sum(r_img_data))
-width = width // downscale
-height = height // downscale
-
-# write an EXR file
-exr_fn = "jwst_psf.exr"
-r_img_data = numpy.asarray(r_img_data, dtype=numpy.float32)
-r_img_data = r_img_data.tobytes()
-g_img_data = numpy.asarray(g_img_data, dtype=numpy.float32)
-g_img_data = g_img_data.tobytes()
-b_img_data = numpy.asarray(b_img_data, dtype=numpy.float32)
-b_img_data = b_img_data.tobytes()
-header = OpenEXR.Header(width, height)
-out_exr = OpenEXR.OutputFile(exr_fn, header)
-out_exr.writePixels({
-    'R': r_img_data,
-    'G': g_img_data,
-    'B': b_img_data,
-})
-print("write the EXR file ",exr_fn," done...")
+downscale = 1
+
+if downscale != 1:
+  r_img_data = block_reduce(r_img_data, block_size=(downscale,downscale), func=numpy.sum, cval=numpy.sum(r_img_data))
+  g_img_data = block_reduce(g_img_data, block_size=(downscale,downscale), func=numpy.sum, cval=numpy.sum(r_img_data))
+  b_img_data = block_reduce(b_img_data, block_size=(downscale,downscale), func=numpy.sum, cval=numpy.sum(r_img_data))
+  width = width // downscale
+  height = height // downscale
+
+coords = numpy.mgrid[0:width, 0:height]
+xcoord = coords[0] - (width / 2)
+ycoord = coords[1] - (height / 2)
+dist_sq = xcoord * xcoord + ycoord * ycoord + 0
+
+r_img_data = r_img_data * dist_sq
+g_img_data = g_img_data * dist_sq
+b_img_data = b_img_data * dist_sq
+
+r_max = numpy.max(r_img_data)
+g_max = numpy.max(g_img_data)
+b_max = numpy.max(b_img_data)
+
+r_img_data = r_img_data / r_max
+g_img_data = g_img_data / g_max
+b_img_data = b_img_data / b_max
+
+b = 1000
+log_b = numpy.log(b)
+r_img_data = numpy.log(1.0 + r_img_data * (b - 1.0)) / log_b
+g_img_data = numpy.log(1.0 + g_img_data * (b - 1.0)) / log_b
+b_img_data = numpy.log(1.0 + b_img_data * (b - 1.0)) / log_b
+
+print("max values: ", r_max, g_max, b_max)
+
+r_img_data = numpy.asarray(r_img_data * 255.0, dtype=numpy.uint8)
+g_img_data = numpy.asarray(g_img_data * 255.0, dtype=numpy.uint8)
+b_img_data = numpy.asarray(b_img_data * 255.0, dtype=numpy.uint8)
+
+rgb = numpy.dstack((r_img_data, g_img_data, b_img_data))
+
+image = Image.fromarray(rgb)
+image.save("jwst.png")