Hello Matt,

Sorry for the dredging up a question from quite a bit ago. I had completely forgotten that I wanted to do this until reminded by the person who initially requested I make these plots.

As far as your recommendation, I do think that should be a workable solution, but I am running into an issue. Currently, I am attempting to just implement the log scaling shown in the example page you linked, and have a question about its interface with the yt-projection plot. I will include my modified code to illustrate my question/issue.

index = 0
for letter in [a,b,c]:
plot = letter.plots[('darkmatter0', 'particle_mass')]
plot.figure = fig
plot.axes = grid[index].axes
plot.cax = grid.cbar_axes[index]#.colorbar(pcm, ax=grid[index].axes)

pcm = grid.cbar_axes[index].pcolor(x, y, z,
norm=colors.LogNorm(vmin=z.min(), vmax=z.max()))

fig.colorbar(pcm, ax=ax[index])
letter._setup_plots()
index = index + 1

For the .pcolor call to modify the norm, it requires an x, y, z inputs, for the x and y axis of the plot (the x-y projections of space from the projectionplot), with the z being the colorbar paramater (particle_mass for this case). As the data is coming from yt.projectionplots, I am not sure where/what the proper inputs for these fields are. I have done some looking around, but am unsure what these should be. Do you have any insight into where these fields are stored/can be accessed to be re passed to this call?

On Fri, Jan 3, 2020 at 6:19 AM Matthew Turk <matthewturk@gmail.com> wrote:

Hi Sean,

I've been trying to think of the easiest way to do this in the
framework you suggest, and I have a few ideas. The simplest way would
likely be to use a different matplotlib normalizer for your particle
colors:

https://matplotlib.org/3.1.1/tutorials/colors/colormapnorms.html
https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.colors.Normalize.html

Since you're already accessing the plot.cax objects, you should be
able to swap it there. You should then be able to avoid doing
anything with derived fields, etc, as you could control the color maps
themselves.

Do you think that could be a workable solution?

-Matt

On Thu, Jan 2, 2020 at 5:52 PM Sean Larkin via yt-users
<yt-users@python.org> wrote:
>
> Hello All,
>
> I have a question about the Projection plot function in yt. I have a script that plots the smallest mass darkmatter particles of a simulation in all 3 of the 2-d coordinate projections, and overplots the halos found by a halo finder. This script works, and currently plots the colorbar based on the mass in a given 2-d bin as done in some of the basic yt plots included in the website. I am wondering if it is possible to have the color scheme of the particles be based on the squared mass or squared density of the particles in any given 2-d bin, so that the higher density regions are highlighted compared to the background lower density regions. I have included the plotting part of my script below, in case it is helpful.
>
> I hope everyone is having a great New Year!
>
> ds = yt.load(VELA_snaps[position[0]])
> domain_width = float(ds.domain_width.in_units('Mpc/h')[0])
> ad = ds.all_data()
> masses = yt.np.unique(ad[('darkmatter', 'particle_mass')])
> #filter out the darkmatter0 particles
> def mass_filter(pfilter, data):
> filter = data[(pfilter.filtered_type, 'particle_mass')] == masses[0]
> return filter
> yt.add_particle_filter('darkmatter0', function=mass_filter, filtered_type='darkmatter', requires=['particle_mass'])
> ds.add_particle_filter('darkmatter0')
>
> scale = float(ds.scale_factor)
>
> fig = plt.figure()
>
> grid = AxesGrid(fig, (0.075,0.075,10,5),
> nrows_ncols = (3, 1),
> axes_pad = 1.0,
> label_mode = "L",
> share_all = False,
> cbar_location="right",
> cbar_mode="each",
> cbar_size="3%",
> cbar_pad="0%")
>
> zoom = 10
>
> x0 = float(consistent.halo_data_sorted[index][0][17]) * scale / domain_width
> y0 = float(consistent.halo_data_sorted[index][0][18]) * scale / domain_width
> z0 = float(consistent.halo_data_sorted[index][0][19]) * scale / domain_width
> center = [x0, y0, z0]
>
> a = yt.ParticlePlot(ds, ('darkmatter0', 'particle_position_x'), ('darkmatter0', 'particle_position_y'),\
> ('darkmatter0', 'particle_mass'), center=center)
> a.set_unit(('darkmatter0','particle_mass'), 'Msun')
> a.zoom(zoom)
> b = yt.ParticlePlot(ds, ('darkmatter0', 'particle_position_y'), ('darkmatter0', 'particle_position_z'),\
> ('darkmatter0', 'particle_mass'), center=center)
> b.set_unit(('darkmatter0','particle_mass'), 'Msun')
> b.zoom(zoom)
> c = yt.ParticlePlot(ds, ('darkmatter0', 'particle_position_z'), ('darkmatter0', 'particle_position_x'),\
> ('darkmatter0', 'particle_mass'), center=center)
> c.set_unit(('darkmatter0','particle_mass'), 'Msun')
> c.zoom(zoom)
>
>
> for halos in consistent.halo_data_sorted[index]:
> x = float(halos[17]) * scale / domain_width
> y = float(halos[18]) * scale / domain_width
> z = float(halos[19]) * scale / domain_width
> r = float(halos[11]) * scale / .7
> center = [x, y, z]
> a.annotate_sphere(center, radius=(r, 'kpc'), circle_args={'color':'red'})
> b.annotate_sphere(center, radius=(r, 'kpc'), circle_args={'color':'red'})
> c.annotate_sphere(center, radius=(r, 'kpc'), circle_args={'color':'red'})
>
>
> index = 0
> for letter in [a,b,c]:
> plot = letter.plots[('darkmatter0', 'particle_mass')]
> plot.figure = fig
> plot.axes = grid[index].axes
> plot.cax = grid.cbar_axes[index]
> letter._setup_plots()
> index = index + 1
>
> plt.savefig('{}/CatalogProjectionYT_VELA{}_Scale{}.png'.format(out_dir, VELA_number, str(scale)[2:5]), bbox_inches='tight')
> plt.close()
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