Particle Data Projections
This tutorial demonstrates advanced projection techniques for stellar and dark matter particle data using MERA.jl. Learn how to create 2D projections from N-body simulations, analyze stellar populations, and investigate galactic structure through particle-based analysis.
Quick Reference
Essential Functions
# Basic particle projection
projection(particles, :variable, :unit)
# Multi-quantity projection
projection(particles, [:var1, :var2], units=[:unit1, :unit2])
# Spatial selection
projection(particles, :sd, :Msol_pc2,
xrange=[-10,10], center=[:boxcenter], range_unit=:kpc)
# Direction control
projection(particles, :sd, :Msol_pc2, direction=:x) # x, y, z directions
# Resolution control
projection(particles, :sd, :Msol_pc2, lmax=9) # AMR level
projection(particles, :sd, :Msol_pc2, res=256) # Effective grid resolution
projection(particles, :sd, :Msol_pc2, pxsize=[100.,:pc]) # Physical pixel size
# Stellar population analysis
projection(particles, :age, :Myr, mask=age_mask)
projection(particles, :birth, :Myr, ref_time=0.)Key Particle Quantities
:sd- Surface density (Σ):vx, :vy, :vz- Velocity components:v- Total velocity magnitude:σ, :σx, :σy, :σz- Velocity dispersions:ekin- Kinetic energy:age- Stellar age (relative to snapshot time):birth- Birth time (absolute):r_cylinder, :vr_cylinder, :vϕ_cylinder- Cylindrical coordinates:ϕ, :σr_cylinder, :σϕ_cylinder- Cylindrical kinematic quantities
Overview
MERA.jl provides comprehensive projection capabilities for particle-based simulations:
- Stellar surface density projections in arbitrary directions
- Kinematic analysis of stellar and dark matter components
- Stellar population studies through age and birth time projections
- Multi-component analysis with customizable units
- Coordinate system transformations (Cartesian, cylindrical, spherical)
- Advanced masking and filtering for population selection
Key Concepts
- Particle Types: Stellar particles, dark matter,...
- Population Analysis: Age-based selection and temporal evolution
- Projection Direction: Control viewing angle (x, y, z directions)
- Grid Resolution: Customize output resolution via
lmax,res, orpxsize - Weighting Schemes: Mass-weighted projections, ...
Environment Setup and Data Loading
Package Configuration and Particle Data
Load particle data from N-body simulations including stellar particles, dark matter, and other particle types.
using Mera
# Load simulation metadata
# Replace with your simulation path and output number
info = getinfo(300, "/Volumes/FASTStorage/Simulations/Mera-Tests/mw_L10")
# Load particle data (stellar particles, dark matter, etc.)
# Includes position, velocity, mass, and stellar population properties
particles = getparticles(info);[Mera]: 2025-08-14T15:02:07.552
Code: RAMSES
output [300] summary:
mtime: 2023-04-09T05:34:09
ctime: 2025-06-21T18:31:24.020
=======================================================
simulation time: 445.89 [Myr]
boxlen: 48.0 [kpc]
ncpu: 640
ndim: 3
-------------------------------------------------------
amr: true
level(s): 6 - 10 --> cellsize(s): 750.0 [pc] - 46.88 [pc]
-------------------------------------------------------
hydro: true
hydro-variables: 7 --> (:rho, :vx, :vy, :vz, :p, :var6, :var7)
hydro-descriptor: (:density, :velocity_x, :velocity_y, :velocity_z, :pressure, :scalar_00, :scalar_01)
γ: 1.6667
-------------------------------------------------------
gravity: true
gravity-variables: (:epot, :ax, :ay, :az)
-------------------------------------------------------
particles: true
- Nstars: 5.445150e+05
particle-variables: 7 --> (:vx, :vy, :vz, :mass, :family, :tag, :birth)
particle-descriptor: (:position_x, :position_y, :position_z, :velocity_x, :velocity_y, :velocity_z, :mass, :identity, :levelp, :family, :tag, :birth_time)
-------------------------------------------------------
rt: false
clumps: false
-------------------------------------------------------
namelist-file: ("&COOLING_PARAMS", "&SF_PARAMS", "&AMR_PARAMS", "&BOUNDARY_PARAMS", "&OUTPUT_PARAMS", "&POISSON_PARAMS", "&RUN_PARAMS", "&FEEDBACK_PARAMS", "&HYDRO_PARAMS", "&INIT_PARAMS", "&REFINE_PARAMS")
-------------------------------------------------------
timer-file: true
compilation-file: false
makefile: true
patchfile: true
=======================================================
[Mera]: Get particle data: 2025-08-14T15:02:11.138
Using threaded processing with 4 threads
Key vars=(:level, :x, :y, :z, :id, :family, :tag)
Using var(s)=(1, 2, 3, 4, 7) = (:vx, :vy, :vz, :mass, :birth)
domain:
xmin::xmax: 0.0 :: 1.0 ==> 0.0 [kpc] :: 48.0 [kpc]
ymin::ymax: 0.0 :: 1.0 ==> 0.0 [kpc] :: 48.0 [kpc]
zmin::zmax: 0.0 :: 1.0 ==> 0.0 [kpc] :: 48.0 [kpc]
Processing 640 CPU files using 4 threads
Mode: Threaded processing
Combining results from 4 thread(s)...
Found 5.445150e+05 particles
Memory used for data table :38.428720474243164 MB
-------------------------------------------------------
# Inspect the loaded particle data structure
# Shows available fields
particles.dataTable with 544515 rows, 12 columns:
Columns:
# colname type
────────────────────
1 level Int32
2 x Float64
3 y Float64
4 z Float64
5 id Int32
6 family Int8
7 tag Int8
8 vx Float64
9 vy Float64
10 vz Float64
11 mass Float64
12 birth Float64Projection of Predefined Quantities
See the possible variables:
# Show all available projection quantities and their descriptions
# Displays particle-specific variables and derived quantities
projection()Predefined vars for projections:
------------------------------------------------
=====================[gas]:=====================
-all the non derived hydro vars-
:cpu, :level, :rho, :cx, :cy, :cz, :vx, :vy, :vz, :p, var6,...
further possibilities: :rho, :density, :ρ
-derived hydro vars-
:x, :y, :z
:sd or :Σ or :surfacedensity
:mass, :cellsize, :freefall_time
:cs, :mach, :machx, :machy, :machz, :jeanslength, :jeansnumber
:t, :Temp, :Temperature with p/rho
==================[particles]:==================
all the non derived vars:
:cpu, :level, :id, :family, :tag
:x, :y, :z, :vx, :vy, :vz, :mass, :birth, :metal....
-derived particle vars-
:age
==============[gas or particles]:===============
:v, :ekin
squared => :vx2, :vy2, :vz2
velocity dispersion => σx, σy, σz, σ
related to a given center:
---------------------------
:vr_cylinder, vr_sphere (radial components)
:vϕ_cylinder, :vθ
squared => :vr_cylinder2, :vϕ_cylinder2
velocity dispersion => σr_cylinder, σϕ_cylinder
2d maps (not projected) => :r_cylinder, :ϕ
------------------------------------------------
Projection of a Single Quantity in Different Directions (z,y,x)
Here we project the surface density in the z-direction of the data within a particular vertical range (domain=[0:1]) on a grid corresponding to level=9. Pass any object of ParticleDataType (here: "particles") to the projection-function and select a variable by a Symbol (here: :sd = :surfacedensity = :Σ in M⊙/pc²).
# Basic surface density projection in z-direction
# Projects stellar particles onto xy-plane with specified resolution
# Z-direction: projects particles onto xy-plane (face-on view)
# X-direction: projects particles onto yz-plane (edge-on view)
proj_z = projection(particles, :sd, :Msol_pc2, lmax=9, zrange=[0.45,0.55], verbose=false)
proj_x = projection(particles, :sd, :Msol_pc2, lmax=9, direction=:x, zrange=[0.45,0.55], verbose=false);Select a Range Related to a Center
See also in the documentation for: load data by selection
# Calculate box center coordinates for spatial selection
cv = (particles.boxlen / 2.) * particles.scale.kpc # provide the box-center in kpc
proj_z = projection(particles, :sd, :Msol_pc2, lmax=9,
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[cv,cv,cv], range_unit=:kpc);[Mera]: 2025-08-14T15:02:22.177
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
Use the short notation for the box center :bc or :boxcenter for all dimensions (x,y,z):
# Use convenient box center notation for spatial selection
proj_z = projection(particles, :sd, :Msol_pc2, lmax=9,
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[:boxcenter], range_unit=:kpc);[Mera]: 2025-08-14T15:02:22.707
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
# Alternative short notation using :bc
proj_z = projection(particles, :sd, :Msol_pc2, lmax=9,
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[:bc], range_unit=:kpc);[Mera]: 2025-08-14T15:02:22.840
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
Use the box center notation for individual dimensions, here x,z:
# Mix explicit coordinates with box center notation
proj_z = projection(particles, :sd, :Msol_pc2, lmax=9,
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[:bc, 24., :bc], range_unit=:kpc);[Mera]: 2025-08-14T15:02:23.289
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
Get Multiple Quantities
Get several quantities with one function call by passing an array containing the selected variables (at least one entry). The keyword name for the units is now in plural.
# Project multiple quantities simultaneously
proj1_x = projection(particles, [:sd], units=[:Msol_pc2], lmax=9,
direction=:x,
xrange=[-10.,10.],
yrange=[-10.,10.],
zrange=[-2.,2.],
center=[24.,24.,24.],
range_unit=:kpc);[Mera]: 2025-08-14T15:02:23.432
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
Pass an array containing several quantities to process and their corresponding units:
# Project surface density and x-velocity component together
proj1_z = projection(particles, [:sd, :vx], units=[:Msol_pc2, :km_s], lmax=9,
direction=:x,
xrange=[-10.,10.],
yrange=[-10.,10.],
zrange=[-2.,2.],
center=[24.,24.,24.],
range_unit=:kpc);[Mera]: 2025-08-14T15:02:23.458
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
The function can be called without any keywords by preserving the following order: dataobject, variables, units
# Simplified syntax without explicit keywords
proj1_z = projection(particles, [:sd, :vx], [:Msol_pc2, :km_s], lmax=9,
direction=:x,
xrange=[-10.,10.],
yrange=[-10.,10.],
zrange=[-2.,2.],
center=[24.,24.,24.],
range_unit=:kpc);[Mera]: 2025-08-14T15:02:24.016
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
If all selected variables should be of the same unit use the following arguments: dataobject, array of quantities, unit (no array needed)
# Project multiple velocity components with unified units
projvel_z = projection(particles, [:vx, :vy, :vz], :km_s, lmax=9,
xrange=[-10.,10.],
yrange=[-10.,10.],
zrange=[-2.,2.],
center=[24.,24.,24.],
range_unit=:kpc);[Mera]: 2025-08-14T15:02:24.078
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
Function Output
List the fields of the assigned object:
# Examine the structure of projection objects
propertynames(projvel_z)(:maps, :maps_unit, :maps_lmax, :maps_mode, :lmax_projected, :lmin, :lmax, :ref_time, :ranges, :extent, :cextent, :ratio, :effres, :pixsize, :boxlen, :scale, :info)The projected 2D maps are stored in a dictionary:
# Access the maps dictionary containing projection data
projvel_z.maps # NaN for empty regionsDataStructures.SortedDict{Any, Any, Base.Order.ForwardOrdering} with 4 entries:
:sd => [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.0 0.0 … 0.0 0.0; 0.0 0.0 …
:vx => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN …
:vy => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN …
:vz => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN NaN …The maps can be accessed by giving the name of the dictionary:
# Access individual projection maps by variable name
proj1_z.maps[:sd]214×44 Matrix{Float64}:
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
⋮ ⋮ ⋱ ⋮
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 … 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0The units of the maps are stored in:
# Check units for each projected quantity
projvel_z.maps_unitDataStructures.SortedDict{Any, Any, Base.Order.ForwardOrdering} with 4 entries:
:sd => :standard
:vx => :km_s
:vy => :km_s
:vz => :km_sThe following fields are helpful for further calculations or plots.
# Get coordinate ranges normalized to domain [0:1]
projvel_z.ranges6-element Vector{Float64}:
0.29166666666647767
0.7083333333328743
0.29166666666647767
0.7083333333328743
0.4583333333330363
0.5416666666663156# Get coordinate ranges in code units
projvel_z.extent4-element Vector{Float64}:
13.96875
34.03125
13.96875
34.03125# Get coordinate ranges relative to center (code units)
projvel_z.cextent4-element Vector{Float64}:
-10.031250000015554
10.031249999984446
-10.031250000015554
10.031249999984446# Get aspect ratio between coordinate ranges
projvel_z.ratio # the ratio between the two ranges1.0Plot Maps with PyPlot
proj_z = projection(particles, :sd, :Msol_pc2, lmax=9,
zrange=[-2.,2.], center=[:boxcenter], range_unit=:kpc,
verbose=false)
proj_x = projection(particles, :sd, :Msol_pc2, lmax=9,
zrange=[-2.,2.], center=[:boxcenter], range_unit=:kpc,
verbose=false,
direction = :x);Python functions can be directly called in Julia, which gives the opportunity, e.g. to use the Matplotlib library.
using PyPlot
using ColorSchemes
cmap = ColorMap(ColorSchemes.lajolla.colors) # See http://www.fabiocrameri.ch/colourmaps.php
cmap2 = ColorMap(reverse(ColorSchemes.roma.colors))ColorMap "cm_9039267583110145682"figure(figsize=(10, 3.5))
subplot(1,2,1)
im = imshow( log10.( permutedims(proj_z.maps[:sd])), cmap=cmap, aspect=proj_z.ratio, origin="lower", extent=proj_z.cextent, vmin=-0.5, vmax=1.5)
xlabel("x [kpc]")
ylabel("y [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(\Sigma) \ [M_{\odot} pc^{-2}]}")
subplot(1,2,2)
im = imshow( log10.( permutedims(proj_x.maps[:sd])), cmap=cmap, origin="lower", extent=proj_x.cextent, vmin=-0.5, vmax=3)
xlabel("x [kpc]")
ylabel("z [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(\Sigma) \ [M_{\odot} pc^{-2}]}",orientation="horizontal", pad=0.2);
tight_layout()
Figure(PyObject <Figure size 1000x350 with 4 Axes>)Project a specific spatial range and plot the axes of the map relative to the box-center (given by keyword: data_center):
proj_z = projection(particles, :sd, :Msol_pc2, lmax=9,
xrange=[-10.,0.], yrange=[-10.,0.], zrange=[-2.,2.], center=[:boxcenter], range_unit=:kpc,
verbose=false,
data_center=[24.,24.,24.], data_center_unit=:kpc)
proj_x = projection(particles, :sd, :Msol_pc2, lmax=9,
xrange=[-10.,0.], yrange=[-10.,0.], zrange=[-2.,2.], center=[:boxcenter], range_unit=:kpc,
verbose=false,
data_center=[24.,24.,24.], data_center_unit=:kpc,
direction = :x);figure(figsize=(10, 3.5))
subplot(1,2,1)
im = imshow( log10.( permutedims(proj_z.maps[:sd])), cmap=cmap, aspect=proj_z.ratio, origin="lower", extent=proj_z.cextent, vmin=-0.5, vmax=2.)
xlabel("x [kpc]")
ylabel("y [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(\Sigma) \ [M_{\odot} pc^{-2}]}")
subplot(1,2,2)
im = imshow( log10.( permutedims(proj_x.maps[:sd])), cmap=cmap, origin="lower", extent=proj_x.cextent, vmin=-0.5, vmax=3)
xlabel("x [kpc]")
ylabel("z [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(\Sigma) \ [M_{\odot} pc^{-2}]}",orientation="horizontal", pad=0.2);
Figure(PyObject <Figure size 1000x350 with 4 Axes>)Plot the axes of the map relative to the map-center (given by keyword: data_center):
proj_z = projection(particles, :sd, :Msol_pc2, lmax=9,
xrange=[-10.,0.], yrange=[-10.,0.], zrange=[-2.,2.], center=[:boxcenter], range_unit=:kpc,
verbose=false,
data_center=[19.,19.,24.], data_center_unit=:kpc)
proj_x = projection(particles, :sd, :Msol_pc2, lmax=9,
xrange=[-10.,0.], yrange=[-10.,0.], zrange=[-2.,2.], center=[:boxcenter], range_unit=:kpc,
verbose=false,
data_center=[19.,19.,24.], data_center_unit=:kpc,
direction = :x);figure(figsize=(10, 3.5))
subplot(1,2,1)
im = imshow( log10.( permutedims(proj_z.maps[:sd])), cmap=cmap, aspect=proj_z.ratio, origin="lower", extent=proj_z.cextent, vmin=-0.5, vmax=2.)
xlabel("x [kpc]")
ylabel("y [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(\Sigma) \ [M_{\odot} pc^{-2}]}")
subplot(1,2,2)
im = imshow( log10.( permutedims(proj_x.maps[:sd])), cmap=cmap, origin="lower", extent=proj_x.cextent, vmin=-0.5, vmax=3)
xlabel("x [kpc]")
ylabel("z [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(\Sigma) \ [M_{\odot} pc^{-2}]}",orientation="horizontal", pad=0.2);
Figure(PyObject <Figure size 1000x350 with 4 Axes>)Projections of Derived Kinematic Data
Use quantities in cartesian coordinates:
Project the following derived data (mass weighted by default): The absolute value of the velocity :v, the velocity dispersion :σ in different directions and the kinetic energy :ekin. The Julia language supports Unicode characters and can be inserted by e.g. "\sigma + tab-key" leading to: σ.
# σ: Total 3D velocity dispersion = √(σx² + σy² + σz²)
# σx, σy, σz: Velocity dispersions along coordinate axes
proj_z = projection(particles, [:v, :σ, :σx, :σy, :σz, :ekin],
units=[:km_s,:km_s,:km_s,:km_s,:km_s,:erg],
lmax=9,
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[24.,24.,24.], range_unit=:kpc);[Mera]: 2025-08-14T15:02:31.788
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 512^2
Pixel size: 93.75 [pc]
Simulation min.: 46.875 [pc]
For the velocity dispersion additional maps are created that lead to the mass-weighted quantity: E. g.: σx = sqrt( <vx^2> - < vx >^2 )
proj_z.maps # NaN indicates that there are no particles for the available grid.DataStructures.SortedDict{Any, Any, Base.Order.ForwardOrdering} with 14 entries:
:ekin => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:sd => [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.0 0.0 … 0.0 0.0; 0.0 0.…
:v => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:v2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:vx => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:vx2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:vy => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:vy2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:vz => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:vz2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:σ => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:σx => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:σy => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…
:σz => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN; NaN Na…proj_z.maps_unitDataStructures.SortedDict{Any, Any, Base.Order.ForwardOrdering} with 14 entries:
:ekin => :erg
:sd => :standard
:v => :km_s
:v2 => :standard
:vx => :standard
:vx2 => :standard
:vy => :standard
:vy2 => :standard
:vz => :standard
:vz2 => :standard
:σ => :km_s
:σx => :km_s
:σy => :km_s
:σz => :km_susedmemory(proj_z);Memory used: 4.919 MB
figure(figsize=(10, 5.5))
subplot(2, 3, 1)
title("v [km/s]")
imshow( (permutedims(proj_z.maps[:v]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent, vmax=300.)
colorbar()
subplot(2, 3, 2)
title("σ [km/s]")
imshow( (permutedims(proj_z.maps[:σ]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(2, 3, 3)
title("Ekin [erg]")
imshow( log10.(permutedims(proj_z.maps[:ekin]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(2, 3, 4)
title("σx [km/s]")
imshow( (permutedims(proj_z.maps[:σx]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(2, 3, 5)
title("σy [km/s]")
imshow( (permutedims(proj_z.maps[:σy]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(2, 3, 6)
title("σz [km/s]")
imshow( (permutedims(proj_z.maps[:σz]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar();
Figure(PyObject <Figure size 1000x550 with 12 Axes>)Use quantities in cylindrical coordinates:
Face-on disc (z-direction)
For the cylindrical or spherical components of a quantity, the center of the coordinate system is used (keywords: datacenter = center default) and can be given with the keyword "datacenter" and its units with "datacenterunit". Additionally, the quantities that are based on cartesian coordinates can be given.
# σr_cylinder: Radial velocity dispersion in cylindrical coordinates
# σϕ_cylinder: Azimuthal velocity dispersion (tangential motions)
proj_z = projection(particles, [:v, :σ, :σx, :σy, :ϕ, :r_cylinder, :vr_cylinder, :vϕ_cylinder, :σr_cylinder, :σϕ_cylinder],
units=[:km_s,:km_s,:km_s, :km_s, :standard, :kpc, :km_s, :km_s, :km_s, :km_s],
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[:boxcenter], range_unit=:kpc,
data_center=[24.,24.,24.], data_center_unit=:kpc);[Mera]: 2025-08-14T15:02:34.805
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 1024^2
Pixel size: 46.875 [pc]
Simulation min.: 46.875 [pc]
proj_z.mapsDataStructures.SortedDict{Any, Any, Base.Order.ForwardOrdering} with 18 entries:
:r_cylinder => [14.0758 14.0427 … 14.1201 14.1534; 14.0427 14.0096 … 14.087…
:sd => [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.0 0.0 … 0.0 0.0…
:v => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:v2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:vr_cylinder => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:vr_cylinder2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:vx => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:vx2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:vy => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:vy2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:vϕ_cylinder => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:vϕ_cylinder2 => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:σ => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:σr_cylinder => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:σx => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:σy => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:σϕ_cylinder => [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … NaN NaN…
:ϕ => [3.92699 3.92463 … 2.35306 2.35073; 3.92935 3.92699 … 2.3507…proj_z.maps_unitDataStructures.SortedDict{Any, Any, Base.Order.ForwardOrdering} with 18 entries:
:r_cylinder => :kpc
:sd => :standard
:v => :km_s
:v2 => :standard
:vr_cylinder => :km_s
:vr_cylinder2 => :standard
:vx => :standard
:vx2 => :standard
:vy => :standard
:vy2 => :standard
:vϕ_cylinder => :km_s
:vϕ_cylinder2 => :standard
:σ => :km_s
:σr_cylinder => :km_s
:σx => :km_s
:σy => :km_s
:σϕ_cylinder => :km_s
:ϕ => :radianfigure(figsize=(10, 8.5))
subplot(3, 3, 1)
title("Radius [kpc]")
imshow( permutedims(proj_z.maps[:r_cylinder] ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 2)
title("vr [km/s]")
imshow( permutedims(proj_z.maps[:vr_cylinder] ), cmap="seismic", origin="lower", extent=proj_z.cextent, vmin=-50.,vmax=50.)
colorbar()
subplot(3, 3, 3)
title("vϕ [km/s]")
imshow( permutedims(proj_z.maps[:vϕ_cylinder] ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 4)
title("ϕ-angle")
imshow( (permutedims(proj_z.maps[:ϕ]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 5)
title("σr [km/s]")
imshow( permutedims(proj_z.maps[:σr_cylinder] ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 6)
title("σϕ [km/s]")
imshow( permutedims(proj_z.maps[:σϕ_cylinder] ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 7)
title("σ [km/s]")
imshow( (permutedims(proj_z.maps[:σ]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 8)
title("σx [km/s]")
imshow( permutedims(proj_z.maps[:σx] ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 9)
title("σy [km/s]")
imshow( permutedims(proj_z.maps[:σy] ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar();
Figure(PyObject <Figure size 1000x850 with 18 Axes>)Project on a Coarser Grid
lmax
The default is the projection on the maximum loaded grid level (always provided in the output). Choose a smaller/larger level with the keyword lmax (independend on the maximum level of the simulation) to project on a coarser/finer grid. By default, the data is assumed to be in the center of the simulation box.
# lmax=6: Uses AMR level 6 → 2^6 = 64x64 effective grid cells
proj_z = projection(particles,
[:v, :σ, :σx, :σy, :σz, :vr_cylinder, :vϕ_cylinder, :σr_cylinder, :σϕ_cylinder],
:km_s,
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[:boxcenter], range_unit=:kpc,
lmax=6);[Mera]: 2025-08-14T15:02:38.034
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 64^2
Pixel size: 750.0 [pc]
Simulation min.: 46.875 [pc]
The projection onto the maximum loaded grid is always provided:
proj_z.mapsDataStructures.SortedDict{Any, Any, Base.Order.ForwardOrdering} with 19 entries:
:sd => [3.6988e-5 6.11725e-5 … 2.84523e-5 4.26785e-6; 0.000146885 8…
:v => [215.727 217.893 … 213.689 211.398; 211.909 214.371 … 215.29…
:v2 => [10.8327 11.0472 … 10.6218 10.3945; 10.4511 10.6909 … 10.782…
:vr_cylinder => [-4.97035 9.83744 … 0.326624 -5.23378; -8.36334 3.71396 … 7.…
:vr_cylinder2 => [0.0231364 0.0346302 … 0.0196151 0.0344127; 0.0339309 0.0312…
:vx => [2.38824 2.13472 … -2.30736 -2.28246; 2.4166 2.28742 … -2.49…
:vx2 => [5.71099 4.56492 … 5.33794 5.23511; 5.844 5.24859 … 6.2532 6…
:vy => [-2.25778 -2.54366 … -2.29652 -2.26901; -2.14059 -2.32778 … …
:vy2 => [5.12035 6.48209 … 5.28291 5.15929; 4.60593 5.44099 … 4.5285…
:vz => [-0.032174 0.00624848 … -0.0229633 0.00636579; -0.0269481 -0…
:vz2 => [0.00140393 0.000219812 … 0.000962049 0.000127987; 0.0011519…
:vϕ_cylinder => [215.479 217.553 … 213.481 211.046; 211.546 214.042 … 214.91…
:vϕ_cylinder2 => [10.8082 11.0124 … 10.6012 10.36; 10.416 10.6583 … 10.7443 1…
:σ => [6.62266 5.19183 … 3.45808 2.9201; 5.96424 4.11213 … 3.61926…
:σr_cylinder => [8.64782 7.22072 … 9.17826 10.9812; 8.71558 10.9816 … 10.005…
:σx => [5.59831 5.83217 … 7.77318 10.4683; 4.16244 8.36784 … 6.5268…
:σy => [9.89835 7.14297 … 6.19364 6.84324; 10.118 9.82426 … 9.42142…
:σz => [1.25926 0.88166 … 1.36727 0.613275; 1.35303 2.02241 … 1.188…
:σϕ_cylinder => [6.7421 5.02267 … 3.52611 2.97303; 6.19423 4.23835 … 3.69795…proj_z.maps_unitDataStructures.SortedDict{Any, Any, Base.Order.ForwardOrdering} with 19 entries:
:sd => :standard
:v => :km_s
:v2 => :standard
:vr_cylinder => :km_s
:vr_cylinder2 => :standard
:vx => :standard
:vx2 => :standard
:vy => :standard
:vy2 => :standard
:vz => :standard
:vz2 => :standard
:vϕ_cylinder => :km_s
:vϕ_cylinder2 => :standard
:σ => :km_s
:σr_cylinder => :km_s
:σx => :km_s
:σy => :km_s
:σz => :km_s
:σϕ_cylinder => :km_sfigure(figsize=(10, 8.5))
subplot(3, 3, 1)
title("|v| [km/s]")
imshow( permutedims(proj_z.maps[:v] ), cmap=cmap2, origin="lower", extent=proj_z.cextent, vmax=300.)
colorbar()
subplot(3, 3, 2)
title("vr [km/s]")
imshow( permutedims(proj_z.maps[:vr_cylinder] ), cmap=cmap2, origin="lower", extent=proj_z.cextent, vmin=-200.,vmax=200.)
colorbar()
subplot(3, 3, 3)
title("vϕ [km/s]")
imshow( permutedims(proj_z.maps[:vϕ_cylinder] ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 4)
title("σz [km/s]")
imshow( (permutedims(proj_z.maps[:σz]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 5)
title("σr [km/s]")
imshow( (permutedims(proj_z.maps[:σr_cylinder] )), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 6)
title("σϕ [km/s]")
imshow( (permutedims(proj_z.maps[:σϕ_cylinder] )), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 7)
title("σ [km/s]")
imshow( (permutedims(proj_z.maps[:σ]) ), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 8)
title("σx [km/s]")
imshow( (permutedims(proj_z.maps[:σx] )), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar()
subplot(3, 3, 9)
title("σy [km/s]")
imshow( (permutedims(proj_z.maps[:σy] )), cmap=cmap2, origin="lower", extent=proj_z.cextent)
colorbar();
Figure(PyObject <Figure size 1000x850 with 18 Axes>)res
Choose the effective resolution (related to the full box) of the projected grid:
# res=100: Forces 100x100 effective grid
proj_z = projection(particles,
[:v, :σ, :σx, :σy, :σz, :vr_cylinder, :vϕ_cylinder, :σr_cylinder, :σϕ_cylinder],
:km_s,
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[:boxcenter], range_unit=:kpc,
res=100);[Mera]: 2025-08-14T15:02:39.426
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 100^2
Pixel size: 480.0 [pc]
Simulation min.: 46.875 [pc]
pxsize
Choose the pixel size in a physical unit, e.g. pixel-size=100 pc. The data is projected to a grid with a pixel-size that is closest to the given number, but not larger:
# pxsize=[100.,:pc]: Each pixel represents ~100 pc physical size
proj_z = projection(particles,
[:v, :σ, :σx, :σy, :σz, :vr_cylinder, :vϕ_cylinder, :σr_cylinder, :σϕ_cylinder],
:km_s,
xrange=[-10.,10.], yrange=[-10.,10.], zrange=[-2.,2.],
center=[:boxcenter], range_unit=:kpc,
pxsize=[100., :pc]);[Mera]: 2025-08-14T15:02:40.202
center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]
domain:
xmin::xmax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
ymin::ymax: 0.2916667 :: 0.7083333 ==> 14.0 [kpc] :: 34.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667 ==> 22.0 [kpc] :: 26.0 [kpc]
Effective resolution: 481^2
Pixel size: 99.792 [pc]
Simulation min.: 46.875 [pc]
Projection of the Birth/Age-Time
Project the average birth-time of the particles to the grid:
# :birth - Absolute formation time in simulation units (when star formed)
proj_z = projection(particles, :birth, :Myr,
lmax=8, zrange=[0.45,0.55], center=[0.,0.,0.], verbose=false);
proj_x = projection(particles, :birth, :Myr,
lmax=8, zrange=[0.45,0.55], center=[0.,0.,0.], direction=:x, verbose=false);figure(figsize=(10, 3.5))
subplot(1,2,1)
im = imshow( log10.( permutedims(proj_z.maps[:birth])), cmap=cmap2, aspect=proj_z.ratio, origin="lower", extent=proj_z.cextent)
xlabel("x [kpc]")
ylabel("y [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(Birth) \ [Myr]}")
subplot(1,2,2)
im = imshow( log10.( permutedims(proj_x.maps[:birth])), cmap=cmap2, origin="lower", extent=proj_x.cextent)
xlabel("x [kpc]")
ylabel("z [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(Birth) \ [Myr]}",orientation="horizontal", pad=0.2);
tight_layout()
Figure(PyObject <Figure size 1000x350 with 4 Axes>)Project the average age of the particles to the grid. The age is taken relative to the loaded snapshot time by default.
# :age - Relative age = snapshot_time - birth_time (how old star is now)
proj_z = projection(particles, :age, :Myr,
lmax=8, zrange=[0.45,0.55], center=[0.,0.,0.], verbose=false);
proj_x = projection(particles, :age, :Myr,
lmax=8, zrange=[0.45,0.55], direction=:x, center=[0.,0.,0.], verbose=false);The reference time (code units) for the age calculation:
proj_z.ref_time29.9031937665063figure(figsize=(10, 3.5))
subplot(1,2,1)
im = imshow( log10.( permutedims(proj_z.maps[:age])), cmap=cmap2, aspect=proj_z.ratio, origin="lower", extent=proj_z.cextent)
xlabel("x [kpc]")
ylabel("y [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(Age) \ [Myr]}")
subplot(1,2,2)
im = imshow( log10.( permutedims(proj_x.maps[:age])), cmap=cmap2, origin="lower", extent=proj_x.cextent)
xlabel("x [kpc]")
ylabel("z [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(Age) \ [Myr]}",orientation="horizontal", pad=0.2);
tight_layout()
Figure(PyObject <Figure size 1000x350 with 4 Axes>)Project the average age of the particles relative to a given reference time:
proj_z = projection(particles, :age, :Myr, ref_time=0.,
lmax=8, zrange=[0.45,0.55], center=[0.,0.,0.], verbose=false);
proj_x = projection(particles, :age, :Myr, ref_time = 0.,
lmax=8, zrange=[0.45,0.55], center=[0.,0.,0.], direction=:x, verbose=false);The reference time (code units) for the age calculation:
proj_z.ref_time0.0figure(figsize=(10, 3.5))
subplot(1,2,1)
im = imshow( ( permutedims(proj_z.maps[:age])), cmap=cmap2, aspect=proj_z.ratio, origin="lower", extent=proj_z.cextent)
xlabel("x [kpc]")
ylabel("y [kpc]")
cb = colorbar(im, label=L"\mathrm{Age \ [Myr]}")
subplot(1,2,2)
im = imshow( ( permutedims(proj_x.maps[:age])), cmap=cmap2, origin="lower", extent=proj_x.cextent)
xlabel("x [kpc]")
ylabel("z [kpc]")
cb = colorbar(im, label=L"\mathrm{Age \ [Myr]}",orientation="horizontal", pad=0.2);
tight_layout()
Figure(PyObject <Figure size 1000x350 with 4 Axes>)Projection of Masked Data
Mask particles with ages higher than 400 Myr by creating a Bool-array where the smaller ages correspond to false entries:
mask = getvar(particles, :age, :Myr) .> 400. ;proj_z = projection(particles, :age, :Myr, mask=mask,
lmax=8, zrange=[0.45,0.55], center=[0.,0.,0.]);
proj_x = projection(particles, :age, :Myr, mask=mask,
lmax=8, zrange=[0.45,0.55], center=[0.,0.,0.], direction=:x);[Mera]: 2025-08-14T15:02:42.255
domain:
xmin::xmax: 0.0 :: 1.0 ==> 0.0 [kpc] :: 48.0 [kpc]
ymin::ymax: 0.0 :: 1.0 ==> 0.0 [kpc] :: 48.0 [kpc]
zmin::zmax: 0.45 :: 0.55 ==> 21.6 [kpc] :: 26.4 [kpc]
Effective resolution: 256^2
Pixel size: 187.5 [pc]
Simulation min.: 46.875 [pc]
:mask provided by function
[Mera]: 2025-08-14T15:02:43.103
domain:
xmin::xmax: 0.0 :: 1.0 ==> 0.0 [kpc] :: 48.0 [kpc]
ymin::ymax: 0.0 :: 1.0 ==> 0.0 [kpc] :: 48.0 [kpc]
zmin::zmax: 0.45 :: 0.55 ==> 21.6 [kpc] :: 26.4 [kpc]
Effective resolution: 256^2
Pixel size: 187.5 [pc]
Simulation min.: 46.875 [pc]
:mask provided by function
figure(figsize=(10, 3.5))
subplot(1,2,1)
im = imshow( log10.( permutedims(proj_z.maps[:age])), cmap=cmap2, aspect=proj_z.ratio, origin="lower", extent=proj_z.cextent)
xlabel("x [kpc]")
ylabel("y [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(Age) \ [Myr]}")
subplot(1,2,2)
im = imshow( log10.( permutedims(proj_x.maps[:age])), cmap=cmap2, origin="lower", extent=proj_x.cextent)
xlabel("x [kpc]")
ylabel("z [kpc]")
cb = colorbar(im, label=L"\mathrm{log10(Age) \ [Myr]}",orientation="horizontal", pad=0.2);
tight_layout()
Figure(PyObject <Figure size 1000x350 with 4 Axes>)Summary
Key Accomplishments
In this tutorial, you have learned to:
- Create basic particle projections with mass weighting
- Configure custom resolution and spatial extent
- Generate projections along different axes
- Perform age-based stellar population analysis
- Access and analyze projection data arrays
Particle Projection Capabilities
- Stellar Properties: Mass, age, metallicity, birth time projections
- Kinematic Analysis: Velocity component projections
- Population Studies: Age gradients and stellar distribution analysis
- Multi-component: Separate star and dark matter projections
- Custom Weighting: Mass-weighted or number-weighted projections