Clump Data: Load Selected Variables and Spatial Ranges

This notebook provides a comprehensive guide to selective clump data loading and spatial filtering in Mera.jl. You'll learn advanced techniques for efficiently loading only the clump data you need from large clump-finding simulations.

Learning Objectives

Master selective clump property loading for memory optimization
Apply spatial filtering and region selection techniques for clump analysis
Work with different coordinate systems and units for clump distributions
Understand center-relative coordinate systems for clump populations
Optimize clump data loading for large simulations

Quick Reference: Clump Data Selection Functions

This section provides a comprehensive reference of Mera.jl functions for selective clump data loading and spatial filtering.

Variable Selection

# Load all variables (default behavior)
clumps = getclumps(info)

# Select specific variables by name
clumps = getclumps(info, vars=[:index, :lev, :parent, :ncell])  # Core properties
clumps = getclumps(info, vars=[:peak_x, :peak_y, :peak_z])      # Position data
clumps = getclumps(info, vars=[:mass_cl, :rho_av, :relevance])  # Physical properties

# Select variables without keyword (order matters: info, variables)
clumps = getclumps(info, [:index, :lev, :parent])              # Multiple variables
clumps = getclumps(info, :mass_cl)                             # Single variable

# Common clump variable categories
# Structural: :index, :lev, :parent, :ncell
# Position: :peak_x, :peak_y, :peak_z
# Physical: :mass_cl, :rho_av, Symbol("rho-"), Symbol("rho+")
# Kinematics: :vx, :vy, :vz
# Analysis: :relevance

Spatial Range Selection

# RAMSES standard notation (domain: [0:1]³)
clumps = getclumps(info, xrange=[0.2, 0.8],         # X-range filter
                         yrange=[0.2, 0.8],         # Y-range filter
                         zrange=[0.4, 0.6])         # Z-range filter

# Center-relative coordinates (RAMSES units)
clumps = getclumps(info, xrange=[-0.3, 0.3],        # Relative to center
                         yrange=[-0.3, 0.3],
                         zrange=[-0.1, 0.1],
                         center=[0.5, 0.5, 0.5])

# Physical units (e.g., kpc)
clumps = getclumps(info, xrange=[2., 22.],           # Physical coordinates
                         yrange=[2., 22.],
                         zrange=[22., 26.],
                         range_unit=:kpc)

# Center-relative with physical units
clumps = getclumps(info, xrange=[-16., 16.],         # Relative to center in kpc
                         yrange=[-16., 16.],
                         zrange=[-2., 2.],
                         center=[24., 24., 24.],
                         range_unit=:kpc)

# Box center shortcuts
clumps = getclumps(info, center=[:boxcenter])       # All dimensions centered
clumps = getclumps(info, center=[:bc])              # Short form
clumps = getclumps(info, center=[:bc, 24., :bc])    # Mixed: center x,z; fixed y

Performance Optimization

# Combined optimizations
clumps = getclumps(info, [:index, :mass_cl, :peak_x, :peak_y, :peak_z], # Select variables
                         xrange=[-10., 10.],         # Spatial range
                         yrange=[-10., 10.],
                         zrange=[-2., 2.],
                         center=[:bc],               # Box center
                         range_unit=:kpc)            # Physical units

Available Physical Units

# Check available units in simulation
viewfields(info.scale)

# Common length units
:m, :km, :cm, :mm, :μm, :Mpc, :kpc, :pc, :ly, :au, :Rsun

Getting Started: Simulation Setup

Before exploring clump data selection techniques, let's load our simulation and examine its properties. This establishes the foundation for all subsequent clump data loading operations.

using Mera
info = getinfo(400, "/Volumes/FASTStorage/Simulations/Mera-Tests/manu_sim_sf_L14");

[Mera]: 2025-08-14T14:14:07.650

Code: RAMSES
output [400] summary:
mtime: 2018-09-05T09:51:55
ctime: 2025-06-29T20:06:45.267
=======================================================
simulation time: 594.98 [Myr]
boxlen: 48.0 [kpc]
ncpu: 2048
ndim: 3
-------------------------------------------------------
amr:           true
level(s): 6 - 14 --> cellsize(s): 750.0 [pc] - 2.93 [pc]
-------------------------------------------------------
hydro:         true
hydro-variables:  7  --> (:rho, :vx, :vy, :vz, :p, :var6, :var7)
hydro-descriptor: (:density, :velocity_x, :velocity_y, :velocity_z, :thermal_pressure, :passive_scalar_1, :passive_scalar_2)
γ: 1.6667
-------------------------------------------------------
gravity:       true
gravity-variables: (:epot, :ax, :ay, :az)
-------------------------------------------------------
particles:     true
- Npart:    5.091500e+05
- Nstars:   5.066030e+05
- Ndm:      2.547000e+03
particle-variables: 5  --> (:vx, :vy, :vz, :mass, :birth)
-------------------------------------------------------
rt:            false
-------------------------------------------------------
clumps:           true
clump-variables: (:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance)
-------------------------------------------------------
namelist-file:    false
timer-file:       false
compilation-file: true
makefile:         true
patchfile:        true
=======================================================

Variable Selection Techniques

Understanding how to selectively load clump properties is crucial for efficient memory usage and faster analysis. Mera provides flexible approaches to clump variable selection, from loading everything to precise property targeting.

Understanding Clump Variable References

Mera automatically reads clump file headers to identify available properties. Understanding these variables enables precise control over clump data loading.

Core Clump Properties:

Category	Variables	Description
Structural	`:index`, `:lev`, `:parent`, `:ncell`	Hierarchy and grid information
Position	`:peak_x`, `:peak_y`, `:peak_z`	Clump peak coordinates
Density	`:rho_av`, `Symbol("rho-")`, `Symbol("rho+")`	Average and extreme densities
Physical	`:mass_cl`, `:relevance`	Mass and significance measures
Kinematics	`:vx`, `:vy`, `:vz`	Velocity components

Key Features:

Variable order must match clump file headers
Automatic header parsing and column identification
Extensible beyond default header length
Consistent naming across all Mera clump functions
Future support for descriptor file variable names

Loading All Variables (Default Behavior)

The simplest approach is to load all available clump properties. This is the default behavior when no specific variables are requested.

clumps = getclumps(info);

[Mera]: Get clump data: 2025-08-14T14:14:10.113

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]

Read 12 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance]
Memory used for data table :61.58203125 KB
-------------------------------------------------------

clumps.data

Table with 644 rows, 12 columns:
Columns:
#   colname    type
──────────────────────
1   index      Float64
2   lev        Float64
3   parent     Float64
4   ncell      Float64
5   peak_x     Float64
6   peak_y     Float64
7   peak_z     Float64
8   rho-       Float64
9   rho+       Float64
10  rho_av     Float64
11  mass_cl    Float64
12  relevance  Float64

Important Note: Column names should be preserved as they are used by internal functions. Future updates will support descriptor file variable names for enhanced flexibility.

Selecting Multiple Variables

Mera provides multiple ways to select specific clump properties. You can use keyword arguments or positional arguments with flexible syntax.

Subset Selection: Load fewer than the default columns found in clump file headers. The order must be consistent with the header structure:

clumps = getclumps(info, vars=[ :index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z]);

[Mera]: Get clump data: 2025-08-14T14:14:12.333

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]

Read 7 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z]
Memory used for data table :35.9912109375 KB
-------------------------------------------------------

Alternative: Use positional arguments without the keyword. The following order must be preserved: InfoType object, then variables:

clumps = getclumps(info, [ :index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z]);

[Mera]: Get clump data: 2025-08-14T14:14:12.895

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]

Read 7 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z]
Memory used for data table :35.9912109375 KB
-------------------------------------------------------

clumps.data

Table with 644 rows, 7 columns:
index   lev  parent  ncell   peak_x   peak_y   peak_z
──────────────────────────────────────────────────────
4.0     0.0  4.0     740.0   20.1094  11.5005  23.9604
5.0     0.0  5.0     1073.0  20.1592  11.5122  23.9253
9.0     0.0  9.0     551.0   21.7852  17.855   23.814
12.0    0.0  12.0    463.0   21.8232  17.8608  23.855
13.0    0.0  13.0    2141.0  21.8906  17.2837  23.5415
18.0    0.0  18.0    691.0   21.7822  16.8823  23.7817
19.0    0.0  19.0    608.0   21.75    16.8589  23.7993
20.0    0.0  20.0    1253.0  21.6006  17.5679  23.7935
25.0    0.0  25.0    1275.0  21.5801  17.6177  23.9341
26.0    0.0  26.0    1212.0  21.5859  17.5796  23.9165
29.0    0.0  29.0    1759.0  21.5625  17.5854  23.8726
46.0    0.0  46.0    4741.0  21.5215  17.6235  23.9458
⋮
2115.0  0.0  2115.0  1071.0  27.7705  13.2788  23.8081
2116.0  0.0  2116.0  839.0   27.7617  13.3081  23.8081
2117.0  0.0  2117.0  753.0   27.7793  13.2993  23.6851
2120.0  0.0  2120.0  866.0   27.7559  13.1792  23.8638
2125.0  0.0  2125.0  181.0   27.7939  13.0298  23.9194
2128.0  0.0  2128.0  296.0   27.791   13.0649  23.9019
2131.0  0.0  2131.0  323.0   28.3037  12.8188  23.9487
2132.0  0.0  2132.0  615.0   28.626   12.8188  23.8755
2137.0  0.0  2137.0  318.0   29.9736  15.0571  23.7202
2140.0  0.0  2140.0  1719.0  27.1436  15.6401  23.9048
2147.0  0.0  2147.0  1535.0  25.1953  9.93604  23.9897

Extended Selection: Load more than the default columns from clump file headers. The order must still be consistent with the header structure:

clumps = getclumps(info, vars=[  :index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance, :vx, :vy, :vz]);

[Mera]: Get clump data: 2025-08-14T14:14:13.538

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]

Read 15 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance, :vx, :vy, :vz]
Memory used for data table :76.9365234375 KB
-------------------------------------------------------

clumps.data

Table with 644 rows, 15 columns:
Columns:
#   colname    type
──────────────────────
1   index      Float64
2   lev        Float64
3   parent     Float64
4   ncell      Float64
5   peak_x     Float64
6   peak_y     Float64
7   peak_z     Float64
8   rho-       Float64
9   rho+       Float64
10  rho_av     Float64
11  mass_cl    Float64
12  relevance  Float64
13  vx         Float64
14  vy         Float64
15  vz         Float64

Spatial Range Selection Techniques

Spatial filtering is essential for focusing clump analysis on specific regions of interest. Mera offers multiple coordinate systems and reference methods to accommodate different clump analysis needs.

Available Coordinate Systems:

RAMSES Standard: Normalized domain [0:1]³
Center-Relative: Coordinates relative to specified points
Physical Units: Real astronomical units (kpc, pc, etc.)
Box-Centered: Convenient shortcuts for simulation center

This flexibility allows precise clump region selection for targeted analysis while optimizing memory usage and computational efficiency.

RAMSES Standard Coordinate System

The RAMSES standard provides a normalized coordinate system that simplifies numerical calculations and ensures consistency across different simulation scales for clump analysis.

Coordinate System Properties:

Domain Range: [0:1]³ in all dimensions
Origin: Located at [0., 0., 0.]
Benefits: Scale-independent, numerically stable
Usage: Ideal for relative positioning and clump hierarchy analysis

Clump Analysis Applications: This notation is particularly useful for comparing clump distributions with grid-based data and analyzing hierarchical structure relationships.

clumps = getclumps(info,
                    xrange=[0.2,0.8],
                    yrange=[0.2,0.8],
                    zrange=[0.4,0.6]);

[Mera]: Get clump data: 2025-08-14T14:14:14.504

domain:
xmin::xmax: 0.2 :: 0.8  	==> 9.6 [kpc] :: 38.4 [kpc]
ymin::ymax: 0.2 :: 0.8  	==> 9.6 [kpc] :: 38.4 [kpc]
zmin::zmax: 0.4 :: 0.6  	==> 19.2 [kpc] :: 28.8 [kpc]

Read 12 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance]
Memory used for data table :61.58203125 KB
-------------------------------------------------------

Range Verification: The loaded clump data ranges are stored in the ranges field using RAMSES standard notation (domain: [0:1]³):

clumps.ranges

6-element Vector{Float64}:
 0.2
 0.8
 0.2
 0.8
 0.4
 0.6

Center-Relative Coordinate Selection

Define spatial ranges relative to a specified center point. This approach is particularly useful for analyzing clump distributions around specific massive objects, galaxies, or regions of interest:

clumps = getclumps(info,
                    xrange=[-0.3, 0.3],
                    yrange=[-0.3, 0.3],
                    zrange=[-0.1, 0.1],
                    center=[0.5, 0.5, 0.5]);

[Mera]: Get clump data: 2025-08-14T14:14:16.062

center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]

domain:
xmin::xmax: 0.2 :: 0.8  	==> 9.6 [kpc] :: 38.4 [kpc]
ymin::ymax: 0.2 :: 0.8  	==> 9.6 [kpc] :: 38.4 [kpc]
zmin::zmax: 0.4 :: 0.6  	==> 19.2 [kpc] :: 28.8 [kpc]

Read 12 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance]
Memory used for data table :61.58203125 KB
-------------------------------------------------------

Physical Unit Coordinate System

Working with physical units provides intuitive scale references for astronomical clump analysis. This system automatically handles unit conversions and maintains physical meaning for clump phenomena.

Key Advantages:

Intuitive Scaling: Use familiar astronomical units (kpc, pc, Mpc)
Automatic Conversion: Mera handles unit transformations internally
Reference Point: Coordinates measured from box corner [0., 0., 0.]
Flexibility: Mix different units as needed for clump analysis

The following example demonstrates kiloparsec (kpc) coordinate selection for clump analysis:

clumps = getclumps(info,
                    xrange=[2.,22.],
                    yrange=[2.,22.],
                    zrange=[22.,26.],
                    range_unit=:kpc);

[Mera]: Get clump data: 2025-08-14T14:14:16.531

domain:
xmin::xmax: 0.0416667 :: 0.4583333  	==> 2.0 [kpc] :: 22.0 [kpc]
ymin::ymax: 0.0416667 :: 0.4583333  	==> 2.0 [kpc] :: 22.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667  	==> 22.0 [kpc] :: 26.0 [kpc]

Read 12 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance]
Memory used for data table :12.64453125 KB
-------------------------------------------------------

Available Physical Units: The range_unit keyword accepts various length units defined in the simulation's scale field:

viewfields(info.scale) # or e.g.: clumps.info.scale


[Mera]: Fields to scale from user/code units to selected units
=======================================================================
Mpc	= 0.0010000000000006482
kpc	= 1.0000000000006481
pc	= 1000.0000000006482
mpc	= 1.0000000000006482e6
ly	= 3261.5637769461323
Au	= 2.0626480623310105e23
km	= 3.0856775812820004e16
m	= 3.085677581282e19
cm	= 3.085677581282e21
mm	= 3.085677581282e22
μm	= 3.085677581282e25
Mpc3	= 1.0000000000019446e-9
kpc3	= 1.0000000000019444
pc3	= 1.0000000000019448e9
mpc3	= 1.0000000000019446e18
ly3	= 3.469585750743794e10
Au3	= 8.775571306099254e69
km3	= 2.9379989454983075e49
m3	= 2.9379989454983063e58
cm3	= 2.9379989454983065e64
mm3	= 2.937998945498306e67
μm3	= 2.937998945498306e76
Msol_pc3	= 0.9997234790001649
Msun_pc3	= 0.9997234790001649
g_cm3	= 6.76838218451376e-23
Msol_pc2	= 999.7234790008131
Msun_pc2	= 999.7234790008131
g_cm2	= 0.20885045168302602
Gyr	= 0.014910986463557083
Myr	= 14.910986463557084
yr	= 1.4910986463557083e7
s	= 4.70554946422349e14
ms	= 4.70554946422349e17
Msol	= 9.99723479002109e8
Msun	= 9.99723479002109e8
Mearth	= 3.329677459032007e14
Mjupiter	= 1.0476363431814971e12
g	= 1.9885499720830952e42
km_s	= 65.57528732282063
m_s	= 65575.28732282063
cm_s	= 6.557528732282063e6
nH	= 30.987773856809987
erg	= 8.551000140274429e55
g_cms2	= 2.9104844143584656e-9
T_mu	= 517017.45993377
K_mu	= 517017.45993377
T	= 680286.1314918026
K	= 680286.1314918026
Ba	= 2.910484414358466e-9
g_cm_s2	= 2.910484414358466e-9
p_kB	= 2.1080552800592083e7
K_cm3	= 2.1080552800592083e7
erg_g_K	= 3.114563011649217e29
keV_cm2	= 1.252773885965637e65
erg_K	= 6.193464189866091e71
J_K	= 6.193464189866091e64
erg_cm3_K	= 2.1080552800592083e7
J_m3_K	= 2.1080552800592083e8
kB_per_particle	= 1.380649e-16
J_s	= 4.023715412864333e70
g_cm2_s	= 4.023715412864333e70
kg_m2_s	= 4.023715412864333e71
Gauss	= 0.00019124389093025845
muG	= 191.24389093025846
microG	= 191.24389093025846
Tesla	= 1.9124389093025845e-8
eV	= 5.3371144971238105e67
keV	= 5.33711449712381e64
MeV	= 5.33711449712381e61
erg_s	= 1.8172160775884043e41
Lsol	= 4.747168436751317e7
Lsun	= 4.747168436751317e7
cm_3	= 3.4036771916893676e-65
pc_3	= 1.158501842524895e-120
n_e	= 30.987773856809987
erg_g_s	= 0.09138397843151959
erg_cm3_s	= 6.185216915658869e-24
erg_cm2_s	= 6.185216915658869e-24
Jy	= 0.6185216915658869
mJy	= 618.5216915658868
microJy	= 618521.6915658868
atoms_cm2	= 1.2581352511025663e23
NH_cm2	= 1.2581352511025663e23
cm_s2	= 1.3935734353956443e-8
m_s2	= 1.3935734353956443e-10
km_s2	= 1.3935734353956443e-13
pc_Myr2	= 3.09843657823729e-9
erg_g	= 4.30011830747048e13
J_kg	= 4.30011830747048e6
km2_s2	= 4300.1183074704795
u_grav	= 2.910484414358466e-9
erg_cell	= 8.55100014027443e55
dyne	= 9.432237612943517e-31
s_2	= 4.516263928056473e-30
lambda_J	= 3.085677581282e21
M_J	= 1.9885499720830952e42
t_ff	= 4.70554946422349e14
alpha_vir	= 1.0
delta_rho	= 2.1915428665e-314
a_mag	= 2.19154289e-314
v_esc	= 2.191542914e-314
ax	= 2.1915429376e-314
ay	= 2.1915429614e-314
az	= 2.191542985e-314
epot	= 2.191543009e-314
a_magnitude	= 2.1915430325e-314
escape_speed	= 2.191543056e-314
gravitational_redshift	= 2.19154308e-314
gravitational_energy_density	= 2.1915431037e-314
gravitational_binding_energy	= 2.1915431274e-314
total_binding_energy	= 2.191543151e-314
specific_gravitational_energy	= 4.30011830747048e13
gravitational_work	= 2.1915431985e-314
jeans_length_gravity	= 3.085677581282e21
jeans_mass_gravity	= 1.9885499720830952e42
jeansmass	= 1.9885499720830952e42
freefall_time_gravity	= 4.70554946422349e14
ekin	= 8.551000140274429e55
etherm	= 8.551000140274429e55
virial_parameter_local	= 1.0
Fg	= 2.191543388e-314
poisson_source	= 2.191543412e-314
ar_cylinder	= 1.3935734353956443e-8
aϕ_cylinder	= 1.3935734353956443e-8
ar_sphere	= 1.3935734353956443e-8
aθ_sphere	= 1.3935734353956443e-8
aϕ_sphere	= 1.3935734353956443e-8
r_cylinder	= 3.085677581282e21
r_sphere	= 3.085677581282e21
ϕ	= 1.0
dimensionless	= 1.0
rad	= 1.0
deg	= 57.29577951308232

Center-Relative with Physical Units: Combine center-relative positioning with physical unit specifications for precise clump analysis:

clumps = getclumps(info,
                    xrange=[-16.,16.],
                    yrange=[-16.,16.],
                    zrange=[-2.,2.],
                    center=[24.,24.,24.],
                    range_unit=:kpc);

[Mera]: Get clump data: 2025-08-14T14:14:16.972

center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]

domain:
xmin::xmax: 0.1666667 :: 0.8333333  	==> 8.0 [kpc] :: 40.0 [kpc]
ymin::ymax: 0.1666667 :: 0.8333333  	==> 8.0 [kpc] :: 40.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667  	==> 22.0 [kpc] :: 26.0 [kpc]

Read 12 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance]
Memory used for data table :61.58203125 KB
-------------------------------------------------------

Box Center Coordinate Shortcuts

Mera provides convenient shortcuts for box-centered coordinate systems, simplifying clump analysis focused on the simulation center.

Available Shortcuts:

:bc or :boxcenter - Center coordinate for all dimensions
Can be applied to individual dimensions selectively
Combines seamlessly with physical units and range specifications
Ideal for symmetric clump analysis around simulation center

Clump Analysis Benefits:

Perfect for studying clump distributions around massive central objects
Eliminates manual center calculation for clump analysis
Ensures precise geometric centering of clump selections
Simplifies symmetric region definitions for hierarchical studies
Reduces coordinate specification errors in clump filtering

clumps = getclumps(info,
                    xrange=[-16.,16.],
                    yrange=[-16.,16.],
                    zrange=[-2.,2.],
                    center=[:boxcenter],
                    range_unit=:kpc);

[Mera]: Get clump data: 2025-08-14T14:14:17.398

center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]

domain:
xmin::xmax: 0.1666667 :: 0.8333333  	==> 8.0 [kpc] :: 40.0 [kpc]
ymin::ymax: 0.1666667 :: 0.8333333  	==> 8.0 [kpc] :: 40.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667  	==> 22.0 [kpc] :: 26.0 [kpc]

Read 12 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance]
Memory used for data table :61.58203125 KB
-------------------------------------------------------

clumps = getclumps(info,
                    xrange=[-16.,16.],
                    yrange=[-16.,16.],
                    zrange=[-2.,2.],
                    center=[:bc],
                    range_unit=:kpc);

[Mera]: Get clump data: 2025-08-14T14:14:17.894

center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]

domain:
xmin::xmax: 0.1666667 :: 0.8333333  	==> 8.0 [kpc] :: 40.0 [kpc]
ymin::ymax: 0.1666667 :: 0.8333333  	==> 8.0 [kpc] :: 40.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667  	==> 22.0 [kpc] :: 26.0 [kpc]

Read 12 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance]
Memory used for data table :61.58203125 KB
-------------------------------------------------------

Selective Dimension Centering: Apply box center notation to specific dimensions while maintaining explicit coordinates for others. This example centers x and z dimensions while fixing y at 24 kpc:

clumps = getclumps(info,
                    xrange=[-16.,16.],
                    yrange=[-16.,16.],
                    zrange=[-2.,2.],
                    center=[:bc, 24., :bc],
                    range_unit=:kpc);

[Mera]: Get clump data: 2025-08-14T14:14:18.320

center: [0.5, 0.5, 0.5] ==> [24.0 [kpc] :: 24.0 [kpc] :: 24.0 [kpc]]

domain:
xmin::xmax: 0.1666667 :: 0.8333333  	==> 8.0 [kpc] :: 40.0 [kpc]
ymin::ymax: 0.1666667 :: 0.8333333  	==> 8.0 [kpc] :: 40.0 [kpc]
zmin::zmax: 0.4583333 :: 0.5416667  	==> 22.0 [kpc] :: 26.0 [kpc]

Read 12 colums:
[:index, :lev, :parent, :ncell, :peak_x, :peak_y, :peak_z, Symbol("rho-"), Symbol("rho+"), :rho_av, :mass_cl, :relevance]
Memory used for data table :61.58203125 KB
-------------------------------------------------------

Summary

This notebook demonstrated comprehensive clump data selection techniques in Mera.jl, covering both variable selection and spatial filtering strategies for clump analysis. Key concepts covered include:

Variable Selection Mastery

Flexible Property Access: Loading structural, positional, physical, and kinematic clump properties
Header-Based Loading: Automatic parsing of clump file headers for available variables
Selective Loading: Optimizing memory usage by loading only required clump properties
Syntax Variations: Keyword and positional argument approaches for different coding styles
Extensible Selection: Supporting both subset and extended variable loading

Spatial Filtering Expertise

Coordinate Systems: RAMSES standard, physical units, center-relative, and box-centered approaches
Clump Focus: Targeting regions with significant clump populations and hierarchical structures
Memory Management: Balancing analysis needs with computational resources for clump studies
Unit Flexibility: Working with various astronomical length scales for clump phenomena
Center Definitions: Absolute positioning and relative coordinate systems for clump distributions

Advanced Clump Techniques

Combined Selection: Integrating variable selection with spatial filtering for clump analysis
Coordinate Shortcuts: Using box center notation for simplified clump positioning
Quality Assurance: Verifying loaded clump data ranges and property consistency
Multi-Physics Integration: Preparing clump data for combined hydrodynamic-structure analysis