From cd1e8abb0069406e817ceec0089722bca66da2be Mon Sep 17 00:00:00 2001
From: John Helly <j.c.helly@durham.ac.uk>
Date: Wed, 10 Jun 2026 10:42:59 +0100
Subject: [PATCH 1/4] Add example reading halo particles

---
 source/service_docs/python_examples.rst |   7 ++-
 source/snapshots/halo_particles.rst     |  80 ++++++++++++++++++++++++
 source/snapshots/images/L1_m10_halo.png | Bin 0 -> 33863 bytes
 source/snapshots/index.rst              |   1 +
 source/snapshots/swiftsimio.rst         |   2 +
 5 files changed, 88 insertions(+), 2 deletions(-)
 create mode 100644 source/snapshots/halo_particles.rst
 create mode 100644 source/snapshots/images/L1_m10_halo.png

diff --git a/source/service_docs/python_examples.rst b/source/service_docs/python_examples.rst
index 65c5b7f..3722b98 100644
--- a/source/service_docs/python_examples.rst
+++ b/source/service_docs/python_examples.rst
@@ -10,11 +10,14 @@ in python. Here, we provide links to these examples.
     specified region, and save part of a snapshot to a local file
     </snapshots/swiftsimio>`
 
+  * :doc:`Read SOAP halo properties using swiftsimio</soap/swiftsimio>`
+
+  * :doc:`Identify a halo of interest and read in the particles
+    belonging to it </snapshots/halo_particles>`
+
   * :doc:`Read and plot the matter power spectrum at several redshifts
     </power_spectra>`
 
-  * :doc:`Read SOAP halo properties using swiftsimio</soap/swiftsimio>`
-
   * :doc:`Identify the same halo between different simulations </soap/soap_matching>`
 
   * :ref:`Plot the evolution of a halo across snapshots <hbt_evolution_example>`
diff --git a/source/snapshots/halo_particles.rst b/source/snapshots/halo_particles.rst
new file mode 100644
index 0000000..da698d4
--- /dev/null
+++ b/source/snapshots/halo_particles.rst
@@ -0,0 +1,80 @@
+Reading particles belonging to a halo
+=====================================
+
+Here we show how to identify a halo of interest in the SOAP halo
+catalogue and then find its particles in the corresponding
+snapshot. This could be used to compute a halo density profile, for
+example.
+
+In the SOAP halo catalogues, each halo is assigned an index which is
+unique within the snapshot (but not between snapshots). This index is
+stored in the HDF5 dataset ``InputHalos/HaloCatalogueIndex``. There is
+a corresponding index ``PartTypeX/HaloCatalogueIndex`` associated with
+each particle in the snapshot.
+
+We can read in all particles belonging to a halo as follows:
+
+  * Use swiftsimio to read the halo catalogue (see :doc:`/soap/swiftsimio`)
+  * Identify a halo of interest in the catalogue
+  * Extract the position and ``HaloCatalogueIndex`` of this halo from the SOAP catalogue
+  * Read particles from the snapshot in some radius around the halo position (see :ref:`swiftsimio_cutouts`)
+  * Discard particles which do not have the required ``HaloCatalogueIndex``
+
+A python example which carries out this operation on the
+``L1_m10_DMO`` simulation is shown below:
+
+.. code-block:: python
+
+   import numpy as np
+   import unyt
+   import hdfstream
+   import swiftsimio as sw
+
+   # Connect to the hdfstream service and open the root directory
+   root_dir = hdfstream.open("cosma", "/")
+
+   # Open the z=0 halo catalogue from the L1_m10_DMO simulation
+   soap_file = root_dir["FLAMINGO/L1_m10/L1_m10_DMO/SOAP-HBT/halo_properties_0077.hdf5"]
+   soap = sw.load(soap_file)
+
+   # Get halo positions, masses and indexes
+   halo_pos = soap.input_halos.halo_centre
+   halo_m200c = soap.spherical_overdensity_200_crit.total_mass
+   halo_index = soap.input_halos.halo_catalogue_index
+
+   # Select the most massive halo
+   i = np.argmax(halo_m200c)
+   target_pos = halo_pos[i,:]
+   target_index = halo_index[i]
+
+   # Choose a region to read in. Note that we need to choose a radius large
+   # enough to enclose all halo particles.
+   radius = 5.0*unyt.Mpc
+   region = [[x-radius, x+radius] for x in target_pos]
+
+   # Open the snapshot and select this region
+   snap_file = root_dir["FLAMINGO/L1_m10/L1_m10_DMO/snapshots/flamingo_0077/flamingo_0077.hdf5"]
+   mask = sw.mask(snap_file)
+   mask.constrain_spatial(region)
+   snap = sw.load(snap_file, mask=mask)
+
+   # Read position and halo index of dark matter particles in this region
+   dm_pos = snap.dark_matter.coordinates
+   dm_halo_index = snap.dark_matter.halo_catalogue_index
+
+   # Of the particles we read, identify those which belong to the halo
+   in_halo = (dm_halo_index == target_index)
+   halo_dm_pos = dm_pos[in_halo,:]
+
+   # Make a simple dotplot as a sanity check
+   import matplotlib.pyplot as plt
+   plt.plot(halo_dm_pos[:,0], halo_dm_pos[:,1], "k,")
+   plt.gca().set_aspect("equal")
+   plt.xlabel("x [Mpc]")
+   plt.ylabel("y [Mpc]")
+   plt.title("Most massive halo in L1_m10_DMO")
+   plt.show()
+
+This results in the following plot showing dark matter particles in the halo:
+
+.. image:: images/L1_m10_halo.png
diff --git a/source/snapshots/images/L1_m10_halo.png b/source/snapshots/images/L1_m10_halo.png
new file mode 100644
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diff --git a/source/snapshots/index.rst b/source/snapshots/index.rst
index fe69e87..6178122 100644
--- a/source/snapshots/index.rst
+++ b/source/snapshots/index.rst
@@ -40,6 +40,7 @@ The following sections describe the layout and contents of the snapshots.
    Output redshifts <snapshot_redshifts>
    Particle properties <snapshot_particle_properties>
    Reading with swiftsimio <swiftsimio.rst>
+   Reading halo particles <halo_particles.rst>
    Partial snapshots <snapshot_partial>
 
 For more information about the SWIFT simulation snapshot format used
diff --git a/source/snapshots/swiftsimio.rst b/source/snapshots/swiftsimio.rst
index bb0a38a..d8bf9d3 100644
--- a/source/snapshots/swiftsimio.rst
+++ b/source/snapshots/swiftsimio.rst
@@ -156,6 +156,8 @@ Mpc::
    [987.07806218 995.72685218  88.39880218]
    [987.07910218 995.73126218  88.39935218]] Mpc (Comoving)
 
+.. _swiftsimio_cutouts:
+
 Cutting out a region
 --------------------
 

From d1b034acdefb06dbe533fbc3d9119d71d9d104c7 Mon Sep 17 00:00:00 2001
From: John Helly <j.c.helly@durham.ac.uk>
Date: Wed, 10 Jun 2026 10:59:51 +0100
Subject: [PATCH 2/4] Add example reading halo particles with h5py

---
 source/snapshots/halo_particles.rst | 164 +++++++++++++++++++---------
 source/soap/swiftsimio.rst          |   3 +
 2 files changed, 113 insertions(+), 54 deletions(-)

diff --git a/source/snapshots/halo_particles.rst b/source/snapshots/halo_particles.rst
index da698d4..4ffc441 100644
--- a/source/snapshots/halo_particles.rst
+++ b/source/snapshots/halo_particles.rst
@@ -20,60 +20,116 @@ We can read in all particles belonging to a halo as follows:
   * Read particles from the snapshot in some radius around the halo position (see :ref:`swiftsimio_cutouts`)
   * Discard particles which do not have the required ``HaloCatalogueIndex``
 
-A python example which carries out this operation on the
-``L1_m10_DMO`` simulation is shown below:
-
-.. code-block:: python
-
-   import numpy as np
-   import unyt
-   import hdfstream
-   import swiftsimio as sw
-
-   # Connect to the hdfstream service and open the root directory
-   root_dir = hdfstream.open("cosma", "/")
-
-   # Open the z=0 halo catalogue from the L1_m10_DMO simulation
-   soap_file = root_dir["FLAMINGO/L1_m10/L1_m10_DMO/SOAP-HBT/halo_properties_0077.hdf5"]
-   soap = sw.load(soap_file)
-
-   # Get halo positions, masses and indexes
-   halo_pos = soap.input_halos.halo_centre
-   halo_m200c = soap.spherical_overdensity_200_crit.total_mass
-   halo_index = soap.input_halos.halo_catalogue_index
-
-   # Select the most massive halo
-   i = np.argmax(halo_m200c)
-   target_pos = halo_pos[i,:]
-   target_index = halo_index[i]
-
-   # Choose a region to read in. Note that we need to choose a radius large
-   # enough to enclose all halo particles.
-   radius = 5.0*unyt.Mpc
-   region = [[x-radius, x+radius] for x in target_pos]
-
-   # Open the snapshot and select this region
-   snap_file = root_dir["FLAMINGO/L1_m10/L1_m10_DMO/snapshots/flamingo_0077/flamingo_0077.hdf5"]
-   mask = sw.mask(snap_file)
-   mask.constrain_spatial(region)
-   snap = sw.load(snap_file, mask=mask)
-
-   # Read position and halo index of dark matter particles in this region
-   dm_pos = snap.dark_matter.coordinates
-   dm_halo_index = snap.dark_matter.halo_catalogue_index
-
-   # Of the particles we read, identify those which belong to the halo
-   in_halo = (dm_halo_index == target_index)
-   halo_dm_pos = dm_pos[in_halo,:]
-
-   # Make a simple dotplot as a sanity check
-   import matplotlib.pyplot as plt
-   plt.plot(halo_dm_pos[:,0], halo_dm_pos[:,1], "k,")
-   plt.gca().set_aspect("equal")
-   plt.xlabel("x [Mpc]")
-   plt.ylabel("y [Mpc]")
-   plt.title("Most massive halo in L1_m10_DMO")
-   plt.show()
+This can be done either by using the :doc:`hdfstream python module
+</service_docs/python_module>` for remote access, or by downloading
+the SOAP catalogue and snapshot as HDF5 files and reading them
+directly with h5py. Both methods are illustrated below.
+
+.. tab-set::
+
+   .. tab-item:: Using remote file access
+
+      .. code-block:: python
+
+         import numpy as np
+         import unyt
+         import hdfstream
+         import swiftsimio as sw
+
+         # Connect to the hdfstream service and open the root directory
+         root_dir = hdfstream.open("cosma", "/")
+
+         # Open the z=0 halo catalogue from the L1_m10_DMO simulation
+         soap_file = root_dir["FLAMINGO/L1_m10/L1_m10_DMO/SOAP-HBT/halo_properties_0077.hdf5"]
+         soap = sw.load(soap_file)
+
+         # Get halo positions, masses and indexes
+         halo_pos = soap.input_halos.halo_centre
+         halo_m200c = soap.spherical_overdensity_200_crit.total_mass
+         halo_index = soap.input_halos.halo_catalogue_index
+
+         # Select the most massive halo
+         i = np.argmax(halo_m200c)
+         target_pos = halo_pos[i,:]
+         target_index = halo_index[i]
+
+         # Choose a region to read in. Note that we need to choose a radius large
+         # enough to enclose all halo particles.
+         radius = 5.0*unyt.Mpc
+         region = [[x-radius, x+radius] for x in target_pos]
+
+         # Open the z=0 snapshot from the L1_m10_DMO simulation and select this region
+         snap_file = root_dir["FLAMINGO/L1_m10/L1_m10_DMO/snapshots/flamingo_0077/flamingo_0077.hdf5"]
+         mask = sw.mask(snap_file)
+         mask.constrain_spatial(region)
+         snap = sw.load(snap_file, mask=mask)
+
+         # Read position and halo index of dark matter particles in this region
+         dm_pos = snap.dark_matter.coordinates
+         dm_halo_index = snap.dark_matter.halo_catalogue_index
+
+         # Of the particles we read, identify those which belong to the halo
+         in_halo = (dm_halo_index == target_index)
+         halo_dm_pos = dm_pos[in_halo,:]
+
+         # Make a simple dotplot as a sanity check
+         import matplotlib.pyplot as plt
+         plt.plot(halo_dm_pos[:,0], halo_dm_pos[:,1], "k,")
+         plt.gca().set_aspect("equal")
+         plt.xlabel("x [Mpc]")
+         plt.ylabel("y [Mpc]")
+         plt.title("Most massive halo in L1_m10_DMO")
+         plt.show()
+
+   .. tab-item:: Reading local HDF5 files
+
+      .. code-block:: python
+
+         import numpy as np
+         import unyt
+         import swiftsimio as sw
+
+         # Open the z=0 halo catalogue from the L1_m10_DMO simulation
+         soap_file = "FLAMINGO/L1_m10/L1_m10_DMO/SOAP-HBT/halo_properties_0077.hdf5"
+         soap = sw.load(soap_file)
+
+         # Get halo positions, masses and indexes
+         halo_pos = soap.input_halos.halo_centre
+         halo_m200c = soap.spherical_overdensity_200_crit.total_mass
+         halo_index = soap.input_halos.halo_catalogue_index
+
+         # Select the most massive halo
+         i = np.argmax(halo_m200c)
+         target_pos = halo_pos[i,:]
+         target_index = halo_index[i]
+
+         # Choose a region to read in. Note that we need to choose a radius large
+         # enough to enclose all halo particles.
+         radius = 5.0*unyt.Mpc
+         region = [[x-radius, x+radius] for x in target_pos]
+
+         # Open the z=0 snapshot from the L1_m10_DMO simulation and select this region
+         snap_file = "./FLAMINGO/L1_m10/L1_m10_DMO/snapshots/flamingo_0077/flamingo_0077.hdf5"
+         mask = sw.mask(snap_file)
+         mask.constrain_spatial(region)
+         snap = sw.load(snap_file, mask=mask)
+
+         # Read position and halo index of dark matter particles in this region
+         dm_pos = snap.dark_matter.coordinates
+         dm_halo_index = snap.dark_matter.halo_catalogue_index
+
+         # Of the particles we read, identify those which belong to the halo
+         in_halo = (dm_halo_index == target_index)
+         halo_dm_pos = dm_pos[in_halo,:]
+
+         # Make a simple dotplot as a sanity check
+         import matplotlib.pyplot as plt
+         plt.plot(halo_dm_pos[:,0], halo_dm_pos[:,1], "k,")
+         plt.gca().set_aspect("equal")
+         plt.xlabel("x [Mpc]")
+         plt.ylabel("y [Mpc]")
+         plt.title("Most massive halo in L1_m10_DMO")
+         plt.show()
 
 This results in the following plot showing dark matter particles in the halo:
 
diff --git a/source/soap/swiftsimio.rst b/source/soap/swiftsimio.rst
index 12aecdc..548a810 100644
--- a/source/soap/swiftsimio.rst
+++ b/source/soap/swiftsimio.rst
@@ -16,6 +16,9 @@ The latter method might be preferable if you only need a small
 fraction of the data, such as a subset of halo properties or halos in
 a small region of interest.
 
+To read the particles associated with a halo selected from a SOAP
+catalogue, see :doc:`/snapshots/halo_particles`.
+
 Installation
 ------------
 

From 8896df55beb701800d7fcca1fb3fb90978e1678c Mon Sep 17 00:00:00 2001
From: John Helly <j.c.helly@durham.ac.uk>
Date: Wed, 10 Jun 2026 11:07:30 +0100
Subject: [PATCH 3/4] Fix inconsistency in new example

---
 source/snapshots/halo_particles.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/source/snapshots/halo_particles.rst b/source/snapshots/halo_particles.rst
index 4ffc441..55d3a36 100644
--- a/source/snapshots/halo_particles.rst
+++ b/source/snapshots/halo_particles.rst
@@ -90,7 +90,7 @@ directly with h5py. Both methods are illustrated below.
          import swiftsimio as sw
 
          # Open the z=0 halo catalogue from the L1_m10_DMO simulation
-         soap_file = "FLAMINGO/L1_m10/L1_m10_DMO/SOAP-HBT/halo_properties_0077.hdf5"
+         soap_file = "./FLAMINGO/L1_m10/L1_m10_DMO/SOAP-HBT/halo_properties_0077.hdf5"
          soap = sw.load(soap_file)
 
          # Get halo positions, masses and indexes

From b02ed5431dea57a6b24f03210063c63678c6d258 Mon Sep 17 00:00:00 2001
From: John Helly <j.c.helly@durham.ac.uk>
Date: Wed, 10 Jun 2026 11:13:09 +0100
Subject: [PATCH 4/4] Add soap and snapshot links to the example

---
 source/snapshots/halo_particles.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/source/snapshots/halo_particles.rst b/source/snapshots/halo_particles.rst
index 55d3a36..40b8c0f 100644
--- a/source/snapshots/halo_particles.rst
+++ b/source/snapshots/halo_particles.rst
@@ -1,10 +1,10 @@
 Reading particles belonging to a halo
 =====================================
 
-Here we show how to identify a halo of interest in the SOAP halo
-catalogue and then find its particles in the corresponding
-snapshot. This could be used to compute a halo density profile, for
-example.
+Here we show how to identify a halo of interest in a :doc:`SOAP halo
+catalogue </soap/index>` and then find its particles in the
+corresponding :doc:`snapshot </snapshots/index>`. This could be used
+to compute a halo density profile, for example.
 
 In the SOAP halo catalogues, each halo is assigned an index which is
 unique within the snapshot (but not between snapshots). This index is