From fd866d3fc4dfdb831fde2804293732ccfffacb59 Mon Sep 17 00:00:00 2001
From: emilyhunt <emily.hunt.physics@gmail.com>
Date: Thu, 16 Oct 2025 12:56:59 +0200
Subject: [PATCH] Notebook to reproduce coolest plots in the paper

---
 examples/figure_9_and_10.ipynb | 658 +++++++++++++++++++++++++++++++++
 1 file changed, 658 insertions(+)
 create mode 100644 examples/figure_9_and_10.ipynb

diff --git a/examples/figure_9_and_10.ipynb b/examples/figure_9_and_10.ipynb
new file mode 100644
index 0000000..aecfb76
--- /dev/null
+++ b/examples/figure_9_and_10.ipynb
@@ -0,0 +1,658 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a414e695",
+   "metadata": {},
+   "source": [
+    "# Reproducing figures 9 & 10 from the paper\n",
+    "\n",
+    "In the paper, we show that proper motion can have a major impact on the detectability of clusters. This notebook presents a quick way to reproduce these figures as an example. By default, the figures use a lower resolution - this makes them a lot quicker to generate."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d516752",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2a3e4aae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import hashlib\n",
+    "import warnings\n",
+    "from dustmaps.bayestar import BayestarWebQuery\n",
+    "from dustmaps.decaps import DECaPSQueryLite\n",
+    "import dustmaps.decaps\n",
+    "from astropy.coordinates import (\n",
+    "    SkyCoord,\n",
+    "    CylindricalDifferential,\n",
+    "    CylindricalRepresentation,\n",
+    ")\n",
+    "from astropy import units as u\n",
+    "from gala.potential import MilkyWayPotential2022\n",
+    "from pathlib import Path\n",
+    "from hr_selection_function import HR24SelectionFunction, NStarsPredictor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42ad502d",
+   "metadata": {},
+   "source": [
+    "## Make a query grid and grab dust\n",
+    "\n",
+    "To start with, we need to look up extinction on a grid in the Milky Way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b4bf2a50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Whether or not to allow a predefined cache file to be used. This will speed up your\n",
+    "# code if you run it multiple times, but if you change other settings about the grid,\n",
+    "# then you might need to switch off the cache file system.\n",
+    "allow_cache_file = True\n",
+    "\n",
+    "# Define grid parameters\n",
+    "res = 50  # 250 in the paper (reduced for runtime improvement)\n",
+    "z_res = 7  # 11 in the paper (reduced for runtime improvement)\n",
+    "\n",
+    "# Setup the grid\n",
+    "x_grid = np.linspace(-13000, -3000, num=res)\n",
+    "y_grid = np.linspace(-5000, 5000, num=res)\n",
+    "z_grid = np.linspace(-100, 100, num=z_res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6af9c40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No cache file found for these settings, or cache file use disallowed; looking up extinction values for this grid.\n",
+      "File '/home/emily/data/dustmaps/decaps/decaps_mean.h5' already exists. Skipping download. To overwrite, use clobber=True.\n",
+      "Loading meta pixel info...\n",
+      "Meta pixel info loaded!\n",
+      "Number of points: 17500\n",
+      "Performing Bayestar query...\n",
+      "Performing decaps query... (this may take a WHILE)\n",
+      "Saving...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Make a unique ID based on the defined grid, which lets us know easily if any settings\n",
+    "# have changed\n",
+    "unique_hash = hashlib.md5(np.concatenate([x_grid, y_grid, z_grid])).hexdigest()\n",
+    "cache_file = Path(f\".cache/extinction_grid_{unique_hash}.parquet\")\n",
+    "\n",
+    "if cache_file.exists() and allow_cache_file:\n",
+    "    points = pd.read_parquet(cache_file)\n",
+    "else:\n",
+    "    print(\n",
+    "        \"No cache file found for these settings, or cache file use disallowed; \"\n",
+    "        \"looking up extinction values for this grid.\"\n",
+    "    )\n",
+    "    bayestar = BayestarWebQuery()\n",
+    "    dustmaps.decaps.fetch(mean_only=True)\n",
+    "    decaps = DECaPSQueryLite(mean_only=True)\n",
+    "\n",
+    "    if z_res == 1:\n",
+    "        z_grid = np.zeros(1)\n",
+    "\n",
+    "    xx, yy, zz = np.meshgrid(x_grid, y_grid, z_grid)\n",
+    "\n",
+    "    x, y, z = xx.flatten(), yy.flatten(), zz.flatten()\n",
+    "\n",
+    "    print(f\"Number of points: {len(x)}\")\n",
+    "\n",
+    "    coords = SkyCoord(\n",
+    "        x * u.pc,\n",
+    "        y * u.pc,\n",
+    "        z * u.pc,\n",
+    "        frame=\"galactocentric\",\n",
+    "        representation_type=\"cartesian\",\n",
+    "    ).transform_to(\"galactic\")\n",
+    "\n",
+    "    points = pd.DataFrame.from_dict(\n",
+    "        {\n",
+    "            \"x\": x,\n",
+    "            \"y\": y,\n",
+    "            \"z\": z,\n",
+    "            \"l\": coords.l.value,\n",
+    "            \"b\": coords.b.value,\n",
+    "            \"distance\": coords.distance.value,\n",
+    "            \"parallax\": 1000 / coords.distance.value,\n",
+    "            \"distance_modulus\": 5 * np.log10(coords.distance.value) - 5,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "    print(\"Performing Bayestar query...\")\n",
+    "    points[\"extinction_bayestar\"] = bayestar.query(coords, mode=\"best\") * 3.1 * 0.86\n",
+    "\n",
+    "    print(\"Performing decaps query... (this may take a WHILE)\")\n",
+    "    points[\"extinction_decaps\"] = decaps.query(coords) * 3.1\n",
+    "\n",
+    "    print(\"Saving...\")\n",
+    "    points[\"extinction\"] = np.where(\n",
+    "        points[\"extinction_bayestar\"].isna(),\n",
+    "        points[\"extinction_decaps\"],\n",
+    "        points[\"extinction_bayestar\"],\n",
+    "    )\n",
+    "    cache_file.parent.mkdir(exist_ok=True)\n",
+    "    points.to_parquet(cache_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e41a2c28",
+   "metadata": {},
+   "source": [
+    "## Query the selection function for a given cluster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed9cc070",
+   "metadata": {},
+   "source": [
+    "Next, we need to query the selection function! Let's use a cluster with these parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "275d9bce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster_parameters = {\n",
+    "    \"mass\": 200,\n",
+    "    \"log_age\": 9.4,  # 2.5 Gyr\n",
+    "    \"metallicity\": 0.0,\n",
+    "    \"differential_extinction\": 0.0,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a45fb0b",
+   "metadata": {},
+   "source": [
+    "Everything else (distance, velocity, extinction, etc) will change on our grid."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a401444b",
+   "metadata": {},
+   "source": [
+    "### Required models\n",
+    "\n",
+    "Let's instantiate the models we need. We need to pick a Galactic potential to sample velocities, use a model to predict the number of stars in each cluster, and then also initialize our selection function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cf97441c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "potential = MilkyWayPotential2022()\n",
+    "selection_function = HR24SelectionFunction()\n",
+    "\n",
+    "# Set number of models higher for more accuracy at expense of speed\n",
+    "n_simulated_predictor = NStarsPredictor(models=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62430d6e",
+   "metadata": {},
+   "source": [
+    "### Helpers\n",
+    "\n",
+    "Before we go any further, we should define some helper functions to do things, like go from a velocity in cylindrical coordinates to an astropy SkyCoord object, or calculate what the detection probability would be end-to-end based on our model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c205151",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_coordinates(data, v_phi_offset=0, v_rho_offset=0, v_z_offset=0):\n",
+    "    \"\"\"Creates an astropy SkyCoord object containing the coordinates of all grid points\n",
+    "    but with a velocity offset added.\n",
+    "    \"\"\"\n",
+    "    coordinates = SkyCoord(\n",
+    "        data[\"x\"].to_numpy() * u.pc,\n",
+    "        data[\"y\"].to_numpy() * u.pc,\n",
+    "        data[\"z\"].to_numpy() * u.pc,\n",
+    "        frame=\"galactocentric\",\n",
+    "        representation_type=\"cartesian\",\n",
+    "    ).represent_as(\"cylindrical\")\n",
+    "\n",
+    "    v_phi = -1 * potential.circular_velocity(\n",
+    "        data[[\"x\", \"y\", \"z\"]].to_numpy().T * u.pc\n",
+    "    ) - (v_phi_offset * u.km / u.s)\n",
+    "\n",
+    "    v_phi_angular = (\n",
+    "        (v_phi.to(u.km / u.s).value / coordinates.rho.to(u.km).value) * u.rad / u.s\n",
+    "    )\n",
+    "    v_rho = v_rho_offset * u.km / u.s\n",
+    "    v_z = v_z_offset * u.km / u.s\n",
+    "\n",
+    "    coordinates_icrs = SkyCoord(\n",
+    "        CylindricalRepresentation(\n",
+    "            rho=coordinates.rho,\n",
+    "            phi=coordinates.phi,\n",
+    "            z=coordinates.z,\n",
+    "            differentials=CylindricalDifferential(\n",
+    "                d_rho=v_rho, d_phi=v_phi_angular, d_z=v_z\n",
+    "            ),\n",
+    "        ),\n",
+    "        frame=\"galactocentric\",\n",
+    "    ).transform_to(\"icrs\")\n",
+    "\n",
+    "    return coordinates_icrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39e449d1",
+   "metadata": {},
+   "source": [
+    "### Calculate the expected number of stars & median parallax error\n",
+    "\n",
+    "Now for the science! Let's first use the NStarsPredictor to estimate the number of stars in a cluster at each location, along with their median parallax error - which is a shortcut to having to otherwise simulate every cluster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "713a9f8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Querying model 10 of 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "coordinates = get_coordinates(points)\n",
+    "\n",
+    "# Assign cluster information to the points dataframe\n",
+    "for col, value in cluster_parameters.items():\n",
+    "    points[col] = value\n",
+    "\n",
+    "# Query the XGBoost model\n",
+    "points[\"n_stars\"], points[\"median_parallax_error\"] = n_simulated_predictor(\n",
+    "    coordinates, data=points\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4d811bd",
+   "metadata": {},
+   "source": [
+    "### Calculate detection probability on different orbits\n",
+    "\n",
+    "Now we have everything to use our selection function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9800d593",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define orbits to investigate. By default, just try a blank (circular) one, and then\n",
+    "# three with varying speed offsets.\n",
+    "orbits = [\n",
+    "    dict(),\n",
+    "    dict(v_phi_offset=25),\n",
+    "    dict(v_rho_offset=25),\n",
+    "    dict(v_z_offset=25),\n",
+    "]\n",
+    "\n",
+    "# Also set a CST threshold (3 in the HR23/HR24 catalogues)\n",
+    "points['threshold'] = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "655d697b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calcualting orbit circular\n",
+      "Calcualting orbit v_phi_offset_25\n",
+      "Calcualting orbit v_rho_offset_25\n",
+      "Calcualting orbit v_z_offset_25\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert the orbits into names we can use for pandas columns\n",
+    "orbit_names = [\"_\".join([f\"{k}_{v}\" for k, v in orbit.items()]) for orbit in orbits]\n",
+    "orbit_names = [\n",
+    "    f\"probability_{name}\" if len(name) > 0 else \"probability_circular\"\n",
+    "    for name in orbit_names\n",
+    "]\n",
+    "\n",
+    "# Calcualte detection probability of each orbit option\n",
+    "good_points = points['distance'] >= 500\n",
+    "points_subset = points.loc[good_points]\n",
+    "for orbit, name in zip(orbits, orbit_names):\n",
+    "    print(f\"Calcualting orbit {name.replace('probability_', '')}\")\n",
+    "    new_coordinates = get_coordinates(points_subset, **orbit)\n",
+    "    points.loc[good_points, name] = selection_function(\n",
+    "        new_coordinates,\n",
+    "        points_subset['n_stars'],\n",
+    "        points_subset['median_parallax_error'],\n",
+    "        points_subset['threshold'],\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d94d30c",
+   "metadata": {},
+   "source": [
+    "## Aggregate the results\n",
+    "\n",
+    "To make plots in two dimensions, we need to aggregate all points in the z direction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "19fe73da",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define which columns to aggregate\n",
+    "columns = [col for col in points.columns if \"probability\" in col or \"n_stars\" in col]\n",
+    "aggregate = {col: pd.NamedAgg(col, \"mean\") for col in columns}\n",
+    "\n",
+    "# Perform groupby\n",
+    "points_aggregated = (\n",
+    "    points.groupby([\"x\", \"y\"])\n",
+    "    .aggregate(\n",
+    "        extinction=pd.NamedAgg(\"extinction\", \"mean\"),\n",
+    "        distance=pd.NamedAgg(\"distance\", \"min\"),\n",
+    "        **aggregate,\n",
+    "    )\n",
+    "    .reset_index()\n",
+    ")\n",
+    "\n",
+    "# Mask bad values\n",
+    "points_aggregated.loc[points_aggregated[\"distance\"] < 500, columns] = np.nan\n",
+    "\n",
+    "# Grab some useful things for matplotlib later\n",
+    "extent = (x_grid[0] * 1e-3, x_grid[-1] * 1e-3, y_grid[0] * 1e-3, y_grid[-1] * 1e-3)\n",
+    "imshow_defaults = dict(origin=\"lower\", cmap=\"inferno\", extent=extent)\n",
+    "\n",
+    "\n",
+    "def get_grid(column):\n",
+    "    \"\"\"Fetch an aggregated points column reshaped to be correct for matplotlib imshow.\n",
+    "    Remember to set origin='lower'!\n",
+    "    \"\"\"\n",
+    "    return points_aggregated[column].to_numpy().reshape(res, res).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc39dc19",
+   "metadata": {},
+   "source": [
+    "## Number of stars predicted in different spots\n",
+    "\n",
+    "Firstly, it's interesting to see how many stars should be in Gaia data for each of our cluster points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "1e9fd043",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "image = ax.imshow(get_grid(\"n_stars\"), vmin=0.0, **imshow_defaults)\n",
+    "cax = fig.colorbar(image, ax=ax, pad=0.02, aspect=20, label=r\"$n_*$\")\n",
+    "cax.minorticks_on()\n",
+    "ax.set(xlabel=\"X [kpc]\", ylabel=\"Y [kpc]\", aspect=\"auto\")\n",
+    "ax.minorticks_on()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b31aae51",
+   "metadata": {},
+   "source": [
+    "## Detection probability (Fig. 9)\n",
+    "\n",
+    "We can look at the detection probability of our cluster across the disk. As expected, it matches up quite a lot with where dust is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a0ceb14f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 0, 'X [kpc]'), Text(0, 0.5, 'Y [kpc]')]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 4), ncols=2, sharex=True)\n",
+    "fig.subplots_adjust(wspace=0.3)\n",
+    "\n",
+    "image = ax[0].imshow(get_grid(\"extinction\"), **imshow_defaults)\n",
+    "cax = fig.colorbar(\n",
+    "    image,\n",
+    "    ax=ax[0],\n",
+    "    pad=0.03,\n",
+    "    orientation=\"horizontal\",\n",
+    "    location=\"top\",\n",
+    "    label=r\"$A_V$ [mag]\",\n",
+    ")\n",
+    "cax.minorticks_on()\n",
+    "\n",
+    "image = ax[1].imshow(\n",
+    "    get_grid(\"probability_circular\"), vmin=0.0, vmax=1.0, **imshow_defaults\n",
+    ")\n",
+    "cax = fig.colorbar(\n",
+    "    image,\n",
+    "    ax=ax[1],\n",
+    "    pad=0.03,\n",
+    "    orientation=\"horizontal\",\n",
+    "    location=\"top\",\n",
+    "    label=r\"$P(\\mathrm{detected})$\",\n",
+    ")\n",
+    "cax.minorticks_on()\n",
+    "\n",
+    "for an_ax in ax:\n",
+    "    an_ax.set_aspect(\"auto\")\n",
+    "    an_ax.minorticks_on()\n",
+    "\n",
+    "ax[0].set(xlabel=\"X [kpc]\", ylabel=\"Y [kpc]\")\n",
+    "ax[1].set(xlabel=\"X [kpc]\", ylabel=\"Y [kpc]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ba4a7d7",
+   "metadata": {},
+   "source": [
+    "## Detection probability ratio (Fig. 10)\n",
+    "\n",
+    "And finally - for the money plot - we can calculate what the ratio in detection probability is between a circular and fast orbit, as a function of cluster position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "97bacd13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 4), ncols=3, sharex=True)\n",
+    "fig.subplots_adjust(wspace=0.05)\n",
+    "\n",
+    "# Precalculate ratios so that we can give every plot the same colormap\n",
+    "p_circular = get_grid(\"probability_circular\")\n",
+    "circular_is_zero = (p_circular == 0).nonzero()\n",
+    "\n",
+    "# Ignore warnings - div by 0 here is fine, NaN values are handled later properly\n",
+    "with warnings.catch_warnings():\n",
+    "    warnings.simplefilter(\"ignore\")\n",
+    "    ratios = [get_grid(name) / p_circular for name in orbit_names[1:4]]\n",
+    "\n",
+    "# Calculate min/max for colourmap\n",
+    "# Min is set to 1.0 because very occasionally things erroneously dip very low (due to\n",
+    "# random chance), which messes up colour scheme.\n",
+    "min_value = 1.0\n",
+    "max_value = np.nanmax(ratios)\n",
+    "\n",
+    "# Add images\n",
+    "for an_ax, col, ratio in zip(ax, orbit_names[1:4], ratios):\n",
+    "    ratio[circular_is_zero] = 1.0  # Handles division by zeros\n",
+    "    image = an_ax.imshow(ratio, vmin=min_value, vmax=max_value, **imshow_defaults)\n",
+    "    an_ax.set_title(\n",
+    "        col.replace(\"probability_\", \"\").replace(\"_offset_\", \": \") + \" km/s faster\"\n",
+    "    )\n",
+    "\n",
+    "# Add a colour bar\n",
+    "cax = fig.colorbar(\n",
+    "    image,\n",
+    "    ax=ax,\n",
+    "    pad=0.2,\n",
+    "    label=r\"$P(\\mathrm{det.}\\,|\\,\\mathrm{fast}) / P(\\mathrm{det.}\\,|\\,\\mathrm{circ.})$\",\n",
+    "    orientation=\"horizontal\",\n",
+    "    location=\"bottom\",\n",
+    "    aspect=40,\n",
+    "    shrink=0.6,\n",
+    ")\n",
+    "cax.minorticks_on()\n",
+    "\n",
+    "# Beautification\n",
+    "# Aspects & minorticks on\n",
+    "for an_ax in ax:\n",
+    "    an_ax.set_aspect(\"auto\")\n",
+    "    an_ax.minorticks_on()\n",
+    "\n",
+    "# Axis labels\n",
+    "ax[0].set_ylabel(\"$Y$ [kpc]\")\n",
+    "ax[-1].set_ylabel(\"$Y$ [kpc]\")\n",
+    "ax[1].set_xlabel(\"$X$ [kpc]\")\n",
+    "\n",
+    "# Configure ticks\n",
+    "for an_ax in ax[1:-1]:\n",
+    "    an_ax.tick_params(labelleft=False, labelright=False)\n",
+    "ax[-1].tick_params(labelright=True, labelleft=False)\n",
+    "ax[-1].yaxis.set_label_position(\"right\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc8769ac",
+   "metadata": {},
+   "source": [
+    "So that's it! This notebook shows you how to reproduce our favourite figure from the paper. Armed with this information, we hope you can do cool things with our work."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hr-selection-function",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
-- 
2.51.2