diff --git a/.gitignore b/.gitignore
index 6aaec371..9cae4f8a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,3 +16,4 @@ docs/_build/
 _version.py
 _autosummary/
 coverage.xml
+.ipynb_checkpoints/
diff --git a/docs/conf.py b/docs/conf.py
index 15107226..26db6d57 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,9 +1,10 @@
 """Configuration for docs."""
 
 import os
-from datetime import datetime, timezone
 import sys
+from datetime import UTC, datetime
 from pathlib import Path
+
 sys.path.insert(0, str(Path(__file__).absolute().parent.parent / "src"))
 
 import tuesday
@@ -45,7 +46,7 @@
 source_suffix = ".rst"
 master_doc = "index"
 project = "tuesday"
-year = str(datetime.now(tz=timezone.utc).year)
+year = str(datetime.now(tz=UTC).year)
 author = "Daniela Breitman and Steven Murray"
 copyright = f"{year}, {author}"
 version = release = tuesday.__version__
@@ -86,4 +87,4 @@
     ".DS_Store",
     "templates",
     "**.ipynb_checkpoints",
-]
\ No newline at end of file
+]
diff --git a/docs/tutorials/computing_and_plotting_power_spectra.ipynb b/docs/tutorials/computing_and_plotting_power_spectra.ipynb
index aa250b41..796fbf5c 100644
--- a/docs/tutorials/computing_and_plotting_power_spectra.ipynb
+++ b/docs/tutorials/computing_and_plotting_power_spectra.ipynb
@@ -185,14 +185,13 @@
     }
    ],
    "source": [
-    "\n",
     "ax = plot_coeval_slice(\n",
     "    delta_x,\n",
     "    box_len,\n",
     "    transform2slice=coeval2slice_x(idx=15),\n",
     "    title=\"Slice in x-direction\",\n",
-    "    v_x=delta_x[15,...]*un.m / un.s,\n",
-    "    v_y=delta_x[15,...]*un.m / un.s,\n",
+    "    v_x=delta_x[15, ...] * un.m / un.s,\n",
+    "    v_y=delta_x[15, ...] * un.m / un.s,\n",
     "    quiver_decimate_factor=3,\n",
     ")"
    ]
@@ -232,7 +231,10 @@
    ],
    "source": [
     "plot_coeval_slice(\n",
-    "    delta_x.value*un.mK, box_len, transform2slice=coeval2slice_y(), title=\"Slice in y-direction\"\n",
+    "    delta_x.value * un.mK,\n",
+    "    box_len,\n",
+    "    transform2slice=coeval2slice_y(),\n",
+    "    title=\"Slice in y-direction\",\n",
     ")"
    ]
   },
@@ -530,16 +532,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tuesday.core import lc2slice_x, lc2slice_y, plot_redshift_slice"
+    "from tuesday.core import lc2slice_x, lc2slice_y, plot_lightcone_slice"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -564,7 +566,7 @@
     }
    ],
    "source": [
-    "plot_redshift_slice(\n",
+    "plot_lightcone_slice(\n",
     "    lc,\n",
     "    box_len,\n",
     "    lc_redshifts,\n",
@@ -583,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -608,7 +610,7 @@
     }
    ],
    "source": [
-    "plot_redshift_slice(\n",
+    "plot_lightcone_slice(\n",
     "    lc,\n",
     "    box_len,\n",
     "    lc_redshifts,\n",
@@ -628,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -653,7 +655,7 @@
     }
    ],
    "source": [
-    "plot_redshift_slice(\n",
+    "plot_lightcone_slice(\n",
     "    lc,\n",
     "    box_len,\n",
     "    lc_redshifts,\n",
@@ -793,16 +795,16 @@
     "fig, ax = plt.subplots(1, 1, figsize=(18, 7))\n",
     "\n",
     "ax = tuesday.core.plot_power_spectrum(\n",
-    "        ps1d,\n",
-    "        at_k=0.05,\n",
-    "        fontsize=20,\n",
-    "        ax=ax,\n",
-    "        color=f\"r\",\n",
-    "        legend=f\"k ~ 0.05\",\n",
-    "        legend_kwargs={\"frameon\": False},\n",
-    "        logx=False,\n",
-    "        logy=False,\n",
-    "    )\n",
+    "    ps1d,\n",
+    "    at_k=0.05,\n",
+    "    fontsize=20,\n",
+    "    ax=ax,\n",
+    "    color=\"r\",\n",
+    "    legend=\"k ~ 0.05\",\n",
+    "    legend_kwargs={\"frameon\": False},\n",
+    "    logx=False,\n",
+    "    logy=False,\n",
+    ")\n",
     "\n",
     "ax = tuesday.core.plot_power_spectrum(\n",
     "    all_analytic,\n",
@@ -840,16 +842,16 @@
     "fig, ax = plt.subplots(1, 1, figsize=(18, 7))\n",
     "\n",
     "ax = tuesday.core.plot_power_spectrum(\n",
-    "        ps1d,\n",
-    "        fontsize=20,\n",
-    "        ax=ax,\n",
-    "        color=f\"r\",\n",
-    "        at_k=at_k,\n",
-    "        legend=f\"k ~ \"+str(at_k),\n",
-    "        legend_kwargs={\"frameon\": False},\n",
-    "        logx=False,\n",
-    "        logy=False,\n",
-    "    )\n",
+    "    ps1d,\n",
+    "    fontsize=20,\n",
+    "    ax=ax,\n",
+    "    color=\"r\",\n",
+    "    at_k=at_k,\n",
+    "    legend=\"k ~ \" + str(at_k),\n",
+    "    legend_kwargs={\"frameon\": False},\n",
+    "    logx=False,\n",
+    "    logy=False,\n",
+    ")\n",
     "\n",
     "ax = tuesday.core.plot_power_spectrum(\n",
     "    all_analytic,\n",
@@ -1064,7 +1066,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "21cmFAST",
+   "display_name": "tuesday",
    "language": "python",
    "name": "python3"
   },
@@ -1078,7 +1080,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.12"
+   "version": "3.12.6"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/docs/tutorials/lc_noise_compare_tools21cm.ipynb b/docs/tutorials/lc_noise_compare_tools21cm.ipynb
index b650063c..6b147bca 100644
--- a/docs/tutorials/lc_noise_compare_tools21cm.ipynb
+++ b/docs/tutorials/lc_noise_compare_tools21cm.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "7fb27b941602401d91542211134fc71a",
    "metadata": {},
    "source": [
     "# Generating lightcone noise samples - Comparing between `tuesday` and `tools21cm`\n",
@@ -13,43 +14,52 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "acae54e37e7d407bbb7b55eff062a284",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import astropy.units as un\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from py21cmsense import Observatory, Observation\n",
+    "from py21cmsense import Observation, Observatory\n",
     "from py21cmsense.conversions import f2z\n",
-    "import astropy.units as un\n",
-    "\n",
-    "from tools21cm.radio_telescope_noise import get_uv_map, get_SKA_Low_layout\n",
-    "import matplotlib.pyplot as plt"
+    "from tools21cm.radio_telescope_noise import get_SKA_Low_layout, get_uv_map"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "id": "9a63283cbaf04dbcab1f6479b197f3a8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def compare(tools_qt, tuesday_qt, qt_name=\"\", f = \"150 MHz\", vmin=None, vmax=None):\n",
-    "    fig, axs = plt.subplots(1, 3, figsize=(12, 6), gridspec_kw={\"width_ratios\":[1,1,0.07]})\n",
+    "def compare(tools_qt, tuesday_qt, qt_name=\"\", f=\"150 MHz\", vmin=None, vmax=None):\n",
+    "    \"\"\"Perform a quick comparison between tools21cm and tuesday quantities.\"\"\"\n",
+    "    fig, axs = plt.subplots(\n",
+    "        1, 3, figsize=(12, 6), gridspec_kw={\"width_ratios\": [1, 1, 0.07]}\n",
+    "    )\n",
     "    if vmin is None:\n",
     "        vmin = np.nanpercentile(tools_qt, 2.5)\n",
     "    if vmax is None:\n",
     "        vmax = np.nanpercentile(tools_qt, 97.5)\n",
     "    axs[0].set_title(f\"tuesday - {qt_name} at {f} for SKA AA4\")\n",
-    "    axs[0].imshow(np.flip(tuesday_qt.T), origin=\"lower\", vmin = vmin, vmax = vmax)\n",
+    "    axs[0].imshow(np.flip(tuesday_qt.T), origin=\"lower\", vmin=vmin, vmax=vmax)\n",
     "    axs[1].set_title(f\"tools21cm - {qt_name} at {f} for SKA AA4\")\n",
-    "    im=axs[1].imshow(tools_qt.T, origin=\"lower\", vmin = vmin, vmax = vmax)\n",
-    "    fig.colorbar(im,  label = qt_name, cax = axs[2],fraction=0.1,shrink=0.6)\n",
+    "    im = axs[1].imshow(tools_qt.T, origin=\"lower\", vmin=vmin, vmax=vmax)\n",
+    "    fig.colorbar(im, label=qt_name, cax=axs[2], fraction=0.1, shrink=0.6)\n",
     "    plt.show()\n",
     "    err = tools_qt.T - np.flip(tuesday_qt.T)\n",
     "    vmin = np.nanpercentile(err, 2.5)\n",
     "    vmax = np.nanpercentile(err, 97.5)\n",
-    "    fig, axs = plt.subplots(1,2, figsize=(12, 6))\n",
-    "    im = axs[0].imshow(err, vmin = vmin, vmax = vmax, origin=\"lower\")\n",
-    "    fig.colorbar(im,  label = f\"tools21cm  {qt_name} - 21cmSense {qt_name}\", fraction=0.1,shrink=0.8)\n",
-    "    axs[1].hist(err.ravel(), bins = np.linspace(vmin,vmax,20))\n",
+    "    fig, axs = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "    im = axs[0].imshow(err, vmin=vmin, vmax=vmax, origin=\"lower\")\n",
+    "    fig.colorbar(\n",
+    "        im,\n",
+    "        label=f\"tools21cm  {qt_name} - 21cmSense {qt_name}\",\n",
+    "        fraction=0.1,\n",
+    "        shrink=0.8,\n",
+    "    )\n",
+    "    axs[1].hist(err.ravel(), bins=np.linspace(vmin, vmax, 20))\n",
     "    axs[1].set_xlabel(f\"tools21cm {qt_name} - 21cmSense {qt_name}\")\n",
     "    plt.show()"
    ]
@@ -70,7 +80,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hours_tracking = 6.*un.hour\n",
+    "hours_tracking = 6.0 * un.hour\n",
     "observatory = Observatory.from_ska(\"AA4\")\n",
     "freqs = np.array([150.0, 250.0]) * un.MHz"
    ]
@@ -111,8 +121,14 @@
     }
    ],
    "source": [
-    "plt.scatter(enu_coords[:,0], enu_coords[:,1], color = \"k\", label = \"tools21cm\")\n",
-    "plt.scatter(observatory.antpos[:,0], observatory.antpos[:,1], color = \"r\", marker = \".\", label = \"tuesday\")\n",
+    "plt.scatter(enu_coords[:, 0], enu_coords[:, 1], color=\"k\", label=\"tools21cm\")\n",
+    "plt.scatter(\n",
+    "    observatory.antpos[:, 0],\n",
+    "    observatory.antpos[:, 1],\n",
+    "    color=\"r\",\n",
+    "    marker=\".\",\n",
+    "    label=\"tuesday\",\n",
+    ")\n",
     "plt.legend()\n",
     "plt.show()"
    ]
@@ -137,8 +153,10 @@
    "outputs": [],
    "source": [
     "observatory = observatory.clone(beam=observatory.beam.clone(frequency=freqs[0]))\n",
-    "integration_time = 60*un.s\n",
-    "time_offsets = observatory.time_offsets_from_obs_int_time(integration_time, hours_tracking)"
+    "integration_time = 60 * un.s\n",
+    "time_offsets = observatory.time_offsets_from_obs_int_time(\n",
+    "    integration_time, hours_tracking\n",
+    ")"
    ]
   },
   {
@@ -203,8 +221,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "proj_bls = observatory.projected_baselines(baselines=baselines, #21cmsense caches them \n",
-    "                                           time_offset = time_offsets, ) #(Nbls, N time_offsets, 3)"
+    "proj_bls = observatory.projected_baselines(\n",
+    "    baselines=baselines,  # 21cmsense caches them\n",
+    "    time_offset=time_offsets,\n",
+    ")  # (Nbls, N time_offsets, 3)"
    ]
   },
   {
@@ -227,8 +247,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "lc_shape = np.array([200,200,1945])\n",
-    "boxlength = 300. *un.Mpc"
+    "lc_shape = np.array([200, 200, 1945])\n",
+    "boxlength = 300.0 * un.Mpc"
    ]
   },
   {
@@ -246,14 +266,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "uv_coverage = np.zeros((lc_shape[0],lc_shape[0],len(freqs)))#Nu, Nv, Nfreqs\n",
-    "for i, freq in enumerate(freqs): \n",
+    "uv_coverage = np.zeros((lc_shape[0], lc_shape[0], len(freqs)))  # Nu, Nv, Nfreqs\n",
+    "for i, freq in enumerate(freqs):\n",
     "    # uv coverage integrated over one field\n",
-    "    uv_coverage[...,i] += grid_baselines_uv(proj_bls[::2]*freq/freqs[0],\n",
-    "                              freq,\n",
-    "                        boxlength, \n",
-    "                        lc_shape,\n",
-    "                        weights[::2])"
+    "    uv_coverage[..., i] += grid_baselines_uv(\n",
+    "        proj_bls[::2] * freq / freqs[0], freq, boxlength, lc_shape, weights[::2]\n",
+    "    )"
    ]
   },
   {
@@ -321,14 +339,19 @@
    "source": [
     "redshifts = [f2z(freq) for freq in freqs]\n",
     "uv_tools = np.zeros_like(uv_coverage)\n",
-    "for i,z in enumerate(redshifts):\n",
-    "    uv, Nant = get_uv_map(lc_shape[0],z, \n",
-    "                                 boxsize=boxlength.value,\n",
-    "                                 int_time=integration_time.value,\n",
-    "                                 total_int_time= hours_tracking.value,\n",
-    "                                 include_mirror_baselines=True,\n",
-    "                                 declination = -26.824722)\n",
-    "    uv_tools[...,i] = np.fft.fftshift(uv)*int(3600*hours_tracking.value/integration_time.value)"
+    "for i, z in enumerate(redshifts):\n",
+    "    uv, Nant = get_uv_map(\n",
+    "        lc_shape[0],\n",
+    "        z,\n",
+    "        boxsize=boxlength.value,\n",
+    "        int_time=integration_time.value,\n",
+    "        total_int_time=hours_tracking.value,\n",
+    "        include_mirror_baselines=True,\n",
+    "        declination=-26.824722,\n",
+    "    )\n",
+    "    uv_tools[..., i] = np.fft.fftshift(uv) * int(\n",
+    "        3600 * hours_tracking.value / integration_time.value\n",
+    "    )"
    ]
   },
   {
@@ -367,7 +390,7 @@
     }
    ],
    "source": [
-    "compare(uv_tools[...,0], uv_coverage[...,0], \"uv coverage\", f = \"150 MHz\")"
+    "compare(uv_tools[..., 0], uv_coverage[..., 0], \"uv coverage\", f=\"150 MHz\")"
    ]
   },
   {
@@ -398,11 +421,12 @@
     }
    ],
    "source": [
-    "compare(uv_tools[...,1], uv_coverage[...,1], \"uv coverage\", f = \"250 MHz\")"
+    "compare(uv_tools[..., 1], uv_coverage[..., 1], \"uv coverage\", f=\"250 MHz\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "8dd0d8092fe74a7c96281538738b07e2",
    "metadata": {},
    "source": [
     "## Calculate the thermal RMS noise with `tuesday`\n",
@@ -413,6 +437,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
+   "id": "72eea5119410473aa328ad9291626812",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,35 +447,41 @@
   {
    "cell_type": "code",
    "execution_count": 16,
+   "id": "8edb47106e1a46a883d545849b8ab81b",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Define an observation\n",
-    "obs = Observation(observatory=observatory.clone(beam=observatory.beam.clone(frequency=freqs[0])), \n",
-    "            time_per_day=hours_tracking,\n",
-    "            lst_bin_size=hours_tracking,\n",
-    "            integration_time=integration_time,\n",
-    "            bandwidth=50 *un.kHz,\n",
-    "            n_days=int(np.ceil(1000/hours_tracking.value)),\n",
-    "            tsky_amplitude=60.*un.K,      # to be consistent with tools21cm\n",
-    "            tsky_ref_freq=300.*un.MHz,\n",
-    "            spectral_index=2.55,\n",
-    "                 )"
+    "obs = Observation(\n",
+    "    observatory=observatory.clone(beam=observatory.beam.clone(frequency=freqs[0])),\n",
+    "    time_per_day=hours_tracking,\n",
+    "    lst_bin_size=hours_tracking,\n",
+    "    integration_time=integration_time,\n",
+    "    bandwidth=50 * un.kHz,\n",
+    "    n_days=int(np.ceil(1000 / hours_tracking.value)),\n",
+    "    tsky_amplitude=60.0 * un.K,  # to be consistent with tools21cm\n",
+    "    tsky_ref_freq=300.0 * un.MHz,\n",
+    "    spectral_index=2.55,\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
+   "id": "10185d26023b46108eb7d9f57d49d2b3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "sigmaN_rms = thermal_noise_per_voxel(obs, freqs, boxlength, lc_shape, antenna_effective_area = [517.7, 186.4]*un.m**2) #A_eff set to match tools21cm\n",
-    "sigmaN = sigmaN_rms / np.sqrt(uv_coverage * obs.n_days)\n",
-    "sigmaN[uv_coverage == 0.] = 0."
+    "sigma_rms = thermal_noise_per_voxel(\n",
+    "    obs, freqs, boxlength, lc_shape, antenna_effective_area=[517.7, 186.4] * un.m**2\n",
+    ")  # A_eff set to match tools21cm\n",
+    "sigma = sigma_rms / np.sqrt(uv_coverage * obs.n_days)\n",
+    "sigma[uv_coverage == 0.0] = 0.0"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "8763a12b2bbd4a93a75aff182afb95dc",
    "metadata": {},
    "source": [
     "## Calculate thermal RMS noise with tools21cm"
@@ -459,16 +490,18 @@
   {
    "cell_type": "code",
    "execution_count": 18,
+   "id": "7623eae2785240b9bd12b16a66d81610",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from tools21cm.radio_telescope_sensitivity import sigma_noise_radio, jansky_2_kelvin, kelvin_2_jansky\n",
-    "from tools21cm.radio_telescope_noise import noise_map"
+    "from tools21cm.radio_telescope_noise import noise_map\n",
+    "from tools21cm.radio_telescope_sensitivity import jansky_2_kelvin, sigma_noise_radio"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
+   "id": "7cdc8c89c7104fffa095e18ddfef8986",
    "metadata": {},
    "outputs": [
     {
@@ -489,24 +522,29 @@
     }
    ],
    "source": [
-    "sigmaN_tools = np.zeros((lc_shape[0],lc_shape[0],len(freqs)))#Nu, Nv, Nfreqs\n",
-    "sigmaN_rms_tools = np.zeros(len(freqs))\n",
+    "sigma_tools = np.zeros((lc_shape[0], lc_shape[0], len(freqs)))  # Nu, Nv, Nfreqs\n",
+    "sigma_rms_tools = np.zeros(len(freqs))\n",
     "\n",
-    "for i, freq in enumerate(freqs): \n",
-    "    sigma, rms_noi = sigma_noise_radio(f2z(freq), \n",
-    "                                       obs.bandwidth.to(un.MHz).value, \n",
-    "                                       obs.n_days*obs.time_per_day.value, \n",
-    "                                       integration_time.value, \n",
-    "                                       uv_map=uv_tools, \n",
-    "                                       N_ant=512) #μJy\n",
-    "    sigmaN_rms_tools[i] = jansky_2_kelvin(rms_noi*1e-6, f2z(freq), boxsize=boxlength.value, ncells=lc_shape[0])[0] #mK\n",
-    "    print(\"RMS tools21cm\",sigmaN_rms_tools[i], \"vs RMS tuesday\", sigmaN_rms[i])\n",
-    "    sigmaN_tools[...,i] = sigmaN_rms_tools[i] / np.sqrt(uv_tools[...,i] * obs.n_days)\n",
-    "sigmaN_tools[uv_tools == 0.] = 0."
+    "for i, freq in enumerate(freqs):\n",
+    "    sigma, rms_noi = sigma_noise_radio(\n",
+    "        f2z(freq),\n",
+    "        obs.bandwidth.to(un.MHz).value,\n",
+    "        obs.n_days * obs.time_per_day.value,\n",
+    "        integration_time.value,\n",
+    "        uv_map=uv_tools,\n",
+    "        N_ant=512,\n",
+    "    )  # μJy\n",
+    "    sigma_rms_tools[i] = jansky_2_kelvin(\n",
+    "        rms_noi * 1e-6, f2z(freq), boxsize=boxlength.value, ncells=lc_shape[0]\n",
+    "    )[0]  # mK\n",
+    "    print(\"RMS tools21cm\", sigma_rms_tools[i], \"vs RMS tuesday\", sigma_rms[i])\n",
+    "    sigma_tools[..., i] = sigma_rms_tools[i] / np.sqrt(uv_tools[..., i] * obs.n_days)\n",
+    "sigma_tools[uv_tools == 0.0] = 0.0"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "b118ea5561624da68c537baed56e602f",
    "metadata": {},
    "source": [
     "## Compare the thermal RMS noise"
@@ -514,7 +552,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
+   "id": "938c804e27f84196a10c8828c723f798",
    "metadata": {},
    "outputs": [
     {
@@ -539,12 +578,13 @@
     }
    ],
    "source": [
-    "compare(sigmaN_tools[...,0], sigmaN[...,0].value, r\"$\\sigma_{N}$ [mK]\")"
+    "compare(sigma_tools[..., 0], sigma[..., 0].value, r\"$\\sigma_{N}$ [mK]\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
+   "id": "504fb2a444614c0babb325280ed9130a",
    "metadata": {},
    "outputs": [
     {
@@ -569,11 +609,12 @@
     }
    ],
    "source": [
-    "compare(sigmaN_tools[...,1], sigmaN[...,1].value, r\"$\\sigma_{N}$ [mK]\", f=\"250 MHz\")"
+    "compare(sigma_tools[..., 1], sigma[..., 1].value, r\"$\\sigma_{N}$ [mK]\", f=\"250 MHz\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "59bbdb311c014d738909a11f9e486628",
    "metadata": {},
    "source": [
     "## Sample lightcone noise with `tools21cm`\n",
@@ -583,15 +624,15 @@
   {
    "cell_type": "code",
    "execution_count": 22,
+   "id": "b43b363d81ae4b689946ece5c682cd59",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from tools21cm.radio_telescope_noise import noise_map"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
+   "id": "8a65eabff63a45729fe45fb5ade58bdc",
    "metadata": {},
    "outputs": [
     {
@@ -643,22 +684,33 @@
     }
    ],
    "source": [
-    "noise_tools = np.zeros_like(sigmaN)\n",
-    "for i, freq in enumerate(freqs): \n",
-    "    n = noise_map(lc_shape[0], \n",
-    "                        f2z(freq), \n",
-    "                        obs.bandwidth.to(un.MHz).value, \n",
-    "                        obs_time=obs.n_days*obs.time_per_day.value, \n",
-    "                        subarray_type=\"AA4\", \n",
-    "                        boxsize=boxlength.value, \n",
-    "                        total_int_time=obs.time_per_day.value, \n",
-    "                        int_time=integration_time.value, \n",
-    "                        declination=-26.824722,)*un.uJy\n",
-    "    noise_tools[...,i] = jansky_2_kelvin(n.to(un.Jy).value, f2z(freq), boxsize=boxlength.value, ncells=lc_shape[0])*un.mK"
+    "noise_tools = np.zeros_like(sigma)\n",
+    "for i, freq in enumerate(freqs):\n",
+    "    n = (\n",
+    "        noise_map(\n",
+    "            lc_shape[0],\n",
+    "            f2z(freq),\n",
+    "            obs.bandwidth.to(un.MHz).value,\n",
+    "            obs_time=obs.n_days * obs.time_per_day.value,\n",
+    "            subarray_type=\"AA4\",\n",
+    "            boxsize=boxlength.value,\n",
+    "            total_int_time=obs.time_per_day.value,\n",
+    "            int_time=integration_time.value,\n",
+    "            declination=-26.824722,\n",
+    "        )\n",
+    "        * un.uJy\n",
+    "    )\n",
+    "    noise_tools[..., i] = (\n",
+    "        jansky_2_kelvin(\n",
+    "            n.to(un.Jy).value, f2z(freq), boxsize=boxlength.value, ncells=lc_shape[0]\n",
+    "        )\n",
+    "        * un.mK\n",
+    "    )"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "c3933fab20d04ec698c2621248eb3be0",
    "metadata": {},
    "source": [
     "## Sample lightcone noise with `tuesday`\n",
@@ -669,6 +721,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
+   "id": "4dd4641cc4064e0191573fe9c69df29b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,17 +730,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
+   "id": "8309879909854d7188b41380fd92a7c3",
    "metadata": {},
    "outputs": [],
    "source": [
-    "noise = sample_lc_noise(sigmaN, \n",
-    "                        seed=4, \n",
-    "                        nsamples=10)"
+    "noise = sample_lc_noise(sigma, seed=4, nsamples=10)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "3ed186c9a28b402fb0bc4494df01f08d",
    "metadata": {},
    "source": [
     "## Compare noise realisations"
@@ -696,6 +749,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
+   "id": "cb1e1581032b452c9409d6c6813c49d1",
    "metadata": {},
    "outputs": [
     {
@@ -720,12 +774,13 @@
     }
    ],
    "source": [
-    "compare(noise_tools.value[...,0], noise[2,...,0].value, \"noise [mK]\")"
+    "compare(noise_tools.value[..., 0], noise[2, ..., 0].value, \"noise [mK]\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 27,
+   "id": "379cbbc1e968416e875cc15c1202d7eb",
    "metadata": {},
    "outputs": [
     {
@@ -750,12 +805,13 @@
     }
    ],
    "source": [
-    "compare(noise_tools.value[...,1], noise[0,...,1].value, \"noise [mK]\", f=\"250 MHz\")"
+    "compare(noise_tools.value[..., 1], noise[0, ..., 1].value, \"noise [mK]\", f=\"250 MHz\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "277c27b1587741f2af2001be3712ef0d",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/pyproject.toml b/pyproject.toml
index 3d2a1da2..3d59f0e7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ dependencies = [
     "numpy>=2",
     "powerbox>=0.8.2",
     "scipy>=1.15.2",
+    "deprecation",
 ]
 dynamic = ['version']
 
diff --git a/src/tuesday/__init__.py b/src/tuesday/__init__.py
index 2c97ffd0..c2e258f6 100644
--- a/src/tuesday/__init__.py
+++ b/src/tuesday/__init__.py
@@ -1,7 +1,6 @@
 """The Ultimate EoR Simulation Data AnalYser (TUESDAY) package."""
 
 __all__ = ["__version__", "core", "simulators"]
-from . import core, simulators
 
 try:
     from ._version import version as __version__
@@ -12,3 +11,5 @@
         __version__ = get_version(root="../..", relative_to=__file__)
     except Exception:
         __version__ = "0.0.0+unknown"
+
+from . import core, simulators
diff --git a/src/tuesday/core/__init__.py b/src/tuesday/core/__init__.py
index 4a75c584..66750cb0 100644
--- a/src/tuesday/core/__init__.py
+++ b/src/tuesday/core/__init__.py
@@ -19,6 +19,7 @@
     "plot_1d_power_spectrum_z",
     "plot_2d_power_spectrum",
     "plot_coeval_slice",
+    "plot_lightcone_slice",
     "plot_pdf",
     "plot_power_spectrum",
     "plot_redshift_slice",
@@ -48,6 +49,7 @@
     lc2slice_x,
     lc2slice_y,
     plot_coeval_slice,
+    plot_lightcone_slice,
     plot_pdf,
     plot_redshift_slice,
 )
diff --git a/src/tuesday/core/plotting/__init__.py b/src/tuesday/core/plotting/__init__.py
index 9e12b291..a54fdb3c 100644
--- a/src/tuesday/core/plotting/__init__.py
+++ b/src/tuesday/core/plotting/__init__.py
@@ -5,9 +5,9 @@
     "plot_1d_power_spectrum_z",
     "plot_2d_power_spectrum",
     "plot_coeval_slice",
+    "plot_lightcone_slice",
     "plot_pdf",
     "plot_power_spectrum",
-    "plot_redshift_slice",
 ]
 
 from .powerspectra import (
@@ -16,4 +16,4 @@
     plot_2d_power_spectrum,
     plot_power_spectrum,
 )
-from .sliceplots import plot_coeval_slice, plot_pdf, plot_redshift_slice
+from .sliceplots import plot_coeval_slice, plot_lightcone_slice, plot_pdf
diff --git a/src/tuesday/core/plotting/sliceplots.py b/src/tuesday/core/plotting/sliceplots.py
index f223026b..24d94c2f 100644
--- a/src/tuesday/core/plotting/sliceplots.py
+++ b/src/tuesday/core/plotting/sliceplots.py
@@ -5,10 +5,13 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from astropy import units as un
+from deprecation import deprecated
 from matplotlib import colormaps, colors, rcParams
 from matplotlib.colors import LogNorm
 from scipy.ndimage import gaussian_filter
 
+from tuesday import __version__
+
 from ..units import validate
 
 try:
@@ -174,7 +177,7 @@ def slice_index(box: un.Quantity) -> un.Quantity:
     return slice_index
 
 
-def plot_redshift_slice(
+def plot_lightcone_slice(
     lightcone: un.Quantity,
     box_length: un.Quantity,
     redshift: np.ndarray | un.Quantity,
@@ -198,6 +201,9 @@ def plot_redshift_slice(
 ) -> plt.Axes:
     """Plot a slice from a lightcone of shape (N_x, N_y, N_redshifts).
 
+    The plot will have redshift on the x-axis and (transverse) distance on the y-axis,
+    so it will show the cosmic evolution of the volume along the line of sight.
+
     Parameters
     ----------
     lightcone : un.Quantity
@@ -236,8 +242,6 @@ def plot_redshift_slice(
         Whether to apply Gaussian smoothing to the lightcone data.
         If True, a default sigma of 1.0 will be used.
         If a float, it will be used as the sigma for the Gaussian filter.
-
-
     """
     validate(box_length, "length")
     rcParams.update({"font.size": fontsize})
@@ -257,7 +261,7 @@ def plot_redshift_slice(
 
     if clabel is None:
         if lightcone.unit.physical_type == un.get_physical_type("temperature"):
-            clabel = "Brightness Temperature " + f" [{lightcone.unit:latex_inline}]"
+            clabel = f"Brightness Temperature  [{lightcone.unit:latex_inline}]"
         elif lightcone.unit.is_equivalent(un.dimensionless_unscaled):
             clabel = "Density Contrast"
         else:
@@ -289,6 +293,20 @@ def plot_redshift_slice(
     )
 
 
+@deprecated(
+    deprecated_in="2.3.2",
+    removed_in="3.0.0",
+    current_version=__version__,
+    details="Please use plot_lightcone_slice instead",
+)
+def plot_redshift_slice(*args, **kwargs):
+    """PLot a slice from a lightcone.
+
+    Deprecated: use plot_lightcone_slice instead.
+    """
+    return plot_lightcone_slice(*args, **kwargs)
+
+
 def plot_coeval_slice(
     coeval: un.Quantity,
     box_length: un.Quantity,
diff --git a/tests/test_dataclasses.py b/tests/test_dataclasses.py
index b988bd34..b1951269 100644
--- a/tests/test_dataclasses.py
+++ b/tests/test_dataclasses.py
@@ -31,11 +31,9 @@ def ps():
 
 
 def test_one_ps_per_obj(ps, ps2):
-    with pytest.raises(ValueError, match="The ps array must be 1D for a SphericalPS."):
+    with pytest.raises(ValueError, match="The ps array must be 1D for a SphericalPS"):
         SphericalPS(np.append(ps.ps[None, ...], ps.ps[None, ...], axis=0), k=ps.k)
-    with pytest.raises(
-        ValueError, match="The ps array must be 2D for a CylindricalPS."
-    ):
+    with pytest.raises(ValueError, match="The ps array must be 2D for a CylindricalPS"):
         CylindricalPS(
             np.append(ps2.ps[None, ...], ps2.ps[None, ...], axis=0),
             kperp=ps2.kperp,
diff --git a/tests/test_lc_noise.py b/tests/test_lc_noise.py
index 4f1a0d63..deb6e6b1 100644
--- a/tests/test_lc_noise.py
+++ b/tests/test_lc_noise.py
@@ -72,10 +72,7 @@ def test_thermal_noise_per_voxel(observation):
         observation, np.array([150.0, 120.0]) * un.MHz, boxlength, lc_shape
     )
     with pytest.raises(
-        ValueError,
-        match="You cannot provide both beam_area "
-        "and antenna_effective_area."
-        " Proceding with beam_area.",
+        ValueError, match="You cannot provide both beam_area and antenna_effective_area"
     ):
         thermal_noise_per_voxel(
             observation,
@@ -87,8 +84,7 @@ def test_thermal_noise_per_voxel(observation):
         )
     with pytest.raises(
         ValueError,
-        match="Antenna effective area must either be a float or have the"
-        " same shape as freqs.",
+        match="Antenna effective area must either be a float or have the same shape",
     ):
         thermal_noise_per_voxel(
             observation,
@@ -98,7 +94,7 @@ def test_thermal_noise_per_voxel(observation):
             antenna_effective_area=[517.7, 200.0] * un.m**2,
         )
     with pytest.raises(
-        ValueError, match="Beam area must be a float or have the same shape as freqs."
+        ValueError, match="Beam area must be a float or have the same shape as freqs"
     ):
         thermal_noise_per_voxel(
             observation,
diff --git a/tests/test_plotting_boxes.py b/tests/test_plotting_boxes.py
index 8372f0f9..13f0f06e 100644
--- a/tests/test_plotting_boxes.py
+++ b/tests/test_plotting_boxes.py
@@ -3,6 +3,7 @@
 import astropy.units as un
 import numpy as np
 import pytest
+from deprecation import fail_if_not_removed
 
 from tuesday.core import (
     coeval2slice_x,
@@ -11,6 +12,7 @@
     lc2slice_x,
     lc2slice_y,
     plot_coeval_slice,
+    plot_lightcone_slice,
     plot_pdf,
     plot_redshift_slice,
 )
@@ -82,7 +84,7 @@ def test_coeval_slice(test_coeval):
 def test_lightcone_slice(test_lc, test_redshifts):
     """Test the plot_lightcone_slice function."""
     box_len = 300 * un.cm
-    ax = plot_redshift_slice(
+    ax = plot_lightcone_slice(
         test_lc.value * un.dimensionless_unscaled,
         box_len,
         test_redshifts,
@@ -93,7 +95,7 @@ def test_lightcone_slice(test_lc, test_redshifts):
     assert ax.get_xlabel() == "Redshift"
     assert ax.get_title() == "tiny lightcone"
 
-    ax = plot_redshift_slice(
+    ax = plot_lightcone_slice(
         test_lc.value * un.mK**2,
         box_len,
         test_redshifts,
@@ -105,14 +107,14 @@ def test_lightcone_slice(test_lc, test_redshifts):
         transform2slice=lc2slice_x(idx=5),
     )
 
-    ax = plot_redshift_slice(
+    ax = plot_lightcone_slice(
         test_lc,
         box_len,
         test_redshifts,
         transform2slice=lc2slice_y(idx=5),
         smooth=True,
     )
-    ax = plot_redshift_slice(
+    ax = plot_lightcone_slice(
         test_lc,
         box_len,
         test_redshifts,
@@ -169,3 +171,16 @@ def test_plot_slice(test_coeval):
         np.linspace(0, box_len.value, test_coeval.shape[0]) * un.cm,
         np.linspace(0, box_len.value, test_coeval.shape[1]) * un.Mpc,
     )
+
+
+@fail_if_not_removed
+def test_deprecated_plot_redshift_slice(test_lc, test_redshifts):
+    """Test the deprecated plot_redshift_slice function."""
+    box_len = 300 * un.cm
+    plot_redshift_slice(
+        test_lc.value * un.dimensionless_unscaled,
+        box_len,
+        test_redshifts,
+        title="tiny lightcone",
+        transform2slice=lc2slice_y(idx=5),
+    )
diff --git a/tests/test_plotting_ps.py b/tests/test_plotting_ps.py
index ea7695f5..42936e4e 100644
--- a/tests/test_plotting_ps.py
+++ b/tests/test_plotting_ps.py
@@ -87,9 +87,7 @@ def test_1d_ps_plot(ps1d: SphericalPS):
 
     with pytest.raises(
         ValueError,
-        match="power_spectrum must be a SphericalPS object or a list of "
-        "SphericalPS objects,"
-        " got <class 'numpy.ndarray'> instead.",
+        match="power_spectrum must be a SphericalPS object or a list of SphericalPS",
     ):
         plot_1d_power_spectrum_z(
             [ps1d, np.ones((10, 10))],