diff --git a/docs/examples.rst b/docs/examples.rst
index 4d4bfaca9..0fc4db1c4 100644
--- a/docs/examples.rst
+++ b/docs/examples.rst
@@ -34,6 +34,7 @@ To get started, these examples focus on simulating one thing at a time.
    examples/1-basic/matter.ipynb
    examples/1-basic/density.ipynb
    examples/1-basic/lensing.ipynb
+   examples/1-basic/lensing-harmonic.ipynb
    examples/1-basic/photoz.ipynb
 
 
diff --git a/examples/1-basic/lensing-harmonic.ipynb b/examples/1-basic/lensing-harmonic.ipynb
new file mode 100644
index 000000000..5a8aad785
--- /dev/null
+++ b/examples/1-basic/lensing-harmonic.ipynb
@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Weak lensing in harmonic space\n",
+    "\n",
+    "This example computes a shear catalogue from a set of matter shells. The computation is carried out in harmonic space."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Simulate the matter fields, and use them to iteratively compute the lensing fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import healpy as hp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# use the CAMB cosmology that generated the matter power spectra\n",
+    "import camb\n",
+    "from cosmology.compat.camb import Cosmology\n",
+    "\n",
+    "# almost all GLASS functionality is available from the `glass` namespace\n",
+    "import glass\n",
+    "import glass.ext.camb\n",
+    "\n",
+    "# creating a numpy random number generator for sampling\n",
+    "rng = np.random.default_rng(seed=42)\n",
+    "\n",
+    "# cosmology for the simulation\n",
+    "h = 0.7\n",
+    "Oc = 0.25\n",
+    "Ob = 0.05\n",
+    "\n",
+    "# basic parameters of the simulation\n",
+    "nside = lmax = 256\n",
+    "\n",
+    "# set up CAMB parameters for matter angular power spectrum\n",
+    "pars = camb.set_params(\n",
+    "    H0=100 * h,\n",
+    "    omch2=Oc * h**2,\n",
+    "    ombh2=Ob * h**2,\n",
+    "    NonLinear=camb.model.NonLinear_both,\n",
+    ")\n",
+    "results = camb.get_background(pars)\n",
+    "\n",
+    "# get the cosmology from CAMB\n",
+    "cosmo = Cosmology(results)\n",
+    "\n",
+    "# shells of 200 Mpc in comoving distance spacing\n",
+    "zb = glass.distance_grid(cosmo, 0.0, 1.0, dx=200.0)\n",
+    "\n",
+    "# linear radial window functions\n",
+    "shells = glass.linear_windows(zb)\n",
+    "\n",
+    "# compute the angular matter power spectra of the shells with CAMB\n",
+    "cls = glass.ext.camb.matter_cls(pars, lmax, shells)\n",
+    "\n",
+    "# apply discretisation to the full set of spectra:\n",
+    "# - maximum angular mode number (`lmax=lmax`)\n",
+    "# - number of correlated shells (`ncorr=3`)\n",
+    "cls = glass.discretized_cls(cls, lmax=lmax, ncorr=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up lognormal fields for simulation\n",
+    "fields = glass.lognormal_fields(shells)\n",
+    "\n",
+    "# compute Gaussian spectra for lognormal fields from discretised spectra\n",
+    "gls = glass.solve_gaussian_spectra(fields, cls)\n",
+    "\n",
+    "# generator for lognormal matter fields\n",
+    "matter = glass.generate(fields, gls, nside, ncorr=3, rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lensing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this will compute the convergence field iteratively\n",
+    "convergence = glass.MultiPlaneConvergence(cosmo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Galaxies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# localised redshift distribution\n",
+    "# normalised to 0.01 galaxies/arcmin2\n",
+    "z = np.linspace(0.0, 1.0, 101)\n",
+    "dndz = np.exp(-((z - 0.5) ** 2) / (0.1) ** 2)\n",
+    "dndz = 0.1 * dndz / np.trapezoid(dndz, z)\n",
+    "\n",
+    "# distribute dN/dz over the radial window functions\n",
+    "ngal = glass.partition(z, dndz, shells)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "The simulation is carried out in harmonic space: the spherical harmonic decomposition of `delta` is computed on the fly. The convergence `kappa` is computed in harmonic space (i.e. $\\kappa_{lm}$) as well. The shear values for a set of galaxies are evaluated from the spherical harmonic decomposition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the integrated complex shear field over the redshift distribution\n",
+    "count = np.zeros(12 * nside**2, int)\n",
+    "gamma = np.zeros(12 * nside**2, complex)\n",
+    "\n",
+    "# main loop to simulate the matter fields iteratively\n",
+    "# delta is converted to harmonic space on the fly\n",
+    "for i, delta in enumerate(glass.harmonic_space(matter)):\n",
+    "    # add lensing plane from the window function of this shell\n",
+    "    convergence.add_window(delta, shells[i])\n",
+    "\n",
+    "    # get convergence field (in harmonic space!)\n",
+    "    kappa = convergence.kappa\n",
+    "\n",
+    "    # get random positions on the sphere\n",
+    "    for lon, lat, _ in glass.uniform_positions(ngal[i], rng=rng):\n",
+    "        # compute gamma in sampled positions from\n",
+    "        # the spherical harmonic decomposition of kappa\n",
+    "        gamma_gal = glass.harmonic_shear(kappa, lon, lat)\n",
+    "\n",
+    "        # add to integrated map\n",
+    "        ipix = hp.ang2pix(nside, lon, lat, lonlat=True)\n",
+    "        np.add.at(count, ipix, 1)\n",
+    "        np.add.at(gamma, ipix, gamma_gal)\n",
+    "\n",
+    "# average shear map\n",
+    "gamma[count > 0] /= count[count > 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysis\n",
+    "\n",
+    "To make sure the simulation works, compute the angular power spectrum of the simulated convergence field, and compare with the expectation (from CAMB) for the given redshift distribution of sources.\n",
+    "\n",
+    "We are not doing the modelling very carefully here, so a bit of discrepancy is to be expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 435,
+       "width": 582
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get the angular power spectra of the lensing maps\n",
+    "sim_cls = hp.anafast(\n",
+    "    [count, gamma.real, gamma.imag],\n",
+    "    pol=True,\n",
+    "    lmax=lmax,\n",
+    "    use_pixel_weights=True,\n",
+    ")\n",
+    "\n",
+    "# get the expected cls from CAMB\n",
+    "pars.min_l = 1\n",
+    "pars.set_for_lmax(lmax)\n",
+    "pars.SourceWindows = [\n",
+    "    camb.sources.SplinedSourceWindow(z=z, W=dndz, source_type=\"lensing\"),\n",
+    "]\n",
+    "theory_cls = camb.get_results(pars).get_source_cls_dict(lmax=lmax, raw_cl=True)\n",
+    "\n",
+    "# get the HEALPix pixel window function, since the lensing fields have it\n",
+    "pw0, pw2 = hp.pixwin(nside, lmax=lmax, pol=True)\n",
+    "\n",
+    "# plot the realised and expected cls\n",
+    "ell = np.arange(2, lmax + 1)\n",
+    "fl = ((ell + 2) * (ell - 1)) / (ell * (ell + 1)) * pw2[2:] ** 2\n",
+    "plt.plot(ell, sim_cls[1][2:], \"-k\", lw=2, label=\"simulation\")\n",
+    "plt.plot(ell, fl * theory_cls[\"W1xW1\"][2:], \"-r\", lw=2, label=\"expectation\")\n",
+    "plt.xscale(\"symlog\", linthresh=10, linscale=0.5, subs=[2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "plt.yscale(\"symlog\", linthresh=1e-9, linscale=0.5, subs=[2, 3, 4, 5, 6, 7, 8, 9])\n",
+    "plt.xlabel(r\"angular mode number $l$\")\n",
+    "plt.ylabel(r\"angular power spectrum $C_l^{\\gamma\\gamma}$\")\n",
+    "plt.legend(frameon=False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/glass/__init__.py b/glass/__init__.py
index 3a25d226e..5e1b77739 100644
--- a/glass/__init__.py
+++ b/glass/__init__.py
@@ -37,6 +37,8 @@
     "getcl",
     "glass_to_healpix_spectra",
     "grf",
+    "harmonic_shear",
+    "harmonic_space",
     "healpix_to_glass_spectra",
     "iternorm",
     "linear_bias",
@@ -92,6 +94,7 @@
     generate_lognormal,
     getcl,
     glass_to_healpix_spectra,
+    harmonic_space,
     healpix_to_glass_spectra,
     iternorm,
     lognormal_fields,
@@ -112,6 +115,7 @@
     MultiPlaneConvergence,
     deflect,
     from_convergence,
+    harmonic_shear,
     multi_plane_matrix,
     multi_plane_weights,
     shear_from_convergence,
diff --git a/glass/fields.py b/glass/fields.py
index bb8ff8265..0ffa694f9 100644
--- a/glass/fields.py
+++ b/glass/fields.py
@@ -1109,3 +1109,29 @@ def regularized_spectra(
 
     # return regularised spectra from cov matrix array
     return [cov[:, i, j] for i, j in spectra_indices(cov.shape[-1])]
+
+
+def harmonic_space(
+    maps: Iterable[NDArray[Any]],
+    lmax: int | None = None,
+) -> Iterator[NDArray[Any]]:
+    """
+    Transform simulated fields to harmonic space.
+
+    This function takes an iterator over maps, such as the one returned by
+    :func:`generate`, and turns it into an iterator over their spherical
+    harmonic coefficients :math:`a_{lm}`.
+
+    Parameters
+    ----------
+    maps
+        Maps to be transformed to harmonic space.
+
+    Returns
+    -------
+    alms
+        Spherical harmonic decomposition of the maps.
+
+    """
+    for m in maps:
+        yield hp.map2alm(m, lmax=lmax, use_pixel_weights=True)
diff --git a/glass/lensing.py b/glass/lensing.py
index 206a6f2d5..977c75f7f 100644
--- a/glass/lensing.py
+++ b/glass/lensing.py
@@ -31,6 +31,7 @@
 
 from __future__ import annotations
 
+import math
 from typing import TYPE_CHECKING, Literal, overload
 
 import healpy as hp
@@ -43,6 +44,7 @@
 if TYPE_CHECKING:
     from collections.abc import Sequence
     from types import ModuleType
+    from typing import Any
 
     from numpy.typing import NDArray
 
@@ -687,3 +689,88 @@ def deflect(
     d = xp.atan2(sa * sg, st * ca - ct * sa * cg)
 
     return lon - uxpx.degrees(d), uxpx.degrees(tp)
+
+
+def harmonic_shear(
+    kappa: NDArray[Any],
+    lon: NDArray[Any],
+    lat: NDArray[Any],
+) -> NDArray[Any]:
+    r"""
+    Compute shear at given positions in harmonic space.
+
+    Takes a convergence field in harmonic space (i.e. spherical harmonic
+    coefficients :math:`\kappa_{lm}`) and returns the *shear* values in the
+    given positions.
+
+    Parameters
+    ----------
+    kappa
+        Spherical harmonic coefficients of the convergence field.
+    lon, lat
+        Longitude and latitude (in degrees) of positions to evaluate.
+
+    Returns
+    -------
+    gamma
+        Complex shear in the given positions.
+
+    Notes
+    -----
+    The spherical harmonic coefficients of convergence and shear for
+    :math:`l \ge 2` are related as [Tessore23]_
+
+    .. math::
+
+        \gamma_{lm}
+        = -\sqrt{\frac{(l+2) \, (l-1)}{l \, (l+1)}} \, \kappa_{lm} \;,
+
+    with :math:`\gamma_{lm} = ` for :math:`l < 2`.
+
+    """
+    try:
+        import ducc0  # noqa: PLC0415
+    except ModuleNotFoundError as exc:
+        raise RuntimeError("function requires the ducc0 package") from exc
+
+    # should be moved to helper module
+    from glass.fields import _multalm  # noqa: PLC0415
+
+    # turn inputs into spherical coordinates
+    theta = (90.0 - lat.reshape(-1)) / 180 * np.pi
+    phi = (lon.reshape(-1) / 180 * np.pi) % (2 * np.pi)
+    if theta.size != phi.size:
+        raise ValueError("inconsistent input shapes")
+
+    # skip empty list of points
+    if theta.size == 0:
+        return np.zeros(0) + 1j * np.zeros(0)
+
+    # compute lmax from size of kappa alms
+    lmax = (math.isqrt(8 * kappa.shape[-1] + 1) - 3) // 2
+
+    # array for kappa to gamma conversion
+    ell = np.arange(2, lmax + 1)
+    fl = np.concat(
+        [
+            np.zeros(2),
+            -np.sqrt(((ell + 2) * (ell - 1)) / (ell * (ell + 1))),
+        ]
+    )
+
+    # compute inputs to transform
+    alm = np.stack([_multalm(kappa, fl), np.zeros_like(kappa)], axis=0)
+    loc = np.stack([theta, phi], axis=1)
+
+    # compute shear values in positions
+    gamma = ducc0.sht.synthesis_general(
+        alm=alm,
+        spin=2,
+        lmax=lmax,
+        loc=loc,
+        epsilon=2e-13,
+        nthreads=0,
+    )
+
+    # return shear as complex array
+    return np.dot(np.asarray([1, 1j]), gamma)  # type: ignore[no-any-return]