diff --git a/docs/source/index.rst b/docs/source/index.rst
index fb1b70a..10feb9c 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -20,6 +20,6 @@ Contents
    :caption: Contents:
 
    introduction
-   notebooks/Basic tutorial
+   tutorials
    generated/api
    dev guide
diff --git a/docs/source/notebooks/Basic tutorial.ipynb b/docs/source/notebooks/Basic tutorial.ipynb
index 07a3d49..3cab9eb 100644
--- a/docs/source/notebooks/Basic tutorial.ipynb	
+++ b/docs/source/notebooks/Basic tutorial.ipynb	
@@ -325,7 +325,7 @@
     "\n",
     "\n",
     "for axis_i, (i,j) in enumerate([(0,0),(1,1),(2,2),(0,1),(0,2),(1,2)]):\n",
-    "    axis = axes[axis_i]\n",
+    "    axis = axes[axis_i] # type: ignore\n",
     "    for interp,sign,color in zip(target_interps,signs,['pink', 'red', 'orange']):\n",
     "        if sign < 0:\n",
     "            print('negative sign')\n",
diff --git a/docs/source/notebooks/Masking.ipynb b/docs/source/notebooks/Masking.ipynb
new file mode 100644
index 0000000..c88665e
--- /dev/null
+++ b/docs/source/notebooks/Masking.ipynb
@@ -0,0 +1,306 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "81c543e7",
+   "metadata": {},
+   "source": [
+    "# Masking"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bef1d38",
+   "metadata": {},
+   "source": [
+    "This tutorial explores ramannoodle's masking features. Ramannoodle's `InterpolationModel`, as well it's subclasses (such as `ARTModel`), can be modified by \"masking\" specified degrees of freedom (DOFs). When a DOF is masked, it is excluded when calculating polarizabilities and therefore will not be accounted for when calculating Raman spectra. Raman spectra computed with masking should be regarded as *partial Raman spectra*. By choosing the masks wisely, quite a bit can be learned about which atom (or groups of atoms) correspond to which features in the simulated Raman spectra."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b8f3299-8cf3-4c00-b2b2-fb514c3aeb0f",
+   "metadata": {},
+   "source": [
+    "We'll import everything we'll need up front, as well add some customizations for matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3956373a-63a1-4128-b0f2-e2711b5a5213",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import matplotlib_inline\n",
+    "\n",
+    "from ramannoodle import io\n",
+    "from ramannoodle.polarizability.art import ARTModel\n",
+    "from ramannoodle.symmetry import symmetry_utils\n",
+    "from ramannoodle.spectrum.spectrum_utils import convolve_intensities\n",
+    "\n",
+    "matplotlib_inline.backend_inline.set_matplotlib_formats('png')\n",
+    "plt.rcParams['figure.dpi'] = 300\n",
+    "plt.rcParams['font.family'] = 'sans-serif'\n",
+    "plt.rcParams[\"mathtext.default\"] = 'regular'\n",
+    "plt.rcParams['axes.linewidth'] = 0.5\n",
+    "plt.rcParams['xtick.major.width'] = 0.5\n",
+    "plt.rcParams['xtick.minor.width'] = 0.5\n",
+    "plt.rcParams['lines.linewidth'] = 1.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7da690f-f4db-4509-8c89-014e7a49c83b",
+   "metadata": {},
+   "source": [
+    "We will be computing TiO2's Raman spectrum using data available in `tests/data/TiO2`. We will be basing this spectrum on frozen phonon calculations and use `ARTModel` to estimate phonon polarizabilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6102769c-0416-497a-b6a9-e451b66ca71d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_dir = \"../../../test/data/TiO2\"\n",
+    "# phonon_OUTCAR contains phonons (duh) as well as the equilibrium TiO2 structure.\n",
+    "phonon_outcar = f\"{data_dir}/phonons_OUTCAR\"\n",
+    "\n",
+    "# Read the phonons\n",
+    "phonons = io.read_phonons(phonon_outcar, file_format = 'outcar')\n",
+    "# Read the symmetry of the structure. This might take a few moments...\n",
+    "symmetry = io.read_structural_symmetry(phonon_outcar, file_format = 'outcar')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6831e95",
+   "metadata": {},
+   "source": [
+    "Now that we have the structure's symmetry, we can start building the polarizability model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f14761e9-96a8-4317-94cb-47153683eb88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "╭──────────────┬──────────────┬─────────────┬────────────────────╮\n",
+       "│   Atom index │ Directions   │ Specified   │   Equivalent atoms │\n",
+       "├──────────────┼──────────────┼─────────────┼────────────────────┤\n",
+       "│            0 │              │ \u001b[1;31;40m0/3\u001b[0m         │                 35 │\n",
+       "│           36 │              │ \u001b[1;31;40m0/3\u001b[0m         │                 71 │\n",
+       "╰──────────────┴──────────────┴─────────────┴────────────────────╯"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We'll need the polarizability of the equilibrium structure. \n",
+    "_, equilibrium_polarizability = io.read_positions_and_polarizability(\n",
+    "   f\"{data_dir}/ref_eps_OUTCAR\", file_format = \"outcar\" \n",
+    ")\n",
+    "model = ARTModel(symmetry, equilibrium_polarizability)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58d82797",
+   "metadata": {},
+   "source": [
+    "The `__repr__` method of `ARTModel` allows us to track out progress as we build up the model. We set up the model by feeding in files containing displacements and polarizabilities. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "147f6a26-a22a-4cff-9659-336d6316e0b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "╭──────────────┬─────────────────────────────────────────────┬─────────────┬────────────────────╮\n",
+       "│   Atom index │ Directions                                  │ Specified   │   Equivalent atoms │\n",
+       "├──────────────┼─────────────────────────────────────────────┼─────────────┼────────────────────┤\n",
+       "│            0 │ [-1. -0. +0.], [-0. -1. -0.], [-0. -0. +1.] │ \u001b[1;32;40m3/3\u001b[0m         │                 35 │\n",
+       "│           36 │ [+0. +0. +1.], [+1. +0. -0.], [+0. +1. -0.] │ \u001b[1;32;40m3/3\u001b[0m         │                 71 │\n",
+       "╰──────────────┴─────────────────────────────────────────────┴─────────────┴────────────────────╯"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# OUTCARS are polarizability calculation where atom 5 (Ti) \n",
+    "# was displaced +0.1 and +0.2 angstrom in the x direction\n",
+    "model.add_art_from_files(\n",
+    "    [f\"{data_dir}/Ti5_0.1x_eps_OUTCAR\"], file_format = 'outcar'\n",
+    "  )\n",
+    "model.add_art_from_files(\n",
+    "    [f\"{data_dir}/Ti5_0.1z_eps_OUTCAR\"],file_format = 'outcar'\n",
+    "  )\n",
+    "model.add_art_from_files(\n",
+    "    [f\"{data_dir}/O43_0.1z_eps_OUTCAR\", f\"{data_dir}/O43_m0.1z_eps_OUTCAR\"], \n",
+    "    file_format=\"outcar\"\n",
+    ")\n",
+    "model.add_art_from_files(\n",
+    "    [f\"{data_dir}/O43_0.1x_eps_OUTCAR\"], file_format = 'outcar'\n",
+    ")\n",
+    "model.add_art_from_files([f\"{data_dir}/O43_0.1y_eps_OUTCAR\"],file_format = 'outcar')\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6c70688",
+   "metadata": {},
+   "source": [
+    "From the \"Specified\" column, we see that all degrees of freedom have been specified. We are now ready to do some Raman calculations!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b1f2cb2c-8444-4b6a-ae8e-f1ac6c6a6e98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1145de810>]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Compute and plot spectrum\n",
+    "spectrum = phonons.get_raman_spectrum(model)\n",
+    "wavenumbers, total_intensities = spectrum.measure(laser_correction = True, \n",
+    "                                           laser_wavelength = 532, \n",
+    "                                           bose_einstein_correction = True, \n",
+    "                                           temperature = 300)\n",
+    "wavenumbers, total_intensities = convolve_intensities(wavenumbers, total_intensities)\n",
+    "fig = plt.figure()\n",
+    "axis = fig.add_subplot(111)\n",
+    "axis.plot(wavenumbers, total_intensities)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "049753d4",
+   "metadata": {},
+   "source": [
+    "Now, one question we could ask is which parts of the Raman spectrum are associated with motion of Ti atoms and which parts are associated more with O atoms. In this case, we can produce two masked `ARTModel`'s, one with the Ti atoms masked and other with the O atoms masked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e0806350-ac5d-4f09-826d-b9dc85ec471b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "axis = fig.add_subplot(111)\n",
+    "\n",
+    "total_model = model\n",
+    "O_dof_indexes = total_model.get_dof_indexes('O')\n",
+    "Ti_model = total_model.get_masked_model(O_dof_indexes) # Mask O to leave only Ti\n",
+    "Ti_dof_indexes = total_model.get_dof_indexes('Ti')\n",
+    "O_model = total_model.get_masked_model(Ti_dof_indexes) # Mask Ti to leave only O\n",
+    "\n",
+    "offset = 0\n",
+    "models = [Ti_model, O_model, total_model]\n",
+    "labels = ['Ti contribution', 'O contribution', 'Total spectrum']\n",
+    "for plot_model, label in zip(models, labels):\n",
+    "\n",
+    "    spectrum = phonons.get_raman_spectrum(plot_model)\n",
+    "    wavenumbers, intensities = spectrum.measure()\n",
+    "    np.savez(f\"{data_dir}/known_art_{label}_spectrum.npz\", wavenumbers = wavenumbers, intensities = intensities)\n",
+    "    wavenumbers, intensities = convolve_intensities(wavenumbers, intensities)\n",
+    "    axis.plot(wavenumbers, intensities + offset, label = label)\n",
+    "    offset = np.max(intensities) * 2.5\n",
+    "\n",
+    "l = axis.legend()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32f6be86",
+   "metadata": {},
+   "source": [
+    "Convince yourself that this makes sense.\n",
+    "\n",
+    "Hint: consider the vibrational frequencies of the heavier Ti atoms vs the lighter O atoms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9972621",
+   "metadata": {},
+   "source": [
+    "Although this a very simple example, model masking is extremely powerful. By isolating the influence of individual atomic motions on the Raman spectrum, we can gain deep insight into the factors regulating the Raman shifts and Raman intensities of certain motions. In addition, masking gives us a straightforward way to isolate the Raman spectra of specific atoms or groups of atoms. This is useful in many cases, for example when considering defects."
+   ]
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/tutorials.rst b/docs/source/tutorials.rst
new file mode 100644
index 0000000..2f1ec61
--- /dev/null
+++ b/docs/source/tutorials.rst
@@ -0,0 +1,9 @@
+Tutorials
+=========
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   notebooks/Basic tutorial
+   notebooks/Masking
diff --git a/ramannoodle/globals.py b/ramannoodle/globals.py
index f441406..68620b6 100644
--- a/ramannoodle/globals.py
+++ b/ramannoodle/globals.py
@@ -242,6 +242,8 @@
     "Og": 118,
 }
 
+ATOM_SYMBOLS = {b: a for a, b in ATOMIC_NUMBERS.items()}
+
 RAMAN_TENSOR_CENTRAL_DIFFERENCE = 0.001
 BOLTZMANN_CONSTANT = 8.617333262e-5  # Units: eV/K
 
diff --git a/ramannoodle/polarizability/art.py b/ramannoodle/polarizability/art.py
index 6d558f4..4b00c2d 100644
--- a/ramannoodle/polarizability/art.py
+++ b/ramannoodle/polarizability/art.py
@@ -229,14 +229,44 @@ def get_specification_tuples(
             )
         return specification_tuples
 
+    def get_dof_indexes(
+        self, atom_indexes_or_symbols: int | str | list[int | str]
+    ) -> list[int]:
+        """Return art (DOF) indexes for certain atoms.
+
+        Parameters
+        ----------
+        atom_indexes_or_symbols
+            If integer or list of integers, specifies atom indexes. If string or list
+            of strings, specifies atom symbols. Mixtures of integers and strings are
+            allowed.
+
+        """
+        if not isinstance(atom_indexes_or_symbols, list):
+            atom_indexes_or_symbols = list([atom_indexes_or_symbols])
+
+        atom_indexes = []
+        for item in atom_indexes_or_symbols:
+            if isinstance(item, str):
+                atom_indexes += self._structural_symmetry.get_atom_indexes(item)
+            else:
+                atom_indexes += [item]
+        atom_indexes = list(set(atom_indexes))
+
+        dof_indexes = []
+        for atom_index in atom_indexes:
+            for index, basis_vector in enumerate(self._cartesian_basis_vectors):
+                direction = basis_vector[atom_index]
+                if not np.isclose(direction, 0, atol=1e-5).all():
+                    dof_indexes.append(index)
+        return dof_indexes
+
     def _get_art_directions(self, atom_index: int) -> list[NDArray[np.float64]]:
         """Return specified art direction vectors for an atom."""
-        directions = []
-        for basis_vector in self._cartesian_basis_vectors:
-            direction = basis_vector[atom_index]
-            if not np.isclose(direction, 0, atol=1e-5).all():
-                directions.append(direction)
-        assert len(directions) <= 3
+        indexes = self.get_dof_indexes(atom_index)
+        directions = [
+            self._cartesian_basis_vectors[index][atom_index] for index in indexes
+        ]
         return directions
 
     def __repr__(self) -> str:
diff --git a/ramannoodle/polarizability/interpolation.py b/ramannoodle/polarizability/interpolation.py
index 2d12b19..e166a76 100644
--- a/ramannoodle/polarizability/interpolation.py
+++ b/ramannoodle/polarizability/interpolation.py
@@ -1,6 +1,8 @@
 """Polarizability models."""
 
 from pathlib import Path
+from typing import Self
+import copy
 
 import numpy as np
 from numpy.typing import NDArray
@@ -65,6 +67,7 @@ def __init__(
         self._equilibrium_polarizability = equilibrium_polarizability
         self._cartesian_basis_vectors: list[NDArray[np.float64]] = []
         self._interpolations: list[BSpline] = []
+        self._mask: NDArray[np.bool] = np.array([], dtype="bool")
 
     def get_polarizability(
         self, cartesian_displacement: NDArray[np.float64]
@@ -83,8 +86,8 @@ def get_polarizability(
 
         """
         delta_polarizability: NDArray[np.float64] = np.zeros((3, 3))
-        for basis_vector, interpolation in zip(
-            self._cartesian_basis_vectors, self._interpolations
+        for basis_vector, interpolation, mask in zip(
+            self._cartesian_basis_vectors, self._interpolations, self._mask
         ):
             try:
                 amplitude = np.dot(
@@ -97,7 +100,9 @@ def get_polarizability(
                     "cartesian_displacement has incompatible length "
                     f"({len(cartesian_displacement)}!={len(basis_vector)})"
                 ) from exc
-            delta_polarizability += interpolation(amplitude)
+            delta_polarizability += mask * np.array(
+                interpolation(amplitude), dtype="float64"
+            )
 
         return delta_polarizability + self._equilibrium_polarizability
 
@@ -126,6 +131,18 @@ def _get_dof(  # pylint: disable=too-many-locals
         :
             3-tuple of the form (basis vectors, interpolation_xs, interpolation_ys)
         """
+        # Check that the parent displacement is orthogonal to existing basis vectors
+        parent_cartesian_basis_vector = (
+            self._structural_symmetry.get_cartesian_displacement(parent_displacement)
+        )
+        result = is_orthogonal_to_all(
+            parent_cartesian_basis_vector, self._cartesian_basis_vectors
+        )
+        if result != -1:
+            raise InvalidDOFException(
+                f"new dof is not orthogonal with existing dof (index={result})"
+            )
+
         displacements_and_transformations = (
             self._structural_symmetry.get_equivalent_displacements(parent_displacement)
         )
@@ -224,6 +241,7 @@ def _construct_and_add_interpolations(
 
         self._cartesian_basis_vectors += basis_vectors_to_add
         self._interpolations += interpolations_to_add
+        self._mask = np.append(self._mask, [True] * len(basis_vectors_to_add))
 
     def add_dof(  # pylint: disable=too-many-arguments
         self,
@@ -275,18 +293,6 @@ def add_dof(  # pylint: disable=too-many-arguments
             "polarizabilities", polarizabilities, (len(amplitudes), 3, 3)
         )
 
-        # Check that the parent displacement is orthogonal to existing basis vectors
-        parent_cartesian_basis_vector = (
-            self._structural_symmetry.get_cartesian_displacement(parent_displacement)
-        )
-        result = is_orthogonal_to_all(
-            parent_cartesian_basis_vector, self._cartesian_basis_vectors
-        )
-        if result != -1:
-            raise InvalidDOFException(
-                f"new dof is not orthogonal with existing dof (index={result})"
-            )
-
         # Get information needed for DOF
         basis_vectors_to_add, interpolation_xs, interpolation_ys = self._get_dof(
             parent_displacement,
@@ -398,6 +404,43 @@ def _read_dof(
             np.array(polarizabilities),
         )
 
+    def get_mask(self) -> NDArray[np.bool]:
+        """Return mask."""
+        return self._mask
+
+    def set_mask(self, mask: NDArray[np.bool]) -> None:
+        """Set mask.
+
+        ..warning:: To avoid unintentional use of masked models, we discourage masking
+                    in-place. Instead, consider using `get masked_model`.
+
+        Parameters
+        ----------
+        mask
+            1D array of size (N,) where N is the number of specified degrees
+            of freedom (DOFs). If an element is False, its corresponding DOF will be
+            "masked" and therefore excluded from polarizability calculations.
+        """
+        verify_ndarray_shape("mask", mask, self._mask.shape)
+        self._mask = mask
+
+    def get_masked_model(self, dof_indexes_to_mask: list[int]) -> Self:
+        """Return new model with certain degrees of freedom deactivated.
+
+        Model masking allows for the calculation of partial Raman spectra in which only
+        certain degrees of freedom are considered.
+        """
+        result = copy.deepcopy(self)
+        new_mask = result.get_mask()
+        new_mask[:] = True
+        new_mask[dof_indexes_to_mask] = False
+        result.set_mask(new_mask)
+        return result
+
+    def unmask(self) -> None:
+        """Clear mask, activating all specified DOFs."""
+        self._mask[:] = True
+
     def __repr__(self) -> str:
         """Return string representation."""
         total_dofs = 3 * len(self._structural_symmetry.get_fractional_positions())
diff --git a/ramannoodle/symmetry/__init__.py b/ramannoodle/symmetry/__init__.py
index d55992e..4f14b85 100644
--- a/ramannoodle/symmetry/__init__.py
+++ b/ramannoodle/symmetry/__init__.py
@@ -4,8 +4,9 @@
 from numpy.typing import NDArray
 import spglib
 
+from ..globals import ATOM_SYMBOLS
 from . import symmetry_utils
-from ..exceptions import SymmetryException, verify_ndarray_shape
+from ..exceptions import SymmetryException, verify_ndarray_shape, get_type_error
 
 
 class StructuralSymmetry:
@@ -182,3 +183,25 @@ def get_cartesian_displacement(
     def get_fractional_positions(self) -> NDArray[np.float64]:
         """Return fractional positions."""
         return self._fractional_positions
+
+    def get_atom_indexes(self, atom_symbols: str | list[str]) -> list[int]:
+        """Return atom indexes with matching symbols.
+
+        Parameters
+        ----------
+        atom_symbols
+            If integer or list of integers, specifies atom indexes. If string or list
+            of strings, specifies atom symbols. Mixtures of integers and strings are
+            allowed.
+        """
+        symbols = [ATOM_SYMBOLS[number] for number in self._atomic_numbers]
+        indexes = []
+        if isinstance(atom_symbols, str):
+            atom_symbols = [atom_symbols]
+        try:
+            for index, symbol in enumerate(symbols):
+                if symbol in atom_symbols:
+                    indexes.append(index)
+        except TypeError as err:
+            raise get_type_error("atom_symbols", atom_symbols, "list") from err
+        return indexes
diff --git a/test/data/TiO2/known_art_O contribution_spectrum.npz b/test/data/TiO2/known_art_O contribution_spectrum.npz
new file mode 100644
index 0000000..5b275e2
Binary files /dev/null and b/test/data/TiO2/known_art_O contribution_spectrum.npz differ
diff --git a/test/data/TiO2/known_art_O_spectrum.npz b/test/data/TiO2/known_art_O_spectrum.npz
new file mode 100644
index 0000000..5b275e2
Binary files /dev/null and b/test/data/TiO2/known_art_O_spectrum.npz differ
diff --git a/test/data/TiO2/known_art_Ti contribution_spectrum.npz b/test/data/TiO2/known_art_Ti contribution_spectrum.npz
new file mode 100644
index 0000000..9d1432a
Binary files /dev/null and b/test/data/TiO2/known_art_Ti contribution_spectrum.npz differ
diff --git a/test/data/TiO2/known_art_Ti_spectrum.npz b/test/data/TiO2/known_art_Ti_spectrum.npz
new file mode 100644
index 0000000..9d1432a
Binary files /dev/null and b/test/data/TiO2/known_art_Ti_spectrum.npz differ
diff --git a/test/data/TiO2/known_art_Total spectrum_spectrum.npz b/test/data/TiO2/known_art_Total spectrum_spectrum.npz
new file mode 100644
index 0000000..ff3314a
Binary files /dev/null and b/test/data/TiO2/known_art_Total spectrum_spectrum.npz differ
diff --git a/test/tests/test_art.py b/test/tests/test_art.py
index b6e722a..e108aa7 100644
--- a/test/tests/test_art.py
+++ b/test/tests/test_art.py
@@ -143,19 +143,21 @@ def test_add_art_exception(
 
 
 @pytest.mark.parametrize(
-    "outcar_symmetry_fixture,outcar_files,exception_type,in_reason",
+    "outcar_symmetry_fixture,outcar_file_groups,exception_type,in_reason",
     [
         (
             "test/data/STO_RATTLED_OUTCAR",
-            ["test/data/TiO2/Ti5_0.1x_eps_OUTCAR"],
+            [["test/data/TiO2/Ti5_0.1x_eps_OUTCAR"]],
             InvalidDOFException,
             "incompatible outcar",
         ),
         (
             "test/data/TiO2/phonons_OUTCAR",
             [
-                "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
-                "test/data/TiO2/Ti5_0.2x_eps_OUTCAR",
+                [
+                    "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
+                    "test/data/TiO2/Ti5_0.2x_eps_OUTCAR",
+                ]
             ],
             InvalidDOFException,
             "wrong number of amplitudes: 4 != 2",
@@ -163,8 +165,10 @@ def test_add_art_exception(
         (
             "test/data/TiO2/phonons_OUTCAR",
             [
-                "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
-                "test/data/TiO2/Ti5_m0.1x_eps_OUTCAR",
+                [
+                    "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
+                    "test/data/TiO2/Ti5_m0.1x_eps_OUTCAR",
+                ]
             ],
             InvalidDOFException,
             "wrong number of amplitudes: 4 != 2",
@@ -172,7 +176,9 @@ def test_add_art_exception(
         (
             "test/data/TiO2/phonons_OUTCAR",
             [
-                "this_outcar_does_not_exist",
+                [
+                    "this_outcar_does_not_exist",
+                ]
             ],
             FileNotFoundError,
             "No such file or directory",
@@ -180,8 +186,10 @@ def test_add_art_exception(
         (
             "test/data/TiO2/phonons_OUTCAR",
             [
-                "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
-                "test/data/TiO2/Ti5_0.1y_eps_OUTCAR",
+                [
+                    "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
+                    "test/data/TiO2/Ti5_0.1y_eps_OUTCAR",
+                ]
             ],
             InvalidDOFException,
             "is not collinear",
@@ -189,17 +197,32 @@ def test_add_art_exception(
         (
             "test/data/TiO2/phonons_OUTCAR",
             [
-                "test/data/TiO2/O43_0.1z_eps_OUTCAR",
+                [
+                    "test/data/TiO2/O43_0.1z_eps_OUTCAR",
+                ]
             ],
             InvalidDOFException,
             "wrong number of amplitudes: 1 != 2",
         ),
+        (
+            "test/data/TiO2/phonons_OUTCAR",
+            [
+                [
+                    "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
+                ],
+                [
+                    "test/data/TiO2/Ti5_0.1y_eps_OUTCAR",
+                ],
+            ],
+            InvalidDOFException,
+            "is not orthogonal",
+        ),
     ],
     indirect=["outcar_symmetry_fixture"],
 )
 def test_add_art_from_files_exception(
     outcar_symmetry_fixture: StructuralSymmetry,
-    outcar_files: list[str],
+    outcar_file_groups: list[str],
     exception_type: Type[Exception],
     in_reason: str,
 ) -> None:
@@ -207,7 +230,8 @@ def test_add_art_from_files_exception(
     symmetry = outcar_symmetry_fixture
     model = ARTModel(symmetry, np.zeros((3, 3)))
     with pytest.raises(exception_type) as error:
-        model.add_art_from_files(outcar_files, "outcar")
+        for outcar_files in outcar_file_groups:
+            model.add_art_from_files(outcar_files, "outcar")
     assert in_reason in str(error.value)
 
 
diff --git a/test/tests/test_interpolation.py b/test/tests/test_interpolation.py
index ecea562..78013a2 100644
--- a/test/tests/test_interpolation.py
+++ b/test/tests/test_interpolation.py
@@ -191,18 +191,19 @@ def test_add_dof_exception(
 
 
 @pytest.mark.parametrize(
-    "outcar_symmetry_fixture,outcar_files,interpolation_order,exception_type,in_reason",
+    "outcar_symmetry_fixture,outcar_file_groups,interpolation_order,exception_type,"
+    "in_reason",
     [
         (
             "test/data/STO_RATTLED_OUTCAR",
-            ["test/data/TiO2/Ti5_0.1x_eps_OUTCAR"],
+            [["test/data/TiO2/Ti5_0.1x_eps_OUTCAR"]],
             1,
             InvalidDOFException,
             "incompatible outcar",
         ),
         (
             "test/data/TiO2/phonons_OUTCAR",
-            ["test/data/TiO2/Ti5_0.1x_eps_OUTCAR"],
+            [["test/data/TiO2/Ti5_0.1x_eps_OUTCAR"]],
             3,
             InvalidDOFException,
             "insufficient points (3)",
@@ -210,8 +211,10 @@ def test_add_dof_exception(
         (
             "test/data/TiO2/phonons_OUTCAR",
             [
-                "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
-                "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
+                [
+                    "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
+                    "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
+                ]
             ],
             1,
             InvalidDOFException,
@@ -220,7 +223,9 @@ def test_add_dof_exception(
         (
             "test/data/TiO2/phonons_OUTCAR",
             [
-                "this_outcar_does_not_exist",
+                [
+                    "this_outcar_does_not_exist",
+                ]
             ],
             1,
             FileNotFoundError,
@@ -229,19 +234,31 @@ def test_add_dof_exception(
         (
             "test/data/TiO2/phonons_OUTCAR",
             [
-                "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
-                "test/data/TiO2/Ti5_0.1y_eps_OUTCAR",
+                [
+                    "test/data/TiO2/Ti5_0.1x_eps_OUTCAR",
+                    "test/data/TiO2/Ti5_0.1y_eps_OUTCAR",
+                ]
             ],
             1,
             InvalidDOFException,
             "is not collinear",
         ),
+        (
+            "test/data/TiO2/phonons_OUTCAR",
+            [
+                ["test/data/TiO2/Ti5_0.1x_eps_OUTCAR"],
+                ["test/data/TiO2/Ti5_0.1x_eps_OUTCAR"],
+            ],
+            1,
+            InvalidDOFException,
+            "new dof is not orthogonal with existing dof (index=0)",
+        ),
     ],
     indirect=["outcar_symmetry_fixture"],
 )
 def test_add_dof_from_files_exception(
     outcar_symmetry_fixture: StructuralSymmetry,
-    outcar_files: list[str],
+    outcar_file_groups: list[str],
     interpolation_order: int,
     exception_type: Type[Exception],
     in_reason: str,
@@ -250,5 +267,6 @@ def test_add_dof_from_files_exception(
     symmetry = outcar_symmetry_fixture
     model = InterpolationModel(symmetry, np.zeros((3, 3)))
     with pytest.raises(exception_type) as error:
-        model.add_dof_from_files(outcar_files, "outcar", interpolation_order)
+        for outcar_files in outcar_file_groups:
+            model.add_dof_from_files(outcar_files, "outcar", interpolation_order)
     assert in_reason in str(error.value)
diff --git a/test/tests/test_spectrum.py b/test/tests/test_spectrum.py
index 0e16126..55af017 100644
--- a/test/tests/test_spectrum.py
+++ b/test/tests/test_spectrum.py
@@ -18,7 +18,7 @@
     get_laser_correction,
 )
 
-# pylint: disable=protected-access
+# pylint: disable=protected-access,too-many-locals
 
 
 def _get_all_eps_outcars(directory: str) -> list[str]:
@@ -48,7 +48,7 @@ def _validate_polarizabilities(model: InterpolationModel, data_directory: str) -
     [
         (
             "test/data/TiO2/phonons_OUTCAR",
-            "test/data/TiO2/",
+            "test/data/TiO2",
             [
                 ["Ti5_0.1z_eps_OUTCAR", "Ti5_0.2z_eps_OUTCAR"],
                 ["Ti5_0.1x_eps_OUTCAR", "Ti5_0.2x_eps_OUTCAR"],
@@ -163,6 +163,81 @@ def test_art_spectrum(
         assert np.isclose(intensities, known_intensities, atol=1e-4).all()
 
 
+@pytest.mark.parametrize(
+    "outcar_symmetry_fixture,data_directory,dof_eps_outcars,atoms_to_mask,"
+    "known_spectrum_file",
+    [
+        (
+            "test/data/TiO2/phonons_OUTCAR",
+            "test/data/TiO2/",
+            [
+                ["Ti5_0.1z_eps_OUTCAR"],
+                ["Ti5_0.1x_eps_OUTCAR"],
+                [
+                    "O43_0.1z_eps_OUTCAR",
+                    "O43_m0.1z_eps_OUTCAR",
+                ],
+                ["O43_0.1x_eps_OUTCAR"],
+                ["O43_0.1y_eps_OUTCAR"],
+            ],
+            "Ti",
+            "known_art_O_spectrum.npz",
+        ),
+        (
+            "test/data/TiO2/phonons_OUTCAR",
+            "test/data/TiO2/",
+            [
+                ["Ti5_0.1z_eps_OUTCAR"],
+                ["Ti5_0.1x_eps_OUTCAR"],
+                [
+                    "O43_0.1z_eps_OUTCAR",
+                    "O43_m0.1z_eps_OUTCAR",
+                ],
+                ["O43_0.1x_eps_OUTCAR"],
+                ["O43_0.1y_eps_OUTCAR"],
+            ],
+            "O",
+            "known_art_Ti_spectrum.npz",
+        ),
+    ],
+    indirect=["outcar_symmetry_fixture"],
+)
+def test_art_masked_spectrum(
+    outcar_symmetry_fixture: StructuralSymmetry,
+    data_directory: str,
+    dof_eps_outcars: list[str],
+    atoms_to_mask: str,
+    known_spectrum_file: str,
+) -> None:
+    """Test a masked spectrum calculation using ARTModel."""
+    # Setup model
+    symmetry = outcar_symmetry_fixture
+    _, polarizability = io.read_positions_and_polarizability(
+        f"{data_directory}/ref_eps_OUTCAR", file_format="outcar"
+    )
+    model = ARTModel(symmetry, polarizability)
+    for outcar_names in dof_eps_outcars:
+        model.add_art_from_files(
+            [f"{data_directory}/{name}" for name in outcar_names], file_format="outcar"
+        )
+    masked_dofs = model.get_dof_indexes(atoms_to_mask)
+    model = model.get_masked_model(masked_dofs)
+
+    # Spectrum test
+    with np.load(f"{data_directory}/{known_spectrum_file}") as known_spectrum:
+        phonons = io.read_phonons(
+            f"{data_directory}/phonons_OUTCAR", file_format="outcar"
+        )
+        spectrum = phonons.get_raman_spectrum(model)
+        wavenumbers, intensities = spectrum.measure()
+
+        known_wavenumbers = known_spectrum["wavenumbers"]
+        known_intensities = known_spectrum["intensities"]
+
+        assert np.isclose(wavenumbers, known_wavenumbers).all()
+        assert np.isclose(intensities, known_intensities, atol=1e-4).all()
+
+
 @pytest.mark.parametrize(
     "spectrum_path,known_gaussian_spectrum_path,known_lorentzian_spectrum_path",
     [
diff --git a/test/tests/test_symmetry.py b/test/tests/test_symmetry.py
index e7dbc7f..4cd60ba 100644
--- a/test/tests/test_symmetry.py
+++ b/test/tests/test_symmetry.py
@@ -257,3 +257,22 @@ def test_structural_symmetry_exception(
     with pytest.raises(exception_type) as error:
         StructuralSymmetry(atomic_numbers, lattice, fractional_positions)
     assert in_reason in str(error.value)
+
+
+@pytest.mark.parametrize(
+    "outcar_symmetry_fixture, atom_symbols, known_atom_indexes",
+    [
+        ("test/data/TiO2/phonons_OUTCAR", "Ti", list(range(0, 36))),
+        ("test/data/STO_RATTLED_OUTCAR", "O", list(range(54, 135))),
+        ("test/data/LLZO/LLZO_OUTCAR", "La", list(range(56, 80))),
+    ],
+    indirect=["outcar_symmetry_fixture"],
+)
+def test_get_atom_indexes(
+    outcar_symmetry_fixture: StructuralSymmetry,
+    atom_symbols: str | list[str],
+    known_atom_indexes: list[int],
+) -> None:
+    """Test get_atom_indexes."""
+    symmetry = outcar_symmetry_fixture
+    assert symmetry.get_atom_indexes(atom_symbols) == known_atom_indexes