diff --git a/cspell.json b/cspell.json
index 26d5f2d..17ad03f 100644
--- a/cspell.json
+++ b/cspell.json
@@ -24,6 +24,7 @@
         "newaxis",
         "NIONS",
         "outcar",
+        "outcars",
         "pathify",
         "phonopy",
         "polarizabilities",
diff --git a/docs/Examples - ramannoodle.ipynb b/docs/Examples - ramannoodle.ipynb
new file mode 100644
index 0000000..5cb8da8
--- /dev/null
+++ b/docs/Examples - ramannoodle.ipynb	
@@ -0,0 +1,415 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "81c543e7",
+   "metadata": {},
+   "source": [
+    "**Greetings, and welcome to ramannoodle!** This notebook contains a short tutorial, showcasing the API's workflow and capabilities. "
+   ]
+  },
+  {
+   "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": 23,
+   "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.interpolation import InterpolationPolarizabilityModel\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 the InterpolationPolarizabilityModel to estimate phonon polarizabilities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6102769c-0416-497a-b6a9-e451b66ca71d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# phononsepseps_OUTCAR contains phonons (duh) as well as the equilibrium TiO2 structure.\n",
+    "phonon_outcar = \"../test/data/TiO2/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": 27,
+   "id": "f14761e9-96a8-4317-94cb-47153683eb88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We'll need the polarizability of the equilibrium structure. \n",
+    "_, equilibrium_polarizability = io.read_positions_and_polarizability(\n",
+    "   \"../test/data/TiO2/ref_eps_OUTCAR\", file_format = \"outcar\" \n",
+    ")\n",
+    "model = InterpolationPolarizabilityModel(symmetry, equilibrium_polarizability)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58d82797",
+   "metadata": {},
+   "source": [
+    "We've set up `model`, but we are not yet ready to use it to predict polarizabilities. We'll need to add data! Specifically, we will walk through the system's symmetrically distinct degrees of freedom and add relevant polarizabilities for displacements along those degrees of freedom.\n",
+    "\n",
+    "You can find the TiO2 supercell we're using for these calculations in `tests/data/TiO2/POSCAR`. Note that there are only **two** symmetrically distinct atoms - one Ti and one O. If we understand how movement of any one atom influences the polarizability, we can use symmetry to derive the behavior of the rest.  \n",
+    "\n",
+    "Let's focus on a single Ti atom: atom 5 in our structure. Visualize the POSCAR mentioned above, for example using [VESTA](https://jp-minerals.org/vesta/en/). Think about displacing atom 5 along the x-axis. Convince yourself that displacements in the `+x` direction can be related by symmetry to displacements in the `-x` direction. We call these displacements **symmetrically equivalent**. \n",
+    "\n",
+    "Atom 5's displacement along the x-axis is a degree of freedom (DOF) of the system. Convince yourself that TiO2 structure contains many DOFs that are **symmetrically equivalent** to this DOF.\n",
+    "\n",
+    " The InterpolationPolarizabilityModel assumes that every DOF modulates the polarizability independently; it estimates this modulation using an interpolation around each DOF. We need to \"add\" each DOF to `model`, specifying specific displacements as well as polarizabilities for these displacements (which we calculate using VASP). The model's routines will take full advantage of symmetry to make our job much easier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "147f6a26-a22a-4cff-9659-336d6316e0b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total interpolations: 72\n"
+     ]
+    }
+   ],
+   "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_dof_from_files(\n",
+    "    [\"../test/data/TiO2/Ti5_0.1x_eps_OUTCAR\", \"../test/data/TiO2/Ti5_0.2x_eps_OUTCAR\"],\n",
+    "    file_format = 'outcar', interpolation_order=2)\n",
+    "\n",
+    "# (36 equivalent Ti atoms) * (2 equivalent directions) --> 72 dofs should be added\n",
+    "print(f'Total interpolations: {len(model._interpolations)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6c70688",
+   "metadata": {},
+   "source": [
+    "We've added a total of 72 interpolations (i.e. 72 DOFs). Convince yourself that this makes sense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "370ffc55-8cdb-4101-84b1-4f3a5940525e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total interpolations: 108\n"
+     ]
+    }
+   ],
+   "source": [
+    "# atom 5 moving +0.1 and +0.2 angstroms in the z direction\n",
+    "model.add_dof_from_files([\"../test/data/TiO2/Ti5_0.1z_eps_OUTCAR\",\n",
+    "                          \"../test/data/TiO2/Ti5_0.2z_eps_OUTCAR\"],\n",
+    "                            file_format = 'outcar', interpolation_order=2)\n",
+    "\n",
+    "# (36 equivalent Ti atoms) * (1 equivalent direction) --> another 36 DOFs\n",
+    "print(f'Total interpolations: {len(model._interpolations)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8613d742",
+   "metadata": {},
+   "source": [
+    "Now, let's move on to the oxygen motions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "25e7947c-b9f5-4d9d-bb33-3fc447217c0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total interpolations: 180\n"
+     ]
+    }
+   ],
+   "source": [
+    "# atom 43 moving in the z direction\n",
+    "# Convince yourself that we need all of these displacements. \n",
+    "model.add_dof_from_files([\"../test/data/TiO2/O43_0.2z_eps_OUTCAR\",\n",
+    "                          \"../test/data/TiO2/O43_0.1z_eps_OUTCAR\", \n",
+    "                          \"../test/data/TiO2/O43_m0.1z_eps_OUTCAR\", \n",
+    "                          \"../test/data/TiO2/O43_m0.2z_eps_OUTCAR\"], file_format= 'outcar', interpolation_order=2)\n",
+    "\n",
+    "print(f'Total interpolations: {len(model._interpolations)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "c4603def-3dcf-469e-b78c-0ab4c239a18d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total interpolations: 252\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.add_dof_from_files([\"../test/data/TiO2/O43_0.1x_eps_OUTCAR\",\n",
+    "                         \"../test/data/TiO2/O43_0.2x_eps_OUTCAR\"], \n",
+    "                         file_format = 'outcar', interpolation_order=2)\n",
+    "\n",
+    "print(f'Total interpolations: {len(model._interpolations)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "7189ea1e-3f41-4347-a3a1-2d6adb5b62e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total interpolations: 324\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.add_dof_from_files([\"../test/data/TiO2/O43_0.1y_eps_OUTCAR\",\n",
+    "                          \"../test/data/TiO2/O43_0.2y_eps_OUTCAR\"],\n",
+    "                            file_format = 'outcar', interpolation_order=2)\n",
+    "\n",
+    "# We should now have specified all 324 DOFs\n",
+    "print(f'Total interpolations: {len(model._interpolations)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d46db59c",
+   "metadata": {},
+   "source": [
+    "The model should contain 324 interpolations, indicating that all 324 DOFs of the system are accounted for. Now, what do these interpolations look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "fcb23aaf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1800x600 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "atom_number = 5 # Feel free to change this parameter to visualize the interpolations below.\n",
+    "\n",
+    "fig = plt.figure(constrained_layout = True, figsize = (6, 2))\n",
+    "gs = fig.add_gridspec(1,6, wspace=0)\n",
+    "axes = gs.subplots(sharex=True, sharey=True)\n",
+    "interp_displacements = np.linspace(-0.21,0.21,50)\n",
+    "\n",
+    "target_interps = []\n",
+    "signs = []\n",
+    "for direction_i in [0,1,2]:\n",
+    "    basis_vector = model._cartesian_basis_vectors[0] * 0\n",
+    "    basis_vector[atom_number-1][direction_i] = 1\n",
+    "    for basis, interp in zip(model._cartesian_basis_vectors,model._interpolations):\n",
+    "        if np.dot(basis.flatten(), basis_vector.flatten()) < -0.9:\n",
+    "            target_interps.append(interp)\n",
+    "            signs.append(-1)\n",
+    "        elif np.dot(basis.flatten(), basis_vector.flatten()) > 0.9:\n",
+    "            target_interps.append(interp)\n",
+    "            signs.append(1)\n",
+    "\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",
+    "    for interp,sign,color in zip(target_interps,signs,['pink', 'red', 'orange']):\n",
+    "        if sign < 0:\n",
+    "            print('negative sign')\n",
+    "        axis.plot(interp_displacements, \n",
+    "                  interp(sign*interp_displacements)[:,i,j], color = color)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f4a9b56",
+   "metadata": {},
+   "source": [
+    "With the model specified, we're ready to calculate a Raman spectrum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b1f2cb2c-8444-4b6a-ae8e-f1ac6c6a6e98",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x154ab07d0>]"
+      ]
+     },
+     "execution_count": 34,
+     "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, intensities = spectrum.measure(laser_correction = True, \n",
+    "                                           laser_wavelength = 532, \n",
+    "                                           bose_einstein_correction = True, \n",
+    "                                           temperature = 300)\n",
+    "fig = plt.figure()\n",
+    "axis = fig.add_subplot(111)\n",
+    "axis.plot(wavenumbers, intensities)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "049753d4",
+   "metadata": {},
+   "source": [
+    "`spectrum.calculate` returns a fairly unprocessed spectrum - literally a list of phonon frequencies and intensities. To make a nicer, more realistic visualization, we can broaden the spectra through convolutions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e0806350-ac5d-4f09-826d-b9dc85ec471b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x16c02d0d0>]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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FihUyf/58+fe//23YDuMS0aJpmjz77LO6dZ29BNeOex501fXXXy8jR46UG2+8UbZs2SIiR7Mv/TMwj1VcXCy33XabXHPNNbqMI3/c9yQ2SskBUeL/C+rY1MpQpaSkBD0nEI5YjUnGfmLy+XxyxRVXSGlpace6rKyskGogd3fM+I8Xo3NGoh2jthibPcdFF10kP/nJTzoNConE7/dlV9piTJrL97///Y5JgUeNGiV33nlnxM7NdRxdZbFYZMaMGXLffffJ8uXLpbS0VJqbm6W1tVXKysrk1VdflWuvvTbgs161apVceumlAQ9yjsW4RDjq6up0GTYiIuvXrxcRkZycHHnvvffklVdekeuuu07mzZsnl1xyiTzwwAOyfft2ueyyy3THLV26NOABfTvGJaLl3Xff1WWpO53OgLGpwj0PuuMb3/iGfPLJJ3Lrrbd2ms07aNAgufXWW+Wyyy4LGhQS4b4n0REYAqLEvwZxOJO3tfN/86OlpaVbfUJii9WYZOwnpkWLFsmbb76pW/fnP/85pLrB3R0z/uNFhLGJQC+++KKcdNJJMmvWrIAyNv7i9fuyK20xJs1j8eLFsmLFChE5+iD+ySef7NLYUeE6jq4444wz5IsvvpCPPvpI7rzzTjnttNOksLBQUlJSJCkpSQoKCmTevHnypz/9SXbs2CEzZ87UHf/666/LE088oTw/4xLhUD3Es9ls8vrrr8vJJ59suD09PV3+/ve/yxlnnKFb/6tf/Sog0CTCuET0+JeRmzdvnuTk5IR0LPc86I4//elPUlJSIg8//HDQFzZERPbv3y8LFy6UIUOGBMz/64/7nsRGYAiIEv/Id/sExOFoa2sLek4gHLEak4z9xPP444/LI488olt32223ySWXXBLS8d0dM/7jxeickWjHqC3Gpjk8+uijomlax1dzc7McOHBAXnvtNVmwYIHu7bIPPvhApk2bJp9++qnyfPH6fdmVthiT5nDo0CG59dZbO5avvvpq5QPOruI6jq6YMWOGjBgxIqR9i4qKZMWKFXLiiSfq1v/yl7+U5uZmw2MYlwiH6t/86quvluOPPz7osVarVf74xz/q3n7fvn27vPfee522w7hEJDQ2NsrSpUt160ItIyfCPQ+6xu12y7e//W25/vrr5dChQyIi0rdvX/nZz34ma9eulZqaGnG5XHLw4EF55ZVX5IILLugoaVldXS0LFiyQRYsWKc/PfU9iIzAERIl/LW6j6Hhn/KPfwep7A52J1Zhk7CeW559/Xm6++Wbduvnz58sDDzwQ8jm6O2aM3hRibCa2lJQUKSoqkrlz58pTTz0ln3/+uUyaNKlje21trZx//vlSW1treHy8fl92pS3GpDnccMMNHeOpf//+8tBDD0W8Da7jiIXk5GR59tlnxW7/ejri8vJyefvttw33Z1wiHKp/82uuuSak44uLi+W0007TrTMKDDEuEQ1LlizpKBcrIpKfny9nn312yMdzz4OuuP7663XzqU2fPl22bNki99xzj0ybNk2ys7PF4XDIgAED5JxzzpGlS5fKsmXLdEGThx9+WP76178anp/7nsRGYAiIEv9fUM3NzYZp7sEc+0eH0TmBcPiPH//x1RlN07p0wWfs916vvfaafPe739V9vhdeeKE89dRTYU28292x6b+/3W43fHuou+0YHcPY7BmGDRsmy5cv15U2LCsrk1//+teG+8dqrKSkpATUCO/u+GdMxt6SJUvkP//5T8fyY489JtnZ2RFvh+s4YmXYsGFy7rnn6taFGhhiXCIYo+teRkaGTJ48OeRznHLKKbplowxgxiWi4W9/+5tu+fLLL9cF0TvDPQ/CtWrVKlm8eHHHcl5enrz22mvSv3//oMede+658oc//EG3btGiRSEFF7nvSSwEhoAoyc3N1T0YdbvdUl5eHtY5ysrKdMt5eXkR6RsSk//4KS0tDev4I0eOiMfj6Vi2Wq2Sm5sbsB9jPzGsXLlSLrroIt2YOP300+WFF17odDJMf90dm/7jpV+/fiG1439cV9pibPYcubm5cs899+jW+d/gt+vumBQROXjwYNBztvMfr90d/4zJ2Du2PMfcuXPl4osvjko7XMcRS6eeeqpuefv27Yb7MS4RLv9/92HDhnU6OfqxRo4cqVs2GgeMS0Ta7t275YMPPtCtC6eMnAj3PAjf448/rlu++eablZ+7v/nz5+tKyVZVVQWUQhThvifRERgCoiQlJUUGDRqkW7d///6wzuG//6hRo7rdLyQu/5uo7o7HwYMHG76hxNjv/dasWSPnnnuuLvV7xowZ8p///KdLk1VGemyqxktxcbHurb6WlhapqKiISlswp2NrboscvYnZt29fwH7dHZPl5eW6nw+n0ynFxcWG+8Zq/CN6ji1J+Prrr4vFYun0a86cObpz7Nu3L2Cfzz77TLcP13HE0rEZliKivF4yLhGu0aNH65YzMzPDOt5//5qamoB9GJeItGeffVaXDTZlyhQZP358WOfgngfh0DRN3n33Xd26c845J+TjrVarzJ07V7fu/fffD9iP+57ERmAIiCL/X1Jbt24N6/ht27YFPR8QjliOR8Z+7/X555/L2WefLY2NjR3rJk+eLG+88YakpaV16ZyxGi8Oh0NKSkq63FZbW5vs3r07pLZgTtnZ2dK3b1/dusOHDwfs5/+57tq1K6zJUf3HZElJibLUCL8vESqu44glh8OhW3a73Yb7MS4RrjFjxuiW/ScT74z/nBSpqakB+zAuEUmapsmzzz6rW3fVVVeFfR7ueRCOmpoaqaur060bOnRoWOfw398oe4z7nsRGYAiIomMnuhYRWb16dcjHHjp0SPbu3dux7HA4Av6IBsIxduxY3U3+3r175dChQyEf/9FHH+mW/cd3sG2M/d5h+/btcvrpp+vezBw9erS89dZbkpWV1eXz+o+XTz75RFe+ozOxGpvr1q3TPbwYMGAA6eu9gP/DTxGR/v3762p3t7W1ybp160I+Z6zGpMfjkbVr14bcFno2ruOIJf+guap0DeMS4ZoyZYpu+ciRI2Ed71+qLScnJ2AfxiUi6b333pM9e/Z0LDudTrnsssvCPg/3PAiHUdA8nDmtRALvc7xeb8A+3PckNgJDQBTNmzdPt7xixYqQJ6P0n+B1zpw5TKyGbsnIyJBZs2bp1i1fvjykYzVNkxUrVujWBUtjZuz3Pvv27ZPTTjtNdzM+dOhQWb58ech1jlVGjRqle6utqakp5D8Sm5qa5OOPP+5YtlgsAePvWP7bQv0ZMNo3nFR+mENDQ4NUV1fr1uXn5xvu6196IVpjxb+d1atXhzwR60cffSTNzc0dyyNGjNDVEkdsvPzyy7J8+fKwvh5++GHdOfLz8wP2GTZsmG4fruOIpQ8//FC37F9arh3jEuGaO3eubk6hPXv2BFybg/F/YOlfmkiEcYnIeuaZZ3TL55xzTkAGeii450E4jILe/nP5dCbUeam470lgGoCo8Xq9Wm5uriYiHV/vvvtuSMeefPLJuuP+8Ic/RLm3MLOVK1fqxsPgwYO7dJ7HHntMd55Zs2aFdNw777yjOy4/P1/zer3K/Rn7vcvBgwe1kpIS3edSWFio7d69O2Jt/PCHP9Sd/zvf+U5Ixy1evFh33LRp04LuX1VVpdnt9o79LRaLtmvXrk7b8fl82pAhQ3Rtvf766yH1Eebxwgsv6D7Dfv36KX+Xvfzyy7p9hwwZovl8vk7b2Llzp2axWDqOczgcWm1tbdBjJk+erGvr6aefDun7ufLKK3XHLVq0KKTjEH9dva5zHUcs1NTUaNnZ2brPcfHixcr9GZcIl//n8Ze//CWk49xut9a/f3/dsf/6178M92VcIhIaGxu19PR03ef06quvdvl83PMgHAMGDNB9Fk899VRYx5944om643/+858b7sd9T+IiMARE2a233qr75XXKKad0+gt2xYoVumMyMjK0ioqKGPUYZhSpwNCRI0e0tLQ03bneeeedoMf4fD5t1qxZumN+/OMfd9oWY793qKqq0saOHRvwMH3r1q0RbWfTpk26PyqdTmenbbS0tGjDhw/X9e1Pf/pTp219+9vf1h1z5ZVXdnrMU089FfAz2NbWFvL3h/hrbm7WRowYofscr7rqKuX+ra2tWlFRUcgPRttdccUVumMuvfTSTo/5/e9/rztmxIgRWktLS9Bjtm7dqjmdzo5jrFartmXLlk7bgjl09brOdRyxsGDBAt3n6HQ6tYMHDyr3Z1wiXP/4xz8Crnutra2dHvfEE0/ojsvMzFQ+hGRcIhKeeeYZ3efUv39/ze12d/l83PMgHP73FePGjQt5/K1atUp3rIhoH3zwgeG+3PckLgJDQJRVVFQEvGFy//33K/cvLS0NeEPjrrvuimGPYUaRCgxpmqbdfvvtunMNHTpUKysrU+5/33336fbPysrSqqqqOm2Hsd/z1dfXa9OmTdN9JtnZ2dqGDRui0t4ll1yia2vatGlaXV2d4b4+n0+79tprdfsXFxdrLper03a2bNmiWa1W3bHPP/980P3935wO920tRM6iRYu0tWvXhnVMVVWVdtppp+k+Q5vNpn3++edBj/vjH/+oO6ZPnz5Bb0Kee+65gDa2b9/eaf/a2tq0QYMG6Y697rrrlA+V6urqtOOOO063/xVXXNFpOzCP7lzXuY4jVPfff7/26aefhry/2+3WfvSjHwU8SLrxxhs7PZZxiXB4vV5t/Pjxus/lu9/9btDMnP/9738Bn31nQRvGJbprzpw5us/plltu6fY5uedBqP773/8GXJOvueaaoL8rNU3TduzYoRUUFOiOGz58uObxeJTHcN+TmAgMATHwq1/9KuCX+fXXX6/7o9Tr9Wr/+c9/An5BFhQUaDU1NfHrPGLqww8/1JYvXx7w9fDDD+vGRX5+vuF+y5cv7/TNiaqqqoASDIMHD9Zefvll3cX4wIEDAX+Eioj20EMPhfz9MPZ7ttmzZwd8fvfee69y7AX7qq6u7rS9HTt2aKmpqbr2Jk6cqK1cuVK33/bt27ULL7wwoG8vvvhiyN/b97//fd2xVqtV++lPf6rrp8vl0v76179qffr00e07YcKEbr0piO6ZOHGiJiLa9OnTtd/85jfahg0bDG+OfT6ftm3bNu3ee+8NKPEiItqtt97aaVsulysgY65v377aM888oxsDVVVV2l133RVw871w4cKQv6/nn38+oI/f/va3tS+//FK33zvvvKNNmDBBt196enpESzsi+roTGOI6jlCdcsopmohoM2bM0B599FFt06ZNhtev2tpa7fnnn9cmTZoU8HmXlJRolZWVnbbFuES4VqxYocucEBHttNNOCwhm1tbWar/5zW8CAi8jRozQ6uvrg7bBuER37N27N2CMbtq0qdvn5Z4H4fAPToqIdtJJJ2krVqwI+HwqKyu1hx9+WMvKygo4ZsmSJUHb4b4nMREYAmLA6/Vq8+bNC/jFZ7PZtOLiYm3y5MkBb2aIiJaSkqJ9+OGH8e4+Ymjw4MEB4yDcr+9+97udtvPee+9pycnJAcdmZ2drkydP1oYOHarZbLaA7eedd15ItWbbMfZ7tu6OxWO//G90VF544YWAGzCRo+Xrpk6dqg0cONBw+w9+8IOwvrempqaAN49EjpZzGDlypDZhwoSABxAiouXm5ob0JhSipz0w5P+5DR06VJs8ebJ2/PHHa2PGjNEyMjKC/p7s7E27dlu3btX69u0bcI709HRt4sSJ2ogRIzSHwxGwffr06Vpzc3NY39v1118fcB6LxaINGjRImzp1qmGAy2q1dnqjB/PpbiYw13GEoj0wdOxXUlKSVlJSok2ZMkWbNm2aVlxcHPBwp/2rf//+AQ9pgmFcIlwPPPCAcuwdd9xx2ujRo3Xlg9q/cnJyOs36bce4RFfde++9us9q6tSpETs39zwI1aFDh7ShQ4ca/q5MT0/Xxo8fr02fPl0rKSkxHDMioWe6cd+TeAgMATHS0tKiXXrppYa/pI2+cnJyQn6Qit4jVoEhTTv69oXRRV/1ddlll4VU+9sfY7/n6u5YPPYrnM/0+eef11JSUkI+96233hrWjXu7qqoq7Rvf+EbI7QwZMiTkhxCIHqPAUKhfmZmZ2hNPPBH2ePnss8/C+v182mmndenNX6/XGzApcbCv1NRU5aTbMLdIlIjlOo7OGAWGQv365je/qR05ciTsNhmXCNfjjz9u+KBR9TVy5MiwApaaxrhE15SUlOg+r9/97ncRPT/3PAjV/v37Dat5dPblcDi0Bx54IKxxw31PYiEwBMTYSy+9ZFimof0rLS1NW7hwYZduxNDzxTIwpGmadvjwYe36668PSGU/9mvy5Mnav//9725/b4z9nqe7Y/HYr3Bvenft2qVddtllQR8UzJo1S1u1alW3vkev16s9+eST2rBhw5Tt9O3bV7vzzju1hoaGbrWFyNi6dav24IMPaqeddpqWmZnZ6dizWCzahAkTtF//+tdaeXl5l9utr6/X7rjjjoAyG8d+DR8+XPvLX/7SpZv2Y7377rvaySefrGzH6XRql19+OWUUerBIzR3IdRzBvP3229p1112njR071jAjwv8rPT1du+iii7T33nuvW+0yLhGubdu2aZdccknQv/uGDh2qPfbYY1pbW1uX2mBcIhwffPBBwN9eocw3FS7ueRAqr9ervfjii9rs2bOVmb7tX1lZWdr111+vffHFF11qi/uexGHRNE0TADG3c+dOWbNmjZSVlYnL5ZLs7GwZPXq0zJw5U5KTk+PdPSSYlpYWWb16tWzbtk1qa2vF6XRKYWGhHH/88TJs2LCItsXYRzjq6+vlww8/lB07dkhDQ4MkJyfLoEGDZObMmVJYWBjRtjZt2iTr16+XQ4cOidfrlZycHBk3bpwcf/zx4nA4ItoWIsPn88mOHTtk586dsn//fqmvrxe32y0ZGRmSlZUlQ4YMkSlTpkhmZmbE2nS73bJmzRrZvHmzVFVVic1mkwEDBsiUKVNk/PjxEWtHRKS0tFRWr14t+/fvl9bWVsnIyJDhw4fLSSedFNHvCT0f13F0prm5WbZu3Sp79+6VQ4cOSWNjo/h8PsnOzpY+ffrImDFjZPz48WKz2SLWJuMS4aqvr5fVq1fLjh07pK6uTtLT0yU/P1+mTJkiI0eOjEgbjEuYEfc8CEdDQ4N8+umnsnv3bqmtrZXW1lbJzMyUnJwcmTBhgowZM0asVmu32+G+p/cjMAQAAAAAAAAAAJAguh8+BAAAAAAAAAAAQI9AYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgARBYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgARBYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgARBYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgARBYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgARBYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgARBYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgARBYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgARBYAgAAAAAAAAAACBBEBgCAAAAAAAAAABIEASGAAAAAAAAAAAAEgSBIQAAAAAAAAAAgATx/wF88OMV1WtqXAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1920x1440 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Let's make it prettier. \n",
+    "wavenumbers, intensities = spectrum.measure(laser_correction = True, \n",
+    "                                           laser_wavelength = 532, \n",
+    "                                           bose_einstein_correction = True, \n",
+    "                                           temperature = 300)\n",
+    "wavenumbers, intensities = convolve_intensities(wavenumbers, intensities)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "axis = fig.add_subplot(111)\n",
+    "axis.plot(wavenumbers, intensities)\n"
+   ]
+  }
+ ],
+ "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/coverage-badge.svg b/docs/coverage-badge.svg
index 3436f9d..a3bab45 100644
--- a/docs/coverage-badge.svg
+++ b/docs/coverage-badge.svg
@@ -1 +1 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="114" height="20" role="img" aria-label="coverage: 80.85%"><title>coverage: 80.85%</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="61" height="20" fill="#555"/><rect x="61" width="53" height="20" fill="#97ca00"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="315" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="510">coverage</text><text x="315" y="140" transform="scale(.1)" fill="#fff" textLength="510">coverage</text><text aria-hidden="true" x="865" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="430">80.85%</text><text x="865" y="140" transform="scale(.1)" fill="#fff" textLength="430">80.85%</text></g></svg>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="114" height="20" role="img" aria-label="coverage: 88.69%"><title>coverage: 88.69%</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="61" height="20" fill="#555"/><rect x="61" width="53" height="20" fill="#97ca00"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="315" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="510">coverage</text><text x="315" y="140" transform="scale(.1)" fill="#fff" textLength="510">coverage</text><text aria-hidden="true" x="865" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="430">88.69%</text><text x="865" y="140" transform="scale(.1)" fill="#fff" textLength="430">88.69%</text></g></svg>
diff --git a/ramannoodle/polarizability/interpolation/__init__.py b/ramannoodle/polarizability/interpolation/__init__.py
index 3fa6e0b..7d8fa19 100644
--- a/ramannoodle/polarizability/interpolation/__init__.py
+++ b/ramannoodle/polarizability/interpolation/__init__.py
@@ -174,7 +174,7 @@ def add_dof(  # pylint: disable=too-many-locals
                     f"due to symmetry, amplitude {duplicate} should not be specified"
                 )
 
-            if len(interpolation_x) <= interpolation_order:
+            if len(interpolation_x) - 1 <= interpolation_order:
                 raise InvalidDOFException(
                     f"insufficient amplitudes ({len(interpolation_x)}) available for"
                     f"{interpolation_order}-order interpolation"
diff --git a/test/data/TiO2/O43_0.1x_OUTCAR b/test/data/TiO2/O43_0.1x_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_0.1x_OUTCAR
rename to test/data/TiO2/O43_0.1x_eps_OUTCAR
diff --git a/test/data/TiO2/O43_0.1y_OUTCAR b/test/data/TiO2/O43_0.1y_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_0.1y_OUTCAR
rename to test/data/TiO2/O43_0.1y_eps_OUTCAR
diff --git a/test/data/TiO2/O43_0.1z_OUTCAR b/test/data/TiO2/O43_0.1z_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_0.1z_OUTCAR
rename to test/data/TiO2/O43_0.1z_eps_OUTCAR
diff --git a/test/data/TiO2/O43_0.2x_OUTCAR b/test/data/TiO2/O43_0.2x_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_0.2x_OUTCAR
rename to test/data/TiO2/O43_0.2x_eps_OUTCAR
diff --git a/test/data/TiO2/O43_0.2y_OUTCAR b/test/data/TiO2/O43_0.2y_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_0.2y_OUTCAR
rename to test/data/TiO2/O43_0.2y_eps_OUTCAR
diff --git a/test/data/TiO2/O43_0.2z_OUTCAR b/test/data/TiO2/O43_0.2z_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_0.2z_OUTCAR
rename to test/data/TiO2/O43_0.2z_eps_OUTCAR
diff --git a/test/data/TiO2/O43_m0.1x_OUTCAR b/test/data/TiO2/O43_m0.1x_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_m0.1x_OUTCAR
rename to test/data/TiO2/O43_m0.1x_eps_OUTCAR
diff --git a/test/data/TiO2/O43_m0.1y_OUTCAR b/test/data/TiO2/O43_m0.1y_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_m0.1y_OUTCAR
rename to test/data/TiO2/O43_m0.1y_eps_OUTCAR
diff --git a/test/data/TiO2/O43_m0.1z_OUTCAR b/test/data/TiO2/O43_m0.1z_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_m0.1z_OUTCAR
rename to test/data/TiO2/O43_m0.1z_eps_OUTCAR
diff --git a/test/data/TiO2/O43_m0.2x_OUTCAR b/test/data/TiO2/O43_m0.2x_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_m0.2x_OUTCAR
rename to test/data/TiO2/O43_m0.2x_eps_OUTCAR
diff --git a/test/data/TiO2/O43_m0.2y_OUTCAR b/test/data/TiO2/O43_m0.2y_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_m0.2y_OUTCAR
rename to test/data/TiO2/O43_m0.2y_eps_OUTCAR
diff --git a/test/data/TiO2/O43_m0.2z_OUTCAR b/test/data/TiO2/O43_m0.2z_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/O43_m0.2z_OUTCAR
rename to test/data/TiO2/O43_m0.2z_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_0.1x_OUTCAR b/test/data/TiO2/Ti5_0.1x_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_0.1x_OUTCAR
rename to test/data/TiO2/Ti5_0.1x_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_0.1y_OUTCAR b/test/data/TiO2/Ti5_0.1y_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_0.1y_OUTCAR
rename to test/data/TiO2/Ti5_0.1y_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_0.1z_OUTCAR b/test/data/TiO2/Ti5_0.1z_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_0.1z_OUTCAR
rename to test/data/TiO2/Ti5_0.1z_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_0.2x_OUTCAR b/test/data/TiO2/Ti5_0.2x_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_0.2x_OUTCAR
rename to test/data/TiO2/Ti5_0.2x_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_0.2y_OUTCAR b/test/data/TiO2/Ti5_0.2y_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_0.2y_OUTCAR
rename to test/data/TiO2/Ti5_0.2y_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_0.2z_OUTCAR b/test/data/TiO2/Ti5_0.2z_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_0.2z_OUTCAR
rename to test/data/TiO2/Ti5_0.2z_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_m0.1x_OUTCAR b/test/data/TiO2/Ti5_m0.1x_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_m0.1x_OUTCAR
rename to test/data/TiO2/Ti5_m0.1x_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_m0.1y_OUTCAR b/test/data/TiO2/Ti5_m0.1y_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_m0.1y_OUTCAR
rename to test/data/TiO2/Ti5_m0.1y_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_m0.1z_OUTCAR b/test/data/TiO2/Ti5_m0.1z_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_m0.1z_OUTCAR
rename to test/data/TiO2/Ti5_m0.1z_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_m0.2x_OUTCAR b/test/data/TiO2/Ti5_m0.2x_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_m0.2x_OUTCAR
rename to test/data/TiO2/Ti5_m0.2x_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_m0.2y_OUTCAR b/test/data/TiO2/Ti5_m0.2y_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_m0.2y_OUTCAR
rename to test/data/TiO2/Ti5_m0.2y_eps_OUTCAR
diff --git a/test/data/TiO2/Ti5_m0.2z_OUTCAR b/test/data/TiO2/Ti5_m0.2z_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/Ti5_m0.2z_OUTCAR
rename to test/data/TiO2/Ti5_m0.2z_eps_OUTCAR
diff --git a/test/data/TiO2/known_spectrum.npz b/test/data/TiO2/known_spectrum.npz
new file mode 100644
index 0000000..4c0eecd
Binary files /dev/null and b/test/data/TiO2/known_spectrum.npz differ
diff --git a/test/data/TiO2/PHONON_OUTCAR b/test/data/TiO2/phonons_OUTCAR
similarity index 100%
rename from test/data/TiO2/PHONON_OUTCAR
rename to test/data/TiO2/phonons_OUTCAR
diff --git a/test/data/TiO2/ref_OUTCAR b/test/data/TiO2/ref_eps_OUTCAR
similarity index 100%
rename from test/data/TiO2/ref_OUTCAR
rename to test/data/TiO2/ref_eps_OUTCAR
diff --git a/test/tests/test_polarizability.py b/test/tests/test_polarizability.py
index 2c6fd8c..badde58 100644
--- a/test/tests/test_polarizability.py
+++ b/test/tests/test_polarizability.py
@@ -11,8 +11,6 @@
 from ramannoodle.polarizability.interpolation import InterpolationPolarizabilityModel
 from ramannoodle.io.vasp import read_structural_symmetry_from_outcar
 from ramannoodle.exceptions import InvalidDOFException
-from ramannoodle.symmetry import StructuralSymmetry
-from ramannoodle import io
 
 # pylint: disable=protected-access
 
@@ -33,7 +31,7 @@ def test_find_duplicates(vectors: list[NDArray[np.float64]], known: bool) -> Non
     "outcar_path_fixture,displaced_atom_index, amplitudes, known_dof_added",
     [
         ("test/data/STO_RATTLED_OUTCAR", 0, np.array([-0.05, 0.05, 0.01, -0.01]), 1),
-        ("test/data/TiO2/PHONON_OUTCAR", 0, np.array([0.01]), 72),
+        ("test/data/TiO2/phonons_OUTCAR", 0, np.array([0.01]), 72),
     ],
     indirect=["outcar_path_fixture"],
 )
@@ -58,7 +56,7 @@ def test_add_dof(
     "outcar_path_fixture,displaced_atom_index, amplitudes",
     [
         ("test/data/STO_RATTLED_OUTCAR", 0, np.array([0.01, 0.01])),
-        ("test/data/TiO2/PHONON_OUTCAR", 0, np.array([-0.01, 0.01])),
+        ("test/data/TiO2/phonons_OUTCAR", 0, np.array([-0.01, 0.01])),
     ],
     indirect=["outcar_path_fixture"],
 )
@@ -77,71 +75,3 @@ def test_overspecified_dof(
         model.add_dof(displacement, amplitudes, polarizabilities, 1)
 
     assert "should not be specified" in str(error.value)
-
-
-@pytest.mark.parametrize(
-    "outcar_symmetry_fixture,ref_eps,to_add,additional_references",
-    [
-        (
-            "test/data/TiO2/PHONON_OUTCAR",
-            "test/data/TiO2/ref_OUTCAR",
-            [
-                ["test/data/TiO2/Ti5_0.1z_OUTCAR", "test/data/TiO2/Ti5_0.2z_OUTCAR"],
-                ["test/data/TiO2/Ti5_0.1x_OUTCAR", "test/data/TiO2/Ti5_0.2x_OUTCAR"],
-                [
-                    "test/data/TiO2/O43_0.1z_OUTCAR",
-                    "test/data/TiO2/O43_0.2z_OUTCAR",
-                    "test/data/TiO2/O43_m0.1z_OUTCAR",
-                    "test/data/TiO2/O43_m0.2z_OUTCAR",
-                ],
-                [
-                    "test/data/TiO2/O43_0.1x_OUTCAR",
-                    "test/data/TiO2/O43_0.2x_OUTCAR",
-                ],
-                [
-                    "test/data/TiO2/O43_0.1y_OUTCAR",
-                    "test/data/TiO2/O43_0.2y_OUTCAR",
-                ],
-            ],
-            [
-                "test/data/TiO2/Ti5_m0.2z_OUTCAR",
-                "test/data/TiO2/Ti5_m0.1z_OUTCAR",
-                "test/data/TiO2/O43_m0.1x_OUTCAR",
-                "test/data/TiO2/O43_m0.2x_OUTCAR",
-                "test/data/TiO2/O43_m0.1y_OUTCAR",
-                "test/data/TiO2/O43_m0.2y_OUTCAR",
-            ],
-        ),
-    ],
-    indirect=["outcar_symmetry_fixture"],
-)
-def test_get_polarizability(
-    outcar_symmetry_fixture: StructuralSymmetry,
-    ref_eps: str,
-    to_add: list[str],
-    additional_references: list[str],
-) -> None:
-    """Test."""
-    symmetry = outcar_symmetry_fixture
-    _, polarizability = io.read_positions_and_polarizability(
-        ref_eps, file_format="outcar"
-    )
-    model = InterpolationPolarizabilityModel(symmetry, polarizability)
-    for outcar_path_list in to_add:
-        model.add_dof_from_files(
-            outcar_path_list, file_format="outcar", interpolation_order=2
-        )
-
-    # Tests
-    total_outcar_paths = additional_references
-    for outcar_path in to_add:
-        total_outcar_paths += outcar_path
-    for outcar_path in total_outcar_paths:
-        positions, known_polarizability = io.read_positions_and_polarizability(
-            outcar_path, file_format="outcar"
-        )
-        cartesian_displacement = symmetry.get_cartesian_displacement(
-            positions - symmetry.get_fractional_positions()
-        )
-        model_polarizability = model.get_polarizability(cartesian_displacement)
-        assert np.isclose(model_polarizability, known_polarizability, atol=1e-4).all()
diff --git a/test/tests/test_spectrum.py b/test/tests/test_spectrum.py
new file mode 100644
index 0000000..9e4f644
--- /dev/null
+++ b/test/tests/test_spectrum.py
@@ -0,0 +1,96 @@
+"""Testing for spectra."""
+
+from pathlib import Path
+
+import numpy as np
+
+import pytest
+
+from ramannoodle.polarizability.interpolation import InterpolationPolarizabilityModel
+from ramannoodle.symmetry import StructuralSymmetry
+from ramannoodle import io
+
+# pylint: disable=protected-access
+
+
+def _get_all_eps_outcars(directory: str) -> list[str]:
+    """Return name of all eps OUTCARs in a directory."""
+    path = Path(directory)
+    return [str(item) for item in path.glob("*eps_OUTCAR")]
+
+
+def _validate_polarizabilities(
+    model: InterpolationPolarizabilityModel, data_directory: str
+) -> None:
+    for outcar_path in _get_all_eps_outcars(data_directory):
+        positions, known_polarizability = io.read_positions_and_polarizability(
+            f"{outcar_path}", file_format="outcar"
+        )
+        cartesian_displacement = model._structural_symmetry.get_cartesian_displacement(
+            positions - model._structural_symmetry.get_fractional_positions()
+        )
+        model_polarizability = model.get_polarizability(cartesian_displacement)
+        assert np.isclose(model_polarizability, known_polarizability, atol=1e-4).all()
+
+
+@pytest.mark.parametrize(
+    "outcar_symmetry_fixture,data_directory,dof_eps_outcars",
+    [
+        (
+            "test/data/TiO2/phonons_OUTCAR",
+            "test/data/TiO2/",
+            [
+                ["Ti5_0.1z_eps_OUTCAR", "Ti5_0.2z_eps_OUTCAR"],
+                ["Ti5_0.1x_eps_OUTCAR", "Ti5_0.2x_eps_OUTCAR"],
+                [
+                    "O43_0.1z_eps_OUTCAR",
+                    "O43_0.2z_eps_OUTCAR",
+                    "O43_m0.1z_eps_OUTCAR",
+                    "O43_m0.2z_eps_OUTCAR",
+                ],
+                ["O43_0.1x_eps_OUTCAR", "O43_0.2x_eps_OUTCAR"],
+                ["O43_0.1y_eps_OUTCAR", "O43_0.2y_eps_OUTCAR"],
+            ],
+        ),
+    ],
+    indirect=["outcar_symmetry_fixture"],
+)
+def test_spectrum(
+    outcar_symmetry_fixture: StructuralSymmetry,
+    data_directory: str,
+    dof_eps_outcars: list[str],
+) -> None:
+    """Test a spectrum calculation."""
+    # Setup model
+    symmetry = outcar_symmetry_fixture
+    _, polarizability = io.read_positions_and_polarizability(
+        f"{data_directory}/ref_eps_OUTCAR", file_format="outcar"
+    )
+    model = InterpolationPolarizabilityModel(symmetry, polarizability)
+    for outcar_names in dof_eps_outcars:
+        model.add_dof_from_files(
+            [f"{data_directory}/{name}" for name in outcar_names],
+            file_format="outcar",
+            interpolation_order=2,
+        )
+
+    _validate_polarizabilities(model, data_directory)
+
+    # Spectrum test
+    with np.load(f"{data_directory}/known_spectrum.npz") 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(
+            laser_correction=True,
+            laser_wavelength=532,
+            bose_einstein_correction=True,
+            temperature=300,
+        )
+
+        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()
diff --git a/test/tests/test_symmetry.py b/test/tests/test_symmetry.py
index 54c570c..90f3025 100644
--- a/test/tests/test_symmetry.py
+++ b/test/tests/test_symmetry.py
@@ -51,7 +51,7 @@ def test_check_orthogonal(
     "outcar_symmetry_fixture, known_nonequivalent_atoms,"
     "known_orthogonal_displacements, known_displacements_shape",
     [
-        ("test/data/TiO2/PHONON_OUTCAR", 2, 36, [2] * 36),
+        ("test/data/TiO2/phonons_OUTCAR", 2, 36, [2] * 36),
         ("test/data/STO_RATTLED_OUTCAR", 135, 1, [1]),
         ("test/data/LLZO_OUTCAR", 9, 32, [1] * 32),
     ],
diff --git a/test/tests/test_vasp.py b/test/tests/test_vasp.py
index 91b9480..45a6e10 100644
--- a/test/tests/test_vasp.py
+++ b/test/tests/test_vasp.py
@@ -19,7 +19,7 @@
     "known_first_displacement, known_last_displacement",
     [
         (
-            "test/data/TiO2/PHONON_OUTCAR",
+            "test/data/TiO2/phonons_OUTCAR",
             PHONONS_OUTCAR_NUM_ATOMS,
             np.array([811.691808, 811.691808, 811.691808, 811.691808]),
             np.array([-0.068172, 0.046409, 0.000000]) / np.sqrt(ATOMIC_WEIGHTS["Ti"]),
diff --git a/test/tests/test_vasp_utils.py b/test/tests/test_vasp_utils.py
index 51bbeca..6de34ff 100644
--- a/test/tests/test_vasp_utils.py
+++ b/test/tests/test_vasp_utils.py
@@ -43,7 +43,7 @@ def test_fail_get_atomic_symbol_from_potcar_line(potcar_line: str) -> None:
 
 @pytest.mark.parametrize(
     "outcar_file_fixture, known",
-    [("test/data/TiO2/PHONON_OUTCAR", ["Ti"] * 36 + ["O"] * 72)],
+    [("test/data/TiO2/phonons_OUTCAR", ["Ti"] * 36 + ["O"] * 72)],
     indirect=["outcar_file_fixture"],
 )
 def test_read_atomic_symbols_from_outcar(
@@ -137,7 +137,7 @@ def test_read_polarizability_from_outcar(
     "outcar_file_fixture, known_lattice",
     [
         (
-            "test/data/TiO2/PHONON_OUTCAR",
+            "test/data/TiO2/phonons_OUTCAR",
             np.array(
                 [
                     [11.3768434223, 0.0000000000, 0.0000000000],