diff --git a/.gitignore b/.gitignore
index 2dbee14..0d9b42a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,9 @@
 __pycache__/
 *.py[cod]
 
+# iPython checkpoints
+**/.ipynb_checkpoints
+
 # Gedit junk
 *~
 
diff --git a/README.rst b/README.md
similarity index 81%
rename from README.rst
rename to README.md
index 4acebfa..9cfc50b 100644
--- a/README.rst
+++ b/README.md
@@ -3,15 +3,19 @@ wimprates
 
 `https://github.com/JelleAalbers/wimprates`
 
-Differential rates of WIMP-nucleus scattering in the standard halo model
+Differential rates of WIMP-nucleus scattering in the standard halo model.
 
 Jelle Aalbers, 2018
 
-Features:
+[Please see this simple example for usage.](https://github.com/JelleAalbers/blob/master/notebooks/Example.ipynb)
+
+Features
+--------
   - Spin-indendent and spin-dependent DM-nucleus scattering
   - Bremsstrahlung and elastic NR detection modes
 
-Limitations:
+Limitations
+-----------
  - Numeric integration is used to compute some differential rates, even in cases where exact expressions are known / could be derived.
  - The earth's motion w.r.t. to the sun is not taken into account: no annual modulation for you!
  - Not all functions are properly vectorized yet
diff --git a/notebooks/Example.ipynb b/notebooks/Example.ipynb
new file mode 100644
index 0000000..8a09d34
--- /dev/null
+++ b/notebooks/Example.ipynb
@@ -0,0 +1,148 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import wimprates\n",
+    "import numericalunits as nu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "energies = np.linspace(0.01, 40, 100)\n",
+    "dr = wimprates.rate_wimp_std(energies, mw=50, sigma_nucleon=1e-45)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 38.047764781135932)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8fd173a8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(energies, dr)\n",
+    "\n",
+    "plt.xlabel(\"Recoil energy [keV]\")\n",
+    "plt.ylabel(\"Rate [events per (keV ton year)]\")\n",
+    "plt.title(\"$m_\\chi = 50$ GeV/c${}^2$, $\\sigma_\\chi = 10^{-45}$ cm${}^2$\")\n",
+    "plt.xlim(0, energies.max())\n",
+    "plt.ylim(0, None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function rate_wimp_std in module wimprates:\n",
+      "\n",
+      "rate_wimp_std(es, mw, sigma_nucleon, **kwargs)\n",
+      "    Differential rate per (ton day keV) of WIMP-nucleus scattering.\n",
+      "    :param es: Energies in keV\n",
+      "    :param mw: WIMP mass in GeV/c^2\n",
+      "    :param sigma_nucleon: WIMP-nucleon cross-section in cm^2\n",
+      "    :returns: numpy array of same length as es\n",
+      "    \n",
+      "    For more information, see documentation of rate_wimp.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(wimprates.rate_wimp_std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function rate_wimp in module wimprates:\n",
+      "\n",
+      "rate_wimp(es, mw, sigma_nucleon, interaction='SI', detection_mechanism='elastic_nr', progress_bar=False, **kwargs)\n",
+      "    Differential rate per unit time, unit detector mass and unit recoil energy of WIMP-nucleus scattering\n",
+      "    Use numericalunits to get variables in/out in the right units.\n",
+      "    \n",
+      "    :param es: Energy of recoil (for elastic_nr) or bremsstrahlung photon (for bremsstrahlung)\n",
+      "    :param mw: Mass of WIMP\n",
+      "    :param sigma_nucleon: WIMP-nucleon cross-section\n",
+      "    :param interaction: string describing DM-nucleus interaction. Options:\n",
+      "            'SI' for spin-independent scattering, equal coupling to protons and neutrons)\n",
+      "            'SD_n_xxx' for spin-dependent scattering\n",
+      "                n can be 'n' or 'p' for neutron or proton coupling (at first order)\n",
+      "                x can be 'central', 'up' or 'down' for theoretical uncertainty on structure function\n",
+      "    :param detection_mechanism: Detection mechanism, can be 'elastic_nr' or 'bremsstrahlung'\n",
+      "    :param progress_bar: if True, show a progress bar during evaluation for multiple energies\n",
+      "    :returns: numpy array of same length as es, differential WIMP-nucleus scattering rates\n",
+      "    \n",
+      "    Further kwargs are passed to scipy.integrate.quad numeric integrator (e.g. error tolerance).\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(wimprates.rate_wimp)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.4.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Spin-dependent.ipynb b/notebooks/Spin-dependent.ipynb
index 4d75fe3..6e456b6 100644
--- a/notebooks/Spin-dependent.ipynb
+++ b/notebooks/Spin-dependent.ipynb
@@ -265,9 +265,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:pax]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-pax-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -283,5 +283,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 2
 }
diff --git a/wimprates.py b/wimprates.py
index 1a5285c..b49da13 100644
--- a/wimprates.py
+++ b/wimprates.py
@@ -346,13 +346,13 @@ def rate_wimp(es, mw, sigma_nucleon, interaction='SI', detection_mechanism='elas
 
     :param es: Energy of recoil (for elastic_nr) or bremsstrahlung photon (for bremsstrahlung)
     :param mw: Mass of WIMP
-    :param sigma_nucleon: WIMP/nucleon cross-section
+    :param sigma_nucleon: WIMP-nucleon cross-section
     :param interaction: string describing DM-nucleus interaction. Options:
-            'SI' for spin-independent scattering, equal coupling to protons and neutrons)
+            'SI' for spin-independent scattering; equal coupling to protons and neutrons
             'SD_n_xxx' for spin-dependent scattering
                 n can be 'n' or 'p' for neutron or proton coupling (at first order)
                 x can be 'central', 'up' or 'down' for theoretical uncertainty on structure function
-    :param modality: Detection mechanism, can be 'elastic_nr' or 'bremsstrahlung'
+    :param detection_mechanism: Detection mechanism, can be 'elastic_nr' or 'bremsstrahlung'
     :param progress_bar: if True, show a progress bar during evaluation for multiple energies
     :returns: numpy array of same length as es, differential WIMP-nucleus scattering rates
 
@@ -363,3 +363,15 @@ def rate_wimp(es, mw, sigma_nucleon, interaction='SI', detection_mechanism='elas
         raise NotImplementedError("Unsupported detection mechanism '%s'" % detection_mechanism)
     return dmechs[detection_mechanism](es, mw=mw, sigma_nucleon=sigma_nucleon, interaction=interaction,
                                        progress_bar=progress_bar, **kwargs)
+
+
+def rate_wimp_std(es, mw, sigma_nucleon, **kwargs):
+    """Differential rate per (ton day keV) of WIMP-nucleus scattering.
+    :param es: Recoil energies in keV
+    :param mw: WIMP mass in GeV/c^2
+    :param sigma_nucleon: WIMP-nucleon cross-section in cm^2
+    :returns: numpy array of same length as es
+
+    For more information, see documentation of rate_wimp.
+    """
+    return rate_wimp(es * nu.keV, mw * nu.GeV/nu.c0**2, sigma_nucleon * nu.cm**2) * (nu.keV * (1000 * nu.kg) * nu.year)