diff --git a/tutorials/GW150914.ipynb b/tutorials/GW150914.ipynb
deleted file mode 100644
index 3d4a994940400f05581d66905d8b690522b20ec8..0000000000000000000000000000000000000000
--- a/tutorials/GW150914.ipynb
+++ /dev/null
@@ -1,170 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# GW150914 analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Analyse GW150914 data using TUPAK"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pylab inline\n",
-    "# %matplotlib notebook\n",
-    "import numpy as np\n",
-    "import pylab as plt\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "import peyote\n",
-    "import corner\n",
-    "\n",
-    "import logging\n",
-    "logging.getLogger().addHandler(logging.StreamHandler())\n",
-    "logging.getLogger().setLevel('DEBUG')\n",
-    "\n",
-    "import matplotlib.mlab as mlab"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## load and plot GW150914 data\n",
-    "data has been downloaded from the LIGO Open Science Center (LOSC) and pre-formatted"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1111c7d50>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110fa8fd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data_file = 'tutorial_data/GW150914_strain_data.npy'\n",
-    "time_series, strain_H1, strain_L1 = np.load(data_file)\n",
-    "\n",
-    "time_of_event = 1126259462.44\n",
-    "\n",
-    "plt.plot(time_series - time_of_event, strain_H1 * 1e18, 'r', label = 'H1')\n",
-    "plt.plot(time_series - time_of_event, strain_L1 * 1e18, 'b', label = 'L1')\n",
-    "\n",
-    "plt.xlabel('time [s] since GW150914')\n",
-    "plt.ylabel(r'strain $[10^{-18}]$')\n",
-    "plt.legend(loc='lower right')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x112a629d0>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1122b2910>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "sampling_frequency = np.int(peyote.utils.sampling_frequency(time_series))\n",
-    "\n",
-    "NFFT = 4 * sampling_frequency\n",
-    "\n",
-    "power_spectral_density_H1, frequency_series = mlab.psd(strain_H1, Fs = sampling_frequency, NFFT = NFFT)\n",
-    "power_spectral_density_L1, frequency_series = mlab.psd(strain_L1, Fs = sampling_frequency, NFFT = NFFT)\n",
-    "\n",
-    "## smoothed power spectral density -- see LOSC tutorial (https://losc.ligo.org/s/events/GW150914/LOSC_Event_tutorial_GW150914.html)\n",
-    "# this can be used as the psd in the searches instead\n",
-    "power_spectral_density_smooted = (1.e-22*(18./(0.1+frequency_series))**2)**2+0.7e-23**2+((frequency_series/2000.)*4.e-23)**2\n",
-    "\n",
-    "plt.loglog(frequency_series, np.sqrt(power_spectral_density_H1), 'r', label='H1')\n",
-    "plt.loglog(frequency_series, np.sqrt(power_spectral_density_L1), 'b', label='L1')\n",
-    "plt.loglog(frequency_series, np.sqrt(power_spectral_density_smooted), 'k')\n",
-    "\n",
-    "plt.grid('on')\n",
-    "\n",
-    "plt.ylabel(r'amplitude spectral density [strain/$\\sqrt{\\rm Hz}$]')\n",
-    "plt.ylabel(r'frequency [Hz]')\n",
-    "plt.ylim(1e-24, 1e-19)\n",
-    "plt.xlim(20, 2000)\n",
-    "\n",
-    "plt.legend(loc='best')\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/tutorials/GW150914.py b/tutorials/GW150914.py
index 35ec7b419c90ee4d3583222a0776f0587cb706e3..9e7d5a85e4677bd4a7c294cecb2ec5f14b75eb6f 100644
--- a/tutorials/GW150914.py
+++ b/tutorials/GW150914.py
@@ -1,78 +1,96 @@
-# coding: utf-8
-
-# # GW150914 analysis
-
-# Analyse GW150914 data using TUPAK
-
-# In[1]:
-
-
+from __future__ import division
+import os
 import numpy as np
-import pylab as plt
+import matplotlib.pyplot as plt
+import matplotlib.mlab as mlab
 
 import peyote
 import corner
 
-import logging
+peyote.setup_logger()
 
-logging.getLogger().addHandler(logging.StreamHandler())
-logging.getLogger().setLevel('DEBUG')
-
-import matplotlib.mlab as mlab
+event = 'GW150914'
+outdir = 'GW150914_results'
+if os.path.isdir(outdir) is False:
+    os.mkdir(outdir)
 
+# Load in the data
 data_file = 'tutorial_data/GW150914_strain_data.npy'
+if os.path.isfile(data_file) is False:
+    os.system('python get_LOSC_event_data.py -e GW150914 -o tutorial_data')
 time_series, strain_H1, strain_L1 = np.load(data_file)
 time_duration = time_series[-1] - time_series[0]
-
 time_of_event = 1126259462.44
 
+# Create and save PSDs
 sampling_frequency = np.int(peyote.utils.sampling_frequency(time_series))
 NFFT = 4 * sampling_frequency
-power_spectral_density_H1, frequency_series = mlab.psd(strain_H1, Fs=sampling_frequency, NFFT=NFFT)
-power_spectral_density_L1, frequency_series = mlab.psd(strain_L1, Fs=sampling_frequency, NFFT=NFFT)
-
-with open('150914_PSD_H1.txt', 'w+') as file:
-    for f, p in zip(frequency_series, power_spectral_density_H1):
+psd_H1, psd_frequencies = mlab.psd(strain_H1, Fs=sampling_frequency, NFFT=NFFT)
+psd_L1, psd_frequencies = mlab.psd(strain_L1, Fs=sampling_frequency, NFFT=NFFT)
+with open('GW150914_PSD_H1.txt', 'w+') as file:
+    for f, p in zip(psd_frequencies, psd_H1):
         file.write('{} {}\n'.format(f, p))
-with open('150914_PSD_L1.txt', 'w+') as file:
-    for f, p in zip(frequency_series, power_spectral_density_L1):
+with open('GW150914_PSD_L1.txt', 'w+') as file:
+    for f, p in zip(psd_frequencies, psd_L1):
         file.write('{} {}\n'.format(f, p))
 
+# Cut out 1 second period around the data and make IFOs with this data
 search_idxs = (time_series > time_of_event - 0.5) * (time_series < time_of_event + 0.5)
 time_series = time_series[search_idxs]
 strain_H1 = strain_H1[search_idxs]
 strain_L1 = strain_L1[search_idxs]
 time_duration = time_series[-1] - time_series[0]
 H1 = peyote.detector.H1
-H1.power_spectral_density = peyote.detector.PowerSpectralDensity(psd_file='./150914_PSD_H1.txt')
+H1.power_spectral_density = peyote.detector.PowerSpectralDensity(
+    psd_file='./150914_PSD_H1.txt')
 H1.set_data(sampling_frequency, time_duration,
-            frequency_domain_strain=peyote.utils.nfft(strain_H1, sampling_frequency)[0])
-
+            frequency_domain_strain=peyote.utils.nfft(
+                strain_H1, sampling_frequency)[0])
 L1 = peyote.detector.L1
-L1.power_spectral_density = peyote.detector.PowerSpectralDensity(psd_file='./150914_PSD_L1.txt')
+L1.power_spectral_density = peyote.detector.PowerSpectralDensity(
+    psd_file='./150914_PSD_L1.txt')
 L1.set_data(sampling_frequency, time_duration,
-            frequency_domain_strain=peyote.utils.nfft(strain_L1, sampling_frequency)[0])
-
+            frequency_domain_strain=peyote.utils.nfft(
+                strain_L1, sampling_frequency)[0])
 IFOs = [H1, L1]
 
-source = peyote.source.BinaryBlackHole('BBH', sampling_frequency, time_duration, spin_1=[0, 0, 0], spin_2=[0, 0, 0],
-                                       luminosity_distance=410., iota=2.97305, phase=1.145,
-                                       waveform_approximant='IMRPhenomPv2', reference_frequency=50., ra=1.375,
-                                       dec=-1.2108, geocent_time=1126259642.413, psi=2.659, mass_1=32, mass_2=32)
-# ignore the fact that I hardcoded in some masses
-likelihood = peyote.likelihood.Likelihood(IFOs, source)
-
-prior = source.copy()
-
-prior.mass_1 = peyote.parameter.Parameter(
+# Plot the data and PSDs
+fig, axes = plt.subplots(nrows=2, figsize=(8, 8))
+for ax, IFO in zip(axes, IFOs):
+    ax.loglog(IFO.frequency_array, IFO.data, '-C0', label=IFO.name, lw=1.5)
+    ax.loglog(IFO.frequency_array,
+              np.abs(IFO.amplitude_spectral_density_array), '-C1', lw=0.5,
+              label=IFO.name+' PSD')
+    ax.grid('on')
+    ax.set_ylabel(r'amplitude spectral density [strain/$\sqrt{\rm Hz}$]')
+    ax.set_xlabel(r'frequency [Hz]')
+    ax.set_ylim(1e-24, 1e-19)
+    ax.set_xlim(20, 2000)
+    ax.legend(loc='best')
+fig.savefig('{}/frequency_domain_data.png'.format(outdir))
+
+# Create the waveformgenerator
+waveformgenerator = peyote.source.WaveformGenerator(
+    'BBH', sampling_frequency, time_duration, peyote.source.LALBinaryBlackHole)
+
+# Define the prior
+prior = dict(spin_1=[0, 0, 0], spin_2=[0, 0, 0], luminosity_distance=410.,
+             iota=2.97305, phase=1.145, waveform_approximant='IMRPhenomPv2',
+             reference_frequency=50., ra=1.375, dec=-1.2108,
+             geocent_time=1126259642.413, psi=2.659, mass_1=32, mass_2=32)
+prior['mass_1'] = peyote.parameter.Parameter(
     'mass_1', prior=peyote.prior.Uniform(lower=32, upper=41),
     latex_label='$m_1$')
-prior.mass_2 = peyote.parameter.Parameter(
+prior['mass_2'] = peyote.parameter.Parameter(
     'mass_2', prior=peyote.prior.Uniform(lower=25, upper=33),
     latex_label='$m_2$')
 
+# Define a likelihood
+likelihood = peyote.likelihood.Likelihood(IFOs, waveformgenerator)
+
+# Run the sampler
 result = peyote.run_sampler(likelihood, prior, sampler='pymultinest',
-                            n_live_points=400, verbose=True)
+                            n_live_points=400, verbose=True, outdir=outdir)
 
 fig = corner.corner(result.samples)
 fig.savefig('test')
diff --git a/tutorials/get_LOSC_event_data.py b/tutorials/get_LOSC_event_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a8836ba110f881e687860a718b0c4092c734a35
--- /dev/null
+++ b/tutorials/get_LOSC_event_data.py
@@ -0,0 +1,59 @@
+""" Helper script to faciliate downloading data from LOSC
+
+Usage: To download the GW150914 data from https://losc.ligo.org/events/ 
+
+$ python get_LOSC_event_data -e GW150914
+
+"""
+
+from __future__ import division
+import numpy as np
+import os
+import argparse
+
+parser = argparse.ArgumentParser(description='Script to download LOSC data.')
+parser.add_argument('-e', '--event', metavar='event', type=str)
+parser.add_argument('-o', '--outdir', metavar='outdir',
+                    default='tutorial_data')
+
+args = parser.parse_args()
+
+url_dictionary = dict(
+    GW150914="https://losc.ligo.org/s/events/GW150914/{}-{}1_LOSC_4_V2-1126259446-32.txt.gz",
+    LVT151012="https://losc.ligo.org/s/events/LVT151012/{}-{}1_LOSC_4_V2-1128678884-32.txt.gz",
+    GW151226="https://losc.ligo.org/s/events/GW151226/{}-{}1_LOSC_4_V2-1135136334-32.txt.gz",
+    GW170104="https://losc.ligo.org/s/events/GW170104/{}-{}1_LOSC_4_V1-1167559920-32.txt.gz",
+    GW170608="https://losc.ligo.org/s/events/GW170608/{}-{}1_LOSC_CLN_4_V1-1180922478-32.txt.gz",
+    GW170814="https://dcc.ligo.org/public/0146/P1700341/001/{}-{}1_LOSC_CLN_4_V1-1186741845-32.txt.gz",
+    GW170817="https://dcc.ligo.org/public/0146/P1700349/001/{}-{}1_LOSC_CLN_4_V1-1187007040-2048.txt.gz")
+
+outdir = 'tutorial_data'
+
+data = []
+for det, in ['H', 'L']:
+    url = url_dictionary[args.event].format(det, det)
+    filename = os.path.basename(url)
+    if os.path.isfile(filename.rstrip('.gz')) is False:
+        print("Downloading data from {}".format(url))
+        os.system("wget {} ".format(url))
+        os.system("gunzip {}".format(filename))
+        filename = filename.rstrip('.gz')
+    data.append(np.loadtxt(filename))
+    with open(filename, 'r') as f:
+        header = f.readlines()[:3]
+        event = header[0].split(' ')[5]
+        detector = header[0].split(' ')[7]
+        sampling_frequency = header[1].split(' ')[4]
+        starttime = header[2].split(' ')[3]
+        duration = header[2].split(' ')[5]
+        print('Loaded data for event={}, detector={}, sampling_frequency={}'
+              ', starttime={}, duration={}'.format(
+                  event, detector, sampling_frequency, starttime, duration))
+    os.remove(filename)
+
+time = np.arange(0, int(duration), 1/int(sampling_frequency)) + int(starttime)
+arr = [time] + data
+
+outfile = '{}/{}_strain_data.npy'.format(args.outdir, args.event)
+np.save(outfile, arr)
+print("Saved data to {}".format(outfile))
diff --git a/tutorials/tutorial_data/GW150914_strain_data.npy b/tutorials/tutorial_data/GW150914_strain_data.npy
deleted file mode 100644
index 0d7f6e2bcf62f95a661ced22f07495fd31603c05..0000000000000000000000000000000000000000
Binary files a/tutorials/tutorial_data/GW150914_strain_data.npy and /dev/null differ