diff --git a/examples/tutorials/compare_samplers.ipynb b/examples/tutorials/compare_samplers.ipynb
index c731fb2b711ed10d18ae607df3fcd37e3804fc65..c1a8fe87cdf43cf38d601d6cf87c0df5acb3e12e 100644
--- a/examples/tutorials/compare_samplers.ipynb
+++ b/examples/tutorials/compare_samplers.ipynb
@@ -186,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -303,7 +303,39 @@
       "11:30 INFO    :   tilt_1 = 0\n",
       "11:30 WARNING : Cannot sample from waveform_approximant, 'str' object has no attribute 'sample'\n",
       "11:30 INFO    : Using sampler Dynesty with kwargs {'dlogz': 0.1, 'verbose': True, 'dynamic': True, 'bound': 'multi', 'sample': 'unif', 'nlive': 250, 'walks': 10, 'update_interval': 100}\n",
-      "iter: 1196 | batch: 0 | bound: 121 | nc: 9262 | ncall: 15825 | eff(%):  7.510 | loglstar:   -inf < 25294.719 <    inf | logz: 25280.580 +/-  0.361 | dlogz: 21.245 >  0.010            "
+      "iter: 18333 | batch: 44 | bound: 974 | nc: 3 | ncall: 106747 | eff(%): 17.174 | loglstar: 25310.772 < 25313.915 < 25313.453 | logz: 25299.482 +/-  0.212 | stop:  0.971                 \n",
+      "11:46 INFO    : Renaming existing file outdir/dynesty_dynamic_result.h5 to outdir/dynesty_dynamic_result.h5.old\n",
+      "11:46 INFO    : Saving result to outdir/dynesty_dynamic_result.h5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Fq5ePX1TEP/+2gdXLinAb46jJ4gRiTGYVHdEae5Y8eQAjrwizaFLSx6oq/p0H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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1d412c7290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nsamples: 18333\n",
+      "noise_logz: -27253.938\n",
+      "logz: -1954.457 +/-  0.212\n",
+      "log_bayes_factor: 25299.481 +/-  0.212\n",
+      "\n",
+      "CPU times: user 17min 36s, sys: 2min 22s, total: 19min 59s\n",
+      "Wall time: 16min 46s\n"
      ]
     }
    ],
@@ -327,9 +359,111 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "11:46 INFO    : waveform_approximant cannot be converted to delta function prior.\n",
+      "11:46 INFO    : Writing priors to outdir/prior.txt\n",
+      "11:46 INFO    : Search parameters:\n",
+      "11:46 INFO    :   ra ~ Uniform(support=6.28318530718, minimum=0, name=ra, maximum=6.28318530718, _Prior__latex_label=$\\mathrm{RA}$)\n",
+      "11:46 INFO    :   dec ~ Uniform(support=3.14159265359, minimum=-1.57079632679, name=dec, maximum=1.57079632679, _Prior__latex_label=$\\mathrm{DEC}$)\n",
+      "11:46 INFO    :   psi = 2.659\n",
+      "11:46 INFO    :   a_2 = 0\n",
+      "11:46 INFO    :   a_1 = 0\n",
+      "11:46 INFO    :   geocent_time = 1126259642.41\n",
+      "11:46 INFO    :   reference_frequency = 50.0\n",
+      "11:46 INFO    :   phi_jl = 0\n",
+      "11:46 INFO    :   phase = 1.3\n",
+      "11:46 INFO    :   mass_2 = 29.0\n",
+      "11:46 INFO    :   mass_1 = 36.0\n",
+      "11:46 INFO    :   phi_12 = 0\n",
+      "11:46 INFO    :   luminosity_distance = 100.0\n",
+      "11:46 INFO    :   tilt_2 = 0\n",
+      "11:46 INFO    :   iota = 0.4\n",
+      "11:46 INFO    :   tilt_1 = 0\n",
+      "11:46 WARNING : Cannot sample from waveform_approximant, 'str' object has no attribute 'sample'\n",
+      "11:46 INFO    : Using sampler Ptemcee with kwargs {'tqdm': 'tqdm_notebook', 'nwalkers': 100, 'nburn': 100, 'ntemps': 2, 'nsteps': 200}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2eeee90408b24ff5a99fff3acfdb5958",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "A Jupyter Widget"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/user1/anaconda2/lib/python2.7/site-packages/tupak/sampler.py:162: RuntimeWarning: divide by zero encountered in log\n",
+      "  zip(self.__search_parameter_keys, theta)])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "11:48 INFO    : Saving walkers plot to filename\n",
+      "11:48 INFO    : Max autocorr time = 54.1336863627\n",
+      "11:48 INFO    : Tswap frac = [0.0262 0.0262]\n",
+      "11:48 INFO    : Renaming existing file outdir/ptemcee_result.h5 to outdir/ptemcee_result.h5.old\n",
+      "11:48 INFO    : Saving result to outdir/ptemcee_result.h5\n",
+      "11:48 WARNING : Parameter $\\mathrm{DEC}$ in chain ptemcee is not constrained\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ILBArEmILhQJPnjxhdXVVvi4UClEqlQgEAuzs7DA3Nydr/q2vr9PtdrFYLNRqNba3t4lE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\ncTqd2O12bDbb2P/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ImZkZGQ7ucDjI5/PY7XZyuRz1ep1Go8H6+jq6rss9JpFs6/P58Pl8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vfddxGLxRAKhRAKhbC8vAybzYa7d+/iV7/6FW7duoWdnR2qQsEHM7/fD13XSfKdJ+j+8Ic/pIEP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\nJcJACQQCgWAo+f82RN6FDWyAOQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1d51f37bd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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MSVc63ewZqUFOUtUe/Hw0wZg8k06Yq4/xXFWmKmKMmZp0wtxygs8ZY3IonTC/IiJfH7tS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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f1d40f6c190>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nsamples: 10000\n",
+      "noise_logz: -27253.938\n",
+      "logz:    nan +/-    nan\n",
+      "log_bayes_factor:    nan +/-    nan\n",
+      "\n",
+      "CPU times: user 1min 26s, sys: 1.01 s, total: 1min 27s\n",
+      "Wall time: 1min 26s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time \n",
     "result = tupak.sampler.run_sampler(\n",
diff --git a/tupak/detector.py b/tupak/detector.py
index ee794189ec425121b0a2df6412c4addc8dee484d..6976844a83ae7f33c021c3687a462f4644576e03 100644
--- a/tupak/detector.py
+++ b/tupak/detector.py
@@ -398,7 +398,7 @@ class PowerSpectralDensity:
 
     def __init__(self, asd_file=None, psd_file='aLIGO_ZERO_DET_high_P_psd.txt'):
         """
-        Instantiate a new PSD object.
+        Instantiate a new PowerSpectralDensity object.
 
         Only one of the asd_file or psd_file needs to be specified.
         If multiple are given, the first will be used.
@@ -498,15 +498,16 @@ def get_empty_interferometer(name):
     The available instruments are:
         H1, L1, V1, GEO600
 
-    Detector positions taken from LIGO-T980044-10 for L1/H1 and from arXiv:gr-qc/0008066 [45] for V1/GEO600
+    Detector positions taken from LIGO-T980044-10 for L1/H1 and from
+    arXiv:gr-qc/0008066 [45] for V1/GEO600
 
     Parameters
     ----------
     name: str
         Interferometer identifier.
 
-    Return
-    ------
+    Returns
+    -------
     interferometer: Interferometer
         Interferometer instance
     """
@@ -546,7 +547,7 @@ def get_interferometer_with_open_data(
         raw_data_file=None, **kwargs):
     """
     Helper function to obtain an Interferometer instance with appropriate
-    PSD and data, given an center_time
+    PSD and data, given an center_time.
 
     Parameters
     ----------
@@ -576,6 +577,7 @@ def get_interferometer_with_open_data(
     -------
     interferometer: `tupak.detector.Interferometer`
         An Interferometer instance with a PSD and frequency-domain strain data.
+
     """
 
     utils.check_directory_exists_and_if_not_mkdir(outdir)
@@ -647,14 +649,15 @@ def get_interferometer_with_fake_noise_and_injection(
         save=True):
     """
     Helper function to obtain an Interferometer instance with appropriate
-    PSD and data, given an center_time
+    power spectral density and data, given an center_time.
 
     Parameters
     ----------
     name: str
         Detector name, e.g., 'H1'.
     injection_polarizations: dict
-        polarizations of waveform to inject, output of waveform_generator.get_frequency_domain_signal
+        polarizations of waveform to inject, output of
+        `waveform_generator.get_frequency_domain_signal`
     injection_parameters: dict
         injection parameters, needed for sky position and timing
     sampling_frequency: float
@@ -672,6 +675,7 @@ def get_interferometer_with_fake_noise_and_injection(
     -------
     interferometer: `tupak.detector.Interferometer`
         An Interferometer instance with a PSD and frequency-domain strain data.
+
     """
 
     utils.check_directory_exists_and_if_not_mkdir(outdir)
@@ -716,9 +720,6 @@ def get_event_data(
     """
     Get open data for a specified event.
 
-    We currently know about:
-        GW150914
-
     Parameters
     ----------
     event: str
diff --git a/tupak/likelihood.py b/tupak/likelihood.py
index 9372cc6ad10db127a8f42b4f307464f297ad8033..dd496935391f813b3ee6ab5429707dc724fb8e02 100644
--- a/tupak/likelihood.py
+++ b/tupak/likelihood.py
@@ -12,6 +12,36 @@ import logging
 
 
 class GravitationalWaveTransient(object):
+    """ A gravitational-wave transient likelihood object
+
+    This is the usual likelihood object to use for transient gravitational
+    wave parameter estimation. It computes the log-likelihood in the frequency
+    domain assuming a colored Gaussian noise model described by a power
+    spectral density
+
+
+    Parameters
+    ----------
+    interferometers: list
+        A list of `tupak.detector.Interferometer` instances - contains the
+        detector data and power spectral densities
+    waveform_generator: `tupak.waveform_generator.WaveformGenerator`
+        An object which computes the frequency-domain strain of the signal,
+        given some set of parameters
+    distance_marginalization: bool
+        If true, analytic distance marginalization
+    phase_marginalization: bool
+        If true, analytic phase marginalization
+    prior: dict
+        If given, used in the distance and phase marginalization.
+
+    Returns
+    -------
+    Likelihood: `tupak.likelihood.Likelihood`
+        A likehood object, able to compute the likelihood of the data given
+        some model parameters
+
+    """
     def __init__(self, interferometers, waveform_generator, distance_marginalization=False, phase_marginalization=False,
                  prior=None):
         # GravitationalWaveTransient.__init__(self, interferometers, waveform_generator)
@@ -106,39 +136,6 @@ class GravitationalWaveTransient(object):
                                                  bounds_error=False, fill_value=-np.inf)
 
 
-class BasicGravitationalWaveTransient(object):
-    def __init__(self, interferometers, waveform_generator):
-        self.interferometers = interferometers
-        self.waveform_generator = waveform_generator
-
-    def noise_log_likelihood(self):
-        log_l = 0
-        for interferometer in self.interferometers:
-            log_l -= 2. / self.waveform_generator.time_duration * np.sum(
-                abs(interferometer.data) ** 2 / interferometer.power_spectral_density_array)
-        return log_l.real
-
-    def log_likelihood(self):
-        log_l = 0
-        waveform_polarizations = self.waveform_generator.frequency_domain_strain()
-        if waveform_polarizations is None:
-            return np.nan_to_num(-np.inf)
-        for interferometer in self.interferometers:
-            log_l += self.log_likelihood_interferometer(waveform_polarizations, interferometer)
-        return log_l.real
-
-    def log_likelihood_interferometer(self, waveform_polarizations, interferometer):
-        signal_ifo = interferometer.get_detector_response(waveform_polarizations, self.waveform_generator.parameters)
-
-        log_l = - 2. / self.waveform_generator.time_duration * np.vdot(interferometer.data - signal_ifo,
-                                                                       (interferometer.data - signal_ifo)
-                                                                       / interferometer.power_spectral_density_array)
-        return log_l.real
-
-    def log_likelihood_ratio(self):
-        return self.log_likelihood() - self.noise_log_likelihood()
-
-
 def get_binary_black_hole_likelihood(interferometers):
     """ A rapper to quickly set up a likelihood for BBH parameter estimation
 
@@ -148,7 +145,9 @@ def get_binary_black_hole_likelihood(interferometers):
         A list of `tupak.detector.Interferometer` instances, typically the
         output of either `tupak.detector.get_interferometer_with_open_data`
         or `tupak.detector.get_interferometer_with_fake_noise_and_injection`
+
     Returns
+    -------
     likelihood: tupak.likelihood.GravitationalWaveTransient
         The likelihood to pass to `run_sampler`
     """
@@ -158,4 +157,3 @@ def get_binary_black_hole_likelihood(interferometers):
         parameters={'waveform_approximant': 'IMRPhenomPv2', 'reference_frequency': 50})
     likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers, waveform_generator)
     return likelihood
-
diff --git a/tupak/waveform_generator.py b/tupak/waveform_generator.py
index 843ae839d3e9c9953efda399fc0f4621022265e0..30667d84050c34500034fbd736ceb6c49fce8f88 100644
--- a/tupak/waveform_generator.py
+++ b/tupak/waveform_generator.py
@@ -1,9 +1,9 @@
 import inspect
 
-import tupak
 from . import utils
 import numpy as np
 
+
 class WaveformGenerator(object):
     """ A waveform generator
 
@@ -24,13 +24,15 @@ class WaveformGenerator(object):
     parameters: dict
         Initial values for the parameters
     parameter_conversion: func
-        Function to convert from sampled parameters to parameters of the waveform generator
+        Function to convert from sampled parameters to parameters of the
+        waveform generator
     non_standard_sampling_parameter_keys: list
         List of parameter name for *non-standard* sampling parameters.
 
-    Note: the arguments of frequency_domain_source_model (except the first, which is the
-    frequencies at which to compute the strain) will be added to the
-    WaveformGenerator object and initialised to `None`.
+    Note: the arguments of frequency_domain_source_model (except the first,
+    which is the frequencies at which to compute the strain) will be added to
+    the WaveformGenerator object and initialised to `None`.
+
     """
 
     def __init__(self, time_duration, sampling_frequency, frequency_domain_source_model=None,
@@ -52,7 +54,7 @@ class WaveformGenerator(object):
         """ Wrapper to source_model """
         if self.parameter_conversion is not None:
             added_keys = self.parameter_conversion(self.parameters, self.non_standard_sampling_parameter_keys)
-            
+
         if self.frequency_domain_source_model is not None:
             model_frequency_strain = self.frequency_domain_source_model(self.frequency_array, **self.parameters)
         elif self.time_domain_source_model is not None: