Updating tutorial notebooks

examples/tutorials/compare_samplers.ipynb

 %% Cell type:markdown id: tags:
 
 # Compare samplers
 
 In this notebook, we'll compare the different samplers implemented in `bilby`. As of this version, we don't compare the outputs, only how to run them and the timings for their default setup.
 
 ## Setup
 
 %% Cell type:code id: tags:
 
 ``` python
 import numpy as np
 import pylab as plt
 
 %load_ext autoreload
 %autoreload 2
 
 import bilby
 
 bilby.utils.setup_logger()
 
 time_duration = 1.
 sampling_frequency = 4096.
 
 injection_parameters = dict(mass_1=36., mass_2=29., a_1=0, a_2=0, tilt_1=0, tilt_2=0, phi_12=0, phi_jl=0,
                             luminosity_distance=100., iota=0.4, phase=1.3, waveform_approximant='IMRPhenomPv2',
                             reference_frequency=50., ra=1.375, dec=-1.2108, geocent_time=1126259642.413,
                             psi=2.659)
 
 
 waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
     sampling_frequency=sampling_frequency,
-    time_duration=time_duration,
+    duration=time_duration,
     frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
     parameters=injection_parameters)
 
 hf_signal = waveform_generator.frequency_domain_strain()
 
 H1 = bilby.gw.detector.get_empty_interferometer('H1')
-H1.set_data(sampling_frequency=sampling_frequency, duration=time_duration,
-            from_power_spectral_density=True)
-H1.inject_signal(waveform_polarizations=hf_signal, parameters=injection_parameters)
+H1.set_strain_data_from_power_spectral_density(sampling_frequency=sampling_frequency, duration=time_duration)
+H1.inject_signal(injection_polarizations=hf_signal, parameters=injection_parameters)
 
 IFOs = [H1]
 
 likelihood = bilby.gw.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Prior
 
 For this test, we will simply search of the sky position, setting the other parameters to their simulated values.
 
 %% Cell type:code id: tags:
 
 ``` python
-priors = bilby.core.prior.fill_priors(injection_parameters, likelihood)
+priors = bilby.gw.prior.BBHPriorDict()
+
+for key in injection_parameters.keys():
+    priors[key] = injection_parameters[key]
+
 priors['ra'] = bilby.prior.Uniform(0, 2*np.pi, 'ra')
 priors['dec'] = bilby.prior.Uniform(-np.pi/2, np.pi/2, 'dec')
 ```
 
 %% Cell type:markdown id: tags:
 
 ## PyMultinest
 
 %% Cell type:code id: tags:
 
 ``` python
 %%time
 result = bilby.core.sampler.run_sampler(
     likelihood, priors=priors, sampler='pymultinest', label='pymultinest',
     npoints=200, verbose=False, resume=False)
 fig = result.plot_corner(save=False)
 plt.show()
 print(result)
 ```
 
 %% Cell type:markdown id: tags:
 
 # dynesty
 
 %% Cell type:code id: tags:
 
 ``` python
 %%time
-result = bilby.gw.sampler.run_sampler(
+result = bilby.core.sampler.run_sampler(
     likelihood, priors=priors, sampler='dynesty', label='dynesty',
     bound='multi', sample='rwalk', npoints=200, walks=1, verbose=False,
     update_interval=100)
 fig = result.plot_corner(save=False)
 plt.show()
 print(result)
 ```
 
 %% Cell type:markdown id: tags:
 
 # Dynamic Nested Sampling (Dynesty)
 
 See [the dynesty docs](http://dynesty.readthedocs.io/en/latest/dynamic.html#). Essentially, this methods improves the posterior estimation over that of standard nested sampling.
 
 %% Cell type:code id: tags:
 
 ``` python
 %%time
-result = bilby.gw.sampler.run_sampler(
+result = bilby.core.sampler.run_sampler(
     likelihood, priors=priors, sampler='dynesty', label='dynesty_dynamic',
     bound='multi', nlive=250, sample='unif', verbose=True,
     update_interval=100, dynamic=True)
 fig = result.plot_corner(save=False)
 plt.show()
 print(result)
 ```
 
 %% Cell type:markdown id: tags:
 
 # ptemcee
 
 %% Cell type:code id: tags:
 
 ``` python
 %%time
-result = bilby.gw.sampler.run_sampler(
+result = bilby.core.sampler.run_sampler(
     likelihood, priors=priors, sampler='ptemcee', label='ptemcee',
     nwalkers=100, nsteps=200, nburn=100, ntemps=2,
     tqdm='tqdm_notebook')
 fig = result.plot_corner(save=False)
 plt.show()
 print(result)
 ```