Merge branch 'master' into project_restructuring

# Conflicts:
#	test/example_tests.py

docs/likelihood.txt

@@ -9,9 +9,169 @@ for some specific set of parameters. In mathematical notation, the likelihood
 can be generically written as :math:`\mathcal{L}(d| \theta)`. How this is
 coded up will depend on the problem, but `tupak` expects all likelihood
 objects to have a `parameters` attribute (a dictionary of key-value pairs) and
-a `log_likelihood()` method.
+a `log_likelihood()` method. In thie page, we'll discuss how to write your own
+Likelihood, and the standard likelihoods in :code:`tupak`.
 
-The default likelihood we use in the examples is `GravitationalWaveTransient`:
+The simplest likelihood
+-----------------------
+
+To start with let's consider perhaps the simplest likelihood we could write
+down, namely a Gaussian likelihood for a set of data :math:`\vec{x}=[x_1, x_2,
+\ldots, x_N]`. The likelihood for a single data point, given the mean
+:math:`\mu` and standard-deviation :math:`\sigma` is given by
+
+.. math::
+
+   \mathcal{L}(x_i| \mu, \sigma) =
+   \frac{1}{\sqrt{2\pi\sigma^2}}\mathrm{exp}\left(
+   \frac{-(x_i - \mu)^2}{2\sigma^2}\right)
+
+Then, the likelihood for all :math:`N` data points is
+
+.. math::
+
+   \mathcal{L}(\vec{x}| \mu, \sigma) = \prod_{i=1}^N
+   \mathcal{L}(x_i| \mu, \sigma)
+
+In practise, we implement the log-likelihood to avoid numerical overflow
+errors. To code this up in :code:`tupak`, we would write a class like this::
+
+   class GaussianLikelihood(tupak.Likelihood):
+       def __init__(self, data):
+           """
+           A very simple Gaussian likelihood
+
+           Parameters
+           ----------
+           data: array_like
+               The data to analyse
+           """
+           self.data = data
+           self.N = len(data)
+           self.parameters = {'mu': None, 'sigma': None}
+
+       def log_likelihood(self):
+           mu = self.parameters['mu']
+           sigma = self.parameters['sigma']
+           res = self.data - mu
+           return -0.5 * (np.sum((res / sigma)**2)
+                          + self.N*np.log(2*np.pi*sigma**2))
+
+
+This demonstrates the two required features of a :code:`tupak`
+:code:`Likelihood` object:
+
+#. It has a `parameters` attribute (a dictionary with
+   keys for all the parameters, in this case, initialised to :code:`None`)
+
+#. It has a :code:`log_likelihood` method which, when called returns the log
+   likelihood for all the data.
+
+You can find an example that uses this likelihood `here <https://git.ligo.org/Monash/tupak/blob/master/examples/other_examples/gaussian_example.py>`_.
+
+.. tip::
+
+   Note that the example above subclasses the :code:`tupak.Likelihood` base
+   class, this simply provides a few in built functions. We recommend you also
+   do this when writing your own likelihood.
+
+
+General likelihood for fitting a function :math:`y(x)` to some data with known noise
+------------------------------------------------------------------------------------
+
+The previous example was rather simplistic, Let's now consider that we have some
+dependent data :math:`\vec{y}=y_1, y_2, \ldots y_N$` measured at
+:math:`\vec{x}=x_1, x_2, \ldots, x_N`. We believe that the data is generated
+by additive Gaussian noise with a known variance :math:`sigma^2` and a function
+:math:`y(x; \theta)` where :math:`\theta`  are some unknown parameters; that is
+
+.. math::
+
+   y_i = y(x_i; \theta) + n_i
+
+where :math:`n_i` is drawn from a normal distribution with zero mean and
+standard deviation :math:`sigma`. As such, :math:`y_i - y(x_i; \theta)`
+itself will have a likelihood
+
+.. math::
+
+   \mathcal{L}(y_i; x_i, \theta) =
+   \frac{1}{\sqrt{2\pi\sigma^{2}}}
+   \mathrm{exp}\left(\frac{-(y_i - y(x_i; \theta))^2}{2\sigma^2}\right)
+
+
+As with the previous case, the likelihood for all the data is the produce over
+the likelihood for each data point.
+
+In :code:`tupak`, we can code this up as a likelihood in the following way::
+
+   class GaussianLikelihood(tupak.Likelihood):
+       def __init__(self, x, y, sigma, function):
+           """
+           A general Gaussian likelihood - the parameters are inferred from the
+           arguments of function
+
+           Parameters
+           ----------
+           x, y: array_like
+               The data to analyse
+           sigma: float
+               The standard deviation of the noise
+           function:
+               The python function to fit to the data. Note, this must take the
+               dependent variable as its first argument. The other arguments are
+               will require a prior and will be sampled over (unless a fixed
+               value is given).
+           """
+           self.x = x
+           self.y = y
+           self.sigma = sigma
+           self.N = len(x)
+           self.function = function
+
+           # These lines of code infer the parameters from the provided function
+           parameters = inspect.getargspec(function).args
+           parameters.pop(0)
+           self.parameters = dict.fromkeys(parameters)
+
+       def log_likelihood(self):
+           res = self.y - self.function(self.x, **self.parameters)
+           return -0.5 * (np.sum((res / self.sigma)**2)
+                          + self.N*np.log(2*np.pi*self.sigma**2))
+
+       def noise_log_likelihood(self):
+           return -0.5 * (np.sum((self.y / self.sigma)**2)
+                          + self.N*np.log(2*np.pi*self.sigma**2))
+
+
+This likelihood can be given any python function, the data (in the form of
+:code:`x` and :code:`y`) and the standard deviation of the noise. The
+parameters are inferred from the arguments to the :code:`function` argument,
+for example if, when instatiating the likelihood you passed in a the following
+function::
+
+   def f(x, a, b):
+       return x**2 + b
+
+Then you would also need to provide priors for :code:`a` and :code:`b`. For
+this likelihood, the first argument to :code:`function` is always assumed to
+be the dependent variable.
+
+.. note::
+    Here we have explicitly defined the :code:`noise_log_likelihood` method
+    as the case when there is no signal (i.e., :math:`y(x; \theta)=0`).
+
+You can see an example of this likelihood in the `linear regression example
+<https://git.ligo.org/Monash/tupak/blob/master/examples/other_examples/linear_regression.py>`_.
+
+
+Likelihood for transient gravitational waves
+--------------------------------------------
+
+In the examples above, we show how to write your own likelihood. However, for
+routine gravitational wave data analysis of transient events, we have in built
+likelihoods.  The default likelihood we use in the examples is
+`GravitationalWaveTransient`:
 
 .. autoclass:: tupak.likelihood.GravitationalWaveTransient
 
@@ -19,6 +179,9 @@ We also provide a simpler likelihood
 
 .. autoclass:: tupak.likelihood.BasicGravitationalWaveTransient
 
+Empty likelihood for subclassing
+--------------------------------
+
 We provide an empty parent class which can be subclassed for alternative use
 cases
 

examples/other_examples/gaussian_example.py

+#!/bin/python
+"""
+An example of how to use tupak to perform paramater estimation for
+non-gravitational wave data consisting of a Gaussian with a mean and variance
+"""
+from __future__ import division
+import tupak
+import numpy as np
+
+# A few simple setup steps
+tupak.utils.setup_logger()
+label = 'gaussian_example'
+outdir = 'outdir'
+
+# Here is minimum requirement for a Likelihood class to run with tupak. In this
+# case, we setup a GaussianLikelihood, which needs to have a log_likelihood
+# method. Note, in this case we will NOT make use of the `tupak`
+# waveform_generator to make the signal.
+
+# Making simulated data: in this case, we consider just a Gaussian
+
+data = np.random.normal(3, 4, 100)
+
+
+class GaussianLikelihood(tupak.Likelihood):
+    def __init__(self, data):
+        """
+        A very simple Gaussian likelihood
+
+        Parameters
+        ----------
+        data: array_like
+            The data to analyse
+        """
+        self.data = data
+        self.N = len(data)
+        self.parameters = {'mu': None, 'sigma': None}
+
+    def log_likelihood(self):
+        mu = self.parameters['mu']
+        sigma = self.parameters['sigma']
+        res = self.data - mu
+        return -0.5 * (np.sum((res / sigma)**2)
+                       + self.N*np.log(2*np.pi*sigma**2))
+
+
+likelihood = GaussianLikelihood(data)
+priors = dict(mu=tupak.prior.Uniform(0, 5, 'mu'),
+              sigma=tupak.prior.Uniform(0, 10, 'sigma'))
+
+# And run sampler
+result = tupak.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
+    walks=10, outdir=outdir, label=label)
+result.plot_corner()
+print(result)

examples/other_examples/linear_regression.py

 #!/bin/python
 """
 An example of how to use tupak to perform paramater estimation for
-non-gravitational wave data
+non-gravitational wave data. In this case, fitting a linear function to
+data with background Gaussian noise
+
 """
 from __future__ import division
 import tupak
 import numpy as np
 import matplotlib.pyplot as plt
+import inspect
 
 # A few simple setup steps
 tupak.utils.setup_logger()
-label = 'linear-regression'
+label = 'linear_regression'
 outdir = 'outdir'
 
-# Here is minimum requirement for a Likelihood class to run linear regression
-# with tupak. In this case, we setup a GaussianLikelihood, which needs to have
-# a log_likelihood method. Note, in this case we make use of the `tupak`
-# waveform_generator to make the signal (more on this later) But, one could
-# make this work without the waveform generator.
-
-# Making simulated data
-
 
-# First, we define our signal model, in this case a simple linear function
+# First, we define our "signal model", in this case a simple linear function
 def model(time, m, c):
     return time * m + c
 
@@ -56,8 +51,10 @@ fig.savefig('{}/{}_data.png'.format(outdir, label))
 
 
 class GaussianLikelihood(tupak.Likelihood):
-    def __init__(self, x, y, sigma, waveform_generator):
+    def __init__(self, x, y, sigma, function):
         """
+        A general Gaussian likelihood - the parameters are inferred from the
+        arguments of function
 
         Parameters
         ----------
@@ -65,19 +62,25 @@ class GaussianLikelihood(tupak.Likelihood):
             The data to analyse
         sigma: float
             The standard deviation of the noise
-        waveform_generator: `tupak.waveform_generator.WaveformGenerator`
-            An object which can generate the 'waveform', which in this case is
-            any arbitrary function
+        function:
+            The python function to fit to the data. Note, this must take the
+            dependent variable as its first argument. The other arguments are
+            will require a prior and will be sampled over (unless a fixed
+            value is given).
         """
         self.x = x
         self.y = y
         self.sigma = sigma
         self.N = len(x)
-        self.waveform_generator = waveform_generator
-        self.parameters = waveform_generator.parameters
+        self.function = function
+
+        # These lines of code infer the parameters from the provided function
+        parameters = inspect.getargspec(function).args
+        parameters.pop(0)
+        self.parameters = dict.fromkeys(parameters)
 
     def log_likelihood(self):
-        res = self.y - self.waveform_generator.time_domain_strain()
+        res = self.y - self.function(self.x, **self.parameters)
         return -0.5 * (np.sum((res / self.sigma)**2)
                        + self.N*np.log(2*np.pi*self.sigma**2))
 
@@ -86,18 +89,9 @@ class GaussianLikelihood(tupak.Likelihood):
                        + self.N*np.log(2*np.pi*self.sigma**2))
 
 
-# Here, we make a `tupak` waveform_generator. In this case, of course, the
-# name doesn't make so much sense. But essentially this is an objects that
-# can generate a signal. We give it information on how to make the time series
-# and the model() we wrote earlier.
-
-waveform_generator = tupak.WaveformGenerator(
-    time_duration=time_duration, sampling_frequency=sampling_frequency,
-    time_domain_source_model=model)
-
-# Now lets instantiate a version of out GravitationalWaveTransient, giving it
-# the time, data and waveform_generator
-likelihood = GaussianLikelihood(time, data, sigma, waveform_generator)
+# Now lets instantiate a version of our GaussianLikelihood, giving it
+# the time, data and signal model
+likelihood = GaussianLikelihood(time, data, sigma, model)
 
 # From hereon, the syntax is exactly equivalent to other tupak examples
 # We make a prior
@@ -109,5 +103,6 @@ priors['c'] = tupak.prior.Uniform(-2, 2, 'c')
 result = tupak.run_sampler(
     likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
     walks=10, injection_parameters=injection_parameters, outdir=outdir,
-    label=label, plot=True)
+    label=label)
+result.plot_corner()
 print(result)

examples/other_examples/linear_regression_unknown_noise.py

+#!/bin/python
+"""
+An example of how to use tupak to perform paramater estimation for
+non-gravitational wave data. In this case, fitting a linear function to
+data with background Gaussian noise with unknown variance.
+
+"""
+from __future__ import division
+import tupak
+import numpy as np
+import matplotlib.pyplot as plt
+import inspect
+
+# A few simple setup steps
+tupak.utils.setup_logger()
+label = 'linear_regression_unknown_noise'
+outdir = 'outdir'
+
+
+# First, we define our "signal model", in this case a simple linear function
+def model(time, m, c):
+    return time * m + c
+
+
+# New we define the injection parameters which we make simulated data with
+injection_parameters = dict(m=0.5, c=0.2)
+
+# For this example, we'll inject standard Gaussian noise
+sigma = 1
+
+# These lines of code generate the fake data. Note the ** just unpacks the
+# contents of the injection_paramsters when calling the model function.
+sampling_frequency = 10
+time_duration = 10
+time = np.arange(0, time_duration, 1/sampling_frequency)
+N = len(time)
+data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
+
+# We quickly plot the data to check it looks sensible
+fig, ax = plt.subplots()
+ax.plot(time, data, 'o', label='data')
+ax.plot(time, model(time, **injection_parameters), '--r', label='signal')
+ax.set_xlabel('time')
+ax.set_ylabel('y')
+ax.legend()
+fig.savefig('{}/{}_data.png'.format(outdir, label))
+
+
+# Parameter estimation: we now define a Gaussian Likelihood class relevant for
+# our model.
+
+class GaussianLikelihood(tupak.Likelihood):
+    def __init__(self, x, y, function, sigma=None):
+        """
+        A general Gaussian likelihood for known or unknown noise - the model
+        parameters are inferred from the arguments of function
+
+        Parameters
+        ----------
+        x, y: array_like
+            The data to analyse
+        function:
+            The python function to fit to the data. Note, this must take the
+            dependent variable as its first argument. The other arguments are
+            will require a prior and will be sampled over (unless a fixed
+            value is given).
+        sigma: None, float, array_like
+            If None, the standard deviation of the noise is unknown and will be
+            estimated (note: this requires a prior to be given for sigma). If
+            not None, this defined the standard-deviation of the data points.
+            This can either be a single float, or an array with length equal
+            to that for `x` and `y`.
+        """
+        self.x = x
+        self.y = y
+        self.N = len(x)
+        self.function = function
+
+        # These lines of code infer the parameters from the provided function
+        parameters = inspect.getargspec(function).args
+        parameters.pop(0)
+        self.parameters = dict.fromkeys(parameters)
+        self.function_keys = self.parameters.keys()
+        self.parameters['sigma'] = None
+
+    def log_likelihood(self):
+        model_parameters = {k: self.parameters[k] for k in self.function_keys}
+        res = self.y - self.function(self.x, **model_parameters)
+        sigma = self.parameters['sigma']
+        return -0.5 * (np.sum((res / sigma)**2)
+                       + self.N*np.log(2*np.pi*sigma**2))
+
+    def noise_log_likelihood(self):
+        return np.nan
+        sigma = self.parameters['sigma']
+        return -0.5 * (np.sum((self.y / sigma)**2)
+                       + self.N*np.log(2*np.pi*sigma**2))
+
+
+# Now lets instantiate a version of our GaussianLikelihood, giving it
+# the time, data and signal model
+likelihood = GaussianLikelihood(time, data, model)
+
+# From hereon, the syntax is exactly equivalent to other tupak examples
+# We make a prior
+priors = {}
+priors['m'] = tupak.prior.Uniform(0, 5, 'm')
+priors['c'] = tupak.prior.Uniform(-2, 2, 'c')
+priors['sigma'] = tupak.prior.Uniform(0, 10, 'sigma')
+
+# And run sampler
+result = tupak.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
+    walks=10, injection_parameters=injection_parameters, outdir=outdir,
+    label=label)
+result.plot_corner()
+print(result)

examples/other_examples/sine_gaussian_example.py

+#!/bin/python
+"""
+Tutorial to demonstrate running parameter estimation on a sine gaussian injected signal.
+
+"""
+from __future__ import division, print_function
+import tupak
+import numpy as np
+
+# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+time_duration = 4.
+sampling_frequency = 2048.
+
+# Specify the output directory and the name of the simulation.
+outdir = 'outdir'
+label = 'sine_gaussian'
+tupak.utils.setup_logger(outdir=outdir, label=label)
+
+# Set up a random seed for result reproducibility.  This is optional!
+np.random.seed(170801)
+
+# We are going to inject a sine gaussian waveform.  We first establish a dictionary of parameters that
+# includes all of the different waveform parameters
+injection_parameters = dict(hrss = 1e-22, Q = 5.0, frequency = 200.0, ra = 1.375, dec = -1.2108, 
+                             geocent_time = 1126259642.413, psi= 2.659)
+
+# Create the waveform_generator using a sine Gaussian source function
+waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
+                                                                sampling_frequency=sampling_frequency,
+                                                                frequency_domain_source_model=tupak.source.sinegaussian,
+                                                                parameters=injection_parameters)
+hf_signal = waveform_generator.frequency_domain_strain()
+
+# Set up interferometers.  In this case we'll use three interferometers (LIGO-Hanford (H1), LIGO-Livingston (L1),
+# and Virgo (V1)).  These default to their design sensitivity
+IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+    name, injection_polarizations=hf_signal, injection_parameters=injection_parameters, time_duration=time_duration,
+    sampling_frequency=sampling_frequency, outdir=outdir) for name in ['H1', 'L1', 'V1']]
+
+# Set up prior, which is a dictionary
+priors = dict()
+# By default we will sample all terms in the signal models.  However, this will take a long time for the calculation,
+# so for this example we will set almost all of the priors to be equall to their injected values.  This implies the
+# prior is a delta function at the true, injected value.  In reality, the sampler implementation is smart enough to
+# not sample any parameter that has a delta-function prior.
+for key in ['psi', 'ra', 'dec', 'geocent_time']:
+    priors[key] = injection_parameters[key]
+
+# The above list does *not* include frequency and Q, which means those are the parameters
+# that will be included in the sampler.  If we do nothing, then the default priors get used.
+#priors['Q'] = tupak.prior.create_default_prior(name='Q')
+#priors['frequency'] = tupak.prior.create_default_prior(name='frequency')
+priors['Q'] = tupak.prior.Uniform(2, 50, 'Q')
+priors['frequency'] = tupak.prior.Uniform(30, 1000, 'frequency')
+priors['hrss'] = tupak.prior.Uniform(1e-23, 1e-21, 'hrss')
+
+# Initialise the likelihood by passing in the interferometer data (IFOs) and the waveoform generator
+likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers=IFOs, waveform_generator=waveform_generator)
+
+# Run sampler.  In this case we're going to use the `dynesty` sampler
+result = tupak.sampler.run_sampler(likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
+                                   injection_parameters=injection_parameters, outdir=outdir, label=label)
+
+# make some plots of the outputs
+result.plot_corner()
+print(result)
+
+
+
+
+
+
+
+
+
+
+

examples/supernova_example/supernova_example.py

+#!/bin/python
+"""
+
+Tutorial to demonstrate running parameter estimation/model selection on an NR
+supernova injected signal.  Signal model is made by applying PCA to a set of
+supernova waveforms. The first few PCs are then linearly combined with a scale
+factor. (See https://arxiv.org/pdf/1202.3256.pdf)
+
+"""
+from __future__ import division, print_function
+import tupak
+import numpy as np
+
+# Set the duration and sampling frequency of the data segment that we're going to inject the signal into
+time_duration = 3.
+sampling_frequency = 4096.
+
+# Specify the output directory and the name of the simulation.
+outdir = 'outdir'
+label = 'supernova'
+tupak.utils.setup_logger(outdir=outdir, label=label)
+
+# Set up a random seed for result reproducibility.  This is optional!
+np.random.seed(170801)
+
+# We are going to inject a supernova waveform.  We first establish a dictionary
+# of parameters that includes all of the different waveform parameters. It will
+# read in a signal to inject from a txt file.
+
+injection_parameters = dict(file_path='MuellerL15_example_inj.txt',
+                            luminosity_distance=10.0, ra=1.375,
+                            dec=-1.2108, geocent_time=1126259642.413,
+                            psi=2.659)
+
+# Create the waveform_generator using a supernova source function
+waveform_generator = tupak.waveform_generator.WaveformGenerator(
+    time_duration=time_duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=tupak.source.supernova,
+    parameters=injection_parameters)
+hf_signal = waveform_generator.frequency_domain_strain()
+
+# Set up interferometers.  In this case we'll use three interferometers
+# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)).  These default to
+# their design sensitivity
+IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
+    name, injection_polarizations=hf_signal,
+    injection_parameters=injection_parameters, time_duration=time_duration,
+    sampling_frequency=sampling_frequency, outdir=outdir)
+    for name in ['H1', 'L1', 'V1']]
+
+# read in from a file the PCs used to create the signal model.
+realPCs = np.loadtxt('SupernovaRealPCs.txt')
+imagPCs = np.loadtxt('SupernovaImagPCs.txt')
+
+# Now we make another waveform_generator because the signal model is
+# not the same as the injection in this case.
+simulation_parameters = dict(
+    realPCs=realPCs, imagPCs=imagPCs)
+
+search_waveform_generator = tupak.waveform_generator.WaveformGenerator(
+    time_duration=time_duration, sampling_frequency=sampling_frequency,
+    frequency_domain_source_model=tupak.source.supernova_pca_model,
+    parameters=simulation_parameters)
+
+# Set up prior
+priors = dict()
+for key in ['psi', 'geocent_time']:
+    priors[key] = injection_parameters[key]
+priors['luminosity_distance'] = tupak.prior.Uniform(
+    2, 20, 'luminosity_distance')
+priors['pc_coeff1'] = tupak.prior.Uniform(-1, 1, 'pc_coeff1')
+priors['pc_coeff2'] = tupak.prior.Uniform(-1, 1, 'pc_coeff2')
+priors['pc_coeff3'] = tupak.prior.Uniform(-1, 1, 'pc_coeff3')
+priors['pc_coeff4'] = tupak.prior.Uniform(-1, 1, 'pc_coeff4')
+priors['pc_coeff5'] = tupak.prior.Uniform(-1, 1, 'pc_coeff5')
+priors['ra'] = tupak.prior.create_default_prior(name='ra')
+priors['dec'] = tupak.prior.create_default_prior(name='dec')
+priors['geocent_time'] = tupak.prior.Uniform(
+    injection_parameters['geocent_time'] - 1,
+    injection_parameters['geocent_time'] + 1,
+    'geocent_time')
+
+# Initialise the likelihood by passing in the interferometer data (IFOs) and
+# the waveoform generator
+likelihood = tupak.GravitationalWaveTransient(
+    interferometers=IFOs, waveform_generator=search_waveform_generator)
+
+# Run sampler.
+result = tupak.run_sampler(
+    likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
+    outdir=outdir, label=label)
+
+# make some plots of the outputs
+result.plot_corner()
+print(result)
+
+
+
+
+
+
+
+
+
+
+

test/example_tests.py

@@ -7,7 +7,10 @@ import logging
 # Required to run the tests
 from past.builtins import execfile
 from test.context import tupak
+
+# Imported to ensure the examples run
 import numpy as np
+import inspect
 
 tupak.utils.command_line_args.test = True
 

tupak/detector.py

@@ -322,8 +322,9 @@ class Interferometer(object):
         return self.power_spectral_density.power_spectral_density_interpolated(self.frequency_array)
 
     def set_data(self, sampling_frequency, duration, epoch=0,
-                 from_power_spectral_density=False,
-                 frequency_domain_strain=None):
+                 from_power_spectral_density=False, zero_noise=False,
+                 frequency_domain_strain=None, frame_file=None,
+                 channel_name=None, overwrite_psd=True, **kwargs):
         """
         Set the interferometer frequency-domain stain and accompanying PSD values.
 
@@ -340,6 +341,18 @@ class Interferometer(object):
         from_power_spectral_density: bool
             If frequency_domain_strain not given, use IFO's PSD object to
             generate noise
+        zero_noise: bool
+            If true and frequency_domain_strain and from_power_spectral_density
+            are false, set the data to be zero.
+        frame_file: str
+            File from which to load data.
+        channel_name: str
+            Channel to read from frame.
+        overwrite_psd: bool
+            Whether to overwrite the psd in the interferometer with one calculated
+            from the loaded data, default=True.
+        kwargs: dict
+            Additional arguments for loading data.
         """
 
         self.epoch = epoch
@@ -348,13 +361,25 @@ class Interferometer(object):
             logging.info(
                 'Setting {} data using provided frequency_domain_strain'.format(self.name))
             frequencies = utils.create_frequency_series(sampling_frequency, duration)
-        elif from_power_spectral_density is True:
+        elif from_power_spectral_density:
             logging.info(
                 'Setting {} data using noise realization from provided'
                 'power_spectal_density'.format(self.name))
             frequency_domain_strain, frequencies = \
                 self.power_spectral_density.get_noise_realisation(
                     sampling_frequency, duration)
+        elif zero_noise:
+            logging.info('Setting zero noise in {}'.format(self.name))
+            frequencies = utils.create_fequency_series(sampling_frequency, duration)
+            frequency_domain_strain = np.zeros_like(frequencies) * (1 + 1j)
+        elif frame_file is not None:
+            logging.info('Reading data from frame, {}.'.format(self.name))
+            strain = tupak.utils.read_frame_file(
+                frame_file, t1=epoch, t2=epoch+duration, channel=channel_name, resample=sampling_frequency)
+            frequency_domain_strain, frequencies = tupak.utils.process_strain_data(strain, **kwargs)
+            if overwrite_psd:
+                self.power_spectral_density = PowerSpectralDensity(
+                    frame_file=frame_file, channel_name=channel_name, epoch=epoch, **kwargs)
         else:
             raise ValueError("No method to set data provided.")
 
@@ -406,16 +431,33 @@ class Interferometer(object):
 
 class PowerSpectralDensity:
 
-    def __init__(self, asd_file=None, psd_file='aLIGO_ZERO_DET_high_P_psd.txt'):
+    def __init__(self, asd_file=None, psd_file='aLIGO_ZERO_DET_high_P_psd.txt', frame_file=None, epoch=0,
+                 psd_duration=1024, psd_offset=16, channel_name=None, filter_freq=1024, alpha=0.25, fft_length=4):
         """
         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.
-        FIXME: Allow reading a frame and then FFT to get PSD, use gwpy?
 
-        :param asd_file: amplitude spectral density, format 'f h_f'
-        :param psd_file: power spectral density, format 'f h_f'
+        Parameters:
+        asd_file: str
+            File containing amplitude spectral density, format 'f h_f'
+        psd_file: str
+            File containing power spectral density, format 'f h_f'
+        frame_file: str
+            Frame file to read data from.
+        epoch: float
+            Beginning of segment to analyse.
+        psd_duration: float
+            Duration of data to generate PSD from.
+        psd_offset: float
+            Offset of data from beginning of analysed segment.
+        channel_name: str
+            Name of channel to use to generate PSD.
+        alpha: float
+            Parameter for Tukey window.
+        fft_length: float
+            Number of seconds in a single fft.
         """
 
         self.frequencies = []
@@ -434,6 +476,24 @@ class PowerSpectralDensity:
                 logging.warning("The minimum of the provided curve is {:.2e}.".format(
                     min(self.amplitude_spectral_density)))
                 logging.warning("You may have intended to provide this as a power spectral density.")
+        elif frame_file is not None:
+            strain = tupak.utils.read_frame_file(frame_file, t1=epoch - psd_duration - psd_offset,
+                                                 t2=epoch - psd_duration, channel=channel_name)
+            sampling_frequency = int(strain.sample_rate.value)
+
+            # Low pass filter
+            bp = filter_design.lowpass(filter_freq, strain.sample_rate)
+            strain = strain.filter(bp, filtfilt=True)
+            strain = strain.crop(*strain.span.contract(1))
+
+            # Create and save PSDs
+            NFFT = int(sampling_frequency * fft_length)
+            window = signal.windows.tukey(NFFT, alpha=alpha)
+            psd = strain.psd(fftlength=fft_length, window=window)
+            self.frequencies = psd.frequencies
+            self.power_spectral_density = psd.value
+            self.amplitude_spectral_density = self.power_spectral_density**0.5
+            self.interpolate_power_spectral_density()
         else:
             self.power_spectral_density_file = psd_file
             self.import_power_spectral_density()
@@ -666,7 +726,7 @@ def get_interferometer_with_open_data(
 def get_interferometer_with_fake_noise_and_injection(
         name, injection_polarizations, injection_parameters,
         sampling_frequency=4096, time_duration=4, outdir='outdir', plot=True,
-        save=True):
+        save=True, zero_noise=False):
     """
     Helper function to obtain an Interferometer instance with appropriate
     power spectral density and data, given an center_time.
@@ -690,6 +750,8 @@ def get_interferometer_with_fake_noise_and_injection(
         If true, create an ASD + strain plot
     save: bool
         If true, save frequency domain data and PSD to file
+    zero_noise: bool
+        If true, set noise to zero.
 
     Returns
     -------
@@ -701,9 +763,16 @@ def get_interferometer_with_fake_noise_and_injection(
     utils.check_directory_exists_and_if_not_mkdir(outdir)
 
     interferometer = get_empty_interferometer(name)
-    interferometer.set_data(
-        sampling_frequency=sampling_frequency, duration=time_duration,
-        from_power_spectral_density=True, epoch=(injection_parameters['geocent_time']+2)-time_duration)
+    if zero_noise:
+        interferometer.set_data(
+            sampling_frequency=sampling_frequency, duration=time_duration,
+            zero_noise=True,
+            epoch=(injection_parameters['geocent_time']+2)-time_duration)
+    else:
+        interferometer.set_data(
+            sampling_frequency=sampling_frequency, duration=time_duration,
+            from_power_spectral_density=True,
+            epoch=(injection_parameters['geocent_time']+2)-time_duration)
     interferometer.inject_signal(
         waveform_polarizations=injection_polarizations,
         parameters=injection_parameters)

tupak/result.py

@@ -154,7 +154,7 @@ class Result(dict):
             kwargs['truths'] = kwargs.pop('truth')
 
         if getattr(self, 'injection_parameters', None) is not None:
-            injection_parameters = [self.injection_parameters[key]
+            injection_parameters = [self.injection_parameters.get(key, None)
                                     for key in self.search_parameter_keys]
             kwargs['truths'] = kwargs.get('truths', injection_parameters)
 

tupak/source.py

 from __future__ import division, print_function
 
 import logging
+import numpy as np
 
 try:
     import lalsimulation as lalsim
@@ -59,6 +60,67 @@ def lal_binary_black_hole(
     return {'plus': h_plus, 'cross': h_cross}
 
 
+def sinegaussian(
+        frequency_array, hrss, Q, frequency, ra, dec, geocent_time, psi):
+
+    tau  = Q / (np.sqrt(2.0)*np.pi*frequency)
+    temp = Q / (4.0*np.sqrt(np.pi)*frequency)
+    t = geocent_time
+    fm = frequency_array - frequency
+    fp = frequency_array + frequency
+
+    h_plus = ((hrss / np.sqrt(temp * (1+np.exp(-Q**2))))
+              * ((np.sqrt(np.pi)*tau)/2.0)
+              * (np.exp(-fm**2 * np.pi**2 * tau**2)
+                  + np.exp(-fp**2 * np.pi**2 * tau**2)))
+
+    h_cross = (-1j*(hrss / np.sqrt(temp * (1-np.exp(-Q**2))))
+               * ((np.sqrt(np.pi)*tau)/2.0)
+               * (np.exp(-fm**2 * np.pi**2 * tau**2)
+                  - np.exp(-fp**2 * np.pi**2 * tau**2)))
+
+    return{'plus': h_plus, 'cross': h_cross}
+
+
+def supernova(
+        frequency_array, realPCs, imagPCs, file_path, luminosity_distance, ra,
+        dec, geocent_time, psi):
+    """ A supernova NR simulation for injections """
+
+    realhplus, imaghplus, realhcross, imaghcross = np.loadtxt(
+        file_path, usecols=(0, 1, 2, 3), unpack=True)
+
+    # waveform in file at 10kpc
+    scaling = 1e-3 * (10.0 / luminosity_distance)
+
+    h_plus = scaling * (realhplus + 1.0j*imaghplus)
+    h_cross = scaling * (realhcross + 1.0j*imaghcross)
+    return {'plus': h_plus, 'cross': h_cross}
+
+
+def supernova_pca_model(
+        frequency_array, realPCs, imagPCs, pc_coeff1, pc_coeff2, pc_coeff3,
+        pc_coeff4, pc_coeff5, luminosity_distance, ra, dec, geocent_time, psi):
+    """ Supernova signal model """
+
+    pc1 = realPCs[:, 0] + 1.0j*imagPCs[:, 0]
+    pc2 = realPCs[:, 1] + 1.0j*imagPCs[:, 1]
+    pc3 = realPCs[:, 2] + 1.0j*imagPCs[:, 2]
+    pc4 = realPCs[:, 3] + 1.0j*imagPCs[:, 3]
+    pc5 = realPCs[:, 4] + 1.0j*imagPCs[:, 5]
+
+    # file at 10kpc
+    scaling = 1e-23 * (10.0 / luminosity_distance)
+
+    h_plus = scaling * (pc_coeff1*pc1 + pc_coeff2*pc2 + pc_coeff3*pc3
+                        + pc_coeff4*pc4 + pc_coeff5*pc5)
+    h_cross = scaling * (pc_coeff1*pc1 + pc_coeff2*pc2 + pc_coeff3*pc3
+                         + pc_coeff4*pc4 + pc_coeff5*pc5)
+
+    return {'plus': h_plus, 'cross': h_cross}
+
+
+
 #class BinaryNeutronStarMergerNumericalRelativity:
 #    """Loads in NR simulations of BNS merger
 #    takes parameters mean_mass, mass_ratio and equation_of_state, directory_path

tupak/utils.py

@@ -4,6 +4,8 @@ import os
 import numpy as np
 from math import fmod
 from gwpy.timeseries import TimeSeries
+from gwpy.signal import filter_design
+from scipy import signal
 import argparse
 
 # Constants
@@ -514,6 +516,112 @@ def get_open_strain_data(
     return strain
 
 
+def read_frame_file(file_name, t1, t2, channel=None, **kwargs):
+    """ A function which accesses the open strain data
+
+    This uses `gwpy` to download the open data and then saves a cached copy for
+    later use
+
+    Parameters
+    ----------
+    file_name: str
+        The name of the frame to read
+    t1, t2: float
+        The GPS time of the start and end of the data
+    channel: str
+        The name of the channel being searched for, some standard channel names are attempted
+        if channel is not specified or if specified channel is not found.
+    **kwargs:
+        Passed to `gwpy.timeseries.TimeSeries.fetch_open_data`
+
+    Returns
+    -----------
+    strain: gwpy.timeseries.TimeSeries
+
+    """
+    loaded = False
+    if channel is not None:
+        try:
+            strain = TimeSeries.read(source=file_name, channel=channel, start=t1, end=t2, **kwargs)
+            loaded = True
+            logging.info('Successfully loaded {}.'.format(channel))
+        except RuntimeError:
+            logging.warning('Channel {} not found. Trying preset channel names'.format(channel))
+    for channel_type in ['GDS-CALIB_STRAIN', 'DCS-CALIB_STRAIN_C01', 'DCS-CALIB_STRAIN_C02']:
+        for ifo_name in ['H1', 'L1']:
+            channel = '{}:{}'.format(ifo_name, channel_type)
+            if loaded:
+                continue
+            try:
+                strain = TimeSeries.read(source=file_name, channel=channel, start=t1, end=t2, **kwargs)
+                loaded = True
+                logging.info('Successfully loaded {}.'.format(channel))
+            except RuntimeError:
+                None
+
+    if loaded:
+        return strain
+    else:
+        logging.warning('No data loaded.')
+        return None
+
+
+def process_strain_data(
+        strain, alpha=0.25, filter_freq=1024, **kwargs):
+    """
+    Helper function to obtain an Interferometer instance with appropriate
+    PSD and data, given an center_time.
+
+    Parameters
+    ----------
+    name: str
+        Detector name, e.g., 'H1'.
+    center_time: float
+        GPS time of the center_time about which to perform the analysis.
+        Note: the analysis data is from `center_time-T/2` to `center_time+T/2`.
+    T: float
+        The total time (in seconds) to analyse. Defaults to 4s.
+    alpha: float
+        The tukey window shape parameter passed to `scipy.signal.tukey`.
+    psd_offset, psd_duration: float
+        The power spectral density (psd) is estimated using data from
+        `center_time+psd_offset` to `center_time+psd_offset + psd_duration`.
+    outdir: str
+        Directory where the psd files are saved
+    plot: bool
+        If true, create an ASD + strain plot
+    filter_freq: float
+        Low pass filter frequency
+    **kwargs:
+        All keyword arguments are passed to
+        `gwpy.timeseries.TimeSeries.fetch_open_data()`.
+
+    Returns
+    -------
+    interferometer: `tupak.detector.Interferometer`
+        An Interferometer instance with a PSD and frequency-domain strain data.
+
+    """
+
+    sampling_frequency = int(strain.sample_rate.value)
+
+    # Low pass filter
+    bp = filter_design.lowpass(filter_freq, strain.sample_rate)
+    strain = strain.filter(bp, filtfilt=True)
+    strain = strain.crop(*strain.span.contract(1))
+
+    time_series = strain.times.value
+    time_duration = time_series[-1] - time_series[0]
+
+    # Apply Tukey window
+    N = len(time_series)
+    strain = strain * signal.windows.tukey(N, alpha=alpha)
+
+    frequency_domain_strain, frequencies = nfft(strain.value, sampling_frequency)
+
+    return frequency_domain_strain, frequencies
+
+
 def set_up_command_line_arguments():
     parser = argparse.ArgumentParser(
         description="Command line interface for tupak scripts")