Remove waveform generator dep. example

examples/other_examples/linear_regression_with_waveform_generator.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. This example illustrates how the
-waveform_generator could be used.
-"""
-from __future__ import division
-import tupak
-import numpy as np
-import matplotlib.pyplot as plt
-
-# A few simple setup steps
-tupak.utils.setup_logger()
-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
-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 use 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, sigma, waveform_generator):
-        """
-
-        Parameters
-        ----------
-        x, y: array_like
-            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
-        """
-        self.x = x
-        self.y = y
-        self.sigma = sigma
-        self.N = len(x)
-        self.waveform_generator = waveform_generator
-        self.parameters = waveform_generator.parameters
-
-    def log_likelihood(self):
-        res = self.y - self.waveform_generator.time_domain_strain()
-        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))
-
-
-# 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)
-
-# 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')
-
-# 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, plot=True)
-print(result)