From eaf8d4d792bb27e49c31ffbe7074f215cc2ad04d Mon Sep 17 00:00:00 2001
From: Gregory Ashton <gregory.ashton@ligo.org>
Date: Tue, 29 May 2018 08:33:15 +1000
Subject: [PATCH] Update example to a more relevant linear regression
---
.../other_examples/alternative_likelihoods.py | 93 --------------
examples/other_examples/linear_regression.py | 113 ++++++++++++++++++
2 files changed, 113 insertions(+), 93 deletions(-)
delete mode 100644 examples/other_examples/alternative_likelihoods.py
create mode 100644 examples/other_examples/linear_regression.py
diff --git a/examples/other_examples/alternative_likelihoods.py b/examples/other_examples/alternative_likelihoods.py
deleted file mode 100644
index 0bc522775..000000000
--- a/examples/other_examples/alternative_likelihoods.py
+++ /dev/null
@@ -1,93 +0,0 @@
-#!/bin/python
-"""
-An example of how to use tupak to perform paramater estimation for
-non-gravitational wave data
-"""
-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 = 'test'
-outdir = 'outdir'
-
-
-# Here is minimum requirement for a GravitationalWaveTransient class needed to run tupak. In
-# this case, we setup a GaussianGravitationalWaveTransient, which needs to have a
-# log_likelihood and noise_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.
-
-class GaussianLikelihood(tupak.likelihood.Likelihood):
- def __init__(self, x, y, waveform_generator):
- self.x = x
- self.y = y
- self.N = len(x)
- self.waveform_generator = waveform_generator
- self.parameters = waveform_generator.parameters
-
- def log_likelihood(self):
- sigma = 1
- res = self.y - self.waveform_generator.time_domain_strain()
- return -0.5 * (np.sum((res / sigma)**2)
- + self.N*np.log(2*np.pi*sigma**2))
-
- def noise_log_likelihood(self):
- sigma = 1
- return -0.5 * (np.sum((self.y / sigma)**2)
- + self.N*np.log(2*np.pi*sigma**2))
-
-
-# Here we define our signal model, in this case a very basic trig. function
-def model(time, A, P):
- return A*np.sin(2*np.pi*time/P)
-
-
-# Here we define the injection parameters which we make simulated data with
-injection_parameters = dict(A=1.5, P=10)
-
-# 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 = 100
-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)
-ax.plot(time, model(time, **injection_parameters), '--r')
-fig.savefig('{}/data.png'.format(outdir))
-
-# 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.waveform_generator.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, waveform_generator)
-
-# From hereon, the syntax is exactly equivalent to other tupak examples
-# We make a prior
-priors = {}
-priors['A'] = tupak.prior.Uniform(0, 5, 'A')
-priors['P'] = tupak.prior.Uniform(0, 20, 'P')
-
-# And run sampler
-result = tupak.sampler.run_sampler(
- likelihood=likelihood, priors=priors, sampler='dynesty', npoints=1000,
- walks=10, injection_parameters=injection_parameters, outdir=outdir,
- label=label, use_ratio=False)
-result.plot_corner()
-print(result)
diff --git a/examples/other_examples/linear_regression.py b/examples/other_examples/linear_regression.py
new file mode 100644
index 000000000..115eb1bb1
--- /dev/null
+++ b/examples/other_examples/linear_regression.py
@@ -0,0 +1,113 @@
+#!/bin/python
+"""
+An example of how to use tupak to perform paramater estimation for
+non-gravitational wave data
+"""
+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))
+
+
+# Parameter estimation: we now define a Gaussian Likelihood class relevant for
+# our model.
+
+
+class GaussianLikelihood(tupak.likelihood.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.waveform_generator.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.sampler.run_sampler(
+ likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
+ walks=10, injection_parameters=injection_parameters, outdir=outdir,
+ label=label, plot=True)
+print(result)
--
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