From e2601cbc00e37de40d3f4ded25b15bcc085f1d0c Mon Sep 17 00:00:00 2001
From: Colm Talbot <colm.talbot@ligo.org>
Date: Thu, 4 Oct 2018 21:31:44 +1000
Subject: [PATCH] flake examples
---
examples/injection_examples/basic_tutorial.py | 3 +--
.../binary_neutron_star_example.py | 7 +++----
examples/injection_examples/calibration_example.py | 3 +--
.../change_sampled_parameters.py | 6 ++----
.../create_your_own_source_model.py | 5 ++---
.../create_your_own_time_domain_source_model.py | 1 -
examples/injection_examples/eccentric_inspiral.py | 1 -
.../injection_examples/how_to_specify_the_prior.py | 1 -
.../injection_examples/marginalized_likelihood.py | 1 -
examples/injection_examples/non_tensor.py | 2 +-
.../injection_examples/sine_gaussian_example.py | 14 +-------------
examples/injection_examples/using_gwin.py | 1 -
examples/logo/sample_logo.py | 4 ++--
examples/open_data_examples/GW150914.py | 1 -
examples/other_examples/add_multiple_results.py | 6 ++----
examples/other_examples/gaussian_example.py | 4 ++--
examples/other_examples/get_LOSC_event_data.py | 4 ++--
examples/other_examples/hyper_parameter_example.py | 6 +++---
examples/other_examples/linear_regression_pymc3.py | 3 +--
.../linear_regression_pymc3_custom_likelihood.py | 5 +++--
.../linear_regression_unknown_noise.py | 2 +-
examples/other_examples/occam_factor_example.py | 4 ++--
examples/other_examples/radioactive_decay.py | 4 ++--
examples/supernova_example/supernova_example.py | 2 +-
24 files changed, 32 insertions(+), 58 deletions(-)
diff --git a/examples/injection_examples/basic_tutorial.py b/examples/injection_examples/basic_tutorial.py
index e22d38056..9fde009d6 100644
--- a/examples/injection_examples/basic_tutorial.py
+++ b/examples/injection_examples/basic_tutorial.py
@@ -50,7 +50,7 @@ waveform_generator = bilby.gw.WaveformGenerator(
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
- start_time=injection_parameters['geocent_time']-3)
+ start_time=injection_parameters['geocent_time'] - 3)
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
@@ -85,4 +85,3 @@ result = bilby.run_sampler(
# Make a corner plot.
result.plot_corner()
-
diff --git a/examples/injection_examples/binary_neutron_star_example.py b/examples/injection_examples/binary_neutron_star_example.py
index 4f826a060..4b3b9b848 100644
--- a/examples/injection_examples/binary_neutron_star_example.py
+++ b/examples/injection_examples/binary_neutron_star_example.py
@@ -25,7 +25,7 @@ np.random.seed(88170235)
# We are going to inject a binary neutron star waveform. We first establish a
# dictionary of parameters that includes all of the different waveform
# parameters, including masses of the two black holes (mass_1, mass_2),
-# spins of both black holes (a_1,a_2) , etc.
+# spins of both black holes (a_1, a_2), etc.
injection_parameters = dict(
mass_1=1.5, mass_2=1.3, a_1=0.0, a_2=0.0, luminosity_distance=50.,
iota=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413,
@@ -35,7 +35,7 @@ injection_parameters = dict(
# to inject the signal into. For the
# TaylorF2 waveform, we cut the signal close to the isco frequency
duration = 8
-sampling_frequency = 2*1570.
+sampling_frequency = 2 * 1570.
start_time = injection_parameters['geocent_time'] + 2 - duration
# Fixed arguments passed into the source model. The analysis starts at 40 Hz.
@@ -64,7 +64,7 @@ priors = bilby.gw.prior.BNSPriorSet()
for key in ['a_1', 'a_2', 'psi', 'geocent_time', 'ra', 'dec',
'iota', 'luminosity_distance', 'phase']:
priors[key] = injection_parameters[key]
-
+
# Initialise the likelihood by passing in the interferometer data (IFOs)
# and the waveoform generator
likelihood = bilby.gw.GravitationalWaveTransient(
@@ -78,4 +78,3 @@ result = bilby.run_sampler(
injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
-
diff --git a/examples/injection_examples/calibration_example.py b/examples/injection_examples/calibration_example.py
index 9f2052ffb..ec3f3491e 100644
--- a/examples/injection_examples/calibration_example.py
+++ b/examples/injection_examples/calibration_example.py
@@ -39,7 +39,7 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
waveform_generator = bilby.gw.WaveformGenerator(
duration=duration, sampling_frequency=sampling_frequency,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
- parameters=injection_parameters,waveform_arguments=waveform_arguments)
+ parameters=injection_parameters, waveform_arguments=waveform_arguments)
# Set up interferometers. In this case we'll use three interferometers
# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)).
@@ -83,4 +83,3 @@ result = bilby.run_sampler(
# make some plots of the outputs
result.plot_corner()
-
diff --git a/examples/injection_examples/change_sampled_parameters.py b/examples/injection_examples/change_sampled_parameters.py
index aabcbfebc..06dfa1740 100644
--- a/examples/injection_examples/change_sampled_parameters.py
+++ b/examples/injection_examples/change_sampled_parameters.py
@@ -34,15 +34,14 @@ waveform_arguments = dict(waveform_approximant='IMRPhenomPv2',
waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
sampling_frequency=sampling_frequency, duration=duration,
frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,
- parameter_conversion=
- bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,
+ parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,
waveform_arguments=waveform_arguments)
# Set up interferometers.
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1', 'V1', 'K1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
- start_time=injection_parameters['geocent_time']-3)
+ start_time=injection_parameters['geocent_time'] - 3)
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
@@ -80,4 +79,3 @@ result = bilby.core.sampler.run_sampler(
injection_parameters=injection_parameters, label='DifferentParameters',
conversion_function=bilby.gw.conversion.generate_all_bbh_parameters)
result.plot_corner()
-
diff --git a/examples/injection_examples/create_your_own_source_model.py b/examples/injection_examples/create_your_own_source_model.py
index 4e0ab71da..ded59e157 100644
--- a/examples/injection_examples/create_your_own_source_model.py
+++ b/examples/injection_examples/create_your_own_source_model.py
@@ -16,9 +16,9 @@ duration = 1
# Here we define out source model - this is the sine-Gaussian model in the
# frequency domain.
def sine_gaussian(f, A, f0, tau, phi0, geocent_time, ra, dec, psi):
- arg = -(np.pi * tau * (f-f0))**2 + 1j * phi0
+ arg = -(np.pi * tau * (f - f0))**2 + 1j * phi0
plus = np.sqrt(np.pi) * A * tau * np.exp(arg) / 2.
- cross = plus * np.exp(1j*np.pi/2)
+ cross = plus * np.exp(1j * np.pi / 2)
return {'plus': plus, 'cross': cross}
@@ -52,4 +52,3 @@ result = bilby.core.sampler.run_sampler(
likelihood, prior, sampler='dynesty', outdir=outdir, label=label,
resume=False, sample='unif', injection_parameters=injection_parameters)
result.plot_corner()
-
diff --git a/examples/injection_examples/create_your_own_time_domain_source_model.py b/examples/injection_examples/create_your_own_time_domain_source_model.py
index 28bae6952..82ca24d18 100644
--- a/examples/injection_examples/create_your_own_time_domain_source_model.py
+++ b/examples/injection_examples/create_your_own_time_domain_source_model.py
@@ -62,4 +62,3 @@ result = bilby.core.sampler.run_sampler(
injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
-
diff --git a/examples/injection_examples/eccentric_inspiral.py b/examples/injection_examples/eccentric_inspiral.py
index 2034df2b5..2709220fc 100644
--- a/examples/injection_examples/eccentric_inspiral.py
+++ b/examples/injection_examples/eccentric_inspiral.py
@@ -88,4 +88,3 @@ result = bilby.run_sampler(
# And finally we make some plots of the output posteriors.
result.plot_corner()
-
diff --git a/examples/injection_examples/how_to_specify_the_prior.py b/examples/injection_examples/how_to_specify_the_prior.py
index 4c9b80635..6a5eb0337 100644
--- a/examples/injection_examples/how_to_specify_the_prior.py
+++ b/examples/injection_examples/how_to_specify_the_prior.py
@@ -72,4 +72,3 @@ result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty', outdir=outdir,
injection_parameters=injection_parameters, label='specify_prior')
result.plot_corner()
-
diff --git a/examples/injection_examples/marginalized_likelihood.py b/examples/injection_examples/marginalized_likelihood.py
index 29eae13c6..05c1def46 100644
--- a/examples/injection_examples/marginalized_likelihood.py
+++ b/examples/injection_examples/marginalized_likelihood.py
@@ -58,4 +58,3 @@ result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty',
injection_parameters=injection_parameters, outdir=outdir, label=label)
result.plot_corner()
-
diff --git a/examples/injection_examples/non_tensor.py b/examples/injection_examples/non_tensor.py
index 6b5bc8e29..c75fcf0db 100644
--- a/examples/injection_examples/non_tensor.py
+++ b/examples/injection_examples/non_tensor.py
@@ -57,7 +57,7 @@ waveform_generator =\
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
- start_time=injection_parameters['geocent_time']-3)
+ start_time=injection_parameters['geocent_time'] - 3)
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
diff --git a/examples/injection_examples/sine_gaussian_example.py b/examples/injection_examples/sine_gaussian_example.py
index 2f0d1a127..07ac1ff0b 100644
--- a/examples/injection_examples/sine_gaussian_example.py
+++ b/examples/injection_examples/sine_gaussian_example.py
@@ -38,7 +38,7 @@ waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1', 'V1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
- start_time=injection_parameters['geocent_time']-3)
+ start_time=injection_parameters['geocent_time'] - 3)
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
@@ -63,15 +63,3 @@ result = bilby.core.sampler.run_sampler(
# make some plots of the outputs
result.plot_corner()
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/examples/injection_examples/using_gwin.py b/examples/injection_examples/using_gwin.py
index dc312ddd8..1539195e6 100644
--- a/examples/injection_examples/using_gwin.py
+++ b/examples/injection_examples/using_gwin.py
@@ -91,4 +91,3 @@ result = bilby.run_sampler(
likelihood=likelihood, priors=priors, sampler='dynesty', npoints=500,
label=label)
result.plot_corner()
-
diff --git a/examples/logo/sample_logo.py b/examples/logo/sample_logo.py
index b82733ebe..59153aeef 100644
--- a/examples/logo/sample_logo.py
+++ b/examples/logo/sample_logo.py
@@ -11,11 +11,11 @@ class Likelihood(bilby.Likelihood):
self.parameters = dict(x=None, y=None)
def log_likelihood(self):
- return -1/(self.interp(self.parameters['x'], self.parameters['y'])[0])
+ return -1 / (self.interp(self.parameters['x'], self.parameters['y'])[0])
for letter in ['t', 'u', 'p', 'a', 'k']:
- img = 1-io.imread('{}.jpg'.format(letter), as_grey=True)[::-1, :]
+ img = 1 - io.imread('{}.jpg'.format(letter), as_grey=True)[::-1, :]
x = np.arange(img.shape[0])
y = np.arange(img.shape[1])
interp = si.interpolate.interp2d(x, y, img.T)
diff --git a/examples/open_data_examples/GW150914.py b/examples/open_data_examples/GW150914.py
index aa5ca44af..f44c9dd1c 100644
--- a/examples/open_data_examples/GW150914.py
+++ b/examples/open_data_examples/GW150914.py
@@ -51,4 +51,3 @@ likelihood = bilby.gw.likelihood.GravitationalWaveTransient(
result = bilby.run_sampler(likelihood, prior, sampler='dynesty',
outdir=outdir, label=label)
result.plot_corner()
-
diff --git a/examples/other_examples/add_multiple_results.py b/examples/other_examples/add_multiple_results.py
index f4c565555..83f57b1a6 100644
--- a/examples/other_examples/add_multiple_results.py
+++ b/examples/other_examples/add_multiple_results.py
@@ -17,14 +17,14 @@ injection_parameters = dict(m=0.5, c=0.2)
sigma = 1
sampling_frequency = 10
time_duration = 10
-time = np.arange(0, time_duration, 1/sampling_frequency)
+time = np.arange(0, time_duration, 1 / sampling_frequency)
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
likelihood = bilby.core.likelihood.GaussianLikelihood(
time, data, model, sigma=sigma)
-priors = {}
+priors = dict()
priors['m'] = bilby.core.prior.Uniform(0, 1, 'm')
priors['c'] = bilby.core.prior.Uniform(-2, 2, 'c')
@@ -40,5 +40,3 @@ resultA.plot_walkers()
result = resultA + resultB
result.plot_corner()
print(result)
-
-
diff --git a/examples/other_examples/gaussian_example.py b/examples/other_examples/gaussian_example.py
index bf7730286..2d9e51938 100644
--- a/examples/other_examples/gaussian_example.py
+++ b/examples/other_examples/gaussian_example.py
@@ -39,8 +39,8 @@ class SimpleGaussianLikelihood(bilby.Likelihood):
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))
+ return -0.5 * (np.sum((res / sigma)**2) +
+ self.N * np.log(2 * np.pi * sigma**2))
likelihood = SimpleGaussianLikelihood(data)
diff --git a/examples/other_examples/get_LOSC_event_data.py b/examples/other_examples/get_LOSC_event_data.py
index 6197426eb..9c7b31479 100644
--- a/examples/other_examples/get_LOSC_event_data.py
+++ b/examples/other_examples/get_LOSC_event_data.py
@@ -1,7 +1,7 @@
#!/usr/bin/env python
""" Helper script to faciliate downloading data from LOSC
-Usage: To download the GW150914 data from https://losc.ligo.org/events/
+Usage: To download the GW150914 data from https://losc.ligo.org/events/
$ python get_LOSC_event_data -e GW150914
@@ -52,7 +52,7 @@ for det, in ['H', 'L']:
event, detector, sampling_frequency, starttime, duration))
os.remove(filename)
-time = np.arange(0, int(duration), 1/int(sampling_frequency)) + int(starttime)
+time = np.arange(0, int(duration), 1 / int(sampling_frequency)) + int(starttime)
arr = [time] + data
outfile = '{}/{}_strain_data.npy'.format(args.outdir, args.event)
diff --git a/examples/other_examples/hyper_parameter_example.py b/examples/other_examples/hyper_parameter_example.py
index 21ad6c24d..ef04e9840 100644
--- a/examples/other_examples/hyper_parameter_example.py
+++ b/examples/other_examples/hyper_parameter_example.py
@@ -15,7 +15,7 @@ outdir = 'outdir'
# Define a model to fit to each data set
def model(x, c0, c1):
- return c0 + c1*x
+ return c0 + c1 * x
N = 10
@@ -64,8 +64,8 @@ fig2.savefig('outdir/hyper_parameter_combined_posteriors.png')
def hyper_prior(data, mu, sigma):
- return np.exp(- (data['c0'] - mu)**2 / (2 * sigma**2))\
- / (2 * np.pi * sigma**2)**0.5
+ return np.exp(- (data['c0'] - mu)**2 / (2 * sigma**2)) /\
+ (2 * np.pi * sigma**2)**0.5
def run_prior(data):
diff --git a/examples/other_examples/linear_regression_pymc3.py b/examples/other_examples/linear_regression_pymc3.py
index df98e5a5b..eb98be1ed 100644
--- a/examples/other_examples/linear_regression_pymc3.py
+++ b/examples/other_examples/linear_regression_pymc3.py
@@ -9,7 +9,6 @@ from __future__ import division
import bilby
import numpy as np
import matplotlib.pyplot as plt
-import inspect
from bilby.core.likelihood import GaussianLikelihood
@@ -34,7 +33,7 @@ sigma = 1
# 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)
+time = np.arange(0, time_duration, 1 / sampling_frequency)
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
diff --git a/examples/other_examples/linear_regression_pymc3_custom_likelihood.py b/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
index f62b187ac..4270e6968 100644
--- a/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
+++ b/examples/other_examples/linear_regression_pymc3_custom_likelihood.py
@@ -36,7 +36,7 @@ sigma = 1
# 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)
+time = np.arange(0, time_duration, 1 / sampling_frequency)
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
@@ -139,9 +139,10 @@ class PriorPyMC3(bilby.core.prior.Prior):
return pm.Uniform(self.name, lower=self.minimum,
upper=self.maximum)
+
# From hereon, the syntax is exactly equivalent to other bilby examples
# We make a prior
-priors = {}
+priors = dict()
priors['m'] = bilby.core.prior.Uniform(0, 5, 'm')
priors['c'] = PriorPyMC3(-2, 2, 'c')
diff --git a/examples/other_examples/linear_regression_unknown_noise.py b/examples/other_examples/linear_regression_unknown_noise.py
index be44d994f..e4427c9e0 100644
--- a/examples/other_examples/linear_regression_unknown_noise.py
+++ b/examples/other_examples/linear_regression_unknown_noise.py
@@ -31,7 +31,7 @@ sigma = 1
# contents of the injection_parameters when calling the model function.
sampling_frequency = 10
time_duration = 10
-time = np.arange(0, time_duration, 1/sampling_frequency)
+time = np.arange(0, time_duration, 1 / sampling_frequency)
N = len(time)
data = model(time, **injection_parameters) + np.random.normal(0, sigma, N)
diff --git a/examples/other_examples/occam_factor_example.py b/examples/other_examples/occam_factor_example.py
index b977eaead..7785a540f 100644
--- a/examples/other_examples/occam_factor_example.py
+++ b/examples/other_examples/occam_factor_example.py
@@ -84,8 +84,8 @@ class Polynomial(bilby.Likelihood):
def log_likelihood(self):
res = self.y - self.polynomial(self.x, self.parameters)
- return -0.5 * (np.sum((res / self.sigma)**2)
- + self.N*np.log(2*np.pi*self.sigma**2))
+ return -0.5 * (np.sum((res / self.sigma)**2) +
+ self.N * np.log(2 * np.pi * self.sigma**2))
def fit(n):
diff --git a/examples/other_examples/radioactive_decay.py b/examples/other_examples/radioactive_decay.py
index b59fad8a9..aceabd156 100644
--- a/examples/other_examples/radioactive_decay.py
+++ b/examples/other_examples/radioactive_decay.py
@@ -45,8 +45,8 @@ def decay_rate(delta_t, halflife, n_init):
n_atoms = n_init * atto * n_avogadro
- rates = (np.exp(-np.log(2) * (times[:-1] / halflife))
- - np.exp(- np.log(2) * (times[1:] / halflife))) * n_atoms / delta_t
+ rates = (np.exp(-np.log(2) * (times[:-1] / halflife)) -
+ np.exp(- np.log(2) * (times[1:] / halflife))) * n_atoms / delta_t
return rates
diff --git a/examples/supernova_example/supernova_example.py b/examples/supernova_example/supernova_example.py
index 05b2eebcd..507c4e4ed 100644
--- a/examples/supernova_example/supernova_example.py
+++ b/examples/supernova_example/supernova_example.py
@@ -45,7 +45,7 @@ waveform_generator = bilby.gw.waveform_generator.WaveformGenerator(
ifos = bilby.gw.detector.InterferometerList(['H1', 'L1'])
ifos.set_strain_data_from_power_spectral_densities(
sampling_frequency=sampling_frequency, duration=duration,
- start_time=injection_parameters['geocent_time']-3)
+ start_time=injection_parameters['geocent_time'] - 3)
ifos.inject_signal(waveform_generator=waveform_generator,
parameters=injection_parameters)
--
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