examples/logo/sample_logo.py

+""" Script used to generate the samples for the tupak logo """
+import tupak
+import numpy as np
+import scipy.interpolate as si
+from skimage import io
+
+
+class Likelihood(tupak.Likelihood):
+    def __init__(self, interp):
+        self.interp = interp
+        self.parameters = dict(x=None, y=None)
+
+    def log_likelihood(self):
+        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, :]
+    x = np.arange(img.shape[0])
+    y = np.arange(img.shape[1])
+    interp = si.interpolate.interp2d(x, y, img.T)
+
+    likelihood = Likelihood(interp)
+
+    priors = {}
+    priors['x'] = tupak.prior.Uniform(0, max(x), 'x')
+    priors['y'] = tupak.prior.Uniform(0, max(y), 'y')
+
+    result = tupak.run_sampler(
+        likelihood=likelihood, priors=priors, sampler='nestle', npoints=5000,
+        label=letter)
+    fig = result.plot_corner(quantiles=None, smooth1d=4)

examples/open_data_examples/GW150914.py

@@ -53,11 +53,7 @@ waveform_generator = tupak.WaveformGenerator(time_duration=interferometers[0].du
 
 # In this step, we define the likelihood. Here we use the standard likelihood
 # function, passing it the data and the waveform generator.
-<<<<<<< HEAD
-likelihood = tupak.likelihood.GravitationalWaveTransient(interferometers, waveform_generator, time_marginalization=False, distance_marginalization=True, prior=prior)
-=======
 likelihood = tupak.GravitationalWaveTransient(interferometers, waveform_generator)
->>>>>>> e8b704afba65886f49cb448402a95ae90c468a24
 
 # Finally, we run the sampler. This function takes the likelihood and prio
 # along with some options for how to do the sampling and how to save the data

requirements.txt

@@ -14,3 +14,4 @@ nestle
 deepdish
 ptemcee
 mock
+lalsuite

tupak/detector.py

@@ -371,7 +371,7 @@ class Interferometer(object):
                     sampling_frequency, duration)
         elif zero_noise:
             logging.info('Setting zero noise in {}'.format(self.name))
-            frequencies = utils.create_fequency_series(sampling_frequency, duration)
+            frequencies = utils.create_frequency_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))