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diff --git a/examples/logo/image.jpg b/examples/logo/image.jpg
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diff --git a/examples/logo/k.jpg b/examples/logo/k.jpg
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diff --git a/examples/logo/p.jpg b/examples/logo/p.jpg
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diff --git a/examples/logo/sample_logo.py b/examples/logo/sample_logo.py
new file mode 100644
index 0000000000000000000000000000000000000000..339c50f1d9d4cb33769b4b549bb40b7876231dd2
--- /dev/null
+++ b/examples/logo/sample_logo.py
@@ -0,0 +1,32 @@
+""" 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)
diff --git a/examples/logo/t.jpg b/examples/logo/t.jpg
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diff --git a/examples/logo/u.jpg b/examples/logo/u.jpg
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diff --git a/requirements.txt b/requirements.txt
index fe8a629b9a049938e04c73d9c49034b19bffc522..356634c1c863b2c80a7d6f84efbf66d16a6056c7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -14,3 +14,4 @@ nestle
 deepdish
 ptemcee
 mock
+lalsuite