Merge pull request #170 from migueldvb/corner

Update the dependency on triangle module to corner

docs/user/line.rst

@@ -134,7 +134,7 @@ use the `scipy.optimize
     m_ml, b_ml, lnf_ml = result["x"]
 
 It's worth noting that the optimize module *minimizes* functions whereas we
-would like the maximize the likelihood.
+would like to maximize the likelihood.
 This goal is equivalent to minimizing the *negative* likelihood (or in this
 case, the negative *log* likelihood).
 The maximum likelihood result is plotted as a solid black line—compared to
@@ -284,13 +284,13 @@ Results
 
 Now that we have this list of samples, let's make one of the most useful plots
 you can make with your MCMC results: *a corner plot*.
-You'll need the `triangle.py module <https://github.com/dfm/triangle.py>`_ but
+You'll need the `corner.py module <https://github.com/dfm/corner.py>`_ but
 once you have it, generating a corner plot is as simple as:
 
 .. code-block:: python
 
-    import triangle
-    fig = triangle.corner(samples, labels=["$m$", "$b$", "$\ln\,f$"],
+    import corner
+    fig = corner.corner(samples, labels=["$m$", "$b$", "$\ln\,f$"],
                           truths=[m_true, b_true, np.log(f_true)])
     fig.savefig("triangle.png")
 

examples/line.py

@@ -4,7 +4,7 @@
 from __future__ import print_function
 
 import emcee
-import triangle
+import corner
 import numpy as np
 import scipy.optimize as op
 import matplotlib.pyplot as pl
@@ -118,7 +118,7 @@ def lnprob(theta, x, y, yerr):
 burnin = 50
 samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))
 
-fig = triangle.corner(samples, labels=["$m$", "$b$", "$\ln\,f$"],
+fig = corner.corner(samples, labels=["$m$", "$b$", "$\ln\,f$"],
                       truths=[m_true, b_true, np.log(f_true)])
 fig.savefig("line-triangle.png")