From e35999b24ba3e070dc245f775c82d5adddebc116 Mon Sep 17 00:00:00 2001
From: mzwiessele <ibinbei@gmail.com>
Date: Wed, 8 Apr 2015 08:24:55 +0200
Subject: [PATCH] [var plots] wrong return values

---
 GPy/plotting/matplot_dep/variational_plots.py | 20 ++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/GPy/plotting/matplot_dep/variational_plots.py b/GPy/plotting/matplot_dep/variational_plots.py
index 5cced10d..55128ec7 100644
--- a/GPy/plotting/matplot_dep/variational_plots.py
+++ b/GPy/plotting/matplot_dep/variational_plots.py
@@ -1,6 +1,6 @@
 import pylab as pb, numpy as np
 
-def plot(parameterized, fignum=None, ax=None, colors=None):
+def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
     """
     Plot latent space X in 1D:
 
@@ -13,13 +13,15 @@ def plot(parameterized, fignum=None, ax=None, colors=None):
 
     """
     if ax is None:
-        fig = pb.figure(num=fignum, figsize=(8, min(12, (2 * parameterized.mean.shape[1]))))
+        fig = pb.figure(num=fignum, figsize=figsize)
     if colors is None:
         colors = pb.gca()._get_lines.color_cycle
         pb.clf()
     else:
         colors = iter(colors)
-    plots = []
+    lines = []
+    fills = []
+    bg_lines = []
     means, variances = parameterized.mean, parameterized.variance
     x = np.arange(means.shape[0])
     for i in range(means.shape[1]):
@@ -29,20 +31,20 @@ def plot(parameterized, fignum=None, ax=None, colors=None):
             a = ax[i]
         else:
             raise ValueError("Need one ax per latent dimension input_dim")
-        a.plot(means, c='k', alpha=.3)
-        plots.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
-        a.fill_between(x,
+        bg_lines.append(a.plot(means, c='k', alpha=.3))
+        lines.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
+        fills.append(a.fill_between(x,
                         means.T[i] - 2 * np.sqrt(variances.T[i]),
                         means.T[i] + 2 * np.sqrt(variances.T[i]),
-                        facecolor=plots[-1].get_color(),
-                        alpha=.3)
+                        facecolor=lines[-1].get_color(),
+                        alpha=.3))
         a.legend(borderaxespad=0.)
         a.set_xlim(x.min(), x.max())
         if i < means.shape[1] - 1:
             a.set_xticklabels('')
     pb.draw()
     fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
-    return fig
+    return dict(lines=lines, fills=fills, bg_lines=bg_lines)
 
 def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_side=True):
     """