From 11f784d47275733debaa0e2cc5c6fb869dfa972f Mon Sep 17 00:00:00 2001
From: Ricardo <acq11ra@sheffield.ac.uk>
Date: Tue, 28 Jan 2014 13:40:24 +0000
Subject: [PATCH] Plotting functions modified

---
 GPy/core/parameterization/priors.py      | 24 ++++--------
 GPy/core/parameterization/variational.py | 47 +++---------------------
 2 files changed, 13 insertions(+), 58 deletions(-)

diff --git a/GPy/core/parameterization/priors.py b/GPy/core/parameterization/priors.py
index 9614ca53..f1208f18 100644
--- a/GPy/core/parameterization/priors.py
+++ b/GPy/core/parameterization/priors.py
@@ -3,7 +3,6 @@
 
 
 import numpy as np
-import pylab as pb
 from scipy.special import gammaln, digamma
 from ...util.linalg import pdinv
 from domains import _REAL, _POSITIVE
@@ -12,16 +11,14 @@ import weakref
 
 class Prior:
     domain = None
-    
+
     def pdf(self, x):
         return np.exp(self.lnpdf(x))
 
     def plot(self):
-        rvs = self.rvs(1000)
-        pb.hist(rvs, 100, normed=True)
-        xmin, xmax = pb.xlim()
-        xx = np.linspace(xmin, xmax, 1000)
-        pb.plot(xx, self.pdf(xx), 'r', linewidth=2)
+        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
+        from ..plotting.matplot_dep import priors_plots
+        priors_plots.univariate_plot(self)
 
 
 class Gaussian(Prior):
@@ -153,16 +150,9 @@ class MultivariateGaussian:
         return np.random.multivariate_normal(self.mu, self.var, n)
 
     def plot(self):
-        if self.input_dim == 2:
-            rvs = self.rvs(200)
-            pb.plot(rvs[:, 0], rvs[:, 1], 'kx', mew=1.5)
-            xmin, xmax = pb.xlim()
-            ymin, ymax = pb.ylim()
-            xx, yy = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
-            xflat = np.vstack((xx.flatten(), yy.flatten())).T
-            zz = self.pdf(xflat).reshape(100, 100)
-            pb.contour(xx, yy, zz, linewidths=2)
-
+        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
+        from ..plotting.matplot_dep import priors_plots
+        priors_plots.multivariate_plot(self)
 
 def gamma_from_EV(E, V):
     warnings.warn("use Gamma.from_EV to create Gamma Prior", FutureWarning)
diff --git a/GPy/core/parameterization/variational.py b/GPy/core/parameterization/variational.py
index 25718fbf..e9868b82 100644
--- a/GPy/core/parameterization/variational.py
+++ b/GPy/core/parameterization/variational.py
@@ -11,7 +11,7 @@ from ...util.misc import param_to_array
 class Normal(Parameterized):
     '''
     Normal distribution for variational approximations.
-    
+
     holds the means and variances for a factorizing multivariate normal distribution
     '''
     def __init__(self, means, variances, name='latent space'):
@@ -20,47 +20,12 @@ class Normal(Parameterized):
         self.variances = Param('variance', variances)
         self.add_parameters(self.means, self.variances)
 
-    def plot(self, fignum=None, ax=None, colors=None):
+    def plot(self, *args):
         """
         Plot latent space X in 1D:
 
-            - if fig is given, create input_dim subplots in fig and plot in these
-            - if ax is given plot input_dim 1D latent space plots of X into each `axis`
-            - if neither fig nor ax is given create a figure with fignum and plot in there
-
-        colors:
-            colors of different latent space dimensions input_dim
-
+        See  GPy.plotting.matplot_dep.variational_plots
         """
-        import pylab
-        if ax is None:
-            fig = pylab.figure(num=fignum, figsize=(8, min(12, (2 * self.means.shape[1]))))
-        if colors is None:
-            colors = pylab.gca()._get_lines.color_cycle
-            pylab.clf()
-        else:
-            colors = iter(colors)
-        plots = []
-        means, variances = param_to_array(self.means, self.variances)
-        x = np.arange(means.shape[0])
-        for i in range(means.shape[1]):
-            if ax is None:
-                a = fig.add_subplot(means.shape[1], 1, i + 1)
-            elif isinstance(ax, (tuple, list)):
-                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,
-                            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)
-            a.legend(borderaxespad=0.)
-            a.set_xlim(x.min(), x.max())
-            if i < means.shape[1] - 1:
-                a.set_xticklabels('')
-        pylab.draw()
-        fig.tight_layout(h_pad=.01) # , rect=(0, 0, 1, .95))
-        return fig
+        assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
+        from ..plotting.matplot_dep import variational_plots
+        return variational_plots.plot(self,*args)