diff --git a/.travis.yml b/.travis.yml
index e7944d8a..6d188401 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -12,9 +12,10 @@ before_install:
   - sudo apt-get install -qq python-matplotlib
 
 install:
+  - pip install --upgrade numpy==1.7.1 
   - pip install sphinx 
   - pip install nose
   - pip install . --use-mirrors
 # command to run tests, e.g. python setup.py test
 script: 
-  - nosetests GPy/testing
\ No newline at end of file
+  - nosetests GPy/testing
diff --git a/GPy/core/model.py b/GPy/core/model.py
index f3542ce8..25c10b42 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -2,17 +2,19 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-import numpy as np
-from scipy import optimize
-import sys, pdb
-import multiprocessing as mp
-from GPy.util.misc import opt_wrapper
-#import numdifftools as ndt
-from parameterised import parameterised, truncate_pad
-import priors
-from ..util.linalg import jitchol
-from ..inference import optimization
 from .. import likelihoods
+from ..inference import optimization
+from ..util.linalg import jitchol
+from GPy.util.misc import opt_wrapper
+from parameterised import parameterised, truncate_pad
+from scipy import optimize
+import multiprocessing as mp
+import numpy as np
+import priors
+import re
+import sys
+import pdb
+# import numdifftools as ndt
 
 class model(parameterised):
     def __init__(self):
@@ -24,14 +26,14 @@ class model(parameterised):
         self.preferred_optimizer = 'tnc'
     def _get_params(self):
         raise NotImplementedError, "this needs to be implemented to use the model class"
-    def _set_params(self,x):
+    def _set_params(self, x):
         raise NotImplementedError, "this needs to be implemented to use the model class"
     def log_likelihood(self):
         raise NotImplementedError, "this needs to be implemented to use the model class"
     def _log_likelihood_gradients(self):
         raise NotImplementedError, "this needs to be implemented to use the model class"
 
-    def set_prior(self,which,what):
+    def set_prior(self, which, what):
         """
         Sets priors on the model parameters.
 
@@ -52,59 +54,59 @@ class model(parameterised):
 
         which = self.grep_param_names(which)
 
-        #check tied situation
-        tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie)==set(which))]
+        # check tied situation
+        tie_partial_matches = [tie for tie in self.tied_indices if (not set(tie).isdisjoint(set(which))) & (not set(tie) == set(which))]
         if len(tie_partial_matches):
             raise ValueError, "cannot place prior across partial ties"
-        tie_matches = [tie for tie in self.tied_indices if set(which)==set(tie) ]
-        if len(tie_matches)>1:
+        tie_matches = [tie for tie in self.tied_indices if set(which) == set(tie) ]
+        if len(tie_matches) > 1:
             raise ValueError, "cannot place prior across multiple ties"
-        elif len(tie_matches)==1:
-            which = which[:1]# just place a prior object on the first parameter
+        elif len(tie_matches) == 1:
+            which = which[:1]  # just place a prior object on the first parameter
 
 
-        #check constraints are okay
+        # check constraints are okay
         if isinstance(what, (priors.gamma, priors.log_Gaussian)):
-            assert not np.any(which[:,None]==self.constrained_negative_indices), "constraint and prior incompatible"
-            assert not np.any(which[:,None]==self.constrained_bounded_indices), "constraint and prior incompatible"
+            assert not np.any(which[:, None] == self.constrained_negative_indices), "constraint and prior incompatible"
+            assert not np.any(which[:, None] == self.constrained_bounded_indices), "constraint and prior incompatible"
             unconst = np.setdiff1d(which, self.constrained_positive_indices)
             if len(unconst):
                 print "Warning: constraining parameters to be positive:"
-                print '\n'.join([n for i,n in enumerate(self._get_param_names()) if i in unconst])
+                print '\n'.join([n for i, n in enumerate(self._get_param_names()) if i in unconst])
                 print '\n'
                 self.constrain_positive(unconst)
-        elif isinstance(what,priors.Gaussian):
-            assert not np.any(which[:,None]==self.all_constrained_indices()), "constraint and prior incompatible"
+        elif isinstance(what, priors.Gaussian):
+            assert not np.any(which[:, None] == self.all_constrained_indices()), "constraint and prior incompatible"
         else:
             raise ValueError, "prior not recognised"
 
 
-        #store the prior in a local list
+        # store the prior in a local list
         for w in which:
             self.priors[w] = what
 
-    def get_gradient(self,name, return_names=False):
+    def get_gradient(self, name, return_names=False):
         """
         Get model gradient(s) by name. The name is applied as a regular expression and all parameters that match that regular expression are returned.
         """
         matches = self.grep_param_names(name)
         if len(matches):
             if return_names:
-                return self._log_likelihood_gradients()[matches],  np.asarray(self._get_param_names())[matches].tolist()
+                return self._log_likelihood_gradients()[matches], np.asarray(self._get_param_names())[matches].tolist()
             else:
                 return self._log_likelihood_gradients()[matches]
         else:
-            raise AttributeError, "no parameter matches %s"%name
+            raise AttributeError, "no parameter matches %s" % name
 
     def log_prior(self):
         """evaluate the prior"""
-        return np.sum([p.lnpdf(x) for p, x in zip(self.priors,self._get_params()) if p is not None])
+        return np.sum([p.lnpdf(x) for p, x in zip(self.priors, self._get_params()) if p is not None])
 
     def _log_prior_gradients(self):
         """evaluate the gradients of the priors"""
         x = self._get_params()
         ret = np.zeros(x.size)
-        [np.put(ret,i,p.lnpdf_grad(xx)) for i,(p,xx) in enumerate(zip(self.priors,x)) if not p is None]
+        [np.put(ret, i, p.lnpdf_grad(xx)) for i, (p, xx) in enumerate(zip(self.priors, x)) if not p is None]
         return ret
 
     def _transform_gradients(self, g):
@@ -113,13 +115,13 @@ class model(parameterised):
         """
 
         x = self._get_params()
-        g[self.constrained_positive_indices] = g[self.constrained_positive_indices]*x[self.constrained_positive_indices]
-        g[self.constrained_negative_indices] = g[self.constrained_negative_indices]*x[self.constrained_negative_indices]
-        [np.put(g,i,g[i]*(x[i]-l)*(h-x[i])/(h-l)) for i,l,h in zip(self.constrained_bounded_indices, self.constrained_bounded_lowers, self.constrained_bounded_uppers)]
-        [np.put(g,i,v) for i,v in [(t[0],np.sum(g[t])) for t in self.tied_indices]]
+        g[self.constrained_positive_indices] = g[self.constrained_positive_indices] * x[self.constrained_positive_indices]
+        g[self.constrained_negative_indices] = g[self.constrained_negative_indices] * x[self.constrained_negative_indices]
+        [np.put(g, i, g[i] * (x[i] - l) * (h - x[i]) / (h - l)) for i, l, h in zip(self.constrained_bounded_indices, self.constrained_bounded_lowers, self.constrained_bounded_uppers)]
+        [np.put(g, i, v) for i, v in [(t[0], np.sum(g[t])) for t in self.tied_indices]]
         if len(self.tied_indices) or len(self.constrained_fixed_indices):
-            to_remove = np.hstack((self.constrained_fixed_indices+[t[1:] for t in self.tied_indices]))
-            return np.delete(g,to_remove)
+            to_remove = np.hstack((self.constrained_fixed_indices + [t[1:] for t in self.tied_indices]))
+            return np.delete(g, to_remove)
         else:
             return g
 
@@ -129,15 +131,15 @@ class model(parameterised):
         Randomize the model.
         Make this draw from the prior if one exists, else draw from N(0,1)
         """
-        #first take care of all parameters (from N(0,1))
+        # first take care of all parameters (from N(0,1))
         x = self._get_params_transformed()
         x = np.random.randn(x.size)
         self._set_params_transformed(x)
-        #now draw from prior where possible
+        # now draw from prior where possible
         x = self._get_params()
-        [np.put(x,i,p.rvs(1)) for i,p in enumerate(self.priors) if not p is None]
+        [np.put(x, i, p.rvs(1)) for i, p in enumerate(self.priors) if not p is None]
         self._set_params(x)
-        self._set_params_transformed(self._get_params_transformed())#makes sure all of the tied parameters get the same init (since there's only one prior object...)
+        self._set_params_transformed(self._get_params_transformed())  # makes sure all of the tied parameters get the same init (since there's only one prior object...)
 
 
     def optimize_restarts(self, Nrestarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
@@ -171,10 +173,10 @@ class model(parameterised):
                 pool = mp.Pool(processes=num_processes)
                 for i in range(Nrestarts):
                     self.randomize()
-                    job = pool.apply_async(opt_wrapper, args = (self,), kwds = kwargs)
+                    job = pool.apply_async(opt_wrapper, args=(self,), kwds=kwargs)
                     jobs.append(job)
 
-                pool.close() # signal that no more data coming in
+                pool.close()  # signal that no more data coming in
                 pool.join()  # wait for all the tasks to complete
             except KeyboardInterrupt:
                 print "Ctrl+c received, terminating and joining pool."
@@ -190,10 +192,10 @@ class model(parameterised):
                     self.optimization_runs.append(jobs[i].get())
 
                 if verbose:
-                    print("Optimization restart {0}/{1}, f = {2}".format(i+1, Nrestarts, self.optimization_runs[-1].f_opt))
+                    print("Optimization restart {0}/{1}, f = {2}".format(i + 1, Nrestarts, self.optimization_runs[-1].f_opt))
             except Exception as e:
                 if robust:
-                    print("Warning - optimization restart {0}/{1} failed".format(i+1, Nrestarts))
+                    print("Warning - optimization restart {0}/{1} failed".format(i + 1, Nrestarts))
                 else:
                     raise e
 
@@ -203,22 +205,22 @@ class model(parameterised):
         else:
             self._set_params_transformed(initial_parameters)
 
-    def ensure_default_constraints(self,warn=False):
+    def ensure_default_constraints(self, warn=False):
         """
         Ensure that any variables which should clearly be positive have been constrained somehow.
         """
-        positive_strings = ['variance','lengthscale', 'precision']
+        positive_strings = ['variance', 'lengthscale', 'precision']
         param_names = self._get_param_names()
         currently_constrained = self.all_constrained_indices()
         to_make_positive = []
         for s in positive_strings:
             for i in self.grep_param_names(s):
                 if not (i in currently_constrained):
-                    to_make_positive.append(param_names[i])
+                    to_make_positive.append(re.escape(param_names[i]))
                     if warn:
-                        print "Warning! constraining %s postive"%name
+                        print "Warning! constraining %s positive" % s
         if len(to_make_positive):
-            self.constrain_positive('('+'|'.join(to_make_positive)+')')
+            self.constrain_positive('(' + '|'.join(to_make_positive) + ')')
 
 
 
@@ -236,14 +238,14 @@ class model(parameterised):
         self._set_params_transformed(x)
         LL_gradients = self._transform_gradients(self._log_likelihood_gradients())
         prior_gradients = self._transform_gradients(self._log_prior_gradients())
-        return - LL_gradients - prior_gradients
+        return -LL_gradients - prior_gradients
 
     def objective_and_gradients(self, x):
         self._set_params_transformed(x)
-        obj_f =  -self.log_likelihood() - self.log_prior()
+        obj_f = -self.log_likelihood() - self.log_prior()
         LL_gradients = self._transform_gradients(self._log_likelihood_gradients())
         prior_gradients = self._transform_gradients(self._log_prior_gradients())
-        obj_grads = - LL_gradients - prior_gradients
+        obj_grads = -LL_gradients - prior_gradients
         return obj_f, obj_grads
 
     def optimize(self, optimizer=None, start=None, **kwargs):
@@ -253,7 +255,7 @@ class model(parameterised):
 
         :max_f_eval: maximum number of function evaluations
         :messages: whether to display during optimisation
-        :param optimzer: whice optimizer to use (defaults to self.preferred optimizer)
+        :param optimzer: which optimizer to use (defaults to self.preferred optimizer)
         :type optimzer: string TODO: valid strings?
         """
         if optimizer is None:
@@ -269,7 +271,7 @@ class model(parameterised):
 
         self._set_params_transformed(opt.x_opt)
 
-    def optimize_SGD(self, momentum = 0.1, learning_rate = 0.01, iterations = 20, **kwargs):
+    def optimize_SGD(self, momentum=0.1, learning_rate=0.01, iterations=20, **kwargs):
         # assert self.Y.shape[1] > 1, "SGD only works with D > 1"
         sgd = SGD.StochasticGD(self, iterations, learning_rate, momentum, **kwargs)
         sgd.run()
@@ -277,8 +279,8 @@ class model(parameterised):
 
     def Laplace_covariance(self):
         """return the covariance matric of a Laplace approximatino at the current (stationary) point"""
-        #TODO add in the prior contributions for MAP estimation
-        #TODO fix the hessian for tied, constrained and fixed components
+        # TODO add in the prior contributions for MAP estimation
+        # TODO fix the hessian for tied, constrained and fixed components
         if hasattr(self, 'log_likelihood_hessian'):
             A = -self.log_likelihood_hessian()
 
@@ -292,8 +294,8 @@ class model(parameterised):
             A = -h(x)
             self._set_params(x)
         # check for almost zero components on the diagonal which screw up the cholesky
-        aa = np.nonzero((np.diag(A)<1e-6) & (np.diag(A)>0.))[0]
-        A[aa,aa] = 0.
+        aa = np.nonzero((np.diag(A) < 1e-6) & (np.diag(A) > 0.))[0]
+        A[aa, aa] = 0.
         return A
 
     def Laplace_evidence(self):
@@ -304,11 +306,11 @@ class model(parameterised):
             hld = np.sum(np.log(np.diag(jitchol(A)[0])))
         except:
             return np.nan
-        return 0.5*self._get_params().size*np.log(2*np.pi) + self.log_likelihood() - hld
+        return 0.5 * self._get_params().size * np.log(2 * np.pi) + self.log_likelihood() - hld
 
     def __str__(self):
         s = parameterised.__str__(self).split('\n')
-        #add priors to the string
+        # add priors to the string
         strs = [str(p) if p is not None else '' for p in self.priors]
         width = np.array(max([len(p) for p in strs] + [5])) + 4
 
@@ -319,16 +321,16 @@ class model(parameterised):
             obj_funct += ', Log prior: {0:.3e}, LL+prior = {0:.3e}'.format(log_prior, log_like + log_prior)
         obj_funct += '\n\n'
         s[0] = obj_funct + s[0]
-        s[0] += "|{h:^{col}}".format(h = 'Prior', col = width)
-        s[1] += '-'*(width + 1)
+        s[0] += "|{h:^{col}}".format(h='Prior', col=width)
+        s[1] += '-' * (width + 1)
 
-        for p in range(2, len(strs)+2):
-            s[p] += '|{prior:^{width}}'.format(prior = strs[p-2], width = width)
+        for p in range(2, len(strs) + 2):
+            s[p] += '|{prior:^{width}}'.format(prior=strs[p - 2], width=width)
 
         return '\n'.join(s)
 
 
-    def checkgrad(self, target_param = None, verbose=False, step=1e-6, tolerance = 1e-3):
+    def checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):
         """
         Check the gradient of the model by comparing to a numerical estimate.
         If the verbose flag is passed, invividual components are tested (and printed)
@@ -348,27 +350,27 @@ class model(parameterised):
         x = self._get_params_transformed().copy()
 
         if not verbose:
-            #just check the global ratio
-            dx = step*np.sign(np.random.uniform(-1,1,x.size))
+            # just check the global ratio
+            dx = step * np.sign(np.random.uniform(-1, 1, x.size))
 
-            #evaulate around the point x
-            f1, g1 = self.objective_and_gradients(x+dx)
-            f2, g2 = self.objective_and_gradients(x-dx)
+            # evaulate around the point x
+            f1, g1 = self.objective_and_gradients(x + dx)
+            f2, g2 = self.objective_and_gradients(x - dx)
             gradient = self.objective_function_gradients(x)
 
-            numerical_gradient = (f1-f2)/(2*dx)
-            global_ratio = (f1-f2)/(2*np.dot(dx,gradient))
+            numerical_gradient = (f1 - f2) / (2 * dx)
+            global_ratio = (f1 - f2) / (2 * np.dot(dx, gradient))
 
-            if (np.abs(1.-global_ratio)<tolerance) and not np.isnan(global_ratio):
+            if (np.abs(1. - global_ratio) < tolerance) and not np.isnan(global_ratio):
                 return True
             else:
                 return False
         else:
-            #check the gradient of each parameter individually, and do some pretty printing
+            # check the gradient of each parameter individually, and do some pretty printing
             try:
                 names = self._get_param_names_transformed()
             except NotImplementedError:
-                names = ['Variable %i'%i for i in range(len(x))]
+                names = ['Variable %i' % i for i in range(len(x))]
 
             # Prepare for pretty-printing
             header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical']
@@ -377,9 +379,9 @@ class model(parameterised):
             cols = [max_names]
             cols.extend([max(float_len, len(header[i])) for i in range(1, len(header))])
             cols = np.array(cols) + 5
-            header_string = ["{h:^{col}}".format(h = header[i], col = cols[i]) for i in range(len(cols))]
+            header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
             header_string = map(lambda x: '|'.join(x), [header_string])
-            separator = '-'*len(header_string[0])
+            separator = '-' * len(header_string[0])
             print '\n'.join([header_string[0], separator])
 
             if target_param is None:
@@ -395,11 +397,11 @@ class model(parameterised):
                 f2, g2 = self.objective_and_gradients(xx)
                 gradient = self.objective_function_gradients(x)[i]
 
-                numerical_gradient = (f1-f2)/(2*step)
-                ratio = (f1-f2)/(2*step*gradient)
-                difference = np.abs((f1-f2)/2/step - gradient)
+                numerical_gradient = (f1 - f2) / (2 * step)
+                ratio = (f1 - f2) / (2 * step * gradient)
+                difference = np.abs((f1 - f2) / 2 / step - gradient)
 
-                if (np.abs(ratio-1)<tolerance):
+                if (np.abs(ratio - 1) < tolerance):
                     formatted_name = "\033[92m {0} \033[0m".format(names[i])
                 else:
                     formatted_name = "\033[91m {0} \033[0m".format(names[i])
@@ -407,7 +409,7 @@ class model(parameterised):
                 d = '%.6f' % float(difference)
                 g = '%.6f' % gradient
                 ng = '%.6f' % float(numerical_gradient)
-                grad_string = "{0:^{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name,r,d,g, ng, c0 = cols[0]+9, c1 = cols[1], c2 = cols[2], c3 = cols[3], c4 = cols[4])
+                grad_string = "{0:^{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
                 print grad_string
 
     def input_sensitivity(self):
@@ -418,21 +420,21 @@ class model(parameterised):
         TODO: proper sensitivity analysis
         """
 
-        if not hasattr(self,'kern'):
+        if not hasattr(self, 'kern'):
             raise ValueError, "this model has no kernel"
 
-        k = [p for p in self.kern.parts if p.name in ['rbf','linear']]
-        if (not len(k)==1) or (not k[0].ARD):
+        k = [p for p in self.kern.parts if p.name in ['rbf', 'linear']]
+        if (not len(k) == 1) or (not k[0].ARD):
             raise ValueError, "cannot determine sensitivity for this kernel"
         k = k[0]
 
-        if k.name=='rbf':
+        if k.name == 'rbf':
             return k.lengthscale
-        elif k.name=='linear':
-            return 1./k.variances
+        elif k.name == 'linear':
+            return 1. / k.variances
 
 
-    def pseudo_EM(self,epsilon=.1,**kwargs):
+    def pseudo_EM(self, epsilon=.1, **kwargs):
         """
         TODO: Should this not bein the GP class?
         EM - like algorithm  for Expectation Propagation and Laplace approximation
@@ -446,7 +448,7 @@ class model(parameterised):
         :type optimzer: string TODO: valid strings?
 
         """
-        assert isinstance(self.likelihood,likelihoods.EP), "EPEM is only available for EP likelihoods"
+        assert isinstance(self.likelihood, likelihoods.EP), "EPEM is only available for EP likelihoods"
         ll_change = epsilon + 1.
         iteration = 0
         last_ll = -np.exp(1000)
@@ -466,9 +468,9 @@ class model(parameterised):
             ll_change = new_ll - last_ll
 
             if ll_change < 0:
-                self.likelihood = last_approximation #restore previous likelihood approximation
-                self._set_params(last_params) #restore model parameters
-                print "Log-likelihood decrement: %s \nLast likelihood update discarded." %ll_change
+                self.likelihood = last_approximation  # restore previous likelihood approximation
+                self._set_params(last_params)  # restore model parameters
+                print "Log-likelihood decrement: %s \nLast likelihood update discarded." % ll_change
                 stop = True
             else:
                 self.optimize(**kwargs)
@@ -477,5 +479,5 @@ class model(parameterised):
                     stop = True
             iteration += 1
             if stop:
-                print "%s iterations." %iteration
+                print "%s iterations." % iteration
 
diff --git a/GPy/core/parameterised.py b/GPy/core/parameterised.py
index 4d1d6992..9d0dfc78 100644
--- a/GPy/core/parameterised.py
+++ b/GPy/core/parameterised.py
@@ -171,10 +171,18 @@ class parameterised(object):
             return expr
 
     def Nparam_transformed(self):
-            ties = 0
-            for ar in self.tied_indices:
-                ties += ar.size - 1
-            return self.Nparam - len(self.constrained_fixed_indices) - ties
+        """
+        Compute the number of parameters after ties and fixing have been performed
+        """
+        ties = 0
+        for ti in self.tied_indices:
+            ties += ti.size - 1
+
+        fixes = 0
+        for fi in self.constrained_fixed_indices:
+            fixes += len(fi)
+
+        return self.Nparam - fixes - ties
 
     def constrain_positive(self, which):
         """
diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index b17628ed..7d7d5fdd 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -81,11 +81,19 @@ def BGPLVM_oil(optimize=True, N=100, Q=10, M=15, max_f_eval=300):
     else:
         m.ensure_default_constraints()
 
-    # plot
-    print(m)
-    m.plot_latent(labels=m.data_labels)
-    pb.figure()
-    pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
+    y = m.likelihood.Y[0, :]
+    fig,(latent_axes,hist_axes) = plt.subplots(1,2)
+    plt.sca(latent_axes)
+    m.plot_latent()
+    data_show = GPy.util.visualize.vector_show(y)
+    lvm_visualizer = GPy.util.visualize.lvm_dimselect(m.X[0, :], m, data_show, latent_axes=latent_axes, hist_axes=hist_axes)
+    raw_input('Press enter to finish')
+    plt.close('all')
+    # # plot
+    # print(m)
+    # m.plot_latent(labels=m.data_labels)
+    # pb.figure()
+    # pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
     return m
 
 def oil_100():
@@ -348,7 +356,7 @@ def brendan_faces():
     ax = m.plot_latent()
     y = m.likelihood.Y[0, :]
     data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
-    lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
+    lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
     raw_input('Press enter to finish')
     plt.close('all')
 
@@ -365,7 +373,29 @@ def stick():
     ax = m.plot_latent()
     y = m.likelihood.Y[0, :]
     data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
-    lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
+    lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
+    raw_input('Press enter to finish')
+    plt.close('all')
+
+    return m
+
+def cmu_mocap(subject='35', motion=['01'], in_place=True):
+
+    data = GPy.util.datasets.cmu_mocap(subject, motion)
+    Y = data['Y']
+    if in_place:
+        # Make figure move in place.
+        data['Y'][:, 0:3]=0.0
+    m = GPy.models.GPLVM(data['Y'], 2, normalize_Y=True)
+
+    # optimize
+    m.ensure_default_constraints()
+    m.optimize(messages=1, max_f_eval=10000)
+
+    ax = m.plot_latent()
+    y = m.likelihood.Y[0, :]
+    data_show = GPy.util.visualize.skeleton_show(y[None, :], data['skel'])
+    lvm_visualizer = GPy.util.visualize.lvm(m.X[0, :], m, data_show, ax)
     raw_input('Press enter to finish')
     plt.close('all')
 
diff --git a/GPy/models/GPLVM.py b/GPy/models/GPLVM.py
index c0d9429a..157fe1c3 100644
--- a/GPy/models/GPLVM.py
+++ b/GPy/models/GPLVM.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+### Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
@@ -91,8 +91,8 @@ class GPLVM(GP):
         Xtest_full[:, :2] = Xtest
         mu, var, low, up = self.predict(Xtest_full)
         var = var[:, :1]
-        ax.imshow(var.reshape(resolution, resolution).T[::-1, :],
-                  extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear')
+        ax.imshow(var.reshape(resolution, resolution).T,
+                  extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear',origin='lower')
 
         for i,ul in enumerate(np.unique(labels)):
             if type(ul) is np.string_:
diff --git a/GPy/util/mocap.py b/GPy/util/mocap.py
index 76650086..174728bd 100644
--- a/GPy/util/mocap.py
+++ b/GPy/util/mocap.py
@@ -532,7 +532,6 @@ class acclaim_skeleton(skeleton):
                 self.vertices[0].meta['orientation'] =  [float(parts[1]),
                                              float(parts[2]),
                                              float(parts[3])]
-                print self.vertices[0].meta['orientation']
             lin = self.read_line(fid)
         return lin
     
diff --git a/GPy/util/visualize.py b/GPy/util/visualize.py
index 9754db63..bd5f112f 100644
--- a/GPy/util/visualize.py
+++ b/GPy/util/visualize.py
@@ -3,121 +3,7 @@ from mpl_toolkits.mplot3d import Axes3D
 import GPy
 import numpy as np
 import matplotlib as mpl
-
-class lvm:
-    def __init__(self, model, data_visualize, latent_axes, latent_index=[0,1]):
-        if isinstance(latent_axes,mpl.axes.Axes):
-            self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
-            self.cid = latent_axes.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
-            self.cid = latent_axes.figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
-            self.cid = latent_axes.figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
-        else:
-            self.cid = latent_axes[0].figure.canvas.mpl_connect('button_press_event', self.on_click)
-            self.cid = latent_axes[0].figure.canvas.mpl_connect('motion_notify_event', self.on_move)
-            self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
-            self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
-        self.data_visualize = data_visualize
-        self.model = model
-        self.latent_axes = latent_axes
-
-        self.called = False
-        self.move_on = False
-        self.latent_index = latent_index
-        self.latent_dim = model.Q
-
-    def on_enter(self,event):
-        pass
-    def on_leave(self,event):
-        pass
-
-    def on_click(self, event):
-        #print 'click', event.xdata, event.ydata
-        if event.inaxes!=self.latent_axes: return
-        self.move_on = not self.move_on
-        # if self.called:
-        #     self.xs.append(event.xdata)
-        #     self.ys.append(event.ydata)
-        #     self.line.set_data(self.xs, self.ys)
-        #     self.line.figure.canvas.draw()
-        # else:
-        #     self.xs = [event.xdata]
-        #     self.ys = [event.ydata]
-        #     self.line, = self.latent_axes.plot(event.xdata, event.ydata)
-        self.called = True
-    def on_move(self, event):
-        if event.inaxes!=self.latent_axes: return
-        if self.called and self.move_on:
-            # Call modify code on move
-            #print 'move', event.xdata, event.ydata
-            latent_values = np.zeros((1,self.latent_dim))
-            latent_values[0,self.latent_index] = np.array([event.xdata, event.ydata])
-            y = self.model.predict(latent_values)[0]
-            self.data_visualize.modify(y)
-            #print 'y', y
-
-class lvm_subplots(lvm):
-    """
-    latent_axes is a np array of dimension np.ceil(Q/2) + 1,
-    one for each pair of the axes, and the last one for the sensitiity histogram
-    """
-    def __init__(self, model, data_visualize, latent_axes=None, latent_index=[0,1]):
-        self.nplots = int(np.ceil(model.Q/2.))+1
-        lvm.__init__(self,model,data_visualize,latent_axes,latent_index)
-        self.latent_values = np.zeros(2*np.ceil(self.model.Q/2.)) # possibly an extra dimension on this
-        assert latent_axes.size == self.nplots
-
-
-class lvm_dimselect(lvm):
-    """
-    A visualizer for latent variable models
-    with selection by clicking on the histogram
-    """
-    def __init__(self, model, data_visualize):
-        self.fig,(latent_axes,self.hist_axes) = plt.subplots(1,2)
-
-        lvm.__init__(self,model,data_visualize,latent_axes,[0,1])
-        self.latent_values_clicked = np.zeros(model.Q)
-        self.clicked_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
-        print "use left and right mouse butons to select dimensions"
-
-    def on_click(self, event):
-        #print "click"
-        if event.inaxes==self.hist_axes:
-            self.hist_axes.cla()
-            self.hist_axes.bar(np.arange(self.model.Q),1./self.model.input_sensitivity(),color='b')
-            new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.Q-1))
-            self.latent_index[(0 if event.button==1 else 1)] = new_index
-            self.hist_axes.bar(np.array(self.latent_index),1./self.model.input_sensitivity()[self.latent_index],color='r')
-            self.latent_axes.cla()
-            self.model.plot_latent(which_indices = self.latent_index,ax=self.latent_axes)
-            self.clicked_handle = self.latent_axes.plot([self.latent_values_clicked[self.latent_index[0]]],self.latent_values_clicked[self.latent_index[1]],'rx',mew=2)[0]
-        if event.inaxes==self.latent_axes:
-            self.clicked_handle.set_visible(False)
-            self.latent_values_clicked[self.latent_index] = np.array([event.xdata,event.ydata])
-            self.clicked_handle = self.latent_axes.plot([self.latent_values_clicked[self.latent_index[0]]],self.latent_values_clicked[self.latent_index[1]],'rx',mew=2)[0]
-        self.fig.canvas.draw()
-        self.move_on=True
-        self.called = True
-
-
-    def on_move(self, event):
-        #print "move"
-        if event.inaxes!=self.latent_axes: return
-        if self.called and self.move_on:
-            latent_values = self.latent_values_clicked.copy()
-            latent_values[self.latent_index] = np.array([event.xdata, event.ydata])
-            y = self.model.predict(latent_values[None,:])[0]
-            self.data_visualize.modify(y)
-
-    def on_leave(self,event):
-        latent_values = self.latent_values_clicked.copy()
-        y = self.model.predict(latent_values[None,:])[0]
-        self.data_visualize.modify(y)
-
-
-
-
-
+import time
 
 class data_show:
     """
@@ -155,6 +41,160 @@ class vector_show(data_show):
         self.handle.set_data(xdata, self.vals)
         self.axes.figure.canvas.draw()
 
+
+class lvm(data_show):
+    def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]):
+        """Visualize a latent variable model
+
+        :param model: the latent variable model to visualize.
+        :param data_visualize: the object used to visualize the data which has been modelled.
+        :type data_visualize: visualize.data_show  type.
+        :param latent_axes: the axes where the latent visualization should be plotted.
+        """
+        if vals == None:
+            vals = model.X[0]
+
+        data_show.__init__(self, vals, axes=latent_axes)
+
+        if isinstance(latent_axes,mpl.axes.Axes):
+            self.cid = latent_axes.figure.canvas.mpl_connect('button_press_event', self.on_click)
+            self.cid = latent_axes.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
+            self.cid = latent_axes.figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
+            self.cid = latent_axes.figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
+        else:
+            self.cid = latent_axes[0].figure.canvas.mpl_connect('button_press_event', self.on_click)
+            self.cid = latent_axes[0].figure.canvas.mpl_connect('motion_notify_event', self.on_move)
+            self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_leave_event', self.on_leave)
+            self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter)
+
+        self.data_visualize = data_visualize
+        self.model = model
+        self.latent_axes = latent_axes
+
+        self.called = False
+        self.move_on = False
+        self.latent_index = latent_index
+        self.latent_dim = model.Q
+
+        # The red cross which shows current latent point.        
+        self.latent_values = vals
+        self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
+        self.modify(vals)
+
+    def modify(self, vals):
+        """When latent values are modified update the latent representation and ulso update the output visualization."""
+        
+        y = self.model.predict(vals)[0]
+        self.data_visualize.modify(y)
+        self.latent_handle.set_data(vals[self.latent_index[0]], vals[self.latent_index[1]])
+        self.axes.figure.canvas.draw()
+
+
+    def on_enter(self,event):
+        pass
+    def on_leave(self,event):
+        pass
+
+    def on_click(self, event):
+        if event.inaxes!=self.latent_axes: return
+        self.move_on = not self.move_on
+        self.called = True
+    def on_move(self, event):
+        if event.inaxes!=self.latent_axes: return
+        if self.called and self.move_on:
+            # Call modify code on move
+            self.latent_values[self.latent_index[0]]=event.xdata
+            self.latent_values[self.latent_index[1]]=event.ydata            
+            self.modify(self.latent_values)
+
+class lvm_subplots(lvm):
+    """
+    latent_axes is a np array of dimension np.ceil(Q/2) + 1,
+    one for each pair of the axes, and the last one for the sensitiity bar chart
+    """
+    def __init__(self, vals, model, data_visualize, latent_axes=None, latent_index=[0,1]):
+        lvm.__init__(self, vals, model,data_visualize,latent_axes,[0,1])
+        self.nplots = int(np.ceil(model.Q/2.))+1
+        lvm.__init__(self,model,data_visualize,latent_axes,latent_index)
+        self.latent_values = np.zeros(2*np.ceil(self.model.Q/2.)) # possibly an extra dimension on this
+        assert latent_axes.size == self.nplots
+
+
+class lvm_dimselect(lvm):
+    """
+    A visualizer for latent variable models which allows selection of the latent dimensions to use by clicking on a bar chart of their length scales.
+    """
+    def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1]):
+        if latent_axes==None and sense_axes==None:
+            self.fig,(latent_axes,self.sense_axes) = plt.subplots(1,2)
+        elif sense_axes==None:
+            fig=plt.figure()
+            self.sense_axes = fig.add_subplot(111)
+        else:
+            self.sense_axes = sense_axes
+        
+        lvm.__init__(self,vals,model,data_visualize,latent_axes,latent_index)
+        self.show_sensitivities()
+        print "use left and right mouse butons to select dimensions"
+
+    def show_sensitivities(self):
+        # A click in the bar chart axis for selection a dimension.
+        self.sense_axes.cla()
+        self.sense_axes.bar(np.arange(self.model.Q),1./self.model.input_sensitivity(),color='b')
+
+        if self.latent_index[1] == self.latent_index[0]:
+            self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='y')
+            self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='y')
+            
+        else:
+            self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='g')
+            self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='r')
+
+        self.sense_axes.figure.canvas.draw()
+
+    def on_click(self, event):
+
+        if event.inaxes==self.sense_axes:
+            new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.Q-1))
+            if event.button == 1:
+                # Make it red if and y-axis (red=port=left) if it is a left button click
+                self.latent_index[1] = new_index                
+            else:
+                # Make it green and x-axis (green=starboard=right) if it is a right button click
+                self.latent_index[0] = new_index
+                
+            self.show_sensitivities()
+
+            self.latent_axes.cla()
+            self.model.plot_latent(which_indices=self.latent_index,
+                                   ax=self.latent_axes)
+            self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0]
+            self.modify(self.latent_values)
+
+        elif event.inaxes==self.latent_axes:
+            self.move_on = not self.move_on
+
+        self.called = True
+
+
+    def on_move(self, event):
+        if event.inaxes!=self.latent_axes: return
+        if self.called and self.move_on:
+            self.latent_values[self.latent_index[0]]=event.xdata
+            self.latent_values[self.latent_index[1]]=event.ydata            
+            self.modify(self.latent_values)
+
+    def on_leave(self,event):
+        latent_values = self.latent_values.copy()
+        y = self.model.predict(latent_values[None,:])[0]
+        self.data_visualize.modify(y)
+
+
+
+
+
+
+
 class image_show(data_show):
     """Show a data vector as an image."""
     def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False):
@@ -269,12 +309,24 @@ class stick_show(mocap_data_show):
 class skeleton_show(mocap_data_show):
     """data_show class for visualizing motion capture data encoded as a skeleton with angles."""
     def __init__(self, vals, skel, padding=0, axes=None):
+        """data_show class for visualizing motion capture data encoded as a skeleton with angles.
+        :param vals: set of modeled angles to use for printing in the axis when it's first created.
+        :type vals: np.array
+        :param skel: skeleton object that has the parameters of the motion capture skeleton associated with it.
+        :type skel: mocap.skeleton object 
+        :param padding:
+        :type int
+        """
         self.skel = skel
         self.padding = padding
         connect = skel.connection_matrix()
         mocap_data_show.__init__(self, vals, axes, connect)
 
     def process_values(self, vals):
+        """Takes a set of angles and converts them to the x,y,z coordinates in the internal prepresentation of the class, ready for plotting.
+
+        :param vals: the values that are being modelled."""
+        
         if self.padding>0:
             channels = np.zeros((vals.shape[0], vals.shape[1]+self.padding))
             channels[:, 0:vals.shape[0]] = vals
@@ -296,3 +348,27 @@ class skeleton_show(mocap_data_show):
                 if nVals[i] != nVals[j]:
                     connect[i, j] = False
         return vals, connect
+
+
+def data_play(Y, visualizer, frame_rate=30):
+    """Play a data set using the data_show object given.
+
+    :Y: the data set to be visualized.
+    :param visualizer: the data show objectwhether to display during optimisation
+    :type visualizer: data_show
+
+    Example usage:
+
+    This example loads in the CMU mocap database (http://mocap.cs.cmu.edu) subject number 35 motion number 01. It then plays it using the mocap_show visualize object.
+    
+    data = GPy.util.datasets.cmu_mocap(subject='35', train_motions=['01'])
+    Y = data['Y']
+    Y[:, 0:3] = 0.   # Make figure walk in place
+    visualize = GPy.util.visualize.skeleton_show(Y[0, :], data['skel'])
+    GPy.util.visualize.data_play(Y, visualize)
+    """
+    
+
+    for y in Y:
+        visualizer.modify(y)
+        time.sleep(1./float(frame_rate))