diff --git a/GPy/core/model.py b/GPy/core/model.py
index 0804f277..9216aea6 100644
--- a/GPy/core/model.py
+++ b/GPy/core/model.py
@@ -23,13 +23,13 @@ class model(parameterised):
         self._set_params(self._get_params())
         self.preferred_optimizer = 'tnc'
     def _get_params(self):
-        raise NotImplementedError, "this needs to be implemented to utilise the model class"
+        raise NotImplementedError, "this needs to be implemented to use the model class"
     def _set_params(self,x):
-        raise NotImplementedError, "this needs to be implemented to utilise the model class"
+        raise NotImplementedError, "this needs to be implemented to use the model class"
     def log_likelihood(self):
-        raise NotImplementedError, "this needs to be implemented to utilise the model class"
+        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 utilise the model class"
+        raise NotImplementedError, "this needs to be implemented to use the model class"
 
     def set_prior(self,which,what):
         """
diff --git a/GPy/examples/dimensionality_reduction.py b/GPy/examples/dimensionality_reduction.py
index d7610acb..e4308465 100644
--- a/GPy/examples/dimensionality_reduction.py
+++ b/GPy/examples/dimensionality_reduction.py
@@ -3,6 +3,8 @@
 
 import numpy as np
 import pylab as pb
+from matplotlib import pyplot as plt
+
 import GPy
 
 default_seed = np.random.seed(123344)
@@ -55,3 +57,51 @@ def GPLVM_oil_100():
     print(m)
     m.plot_latent(labels=data['Y'].argmax(axis=1))
     return m
+
+def oil_100():
+    data = GPy.util.datasets.oil_100()
+    m = GPy.models.GPLVM(data['X'], 2)
+
+    # optimize
+    m.ensure_default_constraints()
+    m.optimize(messages=1, max_iters=2)
+
+    # plot
+    print(m)
+    #m.plot_latent(labels=data['Y'].argmax(axis=1))
+    return m
+
+def brendan_faces():
+    data = GPy.util.datasets.brendan_faces()
+    Y = data['Y'][0:-1:10, :]
+    m = GPy.models.GPLVM(data['Y'], 2)
+
+    # 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.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
+    lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
+    raw_input('Press enter to finish')
+    plt.close('all')
+
+    return m
+
+def stick():
+    data = GPy.util.datasets.stick()
+    m = GPy.models.GPLVM(data['Y'], 2)
+    
+    # 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.stick_show(y[None, :], connect=data['connect'])
+    lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
+    raw_input('Press enter to finish')
+    plt.close('all')
+
+    return m
diff --git a/GPy/examples/regression.py b/GPy/examples/regression.py
index 7de95d20..1a35df2f 100644
--- a/GPy/examples/regression.py
+++ b/GPy/examples/regression.py
@@ -73,7 +73,7 @@ def silhouette():
 
 def coregionalisation_toy2():
     """
-    A simple demonstration of coregionalisation on two sinusoidal functions
+    A simple demonstration of coregionalisation on two sinusoidal functions.
     """
     X1 = np.random.rand(50,1)*8
     X2 = np.random.rand(30,1)*5
@@ -106,7 +106,7 @@ def coregionalisation_toy2():
 
 def coregionalisation_toy():
     """
-    A simple demonstration of coregionalisation on two sinusoidal functions
+    A simple demonstration of coregionalisation on two sinusoidal functions.
     """
     X1 = np.random.rand(50,1)*8
     X2 = np.random.rand(30,1)*5
@@ -139,7 +139,7 @@ def coregionalisation_toy():
 
 def coregionalisation_sparse():
     """
-    A simple demonstration of coregionalisation on two sinusoidal functions
+    A simple demonstration of coregionalisation on two sinusoidal functions using sparse approximations.
     """
     X1 = np.random.rand(500,1)*8
     X2 = np.random.rand(300,1)*5
diff --git a/GPy/kern/__init__.py b/GPy/kern/__init__.py
index 5d8a7d15..f062ee56 100644
--- a/GPy/kern/__init__.py
+++ b/GPy/kern/__init__.py
@@ -2,5 +2,5 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, rbf_sympy, sympykern, periodic_exponential, periodic_Matern32, periodic_Matern52, prod, prod_orthogonal, symmetric, coregionalise, rational_quadratic, fixed
+from constructors import rbf, Matern32, Matern52, exponential, linear, white, bias, finite_dimensional, spline, Brownian, rbf_sympy, sympykern, periodic_exponential, periodic_Matern32, periodic_Matern52, prod, prod_orthogonal, symmetric, coregionalise, rational_quadratic, fixed, rbfcos
 from kern import kern
diff --git a/GPy/kern/constructors.py b/GPy/kern/constructors.py
index 90b13600..6a968da4 100644
--- a/GPy/kern/constructors.py
+++ b/GPy/kern/constructors.py
@@ -24,6 +24,7 @@ from prod_orthogonal import prod_orthogonal as prod_orthogonalpart
 from symmetric import symmetric as symmetric_part
 from coregionalise import coregionalise as coregionalise_part
 from rational_quadratic import rational_quadratic as rational_quadraticpart
+from rbfcos import rbfcos as rbfcospart
 #TODO these s=constructors are not as clean as we'd like. Tidy the code up
 #using meta-classes to make the objects construct properly wthout them.
 
@@ -310,3 +311,10 @@ def fixed(D, K, variance=1.):
     """
     part = fixedpart(D, K, variance)
     return kern(D, [part])
+
+def rbfcos(D,variance=1.,frequencies=None,bandwidths=None,ARD=False):
+    """
+    construct a rbfcos kernel
+    """
+    part = rbfcospart(D,variance,frequencies,bandwidths,ARD)
+    return kern(D,[part])
diff --git a/GPy/kern/rbfcos.py b/GPy/kern/rbfcos.py
new file mode 100644
index 00000000..094b806b
--- /dev/null
+++ b/GPy/kern/rbfcos.py
@@ -0,0 +1,117 @@
+
+# Copyright (c) 2012, James Hensman and Andrew Gordon Wilson
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+
+from kernpart import kernpart
+import numpy as np
+
+class rbfcos(kernpart):
+    def __init__(self,D,variance=1.,frequencies=None,bandwidths=None,ARD=False):
+        self.D = D
+        self.name = 'rbfcos'
+        if self.D>10:
+            print "Warning: the rbfcos kernel requires a lot of memory for high dimensional inputs"
+        self.ARD = ARD
+
+        #set the default frequencies and bandwidths, appropriate Nparam
+        if ARD:
+            self.Nparam = 2*self.D + 1
+            if frequencies is not None:
+                frequencies = np.asarray(frequencies)
+                assert frequencies.size == self.D, "bad number of frequencies"
+            else:
+                frequencies = np.ones(self.D)
+            if bandwidths is not None:
+                bandwidths = np.asarray(bandwidths)
+                assert bandwidths.size == self.D, "bad number of bandwidths"
+            else:
+                bandwidths = np.ones(self.D)
+        else:
+            self.Nparam = 3
+            if frequencies is not None:
+                frequencies = np.asarray(frequencies)
+                assert frequencies.size == 1, "Exactly one frequency needed for non-ARD kernel"
+            else:
+                frequencies = np.ones(1)
+
+            if bandwidths is not None:
+                bandwidths = np.asarray(bandwidths)
+                assert bandwidths.size == 1, "Exactly one bandwidth needed for non-ARD kernel"
+            else:
+                bandwidths = np.ones(1)
+
+        #initialise cache
+        self._X, self._X2, self._params = np.empty(shape=(3,1))
+
+        self._set_params(np.hstack((variance,frequencies.flatten(),bandwidths.flatten())))
+
+
+    def _get_params(self):
+        return np.hstack((self.variance,self.frequencies, self.bandwidths))
+
+    def _set_params(self,x):
+        assert x.size==(self.Nparam)
+        if self.ARD:
+            self.variance = x[0]
+            self.frequencies = x[1:1+self.D]
+            self.bandwidths = x[1+self.D:]
+        else:
+            self.variance, self.frequencies, self.bandwidths = x
+
+    def _get_param_names(self):
+        if self.Nparam == 3:
+            return ['variance','frequency','bandwidth']
+        else:
+            return ['variance']+['frequency_%i'%i for i in range(self.D)]+['bandwidth_%i'%i for i in range(self.D)]
+
+    def K(self,X,X2,target):
+        self._K_computations(X,X2)
+        target += self.variance*self._dvar
+
+    def Kdiag(self,X,target):
+        np.add(target,self.variance,target)
+
+    def dK_dtheta(self,dL_dK,X,X2,target):
+        self._K_computations(X,X2)
+        target[0] += np.sum(dL_dK*self._dvar)
+        if self.ARD:
+            for q in xrange(self.D):
+                target[q+1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.tan(2.*np.pi*self._dist[:,:,q]*self.frequencies[q])*self._dist[:,:,q])
+                target[q+1+self.D] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2[:,:,q])
+        else:
+            target[1] += -2.*np.pi*self.variance*np.sum(dL_dK*self._dvar*np.sum(np.tan(2.*np.pi*self._dist*self.frequencies)*self._dist,-1))
+            target[2] += -2.*np.pi**2*self.variance*np.sum(dL_dK*self._dvar*self._dist2.sum(-1))
+
+
+    def dKdiag_dtheta(self,dL_dKdiag,X,target):
+        target[0] += np.sum(dL_dKdiag)
+
+    def dK_dX(self,dL_dK,X,X2,target):
+        #TODO!!!
+        raise NotImplementedError
+
+    def dKdiag_dX(self,dL_dKdiag,X,target):
+        pass
+
+    def _K_computations(self,X,X2):
+        if not (np.all(X==self._X) and np.all(X2==self._X2)):
+            if X2 is None: X2 = X
+            self._X = X.copy()
+            self._X2 = X2.copy()
+
+            #do the distances: this will be high memory for large D
+            #NB: we don't take the abs of the dist because cos is symmetric
+            self._dist = X[:,None,:] - X2[None,:,:]
+            self._dist2 = np.square(self._dist)
+
+            #ensure the next section is computed:
+            self._params = np.empty(self.Nparam)
+
+        if not np.all(self._params == self._get_params()):
+            self._params == self._get_params().copy()
+
+            self._rbf_part = np.exp(-2.*np.pi**2*np.sum(self._dist2*self.bandwidths,-1))
+            self._cos_part = np.prod(np.cos(2.*np.pi*self._dist*self.frequencies),-1)
+            self._dvar = self._rbf_part*self._cos_part
+
diff --git a/GPy/models/GPLVM.py b/GPy/models/GPLVM.py
index 32594594..03e9b715 100644
--- a/GPy/models/GPLVM.py
+++ b/GPy/models/GPLVM.py
@@ -81,6 +81,7 @@ class GPLVM(GP):
                 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 Atomatically determine which dimensions to plot, please pass 'which_indices'"
+                input_1, input_2 = self.lengthscale_order()
                 k = k[0]
                 if k.name=='rbf':
                     input_1, input_2 = np.argsort(k.lengthscale)[:2]
@@ -92,7 +93,7 @@ class GPLVM(GP):
 	Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
 	Xtest_full[:, :2] = Xtest
 	mu, var, low, up = self.predict(Xtest_full)
-	var = var[:, :2]
+	var = var.mean(axis=1) # this was var[:, :2] edit by Neil
         pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear')
 
 
@@ -122,4 +123,4 @@ class GPLVM(GP):
         pb.xlim(xmin[0],xmax[0])
         pb.ylim(xmin[1],xmax[1])
 
-        return input_1, input_2
+        return pb.gca() #input_1, input_2 temporary removal, to return axes.
diff --git a/GPy/util/__init__.py b/GPy/util/__init__.py
index c91557d0..56dbd5b9 100644
--- a/GPy/util/__init__.py
+++ b/GPy/util/__init__.py
@@ -10,4 +10,6 @@ import Tango
 import misc
 import warping_functions
 import datasets
+import mocap
+import visualize
 import decorators
diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py
index ed808f1b..932690ec 100644
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@@ -15,12 +15,12 @@ def sample_class(f):
     return c
 
 def della_gatta_TRP63_gene_expression(gene_number=None):
-    matData = scipy.io.loadmat(os.path.join(data_path, 'DellaGattadata.mat'))
-    X = np.double(matData['timepoints'])
+    mat_data = scipy.io.loadmat(os.path.join(data_path, 'DellaGattadata.mat'))
+    X = np.double(mat_data['timepoints'])
     if gene_number == None:
-        Y = matData['exprs_tp53_RMA']
+        Y = mat_data['exprs_tp53_RMA']
     else:
-        Y = matData['exprs_tp53_RMA'][:, gene_number]
+        Y = mat_data['exprs_tp53_RMA'][:, gene_number]
         if len(Y.shape) == 1:
             Y = Y[:, None]
     return {'X': X, 'Y': Y, 'info': "The full gene expression data set from della Gatta et al (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413161/) processed by RMA."}
@@ -60,28 +60,42 @@ def pumadyn(seed=default_seed):
     return {'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'info': "The puma robot arm data with 32 inputs. This data is the non linear case with medium noise (pumadyn-32nm). For training 7,168 examples are sampled without replacement."}
 
 
+def brendan_faces():
+    mat_data = scipy.io.loadmat(os.path.join(data_path, 'frey_rawface.mat'))
+    Y = mat_data['ff'].T
+    return {'Y': Y, 'info': "Face data made available by Brendan Frey"}
+
+
+
 
 def silhouette():
     # Ankur Agarwal and Bill Trigg's silhoutte data.
-    matData = scipy.io.loadmat(os.path.join(data_path, 'mocap', 'ankur', 'ankurDataPoseSilhouette.mat'))
-    inMean = np.mean(matData['Y'])
-    inScales = np.sqrt(np.var(matData['Y']))
-    X = matData['Y'] - inMean
+    mat_data = scipy.io.loadmat(os.path.join(data_path, 'mocap', 'ankur', 'ankurDataPoseSilhouette.mat'))
+    inMean = np.mean(mat_data['Y'])
+    inScales = np.sqrt(np.var(mat_data['Y']))
+    X = mat_data['Y'] - inMean
     X = X/inScales
-    Xtest = matData['Y_test'] - inMean
+    Xtest = mat_data['Y_test'] - inMean
     Xtest = Xtest/inScales
-    Y = matData['Z']
-    Ytest = matData['Z_test']
+    Y = mat_data['Z']
+    Ytest = mat_data['Z_test']
     return {'X': X, 'Y': Y, 'Xtest': Xtest, 'Ytest': Ytest, 'info': "Artificial silhouette simulation data developed from Agarwal and Triggs (2004)."}
 
+def stick():
+    Y, connect = GPy.util.mocap.load_text_data('run1', data_path)
+    Y = Y[0:-1:4, :]
+    lbls = 'connect'
+    return {'Y': Y, 'connect' : connect, 'info': "Stick man data from Ohio."}
+
+
 def swiss_roll_1000():
-    matData = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data'))
-    Y = matData['X_data'][:, 0:1000].transpose()
+    mat_data = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data'))
+    Y = mat_data['X_data'][:, 0:1000].transpose()
     return {'Y': Y, 'info': "Subsample of the swiss roll data extracting only the first 1000 values."}
 
 def swiss_roll():
-    matData = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data.mat'))
-    Y = matData['X_data'][:, 0:3000].transpose()
+    mat_data = scipy.io.loadmat(os.path.join(data_path, 'swiss_roll_data.mat'))
+    Y = mat_data['X_data'][:, 0:3000].transpose()
     return {'Y': Y, 'info': "The first 3,000 points from the swiss roll data of Tennenbaum, de Silva and Langford (2001)."}
 
 def toy_rbf_1d(seed=default_seed):
@@ -202,3 +216,4 @@ def creep_data():
     features.extend(range(2, 31))
     X = all_data[:,features].copy()
     return {'X': X, 'y' : y}
+
diff --git a/GPy/util/linalg.py b/GPy/util/linalg.py
index f21502a5..59f598f9 100644
--- a/GPy/util/linalg.py
+++ b/GPy/util/linalg.py
@@ -145,9 +145,10 @@ def PCA(Y, Q):
     """
     if not np.allclose(Y.mean(axis=0), 0.0):
         print "Y is not zero mean, centering it locally (GPy.util.linalg.PCA)"
-        Y -= Y.mean(axis=0)
+        
+        #Y -= Y.mean(axis=0) 
 
-    Z = linalg.svd(Y, full_matrices = False)
+    Z = linalg.svd(Y-Y.mean(axis=0), full_matrices = False)
     [X, W] = [Z[0][:,0:Q], np.dot(np.diag(Z[1]), Z[2]).T[:,0:Q]]
     v = X.std(axis=0)
     X /= v;
diff --git a/GPy/util/mocap.py b/GPy/util/mocap.py
new file mode 100644
index 00000000..e66a36b9
--- /dev/null
+++ b/GPy/util/mocap.py
@@ -0,0 +1,74 @@
+import os
+import numpy as np
+
+def load_text_data(dataset, directory, centre=True):
+    """Load in a data set of marker points from the Ohio State University C3D motion capture files (http://accad.osu.edu/research/mocap/mocap_data.htm)."""
+
+    points, point_names = parse_text(os.path.join(directory, dataset + '.txt'))[0:2]
+    # Remove markers where there is a NaN
+    present_index = [i for i in range(points[0].shape[1]) if not (np.any(np.isnan(points[0][:, i])) or np.any(np.isnan(points[0][:, i])) or np.any(np.isnan(points[0][:, i])))]
+
+    point_names = point_names[present_index]
+    for i in range(3):
+        points[i] = points[i][:, present_index]
+        if centre:
+            points[i] = (points[i].T - points[i].mean(axis=1)).T 
+
+    # Concatanate the X, Y and Z markers together
+    Y = np.concatenate((points[0], points[1], points[2]), axis=1)
+    Y = Y/400.
+    connect = read_connections(os.path.join(directory, 'connections.txt'), point_names)
+    return Y, connect
+
+def parse_text(file_name):
+    """Parse data from Ohio State University text mocap files (http://accad.osu.edu/research/mocap/mocap_data.htm)."""
+
+    # Read the header
+    fid = open(file_name, 'r')
+    point_names = np.array(fid.readline().split())[2:-1:3]
+    fid.close()
+    for i in range(len(point_names)):
+        point_names[i] = point_names[i][0:-2]
+
+    # Read the matrix data
+    S = np.loadtxt(file_name, skiprows=1)
+    field = np.uint(S[:, 0])
+    times = S[:, 1]
+    S = S[:, 2:]
+
+    # Set the -9999.99 markers to be not present
+    S[S==-9999.99] = np.NaN
+
+    # Store x, y and z in different arrays
+    points = []
+    points.append(S[:, 0:-1:3])
+    points.append(S[:, 1:-1:3])
+    points.append(S[:, 2:-1:3])
+
+    return points, point_names, times
+
+def read_connections(file_name, point_names):
+    """Read a file detailing which markers should be connected to which for motion capture data."""
+
+    connections = []
+    fid = open(file_name, 'r')
+    line=fid.readline()
+    while(line):
+        connections.append(np.array(line.split(',')))
+        connections[-1][0] = connections[-1][0].strip()
+        connections[-1][1] = connections[-1][1].strip()
+        line = fid.readline()
+    connect = np.zeros((len(point_names), len(point_names)),dtype=bool)
+    for i in range(len(point_names)):
+        for j in range(len(point_names)):
+            for k in range(len(connections)):
+                if connections[k][0] == point_names[i] and connections[k][1] == point_names[j]:
+                    
+                    connect[i,j]=True
+                    connect[j,i]=True
+                    break
+    
+    return connect
+
+    
+  
diff --git a/GPy/util/visualize.py b/GPy/util/visualize.py
new file mode 100644
index 00000000..dde9cd32
--- /dev/null
+++ b/GPy/util/visualize.py
@@ -0,0 +1,164 @@
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+import GPy
+import numpy as np
+
+class lvm:
+    def __init__(self, model, data_visualize, latent_axis):
+        self.cid = latent_axis.figure.canvas.mpl_connect('button_press_event', self.on_click)
+        self.cid = latent_axis.figure.canvas.mpl_connect('motion_notify_event', self.on_move)
+        self.data_visualize = data_visualize
+        self.model = model
+        self.latent_axis = latent_axis
+        self.called = False
+        self.move_on = False
+
+    def on_click(self, event):
+        #print 'click', event.xdata, event.ydata
+        if event.inaxes!=self.latent_axis: 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_axis.plot(event.xdata, event.ydata)
+        self.called = True
+    def on_move(self, event):
+        if event.inaxes!=self.latent_axis: return
+        if self.called and self.move_on:
+            # Call modify code on move
+            #print 'move', event.xdata, event.ydata
+            latent_values = np.array((event.xdata, event.ydata))
+            y = self.model.predict(latent_values)[0]
+            self.data_visualize.modify(y)
+            #print 'y', y
+
+class data_show:
+    """The data show class is a base class which describes how to visualize a particular data set. For example, motion capture data can be plotted as a stick figure, or images are shown using imshow. This class enables latent to data visualizations for the GP-LVM."""
+
+    def __init__(self, vals, axis=None):
+        self.vals = vals
+        # If no axes are defined, create some.
+        if axis==None:
+            fig = plt.figure()
+            self.axis = fig.add_subplot(111)            
+        else:
+            self.axis = axis
+
+    def modify(self, vals):
+        raise NotImplementedError, "this needs to be implemented to use the data_show class"
+
+class vector_show(data_show):
+    """A base visualization class that just shows a data vector as a plot of vector elements alongside their indices."""
+    def __init__(self, vals, axis=None):
+        data_show.__init__(self, vals, axis)
+        self.vals = vals.T
+        self.handle = plt.plot(np.arange(0, len(vals))[:, None], self.vals)[0]
+
+    def modify(self, vals):
+        xdata, ydata = self.handle.get_data()
+        self.vals = vals.T
+        self.handle.set_data(xdata, self.vals)
+        self.axis.figure.canvas.draw()
+
+class image_show(data_show):
+    """Show a data vector as an image."""
+    def __init__(self, vals, axis=None, dimensions=(16,16), transpose=False, invert=False, scale=False):
+        data_show.__init__(self, vals, axis)
+        self.dimensions = dimensions
+        self.transpose = transpose
+        self.invert = invert
+        self.scale = scale
+        self.set_image(vals/255.)
+        self.handle = self.axis.imshow(self.vals, cmap=plt.cm.gray, interpolation='nearest')
+        plt.show()
+
+    def modify(self, vals):
+        self.set_image(vals/255.)
+        #self.handle.remove()
+        #self.handle = self.axis.imshow(self.vals)
+        self.handle.set_array(self.vals)
+        #self.axis.figure.canvas.draw()
+        plt.show()
+        
+    def set_image(self, vals):
+        self.vals = np.reshape(vals, self.dimensions, order='F')
+        if self.transpose:
+            self.vals = self.vals.T
+        if not self.scale:
+            self.vals = self.vals
+        #if self.invert:
+        #    self.vals = -self.vals
+
+class stick_show(data_show): 
+    """Show a three dimensional point cloud as a figure. Connect elements of the figure together using the matrix connect."""
+
+    def __init__(self, vals, axis=None, connect=None):
+        if axis==None:
+            fig = plt.figure()
+            axis = fig.add_subplot(111, projection='3d')
+        data_show.__init__(self, vals, axis)
+        self.vals = vals.reshape((3, vals.shape[1]/3)).T
+        self.x_lim = np.array([self.vals[:, 0].min(), self.vals[:, 0].max()])
+        self.y_lim = np.array([self.vals[:, 1].min(), self.vals[:, 1].max()])
+        self.z_lim = np.array([self.vals[:, 2].min(), self.vals[:, 2].max()])
+        self.points_handle = self.axis.scatter(self.vals[:, 0], self.vals[:, 1], self.vals[:, 2])
+        self.axis.set_xlim(self.x_lim)
+        self.axis.set_ylim(self.y_lim)
+        self.axis.set_zlim(self.z_lim)
+        self.axis.set_aspect(1)
+        self.axis.autoscale(enable=False)
+
+        self.connect = connect
+        if not self.connect==None:
+            x = []
+            y = []
+            z = []
+            self.I, self.J = np.nonzero(self.connect)
+            for i in range(len(self.I)):
+                x.append(self.vals[self.I[i], 0])
+                x.append(self.vals[self.J[i], 0])
+                x.append(np.NaN)
+                y.append(self.vals[self.I[i], 1])
+                y.append(self.vals[self.J[i], 1])
+                y.append(np.NaN)
+                z.append(self.vals[self.I[i], 2])
+                z.append(self.vals[self.J[i], 2])
+                z.append(np.NaN)
+            self.line_handle = self.axis.plot(np.array(x), np.array(y), np.array(z), 'b-')
+        self.axis.figure.canvas.draw()
+
+    def modify(self, vals):
+        self.points_handle.remove()
+        self.line_handle[0].remove()
+        self.vals = vals.reshape((3, vals.shape[1]/3)).T
+        self.points_handle = self.axis.scatter(self.vals[:, 0], self.vals[:, 1], self.vals[:, 2])
+        self.axis.set_xlim(self.x_lim)
+        self.axis.set_ylim(self.y_lim)
+        self.axis.set_zlim(self.z_lim)
+        self.line_handle = []
+        if not self.connect==None:
+            x = []
+            y = []
+            z = []
+            self.I, self.J = np.nonzero(self.connect)
+            for i in range(len(self.I)):
+                x.append(self.vals[self.I[i], 0])
+                x.append(self.vals[self.J[i], 0])
+                x.append(np.NaN)
+                y.append(self.vals[self.I[i], 1])
+                y.append(self.vals[self.J[i], 1])
+                y.append(np.NaN)
+                z.append(self.vals[self.I[i], 2])
+                z.append(self.vals[self.J[i], 2])
+                z.append(np.NaN)
+            self.line_handle = self.axis.plot(np.array(x), np.array(y), np.array(z), 'b-')
+
+        self.axis.figure.canvas.draw()
+        
+
+