diff --git a/GPy/kern/_src/sympykern.py b/GPy/kern/_src/sympykern.py
index a09d4bfc..3d6517a8 100644
--- a/GPy/kern/_src/sympykern.py
+++ b/GPy/kern/_src/sympykern.py
@@ -1,16 +1,34 @@
-import numpy as np
-import sympy as sp
-from sympy.utilities.codegen import codegen
+# Check Matthew Rocklin's blog post.
+try: 
+    import sympy as sp
+    sympy_available=True
+except ImportError:
+    sympy_available=False
+    exit()
+
 from sympy.core.cache import clear_cache
-from scipy import weave
-import re
+from sympy.utilities.codegen import codegen
+
+try:
+    from scipy import weave
+    weave_available = True
+except ImportError:
+    weave_available = False
+
 import os
-import sys
 current_dir = os.path.dirname(os.path.abspath(os.path.dirname(__file__)))
+import sys
+import numpy as np
+import re
 import tempfile
 import pdb
 import ast
+
 from kernpart import Kernpart
+from ...core.parameterization import Param
+from ...core.parameterization.transformations import Logexp
+# TODO have this set up in a set up file!
+user_code_storage = tempfile.gettempdir()
 
 class spkern(Kernpart):
     """
@@ -28,96 +46,117 @@ class spkern(Kernpart):
      - to handle multpile correlated outputs, you'll need to add parameters with an index, such as lengthscale_i and lengthscale_j.
     """
     def __init__(self, input_dim, k=None, output_dim=1, name=None, param=None):
+
         if name is None:
-            self.name='sympykern'
-        else:
-            self.name = name
+            name='sympykern'
         if k is None:
             raise ValueError, "You must provide an argument for the covariance function."
+        super(spkern, self).__init__(input_dim, name)
+
         self._sp_k = k
+
+        # pull the variable names out of the symbolic covariance function.
         sp_vars = [e for e in k.atoms() if e.is_Symbol]
         self._sp_x= sorted([e for e in sp_vars if e.name[0:2]=='x_'],key=lambda x:int(x.name[2:]))
         self._sp_z= sorted([e for e in sp_vars if e.name[0:2]=='z_'],key=lambda z:int(z.name[2:]))
+
         # Check that variable names make sense.
         assert all([x.name=='x_%i'%i for i,x in enumerate(self._sp_x)])
         assert all([z.name=='z_%i'%i for i,z in enumerate(self._sp_z)])
         assert len(self._sp_x)==len(self._sp_z)
-        self.input_dim = len(self._sp_x)
-        self._real_input_dim = self.input_dim
-        if output_dim > 1:
-            self.input_dim += 1
-        assert self.input_dim == input_dim
+        x_dim=len(self._sp_x)
+
+        # If it is a multi-output covariance, add an input for indexing the outputs.
+        self._real_input_dim = x_dim
+        # Check input dim is number of xs + 1 if output_dim is >1
+        assert self.input_dim == x_dim + int(output_dim > 1)
         self.output_dim = output_dim
-        # extract parameter names
+
+        # extract parameter names from the covariance
         thetas = sorted([e for e in sp_vars if not (e.name[0:2]=='x_' or e.name[0:2]=='z_')],key=lambda e:e.name)
 
 
-        # Look for parameters with index.
+        # Look for parameters with index (subscripts), they are associated with different outputs.
         if self.output_dim>1:
             self._sp_theta_i = sorted([e for e in thetas if (e.name[-2:]=='_i')], key=lambda e:e.name)
             self._sp_theta_j = sorted([e for e in thetas if (e.name[-2:]=='_j')], key=lambda e:e.name)
+
             # Make sure parameter appears with both indices!
             assert len(self._sp_theta_i)==len(self._sp_theta_j)
             assert all([theta_i.name[:-2]==theta_j.name[:-2] for theta_i, theta_j in zip(self._sp_theta_i, self._sp_theta_j)])
 
-            # Extract names of shared parameters
+            # Extract names of shared parameters (those without a subscript)
             self._sp_theta = [theta for theta in thetas if theta not in self._sp_theta_i and theta not in self._sp_theta_j]
             
             self.num_split_params = len(self._sp_theta_i)
             self._split_theta_names = ["%s"%theta.name[:-2] for theta in self._sp_theta_i]
             for theta in self._split_theta_names:
-                setattr(self, theta, np.ones(self.output_dim))
+                setattr(self, theta, Param(theta, np.ones(self.output_dim), None))
+                self.add_parameters(getattr(self, theta))
+
+                #setattr(self, theta, np.ones(self.output_dim))
             
             self.num_shared_params = len(self._sp_theta)
-            self.num_params = self.num_shared_params+self.num_split_params*self.output_dim
+            #self.num_params = self.num_shared_params+self.num_split_params*self.output_dim
             
         else:
             self.num_split_params = 0
             self._split_theta_names = []
             self._sp_theta = thetas
             self.num_shared_params = len(self._sp_theta)
-            self.num_params = self.num_shared_params
-        
+            #self.num_params = self.num_shared_params
+
+        # Add parameters to the model.
         for theta in self._sp_theta:
             val = 1.0
             if param is not None:
                 if param.has_key(theta):
                     val = param[theta]
-            setattr(self, theta.name, val)
+            #setattr(self, theta.name, val)
+            setattr(self, theta.name, Param(theta.name, val, None))
+            self.add_parameters(getattr(self, theta.name))
         #deal with param            
-        self._set_params(self._get_params())
+        #self._set_params(self._get_params())
 
-        #Differentiate!
+        # Differentiate with respect to parameters.
         self._sp_dk_dtheta = [sp.diff(k,theta).simplify() for theta in self._sp_theta]
         if self.output_dim > 1:
             self._sp_dk_dtheta_i = [sp.diff(k,theta).simplify() for theta in self._sp_theta_i]
-            
+
+        # differentiate with respect to input variables.
         self._sp_dk_dx = [sp.diff(k,xi).simplify() for xi in self._sp_x]
 
+        # psi_stats aren't yet implemented.
         if False:
             self.compute_psi_stats()
 
+        self._code = {}
+
+        # generate the code for the covariance functions
         self._gen_code()
 
-        if False:
-            extra_compile_args = ['-ftree-vectorize', '-mssse3', '-ftree-vectorizer-verbose=5']
-        else:
-            extra_compile_args = []
+        if weave_available:
+            if False:
+                extra_compile_args = ['-ftree-vectorize', '-mssse3', '-ftree-vectorizer-verbose=5']
+            else:
+                extra_compile_args = []
             
-        self.weave_kwargs = {
-            'support_code':self._function_code,
-            'include_dirs':[tempfile.gettempdir(), os.path.join(current_dir,'parts/')],
-            'headers':['"sympy_helpers.h"'],
-            'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp")],
-            'extra_compile_args':extra_compile_args,
-            'extra_link_args':['-lgomp'],
-            'verbose':True}
+                self.weave_kwargs = {
+                    'support_code': None, #self._function_code,
+                    'include_dirs':[user_code_storage, os.path.join(current_dir,'parts/')],
+                    'headers':['"sympy_helpers.h"', '"'+self.name+'.h"'],
+                    'sources':[os.path.join(current_dir,"parts/sympy_helpers.cpp"), os.path.join(user_code_storage, self.name+'.cpp')],
+                    'extra_compile_args':extra_compile_args,
+                    'extra_link_args':['-lgomp'],
+                    'verbose':True}
+        self.parameters_changed() # initializes caches
+
 
     def __add__(self,other):
         return spkern(self._sp_k+other._sp_k)
 
     def _gen_code(self):
-        #generate c functions from sympy objects        
+
         argument_sequence = self._sp_x+self._sp_z+self._sp_theta
         code_list = [('k',self._sp_k)]
         # gradients with respect to covariance input
@@ -128,194 +167,268 @@ class spkern(Kernpart):
         if self.output_dim > 1:
             argument_sequence += self._sp_theta_i + self._sp_theta_j
             code_list += [('dk_d%s'%theta.name,dtheta) for theta,dtheta in zip(self._sp_theta_i,self._sp_dk_dtheta_i)]
+        # generate c functions from sympy objects
+        if weave_available:
+            code_type = "C"
+        else:
+            code_type = "PYTHON"
+        # Need to add the sympy_helpers header in here.
         (foo_c,self._function_code), (foo_h,self._function_header) = \
-                                     codegen(code_list, "C",'foobar',argument_sequence=argument_sequence)
-        #put the header file where we can find it
-        f = file(os.path.join(tempfile.gettempdir(),'foobar.h'),'w')
-        f.write(self._function_header)
+                                     codegen(code_list,
+                                             code_type,
+                                             self.name,
+                                             argument_sequence=argument_sequence)
+
+
+        # Use weave to compute the underlying functions.
+        if weave_available:
+            # put the header file where we can find it
+            f = file(os.path.join(user_code_storage, self.name + '.h'),'w')
+            f.write(self._function_header)
+            f.close()
+    
+
+        if weave_available:
+            # Substitute any known derivatives which sympy doesn't compute
+            self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
+            # put the cpp file in user code storage (defaults to temp file location)
+            f = file(os.path.join(user_code_storage, self.name + '.cpp'),'w')
+        else:
+            # put the python file in user code storage
+            f = file(os.path.join(user_code_storage, self.name + '.py'),'w')
+        f.write(self._function_code)
         f.close()
 
-        # Substitute any known derivatives which sympy doesn't compute
-        self._function_code = re.sub('DiracDelta\(.+?,.+?\)','0.0',self._function_code)
+        if weave_available:
+            # arg_list will store the arguments required for the C code.
+            input_arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
+                        + ["Z2(j, %s)"%z.name[2:] for z in self._sp_z])
 
-        # This is the basic argument construction for the C code.
-        #arg_list = (["X[i*input_dim+%s]"%x.name[2:] for x in self._sp_x]
-        #            + ["Z[j*input_dim+%s]"%z.name[2:] for z in self._sp_z])
-        arg_list = (["X2(i, %s)"%x.name[2:] for x in self._sp_x]
-                    + ["Z2(j, %s)"%z.name[2:] for z in self._sp_z])
-        if self.output_dim>1:
-            reverse_arg_list = list(arg_list)
-            reverse_arg_list.reverse()
+            # for multiple outputs reverse argument list is also required
+            if self.output_dim>1:
+                reverse_input_arg_list = list(input_arg_list)
+                reverse_input_arg_list.reverse()
 
-        param_arg_list = [shared_params.name for shared_params in self._sp_theta]
-        arg_list += param_arg_list
+            # This gives the parameters for the arg list.
+            param_arg_list = [shared_params.name for shared_params in self._sp_theta]
+            arg_list = input_arg_list + param_arg_list
 
-        precompute_list=[]
-        if self.output_dim > 1:
-            reverse_arg_list+=list(param_arg_list)
-            split_param_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['ii', 'jj'] for theta in self._sp_theta_i]
-            split_param_reverse_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['jj', 'ii'] for theta in self._sp_theta_i]
-            arg_list += split_param_arg_list
-            reverse_arg_list += split_param_reverse_arg_list
-            # Extract the right output indices from the inputs.
-            c_define_output_indices = [' '*16 + "int %s=(int)%s(%s, %i);"%(index, var, index2, self.input_dim-1) for index, var, index2 in zip(['ii', 'jj'], ['X2', 'Z2'], ['i', 'j'])]
-            precompute_list += c_define_output_indices
-            reverse_arg_string = ", ".join(reverse_arg_list)
-        arg_string = ", ".join(arg_list)
-        precompute_string = "\n".join(precompute_list)
-        # Here's the code to do the looping for K
-        self._K_code =\
-        """
-        // _K_code
-        // Code for computing the covariance function.
-        int i;
-        int j;
-        int N = target_array->dimensions[0];
-        int num_inducing = target_array->dimensions[1];
-        int input_dim = X_array->dimensions[1];
-        //#pragma omp parallel for private(j)
-        for (i=0;i<N;i++){
-            for (j=0;j<num_inducing;j++){
-%s
-                //target[i*num_inducing+j] = 
-                TARGET2(i, j) += k(%s);
+            precompute_list=[]
+            if self.output_dim > 1:
+                reverse_arg_list= reverse_input_arg_list + list(param_arg_list)
+                # For multiple outputs, also need the split parameters.
+                split_param_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['ii', 'jj'] for theta in self._sp_theta_i]
+                split_param_reverse_arg_list = ["%s1(%s)"%(theta.name[:-2].upper(),index) for index in ['jj', 'ii'] for theta in self._sp_theta_i]
+                arg_list += split_param_arg_list
+                reverse_arg_list += split_param_reverse_arg_list
+                # Extract the right output indices from the inputs.
+                c_define_output_indices = [' '*16 + "int %s=(int)%s(%s, %i);"%(index, var, index2, self.input_dim-1) for index, var, index2 in zip(['ii', 'jj'], ['X2', 'Z2'], ['i', 'j'])]
+                precompute_list += c_define_output_indices
+                reverse_arg_string = ", ".join(reverse_arg_list)
+            arg_string = ", ".join(arg_list)
+            precompute_string = "\n".join(precompute_list)
+
+            # Now we use the arguments in code that computes the separate parts.
+
+            # Any precomputations will be done here eventually.
+            self._precompute = \
+                """
+                // Precompute code would go here. It will be called when parameters are updated. 
+                """
+
+            # Here's the code to do the looping for K
+            self._code['K'] =\
+            """
+            // _K_code
+            // Code for computing the covariance function.
+            int i;
+            int j;
+            int n = target_array->dimensions[0];
+            int num_inducing = target_array->dimensions[1];
+            int input_dim = X_array->dimensions[1];
+            //#pragma omp parallel for private(j)
+            for (i=0;i<n;i++){
+                for (j=0;j<num_inducing;j++){
+                    %s
+                    //target[i*num_inducing+j] = 
+                    TARGET2(i, j) += k(%s);
+                }
             }
-        }
-        %s
-        """%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
+            %s
+            """%(precompute_string,arg_string,"/*"+str(self._sp_k)+"*/")
+           # adding a string representation of the function in the
+           # comment forces recompile when needed
+            self._code['K_X'] = self._code['K'].replace('Z2(', 'X2(')
 
-        
-        # Code to compute diagonal of covariance.
-        diag_arg_string = re.sub('Z','X',arg_string)
-        diag_arg_string = re.sub('int jj','//int jj',diag_arg_string)
-        diag_arg_string = re.sub('j','i',diag_arg_string)
-        diag_precompute_string = re.sub('int jj','//int jj',precompute_string)
-        diag_precompute_string = re.sub('Z','X',diag_precompute_string)
-        diag_precompute_string = re.sub('j','i',diag_precompute_string)
-        # Code to do the looping for Kdiag
-        self._Kdiag_code =\
-        """
-        // _Kdiag_code
-        // Code for computing diagonal of covariance function.
-        int i;
-        int N = target_array->dimensions[0];
-        int input_dim = X_array->dimensions[1];
-        //#pragma omp parallel for
-        for (i=0;i<N;i++){
-                %s
-                //target[i] =
-                TARGET1(i)=k(%s);
-        }
-        %s
-        """%(diag_precompute_string,diag_arg_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
 
-        # Code to compute gradients
-        grad_func_list = []
-        if self.output_dim>1:
-            grad_func_list += c_define_output_indices
-            grad_func_list += [' '*16 + 'TARGET1(%i+ii) += partial[i*num_inducing+j]*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, arg_string) for i, theta in enumerate(self._sp_theta_i)]
-            grad_func_list += [' '*16 + 'TARGET1(%i+jj) += partial[i*num_inducing+j]*dk_d%s(%s);'%(self.num_shared_params+i*self.output_dim, theta.name, reverse_arg_string) for i, theta in enumerate(self._sp_theta_i)]
-        grad_func_list += ([' '*16 + 'TARGET1(%i) += partial[i*num_inducing+j]*dk_d%s(%s);'%(i,theta.name,arg_string) for i,theta in  enumerate(self._sp_theta)])
-        grad_func_string = '\n'.join(grad_func_list) 
-
-        self._dK_dtheta_code =\
-        """
-        // _dK_dtheta_code
-        // Code for computing gradient of covariance with respect to parameters.
-        int i;
-        int j;
-        int N = partial_array->dimensions[0];
-        int num_inducing = partial_array->dimensions[1];
-        int input_dim = X_array->dimensions[1];
-        //#pragma omp parallel for private(j)
-        for (i=0;i<N;i++){
-            for (j=0;j<num_inducing;j++){
-%s
+            # Code to compute diagonal of covariance.
+            diag_arg_string = re.sub('Z','X',arg_string)
+            diag_arg_string = re.sub('int jj','//int jj',diag_arg_string)
+            diag_arg_string = re.sub('j','i',diag_arg_string)
+            diag_precompute_string = re.sub('int jj','//int jj',precompute_string)
+            diag_precompute_string = re.sub('Z','X',diag_precompute_string)
+            diag_precompute_string = re.sub('j','i',diag_precompute_string)
+            # Code to do the looping for Kdiag
+            self._code['Kdiag'] =\
+            """
+            // _code['Kdiag']
+            // Code for computing diagonal of covariance function.
+            int i;
+            int n = target_array->dimensions[0];
+            int input_dim = X_array->dimensions[1];
+            //#pragma omp parallel for
+            for (i=0;i<n;i++){
+                    %s
+                    //target[i] =
+                    TARGET1(i)=k(%s);
             }
-        }
-        %s
-        """%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
+            %s
+            """%(diag_precompute_string,diag_arg_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
+
+            # Code to compute gradients
+            if self.output_dim>1:
+                for i, theta in enumerate(self._sp_theta_i):
+                    grad_func_list = [' '*26 + 'TARGET1(ii) += PARTIAL2(i, j)*dk_d%s(%s);'%(theta.name, arg_string)]
+                    grad_func_list += [' '*26 + 'TARGET1(jj) += PARTIAL2(i, j)*dk_d%s(%s);'%(theta.name, reverse_arg_string)]
+                    grad_func_list = c_define_output_indices+grad_func_list
+
+                    grad_func_string = '\n'.join(grad_func_list) 
+                    self._code['dK_d' + theta.name] =\
+                      """
+                      int i;
+                      int j;
+                      int n = partial_array->dimensions[0];
+                      int num_inducing = partial_array->dimensions[1];
+                      int input_dim = X_array->dimensions[1];
+                      //#pragma omp parallel for private(j)
+                      for (i=0;i<n;i++){
+                        for (j=0;j<num_inducing;j++){
+%s
+                        }
+                      }
+                      %s
+                      """%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
+                    self._code['dK_d' +theta.name + '_X'] = self._code['dK_d' + theta.name].replace('Z2(', 'X2(')
+                    # Code to compute gradients for Kdiag TODO: needs clean up
+                    diag_grad_func_string = re.sub('Z','X',grad_func_string,count=0)
+                    diag_grad_func_string = re.sub('int jj','//int jj',diag_grad_func_string)
+                    diag_grad_func_string = re.sub('j','i',diag_grad_func_string)
+                    diag_grad_func_string = re.sub('PARTIAL2\(i, i\)','PARTIAL(i)',diag_grad_func_string)
+                    self._code['dKdiag_d' + theta.name] =\
+                      """
+                      // _dKdiag_dtheta_code
+                      // Code for computing gradient of diagonal with respect to parameters.
+                      int i;
+                      int n = partial_array->dimensions[0];
+                      int input_dim = X_array->dimensions[1];
+                      for (i=0;i<n;i++){
+                        %s
+                      }
+                      %s
+                      """%(diag_grad_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
+
+            for i, theta in enumerate(self._sp_theta):
+                grad_func_list = [' '*26 + 'TARGET1(%i) += PARTIAL2(i, j)*dk_d%s(%s);'%(i,theta.name,arg_string)]
+                grad_func_string = '\n'.join(grad_func_list) 
+
+                self._code['dK_d' + theta.name] =\
+                  """
+                  // _dK_dtheta_code
+                  // Code for computing gradient of covariance with respect to parameters.
+                  int i;
+                  int j;
+                  int n = partial_array->dimensions[0];
+                  int num_inducing = partial_array->dimensions[1];
+                  int input_dim = X_array->dimensions[1];
+                  //#pragma omp parallel for private(j)
+                  for (i=0;i<n;i++){
+                    for (j=0;j<num_inducing;j++){
+                      %s
+                    }
+                  }
+                  %s
+                  """%(grad_func_string,"/*"+str(self._sp_k)+"*/") # adding a string representation forces recompile when needed
+                self._code['dK_d' + theta.name +'_X'] = self._code['dK_d' + theta.name].replace('Z2(', 'X2(')
+                # Code to compute gradients for Kdiag TODO: needs clean up
+                diag_grad_func_string = re.sub('Z','X',grad_func_string,count=0)
+                diag_grad_func_string = re.sub('int jj','//int jj',diag_grad_func_string)
+                diag_grad_func_string = re.sub('j','i',diag_grad_func_string)
+                diag_grad_func_string = re.sub('PARTIAL2\(i, i\)','PARTIAL(i)',diag_grad_func_string)
+                self._code['dKdiag_d' + theta.name] =\
+                   """
+                   // _dKdiag_dtheta_code
+                   // Code for computing gradient of diagonal with respect to parameters.
+                   int i;
+                   int n = partial_array->dimensions[0];
+                   int input_dim = X_array->dimensions[1];
+                   for (i=0;i<n;i++){
+                     %s
+                   }
+                   %s
+                   """%(diag_grad_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
 
 
-        # Code to compute gradients for Kdiag TODO: needs clean up
-        diag_grad_func_string = re.sub('Z','X',grad_func_string,count=0)
-        diag_grad_func_string = re.sub('int jj','//int jj',diag_grad_func_string)
-        diag_grad_func_string = re.sub('j','i',diag_grad_func_string)
-        diag_grad_func_string = re.sub('partial\[i\*num_inducing\+i\]','partial[i]',diag_grad_func_string)
-        self._dKdiag_dtheta_code =\
-        """
-        // _dKdiag_dtheta_code
-        // Code for computing gradient of diagonal with respect to parameters.
-        int i;
-        int N = partial_array->dimensions[0];
-        int input_dim = X_array->dimensions[1];
-        for (i=0;i<N;i++){
+
+            # Code for gradients wrt X, TODO: may need to deal with special case where one input is actually an output.
+            gradX_func_list = []
+            if self.output_dim>1:
+                gradX_func_list += c_define_output_indices
+            gradX_func_list += ["TARGET2(i, %i) += partial[i*num_inducing+j]*dk_dx_%i(%s);"%(q,q,arg_string) for q in range(self._real_input_dim)]
+            gradX_func_string = "\n".join(gradX_func_list)
+
+            self._code['dK_dX'] = \
+            """
+            // _dK_dX_code
+            // Code for computing gradient of covariance with respect to inputs.
+            int i;
+            int j;
+            int n = partial_array->dimensions[0];
+            int num_inducing = partial_array->dimensions[1];
+            int input_dim = X_array->dimensions[1];
+            //#pragma omp parallel for private(j)
+            for (i=0;i<n; i++){
+              for (j=0; j<num_inducing; j++){
                 %s
-        }
-        %s
-        """%(diag_grad_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-
-        # Code for gradients wrt X, TODO: may need to deal with special case where one input is actually an output.
-        gradX_func_list = []
-        if self.output_dim>1:
-            gradX_func_list += c_define_output_indices
-        gradX_func_list += ["TARGET2(i, %i) += partial[i*num_inducing+j]*dk_dx_%i(%s);"%(q,q,arg_string) for q in range(self._real_input_dim)]
-        gradX_func_string = "\n".join(gradX_func_list)
-
-        self._dK_dX_code = \
-        """
-        // _dK_dX_code
-        // Code for computing gradient of covariance with respect to inputs.
-        int i;
-        int j;
-        int N = partial_array->dimensions[0];
-        int num_inducing = partial_array->dimensions[1];
-        int input_dim = X_array->dimensions[1];
-        //#pragma omp parallel for private(j)
-        for (i=0;i<N; i++){
-          for (j=0; j<num_inducing; j++){
+              }
+            }
             %s
-          }
-        }
-        %s
-        """%(gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
-  
+            """%(gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a string representation forces recompile when needed
+            self._code['dK_dX_X'] = self._code['dK_dX'].replace('Z2(', 'X2(')
 
-        diag_gradX_func_string = re.sub('Z','X',gradX_func_string,count=0)
-        diag_gradX_func_string = re.sub('int jj','//int jj',diag_gradX_func_string)
-        diag_gradX_func_string = re.sub('j','i',diag_gradX_func_string)
-        diag_gradX_func_string = re.sub('partial\[i\*num_inducing\+i\]','2*partial[i]',diag_gradX_func_string)
 
-        # Code for gradients of Kdiag wrt X
-        self._dKdiag_dX_code= \
-        """
-        // _dKdiag_dX_code
-        // Code for computing gradient of diagonal with respect to inputs.
-        int N = partial_array->dimensions[0];
-        int input_dim = X_array->dimensions[1];
-        for (int i=0;i<N; i++){
+            diag_gradX_func_string = re.sub('Z','X',gradX_func_string,count=0)
+            diag_gradX_func_string = re.sub('int jj','//int jj',diag_gradX_func_string)
+            diag_gradX_func_string = re.sub('j','i',diag_gradX_func_string)
+            diag_gradX_func_string = re.sub('PARTIAL2\(i\, i\)','2*PARTIAL(i)',diag_gradX_func_string)
+
+            # Code for gradients of Kdiag wrt X
+            self._code['dKdiag_dX'] = \
+            """
+            // _dKdiag_dX_code
+            // Code for computing gradient of diagonal with respect to inputs.
+            int n = partial_array->dimensions[0];
+            int input_dim = X_array->dimensions[1];
+            for (int i=0;i<n; i++){
+                %s
+            }
             %s
-        }
-        %s
-        """%(diag_gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a
-        # string representation forces recompile when needed Get rid
-        # of Zs in argument for diagonal. TODO: Why wasn't
-        # diag_func_string called here? Need to check that.
-        #self._dKdiag_dX_code = self._dKdiag_dX_code.replace('Z[j', 'X[i')
+            """%(diag_gradX_func_string,"/*"+str(self._sp_k)+"*/") #adding a
+            # string representation forces recompile when needed Get rid
+            # of Zs in argument for diagonal. TODO: Why wasn't
+            # diag_func_string called here? Need to check that.
 
-        # Code to use when only X is provided. 
-        self._K_code_X = self._K_code.replace('Z[', 'X[')
-        self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z[', 'X[')
-        self._dK_dX_code_X = self._dK_dX_code.replace('Z[', 'X[').replace('+= partial[', '+= 2*partial[')
-        self._K_code_X = self._K_code.replace('Z2(', 'X2(')
-        self._dK_dtheta_code_X = self._dK_dtheta_code.replace('Z2(', 'X2(')
-        self._dK_dX_code_X = self._dK_dX_code.replace('Z2(', 'X2(')
+            
 
 
-        #TODO: insert multiple functions here via string manipulation
-        #TODO: similar functions for psi_stats
-    def _get_arg_names(self, Z=None, partial=None):
-        arg_names = ['target','X']
+            #TODO: insert multiple functions here via string manipulation
+            #TODO: similar functions for psi_stats
+            #TODO: similar functions when cython available.
+            #TODO: similar functions when only python available.
+            
+    def _get_arg_names(self, target=None, Z=None, partial=None):
+        arg_names = ['X']
+        if target is not None:
+            arg_names += ['target']
         for shared_params in self._sp_theta:
             arg_names += [shared_params.name]
         if Z is not None:
@@ -326,45 +439,49 @@ class spkern(Kernpart):
             arg_names += self._split_theta_names
             arg_names += ['output_dim']
         return arg_names
-        
-    def _weave_inline(self, code, X, target, Z=None, partial=None):
+
+    def _generate_inline(self, code, X, target=None, Z=None, partial=None):
         output_dim = self.output_dim
+        # Need to extract parameters to local variables first
         for shared_params in self._sp_theta:
             locals()[shared_params.name] = getattr(self, shared_params.name)
-
-        # Need to extract parameters first
+            
         for split_params in self._split_theta_names:
-            locals()[split_params] = getattr(self, split_params)
-        arg_names = self._get_arg_names(Z, partial)        
-        weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
+            locals()[split_params] = np.asarray(getattr(self, split_params))
+        arg_names = self._get_arg_names(target, Z, partial)        
+
+        if weave_available:
+            return weave.inline(code=code, arg_names=arg_names,**self.weave_kwargs)
+        else:
+            raise RuntimeError('Weave not available and other variants of sympy covariance not yet implemented')
 
     def K(self,X,Z,target):        
         if Z is None:
-            self._weave_inline(self._K_code_X, X, target)
+            self._generate_inline(self._code['K_X'], X, target)
         else:
-            self._weave_inline(self._K_code, X, target, Z)
+            self._generate_inline(self._code['K'], X, target, Z)
 
 
     def Kdiag(self,X,target):
-        self._weave_inline(self._Kdiag_code, X, target)
+        self._generate_inline(self._code['Kdiag'], X, target)
 
     def _param_grad_helper(self,partial,X,Z,target):
         if Z is None:
-            self._weave_inline(self._dK_dtheta_code_X, X, target, Z, partial)
+            self._generate_inline(self._code['dK_dtheta_X'], X, target, Z, partial)
         else:
-            self._weave_inline(self._dK_dtheta_code, X, target, Z, partial)
-            
+            self._generate_inline(self._code['dK_dtheta'], X, target, Z, partial)
+
     def dKdiag_dtheta(self,partial,X,target):
-        self._weave_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial)
+        self._generate_inline(self._code['dKdiag_dtheta'], X, target, Z=None, partial=partial).namelocals()[shared_params.name] = getattr(self, shared_params.name)
                
     def gradients_X(self,partial,X,Z,target):
         if Z is None:
-            self._weave_inline(self._dK_dX_code_X, X, target, Z, partial)
+            self._generate_inline(self._code['dK_dX_X'], X, target, Z, partial)
         else:
-            self._weave_inline(self._dK_dX_code, X, target, Z, partial)
+            self._generate_inline(self._code['dK_dX'], X, target, Z, partial)
 
     def dKdiag_dX(self,partial,X,target):
-        self._weave.inline(self._dKdiag_dX_code, X, target, Z, partial)
+        self._generate_inline(self._code['dKdiag_dX'], X, target, Z, partial)
 
     def compute_psi_stats(self):
         #define some normal distributions
@@ -394,30 +511,53 @@ class spkern(Kernpart):
             clear_cache()
         self._sp_psi2 = self._sp_psi2.simplify()
 
+    def parameters_changed(self):
+        # Reset the caches
+        self._cache, self._cache2 = np.empty(shape=(2, 1))
+        self._cache3, self._cache4, self._cache5 = np.empty(shape=(3, 1)) 
 
-    def _set_params(self,param):        
-        assert param.size == (self.num_params)
-        for i, shared_params in enumerate(self._sp_theta):
-            setattr(self, shared_params.name, param[i])
-            
-        if self.output_dim>1:
-            for i, split_params in enumerate(self._split_theta_names):
-                start = self.num_shared_params + i*self.output_dim
-                end = self.num_shared_params + (i+1)*self.output_dim
-                setattr(self, split_params, param[start:end])
-
-
-    def _get_params(self):
-        params = np.zeros(0)
+    def update_gradients_full(self, dL_dK, X):
+        # Need to extract parameters to local variables first
+        self._K_computations(X, None)
         for shared_params in self._sp_theta:
-            params = np.hstack((params, getattr(self, shared_params.name)))
-        if self.output_dim>1:
-            for split_params in self._split_theta_names:
-                params = np.hstack((params, getattr(self, split_params).flatten()))
-        return params
+            parameter = getattr(self, shared_params.name)
+            code = self._code['dK_d' + shared_params.name]
+            setattr(parameter, 'gradient', self._generate_inline(code, X, target=None, Z=None, partial=dL_dK))
+            
+        for split_params in self._split_theta_names:
+            parameter = getattr(self, split_params.name)
+            code = self._code['dK_d' + split_params.name]
+            setattr(parameter, 'gradient', self._generate_inline(code, X, target=None, Z=None, partial=dL_dK))
+    
 
-    def _get_param_names(self):
-        if self.output_dim>1:
-            return [x.name for x in self._sp_theta] + [x.name[:-2] + str(i)  for x in self._sp_theta_i for i in range(self.output_dim)]
+    # def update_gradients_sparse(self, dL_dKmm, dL_dKnm, dL_dKdiag, X, Z):
+    #     #contributions from Kdiag
+    #     self.variance.gradient = np.sum(dL_dKdiag)
+        
+    #     #from Knm
+    #     self._K_computations(X, Z)
+    #     self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
+    #     if self.ARD:
+    #         self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
+            
+    #     else:
+    #         self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
+            
+    #     #from Kmm
+    #     self._K_computations(Z, None)
+    #     self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
+    #     if self.ARD:
+    #         self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
+    #     else:
+    #         self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
+            
+            
+    #---------------------------------------#
+    #            Precomputations            #
+    #---------------------------------------#
+
+    def _K_computations(self, X, Z):
+        if Z is None:
+            self._generate_inline(self._precompute, X)
         else:
-            return [x.name for x in self._sp_theta]
+            self._generate_inline(self._precompute, X, Z=Z)