diff --git a/Software/Send/interp3d.py b/Software/Send/interp3d.py
new file mode 100644
index 0000000..bae7616
--- /dev/null
+++ b/Software/Send/interp3d.py
@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+"""
+Title: interp3d.py - interpolate to a set of points in 3-dimensional space
+Author: Craig Arthur, craig.arthur@ga.gov.au
+CreationDate: 2012-08-22
+Description: Use scipy.ndimage.interpolation.map-coordinates to interpolate
+             data in three dimensions.
+
+Version: $Rev$
+Id: $Id$
+"""
+
+import numpy
+import pdb
+from scipy.ndimage.interpolation import map_coordinates
+
+def interp3d(input_array, coords, 
+             scale=[360., 180., 365.],
+             offset=[0.,-90.,0.]):
+    """
+    Wrapper to scipy.ndimage.interpolation.map_coordinates, which
+    converts coordinates of points to indices that correspond to the
+    array.  We assume that one is working with lon, lat, day data
+    (i.e. initially designed to work with daily long term mean sea
+    level pressure)
+
+    Input:
+    input_array - a 3-d array of data at regular intervals, representing the 
+                  data to be evaluated
+    coords - a 3xn array of coordinates at which the data in input_array
+             will be interpolated
+    scale - a scale factor that reduces the coords values to the range of
+            indices in input_array
+    offset - an offset factor that is subtracted from the coords values
+             before adjusting the scale (above)
+
+    Output:
+    1-d array of values corresponding to the interpolated values 
+    at the points given in 'coords'
+
+    Example: vals = interp3d( data, coords, 
+                              scale=[360., 180., 365.],
+                              offset=[0.,-90.,0.] )
+
+    """
+
+    if input_array.ndim != 3:
+        raise ValueError('Input array has incorrect shape')
+    if coords.shape[0] != 3:
+        raise ValueError('Coordinates of points must be 3-d')
+
+    dims = input_array.shape
+    indices = [d*(c - o) / s for d,c,o,s in 
+               zip(dims,coords,offset,scale)]
+
+    values = map_coordinates(input_array,indices, mode='wrap')
+
+    return values
+
diff --git a/Software/Send/sciPy_test.py b/Software/Send/sciPy_test.py
new file mode 100644
index 0000000..d09ce3b
--- /dev/null
+++ b/Software/Send/sciPy_test.py
@@ -0,0 +1,29 @@
+#From http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html
+
+import numpy as np
+from scipy import interpolate
+import matplotlib.pyplot as plt
+
+# Define function over sparse 20x20 grid
+
+x,y = np.mgrid[-1:1:20j,-1:1:20j]
+z = (x+y)*np.exp(-6.0*(x*x+y*y))
+
+#plt.figure()
+#plt.pcolor(x,y,z)
+#plt.colorbar()
+#plt.title("Sparsely sampled function.")
+#plt.show()
+
+# Interpolate function over new 70x70 grid
+
+xnew,ynew = np.mgrid[-1:1:70j,-1:1:70j]
+tck = interpolate.bisplrep(x,y,z,s=0)
+znew = interpolate.bisplev(xnew[:,0],ynew[0,:],tck)
+
+plt.figure()
+plt.pcolor(xnew,ynew,znew)
+plt.colorbar()
+plt.title("Interpolated function.")
+plt.show()
+