diff --git a/script/scitest/numpy_test.py b/script/scitest/numpy_test.py
index 95afb86..a661f94 100644
--- a/script/scitest/numpy_test.py
+++ b/script/scitest/numpy_test.py
@@ -344,13 +344,15 @@ print p.order #2
 print  p[1]  #2
 #Polynomials can be added, subtracted, multiplied, and divided (returns quotient and remainder):
 
-print  p * p #poly1d([ 1,  4, 10, 12,  9])
-print  (p**3 + 4) / p    #(poly1d([ 1.,  4., 10., 12.,  9.]), poly1d([4.]))
+###TODO: Not supported
+#print  p * p #poly1d([ 1,  4, 10, 12,  9])
+#print  (p**3 + 4) / p    #(poly1d([ 1.,  4., 10., 12.,  9.]), poly1d([4.]))
 #asarray(p) gives the coefficient array, so polynomials can be used in all functions that accept arrays:
+#print  np.square(p) # square of individual coefficients   array([1, 4, 9])
+
+#print  p**2 # square of polynomial
+print np.poly1d([ 1,  4, 10, 12,  9])
 
-print  p**2 # square of polynomial
-print poly1d([ 1,  4, 10, 12,  9])
-print  np.square(p) # square of individual coefficients   array([1, 4, 9])
 #The variable used in the string representation of p can be modified, using the variable parameter:
 
 p = np.poly1d([1,2,3], variable='z')
@@ -359,5 +361,49 @@ print(p) #   2
 #Construct a polynomial from its roots:
 print np.poly1d([1, 2], True)   #poly1d([ 1., -3.,  2.])
 
-3This is the same polynomial as obtained by:
-print np.poly1d([1, -1]) * np.poly1d([1, -2])
+###TODO: Not supported
+#This is the same polynomial as obtained by:
+#print np.poly1d([1, -1]) * np.poly1d([1, -2])
+
+
+
+coeff = [3.2, 2, 1]
+print np.roots(coeff)  #array([-0.3125+0.46351241j, -0.3125-0.46351241j])
+
+################################################################################
+# Fit
+################################################################################   
+
+
+x = np.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
+y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
+z =  np.fit.polyfit(x, y, 3) 
+print z #array([ 0.08703704, -0.81349206,  1.69312169, -0.03968254]) # may vary
+
+p = np.poly1d(z)
+p(0.5) #0.6143849206349179 
+
+
+################################################################################
+# Convolution
+################################################################################   
+#Note how the convolution operator flips the second array before “sliding” the two across one another:
+print np.convolve([1, 2, 3], [0, 1, 0.5]) #array([0. , 1. , 2.5, 4. , 1.5])
+
+#Only return the middle values of the convolution. Contains boundary effects, where zeros are taken into account:
+print np.convolve([1,2,3],[0,1,0.5], 'same') #array([1. ,  2.5,  4. ])
+
+#The two arrays are of the same length, so there is only one position where they completely overlap:
+print  np.convolve([1,2,3],[0,1,0.5], 'valid') #array([2.5])
+
+
+
+print  np.correlate([1, 2, 3], [0, 1, 0.5]) #array([3.5])
+print  np.correlate([1, 2, 3], [0, 1, 0.5], "same") #array([2. ,  3.5,  3. ])
+print  np.correlate([1, 2, 3], [0, 1, 0.5], "full") #array([0.5,  2. ,  3.5,  3. ,  0. ])
+
+###TODO: Not supported
+#Using complex sequences:
+#print  np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full') #array([ 0.5-0.5j,  1.0+0.j ,  1.5-1.5j,  3.0-1.j ,  0.0+0.j ])
+#Note that you get the time reversed, complex conjugated result when the two input sequences change places, i.e., c_{va}[k] = c^{*}_{av}[-k]:
+#print  np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full') #array([ 0.0+0.j ,  3.0+1.j ,  1.5+1.5j,  1.0+0.j ,  0.5+0.5j])