eucledian dist calculations

[nodrogbrown]

hi
i am using python to do some image data calculations..I use the
following numpy.ndarrays ,(i have given their shapes and ranks)

weights=ndarray :shape(100,30),ndim=2   will have vals like
2458121847.49 (of  type 'numpy.float64')
input_weight=ndarray :shape(30,),ndim=1 (similar to above but diff
vals)
distance =ndarray :shape(30,),ndim=1
mindistance==ndarray :shape(30,),ndim=1

now i am calculating the euclidian distance of 'input_weight' from
'weight'
since this is the cumulative diff i do this in this way

<code>
for image in range(100):
   temp=0.0
   for j in range(30):
	distance[j]=abs(input_weight[j]-weights[image,j])

   if(image==0):
	#at the start copy from distance to mindistance
	mindistance=distance.copy()
   if (sum(mindistance) > sum(distance)):
	imgindex=image # i use this  later to access a list
	mindistance=distance.copy()

# now normalise the mindistance
array
if (max(mindistance) > 0.0):
	mindistance=mindistance/(max(mindistance))

dist=sum(mindistance)

<code>

this gives me the correct results but i am worried if this is a bit
unpythonish?
(been a java programmer for a long time..) i wd like to know if there
is a better way

gordon