If you have an array of numbers and you want an array of strings, you can write:
strings = ["%.2f" % number for number in numbers]
If your numbers are floats, the array would be an array with the same numbers as strings with two decimals.
>>> a = [1,2,3,4,5]
>>> min_a, max_a = min(a), max(a)
>>> a_normalized = [float(x-min_a)/(max_a-min_a) for x in a]
>>> a_normalized
[0.0, 0.25, 0.5, 0.75, 1.0]
>>> a_strings = ["%.2f" % x for x in a_normalized]
>>> a_strings
['0.00', '0.25', '0.50', '0.75', '1.00']
Notice that it also works with numpy arrays:
>>> a = numpy.array([0.0, 0.25, 0.75, 1.0])
>>> print ["%.2f" % x for x in a]
['0.00', '0.25', '0.50', '0.75', '1.00']
A similar methodology can be used if you have a multi-dimensional array:
new_array = numpy.array(["%.2f" % x for x in old_array.reshape(old_array.size)])
new_array = new_array.reshape(old_array.shape)
Example:
>>> x = numpy.array([[0,0.1,0.2],[0.3,0.4,0.5],[0.6, 0.7, 0.8]])
>>> y = numpy.array(["%.2f" % w for w in x.reshape(x.size)])
>>> y = y.reshape(x.shape)
>>> print y
[['0.00' '0.10' '0.20']
['0.30' '0.40' '0.50']
['0.60' '0.70' '0.80']]
If you check the Matplotlib example for the function you are using, you will notice they use a similar methodology: build empty matrix and fill it with strings built with the interpolation method. The relevant part of the referenced code is:
colortuple = ('y', 'b')
colors = np.empty(X.shape, dtype=str)
for y in range(ylen):
for x in range(xlen):
colors[x, y] = colortuple[(x + y) % len(colortuple)]
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=colors,
linewidth=0, antialiased=False)