Quite a busy one-liner, but here it is:

1. First ensure your NumPy array, myarray, is normalised with the max value at 1.0.
2. Apply the colormap directly to myarray.
3. Rescale to the 0-255 range.
4. Convert to integers, using np.uint8().
5. Use Image.fromarray().

And you're done:

from PIL import Image
from matplotlib import cm
im = Image.fromarray(np.uint8(cm.gist_earth(myarray)*255))

with plt.savefig():

with im.save():

You're not saying how exactly putdata() is not behaving. I'm assuming you're doing

>>> pic.putdata(a)
Traceback (most recent call last):
  File "...blablabla.../PIL/Image.py", line 1185, in putdata
    self.im.putdata(data, scale, offset)
SystemError: new style getargs format but argument is not a tuple

This is because putdata expects a sequence of tuples and you're giving it a numpy array. This

>>> data = list(tuple(pixel) for pixel in pix)
>>> pic.putdata(data)

will work but it is very slow.

As of PIL 1.1.6, the "proper" way to convert between images and numpy arrays is simply

>>> pix = numpy.array(pic)

although the resulting array is in a different format than yours (3-d array or rows/columns/rgb in this case).

Then, after you make your changes to the array, you should be able to do either pic.putdata(pix) or create a new image with Image.fromarray(pix).

You don't need to reshape. This is what rollaxis is for:

Image.fromarray(np.rollaxis(img, 0,3))