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).

I highly recommend you use the tobytes method of the Image object. After some timing checks, this is much more efficient.

def jpg_image_to_array(image_path):
  """
  Loads JPEG image into 3D Numpy array of shape 
  (width, height, channels)
  """
  with Image.open(image_path) as image:         
    im_arr = np.fromstring(image.tobytes(), dtype=np.uint8)
    im_arr = im_arr.reshape((image.size[1], image.size[0], 3))                                   
  return im_arr

The timings I ran on my laptop show

In [76]: %timeit np.fromstring(im.tobytes(), dtype=np.uint8)
1000 loops, best of 3: 230 µs per loop

In [77]: %timeit np.array(im.getdata(), dtype=np.uint8)
10 loops, best of 3: 114 ms per loop

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():

Not entirely sure what you are trying to achieve but if you just want to transform the image to a numpy array and back, the following works:

from PIL import Image
import numpy as np

def array_image_save(array, image_path ='plane_2.bmp'):
  image = Image.fromarray(array)
  image.save(image_path)
  print("Saved image: {}".format(image_path))

im = Image.open('plane.bmp')
array_image_save(np.array(im))

You can just pass a PIL image to np.array and it takes care of the proper shaping. The reason you get distorted data is because you convert the pil image to greyscale (.convert('L')) but then try to save it as RGB.