I have an array of floats that I have normalised to one (i.e. the largest number in the array is 1), and I wanted to use it as colour indices for a graph. In using matplotlib to use grayscale, this requires using strings between 0 and 1, so I wanted to convert the array of floats to an array of strings. I was attempting to do this by using "astype('str')", but this appears to create some values that are not the same (or even close) to the originals.
I notice this because matplotlib complains about finding the number 8 in the array, which is odd as it was normalised to one!
In short, I have an array phis, of float64, such that:
numpy.where(phis.astype('str').astype('float64') != phis)
is non empty. This is puzzling as (hopefully naively) it appears to be a bug in numpy, is there anything that I could have done wrong to cause this?
Edit: after investigation this appears to be due to the way the string function handles high precision floats. Using a vectorized toString function (as from robbles answer), this is also the case, however if the lambda function is:
lambda x: "%.2f" % x
Then the graphing works - curiouser and curiouser. (Obviously the arrays are no longer equal however!)