I have a data file with only one line like:
1.2 2.1 3.2
I used numpy version 1.3.0 loadtxt to load it
a,b,c = loadtxt("data.dat", usecols(0,1,2), unpack=True)
The output was a float instead of array like
a = 1.2
I expect it would be:
a = array([1.2])
If i read a file with multiple lines, it's working.
Simply use the numpy's inbuit loadtxt parameter ndmin.
a,b,c=np.loadtxt('data.dat',ndmin=2,unpack=True)
output
a=[1.2]
ndim was implemented in numpy 1.6 which is not the version used by OP.
What is happening is that when you load the array you obtain a monodimensional one. When you unpack it, it obtain a set of numbers, i.e. array without dimension. This is because when you unpack an array, it decrease it's number of dimension by one. starting with a monodimensional array, it boil down to a simple number.
If you test for the type of a, it is not a float, but a numpy.float, that has all the properties of an array but a void tuple as shape. So it is an array, just is not represented as one.
If what you need is a monodimensional array with just one element, the simplest way is to reshape your array before unpacking it:
#note the reshape function to transform the shape
a,b,c = loadtxt("text.txt").reshape((-1,1))
This gives you the expected result. What is happening is that whe reshaped it into a bidimensional array, so that when you unpack it, the number of dimensions go down to one.
If you need it to work normally for multidimensional array and to keep one-dimensional when you read onedimensional array, I thik that the best way is to read normally with loadtxt and reshape you arrays in a second phase, converting them to monodimensional if they are pure numbers
a,b,c = loadtxt("text.txt",unpack=True)
for e in [a,b,c]
e.reshape(e.shape if e.shape else (-1,))
The simple way without using reshape is, to explicitly typecast the list
a,b,c = loadtxt("data.dat", usecols(0,1,2), unpack=True)
a,b,c = (a,b,c) if usi.shape else ([a], [b], [c])
This works faster than the reshape!