Fast ascii loader to NumPy arrays

It is well known [1] [2] that numpy.loadtxt is not particularly fast in loading simple text files containing numbers.

I have been googling around for alternatives, and of course I stumbled across pandas.read_csv and astropy io.ascii. However, these readers don’t appear to be easy to decouple from their library, and I’d like to avoid adding a 200 MB, 5-seconds-import-time gorilla just for reading some ascii files.

The files I usually read are simple, no missing data, no malformed rows, no NaNs, floating point only, space or comma separated. But I need numpy arrays as output.

Does anyone know if any of the parsers above can be used standalone or about any other quick parser I could use?

Thank you in advance.

[1] Numpy loading csv TOO slow compared to Matlab

[2] http://wesmckinney.com/blog/a-new-high-performance-memory-efficient-file-parser-engine-for-pandas/

[Edit 1]

For the sake of clarity and to reduce background noise: as I stated at the beginning, my ascii files contain simple floats, no scientific notation, no fortran specific data, no funny stuff, no nothing but simple floats.

Sample:

{

arr = np.random.rand(1000,100)
np.savetxt('float.csv',arr)

}