## Automatically adapted for scipy Oct 19, 2005 by convertcode.py

#!/usr/bin/env python

# This python script tests the numpyio module.
# also check out numpyio.fread.__doc__ and other method docstrings.

import os
from numpy.testing import *
set_package_path()
import io
from io import numpyio
restore_path()


import numpy.oldnumeric as N
import tempfile

class test_numpyio(NumpyTestCase):
    def check_basic(self):
        # Generate some data
        a = 255*rand(20)
        # Open a file
        fname = tempfile.mktemp('.dat')
        fid = open(fname,"wb")
        # Write the data as shorts
        numpyio.fwrite(fid,20,a,N.Int16)
        fid.close()
        # Reopen the file and read in data
        fid = open(fname,"rb")
        print "\nDon't worry about a warning regarding the number of bytes read."
        b = numpyio.fread(fid,1000000,N.Int16,N.Int)
        fid.close()
        assert(N.product(a.astype(N.Int16) == b,axis=0))
        os.remove(fname)

class test_read_array(NumpyTestCase):
    def check_complex(self):
        a = rand(13,4) + 1j*rand(13,4)
        fname = tempfile.mktemp('.dat')
        io.write_array(fname,a)
        b = io.read_array(fname,atype=N.Complex)
        assert_array_almost_equal(a,b,decimal=4)
        os.remove(fname)

    def check_float(self):
        a = rand(3,4)*30
        fname = tempfile.mktemp('.dat')
        io.write_array(fname,a)
        b = io.read_array(fname)
        assert_array_almost_equal(a,b,decimal=4)
        os.remove(fname)

    def check_integer(self):
        from scipy import stats
        a = stats.randint.rvs(1,20,size=(3,4))
        fname = tempfile.mktemp('.dat')
        io.write_array(fname,a)
        b = io.read_array(fname,atype=a.dtype.char)
        assert_array_equal(a,b)
        os.remove(fname)

if __name__ == "__main__":
    NumpyTest().run()