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## 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()
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