8000 BUG: open genfromtxt file as binary; add test for filename use by matthew-brett · Pull Request #64 · numpy/numpy · GitHub
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BUG: open genfromtxt file as binary; add test for filename use #64

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Merged
merged 1 commit into from
Mar 31, 2011
Merged

BUG: open genfromtxt file as binary; add test for filename use #64

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2 chang 8000 es: 1 addition & 1 deletion numpy/lib/npyio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1202,7 +1202,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
# Initialize the filehandle, the LineSplitter and the NameValidator
own_fhd = False
if isinstance(fname, basestring):
fhd = np.lib._datasource.open(fname, 'U')
fhd = np.lib._datasource.open(fname, 'Ub')
own_fhd = True
elif not hasattr(fname, 'read'):
raise TypeError("The input should be a string or a filehandle. "\
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21 changes: 20 additions & 1 deletion numpy/lib/tests/test_io.py
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9535
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
import numpy as np
import numpy.ma as ma
from numpy.ma.testutils import *
from numpy.ma.testutils import (TestCase, assert_equal, assert_array_equal,
assert_raises, run_module_suite)
from numpy.testing import assert_warns

import sys
Expand Down Expand Up @@ -1248,6 +1249,24 @@ def test_recfromcsv(self):
self.assertTrue(isinstance(test, np.recarray))
assert_equal(test, control)

def test_gft_filename(self):
# Test that we can load data from a filename as well as a file object
data = '0 1 2\n3 4 5'
exp_res = np.arange(6).reshape((2,3))
assert_array_equal(np.genfromtxt(StringIO(data)), exp_res)
f, name = mkstemp()
# Thanks to another windows brokeness, we can't use
# NamedTemporaryFile: a file created from this function cannot be
# reopened by another open call. So we first put the string
# of the test reference array, write it to a securely opened file,
# which is then read from by the loadtxt function
try:
os.write(f, asbytes(data))
assert_array_equal(np.genfromtxt(name), exp_res)
finally:
os.close(f)
os.unlink(name)


def test_gzip_load():
a = np.random.random((5, 5))
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