How to create structured char arrays?
Hi, I think that I have resolved my issue down to creating a structured string array. I am using numpy version '2.0.0.dev-3c338cb'. Without a structured array, it should be a 2 by 2 array:
np.array([('a','b'),('c','d')]) array([['a', 'b'], ['c', 'd']], dtype='|S1') np.array([('a','b'),('c','d')], dtype='a2') array([['a', 'b'], ['c', 'd']], dtype='|S2')
It does not work as a structured array:
np.array([('a','b'),('c','d')], dtype=[('label','a2'), ('Value', 'a4')]) array([('a', 'b'), ('c', 'd')], dtype=[('label', '|S2'), ('Value', '|S4')])
What is the correct dtype argument to use in this case? Thanks Bruce
Hi Bruce,
I think that I have resolved my issue down to creating a structured string array. I am using numpy version '2.0.0.dev-3c338cb'.
Without a structured array, it should be a 2 by 2 array:
np.array([('a','b'),('c','d')]) array([['a', 'b'], ['c', 'd']], dtype='|S1') np.array([('a','b'),('c','d')], dtype='a2') array([['a', 'b'], ['c', 'd']], dtype='|S2')
It does not work as a structured array:
np.array([('a','b'),('c','d')], dtype=[('label','a2'), ('Value', 'a4')]) array([('a', 'b'), ('c', 'd')], dtype=[('label', '|S2'), ('Value', '|S4')])
What is the correct dtype argument to use in this case?
what do you mean by "does not work" - this should give you a 1 by 2 array with 2 fields, i.e.
c['label'] array(['a', 'c'], dtype='|S2') c['Value'] array(['b', 'd'], dtype='|S4')
and this should be the same as returned by loadtxt for
dt=np.dtype([('label', 'S2'), ('Value', 'S4')]) d = np.loadtxt(StringIO("a\tb\nc\td"), delimiter="\t", dtype=dt)
right? Maybe I should modify my test in #1071 to compare to the full array, like a = np.array([(1,2,3, asbytes('start ')), (4,5,6, asbytes(' ')), (7,8,9.5, asbytes(''))]) assert_array_equal(x, a) HTH, Derek
On 04/04/2011 02:30 PM, Derek Homeier wrote:
Hi Bruce,
I think that I have resolved my issue down to creating a structured string array. I am using numpy version '2.0.0.dev-3c338cb'.
Without a structured array, it should be a 2 by 2 array:
np.array([('a','b'),('c','d')]) array([['a', 'b'], ['c', 'd']], dtype='|S1') np.array([('a','b'),('c','d')], dtype='a2') array([['a', 'b'], ['c', 'd']], dtype='|S2')
It does not work as a structured array:
np.array([('a','b'),('c','d')], dtype=[('label','a2'), ('Value', 'a4')]) array([('a', 'b'), ('c', 'd')], dtype=[('label', '|S2'), ('Value', '|S4')])
What is the correct dtype argument to use in this case?
what do you mean by "does not work" - this should give you a 1 by 2 array with 2 fields, i.e.
c['label'] array(['a', 'c'], dtype='|S2') c['Value'] array(['b', 'd'], dtype='|S4')
and this should be the same as returned by loadtxt for
dt=np.dtype([('label', 'S2'), ('Value', 'S4')]) d = np.loadtxt(StringIO("a\tb\nc\td"), delimiter="\t", dtype=dt)
right? Maybe I should modify my test in #1071 to compare to the full array, like
a = np.array([(1,2,3, asbytes('start ')), (4,5,6, asbytes(' ')), (7,8,9.5, asbytes(''))]) assert_array_equal(x, a)
HTH, Derek
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Thanks, Yes I was not thinking carefully enough.
I think it is good to compare the full array because it ensures all elements should be the same in value and dtype. Bruce
participants (2)
-
Bruce Southey -
Derek Homeier