diff options
Diffstat (limited to 'numpy/lib/npyio.py')
| -rw-r--r-- | numpy/lib/npyio.py | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index b91bf440f..6c34e95fe 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -248,7 +248,6 @@ class NpzFile(Mapping): else: raise KeyError("%s is not a file in the archive" % key) - # deprecate the python 2 dict apis that we supported by accident in # python 3. We forgot to implement itervalues() at all in earlier # versions of numpy, so no need to deprecated it here. @@ -1465,7 +1464,7 @@ def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', @set_module('numpy') def fromregex(file, regexp, dtype, encoding=None): - """ + r""" Construct an array from a text file, using regular expression parsing. The returned array is always a structured array, and is constructed from @@ -1483,7 +1482,7 @@ def fromregex(file, regexp, dtype, encoding=None): Regular expression used to parse the file. Groups in the regular expression correspond to fields in the dtype. dtype : dtype or list of dtypes - Dtype for the structured array. + Dtype for the structured array; must be a structured datatype. encoding : str, optional Encoding used to decode the inputfile. Does not apply to input streams. @@ -1512,12 +1511,11 @@ def fromregex(file, regexp, dtype, encoding=None): Examples -------- - >>> f = open('test.dat', 'w') - >>> _ = f.write("1312 foo\\n1534 bar\\n444 qux") - >>> f.close() + >>> from io import StringIO + >>> text = StringIO("1312 foo\n1534 bar\n444 qux") - >>> regexp = r"(\\d+)\\s+(...)" # match [digits, whitespace, anything] - >>> output = np.fromregex('test.dat', regexp, + >>> regexp = r"(\d+)\s+(...)" # match [digits, whitespace, anything] + >>> output = np.fromregex(text, regexp, ... [('num', np.int64), ('key', 'S3')]) >>> output array([(1312, b'foo'), (1534, b'bar'), ( 444, b'qux')], @@ -1535,6 +1533,8 @@ def fromregex(file, regexp, dtype, encoding=None): try: if not isinstance(dtype, np.dtype): dtype = np.dtype(dtype) + if dtype.names is None: + raise TypeError('dtype must be a structured datatype.') content = file.read() if isinstance(content, bytes) and isinstance(regexp, str): @@ -1634,7 +1634,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns. names : {None, True, str, sequence}, optional If `names` is True, the field names are read from the first line after - the first `skip_header` lines. This line can optionally be preceeded + the first `skip_header` lines. This line can optionally be preceded by a comment delimiter. If `names` is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If `names` is None, the names of the dtype |