[NumPy-Tickets] [NumPy] #1562: potentially unexpected results with loadtxt()

[NumPy Trac]

#1562: potentially unexpected results with loadtxt()
------------------------+---------------------------------------------------
 Reporter:  weathergod  |       Owner:  somebody    
     Type:  defect      |      Status:  needs_review
 Priority:  normal      |   Milestone:  1.5.1       
Component:  Other       |     Version:              
 Keywords:              |  
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Changes (by dynetrekk):

 * cc: paul.anton.letnes@… (added)
  * status:  new => needs_review


Comment:

 A suggested implementation can be found here. If requested, the shape is
 simply modified to contain enough ones to ensure the minimum number of
 dimensions.

 {{{
 diff -r 2763b87dd7e8 -r 438a92342252 numpy/lib/npyio.py
 --- a/numpy/lib/npyio.py        Fri Mar 25 22:37:19 2011 -0600
 +++ b/numpy/lib/npyio.py        Sat Mar 26 12:23:25 2011 +0100
 @@ -579,7 +579,8 @@


  def loadtxt(fname, dtype=float, comments='#', delimiter=None,
 -            converters=None, skiprows=0, usecols=None, unpack=False):
 +            converters=None, skiprows=0, usecols=None, unpack=False,
 +            ndmin=None):
      """
      Load data from a text file.

 @@ -616,6 +617,9 @@
      unpack : bool, optional
          If True, the returned array is transposed, so that arguments may
 be
          unpacked using ``x, y, z = loadtxt(...)``.  The default is False.
 +    ndmin : int, optional
 +        If not None, the returned array must have at least `ndmin`
 dimensions.
 +        Legal values: 1 or 2.

      Returns
      -------
 @@ -790,8 +794,20 @@
              fh.close()

      X = np.array(X, dtype)
 +    X = np.squeeze(X)

 -    X = np.squeeze(X)
 +    # Verify that the array has at least dimensions `ndmin`.
 +    if not (ndmin is None):
 +        # Check correctness of the values of `ndmin`
 +        if not (ndmin in (1, 2)):
 +            msg = 'Illegal value of ndmin keyword: {0}'
 +            raise ValueError(msg.format(ndmin))
 +        # Tweak the size and shape of the arrays
 +        if not (len(X.shape) >= ndmin):
 +            if ndmin == 1:
 +                X.shape = (X.size, )
 +            elif ndmin == 2:
 +                X.shape = (X.size, 1)
      if unpack:
          return X.T
      else:

 }}}

-- 
Ticket URL: <http://projects.scipy.org/numpy/ticket/1562#comment:1>
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