[Numpy-discussion] loadtxt stop

Zachary Pincus zachary.pincus at yale.edu
Fri Sep 17 15:50:27 EDT 2010


> Though, really, it's annoying that numpy.loadtxt needs both the
> readline function *and* the iterator protocol. If it just used
> iterators, you could do:
>
> def truncator(fh, delimiter='END'):
>   for line in fh:
>     if line.strip() == delimiter:
>       break
>     yield line
>
> numpy.loadtxt(truncator(c))
>
> Maybe I'll try to work up a patch for this.


That seemed easy... worth applying? Won't break compatibility, because  
the previous loadtxt required both fname.readline and fname.__iter__,  
while this requires only the latter.


Index: numpy/lib/npyio.py
===================================================================
--- numpy/lib/npyio.py	(revision 8716)
+++ numpy/lib/npyio.py	(working copy)
@@ -597,10 +597,11 @@
              fh = bz2.BZ2File(fname)
          else:
              fh = open(fname, 'U')
-    elif hasattr(fname, 'readline'):
-        fh = fname
      else:
-        raise ValueError('fname must be a string or file handle')
+      try:
+          fh = iter(fname)
+      except:
+          raise ValueError('fname must be a string or file handle')
      X = []

      def flatten_dtype(dt):
@@ -633,14 +634,18 @@

          # Skip the first `skiprows` lines
          for i in xrange(skiprows):
-            fh.readline()
+            try:
+                fh.next()
+            except StopIteration:
+                raise IOError('End-of-file reached before  
encountering data.')

          # Read until we find a line with some values, and use
          # it to estimate the number of columns, N.
          first_vals = None
          while not first_vals:
-            first_line = fh.readline()
-            if not first_line: # EOF reached
+            try:
+                first_line = fh.next()
+            except StopIteration:
                  raise IOError('End-of-file reached before  
encountering data.')
              first_vals = split_line(first_line)
          N = len(usecols or first_vals)




More information about the NumPy-Discussion mailing list