From bcbba6f0917ad004b1468294128e3269c38f21c3 Mon Sep 17 00:00:00 2001
From: Robert Kern <robert.kern@gmail.com>
Date: Thu, 28 Feb 2008 06:42:48 +0000
Subject: loadtxt and savetxt moved to lib/io.py

---
 numpy/core/numeric.py | 171 --------------------------------------------------
 1 file changed, 171 deletions(-)

(limited to 'numpy/core/numeric.py')

diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index 00c70256f..e77b4098a 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -636,177 +636,6 @@ def load(file):
         file = _file(file,"rb")
     return _cload(file)
 
-# Adapted from matplotlib
-
-def _getconv(dtype):
-    typ = dtype.type
-    if issubclass(typ, bool_):
-        return lambda x: bool(int(x))
-    if issubclass(typ, integer):
-        return int
-    elif issubclass(typ, floating):
-        return float
-    elif issubclass(typ, complex):
-        return complex
-    else:
-        return str
-
-
-def _string_like(obj):
-    try: obj + ''
-    except (TypeError, ValueError): return 0
-    return 1
-
-def loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None,
-            skiprows=0, usecols=None, unpack=False):
-    """
-    Load ASCII data from fname into an array and return the array.
-
-    The data must be regular, same number of values in every row
-
-    fname can be a filename or a file handle.  Support for gzipped files is
-    automatic, if the filename ends in .gz
-
-    See scipy.io.loadmat to read and write matfiles.
-
-    Example usage:
-
-      X = loadtxt('test.dat')  # data in two columns
-      t = X[:,0]
-      y = X[:,1]
-
-    Alternatively, you can do the same with "unpack"; see below
-
-      X = loadtxt('test.dat')    # a matrix of data
-      x = loadtxt('test.dat')    # a single column of data
-
-
-    dtype - the data-type of the resulting array.  If this is a
-    record data-type, the the resulting array will be 1-d and each row will
-    be interpreted as an element of the array. The number of columns
-    used must match the number of fields in the data-type in this case.
-
-    comments - the character used to indicate the start of a comment
-    in the file
-
-    delimiter is a string-like character used to seperate values in the
-    file. If delimiter is unspecified or none, any whitespace string is
-    a separator.
-
-    converters, if not None, is a dictionary mapping column number to
-    a function that will convert that column to a float.  Eg, if
-    column 0 is a date string: converters={0:datestr2num}
-
-    skiprows is the number of rows from the top to skip
-
-    usecols, if not None, is a sequence of integer column indexes to
-    extract where 0 is the first column, eg usecols=(1,4,5) to extract
-    just the 2nd, 5th and 6th columns
-
-    unpack, if True, will transpose the matrix allowing you to unpack
-    into named arguments on the left hand side
-
-        t,y = load('test.dat', unpack=True) # for  two column data
-        x,y,z = load('somefile.dat', usecols=(3,5,7), unpack=True)
-
-    """
-
-    if _string_like(fname):
-        if fname.endswith('.gz'):
-            import gzip
-            fh = gzip.open(fname)
-        else:
-            fh = file(fname)
-    elif hasattr(fname, 'seek'):
-        fh = fname
-    else:
-        raise ValueError('fname must be a string or file handle')
-    X = []
-
-    dtype = multiarray.dtype(dtype)
-    defconv = _getconv(dtype)
-    converterseq = None
-    if converters is None:
-        converters = {}
-        if dtype.names is not None:
-            converterseq = [_getconv(dtype.fields[name][0]) \
-                            for name in dtype.names]
-
-    for i,line in enumerate(fh):
-        if i<skiprows: continue
-        line = line[:line.find(comments)].strip()
-        if not len(line): continue
-        vals = line.split(delimiter)
-        if converterseq is None:
-            converterseq = [converters.get(j,defconv) \
-                            for j in xrange(len(vals))]
-        if usecols is not None:
-            row = [converterseq[j](vals[j]) for j in usecols]
-        else:
-            row = [converterseq[j](val) for j,val in enumerate(vals)]
-        if dtype.names is not None:
-            row = tuple(row)
-        X.append(row)
-
-    X = array(X, dtype)
-    r,c = X.shape
-    if r==1 or c==1:
-        X.shape = max([r,c]),
-    if unpack: return X.T
-    else:  return X
-
-
-# adjust so that fmt can change across columns if desired.
-
-def savetxt(fname, X, fmt='%.18e',delimiter=' '):
-    """
-    Save the data in X to file fname using fmt string to convert the
-    data to strings
-
-    fname can be a filename or a file handle.  If the filename ends in .gz,
-    the file is automatically saved in compressed gzip format.  The load()
-    command understands gzipped files transparently.
-
-    Example usage:
-
-    save('test.out', X)         # X is an array
-    save('test1.out', (x,y,z))  # x,y,z equal sized 1D arrays
-    save('test2.out', x)        # x is 1D
-    save('test3.out', x, fmt='%1.4e')  # use exponential notation
-
-    delimiter is used to separate the fields, eg delimiter ',' for
-    comma-separated values
-    """
-
-    if _string_like(fname):
-        if fname.endswith('.gz'):
-            import gzip
-            fh = gzip.open(fname,'wb')
-        else:
-            fh = file(fname,'w')
-    elif hasattr(fname, 'seek'):
-        fh = fname
-    else:
-        raise ValueError('fname must be a string or file handle')
-
-
-    X = asarray(X)
-    origShape = None
-    if len(X.shape)==1:
-        origShape = X.shape
-        X.shape = len(X), 1
-    for row in X:
-        fh.write(delimiter.join([fmt%val for val in row]) + '\n')
-
-    if origShape is not None:
-        X.shape = origShape
-
-
-
-
-
-
-
 # These are all essentially abbreviations
 # These might wind up in a special abbreviations module
 
-- 
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