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Diffstat (limited to 'numpy/core/numeric.py')
| -rw-r--r-- | numpy/core/numeric.py | 171 |
1 files changed, 0 insertions, 171 deletions
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 |