@@ -15,7 +15,7 @@ but always 1-d) --- only has .copy and .__array__ attributes of an array!!! If you used typecode characters: -'c' -> 'S1' +'c' -> 'S1' or 'c' 'b' -> 'B' '1' -> 'b' 's' -> 'h' @@ -35,12 +35,12 @@ using PyMemData_FREE(ptr); a->descr->zero --> PyArray_Zero(a) a->descr->one --> PyArray_One(a) -Numeric/arrayobject.h --> numpy/arrayobject.h +Numeric/arrayobject.h --> numpy/oldnumeric.h # These will actually work and are defines for PyArray_BYTE, # but you really should change it in your code -PyArray_CHAR --> PyArray_BYTE +PyArray_CHAR --> PyArray_CHAR (or PyArray_STRING which is more flexible) PyArray_SBYTE --> PyArray_BYTE COMPATIBILITY @@ -12,7 +12,7 @@ __all__ = ['newaxis', 'ndarray', 'flatiter', 'ufunc', 'array_repr', 'array_str', 'set_string_function', 'little_endian', 'require', 'fromiter', 'array_equal', 'array_equiv', - 'indices', 'fromfunction', + 'indices', 'fromfunction', 'loadtxt', 'savetxt', 'load', 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones', 'identity', 'allclose', 'compare_chararrays', 'putmask', 'seterr', 'geterr', 'setbufsize', 'getbufsize', @@ -610,6 +610,177 @@ 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.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 numpy/core/numeric.py aba6f27ca5f699de521fbb1a1784b51006bba559 oliphant oliphant@94b884b6-d6fd-0310-90d3-974f1d3f35e1 http://github.com/cournape/numpy/commit/31d778a494341843f83951c854cb60567e641e91 31d778a494341843f83951c854cb60567e641e91 2007-04-21T21:29:42-07:00 2007-04-21T21:29:42-07:00 Add loadtxt and savetxt adapted from matplotlib. git-svn-id: http://svn.scipy.org/svn/numpy/trunk@3722 94b884b6-d6fd-0310-90d3-974f1d3f35e1 c4ace75a4975175b5b2b4d12690c7d64be161799 oliphant oliphant@94b884b6-d6fd-0310-90d3-974f1d3f35e1