diff options
Diffstat (limited to 'numpy/lib/npyio.py')
| -rw-r--r-- | numpy/lib/npyio.py | 29 |
1 files changed, 16 insertions, 13 deletions
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index dc1c951e7..7598b2c6b 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -424,7 +424,7 @@ def load(file, mmap_mode=None, allow_pickle=True, fix_imports=True, "non-pickled data") try: return pickle.load(fid, **pickle_kwargs) - except: + except Exception: raise IOError( "Failed to interpret file %s as a pickle" % repr(file)) finally: @@ -443,6 +443,8 @@ def save(file, arr, allow_pickle=True, fix_imports=True): then the filename is unchanged. If file is a string or Path, a ``.npy`` extension will be appended to the file name if it does not already have one. + arr : array_like + Array data to be saved. allow_pickle : bool, optional Allow saving object arrays using Python pickles. Reasons for disallowing pickles include security (loading pickled data can execute arbitrary @@ -456,8 +458,6 @@ def save(file, arr, allow_pickle=True, fix_imports=True): pickled in a Python 2 compatible way. If `fix_imports` is True, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2. - arr : array_like - Array data to be saved. See Also -------- @@ -737,7 +737,7 @@ def _getconv(dtype): return np.longdouble elif issubclass(typ, np.floating): return floatconv - elif issubclass(typ, np.complex): + elif issubclass(typ, complex): return lambda x: complex(asstr(x)) elif issubclass(typ, np.bytes_): return asbytes @@ -1014,7 +1014,7 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, if len(vals) == 0: continue if usecols: - vals = [vals[i] for i in usecols] + vals = [vals[j] for j in usecols] if len(vals) != N: line_num = i + skiprows + 1 raise ValueError("Wrong number of columns at line %d" @@ -1071,7 +1071,7 @@ def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', If the filename ends in ``.gz``, the file is automatically saved in compressed gzip format. `loadtxt` understands gzipped files transparently. - X : array_like + X : 1D or 2D array_like Data to be saved to a text file. fmt : str or sequence of strs, optional A single format (%10.5f), a sequence of formats, or a @@ -1201,7 +1201,10 @@ def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='', X = np.asarray(X) # Handle 1-dimensional arrays - if X.ndim == 1: + if X.ndim == 0 or X.ndim > 2: + raise ValueError( + "Expected 1D or 2D array, got %dD array instead" % X.ndim) + elif X.ndim == 1: # Common case -- 1d array of numbers if X.dtype.names is None: X = np.atleast_2d(X).T @@ -1902,16 +1905,16 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, # If the dtype is uniform, don't define names, else use '' base = set([c.type for c in converters if c._checked]) if len(base) == 1: - (ddtype, mdtype) = (list(base)[0], np.bool) + (ddtype, mdtype) = (list(base)[0], bool) else: ddtype = [(defaultfmt % i, dt) for (i, dt) in enumerate(column_types)] if usemask: - mdtype = [(defaultfmt % i, np.bool) + mdtype = [(defaultfmt % i, bool) for (i, dt) in enumerate(column_types)] else: ddtype = list(zip(names, column_types)) - mdtype = list(zip(names, [np.bool] * len(column_types))) + mdtype = list(zip(names, [bool] * len(column_types))) output = np.array(data, dtype=ddtype) if usemask: outputmask = np.array(masks, dtype=mdtype) @@ -1937,7 +1940,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, # Now, process the rowmasks the same way if usemask: rowmasks = np.array( - masks, dtype=np.dtype([('', np.bool) for t in dtype_flat])) + masks, dtype=np.dtype([('', bool) for t in dtype_flat])) # Construct the new dtype mdtype = make_mask_descr(dtype) outputmask = rowmasks.view(mdtype) @@ -1968,9 +1971,9 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, output = np.array(data, dtype) if usemask: if dtype.names: - mdtype = [(_, np.bool) for _ in dtype.names] + mdtype = [(_, bool) for _ in dtype.names] else: - mdtype = np.bool + mdtype = bool outputmask = np.array(masks, dtype=mdtype) # Try to take care of the missing data we missed names = output.dtype.names |