diff options
Diffstat (limited to 'numpy/lib/npyio.py')
| -rw-r--r-- | numpy/lib/npyio.py | 94 |
1 files changed, 56 insertions, 38 deletions
diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index aeccc2a24..9fbacaa22 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -500,7 +500,7 @@ def savez_compressed(file, *args, **kwds): Parameters ---------- - file : string + file : str File name of .npz file. args : Arguments Function arguments. @@ -683,19 +683,44 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, X = [] def flatten_dtype(dt): - """Unpack a structured data-type.""" + """Unpack a structured data-type, and produce re-packing info.""" if dt.names is None: # If the dtype is flattened, return. # If the dtype has a shape, the dtype occurs # in the list more than once. - return [dt.base] * int(np.prod(dt.shape)) + shape = dt.shape + if len(shape) == 0: + return ([dt.base], None) + else: + packing = [(shape[-1], tuple)] + if len(shape) > 1: + for dim in dt.shape[-2:0:-1]: + packing = [(dim*packing[0][0],packing*dim)] + packing = packing*shape[0] + return ([dt.base] * int(np.prod(dt.shape)), packing) else: types = [] + packing = [] for field in dt.names: tp, bytes = dt.fields[field] - flat_dt = flatten_dtype(tp) + flat_dt, flat_packing = flatten_dtype(tp) types.extend(flat_dt) - return types + packing.append((len(flat_dt),flat_packing)) + return (types, packing) + + def pack_items(items, packing): + """Pack items into nested lists based on re-packing info.""" + if packing == None: + return items[0] + elif packing is tuple: + return tuple(items) + else: + start = 0 + ret = [] + for length, subpacking in packing: + ret.append(pack_items(items[start:start+length], subpacking)) + start += length + return tuple(ret) def split_line(line): """Chop off comments, strip, and split at delimiter.""" @@ -724,7 +749,7 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, first_vals = split_line(first_line) N = len(usecols or first_vals) - dtype_types = flatten_dtype(dtype) + dtype_types, packing = flatten_dtype(dtype) if len(dtype_types) > 1: # We're dealing with a structured array, each field of # the dtype matches a column @@ -732,6 +757,8 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, else: # All fields have the same dtype converters = [defconv for i in xrange(N)] + if N > 1: + packing = [(N, tuple)] # By preference, use the converters specified by the user for i, conv in (user_converters or {}).iteritems(): @@ -753,27 +780,16 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, vals = [vals[i] for i in usecols] # Convert each value according to its column and store - X.append(tuple([conv(val) for (conv, val) in zip(converters, vals)])) + items = [conv(val) for (conv, val) in zip(converters, vals)] + # Then pack it according to the dtype's nesting + items = pack_items(items, packing) + + X.append(items) finally: if own_fh: fh.close() - if len(dtype_types) > 1: - # We're dealing with a structured array, with a dtype such as - # [('x', int), ('y', [('s', int), ('t', float)])] - # - # First, create the array using a flattened dtype: - # [('x', int), ('s', int), ('t', float)] - # - # Then, view the array using the specified dtype. - try: - X = np.array(X, dtype=np.dtype([('', t) for t in dtype_types])) - X = X.view(dtype) - except TypeError: - # In the case we have an object dtype - X = np.array(X, dtype=dtype) - else: - X = np.array(X, dtype) + X = np.array(X, dtype) X = np.squeeze(X) if unpack: @@ -1012,7 +1028,7 @@ def fromregex(file, regexp, dtype): return output finally: if own_fh: - fh.close() + file.close() @@ -1033,7 +1049,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, """ Load data from a text file, with missing values handled as specified. - Each line past the first `skiprows` lines is split at the `delimiter` + Each line past the first `skip_header` lines is split at the `delimiter` character, and characters following the `comments` character are discarded. Parameters @@ -1056,20 +1072,20 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, The numbers of lines to skip at the beginning of the file. skip_footer : int, optional The numbers of lines to skip at the end of the file - converters : variable or None, optional + converters : variable, optional The set of functions that convert the data of a column to a value. The converters can also be used to provide a default value for missing data: ``converters = {3: lambda s: float(s or 0)}``. - missing_values : variable or None, optional + missing_values : variable, optional The set of strings corresponding to missing data. - filling_values : variable or None, optional + filling_values : variable, optional The set of values to be used as default when the data are missing. - usecols : sequence or None, optional + usecols : sequence, optional Which columns to read, with 0 being the first. For example, ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns. names : {None, True, str, sequence}, optional If `names` is True, the field names are read from the first valid line - after the first `skiprows` lines. + after the first `skip_header` lines. If `names` is a sequence or a single-string of comma-separated names, the names will be used to define the field names in a structured dtype. If `names` is None, the names of the dtype fields will be used, if any. @@ -1202,9 +1218,10 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, # Get the first valid lines after the first skiprows ones .. if skiprows: - warnings.warn("The use of `skiprows` is deprecated.\n"\ - "Please use `skip_header` instead.", - DeprecationWarning) + warnings.warn(\ + "The use of `skiprows` is deprecated, it will be removed in numpy 2.0.\n" \ + "Please use `skip_header` instead.", + DeprecationWarning) skip_header = skiprows # Skip the first `skip_header` rows for i in xrange(skip_header): @@ -1269,7 +1286,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, elif (names is not None) and (len(names) > nbcols): names = [names[_] for _ in usecols] elif (names is not None) and (dtype is not None): - names = dtype.names + names = list(dtype.names) # Process the missing values ............................... @@ -1327,9 +1344,10 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, # Process the deprecated `missing` if missing != asbytes(''): - warnings.warn("The use of `missing` is deprecated.\n"\ - "Please use `missing_values` instead.", - DeprecationWarning) + warnings.warn(\ + "The use of `missing` is deprecated, it will be removed in Numpy 2.0.\n" \ + "Please use `missing_values` instead.", + DeprecationWarning) values = [str(_) for _ in missing.split(asbytes(","))] for entry in missing_values: entry.extend(values) @@ -1486,7 +1504,7 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, if _[0] > nbrows + skip_header]) invalid = invalid[:nbinvalid - nbinvalid_skipped] skip_footer -= nbinvalid_skipped -# +# # nbrows -= skip_footer # errmsg = [template % (i, nb) # for (i, nb) in invalid if i < nbrows] |