Python 3 builtins via six

docs/iris/example_code/General/custom_file_loading.py

@@ -65,7 +65,7 @@ def load_NAME_III(filename):
 
     # loading a file gives a generator of lines which can be progressed using the next() method.
     # This will come in handy as we wish to progress through the file line by line.
-    file_handle = file(filename)
+    file_handle = open(filename)
 
     # define a dictionary which can hold the header metadata about this file
     headers = {}

docs/iris/example_code/Meteorology/COP_maps.py

@@ -18,8 +18,9 @@
 
 
 """
+from six.moves import zip
+
 import os.path
-import itertools
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -65,7 +66,8 @@ def main():
 
 
     # Iterate over each latitude longitude slice for both e1 and a1b scenarios simultaneously
-    for e1_slice, a1b_slice in itertools.izip(e1.slices(['latitude', 'longitude']), a1b.slices(['latitude', 'longitude'])):
+    for e1_slice, a1b_slice in zip(e1.slices(['latitude', 'longitude']),
+                                   a1b.slices(['latitude', 'longitude'])):
 
         time_coord = a1b_slice.coord('time')
 

docs/iris/src/sphinxext/generate_package_rst.py

@@ -1,4 +1,4 @@
-# (C) British Crown Copyright 2010 - 2014, Met Office
+# (C) British Crown Copyright 2010 - 2015, Met Office
 #
 # This file is part of Iris.
 #
@@ -16,6 +16,7 @@
 # along with Iris.  If not, see <http://www.gnu.org/licenses/>.
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import filter
 
 import os
 import sys

docs/iris/src/userguide/regridding_plots/regridded_to_global_area_weighted.py

@@ -1,5 +1,6 @@
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import zip
 
 import iris
 import iris.analysis

lib/iris/__init__.py

@@ -369,7 +369,7 @@ def load_cubes(uris, constraints=None, callback=None):
     collection = _load_collection(uris, constraints, callback).merged()
 
     # Make sure we have exactly one merged cube per constraint
-    bad_pairs = filter(lambda pair: len(pair) != 1, collection.pairs)
+    bad_pairs = [pair for pair in collection.pairs if len(pair) != 1]
     if bad_pairs:
         fmt = '   {} -> {} cubes'
         bits = [fmt.format(pair.constraint, len(pair)) for pair in bad_pairs]

lib/iris/_concatenate.py

@@ -20,6 +20,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import range, zip
 
 from collections import defaultdict, namedtuple
 

lib/iris/_constraints.py

@@ -1,4 +1,4 @@
-# (C) British Crown Copyright 2010 - 2014, Met Office
+# (C) British Crown Copyright 2010 - 2015, Met Office
 #
 # This file is part of Iris.
 #
@@ -20,6 +20,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import range, zip
 
 import collections
 import operator

lib/iris/_merge.py

@@ -23,6 +23,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import map, range, zip
 
 from collections import namedtuple, OrderedDict
 from copy import deepcopy
@@ -1171,7 +1172,7 @@ def merge(self, unique=True):
             self._report_duplicate(nd_indexes, group_by_nd_index)
 
         # Generate group-depth merged cubes from the source-cubes.
-        for level in xrange(group_depth):
+        for level in range(group_depth):
             # Stack up all the data from all of the relevant source
             # cubes in a single biggus ArrayStack.
             # If it turns out that all the source cubes already had

lib/iris/analysis/__init__.py

@@ -47,6 +47,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import filter, range, zip
 
 import collections
 
@@ -92,8 +93,8 @@ def _first_coord_w_cube(self):
         as (cube, coord).
 
         """
-        return filter(lambda cube_coord: cube_coord[1] is not None,
-                      zip(self.cubes, self.coords))[0]
+        return next(filter(lambda cube_coord: cube_coord[1] is not None,
+                           zip(self.cubes, self.coords)))
 
     def __repr__(self):
         # No exact repr, so a helpful string is given instead
@@ -1074,7 +1075,7 @@ def interp_order(length):
                 column_peaks.append(column[0])
                 continue
 
-            tck = scipy.interpolate.splrep(range(column.size), column, k=k)
+            tck = scipy.interpolate.splrep(np.arange(column.size), column, k=k)
             npoints = column.size * 100
             points = np.linspace(0, column.size - 1, npoints)
             spline = scipy.interpolate.splev(points, tck)

lib/iris/analysis/_interpolation.py

@@ -1,4 +1,4 @@
-# (C) British Crown Copyright 2014, Met Office
+# (C) British Crown Copyright 2014 - 2015, Met Office
 #
 # This file is part of Iris.
 #
@@ -17,6 +17,7 @@
 """A collection of helpers for interpolation."""
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import range, zip
 
 from collections import namedtuple
 from itertools import product
@@ -500,7 +501,7 @@ def _points(self, sample_points, data, data_dims=None):
             instance of the interpolated data.
 
         """
-        dims = range(self._src_cube.ndim)
+        dims = list(range(self._src_cube.ndim))
         data_dims = data_dims or dims
 
         if len(data_dims) != data.ndim:
@@ -514,7 +515,7 @@ def _points(self, sample_points, data, data_dims=None):
             raise NotImplementedError(msg)
 
         # Broadcast the data into the shape of the original cube.
-        if data_dims != range(self._src_cube.ndim):
+        if data_dims != list(range(self._src_cube.ndim)):
             strides = list(data.strides)
             for dim in range(self._src_cube.ndim):
                 if dim not in data_dims:

lib/iris/analysis/_scipy_interpolate.py

@@ -1,5 +1,6 @@
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import range, zip
 
 import numpy as np
 
@@ -22,7 +23,7 @@ def _ndim_coords_from_arrays(points, ndim=None):
         points = points[0]
     if isinstance(points, tuple):
         p = np.broadcast_arrays(*points)
-        for j in xrange(1, len(p)):
+        for j in range(1, len(p)):
             if p[j].shape != p[0].shape:
                 raise ValueError(
                     "coordinate arrays do not have the same shape")

lib/iris/analysis/calculus.py

@@ -22,6 +22,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import filter, zip
 
 import re
 import warnings

lib/iris/analysis/cartography.py

@@ -20,9 +20,9 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import zip
 
 import copy
-import itertools
 import warnings
 
 import numpy as np
@@ -108,10 +108,10 @@ def rotate_pole(lons, lats, pole_lon, pole_lat):
 
 
 def _get_lat_lon_coords(cube):
-    lat_coords = filter(lambda coord: "latitude" in coord.name(),
-                        cube.coords())
-    lon_coords = filter(lambda coord: "longitude" in coord.name(),
-                        cube.coords())
+    lat_coords = [coord for coord in cube.coords()
+                  if "latitude" in coord.name()]
+    lon_coords = [coord for coord in cube.coords()
+                  if "longitude" in coord.name()]
     if len(lat_coords) > 1 or len(lon_coords) > 1:
         raise ValueError(
             "Calling _get_lat_lon_coords() with multiple lat or lon coords"
@@ -372,7 +372,7 @@ def area_weights(cube, normalize=False):
     # Now we create an array of weights for each cell. This process will
     # handle adding the required extra dimensions and also take care of
     # the order of dimensions.
-    broadcast_dims = filter(lambda x: x is not None, (lat_dim, lon_dim))
+    broadcast_dims = [x for x in (lat_dim, lon_dim) if x is not None]
     wshape = []
     for idim, dim in zip((0, 1), (lat_dim, lon_dim)):
         if dim is not None:
@@ -421,8 +421,8 @@ def cosine_latitude_weights(cube):
 
     """
     # Find all latitude coordinates, we want one and only one.
-    lat_coords = filter(lambda coord: "latitude" in coord.name(),
-                        cube.coords())
+    lat_coords = [coord for coord in cube.coords()
+                  if "latitude" in coord.name()]
     if len(lat_coords) > 1:
         raise ValueError("Multiple latitude coords are currently disallowed.")
     try:
@@ -455,7 +455,7 @@ def cosine_latitude_weights(cube):
 
     # Create weights for each grid point. This operation handles adding extra
     # dimensions and also the order of the dimensions.
-    broadcast_dims = filter(lambda x: x is not None, lat_dims)
+    broadcast_dims = [x for x in lat_dims if x is not None]
     wshape = []
     for idim, dim in enumerate(lat_dims):
         if dim is not None:
@@ -641,7 +641,7 @@ def project(cube, target_proj, nx=None, ny=None):
 
     # Step through cube data, regrid onto desired projection and insert results
     # in new_data array
-    for index, ll_slice in itertools.izip(index_it, slice_it):
+    for index, ll_slice in zip(index_it, slice_it):
         # Regrid source data onto target grid
         index = list(index)
         index[xdim] = slice(None, None)

lib/iris/analysis/geometry.py

@@ -1,4 +1,4 @@
-# (C) British Crown Copyright 2010 - 2014, Met Office
+# (C) British Crown Copyright 2010 - 2015, Met Office
 #
 # This file is part of Iris.
 #
@@ -23,6 +23,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import range
 
 import warnings
 

lib/iris/analysis/interpolate.py

@@ -22,6 +22,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import map, range, zip
 
 import collections
 import warnings
@@ -58,7 +59,7 @@ def _cartesian_sample_points(sample_points, sample_point_coord_names):
 
     # Find lat and lon coord indices
     i_lat = i_lon = None
-    i_non_latlon = range(len(sample_point_coord_names))
+    i_non_latlon = list(range(len(sample_point_coord_names)))
     for i, name in enumerate(sample_point_coord_names):
         if "latitude" in name:
             i_lat = i
@@ -730,7 +731,7 @@ def __call__(self, requested_x):
 
                 r = self._interpolator(requested_x[ok])
                 # Reshape the properly formed array to put the interpolator.axis last i.e. dims 0, 1, 2 -> 0, 2, 1 if axis = 1
-                axes = range(r.ndim)
+                axes = list(range(r.ndim))
                 del axes[self._interpolator.axis]
                 axes.append(self._interpolator.axis)
 
@@ -748,7 +749,7 @@ def __call__(self, requested_x):
                 grad = (self.y[..., -1:] - self.y[..., -2:-1]) / (self.x[-1] - self.x[-2])
                 result[interpolator_result_index] = self.y[..., -1:] + (requested_x[gt] - self.x[-1]) * grad
 
-            axes = range(len(interpolator_result_index))
+            axes = list(range(len(interpolator_result_index)))
             axes.insert(self._interpolator.axis, axes.pop(axes[-1]))
             result = result.transpose(axes)
 

lib/iris/analysis/maths.py

@@ -20,6 +20,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import filter
 
 import warnings
 import math

lib/iris/analysis/trajectory.py

@@ -1,4 +1,4 @@
-# (C) British Crown Copyright 2010 - 2014, Met Office
+# (C) British Crown Copyright 2010 - 2015, Met Office
 #
 # This file is part of Iris.
 #
@@ -20,6 +20,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import range
 
 import math
 
@@ -76,7 +77,8 @@ def __init__(self, waypoints, sample_count=10):
         self.sample_count = sample_count
 
         # create line segments from the waypoints
-        segments = [_Segment(self.waypoints[i], self.waypoints[i+1]) for i in range(len(self.waypoints) - 1)]
+        segments = [_Segment(self.waypoints[i], self.waypoints[i+1])
+                    for i in range(len(self.waypoints) - 1)]
 
         # calculate our total length
         self.length = sum([seg.length for seg in segments])

lib/iris/aux_factory.py

@@ -20,6 +20,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import map, range, zip
 
 from abc import ABCMeta, abstractmethod, abstractproperty
 import warnings
@@ -288,7 +289,7 @@ def _nd_points(coord, dims, ndim):
         sorted_pairs = sorted(enumerate(dims), key=lambda pair: pair[1])
         transpose_order = [pair[0] for pair in sorted_pairs]
         points = coord._points
-        if dims and transpose_order != range(len(dims)):
+        if dims and transpose_order != list(range(len(dims))):
             points = points.transpose(transpose_order)
 
         # Expand dimensionality to be consistent with the Cube.
@@ -322,7 +323,7 @@ def _remap(self, dependency_dims, derived_dims):
                 # no transpose is needed.
                 if derived_dims:
                     keys = tuple(slice(None) if dim in derived_dims else 0 for
-                                 dim in xrange(ndim))
+                                 dim in range(ndim))
                     nd_points = nd_points[keys]
             else:
                 # If no coord, treat value as zero.

lib/iris/coord_categorisation.py

@@ -28,6 +28,7 @@
 """
 
 from __future__ import (absolute_import, division, print_function)
+from six.moves import range
 
 import calendar
 import collections
@@ -216,7 +217,7 @@ def _months_in_season(season):
         # Can't match the season, raise an error.
         raise ValueError('unrecognised season: {!s}'.format(season))
     m1 = m0 + len(season)
-    return map(lambda month: (month % 12) + 1, range(m0, m1))
+    return [(month % 12) + 1 for month in range(m0, m1)]
 
 
 def _validate_seasons(seasons):
@@ -233,13 +234,13 @@ def _validate_seasons(seasons):
     for season in seasons:
         c.update(_months_in_season(season))
     # Make a list of months that are not present...
-    not_present = [calendar.month_abbr[month] for month in xrange(1, 13)
+    not_present = [calendar.month_abbr[month] for month in range(1, 13)
                    if month not in c]
     if not_present:
         raise ValueError('some months do not appear in any season: '
                          '{!s}'.format(', '.join(not_present)))
     # Make a list of months that appear multiple times...
-    multi_present = [calendar.month_abbr[month] for month in xrange(1, 13)
+    multi_present = [calendar.month_abbr[month] for month in range(1, 13)
                      if c[month] > 1]
     if multi_present:
         raise ValueError('some months appear in more than one season: '
@@ -258,8 +259,9 @@ def _month_year_adjusts(seasons):
     month_year_adjusts = [None, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
     for season in seasons:
         months = _months_in_season(season)
-        for month in filter(lambda m: m > months[-1], months):
-            month_year_adjusts[month] = 1
+        for month in months:
+            if month > months[-1]:
+                month_year_adjusts[month] = 1
     return month_year_adjusts