Merge pull request #177 from aykuznetsova/add-python2.6-compatibility

Add python2.6 compatibility

.travis.yml

@@ -1,6 +1,7 @@
 # Based on https://github.com/Jorge-C/ordination/blob/master/.travis.yml
 language: python
 python:
+  - "2.6"
   - "2.7"
   - "3.3"
 notifications:
@@ -28,11 +29,15 @@ matrix:
       env: UPDATE_PYENV='pip install pydap'
     - python: "2.7"
       env: UPDATE_PYENV=''
+    - python: "2.6"
+      env: UPDATE_PYENV=''
 
 # Install packages
 install:
   - conda create --yes -n test_env python=$TRAVIS_PYTHON_VERSION pip nose mock numpy pandas scipy netCDF4
   - source activate test_env
+  # install unittest2 ONLY if running 2.6
+  - if [ ${TRAVIS_PYTHON_VERSION:0:3} == "2.6" ]; then pip install unittest2; fi
   - echo $UPDATE_PYENV; $UPDATE_PYENV
   - python setup.py install
 # Run test

setup.py

@@ -28,6 +28,7 @@
     'Intended Audience :: Science/Research',
     'Programming Language :: Python',
     'Programming Language :: Python :: 2',
+    'Programming Language :: Python :: 2.6',
     'Programming Language :: Python :: 2.7',
     'Programming Language :: Python :: 3',
     'Programming Language :: Python :: 3.3',
@@ -37,6 +38,8 @@
 INSTALL_REQUIRES = ['numpy >= 1.7', 'pandas >= 0.13.1']
 TESTS_REQUIRE = ['nose >= 1.0']
 
+if sys.version_info[:2] < (2, 7):
+    TESTS_REQUIRE += ["unittest2 == 0.5.1"]
 
 DESCRIPTION = "Extended arrays for working with scientific datasets in Python"
 LONG_DESCRIPTION = """

test/__init__.py

@@ -1,12 +1,15 @@
-import unittest
-
 import numpy as np
 from numpy.testing import assert_array_equal
 
 from xray import utils, DataArray
 from xray.variable import as_variable
 from xray.pycompat import PY3
 
+try:
+    import unittest2 as unittest
+except ImportError:
+    import unittest
+
 try:
     import scipy
     has_scipy = True

test/test_backends.py

@@ -239,8 +239,8 @@ def test_open_encodings(self):
             actual = open_dataset(tmp_file)
 
             self.assertVariableEqual(actual['time'], expected['time'])
-            actual_encoding = {k: v for k, v in iteritems(actual['time'].encoding)
-                               if k in expected['time'].encoding}
+            actual_encoding = dict((k, v) for k, v in iteritems(actual['time'].encoding)
+                                   if k in expected['time'].encoding)
             self.assertDictEqual(actual_encoding, expected['time'].encoding)
 
     def test_open_group(self):

test/test_data_array.py

@@ -2,10 +2,9 @@
 import pandas as pd
 from copy import deepcopy
 from textwrap import dedent
-from collections import OrderedDict
 
 from xray import Dataset, DataArray, Index, Variable, align
-from xray.pycompat import iteritems
+from xray.pycompat import iteritems, OrderedDict
 from . import TestCase, ReturnItem, source_ndarray
 
 

test/test_dataset.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 from copy import copy, deepcopy
 from textwrap import dedent
 try:
@@ -11,7 +10,7 @@
 
 from xray import (Dataset, DataArray, Index, Variable,
                   backends, utils, align, indexing)
-from xray.pycompat import iteritems
+from xray.pycompat import iteritems, OrderedDict
 
 from . import TestCase
 
@@ -164,7 +163,7 @@ def test_indexes_properties(self):
 
         self.assertEquals(2, len(data.indexes))
 
-        self.assertEquals({'x', 'y'}, set(data.indexes))
+        self.assertEquals(set(['x', 'y']), set(data.indexes))
 
         self.assertVariableIdentical(data.indexes['x'], data['x'].variable)
         self.assertVariableIdentical(data.indexes['y'], data['y'].variable)
@@ -367,11 +366,11 @@ def test_drop_vars(self):
 
         self.assertEqual(data, data.drop_vars())
 
-        expected = Dataset({k: data[k] for k in data if k != 'time'})
+        expected = Dataset(dict((k, data[k]) for k in data if k != 'time'))
         actual = data.drop_vars('time')
         self.assertEqual(expected, actual)
 
-        expected = Dataset({k: data[k] for k in ['dim2', 'dim3', 'time']})
+        expected = Dataset(dict((k, data[k]) for k in ['dim2', 'dim3', 'time']))
         actual = data.drop_vars('dim1')
         self.assertEqual(expected, actual)
 
@@ -510,20 +509,20 @@ def test_setitem(self):
 
     def test_delitem(self):
         data = create_test_data()
-        all_items = {'time', 'dim1', 'dim2', 'dim3', 'var1', 'var2', 'var3'}
+        all_items = set(['time', 'dim1', 'dim2', 'dim3', 'var1', 'var2', 'var3'])
         self.assertItemsEqual(data, all_items)
         del data['var1']
-        self.assertItemsEqual(data, all_items - {'var1'})
+        self.assertItemsEqual(data, all_items - set(['var1']))
         del data['dim1']
-        self.assertItemsEqual(data, {'time', 'dim2', 'dim3'})
+        self.assertItemsEqual(data, set(['time', 'dim2', 'dim3']))
 
     def test_squeeze(self):
         data = Dataset({'foo': (['x', 'y', 'z'], [[[1], [2]]])})
         for args in [[], [['x']], [['x', 'z']]]:
             def get_args(v):
                 return [set(args[0]) & set(v.dimensions)] if args else []
-            expected = Dataset({k: v.squeeze(*get_args(v))
-                               for k, v in iteritems(data.variables)})
+            expected = Dataset(dict((k, v.squeeze(*get_args(v)))
+                                    for k, v in iteritems(data.variables)))
             self.assertDatasetIdentical(expected, data.squeeze(*args))
         # invalid squeeze
         with self.assertRaisesRegexp(ValueError, 'cannot select a dimension'):
@@ -595,8 +594,8 @@ def test_concat(self):
         def rectify_dim_order(dataset):
             # return a new dataset with all variable dimensions tranposed into
             # the order in which they are found in `data`
-            return Dataset({k: v.transpose(*data[k].dimensions)
-                           for k, v in iteritems(dataset.variables)},
+            return Dataset(dict((k, v.transpose(*data[k].dimensions))
+                                for k, v in iteritems(dataset.variables)),
                            dataset.attrs)
 
         for dim in ['dim1', 'dim2', 'dim3']:

test/test_utils.py

@@ -1,8 +1,8 @@
-from collections import OrderedDict
 import numpy as np
 import pandas as pd
 
 from xray import utils
+from xray.pycompat import OrderedDict
 from . import TestCase
 
 

test/test_variable.py

@@ -1,4 +1,4 @@
-from collections import namedtuple, OrderedDict
+from collections import namedtuple
 from copy import copy, deepcopy
 from datetime import datetime
 from textwrap import dedent
@@ -9,7 +9,7 @@
 from xray import Variable, Dataset, DataArray, indexing
 from xray.variable import (Index, as_variable, NumpyArrayAdapter,
                            PandasIndexAdapter, _as_compatible_data)
-from xray.pycompat import PY3
+from xray.pycompat import PY3, OrderedDict
 
 from . import TestCase, source_ndarray
 

xray/backends/memory.py

@@ -1,4 +1,4 @@
-from collections import OrderedDict
+from xray.pycompat import OrderedDict
 
 from .common import AbstractWritableDataStore
 

xray/backends/netCDF4_.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 import warnings
 
 import numpy as np
@@ -9,7 +8,7 @@
 from xray.conventions import encode_cf_variable
 from xray.utils import FrozenOrderedDict, NDArrayMixin
 from xray import indexing
-from xray.pycompat import iteritems, basestring, bytes_type
+from xray.pycompat import iteritems, basestring, bytes_type, OrderedDict
 
 
 class NetCDF4ArrayWrapper(NDArrayMixin):

xray/backends/scipy_.py

@@ -1,4 +1,3 @@
-from collections import OrderedDict
 try: # Python 2
     from cStringIO import StringIO as BytesIO
 except ImportError: # Python 3
@@ -9,7 +8,7 @@
 import xray
 from xray.backends.common import AbstractWritableDataStore
 from xray.utils import Frozen
-from xray.pycompat import iteritems, basestring, unicode_type
+from xray.pycompat import iteritems, basestring, unicode_type, OrderedDict
 
 from .. import conventions
 from .netcdf3 import is_valid_nc3_name, coerce_nc3_dtype, encode_nc3_variable

xray/conventions.py

@@ -1,16 +1,16 @@
 import numpy as np
 import pandas as pd
 import warnings
-from collections import defaultdict, OrderedDict
+from collections import defaultdict
 from datetime import datetime
 
 from . import indexing
 from . import utils
-from .pycompat import iteritems, bytes_type, unicode_type
+from .pycompat import iteritems, bytes_type, unicode_type, OrderedDict
 import xray
 
 # standard calendars recognized by netcdftime
-_STANDARD_CALENDARS = {'standard', 'gregorian', 'proleptic_gregorian'}
+_STANDARD_CALENDARS = set(['standard', 'gregorian', 'proleptic_gregorian'])
 
 
 def mask_and_scale(array, fill_value=None, scale_factor=None, add_offset=None,

xray/data_array.py

@@ -1,7 +1,7 @@
 import functools
 import operator
 import warnings
-from collections import defaultdict, OrderedDict
+from collections import defaultdict
 
 import numpy as np
 import pandas as pd
@@ -14,7 +14,7 @@
 from . import variable
 from .common import AbstractArray, AbstractIndexes
 from .utils import multi_index_from_product
-from .pycompat import iteritems, basestring
+from .pycompat import iteritems, basestring, OrderedDict
 
 
 def _is_dict_like(value):
@@ -639,8 +639,8 @@ def reduce(self, func, dimension=None, axis=None, keep_attrs=False,
         # For now, take an aggressive strategy of removing all variables
         # associated with any dropped dimensions
         # TODO: save some summary (mean? bounds?) of dropped variables
-        drop |= {k for k, v in iteritems(self.dataset.variables)
-                 if any(dim in drop for dim in v.dimensions)}
+        drop |= set(k for k, v in iteritems(self.dataset.variables)
+                    if any(dim in drop for dim in v.dimensions))
         ds = self.dataset.drop_vars(*drop)
         ds[self.name] = var
 
@@ -702,7 +702,9 @@ def concat(cls, arrays, dimension='concat_dimension', indexers=None,
             datasets.append(arr.dataset)
         if concat_over is None:
             concat_over = set()
-        concat_over = set(concat_over) | {name}
+        elif isinstance(concat_over, basestring):
+            concat_over = set([concat_over])
+        concat_over = set(concat_over) | set([name])
         ds = xray.Dataset.concat(datasets, dimension, indexers,
                                      concat_over=concat_over)
         return ds[name]
@@ -897,7 +899,7 @@ def align(*objects, **kwargs):
 
     # Exclude dimensions with all equal indices to avoid unnecessary reindexing
     # work.
-    joined_indexes = {k: join_indices(v) for k, v in iteritems(all_indexes)
-                      if any(not v[0].equals(idx) for idx in v[1:])}
+    joined_indexes = dict((k, join_indices(v)) for k, v in iteritems(all_indexes)
+                          if any(not v[0].equals(idx) for idx in v[1:]))
 
     return tuple(obj.reindex(copy=copy, **joined_indexes) for obj in objects)

xray/dataset.py

@@ -5,7 +5,7 @@
     from cStringIO import StringIO as BytesIO
 except ImportError:  # Python 3
     from io import BytesIO
-from collections import OrderedDict, Mapping
+from collections import Mapping
 
 from . import backends
 from . import conventions
@@ -18,7 +18,7 @@
 from . import ops
 from .utils import (FrozenOrderedDict, Frozen, SortedKeysDict, ChainMap,
                     multi_index_from_product)
-from .pycompat import iteritems, itervalues, basestring
+from .pycompat import iteritems, itervalues, basestring, OrderedDict
 
 
 def open_dataset(nc, decode_cf=True, mask_and_scale=True, decode_times=True,
@@ -623,13 +623,12 @@ def isel(self, **indexers):
             raise ValueError("dimensions %r do not exist" % invalid)
 
         # all indexers should be int, slice or np.ndarrays
-        indexers = {k: np.asarray(v) if not isinstance(v, (int, np.integer, slice)) else v
-                   for k, v in iteritems(indexers)}
+        indexers = dict((k, np.asarray(v) if not isinstance(v, (int, np.integer, slice)) else v)
+                         for k, v in iteritems(indexers))
 
         variables = OrderedDict()
         for name, var in iteritems(self.variables):
-            var_indexers = {k: v for k, v in iteritems(indexers)
-                            if k in var.dimensions}
+            var_indexers = dict((k, v) for k, v in iteritems(indexers) if k in var.dimensions)
             variables[name] = var.isel(**var_indexers)
         return type(self)(variables, self.attrs)
 
@@ -923,11 +922,11 @@ def merge(self, other, inplace=False, overwrite_vars=set(),
             potential_conflicts = self.variables
         else:
             if isinstance(overwrite_vars, basestring):
-                overwrite_vars = {overwrite_vars}
+                overwrite_vars = set([overwrite_vars])
             else:
                 overwrite_vars = set(overwrite_vars)
-            potential_conflicts = {k: v for k, v in iteritems(self.variables)
-                                   if k not in overwrite_vars}
+            potential_conflicts = dict((k, v) for k, v in iteritems(self.variables)
+                                       if k not in overwrite_vars)
 
         # update variables
         new_variables = _expand_variables(other_variables, potential_conflicts,
@@ -975,8 +974,8 @@ def drop_vars(self, *names):
             raise ValueError('One or more of the specified variable '
                              'names does not exist in this dataset')
         drop = set(names)
-        drop |= {k for k, v in iteritems(self.variables)
-                 if any(name in v.dimensions for name in names)}
+        drop |= set(k for k, v in iteritems(self.variables)
+                    if any(name in v.dimensions for name in names))
         variables = OrderedDict((k, v) for k, v in iteritems(self.variables)
                                 if k not in drop)
         return type(self)(variables, self.attrs)
@@ -1150,7 +1149,7 @@ def concat(cls, datasets, dimension='concat_dimension', indexers=None,
         if concat_over is None:
             concat_over = set()
         elif isinstance(concat_over, basestring):
-            concat_over = {concat_over}
+            concat_over = set([concat_over])
         else:
             concat_over = set(concat_over)
 
@@ -1180,7 +1179,7 @@ def differs(vname, v):
                              % (concat_over, datasets[0]))
 
         # automatically concatenate over variables along the dimension
-        auto_concat_dims = {dim_name}
+        auto_concat_dims = set([dim_name])
         if hasattr(dimension, 'dimensions'):
             auto_concat_dims |= set(dimension.dimensions)
         for k, v in iteritems(datasets[0]):

xray/indexing.py

@@ -129,9 +129,8 @@ def remap_label_indexers(data_obj, indexers):
     """Given an xray data object and label based indexers, return a mapping
     of equivalent location based indexers.
     """
-    return {dim: convert_label_indexer(data_obj.indexes[dim], label, dim)
-            for dim, label in iteritems(indexers)}
-
+    return dict((dim, convert_label_indexer(data_obj.indexes[dim], label, dim))
+                for dim, label in iteritems(indexers))
 
 def _expand_slice(slice_, size):
     return np.arange(*slice_.indices(size))

xray/pycompat.py

@@ -11,6 +11,7 @@ def iteritems(d):
     def itervalues(d):
         return iter(d.values())
     xrange = range
+    from collections import OrderedDict
 else:
     # Python 2
     basestring = basestring
@@ -21,3 +22,7 @@ def iteritems(d):
     def itervalues(d):
         return d.itervalues()
     xrange = xrange
+    try:
+        from collections import OrderedDict
+    except ImportError:
+        from ordereddict import OrderedDict