Dask maskarray merge master

.travis.yml

@@ -83,7 +83,6 @@ install:
     fi
 
 # prepare iris build directory
-  - python setup.py --with-unpack build_ext --include-dirs=${PREFIX}/include --library-dirs=${PREFIX}/lib
   - if [[ $TEST_TARGET -ne 'coding' ]]; then
       IRIS=$(ls -d1 build/lib*/iris);
       mkdir $IRIS/etc;
@@ -107,8 +106,8 @@ install:
     fi
 
 # iris
-  - python setup.py --quiet --with-unpack build
-  - python setup.py --quiet --with-unpack install
+  - python setup.py --quiet build
+  - python setup.py --quiet install
 
 script:
   - if [[ $TEST_TARGET == 'default' ]]; then

docs/iris/src/userguide/saving_iris_cubes.rst

@@ -82,7 +82,7 @@ For example, a GRIB2 message with a particular known long_name may need to be sa
 Similarly a PP field may need to be written out with a specific value for LBEXP.  This can be achieved by::
 
         def tweaked_fields(cube):
-            for cube, field in iris.fileformats.pp.as_pairs(cube):
+            for cube, field in iris.fileformats.pp.save_pairs_from_cube(cube):
                 # post process the PP field, prior to saving
                 if cube.name() == 'air_pressure':
                     field.lbexp = 'meaxp'

docs/iris/src/whatsnew/contributions_v2.0.0/incompatiblechange_2017-Oct-17_rename-pp-rules.txt

@@ -0,0 +1,2 @@
+`iris.fileformats.pp_rules` has been renamed to `iris.fileformats.pp_load_rules`.
+This has been done for the sake of clarity following the introduction of `iris.fileformats.pp_save_rules`.

lib/iris/__init__.py

@@ -454,11 +454,8 @@ def sample_data_path(*path_to_join):
     if iris_sample_data is not None:
         target = os.path.join(iris_sample_data.path, target)
     else:
-        wmsg = ("iris.config.SAMPLE_DATA_DIR was deprecated in v1.10.0 and "
-                "will be removed in a future Iris release. Install the "
-                "'iris_sample_data' package.")
-        warn_deprecated(wmsg)
-        target = os.path.join(iris.config.SAMPLE_DATA_DIR, target)
+        raise ImportError("Please install the 'iris_sample_data' package to "
+                          "access sample data.")
     if not glob.glob(target):
         raise ValueError('Sample data file(s) at {!r} not found.\n'
                          'NB. This function is only for locating files in the '

lib/iris/analysis/__init__.py

@@ -1015,7 +1015,8 @@ def post_process(self, collapsed_cube, data_result, coords, **kwargs):
         return result
 
 
-def _percentile(data, axis, percent, **kwargs):
+def _percentile(data, axis, percent, fast_percentile_method=False,
+                **kwargs):
     """
     The percentile aggregator is an additive operation. This means that
     it *may* introduce a new dimension to the data for the statistic being
@@ -1024,18 +1025,34 @@ def _percentile(data, axis, percent, **kwargs):
     If a new additive dimension is formed, then it will always be the last
     dimension of the resulting percentile data payload.
 
+    Kwargs:
+
+    * fast_percentile_method (boolean) :
+        When set to True, uses the numpy.percentiles method as a faster
+        alternative to the scipy.mstats.mquantiles method. Does not handle
+        masked arrays.
+
     """
     # Ensure that the target axis is the last dimension.
     data = np.rollaxis(data, axis, start=data.ndim)
-    quantiles = np.array(percent) / 100.
     shape = data.shape[:-1]
     # Flatten any leading dimensions.
     if shape:
         data = data.reshape([np.prod(shape), data.shape[-1]])
     # Perform the percentile calculation.
-    result = scipy.stats.mstats.mquantiles(data, quantiles, axis=-1, **kwargs)
+    if fast_percentile_method:
+        msg = 'Cannot use fast np.percentile method with masked array.'
+        if ma.isMaskedArray(data):
+            raise TypeError(msg)
+        result = np.percentile(data, percent, axis=-1)
+        result = result.T
+    else:
+        quantiles = np.array(percent) / 100.
+        result = scipy.stats.mstats.mquantiles(data, quantiles, axis=-1,
+                                               **kwargs)
     if not ma.isMaskedArray(data) and not ma.is_masked(result):
         result = np.asarray(result)
+
     # Ensure to unflatten any leading dimensions.
     if shape:
         if not isinstance(percent, collections.Iterable):
@@ -2477,8 +2494,3 @@ def regridder(self, src_cube, target_grid):
         from iris.analysis.trajectory import \
             UnstructuredNearestNeigbourRegridder
         return UnstructuredNearestNeigbourRegridder(src_cube, target_grid)
-
-
-# Import "iris.analysis.interpolate" to replicate older automatic imports.
-# NOTE: do this at end, as otherwise its import of 'Linear' will fail.
-from . import _interpolate_backdoor as interpolate