Approxdata

lib/iris/tests/__init__.py

@@ -45,6 +45,7 @@
 import json
 import io
 import logging
+import math
 import os
 import os.path
 import shutil
@@ -281,12 +282,11 @@ def assertCMLApproxData(self, cubes, reference_filename=None, *args,
             reference_filename = [self.get_result_path(reference_filename)]
         for i, cube in enumerate(cubes):
             fname = list(reference_filename)
-            # don't want the ".cml" for the numpy data file
+            # don't want the ".cml" for the json stats file
             if fname[-1].endswith(".cml"):
                 fname[-1] = fname[-1][:-4]
-            fname[-1] += '.data.%d.npy' % i
+            fname[-1] += '.data.%d.json' % i
             self.assertCubeDataAlmostEqual(cube, fname, *args, **kwargs)
-
         self.assertCML(cubes, reference_filename, checksum=False)
 
     def assertCDL(self, netcdf_filename, reference_filename=None, flags='-h'):
@@ -398,32 +398,38 @@ def assertTextFile(self, source_filename, reference_filename, desc="text file"):
             diff = ''.join(difflib.unified_diff(reference_text, source_text, 'Reference', 'Test result', '', '', 0))
             self.fail("%s does not match reference file: %s\n%s" % (desc, reference_filename, diff))
 
-    def assertCubeDataAlmostEqual(self, cube, reference_filename, *args, **kwargs):
+    def assertCubeDataAlmostEqual(self, cube, reference_filename, *args,
+                                  **kwargs):
         reference_path = self.get_result_path(reference_filename)
         if self._check_reference_file(reference_path):
             kwargs.setdefault('err_msg', 'Reference file %s' % reference_path)
-
-            result = np.load(reference_path)
-            if isinstance(result, np.lib.npyio.NpzFile):
-                self.assertIsInstance(cube.data, ma.MaskedArray, 'Cube data was not a masked array.')
-                # Avoid comparing any non-initialised array data.
-                data = cube.data.filled()
-                np.testing.assert_array_almost_equal(data, result['data'],
-                                                     *args, **kwargs)
-                np.testing.assert_array_equal(cube.data.mask, result['mask'])
-            else:
-                np.testing.assert_array_almost_equal(cube.data, result, *args, **kwargs)
+            with open(reference_path, 'r') as reference_file:
+                stats = json.load(reference_file)
+                self.assertEqual(stats.get('shape', []), list(cube.shape))
+                self.assertEqual(stats.get('masked', False),
+                                       isinstance(cube.data, ma.MaskedArray))
+                nstats = np.array((stats.get('mean', 0.), stats.get('std', 0.),
+                                   stats.get('max', 0), stats.get('min', 0)))
+                if math.isnan(stats.get('mean', 0)):
+                    self.assertEqual(math.isnan(stats.get('mean', 0)),
+                                     math.isnan(cube.data.mean()))
+                else:
+                    cube_stats = np.array((cube.data.mean(), cube.data.std(),
+                                           cube.data.max(), cube.data.min()))
+                    self.assertArrayAllClose(nstats, cube_stats, **kwargs)
         else:
             self._ensure_folder(reference_path)
             logger.warning('Creating result file: %s', reference_path)
+            masked = False
             if isinstance(cube.data, ma.MaskedArray):
-                # Avoid recording any non-initialised array data.
-                data = cube.data.filled()
-                with open(reference_path, 'wb') as reference_file:
-                    np.savez(reference_file, data=data, mask=cube.data.mask)
-            else:
-                with open(reference_path, 'wb') as reference_file:
-                    np.save(reference_file, cube.data)
+                masked = True
+            stats = {'mean': np.float_(cube.data.mean()),
+                     'std': np.float_(cube.data.std()),
+                     'max': np.float_(cube.data.max()),
+                     'min': np.float_(cube.data.min()),
+                     'shape': cube.shape, 'masked': masked}
+            with open(reference_path, 'w') as reference_file:
+                reference_file.write(json.dumps(stats))
 
     def assertFilesEqual(self, test_filename, reference_filename):
         reference_path = self.get_result_path(reference_filename)

lib/iris/tests/results/analysis/apply_ifunc.data.0.json

@@ -0,0 +1 @@
+{"std": 600.0958251953125, "min": 5.4993666708469391e-05, "max": 2069.03369140625, "shape": [73, 96], "masked": false, "mean": 664.99627685546875}

lib/iris/tests/results/analysis/apply_ifunc_frompyfunc.data.0.json

@@ -0,0 +1 @@
+{"std": 848.63707924732125, "min": 2.0000550754478277, "max": 2927.4700625478599, "shape": [73, 96], "masked": false, "mean": 941.88598170710736}

lib/iris/tests/results/analysis/apply_ifunc_original.data.0.json

@@ -0,0 +1 @@
+{"std": 16.345842361450195, "min": -15.285690307617188, "max": 45.48663330078125, "shape": [73, 96], "masked": false, "mean": 19.945165634155273}

lib/iris/tests/results/analysis/apply_ufunc.data.0.json

@@ -0,0 +1 @@
+{"std": 600.0958251953125, "min": 5.4993666708469391e-05, "max": 2069.03369140625, "shape": [73, 96], "masked": false, "mean": 664.99627685546875}

lib/iris/tests/results/analysis/apply_ufunc_frompyfunc.data.0.json

@@ -0,0 +1 @@
+{"std": 848.63707924732125, "min": 2.0000550754478277, "max": 2927.4700625478599, "shape": [73, 96], "masked": false, "mean": 941.88598170710736}

lib/iris/tests/results/analysis/apply_ufunc_original.data.0.json

@@ -0,0 +1 @@
+{"std": 16.345842361450195, "min": -15.285690307617188, "max": 45.48663330078125, "shape": [73, 96], "masked": false, "mean": 19.945165634155273}

lib/iris/tests/results/analysis/calculus/delta_handmade_simple_wrt_x.data.0.json

@@ -0,0 +1 @@
+{"std": 0.21794494986534119, "min": 1.0, "max": 2.0, "shape": [4, 5], "masked": false, "mean": 1.0499999523162842}

lib/iris/tests/results/analysis/calculus/delta_handmade_wrt_lat.data.0.json

@@ -0,0 +1 @@
+{"std": 0.24206146597862244, "min": 1.0, "max": 2.0, "shape": [4, 4], "masked": false, "mean": 1.0625}

lib/iris/tests/results/analysis/calculus/delta_handmade_wrt_lon.data.0.json

@@ -0,0 +1 @@
+{"std": 1.8708287477493286, "min": -4.0, "max": 2.0, "shape": [4, 5], "masked": false, "mean": 0.0}

lib/iris/tests/results/analysis/calculus/delta_handmade_wrt_x.data.0.json

@@ -0,0 +1 @@
+{"std": 1.8708287477493286, "min": -4.0, "max": 2.0, "shape": [4, 5], "masked": false, "mean": 0.0}

lib/iris/tests/results/analysis/calculus/delta_handmade_wrt_y.data.0.json

@@ -0,0 +1 @@
+{"std": 0.24206146597862244, "min": 1.0, "max": 2.0, "shape": [4, 4], "masked": false, "mean": 1.0625}

lib/iris/tests/results/analysis/calculus/handmade2_wrt_lat.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 0.2222222238779068, "max": 0.2222222238779068, "shape": [50, 49], "masked": false, "mean": 0.2222222238779068}

lib/iris/tests/results/analysis/calculus/handmade2_wrt_lon.data.0.json

@@ -0,0 +1 @@
+{"std": 9.5367431640625e-07, "min": 11.111110687255859, "max": 11.111110687255859, "shape": [49, 50], "masked": false, "mean": 11.111111640930176}

lib/iris/tests/results/analysis/calculus/handmade_simple_wrt_x.data.0.json

@@ -0,0 +1 @@
+{"std": 0.21794494986534119, "min": 1.0, "max": 2.0, "shape": [4, 5], "masked": false, "mean": 1.0499999523162842}

lib/iris/tests/results/analysis/calculus/handmade_wrt_lat.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0053791436366736889, "min": 0.02222222276031971, "max": 0.04444444552063942, "shape": [4, 4], "masked": false, "mean": 0.023611113429069519}

lib/iris/tests/results/analysis/calculus/handmade_wrt_lon.data.0.json

@@ -0,0 +1 @@
+{"std": 0.020786985754966736, "min": -0.04444444552063942, "max": 0.02222222276031971, "shape": [4, 5], "masked": false, "mean": 3.7252903539730653e-10}

lib/iris/tests/results/analysis/calculus/handmade_wrt_x.data.0.json

@@ -0,0 +1 @@
+{"std": 0.22607769072055817, "min": 1.0, "max": 2.0, "shape": [4, 5], "masked": false, "mean": 1.0666667222976685}

lib/iris/tests/results/analysis/calculus/handmade_wrt_y.data.0.json

@@ -0,0 +1 @@
+{"std": 0.24206146597862244, "min": 1.0, "max": 2.0, "shape": [4, 4], "masked": false, "mean": 1.0625}

lib/iris/tests/results/analysis/exp.data.0.json

@@ -0,0 +1 @@
+{"std": 6401.1652629017917, "min": 1.0, "max": 22026.465794806718, "shape": [11], "masked": false, "mean": 3167.7067132119382}

lib/iris/tests/results/analysis/exponentiate.data.0.json

@@ -0,0 +1 @@
+{"std": 978472.15716317657, "min": 3.0243033780422146e-09, "max": 4280900.9029495027, "shape": [73, 96], "masked": false, "mean": 802335.05663924129}

lib/iris/tests/results/analysis/gmean_latitude.data.0.json

@@ -0,0 +1 @@
+{"std": 306.9084856481536, "min": 100878.38609750666, "max": 101988.84048626159, "shape": [10, 720], "masked": false, "mean": 101441.5793318709}

lib/iris/tests/results/analysis/gmean_latitude_longitude.data.0.json

@@ -0,0 +1 @@
+{"std": 1.4551915228366852e-11, "min": 101441.11521734977, "max": 101441.11521734977, "shape": [10], "masked": false, "mean": 101441.11521734975}

lib/iris/tests/results/analysis/gmean_latitude_longitude_1call.data.0.json

@@ -0,0 +1 @@
+{"std": 1.4551915228366852e-11, "min": 101441.11521735013, "max": 101441.11521735013, "shape": [10], "masked": false, "mean": 101441.11521735015}

lib/iris/tests/results/analysis/hmean_latitude.data.0.json

@@ -0,0 +1 @@
+{"std": 62791732.052807324, "min": 10173628652.643913, "max": 10401222118.976389, "shape": [10, 720], "masked": false, "mean": 10289063259.465273}

lib/iris/tests/results/analysis/hmean_latitude_longitude.data.0.json

@@ -0,0 +1 @@
+{"std": 1.9073486328125e-06, "min": 10288680426.032187, "max": 10288680426.032187, "shape": [10], "masked": false, "mean": 10288680426.032185}

lib/iris/tests/results/analysis/hmean_latitude_longitude_1call.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 10288680426.032185, "max": 10288680426.032185, "shape": [10], "masked": false, "mean": 10288680426.032185}

lib/iris/tests/results/analysis/interpolation/linear/circular_wrapping/positive.data.0.json

@@ -0,0 +1 @@
+{"std": 0.33973028517732512, "min": 0.0, "max": 1.1000000000000001, "shape": [3, 24], "masked": false, "mean": 0.55000000000000004}

lib/iris/tests/results/analysis/interpolation/linear/circular_wrapping/symmetric.data.0.json

@@ -0,0 +1 @@
+{"std": 0.33973028517732512, "min": 0.0, "max": 1.1000000000000001, "shape": [3, 24], "masked": false, "mean": 0.55000000000000004}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_many_0.data.0.json

@@ -0,0 +1 @@
+{"std": 3.2659863237109041, "min": 2.0, "max": 10.0, "shape": [3, 4], "masked": false, "mean": 6.0}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_many_1.data.0.json

@@ -0,0 +1 @@
+{"std": 1.1180339887498949, "min": 4.0, "max": 7.0, "shape": [4, 4], "masked": false, "mean": 5.5}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_many_nan.data.0.json

@@ -0,0 +1 @@
+{"std": NaN, "min": NaN, "max": NaN, "shape": [3, 3], "masked": false, "mean": NaN}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_many_same.data.0.json

@@ -0,0 +1 @@
+{"std": 3.2659863237109041, "min": 2.0, "max": 10.0, "shape": [3, 3], "masked": false, "mean": 6.0}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_same_pt.data.0.json

@@ -0,0 +1 @@
+{"std": 3.2659863237109041, "min": 2.0, "max": 10.0, "shape": [3, 1], "masked": false, "mean": 6.0}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_scalar_0.data.0.json

@@ -0,0 +1 @@
+{"std": 3.2659863237109041, "min": 2.0, "max": 10.0, "shape": [3], "masked": false, "mean": 6.0}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_scalar_1.data.0.json

@@ -0,0 +1 @@
+{"std": 1.1180339887498949, "min": 4.0, "max": 7.0, "shape": [4], "masked": false, "mean": 5.5}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_scalar_nan.data.0.json

@@ -0,0 +1 @@
+{"std": NaN, "min": NaN, "max": NaN, "shape": [3], "masked": false, "mean": NaN}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_single_pt_0.data.0.json

@@ -0,0 +1 @@
+{"std": 3.2659863237109041, "min": 2.0, "max": 10.0, "shape": [3, 1], "masked": false, "mean": 6.0}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_single_pt_1.data.0.json

@@ -0,0 +1 @@
+{"std": 1.1180339887498949, "min": 4.0, "max": 7.0, "shape": [1, 4], "masked": false, "mean": 5.5}

lib/iris/tests/results/analysis/interpolation/linear/single_pt_to_single_pt_nan.data.0.json

@@ -0,0 +1 @@
+{"std": NaN, "min": NaN, "max": NaN, "shape": [3, 1], "masked": false, "mean": NaN}

lib/iris/tests/results/analysis/log.data.0.json

@@ -0,0 +1 @@
+{"std": 0.059119906276464462, "min": 5.500091552734375, "max": 5.7219061851501465, "shape": [73, 96], "masked": false, "mean": 5.6328606605529785}

lib/iris/tests/results/analysis/log10.data.0.json

@@ -0,0 +1 @@
+{"std": 0.025675451382994652, "min": 2.3886594772338867, "max": 2.4849922657012939, "shape": [73, 96], "masked": false, "mean": 2.4463202953338623}

lib/iris/tests/results/analysis/log2.data.0.json

@@ -0,0 +1 @@
+{"std": 0.085292011499404907, "min": 7.9349546432495117, "max": 8.2549657821655273, "shape": [73, 96], "masked": false, "mean": 8.126500129699707}

lib/iris/tests/results/analysis/max_latitude.data.0.json

@@ -0,0 +1 @@
+{"std": 331.70321238027685, "min": 101864.0, "max": 102977.0, "shape": [10, 720], "masked": true, "mean": 102466.40277777778}

lib/iris/tests/results/analysis/max_latitude_longitude.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 102977.0, "max": 102977.0, "shape": [10], "masked": true, "mean": 102977.0}

lib/iris/tests/results/analysis/max_latitude_longitude_1call.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 102977.0, "max": 102977.0, "shape": [10], "masked": true, "mean": 102977.0}

lib/iris/tests/results/analysis/mean_latitude.data.0.json

@@ -0,0 +1 @@
+{"std": 305.67198815976997, "min": 100885.3425925926, "max": 101990.07175925926, "shape": [10, 720], "masked": true, "mean": 101445.07356931583}

lib/iris/tests/results/analysis/mean_latitude_longitude.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 101445.07356931586, "max": 101445.07356931586, "shape": [10], "masked": true, "mean": 101445.07356931586}

lib/iris/tests/results/analysis/mean_latitude_longitude_1call.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 101445.07356931585, "max": 101445.07356931585, "shape": [10], "masked": true, "mean": 101445.07356931585}

lib/iris/tests/results/analysis/median_latitude.data.0.json

@@ -0,0 +1 @@
+{"std": 247.17003116388435, "min": 101023.5, "max": 102126.0, "shape": [10, 720], "masked": true, "mean": 101629.08333333333}

lib/iris/tests/results/analysis/median_latitude_longitude.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 101658.25, "max": 101658.25, "shape": [10], "masked": true, "mean": 101658.25}

lib/iris/tests/results/analysis/median_latitude_longitude_1call.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 101604.0, "max": 101604.0, "shape": [10], "masked": true, "mean": 101604.0}

lib/iris/tests/results/analysis/min_latitude.data.0.json

@@ -0,0 +1 @@
+{"std": 812.32687602393321, "min": 98385.0, "max": 101362.0, "shape": [10, 720], "masked": true, "mean": 99766.430555555562}

lib/iris/tests/results/analysis/min_latitude_longitude.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 98385.0, "max": 98385.0, "shape": [10], "masked": true, "mean": 98385.0}

lib/iris/tests/results/analysis/min_latitude_longitude_1call.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 98385.0, "max": 98385.0, "shape": [10], "masked": true, "mean": 98385.0}

lib/iris/tests/results/analysis/regrid/linear_both_circular.data.0.json

@@ -0,0 +1 @@
+{"std": 2.5880997180938721, "min": 254.64399719238281, "max": 261.7274169921875, "shape": [4, 4], "masked": false, "mean": 258.11480712890625}

lib/iris/tests/results/analysis/regrid/linear_circular_grid.data.0.json

@@ -0,0 +1 @@
+{"std": 2.5862049308282899, "min": 254.64399719238281, "max": 261.68328857421875, "shape": [4, 4], "masked": true, "mean": 258.11273193359375}

lib/iris/tests/results/analysis/regrid/linear_circular_src.data.0.json

@@ -0,0 +1 @@
+{"std": 2.5880997180938721, "min": 254.64399719238281, "max": 261.7274169921875, "shape": [4, 4], "masked": false, "mean": 258.11480712890625}

lib/iris/tests/results/analysis/regrid/linear_circular_srcmissingmask.cml

@@ -0,0 +1,72 @@
+<?xml version="1.0" ?>
+<cubes xmlns="urn:x-iris:cubeml-0.2">
+  <cube standard_name="air_temperature" units="K">
+    <attributes>
+      <attribute name="STASH" value="m01s16i203"/>
+      <attribute name="source" value="Data from Met Office Unified Model"/>
+    </attributes>
+    <coords>
+      <coord datadims="[0, 1]">
+        <auxCoord id="9041e969" points="[[260.259246826, 260.259246826, 260.259246826,
+ 		260.259246826],
+		[373.731048584, 378.316101074, 377.580688477,
+ 		374.184997559],
+		[487.202819824, 496.37298584, 494.902130127,
+ 		488.110778809],
+		[600.674621582, 614.42980957, 612.223571777,
+ 		602.036560059]]" shape="(4, 4)" standard_name="altitude" units="Unit('m')" value_type="float64">
+          <attributes>
+            <attribute name="positive" value="up"/>
+          </attributes>
+        </auxCoord>
+      </coord>
+      <coord>
+        <dimCoord bounds="[[-28083.0, 6477.0]]" id="1d45e087" points="[6477]" shape="(1,)" standard_name="forecast_period" units="Unit('hours')" value_type="int32"/>
+      </coord>
+      <coord>
+        <dimCoord id="9c8bdf81" points="[243363.0]" shape="(1,)" standard_name="forecast_reference_time" units="Unit('hours since 1970-01-01 00:00:00', calendar='360_day')" value_type="float64"/>
+      </coord>
+      <coord datadims="[0]">
+        <dimCoord id="77a50eb5" points="[90.0, 87.5, 85.0, 82.5]" shape="(4,)" standard_name="latitude" units="Unit('degrees')" value_type="float32">
+          <geogCS earth_radius="6371229.0"/>
+        </dimCoord>
+      </coord>
+      <coord>
+        <auxCoord id="fb105c19" long_name="level_height" points="[0]" shape="(1,)" units="Unit('m')" value_type="int64">
+          <attributes>
+            <attribute name="positive" value="up"/>
+          </attributes>
+        </auxCoord>
+      </coord>
+      <coord datadims="[1]">
+        <dimCoord id="f913a8b3" points="[-5.0, -1.25, 2.5, 6.25]" shape="(4,)" standard_name="longitude" units="Unit('degrees')" value_type="float32">
+          <geogCS earth_radius="6371229.0"/>
+        </dimCoord>
+      </coord>
+      <coord>
+        <dimCoord id="6eef7051" long_name="pressure" points="[1000.0]" shape="(1,)" units="Unit('hPa')" value_type="float32"/>
+      </coord>
+      <coord>
+        <auxCoord id="a5c170db" long_name="sigma" points="[1]" shape="(1,)" units="Unit('1')" value_type="int64"/>
+      </coord>
+      <coord datadims="[0, 1]">
+        <auxCoord id="b15e3a2e" points="[[260.259, 260.259, 260.259, 260.259],
+		[373.731, 378.316, 377.581, 374.185],
+		[487.203, 496.373, 494.902, 488.111],
+		[600.675, 614.43, 612.224, 602.037]]" shape="(4, 4)" standard_name="surface_altitude" units="Unit('m')" value_type="float32"/>
+      </coord>
+      <coord>
+        <dimCoord bounds="[[215280.0, 249840.0]]" id="cb784457" points="[249840.0]" shape="(1,)" standard_name="time" units="Unit('hours since 1970-01-01 00:00:00', calendar='360_day')" value_type="float64"/>
+      </coord>
+    </coords>
+    <cellMethods>
+      <cellMethod method="mean within years">
+        <coord name="time"/>
+      </cellMethod>
+      <cellMethod method="mean over years">
+        <coord name="time"/>
+      </cellMethod>
+    </cellMethods>
+    <data dtype="float32" shape="(4, 4)" state="loaded"/>
+  </cube>
+</cubes>

lib/iris/tests/results/analysis/regrid/linear_circular_srcmissingmask.data.0.json

@@ -0,0 +1 @@
+{"std": 2.588099748331198, "min": 254.64399719238281, "max": 261.7274169921875, "shape": [4, 4], "masked": true, "mean": 258.11480712890625}

lib/iris/tests/results/analysis/regrid/linear_masked_altitude.data.0.json

@@ -0,0 +1 @@
+{"std": 0.33354389667510986, "min": 286.5152587890625, "max": 288.0738525390625, "shape": [5, 2, 4, 5], "masked": false, "mean": 287.3565673828125}

lib/iris/tests/results/analysis/regrid/linear_non_circular.data.0.json

@@ -0,0 +1 @@
+{"std": 2.5862049308282899, "min": 254.64399719238281, "max": 261.68328857421875, "shape": [4, 4], "masked": true, "mean": 258.11273193359375}

lib/iris/tests/results/analysis/regrid/linear_partial_overlap.data.0.json

@@ -0,0 +1 @@
+{"std": 0.33802566423192859, "min": 286.60049438476562, "max": 287.97402954101562, "shape": [5, 2, 4, 4], "masked": true, "mean": 287.31777343750002}

lib/iris/tests/results/analysis/regrid/linear_subset.data.0.json

@@ -0,0 +1 @@
+{"std": 0.33354389667510986, "min": 286.5152587890625, "max": 288.0738525390625, "shape": [5, 2, 4, 5], "masked": false, "mean": 287.3565673828125}

lib/iris/tests/results/analysis/regrid/linear_subset_anon.data.0.json

@@ -0,0 +1 @@
+{"std": 0.33354389667510986, "min": 286.5152587890625, "max": 288.0738525390625, "shape": [5, 2, 4, 5], "masked": false, "mean": 287.3565673828125}

lib/iris/tests/results/analysis/regrid/linear_subset_masked_1.data.0.json

@@ -0,0 +1 @@
+{"std": 0.32071127247937192, "min": 286.5152587890625, "max": 287.922607421875, "shape": [5, 2, 4, 5], "masked": true, "mean": 287.31103515625}

lib/iris/tests/results/analysis/regrid/linear_subset_masked_2.data.0.json

@@ -0,0 +1 @@
+{"std": 0.30926843652344405, "min": 286.81167602539062, "max": 288.0738525390625, "shape": [5, 2, 4, 5], "masked": true, "mean": 287.44599609375001}

lib/iris/tests/results/analysis/regrid/nearest_both_circular.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 254.64399719238281, "max": 254.64399719238281, "shape": [4, 4], "masked": false, "mean": 254.64399719238281}

lib/iris/tests/results/analysis/regrid/nearest_circular_grid.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 254.64399719238281, "max": 254.64399719238281, "shape": [4, 4], "masked": true, "mean": 254.64399719238281}

lib/iris/tests/results/analysis/regrid/nearest_circular_src.data.0.json

@@ -0,0 +1 @@
+{"std": 0.0, "min": 254.64399719238281, "max": 254.64399719238281, "shape": [4, 4], "masked": false, "mean": 254.64399719238281}