Unpin matplotlib to support >=2.0

.gitignore

@@ -1,7 +1,7 @@
 *.py[co]
 
 # Packages
-*.egg
+*.egg?
 *.egg-info
 dist
 build
@@ -32,6 +32,7 @@ lib/iris/std_names.py
 # Iris test result files
 lib/iris/tests/result_image_comparison/
 iris_image_test_output/
+*.ESMF_LogFile
 
 # Iris test lock file
 lib/iris/tests/results/imagerepo.lock

.travis.yml

@@ -76,13 +76,14 @@ install:
     CONDA_REQS_FILE=conda-requirements.txt;
     python requirements/gen_conda_requirements.py ${CONDA_REQS_FLAGS} --groups ${CONDA_REQS_GROUPS} > ${CONDA_REQS_FILE};
     cat ${CONDA_REQS_FILE};
-    conda install --quiet --file ${CONDA_REQS_FILE};
+    conda install --quiet -n $ENV_NAME --file ${CONDA_REQS_FILE};
 
   - PREFIX=$HOME/miniconda/envs/$ENV_NAME
 
   # Output debug info
   - >
-    conda list;
+    conda list -n $ENV_NAME;
+    conda list -n $ENV_NAME --explicit;
     conda info -a;
 
 # Pre-load Natural Earth data to avoid multiple, overlapping downloads.
@@ -111,7 +112,7 @@ install:
   # JUST FOR NOW : Install latest version of iris-grib.
   # TODO : remove when iris doesn't do an integration test requiring iris-grib.
   - if [[ "$TEST_MINIMAL" != true && ${TRAVIS_PYTHON_VERSION} == 2* ]]; then
-        conda install python-ecmwf_grib;
+        conda install --quiet -n $ENV_NAME python-ecmwf_grib;
         pip install git+https://github.com/SciTools/iris-grib.git@v0.11.0;
     fi
 

docs/iris/example_code/Meteorology/COP_maps.py

@@ -104,8 +104,7 @@ def main():
         # Add the first subplot showing the E1 scenario
         plt.subplot(121)
         plt.title('HadGEM2 E1 Scenario',  fontsize=10)
-        iplt.contourf(delta_e1, levels, colors=colors, linewidth=0,
-                      extend='both')
+        iplt.contourf(delta_e1, levels, colors=colors, extend='both')
         plt.gca().coastlines()
         # get the current axes' subplot for use later on
         plt1_ax = plt.gca()
@@ -114,7 +113,7 @@ def main():
         plt.subplot(122)
         plt.title('HadGEM2 A1B-Image Scenario',  fontsize=10)
         contour_result = iplt.contourf(delta_a1b, levels, colors=colors,
-                                       linewidth=0, extend='both')
+                                       extend='both')
         plt.gca().coastlines()
         # get the current axes' subplot for use later on
         plt2_ax = plt.gca()

docs/iris/src/userguide/cube_statistics.rst

@@ -77,9 +77,9 @@ we can pass the coordinate name and the aggregation definition to the
               altitude                       -                 x                    x
          Scalar coordinates:
               forecast_reference_time: 2009-11-19 04:00:00
-              level_height: 696.667 m, bound=(0.0, 1393.33) m
+              level_height: 696.6666 m, bound=(0.0, 1393.3333) m
               model_level_number: 10, bound=(1, 19)
-              sigma: 0.92293, bound=(0.84586, 1.0)
+              sigma: 0.92292976, bound=(0.8458596, 1.0)
          Attributes:
               STASH: m01s00i004
               source: Data from Met Office Unified Model
@@ -139,8 +139,8 @@ These areas can now be passed to the ``collapsed`` method as weights:
               altitude                       -                      x
          Scalar coordinates:
               forecast_reference_time: 2009-11-19 04:00:00
-              grid_latitude: 1.51455 degrees, bound=(0.1443, 2.8848) degrees
-              grid_longitude: 358.749 degrees, bound=(357.494, 360.005) degrees
+              grid_latitude: 1.5145501 degrees, bound=(0.14430022, 2.8848) degrees
+              grid_longitude: 358.74948 degrees, bound=(357.494, 360.00497) degrees
               surface_altitude: 399.625 m, bound=(-14.0, 813.25) m
          Attributes:
               STASH: m01s00i004

docs/iris/src/userguide/interpolation_and_regridding.rst

@@ -132,9 +132,9 @@ This cube has a "hybrid-height" vertical coordinate system, meaning that the ver
 coordinate is unevenly spaced in altitude:
 
    >>> print(column.coord('altitude').points)
-   [  418.69836426   434.57049561   456.79278564   485.3664856    520.29327393
-      561.57519531   609.21447754   663.21411133   723.57696533   790.30664062
-      863.40722656   942.88232422  1028.73706055  1120.97644043  1219.60510254]
+   [ 418.69836  434.5705   456.7928   485.3665   520.2933   561.5752
+     609.2145   663.2141   723.57697  790.30664  863.4072   942.8823
+    1028.737   1120.9764  1219.6051 ]
 
 We could regularise the vertical coordinate by defining 10 equally spaced altitude
 sample points between 400 and 1250 and interpolating our vertical coordinate onto
@@ -177,8 +177,8 @@ For example, to mask values that lie beyond the range of the original data:
    >>> scheme = iris.analysis.Linear(extrapolation_mode='mask')
    >>> new_column = column.interpolate(sample_points, scheme)
    >>> print(new_column.coord('altitude').points)
-   [           nan   494.44451904   588.88891602   683.33325195   777.77783203
-      872.222229     966.66674805  1061.11108398  1155.55541992            nan]
+   [       nan  494.44452  588.8889   683.33325  777.77783  872.2222
+     966.66675 1061.1111  1155.5554         nan]
 
 
 .. _caching_an_interpolator:

docs/iris/src/userguide/subsetting_a_cube.rst

@@ -91,9 +91,9 @@ same way as loading with constraints:
          Scalar coordinates:
               forecast_period: 6.0 hours
               forecast_reference_time: 2009-11-19 04:00:00
-              level_height: 395.0 m, bound=(360.0, 433.333) m
+              level_height: 395.0 m, bound=(360.0, 433.3332) m
               model_level_number: 10
-              sigma: 0.954993, bound=(0.958939, 0.95068)
+              sigma: 0.9549927, bound=(0.9589389, 0.95068014)
               time: 2009-11-19 10:00:00
          Attributes:
               STASH: m01s00i004

docs/iris/src/whitepapers/um_files_loading.rst

@@ -150,7 +150,7 @@ For example:
     >>> # Show grid details and first 5 longitude values.
     >>> print(' '.join(str(_) for _ in (field.lbcode, field.lbnpt, field.bzx,
     ...                                 field.bdx)))
-    1 96 -3.75 3.75
+    1 96 -3.749999 3.749999
     >>> print(field.bzx + field.bdx * np.arange(1, 6))
     [ 0.    3.75  7.5  11.25 15.  ]
     >>> 

lib/iris/experimental/regrid.py

@@ -390,7 +390,7 @@ def _weighted_mean_with_mdtol(data, weights, axis=None, mdtol=0):
             weights_sum = weights.sum(axis=axis)
             frac_masked = 1 - np.true_divide(unmasked_weights_sum, weights_sum)
             mask_pt = frac_masked > mdtol
-            if np.any(mask_pt):
+            if np.any(mask_pt) and not isinstance(res, ma.core.MaskedConstant):
                 if np.isscalar(res):
                     res = ma.masked
                 elif ma.isMaskedArray(res):

lib/iris/plot.py

@@ -1,4 +1,4 @@
-# (C) British Crown Copyright 2010 - 2017, Met Office
+# (C) British Crown Copyright 2010 - 2018, Met Office
 #
 # This file is part of Iris.
 #
@@ -34,10 +34,10 @@
 import matplotlib.axes
 import matplotlib.collections as mpl_collections
 import matplotlib.dates as mpl_dates
-import matplotlib.transforms as mpl_transforms
 import matplotlib.pyplot as plt
+from matplotlib.offsetbox import AnchoredText
 import matplotlib.ticker as mpl_ticker
-from mpl_toolkits.axes_grid.anchored_artists import AnchoredText
+import matplotlib.transforms as mpl_transforms
 import netcdftime
 import numpy as np
 import numpy.ma as ma
@@ -343,6 +343,12 @@ def _draw_2d_from_points(draw_method_name, arg_func, cube, *args, **kwargs):
         data = cube.data
         if plot_defn.transpose:
             data = data.T
+            # Also transpose the scatter marker color array,
+            # as now mpl 2.x does not do this for free.
+            if draw_method_name == 'scatter' and 'c' in kwargs:
+                c = kwargs['c']
+                if hasattr(c, 'T') and cube.data.shape == c.shape:
+                    kwargs['c'] = c.T
 
         # Obtain U and V coordinates
         v_coord, u_coord = plot_defn.coords

lib/iris/tests/__init__.py

@@ -78,6 +78,9 @@
     import matplotlib
     matplotlib.use('agg')
     matplotlib.rcdefaults()
+    # Standardise the figure size across matplotlib versions.
+    # This permits matplotlib png image comparison.
+    matplotlib.rcParams['figure.figsize'] = [8.0, 6.0]
     import matplotlib.testing.compare as mcompare
     import matplotlib.pyplot as plt
 except ImportError:
@@ -835,7 +838,7 @@ def _create_missing():
                                'for test {}.')
                         msg = msg.format(phash, distances, unique_id)
                         if _DISPLAY_FIGURES:
-                            emsg = 'Image comparion would have failed: {}'
+                            emsg = 'Image comparison would have failed: {}'
                             print(emsg.format(msg))
                         else:
                             emsg = 'Image comparison failed: {}'
@@ -1174,7 +1177,7 @@ class MyPlotTests(test.GraphicsTest):
 
 
 skip_sample_data = unittest.skipIf(not SAMPLE_DATA_AVAILABLE,
-                                   ('Test(s) require "iris_sample_data", '
+                                   ('Test(s) require "iris-sample-data", '
                                     'which is not available.'))
 
 

lib/iris/tests/idiff.py

@@ -35,23 +35,24 @@
 import sys
 import warnings
 
-from PIL import Image
-import filelock
-import imagehash
-import matplotlib.pyplot as plt
-import matplotlib.image as mimg
-import matplotlib.testing.compare as mcompare
-from matplotlib.testing.exceptions import ImageComparisonFailure
-import matplotlib.widgets as mwidget
-import numpy as np
-import requests
-
 # Force iris.tests to use the ```tkagg``` backend by using the '-d'
 # command-line argument as idiff is an interactive tool that requires a
 # gui interface.
 sys.argv.append('-d')
-import iris.tests
-import iris.util as iutil
+import iris.tests  # noqa
+
+from PIL import Image  # noqa
+import filelock  # noqa
+import imagehash  # noqa
+import matplotlib.pyplot as plt  # noqa
+import matplotlib.image as mimg  # noqa
+import matplotlib.testing.compare as mcompare  # noqa
+from matplotlib.testing.exceptions import ImageComparisonFailure  # noqa
+import matplotlib.widgets as mwidget  # noqa
+import numpy as np  # noqa
+import requests  # noqa
+
+import iris.util as iutil  # noqa
 
 
 _POSTFIX_DIFF = '-failed-diff.png'
@@ -246,7 +247,7 @@ def step_over_diffs(result_dir, action, display=True):
                     if isinstance(e,  ValueError) or \
                             isinstance(e, ImageComparisonFailure):
                         print('Could not compare {}: {}'.format(result_fname,
-                                                                e.message))
+                                                                e))
                         continue
                     else:
                         # Propagate the exception, keeping the stack trace