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Fix deprecations from NumPy #1122

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merged 2 commits into from
Sep 25, 2018
Merged

Fix deprecations from NumPy #1122

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d54dfe3
Make bool-to-int conversion explicit.
QuLogic Sep 25, 2018
eb99c9b
Ensure array shapes are always integers.
QuLogic Sep 25, 2018
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4 changes: 2 additions & 2 deletions lib/cartopy/img_transform.py
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Expand Up @@ -42,9 +42,9 @@ def mesh_projection(projection, nx, ny,
----------
projection
A :class:`~cartopy.crs.Projection` instance.
nx
nx: int
The number of sample points in the projection x-direction.
ny
ny: int
The number of sample points in the projection y-direction.
x_extents: optional
The (lower, upper) x-direction extent of the projection.
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3 changes: 2 additions & 1 deletion lib/cartopy/io/ogc_clients.py
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Expand Up @@ -122,7 +122,8 @@ def _warped_located_image(image, source_projection, source_extent,
source_proj=source_projection,
source_extent=source_extent,
target_proj=output_projection,
target_res=target_resolution,
target_res=np.asarray(target_resolution,
dtype=int),
target_extent=output_extent,
mask_extrapolated=True)

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4 changes: 2 additions & 2 deletions lib/cartopy/mpl/geoaxes.py
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Expand Up @@ -1052,9 +1052,9 @@ def _regrid_shape_aspect(self, regrid_shape, target_extent):
x_range, y_range = np.diff(target_extent)[::2]
desired_aspect = x_range / y_range
if x_range >= y_range:
regrid_shape = (target_size * desired_aspect, target_size)
regrid_shape = (int(target_size * desired_aspect), target_size)
else:
regrid_shape = (target_size, target_size / desired_aspect)
regrid_shape = (target_size, int(target_size / desired_aspect))
return regrid_shape

def imshow(self, img, *args, **kwargs):
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4 changes: 2 additions & 2 deletions lib/cartopy/tests/test_polygon.py
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@@ -1,4 +1,4 @@
# (C) British Crown Copyright 2011 - 2017, Met Office
# (C) British Crown Copyright 2011 - 2018, Met Office
#
# This file is part of cartopy.
#
Expand Down Expand Up @@ -254,7 +254,7 @@ def test_symmetry(self):

# For each region, check if the number of increasing steps is roughly
# equal to the number of decreasing steps.
for i in range(boundary[0], regions.max(), 2):
for i in range(int(boundary[0]), regions.max(), 2):
indices = np.where(regions == i)
x = xy[indices, 0]
delta = np.diff(x)
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