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Merged
merged 3 commits into from
Aug 8, 2015
Merged

Two bugs in colors.BoundaryNorm #4824

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32e727b
bugs in colors.BoundaryNorm
fmaussion Jul 30, 2015
7fab129
added tests for masked array and clipping
fmaussion Aug 1, 2015
be12dc7
now consistent with other Normalize classes
fmaussion Aug 3, 2015
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29 changes: 18 additions & 11 deletions lib/matplotlib/colors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1252,9 +1252,12 @@ def __init__(self, boundaries, ncolors, clip=False):
as i varies from 0 to len(boundaries)-2,
j goes from 0 to ncolors-1.

Out-of-range values are mapped to -1 if low and ncolors
if high; these are converted to valid indices by
Out-of-range values are mapped
to -1 if low and ncolors if high; these are converted
to valid indices by
:meth:`Colormap.__call__` .
If clip == True, out-of-range values
are mapped to 0 if low and ncolors-1 if high.
"""
self.clip = clip
self.vmin = boundaries[0]
Expand All @@ -1267,25 +1270,29 @@ def __init__(self, boundaries, ncolors, clip=False):
else:
self._interp = True

def __call__(self, x, clip=None):
def __call__(self, value, clip=None):
if clip is None:
clip = self.clip
x = ma.asarray(x)
mask = ma.getmaskarray(x)
xx = x.filled(self.vmax + 1)

xx, is_scalar = self.process_value(value)
mask = ma.getmaskarray(xx)
xx = np.atleast_1d(xx.filled(self.vmax + 1))
if clip:
np.clip(xx, self.vmin, self.vmax)
iret = np.zeros(x.shape, dtype=np.int16)
np.clip(xx, self.vmin, self.vmax, out=xx)
max_col = self.Ncmap - 1
else:
max_col = self.Ncmap
iret = np.zeros(xx.shape, dtype=np.int16)
for i, b in enumerate(self.boundaries):
iret[xx >= b] = i
if self._interp:
scalefac = float(self.Ncmap - 1) / (self.N - 2)
iret = (iret * scalefac).astype(np.int16)
iret[xx < self.vmin] = -1
iret[xx >= self.vmax] = self.Ncmap
iret[xx >= self.vmax] = max_col
ret = ma.array(iret, mask=mask)
if ret.shape == () and not mask:
ret = int(ret) # assume python scalar
if is_scalar:
ret = int(ret[0]) # assume python scalar
return ret

def inverse(self, value):
Expand Down
73 changes: 69 additions & 4 deletions lib/matplotlib/tests/test_colors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
import itertools
from distutils.version import LooseVersion as V

from nose.tools import assert_raises, assert_equal
from nose.tools import assert_raises, assert_equal, assert_true

import numpy as np
from numpy.testing.utils import assert_array_equal, assert_array_almost_equal
Expand Down Expand Up @@ -39,15 +39,80 @@ def test_BoundaryNorm():
Github issue #1258: interpolation was failing with numpy
1.7 pre-release.
"""
# TODO: expand this into a more general test of BoundaryNorm.

boundaries = [0, 1.1, 2.2]
vals = [-1, 0, 2, 2.2, 4]
expected = [-1, 0, 2, 3, 3]
vals = [-1, 0, 1, 2, 2.2, 4]

# Without interpolation
expected = [-1, 0, 0, 1, 2, 2]
ncolors = len(boundaries) - 1
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)

# ncolors != len(boundaries) - 1 triggers interpolation
expected = [-1, 0, 0, 2, 3, 3]
ncolors = len(boundaries)
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)

# more boundaries for a third color
boundaries = [0, 1, 2, 3]
vals = [-1, 0.1, 1.1, 2.2, 4]
ncolors = 5
expected = [-1, 0, 2, 4, 5]
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)

# a scalar as input should not trigger an error and should return a scalar
boundaries = [0, 1, 2]
vals = [-1, 0.1, 1.1, 2.2]
bn = mcolors.BoundaryNorm(boundaries, 2)
expected = [-1, 0, 1, 2]
for v, ex in zip(vals, expected):
ret = bn(v)
assert_true(isinstance(ret, six.integer_types))
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)

# same with interp
bn = mcolors.BoundaryNorm(boundaries, 3)
expected = [-1, 0, 2, 3]
for v, ex in zip(vals, expected):
ret = bn(v)
assert_true(isinstance(ret, six.integer_types))
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)

# Clipping
bn = mcolors.BoundaryNorm(boundaries, 3, clip=True)
expected = [0, 0, 2, 2]
for v, ex in zip(vals, expected):
ret = bn(v)
assert_true(isinstance(ret, six.integer_types))
assert_array_equal(ret, ex)
assert_array_equal(bn([v]), ex)

# Masked arrays
boundaries = [0, 1.1, 2.2]
vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9])

# Without interpolation
ncolors = len(boundaries) - 1
bn = mcolors.BoundaryNorm(boundaries, ncolors)
expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0])
assert_array_equal(bn(vals), expected)

# With interpolation
bn = mcolors.BoundaryNorm(boundaries, len(boundaries))
expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0])
assert_array_equal(bn(vals), expected)

# Non-trivial masked arrays
vals = np.ma.masked_invalid([np.Inf, np.NaN])
assert_true(np.all(bn(vals).mask))
vals = np.ma.masked_invalid([np.Inf])
assert_true(np.all(bn(vals).mask))


def test_LogNorm():
"""
Expand Down