def test_cmap_scale_sqrt(self):
params = _ScatterParams(x=self.x,
y=self.y,
c=self.c,
cmap_scale='sqrt')
assert params.cmap_scale == 'sqrt'
assert isinstance(params.norm, matplotlib.colors.PowerNorm)
assert params.norm.gamma == 0.5
def test_data_3d(self):
params = _ScatterParams(x=self.x, y=self.y, z=self.z)
np.testing.assert_equal(params._data, [self.x, self.y, self.z])
np.testing.assert_equal(params.data, [
self.x[params.plot_idx], self.y[params.plot_idx],
self.z[params.plot_idx]
])
assert params.subplot_kw == {'projection': '3d'}
def test_list_cmap(self):
params = _ScatterParams(x=self.x,
y=self.y,
c=self.c,
cmap=['red', 'black'])
assert params.list_cmap()
np.testing.assert_equal(params.cmap([0, 255]),
[[1, 0, 0, 1], [0, 0, 0, 1]])
def test_continuous(self):
params = _ScatterParams(x=self.x, y=self.y, c=self.c)
assert not params.array_c()
assert not params.constant_c()
assert params.discrete is False
assert params.legend is True
assert params.cmap_scale == 'linear'
assert params.cmap == 'inferno'
params = _ScatterParams(x=self.x,
y=self.y,
discrete=False,
c=np.round(self.c % 1, 1))
assert not params.array_c()
assert not params.constant_c()
assert params.discrete is False
assert params.legend is True
assert params.labels is None
assert params.cmap_scale == 'linear'
assert params.cmap == 'inferno'
def test_extend(self):
params = _ScatterParams(x=self.x,
y=self.y,
c=self.c,
vmin=np.mean(self.c))
assert params.extend == 'min'
params = _ScatterParams(x=self.x,
y=self.y,
c=self.c,
vmax=np.mean(self.c))
assert params.extend == 'max'
params = _ScatterParams(x=self.x,
y=self.y,
c=self.c,
vmin=(np.min(self.c) + np.mean(self.c)) / 2,
vmax=(np.max(self.c) + np.mean(self.c)) / 2)
assert params.extend == 'both'
params = _ScatterParams(x=self.x, y=self.y, c=self.c)
assert params.extend == 'neither'
def test_dict_cmap(self):
params = _ScatterParams(x=self.x,
y=self.y,
c=np.where(self.c > 0, '+', '-'),
cmap={
'+': 'k',
'-': 'r'
})
assert not params.list_cmap()
if sys.version_info[1] > 5:
np.testing.assert_equal(params.cmap.colors,
[[0, 0, 0, 1], [1, 0, 0, 1]])
assert np.all(params._labels == np.array(['+', '-']))
else:
try:
np.testing.assert_equal(params.cmap.colors,
[[0, 0, 0, 1], [1, 0, 0, 1]])
assert np.all(params._labels == np.array(['+', '-']))
except AssertionError:
np.testing.assert_equal(params.cmap.colors,
[[1, 0, 0, 1], [0, 0, 0, 1]])
assert np.all(params._labels == np.array(['-', '+']))
params = _ScatterParams(x=self.x,
y=self.y,
c=np.where(self.c > 0, '+', '-'),
cmap={
'-': 'k',
'+': 'r'
})
if sys.version_info[1] > 5:
np.testing.assert_equal(params.cmap.colors,
[[0, 0, 0, 1], [1, 0, 0, 1]])
assert np.all(params._labels == np.array(['-', '+']))
else:
try:
np.testing.assert_equal(params.cmap.colors,
[[0, 0, 0, 1], [1, 0, 0, 1]])
assert np.all(params._labels == np.array(['-', '+']))
except AssertionError:
np.testing.assert_equal(params.cmap.colors,
[[1, 0, 0, 1], [0, 0, 0, 1]])
assert np.all(params._labels == np.array(['+', '-']))
def test_c_none(self):
params = _ScatterParams(x=self.x, y=self.y)
assert params.constant_c()
assert not params.array_c()
assert params.discrete is None
assert params.legend is False
assert params.vmin is None
assert params.vmax is None
assert params.cmap is None
assert params.cmap_scale is None
assert params.extend is None
def test_plot_idx_shuffle(self):
params = _ScatterParams(x=self.x,
y=self.y,
z=self.z,
c=self.c,
s=np.abs(self.x))
assert not np.all(params.plot_idx == np.arange(params.size))
np.testing.assert_equal(params.x, self.x[params.plot_idx])
np.testing.assert_equal(params.y, self.y[params.plot_idx])
np.testing.assert_equal(params.z, self.z[params.plot_idx])
np.testing.assert_equal(params.c, self.c[params.plot_idx])
np.testing.assert_equal(params.s, np.abs(self.x)[params.plot_idx])
def test_plot_idx_no_shuffle(self):
params = _ScatterParams(x=self.x,
y=self.y,
z=self.z,
c=self.c,
s=np.abs(self.x),
shuffle=False)
np.testing.assert_equal(params.plot_idx, np.arange(params.size))
np.testing.assert_equal(params.x, self.x)
np.testing.assert_equal(params.y, self.y)
np.testing.assert_equal(params.z, self.z)
np.testing.assert_equal(params.c, self.c)
np.testing.assert_equal(params.s, np.abs(self.x))
def test_vmin_default(self):
params = _ScatterParams(x=self.x, y=self.y, c=self.c)
assert params.vmin == np.min(self.c)
def test_s_default(self):
params = _ScatterParams(x=self.x, y=self.y)
assert params.s == 200 / np.sqrt(params.size)
def test_size(self):
params = _ScatterParams(x=self.x, y=self.y)
assert params.size == len(self.x)
def test_cmap_given(self):
params = _ScatterParams(x=self.x, y=self.y, c=self.c, cmap='viridis')
assert params.cmap == 'viridis'
assert not params.list_cmap()
def test_s_given(self):
params = _ScatterParams(x=self.x, y=self.y, s=3)
assert params.s == 3
def test_c_discrete(self):
c = np.where(self.c > 0, 'a', 'b')
params = _ScatterParams(x=self.x, y=self.y, c=c)
np.testing.assert_equal(params.c_discrete, np.where(c == 'a', 0, 1))
np.testing.assert_equal(params.labels, ['a', 'b'])
def test_legend(self):
params = _ScatterParams(x=self.x, y=self.y, c=self.c, legend=False)
assert params.legend is False
params = _ScatterParams(x=self.x, y=self.y, c=self.c, colorbar=False)
assert params.legend is False
def test_vmax_default(self):
params = _ScatterParams(x=self.x, y=self.y, c=self.c)
assert params.vmax == np.max(self.c)
def test_vmax_given(self):
params = _ScatterParams(x=self.x, y=self.y, c=self.c, vmax=0)
assert params.vmax == 0
def test_data_2d(self):
params = _ScatterParams(x=self.x, y=self.y)
np.testing.assert_equal(params._data, [self.x, self.y])
np.testing.assert_equal(
params.data, [self.x[params.plot_idx], self.y[params.plot_idx]])
assert params.subplot_kw == {}