def test_binning2d_iadd():
x_axis = core.Axis(np.linspace(-180, 180, 1), is_circle=True)
y_axis = core.Axis(np.linspace(-90, 90, 1))
binning = core.Binning2DFloat64(x_axis, y_axis, None)
binning.push([-180], [-90], [np.pi])
other = copy.copy(binning)
other += binning
assert np.all(binning.count() == 1)
assert np.all(binning.mean() == np.pi)
assert np.all(binning.min() == np.pi)
assert np.all(binning.max() == np.pi)
assert np.all(binning.sum() == np.pi)
assert np.all(binning.sum_of_weights() == 1)
assert np.all(binning.variance() == 0)
assert np.all(np.isnan(other.skewness()))
assert np.all(np.isnan(other.kurtosis()))
assert np.all(other.count() == 2)
assert np.all(other.mean() == np.pi)
assert np.all(other.min() == np.pi)
assert np.all(other.max() == np.pi)
assert np.all(other.sum() == np.pi * 2)
assert np.all(other.sum_of_weights() == 2)
assert np.all(other.variance() == 0)
assert np.all(np.isnan(other.skewness()))
assert np.all(np.isnan(other.kurtosis()))
def test_binning2d_acessors(self):
x_axis = core.Axis(np.linspace(-180, 180, 10), is_circle=True)
y_axis = core.Axis(np.linspace(-90, 90, 10))
binning = core.Binning2DFloat64(x_axis, y_axis)
self.assertIsInstance(binning.x, core.Axis)
self.assertIsInstance(binning.y, core.Axis)
# The class must return a reference on the axes provided during
# construction
self.assertEqual(id(x_axis), id(binning.x))
self.assertEqual(id(y_axis), id(binning.y))
binning.clear()
count = binning.count()
self.assertIsInstance(count, np.ndarray)
self.assertEqual(count.size, len(x_axis) * len(y_axis))
self.assertEqual(count.mean(), 0)
def test_binning2d_acessors():
x_axis = core.Axis(np.linspace(-180, 180, 10), is_circle=True)
y_axis = core.Axis(np.linspace(-90, 90, 10))
binning = core.Binning2DFloat64(x_axis, y_axis)
assert isinstance(binning.x, core.Axis)
assert isinstance(binning.y, core.Axis)
# The class must return a reference on the axes provided during
# construction
assert id(x_axis) == id(binning.x)
assert id(y_axis) == id(binning.y)
binning.clear()
count = binning.count()
assert isinstance(count, np.ndarray)
assert count.size == len(x_axis) * len(y_axis)
assert count.mean() == 0
def test_binning2d_methods(self):
x_axis = core.Axis(np.linspace(-180, 180, 361 // 4), is_circle=True)
y_axis = core.Axis(np.linspace(-90, 90, 180 // 4))
binning = core.Binning2DFloat64(x_axis, y_axis, None)
x, y, z = self.load_data()
mx, my = np.meshgrid(x, y, indexing='ij')
binning.push(mx.flatten(), my.flatten(), z.flatten())
count = binning.count()
self.assertNotEqual(count.max(), 0)
simple_mean = np.ma.fix_invalid(binning.mean())
if HAVE_PLT:
mx, my = np.meshgrid(x_axis[:], y_axis[:], indexing='ij')
plot(mx, my, simple_mean, "binning2d_simple.png")
mx, my = np.meshgrid(x, y, indexing='ij')
binning.clear()
binning.push(mx.flatten(), my.flatten(), z.flatten(), simple=False)
count = binning.count()
self.assertNotEqual(count.max(), 0)
linear_mean = np.ma.fix_invalid(binning.mean())
if HAVE_PLT:
mx, my = np.meshgrid(x_axis[:], y_axis[:], indexing='ij')
plot(mx, my, linear_mean, "binning2d_linear.png")
self.assertFalse(np.all(linear_mean == simple_mean))
# Test of access to statistical variables
self.assertIsInstance(binning.kurtosis(), np.ndarray)
self.assertIsInstance(binning.max(), np.ndarray)
self.assertIsInstance(binning.median(), np.ndarray)
self.assertIsInstance(binning.min(), np.ndarray)
self.assertIsInstance(binning.skewness(), np.ndarray)
self.assertIsInstance(binning.sum(), np.ndarray)
self.assertIsInstance(binning.sum_of_weights(), np.ndarray)
self.assertIsInstance(binning.variance(), np.ndarray)