def test_biweight_midvariance_small():
data = [1, 3, 5, 500, 2]
var = biweight_midvariance(data)
assert_allclose(var, 2.9238456) # verified with R
var = biweight_midvariance(data, modify_sample_size=True)
assert_allclose(var, 2.3390765)
def test_biweight_32bit_runtime_warnings():
"""Regression test for #6905."""
with NumpyRNGContext(12345):
data = np.random.random(100).astype(np.float32)
data[50] = 30000.
biweight_scale(data)
biweight_midvariance(data)
def test_biweight_midvariance_small():
data = [1, 3, 5, 500, 2]
var = biweight_midvariance(data)
assert_allclose(var, 2.9238456) # verified with R
var = biweight_midvariance(data, modify_sample_size=True)
assert_allclose(var, 2.3390765)
def test_biweight_midvariance_constant_axis_3d():
shape = (10, 5, 2)
data = np.ones(shape)
cbl = biweight_midvariance(data, axis=0)
assert_allclose(cbl, np.zeros((shape[1], shape[2])))
cbl = biweight_midvariance(data, axis=1)
assert_allclose(cbl, np.zeros((shape[0], shape[2])))
cbl = biweight_midvariance(data, axis=2)
assert_allclose(cbl, np.zeros((shape[0], shape[1])))
def test_biweight_midvariance_constant_axis_3d():
shape = (10, 5, 2)
data = np.ones(shape)
cbl = biweight_midvariance(data, axis=0)
assert_allclose(cbl, np.zeros((shape[1], shape[2])))
cbl = biweight_midvariance(data, axis=1)
assert_allclose(cbl, np.zeros((shape[0], shape[2])))
cbl = biweight_midvariance(data, axis=2)
assert_allclose(cbl, np.zeros((shape[0], shape[1])))
def test_biweight_scale():
# NOTE: biweight_scale is covered by biweight_midvariance tests
data = [1, 3, 5, 500, 2]
scl = biweight_scale(data)
var = biweight_midvariance(data)
assert_allclose(scl, np.sqrt(var))
data = np.ma.masked_invalid([1, 3, 5, 500, 2, np.nan])
data[0] = np.nan
scl = biweight_scale(data, ignore_nan=True)
var = biweight_midvariance(data, ignore_nan=True)
assert_allclose(scl, np.sqrt(var))
def test_biweight_32bit_runtime_warnings():
"""Regression test for #6905."""
with NumpyRNGContext(12345):
data = np.random.random(100).astype(np.float32)
data[50] = 30000.
with catch_warnings(RuntimeWarning) as warning_lines:
biweight_scale(data)
assert len(warning_lines) == 0
with catch_warnings(RuntimeWarning) as warning_lines:
biweight_midvariance(data)
assert len(warning_lines) == 0
def test_biweight_32bit_runtime_warnings():
"""Regression test for #6905."""
with NumpyRNGContext(12345):
data = np.random.random(100).astype(np.float32)
data[50] = 30000.
with catch_warnings(RuntimeWarning) as warning_lines:
biweight_scale(data)
assert len(warning_lines) == 0
with catch_warnings(RuntimeWarning) as warning_lines:
biweight_midvariance(data)
assert len(warning_lines) == 0
def test_biweight_midvariance_constant_axis_2d():
shape = (10, 5)
data = np.ones(shape)
cbl = biweight_midvariance(data, axis=0)
assert_allclose(cbl, np.zeros(shape[1]))
cbl = biweight_midvariance(data, axis=1)
assert_allclose(cbl, np.zeros(shape[0]))
data = np.arange(50).reshape(10, 5)
data[2] = 100.
data[7] = 2.
bw = biweight_midvariance(data, axis=1)
assert_allclose(bw[2], 0.)
assert_allclose(bw[7], 0.)
def test_biweight_midvariance_ignore_nan():
data1d = np.array([1, 3, 5, 500, 2, np.nan])
data2d = np.array([data1d, data1d])
assert np.isnan(biweight_midvariance(data1d, ignore_nan=False))
biw_var = biweight_midvariance(data1d[:-1], ignore_nan=False)
biw_var_nonan = biweight_midvariance(data1d, ignore_nan=True)
assert_equal(biw_var_nonan, biw_var)
assert_equal(biweight_midvariance(data2d, axis=0, ignore_nan=True),
[0., 0., 0., 0., 0., np.nan])
assert_equal(biweight_midvariance(data2d, axis=1, ignore_nan=True),
[biw_var_nonan, biw_var_nonan])
def test_biweight_midvariance_constant_axis_2d():
shape = (10, 5)
data = np.ones(shape)
cbl = biweight_midvariance(data, axis=0)
assert_allclose(cbl, np.zeros(shape[1]))
cbl = biweight_midvariance(data, axis=1)
assert_allclose(cbl, np.zeros(shape[0]))
data = np.arange(50).reshape(10, 5)
data[2] = 100.
data[7] = 2.
bw = biweight_midvariance(data, axis=1)
assert_allclose(bw[2], 0.)
assert_allclose(bw[7], 0.)
def test_biweight_midcovariance_midvariance():
"""
Test that biweight_midcovariance diagonal elements agree with
biweight_midvariance.
"""
rng = np.random.RandomState(1)
d = rng.normal(0, 2, size=(100, 3))
cov = biweight_midcovariance(d)
var = [biweight_midvariance(a) for a in d]
assert_allclose(cov.diagonal(), var)
cov2 = biweight_midcovariance(d, modify_sample_size=True)
var2 = [biweight_midvariance(a, modify_sample_size=True) for a in d]
assert_allclose(cov2.diagonal(), var2)
def test_biweight_midvariance_axis_3d():
"""Test a 3D array with the axis keyword."""
with NumpyRNGContext(12345):
nz = 3
ny = 4
nx = 5
data = np.random.normal(5, 2, (nz, ny, nx))
bw = biweight_midvariance(data, axis=0)
assert bw.shape == (ny, nx)
y = 0
bwi = []
for i in range(nx):
bwi.append(biweight_midvariance(data[:, y, i]))
bwi = np.array(bwi)
assert_allclose(bw[y], bwi)
def test_biweight_midvariance_axis_3d():
"""Test a 3D array with the axis keyword."""
with NumpyRNGContext(12345):
nz = 3
ny = 4
nx = 5
data = np.random.normal(5, 2, (nz, ny, nx))
bw = biweight_midvariance(data, axis=0)
assert bw.shape == (ny, nx)
y = 0
bwi = []
for i in range(nx):
bwi.append(biweight_midvariance(data[:, y, i]))
bwi = np.array(bwi)
assert_allclose(bw[y], bwi)
def test_biweight_midcovariance_midvariance():
"""
Test that biweight_midcovariance diagonal elements agree with
biweight_midvariance.
"""
rng = np.random.RandomState(1)
d = rng.normal(0, 2, size=(100, 3))
cov = biweight_midcovariance(d)
var = [biweight_midvariance(a) for a in d]
assert_allclose(cov.diagonal(), var)
cov2 = biweight_midcovariance(d, modify_sample_size=True)
var2 = [biweight_midvariance(a, modify_sample_size=True)
for a in d]
assert_allclose(cov2.diagonal(), var2)
def test_biweight_midvariance_axis():
"""Test a 2D array with the axis keyword."""
with NumpyRNGContext(12345):
ny = 100
nx = 200
data = np.random.normal(5, 2, (ny, nx))
bw = biweight_midvariance(data, axis=0)
bwi = []
for i in range(nx):
bwi.append(biweight_midvariance(data[:, i]))
bwi = np.array(bwi)
assert_allclose(bw, bwi)
bw = biweight_midvariance(data, axis=1)
bwi = []
for i in range(ny):
bwi.append(biweight_midvariance(data[i, :]))
bwi = np.array(bwi)
assert_allclose(bw, bwi)
def test_biweight_midvariance_axis():
"""Test a 2D array with the axis keyword."""
with NumpyRNGContext(12345):
ny = 100
nx = 200
data = np.random.normal(5, 2, (ny, nx))
bw = biweight_midvariance(data, axis=0)
bwi = []
for i in range(nx):
bwi.append(biweight_midvariance(data[:, i]))
bwi = np.array(bwi)
assert_allclose(bw, bwi)
bw = biweight_midvariance(data, axis=1)
bwi = []
for i in range(ny):
bwi.append(biweight_midvariance(data[i, :]))
bwi = np.array(bwi)
assert_allclose(bw, bwi)
def test_biweight_midvariance_5127():
# test a regression introduced in #5127
rand = np.random.default_rng(12345)
data = rand.normal(loc=0., scale=20., size=(100, 100))
var = biweight_midvariance(data)
assert_allclose(var, 409.87135608846205)
def test_biweight_midcovariance_1d():
d = [0, 1, 2]
cov = biweight_midcovariance(d)
var = biweight_midvariance(d)
assert_allclose(cov, [[var]])
def test_biweight_scale():
# NOTE: biweight_scale is covered by biweight_midvariance tests
data = [1, 3, 5, 500, 2]
scl = biweight_scale(data)
var = biweight_midvariance(data)
assert_allclose(scl, np.sqrt(var))
def test_biweight_midvariance():
with NumpyRNGContext(12345):
# test that it runs
randvar = np.random.randn(10000)
var = biweight_midvariance(randvar)
assert_allclose(var, 1.0, rtol=0.02)
def test_biweight_midvariance_constant_axis():
bw = biweight_midvariance(np.ones((10, 5)))
assert bw == 0.0
def test_biweight_midvariance_masked():
data1d = np.array([1, 3, 5, 500, 2, np.nan])
data2d = np.array([data1d, data1d])
data1d_masked = np.ma.masked_invalid(data1d)
data2d_masked = np.ma.masked_invalid(data2d)
assert_equal(biweight_midvariance(data1d, ignore_nan=True),
biweight_midvariance(data1d_masked))
assert_equal(biweight_midvariance(data2d, ignore_nan=True),
biweight_midvariance(data2d_masked))
bw_loc = biweight_midvariance(data2d, ignore_nan=True, axis=1)
bw_loc_masked = biweight_midvariance(data2d_masked, axis=1)
assert isinstance(bw_loc_masked, np.ma.MaskedArray)
assert ~np.any(bw_loc_masked.mask) # mask is all False
assert_equal(bw_loc, bw_loc_masked.data)
bw_loc = biweight_midvariance(data2d, ignore_nan=True, axis=0)
bw_loc_masked = biweight_midvariance(data2d_masked, axis=0)
assert_equal(bw_loc_masked.data[:-1], bw_loc[:-1])
assert bw_loc_masked.mask[-1] # last mask element is True
data1d_masked.data[0] = np.nan # unmasked NaN
assert np.isnan(biweight_midvariance(data1d_masked))
assert_equal(biweight_midvariance(data1d_masked, ignore_nan=True),
biweight_midvariance(data1d[1:], ignore_nan=True))
# ensure that input masked array is not modified
assert np.isnan(data1d_masked[0])
def test_biweight_midvariance_5127():
# test a regression introduced in #5127
rand = np.random.RandomState(12345)
data = rand.normal(loc=0., scale=20., size=(100, 100))
var = biweight_midvariance(data)
assert_allclose(var, 406.86938710817344) # verified with R
def test_biweight_scale():
# NOTE: biweight_scale is covered by biweight_midvariance tests
data = [1, 3, 5, 500, 2]
scl = biweight_scale(data)
var = biweight_midvariance(data)
assert_allclose(scl, np.sqrt(var))
def test_biweight_midcovariance_1d():
d = [0, 1, 2]
cov = biweight_midcovariance(d)
var = biweight_midvariance(d)
assert_allclose(cov, [[var]])
def test_biweight_midvariance_5127():
# test a regression introduced in #5127
rand = np.random.RandomState(12345)
data = rand.normal(loc=0., scale=20., size=(100, 100))
var = biweight_midvariance(data)
assert_allclose(var, 406.86938710817344) # verified with R
def test_biweight_midvariance():
with NumpyRNGContext(12345):
# test that it runs
randvar = np.random.randn(10000)
var = biweight_midvariance(randvar)
assert_allclose(var, 1.0, rtol=0.02)
def test_biweight_midvariance_constant_axis():
bw = biweight_midvariance(np.ones((10, 5)))
assert bw == 0.0
