Python numpy.nanvar() Examples
The following are 30
code examples of numpy.nanvar().
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Example #1
| Source File: test_nanfunctions.py From recruit with Apache License 2.0 | 7 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example #2
| Source File: test_nanfunctions.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example #3
| Source File: test_nanfunctions.py From mxnet-lambda with Apache License 2.0 | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
sup.filter(np.ComplexWarning)
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(sup.log) == 1)
else:
assert_(len(sup.log) == 0)
Example #4
| Source File: test_nanfunctions.py From lambda-packs with MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
sup.filter(np.ComplexWarning)
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(sup.log) == 1)
else:
assert_(len(sup.log) == 0)
Example #5
| Source File: subjective_model_test.py From sureal with Apache License 2.0 | 6 votes |
|
def test_observer_content_aware_subjective_model(self):
subjective_model = MaximumLikelihoodEstimationModel.from_dataset_file(
self.dataset_filepath)
result = subjective_model.run_modeling(force_subjbias_zeromean=False)
self.assertAlmostEqual(float(np.nansum(result['content_ambiguity'])), 2.653508643860357, places=4)
self.assertAlmostEqual(float(np.nanvar(result['content_ambiguity'])), 0.0092892978862108271, places=4)
self.assertAlmostEqual(float(np.sum(result['observer_bias'])), -0.020313188445860726, places=4)
self.assertAlmostEqual(float(np.var(result['observer_bias'])), 0.091830942654165318, places=4)
self.assertAlmostEqual(float(np.sum(result['observer_inconsistency'])), 11.232923468639161, places=4)
self.assertAlmostEqual(float(np.var(result['observer_inconsistency'])), 0.027721095664357907, places=4)
self.assertAlmostEqual(float(np.sum(result['quality_scores'])), 177.88599894484821, places=4)
self.assertAlmostEqual(float(np.var(result['quality_scores'])), 1.4896077857605587, places=4)
# self.assertAlmostEqual(np.nansum(result['content_ambiguity_std']), 0.30465244947706538, places=4)
self.assertAlmostEqual(float(np.sum(result['observer_bias_std'])), 2.165903882505483, places=4)
self.assertAlmostEqual(float(np.sum(result['observer_inconsistency_std'])), 27.520643824238352, places=4)
self.assertAlmostEqual(float(np.sum(result['quality_scores_std'])), 5.7355563435912256, places=4)
Example #6
| Source File: test_nanfunctions.py From lambda-packs with MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example #7
| Source File: test_nanfunctions.py From lambda-packs with MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example #8
| Source File: test_nanfunctions.py From ImageFusion with MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
else:
assert_(len(w) == 0)
Example #9
| Source File: test_nanfunctions.py From recruit with Apache License 2.0 | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example #10
| Source File: moments.py From dynamo-release with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def calc_12_mom_labeling(data, t, calculate_2_mom=True):
t_uniq = np.unique(t)
m = np.zeros((data.shape[0], len(t_uniq)))
if calculate_2_mom: v =np.zeros((data.shape[0], len(t_uniq)))
for i in range(data.shape[0]):
data_ = (
np.array(data[i].A.flatten(), dtype=float)
if issparse(data)
else np.array(data[i], dtype=float)
) # consider using the `adata.obs_vector`, `adata.var_vector` methods or accessing the array directly.
m[i] = strat_mom(data_, t, np.nanmean)
if calculate_2_mom: v[i] = strat_mom(data_, t, np.nanvar)
return (m, v, t_uniq) if calculate_2_mom else (m, t_uniq)
Example #11
| Source File: moments.py From dynamo-release with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def __init__(self, adata, time_key="Time", has_nan=False):
# self.data = adata
self.__dict__ = adata.__dict__
# calculate first and second moments from data
self.times = np.array(self.obs[time_key].values, dtype=float)
self.uniq_times = np.unique(self.times)
nT = self.get_n_times()
ng = self.get_n_genes()
self.M = np.zeros((ng, nT)) # first moments (data)
self.V = np.zeros((ng, nT)) # second moments (data)
for g in tqdm(range(ng), desc="calculating 1/2 moments"):
tmp = self[:, g].layers["new"]
L = (
np.array(tmp.A, dtype=float)
if issparse(tmp)
else np.array(tmp, dtype=float)
) # consider using the `adata.obs_vector`, `adata.var_vector` methods or accessing the array directly.
if has_nan:
self.M[g] = strat_mom(L, self.times, np.nanmean)
self.V[g] = strat_mom(L, self.times, np.nanvar)
else:
self.M[g] = strat_mom(L, self.times, np.mean)
self.V[g] = strat_mom(L, self.times, np.var)
Example #12
| Source File: test_nanfunctions.py From auto-alt-text-lambda-api with MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
else:
assert_(len(w) == 0)
Example #13
| Source File: test_nanfunctions.py From vnpy_crypto with MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example #14
| Source File: test_nanfunctions.py From vnpy_crypto with MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example #15
| Source File: test_nanfunctions.py From mxnet-lambda with Apache License 2.0 | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example #16
| Source File: test_nanfunctions.py From mxnet-lambda with Apache License 2.0 | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example #17
| Source File: test_nanfunctions.py From Computable with MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(w) == 1)
assert_(issubclass(w[0].category, RuntimeWarning))
else:
assert_(len(w) == 0)
Example #18
| Source File: scaler.py From fancyimpute with Apache License 2.0 | 6 votes |
|
def residual(self, X_normalized):
total = 0
if self.center_rows:
row_means = np.nanmean(X_normalized, axis=1)
total += (row_means ** 2).sum()
if self.center_columns:
column_means = np.nanmean(X_normalized, axis=0)
total += (column_means ** 2).sum()
if self.scale_rows:
row_variances = np.nanvar(X_normalized, axis=1)
row_variances[row_variances == 0] = 1.0
total += (np.log(row_variances) ** 2).sum()
if self.scale_columns:
column_variances = np.nanvar(X_normalized, axis=0)
column_variances[column_variances == 0] = 1.0
total += (np.log(column_variances) ** 2).sum()
return total
Example #19
| Source File: test_nanfunctions.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example #20
| Source File: test_nanfunctions.py From Mastering-Elasticsearch-7.0 with MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example #21
| Source File: expressions.py From ad_examples with MIT License | 6 votes |
|
def __init__(self, vals=None):
"""Initializes the mean and variance of the Gaussian variable."""
DType.__init__(self)
if vals is None:
vals = [0, 1] # some dummy. This is more for information.
# Ignore NaNs
n = np.count_nonzero(~np.isnan(vals))
if n > 0:
self.mean = np.nanmean(vals)
self.variance = np.nanvar(vals)
else:
self.mean = 0
self.variance = 0
Example #22
| Source File: scaler.py From ME-Net with MIT License | 6 votes |
|
def residual(self, X_normalized):
total = 0
if self.center_rows:
row_means = np.nanmean(X_normalized, axis=1)
total += (row_means ** 2).sum()
if self.center_columns:
column_means = np.nanmean(X_normalized, axis=0)
total += (column_means ** 2).sum()
if self.scale_rows:
row_variances = np.nanvar(X_normalized, axis=1)
row_variances[row_variances == 0] = 1.0
total += (np.log(row_variances) ** 2).sum()
if self.scale_columns:
column_variances = np.nanvar(X_normalized, axis=0)
column_variances[column_variances == 0] = 1.0
total += (np.log(column_variances) ** 2).sum()
return total
Example #23
| Source File: test_nanfunctions.py From pySINDy with MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example #24
| Source File: test_nanfunctions.py From pySINDy with MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example #25
| Source File: meanvar.py From cupy with MIT License | 6 votes |
|
def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
"""Returns the variance along an axis ignoring NaN values.
Args:
a (cupy.ndarray): Array to compute variance.
axis (int): Along which axis to compute variance. The flattened array
is used by default.
dtype: Data type specifier.
out (cupy.ndarray): Output array.
keepdims (bool): If ``True``, the axis is remained as an axis of
size one.
Returns:
cupy.ndarray: The variance of the input array along the axis.
.. seealso:: :func:`numpy.nanvar`
"""
if a.dtype.kind in 'biu':
return a.var(axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
# TODO(okuta): check type
return _statistics._nanvar(
a, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
Example #26
| Source File: test_nanfunctions.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_dtype_from_dtype(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
assert_(res is tgt)
Example #27
| Source File: test_nanfunctions.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example #28
| Source File: test_nanfunctions.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_dtype_from_char(self):
mat = np.eye(3)
codes = 'efdgFDG'
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
for c in codes:
with suppress_warnings() as sup:
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
# Giving the warning is a small bug, see gh-8000
sup.filter(np.ComplexWarning)
tgt = rf(mat, dtype=c, axis=1).dtype.type
res = nf(mat, dtype=c, axis=1).dtype.type
assert_(res is tgt)
# scalar case
tgt = rf(mat, dtype=c, axis=None).dtype.type
res = nf(mat, dtype=c, axis=None).dtype.type
assert_(res is tgt)
Example #29
| Source File: test_nanfunctions.py From vnpy_crypto with MIT License | 6 votes |
|
def test_ddof_too_big(self):
nanfuncs = [np.nanvar, np.nanstd]
stdfuncs = [np.var, np.std]
dsize = [len(d) for d in _rdat]
for nf, rf in zip(nanfuncs, stdfuncs):
for ddof in range(5):
with suppress_warnings() as sup:
sup.record(RuntimeWarning)
sup.filter(np.ComplexWarning)
tgt = [ddof >= d for d in dsize]
res = nf(_ndat, axis=1, ddof=ddof)
assert_equal(np.isnan(res), tgt)
if any(tgt):
assert_(len(sup.log) == 1)
else:
assert_(len(sup.log) == 0)
Example #30
| Source File: test_sdc_numpy.py From sdc with BSD 2-Clause "Simplified" License | 5 votes |
|
def test_nanvar(self):
def ref_impl(a):
return np.nanvar(a)
def sdc_impl(a):
return numpy_like.nanvar(a)
self.check_reduction_basic(ref_impl, sdc_impl,
comparator=np.testing.assert_array_almost_equal)
