290 lines
12 KiB
Python
290 lines
12 KiB
Python
import pytest
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import numpy as np
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from scipy import stats
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from scipy._lib._array_api import xp_assert_close, xp_assert_equal
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from scipy.stats._stats_py import _xp_mean, _xp_var, _length_nonmasked
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from scipy.stats._axis_nan_policy import _axis_nan_policy_factory
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marray = pytest.importorskip('marray')
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skip_backend = pytest.mark.skip_xp_backends
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def get_arrays(n_arrays, *, dtype='float64', xp=np, shape=(7, 8), seed=84912165484321):
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mxp = marray._get_namespace(xp)
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rng = np.random.default_rng(seed)
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datas, masks = [], []
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for i in range(n_arrays):
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data = rng.random(size=shape)
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if dtype.startswith('complex'):
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data = 10*data * 10j*rng.standard_normal(size=shape)
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data = data.astype(dtype)
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datas.append(data)
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mask = rng.random(size=shape) > 0.75
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masks.append(mask)
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marrays = []
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nan_arrays = []
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for array, mask in zip(datas, masks):
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marrays.append(mxp.asarray(array, mask=mask))
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nan_array = array.copy()
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nan_array[mask] = xp.nan
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nan_arrays.append(nan_array)
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return mxp, marrays, nan_arrays
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="marray#99")
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@pytest.mark.parametrize('fun, kwargs', [(stats.gmean, {}),
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(stats.hmean, {}),
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(stats.pmean, {'p': 2})])
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@pytest.mark.parametrize('axis', [0, 1])
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def test_xmean(fun, kwargs, axis, xp):
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mxp, marrays, narrays = get_arrays(2, xp=xp)
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res = fun(marrays[0], weights=marrays[1], axis=axis, **kwargs)
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ref = fun(narrays[0], weights=narrays[1], nan_policy='omit', axis=axis, **kwargs)
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xp_assert_close(res.data, xp.asarray(ref))
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="marray#99")
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@pytest.mark.parametrize('axis', [0, 1, None])
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@pytest.mark.parametrize('keepdims', [False, True])
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def test_xp_mean(axis, keepdims, xp):
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mxp, marrays, narrays = get_arrays(2, xp=xp)
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kwargs = dict(axis=axis, keepdims=keepdims)
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res = _xp_mean(marrays[0], weights=marrays[1], **kwargs)
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ref = _xp_mean(narrays[0], weights=narrays[1], nan_policy='omit', **kwargs)
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xp_assert_close(res.data, xp.asarray(ref))
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="array-api-compat#242")
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@pytest.mark.parametrize('fun, kwargs',
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[(stats.moment, {'order': 2}),
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(stats.skew, {}),
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(stats.skew, {'bias': False}),
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(stats.kurtosis, {}),
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(stats.kurtosis, {'bias': False}),
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(stats.sem, {}),
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(stats.kstat, {'n': 1}),
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(stats.kstat, {'n': 2}),
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(stats.kstat, {'n': 3}),
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(stats.kstat, {'n': 4}),
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(stats.kstatvar, {'n': 1}),
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(stats.kstatvar, {'n': 2}),
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(stats.circmean, {}),
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(stats.circvar, {}),
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(stats.circstd, {}),
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(_xp_var, {}),
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(stats.tmean, {'limits': (0.1, 0.9)}),
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(stats.tvar, {'limits': (0.1, 0.9)}),
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(stats.tmin, {'lowerlimit': 0.5}),
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(stats.tmax, {'upperlimit': 0.5}),
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(stats.tstd, {'limits': (0.1, 0.9)}),
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(stats.tsem, {'limits': (0.1, 0.9)}),
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])
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@pytest.mark.parametrize('axis', [0, 1, None])
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def test_several(fun, kwargs, axis, xp):
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mxp, marrays, narrays = get_arrays(1, xp=xp)
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kwargs = dict(axis=axis) | kwargs
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res = fun(marrays[0], **kwargs)
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ref = fun(narrays[0], nan_policy='omit', **kwargs)
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xp_assert_close(res.data, xp.asarray(ref))
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="array-api-compat#242")
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@pytest.mark.parametrize('axis', [0, 1])
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@pytest.mark.parametrize('kwargs', [{}])
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def test_describe(axis, kwargs, xp):
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mxp, marrays, narrays = get_arrays(1, xp=xp)
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kwargs = dict(axis=axis) | kwargs
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res = stats.describe(marrays[0], **kwargs)
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ref = stats.describe(narrays[0], nan_policy='omit', **kwargs)
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xp_assert_close(res.nobs.data, xp.asarray(ref.nobs))
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xp_assert_close(res.minmax[0].data, xp.asarray(ref.minmax[0].data))
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xp_assert_close(res.minmax[1].data, xp.asarray(ref.minmax[1].data))
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xp_assert_close(res.variance.data, xp.asarray(ref.variance.data))
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xp_assert_close(res.skewness.data, xp.asarray(ref.skewness.data))
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xp_assert_close(res.kurtosis.data, xp.asarray(ref.kurtosis.data))
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="array-api-compat#242")
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@pytest.mark.parametrize('fun', [stats.zscore, stats.gzscore, stats.zmap])
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@pytest.mark.parametrize('axis', [0, 1, None])
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def test_zscore(fun, axis, xp):
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mxp, marrays, narrays = (get_arrays(2, xp=xp) if fun == stats.zmap
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else get_arrays(1, xp=xp))
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res = fun(*marrays, axis=axis)
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ref = xp.asarray(fun(*narrays, nan_policy='omit', axis=axis))
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xp_assert_close(res.data[~res.mask], ref[~xp.isnan(ref)])
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xp_assert_equal(res.mask, marrays[0].mask)
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="array-api-compat#242")
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@skip_backend('cupy', reason="special functions won't work")
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@pytest.mark.parametrize('f_name', ['ttest_1samp', 'ttest_rel', 'ttest_ind'])
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@pytest.mark.parametrize('axis', [0, 1, None])
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def test_ttest(f_name, axis, xp):
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f = getattr(stats, f_name)
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mxp, marrays, narrays = get_arrays(2, xp=xp)
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if f_name == 'ttest_1samp':
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marrays[1] = mxp.mean(marrays[1], axis=axis, keepdims=axis is not None)
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narrays[1] = np.nanmean(narrays[1], axis=axis, keepdims=axis is not None)
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res = f(*marrays, axis=axis)
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ref = f(*narrays, nan_policy='omit', axis=axis)
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xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
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xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
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res_ci = res.confidence_interval()
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ref_ci = ref.confidence_interval()
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xp_assert_close(res_ci.low.data, xp.asarray(ref_ci.low))
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xp_assert_close(res_ci.high.data, xp.asarray(ref_ci.high))
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="array-api-compat#242")
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@skip_backend('cupy', reason="special functions won't work")
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@pytest.mark.filterwarnings("ignore::scipy.stats._axis_nan_policy.SmallSampleWarning")
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@pytest.mark.parametrize('f_name', ['skewtest', 'kurtosistest',
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'normaltest', 'jarque_bera'])
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@pytest.mark.parametrize('axis', [0, 1, None])
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def test_normality_tests(f_name, axis, xp):
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f = getattr(stats, f_name)
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mxp, marrays, narrays = get_arrays(1, xp=xp, shape=(10, 11))
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res = f(*marrays, axis=axis)
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ref = f(*narrays, nan_policy='omit', axis=axis)
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xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
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xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
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def pd_nsamples(kwargs):
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return 2 if kwargs.get('f_exp', None) is not None else 1
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@_axis_nan_policy_factory(lambda *args: tuple(args), paired=True, n_samples=pd_nsamples)
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def power_divergence_ref(f_obs, f_exp=None, *, ddof, lambda_, axis=0):
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return stats.power_divergence(f_obs, f_exp, axis=axis, ddof=ddof, lambda_=lambda_)
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="array-api-compat#242")
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@skip_backend('cupy', reason="special functions won't work")
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@pytest.mark.parametrize('lambda_', ['pearson', 'log-likelihood', 'freeman-tukey',
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'mod-log-likelihood', 'neyman', 'cressie-read',
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'chisquare'])
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@pytest.mark.parametrize('ddof', [0, 1])
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@pytest.mark.parametrize('axis', [0, 1, None])
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def test_power_divergence_chisquare(lambda_, ddof, axis, xp):
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mxp, marrays, narrays = get_arrays(2, xp=xp, shape=(5, 6))
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kwargs = dict(axis=axis, ddof=ddof)
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if lambda_ == 'chisquare':
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lambda_ = "pearson"
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def f(*args, **kwargs):
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return stats.chisquare(*args, **kwargs)
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else:
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def f(*args, **kwargs):
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return stats.power_divergence(*args, lambda_=lambda_, **kwargs)
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# test 1-arg
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res = f(marrays[0], **kwargs)
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ref = power_divergence_ref(narrays[0], nan_policy='omit', lambda_=lambda_, **kwargs)
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xp_assert_close(res.statistic.data, xp.asarray(ref[0]))
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xp_assert_close(res.pvalue.data, xp.asarray(ref[1]))
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# test 2-arg
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common_mask = np.isnan(narrays[0]) | np.isnan(narrays[1])
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normalize = (np.nansum(narrays[1] * ~common_mask, axis=axis, keepdims=True)
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/ np.nansum(narrays[0] * ~common_mask, axis=axis, keepdims=True))
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marrays[0] *= xp.asarray(normalize)
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narrays[0] *= normalize
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res = f(*marrays, **kwargs)
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ref = power_divergence_ref(*narrays, nan_policy='omit', lambda_=lambda_, **kwargs)
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xp_assert_close(res.statistic.data, xp.asarray(ref[0]))
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xp_assert_close(res.pvalue.data, xp.asarray(ref[1]))
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="array-api-compat#242")
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@skip_backend('cupy', reason="special functions won't work")
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@pytest.mark.parametrize('method', ['fisher', 'pearson', 'mudholkar_george',
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'tippett', 'stouffer'])
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@pytest.mark.parametrize('axis', [0, 1, None])
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def test_combine_pvalues(method, axis, xp):
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mxp, marrays, narrays = get_arrays(2, xp=xp, shape=(10, 11))
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kwargs = dict(method=method, axis=axis)
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res = stats.combine_pvalues(marrays[0], **kwargs)
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ref = stats.combine_pvalues(narrays[0], nan_policy='omit', **kwargs)
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xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
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xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
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if method != 'stouffer':
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return
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res = stats.combine_pvalues(marrays[0], weights=marrays[1], **kwargs)
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ref = stats.combine_pvalues(narrays[0], weights=narrays[1],
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nan_policy='omit', **kwargs)
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xp_assert_close(res.statistic.data, xp.asarray(ref.statistic))
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xp_assert_close(res.pvalue.data, xp.asarray(ref.pvalue))
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@skip_backend('dask.array', reason='Arrays need `device` attribute: dask/dask#11711')
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@skip_backend('jax.numpy', reason="JAX doesn't allow item assignment.")
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@skip_backend('torch', reason="array-api-compat#242")
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@skip_backend('cupy', reason="special functions won't work")
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def test_ttest_ind_from_stats(xp):
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shape = (10, 11)
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mxp, marrays, narrays = get_arrays(6, xp=xp, shape=shape)
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mask = np.astype(np.sum(np.stack([np.isnan(arg) for arg in narrays]), axis=0), bool)
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narrays = [arg[~mask] for arg in narrays]
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marrays[2], marrays[5] = marrays[2] * 100, marrays[5] * 100
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narrays[2], narrays[5] = narrays[2] * 100, narrays[5] * 100
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res = stats.ttest_ind_from_stats(*marrays)
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ref = stats.ttest_ind_from_stats(*narrays)
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mask = xp.asarray(mask)
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assert xp.any(mask) and xp.any(~mask)
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xp_assert_close(res.statistic.data[~mask], xp.asarray(ref.statistic))
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xp_assert_close(res.pvalue.data[~mask], xp.asarray(ref.pvalue))
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xp_assert_close(res.statistic.mask, mask)
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xp_assert_close(res.pvalue.mask, mask)
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assert res.statistic.shape == shape
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assert res.pvalue.shape == shape
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def test_length_nonmasked_marray_iterable_axis_raises():
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xp = marray._get_namespace(np)
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data = [[1.0, 2.0], [3.0, 4.0]]
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mask = [[False, False], [True, False]]
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marr = xp.asarray(data, mask=mask)
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# Axis tuples are not currently supported for MArray input.
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# This test can be removed after support is added.
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with pytest.raises(NotImplementedError,
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match="`axis` must be an integer or None for use with `MArray`"):
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_length_nonmasked(marr, axis=(0, 1), xp=xp)
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