U f{j @sfdZddlmZddlZddlmZmZmZddl m Z ddZ edd d Z dddZ dddZdS)zT-test with permutations.)sqrtN)check_random_stateverboselogger) parallel_funccCsbt|}t||t|}t|dddf|||}tjt||t|dd}|S)z8Aux function for permutation_t_test (for parallel comp).NZaxis)lennpdotfloatrmaxabs)XX2perms dof_scaling n_samplesZmusZstdsmax_absrU/var/www/html/bwave/python/venv/lib/python3.8/site-packages/mne/stats/permutations.py _max_stats " r'csddlm}j\}}tjdddtjdd} t||dt| d} tjdd| t|} t|} ||||| \} }}dt| d}t dt | |ft t |\}}t |fdd t||D}t |t| gf}t|}|dkrF|t| d d tjfkd }nP|dkrn|| d d tjfkd }n(|d kr| | d d tjfkd }| ||fS) a@One sample/paired sample permutation test based on a t-statistic. This function can perform the test on one variable or simultaneously on multiple variables. When applying the test to multiple variables, the "tmax" method is used for adjusting the p-values of each variable for multiple comparisons. Like Bonferroni correction, this method adjusts p-values in a way that controls the family-wise error rate. However, the permutation method will be more powerful than Bonferroni correction when different variables in the test are correlated (see :footcite:`NicholsHolmes2002`). Parameters ---------- X : array, shape (n_samples, n_tests) Samples (observations) by number of tests (variables). n_permutations : int | 'all' Number of permutations. If n_permutations is 'all' all possible permutations are tested. It's the exact test, that can be untractable when the number of samples is big (e.g. > 20). If n_permutations >= 2**n_samples then the exact test is performed. tail : -1 or 0 or 1 (default = 0) If tail is 1, the alternative hypothesis is that the mean of the data is greater than 0 (upper tailed test). If tail is 0, the alternative hypothesis is that the mean of the data is different than 0 (two tailed test). If tail is -1, the alternative hypothesis is that the mean of the data is less than 0 (lower tailed test). %(n_jobs)s %(seed)s %(verbose)s Returns ------- T_obs : array of shape [n_tests] T-statistic observed for all variables. p_values : array of shape [n_tests] P-values for all the tests (a.k.a. variables). H0 : array of shape [n_permutations] T-statistic obtained by permutations and t-max trick for multiple comparison. Notes ----- If ``n_permutations >= 2 ** (n_samples - (tail == 0))``, ``n_permutations`` and ``seed`` will be ignored since an exact test (full permutation test) will be performed. References ---------- .. footbibliography:: r)_get_1samp_ordersrrr g?zPermuting %d times%s...c3s|]}|VqdS)Nr).0prrrZ my_max_statrr Xsz%permutation_t_test..N)Z cluster_levelrshaper meanrrarrayrinfor rrZ concatenateZ array_splitrrsortZnewaxis)rZn_permutationstailZn_jobsseedrrrZn_testsZmu0Zstd0ZT_obsrngZorders_extrarparallelrZH0Zp_valuesrrrpermutation_t_tests05     $  r+ffffff?r!c sdkrddn"dkr$ddnts4tdjd}tj|td}t|}|j|d |t|fd }t fd d |D} d |ddd d |ddf}tj | |dd\} } t | | gS)aGet confidence intervals from non-parametric bootstrap. Parameters ---------- arr : ndarray, shape (n_samples, ...) The input data on which to calculate the confidence interval. ci : float Level of the confidence interval between 0 and 1. n_bootstraps : int Number of bootstraps. stat_fun : str | callable Can be "mean", "median", or a callable operating along ``axis=0``. random_state : int | float | array_like | None The seed at which to initialize the bootstrap. Returns ------- cis : ndarray, shape (2, ...) Containing the lower boundary of the CI at ``cis[0, ...]`` and the upper boundary of the CI at ``cis[1, ...]``. r!cSs |jddSNrr )r!xrrrstat_fun}sz/bootstrap_confidence_interval..stat_funmediancSstj|ddSr.)r r2r/rrrr1sz.stat_fun must be 'mean', 'median' or callable.r)ZdtypeT)replacesizecsg|]}|qSrr)rZindsarrr1rr sz1bootstrap_confidence_interval..rrdr ) callable ValueErrorr r Zarangeintrchoicer r"Z percentile) r6ci n_bootstrapsr1 random_stateZn_trialsindicesr'Z boot_indicesstatZci_lowZci_uprr5rbootstrap_confidence_intervales     $rB bootstrapcCs4|dkrt||||dSddlm}|||dSdS)zECalculate confidence interval. Aux function for plot_compare_evokeds.rC)r=r>r?r)_parametric_ci)r=N)rBrD)r6r=methodr>r?rDrrr_cis rG)rrNNN)r,r-r!N)r,rCr-N)__doc__mathrnumpyr utilsrrrr*rrr+rBrGrrrrs    M *