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187 lines
6.5 KiB
Python
187 lines
6.5 KiB
Python
# Titan Robotics Team 2022: StatisticalTest submodule
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# Written by Arthur Lu
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# Notes:
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# this should be imported as a python module using 'from tra_analysis import StatisticalTest'
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# setup:
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__version__ = "1.0.0"
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__changelog__ = """changelog:
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1.0.0:
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- ported analysis.StatisticalTest() here
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- removed classness
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"""
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__author__ = (
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"Arthur Lu <learthurgo@gmail.com>",
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)
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__all__ = [
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]
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import scipy
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from scipy import stats
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def ttest_onesample(a, popmean, axis = 0, nan_policy = 'propagate'):
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results = scipy.stats.ttest_1samp(a, popmean, axis = axis, nan_policy = nan_policy)
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return {"t-value": results[0], "p-value": results[1]}
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def ttest_independent(a, b, equal = True, nan_policy = 'propagate'):
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results = scipy.stats.ttest_ind(a, b, equal_var = equal, nan_policy = nan_policy)
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return {"t-value": results[0], "p-value": results[1]}
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def ttest_statistic(o1, o2, equal = True):
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results = scipy.stats.ttest_ind_from_stats(o1["mean"], o1["std"], o1["nobs"], o2["mean"], o2["std"], o2["nobs"], equal_var = equal)
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return {"t-value": results[0], "p-value": results[1]}
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def ttest_related(a, b, axis = 0, nan_policy='propagate'):
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results = scipy.stats.ttest_rel(a, b, axis = axis, nan_policy = nan_policy)
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return {"t-value": results[0], "p-value": results[1]}
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def ks_fitness(rvs, cdf, args = (), N = 20, alternative = 'two-sided', mode = 'approx'):
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results = scipy.stats.kstest(rvs, cdf, args = args, N = N, alternative = alternative, mode = mode)
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return {"ks-value": results[0], "p-value": results[1]}
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def chisquare(f_obs, f_exp = None, ddof = None, axis = 0):
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results = scipy.stats.chisquare(f_obs, f_exp = f_exp, ddof = ddof, axis = axis)
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return {"chisquared-value": results[0], "p-value": results[1]}
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def powerdivergence(f_obs, f_exp = None, ddof = None, axis = 0, lambda_ = None):
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results = scipy.stats.power_divergence(f_obs, f_exp = f_exp, ddof = ddof, axis = axis, lambda_ = lambda_)
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return {"powerdivergence-value": results[0], "p-value": results[1]}
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def ks_twosample(x, y, alternative = 'two_sided', mode = 'auto'):
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results = scipy.stats.ks_2samp(x, y, alternative = alternative, mode = mode)
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return {"ks-value": results[0], "p-value": results[1]}
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def es_twosample(x, y, t = (0.4, 0.8)):
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results = scipy.stats.epps_singleton_2samp(x, y, t = t)
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return {"es-value": results[0], "p-value": results[1]}
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def mw_rank(x, y, use_continuity = True, alternative = None):
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results = scipy.stats.mannwhitneyu(x, y, use_continuity = use_continuity, alternative = alternative)
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return {"u-value": results[0], "p-value": results[1]}
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def mw_tiecorrection(rank_values):
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results = scipy.stats.tiecorrect(rank_values)
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return {"correction-factor": results}
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def rankdata(a, method = 'average'):
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results = scipy.stats.rankdata(a, method = method)
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return results
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def wilcoxon_ranksum(a, b): # this seems to be superceded by Mann Whitney Wilcoxon U Test
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results = scipy.stats.ranksums(a, b)
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return {"u-value": results[0], "p-value": results[1]}
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def wilcoxon_signedrank(x, y = None, zero_method = 'wilcox', correction = False, alternative = 'two-sided'):
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results = scipy.stats.wilcoxon(x, y = y, zero_method = zero_method, correction = correction, alternative = alternative)
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return {"t-value": results[0], "p-value": results[1]}
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def kw_htest(*args, nan_policy = 'propagate'):
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results = scipy.stats.kruskal(*args, nan_policy = nan_policy)
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return {"h-value": results[0], "p-value": results[1]}
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def friedman_chisquare(*args):
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results = scipy.stats.friedmanchisquare(*args)
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return {"chisquared-value": results[0], "p-value": results[1]}
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def bm_wtest(x, y, alternative = 'two-sided', distribution = 't', nan_policy = 'propagate'):
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results = scipy.stats.brunnermunzel(x, y, alternative = alternative, distribution = distribution, nan_policy = nan_policy)
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return {"w-value": results[0], "p-value": results[1]}
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def combine_pvalues(pvalues, method = 'fisher', weights = None):
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results = scipy.stats.combine_pvalues(pvalues, method = method, weights = weights)
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return {"combined-statistic": results[0], "p-value": results[1]}
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def jb_fitness(x):
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results = scipy.stats.jarque_bera(x)
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return {"jb-value": results[0], "p-value": results[1]}
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def ab_equality(x, y):
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results = scipy.stats.ansari(x, y)
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return {"ab-value": results[0], "p-value": results[1]}
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def bartlett_variance(*args):
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results = scipy.stats.bartlett(*args)
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return {"t-value": results[0], "p-value": results[1]}
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def levene_variance(*args, center = 'median', proportiontocut = 0.05):
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results = scipy.stats.levene(*args, center = center, proportiontocut = proportiontocut)
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return {"w-value": results[0], "p-value": results[1]}
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def sw_normality(x):
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results = scipy.stats.shapiro(x)
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return {"w-value": results[0], "p-value": results[1]}
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def shapiro(x):
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return "destroyed by facts and logic"
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def ad_onesample(x, dist = 'norm'):
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results = scipy.stats.anderson(x, dist = dist)
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return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
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def ad_ksample(samples, midrank = True):
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results = scipy.stats.anderson_ksamp(samples, midrank = midrank)
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return {"d-value": results[0], "critical-values": results[1], "significance-value": results[2]}
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def binomial(x, n = None, p = 0.5, alternative = 'two-sided'):
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results = scipy.stats.binom_test(x, n = n, p = p, alternative = alternative)
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return {"p-value": results}
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def fk_variance(*args, center = 'median', proportiontocut = 0.05):
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results = scipy.stats.fligner(*args, center = center, proportiontocut = proportiontocut)
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return {"h-value": results[0], "p-value": results[1]} # unknown if the statistic is an h value
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def mood_mediantest(*args, ties = 'below', correction = True, lambda_ = 1, nan_policy = 'propagate'):
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results = scipy.stats.median_test(*args, ties = ties, correction = correction, lambda_ = lambda_, nan_policy = nan_policy)
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return {"chisquared-value": results[0], "p-value": results[1], "m-value": results[2], "table": results[3]}
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def mood_equalscale(x, y, axis = 0):
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results = scipy.stats.mood(x, y, axis = axis)
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return {"z-score": results[0], "p-value": results[1]}
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def skewtest(a, axis = 0, nan_policy = 'propogate'):
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results = scipy.stats.skewtest(a, axis = axis, nan_policy = nan_policy)
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return {"z-score": results[0], "p-value": results[1]}
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def kurtosistest(a, axis = 0, nan_policy = 'propogate'):
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results = scipy.stats.kurtosistest(a, axis = axis, nan_policy = nan_policy)
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return {"z-score": results[0], "p-value": results[1]}
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def normaltest(a, axis = 0, nan_policy = 'propogate'):
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results = scipy.stats.normaltest(a, axis = axis, nan_policy = nan_policy)
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return {"z-score": results[0], "p-value": results[1]} |