Working with circular data
| Circular([low, upp, units, modulo, rad_low, ...]) | Class to keep track of circular data |
| circ_axial(alpha[, m]) | Transform p-axial data to a common scale (0, 2pi) in radians. |
| degrees | Predefined instance of Circular for degrees in (0, 360) |
| clock24 | Predefined instance of Circular for time or clock in (0, 24) |
| radshift | Predefined instance of Circular for radians in (0, 2pi) |
| circ_resultant(alpha[, w, d, axis]) | Compute mean resultant vector length for circular data |
| circ_mean(alpha[, w, return_ci, xi, axis]) | Compute the mean direction for circular data. |
| circ_median(alpha[, axis]) | Compute the median direction for circular data. |
| circ_var(alpha[, w, d, axis]) | Compute circular variance for circular data (equ. |
| circ_std(alpha[, w, d, axis]) | Compute circular standard deviation for circular data (equ. |
| circ_skewness(alpha[, w, axis]) | Compute measures of angular skewness. |
| circ_kurtosis(alpha[, w, axis]) | Compute measures of angular kurtosis. |
| circ_moment(alpha[, w, p, cent, axis]) | Compute the complex p-th centered or non-centered circular moment. |
| circ_kappa(alpha[, w, method]) | Estimate kappa of the von Mises distribution |
| circ_dist(x, y[, pairwise]) | Compute pairwise distance, difference x_i-y_i, around the circle |
| circ_quantile(alpha, p[, mu]) | quantiles of distribution centered at mu |
| circ_axialmean(alpha[, m, axis]) | Compute the mean direction for circular data with axial correction. |
| circ_clust(alpha[, numclust, disp]) | Perform a simple agglomerative clustering of angular data. |
| circ_corrcc(alpha1, alpha2) | Circular correlation coefficient for two circular random variables. |
| circ_corrcl(alpha, x) | Correlation coefficient between one circular and one linear random variable. |
| circ_medtest(alpha, md) | One sample test for the median angle. |
| circ_mtest(alpha, mu0[, xi, w, d]) | One sample test for the mean angle. |
| circ_symtest(alpha) | Test for symmetry about the median. |
| circ_otest(alpha[, sz, w]) | Omnibus or Hodges-Ajne test for uniform distribution of circular data. |
| circ_raotest(alpha) | Rao’s spacing test for uniform distribution of circular data. |
| circ_rtest(alpha[, w, d, approx]) | Rayleigh test for uniform distribution of circular data. |
| circ_vtest(alpha, mu0[, w, d]) | V test for uniformity of circular data with a specified mean alternative. |
| circ_cmtest(alpha, groups) | Nonparametric multi-sample test for equal medians. |
| circ_hktest(alpha, idp, idq[, inter, fn]) | Parametric two-way ANOVA for circular data with interations. |
| circ_ktest(alpha1, alpha2) | A parametric two-sample test for equality of two concentration parameters. |
| circ_wwtest(alpha, groups[, w]) | Parametric Watson-Williams multi-sample test for equal means. |
| circ_kuipertest(alpha1, alpha2[, res, vis_on]) | Kuiper two-sample test for equality of distributions |
| pdf_from_data(data[, k_comp]) | get a pdf function for trigonometric series density |
| circ_pdf_from_moments(x, coeff_cos[, coeff_sin]) | pdf evaluated at x for circular distribution given by moments |
| circ_cdf_from_moments(x, coeff_cos[, coeff_sin]) | cdf evaluated at x for circular distribution given by moments |
| circ_moments_emp(x[, orders]) | calculate circular moments for the data |
| circ_moments_from_pdf(pdf_func[, k_comp, ...]) | calculate circular moments for a distribution given by its density |
| pdf_wrapcauchy(x, mean, a[, k_comp, neg]) | pdf of wrapped cauchy distribution |
| cdf_wrapcauchy(x, mean, a[, k_comp]) | cdf of wrapped cauchy distribution |
| coeffs_wrapcauchy(a, mu[, k_comp, neg]) | coefficients for series expansion of wrapped cauchy distribution |
| pdf_wrapnormal(x, mean, var[, k_comp]) | pdf of wrapped cauchy distribution |
| pdf_wrapstable(x, a, b, mu[, k_comp]) | density of wrapped stable distribution |
| coeffs_wrapnormal(mean, var[, k_comp]) | coefficients for series expansion of wrapped normal distribution |
| chf_cauchy(t, a, mu) | characteristic function of cauchy distribution |
| chf_normal(t, mean, var) | characteristic function of normal distribution |
| chf_stable(t, a, lambd, gamma, mu) | characteristic function of stable distribution |
| pdf_wrapped_from_charfunc(chfunc[, args, k_comp]) | returns a function that is the density of a wrapped distribution |
| cdf_wrapped_from_charfunc(chfunc[, args, k_comp]) | returns a function that is the cumulative density of a wrapped distribution |