jax.scipy module

jax.scipy.fft

dct(x[, type, n, axis, norm])	Return the Discrete Cosine Transform of arbitrary type sequence x.
dctn(x[, type, s, axes, norm])	Return multidimensional Discrete Cosine Transform along the specified axes.
idct(x[, type, n, axis, norm])	Return the Discrete Cosine Transform of arbitrary type sequence x.
idctn(x[, type, s, axes, norm])	Return multidimensional Inverse Discrete Cosine Transform along the specified axes.

jax.scipy.linalg

block_diag(*arrs)	Create a block diagonal matrix from provided arrays.
cho_factor(a[, lower, overwrite_a, check_finite])	Compute the Cholesky decomposition of a matrix, to use in cho_solve
cho_solve(c_and_lower, b[, overwrite_b, ...])	Solve the linear equations A x = b, given the Cholesky factorization of A.
cholesky(a[, lower, overwrite_a, check_finite])	Compute the Cholesky decomposition of a matrix.
det(a[, overwrite_a, check_finite])	Compute the determinant of a matrix
eigh(a[, b, lower, eigvals_only, ...])	Solve a standard or generalized eigenvalue problem for a complex
eigh_tridiagonal(d, e, *[, eigvals_only, ...])	Solve eigenvalue problem for a real symmetric tridiagonal matrix.
expm(A, *[, upper_triangular, max_squarings])	Compute the matrix exponential of an array.
expm_frechet(A, E, *[, method, compute_expm])	Frechet derivative of the matrix exponential of A in the direction E.
funm(A, func[, disp])	Evaluate a matrix function specified by a callable.
hessenberg(a, *[, calc_q, overwrite_a, ...])	Compute Hessenberg form of a matrix.
inv(a[, overwrite_a, check_finite])	Compute the inverse of a matrix.
lu(a[, permute_l, overwrite_a, check_finite])	Compute pivoted LU decomposition of a matrix.
lu_factor(a[, overwrite_a, check_finite])	Compute pivoted LU decomposition of a matrix.
lu_solve(lu_and_piv, b[, trans, ...])	Solve an equation system, a x = b, given the LU factorization of a
polar(a[, side, method, eps, max_iterations])	Computes the polar decomposition.
qr(a[, overwrite_a, lwork, mode, pivoting, ...])	Compute QR decomposition of a matrix.
rsf2csf(T, Z[, check_finite])	Convert real Schur form to complex Schur form.
schur(a[, output])	Compute Schur decomposition of a matrix.
sqrtm(A[, blocksize])	Matrix square root.
solve(a, b[, sym_pos, lower, overwrite_a, ...])	Solves the linear equation set a @ x == b for the unknown x
solve_triangular(a, b[, trans, lower, ...])	Solve the equation a x = b for x, assuming a is a triangular matrix.
sqrtm(A[, blocksize])	Matrix square root.
svd(a[, full_matrices, compute_uv, ...])	Singular Value Decomposition.
toeplitz(c[, r])	Construct a Toeplitz matrix.
tril(m[, k])	Make a copy of a matrix with elements above the kth diagonal zeroed.
triu(m[, k])	Make a copy of a matrix with elements below the kth diagonal zeroed.

jax.scipy.ndimage

map_coordinates(input, coordinates, order[, ...])

Map the input array to new coordinates by interpolation.

jax.scipy.optimize

minimize(fun, x0[, args, tol, options])	Minimization of scalar function of one or more variables.
OptimizeResults(x, success, status, fun, ...)	Object holding optimization results.

jax.scipy.signal

fftconvolve(in1, in2[, mode, axes])	Convolve two N-dimensional arrays using FFT.
convolve(in1, in2[, mode, method, precision])	Convolve two N-dimensional arrays.
convolve2d(in1, in2[, mode, boundary, ...])	Convolve two 2-dimensional arrays.
correlate(in1, in2[, mode, method, precision])	Cross-correlate two N-dimensional arrays.
correlate2d(in1, in2[, mode, boundary, ...])	Cross-correlate two 2-dimensional arrays.
csd(x, y[, fs, window, nperseg, noverlap, ...])	Estimate the cross power spectral density, Pxy, using Welch's method.
istft(Zxx[, fs, window, nperseg, noverlap, ...])	Perform the inverse Short Time Fourier transform (iSTFT).
stft(x[, fs, window, nperseg, noverlap, ...])	Compute the Short Time Fourier Transform (STFT).
welch(x[, fs, window, nperseg, noverlap, ...])	Estimate power spectral density using Welch's method.

jax.scipy.sparse.linalg

bicgstab(A, b[, x0, tol, atol, maxiter, M])	Use Bi-Conjugate Gradient Stable iteration to solve Ax = b.
cg(A, b[, x0, tol, atol, maxiter, M])	Use Conjugate Gradient iteration to solve Ax = b.
gmres(A, b[, x0, tol, atol, restart, ...])	GMRES solves the linear system A x = b for x, given A and b.

jax.scipy.special

betainc(a, b, x)	Regularized incomplete beta function.
betaln(a, b)	Natural logarithm of absolute value of beta function.
digamma(x)	The digamma function.
entr(x)	Elementwise function for computing entropy.
erf(x)	Returns the error function of complex argument.
erfc(x)	Complementary error function, 1 - erf(x).
erfinv(x)	Inverse of the error function.
exp1(x[, module])	Exponential integral E1.
expi(x)	Exponential integral Ei.
expit(x)	Expit (a.k.a.
expn(n, x)	Generalized exponential integral En.
gamma(x)	Logarithm of the absolute value of the gamma function.
gammainc(a, x)	Regularized lower incomplete gamma function.
gammaincc(a, x)	Regularized upper incomplete gamma function.
gammaln(x)	Logarithm of the absolute value of the gamma function.
i0(x)	Modified Bessel function of order 0.
i0e(x)	Exponentially scaled modified Bessel function of order 0.
i1(x)	Modified Bessel function of order 1.
i1e(x)	Exponentially scaled modified Bessel function of order 1.
log_ndtr(x[, series_order])	Log Normal distribution function.
logit(x)	Logit ufunc for ndarrays.
logsumexp(a[, axis, b, keepdims, return_sign])	Compute the log of the sum of exponentials of input elements.
lpmn(m, n, z)	The associated Legendre functions (ALFs) of the first kind.
lpmn_values(m, n, z, is_normalized)	The associated Legendre functions (ALFs) of the first kind.
multigammaln(a, d)	Returns the log of multivariate gamma, also sometimes called the
ndtr(x)	Normal distribution function.
ndtri(p)	The inverse of the CDF of the Normal distribution function.
polygamma(n, x)	Polygamma functions.
spence(x)	Spence's function, also known as the dilogarithm for real values.
sph_harm(m, n, theta, phi[, n_max])	Computes the spherical harmonics.
xlog1py(x, y)	Compute x*log1p(y) so that the result is 0 if x = 0.
xlogy(x, y)	Compute x*log(y) so that the result is 0 if x = 0.
zeta(x[, q])	Riemann or Hurwitz zeta function.

jax.scipy.stats

mode(a[, axis, nan_policy, keepdims])	Return an array of the modal (most common) value in the passed array.
rankdata(a[, method, axis, nan_policy])	Assign ranks to data, dealing with ties appropriately.

jax.scipy.stats.bernoulli

logpmf(k, p[, loc])	Log of the probability mass function at k of the given RV.
pmf(k, p[, loc])	Probability mass function at k of the given RV.
cdf(k, p)	Cumulative distribution function of the given RV.
ppf(q, p)	Percent point function (inverse of cdf) at q of the given RV.

jax.scipy.stats.beta

logpdf(x, a, b[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, a, b[, loc, scale])	Probability density function at x of the given RV.
cdf(x, a, b[, loc, scale])	Cumulative distribution function of the given RV.
logcdf(x, a, b[, loc, scale])	Log of the cumulative distribution function at x of the given RV.
sf(x, a, b[, loc, scale])	Survival function (1 - cdf) at x of the given RV.

jax.scipy.stats.betabinom

logpmf(k, n, a, b[, loc])	Log of the probability mass function at k of the given RV.
pmf(k, n, a, b[, loc])	Probability mass function at k of the given RV.

jax.scipy.stats.cauchy

logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.
cdf(x[, loc, scale])	Cumulative distribution function of the given RV.
logcdf(x[, loc, scale])	Log of the cumulative distribution function at x of the given RV.
sf(x[, loc, scale])	Survival function (1 - cdf) at x of the given RV.
isf(q[, loc, scale])	Inverse survival function (inverse of sf) at q of the given RV.
ppf(q[, loc, scale])	Percent point function (inverse of cdf) at q of the given RV.

jax.scipy.stats.chi2

logpdf(x, df[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, df[, loc, scale])	Probability density function at x of the given RV.
cdf(x, df[, loc, scale])	Cumulative distribution function of the given RV.
logcdf(x, df[, loc, scale])	Log of the cumulative distribution function at x of the given RV.
sf(x, df[, loc, scale])	Survival function (1 - cdf) at x of the given RV.

jax.scipy.stats.dirichlet

logpdf(x, alpha)	Log of the Dirichlet probability density function.
pdf(x, alpha)	The Dirichlet probability density function.

jax.scipy.stats.expon

logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.gamma

logpdf(x, a[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, a[, loc, scale])	Probability density function at x of the given RV.
cdf(x, a[, loc, scale])	Cumulative distribution function of the given RV.
logcdf(x, a[, loc, scale])	Log of the cumulative distribution function at x of the given RV.
sf(x, a[, loc, scale])	Survival function (1 - cdf) at x of the given RV.

jax.scipy.stats.gennorm

cdf(x, p)	Cumulative distribution function of the given RV.
logpdf(x, p)	Log of the probability density function at x of the given RV.
pdf(x, p)	Probability density function at x of the given RV.

jax.scipy.stats.geom

logpmf(k, p[, loc])	Log of the probability mass function at k of the given RV.
pmf(k, p[, loc])	Probability mass function at k of the given RV.

jax.scipy.stats.laplace

cdf(x[, loc, scale])	Cumulative distribution function of the given RV.
logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.logistic

cdf(x[, loc, scale])	Cumulative distribution function of the given RV.
isf(x[, loc, scale])	Inverse survival function (inverse of sf) at q of the given RV.
logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.
ppf(x[, loc, scale])	Percent point function (inverse of cdf) at q of the given RV.
sf(x[, loc, scale])	Survival function (1 - cdf) at x of the given RV.

jax.scipy.stats.multivariate_normal

logpdf(x, mean, cov[, allow_singular])	Log of the multivariate normal probability density function.
pdf(x, mean, cov)	Multivariate normal probability density function.

jax.scipy.stats.norm

cdf(x[, loc, scale])	Cumulative distribution function of the given RV.
logcdf(x[, loc, scale])	Log of the cumulative distribution function at x of the given RV.
logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.
ppf(q[, loc, scale])	Percent point function (inverse of cdf) at q of the given RV.
sf(x[, loc, scale])	Survival function (1 - cdf) at x of the given RV.
isf(q[, loc, scale])	Inverse survival function (inverse of sf) at q of the given RV.

jax.scipy.stats.pareto

logpdf(x, b[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, b[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.poisson

logpmf(k, mu[, loc])	Log of the probability mass function at k of the given RV.
pmf(k, mu[, loc])	Probability mass function at k of the given RV.

jax.scipy.stats.t

logpdf(x, df[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x, df[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.truncnorm

cdf(x, a, b[, loc, scale])	Cumulative distribution function of the given RV.
logcdf(x, a, b[, loc, scale])	Log of the cumulative distribution function at x of the given RV.
logpdf(x, a, b[, loc, scale])	Log of the probability density function at x of the given RV.
logsf(x, a, b[, loc, scale])	Log of the survival function of the given RV.
pdf(x, a, b[, loc, scale])	Probability density function at x of the given RV.
sf(x, a, b[, loc, scale])	Survival function (1 - cdf) at x of the given RV.

jax.scipy.stats.uniform

logpdf(x[, loc, scale])	Log of the probability density function at x of the given RV.
pdf(x[, loc, scale])	Probability density function at x of the given RV.

jax.scipy.stats.gaussian_kde

gaussian_kde(dataset[, bw_method, weights])	Representation of a kernel-density estimate using Gaussian kernels.
gaussian_kde.evaluate(points)	Evaluate the estimated pdf on a set of points.
gaussian_kde.integrate_gaussian(mean, cov)	Multiply estimated density by a multivariate Gaussian and integrate
gaussian_kde.integrate_box_1d(low, high)	Computes the integral of a 1D pdf between two bounds.
gaussian_kde.integrate_kde(other)	Computes the integral of the product of this kernel density estimate
gaussian_kde.resample(key[, shape])	Randomly sample a dataset from the estimated pdf
gaussian_kde.pdf(x)	Evaluate the estimated pdf on a provided set of points.
gaussian_kde.logpdf(x)	Evaluate the log of the estimated pdf on a provided set of points.

jax.scipy.stats.vonmises

logpdf(x, kappa)	Log of the probability density function at x of the given RV.
pdf(x, kappa)	Probability density function at x of the given RV.