# jax.scipy package#

## 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.

## 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. `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. `polar_unitary`(a, *[, method, eps, ...]) Computes the unitary factor u in the polar decomposition `a = u p` (or `a = p u`). `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. `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#

 `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) Incomplete beta function. The digamma function. Elementwise function for computing entropy. Returns the error function of complex argument. Complementary error function, `1 - erf(x)`. Inverse of the error function. `exp1`(x[, module]) Exponential integral E1. `expi` Exponential integral Ei. Expit (a.k.a. `expn` Generalized exponential integral En. `gammainc`(a, x) Regularized lower incomplete gamma function. `gammaincc`(a, x) Regularized upper incomplete gamma function. Logarithm of the absolute value of the gamma function. `i0`(x) Modified Bessel function of order 0. Exponentially scaled modified Bessel function of order 0. `i1`(x) Modified Bessel function of order 1. Exponentially scaled modified Bessel function of order 1. `log_ndtr` Log Normal distribution function. `logit` 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 Normal distribution function. The inverse of the CDF of the Normal distribution function. `polygamma`(n, x) Polygamma functions. `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#

### 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.

### 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.

### 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.

### 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.

### 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.

### 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#

 Cumulative distribution function of the given RV. Inverse survival function (inverse of sf) at q of the given RV. Log of the probability density function at x of the given RV. Probability density function at x of the given RV. Percent point function (inverse of cdf) at q of the given RV. `sf`(x) 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.

### 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.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. Computes the integral of the product of this kernel density estimate `gaussian_kde.resample`(key[, shape]) Randomly sample a dataset from the estimated pdf Evaluate the estimated pdf on a provided set of points. Evaluate the log of the estimated pdf on a provided set of points.