diff options
| -rw-r--r-- | numpy/lib/arraysetops.py | 16 | ||||
| -rw-r--r-- | numpy/lib/function_base.py | 11 | ||||
| -rw-r--r-- | numpy/lib/type_check.py | 12 | ||||
| -rw-r--r-- | numpy/linalg/linalg.py | 81 |
4 files changed, 63 insertions, 57 deletions
diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index 91dd96f9c..20a0e7151 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -44,7 +44,7 @@ def ediff1d(ary, to_end=None, to_begin=None): Returns ------- - ed : ndarray + ediff1d : ndarray The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``. See Also @@ -212,7 +212,7 @@ def intersect1d(ar1, ar2, assume_unique=False): Returns ------- - out : ndarray + intersect1d : ndarray Sorted 1D array of common and unique elements. See Also @@ -251,7 +251,7 @@ def setxor1d(ar1, ar2, assume_unique=False): Returns ------- - xor : ndarray + setxor1d : ndarray Sorted 1D array of unique values that are in only one of the input arrays. @@ -287,7 +287,7 @@ def in1d(ar1, ar2, assume_unique=False): Parameters ---------- - ar1 : array_like, shape (M,) + ar1 : (M,) array_like Input array. ar2 : array_like The values against which to test each value of `ar1`. @@ -297,8 +297,8 @@ def in1d(ar1, ar2, assume_unique=False): Returns ------- - mask : ndarray of bools, shape(M,) - The values `ar1[mask]` are in `ar2`. + in1d : (M,) ndarray, bool + The values `ar1[in1d]` are in `ar2`. See Also -------- @@ -365,7 +365,7 @@ def union1d(ar1, ar2): Returns ------- - union : ndarray + union1d : ndarray Unique, sorted union of the input arrays. See Also @@ -399,7 +399,7 @@ def setdiff1d(ar1, ar2, assume_unique=False): Returns ------- - difference : ndarray + setdiff1d : ndarray Sorted 1D array of values in `ar1` that are not in `ar2`. See Also diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 6a457010b..a0781ebf9 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -843,7 +843,7 @@ def gradient(f, *varargs): Returns ------- - g : ndarray + gradient : ndarray N arrays of the same shape as `f` giving the derivative of `f` with respect to each dimension. @@ -948,7 +948,7 @@ def diff(a, n=1, axis=-1): Returns ------- - out : ndarray + diff : ndarray The `n` order differences. The shape of the output is the same as `a` except along `axis` where the dimension is smaller by `n`. @@ -1284,6 +1284,11 @@ def extract(condition, arr): arr : array_like Input array of the same size as `condition`. + Returns + ------- + extract : ndarray + Rank 1 array of values from `arr` where `condition` is True. + See Also -------- take, put, copyto, compress @@ -2714,7 +2719,7 @@ def kaiser(M,beta): A beta value of 14 is probably a good starting point. Note that as beta gets large, the window narrows, and so the number of samples needs to be - large enough to sample the increasingly narrow spike, otherwise nans will + large enough to sample the increasingly narrow spike, otherwise NaNs will get returned. Most references to the Kaiser window come from the signal processing diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py index c116c7e4a..e22d63156 100644 --- a/numpy/lib/type_check.py +++ b/numpy/lib/type_check.py @@ -233,11 +233,10 @@ def isreal(x): def iscomplexobj(x): """ - Return True if x is a complex type or an array of complex numbers. + Check for a complex type or an array of complex numbers. - The type of the input is checked, not the value. So even if the input - has an imaginary part equal to zero, `iscomplexobj` evaluates to True - if the data type is complex. + The type of the input is checked, not the value. Even if the input + has an imaginary part equal to zero, `iscomplexobj` evaluates to True. Parameters ---------- @@ -246,8 +245,9 @@ def iscomplexobj(x): Returns ------- - y : bool - The return value, True if `x` is of a complex type. + iscomplexobj : bool + The return value, True if `x` is of a complex type or has at least + one complex element. See Also -------- diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index aba656b5e..f25452064 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -250,15 +250,15 @@ def solve(a, b): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like Coefficient matrix. - b : array_like, shape (M,) or (M, N) + b : {(M,), (M, N)}, array_like Ordinate or "dependent variable" values. Returns ------- - x : ndarray, shape (M,) or (M, N) depending on b - Solution to the system a x = b + x : {(M,), (M, N)} ndarray + Solution to the system a x = b. Returned shape is identical to `b`. Raises ------ @@ -410,12 +410,12 @@ def inv(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like Matrix to be inverted. Returns ------- - ainv : ndarray or matrix, shape (M, M) + ainv : (M, M) ndarray or matrix (Multiplicative) inverse of the matrix `a`. Raises @@ -459,14 +459,15 @@ def cholesky(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like Hermitian (symmetric if all elements are real), positive-definite input matrix. Returns ------- - L : ndarray, or matrix object if `a` is, shape (M, M) - Lower-triangular Cholesky factor of a. + L : {(M, M) ndarray, (M, M) matrix} + Lower-triangular Cholesky factor of `a`. Returns a matrix object + if `a` is a matrix object. Raises ------ @@ -709,12 +710,12 @@ def eigvals(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like A complex- or real-valued matrix whose eigenvalues will be computed. Returns ------- - w : ndarray, shape (M,) + w : (M,) ndarray The eigenvalues, each repeated according to its multiplicity. They are not necessarily ordered, nor are they necessarily real for real matrices. @@ -815,7 +816,7 @@ def eigvalsh(a, UPLO='L'): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like A complex- or real-valued matrix whose eigenvalues are to be computed. UPLO : {'L', 'U'}, optional @@ -824,7 +825,7 @@ def eigvalsh(a, UPLO='L'): Returns ------- - w : ndarray, shape (M,) + w : (M,) ndarray The eigenvalues, not necessarily ordered, each repeated according to its multiplicity. @@ -910,18 +911,17 @@ def eig(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like A square array of real or complex elements. Returns ------- - w : ndarray, shape (M,) + w : (M,) ndarray The eigenvalues, each repeated according to its multiplicity. The eigenvalues are not necessarily ordered, nor are they necessarily real for real arrays (though for real arrays complex-valued eigenvalues should occur in conjugate pairs). - - v : ndarray, shape (M, M) + v : (M, M) ndarray The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]``. @@ -1077,7 +1077,7 @@ def eigh(a, UPLO='L'): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like A complex Hermitian or real symmetric matrix. UPLO : {'L', 'U'}, optional Specifies whether the calculation is done with the lower triangular @@ -1085,11 +1085,12 @@ def eigh(a, UPLO='L'): Returns ------- - w : ndarray, shape (M,) + w : (M,) ndarray The eigenvalues, not necessarily ordered. - v : ndarray, or matrix object if `a` is, shape (M, M) + v : {(M, M) ndarray, (M, M) matrix} The column ``v[:, i]`` is the normalized eigenvector corresponding - to the eigenvalue ``w[i]``. + to the eigenvalue ``w[i]``. Will return a matrix object if `a` is + a matrix object. Raises ------ @@ -1338,7 +1339,7 @@ def cond(x, p=None): Parameters ---------- - x : array_like, shape (M, N) + x : (M, N) array_like The matrix whose condition number is sought. p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional Order of the norm: @@ -1424,9 +1425,9 @@ def matrix_rank(M, tol=None): Parameters ---------- - M : array_like + M : {(M,), (M, N)} array_like array of <=2 dimensions - tol : {None, float} + tol : {None, float}, optional threshold below which SVD values are considered zero. If `tol` is None, and ``S`` is an array with singular values for `M`, and ``eps`` is the epsilon value for datatype of ``S``, then `tol` is @@ -1489,7 +1490,7 @@ def pinv(a, rcond=1e-15 ): Parameters ---------- - a : array_like, shape (M, N) + a : (M, N) array_like Matrix to be pseudo-inverted. rcond : float Cutoff for small singular values. @@ -1499,7 +1500,7 @@ def pinv(a, rcond=1e-15 ): Returns ------- - B : ndarray, shape (N, M) + B : (N, M) ndarray The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so is `B`. @@ -1647,14 +1648,19 @@ def det(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like Input array. Returns ------- - det : ndarray + det : float Determinant of `a`. + See Also + -------- + slogdet : Another way to representing the determinant, more suitable + for large matrices where underflow/overflow may occur. + Notes ----- The determinant is computed via LU factorization using the LAPACK @@ -1668,11 +1674,6 @@ def det(a): >>> np.linalg.det(a) -2.0 - See Also - -------- - slogdet : Another way to representing the determinant, more suitable - for large matrices where underflow/overflow may occur. - """ sign, logdet = slogdet(a) return sign * exp(logdet) @@ -1693,9 +1694,9 @@ def lstsq(a, b, rcond=-1): Parameters ---------- - a : array_like, shape (M, N) + a : (M, N) array_like "Coefficient" matrix. - b : array_like, shape (M,) or (M, K) + b : {(M,), (M, K)} array_like Ordinate or "dependent variable" values. If `b` is two-dimensional, the least-squares solution is calculated for each of the `K` columns of `b`. @@ -1706,18 +1707,18 @@ def lstsq(a, b, rcond=-1): Returns ------- - x : ndarray, shape (N,) or (N, K) + x : {(M,), (M, K)} ndarray Least-squares solution. The shape of `x` depends on the shape of `b`. - residues : ndarray, shape (), (1,), or (K,) - Sums of residues; squared Euclidean 2-norm for each column in + residuals : {(), (1,), (K,)} ndarray + Sums of residuals; squared Euclidean 2-norm for each column in ``b - a*x``. If the rank of `a` is < N or > M, this is an empty array. If `b` is 1-dimensional, this is a (1,) shape array. Otherwise the shape is (K,). rank : int Rank of matrix `a`. - s : ndarray, shape (min(M,N),) + s : (min(M, N),) ndarray Singular values of `a`. Raises @@ -1849,7 +1850,7 @@ def norm(x, ord=None): Parameters ---------- - x : array_like, shape (M,) or (M, N) + x : {(M,), (M, N)} array_like Input array. ord : {non-zero int, inf, -inf, 'fro'}, optional Order of the norm (see table under ``Notes``). inf means numpy's |