diff --git a/README.md b/README.md index f91cdb42..20b017d0 100644 --- a/README.md +++ b/README.md @@ -52,6 +52,7 @@ Stubs for the following libraries now exist in typeshed or the libraries themsel - pygame - pywin32 (pythonwin, win32 and win32com packages) - retry +- scipy (see https://github.com/jorenham/scipy-stubs) - slugify - SQLAlchemy (see for SQLAlchemy 1.4; 2.0.0 and above include type annotations) - tenacity diff --git a/stubs/scipy-stubs/_lib/_ccallback_c.pyi b/stubs/scipy-stubs/_lib/_ccallback_c.pyi deleted file mode 100644 index 5f58ba65..00000000 --- a/stubs/scipy-stubs/_lib/_ccallback_c.pyi +++ /dev/null @@ -1,48 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy._lib._ccallback_c, version: unspecified -import builtins as _mod_builtins -import ctypes as _mod_ctypes -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def check_capsule(item) -> typing.Any: - "\n check_capsule(item)\n\n Return True if the given object is a PyCapsule.\n" - ... - -def get_capsule_signature() -> typing.Any: ... -def get_raw_capsule(ptr, name, context) -> typing.Any: - "\n get_raw_capsule(ptr, name, context)\n\n Create a new PyCapsule with given pointer, name, and context.\n\n Parameters\n ----------\n ptr : {PyCapsule, int}\n Memory address of the pointer.\n name : str\n Python string containing the signature.\n context : {PyCapsule, int}\n Memory address of the context.\n If NULL and ptr is a PyCapsule, use the one from the context of ptr.\n\n" - ... - -idx: int - -def plus1_ctypes() -> typing.Any: ... - -plus1_t = _mod_ctypes.CFunctionType - -def plus1b_ctypes() -> typing.Any: ... - -plus1b_t = _mod_ctypes.CFunctionType - -def plus1bc_ctypes() -> typing.Any: ... - -plus1bc_t = _mod_ctypes.CFunctionType -sig: tuple -sigs: list - -def sine_ctypes() -> typing.Any: ... - -sine_t = _mod_ctypes.CFunctionType - -def test_call_cython() -> typing.Any: - "\n Implementation of a caller routine in Cython\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/_lib/_fpumode.pyi b/stubs/scipy-stubs/_lib/_fpumode.pyi deleted file mode 100644 index 2b3c3c21..00000000 --- a/stubs/scipy-stubs/_lib/_fpumode.pyi +++ /dev/null @@ -1,16 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy._lib._fpumode, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def get_fpu_mode() -> typing.Any: - "get_fpu_mode()\n\nGet the current FPU control word, in a platform-dependent format.\nReturns None if not implemented on current platform." - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/_lib/_test_ccallback.pyi b/stubs/scipy-stubs/_lib/_test_ccallback.pyi deleted file mode 100644 index 00a51d42..00000000 --- a/stubs/scipy-stubs/_lib/_test_ccallback.pyi +++ /dev/null @@ -1,19 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy._lib._test_ccallback, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def test_call_nodata() -> typing.Any: ... -def test_call_nonlocal() -> typing.Any: ... -def test_call_simple() -> typing.Any: ... -def test_get_data_capsule() -> typing.Any: ... -def test_get_plus1_capsule() -> typing.Any: ... -def test_get_plus1b_capsule() -> typing.Any: ... -def test_get_plus1bc_capsule() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/_lib/_test_deprecation_call.pyi b/stubs/scipy-stubs/_lib/_test_deprecation_call.pyi deleted file mode 100644 index 04791029..00000000 --- a/stubs/scipy-stubs/_lib/_test_deprecation_call.pyi +++ /dev/null @@ -1,14 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy._lib._test_deprecation_call, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__test__: dict - -def call() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/_lib/_test_deprecation_def.pyi b/stubs/scipy-stubs/_lib/_test_deprecation_def.pyi deleted file mode 100644 index b8b94b77..00000000 --- a/stubs/scipy-stubs/_lib/_test_deprecation_def.pyi +++ /dev/null @@ -1,14 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy._lib._test_deprecation_def, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/_lib/_uarray/_uarray.pyi b/stubs/scipy-stubs/_lib/_uarray/_uarray.pyi deleted file mode 100644 index 3c319d46..00000000 --- a/stubs/scipy-stubs/_lib/_uarray/_uarray.pyi +++ /dev/null @@ -1,22 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy._lib._uarray._uarray, version: unspecified -import builtins as _mod_builtins -import typing - -import uarray as _mod_uarray - -BackendNotImplementedError = _mod_uarray.BackendNotImplementedError -_Function = _mod_uarray._Function -_SetBackendContext = _mod_uarray._SetBackendContext -_SkipBackendContext = _mod_uarray._SkipBackendContext -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def clear_all_globals() -> typing.Any: ... -def clear_backends() -> typing.Any: ... -def register_backend() -> typing.Any: ... -def set_global_backend() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/_lib/messagestream.pyi b/stubs/scipy-stubs/_lib/messagestream.pyi deleted file mode 100644 index 2d196014..00000000 --- a/stubs/scipy-stubs/_lib/messagestream.pyi +++ /dev/null @@ -1,37 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy._lib.messagestream, version: unspecified -import builtins as _mod_builtins -import typing - -class MessageStream(_mod_builtins.object): - "\n Capture messages emitted to FILE* streams. Do this by directing them\n to a temporary file, residing in memory (if possible) or on disk.\n" - def __del__(self) -> None: ... - def __init__(self, *args, **kwargs) -> None: - "\n Capture messages emitted to FILE* streams. Do this by directing them\n to a temporary file, residing in memory (if possible) or on disk.\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def clear(self) -> typing.Any: ... - def close(self) -> typing.Any: ... - def get(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__test__: dict - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/cluster/_hierarchy.pyi b/stubs/scipy-stubs/cluster/_hierarchy.pyi deleted file mode 100644 index dbeb904c..00000000 --- a/stubs/scipy-stubs/cluster/_hierarchy.pyi +++ /dev/null @@ -1,125 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.cluster._hierarchy, version: unspecified -import builtins as _mod_builtins -import typing - -class Heap(_mod_builtins.object): - "Binary heap.\n\n Heap stores values and keys. Values are passed explicitly, whereas keys\n are assigned implicitly to natural numbers (from 0 to n - 1).\n\n The supported operations (all have O(log n) time complexity):\n\n * Return the current minimum value and the corresponding key.\n * Remove the current minimum value.\n * Change the value of the given key. Note that the key must be still\n in the heap.\n\n The heap is stored as an array, where children of parent i have indices\n 2 * i + 1 and 2 * i + 2. All public methods are based on `sift_down` and\n `sift_up` methods, which restore the heap property by moving an element\n down or up in the heap.\n" - def __init__(self, *args, **kwargs) -> None: - "Binary heap.\n\n Heap stores values and keys. Values are passed explicitly, whereas keys\n are assigned implicitly to natural numbers (from 0 to n - 1).\n\n The supported operations (all have O(log n) time complexity):\n\n * Return the current minimum value and the corresponding key.\n * Remove the current minimum value.\n * Change the value of the given key. Note that the key must be still\n in the heap.\n\n The heap is stored as an array, where children of parent i have indices\n 2 * i + 1 and 2 * i + 2. All public methods are based on `sift_down` and\n `sift_up` methods, which restore the heap property by moving an element\n down or up in the heap.\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __pyx_vtable__: PyCapsule - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def change_value(self) -> typing.Any: ... - def get_min(self) -> typing.Any: ... - def remove_min(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class LinkageUnionFind(_mod_builtins.object): - "Structure for fast cluster labeling in unsorted dendrogram." - def __init__(self, *args, **kwargs) -> None: - "Structure for fast cluster labeling in unsorted dendrogram." - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __pyx_vtable__: PyCapsule - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def __getattr__(self, name) -> typing.Any: ... - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... -def __pyx_unpickle_Heap() -> typing.Any: ... -def __pyx_unpickle_LinkageUnionFind() -> typing.Any: ... - -__test__: dict - -def calculate_cluster_sizes() -> typing.Any: - "\n Calculate the size of each cluster. The result is the fourth column of\n the linkage matrix.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix. The fourth column can be empty.\n cs : ndarray\n The array to store the sizes.\n n : ndarray\n The number of observations.\n" - ... - -def cluster_dist() -> typing.Any: - "\n Form flat clusters by distance criterion.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n T : ndarray\n The array to store the cluster numbers. The i'th observation belongs to\n cluster `T[i]`.\n cutoff : double\n Clusters are formed when distances are less than or equal to `cutoff`.\n n : int\n The number of observations.\n" - ... - -def cluster_in() -> typing.Any: - "\n Form flat clusters by inconsistent criterion.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n R : ndarray\n The inconsistent matrix.\n T : ndarray\n The array to store the cluster numbers. The i'th observation belongs to\n cluster `T[i]`.\n cutoff : double\n Clusters are formed when the inconsistent values are less than or\n or equal to `cutoff`.\n n : int\n The number of observations.\n" - ... - -def cluster_maxclust_dist() -> typing.Any: - "\n Form flat clusters by maxclust criterion.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n T : ndarray\n The array to store the cluster numbers. The i'th observation belongs to\n cluster `T[i]`.\n n : int\n The number of observations.\n mc : int\n The maximum number of clusters.\n" - ... - -def cluster_maxclust_monocrit() -> typing.Any: - "\n Form flat clusters by maxclust_monocrit criterion.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n MC : ndarray\n The monotonic criterion array.\n T : ndarray\n The array to store the cluster numbers. The i'th observation belongs to\n cluster `T[i]`.\n n : int\n The number of observations.\n max_nc : int\n The maximum number of clusters.\n" - ... - -def cluster_monocrit() -> typing.Any: - "\n Form flat clusters by monocrit criterion.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n MC : ndarray\n The monotonic criterion array.\n T : ndarray\n The array to store the cluster numbers. The i'th observation belongs to\n cluster `T[i]`.\n cutoff : double\n Clusters are formed when the MC values are less than or equal to\n `cutoff`.\n n : int\n The number of observations.\n" - ... - -def cophenetic_distances() -> typing.Any: - "\n Calculate the cophenetic distances between each observation\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n d : ndarray\n The condensed matrix to store the cophenetic distances.\n n : int\n The number of observations.\n" - ... - -def fast_linkage() -> typing.Any: - 'Perform hierarchy clustering.\n\n It implements "Generic Clustering Algorithm" from [1]. The worst case\n time complexity is O(N^3), but the best case time complexity is O(N^2) and\n it usually works quite close to the best case.\n\n Parameters\n ----------\n dists : ndarray\n A condensed matrix stores the pairwise distances of the observations.\n n : int\n The number of observations.\n method : int\n The linkage method. 0: single 1: complete 2: average 3: centroid\n 4: median 5: ward 6: weighted\n\n Returns\n -------\n Z : ndarray, shape (n - 1, 4)\n Computed linkage matrix.\n\n References\n ----------\n .. [1] Daniel Mullner, "Modern hierarchical, agglomerative clustering\n algorithms", :arXiv:`1109.2378v1`.\n' - ... - -def get_max_Rfield_for_each_cluster() -> typing.Any: - "\n Get the maximum statistic for each non-singleton cluster. For the i'th\n non-singleton cluster, max_rfs[i] = max{R[j, rf] j is a descendent of i}.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n R : ndarray\n The R matrix.\n max_rfs : ndarray\n The array to store the result.\n n : int\n The number of observations.\n rf : int\n Indicate which column of `R` is used.\n" - ... - -def get_max_dist_for_each_cluster() -> typing.Any: - "\n Get the maximum inconsistency coefficient for each non-singleton cluster.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n MD : ndarray\n The array to store the result.\n n : int\n The number of observations.\n" - ... - -def inconsistent() -> typing.Any: - "\n Calculate the inconsistency statistics.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n R : ndarray\n A (n - 1) x 4 matrix to store the result. The inconsistency statistics\n `R[i]` are calculated over `d` levels below cluster i. `R[i, 0]` is the\n mean of distances. `R[i, 1]` is the standard deviation of distances.\n `R[i, 2]` is the number of clusters included. `R[i, 3]` is the\n inconsistency coefficient.\n\n .. math:: \\frac{\\mathtt{Z[i,2]}-\\mathtt{R[i,0]}} {R[i,1]}\n\n n : int\n The number of observations.\n d : int\n The number of levels included in calculation below a node.\n" - ... - -def leaders() -> typing.Any: - "\n Find the leader (root) of each flat cluster.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n T : ndarray\n The flat clusters assignment returned by `fcluster` or `fclusterdata`.\n L : ndarray\n `L` and `M` store the result. The leader of flat cluster `L[i]` is\n node `M[i]`.\n M : ndarray\n `L` and `M` store the result. The leader of flat cluster `L[i]` is\n node `M[i]`.\n nc : int\n The number of flat clusters.\n n : int\n The number of observations.\n\n Returns\n -------\n err_node : int\n Found that `T` is invalid when examining node `err_node`.\n `-1` indicates success.\n" - ... - -def linkage() -> typing.Any: - "\n Perform hierarchy clustering.\n\n Parameters\n ----------\n dists : ndarray\n A condensed matrix stores the pairwise distances of the observations.\n n : int\n The number of observations.\n method : int\n The linkage method. 0: single 1: complete 2: average 3: centroid\n 4: median 5: ward 6: weighted\n\n Returns\n -------\n Z : ndarray, shape (n - 1, 4)\n Computed linkage matrix.\n" - ... - -def mst_single_linkage() -> typing.Any: - "Perform hierarchy clustering using MST algorithm for single linkage.\n\n Parameters\n ----------\n dists : ndarray\n A condensed matrix stores the pairwise distances of the observations.\n n : int\n The number of observations.\n\n Returns\n -------\n Z : ndarray, shape (n - 1, 4)\n Computed linkage matrix.\n" - ... - -def nn_chain() -> typing.Any: - "Perform hierarchy clustering using nearest-neighbor chain algorithm.\n\n Parameters\n ----------\n dists : ndarray\n A condensed matrix stores the pairwise distances of the observations.\n n : int\n The number of observations.\n method : int\n The linkage method. 0: single 1: complete 2: average 3: centroid\n 4: median 5: ward 6: weighted\n\n Returns\n -------\n Z : ndarray, shape (n - 1, 4)\n Computed linkage matrix.\n" - ... - -def prelist() -> typing.Any: - "\n Perform a pre-order traversal on the linkage tree and get a list of ids\n of the leaves.\n\n Parameters\n ----------\n Z : ndarray\n The linkage matrix.\n members : ndarray\n The array to store the result.\n n : int\n The number of observations.\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/cluster/_optimal_leaf_ordering.pyi b/stubs/scipy-stubs/cluster/_optimal_leaf_ordering.pyi deleted file mode 100644 index fe496642..00000000 --- a/stubs/scipy-stubs/cluster/_optimal_leaf_ordering.pyi +++ /dev/null @@ -1,32 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.cluster._optimal_leaf_ordering, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def is_valid_dm(D, tol, throw, name, warning) -> typing.Any: - "\n Return True if input array is a valid distance matrix.\n\n Distance matrices must be 2-dimensional numpy arrays.\n They must have a zero-diagonal, and they must be symmetric.\n\n Parameters\n ----------\n D : ndarray\n The candidate object to test for validity.\n tol : float, optional\n The distance matrix should be symmetric. `tol` is the maximum\n difference between entries ``ij`` and ``ji`` for the distance\n metric to be considered symmetric.\n throw : bool, optional\n An exception is thrown if the distance matrix passed is not valid.\n name : str, optional\n The name of the variable to checked. This is useful if\n throw is set to True so the offending variable can be identified\n in the exception message when an exception is thrown.\n warning : bool, optional\n Instead of throwing an exception, a warning message is\n raised.\n\n Returns\n -------\n valid : bool\n True if the variable `D` passed is a valid distance matrix.\n\n Notes\n -----\n Small numerical differences in `D` and `D.T` and non-zeroness of\n the diagonal are ignored if they are within the tolerance specified\n by `tol`.\n\n" - ... - -def is_valid_y(y, warning, throw, name) -> typing.Any: - "\n Return True if the input array is a valid condensed distance matrix.\n\n Condensed distance matrices must be 1-dimensional numpy arrays.\n Their length must be a binomial coefficient :math:`{n \\choose 2}`\n for some positive integer n.\n\n Parameters\n ----------\n y : ndarray\n The condensed distance matrix.\n warning : bool, optional\n Invokes a warning if the variable passed is not a valid\n condensed distance matrix. The warning message explains why\n the distance matrix is not valid. `name` is used when\n referencing the offending variable.\n throw : bool, optional\n Throws an exception if the variable passed is not a valid\n condensed distance matrix.\n name : bool, optional\n Used when referencing the offending variable in the\n warning or exception message.\n\n" - ... - -def optimal_leaf_ordering() -> typing.Any: - "\n Compute the optimal leaf order for Z (according to D) and return an \n optimally sorted Z. \n\n We start by sorting and relabelling Z and D according to the current leaf \n order in Z.\n \n This is because when everything is sorted each cluster (including\n singletons) can be defined by its range over (0...n_points).\n\n This is used extensively to loop efficiently over the various arrays in the \n algorithm.\n\n" - ... - -def squareform(X, force, checks) -> typing.Any: - "\n Convert a vector-form distance vector to a square-form distance\n matrix, and vice-versa.\n\n Parameters\n ----------\n X : ndarray\n Either a condensed or redundant distance matrix.\n force : str, optional\n As with MATLAB(TM), if force is equal to ``'tovector'`` or\n ``'tomatrix'``, the input will be treated as a distance matrix or\n distance vector respectively.\n checks : bool, optional\n If set to False, no checks will be made for matrix\n symmetry nor zero diagonals. This is useful if it is known that\n ``X - X.T1`` is small and ``diag(X)`` is close to zero.\n These values are ignored any way so they do not disrupt the\n squareform transformation.\n\n Returns\n -------\n Y : ndarray\n If a condensed distance matrix is passed, a redundant one is\n returned, or if a redundant one is passed, a condensed distance\n matrix is returned.\n\n Notes\n -----\n 1. ``v = squareform(X)``\n\n Given a square n-by-n symmetric distance matrix ``X``,\n ``v = squareform(X)`` returns a ``n * (n-1) / 2``\n (i.e. binomial coefficient n choose 2) sized vector `v`\n where :math:`v[{n \\choose 2} - {n-i \\choose 2} + (j-i-1)]`\n is the distance between distinct points ``i`` and ``j``.\n If ``X`` is non-square or asymmetric, an error is raised.\n\n 2. ``X = squareform(v)``\n\n Given a ``n * (n-1) / 2`` sized vector ``v``\n for some integer ``n >= 1`` encoding distances as described,\n ``X = squareform(v)`` returns a n-by-n distance matrix ``X``.\n The ``X[i, j]`` and ``X[j, i]`` values are set to\n :math:`v[{n \\choose 2} - {n-i \\choose 2} + (j-i-1)]`\n and all diagonal elements are zero.\n\n In SciPy 0.19.0, ``squareform`` stopped casting all input types to\n float64, and started returning arrays of the same dtype as the input.\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/cluster/_vq.pyi b/stubs/scipy-stubs/cluster/_vq.pyi deleted file mode 100644 index 1b28c9b9..00000000 --- a/stubs/scipy-stubs/cluster/_vq.pyi +++ /dev/null @@ -1,21 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.cluster._vq, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__test__: dict - -def update_cluster_means() -> typing.Any: - "\n The update-step of K-means. Calculate the mean of observations in each\n cluster.\n\n Parameters\n ----------\n obs : ndarray\n The observation matrix. Each row is an observation. Its dtype must be\n float32 or float64.\n labels : ndarray\n The label of each observation. Must be an 1d array.\n nc : int\n The number of centroids.\n\n Returns\n -------\n cb : ndarray\n The new code book.\n has_members : ndarray\n A boolean array indicating which clusters have members.\n\n Notes\n -----\n The empty clusters will be set to all zeros and the corresponding elements\n in `has_members` will be `False`. The upper level function should decide\n how to deal with them.\n" - ... - -def vq() -> typing.Any: - "\n Vector quantization ndarray wrapper. Only support float32 and float64.\n\n Parameters\n ----------\n obs : ndarray\n The observation matrix. Each row is an observation.\n codes : ndarray\n The code book matrix.\n\n Notes\n -----\n The observation matrix and code book matrix should have same ndim and\n same number of columns (features). Only 1-dimensional and 2-dimensional\n arrays are supported.\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/fft/_pocketfft/pypocketfft.pyi b/stubs/scipy-stubs/fft/_pocketfft/pypocketfft.pyi deleted file mode 100644 index 196eef2a..00000000 --- a/stubs/scipy-stubs/fft/_pocketfft/pypocketfft.pyi +++ /dev/null @@ -1,48 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.fft._pocketfft.pypocketfft, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str - -def c2c(a, axes=..., forward=..., inorm=..., out=..., nthreads=...) -> typing.Any: - "c2c(a: array, axes: object = None, forward: bool = True, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms a complex FFT.\n\nParameters\n----------\na : numpy.ndarray (any complex or real type)\n The input data. If its type is real, a more efficient real-to-complex\n transform will be used.\naxes : list of integers\n The axes along which the FFT is carried out.\n If not set, all axes will be transformed.\nforward : bool\n If `True`, a negative sign is used in the exponent, else a positive one.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : divide by sqrt(N)\n 2 : divide by N\n where N is the product of the lengths of the transformed axes.\nout : numpy.ndarray (same shape as `a`, complex type with same accuracy as `a`)\n May be identical to `a`, but if it isn't, it must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (same shape as `a`, complex type with same accuracy as `a`)\n The transformed data.\n\n" - ... - -def c2r(a, axes=..., lastsize=..., forward=..., inorm=..., out=..., nthreads=...) -> typing.Any: - "c2r(a: array, axes: object = None, lastsize: int = 0, forward: bool = True, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms an FFT whose output is strictly real.\n\nParameters\n----------\na : numpy.ndarray (any complex type)\n The input data\naxes : list of integers\n The axes along which the FFT is carried out.\n If not set, all axes will be transformed in ascending order.\nlastsize : the output size of the last axis to be transformed.\n If the corresponding input axis has size n, this can be 2*n-2 or 2*n-1.\nforward : bool\n If `True`, a negative sign is used in the exponent, else a positive one.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : divide by sqrt(N)\n 2 : divide by N\n where N is the product of the lengths of the transformed output axes.\nout : numpy.ndarray (real type with same accuracy as `a`)\n For the required shape, see the `Returns` section.\n Must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (real type with same accuracy as `a`)\n The transformed data. The shape is identical to that of the input array,\n except for the axis that was transformed last, which has now `lastsize`\n entries.\n\n" - ... - -def dct(a, type, axes=..., inorm=..., out=..., nthreads=...) -> typing.Any: - "dct(a: array, type: int, axes: object = None, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms a discrete cosine transform.\n\nParameters\n----------\na : numpy.ndarray (any real type)\n The input data\ntype : integer\n the type of DCT. Must be in [1; 4].\naxes : list of integers\n The axes along which the transform is carried out.\n If not set, all axes will be transformed.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : make transform orthogonal and divide by sqrt(N)\n 2 : divide by N\n where N is the product of n_i for every transformed axis i.\n n_i is 2*(-1 for type 1 and 2*\n for types 2, 3, 4.\n Making the transform orthogonal involves the following additional steps\n for every 1D sub-transform:\n Type 1 : multiply first and last input value by sqrt(2)\n divide first and last output value by sqrt(2)\n Type 2 : divide first output value by sqrt(2)\n Type 3 : multiply first input value by sqrt(2)\n Type 4 : nothing\nout : numpy.ndarray (same shape and data type as `a`)\n May be identical to `a`, but if it isn't, it must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (same shape and data type as `a`)\n The transformed data\n\n" - ... - -def dst(a, type, axes=..., inorm=..., out=..., nthreads=...) -> typing.Any: - "dst(a: array, type: int, axes: object = None, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms a discrete sine transform.\n\nParameters\n----------\na : numpy.ndarray (any real type)\n The input data\ntype : integer\n the type of DST. Must be in [1; 4].\naxes : list of integers\n The axes along which the transform is carried out.\n If not set, all axes will be transformed.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : make transform orthogonal and divide by sqrt(N)\n 2 : divide by N\n where N is the product of n_i for every transformed axis i.\n n_i is 2*(+1 for type 1 and 2*\n for types 2, 3, 4.\n Making the transform orthogonal involves the following additional steps\n for every 1D sub-transform:\n Type 1 : nothing\n Type 2 : divide first output value by sqrt(2)\n Type 3 : multiply first input value by sqrt(2)\n Type 4 : nothing\nout : numpy.ndarray (same shape and data type as `a`)\n May be identical to `a`, but if it isn't, it must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (same shape and data type as `a`)\n The transformed data\n\n" - ... - -def genuine_hartley(a, axes=..., inorm=..., out=..., nthreads=...) -> typing.Any: - "genuine_hartley(a: array, axes: object = None, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms a full Hartley transform.\nA full Fourier transform is carried out over the requested axes, and the\nsum of real and imaginary parts of the result is stored in the output\narray. For a single transformed axis, this is identical to `separable_hartley`,\nbut when transforming multiple axes, the results are different.\n\nParameters\n----------\na : numpy.ndarray (any real type)\n The input data\naxes : list of integers\n The axes along which the transform is carried out.\n If not set, all axes will be transformed.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : divide by sqrt(N)\n 2 : divide by N\n where N is the product of the lengths of the transformed axes.\nout : numpy.ndarray (same shape and data type as `a`)\n May be identical to `a`, but if it isn't, it must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (same shape and data type as `a`)\n The transformed data\n\n" - ... - -def good_size() -> typing.Any: - "Returns a good length to pad an FFT to.\n\nParameters\n----------\ntarget : int\n Minimum transform length\nreal : bool, optional\n True if either input or output of FFT should be fully real.\n\nReturns\n-------\nout : int\n The smallest fast size >= n\n\n" - ... - -def r2c(a, axes=..., forward=..., inorm=..., out=..., nthreads=...) -> typing.Any: - "r2c(a: array, axes: object = None, forward: bool = True, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms an FFT whose input is strictly real.\n\nParameters\n----------\na : numpy.ndarray (any real type)\n The input data\naxes : list of integers\n The axes along which the FFT is carried out.\n If not set, all axes will be transformed in ascending order.\nforward : bool\n If `True`, a negative sign is used in the exponent, else a positive one.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : divide by sqrt(N)\n 2 : divide by N\n where N is the product of the lengths of the transformed input axes.\nout : numpy.ndarray (complex type with same accuracy as `a`)\n For the required shape, see the `Returns` section.\n Must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (complex type with same accuracy as `a`)\n The transformed data. The shape is identical to that of the input array,\n except for the axis that was transformed last. If the length of that axis\n was n on input, it is n//2+1 on output.\n\n" - ... - -def r2r_fftpack(a, axes, real2hermitian, forward, inorm=..., out=..., nthreads=...) -> typing.Any: - "r2r_fftpack(a: array, axes: object, real2hermitian: bool, forward: bool, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms a real-valued FFT using the FFTPACK storage scheme.\n\nParameters\n----------\na : numpy.ndarray (any real type)\n The input data\naxes : list of integers\n The axes along which the FFT is carried out.\n If not set, all axes will be transformed.\nreal2hermitian : bool\n if True, the input is purely real and the output will have Hermitian\n symmetry and be stored in FFTPACK's halfcomplex ordering, otherwise the\n opposite.\nforward : bool\n If `True`, a negative sign is used in the exponent, else a positive one.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : divide by sqrt(N)\n 2 : divide by N\n where N is the length of `axis`.\nout : numpy.ndarray (same shape and data type as `a`)\n May be identical to `a`, but if it isn't, it must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (same shape and data type as `a`)\n The transformed data. The shape is identical to that of the input array.\n\n" - ... - -def separable_hartley(a, axes=..., inorm=..., out=..., nthreads=...) -> typing.Any: - "separable_hartley(a: array, axes: object = None, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms a separable Hartley transform.\nFor every requested axis, a 1D forward Fourier transform is carried out, and\nthe real and imaginary parts of the result are added before the next axis is\nprocessed.\n\nParameters\n----------\na : numpy.ndarray (any real type)\n The input data\naxes : list of integers\n The axes along which the transform is carried out.\n If not set, all axes will be transformed.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : divide by sqrt(N)\n 2 : divide by N\n where N is the product of the lengths of the transformed axes.\nout : numpy.ndarray (same shape and data type as `a`)\n May be identical to `a`, but if it isn't, it must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (same shape and data type as `a`)\n The transformed data\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/fftpack/convolve.pyi b/stubs/scipy-stubs/fftpack/convolve.pyi deleted file mode 100644 index 63313dc5..00000000 --- a/stubs/scipy-stubs/fftpack/convolve.pyi +++ /dev/null @@ -1,34 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.fftpack.convolve, version: unspecified -import builtins as _mod_builtins -import typing - -__all__: list -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def convolve(x, omega, swap_real_imag=..., overwrite_x=...) -> typing.Any: - "y = convolve(x,omega,[swap_real_imag,overwrite_x])\n\n Wrapper for ``convolve``.\n\n Parameters\n ----------\n x : input rank-1 array('d') with bounds (n)\n omega : input rank-1 array('d') with bounds (n)\n\n Other Parameters\n ----------------\n overwrite_x : input int, optional\n Default: 0\n swap_real_imag : input int, optional\n Default: 0\n\n Returns\n -------\n y : rank-1 array('d') with bounds (n) and x storage\n" - ... - -def convolve_z(x, omega_real, omega_imag, overwrite_x=...) -> typing.Any: - "y = convolve_z(x,omega_real,omega_imag,[overwrite_x])\n\n Wrapper for ``convolve_z``.\n\n Parameters\n ----------\n x : input rank-1 array('d') with bounds (n)\n omega_real : input rank-1 array('d') with bounds (n)\n omega_imag : input rank-1 array('d') with bounds (n)\n\n Other Parameters\n ----------------\n overwrite_x : input int, optional\n Default: 0\n\n Returns\n -------\n y : rank-1 array('d') with bounds (n) and x storage\n" - ... - -def destroy_convolve_cache() -> typing.Any: ... -def init_convolution_kernel(n, kernel_func, d=..., zero_nyquist=..., kernel_func_extra_args=...) -> typing.Any: - "omega = init_convolution_kernel(n,kernel_func,[d,zero_nyquist,kernel_func_extra_args])\n\n Wrapper for ``init_convolution_kernel``.\n\n Parameters\n ----------\n n : input int\n kernel_func : call-back function\n\n Other Parameters\n ----------------\n d : input int, optional\n Default: 0\n kernel_func_extra_args : input tuple, optional\n Default: ()\n zero_nyquist : input int, optional\n Default: d%2\n\n Returns\n -------\n omega : rank-1 array('d') with bounds (n)\n\n Notes\n -----\n Call-back functions::\n\n def kernel_func(k): return kernel_func\n Required arguments:\n k : input int\n Return objects:\n kernel_func : float\n" - ... - -def r2r_fftpack(a, axes, real2hermitian, forward, inorm=..., out=..., nthreads=...) -> typing.Any: - "r2r_fftpack(a: array, axes: object, real2hermitian: bool, forward: bool, inorm: int = 0, out: object = None, nthreads: int = 1) -> array\n\nPerforms a real-valued FFT using the FFTPACK storage scheme.\n\nParameters\n----------\na : numpy.ndarray (any real type)\n The input data\naxes : list of integers\n The axes along which the FFT is carried out.\n If not set, all axes will be transformed.\nreal2hermitian : bool\n if True, the input is purely real and the output will have Hermitian\n symmetry and be stored in FFTPACK's halfcomplex ordering, otherwise the\n opposite.\nforward : bool\n If `True`, a negative sign is used in the exponent, else a positive one.\ninorm : int\n Normalization type\n 0 : no normalization\n 1 : divide by sqrt(N)\n 2 : divide by N\n where N is the length of `axis`.\nout : numpy.ndarray (same shape and data type as `a`)\n May be identical to `a`, but if it isn't, it must not overlap with `a`.\n If None, a new array is allocated to store the output.\nnthreads : int\n Number of threads to use. If 0, use the system default (typically governed\n by the `OMP_NUM_THREADS` environment variable).\n\nReturns\n-------\nnumpy.ndarray (same shape and data type as `a`)\n The transformed data. The shape is identical to that of the input array.\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/integrate/_dop.pyi b/stubs/scipy-stubs/integrate/_dop.pyi deleted file mode 100644 index 72c1b8de..00000000 --- a/stubs/scipy-stubs/integrate/_dop.pyi +++ /dev/null @@ -1,29 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.integrate._dop, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def dop853( - fcn, x, y, xend, rtol, atol, solout, iout, work, iwork, fcn_extra_args=..., overwrite_y=..., solout_extra_args=... -) -> typing.Any: - "x,y,iwork,idid = dop853(fcn,x,y,xend,rtol,atol,solout,iout,work,iwork,[fcn_extra_args,overwrite_y,solout_extra_args])\n\nWrapper for ``dop853``.\n\nParameters\n----------\nfcn : call-back function\nx : input float\ny : input rank-1 array('d') with bounds (n)\nxend : input float\nrtol : input rank-1 array('d') with bounds (*)\natol : input rank-1 array('d') with bounds (*)\nsolout : call-back function\niout : input int\nwork : input rank-1 array('d') with bounds (*)\niwork : input rank-1 array('i') with bounds (*)\n\nOther Parameters\n----------------\nfcn_extra_args : input tuple, optional\n Default: ()\noverwrite_y : input int, optional\n Default: 0\nsolout_extra_args : input tuple, optional\n Default: ()\n\nReturns\n-------\nx : float\ny : rank-1 array('d') with bounds (n)\niwork : rank-1 array('i') with bounds (*)\nidid : int\n\nNotes\n-----\nCall-back functions::\n\n def fcn(x,y): return f\n Required arguments:\n x : input float\n y : input rank-1 array('d') with bounds (n)\n Return objects:\n f : rank-1 array('d') with bounds (n)\n def solout(nr,xold,x,y,con,icomp,[nd]): return irtn\n Required arguments:\n nr : input int\n xold : input float\n x : input float\n y : input rank-1 array('d') with bounds (n)\n con : input rank-1 array('d') with bounds (5 * nd)\n icomp : input rank-1 array('i') with bounds (nd)\n Optional arguments:\n nd : input int, optional\n Default: (len(con))/(5)\n Return objects:\n irtn : int\n" - ... - -def dopri5( - fcn, x, y, xend, rtol, atol, solout, iout, work, iwork, fcn_extra_args=..., overwrite_y=..., solout_extra_args=... -) -> typing.Any: - "x,y,iwork,idid = dopri5(fcn,x,y,xend,rtol,atol,solout,iout,work,iwork,[fcn_extra_args,overwrite_y,solout_extra_args])\n\nWrapper for ``dopri5``.\n\nParameters\n----------\nfcn : call-back function\nx : input float\ny : input rank-1 array('d') with bounds (n)\nxend : input float\nrtol : input rank-1 array('d') with bounds (*)\natol : input rank-1 array('d') with bounds (*)\nsolout : call-back function\niout : input int\nwork : input rank-1 array('d') with bounds (*)\niwork : input rank-1 array('i') with bounds (*)\n\nOther Parameters\n----------------\nfcn_extra_args : input tuple, optional\n Default: ()\noverwrite_y : input int, optional\n Default: 0\nsolout_extra_args : input tuple, optional\n Default: ()\n\nReturns\n-------\nx : float\ny : rank-1 array('d') with bounds (n)\niwork : rank-1 array('i') with bounds (*)\nidid : int\n\nNotes\n-----\nCall-back functions::\n\n def fcn(x,y): return f\n Required arguments:\n x : input float\n y : input rank-1 array('d') with bounds (n)\n Return objects:\n f : rank-1 array('d') with bounds (n)\n def solout(nr,xold,x,y,con,icomp,[nd]): return irtn\n Required arguments:\n nr : input int\n xold : input float\n x : input float\n y : input rank-1 array('d') with bounds (n)\n con : input rank-1 array('d') with bounds (5 * nd)\n icomp : input rank-1 array('i') with bounds (nd)\n Optional arguments:\n nd : input int, optional\n Default: (len(con))/(5)\n Return objects:\n irtn : int\n" - ... - -def types() -> typing.Any: - "'i'-scalar\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/integrate/_odepack.pyi b/stubs/scipy-stubs/integrate/_odepack.pyi deleted file mode 100644 index 0cf8c662..00000000 --- a/stubs/scipy-stubs/integrate/_odepack.pyi +++ /dev/null @@ -1,41 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.integrate._odepack, version: 1.9 -import builtins as _mod_builtins -import typing - -import odepack as _mod_odepack - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__version__: str -error = _mod_odepack.error - -def odeint( - fun, - y0, - t, - args=..., - Dfun=..., - col_deriv=..., - ml=..., - mu=..., - full_output=..., - rtol=..., - atol=..., - tcrit=..., - h0=..., - hmax=..., - hmin=..., - ixpr=..., - mxstep=..., - mxhnil=..., - mxordn=..., - mxords=..., -) -> typing.Any: - "[y,{infodict,}istate] = odeint(fun, y0, t, args=(), Dfun=None, col_deriv=0, ml=, mu=, full_output=0, rtol=, atol=, tcrit=, h0=0.0, hmax=0.0, hmin=0.0, ixpr=0.0, mxstep=0.0, mxhnil=0, mxordn=0, mxords=0)\n yprime = fun(y,t,...)" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/integrate/_quadpack.pyi b/stubs/scipy-stubs/integrate/_quadpack.pyi deleted file mode 100644 index 1ef26055..00000000 --- a/stubs/scipy-stubs/integrate/_quadpack.pyi +++ /dev/null @@ -1,45 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.integrate._quadpack, version: 1.13 -import builtins as _mod_builtins -import typing - -import quadpack as _mod_quadpack - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__version__: str - -def _qagie(fun, bound, inf, args, full_output, epsabs, epsrel, limit) -> typing.Any: - "[result,abserr,infodict,ier] = _qagie(fun, bound, inf, | args, full_output, epsabs, epsrel, limit)" - ... - -def _qagpe(fun, a, b, points, args, full_output, epsabs, epsrel, limit) -> typing.Any: - "[result,abserr,infodict,ier] = _qagpe(fun, a, b, points, | args, full_output, epsabs, epsrel, limit)" - ... - -def _qagse(fun, a, b, args, full_output, epsabs, epsrel, limit) -> typing.Any: - "[result,abserr,infodict,ier] = _qagse(fun, a, b, | args, full_output, epsabs, epsrel, limit)" - ... - -def _qawce(fun, a, b, c, args, full_output, epsabs, epsrel, limit) -> typing.Any: - "[result,abserr,infodict,ier] = _qawce(fun, a, b, c, | args, full_output, epsabs, epsrel, limit)" - ... - -def _qawfe(fun, a, omega, integr, args, full_output, epsabs, limlst, limit, maxp1) -> typing.Any: - "[result,abserr,infodict,ier] = _qawfe(fun, a, omega, integr, | args, full_output, epsabs, limlst, limit, maxp1)" - ... - -def _qawoe(fun, a, b, omega, integr, args, full_output, epsabs, epsrel, limit, maxp1, icall, momcom, chebmo) -> typing.Any: - "[result,abserr,infodict,ier] = _qawoe(fun, a, b, omega, integr, | args, full_output, epsabs, epsrel, limit, maxp1, icall, momcom, chebmo)" - ... - -def _qawse(fun, a, b, alfa, beta) -> typing.Any: - "[result,abserr,infodict,ier] = _qawse(fun, a, b, (alfa, beta), integr, | args, full_output, epsabs, epsrel, limit)" - ... - -error = _mod_quadpack.error - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/integrate/_test_multivariate.pyi b/stubs/scipy-stubs/integrate/_test_multivariate.pyi deleted file mode 100644 index 51c5bd7d..00000000 --- a/stubs/scipy-stubs/integrate/_test_multivariate.pyi +++ /dev/null @@ -1,19 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.integrate._test_multivariate, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -_multivariate_indefinite: int -_multivariate_sin: int -_multivariate_typical: int -_sin_0: int -_sin_1: int -_sin_2: int -_sin_3: int - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/integrate/_test_odeint_banded.pyi b/stubs/scipy-stubs/integrate/_test_odeint_banded.pyi deleted file mode 100644 index 9d97d202..00000000 --- a/stubs/scipy-stubs/integrate/_test_odeint_banded.pyi +++ /dev/null @@ -1,37 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.integrate._test_odeint_banded, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def banded5x5(t, y, f, n=...) -> typing.Any: - "banded5x5(t,y,f,[n])\n\nWrapper for ``banded5x5``.\n\nParameters\n----------\nt : input float\ny : input rank-1 array('d') with bounds (n)\nf : input rank-1 array('d') with bounds (n)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(y)\n" - ... - -def banded5x5_bjac(t, y, ml, mu, bjac, n=..., nrowpd=...) -> typing.Any: - "banded5x5_bjac(t,y,ml,mu,bjac,[n,nrowpd])\n\nWrapper for ``banded5x5_bjac``.\n\nParameters\n----------\nt : input float\ny : input rank-1 array('d') with bounds (5)\nml : input int\nmu : input int\nbjac : input rank-2 array('d') with bounds (nrowpd,n)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: shape(bjac,1)\nnrowpd : input int, optional\n Default: shape(bjac,0)\n" - ... - -def banded5x5_jac(t, y, ml, mu, jac, n=..., nrowpd=...) -> typing.Any: - "banded5x5_jac(t,y,ml,mu,jac,[n,nrowpd])\n\nWrapper for ``banded5x5_jac``.\n\nParameters\n----------\nt : input float\ny : input rank-1 array('d') with bounds (n)\nml : input int\nmu : input int\njac : input rank-2 array('d') with bounds (nrowpd,n)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(y)\nnrowpd : input int, optional\n Default: shape(jac,0)\n" - ... - -def banded5x5_solve(y, nsteps, dt, jt) -> typing.Any: - "nst,nfe,nje = banded5x5_solve(y,nsteps,dt,jt)\n\nWrapper for ``banded5x5_solve``.\n\nParameters\n----------\ny : in/output rank-1 array('d') with bounds (5)\nnsteps : input int\ndt : input float\njt : input int\n\nReturns\n-------\nnst : int\nnfe : int\nnje : int\n" - ... - -def getbands() -> typing.Any: - "jac = getbands()\n\nWrapper for ``getbands``.\n\nReturns\n-------\njac : rank-2 array('d') with bounds (4,5)\n" - ... - -def jac() -> typing.Any: - "'d'-array(4,5)\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/integrate/lsoda.pyi b/stubs/scipy-stubs/integrate/lsoda.pyi deleted file mode 100644 index 983a5648..00000000 --- a/stubs/scipy-stubs/integrate/lsoda.pyi +++ /dev/null @@ -1,23 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.integrate.lsoda, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def lsoda( - f, y, t, tout, rtol, atol, itask, istate, rwork, iwork, jac, jt, f_extra_args=..., overwrite_y=..., jac_extra_args=... -) -> typing.Any: - "y,t,istate = lsoda(f,y,t,tout,rtol,atol,itask,istate,rwork,iwork,jac,jt,[f_extra_args,overwrite_y,jac_extra_args])\n\nWrapper for ``lsoda``.\n\nParameters\n----------\nf : call-back function\ny : input rank-1 array('d') with bounds (neq)\nt : input float\ntout : input float\nrtol : input rank-1 array('d') with bounds (*)\natol : input rank-1 array('d') with bounds (*)\nitask : input int\nistate : input int\nrwork : input rank-1 array('d') with bounds (lrw)\niwork : input rank-1 array('i') with bounds (liw)\njac : call-back function\njt : input int\n\nOther Parameters\n----------------\nf_extra_args : input tuple, optional\n Default: ()\noverwrite_y : input int, optional\n Default: 0\njac_extra_args : input tuple, optional\n Default: ()\n\nReturns\n-------\ny : rank-1 array('d') with bounds (neq)\nt : float\nistate : int\n\nNotes\n-----\nCall-back functions::\n\n def f(t,y): return ydot\n Required arguments:\n t : input float\n y : input rank-1 array('d') with bounds (n)\n Return objects:\n ydot : rank-1 array('d') with bounds (n)\n def jac(t,y): return jac\n Required arguments:\n t : input float\n y : input rank-1 array('d') with bounds (n)\n Return objects:\n jac : rank-2 array('d') with bounds (nrowpd,n)\n" - ... - -def types() -> typing.Any: - "'i'-scalar\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/integrate/vode.pyi b/stubs/scipy-stubs/integrate/vode.pyi deleted file mode 100644 index 6442b54b..00000000 --- a/stubs/scipy-stubs/integrate/vode.pyi +++ /dev/null @@ -1,29 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.integrate.vode, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def dvode( - f, jac, y, t, tout, rtol, atol, itask, istate, rwork, iwork, mf, f_extra_args=..., jac_extra_args=..., overwrite_y=... -) -> typing.Any: - "y,t,istate = dvode(f,jac,y,t,tout,rtol,atol,itask,istate,rwork,iwork,mf,[f_extra_args,jac_extra_args,overwrite_y])\n\nWrapper for ``dvode``.\n\nParameters\n----------\nf : call-back function\njac : call-back function\ny : input rank-1 array('d') with bounds (neq)\nt : input float\ntout : input float\nrtol : input rank-1 array('d') with bounds (*)\natol : input rank-1 array('d') with bounds (*)\nitask : input int\nistate : input int\nrwork : input rank-1 array('d') with bounds (lrw)\niwork : input rank-1 array('i') with bounds (liw)\nmf : input int\n\nOther Parameters\n----------------\nf_extra_args : input tuple, optional\n Default: ()\njac_extra_args : input tuple, optional\n Default: ()\noverwrite_y : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('d') with bounds (neq)\nt : float\nistate : int\n\nNotes\n-----\nCall-back functions::\n\n def f(t,y): return ydot\n Required arguments:\n t : input float\n y : input rank-1 array('d') with bounds (n)\n Return objects:\n ydot : rank-1 array('d') with bounds (n)\n def jac(t,y): return jac\n Required arguments:\n t : input float\n y : input rank-1 array('d') with bounds (n)\n Return objects:\n jac : rank-2 array('d') with bounds (nrowpd,n)\n" - ... - -def types() -> typing.Any: - "'i'-scalar\n" - ... - -def zvode( - f, jac, y, t, tout, rtol, atol, itask, istate, zwork, rwork, iwork, mf, f_extra_args=..., jac_extra_args=..., overwrite_y=... -) -> typing.Any: - "y,t,istate = zvode(f,jac,y,t,tout,rtol,atol,itask,istate,zwork,rwork,iwork,mf,[f_extra_args,jac_extra_args,overwrite_y])\n\nWrapper for ``zvode``.\n\nParameters\n----------\nf : call-back function\njac : call-back function\ny : input rank-1 array('D') with bounds (neq)\nt : input float\ntout : input float\nrtol : input rank-1 array('d') with bounds (*)\natol : input rank-1 array('d') with bounds (*)\nitask : input int\nistate : input int\nzwork : input rank-1 array('D') with bounds (lzw)\nrwork : input rank-1 array('d') with bounds (lrw)\niwork : input rank-1 array('i') with bounds (liw)\nmf : input int\n\nOther Parameters\n----------------\nf_extra_args : input tuple, optional\n Default: ()\njac_extra_args : input tuple, optional\n Default: ()\noverwrite_y : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('D') with bounds (neq)\nt : float\nistate : int\n\nNotes\n-----\nCall-back functions::\n\n def f(t,y): return ydot\n Required arguments:\n t : input float\n y : input rank-1 array('D') with bounds (n)\n Return objects:\n ydot : rank-1 array('D') with bounds (n)\n def jac(t,y): return jac\n Required arguments:\n t : input float\n y : input rank-1 array('D') with bounds (n)\n Return objects:\n jac : rank-2 array('D') with bounds (nrowpd,n)\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/interpolate/_bspl.pyi b/stubs/scipy-stubs/interpolate/_bspl.pyi deleted file mode 100644 index e6dfbd5f..00000000 --- a/stubs/scipy-stubs/interpolate/_bspl.pyi +++ /dev/null @@ -1,36 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.interpolate._bspl, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _colloc() -> typing.Any: - "Build the B-spline collocation matrix.\n\n The collocation matrix is defined as :math:`B_{j,l} = B_l(x_j)`,\n so that row ``j`` contains all the B-splines which are non-zero\n at ``x_j``.\n\n The matrix is constructed in the LAPACK banded storage.\n Basically, for an N-by-N matrix A with ku upper diagonals and\n kl lower diagonals, the shape of the array Ab is (2*kl + ku +1, N),\n where the last kl+ku+1 rows of Ab contain the diagonals of A, and\n the first kl rows of Ab are not referenced.\n For more info see, e.g. the docs for the ``*gbsv`` routine.\n\n This routine is not supposed to be called directly, and\n does no error checking.\n\n Parameters\n ----------\n x : ndarray, shape (n,)\n sorted 1D array of x values\n t : ndarray, shape (nt + k + 1,)\n sorted 1D array of knots\n k : int\n spline order\n ab : ndarray, shape (2*kl + ku + 1, nt), F-order\n This parameter is modified in-place.\n On exit: zeroed out.\n On exit: B-spline collocation matrix in the band storage with\n ``ku`` upper diagonals and ``kl`` lower diagonals.\n Here ``kl = ku = k``.\n offset : int, optional\n skip this many rows\n\n" - ... - -def _handle_lhs_derivatives() -> typing.Any: - "Fill in the entries of the collocation matrix corresponding to known\n derivatives at xval.\n\n The collocation matrix is in the banded storage, as prepared by _colloc.\n No error checking.\n\n Parameters\n ----------\n t : ndarray, shape (nt + k + 1,)\n knots\n k : integer\n B-spline order\n xval : float\n The value at which to evaluate the derivatives at.\n ab : ndarray, shape(2*kl + ku + 1, nt), Fortran order\n B-spline collocation matrix.\n This argument is modified *in-place*.\n kl : integer\n Number of lower diagonals of ab.\n ku : integer\n Number of upper diagonals of ab.\n deriv_ords : 1D ndarray\n Orders of derivatives known at xval\n offset : integer, optional\n Skip this many rows of the matrix ab.\n\n" - ... - -def _norm_eq_lsq(x, t, k, y, w, ab, rhs) -> typing.Any: - "Construct the normal equations for the B-spline LSQ problem.\n\n The observation equations are ``A @ c = y``, and the normal equations are\n ``A.T @ A @ c = A.T @ y``. This routine fills in the rhs and lhs for the\n latter.\n\n The B-spline collocation matrix is defined as :math:`A_{j,l} = B_l(x_j)`,\n so that row ``j`` contains all the B-splines which are non-zero\n at ``x_j``.\n\n The normal eq matrix has at most `2k+1` bands and is constructed in the\n LAPACK symmetrix banded storage: ``A[i, j] == ab[i-j, j]`` with `i >= j`.\n See the doctsring for `scipy.linalg.cholesky_banded` for more info.\n\n This routine is not supposed to be called directly, and\n does no error checking.\n\n Parameters\n ----------\n x : ndarray, shape (n,)\n sorted 1D array of x values\n t : ndarray, shape (nt + k + 1,)\n sorted 1D array of knots\n k : int\n spline order\n y : ndarray, shape (n, s)\n a 2D array of y values. The second dimension contains all trailing\n dimensions of the original array of ordinates.\n w : ndarray, shape(n,)\n Weights.\n ab : ndarray, shape (k+1, n), in Fortran order.\n This parameter is modified in-place.\n On entry: should be zeroed out.\n On exit: LHS of the normal equations.\n rhs : ndarray, shape (n, s), in Fortran order.\n This parameter is modified in-place.\n On entry: should be zeroed out.\n On exit: RHS of the normal equations.\n\n" - ... - -def evaluate_all_bspl() -> typing.Any: - "Evaluate the ``k+1`` B-splines which are non-zero on interval ``m``.\n\n Parameters\n ----------\n t : ndarray, shape (nt + k + 1,)\n sorted 1D array of knots\n k : int\n spline order\n xval: float\n argument at which to evaluate the B-splines\n m : int\n index of the left edge of the evaluation interval, ``t[m] <= x < t[m+1]``\n nu : int, optional\n Evaluate derivatives order `nu`. Default is zero.\n\n Returns\n -------\n ndarray, shape (k+1,)\n The values of B-splines :math:`[B_{m-k}(xval), ..., B_{m}(xval)]` if\n `nu` is zero, otherwise the derivatives of order `nu`.\n\n Examples\n --------\n\n A textbook use of this sort of routine is plotting the ``k+1`` polynomial\n pieces which make up a B-spline of order `k`.\n\n Consider a cubic spline\n\n >>> k = 3\n >>> t = [0., 2., 2., 3., 4.] # internal knots\n >>> a, b = t[0], t[-1] # base interval is [a, b)\n >>> t = [a]*k + t + [b]*k # add boundary knots\n\n >>> import matplotlib.pyplot as plt\n >>> xx = np.linspace(a, b, 100)\n >>> plt.plot(xx, BSpline.basis_element(t[k:-k])(xx),\n ... 'r-', lw=5, alpha=0.5)\n >>> c = ['b', 'g', 'c', 'k']\n\n Now we use slide an interval ``t[m]..t[m+1]`` along the base interval\n ``a..b`` and use `evaluate_all_bspl` to compute the restriction of\n the B-spline of interest to this interval:\n\n >>> for i in range(k+1):\n ... x1, x2 = t[2*k - i], t[2*k - i + 1]\n ... xx = np.linspace(x1 - 0.5, x2 + 0.5)\n ... yy = [evaluate_all_bspl(t, k, x, 2*k - i)[i] for x in xx]\n ... plt.plot(xx, yy, c[i] + '--', lw=3, label=str(i))\n ...\n >>> plt.grid(True)\n >>> plt.legend()\n >>> plt.show()\n\n" - ... - -def evaluate_spline(t, c, k, xp, nu, extrapolate, out) -> typing.Any: - "\n Evaluate a spline in the B-spline basis.\n\n Parameters\n ----------\n t : ndarray, shape (n+k+1)\n knots\n c : ndarray, shape (n, m)\n B-spline coefficients\n xp : ndarray, shape (s,)\n Points to evaluate the spline at.\n nu : int\n Order of derivative to evaluate.\n extrapolate : int, optional\n Whether to extrapolate to ouf-of-bounds points, or to return NaNs.\n out : ndarray, shape (s, m)\n Computed values of the spline at each of the input points.\n This argument is modified in-place.\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/interpolate/_fitpack.pyi b/stubs/scipy-stubs/interpolate/_fitpack.pyi deleted file mode 100644 index 897df1c6..00000000 --- a/stubs/scipy-stubs/interpolate/_fitpack.pyi +++ /dev/null @@ -1,61 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.interpolate._fitpack, version: 1.7 -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__version__: str - -def _bispev(tx, ty, c, kx, ky, x, y, nux, nuy) -> typing.Any: - "[z,ier] = _bispev(tx,ty,c,kx,ky,x,y,nux,nuy)" - ... - -def _bspldismat(order, xk) -> typing.Any: - "B = _bspldismat(order,xk)\nConstruct the kth derivative discontinuity jump constraint matrix \nfor spline fitting of order k given sample positions in xk.\n\nIf xk is an integer (N+1), then the result is equivalent to\nxk=arange(N+1)+x0 for any value of x0. This produces the\ninteger-spaced matrix a bit faster. If xk is a 2-tuple (N+1,dx)\nthen it produces the result as if the sample distance were dx" - ... - -def _bspleval(xx, xk, coef, k, deriv) -> typing.Any: - "y = _bspleval(xx,xk,coef,k,{deriv (0)})\n\nThe spline is defined by the approximation interval xk[0] to xk[-1],\nthe length of xk (N+1), the order of the spline, k, and \nthe number of coeficients N+k. The coefficients range from xk_{-K}\nto xk_{N-1} inclusive and are all the coefficients needed to define\nan arbitrary spline of order k, on the given approximation interval\n\nExtra knot points are internally added using knot-point symmetry \naround xk[0] and xk[-1]" - ... - -def _bsplmat(order, xk) -> typing.Any: - "B = _bsplmat(order,xk)\nConstruct the constraint matrix for spline fitting of order k\ngiven sample positions in xk.\n\nIf xk is an integer (N+1), then the result is equivalent to\nxk=arange(N+1)+x0 for any value of x0. This produces the\ninteger-spaced, or cardinal spline matrix a bit faster." - ... - -def _curfit(x, y, w, xb, xe, k, iopt, s, t, nest, wrk, iwrk, per) -> typing.Any: - "[t,c,o] = _curfit(x,y,w,xb,xe,k,iopt,s,t,nest,wrk,iwrk,per)" - ... - -def _insert(iopt, t, c, k, x, m) -> typing.Any: - "[tt,cc,ier] = _insert(iopt,t,c,k,x,m)" - ... - -def _parcur(x, w, u, ub, ue, k, iopt, ipar, s, t, nest, wrk, iwrk, per) -> typing.Any: - "[t,c,o] = _parcur(x,w,u,ub,ue,k,iopt,ipar,s,t,nest,wrk,iwrk,per)" - ... - -def _spalde(t, c, k, x) -> typing.Any: - "[d,ier] = _spalde(t,c,k,x)" - ... - -def _spl_(x, nu, t, c, k, e) -> typing.Any: - "[y,ier] = _spl_(x,nu,t,c,k,e)" - ... - -def _splint(t, c, k, a, b) -> typing.Any: - "[aint,wrk] = _splint(t,c,k,a,b)" - ... - -def _sproot(t, c, k, mest) -> typing.Any: - "[z,ier] = _sproot(t,c,k,mest)" - ... - -def _surfit(x, y, z, w, xb, xe, yb, ye, kx, ky, iopt, s, eps, tx, ty, nxest, nyest, wrk, lwrk1, lwrk2) -> typing.Any: - "[tx,ty,c,o] = _surfit(x, y, z, w, xb, xe, yb, ye, kx,ky,iopt,s,eps,tx,ty,nxest,nyest,wrk,lwrk1,lwrk2)" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/interpolate/_ppoly.pyi b/stubs/scipy-stubs/interpolate/_ppoly.pyi deleted file mode 100644 index d8011f9c..00000000 --- a/stubs/scipy-stubs/interpolate/_ppoly.pyi +++ /dev/null @@ -1,44 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.interpolate._ppoly, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _croots_poly1() -> typing.Any: - "\n Find roots of polynomials.\n\n This function is for testing croots_poly1\n\n Parameters\n ----------\n c : ndarray, (k, m, n)\n Coefficients of several order-k polynomials\n w : ndarray, (k, m, n)\n Output argument --- roots of the polynomials.\n\n" - ... - -def evaluate(c, x, xp, dx, extrapolate, out) -> typing.Any: - "\n Evaluate a piecewise polynomial.\n\n Parameters\n ----------\n c : ndarray, shape (k, m, n)\n Coefficients local polynomials of order `k-1` in `m` intervals.\n There are `n` polynomials in each interval.\n Coefficient of highest order-term comes first.\n x : ndarray, shape (m+1,)\n Breakpoints of polynomials.\n xp : ndarray, shape (r,)\n Points to evaluate the piecewise polynomial at.\n dx : int\n Order of derivative to evaluate. The derivative is evaluated\n piecewise and may have discontinuities.\n extrapolate : bint\n Whether to extrapolate to out-of-bounds points based on first\n and last intervals, or to return NaNs.\n out : ndarray, shape (r, n)\n Value of each polynomial at each of the input points.\n This argument is modified in-place.\n\n" - ... - -def evaluate_bernstein(c, x, xp, nu, extrapolate, out) -> typing.Any: - "\n Evaluate a piecewise polynomial in the Bernstein basis.\n\n Parameters\n ----------\n c : ndarray, shape (k, m, n)\n Coefficients local polynomials of order `k-1` in `m` intervals.\n There are `n` polynomials in each interval.\n Coefficient of highest order-term comes first.\n x : ndarray, shape (m+1,)\n Breakpoints of polynomials\n xp : ndarray, shape (r,)\n Points to evaluate the piecewise polynomial at.\n nu : int\n Order of derivative to evaluate. The derivative is evaluated\n piecewise and may have discontinuities.\n extrapolate : bint, optional\n Whether to extrapolate to out-of-bounds points based on first\n and last intervals, or to return NaNs.\n out : ndarray, shape (r, n)\n Value of each polynomial at each of the input points.\n This argument is modified in-place.\n\n" - ... - -def evaluate_nd(c, xs, ks, xp, dx, extrapolate, out) -> typing.Any: - "\n Evaluate a piecewise tensor-product polynomial.\n\n Parameters\n ----------\n c : ndarray, shape (k_1*...*k_d, m_1*...*m_d, n)\n Coefficients local polynomials of order `k-1` in\n `m_1`, ..., `m_d` intervals. There are `n` polynomials\n in each interval.\n ks : ndarray of int, shape (d,)\n Orders of polynomials in each dimension\n xs : d-tuple of ndarray of shape (m_d+1,) each\n Breakpoints of polynomials\n xp : ndarray, shape (r, d)\n Points to evaluate the piecewise polynomial at.\n dx : ndarray of int, shape (d,)\n Orders of derivative to evaluate. The derivative is evaluated\n piecewise and may have discontinuities.\n extrapolate : int, optional\n Whether to extrapolate to out-of-bounds points based on first\n and last intervals, or to return NaNs.\n out : ndarray, shape (r, n)\n Value of each polynomial at each of the input points.\n For points outside the span ``x[0] ... x[-1]``,\n ``nan`` is returned.\n This argument is modified in-place.\n\n" - ... - -def fix_continuity(c, x, order) -> typing.Any: - "\n Make a piecewise polynomial continuously differentiable to given order.\n\n Parameters\n ----------\n c : ndarray, shape (k, m, n)\n Coefficients local polynomials of order `k-1` in `m` intervals.\n There are `n` polynomials in each interval.\n Coefficient of highest order-term comes first.\n\n Coefficients c[-order-1:] are modified in-place.\n x : ndarray, shape (m+1,)\n Breakpoints of polynomials\n order : int\n Order up to which enforce piecewise differentiability.\n\n" - ... - -def integrate(c, x, a, b, extrapolate, out) -> typing.Any: - "\n Compute integral over a piecewise polynomial.\n\n Parameters\n ----------\n c : ndarray, shape (k, m, n)\n Coefficients local polynomials of order `k-1` in `m` intervals.\n x : ndarray, shape (m+1,)\n Breakpoints of polynomials\n a : double\n Start point of integration.\n b : double\n End point of integration.\n extrapolate : bint, optional\n Whether to extrapolate to out-of-bounds points based on first\n and last intervals, or to return NaNs.\n out : ndarray, shape (n,)\n Integral of the piecewise polynomial, assuming the polynomial\n is zero outside the range (x[0], x[-1]).\n This argument is modified in-place.\n\n" - ... - -def real_roots() -> typing.Any: - "\n Compute real roots of a real-valued piecewise polynomial function.\n\n If a section of the piecewise polynomial is identically zero, the\n values (x[begin], nan) are appended to the root list.\n\n If the piecewise polynomial is not continuous, and the sign\n changes across a breakpoint, the breakpoint is added to the root\n set if `report_discont` is True.\n\n Parameters\n ----------\n c, x\n Polynomial coefficients, as above\n y : float\n Find roots of ``pp(x) == y``.\n report_discont : bint, optional\n Whether to report discontinuities across zero at breakpoints\n as roots\n extrapolate : bint, optional\n Whether to consider roots obtained by extrapolating based\n on first and last intervals.\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/interpolate/dfitpack.pyi b/stubs/scipy-stubs/interpolate/dfitpack.pyi deleted file mode 100644 index 0ca3b266..00000000 --- a/stubs/scipy-stubs/interpolate/dfitpack.pyi +++ /dev/null @@ -1,152 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.interpolate.dfitpack, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def bispeu(tx, ty, c, kx, ky, x, y) -> typing.Any: - "z,ier = bispeu(tx,ty,c,kx,ky,x,y)\n\nWrapper for ``bispeu``.\n\nParameters\n----------\ntx : input rank-1 array('d') with bounds (nx)\nty : input rank-1 array('d') with bounds (ny)\nc : input rank-1 array('d') with bounds ((nx-kx-1)*(ny-ky-1))\nkx : input int\nky : input int\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\n\nReturns\n-------\nz : rank-1 array('d') with bounds (m)\nier : int\n" - ... - -def bispev(tx, ty, c, kx, ky, x, y) -> typing.Any: - "z,ier = bispev(tx,ty,c,kx,ky,x,y)\n\nWrapper for ``bispev``.\n\nParameters\n----------\ntx : input rank-1 array('d') with bounds (nx)\nty : input rank-1 array('d') with bounds (ny)\nc : input rank-1 array('d') with bounds ((nx-kx-1)*(ny-ky-1))\nkx : input int\nky : input int\nx : input rank-1 array('d') with bounds (mx)\ny : input rank-1 array('d') with bounds (my)\n\nReturns\n-------\nz : rank-2 array('d') with bounds (mx,my)\nier : int\n" - ... - -def curfit(iopt, x, y, w, t, wrk, iwrk, xb=..., xe=..., k=..., s=...) -> typing.Any: - "n,c,fp,ier = curfit(iopt,x,y,w,t,wrk,iwrk,[xb,xe,k,s])\n\nWrapper for ``curfit``.\n\nParameters\n----------\niopt : input int\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\nw : input rank-1 array('d') with bounds (m)\nt : in/output rank-1 array('d') with bounds (nest)\nwrk : in/output rank-1 array('d') with bounds (lwrk)\niwrk : in/output rank-1 array('i') with bounds (nest)\n\nOther Parameters\n----------------\nxb : input float, optional\n Default: x[0]\nxe : input float, optional\n Default: x[m-1]\nk : input int, optional\n Default: 3\ns : input float, optional\n Default: 0.0\n\nReturns\n-------\nn : int\nc : rank-1 array('d') with bounds (n)\nfp : float\nier : int\n" - ... - -def dblint(tx, ty, c, kx, ky, xb, xe, yb, ye) -> typing.Any: - "dblint = dblint(tx,ty,c,kx,ky,xb,xe,yb,ye)\n\nWrapper for ``dblint``.\n\nParameters\n----------\ntx : input rank-1 array('d') with bounds (nx)\nty : input rank-1 array('d') with bounds (ny)\nc : input rank-1 array('d') with bounds ((nx-kx-1)*(ny-ky-1))\nkx : input int\nky : input int\nxb : input float\nxe : input float\nyb : input float\nye : input float\n\nReturns\n-------\ndblint : float\n" - ... - -def fpchec(x, t, k) -> typing.Any: - "ier = fpchec(x,t,k)\n\nWrapper for ``fpchec``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (m)\nt : input rank-1 array('d') with bounds (n)\nk : input int\n\nReturns\n-------\nier : int\n" - ... - -def fpcurf0(x, y, k, w=..., xb=..., xe=..., s=..., nest=...) -> typing.Any: - "x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurf0(x,y,k,[w,xb,xe,s,nest])\n\nWrapper for ``fpcurf0``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\nk : input int\n\nOther Parameters\n----------------\nw : input rank-1 array('d') with bounds (m), optional\n Default: 1.0\nxb : input float, optional\n Default: x[0]\nxe : input float, optional\n Default: x[m-1]\ns : input float, optional\n Default: m\nnest : input int, optional\n Default: (s==0.0?m+k+1:MAX(m/2,2*k1))\n\nReturns\n-------\nx : rank-1 array('d') with bounds (m)\ny : rank-1 array('d') with bounds (m)\nw : rank-1 array('d') with bounds (m)\nxb : float\nxe : float\nk : int\ns : float\nn : int\nt : rank-1 array('d') with bounds (nest)\nc : rank-1 array('d') with bounds (nest)\nfp : float\nfpint : rank-1 array('d') with bounds (nest)\nnrdata : rank-1 array('i') with bounds (nest)\nier : int\n" - ... - -def fpcurf1( - x, - y, - w, - xb, - xe, - k, - s, - n, - t, - c, - fp, - fpint, - nrdata, - ier, - overwrite_x=..., - overwrite_y=..., - overwrite_w=..., - overwrite_t=..., - overwrite_c=..., - overwrite_fpint=..., - overwrite_nrdata=..., -) -> typing.Any: - "x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurf1(x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier,[overwrite_x,overwrite_y,overwrite_w,overwrite_t,overwrite_c,overwrite_fpint,overwrite_nrdata])\n\nWrapper for ``fpcurf1``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\nw : input rank-1 array('d') with bounds (m)\nxb : input float\nxe : input float\nk : input int\ns : input float\nn : input int\nt : input rank-1 array('d') with bounds (nest)\nc : input rank-1 array('d') with bounds (nest)\nfp : input float\nfpint : input rank-1 array('d') with bounds (nest)\nnrdata : input rank-1 array('i') with bounds (nest)\nier : input int\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 1\noverwrite_w : input int, optional\n Default: 1\noverwrite_t : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 1\noverwrite_fpint : input int, optional\n Default: 1\noverwrite_nrdata : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('d') with bounds (m)\ny : rank-1 array('d') with bounds (m)\nw : rank-1 array('d') with bounds (m)\nxb : float\nxe : float\nk : int\ns : float\nn : int\nt : rank-1 array('d') with bounds (nest)\nc : rank-1 array('d') with bounds (nest)\nfp : float\nfpint : rank-1 array('d') with bounds (nest)\nnrdata : rank-1 array('i') with bounds (nest)\nier : int\n" - ... - -def fpcurfm1(x, y, k, t, w=..., xb=..., xe=..., overwrite_t=...) -> typing.Any: - "x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier = fpcurfm1(x,y,k,t,[w,xb,xe,overwrite_t])\n\nWrapper for ``fpcurfm1``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\nk : input int\nt : input rank-1 array('d') with bounds (n)\n\nOther Parameters\n----------------\nw : input rank-1 array('d') with bounds (m), optional\n Default: 1.0\nxb : input float, optional\n Default: x[0]\nxe : input float, optional\n Default: x[m-1]\noverwrite_t : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('d') with bounds (m)\ny : rank-1 array('d') with bounds (m)\nw : rank-1 array('d') with bounds (m)\nxb : float\nxe : float\nk : int\ns : float\nn : int\nt : rank-1 array('d') with bounds (n)\nc : rank-1 array('d') with bounds (nest)\nfp : float\nfpint : rank-1 array('d') with bounds (nest)\nnrdata : rank-1 array('i') with bounds (nest)\nier : int\n" - ... - -def parcur(iopt, ipar, idim, u, x, w, ub, ue, t, wrk, iwrk, k=..., s=...) -> typing.Any: - "n,c,fp,ier = parcur(iopt,ipar,idim,u,x,w,ub,ue,t,wrk,iwrk,[k,s])\n\nWrapper for ``parcur``.\n\nParameters\n----------\niopt : input int\nipar : input int\nidim : input int\nu : in/output rank-1 array('d') with bounds (m)\nx : input rank-1 array('d') with bounds (mx)\nw : input rank-1 array('d') with bounds (m)\nub : input float\nue : input float\nt : in/output rank-1 array('d') with bounds (nest)\nwrk : in/output rank-1 array('d') with bounds (lwrk)\niwrk : in/output rank-1 array('i') with bounds (nest)\n\nOther Parameters\n----------------\nk : input int, optional\n Default: 3.0\ns : input float, optional\n Default: 0.0\n\nReturns\n-------\nn : int\nc : rank-1 array('d') with bounds (nc)\nfp : float\nier : int\n" - ... - -def parder(tx, ty, c, kx, ky, nux, nuy, x, y) -> typing.Any: - "z,ier = parder(tx,ty,c,kx,ky,nux,nuy,x,y)\n\nWrapper for ``parder``.\n\nParameters\n----------\ntx : input rank-1 array('d') with bounds (nx)\nty : input rank-1 array('d') with bounds (ny)\nc : input rank-1 array('d') with bounds ((nx-kx-1)*(ny-ky-1))\nkx : input int\nky : input int\nnux : input int\nnuy : input int\nx : input rank-1 array('d') with bounds (mx)\ny : input rank-1 array('d') with bounds (my)\n\nReturns\n-------\nz : rank-2 array('d') with bounds (mx,my)\nier : int\n" - ... - -def pardeu(tx, ty, c, kx, ky, nux, nuy, x, y) -> typing.Any: - "z,ier = pardeu(tx,ty,c,kx,ky,nux,nuy,x,y)\n\nWrapper for ``pardeu``.\n\nParameters\n----------\ntx : input rank-1 array('d') with bounds (nx)\nty : input rank-1 array('d') with bounds (ny)\nc : input rank-1 array('d') with bounds ((nx-kx-1)*(ny-ky-1))\nkx : input int\nky : input int\nnux : input int\nnuy : input int\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\n\nReturns\n-------\nz : rank-1 array('d') with bounds (m)\nier : int\n" - ... - -def percur(iopt, x, y, w, t, wrk, iwrk, k=..., s=...) -> typing.Any: - "n,c,fp,ier = percur(iopt,x,y,w,t,wrk,iwrk,[k,s])\n\nWrapper for ``percur``.\n\nParameters\n----------\niopt : input int\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\nw : input rank-1 array('d') with bounds (m)\nt : in/output rank-1 array('d') with bounds (nest)\nwrk : in/output rank-1 array('d') with bounds (lwrk)\niwrk : in/output rank-1 array('i') with bounds (nest)\n\nOther Parameters\n----------------\nk : input int, optional\n Default: 3\ns : input float, optional\n Default: 0.0\n\nReturns\n-------\nn : int\nc : rank-1 array('d') with bounds (n)\nfp : float\nier : int\n" - ... - -def regrid_smth(x, y, z, xb=..., xe=..., yb=..., ye=..., kx=..., ky=..., s=...) -> typing.Any: - "nx,tx,ny,ty,c,fp,ier = regrid_smth(x,y,z,[xb,xe,yb,ye,kx,ky,s])\n\nWrapper for ``regrid_smth``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (mx)\ny : input rank-1 array('d') with bounds (my)\nz : input rank-1 array('d') with bounds (mx*my)\n\nOther Parameters\n----------------\nxb : input float, optional\n Default: dmin(x,mx)\nxe : input float, optional\n Default: dmax(x,mx)\nyb : input float, optional\n Default: dmin(y,my)\nye : input float, optional\n Default: dmax(y,my)\nkx : input int, optional\n Default: 3\nky : input int, optional\n Default: 3\ns : input float, optional\n Default: 0.0\n\nReturns\n-------\nnx : int\ntx : rank-1 array('d') with bounds (nxest)\nny : int\nty : rank-1 array('d') with bounds (nyest)\nc : rank-1 array('d') with bounds ((nxest-kx-1)*(nyest-ky-1))\nfp : float\nier : int\n" - ... - -def regrid_smth_spher(iopt, ider, u, v, r, r0=..., r1=..., s=...) -> typing.Any: - "nu,tu,nv,tv,c,fp,ier = regrid_smth_spher(iopt,ider,u,v,r,[r0,r1,s])\n\nWrapper for ``regrid_smth_spher``.\n\nParameters\n----------\niopt : input rank-1 array('i') with bounds (3)\nider : input rank-1 array('i') with bounds (4)\nu : input rank-1 array('d') with bounds (mu)\nv : input rank-1 array('d') with bounds (mv)\nr : input rank-1 array('d') with bounds (mu*mv)\n\nOther Parameters\n----------------\nr0 : input float\nr1 : input float\ns : input float, optional\n Default: 0.0\n\nReturns\n-------\nnu : int\ntu : rank-1 array('d') with bounds (nuest)\nnv : int\ntv : rank-1 array('d') with bounds (nvest)\nc : rank-1 array('d') with bounds ((nuest-4)*(nvest-4))\nfp : float\nier : int\n" - ... - -def spalde(t, c, k, x) -> typing.Any: - "d,ier = spalde(t,c,k,x)\n\nWrapper for ``spalde``.\n\nParameters\n----------\nt : input rank-1 array('d') with bounds (n)\nc : input rank-1 array('d') with bounds (n)\nk : input int\nx : input float\n\nReturns\n-------\nd : rank-1 array('d') with bounds (k + 1)\nier : int\n" - ... - -def spherfit_lsq(teta, phi, r, tt, tp, w=..., eps=..., overwrite_tt=..., overwrite_tp=...) -> typing.Any: - "tt,tp,c,fp,ier = spherfit_lsq(teta,phi,r,tt,tp,[w,eps,overwrite_tt,overwrite_tp])\n\nWrapper for ``spherfit_lsq``.\n\nParameters\n----------\nteta : input rank-1 array('d') with bounds (m)\nphi : input rank-1 array('d') with bounds (m)\nr : input rank-1 array('d') with bounds (m)\ntt : input rank-1 array('d') with bounds (ntest)\ntp : input rank-1 array('d') with bounds (npest)\n\nOther Parameters\n----------------\nw : input rank-1 array('d') with bounds (m), optional\n Default: 1.0\neps : input float, optional\n Default: 1e-16\noverwrite_tt : input int, optional\n Default: 1\noverwrite_tp : input int, optional\n Default: 1\n\nReturns\n-------\ntt : rank-1 array('d') with bounds (ntest)\ntp : rank-1 array('d') with bounds (npest)\nc : rank-1 array('d') with bounds ((nt-4)*(np-4))\nfp : float\nier : int\n" - ... - -def spherfit_smth(teta, phi, r, w=..., s=..., eps=...) -> typing.Any: - "nt,tt,np,tp,c,fp,ier = spherfit_smth(teta,phi,r,[w,s,eps])\n\nWrapper for ``spherfit_smth``.\n\nParameters\n----------\nteta : input rank-1 array('d') with bounds (m)\nphi : input rank-1 array('d') with bounds (m)\nr : input rank-1 array('d') with bounds (m)\n\nOther Parameters\n----------------\nw : input rank-1 array('d') with bounds (m), optional\n Default: 1.0\ns : input float, optional\n Default: m\neps : input float, optional\n Default: 1e-16\n\nReturns\n-------\nnt : int\ntt : rank-1 array('d') with bounds (ntest)\nnp : int\ntp : rank-1 array('d') with bounds (npest)\nc : rank-1 array('d') with bounds ((ntest-4)*(npest-4))\nfp : float\nier : int\n" - ... - -def splder(t, c, k, x, nu=..., e=...) -> typing.Any: - "y = splder(t,c,k,x,[nu,e])\n\nWrapper for ``splder``.\n\nParameters\n----------\nt : input rank-1 array('d') with bounds (n)\nc : input rank-1 array('d') with bounds (n)\nk : input int\nx : input rank-1 array('d') with bounds (m)\n\nOther Parameters\n----------------\nnu : input int, optional\n Default: 1\ne : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('d') with bounds (m)\n" - ... - -def splev(t, c, k, x, e=...) -> typing.Any: - "y = splev(t,c,k,x,[e])\n\nWrapper for ``splev``.\n\nParameters\n----------\nt : input rank-1 array('d') with bounds (n)\nc : input rank-1 array('d') with bounds (n)\nk : input int\nx : input rank-1 array('d') with bounds (m)\n\nOther Parameters\n----------------\ne : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('d') with bounds (m)\n" - ... - -def splint(t, c, k, a, b) -> typing.Any: - "splint = splint(t,c,k,a,b)\n\nWrapper for ``splint``.\n\nParameters\n----------\nt : input rank-1 array('d') with bounds (n)\nc : input rank-1 array('d') with bounds (n)\nk : input int\na : input float\nb : input float\n\nReturns\n-------\nsplint : float\n" - ... - -def sproot(t, c, mest=...) -> typing.Any: - "zero,m,ier = sproot(t,c,[mest])\n\nWrapper for ``sproot``.\n\nParameters\n----------\nt : input rank-1 array('d') with bounds (n)\nc : input rank-1 array('d') with bounds (n)\n\nOther Parameters\n----------------\nmest : input int, optional\n Default: 3*(n-7)\n\nReturns\n-------\nzero : rank-1 array('d') with bounds (mest)\nm : int\nier : int\n" - ... - -def surfit_lsq( - x, - y, - z, - nx, - tx, - ny, - ty, - w=..., - xb=..., - xe=..., - yb=..., - ye=..., - kx=..., - ky=..., - eps=..., - lwrk2=..., - overwrite_tx=..., - overwrite_ty=..., -) -> typing.Any: - "tx,ty,c,fp,ier = surfit_lsq(x,y,z,nx,tx,ny,ty,[w,xb,xe,yb,ye,kx,ky,eps,lwrk2,overwrite_tx,overwrite_ty])\n\nWrapper for ``surfit_lsq``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\nz : input rank-1 array('d') with bounds (m)\nnx : input int\ntx : input rank-1 array('d') with bounds (nmax)\nny : input int\nty : input rank-1 array('d') with bounds (nmax)\n\nOther Parameters\n----------------\nw : input rank-1 array('d') with bounds (m), optional\n Default: 1.0\nxb : input float, optional\n Default: calc_b(x,m,tx,nx)\nxe : input float, optional\n Default: calc_e(x,m,tx,nx)\nyb : input float, optional\n Default: calc_b(y,m,ty,ny)\nye : input float, optional\n Default: calc_e(y,m,ty,ny)\nkx : input int, optional\n Default: 3\nky : input int, optional\n Default: 3\neps : input float, optional\n Default: 1e-16\noverwrite_tx : input int, optional\n Default: 1\noverwrite_ty : input int, optional\n Default: 1\nlwrk2 : input int, optional\n Default: calc_surfit_lwrk2(m,kx,ky,nxest,nyest)\n\nReturns\n-------\ntx : rank-1 array('d') with bounds (nmax)\nty : rank-1 array('d') with bounds (nmax)\nc : rank-1 array('d') with bounds ((nx-kx-1)*(ny-ky-1))\nfp : float\nier : int\n" - ... - -def surfit_smth( - x, y, z, w=..., xb=..., xe=..., yb=..., ye=..., kx=..., ky=..., s=..., nxest=..., nyest=..., eps=..., lwrk2=... -) -> typing.Any: - "nx,tx,ny,ty,c,fp,wrk1,ier = surfit_smth(x,y,z,[w,xb,xe,yb,ye,kx,ky,s,nxest,nyest,eps,lwrk2])\n\nWrapper for ``surfit_smth``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (m)\nz : input rank-1 array('d') with bounds (m)\n\nOther Parameters\n----------------\nw : input rank-1 array('d') with bounds (m), optional\n Default: 1.0\nxb : input float, optional\n Default: dmin(x,m)\nxe : input float, optional\n Default: dmax(x,m)\nyb : input float, optional\n Default: dmin(y,m)\nye : input float, optional\n Default: dmax(y,m)\nkx : input int, optional\n Default: 3\nky : input int, optional\n Default: 3\ns : input float, optional\n Default: m\nnxest : input int, optional\n Default: imax(kx+1+sqrt(m/2),2*(kx+1))\nnyest : input int, optional\n Default: imax(ky+1+sqrt(m/2),2*(ky+1))\neps : input float, optional\n Default: 1e-16\nlwrk2 : input int, optional\n Default: calc_surfit_lwrk2(m,kx,ky,nxest,nyest)\n\nReturns\n-------\nnx : int\ntx : rank-1 array('d') with bounds (nmax)\nny : int\nty : rank-1 array('d') with bounds (nmax)\nc : rank-1 array('d') with bounds ((nxest-kx-1)*(nyest-ky-1))\nfp : float\nwrk1 : rank-1 array('d') with bounds (lwrk1)\nier : int\n" - ... - -def types() -> typing.Any: - "'i'-scalar\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/interpolate/interpnd.pyi b/stubs/scipy-stubs/interpolate/interpnd.pyi deleted file mode 100644 index 92470bbd..00000000 --- a/stubs/scipy-stubs/interpolate/interpnd.pyi +++ /dev/null @@ -1,114 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.interpolate.interpnd, version: unspecified -import builtins as _mod_builtins -import typing - -class CloughTocher2DInterpolator(NDInterpolatorBase): - "\n CloughTocher2DInterpolator(points, values, tol=1e-6)\n\n Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D.\n\n .. versionadded:: 0.9\n\n Methods\n -------\n __call__\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndims); or Delaunay\n Data point coordinates, or a precomputed Delaunay triangulation.\n values : ndarray of float or complex, shape (npoints, ...)\n Data values.\n fill_value : float, optional\n Value used to fill in for requested points outside of the\n convex hull of the input points. If not provided, then\n the default is ``nan``.\n tol : float, optional\n Absolute/relative tolerance for gradient estimation.\n maxiter : int, optional\n Maximum number of iterations in gradient estimation.\n rescale : bool, optional\n Rescale points to unit cube before performing interpolation.\n This is useful if some of the input dimensions have\n incommensurable units and differ by many orders of magnitude.\n\n Notes\n -----\n The interpolant is constructed by triangulating the input data\n with Qhull [1]_, and constructing a piecewise cubic\n interpolating Bezier polynomial on each triangle, using a\n Clough-Tocher scheme [CT]_. The interpolant is guaranteed to be\n continuously differentiable.\n\n The gradients of the interpolant are chosen so that the curvature\n of the interpolating surface is approximatively minimized. The\n gradients necessary for this are estimated using the global\n algorithm described in [Nielson83]_ and [Renka84]_.\n\n Examples\n --------\n We can interpolate values on a 2D plane:\n\n >>> from scipy.interpolate import CloughTocher2DInterpolator\n >>> import matplotlib.pyplot as plt\n >>> np.random.seed(0)\n >>> x = np.random.random(10) - 0.5\n >>> y = np.random.random(10) - 0.5\n >>> z = np.hypot(x, y)\n >>> X = np.linspace(min(x), max(x))\n >>> Y = np.linspace(min(y), max(y))\n >>> X, Y = np.meshgrid(X, Y) # 2D grid for interpolation\n >>> interp = CloughTocher2DInterpolator(list(zip(x, y)), z)\n >>> Z = interp(X, Y)\n >>> plt.pcolormesh(X, Y, Z, shading='auto')\n >>> plt.plot(x, y, \"ok\", label=\"input point\")\n >>> plt.legend()\n >>> plt.colorbar()\n >>> plt.axis(\"equal\")\n >>> plt.show()\n\n See also\n --------\n griddata :\n Interpolate unstructured D-D data.\n LinearNDInterpolator :\n Piecewise linear interpolant in N dimensions.\n NearestNDInterpolator :\n Nearest-neighbor interpolation in N dimensions.\n\n References\n ----------\n .. [1] http://www.qhull.org/\n\n .. [CT] See, for example,\n P. Alfeld,\n ''A trivariate Clough-Tocher scheme for tetrahedral data''.\n Computer Aided Geometric Design, 1, 169 (1984);\n G. Farin,\n ''Triangular Bernstein-Bezier patches''.\n Computer Aided Geometric Design, 3, 83 (1986).\n\n .. [Nielson83] G. Nielson,\n ''A method for interpolating scattered data based upon a minimum norm\n network''.\n Math. Comp., 40, 253 (1983).\n\n .. [Renka84] R. J. Renka and A. K. Cline.\n ''A Triangle-based C1 interpolation method.'',\n Rocky Mountain J. Math., 14, 223 (1984).\n\n" - __dict__: typing.Dict[str, typing.Any] - def __init__(self, points, values, tol=...) -> None: - "\n CloughTocher2DInterpolator(points, values, tol=1e-6)\n\n Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D.\n\n .. versionadded:: 0.9\n\n Methods\n -------\n __call__\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndims); or Delaunay\n Data point coordinates, or a precomputed Delaunay triangulation.\n values : ndarray of float or complex, shape (npoints, ...)\n Data values.\n fill_value : float, optional\n Value used to fill in for requested points outside of the\n convex hull of the input points. If not provided, then\n the default is ``nan``.\n tol : float, optional\n Absolute/relative tolerance for gradient estimation.\n maxiter : int, optional\n Maximum number of iterations in gradient estimation.\n rescale : bool, optional\n Rescale points to unit cube before performing interpolation.\n This is useful if some of the input dimensions have\n incommensurable units and differ by many orders of magnitude.\n\n Notes\n -----\n The interpolant is constructed by triangulating the input data\n with Qhull [1]_, and constructing a piecewise cubic\n interpolating Bezier polynomial on each triangle, using a\n Clough-Tocher scheme [CT]_. The interpolant is guaranteed to be\n continuously differentiable.\n\n The gradients of the interpolant are chosen so that the curvature\n of the interpolating surface is approximatively minimized. The\n gradients necessary for this are estimated using the global\n algorithm described in [Nielson83]_ and [Renka84]_.\n\n Examples\n --------\n We can interpolate values on a 2D plane:\n\n >>> from scipy.interpolate import CloughTocher2DInterpolator\n >>> import matplotlib.pyplot as plt\n >>> np.random.seed(0)\n >>> x = np.random.random(10) - 0.5\n >>> y = np.random.random(10) - 0.5\n >>> z = np.hypot(x, y)\n >>> X = np.linspace(min(x), max(x))\n >>> Y = np.linspace(min(y), max(y))\n >>> X, Y = np.meshgrid(X, Y) # 2D grid for interpolation\n >>> interp = CloughTocher2DInterpolator(list(zip(x, y)), z)\n >>> Z = interp(X, Y)\n >>> plt.pcolormesh(X, Y, Z, shading='auto')\n >>> plt.plot(x, y, \"ok\", label=\"input point\")\n >>> plt.legend()\n >>> plt.colorbar()\n >>> plt.axis(\"equal\")\n >>> plt.show()\n\n See also\n --------\n griddata :\n Interpolate unstructured D-D data.\n LinearNDInterpolator :\n Piecewise linear interpolant in N dimensions.\n NearestNDInterpolator :\n Nearest-neighbor interpolation in N dimensions.\n\n References\n ----------\n .. [1] http://www.qhull.org/\n\n .. [CT] See, for example,\n P. Alfeld,\n ''A trivariate Clough-Tocher scheme for tetrahedral data''.\n Computer Aided Geometric Design, 1, 169 (1984);\n G. Farin,\n ''Triangular Bernstein-Bezier patches''.\n Computer Aided Geometric Design, 3, 83 (1986).\n\n .. [Nielson83] G. Nielson,\n ''A method for interpolating scattered data based upon a minimum norm\n network''.\n Math. Comp., 40, 253 (1983).\n\n .. [Renka84] R. J. Renka and A. K. Cline.\n ''A Triangle-based C1 interpolation method.'',\n Rocky Mountain J. Math., 14, 223 (1984).\n\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def _do_evaluate(self, xi, dummy) -> typing.Any: ... - def _evaluate_complex(self, xi) -> typing.Any: ... - def _evaluate_double(self, xi) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class GradientEstimationWarning(_mod_builtins.Warning): - __dict__: typing.Dict[str, typing.Any] - def __init__(self, *args, **kwargs) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def __getattr__(self, name) -> typing.Any: ... - -class LinearNDInterpolator(NDInterpolatorBase): - '\n LinearNDInterpolator(points, values, fill_value=np.nan, rescale=False)\n\n Piecewise linear interpolant in N dimensions.\n\n .. versionadded:: 0.9\n\n Methods\n -------\n __call__\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndims); or Delaunay\n Data point coordinates, or a precomputed Delaunay triangulation.\n values : ndarray of float or complex, shape (npoints, ...)\n Data values.\n fill_value : float, optional\n Value used to fill in for requested points outside of the\n convex hull of the input points. If not provided, then\n the default is ``nan``.\n rescale : bool, optional\n Rescale points to unit cube before performing interpolation.\n This is useful if some of the input dimensions have\n incommensurable units and differ by many orders of magnitude.\n\n Notes\n -----\n The interpolant is constructed by triangulating the input data\n with Qhull [1]_, and on each triangle performing linear\n barycentric interpolation.\n\n Examples\n --------\n We can interpolate values on a 2D plane:\n\n >>> from scipy.interpolate import LinearNDInterpolator\n >>> import matplotlib.pyplot as plt\n >>> np.random.seed(0)\n >>> x = np.random.random(10) - 0.5\n >>> y = np.random.random(10) - 0.5\n >>> z = np.hypot(x, y)\n >>> X = np.linspace(min(x), max(x))\n >>> Y = np.linspace(min(y), max(y))\n >>> X, Y = np.meshgrid(X, Y) # 2D grid for interpolation\n >>> interp = LinearNDInterpolator(list(zip(x, y)), z)\n >>> Z = interp(X, Y)\n >>> plt.pcolormesh(X, Y, Z, shading=\'auto\')\n >>> plt.plot(x, y, "ok", label="input point")\n >>> plt.legend()\n >>> plt.colorbar()\n >>> plt.axis("equal")\n >>> plt.show()\n\n See also\n --------\n griddata :\n Interpolate unstructured D-D data.\n NearestNDInterpolator :\n Nearest-neighbor interpolation in N dimensions.\n CloughTocher2DInterpolator :\n Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D.\n\n References\n ----------\n .. [1] http://www.qhull.org/\n\n' - __dict__: typing.Dict[str, typing.Any] - def __init__(self, points, values, fill_value=..., rescale=...) -> None: - '\n LinearNDInterpolator(points, values, fill_value=np.nan, rescale=False)\n\n Piecewise linear interpolant in N dimensions.\n\n .. versionadded:: 0.9\n\n Methods\n -------\n __call__\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndims); or Delaunay\n Data point coordinates, or a precomputed Delaunay triangulation.\n values : ndarray of float or complex, shape (npoints, ...)\n Data values.\n fill_value : float, optional\n Value used to fill in for requested points outside of the\n convex hull of the input points. If not provided, then\n the default is ``nan``.\n rescale : bool, optional\n Rescale points to unit cube before performing interpolation.\n This is useful if some of the input dimensions have\n incommensurable units and differ by many orders of magnitude.\n\n Notes\n -----\n The interpolant is constructed by triangulating the input data\n with Qhull [1]_, and on each triangle performing linear\n barycentric interpolation.\n\n Examples\n --------\n We can interpolate values on a 2D plane:\n\n >>> from scipy.interpolate import LinearNDInterpolator\n >>> import matplotlib.pyplot as plt\n >>> np.random.seed(0)\n >>> x = np.random.random(10) - 0.5\n >>> y = np.random.random(10) - 0.5\n >>> z = np.hypot(x, y)\n >>> X = np.linspace(min(x), max(x))\n >>> Y = np.linspace(min(y), max(y))\n >>> X, Y = np.meshgrid(X, Y) # 2D grid for interpolation\n >>> interp = LinearNDInterpolator(list(zip(x, y)), z)\n >>> Z = interp(X, Y)\n >>> plt.pcolormesh(X, Y, Z, shading=\'auto\')\n >>> plt.plot(x, y, "ok", label="input point")\n >>> plt.legend()\n >>> plt.colorbar()\n >>> plt.axis("equal")\n >>> plt.show()\n\n See also\n --------\n griddata :\n Interpolate unstructured D-D data.\n NearestNDInterpolator :\n Nearest-neighbor interpolation in N dimensions.\n CloughTocher2DInterpolator :\n Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D.\n\n References\n ----------\n .. [1] http://www.qhull.org/\n\n' - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def _do_evaluate(self, xi, dummy) -> typing.Any: ... - def _evaluate_complex(self, xi) -> typing.Any: ... - def _evaluate_double(self, xi) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class NDInterpolatorBase(_mod_builtins.object): - "\n Common routines for interpolators.\n\n .. versionadded:: 0.9\n\n" - def __call__(self, *args) -> typing.Any: - "\n interpolator(xi)\n\n Evaluate interpolator at given points.\n\n Parameters\n ----------\n x1, x2, ... xn: array-like of float\n Points where to interpolate data at.\n x1, x2, ... xn can be array-like of float with broadcastable shape.\n or x1 can be array-like of float with shape ``(..., ndim)``\n\n" - ... - __dict__: typing.Dict[str, typing.Any] - def __init__(self, points, values, fill_value, ndim, rescale, need_contiguous, need_values) -> None: - "\n Check shape of points and values arrays, and reshape values to\n (npoints, nvalues). Ensure the `points` and values arrays are\n C-contiguous, and of correct type.\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def _check_call_shape(self, xi) -> typing.Any: ... - def _scale_x(self, xi) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _ndim_coords_from_arrays() -> typing.Any: - "\n Convert a tuple of coordinate arrays to a (..., ndim)-shaped array.\n\n" - ... - -def estimate_gradients_2d_global() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/io/_test_fortran.pyi b/stubs/scipy-stubs/io/_test_fortran.pyi deleted file mode 100644 index 34167807..00000000 --- a/stubs/scipy-stubs/io/_test_fortran.pyi +++ /dev/null @@ -1,25 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.io._test_fortran, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def read_unformatted_double(m, n, k, filename) -> typing.Any: - "a = read_unformatted_double(m,n,k,filename)\n\nWrapper for ``read_unformatted_double``.\n\nParameters\n----------\nm : input int\nn : input int\nk : input int\nfilename : input string(len=4096)\n\nReturns\n-------\na : rank-3 array('d') with bounds (m,n,k)\n" - ... - -def read_unformatted_int(m, n, k, filename) -> typing.Any: - "a = read_unformatted_int(m,n,k,filename)\n\nWrapper for ``read_unformatted_int``.\n\nParameters\n----------\nm : input int\nn : input int\nk : input int\nfilename : input string(len=4096)\n\nReturns\n-------\na : rank-3 array('i') with bounds (m,n,k)\n" - ... - -def read_unformatted_mixed(m, n, k, filename) -> typing.Any: - "a,b = read_unformatted_mixed(m,n,k,filename)\n\nWrapper for ``read_unformatted_mixed``.\n\nParameters\n----------\nm : input int\nn : input int\nk : input int\nfilename : input string(len=4096)\n\nReturns\n-------\na : rank-2 array('d') with bounds (m,n)\nb : rank-1 array('i') with bounds (k)\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/io/matlab/mio5_utils.pyi b/stubs/scipy-stubs/io/matlab/mio5_utils.pyi deleted file mode 100644 index 6dc6f32d..00000000 --- a/stubs/scipy-stubs/io/matlab/mio5_utils.pyi +++ /dev/null @@ -1,138 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.io.matlab.mio5_utils, version: unspecified -import builtins as _mod_builtins -import typing - -import scipy.sparse.csc as _mod_scipy_sparse_csc - -class VarHeader5(_mod_builtins.object): - def __init__(self, *args, **kwargs) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def dims(self) -> typing.Any: ... - @property - def is_global(self) -> typing.Any: ... - @property - def is_logical(self) -> typing.Any: ... - @property - def mclass(self) -> typing.Any: ... - @property - def name(self) -> typing.Any: ... - @property - def nzmax(self) -> typing.Any: ... - def set_dims(self) -> typing.Any: - "Allow setting of dimensions from python\n\n This is for constructing headers for tests\n" - ... - - def __getattr__(self, name) -> typing.Any: ... - -class VarReader5(_mod_builtins.object): - def __init__(self, *args, **kwargs) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __pyx_vtable__: PyCapsule - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def array_from_header(self) -> typing.Any: - "Read array of any class, given matrix `header`\n\n Parameters\n ----------\n header : VarHeader5\n array header object\n process : int, optional\n If not zero, apply post-processing on returned array\n \n Returns\n -------\n arr : array or sparse array\n read array\n" - ... - - @property - def is_swapped(self) -> typing.Any: ... - @property - def little_endian(self) -> typing.Any: ... - def read_cells(self) -> typing.Any: - "Read cell array from stream" - ... - - def read_char(self) -> typing.Any: - "Read char matrices from stream as arrays\n\n Matrices of char are likely to be converted to matrices of\n string by later processing in ``array_from_header``\n" - ... - - def read_fieldnames(self) -> typing.Any: - "Read fieldnames for struct-like matrix." - ... - - def read_full_tag(self) -> typing.Any: - "Python method for reading full u4, u4 tag from stream\n\n Returns\n -------\n mdtype : int32\n matlab data type code\n byte_count : int32\n number of data bytes following\n\n Notes\n -----\n Assumes tag is in fact full, that is, is not a small data\n element. This means it can skip some checks and makes it\n slightly faster than ``read_tag``\n" - ... - - def read_header(self) -> typing.Any: - "Return matrix header for current stream position\n\n Returns matrix headers at top level and sub levels\n\n Parameters\n ----------\n check_stream_limit : if True, then if the returned header\n is passed to array_from_header, it will be verified that\n the length of the uncompressed data is not overlong (which\n can indicate .mat file corruption)\n" - ... - - def read_numeric(self) -> typing.Any: - "Read numeric data element into ndarray\n\n Reads element, then casts to ndarray.\n\n The type of the array is usually given by the ``mdtype`` returned via\n ``read_element``. Sparse logical arrays are an exception, where the\n type of the array may be ``np.bool_`` even if the ``mdtype`` claims the\n data is of float64 type.\n\n Parameters\n ----------\n copy : bool, optional\n Whether to copy the array before returning. If False, return array\n backed by bytes read from file.\n nnz : int, optional\n Number of non-zero values when reading numeric data from sparse\n matrices. -1 if not reading sparse matrices, or to disable check\n for bytes data instead of declared data type (see Notes).\n\n Returns\n -------\n arr : array\n Numeric array\n\n Notes\n -----\n MATLAB apparently likes to store sparse logical matrix data as bytes\n instead of miDOUBLE (float64) data type, even though the data element\n still declares its type as miDOUBLE. We can guess this has happened by\n looking for the length of the data compared to the expected number of\n elements, using the `nnz` input parameter.\n" - ... - - def read_opaque(self) -> typing.Any: - "Read opaque (function workspace) type\n\n Looking at some mat files, the structure of this type seems to\n be:\n\n * array flags as usual (already read into `hdr`)\n * 3 int8 strings\n * a matrix\n\n Then there's a matrix at the end of the mat file that seems have\n the anonymous founction workspaces - we load it as\n ``__function_workspace__``\n\n See the comments at the beginning of ``mio5.py``\n" - ... - - def read_real_complex(self) -> typing.Any: - "Read real / complex matrices from stream" - ... - - def read_struct(self) -> typing.Any: - "Read struct or object array from stream\n\n Objects are just structs with an extra field *classname*,\n defined before (this here) struct format structure\n" - ... - - def read_tag(self) -> typing.Any: - "Read tag mdtype and byte_count\n\n Does necessary swapping and takes account of SDE formats.\n\n See also ``read_full_tag`` method.\n \n Returns\n -------\n mdtype : int\n matlab data type code\n byte_count : int\n number of bytes following that comprise the data\n tag_data : None or str\n Any data from the tag itself. This is None for a full tag,\n and string length `byte_count` if this is a small data\n element.\n" - ... - - def set_stream(self) -> typing.Any: - "Set stream of best type from file-like `fobj`\n\n Called from Python when initiating a variable read\n" - ... - - def shape_from_header(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_VarHeader5() -> typing.Any: ... - -__test__: dict - -def asbytes(s) -> typing.Any: ... -def asstr(s) -> typing.Any: ... -def byteswap_u4() -> typing.Any: ... -def chars_to_strings() -> typing.Any: - "Convert final axis of char array to strings\n\n Parameters\n ----------\n in_arr : array\n dtype of 'U1'\n\n Returns\n -------\n str_arr : array\n dtype of 'UN' where N is the length of the last dimension of\n ``arr``\n" - ... - -csc_matrix = _mod_scipy_sparse_csc.csc_matrix - -def pycopy(x) -> typing.Any: - "Shallow copy operation on arbitrary Python objects.\n\n See the module's __doc__ string for more info.\n" - ... - -def squeeze_element() -> typing.Any: - "Return squeezed element\n\n The returned object may not be an ndarray - for example if we do\n ``arr.item`` to return a ``mat_struct`` object from a struct array" - ... - -swapped_code: str - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/io/matlab/mio_utils.pyi b/stubs/scipy-stubs/io/matlab/mio_utils.pyi deleted file mode 100644 index 3176b265..00000000 --- a/stubs/scipy-stubs/io/matlab/mio_utils.pyi +++ /dev/null @@ -1,22 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.io.matlab.mio_utils, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def chars_to_strings() -> typing.Any: - "Convert final axis of char array to strings\n\n Parameters\n ----------\n in_arr : array\n dtype of 'U1'\n\n Returns\n -------\n str_arr : array\n dtype of 'UN' where N is the length of the last dimension of\n ``arr``\n" - ... - -def squeeze_element() -> typing.Any: - "Return squeezed element\n\n The returned object may not be an ndarray - for example if we do\n ``arr.item`` to return a ``mat_struct`` object from a struct array" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/io/matlab/streams.pyi b/stubs/scipy-stubs/io/matlab/streams.pyi deleted file mode 100644 index 7b3d9456..00000000 --- a/stubs/scipy-stubs/io/matlab/streams.pyi +++ /dev/null @@ -1,70 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.io.matlab.streams, version: unspecified -import builtins as _mod_builtins -import typing - -BLOCK_SIZE: int - -class GenericStream(_mod_builtins.object): - def __init__(self, *args, **kwargs) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __pyx_vtable__: PyCapsule - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def all_data_read(self) -> typing.Any: ... - def read(self) -> typing.Any: ... - def seek(self) -> typing.Any: ... - def tell(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class ZlibInputStream(GenericStream): - "\n File-like object uncompressing bytes from a zlib compressed stream.\n\n Parameters\n ----------\n stream : file-like\n Stream to read compressed data from.\n max_length : int\n Maximum number of bytes to read from the stream.\n\n Notes\n -----\n Some matlab files contain zlib streams without valid Z_STREAM_END\n termination. To get round this, we use the decompressobj object, that\n allows you to decode an incomplete stream. See discussion at\n https://bugs.python.org/issue8672\n\n" - def __init__(self, *args, **kwargs) -> None: - "\n File-like object uncompressing bytes from a zlib compressed stream.\n\n Parameters\n ----------\n stream : file-like\n Stream to read compressed data from.\n max_length : int\n Maximum number of bytes to read from the stream.\n\n Notes\n -----\n Some matlab files contain zlib streams without valid Z_STREAM_END\n termination. To get round this, we use the decompressobj object, that\n allows you to decode an incomplete stream. See discussion at\n https://bugs.python.org/issue8672\n\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __pyx_vtable__: PyCapsule - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def all_data_read(self) -> typing.Any: ... - def read(self) -> typing.Any: ... - def seek(self) -> typing.Any: ... - def tell(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict - -def __pyx_unpickle_GenericStream() -> typing.Any: ... -def __pyx_unpickle_ZlibInputStream() -> typing.Any: ... - -__test__: dict - -def _read_into() -> typing.Any: ... -def _read_string() -> typing.Any: ... -def make_stream() -> typing.Any: - "Make stream of correct type for file-like `fobj`\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/_decomp_update.pyi b/stubs/scipy-stubs/linalg/_decomp_update.pyi deleted file mode 100644 index 5e4561e7..00000000 --- a/stubs/scipy-stubs/linalg/_decomp_update.pyi +++ /dev/null @@ -1,33 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg._decomp_update, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy.linalg as _mod_numpy_linalg - -LinAlgError = _mod_numpy_linalg.LinAlgError -__all__: list -__doc__: str -__file__: str -__name__: str -__package__: str -__test__: dict - -def _form_qTu() -> typing.Any: - "this function only exists to expose the cdef version below for testing\n purposes. Here we perform minimal input validation to ensure that the\n inputs meet the requirements below.\n" - ... - -def qr_delete(Q, R, k, intp=..., which=..., overwrite_qr=..., check_finite=...) -> typing.Any: - "qr_delete(Q, R, k, int p=1, which=u'row', overwrite_qr=False, check_finite=True)\n\n QR downdate on row or column deletions\n\n If ``A = Q R`` is the QR factorization of ``A``, return the QR\n factorization of ``A`` where ``p`` rows or columns have been removed\n starting at row or column ``k``.\n\n Parameters\n ----------\n Q : (M, M) or (M, N) array_like\n Unitary/orthogonal matrix from QR decomposition.\n R : (M, N) or (N, N) array_like\n Upper triangular matrix from QR decomposition.\n k : int\n Index of the first row or column to delete.\n p : int, optional\n Number of rows or columns to delete, defaults to 1.\n which: {'row', 'col'}, optional\n Determines if rows or columns will be deleted, defaults to 'row'\n overwrite_qr : bool, optional\n If True, consume Q and R, overwriting their contents with their\n downdated versions, and returning approriately sized views.\n Defaults to False.\n check_finite : bool, optional\n Whether to check that the input matrix contains only finite numbers.\n Disabling may give a performance gain, but may result in problems\n (crashes, non-termination) if the inputs do contain infinities or NaNs.\n Default is True.\n\n Returns\n -------\n Q1 : ndarray\n Updated unitary/orthogonal factor\n R1 : ndarray\n Updated upper triangular factor\n\n See Also\n --------\n qr, qr_multiply, qr_insert, qr_update\n\n Notes\n -----\n This routine does not guarantee that the diagonal entries of ``R1`` are\n positive.\n\n .. versionadded:: 0.16.0\n\n References\n ----------\n .. [1] Golub, G. H. & Van Loan, C. F. Matrix Computations, 3rd Ed.\n (Johns Hopkins University Press, 1996).\n\n .. [2] Daniel, J. W., Gragg, W. B., Kaufman, L. & Stewart, G. W.\n Reorthogonalization and stable algorithms for updating the\n Gram-Schmidt QR factorization. Math. Comput. 30, 772-795 (1976).\n\n .. [3] Reichel, L. & Gragg, W. B. Algorithm 686: FORTRAN Subroutines for\n Updating the QR Decomposition. ACM Trans. Math. Softw. 16, 369-377\n (1990).\n\n Examples\n --------\n >>> from scipy import linalg\n >>> a = np.array([[ 3., -2., -2.],\n ... [ 6., -9., -3.],\n ... [ -3., 10., 1.],\n ... [ 6., -7., 4.],\n ... [ 7., 8., -6.]])\n >>> q, r = linalg.qr(a)\n\n Given this QR decomposition, update q and r when 2 rows are removed.\n\n >>> q1, r1 = linalg.qr_delete(q, r, 2, 2, 'row', False)\n >>> q1\n array([[ 0.30942637, 0.15347579, 0.93845645], # may vary (signs)\n [ 0.61885275, 0.71680171, -0.32127338],\n [ 0.72199487, -0.68017681, -0.12681844]])\n >>> r1\n array([[ 9.69535971, -0.4125685 , -6.80738023], # may vary (signs)\n [ 0. , -12.19958144, 1.62370412],\n [ 0. , 0. , -0.15218213]])\n\n The update is equivalent, but faster than the following.\n\n >>> a1 = np.delete(a, slice(2,4), 0)\n >>> a1\n array([[ 3., -2., -2.],\n [ 6., -9., -3.],\n [ 7., 8., -6.]])\n >>> q_direct, r_direct = linalg.qr(a1)\n\n Check that we have equivalent results:\n\n >>> np.dot(q1, r1)\n array([[ 3., -2., -2.],\n [ 6., -9., -3.],\n [ 7., 8., -6.]])\n >>> np.allclose(np.dot(q1, r1), a1)\n True\n\n And the updated Q is still unitary:\n\n >>> np.allclose(np.dot(q1.T, q1), np.eye(3))\n True\n\n" - ... - -def qr_insert(Q, R, u, k, which=..., rcond=..., overwrite_qru=..., check_finite=...) -> typing.Any: - "qr_insert(Q, R, u, k, which=u'row', rcond=None, overwrite_qru=False, check_finite=True)\n\n QR update on row or column insertions\n\n If ``A = Q R`` is the QR factorization of ``A``, return the QR\n factorization of ``A`` where rows or columns have been inserted starting\n at row or column ``k``.\n\n Parameters\n ----------\n Q : (M, M) array_like\n Unitary/orthogonal matrix from the QR decomposition of A.\n R : (M, N) array_like\n Upper triangular matrix from the QR decomposition of A.\n u : (N,), (p, N), (M,), or (M, p) array_like\n Rows or columns to insert\n k : int\n Index before which `u` is to be inserted.\n which: {'row', 'col'}, optional\n Determines if rows or columns will be inserted, defaults to 'row'\n rcond : float\n Lower bound on the reciprocal condition number of ``Q`` augmented with\n ``u/||u||`` Only used when updating economic mode (thin, (M,N) (N,N))\n decompositions. If None, machine precision is used. Defaults to\n None.\n overwrite_qru : bool, optional\n If True, consume Q, R, and u, if possible, while performing the update,\n otherwise make copies as necessary. Defaults to False.\n check_finite : bool, optional\n Whether to check that the input matrices contain only finite numbers.\n Disabling may give a performance gain, but may result in problems\n (crashes, non-termination) if the inputs do contain infinities or NaNs.\n Default is True.\n\n Returns\n -------\n Q1 : ndarray\n Updated unitary/orthogonal factor\n R1 : ndarray\n Updated upper triangular factor\n\n Raises\n ------\n LinAlgError :\n If updating a (M,N) (N,N) factorization and the reciprocal condition\n number of Q augmented with u/||u|| is smaller than rcond.\n\n See Also\n --------\n qr, qr_multiply, qr_delete, qr_update\n\n Notes\n -----\n This routine does not guarantee that the diagonal entries of ``R1`` are\n positive.\n\n .. versionadded:: 0.16.0\n\n References\n ----------\n\n .. [1] Golub, G. H. & Van Loan, C. F. Matrix Computations, 3rd Ed.\n (Johns Hopkins University Press, 1996).\n\n .. [2] Daniel, J. W., Gragg, W. B., Kaufman, L. & Stewart, G. W.\n Reorthogonalization and stable algorithms for updating the\n Gram-Schmidt QR factorization. Math. Comput. 30, 772-795 (1976).\n\n .. [3] Reichel, L. & Gragg, W. B. Algorithm 686: FORTRAN Subroutines for\n Updating the QR Decomposition. ACM Trans. Math. Softw. 16, 369-377\n (1990).\n\n Examples\n --------\n >>> from scipy import linalg\n >>> a = np.array([[ 3., -2., -2.],\n ... [ 6., -7., 4.],\n ... [ 7., 8., -6.]])\n >>> q, r = linalg.qr(a)\n\n Given this QR decomposition, update q and r when 2 rows are inserted.\n\n >>> u = np.array([[ 6., -9., -3.],\n ... [ -3., 10., 1.]])\n >>> q1, r1 = linalg.qr_insert(q, r, u, 2, 'row')\n >>> q1\n array([[-0.25445668, 0.02246245, 0.18146236, -0.72798806, 0.60979671], # may vary (signs)\n [-0.50891336, 0.23226178, -0.82836478, -0.02837033, -0.00828114],\n [-0.50891336, 0.35715302, 0.38937158, 0.58110733, 0.35235345],\n [ 0.25445668, -0.52202743, -0.32165498, 0.36263239, 0.65404509],\n [-0.59373225, -0.73856549, 0.16065817, -0.0063658 , -0.27595554]])\n >>> r1\n array([[-11.78982612, 6.44623587, 3.81685018], # may vary (signs)\n [ 0. , -16.01393278, 3.72202865],\n [ 0. , 0. , -6.13010256],\n [ 0. , 0. , 0. ],\n [ 0. , 0. , 0. ]])\n\n The update is equivalent, but faster than the following.\n\n >>> a1 = np.insert(a, 2, u, 0)\n >>> a1\n array([[ 3., -2., -2.],\n [ 6., -7., 4.],\n [ 6., -9., -3.],\n [ -3., 10., 1.],\n [ 7., 8., -6.]])\n >>> q_direct, r_direct = linalg.qr(a1)\n\n Check that we have equivalent results:\n\n >>> np.dot(q1, r1)\n array([[ 3., -2., -2.],\n [ 6., -7., 4.],\n [ 6., -9., -3.],\n [ -3., 10., 1.],\n [ 7., 8., -6.]])\n\n >>> np.allclose(np.dot(q1, r1), a1)\n True\n\n And the updated Q is still unitary:\n\n >>> np.allclose(np.dot(q1.T, q1), np.eye(5))\n True\n\n" - ... - -def qr_update(Q, R, u, v, overwrite_qruv=..., check_finite=...) -> typing.Any: - "qr_update(Q, R, u, v, overwrite_qruv=False, check_finite=True)\n\n Rank-k QR update\n\n If ``A = Q R`` is the QR factorization of ``A``, return the QR\n factorization of ``A + u v**T`` for real ``A`` or ``A + u v**H``\n for complex ``A``.\n\n Parameters\n ----------\n Q : (M, M) or (M, N) array_like\n Unitary/orthogonal matrix from the qr decomposition of A.\n R : (M, N) or (N, N) array_like\n Upper triangular matrix from the qr decomposition of A.\n u : (M,) or (M, k) array_like\n Left update vector\n v : (N,) or (N, k) array_like\n Right update vector\n overwrite_qruv : bool, optional\n If True, consume Q, R, u, and v, if possible, while performing the\n update, otherwise make copies as necessary. Defaults to False.\n check_finite : bool, optional\n Whether to check that the input matrix contains only finite numbers.\n Disabling may give a performance gain, but may result in problems\n (crashes, non-termination) if the inputs do contain infinities or NaNs.\n Default is True.\n\n Returns\n -------\n Q1 : ndarray\n Updated unitary/orthogonal factor\n R1 : ndarray\n Updated upper triangular factor\n\n See Also\n --------\n qr, qr_multiply, qr_delete, qr_insert\n\n Notes\n -----\n This routine does not guarantee that the diagonal entries of `R1` are\n real or positive.\n\n .. versionadded:: 0.16.0\n\n References\n ----------\n .. [1] Golub, G. H. & Van Loan, C. F. Matrix Computations, 3rd Ed.\n (Johns Hopkins University Press, 1996).\n\n .. [2] Daniel, J. W., Gragg, W. B., Kaufman, L. & Stewart, G. W.\n Reorthogonalization and stable algorithms for updating the\n Gram-Schmidt QR factorization. Math. Comput. 30, 772-795 (1976).\n\n .. [3] Reichel, L. & Gragg, W. B. Algorithm 686: FORTRAN Subroutines for\n Updating the QR Decomposition. ACM Trans. Math. Softw. 16, 369-377\n (1990).\n\n Examples\n --------\n >>> from scipy import linalg\n >>> a = np.array([[ 3., -2., -2.],\n ... [ 6., -9., -3.],\n ... [ -3., 10., 1.],\n ... [ 6., -7., 4.],\n ... [ 7., 8., -6.]])\n >>> q, r = linalg.qr(a)\n\n Given this q, r decomposition, perform a rank 1 update.\n\n >>> u = np.array([7., -2., 4., 3., 5.])\n >>> v = np.array([1., 3., -5.])\n >>> q_up, r_up = linalg.qr_update(q, r, u, v, False)\n >>> q_up\n array([[ 0.54073807, 0.18645997, 0.81707661, -0.02136616, 0.06902409], # may vary (signs)\n [ 0.21629523, -0.63257324, 0.06567893, 0.34125904, -0.65749222],\n [ 0.05407381, 0.64757787, -0.12781284, -0.20031219, -0.72198188],\n [ 0.48666426, -0.30466718, -0.27487277, -0.77079214, 0.0256951 ],\n [ 0.64888568, 0.23001 , -0.4859845 , 0.49883891, 0.20253783]])\n >>> r_up\n array([[ 18.49324201, 24.11691794, -44.98940746], # may vary (signs)\n [ 0. , 31.95894662, -27.40998201],\n [ 0. , 0. , -9.25451794],\n [ 0. , 0. , 0. ],\n [ 0. , 0. , 0. ]])\n\n The update is equivalent, but faster than the following.\n\n >>> a_up = a + np.outer(u, v)\n >>> q_direct, r_direct = linalg.qr(a_up)\n\n Check that we have equivalent results:\n\n >>> np.allclose(np.dot(q_up, r_up), a_up)\n True\n\n And the updated Q is still unitary:\n\n >>> np.allclose(np.dot(q_up.T, q_up), np.eye(5))\n True\n\n Updating economic (reduced, thin) decompositions is also possible:\n\n >>> qe, re = linalg.qr(a, mode='economic')\n >>> qe_up, re_up = linalg.qr_update(qe, re, u, v, False)\n >>> qe_up\n array([[ 0.54073807, 0.18645997, 0.81707661], # may vary (signs)\n [ 0.21629523, -0.63257324, 0.06567893],\n [ 0.05407381, 0.64757787, -0.12781284],\n [ 0.48666426, -0.30466718, -0.27487277],\n [ 0.64888568, 0.23001 , -0.4859845 ]])\n >>> re_up\n array([[ 18.49324201, 24.11691794, -44.98940746], # may vary (signs)\n [ 0. , 31.95894662, -27.40998201],\n [ 0. , 0. , -9.25451794]])\n >>> np.allclose(np.dot(qe_up, re_up), a_up)\n True\n >>> np.allclose(np.dot(qe_up.T, qe_up), np.eye(3))\n True\n\n Similarly to the above, perform a rank 2 update.\n\n >>> u2 = np.array([[ 7., -1,],\n ... [-2., 4.],\n ... [ 4., 2.],\n ... [ 3., -6.],\n ... [ 5., 3.]])\n >>> v2 = np.array([[ 1., 2.],\n ... [ 3., 4.],\n ... [-5., 2]])\n >>> q_up2, r_up2 = linalg.qr_update(q, r, u2, v2, False)\n >>> q_up2\n array([[-0.33626508, -0.03477253, 0.61956287, -0.64352987, -0.29618884], # may vary (signs)\n [-0.50439762, 0.58319694, -0.43010077, -0.33395279, 0.33008064],\n [-0.21016568, -0.63123106, 0.0582249 , -0.13675572, 0.73163206],\n [ 0.12609941, 0.49694436, 0.64590024, 0.31191919, 0.47187344],\n [-0.75659643, -0.11517748, 0.10284903, 0.5986227 , -0.21299983]])\n >>> r_up2\n array([[-23.79075451, -41.1084062 , 24.71548348], # may vary (signs)\n [ 0. , -33.83931057, 11.02226551],\n [ 0. , 0. , 48.91476811],\n [ 0. , 0. , 0. ],\n [ 0. , 0. , 0. ]])\n\n This update is also a valid qr decomposition of ``A + U V**T``.\n\n >>> a_up2 = a + np.dot(u2, v2.T)\n >>> np.allclose(a_up2, np.dot(q_up2, r_up2))\n True\n >>> np.allclose(np.dot(q_up2.T, q_up2), np.eye(5))\n True\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/_fblas.pyi b/stubs/scipy-stubs/linalg/_fblas.pyi deleted file mode 100644 index 305172fe..00000000 --- a/stubs/scipy-stubs/linalg/_fblas.pyi +++ /dev/null @@ -1,621 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg._fblas, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def caxpy(x, y, n=..., a=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "z = caxpy(x,y,[n,a,offx,incx,offy,incy])\n\nWrapper for ``caxpy``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\ny : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\na : input complex, optional\n Default: (1.0, 0.0)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nz : rank-1 array('F') with bounds (*) and y storage\n" - ... - -def ccopy(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "y = ccopy(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``ccopy``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\ny : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\ny : rank-1 array('F') with bounds (*)\n" - ... - -def cdotc(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "xy = cdotc(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``cdotc``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\ny : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nxy : complex\n" - ... - -def cdotu(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "xy = cdotu(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``cdotu``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\ny : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nxy : complex\n" - ... - -def cgbmv( - m, n, kl, ku, alpha, a, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., trans=..., overwrite_y=... -) -> typing.Any: - "yout = cgbmv(m,n,kl,ku,alpha,a,x,[incx,offx,beta,y,incy,offy,trans,overwrite_y])\n\nWrapper for ``cgbmv``.\n\nParameters\n----------\nm : input int\nn : input int\nkl : input int\nku : input int\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,n)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('F') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('F') with bounds (ly) and y storage\n" - ... - -def cgemm(alpha, a, b, beta=..., c=..., trans_a=..., trans_b=..., overwrite_c=...) -> typing.Any: - "c = cgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])\n\nWrapper for ``cgemm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,ka)\nb : input rank-2 array('F') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('F') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ntrans_b : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (m,n)\n" - ... - -def cgemv(alpha, a, x, beta=..., y=..., offx=..., incx=..., offy=..., incy=..., trans=..., overwrite_y=...) -> typing.Any: - "y = cgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])\n\nWrapper for ``cgemv``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (m,n)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('F') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('F') with bounds (ly)\n" - ... - -def cgerc(alpha, x, y, incx=..., incy=..., a=..., overwrite_x=..., overwrite_y=..., overwrite_a=...) -> typing.Any: - "a = cgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])\n\nWrapper for ``cgerc``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('F') with bounds (m)\ny : input rank-1 array('F') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 1\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 1\nincy : input int, optional\n Default: 1\na : input rank-2 array('F') with bounds (m,n), optional\n Default: (0.0,0.0)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (m,n)\n" - ... - -def cgeru(alpha, x, y, incx=..., incy=..., a=..., overwrite_x=..., overwrite_y=..., overwrite_a=...) -> typing.Any: - "a = cgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])\n\nWrapper for ``cgeru``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('F') with bounds (m)\ny : input rank-1 array('F') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 1\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 1\nincy : input int, optional\n Default: 1\na : input rank-2 array('F') with bounds (m,n), optional\n Default: (0.0,0.0)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (m,n)\n" - ... - -def chbmv(k, alpha, a, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = chbmv(k,alpha,a,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``chbmv``.\n\nParameters\n----------\nk : input int\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,n)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('F') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('F') with bounds (ly) and y storage\n" - ... - -def chemm(alpha, a, b, beta=..., c=..., side=..., lower=..., overwrite_c=...) -> typing.Any: - "c = chemm(alpha,a,b,[beta,c,side,lower,overwrite_c])\n\nWrapper for ``chemm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,ka)\nb : input rank-2 array('F') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('F') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (m,n)\n" - ... - -def chemv(alpha, a, x, beta=..., y=..., offx=..., incx=..., offy=..., incy=..., lower=..., overwrite_y=...) -> typing.Any: - "y = chemv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])\n\nWrapper for ``chemv``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (n,n)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('F') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('F') with bounds (ly)\n" - ... - -def cher(alpha, x, lower=..., incx=..., offx=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = cher(alpha,x,[lower,incx,offx,n,a,overwrite_a])\n\nWrapper for ``cher``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\na : input rank-2 array('F') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (n,n)\n" - ... - -def cher2(alpha, x, y, lower=..., incx=..., offx=..., incy=..., offy=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = cher2(alpha,x,y,[lower,incx,offx,incy,offy,n,a,overwrite_a])\n\nWrapper for ``cher2``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('F') with bounds (*)\ny : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nn : input int, optional\n Default: ((len(x)-1-offx)/abs(incx)+1 <=(len(y)-1-offy)/abs(incy)+1 ?(len(x)-1-offx)/abs(incx)+1 :(len(y)-1-offy)/abs(incy)+1)\na : input rank-2 array('F') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (n,n)\n" - ... - -def cher2k(alpha, a, b, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = cher2k(alpha,a,b,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``cher2k``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,ka)\nb : input rank-2 array('F') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('F') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n)\n" - ... - -def cherk(alpha, a, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = cherk(alpha,a,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``cherk``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,ka)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('F') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n)\n" - ... - -def chpmv(n, alpha, ap, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = chpmv(n,alpha,ap,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``chpmv``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nap : input rank-1 array('F') with bounds (*)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('F') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('F') with bounds (ly) and y storage\n" - ... - -def chpr(n, alpha, x, ap, incx=..., offx=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = chpr(n,alpha,x,ap,[incx,offx,lower,overwrite_ap])\n\nWrapper for ``chpr``.\n\nParameters\n----------\nn : input int\nalpha : input float\nx : input rank-1 array('F') with bounds (*)\nap : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('F') with bounds (*) and ap storage\n" - ... - -def chpr2(n, alpha, x, y, ap, incx=..., offx=..., incy=..., offy=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = chpr2(n,alpha,x,y,ap,[incx,offx,incy,offy,lower,overwrite_ap])\n\nWrapper for ``chpr2``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nx : input rank-1 array('F') with bounds (*)\ny : input rank-1 array('F') with bounds (*)\nap : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('F') with bounds (*) and ap storage\n" - ... - -def crotg(a, b) -> typing.Any: - "c,s = crotg(a,b)\n\nWrapper for ``crotg``.\n\nParameters\n----------\na : input complex\nb : input complex\n\nReturns\n-------\nc : complex\ns : complex\n" - ... - -def cscal(a, x, n=..., offx=..., incx=...) -> typing.Any: - "x = cscal(a,x,[n,offx,incx])\n\nWrapper for ``cscal``.\n\nParameters\n----------\na : input complex\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('F') with bounds (*)\n" - ... - -def cspmv(n, alpha, ap, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = cspmv(n,alpha,ap,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``cspmv``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nap : input rank-1 array('F') with bounds (*)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('F') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('F') with bounds (ly) and y storage\n" - ... - -def cspr(n, alpha, x, ap, incx=..., offx=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = cspr(n,alpha,x,ap,[incx,offx,lower,overwrite_ap])\n\nWrapper for ``cspr``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nx : input rank-1 array('F') with bounds (*)\nap : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('F') with bounds (*) and ap storage\n" - ... - -def csrot(x, y, c, s, n=..., offx=..., incx=..., offy=..., incy=..., overwrite_x=..., overwrite_y=...) -> typing.Any: - "x,y = csrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])\n\nWrapper for ``csrot``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\ny : input rank-1 array('F') with bounds (*)\nc : input float\ns : input float\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 0\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('F') with bounds (*)\ny : rank-1 array('F') with bounds (*)\n" - ... - -def csscal(a, x, n=..., offx=..., incx=..., overwrite_x=...) -> typing.Any: - "x = csscal(a,x,[n,offx,incx,overwrite_x])\n\nWrapper for ``csscal``.\n\nParameters\n----------\na : input float\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('F') with bounds (*)\n" - ... - -def cswap(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "x,y = cswap(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``cswap``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\ny : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('F') with bounds (*)\ny : rank-1 array('F') with bounds (*)\n" - ... - -def csymm(alpha, a, b, beta=..., c=..., side=..., lower=..., overwrite_c=...) -> typing.Any: - "c = csymm(alpha,a,b,[beta,c,side,lower,overwrite_c])\n\nWrapper for ``csymm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,ka)\nb : input rank-2 array('F') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('F') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (m,n)\n" - ... - -def csyr(alpha, x, lower=..., incx=..., offx=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = csyr(alpha,x,[lower,incx,offx,n,a,overwrite_a])\n\nWrapper for ``csyr``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\na : input rank-2 array('F') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (n,n)\n" - ... - -def csyr2k(alpha, a, b, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = csyr2k(alpha,a,b,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``csyr2k``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,ka)\nb : input rank-2 array('F') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('F') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n)\n" - ... - -def csyrk(alpha, a, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = csyrk(alpha,a,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``csyrk``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,ka)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('F') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n)\n" - ... - -def ctbmv(k, a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ctbmv(k,a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ctbmv``.\n\nParameters\n----------\nk : input int\na : input rank-2 array('F') with bounds (lda,n)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('F') with bounds (*) and x storage\n" - ... - -def ctbsv(k, a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ctbsv(k,a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ctbsv``.\n\nParameters\n----------\nk : input int\na : input rank-2 array('F') with bounds (lda,n)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('F') with bounds (*) and x storage\n" - ... - -def ctpmv(n, ap, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ctpmv(n,ap,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ctpmv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (*)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('F') with bounds (*) and x storage\n" - ... - -def ctpsv(n, ap, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ctpsv(n,ap,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ctpsv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (*)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('F') with bounds (*) and x storage\n" - ... - -def ctrmm(alpha, a, b, side=..., lower=..., trans_a=..., diag=..., overwrite_b=...) -> typing.Any: - "b = ctrmm(alpha,a,b,[side,lower,trans_a,diag,overwrite_b])\n\nWrapper for ``ctrmm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,k)\nb : input rank-2 array('F') with bounds (ldb,n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nb : rank-2 array('F') with bounds (ldb,n)\n" - ... - -def ctrmv(a, x, offx=..., incx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "x = ctrmv(a,x,[offx,incx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ctrmv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('F') with bounds (*)\n" - ... - -def ctrsm(alpha, a, b, side=..., lower=..., trans_a=..., diag=..., overwrite_b=...) -> typing.Any: - "x = ctrsm(alpha,a,b,[side,lower,trans_a,diag,overwrite_b])\n\nWrapper for ``ctrsm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('F') with bounds (lda,*)\nb : input rank-2 array('F') with bounds (ldb,n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,n) and b storage\n" - ... - -def ctrsv(a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ctrsv(a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ctrsv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('F') with bounds (*) and x storage\n" - ... - -def dasum(x, n=..., offx=..., incx=...) -> typing.Any: - "s = dasum(x,[n,offx,incx])\n\nWrapper for ``dasum``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\ns : float\n" - ... - -def daxpy(x, y, n=..., a=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "z = daxpy(x,y,[n,a,offx,incx,offy,incy])\n\nWrapper for ``daxpy``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\ny : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\na : input float, optional\n Default: 1.0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nz : rank-1 array('d') with bounds (*) and y storage\n" - ... - -def dcopy(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "y = dcopy(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``dcopy``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\ny : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\ny : rank-1 array('d') with bounds (*)\n" - ... - -def ddot(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "xy = ddot(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``ddot``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\ny : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nxy : float\n" - ... - -def dgbmv( - m, n, kl, ku, alpha, a, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., trans=..., overwrite_y=... -) -> typing.Any: - "yout = dgbmv(m,n,kl,ku,alpha,a,x,[incx,offx,beta,y,incy,offy,trans,overwrite_y])\n\nWrapper for ``dgbmv``.\n\nParameters\n----------\nm : input int\nn : input int\nkl : input int\nku : input int\nalpha : input float\na : input rank-2 array('d') with bounds (lda,n)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('d') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('d') with bounds (ly) and y storage\n" - ... - -def dgemm(alpha, a, b, beta=..., c=..., trans_a=..., trans_b=..., overwrite_c=...) -> typing.Any: - "c = dgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])\n\nWrapper for ``dgemm``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('d') with bounds (lda,ka)\nb : input rank-2 array('d') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\nc : input rank-2 array('d') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ntrans_b : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (m,n)\n" - ... - -def dgemv(alpha, a, x, beta=..., y=..., offx=..., incx=..., offy=..., incy=..., trans=..., overwrite_y=...) -> typing.Any: - "y = dgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])\n\nWrapper for ``dgemv``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('d') with bounds (m,n)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('d') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('d') with bounds (ly)\n" - ... - -def dger(alpha, x, y, incx=..., incy=..., a=..., overwrite_x=..., overwrite_y=..., overwrite_a=...) -> typing.Any: - "a = dger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])\n\nWrapper for ``dger``.\n\nParameters\n----------\nalpha : input float\nx : input rank-1 array('d') with bounds (m)\ny : input rank-1 array('d') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 1\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 1\nincy : input int, optional\n Default: 1\na : input rank-2 array('d') with bounds (m,n), optional\n Default: 0.0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('d') with bounds (m,n)\n" - ... - -def dnrm2(x, n=..., offx=..., incx=...) -> typing.Any: - "n2 = dnrm2(x,[n,offx,incx])\n\nWrapper for ``dnrm2``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nn2 : float\n" - ... - -def drot(x, y, c, s, n=..., offx=..., incx=..., offy=..., incy=..., overwrite_x=..., overwrite_y=...) -> typing.Any: - "x,y = drot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])\n\nWrapper for ``drot``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\ny : input rank-1 array('d') with bounds (*)\nc : input float\ns : input float\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 0\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('d') with bounds (*)\ny : rank-1 array('d') with bounds (*)\n" - ... - -def drotg(a, b) -> typing.Any: - "c,s = drotg(a,b)\n\nWrapper for ``drotg``.\n\nParameters\n----------\na : input float\nb : input float\n\nReturns\n-------\nc : float\ns : float\n" - ... - -def drotm(x, y, param, n=..., offx=..., incx=..., offy=..., incy=..., overwrite_x=..., overwrite_y=...) -> typing.Any: - "x,y = drotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])\n\nWrapper for ``drotm``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\ny : input rank-1 array('d') with bounds (*)\nparam : input rank-1 array('d') with bounds (5)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 0\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('d') with bounds (*)\ny : rank-1 array('d') with bounds (*)\n" - ... - -def drotmg(d1, d2, x1, y1) -> typing.Any: - "param = drotmg(d1,d2,x1,y1)\n\nWrapper for ``drotmg``.\n\nParameters\n----------\nd1 : input float\nd2 : input float\nx1 : input float\ny1 : input float\n\nReturns\n-------\nparam : rank-1 array('d') with bounds (5)\n" - ... - -def dsbmv(k, alpha, a, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = dsbmv(k,alpha,a,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``dsbmv``.\n\nParameters\n----------\nk : input int\nalpha : input float\na : input rank-2 array('d') with bounds (lda,n)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('d') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('d') with bounds (ly) and y storage\n" - ... - -def dscal(a, x, n=..., offx=..., incx=...) -> typing.Any: - "x = dscal(a,x,[n,offx,incx])\n\nWrapper for ``dscal``.\n\nParameters\n----------\na : input float\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('d') with bounds (*)\n" - ... - -def dspmv(n, alpha, ap, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = dspmv(n,alpha,ap,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``dspmv``.\n\nParameters\n----------\nn : input int\nalpha : input float\nap : input rank-1 array('d') with bounds (*)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('d') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('d') with bounds (ly) and y storage\n" - ... - -def dspr(n, alpha, x, ap, incx=..., offx=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = dspr(n,alpha,x,ap,[incx,offx,lower,overwrite_ap])\n\nWrapper for ``dspr``.\n\nParameters\n----------\nn : input int\nalpha : input float\nx : input rank-1 array('d') with bounds (*)\nap : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('d') with bounds (*) and ap storage\n" - ... - -def dspr2(n, alpha, x, y, ap, incx=..., offx=..., incy=..., offy=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = dspr2(n,alpha,x,y,ap,[incx,offx,incy,offy,lower,overwrite_ap])\n\nWrapper for ``dspr2``.\n\nParameters\n----------\nn : input int\nalpha : input float\nx : input rank-1 array('d') with bounds (*)\ny : input rank-1 array('d') with bounds (*)\nap : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('d') with bounds (*) and ap storage\n" - ... - -def dswap(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "x,y = dswap(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``dswap``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\ny : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('d') with bounds (*)\ny : rank-1 array('d') with bounds (*)\n" - ... - -def dsymm(alpha, a, b, beta=..., c=..., side=..., lower=..., overwrite_c=...) -> typing.Any: - "c = dsymm(alpha,a,b,[beta,c,side,lower,overwrite_c])\n\nWrapper for ``dsymm``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('d') with bounds (lda,ka)\nb : input rank-2 array('d') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\nc : input rank-2 array('d') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (m,n)\n" - ... - -def dsymv(alpha, a, x, beta=..., y=..., offx=..., incx=..., offy=..., incy=..., lower=..., overwrite_y=...) -> typing.Any: - "y = dsymv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])\n\nWrapper for ``dsymv``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('d') with bounds (n,n)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('d') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('d') with bounds (ly)\n" - ... - -def dsyr(alpha, x, lower=..., incx=..., offx=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = dsyr(alpha,x,[lower,incx,offx,n,a,overwrite_a])\n\nWrapper for ``dsyr``.\n\nParameters\n----------\nalpha : input float\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\na : input rank-2 array('d') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('d') with bounds (n,n)\n" - ... - -def dsyr2(alpha, x, y, lower=..., incx=..., offx=..., incy=..., offy=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = dsyr2(alpha,x,y,[lower,incx,offx,incy,offy,n,a,overwrite_a])\n\nWrapper for ``dsyr2``.\n\nParameters\n----------\nalpha : input float\nx : input rank-1 array('d') with bounds (*)\ny : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nn : input int, optional\n Default: ((len(x)-1-offx)/abs(incx)+1 <=(len(y)-1-offy)/abs(incy)+1 ?(len(x)-1-offx)/abs(incx)+1 :(len(y)-1-offy)/abs(incy)+1)\na : input rank-2 array('d') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('d') with bounds (n,n)\n" - ... - -def dsyr2k(alpha, a, b, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = dsyr2k(alpha,a,b,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``dsyr2k``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('d') with bounds (lda,ka)\nb : input rank-2 array('d') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\nc : input rank-2 array('d') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (n,n)\n" - ... - -def dsyrk(alpha, a, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = dsyrk(alpha,a,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``dsyrk``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('d') with bounds (lda,ka)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\nc : input rank-2 array('d') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (n,n)\n" - ... - -def dtbmv(k, a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = dtbmv(k,a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``dtbmv``.\n\nParameters\n----------\nk : input int\na : input rank-2 array('d') with bounds (lda,n)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('d') with bounds (*) and x storage\n" - ... - -def dtbsv(k, a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = dtbsv(k,a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``dtbsv``.\n\nParameters\n----------\nk : input int\na : input rank-2 array('d') with bounds (lda,n)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('d') with bounds (*) and x storage\n" - ... - -def dtpmv(n, ap, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = dtpmv(n,ap,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``dtpmv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (*)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('d') with bounds (*) and x storage\n" - ... - -def dtpsv(n, ap, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = dtpsv(n,ap,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``dtpsv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (*)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('d') with bounds (*) and x storage\n" - ... - -def dtrmm(alpha, a, b, side=..., lower=..., trans_a=..., diag=..., overwrite_b=...) -> typing.Any: - "b = dtrmm(alpha,a,b,[side,lower,trans_a,diag,overwrite_b])\n\nWrapper for ``dtrmm``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('d') with bounds (lda,k)\nb : input rank-2 array('d') with bounds (ldb,n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nb : rank-2 array('d') with bounds (ldb,n)\n" - ... - -def dtrmv(a, x, offx=..., incx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "x = dtrmv(a,x,[offx,incx,lower,trans,diag,overwrite_x])\n\nWrapper for ``dtrmv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('d') with bounds (*)\n" - ... - -def dtrsm(alpha, a, b, side=..., lower=..., trans_a=..., diag=..., overwrite_b=...) -> typing.Any: - "x = dtrsm(alpha,a,b,[side,lower,trans_a,diag,overwrite_b])\n\nWrapper for ``dtrsm``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('d') with bounds (lda,*)\nb : input rank-2 array('d') with bounds (ldb,n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,n) and b storage\n" - ... - -def dtrsv(a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = dtrsv(a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``dtrsv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('d') with bounds (*) and x storage\n" - ... - -def dzasum(x, n=..., offx=..., incx=...) -> typing.Any: - "s = dzasum(x,[n,offx,incx])\n\nWrapper for ``dzasum``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\ns : float\n" - ... - -def dznrm2(x, n=..., offx=..., incx=...) -> typing.Any: - "n2 = dznrm2(x,[n,offx,incx])\n\nWrapper for ``dznrm2``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nn2 : float\n" - ... - -def icamax(x, n=..., offx=..., incx=...) -> typing.Any: - "k = icamax(x,[n,offx,incx])\n\nWrapper for ``icamax``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nk : int\n" - ... - -def idamax(x, n=..., offx=..., incx=...) -> typing.Any: - "k = idamax(x,[n,offx,incx])\n\nWrapper for ``idamax``.\n\nParameters\n----------\nx : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nk : int\n" - ... - -def isamax(x, n=..., offx=..., incx=...) -> typing.Any: - "k = isamax(x,[n,offx,incx])\n\nWrapper for ``isamax``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nk : int\n" - ... - -def izamax(x, n=..., offx=..., incx=...) -> typing.Any: - "k = izamax(x,[n,offx,incx])\n\nWrapper for ``izamax``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nk : int\n" - ... - -def sasum(x, n=..., offx=..., incx=...) -> typing.Any: - "s = sasum(x,[n,offx,incx])\n\nWrapper for ``sasum``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\ns : float\n" - ... - -def saxpy(x, y, n=..., a=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "z = saxpy(x,y,[n,a,offx,incx,offy,incy])\n\nWrapper for ``saxpy``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\ny : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\na : input float, optional\n Default: 1.0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nz : rank-1 array('f') with bounds (*) and y storage\n" - ... - -def scasum(x, n=..., offx=..., incx=...) -> typing.Any: - "s = scasum(x,[n,offx,incx])\n\nWrapper for ``scasum``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\ns : float\n" - ... - -def scnrm2(x, n=..., offx=..., incx=...) -> typing.Any: - "n2 = scnrm2(x,[n,offx,incx])\n\nWrapper for ``scnrm2``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nn2 : float\n" - ... - -def scopy(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "y = scopy(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``scopy``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\ny : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\ny : rank-1 array('f') with bounds (*)\n" - ... - -def sdot(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "xy = sdot(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``sdot``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\ny : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nxy : float\n" - ... - -def sgbmv( - m, n, kl, ku, alpha, a, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., trans=..., overwrite_y=... -) -> typing.Any: - "yout = sgbmv(m,n,kl,ku,alpha,a,x,[incx,offx,beta,y,incy,offy,trans,overwrite_y])\n\nWrapper for ``sgbmv``.\n\nParameters\n----------\nm : input int\nn : input int\nkl : input int\nku : input int\nalpha : input float\na : input rank-2 array('f') with bounds (lda,n)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('f') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('f') with bounds (ly) and y storage\n" - ... - -def sgemm(alpha, a, b, beta=..., c=..., trans_a=..., trans_b=..., overwrite_c=...) -> typing.Any: - "c = sgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])\n\nWrapper for ``sgemm``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('f') with bounds (lda,ka)\nb : input rank-2 array('f') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\nc : input rank-2 array('f') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ntrans_b : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (m,n)\n" - ... - -def sgemv(alpha, a, x, beta=..., y=..., offx=..., incx=..., offy=..., incy=..., trans=..., overwrite_y=...) -> typing.Any: - "y = sgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])\n\nWrapper for ``sgemv``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('f') with bounds (m,n)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('f') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('f') with bounds (ly)\n" - ... - -def sger(alpha, x, y, incx=..., incy=..., a=..., overwrite_x=..., overwrite_y=..., overwrite_a=...) -> typing.Any: - "a = sger(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])\n\nWrapper for ``sger``.\n\nParameters\n----------\nalpha : input float\nx : input rank-1 array('f') with bounds (m)\ny : input rank-1 array('f') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 1\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 1\nincy : input int, optional\n Default: 1\na : input rank-2 array('f') with bounds (m,n), optional\n Default: 0.0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('f') with bounds (m,n)\n" - ... - -def snrm2(x, n=..., offx=..., incx=...) -> typing.Any: - "n2 = snrm2(x,[n,offx,incx])\n\nWrapper for ``snrm2``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nn2 : float\n" - ... - -def srot(x, y, c, s, n=..., offx=..., incx=..., offy=..., incy=..., overwrite_x=..., overwrite_y=...) -> typing.Any: - "x,y = srot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])\n\nWrapper for ``srot``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\ny : input rank-1 array('f') with bounds (*)\nc : input float\ns : input float\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 0\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('f') with bounds (*)\ny : rank-1 array('f') with bounds (*)\n" - ... - -def srotg(a, b) -> typing.Any: - "c,s = srotg(a,b)\n\nWrapper for ``srotg``.\n\nParameters\n----------\na : input float\nb : input float\n\nReturns\n-------\nc : float\ns : float\n" - ... - -def srotm(x, y, param, n=..., offx=..., incx=..., offy=..., incy=..., overwrite_x=..., overwrite_y=...) -> typing.Any: - "x,y = srotm(x,y,param,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])\n\nWrapper for ``srotm``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\ny : input rank-1 array('f') with bounds (*)\nparam : input rank-1 array('f') with bounds (5)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 0\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('f') with bounds (*)\ny : rank-1 array('f') with bounds (*)\n" - ... - -def srotmg(d1, d2, x1, y1) -> typing.Any: - "param = srotmg(d1,d2,x1,y1)\n\nWrapper for ``srotmg``.\n\nParameters\n----------\nd1 : input float\nd2 : input float\nx1 : input float\ny1 : input float\n\nReturns\n-------\nparam : rank-1 array('f') with bounds (5)\n" - ... - -def ssbmv(k, alpha, a, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = ssbmv(k,alpha,a,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``ssbmv``.\n\nParameters\n----------\nk : input int\nalpha : input float\na : input rank-2 array('f') with bounds (lda,n)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('f') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('f') with bounds (ly) and y storage\n" - ... - -def sscal(a, x, n=..., offx=..., incx=...) -> typing.Any: - "x = sscal(a,x,[n,offx,incx])\n\nWrapper for ``sscal``.\n\nParameters\n----------\na : input float\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('f') with bounds (*)\n" - ... - -def sspmv(n, alpha, ap, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = sspmv(n,alpha,ap,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``sspmv``.\n\nParameters\n----------\nn : input int\nalpha : input float\nap : input rank-1 array('f') with bounds (*)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('f') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('f') with bounds (ly) and y storage\n" - ... - -def sspr(n, alpha, x, ap, incx=..., offx=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = sspr(n,alpha,x,ap,[incx,offx,lower,overwrite_ap])\n\nWrapper for ``sspr``.\n\nParameters\n----------\nn : input int\nalpha : input float\nx : input rank-1 array('f') with bounds (*)\nap : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('f') with bounds (*) and ap storage\n" - ... - -def sspr2(n, alpha, x, y, ap, incx=..., offx=..., incy=..., offy=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = sspr2(n,alpha,x,y,ap,[incx,offx,incy,offy,lower,overwrite_ap])\n\nWrapper for ``sspr2``.\n\nParameters\n----------\nn : input int\nalpha : input float\nx : input rank-1 array('f') with bounds (*)\ny : input rank-1 array('f') with bounds (*)\nap : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('f') with bounds (*) and ap storage\n" - ... - -def sswap(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "x,y = sswap(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``sswap``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (*)\ny : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('f') with bounds (*)\ny : rank-1 array('f') with bounds (*)\n" - ... - -def ssymm(alpha, a, b, beta=..., c=..., side=..., lower=..., overwrite_c=...) -> typing.Any: - "c = ssymm(alpha,a,b,[beta,c,side,lower,overwrite_c])\n\nWrapper for ``ssymm``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('f') with bounds (lda,ka)\nb : input rank-2 array('f') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\nc : input rank-2 array('f') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (m,n)\n" - ... - -def ssymv(alpha, a, x, beta=..., y=..., offx=..., incx=..., offy=..., incy=..., lower=..., overwrite_y=...) -> typing.Any: - "y = ssymv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])\n\nWrapper for ``ssymv``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('f') with bounds (n,n)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\ny : input rank-1 array('f') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('f') with bounds (ly)\n" - ... - -def ssyr(alpha, x, lower=..., incx=..., offx=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = ssyr(alpha,x,[lower,incx,offx,n,a,overwrite_a])\n\nWrapper for ``ssyr``.\n\nParameters\n----------\nalpha : input float\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\na : input rank-2 array('f') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('f') with bounds (n,n)\n" - ... - -def ssyr2(alpha, x, y, lower=..., incx=..., offx=..., incy=..., offy=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = ssyr2(alpha,x,y,[lower,incx,offx,incy,offy,n,a,overwrite_a])\n\nWrapper for ``ssyr2``.\n\nParameters\n----------\nalpha : input float\nx : input rank-1 array('f') with bounds (*)\ny : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nn : input int, optional\n Default: ((len(x)-1-offx)/abs(incx)+1 <=(len(y)-1-offy)/abs(incy)+1 ?(len(x)-1-offx)/abs(incx)+1 :(len(y)-1-offy)/abs(incy)+1)\na : input rank-2 array('f') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('f') with bounds (n,n)\n" - ... - -def ssyr2k(alpha, a, b, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = ssyr2k(alpha,a,b,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``ssyr2k``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('f') with bounds (lda,ka)\nb : input rank-2 array('f') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\nc : input rank-2 array('f') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (n,n)\n" - ... - -def ssyrk(alpha, a, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = ssyrk(alpha,a,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``ssyrk``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('f') with bounds (lda,ka)\n\nOther Parameters\n----------------\nbeta : input float, optional\n Default: 0.0\nc : input rank-2 array('f') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (n,n)\n" - ... - -def stbmv(k, a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = stbmv(k,a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``stbmv``.\n\nParameters\n----------\nk : input int\na : input rank-2 array('f') with bounds (lda,n)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('f') with bounds (*) and x storage\n" - ... - -def stbsv(k, a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = stbsv(k,a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``stbsv``.\n\nParameters\n----------\nk : input int\na : input rank-2 array('f') with bounds (lda,n)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('f') with bounds (*) and x storage\n" - ... - -def stpmv(n, ap, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = stpmv(n,ap,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``stpmv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (*)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('f') with bounds (*) and x storage\n" - ... - -def stpsv(n, ap, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = stpsv(n,ap,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``stpsv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (*)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('f') with bounds (*) and x storage\n" - ... - -def strmm(alpha, a, b, side=..., lower=..., trans_a=..., diag=..., overwrite_b=...) -> typing.Any: - "b = strmm(alpha,a,b,[side,lower,trans_a,diag,overwrite_b])\n\nWrapper for ``strmm``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('f') with bounds (lda,k)\nb : input rank-2 array('f') with bounds (ldb,n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nb : rank-2 array('f') with bounds (ldb,n)\n" - ... - -def strmv(a, x, offx=..., incx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "x = strmv(a,x,[offx,incx,lower,trans,diag,overwrite_x])\n\nWrapper for ``strmv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('f') with bounds (*)\n" - ... - -def strsm(alpha, a, b, side=..., lower=..., trans_a=..., diag=..., overwrite_b=...) -> typing.Any: - "x = strsm(alpha,a,b,[side,lower,trans_a,diag,overwrite_b])\n\nWrapper for ``strsm``.\n\nParameters\n----------\nalpha : input float\na : input rank-2 array('f') with bounds (lda,*)\nb : input rank-2 array('f') with bounds (ldb,n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,n) and b storage\n" - ... - -def strsv(a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = strsv(a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``strsv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nx : input rank-1 array('f') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('f') with bounds (*) and x storage\n" - ... - -def zaxpy(x, y, n=..., a=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "z = zaxpy(x,y,[n,a,offx,incx,offy,incy])\n\nWrapper for ``zaxpy``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\ny : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\na : input complex, optional\n Default: (1.0, 0.0)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nz : rank-1 array('D') with bounds (*) and y storage\n" - ... - -def zcopy(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "y = zcopy(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``zcopy``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\ny : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\ny : rank-1 array('D') with bounds (*)\n" - ... - -def zdotc(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "xy = zdotc(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``zdotc``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\ny : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nxy : complex\n" - ... - -def zdotu(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "xy = zdotu(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``zdotu``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\ny : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nxy : complex\n" - ... - -def zdrot(x, y, c, s, n=..., offx=..., incx=..., offy=..., incy=..., overwrite_x=..., overwrite_y=...) -> typing.Any: - "x,y = zdrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])\n\nWrapper for ``zdrot``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\ny : input rank-1 array('D') with bounds (*)\nc : input float\ns : input float\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 0\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('D') with bounds (*)\ny : rank-1 array('D') with bounds (*)\n" - ... - -def zdscal(a, x, n=..., offx=..., incx=..., overwrite_x=...) -> typing.Any: - "x = zdscal(a,x,[n,offx,incx,overwrite_x])\n\nWrapper for ``zdscal``.\n\nParameters\n----------\na : input float\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('D') with bounds (*)\n" - ... - -def zgbmv( - m, n, kl, ku, alpha, a, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., trans=..., overwrite_y=... -) -> typing.Any: - "yout = zgbmv(m,n,kl,ku,alpha,a,x,[incx,offx,beta,y,incy,offy,trans,overwrite_y])\n\nWrapper for ``zgbmv``.\n\nParameters\n----------\nm : input int\nn : input int\nkl : input int\nku : input int\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,n)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('D') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('D') with bounds (ly) and y storage\n" - ... - -def zgemm(alpha, a, b, beta=..., c=..., trans_a=..., trans_b=..., overwrite_c=...) -> typing.Any: - "c = zgemm(alpha,a,b,[beta,c,trans_a,trans_b,overwrite_c])\n\nWrapper for ``zgemm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,ka)\nb : input rank-2 array('D') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('D') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ntrans_b : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (m,n)\n" - ... - -def zgemv(alpha, a, x, beta=..., y=..., offx=..., incx=..., offy=..., incy=..., trans=..., overwrite_y=...) -> typing.Any: - "y = zgemv(alpha,a,x,[beta,y,offx,incx,offy,incy,trans,overwrite_y])\n\nWrapper for ``zgemv``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (m,n)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('D') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('D') with bounds (ly)\n" - ... - -def zgerc(alpha, x, y, incx=..., incy=..., a=..., overwrite_x=..., overwrite_y=..., overwrite_a=...) -> typing.Any: - "a = zgerc(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])\n\nWrapper for ``zgerc``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('D') with bounds (m)\ny : input rank-1 array('D') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 1\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 1\nincy : input int, optional\n Default: 1\na : input rank-2 array('D') with bounds (m,n), optional\n Default: (0.0,0.0)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (m,n)\n" - ... - -def zgeru(alpha, x, y, incx=..., incy=..., a=..., overwrite_x=..., overwrite_y=..., overwrite_a=...) -> typing.Any: - "a = zgeru(alpha,x,y,[incx,incy,a,overwrite_x,overwrite_y,overwrite_a])\n\nWrapper for ``zgeru``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('D') with bounds (m)\ny : input rank-1 array('D') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 1\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 1\nincy : input int, optional\n Default: 1\na : input rank-2 array('D') with bounds (m,n), optional\n Default: (0.0,0.0)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (m,n)\n" - ... - -def zhbmv(k, alpha, a, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = zhbmv(k,alpha,a,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``zhbmv``.\n\nParameters\n----------\nk : input int\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,n)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('D') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('D') with bounds (ly) and y storage\n" - ... - -def zhemm(alpha, a, b, beta=..., c=..., side=..., lower=..., overwrite_c=...) -> typing.Any: - "c = zhemm(alpha,a,b,[beta,c,side,lower,overwrite_c])\n\nWrapper for ``zhemm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,ka)\nb : input rank-2 array('D') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('D') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (m,n)\n" - ... - -def zhemv(alpha, a, x, beta=..., y=..., offx=..., incx=..., offy=..., incy=..., lower=..., overwrite_y=...) -> typing.Any: - "y = zhemv(alpha,a,x,[beta,y,offx,incx,offy,incy,lower,overwrite_y])\n\nWrapper for ``zhemv``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (n,n)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('D') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ny : rank-1 array('D') with bounds (ly)\n" - ... - -def zher(alpha, x, lower=..., incx=..., offx=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = zher(alpha,x,[lower,incx,offx,n,a,overwrite_a])\n\nWrapper for ``zher``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\na : input rank-2 array('D') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (n,n)\n" - ... - -def zher2(alpha, x, y, lower=..., incx=..., offx=..., incy=..., offy=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = zher2(alpha,x,y,[lower,incx,offx,incy,offy,n,a,overwrite_a])\n\nWrapper for ``zher2``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('D') with bounds (*)\ny : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nn : input int, optional\n Default: ((len(x)-1-offx)/abs(incx)+1 <=(len(y)-1-offy)/abs(incy)+1 ?(len(x)-1-offx)/abs(incx)+1 :(len(y)-1-offy)/abs(incy)+1)\na : input rank-2 array('D') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (n,n)\n" - ... - -def zher2k(alpha, a, b, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = zher2k(alpha,a,b,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``zher2k``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,ka)\nb : input rank-2 array('D') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('D') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n)\n" - ... - -def zherk(alpha, a, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = zherk(alpha,a,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``zherk``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,ka)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('D') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n)\n" - ... - -def zhpmv(n, alpha, ap, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = zhpmv(n,alpha,ap,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``zhpmv``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nap : input rank-1 array('D') with bounds (*)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('D') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('D') with bounds (ly) and y storage\n" - ... - -def zhpr(n, alpha, x, ap, incx=..., offx=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = zhpr(n,alpha,x,ap,[incx,offx,lower,overwrite_ap])\n\nWrapper for ``zhpr``.\n\nParameters\n----------\nn : input int\nalpha : input float\nx : input rank-1 array('D') with bounds (*)\nap : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('D') with bounds (*) and ap storage\n" - ... - -def zhpr2(n, alpha, x, y, ap, incx=..., offx=..., incy=..., offy=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = zhpr2(n,alpha,x,y,ap,[incx,offx,incy,offy,lower,overwrite_ap])\n\nWrapper for ``zhpr2``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nx : input rank-1 array('D') with bounds (*)\ny : input rank-1 array('D') with bounds (*)\nap : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('D') with bounds (*) and ap storage\n" - ... - -def zrotg(a, b) -> typing.Any: - "c,s = zrotg(a,b)\n\nWrapper for ``zrotg``.\n\nParameters\n----------\na : input complex\nb : input complex\n\nReturns\n-------\nc : complex\ns : complex\n" - ... - -def zscal(a, x, n=..., offx=..., incx=...) -> typing.Any: - "x = zscal(a,x,[n,offx,incx])\n\nWrapper for ``zscal``.\n\nParameters\n----------\na : input complex\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('D') with bounds (*)\n" - ... - -def zspmv(n, alpha, ap, x, incx=..., offx=..., beta=..., y=..., incy=..., offy=..., lower=..., overwrite_y=...) -> typing.Any: - "yout = zspmv(n,alpha,ap,x,[incx,offx,beta,y,incy,offy,lower,overwrite_y])\n\nWrapper for ``zspmv``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nap : input rank-1 array('D') with bounds (*)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nbeta : input complex, optional\n Default: (0.0, 0.0)\ny : input rank-1 array('D') with bounds (ly)\noverwrite_y : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nyout : rank-1 array('D') with bounds (ly) and y storage\n" - ... - -def zspr(n, alpha, x, ap, incx=..., offx=..., lower=..., overwrite_ap=...) -> typing.Any: - "apu = zspr(n,alpha,x,ap,[incx,offx,lower,overwrite_ap])\n\nWrapper for ``zspr``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nx : input rank-1 array('D') with bounds (*)\nap : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\napu : rank-1 array('D') with bounds (*) and ap storage\n" - ... - -def zswap(x, y, n=..., offx=..., incx=..., offy=..., incy=...) -> typing.Any: - "x,y = zswap(x,y,[n,offx,incx,offy,incy])\n\nWrapper for ``zswap``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (*)\ny : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (len(x)-offx)/abs(incx)\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('D') with bounds (*)\ny : rank-1 array('D') with bounds (*)\n" - ... - -def zsymm(alpha, a, b, beta=..., c=..., side=..., lower=..., overwrite_c=...) -> typing.Any: - "c = zsymm(alpha,a,b,[beta,c,side,lower,overwrite_c])\n\nWrapper for ``zsymm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,ka)\nb : input rank-2 array('D') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('D') with bounds (m,n)\noverwrite_c : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (m,n)\n" - ... - -def zsyr(alpha, x, lower=..., incx=..., offx=..., n=..., a=..., overwrite_a=...) -> typing.Any: - "a = zsyr(alpha,x,[lower,incx,offx,n,a,overwrite_a])\n\nWrapper for ``zsyr``.\n\nParameters\n----------\nalpha : input complex\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nn : input int, optional\n Default: (len(x)-1-offx)/abs(incx)+1\na : input rank-2 array('D') with bounds (n,n)\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (n,n)\n" - ... - -def zsyr2k(alpha, a, b, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = zsyr2k(alpha,a,b,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``zsyr2k``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,ka)\nb : input rank-2 array('D') with bounds (ldb,kb)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('D') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n)\n" - ... - -def zsyrk(alpha, a, beta=..., c=..., trans=..., lower=..., overwrite_c=...) -> typing.Any: - "c = zsyrk(alpha,a,[beta,c,trans,lower,overwrite_c])\n\nWrapper for ``zsyrk``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,ka)\n\nOther Parameters\n----------------\nbeta : input complex, optional\n Default: (0.0, 0.0)\nc : input rank-2 array('D') with bounds (n,n)\noverwrite_c : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n)\n" - ... - -def ztbmv(k, a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ztbmv(k,a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ztbmv``.\n\nParameters\n----------\nk : input int\na : input rank-2 array('D') with bounds (lda,n)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('D') with bounds (*) and x storage\n" - ... - -def ztbsv(k, a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ztbsv(k,a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ztbsv``.\n\nParameters\n----------\nk : input int\na : input rank-2 array('D') with bounds (lda,n)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('D') with bounds (*) and x storage\n" - ... - -def ztpmv(n, ap, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ztpmv(n,ap,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ztpmv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (*)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('D') with bounds (*) and x storage\n" - ... - -def ztpsv(n, ap, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ztpsv(n,ap,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ztpsv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (*)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('D') with bounds (*) and x storage\n" - ... - -def ztrmm(alpha, a, b, side=..., lower=..., trans_a=..., diag=..., overwrite_b=...) -> typing.Any: - "b = ztrmm(alpha,a,b,[side,lower,trans_a,diag,overwrite_b])\n\nWrapper for ``ztrmm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,k)\nb : input rank-2 array('D') with bounds (ldb,n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nb : rank-2 array('D') with bounds (ldb,n)\n" - ... - -def ztrmv(a, x, offx=..., incx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "x = ztrmv(a,x,[offx,incx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ztrmv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('D') with bounds (*)\n" - ... - -def ztrsm(alpha, a, b, side=..., lower=..., trans_a=..., diag=..., overwrite_b=...) -> typing.Any: - "x = ztrsm(alpha,a,b,[side,lower,trans_a,diag,overwrite_b])\n\nWrapper for ``ztrsm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-2 array('D') with bounds (lda,*)\nb : input rank-2 array('D') with bounds (ldb,n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nside : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans_a : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,n) and b storage\n" - ... - -def ztrsv(a, x, incx=..., offx=..., lower=..., trans=..., diag=..., overwrite_x=...) -> typing.Any: - "xout = ztrsv(a,x,[incx,offx,lower,trans,diag,overwrite_x])\n\nWrapper for ``ztrsv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nx : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noffx : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\ndiag : input int, optional\n Default: 0\n\nReturns\n-------\nxout : rank-1 array('D') with bounds (*) and x storage\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/_flapack.pyi b/stubs/scipy-stubs/linalg/_flapack.pyi deleted file mode 100644 index c2fc12ec..00000000 --- a/stubs/scipy-stubs/linalg/_flapack.pyi +++ /dev/null @@ -1,2535 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg._flapack, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def cgbsv(kl, ku, ab, b, overwrite_ab=..., overwrite_b=...) -> typing.Any: - "lub,piv,x,info = cgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])\n\nWrapper for ``cgbsv``.\n\nParameters\n----------\nkl : input int\nku : input int\nab : input rank-2 array('F') with bounds (2*kl+ku+1,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nlub : rank-2 array('F') with bounds (2*kl+ku+1,n) and ab storage\npiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def cgbtrf(ab, kl, ku, m=..., n=..., ldab=..., overwrite_ab=...) -> typing.Any: - "lu,ipiv,info = cgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])\n\nWrapper for ``cgbtrf``.\n\nParameters\n----------\nab : input rank-2 array('F') with bounds (ldab,n)\nkl : input int\nku : input int\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(ab,1)\nn : input int, optional\n Default: shape(ab,1)\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: max(shape(ab,0),1)\n\nReturns\n-------\nlu : rank-2 array('F') with bounds (ldab,n) and ab storage\nipiv : rank-1 array('i') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def cgbtrs(ab, kl, ku, b, ipiv, trans=..., n=..., ldab=..., ldb=..., overwrite_b=...) -> typing.Any: - "x,info = cgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])\n\nWrapper for ``cgbtrs``.\n\nParameters\n----------\nab : input rank-2 array('F') with bounds (ldab,n)\nkl : input int\nku : input int\nb : input rank-2 array('F') with bounds (ldb,nrhs)\nipiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nn : input int, optional\n Default: shape(ab,1)\nldab : input int, optional\n Default: shape(ab,0)\nldb : input int, optional\n Default: shape(b,0)\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def cgebal(a, scale=..., permute=..., overwrite_a=...) -> typing.Any: - "ba,lo,hi,pivscale,info = cgebal(a,[scale,permute,overwrite_a])\n\nWrapper for ``cgebal``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\nscale : input int, optional\n Default: 0\npermute : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nba : rank-2 array('F') with bounds (m,n) and a storage\nlo : int\nhi : int\npivscale : rank-1 array('f') with bounds (n)\ninfo : int\n" - ... - -def cgecon(a, anorm, norm=...) -> typing.Any: - "rcond,info = cgecon(a,anorm,[norm])\n\nWrapper for ``cgecon``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nanorm : input float\n\nOther Parameters\n----------------\nnorm : input string(len=1), optional\n Default: '1'\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def cgeequ(a) -> typing.Any: - "r,c,rowcnd,colcnd,amax,info = cgeequ(a)\n\nWrapper for ``cgeequ``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nReturns\n-------\nr : rank-1 array('f') with bounds (m)\nc : rank-1 array('f') with bounds (n)\nrowcnd : float\ncolcnd : float\namax : float\ninfo : int\n" - ... - -def cgeequb(a) -> typing.Any: - "r,c,rowcnd,colcnd,amax,info = cgeequb(a)\n\nWrapper for ``cgeequb``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nReturns\n-------\nr : rank-1 array('f') with bounds (m)\nc : rank-1 array('f') with bounds (n)\nrowcnd : float\ncolcnd : float\namax : float\ninfo : int\n" - ... - -def cgees(cselect, a, compute_v=..., sort_t=..., lwork=..., cselect_extra_args=..., overwrite_a=...) -> typing.Any: - "t,sdim,w,vs,work,info = cgees(cselect,a,[compute_v,sort_t,lwork,cselect_extra_args,overwrite_a])\n\nWrapper for ``cgees``.\n\nParameters\n----------\ncselect : call-back function\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nsort_t : input int, optional\n Default: 0\ncselect_extra_args : input tuple, optional\n Default: ()\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nt : rank-2 array('F') with bounds (n,n) and a storage\nsdim : int\nw : rank-1 array('F') with bounds (n)\nvs : rank-2 array('F') with bounds (ldvs,n)\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n\nNotes\n-----\nCall-back functions::\n\n def cselect(arg): return cselect\n Required arguments:\n arg : input complex\n Return objects:\n cselect : int\n" - ... - -def cgeev(a, compute_vl=..., compute_vr=..., lwork=..., overwrite_a=...) -> typing.Any: - "w,vl,vr,info = cgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])\n\nWrapper for ``cgeev``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\nw : rank-1 array('F') with bounds (n)\nvl : rank-2 array('F') with bounds (ldvl,n)\nvr : rank-2 array('F') with bounds (ldvr,n)\ninfo : int\n" - ... - -def cgeev_lwork(n, compute_vl=..., compute_vr=...) -> typing.Any: - "work,info = cgeev_lwork(n,[compute_vl,compute_vr])\n\nWrapper for ``cgeev_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgegv(*args, **kwds) -> typing.Any: - "`cgegv` is deprecated!\nThe `*gegv` family of routines has been deprecated in\nLAPACK 3.6.0 in favor of the `*ggev` family of routines.\nThe corresponding wrappers will be removed from SciPy in\na future release.\n\nalpha,beta,vl,vr,info = cgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``cgegv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\nalpha : rank-1 array('F') with bounds (n)\nbeta : rank-1 array('F') with bounds (n)\nvl : rank-2 array('F') with bounds (ldvl,n)\nvr : rank-2 array('F') with bounds (ldvr,n)\ninfo : int\n" - ... - -def cgehrd(a, lo=..., hi=..., lwork=..., overwrite_a=...) -> typing.Any: - "ht,tau,info = cgehrd(a,[lo,hi,lwork,overwrite_a])\n\nWrapper for ``cgehrd``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(n,1)\n\nReturns\n-------\nht : rank-2 array('F') with bounds (n,n) and a storage\ntau : rank-1 array('F') with bounds (n - 1)\ninfo : int\n" - ... - -def cgehrd_lwork(n, lo=..., hi=...) -> typing.Any: - "work,info = cgehrd_lwork(n,[lo,hi])\n\nWrapper for ``cgehrd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgels(a, b, trans=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "lqr,x,info = cgels(a,b,[trans,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``cgels``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\nb : input rank-2 array('F') with bounds (MAX(m,n),nrhs)\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(MIN(m,n)+MAX(MIN(m,n),nrhs),1)\n\nReturns\n-------\nlqr : rank-2 array('F') with bounds (m,n) and a storage\nx : rank-2 array('F') with bounds (MAX(m,n),nrhs) and b storage\ninfo : int\n" - ... - -def cgels_lwork(m, n, nrhs, trans=...) -> typing.Any: - "work,info = cgels_lwork(m,n,nrhs,[trans])\n\nWrapper for ``cgels_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgelsd(a, b, lwork, size_rwork, size_iwork, cond=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "x,s,rank,info = cgelsd(a,b,lwork,size_rwork,size_iwork,[cond,overwrite_a,overwrite_b])\n\nWrapper for ``cgelsd``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\nb : input rank-2 array('F') with bounds (maxmn,nrhs)\nlwork : input int\nsize_rwork : input int\nsize_iwork : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\ncond : input float, optional\n Default: -1.0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (maxmn,nrhs) and b storage\ns : rank-1 array('f') with bounds (minmn)\nrank : int\ninfo : int\n" - ... - -def cgelsd_lwork(m, n, nrhs, cond=..., lwork=...) -> typing.Any: - "work,rwork,iwork,info = cgelsd_lwork(m,n,nrhs,[cond,lwork])\n\nWrapper for ``cgelsd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : complex\nrwork : float\niwork : int\ninfo : int\n" - ... - -def cgelss(a, b, cond=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "v,x,s,rank,work,info = cgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``cgelss``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\nb : input rank-2 array('F') with bounds (maxmn,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: max(2*minmn+MAX(maxmn,nrhs),1)\n\nReturns\n-------\nv : rank-2 array('F') with bounds (m,n) and a storage\nx : rank-2 array('F') with bounds (maxmn,nrhs) and b storage\ns : rank-1 array('f') with bounds (minmn)\nrank : int\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def cgelss_lwork(m, n, nrhs, cond=..., lwork=...) -> typing.Any: - "work,info = cgelss_lwork(m,n,nrhs,[cond,lwork])\n\nWrapper for ``cgelss_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgelsy(a, b, jptv, cond, lwork, overwrite_a=..., overwrite_b=...) -> typing.Any: - "v,x,j,rank,info = cgelsy(a,b,jptv,cond,lwork,[overwrite_a,overwrite_b])\n\nWrapper for ``cgelsy``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\nb : input rank-2 array('F') with bounds (maxmn,nrhs)\njptv : input rank-1 array('i') with bounds (n)\ncond : input float\nlwork : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nv : rank-2 array('F') with bounds (m,n) and a storage\nx : rank-2 array('F') with bounds (maxmn,nrhs) and b storage\nj : rank-1 array('i') with bounds (n) and jptv storage\nrank : int\ninfo : int\n" - ... - -def cgelsy_lwork(m, n, nrhs, cond, lwork=...) -> typing.Any: - "work,info = cgelsy_lwork(m,n,nrhs,cond,[lwork])\n\nWrapper for ``cgelsy_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\ncond : input float\n\nOther Parameters\n----------------\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgemqrt(v, t, c, side=..., trans=..., overwrite_c=...) -> typing.Any: - "c,info = cgemqrt(v,t,c,[side,trans,overwrite_c])\n\nWrapper for ``cgemqrt``.\n\nParameters\n----------\nv : input rank-2 array('F') with bounds ((side[0]=='L'?m:n),k)\nt : input rank-2 array('F') with bounds (nb,k)\nc : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (m,n)\ninfo : int\n" - ... - -def cgeqp3(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,jpvt,tau,work,info = cgeqp3(a,[lwork,overwrite_a])\n\nWrapper for ``cgeqp3``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*(n+1),1)\n\nReturns\n-------\nqr : rank-2 array('F') with bounds (m,n) and a storage\njpvt : rank-1 array('i') with bounds (n)\ntau : rank-1 array('F') with bounds (MIN(m,n))\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def cgeqrf(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,work,info = cgeqrf(a,[lwork,overwrite_a])\n\nWrapper for ``cgeqrf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nqr : rank-2 array('F') with bounds (m,n) and a storage\ntau : rank-1 array('F') with bounds (MIN(m,n))\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def cgeqrf_lwork(m, n) -> typing.Any: - "work,info = cgeqrf_lwork(m,n)\n\nWrapper for ``cgeqrf_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgeqrfp(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,info = cgeqrfp(a,[lwork,overwrite_a])\n\nWrapper for ``cgeqrfp``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(1, n)\n\nReturns\n-------\nqr : rank-2 array('F') with bounds (m,n) and a storage\ntau : rank-1 array('F') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def cgeqrfp_lwork(m, n) -> typing.Any: - "work,info = cgeqrfp_lwork(m,n)\n\nWrapper for ``cgeqrfp_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgeqrt(nb, a, overwrite_a=...) -> typing.Any: - "a,t,info = cgeqrt(nb,a,[overwrite_a])\n\nWrapper for ``cgeqrt``.\n\nParameters\n----------\nnb : input int\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (m,n)\nt : rank-2 array('F') with bounds (nb,MIN(m,n))\ninfo : int\n" - ... - -def cgerqf(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,work,info = cgerqf(a,[lwork,overwrite_a])\n\nWrapper for ``cgerqf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*m,1)\n\nReturns\n-------\nqr : rank-2 array('F') with bounds (m,n) and a storage\ntau : rank-1 array('F') with bounds (MIN(m,n))\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def cgesc2(lu, rhs, ipiv, jpiv, overwrite_rhs=...) -> typing.Any: - "x,scale = cgesc2(lu,rhs,ipiv,jpiv,[overwrite_rhs])\n\nWrapper for ``cgesc2``.\n\nParameters\n----------\nlu : input rank-2 array('F') with bounds (n,n)\nrhs : input rank-1 array('F') with bounds (n)\nipiv : input rank-1 array('i') with bounds (n)\njpiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_rhs : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('F') with bounds (n) and rhs storage\nscale : float\n" - ... - -def cgesdd(a, compute_uv=..., full_matrices=..., lwork=..., overwrite_a=...) -> typing.Any: - "u,s,vt,info = cgesdd(a,[compute_uv,full_matrices,lwork,overwrite_a])\n\nWrapper for ``cgesdd``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\nlwork : input int, optional\n Default: max((compute_uv?2*minmn*minmn+MAX(m,n)+2*minmn:2*minmn+MAX(m,n)),1)\n\nReturns\n-------\nu : rank-2 array('F') with bounds (u0,u1)\ns : rank-1 array('f') with bounds (minmn)\nvt : rank-2 array('F') with bounds (vt0,vt1)\ninfo : int\n" - ... - -def cgesdd_lwork(m, n, compute_uv=..., full_matrices=...) -> typing.Any: - "work,info = cgesdd_lwork(m,n,[compute_uv,full_matrices])\n\nWrapper for ``cgesdd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgesv(a, b, overwrite_a=..., overwrite_b=...) -> typing.Any: - "lu,piv,x,info = cgesv(a,b,[overwrite_a,overwrite_b])\n\nWrapper for ``cgesv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('F') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def cgesvd(a, compute_uv=..., full_matrices=..., lwork=..., overwrite_a=...) -> typing.Any: - "u,s,vt,info = cgesvd(a,[compute_uv,full_matrices,lwork,overwrite_a])\n\nWrapper for ``cgesvd``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\nlwork : input int, optional\n Default: MAX(2*minmn+MAX(m,n),1)\n\nReturns\n-------\nu : rank-2 array('F') with bounds (u0,u1)\ns : rank-1 array('f') with bounds (minmn)\nvt : rank-2 array('F') with bounds (vt0,vt1)\ninfo : int\n" - ... - -def cgesvd_lwork(m, n, compute_uv=..., full_matrices=...) -> typing.Any: - "work,info = cgesvd_lwork(m,n,[compute_uv,full_matrices])\n\nWrapper for ``cgesvd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgesvx(a, b, fact=..., trans=..., af=..., ipiv=..., equed=..., r=..., c=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "as,lu,ipiv,equed,rs,cs,bs,x,rcond,ferr,berr,info = cgesvx(a,b,[fact,trans,af,ipiv,equed,r,c,overwrite_a,overwrite_b])\n\nWrapper for ``cgesvx``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'E'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('F') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nequed : input string(len=1), optional\n Default: 'B'\nr : input rank-1 array('f') with bounds (n)\nc : input rank-1 array('f') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nas : rank-2 array('F') with bounds (n,n) and a storage\nlu : rank-2 array('F') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nequed : string(len=1)\nrs : rank-1 array('f') with bounds (n) and r storage\ncs : rank-1 array('f') with bounds (n) and c storage\nbs : rank-2 array('F') with bounds (n,nrhs) and b storage\nx : rank-2 array('F') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def cgetc2(a, overwrite_a=...) -> typing.Any: - "lu,ipiv,jpiv,info = cgetc2(a,[overwrite_a])\n\nWrapper for ``cgetc2``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('F') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\njpiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def cgetrf(a, overwrite_a=...) -> typing.Any: - "lu,piv,info = cgetrf(a,[overwrite_a])\n\nWrapper for ``cgetrf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('F') with bounds (m,n) and a storage\npiv : rank-1 array('i') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def cgetri(lu, piv, lwork=..., overwrite_lu=...) -> typing.Any: - "inv_a,info = cgetri(lu,piv,[lwork,overwrite_lu])\n\nWrapper for ``cgetri``.\n\nParameters\n----------\nlu : input rank-2 array('F') with bounds (n,n)\npiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_lu : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\ninv_a : rank-2 array('F') with bounds (n,n) and lu storage\ninfo : int\n" - ... - -def cgetri_lwork(n) -> typing.Any: - "work,info = cgetri_lwork(n)\n\nWrapper for ``cgetri_lwork``.\n\nParameters\n----------\nn : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgetrs(lu, piv, b, trans=..., overwrite_b=...) -> typing.Any: - "x,info = cgetrs(lu,piv,b,[trans,overwrite_b])\n\nWrapper for ``cgetrs``.\n\nParameters\n----------\nlu : input rank-2 array('F') with bounds (n,n)\npiv : input rank-1 array('i') with bounds (n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def cgges( - cselect, - a, - b, - jobvsl=..., - jobvsr=..., - sort_t=..., - ldvsl=..., - ldvsr=..., - lwork=..., - cselect_extra_args=..., - overwrite_a=..., - overwrite_b=..., -) -> typing.Any: - "a,b,sdim,alpha,beta,vsl,vsr,work,info = cgges(cselect,a,b,[jobvsl,jobvsr,sort_t,ldvsl,ldvsr,lwork,cselect_extra_args,overwrite_a,overwrite_b])\n\nWrapper for ``cgges``.\n\nParameters\n----------\ncselect : call-back function\na : input rank-2 array('F') with bounds (lda,n)\nb : input rank-2 array('F') with bounds (ldb,n)\n\nOther Parameters\n----------------\njobvsl : input int, optional\n Default: 1\njobvsr : input int, optional\n Default: 1\nsort_t : input int, optional\n Default: 0\ncselect_extra_args : input tuple, optional\n Default: ()\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nldvsl : input int, optional\n Default: ((jobvsl==1)?n:1)\nldvsr : input int, optional\n Default: ((jobvsr==1)?n:1)\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\na : rank-2 array('F') with bounds (lda,n)\nb : rank-2 array('F') with bounds (ldb,n)\nsdim : int\nalpha : rank-1 array('F') with bounds (n)\nbeta : rank-1 array('F') with bounds (n)\nvsl : rank-2 array('F') with bounds (ldvsl,n)\nvsr : rank-2 array('F') with bounds (ldvsr,n)\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n\nNotes\n-----\nCall-back functions::\n\n def cselect(alpha,beta): return cselect\n Required arguments:\n alpha : input complex\n beta : input complex\n Return objects:\n cselect : int\n" - ... - -def cggev(a, b, compute_vl=..., compute_vr=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "alpha,beta,vl,vr,work,info = cggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``cggev``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\nalpha : rank-1 array('F') with bounds (n)\nbeta : rank-1 array('F') with bounds (n)\nvl : rank-2 array('F') with bounds (ldvl,n)\nvr : rank-2 array('F') with bounds (ldvr,n)\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def cgglse(a, b, c, d, lwork=..., overwrite_a=..., overwrite_b=..., overwrite_c=..., overwrite_d=...) -> typing.Any: - "t,r,res,x,info = cgglse(a,b,c,d,[lwork,overwrite_a,overwrite_b,overwrite_c,overwrite_d])\n\nWrapper for ``cgglse``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\nb : input rank-2 array('F') with bounds (p,n)\nc : input rank-1 array('F') with bounds (m)\nd : input rank-1 array('F') with bounds (p)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\noverwrite_c : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(m+n+p,1)\n\nReturns\n-------\nt : rank-2 array('F') with bounds (m,n) and a storage\nr : rank-2 array('F') with bounds (p,n) and b storage\nres : rank-1 array('F') with bounds (m) and c storage\nx : rank-1 array('F') with bounds (n)\ninfo : int\n" - ... - -def cgglse_lwork(m, n, p) -> typing.Any: - "work,info = cgglse_lwork(m,n,p)\n\nWrapper for ``cgglse_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\np : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cgtsv(dl, d, du, b, overwrite_dl=..., overwrite_d=..., overwrite_du=..., overwrite_b=...) -> typing.Any: - "du2,d,du,x,info = cgtsv(dl,d,du,b,[overwrite_dl,overwrite_d,overwrite_du,overwrite_b])\n\nWrapper for ``cgtsv``.\n\nParameters\n----------\ndl : input rank-1 array('F') with bounds (n - 1)\nd : input rank-1 array('F') with bounds (n)\ndu : input rank-1 array('F') with bounds (n - 1)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_dl : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_du : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\ndu2 : rank-1 array('F') with bounds (n - 1) and dl storage\nd : rank-1 array('F') with bounds (n)\ndu : rank-1 array('F') with bounds (n - 1)\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def cgtsvx(dl, d, du, b, fact=..., trans=..., dlf=..., df=..., duf=..., du2=..., ipiv=...) -> typing.Any: - "dlf,df,duf,du2,ipiv,x,rcond,ferr,berr,info = cgtsvx(dl,d,du,b,[fact,trans,dlf,df,duf,du2,ipiv])\n\nWrapper for ``cgtsvx``.\n\nParameters\n----------\ndl : input rank-1 array('F') with bounds (MAX(0, n-1))\nd : input rank-1 array('F') with bounds (n)\ndu : input rank-1 array('F') with bounds (MAX(0, n-1))\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'N'\ntrans : input string(len=1), optional\n Default: 'N'\ndlf : input rank-1 array('F') with bounds (MAX(0,n-1))\ndf : input rank-1 array('F') with bounds (n)\nduf : input rank-1 array('F') with bounds (MAX(0,n-1))\ndu2 : input rank-1 array('F') with bounds (MAX(0,n-2))\nipiv : input rank-1 array('i') with bounds (n)\n\nReturns\n-------\ndlf : rank-1 array('F') with bounds (MAX(0,n-1))\ndf : rank-1 array('F') with bounds (n)\nduf : rank-1 array('F') with bounds (MAX(0,n-1))\ndu2 : rank-1 array('F') with bounds (MAX(0,n-2))\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('F') with bounds (ldx,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def cgttrf(dl, d, du, overwrite_dl=..., overwrite_d=..., overwrite_du=...) -> typing.Any: - "dl,d,du,du2,ipiv,info = cgttrf(dl,d,du,[overwrite_dl,overwrite_d,overwrite_du])\n\nWrapper for ``cgttrf``.\n\nParameters\n----------\ndl : input rank-1 array('F') with bounds (n - 1)\nd : input rank-1 array('F') with bounds (n)\ndu : input rank-1 array('F') with bounds (n - 1)\n\nOther Parameters\n----------------\noverwrite_dl : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_du : input int, optional\n Default: 0\n\nReturns\n-------\ndl : rank-1 array('F') with bounds (n - 1)\nd : rank-1 array('F') with bounds (n)\ndu : rank-1 array('F') with bounds (n - 1)\ndu2 : rank-1 array('F') with bounds (n - 2)\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def cgttrs(dl, d, du, du2, ipiv, b, trans=..., overwrite_b=...) -> typing.Any: - "x,info = cgttrs(dl,d,du,du2,ipiv,b,[trans,overwrite_b])\n\nWrapper for ``cgttrs``.\n\nParameters\n----------\ndl : input rank-1 array('F') with bounds (n - 1)\nd : input rank-1 array('F') with bounds (n)\ndu : input rank-1 array('F') with bounds (n - 1)\ndu2 : input rank-1 array('F') with bounds (n - 2)\nipiv : input rank-1 array('i') with bounds (n)\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def chbevd(ab, compute_v=..., lower=..., ldab=..., lrwork=..., liwork=..., overwrite_ab=...) -> typing.Any: - "w,z,info = chbevd(ab,[compute_v,lower,ldab,lrwork,liwork,overwrite_ab])\n\nWrapper for ``chbevd``.\n\nParameters\n----------\nab : input rank-2 array('F') with bounds (ldab,n)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\nlrwork : input int, optional\n Default: (compute_v?1+5*n+2*n*n:n)\nliwork : input int, optional\n Default: (compute_v?3+5*n:1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nz : rank-2 array('F') with bounds (ldz,ldz)\ninfo : int\n" - ... - -def chbevx( - ab, vl, vu, il, iu, ldab=..., compute_v=..., range=..., lower=..., abstol=..., mmax=..., overwrite_ab=... -) -> typing.Any: - "w,z,m,ifail,info = chbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])\n\nWrapper for ``chbevx``.\n\nParameters\n----------\nab : input rank-2 array('F') with bounds (ldab,n)\nvl : input float\nvu : input float\nil : input int\niu : input int\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\nldab : input int, optional\n Default: shape(ab,0)\ncompute_v : input int, optional\n Default: 1\nrange : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nabstol : input float, optional\n Default: 0.0\nmmax : input int, optional\n Default: (compute_v?(range==2?(iu-il+1):n):1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nz : rank-2 array('F') with bounds (ldz,mmax)\nm : int\nifail : rank-1 array('i') with bounds ((compute_v?n:1))\ninfo : int\n" - ... - -def checon(a, ipiv, anorm, lower=...) -> typing.Any: - "rcond,info = checon(a,ipiv,anorm,[lower])\n\nWrapper for ``checon``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def cheequb(a, lower=...) -> typing.Any: - "s,scond,amax,info = cheequb(a,[lower])\n\nWrapper for ``cheequb``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ns : rank-1 array('f') with bounds (n)\nscond : float\namax : float\ninfo : int\n" - ... - -def cheev(a, compute_v=..., lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "w,v,info = cheev(a,[compute_v,lower,lwork,overwrite_a])\n\nWrapper for ``cheev``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n-1,1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nv : rank-2 array('F') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def cheev_lwork(n, lower=...) -> typing.Any: - "work,info = cheev_lwork(n,[lower])\n\nWrapper for ``cheev_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cheevd(a, compute_v=..., lower=..., lwork=..., liwork=..., lrwork=..., overwrite_a=...) -> typing.Any: - "w,v,info = cheevd(a,[compute_v,lower,lwork,liwork,lrwork,overwrite_a])\n\nWrapper for ``cheevd``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max((compute_v?2*n+n*n:n+1),1)\nliwork : input int, optional\n Default: (compute_v?3+5*n:1)\nlrwork : input int, optional\n Default: (compute_v?1+5*n+2*n*n:n)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nv : rank-2 array('F') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def cheevd_lwork(n, compute_v=..., lower=...) -> typing.Any: - "work,iwork,rwork,info = cheevd_lwork(n,[compute_v,lower])\n\nWrapper for ``cheevd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\niwork : int\nrwork : float\ninfo : int\n" - ... - -def cheevr( - a, - compute_v=..., - range=..., - lower=..., - vl=..., - vu=..., - il=..., - iu=..., - abstol=..., - lwork=..., - lrwork=..., - liwork=..., - overwrite_a=..., -) -> typing.Any: - "w,z,m,isuppz,info = cheevr(a,[compute_v,range,lower,vl,vu,il,iu,abstol,lwork,lrwork,liwork,overwrite_a])\n\nWrapper for ``cheevr``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds ``(n,n)``\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default ``1``\nrange : input string(len=1), optional\n Default ``'A'``\nlower : input int, optional\n Default ``0``\noverwrite_a : input int, optional\n Default ``0``\nvl : input float, optional\n Default ``0.0``\nvu : input float, optional\n Default ``1.0``\nil : input int, optional\n Default ``1``\niu : input int, optional\n Default ``n``\nabstol : input float, optional\n Default ``0.0``\nlwork : input int, optional\n Default ``max(2*n,1)``\nlrwork : input int, optional\n Default ``max(24*n,1)``\nliwork : input int, optional\n Default ``max(1,10*n)``\n\nReturns\n-------\nw : rank-1 array('f') with bounds ``(n)``\nz : rank-2 array('F') with bounds ``((compute_v?MAX(0,n):0),(compute_v?(*range=='I'?iu-il+1:MAX(1,n)):0))``\nm : int\nisuppz : rank-1 array('i') with bounds ``(2*max(1,n))``\ninfo : int\n" - ... - -def cheevr_lwork(n, lower=...) -> typing.Any: - "work,rwork,iwork,info = cheevr_lwork(n,[lower])\n\nWrapper for ``cheevr_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\nrwork : float\niwork : int\ninfo : int\n" - ... - -def cheevx( - a, compute_v=..., range=..., lower=..., vl=..., vu=..., il=..., iu=..., abstol=..., lwork=..., overwrite_a=... -) -> typing.Any: - "w,z,m,ifail,info = cheevx(a,[compute_v,range,lower,vl,vu,il,iu,abstol,lwork,overwrite_a])\n\nWrapper for ``cheevx``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds ``(n,n)``\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default ``1``\nrange : input string(len=1), optional\n Default ``'A'``\nlower : input int, optional\n Default ``0``\noverwrite_a : input int, optional\n Default ``0``\nvl : input float, optional\n Default ``0.0``\nvu : input float, optional\n Default ``1.0``\nil : input int, optional\n Default ``1``\niu : input int, optional\n Default ``n``\nabstol : input float, optional\n Default ``0.0``\nlwork : input int, optional\n Default ``max(2*n,1)``\n\nReturns\n-------\nw : rank-1 array('f') with bounds ``(n)``\nz : rank-2 array('F') with bounds ``((compute_v*n),(compute_v?(*range=='I'?iu-il+1:MAX(1,n)):0))``\nm : int\nifail : rank-1 array('i') with bounds ``(compute_v*n)``\ninfo : int\n" - ... - -def cheevx_lwork(n, lower=...) -> typing.Any: - "work,info = cheevx_lwork(n,[lower])\n\nWrapper for ``cheevx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def chegst(a, b, itype=..., lower=..., overwrite_a=...) -> typing.Any: - "c,info = chegst(a,b,[itype,lower,overwrite_a])\n\nWrapper for ``chegst``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nitype : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def chegv(a, b, itype=..., jobz=..., uplo=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "w,v,info = chegv(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``chegv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\nitype : input int, optional\n Default: 1\njobz : input string(len=1), optional\n Default: 'V'\nuplo : input string(len=1), optional\n Default: 'L'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n-1,1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nv : rank-2 array('F') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def chegv_lwork(n, uplo=...) -> typing.Any: - "work,info = chegv_lwork(n,[uplo])\n\nWrapper for ``chegv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'L'\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def chegvd( - a, b, itype=..., jobz=..., uplo=..., lwork=..., lrwork=..., liwork=..., overwrite_a=..., overwrite_b=... -) -> typing.Any: - "w,v,info = chegvd(a,b,[itype,jobz,uplo,lwork,lrwork,liwork,overwrite_a,overwrite_b])\n\nWrapper for ``chegvd``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds ``(n,n)``\nb : input rank-2 array('F') with bounds ``(n,n)``\n\nOther Parameters\n----------------\nitype : input int, optional\n Default ``1``\njobz : input string(len=1), optional\n Default ``'V'``\nuplo : input string(len=1), optional\n Default ``'L'``\noverwrite_a : input int, optional\n Default ``0``\noverwrite_b : input int, optional\n Default ``0``\nlwork : input int, optional\n Default ``(*jobz=='N'?n+1:n*(n+2))``\nlrwork : input int, optional\n Default ``max((*jobz=='N'?n:2*n*n+5*n+1),1)``\nliwork : input int, optional\n Default ``(*jobz=='N'?1:5*n+3)``\n\nReturns\n-------\nw : rank-1 array('f') with bounds ``(n)``\nv : rank-2 array('F') with bounds ``(n,n)`` with ``a`` storage\ninfo : int\n" - ... - -def chegvx( - a, - b, - itype=..., - jobz=..., - range=..., - uplo=..., - vl=..., - vu=..., - il=..., - iu=..., - abstol=..., - lwork=..., - overwrite_a=..., - overwrite_b=..., -) -> typing.Any: - "w,z,m,ifail,info = chegvx(a,b,[itype,jobz,range,uplo,vl,vu,il,iu,abstol,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``chegvx``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\nitype : input int, optional\n Default: 1\njobz : input string(len=1), optional\n Default: 'V'\nrange : input string(len=1), optional\n Default: 'A'\nuplo : input string(len=1), optional\n Default: 'L'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nvl : input float, optional\n Default: 0.0\nvu : input float, optional\n Default: 1.0\nil : input int, optional\n Default: 1\niu : input int, optional\n Default: n\nabstol : input float, optional\n Default: 0.0\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nz : rank-2 array('F') with bounds ((jobz[0]=='V'?MAX(0,n):0),(jobz[0]=='V'?(range[0]=='I'?iu-il+1:MAX(1,n)):0))\nm : int\nifail : rank-1 array('i') with bounds ((jobz[0]=='N'?0:n))\ninfo : int\n" - ... - -def chegvx_lwork(n, uplo=...) -> typing.Any: - "work,info = chegvx_lwork(n,[uplo])\n\nWrapper for ``chegvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'L'\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def chesv(a, b, lwork=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "uduh,ipiv,x,info = chesv(a,b,[lwork,lower,overwrite_a,overwrite_b])\n\nWrapper for ``chesv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nuduh : rank-2 array('F') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def chesv_lwork(n, lower=...) -> typing.Any: - "work,info = chesv_lwork(n,[lower])\n\nWrapper for ``chesv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def chesvx(a, b, af=..., ipiv=..., lwork=..., factored=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "uduh,ipiv,x,rcond,ferr,berr,info = chesvx(a,b,[af,ipiv,lwork,factored,lower,overwrite_a,overwrite_b])\n\nWrapper for ``chesvx``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('F') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n,1)\nfactored : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nuduh : rank-2 array('F') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('F') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def chesvx_lwork(n, lower=...) -> typing.Any: - "work,info = chesvx_lwork(n,[lower])\n\nWrapper for ``chesvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def chetrd(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "c,d,e,tau,info = chetrd(a,[lower,lwork,overwrite_a])\n\nWrapper for ``chetrd``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(n,1)\n\nReturns\n-------\nc : rank-2 array('F') with bounds (lda,n) and a storage\nd : rank-1 array('f') with bounds (n)\ne : rank-1 array('f') with bounds (n - 1)\ntau : rank-1 array('F') with bounds (n - 1)\ninfo : int\n" - ... - -def chetrd_lwork(n, lower=...) -> typing.Any: - "work,info = chetrd_lwork(n,[lower])\n\nWrapper for ``chetrd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def chetrf(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = chetrf(a,[lower,lwork,overwrite_a])\n\nWrapper for ``chetrf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\n\nReturns\n-------\nldu : rank-2 array('F') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def chetrf_lwork(n, lower=...) -> typing.Any: - "work,info = chetrf_lwork(n,[lower])\n\nWrapper for ``chetrf_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def chfrk(n, k, alpha, a, beta, c, transr=..., uplo=..., trans=..., overwrite_c=...) -> typing.Any: - "cout = chfrk(n,k,alpha,a,beta,c,[transr,uplo,trans,overwrite_c])\n\nWrapper for ``chfrk``.\n\nParameters\n----------\nn : input int\nk : input int\nalpha : input float\na : input rank-2 array('F') with bounds (lda,ka)\nbeta : input float\nc : input rank-1 array('F') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\ncout : rank-1 array('F') with bounds (nt) and c storage\n" - ... - -def clange(norm, a) -> typing.Any: - "n2 = clange(norm,a)\n\nWrapper for ``clange``.\n\nParameters\n----------\nnorm : input string(len=1)\na : input rank-2 array('F') with bounds (m,n)\n\nReturns\n-------\nn2 : float\n" - ... - -def clarf(v, tau, c, work, side=..., incv=..., overwrite_c=...) -> typing.Any: - "c = clarf(v,tau,c,work,[side,incv,overwrite_c])\n\nWrapper for ``clarf``.\n\nParameters\n----------\nv : input rank-1 array('F') with bounds ((side[0]=='L'?(1 + (m-1)*abs(incv)):(1 + (n-1)*abs(incv))))\ntau : input complex\nc : input rank-2 array('F') with bounds (m,n)\nwork : input rank-1 array('F') with bounds (lwork)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\nincv : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (m,n)\n" - ... - -def clarfg(n, alpha, x, incx=..., overwrite_x=...) -> typing.Any: - "alpha,x,tau = clarfg(n,alpha,x,[incx,overwrite_x])\n\nWrapper for ``clarfg``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nx : input rank-1 array('F') with bounds (lx)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nalpha : complex\nx : rank-1 array('F') with bounds (lx)\ntau : complex\n" - ... - -def clartg(f, g) -> typing.Any: - "cs,sn,r = clartg(f,g)\n\nWrapper for ``clartg``.\n\nParameters\n----------\nf : input complex\ng : input complex\n\nReturns\n-------\ncs : float\nsn : complex\nr : complex\n" - ... - -def claswp(a, piv, k1=..., k2=..., off=..., inc=..., overwrite_a=...) -> typing.Any: - "a = claswp(a,piv,[k1,k2,off,inc,overwrite_a])\n\nWrapper for ``claswp``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (nrows,n)\npiv : input rank-1 array('i') with bounds (npiv)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nk1 : input int, optional\n Default: 0\nk2 : input int, optional\n Default: npiv-1\noff : input int, optional\n Default: 0\ninc : input int, optional\n Default: 1\n\nReturns\n-------\na : rank-2 array('F') with bounds (nrows,n)\n" - ... - -def clauum(c, lower=..., overwrite_c=...) -> typing.Any: - "a,info = clauum(c,[lower,overwrite_c])\n\nWrapper for ``clauum``.\n\nParameters\n----------\nc : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def cpbsv(ab, b, lower=..., ldab=..., overwrite_ab=..., overwrite_b=...) -> typing.Any: - "c,x,info = cpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])\n\nWrapper for ``cpbsv``.\n\nParameters\n----------\nab : input rank-2 array('F') with bounds (ldab,n)\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (ldab,n) and ab storage\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def cpbtrf(ab, lower=..., ldab=..., overwrite_ab=...) -> typing.Any: - "c,info = cpbtrf(ab,[lower,ldab,overwrite_ab])\n\nWrapper for ``cpbtrf``.\n\nParameters\n----------\nab : input rank-2 array('F') with bounds (ldab,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\n\nReturns\n-------\nc : rank-2 array('F') with bounds (ldab,n) and ab storage\ninfo : int\n" - ... - -def cpbtrs(ab, b, lower=..., ldab=..., overwrite_b=...) -> typing.Any: - "x,info = cpbtrs(ab,b,[lower,ldab,overwrite_b])\n\nWrapper for ``cpbtrs``.\n\nParameters\n----------\nab : input rank-2 array('F') with bounds (ldab,n)\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def cpftrf(n, a, transr=..., uplo=..., overwrite_a=...) -> typing.Any: - "achol,info = cpftrf(n,a,[transr,uplo,overwrite_a])\n\nWrapper for ``cpftrf``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('F') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nachol : rank-1 array('F') with bounds (nt) and a storage\ninfo : int\n" - ... - -def cpftri(n, a, transr=..., uplo=..., overwrite_a=...) -> typing.Any: - "ainv,info = cpftri(n,a,[transr,uplo,overwrite_a])\n\nWrapper for ``cpftri``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('F') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nainv : rank-1 array('F') with bounds (nt) and a storage\ninfo : int\n" - ... - -def cpftrs(n, a, b, transr=..., uplo=..., overwrite_b=...) -> typing.Any: - "x,info = cpftrs(n,a,b,[transr,uplo,overwrite_b])\n\nWrapper for ``cpftrs``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('F') with bounds (nt)\nb : input rank-2 array('F') with bounds (ldb,nhrs)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nhrs) and b storage\ninfo : int\n" - ... - -def cpocon(a, anorm, uplo=...) -> typing.Any: - "rcond,info = cpocon(a,anorm,[uplo])\n\nWrapper for ``cpocon``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nanorm : input float\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def cposv(a, b, lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "c,x,info = cposv(a,b,[lower,overwrite_a,overwrite_b])\n\nWrapper for ``cposv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n) and a storage\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def cposvx(a, b, fact=..., af=..., equed=..., s=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a_s,lu,equed,s,b_s,x,rcond,ferr,berr,info = cposvx(a,b,[fact,af,equed,s,lower,overwrite_a,overwrite_b])\n\nWrapper for ``cposvx``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'E'\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('F') with bounds (n,n)\nequed : input string(len=1), optional\n Default: 'Y'\ns : input rank-1 array('f') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na_s : rank-2 array('F') with bounds (n,n) and a storage\nlu : rank-2 array('F') with bounds (n,n) and af storage\nequed : string(len=1)\ns : rank-1 array('f') with bounds (n)\nb_s : rank-2 array('F') with bounds (n,nrhs) and b storage\nx : rank-2 array('F') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def cpotrf(a, lower=..., clean=..., overwrite_a=...) -> typing.Any: - "c,info = cpotrf(a,[lower,clean,overwrite_a])\n\nWrapper for ``cpotrf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nclean : input int, optional\n Default: 1\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def cpotri(c, lower=..., overwrite_c=...) -> typing.Any: - "inv_a,info = cpotri(c,[lower,overwrite_c])\n\nWrapper for ``cpotri``.\n\nParameters\n----------\nc : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ninv_a : rank-2 array('F') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def cpotrs(c, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = cpotrs(c,b,[lower,overwrite_b])\n\nWrapper for ``cpotrs``.\n\nParameters\n----------\nc : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def cppcon(n, ap, anorm, lower=...) -> typing.Any: - "rcond,info = cppcon(n,ap,anorm,[lower])\n\nWrapper for ``cppcon``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (L)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def cppsv(n, ap, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = cppsv(n,ap,b,[lower,overwrite_b])\n\nWrapper for ``cppsv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (L)\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def cpptrf(n, ap, lower=..., overwrite_ap=...) -> typing.Any: - "ul,info = cpptrf(n,ap,[lower,overwrite_ap])\n\nWrapper for ``cpptrf``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (L)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\n\nReturns\n-------\nul : rank-1 array('F') with bounds (L) and ap storage\ninfo : int\n" - ... - -def cpptri(n, ap, lower=..., overwrite_ap=...) -> typing.Any: - "uli,info = cpptri(n,ap,[lower,overwrite_ap])\n\nWrapper for ``cpptri``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (L)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\n\nReturns\n-------\nuli : rank-1 array('F') with bounds (L) and ap storage\ninfo : int\n" - ... - -def cpptrs(n, ap, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = cpptrs(n,ap,b,[lower,overwrite_b])\n\nWrapper for ``cpptrs``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (L)\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def cpstf2(a, tol=..., lower=..., overwrite_a=...) -> typing.Any: - "c,piv,rank_c,info = cpstf2(a,[tol,lower,overwrite_a])\n\nWrapper for ``cpstf2``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ntol : input float, optional\n Default: -1.0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nrank_c : int\ninfo : int\n" - ... - -def cpstrf(a, tol=..., lower=..., overwrite_a=...) -> typing.Any: - "c,piv,rank_c,info = cpstrf(a,[tol,lower,overwrite_a])\n\nWrapper for ``cpstrf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ntol : input float, optional\n Default: -1.0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('F') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nrank_c : int\ninfo : int\n" - ... - -def cpteqr(d, e, z, compute_z=..., overwrite_d=..., overwrite_e=..., overwrite_z=...) -> typing.Any: - "d,e,z,info = cpteqr(d,e,z,[compute_z,overwrite_d,overwrite_e,overwrite_z])\n\nWrapper for ``cpteqr``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds ((n>0?n-1:0))\nz : input rank-2 array('F') with bounds ((compute_z==0?shape(z, 0):max(1,n)),(compute_z==0?shape(z, 1):n))\n\nOther Parameters\n----------------\ncompute_z : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\noverwrite_z : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('f') with bounds (n)\ne : rank-1 array('f') with bounds ((n>0?n-1:0))\nz : rank-2 array('F') with bounds ((compute_z==0?shape(z, 0):max(1,n)),(compute_z==0?shape(z, 1):n))\ninfo : int\n" - ... - -def cptsv(d, e, b, overwrite_d=..., overwrite_e=..., overwrite_b=...) -> typing.Any: - "d,du,x,info = cptsv(d,e,b,[overwrite_d,overwrite_e,overwrite_b])\n\nWrapper for ``cptsv``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('F') with bounds (n - 1)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('f') with bounds (n)\ndu : rank-1 array('F') with bounds (n - 1) and e storage\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def cptsvx(d, e, b, fact=..., df=..., ef=...) -> typing.Any: - "df,ef,x,rcond,ferr,berr,info = cptsvx(d,e,b,[fact,df,ef])\n\nWrapper for ``cptsvx``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('F') with bounds (max(0, n-1))\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'N'\ndf : input rank-1 array('f') with bounds (n)\nef : input rank-1 array('F') with bounds (max(0, n-1))\n\nReturns\n-------\ndf : rank-1 array('f') with bounds (n)\nef : rank-1 array('F') with bounds (max(0, n-1))\nx : rank-2 array('F') with bounds (ldx,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def cpttrf(d, e, overwrite_d=..., overwrite_e=...) -> typing.Any: - "d,e,info = cpttrf(d,e,[overwrite_d,overwrite_e])\n\nWrapper for ``cpttrf``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('F') with bounds ((n>0?n-1:0))\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('f') with bounds (n)\ne : rank-1 array('F') with bounds ((n>0?n-1:0))\ninfo : int\n" - ... - -def cpttrs(d, e, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = cpttrs(d,e,b,[lower,overwrite_b])\n\nWrapper for ``cpttrs``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('F') with bounds ((n>0?n-1:0))\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def crot(x, y, c, s, n=..., offx=..., incx=..., offy=..., incy=..., overwrite_x=..., overwrite_y=...) -> typing.Any: - "x,y = crot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])\n\nWrapper for ``crot``.\n\nParameters\n----------\nx : input rank-1 array('F') with bounds (lx)\ny : input rank-1 array('F') with bounds (ly)\nc : input float\ns : input complex\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (lx-1-offx)/abs(incx)+1\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 0\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('F') with bounds (lx)\ny : rank-1 array('F') with bounds (ly)\n" - ... - -def csycon(a, ipiv, anorm, lower=...) -> typing.Any: - "rcond,info = csycon(a,ipiv,anorm,[lower])\n\nWrapper for ``csycon``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def csyconv(a, ipiv, lower=..., way=..., overwrite_a=...) -> typing.Any: - "a,e,info = csyconv(a,ipiv,[lower,way,overwrite_a])\n\nWrapper for ``csyconv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nway : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (n,n)\ne : rank-1 array('F') with bounds (n)\ninfo : int\n" - ... - -def csyequb(a, lower=...) -> typing.Any: - "s,scond,amax,info = csyequb(a,[lower])\n\nWrapper for ``csyequb``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ns : rank-1 array('f') with bounds (n)\nscond : float\namax : float\ninfo : int\n" - ... - -def csysv(a, b, lwork=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "udut,ipiv,x,info = csysv(a,b,[lwork,lower,overwrite_a,overwrite_b])\n\nWrapper for ``csysv``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nudut : rank-2 array('F') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('F') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def csysv_lwork(n, lower=...) -> typing.Any: - "work,info = csysv_lwork(n,[lower])\n\nWrapper for ``csysv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def csysvx(a, b, af=..., ipiv=..., lwork=..., factored=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a_s,udut,ipiv,b_s,x,rcond,ferr,berr,info = csysvx(a,b,[af,ipiv,lwork,factored,lower,overwrite_a,overwrite_b])\n\nWrapper for ``csysvx``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('F') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\nfactored : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na_s : rank-2 array('F') with bounds (n,n) and a storage\nudut : rank-2 array('F') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nb_s : rank-2 array('F') with bounds (n,nrhs) and b storage\nx : rank-2 array('F') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def csysvx_lwork(n, lower=...) -> typing.Any: - "work,info = csysvx_lwork(n,[lower])\n\nWrapper for ``csysvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def csytf2(a, lower=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = csytf2(a,[lower,overwrite_a])\n\nWrapper for ``csytf2``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nldu : rank-2 array('F') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def csytrf(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = csytrf(a,[lower,lwork,overwrite_a])\n\nWrapper for ``csytrf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\n\nReturns\n-------\nldu : rank-2 array('F') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def csytrf_lwork(n, lower=...) -> typing.Any: - "work,info = csytrf_lwork(n,[lower])\n\nWrapper for ``csytrf_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def ctbtrs(ab, b, uplo=..., trans=..., diag=..., overwrite_b=...) -> typing.Any: - "x,info = ctbtrs(ab,b,[uplo,trans,diag,overwrite_b])\n\nWrapper for ``ctbtrs``.\n\nParameters\n----------\nab : input rank-2 array('F') with bounds (ldab,n)\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\ndiag : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def ctfsm(alpha, a, b, transr=..., side=..., uplo=..., trans=..., diag=..., overwrite_b=...) -> typing.Any: - "x = ctfsm(alpha,a,b,[transr,side,uplo,trans,diag,overwrite_b])\n\nWrapper for ``ctfsm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-1 array('F') with bounds (nt)\nb : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nside : input string(len=1), optional\n Default: 'L'\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\ndiag : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (m,n) and b storage\n" - ... - -def ctfttp(n, arf, transr=..., uplo=...) -> typing.Any: - "ap,info = ctfttp(n,arf,[transr,uplo])\n\nWrapper for ``ctfttp``.\n\nParameters\n----------\nn : input int\narf : input rank-1 array('F') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nap : rank-1 array('F') with bounds (nt)\ninfo : int\n" - ... - -def ctfttr(n, arf, transr=..., uplo=...) -> typing.Any: - "a,info = ctfttr(n,arf,[transr,uplo])\n\nWrapper for ``ctfttr``.\n\nParameters\n----------\nn : input int\narf : input rank-1 array('F') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\na : rank-2 array('F') with bounds (lda,n)\ninfo : int\n" - ... - -def ctgsen( - select, a, b, q, z, lwork=..., liwork=..., overwrite_a=..., overwrite_b=..., overwrite_q=..., overwrite_z=... -) -> typing.Any: - "a,b,alpha,beta,q,z,m,pl,pr,dif,work,iwork,info = ctgsen(select,a,b,q,z,[lwork,liwork,overwrite_a,overwrite_b,overwrite_q,overwrite_z])\n\nWrapper for ``ctgsen``.\n\nParameters\n----------\nselect : input rank-1 array('i') with bounds (n)\na : input rank-2 array('F') with bounds (lda,n)\nb : input rank-2 array('F') with bounds (ldb,n)\nq : input rank-2 array('F') with bounds (ldq,n)\nz : input rank-2 array('F') with bounds (ldz,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\noverwrite_q : input int, optional\n Default: 0\noverwrite_z : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*m*(n-m),1)\nliwork : input int, optional\n Default: n+2\n\nReturns\n-------\na : rank-2 array('F') with bounds (lda,n)\nb : rank-2 array('F') with bounds (ldb,n)\nalpha : rank-1 array('F') with bounds (n)\nbeta : rank-1 array('F') with bounds (n)\nq : rank-2 array('F') with bounds (ldq,n)\nz : rank-2 array('F') with bounds (ldz,n)\nm : int\npl : float\npr : float\ndif : rank-1 array('f') with bounds (2)\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\niwork : rank-1 array('i') with bounds (MAX(1,liwork))\ninfo : int\n" - ... - -def ctpmqrt(l, v, t, a, b, side=..., trans=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a,b,info = ctpmqrt(l,v,t,a,b,[side,trans,overwrite_a,overwrite_b])\n\nWrapper for ``ctpmqrt``.\n\nParameters\n----------\nl : input int\nv : input rank-2 array('F') with bounds ((side[0]=='L'?m:n),k)\nt : input rank-2 array('F') with bounds (nb,k)\na : input rank-2 array('F') with bounds ((side[0]=='L'?k:m),(side[0]=='L'?n:k))\nb : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds ((side[0]=='L'?k:m),(side[0]=='L'?n:k))\nb : rank-2 array('F') with bounds (m,n)\ninfo : int\n" - ... - -def ctpqrt(l, nb, a, b, overwrite_a=..., overwrite_b=...) -> typing.Any: - "a,b,t,info = ctpqrt(l,nb,a,b,[overwrite_a,overwrite_b])\n\nWrapper for ``ctpqrt``.\n\nParameters\n----------\nl : input int\nnb : input int\na : input rank-2 array('F') with bounds (n,n)\nb : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('F') with bounds (n,n)\nb : rank-2 array('F') with bounds (m,n)\nt : rank-2 array('F') with bounds (nb,n)\ninfo : int\n" - ... - -def ctpttf(n, ap, transr=..., uplo=...) -> typing.Any: - "arf,info = ctpttf(n,ap,[transr,uplo])\n\nWrapper for ``ctpttf``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\narf : rank-1 array('F') with bounds (nt)\ninfo : int\n" - ... - -def ctpttr(n, ap, uplo=...) -> typing.Any: - "a,info = ctpttr(n,ap,[uplo])\n\nWrapper for ``ctpttr``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('F') with bounds (nt)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\na : rank-2 array('F') with bounds (n,n)\ninfo : int\n" - ... - -def ctrsyl(a, b, c, trana=..., tranb=..., isgn=..., overwrite_c=...) -> typing.Any: - "x,scale,info = ctrsyl(a,b,c,[trana,tranb,isgn,overwrite_c])\n\nWrapper for ``ctrsyl``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,m)\nb : input rank-2 array('F') with bounds (n,n)\nc : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\ntrana : input string(len=1), optional\n Default: 'N'\ntranb : input string(len=1), optional\n Default: 'N'\nisgn : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (m,n) and c storage\nscale : float\ninfo : int\n" - ... - -def ctrtri(c, lower=..., unitdiag=..., overwrite_c=...) -> typing.Any: - "inv_c,info = ctrtri(c,[lower,unitdiag,overwrite_c])\n\nWrapper for ``ctrtri``.\n\nParameters\n----------\nc : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nunitdiag : input int, optional\n Default: 0\n\nReturns\n-------\ninv_c : rank-2 array('F') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def ctrtrs(a, b, lower=..., trans=..., unitdiag=..., lda=..., overwrite_b=...) -> typing.Any: - "x,info = ctrtrs(a,b,[lower,trans,unitdiag,lda,overwrite_b])\n\nWrapper for ``ctrtrs``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (lda,n)\nb : input rank-2 array('F') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nunitdiag : input int, optional\n Default: 0\nlda : input int, optional\n Default: shape(a,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('F') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def ctrttf(a, transr=..., uplo=...) -> typing.Any: - "arf,info = ctrttf(a,[transr,uplo])\n\nWrapper for ``ctrttf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (lda,n)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\narf : rank-1 array('F') with bounds (n*(n+1)/2)\ninfo : int\n" - ... - -def ctrttp(a, uplo=...) -> typing.Any: - "ap,info = ctrttp(a,[uplo])\n\nWrapper for ``ctrttp``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (lda,n)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nap : rank-1 array('F') with bounds (n*(n+1)/2)\ninfo : int\n" - ... - -def ctzrzf(a, lwork=..., overwrite_a=...) -> typing.Any: - "rz,tau,info = ctzrzf(a,[lwork,overwrite_a])\n\nWrapper for ``ctzrzf``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(m,1)\n\nReturns\n-------\nrz : rank-2 array('F') with bounds (m,n) and a storage\ntau : rank-1 array('F') with bounds (m)\ninfo : int\n" - ... - -def ctzrzf_lwork(m, n) -> typing.Any: - "work,info = ctzrzf_lwork(m,n)\n\nWrapper for ``ctzrzf_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cuncsd( - x11, - x12, - x21, - x22, - compute_u1=..., - compute_u2=..., - compute_v1t=..., - compute_v2t=..., - trans=..., - signs=..., - lwork=..., - lrwork=..., - overwrite_x11=..., - overwrite_x12=..., - overwrite_x21=..., - overwrite_x22=..., -) -> typing.Any: - "cs11,cs12,cs21,cs22,theta,u1,u2,v1t,v2t,info = cuncsd(x11,x12,x21,x22,[compute_u1,compute_u2,compute_v1t,compute_v2t,trans,signs,lwork,lrwork,overwrite_x11,overwrite_x12,overwrite_x21,overwrite_x22])\n\nWrapper for ``cuncsd``.\n\nParameters\n----------\nx11 : input rank-2 array('F') with bounds (p,q)\nx12 : input rank-2 array('F') with bounds (p,mmq)\nx21 : input rank-2 array('F') with bounds (mmp,q)\nx22 : input rank-2 array('F') with bounds (mmp,mmq)\n\nOther Parameters\n----------------\ncompute_u1 : input int, optional\n Default: 1\ncompute_u2 : input int, optional\n Default: 1\ncompute_v1t : input int, optional\n Default: 1\ncompute_v2t : input int, optional\n Default: 1\ntrans : input int, optional\n Default: 0\nsigns : input int, optional\n Default: 0\noverwrite_x11 : input int, optional\n Default: 0\noverwrite_x12 : input int, optional\n Default: 0\noverwrite_x21 : input int, optional\n Default: 0\noverwrite_x22 : input int, optional\n Default: 0\nlwork : input int, optional\n Default: 2*m+MAX(1,MAX(mmp,mmq))+1\nlrwork : input int, optional\n Default: 5*MAX(1,q-1)+4*MAX(1,q)+8*q+1\n\nReturns\n-------\ncs11 : rank-2 array('F') with bounds (p,q) and x11 storage\ncs12 : rank-2 array('F') with bounds (p,mmq) and x12 storage\ncs21 : rank-2 array('F') with bounds (mmp,q) and x21 storage\ncs22 : rank-2 array('F') with bounds (mmp,mmq) and x22 storage\ntheta : rank-1 array('f') with bounds (min(min(p,mmp),min(q,mmq)))\nu1 : rank-2 array('F') with bounds ((compute_u1?p:0),(compute_u1?p:0))\nu2 : rank-2 array('F') with bounds ((compute_u2?mmp:0),(compute_u2?mmp:0))\nv1t : rank-2 array('F') with bounds ((compute_v1t?q:0),(compute_v1t?q:0))\nv2t : rank-2 array('F') with bounds ((compute_v2t?mmq:0),(compute_v2t?mmq:0))\ninfo : int\n" - ... - -def cuncsd_lwork(m, p, q) -> typing.Any: - "work,rwork,info = cuncsd_lwork(m,p,q)\n\nWrapper for ``cuncsd_lwork``.\n\nParameters\n----------\nm : input int\np : input int\nq : input int\n\nReturns\n-------\nwork : complex\nrwork : float\ninfo : int\n" - ... - -def cunghr(a, tau, lo=..., hi=..., lwork=..., overwrite_a=...) -> typing.Any: - "ht,info = cunghr(a,tau,[lo,hi,lwork,overwrite_a])\n\nWrapper for ``cunghr``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\ntau : input rank-1 array('F') with bounds (n - 1)\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(hi-lo,1)\n\nReturns\n-------\nht : rank-2 array('F') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def cunghr_lwork(n, lo=..., hi=...) -> typing.Any: - "work,info = cunghr_lwork(n,[lo,hi])\n\nWrapper for ``cunghr_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def cungqr(a, tau, lwork=..., overwrite_a=...) -> typing.Any: - "q,work,info = cungqr(a,tau,[lwork,overwrite_a])\n\nWrapper for ``cungqr``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\ntau : input rank-1 array('F') with bounds (k)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nq : rank-2 array('F') with bounds (m,n) and a storage\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def cungrq(a, tau, lwork=..., overwrite_a=...) -> typing.Any: - "q,work,info = cungrq(a,tau,[lwork,overwrite_a])\n\nWrapper for ``cungrq``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\ntau : input rank-1 array('F') with bounds (k)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*m,1)\n\nReturns\n-------\nq : rank-2 array('F') with bounds (m,n) and a storage\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def cunmqr(side, trans, a, tau, c, lwork, overwrite_c=...) -> typing.Any: - "cq,work,info = cunmqr(side,trans,a,tau,c,lwork,[overwrite_c])\n\nWrapper for ``cunmqr``.\n\nParameters\n----------\nside : input string(len=1)\ntrans : input string(len=1)\na : input rank-2 array('F') with bounds (lda,k)\ntau : input rank-1 array('F') with bounds (k)\nc : input rank-2 array('F') with bounds (ldc,n)\nlwork : input int\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\ncq : rank-2 array('F') with bounds (ldc,n) and c storage\nwork : rank-1 array('F') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def cunmrz(a, tau, c, side=..., trans=..., lwork=..., overwrite_c=...) -> typing.Any: - "cq,info = cunmrz(a,tau,c,[side,trans,lwork,overwrite_c])\n\nWrapper for ``cunmrz``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (k,nt)\ntau : input rank-1 array('F') with bounds (k)\nc : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX((side[0]=='L'?n:m),1)\n\nReturns\n-------\ncq : rank-2 array('F') with bounds (m,n) and c storage\ninfo : int\n" - ... - -def cunmrz_lwork(m, n, side=..., trans=...) -> typing.Any: - "work,info = cunmrz_lwork(m,n,[side,trans])\n\nWrapper for ``cunmrz_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def dgbsv(kl, ku, ab, b, overwrite_ab=..., overwrite_b=...) -> typing.Any: - "lub,piv,x,info = dgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])\n\nWrapper for ``dgbsv``.\n\nParameters\n----------\nkl : input int\nku : input int\nab : input rank-2 array('d') with bounds (2*kl+ku+1,n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nlub : rank-2 array('d') with bounds (2*kl+ku+1,n) and ab storage\npiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('d') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def dgbtrf(ab, kl, ku, m=..., n=..., ldab=..., overwrite_ab=...) -> typing.Any: - "lu,ipiv,info = dgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])\n\nWrapper for ``dgbtrf``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\nkl : input int\nku : input int\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(ab,1)\nn : input int, optional\n Default: shape(ab,1)\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: max(shape(ab,0),1)\n\nReturns\n-------\nlu : rank-2 array('d') with bounds (ldab,n) and ab storage\nipiv : rank-1 array('i') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def dgbtrs(ab, kl, ku, b, ipiv, trans=..., n=..., ldab=..., ldb=..., overwrite_b=...) -> typing.Any: - "x,info = dgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])\n\nWrapper for ``dgbtrs``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\nkl : input int\nku : input int\nb : input rank-2 array('d') with bounds (ldb,nrhs)\nipiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nn : input int, optional\n Default: shape(ab,1)\nldab : input int, optional\n Default: shape(ab,0)\nldb : input int, optional\n Default: shape(b,0)\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dgebal(a, scale=..., permute=..., overwrite_a=...) -> typing.Any: - "ba,lo,hi,pivscale,info = dgebal(a,[scale,permute,overwrite_a])\n\nWrapper for ``dgebal``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\nscale : input int, optional\n Default: 0\npermute : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nba : rank-2 array('d') with bounds (m,n) and a storage\nlo : int\nhi : int\npivscale : rank-1 array('d') with bounds (n)\ninfo : int\n" - ... - -def dgecon(a, anorm, norm=...) -> typing.Any: - "rcond,info = dgecon(a,anorm,[norm])\n\nWrapper for ``dgecon``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nanorm : input float\n\nOther Parameters\n----------------\nnorm : input string(len=1), optional\n Default: '1'\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def dgeequ(a) -> typing.Any: - "r,c,rowcnd,colcnd,amax,info = dgeequ(a)\n\nWrapper for ``dgeequ``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nReturns\n-------\nr : rank-1 array('d') with bounds (m)\nc : rank-1 array('d') with bounds (n)\nrowcnd : float\ncolcnd : float\namax : float\ninfo : int\n" - ... - -def dgeequb(a) -> typing.Any: - "r,c,rowcnd,colcnd,amax,info = dgeequb(a)\n\nWrapper for ``dgeequb``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nReturns\n-------\nr : rank-1 array('d') with bounds (m)\nc : rank-1 array('d') with bounds (n)\nrowcnd : float\ncolcnd : float\namax : float\ninfo : int\n" - ... - -def dgees(dselect, a, compute_v=..., sort_t=..., lwork=..., dselect_extra_args=..., overwrite_a=...) -> typing.Any: - "t,sdim,wr,wi,vs,work,info = dgees(dselect,a,[compute_v,sort_t,lwork,dselect_extra_args,overwrite_a])\n\nWrapper for ``dgees``.\n\nParameters\n----------\ndselect : call-back function\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nsort_t : input int, optional\n Default: 0\ndselect_extra_args : input tuple, optional\n Default: ()\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nt : rank-2 array('d') with bounds (n,n) and a storage\nsdim : int\nwr : rank-1 array('d') with bounds (n)\nwi : rank-1 array('d') with bounds (n)\nvs : rank-2 array('d') with bounds (ldvs,n)\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n\nNotes\n-----\nCall-back functions::\n\n def dselect(arg1,arg2): return dselect\n Required arguments:\n arg1 : input float\n arg2 : input float\n Return objects:\n dselect : int\n" - ... - -def dgeev(a, compute_vl=..., compute_vr=..., lwork=..., overwrite_a=...) -> typing.Any: - "wr,wi,vl,vr,info = dgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])\n\nWrapper for ``dgeev``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(4*n,1)\n\nReturns\n-------\nwr : rank-1 array('d') with bounds (n)\nwi : rank-1 array('d') with bounds (n)\nvl : rank-2 array('d') with bounds (ldvl,n)\nvr : rank-2 array('d') with bounds (ldvr,n)\ninfo : int\n" - ... - -def dgeev_lwork(n, compute_vl=..., compute_vr=...) -> typing.Any: - "work,info = dgeev_lwork(n,[compute_vl,compute_vr])\n\nWrapper for ``dgeev_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgegv(*args, **kwds) -> typing.Any: - "`dgegv` is deprecated!\nThe `*gegv` family of routines has been deprecated in\nLAPACK 3.6.0 in favor of the `*ggev` family of routines.\nThe corresponding wrappers will be removed from SciPy in\na future release.\n\nalphar,alphai,beta,vl,vr,info = dgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``dgegv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(8*n,1)\n\nReturns\n-------\nalphar : rank-1 array('d') with bounds (n)\nalphai : rank-1 array('d') with bounds (n)\nbeta : rank-1 array('d') with bounds (n)\nvl : rank-2 array('d') with bounds (ldvl,n)\nvr : rank-2 array('d') with bounds (ldvr,n)\ninfo : int\n" - ... - -def dgehrd(a, lo=..., hi=..., lwork=..., overwrite_a=...) -> typing.Any: - "ht,tau,info = dgehrd(a,[lo,hi,lwork,overwrite_a])\n\nWrapper for ``dgehrd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(n,1)\n\nReturns\n-------\nht : rank-2 array('d') with bounds (n,n) and a storage\ntau : rank-1 array('d') with bounds (n - 1)\ninfo : int\n" - ... - -def dgehrd_lwork(n, lo=..., hi=...) -> typing.Any: - "work,info = dgehrd_lwork(n,[lo,hi])\n\nWrapper for ``dgehrd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgejsv(a, joba=..., jobu=..., jobv=..., jobr=..., jobt=..., jobp=..., lwork=..., overwrite_a=...) -> typing.Any: - "sva,u,v,workout,iworkout,info = dgejsv(a,[joba,jobu,jobv,jobr,jobt,jobp,lwork,overwrite_a])\n\nWrapper for ``dgejsv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (lda,n)\n\nOther Parameters\n----------------\njoba : input int, optional\n Default: 4\njobu : input int, optional\n Default: 0\njobv : input int, optional\n Default: 0\njobr : input int, optional\n Default: 1\njobt : input int, optional\n Default: 0\njobp : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(6*n+2*n*n, max(2*m+n, max(4*n+n*n, max(2*n+n*n+6, 7))))\n\nReturns\n-------\nsva : rank-1 array('d') with bounds (n)\nu : rank-2 array('d') with bounds (((jobt == 0)&&(jobu == 3)?0:m),((jobt == 0)&&(jobu == 3)?0:(jobu == 1?m:n)))\nv : rank-2 array('d') with bounds (((jobt == 0)&&(jobv == 3)?0:ldv),((jobt == 0)&&(jobv == 3)?0:n))\nworkout : rank-1 array('d') with bounds (7)\niworkout : rank-1 array('i') with bounds (3)\ninfo : int\n" - ... - -def dgels(a, b, trans=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "lqr,x,info = dgels(a,b,[trans,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``dgels``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nb : input rank-2 array('d') with bounds (MAX(m,n),nrhs)\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(MIN(m,n)+MAX(MIN(m,n),nrhs),1)\n\nReturns\n-------\nlqr : rank-2 array('d') with bounds (m,n) and a storage\nx : rank-2 array('d') with bounds (MAX(m,n),nrhs) and b storage\ninfo : int\n" - ... - -def dgels_lwork(m, n, nrhs, trans=...) -> typing.Any: - "work,info = dgels_lwork(m,n,nrhs,[trans])\n\nWrapper for ``dgels_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgelsd(a, b, lwork, size_iwork, cond=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "x,s,rank,info = dgelsd(a,b,lwork,size_iwork,[cond,overwrite_a,overwrite_b])\n\nWrapper for ``dgelsd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nb : input rank-2 array('d') with bounds (maxmn,nrhs)\nlwork : input int\nsize_iwork : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\ncond : input float, optional\n Default: -1.0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (maxmn,nrhs) and b storage\ns : rank-1 array('d') with bounds (minmn)\nrank : int\ninfo : int\n" - ... - -def dgelsd_lwork(m, n, nrhs, cond=..., lwork=...) -> typing.Any: - "work,iwork,info = dgelsd_lwork(m,n,nrhs,[cond,lwork])\n\nWrapper for ``dgelsd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : float\niwork : int\ninfo : int\n" - ... - -def dgelss(a, b, cond=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "v,x,s,rank,work,info = dgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``dgelss``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nb : input rank-2 array('d') with bounds (maxmn,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: max(3*minmn+MAX(2*minmn,MAX(maxmn,nrhs)),1)\n\nReturns\n-------\nv : rank-2 array('d') with bounds (m,n) and a storage\nx : rank-2 array('d') with bounds (maxmn,nrhs) and b storage\ns : rank-1 array('d') with bounds (minmn)\nrank : int\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def dgelss_lwork(m, n, nrhs, cond=..., lwork=...) -> typing.Any: - "work,info = dgelss_lwork(m,n,nrhs,[cond,lwork])\n\nWrapper for ``dgelss_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgelsy(a, b, jptv, cond, lwork, overwrite_a=..., overwrite_b=...) -> typing.Any: - "v,x,j,rank,info = dgelsy(a,b,jptv,cond,lwork,[overwrite_a,overwrite_b])\n\nWrapper for ``dgelsy``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nb : input rank-2 array('d') with bounds (maxmn,nrhs)\njptv : input rank-1 array('i') with bounds (n)\ncond : input float\nlwork : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nv : rank-2 array('d') with bounds (m,n) and a storage\nx : rank-2 array('d') with bounds (maxmn,nrhs) and b storage\nj : rank-1 array('i') with bounds (n) and jptv storage\nrank : int\ninfo : int\n" - ... - -def dgelsy_lwork(m, n, nrhs, cond, lwork=...) -> typing.Any: - "work,info = dgelsy_lwork(m,n,nrhs,cond,[lwork])\n\nWrapper for ``dgelsy_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\ncond : input float\n\nOther Parameters\n----------------\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgemqrt(v, t, c, side=..., trans=..., overwrite_c=...) -> typing.Any: - "c,info = dgemqrt(v,t,c,[side,trans,overwrite_c])\n\nWrapper for ``dgemqrt``.\n\nParameters\n----------\nv : input rank-2 array('d') with bounds ((side[0]=='L'?m:n),k)\nt : input rank-2 array('d') with bounds (nb,k)\nc : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (m,n)\ninfo : int\n" - ... - -def dgeqp3(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,jpvt,tau,work,info = dgeqp3(a,[lwork,overwrite_a])\n\nWrapper for ``dgeqp3``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*(n+1),1)\n\nReturns\n-------\nqr : rank-2 array('d') with bounds (m,n) and a storage\njpvt : rank-1 array('i') with bounds (n)\ntau : rank-1 array('d') with bounds (MIN(m,n))\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def dgeqrf(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,work,info = dgeqrf(a,[lwork,overwrite_a])\n\nWrapper for ``dgeqrf``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nqr : rank-2 array('d') with bounds (m,n) and a storage\ntau : rank-1 array('d') with bounds (MIN(m,n))\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def dgeqrf_lwork(m, n) -> typing.Any: - "work,info = dgeqrf_lwork(m,n)\n\nWrapper for ``dgeqrf_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgeqrfp(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,info = dgeqrfp(a,[lwork,overwrite_a])\n\nWrapper for ``dgeqrfp``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(1, n)\n\nReturns\n-------\nqr : rank-2 array('d') with bounds (m,n) and a storage\ntau : rank-1 array('d') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def dgeqrfp_lwork(m, n) -> typing.Any: - "work,info = dgeqrfp_lwork(m,n)\n\nWrapper for ``dgeqrfp_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgeqrt(nb, a, overwrite_a=...) -> typing.Any: - "a,t,info = dgeqrt(nb,a,[overwrite_a])\n\nWrapper for ``dgeqrt``.\n\nParameters\n----------\nnb : input int\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('d') with bounds (m,n)\nt : rank-2 array('d') with bounds (nb,MIN(m,n))\ninfo : int\n" - ... - -def dgerqf(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,work,info = dgerqf(a,[lwork,overwrite_a])\n\nWrapper for ``dgerqf``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*m,1)\n\nReturns\n-------\nqr : rank-2 array('d') with bounds (m,n) and a storage\ntau : rank-1 array('d') with bounds (MIN(m,n))\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def dgesc2(lu, rhs, ipiv, jpiv, overwrite_rhs=...) -> typing.Any: - "x,scale = dgesc2(lu,rhs,ipiv,jpiv,[overwrite_rhs])\n\nWrapper for ``dgesc2``.\n\nParameters\n----------\nlu : input rank-2 array('d') with bounds (n,n)\nrhs : input rank-1 array('d') with bounds (n)\nipiv : input rank-1 array('i') with bounds (n)\njpiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_rhs : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n) and rhs storage\nscale : float\n" - ... - -def dgesdd(a, compute_uv=..., full_matrices=..., lwork=..., overwrite_a=...) -> typing.Any: - "u,s,vt,info = dgesdd(a,[compute_uv,full_matrices,lwork,overwrite_a])\n\nWrapper for ``dgesdd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\nlwork : input int, optional\n Default: max((compute_uv?4*minmn*minmn+MAX(m,n)+9*minmn:MAX(14*minmn+4,10*minmn+2+25*(25+8))+MAX(m,n)),1)\n\nReturns\n-------\nu : rank-2 array('d') with bounds (u0,u1)\ns : rank-1 array('d') with bounds (minmn)\nvt : rank-2 array('d') with bounds (vt0,vt1)\ninfo : int\n" - ... - -def dgesdd_lwork(m, n, compute_uv=..., full_matrices=...) -> typing.Any: - "work,info = dgesdd_lwork(m,n,[compute_uv,full_matrices])\n\nWrapper for ``dgesdd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgesv(a, b, overwrite_a=..., overwrite_b=...) -> typing.Any: - "lu,piv,x,info = dgesv(a,b,[overwrite_a,overwrite_b])\n\nWrapper for ``dgesv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('d') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('d') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def dgesvd(a, compute_uv=..., full_matrices=..., lwork=..., overwrite_a=...) -> typing.Any: - "u,s,vt,info = dgesvd(a,[compute_uv,full_matrices,lwork,overwrite_a])\n\nWrapper for ``dgesvd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\nlwork : input int, optional\n Default: max(MAX(3*minmn+MAX(m,n),5*minmn),1)\n\nReturns\n-------\nu : rank-2 array('d') with bounds (u0,u1)\ns : rank-1 array('d') with bounds (minmn)\nvt : rank-2 array('d') with bounds (vt0,vt1)\ninfo : int\n" - ... - -def dgesvd_lwork(m, n, compute_uv=..., full_matrices=...) -> typing.Any: - "work,info = dgesvd_lwork(m,n,[compute_uv,full_matrices])\n\nWrapper for ``dgesvd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgesvx(a, b, fact=..., trans=..., af=..., ipiv=..., equed=..., r=..., c=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "as,lu,ipiv,equed,rs,cs,bs,x,rcond,ferr,berr,info = dgesvx(a,b,[fact,trans,af,ipiv,equed,r,c,overwrite_a,overwrite_b])\n\nWrapper for ``dgesvx``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'E'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('d') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nequed : input string(len=1), optional\n Default: 'B'\nr : input rank-1 array('d') with bounds (n)\nc : input rank-1 array('d') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nas : rank-2 array('d') with bounds (n,n) and a storage\nlu : rank-2 array('d') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nequed : string(len=1)\nrs : rank-1 array('d') with bounds (n) and r storage\ncs : rank-1 array('d') with bounds (n) and c storage\nbs : rank-2 array('d') with bounds (n,nrhs) and b storage\nx : rank-2 array('d') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def dgetc2(a, overwrite_a=...) -> typing.Any: - "lu,ipiv,jpiv,info = dgetc2(a,[overwrite_a])\n\nWrapper for ``dgetc2``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('d') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\njpiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def dgetrf(a, overwrite_a=...) -> typing.Any: - "lu,piv,info = dgetrf(a,[overwrite_a])\n\nWrapper for ``dgetrf``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('d') with bounds (m,n) and a storage\npiv : rank-1 array('i') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def dgetri(lu, piv, lwork=..., overwrite_lu=...) -> typing.Any: - "inv_a,info = dgetri(lu,piv,[lwork,overwrite_lu])\n\nWrapper for ``dgetri``.\n\nParameters\n----------\nlu : input rank-2 array('d') with bounds (n,n)\npiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_lu : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\ninv_a : rank-2 array('d') with bounds (n,n) and lu storage\ninfo : int\n" - ... - -def dgetri_lwork(n) -> typing.Any: - "work,info = dgetri_lwork(n)\n\nWrapper for ``dgetri_lwork``.\n\nParameters\n----------\nn : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgetrs(lu, piv, b, trans=..., overwrite_b=...) -> typing.Any: - "x,info = dgetrs(lu,piv,b,[trans,overwrite_b])\n\nWrapper for ``dgetrs``.\n\nParameters\n----------\nlu : input rank-2 array('d') with bounds (n,n)\npiv : input rank-1 array('i') with bounds (n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def dgges( - dselect, - a, - b, - jobvsl=..., - jobvsr=..., - sort_t=..., - ldvsl=..., - ldvsr=..., - lwork=..., - dselect_extra_args=..., - overwrite_a=..., - overwrite_b=..., -) -> typing.Any: - "a,b,sdim,alphar,alphai,beta,vsl,vsr,work,info = dgges(dselect,a,b,[jobvsl,jobvsr,sort_t,ldvsl,ldvsr,lwork,dselect_extra_args,overwrite_a,overwrite_b])\n\nWrapper for ``dgges``.\n\nParameters\n----------\ndselect : call-back function\na : input rank-2 array('d') with bounds (lda,n)\nb : input rank-2 array('d') with bounds (ldb,n)\n\nOther Parameters\n----------------\njobvsl : input int, optional\n Default: 1\njobvsr : input int, optional\n Default: 1\nsort_t : input int, optional\n Default: 0\ndselect_extra_args : input tuple, optional\n Default: ()\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nldvsl : input int, optional\n Default: ((jobvsl==1)?n:1)\nldvsr : input int, optional\n Default: ((jobvsr==1)?n:1)\nlwork : input int, optional\n Default: max(8*n+16,1)\n\nReturns\n-------\na : rank-2 array('d') with bounds (lda,n)\nb : rank-2 array('d') with bounds (ldb,n)\nsdim : int\nalphar : rank-1 array('d') with bounds (n)\nalphai : rank-1 array('d') with bounds (n)\nbeta : rank-1 array('d') with bounds (n)\nvsl : rank-2 array('d') with bounds (ldvsl,n)\nvsr : rank-2 array('d') with bounds (ldvsr,n)\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n\nNotes\n-----\nCall-back functions::\n\n def dselect(alphar,alphai,beta): return dselect\n Required arguments:\n alphar : input float\n alphai : input float\n beta : input float\n Return objects:\n dselect : int\n" - ... - -def dggev(a, b, compute_vl=..., compute_vr=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "alphar,alphai,beta,vl,vr,work,info = dggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``dggev``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(8*n,1)\n\nReturns\n-------\nalphar : rank-1 array('d') with bounds (n)\nalphai : rank-1 array('d') with bounds (n)\nbeta : rank-1 array('d') with bounds (n)\nvl : rank-2 array('d') with bounds (ldvl,n)\nvr : rank-2 array('d') with bounds (ldvr,n)\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def dgglse(a, b, c, d, lwork=..., overwrite_a=..., overwrite_b=..., overwrite_c=..., overwrite_d=...) -> typing.Any: - "t,r,res,x,info = dgglse(a,b,c,d,[lwork,overwrite_a,overwrite_b,overwrite_c,overwrite_d])\n\nWrapper for ``dgglse``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nb : input rank-2 array('d') with bounds (p,n)\nc : input rank-1 array('d') with bounds (m)\nd : input rank-1 array('d') with bounds (p)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\noverwrite_c : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(m+n+p,1)\n\nReturns\n-------\nt : rank-2 array('d') with bounds (m,n) and a storage\nr : rank-2 array('d') with bounds (p,n) and b storage\nres : rank-1 array('d') with bounds (m) and c storage\nx : rank-1 array('d') with bounds (n)\ninfo : int\n" - ... - -def dgglse_lwork(m, n, p) -> typing.Any: - "work,info = dgglse_lwork(m,n,p)\n\nWrapper for ``dgglse_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\np : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dgtsv(dl, d, du, b, overwrite_dl=..., overwrite_d=..., overwrite_du=..., overwrite_b=...) -> typing.Any: - "du2,d,du,x,info = dgtsv(dl,d,du,b,[overwrite_dl,overwrite_d,overwrite_du,overwrite_b])\n\nWrapper for ``dgtsv``.\n\nParameters\n----------\ndl : input rank-1 array('d') with bounds (n - 1)\nd : input rank-1 array('d') with bounds (n)\ndu : input rank-1 array('d') with bounds (n - 1)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_dl : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_du : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\ndu2 : rank-1 array('d') with bounds (n - 1) and dl storage\nd : rank-1 array('d') with bounds (n)\ndu : rank-1 array('d') with bounds (n - 1)\nx : rank-2 array('d') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def dgtsvx(dl, d, du, b, fact=..., trans=..., dlf=..., df=..., duf=..., du2=..., ipiv=...) -> typing.Any: - "dlf,df,duf,du2,ipiv,x,rcond,ferr,berr,info = dgtsvx(dl,d,du,b,[fact,trans,dlf,df,duf,du2,ipiv])\n\nWrapper for ``dgtsvx``.\n\nParameters\n----------\ndl : input rank-1 array('d') with bounds (MAX(0, n-1))\nd : input rank-1 array('d') with bounds (n)\ndu : input rank-1 array('d') with bounds (MAX(0, n-1))\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'N'\ntrans : input string(len=1), optional\n Default: 'N'\ndlf : input rank-1 array('d') with bounds (MAX(0,n-1))\ndf : input rank-1 array('d') with bounds (n)\nduf : input rank-1 array('d') with bounds (MAX(0,n-1))\ndu2 : input rank-1 array('d') with bounds (MAX(0,n-2))\nipiv : input rank-1 array('i') with bounds (n)\n\nReturns\n-------\ndlf : rank-1 array('d') with bounds (MAX(0,n-1))\ndf : rank-1 array('d') with bounds (n)\nduf : rank-1 array('d') with bounds (MAX(0,n-1))\ndu2 : rank-1 array('d') with bounds (MAX(0,n-2))\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('d') with bounds (ldx,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def dgttrf(dl, d, du, overwrite_dl=..., overwrite_d=..., overwrite_du=...) -> typing.Any: - "dl,d,du,du2,ipiv,info = dgttrf(dl,d,du,[overwrite_dl,overwrite_d,overwrite_du])\n\nWrapper for ``dgttrf``.\n\nParameters\n----------\ndl : input rank-1 array('d') with bounds (n - 1)\nd : input rank-1 array('d') with bounds (n)\ndu : input rank-1 array('d') with bounds (n - 1)\n\nOther Parameters\n----------------\noverwrite_dl : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_du : input int, optional\n Default: 0\n\nReturns\n-------\ndl : rank-1 array('d') with bounds (n - 1)\nd : rank-1 array('d') with bounds (n)\ndu : rank-1 array('d') with bounds (n - 1)\ndu2 : rank-1 array('d') with bounds (n - 2)\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def dgttrs(dl, d, du, du2, ipiv, b, trans=..., overwrite_b=...) -> typing.Any: - "x,info = dgttrs(dl,d,du,du2,ipiv,b,[trans,overwrite_b])\n\nWrapper for ``dgttrs``.\n\nParameters\n----------\ndl : input rank-1 array('d') with bounds (n - 1)\nd : input rank-1 array('d') with bounds (n)\ndu : input rank-1 array('d') with bounds (n - 1)\ndu2 : input rank-1 array('d') with bounds (n - 2)\nipiv : input rank-1 array('i') with bounds (n)\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dlamch(cmach) -> typing.Any: - "dlamch = dlamch(cmach)\n\nWrapper for ``dlamch``.\n\nParameters\n----------\ncmach : input string(len=1)\n\nReturns\n-------\ndlamch : float\n" - ... - -def dlange(norm, a) -> typing.Any: - "n2 = dlange(norm,a)\n\nWrapper for ``dlange``.\n\nParameters\n----------\nnorm : input string(len=1)\na : input rank-2 array('d') with bounds (m,n)\n\nReturns\n-------\nn2 : float\n" - ... - -def dlarf(v, tau, c, work, side=..., incv=..., overwrite_c=...) -> typing.Any: - "c = dlarf(v,tau,c,work,[side,incv,overwrite_c])\n\nWrapper for ``dlarf``.\n\nParameters\n----------\nv : input rank-1 array('d') with bounds ((side[0]=='L'?(1 + (m-1)*abs(incv)):(1 + (n-1)*abs(incv))))\ntau : input float\nc : input rank-2 array('d') with bounds (m,n)\nwork : input rank-1 array('d') with bounds (lwork)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\nincv : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (m,n)\n" - ... - -def dlarfg(n, alpha, x, incx=..., overwrite_x=...) -> typing.Any: - "alpha,x,tau = dlarfg(n,alpha,x,[incx,overwrite_x])\n\nWrapper for ``dlarfg``.\n\nParameters\n----------\nn : input int\nalpha : input float\nx : input rank-1 array('d') with bounds (lx)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nalpha : float\nx : rank-1 array('d') with bounds (lx)\ntau : float\n" - ... - -def dlartg(f, g) -> typing.Any: - "cs,sn,r = dlartg(f,g)\n\nWrapper for ``dlartg``.\n\nParameters\n----------\nf : input float\ng : input float\n\nReturns\n-------\ncs : float\nsn : float\nr : float\n" - ... - -def dlasd4(i, d, z, rho=...) -> typing.Any: - "delta,sigma,work,info = dlasd4(i,d,z,[rho])\n\nWrapper for ``dlasd4``.\n\nParameters\n----------\ni : input int\nd : input rank-1 array('d') with bounds (n)\nz : input rank-1 array('d') with bounds (n)\n\nOther Parameters\n----------------\nrho : input float, optional\n Default: 1.0\n\nReturns\n-------\ndelta : rank-1 array('d') with bounds (n)\nsigma : float\nwork : rank-1 array('d') with bounds (n)\ninfo : int\n" - ... - -def dlaswp(a, piv, k1=..., k2=..., off=..., inc=..., overwrite_a=...) -> typing.Any: - "a = dlaswp(a,piv,[k1,k2,off,inc,overwrite_a])\n\nWrapper for ``dlaswp``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (nrows,n)\npiv : input rank-1 array('i') with bounds (npiv)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nk1 : input int, optional\n Default: 0\nk2 : input int, optional\n Default: npiv-1\noff : input int, optional\n Default: 0\ninc : input int, optional\n Default: 1\n\nReturns\n-------\na : rank-2 array('d') with bounds (nrows,n)\n" - ... - -def dlauum(c, lower=..., overwrite_c=...) -> typing.Any: - "a,info = dlauum(c,[lower,overwrite_c])\n\nWrapper for ``dlauum``.\n\nParameters\n----------\nc : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('d') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def dorcsd( - x11, - x12, - x21, - x22, - compute_u1=..., - compute_u2=..., - compute_v1t=..., - compute_v2t=..., - trans=..., - signs=..., - lwork=..., - overwrite_x11=..., - overwrite_x12=..., - overwrite_x21=..., - overwrite_x22=..., -) -> typing.Any: - "cs11,cs12,cs21,cs22,theta,u1,u2,v1t,v2t,info = dorcsd(x11,x12,x21,x22,[compute_u1,compute_u2,compute_v1t,compute_v2t,trans,signs,lwork,overwrite_x11,overwrite_x12,overwrite_x21,overwrite_x22])\n\nWrapper for ``dorcsd``.\n\nParameters\n----------\nx11 : input rank-2 array('d') with bounds (p,q)\nx12 : input rank-2 array('d') with bounds (p,mmq)\nx21 : input rank-2 array('d') with bounds (mmp,q)\nx22 : input rank-2 array('d') with bounds (mmp,mmq)\n\nOther Parameters\n----------------\ncompute_u1 : input int, optional\n Default: 1\ncompute_u2 : input int, optional\n Default: 1\ncompute_v1t : input int, optional\n Default: 1\ncompute_v2t : input int, optional\n Default: 1\ntrans : input int, optional\n Default: 0\nsigns : input int, optional\n Default: 0\noverwrite_x11 : input int, optional\n Default: 0\noverwrite_x12 : input int, optional\n Default: 0\noverwrite_x21 : input int, optional\n Default: 0\noverwrite_x22 : input int, optional\n Default: 0\nlwork : input int, optional\n Default: 2+2*m+5*MAX(1,q-1)+4*MAX(1,q)+8*q\n\nReturns\n-------\ncs11 : rank-2 array('d') with bounds (p,q) and x11 storage\ncs12 : rank-2 array('d') with bounds (p,mmq) and x12 storage\ncs21 : rank-2 array('d') with bounds (mmp,q) and x21 storage\ncs22 : rank-2 array('d') with bounds (mmp,mmq) and x22 storage\ntheta : rank-1 array('d') with bounds (min(min(p,mmp),min(q,mmq)))\nu1 : rank-2 array('d') with bounds ((compute_u1?p:0),(compute_u1?p:0))\nu2 : rank-2 array('d') with bounds ((compute_u2?mmp:0),(compute_u2?mmp:0))\nv1t : rank-2 array('d') with bounds ((compute_v1t?q:0),(compute_v1t?q:0))\nv2t : rank-2 array('d') with bounds ((compute_v2t?mmq:0),(compute_v2t?mmq:0))\ninfo : int\n" - ... - -def dorcsd_lwork(m, p, q) -> typing.Any: - "work,info = dorcsd_lwork(m,p,q)\n\nWrapper for ``dorcsd_lwork``.\n\nParameters\n----------\nm : input int\np : input int\nq : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dorghr(a, tau, lo=..., hi=..., lwork=..., overwrite_a=...) -> typing.Any: - "ht,info = dorghr(a,tau,[lo,hi,lwork,overwrite_a])\n\nWrapper for ``dorghr``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\ntau : input rank-1 array('d') with bounds (n - 1)\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(hi-lo,1)\n\nReturns\n-------\nht : rank-2 array('d') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def dorghr_lwork(n, lo=..., hi=...) -> typing.Any: - "work,info = dorghr_lwork(n,[lo,hi])\n\nWrapper for ``dorghr_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dorgqr(a, tau, lwork=..., overwrite_a=...) -> typing.Any: - "q,work,info = dorgqr(a,tau,[lwork,overwrite_a])\n\nWrapper for ``dorgqr``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\ntau : input rank-1 array('d') with bounds (k)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nq : rank-2 array('d') with bounds (m,n) and a storage\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def dorgrq(a, tau, lwork=..., overwrite_a=...) -> typing.Any: - "q,work,info = dorgrq(a,tau,[lwork,overwrite_a])\n\nWrapper for ``dorgrq``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\ntau : input rank-1 array('d') with bounds (k)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*m,1)\n\nReturns\n-------\nq : rank-2 array('d') with bounds (m,n) and a storage\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def dormqr(side, trans, a, tau, c, lwork, overwrite_c=...) -> typing.Any: - "cq,work,info = dormqr(side,trans,a,tau,c,lwork,[overwrite_c])\n\nWrapper for ``dormqr``.\n\nParameters\n----------\nside : input string(len=1)\ntrans : input string(len=1)\na : input rank-2 array('d') with bounds (lda,k)\ntau : input rank-1 array('d') with bounds (k)\nc : input rank-2 array('d') with bounds (ldc,n)\nlwork : input int\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\ncq : rank-2 array('d') with bounds (ldc,n) and c storage\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def dormrz(a, tau, c, side=..., trans=..., lwork=..., overwrite_c=...) -> typing.Any: - "cq,info = dormrz(a,tau,c,[side,trans,lwork,overwrite_c])\n\nWrapper for ``dormrz``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (k,nt)\ntau : input rank-1 array('d') with bounds (k)\nc : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX((side[0]=='L'?n:m),1)\n\nReturns\n-------\ncq : rank-2 array('d') with bounds (m,n) and c storage\ninfo : int\n" - ... - -def dormrz_lwork(m, n, side=..., trans=...) -> typing.Any: - "work,info = dormrz_lwork(m,n,[side,trans])\n\nWrapper for ``dormrz_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dpbsv(ab, b, lower=..., ldab=..., overwrite_ab=..., overwrite_b=...) -> typing.Any: - "c,x,info = dpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])\n\nWrapper for ``dpbsv``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (ldab,n) and ab storage\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dpbtrf(ab, lower=..., ldab=..., overwrite_ab=...) -> typing.Any: - "c,info = dpbtrf(ab,[lower,ldab,overwrite_ab])\n\nWrapper for ``dpbtrf``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\n\nReturns\n-------\nc : rank-2 array('d') with bounds (ldab,n) and ab storage\ninfo : int\n" - ... - -def dpbtrs(ab, b, lower=..., ldab=..., overwrite_b=...) -> typing.Any: - "x,info = dpbtrs(ab,b,[lower,ldab,overwrite_b])\n\nWrapper for ``dpbtrs``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dpftrf(n, a, transr=..., uplo=..., overwrite_a=...) -> typing.Any: - "achol,info = dpftrf(n,a,[transr,uplo,overwrite_a])\n\nWrapper for ``dpftrf``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('d') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nachol : rank-1 array('d') with bounds (nt) and a storage\ninfo : int\n" - ... - -def dpftri(n, a, transr=..., uplo=..., overwrite_a=...) -> typing.Any: - "ainv,info = dpftri(n,a,[transr,uplo,overwrite_a])\n\nWrapper for ``dpftri``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('d') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nainv : rank-1 array('d') with bounds (nt) and a storage\ninfo : int\n" - ... - -def dpftrs(n, a, b, transr=..., uplo=..., overwrite_b=...) -> typing.Any: - "x,info = dpftrs(n,a,b,[transr,uplo,overwrite_b])\n\nWrapper for ``dpftrs``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('d') with bounds (nt)\nb : input rank-2 array('d') with bounds (ldb,nhrs)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nhrs) and b storage\ninfo : int\n" - ... - -def dpocon(a, anorm, uplo=...) -> typing.Any: - "rcond,info = dpocon(a,anorm,[uplo])\n\nWrapper for ``dpocon``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nanorm : input float\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def dposv(a, b, lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "c,x,info = dposv(a,b,[lower,overwrite_a,overwrite_b])\n\nWrapper for ``dposv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (n,n) and a storage\nx : rank-2 array('d') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def dposvx(a, b, fact=..., af=..., equed=..., s=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a_s,lu,equed,s,b_s,x,rcond,ferr,berr,info = dposvx(a,b,[fact,af,equed,s,lower,overwrite_a,overwrite_b])\n\nWrapper for ``dposvx``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'E'\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('d') with bounds (n,n)\nequed : input string(len=1), optional\n Default: 'Y'\ns : input rank-1 array('d') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na_s : rank-2 array('d') with bounds (n,n) and a storage\nlu : rank-2 array('d') with bounds (n,n) and af storage\nequed : string(len=1)\ns : rank-1 array('d') with bounds (n)\nb_s : rank-2 array('d') with bounds (n,nrhs) and b storage\nx : rank-2 array('d') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def dpotrf(a, lower=..., clean=..., overwrite_a=...) -> typing.Any: - "c,info = dpotrf(a,[lower,clean,overwrite_a])\n\nWrapper for ``dpotrf``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nclean : input int, optional\n Default: 1\n\nReturns\n-------\nc : rank-2 array('d') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def dpotri(c, lower=..., overwrite_c=...) -> typing.Any: - "inv_a,info = dpotri(c,[lower,overwrite_c])\n\nWrapper for ``dpotri``.\n\nParameters\n----------\nc : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ninv_a : rank-2 array('d') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def dpotrs(c, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = dpotrs(c,b,[lower,overwrite_b])\n\nWrapper for ``dpotrs``.\n\nParameters\n----------\nc : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def dppcon(n, ap, anorm, lower=...) -> typing.Any: - "rcond,info = dppcon(n,ap,anorm,[lower])\n\nWrapper for ``dppcon``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (L)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def dppsv(n, ap, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = dppsv(n,ap,b,[lower,overwrite_b])\n\nWrapper for ``dppsv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (L)\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dpptrf(n, ap, lower=..., overwrite_ap=...) -> typing.Any: - "ul,info = dpptrf(n,ap,[lower,overwrite_ap])\n\nWrapper for ``dpptrf``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (L)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\n\nReturns\n-------\nul : rank-1 array('d') with bounds (L) and ap storage\ninfo : int\n" - ... - -def dpptri(n, ap, lower=..., overwrite_ap=...) -> typing.Any: - "uli,info = dpptri(n,ap,[lower,overwrite_ap])\n\nWrapper for ``dpptri``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (L)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\n\nReturns\n-------\nuli : rank-1 array('d') with bounds (L) and ap storage\ninfo : int\n" - ... - -def dpptrs(n, ap, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = dpptrs(n,ap,b,[lower,overwrite_b])\n\nWrapper for ``dpptrs``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (L)\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dpstf2(a, tol=..., lower=..., overwrite_a=...) -> typing.Any: - "c,piv,rank_c,info = dpstf2(a,[tol,lower,overwrite_a])\n\nWrapper for ``dpstf2``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ntol : input float, optional\n Default: -1.0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nrank_c : int\ninfo : int\n" - ... - -def dpstrf(a, tol=..., lower=..., overwrite_a=...) -> typing.Any: - "c,piv,rank_c,info = dpstrf(a,[tol,lower,overwrite_a])\n\nWrapper for ``dpstrf``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ntol : input float, optional\n Default: -1.0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nrank_c : int\ninfo : int\n" - ... - -def dpteqr(d, e, z, compute_z=..., overwrite_d=..., overwrite_e=..., overwrite_z=...) -> typing.Any: - "d,e,z,info = dpteqr(d,e,z,[compute_z,overwrite_d,overwrite_e,overwrite_z])\n\nWrapper for ``dpteqr``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds ((n>0?n-1:0))\nz : input rank-2 array('d') with bounds ((compute_z==0?shape(z, 0):max(1,n)),(compute_z==0?shape(z, 1):n))\n\nOther Parameters\n----------------\ncompute_z : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\noverwrite_z : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('d') with bounds (n)\ne : rank-1 array('d') with bounds ((n>0?n-1:0))\nz : rank-2 array('d') with bounds ((compute_z==0?shape(z, 0):max(1,n)),(compute_z==0?shape(z, 1):n))\ninfo : int\n" - ... - -def dptsv(d, e, b, overwrite_d=..., overwrite_e=..., overwrite_b=...) -> typing.Any: - "d,du,x,info = dptsv(d,e,b,[overwrite_d,overwrite_e,overwrite_b])\n\nWrapper for ``dptsv``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds (n - 1)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('d') with bounds (n)\ndu : rank-1 array('d') with bounds (n - 1) and e storage\nx : rank-2 array('d') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def dptsvx(d, e, b, fact=..., df=..., ef=...) -> typing.Any: - "df,ef,x,rcond,ferr,berr,info = dptsvx(d,e,b,[fact,df,ef])\n\nWrapper for ``dptsvx``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds (max(0, n-1))\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'N'\ndf : input rank-1 array('d') with bounds (n)\nef : input rank-1 array('d') with bounds (max(0, n-1))\n\nReturns\n-------\ndf : rank-1 array('d') with bounds (n)\nef : rank-1 array('d') with bounds (max(0, n-1))\nx : rank-2 array('d') with bounds (ldx,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def dpttrf(d, e, overwrite_d=..., overwrite_e=...) -> typing.Any: - "d,e,info = dpttrf(d,e,[overwrite_d,overwrite_e])\n\nWrapper for ``dpttrf``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds ((n>0?n-1:0))\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('d') with bounds (n)\ne : rank-1 array('d') with bounds ((n>0?n-1:0))\ninfo : int\n" - ... - -def dpttrs(d, e, b, overwrite_b=...) -> typing.Any: - "x,info = dpttrs(d,e,b,[overwrite_b])\n\nWrapper for ``dpttrs``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds ((n>0?n-1:0))\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dsbev(ab, compute_v=..., lower=..., ldab=..., overwrite_ab=...) -> typing.Any: - "w,z,info = dsbev(ab,[compute_v,lower,ldab,overwrite_ab])\n\nWrapper for ``dsbev``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nz : rank-2 array('d') with bounds (ldz,ldz)\ninfo : int\n" - ... - -def dsbevd(ab, compute_v=..., lower=..., ldab=..., liwork=..., overwrite_ab=...) -> typing.Any: - "w,z,info = dsbevd(ab,[compute_v,lower,ldab,liwork,overwrite_ab])\n\nWrapper for ``dsbevd``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\nliwork : input int, optional\n Default: (compute_v?3+5*n:1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nz : rank-2 array('d') with bounds (ldz,ldz)\ninfo : int\n" - ... - -def dsbevx( - ab, vl, vu, il, iu, ldab=..., compute_v=..., range=..., lower=..., abstol=..., mmax=..., overwrite_ab=... -) -> typing.Any: - "w,z,m,ifail,info = dsbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])\n\nWrapper for ``dsbevx``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\nvl : input float\nvu : input float\nil : input int\niu : input int\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\nldab : input int, optional\n Default: shape(ab,0)\ncompute_v : input int, optional\n Default: 1\nrange : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nabstol : input float, optional\n Default: 0.0\nmmax : input int, optional\n Default: (compute_v?(range==2?(iu-il+1):n):1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nz : rank-2 array('d') with bounds (ldz,mmax)\nm : int\nifail : rank-1 array('i') with bounds ((compute_v?n:1))\ninfo : int\n" - ... - -def dsfrk(n, k, alpha, a, beta, c, transr=..., uplo=..., trans=..., overwrite_c=...) -> typing.Any: - "cout = dsfrk(n,k,alpha,a,beta,c,[transr,uplo,trans,overwrite_c])\n\nWrapper for ``dsfrk``.\n\nParameters\n----------\nn : input int\nk : input int\nalpha : input float\na : input rank-2 array('d') with bounds (lda,ka)\nbeta : input float\nc : input rank-1 array('d') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\ncout : rank-1 array('d') with bounds (nt) and c storage\n" - ... - -def dstebz(d, e, range, vl, vu, il, iu, tol, order) -> typing.Any: - "m,w,iblock,isplit,info = dstebz(d,e,range,vl,vu,il,iu,tol,order)\n\nWrapper for ``dstebz``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds (n - 1)\nrange : input int\nvl : input float\nvu : input float\nil : input int\niu : input int\ntol : input float\norder : input string(len=1)\n\nReturns\n-------\nm : int\nw : rank-1 array('d') with bounds (n)\niblock : rank-1 array('i') with bounds (n)\nisplit : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def dstein(d, e, w, iblock, isplit) -> typing.Any: - "z,info = dstein(d,e,w,iblock,isplit)\n\nWrapper for ``dstein``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds (n - 1)\nw : input rank-1 array('d') with bounds (m)\niblock : input rank-1 array('i') with bounds (n)\nisplit : input rank-1 array('i') with bounds (n)\n\nReturns\n-------\nz : rank-2 array('d') with bounds (ldz,m)\ninfo : int\n" - ... - -def dstemr(d, e, range, vl, vu, il, iu, compute_v=..., lwork=..., liwork=..., overwrite_d=...) -> typing.Any: - "m,w,z,info = dstemr(d,e,range,vl,vu,il,iu,[compute_v,lwork,liwork,overwrite_d])\n\nWrapper for ``dstemr``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds (n)\nrange : input int\nvl : input float\nvu : input float\nil : input int\niu : input int\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\ncompute_v : input int, optional\n Default: 1\nlwork : input int, optional\n Default: max((compute_v?18*n:12*n),1)\nliwork : input int, optional\n Default: (compute_v?10*n:8*n)\n\nReturns\n-------\nm : int\nw : rank-1 array('d') with bounds (n)\nz : rank-2 array('d') with bounds (n,n)\ninfo : int\n" - ... - -def dstemr_lwork(d, e, range, vl, vu, il, iu, compute_v=..., overwrite_d=..., overwrite_e=...) -> typing.Any: - "work,iwork,info = dstemr_lwork(d,e,range,vl,vu,il,iu,[compute_v,overwrite_d,overwrite_e])\n\nWrapper for ``dstemr_lwork``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds (n)\nrange : input int\nvl : input float\nvu : input float\nil : input int\niu : input int\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\ncompute_v : input int, optional\n Default: 1\n\nReturns\n-------\nwork : float\niwork : int\ninfo : int\n" - ... - -def dsterf(d, e, overwrite_d=..., overwrite_e=...) -> typing.Any: - "vals,info = dsterf(d,e,[overwrite_d,overwrite_e])\n\nWrapper for ``dsterf``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds (n - 1)\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\n\nReturns\n-------\nvals : rank-1 array('d') with bounds (n) and d storage\ninfo : int\n" - ... - -def dstev(d, e, compute_v=..., overwrite_d=..., overwrite_e=...) -> typing.Any: - "vals,z,info = dstev(d,e,[compute_v,overwrite_d,overwrite_e])\n\nWrapper for ``dstev``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds (MAX(n-1,1))\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\ncompute_v : input int, optional\n Default: 1\n\nReturns\n-------\nvals : rank-1 array('d') with bounds (n) and d storage\nz : rank-2 array('d') with bounds (ldz,(compute_v?n:1))\ninfo : int\n" - ... - -def dsycon(a, ipiv, anorm, lower=...) -> typing.Any: - "rcond,info = dsycon(a,ipiv,anorm,[lower])\n\nWrapper for ``dsycon``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def dsyconv(a, ipiv, lower=..., way=..., overwrite_a=...) -> typing.Any: - "a,e,info = dsyconv(a,ipiv,[lower,way,overwrite_a])\n\nWrapper for ``dsyconv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nway : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('d') with bounds (n,n)\ne : rank-1 array('d') with bounds (n)\ninfo : int\n" - ... - -def dsyequb(a, lower=...) -> typing.Any: - "s,scond,amax,info = dsyequb(a,[lower])\n\nWrapper for ``dsyequb``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ns : rank-1 array('d') with bounds (n)\nscond : float\namax : float\ninfo : int\n" - ... - -def dsyev(a, compute_v=..., lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "w,v,info = dsyev(a,[compute_v,lower,lwork,overwrite_a])\n\nWrapper for ``dsyev``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n-1,1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nv : rank-2 array('d') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def dsyev_lwork(n, lower=...) -> typing.Any: - "work,info = dsyev_lwork(n,[lower])\n\nWrapper for ``dsyev_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dsyevd(a, compute_v=..., lower=..., lwork=..., liwork=..., overwrite_a=...) -> typing.Any: - "w,v,info = dsyevd(a,[compute_v,lower,lwork,liwork,overwrite_a])\n\nWrapper for ``dsyevd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max((compute_v?1+6*n+2*n*n:2*n+1),1)\nliwork : input int, optional\n Default: (compute_v?3+5*n:1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nv : rank-2 array('d') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def dsyevd_lwork(n, compute_v=..., lower=...) -> typing.Any: - "work,iwork,info = dsyevd_lwork(n,[compute_v,lower])\n\nWrapper for ``dsyevd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\niwork : int\ninfo : int\n" - ... - -def dsyevr( - a, compute_v=..., range=..., lower=..., vl=..., vu=..., il=..., iu=..., abstol=..., lwork=..., liwork=..., overwrite_a=... -) -> typing.Any: - "w,z,m,isuppz,info = dsyevr(a,[compute_v,range,lower,vl,vu,il,iu,abstol,lwork,liwork,overwrite_a])\n\nWrapper for ``dsyevr``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds ``(n,n)``\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default ``1``\nrange : input string(len=1), optional\n Default ``'A'``\nlower : input int, optional\n Default ``0``\noverwrite_a : input int, optional\n Default ``0``\nvl : input float, optional\n Default ``0.0``\nvu : input float, optional\n Default ``1.0``\nil : input int, optional\n Default ``1``\niu : input int, optional\n Default ``n``\nabstol : input float, optional\n Default ``0.0``\nlwork : input int, optional\n Default ``max(26*n,1)``\nliwork : input int, optional\n Default ``max(1,10*n)``\n\nReturns\n-------\nw : rank-1 array('d') with bounds ``(n)``\nz : rank-2 array('d') with bounds ``((compute_v?MAX(0,n):0),(compute_v?(*range=='I'?iu-il+1:MAX(1,n)):0))``\nm : int\nisuppz : rank-1 array('i') with bounds ``((compute_v?(2*(*range=='A'||(*range=='I' && iu-il+1==n)?n:0)):0))``\ninfo : int\n" - ... - -def dsyevr_lwork(n, lower=...) -> typing.Any: - "work,iwork,info = dsyevr_lwork(n,[lower])\n\nWrapper for ``dsyevr_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\niwork : int\ninfo : int\n" - ... - -def dsyevx( - a, compute_v=..., range=..., lower=..., vl=..., vu=..., il=..., iu=..., abstol=..., lwork=..., overwrite_a=... -) -> typing.Any: - "w,z,m,ifail,info = dsyevx(a,[compute_v,range,lower,vl,vu,il,iu,abstol,lwork,overwrite_a])\n\nWrapper for ``dsyevx``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds ``(n,n)``\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default ``1``\nrange : input string(len=1), optional\n Default ``'A'``\nlower : input int, optional\n Default ``0``\noverwrite_a : input int, optional\n Default ``0``\nvl : input float, optional\n Default ``0.0``\nvu : input float, optional\n Default ``1.0``\nil : input int, optional\n Default ``1``\niu : input int, optional\n Default ``n``\nabstol : input float, optional\n Default ``0.0``\nlwork : input int, optional\n Default ``max(8*n,1)``\n\nReturns\n-------\nw : rank-1 array('d') with bounds ``(n)``\nz : rank-2 array('d') with bounds ``((compute_v?MAX(0,n):0),(compute_v?(*range=='I'?iu-il+1:MAX(1,n)):0))``\nm : int\nifail : rank-1 array('i') with bounds ``((compute_v?n:0))``\ninfo : int\n" - ... - -def dsyevx_lwork(n, lower=...) -> typing.Any: - "work,info = dsyevx_lwork(n,[lower])\n\nWrapper for ``dsyevx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dsygst(a, b, itype=..., lower=..., overwrite_a=...) -> typing.Any: - "c,info = dsygst(a,b,[itype,lower,overwrite_a])\n\nWrapper for ``dsygst``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nitype : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('d') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def dsygv(a, b, itype=..., jobz=..., uplo=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "w,v,info = dsygv(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``dsygv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\nitype : input int, optional\n Default: 1\njobz : input string(len=1), optional\n Default: 'V'\nuplo : input string(len=1), optional\n Default: 'L'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n-1,1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nv : rank-2 array('d') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def dsygv_lwork(n, uplo=...) -> typing.Any: - "work,info = dsygv_lwork(n,[uplo])\n\nWrapper for ``dsygv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'L'\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dsygvd(a, b, itype=..., jobz=..., uplo=..., lwork=..., liwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "w,v,info = dsygvd(a,b,[itype,jobz,uplo,lwork,liwork,overwrite_a,overwrite_b])\n\nWrapper for ``dsygvd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds ``(n,n)``\nb : input rank-2 array('d') with bounds ``(n,n)``\n\nOther Parameters\n----------------\nitype : input int, optional\n Default ``1``\njobz : input string(len=1), optional\n Default ``'V'``\nuplo : input string(len=1), optional\n Default ``'L'``\noverwrite_a : input int, optional\n Default ``0``\noverwrite_b : input int, optional\n Default ``0``\nlwork : input int, optional\n Default ``(*jobz=='N'?2*n+1:1+6*n+2*n*n)``\nliwork : input int, optional\n Default ``(*jobz=='N'?1:5*n+3)``\n\nReturns\n-------\nw : rank-1 array('d') with bounds ``(n)``\nv : rank-2 array('d') with bounds ``(n,n)`` with ``a`` storage\ninfo : int\n" - ... - -def dsygvx( - a, - b, - itype=..., - jobz=..., - range=..., - uplo=..., - vl=..., - vu=..., - il=..., - iu=..., - abstol=..., - lwork=..., - overwrite_a=..., - overwrite_b=..., -) -> typing.Any: - "w,z,m,ifail,info = dsygvx(a,b,[itype,jobz,range,uplo,vl,vu,il,iu,abstol,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``dsygvx``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\nitype : input int, optional\n Default: 1\njobz : input string(len=1), optional\n Default: 'V'\nrange : input string(len=1), optional\n Default: 'A'\nuplo : input string(len=1), optional\n Default: 'L'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nvl : input float, optional\n Default: 0.0\nvu : input float, optional\n Default: 1.0\nil : input int, optional\n Default: 1\niu : input int, optional\n Default: n\nabstol : input float, optional\n Default: 0.0\nlwork : input int, optional\n Default: max(8*n,1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nz : rank-2 array('d') with bounds ((jobz[0]=='V'?MAX(0,n):0),(jobz[0]=='V'?(range[0]=='I'?iu-il+1:MAX(1,n)):0))\nm : int\nifail : rank-1 array('i') with bounds ((jobz[0]=='N'?0:n))\ninfo : int\n" - ... - -def dsygvx_lwork(n, uplo=...) -> typing.Any: - "work,info = dsygvx_lwork(n,[uplo])\n\nWrapper for ``dsygvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'L'\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dsysv(a, b, lwork=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "udut,ipiv,x,info = dsysv(a,b,[lwork,lower,overwrite_a,overwrite_b])\n\nWrapper for ``dsysv``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nudut : rank-2 array('d') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('d') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def dsysv_lwork(n, lower=...) -> typing.Any: - "work,info = dsysv_lwork(n,[lower])\n\nWrapper for ``dsysv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dsysvx(a, b, af=..., ipiv=..., lwork=..., factored=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a_s,udut,ipiv,b_s,x,rcond,ferr,berr,info = dsysvx(a,b,[af,ipiv,lwork,factored,lower,overwrite_a,overwrite_b])\n\nWrapper for ``dsysvx``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('d') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\nfactored : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na_s : rank-2 array('d') with bounds (n,n) and a storage\nudut : rank-2 array('d') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nb_s : rank-2 array('d') with bounds (n,nrhs) and b storage\nx : rank-2 array('d') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def dsysvx_lwork(n, lower=...) -> typing.Any: - "work,info = dsysvx_lwork(n,[lower])\n\nWrapper for ``dsysvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dsytf2(a, lower=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = dsytf2(a,[lower,overwrite_a])\n\nWrapper for ``dsytf2``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nldu : rank-2 array('d') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def dsytrd(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "c,d,e,tau,info = dsytrd(a,[lower,lwork,overwrite_a])\n\nWrapper for ``dsytrd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(n,1)\n\nReturns\n-------\nc : rank-2 array('d') with bounds (lda,n) and a storage\nd : rank-1 array('d') with bounds (n)\ne : rank-1 array('d') with bounds (n - 1)\ntau : rank-1 array('d') with bounds (n - 1)\ninfo : int\n" - ... - -def dsytrd_lwork(n, lower=...) -> typing.Any: - "work,info = dsytrd_lwork(n,[lower])\n\nWrapper for ``dsytrd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dsytrf(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = dsytrf(a,[lower,lwork,overwrite_a])\n\nWrapper for ``dsytrf``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\n\nReturns\n-------\nldu : rank-2 array('d') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def dsytrf_lwork(n, lower=...) -> typing.Any: - "work,info = dsytrf_lwork(n,[lower])\n\nWrapper for ``dsytrf_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def dtbtrs(ab, b, uplo=..., trans=..., diag=..., overwrite_b=...) -> typing.Any: - "x,info = dtbtrs(ab,b,[uplo,trans,diag,overwrite_b])\n\nWrapper for ``dtbtrs``.\n\nParameters\n----------\nab : input rank-2 array('d') with bounds (ldab,n)\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\ndiag : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dtfsm(alpha, a, b, transr=..., side=..., uplo=..., trans=..., diag=..., overwrite_b=...) -> typing.Any: - "x = dtfsm(alpha,a,b,[transr,side,uplo,trans,diag,overwrite_b])\n\nWrapper for ``dtfsm``.\n\nParameters\n----------\nalpha : input float\na : input rank-1 array('d') with bounds (nt)\nb : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nside : input string(len=1), optional\n Default: 'L'\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\ndiag : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (m,n) and b storage\n" - ... - -def dtfttp(n, arf, transr=..., uplo=...) -> typing.Any: - "ap,info = dtfttp(n,arf,[transr,uplo])\n\nWrapper for ``dtfttp``.\n\nParameters\n----------\nn : input int\narf : input rank-1 array('d') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nap : rank-1 array('d') with bounds (nt)\ninfo : int\n" - ... - -def dtfttr(n, arf, transr=..., uplo=...) -> typing.Any: - "a,info = dtfttr(n,arf,[transr,uplo])\n\nWrapper for ``dtfttr``.\n\nParameters\n----------\nn : input int\narf : input rank-1 array('d') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\na : rank-2 array('d') with bounds (lda,n)\ninfo : int\n" - ... - -def dtgsen( - select, a, b, q, z, lwork=..., liwork=..., overwrite_a=..., overwrite_b=..., overwrite_q=..., overwrite_z=... -) -> typing.Any: - "a,b,alphar,alphai,beta,q,z,m,pl,pr,dif,work,iwork,info = dtgsen(select,a,b,q,z,[lwork,liwork,overwrite_a,overwrite_b,overwrite_q,overwrite_z])\n\nWrapper for ``dtgsen``.\n\nParameters\n----------\nselect : input rank-1 array('i') with bounds (n)\na : input rank-2 array('d') with bounds (lda,n)\nb : input rank-2 array('d') with bounds (ldb,n)\nq : input rank-2 array('d') with bounds (ldq,n)\nz : input rank-2 array('d') with bounds (ldz,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\noverwrite_q : input int, optional\n Default: 0\noverwrite_z : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(MAX(4*n+16,2*m*(n-m)),1)\nliwork : input int, optional\n Default: n+6\n\nReturns\n-------\na : rank-2 array('d') with bounds (lda,n)\nb : rank-2 array('d') with bounds (ldb,n)\nalphar : rank-1 array('d') with bounds (n)\nalphai : rank-1 array('d') with bounds (n)\nbeta : rank-1 array('d') with bounds (n)\nq : rank-2 array('d') with bounds (ldq,n)\nz : rank-2 array('d') with bounds (ldz,n)\nm : int\npl : float\npr : float\ndif : rank-1 array('d') with bounds (2)\nwork : rank-1 array('d') with bounds (MAX(lwork,1))\niwork : rank-1 array('i') with bounds (MAX(1,liwork))\ninfo : int\n" - ... - -def dtpmqrt(l, v, t, a, b, side=..., trans=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a,b,info = dtpmqrt(l,v,t,a,b,[side,trans,overwrite_a,overwrite_b])\n\nWrapper for ``dtpmqrt``.\n\nParameters\n----------\nl : input int\nv : input rank-2 array('d') with bounds ((side[0]=='L'?m:n),k)\nt : input rank-2 array('d') with bounds (nb,k)\na : input rank-2 array('d') with bounds ((side[0]=='L'?k:m),(side[0]=='L'?n:k))\nb : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('d') with bounds ((side[0]=='L'?k:m),(side[0]=='L'?n:k))\nb : rank-2 array('d') with bounds (m,n)\ninfo : int\n" - ... - -def dtpqrt(l, nb, a, b, overwrite_a=..., overwrite_b=...) -> typing.Any: - "a,b,t,info = dtpqrt(l,nb,a,b,[overwrite_a,overwrite_b])\n\nWrapper for ``dtpqrt``.\n\nParameters\n----------\nl : input int\nnb : input int\na : input rank-2 array('d') with bounds (n,n)\nb : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('d') with bounds (n,n)\nb : rank-2 array('d') with bounds (m,n)\nt : rank-2 array('d') with bounds (nb,n)\ninfo : int\n" - ... - -def dtpttf(n, ap, transr=..., uplo=...) -> typing.Any: - "arf,info = dtpttf(n,ap,[transr,uplo])\n\nWrapper for ``dtpttf``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\narf : rank-1 array('d') with bounds (nt)\ninfo : int\n" - ... - -def dtpttr(n, ap, uplo=...) -> typing.Any: - "a,info = dtpttr(n,ap,[uplo])\n\nWrapper for ``dtpttr``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('d') with bounds (nt)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\na : rank-2 array('d') with bounds (n,n)\ninfo : int\n" - ... - -def dtrsyl(a, b, c, trana=..., tranb=..., isgn=..., overwrite_c=...) -> typing.Any: - "x,scale,info = dtrsyl(a,b,c,[trana,tranb,isgn,overwrite_c])\n\nWrapper for ``dtrsyl``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,m)\nb : input rank-2 array('d') with bounds (n,n)\nc : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\ntrana : input string(len=1), optional\n Default: 'N'\ntranb : input string(len=1), optional\n Default: 'N'\nisgn : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (m,n) and c storage\nscale : float\ninfo : int\n" - ... - -def dtrtri(c, lower=..., unitdiag=..., overwrite_c=...) -> typing.Any: - "inv_c,info = dtrtri(c,[lower,unitdiag,overwrite_c])\n\nWrapper for ``dtrtri``.\n\nParameters\n----------\nc : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nunitdiag : input int, optional\n Default: 0\n\nReturns\n-------\ninv_c : rank-2 array('d') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def dtrtrs(a, b, lower=..., trans=..., unitdiag=..., lda=..., overwrite_b=...) -> typing.Any: - "x,info = dtrtrs(a,b,[lower,trans,unitdiag,lda,overwrite_b])\n\nWrapper for ``dtrtrs``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (lda,n)\nb : input rank-2 array('d') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nunitdiag : input int, optional\n Default: 0\nlda : input int, optional\n Default: shape(a,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('d') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def dtrttf(a, transr=..., uplo=...) -> typing.Any: - "arf,info = dtrttf(a,[transr,uplo])\n\nWrapper for ``dtrttf``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (lda,n)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\narf : rank-1 array('d') with bounds (n*(n+1)/2)\ninfo : int\n" - ... - -def dtrttp(a, uplo=...) -> typing.Any: - "ap,info = dtrttp(a,[uplo])\n\nWrapper for ``dtrttp``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (lda,n)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nap : rank-1 array('d') with bounds (n*(n+1)/2)\ninfo : int\n" - ... - -def dtzrzf(a, lwork=..., overwrite_a=...) -> typing.Any: - "rz,tau,info = dtzrzf(a,[lwork,overwrite_a])\n\nWrapper for ``dtzrzf``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(m,1)\n\nReturns\n-------\nrz : rank-2 array('d') with bounds (m,n) and a storage\ntau : rank-1 array('d') with bounds (m)\ninfo : int\n" - ... - -def dtzrzf_lwork(m, n) -> typing.Any: - "work,info = dtzrzf_lwork(m,n)\n\nWrapper for ``dtzrzf_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def ilaver() -> typing.Any: - "major,minor,patch = ilaver()\n\nWrapper for ``ilaver``.\n\nReturns\n-------\nmajor : int\nminor : int\npatch : int\n" - ... - -def sgbsv(kl, ku, ab, b, overwrite_ab=..., overwrite_b=...) -> typing.Any: - "lub,piv,x,info = sgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])\n\nWrapper for ``sgbsv``.\n\nParameters\n----------\nkl : input int\nku : input int\nab : input rank-2 array('f') with bounds (2*kl+ku+1,n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nlub : rank-2 array('f') with bounds (2*kl+ku+1,n) and ab storage\npiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('f') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def sgbtrf(ab, kl, ku, m=..., n=..., ldab=..., overwrite_ab=...) -> typing.Any: - "lu,ipiv,info = sgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])\n\nWrapper for ``sgbtrf``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\nkl : input int\nku : input int\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(ab,1)\nn : input int, optional\n Default: shape(ab,1)\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: max(shape(ab,0),1)\n\nReturns\n-------\nlu : rank-2 array('f') with bounds (ldab,n) and ab storage\nipiv : rank-1 array('i') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def sgbtrs(ab, kl, ku, b, ipiv, trans=..., n=..., ldab=..., ldb=..., overwrite_b=...) -> typing.Any: - "x,info = sgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])\n\nWrapper for ``sgbtrs``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\nkl : input int\nku : input int\nb : input rank-2 array('f') with bounds (ldb,nrhs)\nipiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nn : input int, optional\n Default: shape(ab,1)\nldab : input int, optional\n Default: shape(ab,0)\nldb : input int, optional\n Default: shape(b,0)\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def sgebal(a, scale=..., permute=..., overwrite_a=...) -> typing.Any: - "ba,lo,hi,pivscale,info = sgebal(a,[scale,permute,overwrite_a])\n\nWrapper for ``sgebal``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\nscale : input int, optional\n Default: 0\npermute : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nba : rank-2 array('f') with bounds (m,n) and a storage\nlo : int\nhi : int\npivscale : rank-1 array('f') with bounds (n)\ninfo : int\n" - ... - -def sgecon(a, anorm, norm=...) -> typing.Any: - "rcond,info = sgecon(a,anorm,[norm])\n\nWrapper for ``sgecon``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nanorm : input float\n\nOther Parameters\n----------------\nnorm : input string(len=1), optional\n Default: '1'\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def sgeequ(a) -> typing.Any: - "r,c,rowcnd,colcnd,amax,info = sgeequ(a)\n\nWrapper for ``sgeequ``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nReturns\n-------\nr : rank-1 array('f') with bounds (m)\nc : rank-1 array('f') with bounds (n)\nrowcnd : float\ncolcnd : float\namax : float\ninfo : int\n" - ... - -def sgeequb(a) -> typing.Any: - "r,c,rowcnd,colcnd,amax,info = sgeequb(a)\n\nWrapper for ``sgeequb``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nReturns\n-------\nr : rank-1 array('f') with bounds (m)\nc : rank-1 array('f') with bounds (n)\nrowcnd : float\ncolcnd : float\namax : float\ninfo : int\n" - ... - -def sgees(sselect, a, compute_v=..., sort_t=..., lwork=..., sselect_extra_args=..., overwrite_a=...) -> typing.Any: - "t,sdim,wr,wi,vs,work,info = sgees(sselect,a,[compute_v,sort_t,lwork,sselect_extra_args,overwrite_a])\n\nWrapper for ``sgees``.\n\nParameters\n----------\nsselect : call-back function\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nsort_t : input int, optional\n Default: 0\nsselect_extra_args : input tuple, optional\n Default: ()\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nt : rank-2 array('f') with bounds (n,n) and a storage\nsdim : int\nwr : rank-1 array('f') with bounds (n)\nwi : rank-1 array('f') with bounds (n)\nvs : rank-2 array('f') with bounds (ldvs,n)\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n\nNotes\n-----\nCall-back functions::\n\n def sselect(arg1,arg2): return sselect\n Required arguments:\n arg1 : input float\n arg2 : input float\n Return objects:\n sselect : int\n" - ... - -def sgeev(a, compute_vl=..., compute_vr=..., lwork=..., overwrite_a=...) -> typing.Any: - "wr,wi,vl,vr,info = sgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])\n\nWrapper for ``sgeev``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(4*n,1)\n\nReturns\n-------\nwr : rank-1 array('f') with bounds (n)\nwi : rank-1 array('f') with bounds (n)\nvl : rank-2 array('f') with bounds (ldvl,n)\nvr : rank-2 array('f') with bounds (ldvr,n)\ninfo : int\n" - ... - -def sgeev_lwork(n, compute_vl=..., compute_vr=...) -> typing.Any: - "work,info = sgeev_lwork(n,[compute_vl,compute_vr])\n\nWrapper for ``sgeev_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgegv(*args, **kwds) -> typing.Any: - "`sgegv` is deprecated!\nThe `*gegv` family of routines has been deprecated in\nLAPACK 3.6.0 in favor of the `*ggev` family of routines.\nThe corresponding wrappers will be removed from SciPy in\na future release.\n\nalphar,alphai,beta,vl,vr,info = sgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``sgegv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(8*n,1)\n\nReturns\n-------\nalphar : rank-1 array('f') with bounds (n)\nalphai : rank-1 array('f') with bounds (n)\nbeta : rank-1 array('f') with bounds (n)\nvl : rank-2 array('f') with bounds (ldvl,n)\nvr : rank-2 array('f') with bounds (ldvr,n)\ninfo : int\n" - ... - -def sgehrd(a, lo=..., hi=..., lwork=..., overwrite_a=...) -> typing.Any: - "ht,tau,info = sgehrd(a,[lo,hi,lwork,overwrite_a])\n\nWrapper for ``sgehrd``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(n,1)\n\nReturns\n-------\nht : rank-2 array('f') with bounds (n,n) and a storage\ntau : rank-1 array('f') with bounds (n - 1)\ninfo : int\n" - ... - -def sgehrd_lwork(n, lo=..., hi=...) -> typing.Any: - "work,info = sgehrd_lwork(n,[lo,hi])\n\nWrapper for ``sgehrd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgejsv(a, joba=..., jobu=..., jobv=..., jobr=..., jobt=..., jobp=..., lwork=..., overwrite_a=...) -> typing.Any: - "sva,u,v,workout,iworkout,info = sgejsv(a,[joba,jobu,jobv,jobr,jobt,jobp,lwork,overwrite_a])\n\nWrapper for ``sgejsv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (lda,n)\n\nOther Parameters\n----------------\njoba : input int, optional\n Default: 4\njobu : input int, optional\n Default: 0\njobv : input int, optional\n Default: 0\njobr : input int, optional\n Default: 1\njobt : input int, optional\n Default: 0\njobp : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(6*n+2*n*n, max(2*m+n, max(4*n+n*n, max(2*n+n*n+6, 7))))\n\nReturns\n-------\nsva : rank-1 array('f') with bounds (n)\nu : rank-2 array('f') with bounds (((jobt == 0)&&(jobu == 3)?0:m),((jobt == 0)&&(jobu == 3)?0:(jobu == 1?m:n)))\nv : rank-2 array('f') with bounds (((jobt == 0)&&(jobv == 3)?0:ldv),((jobt == 0)&&(jobv == 3)?0:n))\nworkout : rank-1 array('f') with bounds (7)\niworkout : rank-1 array('i') with bounds (3)\ninfo : int\n" - ... - -def sgels(a, b, trans=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "lqr,x,info = sgels(a,b,[trans,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``sgels``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\nb : input rank-2 array('f') with bounds (MAX(m,n),nrhs)\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(MIN(m,n)+MAX(MIN(m,n),nrhs),1)\n\nReturns\n-------\nlqr : rank-2 array('f') with bounds (m,n) and a storage\nx : rank-2 array('f') with bounds (MAX(m,n),nrhs) and b storage\ninfo : int\n" - ... - -def sgels_lwork(m, n, nrhs, trans=...) -> typing.Any: - "work,info = sgels_lwork(m,n,nrhs,[trans])\n\nWrapper for ``sgels_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgelsd(a, b, lwork, size_iwork, cond=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "x,s,rank,info = sgelsd(a,b,lwork,size_iwork,[cond,overwrite_a,overwrite_b])\n\nWrapper for ``sgelsd``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\nb : input rank-2 array('f') with bounds (maxmn,nrhs)\nlwork : input int\nsize_iwork : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\ncond : input float, optional\n Default: -1.0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (maxmn,nrhs) and b storage\ns : rank-1 array('f') with bounds (minmn)\nrank : int\ninfo : int\n" - ... - -def sgelsd_lwork(m, n, nrhs, cond=..., lwork=...) -> typing.Any: - "work,iwork,info = sgelsd_lwork(m,n,nrhs,[cond,lwork])\n\nWrapper for ``sgelsd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : float\niwork : int\ninfo : int\n" - ... - -def sgelss(a, b, cond=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "v,x,s,rank,work,info = sgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``sgelss``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\nb : input rank-2 array('f') with bounds (maxmn,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: max(3*minmn+MAX(2*minmn,MAX(maxmn,nrhs)),1)\n\nReturns\n-------\nv : rank-2 array('f') with bounds (m,n) and a storage\nx : rank-2 array('f') with bounds (maxmn,nrhs) and b storage\ns : rank-1 array('f') with bounds (minmn)\nrank : int\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def sgelss_lwork(m, n, nrhs, cond=..., lwork=...) -> typing.Any: - "work,info = sgelss_lwork(m,n,nrhs,[cond,lwork])\n\nWrapper for ``sgelss_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgelsy(a, b, jptv, cond, lwork, overwrite_a=..., overwrite_b=...) -> typing.Any: - "v,x,j,rank,info = sgelsy(a,b,jptv,cond,lwork,[overwrite_a,overwrite_b])\n\nWrapper for ``sgelsy``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\nb : input rank-2 array('f') with bounds (maxmn,nrhs)\njptv : input rank-1 array('i') with bounds (n)\ncond : input float\nlwork : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nv : rank-2 array('f') with bounds (m,n) and a storage\nx : rank-2 array('f') with bounds (maxmn,nrhs) and b storage\nj : rank-1 array('i') with bounds (n) and jptv storage\nrank : int\ninfo : int\n" - ... - -def sgelsy_lwork(m, n, nrhs, cond, lwork=...) -> typing.Any: - "work,info = sgelsy_lwork(m,n,nrhs,cond,[lwork])\n\nWrapper for ``sgelsy_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\ncond : input float\n\nOther Parameters\n----------------\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgemqrt(v, t, c, side=..., trans=..., overwrite_c=...) -> typing.Any: - "c,info = sgemqrt(v,t,c,[side,trans,overwrite_c])\n\nWrapper for ``sgemqrt``.\n\nParameters\n----------\nv : input rank-2 array('f') with bounds ((side[0]=='L'?m:n),k)\nt : input rank-2 array('f') with bounds (nb,k)\nc : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (m,n)\ninfo : int\n" - ... - -def sgeqp3(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,jpvt,tau,work,info = sgeqp3(a,[lwork,overwrite_a])\n\nWrapper for ``sgeqp3``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*(n+1),1)\n\nReturns\n-------\nqr : rank-2 array('f') with bounds (m,n) and a storage\njpvt : rank-1 array('i') with bounds (n)\ntau : rank-1 array('f') with bounds (MIN(m,n))\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def sgeqrf(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,work,info = sgeqrf(a,[lwork,overwrite_a])\n\nWrapper for ``sgeqrf``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nqr : rank-2 array('f') with bounds (m,n) and a storage\ntau : rank-1 array('f') with bounds (MIN(m,n))\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def sgeqrf_lwork(m, n) -> typing.Any: - "work,info = sgeqrf_lwork(m,n)\n\nWrapper for ``sgeqrf_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgeqrfp(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,info = sgeqrfp(a,[lwork,overwrite_a])\n\nWrapper for ``sgeqrfp``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(1, n)\n\nReturns\n-------\nqr : rank-2 array('f') with bounds (m,n) and a storage\ntau : rank-1 array('f') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def sgeqrfp_lwork(m, n) -> typing.Any: - "work,info = sgeqrfp_lwork(m,n)\n\nWrapper for ``sgeqrfp_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgeqrt(nb, a, overwrite_a=...) -> typing.Any: - "a,t,info = sgeqrt(nb,a,[overwrite_a])\n\nWrapper for ``sgeqrt``.\n\nParameters\n----------\nnb : input int\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('f') with bounds (m,n)\nt : rank-2 array('f') with bounds (nb,MIN(m,n))\ninfo : int\n" - ... - -def sgerqf(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,work,info = sgerqf(a,[lwork,overwrite_a])\n\nWrapper for ``sgerqf``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*m,1)\n\nReturns\n-------\nqr : rank-2 array('f') with bounds (m,n) and a storage\ntau : rank-1 array('f') with bounds (MIN(m,n))\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def sgesc2(lu, rhs, ipiv, jpiv, overwrite_rhs=...) -> typing.Any: - "x,scale = sgesc2(lu,rhs,ipiv,jpiv,[overwrite_rhs])\n\nWrapper for ``sgesc2``.\n\nParameters\n----------\nlu : input rank-2 array('f') with bounds (n,n)\nrhs : input rank-1 array('f') with bounds (n)\nipiv : input rank-1 array('i') with bounds (n)\njpiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_rhs : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('f') with bounds (n) and rhs storage\nscale : float\n" - ... - -def sgesdd(a, compute_uv=..., full_matrices=..., lwork=..., overwrite_a=...) -> typing.Any: - "u,s,vt,info = sgesdd(a,[compute_uv,full_matrices,lwork,overwrite_a])\n\nWrapper for ``sgesdd``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\nlwork : input int, optional\n Default: max((compute_uv?4*minmn*minmn+MAX(m,n)+9*minmn:MAX(14*minmn+4,10*minmn+2+25*(25+8))+MAX(m,n)),1)\n\nReturns\n-------\nu : rank-2 array('f') with bounds (u0,u1)\ns : rank-1 array('f') with bounds (minmn)\nvt : rank-2 array('f') with bounds (vt0,vt1)\ninfo : int\n" - ... - -def sgesdd_lwork(m, n, compute_uv=..., full_matrices=...) -> typing.Any: - "work,info = sgesdd_lwork(m,n,[compute_uv,full_matrices])\n\nWrapper for ``sgesdd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgesv(a, b, overwrite_a=..., overwrite_b=...) -> typing.Any: - "lu,piv,x,info = sgesv(a,b,[overwrite_a,overwrite_b])\n\nWrapper for ``sgesv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('f') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('f') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def sgesvd(a, compute_uv=..., full_matrices=..., lwork=..., overwrite_a=...) -> typing.Any: - "u,s,vt,info = sgesvd(a,[compute_uv,full_matrices,lwork,overwrite_a])\n\nWrapper for ``sgesvd``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\nlwork : input int, optional\n Default: max(MAX(3*minmn+MAX(m,n),5*minmn),1)\n\nReturns\n-------\nu : rank-2 array('f') with bounds (u0,u1)\ns : rank-1 array('f') with bounds (minmn)\nvt : rank-2 array('f') with bounds (vt0,vt1)\ninfo : int\n" - ... - -def sgesvd_lwork(m, n, compute_uv=..., full_matrices=...) -> typing.Any: - "work,info = sgesvd_lwork(m,n,[compute_uv,full_matrices])\n\nWrapper for ``sgesvd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgesvx(a, b, fact=..., trans=..., af=..., ipiv=..., equed=..., r=..., c=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "as,lu,ipiv,equed,rs,cs,bs,x,rcond,ferr,berr,info = sgesvx(a,b,[fact,trans,af,ipiv,equed,r,c,overwrite_a,overwrite_b])\n\nWrapper for ``sgesvx``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'E'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('f') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nequed : input string(len=1), optional\n Default: 'B'\nr : input rank-1 array('f') with bounds (n)\nc : input rank-1 array('f') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nas : rank-2 array('f') with bounds (n,n) and a storage\nlu : rank-2 array('f') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nequed : string(len=1)\nrs : rank-1 array('f') with bounds (n) and r storage\ncs : rank-1 array('f') with bounds (n) and c storage\nbs : rank-2 array('f') with bounds (n,nrhs) and b storage\nx : rank-2 array('f') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def sgetc2(a, overwrite_a=...) -> typing.Any: - "lu,ipiv,jpiv,info = sgetc2(a,[overwrite_a])\n\nWrapper for ``sgetc2``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('f') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\njpiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def sgetrf(a, overwrite_a=...) -> typing.Any: - "lu,piv,info = sgetrf(a,[overwrite_a])\n\nWrapper for ``sgetrf``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('f') with bounds (m,n) and a storage\npiv : rank-1 array('i') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def sgetri(lu, piv, lwork=..., overwrite_lu=...) -> typing.Any: - "inv_a,info = sgetri(lu,piv,[lwork,overwrite_lu])\n\nWrapper for ``sgetri``.\n\nParameters\n----------\nlu : input rank-2 array('f') with bounds (n,n)\npiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_lu : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\ninv_a : rank-2 array('f') with bounds (n,n) and lu storage\ninfo : int\n" - ... - -def sgetri_lwork(n) -> typing.Any: - "work,info = sgetri_lwork(n)\n\nWrapper for ``sgetri_lwork``.\n\nParameters\n----------\nn : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgetrs(lu, piv, b, trans=..., overwrite_b=...) -> typing.Any: - "x,info = sgetrs(lu,piv,b,[trans,overwrite_b])\n\nWrapper for ``sgetrs``.\n\nParameters\n----------\nlu : input rank-2 array('f') with bounds (n,n)\npiv : input rank-1 array('i') with bounds (n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def sgges( - sselect, - a, - b, - jobvsl=..., - jobvsr=..., - sort_t=..., - ldvsl=..., - ldvsr=..., - lwork=..., - sselect_extra_args=..., - overwrite_a=..., - overwrite_b=..., -) -> typing.Any: - "a,b,sdim,alphar,alphai,beta,vsl,vsr,work,info = sgges(sselect,a,b,[jobvsl,jobvsr,sort_t,ldvsl,ldvsr,lwork,sselect_extra_args,overwrite_a,overwrite_b])\n\nWrapper for ``sgges``.\n\nParameters\n----------\nsselect : call-back function\na : input rank-2 array('f') with bounds (lda,n)\nb : input rank-2 array('f') with bounds (ldb,n)\n\nOther Parameters\n----------------\njobvsl : input int, optional\n Default: 1\njobvsr : input int, optional\n Default: 1\nsort_t : input int, optional\n Default: 0\nsselect_extra_args : input tuple, optional\n Default: ()\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nldvsl : input int, optional\n Default: ((jobvsl==1)?n:1)\nldvsr : input int, optional\n Default: ((jobvsr==1)?n:1)\nlwork : input int, optional\n Default: max(8*n+16,1)\n\nReturns\n-------\na : rank-2 array('f') with bounds (lda,n)\nb : rank-2 array('f') with bounds (ldb,n)\nsdim : int\nalphar : rank-1 array('f') with bounds (n)\nalphai : rank-1 array('f') with bounds (n)\nbeta : rank-1 array('f') with bounds (n)\nvsl : rank-2 array('f') with bounds (ldvsl,n)\nvsr : rank-2 array('f') with bounds (ldvsr,n)\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n\nNotes\n-----\nCall-back functions::\n\n def sselect(alphar,alphai,beta): return sselect\n Required arguments:\n alphar : input float\n alphai : input float\n beta : input float\n Return objects:\n sselect : int\n" - ... - -def sggev(a, b, compute_vl=..., compute_vr=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "alphar,alphai,beta,vl,vr,work,info = sggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``sggev``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(8*n,1)\n\nReturns\n-------\nalphar : rank-1 array('f') with bounds (n)\nalphai : rank-1 array('f') with bounds (n)\nbeta : rank-1 array('f') with bounds (n)\nvl : rank-2 array('f') with bounds (ldvl,n)\nvr : rank-2 array('f') with bounds (ldvr,n)\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def sgglse(a, b, c, d, lwork=..., overwrite_a=..., overwrite_b=..., overwrite_c=..., overwrite_d=...) -> typing.Any: - "t,r,res,x,info = sgglse(a,b,c,d,[lwork,overwrite_a,overwrite_b,overwrite_c,overwrite_d])\n\nWrapper for ``sgglse``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\nb : input rank-2 array('f') with bounds (p,n)\nc : input rank-1 array('f') with bounds (m)\nd : input rank-1 array('f') with bounds (p)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\noverwrite_c : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(m+n+p,1)\n\nReturns\n-------\nt : rank-2 array('f') with bounds (m,n) and a storage\nr : rank-2 array('f') with bounds (p,n) and b storage\nres : rank-1 array('f') with bounds (m) and c storage\nx : rank-1 array('f') with bounds (n)\ninfo : int\n" - ... - -def sgglse_lwork(m, n, p) -> typing.Any: - "work,info = sgglse_lwork(m,n,p)\n\nWrapper for ``sgglse_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\np : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sgtsv(dl, d, du, b, overwrite_dl=..., overwrite_d=..., overwrite_du=..., overwrite_b=...) -> typing.Any: - "du2,d,du,x,info = sgtsv(dl,d,du,b,[overwrite_dl,overwrite_d,overwrite_du,overwrite_b])\n\nWrapper for ``sgtsv``.\n\nParameters\n----------\ndl : input rank-1 array('f') with bounds (n - 1)\nd : input rank-1 array('f') with bounds (n)\ndu : input rank-1 array('f') with bounds (n - 1)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_dl : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_du : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\ndu2 : rank-1 array('f') with bounds (n - 1) and dl storage\nd : rank-1 array('f') with bounds (n)\ndu : rank-1 array('f') with bounds (n - 1)\nx : rank-2 array('f') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def sgtsvx(dl, d, du, b, fact=..., trans=..., dlf=..., df=..., duf=..., du2=..., ipiv=...) -> typing.Any: - "dlf,df,duf,du2,ipiv,x,rcond,ferr,berr,info = sgtsvx(dl,d,du,b,[fact,trans,dlf,df,duf,du2,ipiv])\n\nWrapper for ``sgtsvx``.\n\nParameters\n----------\ndl : input rank-1 array('f') with bounds (MAX(0, n-1))\nd : input rank-1 array('f') with bounds (n)\ndu : input rank-1 array('f') with bounds (MAX(0, n-1))\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'N'\ntrans : input string(len=1), optional\n Default: 'N'\ndlf : input rank-1 array('f') with bounds (MAX(0,n-1))\ndf : input rank-1 array('f') with bounds (n)\nduf : input rank-1 array('f') with bounds (MAX(0,n-1))\ndu2 : input rank-1 array('f') with bounds (MAX(0,n-2))\nipiv : input rank-1 array('i') with bounds (n)\n\nReturns\n-------\ndlf : rank-1 array('f') with bounds (MAX(0,n-1))\ndf : rank-1 array('f') with bounds (n)\nduf : rank-1 array('f') with bounds (MAX(0,n-1))\ndu2 : rank-1 array('f') with bounds (MAX(0,n-2))\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('f') with bounds (ldx,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def sgttrf(dl, d, du, overwrite_dl=..., overwrite_d=..., overwrite_du=...) -> typing.Any: - "dl,d,du,du2,ipiv,info = sgttrf(dl,d,du,[overwrite_dl,overwrite_d,overwrite_du])\n\nWrapper for ``sgttrf``.\n\nParameters\n----------\ndl : input rank-1 array('f') with bounds (n - 1)\nd : input rank-1 array('f') with bounds (n)\ndu : input rank-1 array('f') with bounds (n - 1)\n\nOther Parameters\n----------------\noverwrite_dl : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_du : input int, optional\n Default: 0\n\nReturns\n-------\ndl : rank-1 array('f') with bounds (n - 1)\nd : rank-1 array('f') with bounds (n)\ndu : rank-1 array('f') with bounds (n - 1)\ndu2 : rank-1 array('f') with bounds (n - 2)\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def sgttrs(dl, d, du, du2, ipiv, b, trans=..., overwrite_b=...) -> typing.Any: - "x,info = sgttrs(dl,d,du,du2,ipiv,b,[trans,overwrite_b])\n\nWrapper for ``sgttrs``.\n\nParameters\n----------\ndl : input rank-1 array('f') with bounds (n - 1)\nd : input rank-1 array('f') with bounds (n)\ndu : input rank-1 array('f') with bounds (n - 1)\ndu2 : input rank-1 array('f') with bounds (n - 2)\nipiv : input rank-1 array('i') with bounds (n)\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def slamch(cmach) -> typing.Any: - "slamch = slamch(cmach)\n\nWrapper for ``slamch``.\n\nParameters\n----------\ncmach : input string(len=1)\n\nReturns\n-------\nslamch : float\n" - ... - -def slange(norm, a) -> typing.Any: - "n2 = slange(norm,a)\n\nWrapper for ``slange``.\n\nParameters\n----------\nnorm : input string(len=1)\na : input rank-2 array('f') with bounds (m,n)\n\nReturns\n-------\nn2 : float\n" - ... - -def slarf(v, tau, c, work, side=..., incv=..., overwrite_c=...) -> typing.Any: - "c = slarf(v,tau,c,work,[side,incv,overwrite_c])\n\nWrapper for ``slarf``.\n\nParameters\n----------\nv : input rank-1 array('f') with bounds ((side[0]=='L'?(1 + (m-1)*abs(incv)):(1 + (n-1)*abs(incv))))\ntau : input float\nc : input rank-2 array('f') with bounds (m,n)\nwork : input rank-1 array('f') with bounds (lwork)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\nincv : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (m,n)\n" - ... - -def slarfg(n, alpha, x, incx=..., overwrite_x=...) -> typing.Any: - "alpha,x,tau = slarfg(n,alpha,x,[incx,overwrite_x])\n\nWrapper for ``slarfg``.\n\nParameters\n----------\nn : input int\nalpha : input float\nx : input rank-1 array('f') with bounds (lx)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nalpha : float\nx : rank-1 array('f') with bounds (lx)\ntau : float\n" - ... - -def slartg(f, g) -> typing.Any: - "cs,sn,r = slartg(f,g)\n\nWrapper for ``slartg``.\n\nParameters\n----------\nf : input float\ng : input float\n\nReturns\n-------\ncs : float\nsn : float\nr : float\n" - ... - -def slasd4(i, d, z, rho=...) -> typing.Any: - "delta,sigma,work,info = slasd4(i,d,z,[rho])\n\nWrapper for ``slasd4``.\n\nParameters\n----------\ni : input int\nd : input rank-1 array('f') with bounds (n)\nz : input rank-1 array('f') with bounds (n)\n\nOther Parameters\n----------------\nrho : input float, optional\n Default: 1.0\n\nReturns\n-------\ndelta : rank-1 array('f') with bounds (n)\nsigma : float\nwork : rank-1 array('f') with bounds (n)\ninfo : int\n" - ... - -def slaswp(a, piv, k1=..., k2=..., off=..., inc=..., overwrite_a=...) -> typing.Any: - "a = slaswp(a,piv,[k1,k2,off,inc,overwrite_a])\n\nWrapper for ``slaswp``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (nrows,n)\npiv : input rank-1 array('i') with bounds (npiv)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nk1 : input int, optional\n Default: 0\nk2 : input int, optional\n Default: npiv-1\noff : input int, optional\n Default: 0\ninc : input int, optional\n Default: 1\n\nReturns\n-------\na : rank-2 array('f') with bounds (nrows,n)\n" - ... - -def slauum(c, lower=..., overwrite_c=...) -> typing.Any: - "a,info = slauum(c,[lower,overwrite_c])\n\nWrapper for ``slauum``.\n\nParameters\n----------\nc : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('f') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def sorcsd( - x11, - x12, - x21, - x22, - compute_u1=..., - compute_u2=..., - compute_v1t=..., - compute_v2t=..., - trans=..., - signs=..., - lwork=..., - overwrite_x11=..., - overwrite_x12=..., - overwrite_x21=..., - overwrite_x22=..., -) -> typing.Any: - "cs11,cs12,cs21,cs22,theta,u1,u2,v1t,v2t,info = sorcsd(x11,x12,x21,x22,[compute_u1,compute_u2,compute_v1t,compute_v2t,trans,signs,lwork,overwrite_x11,overwrite_x12,overwrite_x21,overwrite_x22])\n\nWrapper for ``sorcsd``.\n\nParameters\n----------\nx11 : input rank-2 array('f') with bounds (p,q)\nx12 : input rank-2 array('f') with bounds (p,mmq)\nx21 : input rank-2 array('f') with bounds (mmp,q)\nx22 : input rank-2 array('f') with bounds (mmp,mmq)\n\nOther Parameters\n----------------\ncompute_u1 : input int, optional\n Default: 1\ncompute_u2 : input int, optional\n Default: 1\ncompute_v1t : input int, optional\n Default: 1\ncompute_v2t : input int, optional\n Default: 1\ntrans : input int, optional\n Default: 0\nsigns : input int, optional\n Default: 0\noverwrite_x11 : input int, optional\n Default: 0\noverwrite_x12 : input int, optional\n Default: 0\noverwrite_x21 : input int, optional\n Default: 0\noverwrite_x22 : input int, optional\n Default: 0\nlwork : input int, optional\n Default: 2+2*m+5*MAX(1,q-1)+4*MAX(1,q)+8*q\n\nReturns\n-------\ncs11 : rank-2 array('f') with bounds (p,q) and x11 storage\ncs12 : rank-2 array('f') with bounds (p,mmq) and x12 storage\ncs21 : rank-2 array('f') with bounds (mmp,q) and x21 storage\ncs22 : rank-2 array('f') with bounds (mmp,mmq) and x22 storage\ntheta : rank-1 array('f') with bounds (min(min(p,mmp),min(q,mmq)))\nu1 : rank-2 array('f') with bounds ((compute_u1?p:0),(compute_u1?p:0))\nu2 : rank-2 array('f') with bounds ((compute_u2?mmp:0),(compute_u2?mmp:0))\nv1t : rank-2 array('f') with bounds ((compute_v1t?q:0),(compute_v1t?q:0))\nv2t : rank-2 array('f') with bounds ((compute_v2t?mmq:0),(compute_v2t?mmq:0))\ninfo : int\n" - ... - -def sorcsd_lwork(m, p, q) -> typing.Any: - "work,info = sorcsd_lwork(m,p,q)\n\nWrapper for ``sorcsd_lwork``.\n\nParameters\n----------\nm : input int\np : input int\nq : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sorghr(a, tau, lo=..., hi=..., lwork=..., overwrite_a=...) -> typing.Any: - "ht,info = sorghr(a,tau,[lo,hi,lwork,overwrite_a])\n\nWrapper for ``sorghr``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\ntau : input rank-1 array('f') with bounds (n - 1)\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(hi-lo,1)\n\nReturns\n-------\nht : rank-2 array('f') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def sorghr_lwork(n, lo=..., hi=...) -> typing.Any: - "work,info = sorghr_lwork(n,[lo,hi])\n\nWrapper for ``sorghr_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def sorgqr(a, tau, lwork=..., overwrite_a=...) -> typing.Any: - "q,work,info = sorgqr(a,tau,[lwork,overwrite_a])\n\nWrapper for ``sorgqr``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\ntau : input rank-1 array('f') with bounds (k)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nq : rank-2 array('f') with bounds (m,n) and a storage\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def sorgrq(a, tau, lwork=..., overwrite_a=...) -> typing.Any: - "q,work,info = sorgrq(a,tau,[lwork,overwrite_a])\n\nWrapper for ``sorgrq``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\ntau : input rank-1 array('f') with bounds (k)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*m,1)\n\nReturns\n-------\nq : rank-2 array('f') with bounds (m,n) and a storage\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def sormqr(side, trans, a, tau, c, lwork, overwrite_c=...) -> typing.Any: - "cq,work,info = sormqr(side,trans,a,tau,c,lwork,[overwrite_c])\n\nWrapper for ``sormqr``.\n\nParameters\n----------\nside : input string(len=1)\ntrans : input string(len=1)\na : input rank-2 array('f') with bounds (lda,k)\ntau : input rank-1 array('f') with bounds (k)\nc : input rank-2 array('f') with bounds (ldc,n)\nlwork : input int\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\ncq : rank-2 array('f') with bounds (ldc,n) and c storage\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def sormrz(a, tau, c, side=..., trans=..., lwork=..., overwrite_c=...) -> typing.Any: - "cq,info = sormrz(a,tau,c,[side,trans,lwork,overwrite_c])\n\nWrapper for ``sormrz``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (k,nt)\ntau : input rank-1 array('f') with bounds (k)\nc : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX((side[0]=='L'?n:m),1)\n\nReturns\n-------\ncq : rank-2 array('f') with bounds (m,n) and c storage\ninfo : int\n" - ... - -def sormrz_lwork(m, n, side=..., trans=...) -> typing.Any: - "work,info = sormrz_lwork(m,n,[side,trans])\n\nWrapper for ``sormrz_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def spbsv(ab, b, lower=..., ldab=..., overwrite_ab=..., overwrite_b=...) -> typing.Any: - "c,x,info = spbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])\n\nWrapper for ``spbsv``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (ldab,n) and ab storage\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def spbtrf(ab, lower=..., ldab=..., overwrite_ab=...) -> typing.Any: - "c,info = spbtrf(ab,[lower,ldab,overwrite_ab])\n\nWrapper for ``spbtrf``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\n\nReturns\n-------\nc : rank-2 array('f') with bounds (ldab,n) and ab storage\ninfo : int\n" - ... - -def spbtrs(ab, b, lower=..., ldab=..., overwrite_b=...) -> typing.Any: - "x,info = spbtrs(ab,b,[lower,ldab,overwrite_b])\n\nWrapper for ``spbtrs``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def spftrf(n, a, transr=..., uplo=..., overwrite_a=...) -> typing.Any: - "achol,info = spftrf(n,a,[transr,uplo,overwrite_a])\n\nWrapper for ``spftrf``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('f') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nachol : rank-1 array('f') with bounds (nt) and a storage\ninfo : int\n" - ... - -def spftri(n, a, transr=..., uplo=..., overwrite_a=...) -> typing.Any: - "ainv,info = spftri(n,a,[transr,uplo,overwrite_a])\n\nWrapper for ``spftri``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('f') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nainv : rank-1 array('f') with bounds (nt) and a storage\ninfo : int\n" - ... - -def spftrs(n, a, b, transr=..., uplo=..., overwrite_b=...) -> typing.Any: - "x,info = spftrs(n,a,b,[transr,uplo,overwrite_b])\n\nWrapper for ``spftrs``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('f') with bounds (nt)\nb : input rank-2 array('f') with bounds (ldb,nhrs)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nhrs) and b storage\ninfo : int\n" - ... - -def spocon(a, anorm, uplo=...) -> typing.Any: - "rcond,info = spocon(a,anorm,[uplo])\n\nWrapper for ``spocon``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nanorm : input float\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def sposv(a, b, lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "c,x,info = sposv(a,b,[lower,overwrite_a,overwrite_b])\n\nWrapper for ``sposv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (n,n) and a storage\nx : rank-2 array('f') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def sposvx(a, b, fact=..., af=..., equed=..., s=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a_s,lu,equed,s,b_s,x,rcond,ferr,berr,info = sposvx(a,b,[fact,af,equed,s,lower,overwrite_a,overwrite_b])\n\nWrapper for ``sposvx``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'E'\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('f') with bounds (n,n)\nequed : input string(len=1), optional\n Default: 'Y'\ns : input rank-1 array('f') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na_s : rank-2 array('f') with bounds (n,n) and a storage\nlu : rank-2 array('f') with bounds (n,n) and af storage\nequed : string(len=1)\ns : rank-1 array('f') with bounds (n)\nb_s : rank-2 array('f') with bounds (n,nrhs) and b storage\nx : rank-2 array('f') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def spotrf(a, lower=..., clean=..., overwrite_a=...) -> typing.Any: - "c,info = spotrf(a,[lower,clean,overwrite_a])\n\nWrapper for ``spotrf``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nclean : input int, optional\n Default: 1\n\nReturns\n-------\nc : rank-2 array('f') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def spotri(c, lower=..., overwrite_c=...) -> typing.Any: - "inv_a,info = spotri(c,[lower,overwrite_c])\n\nWrapper for ``spotri``.\n\nParameters\n----------\nc : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ninv_a : rank-2 array('f') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def spotrs(c, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = spotrs(c,b,[lower,overwrite_b])\n\nWrapper for ``spotrs``.\n\nParameters\n----------\nc : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def sppcon(n, ap, anorm, lower=...) -> typing.Any: - "rcond,info = sppcon(n,ap,anorm,[lower])\n\nWrapper for ``sppcon``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (L)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def sppsv(n, ap, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = sppsv(n,ap,b,[lower,overwrite_b])\n\nWrapper for ``sppsv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (L)\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def spptrf(n, ap, lower=..., overwrite_ap=...) -> typing.Any: - "ul,info = spptrf(n,ap,[lower,overwrite_ap])\n\nWrapper for ``spptrf``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (L)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\n\nReturns\n-------\nul : rank-1 array('f') with bounds (L) and ap storage\ninfo : int\n" - ... - -def spptri(n, ap, lower=..., overwrite_ap=...) -> typing.Any: - "uli,info = spptri(n,ap,[lower,overwrite_ap])\n\nWrapper for ``spptri``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (L)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\n\nReturns\n-------\nuli : rank-1 array('f') with bounds (L) and ap storage\ninfo : int\n" - ... - -def spptrs(n, ap, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = spptrs(n,ap,b,[lower,overwrite_b])\n\nWrapper for ``spptrs``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (L)\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def spstf2(a, tol=..., lower=..., overwrite_a=...) -> typing.Any: - "c,piv,rank_c,info = spstf2(a,[tol,lower,overwrite_a])\n\nWrapper for ``spstf2``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ntol : input float, optional\n Default: -1.0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nrank_c : int\ninfo : int\n" - ... - -def spstrf(a, tol=..., lower=..., overwrite_a=...) -> typing.Any: - "c,piv,rank_c,info = spstrf(a,[tol,lower,overwrite_a])\n\nWrapper for ``spstrf``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ntol : input float, optional\n Default: -1.0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nrank_c : int\ninfo : int\n" - ... - -def spteqr(d, e, z, compute_z=..., overwrite_d=..., overwrite_e=..., overwrite_z=...) -> typing.Any: - "d,e,z,info = spteqr(d,e,z,[compute_z,overwrite_d,overwrite_e,overwrite_z])\n\nWrapper for ``spteqr``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds ((n>0?n-1:0))\nz : input rank-2 array('f') with bounds ((compute_z==0?shape(z, 0):max(1,n)),(compute_z==0?shape(z, 1):n))\n\nOther Parameters\n----------------\ncompute_z : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\noverwrite_z : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('f') with bounds (n)\ne : rank-1 array('f') with bounds ((n>0?n-1:0))\nz : rank-2 array('f') with bounds ((compute_z==0?shape(z, 0):max(1,n)),(compute_z==0?shape(z, 1):n))\ninfo : int\n" - ... - -def sptsv(d, e, b, overwrite_d=..., overwrite_e=..., overwrite_b=...) -> typing.Any: - "d,du,x,info = sptsv(d,e,b,[overwrite_d,overwrite_e,overwrite_b])\n\nWrapper for ``sptsv``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds (n - 1)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('f') with bounds (n)\ndu : rank-1 array('f') with bounds (n - 1) and e storage\nx : rank-2 array('f') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def sptsvx(d, e, b, fact=..., df=..., ef=...) -> typing.Any: - "df,ef,x,rcond,ferr,berr,info = sptsvx(d,e,b,[fact,df,ef])\n\nWrapper for ``sptsvx``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds (max(0, n-1))\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'N'\ndf : input rank-1 array('f') with bounds (n)\nef : input rank-1 array('f') with bounds (max(0, n-1))\n\nReturns\n-------\ndf : rank-1 array('f') with bounds (n)\nef : rank-1 array('f') with bounds (max(0, n-1))\nx : rank-2 array('f') with bounds (ldx,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def spttrf(d, e, overwrite_d=..., overwrite_e=...) -> typing.Any: - "d,e,info = spttrf(d,e,[overwrite_d,overwrite_e])\n\nWrapper for ``spttrf``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds ((n>0?n-1:0))\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('f') with bounds (n)\ne : rank-1 array('f') with bounds ((n>0?n-1:0))\ninfo : int\n" - ... - -def spttrs(d, e, b, overwrite_b=...) -> typing.Any: - "x,info = spttrs(d,e,b,[overwrite_b])\n\nWrapper for ``spttrs``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds ((n>0?n-1:0))\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def ssbev(ab, compute_v=..., lower=..., ldab=..., overwrite_ab=...) -> typing.Any: - "w,z,info = ssbev(ab,[compute_v,lower,ldab,overwrite_ab])\n\nWrapper for ``ssbev``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nz : rank-2 array('f') with bounds (ldz,ldz)\ninfo : int\n" - ... - -def ssbevd(ab, compute_v=..., lower=..., ldab=..., liwork=..., overwrite_ab=...) -> typing.Any: - "w,z,info = ssbevd(ab,[compute_v,lower,ldab,liwork,overwrite_ab])\n\nWrapper for ``ssbevd``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\nliwork : input int, optional\n Default: (compute_v?3+5*n:1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nz : rank-2 array('f') with bounds (ldz,ldz)\ninfo : int\n" - ... - -def ssbevx( - ab, vl, vu, il, iu, ldab=..., compute_v=..., range=..., lower=..., abstol=..., mmax=..., overwrite_ab=... -) -> typing.Any: - "w,z,m,ifail,info = ssbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])\n\nWrapper for ``ssbevx``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\nvl : input float\nvu : input float\nil : input int\niu : input int\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\nldab : input int, optional\n Default: shape(ab,0)\ncompute_v : input int, optional\n Default: 1\nrange : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nabstol : input float, optional\n Default: 0.0\nmmax : input int, optional\n Default: (compute_v?(range==2?(iu-il+1):n):1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nz : rank-2 array('f') with bounds (ldz,mmax)\nm : int\nifail : rank-1 array('i') with bounds ((compute_v?n:1))\ninfo : int\n" - ... - -def ssfrk(n, k, alpha, a, beta, c, transr=..., uplo=..., trans=..., overwrite_c=...) -> typing.Any: - "cout = ssfrk(n,k,alpha,a,beta,c,[transr,uplo,trans,overwrite_c])\n\nWrapper for ``ssfrk``.\n\nParameters\n----------\nn : input int\nk : input int\nalpha : input float\na : input rank-2 array('f') with bounds (lda,ka)\nbeta : input float\nc : input rank-1 array('f') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\ncout : rank-1 array('f') with bounds (nt) and c storage\n" - ... - -def sstebz(d, e, range, vl, vu, il, iu, tol, order) -> typing.Any: - "m,w,iblock,isplit,info = sstebz(d,e,range,vl,vu,il,iu,tol,order)\n\nWrapper for ``sstebz``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds (n - 1)\nrange : input int\nvl : input float\nvu : input float\nil : input int\niu : input int\ntol : input float\norder : input string(len=1)\n\nReturns\n-------\nm : int\nw : rank-1 array('f') with bounds (n)\niblock : rank-1 array('i') with bounds (n)\nisplit : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def sstein(d, e, w, iblock, isplit) -> typing.Any: - "z,info = sstein(d,e,w,iblock,isplit)\n\nWrapper for ``sstein``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds (n - 1)\nw : input rank-1 array('f') with bounds (m)\niblock : input rank-1 array('i') with bounds (n)\nisplit : input rank-1 array('i') with bounds (n)\n\nReturns\n-------\nz : rank-2 array('f') with bounds (ldz,m)\ninfo : int\n" - ... - -def sstemr(d, e, range, vl, vu, il, iu, compute_v=..., lwork=..., liwork=..., overwrite_d=...) -> typing.Any: - "m,w,z,info = sstemr(d,e,range,vl,vu,il,iu,[compute_v,lwork,liwork,overwrite_d])\n\nWrapper for ``sstemr``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds (n)\nrange : input int\nvl : input float\nvu : input float\nil : input int\niu : input int\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\ncompute_v : input int, optional\n Default: 1\nlwork : input int, optional\n Default: max((compute_v?18*n:12*n),1)\nliwork : input int, optional\n Default: (compute_v?10*n:8*n)\n\nReturns\n-------\nm : int\nw : rank-1 array('f') with bounds (n)\nz : rank-2 array('f') with bounds (n,n)\ninfo : int\n" - ... - -def sstemr_lwork(d, e, range, vl, vu, il, iu, compute_v=..., overwrite_d=..., overwrite_e=...) -> typing.Any: - "work,iwork,info = sstemr_lwork(d,e,range,vl,vu,il,iu,[compute_v,overwrite_d,overwrite_e])\n\nWrapper for ``sstemr_lwork``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds (n)\nrange : input int\nvl : input float\nvu : input float\nil : input int\niu : input int\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\ncompute_v : input int, optional\n Default: 1\n\nReturns\n-------\nwork : float\niwork : int\ninfo : int\n" - ... - -def ssterf(d, e, overwrite_d=..., overwrite_e=...) -> typing.Any: - "vals,info = ssterf(d,e,[overwrite_d,overwrite_e])\n\nWrapper for ``ssterf``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds (n - 1)\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\n\nReturns\n-------\nvals : rank-1 array('f') with bounds (n) and d storage\ninfo : int\n" - ... - -def sstev(d, e, compute_v=..., overwrite_d=..., overwrite_e=...) -> typing.Any: - "vals,z,info = sstev(d,e,[compute_v,overwrite_d,overwrite_e])\n\nWrapper for ``sstev``.\n\nParameters\n----------\nd : input rank-1 array('f') with bounds (n)\ne : input rank-1 array('f') with bounds (MAX(n-1,1))\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\ncompute_v : input int, optional\n Default: 1\n\nReturns\n-------\nvals : rank-1 array('f') with bounds (n) and d storage\nz : rank-2 array('f') with bounds (ldz,(compute_v?n:1))\ninfo : int\n" - ... - -def ssycon(a, ipiv, anorm, lower=...) -> typing.Any: - "rcond,info = ssycon(a,ipiv,anorm,[lower])\n\nWrapper for ``ssycon``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def ssyconv(a, ipiv, lower=..., way=..., overwrite_a=...) -> typing.Any: - "a,e,info = ssyconv(a,ipiv,[lower,way,overwrite_a])\n\nWrapper for ``ssyconv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nway : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('f') with bounds (n,n)\ne : rank-1 array('f') with bounds (n)\ninfo : int\n" - ... - -def ssyequb(a, lower=...) -> typing.Any: - "s,scond,amax,info = ssyequb(a,[lower])\n\nWrapper for ``ssyequb``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ns : rank-1 array('f') with bounds (n)\nscond : float\namax : float\ninfo : int\n" - ... - -def ssyev(a, compute_v=..., lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "w,v,info = ssyev(a,[compute_v,lower,lwork,overwrite_a])\n\nWrapper for ``ssyev``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n-1,1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nv : rank-2 array('f') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def ssyev_lwork(n, lower=...) -> typing.Any: - "work,info = ssyev_lwork(n,[lower])\n\nWrapper for ``ssyev_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def ssyevd(a, compute_v=..., lower=..., lwork=..., liwork=..., overwrite_a=...) -> typing.Any: - "w,v,info = ssyevd(a,[compute_v,lower,lwork,liwork,overwrite_a])\n\nWrapper for ``ssyevd``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max((compute_v?1+6*n+2*n*n:2*n+1),1)\nliwork : input int, optional\n Default: (compute_v?3+5*n:1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nv : rank-2 array('f') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def ssyevd_lwork(n, compute_v=..., lower=...) -> typing.Any: - "work,iwork,info = ssyevd_lwork(n,[compute_v,lower])\n\nWrapper for ``ssyevd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\niwork : int\ninfo : int\n" - ... - -def ssyevr( - a, compute_v=..., range=..., lower=..., vl=..., vu=..., il=..., iu=..., abstol=..., lwork=..., liwork=..., overwrite_a=... -) -> typing.Any: - "w,z,m,isuppz,info = ssyevr(a,[compute_v,range,lower,vl,vu,il,iu,abstol,lwork,liwork,overwrite_a])\n\nWrapper for ``ssyevr``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds ``(n,n)``\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default ``1``\nrange : input string(len=1), optional\n Default ``'A'``\nlower : input int, optional\n Default ``0``\noverwrite_a : input int, optional\n Default ``0``\nvl : input float, optional\n Default ``0.0``\nvu : input float, optional\n Default ``1.0``\nil : input int, optional\n Default ``1``\niu : input int, optional\n Default ``n``\nabstol : input float, optional\n Default ``0.0``\nlwork : input int, optional\n Default ``max(26*n,1)``\nliwork : input int, optional\n Default ``max(1,10*n)``\n\nReturns\n-------\nw : rank-1 array('f') with bounds ``(n)``\nz : rank-2 array('f') with bounds ``((compute_v?MAX(0,n):0),(compute_v?(*range=='I'?iu-il+1:MAX(1,n)):0))``\nm : int\nisuppz : rank-1 array('i') with bounds ``((compute_v?(2*(*range=='A'||(*range=='I' && iu-il+1==n)?n:0)):0))``\ninfo : int\n" - ... - -def ssyevr_lwork(n, lower=...) -> typing.Any: - "work,iwork,info = ssyevr_lwork(n,[lower])\n\nWrapper for ``ssyevr_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\niwork : int\ninfo : int\n" - ... - -def ssyevx( - a, compute_v=..., range=..., lower=..., vl=..., vu=..., il=..., iu=..., abstol=..., lwork=..., overwrite_a=... -) -> typing.Any: - "w,z,m,ifail,info = ssyevx(a,[compute_v,range,lower,vl,vu,il,iu,abstol,lwork,overwrite_a])\n\nWrapper for ``ssyevx``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds ``(n,n)``\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default ``1``\nrange : input string(len=1), optional\n Default ``'A'``\nlower : input int, optional\n Default ``0``\noverwrite_a : input int, optional\n Default ``0``\nvl : input float, optional\n Default ``0.0``\nvu : input float, optional\n Default ``1.0``\nil : input int, optional\n Default ``1``\niu : input int, optional\n Default ``n``\nabstol : input float, optional\n Default ``0.0``\nlwork : input int, optional\n Default ``max(8*n,1)``\n\nReturns\n-------\nw : rank-1 array('f') with bounds ``(n)``\nz : rank-2 array('f') with bounds ``((compute_v?MAX(0,n):0),(compute_v?(*range=='I'?iu-il+1:MAX(1,n)):0))``\nm : int\nifail : rank-1 array('i') with bounds ``((compute_v?n:0))``\ninfo : int\n" - ... - -def ssyevx_lwork(n, lower=...) -> typing.Any: - "work,info = ssyevx_lwork(n,[lower])\n\nWrapper for ``ssyevx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def ssygst(a, b, itype=..., lower=..., overwrite_a=...) -> typing.Any: - "c,info = ssygst(a,b,[itype,lower,overwrite_a])\n\nWrapper for ``ssygst``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nitype : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('f') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def ssygv(a, b, itype=..., jobz=..., uplo=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "w,v,info = ssygv(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``ssygv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\nitype : input int, optional\n Default: 1\njobz : input string(len=1), optional\n Default: 'V'\nuplo : input string(len=1), optional\n Default: 'L'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n-1,1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nv : rank-2 array('f') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def ssygv_lwork(n, uplo=...) -> typing.Any: - "work,info = ssygv_lwork(n,[uplo])\n\nWrapper for ``ssygv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'L'\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def ssygvd(a, b, itype=..., jobz=..., uplo=..., lwork=..., liwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "w,v,info = ssygvd(a,b,[itype,jobz,uplo,lwork,liwork,overwrite_a,overwrite_b])\n\nWrapper for ``ssygvd``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds ``(n,n)``\nb : input rank-2 array('f') with bounds ``(n,n)``\n\nOther Parameters\n----------------\nitype : input int, optional\n Default ``1``\njobz : input string(len=1), optional\n Default ``'V'``\nuplo : input string(len=1), optional\n Default ``'L'``\noverwrite_a : input int, optional\n Default ``0``\noverwrite_b : input int, optional\n Default ``0``\nlwork : input int, optional\n Default ``(*jobz=='N'?2*n+1:1+6*n+2*n*n)``\nliwork : input int, optional\n Default ``(*jobz=='N'?1:5*n+3)``\n\nReturns\n-------\nw : rank-1 array('f') with bounds ``(n)``\nv : rank-2 array('f') with bounds ``(n,n)`` with ``a`` storage\ninfo : int\n" - ... - -def ssygvx( - a, - b, - itype=..., - jobz=..., - range=..., - uplo=..., - vl=..., - vu=..., - il=..., - iu=..., - abstol=..., - lwork=..., - overwrite_a=..., - overwrite_b=..., -) -> typing.Any: - "w,z,m,ifail,info = ssygvx(a,b,[itype,jobz,range,uplo,vl,vu,il,iu,abstol,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``ssygvx``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\nitype : input int, optional\n Default: 1\njobz : input string(len=1), optional\n Default: 'V'\nrange : input string(len=1), optional\n Default: 'A'\nuplo : input string(len=1), optional\n Default: 'L'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nvl : input float, optional\n Default: 0.0\nvu : input float, optional\n Default: 1.0\nil : input int, optional\n Default: 1\niu : input int, optional\n Default: n\nabstol : input float, optional\n Default: 0.0\nlwork : input int, optional\n Default: max(8*n,1)\n\nReturns\n-------\nw : rank-1 array('f') with bounds (n)\nz : rank-2 array('f') with bounds ((jobz[0]=='V'?MAX(0,n):0),(jobz[0]=='V'?(range[0]=='I'?iu-il+1:MAX(1,n)):0))\nm : int\nifail : rank-1 array('i') with bounds ((jobz[0]=='N'?0:n))\ninfo : int\n" - ... - -def ssygvx_lwork(n, uplo=...) -> typing.Any: - "work,info = ssygvx_lwork(n,[uplo])\n\nWrapper for ``ssygvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'L'\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def ssysv(a, b, lwork=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "udut,ipiv,x,info = ssysv(a,b,[lwork,lower,overwrite_a,overwrite_b])\n\nWrapper for ``ssysv``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nudut : rank-2 array('f') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('f') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def ssysv_lwork(n, lower=...) -> typing.Any: - "work,info = ssysv_lwork(n,[lower])\n\nWrapper for ``ssysv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def ssysvx(a, b, af=..., ipiv=..., lwork=..., factored=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a_s,udut,ipiv,b_s,x,rcond,ferr,berr,info = ssysvx(a,b,[af,ipiv,lwork,factored,lower,overwrite_a,overwrite_b])\n\nWrapper for ``ssysvx``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('f') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\nfactored : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na_s : rank-2 array('f') with bounds (n,n) and a storage\nudut : rank-2 array('f') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nb_s : rank-2 array('f') with bounds (n,nrhs) and b storage\nx : rank-2 array('f') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('f') with bounds (nrhs)\nberr : rank-1 array('f') with bounds (nrhs)\ninfo : int\n" - ... - -def ssysvx_lwork(n, lower=...) -> typing.Any: - "work,info = ssysvx_lwork(n,[lower])\n\nWrapper for ``ssysvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def ssytf2(a, lower=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = ssytf2(a,[lower,overwrite_a])\n\nWrapper for ``ssytf2``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nldu : rank-2 array('f') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def ssytrd(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "c,d,e,tau,info = ssytrd(a,[lower,lwork,overwrite_a])\n\nWrapper for ``ssytrd``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(n,1)\n\nReturns\n-------\nc : rank-2 array('f') with bounds (lda,n) and a storage\nd : rank-1 array('f') with bounds (n)\ne : rank-1 array('f') with bounds (n - 1)\ntau : rank-1 array('f') with bounds (n - 1)\ninfo : int\n" - ... - -def ssytrd_lwork(n, lower=...) -> typing.Any: - "work,info = ssytrd_lwork(n,[lower])\n\nWrapper for ``ssytrd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def ssytrf(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = ssytrf(a,[lower,lwork,overwrite_a])\n\nWrapper for ``ssytrf``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\n\nReturns\n-------\nldu : rank-2 array('f') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def ssytrf_lwork(n, lower=...) -> typing.Any: - "work,info = ssytrf_lwork(n,[lower])\n\nWrapper for ``ssytrf_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def stbtrs(ab, b, uplo=..., trans=..., diag=..., overwrite_b=...) -> typing.Any: - "x,info = stbtrs(ab,b,[uplo,trans,diag,overwrite_b])\n\nWrapper for ``stbtrs``.\n\nParameters\n----------\nab : input rank-2 array('f') with bounds (ldab,n)\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\ndiag : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def stfsm(alpha, a, b, transr=..., side=..., uplo=..., trans=..., diag=..., overwrite_b=...) -> typing.Any: - "x = stfsm(alpha,a,b,[transr,side,uplo,trans,diag,overwrite_b])\n\nWrapper for ``stfsm``.\n\nParameters\n----------\nalpha : input float\na : input rank-1 array('f') with bounds (nt)\nb : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nside : input string(len=1), optional\n Default: 'L'\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\ndiag : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (m,n) and b storage\n" - ... - -def stfttp(n, arf, transr=..., uplo=...) -> typing.Any: - "ap,info = stfttp(n,arf,[transr,uplo])\n\nWrapper for ``stfttp``.\n\nParameters\n----------\nn : input int\narf : input rank-1 array('f') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nap : rank-1 array('f') with bounds (nt)\ninfo : int\n" - ... - -def stfttr(n, arf, transr=..., uplo=...) -> typing.Any: - "a,info = stfttr(n,arf,[transr,uplo])\n\nWrapper for ``stfttr``.\n\nParameters\n----------\nn : input int\narf : input rank-1 array('f') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\na : rank-2 array('f') with bounds (lda,n)\ninfo : int\n" - ... - -def stgsen( - select, a, b, q, z, lwork=..., liwork=..., overwrite_a=..., overwrite_b=..., overwrite_q=..., overwrite_z=... -) -> typing.Any: - "a,b,alphar,alphai,beta,q,z,m,pl,pr,dif,work,iwork,info = stgsen(select,a,b,q,z,[lwork,liwork,overwrite_a,overwrite_b,overwrite_q,overwrite_z])\n\nWrapper for ``stgsen``.\n\nParameters\n----------\nselect : input rank-1 array('i') with bounds (n)\na : input rank-2 array('f') with bounds (lda,n)\nb : input rank-2 array('f') with bounds (ldb,n)\nq : input rank-2 array('f') with bounds (ldq,n)\nz : input rank-2 array('f') with bounds (ldz,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\noverwrite_q : input int, optional\n Default: 0\noverwrite_z : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(MAX(4*n+16,2*m*(n-m)),1)\nliwork : input int, optional\n Default: n+6\n\nReturns\n-------\na : rank-2 array('f') with bounds (lda,n)\nb : rank-2 array('f') with bounds (ldb,n)\nalphar : rank-1 array('f') with bounds (n)\nalphai : rank-1 array('f') with bounds (n)\nbeta : rank-1 array('f') with bounds (n)\nq : rank-2 array('f') with bounds (ldq,n)\nz : rank-2 array('f') with bounds (ldz,n)\nm : int\npl : float\npr : float\ndif : rank-1 array('f') with bounds (2)\nwork : rank-1 array('f') with bounds (MAX(lwork,1))\niwork : rank-1 array('i') with bounds (MAX(1,liwork))\ninfo : int\n" - ... - -def stpmqrt(l, v, t, a, b, side=..., trans=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a,b,info = stpmqrt(l,v,t,a,b,[side,trans,overwrite_a,overwrite_b])\n\nWrapper for ``stpmqrt``.\n\nParameters\n----------\nl : input int\nv : input rank-2 array('f') with bounds ((side[0]=='L'?m:n),k)\nt : input rank-2 array('f') with bounds (nb,k)\na : input rank-2 array('f') with bounds ((side[0]=='L'?k:m),(side[0]=='L'?n:k))\nb : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('f') with bounds ((side[0]=='L'?k:m),(side[0]=='L'?n:k))\nb : rank-2 array('f') with bounds (m,n)\ninfo : int\n" - ... - -def stpqrt(l, nb, a, b, overwrite_a=..., overwrite_b=...) -> typing.Any: - "a,b,t,info = stpqrt(l,nb,a,b,[overwrite_a,overwrite_b])\n\nWrapper for ``stpqrt``.\n\nParameters\n----------\nl : input int\nnb : input int\na : input rank-2 array('f') with bounds (n,n)\nb : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('f') with bounds (n,n)\nb : rank-2 array('f') with bounds (m,n)\nt : rank-2 array('f') with bounds (nb,n)\ninfo : int\n" - ... - -def stpttf(n, ap, transr=..., uplo=...) -> typing.Any: - "arf,info = stpttf(n,ap,[transr,uplo])\n\nWrapper for ``stpttf``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\narf : rank-1 array('f') with bounds (nt)\ninfo : int\n" - ... - -def stpttr(n, ap, uplo=...) -> typing.Any: - "a,info = stpttr(n,ap,[uplo])\n\nWrapper for ``stpttr``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('f') with bounds (nt)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\na : rank-2 array('f') with bounds (n,n)\ninfo : int\n" - ... - -def strsyl(a, b, c, trana=..., tranb=..., isgn=..., overwrite_c=...) -> typing.Any: - "x,scale,info = strsyl(a,b,c,[trana,tranb,isgn,overwrite_c])\n\nWrapper for ``strsyl``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,m)\nb : input rank-2 array('f') with bounds (n,n)\nc : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\ntrana : input string(len=1), optional\n Default: 'N'\ntranb : input string(len=1), optional\n Default: 'N'\nisgn : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (m,n) and c storage\nscale : float\ninfo : int\n" - ... - -def strtri(c, lower=..., unitdiag=..., overwrite_c=...) -> typing.Any: - "inv_c,info = strtri(c,[lower,unitdiag,overwrite_c])\n\nWrapper for ``strtri``.\n\nParameters\n----------\nc : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nunitdiag : input int, optional\n Default: 0\n\nReturns\n-------\ninv_c : rank-2 array('f') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def strtrs(a, b, lower=..., trans=..., unitdiag=..., lda=..., overwrite_b=...) -> typing.Any: - "x,info = strtrs(a,b,[lower,trans,unitdiag,lda,overwrite_b])\n\nWrapper for ``strtrs``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (lda,n)\nb : input rank-2 array('f') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nunitdiag : input int, optional\n Default: 0\nlda : input int, optional\n Default: shape(a,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('f') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def strttf(a, transr=..., uplo=...) -> typing.Any: - "arf,info = strttf(a,[transr,uplo])\n\nWrapper for ``strttf``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (lda,n)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\narf : rank-1 array('f') with bounds (n*(n+1)/2)\ninfo : int\n" - ... - -def strttp(a, uplo=...) -> typing.Any: - "ap,info = strttp(a,[uplo])\n\nWrapper for ``strttp``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (lda,n)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nap : rank-1 array('f') with bounds (n*(n+1)/2)\ninfo : int\n" - ... - -def stzrzf(a, lwork=..., overwrite_a=...) -> typing.Any: - "rz,tau,info = stzrzf(a,[lwork,overwrite_a])\n\nWrapper for ``stzrzf``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(m,1)\n\nReturns\n-------\nrz : rank-2 array('f') with bounds (m,n) and a storage\ntau : rank-1 array('f') with bounds (m)\ninfo : int\n" - ... - -def stzrzf_lwork(m, n) -> typing.Any: - "work,info = stzrzf_lwork(m,n)\n\nWrapper for ``stzrzf_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : float\ninfo : int\n" - ... - -def zgbsv(kl, ku, ab, b, overwrite_ab=..., overwrite_b=...) -> typing.Any: - "lub,piv,x,info = zgbsv(kl,ku,ab,b,[overwrite_ab,overwrite_b])\n\nWrapper for ``zgbsv``.\n\nParameters\n----------\nkl : input int\nku : input int\nab : input rank-2 array('D') with bounds (2*kl+ku+1,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nlub : rank-2 array('D') with bounds (2*kl+ku+1,n) and ab storage\npiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zgbtrf(ab, kl, ku, m=..., n=..., ldab=..., overwrite_ab=...) -> typing.Any: - "lu,ipiv,info = zgbtrf(ab,kl,ku,[m,n,ldab,overwrite_ab])\n\nWrapper for ``zgbtrf``.\n\nParameters\n----------\nab : input rank-2 array('D') with bounds (ldab,n)\nkl : input int\nku : input int\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(ab,1)\nn : input int, optional\n Default: shape(ab,1)\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: max(shape(ab,0),1)\n\nReturns\n-------\nlu : rank-2 array('D') with bounds (ldab,n) and ab storage\nipiv : rank-1 array('i') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def zgbtrs(ab, kl, ku, b, ipiv, trans=..., n=..., ldab=..., ldb=..., overwrite_b=...) -> typing.Any: - "x,info = zgbtrs(ab,kl,ku,b,ipiv,[trans,n,ldab,ldb,overwrite_b])\n\nWrapper for ``zgbtrs``.\n\nParameters\n----------\nab : input rank-2 array('D') with bounds (ldab,n)\nkl : input int\nku : input int\nb : input rank-2 array('D') with bounds (ldb,nrhs)\nipiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nn : input int, optional\n Default: shape(ab,1)\nldab : input int, optional\n Default: shape(ab,0)\nldb : input int, optional\n Default: shape(b,0)\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def zgebal(a, scale=..., permute=..., overwrite_a=...) -> typing.Any: - "ba,lo,hi,pivscale,info = zgebal(a,[scale,permute,overwrite_a])\n\nWrapper for ``zgebal``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\nscale : input int, optional\n Default: 0\npermute : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nba : rank-2 array('D') with bounds (m,n) and a storage\nlo : int\nhi : int\npivscale : rank-1 array('d') with bounds (n)\ninfo : int\n" - ... - -def zgecon(a, anorm, norm=...) -> typing.Any: - "rcond,info = zgecon(a,anorm,[norm])\n\nWrapper for ``zgecon``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nanorm : input float\n\nOther Parameters\n----------------\nnorm : input string(len=1), optional\n Default: '1'\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def zgeequ(a) -> typing.Any: - "r,c,rowcnd,colcnd,amax,info = zgeequ(a)\n\nWrapper for ``zgeequ``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nReturns\n-------\nr : rank-1 array('d') with bounds (m)\nc : rank-1 array('d') with bounds (n)\nrowcnd : float\ncolcnd : float\namax : float\ninfo : int\n" - ... - -def zgeequb(a) -> typing.Any: - "r,c,rowcnd,colcnd,amax,info = zgeequb(a)\n\nWrapper for ``zgeequb``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nReturns\n-------\nr : rank-1 array('d') with bounds (m)\nc : rank-1 array('d') with bounds (n)\nrowcnd : float\ncolcnd : float\namax : float\ninfo : int\n" - ... - -def zgees(zselect, a, compute_v=..., sort_t=..., lwork=..., zselect_extra_args=..., overwrite_a=...) -> typing.Any: - "t,sdim,w,vs,work,info = zgees(zselect,a,[compute_v,sort_t,lwork,zselect_extra_args,overwrite_a])\n\nWrapper for ``zgees``.\n\nParameters\n----------\nzselect : call-back function\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nsort_t : input int, optional\n Default: 0\nzselect_extra_args : input tuple, optional\n Default: ()\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nt : rank-2 array('D') with bounds (n,n) and a storage\nsdim : int\nw : rank-1 array('D') with bounds (n)\nvs : rank-2 array('D') with bounds (ldvs,n)\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n\nNotes\n-----\nCall-back functions::\n\n def zselect(arg): return zselect\n Required arguments:\n arg : input complex\n Return objects:\n zselect : int\n" - ... - -def zgeev(a, compute_vl=..., compute_vr=..., lwork=..., overwrite_a=...) -> typing.Any: - "w,vl,vr,info = zgeev(a,[compute_vl,compute_vr,lwork,overwrite_a])\n\nWrapper for ``zgeev``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\nw : rank-1 array('D') with bounds (n)\nvl : rank-2 array('D') with bounds (ldvl,n)\nvr : rank-2 array('D') with bounds (ldvr,n)\ninfo : int\n" - ... - -def zgeev_lwork(n, compute_vl=..., compute_vr=...) -> typing.Any: - "work,info = zgeev_lwork(n,[compute_vl,compute_vr])\n\nWrapper for ``zgeev_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgegv(*args, **kwds) -> typing.Any: - "`zgegv` is deprecated!\nThe `*gegv` family of routines has been deprecated in\nLAPACK 3.6.0 in favor of the `*ggev` family of routines.\nThe corresponding wrappers will be removed from SciPy in\na future release.\n\nalpha,beta,vl,vr,info = zgegv(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``zgegv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\nalpha : rank-1 array('D') with bounds (n)\nbeta : rank-1 array('D') with bounds (n)\nvl : rank-2 array('D') with bounds (ldvl,n)\nvr : rank-2 array('D') with bounds (ldvr,n)\ninfo : int\n" - ... - -def zgehrd(a, lo=..., hi=..., lwork=..., overwrite_a=...) -> typing.Any: - "ht,tau,info = zgehrd(a,[lo,hi,lwork,overwrite_a])\n\nWrapper for ``zgehrd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(n,1)\n\nReturns\n-------\nht : rank-2 array('D') with bounds (n,n) and a storage\ntau : rank-1 array('D') with bounds (n - 1)\ninfo : int\n" - ... - -def zgehrd_lwork(n, lo=..., hi=...) -> typing.Any: - "work,info = zgehrd_lwork(n,[lo,hi])\n\nWrapper for ``zgehrd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgels(a, b, trans=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "lqr,x,info = zgels(a,b,[trans,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``zgels``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nb : input rank-2 array('D') with bounds (MAX(m,n),nrhs)\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(MIN(m,n)+MAX(MIN(m,n),nrhs),1)\n\nReturns\n-------\nlqr : rank-2 array('D') with bounds (m,n) and a storage\nx : rank-2 array('D') with bounds (MAX(m,n),nrhs) and b storage\ninfo : int\n" - ... - -def zgels_lwork(m, n, nrhs, trans=...) -> typing.Any: - "work,info = zgels_lwork(m,n,nrhs,[trans])\n\nWrapper for ``zgels_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgelsd(a, b, lwork, size_rwork, size_iwork, cond=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "x,s,rank,info = zgelsd(a,b,lwork,size_rwork,size_iwork,[cond,overwrite_a,overwrite_b])\n\nWrapper for ``zgelsd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nb : input rank-2 array('D') with bounds (maxmn,nrhs)\nlwork : input int\nsize_rwork : input int\nsize_iwork : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\ncond : input float, optional\n Default: -1.0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (maxmn,nrhs) and b storage\ns : rank-1 array('d') with bounds (minmn)\nrank : int\ninfo : int\n" - ... - -def zgelsd_lwork(m, n, nrhs, cond=..., lwork=...) -> typing.Any: - "work,rwork,iwork,info = zgelsd_lwork(m,n,nrhs,[cond,lwork])\n\nWrapper for ``zgelsd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : complex\nrwork : float\niwork : int\ninfo : int\n" - ... - -def zgelss(a, b, cond=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "v,x,s,rank,work,info = zgelss(a,b,[cond,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``zgelss``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nb : input rank-2 array('D') with bounds (maxmn,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: max(2*minmn+MAX(maxmn,nrhs),1)\n\nReturns\n-------\nv : rank-2 array('D') with bounds (m,n) and a storage\nx : rank-2 array('D') with bounds (maxmn,nrhs) and b storage\ns : rank-1 array('d') with bounds (minmn)\nrank : int\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def zgelss_lwork(m, n, nrhs, cond=..., lwork=...) -> typing.Any: - "work,info = zgelss_lwork(m,n,nrhs,[cond,lwork])\n\nWrapper for ``zgelss_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\n\nOther Parameters\n----------------\ncond : input float, optional\n Default: -1.0\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgelsy(a, b, jptv, cond, lwork, overwrite_a=..., overwrite_b=...) -> typing.Any: - "v,x,j,rank,info = zgelsy(a,b,jptv,cond,lwork,[overwrite_a,overwrite_b])\n\nWrapper for ``zgelsy``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nb : input rank-2 array('D') with bounds (maxmn,nrhs)\njptv : input rank-1 array('i') with bounds (n)\ncond : input float\nlwork : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nv : rank-2 array('D') with bounds (m,n) and a storage\nx : rank-2 array('D') with bounds (maxmn,nrhs) and b storage\nj : rank-1 array('i') with bounds (n) and jptv storage\nrank : int\ninfo : int\n" - ... - -def zgelsy_lwork(m, n, nrhs, cond, lwork=...) -> typing.Any: - "work,info = zgelsy_lwork(m,n,nrhs,cond,[lwork])\n\nWrapper for ``zgelsy_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\nnrhs : input int\ncond : input float\n\nOther Parameters\n----------------\nlwork : input int, optional\n Default: -1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgemqrt(v, t, c, side=..., trans=..., overwrite_c=...) -> typing.Any: - "c,info = zgemqrt(v,t,c,[side,trans,overwrite_c])\n\nWrapper for ``zgemqrt``.\n\nParameters\n----------\nv : input rank-2 array('D') with bounds ((side[0]=='L'?m:n),k)\nt : input rank-2 array('D') with bounds (nb,k)\nc : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (m,n)\ninfo : int\n" - ... - -def zgeqp3(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,jpvt,tau,work,info = zgeqp3(a,[lwork,overwrite_a])\n\nWrapper for ``zgeqp3``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*(n+1),1)\n\nReturns\n-------\nqr : rank-2 array('D') with bounds (m,n) and a storage\njpvt : rank-1 array('i') with bounds (n)\ntau : rank-1 array('D') with bounds (MIN(m,n))\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def zgeqrf(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,work,info = zgeqrf(a,[lwork,overwrite_a])\n\nWrapper for ``zgeqrf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nqr : rank-2 array('D') with bounds (m,n) and a storage\ntau : rank-1 array('D') with bounds (MIN(m,n))\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def zgeqrf_lwork(m, n) -> typing.Any: - "work,info = zgeqrf_lwork(m,n)\n\nWrapper for ``zgeqrf_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgeqrfp(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,info = zgeqrfp(a,[lwork,overwrite_a])\n\nWrapper for ``zgeqrfp``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(1, n)\n\nReturns\n-------\nqr : rank-2 array('D') with bounds (m,n) and a storage\ntau : rank-1 array('D') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def zgeqrfp_lwork(m, n) -> typing.Any: - "work,info = zgeqrfp_lwork(m,n)\n\nWrapper for ``zgeqrfp_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgeqrt(nb, a, overwrite_a=...) -> typing.Any: - "a,t,info = zgeqrt(nb,a,[overwrite_a])\n\nWrapper for ``zgeqrt``.\n\nParameters\n----------\nnb : input int\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (m,n)\nt : rank-2 array('D') with bounds (nb,MIN(m,n))\ninfo : int\n" - ... - -def zgerqf(a, lwork=..., overwrite_a=...) -> typing.Any: - "qr,tau,work,info = zgerqf(a,[lwork,overwrite_a])\n\nWrapper for ``zgerqf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*m,1)\n\nReturns\n-------\nqr : rank-2 array('D') with bounds (m,n) and a storage\ntau : rank-1 array('D') with bounds (MIN(m,n))\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def zgesc2(lu, rhs, ipiv, jpiv, overwrite_rhs=...) -> typing.Any: - "x,scale = zgesc2(lu,rhs,ipiv,jpiv,[overwrite_rhs])\n\nWrapper for ``zgesc2``.\n\nParameters\n----------\nlu : input rank-2 array('D') with bounds (n,n)\nrhs : input rank-1 array('D') with bounds (n)\nipiv : input rank-1 array('i') with bounds (n)\njpiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_rhs : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('D') with bounds (n) and rhs storage\nscale : float\n" - ... - -def zgesdd(a, compute_uv=..., full_matrices=..., lwork=..., overwrite_a=...) -> typing.Any: - "u,s,vt,info = zgesdd(a,[compute_uv,full_matrices,lwork,overwrite_a])\n\nWrapper for ``zgesdd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\nlwork : input int, optional\n Default: max((compute_uv?2*minmn*minmn+MAX(m,n)+2*minmn:2*minmn+MAX(m,n)),1)\n\nReturns\n-------\nu : rank-2 array('D') with bounds (u0,u1)\ns : rank-1 array('d') with bounds (minmn)\nvt : rank-2 array('D') with bounds (vt0,vt1)\ninfo : int\n" - ... - -def zgesdd_lwork(m, n, compute_uv=..., full_matrices=...) -> typing.Any: - "work,info = zgesdd_lwork(m,n,[compute_uv,full_matrices])\n\nWrapper for ``zgesdd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgesv(a, b, overwrite_a=..., overwrite_b=...) -> typing.Any: - "lu,piv,x,info = zgesv(a,b,[overwrite_a,overwrite_b])\n\nWrapper for ``zgesv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('D') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zgesvd(a, compute_uv=..., full_matrices=..., lwork=..., overwrite_a=...) -> typing.Any: - "u,s,vt,info = zgesvd(a,[compute_uv,full_matrices,lwork,overwrite_a])\n\nWrapper for ``zgesvd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\nlwork : input int, optional\n Default: MAX(2*minmn+MAX(m,n),1)\n\nReturns\n-------\nu : rank-2 array('D') with bounds (u0,u1)\ns : rank-1 array('d') with bounds (minmn)\nvt : rank-2 array('D') with bounds (vt0,vt1)\ninfo : int\n" - ... - -def zgesvd_lwork(m, n, compute_uv=..., full_matrices=...) -> typing.Any: - "work,info = zgesvd_lwork(m,n,[compute_uv,full_matrices])\n\nWrapper for ``zgesvd_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\ncompute_uv : input int, optional\n Default: 1\nfull_matrices : input int, optional\n Default: 1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgesvx(a, b, fact=..., trans=..., af=..., ipiv=..., equed=..., r=..., c=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "as,lu,ipiv,equed,rs,cs,bs,x,rcond,ferr,berr,info = zgesvx(a,b,[fact,trans,af,ipiv,equed,r,c,overwrite_a,overwrite_b])\n\nWrapper for ``zgesvx``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'E'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('D') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nequed : input string(len=1), optional\n Default: 'B'\nr : input rank-1 array('d') with bounds (n)\nc : input rank-1 array('d') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nas : rank-2 array('D') with bounds (n,n) and a storage\nlu : rank-2 array('D') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nequed : string(len=1)\nrs : rank-1 array('d') with bounds (n) and r storage\ncs : rank-1 array('d') with bounds (n) and c storage\nbs : rank-2 array('D') with bounds (n,nrhs) and b storage\nx : rank-2 array('D') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def zgetc2(a, overwrite_a=...) -> typing.Any: - "lu,ipiv,jpiv,info = zgetc2(a,[overwrite_a])\n\nWrapper for ``zgetc2``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('D') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\njpiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def zgetrf(a, overwrite_a=...) -> typing.Any: - "lu,piv,info = zgetrf(a,[overwrite_a])\n\nWrapper for ``zgetrf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nlu : rank-2 array('D') with bounds (m,n) and a storage\npiv : rank-1 array('i') with bounds (MIN(m,n))\ninfo : int\n" - ... - -def zgetri(lu, piv, lwork=..., overwrite_lu=...) -> typing.Any: - "inv_a,info = zgetri(lu,piv,[lwork,overwrite_lu])\n\nWrapper for ``zgetri``.\n\nParameters\n----------\nlu : input rank-2 array('D') with bounds (n,n)\npiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\noverwrite_lu : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\ninv_a : rank-2 array('D') with bounds (n,n) and lu storage\ninfo : int\n" - ... - -def zgetri_lwork(n) -> typing.Any: - "work,info = zgetri_lwork(n)\n\nWrapper for ``zgetri_lwork``.\n\nParameters\n----------\nn : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgetrs(lu, piv, b, trans=..., overwrite_b=...) -> typing.Any: - "x,info = zgetrs(lu,piv,b,[trans,overwrite_b])\n\nWrapper for ``zgetrs``.\n\nParameters\n----------\nlu : input rank-2 array('D') with bounds (n,n)\npiv : input rank-1 array('i') with bounds (n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zgges( - zselect, - a, - b, - jobvsl=..., - jobvsr=..., - sort_t=..., - ldvsl=..., - ldvsr=..., - lwork=..., - zselect_extra_args=..., - overwrite_a=..., - overwrite_b=..., -) -> typing.Any: - "a,b,sdim,alpha,beta,vsl,vsr,work,info = zgges(zselect,a,b,[jobvsl,jobvsr,sort_t,ldvsl,ldvsr,lwork,zselect_extra_args,overwrite_a,overwrite_b])\n\nWrapper for ``zgges``.\n\nParameters\n----------\nzselect : call-back function\na : input rank-2 array('D') with bounds (lda,n)\nb : input rank-2 array('D') with bounds (ldb,n)\n\nOther Parameters\n----------------\njobvsl : input int, optional\n Default: 1\njobvsr : input int, optional\n Default: 1\nsort_t : input int, optional\n Default: 0\nzselect_extra_args : input tuple, optional\n Default: ()\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nldvsl : input int, optional\n Default: ((jobvsl==1)?n:1)\nldvsr : input int, optional\n Default: ((jobvsr==1)?n:1)\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\na : rank-2 array('D') with bounds (lda,n)\nb : rank-2 array('D') with bounds (ldb,n)\nsdim : int\nalpha : rank-1 array('D') with bounds (n)\nbeta : rank-1 array('D') with bounds (n)\nvsl : rank-2 array('D') with bounds (ldvsl,n)\nvsr : rank-2 array('D') with bounds (ldvsr,n)\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n\nNotes\n-----\nCall-back functions::\n\n def zselect(alpha,beta): return zselect\n Required arguments:\n alpha : input complex\n beta : input complex\n Return objects:\n zselect : int\n" - ... - -def zggev(a, b, compute_vl=..., compute_vr=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "alpha,beta,vl,vr,work,info = zggev(a,b,[compute_vl,compute_vr,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``zggev``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_vl : input int, optional\n Default: 1\ncompute_vr : input int, optional\n Default: 1\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\nalpha : rank-1 array('D') with bounds (n)\nbeta : rank-1 array('D') with bounds (n)\nvl : rank-2 array('D') with bounds (ldvl,n)\nvr : rank-2 array('D') with bounds (ldvr,n)\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def zgglse(a, b, c, d, lwork=..., overwrite_a=..., overwrite_b=..., overwrite_c=..., overwrite_d=...) -> typing.Any: - "t,r,res,x,info = zgglse(a,b,c,d,[lwork,overwrite_a,overwrite_b,overwrite_c,overwrite_d])\n\nWrapper for ``zgglse``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nb : input rank-2 array('D') with bounds (p,n)\nc : input rank-1 array('D') with bounds (m)\nd : input rank-1 array('D') with bounds (p)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\noverwrite_c : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(m+n+p,1)\n\nReturns\n-------\nt : rank-2 array('D') with bounds (m,n) and a storage\nr : rank-2 array('D') with bounds (p,n) and b storage\nres : rank-1 array('D') with bounds (m) and c storage\nx : rank-1 array('D') with bounds (n)\ninfo : int\n" - ... - -def zgglse_lwork(m, n, p) -> typing.Any: - "work,info = zgglse_lwork(m,n,p)\n\nWrapper for ``zgglse_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\np : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zgtsv(dl, d, du, b, overwrite_dl=..., overwrite_d=..., overwrite_du=..., overwrite_b=...) -> typing.Any: - "du2,d,du,x,info = zgtsv(dl,d,du,b,[overwrite_dl,overwrite_d,overwrite_du,overwrite_b])\n\nWrapper for ``zgtsv``.\n\nParameters\n----------\ndl : input rank-1 array('D') with bounds (n - 1)\nd : input rank-1 array('D') with bounds (n)\ndu : input rank-1 array('D') with bounds (n - 1)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_dl : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_du : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\ndu2 : rank-1 array('D') with bounds (n - 1) and dl storage\nd : rank-1 array('D') with bounds (n)\ndu : rank-1 array('D') with bounds (n - 1)\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zgtsvx(dl, d, du, b, fact=..., trans=..., dlf=..., df=..., duf=..., du2=..., ipiv=...) -> typing.Any: - "dlf,df,duf,du2,ipiv,x,rcond,ferr,berr,info = zgtsvx(dl,d,du,b,[fact,trans,dlf,df,duf,du2,ipiv])\n\nWrapper for ``zgtsvx``.\n\nParameters\n----------\ndl : input rank-1 array('D') with bounds (MAX(0, n-1))\nd : input rank-1 array('D') with bounds (n)\ndu : input rank-1 array('D') with bounds (MAX(0, n-1))\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'N'\ntrans : input string(len=1), optional\n Default: 'N'\ndlf : input rank-1 array('D') with bounds (MAX(0,n-1))\ndf : input rank-1 array('D') with bounds (n)\nduf : input rank-1 array('D') with bounds (MAX(0,n-1))\ndu2 : input rank-1 array('D') with bounds (MAX(0,n-2))\nipiv : input rank-1 array('i') with bounds (n)\n\nReturns\n-------\ndlf : rank-1 array('D') with bounds (MAX(0,n-1))\ndf : rank-1 array('D') with bounds (n)\nduf : rank-1 array('D') with bounds (MAX(0,n-1))\ndu2 : rank-1 array('D') with bounds (MAX(0,n-2))\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('D') with bounds (ldx,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def zgttrf(dl, d, du, overwrite_dl=..., overwrite_d=..., overwrite_du=...) -> typing.Any: - "dl,d,du,du2,ipiv,info = zgttrf(dl,d,du,[overwrite_dl,overwrite_d,overwrite_du])\n\nWrapper for ``zgttrf``.\n\nParameters\n----------\ndl : input rank-1 array('D') with bounds (n - 1)\nd : input rank-1 array('D') with bounds (n)\ndu : input rank-1 array('D') with bounds (n - 1)\n\nOther Parameters\n----------------\noverwrite_dl : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_du : input int, optional\n Default: 0\n\nReturns\n-------\ndl : rank-1 array('D') with bounds (n - 1)\nd : rank-1 array('D') with bounds (n)\ndu : rank-1 array('D') with bounds (n - 1)\ndu2 : rank-1 array('D') with bounds (n - 2)\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def zgttrs(dl, d, du, du2, ipiv, b, trans=..., overwrite_b=...) -> typing.Any: - "x,info = zgttrs(dl,d,du,du2,ipiv,b,[trans,overwrite_b])\n\nWrapper for ``zgttrs``.\n\nParameters\n----------\ndl : input rank-1 array('D') with bounds (n - 1)\nd : input rank-1 array('D') with bounds (n)\ndu : input rank-1 array('D') with bounds (n - 1)\ndu2 : input rank-1 array('D') with bounds (n - 2)\nipiv : input rank-1 array('i') with bounds (n)\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def zhbevd(ab, compute_v=..., lower=..., ldab=..., lrwork=..., liwork=..., overwrite_ab=...) -> typing.Any: - "w,z,info = zhbevd(ab,[compute_v,lower,ldab,lrwork,liwork,overwrite_ab])\n\nWrapper for ``zhbevd``.\n\nParameters\n----------\nab : input rank-2 array('D') with bounds (ldab,n)\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\nlrwork : input int, optional\n Default: (compute_v?1+5*n+2*n*n:n)\nliwork : input int, optional\n Default: (compute_v?3+5*n:1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nz : rank-2 array('D') with bounds (ldz,ldz)\ninfo : int\n" - ... - -def zhbevx( - ab, vl, vu, il, iu, ldab=..., compute_v=..., range=..., lower=..., abstol=..., mmax=..., overwrite_ab=... -) -> typing.Any: - "w,z,m,ifail,info = zhbevx(ab,vl,vu,il,iu,[ldab,compute_v,range,lower,abstol,mmax,overwrite_ab])\n\nWrapper for ``zhbevx``.\n\nParameters\n----------\nab : input rank-2 array('D') with bounds (ldab,n)\nvl : input float\nvu : input float\nil : input int\niu : input int\n\nOther Parameters\n----------------\noverwrite_ab : input int, optional\n Default: 1\nldab : input int, optional\n Default: shape(ab,0)\ncompute_v : input int, optional\n Default: 1\nrange : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nabstol : input float, optional\n Default: 0.0\nmmax : input int, optional\n Default: (compute_v?(range==2?(iu-il+1):n):1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nz : rank-2 array('D') with bounds (ldz,mmax)\nm : int\nifail : rank-1 array('i') with bounds ((compute_v?n:1))\ninfo : int\n" - ... - -def zhecon(a, ipiv, anorm, lower=...) -> typing.Any: - "rcond,info = zhecon(a,ipiv,anorm,[lower])\n\nWrapper for ``zhecon``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def zheequb(a, lower=...) -> typing.Any: - "s,scond,amax,info = zheequb(a,[lower])\n\nWrapper for ``zheequb``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ns : rank-1 array('d') with bounds (n)\nscond : float\namax : float\ninfo : int\n" - ... - -def zheev(a, compute_v=..., lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "w,v,info = zheev(a,[compute_v,lower,lwork,overwrite_a])\n\nWrapper for ``zheev``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n-1,1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nv : rank-2 array('D') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def zheev_lwork(n, lower=...) -> typing.Any: - "work,info = zheev_lwork(n,[lower])\n\nWrapper for ``zheev_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zheevd(a, compute_v=..., lower=..., lwork=..., liwork=..., lrwork=..., overwrite_a=...) -> typing.Any: - "w,v,info = zheevd(a,[compute_v,lower,lwork,liwork,lrwork,overwrite_a])\n\nWrapper for ``zheevd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max((compute_v?2*n+n*n:n+1),1)\nliwork : input int, optional\n Default: (compute_v?3+5*n:1)\nlrwork : input int, optional\n Default: (compute_v?1+5*n+2*n*n:n)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nv : rank-2 array('D') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def zheevd_lwork(n, compute_v=..., lower=...) -> typing.Any: - "work,iwork,rwork,info = zheevd_lwork(n,[compute_v,lower])\n\nWrapper for ``zheevd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\niwork : int\nrwork : float\ninfo : int\n" - ... - -def zheevr( - a, - compute_v=..., - range=..., - lower=..., - vl=..., - vu=..., - il=..., - iu=..., - abstol=..., - lwork=..., - lrwork=..., - liwork=..., - overwrite_a=..., -) -> typing.Any: - "w,z,m,isuppz,info = zheevr(a,[compute_v,range,lower,vl,vu,il,iu,abstol,lwork,lrwork,liwork,overwrite_a])\n\nWrapper for ``zheevr``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds ``(n,n)``\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default ``1``\nrange : input string(len=1), optional\n Default ``'A'``\nlower : input int, optional\n Default ``0``\noverwrite_a : input int, optional\n Default ``0``\nvl : input float, optional\n Default ``0.0``\nvu : input float, optional\n Default ``1.0``\nil : input int, optional\n Default ``1``\niu : input int, optional\n Default ``n``\nabstol : input float, optional\n Default ``0.0``\nlwork : input int, optional\n Default ``max(2*n,1)``\nlrwork : input int, optional\n Default ``max(24*n,1)``\nliwork : input int, optional\n Default ``max(1,10*n)``\n\nReturns\n-------\nw : rank-1 array('d') with bounds ``(n)``\nz : rank-2 array('D') with bounds ``((compute_v?MAX(0,n):0),(compute_v?(*range=='I'?iu-il+1:MAX(1,n)):0))``\nm : int\nisuppz : rank-1 array('i') with bounds ``(2*max(1,n))``\ninfo : int\n" - ... - -def zheevr_lwork(n, lower=...) -> typing.Any: - "work,rwork,iwork,info = zheevr_lwork(n,[lower])\n\nWrapper for ``zheevr_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\nrwork : float\niwork : int\ninfo : int\n" - ... - -def zheevx( - a, compute_v=..., range=..., lower=..., vl=..., vu=..., il=..., iu=..., abstol=..., lwork=..., overwrite_a=... -) -> typing.Any: - "w,z,m,ifail,info = zheevx(a,[compute_v,range,lower,vl,vu,il,iu,abstol,lwork,overwrite_a])\n\nWrapper for ``zheevx``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds ``(n,n)``\n\nOther Parameters\n----------------\ncompute_v : input int, optional\n Default ``1``\nrange : input string(len=1), optional\n Default ``'A'``\nlower : input int, optional\n Default ``0``\noverwrite_a : input int, optional\n Default ``0``\nvl : input float, optional\n Default ``0.0``\nvu : input float, optional\n Default ``1.0``\nil : input int, optional\n Default ``1``\niu : input int, optional\n Default ``n``\nabstol : input float, optional\n Default ``0.0``\nlwork : input int, optional\n Default ``max(2*n,1)``\n\nReturns\n-------\nw : rank-1 array('d') with bounds ``(n)``\nz : rank-2 array('D') with bounds ``((compute_v*n),(compute_v?(*range=='I'?iu-il+1:MAX(1,n)):0))``\nm : int\nifail : rank-1 array('i') with bounds ``(compute_v*n)``\ninfo : int\n" - ... - -def zheevx_lwork(n, lower=...) -> typing.Any: - "work,info = zheevx_lwork(n,[lower])\n\nWrapper for ``zheevx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zhegst(a, b, itype=..., lower=..., overwrite_a=...) -> typing.Any: - "c,info = zhegst(a,b,[itype,lower,overwrite_a])\n\nWrapper for ``zhegst``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nitype : input int, optional\n Default: 1\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def zhegv(a, b, itype=..., jobz=..., uplo=..., lwork=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "w,v,info = zhegv(a,b,[itype,jobz,uplo,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``zhegv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\nitype : input int, optional\n Default: 1\njobz : input string(len=1), optional\n Default: 'V'\nuplo : input string(len=1), optional\n Default: 'L'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n-1,1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nv : rank-2 array('D') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def zhegv_lwork(n, uplo=...) -> typing.Any: - "work,info = zhegv_lwork(n,[uplo])\n\nWrapper for ``zhegv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'L'\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zhegvd( - a, b, itype=..., jobz=..., uplo=..., lwork=..., lrwork=..., liwork=..., overwrite_a=..., overwrite_b=... -) -> typing.Any: - "w,v,info = zhegvd(a,b,[itype,jobz,uplo,lwork,lrwork,liwork,overwrite_a,overwrite_b])\n\nWrapper for ``zhegvd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds ``(n,n)``\nb : input rank-2 array('D') with bounds ``(n,n)``\n\nOther Parameters\n----------------\nitype : input int, optional\n Default ``1``\njobz : input string(len=1), optional\n Default ``'V'``\nuplo : input string(len=1), optional\n Default ``'L'``\noverwrite_a : input int, optional\n Default ``0``\noverwrite_b : input int, optional\n Default ``0``\nlwork : input int, optional\n Default ``(*jobz=='N'?n+1:n*(n+2))``\nlrwork : input int, optional\n Default ``max((*jobz=='N'?n:2*n*n+5*n+1),1)``\nliwork : input int, optional\n Default ``(*jobz=='N'?1:5*n+3)``\n\nReturns\n-------\nw : rank-1 array('d') with bounds ``(n)``\nv : rank-2 array('D') with bounds ``(n,n)`` with ``a`` storage\ninfo : int\n" - ... - -def zhegvx( - a, - b, - itype=..., - jobz=..., - range=..., - uplo=..., - vl=..., - vu=..., - il=..., - iu=..., - abstol=..., - lwork=..., - overwrite_a=..., - overwrite_b=..., -) -> typing.Any: - "w,z,m,ifail,info = zhegvx(a,b,[itype,jobz,range,uplo,vl,vu,il,iu,abstol,lwork,overwrite_a,overwrite_b])\n\nWrapper for ``zhegvx``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\nitype : input int, optional\n Default: 1\njobz : input string(len=1), optional\n Default: 'V'\nrange : input string(len=1), optional\n Default: 'A'\nuplo : input string(len=1), optional\n Default: 'L'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nvl : input float, optional\n Default: 0.0\nvu : input float, optional\n Default: 1.0\nil : input int, optional\n Default: 1\niu : input int, optional\n Default: n\nabstol : input float, optional\n Default: 0.0\nlwork : input int, optional\n Default: max(2*n,1)\n\nReturns\n-------\nw : rank-1 array('d') with bounds (n)\nz : rank-2 array('D') with bounds ((jobz[0]=='V'?MAX(0,n):0),(jobz[0]=='V'?(range[0]=='I'?iu-il+1:MAX(1,n)):0))\nm : int\nifail : rank-1 array('i') with bounds ((jobz[0]=='N'?0:n))\ninfo : int\n" - ... - -def zhegvx_lwork(n, uplo=...) -> typing.Any: - "work,info = zhegvx_lwork(n,[uplo])\n\nWrapper for ``zhegvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'L'\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zhesv(a, b, lwork=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "uduh,ipiv,x,info = zhesv(a,b,[lwork,lower,overwrite_a,overwrite_b])\n\nWrapper for ``zhesv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nuduh : rank-2 array('D') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zhesv_lwork(n, lower=...) -> typing.Any: - "work,info = zhesv_lwork(n,[lower])\n\nWrapper for ``zhesv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zhesvx(a, b, af=..., ipiv=..., lwork=..., factored=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "uduh,ipiv,x,rcond,ferr,berr,info = zhesvx(a,b,[af,ipiv,lwork,factored,lower,overwrite_a,overwrite_b])\n\nWrapper for ``zhesvx``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('D') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*n,1)\nfactored : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nuduh : rank-2 array('D') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('D') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def zhesvx_lwork(n, lower=...) -> typing.Any: - "work,info = zhesvx_lwork(n,[lower])\n\nWrapper for ``zhesvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zhetrd(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "c,d,e,tau,info = zhetrd(a,[lower,lwork,overwrite_a])\n\nWrapper for ``zhetrd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(n,1)\n\nReturns\n-------\nc : rank-2 array('D') with bounds (lda,n) and a storage\nd : rank-1 array('d') with bounds (n)\ne : rank-1 array('d') with bounds (n - 1)\ntau : rank-1 array('D') with bounds (n - 1)\ninfo : int\n" - ... - -def zhetrd_lwork(n, lower=...) -> typing.Any: - "work,info = zhetrd_lwork(n,[lower])\n\nWrapper for ``zhetrd_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zhetrf(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = zhetrf(a,[lower,lwork,overwrite_a])\n\nWrapper for ``zhetrf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\n\nReturns\n-------\nldu : rank-2 array('D') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def zhetrf_lwork(n, lower=...) -> typing.Any: - "work,info = zhetrf_lwork(n,[lower])\n\nWrapper for ``zhetrf_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zhfrk(n, k, alpha, a, beta, c, transr=..., uplo=..., trans=..., overwrite_c=...) -> typing.Any: - "cout = zhfrk(n,k,alpha,a,beta,c,[transr,uplo,trans,overwrite_c])\n\nWrapper for ``zhfrk``.\n\nParameters\n----------\nn : input int\nk : input int\nalpha : input float\na : input rank-2 array('D') with bounds (lda,ka)\nbeta : input float\nc : input rank-1 array('D') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\ncout : rank-1 array('D') with bounds (nt) and c storage\n" - ... - -def zlange(norm, a) -> typing.Any: - "n2 = zlange(norm,a)\n\nWrapper for ``zlange``.\n\nParameters\n----------\nnorm : input string(len=1)\na : input rank-2 array('D') with bounds (m,n)\n\nReturns\n-------\nn2 : float\n" - ... - -def zlarf(v, tau, c, work, side=..., incv=..., overwrite_c=...) -> typing.Any: - "c = zlarf(v,tau,c,work,[side,incv,overwrite_c])\n\nWrapper for ``zlarf``.\n\nParameters\n----------\nv : input rank-1 array('D') with bounds ((side[0]=='L'?(1 + (m-1)*abs(incv)):(1 + (n-1)*abs(incv))))\ntau : input complex\nc : input rank-2 array('D') with bounds (m,n)\nwork : input rank-1 array('D') with bounds (lwork)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\nincv : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (m,n)\n" - ... - -def zlarfg(n, alpha, x, incx=..., overwrite_x=...) -> typing.Any: - "alpha,x,tau = zlarfg(n,alpha,x,[incx,overwrite_x])\n\nWrapper for ``zlarfg``.\n\nParameters\n----------\nn : input int\nalpha : input complex\nx : input rank-1 array('D') with bounds (lx)\n\nOther Parameters\n----------------\noverwrite_x : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\n\nReturns\n-------\nalpha : complex\nx : rank-1 array('D') with bounds (lx)\ntau : complex\n" - ... - -def zlartg(f, g) -> typing.Any: - "cs,sn,r = zlartg(f,g)\n\nWrapper for ``zlartg``.\n\nParameters\n----------\nf : input complex\ng : input complex\n\nReturns\n-------\ncs : float\nsn : complex\nr : complex\n" - ... - -def zlaswp(a, piv, k1=..., k2=..., off=..., inc=..., overwrite_a=...) -> typing.Any: - "a = zlaswp(a,piv,[k1,k2,off,inc,overwrite_a])\n\nWrapper for ``zlaswp``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (nrows,n)\npiv : input rank-1 array('i') with bounds (npiv)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nk1 : input int, optional\n Default: 0\nk2 : input int, optional\n Default: npiv-1\noff : input int, optional\n Default: 0\ninc : input int, optional\n Default: 1\n\nReturns\n-------\na : rank-2 array('D') with bounds (nrows,n)\n" - ... - -def zlauum(c, lower=..., overwrite_c=...) -> typing.Any: - "a,info = zlauum(c,[lower,overwrite_c])\n\nWrapper for ``zlauum``.\n\nParameters\n----------\nc : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def zpbsv(ab, b, lower=..., ldab=..., overwrite_ab=..., overwrite_b=...) -> typing.Any: - "c,x,info = zpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b])\n\nWrapper for ``zpbsv``.\n\nParameters\n----------\nab : input rank-2 array('D') with bounds (ldab,n)\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (ldab,n) and ab storage\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def zpbtrf(ab, lower=..., ldab=..., overwrite_ab=...) -> typing.Any: - "c,info = zpbtrf(ab,[lower,ldab,overwrite_ab])\n\nWrapper for ``zpbtrf``.\n\nParameters\n----------\nab : input rank-2 array('D') with bounds (ldab,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ab : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\n\nReturns\n-------\nc : rank-2 array('D') with bounds (ldab,n) and ab storage\ninfo : int\n" - ... - -def zpbtrs(ab, b, lower=..., ldab=..., overwrite_b=...) -> typing.Any: - "x,info = zpbtrs(ab,b,[lower,ldab,overwrite_b])\n\nWrapper for ``zpbtrs``.\n\nParameters\n----------\nab : input rank-2 array('D') with bounds (ldab,n)\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nldab : input int, optional\n Default: shape(ab,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def zpftrf(n, a, transr=..., uplo=..., overwrite_a=...) -> typing.Any: - "achol,info = zpftrf(n,a,[transr,uplo,overwrite_a])\n\nWrapper for ``zpftrf``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('D') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nachol : rank-1 array('D') with bounds (nt) and a storage\ninfo : int\n" - ... - -def zpftri(n, a, transr=..., uplo=..., overwrite_a=...) -> typing.Any: - "ainv,info = zpftri(n,a,[transr,uplo,overwrite_a])\n\nWrapper for ``zpftri``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('D') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nainv : rank-1 array('D') with bounds (nt) and a storage\ninfo : int\n" - ... - -def zpftrs(n, a, b, transr=..., uplo=..., overwrite_b=...) -> typing.Any: - "x,info = zpftrs(n,a,b,[transr,uplo,overwrite_b])\n\nWrapper for ``zpftrs``.\n\nParameters\n----------\nn : input int\na : input rank-1 array('D') with bounds (nt)\nb : input rank-2 array('D') with bounds (ldb,nhrs)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nhrs) and b storage\ninfo : int\n" - ... - -def zpocon(a, anorm, uplo=...) -> typing.Any: - "rcond,info = zpocon(a,anorm,[uplo])\n\nWrapper for ``zpocon``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nanorm : input float\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def zposv(a, b, lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "c,x,info = zposv(a,b,[lower,overwrite_a,overwrite_b])\n\nWrapper for ``zposv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n) and a storage\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zposvx(a, b, fact=..., af=..., equed=..., s=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a_s,lu,equed,s,b_s,x,rcond,ferr,berr,info = zposvx(a,b,[fact,af,equed,s,lower,overwrite_a,overwrite_b])\n\nWrapper for ``zposvx``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'E'\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('D') with bounds (n,n)\nequed : input string(len=1), optional\n Default: 'Y'\ns : input rank-1 array('d') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na_s : rank-2 array('D') with bounds (n,n) and a storage\nlu : rank-2 array('D') with bounds (n,n) and af storage\nequed : string(len=1)\ns : rank-1 array('d') with bounds (n)\nb_s : rank-2 array('D') with bounds (n,nrhs) and b storage\nx : rank-2 array('D') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def zpotrf(a, lower=..., clean=..., overwrite_a=...) -> typing.Any: - "c,info = zpotrf(a,[lower,clean,overwrite_a])\n\nWrapper for ``zpotrf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nclean : input int, optional\n Default: 1\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def zpotri(c, lower=..., overwrite_c=...) -> typing.Any: - "inv_a,info = zpotri(c,[lower,overwrite_c])\n\nWrapper for ``zpotri``.\n\nParameters\n----------\nc : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ninv_a : rank-2 array('D') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def zpotrs(c, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = zpotrs(c,b,[lower,overwrite_b])\n\nWrapper for ``zpotrs``.\n\nParameters\n----------\nc : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_b : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zppcon(n, ap, anorm, lower=...) -> typing.Any: - "rcond,info = zppcon(n,ap,anorm,[lower])\n\nWrapper for ``zppcon``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (L)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def zppsv(n, ap, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = zppsv(n,ap,b,[lower,overwrite_b])\n\nWrapper for ``zppsv``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (L)\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def zpptrf(n, ap, lower=..., overwrite_ap=...) -> typing.Any: - "ul,info = zpptrf(n,ap,[lower,overwrite_ap])\n\nWrapper for ``zpptrf``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (L)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\n\nReturns\n-------\nul : rank-1 array('D') with bounds (L) and ap storage\ninfo : int\n" - ... - -def zpptri(n, ap, lower=..., overwrite_ap=...) -> typing.Any: - "uli,info = zpptri(n,ap,[lower,overwrite_ap])\n\nWrapper for ``zpptri``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (L)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_ap : input int, optional\n Default: 0\n\nReturns\n-------\nuli : rank-1 array('D') with bounds (L) and ap storage\ninfo : int\n" - ... - -def zpptrs(n, ap, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = zpptrs(n,ap,b,[lower,overwrite_b])\n\nWrapper for ``zpptrs``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (L)\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def zpstf2(a, tol=..., lower=..., overwrite_a=...) -> typing.Any: - "c,piv,rank_c,info = zpstf2(a,[tol,lower,overwrite_a])\n\nWrapper for ``zpstf2``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ntol : input float, optional\n Default: -1.0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nrank_c : int\ninfo : int\n" - ... - -def zpstrf(a, tol=..., lower=..., overwrite_a=...) -> typing.Any: - "c,piv,rank_c,info = zpstrf(a,[tol,lower,overwrite_a])\n\nWrapper for ``zpstrf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\ntol : input float, optional\n Default: -1.0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nc : rank-2 array('D') with bounds (n,n) and a storage\npiv : rank-1 array('i') with bounds (n)\nrank_c : int\ninfo : int\n" - ... - -def zpteqr(d, e, z, compute_z=..., overwrite_d=..., overwrite_e=..., overwrite_z=...) -> typing.Any: - "d,e,z,info = zpteqr(d,e,z,[compute_z,overwrite_d,overwrite_e,overwrite_z])\n\nWrapper for ``zpteqr``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('d') with bounds ((n>0?n-1:0))\nz : input rank-2 array('D') with bounds ((compute_z==0?shape(z, 0):max(1,n)),(compute_z==0?shape(z, 1):n))\n\nOther Parameters\n----------------\ncompute_z : input int, optional\n Default: 0\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\noverwrite_z : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('d') with bounds (n)\ne : rank-1 array('d') with bounds ((n>0?n-1:0))\nz : rank-2 array('D') with bounds ((compute_z==0?shape(z, 0):max(1,n)),(compute_z==0?shape(z, 1):n))\ninfo : int\n" - ... - -def zptsv(d, e, b, overwrite_d=..., overwrite_e=..., overwrite_b=...) -> typing.Any: - "d,du,x,info = zptsv(d,e,b,[overwrite_d,overwrite_e,overwrite_b])\n\nWrapper for ``zptsv``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('D') with bounds (n - 1)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('d') with bounds (n)\ndu : rank-1 array('D') with bounds (n - 1) and e storage\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zptsvx(d, e, b, fact=..., df=..., ef=...) -> typing.Any: - "df,ef,x,rcond,ferr,berr,info = zptsvx(d,e,b,[fact,df,ef])\n\nWrapper for ``zptsvx``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('D') with bounds (max(0, n-1))\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nfact : input string(len=1), optional\n Default: 'N'\ndf : input rank-1 array('d') with bounds (n)\nef : input rank-1 array('D') with bounds (max(0, n-1))\n\nReturns\n-------\ndf : rank-1 array('d') with bounds (n)\nef : rank-1 array('D') with bounds (max(0, n-1))\nx : rank-2 array('D') with bounds (ldx,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def zpttrf(d, e, overwrite_d=..., overwrite_e=...) -> typing.Any: - "d,e,info = zpttrf(d,e,[overwrite_d,overwrite_e])\n\nWrapper for ``zpttrf``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('D') with bounds ((n>0?n-1:0))\n\nOther Parameters\n----------------\noverwrite_d : input int, optional\n Default: 0\noverwrite_e : input int, optional\n Default: 0\n\nReturns\n-------\nd : rank-1 array('d') with bounds (n)\ne : rank-1 array('D') with bounds ((n>0?n-1:0))\ninfo : int\n" - ... - -def zpttrs(d, e, b, lower=..., overwrite_b=...) -> typing.Any: - "x,info = zpttrs(d,e,b,[lower,overwrite_b])\n\nWrapper for ``zpttrs``.\n\nParameters\n----------\nd : input rank-1 array('d') with bounds (n)\ne : input rank-1 array('D') with bounds ((n>0?n-1:0))\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def zrot(x, y, c, s, n=..., offx=..., incx=..., offy=..., incy=..., overwrite_x=..., overwrite_y=...) -> typing.Any: - "x,y = zrot(x,y,c,s,[n,offx,incx,offy,incy,overwrite_x,overwrite_y])\n\nWrapper for ``zrot``.\n\nParameters\n----------\nx : input rank-1 array('D') with bounds (lx)\ny : input rank-1 array('D') with bounds (ly)\nc : input float\ns : input complex\n\nOther Parameters\n----------------\nn : input int, optional\n Default: (lx-1-offx)/abs(incx)+1\noverwrite_x : input int, optional\n Default: 0\noffx : input int, optional\n Default: 0\nincx : input int, optional\n Default: 1\noverwrite_y : input int, optional\n Default: 0\noffy : input int, optional\n Default: 0\nincy : input int, optional\n Default: 1\n\nReturns\n-------\nx : rank-1 array('D') with bounds (lx)\ny : rank-1 array('D') with bounds (ly)\n" - ... - -def zsycon(a, ipiv, anorm, lower=...) -> typing.Any: - "rcond,info = zsycon(a,ipiv,anorm,[lower])\n\nWrapper for ``zsycon``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\nanorm : input float\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nrcond : float\ninfo : int\n" - ... - -def zsyconv(a, ipiv, lower=..., way=..., overwrite_a=...) -> typing.Any: - "a,e,info = zsyconv(a,ipiv,[lower,way,overwrite_a])\n\nWrapper for ``zsyconv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\nway : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (n,n)\ne : rank-1 array('D') with bounds (n)\ninfo : int\n" - ... - -def zsyequb(a, lower=...) -> typing.Any: - "s,scond,amax,info = zsyequb(a,[lower])\n\nWrapper for ``zsyequb``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (lda,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\ns : rank-1 array('d') with bounds (n)\nscond : float\namax : float\ninfo : int\n" - ... - -def zsysv(a, b, lwork=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "udut,ipiv,x,info = zsysv(a,b,[lwork,lower,overwrite_a,overwrite_b])\n\nWrapper for ``zsysv``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nudut : rank-2 array('D') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\nx : rank-2 array('D') with bounds (n,nrhs) and b storage\ninfo : int\n" - ... - -def zsysv_lwork(n, lower=...) -> typing.Any: - "work,info = zsysv_lwork(n,[lower])\n\nWrapper for ``zsysv_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zsysvx(a, b, af=..., ipiv=..., lwork=..., factored=..., lower=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a_s,udut,ipiv,b_s,x,rcond,ferr,berr,info = zsysvx(a,b,[af,ipiv,lwork,factored,lower,overwrite_a,overwrite_b])\n\nWrapper for ``zsysvx``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (n,nrhs)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\naf : input rank-2 array('D') with bounds (n,n)\nipiv : input rank-1 array('i') with bounds (n)\noverwrite_b : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\nfactored : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\n\nReturns\n-------\na_s : rank-2 array('D') with bounds (n,n) and a storage\nudut : rank-2 array('D') with bounds (n,n) and af storage\nipiv : rank-1 array('i') with bounds (n)\nb_s : rank-2 array('D') with bounds (n,nrhs) and b storage\nx : rank-2 array('D') with bounds (n,nrhs)\nrcond : float\nferr : rank-1 array('d') with bounds (nrhs)\nberr : rank-1 array('d') with bounds (nrhs)\ninfo : int\n" - ... - -def zsysvx_lwork(n, lower=...) -> typing.Any: - "work,info = zsysvx_lwork(n,[lower])\n\nWrapper for ``zsysvx_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zsytf2(a, lower=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = zsytf2(a,[lower,overwrite_a])\n\nWrapper for ``zsytf2``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\nldu : rank-2 array('D') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def zsytrf(a, lower=..., lwork=..., overwrite_a=...) -> typing.Any: - "ldu,ipiv,info = zsytrf(a,[lower,lwork,overwrite_a])\n\nWrapper for ``zsytrf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(n,1)\n\nReturns\n-------\nldu : rank-2 array('D') with bounds (n,n) and a storage\nipiv : rank-1 array('i') with bounds (n)\ninfo : int\n" - ... - -def zsytrf_lwork(n, lower=...) -> typing.Any: - "work,info = zsytrf_lwork(n,[lower])\n\nWrapper for ``zsytrf_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def ztbtrs(ab, b, uplo=..., trans=..., diag=..., overwrite_b=...) -> typing.Any: - "x,info = ztbtrs(ab,b,[uplo,trans,diag,overwrite_b])\n\nWrapper for ``ztbtrs``.\n\nParameters\n----------\nab : input rank-2 array('D') with bounds (ldab,n)\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\ndiag : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def ztfsm(alpha, a, b, transr=..., side=..., uplo=..., trans=..., diag=..., overwrite_b=...) -> typing.Any: - "x = ztfsm(alpha,a,b,[transr,side,uplo,trans,diag,overwrite_b])\n\nWrapper for ``ztfsm``.\n\nParameters\n----------\nalpha : input complex\na : input rank-1 array('D') with bounds (nt)\nb : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nside : input string(len=1), optional\n Default: 'L'\nuplo : input string(len=1), optional\n Default: 'U'\ntrans : input string(len=1), optional\n Default: 'N'\ndiag : input string(len=1), optional\n Default: 'N'\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (m,n) and b storage\n" - ... - -def ztfttp(n, arf, transr=..., uplo=...) -> typing.Any: - "ap,info = ztfttp(n,arf,[transr,uplo])\n\nWrapper for ``ztfttp``.\n\nParameters\n----------\nn : input int\narf : input rank-1 array('D') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nap : rank-1 array('D') with bounds (nt)\ninfo : int\n" - ... - -def ztfttr(n, arf, transr=..., uplo=...) -> typing.Any: - "a,info = ztfttr(n,arf,[transr,uplo])\n\nWrapper for ``ztfttr``.\n\nParameters\n----------\nn : input int\narf : input rank-1 array('D') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\na : rank-2 array('D') with bounds (lda,n)\ninfo : int\n" - ... - -def ztgsen( - select, a, b, q, z, lwork=..., liwork=..., overwrite_a=..., overwrite_b=..., overwrite_q=..., overwrite_z=... -) -> typing.Any: - "a,b,alpha,beta,q,z,m,pl,pr,dif,work,iwork,info = ztgsen(select,a,b,q,z,[lwork,liwork,overwrite_a,overwrite_b,overwrite_q,overwrite_z])\n\nWrapper for ``ztgsen``.\n\nParameters\n----------\nselect : input rank-1 array('i') with bounds (n)\na : input rank-2 array('D') with bounds (lda,n)\nb : input rank-2 array('D') with bounds (ldb,n)\nq : input rank-2 array('D') with bounds (ldq,n)\nz : input rank-2 array('D') with bounds (ldz,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\noverwrite_q : input int, optional\n Default: 0\noverwrite_z : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(2*m*(n-m),1)\nliwork : input int, optional\n Default: n+2\n\nReturns\n-------\na : rank-2 array('D') with bounds (lda,n)\nb : rank-2 array('D') with bounds (ldb,n)\nalpha : rank-1 array('D') with bounds (n)\nbeta : rank-1 array('D') with bounds (n)\nq : rank-2 array('D') with bounds (ldq,n)\nz : rank-2 array('D') with bounds (ldz,n)\nm : int\npl : float\npr : float\ndif : rank-1 array('d') with bounds (2)\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\niwork : rank-1 array('i') with bounds (MAX(1,liwork))\ninfo : int\n" - ... - -def ztpmqrt(l, v, t, a, b, side=..., trans=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "a,b,info = ztpmqrt(l,v,t,a,b,[side,trans,overwrite_a,overwrite_b])\n\nWrapper for ``ztpmqrt``.\n\nParameters\n----------\nl : input int\nv : input rank-2 array('D') with bounds ((side[0]=='L'?m:n),k)\nt : input rank-2 array('D') with bounds (nb,k)\na : input rank-2 array('D') with bounds ((side[0]=='L'?k:m),(side[0]=='L'?n:k))\nb : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds ((side[0]=='L'?k:m),(side[0]=='L'?n:k))\nb : rank-2 array('D') with bounds (m,n)\ninfo : int\n" - ... - -def ztpqrt(l, nb, a, b, overwrite_a=..., overwrite_b=...) -> typing.Any: - "a,b,t,info = ztpqrt(l,nb,a,b,[overwrite_a,overwrite_b])\n\nWrapper for ``ztpqrt``.\n\nParameters\n----------\nl : input int\nnb : input int\na : input rank-2 array('D') with bounds (n,n)\nb : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\na : rank-2 array('D') with bounds (n,n)\nb : rank-2 array('D') with bounds (m,n)\nt : rank-2 array('D') with bounds (nb,n)\ninfo : int\n" - ... - -def ztpttf(n, ap, transr=..., uplo=...) -> typing.Any: - "arf,info = ztpttf(n,ap,[transr,uplo])\n\nWrapper for ``ztpttf``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (nt)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\narf : rank-1 array('D') with bounds (nt)\ninfo : int\n" - ... - -def ztpttr(n, ap, uplo=...) -> typing.Any: - "a,info = ztpttr(n,ap,[uplo])\n\nWrapper for ``ztpttr``.\n\nParameters\n----------\nn : input int\nap : input rank-1 array('D') with bounds (nt)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\na : rank-2 array('D') with bounds (n,n)\ninfo : int\n" - ... - -def ztrsyl(a, b, c, trana=..., tranb=..., isgn=..., overwrite_c=...) -> typing.Any: - "x,scale,info = ztrsyl(a,b,c,[trana,tranb,isgn,overwrite_c])\n\nWrapper for ``ztrsyl``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,m)\nb : input rank-2 array('D') with bounds (n,n)\nc : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\ntrana : input string(len=1), optional\n Default: 'N'\ntranb : input string(len=1), optional\n Default: 'N'\nisgn : input int, optional\n Default: 1\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (m,n) and c storage\nscale : float\ninfo : int\n" - ... - -def ztrtri(c, lower=..., unitdiag=..., overwrite_c=...) -> typing.Any: - "inv_c,info = ztrtri(c,[lower,unitdiag,overwrite_c])\n\nWrapper for ``ztrtri``.\n\nParameters\n----------\nc : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\nlower : input int, optional\n Default: 0\nunitdiag : input int, optional\n Default: 0\n\nReturns\n-------\ninv_c : rank-2 array('D') with bounds (n,n) and c storage\ninfo : int\n" - ... - -def ztrtrs(a, b, lower=..., trans=..., unitdiag=..., lda=..., overwrite_b=...) -> typing.Any: - "x,info = ztrtrs(a,b,[lower,trans,unitdiag,lda,overwrite_b])\n\nWrapper for ``ztrtrs``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (lda,n)\nb : input rank-2 array('D') with bounds (ldb,nrhs)\n\nOther Parameters\n----------------\nlower : input int, optional\n Default: 0\ntrans : input int, optional\n Default: 0\nunitdiag : input int, optional\n Default: 0\nlda : input int, optional\n Default: shape(a,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-2 array('D') with bounds (ldb,nrhs) and b storage\ninfo : int\n" - ... - -def ztrttf(a, transr=..., uplo=...) -> typing.Any: - "arf,info = ztrttf(a,[transr,uplo])\n\nWrapper for ``ztrttf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (lda,n)\n\nOther Parameters\n----------------\ntransr : input string(len=1), optional\n Default: 'N'\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\narf : rank-1 array('D') with bounds (n*(n+1)/2)\ninfo : int\n" - ... - -def ztrttp(a, uplo=...) -> typing.Any: - "ap,info = ztrttp(a,[uplo])\n\nWrapper for ``ztrttp``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (lda,n)\n\nOther Parameters\n----------------\nuplo : input string(len=1), optional\n Default: 'U'\n\nReturns\n-------\nap : rank-1 array('D') with bounds (n*(n+1)/2)\ninfo : int\n" - ... - -def ztzrzf(a, lwork=..., overwrite_a=...) -> typing.Any: - "rz,tau,info = ztzrzf(a,[lwork,overwrite_a])\n\nWrapper for ``ztzrzf``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX(m,1)\n\nReturns\n-------\nrz : rank-2 array('D') with bounds (m,n) and a storage\ntau : rank-1 array('D') with bounds (m)\ninfo : int\n" - ... - -def ztzrzf_lwork(m, n) -> typing.Any: - "work,info = ztzrzf_lwork(m,n)\n\nWrapper for ``ztzrzf_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zuncsd( - x11, - x12, - x21, - x22, - compute_u1=..., - compute_u2=..., - compute_v1t=..., - compute_v2t=..., - trans=..., - signs=..., - lwork=..., - lrwork=..., - overwrite_x11=..., - overwrite_x12=..., - overwrite_x21=..., - overwrite_x22=..., -) -> typing.Any: - "cs11,cs12,cs21,cs22,theta,u1,u2,v1t,v2t,info = zuncsd(x11,x12,x21,x22,[compute_u1,compute_u2,compute_v1t,compute_v2t,trans,signs,lwork,lrwork,overwrite_x11,overwrite_x12,overwrite_x21,overwrite_x22])\n\nWrapper for ``zuncsd``.\n\nParameters\n----------\nx11 : input rank-2 array('D') with bounds (p,q)\nx12 : input rank-2 array('D') with bounds (p,mmq)\nx21 : input rank-2 array('D') with bounds (mmp,q)\nx22 : input rank-2 array('D') with bounds (mmp,mmq)\n\nOther Parameters\n----------------\ncompute_u1 : input int, optional\n Default: 1\ncompute_u2 : input int, optional\n Default: 1\ncompute_v1t : input int, optional\n Default: 1\ncompute_v2t : input int, optional\n Default: 1\ntrans : input int, optional\n Default: 0\nsigns : input int, optional\n Default: 0\noverwrite_x11 : input int, optional\n Default: 0\noverwrite_x12 : input int, optional\n Default: 0\noverwrite_x21 : input int, optional\n Default: 0\noverwrite_x22 : input int, optional\n Default: 0\nlwork : input int, optional\n Default: 2*m+MAX(1,MAX(mmp,mmq))+1\nlrwork : input int, optional\n Default: 5*MAX(1,q-1)+4*MAX(1,q)+8*q+1\n\nReturns\n-------\ncs11 : rank-2 array('D') with bounds (p,q) and x11 storage\ncs12 : rank-2 array('D') with bounds (p,mmq) and x12 storage\ncs21 : rank-2 array('D') with bounds (mmp,q) and x21 storage\ncs22 : rank-2 array('D') with bounds (mmp,mmq) and x22 storage\ntheta : rank-1 array('d') with bounds (min(min(p,mmp),min(q,mmq)))\nu1 : rank-2 array('D') with bounds ((compute_u1?p:0),(compute_u1?p:0))\nu2 : rank-2 array('D') with bounds ((compute_u2?mmp:0),(compute_u2?mmp:0))\nv1t : rank-2 array('D') with bounds ((compute_v1t?q:0),(compute_v1t?q:0))\nv2t : rank-2 array('D') with bounds ((compute_v2t?mmq:0),(compute_v2t?mmq:0))\ninfo : int\n" - ... - -def zuncsd_lwork(m, p, q) -> typing.Any: - "work,rwork,info = zuncsd_lwork(m,p,q)\n\nWrapper for ``zuncsd_lwork``.\n\nParameters\n----------\nm : input int\np : input int\nq : input int\n\nReturns\n-------\nwork : complex\nrwork : float\ninfo : int\n" - ... - -def zunghr(a, tau, lo=..., hi=..., lwork=..., overwrite_a=...) -> typing.Any: - "ht,info = zunghr(a,tau,[lo,hi,lwork,overwrite_a])\n\nWrapper for ``zunghr``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\ntau : input rank-1 array('D') with bounds (n - 1)\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(hi-lo,1)\n\nReturns\n-------\nht : rank-2 array('D') with bounds (n,n) and a storage\ninfo : int\n" - ... - -def zunghr_lwork(n, lo=..., hi=...) -> typing.Any: - "work,info = zunghr_lwork(n,[lo,hi])\n\nWrapper for ``zunghr_lwork``.\n\nParameters\n----------\nn : input int\n\nOther Parameters\n----------------\nlo : input int, optional\n Default: 0\nhi : input int, optional\n Default: n-1\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def zungqr(a, tau, lwork=..., overwrite_a=...) -> typing.Any: - "q,work,info = zungqr(a,tau,[lwork,overwrite_a])\n\nWrapper for ``zungqr``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\ntau : input rank-1 array('D') with bounds (k)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*n,1)\n\nReturns\n-------\nq : rank-2 array('D') with bounds (m,n) and a storage\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def zungrq(a, tau, lwork=..., overwrite_a=...) -> typing.Any: - "q,work,info = zungrq(a,tau,[lwork,overwrite_a])\n\nWrapper for ``zungrq``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\ntau : input rank-1 array('D') with bounds (k)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nlwork : input int, optional\n Default: max(3*m,1)\n\nReturns\n-------\nq : rank-2 array('D') with bounds (m,n) and a storage\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def zunmqr(side, trans, a, tau, c, lwork, overwrite_c=...) -> typing.Any: - "cq,work,info = zunmqr(side,trans,a,tau,c,lwork,[overwrite_c])\n\nWrapper for ``zunmqr``.\n\nParameters\n----------\nside : input string(len=1)\ntrans : input string(len=1)\na : input rank-2 array('D') with bounds (lda,k)\ntau : input rank-1 array('D') with bounds (k)\nc : input rank-2 array('D') with bounds (ldc,n)\nlwork : input int\n\nOther Parameters\n----------------\noverwrite_c : input int, optional\n Default: 0\n\nReturns\n-------\ncq : rank-2 array('D') with bounds (ldc,n) and c storage\nwork : rank-1 array('D') with bounds (MAX(lwork,1))\ninfo : int\n" - ... - -def zunmrz(a, tau, c, side=..., trans=..., lwork=..., overwrite_c=...) -> typing.Any: - "cq,info = zunmrz(a,tau,c,[side,trans,lwork,overwrite_c])\n\nWrapper for ``zunmrz``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (k,nt)\ntau : input rank-1 array('D') with bounds (k)\nc : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\noverwrite_c : input int, optional\n Default: 0\nlwork : input int, optional\n Default: MAX((side[0]=='L'?n:m),1)\n\nReturns\n-------\ncq : rank-2 array('D') with bounds (m,n) and c storage\ninfo : int\n" - ... - -def zunmrz_lwork(m, n, side=..., trans=...) -> typing.Any: - "work,info = zunmrz_lwork(m,n,[side,trans])\n\nWrapper for ``zunmrz_lwork``.\n\nParameters\n----------\nm : input int\nn : input int\n\nOther Parameters\n----------------\nside : input string(len=1), optional\n Default: 'L'\ntrans : input string(len=1), optional\n Default: 'N'\n\nReturns\n-------\nwork : complex\ninfo : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/_flinalg.pyi b/stubs/scipy-stubs/linalg/_flinalg.pyi deleted file mode 100644 index 2b245fcd..00000000 --- a/stubs/scipy-stubs/linalg/_flinalg.pyi +++ /dev/null @@ -1,61 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg._flinalg, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def cdet_c(a, overwrite_a=...) -> typing.Any: - "det,info = cdet_c(a,[overwrite_a])\n\nWrapper for ``cdet_c``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\ndet : complex\ninfo : int\n" - ... - -def cdet_r(a, overwrite_a=...) -> typing.Any: - "det,info = cdet_r(a,[overwrite_a])\n\nWrapper for ``cdet_r``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\ndet : complex\ninfo : int\n" - ... - -def clu_c(a, permute_l=..., overwrite_a=...) -> typing.Any: - "p,l,u,info = clu_c(a,[permute_l,overwrite_a])\n\nWrapper for ``clu_c``.\n\nParameters\n----------\na : input rank-2 array('F') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\npermute_l : input int, optional\n Default: 0\n\nReturns\n-------\np : rank-2 array('f') with bounds (m1,m1)\nl : rank-2 array('F') with bounds (m,k)\nu : rank-2 array('F') with bounds (k,n)\ninfo : int\n" - ... - -def ddet_c(a, overwrite_a=...) -> typing.Any: - "det,info = ddet_c(a,[overwrite_a])\n\nWrapper for ``ddet_c``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\ndet : float\ninfo : int\n" - ... - -def ddet_r(a, overwrite_a=...) -> typing.Any: - "det,info = ddet_r(a,[overwrite_a])\n\nWrapper for ``ddet_r``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\ndet : float\ninfo : int\n" - ... - -def dlu_c(a, permute_l=..., overwrite_a=...) -> typing.Any: - "p,l,u,info = dlu_c(a,[permute_l,overwrite_a])\n\nWrapper for ``dlu_c``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\npermute_l : input int, optional\n Default: 0\n\nReturns\n-------\np : rank-2 array('d') with bounds (m1,m1)\nl : rank-2 array('d') with bounds (m,k)\nu : rank-2 array('d') with bounds (k,n)\ninfo : int\n" - ... - -def sdet_c(a, overwrite_a=...) -> typing.Any: - "det,info = sdet_c(a,[overwrite_a])\n\nWrapper for ``sdet_c``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\ndet : float\ninfo : int\n" - ... - -def sdet_r(a, overwrite_a=...) -> typing.Any: - "det,info = sdet_r(a,[overwrite_a])\n\nWrapper for ``sdet_r``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\ndet : float\ninfo : int\n" - ... - -def slu_c(a, permute_l=..., overwrite_a=...) -> typing.Any: - "p,l,u,info = slu_c(a,[permute_l,overwrite_a])\n\nWrapper for ``slu_c``.\n\nParameters\n----------\na : input rank-2 array('f') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\npermute_l : input int, optional\n Default: 0\n\nReturns\n-------\np : rank-2 array('f') with bounds (m1,m1)\nl : rank-2 array('f') with bounds (m,k)\nu : rank-2 array('f') with bounds (k,n)\ninfo : int\n" - ... - -def zdet_c(a, overwrite_a=...) -> typing.Any: - "det,info = zdet_c(a,[overwrite_a])\n\nWrapper for ``zdet_c``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\ndet : complex\ninfo : int\n" - ... - -def zdet_r(a, overwrite_a=...) -> typing.Any: - "det,info = zdet_r(a,[overwrite_a])\n\nWrapper for ``zdet_r``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (n,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\n\nReturns\n-------\ndet : complex\ninfo : int\n" - ... - -def zlu_c(a, permute_l=..., overwrite_a=...) -> typing.Any: - "p,l,u,info = zlu_c(a,[permute_l,overwrite_a])\n\nWrapper for ``zlu_c``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\npermute_l : input int, optional\n Default: 0\n\nReturns\n-------\np : rank-2 array('d') with bounds (m1,m1)\nl : rank-2 array('D') with bounds (m,k)\nu : rank-2 array('D') with bounds (k,n)\ninfo : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/_interpolative.pyi b/stubs/scipy-stubs/linalg/_interpolative.pyi deleted file mode 100644 index 638ce83d..00000000 --- a/stubs/scipy-stubs/linalg/_interpolative.pyi +++ /dev/null @@ -1,383 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg._interpolative, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def id_srand(n) -> typing.Any: - "r = id_srand(n)\n\nWrapper for ``id_srand``.\n\nParameters\n----------\nn : input int\n\nReturns\n-------\nr : rank-1 array('d') with bounds (n)\n" - ... - -def id_srandi(t) -> typing.Any: - "id_srandi(t)\n\nWrapper for ``id_srandi``.\n\nParameters\n----------\nt : input rank-1 array('d') with bounds (55)\n" - ... - -def id_srando() -> typing.Any: - "id_srando()\n\nWrapper for ``id_srando``.\n\n" - ... - -def idd_copycols(a, krank, list, m=..., n=...) -> typing.Any: - "col = idd_copycols(a,krank,list,[m,n])\n\nWrapper for ``idd_copycols``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nkrank : input int\nlist : input rank-1 array('i') with bounds (*)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\ncol : rank-2 array('d') with bounds (m,krank)\n" - ... - -def idd_diffsnorm( - m, - n, - matvect, - matvect2, - matvec, - matvec2, - its, - p1t=..., - p2t=..., - p3t=..., - p4t=..., - p1t2=..., - p2t2=..., - p3t2=..., - p4t2=..., - p1=..., - p2=..., - p3=..., - p4=..., - p12=..., - p22=..., - p32=..., - p42=..., - w=..., - matvect_extra_args=..., - matvect2_extra_args=..., - matvec_extra_args=..., - matvec2_extra_args=..., -) -> typing.Any: - "snorm = idd_diffsnorm(m,n,matvect,matvect2,matvec,matvec2,its,[p1t,p2t,p3t,p4t,p1t2,p2t2,p3t2,p4t2,p1,p2,p3,p4,p12,p22,p32,p42,w,matvect_extra_args,matvect2_extra_args,matvec_extra_args,matvec2_extra_args])\n\nWrapper for ``idd_diffsnorm``.\n\nParameters\n----------\nm : input int\nn : input int\nmatvect : call-back function\nmatvect2 : call-back function\nmatvec : call-back function\nmatvec2 : call-back function\nits : input int\n\nOther Parameters\n----------------\nmatvect_extra_args : input tuple, optional\n Default: ()\np1t : input float\np2t : input float\np3t : input float\np4t : input float\nmatvect2_extra_args : input tuple, optional\n Default: ()\np1t2 : input float\np2t2 : input float\np3t2 : input float\np4t2 : input float\nmatvec_extra_args : input tuple, optional\n Default: ()\np1 : input float\np2 : input float\np3 : input float\np4 : input float\nmatvec2_extra_args : input tuple, optional\n Default: ()\np12 : input float\np22 : input float\np32 : input float\np42 : input float\nw : input rank-1 array('d') with bounds (3*(m+n))\n\nReturns\n-------\nsnorm : float\n\nNotes\n-----\nCall-back functions::\n\n def matvect(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (n)\n def matvect2(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (n)\n def matvec(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (m)\n def matvec2(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (m)\n" - ... - -def idd_estrank(eps, a, w, ra, m=..., n=...) -> typing.Any: - "krank,ra = idd_estrank(eps,a,w,ra,[m,n])\n\nWrapper for ``idd_estrank``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('d') with bounds (m,n)\nw : input rank-1 array('d') with bounds (17 * m + 70)\nra : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\nra : rank-1 array('d') with bounds (*)\n" - ... - -def idd_findrank(eps, m, n, matvect, p1=..., p2=..., p3=..., p4=..., w=..., matvect_extra_args=...) -> typing.Any: - "krank,ra,ier = idd_findrank(eps,m,n,matvect,[p1,p2,p3,p4,w,matvect_extra_args])\n\nWrapper for ``idd_findrank``.\n\nParameters\n----------\neps : input float\nm : input int\nn : input int\nmatvect : call-back function\n\nOther Parameters\n----------------\nmatvect_extra_args : input tuple, optional\n Default: ()\np1 : input float\np2 : input float\np3 : input float\np4 : input float\nw : input rank-1 array('d') with bounds (m+2*n+1)\n\nReturns\n-------\nkrank : int\nra : rank-1 array('d') with bounds (2*n*min(m,n))\nier : int\n\nNotes\n-----\nCall-back functions::\n\n def matvect(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (n)\n" - ... - -def idd_frm(n, w, x, m=...) -> typing.Any: - "y = idd_frm(n,w,x,[m])\n\nWrapper for ``idd_frm``.\n\nParameters\n----------\nn : input int\nw : input rank-1 array('d') with bounds (17 * m + 70)\nx : input rank-1 array('d') with bounds (m)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: len(x)\n\nReturns\n-------\ny : rank-1 array('d') with bounds (n)\n" - ... - -def idd_frmi(m) -> typing.Any: - "n,w = idd_frmi(m)\n\nWrapper for ``idd_frmi``.\n\nParameters\n----------\nm : input int\n\nReturns\n-------\nn : int\nw : rank-1 array('d') with bounds (17 * m + 70)\n" - ... - -def idd_id2svd(b, list, proj, m=..., krank=..., n=..., w=...) -> typing.Any: - "u,v,s,ier = idd_id2svd(b,list,proj,[m,krank,n,w])\n\nWrapper for ``idd_id2svd``.\n\nParameters\n----------\nb : input rank-2 array('d') with bounds (m,krank)\nlist : input rank-1 array('i') with bounds (n)\nproj : input rank-2 array('d') with bounds (krank,n-krank)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(b,0)\nkrank : input int, optional\n Default: shape(b,1)\nn : input int, optional\n Default: len(list)\nw : input rank-1 array('d') with bounds ((krank+1)*(m+3*n)+26*pow(krank,2))\n\nReturns\n-------\nu : rank-2 array('d') with bounds (m,krank)\nv : rank-2 array('d') with bounds (n,krank)\ns : rank-1 array('d') with bounds (krank)\nier : int\n" - ... - -def idd_reconid(col, list, proj, m=..., krank=..., n=...) -> typing.Any: - "approx = idd_reconid(col,list,proj,[m,krank,n])\n\nWrapper for ``idd_reconid``.\n\nParameters\n----------\ncol : input rank-2 array('d') with bounds (m,krank)\nlist : input rank-1 array('i') with bounds (n)\nproj : input rank-2 array('d') with bounds (krank,n-krank)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(col,0)\nkrank : input int, optional\n Default: shape(col,1)\nn : input int, optional\n Default: len(list)\n\nReturns\n-------\napprox : rank-2 array('d') with bounds (m,n)\n" - ... - -def idd_reconint(list, proj, n=..., krank=...) -> typing.Any: - "p = idd_reconint(list,proj,[n,krank])\n\nWrapper for ``idd_reconint``.\n\nParameters\n----------\nlist : input rank-1 array('i') with bounds (n)\nproj : input rank-2 array('d') with bounds (krank,n-krank)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(list)\nkrank : input int, optional\n Default: shape(proj,0)\n\nReturns\n-------\np : rank-2 array('d') with bounds (krank,n)\n" - ... - -def idd_sfrm(l, n, w, x, m=...) -> typing.Any: - "y = idd_sfrm(l,n,w,x,[m])\n\nWrapper for ``idd_sfrm``.\n\nParameters\n----------\nl : input int\nn : input int\nw : input rank-1 array('d') with bounds (27 * m + 90)\nx : input rank-1 array('d') with bounds (m)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: len(x)\n\nReturns\n-------\ny : rank-1 array('d') with bounds (l)\n" - ... - -def idd_sfrmi(l, m) -> typing.Any: - "n,w = idd_sfrmi(l,m)\n\nWrapper for ``idd_sfrmi``.\n\nParameters\n----------\nl : input int\nm : input int\n\nReturns\n-------\nn : int\nw : rank-1 array('d') with bounds (27 * m + 90)\n" - ... - -def idd_snorm( - m, - n, - matvect, - matvec, - its, - p1t=..., - p2t=..., - p3t=..., - p4t=..., - p1=..., - p2=..., - p3=..., - p4=..., - u=..., - matvect_extra_args=..., - matvec_extra_args=..., -) -> typing.Any: - "snorm,v = idd_snorm(m,n,matvect,matvec,its,[p1t,p2t,p3t,p4t,p1,p2,p3,p4,u,matvect_extra_args,matvec_extra_args])\n\nWrapper for ``idd_snorm``.\n\nParameters\n----------\nm : input int\nn : input int\nmatvect : call-back function\nmatvec : call-back function\nits : input int\n\nOther Parameters\n----------------\nmatvect_extra_args : input tuple, optional\n Default: ()\np1t : input float\np2t : input float\np3t : input float\np4t : input float\nmatvec_extra_args : input tuple, optional\n Default: ()\np1 : input float\np2 : input float\np3 : input float\np4 : input float\nu : input rank-1 array('d') with bounds (m)\n\nReturns\n-------\nsnorm : float\nv : rank-1 array('d') with bounds (n)\n\nNotes\n-----\nCall-back functions::\n\n def matvect(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (n)\n def matvec(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (m)\n" - ... - -def iddp_aid(eps, a, work, proj, m=..., n=...) -> typing.Any: - "krank,list,proj = iddp_aid(eps,a,work,proj,[m,n])\n\nWrapper for ``iddp_aid``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('d') with bounds (m,n)\nwork : input rank-1 array('d') with bounds (17 * m + 70)\nproj : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\nlist : rank-1 array('i') with bounds (n)\nproj : rank-1 array('d') with bounds (*)\n" - ... - -def iddp_asvd(eps, a, winit, w, m=..., n=...) -> typing.Any: - "krank,iu,iv,is,w,ier = iddp_asvd(eps,a,winit,w,[m,n])\n\nWrapper for ``iddp_asvd``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('d') with bounds (m,n)\nwinit : input rank-1 array('d') with bounds (17 * m + 70)\nw : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\niu : int\niv : int\nis : int\nw : rank-1 array('d') with bounds (*)\nier : int\n" - ... - -def iddp_id(eps, a, m=..., n=...) -> typing.Any: - "krank,list,rnorms = iddp_id(eps,a,[m,n])\n\nWrapper for ``iddp_id``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\nlist : rank-1 array('i') with bounds (n)\nrnorms : rank-1 array('d') with bounds (n)\n" - ... - -def iddp_rid(eps, m, n, matvect, proj, p1=..., p2=..., p3=..., p4=..., matvect_extra_args=...) -> typing.Any: - "krank,list,proj,ier = iddp_rid(eps,m,n,matvect,proj,[p1,p2,p3,p4,matvect_extra_args])\n\nWrapper for ``iddp_rid``.\n\nParameters\n----------\neps : input float\nm : input int\nn : input int\nmatvect : call-back function\nproj : input rank-1 array('d') with bounds (*)\n\nOther Parameters\n----------------\nmatvect_extra_args : input tuple, optional\n Default: ()\np1 : input float\np2 : input float\np3 : input float\np4 : input float\n\nReturns\n-------\nkrank : int\nlist : rank-1 array('i') with bounds (n)\nproj : rank-1 array('d') with bounds (*)\nier : int\n\nNotes\n-----\nCall-back functions::\n\n def matvect(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (n)\n" - ... - -def iddp_rsvd( - eps, - m, - n, - matvect, - matvec, - p1t=..., - p2t=..., - p3t=..., - p4t=..., - p1=..., - p2=..., - p3=..., - p4=..., - matvect_extra_args=..., - matvec_extra_args=..., -) -> typing.Any: - "krank,iu,iv,is,w,ier = iddp_rsvd(eps,m,n,matvect,matvec,[p1t,p2t,p3t,p4t,p1,p2,p3,p4,matvect_extra_args,matvec_extra_args])\n\nWrapper for ``iddp_rsvd``.\n\nParameters\n----------\neps : input float\nm : input int\nn : input int\nmatvect : call-back function\nmatvec : call-back function\n\nOther Parameters\n----------------\nmatvect_extra_args : input tuple, optional\n Default: ()\np1t : input float\np2t : input float\np3t : input float\np4t : input float\nmatvec_extra_args : input tuple, optional\n Default: ()\np1 : input float\np2 : input float\np3 : input float\np4 : input float\n\nReturns\n-------\nkrank : int\niu : int\niv : int\nis : int\nw : rank-1 array('d') with bounds ((min(m,n)+1)*(3*m+5*n+1)+25*pow(min(m,n),2))\nier : int\n\nNotes\n-----\nCall-back functions::\n\n def matvect(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (n)\n def matvec(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (m)\n" - ... - -def iddp_svd(eps, a, m=..., n=...) -> typing.Any: - "krank,iu,iv,is,w,ier = iddp_svd(eps,a,[m,n])\n\nWrapper for ``iddp_svd``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('d') with bounds (m,n)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\niu : int\niv : int\nis : int\nw : rank-1 array('d') with bounds ((min(m,n)+1)*(m+2*n+9)+8*min(m,n)+15*pow(min(m,n),2))\nier : int\n" - ... - -def iddr_aid(a, krank, w, m=..., n=...) -> typing.Any: - "list,proj = iddr_aid(a,krank,w,[m,n])\n\nWrapper for ``iddr_aid``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nkrank : input int\nw : input rank-1 array('d') with bounds ((2*krank+17)*n+27*m+100)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nlist : rank-1 array('i') with bounds (n)\nproj : rank-1 array('d') with bounds (max(krank*(n-krank),1))\n" - ... - -def iddr_aidi(m, n, krank) -> typing.Any: - "w = iddr_aidi(m,n,krank)\n\nWrapper for ``iddr_aidi``.\n\nParameters\n----------\nm : input int\nn : input int\nkrank : input int\n\nReturns\n-------\nw : rank-1 array('d') with bounds ((2*krank+17)*n+27*m+100)\n" - ... - -def iddr_asvd(a, krank, w, m=..., n=...) -> typing.Any: - "u,v,s,ier = iddr_asvd(a,krank,w,[m,n])\n\nWrapper for ``iddr_asvd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nkrank : input int\nw : input rank-1 array('d') with bounds ((2*krank+28)*m+(6*krank+21)*n+25*pow(krank,2)+100)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nu : rank-2 array('d') with bounds (m,krank)\nv : rank-2 array('d') with bounds (n,krank)\ns : rank-1 array('d') with bounds (krank)\nier : int\n" - ... - -def iddr_id(a, krank, m=..., n=...) -> typing.Any: - "list,rnorms = iddr_id(a,krank,[m,n])\n\nWrapper for ``iddr_id``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nkrank : input int\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nlist : rank-1 array('i') with bounds (n)\nrnorms : rank-1 array('d') with bounds (n)\n" - ... - -def iddr_rid(m, n, matvect, krank, p1=..., p2=..., p3=..., p4=..., matvect_extra_args=...) -> typing.Any: - "list,proj = iddr_rid(m,n,matvect,krank,[p1,p2,p3,p4,matvect_extra_args])\n\nWrapper for ``iddr_rid``.\n\nParameters\n----------\nm : input int\nn : input int\nmatvect : call-back function\nkrank : input int\n\nOther Parameters\n----------------\nmatvect_extra_args : input tuple, optional\n Default: ()\np1 : input float\np2 : input float\np3 : input float\np4 : input float\n\nReturns\n-------\nlist : rank-1 array('i') with bounds (n)\nproj : rank-1 array('d') with bounds (m+(krank+3)*n)\n\nNotes\n-----\nCall-back functions::\n\n def matvect(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (n)\n" - ... - -def iddr_rsvd( - m, - n, - matvect, - matvec, - krank, - p1t=..., - p2t=..., - p3t=..., - p4t=..., - p1=..., - p2=..., - p3=..., - p4=..., - w=..., - matvect_extra_args=..., - matvec_extra_args=..., -) -> typing.Any: - "u,v,s,ier = iddr_rsvd(m,n,matvect,matvec,krank,[p1t,p2t,p3t,p4t,p1,p2,p3,p4,w,matvect_extra_args,matvec_extra_args])\n\nWrapper for ``iddr_rsvd``.\n\nParameters\n----------\nm : input int\nn : input int\nmatvect : call-back function\nmatvec : call-back function\nkrank : input int\n\nOther Parameters\n----------------\nmatvect_extra_args : input tuple, optional\n Default: ()\np1t : input float\np2t : input float\np3t : input float\np4t : input float\nmatvec_extra_args : input tuple, optional\n Default: ()\np1 : input float\np2 : input float\np3 : input float\np4 : input float\nw : input rank-1 array('d') with bounds ((krank+1)*(2*m+4*n)+25*pow(krank,2))\n\nReturns\n-------\nu : rank-2 array('d') with bounds (m,krank)\nv : rank-2 array('d') with bounds (n,krank)\ns : rank-1 array('d') with bounds (krank)\nier : int\n\nNotes\n-----\nCall-back functions::\n\n def matvect(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (n)\n def matvec(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('d') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input float\n p2 : input float\n p3 : input float\n p4 : input float\n Return objects:\n y : rank-1 array('d') with bounds (m)\n" - ... - -def iddr_svd(a, krank, m=..., n=..., r=...) -> typing.Any: - "u,v,s,ier = iddr_svd(a,krank,[m,n,r])\n\nWrapper for ``iddr_svd``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (m,n)\nkrank : input int\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\nr : input rank-1 array('d') with bounds ((krank+2)*n+8*min(m,n)+15*pow(krank,2)+8*krank)\n\nReturns\n-------\nu : rank-2 array('d') with bounds (m,krank)\nv : rank-2 array('d') with bounds (n,krank)\ns : rank-1 array('d') with bounds (krank)\nier : int\n" - ... - -def idz_copycols(a, krank, list, m=..., n=...) -> typing.Any: - "col = idz_copycols(a,krank,list,[m,n])\n\nWrapper for ``idz_copycols``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nkrank : input int\nlist : input rank-1 array('i') with bounds (*)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\ncol : rank-2 array('D') with bounds (m,krank)\n" - ... - -def idz_diffsnorm( - m, - n, - matveca, - matveca2, - matvec, - matvec2, - its, - p1a=..., - p2a=..., - p3a=..., - p4a=..., - p1a2=..., - p2a2=..., - p3a2=..., - p4a2=..., - p1=..., - p2=..., - p3=..., - p4=..., - p12=..., - p22=..., - p32=..., - p42=..., - w=..., - matveca_extra_args=..., - matveca2_extra_args=..., - matvec_extra_args=..., - matvec2_extra_args=..., -) -> typing.Any: - "snorm = idz_diffsnorm(m,n,matveca,matveca2,matvec,matvec2,its,[p1a,p2a,p3a,p4a,p1a2,p2a2,p3a2,p4a2,p1,p2,p3,p4,p12,p22,p32,p42,w,matveca_extra_args,matveca2_extra_args,matvec_extra_args,matvec2_extra_args])\n\nWrapper for ``idz_diffsnorm``.\n\nParameters\n----------\nm : input int\nn : input int\nmatveca : call-back function\nmatveca2 : call-back function\nmatvec : call-back function\nmatvec2 : call-back function\nits : input int\n\nOther Parameters\n----------------\nmatveca_extra_args : input tuple, optional\n Default: ()\np1a : input complex\np2a : input complex\np3a : input complex\np4a : input complex\nmatveca2_extra_args : input tuple, optional\n Default: ()\np1a2 : input complex\np2a2 : input complex\np3a2 : input complex\np4a2 : input complex\nmatvec_extra_args : input tuple, optional\n Default: ()\np1 : input complex\np2 : input complex\np3 : input complex\np4 : input complex\nmatvec2_extra_args : input tuple, optional\n Default: ()\np12 : input complex\np22 : input complex\np32 : input complex\np42 : input complex\nw : input rank-1 array('D') with bounds (3*(m+n))\n\nReturns\n-------\nsnorm : float\n\nNotes\n-----\nCall-back functions::\n\n def matveca(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (n)\n def matveca2(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (n)\n def matvec(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (m)\n def matvec2(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (m)\n" - ... - -def idz_estrank(eps, a, w, ra, m=..., n=...) -> typing.Any: - "krank,ra = idz_estrank(eps,a,w,ra,[m,n])\n\nWrapper for ``idz_estrank``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('D') with bounds (m,n)\nw : input rank-1 array('D') with bounds (17 * m + 70)\nra : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\nra : rank-1 array('D') with bounds (*)\n" - ... - -def idz_findrank(eps, m, n, matveca, p1=..., p2=..., p3=..., p4=..., w=..., matveca_extra_args=...) -> typing.Any: - "krank,ra,ier = idz_findrank(eps,m,n,matveca,[p1,p2,p3,p4,w,matveca_extra_args])\n\nWrapper for ``idz_findrank``.\n\nParameters\n----------\neps : input float\nm : input int\nn : input int\nmatveca : call-back function\n\nOther Parameters\n----------------\nmatveca_extra_args : input tuple, optional\n Default: ()\np1 : input complex\np2 : input complex\np3 : input complex\np4 : input complex\nw : input rank-1 array('D') with bounds (m+2*n+1)\n\nReturns\n-------\nkrank : int\nra : rank-1 array('D') with bounds (2*n*min(m,n))\nier : int\n\nNotes\n-----\nCall-back functions::\n\n def matveca(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (n)\n" - ... - -def idz_frm(n, w, x, m=...) -> typing.Any: - "y = idz_frm(n,w,x,[m])\n\nWrapper for ``idz_frm``.\n\nParameters\n----------\nn : input int\nw : input rank-1 array('D') with bounds (17 * m + 70)\nx : input rank-1 array('D') with bounds (m)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: len(x)\n\nReturns\n-------\ny : rank-1 array('D') with bounds (n)\n" - ... - -def idz_frmi(m) -> typing.Any: - "n,w = idz_frmi(m)\n\nWrapper for ``idz_frmi``.\n\nParameters\n----------\nm : input int\n\nReturns\n-------\nn : int\nw : rank-1 array('D') with bounds (17 * m + 70)\n" - ... - -def idz_id2svd(b, list, proj, m=..., krank=..., n=..., w=...) -> typing.Any: - "u,v,s,ier = idz_id2svd(b,list,proj,[m,krank,n,w])\n\nWrapper for ``idz_id2svd``.\n\nParameters\n----------\nb : input rank-2 array('D') with bounds (m,krank)\nlist : input rank-1 array('i') with bounds (n)\nproj : input rank-2 array('D') with bounds (krank,n-krank)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(b,0)\nkrank : input int, optional\n Default: shape(b,1)\nn : input int, optional\n Default: len(list)\nw : input rank-1 array('D') with bounds ((krank+1)*(m+3*n+10)+9*pow(krank,2))\n\nReturns\n-------\nu : rank-2 array('D') with bounds (m,krank)\nv : rank-2 array('D') with bounds (n,krank)\ns : rank-1 array('d') with bounds (krank)\nier : int\n" - ... - -def idz_reconid(col, list, proj, m=..., krank=..., n=...) -> typing.Any: - "approx = idz_reconid(col,list,proj,[m,krank,n])\n\nWrapper for ``idz_reconid``.\n\nParameters\n----------\ncol : input rank-2 array('D') with bounds (m,krank)\nlist : input rank-1 array('i') with bounds (n)\nproj : input rank-2 array('D') with bounds (krank,n-krank)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(col,0)\nkrank : input int, optional\n Default: shape(col,1)\nn : input int, optional\n Default: len(list)\n\nReturns\n-------\napprox : rank-2 array('D') with bounds (m,n)\n" - ... - -def idz_reconint(list, proj, n=..., krank=...) -> typing.Any: - "p = idz_reconint(list,proj,[n,krank])\n\nWrapper for ``idz_reconint``.\n\nParameters\n----------\nlist : input rank-1 array('i') with bounds (n)\nproj : input rank-2 array('D') with bounds (krank,n-krank)\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(list)\nkrank : input int, optional\n Default: shape(proj,0)\n\nReturns\n-------\np : rank-2 array('D') with bounds (krank,n)\n" - ... - -def idz_sfrm(l, n, w, x, m=...) -> typing.Any: - "y = idz_sfrm(l,n,w,x,[m])\n\nWrapper for ``idz_sfrm``.\n\nParameters\n----------\nl : input int\nn : input int\nw : input rank-1 array('D') with bounds (27 * m + 90)\nx : input rank-1 array('D') with bounds (m)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: len(x)\n\nReturns\n-------\ny : rank-1 array('D') with bounds (l)\n" - ... - -def idz_sfrmi(l, m) -> typing.Any: - "n,w = idz_sfrmi(l,m)\n\nWrapper for ``idz_sfrmi``.\n\nParameters\n----------\nl : input int\nm : input int\n\nReturns\n-------\nn : int\nw : rank-1 array('D') with bounds (27 * m + 90)\n" - ... - -def idz_snorm( - m, - n, - matveca, - matvec, - its, - p1a=..., - p2a=..., - p3a=..., - p4a=..., - p1=..., - p2=..., - p3=..., - p4=..., - u=..., - matveca_extra_args=..., - matvec_extra_args=..., -) -> typing.Any: - "snorm,v = idz_snorm(m,n,matveca,matvec,its,[p1a,p2a,p3a,p4a,p1,p2,p3,p4,u,matveca_extra_args,matvec_extra_args])\n\nWrapper for ``idz_snorm``.\n\nParameters\n----------\nm : input int\nn : input int\nmatveca : call-back function\nmatvec : call-back function\nits : input int\n\nOther Parameters\n----------------\nmatveca_extra_args : input tuple, optional\n Default: ()\np1a : input complex\np2a : input complex\np3a : input complex\np4a : input complex\nmatvec_extra_args : input tuple, optional\n Default: ()\np1 : input complex\np2 : input complex\np3 : input complex\np4 : input complex\nu : input rank-1 array('D') with bounds (m)\n\nReturns\n-------\nsnorm : float\nv : rank-1 array('D') with bounds (n)\n\nNotes\n-----\nCall-back functions::\n\n def matveca(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (n)\n def matvec(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (m)\n" - ... - -def idzp_aid(eps, a, work, proj, m=..., n=...) -> typing.Any: - "krank,list,proj = idzp_aid(eps,a,work,proj,[m,n])\n\nWrapper for ``idzp_aid``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('D') with bounds (m,n)\nwork : input rank-1 array('D') with bounds (17 * m + 70)\nproj : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\nlist : rank-1 array('i') with bounds (n)\nproj : rank-1 array('D') with bounds (*)\n" - ... - -def idzp_asvd(eps, a, winit, w, m=..., n=...) -> typing.Any: - "krank,iu,iv,is,w,ier = idzp_asvd(eps,a,winit,w,[m,n])\n\nWrapper for ``idzp_asvd``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('D') with bounds (m,n)\nwinit : input rank-1 array('D') with bounds (17 * m + 70)\nw : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\niu : int\niv : int\nis : int\nw : rank-1 array('D') with bounds (*)\nier : int\n" - ... - -def idzp_id(eps, a, m=..., n=...) -> typing.Any: - "krank,list,rnorms = idzp_id(eps,a,[m,n])\n\nWrapper for ``idzp_id``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\nlist : rank-1 array('i') with bounds (n)\nrnorms : rank-1 array('d') with bounds (n)\n" - ... - -def idzp_rid(eps, m, n, matveca, proj, p1=..., p2=..., p3=..., p4=..., matveca_extra_args=...) -> typing.Any: - "krank,list,proj,ier = idzp_rid(eps,m,n,matveca,proj,[p1,p2,p3,p4,matveca_extra_args])\n\nWrapper for ``idzp_rid``.\n\nParameters\n----------\neps : input float\nm : input int\nn : input int\nmatveca : call-back function\nproj : input rank-1 array('D') with bounds (*)\n\nOther Parameters\n----------------\nmatveca_extra_args : input tuple, optional\n Default: ()\np1 : input complex\np2 : input complex\np3 : input complex\np4 : input complex\n\nReturns\n-------\nkrank : int\nlist : rank-1 array('i') with bounds (n)\nproj : rank-1 array('D') with bounds (*)\nier : int\n\nNotes\n-----\nCall-back functions::\n\n def matveca(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (n)\n" - ... - -def idzp_rsvd( - eps, - m, - n, - matveca, - matvec, - p1a=..., - p2a=..., - p3a=..., - p4a=..., - p1=..., - p2=..., - p3=..., - p4=..., - matveca_extra_args=..., - matvec_extra_args=..., -) -> typing.Any: - "krank,iu,iv,is,w,ier = idzp_rsvd(eps,m,n,matveca,matvec,[p1a,p2a,p3a,p4a,p1,p2,p3,p4,matveca_extra_args,matvec_extra_args])\n\nWrapper for ``idzp_rsvd``.\n\nParameters\n----------\neps : input float\nm : input int\nn : input int\nmatveca : call-back function\nmatvec : call-back function\n\nOther Parameters\n----------------\nmatveca_extra_args : input tuple, optional\n Default: ()\np1a : input complex\np2a : input complex\np3a : input complex\np4a : input complex\nmatvec_extra_args : input tuple, optional\n Default: ()\np1 : input complex\np2 : input complex\np3 : input complex\np4 : input complex\n\nReturns\n-------\nkrank : int\niu : int\niv : int\nis : int\nw : rank-1 array('D') with bounds ((min(m,n)+1)*(3*m+5*n+11)+8*pow(min(m,n),2))\nier : int\n\nNotes\n-----\nCall-back functions::\n\n def matveca(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (n)\n def matvec(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (m)\n" - ... - -def idzp_svd(eps, a, m=..., n=...) -> typing.Any: - "krank,iu,iv,is,w,ier = idzp_svd(eps,a,[m,n])\n\nWrapper for ``idzp_svd``.\n\nParameters\n----------\neps : input float\na : input rank-2 array('D') with bounds (m,n)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nkrank : int\niu : int\niv : int\nis : int\nw : rank-1 array('D') with bounds ((min(m,n)+1)*(m+2*n+9)+8*min(m,n)+6*pow(min(m,n),2))\nier : int\n" - ... - -def idzr_aid(a, krank, w, m=..., n=...) -> typing.Any: - "list,proj = idzr_aid(a,krank,w,[m,n])\n\nWrapper for ``idzr_aid``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nkrank : input int\nw : input rank-1 array('D') with bounds ((2*krank+17)*n+21*m+80)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nlist : rank-1 array('i') with bounds (n)\nproj : rank-1 array('D') with bounds (max(krank*(n-krank),1))\n" - ... - -def idzr_aidi(m, n, krank) -> typing.Any: - "w = idzr_aidi(m,n,krank)\n\nWrapper for ``idzr_aidi``.\n\nParameters\n----------\nm : input int\nn : input int\nkrank : input int\n\nReturns\n-------\nw : rank-1 array('D') with bounds ((2*krank+17)*n+21*m+80)\n" - ... - -def idzr_asvd(a, krank, w, m=..., n=...) -> typing.Any: - "u,v,s,ier = idzr_asvd(a,krank,w,[m,n])\n\nWrapper for ``idzr_asvd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nkrank : input int\nw : input rank-1 array('D') with bounds ((2*krank+22)*m+(6*krank+21)*n+8*pow(krank,2)+10*krank+90)\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nu : rank-2 array('D') with bounds (m,krank)\nv : rank-2 array('D') with bounds (n,krank)\ns : rank-1 array('d') with bounds (krank)\nier : int\n" - ... - -def idzr_id(a, krank, m=..., n=...) -> typing.Any: - "list,rnorms = idzr_id(a,krank,[m,n])\n\nWrapper for ``idzr_id``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nkrank : input int\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\n\nReturns\n-------\nlist : rank-1 array('i') with bounds (n)\nrnorms : rank-1 array('d') with bounds (n)\n" - ... - -def idzr_rid(m, n, matveca, krank, p1=..., p2=..., p3=..., p4=..., matveca_extra_args=...) -> typing.Any: - "list,proj = idzr_rid(m,n,matveca,krank,[p1,p2,p3,p4,matveca_extra_args])\n\nWrapper for ``idzr_rid``.\n\nParameters\n----------\nm : input int\nn : input int\nmatveca : call-back function\nkrank : input int\n\nOther Parameters\n----------------\nmatveca_extra_args : input tuple, optional\n Default: ()\np1 : input complex\np2 : input complex\np3 : input complex\np4 : input complex\n\nReturns\n-------\nlist : rank-1 array('i') with bounds (n)\nproj : rank-1 array('D') with bounds (m+(krank+3)*n)\n\nNotes\n-----\nCall-back functions::\n\n def matveca(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (n)\n" - ... - -def idzr_rsvd( - m, - n, - matveca, - matvec, - krank, - p1a=..., - p2a=..., - p3a=..., - p4a=..., - p1=..., - p2=..., - p3=..., - p4=..., - w=..., - matveca_extra_args=..., - matvec_extra_args=..., -) -> typing.Any: - "u,v,s,ier = idzr_rsvd(m,n,matveca,matvec,krank,[p1a,p2a,p3a,p4a,p1,p2,p3,p4,w,matveca_extra_args,matvec_extra_args])\n\nWrapper for ``idzr_rsvd``.\n\nParameters\n----------\nm : input int\nn : input int\nmatveca : call-back function\nmatvec : call-back function\nkrank : input int\n\nOther Parameters\n----------------\nmatveca_extra_args : input tuple, optional\n Default: ()\np1a : input complex\np2a : input complex\np3a : input complex\np4a : input complex\nmatvec_extra_args : input tuple, optional\n Default: ()\np1 : input complex\np2 : input complex\np3 : input complex\np4 : input complex\nw : input rank-1 array('D') with bounds ((krank+1)*(2*m+4*n+10)+8*pow(krank,2))\n\nReturns\n-------\nu : rank-2 array('D') with bounds (m,krank)\nv : rank-2 array('D') with bounds (n,krank)\ns : rank-1 array('d') with bounds (krank)\nier : int\n\nNotes\n-----\nCall-back functions::\n\n def matveca(x,[m,n,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (m)\n Optional arguments:\n m : input int, optional\n Default: len(x)\n n : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (n)\n def matvec(x,[n,m,p1,p2,p3,p4]): return y\n Required arguments:\n x : input rank-1 array('D') with bounds (n)\n Optional arguments:\n n : input int, optional\n Default: len(x)\n m : input int\n p1 : input complex\n p2 : input complex\n p3 : input complex\n p4 : input complex\n Return objects:\n y : rank-1 array('D') with bounds (m)\n" - ... - -def idzr_svd(a, krank, m=..., n=..., r=...) -> typing.Any: - "u,v,s,ier = idzr_svd(a,krank,[m,n,r])\n\nWrapper for ``idzr_svd``.\n\nParameters\n----------\na : input rank-2 array('D') with bounds (m,n)\nkrank : input int\n\nOther Parameters\n----------------\nm : input int, optional\n Default: shape(a,0)\nn : input int, optional\n Default: shape(a,1)\nr : input rank-1 array('D') with bounds ((krank+2)*n+8*min(m,n)+6*pow(krank,2)+8*krank)\n\nReturns\n-------\nu : rank-2 array('D') with bounds (m,krank)\nv : rank-2 array('D') with bounds (n,krank)\ns : rank-1 array('d') with bounds (krank)\nier : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/_matfuncs_sqrtm_triu.pyi b/stubs/scipy-stubs/linalg/_matfuncs_sqrtm_triu.pyi deleted file mode 100644 index 08a77b3c..00000000 --- a/stubs/scipy-stubs/linalg/_matfuncs_sqrtm_triu.pyi +++ /dev/null @@ -1,20 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg._matfuncs_sqrtm_triu, version: unspecified -import builtins as _mod_builtins -import typing - -import scipy.linalg._matfuncs_sqrtm as _mod_scipy_linalg__matfuncs_sqrtm - -SqrtmError = _mod_scipy_linalg__matfuncs_sqrtm.SqrtmError -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def within_block_loop(R, T, start_stop_pairs, nblocks) -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/_solve_toeplitz.pyi b/stubs/scipy-stubs/linalg/_solve_toeplitz.pyi deleted file mode 100644 index 87a4a9f6..00000000 --- a/stubs/scipy-stubs/linalg/_solve_toeplitz.pyi +++ /dev/null @@ -1,35 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg._solve_toeplitz, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy as _mod_numpy -import numpy.linalg as _mod_numpy_linalg - -LinAlgError = _mod_numpy_linalg.LinAlgError -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def asarray(a, dtype, order) -> typing.Any: - "Convert the input to an array.\n\n Parameters\n ----------\n a : array_like\n Input data, in any form that can be converted to an array. This\n includes lists, lists of tuples, tuples, tuples of tuples, tuples\n of lists and ndarrays.\n dtype : data-type, optional\n By default, the data-type is inferred from the input data.\n order : {'C', 'F', 'A', 'K'}, optional\n Memory layout. 'A' and 'K' depend on the order of input array a.\n 'C' row-major (C-style), \n 'F' column-major (Fortran-style) memory representation.\n 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise\n 'K' (keep) preserve input order\n Defaults to 'C'.\n like : array_like\n Reference object to allow the creation of arrays which are not\n NumPy arrays. If an array-like passed in as ``like`` supports\n the ``__array_function__`` protocol, the result will be defined\n by it. In this case, it ensures the creation of an array object\n compatible with that passed in via this argument.\n\n .. note::\n The ``like`` keyword is an experimental feature pending on\n acceptance of :ref:`NEP 35 `.\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n out : ndarray\n Array interpretation of `a`. No copy is performed if the input\n is already an ndarray with matching dtype and order. If `a` is a\n subclass of ndarray, a base class ndarray is returned.\n\n See Also\n --------\n asanyarray : Similar function which passes through subclasses.\n ascontiguousarray : Convert input to a contiguous array.\n asfarray : Convert input to a floating point ndarray.\n asfortranarray : Convert input to an ndarray with column-major\n memory order.\n asarray_chkfinite : Similar function which checks input for NaNs and Infs.\n fromiter : Create an array from an iterator.\n fromfunction : Construct an array by executing a function on grid\n positions.\n\n Examples\n --------\n Convert a list into an array:\n\n >>> a = [1, 2]\n >>> np.asarray(a)\n array([1, 2])\n\n Existing arrays are not copied:\n\n >>> a = np.array([1, 2])\n >>> np.asarray(a) is a\n True\n\n If `dtype` is set, array is copied only if dtype does not match:\n\n >>> a = np.array([1, 2], dtype=np.float32)\n >>> np.asarray(a, dtype=np.float32) is a\n True\n >>> np.asarray(a, dtype=np.float64) is a\n False\n\n Contrary to `asanyarray`, ndarray subclasses are not passed through:\n\n >>> issubclass(np.recarray, np.ndarray)\n True\n >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray)\n >>> np.asarray(a) is a\n False\n >>> np.asanyarray(a) is a\n True\n\n" - ... - -complex128 = _mod_numpy.complex128 -float64 = _mod_numpy.float64 - -def levinson(a, b) -> typing.Any: - "Solve a linear Toeplitz system using Levinson recursion.\n\n Parameters\n ----------\n a : array, dtype=double or complex128, shape=(2n-1,)\n The first column of the matrix in reverse order (without the diagonal)\n followed by the first (see below)\n b : array, dtype=double or complex128, shape=(n,)\n The right hand side vector. Both a and b must have the same type\n (double or complex128).\n\n Notes\n -----\n For example, the 5x5 toeplitz matrix below should be represented as\n the linear array ``a`` on the right ::\n\n [ a0 a1 a2 a3 a4 ]\n [ a-1 a0 a1 a2 a3 ]\n [ a-2 a-1 a0 a1 a2 ] -> [a-4 a-3 a-2 a-1 a0 a1 a2 a3 a4]\n [ a-3 a-2 a-1 a0 a1 ]\n [ a-4 a-3 a-2 a-1 a0 ]\n\n Returns\n -------\n x : arrray, shape=(n,)\n The solution vector\n reflection_coeff : array, shape=(n+1,)\n Toeplitz reflection coefficients. When a is symmetric Toeplitz and\n ``b`` is ``a[n:]``, as in the solution of autoregressive systems,\n then ``reflection_coeff`` also correspond to the partial\n autocorrelation function.\n" - ... - -def zeros(shape, dtype=..., order=..., *, like=...) -> typing.Any: - "zeros(shape, dtype=float, order='C', *, like=None)\n\n Return a new array of given shape and type, filled with zeros.\n\n Parameters\n ----------\n shape : int or tuple of ints\n Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n dtype : data-type, optional\n The desired data-type for the array, e.g., `numpy.int8`. Default is\n `numpy.float64`.\n order : {'C', 'F'}, optional, default: 'C'\n Whether to store multi-dimensional data in row-major\n (C-style) or column-major (Fortran-style) order in\n memory.\n like : array_like\n Reference object to allow the creation of arrays which are not\n NumPy arrays. If an array-like passed in as ``like`` supports\n the ``__array_function__`` protocol, the result will be defined\n by it. In this case, it ensures the creation of an array object\n compatible with that passed in via this argument.\n\n .. note::\n The ``like`` keyword is an experimental feature pending on\n acceptance of :ref:`NEP 35 `.\n\n .. versionadded:: 1.20.0\n\n Returns\n -------\n out : ndarray\n Array of zeros with the given shape, dtype, and order.\n\n See Also\n --------\n zeros_like : Return an array of zeros with shape and type of input.\n empty : Return a new uninitialized array.\n ones : Return a new array setting values to one.\n full : Return a new array of given shape filled with value.\n\n Examples\n --------\n >>> np.zeros(5)\n array([ 0., 0., 0., 0., 0.])\n\n >>> np.zeros((5,), dtype=int)\n array([0, 0, 0, 0, 0])\n\n >>> np.zeros((2, 1))\n array([[ 0.],\n [ 0.]])\n\n >>> s = (2,2)\n >>> np.zeros(s)\n array([[ 0., 0.],\n [ 0., 0.]])\n\n >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype\n array([(0, 0), (0, 0)],\n dtype=[('x', ' typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/cython_blas.pyi b/stubs/scipy-stubs/linalg/cython_blas.pyi deleted file mode 100644 index ca2c42e9..00000000 --- a/stubs/scipy-stubs/linalg/cython_blas.pyi +++ /dev/null @@ -1,36 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg.cython_blas, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _test_cdotc() -> typing.Any: ... -def _test_cdotu() -> typing.Any: ... -def _test_dasum() -> typing.Any: ... -def _test_ddot() -> typing.Any: ... -def _test_dgemm() -> typing.Any: ... -def _test_dnrm2() -> typing.Any: ... -def _test_dzasum() -> typing.Any: ... -def _test_dznrm2() -> typing.Any: ... -def _test_icamax() -> typing.Any: ... -def _test_idamax() -> typing.Any: ... -def _test_isamax() -> typing.Any: ... -def _test_izamax() -> typing.Any: ... -def _test_sasum() -> typing.Any: ... -def _test_scasum() -> typing.Any: ... -def _test_scnrm2() -> typing.Any: ... -def _test_sdot() -> typing.Any: ... -def _test_snrm2() -> typing.Any: ... -def _test_zdotc() -> typing.Any: ... -def _test_zdotu() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/linalg/cython_lapack.pyi b/stubs/scipy-stubs/linalg/cython_lapack.pyi deleted file mode 100644 index 419afa01..00000000 --- a/stubs/scipy-stubs/linalg/cython_lapack.pyi +++ /dev/null @@ -1,16 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.linalg.cython_lapack, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def _test_dlamch() -> typing.Any: ... -def _test_slamch() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/ndimage/_ctest.pyi b/stubs/scipy-stubs/ndimage/_ctest.pyi deleted file mode 100644 index c8cb3c43..00000000 --- a/stubs/scipy-stubs/ndimage/_ctest.pyi +++ /dev/null @@ -1,15 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.ndimage._ctest, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def filter1d() -> typing.Any: ... -def filter2d() -> typing.Any: ... -def transform() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/ndimage/_cytest.pyi b/stubs/scipy-stubs/ndimage/_cytest.pyi deleted file mode 100644 index 1dfc213b..00000000 --- a/stubs/scipy-stubs/ndimage/_cytest.pyi +++ /dev/null @@ -1,20 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.ndimage._cytest, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def filter1d() -> typing.Any: ... -def filter1d_capsule() -> typing.Any: ... -def filter2d() -> typing.Any: ... -def filter2d_capsule() -> typing.Any: ... -def transform() -> typing.Any: ... -def transform_capsule() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/ndimage/_nd_image.pyi b/stubs/scipy-stubs/ndimage/_nd_image.pyi deleted file mode 100644 index ccc9a1eb..00000000 --- a/stubs/scipy-stubs/ndimage/_nd_image.pyi +++ /dev/null @@ -1,32 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.ndimage._nd_image, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def binary_erosion() -> typing.Any: ... -def binary_erosion2() -> typing.Any: ... -def correlate() -> typing.Any: ... -def correlate1d() -> typing.Any: ... -def distance_transform_bf() -> typing.Any: ... -def distance_transform_op() -> typing.Any: ... -def euclidean_feature_transform() -> typing.Any: ... -def find_objects() -> typing.Any: ... -def fourier_filter() -> typing.Any: ... -def fourier_shift() -> typing.Any: ... -def generic_filter() -> typing.Any: ... -def generic_filter1d() -> typing.Any: ... -def geometric_transform() -> typing.Any: ... -def min_or_max_filter() -> typing.Any: ... -def min_or_max_filter1d() -> typing.Any: ... -def rank_filter() -> typing.Any: ... -def spline_filter1d() -> typing.Any: ... -def uniform_filter1d() -> typing.Any: ... -def watershed_ift() -> typing.Any: ... -def zoom_shift() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/ndimage/_ni_label.pyi b/stubs/scipy-stubs/ndimage/_ni_label.pyi deleted file mode 100644 index f0b6f75d..00000000 --- a/stubs/scipy-stubs/ndimage/_ni_label.pyi +++ /dev/null @@ -1,23 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.ndimage._ni_label, version: unspecified -import builtins as _mod_builtins -import typing - -import _ni_label as _mod__ni_label - -NeedMoreBits = _mod__ni_label.NeedMoreBits -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _label() -> typing.Any: ... -def get_nonzero_line(a) -> typing.Any: ... -def get_read_line(a) -> typing.Any: ... -def get_write_line(a) -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/odr/__odrpack.pyi b/stubs/scipy-stubs/odr/__odrpack.pyi deleted file mode 100644 index c6d564cc..00000000 --- a/stubs/scipy-stubs/odr/__odrpack.pyi +++ /dev/null @@ -1,48 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: _odrpack, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def _set_exceptions(odr_error, odr_stop) -> typing.Any: - "_set_exceptions(odr_error, odr_stop)\n\n Internal function: set exception classes." - ... - -def odr( - fcn, - beta0, - y, - x, - we=..., - wd=..., - fjacb=..., - fjacd=..., - extra_args=..., - ifixx=..., - ifixb=..., - job=..., - iprint=..., - errfile=..., - rptfile=..., - ndigit=..., - taufac=..., - sstol=..., - partol=..., - maxit=..., - stpb=..., - stpd=..., - sclb=..., - scld=..., - work=..., - iwork=..., - full_output=..., -) -> typing.Any: - "odr(fcn, beta0, y, x, we=None, wd=None, fjacb=None, fjacd=None, extra_args=None, ifixx=None, ifixb=None, job=0, iprint=0, errfile=None, rptfile=None, ndigit=0, taufac=0.0, sstol=-1.0, partol=-1.0, maxit=-1, stpb=None, stpd=None, sclb=None, scld=None, work=None, iwork=None, full_output=0)\n\n Low-level function for ODR.\n\n See Also\n --------\n ODR : The ODR class gathers all information and coordinates the running of the main fitting routine.\n Model : The Model class stores information about the function you wish to fit.\n Data : The data to fit.\n RealData : Data with weights as actual std. dev.s and/or covariances.\n\n Notes\n -----\n This is a function performing the same operation as the `ODR`,\n `Model`, and `Data` classes together. The parameters of this\n function are explained in the class documentation." - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/__nnls.pyi b/stubs/scipy-stubs/optimize/__nnls.pyi deleted file mode 100644 index 0f433f60..00000000 --- a/stubs/scipy-stubs/optimize/__nnls.pyi +++ /dev/null @@ -1,17 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize.__nnls, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def nnls(a, m, n, b, w, zz, index_bn, maxiter, mda=..., overwrite_a=..., overwrite_b=...) -> typing.Any: - "x,rnorm,mode = nnls(a,m,n,b,w,zz,index_bn,maxiter,[mda,overwrite_a,overwrite_b])\n\nWrapper for ``nnls``.\n\nParameters\n----------\na : input rank-2 array('d') with bounds (mda,*)\nm : input int\nn : input int\nb : input rank-1 array('d') with bounds (*)\nw : input rank-1 array('d') with bounds (*)\nzz : input rank-1 array('d') with bounds (*)\nindex_bn : input rank-1 array('i') with bounds (*)\nmaxiter : input int\n\nOther Parameters\n----------------\noverwrite_a : input int, optional\n Default: 0\nmda : input int, optional\n Default: shape(a,0)\noverwrite_b : input int, optional\n Default: 0\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n)\nrnorm : float\nmode : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_bglu_dense.pyi b/stubs/scipy-stubs/optimize/_bglu_dense.pyi deleted file mode 100644 index 025c9402..00000000 --- a/stubs/scipy-stubs/optimize/_bglu_dense.pyi +++ /dev/null @@ -1,136 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._bglu_dense, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy.linalg as _mod_numpy_linalg - -class BGLU(LU): - '\n Represents PLU factorization with Golub rank-one updates from\n Bartels, Richard H. "A stabilization of the simplex method."\n Numerische Mathematik 16.5 (1971): 414-434.\n' - @property - def L(self) -> typing.Any: ... - @property - def U(self) -> typing.Any: ... - def __init__(self) -> None: - "\n Given matrix A and basis indices b, perform PLU factorization of\n basis matrix B\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def average_solve_times(self) -> typing.Any: ... - @property - def bglu_time(self) -> typing.Any: ... - @property - def mast(self) -> typing.Any: ... - @property - def max_updates(self) -> typing.Any: ... - @property - def ops_list(self) -> typing.Any: ... - def perform_perm(self) -> typing.Any: - "\n Perform individual row swaps defined in p returned by factor_lu to\n generate final permutation indices pi\n" - ... - - @property - def pi(self) -> typing.Any: ... - @property - def pit(self) -> typing.Any: ... - @property - def plu(self) -> typing.Any: ... - def refactor(self, *args, **kwargs) -> typing.Any: ... - def solve(self, *args, **kwargs) -> typing.Any: ... - @property - def solves(self) -> typing.Any: ... - def update(self, *args, **kwargs) -> typing.Any: ... - def update_basis(self) -> typing.Any: ... - @property - def updates(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class LU(_mod_builtins.object): - "\n Represents PLU factorization of a basis matrix with naive rank-one updates\n" - @property - def A(self) -> typing.Any: ... - @property - def B(self) -> typing.Any: ... - def __init__(self) -> None: - "Given matrix A and basis indices b, form basis matrix B" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def b(self) -> typing.Any: ... - @property - def m(self) -> typing.Any: ... - @property - def n(self) -> typing.Any: ... - def solve(self) -> typing.Any: - "\n Solve B @ v = q\n" - ... - - def update(self) -> typing.Any: - "Rank-one update to basis and basis matrix" - ... - - def __getattr__(self, name) -> typing.Any: ... - -LinAlgError = _mod_numpy_linalg.LinAlgError -__all__: list -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_BGLU() -> typing.Any: ... -def __pyx_unpickle_Enum() -> typing.Any: ... -def __pyx_unpickle_LU() -> typing.Any: ... - -__test__: dict - -def _consider_refactor() -> typing.Any: - "\n This decorator records the time spent in the major BGLU\n routines - refactor, update, and solve - in order to\n calculate the average time required to solve a system.\n It also forces PLU factorization of the basis matrix from\n scratch to minimize the average solve time and to\n accumulation of roundoff error.\n\n Immediately after PLU factorization, the average solve time\n will be rather high because PLU factorization is slow. For\n some number of factor updates, the average solve time is\n expected to decrease because the updates and solves are fast.\n However, updates increase the compexity of the factorization,\n so solve times are expected to increase with each update.\n When the average solve time stops decreasing and begins\n increasing, we perform PLU factorization from scratch rather\n than updating. PLU factorization is also performed after the\n maximum permitted number of updates is reached to prevent\n further accumulation of roundoff error.\n" - ... - -def lu_factor(a, overwrite_a, check_finite) -> typing.Any: - "\n Compute pivoted LU decomposition of a matrix.\n\n The decomposition is::\n\n A = P L U\n\n where P is a permutation matrix, L lower triangular with unit\n diagonal elements, and U upper triangular.\n\n Parameters\n ----------\n a : (M, M) array_like\n Matrix to decompose\n overwrite_a : bool, optional\n Whether to overwrite data in A (may increase performance)\n check_finite : bool, optional\n Whether to check that the input matrix contains only finite numbers.\n Disabling may give a performance gain, but may result in problems\n (crashes, non-termination) if the inputs do contain infinities or NaNs.\n\n Returns\n -------\n lu : (N, N) ndarray\n Matrix containing U in its upper triangle, and L in its lower triangle.\n The unit diagonal elements of L are not stored.\n piv : (N,) ndarray\n Pivot indices representing the permutation matrix P:\n row i of matrix was interchanged with row piv[i].\n\n See also\n --------\n lu_solve : solve an equation system using the LU factorization of a matrix\n\n Notes\n -----\n This is a wrapper to the ``*GETRF`` routines from LAPACK.\n\n Examples\n --------\n >>> from scipy.linalg import lu_factor\n >>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]])\n >>> lu, piv = lu_factor(A)\n >>> piv\n array([2, 2, 3, 3], dtype=int32)\n\n Convert LAPACK's ``piv`` array to NumPy index and test the permutation\n\n >>> piv_py = [2, 0, 3, 1]\n >>> L, U = np.tril(lu, k=-1) + np.eye(4), np.triu(lu)\n >>> np.allclose(A[piv_py] - L @ U, np.zeros((4, 4)))\n True\n" - ... - -def lu_solve(lu_and_piv, b, trans, overwrite_b, check_finite) -> typing.Any: - "Solve an equation system, a x = b, given the LU factorization of a\n\n Parameters\n ----------\n (lu, piv)\n Factorization of the coefficient matrix a, as given by lu_factor\n b : array\n Right-hand side\n trans : {0, 1, 2}, optional\n Type of system to solve:\n\n ===== =========\n trans system\n ===== =========\n 0 a x = b\n 1 a^T x = b\n 2 a^H x = b\n ===== =========\n overwrite_b : bool, optional\n Whether to overwrite data in b (may increase performance)\n check_finite : bool, optional\n Whether to check that the input matrices contain only finite numbers.\n Disabling may give a performance gain, but may result in problems\n (crashes, non-termination) if the inputs do contain infinities or NaNs.\n\n Returns\n -------\n x : array\n Solution to the system\n\n See also\n --------\n lu_factor : LU factorize a matrix\n\n Examples\n --------\n >>> from scipy.linalg import lu_factor, lu_solve\n >>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]])\n >>> b = np.array([1, 1, 1, 1])\n >>> lu, piv = lu_factor(A)\n >>> x = lu_solve((lu, piv), b)\n >>> np.allclose(A @ x - b, np.zeros((4,)))\n True\n\n" - ... - -def solve(a, b, sym_pos, lower, overwrite_a, overwrite_b, debug, check_finite, assume_a, transposed) -> typing.Any: - "\n Solves the linear equation set ``a * x = b`` for the unknown ``x``\n for square ``a`` matrix.\n\n If the data matrix is known to be a particular type then supplying the\n corresponding string to ``assume_a`` key chooses the dedicated solver.\n The available options are\n\n =================== ========\n generic matrix 'gen'\n symmetric 'sym'\n hermitian 'her'\n positive definite 'pos'\n =================== ========\n\n If omitted, ``'gen'`` is the default structure.\n\n The datatype of the arrays define which solver is called regardless\n of the values. In other words, even when the complex array entries have\n precisely zero imaginary parts, the complex solver will be called based\n on the data type of the array.\n\n Parameters\n ----------\n a : (N, N) array_like\n Square input data\n b : (N, NRHS) array_like\n Input data for the right hand side.\n sym_pos : bool, optional\n Assume `a` is symmetric and positive definite. This key is deprecated\n and assume_a = 'pos' keyword is recommended instead. The functionality\n is the same. It will be removed in the future.\n lower : bool, optional\n If True, only the data contained in the lower triangle of `a`. Default\n is to use upper triangle. (ignored for ``'gen'``)\n overwrite_a : bool, optional\n Allow overwriting data in `a` (may enhance performance).\n Default is False.\n overwrite_b : bool, optional\n Allow overwriting data in `b` (may enhance performance).\n Default is False.\n check_finite : bool, optional\n Whether to check that the input matrices contain only finite numbers.\n Disabling may give a performance gain, but may result in problems\n (crashes, non-termination) if the inputs do contain infinities or NaNs.\n assume_a : str, optional\n Valid entries are explained above.\n transposed: bool, optional\n If True, ``a^T x = b`` for real matrices, raises `NotImplementedError`\n for complex matrices (only for True).\n\n Returns\n -------\n x : (N, NRHS) ndarray\n The solution array.\n\n Raises\n ------\n ValueError\n If size mismatches detected or input a is not square.\n LinAlgError\n If the matrix is singular.\n LinAlgWarning\n If an ill-conditioned input a is detected.\n NotImplementedError\n If transposed is True and input a is a complex matrix.\n\n Examples\n --------\n Given `a` and `b`, solve for `x`:\n\n >>> a = np.array([[3, 2, 0], [1, -1, 0], [0, 5, 1]])\n >>> b = np.array([2, 4, -1])\n >>> from scipy import linalg\n >>> x = linalg.solve(a, b)\n >>> x\n array([ 2., -2., 9.])\n >>> np.dot(a, x) == b\n array([ True, True, True], dtype=bool)\n\n Notes\n -----\n If the input b matrix is a 1-D array with N elements, when supplied\n together with an NxN input a, it is assumed as a valid column vector\n despite the apparent size mismatch. This is compatible with the\n numpy.dot() behavior and the returned result is still 1-D array.\n\n The generic, symmetric, Hermitian and positive definite solutions are\n obtained via calling ?GESV, ?SYSV, ?HESV, and ?POSV routines of\n LAPACK respectively.\n" - ... - -def solve_triangular(a, b, trans, lower, unit_diagonal, overwrite_b, debug, check_finite) -> typing.Any: - "\n Solve the equation `a x = b` for `x`, assuming a is a triangular matrix.\n\n Parameters\n ----------\n a : (M, M) array_like\n A triangular matrix\n b : (M,) or (M, N) array_like\n Right-hand side matrix in `a x = b`\n lower : bool, optional\n Use only data contained in the lower triangle of `a`.\n Default is to use upper triangle.\n trans : {0, 1, 2, 'N', 'T', 'C'}, optional\n Type of system to solve:\n\n ======== =========\n trans system\n ======== =========\n 0 or 'N' a x = b\n 1 or 'T' a^T x = b\n 2 or 'C' a^H x = b\n ======== =========\n unit_diagonal : bool, optional\n If True, diagonal elements of `a` are assumed to be 1 and\n will not be referenced.\n overwrite_b : bool, optional\n Allow overwriting data in `b` (may enhance performance)\n check_finite : bool, optional\n Whether to check that the input matrices contain only finite numbers.\n Disabling may give a performance gain, but may result in problems\n (crashes, non-termination) if the inputs do contain infinities or NaNs.\n\n Returns\n -------\n x : (M,) or (M, N) ndarray\n Solution to the system `a x = b`. Shape of return matches `b`.\n\n Raises\n ------\n LinAlgError\n If `a` is singular\n\n Notes\n -----\n .. versionadded:: 0.9.0\n\n Examples\n --------\n Solve the lower triangular system a x = b, where::\n\n [3 0 0 0] [4]\n a = [2 1 0 0] b = [2]\n [1 0 1 0] [4]\n [1 1 1 1] [2]\n\n >>> from scipy.linalg import solve_triangular\n >>> a = np.array([[3, 0, 0, 0], [2, 1, 0, 0], [1, 0, 1, 0], [1, 1, 1, 1]])\n >>> b = np.array([4, 2, 4, 2])\n >>> x = solve_triangular(a, b, lower=True)\n >>> x\n array([ 1.33333333, -0.66666667, 2.66666667, -1.33333333])\n >>> a.dot(x) # Check the result\n array([ 4., 2., 4., 2.])\n\n" - ... - -def timer() -> float: - "process_time() -> float\n\nProcess time for profiling: sum of the kernel and user-space CPU time." - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_cobyla.pyi b/stubs/scipy-stubs/optimize/_cobyla.pyi deleted file mode 100644 index 2a866e7e..00000000 --- a/stubs/scipy-stubs/optimize/_cobyla.pyi +++ /dev/null @@ -1,17 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._cobyla, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def minimize(calcfc, m, x, rhobeg, rhoend, dinfo, iprint=..., maxfun=..., calcfc_extra_args=...) -> typing.Any: - "x,dinfo = minimize(calcfc,m,x,rhobeg,rhoend,dinfo,[iprint,maxfun,calcfc_extra_args])\n\nWrapper for ``minimize``.\n\nParameters\n----------\ncalcfc : call-back function\nm : input int\nx : input rank-1 array('d') with bounds (n)\nrhobeg : input float\nrhoend : input float\ndinfo : input rank-1 array('d') with bounds (4)\n\nOther Parameters\n----------------\ncalcfc_extra_args : input tuple, optional\n Default: ()\niprint : input int, optional\n Default: 1\nmaxfun : input int, optional\n Default: 100\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n)\ndinfo : rank-1 array('d') with bounds (4)\n\nNotes\n-----\nCall-back functions::\n\n def calcfc(x,con): return f\n Required arguments:\n x : input rank-1 array('d') with bounds (n)\n con : input rank-1 array('d') with bounds (m)\n Return objects:\n f : float\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_group_columns.pyi b/stubs/scipy-stubs/optimize/_group_columns.pyi deleted file mode 100644 index ffc70018..00000000 --- a/stubs/scipy-stubs/optimize/_group_columns.pyi +++ /dev/null @@ -1,18 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._group_columns, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def group_dense() -> typing.Any: ... -def group_sparse() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_highs/_highs_constants.pyi b/stubs/scipy-stubs/optimize/_highs/_highs_constants.pyi deleted file mode 100644 index d4b9b6b5..00000000 --- a/stubs/scipy-stubs/optimize/_highs/_highs_constants.pyi +++ /dev/null @@ -1,45 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._highs._highs_constants, version: unspecified -import builtins as _mod_builtins -import typing - -CONST_INF: float -CONST_I_INF: int -HIGHS_SIMPLEX_CRASH_STRATEGY_BIXBY: int -HIGHS_SIMPLEX_CRASH_STRATEGY_LTSF: int -HIGHS_SIMPLEX_CRASH_STRATEGY_OFF: int -HIGHS_SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_DANTZIG: int -HIGHS_SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_DEVEX: int -HIGHS_SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_STEEPEST_EDGE: int -HIGHS_SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_STEEPEST_EDGE_TO_DEVEX_SWITCH: int -HIGHS_SIMPLEX_PRIMAL_EDGE_WEIGHT_STRATEGY_DANTZIG: int -HIGHS_SIMPLEX_PRIMAL_EDGE_WEIGHT_STRATEGY_DEVEX: int -HIGHS_SIMPLEX_STRATEGY_CHOOSE: int -HIGHS_SIMPLEX_STRATEGY_DUAL: int -HIGHS_SIMPLEX_STRATEGY_PRIMAL: int -MESSAGE_LEVEL_ALWAYS: int -MESSAGE_LEVEL_DETAILED: int -MESSAGE_LEVEL_MINIMAL: int -MESSAGE_LEVEL_NONE: int -MESSAGE_LEVEL_VERBOSE: int -MODEL_STATUS_LOAD_ERROR: int -MODEL_STATUS_MODEL_EMPTY: int -MODEL_STATUS_MODEL_ERROR: int -MODEL_STATUS_NOTSET: int -MODEL_STATUS_OPTIMAL: int -MODEL_STATUS_POSTSOLVE_ERROR: int -MODEL_STATUS_PRESOLVE_ERROR: int -MODEL_STATUS_PRIMAL_INFEASIBLE: int -MODEL_STATUS_PRIMAL_UNBOUNDED: int -MODEL_STATUS_REACHED_DUAL_OBJECTIVE_VALUE_UPPER_BOUND: int -MODEL_STATUS_REACHED_ITERATION_LIMIT: int -MODEL_STATUS_REACHED_TIME_LIMIT: int -MODEL_STATUS_SOLVE_ERROR: int -__doc__: str -__file__: str -__name__: str -__package__: str -__test__: dict - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_highs/_highs_wrapper.pyi b/stubs/scipy-stubs/optimize/_highs/_highs_wrapper.pyi deleted file mode 100644 index 53f85591..00000000 --- a/stubs/scipy-stubs/optimize/_highs/_highs_wrapper.pyi +++ /dev/null @@ -1,27 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._highs._highs_wrapper, version: unspecified -import builtins as _mod_builtins -import typing - -import scipy.optimize.optimize as _mod_scipy_optimize_optimize - -OptimizeWarning = _mod_scipy_optimize_optimize.OptimizeWarning -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _highs_wrapper() -> typing.Any: - "Solve linear programs using HiGHS [1]_.\n\n Assume problems of the form:\n\n MIN c.T @ x\n s.t. lhs <= A @ x <= rhs\n lb <= x <= ub\n\n Parameters\n ----------\n c : 1-D array, (n,)\n Array of objective value coefficients.\n astart : 1-D array\n CSC format index array.\n aindex : 1-D array\n CSC format index array.\n avalue : 1-D array\n Data array of the matrix.\n lhs : 1-D array (or None), (m,)\n Array of left hand side values of the inequality constraints.\n If ``lhs=None``, then an array of ``-inf`` is assumed.\n rhs : 1-D array, (m,)\n Array of right hand side values of the inequality constraints.\n lb : 1-D array (or None), (n,)\n Lower bounds on solution variables x. If ``lb=None``, then an\n array of all `0` is assumed.\n ub : 1-D array (or None), (n,)\n Upper bounds on solution variables x. If ``ub=None``, then an\n array of ``inf`` is assumed.\n options : dict\n A dictionary of solver options with the following fields:\n\n - allowed_simplex_cost_scale_factor : int\n Undocumented advanced option.\n\n - allowed_simplex_matrix_scale_factor : int\n Undocumented advanced option.\n\n - dual_feasibility_tolerance : double\n Dual feasibility tolerance for simplex.\n ``min(dual_feasibility_tolerance,\n primal_feasibility_tolerance)`` will be used for\n ipm feasibility tolerance.\n\n - dual_objective_value_upper_bound : double\n Upper bound on objective value for dual simplex:\n algorithm terminates if reached\n\n - dual_simplex_cleanup_strategy : int\n Undocumented advanced option.\n\n - dual_simplex_cost_perturbation_multiplier : double\n Undocumented advanced option.\n\n - dual_steepest_edge_weight_log_error_threshhold : double\n Undocumented advanced option.\n\n - infinite_bound : double\n Limit on abs(constraint bound): values larger than\n this will be treated as infinite\n\n - infinite_cost : double\n Limit on cost coefficient: values larger than this\n will be treated as infinite.\n\n - ipm_iteration_limit : int\n Iteration limit for interior-point solver.\n\n - ipm_optimality_tolerance : double\n Optimality tolerance for IPM.\n\n - keep_n_rows : int {-1, 0, 1}\n Undocumented advanced option.\n\n - ``-1``: ``KEEP_N_ROWS_DELETE_ROWS``\n - ``0``: ``KEEP_N_ROWS_DELETE_ENTRIES``\n - ``1``: ``KEEP_N_ROWS_KEEP_ROWS``\n\n - large_matrix_value : double\n Upper limit on abs(matrix entries): values larger than\n this will be treated as infinite\n\n - less_infeasible_DSE_check : bool\n Undocumented advanced option.\n\n - less_infeasible_DSE_choose_row : bool\n Undocumented advanced option.\n\n - max_threads : int\n Maximum number of threads in parallel execution.\n\n - message_level : int {0, 1, 2, 4, 7}\n Verbosity level, corresponds to:\n\n - ``0``: ``ML_NONE``\n All messaging to stdout is supressed.\n\n - ``1``: ``ML_VERBOSE``\n Includes a once-per-iteration report on simplex/ipm\n progress and information about each nonzero row and\n column.\n\n - ``2``: ``ML_DETAILED``\n Includes technical information about progress and\n events in applying the simplex method.\n\n - ``4``: ``ML_MINIMAL``\n Once-per-solve information about progress as well as a\n once-per-basis-matrix-reinversion report on progress in\n simplex or a once-per-iteration report on progress in IPX.\n\n ``message_level`` behaves like a bitmask, i.e., any\n combination of levels is possible using the bit-or\n operator.\n\n - min_threads : int\n Minimum number of threads in parallel execution.\n\n - mps_parser_type_free : bool\n Use free format MPS parsing.\n\n - parallel : bool\n Run the solver in serial (False) or parallel (True).\n\n - presolve : bool\n Run the presolve or not (or if ``None``, then choose).\n\n - primal_feasibility_tolerance : double\n Primal feasibility tolerance.\n ``min(dual_feasibility_tolerance,\n primal_feasibility_tolerance)`` will be used for\n ipm feasibility tolerance.\n\n - run_as_hsol : bool\n Undocumented advanced option.\n\n - run_crossover : bool\n Advanced option. Toggles running the crossover routine\n for IPX.\n\n - sense : int {1, -1}\n ``sense=1`` corresponds to the MIN problem, ``sense=-1``\n corresponds to the MAX problem. TODO: NOT IMPLEMENTED\n\n - simplex_crash_strategy : int {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}\n Strategy for simplex crash: off / LTSSF / Bixby (0/1/2).\n Default is ``0``. Corresponds to the following:\n\n - ``0``: ``SIMPLEX_CRASH_STRATEGY_OFF``\n - ``1``: ``SIMPLEX_CRASH_STRATEGY_LTSSF_K``\n - ``2``: ``SIMPLEX_CRASH_STRATEGY_BIXBY``\n - ``3``: ``SIMPLEX_CRASH_STRATEGY_LTSSF_PRI``\n - ``4``: ``SIMPLEX_CRASH_STRATEGY_LTSF_K``\n - ``5``: ``SIMPLEX_CRASH_STRATEGY_LTSF_PRI``\n - ``6``: ``SIMPLEX_CRASH_STRATEGY_LTSF``\n - ``7``: ``SIMPLEX_CRASH_STRATEGY_BIXBY_NO_NONZERO_COL_COSTS``\n - ``8``: ``SIMPLEX_CRASH_STRATEGY_BASIC``\n - ``9``: ``SIMPLE_CRASH_STRATEGY_TEST_SING``\n\n - simplex_dualise_strategy : int\n Undocumented advanced option.\n\n - simplex_dual_edge_weight_strategy : int {0, 1, 2, 3, 4}\n Strategy for simplex dual edge weights:\n Dantzig / Devex / Steepest Edge. Corresponds\n to the following:\n\n - ``0``: ``SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_DANTZIG``\n - ``1``: ``SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_DEVEX``\n - ``2``: ``SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_STEEPEST_EDGE_TO_DEVEX_SWITCH``\n - ``3``: ``SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_STEEPEST_EDGE``\n - ``4``: ``SIMPLEX_DUAL_EDGE_WEIGHT_STRATEGY_STEEPEST_EDGE_UNIT_INITIAL``\n\n - simplex_initial_condition_check : bool\n Undocumented advanced option.\n\n - simplex_initial_condition_tolerance : double\n Undocumented advanced option.\n\n - simplex_iteration_limit : int\n Iteration limit for simplex solver.\n\n - simplex_permute_strategy : int\n Undocumented advanced option.\n\n - simplex_price_strategy : int\n Undocumented advanced option.\n\n - simplex_primal_edge_weight_strategy : int {0, 1}\n Strategy for simplex primal edge weights:\n Dantzig / Devex. Corresponds to the following:\n\n - ``0``: ``SIMPLEX_PRIMAL_EDGE_WEIGHT_STRATEGY_DANTZIG``\n - ``1``: ``SIMPLEX_PRIMAL_EDGE_WEIGHT_STRATEGY_DEVEX``\n\n - simplex_scale_strategy : int {0, 1, 2, 3, 4, 5}\n Strategy for scaling before simplex solver:\n off / on (0/1)\n\n - ``0``: ``SIMPLEX_SCALE_STRATEGY_OFF``\n - ``1``: ``SIMPLEX_SCALE_STRATEGY_HIGHS``\n - ``2``: ``SIMPLEX_SCALE_STRATEGY_HIGHS_FORCED``\n - ``3``: ``SIMPLEX_SCALE_STRATEGY_HIGHS_015``\n - ``4``: ``SIMPLEX_SCALE_STRATEGY_HIGHS_0157``\n - ``5``: ``SIMPLEX_SCALE_STRATEGY_HSOL``\n\n - simplex_strategy : int {0, 1, 2, 3, 4}\n Strategy for simplex solver. Default: 1. Corresponds\n to the following:\n\n - ``0``: ``SIMPLEX_STRATEGY_MIN``\n - ``1``: ``SIMPLEX_STRATEGY_DUAL``\n - ``2``: ``SIMPLEX_STRATEGY_DUAL_TASKS``\n - ``3``: ``SIMPLEX_STRATEGY_DUAL_MULTI``\n - ``4``: ``SIMPLEX_STRATEGY_PRIMAL``\n\n - simplex_update_limit : int\n Limit on the number of simplex UPDATE operations.\n\n - small_matrix_value : double\n Lower limit on abs(matrix entries): values smaller\n than this will be treated as zero.\n\n - solution_file : str\n Solution file\n\n - solver : str {'simplex', 'ipm'}\n Choose which solver to use. If ``solver='simplex'``\n and ``parallel=True`` then PAMI will be used.\n\n - time_limit : double\n Max number of seconds to run the solver for.\n\n - use_original_HFactor_logic : bool\n Undocumented advanced option.\n\n - write_solution_to_file : bool\n Write the primal and dual solution to a file\n\n - write_solution_pretty : bool\n Write the primal and dual solution in a pretty\n (human-readable) format\n\n See [2]_ for a list of all non-advanced options.\n\n Returns\n -------\n res : dict\n\n If model_status is one of OPTIMAL,\n REACHED_DUAL_OBJECTIVE_VALUE_UPPER_BOUND, REACHED_TIME_LIMIT,\n REACHED_ITERATION_LIMIT:\n\n - ``status`` : int\n Model status code.\n\n - ``message`` : str\n Message corresponding to model status code.\n\n - ``x`` : list\n Solution variables.\n\n - ``slack`` : list\n Slack variables.\n\n - ``lambda`` : list\n Lagrange multipliers assoicated with the constraints\n Ax = b.\n\n - ``s`` : list\n Lagrange multipliers associated with the constraints\n x >= 0.\n\n - ``fun``\n Final objective value.\n\n - ``simplex_nit`` : int\n Number of iterations accomplished by the simplex\n solver.\n\n - ``ipm_nit`` : int\n Number of iterations accomplished by the interior-\n point solver.\n\n If model_status is not one of the above:\n\n - ``status`` : int\n Model status code.\n\n - ``message`` : str\n Message corresponding to model status code.\n\n Notes\n -----\n If ``options['write_solution_to_file']`` is ``True`` but\n ``options['solution_file']`` is unset or ``''``, then the solution\n will be printed to ``stdout``.\n\n If any iteration limit is reached, no solution will be\n available.\n\n ``OptimizeWarning`` will be raised if any option value set by\n the user is found to be incorrect.\n\n References\n ----------\n .. [1] https://www.maths.ed.ac.uk/hall/HiGHS\n .. [2] https://www.maths.ed.ac.uk/hall/HiGHS/HighsOptions.html\n" - ... - -def warn(message, category, stacklevel, source) -> typing.Any: - "Issue a warning, or maybe ignore it or raise an exception." - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_highs/_mpswriter.pyi b/stubs/scipy-stubs/optimize/_highs/_mpswriter.pyi deleted file mode 100644 index 39299eda..00000000 --- a/stubs/scipy-stubs/optimize/_highs/_mpswriter.pyi +++ /dev/null @@ -1,23 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._highs._mpswriter, version: unspecified -import builtins as _mod_builtins -import typing - -import scipy.sparse.csc as _mod_scipy_sparse_csc - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict -csc_matrix = _mod_scipy_sparse_csc.csc_matrix - -def mpswriter() -> typing.Any: - "MPS writer: create MPS file from matrices.\n\n Parameters\n ----------\n filename : bytes (string)\n Name of MPS file to write out to. Will be overwritten.\n c : 1-D array (numcol,)\n Objective coefficient values (assumes minimization).\n A : 2-D array (numrow, numcol), scipy.sparse.csc_matrix\n Sparse inequality constraint matrix.\n lhs : 1-D array (numrow,)\n Left hand side inequality values.\n rhs : 1-D array (numrow,)\n Right hand side inequality values.\n lb : 1-D array (numcol,)\n Lower bounds of solution variables.\n ub : 1-D array (numcol,)\n Upper bounds of solution variables.\n integer_valued : 1-D array (numint,)\n Indices of integer valued solution variables.\n use_free_format : bool, optional\n Use MPS free format. Default is False.\n\n Notes\n -----\n Wrapper over HiGHS `writeMPS` function.\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_lbfgsb.pyi b/stubs/scipy-stubs/optimize/_lbfgsb.pyi deleted file mode 100644 index 752957f9..00000000 --- a/stubs/scipy-stubs/optimize/_lbfgsb.pyi +++ /dev/null @@ -1,21 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._lbfgsb, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def setulb(m, x, l, u, nbd, f, g, factr, pgtol, wa, iwa, task, iprint, csave, lsave, isave, dsave, maxls, n=...) -> typing.Any: - "setulb(m,x,l,u,nbd,f,g,factr,pgtol,wa,iwa,task,iprint,csave,lsave,isave,dsave,maxls,[n])\n\nWrapper for ``setulb``.\n\nParameters\n----------\nm : input int\nx : in/output rank-1 array('d') with bounds (n)\nl : input rank-1 array('d') with bounds (n)\nu : input rank-1 array('d') with bounds (n)\nnbd : input rank-1 array('i') with bounds (n)\nf : in/output rank-0 array(float,'d')\ng : in/output rank-1 array('d') with bounds (n)\nfactr : input float\npgtol : input float\nwa : in/output rank-1 array('d') with bounds (2*m*n+5*n+11*m*m+8*m)\niwa : in/output rank-1 array('i') with bounds (3 * n)\ntask : in/output rank-0 array(string(len=60),'c')\niprint : input int\ncsave : in/output rank-0 array(string(len=60),'c')\nlsave : in/output rank-1 array('i') with bounds (4)\nisave : in/output rank-1 array('i') with bounds (44)\ndsave : in/output rank-1 array('d') with bounds (29)\nmaxls : input int\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(x)\n" - ... - -def types() -> typing.Any: - "'i'-scalar\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_lsap_module.pyi b/stubs/scipy-stubs/optimize/_lsap_module.pyi deleted file mode 100644 index f14dd73b..00000000 --- a/stubs/scipy-stubs/optimize/_lsap_module.pyi +++ /dev/null @@ -1,16 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._lsap_module, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str - -def calculate_assignment() -> typing.Any: - "Solves the rectangular linear sum assignment problem." - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_lsq/givens_elimination.pyi b/stubs/scipy-stubs/optimize/_lsq/givens_elimination.pyi deleted file mode 100644 index 217bcfdd..00000000 --- a/stubs/scipy-stubs/optimize/_lsq/givens_elimination.pyi +++ /dev/null @@ -1,20 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._lsq.givens_elimination, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def givens_elimination() -> typing.Any: - "Zero out a diagonal block of a matrix by series of Givens rotations.\n\n The matrix has the structure::\n\n [ S ]\n [ D ]\n\n Where S is an upper triangular matrix with shape (n, n) and D is a\n diagonal matrix with shape (n, n) with elements from `diag`. This function\n applies Givens rotations to it such that the resulting matrix has zeros\n in place of D.\n\n Array `S` will be modified in-place.\n\n Array `v` of shape (n,) is the part of the full vector with shape (2*n,)::\n\n [ v ]\n [ 0 ]\n\n to which Givens rotations are applied. This array is modified in place,\n such that on exit it contains the first n components of the above\n mentioned vector after rotations were applied.\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_minpack.pyi b/stubs/scipy-stubs/optimize/_minpack.pyi deleted file mode 100644 index 31fafadf..00000000 --- a/stubs/scipy-stubs/optimize/_minpack.pyi +++ /dev/null @@ -1,37 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._minpack, version: 1.10 -import builtins as _mod_builtins -import typing - -import minpack as _mod_minpack - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__version__: str - -def _chkder(m, n, x, fvec, fjac, ldfjac, xp, fvecp, mode, err) -> typing.Any: - "_chkder(m,n,x,fvec,fjac,ldfjac,xp,fvecp,mode,err)" - ... - -def _hybrd(fun, x0, args, full_output, xtol, maxfev, ml, mu, epsfcn, factor, diag) -> typing.Any: - "[x,infodict,info] = _hybrd(fun, x0, args, full_output, xtol, maxfev, ml, mu, epsfcn, factor, diag)" - ... - -def _hybrj(fun, Dfun, x0, args, full_output, col_deriv, xtol, maxfev, factor, diag) -> typing.Any: - "[x,infodict,info] = _hybrj(fun, Dfun, x0, args, full_output, col_deriv, xtol, maxfev, factor, diag)" - ... - -def _lmder(fun, Dfun, x0, args, full_output, col_deriv, ftol, xtol, gtol, maxfev, factor, diag) -> typing.Any: - "[x,infodict,info] = _lmder(fun, Dfun, x0, args, full_output, col_deriv, ftol, xtol, gtol, maxfev, factor, diag)" - ... - -def _lmdif(fun, x0, args, full_output, ftol, xtol, gtol, maxfev, epsfcn, factor, diag) -> typing.Any: - "[x,infodict,info] = _lmdif(fun, x0, args, full_output, ftol, xtol, gtol, maxfev, epsfcn, factor, diag)" - ... - -error = _mod_minpack.error - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_slsqp.pyi b/stubs/scipy-stubs/optimize/_slsqp.pyi deleted file mode 100644 index c844c486..00000000 --- a/stubs/scipy-stubs/optimize/_slsqp.pyi +++ /dev/null @@ -1,54 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._slsqp, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def slsqp( - m, - meq, - x, - xl, - xu, - f, - c, - g, - a, - acc, - iter, - mode, - w, - jw, - alpha, - f0, - gs, - h1, - h2, - h3, - h4, - t, - t0, - tol, - iexact, - incons, - ireset, - itermx, - line, - n1, - n2, - n3, - la=..., - n=..., - l_w=..., - l_jw=..., -) -> typing.Any: - "slsqp(m,meq,x,xl,xu,f,c,g,a,acc,iter,mode,w,jw,alpha,f0,gs,h1,h2,h3,h4,t,t0,tol,iexact,incons,ireset,itermx,line,n1,n2,n3,[la,n,l_w,l_jw])\n\nWrapper for ``slsqp``.\n\nParameters\n----------\nm : input int\nmeq : input int\nx : in/output rank-1 array('d') with bounds (n)\nxl : input rank-1 array('d') with bounds (n)\nxu : input rank-1 array('d') with bounds (n)\nf : input float\nc : input rank-1 array('d') with bounds (la)\ng : input rank-1 array('d') with bounds (n + 1)\na : input rank-2 array('d') with bounds (la,n + 1)\nacc : in/output rank-0 array(float,'d')\niter : in/output rank-0 array(int,'i')\nmode : in/output rank-0 array(int,'i')\nw : input rank-1 array('d') with bounds (l_w)\njw : input rank-1 array('i') with bounds (l_jw)\nalpha : in/output rank-0 array(float,'d')\nf0 : in/output rank-0 array(float,'d')\ngs : in/output rank-0 array(float,'d')\nh1 : in/output rank-0 array(float,'d')\nh2 : in/output rank-0 array(float,'d')\nh3 : in/output rank-0 array(float,'d')\nh4 : in/output rank-0 array(float,'d')\nt : in/output rank-0 array(float,'d')\nt0 : in/output rank-0 array(float,'d')\ntol : in/output rank-0 array(float,'d')\niexact : in/output rank-0 array(int,'i')\nincons : in/output rank-0 array(int,'i')\nireset : in/output rank-0 array(int,'i')\nitermx : in/output rank-0 array(int,'i')\nline : in/output rank-0 array(int,'i')\nn1 : in/output rank-0 array(int,'i')\nn2 : in/output rank-0 array(int,'i')\nn3 : in/output rank-0 array(int,'i')\n\nOther Parameters\n----------------\nla : input int, optional\n Default: len(c)\nn : input int, optional\n Default: len(x)\nl_w : input int, optional\n Default: len(w)\nl_jw : input int, optional\n Default: len(jw)\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_trlib/_trlib.pyi b/stubs/scipy-stubs/optimize/_trlib/_trlib.pyi deleted file mode 100644 index fb2113db..00000000 --- a/stubs/scipy-stubs/optimize/_trlib/_trlib.pyi +++ /dev/null @@ -1,58 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._trlib._trlib, version: unspecified -import builtins as _mod_builtins -import typing - -import scipy.optimize._trustregion as _mod_scipy_optimize__trustregion - -BaseQuadraticSubproblem = _mod_scipy_optimize__trustregion.BaseQuadraticSubproblem - -class TRLIBQuadraticSubproblem(_mod_scipy_optimize__trustregion.BaseQuadraticSubproblem): - __dict__: typing.Dict[str, typing.Any] - def __init__(self, x, fun, jac, hess, hessp, tol_rel_i, tol_rel_b, disp) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def solve(self, trust_radius) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _minimize_trust_region( - fun, - x0, - args, - jac, - hess, - hessp, - subproblem, - initial_trust_radius, - max_trust_radius, - eta, - gtol, - maxiter, - disp, - return_all, - callback, - inexact, - **unknown_options, -) -> typing.Any: - "\n Minimization of scalar function of one or more variables using a\n trust-region algorithm.\n\n Options for the trust-region algorithm are:\n initial_trust_radius : float\n Initial trust radius.\n max_trust_radius : float\n Never propose steps that are longer than this value.\n eta : float\n Trust region related acceptance stringency for proposed steps.\n gtol : float\n Gradient norm must be less than `gtol`\n before successful termination.\n maxiter : int\n Maximum number of iterations to perform.\n disp : bool\n If True, print convergence message.\n inexact : bool\n Accuracy to solve subproblems. If True requires less nonlinear\n iterations, but more vector products. Only effective for method\n trust-krylov.\n\n This function is called by the `minimize` function.\n It is not supposed to be called directly.\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/_zeros.pyi b/stubs/scipy-stubs/optimize/_zeros.pyi deleted file mode 100644 index 488e73ef..00000000 --- a/stubs/scipy-stubs/optimize/_zeros.pyi +++ /dev/null @@ -1,28 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize._zeros, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def _bisect() -> typing.Any: - "a" - ... - -def _brenth() -> typing.Any: - "a" - ... - -def _brentq() -> typing.Any: - "a" - ... - -def _ridder() -> typing.Any: - "a" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/cython_optimize/_zeros.pyi b/stubs/scipy-stubs/optimize/cython_optimize/_zeros.pyi deleted file mode 100644 index e043a825..00000000 --- a/stubs/scipy-stubs/optimize/cython_optimize/_zeros.pyi +++ /dev/null @@ -1,23 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize.cython_optimize._zeros, version: unspecified -import builtins as _mod_builtins -import typing - -EXAMPLES_MAP: dict -__doc__: str -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def full_output_example() -> typing.Any: - "\n Example of Cython optimize zeros functions with full output.\n\n Parameters\n ----------\n args : sequence of float\n extra arguments of zero function\n xa : float\n first boundary of zero function\n xb : float\n second boundary of zero function\n xtol : float\n absolute tolerance of zero function\n rtol : float\n relative tolerance of zero function\n mitr : int\n max. iteration of zero function\n\n Returns\n -------\n full_output : dict\n the root, number of function calls, number of iterations, and the zero\n function error number \n\n This example finds the roots of a 3rd order polynomial with coefficients\n given as `args`.\n" - ... - -def loop_example() -> typing.Any: - "\n Example of Cython optimize zeros functions with map.\n\n Parameters\n ----------\n method : str\n name of the Cython optimize zeros function to call\n a0 : sequence of float\n first extra argument which is mapped to output\n args : sequence of float\n the remaining extra arguments which are constant\n xa : float\n first bound of zero function\n xb : float\n second bound of zero function\n xtol : float\n absolute tolerance of zero function\n rtol : float\n relative tolerance of zero function\n mitr : int\n max. iteration of zero function\n\n Returns\n -------\n roots : sequence of float\n the root for each of the values of `a0`\n\n This example finds the roots of a 3rd order polynomial given a sequence of\n constant terms as `a0` and fixed 1st, 2nd, and 3rd order terms in `args`.\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/minpack2.pyi b/stubs/scipy-stubs/optimize/minpack2.pyi deleted file mode 100644 index dc389864..00000000 --- a/stubs/scipy-stubs/optimize/minpack2.pyi +++ /dev/null @@ -1,21 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize.minpack2, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def dcsrch(stp, f, g, ftol, gtol, xtol, task, stpmin, stpmax, isave, dsave) -> typing.Any: - "stp,f,g,task = dcsrch(stp,f,g,ftol,gtol,xtol,task,stpmin,stpmax,isave,dsave)\n\nWrapper for ``dcsrch``.\n\nParameters\n----------\nstp : input float\nf : input float\ng : input float\nftol : input float\ngtol : input float\nxtol : input float\ntask : input string(len=60)\nstpmin : input float\nstpmax : input float\nisave : in/output rank-1 array('i') with bounds (2)\ndsave : in/output rank-1 array('d') with bounds (13)\n\nReturns\n-------\nstp : float\nf : float\ng : float\ntask : string(len=60)\n" - ... - -def dcstep(stx, fx, dx, sty, fy, dy, stp, fp, dp, brackt, stpmin, stpmax) -> typing.Any: - "stx,fx,dx,sty,fy,dy,stp,brackt = dcstep(stx,fx,dx,sty,fy,dy,stp,fp,dp,brackt,stpmin,stpmax)\n\nWrapper for ``dcstep``.\n\nParameters\n----------\nstx : input float\nfx : input float\ndx : input float\nsty : input float\nfy : input float\ndy : input float\nstp : input float\nfp : input float\ndp : input float\nbrackt : input int\nstpmin : input float\nstpmax : input float\n\nReturns\n-------\nstx : float\nfx : float\ndx : float\nsty : float\nfy : float\ndy : float\nstp : float\nbrackt : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/optimize/moduleTNC.pyi b/stubs/scipy-stubs/optimize/moduleTNC.pyi deleted file mode 100644 index 1ee7c5b0..00000000 --- a/stubs/scipy-stubs/optimize/moduleTNC.pyi +++ /dev/null @@ -1,13 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.optimize.moduleTNC, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def minimize() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/py.typed b/stubs/scipy-stubs/py.typed deleted file mode 100644 index b648ac92..00000000 --- a/stubs/scipy-stubs/py.typed +++ /dev/null @@ -1 +0,0 @@ -partial diff --git a/stubs/scipy-stubs/signal/_max_len_seq_inner.pyi b/stubs/scipy-stubs/signal/_max_len_seq_inner.pyi deleted file mode 100644 index 6830d4a6..00000000 --- a/stubs/scipy-stubs/signal/_max_len_seq_inner.pyi +++ /dev/null @@ -1,17 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.signal._max_len_seq_inner, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _max_len_seq_inner() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/signal/_peak_finding_utils.pyi b/stubs/scipy-stubs/signal/_peak_finding_utils.pyi deleted file mode 100644 index 9c7f81ea..00000000 --- a/stubs/scipy-stubs/signal/_peak_finding_utils.pyi +++ /dev/null @@ -1,57 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.signal._peak_finding_utils, version: unspecified -import builtins as _mod_builtins -import typing - -class PeakPropertyWarning(_mod_builtins.RuntimeWarning): - "Calculated property of a peak has unexpected value." - __dict__: typing.Dict[str, typing.Any] - def __init__(self, *args, **kwargs) -> None: - "Calculated property of a peak has unexpected value." - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def __getattr__(self, name) -> typing.Any: ... - -__all__: list -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _local_maxima_1d() -> typing.Any: - "\n Find local maxima in a 1D array.\n\n This function finds all local maxima in a 1D array and returns the indices\n for their edges and midpoints (rounded down for even plateau sizes).\n\n Parameters\n ----------\n x : ndarray\n The array to search for local maxima.\n\n Returns\n -------\n midpoints : ndarray\n Indices of midpoints of local maxima in `x`.\n left_edges : ndarray\n Indices of edges to the left of local maxima in `x`.\n right_edges : ndarray\n Indices of edges to the right of local maxima in `x`.\n\n Notes\n -----\n - Compared to `argrelmax` this function is significantly faster and can\n detect maxima that are more than one sample wide. However this comes at\n the cost of being only applicable to 1D arrays.\n - A maxima is defined as one or more samples of equal value that are\n surrounded on both sides by at least one smaller sample.\n\n .. versionadded:: 1.1.0\n" - ... - -def _peak_prominences() -> typing.Any: - "\n Calculate the prominence of each peak in a signal.\n\n Parameters\n ----------\n x : ndarray\n A signal with peaks.\n peaks : ndarray\n Indices of peaks in `x`.\n wlen : np.intp\n A window length in samples (see `peak_prominences`) which is rounded up\n to the nearest odd integer. If smaller than 2 the entire signal `x` is\n used.\n\n Returns\n -------\n prominences : ndarray\n The calculated prominences for each peak in `peaks`.\n left_bases, right_bases : ndarray\n The peaks' bases as indices in `x` to the left and right of each peak.\n\n Raises\n ------\n ValueError\n If a value in `peaks` is an invalid index for `x`.\n\n Warns\n -----\n PeakPropertyWarning\n If a prominence of 0 was calculated for any peak.\n\n Notes\n -----\n This is the inner function to `peak_prominences`.\n\n .. versionadded:: 1.1.0\n" - ... - -def _peak_widths() -> typing.Any: - "\n Calculate the width of each each peak in a signal.\n\n Parameters\n ----------\n x : ndarray\n A signal with peaks.\n peaks : ndarray\n Indices of peaks in `x`.\n rel_height : np.float64\n Chooses the relative height at which the peak width is measured as a\n percentage of its prominence (see `peak_widths`).\n prominences : ndarray\n Prominences of each peak in `peaks` as returned by `peak_prominences`.\n left_bases, right_bases : ndarray\n Left and right bases of each peak in `peaks` as returned by\n `peak_prominences`.\n\n Returns\n -------\n widths : ndarray\n The widths for each peak in samples.\n width_heights : ndarray\n The height of the contour lines at which the `widths` where evaluated.\n left_ips, right_ips : ndarray\n Interpolated positions of left and right intersection points of a\n horizontal line at the respective evaluation height.\n\n Raises\n ------\n ValueError\n If the supplied prominence data doesn't satisfy the condition\n ``0 <= left_base <= peak <= right_base < x.shape[0]`` for each peak or\n if `peaks`, `left_bases` and `right_bases` don't share the same shape.\n Or if `rel_height` is not at least 0.\n\n Warnings\n --------\n PeakPropertyWarning\n If a width of 0 was calculated for any peak.\n\n Notes\n -----\n This is the inner function to `peak_widths`.\n\n .. versionadded:: 1.1.0\n" - ... - -def _select_by_peak_distance() -> typing.Any: - "\n Evaluate which peaks fulfill the distance condition.\n\n Parameters\n ----------\n peaks : ndarray\n Indices of peaks in `vector`.\n priority : ndarray\n An array matching `peaks` used to determine priority of each peak. A\n peak with a higher priority value is kept over one with a lower one.\n distance : np.float64\n Minimal distance that peaks must be spaced.\n\n Returns\n -------\n keep : ndarray[bool]\n A boolean mask evaluating to true where `peaks` fulfill the distance\n condition.\n\n Notes\n -----\n Declaring the input arrays as C-contiguous doesn't seem to have performance\n advantages.\n\n .. versionadded:: 1.1.0\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/signal/_sosfilt.pyi b/stubs/scipy-stubs/signal/_sosfilt.pyi deleted file mode 100644 index fba914e4..00000000 --- a/stubs/scipy-stubs/signal/_sosfilt.pyi +++ /dev/null @@ -1,18 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.signal._sosfilt, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _sosfilt(sos, x, zi) -> typing.Any: ... -def _sosfilt_object() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/signal/_spectral.pyi b/stubs/scipy-stubs/signal/_spectral.pyi deleted file mode 100644 index e22592b1..00000000 --- a/stubs/scipy-stubs/signal/_spectral.pyi +++ /dev/null @@ -1,18 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.signal._spectral, version: unspecified -import builtins as _mod_builtins -import typing - -__all__: list -__doc__: str -__file__: str -__name__: str -__package__: str -__test__: dict - -def _lombscargle(x, y, freqs) -> typing.Any: - "\n _lombscargle(x, y, freqs)\n\n Computes the Lomb-Scargle periodogram.\n\n Parameters\n ----------\n x : array_like\n Sample times.\n y : array_like\n Measurement values (must be registered so the mean is zero).\n freqs : array_like\n Angular frequencies for output periodogram.\n\n Returns\n -------\n pgram : array_like\n Lomb-Scargle periodogram.\n\n Raises\n ------\n ValueError\n If the input arrays `x` and `y` do not have the same shape.\n\n See also\n --------\n lombscargle\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/signal/_upfirdn_apply.pyi b/stubs/scipy-stubs/signal/_upfirdn_apply.pyi deleted file mode 100644 index 3b1f4fb0..00000000 --- a/stubs/scipy-stubs/signal/_upfirdn_apply.pyi +++ /dev/null @@ -1,26 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.signal._upfirdn_apply, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _apply(data, h_trans_flip, out, up, down, axis, mode, cval) -> typing.Any: ... -def _output_len() -> typing.Any: - "The output length that results from a given input" - ... - -def _pad_test() -> typing.Any: - "1D test function for signal extension modes.\n\n Returns ``data extended by ``npre``, ``npost`` at the beginning, end.\n" - ... - -def mode_enum() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/signal/sigtools.pyi b/stubs/scipy-stubs/signal/sigtools.pyi deleted file mode 100644 index 9000aa70..00000000 --- a/stubs/scipy-stubs/signal/sigtools.pyi +++ /dev/null @@ -1,36 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.signal.sigtools, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def _convolve2d(in1, in2, flip, mode, boundary, fillvalue) -> typing.Any: - "out = _convolve2d(in1, in2, flip, mode, boundary, fillvalue)" - ... - -def _correlateND(a, kernel, mode) -> typing.Any: - "out = _correlateND(a,kernel,mode) \n\n mode = 0 - 'valid', 1 - 'same', \n 2 - 'full' (default)" - ... - -def _linear_filter() -> typing.Any: - "(y,Vf) = _linear_filter(b,a,X,Dim=-1,Vi=None) implemented using Direct Form II transposed flow diagram. If Vi is not given, Vf is not returned." - ... - -def _medfilt2d() -> typing.Any: - "filt = _median2d(data, size)" - ... - -def _order_filterND(a, domain, order) -> typing.Any: - "out = _order_filterND(a,domain,order)" - ... - -def _remez(numtaps, bands, des, weight, type, fs, maxiter, grid_density) -> typing.Any: - "h = _remez(numtaps, bands, des, weight, type, fs, maxiter, grid_density)\n returns the optimal (in the Chebyshev/minimax sense) FIR filter impulse\n response given a set of band edges, the desired response on those bands,\n and the weight given to the error in those bands. Bands is a monotonic\n vector with band edges given in frequency domain where fs is the sampling\n frequency." - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/signal/spline.pyi b/stubs/scipy-stubs/signal/spline.pyi deleted file mode 100644 index 254b33b6..00000000 --- a/stubs/scipy-stubs/signal/spline.pyi +++ /dev/null @@ -1,33 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.signal.spline, version: 0.2 -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__version__: str - -def cspline2d(input, lambda_=..., precision=...) -> typing.Any: - "out = cspline2d(input, lambda=0.0, precision=-1.0)\n\n Coefficients for 2-D cubic (3rd order) B-spline.\n\n Return the third-order B-spline coefficients over a regularly spaced\n input grid for the two-dimensional input image.\n\n Parameters\n ----------\n input : ndarray\n The input signal.\n lambda : float\n Specifies the amount of smoothing in the transfer function.\n precision : float\n Specifies the precision for computing the infinite sum needed to apply mirror-\n symmetric boundary conditions.\n\n Returns\n -------\n output : ndarray\n The filtered signal.\n\n Examples\n --------\n Examples are given :ref:`in the tutorial `.\n\n" - ... - -def qspline2d(input, lambda_=..., precision=...) -> typing.Any: - "out = qspline2d(input, lambda=0.0, precision=-1.0)\n\n Coefficients for 2-D quadratic (2nd order) B-spline:\n\n Return the second-order B-spline coefficients over a regularly spaced\n input grid for the two-dimensional input image.\n\n Parameters\n ----------\n input : ndarray\n The input signal.\n lambda : float\n Specifies the amount of smoothing in the transfer function.\n precision : float\n Specifies the precision for computing the infinite sum needed to apply mirror-\n symmetric boundary conditions.\n\n Returns\n -------\n output : ndarray\n The filtered signal.\n\n Examples\n --------\n Examples are given :ref:`in the tutorial `.\n\n" - ... - -def sepfir2d(input, hrow, hcol) -> typing.Any: - "out = sepfir2d(input, hrow, hcol)\n\n Convolve with a 2-D separable FIR filter.\n\n Convolve the rank-2 input array with the separable filter defined by the\n rank-1 arrays hrow, and hcol. Mirror symmetric boundary conditions are\n assumed. This function can be used to find an image given its B-spline\n representation.\n\n Parameters\n ----------\n input : ndarray\n The input signal. Must be a rank-2 array.\n hrow : ndarray\n A rank-1 array defining the row direction of the filter.\n Must be odd-length\n hcol : ndarray\n A rank-1 array defining the column direction of the filter.\n Must be odd-length\n\n Returns\n -------\n output : ndarray\n The filtered signal.\n\n Examples\n --------\n Examples are given :ref:`in the tutorial `.\n\n" - ... - -def symiirorder1(input, c0, z1, precision=...) -> typing.Any: - "out = symiirorder1(input, c0, z1, precision=-1.0)\n\n Implement a smoothing IIR filter with mirror-symmetric boundary conditions\n using a cascade of first-order sections. The second section uses a\n reversed sequence. This implements a system with the following\n transfer function and mirror-symmetric boundary conditions::\n\n c0 \n H(z) = --------------------- \n (1-z1/z) (1 - z1 z) \n\n The resulting signal will have mirror symmetric boundary conditions as well.\n\n Parameters\n ----------\n input : ndarray\n The input signal.\n c0, z1 : scalar\n Parameters in the transfer function.\n precision :\n Specifies the precision for calculating initial conditions\n of the recursive filter based on mirror-symmetric input.\n\n Returns\n -------\n output : ndarray\n The filtered signal." - ... - -def symiirorder2(input, r, omega, precision=...) -> typing.Any: - "out = symiirorder2(input, r, omega, precision=-1.0)\n\n Implement a smoothing IIR filter with mirror-symmetric boundary conditions\n using a cascade of second-order sections. The second section uses a\n reversed sequence. This implements the following transfer function::\n\n cs^2\n H(z) = ---------------------------------------\n (1 - a2/z - a3/z^2) (1 - a2 z - a3 z^2 )\n\n where::\n\n a2 = (2 r cos omega)\n a3 = - r^2\n cs = 1 - 2 r cos omega + r^2\n\n Parameters\n ----------\n input : ndarray\n The input signal.\n r, omega : float\n Parameters in the transfer function.\n precision : float\n Specifies the precision for calculating initial conditions\n of the recursive filter based on mirror-symmetric input.\n\n Returns\n -------\n output : ndarray\n The filtered signal." - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/_csparsetools.pyi b/stubs/scipy-stubs/sparse/_csparsetools.pyi deleted file mode 100644 index 0d180c7f..00000000 --- a/stubs/scipy-stubs/sparse/_csparsetools.pyi +++ /dev/null @@ -1,87 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse._csparsetools, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _lil_fancy_get_int32() -> typing.Any: ... -def _lil_fancy_get_int64() -> typing.Any: ... -def _lil_fancy_set_int32_bool_() -> typing.Any: ... -def _lil_fancy_set_int32_clongdouble() -> typing.Any: ... -def _lil_fancy_set_int32_complex128() -> typing.Any: ... -def _lil_fancy_set_int32_complex64() -> typing.Any: ... -def _lil_fancy_set_int32_float32() -> typing.Any: ... -def _lil_fancy_set_int32_float64() -> typing.Any: ... -def _lil_fancy_set_int32_int16() -> typing.Any: ... -def _lil_fancy_set_int32_int32() -> typing.Any: ... -def _lil_fancy_set_int32_int64() -> typing.Any: ... -def _lil_fancy_set_int32_int8() -> typing.Any: ... -def _lil_fancy_set_int32_longdouble() -> typing.Any: ... -def _lil_fancy_set_int32_uint16() -> typing.Any: ... -def _lil_fancy_set_int32_uint32() -> typing.Any: ... -def _lil_fancy_set_int32_uint64() -> typing.Any: ... -def _lil_fancy_set_int32_uint8() -> typing.Any: ... -def _lil_fancy_set_int64_bool_() -> typing.Any: ... -def _lil_fancy_set_int64_clongdouble() -> typing.Any: ... -def _lil_fancy_set_int64_complex128() -> typing.Any: ... -def _lil_fancy_set_int64_complex64() -> typing.Any: ... -def _lil_fancy_set_int64_float32() -> typing.Any: ... -def _lil_fancy_set_int64_float64() -> typing.Any: ... -def _lil_fancy_set_int64_int16() -> typing.Any: ... -def _lil_fancy_set_int64_int32() -> typing.Any: ... -def _lil_fancy_set_int64_int64() -> typing.Any: ... -def _lil_fancy_set_int64_int8() -> typing.Any: ... -def _lil_fancy_set_int64_longdouble() -> typing.Any: ... -def _lil_fancy_set_int64_uint16() -> typing.Any: ... -def _lil_fancy_set_int64_uint32() -> typing.Any: ... -def _lil_fancy_set_int64_uint64() -> typing.Any: ... -def _lil_fancy_set_int64_uint8() -> typing.Any: ... -def _lil_flatten_to_array_bool_() -> typing.Any: ... -def _lil_flatten_to_array_clongdouble() -> typing.Any: ... -def _lil_flatten_to_array_complex128() -> typing.Any: ... -def _lil_flatten_to_array_complex64() -> typing.Any: ... -def _lil_flatten_to_array_float32() -> typing.Any: ... -def _lil_flatten_to_array_float64() -> typing.Any: ... -def _lil_flatten_to_array_int16() -> typing.Any: ... -def _lil_flatten_to_array_int32() -> typing.Any: ... -def _lil_flatten_to_array_int64() -> typing.Any: ... -def _lil_flatten_to_array_int8() -> typing.Any: ... -def _lil_flatten_to_array_longdouble() -> typing.Any: ... -def _lil_flatten_to_array_uint16() -> typing.Any: ... -def _lil_flatten_to_array_uint32() -> typing.Any: ... -def _lil_flatten_to_array_uint64() -> typing.Any: ... -def _lil_flatten_to_array_uint8() -> typing.Any: ... -def _lil_get_lengths_int32() -> typing.Any: ... -def _lil_get_lengths_int64() -> typing.Any: ... -def lil_fancy_get() -> typing.Any: - "\n Get multiple items at given indices in LIL matrix and store to\n another LIL.\n\n Parameters\n ----------\n M, N, rows, data\n LIL matrix data, initially empty\n new_rows, new_idx\n Data for LIL matrix to insert to.\n Must be preallocated to shape `i_idx.shape`!\n i_idx, j_idx\n Indices of elements to insert to the new LIL matrix.\n\n" - ... - -def lil_fancy_set() -> typing.Any: - "\n Set multiple items to a LIL matrix.\n\n Checks for zero elements and deletes them.\n\n Parameters\n ----------\n M, N, rows, data\n LIL matrix data\n i_idx, j_idx\n Indices of elements to insert to the new LIL matrix.\n values\n Values of items to set.\n\n" - ... - -def lil_flatten_to_array() -> typing.Any: ... -def lil_get1() -> typing.Any: - "\n Get a single item from LIL matrix.\n\n Doesn't do output type conversion. Checks for bounds errors.\n\n Parameters\n ----------\n M, N, rows, datas\n Shape and data arrays for a LIL matrix\n i, j : int\n Indices at which to get\n\n Returns\n -------\n x\n Value at indices.\n\n" - ... - -def lil_get_lengths() -> typing.Any: ... -def lil_get_row_ranges() -> typing.Any: - "\n Column-slicing fast path for LIL matrices.\n Extracts values from rows/datas and inserts in to\n new_rows/new_datas.\n Parameters\n ----------\n M, N\n Shape of input array\n rows, datas\n LIL data for input array, shape (M, N)\n new_rows, new_datas\n LIL data for output array, shape (len(irows), nj)\n irows : iterator\n Iterator yielding row indices\n j_start, j_stop, j_stride\n Column range(j_start, j_stop, j_stride) to get\n nj : int\n Number of columns corresponding to j_* variables.\n" - ... - -def lil_insert() -> typing.Any: - "\n Insert a single item to LIL matrix.\n\n Checks for bounds errors and deletes item if x is zero.\n\n Parameters\n ----------\n M, N, rows, datas\n Shape and data arrays for a LIL matrix\n i, j : int\n Indices at which to get\n x\n Value to insert.\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/_sparsetools.pyi b/stubs/scipy-stubs/sparse/_sparsetools.pyi deleted file mode 100644 index f6ba1c38..00000000 --- a/stubs/scipy-stubs/sparse/_sparsetools.pyi +++ /dev/null @@ -1,90 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse._sparsetools, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def bsr_diagonal() -> typing.Any: ... -def bsr_eldiv_bsr() -> typing.Any: ... -def bsr_elmul_bsr() -> typing.Any: ... -def bsr_ge_bsr() -> typing.Any: ... -def bsr_gt_bsr() -> typing.Any: ... -def bsr_le_bsr() -> typing.Any: ... -def bsr_lt_bsr() -> typing.Any: ... -def bsr_matmat() -> typing.Any: ... -def bsr_matvec() -> typing.Any: ... -def bsr_matvecs() -> typing.Any: ... -def bsr_maximum_bsr() -> typing.Any: ... -def bsr_minimum_bsr() -> typing.Any: ... -def bsr_minus_bsr() -> typing.Any: ... -def bsr_ne_bsr() -> typing.Any: ... -def bsr_plus_bsr() -> typing.Any: ... -def bsr_scale_columns() -> typing.Any: ... -def bsr_scale_rows() -> typing.Any: ... -def bsr_sort_indices() -> typing.Any: ... -def bsr_tocsr() -> typing.Any: ... -def bsr_transpose() -> typing.Any: ... -def coo_matvec() -> typing.Any: ... -def coo_tocsr() -> typing.Any: ... -def coo_todense() -> typing.Any: ... -def cs_graph_components() -> typing.Any: ... -def csc_diagonal() -> typing.Any: ... -def csc_eldiv_csc() -> typing.Any: ... -def csc_elmul_csc() -> typing.Any: ... -def csc_ge_csc() -> typing.Any: ... -def csc_gt_csc() -> typing.Any: ... -def csc_le_csc() -> typing.Any: ... -def csc_lt_csc() -> typing.Any: ... -def csc_matmat() -> typing.Any: ... -def csc_matmat_maxnnz() -> typing.Any: ... -def csc_matvec() -> typing.Any: ... -def csc_matvecs() -> typing.Any: ... -def csc_maximum_csc() -> typing.Any: ... -def csc_minimum_csc() -> typing.Any: ... -def csc_minus_csc() -> typing.Any: ... -def csc_ne_csc() -> typing.Any: ... -def csc_plus_csc() -> typing.Any: ... -def csc_tocsr() -> typing.Any: ... -def csr_column_index1() -> typing.Any: ... -def csr_column_index2() -> typing.Any: ... -def csr_count_blocks() -> typing.Any: ... -def csr_diagonal() -> typing.Any: ... -def csr_eldiv_csr() -> typing.Any: ... -def csr_eliminate_zeros() -> typing.Any: ... -def csr_elmul_csr() -> typing.Any: ... -def csr_ge_csr() -> typing.Any: ... -def csr_gt_csr() -> typing.Any: ... -def csr_has_canonical_format() -> typing.Any: ... -def csr_has_sorted_indices() -> typing.Any: ... -def csr_le_csr() -> typing.Any: ... -def csr_lt_csr() -> typing.Any: ... -def csr_matmat() -> typing.Any: ... -def csr_matmat_maxnnz() -> typing.Any: ... -def csr_matvec() -> typing.Any: ... -def csr_matvecs() -> typing.Any: ... -def csr_maximum_csr() -> typing.Any: ... -def csr_minimum_csr() -> typing.Any: ... -def csr_minus_csr() -> typing.Any: ... -def csr_ne_csr() -> typing.Any: ... -def csr_plus_csr() -> typing.Any: ... -def csr_row_index() -> typing.Any: ... -def csr_row_slice() -> typing.Any: ... -def csr_sample_offsets() -> typing.Any: ... -def csr_sample_values() -> typing.Any: ... -def csr_scale_columns() -> typing.Any: ... -def csr_scale_rows() -> typing.Any: ... -def csr_sort_indices() -> typing.Any: ... -def csr_sum_duplicates() -> typing.Any: ... -def csr_tobsr() -> typing.Any: ... -def csr_tocsc() -> typing.Any: ... -def csr_todense() -> typing.Any: ... -def dia_matvec() -> typing.Any: ... -def expandptr() -> typing.Any: ... -def get_csr_submatrix() -> typing.Any: ... -def test_throw_error() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/csgraph/_flow.pyi b/stubs/scipy-stubs/sparse/csgraph/_flow.pyi deleted file mode 100644 index 2ecd2792..00000000 --- a/stubs/scipy-stubs/sparse/csgraph/_flow.pyi +++ /dev/null @@ -1,65 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.csgraph._flow, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy as _mod_numpy -import scipy.sparse.csr as _mod_scipy_sparse_csr - -DTYPE = _mod_numpy.float64 -ITYPE = _mod_numpy.int32 - -class MaximumFlowResult(_mod_builtins.object): - "Represents the result of a maximum flow calculation.\n\n Attributes\n ----------\n flow_value : int\n The value of the maximum flow.\n residual : csr_matrix\n The residual graph with respect to the maximum flow.\n" - __dict__: typing.Dict[str, typing.Any] - def __init__(self, flow_value, residual) -> None: - "Represents the result of a maximum flow calculation.\n\n Attributes\n ----------\n flow_value : int\n The value of the maximum flow.\n residual : csr_matrix\n The residual graph with respect to the maximum flow.\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - def __repr__(self) -> str: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def __getattr__(self, name) -> typing.Any: ... - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _add_reverse_edges() -> typing.Any: - "Add reversed edges to all edges in a graph.\n\n This adds to a given directed weighted graph all edges in the reverse\n direction and give them weight 0, unless they already exist.\n\n Parameters\n ----------\n a : csr_matrix\n The square matrix in CSR format representing a directed graph\n\n Returns\n -------\n res : csr_matrix\n A new matrix in CSR format in which the missing edges are represented\n by explicit zeros.\n\n" - ... - -def _make_edge_pointers() -> typing.Any: - "Create for each edge pointers to its reverse and its tail." - ... - -csr_matrix = _mod_scipy_sparse_csr.csr_matrix - -def isspmatrix_csr(x) -> typing.Any: - "Is x of csr_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csr matrix\n\n Returns\n -------\n bool\n True if x is a csr matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix_csr\n >>> isspmatrix_csr(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csr(csc_matrix([[5]]))\n False\n" - ... - -def maximum_flow(csgraph, source, sink) -> typing.Any: - "\n maximum_flow(csgraph, source, sink)\n\n Maximize the flow between two vertices in a graph.\n\n .. versionadded:: 1.4.0\n\n Parameters\n ----------\n csgraph : csr_matrix\n The square matrix representing a directed graph whose (i, j)'th entry\n is an integer representing the capacity of the edge between\n vertices i and j.\n source : int\n The source vertex from which the flow flows.\n sink : int\n The sink vertex to which the flow flows.\n\n Returns\n -------\n res : MaximumFlowResult\n A maximum flow represented by a ``MaximumFlowResult``\n which includes the value of the flow in ``flow_value``,\n and the residual graph in ``residual``.\n\n Raises\n ------\n TypeError:\n if the input graph is not in CSR format.\n\n ValueError:\n if the capacity values are not integers, or the source or sink are out\n of bounds.\n\n Notes\n -----\n This solves the maximum flow problem on a given directed weighted graph:\n A flow associates to every edge a value, also called a flow, less than the\n capacity of the edge, so that for every vertex (apart from the source and\n the sink vertices), the total incoming flow is equal to the total outgoing\n flow. The value of a flow is the sum of the flow of all edges leaving the\n source vertex, and the maximum flow problem consists of finding a flow\n whose value is maximal.\n\n By the max-flow min-cut theorem, the maximal value of the flow is also the\n total weight of the edges in a minimum cut.\n\n To solve the problem, we use the Edmonds--Karp algorithm. [1]_ This\n particular implementation strives to exploit sparsity. Its time complexity\n is :math:`O(VE^2)` and its space complexity is :math:`O(E)`.\n\n The maximum flow problem is usually defined with real valued capacities,\n but we require that all capacities are integral to ensure convergence. When\n dealing with rational capacities, or capacities belonging to\n :math:`x\\mathbb{Q}` for some fixed :math:`x \\in \\mathbb{R}`, it is possible\n to reduce the problem to the integral case by scaling all capacities\n accordingly.\n\n Solving a maximum-flow problem can be used for example for graph cuts\n optimization in computer vision [3]_.\n\n References\n ----------\n .. [1] Edmonds, J. and Karp, R. M.\n Theoretical improvements in algorithmic efficiency for network flow\n problems. 1972. Journal of the ACM. 19 (2): pp. 248-264\n .. [2] Cormen, T. H. and Leiserson, C. E. and Rivest, R. L. and Stein C.\n Introduction to Algorithms. Second Edition. 2001. MIT Press.\n .. [3] https://en.wikipedia.org/wiki/Graph_cuts_in_computer_vision\n\n Examples\n --------\n Perhaps the simplest flow problem is that of a graph of only two vertices\n with an edge from source (0) to sink (1)::\n\n (0) --5--> (1)\n\n Here, the maximum flow is simply the capacity of the edge:\n\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import maximum_flow\n >>> graph = csr_matrix([[0, 5], [0, 0]])\n >>> maximum_flow(graph, 0, 1).flow_value\n 5\n\n If, on the other hand, there is a bottleneck between source and sink, that\n can reduce the maximum flow::\n\n (0) --5--> (1) --3--> (2)\n\n >>> graph = csr_matrix([[0, 5, 0], [0, 0, 3], [0, 0, 0]])\n >>> maximum_flow(graph, 0, 2).flow_value\n 3\n\n A less trivial example is given in [2]_, Chapter 26.1:\n\n >>> graph = csr_matrix([[0, 16, 13, 0, 0, 0],\n ... [0, 10, 0, 12, 0, 0],\n ... [0, 4, 0, 0, 14, 0],\n ... [0, 0, 9, 0, 0, 20],\n ... [0, 0, 0, 7, 0, 4],\n ... [0, 0, 0, 0, 0, 0]])\n >>> maximum_flow(graph, 0, 5).flow_value\n 23\n\n It is possible to reduce the problem of finding a maximum matching in a\n bipartite graph to a maximum flow problem: Let :math:`G = ((U, V), E)` be a\n bipartite graph. Then, add to the graph a source vertex with edges to every\n vertex in :math:`U` and a sink vertex with edges from every vertex in\n :math:`V`. Finally, give every edge in the resulting graph a capacity of 1.\n Then, a maximum flow in the new graph gives a maximum matching in the\n original graph consisting of the edges in :math:`E` whose flow is positive.\n\n Assume that the edges are represented by a\n :math:`\\lvert U \\rvert \\times \\lvert V \\rvert` matrix in CSR format whose\n :math:`(i, j)`'th entry is 1 if there is an edge from :math:`i \\in U` to\n :math:`j \\in V` and 0 otherwise; that is, the input is of the form required\n by :func:`maximum_bipartite_matching`. Then the CSR representation of the\n graph constructed above contains this matrix as a block. Here's an example:\n\n >>> graph = csr_matrix([[0, 1, 0, 1], [1, 0, 1, 0], [0, 1, 1, 0]])\n >>> print(graph.toarray())\n [[0 1 0 1]\n [1 0 1 0]\n [0 1 1 0]]\n >>> i, j = graph.shape\n >>> n = graph.nnz\n >>> indptr = np.concatenate([[0],\n ... graph.indptr + i,\n ... np.arange(n + i + 1, n + i + j + 1),\n ... [n + i + j]])\n >>> indices = np.concatenate([np.arange(1, i + 1),\n ... graph.indices + i + 1,\n ... np.repeat(i + j + 1, j)])\n >>> data = np.ones(n + i + j, dtype=int)\n >>>\n >>> graph_flow = csr_matrix((data, indices, indptr))\n >>> print(graph_flow.toarray())\n [[0 1 1 1 0 0 0 0 0]\n [0 0 0 0 0 1 0 1 0]\n [0 0 0 0 1 0 1 0 0]\n [0 0 0 0 0 1 1 0 0]\n [0 0 0 0 0 0 0 0 1]\n [0 0 0 0 0 0 0 0 1]\n [0 0 0 0 0 0 0 0 1]\n [0 0 0 0 0 0 0 0 1]\n [0 0 0 0 0 0 0 0 0]]\n\n At this point, we can find the maximum flow between the added sink and the\n added source and the desired matching can be obtained by restricting the\n residual graph to the block corresponding to the original graph:\n\n >>> flow = maximum_flow(graph_flow, 0, i+j+1)\n >>> matching = flow.residual[1:i+1, i+1:i+j+1]\n >>> print(matching.toarray())\n [[0 1 0 0]\n [1 0 0 0]\n [0 0 1 0]]\n\n This tells us that the first, second, and third vertex in :math:`U` are\n matched with the second, first, and third vertex in :math:`V` respectively.\n\n While this solves the maximum bipartite matching problem in general, note\n that algorithms specialized to that problem, such as\n :func:`maximum_bipartite_matching`, will generally perform better.\n\n This approach can also be used to solve various common generalizations of\n the maximum bipartite matching problem. If, for instance, some vertices can\n be matched with more than one other vertex, this may be handled by\n modifying the capacities of the new graph appropriately.\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/csgraph/_matching.pyi b/stubs/scipy-stubs/sparse/csgraph/_matching.pyi deleted file mode 100644 index 95ba37ba..00000000 --- a/stubs/scipy-stubs/sparse/csgraph/_matching.pyi +++ /dev/null @@ -1,43 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.csgraph._matching, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy as _mod_numpy -import scipy.sparse.csr as _mod_scipy_sparse_csr - -BTYPE = _mod_numpy.uint8 -DTYPE = _mod_numpy.float64 -ITYPE = _mod_numpy.int32 -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict -csr_matrix = _mod_scipy_sparse_csr.csr_matrix - -def isspmatrix_coo(x) -> typing.Any: - "Is x of coo_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a coo matrix\n\n Returns\n -------\n bool\n True if x is a coo matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import coo_matrix, isspmatrix_coo\n >>> isspmatrix_coo(coo_matrix([[5]]))\n True\n\n >>> from scipy.sparse import coo_matrix, csr_matrix, isspmatrix_coo\n >>> isspmatrix_coo(csr_matrix([[5]]))\n False\n" - ... - -def isspmatrix_csc(x) -> typing.Any: - "Is x of csc_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csc matrix\n\n Returns\n -------\n bool\n True if x is a csc matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csc_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csc_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csr_matrix([[5]]))\n False\n" - ... - -def isspmatrix_csr(x) -> typing.Any: - "Is x of csr_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csr matrix\n\n Returns\n -------\n bool\n True if x is a csr matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix_csr\n >>> isspmatrix_csr(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csr(csc_matrix([[5]]))\n False\n" - ... - -def maximum_bipartite_matching(graph, perm_type=...) -> typing.Any: - "\n maximum_bipartite_matching(graph, perm_type='row')\n\n Returns a matching of a bipartite graph whose cardinality is as least that\n of any given matching of the graph.\n\n Parameters\n ----------\n graph : sparse matrix\n Input sparse in CSR format whose rows represent one partition of the\n graph and whose columns represent the other partition. An edge between\n two vertices is indicated by the corresponding entry in the matrix\n existing in its sparse representation.\n perm_type : str, {'row', 'column'}\n Which partition to return the matching in terms of: If ``'row'``, the\n function produces an array whose length is the number of columns in the\n input, and whose :math:`j`'th element is the row matched to the\n :math:`j`'th column. Conversely, if ``perm_type`` is ``'column'``, this\n returns the columns matched to each row.\n\n Returns\n -------\n perm : ndarray\n A matching of the vertices in one of the two partitions. Unmatched\n vertices are represented by a ``-1`` in the result.\n\n Notes\n -----\n This function implements the Hopcroft--Karp algorithm [1]_. Its time\n complexity is :math:`O(\\lvert E \\rvert \\sqrt{\\lvert V \\rvert})`, and its\n space complexity is linear in the number of rows. In practice, this\n asymmetry between rows and columns means that it can be more efficient to\n transpose the input if it contains more columns than rows.\n\n By Konig's theorem, the cardinality of the matching is also the number of\n vertices appearing in a minimum vertex cover of the graph.\n\n Note that if the sparse representation contains explicit zeros, these are\n still counted as edges.\n\n The implementation was changed in SciPy 1.4.0 to allow matching of general\n bipartite graphs, where previous versions would assume that a perfect\n matching existed. As such, code written against 1.4.0 will not necessarily\n work on older versions.\n\n References\n ----------\n .. [1] John E. Hopcroft and Richard M. Karp. \"An n^{5 / 2} Algorithm for\n Maximum Matchings in Bipartite Graphs\" In: SIAM Journal of Computing\n 2.4 (1973), pp. 225--231. :doi:`10.1137/0202019`\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import maximum_bipartite_matching\n\n As a simple example, consider a bipartite graph in which the partitions\n contain 2 and 3 elements respectively. Suppose that one partition contains\n vertices labelled 0 and 1, and that the other partition contains vertices\n labelled A, B, and C. Suppose that there are edges connecting 0 and C,\n 1 and A, and 1 and B. This graph would then be represented by the following\n sparse matrix:\n\n >>> graph = csr_matrix([[0, 0, 1], [1, 1, 0]])\n\n Here, the 1s could be anything, as long as they end up being stored as\n elements in the sparse matrix. We can now calculate maximum matchings as\n follows:\n\n >>> print(maximum_bipartite_matching(graph, perm_type='column'))\n [2 0]\n >>> print(maximum_bipartite_matching(graph, perm_type='row'))\n [ 1 -1 0]\n\n The first output tells us that 1 and 2 are matched with C and A\n respectively, and the second output tells us that A, B, and C are matched\n with 1, nothing, and 0 respectively.\n\n Note that explicit zeros are still converted to edges. This means that a\n different way to represent the above graph is by using the CSR structure\n directly as follows:\n\n >>> data = [0, 0, 0]\n >>> indices = [2, 0, 1]\n >>> indptr = [0, 1, 3]\n >>> graph = csr_matrix((data, indices, indptr))\n >>> print(maximum_bipartite_matching(graph, perm_type='column'))\n [2 0]\n >>> print(maximum_bipartite_matching(graph, perm_type='row'))\n [ 1 -1 0]\n\n When one or both of the partitions are empty, the matching is empty as\n well:\n\n >>> graph = csr_matrix((2, 0))\n >>> print(maximum_bipartite_matching(graph, perm_type='column'))\n [-1 -1]\n >>> print(maximum_bipartite_matching(graph, perm_type='row'))\n []\n\n When the input matrix is square, and the graph is known to admit a perfect\n matching, i.e. a matching with the property that every vertex in the graph\n belongs to some edge in the matching, then one can view the output as the\n permutation of rows (or columns) turning the input matrix into one with the\n property that all diagonal elements are non-empty:\n\n >>> a = [[0, 1, 2, 0], [1, 0, 0, 1], [2, 0, 0, 3], [0, 1, 3, 0]]\n >>> graph = csr_matrix(a)\n >>> perm = maximum_bipartite_matching(graph, perm_type='row')\n >>> print(graph[perm].toarray())\n [[1 0 0 1]\n [0 1 2 0]\n [0 1 3 0]\n [2 0 0 3]]\n\n" - ... - -def min_weight_full_bipartite_matching(biadjacency_matrix, maximize=...) -> typing.Any: - "\n min_weight_full_bipartite_matching(biadjacency_matrix, maximize=False)\n\n Returns the minimum weight full matching of a bipartite graph.\n\n .. versionadded:: 1.6.0\n\n Parameters\n ----------\n biadjacency_matrix : sparse matrix\n Biadjacency matrix of the bipartite graph: A sparse matrix in CSR, CSC,\n or COO format whose rows represent one partition of the graph and whose\n columns represent the other partition. An edge between two vertices is\n indicated by the corresponding entry in the matrix, and the weight of\n the edge is given by the value of that entry. This should not be\n confused with the full adjacency matrix of the graph, as we only need\n the submatrix defining the bipartite structure.\n\n maximize : bool (default: False)\n Calculates a maximum weight matching if true.\n\n Returns\n -------\n row_ind, col_ind : array\n An array of row indices and one of corresponding column indices giving\n the optimal matching. The total weight of the matching can be computed\n as ``graph[row_ind, col_ind].sum()``. The row indices will be\n sorted; in the case of a square matrix they will be equal to\n ``numpy.arange(graph.shape[0])``.\n\n Notes\n -----\n\n Let :math:`G = ((U, V), E)` be a weighted bipartite graph with non-zero\n weights :math:`w : E \\to \\mathbb{R} \\setminus \\{0\\}`. This function then\n produces a matching :math:`M \\subseteq E` with cardinality\n\n .. math::\n \\lvert M \\rvert = \\min(\\lvert U \\rvert, \\lvert V \\rvert),\n\n which minimizes the sum of the weights of the edges included in the\n matching, :math:`\\sum_{e \\in M} w(e)`, or raises an error if no such\n matching exists.\n\n When :math:`\\lvert U \\rvert = \\lvert V \\rvert`, this is commonly\n referred to as a perfect matching; here, since we allow\n :math:`\\lvert U \\rvert` and :math:`\\lvert V \\rvert` to differ, we\n follow Karp [1]_ and refer to the matching as *full*.\n\n This function implements the LAPJVsp algorithm [2]_, short for \"Linear\n assignment problem, Jonker--Volgenant, sparse\".\n\n The problem it solves is equivalent to the rectangular linear assignment\n problem. [3]_ As such, this function can be used to solve the same problems\n as :func:`scipy.optimize.linear_sum_assignment`. That function may perform\n better when the input is dense, or for certain particular types of inputs,\n such as those for which the :math:`(i, j)`'th entry is the distance between\n two points in Euclidean space.\n\n If no full matching exists, this function raises a ``ValueError``. For\n determining the size of the largest matching in the graph, see\n :func:`maximum_bipartite_matching`.\n\n We require that weights are non-zero only to avoid issues with the handling\n of explicit zeros when converting between different sparse representations.\n Zero weights can be handled by adding a constant to all weights, so that\n the resulting matrix contains no zeros.\n\n References\n ----------\n .. [1] Richard Manning Karp:\n An algorithm to Solve the m x n Assignment Problem in Expected Time\n O(mn log n).\n Networks, 10(2):143-152, 1980.\n .. [2] Roy Jonker and Anton Volgenant:\n A Shortest Augmenting Path Algorithm for Dense and Sparse Linear\n Assignment Problems.\n Computing 38:325-340, 1987.\n .. [3] https://en.wikipedia.org/wiki/Assignment_problem\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import min_weight_full_bipartite_matching\n\n Let us first consider an example in which all weights are equal:\n\n >>> biadjacency_matrix = csr_matrix([[1, 1, 1], [1, 0, 0], [0, 1, 0]])\n\n Here, all we get is a perfect matching of the graph:\n\n >>> print(min_weight_full_bipartite_matching(biadjacency_matrix)[1])\n [2 0 1]\n\n That is, the first, second, and third rows are matched with the third,\n first, and second column respectively. Note that in this example, the 0\n in the input matrix does *not* correspond to an edge with weight 0, but\n rather a pair of vertices not paired by an edge.\n\n Note also that in this case, the output matches the result of applying\n :func:`maximum_bipartite_matching`:\n\n >>> from scipy.sparse.csgraph import maximum_bipartite_matching\n >>> biadjacency = csr_matrix([[1, 1, 1], [1, 0, 0], [0, 1, 0]])\n >>> print(maximum_bipartite_matching(biadjacency, perm_type='column'))\n [2 0 1]\n\n When multiple edges are available, the ones with lowest weights are\n preferred:\n\n >>> biadjacency = csr_matrix([[3, 3, 6], [4, 3, 5], [10, 1, 8]])\n >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency)\n >>> print(col_ind)\n [0 2 1]\n\n The total weight in this case is :math:`3 + 5 + 1 = 9`:\n\n >>> print(biadjacency[row_ind, col_ind].sum())\n 9\n\n When the matrix is not square, i.e. when the two partitions have different\n cardinalities, the matching is as large as the smaller of the two\n partitions:\n\n >>> biadjacency = csr_matrix([[0, 1, 1], [0, 2, 3]])\n >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency)\n >>> print(row_ind, col_ind)\n [0 1] [2 1]\n >>> biadjacency = csr_matrix([[0, 1], [3, 1], [1, 4]])\n >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency)\n >>> print(row_ind, col_ind)\n [0 2] [1 0]\n\n When one or both of the partitions are empty, the matching is empty as\n well:\n\n >>> biadjacency = csr_matrix((2, 0))\n >>> row_ind, col_ind = min_weight_full_bipartite_matching(biadjacency)\n >>> print(row_ind, col_ind)\n [] []\n\n In general, we will always reach the same sum of weights as if we had used\n :func:`scipy.optimize.linear_sum_assignment` but note that for that one,\n missing edges are represented by a matrix entry of ``float('inf')``. Let us\n generate a random sparse matrix with integer entries between 1 and 10:\n\n >>> import numpy as np\n >>> from scipy.sparse import random\n >>> from scipy.optimize import linear_sum_assignment\n >>> sparse = random(10, 10, random_state=42, density=.5, format='coo') * 10\n >>> sparse.data = np.ceil(sparse.data)\n >>> dense = sparse.toarray()\n >>> dense = np.full(sparse.shape, np.inf)\n >>> dense[sparse.row, sparse.col] = sparse.data\n >>> sparse = sparse.tocsr()\n >>> row_ind, col_ind = linear_sum_assignment(dense)\n >>> print(dense[row_ind, col_ind].sum())\n 28.0\n >>> row_ind, col_ind = min_weight_full_bipartite_matching(sparse)\n >>> print(sparse[row_ind, col_ind].sum())\n 28.0\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/csgraph/_min_spanning_tree.pyi b/stubs/scipy-stubs/sparse/csgraph/_min_spanning_tree.pyi deleted file mode 100644 index 983736ff..00000000 --- a/stubs/scipy-stubs/sparse/csgraph/_min_spanning_tree.pyi +++ /dev/null @@ -1,50 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.csgraph._min_spanning_tree, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy as _mod_numpy -import scipy.sparse.csr as _mod_scipy_sparse_csr - -DTYPE = _mod_numpy.float64 -ITYPE = _mod_numpy.int32 -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict -csr_matrix = _mod_scipy_sparse_csr.csr_matrix - -def isspmatrix(x) -> typing.Any: - "Is x of a sparse matrix type?\n\n Parameters\n ----------\n x\n object to check for being a sparse matrix\n\n Returns\n -------\n bool\n True if x is a sparse matrix, False otherwise\n\n Notes\n -----\n issparse and isspmatrix are aliases for the same function.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix\n >>> isspmatrix(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import isspmatrix\n >>> isspmatrix(5)\n False\n" - ... - -def isspmatrix_csc(x) -> typing.Any: - "Is x of csc_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csc matrix\n\n Returns\n -------\n bool\n True if x is a csc matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csc_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csc_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csr_matrix([[5]]))\n False\n" - ... - -def minimum_spanning_tree(csgraph, overwrite=...) -> typing.Any: - "\n minimum_spanning_tree(csgraph, overwrite=False)\n\n Return a minimum spanning tree of an undirected graph\n\n A minimum spanning tree is a graph consisting of the subset of edges\n which together connect all connected nodes, while minimizing the total\n sum of weights on the edges. This is computed using the Kruskal algorithm.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array_like or sparse matrix, 2 dimensions\n The N x N matrix representing an undirected graph over N nodes\n (see notes below).\n overwrite : bool, optional\n if true, then parts of the input graph will be overwritten for\n efficiency.\n\n Returns\n -------\n span_tree : csr matrix\n The N x N compressed-sparse representation of the undirected minimum\n spanning tree over the input (see notes below).\n\n Notes\n -----\n This routine uses undirected graphs as input and output. That is, if\n graph[i, j] and graph[j, i] are both zero, then nodes i and j do not\n have an edge connecting them. If either is nonzero, then the two are\n connected by the minimum nonzero value of the two.\n\n This routine loses precision when users input a dense matrix.\n Small elements < 1E-8 of the dense matrix are rounded to zero.\n All users should input sparse matrices if possible to avoid it.\n\n\n Examples\n --------\n The following example shows the computation of a minimum spanning tree\n over a simple four-component graph::\n\n input graph minimum spanning tree\n\n (0) (0)\n / \\ /\n 3 8 3\n / \\ /\n (3)---5---(1) (3)---5---(1)\n \\ / /\n 6 2 2\n \\ / /\n (2) (2)\n\n It is easy to see from inspection that the minimum spanning tree involves\n removing the edges with weights 8 and 6. In compressed sparse\n representation, the solution looks like this:\n\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import minimum_spanning_tree\n >>> X = csr_matrix([[0, 8, 0, 3],\n ... [0, 0, 2, 5],\n ... [0, 0, 0, 6],\n ... [0, 0, 0, 0]])\n >>> Tcsr = minimum_spanning_tree(X)\n >>> Tcsr.toarray().astype(int)\n array([[0, 0, 0, 3],\n [0, 0, 2, 5],\n [0, 0, 0, 0],\n [0, 0, 0, 0]])\n" - ... - -def validate_graph( - csgraph, - directed, - dtype, - csr_output, - dense_output, - copy_if_dense, - copy_if_sparse, - null_value_in, - null_value_out, - infinity_null, - nan_null, -) -> typing.Any: - "Routine for validation and conversion of csgraph inputs" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/csgraph/_reordering.pyi b/stubs/scipy-stubs/sparse/csgraph/_reordering.pyi deleted file mode 100644 index 6d1798ea..00000000 --- a/stubs/scipy-stubs/sparse/csgraph/_reordering.pyi +++ /dev/null @@ -1,63 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.csgraph._reordering, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy as _mod_numpy -import scipy.sparse.base as _mod_scipy_sparse_base -import scipy.sparse.csc as _mod_scipy_sparse_csc -import scipy.sparse.csr as _mod_scipy_sparse_csr - -DTYPE = _mod_numpy.float64 -ITYPE = _mod_numpy.int32 -SparseEfficiencyWarning = _mod_scipy_sparse_base.SparseEfficiencyWarning -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _reverse_cuthill_mckee(ind, ptr, num_rows) -> typing.Any: - "\n Reverse Cuthill-McKee ordering of a sparse symmetric CSR or CSC matrix. \n We follow the original Cuthill-McKee paper and always start the routine\n at a node of lowest degree for each connected component.\n" - ... - -csc_matrix = _mod_scipy_sparse_csc.csc_matrix -csr_matrix = _mod_scipy_sparse_csr.csr_matrix - -def isspmatrix(x) -> typing.Any: - "Is x of a sparse matrix type?\n\n Parameters\n ----------\n x\n object to check for being a sparse matrix\n\n Returns\n -------\n bool\n True if x is a sparse matrix, False otherwise\n\n Notes\n -----\n issparse and isspmatrix are aliases for the same function.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix\n >>> isspmatrix(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import isspmatrix\n >>> isspmatrix(5)\n False\n" - ... - -def isspmatrix_coo(x) -> typing.Any: - "Is x of coo_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a coo matrix\n\n Returns\n -------\n bool\n True if x is a coo matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import coo_matrix, isspmatrix_coo\n >>> isspmatrix_coo(coo_matrix([[5]]))\n True\n\n >>> from scipy.sparse import coo_matrix, csr_matrix, isspmatrix_coo\n >>> isspmatrix_coo(csr_matrix([[5]]))\n False\n" - ... - -def isspmatrix_csc(x) -> typing.Any: - "Is x of csc_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csc matrix\n\n Returns\n -------\n bool\n True if x is a csc matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csc_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csc_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csr_matrix([[5]]))\n False\n" - ... - -def isspmatrix_csr(x) -> typing.Any: - "Is x of csr_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csr matrix\n\n Returns\n -------\n bool\n True if x is a csr matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix_csr\n >>> isspmatrix_csr(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csr(csc_matrix([[5]]))\n False\n" - ... - -def maximum_bipartite_matching(graph, perm_type=...) -> typing.Any: - "\n maximum_bipartite_matching(graph, perm_type='row')\n\n Returns a matching of a bipartite graph whose cardinality is as least that\n of any given matching of the graph.\n\n Parameters\n ----------\n graph : sparse matrix\n Input sparse in CSR format whose rows represent one partition of the\n graph and whose columns represent the other partition. An edge between\n two vertices is indicated by the corresponding entry in the matrix\n existing in its sparse representation.\n perm_type : str, {'row', 'column'}\n Which partition to return the matching in terms of: If ``'row'``, the\n function produces an array whose length is the number of columns in the\n input, and whose :math:`j`'th element is the row matched to the\n :math:`j`'th column. Conversely, if ``perm_type`` is ``'column'``, this\n returns the columns matched to each row.\n\n Returns\n -------\n perm : ndarray\n A matching of the vertices in one of the two partitions. Unmatched\n vertices are represented by a ``-1`` in the result.\n\n Notes\n -----\n This function implements the Hopcroft--Karp algorithm [1]_. Its time\n complexity is :math:`O(\\lvert E \\rvert \\sqrt{\\lvert V \\rvert})`, and its\n space complexity is linear in the number of rows. In practice, this\n asymmetry between rows and columns means that it can be more efficient to\n transpose the input if it contains more columns than rows.\n\n By Konig's theorem, the cardinality of the matching is also the number of\n vertices appearing in a minimum vertex cover of the graph.\n\n Note that if the sparse representation contains explicit zeros, these are\n still counted as edges.\n\n The implementation was changed in SciPy 1.4.0 to allow matching of general\n bipartite graphs, where previous versions would assume that a perfect\n matching existed. As such, code written against 1.4.0 will not necessarily\n work on older versions.\n\n References\n ----------\n .. [1] John E. Hopcroft and Richard M. Karp. \"An n^{5 / 2} Algorithm for\n Maximum Matchings in Bipartite Graphs\" In: SIAM Journal of Computing\n 2.4 (1973), pp. 225--231. :doi:`10.1137/0202019`\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import maximum_bipartite_matching\n\n As a simple example, consider a bipartite graph in which the partitions\n contain 2 and 3 elements respectively. Suppose that one partition contains\n vertices labelled 0 and 1, and that the other partition contains vertices\n labelled A, B, and C. Suppose that there are edges connecting 0 and C,\n 1 and A, and 1 and B. This graph would then be represented by the following\n sparse matrix:\n\n >>> graph = csr_matrix([[0, 0, 1], [1, 1, 0]])\n\n Here, the 1s could be anything, as long as they end up being stored as\n elements in the sparse matrix. We can now calculate maximum matchings as\n follows:\n\n >>> print(maximum_bipartite_matching(graph, perm_type='column'))\n [2 0]\n >>> print(maximum_bipartite_matching(graph, perm_type='row'))\n [ 1 -1 0]\n\n The first output tells us that 1 and 2 are matched with C and A\n respectively, and the second output tells us that A, B, and C are matched\n with 1, nothing, and 0 respectively.\n\n Note that explicit zeros are still converted to edges. This means that a\n different way to represent the above graph is by using the CSR structure\n directly as follows:\n\n >>> data = [0, 0, 0]\n >>> indices = [2, 0, 1]\n >>> indptr = [0, 1, 3]\n >>> graph = csr_matrix((data, indices, indptr))\n >>> print(maximum_bipartite_matching(graph, perm_type='column'))\n [2 0]\n >>> print(maximum_bipartite_matching(graph, perm_type='row'))\n [ 1 -1 0]\n\n When one or both of the partitions are empty, the matching is empty as\n well:\n\n >>> graph = csr_matrix((2, 0))\n >>> print(maximum_bipartite_matching(graph, perm_type='column'))\n [-1 -1]\n >>> print(maximum_bipartite_matching(graph, perm_type='row'))\n []\n\n When the input matrix is square, and the graph is known to admit a perfect\n matching, i.e. a matching with the property that every vertex in the graph\n belongs to some edge in the matching, then one can view the output as the\n permutation of rows (or columns) turning the input matrix into one with the\n property that all diagonal elements are non-empty:\n\n >>> a = [[0, 1, 2, 0], [1, 0, 0, 1], [2, 0, 0, 3], [0, 1, 3, 0]]\n >>> graph = csr_matrix(a)\n >>> perm = maximum_bipartite_matching(graph, perm_type='row')\n >>> print(graph[perm].toarray())\n [[1 0 0 1]\n [0 1 2 0]\n [0 1 3 0]\n [2 0 0 3]]\n\n" - ... - -def reverse_cuthill_mckee(graph, symmetric_mode=...) -> typing.Any: - '\n reverse_cuthill_mckee(graph, symmetric_mode=False)\n \n Returns the permutation array that orders a sparse CSR or CSC matrix\n in Reverse-Cuthill McKee ordering. \n \n It is assumed by default, ``symmetric_mode=False``, that the input matrix \n is not symmetric and works on the matrix ``A+A.T``. If you are \n guaranteed that the matrix is symmetric in structure (values of matrix \n elements do not matter) then set ``symmetric_mode=True``.\n \n Parameters\n ----------\n graph : sparse matrix\n Input sparse in CSC or CSR sparse matrix format.\n symmetric_mode : bool, optional\n Is input matrix guaranteed to be symmetric.\n\n Returns\n -------\n perm : ndarray\n Array of permuted row and column indices.\n \n Notes\n -----\n .. versionadded:: 0.15.0\n\n References\n ----------\n E. Cuthill and J. McKee, "Reducing the Bandwidth of Sparse Symmetric Matrices",\n ACM \'69 Proceedings of the 1969 24th national conference, (1969).\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import reverse_cuthill_mckee\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [2, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 0)\t2\n (2, 3)\t3\n\n >>> reverse_cuthill_mckee(graph)\n array([3, 2, 1, 0], dtype=int32)\n \n' - ... - -def structural_rank(graph) -> typing.Any: - '\n structural_rank(graph)\n \n Compute the structural rank of a graph (matrix) with a given \n sparsity pattern.\n\n The structural rank of a matrix is the number of entries in the maximum \n transversal of the corresponding bipartite graph, and is an upper bound \n on the numerical rank of the matrix. A graph has full structural rank \n if it is possible to permute the elements to make the diagonal zero-free.\n\n .. versionadded:: 0.19.0\n\n Parameters\n ----------\n graph : sparse matrix\n Input sparse matrix.\n\n Returns\n -------\n rank : int\n The structural rank of the sparse graph.\n \n References\n ----------\n .. [1] I. S. Duff, "Computing the Structural Index", SIAM J. Alg. Disc. \n Meth., Vol. 7, 594 (1986).\n \n .. [2] http://www.cise.ufl.edu/research/sparse/matrices/legend.html\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import structural_rank\n\n >>> graph = [\n ... [0, 1, 2, 0],\n ... [1, 0, 0, 1],\n ... [2, 0, 0, 3],\n ... [0, 1, 3, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 0)\t1\n (1, 3)\t1\n (2, 0)\t2\n (2, 3)\t3\n (3, 1)\t1\n (3, 2)\t3\n\n >>> structural_rank(graph)\n 4\n\n' - ... - -def warn(message, category, stacklevel, source) -> typing.Any: - "Issue a warning, or maybe ignore it or raise an exception." - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/csgraph/_shortest_path.pyi b/stubs/scipy-stubs/sparse/csgraph/_shortest_path.pyi deleted file mode 100644 index e0e86307..00000000 --- a/stubs/scipy-stubs/sparse/csgraph/_shortest_path.pyi +++ /dev/null @@ -1,94 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.csgraph._shortest_path, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy as _mod_numpy -import scipy.sparse.csr as _mod_scipy_sparse_csr - -DTYPE = _mod_numpy.float64 -ITYPE = _mod_numpy.int32 - -class NegativeCycleError(_mod_builtins.Exception): - __dict__: typing.Dict[str, typing.Any] - def __init__(self, message) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def __getattr__(self, name) -> typing.Any: ... - -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def bellman_ford(csgraph, directed=..., indices=..., return_predecessors=..., unweighted=...) -> typing.Any: - "\n bellman_ford(csgraph, directed=True, indices=None, return_predecessors=False,\n unweighted=False)\n\n Compute the shortest path lengths using the Bellman-Ford algorithm.\n\n The Bellman-Ford algorithm can robustly deal with graphs with negative\n weights. If a negative cycle is detected, an error is raised. For\n graphs without negative edge weights, Dijkstra's algorithm may be faster.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array, matrix, or sparse matrix, 2 dimensions\n The N x N array of distances representing the input graph.\n directed : bool, optional\n If True (default), then find the shortest path on a directed graph:\n only move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i]\n indices : array_like or int, optional\n if specified, only compute the paths from the points at the given\n indices.\n return_predecessors : bool, optional\n If True, return the size (N, N) predecesor matrix\n unweighted : bool, optional\n If True, then find unweighted distances. That is, rather than finding\n the path between each point such that the sum of weights is minimized,\n find the path such that the number of edges is minimized.\n\n Returns\n -------\n dist_matrix : ndarray\n The N x N matrix of distances between graph nodes. dist_matrix[i,j]\n gives the shortest distance from point i to point j along the graph.\n\n predecessors : ndarray\n Returned only if return_predecessors == True.\n The N x N matrix of predecessors, which can be used to reconstruct\n the shortest paths. Row i of the predecessor matrix contains\n information on the shortest paths from point i: each entry\n predecessors[i, j] gives the index of the previous node in the\n path from point i to point j. If no path exists between point\n i and j, then predecessors[i, j] = -9999\n\n Raises\n ------\n NegativeCycleError:\n if there are negative cycles in the graph\n\n Notes\n -----\n This routine is specially designed for graphs with negative edge weights.\n If all edge weights are positive, then Dijkstra's algorithm is a better\n choice.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import bellman_ford\n\n >>> graph = [\n ... [0, 1 ,2, 0],\n ... [0, 0, 0, 1],\n ... [2, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 0)\t2\n (2, 3)\t3\n\n >>> dist_matrix, predecessors = bellman_ford(csgraph=graph, directed=False, indices=0, return_predecessors=True)\n >>> dist_matrix\n array([ 0., 1., 2., 2.])\n >>> predecessors\n array([-9999, 0, 0, 1], dtype=int32)\n\n" - ... - -csr_matrix = _mod_scipy_sparse_csr.csr_matrix - -def dijkstra(csgraph, directed=..., indices=..., return_predecessors=..., unweighted=..., limit=..., min_only=...) -> typing.Any: - "\n dijkstra(csgraph, directed=True, indices=None, return_predecessors=False,\n unweighted=False, limit=np.inf, min_only=False)\n\n Dijkstra algorithm using Fibonacci Heaps\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array, matrix, or sparse matrix, 2 dimensions\n The N x N array of non-negative distances representing the input graph.\n directed : bool, optional\n If True (default), then find the shortest path on a directed graph:\n only move from point i to point j along paths csgraph[i, j] and from\n point j to i along paths csgraph[j, i].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j or j to i along either\n csgraph[i, j] or csgraph[j, i].\n indices : array_like or int, optional\n if specified, only compute the paths from the points at the given\n indices.\n return_predecessors : bool, optional\n If True, return the size (N, N) predecesor matrix\n unweighted : bool, optional\n If True, then find unweighted distances. That is, rather than finding\n the path between each point such that the sum of weights is minimized,\n find the path such that the number of edges is minimized.\n limit : float, optional\n The maximum distance to calculate, must be >= 0. Using a smaller limit\n will decrease computation time by aborting calculations between pairs\n that are separated by a distance > limit. For such pairs, the distance\n will be equal to np.inf (i.e., not connected).\n\n .. versionadded:: 0.14.0\n min_only : bool, optional\n If False (default), for every node in the graph, find the shortest path\n from every node in indices.\n If True, for every node in the graph, find the shortest path from any\n of the nodes in indices (which can be substantially faster).\n\n .. versionadded:: 1.3.0\n\n Returns\n -------\n dist_matrix : ndarray, shape ([n_indices, ]n_nodes,)\n The matrix of distances between graph nodes. If min_only=False,\n dist_matrix has shape (n_indices, n_nodes) and dist_matrix[i, j]\n gives the shortest distance from point i to point j along the graph.\n If min_only=True, dist_matrix has shape (n_nodes,) and contains for\n a given node the shortest path to that node from any of the nodes\n in indices.\n predecessors : ndarray, shape ([n_indices, ]n_nodes,)\n If min_only=False, this has shape (n_indices, n_nodes),\n otherwise it has shape (n_nodes,).\n Returned only if return_predecessors == True.\n The matrix of predecessors, which can be used to reconstruct\n the shortest paths. Row i of the predecessor matrix contains\n information on the shortest paths from point i: each entry\n predecessors[i, j] gives the index of the previous node in the\n path from point i to point j. If no path exists between point\n i and j, then predecessors[i, j] = -9999\n\n sources : ndarray, shape (n_nodes,)\n Returned only if min_only=True and return_predecessors=True.\n Contains the index of the source which had the shortest path\n to each target. If no path exists within the limit,\n this will contain -9999. The value at the indices passed\n will be equal to that index (i.e. the fastest way to reach\n node i, is to start on node i).\n\n Notes\n -----\n As currently implemented, Dijkstra's algorithm does not work for\n graphs with direction-dependent distances when directed == False.\n i.e., if csgraph[i,j] and csgraph[j,i] are not equal and\n both are nonzero, setting directed=False will not yield the correct\n result.\n\n Also, this routine does not work for graphs with negative\n distances. Negative distances can lead to infinite cycles that must\n be handled by specialized algorithms such as Bellman-Ford's algorithm\n or Johnson's algorithm.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import dijkstra\n\n >>> graph = [\n ... [0, 1, 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 3)\t3\n\n >>> dist_matrix, predecessors = dijkstra(csgraph=graph, directed=False, indices=0, return_predecessors=True)\n >>> dist_matrix\n array([ 0., 1., 2., 2.])\n >>> predecessors\n array([-9999, 0, 0, 1], dtype=int32)\n\n" - ... - -def floyd_warshall(csgraph, directed=..., return_predecessors=..., unweighted=..., overwrite=...) -> typing.Any: - "\n floyd_warshall(csgraph, directed=True, return_predecessors=False,\n unweighted=False, overwrite=False)\n\n Compute the shortest path lengths using the Floyd-Warshall algorithm\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array, matrix, or sparse matrix, 2 dimensions\n The N x N array of distances representing the input graph.\n directed : bool, optional\n If True (default), then find the shortest path on a directed graph:\n only move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i]\n return_predecessors : bool, optional\n If True, return the size (N, N) predecesor matrix\n unweighted : bool, optional\n If True, then find unweighted distances. That is, rather than finding\n the path between each point such that the sum of weights is minimized,\n find the path such that the number of edges is minimized.\n overwrite : bool, optional\n If True, overwrite csgraph with the result. This applies only if\n csgraph is a dense, c-ordered array with dtype=float64.\n\n Returns\n -------\n dist_matrix : ndarray\n The N x N matrix of distances between graph nodes. dist_matrix[i,j]\n gives the shortest distance from point i to point j along the graph.\n\n predecessors : ndarray\n Returned only if return_predecessors == True.\n The N x N matrix of predecessors, which can be used to reconstruct\n the shortest paths. Row i of the predecessor matrix contains\n information on the shortest paths from point i: each entry\n predecessors[i, j] gives the index of the previous node in the\n path from point i to point j. If no path exists between point\n i and j, then predecessors[i, j] = -9999\n\n Raises\n ------\n NegativeCycleError:\n if there are negative cycles in the graph\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import floyd_warshall\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [2, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 0)\t2\n (2, 3)\t3\n\n\n >>> dist_matrix, predecessors = floyd_warshall(csgraph=graph, directed=False, return_predecessors=True)\n >>> dist_matrix\n array([[ 0., 1., 2., 2.],\n [ 1., 0., 3., 1.],\n [ 2., 3., 0., 3.],\n [ 2., 1., 3., 0.]])\n >>> predecessors\n array([[-9999, 0, 0, 1],\n [ 1, -9999, 0, 1],\n [ 2, 0, -9999, 2],\n [ 1, 3, 3, -9999]], dtype=int32)\n\n" - ... - -def isspmatrix(x) -> typing.Any: - "Is x of a sparse matrix type?\n\n Parameters\n ----------\n x\n object to check for being a sparse matrix\n\n Returns\n -------\n bool\n True if x is a sparse matrix, False otherwise\n\n Notes\n -----\n issparse and isspmatrix are aliases for the same function.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix\n >>> isspmatrix(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import isspmatrix\n >>> isspmatrix(5)\n False\n" - ... - -def isspmatrix_csc(x) -> typing.Any: - "Is x of csc_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csc matrix\n\n Returns\n -------\n bool\n True if x is a csc matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csc_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csc_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csr_matrix([[5]]))\n False\n" - ... - -def isspmatrix_csr(x) -> typing.Any: - "Is x of csr_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csr matrix\n\n Returns\n -------\n bool\n True if x is a csr matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix_csr\n >>> isspmatrix_csr(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csr(csc_matrix([[5]]))\n False\n" - ... - -def johnson(csgraph, directed=..., indices=..., return_predecessors=..., unweighted=...) -> typing.Any: - "\n johnson(csgraph, directed=True, indices=None, return_predecessors=False,\n unweighted=False)\n\n Compute the shortest path lengths using Johnson's algorithm.\n\n Johnson's algorithm combines the Bellman-Ford algorithm and Dijkstra's\n algorithm to quickly find shortest paths in a way that is robust to\n the presence of negative cycles. If a negative cycle is detected,\n an error is raised. For graphs without negative edge weights,\n dijkstra may be faster.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array, matrix, or sparse matrix, 2 dimensions\n The N x N array of distances representing the input graph.\n directed : bool, optional\n If True (default), then find the shortest path on a directed graph:\n only move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i]\n indices : array_like or int, optional\n if specified, only compute the paths from the points at the given\n indices.\n return_predecessors : bool, optional\n If True, return the size (N, N) predecesor matrix\n unweighted : bool, optional\n If True, then find unweighted distances. That is, rather than finding\n the path between each point such that the sum of weights is minimized,\n find the path such that the number of edges is minimized.\n\n Returns\n -------\n dist_matrix : ndarray\n The N x N matrix of distances between graph nodes. dist_matrix[i,j]\n gives the shortest distance from point i to point j along the graph.\n\n predecessors : ndarray\n Returned only if return_predecessors == True.\n The N x N matrix of predecessors, which can be used to reconstruct\n the shortest paths. Row i of the predecessor matrix contains\n information on the shortest paths from point i: each entry\n predecessors[i, j] gives the index of the previous node in the\n path from point i to point j. If no path exists between point\n i and j, then predecessors[i, j] = -9999\n\n Raises\n ------\n NegativeCycleError:\n if there are negative cycles in the graph\n\n Notes\n -----\n This routine is specially designed for graphs with negative edge weights.\n If all edge weights are positive, then Dijkstra's algorithm is a better\n choice.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import johnson\n\n >>> graph = [\n ... [0, 1, 2, 0],\n ... [0, 0, 0, 1],\n ... [2, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 0)\t2\n (2, 3)\t3\n\n >>> dist_matrix, predecessors = johnson(csgraph=graph, directed=False, indices=0, return_predecessors=True)\n >>> dist_matrix\n array([ 0., 1., 2., 2.])\n >>> predecessors\n array([-9999, 0, 0, 1], dtype=int32)\n\n" - ... - -def shortest_path( - csgraph, method=..., directed=..., return_predecessors=..., unweighted=..., overwrite=..., indices=... -) -> typing.Any: - "\n shortest_path(csgraph, method='auto', directed=True, return_predecessors=False,\n unweighted=False, overwrite=False, indices=None)\n\n Perform a shortest-path graph search on a positive directed or\n undirected graph.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array, matrix, or sparse matrix, 2 dimensions\n The N x N array of distances representing the input graph.\n method : string ['auto'|'FW'|'D'], optional\n Algorithm to use for shortest paths. Options are:\n\n 'auto' -- (default) select the best among 'FW', 'D', 'BF', or 'J'\n based on the input data.\n\n 'FW' -- Floyd-Warshall algorithm. Computational cost is\n approximately ``O[N^3]``. The input csgraph will be\n converted to a dense representation.\n\n 'D' -- Dijkstra's algorithm with Fibonacci heaps. Computational\n cost is approximately ``O[N(N*k + N*log(N))]``, where\n ``k`` is the average number of connected edges per node.\n The input csgraph will be converted to a csr\n representation.\n\n 'BF' -- Bellman-Ford algorithm. This algorithm can be used when\n weights are negative. If a negative cycle is encountered,\n an error will be raised. Computational cost is\n approximately ``O[N(N^2 k)]``, where ``k`` is the average\n number of connected edges per node. The input csgraph will\n be converted to a csr representation.\n\n 'J' -- Johnson's algorithm. Like the Bellman-Ford algorithm,\n Johnson's algorithm is designed for use when the weights\n are negative. It combines the Bellman-Ford algorithm\n with Dijkstra's algorithm for faster computation.\n\n directed : bool, optional\n If True (default), then find the shortest path on a directed graph:\n only move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i]\n return_predecessors : bool, optional\n If True, return the size (N, N) predecesor matrix\n unweighted : bool, optional\n If True, then find unweighted distances. That is, rather than finding\n the path between each point such that the sum of weights is minimized,\n find the path such that the number of edges is minimized.\n overwrite : bool, optional\n If True, overwrite csgraph with the result. This applies only if\n method == 'FW' and csgraph is a dense, c-ordered array with\n dtype=float64.\n indices : array_like or int, optional\n If specified, only compute the paths from the points at the given\n indices. Incompatible with method == 'FW'.\n\n Returns\n -------\n dist_matrix : ndarray\n The N x N matrix of distances between graph nodes. dist_matrix[i,j]\n gives the shortest distance from point i to point j along the graph.\n predecessors : ndarray\n Returned only if return_predecessors == True.\n The N x N matrix of predecessors, which can be used to reconstruct\n the shortest paths. Row i of the predecessor matrix contains\n information on the shortest paths from point i: each entry\n predecessors[i, j] gives the index of the previous node in the\n path from point i to point j. If no path exists between point\n i and j, then predecessors[i, j] = -9999\n\n Raises\n ------\n NegativeCycleError:\n if there are negative cycles in the graph\n\n Notes\n -----\n As currently implemented, Dijkstra's algorithm and Johnson's algorithm\n do not work for graphs with direction-dependent distances when\n directed == False. i.e., if csgraph[i,j] and csgraph[j,i] are non-equal\n edges, method='D' may yield an incorrect result.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import shortest_path\n\n >>> graph = [\n ... [0, 1, 2, 0],\n ... [0, 0, 0, 1],\n ... [2, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 0)\t2\n (2, 3)\t3\n\n >>> dist_matrix, predecessors = shortest_path(csgraph=graph, directed=False, indices=0, return_predecessors=True)\n >>> dist_matrix\n array([ 0., 1., 2., 2.])\n >>> predecessors\n array([-9999, 0, 0, 1], dtype=int32)\n\n" - ... - -def validate_graph( - csgraph, - directed, - dtype, - csr_output, - dense_output, - copy_if_dense, - copy_if_sparse, - null_value_in, - null_value_out, - infinity_null, - nan_null, -) -> typing.Any: - "Routine for validation and conversion of csgraph inputs" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/csgraph/_tools.pyi b/stubs/scipy-stubs/sparse/csgraph/_tools.pyi deleted file mode 100644 index bf3d57b9..00000000 --- a/stubs/scipy-stubs/sparse/csgraph/_tools.pyi +++ /dev/null @@ -1,64 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.csgraph._tools, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy as _mod_numpy -import scipy.sparse.csr as _mod_scipy_sparse_csr - -DTYPE = _mod_numpy.float64 -ITYPE = _mod_numpy.int32 -__doc__: str -__file__: str -__name__: str -__package__: str -__test__: dict - -def construct_dist_matrix(graph, predecessors, directed=..., null_value=...) -> typing.Any: - "\n construct_dist_matrix(graph, predecessors, directed=True, null_value=np.inf)\n\n Construct distance matrix from a predecessor matrix\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n graph : array_like or sparse\n The N x N matrix representation of a directed or undirected graph.\n If dense, then non-edges are indicated by zeros or infinities.\n predecessors : array_like\n The N x N matrix of predecessors of each node (see Notes below).\n directed : bool, optional\n If True (default), then operate on a directed graph: only move from\n point i to point j along paths csgraph[i, j].\n If False, then operate on an undirected graph: the algorithm can\n progress from point i to j along csgraph[i, j] or csgraph[j, i].\n null_value : bool, optional\n value to use for distances between unconnected nodes. Default is\n np.inf\n\n Returns\n -------\n dist_matrix : ndarray\n The N x N matrix of distances between nodes along the path specified\n by the predecessor matrix. If no path exists, the distance is zero.\n\n Notes\n -----\n The predecessor matrix is of the form returned by\n `shortest_path`. Row i of the predecessor matrix contains\n information on the shortest paths from point i: each entry\n predecessors[i, j] gives the index of the previous node in the path from\n point i to point j. If no path exists between point i and j, then\n predecessors[i, j] = -9999\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import construct_dist_matrix\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 3)\t3\n\n >>> pred = np.array([[-9999, 0, 0, 2],\n ... [1, -9999, 0, 1],\n ... [2, 0, -9999, 2],\n ... [1, 3, 3, -9999]], dtype=np.int32)\n\n >>> construct_dist_matrix(graph=graph, predecessors=pred, directed=False)\n array([[ 0., 1., 2., 5.],\n [ 1., 0., 3., 1.],\n [ 2., 3., 0., 3.],\n [ 2., 1., 3., 0.]])\n\n" - ... - -def csgraph_from_dense(graph, null_value=..., nan_null=..., infinity_null=...) -> typing.Any: - "\n csgraph_from_dense(graph, null_value=0, nan_null=True, infinity_null=True)\n\n Construct a CSR-format sparse graph from a dense matrix.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n graph : array_like\n Input graph. Shape should be (n_nodes, n_nodes).\n null_value : float or None (optional)\n Value that denotes non-edges in the graph. Default is zero.\n infinity_null : bool\n If True (default), then infinite entries (both positive and negative)\n are treated as null edges.\n nan_null : bool\n If True (default), then NaN entries are treated as non-edges\n\n Returns\n -------\n csgraph : csr_matrix\n Compressed sparse representation of graph,\n\n Examples\n --------\n >>> from scipy.sparse.csgraph import csgraph_from_dense\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n\n >>> csgraph_from_dense(graph)\n <4x4 sparse matrix of type ''\n with 4 stored elements in Compressed Sparse Row format>\n\n" - ... - -def csgraph_from_masked(graph) -> typing.Any: - "\n csgraph_from_masked(graph)\n\n Construct a CSR-format graph from a masked array.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n graph : MaskedArray\n Input graph. Shape should be (n_nodes, n_nodes).\n\n Returns\n -------\n csgraph : csr_matrix\n Compressed sparse representation of graph,\n\n Examples\n --------\n >>> import numpy as np\n >>> from scipy.sparse.csgraph import csgraph_from_masked\n\n >>> graph_masked = np.ma.masked_array(data =[\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ],\n ... mask=[[ True, False, False , True],\n ... [ True, True , True, False],\n ... [ True , True, True ,False],\n ... [ True ,True , True , True]],\n ... fill_value = 0)\n\n >>> csgraph_from_masked(graph_masked)\n <4x4 sparse matrix of type ''\n with 4 stored elements in Compressed Sparse Row format>\n\n" - ... - -def csgraph_masked_from_dense(graph, null_value=..., nan_null=..., infinity_null=..., copy=...) -> typing.Any: - "\n csgraph_masked_from_dense(graph, null_value=0, nan_null=True,\n infinity_null=True, copy=True)\n\n Construct a masked array graph representation from a dense matrix.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n graph : array_like\n Input graph. Shape should be (n_nodes, n_nodes).\n null_value : float or None (optional)\n Value that denotes non-edges in the graph. Default is zero.\n infinity_null : bool\n If True (default), then infinite entries (both positive and negative)\n are treated as null edges.\n nan_null : bool\n If True (default), then NaN entries are treated as non-edges\n\n Returns\n -------\n csgraph : MaskedArray\n masked array representation of graph\n\n Examples\n --------\n >>> from scipy.sparse.csgraph import csgraph_masked_from_dense\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n\n >>> csgraph_masked_from_dense(graph)\n masked_array(\n data=[[--, 1, 2, --],\n [--, --, --, 1],\n [--, --, --, 3],\n [--, --, --, --]],\n mask=[[ True, False, False, True],\n [ True, True, True, False],\n [ True, True, True, False],\n [ True, True, True, True]],\n fill_value=0)\n\n" - ... - -def csgraph_to_dense(csgraph, null_value=...) -> typing.Any: - "\n csgraph_to_dense(csgraph, null_value=0)\n\n Convert a sparse graph representation to a dense representation\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : csr_matrix, csc_matrix, or lil_matrix\n Sparse representation of a graph.\n null_value : float, optional\n The value used to indicate null edges in the dense representation.\n Default is 0.\n\n Returns\n -------\n graph : ndarray\n The dense representation of the sparse graph.\n\n Notes\n -----\n For normal sparse graph representations, calling csgraph_to_dense with\n null_value=0 produces an equivalent result to using dense format\n conversions in the main sparse package. When the sparse representations\n have repeated values, however, the results will differ. The tools in\n scipy.sparse will add repeating values to obtain a final value. This\n function will select the minimum among repeating values to obtain a\n final value. For example, here we'll create a two-node directed sparse\n graph with multiple edges from node 0 to node 1, of weights 2 and 3.\n This illustrates the difference in behavior:\n\n >>> from scipy.sparse import csr_matrix, csgraph\n >>> data = np.array([2, 3])\n >>> indices = np.array([1, 1])\n >>> indptr = np.array([0, 2, 2])\n >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))\n >>> M.toarray()\n array([[0, 5],\n [0, 0]])\n >>> csgraph.csgraph_to_dense(M)\n array([[0., 2.],\n [0., 0.]])\n\n The reason for this difference is to allow a compressed sparse graph to\n represent multiple edges between any two nodes. As most sparse graph\n algorithms are concerned with the single lowest-cost edge between any\n two nodes, the default scipy.sparse behavior of summming multiple weights\n does not make sense in this context.\n\n The other reason for using this routine is to allow for graphs with\n zero-weight edges. Let's look at the example of a two-node directed\n graph, connected by an edge of weight zero:\n\n >>> from scipy.sparse import csr_matrix, csgraph\n >>> data = np.array([0.0])\n >>> indices = np.array([1])\n >>> indptr = np.array([0, 1, 1])\n >>> M = csr_matrix((data, indices, indptr), shape=(2, 2))\n >>> M.toarray()\n array([[0, 0],\n [0, 0]])\n >>> csgraph.csgraph_to_dense(M, np.inf)\n array([[ inf, 0.],\n [ inf, inf]])\n\n In the first case, the zero-weight edge gets lost in the dense\n representation. In the second case, we can choose a different null value\n and see the true form of the graph.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import csgraph_to_dense\n\n >>> graph = csr_matrix( [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ])\n >>> graph\n <4x4 sparse matrix of type ''\n with 4 stored elements in Compressed Sparse Row format>\n\n >>> csgraph_to_dense(graph)\n array([[ 0., 1., 2., 0.],\n [ 0., 0., 0., 1.],\n [ 0., 0., 0., 3.],\n [ 0., 0., 0., 0.]])\n\n" - ... - -def csgraph_to_masked(csgraph) -> typing.Any: - "\n csgraph_to_masked(csgraph)\n\n Convert a sparse graph representation to a masked array representation\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : csr_matrix, csc_matrix, or lil_matrix\n Sparse representation of a graph.\n\n Returns\n -------\n graph : MaskedArray\n The masked dense representation of the sparse graph.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import csgraph_to_masked\n\n >>> graph = csr_matrix( [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ])\n >>> graph\n <4x4 sparse matrix of type ''\n with 4 stored elements in Compressed Sparse Row format>\n\n >>> csgraph_to_masked(graph)\n masked_array(\n data=[[--, 1.0, 2.0, --],\n [--, --, --, 1.0],\n [--, --, --, 3.0],\n [--, --, --, --]],\n mask=[[ True, False, False, True],\n [ True, True, True, False],\n [ True, True, True, False],\n [ True, True, True, True]],\n fill_value=1e+20)\n\n" - ... - -csr_matrix = _mod_scipy_sparse_csr.csr_matrix - -def isspmatrix(x) -> typing.Any: - "Is x of a sparse matrix type?\n\n Parameters\n ----------\n x\n object to check for being a sparse matrix\n\n Returns\n -------\n bool\n True if x is a sparse matrix, False otherwise\n\n Notes\n -----\n issparse and isspmatrix are aliases for the same function.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix\n >>> isspmatrix(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import isspmatrix\n >>> isspmatrix(5)\n False\n" - ... - -def isspmatrix_csc(x) -> typing.Any: - "Is x of csc_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csc matrix\n\n Returns\n -------\n bool\n True if x is a csc matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csc_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csc_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csr_matrix([[5]]))\n False\n" - ... - -def isspmatrix_csr(x) -> typing.Any: - "Is x of csr_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csr matrix\n\n Returns\n -------\n bool\n True if x is a csr matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix_csr\n >>> isspmatrix_csr(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csr(csc_matrix([[5]]))\n False\n" - ... - -def isspmatrix_lil(x) -> typing.Any: - "Is x of lil_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a lil matrix\n\n Returns\n -------\n bool\n True if x is a lil matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import lil_matrix, isspmatrix_lil\n >>> isspmatrix_lil(lil_matrix([[5]]))\n True\n\n >>> from scipy.sparse import lil_matrix, csr_matrix, isspmatrix_lil\n >>> isspmatrix_lil(csr_matrix([[5]]))\n False\n" - ... - -def reconstruct_path(csgraph, predecessors, directed=...) -> typing.Any: - "\n reconstruct_path(csgraph, predecessors, directed=True)\n\n Construct a tree from a graph and a predecessor list.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array_like or sparse matrix\n The N x N matrix representing the directed or undirected graph\n from which the predecessors are drawn.\n predecessors : array_like, one dimension\n The length-N array of indices of predecessors for the tree. The\n index of the parent of node i is given by predecessors[i].\n directed : bool, optional\n If True (default), then operate on a directed graph: only move from\n point i to point j along paths csgraph[i, j].\n If False, then operate on an undirected graph: the algorithm can\n progress from point i to j along csgraph[i, j] or csgraph[j, i].\n\n Returns\n -------\n cstree : csr matrix\n The N x N directed compressed-sparse representation of the tree drawn\n from csgraph which is encoded by the predecessor list.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import reconstruct_path\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 3)\t3\n\n >>> pred = np.array([-9999, 0, 0, 1], dtype=np.int32)\n\n >>> cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False)\n >>> cstree.todense()\n matrix([[ 0., 1., 2., 0.],\n [ 0., 0., 0., 1.],\n [ 0., 0., 0., 0.],\n [ 0., 0., 0., 0.]])\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/csgraph/_traversal.pyi b/stubs/scipy-stubs/sparse/csgraph/_traversal.pyi deleted file mode 100644 index 7dd3fac2..00000000 --- a/stubs/scipy-stubs/sparse/csgraph/_traversal.pyi +++ /dev/null @@ -1,72 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.csgraph._traversal, version: unspecified -import builtins as _mod_builtins -import typing - -import numpy as _mod_numpy -import scipy.sparse.csr as _mod_scipy_sparse_csr - -DTYPE = _mod_numpy.float64 -ITYPE = _mod_numpy.int32 -__doc__: str -__file__: str -__name__: str -__package__: str -__test__: dict - -def breadth_first_order(csgraph, i_start, directed=..., return_predecessors=...) -> typing.Any: - "\n breadth_first_order(csgraph, i_start, directed=True, return_predecessors=True)\n\n Return a breadth-first ordering starting with specified node.\n\n Note that a breadth-first order is not unique, but the tree which it\n generates is unique.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array_like or sparse matrix\n The N x N compressed sparse graph. The input csgraph will be\n converted to csr format for the calculation.\n i_start : int\n The index of starting node.\n directed : bool, optional\n If True (default), then operate on a directed graph: only\n move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i].\n return_predecessors : bool, optional\n If True (default), then return the predecesor array (see below).\n\n Returns\n -------\n node_array : ndarray, one dimension\n The breadth-first list of nodes, starting with specified node. The\n length of node_array is the number of nodes reachable from the\n specified node.\n predecessors : ndarray, one dimension\n Returned only if return_predecessors is True.\n The length-N list of predecessors of each node in a breadth-first\n tree. If node i is in the tree, then its parent is given by\n predecessors[i]. If node i is not in the tree (and for the parent\n node) then predecessors[i] = -9999.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import breadth_first_order\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [2, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1) 1\n (0, 2) 2\n (1, 3) 1\n (2, 0) 2\n (2, 3) 3\n\n >>> breadth_first_order(graph,0)\n (array([0, 1, 2, 3], dtype=int32), array([-9999, 0, 0, 1], dtype=int32))\n\n" - ... - -def breadth_first_tree(csgraph, i_start, directed=...) -> typing.Any: - "\n breadth_first_tree(csgraph, i_start, directed=True)\n\n Return the tree generated by a breadth-first search\n\n Note that a breadth-first tree from a specified node is unique.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array_like or sparse matrix\n The N x N matrix representing the compressed sparse graph. The input\n csgraph will be converted to csr format for the calculation.\n i_start : int\n The index of starting node.\n directed : bool, optional\n If True (default), then operate on a directed graph: only\n move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i].\n\n Returns\n -------\n cstree : csr matrix\n The N x N directed compressed-sparse representation of the breadth-\n first tree drawn from csgraph, starting at the specified node.\n\n Examples\n --------\n The following example shows the computation of a depth-first tree\n over a simple four-component graph, starting at node 0::\n\n input graph breadth first tree from (0)\n\n (0) (0)\n / \\ / \\\n 3 8 3 8\n / \\ / \\\n (3)---5---(1) (3) (1)\n \\ / /\n 6 2 2\n \\ / /\n (2) (2)\n\n In compressed sparse representation, the solution looks like this:\n\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import breadth_first_tree\n >>> X = csr_matrix([[0, 8, 0, 3],\n ... [0, 0, 2, 5],\n ... [0, 0, 0, 6],\n ... [0, 0, 0, 0]])\n >>> Tcsr = breadth_first_tree(X, 0, directed=False)\n >>> Tcsr.toarray().astype(int)\n array([[0, 8, 0, 3],\n [0, 0, 2, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0]])\n\n Note that the resulting graph is a Directed Acyclic Graph which spans\n the graph. A breadth-first tree from a given node is unique.\n" - ... - -def connected_components(csgraph, directed=..., connection=..., return_labels=...) -> typing.Any: - "\n connected_components(csgraph, directed=True, connection='weak',\n return_labels=True)\n\n Analyze the connected components of a sparse graph\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array_like or sparse matrix\n The N x N matrix representing the compressed sparse graph. The input\n csgraph will be converted to csr format for the calculation.\n directed : bool, optional\n If True (default), then operate on a directed graph: only\n move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i].\n connection : str, optional\n ['weak'|'strong']. For directed graphs, the type of connection to\n use. Nodes i and j are strongly connected if a path exists both\n from i to j and from j to i. A directed graph is weakly connected\n if replacing all of its directed edges with undirected edges produces\n a connected (undirected) graph. If directed == False, this keyword\n is not referenced.\n return_labels : bool, optional\n If True (default), then return the labels for each of the connected\n components.\n\n Returns\n -------\n n_components: int\n The number of connected components.\n labels: ndarray\n The length-N array of labels of the connected components.\n\n References\n ----------\n .. [1] D. J. Pearce, \"An Improved Algorithm for Finding the Strongly\n Connected Components of a Directed Graph\", Technical Report, 2005\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import connected_components\n\n >>> graph = [\n ... [ 0, 1 , 1, 0 , 0 ],\n ... [ 0, 0 , 1 , 0 ,0 ],\n ... [ 0, 0, 0, 0, 0],\n ... [0, 0 , 0, 0, 1],\n ... [0, 0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t1\n (1, 2)\t1\n (3, 4)\t1\n\n >>> n_components, labels = connected_components(csgraph=graph, directed=False, return_labels=True)\n >>> n_components\n 2\n >>> labels\n array([0, 0, 0, 1, 1], dtype=int32)\n\n" - ... - -csr_matrix = _mod_scipy_sparse_csr.csr_matrix - -def depth_first_order(csgraph, i_start, directed=..., return_predecessors=...) -> typing.Any: - "\n depth_first_order(csgraph, i_start, directed=True, return_predecessors=True)\n\n Return a depth-first ordering starting with specified node.\n\n Note that a depth-first order is not unique. Furthermore, for graphs\n with cycles, the tree generated by a depth-first search is not\n unique either.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array_like or sparse matrix\n The N x N compressed sparse graph. The input csgraph will be\n converted to csr format for the calculation.\n i_start : int\n The index of starting node.\n directed : bool, optional\n If True (default), then operate on a directed graph: only\n move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i].\n return_predecessors : bool, optional\n If True (default), then return the predecesor array (see below).\n\n Returns\n -------\n node_array : ndarray, one dimension\n The depth-first list of nodes, starting with specified node. The\n length of node_array is the number of nodes reachable from the\n specified node.\n predecessors : ndarray, one dimension\n Returned only if return_predecessors is True.\n The length-N list of predecessors of each node in a depth-first\n tree. If node i is in the tree, then its parent is given by\n predecessors[i]. If node i is not in the tree (and for the parent\n node) then predecessors[i] = -9999.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import depth_first_order\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [2, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 0)\t2\n (2, 3)\t3\n\n >>> depth_first_order(graph,0)\n (array([0, 1, 3, 2], dtype=int32), array([-9999, 0, 0, 1], dtype=int32))\n\n" - ... - -def depth_first_tree(csgraph, i_start, directed=...) -> typing.Any: - "\n depth_first_tree(csgraph, i_start, directed=True)\n\n Return a tree generated by a depth-first search.\n\n Note that a tree generated by a depth-first search is not unique:\n it depends on the order that the children of each node are searched.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array_like or sparse matrix\n The N x N matrix representing the compressed sparse graph. The input\n csgraph will be converted to csr format for the calculation.\n i_start : int\n The index of starting node.\n directed : bool, optional\n If True (default), then operate on a directed graph: only\n move from point i to point j along paths csgraph[i, j].\n If False, then find the shortest path on an undirected graph: the\n algorithm can progress from point i to j along csgraph[i, j] or\n csgraph[j, i].\n\n Returns\n -------\n cstree : csr matrix\n The N x N directed compressed-sparse representation of the depth-\n first tree drawn from csgraph, starting at the specified node.\n\n Examples\n --------\n The following example shows the computation of a depth-first tree\n over a simple four-component graph, starting at node 0::\n\n input graph depth first tree from (0)\n\n (0) (0)\n / \\ \\\n 3 8 8\n / \\ \\\n (3)---5---(1) (3) (1)\n \\ / \\ /\n 6 2 6 2\n \\ / \\ /\n (2) (2)\n\n In compressed sparse representation, the solution looks like this:\n\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import depth_first_tree\n >>> X = csr_matrix([[0, 8, 0, 3],\n ... [0, 0, 2, 5],\n ... [0, 0, 0, 6],\n ... [0, 0, 0, 0]])\n >>> Tcsr = depth_first_tree(X, 0, directed=False)\n >>> Tcsr.toarray().astype(int)\n array([[0, 8, 0, 0],\n [0, 0, 2, 0],\n [0, 0, 0, 6],\n [0, 0, 0, 0]])\n\n Note that the resulting graph is a Directed Acyclic Graph which spans\n the graph. Unlike a breadth-first tree, a depth-first tree of a given\n graph is not unique if the graph contains cycles. If the above solution\n had begun with the edge connecting nodes 0 and 3, the result would have\n been different.\n" - ... - -def isspmatrix(x) -> typing.Any: - "Is x of a sparse matrix type?\n\n Parameters\n ----------\n x\n object to check for being a sparse matrix\n\n Returns\n -------\n bool\n True if x is a sparse matrix, False otherwise\n\n Notes\n -----\n issparse and isspmatrix are aliases for the same function.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix\n >>> isspmatrix(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import isspmatrix\n >>> isspmatrix(5)\n False\n" - ... - -def isspmatrix_csc(x) -> typing.Any: - "Is x of csc_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csc matrix\n\n Returns\n -------\n bool\n True if x is a csc matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csc_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csc_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csc(csr_matrix([[5]]))\n False\n" - ... - -def isspmatrix_csr(x) -> typing.Any: - "Is x of csr_matrix type?\n\n Parameters\n ----------\n x\n object to check for being a csr matrix\n\n Returns\n -------\n bool\n True if x is a csr matrix, False otherwise\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix, isspmatrix_csr\n >>> isspmatrix_csr(csr_matrix([[5]]))\n True\n\n >>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc\n >>> isspmatrix_csr(csc_matrix([[5]]))\n False\n" - ... - -def reconstruct_path(csgraph, predecessors, directed=...) -> typing.Any: - "\n reconstruct_path(csgraph, predecessors, directed=True)\n\n Construct a tree from a graph and a predecessor list.\n\n .. versionadded:: 0.11.0\n\n Parameters\n ----------\n csgraph : array_like or sparse matrix\n The N x N matrix representing the directed or undirected graph\n from which the predecessors are drawn.\n predecessors : array_like, one dimension\n The length-N array of indices of predecessors for the tree. The\n index of the parent of node i is given by predecessors[i].\n directed : bool, optional\n If True (default), then operate on a directed graph: only move from\n point i to point j along paths csgraph[i, j].\n If False, then operate on an undirected graph: the algorithm can\n progress from point i to j along csgraph[i, j] or csgraph[j, i].\n\n Returns\n -------\n cstree : csr matrix\n The N x N directed compressed-sparse representation of the tree drawn\n from csgraph which is encoded by the predecessor list.\n\n Examples\n --------\n >>> from scipy.sparse import csr_matrix\n >>> from scipy.sparse.csgraph import reconstruct_path\n\n >>> graph = [\n ... [0, 1 , 2, 0],\n ... [0, 0, 0, 1],\n ... [0, 0, 0, 3],\n ... [0, 0, 0, 0]\n ... ]\n >>> graph = csr_matrix(graph)\n >>> print(graph)\n (0, 1)\t1\n (0, 2)\t2\n (1, 3)\t1\n (2, 3)\t3\n\n >>> pred = np.array([-9999, 0, 0, 1], dtype=np.int32)\n\n >>> cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False)\n >>> cstree.todense()\n matrix([[ 0., 1., 2., 0.],\n [ 0., 0., 0., 1.],\n [ 0., 0., 0., 0.],\n [ 0., 0., 0., 0.]])\n\n" - ... - -def validate_graph( - csgraph, - directed, - dtype, - csr_output, - dense_output, - copy_if_dense, - copy_if_sparse, - null_value_in, - null_value_out, - infinity_null, - nan_null, -) -> typing.Any: - "Routine for validation and conversion of csgraph inputs" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/linalg/_eigen/lobpcg/lobpcg.pyi b/stubs/scipy-stubs/sparse/linalg/_eigen/lobpcg/lobpcg.pyi deleted file mode 100644 index 92efbc31..00000000 --- a/stubs/scipy-stubs/sparse/linalg/_eigen/lobpcg/lobpcg.pyi +++ /dev/null @@ -1,170 +0,0 @@ -import typing - -def lobpcg( - A, - X, - B=..., - M=..., - Y=..., - tol=..., - maxiter=..., - largest=..., - verbosityLevel=..., - retLambdaHistory=..., - retResidualNormsHistory=..., -) -> typing.Any: - """Locally Optimal Block Preconditioned Conjugate Gradient Method (LOBPCG) - - LOBPCG is a preconditioned eigensolver for large symmetric positive - definite (SPD) generalized eigenproblems. - - Parameters - ---------- - A : {sparse matrix, dense matrix, LinearOperator} - The symmetric linear operator of the problem, usually a - sparse matrix. Often called the "stiffness matrix". - X : ndarray, float32 or float64 - Initial approximation to the ``k`` eigenvectors (non-sparse). If `A` - has ``shape=(n,n)`` then `X` should have shape ``shape=(n,k)``. - B : {dense matrix, sparse matrix, LinearOperator}, optional - The right hand side operator in a generalized eigenproblem. - By default, ``B = Identity``. Often called the "mass matrix". - M : {dense matrix, sparse matrix, LinearOperator}, optional - Preconditioner to `A`; by default ``M = Identity``. - `M` should approximate the inverse of `A`. - Y : ndarray, float32 or float64, optional - n-by-sizeY matrix of constraints (non-sparse), sizeY < n - The iterations will be performed in the B-orthogonal complement - of the column-space of Y. Y must be full rank. - tol : scalar, optional - Solver tolerance (stopping criterion). - The default is ``tol=n*sqrt(eps)``. - maxiter : int, optional - Maximum number of iterations. The default is ``maxiter = 20``. - largest : bool, optional - When True, solve for the largest eigenvalues, otherwise the smallest. - verbosityLevel : int, optional - Controls solver output. The default is ``verbosityLevel=0``. - retLambdaHistory : bool, optional - Whether to return eigenvalue history. Default is False. - retResidualNormsHistory : bool, optional - Whether to return history of residual norms. Default is False. - - Returns - ------- - w : ndarray - Array of ``k`` eigenvalues - v : ndarray - An array of ``k`` eigenvectors. `v` has the same shape as `X`. - lambdas : list of ndarray, optional - The eigenvalue history, if `retLambdaHistory` is True. - rnorms : list of ndarray, optional - The history of residual norms, if `retResidualNormsHistory` is True. - - Notes - ----- - If both ``retLambdaHistory`` and ``retResidualNormsHistory`` are True, - the return tuple has the following format - ``(lambda, V, lambda history, residual norms history)``. - - In the following ``n`` denotes the matrix size and ``m`` the number - of required eigenvalues (smallest or largest). - - The LOBPCG code internally solves eigenproblems of the size ``3m`` on every - iteration by calling the "standard" dense eigensolver, so if ``m`` is not - small enough compared to ``n``, it does not make sense to call the LOBPCG - code, but rather one should use the "standard" eigensolver, e.g. numpy or - scipy function in this case. - If one calls the LOBPCG algorithm for ``5m > n``, it will most likely break - internally, so the code tries to call the standard function instead. - - It is not that ``n`` should be large for the LOBPCG to work, but rather the - ratio ``n / m`` should be large. It you call LOBPCG with ``m=1`` - and ``n=10``, it works though ``n`` is small. The method is intended - for extremely large ``n / m``. - - The convergence speed depends basically on two factors: - - 1. How well relatively separated the seeking eigenvalues are from the rest - of the eigenvalues. One can try to vary ``m`` to make this better. - - 2. How well conditioned the problem is. This can be changed by using proper - preconditioning. For example, a rod vibration test problem (under tests - directory) is ill-conditioned for large ``n``, so convergence will be - slow, unless efficient preconditioning is used. For this specific - problem, a good simple preconditioner function would be a linear solve - for `A`, which is easy to code since A is tridiagonal. - - References - ---------- - .. [1] A. V. Knyazev (2001), - Toward the Optimal Preconditioned Eigensolver: Locally Optimal - Block Preconditioned Conjugate Gradient Method. - SIAM Journal on Scientific Computing 23, no. 2, - pp. 517-541. :doi:`10.1137/S1064827500366124` - - .. [2] A. V. Knyazev, I. Lashuk, M. E. Argentati, and E. Ovchinnikov - (2007), Block Locally Optimal Preconditioned Eigenvalue Xolvers - (BLOPEX) in hypre and PETSc. :arxiv:`0705.2626` - - .. [3] A. V. Knyazev's C and MATLAB implementations: - https://github.com/lobpcg/blopex - - Examples - -------- - - Solve ``A x = lambda x`` with constraints and preconditioning. - - >>> import numpy as np - >>> from scipy.sparse import spdiags, issparse - >>> from scipy.sparse.linalg import lobpcg, LinearOperator - >>> n = 100 - >>> vals = np.arange(1, n + 1) - >>> A = spdiags(vals, 0, n, n) - >>> A.toarray() - array([[ 1., 0., 0., ..., 0., 0., 0.], - [ 0., 2., 0., ..., 0., 0., 0.], - [ 0., 0., 3., ..., 0., 0., 0.], - ..., - [ 0., 0., 0., ..., 98., 0., 0.], - [ 0., 0., 0., ..., 0., 99., 0.], - [ 0., 0., 0., ..., 0., 0., 100.]]) - - Constraints: - - >>> Y = np.eye(n, 3) - - Initial guess for eigenvectors, should have linearly independent - columns. Column dimension = number of requested eigenvalues. - - >>> rng = np.random.default_rng() - >>> X = rng.random((n, 3)) - - Preconditioner in the inverse of A in this example: - - >>> invA = spdiags([1./vals], 0, n, n) - - The preconditiner must be defined by a function: - - >>> def precond( x ): - ... return invA @ x - - The argument x of the preconditioner function is a matrix inside `lobpcg`, - thus the use of matrix-matrix product ``@``. - - The preconditioner function is passed to lobpcg as a `LinearOperator`: - - >>> M = LinearOperator(matvec=precond, matmat=precond, - ... shape=(n, n), dtype=np.float64) - - Let us now solve the eigenvalue problem for the matrix A: - - >>> eigenvalues, _ = lobpcg(A, X, Y=Y, M=M, largest=False) - >>> eigenvalues - array([4., 5., 6.]) - - Note that the vectors passed in Y are the eigenvectors of the 3 smallest - eigenvalues. The results returned are orthogonal to those. - - """ - ... diff --git a/stubs/scipy-stubs/sparse/linalg/dsolve/_superlu.pyi b/stubs/scipy-stubs/sparse/linalg/dsolve/_superlu.pyi deleted file mode 100644 index 48c6cd9b..00000000 --- a/stubs/scipy-stubs/sparse/linalg/dsolve/_superlu.pyi +++ /dev/null @@ -1,21 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.linalg.dsolve._superlu, version: unspecified -import builtins as _mod_builtins -import typing - -SuperLU = _mod_builtins.SuperLU -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def gssv() -> typing.Any: - "Direct inversion of sparse matrix.\n\nX = gssv(A,B) solves A*X = B for X." - ... - -def gstrf() -> typing.Any: - "gstrf(A, ...)\n\nperforms a factorization of the sparse matrix A=*(N,nnz,nzvals,rowind,colptr) and \nreturns a factored_lu object.\n\narguments\n---------\n\nMatrix to be factorized is represented as N,nnz,nzvals,rowind,colptr\n as separate arguments. This is compressed sparse column representation.\n\nN number of rows and columns \nnnz number of non-zero elements\nnzvals non-zero values \nrowind row-index for this column (same size as nzvals)\ncolptr index into rowind for first non-zero value in this column\n size is (N+1). Last value should be nnz. \n\nadditional keyword arguments:\n-----------------------------\noptions specifies additional options for SuperLU\n (same keys and values as in superlu_options_t C structure,\n and additionally 'Relax' and 'PanelSize')\n\nilu whether to perform an incomplete LU decomposition\n (default: false)\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/linalg/eigen/arpack/_arpack.pyi b/stubs/scipy-stubs/sparse/linalg/eigen/arpack/_arpack.pyi deleted file mode 100644 index b98d36cc..00000000 --- a/stubs/scipy-stubs/sparse/linalg/eigen/arpack/_arpack.pyi +++ /dev/null @@ -1,215 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.linalg.eigen.arpack._arpack, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def cnaupd( - ido, bmat, which, nev, tol, resid, v, iparam, ipntr, workd, workl, rwork, info, n=..., ncv=..., ldv=..., lworkl=... -) -> typing.Any: - "ido,tol,resid,v,iparam,ipntr,info = cnaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[n,ncv,ldv,lworkl])\n\nWrapper for ``cnaupd``.\n\nParameters\n----------\nido : input int\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('F') with bounds (n)\nv : input rank-2 array('F') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (14)\nworkd : in/output rank-1 array('F') with bounds (3 * n)\nworkl : in/output rank-1 array('F') with bounds (lworkl)\nrwork : in/output rank-1 array('f') with bounds (ncv)\ninfo : input int\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: shape(v,1)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nido : int\ntol : float\nresid : rank-1 array('F') with bounds (n)\nv : rank-2 array('F') with bounds (ldv,ncv)\niparam : rank-1 array('i') with bounds (11)\nipntr : rank-1 array('i') with bounds (14)\ninfo : int\n" - ... - -def cneupd( - rvec, - howmny, - select, - sigma, - workev, - bmat, - which, - nev, - tol, - resid, - v, - iparam, - ipntr, - workd, - workl, - rwork, - info, - ldz=..., - n=..., - ncv=..., - ldv=..., - lworkl=..., -) -> typing.Any: - "d,z,info = cneupd(rvec,howmny,select,sigma,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[ldz,n,ncv,ldv,lworkl])\n\nWrapper for ``cneupd``.\n\nParameters\n----------\nrvec : input int\nhowmny : input string(len=1)\nselect : input rank-1 array('i') with bounds (ncv)\nsigma : input complex\nworkev : input rank-1 array('F') with bounds (3 * ncv)\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('F') with bounds (n)\nv : input rank-2 array('F') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (14)\nworkd : input rank-1 array('F') with bounds (3 * n)\nworkl : input rank-1 array('F') with bounds (lworkl)\nrwork : input rank-1 array('f') with bounds (ncv)\ninfo : input int\n\nOther Parameters\n----------------\nldz : input int, optional\n Default: shape(z,0)\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: len(select)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nd : rank-1 array('F') with bounds (nev)\nz : rank-2 array('F') with bounds (n,nev)\ninfo : int\n" - ... - -def debug() -> typing.Any: - "'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n" - ... - -def dnaupd( - ido, bmat, which, nev, tol, resid, v, iparam, ipntr, workd, workl, info, n=..., ncv=..., ldv=..., lworkl=... -) -> typing.Any: - "ido,tol,resid,v,iparam,ipntr,info = dnaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])\n\nWrapper for ``dnaupd``.\n\nParameters\n----------\nido : input int\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('d') with bounds (n)\nv : input rank-2 array('d') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (14)\nworkd : in/output rank-1 array('d') with bounds (3 * n)\nworkl : in/output rank-1 array('d') with bounds (lworkl)\ninfo : input int\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: shape(v,1)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nido : int\ntol : float\nresid : rank-1 array('d') with bounds (n)\nv : rank-2 array('d') with bounds (ldv,ncv)\niparam : rank-1 array('i') with bounds (11)\nipntr : rank-1 array('i') with bounds (14)\ninfo : int\n" - ... - -def dneupd( - rvec, - howmny, - select, - sigmar, - sigmai, - workev, - bmat, - which, - nev, - tol, - resid, - v, - iparam, - ipntr, - workd, - workl, - info, - ldz=..., - n=..., - ncv=..., - ldv=..., - lworkl=..., -) -> typing.Any: - "dr,di,z,info = dneupd(rvec,howmny,select,sigmar,sigmai,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])\n\nWrapper for ``dneupd``.\n\nParameters\n----------\nrvec : input int\nhowmny : input string(len=1)\nselect : input rank-1 array('i') with bounds (ncv)\nsigmar : input float\nsigmai : input float\nworkev : input rank-1 array('d') with bounds (3 * ncv)\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('d') with bounds (n)\nv : input rank-2 array('d') with bounds (n,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (14)\nworkd : input rank-1 array('d') with bounds (3 * n)\nworkl : input rank-1 array('d') with bounds (lworkl)\ninfo : input int\n\nOther Parameters\n----------------\nldz : input int, optional\n Default: shape(z,0)\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: len(select)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\ndr : rank-1 array('d') with bounds (nev + 1)\ndi : rank-1 array('d') with bounds (nev + 1)\nz : rank-2 array('d') with bounds (n,nev + 1)\ninfo : int\n" - ... - -def dsaupd( - ido, bmat, which, nev, tol, resid, v, iparam, ipntr, workd, workl, info, n=..., ncv=..., ldv=..., lworkl=... -) -> typing.Any: - "ido,tol,resid,v,iparam,ipntr,info = dsaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])\n\nWrapper for ``dsaupd``.\n\nParameters\n----------\nido : input int\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('d') with bounds (n)\nv : input rank-2 array('d') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (11)\nworkd : in/output rank-1 array('d') with bounds (3 * n)\nworkl : in/output rank-1 array('d') with bounds (lworkl)\ninfo : input int\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: shape(v,1)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nido : int\ntol : float\nresid : rank-1 array('d') with bounds (n)\nv : rank-2 array('d') with bounds (ldv,ncv)\niparam : rank-1 array('i') with bounds (11)\nipntr : rank-1 array('i') with bounds (11)\ninfo : int\n" - ... - -def dseupd( - rvec, - howmny, - select, - sigma, - bmat, - which, - nev, - tol, - resid, - v, - iparam, - ipntr, - workd, - workl, - info, - ldz=..., - n=..., - ncv=..., - ldv=..., - lworkl=..., -) -> typing.Any: - "d,z,info = dseupd(rvec,howmny,select,sigma,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])\n\nWrapper for ``dseupd``.\n\nParameters\n----------\nrvec : input int\nhowmny : input string(len=1)\nselect : input rank-1 array('i') with bounds (ncv)\nsigma : input float\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('d') with bounds (n)\nv : input rank-2 array('d') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (7)\nipntr : input rank-1 array('i') with bounds (11)\nworkd : input rank-1 array('d') with bounds (2 * n)\nworkl : input rank-1 array('d') with bounds (lworkl)\ninfo : input int\n\nOther Parameters\n----------------\nldz : input int, optional\n Default: shape(z,0)\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: len(select)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nd : rank-1 array('d') with bounds (nev)\nz : rank-2 array('d') with bounds (n,nev)\ninfo : int\n" - ... - -def snaupd( - ido, bmat, which, nev, tol, resid, v, iparam, ipntr, workd, workl, info, n=..., ncv=..., ldv=..., lworkl=... -) -> typing.Any: - "ido,tol,resid,v,iparam,ipntr,info = snaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])\n\nWrapper for ``snaupd``.\n\nParameters\n----------\nido : input int\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('f') with bounds (n)\nv : input rank-2 array('f') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (14)\nworkd : in/output rank-1 array('f') with bounds (3 * n)\nworkl : in/output rank-1 array('f') with bounds (lworkl)\ninfo : input int\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: shape(v,1)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nido : int\ntol : float\nresid : rank-1 array('f') with bounds (n)\nv : rank-2 array('f') with bounds (ldv,ncv)\niparam : rank-1 array('i') with bounds (11)\nipntr : rank-1 array('i') with bounds (14)\ninfo : int\n" - ... - -def sneupd( - rvec, - howmny, - select, - sigmar, - sigmai, - workev, - bmat, - which, - nev, - tol, - resid, - v, - iparam, - ipntr, - workd, - workl, - info, - ldz=..., - n=..., - ncv=..., - ldv=..., - lworkl=..., -) -> typing.Any: - "dr,di,z,info = sneupd(rvec,howmny,select,sigmar,sigmai,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])\n\nWrapper for ``sneupd``.\n\nParameters\n----------\nrvec : input int\nhowmny : input string(len=1)\nselect : input rank-1 array('i') with bounds (ncv)\nsigmar : input float\nsigmai : input float\nworkev : input rank-1 array('f') with bounds (3 * ncv)\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('f') with bounds (n)\nv : input rank-2 array('f') with bounds (n,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (14)\nworkd : input rank-1 array('f') with bounds (3 * n)\nworkl : input rank-1 array('f') with bounds (lworkl)\ninfo : input int\n\nOther Parameters\n----------------\nldz : input int, optional\n Default: shape(z,0)\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: len(select)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\ndr : rank-1 array('f') with bounds (nev + 1)\ndi : rank-1 array('f') with bounds (nev + 1)\nz : rank-2 array('f') with bounds (n,nev + 1)\ninfo : int\n" - ... - -def ssaupd( - ido, bmat, which, nev, tol, resid, v, iparam, ipntr, workd, workl, info, n=..., ncv=..., ldv=..., lworkl=... -) -> typing.Any: - "ido,tol,resid,v,iparam,ipntr,info = ssaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[n,ncv,ldv,lworkl])\n\nWrapper for ``ssaupd``.\n\nParameters\n----------\nido : input int\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('f') with bounds (n)\nv : input rank-2 array('f') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (11)\nworkd : in/output rank-1 array('f') with bounds (3 * n)\nworkl : in/output rank-1 array('f') with bounds (lworkl)\ninfo : input int\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: shape(v,1)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nido : int\ntol : float\nresid : rank-1 array('f') with bounds (n)\nv : rank-2 array('f') with bounds (ldv,ncv)\niparam : rank-1 array('i') with bounds (11)\nipntr : rank-1 array('i') with bounds (11)\ninfo : int\n" - ... - -def sseupd( - rvec, - howmny, - select, - sigma, - bmat, - which, - nev, - tol, - resid, - v, - iparam, - ipntr, - workd, - workl, - info, - ldz=..., - n=..., - ncv=..., - ldv=..., - lworkl=..., -) -> typing.Any: - "d,z,info = sseupd(rvec,howmny,select,sigma,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,info,[ldz,n,ncv,ldv,lworkl])\n\nWrapper for ``sseupd``.\n\nParameters\n----------\nrvec : input int\nhowmny : input string(len=1)\nselect : input rank-1 array('i') with bounds (ncv)\nsigma : input float\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('f') with bounds (n)\nv : input rank-2 array('f') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (7)\nipntr : input rank-1 array('i') with bounds (11)\nworkd : input rank-1 array('f') with bounds (2 * n)\nworkl : input rank-1 array('f') with bounds (lworkl)\ninfo : input int\n\nOther Parameters\n----------------\nldz : input int, optional\n Default: shape(z,0)\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: len(select)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nd : rank-1 array('f') with bounds (nev)\nz : rank-2 array('f') with bounds (n,nev)\ninfo : int\n" - ... - -def timing() -> typing.Any: - "'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'i'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n'f'-scalar\n" - ... - -def znaupd( - ido, bmat, which, nev, tol, resid, v, iparam, ipntr, workd, workl, rwork, info, n=..., ncv=..., ldv=..., lworkl=... -) -> typing.Any: - "ido,tol,resid,v,iparam,ipntr,info = znaupd(ido,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[n,ncv,ldv,lworkl])\n\nWrapper for ``znaupd``.\n\nParameters\n----------\nido : input int\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('D') with bounds (n)\nv : input rank-2 array('D') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (14)\nworkd : in/output rank-1 array('D') with bounds (3 * n)\nworkl : in/output rank-1 array('D') with bounds (lworkl)\nrwork : in/output rank-1 array('d') with bounds (ncv)\ninfo : input int\n\nOther Parameters\n----------------\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: shape(v,1)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nido : int\ntol : float\nresid : rank-1 array('D') with bounds (n)\nv : rank-2 array('D') with bounds (ldv,ncv)\niparam : rank-1 array('i') with bounds (11)\nipntr : rank-1 array('i') with bounds (14)\ninfo : int\n" - ... - -def zneupd( - rvec, - howmny, - select, - sigma, - workev, - bmat, - which, - nev, - tol, - resid, - v, - iparam, - ipntr, - workd, - workl, - rwork, - info, - ldz=..., - n=..., - ncv=..., - ldv=..., - lworkl=..., -) -> typing.Any: - "d,z,info = zneupd(rvec,howmny,select,sigma,workev,bmat,which,nev,tol,resid,v,iparam,ipntr,workd,workl,rwork,info,[ldz,n,ncv,ldv,lworkl])\n\nWrapper for ``zneupd``.\n\nParameters\n----------\nrvec : input int\nhowmny : input string(len=1)\nselect : input rank-1 array('i') with bounds (ncv)\nsigma : input complex\nworkev : input rank-1 array('D') with bounds (3 * ncv)\nbmat : input string(len=1)\nwhich : input string(len=2)\nnev : input int\ntol : input float\nresid : input rank-1 array('D') with bounds (n)\nv : input rank-2 array('D') with bounds (ldv,ncv)\niparam : input rank-1 array('i') with bounds (11)\nipntr : input rank-1 array('i') with bounds (14)\nworkd : input rank-1 array('D') with bounds (3 * n)\nworkl : input rank-1 array('D') with bounds (lworkl)\nrwork : input rank-1 array('d') with bounds (ncv)\ninfo : input int\n\nOther Parameters\n----------------\nldz : input int, optional\n Default: shape(z,0)\nn : input int, optional\n Default: len(resid)\nncv : input int, optional\n Default: len(select)\nldv : input int, optional\n Default: shape(v,0)\nlworkl : input int, optional\n Default: len(workl)\n\nReturns\n-------\nd : rank-1 array('D') with bounds (nev)\nz : rank-2 array('D') with bounds (n,nev)\ninfo : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/sparse/linalg/isolve/_iterative.pyi b/stubs/scipy-stubs/sparse/linalg/isolve/_iterative.pyi deleted file mode 100644 index 11c9e208..00000000 --- a/stubs/scipy-stubs/sparse/linalg/isolve/_iterative.pyi +++ /dev/null @@ -1,109 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.sparse.linalg.isolve._iterative, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def cbicgrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``cbicgrevcom``.\n\nParameters\n----------\nb : input rank-1 array('F') with bounds (n)\nx : input rank-1 array('F') with bounds (n)\nwork : in/output rank-1 array('F') with bounds (6 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('F') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def cbicgstabrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``cbicgstabrevcom``.\n\nParameters\n----------\nb : input rank-1 array('F') with bounds (n)\nx : input rank-1 array('F') with bounds (n)\nwork : in/output rank-1 array('F') with bounds (7 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('F') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def ccgrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = ccgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``ccgrevcom``.\n\nParameters\n----------\nb : input rank-1 array('F') with bounds (n)\nx : input rank-1 array('F') with bounds (n)\nwork : in/output rank-1 array('F') with bounds (4 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('F') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def ccgsrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = ccgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``ccgsrevcom``.\n\nParameters\n----------\nb : input rank-1 array('F') with bounds (n)\nx : input rank-1 array('F') with bounds (n)\nwork : in/output rank-1 array('F') with bounds (7 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('F') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def cgmresrevcom(b, x, restrt, work, work2, iter, resid, info, ndx1, ndx2, ijob, tol) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob,tol)\n\nWrapper for ``cgmresrevcom``.\n\nParameters\n----------\nb : input rank-1 array('F') with bounds (n)\nx : input rank-1 array('F') with bounds (n)\nrestrt : input int\nwork : in/output rank-1 array('F') with bounds (ldw*(6+restrt))\nwork2 : in/output rank-1 array('F') with bounds (ldw2*(2*restrt+2))\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\ntol : input float\n\nReturns\n-------\nx : rank-1 array('F') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def cqmrrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = cqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``cqmrrevcom``.\n\nParameters\n----------\nb : input rank-1 array('F') with bounds (n)\nx : input rank-1 array('F') with bounds (n)\nwork : in/output rank-1 array('F') with bounds (11 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('F') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def dbicgrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``dbicgrevcom``.\n\nParameters\n----------\nb : input rank-1 array('d') with bounds (n)\nx : input rank-1 array('d') with bounds (n)\nwork : in/output rank-1 array('d') with bounds (6 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def dbicgstabrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``dbicgstabrevcom``.\n\nParameters\n----------\nb : input rank-1 array('d') with bounds (n)\nx : input rank-1 array('d') with bounds (n)\nwork : in/output rank-1 array('d') with bounds (7 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def dcgrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dcgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``dcgrevcom``.\n\nParameters\n----------\nb : input rank-1 array('d') with bounds (n)\nx : input rank-1 array('d') with bounds (n)\nwork : in/output rank-1 array('d') with bounds (4 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def dcgsrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dcgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``dcgsrevcom``.\n\nParameters\n----------\nb : input rank-1 array('d') with bounds (n)\nx : input rank-1 array('d') with bounds (n)\nwork : in/output rank-1 array('d') with bounds (7 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def dgmresrevcom(b, x, restrt, work, work2, iter, resid, info, ndx1, ndx2, ijob, tol) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob,tol)\n\nWrapper for ``dgmresrevcom``.\n\nParameters\n----------\nb : input rank-1 array('d') with bounds (n)\nx : input rank-1 array('d') with bounds (n)\nrestrt : input int\nwork : in/output rank-1 array('d') with bounds (ldw*(6+restrt))\nwork2 : in/output rank-1 array('d') with bounds (ldw2*(2*restrt+2))\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\ntol : input float\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def dqmrrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = dqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``dqmrrevcom``.\n\nParameters\n----------\nb : input rank-1 array('d') with bounds (n)\nx : input rank-1 array('d') with bounds (n)\nwork : in/output rank-1 array('d') with bounds (11 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('d') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def sbicgrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``sbicgrevcom``.\n\nParameters\n----------\nb : input rank-1 array('f') with bounds (n)\nx : input rank-1 array('f') with bounds (n)\nwork : in/output rank-1 array('f') with bounds (6 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('f') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def sbicgstabrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``sbicgstabrevcom``.\n\nParameters\n----------\nb : input rank-1 array('f') with bounds (n)\nx : input rank-1 array('f') with bounds (n)\nwork : in/output rank-1 array('f') with bounds (7 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('f') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def scgrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = scgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``scgrevcom``.\n\nParameters\n----------\nb : input rank-1 array('f') with bounds (n)\nx : input rank-1 array('f') with bounds (n)\nwork : in/output rank-1 array('f') with bounds (4 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('f') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def scgsrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = scgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``scgsrevcom``.\n\nParameters\n----------\nb : input rank-1 array('f') with bounds (n)\nx : input rank-1 array('f') with bounds (n)\nwork : in/output rank-1 array('f') with bounds (7 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('f') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def sgmresrevcom(b, x, restrt, work, work2, iter, resid, info, ndx1, ndx2, ijob, tol) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob,tol)\n\nWrapper for ``sgmresrevcom``.\n\nParameters\n----------\nb : input rank-1 array('f') with bounds (n)\nx : input rank-1 array('f') with bounds (n)\nrestrt : input int\nwork : in/output rank-1 array('f') with bounds (ldw*(6+restrt))\nwork2 : in/output rank-1 array('f') with bounds (ldw2*(2*restrt+2))\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\ntol : input float\n\nReturns\n-------\nx : rank-1 array('f') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def sqmrrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = sqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``sqmrrevcom``.\n\nParameters\n----------\nb : input rank-1 array('f') with bounds (n)\nx : input rank-1 array('f') with bounds (n)\nwork : in/output rank-1 array('f') with bounds (11 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('f') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : float\nsclr2 : float\nijob : int\n" - ... - -def zbicgrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zbicgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``zbicgrevcom``.\n\nParameters\n----------\nb : input rank-1 array('D') with bounds (n)\nx : input rank-1 array('D') with bounds (n)\nwork : in/output rank-1 array('D') with bounds (6 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('D') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def zbicgstabrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zbicgstabrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``zbicgstabrevcom``.\n\nParameters\n----------\nb : input rank-1 array('D') with bounds (n)\nx : input rank-1 array('D') with bounds (n)\nwork : in/output rank-1 array('D') with bounds (7 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('D') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def zcgrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zcgrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``zcgrevcom``.\n\nParameters\n----------\nb : input rank-1 array('D') with bounds (n)\nx : input rank-1 array('D') with bounds (n)\nwork : in/output rank-1 array('D') with bounds (4 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('D') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def zcgsrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zcgsrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``zcgsrevcom``.\n\nParameters\n----------\nb : input rank-1 array('D') with bounds (n)\nx : input rank-1 array('D') with bounds (n)\nwork : in/output rank-1 array('D') with bounds (7 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('D') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def zgmresrevcom(b, x, restrt, work, work2, iter, resid, info, ndx1, ndx2, ijob, tol) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zgmresrevcom(b,x,restrt,work,work2,iter,resid,info,ndx1,ndx2,ijob,tol)\n\nWrapper for ``zgmresrevcom``.\n\nParameters\n----------\nb : input rank-1 array('D') with bounds (n)\nx : input rank-1 array('D') with bounds (n)\nrestrt : input int\nwork : in/output rank-1 array('D') with bounds (ldw*(6+restrt))\nwork2 : in/output rank-1 array('D') with bounds (ldw2*(2*restrt+2))\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\ntol : input float\n\nReturns\n-------\nx : rank-1 array('D') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def zqmrrevcom(b, x, work, iter, resid, info, ndx1, ndx2, ijob) -> typing.Any: - "x,iter,resid,info,ndx1,ndx2,sclr1,sclr2,ijob = zqmrrevcom(b,x,work,iter,resid,info,ndx1,ndx2,ijob)\n\nWrapper for ``zqmrrevcom``.\n\nParameters\n----------\nb : input rank-1 array('D') with bounds (n)\nx : input rank-1 array('D') with bounds (n)\nwork : in/output rank-1 array('D') with bounds (11 * ldw)\niter : input int\nresid : input float\ninfo : input int\nndx1 : input int\nndx2 : input int\nijob : input int\n\nReturns\n-------\nx : rank-1 array('D') with bounds (n)\niter : int\nresid : float\ninfo : int\nndx1 : int\nndx2 : int\nsclr1 : complex\nsclr2 : complex\nijob : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/spatial/_distance_wrap.pyi b/stubs/scipy-stubs/spatial/_distance_wrap.pyi deleted file mode 100644 index 9f8730fc..00000000 --- a/stubs/scipy-stubs/spatial/_distance_wrap.pyi +++ /dev/null @@ -1,66 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.spatial._distance_wrap, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def cdist_braycurtis_double_wrap() -> typing.Any: ... -def cdist_canberra_double_wrap() -> typing.Any: ... -def cdist_chebyshev_double_wrap() -> typing.Any: ... -def cdist_cityblock_double_wrap() -> typing.Any: ... -def cdist_correlation_double_wrap(XA, XB, dm, **kwargs) -> typing.Any: ... -def cdist_cosine_double_wrap() -> typing.Any: ... -def cdist_dice_bool_wrap() -> typing.Any: ... -def cdist_euclidean_double_wrap() -> typing.Any: ... -def cdist_hamming_bool_wrap() -> typing.Any: ... -def cdist_hamming_double_wrap() -> typing.Any: ... -def cdist_jaccard_bool_wrap() -> typing.Any: ... -def cdist_jaccard_double_wrap() -> typing.Any: ... -def cdist_jensenshannon_double_wrap() -> typing.Any: ... -def cdist_kulsinski_bool_wrap() -> typing.Any: ... -def cdist_mahalanobis_double_wrap() -> typing.Any: ... -def cdist_minkowski_double_wrap() -> typing.Any: ... -def cdist_old_weighted_minkowski_double_wrap() -> typing.Any: ... -def cdist_rogerstanimoto_bool_wrap() -> typing.Any: ... -def cdist_russellrao_bool_wrap() -> typing.Any: ... -def cdist_seuclidean_double_wrap() -> typing.Any: ... -def cdist_sokalmichener_bool_wrap() -> typing.Any: ... -def cdist_sokalsneath_bool_wrap() -> typing.Any: ... -def cdist_sqeuclidean_double_wrap() -> typing.Any: ... -def cdist_weighted_chebyshev_double_wrap() -> typing.Any: ... -def cdist_weighted_minkowski_double_wrap() -> typing.Any: ... -def cdist_yule_bool_wrap() -> typing.Any: ... -def pdist_braycurtis_double_wrap() -> typing.Any: ... -def pdist_canberra_double_wrap() -> typing.Any: ... -def pdist_chebyshev_double_wrap() -> typing.Any: ... -def pdist_cityblock_double_wrap() -> typing.Any: ... -def pdist_correlation_double_wrap(X, dm, **kwargs) -> typing.Any: ... -def pdist_cosine_double_wrap() -> typing.Any: ... -def pdist_dice_bool_wrap() -> typing.Any: ... -def pdist_euclidean_double_wrap() -> typing.Any: ... -def pdist_hamming_bool_wrap() -> typing.Any: ... -def pdist_hamming_double_wrap() -> typing.Any: ... -def pdist_jaccard_bool_wrap() -> typing.Any: ... -def pdist_jaccard_double_wrap() -> typing.Any: ... -def pdist_jensenshannon_double_wrap() -> typing.Any: ... -def pdist_kulsinski_bool_wrap() -> typing.Any: ... -def pdist_mahalanobis_double_wrap() -> typing.Any: ... -def pdist_minkowski_double_wrap() -> typing.Any: ... -def pdist_old_weighted_minkowski_double_wrap() -> typing.Any: ... -def pdist_rogerstanimoto_bool_wrap() -> typing.Any: ... -def pdist_russellrao_bool_wrap() -> typing.Any: ... -def pdist_seuclidean_double_wrap() -> typing.Any: ... -def pdist_sokalmichener_bool_wrap() -> typing.Any: ... -def pdist_sokalsneath_bool_wrap() -> typing.Any: ... -def pdist_sqeuclidean_double_wrap() -> typing.Any: ... -def pdist_weighted_chebyshev_double_wrap() -> typing.Any: ... -def pdist_weighted_minkowski_double_wrap() -> typing.Any: ... -def pdist_yule_bool_wrap() -> typing.Any: ... -def to_squareform_from_vector_wrap() -> typing.Any: ... -def to_vector_from_squareform_wrap() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/spatial/_hausdorff.pyi b/stubs/scipy-stubs/spatial/_hausdorff.pyi deleted file mode 100644 index bc532462..00000000 --- a/stubs/scipy-stubs/spatial/_hausdorff.pyi +++ /dev/null @@ -1,18 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.spatial._hausdorff, version: unspecified -import builtins as _mod_builtins -import typing - -__all__: list -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def directed_hausdorff() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/spatial/_voronoi.pyi b/stubs/scipy-stubs/spatial/_voronoi.pyi deleted file mode 100644 index b2b54ec6..00000000 --- a/stubs/scipy-stubs/spatial/_voronoi.pyi +++ /dev/null @@ -1,18 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.spatial._voronoi, version: unspecified -import builtins as _mod_builtins -import typing - -__all__: list -__doc__: str -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def sort_vertices_of_regions() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/spatial/ckdtree.pyi b/stubs/scipy-stubs/spatial/ckdtree.pyi deleted file mode 100644 index 840dddfc..00000000 --- a/stubs/scipy-stubs/spatial/ckdtree.pyi +++ /dev/null @@ -1,170 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.spatial.ckdtree, version: unspecified -import builtins as _mod_builtins -import typing - -__all__: list -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... -def __pyx_unpickle_cKDTreeNode() -> typing.Any: ... - -__test__: dict - -class cKDTree(_mod_builtins.object): - '\n cKDTree(data, leafsize=16, compact_nodes=True, copy_data=False,\n balanced_tree=True, boxsize=None)\n\n kd-tree for quick nearest-neighbor lookup\n\n This class provides an index into a set of k-dimensional points\n which can be used to rapidly look up the nearest neighbors of any\n point.\n\n Parameters\n ----------\n data : array_like, shape (n,m)\n The n data points of dimension m to be indexed. This array is\n not copied unless this is necessary to produce a contiguous\n array of doubles, and so modifying this data will result in\n bogus results. The data are also copied if the kd-tree is built\n with copy_data=True.\n leafsize : positive int, optional\n The number of points at which the algorithm switches over to\n brute-force. Default: 16.\n compact_nodes : bool, optional\n If True, the kd-tree is built to shrink the hyperrectangles to\n the actual data range. This usually gives a more compact tree that\n is robust against degenerated input data and gives faster queries\n at the expense of longer build time. Default: True.\n copy_data : bool, optional\n If True the data is always copied to protect the kd-tree against\n data corruption. Default: False.\n balanced_tree : bool, optional\n If True, the median is used to split the hyperrectangles instead of\n the midpoint. This usually gives a more compact tree and\n faster queries at the expense of longer build time. Default: True.\n boxsize : array_like or scalar, optional\n Apply a m-d toroidal topology to the KDTree.. The topology is generated\n by :math:`x_i + n_i L_i` where :math:`n_i` are integers and :math:`L_i`\n is the boxsize along i-th dimension. The input data shall be wrapped\n into :math:`[0, L_i)`. A ValueError is raised if any of the data is\n outside of this bound.\n\n Notes\n -----\n The algorithm used is described in Maneewongvatana and Mount 1999.\n The general idea is that the kd-tree is a binary tree, each of whose\n nodes represents an axis-aligned hyperrectangle. Each node specifies\n an axis and splits the set of points based on whether their coordinate\n along that axis is greater than or less than a particular value.\n\n During construction, the axis and splitting point are chosen by the\n "sliding midpoint" rule, which ensures that the cells do not all\n become long and thin.\n\n The tree can be queried for the r closest neighbors of any given point\n (optionally returning only those within some maximum distance of the\n point). It can also be queried, with a substantial gain in efficiency,\n for the r approximate closest neighbors.\n\n For large dimensions (20 is already large) do not expect this to run\n significantly faster than brute force. High-dimensional nearest-neighbor\n queries are a substantial open problem in computer science.\n\n Attributes\n ----------\n data : ndarray, shape (n,m)\n The n data points of dimension m to be indexed. This array is\n not copied unless this is necessary to produce a contiguous\n array of doubles. The data are also copied if the kd-tree is built\n with `copy_data=True`.\n leafsize : positive int\n The number of points at which the algorithm switches over to\n brute-force.\n m : int\n The dimension of a single data-point.\n n : int\n The number of data points.\n maxes : ndarray, shape (m,)\n The maximum value in each dimension of the n data points.\n mins : ndarray, shape (m,)\n The minimum value in each dimension of the n data points.\n tree : object, class cKDTreeNode\n This attribute exposes a Python view of the root node in the cKDTree\n object. A full Python view of the kd-tree is created dynamically\n on the first access. This attribute allows you to create your own\n query functions in Python.\n size : int\n The number of nodes in the tree.\n\n' - def __getstate__(self) -> typing.Any: ... - def __init__(self, data, leafsize=..., compact_nodes=..., copy_data=..., balanced_tree=..., boxsize=...) -> None: - '\n cKDTree(data, leafsize=16, compact_nodes=True, copy_data=False,\n balanced_tree=True, boxsize=None)\n\n kd-tree for quick nearest-neighbor lookup\n\n This class provides an index into a set of k-dimensional points\n which can be used to rapidly look up the nearest neighbors of any\n point.\n\n Parameters\n ----------\n data : array_like, shape (n,m)\n The n data points of dimension m to be indexed. This array is\n not copied unless this is necessary to produce a contiguous\n array of doubles, and so modifying this data will result in\n bogus results. The data are also copied if the kd-tree is built\n with copy_data=True.\n leafsize : positive int, optional\n The number of points at which the algorithm switches over to\n brute-force. Default: 16.\n compact_nodes : bool, optional\n If True, the kd-tree is built to shrink the hyperrectangles to\n the actual data range. This usually gives a more compact tree that\n is robust against degenerated input data and gives faster queries\n at the expense of longer build time. Default: True.\n copy_data : bool, optional\n If True the data is always copied to protect the kd-tree against\n data corruption. Default: False.\n balanced_tree : bool, optional\n If True, the median is used to split the hyperrectangles instead of\n the midpoint. This usually gives a more compact tree and\n faster queries at the expense of longer build time. Default: True.\n boxsize : array_like or scalar, optional\n Apply a m-d toroidal topology to the KDTree.. The topology is generated\n by :math:`x_i + n_i L_i` where :math:`n_i` are integers and :math:`L_i`\n is the boxsize along i-th dimension. The input data shall be wrapped\n into :math:`[0, L_i)`. A ValueError is raised if any of the data is\n outside of this bound.\n\n Notes\n -----\n The algorithm used is described in Maneewongvatana and Mount 1999.\n The general idea is that the kd-tree is a binary tree, each of whose\n nodes represents an axis-aligned hyperrectangle. Each node specifies\n an axis and splits the set of points based on whether their coordinate\n along that axis is greater than or less than a particular value.\n\n During construction, the axis and splitting point are chosen by the\n "sliding midpoint" rule, which ensures that the cells do not all\n become long and thin.\n\n The tree can be queried for the r closest neighbors of any given point\n (optionally returning only those within some maximum distance of the\n point). It can also be queried, with a substantial gain in efficiency,\n for the r approximate closest neighbors.\n\n For large dimensions (20 is already large) do not expect this to run\n significantly faster than brute force. High-dimensional nearest-neighbor\n queries are a substantial open problem in computer science.\n\n Attributes\n ----------\n data : ndarray, shape (n,m)\n The n data points of dimension m to be indexed. This array is\n not copied unless this is necessary to produce a contiguous\n array of doubles. The data are also copied if the kd-tree is built\n with `copy_data=True`.\n leafsize : positive int\n The number of points at which the algorithm switches over to\n brute-force.\n m : int\n The dimension of a single data-point.\n n : int\n The number of data points.\n maxes : ndarray, shape (m,)\n The maximum value in each dimension of the n data points.\n mins : ndarray, shape (m,)\n The minimum value in each dimension of the n data points.\n tree : object, class cKDTreeNode\n This attribute exposes a Python view of the root node in the cKDTree\n object. A full Python view of the kd-tree is created dynamically\n on the first access. This attribute allows you to create your own\n query functions in Python.\n size : int\n The number of nodes in the tree.\n\n' - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __pyx_vtable__: PyCapsule - def __reduce_cython__(self) -> typing.Any: ... - def __setstate__(self, state: typing.Any) -> None: ... - def __setstate_cython__(self) -> typing.Any: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def _build_weights(self, weights) -> typing.Any: - "\n _build_weights(weights)\n\n Compute weights of nodes from weights of data points. This will sum\n up the total weight per node. This function is used internally.\n\n Parameters\n ----------\n weights : array_like\n weights of data points; must be the same length as the data points.\n currently only scalar weights are supported. Therefore the weights\n array must be 1 dimensional.\n\n Returns\n -------\n node_weights : array_like\n total weight for each KD-Tree node.\n\n" - ... - - @property - def boxsize(self) -> typing.Any: ... - def count_neighbors(self, other, r, p=..., weights=..., cumulative=...) -> typing.Any: - '\n count_neighbors(self, other, r, p=2., weights=None, cumulative=True)\n\n Count how many nearby pairs can be formed.\n\n Count the number of pairs ``(x1,x2)`` can be formed, with ``x1`` drawn\n from ``self`` and ``x2`` drawn from ``other``, and where\n ``distance(x1, x2, p) <= r``.\n\n Data points on ``self`` and ``other`` are optionally weighted by the\n ``weights`` argument. (See below)\n\n This is adapted from the "two-point correlation" algorithm described by\n Gray and Moore [1]_. See notes for further discussion.\n\n Parameters\n ----------\n other : cKDTree instance\n The other tree to draw points from, can be the same tree as self.\n r : float or one-dimensional array of floats\n The radius to produce a count for. Multiple radii are searched with\n a single tree traversal.\n If the count is non-cumulative(``cumulative=False``), ``r`` defines\n the edges of the bins, and must be non-decreasing.\n p : float, optional\n 1<=p<=infinity.\n Which Minkowski p-norm to use.\n Default 2.0.\n A finite large p may cause a ValueError if overflow can occur.\n weights : tuple, array_like, or None, optional\n If None, the pair-counting is unweighted.\n If given as a tuple, weights[0] is the weights of points in ``self``, and\n weights[1] is the weights of points in ``other``; either can be None to\n indicate the points are unweighted.\n If given as an array_like, weights is the weights of points in ``self``\n and ``other``. For this to make sense, ``self`` and ``other`` must be the\n same tree. If ``self`` and ``other`` are two different trees, a ``ValueError``\n is raised.\n Default: None\n cumulative : bool, optional\n Whether the returned counts are cumulative. When cumulative is set to ``False``\n the algorithm is optimized to work with a large number of bins (>10) specified\n by ``r``. When ``cumulative`` is set to True, the algorithm is optimized to work\n with a small number of ``r``. Default: True\n\n Returns\n -------\n result : scalar or 1-D array\n The number of pairs. For unweighted counts, the result is integer.\n For weighted counts, the result is float.\n If cumulative is False, ``result[i]`` contains the counts with\n ``(-inf if i == 0 else r[i-1]) < R <= r[i]``\n\n Notes\n -----\n Pair-counting is the basic operation used to calculate the two point\n correlation functions from a data set composed of position of objects.\n\n Two point correlation function measures the clustering of objects and\n is widely used in cosmology to quantify the large scale structure\n in our Universe, but it may be useful for data analysis in other fields\n where self-similar assembly of objects also occur.\n\n The Landy-Szalay estimator for the two point correlation function of\n ``D`` measures the clustering signal in ``D``. [2]_\n\n For example, given the position of two sets of objects,\n\n - objects ``D`` (data) contains the clustering signal, and\n\n - objects ``R`` (random) that contains no signal,\n\n .. math::\n\n \\xi(r) = \\frac{ - 2 f + f^2}{f^2},\n\n where the brackets represents counting pairs between two data sets\n in a finite bin around ``r`` (distance), corresponding to setting\n `cumulative=False`, and ``f = float(len(D)) / float(len(R))`` is the\n ratio between number of objects from data and random.\n\n The algorithm implemented here is loosely based on the dual-tree\n algorithm described in [1]_. We switch between two different\n pair-cumulation scheme depending on the setting of ``cumulative``.\n The computing time of the method we use when for\n ``cumulative == False`` does not scale with the total number of bins.\n The algorithm for ``cumulative == True`` scales linearly with the\n number of bins, though it is slightly faster when only\n 1 or 2 bins are used. [5]_.\n\n As an extension to the naive pair-counting,\n weighted pair-counting counts the product of weights instead\n of number of pairs.\n Weighted pair-counting is used to estimate marked correlation functions\n ([3]_, section 2.2),\n or to properly calculate the average of data per distance bin\n (e.g. [4]_, section 2.1 on redshift).\n\n .. [1] Gray and Moore,\n "N-body problems in statistical learning",\n Mining the sky, 2000, :arxiv:`astro-ph/0012333`\n\n .. [2] Landy and Szalay,\n "Bias and variance of angular correlation functions",\n The Astrophysical Journal, 1993, :doi:`10.1086/172900`\n\n .. [3] Sheth, Connolly and Skibba,\n "Marked correlations in galaxy formation models",\n 2005, :arxiv:`astro-ph/0511773`\n\n .. [4] Hawkins, et al.,\n "The 2dF Galaxy Redshift Survey: correlation functions,\n peculiar velocities and the matter density of the Universe",\n Monthly Notices of the Royal Astronomical Society, 2002,\n :doi:`10.1046/j.1365-2966.2003.07063.x`\n\n .. [5] https://github.com/scipy/scipy/pull/5647#issuecomment-168474926\n\n Examples\n --------\n You can count neighbors number between two kd-trees within a distance:\n\n >>> import numpy as np\n >>> from scipy.spatial import cKDTree\n >>> np.random.seed(21701)\n >>> points1 = np.random.random((5, 2))\n >>> points2 = np.random.random((5, 2))\n >>> kd_tree1 = cKDTree(points1)\n >>> kd_tree2 = cKDTree(points2)\n >>> kd_tree1.count_neighbors(kd_tree2, 0.2)\n 9\n\n This number is same as the total pair number calculated by\n `query_ball_tree`:\n\n >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)\n >>> sum([len(i) for i in indexes])\n 9\n\n' - ... - - @property - def data(self) -> typing.Any: ... - @property - def indices(self) -> typing.Any: ... - @property - def leafsize(self) -> typing.Any: ... - @property - def m(self) -> typing.Any: ... - @property - def maxes(self) -> typing.Any: ... - @property - def mins(self) -> typing.Any: ... - @property - def n(self) -> typing.Any: ... - def query(self, x, k=..., eps=..., p=..., distance_upper_bound=..., workers=...) -> typing.Any: - '\n query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf, workers=1)\n\n Query the kd-tree for nearest neighbors\n\n Parameters\n ----------\n x : array_like, last dimension self.m\n An array of points to query.\n k : list of integer or integer\n The list of k-th nearest neighbors to return. If k is an\n integer it is treated as a list of [1, ... k] (range(1, k+1)).\n Note that the counting starts from 1.\n eps : non-negative float\n Return approximate nearest neighbors; the k-th returned value\n is guaranteed to be no further than (1+eps) times the\n distance to the real k-th nearest neighbor.\n p : float, 1<=p<=infinity\n Which Minkowski p-norm to use.\n 1 is the sum-of-absolute-values "Manhattan" distance\n 2 is the usual Euclidean distance\n infinity is the maximum-coordinate-difference distance\n A finite large p may cause a ValueError if overflow can occur.\n distance_upper_bound : nonnegative float\n Return only neighbors within this distance. This is used to prune\n tree searches, so if you are doing a series of nearest-neighbor\n queries, it may help to supply the distance to the nearest neighbor\n of the most recent point.\n workers : int, optional\n Number of workers to use for parallel processing. If -1 is given\n all CPU threads are used. Default: 1.\n\n .. versionchanged:: 1.6.0\n The "n_jobs" argument was renamed "workers". The old name\n "n_jobs" is deprecated and will stop working in SciPy 1.8.0.\n\n Returns\n -------\n d : array of floats\n The distances to the nearest neighbors.\n If ``x`` has shape ``tuple+(self.m,)``, then ``d`` has shape ``tuple+(k,)``.\n When k == 1, the last dimension of the output is squeezed.\n Missing neighbors are indicated with infinite distances.\n i : ndarray of ints\n The index of each neighbor in ``self.data``.\n If ``x`` has shape ``tuple+(self.m,)``, then ``i`` has shape ``tuple+(k,)``.\n When k == 1, the last dimension of the output is squeezed.\n Missing neighbors are indicated with ``self.n``.\n\n Notes\n -----\n If the KD-Tree is periodic, the position ``x`` is wrapped into the\n box.\n\n When the input k is a list, a query for arange(max(k)) is performed, but\n only columns that store the requested values of k are preserved. This is\n implemented in a manner that reduces memory usage.\n\n Examples\n --------\n\n >>> import numpy as np\n >>> from scipy.spatial import cKDTree\n >>> x, y = np.mgrid[0:5, 2:8]\n >>> tree = cKDTree(np.c_[x.ravel(), y.ravel()])\n\n To query the nearest neighbours and return squeezed result, use\n\n >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=1)\n >>> print(dd, ii)\n [2. 0.14142136] [ 0 13]\n\n To query the nearest neighbours and return unsqueezed result, use\n\n >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[1])\n >>> print(dd, ii)\n [[2. ]\n [0.14142136]] [[ 0]\n [13]]\n\n To query the second nearest neighbours and return unsqueezed result, use\n\n >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[2])\n >>> print(dd, ii)\n [[2.23606798]\n [0.90553851]] [[ 6]\n [12]]\n\n To query the first and second nearest neighbours, use\n\n >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=2)\n >>> print(dd, ii)\n [[2. 2.23606798]\n [0.14142136 0.90553851]] [[ 0 6]\n [13 12]]\n\n or, be more specific\n\n >>> dd, ii = tree.query([[0, 0], [2.1, 2.9]], k=[1, 2])\n >>> print(dd, ii)\n [[2. 2.23606798]\n [0.14142136 0.90553851]] [[ 0 6]\n [13 12]]\n\n' - ... - - def query_ball_point(self, x, r, p=..., eps=..., workers=..., return_sorted=..., return_length=...) -> typing.Any: - '\n query_ball_point(self, x, r, p=2., eps=0, workers=1, return_sorted=None,\n return_length=False)\n\n Find all points within distance r of point(s) x.\n\n Parameters\n ----------\n x : array_like, shape tuple + (self.m,)\n The point or points to search for neighbors of.\n r : array_like, float\n The radius of points to return, shall broadcast to the length of x.\n p : float, optional\n Which Minkowski p-norm to use. Should be in the range [1, inf].\n A finite large p may cause a ValueError if overflow can occur.\n eps : nonnegative float, optional\n Approximate search. Branches of the tree are not explored if their\n nearest points are further than ``r / (1 + eps)``, and branches are\n added in bulk if their furthest points are nearer than\n ``r * (1 + eps)``.\n workers : int, optional\n Number of jobs to schedule for parallel processing. If -1 is given\n all processors are used. Default: 1.\n\n .. versionchanged:: 1.6.0\n The "n_jobs" argument was renamed "workers". The old name\n "n_jobs" is deprecated and will stop working in SciPy 1.8.0.\n\n return_sorted : bool, optional\n Sorts returned indicies if True and does not sort them if False. If\n None, does not sort single point queries, but does sort\n multi-point queries which was the behavior before this option\n was added.\n\n .. versionadded:: 1.2.0\n return_length: bool, optional\n Return the number of points inside the radius instead of a list\n of the indices.\n .. versionadded:: 1.3.0\n\n Returns\n -------\n results : list or array of lists\n If `x` is a single point, returns a list of the indices of the\n neighbors of `x`. If `x` is an array of points, returns an object\n array of shape tuple containing lists of neighbors.\n\n Notes\n -----\n If you have many points whose neighbors you want to find, you may save\n substantial amounts of time by putting them in a cKDTree and using\n query_ball_tree.\n\n Examples\n --------\n >>> from scipy import spatial\n >>> x, y = np.mgrid[0:4, 0:4]\n >>> points = np.c_[x.ravel(), y.ravel()]\n >>> tree = spatial.cKDTree(points)\n >>> tree.query_ball_point([2, 0], 1)\n [4, 8, 9, 12]\n\n Query multiple points and plot the results:\n\n >>> import matplotlib.pyplot as plt\n >>> points = np.asarray(points)\n >>> plt.plot(points[:,0], points[:,1], \'.\')\n >>> for results in tree.query_ball_point(([2, 0], [3, 3]), 1):\n ... nearby_points = points[results]\n ... plt.plot(nearby_points[:,0], nearby_points[:,1], \'o\')\n >>> plt.margins(0.1, 0.1)\n >>> plt.show()\n\n' - ... - - def query_ball_tree(self, other, r, p=..., eps=...) -> typing.Any: - '\n query_ball_tree(self, other, r, p=2., eps=0)\n\n Find all pairs of points between `self` and `other` whose distance is at most r\n\n Parameters\n ----------\n other : cKDTree instance\n The tree containing points to search against.\n r : float\n The maximum distance, has to be positive.\n p : float, optional\n Which Minkowski norm to use. `p` has to meet the condition\n ``1 <= p <= infinity``.\n A finite large p may cause a ValueError if overflow can occur.\n eps : float, optional\n Approximate search. Branches of the tree are not explored\n if their nearest points are further than ``r/(1+eps)``, and\n branches are added in bulk if their furthest points are nearer\n than ``r * (1+eps)``. `eps` has to be non-negative.\n\n Returns\n -------\n results : list of lists\n For each element ``self.data[i]`` of this tree, ``results[i]`` is a\n list of the indices of its neighbors in ``other.data``.\n\n Examples\n --------\n You can search all pairs of points between two kd-trees within a distance:\n\n >>> import matplotlib.pyplot as plt\n >>> import numpy as np\n >>> from scipy.spatial import cKDTree\n >>> np.random.seed(21701)\n >>> points1 = np.random.random((15, 2))\n >>> points2 = np.random.random((15, 2))\n >>> plt.figure(figsize=(6, 6))\n >>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14)\n >>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14)\n >>> kd_tree1 = cKDTree(points1)\n >>> kd_tree2 = cKDTree(points2)\n >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2)\n >>> for i in range(len(indexes)):\n ... for j in indexes[i]:\n ... plt.plot([points1[i, 0], points2[j, 0]],\n ... [points1[i, 1], points2[j, 1]], "-r")\n >>> plt.show()\n\n' - ... - - def query_pairs(self, r, p=..., eps=...) -> typing.Any: - "\n query_pairs(self, r, p=2., eps=0)\n\n Find all pairs of points in `self` whose distance is at most r.\n\n Parameters\n ----------\n r : positive float\n The maximum distance.\n p : float, optional\n Which Minkowski norm to use. ``p`` has to meet the condition\n ``1 <= p <= infinity``.\n A finite large p may cause a ValueError if overflow can occur.\n eps : float, optional\n Approximate search. Branches of the tree are not explored\n if their nearest points are further than ``r/(1+eps)``, and\n branches are added in bulk if their furthest points are nearer\n than ``r * (1+eps)``. `eps` has to be non-negative.\n output_type : string, optional\n Choose the output container, 'set' or 'ndarray'. Default: 'set'\n\n Returns\n -------\n results : set or ndarray\n Set of pairs ``(i,j)``, with ``i < j``, for which the corresponding\n positions are close. If output_type is 'ndarray', an ndarry is\n returned instead of a set.\n\n Examples\n --------\n You can search all pairs of points in a kd-tree within a distance:\n\n >>> import matplotlib.pyplot as plt\n >>> import numpy as np\n >>> from scipy.spatial import cKDTree\n >>> np.random.seed(21701)\n >>> points = np.random.random((20, 2))\n >>> plt.figure(figsize=(6, 6))\n >>> plt.plot(points[:, 0], points[:, 1], \"xk\", markersize=14)\n >>> kd_tree = cKDTree(points)\n >>> pairs = kd_tree.query_pairs(r=0.2)\n >>> for (i, j) in pairs:\n ... plt.plot([points[i, 0], points[j, 0]],\n ... [points[i, 1], points[j, 1]], \"-r\")\n >>> plt.show()\n\n" - ... - - @property - def size(self) -> typing.Any: ... - def sparse_distance_matrix(self, other, max_distance, p=...) -> typing.Any: - "\n sparse_distance_matrix(self, other, max_distance, p=2.)\n\n Compute a sparse distance matrix\n\n Computes a distance matrix between two cKDTrees, leaving as zero\n any distance greater than max_distance.\n\n Parameters\n ----------\n other : cKDTree\n\n max_distance : positive float\n\n p : float, 1<=p<=infinity\n Which Minkowski p-norm to use.\n A finite large p may cause a ValueError if overflow can occur.\n\n output_type : string, optional\n Which container to use for output data. Options: 'dok_matrix',\n 'coo_matrix', 'dict', or 'ndarray'. Default: 'dok_matrix'.\n\n Returns\n -------\n result : dok_matrix, coo_matrix, dict or ndarray\n Sparse matrix representing the results in \"dictionary of keys\"\n format. If a dict is returned the keys are (i,j) tuples of indices.\n If output_type is 'ndarray' a record array with fields 'i', 'j',\n and 'v' is returned,\n\n Examples\n --------\n You can compute a sparse distance matrix between two kd-trees:\n\n >>> import numpy as np\n >>> from scipy.spatial import cKDTree\n >>> np.random.seed(21701)\n >>> points1 = np.random.random((5, 2))\n >>> points2 = np.random.random((5, 2))\n >>> kd_tree1 = cKDTree(points1)\n >>> kd_tree2 = cKDTree(points2)\n >>> sdm = kd_tree1.sparse_distance_matrix(kd_tree2, 0.3)\n >>> sdm.toarray()\n array([[0.20220215, 0.14538496, 0., 0.10257199, 0. ],\n [0.13491385, 0.27251306, 0., 0.18793787, 0. ],\n [0.19262396, 0., 0., 0.25795122, 0. ],\n [0.14859639, 0.07076002, 0., 0.04065851, 0. ],\n [0.17308768, 0., 0., 0.24823138, 0. ]])\n\n You can check distances above the `max_distance` are zeros:\n\n >>> from scipy.spatial import distance_matrix\n >>> distance_matrix(points1, points2)\n array([[0.20220215, 0.14538496, 0.43588092, 0.10257199, 0.4555495 ],\n [0.13491385, 0.27251306, 0.65944131, 0.18793787, 0.68184154],\n [0.19262396, 0.34121593, 0.72176889, 0.25795122, 0.74538858],\n [0.14859639, 0.07076002, 0.48505773, 0.04065851, 0.50043591],\n [0.17308768, 0.32837991, 0.72760803, 0.24823138, 0.75017239]])\n\n" - ... - - @property - def tree(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class cKDTreeNode(_mod_builtins.object): - "\n class cKDTreeNode\n\n This class exposes a Python view of a node in the cKDTree object.\n\n All attributes are read-only.\n\n Attributes\n ----------\n level : int\n The depth of the node. 0 is the level of the root node.\n split_dim : int\n The dimension along which this node is split. If this value is -1\n the node is a leafnode in the kd-tree. Leafnodes are not split further\n and scanned by brute force.\n split : float\n The value used to separate split this node. Points with value >= split\n in the split_dim dimension are sorted to the 'greater' subnode\n whereas those with value < split are sorted to the 'lesser' subnode.\n children : int\n The number of data points sorted to this node.\n data_points : ndarray of float64\n An array with the data points sorted to this node.\n indices : ndarray of intp\n An array with the indices of the data points sorted to this node. The\n indices refer to the position in the data set used to construct the\n kd-tree.\n lesser : cKDTreeNode or None\n Subnode with the 'lesser' data points. This attribute is None for\n leafnodes.\n greater : cKDTreeNode or None\n Subnode with the 'greater' data points. This attribute is None for\n leafnodes.\n\n" - def __init__(self, *args, **kwargs) -> None: - "\n class cKDTreeNode\n\n This class exposes a Python view of a node in the cKDTree object.\n\n All attributes are read-only.\n\n Attributes\n ----------\n level : int\n The depth of the node. 0 is the level of the root node.\n split_dim : int\n The dimension along which this node is split. If this value is -1\n the node is a leafnode in the kd-tree. Leafnodes are not split further\n and scanned by brute force.\n split : float\n The value used to separate split this node. Points with value >= split\n in the split_dim dimension are sorted to the 'greater' subnode\n whereas those with value < split are sorted to the 'lesser' subnode.\n children : int\n The number of data points sorted to this node.\n data_points : ndarray of float64\n An array with the data points sorted to this node.\n indices : ndarray of intp\n An array with the indices of the data points sorted to this node. The\n indices refer to the position in the data set used to construct the\n kd-tree.\n lesser : cKDTreeNode or None\n Subnode with the 'lesser' data points. This attribute is None for\n leafnodes.\n greater : cKDTreeNode or None\n Subnode with the 'greater' data points. This attribute is None for\n leafnodes.\n\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __pyx_vtable__: PyCapsule - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def children(self) -> typing.Any: ... - @property - def data_points(self) -> typing.Any: ... - @property - def end_idx(self) -> typing.Any: ... - @property - def greater(self) -> typing.Any: ... - @property - def indices(self) -> typing.Any: ... - @property - def lesser(self) -> typing.Any: ... - @property - def level(self) -> typing.Any: ... - @property - def split(self) -> typing.Any: ... - @property - def split_dim(self) -> typing.Any: ... - @property - def start_idx(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class coo_entries(_mod_builtins.object): - @property - def __array_interface__(self) -> typing.Any: ... - def __init__(self, *args, **kwargs) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def coo_matrix(self) -> typing.Any: ... - def dict(self) -> typing.Any: ... - def dok_matrix(self) -> typing.Any: ... - def ndarray(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class ordered_pairs(_mod_builtins.object): - @property - def __array_interface__(self) -> typing.Any: ... - def __init__(self, *args, **kwargs) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def ndarray(self) -> typing.Any: ... - def set(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/spatial/qhull.pyi b/stubs/scipy-stubs/spatial/qhull.pyi deleted file mode 100644 index 267adec3..00000000 --- a/stubs/scipy-stubs/spatial/qhull.pyi +++ /dev/null @@ -1,251 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.spatial.qhull, version: unspecified -import builtins as _mod_builtins -import typing - -class ConvexHull(_QhullUser): - "\n ConvexHull(points, incremental=False, qhull_options=None)\n\n Convex hulls in N dimensions.\n\n .. versionadded:: 0.12.0\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndim)\n Coordinates of points to construct a convex hull from\n incremental : bool, optional\n Allow adding new points incrementally. This takes up some additional\n resources.\n qhull_options : str, optional\n Additional options to pass to Qhull. See Qhull manual\n for details. (Default: \"Qx\" for ndim > 4 and \"\" otherwise)\n Option \"Qt\" is always enabled.\n\n Attributes\n ----------\n points : ndarray of double, shape (npoints, ndim)\n Coordinates of input points.\n vertices : ndarray of ints, shape (nvertices,)\n Indices of points forming the vertices of the convex hull.\n For 2-D convex hulls, the vertices are in counterclockwise order.\n For other dimensions, they are in input order.\n simplices : ndarray of ints, shape (nfacet, ndim)\n Indices of points forming the simplical facets of the convex hull.\n neighbors : ndarray of ints, shape (nfacet, ndim)\n Indices of neighbor facets for each facet.\n The kth neighbor is opposite to the kth vertex.\n -1 denotes no neighbor.\n equations : ndarray of double, shape (nfacet, ndim+1)\n [normal, offset] forming the hyperplane equation of the facet\n (see `Qhull documentation `__ for more).\n coplanar : ndarray of int, shape (ncoplanar, 3)\n Indices of coplanar points and the corresponding indices of\n the nearest facets and nearest vertex indices. Coplanar\n points are input points which were *not* included in the\n triangulation due to numerical precision issues.\n\n If option \"Qc\" is not specified, this list is not computed.\n good : ndarray of bool or None\n A one-dimensional Boolean array indicating which facets are\n good. Used with options that compute good facets, e.g. QGn\n and QG-n. Good facets are defined as those that are\n visible (n) or invisible (-n) from point n, where\n n is the nth point in 'points'. The 'good' attribute may be\n used as an index into 'simplices' to return the good (visible)\n facets: simplices[good]. A facet is visible from the outside\n of the hull only, and neither coplanarity nor degeneracy count\n as cases of visibility.\n\n If a \"QGn\" or \"QG-n\" option is not specified, None is returned.\n\n .. versionadded:: 1.3.0\n area : float\n Surface area of the convex hull when input dimension > 2. \n When input `points` are 2-dimensional, this is the perimeter of the convex hull.\n\n .. versionadded:: 0.17.0\n volume : float\n Volume of the convex hull when input dimension > 2. \n When input `points` are 2-dimensional, this is the area of the convex hull. \n\n .. versionadded:: 0.17.0\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n ValueError\n Raised if an incompatible array is given as input.\n\n Notes\n -----\n The convex hull is computed using the\n `Qhull library `__.\n\n Examples\n --------\n\n Convex hull of a random set of points:\n\n >>> from scipy.spatial import ConvexHull, convex_hull_plot_2d\n >>> points = np.random.rand(30, 2) # 30 random points in 2-D\n >>> hull = ConvexHull(points)\n\n Plot it:\n\n >>> import matplotlib.pyplot as plt\n >>> plt.plot(points[:,0], points[:,1], 'o')\n >>> for simplex in hull.simplices:\n ... plt.plot(points[simplex, 0], points[simplex, 1], 'k-')\n\n We could also have directly used the vertices of the hull, which\n for 2-D are guaranteed to be in counterclockwise order:\n\n >>> plt.plot(points[hull.vertices,0], points[hull.vertices,1], 'r--', lw=2)\n >>> plt.plot(points[hull.vertices[0],0], points[hull.vertices[0],1], 'ro')\n >>> plt.show()\n\n Facets visible from a point:\n\n Create a square and add a point above the square.\n\n >>> generators = np.array([[0.2, 0.2],\n ... [0.2, 0.4],\n ... [0.4, 0.4],\n ... [0.4, 0.2],\n ... [0.3, 0.6]])\n\n Call ConvexHull with the QG option. QG4 means\n compute the portions of the hull not including\n point 4, indicating the facets that are visible\n from point 4.\n\n >>> hull = ConvexHull(points=generators,\n ... qhull_options='QG4')\n\n The \"good\" array indicates which facets are\n visible from point 4.\n\n >>> print(hull.simplices)\n [[1 0]\n [1 2]\n [3 0]\n [3 2]]\n >>> print(hull.good)\n [False True False False]\n\n Now plot it, highlighting the visible facets.\n\n >>> fig = plt.figure()\n >>> ax = fig.add_subplot(1,1,1)\n >>> for visible_facet in hull.simplices[hull.good]:\n ... ax.plot(hull.points[visible_facet, 0],\n ... hull.points[visible_facet, 1],\n ... color='violet',\n ... lw=6)\n >>> convex_hull_plot_2d(hull, ax=ax)\n
# may vary\n >>> plt.show()\n\n References\n ----------\n .. [Qhull] http://www.qhull.org/\n\n" - __dict__: typing.Dict[str, typing.Any] - def __init__(self, points, incremental=..., qhull_options=...) -> None: - "\n ConvexHull(points, incremental=False, qhull_options=None)\n\n Convex hulls in N dimensions.\n\n .. versionadded:: 0.12.0\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndim)\n Coordinates of points to construct a convex hull from\n incremental : bool, optional\n Allow adding new points incrementally. This takes up some additional\n resources.\n qhull_options : str, optional\n Additional options to pass to Qhull. See Qhull manual\n for details. (Default: \"Qx\" for ndim > 4 and \"\" otherwise)\n Option \"Qt\" is always enabled.\n\n Attributes\n ----------\n points : ndarray of double, shape (npoints, ndim)\n Coordinates of input points.\n vertices : ndarray of ints, shape (nvertices,)\n Indices of points forming the vertices of the convex hull.\n For 2-D convex hulls, the vertices are in counterclockwise order.\n For other dimensions, they are in input order.\n simplices : ndarray of ints, shape (nfacet, ndim)\n Indices of points forming the simplical facets of the convex hull.\n neighbors : ndarray of ints, shape (nfacet, ndim)\n Indices of neighbor facets for each facet.\n The kth neighbor is opposite to the kth vertex.\n -1 denotes no neighbor.\n equations : ndarray of double, shape (nfacet, ndim+1)\n [normal, offset] forming the hyperplane equation of the facet\n (see `Qhull documentation `__ for more).\n coplanar : ndarray of int, shape (ncoplanar, 3)\n Indices of coplanar points and the corresponding indices of\n the nearest facets and nearest vertex indices. Coplanar\n points are input points which were *not* included in the\n triangulation due to numerical precision issues.\n\n If option \"Qc\" is not specified, this list is not computed.\n good : ndarray of bool or None\n A one-dimensional Boolean array indicating which facets are\n good. Used with options that compute good facets, e.g. QGn\n and QG-n. Good facets are defined as those that are\n visible (n) or invisible (-n) from point n, where\n n is the nth point in 'points'. The 'good' attribute may be\n used as an index into 'simplices' to return the good (visible)\n facets: simplices[good]. A facet is visible from the outside\n of the hull only, and neither coplanarity nor degeneracy count\n as cases of visibility.\n\n If a \"QGn\" or \"QG-n\" option is not specified, None is returned.\n\n .. versionadded:: 1.3.0\n area : float\n Surface area of the convex hull when input dimension > 2. \n When input `points` are 2-dimensional, this is the perimeter of the convex hull.\n\n .. versionadded:: 0.17.0\n volume : float\n Volume of the convex hull when input dimension > 2. \n When input `points` are 2-dimensional, this is the area of the convex hull. \n\n .. versionadded:: 0.17.0\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n ValueError\n Raised if an incompatible array is given as input.\n\n Notes\n -----\n The convex hull is computed using the\n `Qhull library `__.\n\n Examples\n --------\n\n Convex hull of a random set of points:\n\n >>> from scipy.spatial import ConvexHull, convex_hull_plot_2d\n >>> points = np.random.rand(30, 2) # 30 random points in 2-D\n >>> hull = ConvexHull(points)\n\n Plot it:\n\n >>> import matplotlib.pyplot as plt\n >>> plt.plot(points[:,0], points[:,1], 'o')\n >>> for simplex in hull.simplices:\n ... plt.plot(points[simplex, 0], points[simplex, 1], 'k-')\n\n We could also have directly used the vertices of the hull, which\n for 2-D are guaranteed to be in counterclockwise order:\n\n >>> plt.plot(points[hull.vertices,0], points[hull.vertices,1], 'r--', lw=2)\n >>> plt.plot(points[hull.vertices[0],0], points[hull.vertices[0],1], 'ro')\n >>> plt.show()\n\n Facets visible from a point:\n\n Create a square and add a point above the square.\n\n >>> generators = np.array([[0.2, 0.2],\n ... [0.2, 0.4],\n ... [0.4, 0.4],\n ... [0.4, 0.2],\n ... [0.3, 0.6]])\n\n Call ConvexHull with the QG option. QG4 means\n compute the portions of the hull not including\n point 4, indicating the facets that are visible\n from point 4.\n\n >>> hull = ConvexHull(points=generators,\n ... qhull_options='QG4')\n\n The \"good\" array indicates which facets are\n visible from point 4.\n\n >>> print(hull.simplices)\n [[1 0]\n [1 2]\n [3 0]\n [3 2]]\n >>> print(hull.good)\n [False True False False]\n\n Now plot it, highlighting the visible facets.\n\n >>> fig = plt.figure()\n >>> ax = fig.add_subplot(1,1,1)\n >>> for visible_facet in hull.simplices[hull.good]:\n ... ax.plot(hull.points[visible_facet, 0],\n ... hull.points[visible_facet, 1],\n ... color='violet',\n ... lw=6)\n >>> convex_hull_plot_2d(hull, ax=ax)\n
# may vary\n >>> plt.show()\n\n References\n ----------\n .. [Qhull] http://www.qhull.org/\n\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def _update(self, qhull) -> typing.Any: ... - def add_points(self, points, restart) -> typing.Any: - "\n add_points(points, restart=False)\n\n Process a set of additional new points.\n\n Parameters\n ----------\n points : ndarray\n New points to add. The dimensionality should match that of the\n initial points.\n restart : bool, optional\n Whether to restart processing from scratch, rather than\n adding points incrementally.\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n\n See Also\n --------\n close\n\n Notes\n -----\n You need to specify ``incremental=True`` when constructing the\n object to be able to add points incrementally. Incremental addition\n of points is also not possible after `close` has been called.\n\n" - ... - points: property - vertices: property - def __getattr__(self, name) -> typing.Any: ... - -class Delaunay(_QhullUser): - '\n Delaunay(points, furthest_site=False, incremental=False, qhull_options=None)\n\n Delaunay tessellation in N dimensions.\n\n .. versionadded:: 0.9\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndim)\n Coordinates of points to triangulate\n furthest_site : bool, optional\n Whether to compute a furthest-site Delaunay triangulation.\n Default: False\n\n .. versionadded:: 0.12.0\n incremental : bool, optional\n Allow adding new points incrementally. This takes up some additional\n resources.\n qhull_options : str, optional\n Additional options to pass to Qhull. See Qhull manual for\n details. Option "Qt" is always enabled.\n Default:"Qbb Qc Qz Qx Q12" for ndim > 4 and "Qbb Qc Qz Q12" otherwise.\n Incremental mode omits "Qz".\n\n .. versionadded:: 0.12.0\n\n Attributes\n ----------\n points : ndarray of double, shape (npoints, ndim)\n Coordinates of input points.\n simplices : ndarray of ints, shape (nsimplex, ndim+1)\n Indices of the points forming the simplices in the triangulation.\n For 2-D, the points are oriented counterclockwise.\n neighbors : ndarray of ints, shape (nsimplex, ndim+1)\n Indices of neighbor simplices for each simplex.\n The kth neighbor is opposite to the kth vertex.\n For simplices at the boundary, -1 denotes no neighbor.\n equations : ndarray of double, shape (nsimplex, ndim+2)\n [normal, offset] forming the hyperplane equation of the facet\n on the paraboloid\n (see `Qhull documentation `__ for more).\n paraboloid_scale, paraboloid_shift : float\n Scale and shift for the extra paraboloid dimension\n (see `Qhull documentation `__ for more).\n transform : ndarray of double, shape (nsimplex, ndim+1, ndim)\n Affine transform from ``x`` to the barycentric coordinates ``c``.\n This is defined by::\n\n T c = x - r\n\n At vertex ``j``, ``c_j = 1`` and the other coordinates zero.\n\n For simplex ``i``, ``transform[i,:ndim,:ndim]`` contains\n inverse of the matrix ``T``, and ``transform[i,ndim,:]``\n contains the vector ``r``.\n\n If the simplex is degenerate or nearly degenerate, its\n barycentric transform contains NaNs.\n vertex_to_simplex : ndarray of int, shape (npoints,)\n Lookup array, from a vertex, to some simplex which it is a part of.\n If qhull option "Qc" was not specified, the list will contain -1\n for points that are not vertices of the tessellation.\n convex_hull : ndarray of int, shape (nfaces, ndim)\n Vertices of facets forming the convex hull of the point set.\n The array contains the indices of the points belonging to\n the (N-1)-dimensional facets that form the convex hull\n of the triangulation.\n\n .. note::\n\n Computing convex hulls via the Delaunay triangulation is\n inefficient and subject to increased numerical instability.\n Use `ConvexHull` instead.\n coplanar : ndarray of int, shape (ncoplanar, 3)\n Indices of coplanar points and the corresponding indices of\n the nearest facet and the nearest vertex. Coplanar\n points are input points which were *not* included in the\n triangulation due to numerical precision issues.\n\n If option "Qc" is not specified, this list is not computed.\n\n .. versionadded:: 0.12.0\n vertices\n Same as `simplices`, but deprecated.\n vertex_neighbor_vertices : tuple of two ndarrays of int; (indptr, indices)\n Neighboring vertices of vertices. The indices of neighboring\n vertices of vertex `k` are ``indices[indptr[k]:indptr[k+1]]``.\n furthest_site\n True if this was a furthest site triangulation and False if not.\n\n .. versionadded:: 1.4.0\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n ValueError\n Raised if an incompatible array is given as input.\n\n Notes\n -----\n The tessellation is computed using the Qhull library\n `Qhull library `__.\n\n .. note::\n\n Unless you pass in the Qhull option "QJ", Qhull does not\n guarantee that each input point appears as a vertex in the\n Delaunay triangulation. Omitted points are listed in the\n `coplanar` attribute.\n\n Examples\n --------\n Triangulation of a set of points:\n\n >>> points = np.array([[0, 0], [0, 1.1], [1, 0], [1, 1]])\n >>> from scipy.spatial import Delaunay\n >>> tri = Delaunay(points)\n\n We can plot it:\n\n >>> import matplotlib.pyplot as plt\n >>> plt.triplot(points[:,0], points[:,1], tri.simplices)\n >>> plt.plot(points[:,0], points[:,1], \'o\')\n >>> plt.show()\n\n Point indices and coordinates for the two triangles forming the\n triangulation:\n\n >>> tri.simplices\n array([[2, 3, 0], # may vary\n [3, 1, 0]], dtype=int32)\n\n Note that depending on how rounding errors go, the simplices may\n be in a different order than above.\n\n >>> points[tri.simplices]\n array([[[ 1. , 0. ], # may vary\n [ 1. , 1. ],\n [ 0. , 0. ]],\n [[ 1. , 1. ],\n [ 0. , 1.1],\n [ 0. , 0. ]]])\n\n Triangle 0 is the only neighbor of triangle 1, and it\'s opposite to\n vertex 1 of triangle 1:\n\n >>> tri.neighbors[1]\n array([-1, 0, -1], dtype=int32)\n >>> points[tri.simplices[1,1]]\n array([ 0. , 1.1])\n\n We can find out which triangle points are in:\n\n >>> p = np.array([(0.1, 0.2), (1.5, 0.5), (0.5, 1.05)])\n >>> tri.find_simplex(p)\n array([ 1, -1, 1], dtype=int32)\n\n The returned integers in the array are the indices of the simplex the\n corresponding point is in. If -1 is returned, the point is in no simplex.\n Be aware that the shortcut in the following example only works corretcly\n for valid points as invalid points result in -1 which is itself a valid\n index for the last simplex in the list.\n\n >>> p_valids = np.array([(0.1, 0.2), (0.5, 1.05)])\n >>> tri.simplices[tri.find_simplex(p_valids)]\n array([[3, 1, 0], # may vary\n [3, 1, 0]], dtype=int32)\n\n We can also compute barycentric coordinates in triangle 1 for\n these points:\n\n >>> b = tri.transform[1,:2].dot(np.transpose(p - tri.transform[1,2]))\n >>> np.c_[np.transpose(b), 1 - b.sum(axis=0)]\n array([[ 0.1 , 0.09090909, 0.80909091],\n [ 1.5 , -0.90909091, 0.40909091],\n [ 0.5 , 0.5 , 0. ]])\n\n The coordinates for the first point are all positive, meaning it\n is indeed inside the triangle. The third point is on a vertex,\n hence its null third coordinate.\n\n' - __dict__: typing.Dict[str, typing.Any] - def __init__(self, points, furthest_site=..., incremental=..., qhull_options=...) -> None: - '\n Delaunay(points, furthest_site=False, incremental=False, qhull_options=None)\n\n Delaunay tessellation in N dimensions.\n\n .. versionadded:: 0.9\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndim)\n Coordinates of points to triangulate\n furthest_site : bool, optional\n Whether to compute a furthest-site Delaunay triangulation.\n Default: False\n\n .. versionadded:: 0.12.0\n incremental : bool, optional\n Allow adding new points incrementally. This takes up some additional\n resources.\n qhull_options : str, optional\n Additional options to pass to Qhull. See Qhull manual for\n details. Option "Qt" is always enabled.\n Default:"Qbb Qc Qz Qx Q12" for ndim > 4 and "Qbb Qc Qz Q12" otherwise.\n Incremental mode omits "Qz".\n\n .. versionadded:: 0.12.0\n\n Attributes\n ----------\n points : ndarray of double, shape (npoints, ndim)\n Coordinates of input points.\n simplices : ndarray of ints, shape (nsimplex, ndim+1)\n Indices of the points forming the simplices in the triangulation.\n For 2-D, the points are oriented counterclockwise.\n neighbors : ndarray of ints, shape (nsimplex, ndim+1)\n Indices of neighbor simplices for each simplex.\n The kth neighbor is opposite to the kth vertex.\n For simplices at the boundary, -1 denotes no neighbor.\n equations : ndarray of double, shape (nsimplex, ndim+2)\n [normal, offset] forming the hyperplane equation of the facet\n on the paraboloid\n (see `Qhull documentation `__ for more).\n paraboloid_scale, paraboloid_shift : float\n Scale and shift for the extra paraboloid dimension\n (see `Qhull documentation `__ for more).\n transform : ndarray of double, shape (nsimplex, ndim+1, ndim)\n Affine transform from ``x`` to the barycentric coordinates ``c``.\n This is defined by::\n\n T c = x - r\n\n At vertex ``j``, ``c_j = 1`` and the other coordinates zero.\n\n For simplex ``i``, ``transform[i,:ndim,:ndim]`` contains\n inverse of the matrix ``T``, and ``transform[i,ndim,:]``\n contains the vector ``r``.\n\n If the simplex is degenerate or nearly degenerate, its\n barycentric transform contains NaNs.\n vertex_to_simplex : ndarray of int, shape (npoints,)\n Lookup array, from a vertex, to some simplex which it is a part of.\n If qhull option "Qc" was not specified, the list will contain -1\n for points that are not vertices of the tessellation.\n convex_hull : ndarray of int, shape (nfaces, ndim)\n Vertices of facets forming the convex hull of the point set.\n The array contains the indices of the points belonging to\n the (N-1)-dimensional facets that form the convex hull\n of the triangulation.\n\n .. note::\n\n Computing convex hulls via the Delaunay triangulation is\n inefficient and subject to increased numerical instability.\n Use `ConvexHull` instead.\n coplanar : ndarray of int, shape (ncoplanar, 3)\n Indices of coplanar points and the corresponding indices of\n the nearest facet and the nearest vertex. Coplanar\n points are input points which were *not* included in the\n triangulation due to numerical precision issues.\n\n If option "Qc" is not specified, this list is not computed.\n\n .. versionadded:: 0.12.0\n vertices\n Same as `simplices`, but deprecated.\n vertex_neighbor_vertices : tuple of two ndarrays of int; (indptr, indices)\n Neighboring vertices of vertices. The indices of neighboring\n vertices of vertex `k` are ``indices[indptr[k]:indptr[k+1]]``.\n furthest_site\n True if this was a furthest site triangulation and False if not.\n\n .. versionadded:: 1.4.0\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n ValueError\n Raised if an incompatible array is given as input.\n\n Notes\n -----\n The tessellation is computed using the Qhull library\n `Qhull library `__.\n\n .. note::\n\n Unless you pass in the Qhull option "QJ", Qhull does not\n guarantee that each input point appears as a vertex in the\n Delaunay triangulation. Omitted points are listed in the\n `coplanar` attribute.\n\n Examples\n --------\n Triangulation of a set of points:\n\n >>> points = np.array([[0, 0], [0, 1.1], [1, 0], [1, 1]])\n >>> from scipy.spatial import Delaunay\n >>> tri = Delaunay(points)\n\n We can plot it:\n\n >>> import matplotlib.pyplot as plt\n >>> plt.triplot(points[:,0], points[:,1], tri.simplices)\n >>> plt.plot(points[:,0], points[:,1], \'o\')\n >>> plt.show()\n\n Point indices and coordinates for the two triangles forming the\n triangulation:\n\n >>> tri.simplices\n array([[2, 3, 0], # may vary\n [3, 1, 0]], dtype=int32)\n\n Note that depending on how rounding errors go, the simplices may\n be in a different order than above.\n\n >>> points[tri.simplices]\n array([[[ 1. , 0. ], # may vary\n [ 1. , 1. ],\n [ 0. , 0. ]],\n [[ 1. , 1. ],\n [ 0. , 1.1],\n [ 0. , 0. ]]])\n\n Triangle 0 is the only neighbor of triangle 1, and it\'s opposite to\n vertex 1 of triangle 1:\n\n >>> tri.neighbors[1]\n array([-1, 0, -1], dtype=int32)\n >>> points[tri.simplices[1,1]]\n array([ 0. , 1.1])\n\n We can find out which triangle points are in:\n\n >>> p = np.array([(0.1, 0.2), (1.5, 0.5), (0.5, 1.05)])\n >>> tri.find_simplex(p)\n array([ 1, -1, 1], dtype=int32)\n\n The returned integers in the array are the indices of the simplex the\n corresponding point is in. If -1 is returned, the point is in no simplex.\n Be aware that the shortcut in the following example only works corretcly\n for valid points as invalid points result in -1 which is itself a valid\n index for the last simplex in the list.\n\n >>> p_valids = np.array([(0.1, 0.2), (0.5, 1.05)])\n >>> tri.simplices[tri.find_simplex(p_valids)]\n array([[3, 1, 0], # may vary\n [3, 1, 0]], dtype=int32)\n\n We can also compute barycentric coordinates in triangle 1 for\n these points:\n\n >>> b = tri.transform[1,:2].dot(np.transpose(p - tri.transform[1,2]))\n >>> np.c_[np.transpose(b), 1 - b.sum(axis=0)]\n array([[ 0.1 , 0.09090909, 0.80909091],\n [ 1.5 , -0.90909091, 0.40909091],\n [ 0.5 , 0.5 , 0. ]])\n\n The coordinates for the first point are all positive, meaning it\n is indeed inside the triangle. The third point is on a vertex,\n hence its null third coordinate.\n\n' - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def _update(self, qhull) -> typing.Any: ... - def add_points(self, points, restart) -> typing.Any: - "\n add_points(points, restart=False)\n\n Process a set of additional new points.\n\n Parameters\n ----------\n points : ndarray\n New points to add. The dimensionality should match that of the\n initial points.\n restart : bool, optional\n Whether to restart processing from scratch, rather than\n adding points incrementally.\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n\n See Also\n --------\n close\n\n Notes\n -----\n You need to specify ``incremental=True`` when constructing the\n object to be able to add points incrementally. Incremental addition\n of points is also not possible after `close` has been called.\n\n" - ... - convex_hull: property - def find_simplex(self, xi, bruteforce, tol) -> typing.Any: - "\n find_simplex(self, xi, bruteforce=False, tol=None)\n\n Find the simplices containing the given points.\n\n Parameters\n ----------\n tri : DelaunayInfo\n Delaunay triangulation\n xi : ndarray of double, shape (..., ndim)\n Points to locate\n bruteforce : bool, optional\n Whether to only perform a brute-force search\n tol : float, optional\n Tolerance allowed in the inside-triangle check.\n Default is ``100*eps``.\n\n Returns\n -------\n i : ndarray of int, same shape as `xi`\n Indices of simplices containing each point.\n Points outside the triangulation get the value -1.\n\n Notes\n -----\n This uses an algorithm adapted from Qhull's ``qh_findbestfacet``,\n which makes use of the connection between a convex hull and a\n Delaunay triangulation. After finding the simplex closest to\n the point in N+1 dimensions, the algorithm falls back to\n directed search in N dimensions.\n\n" - ... - - def lift_points(self, x) -> typing.Any: - "\n lift_points(self, x)\n\n Lift points to the Qhull paraboloid.\n\n" - ... - - def plane_distance(self, xi) -> typing.Any: - "\n plane_distance(self, xi)\n\n Compute hyperplane distances to the point `xi` from all simplices.\n\n" - ... - points: property - transform: property - vertex_neighbor_vertices: property - vertex_to_simplex: property - def __getattr__(self, name) -> typing.Any: ... - -class HalfspaceIntersection(_QhullUser): - '\n HalfspaceIntersection(halfspaces, interior_point, incremental=False, qhull_options=None)\n\n Halfspace intersections in N dimensions.\n\n .. versionadded:: 0.19.0\n\n Parameters\n ----------\n halfspaces : ndarray of floats, shape (nineq, ndim+1)\n Stacked Inequalities of the form Ax + b <= 0 in format [A; b]\n interior_point : ndarray of floats, shape (ndim,)\n Point clearly inside the region defined by halfspaces. Also called a feasible\n point, it can be obtained by linear programming.\n incremental : bool, optional\n Allow adding new halfspaces incrementally. This takes up some additional\n resources.\n qhull_options : str, optional\n Additional options to pass to Qhull. See Qhull manual\n for details. (Default: "Qx" for ndim > 4 and "" otherwise)\n Option "H" is always enabled.\n\n Attributes\n ----------\n halfspaces : ndarray of double, shape (nineq, ndim+1)\n Input halfspaces.\n interior_point :ndarray of floats, shape (ndim,)\n Input interior point.\n intersections : ndarray of double, shape (ninter, ndim)\n Intersections of all halfspaces.\n dual_points : ndarray of double, shape (nineq, ndim)\n Dual points of the input halfspaces.\n dual_facets : list of lists of ints\n Indices of points forming the (non necessarily simplicial) facets of\n the dual convex hull.\n dual_vertices : ndarray of ints, shape (nvertices,)\n Indices of halfspaces forming the vertices of the dual convex hull.\n For 2-D convex hulls, the vertices are in counterclockwise order.\n For other dimensions, they are in input order.\n dual_equations : ndarray of double, shape (nfacet, ndim+1)\n [normal, offset] forming the hyperplane equation of the dual facet\n (see `Qhull documentation `__ for more).\n dual_area : float\n Area of the dual convex hull\n dual_volume : float\n Volume of the dual convex hull\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n ValueError\n Raised if an incompatible array is given as input.\n\n Notes\n -----\n The intersections are computed using the\n `Qhull library `__.\n This reproduces the "qhalf" functionality of Qhull.\n\n Examples\n --------\n\n Halfspace intersection of planes forming some polygon\n\n >>> from scipy.spatial import HalfspaceIntersection\n >>> halfspaces = np.array([[-1, 0., 0.],\n ... [0., -1., 0.],\n ... [2., 1., -4.],\n ... [-0.5, 1., -2.]])\n >>> feasible_point = np.array([0.5, 0.5])\n >>> hs = HalfspaceIntersection(halfspaces, feasible_point)\n\n Plot halfspaces as filled regions and intersection points:\n\n >>> import matplotlib.pyplot as plt\n >>> fig = plt.figure()\n >>> ax = fig.add_subplot(1, 1, 1, aspect=\'equal\')\n >>> xlim, ylim = (-1, 3), (-1, 3)\n >>> ax.set_xlim(xlim)\n >>> ax.set_ylim(ylim)\n >>> x = np.linspace(-1, 3, 100)\n >>> symbols = [\'-\', \'+\', \'x\', \'*\']\n >>> signs = [0, 0, -1, -1]\n >>> fmt = {"color": None, "edgecolor": "b", "alpha": 0.5}\n >>> for h, sym, sign in zip(halfspaces, symbols, signs):\n ... hlist = h.tolist()\n ... fmt["hatch"] = sym\n ... if h[1]== 0:\n ... ax.axvline(-h[2]/h[0], label=\'{}x+{}y+{}=0\'.format(*hlist))\n ... xi = np.linspace(xlim[sign], -h[2]/h[0], 100)\n ... ax.fill_between(xi, ylim[0], ylim[1], **fmt)\n ... else:\n ... ax.plot(x, (-h[2]-h[0]*x)/h[1], label=\'{}x+{}y+{}=0\'.format(*hlist))\n ... ax.fill_between(x, (-h[2]-h[0]*x)/h[1], ylim[sign], **fmt)\n >>> x, y = zip(*hs.intersections)\n >>> ax.plot(x, y, \'o\', markersize=8)\n\n By default, qhull does not provide with a way to compute an interior point.\n This can easily be computed using linear programming. Considering halfspaces\n of the form :math:`Ax + b \\leq 0`, solving the linear program:\n\n .. math::\n\n max \\: y\n\n s.t. Ax + y ||A_i|| \\leq -b\n\n With :math:`A_i` being the rows of A, i.e. the normals to each plane.\n\n Will yield a point x that is furthest inside the convex polyhedron. To\n be precise, it is the center of the largest hypersphere of radius y\n inscribed in the polyhedron. This point is called the Chebyshev center\n of the polyhedron (see [1]_ 4.3.1, pp148-149). The\n equations outputted by Qhull are always normalized.\n\n >>> from scipy.optimize import linprog\n >>> from matplotlib.patches import Circle\n >>> norm_vector = np.reshape(np.linalg.norm(halfspaces[:, :-1], axis=1),\n ... (halfspaces.shape[0], 1))\n >>> c = np.zeros((halfspaces.shape[1],))\n >>> c[-1] = -1\n >>> A = np.hstack((halfspaces[:, :-1], norm_vector))\n >>> b = - halfspaces[:, -1:]\n >>> res = linprog(c, A_ub=A, b_ub=b, bounds=(None, None))\n >>> x = res.x[:-1]\n >>> y = res.x[-1]\n >>> circle = Circle(x, radius=y, alpha=0.3)\n >>> ax.add_patch(circle)\n >>> plt.legend(bbox_to_anchor=(1.6, 1.0))\n >>> plt.show()\n\n References\n ----------\n .. [Qhull] http://www.qhull.org/\n .. [1] S. Boyd, L. Vandenberghe, Convex Optimization, available\n at http://stanford.edu/~boyd/cvxbook/\n\n' - __dict__: typing.Dict[str, typing.Any] - def __init__(self, halfspaces, interior_point, incremental=..., qhull_options=...) -> None: - '\n HalfspaceIntersection(halfspaces, interior_point, incremental=False, qhull_options=None)\n\n Halfspace intersections in N dimensions.\n\n .. versionadded:: 0.19.0\n\n Parameters\n ----------\n halfspaces : ndarray of floats, shape (nineq, ndim+1)\n Stacked Inequalities of the form Ax + b <= 0 in format [A; b]\n interior_point : ndarray of floats, shape (ndim,)\n Point clearly inside the region defined by halfspaces. Also called a feasible\n point, it can be obtained by linear programming.\n incremental : bool, optional\n Allow adding new halfspaces incrementally. This takes up some additional\n resources.\n qhull_options : str, optional\n Additional options to pass to Qhull. See Qhull manual\n for details. (Default: "Qx" for ndim > 4 and "" otherwise)\n Option "H" is always enabled.\n\n Attributes\n ----------\n halfspaces : ndarray of double, shape (nineq, ndim+1)\n Input halfspaces.\n interior_point :ndarray of floats, shape (ndim,)\n Input interior point.\n intersections : ndarray of double, shape (ninter, ndim)\n Intersections of all halfspaces.\n dual_points : ndarray of double, shape (nineq, ndim)\n Dual points of the input halfspaces.\n dual_facets : list of lists of ints\n Indices of points forming the (non necessarily simplicial) facets of\n the dual convex hull.\n dual_vertices : ndarray of ints, shape (nvertices,)\n Indices of halfspaces forming the vertices of the dual convex hull.\n For 2-D convex hulls, the vertices are in counterclockwise order.\n For other dimensions, they are in input order.\n dual_equations : ndarray of double, shape (nfacet, ndim+1)\n [normal, offset] forming the hyperplane equation of the dual facet\n (see `Qhull documentation `__ for more).\n dual_area : float\n Area of the dual convex hull\n dual_volume : float\n Volume of the dual convex hull\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n ValueError\n Raised if an incompatible array is given as input.\n\n Notes\n -----\n The intersections are computed using the\n `Qhull library `__.\n This reproduces the "qhalf" functionality of Qhull.\n\n Examples\n --------\n\n Halfspace intersection of planes forming some polygon\n\n >>> from scipy.spatial import HalfspaceIntersection\n >>> halfspaces = np.array([[-1, 0., 0.],\n ... [0., -1., 0.],\n ... [2., 1., -4.],\n ... [-0.5, 1., -2.]])\n >>> feasible_point = np.array([0.5, 0.5])\n >>> hs = HalfspaceIntersection(halfspaces, feasible_point)\n\n Plot halfspaces as filled regions and intersection points:\n\n >>> import matplotlib.pyplot as plt\n >>> fig = plt.figure()\n >>> ax = fig.add_subplot(1, 1, 1, aspect=\'equal\')\n >>> xlim, ylim = (-1, 3), (-1, 3)\n >>> ax.set_xlim(xlim)\n >>> ax.set_ylim(ylim)\n >>> x = np.linspace(-1, 3, 100)\n >>> symbols = [\'-\', \'+\', \'x\', \'*\']\n >>> signs = [0, 0, -1, -1]\n >>> fmt = {"color": None, "edgecolor": "b", "alpha": 0.5}\n >>> for h, sym, sign in zip(halfspaces, symbols, signs):\n ... hlist = h.tolist()\n ... fmt["hatch"] = sym\n ... if h[1]== 0:\n ... ax.axvline(-h[2]/h[0], label=\'{}x+{}y+{}=0\'.format(*hlist))\n ... xi = np.linspace(xlim[sign], -h[2]/h[0], 100)\n ... ax.fill_between(xi, ylim[0], ylim[1], **fmt)\n ... else:\n ... ax.plot(x, (-h[2]-h[0]*x)/h[1], label=\'{}x+{}y+{}=0\'.format(*hlist))\n ... ax.fill_between(x, (-h[2]-h[0]*x)/h[1], ylim[sign], **fmt)\n >>> x, y = zip(*hs.intersections)\n >>> ax.plot(x, y, \'o\', markersize=8)\n\n By default, qhull does not provide with a way to compute an interior point.\n This can easily be computed using linear programming. Considering halfspaces\n of the form :math:`Ax + b \\leq 0`, solving the linear program:\n\n .. math::\n\n max \\: y\n\n s.t. Ax + y ||A_i|| \\leq -b\n\n With :math:`A_i` being the rows of A, i.e. the normals to each plane.\n\n Will yield a point x that is furthest inside the convex polyhedron. To\n be precise, it is the center of the largest hypersphere of radius y\n inscribed in the polyhedron. This point is called the Chebyshev center\n of the polyhedron (see [1]_ 4.3.1, pp148-149). The\n equations outputted by Qhull are always normalized.\n\n >>> from scipy.optimize import linprog\n >>> from matplotlib.patches import Circle\n >>> norm_vector = np.reshape(np.linalg.norm(halfspaces[:, :-1], axis=1),\n ... (halfspaces.shape[0], 1))\n >>> c = np.zeros((halfspaces.shape[1],))\n >>> c[-1] = -1\n >>> A = np.hstack((halfspaces[:, :-1], norm_vector))\n >>> b = - halfspaces[:, -1:]\n >>> res = linprog(c, A_ub=A, b_ub=b, bounds=(None, None))\n >>> x = res.x[:-1]\n >>> y = res.x[-1]\n >>> circle = Circle(x, radius=y, alpha=0.3)\n >>> ax.add_patch(circle)\n >>> plt.legend(bbox_to_anchor=(1.6, 1.0))\n >>> plt.show()\n\n References\n ----------\n .. [Qhull] http://www.qhull.org/\n .. [1] S. Boyd, L. Vandenberghe, Convex Optimization, available\n at http://stanford.edu/~boyd/cvxbook/\n\n' - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def _update(self, qhull) -> typing.Any: ... - def add_halfspaces(self, halfspaces, restart) -> typing.Any: - "\n add_halfspaces(halfspaces, restart=False)\n\n Process a set of additional new halfspaces.\n\n Parameters\n ----------\n halfspaces : ndarray\n New halfspaces to add. The dimensionality should match that of the\n initial halfspaces.\n restart : bool, optional\n Whether to restart processing from scratch, rather than\n adding halfspaces incrementally.\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n\n See Also\n --------\n close\n\n Notes\n -----\n You need to specify ``incremental=True`` when constructing the\n object to be able to add halfspaces incrementally. Incremental addition\n of halfspaces is also not possible after `close` has been called.\n\n" - ... - dual_vertices: property - halfspaces: property - def __getattr__(self, name) -> typing.Any: ... - -class QhullError(_mod_builtins.RuntimeError): - __dict__: typing.Dict[str, typing.Any] - def __init__(self, *args, **kwargs) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def __getattr__(self, name) -> typing.Any: ... - -class Voronoi(_QhullUser): - '\n Voronoi(points, furthest_site=False, incremental=False, qhull_options=None)\n\n Voronoi diagrams in N dimensions.\n\n .. versionadded:: 0.12.0\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndim)\n Coordinates of points to construct a convex hull from\n furthest_site : bool, optional\n Whether to compute a furthest-site Voronoi diagram. Default: False\n incremental : bool, optional\n Allow adding new points incrementally. This takes up some additional\n resources.\n qhull_options : str, optional\n Additional options to pass to Qhull. See Qhull manual\n for details. (Default: "Qbb Qc Qz Qx" for ndim > 4 and\n "Qbb Qc Qz" otherwise. Incremental mode omits "Qz".)\n\n Attributes\n ----------\n points : ndarray of double, shape (npoints, ndim)\n Coordinates of input points.\n vertices : ndarray of double, shape (nvertices, ndim)\n Coordinates of the Voronoi vertices.\n ridge_points : ndarray of ints, shape ``(nridges, 2)``\n Indices of the points between which each Voronoi ridge lies.\n ridge_vertices : list of list of ints, shape ``(nridges, *)``\n Indices of the Voronoi vertices forming each Voronoi ridge.\n regions : list of list of ints, shape ``(nregions, *)``\n Indices of the Voronoi vertices forming each Voronoi region.\n -1 indicates vertex outside the Voronoi diagram.\n point_region : list of ints, shape (npoints)\n Index of the Voronoi region for each input point.\n If qhull option "Qc" was not specified, the list will contain -1\n for points that are not associated with a Voronoi region.\n furthest_site\n True if this was a furthest site triangulation and False if not.\n\n .. versionadded:: 1.4.0\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n ValueError\n Raised if an incompatible array is given as input.\n\n Notes\n -----\n The Voronoi diagram is computed using the\n `Qhull library `__.\n\n Examples\n --------\n Voronoi diagram for a set of point:\n\n >>> points = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2],\n ... [2, 0], [2, 1], [2, 2]])\n >>> from scipy.spatial import Voronoi, voronoi_plot_2d\n >>> vor = Voronoi(points)\n\n Plot it:\n\n >>> import matplotlib.pyplot as plt\n >>> fig = voronoi_plot_2d(vor)\n >>> plt.show()\n\n The Voronoi vertices:\n\n >>> vor.vertices\n array([[0.5, 0.5],\n [0.5, 1.5],\n [1.5, 0.5],\n [1.5, 1.5]])\n\n There is a single finite Voronoi region, and four finite Voronoi\n ridges:\n\n >>> vor.regions\n [[], [-1, 0], [-1, 1], [1, -1, 0], [3, -1, 2], [-1, 3], [-1, 2], [0, 1, 3, 2], [2, -1, 0], [3, -1, 1]]\n >>> vor.ridge_vertices\n [[-1, 0], [-1, 0], [-1, 1], [-1, 1], [0, 1], [-1, 3], [-1, 2], [2, 3], [-1, 3], [-1, 2], [1, 3], [0, 2]]\n\n The ridges are perpendicular between lines drawn between the following\n input points:\n\n >>> vor.ridge_points\n array([[0, 3],\n [0, 1],\n [2, 5],\n [2, 1],\n [1, 4],\n [7, 8],\n [7, 6],\n [7, 4],\n [8, 5],\n [6, 3],\n [4, 5],\n [4, 3]], dtype=int32)\n\n' - __dict__: typing.Dict[str, typing.Any] - def __init__(self, points, furthest_site=..., incremental=..., qhull_options=...) -> None: - '\n Voronoi(points, furthest_site=False, incremental=False, qhull_options=None)\n\n Voronoi diagrams in N dimensions.\n\n .. versionadded:: 0.12.0\n\n Parameters\n ----------\n points : ndarray of floats, shape (npoints, ndim)\n Coordinates of points to construct a convex hull from\n furthest_site : bool, optional\n Whether to compute a furthest-site Voronoi diagram. Default: False\n incremental : bool, optional\n Allow adding new points incrementally. This takes up some additional\n resources.\n qhull_options : str, optional\n Additional options to pass to Qhull. See Qhull manual\n for details. (Default: "Qbb Qc Qz Qx" for ndim > 4 and\n "Qbb Qc Qz" otherwise. Incremental mode omits "Qz".)\n\n Attributes\n ----------\n points : ndarray of double, shape (npoints, ndim)\n Coordinates of input points.\n vertices : ndarray of double, shape (nvertices, ndim)\n Coordinates of the Voronoi vertices.\n ridge_points : ndarray of ints, shape ``(nridges, 2)``\n Indices of the points between which each Voronoi ridge lies.\n ridge_vertices : list of list of ints, shape ``(nridges, *)``\n Indices of the Voronoi vertices forming each Voronoi ridge.\n regions : list of list of ints, shape ``(nregions, *)``\n Indices of the Voronoi vertices forming each Voronoi region.\n -1 indicates vertex outside the Voronoi diagram.\n point_region : list of ints, shape (npoints)\n Index of the Voronoi region for each input point.\n If qhull option "Qc" was not specified, the list will contain -1\n for points that are not associated with a Voronoi region.\n furthest_site\n True if this was a furthest site triangulation and False if not.\n\n .. versionadded:: 1.4.0\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n ValueError\n Raised if an incompatible array is given as input.\n\n Notes\n -----\n The Voronoi diagram is computed using the\n `Qhull library `__.\n\n Examples\n --------\n Voronoi diagram for a set of point:\n\n >>> points = np.array([[0, 0], [0, 1], [0, 2], [1, 0], [1, 1], [1, 2],\n ... [2, 0], [2, 1], [2, 2]])\n >>> from scipy.spatial import Voronoi, voronoi_plot_2d\n >>> vor = Voronoi(points)\n\n Plot it:\n\n >>> import matplotlib.pyplot as plt\n >>> fig = voronoi_plot_2d(vor)\n >>> plt.show()\n\n The Voronoi vertices:\n\n >>> vor.vertices\n array([[0.5, 0.5],\n [0.5, 1.5],\n [1.5, 0.5],\n [1.5, 1.5]])\n\n There is a single finite Voronoi region, and four finite Voronoi\n ridges:\n\n >>> vor.regions\n [[], [-1, 0], [-1, 1], [1, -1, 0], [3, -1, 2], [-1, 3], [-1, 2], [0, 1, 3, 2], [2, -1, 0], [3, -1, 1]]\n >>> vor.ridge_vertices\n [[-1, 0], [-1, 0], [-1, 1], [-1, 1], [0, 1], [-1, 3], [-1, 2], [2, 3], [-1, 3], [-1, 2], [1, 3], [0, 2]]\n\n The ridges are perpendicular between lines drawn between the following\n input points:\n\n >>> vor.ridge_points\n array([[0, 3],\n [0, 1],\n [2, 5],\n [2, 1],\n [1, 4],\n [7, 8],\n [7, 6],\n [7, 4],\n [8, 5],\n [6, 3],\n [4, 5],\n [4, 3]], dtype=int32)\n\n' - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def _update(self, qhull) -> typing.Any: ... - def add_points(self, points, restart) -> typing.Any: - "\n add_points(points, restart=False)\n\n Process a set of additional new points.\n\n Parameters\n ----------\n points : ndarray\n New points to add. The dimensionality should match that of the\n initial points.\n restart : bool, optional\n Whether to restart processing from scratch, rather than\n adding points incrementally.\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n\n See Also\n --------\n close\n\n Notes\n -----\n You need to specify ``incremental=True`` when constructing the\n object to be able to add points incrementally. Incremental addition\n of points is also not possible after `close` has been called.\n\n" - ... - points: property - ridge_dict: property - def __getattr__(self, name) -> typing.Any: ... - -class _Qhull(_mod_builtins.object): - def __init__(self, *args, **kwargs) -> None: ... - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... - def __setstate__(self, state: typing.Any) -> None: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - def add_points(self) -> typing.Any: ... - def check_active(self) -> typing.Any: ... - def close(self) -> typing.Any: - "\n Uninitialize this instance\n" - ... - - @property - def furthest_site(self) -> typing.Any: ... - def get_extremes_2d(self) -> typing.Any: - "\n Compute the extremal points in a 2-D convex hull, i.e. the\n vertices of the convex hull, ordered counterclockwise.\n\n See qhull/io.c:qh_printextremes_2d\n\n" - ... - - def get_hull_facets(self) -> typing.Any: - "Returns the facets contained in the current Qhull.\n This function does not assume that the hull is simplicial,\n meaning that facets will have different number of vertices.\n It is thus less efficient but more general than get_simplex_facet_array.\n\n Returns\n -------\n facets: list of lists of ints\n The indices of the vertices forming each facet.\n" - ... - - def get_hull_points(self) -> typing.Any: - "Returns all points currently contained in Qhull.\n It is equivalent to retrieving the input in most cases, except in\n halfspace mode, where the points are in fact the points of the dual\n hull.\n\n Returns\n -------\n points: array of double, shape (nrpoints, ndim)\n The array of points contained in Qhull.\n\n" - ... - - def get_paraboloid_shift_scale(self) -> typing.Any: ... - def get_points(self) -> typing.Any: ... - def get_simplex_facet_array(self) -> typing.Any: - "\n Return array of simplical facets currently in Qhull.\n\n Returns\n -------\n facets : array of int, shape (nfacets, ndim+1)\n Indices of coordinates of vertices forming the simplical facets\n neighbors : array of int, shape (nfacets, ndim)\n Indices of neighboring facets. The kth neighbor is opposite\n the kth vertex, and the first neighbor is the horizon facet\n for the first vertex.\n\n Facets extending to infinity are denoted with index -1.\n equations : array of double, shape (nfacets, ndim+2)\n\n" - ... - - def get_voronoi_diagram(self) -> typing.Any: - "\n Return the voronoi diagram currently in Qhull.\n\n Returns\n -------\n voronoi_vertices : array of double, shape (nvoronoi_vertices, ndim)\n Coordinates of the Voronoi vertices\n\n ridge_points : array of double, shape (nridges, 2)\n Voronoi ridges, as indices to the points array.\n\n ridge_vertices : list of lists, shape (nridges, *)\n Voronoi vertices for each Voronoi ridge, as indices to\n the Voronoi vertices array.\n Infinity is indicated by index ``-1``.\n\n regions : list of lists, shape (nregion, *)\n Voronoi vertices of all regions.\n\n point_region : array of int, shape (npoint,)\n Index of the Voronoi region for each input point.\n\n" - ... - - @property - def mode_option(self) -> typing.Any: ... - @property - def ndim(self) -> typing.Any: ... - @property - def options(self) -> typing.Any: ... - def triangulate(self) -> typing.Any: ... - def volume_area(self) -> typing.Any: ... - def __getattr__(self, name) -> typing.Any: ... - -class _QhullUser(_mod_builtins.object): - "\n Takes care of basic dealings with the Qhull objects\n" - def __del__(self) -> None: ... - - __dict__: typing.Dict[str, typing.Any] - def __init__(self, qhull, incremental) -> None: - "\n Takes care of basic dealings with the Qhull objects\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def _add_points(self, points, restart, interior_point) -> typing.Any: - "\n add_points(points, restart=False)\n\n Process a set of additional new points.\n\n Parameters\n ----------\n points : ndarray\n New points to add. The dimensionality should match that of the\n initial points.\n restart : bool, optional\n Whether to restart processing from scratch, rather than\n adding points incrementally.\n\n Raises\n ------\n QhullError\n Raised when Qhull encounters an error condition, such as\n geometrical degeneracy when options to resolve are not enabled.\n\n See Also\n --------\n close\n\n Notes\n -----\n You need to specify ``incremental=True`` when constructing the\n object to be able to add points incrementally. Incremental addition\n of points is also not possible after `close` has been called.\n\n" - ... - _qhull: typing.Any - def _update(self, qhull) -> typing.Any: ... - def close(self) -> typing.Any: - "\n close()\n\n Finish incremental processing.\n\n Call this to free resources taken up by Qhull, when using the\n incremental mode. After calling this, adding more points is no\n longer possible.\n" - ... - - def __getattr__(self, name) -> typing.Any: ... - -__all__: list -__doc__: str -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _copy_docstr() -> typing.Any: ... -def _get_barycentric_transforms() -> typing.Any: - "\n Compute barycentric affine coordinate transformations for given\n simplices.\n\n Returns\n -------\n Tinvs : array, shape (nsimplex, ndim+1, ndim)\n Barycentric transforms for each simplex.\n\n Tinvs[i,:ndim,:ndim] contains inverse of the matrix ``T``,\n and Tinvs[i,ndim,:] contains the vector ``r_n`` (see below).\n\n Notes\n -----\n Barycentric transform from ``x`` to ``c`` is defined by::\n\n T c = x - r_n\n\n where the ``r_1, ..., r_n`` are the vertices of the simplex.\n The matrix ``T`` is defined by the condition::\n\n T e_j = r_j - r_n\n\n where ``e_j`` is the unit axis vector, e.g, ``e_2 = [0,1,0,0,...]``\n This implies that ``T_ij = (r_j - r_n)_i``.\n\n For the barycentric transforms, we need to compute the inverse\n matrix ``T^-1`` and store the vectors ``r_n`` for each vertex.\n These are stacked into the `Tinvs` returned.\n\n" - ... - -def asbytes(s) -> typing.Any: ... -def tsearch(tri, xi) -> typing.Any: - "\n tsearch(tri, xi)\n\n Find simplices containing the given points. This function does the\n same thing as `Delaunay.find_simplex`.\n\n .. versionadded:: 0.9\n\n See Also\n --------\n Delaunay.find_simplex\n\n\n Examples\n --------\n\n >>> import numpy as np\n >>> import matplotlib.pyplot as plt\n >>> from scipy.spatial import Delaunay, delaunay_plot_2d, tsearch\n\n The Delaunay triangulation of a set of random points:\n\n >>> pts = np.random.rand(20, 2)\n >>> tri = Delaunay(pts)\n >>> _ = delaunay_plot_2d(tri)\n\n Find the simplices containing a given set of points:\n\n >>> loc = np.random.uniform(0.2, 0.8, (5, 2))\n >>> s = tsearch(tri, loc)\n >>> plt.triplot(pts[:, 0], pts[:, 1], tri.simplices[s], 'b-', mask=s==-1)\n >>> plt.scatter(loc[:, 0], loc[:, 1], c='r', marker='x')\n >>> plt.show()\n\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/spatial/transform/rotation.pyi b/stubs/scipy-stubs/spatial/transform/rotation.pyi deleted file mode 100644 index 298fd1f8..00000000 --- a/stubs/scipy-stubs/spatial/transform/rotation.pyi +++ /dev/null @@ -1,177 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.spatial.transform.rotation, version: unspecified -import builtins as _mod_builtins -import typing - -class Rotation(_mod_builtins.object): - "Rotation in 3 dimensions.\n\n This class provides an interface to initialize from and represent rotations\n with:\n\n - Quaternions\n - Rotation Matrices\n - Rotation Vectors\n - Modified Rodrigues Parameters\n - Euler Angles\n\n The following operations on rotations are supported:\n\n - Application on vectors\n - Rotation Composition\n - Rotation Inversion\n - Rotation Indexing\n\n Indexing within a rotation is supported since multiple rotation transforms\n can be stored within a single `Rotation` instance.\n\n To create `Rotation` objects use ``from_...`` methods (see examples below).\n ``Rotation(...)`` is not supposed to be instantiated directly.\n\n Attributes\n ----------\n single\n\n Methods\n -------\n __len__\n from_quat\n from_matrix\n from_rotvec\n from_mrp\n from_euler\n as_quat\n as_matrix\n as_rotvec\n as_mrp\n as_euler\n apply\n __mul__\n inv\n magnitude\n mean\n reduce\n create_group\n __getitem__\n identity\n random\n align_vectors\n\n See Also\n --------\n Slerp\n\n Notes\n -----\n .. versionadded: 1.2.0\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n A `Rotation` instance can be initialized in any of the above formats and\n converted to any of the others. The underlying object is independent of the\n representation used for initialization.\n\n Consider a counter-clockwise rotation of 90 degrees about the z-axis. This\n corresponds to the following quaternion (in scalar-last format):\n\n >>> r = R.from_quat([0, 0, np.sin(np.pi/4), np.cos(np.pi/4)])\n\n The rotation can be expressed in any of the other formats:\n\n >>> r.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> r.as_rotvec()\n array([0. , 0. , 1.57079633])\n >>> r.as_euler('zyx', degrees=True)\n array([90., 0., 0.])\n\n The same rotation can be initialized using a rotation matrix:\n\n >>> r = R.from_matrix([[0, -1, 0],\n ... [1, 0, 0],\n ... [0, 0, 1]])\n\n Representation in other formats:\n\n >>> r.as_quat()\n array([0. , 0. , 0.70710678, 0.70710678])\n >>> r.as_rotvec()\n array([0. , 0. , 1.57079633])\n >>> r.as_euler('zyx', degrees=True)\n array([90., 0., 0.])\n\n The rotation vector corresponding to this rotation is given by:\n\n >>> r = R.from_rotvec(np.pi/2 * np.array([0, 0, 1]))\n\n Representation in other formats:\n\n >>> r.as_quat()\n array([0. , 0. , 0.70710678, 0.70710678])\n >>> r.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> r.as_euler('zyx', degrees=True)\n array([90., 0., 0.])\n\n The ``from_euler`` method is quite flexible in the range of input formats\n it supports. Here we initialize a single rotation about a single axis:\n\n >>> r = R.from_euler('z', 90, degrees=True)\n\n Again, the object is representation independent and can be converted to any\n other format:\n\n >>> r.as_quat()\n array([0. , 0. , 0.70710678, 0.70710678])\n >>> r.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> r.as_rotvec()\n array([0. , 0. , 1.57079633])\n\n It is also possible to initialize multiple rotations in a single instance\n using any of the `from_...` functions. Here we initialize a stack of 3\n rotations using the ``from_euler`` method:\n\n >>> r = R.from_euler('zyx', [\n ... [90, 0, 0],\n ... [0, 45, 0],\n ... [45, 60, 30]], degrees=True)\n\n The other representations also now return a stack of 3 rotations. For\n example:\n\n >>> r.as_quat()\n array([[0. , 0. , 0.70710678, 0.70710678],\n [0. , 0.38268343, 0. , 0.92387953],\n [0.39190384, 0.36042341, 0.43967974, 0.72331741]])\n\n Applying the above rotations onto a vector:\n\n >>> v = [1, 2, 3]\n >>> r.apply(v)\n array([[-2. , 1. , 3. ],\n [ 2.82842712, 2. , 1.41421356],\n [ 2.24452282, 0.78093109, 2.89002836]])\n\n A `Rotation` instance can be indexed and sliced as if it were a single\n 1D array or list:\n\n >>> r.as_quat()\n array([[0. , 0. , 0.70710678, 0.70710678],\n [0. , 0.38268343, 0. , 0.92387953],\n [0.39190384, 0.36042341, 0.43967974, 0.72331741]])\n >>> p = r[0]\n >>> p.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> q = r[1:3]\n >>> q.as_quat()\n array([[0. , 0.38268343, 0. , 0.92387953],\n [0.39190384, 0.36042341, 0.43967974, 0.72331741]])\n\n In fact it can be converted to numpy.array:\n\n >>> r_array = np.asarray(r)\n >>> r_array.shape\n (3,)\n >>> r_array[0].as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n\n Multiple rotations can be composed using the ``*`` operator:\n\n >>> r1 = R.from_euler('z', 90, degrees=True)\n >>> r2 = R.from_rotvec([np.pi/4, 0, 0])\n >>> v = [1, 2, 3]\n >>> r2.apply(r1.apply(v))\n array([-2. , -1.41421356, 2.82842712])\n >>> r3 = r2 * r1 # Note the order\n >>> r3.apply(v)\n array([-2. , -1.41421356, 2.82842712])\n\n Finally, it is also possible to invert rotations:\n\n >>> r1 = R.from_euler('z', [90, 45], degrees=True)\n >>> r2 = r1.inv()\n >>> r2.as_euler('zyx', degrees=True)\n array([[-90., 0., 0.],\n [-45., 0., 0.]])\n\n These examples serve as an overview into the `Rotation` class and highlight\n major functionalities. For more thorough examples of the range of input and\n output formats supported, consult the individual method's examples.\n\n" - def __getitem__(self, index: int) -> typing.Any: - "Extract rotation(s) at given index(es) from object.\n\n Create a new `Rotation` instance containing a subset of rotations\n stored in this object.\n\n Parameters\n ----------\n indexer : index, slice, or index array\n Specifies which rotation(s) to extract. A single indexer must be\n specified, i.e. as if indexing a 1 dimensional array or list.\n\n Returns\n -------\n rotation : `Rotation` instance\n Contains\n - a single rotation, if `indexer` is a single index\n - a stack of rotation(s), if `indexer` is a slice, or and index\n array.\n\n Raises\n ------\n TypeError if the instance was created as a single rotation.\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n >>> r = R.from_quat([\n ... [1, 1, 0, 0],\n ... [0, 1, 0, 1],\n ... [1, 1, -1, 0]])\n >>> r.as_quat()\n array([[ 0.70710678, 0.70710678, 0. , 0. ],\n [ 0. , 0.70710678, 0. , 0.70710678],\n [ 0.57735027, 0.57735027, -0.57735027, 0. ]])\n\n Indexing using a single index:\n\n >>> p = r[0]\n >>> p.as_quat()\n array([0.70710678, 0.70710678, 0. , 0. ])\n\n Array slicing:\n\n >>> q = r[1:3]\n >>> q.as_quat()\n array([[ 0. , 0.70710678, 0. , 0.70710678],\n [ 0.57735027, 0.57735027, -0.57735027, 0. ]])\n\n" - ... - - def __getstate__(self) -> typing.Any: ... - def __init__(self, *args, **kwargs) -> None: - "Rotation in 3 dimensions.\n\n This class provides an interface to initialize from and represent rotations\n with:\n\n - Quaternions\n - Rotation Matrices\n - Rotation Vectors\n - Modified Rodrigues Parameters\n - Euler Angles\n\n The following operations on rotations are supported:\n\n - Application on vectors\n - Rotation Composition\n - Rotation Inversion\n - Rotation Indexing\n\n Indexing within a rotation is supported since multiple rotation transforms\n can be stored within a single `Rotation` instance.\n\n To create `Rotation` objects use ``from_...`` methods (see examples below).\n ``Rotation(...)`` is not supposed to be instantiated directly.\n\n Attributes\n ----------\n single\n\n Methods\n -------\n __len__\n from_quat\n from_matrix\n from_rotvec\n from_mrp\n from_euler\n as_quat\n as_matrix\n as_rotvec\n as_mrp\n as_euler\n apply\n __mul__\n inv\n magnitude\n mean\n reduce\n create_group\n __getitem__\n identity\n random\n align_vectors\n\n See Also\n --------\n Slerp\n\n Notes\n -----\n .. versionadded: 1.2.0\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n A `Rotation` instance can be initialized in any of the above formats and\n converted to any of the others. The underlying object is independent of the\n representation used for initialization.\n\n Consider a counter-clockwise rotation of 90 degrees about the z-axis. This\n corresponds to the following quaternion (in scalar-last format):\n\n >>> r = R.from_quat([0, 0, np.sin(np.pi/4), np.cos(np.pi/4)])\n\n The rotation can be expressed in any of the other formats:\n\n >>> r.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> r.as_rotvec()\n array([0. , 0. , 1.57079633])\n >>> r.as_euler('zyx', degrees=True)\n array([90., 0., 0.])\n\n The same rotation can be initialized using a rotation matrix:\n\n >>> r = R.from_matrix([[0, -1, 0],\n ... [1, 0, 0],\n ... [0, 0, 1]])\n\n Representation in other formats:\n\n >>> r.as_quat()\n array([0. , 0. , 0.70710678, 0.70710678])\n >>> r.as_rotvec()\n array([0. , 0. , 1.57079633])\n >>> r.as_euler('zyx', degrees=True)\n array([90., 0., 0.])\n\n The rotation vector corresponding to this rotation is given by:\n\n >>> r = R.from_rotvec(np.pi/2 * np.array([0, 0, 1]))\n\n Representation in other formats:\n\n >>> r.as_quat()\n array([0. , 0. , 0.70710678, 0.70710678])\n >>> r.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> r.as_euler('zyx', degrees=True)\n array([90., 0., 0.])\n\n The ``from_euler`` method is quite flexible in the range of input formats\n it supports. Here we initialize a single rotation about a single axis:\n\n >>> r = R.from_euler('z', 90, degrees=True)\n\n Again, the object is representation independent and can be converted to any\n other format:\n\n >>> r.as_quat()\n array([0. , 0. , 0.70710678, 0.70710678])\n >>> r.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> r.as_rotvec()\n array([0. , 0. , 1.57079633])\n\n It is also possible to initialize multiple rotations in a single instance\n using any of the `from_...` functions. Here we initialize a stack of 3\n rotations using the ``from_euler`` method:\n\n >>> r = R.from_euler('zyx', [\n ... [90, 0, 0],\n ... [0, 45, 0],\n ... [45, 60, 30]], degrees=True)\n\n The other representations also now return a stack of 3 rotations. For\n example:\n\n >>> r.as_quat()\n array([[0. , 0. , 0.70710678, 0.70710678],\n [0. , 0.38268343, 0. , 0.92387953],\n [0.39190384, 0.36042341, 0.43967974, 0.72331741]])\n\n Applying the above rotations onto a vector:\n\n >>> v = [1, 2, 3]\n >>> r.apply(v)\n array([[-2. , 1. , 3. ],\n [ 2.82842712, 2. , 1.41421356],\n [ 2.24452282, 0.78093109, 2.89002836]])\n\n A `Rotation` instance can be indexed and sliced as if it were a single\n 1D array or list:\n\n >>> r.as_quat()\n array([[0. , 0. , 0.70710678, 0.70710678],\n [0. , 0.38268343, 0. , 0.92387953],\n [0.39190384, 0.36042341, 0.43967974, 0.72331741]])\n >>> p = r[0]\n >>> p.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> q = r[1:3]\n >>> q.as_quat()\n array([[0. , 0.38268343, 0. , 0.92387953],\n [0.39190384, 0.36042341, 0.43967974, 0.72331741]])\n\n In fact it can be converted to numpy.array:\n\n >>> r_array = np.asarray(r)\n >>> r_array.shape\n (3,)\n >>> r_array[0].as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n\n Multiple rotations can be composed using the ``*`` operator:\n\n >>> r1 = R.from_euler('z', 90, degrees=True)\n >>> r2 = R.from_rotvec([np.pi/4, 0, 0])\n >>> v = [1, 2, 3]\n >>> r2.apply(r1.apply(v))\n array([-2. , -1.41421356, 2.82842712])\n >>> r3 = r2 * r1 # Note the order\n >>> r3.apply(v)\n array([-2. , -1.41421356, 2.82842712])\n\n Finally, it is also possible to invert rotations:\n\n >>> r1 = R.from_euler('z', [90, 45], degrees=True)\n >>> r2 = r1.inv()\n >>> r2.as_euler('zyx', degrees=True)\n array([[-90., 0., 0.],\n [-45., 0., 0.]])\n\n These examples serve as an overview into the `Rotation` class and highlight\n major functionalities. For more thorough examples of the range of input and\n output formats supported, consult the individual method's examples.\n\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - - def __len__(self) -> int: - "Number of rotations contained in this object.\n\n Multiple rotations can be stored in a single instance.\n\n Returns\n -------\n length : int\n Number of rotations stored in object.\n\n Raises\n ------\n TypeError if the instance was created as a single rotation.\n" - ... - - def __mul__(self) -> Rotation: - "Compose this rotation with the other.\n\n If `p` and `q` are two rotations, then the composition of 'q followed\n by p' is equivalent to `p * q`. In terms of rotation matrices,\n the composition can be expressed as\n ``p.as_matrix().dot(q.as_matrix())``.\n\n Parameters\n ----------\n other : `Rotation` instance\n Object containing the rotations to be composed with this one. Note\n that rotation compositions are not commutative, so ``p * q`` is\n different from ``q * p``.\n\n Returns\n -------\n composition : `Rotation` instance\n This function supports composition of multiple rotations at a time.\n The following cases are possible:\n\n - Either ``p`` or ``q`` contains a single rotation. In this case\n `composition` contains the result of composing each rotation in\n the other object with the single rotation.\n - Both ``p`` and ``q`` contain ``N`` rotations. In this case each\n rotation ``p[i]`` is composed with the corresponding rotation\n ``q[i]`` and `output` contains ``N`` rotations.\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Composition of two single rotations:\n\n >>> p = R.from_quat([0, 0, 1, 1])\n >>> q = R.from_quat([1, 0, 0, 1])\n >>> p.as_matrix()\n array([[ 0., -1., 0.],\n [ 1., 0., 0.],\n [ 0., 0., 1.]])\n >>> q.as_matrix()\n array([[ 1., 0., 0.],\n [ 0., 0., -1.],\n [ 0., 1., 0.]])\n >>> r = p * q\n >>> r.as_matrix()\n array([[0., 0., 1.],\n [1., 0., 0.],\n [0., 1., 0.]])\n\n Composition of two objects containing equal number of rotations:\n\n >>> p = R.from_quat([[0, 0, 1, 1], [1, 0, 0, 1]])\n >>> q = R.from_rotvec([[np.pi/4, 0, 0], [-np.pi/4, 0, np.pi/4]])\n >>> p.as_quat()\n array([[0. , 0. , 0.70710678, 0.70710678],\n [0.70710678, 0. , 0. , 0.70710678]])\n >>> q.as_quat()\n array([[ 0.38268343, 0. , 0. , 0.92387953],\n [-0.37282173, 0. , 0.37282173, 0.84971049]])\n >>> r = p * q\n >>> r.as_quat()\n array([[ 0.27059805, 0.27059805, 0.65328148, 0.65328148],\n [ 0.33721128, -0.26362477, 0.26362477, 0.86446082]])\n\n" - ... - - def __reduce_cython__(self) -> typing.Any: ... - def __rmul__(self, value) -> Rotation: - "Return value*self." - ... - - def __setstate__(self, state: typing.Any) -> None: ... - def __setstate_cython__(self) -> typing.Any: ... - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @classmethod - def align_vectors(cls) -> typing.Any: - 'Estimate a rotation to optimally align two sets of vectors.\n\n Find a rotation between frames A and B which best aligns a set of\n vectors `a` and `b` observed in these frames. The following loss\n function is minimized to solve for the rotation matrix\n :math:`C`:\n\n .. math::\n\n L(C) = \\frac{1}{2} \\sum_{i = 1}^{n} w_i \\lVert \\mathbf{a}_i -\n C \\mathbf{b}_i \\rVert^2 ,\n\n where :math:`w_i`\'s are the `weights` corresponding to each vector.\n\n The rotation is estimated with Kabsch algorithm [1]_.\n\n Parameters\n ----------\n a : array_like, shape (N, 3)\n Vector components observed in initial frame A. Each row of `a`\n denotes a vector.\n b : array_like, shape (N, 3)\n Vector components observed in another frame B. Each row of `b`\n denotes a vector.\n weights : array_like shape (N,), optional\n Weights describing the relative importance of the vector\n observations. If None (default), then all values in `weights` are\n assumed to be 1.\n return_sensitivity : bool, optional\n Whether to return the sensitivity matrix. See Notes for details.\n Default is False.\n\n Returns\n -------\n estimated_rotation : `Rotation` instance\n Best estimate of the rotation that transforms `b` to `a`.\n rmsd : float\n Root mean square distance (weighted) between the given set of\n vectors after alignment. It is equal to ``sqrt(2 * minimum_loss)``,\n where ``minimum_loss`` is the loss function evaluated for the\n found optimal rotation.\n sensitivity_matrix : ndarray, shape (3, 3)\n Sensitivity matrix of the estimated rotation estimate as explained\n in Notes. Returned only when `return_sensitivity` is True.\n\n Notes\n -----\n This method can also compute the sensitivity of the estimated rotation\n to small perturbations of the vector measurements. Specifically we\n consider the rotation estimate error as a small rotation vector of\n frame A. The sensitivity matrix is proportional to the covariance of\n this rotation vector assuming that the vectors in `a` was measured with\n errors significantly less than their lengths. To get the true\n covariance matrix, the returned sensitivity matrix must be multiplied\n by harmonic mean [3]_ of variance in each observation. Note that\n `weights` are supposed to be inversely proportional to the observation\n variances to get consistent results. For example, if all vectors are\n measured with the same accuracy of 0.01 (`weights` must be all equal),\n then you should multiple the sensitivity matrix by 0.01**2 to get the\n covariance.\n\n Refer to [2]_ for more rigorous discussion of the covariance\n estimation.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Kabsch_algorithm\n .. [2] F. Landis Markley,\n "Attitude determination using vector observations: a fast\n optimal matrix algorithm", Journal of Astronautical Sciences,\n Vol. 41, No.2, 1993, pp. 261-280.\n .. [3] https://en.wikipedia.org/wiki/Harmonic_mean\n' - ... - - def apply(self) -> typing.Any: - "Apply this rotation to a set of vectors.\n\n If the original frame rotates to the final frame by this rotation, then\n its application to a vector can be seen in two ways:\n\n - As a projection of vector components expressed in the final frame\n to the original frame.\n - As the physical rotation of a vector being glued to the original\n frame as it rotates. In this case the vector components are\n expressed in the original frame before and after the rotation.\n\n In terms of rotation matricies, this application is the same as\n ``self.as_matrix().dot(vectors)``.\n\n Parameters\n ----------\n vectors : array_like, shape (3,) or (N, 3)\n Each `vectors[i]` represents a vector in 3D space. A single vector\n can either be specified with shape `(3, )` or `(1, 3)`. The number\n of rotations and number of vectors given must follow standard numpy\n broadcasting rules: either one of them equals unity or they both\n equal each other.\n inverse : boolean, optional\n If True then the inverse of the rotation(s) is applied to the input\n vectors. Default is False.\n\n Returns\n -------\n rotated_vectors : ndarray, shape (3,) or (N, 3)\n Result of applying rotation on input vectors.\n Shape depends on the following cases:\n\n - If object contains a single rotation (as opposed to a stack\n with a single rotation) and a single vector is specified with\n shape ``(3,)``, then `rotated_vectors` has shape ``(3,)``.\n - In all other cases, `rotated_vectors` has shape ``(N, 3)``,\n where ``N`` is either the number of rotations or vectors.\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Single rotation applied on a single vector:\n\n >>> vector = np.array([1, 0, 0])\n >>> r = R.from_rotvec([0, 0, np.pi/2])\n >>> r.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> r.apply(vector)\n array([2.22044605e-16, 1.00000000e+00, 0.00000000e+00])\n >>> r.apply(vector).shape\n (3,)\n\n Single rotation applied on multiple vectors:\n\n >>> vectors = np.array([\n ... [1, 0, 0],\n ... [1, 2, 3]])\n >>> r = R.from_rotvec([0, 0, np.pi/4])\n >>> r.as_matrix()\n array([[ 0.70710678, -0.70710678, 0. ],\n [ 0.70710678, 0.70710678, 0. ],\n [ 0. , 0. , 1. ]])\n >>> r.apply(vectors)\n array([[ 0.70710678, 0.70710678, 0. ],\n [-0.70710678, 2.12132034, 3. ]])\n >>> r.apply(vectors).shape\n (2, 3)\n\n Multiple rotations on a single vector:\n\n >>> r = R.from_rotvec([[0, 0, np.pi/4], [np.pi/2, 0, 0]])\n >>> vector = np.array([1,2,3])\n >>> r.as_matrix()\n array([[[ 7.07106781e-01, -7.07106781e-01, 0.00000000e+00],\n [ 7.07106781e-01, 7.07106781e-01, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]],\n [[ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n [ 0.00000000e+00, 2.22044605e-16, -1.00000000e+00],\n [ 0.00000000e+00, 1.00000000e+00, 2.22044605e-16]]])\n >>> r.apply(vector)\n array([[-0.70710678, 2.12132034, 3. ],\n [ 1. , -3. , 2. ]])\n >>> r.apply(vector).shape\n (2, 3)\n\n Multiple rotations on multiple vectors. Each rotation is applied on the\n corresponding vector:\n\n >>> r = R.from_euler('zxy', [\n ... [0, 0, 90],\n ... [45, 30, 60]], degrees=True)\n >>> vectors = [\n ... [1, 2, 3],\n ... [1, 0, -1]]\n >>> r.apply(vectors)\n array([[ 3. , 2. , -1. ],\n [-0.09026039, 1.11237244, -0.86860844]])\n >>> r.apply(vectors).shape\n (2, 3)\n\n It is also possible to apply the inverse rotation:\n\n >>> r = R.from_euler('zxy', [\n ... [0, 0, 90],\n ... [45, 30, 60]], degrees=True)\n >>> vectors = [\n ... [1, 2, 3],\n ... [1, 0, -1]]\n >>> r.apply(vectors, inverse=True)\n array([[-3. , 2. , 1. ],\n [ 1.09533535, -0.8365163 , 0.3169873 ]])\n\n" - ... - - def as_euler(self) -> typing.Any: - "Represent as Euler angles.\n\n Any orientation can be expressed as a composition of 3 elementary\n rotations. Once the axis sequence has been chosen, Euler angles define\n the angle of rotation around each respective axis [1]_.\n\n The algorithm from [2]_ has been used to calculate Euler angles for the\n rotation about a given sequence of axes.\n\n Euler angles suffer from the problem of gimbal lock [3]_, where the\n representation loses a degree of freedom and it is not possible to\n determine the first and third angles uniquely. In this case,\n a warning is raised, and the third angle is set to zero. Note however\n that the returned angles still represent the correct rotation.\n\n Parameters\n ----------\n seq : string, length 3\n 3 characters belonging to the set {'X', 'Y', 'Z'} for intrinsic\n rotations, or {'x', 'y', 'z'} for extrinsic rotations [1]_.\n Adjacent axes cannot be the same.\n Extrinsic and intrinsic rotations cannot be mixed in one function\n call.\n degrees : boolean, optional\n Returned angles are in degrees if this flag is True, else they are\n in radians. Default is False.\n\n Returns\n -------\n angles : ndarray, shape (3,) or (N, 3)\n Shape depends on shape of inputs used to initialize object.\n The returned angles are in the range:\n\n - First angle belongs to [-180, 180] degrees (both inclusive)\n - Third angle belongs to [-180, 180] degrees (both inclusive)\n - Second angle belongs to:\n\n - [-90, 90] degrees if all axes are different (like xyz)\n - [0, 180] degrees if first and third axes are the same\n (like zxz)\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Euler_angles#Definition_by_intrinsic_rotations\n .. [2] Malcolm D. Shuster, F. Landis Markley, \"General formula for\n extraction the Euler angles\", Journal of guidance, control, and\n dynamics, vol. 29.1, pp. 215-221. 2006\n .. [3] https://en.wikipedia.org/wiki/Gimbal_lock#In_applied_mathematics\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Represent a single rotation:\n\n >>> r = R.from_rotvec([0, 0, np.pi/2])\n >>> r.as_euler('zxy', degrees=True)\n array([90., 0., 0.])\n >>> r.as_euler('zxy', degrees=True).shape\n (3,)\n\n Represent a stack of single rotation:\n\n >>> r = R.from_rotvec([[0, 0, np.pi/2]])\n >>> r.as_euler('zxy', degrees=True)\n array([[90., 0., 0.]])\n >>> r.as_euler('zxy', degrees=True).shape\n (1, 3)\n\n Represent multiple rotations in a single object:\n\n >>> r = R.from_rotvec([\n ... [0, 0, np.pi/2],\n ... [0, -np.pi/3, 0],\n ... [np.pi/4, 0, 0]])\n >>> r.as_euler('zxy', degrees=True)\n array([[ 90., 0., 0.],\n [ 0., 0., -60.],\n [ 0., 45., 0.]])\n >>> r.as_euler('zxy', degrees=True).shape\n (3, 3)\n\n" - ... - - def as_matrix(self) -> typing.Any: - "Represent as rotation matrix.\n\n 3D rotations can be represented using rotation matrices, which\n are 3 x 3 real orthogonal matrices with determinant equal to +1 [1]_.\n\n Returns\n -------\n matrix : ndarray, shape (3, 3) or (N, 3, 3)\n Shape depends on shape of inputs used for initialization.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Rotation_matrix#In_three_dimensions\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Represent a single rotation:\n\n >>> r = R.from_rotvec([0, 0, np.pi/2])\n >>> r.as_matrix()\n array([[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])\n >>> r.as_matrix().shape\n (3, 3)\n\n Represent a stack with a single rotation:\n\n >>> r = R.from_quat([[1, 1, 0, 0]])\n >>> r.as_matrix()\n array([[[ 0., 1., 0.],\n [ 1., 0., 0.],\n [ 0., 0., -1.]]])\n >>> r.as_matrix().shape\n (1, 3, 3)\n\n Represent multiple rotations:\n\n >>> r = R.from_rotvec([[np.pi/2, 0, 0], [0, 0, np.pi/2]])\n >>> r.as_matrix()\n array([[[ 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n [ 0.00000000e+00, 2.22044605e-16, -1.00000000e+00],\n [ 0.00000000e+00, 1.00000000e+00, 2.22044605e-16]],\n [[ 2.22044605e-16, -1.00000000e+00, 0.00000000e+00],\n [ 1.00000000e+00, 2.22044605e-16, 0.00000000e+00],\n [ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]]])\n >>> r.as_matrix().shape\n (2, 3, 3)\n\n Notes\n -----\n This function was called as_dcm before.\n\n .. versionadded:: 1.4.0\n" - ... - - def as_mrp(self) -> typing.Any: - "Represent as Modified Rodrigues Parameters (MRPs).\n\n MRPs are a 3 dimensional vector co-directional to the axis of rotation and whose\n magnitude is equal to ``tan(theta / 4)``, where ``theta`` is the angle of rotation\n (in radians) [1]_.\n\n MRPs have a singuarity at 360 degrees which can be avoided by ensuring the angle of\n rotation does not exceed 180 degrees, i.e. switching the direction of the rotation when\n it is past 180 degrees. This function will always return MRPs corresponding to a rotation\n of less than or equal to 180 degrees.\n\n Returns\n -------\n mrps : ndarray, shape (3,) or (N, 3)\n Shape depends on shape of inputs used for initialization.\n\n References\n ----------\n .. [1] Shuster, M. D. \"A Survery of Attitude Representations\",\n The Journal of Astronautical Sciences, Vol. 41, No.4, 1993,\n pp. 475-476\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Represent a single rotation:\n\n >>> r = R.from_rotvec([0, 0, np.pi])\n >>> r.as_mrp()\n array([0. , 0. , 1. ])\n >>> r.as_mrp().shape\n (3,)\n\n Represent a stack with a single rotation:\n\n >>> r = R.from_euler('xyz', [[180, 0, 0]], degrees=True)\n >>> r.as_mrp()\n array([[1. , 0. , 0. ]])\n >>> r.as_mrp().shape\n (1, 3)\n\n Represent multiple rotations:\n\n >>> r = R.from_rotvec([[np.pi/2, 0, 0], [0, 0, np.pi/2]])\n >>> r.as_mrp()\n array([[0.41421356, 0. , 0. ],\n [0. , 0. , 0.41421356]])\n >>> r.as_mrp().shape\n (2, 3)\n\n Notes\n -----\n\n .. versionadded:: 1.6.0\n" - ... - - def as_quat(self) -> typing.Any: - "Represent as quaternions.\n\n Rotations in 3 dimensions can be represented using unit norm\n quaternions [1]_. The mapping from quaternions to rotations is\n two-to-one, i.e. quaternions ``q`` and ``-q``, where ``-q`` simply\n reverses the sign of each component, represent the same spatial\n rotation. The returned value is in scalar-last (x, y, z, w) format.\n\n Returns\n -------\n quat : `numpy.ndarray`, shape (4,) or (N, 4)\n Shape depends on shape of inputs used for initialization.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Represent a single rotation:\n\n >>> r = R.from_matrix([[0, -1, 0],\n ... [1, 0, 0],\n ... [0, 0, 1]])\n >>> r.as_quat()\n array([0. , 0. , 0.70710678, 0.70710678])\n >>> r.as_quat().shape\n (4,)\n\n Represent a stack with a single rotation:\n\n >>> r = R.from_quat([[0, 0, 0, 1]])\n >>> r.as_quat().shape\n (1, 4)\n\n Represent multiple rotations in a single object:\n\n >>> r = R.from_rotvec([[np.pi, 0, 0], [0, 0, np.pi/2]])\n >>> r.as_quat().shape\n (2, 4)\n\n" - ... - - def as_rotvec(self) -> typing.Any: - "Represent as rotation vectors.\n\n A rotation vector is a 3 dimensional vector which is co-directional to\n the axis of rotation and whose norm gives the angle of rotation (in\n radians) [1]_.\n\n Returns\n -------\n rotvec : ndarray, shape (3,) or (N, 3)\n Shape depends on shape of inputs used for initialization.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Axis%E2%80%93angle_representation#Rotation_vector\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Represent a single rotation:\n\n >>> r = R.from_euler('z', 90, degrees=True)\n >>> r.as_rotvec()\n array([0. , 0. , 1.57079633])\n >>> r.as_rotvec().shape\n (3,)\n\n Represent a stack with a single rotation:\n\n >>> r = R.from_quat([[0, 0, 1, 1]])\n >>> r.as_rotvec()\n array([[0. , 0. , 1.57079633]])\n >>> r.as_rotvec().shape\n (1, 3)\n\n Represent multiple rotations in a single object:\n\n >>> r = R.from_quat([[0, 0, 1, 1], [1, 1, 0, 1]])\n >>> r.as_rotvec()\n array([[0. , 0. , 1.57079633],\n [1.35102172, 1.35102172, 0. ]])\n >>> r.as_rotvec().shape\n (2, 3)\n\n" - ... - - @classmethod - def create_group(cls) -> typing.Any: - "Create a 3D rotation group.\n\n Parameters\n ----------\n group : string\n The name of the group. Must be one of 'I', 'O', 'T', 'Dn', 'Cn',\n where `n` is a positive integer. The groups are:\n\n * I: Icosahedral group\n * O: Octahedral group\n * T: Tetrahedral group\n * D: Dicyclic group\n * C: Cyclic group\n\n axis : integer\n The cyclic rotation axis. Must be one of ['X', 'Y', 'Z'] (or\n lowercase). Default is 'Z'. Ignored for groups 'I', 'O', and 'T'.\n\n Returns\n -------\n rotation : `Rotation` instance\n Object containing the elements of the rotation group.\n\n Notes\n -----\n This method generates rotation groups only. The full 3-dimensional\n point groups [PointGroups]_ also contain reflections.\n\n References\n ----------\n .. [PointGroups] `Point groups\n `_\n on Wikipedia.\n" - ... - - @classmethod - def from_euler(cls) -> typing.Any: - "Initialize from Euler angles.\n\n Rotations in 3-D can be represented by a sequence of 3\n rotations around a sequence of axes. In theory, any three axes spanning\n the 3-D Euclidean space are enough. In practice, the axes of rotation are\n chosen to be the basis vectors.\n\n The three rotations can either be in a global frame of reference\n (extrinsic) or in a body centred frame of reference (intrinsic), which\n is attached to, and moves with, the object under rotation [1]_.\n\n Parameters\n ----------\n seq : string\n Specifies sequence of axes for rotations. Up to 3 characters\n belonging to the set {'X', 'Y', 'Z'} for intrinsic rotations, or\n {'x', 'y', 'z'} for extrinsic rotations. Extrinsic and intrinsic\n rotations cannot be mixed in one function call.\n angles : float or array_like, shape (N,) or (N, [1 or 2 or 3])\n Euler angles specified in radians (`degrees` is False) or degrees\n (`degrees` is True).\n For a single character `seq`, `angles` can be:\n\n - a single value\n - array_like with shape (N,), where each `angle[i]`\n corresponds to a single rotation\n - array_like with shape (N, 1), where each `angle[i, 0]`\n corresponds to a single rotation\n\n For 2- and 3-character wide `seq`, `angles` can be:\n\n - array_like with shape (W,) where `W` is the width of\n `seq`, which corresponds to a single rotation with `W` axes\n - array_like with shape (N, W) where each `angle[i]`\n corresponds to a sequence of Euler angles describing a single\n rotation\n\n degrees : bool, optional\n If True, then the given angles are assumed to be in degrees.\n Default is False.\n\n Returns\n -------\n rotation : `Rotation` instance\n Object containing the rotation represented by the sequence of\n rotations around given axes with given angles.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Euler_angles#Definition_by_intrinsic_rotations\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Initialize a single rotation along a single axis:\n\n >>> r = R.from_euler('x', 90, degrees=True)\n >>> r.as_quat().shape\n (4,)\n\n Initialize a single rotation with a given axis sequence:\n\n >>> r = R.from_euler('zyx', [90, 45, 30], degrees=True)\n >>> r.as_quat().shape\n (4,)\n\n Initialize a stack with a single rotation around a single axis:\n\n >>> r = R.from_euler('x', [90], degrees=True)\n >>> r.as_quat().shape\n (1, 4)\n\n Initialize a stack with a single rotation with an axis sequence:\n\n >>> r = R.from_euler('zyx', [[90, 45, 30]], degrees=True)\n >>> r.as_quat().shape\n (1, 4)\n\n Initialize multiple elementary rotations in one object:\n\n >>> r = R.from_euler('x', [90, 45, 30], degrees=True)\n >>> r.as_quat().shape\n (3, 4)\n\n Initialize multiple rotations in one object:\n\n >>> r = R.from_euler('zyx', [[90, 45, 30], [35, 45, 90]], degrees=True)\n >>> r.as_quat().shape\n (2, 4)\n\n" - ... - - @classmethod - def from_matrix(cls) -> typing.Any: - 'Initialize from rotation matrix.\n\n Rotations in 3 dimensions can be represented with 3 x 3 proper\n orthogonal matrices [1]_. If the input is not proper orthogonal,\n an approximation is created using the method described in [2]_.\n\n Parameters\n ----------\n matrix : array_like, shape (N, 3, 3) or (3, 3)\n A single matrix or a stack of matrices, where ``matrix[i]`` is\n the i-th matrix.\n\n Returns\n -------\n rotation : `Rotation` instance\n Object containing the rotations represented by the rotation\n matrices.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Rotation_matrix#In_three_dimensions\n .. [2] F. Landis Markley, "Unit Quaternion from Rotation Matrix",\n Journal of guidance, control, and dynamics vol. 31.2, pp.\n 440-442, 2008.\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Initialize a single rotation:\n\n >>> r = R.from_matrix([\n ... [0, -1, 0],\n ... [1, 0, 0],\n ... [0, 0, 1]])\n >>> r.as_matrix().shape\n (3, 3)\n\n Initialize multiple rotations in a single object:\n\n >>> r = R.from_matrix([\n ... [\n ... [0, -1, 0],\n ... [1, 0, 0],\n ... [0, 0, 1],\n ... ],\n ... [\n ... [1, 0, 0],\n ... [0, 0, -1],\n ... [0, 1, 0],\n ... ]])\n >>> r.as_matrix().shape\n (2, 3, 3)\n\n If input matrices are not special orthogonal (orthogonal with\n determinant equal to +1), then a special orthogonal estimate is stored:\n\n >>> a = np.array([\n ... [0, -0.5, 0],\n ... [0.5, 0, 0],\n ... [0, 0, 0.5]])\n >>> np.linalg.det(a)\n 0.12500000000000003\n >>> r = R.from_matrix(a)\n >>> matrix = r.as_matrix()\n >>> matrix\n array([[-0.38461538, -0.92307692, 0. ],\n [ 0.92307692, -0.38461538, 0. ],\n [ 0. , 0. , 1. ]])\n >>> np.linalg.det(matrix)\n 1.0000000000000002\n\n It is also possible to have a stack containing a single rotation:\n\n >>> r = R.from_matrix([[\n ... [0, -1, 0],\n ... [1, 0, 0],\n ... [0, 0, 1]]])\n >>> r.as_matrix()\n array([[[ 0., -1., 0.],\n [ 1., 0., 0.],\n [ 0., 0., 1.]]])\n >>> r.as_matrix().shape\n (1, 3, 3)\n\n Notes\n -----\n This function was called from_dcm before.\n\n .. versionadded:: 1.4.0\n' - ... - - @classmethod - def from_mrp(cls) -> typing.Any: - "Initialize from Modified Rodrigues Parameters (MRPs).\n\n MRPs are a 3 dimensional vector co-directional to the axis of rotation and whose\n magnitude is equal to ``tan(theta / 4)``, where ``theta`` is the angle of rotation\n (in radians) [1]_.\n\n MRPs have a singuarity at 360 degrees which can be avoided by ensuring the angle of\n rotation does not exceed 180 degrees, i.e. switching the direction of the rotation when\n it is past 180 degrees.\n\n Parameters\n ----------\n mrp : array_like, shape (N, 3) or (3,)\n A single vector or a stack of vectors, where `mrp[i]` gives\n the ith set of MRPs.\n\n Returns\n -------\n rotation : `Rotation` instance\n Object containing the rotations represented by input MRPs.\n\n References\n ----------\n .. [1] Shuster, M. D. \"A Survery of Attitude Representations\",\n The Journal of Astronautical Sciences, Vol. 41, No.4, 1993,\n pp. 475-476\n\n Notes\n -----\n\n .. versionadded:: 1.6.0\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Initialize a single rotation:\n\n >>> r = R.from_mrp([0, 0, 1])\n >>> r.as_euler('xyz', degrees=True)\n array([0. , 0. , 180. ])\n >>> r.as_euler('xyz').shape\n (3,)\n\n Initialize multiple rotations in one object:\n\n >>> r = R.from_mrp([\n ... [0, 0, 1],\n ... [1, 0, 0]])\n >>> r.as_euler('xyz', degrees=True)\n array([[0. , 0. , 180. ],\n [180.0 , 0. , 0. ]])\n >>> r.as_euler('xyz').shape\n (2, 3)\n\n It is also possible to have a stack of a single rotation:\n\n >>> r = R.from_mrp([[0, 0, np.pi/2]])\n >>> r.as_euler('xyz').shape\n (1, 3)\n\n" - ... - - @classmethod - def from_quat(cls) -> typing.Any: - "Initialize from quaternions.\n\n 3D rotations can be represented using unit-norm quaternions [1]_.\n\n Parameters\n ----------\n quat : array_like, shape (N, 4) or (4,)\n Each row is a (possibly non-unit norm) quaternion in scalar-last\n (x, y, z, w) format. Each quaternion will be normalized to unit\n norm.\n\n Returns\n -------\n rotation : `Rotation` instance\n Object containing the rotations represented by input quaternions.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Initialize a single rotation:\n\n >>> r = R.from_quat([1, 0, 0, 0])\n >>> r.as_quat()\n array([1., 0., 0., 0.])\n >>> r.as_quat().shape\n (4,)\n\n Initialize multiple rotations in a single object:\n\n >>> r = R.from_quat([\n ... [1, 0, 0, 0],\n ... [0, 0, 0, 1]\n ... ])\n >>> r.as_quat()\n array([[1., 0., 0., 0.],\n [0., 0., 0., 1.]])\n >>> r.as_quat().shape\n (2, 4)\n\n It is also possible to have a stack of a single rotation:\n\n >>> r = R.from_quat([[0, 0, 0, 1]])\n >>> r.as_quat()\n array([[0., 0., 0., 1.]])\n >>> r.as_quat().shape\n (1, 4)\n\n Quaternions are normalized before initialization.\n\n >>> r = R.from_quat([0, 0, 1, 1])\n >>> r.as_quat()\n array([0. , 0. , 0.70710678, 0.70710678])\n" - ... - - @classmethod - def from_rotvec(cls) -> typing.Any: - "Initialize from rotation vectors.\n\n A rotation vector is a 3 dimensional vector which is co-directional to\n the axis of rotation and whose norm gives the angle of rotation (in\n radians) [1]_.\n\n Parameters\n ----------\n rotvec : array_like, shape (N, 3) or (3,)\n A single vector or a stack of vectors, where `rot_vec[i]` gives\n the ith rotation vector.\n\n Returns\n -------\n rotation : `Rotation` instance\n Object containing the rotations represented by input rotation\n vectors.\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Axis%E2%80%93angle_representation#Rotation_vector\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Initialize a single rotation:\n\n >>> r = R.from_rotvec(np.pi/2 * np.array([0, 0, 1]))\n >>> r.as_rotvec()\n array([0. , 0. , 1.57079633])\n >>> r.as_rotvec().shape\n (3,)\n\n Initialize multiple rotations in one object:\n\n >>> r = R.from_rotvec([\n ... [0, 0, np.pi/2],\n ... [np.pi/2, 0, 0]])\n >>> r.as_rotvec()\n array([[0. , 0. , 1.57079633],\n [1.57079633, 0. , 0. ]])\n >>> r.as_rotvec().shape\n (2, 3)\n\n It is also possible to have a stack of a single rotaton:\n\n >>> r = R.from_rotvec([[0, 0, np.pi/2]])\n >>> r.as_rotvec().shape\n (1, 3)\n\n" - ... - - @classmethod - def identity(cls) -> typing.Any: - "Get identity rotation(s).\n\n Composition with the identity rotation has no effect.\n\n Parameters\n ----------\n num : int or None, optional\n Number of identity rotations to generate. If None (default), then a\n single rotation is generated.\n\n Returns\n -------\n identity : Rotation object\n The identity rotation.\n" - ... - - def inv(self) -> typing.Any: - "Invert this rotation.\n\n Composition of a rotation with its inverse results in an identity\n transformation.\n\n Returns\n -------\n inverse : `Rotation` instance\n Object containing inverse of the rotations in the current instance.\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Inverting a single rotation:\n\n >>> p = R.from_euler('z', 45, degrees=True)\n >>> q = p.inv()\n >>> q.as_euler('zyx', degrees=True)\n array([-45., 0., 0.])\n\n Inverting multiple rotations:\n\n >>> p = R.from_rotvec([[0, 0, np.pi/3], [-np.pi/4, 0, 0]])\n >>> q = p.inv()\n >>> q.as_rotvec()\n array([[-0. , -0. , -1.04719755],\n [ 0.78539816, -0. , -0. ]])\n\n" - ... - - def magnitude(self) -> typing.Any: - "Get the magnitude(s) of the rotation(s).\n\n Returns\n -------\n magnitude : ndarray or float\n Angle(s) in radians, float if object contains a single rotation\n and ndarray if object contains multiple rotations.\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n >>> r = R.from_quat(np.eye(4))\n >>> r.magnitude()\n array([3.14159265, 3.14159265, 3.14159265, 0. ])\n\n Magnitude of a single rotation:\n\n >>> r[0].magnitude()\n 3.141592653589793\n" - ... - - def mean(self) -> typing.Any: - "Get the mean of the rotations.\n\n Parameters\n ----------\n weights : array_like shape (N,), optional\n Weights describing the relative importance of the rotations. If\n None (default), then all values in `weights` are assumed to be\n equal.\n\n Returns\n -------\n mean : `Rotation` instance\n Object containing the mean of the rotations in the current\n instance.\n\n Notes\n -----\n The mean used is the chordal L2 mean (also called the projected or\n induced arithmetic mean). If ``p`` is a set of rotations with mean\n ``m``, then ``m`` is the rotation which minimizes\n ``(weights[:, None, None] * (p.as_matrix() - m.as_matrix())**2).sum()``.\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n >>> r = R.from_euler('zyx', [[0, 0, 0],\n ... [1, 0, 0],\n ... [0, 1, 0],\n ... [0, 0, 1]], degrees=True)\n >>> r.mean().as_euler('zyx', degrees=True)\n array([0.24945696, 0.25054542, 0.24945696])\n" - ... - - @classmethod - def random(cls) -> typing.Any: - "Generate uniformly distributed rotations.\n\n Parameters\n ----------\n num : int or None, optional\n Number of random rotations to generate. If None (default), then a\n single rotation is generated.\n random_state : int, RandomState instance or None, optional\n Accepts an integer as a seed for the random generator or a\n RandomState object. If None (default), uses global `numpy.random`\n random state.\n\n Returns\n -------\n random_rotation : `Rotation` instance\n Contains a single rotation if `num` is None. Otherwise contains a\n stack of `num` rotations.\n\n Notes\n -----\n This function is optimized for efficiently sampling random rotation\n matrices in three dimensions. For generating random rotation matrices\n in higher dimensions, see `scipy.stats.special_ortho_group`.\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n\n Sample a single rotation:\n\n >>> R.random(random_state=1234).as_euler('zxy', degrees=True)\n array([-110.5976185 , 55.32758512, 76.3289269 ])\n\n Sample a stack of rotations:\n\n >>> R.random(5, random_state=1234).as_euler('zxy', degrees=True)\n array([[-110.5976185 , 55.32758512, 76.3289269 ],\n [ -91.59132005, -14.3629884 , -93.91933182],\n [ 25.23835501, 45.02035145, -121.67867086],\n [ -51.51414184, -15.29022692, -172.46870023],\n [ -81.63376847, -27.39521579, 2.60408416]])\n\n See Also\n --------\n scipy.stats.special_ortho_group\n\n" - ... - - def reduce(self) -> typing.Any: - "Reduce this rotation with the provided rotation groups.\n\n Reduction of a rotation ``p`` is a transformation of the form\n ``q = l * p * r``, where ``l`` and ``r`` are chosen from `left` and\n `right` respectively, such that rotation ``q`` has the smallest\n magnitude.\n\n If `left` and `right` are rotation groups representing symmetries of\n two objects rotated by ``p``, then ``q`` is the rotation of the\n smallest magnitude to align these objects considering their symmetries.\n\n Parameters\n ----------\n left : `Rotation` instance, optional\n Object containing the left rotation(s). Default value (None)\n corresponds to the identity rotation.\n right : `Rotation` instance, optional\n Object containing the right rotation(s). Default value (None)\n corresponds to the identity rotation.\n return_indices : bool, optional\n Whether to return the indices of the rotations from `left` and\n `right` used for reduction.\n\n Returns\n -------\n reduced : `Rotation` instance\n Object containing reduced rotations.\n left_best, right_best: integer ndarray\n Indices of elements from `left` and `right` used for reduction.\n" - ... - - @property - def single(self) -> typing.Any: - "Whether this instance represents a single rotation." - ... - - def __getattr__(self, name) -> typing.Any: ... - -class Slerp(_mod_builtins.object): - "Spherical Linear Interpolation of Rotations.\n\n The interpolation between consecutive rotations is performed as a rotation\n around a fixed axis with a constant angular velocity [1]_. This ensures\n that the interpolated rotations follow the shortest path between initial\n and final orientations.\n\n Parameters\n ----------\n times : array_like, shape (N,)\n Times of the known rotations. At least 2 times must be specified.\n rotations : `Rotation` instance\n Rotations to perform the interpolation between. Must contain N\n rotations.\n\n Methods\n -------\n __call__\n\n See Also\n --------\n Rotation\n\n Notes\n -----\n .. versionadded:: 1.2.0\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Slerp#Quaternion_Slerp\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n >>> from scipy.spatial.transform import Slerp\n\n Setup the fixed keyframe rotations and times:\n\n >>> key_rots = R.random(5, random_state=2342345)\n >>> key_times = [0, 1, 2, 3, 4]\n\n Create the interpolator object:\n\n >>> slerp = Slerp(key_times, key_rots)\n\n Interpolate the rotations at the given times:\n\n >>> times = [0, 0.5, 0.25, 1, 1.5, 2, 2.75, 3, 3.25, 3.60, 4]\n >>> interp_rots = slerp(times)\n\n The keyframe rotations expressed as Euler angles:\n\n >>> key_rots.as_euler('xyz', degrees=True)\n array([[ 14.31443779, -27.50095894, -3.7275787 ],\n [ -1.79924227, -24.69421529, 164.57701743],\n [146.15020772, 43.22849451, -31.34891088],\n [ 46.39959442, 11.62126073, -45.99719267],\n [-88.94647804, -49.64400082, -65.80546984]])\n\n The interpolated rotations expressed as Euler angles. These agree with the\n keyframe rotations at both endpoints of the range of keyframe times.\n\n >>> interp_rots.as_euler('xyz', degrees=True)\n array([[ 14.31443779, -27.50095894, -3.7275787 ],\n [ 4.74588574, -32.44683966, 81.25139984],\n [ 10.71094749, -31.56690154, 38.06896408],\n [ -1.79924227, -24.69421529, 164.57701743],\n [ 11.72796022, 51.64207311, -171.7374683 ],\n [ 146.15020772, 43.22849451, -31.34891088],\n [ 68.10921869, 20.67625074, -48.74886034],\n [ 46.39959442, 11.62126073, -45.99719267],\n [ 12.35552615, 4.21525086, -64.89288124],\n [ -30.08117143, -19.90769513, -78.98121326],\n [ -88.94647804, -49.64400082, -65.80546984]])\n\n" - def __call__(self, times) -> typing.Any: - "Interpolate rotations.\n\n Compute the interpolated rotations at the given `times`.\n\n Parameters\n ----------\n times : array_like\n Times to compute the interpolations at. Can be a scalar or\n 1-dimensional.\n\n Returns\n -------\n interpolated_rotation : `Rotation` instance\n Object containing the rotations computed at given `times`.\n\n" - ... - __dict__: typing.Dict[str, typing.Any] - def __init__(self, times, rotations) -> None: - "Spherical Linear Interpolation of Rotations.\n\n The interpolation between consecutive rotations is performed as a rotation\n around a fixed axis with a constant angular velocity [1]_. This ensures\n that the interpolated rotations follow the shortest path between initial\n and final orientations.\n\n Parameters\n ----------\n times : array_like, shape (N,)\n Times of the known rotations. At least 2 times must be specified.\n rotations : `Rotation` instance\n Rotations to perform the interpolation between. Must contain N\n rotations.\n\n Methods\n -------\n __call__\n\n See Also\n --------\n Rotation\n\n Notes\n -----\n .. versionadded:: 1.2.0\n\n References\n ----------\n .. [1] https://en.wikipedia.org/wiki/Slerp#Quaternion_Slerp\n\n Examples\n --------\n >>> from scipy.spatial.transform import Rotation as R\n >>> from scipy.spatial.transform import Slerp\n\n Setup the fixed keyframe rotations and times:\n\n >>> key_rots = R.random(5, random_state=2342345)\n >>> key_times = [0, 1, 2, 3, 4]\n\n Create the interpolator object:\n\n >>> slerp = Slerp(key_times, key_rots)\n\n Interpolate the rotations at the given times:\n\n >>> times = [0, 0.5, 0.25, 1, 1.5, 2, 2.75, 3, 3.25, 3.60, 4]\n >>> interp_rots = slerp(times)\n\n The keyframe rotations expressed as Euler angles:\n\n >>> key_rots.as_euler('xyz', degrees=True)\n array([[ 14.31443779, -27.50095894, -3.7275787 ],\n [ -1.79924227, -24.69421529, 164.57701743],\n [146.15020772, 43.22849451, -31.34891088],\n [ 46.39959442, 11.62126073, -45.99719267],\n [-88.94647804, -49.64400082, -65.80546984]])\n\n The interpolated rotations expressed as Euler angles. These agree with the\n keyframe rotations at both endpoints of the range of keyframe times.\n\n >>> interp_rots.as_euler('xyz', degrees=True)\n array([[ 14.31443779, -27.50095894, -3.7275787 ],\n [ 4.74588574, -32.44683966, 81.25139984],\n [ 10.71094749, -31.56690154, 38.06896408],\n [ -1.79924227, -24.69421529, 164.57701743],\n [ 11.72796022, 51.64207311, -171.7374683 ],\n [ 146.15020772, 43.22849451, -31.34891088],\n [ 68.10921869, 20.67625074, -48.74886034],\n [ 46.39959442, 11.62126073, -45.99719267],\n [ 12.35552615, 4.21525086, -64.89288124],\n [ -30.08117143, -19.90769513, -78.98121326],\n [ -88.94647804, -49.64400082, -65.80546984]])\n\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def __getattr__(self, name) -> typing.Any: ... - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... -def __pyx_unpickle_Rotation() -> typing.Any: ... - -__test__: dict - -def check_random_state(seed) -> typing.Any: - "Turn seed into a np.random.RandomState instance\n\n If seed is None (or np.random), return the RandomState singleton used\n by np.random.\n If seed is an int, return a new RandomState instance seeded with seed.\n If seed is already a RandomState instance, return it.\n If seed is a new-style np.random.Generator, return it.\n Otherwise, raise ValueError.\n" - ... - -def create_group(cls, group, axis) -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/special/_comb.pyi b/stubs/scipy-stubs/special/_comb.pyi deleted file mode 100644 index 61a770e7..00000000 --- a/stubs/scipy-stubs/special/_comb.pyi +++ /dev/null @@ -1,14 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.special._comb, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__test__: dict - -def _comb_int() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/special/_ellip_harm_2.pyi b/stubs/scipy-stubs/special/_ellip_harm_2.pyi deleted file mode 100644 index 9faa3c6b..00000000 --- a/stubs/scipy-stubs/special/_ellip_harm_2.pyi +++ /dev/null @@ -1,22 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.special._ellip_harm_2, version: unspecified -import builtins as _mod_builtins -import typing - -import scipy._lib._ccallback as _mod_scipy__lib__ccallback - -LowLevelCallable = _mod_scipy__lib__ccallback.LowLevelCallable -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def _ellipsoid() -> typing.Any: ... -def _ellipsoid_norm() -> typing.Any: ... - -nan: float - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/special/_test_round.pyi b/stubs/scipy-stubs/special/_test_round.pyi deleted file mode 100644 index 05e43a64..00000000 --- a/stubs/scipy-stubs/special/_test_round.pyi +++ /dev/null @@ -1,23 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.special._test_round, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def assert_(val, msg) -> typing.Any: - "\n Assert that works in release mode.\n Accepts callable msg to allow deferring evaluation until failure.\n\n The Python built-in ``assert`` does not work when executing code in\n optimized mode (the ``-O`` flag) - no byte-code is generated for it.\n\n For documentation on usage, refer to the Python documentation.\n\n" - ... - -def have_fenv() -> typing.Any: ... -def random_double() -> typing.Any: ... -def test_add_round() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/special/_ufuncs.pyi b/stubs/scipy-stubs/special/_ufuncs.pyi deleted file mode 100644 index 76974e43..00000000 --- a/stubs/scipy-stubs/special/_ufuncs.pyi +++ /dev/null @@ -1,1571 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.special._ufuncs, version: unspecified -import builtins as _mod_builtins -import typing - -__all__: list -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__test__: dict - -def _cospi(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_cospi(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _ellip_harm( - x1, x2, x3, x4, x5, x6, x7, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_ellip_harm(x1, x2, x3, x4, x5, x6, x7, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `ellip_harm` instead." - ... - -def _factorial(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_factorial(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _igam_fac( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_igam_fac(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _kolmogc(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_kolmogc(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _kolmogci(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_kolmogci(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _kolmogp(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_kolmogp(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _lambertw( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_lambertw(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `lambertw` instead." - ... - -def _lanczos_sum_expg_scaled( - x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_lanczos_sum_expg_scaled(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _lgam1p(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_lgam1p(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _log1pmx(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_log1pmx(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _riemann_zeta( - x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_riemann_zeta(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `zeta` instead." - ... - -_sf_error_action_map: dict -_sf_error_code_map: dict - -def _sf_error_test_function( - x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_sf_error_test_function(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nPrivate function; do not use." - ... - -def _sinpi(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_sinpi(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _smirnovc( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_smirnovc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\n_smirnovc(n, d)\n Internal function, do not use." - ... - -def _smirnovci( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_smirnovci(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, do not use." - ... - -def _smirnovp( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_smirnovp(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\n_smirnovp(n, p)\n Internal function, do not use." - ... - -def _spherical_in( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_spherical_in(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `spherical_in` instead." - ... - -def _spherical_in_d( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_spherical_in_d(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `spherical_in` instead." - ... - -def _spherical_jn( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_spherical_jn(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `spherical_jn` instead." - ... - -def _spherical_jn_d( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_spherical_jn_d(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `spherical_jn` instead." - ... - -def _spherical_kn( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_spherical_kn(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `spherical_kn` instead." - ... - -def _spherical_kn_d( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_spherical_kn_d(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `spherical_kn` instead." - ... - -def _spherical_yn( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_spherical_yn(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `spherical_yn` instead." - ... - -def _spherical_yn_d( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_spherical_yn_d(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInternal function, use `spherical_yn` instead." - ... - -def _struve_asymp_large_z( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_struve_asymp_large_z(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\n_struve_asymp_large_z(v, z, is_h)\n\nInternal function for testing `struve` & `modstruve`\n\nEvaluates using asymptotic expansion\n\nReturns\n-------\nv, err" - ... - -def _struve_bessel_series( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_struve_bessel_series(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\n_struve_bessel_series(v, z, is_h)\n\nInternal function for testing `struve` & `modstruve`\n\nEvaluates using Bessel function series\n\nReturns\n-------\nv, err" - ... - -def _struve_power_series( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "_struve_power_series(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\n_struve_power_series(v, z, is_h)\n\nInternal function for testing `struve` & `modstruve`\n\nEvaluates using power series\n\nReturns\n-------\nv, err" - ... - -def _zeta(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "_zeta(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\n_zeta(x, q)\n\nInternal function, Hurwitz zeta." - ... - -def agm(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "agm(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nagm(a, b)\n\nCompute the arithmetic-geometric mean of `a` and `b`.\n\nStart with a_0 = a and b_0 = b and iteratively compute::\n\n a_{n+1} = (a_n + b_n)/2\n b_{n+1} = sqrt(a_n*b_n)\n\na_n and b_n converge to the same limit as n increases; their common\nlimit is agm(a, b).\n\nParameters\n----------\na, b : array_like\n Real values only. If the values are both negative, the result\n is negative. If one value is negative and the other is positive,\n `nan` is returned.\n\nReturns\n-------\nfloat\n The arithmetic-geometric mean of `a` and `b`.\n\nExamples\n--------\n>>> from scipy.special import agm\n>>> a, b = 24.0, 6.0\n>>> agm(a, b)\n13.458171481725614\n\nCompare that result to the iteration:\n\n>>> while a != b:\n... a, b = (a + b)/2, np.sqrt(a*b)\n... print(\"a = %19.16f b=%19.16f\" % (a, b))\n...\na = 15.0000000000000000 b=12.0000000000000000\na = 13.5000000000000000 b=13.4164078649987388\na = 13.4582039324993694 b=13.4581390309909850\na = 13.4581714817451772 b=13.4581714817060547\na = 13.4581714817256159 b=13.4581714817256159\n\nWhen array-like arguments are given, broadcasting applies:\n\n>>> a = np.array([[1.5], [3], [6]]) # a has shape (3, 1).\n>>> b = np.array([6, 12, 24, 48]) # b has shape (4,).\n>>> agm(a, b)\narray([[ 3.36454287, 5.42363427, 9.05798751, 15.53650756],\n [ 4.37037309, 6.72908574, 10.84726853, 18.11597502],\n [ 6. , 8.74074619, 13.45817148, 21.69453707]])" - ... - -def airy( - x, - out1=..., - out2=..., - out3=..., - out4=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "airy(x[, out1, out2, out3, out4], / [, out=(None, None, None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nairy(z)\n\nAiry functions and their derivatives.\n\nParameters\n----------\nz : array_like\n Real or complex argument.\n\nReturns\n-------\nAi, Aip, Bi, Bip : ndarrays\n Airy functions Ai and Bi, and their derivatives Aip and Bip.\n\nNotes\n-----\nThe Airy functions Ai and Bi are two independent solutions of\n\n.. math:: y''(x) = x y(x).\n\nFor real `z` in [-10, 10], the computation is carried out by calling\nthe Cephes [1]_ `airy` routine, which uses power series summation\nfor small `z` and rational minimax approximations for large `z`.\n\nOutside this range, the AMOS [2]_ `zairy` and `zbiry` routines are\nemployed. They are computed using power series for :math:`|z| < 1` and\nthe following relations to modified Bessel functions for larger `z`\n(where :math:`t \\equiv 2 z^{3/2}/3`):\n\n.. math::\n\n Ai(z) = \\frac{1}{\\pi \\sqrt{3}} K_{1/3}(t)\n\n Ai'(z) = -\\frac{z}{\\pi \\sqrt{3}} K_{2/3}(t)\n\n Bi(z) = \\sqrt{\\frac{z}{3}} \\left(I_{-1/3}(t) + I_{1/3}(t) \\right)\n\n Bi'(z) = \\frac{z}{\\sqrt{3}} \\left(I_{-2/3}(t) + I_{2/3}(t)\\right)\n\nSee also\n--------\nairye : exponentially scaled Airy functions.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/\n.. [2] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/\n\nExamples\n--------\nCompute the Airy functions on the interval [-15, 5].\n\n>>> from scipy import special\n>>> x = np.linspace(-15, 5, 201)\n>>> ai, aip, bi, bip = special.airy(x)\n\nPlot Ai(x) and Bi(x).\n\n>>> import matplotlib.pyplot as plt\n>>> plt.plot(x, ai, 'r', label='Ai(x)')\n>>> plt.plot(x, bi, 'b--', label='Bi(x)')\n>>> plt.ylim(-0.5, 1.0)\n>>> plt.grid()\n>>> plt.legend(loc='upper left')\n>>> plt.show()" - ... - -def airye( - x, - out1=..., - out2=..., - out3=..., - out4=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - 'airye(x[, out1, out2, out3, out4], / [, out=(None, None, None, None)], *, where=True, casting=\'same_kind\', order=\'K\', dtype=None, subok=True[, signature, extobj])\n\nairye(z)\n\nExponentially scaled Airy functions and their derivatives.\n\nScaling::\n\n eAi = Ai * exp(2.0/3.0*z*sqrt(z))\n eAip = Aip * exp(2.0/3.0*z*sqrt(z))\n eBi = Bi * exp(-abs(2.0/3.0*(z*sqrt(z)).real))\n eBip = Bip * exp(-abs(2.0/3.0*(z*sqrt(z)).real))\n\nParameters\n----------\nz : array_like\n Real or complex argument.\n\nReturns\n-------\neAi, eAip, eBi, eBip : array_like\n Exponentially scaled Airy functions eAi and eBi, and their derivatives \n eAip and eBip\n\nNotes\n-----\nWrapper for the AMOS [1]_ routines `zairy` and `zbiry`.\n\nSee also\n--------\nairy\n\nReferences\n----------\n.. [1] Donald E. Amos, "AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order",\n http://netlib.org/amos/\n\nExamples\n--------\nWe can compute exponentially scaled Airy functions and their derivatives:\n\n>>> from scipy.special import airye\n>>> import matplotlib.pyplot as plt\n>>> z = np.linspace(0, 50, 500)\n>>> eAi, eAip, eBi, eBip = airye(z)\n>>> f, ax = plt.subplots(2, 1, sharex=True)\n>>> for ind, data in enumerate([[eAi, eAip, ["eAi", "eAip"]],\n... [eBi, eBip, ["eBi", "eBip"]]]):\n... ax[ind].plot(z, data[0], "-r", z, data[1], "-b")\n... ax[ind].legend(data[2])\n... ax[ind].grid(True)\n>>> plt.show()\n\nWe can compute these using usual non-scaled Airy functions by:\n\n>>> from scipy.special import airy\n>>> Ai, Aip, Bi, Bip = airy(z)\n>>> np.allclose(eAi, Ai * np.exp(2.0 / 3.0 * z * np.sqrt(z)))\nTrue\n>>> np.allclose(eAip, Aip * np.exp(2.0 / 3.0 * z * np.sqrt(z)))\nTrue\n>>> np.allclose(eBi, Bi * np.exp(-abs(np.real(2.0 / 3.0 * z * np.sqrt(z)))))\nTrue\n>>> np.allclose(eBip, Bip * np.exp(-abs(np.real(2.0 / 3.0 * z * np.sqrt(z)))))\nTrue\n\nComparing non-scaled and exponentially scaled ones, the usual non-scaled \nfunction quickly underflows for large values, whereas the exponentially\nscaled function does not.\n\n>>> airy(200)\n(0.0, 0.0, nan, nan)\n>>> airye(200)\n(0.07501041684381093, -1.0609012305109042, 0.15003188417418148, 2.1215836725571093)' - ... - -def bdtr( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "bdtr(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbdtr(k, n, p)\n\nBinomial distribution cumulative distribution function.\n\nSum of the terms 0 through `floor(k)` of the Binomial probability density.\n\n.. math::\n \\mathrm{bdtr}(k, n, p) = \\sum_{j=0}^{\\lfloor k \\rfloor} {{n}\\choose{j}} p^j (1-p)^{n-j}\n\nParameters\n----------\nk : array_like\n Number of successes (double), rounded down to the nearest integer.\nn : array_like\n Number of events (int).\np : array_like\n Probability of success in a single event (float).\n\nReturns\n-------\ny : ndarray\n Probability of `floor(k)` or fewer successes in `n` independent events with\n success probabilities of `p`.\n\nNotes\n-----\nThe terms are not summed directly; instead the regularized incomplete beta\nfunction is employed, according to the formula,\n\n.. math::\n \\mathrm{bdtr}(k, n, p) = I_{1 - p}(n - \\lfloor k \\rfloor, \\lfloor k \\rfloor + 1).\n\nWrapper for the Cephes [1]_ routine `bdtr`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def bdtrc( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "bdtrc(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbdtrc(k, n, p)\n\nBinomial distribution survival function.\n\nSum of the terms `floor(k) + 1` through `n` of the binomial probability\ndensity,\n\n.. math::\n \\mathrm{bdtrc}(k, n, p) = \\sum_{j=\\lfloor k \\rfloor +1}^n {{n}\\choose{j}} p^j (1-p)^{n-j}\n\nParameters\n----------\nk : array_like\n Number of successes (double), rounded down to nearest integer.\nn : array_like\n Number of events (int)\np : array_like\n Probability of success in a single event.\n\nReturns\n-------\ny : ndarray\n Probability of `floor(k) + 1` or more successes in `n` independent\n events with success probabilities of `p`.\n\nSee also\n--------\nbdtr\nbetainc\n\nNotes\n-----\nThe terms are not summed directly; instead the regularized incomplete beta\nfunction is employed, according to the formula,\n\n.. math::\n \\mathrm{bdtrc}(k, n, p) = I_{p}(\\lfloor k \\rfloor + 1, n - \\lfloor k \\rfloor).\n\nWrapper for the Cephes [1]_ routine `bdtrc`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def bdtri( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "bdtri(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbdtri(k, n, y)\n\nInverse function to `bdtr` with respect to `p`.\n\nFinds the event probability `p` such that the sum of the terms 0 through\n`k` of the binomial probability density is equal to the given cumulative\nprobability `y`.\n\nParameters\n----------\nk : array_like\n Number of successes (float), rounded down to the nearest integer.\nn : array_like\n Number of events (float)\ny : array_like\n Cumulative probability (probability of `k` or fewer successes in `n`\n events).\n\nReturns\n-------\np : ndarray\n The event probability such that `bdtr(\\lfloor k \\rfloor, n, p) = y`.\n\nSee also\n--------\nbdtr\nbetaincinv\n\nNotes\n-----\nThe computation is carried out using the inverse beta integral function\nand the relation,::\n\n 1 - p = betaincinv(n - k, k + 1, y).\n\nWrapper for the Cephes [1]_ routine `bdtri`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def bdtrik( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "bdtrik(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbdtrik(y, n, p)\n\nInverse function to `bdtr` with respect to `k`.\n\nFinds the number of successes `k` such that the sum of the terms 0 through\n`k` of the Binomial probability density for `n` events with probability\n`p` is equal to the given cumulative probability `y`.\n\nParameters\n----------\ny : array_like\n Cumulative probability (probability of `k` or fewer successes in `n`\n events).\nn : array_like\n Number of events (float).\np : array_like\n Success probability (float).\n\nReturns\n-------\nk : ndarray\n The number of successes `k` such that `bdtr(k, n, p) = y`.\n\nSee also\n--------\nbdtr\n\nNotes\n-----\nFormula 26.5.24 of [1]_ is used to reduce the binomial distribution to the\ncumulative incomplete beta distribution.\n\nComputation of `k` involves a search for a value that produces the desired\nvalue of `y`. The search relies on the monotonicity of `y` with `k`.\n\nWrapper for the CDFLIB [2]_ Fortran routine `cdfbin`.\n\nReferences\n----------\n.. [1] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972.\n.. [2] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters." - ... - -def bdtrin( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "bdtrin(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbdtrin(k, y, p)\n\nInverse function to `bdtr` with respect to `n`.\n\nFinds the number of events `n` such that the sum of the terms 0 through\n`k` of the Binomial probability density for events with probability `p` is\nequal to the given cumulative probability `y`.\n\nParameters\n----------\nk : array_like\n Number of successes (float).\ny : array_like\n Cumulative probability (probability of `k` or fewer successes in `n`\n events).\np : array_like\n Success probability (float).\n\nReturns\n-------\nn : ndarray\n The number of events `n` such that `bdtr(k, n, p) = y`.\n\nSee also\n--------\nbdtr\n\nNotes\n-----\nFormula 26.5.24 of [1]_ is used to reduce the binomial distribution to the\ncumulative incomplete beta distribution.\n\nComputation of `n` involves a search for a value that produces the desired\nvalue of `y`. The search relies on the monotonicity of `y` with `n`.\n\nWrapper for the CDFLIB [2]_ Fortran routine `cdfbin`.\n\nReferences\n----------\n.. [1] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972.\n.. [2] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters." - ... - -def bei(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "bei(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbei(x, out=None)\n\nKelvin function bei.\n\nDefined as\n\n.. math::\n\n \\mathrm{bei}(x) = \\Im[J_0(x e^{3 \\pi i / 4})]\n\nwhere :math:`J_0` is the Bessel function of the first kind of\norder zero (see `jv`). See [dlmf]_ for more details.\n\nParameters\n----------\nx : array_like\n Real argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of the Kelvin function.\n\nSee Also\n--------\nber : the corresponding real part\nbeip : the derivative of bei\njv : Bessel function of the first kind\n\nReferences\n----------\n.. [dlmf] NIST, Digital Library of Mathematical Functions,\n https://dlmf.nist.gov/10.61\n\nExamples\n--------\nIt can be expressed using Bessel functions.\n\n>>> import scipy.special as sc\n>>> x = np.array([1.0, 2.0, 3.0, 4.0])\n>>> sc.jv(0, x * np.exp(3 * np.pi * 1j / 4)).imag\narray([0.24956604, 0.97229163, 1.93758679, 2.29269032])\n>>> sc.bei(x)\narray([0.24956604, 0.97229163, 1.93758679, 2.29269032])" - ... - -def beip(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "beip(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbeip(x, out=None)\n\nDerivative of the Kelvin function bei.\n\nParameters\n----------\nx : array_like\n Real argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n The values of the derivative of bei.\n\nSee Also\n--------\nbei\n\nReferences\n----------\n.. [dlmf] NIST, Digital Library of Mathematical Functions,\n https://dlmf.nist.gov/10#PT5" - ... - -def ber(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "ber(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nber(x, out=None)\n\nKelvin function ber.\n\nDefined as\n\n.. math::\n\n \\mathrm{ber}(x) = \\Re[J_0(x e^{3 \\pi i / 4})]\n\nwhere :math:`J_0` is the Bessel function of the first kind of\norder zero (see `jv`). See [dlmf]_ for more details.\n\nParameters\n----------\nx : array_like\n Real argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of the Kelvin function.\n\nSee Also\n--------\nbei : the corresponding real part\nberp : the derivative of bei\njv : Bessel function of the first kind\n\nReferences\n----------\n.. [dlmf] NIST, Digital Library of Mathematical Functions,\n https://dlmf.nist.gov/10.61\n\nExamples\n--------\nIt can be expressed using Bessel functions.\n\n>>> import scipy.special as sc\n>>> x = np.array([1.0, 2.0, 3.0, 4.0])\n>>> sc.jv(0, x * np.exp(3 * np.pi * 1j / 4)).real\narray([ 0.98438178, 0.75173418, -0.22138025, -2.56341656])\n>>> sc.ber(x)\narray([ 0.98438178, 0.75173418, -0.22138025, -2.56341656])" - ... - -def berp(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "berp(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nberp(x, out=None)\n\nDerivative of the Kelvin function ber.\n\nParameters\n----------\nx : array_like\n Real argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n The values of the derivative of ber.\n\nSee Also\n--------\nber\n\nReferences\n----------\n.. [dlmf] NIST, Digital Library of Mathematical Functions,\n https://dlmf.nist.gov/10#PT5" - ... - -def besselpoly( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "besselpoly(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbesselpoly(a, lmb, nu, out=None)\n\nWeighted integral of the Bessel function of the first kind.\n\nComputes\n\n.. math::\n\n \\int_0^1 x^\\lambda J_\\nu(2 a x) \\, dx\n\nwhere :math:`J_\\nu` is a Bessel function and :math:`\\lambda=lmb`,\n:math:`\\nu=nu`.\n\nParameters\n----------\na : array_like\n Scale factor inside the Bessel function.\nlmb : array_like\n Power of `x`\nnu : array_like\n Order of the Bessel function.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Value of the integral." - ... - -def beta(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "beta(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbeta(a, b, out=None)\n\nBeta function.\n\nThis function is defined in [1]_ as\n\n.. math::\n\n B(a, b) = \\int_0^1 t^{a-1}(1-t)^{b-1}dt\n = \\frac{\\Gamma(a)\\Gamma(b)}{\\Gamma(a+b)},\n\nwhere :math:`\\Gamma` is the gamma function.\n\nParameters\n----------\na, b : array-like\n Real-valued arguments\nout : ndarray, optional\n Optional output array for the function result\n\nReturns\n-------\nscalar or ndarray\n Value of the beta function\n\nSee Also\n--------\ngamma : the gamma function\nbetainc : the incomplete beta function\nbetaln : the natural logarithm of the absolute\n value of the beta function\n\nReferences\n----------\n.. [1] NIST Digital Library of Mathematical Functions,\n Eq. 5.12.1. https://dlmf.nist.gov/5.12\n\nExamples\n--------\n>>> import scipy.special as sc\n\nThe beta function relates to the gamma function by the\ndefinition given above:\n\n>>> sc.beta(2, 3)\n0.08333333333333333\n>>> sc.gamma(2)*sc.gamma(3)/sc.gamma(2 + 3)\n0.08333333333333333\n\nAs this relationship demonstrates, the beta function\nis symmetric:\n\n>>> sc.beta(1.7, 2.4)\n0.16567527689031739\n>>> sc.beta(2.4, 1.7)\n0.16567527689031739\n\nThis function satisfies :math:`B(1, b) = 1/b`:\n\n>>> sc.beta(1, 4)\n0.25" - ... - -def betainc( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "betainc(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbetainc(a, b, x, out=None)\n\nIncomplete beta function.\n\nComputes the incomplete beta function, defined as [1]_:\n\n.. math::\n\n I_x(a, b) = \\frac{\\Gamma(a+b)}{\\Gamma(a)\\Gamma(b)} \\int_0^x\n t^{a-1}(1-t)^{b-1}dt,\n\nfor :math:`0 \\leq x \\leq 1`.\n\nParameters\n----------\na, b : array-like\n Positive, real-valued parameters\nx : array-like\n Real-valued such that :math:`0 \\leq x \\leq 1`,\n the upper limit of integration\nout : ndarray, optional\n Optional output array for the function values\n\nReturns\n-------\narray-like\n Value of the incomplete beta function\n\nSee Also\n--------\nbeta : beta function\nbetaincinv : inverse of the incomplete beta function\n\nNotes\n-----\nThe incomplete beta function is also sometimes defined\nwithout the `gamma` terms, in which case the above\ndefinition is the so-called regularized incomplete beta\nfunction. Under this definition, you can get the incomplete\nbeta function by multiplying the result of the SciPy\nfunction by `beta`.\n\nReferences\n----------\n.. [1] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/8.17\n\nExamples\n--------\n\nLet :math:`B(a, b)` be the `beta` function.\n\n>>> import scipy.special as sc\n\nThe coefficient in terms of `gamma` is equal to\n:math:`1/B(a, b)`. Also, when :math:`x=1`\nthe integral is equal to :math:`B(a, b)`.\nTherefore, :math:`I_{x=1}(a, b) = 1` for any :math:`a, b`.\n\n>>> sc.betainc(0.2, 3.5, 1.0)\n1.0\n\nIt satisfies\n:math:`I_x(a, b) = x^a F(a, 1-b, a+1, x)/ (aB(a, b))`,\nwhere :math:`F` is the hypergeometric function `hyp2f1`:\n\n>>> a, b, x = 1.4, 3.1, 0.5\n>>> x**a * sc.hyp2f1(a, 1 - b, a + 1, x)/(a * sc.beta(a, b))\n0.8148904036225295\n>>> sc.betainc(a, b, x)\n0.8148904036225296\n\nThis functions satisfies the relationship\n:math:`I_x(a, b) = 1 - I_{1-x}(b, a)`:\n\n>>> sc.betainc(2.2, 3.1, 0.4)\n0.49339638807619446\n>>> 1 - sc.betainc(3.1, 2.2, 1 - 0.4)\n0.49339638807619446" - ... - -def betaincinv( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "betaincinv(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbetaincinv(a, b, y, out=None)\n\nInverse of the incomplete beta function.\n\nComputes :math:`x` such that:\n\n.. math::\n\n y = I_x(a, b) = \\frac{\\Gamma(a+b)}{\\Gamma(a)\\Gamma(b)}\n \\int_0^x t^{a-1}(1-t)^{b-1}dt,\n\nwhere :math:`I_x` is the normalized incomplete beta\nfunction `betainc` and\n:math:`\\Gamma` is the `gamma` function [1]_.\n\nParameters\n----------\na, b : array-like\n Positive, real-valued parameters\ny : array-like\n Real-valued input\nout : ndarray, optional\n Optional output array for function values\n\nReturns\n-------\narray-like\n Value of the inverse of the incomplete beta function\n\nSee Also\n--------\nbetainc : incomplete beta function\ngamma : gamma function\n\nReferences\n----------\n.. [1] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/8.17\n\nExamples\n--------\n>>> import scipy.special as sc\n\nThis function is the inverse of `betainc` for fixed\nvalues of :math:`a` and :math:`b`.\n\n>>> a, b = 1.2, 3.1\n>>> y = sc.betainc(a, b, 0.2)\n>>> sc.betaincinv(a, b, y)\n0.2\n>>>\n>>> a, b = 7.5, 0.4\n>>> x = sc.betaincinv(a, b, 0.5)\n>>> sc.betainc(a, b, x)\n0.5" - ... - -def betaln(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "betaln(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbetaln(a, b)\n\nNatural logarithm of absolute value of beta function.\n\nComputes ``ln(abs(beta(a, b)))``." - ... - -def binom(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "binom(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbinom(n, k)\n\nBinomial coefficient\n\nSee Also\n--------\ncomb : The number of combinations of N things taken k at a time." - ... - -def boxcox(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "boxcox(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nboxcox(x, lmbda)\n\nCompute the Box-Cox transformation.\n\nThe Box-Cox transformation is::\n\n y = (x**lmbda - 1) / lmbda if lmbda != 0\n log(x) if lmbda == 0\n\nReturns `nan` if ``x < 0``.\nReturns `-inf` if ``x == 0`` and ``lmbda < 0``.\n\nParameters\n----------\nx : array_like\n Data to be transformed.\nlmbda : array_like\n Power parameter of the Box-Cox transform.\n\nReturns\n-------\ny : array\n Transformed data.\n\nNotes\n-----\n\n.. versionadded:: 0.14.0\n\nExamples\n--------\n>>> from scipy.special import boxcox\n>>> boxcox([1, 4, 10], 2.5)\narray([ 0. , 12.4 , 126.09110641])\n>>> boxcox(2, [0, 1, 2])\narray([ 0.69314718, 1. , 1.5 ])" - ... - -def boxcox1p( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "boxcox1p(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nboxcox1p(x, lmbda)\n\nCompute the Box-Cox transformation of 1 + `x`.\n\nThe Box-Cox transformation computed by `boxcox1p` is::\n\n y = ((1+x)**lmbda - 1) / lmbda if lmbda != 0\n log(1+x) if lmbda == 0\n\nReturns `nan` if ``x < -1``.\nReturns `-inf` if ``x == -1`` and ``lmbda < 0``.\n\nParameters\n----------\nx : array_like\n Data to be transformed.\nlmbda : array_like\n Power parameter of the Box-Cox transform.\n\nReturns\n-------\ny : array\n Transformed data.\n\nNotes\n-----\n\n.. versionadded:: 0.14.0\n\nExamples\n--------\n>>> from scipy.special import boxcox1p\n>>> boxcox1p(1e-4, [0, 0.5, 1])\narray([ 9.99950003e-05, 9.99975001e-05, 1.00000000e-04])\n>>> boxcox1p([0.01, 0.1], 0.25)\narray([ 0.00996272, 0.09645476])" - ... - -def btdtr( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "btdtr(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbtdtr(a, b, x)\n\nCumulative distribution function of the beta distribution.\n\nReturns the integral from zero to `x` of the beta probability density\nfunction,\n\n.. math::\n I = \\int_0^x \\frac{\\Gamma(a + b)}{\\Gamma(a)\\Gamma(b)} t^{a-1} (1-t)^{b-1}\\,dt\n\nwhere :math:`\\Gamma` is the gamma function.\n\nParameters\n----------\na : array_like\n Shape parameter (a > 0).\nb : array_like\n Shape parameter (b > 0).\nx : array_like\n Upper limit of integration, in [0, 1].\n\nReturns\n-------\nI : ndarray\n Cumulative distribution function of the beta distribution with\n parameters `a` and `b` at `x`.\n\nSee Also\n--------\nbetainc\n\nNotes\n-----\nThis function is identical to the incomplete beta integral function\n`betainc`.\n\nWrapper for the Cephes [1]_ routine `btdtr`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def btdtri( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "btdtri(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbtdtri(a, b, p)\n\nThe `p`-th quantile of the beta distribution.\n\nThis function is the inverse of the beta cumulative distribution function,\n`btdtr`, returning the value of `x` for which `btdtr(a, b, x) = p`, or\n\n.. math::\n p = \\int_0^x \\frac{\\Gamma(a + b)}{\\Gamma(a)\\Gamma(b)} t^{a-1} (1-t)^{b-1}\\,dt\n\nParameters\n----------\na : array_like\n Shape parameter (`a` > 0).\nb : array_like\n Shape parameter (`b` > 0).\np : array_like\n Cumulative probability, in [0, 1].\n\nReturns\n-------\nx : ndarray\n The quantile corresponding to `p`.\n\nSee Also\n--------\nbetaincinv\nbtdtr\n\nNotes\n-----\nThe value of `x` is found by interval halving or Newton iterations.\n\nWrapper for the Cephes [1]_ routine `incbi`, which solves the equivalent\nproblem of finding the inverse of the incomplete beta integral.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def btdtria( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "btdtria(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbtdtria(p, b, x)\n\nInverse of `btdtr` with respect to `a`.\n\nThis is the inverse of the beta cumulative distribution function, `btdtr`,\nconsidered as a function of `a`, returning the value of `a` for which\n`btdtr(a, b, x) = p`, or\n\n.. math::\n p = \\int_0^x \\frac{\\Gamma(a + b)}{\\Gamma(a)\\Gamma(b)} t^{a-1} (1-t)^{b-1}\\,dt\n\nParameters\n----------\np : array_like\n Cumulative probability, in [0, 1].\nb : array_like\n Shape parameter (`b` > 0).\nx : array_like\n The quantile, in [0, 1].\n\nReturns\n-------\na : ndarray\n The value of the shape parameter `a` such that `btdtr(a, b, x) = p`.\n\nSee Also\n--------\nbtdtr : Cumulative distribution function of the beta distribution.\nbtdtri : Inverse with respect to `x`.\nbtdtrib : Inverse with respect to `b`.\n\nNotes\n-----\nWrapper for the CDFLIB [1]_ Fortran routine `cdfbet`.\n\nThe cumulative distribution function `p` is computed using a routine by\nDiDinato and Morris [2]_. Computation of `a` involves a search for a value\nthat produces the desired value of `p`. The search relies on the\nmonotonicity of `p` with `a`.\n\nReferences\n----------\n.. [1] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters.\n.. [2] DiDinato, A. R. and Morris, A. H.,\n Algorithm 708: Significant Digit Computation of the Incomplete Beta\n Function Ratios. ACM Trans. Math. Softw. 18 (1993), 360-373." - ... - -def btdtrib( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "btdtrib(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nbtdtria(a, p, x)\n\nInverse of `btdtr` with respect to `b`.\n\nThis is the inverse of the beta cumulative distribution function, `btdtr`,\nconsidered as a function of `b`, returning the value of `b` for which\n`btdtr(a, b, x) = p`, or\n\n.. math::\n p = \\int_0^x \\frac{\\Gamma(a + b)}{\\Gamma(a)\\Gamma(b)} t^{a-1} (1-t)^{b-1}\\,dt\n\nParameters\n----------\na : array_like\n Shape parameter (`a` > 0).\np : array_like\n Cumulative probability, in [0, 1].\nx : array_like\n The quantile, in [0, 1].\n\nReturns\n-------\nb : ndarray\n The value of the shape parameter `b` such that `btdtr(a, b, x) = p`.\n\nSee Also\n--------\nbtdtr : Cumulative distribution function of the beta distribution.\nbtdtri : Inverse with respect to `x`.\nbtdtria : Inverse with respect to `a`.\n\nNotes\n-----\nWrapper for the CDFLIB [1]_ Fortran routine `cdfbet`.\n\nThe cumulative distribution function `p` is computed using a routine by\nDiDinato and Morris [2]_. Computation of `b` involves a search for a value\nthat produces the desired value of `p`. The search relies on the\nmonotonicity of `p` with `b`.\n\nReferences\n----------\n.. [1] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters.\n.. [2] DiDinato, A. R. and Morris, A. H.,\n Algorithm 708: Significant Digit Computation of the Incomplete Beta\n Function Ratios. ACM Trans. Math. Softw. 18 (1993), 360-373." - ... - -def cbrt(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "cbrt(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ncbrt(x)\n\nElement-wise cube root of `x`.\n\nParameters\n----------\nx : array_like\n `x` must contain real numbers.\n\nReturns\n-------\nfloat\n The cube root of each value in `x`.\n\nExamples\n--------\n>>> from scipy.special import cbrt\n\n>>> cbrt(8)\n2.0\n>>> cbrt([-8, -3, 0.125, 1.331])\narray([-2. , -1.44224957, 0.5 , 1.1 ])" - ... - -def chdtr(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "chdtr(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nchdtr(v, x, out=None)\n\nChi square cumulative distribution function.\n\nReturns the area under the left tail (from 0 to `x`) of the Chi\nsquare probability density function with `v` degrees of freedom:\n\n.. math::\n\n \\frac{1}{2^{v/2} \\Gamma(v/2)} \\int_0^x t^{v/2 - 1} e^{-t/2} dt\n\nHere :math:`\\Gamma` is the Gamma function; see `gamma`. This\nintegral can be expressed in terms of the regularized lower\nincomplete gamma function `gammainc` as\n``gammainc(v / 2, x / 2)``. [1]_\n\nParameters\n----------\nv : array_like\n Degrees of freedom.\nx : array_like\n Upper bound of the integral.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of the cumulative distribution function.\n\nSee Also\n--------\nchdtrc, chdtri, chdtriv, gammainc\n\nReferences\n----------\n.. [1] Chi-Square distribution,\n https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt can be expressed in terms of the regularized lower incomplete\ngamma function.\n\n>>> v = 1\n>>> x = np.arange(4)\n>>> sc.chdtr(v, x)\narray([0. , 0.68268949, 0.84270079, 0.91673548])\n>>> sc.gammainc(v / 2, x / 2)\narray([0. , 0.68268949, 0.84270079, 0.91673548])" - ... - -def chdtrc(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "chdtrc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nchdtrc(v, x, out=None)\n\nChi square survival function.\n\nReturns the area under the right hand tail (from `x` to infinity)\nof the Chi square probability density function with `v` degrees of\nfreedom:\n\n.. math::\n\n \\frac{1}{2^{v/2} \\Gamma(v/2)} \\int_x^\\infty t^{v/2 - 1} e^{-t/2} dt\n\nHere :math:`\\Gamma` is the Gamma function; see `gamma`. This\nintegral can be expressed in terms of the regularized upper\nincomplete gamma function `gammaincc` as\n``gammaincc(v / 2, x / 2)``. [1]_\n\nParameters\n----------\nv : array_like\n Degrees of freedom.\nx : array_like\n Lower bound of the integral.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of the survival function.\n\nSee Also\n--------\nchdtr, chdtri, chdtriv, gammaincc\n\nReferences\n----------\n.. [1] Chi-Square distribution,\n https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt can be expressed in terms of the regularized upper incomplete\ngamma function.\n\n>>> v = 1\n>>> x = np.arange(4)\n>>> sc.chdtrc(v, x)\narray([1. , 0.31731051, 0.15729921, 0.08326452])\n>>> sc.gammaincc(v / 2, x / 2)\narray([1. , 0.31731051, 0.15729921, 0.08326452])" - ... - -def chdtri(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "chdtri(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nchdtri(v, p, out=None)\n\nInverse to `chdtrc` with respect to `x`.\n\nReturns `x` such that ``chdtrc(v, x) == p``.\n\nParameters\n----------\nv : array_like\n Degrees of freedom.\np : array_like\n Probability.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nx : scalar or ndarray\n Value so that the probability a Chi square random variable\n with `v` degrees of freedom is greater than `x` equals `p`.\n\nSee Also\n--------\nchdtrc, chdtr, chdtriv\n\nReferences\n----------\n.. [1] Chi-Square distribution,\n https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt inverts `chdtrc`.\n\n>>> v, p = 1, 0.3\n>>> sc.chdtrc(v, sc.chdtri(v, p))\n0.3\n>>> x = 1\n>>> sc.chdtri(v, sc.chdtrc(v, x))\n1.0" - ... - -def chdtriv(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "chdtriv(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nchdtriv(p, x, out=None)\n\nInverse to `chdtr` with respect to `v`.\n\nReturns `v` such that ``chdtr(v, x) == p``.\n\nParameters\n----------\np : array_like\n Probability that the Chi square random variable is less than\n or equal to `x`.\nx : array_like\n Nonnegative input.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Degrees of freedom.\n\nSee Also\n--------\nchdtr, chdtrc, chdtri\n\nReferences\n----------\n.. [1] Chi-Square distribution,\n https://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt inverts `chdtr`.\n\n>>> p, x = 0.5, 1\n>>> sc.chdtr(sc.chdtriv(p, x), x)\n0.5000000000202172\n>>> v = 1\n>>> sc.chdtriv(sc.chdtr(v, x), v)\n1.0000000000000013" - ... - -def chndtr( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "chndtr(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nchndtr(x, df, nc)\n\nNon-central chi square cumulative distribution function" - ... - -def chndtridf( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "chndtridf(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nchndtridf(x, p, nc)\n\nInverse to `chndtr` vs `df`" - ... - -def chndtrinc( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "chndtrinc(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nchndtrinc(x, df, p)\n\nInverse to `chndtr` vs `nc`" - ... - -def chndtrix( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "chndtrix(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nchndtrix(p, df, nc)\n\nInverse to `chndtr` vs `x`" - ... - -def cosdg(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "cosdg(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ncosdg(x, out=None)\n\nCosine of the angle `x` given in degrees.\n\nParameters\n----------\nx : array_like\n Angle, given in degrees.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Cosine of the input.\n\nSee Also\n--------\nsindg, tandg, cotdg\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is more accurate than using cosine directly.\n\n>>> x = 90 + 180 * np.arange(3)\n>>> sc.cosdg(x)\narray([-0., 0., -0.])\n>>> np.cos(x * np.pi / 180)\narray([ 6.1232340e-17, -1.8369702e-16, 3.0616170e-16])" - ... - -def cosm1(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "cosm1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ncosm1(x, out=None)\n\ncos(x) - 1 for use when `x` is near zero.\n\nParameters\n----------\nx : array_like\n Real valued argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of ``cos(x) - 1``.\n\nSee Also\n--------\nexpm1, log1p\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is more accurate than computing ``cos(x) - 1`` directly for\n``x`` around 0.\n\n>>> x = 1e-30\n>>> np.cos(x) - 1\n0.0\n>>> sc.cosm1(x)\n-5.0000000000000005e-61" - ... - -def cotdg(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "cotdg(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ncotdg(x, out=None)\n\nCotangent of the angle `x` given in degrees.\n\nParameters\n----------\nx : array_like\n Angle, given in degrees.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Cotangent at the input.\n\nSee Also\n--------\nsindg, cosdg, tandg\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is more accurate than using cotangent directly.\n\n>>> x = 90 + 180 * np.arange(3)\n>>> sc.cotdg(x)\narray([0., 0., 0.])\n>>> 1 / np.tan(x * np.pi / 180)\narray([6.1232340e-17, 1.8369702e-16, 3.0616170e-16])" - ... - -def dawsn(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "dawsn(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ndawsn(x)\n\nDawson's integral.\n\nComputes::\n\n exp(-x**2) * integral(exp(t**2), t=0..x).\n\nSee Also\n--------\nwofz, erf, erfc, erfcx, erfi\n\nReferences\n----------\n.. [1] Steven G. Johnson, Faddeeva W function implementation.\n http://ab-initio.mit.edu/Faddeeva\n\nExamples\n--------\n>>> from scipy import special\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(-15, 15, num=1000)\n>>> plt.plot(x, special.dawsn(x))\n>>> plt.xlabel('$x$')\n>>> plt.ylabel('$dawsn(x)$')\n>>> plt.show()" - ... - -def ellipe(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "ellipe(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nellipe(m)\n\nComplete elliptic integral of the second kind\n\nThis function is defined as\n\n.. math:: E(m) = \\int_0^{\\pi/2} [1 - m \\sin(t)^2]^{1/2} dt\n\nParameters\n----------\nm : array_like\n Defines the parameter of the elliptic integral.\n\nReturns\n-------\nE : ndarray\n Value of the elliptic integral.\n\nNotes\n-----\nWrapper for the Cephes [1]_ routine `ellpe`.\n\nFor `m > 0` the computation uses the approximation,\n\n.. math:: E(m) \\approx P(1-m) - (1-m) \\log(1-m) Q(1-m),\n\nwhere :math:`P` and :math:`Q` are tenth-order polynomials. For\n`m < 0`, the relation\n\n.. math:: E(m) = E(m/(m - 1)) \\sqrt(1-m)\n\nis used.\n\nThe parameterization in terms of :math:`m` follows that of section\n17.2 in [2]_. Other parameterizations in terms of the\ncomplementary parameter :math:`1 - m`, modular angle\n:math:`\\sin^2(\\alpha) = m`, or modulus :math:`k^2 = m` are also\nused, so be careful that you choose the correct parameter.\n\nSee Also\n--------\nellipkm1 : Complete elliptic integral of the first kind, near `m` = 1\nellipk : Complete elliptic integral of the first kind\nellipkinc : Incomplete elliptic integral of the first kind\nellipeinc : Incomplete elliptic integral of the second kind\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/\n.. [2] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972.\n\nExamples\n--------\nThis function is used in finding the circumference of an\nellipse with semi-major axis `a` and semi-minor axis `b`.\n\n>>> from scipy import special\n\n>>> a = 3.5\n>>> b = 2.1\n>>> e_sq = 1.0 - b**2/a**2 # eccentricity squared\n\nThen the circumference is found using the following:\n\n>>> C = 4*a*special.ellipe(e_sq) # circumference formula\n>>> C\n17.868899204378693\n\nWhen `a` and `b` are the same (meaning eccentricity is 0),\nthis reduces to the circumference of a circle.\n\n>>> 4*a*special.ellipe(0.0) # formula for ellipse with a = b\n21.991148575128552\n>>> 2*np.pi*a # formula for circle of radius a\n21.991148575128552" - ... - -def ellipeinc( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "ellipeinc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nellipeinc(phi, m)\n\nIncomplete elliptic integral of the second kind\n\nThis function is defined as\n\n.. math:: E(\\phi, m) = \\int_0^{\\phi} [1 - m \\sin(t)^2]^{1/2} dt\n\nParameters\n----------\nphi : array_like\n amplitude of the elliptic integral.\n\nm : array_like\n parameter of the elliptic integral.\n\nReturns\n-------\nE : ndarray\n Value of the elliptic integral.\n\nNotes\n-----\nWrapper for the Cephes [1]_ routine `ellie`.\n\nComputation uses arithmetic-geometric means algorithm.\n\nThe parameterization in terms of :math:`m` follows that of section\n17.2 in [2]_. Other parameterizations in terms of the\ncomplementary parameter :math:`1 - m`, modular angle\n:math:`\\sin^2(\\alpha) = m`, or modulus :math:`k^2 = m` are also\nused, so be careful that you choose the correct parameter.\n\nSee Also\n--------\nellipkm1 : Complete elliptic integral of the first kind, near `m` = 1\nellipk : Complete elliptic integral of the first kind\nellipkinc : Incomplete elliptic integral of the first kind\nellipe : Complete elliptic integral of the second kind\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/\n.. [2] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def ellipj( - x1, - x2, - out1=..., - out2=..., - out3=..., - out4=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "ellipj(x1, x2[, out1, out2, out3, out4], / [, out=(None, None, None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nellipj(u, m)\n\nJacobian elliptic functions\n\nCalculates the Jacobian elliptic functions of parameter `m` between\n0 and 1, and real argument `u`.\n\nParameters\n----------\nm : array_like\n Parameter.\nu : array_like\n Argument.\n\nReturns\n-------\nsn, cn, dn, ph : ndarrays\n The returned functions::\n\n sn(u|m), cn(u|m), dn(u|m)\n\n The value `ph` is such that if `u = ellipkinc(ph, m)`,\n then `sn(u|m) = sin(ph)` and `cn(u|m) = cos(ph)`.\n\nNotes\n-----\nWrapper for the Cephes [1]_ routine `ellpj`.\n\nThese functions are periodic, with quarter-period on the real axis\nequal to the complete elliptic integral `ellipk(m)`.\n\nRelation to incomplete elliptic integral: If `u = ellipkinc(phi,m)`, then\n`sn(u|m) = sin(phi)`, and `cn(u|m) = cos(phi)`. The `phi` is called\nthe amplitude of `u`.\n\nComputation is by means of the arithmetic-geometric mean algorithm,\nexcept when `m` is within 1e-9 of 0 or 1. In the latter case with `m`\nclose to 1, the approximation applies only for `phi < pi/2`.\n\nSee also\n--------\nellipk : Complete elliptic integral of the first kind\nellipkinc : Incomplete elliptic integral of the first kind\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def ellipk(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "ellipk(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nellipk(m)\n\nComplete elliptic integral of the first kind.\n\nThis function is defined as\n\n.. math:: K(m) = \\int_0^{\\pi/2} [1 - m \\sin(t)^2]^{-1/2} dt\n\nParameters\n----------\nm : array_like\n The parameter of the elliptic integral.\n\nReturns\n-------\nK : array_like\n Value of the elliptic integral.\n\nNotes\n-----\nFor more precision around point m = 1, use `ellipkm1`, which this\nfunction calls.\n\nThe parameterization in terms of :math:`m` follows that of section\n17.2 in [1]_. Other parameterizations in terms of the\ncomplementary parameter :math:`1 - m`, modular angle\n:math:`\\sin^2(\\alpha) = m`, or modulus :math:`k^2 = m` are also\nused, so be careful that you choose the correct parameter.\n\nSee Also\n--------\nellipkm1 : Complete elliptic integral of the first kind around m = 1\nellipkinc : Incomplete elliptic integral of the first kind\nellipe : Complete elliptic integral of the second kind\nellipeinc : Incomplete elliptic integral of the second kind\n\nReferences\n----------\n.. [1] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def ellipkinc( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "ellipkinc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nellipkinc(phi, m)\n\nIncomplete elliptic integral of the first kind\n\nThis function is defined as\n\n.. math:: K(\\phi, m) = \\int_0^{\\phi} [1 - m \\sin(t)^2]^{-1/2} dt\n\nThis function is also called `F(phi, m)`.\n\nParameters\n----------\nphi : array_like\n amplitude of the elliptic integral\n\nm : array_like\n parameter of the elliptic integral\n\nReturns\n-------\nK : ndarray\n Value of the elliptic integral\n\nNotes\n-----\nWrapper for the Cephes [1]_ routine `ellik`. The computation is\ncarried out using the arithmetic-geometric mean algorithm.\n\nThe parameterization in terms of :math:`m` follows that of section\n17.2 in [2]_. Other parameterizations in terms of the\ncomplementary parameter :math:`1 - m`, modular angle\n:math:`\\sin^2(\\alpha) = m`, or modulus :math:`k^2 = m` are also\nused, so be careful that you choose the correct parameter.\n\nSee Also\n--------\nellipkm1 : Complete elliptic integral of the first kind, near `m` = 1\nellipk : Complete elliptic integral of the first kind\nellipe : Complete elliptic integral of the second kind\nellipeinc : Incomplete elliptic integral of the second kind\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/\n.. [2] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def ellipkm1(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "ellipkm1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nellipkm1(p)\n\nComplete elliptic integral of the first kind around `m` = 1\n\nThis function is defined as\n\n.. math:: K(p) = \\int_0^{\\pi/2} [1 - m \\sin(t)^2]^{-1/2} dt\n\nwhere `m = 1 - p`.\n\nParameters\n----------\np : array_like\n Defines the parameter of the elliptic integral as `m = 1 - p`.\n\nReturns\n-------\nK : ndarray\n Value of the elliptic integral.\n\nNotes\n-----\nWrapper for the Cephes [1]_ routine `ellpk`.\n\nFor `p <= 1`, computation uses the approximation,\n\n.. math:: K(p) \\approx P(p) - \\log(p) Q(p),\n\nwhere :math:`P` and :math:`Q` are tenth-order polynomials. The\nargument `p` is used internally rather than `m` so that the logarithmic\nsingularity at `m = 1` will be shifted to the origin; this preserves\nmaximum accuracy. For `p > 1`, the identity\n\n.. math:: K(p) = K(1/p)/\\sqrt(p)\n\nis used.\n\nSee Also\n--------\nellipk : Complete elliptic integral of the first kind\nellipkinc : Incomplete elliptic integral of the first kind\nellipe : Complete elliptic integral of the second kind\nellipeinc : Incomplete elliptic integral of the second kind\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def entr(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "entr(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nentr(x)\n\nElementwise function for computing entropy.\n\n.. math:: \\text{entr}(x) = \\begin{cases} - x \\log(x) & x > 0 \\\\ 0 & x = 0 \\\\ -\\infty & \\text{otherwise} \\end{cases}\n\nParameters\n----------\nx : ndarray\n Input array.\n\nReturns\n-------\nres : ndarray\n The value of the elementwise entropy function at the given points `x`.\n\nSee Also\n--------\nkl_div, rel_entr\n\nNotes\n-----\nThis function is concave.\n\n.. versionadded:: 0.15.0" - ... - -def erf(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "erf(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nerf(z)\n\nReturns the error function of complex argument.\n\nIt is defined as ``2/sqrt(pi)*integral(exp(-t**2), t=0..z)``.\n\nParameters\n----------\nx : ndarray\n Input array.\n\nReturns\n-------\nres : ndarray\n The values of the error function at the given points `x`.\n\nSee Also\n--------\nerfc, erfinv, erfcinv, wofz, erfcx, erfi\n\nNotes\n-----\nThe cumulative of the unit normal distribution is given by\n``Phi(z) = 1/2[1 + erf(z/sqrt(2))]``.\n\nReferences\n----------\n.. [1] https://en.wikipedia.org/wiki/Error_function\n.. [2] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover,\n 1972. http://www.math.sfu.ca/~cbm/aands/page_297.htm\n.. [3] Steven G. Johnson, Faddeeva W function implementation.\n http://ab-initio.mit.edu/Faddeeva\n\nExamples\n--------\n>>> from scipy import special\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(-3, 3)\n>>> plt.plot(x, special.erf(x))\n>>> plt.xlabel('$x$')\n>>> plt.ylabel('$erf(x)$')\n>>> plt.show()" - ... - -def erfc(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "erfc(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nerfc(x, out=None)\n\nComplementary error function, ``1 - erf(x)``.\n\nParameters\n----------\nx : array_like\n Real or complex valued argument\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the complementary error function\n\nSee Also\n--------\nerf, erfi, erfcx, dawsn, wofz\n\nReferences\n----------\n.. [1] Steven G. Johnson, Faddeeva W function implementation.\n http://ab-initio.mit.edu/Faddeeva\n\nExamples\n--------\n>>> from scipy import special\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(-3, 3)\n>>> plt.plot(x, special.erfc(x))\n>>> plt.xlabel('$x$')\n>>> plt.ylabel('$erfc(x)$')\n>>> plt.show()" - ... - -def erfcinv(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "erfcinv(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInverse of the complementary error function.\n\n Computes the inverse of the complementary error function.\n\n In the complex domain, there is no unique complex number w satisfying\n erfc(w)=z. This indicates a true inverse function would have multi-value.\n When the domain restricts to the real, 0 < x < 2, there is a unique real\n number satisfying erfc(erfcinv(x)) = erfcinv(erfc(x)).\n\n It is related to inverse of the error function by erfcinv(1-x) = erfinv(x)\n\n Parameters\n ----------\n y : ndarray\n Argument at which to evaluate. Domain: [0, 2]\n\n Returns\n -------\n erfcinv : ndarray\n The inverse of erfc of y, element-wise\n\n See Also\n --------\n erf : Error function of a complex argument\n erfc : Complementary error function, ``1 - erf(x)``\n erfinv : Inverse of the error function\n\n Examples\n --------\n 1) evaluating a float number\n\n >>> from scipy import special\n >>> special.erfcinv(0.5)\n 0.4769362762044698\n\n 2) evaluating an ndarray\n\n >>> from scipy import special\n >>> y = np.linspace(0.0, 2.0, num=11)\n >>> special.erfcinv(y)\n array([ inf, 0.9061938 , 0.59511608, 0.37080716, 0.17914345,\n -0. , -0.17914345, -0.37080716, -0.59511608, -0.9061938 ,\n -inf])" - ... - -def erfcx(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "erfcx(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nerfcx(x, out=None)\n\nScaled complementary error function, ``exp(x**2) * erfc(x)``.\n\nParameters\n----------\nx : array_like\n Real or complex valued argument\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the scaled complementary error function\n\n\nSee Also\n--------\nerf, erfc, erfi, dawsn, wofz\n\nNotes\n-----\n\n.. versionadded:: 0.12.0\n\nReferences\n----------\n.. [1] Steven G. Johnson, Faddeeva W function implementation.\n http://ab-initio.mit.edu/Faddeeva\n\nExamples\n--------\n>>> from scipy import special\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(-3, 3)\n>>> plt.plot(x, special.erfcx(x))\n>>> plt.xlabel('$x$')\n>>> plt.ylabel('$erfcx(x)$')\n>>> plt.show()" - ... - -def erfi(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "erfi(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nerfi(z, out=None)\n\nImaginary error function, ``-i erf(i z)``.\n\nParameters\n----------\nz : array_like\n Real or complex valued argument\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the imaginary error function\n\nSee Also\n--------\nerf, erfc, erfcx, dawsn, wofz\n\nNotes\n-----\n\n.. versionadded:: 0.12.0\n\nReferences\n----------\n.. [1] Steven G. Johnson, Faddeeva W function implementation.\n http://ab-initio.mit.edu/Faddeeva\n\nExamples\n--------\n>>> from scipy import special\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(-3, 3)\n>>> plt.plot(x, special.erfi(x))\n>>> plt.xlabel('$x$')\n>>> plt.ylabel('$erfi(x)$')\n>>> plt.show()" - ... - -def erfinv(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "erfinv(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nInverse of the error function.\n\n Computes the inverse of the error function.\n\n In the complex domain, there is no unique complex number w satisfying\n erf(w)=z. This indicates a true inverse function would have multi-value.\n When the domain restricts to the real, -1 < x < 1, there is a unique real\n number satisfying erf(erfinv(x)) = x.\n\n Parameters\n ----------\n y : ndarray\n Argument at which to evaluate. Domain: [-1, 1]\n\n Returns\n -------\n erfinv : ndarray\n The inverse of erf of y, element-wise)\n\n See Also\n --------\n erf : Error function of a complex argument\n erfc : Complementary error function, ``1 - erf(x)``\n erfcinv : Inverse of the complementary error function\n\n Examples\n --------\n 1) evaluating a float number\n\n >>> from scipy import special\n >>> special.erfinv(0.5)\n 0.4769362762044698\n\n 2) evaluating an ndarray\n\n >>> from scipy import special\n >>> y = np.linspace(-1.0, 1.0, num=10)\n >>> special.erfinv(y)\n array([ -inf, -0.86312307, -0.5407314 , -0.30457019, -0.0987901 ,\n 0.0987901 , 0.30457019, 0.5407314 , 0.86312307, inf])" - ... - -class errstate(_mod_builtins.object): - "Context manager for special-function error handling.\n\n Using an instance of `errstate` as a context manager allows\n statements in that context to execute with a known error handling\n behavior. Upon entering the context the error handling is set with\n `seterr`, and upon exiting it is restored to what it was before.\n\n Parameters\n ----------\n kwargs : {all, singular, underflow, overflow, slow, loss, no_result, domain, arg, other}\n Keyword arguments. The valid keywords are possible\n special-function errors. Each keyword should have a string\n value that defines the treatement for the particular type of\n error. Values must be 'ignore', 'warn', or 'other'. See\n `seterr` for details.\n\n See Also\n --------\n geterr : get the current way of handling special-function errors\n seterr : set how special-function errors are handled\n numpy.errstate : similar numpy function for floating-point errors\n\n Examples\n --------\n >>> import scipy.special as sc\n >>> from pytest import raises\n >>> sc.gammaln(0)\n inf\n >>> with sc.errstate(singular='raise'):\n ... with raises(sc.SpecialFunctionError):\n ... sc.gammaln(0)\n ...\n >>> sc.gammaln(0)\n inf\n\n We can also raise on every category except one.\n\n >>> with sc.errstate(all='raise', singular='ignore'):\n ... sc.gammaln(0)\n ... with raises(sc.SpecialFunctionError):\n ... sc.spence(-1)\n ...\n inf\n\n" - __dict__: typing.Dict[str, typing.Any] - def __enter__(self) -> typing.Any: ... - def __exit__(self, exc_type, exc_value, traceback) -> typing.Any: ... - def __init__(self, **kwargs) -> None: - "Context manager for special-function error handling.\n\n Using an instance of `errstate` as a context manager allows\n statements in that context to execute with a known error handling\n behavior. Upon entering the context the error handling is set with\n `seterr`, and upon exiting it is restored to what it was before.\n\n Parameters\n ----------\n kwargs : {all, singular, underflow, overflow, slow, loss, no_result, domain, arg, other}\n Keyword arguments. The valid keywords are possible\n special-function errors. Each keyword should have a string\n value that defines the treatement for the particular type of\n error. Values must be 'ignore', 'warn', or 'other'. See\n `seterr` for details.\n\n See Also\n --------\n geterr : get the current way of handling special-function errors\n seterr : set how special-function errors are handled\n numpy.errstate : similar numpy function for floating-point errors\n\n Examples\n --------\n >>> import scipy.special as sc\n >>> from pytest import raises\n >>> sc.gammaln(0)\n inf\n >>> with sc.errstate(singular='raise'):\n ... with raises(sc.SpecialFunctionError):\n ... sc.gammaln(0)\n ...\n >>> sc.gammaln(0)\n inf\n\n We can also raise on every category except one.\n\n >>> with sc.errstate(all='raise', singular='ignore'):\n ... sc.gammaln(0)\n ... with raises(sc.SpecialFunctionError):\n ... sc.spence(-1)\n ...\n inf\n\n" - ... - - @classmethod - def __init_subclass__(cls) -> None: - "This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n" - ... - __module__: str - @classmethod - def __subclasshook__(cls, subclass: typing.Any) -> bool: - "Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n" - ... - - @property - def __weakref__(self) -> typing.Any: - "list of weak references to the object (if defined)" - ... - - def __getattr__(self, name) -> typing.Any: ... - -def eval_chebyc( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_chebyc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_chebyc(n, x, out=None)\n\nEvaluate Chebyshev polynomial of the first kind on [-2, 2] at a\npoint.\n\nThese polynomials are defined as\n\n.. math::\n\n C_n(x) = 2 T_n(x/2)\n\nwhere :math:`T_n` is a Chebyshev polynomial of the first kind. See\n22.5.11 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to `eval_chebyt`.\nx : array_like\n Points at which to evaluate the Chebyshev polynomial\n\nReturns\n-------\nC : ndarray\n Values of the Chebyshev polynomial\n\nSee Also\n--------\nroots_chebyc : roots and quadrature weights of Chebyshev\n polynomials of the first kind on [-2, 2]\nchebyc : Chebyshev polynomial object\nnumpy.polynomial.chebyshev.Chebyshev : Chebyshev series\neval_chebyt : evaluate Chebycshev polynomials of the first kind\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972.\n\nExamples\n--------\n>>> import scipy.special as sc\n\nThey are a scaled version of the Chebyshev polynomials of the\nfirst kind.\n\n>>> x = np.linspace(-2, 2, 6)\n>>> sc.eval_chebyc(3, x)\narray([-2. , 1.872, 1.136, -1.136, -1.872, 2. ])\n>>> 2 * sc.eval_chebyt(3, x / 2)\narray([-2. , 1.872, 1.136, -1.136, -1.872, 2. ])" - ... - -def eval_chebys( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_chebys(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_chebys(n, x, out=None)\n\nEvaluate Chebyshev polynomial of the second kind on [-2, 2] at a\npoint.\n\nThese polynomials are defined as\n\n.. math::\n\n S_n(x) = U_n(x/2)\n\nwhere :math:`U_n` is a Chebyshev polynomial of the second\nkind. See 22.5.13 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to `eval_chebyu`.\nx : array_like\n Points at which to evaluate the Chebyshev polynomial\n\nReturns\n-------\nS : ndarray\n Values of the Chebyshev polynomial\n\nSee Also\n--------\nroots_chebys : roots and quadrature weights of Chebyshev\n polynomials of the second kind on [-2, 2]\nchebys : Chebyshev polynomial object\neval_chebyu : evaluate Chebyshev polynomials of the second kind\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972.\n\nExamples\n--------\n>>> import scipy.special as sc\n\nThey are a scaled version of the Chebyshev polynomials of the\nsecond kind.\n\n>>> x = np.linspace(-2, 2, 6)\n>>> sc.eval_chebys(3, x)\narray([-4. , 0.672, 0.736, -0.736, -0.672, 4. ])\n>>> sc.eval_chebyu(3, x / 2)\narray([-4. , 0.672, 0.736, -0.736, -0.672, 4. ])" - ... - -def eval_chebyt( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_chebyt(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_chebyt(n, x, out=None)\n\nEvaluate Chebyshev polynomial of the first kind at a point.\n\nThe Chebyshev polynomials of the first kind can be defined via the\nGauss hypergeometric function :math:`{}_2F_1` as\n\n.. math::\n\n T_n(x) = {}_2F_1(n, -n; 1/2; (1 - x)/2).\n\nWhen :math:`n` is an integer the result is a polynomial of degree\n:math:`n`. See 22.5.47 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to the Gauss hypergeometric\n function.\nx : array_like\n Points at which to evaluate the Chebyshev polynomial\n\nReturns\n-------\nT : ndarray\n Values of the Chebyshev polynomial\n\nSee Also\n--------\nroots_chebyt : roots and quadrature weights of Chebyshev\n polynomials of the first kind\nchebyu : Chebychev polynomial object\neval_chebyu : evaluate Chebyshev polynomials of the second kind\nhyp2f1 : Gauss hypergeometric function\nnumpy.polynomial.chebyshev.Chebyshev : Chebyshev series\n\nNotes\n-----\nThis routine is numerically stable for `x` in ``[-1, 1]`` at least\nup to order ``10000``.\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_chebyu( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_chebyu(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_chebyu(n, x, out=None)\n\nEvaluate Chebyshev polynomial of the second kind at a point.\n\nThe Chebyshev polynomials of the second kind can be defined via\nthe Gauss hypergeometric function :math:`{}_2F_1` as\n\n.. math::\n\n U_n(x) = (n + 1) {}_2F_1(-n, n + 2; 3/2; (1 - x)/2).\n\nWhen :math:`n` is an integer the result is a polynomial of degree\n:math:`n`. See 22.5.48 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to the Gauss hypergeometric\n function.\nx : array_like\n Points at which to evaluate the Chebyshev polynomial\n\nReturns\n-------\nU : ndarray\n Values of the Chebyshev polynomial\n\nSee Also\n--------\nroots_chebyu : roots and quadrature weights of Chebyshev\n polynomials of the second kind\nchebyu : Chebyshev polynomial object\neval_chebyt : evaluate Chebyshev polynomials of the first kind\nhyp2f1 : Gauss hypergeometric function\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_gegenbauer( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_gegenbauer(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_gegenbauer(n, alpha, x, out=None)\n\nEvaluate Gegenbauer polynomial at a point.\n\nThe Gegenbauer polynomials can be defined via the Gauss\nhypergeometric function :math:`{}_2F_1` as\n\n.. math::\n\n C_n^{(\\alpha)} = \\frac{(2\\alpha)_n}{\\Gamma(n + 1)}\n {}_2F_1(-n, 2\\alpha + n; \\alpha + 1/2; (1 - z)/2).\n\nWhen :math:`n` is an integer the result is a polynomial of degree\n:math:`n`. See 22.5.46 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to the Gauss hypergeometric\n function.\nalpha : array_like\n Parameter\nx : array_like\n Points at which to evaluate the Gegenbauer polynomial\n\nReturns\n-------\nC : ndarray\n Values of the Gegenbauer polynomial\n\nSee Also\n--------\nroots_gegenbauer : roots and quadrature weights of Gegenbauer\n polynomials\ngegenbauer : Gegenbauer polynomial object\nhyp2f1 : Gauss hypergeometric function\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_genlaguerre( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_genlaguerre(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_genlaguerre(n, alpha, x, out=None)\n\nEvaluate generalized Laguerre polynomial at a point.\n\nThe generalized Laguerre polynomials can be defined via the\nconfluent hypergeometric function :math:`{}_1F_1` as\n\n.. math::\n\n L_n^{(\\alpha)}(x) = \\binom{n + \\alpha}{n}\n {}_1F_1(-n, \\alpha + 1, x).\n\nWhen :math:`n` is an integer the result is a polynomial of degree\n:math:`n`. See 22.5.54 in [AS]_ for details. The Laguerre\npolynomials are the special case where :math:`\\alpha = 0`.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to the confluent hypergeometric\n function.\nalpha : array_like\n Parameter; must have ``alpha > -1``\nx : array_like\n Points at which to evaluate the generalized Laguerre\n polynomial\n\nReturns\n-------\nL : ndarray\n Values of the generalized Laguerre polynomial\n\nSee Also\n--------\nroots_genlaguerre : roots and quadrature weights of generalized\n Laguerre polynomials\ngenlaguerre : generalized Laguerre polynomial object\nhyp1f1 : confluent hypergeometric function\neval_laguerre : evaluate Laguerre polynomials\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_hermite( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_hermite(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_hermite(n, x, out=None)\n\nEvaluate physicist's Hermite polynomial at a point.\n\nDefined by\n\n.. math::\n\n H_n(x) = (-1)^n e^{x^2} \\frac{d^n}{dx^n} e^{-x^2};\n\n:math:`H_n` is a polynomial of degree :math:`n`. See 22.11.7 in\n[AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial\nx : array_like\n Points at which to evaluate the Hermite polynomial\n\nReturns\n-------\nH : ndarray\n Values of the Hermite polynomial\n\nSee Also\n--------\nroots_hermite : roots and quadrature weights of physicist's\n Hermite polynomials\nhermite : physicist's Hermite polynomial object\nnumpy.polynomial.hermite.Hermite : Physicist's Hermite series\neval_hermitenorm : evaluate Probabilist's Hermite polynomials\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_hermitenorm( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_hermitenorm(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_hermitenorm(n, x, out=None)\n\nEvaluate probabilist's (normalized) Hermite polynomial at a\npoint.\n\nDefined by\n\n.. math::\n\n He_n(x) = (-1)^n e^{x^2/2} \\frac{d^n}{dx^n} e^{-x^2/2};\n\n:math:`He_n` is a polynomial of degree :math:`n`. See 22.11.8 in\n[AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial\nx : array_like\n Points at which to evaluate the Hermite polynomial\n\nReturns\n-------\nHe : ndarray\n Values of the Hermite polynomial\n\nSee Also\n--------\nroots_hermitenorm : roots and quadrature weights of probabilist's\n Hermite polynomials\nhermitenorm : probabilist's Hermite polynomial object\nnumpy.polynomial.hermite_e.HermiteE : Probabilist's Hermite series\neval_hermite : evaluate physicist's Hermite polynomials\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_jacobi( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_jacobi(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_jacobi(n, alpha, beta, x, out=None)\n\nEvaluate Jacobi polynomial at a point.\n\nThe Jacobi polynomials can be defined via the Gauss hypergeometric\nfunction :math:`{}_2F_1` as\n\n.. math::\n\n P_n^{(\\alpha, \\beta)}(x) = \\frac{(\\alpha + 1)_n}{\\Gamma(n + 1)}\n {}_2F_1(-n, 1 + \\alpha + \\beta + n; \\alpha + 1; (1 - z)/2)\n\nwhere :math:`(\\cdot)_n` is the Pochhammer symbol; see `poch`. When\n:math:`n` is an integer the result is a polynomial of degree\n:math:`n`. See 22.5.42 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer the result is\n determined via the relation to the Gauss hypergeometric\n function.\nalpha : array_like\n Parameter\nbeta : array_like\n Parameter\nx : array_like\n Points at which to evaluate the polynomial\n\nReturns\n-------\nP : ndarray\n Values of the Jacobi polynomial\n\nSee Also\n--------\nroots_jacobi : roots and quadrature weights of Jacobi polynomials\njacobi : Jacobi polynomial object\nhyp2f1 : Gauss hypergeometric function\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_laguerre( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_laguerre(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_laguerre(n, x, out=None)\n\nEvaluate Laguerre polynomial at a point.\n\nThe Laguerre polynomials can be defined via the confluent\nhypergeometric function :math:`{}_1F_1` as\n\n.. math::\n\n L_n(x) = {}_1F_1(-n, 1, x).\n\nSee 22.5.16 and 22.5.54 in [AS]_ for details. When :math:`n` is an\ninteger the result is a polynomial of degree :math:`n`.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer the result is\n determined via the relation to the confluent hypergeometric\n function.\nx : array_like\n Points at which to evaluate the Laguerre polynomial\n\nReturns\n-------\nL : ndarray\n Values of the Laguerre polynomial\n\nSee Also\n--------\nroots_laguerre : roots and quadrature weights of Laguerre\n polynomials\nlaguerre : Laguerre polynomial object\nnumpy.polynomial.laguerre.Laguerre : Laguerre series\neval_genlaguerre : evaluate generalized Laguerre polynomials\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_legendre( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_legendre(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_legendre(n, x, out=None)\n\nEvaluate Legendre polynomial at a point.\n\nThe Legendre polynomials can be defined via the Gauss\nhypergeometric function :math:`{}_2F_1` as\n\n.. math::\n\n P_n(x) = {}_2F_1(-n, n + 1; 1; (1 - x)/2).\n\nWhen :math:`n` is an integer the result is a polynomial of degree\n:math:`n`. See 22.5.49 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to the Gauss hypergeometric\n function.\nx : array_like\n Points at which to evaluate the Legendre polynomial\n\nReturns\n-------\nP : ndarray\n Values of the Legendre polynomial\n\nSee Also\n--------\nroots_legendre : roots and quadrature weights of Legendre\n polynomials\nlegendre : Legendre polynomial object\nhyp2f1 : Gauss hypergeometric function\nnumpy.polynomial.legendre.Legendre : Legendre series\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972.\n\nExamples\n--------\n>>> from scipy.special import eval_legendre\n\nEvaluate the zero-order Legendre polynomial at x = 0\n\n>>> eval_legendre(0, 0)\n1.0\n\nEvaluate the first-order Legendre polynomial between -1 and 1\n\n>>> import numpy as np\n>>> X = np.linspace(-1, 1, 5) # Domain of Legendre polynomials\n>>> eval_legendre(1, X)\narray([-1. , -0.5, 0. , 0.5, 1. ])\n\nEvaluate Legendre polynomials of order 0 through 4 at x = 0\n\n>>> N = range(0, 5)\n>>> eval_legendre(N, 0)\narray([ 1. , 0. , -0.5 , 0. , 0.375])\n\nPlot Legendre polynomials of order 0 through 4\n\n>>> X = np.linspace(-1, 1)\n\n>>> import matplotlib.pyplot as plt\n>>> for n in range(0, 5):\n... y = eval_legendre(n, X)\n... plt.plot(X, y, label=r'$P_{}(x)$'.format(n))\n\n>>> plt.title(\"Legendre Polynomials\")\n>>> plt.xlabel(\"x\")\n>>> plt.ylabel(r'$P_n(x)$')\n>>> plt.legend(loc='lower right')\n>>> plt.show()" - ... - -def eval_sh_chebyt( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_sh_chebyt(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_sh_chebyt(n, x, out=None)\n\nEvaluate shifted Chebyshev polynomial of the first kind at a\npoint.\n\nThese polynomials are defined as\n\n.. math::\n\n T_n^*(x) = T_n(2x - 1)\n\nwhere :math:`T_n` is a Chebyshev polynomial of the first kind. See\n22.5.14 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to `eval_chebyt`.\nx : array_like\n Points at which to evaluate the shifted Chebyshev polynomial\n\nReturns\n-------\nT : ndarray\n Values of the shifted Chebyshev polynomial\n\nSee Also\n--------\nroots_sh_chebyt : roots and quadrature weights of shifted\n Chebyshev polynomials of the first kind\nsh_chebyt : shifted Chebyshev polynomial object\neval_chebyt : evaluate Chebyshev polynomials of the first kind\nnumpy.polynomial.chebyshev.Chebyshev : Chebyshev series\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_sh_chebyu( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_sh_chebyu(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_sh_chebyu(n, x, out=None)\n\nEvaluate shifted Chebyshev polynomial of the second kind at a\npoint.\n\nThese polynomials are defined as\n\n.. math::\n\n U_n^*(x) = U_n(2x - 1)\n\nwhere :math:`U_n` is a Chebyshev polynomial of the first kind. See\n22.5.15 in [AS]_ for details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to `eval_chebyu`.\nx : array_like\n Points at which to evaluate the shifted Chebyshev polynomial\n\nReturns\n-------\nU : ndarray\n Values of the shifted Chebyshev polynomial\n\nSee Also\n--------\nroots_sh_chebyu : roots and quadrature weights of shifted\n Chebychev polynomials of the second kind\nsh_chebyu : shifted Chebyshev polynomial object\neval_chebyu : evaluate Chebyshev polynomials of the second kind\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_sh_jacobi( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_sh_jacobi(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_sh_jacobi(n, p, q, x, out=None)\n\nEvaluate shifted Jacobi polynomial at a point.\n\nDefined by\n\n.. math::\n\n G_n^{(p, q)}(x)\n = \\binom{2n + p - 1}{n}^{-1} P_n^{(p - q, q - 1)}(2x - 1),\n\nwhere :math:`P_n^{(\\cdot, \\cdot)}` is the n-th Jacobi\npolynomial. See 22.5.2 in [AS]_ for details.\n\nParameters\n----------\nn : int\n Degree of the polynomial. If not an integer, the result is\n determined via the relation to `binom` and `eval_jacobi`.\np : float\n Parameter\nq : float\n Parameter\n\nReturns\n-------\nG : ndarray\n Values of the shifted Jacobi polynomial.\n\nSee Also\n--------\nroots_sh_jacobi : roots and quadrature weights of shifted Jacobi\n polynomials\nsh_jacobi : shifted Jacobi polynomial object\neval_jacobi : evaluate Jacobi polynomials\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def eval_sh_legendre( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "eval_sh_legendre(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\neval_sh_legendre(n, x, out=None)\n\nEvaluate shifted Legendre polynomial at a point.\n\nThese polynomials are defined as\n\n.. math::\n\n P_n^*(x) = P_n(2x - 1)\n\nwhere :math:`P_n` is a Legendre polynomial. See 2.2.11 in [AS]_\nfor details.\n\nParameters\n----------\nn : array_like\n Degree of the polynomial. If not an integer, the value is\n determined via the relation to `eval_legendre`.\nx : array_like\n Points at which to evaluate the shifted Legendre polynomial\n\nReturns\n-------\nP : ndarray\n Values of the shifted Legendre polynomial\n\nSee Also\n--------\nroots_sh_legendre : roots and quadrature weights of shifted\n Legendre polynomials\nsh_legendre : shifted Legendre polynomial object\neval_legendre : evaluate Legendre polynomials\nnumpy.polynomial.legendre.Legendre : Legendre series\n\nReferences\n----------\n.. [AS] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def exp1(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "exp1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nexp1(z, out=None)\n\nExponential integral E1.\n\nFor complex :math:`z \\ne 0` the exponential integral can be defined as\n[1]_\n\n.. math::\n\n E_1(z) = \\int_z^\\infty \\frac{e^{-t}}{t} dt,\n\nwhere the path of the integral does not cross the negative real\naxis or pass through the origin.\n\nParameters\n----------\nz: array_like\n Real or complex argument.\nout: ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the exponential integral E1\n\nSee Also\n--------\nexpi : exponential integral :math:`Ei`\nexpn : generalization of :math:`E_1`\n\nNotes\n-----\nFor :math:`x > 0` it is related to the exponential integral\n:math:`Ei` (see `expi`) via the relation\n\n.. math::\n\n E_1(x) = -Ei(-x).\n\nReferences\n----------\n.. [1] Digital Library of Mathematical Functions, 6.2.1\n https://dlmf.nist.gov/6.2#E1\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt has a pole at 0.\n\n>>> sc.exp1(0)\ninf\n\nIt has a branch cut on the negative real axis.\n\n>>> sc.exp1(-1)\nnan\n>>> sc.exp1(complex(-1, 0))\n(-1.8951178163559368-3.141592653589793j)\n>>> sc.exp1(complex(-1, -0.0))\n(-1.8951178163559368+3.141592653589793j)\n\nIt approaches 0 along the positive real axis.\n\n>>> sc.exp1([1, 10, 100, 1000])\narray([2.19383934e-01, 4.15696893e-06, 3.68359776e-46, 0.00000000e+00])\n\nIt is related to `expi`.\n\n>>> x = np.array([1, 2, 3, 4])\n>>> sc.exp1(x)\narray([0.21938393, 0.04890051, 0.01304838, 0.00377935])\n>>> -sc.expi(-x)\narray([0.21938393, 0.04890051, 0.01304838, 0.00377935])" - ... - -def exp10(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "exp10(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nexp10(x)\n\nCompute ``10**x`` element-wise.\n\nParameters\n----------\nx : array_like\n `x` must contain real numbers.\n\nReturns\n-------\nfloat\n ``10**x``, computed element-wise.\n\nExamples\n--------\n>>> from scipy.special import exp10\n\n>>> exp10(3)\n1000.0\n>>> x = np.array([[-1, -0.5, 0], [0.5, 1, 1.5]])\n>>> exp10(x)\narray([[ 0.1 , 0.31622777, 1. ],\n [ 3.16227766, 10. , 31.6227766 ]])" - ... - -def exp2(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "exp2(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nexp2(x)\n\nCompute ``2**x`` element-wise.\n\nParameters\n----------\nx : array_like\n `x` must contain real numbers.\n\nReturns\n-------\nfloat\n ``2**x``, computed element-wise.\n\nExamples\n--------\n>>> from scipy.special import exp2\n\n>>> exp2(3)\n8.0\n>>> x = np.array([[-1, -0.5, 0], [0.5, 1, 1.5]])\n>>> exp2(x)\narray([[ 0.5 , 0.70710678, 1. ],\n [ 1.41421356, 2. , 2.82842712]])" - ... - -def expi(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "expi(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nexpi(x, out=None)\n\nExponential integral Ei.\n\nFor real :math:`x`, the exponential integral is defined as [1]_\n\n.. math::\n\n Ei(x) = \\int_{-\\infty}^x \\frac{e^t}{t} dt.\n\nFor :math:`x > 0` the integral is understood as a Cauchy principle\nvalue.\n\nIt is extended to the complex plane by analytic continuation of\nthe function on the interval :math:`(0, \\infty)`. The complex\nvariant has a branch cut on the negative real axis.\n\nParameters\n----------\nx: array_like\n Real or complex valued argument\nout: ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the exponential integral\n\nNotes\n-----\nThe exponential integrals :math:`E_1` and :math:`Ei` satisfy the\nrelation\n\n.. math::\n\n E_1(x) = -Ei(-x)\n\nfor :math:`x > 0`.\n\nSee Also\n--------\nexp1 : Exponential integral :math:`E_1`\nexpn : Generalized exponential integral :math:`E_n`\n\nReferences\n----------\n.. [1] Digital Library of Mathematical Functions, 6.2.5\n https://dlmf.nist.gov/6.2#E5\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is related to `exp1`.\n\n>>> x = np.array([1, 2, 3, 4])\n>>> -sc.expi(-x)\narray([0.21938393, 0.04890051, 0.01304838, 0.00377935])\n>>> sc.exp1(x)\narray([0.21938393, 0.04890051, 0.01304838, 0.00377935])\n\nThe complex variant has a branch cut on the negative real axis.\n\n>>> import scipy.special as sc\n>>> sc.expi(-1 + 1e-12j)\n(-0.21938393439552062+3.1415926535894254j)\n>>> sc.expi(-1 - 1e-12j)\n(-0.21938393439552062-3.1415926535894254j)\n\nAs the complex variant approaches the branch cut, the real parts\napproach the value of the real variant.\n\n>>> sc.expi(-1)\n-0.21938393439552062\n\nThe SciPy implementation returns the real variant for complex\nvalues on the branch cut.\n\n>>> sc.expi(complex(-1, 0.0))\n(-0.21938393439552062-0j)\n>>> sc.expi(complex(-1, -0.0))\n(-0.21938393439552062-0j)" - ... - -def expit(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "expit(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nexpit(x)\n\nExpit (a.k.a. logistic sigmoid) ufunc for ndarrays.\n\nThe expit function, also known as the logistic sigmoid function, is\ndefined as ``expit(x) = 1/(1+exp(-x))``. It is the inverse of the\nlogit function.\n\nParameters\n----------\nx : ndarray\n The ndarray to apply expit to element-wise.\n\nReturns\n-------\nout : ndarray\n An ndarray of the same shape as x. Its entries\n are `expit` of the corresponding entry of x.\n\nSee Also\n--------\nlogit\n\nNotes\n-----\nAs a ufunc expit takes a number of optional\nkeyword arguments. For more information\nsee `ufuncs `_\n\n.. versionadded:: 0.10.0\n\nExamples\n--------\n>>> from scipy.special import expit, logit\n\n>>> expit([-np.inf, -1.5, 0, 1.5, np.inf])\narray([ 0. , 0.18242552, 0.5 , 0.81757448, 1. ])\n\n`logit` is the inverse of `expit`:\n\n>>> logit(expit([-2.5, 0, 3.1, 5.0]))\narray([-2.5, 0. , 3.1, 5. ])\n\nPlot expit(x) for x in [-6, 6]:\n\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(-6, 6, 121)\n>>> y = expit(x)\n>>> plt.plot(x, y)\n>>> plt.grid()\n>>> plt.xlim(-6, 6)\n>>> plt.xlabel('x')\n>>> plt.title('expit(x)')\n>>> plt.show()" - ... - -def expm1(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "expm1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nexpm1(x)\n\nCompute ``exp(x) - 1``.\n\nWhen `x` is near zero, ``exp(x)`` is near 1, so the numerical calculation\nof ``exp(x) - 1`` can suffer from catastrophic loss of precision.\n``expm1(x)`` is implemented to avoid the loss of precision that occurs when\n`x` is near zero.\n\nParameters\n----------\nx : array_like\n `x` must contain real numbers.\n\nReturns\n-------\nfloat\n ``exp(x) - 1`` computed element-wise.\n\nExamples\n--------\n>>> from scipy.special import expm1\n\n>>> expm1(1.0)\n1.7182818284590451\n>>> expm1([-0.2, -0.1, 0, 0.1, 0.2])\narray([-0.18126925, -0.09516258, 0. , 0.10517092, 0.22140276])\n\nThe exact value of ``exp(7.5e-13) - 1`` is::\n\n 7.5000000000028125000000007031250000001318...*10**-13.\n\nHere is what ``expm1(7.5e-13)`` gives:\n\n>>> expm1(7.5e-13)\n7.5000000000028135e-13\n\nCompare that to ``exp(7.5e-13) - 1``, where the subtraction results in\na \"catastrophic\" loss of precision:\n\n>>> np.exp(7.5e-13) - 1\n7.5006667543675576e-13" - ... - -def expn(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "expn(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nexpn(n, x, out=None)\n\nGeneralized exponential integral En.\n\nFor integer :math:`n \\geq 0` and real :math:`x \\geq 0` the\ngeneralized exponential integral is defined as [dlmf]_\n\n.. math::\n\n E_n(x) = x^{n - 1} \\int_x^\\infty \\frac{e^{-t}}{t^n} dt.\n\nParameters\n----------\nn: array_like\n Non-negative integers\nx: array_like\n Real argument\nout: ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the generalized exponential integral\n\nSee Also\n--------\nexp1 : special case of :math:`E_n` for :math:`n = 1`\nexpi : related to :math:`E_n` when :math:`n = 1`\n\nReferences\n----------\n.. [dlmf] Digital Library of Mathematical Functions, 8.19.2\n https://dlmf.nist.gov/8.19#E2\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIts domain is nonnegative n and x.\n\n>>> sc.expn(-1, 1.0), sc.expn(1, -1.0)\n(nan, nan)\n\nIt has a pole at ``x = 0`` for ``n = 1, 2``; for larger ``n`` it\nis equal to ``1 / (n - 1)``.\n\n>>> sc.expn([0, 1, 2, 3, 4], 0)\narray([ inf, inf, 1. , 0.5 , 0.33333333])\n\nFor n equal to 0 it reduces to ``exp(-x) / x``.\n\n>>> x = np.array([1, 2, 3, 4])\n>>> sc.expn(0, x)\narray([0.36787944, 0.06766764, 0.01659569, 0.00457891])\n>>> np.exp(-x) / x\narray([0.36787944, 0.06766764, 0.01659569, 0.00457891])\n\nFor n equal to 1 it reduces to `exp1`.\n\n>>> sc.expn(1, x)\narray([0.21938393, 0.04890051, 0.01304838, 0.00377935])\n>>> sc.exp1(x)\narray([0.21938393, 0.04890051, 0.01304838, 0.00377935])" - ... - -def exprel(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "exprel(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nexprel(x)\n\nRelative error exponential, ``(exp(x) - 1)/x``.\n\nWhen `x` is near zero, ``exp(x)`` is near 1, so the numerical calculation\nof ``exp(x) - 1`` can suffer from catastrophic loss of precision.\n``exprel(x)`` is implemented to avoid the loss of precision that occurs when\n`x` is near zero.\n\nParameters\n----------\nx : ndarray\n Input array. `x` must contain real numbers.\n\nReturns\n-------\nfloat\n ``(exp(x) - 1)/x``, computed element-wise.\n\nSee Also\n--------\nexpm1\n\nNotes\n-----\n.. versionadded:: 0.17.0\n\nExamples\n--------\n>>> from scipy.special import exprel\n\n>>> exprel(0.01)\n1.0050167084168056\n>>> exprel([-0.25, -0.1, 0, 0.1, 0.25])\narray([ 0.88479687, 0.95162582, 1. , 1.05170918, 1.13610167])\n\nCompare ``exprel(5e-9)`` to the naive calculation. The exact value\nis ``1.00000000250000000416...``.\n\n>>> exprel(5e-9)\n1.0000000025\n\n>>> (np.exp(5e-9) - 1)/5e-9\n0.99999999392252903" - ... - -def fdtr( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "fdtr(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nfdtr(dfn, dfd, x)\n\nF cumulative distribution function.\n\nReturns the value of the cumulative distribution function of the\nF-distribution, also known as Snedecor's F-distribution or the\nFisher-Snedecor distribution.\n\nThe F-distribution with parameters :math:`d_n` and :math:`d_d` is the\ndistribution of the random variable,\n\n.. math::\n X = \\frac{U_n/d_n}{U_d/d_d},\n\nwhere :math:`U_n` and :math:`U_d` are random variables distributed\n:math:`\\chi^2`, with :math:`d_n` and :math:`d_d` degrees of freedom,\nrespectively.\n\nParameters\n----------\ndfn : array_like\n First parameter (positive float).\ndfd : array_like\n Second parameter (positive float).\nx : array_like\n Argument (nonnegative float).\n\nReturns\n-------\ny : ndarray\n The CDF of the F-distribution with parameters `dfn` and `dfd` at `x`.\n\nNotes\n-----\nThe regularized incomplete beta function is used, according to the\nformula,\n\n.. math::\n F(d_n, d_d; x) = I_{xd_n/(d_d + xd_n)}(d_n/2, d_d/2).\n\nWrapper for the Cephes [1]_ routine `fdtr`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def fdtrc( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "fdtrc(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nfdtrc(dfn, dfd, x)\n\nF survival function.\n\nReturns the complemented F-distribution function (the integral of the\ndensity from `x` to infinity).\n\nParameters\n----------\ndfn : array_like\n First parameter (positive float).\ndfd : array_like\n Second parameter (positive float).\nx : array_like\n Argument (nonnegative float).\n\nReturns\n-------\ny : ndarray\n The complemented F-distribution function with parameters `dfn` and\n `dfd` at `x`.\n\nSee also\n--------\nfdtr\n\nNotes\n-----\nThe regularized incomplete beta function is used, according to the\nformula,\n\n.. math::\n F(d_n, d_d; x) = I_{d_d/(d_d + xd_n)}(d_d/2, d_n/2).\n\nWrapper for the Cephes [1]_ routine `fdtrc`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def fdtri( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "fdtri(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nfdtri(dfn, dfd, p)\n\nThe `p`-th quantile of the F-distribution.\n\nThis function is the inverse of the F-distribution CDF, `fdtr`, returning\nthe `x` such that `fdtr(dfn, dfd, x) = p`.\n\nParameters\n----------\ndfn : array_like\n First parameter (positive float).\ndfd : array_like\n Second parameter (positive float).\np : array_like\n Cumulative probability, in [0, 1].\n\nReturns\n-------\nx : ndarray\n The quantile corresponding to `p`.\n\nNotes\n-----\nThe computation is carried out using the relation to the inverse\nregularized beta function, :math:`I^{-1}_x(a, b)`. Let\n:math:`z = I^{-1}_p(d_d/2, d_n/2).` Then,\n\n.. math::\n x = \\frac{d_d (1 - z)}{d_n z}.\n\nIf `p` is such that :math:`x < 0.5`, the following relation is used\ninstead for improved stability: let\n:math:`z' = I^{-1}_{1 - p}(d_n/2, d_d/2).` Then,\n\n.. math::\n x = \\frac{d_d z'}{d_n (1 - z')}.\n\nWrapper for the Cephes [1]_ routine `fdtri`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def fdtridfd( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "fdtridfd(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nfdtridfd(dfn, p, x)\n\nInverse to `fdtr` vs dfd\n\nFinds the F density argument dfd such that ``fdtr(dfn, dfd, x) == p``." - ... - -def fresnel( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "fresnel(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nfresnel(z, out=None)\n\nFresnel integrals.\n\nThe Fresnel integrals are defined as\n\n.. math::\n\n S(z) &= \\int_0^z \\sin(\\pi t^2 /2) dt \\\\\n C(z) &= \\int_0^z \\cos(\\pi t^2 /2) dt.\n\nSee [dlmf]_ for details.\n\nParameters\n----------\nz : array_like\n Real or complex valued argument\nout : 2-tuple of ndarrays, optional\n Optional output arrays for the function results\n\nReturns\n-------\nS, C : 2-tuple of scalar or ndarray\n Values of the Fresnel integrals\n\nSee Also\n--------\nfresnel_zeros : zeros of the Fresnel integrals\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/7.2#iii\n\nExamples\n--------\n>>> import scipy.special as sc\n\nAs z goes to infinity along the real axis, S and C converge to 0.5.\n\n>>> S, C = sc.fresnel([0.1, 1, 10, 100, np.inf])\n>>> S\narray([0.00052359, 0.43825915, 0.46816998, 0.4968169 , 0.5 ])\n>>> C\narray([0.09999753, 0.7798934 , 0.49989869, 0.4999999 , 0.5 ])\n\nThey are related to the error function `erf`.\n\n>>> z = np.array([1, 2, 3, 4])\n>>> zeta = 0.5 * np.sqrt(np.pi) * (1 - 1j) * z\n>>> S, C = sc.fresnel(z)\n>>> C + 1j*S\narray([0.7798934 +0.43825915j, 0.48825341+0.34341568j,\n 0.60572079+0.496313j , 0.49842603+0.42051575j])\n>>> 0.5 * (1 + 1j) * sc.erf(zeta)\narray([0.7798934 +0.43825915j, 0.48825341+0.34341568j,\n 0.60572079+0.496313j , 0.49842603+0.42051575j])" - ... - -def gamma(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "gamma(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngamma(z)\n\ngamma function.\n\nThe gamma function is defined as\n\n.. math::\n\n \\Gamma(z) = \\int_0^\\infty t^{z-1} e^{-t} dt\n\nfor :math:`\\Re(z) > 0` and is extended to the rest of the complex\nplane by analytic continuation. See [dlmf]_ for more details.\n\nParameters\n----------\nz : array_like\n Real or complex valued argument\n\nReturns\n-------\nscalar or ndarray\n Values of the gamma function\n\nNotes\n-----\nThe gamma function is often referred to as the generalized\nfactorial since :math:`\\Gamma(n + 1) = n!` for natural numbers\n:math:`n`. More generally it satisfies the recurrence relation\n:math:`\\Gamma(z + 1) = z \\cdot \\Gamma(z)` for complex :math:`z`,\nwhich, combined with the fact that :math:`\\Gamma(1) = 1`, implies\nthe above identity for :math:`z = n`.\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/5.2#E1\n\nExamples\n--------\n>>> from scipy.special import gamma, factorial\n\n>>> gamma([0, 0.5, 1, 5])\narray([ inf, 1.77245385, 1. , 24. ])\n\n>>> z = 2.5 + 1j\n>>> gamma(z)\n(0.77476210455108352+0.70763120437959293j)\n>>> gamma(z+1), z*gamma(z) # Recurrence property\n((1.2292740569981171+2.5438401155000685j),\n (1.2292740569981158+2.5438401155000658j))\n\n>>> gamma(0.5)**2 # gamma(0.5) = sqrt(pi)\n3.1415926535897927\n\nPlot gamma(x) for real x\n\n>>> x = np.linspace(-3.5, 5.5, 2251)\n>>> y = gamma(x)\n\n>>> import matplotlib.pyplot as plt\n>>> plt.plot(x, y, 'b', alpha=0.6, label='gamma(x)')\n>>> k = np.arange(1, 7)\n>>> plt.plot(k, factorial(k-1), 'k*', alpha=0.6,\n... label='(x-1)!, x = 1, 2, ...')\n>>> plt.xlim(-3.5, 5.5)\n>>> plt.ylim(-10, 25)\n>>> plt.grid()\n>>> plt.xlabel('x')\n>>> plt.legend(loc='lower right')\n>>> plt.show()" - ... - -def gammainc( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gammainc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngammainc(a, x)\n\nRegularized lower incomplete gamma function.\n\nIt is defined as\n\n.. math::\n\n P(a, x) = \\frac{1}{\\Gamma(a)} \\int_0^x t^{a - 1}e^{-t} dt\n\nfor :math:`a > 0` and :math:`x \\geq 0`. See [dlmf]_ for details.\n\nParameters\n----------\na : array_like\n Positive parameter\nx : array_like\n Nonnegative argument\n\nReturns\n-------\nscalar or ndarray\n Values of the lower incomplete gamma function\n\nNotes\n-----\nThe function satisfies the relation ``gammainc(a, x) +\ngammaincc(a, x) = 1`` where `gammaincc` is the regularized upper\nincomplete gamma function.\n\nThe implementation largely follows that of [boost]_.\n\nSee also\n--------\ngammaincc : regularized upper incomplete gamma function\ngammaincinv : inverse of the regularized lower incomplete gamma\n function with respect to `x`\ngammainccinv : inverse of the regularized upper incomplete gamma\n function with respect to `x`\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical functions\n https://dlmf.nist.gov/8.2#E4\n.. [boost] Maddock et. al., \"Incomplete Gamma Functions\",\n https://www.boost.org/doc/libs/1_61_0/libs/math/doc/html/math_toolkit/sf_gamma/igamma.html\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is the CDF of the gamma distribution, so it starts at 0 and\nmonotonically increases to 1.\n\n>>> sc.gammainc(0.5, [0, 1, 10, 100])\narray([0. , 0.84270079, 0.99999226, 1. ])\n\nIt is equal to one minus the upper incomplete gamma function.\n\n>>> a, x = 0.5, 0.4\n>>> sc.gammainc(a, x)\n0.6289066304773024\n>>> 1 - sc.gammaincc(a, x)\n0.6289066304773024" - ... - -def gammaincc( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gammaincc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngammaincc(a, x)\n\nRegularized upper incomplete gamma function.\n\nIt is defined as\n\n.. math::\n\n Q(a, x) = \\frac{1}{\\Gamma(a)} \\int_x^\\infty t^{a - 1}e^{-t} dt\n\nfor :math:`a > 0` and :math:`x \\geq 0`. See [dlmf]_ for details.\n\nParameters\n----------\na : array_like\n Positive parameter\nx : array_like\n Nonnegative argument\n\nReturns\n-------\nscalar or ndarray\n Values of the upper incomplete gamma function\n\nNotes\n-----\nThe function satisfies the relation ``gammainc(a, x) +\ngammaincc(a, x) = 1`` where `gammainc` is the regularized lower\nincomplete gamma function.\n\nThe implementation largely follows that of [boost]_.\n\nSee also\n--------\ngammainc : regularized lower incomplete gamma function\ngammaincinv : inverse of the regularized lower incomplete gamma\n function with respect to `x`\ngammainccinv : inverse to of the regularized upper incomplete\n gamma function with respect to `x`\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical functions\n https://dlmf.nist.gov/8.2#E4\n.. [boost] Maddock et. al., \"Incomplete Gamma Functions\",\n https://www.boost.org/doc/libs/1_61_0/libs/math/doc/html/math_toolkit/sf_gamma/igamma.html\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is the survival function of the gamma distribution, so it\nstarts at 1 and monotonically decreases to 0.\n\n>>> sc.gammaincc(0.5, [0, 1, 10, 100, 1000])\narray([1.00000000e+00, 1.57299207e-01, 7.74421643e-06, 2.08848758e-45,\n 0.00000000e+00])\n\nIt is equal to one minus the lower incomplete gamma function.\n\n>>> a, x = 0.5, 0.4\n>>> sc.gammaincc(a, x)\n0.37109336952269756\n>>> 1 - sc.gammainc(a, x)\n0.37109336952269756" - ... - -def gammainccinv( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gammainccinv(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngammainccinv(a, y)\n\nInverse of the upper incomplete gamma function with respect to `x`\n\nGiven an input :math:`y` between 0 and 1, returns :math:`x` such\nthat :math:`y = Q(a, x)`. Here :math:`Q` is the upper incomplete\ngamma function; see `gammaincc`. This is well-defined because the\nupper incomplete gamma function is monotonic as can be seen from\nits definition in [dlmf]_.\n\nParameters\n----------\na : array_like\n Positive parameter\ny : array_like\n Argument between 0 and 1, inclusive\n\nReturns\n-------\nscalar or ndarray\n Values of the inverse of the upper incomplete gamma function\n\nSee Also\n--------\ngammaincc : regularized upper incomplete gamma function\ngammainc : regularized lower incomplete gamma function\ngammaincinv : inverse of the regularized lower incomplete gamma\n function with respect to `x`\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/8.2#E4\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt starts at infinity and monotonically decreases to 0.\n\n>>> sc.gammainccinv(0.5, [0, 0.1, 0.5, 1])\narray([ inf, 1.35277173, 0.22746821, 0. ])\n\nIt inverts the upper incomplete gamma function.\n\n>>> a, x = 0.5, [0, 0.1, 0.5, 1]\n>>> sc.gammaincc(a, sc.gammainccinv(a, x))\narray([0. , 0.1, 0.5, 1. ])\n\n>>> a, x = 0.5, [0, 10, 50]\n>>> sc.gammainccinv(a, sc.gammaincc(a, x))\narray([ 0., 10., 50.])" - ... - -def gammaincinv( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gammaincinv(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngammaincinv(a, y)\n\nInverse to the lower incomplete gamma function with respect to `x`.\n\nGiven an input :math:`y` between 0 and 1, returns :math:`x` such\nthat :math:`y = P(a, x)`. Here :math:`P` is the regularized lower\nincomplete gamma function; see `gammainc`. This is well-defined\nbecause the lower incomplete gamma function is monotonic as can be\nseen from its definition in [dlmf]_.\n\nParameters\n----------\na : array_like\n Positive parameter\ny : array_like\n Parameter between 0 and 1, inclusive\n\nReturns\n-------\nscalar or ndarray\n Values of the inverse of the lower incomplete gamma function\n\nSee Also\n--------\ngammainc : regularized lower incomplete gamma function\ngammaincc : regularized upper incomplete gamma function\ngammainccinv : inverse of the regualizred upper incomplete gamma\n function with respect to `x`\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/8.2#E4\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt starts at 0 and monotonically increases to infinity.\n\n>>> sc.gammaincinv(0.5, [0, 0.1 ,0.5, 1])\narray([0. , 0.00789539, 0.22746821, inf])\n\nIt inverts the lower incomplete gamma function.\n\n>>> a, x = 0.5, [0, 0.1, 0.5, 1]\n>>> sc.gammainc(a, sc.gammaincinv(a, x))\narray([0. , 0.1, 0.5, 1. ])\n\n>>> a, x = 0.5, [0, 10, 25]\n>>> sc.gammaincinv(a, sc.gammainc(a, x))\narray([ 0. , 10. , 25.00001465])" - ... - -def gammaln(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "gammaln(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngammaln(x, out=None)\n\nLogarithm of the absolute value of the gamma function.\n\nDefined as\n\n.. math::\n\n \\ln(\\lvert\\Gamma(x)\\rvert)\n\nwhere :math:`\\Gamma` is the gamma function. For more details on\nthe gamma function, see [dlmf]_.\n\nParameters\n----------\nx : array_like\n Real argument\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the log of the absolute value of gamma\n\nSee Also\n--------\ngammasgn : sign of the gamma function\nloggamma : principal branch of the logarithm of the gamma function\n\nNotes\n-----\nIt is the same function as the Python standard library function\n:func:`math.lgamma`.\n\nWhen used in conjunction with `gammasgn`, this function is useful\nfor working in logspace on the real axis without having to deal\nwith complex numbers via the relation ``exp(gammaln(x)) =\ngammasgn(x) * gamma(x)``.\n\nFor complex-valued log-gamma, use `loggamma` instead of `gammaln`.\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/5\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt has two positive zeros.\n\n>>> sc.gammaln([1, 2])\narray([0., 0.])\n\nIt has poles at nonpositive integers.\n\n>>> sc.gammaln([0, -1, -2, -3, -4])\narray([inf, inf, inf, inf, inf])\n\nIt asymptotically approaches ``x * log(x)`` (Stirling's formula).\n\n>>> x = np.array([1e10, 1e20, 1e40, 1e80])\n>>> sc.gammaln(x)\narray([2.20258509e+11, 4.50517019e+21, 9.11034037e+41, 1.83206807e+82])\n>>> x * np.log(x)\narray([2.30258509e+11, 4.60517019e+21, 9.21034037e+41, 1.84206807e+82])" - ... - -def gammasgn(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "gammasgn(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngammasgn(x)\n\nSign of the gamma function.\n\nIt is defined as\n\n.. math::\n\n \\text{gammasgn}(x) =\n \\begin{cases}\n +1 & \\Gamma(x) > 0 \\\\\n -1 & \\Gamma(x) < 0\n \\end{cases}\n\nwhere :math:`\\Gamma` is the gamma function; see `gamma`. This\ndefinition is complete since the gamma function is never zero;\nsee the discussion after [dlmf]_.\n\nParameters\n----------\nx : array_like\n Real argument\n\nReturns\n-------\nscalar or ndarray\n Sign of the gamma function\n\nNotes\n-----\nThe gamma function can be computed as ``gammasgn(x) *\nnp.exp(gammaln(x))``.\n\nSee Also\n--------\ngamma : the gamma function\ngammaln : log of the absolute value of the gamma function\nloggamma : analytic continuation of the log of the gamma function\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/5.2#E1\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is 1 for `x > 0`.\n\n>>> sc.gammasgn([1, 2, 3, 4])\narray([1., 1., 1., 1.])\n\nIt alternates between -1 and 1 for negative integers.\n\n>>> sc.gammasgn([-0.5, -1.5, -2.5, -3.5])\narray([-1., 1., -1., 1.])\n\nIt can be used to compute the gamma function.\n\n>>> x = [1.5, 0.5, -0.5, -1.5]\n>>> sc.gammasgn(x) * np.exp(sc.gammaln(x))\narray([ 0.88622693, 1.77245385, -3.5449077 , 2.3632718 ])\n>>> sc.gamma(x)\narray([ 0.88622693, 1.77245385, -3.5449077 , 2.3632718 ])" - ... - -def gdtr( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gdtr(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngdtr(a, b, x)\n\nGamma distribution cumulative distribution function.\n\nReturns the integral from zero to `x` of the gamma probability density\nfunction,\n\n.. math::\n\n F = \\int_0^x \\frac{a^b}{\\Gamma(b)} t^{b-1} e^{-at}\\,dt,\n\nwhere :math:`\\Gamma` is the gamma function.\n\nParameters\n----------\na : array_like\n The rate parameter of the gamma distribution, sometimes denoted\n :math:`\\beta` (float). It is also the reciprocal of the scale\n parameter :math:`\\theta`.\nb : array_like\n The shape parameter of the gamma distribution, sometimes denoted\n :math:`\\alpha` (float).\nx : array_like\n The quantile (upper limit of integration; float).\n\nSee also\n--------\ngdtrc : 1 - CDF of the gamma distribution.\n\nReturns\n-------\nF : ndarray\n The CDF of the gamma distribution with parameters `a` and `b`\n evaluated at `x`.\n\nNotes\n-----\nThe evaluation is carried out using the relation to the incomplete gamma\nintegral (regularized gamma function).\n\nWrapper for the Cephes [1]_ routine `gdtr`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def gdtrc( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gdtrc(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngdtrc(a, b, x)\n\nGamma distribution survival function.\n\nIntegral from `x` to infinity of the gamma probability density function,\n\n.. math::\n\n F = \\int_x^\\infty \\frac{a^b}{\\Gamma(b)} t^{b-1} e^{-at}\\,dt,\n\nwhere :math:`\\Gamma` is the gamma function.\n\nParameters\n----------\na : array_like\n The rate parameter of the gamma distribution, sometimes denoted\n :math:`\\beta` (float). It is also the reciprocal of the scale\n parameter :math:`\\theta`.\nb : array_like\n The shape parameter of the gamma distribution, sometimes denoted\n :math:`\\alpha` (float).\nx : array_like\n The quantile (lower limit of integration; float).\n\nReturns\n-------\nF : ndarray\n The survival function of the gamma distribution with parameters `a`\n and `b` evaluated at `x`.\n\nSee Also\n--------\ngdtr, gdtrix\n\nNotes\n-----\nThe evaluation is carried out using the relation to the incomplete gamma\nintegral (regularized gamma function).\n\nWrapper for the Cephes [1]_ routine `gdtrc`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def gdtria( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gdtria(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngdtria(p, b, x, out=None)\n\nInverse of `gdtr` vs a.\n\nReturns the inverse with respect to the parameter `a` of ``p =\ngdtr(a, b, x)``, the cumulative distribution function of the gamma\ndistribution.\n\nParameters\n----------\np : array_like\n Probability values.\nb : array_like\n `b` parameter values of `gdtr(a, b, x)`. `b` is the \"shape\" parameter\n of the gamma distribution.\nx : array_like\n Nonnegative real values, from the domain of the gamma distribution.\nout : ndarray, optional\n If a fourth argument is given, it must be a numpy.ndarray whose size\n matches the broadcast result of `a`, `b` and `x`. `out` is then the\n array returned by the function.\n\nReturns\n-------\na : ndarray\n Values of the `a` parameter such that `p = gdtr(a, b, x)`. `1/a`\n is the \"scale\" parameter of the gamma distribution.\n\nSee Also\n--------\ngdtr : CDF of the gamma distribution.\ngdtrib : Inverse with respect to `b` of `gdtr(a, b, x)`.\ngdtrix : Inverse with respect to `x` of `gdtr(a, b, x)`.\n\nNotes\n-----\nWrapper for the CDFLIB [1]_ Fortran routine `cdfgam`.\n\nThe cumulative distribution function `p` is computed using a routine by\nDiDinato and Morris [2]_. Computation of `a` involves a search for a value\nthat produces the desired value of `p`. The search relies on the\nmonotonicity of `p` with `a`.\n\nReferences\n----------\n.. [1] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters.\n.. [2] DiDinato, A. R. and Morris, A. H.,\n Computation of the incomplete gamma function ratios and their\n inverse. ACM Trans. Math. Softw. 12 (1986), 377-393.\n\nExamples\n--------\nFirst evaluate `gdtr`.\n\n>>> from scipy.special import gdtr, gdtria\n>>> p = gdtr(1.2, 3.4, 5.6)\n>>> print(p)\n0.94378087442\n\nVerify the inverse.\n\n>>> gdtria(p, 3.4, 5.6)\n1.2" - ... - -def gdtrib( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gdtrib(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngdtrib(a, p, x, out=None)\n\nInverse of `gdtr` vs b.\n\nReturns the inverse with respect to the parameter `b` of ``p =\ngdtr(a, b, x)``, the cumulative distribution function of the gamma\ndistribution.\n\nParameters\n----------\na : array_like\n `a` parameter values of `gdtr(a, b, x)`. `1/a` is the \"scale\"\n parameter of the gamma distribution.\np : array_like\n Probability values.\nx : array_like\n Nonnegative real values, from the domain of the gamma distribution.\nout : ndarray, optional\n If a fourth argument is given, it must be a numpy.ndarray whose size\n matches the broadcast result of `a`, `b` and `x`. `out` is then the\n array returned by the function.\n\nReturns\n-------\nb : ndarray\n Values of the `b` parameter such that `p = gdtr(a, b, x)`. `b` is\n the \"shape\" parameter of the gamma distribution.\n\nSee Also\n--------\ngdtr : CDF of the gamma distribution.\ngdtria : Inverse with respect to `a` of `gdtr(a, b, x)`.\ngdtrix : Inverse with respect to `x` of `gdtr(a, b, x)`.\n\nNotes\n-----\nWrapper for the CDFLIB [1]_ Fortran routine `cdfgam`.\n\nThe cumulative distribution function `p` is computed using a routine by\nDiDinato and Morris [2]_. Computation of `b` involves a search for a value\nthat produces the desired value of `p`. The search relies on the\nmonotonicity of `p` with `b`.\n\nReferences\n----------\n.. [1] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters.\n.. [2] DiDinato, A. R. and Morris, A. H.,\n Computation of the incomplete gamma function ratios and their\n inverse. ACM Trans. Math. Softw. 12 (1986), 377-393.\n\nExamples\n--------\nFirst evaluate `gdtr`.\n\n>>> from scipy.special import gdtr, gdtrib\n>>> p = gdtr(1.2, 3.4, 5.6)\n>>> print(p)\n0.94378087442\n\nVerify the inverse.\n\n>>> gdtrib(1.2, p, 5.6)\n3.3999999999723882" - ... - -def gdtrix( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "gdtrix(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ngdtrix(a, b, p, out=None)\n\nInverse of `gdtr` vs x.\n\nReturns the inverse with respect to the parameter `x` of ``p =\ngdtr(a, b, x)``, the cumulative distribution function of the gamma\ndistribution. This is also known as the pth quantile of the\ndistribution.\n\nParameters\n----------\na : array_like\n `a` parameter values of `gdtr(a, b, x)`. `1/a` is the \"scale\"\n parameter of the gamma distribution.\nb : array_like\n `b` parameter values of `gdtr(a, b, x)`. `b` is the \"shape\" parameter\n of the gamma distribution.\np : array_like\n Probability values.\nout : ndarray, optional\n If a fourth argument is given, it must be a numpy.ndarray whose size\n matches the broadcast result of `a`, `b` and `x`. `out` is then the\n array returned by the function.\n\nReturns\n-------\nx : ndarray\n Values of the `x` parameter such that `p = gdtr(a, b, x)`.\n\nSee Also\n--------\ngdtr : CDF of the gamma distribution.\ngdtria : Inverse with respect to `a` of `gdtr(a, b, x)`.\ngdtrib : Inverse with respect to `b` of `gdtr(a, b, x)`.\n\nNotes\n-----\nWrapper for the CDFLIB [1]_ Fortran routine `cdfgam`.\n\nThe cumulative distribution function `p` is computed using a routine by\nDiDinato and Morris [2]_. Computation of `x` involves a search for a value\nthat produces the desired value of `p`. The search relies on the\nmonotonicity of `p` with `x`.\n\nReferences\n----------\n.. [1] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters.\n.. [2] DiDinato, A. R. and Morris, A. H.,\n Computation of the incomplete gamma function ratios and their\n inverse. ACM Trans. Math. Softw. 12 (1986), 377-393.\n\nExamples\n--------\nFirst evaluate `gdtr`.\n\n>>> from scipy.special import gdtr, gdtrix\n>>> p = gdtr(1.2, 3.4, 5.6)\n>>> print(p)\n0.94378087442\n\nVerify the inverse.\n\n>>> gdtrix(1.2, 3.4, p)\n5.5999999999999996" - ... - -def geterr() -> typing.Any: - 'Get the current way of handling special-function errors.\n\n Returns\n -------\n err : dict\n A dictionary with keys "singular", "underflow", "overflow",\n "slow", "loss", "no_result", "domain", "arg", and "other",\n whose values are from the strings "ignore", "warn", and\n "raise". The keys represent possible special-function errors,\n and the values define how these errors are handled.\n\n See Also\n --------\n seterr : set how special-function errors are handled\n errstate : context manager for special-function error handling\n numpy.geterr : similar numpy function for floating-point errors\n\n Notes\n -----\n For complete documentation of the types of special-function errors\n and treatment options, see `seterr`.\n\n Examples\n --------\n By default all errors are ignored.\n\n >>> import scipy.special as sc\n >>> for key, value in sorted(sc.geterr().items()):\n ... print("{}: {}".format(key, value))\n ...\n arg: ignore\n domain: ignore\n loss: ignore\n no_result: ignore\n other: ignore\n overflow: ignore\n singular: ignore\n slow: ignore\n underflow: ignore\n\n' - ... - -def hankel1(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "hankel1(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhankel1(v, z)\n\nHankel function of the first kind\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nout : Values of the Hankel function of the first kind.\n\nNotes\n-----\nA wrapper for the AMOS [1]_ routine `zbesh`, which carries out the\ncomputation using the relation,\n\n.. math:: H^{(1)}_v(z) = \\frac{2}{\\imath\\pi} \\exp(-\\imath \\pi v/2) K_v(z \\exp(-\\imath\\pi/2))\n\nwhere :math:`K_v` is the modified Bessel function of the second kind.\nFor negative orders, the relation\n\n.. math:: H^{(1)}_{-v}(z) = H^{(1)}_v(z) \\exp(\\imath\\pi v)\n\nis used.\n\nSee also\n--------\nhankel1e : this function with leading exponential behavior stripped off.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def hankel1e( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "hankel1e(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhankel1e(v, z)\n\nExponentially scaled Hankel function of the first kind\n\nDefined as::\n\n hankel1e(v, z) = hankel1(v, z) * exp(-1j * z)\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nout : Values of the exponentially scaled Hankel function.\n\nNotes\n-----\nA wrapper for the AMOS [1]_ routine `zbesh`, which carries out the\ncomputation using the relation,\n\n.. math:: H^{(1)}_v(z) = \\frac{2}{\\imath\\pi} \\exp(-\\imath \\pi v/2) K_v(z \\exp(-\\imath\\pi/2))\n\nwhere :math:`K_v` is the modified Bessel function of the second kind.\nFor negative orders, the relation\n\n.. math:: H^{(1)}_{-v}(z) = H^{(1)}_v(z) \\exp(\\imath\\pi v)\n\nis used.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def hankel2(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "hankel2(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhankel2(v, z)\n\nHankel function of the second kind\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nout : Values of the Hankel function of the second kind.\n\nNotes\n-----\nA wrapper for the AMOS [1]_ routine `zbesh`, which carries out the\ncomputation using the relation,\n\n.. math:: H^{(2)}_v(z) = -\\frac{2}{\\imath\\pi} \\exp(\\imath \\pi v/2) K_v(z \\exp(\\imath\\pi/2))\n\nwhere :math:`K_v` is the modified Bessel function of the second kind.\nFor negative orders, the relation\n\n.. math:: H^{(2)}_{-v}(z) = H^{(2)}_v(z) \\exp(-\\imath\\pi v)\n\nis used.\n\nSee also\n--------\nhankel2e : this function with leading exponential behavior stripped off.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def hankel2e( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "hankel2e(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhankel2e(v, z)\n\nExponentially scaled Hankel function of the second kind\n\nDefined as::\n\n hankel2e(v, z) = hankel2(v, z) * exp(1j * z)\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nout : Values of the exponentially scaled Hankel function of the second kind.\n\nNotes\n-----\nA wrapper for the AMOS [1]_ routine `zbesh`, which carries out the\ncomputation using the relation,\n\n.. math:: H^{(2)}_v(z) = -\\frac{2}{\\imath\\pi} \\exp(\\frac{\\imath \\pi v}{2}) K_v(z exp(\\frac{\\imath\\pi}{2}))\n\nwhere :math:`K_v` is the modified Bessel function of the second kind.\nFor negative orders, the relation\n\n.. math:: H^{(2)}_{-v}(z) = H^{(2)}_v(z) \\exp(-\\imath\\pi v)\n\nis used.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def huber(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "huber(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhuber(delta, r)\n\nHuber loss function.\n\n.. math:: \\text{huber}(\\delta, r) = \\begin{cases} \\infty & \\delta < 0 \\\\ \\frac{1}{2}r^2 & 0 \\le \\delta, | r | \\le \\delta \\\\ \\delta ( |r| - \\frac{1}{2}\\delta ) & \\text{otherwise} \\end{cases}\n\nParameters\n----------\ndelta : ndarray\n Input array, indicating the quadratic vs. linear loss changepoint.\nr : ndarray\n Input array, possibly representing residuals.\n\nReturns\n-------\nres : ndarray\n The computed Huber loss function values.\n\nNotes\n-----\nThis function is convex in r.\n\n.. versionadded:: 0.15.0" - ... - -def hyp0f1(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "hyp0f1(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhyp0f1(v, z, out=None)\n\nConfluent hypergeometric limit function 0F1.\n\nParameters\n----------\nv : array_like\n Real-valued parameter\nz : array_like\n Real- or complex-valued argument\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n The confluent hypergeometric limit function\n\nNotes\n-----\nThis function is defined as:\n\n.. math:: _0F_1(v, z) = \\sum_{k=0}^{\\infty}\\frac{z^k}{(v)_k k!}.\n\nIt's also the limit as :math:`q \\to \\infty` of :math:`_1F_1(q; v; z/q)`,\nand satisfies the differential equation :math:`f''(z) + vf'(z) =\nf(z)`. See [1]_ for more information.\n\nReferences\n----------\n.. [1] Wolfram MathWorld, \"Confluent Hypergeometric Limit Function\",\n http://mathworld.wolfram.com/ConfluentHypergeometricLimitFunction.html\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is one when `z` is zero.\n\n>>> sc.hyp0f1(1, 0)\n1.0\n\nIt is the limit of the confluent hypergeometric function as `q`\ngoes to infinity.\n\n>>> q = np.array([1, 10, 100, 1000])\n>>> v = 1\n>>> z = 1\n>>> sc.hyp1f1(q, v, z / q)\narray([2.71828183, 2.31481985, 2.28303778, 2.27992985])\n>>> sc.hyp0f1(v, z)\n2.2795853023360673\n\nIt is related to Bessel functions.\n\n>>> n = 1\n>>> x = np.linspace(0, 1, 5)\n>>> sc.jv(n, x)\narray([0. , 0.12402598, 0.24226846, 0.3492436 , 0.44005059])\n>>> (0.5 * x)**n / sc.factorial(n) * sc.hyp0f1(n + 1, -0.25 * x**2)\narray([0. , 0.12402598, 0.24226846, 0.3492436 , 0.44005059])" - ... - -def hyp1f1( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "hyp1f1(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhyp1f1(a, b, x, out=None)\n\nConfluent hypergeometric function 1F1.\n\nThe confluent hypergeometric function is defined by the series\n\n.. math::\n\n {}_1F_1(a; b; x) = \\sum_{k = 0}^\\infty \\frac{(a)_k}{(b)_k k!} x^k.\n\nSee [dlmf]_ for more details. Here :math:`(\\cdot)_k` is the\nPochhammer symbol; see `poch`.\n\nParameters\n----------\na, b : array_like\n Real parameters\nx : array_like\n Real or complex argument\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the confluent hypergeometric function\n\nSee also\n--------\nhyperu : another confluent hypergeometric function\nhyp0f1 : confluent hypergeometric limit function\nhyp2f1 : Gaussian hypergeometric function\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/13.2#E2\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is one when `x` is zero:\n\n>>> sc.hyp1f1(0.5, 0.5, 0)\n1.0\n\nIt is singular when `b` is a nonpositive integer.\n\n>>> sc.hyp1f1(0.5, -1, 0)\ninf\n\nIt is a polynomial when `a` is a nonpositive integer.\n\n>>> a, b, x = -1, 0.5, np.array([1.0, 2.0, 3.0, 4.0])\n>>> sc.hyp1f1(a, b, x)\narray([-1., -3., -5., -7.])\n>>> 1 + (a / b) * x\narray([-1., -3., -5., -7.])\n\nIt reduces to the exponential function when `a = b`.\n\n>>> sc.hyp1f1(2, 2, [1, 2, 3, 4])\narray([ 2.71828183, 7.3890561 , 20.08553692, 54.59815003])\n>>> np.exp([1, 2, 3, 4])\narray([ 2.71828183, 7.3890561 , 20.08553692, 54.59815003])" - ... - -def hyp2f1( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "hyp2f1(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhyp2f1(a, b, c, z)\n\nGauss hypergeometric function 2F1(a, b; c; z)\n\nParameters\n----------\na, b, c : array_like\n Arguments, should be real-valued.\nz : array_like\n Argument, real or complex.\n\nReturns\n-------\nhyp2f1 : scalar or ndarray\n The values of the gaussian hypergeometric function.\n\nSee also\n--------\nhyp0f1 : confluent hypergeometric limit function.\nhyp1f1 : Kummer's (confluent hypergeometric) function.\n\nNotes\n-----\nThis function is defined for :math:`|z| < 1` as\n\n.. math::\n\n \\mathrm{hyp2f1}(a, b, c, z) = \\sum_{n=0}^\\infty\n \\frac{(a)_n (b)_n}{(c)_n}\\frac{z^n}{n!},\n\nand defined on the rest of the complex z-plane by analytic\ncontinuation [1]_.\nHere :math:`(\\cdot)_n` is the Pochhammer symbol; see `poch`. When\n:math:`n` is an integer the result is a polynomial of degree :math:`n`.\n\nThe implementation for complex values of ``z`` is described in [2]_.\n\nReferences\n----------\n.. [1] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/15.2\n.. [2] S. Zhang and J.M. Jin, \"Computation of Special Functions\", Wiley 1996\n.. [3] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt has poles when `c` is a negative integer.\n\n>>> sc.hyp2f1(1, 1, -2, 1)\ninf\n\nIt is a polynomial when `a` or `b` is a negative integer.\n\n>>> a, b, c = -1, 1, 1.5\n>>> z = np.linspace(0, 1, 5)\n>>> sc.hyp2f1(a, b, c, z)\narray([1. , 0.83333333, 0.66666667, 0.5 , 0.33333333])\n>>> 1 + a * b * z / c\narray([1. , 0.83333333, 0.66666667, 0.5 , 0.33333333])\n\nIt is symmetric in `a` and `b`.\n\n>>> a = np.linspace(0, 1, 5)\n>>> b = np.linspace(0, 1, 5)\n>>> sc.hyp2f1(a, b, 1, 0.5)\narray([1. , 1.03997334, 1.1803406 , 1.47074441, 2. ])\n>>> sc.hyp2f1(b, a, 1, 0.5)\narray([1. , 1.03997334, 1.1803406 , 1.47074441, 2. ])\n\nIt contains many other functions as special cases.\n\n>>> z = 0.5\n>>> sc.hyp2f1(1, 1, 2, z)\n1.3862943611198901\n>>> -np.log(1 - z) / z\n1.3862943611198906\n\n>>> sc.hyp2f1(0.5, 1, 1.5, z**2)\n1.098612288668109\n>>> np.log((1 + z) / (1 - z)) / (2 * z)\n1.0986122886681098\n\n>>> sc.hyp2f1(0.5, 1, 1.5, -z**2)\n0.9272952180016117\n>>> np.arctan(z) / z\n0.9272952180016123" - ... - -def hyperu( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "hyperu(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nhyperu(a, b, x, out=None)\n\nConfluent hypergeometric function U\n\nIt is defined as the solution to the equation\n\n.. math::\n\n x \\frac{d^2w}{dx^2} + (b - x) \\frac{dw}{dx} - aw = 0\n\nwhich satisfies the property\n\n.. math::\n\n U(a, b, x) \\sim x^{-a}\n\nas :math:`x \\to \\infty`. See [dlmf]_ for more details.\n\nParameters\n----------\na, b : array_like\n Real-valued parameters\nx : array_like\n Real-valued argument\nout : ndarray\n Optional output array for the function values\n\nReturns\n-------\nscalar or ndarray\n Values of `U`\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematics Functions\n https://dlmf.nist.gov/13.2#E6\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt has a branch cut along the negative `x` axis.\n\n>>> x = np.linspace(-0.1, -10, 5)\n>>> sc.hyperu(1, 1, x)\narray([nan, nan, nan, nan, nan])\n\nIt approaches zero as `x` goes to infinity.\n\n>>> x = np.array([1, 10, 100])\n>>> sc.hyperu(1, 1, x)\narray([0.59634736, 0.09156333, 0.00990194])\n\nIt satisfies Kummer's transformation.\n\n>>> a, b, x = 2, 1, 1\n>>> sc.hyperu(a, b, x)\n0.1926947246463881\n>>> x**(1 - b) * sc.hyperu(a - b + 1, 2 - b, x)\n0.1926947246463881" - ... - -def i0(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "i0(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ni0(x)\n\nModified Bessel function of order 0.\n\nDefined as,\n\n.. math::\n I_0(x) = \\sum_{k=0}^\\infty \\frac{(x^2/4)^k}{(k!)^2} = J_0(\\imath x),\n\nwhere :math:`J_0` is the Bessel function of the first kind of order 0.\n\nParameters\n----------\nx : array_like\n Argument (float)\n\nReturns\n-------\nI : ndarray\n Value of the modified Bessel function of order 0 at `x`.\n\nNotes\n-----\nThe range is partitioned into the two intervals [0, 8] and (8, infinity).\nChebyshev polynomial expansions are employed in each interval.\n\nThis function is a wrapper for the Cephes [1]_ routine `i0`.\n\nSee also\n--------\niv\ni0e\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def i0e(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "i0e(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ni0e(x)\n\nExponentially scaled modified Bessel function of order 0.\n\nDefined as::\n\n i0e(x) = exp(-abs(x)) * i0(x).\n\nParameters\n----------\nx : array_like\n Argument (float)\n\nReturns\n-------\nI : ndarray\n Value of the exponentially scaled modified Bessel function of order 0\n at `x`.\n\nNotes\n-----\nThe range is partitioned into the two intervals [0, 8] and (8, infinity).\nChebyshev polynomial expansions are employed in each interval. The\npolynomial expansions used are the same as those in `i0`, but\nthey are not multiplied by the dominant exponential factor.\n\nThis function is a wrapper for the Cephes [1]_ routine `i0e`.\n\nSee also\n--------\niv\ni0\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def i1(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "i1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ni1(x)\n\nModified Bessel function of order 1.\n\nDefined as,\n\n.. math::\n I_1(x) = \\frac{1}{2}x \\sum_{k=0}^\\infty \\frac{(x^2/4)^k}{k! (k + 1)!}\n = -\\imath J_1(\\imath x),\n\nwhere :math:`J_1` is the Bessel function of the first kind of order 1.\n\nParameters\n----------\nx : array_like\n Argument (float)\n\nReturns\n-------\nI : ndarray\n Value of the modified Bessel function of order 1 at `x`.\n\nNotes\n-----\nThe range is partitioned into the two intervals [0, 8] and (8, infinity).\nChebyshev polynomial expansions are employed in each interval.\n\nThis function is a wrapper for the Cephes [1]_ routine `i1`.\n\nSee also\n--------\niv\ni1e\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def i1e(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "i1e(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ni1e(x)\n\nExponentially scaled modified Bessel function of order 1.\n\nDefined as::\n\n i1e(x) = exp(-abs(x)) * i1(x)\n\nParameters\n----------\nx : array_like\n Argument (float)\n\nReturns\n-------\nI : ndarray\n Value of the exponentially scaled modified Bessel function of order 1\n at `x`.\n\nNotes\n-----\nThe range is partitioned into the two intervals [0, 8] and (8, infinity).\nChebyshev polynomial expansions are employed in each interval. The\npolynomial expansions used are the same as those in `i1`, but\nthey are not multiplied by the dominant exponential factor.\n\nThis function is a wrapper for the Cephes [1]_ routine `i1e`.\n\nSee also\n--------\niv\ni1\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def inv_boxcox( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "inv_boxcox(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ninv_boxcox(y, lmbda)\n\nCompute the inverse of the Box-Cox transformation.\n\nFind ``x`` such that::\n\n y = (x**lmbda - 1) / lmbda if lmbda != 0\n log(x) if lmbda == 0\n\nParameters\n----------\ny : array_like\n Data to be transformed.\nlmbda : array_like\n Power parameter of the Box-Cox transform.\n\nReturns\n-------\nx : array\n Transformed data.\n\nNotes\n-----\n\n.. versionadded:: 0.16.0\n\nExamples\n--------\n>>> from scipy.special import boxcox, inv_boxcox\n>>> y = boxcox([1, 4, 10], 2.5)\n>>> inv_boxcox(y, 2.5)\narray([1., 4., 10.])" - ... - -def inv_boxcox1p( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "inv_boxcox1p(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ninv_boxcox1p(y, lmbda)\n\nCompute the inverse of the Box-Cox transformation.\n\nFind ``x`` such that::\n\n y = ((1+x)**lmbda - 1) / lmbda if lmbda != 0\n log(1+x) if lmbda == 0\n\nParameters\n----------\ny : array_like\n Data to be transformed.\nlmbda : array_like\n Power parameter of the Box-Cox transform.\n\nReturns\n-------\nx : array\n Transformed data.\n\nNotes\n-----\n\n.. versionadded:: 0.16.0\n\nExamples\n--------\n>>> from scipy.special import boxcox1p, inv_boxcox1p\n>>> y = boxcox1p([1, 4, 10], 2.5)\n>>> inv_boxcox1p(y, 2.5)\narray([1., 4., 10.])" - ... - -def it2i0k0( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "it2i0k0(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nit2i0k0(x, out=None)\n\nIntegrals related to modified Bessel functions of order 0.\n\nComputes the integrals\n\n.. math::\n\n \\int_0^x \\frac{I_0(t) - 1}{t} dt \\\\\n \\int_x^\\infty \\frac{K_0(t)}{t} dt.\n\nParameters\n----------\nx : array_like\n Values at which to evaluate the integrals.\nout : tuple of ndarrays, optional\n Optional output arrays for the function results.\n\nReturns\n-------\nii0 : scalar or ndarray\n The integral for `i0`\nik0 : scalar or ndarray\n The integral for `k0`" - ... - -def it2j0y0( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "it2j0y0(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nit2j0y0(x, out=None)\n\nIntegrals related to Bessel functions of the first kind of order 0.\n\nComputes the integrals\n\n.. math::\n\n \\int_0^x \\frac{1 - J_0(t)}{t} dt \\\\\n \\int_x^\\infty \\frac{Y_0(t)}{t} dt.\n\nFor more on :math:`J_0` and :math:`Y_0` see `j0` and `y0`.\n\nParameters\n----------\nx : array_like\n Values at which to evaluate the integrals.\nout : tuple of ndarrays, optional\n Optional output arrays for the function results.\n\nReturns\n-------\nij0 : scalar or ndarray\n The integral for `j0`\niy0 : scalar or ndarray\n The integral for `y0`" - ... - -def it2struve0(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "it2struve0(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nit2struve0(x)\n\nIntegral related to the Struve function of order 0.\n\nReturns the integral,\n\n.. math::\n \\int_x^\\infty \\frac{H_0(t)}{t}\\,dt\n\nwhere :math:`H_0` is the Struve function of order 0.\n\nParameters\n----------\nx : array_like\n Lower limit of integration.\n\nReturns\n-------\nI : ndarray\n The value of the integral.\n\nSee also\n--------\nstruve\n\nNotes\n-----\nWrapper for a Fortran routine created by Shanjie Zhang and Jianming\nJin [1]_.\n\nReferences\n----------\n.. [1] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n Functions\", John Wiley and Sons, 1996.\n https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html" - ... - -def itairy( - x, - out1=..., - out2=..., - out3=..., - out4=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "itairy(x[, out1, out2, out3, out4], / [, out=(None, None, None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nitairy(x)\n\nIntegrals of Airy functions\n\nCalculates the integrals of Airy functions from 0 to `x`.\n\nParameters\n----------\n\nx: array_like\n Upper limit of integration (float).\n\nReturns\n-------\nApt\n Integral of Ai(t) from 0 to x.\nBpt\n Integral of Bi(t) from 0 to x.\nAnt\n Integral of Ai(-t) from 0 to x.\nBnt\n Integral of Bi(-t) from 0 to x.\n\nNotes\n-----\n\nWrapper for a Fortran routine created by Shanjie Zhang and Jianming\nJin [1]_.\n\nReferences\n----------\n\n.. [1] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n Functions\", John Wiley and Sons, 1996.\n https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html" - ... - -def iti0k0( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "iti0k0(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\niti0k0(x, out=None)\n\nIntegrals of modified Bessel functions of order 0.\n\nComputes the integrals\n\n.. math::\n\n \\int_0^x I_0(t) dt \\\\\n \\int_0^x K_0(t) dt.\n\nFor more on :math:`I_0` and :math:`K_0` see `i0` and `k0`.\n\nParameters\n----------\nx : array_like\n Values at which to evaluate the integrals.\nout : tuple of ndarrays, optional\n Optional output arrays for the function results.\n\nReturns\n-------\nii0 : scalar or ndarray\n The integral for `i0`\nik0 : scalar or ndarray\n The integral for `k0`" - ... - -def itj0y0( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "itj0y0(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nitj0y0(x, out=None)\n\nIntegrals of Bessel functions of the first kind of order 0.\n\nComputes the integrals\n\n.. math::\n\n \\int_0^x J_0(t) dt \\\\\n \\int_0^x Y_0(t) dt.\n\nFor more on :math:`J_0` and :math:`Y_0` see `j0` and `y0`.\n\nParameters\n----------\nx : array_like\n Values at which to evaluate the integrals.\nout : tuple of ndarrays, optional\n Optional output arrays for the function results.\n\nReturns\n-------\nij0 : scalar or ndarray\n The integral of `j0`\niy0 : scalar or ndarray\n The integral of `y0`" - ... - -def itmodstruve0(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "itmodstruve0(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nitmodstruve0(x)\n\nIntegral of the modified Struve function of order 0.\n\n.. math::\n I = \\int_0^x L_0(t)\\,dt\n\nParameters\n----------\nx : array_like\n Upper limit of integration (float).\n\nReturns\n-------\nI : ndarray\n The integral of :math:`L_0` from 0 to `x`.\n\nNotes\n-----\nWrapper for a Fortran routine created by Shanjie Zhang and Jianming\nJin [1]_.\n\nReferences\n----------\n.. [1] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n Functions\", John Wiley and Sons, 1996.\n https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html" - ... - -def itstruve0(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "itstruve0(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nitstruve0(x)\n\nIntegral of the Struve function of order 0.\n\n.. math::\n I = \\int_0^x H_0(t)\\,dt\n\nParameters\n----------\nx : array_like\n Upper limit of integration (float).\n\nReturns\n-------\nI : ndarray\n The integral of :math:`H_0` from 0 to `x`.\n\nSee also\n--------\nstruve\n\nNotes\n-----\nWrapper for a Fortran routine created by Shanjie Zhang and Jianming\nJin [1]_.\n\nReferences\n----------\n.. [1] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n Functions\", John Wiley and Sons, 1996.\n https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html" - ... - -def iv(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "iv(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\niv(v, z)\n\nModified Bessel function of the first kind of real order.\n\nParameters\n----------\nv : array_like\n Order. If `z` is of real type and negative, `v` must be integer\n valued.\nz : array_like of float or complex\n Argument.\n\nReturns\n-------\nout : ndarray\n Values of the modified Bessel function.\n\nNotes\n-----\nFor real `z` and :math:`v \\in [-50, 50]`, the evaluation is carried out\nusing Temme's method [1]_. For larger orders, uniform asymptotic\nexpansions are applied.\n\nFor complex `z` and positive `v`, the AMOS [2]_ `zbesi` routine is\ncalled. It uses a power series for small `z`, the asymptotic expansion\nfor large `abs(z)`, the Miller algorithm normalized by the Wronskian\nand a Neumann series for intermediate magnitudes, and the uniform\nasymptotic expansions for :math:`I_v(z)` and :math:`J_v(z)` for large\norders. Backward recurrence is used to generate sequences or reduce\norders when necessary.\n\nThe calculations above are done in the right half plane and continued\ninto the left half plane by the formula,\n\n.. math:: I_v(z \\exp(\\pm\\imath\\pi)) = \\exp(\\pm\\pi v) I_v(z)\n\n(valid when the real part of `z` is positive). For negative `v`, the\nformula\n\n.. math:: I_{-v}(z) = I_v(z) + \\frac{2}{\\pi} \\sin(\\pi v) K_v(z)\n\nis used, where :math:`K_v(z)` is the modified Bessel function of the\nsecond kind, evaluated using the AMOS routine `zbesk`.\n\nSee also\n--------\nkve : This function with leading exponential behavior stripped off.\n\nReferences\n----------\n.. [1] Temme, Journal of Computational Physics, vol 21, 343 (1976)\n.. [2] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def ive(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "ive(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nive(v, z)\n\nExponentially scaled modified Bessel function of the first kind\n\nDefined as::\n\n ive(v, z) = iv(v, z) * exp(-abs(z.real))\n\nParameters\n----------\nv : array_like of float\n Order.\nz : array_like of float or complex\n Argument.\n\nReturns\n-------\nout : ndarray\n Values of the exponentially scaled modified Bessel function.\n\nNotes\n-----\nFor positive `v`, the AMOS [1]_ `zbesi` routine is called. It uses a\npower series for small `z`, the asymptotic expansion for large\n`abs(z)`, the Miller algorithm normalized by the Wronskian and a\nNeumann series for intermediate magnitudes, and the uniform asymptotic\nexpansions for :math:`I_v(z)` and :math:`J_v(z)` for large orders.\nBackward recurrence is used to generate sequences or reduce orders when\nnecessary.\n\nThe calculations above are done in the right half plane and continued\ninto the left half plane by the formula,\n\n.. math:: I_v(z \\exp(\\pm\\imath\\pi)) = \\exp(\\pm\\pi v) I_v(z)\n\n(valid when the real part of `z` is positive). For negative `v`, the\nformula\n\n.. math:: I_{-v}(z) = I_v(z) + \\frac{2}{\\pi} \\sin(\\pi v) K_v(z)\n\nis used, where :math:`K_v(z)` is the modified Bessel function of the\nsecond kind, evaluated using the AMOS routine `zbesk`.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def j0(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "j0(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nj0(x)\n\nBessel function of the first kind of order 0.\n\nParameters\n----------\nx : array_like\n Argument (float).\n\nReturns\n-------\nJ : ndarray\n Value of the Bessel function of the first kind of order 0 at `x`.\n\nNotes\n-----\nThe domain is divided into the intervals [0, 5] and (5, infinity). In the\nfirst interval the following rational approximation is used:\n\n.. math::\n\n J_0(x) \\approx (w - r_1^2)(w - r_2^2) \\frac{P_3(w)}{Q_8(w)},\n\nwhere :math:`w = x^2` and :math:`r_1`, :math:`r_2` are the zeros of\n:math:`J_0`, and :math:`P_3` and :math:`Q_8` are polynomials of degrees 3\nand 8, respectively.\n\nIn the second interval, the Hankel asymptotic expansion is employed with\ntwo rational functions of degree 6/6 and 7/7.\n\nThis function is a wrapper for the Cephes [1]_ routine `j0`.\nIt should not be confused with the spherical Bessel functions (see\n`spherical_jn`).\n\nSee also\n--------\njv : Bessel function of real order and complex argument.\nspherical_jn : spherical Bessel functions.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def j1(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "j1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nj1(x)\n\nBessel function of the first kind of order 1.\n\nParameters\n----------\nx : array_like\n Argument (float).\n\nReturns\n-------\nJ : ndarray\n Value of the Bessel function of the first kind of order 1 at `x`.\n\nNotes\n-----\nThe domain is divided into the intervals [0, 8] and (8, infinity). In the\nfirst interval a 24 term Chebyshev expansion is used. In the second, the\nasymptotic trigonometric representation is employed using two rational\nfunctions of degree 5/5.\n\nThis function is a wrapper for the Cephes [1]_ routine `j1`.\nIt should not be confused with the spherical Bessel functions (see\n`spherical_jn`).\n\nSee also\n--------\njv\nspherical_jn : spherical Bessel functions.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def jn(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "jv(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\njv(v, z)\n\nBessel function of the first kind of real order and complex argument.\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nJ : ndarray\n Value of the Bessel function, :math:`J_v(z)`.\n\nNotes\n-----\nFor positive `v` values, the computation is carried out using the AMOS\n[1]_ `zbesj` routine, which exploits the connection to the modified\nBessel function :math:`I_v`,\n\n.. math::\n J_v(z) = \\exp(v\\pi\\imath/2) I_v(-\\imath z)\\qquad (\\Im z > 0)\n\n J_v(z) = \\exp(-v\\pi\\imath/2) I_v(\\imath z)\\qquad (\\Im z < 0)\n\nFor negative `v` values the formula,\n\n.. math:: J_{-v}(z) = J_v(z) \\cos(\\pi v) - Y_v(z) \\sin(\\pi v)\n\nis used, where :math:`Y_v(z)` is the Bessel function of the second\nkind, computed using the AMOS routine `zbesy`. Note that the second\nterm is exactly zero for integer `v`; to improve accuracy the second\nterm is explicitly omitted for `v` values such that `v = floor(v)`.\n\nNot to be confused with the spherical Bessel functions (see `spherical_jn`).\n\nSee also\n--------\njve : :math:`J_v` with leading exponential behavior stripped off.\nspherical_jn : spherical Bessel functions.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def jv(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "jv(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\njv(v, z)\n\nBessel function of the first kind of real order and complex argument.\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nJ : ndarray\n Value of the Bessel function, :math:`J_v(z)`.\n\nNotes\n-----\nFor positive `v` values, the computation is carried out using the AMOS\n[1]_ `zbesj` routine, which exploits the connection to the modified\nBessel function :math:`I_v`,\n\n.. math::\n J_v(z) = \\exp(v\\pi\\imath/2) I_v(-\\imath z)\\qquad (\\Im z > 0)\n\n J_v(z) = \\exp(-v\\pi\\imath/2) I_v(\\imath z)\\qquad (\\Im z < 0)\n\nFor negative `v` values the formula,\n\n.. math:: J_{-v}(z) = J_v(z) \\cos(\\pi v) - Y_v(z) \\sin(\\pi v)\n\nis used, where :math:`Y_v(z)` is the Bessel function of the second\nkind, computed using the AMOS routine `zbesy`. Note that the second\nterm is exactly zero for integer `v`; to improve accuracy the second\nterm is explicitly omitted for `v` values such that `v = floor(v)`.\n\nNot to be confused with the spherical Bessel functions (see `spherical_jn`).\n\nSee also\n--------\njve : :math:`J_v` with leading exponential behavior stripped off.\nspherical_jn : spherical Bessel functions.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def jve(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "jve(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\njve(v, z)\n\nExponentially scaled Bessel function of order `v`.\n\nDefined as::\n\n jve(v, z) = jv(v, z) * exp(-abs(z.imag))\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nJ : ndarray\n Value of the exponentially scaled Bessel function.\n\nNotes\n-----\nFor positive `v` values, the computation is carried out using the AMOS\n[1]_ `zbesj` routine, which exploits the connection to the modified\nBessel function :math:`I_v`,\n\n.. math::\n J_v(z) = \\exp(v\\pi\\imath/2) I_v(-\\imath z)\\qquad (\\Im z > 0)\n\n J_v(z) = \\exp(-v\\pi\\imath/2) I_v(\\imath z)\\qquad (\\Im z < 0)\n\nFor negative `v` values the formula,\n\n.. math:: J_{-v}(z) = J_v(z) \\cos(\\pi v) - Y_v(z) \\sin(\\pi v)\n\nis used, where :math:`Y_v(z)` is the Bessel function of the second\nkind, computed using the AMOS routine `zbesy`. Note that the second\nterm is exactly zero for integer `v`; to improve accuracy the second\nterm is explicitly omitted for `v` values such that `v = floor(v)`.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def k0(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "k0(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nk0(x)\n\nModified Bessel function of the second kind of order 0, :math:`K_0`.\n\nThis function is also sometimes referred to as the modified Bessel\nfunction of the third kind of order 0.\n\nParameters\n----------\nx : array_like\n Argument (float).\n\nReturns\n-------\nK : ndarray\n Value of the modified Bessel function :math:`K_0` at `x`.\n\nNotes\n-----\nThe range is partitioned into the two intervals [0, 2] and (2, infinity).\nChebyshev polynomial expansions are employed in each interval.\n\nThis function is a wrapper for the Cephes [1]_ routine `k0`.\n\nSee also\n--------\nkv\nk0e\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def k0e(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "k0e(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nk0e(x)\n\nExponentially scaled modified Bessel function K of order 0\n\nDefined as::\n\n k0e(x) = exp(x) * k0(x).\n\nParameters\n----------\nx : array_like\n Argument (float)\n\nReturns\n-------\nK : ndarray\n Value of the exponentially scaled modified Bessel function K of order\n 0 at `x`.\n\nNotes\n-----\nThe range is partitioned into the two intervals [0, 2] and (2, infinity).\nChebyshev polynomial expansions are employed in each interval.\n\nThis function is a wrapper for the Cephes [1]_ routine `k0e`.\n\nSee also\n--------\nkv\nk0\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def k1(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "k1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nk1(x)\n\nModified Bessel function of the second kind of order 1, :math:`K_1(x)`.\n\nParameters\n----------\nx : array_like\n Argument (float)\n\nReturns\n-------\nK : ndarray\n Value of the modified Bessel function K of order 1 at `x`.\n\nNotes\n-----\nThe range is partitioned into the two intervals [0, 2] and (2, infinity).\nChebyshev polynomial expansions are employed in each interval.\n\nThis function is a wrapper for the Cephes [1]_ routine `k1`.\n\nSee also\n--------\nkv\nk1e\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def k1e(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "k1e(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nk1e(x)\n\nExponentially scaled modified Bessel function K of order 1\n\nDefined as::\n\n k1e(x) = exp(x) * k1(x)\n\nParameters\n----------\nx : array_like\n Argument (float)\n\nReturns\n-------\nK : ndarray\n Value of the exponentially scaled modified Bessel function K of order\n 1 at `x`.\n\nNotes\n-----\nThe range is partitioned into the two intervals [0, 2] and (2, infinity).\nChebyshev polynomial expansions are employed in each interval.\n\nThis function is a wrapper for the Cephes [1]_ routine `k1e`.\n\nSee also\n--------\nkv\nk1\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def kei(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "kei(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkei(x, out=None)\n\nKelvin function kei.\n\nDefined as\n\n.. math::\n\n \\mathrm{kei}(x) = \\Im[K_0(x e^{\\pi i / 4})]\n\nwhere :math:`K_0` is the modified Bessel function of the second\nkind (see `kv`). See [dlmf]_ for more details.\n\nParameters\n----------\nx : array_like\n Real argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of the Kelvin function.\n\nSee Also\n--------\nker : the corresponding real part\nkeip : the derivative of kei\nkv : modified Bessel function of the second kind\n\nReferences\n----------\n.. [dlmf] NIST, Digital Library of Mathematical Functions,\n https://dlmf.nist.gov/10.61\n\nExamples\n--------\nIt can be expressed using the modified Bessel function of the\nsecond kind.\n\n>>> import scipy.special as sc\n>>> x = np.array([1.0, 2.0, 3.0, 4.0])\n>>> sc.kv(0, x * np.exp(np.pi * 1j / 4)).imag\narray([-0.49499464, -0.20240007, -0.05112188, 0.0021984 ])\n>>> sc.kei(x)\narray([-0.49499464, -0.20240007, -0.05112188, 0.0021984 ])" - ... - -def keip(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "keip(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkeip(x, out=None)\n\nDerivative of the Kelvin function kei.\n\nParameters\n----------\nx : array_like\n Real argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n The values of the derivative of kei.\n\nSee Also\n--------\nkei\n\nReferences\n----------\n.. [dlmf] NIST, Digital Library of Mathematical Functions,\n https://dlmf.nist.gov/10#PT5" - ... - -def kelvin( - x, - out1=..., - out2=..., - out3=..., - out4=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "kelvin(x[, out1, out2, out3, out4], / [, out=(None, None, None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkelvin(x)\n\nKelvin functions as complex numbers\n\nReturns\n-------\nBe, Ke, Bep, Kep\n The tuple (Be, Ke, Bep, Kep) contains complex numbers\n representing the real and imaginary Kelvin functions and their\n derivatives evaluated at `x`. For example, kelvin(x)[0].real =\n ber x and kelvin(x)[0].imag = bei x with similar relationships\n for ker and kei." - ... - -def ker(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "ker(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nker(x, out=None)\n\nKelvin function ker.\n\nDefined as\n\n.. math::\n\n \\mathrm{ker}(x) = \\Re[K_0(x e^{\\pi i / 4})]\n\nWhere :math:`K_0` is the modified Bessel function of the second\nkind (see `kv`). See [dlmf]_ for more details.\n\nParameters\n----------\nx : array_like\n Real argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nSee Also\n--------\nkei : the corresponding imaginary part\nkerp : the derivative of ker\nkv : modified Bessel function of the second kind\n\nReturns\n-------\nscalar or ndarray\n Values of the Kelvin function.\n\nReferences\n----------\n.. [dlmf] NIST, Digital Library of Mathematical Functions,\n https://dlmf.nist.gov/10.61\n\nExamples\n--------\nIt can be expressed using the modified Bessel function of the\nsecond kind.\n\n>>> import scipy.special as sc\n>>> x = np.array([1.0, 2.0, 3.0, 4.0])\n>>> sc.kv(0, x * np.exp(np.pi * 1j / 4)).real\narray([ 0.28670621, -0.04166451, -0.06702923, -0.03617885])\n>>> sc.ker(x)\narray([ 0.28670621, -0.04166451, -0.06702923, -0.03617885])" - ... - -def kerp(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "kerp(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkerp(x, out=None)\n\nDerivative of the Kelvin function ker.\n\nParameters\n----------\nx : array_like\n Real argument.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of the derivative of ker.\n\nSee Also\n--------\nker\n\nReferences\n----------\n.. [dlmf] NIST, Digital Library of Mathematical Functions,\n https://dlmf.nist.gov/10#PT5" - ... - -def kl_div(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "kl_div(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkl_div(x, y, out=None)\n\nElementwise function for computing Kullback-Leibler divergence.\n\n.. math::\n\n \\mathrm{kl\\_div}(x, y) =\n \\begin{cases}\n x \\log(x / y) - x + y & x > 0, y > 0 \\\\\n y & x = 0, y \\ge 0 \\\\\n \\infty & \\text{otherwise}\n \\end{cases}\n\nParameters\n----------\nx, y : array_like\n Real arguments\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Values of the Kullback-Liebler divergence.\n\nSee Also\n--------\nentr, rel_entr\n\nNotes\n-----\n.. versionadded:: 0.15.0\n\nThis function is non-negative and is jointly convex in `x` and `y`.\n\nThe origin of this function is in convex programming; see [1]_ for\ndetails. This is why the the function contains the extra :math:`-x\n+ y` terms over what might be expected from the Kullback-Leibler\ndivergence. For a version of the function without the extra terms,\nsee `rel_entr`.\n\nReferences\n----------\n.. [1] Grant, Boyd, and Ye, \"CVX: Matlab Software for Disciplined Convex\n Programming\", http://cvxr.com/cvx/" - ... - -def kn(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "kn(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkn(n, x)\n\nModified Bessel function of the second kind of integer order `n`\n\nReturns the modified Bessel function of the second kind for integer order\n`n` at real `z`.\n\nThese are also sometimes called functions of the third kind, Basset\nfunctions, or Macdonald functions.\n\nParameters\n----------\nn : array_like of int\n Order of Bessel functions (floats will truncate with a warning)\nz : array_like of float\n Argument at which to evaluate the Bessel functions\n\nReturns\n-------\nout : ndarray\n The results\n\nNotes\n-----\nWrapper for AMOS [1]_ routine `zbesk`. For a discussion of the\nalgorithm used, see [2]_ and the references therein.\n\nSee Also\n--------\nkv : Same function, but accepts real order and complex argument\nkvp : Derivative of this function\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/\n.. [2] Donald E. Amos, \"Algorithm 644: A portable package for Bessel\n functions of a complex argument and nonnegative order\", ACM\n TOMS Vol. 12 Issue 3, Sept. 1986, p. 265\n\nExamples\n--------\nPlot the function of several orders for real input:\n\n>>> from scipy.special import kn\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(0, 5, 1000)\n>>> for N in range(6):\n... plt.plot(x, kn(N, x), label='$K_{}(x)$'.format(N))\n>>> plt.ylim(0, 10)\n>>> plt.legend()\n>>> plt.title(r'Modified Bessel function of the second kind $K_n(x)$')\n>>> plt.show()\n\nCalculate for a single value at multiple orders:\n\n>>> kn([4, 5, 6], 1)\narray([ 44.23241585, 360.9605896 , 3653.83831186])" - ... - -def kolmogi(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "kolmogi(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkolmogi(p)\n\nInverse Survival Function of Kolmogorov distribution\n\nIt is the inverse function to `kolmogorov`.\nReturns y such that ``kolmogorov(y) == p``.\n\nParameters\n----------\np : float array_like\n Probability\n\nReturns\n-------\nfloat\n The value(s) of kolmogi(p)\n\nNotes\n-----\n`kolmogorov` is used by `stats.kstest` in the application of the\nKolmogorov-Smirnov Goodness of Fit test. For historial reasons this\nfunction is exposed in `scpy.special`, but the recommended way to achieve\nthe most accurate CDF/SF/PDF/PPF/ISF computations is to use the\n`stats.kstwobign` distribution.\n\nSee Also\n--------\nkolmogorov : The Survival Function for the distribution\nscipy.stats.kstwobign : Provides the functionality as a continuous distribution\nsmirnov, smirnovi : Functions for the one-sided distribution\n\nExamples\n--------\n>>> from scipy.special import kolmogi\n>>> kolmogi([0, 0.1, 0.25, 0.5, 0.75, 0.9, 1.0])\narray([ inf, 1.22384787, 1.01918472, 0.82757356, 0.67644769,\n 0.57117327, 0. ])" - ... - -def kolmogorov(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "kolmogorov(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkolmogorov(y)\n\nComplementary cumulative distribution (Survival Function) function of\nKolmogorov distribution.\n\nReturns the complementary cumulative distribution function of\nKolmogorov's limiting distribution (``D_n*\\sqrt(n)`` as n goes to infinity)\nof a two-sided test for equality between an empirical and a theoretical\ndistribution. It is equal to the (limit as n->infinity of the)\nprobability that ``sqrt(n) * max absolute deviation > y``.\n\nParameters\n----------\ny : float array_like\n Absolute deviation between the Empirical CDF (ECDF) and the target CDF,\n multiplied by sqrt(n).\n\nReturns\n-------\nfloat\n The value(s) of kolmogorov(y)\n\nNotes\n-----\n`kolmogorov` is used by `stats.kstest` in the application of the\nKolmogorov-Smirnov Goodness of Fit test. For historial reasons this\nfunction is exposed in `scpy.special`, but the recommended way to achieve\nthe most accurate CDF/SF/PDF/PPF/ISF computations is to use the\n`stats.kstwobign` distribution.\n\nSee Also\n--------\nkolmogi : The Inverse Survival Function for the distribution\nscipy.stats.kstwobign : Provides the functionality as a continuous distribution\nsmirnov, smirnovi : Functions for the one-sided distribution\n\nExamples\n--------\nShow the probability of a gap at least as big as 0, 0.5 and 1.0.\n\n>>> from scipy.special import kolmogorov\n>>> from scipy.stats import kstwobign\n>>> kolmogorov([0, 0.5, 1.0])\narray([ 1. , 0.96394524, 0.26999967])\n\nCompare a sample of size 1000 drawn from a Laplace(0, 1) distribution against\nthe target distribution, a Normal(0, 1) distribution.\n\n>>> from scipy.stats import norm, laplace\n>>> n = 1000\n>>> np.random.seed(seed=233423)\n>>> lap01 = laplace(0, 1)\n>>> x = np.sort(lap01.rvs(n))\n>>> np.mean(x), np.std(x)\n(-0.083073685397609842, 1.3676426568399822)\n\nConstruct the Empirical CDF and the K-S statistic Dn.\n\n>>> target = norm(0,1) # Normal mean 0, stddev 1\n>>> cdfs = target.cdf(x)\n>>> ecdfs = np.arange(n+1, dtype=float)/n\n>>> gaps = np.column_stack([cdfs - ecdfs[:n], ecdfs[1:] - cdfs])\n>>> Dn = np.max(gaps)\n>>> Kn = np.sqrt(n) * Dn\n>>> print('Dn=%f, sqrt(n)*Dn=%f' % (Dn, Kn))\nDn=0.058286, sqrt(n)*Dn=1.843153\n>>> print(chr(10).join(['For a sample of size n drawn from a N(0, 1) distribution:',\n... ' the approximate Kolmogorov probability that sqrt(n)*Dn>=%f is %f' % (Kn, kolmogorov(Kn)),\n... ' the approximate Kolmogorov probability that sqrt(n)*Dn<=%f is %f' % (Kn, kstwobign.cdf(Kn))]))\nFor a sample of size n drawn from a N(0, 1) distribution:\n the approximate Kolmogorov probability that sqrt(n)*Dn>=1.843153 is 0.002240\n the approximate Kolmogorov probability that sqrt(n)*Dn<=1.843153 is 0.997760\n\nPlot the Empirical CDF against the target N(0, 1) CDF.\n\n>>> import matplotlib.pyplot as plt\n>>> plt.step(np.concatenate([[-3], x]), ecdfs, where='post', label='Empirical CDF')\n>>> x3 = np.linspace(-3, 3, 100)\n>>> plt.plot(x3, target.cdf(x3), label='CDF for N(0, 1)')\n>>> plt.ylim([0, 1]); plt.grid(True); plt.legend();\n>>> # Add vertical lines marking Dn+ and Dn-\n>>> iminus, iplus = np.argmax(gaps, axis=0)\n>>> plt.vlines([x[iminus]], ecdfs[iminus], cdfs[iminus], color='r', linestyle='dashed', lw=4)\n>>> plt.vlines([x[iplus]], cdfs[iplus], ecdfs[iplus+1], color='r', linestyle='dashed', lw=4)\n>>> plt.show()" - ... - -def kv(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "kv(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkv(v, z)\n\nModified Bessel function of the second kind of real order `v`\n\nReturns the modified Bessel function of the second kind for real order\n`v` at complex `z`.\n\nThese are also sometimes called functions of the third kind, Basset\nfunctions, or Macdonald functions. They are defined as those solutions\nof the modified Bessel equation for which,\n\n.. math::\n K_v(x) \\sim \\sqrt{\\pi/(2x)} \\exp(-x)\n\nas :math:`x \\to \\infty` [3]_.\n\nParameters\n----------\nv : array_like of float\n Order of Bessel functions\nz : array_like of complex\n Argument at which to evaluate the Bessel functions\n\nReturns\n-------\nout : ndarray\n The results. Note that input must be of complex type to get complex\n output, e.g. ``kv(3, -2+0j)`` instead of ``kv(3, -2)``.\n\nNotes\n-----\nWrapper for AMOS [1]_ routine `zbesk`. For a discussion of the\nalgorithm used, see [2]_ and the references therein.\n\nSee Also\n--------\nkve : This function with leading exponential behavior stripped off.\nkvp : Derivative of this function\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/\n.. [2] Donald E. Amos, \"Algorithm 644: A portable package for Bessel\n functions of a complex argument and nonnegative order\", ACM\n TOMS Vol. 12 Issue 3, Sept. 1986, p. 265\n.. [3] NIST Digital Library of Mathematical Functions,\n Eq. 10.25.E3. https://dlmf.nist.gov/10.25.E3\n\nExamples\n--------\nPlot the function of several orders for real input:\n\n>>> from scipy.special import kv\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(0, 5, 1000)\n>>> for N in np.linspace(0, 6, 5):\n... plt.plot(x, kv(N, x), label='$K_{{{}}}(x)$'.format(N))\n>>> plt.ylim(0, 10)\n>>> plt.legend()\n>>> plt.title(r'Modified Bessel function of the second kind $K_\\nu(x)$')\n>>> plt.show()\n\nCalculate for a single value at multiple orders:\n\n>>> kv([4, 4.5, 5], 1+2j)\narray([ 0.1992+2.3892j, 2.3493+3.6j , 7.2827+3.8104j])" - ... - -def kve(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "kve(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nkve(v, z)\n\nExponentially scaled modified Bessel function of the second kind.\n\nReturns the exponentially scaled, modified Bessel function of the\nsecond kind (sometimes called the third kind) for real order `v` at\ncomplex `z`::\n\n kve(v, z) = kv(v, z) * exp(z)\n\nParameters\n----------\nv : array_like of float\n Order of Bessel functions\nz : array_like of complex\n Argument at which to evaluate the Bessel functions\n\nReturns\n-------\nout : ndarray\n The exponentially scaled modified Bessel function of the second kind.\n\nNotes\n-----\nWrapper for AMOS [1]_ routine `zbesk`. For a discussion of the\nalgorithm used, see [2]_ and the references therein.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/\n.. [2] Donald E. Amos, \"Algorithm 644: A portable package for Bessel\n functions of a complex argument and nonnegative order\", ACM\n TOMS Vol. 12 Issue 3, Sept. 1986, p. 265" - ... - -def log1p(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "log1p(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nlog1p(x, out=None)\n\nCalculates log(1 + x) for use when `x` is near zero.\n\nParameters\n----------\nx : array_like\n Real or complex valued input.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of ``log(1 + x)``.\n\nSee Also\n--------\nexpm1, cosm1\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is more accurate than using ``log(1 + x)`` directly for ``x``\nnear 0. Note that in the below example ``1 + 1e-17 == 1`` to\ndouble precision.\n\n>>> sc.log1p(1e-17)\n1e-17\n>>> np.log(1 + 1e-17)\n0.0" - ... - -def log_ndtr(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "log_ndtr(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nlog_ndtr(x)\n\nLogarithm of Gaussian cumulative distribution function.\n\nReturns the log of the area under the standard Gaussian probability\ndensity function, integrated from minus infinity to `x`::\n\n log(1/sqrt(2*pi) * integral(exp(-t**2 / 2), t=-inf..x))\n\nParameters\n----------\nx : array_like, real or complex\n Argument\n\nReturns\n-------\nndarray\n The value of the log of the normal CDF evaluated at `x`\n\nSee Also\n--------\nerf\nerfc\nscipy.stats.norm\nndtr" - ... - -def loggamma(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "loggamma(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nloggamma(z, out=None)\n\nPrincipal branch of the logarithm of the gamma function.\n\nDefined to be :math:`\\log(\\Gamma(x))` for :math:`x > 0` and\nextended to the complex plane by analytic continuation. The\nfunction has a single branch cut on the negative real axis.\n\n.. versionadded:: 0.18.0\n\nParameters\n----------\nz : array-like\n Values in the complex plain at which to compute ``loggamma``\nout : ndarray, optional\n Output array for computed values of ``loggamma``\n\nReturns\n-------\nloggamma : ndarray\n Values of ``loggamma`` at z.\n\nNotes\n-----\nIt is not generally true that :math:`\\log\\Gamma(z) =\n\\log(\\Gamma(z))`, though the real parts of the functions do\nagree. The benefit of not defining `loggamma` as\n:math:`\\log(\\Gamma(z))` is that the latter function has a\ncomplicated branch cut structure whereas `loggamma` is analytic\nexcept for on the negative real axis.\n\nThe identities\n\n.. math::\n \\exp(\\log\\Gamma(z)) &= \\Gamma(z) \\\\\n \\log\\Gamma(z + 1) &= \\log(z) + \\log\\Gamma(z)\n\nmake `loggamma` useful for working in complex logspace.\n\nOn the real line `loggamma` is related to `gammaln` via\n``exp(loggamma(x + 0j)) = gammasgn(x)*exp(gammaln(x))``, up to\nrounding error.\n\nThe implementation here is based on [hare1997]_.\n\nSee also\n--------\ngammaln : logarithm of the absolute value of the gamma function\ngammasgn : sign of the gamma function\n\nReferences\n----------\n.. [hare1997] D.E.G. Hare,\n *Computing the Principal Branch of log-Gamma*,\n Journal of Algorithms, Volume 25, Issue 2, November 1997, pages 221-236." - ... - -def logit(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "logit(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nlogit(x)\n\nLogit ufunc for ndarrays.\n\nThe logit function is defined as logit(p) = log(p/(1-p)).\nNote that logit(0) = -inf, logit(1) = inf, and logit(p)\nfor p<0 or p>1 yields nan.\n\nParameters\n----------\nx : ndarray\n The ndarray to apply logit to element-wise.\n\nReturns\n-------\nout : ndarray\n An ndarray of the same shape as x. Its entries\n are logit of the corresponding entry of x.\n\nSee Also\n--------\nexpit\n\nNotes\n-----\nAs a ufunc logit takes a number of optional\nkeyword arguments. For more information\nsee `ufuncs `_\n\n.. versionadded:: 0.10.0\n\nExamples\n--------\n>>> from scipy.special import logit, expit\n\n>>> logit([0, 0.25, 0.5, 0.75, 1])\narray([ -inf, -1.09861229, 0. , 1.09861229, inf])\n\n`expit` is the inverse of `logit`:\n\n>>> expit(logit([0.1, 0.75, 0.999]))\narray([ 0.1 , 0.75 , 0.999])\n\nPlot logit(x) for x in [0, 1]:\n\n>>> import matplotlib.pyplot as plt\n>>> x = np.linspace(0, 1, 501)\n>>> y = logit(x)\n>>> plt.plot(x, y)\n>>> plt.grid()\n>>> plt.ylim(-6, 6)\n>>> plt.xlabel('x')\n>>> plt.title('logit(x)')\n>>> plt.show()" - ... - -def lpmv( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "lpmv(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nlpmv(m, v, x)\n\nAssociated Legendre function of integer order and real degree.\n\nDefined as\n\n.. math::\n\n P_v^m = (-1)^m (1 - x^2)^{m/2} \\frac{d^m}{dx^m} P_v(x)\n\nwhere\n\n.. math::\n\n P_v = \\sum_{k = 0}^\\infty \\frac{(-v)_k (v + 1)_k}{(k!)^2}\n \\left(\\frac{1 - x}{2}\\right)^k\n\nis the Legendre function of the first kind. Here :math:`(\\cdot)_k`\nis the Pochhammer symbol; see `poch`.\n\nParameters\n----------\nm : array_like\n Order (int or float). If passed a float not equal to an\n integer the function returns NaN.\nv : array_like\n Degree (float).\nx : array_like\n Argument (float). Must have ``|x| <= 1``.\n\nReturns\n-------\npmv : ndarray\n Value of the associated Legendre function.\n\nSee Also\n--------\nlpmn : Compute the associated Legendre function for all orders\n ``0, ..., m`` and degrees ``0, ..., n``.\nclpmn : Compute the associated Legendre function at complex\n arguments.\n\nNotes\n-----\nNote that this implementation includes the Condon-Shortley phase.\n\nReferences\n----------\n.. [1] Zhang, Jin, \"Computation of Special Functions\", John Wiley\n and Sons, Inc, 1996." - ... - -def mathieu_a( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "mathieu_a(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmathieu_a(m, q)\n\nCharacteristic value of even Mathieu functions\n\nReturns the characteristic value for the even solution,\n``ce_m(z, q)``, of Mathieu's equation." - ... - -def mathieu_b( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "mathieu_b(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmathieu_b(m, q)\n\nCharacteristic value of odd Mathieu functions\n\nReturns the characteristic value for the odd solution,\n``se_m(z, q)``, of Mathieu's equation." - ... - -def mathieu_cem( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "mathieu_cem(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmathieu_cem(m, q, x)\n\nEven Mathieu function and its derivative\n\nReturns the even Mathieu function, ``ce_m(x, q)``, of order `m` and\nparameter `q` evaluated at `x` (given in degrees). Also returns the\nderivative with respect to `x` of ce_m(x, q)\n\nParameters\n----------\nm\n Order of the function\nq\n Parameter of the function\nx\n Argument of the function, *given in degrees, not radians*\n\nReturns\n-------\ny\n Value of the function\nyp\n Value of the derivative vs x" - ... - -def mathieu_modcem1( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "mathieu_modcem1(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmathieu_modcem1(m, q, x)\n\nEven modified Mathieu function of the first kind and its derivative\n\nEvaluates the even modified Mathieu function of the first kind,\n``Mc1m(x, q)``, and its derivative at `x` for order `m` and parameter\n`q`.\n\nReturns\n-------\ny\n Value of the function\nyp\n Value of the derivative vs x" - ... - -def mathieu_modcem2( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "mathieu_modcem2(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmathieu_modcem2(m, q, x)\n\nEven modified Mathieu function of the second kind and its derivative\n\nEvaluates the even modified Mathieu function of the second kind,\nMc2m(x, q), and its derivative at `x` (given in degrees) for order `m`\nand parameter `q`.\n\nReturns\n-------\ny\n Value of the function\nyp\n Value of the derivative vs x" - ... - -def mathieu_modsem1( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "mathieu_modsem1(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmathieu_modsem1(m, q, x)\n\nOdd modified Mathieu function of the first kind and its derivative\n\nEvaluates the odd modified Mathieu function of the first kind,\nMs1m(x, q), and its derivative at `x` (given in degrees) for order `m`\nand parameter `q`.\n\nReturns\n-------\ny\n Value of the function\nyp\n Value of the derivative vs x" - ... - -def mathieu_modsem2( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "mathieu_modsem2(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmathieu_modsem2(m, q, x)\n\nOdd modified Mathieu function of the second kind and its derivative\n\nEvaluates the odd modified Mathieu function of the second kind,\nMs2m(x, q), and its derivative at `x` (given in degrees) for order `m`\nand parameter q.\n\nReturns\n-------\ny\n Value of the function\nyp\n Value of the derivative vs x" - ... - -def mathieu_sem( - x1, x2, x3, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "mathieu_sem(x1, x2, x3[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmathieu_sem(m, q, x)\n\nOdd Mathieu function and its derivative\n\nReturns the odd Mathieu function, se_m(x, q), of order `m` and\nparameter `q` evaluated at `x` (given in degrees). Also returns the\nderivative with respect to `x` of se_m(x, q).\n\nParameters\n----------\nm\n Order of the function\nq\n Parameter of the function\nx\n Argument of the function, *given in degrees, not radians*.\n\nReturns\n-------\ny\n Value of the function\nyp\n Value of the derivative vs x" - ... - -def modfresnelm( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "modfresnelm(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmodfresnelm(x)\n\nModified Fresnel negative integrals\n\nReturns\n-------\nfm\n Integral ``F_-(x)``: ``integral(exp(-1j*t*t), t=x..inf)``\nkm\n Integral ``K_-(x)``: ``1/sqrt(pi)*exp(1j*(x*x+pi/4))*fp``" - ... - -def modfresnelp( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "modfresnelp(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmodfresnelp(x)\n\nModified Fresnel positive integrals\n\nReturns\n-------\nfp\n Integral ``F_+(x)``: ``integral(exp(1j*t*t), t=x..inf)``\nkp\n Integral ``K_+(x)``: ``1/sqrt(pi)*exp(-1j*(x*x+pi/4))*fp``" - ... - -def modstruve( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "modstruve(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nmodstruve(v, x)\n\nModified Struve function.\n\nReturn the value of the modified Struve function of order `v` at `x`. The\nmodified Struve function is defined as,\n\n.. math::\n L_v(x) = -\\imath \\exp(-\\pi\\imath v/2) H_v(\\imath x),\n\nwhere :math:`H_v` is the Struve function.\n\nParameters\n----------\nv : array_like\n Order of the modified Struve function (float).\nx : array_like\n Argument of the Struve function (float; must be positive unless `v` is\n an integer).\n\nReturns\n-------\nL : ndarray\n Value of the modified Struve function of order `v` at `x`.\n\nNotes\n-----\nThree methods discussed in [1]_ are used to evaluate the function:\n\n- power series\n- expansion in Bessel functions (if :math:`|x| < |v| + 20`)\n- asymptotic large-x expansion (if :math:`x \\geq 0.7v + 12`)\n\nRounding errors are estimated based on the largest terms in the sums, and\nthe result associated with the smallest error is returned.\n\nSee also\n--------\nstruve\n\nReferences\n----------\n.. [1] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/11" - ... - -def nbdtr( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nbdtr(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnbdtr(k, n, p)\n\nNegative binomial cumulative distribution function.\n\nReturns the sum of the terms 0 through `k` of the negative binomial\ndistribution probability mass function,\n\n.. math::\n\n F = \\sum_{j=0}^k {{n + j - 1}\\choose{j}} p^n (1 - p)^j.\n\nIn a sequence of Bernoulli trials with individual success probabilities\n`p`, this is the probability that `k` or fewer failures precede the nth\nsuccess.\n\nParameters\n----------\nk : array_like\n The maximum number of allowed failures (nonnegative int).\nn : array_like\n The target number of successes (positive int).\np : array_like\n Probability of success in a single event (float).\n\nReturns\n-------\nF : ndarray\n The probability of `k` or fewer failures before `n` successes in a\n sequence of events with individual success probability `p`.\n\nSee also\n--------\nnbdtrc\n\nNotes\n-----\nIf floating point values are passed for `k` or `n`, they will be truncated\nto integers.\n\nThe terms are not summed directly; instead the regularized incomplete beta\nfunction is employed, according to the formula,\n\n.. math::\n \\mathrm{nbdtr}(k, n, p) = I_{p}(n, k + 1).\n\nWrapper for the Cephes [1]_ routine `nbdtr`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def nbdtrc( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nbdtrc(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnbdtrc(k, n, p)\n\nNegative binomial survival function.\n\nReturns the sum of the terms `k + 1` to infinity of the negative binomial\ndistribution probability mass function,\n\n.. math::\n\n F = \\sum_{j=k + 1}^\\infty {{n + j - 1}\\choose{j}} p^n (1 - p)^j.\n\nIn a sequence of Bernoulli trials with individual success probabilities\n`p`, this is the probability that more than `k` failures precede the nth\nsuccess.\n\nParameters\n----------\nk : array_like\n The maximum number of allowed failures (nonnegative int).\nn : array_like\n The target number of successes (positive int).\np : array_like\n Probability of success in a single event (float).\n\nReturns\n-------\nF : ndarray\n The probability of `k + 1` or more failures before `n` successes in a\n sequence of events with individual success probability `p`.\n\nNotes\n-----\nIf floating point values are passed for `k` or `n`, they will be truncated\nto integers.\n\nThe terms are not summed directly; instead the regularized incomplete beta\nfunction is employed, according to the formula,\n\n.. math::\n \\mathrm{nbdtrc}(k, n, p) = I_{1 - p}(k + 1, n).\n\nWrapper for the Cephes [1]_ routine `nbdtrc`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def nbdtri( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nbdtri(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnbdtri(k, n, y)\n\nInverse of `nbdtr` vs `p`.\n\nReturns the inverse with respect to the parameter `p` of\n`y = nbdtr(k, n, p)`, the negative binomial cumulative distribution\nfunction.\n\nParameters\n----------\nk : array_like\n The maximum number of allowed failures (nonnegative int).\nn : array_like\n The target number of successes (positive int).\ny : array_like\n The probability of `k` or fewer failures before `n` successes (float).\n\nReturns\n-------\np : ndarray\n Probability of success in a single event (float) such that\n `nbdtr(k, n, p) = y`.\n\nSee also\n--------\nnbdtr : Cumulative distribution function of the negative binomial.\nnbdtrik : Inverse with respect to `k` of `nbdtr(k, n, p)`.\nnbdtrin : Inverse with respect to `n` of `nbdtr(k, n, p)`.\n\nNotes\n-----\nWrapper for the Cephes [1]_ routine `nbdtri`.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def nbdtrik( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nbdtrik(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnbdtrik(y, n, p)\n\nInverse of `nbdtr` vs `k`.\n\nReturns the inverse with respect to the parameter `k` of\n`y = nbdtr(k, n, p)`, the negative binomial cumulative distribution\nfunction.\n\nParameters\n----------\ny : array_like\n The probability of `k` or fewer failures before `n` successes (float).\nn : array_like\n The target number of successes (positive int).\np : array_like\n Probability of success in a single event (float).\n\nReturns\n-------\nk : ndarray\n The maximum number of allowed failures such that `nbdtr(k, n, p) = y`.\n\nSee also\n--------\nnbdtr : Cumulative distribution function of the negative binomial.\nnbdtri : Inverse with respect to `p` of `nbdtr(k, n, p)`.\nnbdtrin : Inverse with respect to `n` of `nbdtr(k, n, p)`.\n\nNotes\n-----\nWrapper for the CDFLIB [1]_ Fortran routine `cdfnbn`.\n\nFormula 26.5.26 of [2]_,\n\n.. math::\n \\sum_{j=k + 1}^\\infty {{n + j - 1}\\choose{j}} p^n (1 - p)^j = I_{1 - p}(k + 1, n),\n\nis used to reduce calculation of the cumulative distribution function to\nthat of a regularized incomplete beta :math:`I`.\n\nComputation of `k` involves a search for a value that produces the desired\nvalue of `y`. The search relies on the monotonicity of `y` with `k`.\n\nReferences\n----------\n.. [1] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters.\n.. [2] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def nbdtrin( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nbdtrin(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnbdtrin(k, y, p)\n\nInverse of `nbdtr` vs `n`.\n\nReturns the inverse with respect to the parameter `n` of\n`y = nbdtr(k, n, p)`, the negative binomial cumulative distribution\nfunction.\n\nParameters\n----------\nk : array_like\n The maximum number of allowed failures (nonnegative int).\ny : array_like\n The probability of `k` or fewer failures before `n` successes (float).\np : array_like\n Probability of success in a single event (float).\n\nReturns\n-------\nn : ndarray\n The number of successes `n` such that `nbdtr(k, n, p) = y`.\n\nSee also\n--------\nnbdtr : Cumulative distribution function of the negative binomial.\nnbdtri : Inverse with respect to `p` of `nbdtr(k, n, p)`.\nnbdtrik : Inverse with respect to `k` of `nbdtr(k, n, p)`.\n\nNotes\n-----\nWrapper for the CDFLIB [1]_ Fortran routine `cdfnbn`.\n\nFormula 26.5.26 of [2]_,\n\n.. math::\n \\sum_{j=k + 1}^\\infty {{n + j - 1}\\choose{j}} p^n (1 - p)^j = I_{1 - p}(k + 1, n),\n\nis used to reduce calculation of the cumulative distribution function to\nthat of a regularized incomplete beta :math:`I`.\n\nComputation of `n` involves a search for a value that produces the desired\nvalue of `y`. The search relies on the monotonicity of `y` with `n`.\n\nReferences\n----------\n.. [1] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters.\n.. [2] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972." - ... - -def ncfdtr( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "ncfdtr(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nncfdtr(dfn, dfd, nc, f)\n\nCumulative distribution function of the non-central F distribution.\n\nThe non-central F describes the distribution of,\n\n.. math::\n Z = \\frac{X/d_n}{Y/d_d}\n\nwhere :math:`X` and :math:`Y` are independently distributed, with\n:math:`X` distributed non-central :math:`\\chi^2` with noncentrality\nparameter `nc` and :math:`d_n` degrees of freedom, and :math:`Y`\ndistributed :math:`\\chi^2` with :math:`d_d` degrees of freedom.\n\nParameters\n----------\ndfn : array_like\n Degrees of freedom of the numerator sum of squares. Range (0, inf).\ndfd : array_like\n Degrees of freedom of the denominator sum of squares. Range (0, inf).\nnc : array_like\n Noncentrality parameter. Should be in range (0, 1e4).\nf : array_like\n Quantiles, i.e. the upper limit of integration.\n\nReturns\n-------\ncdf : float or ndarray\n The calculated CDF. If all inputs are scalar, the return will be a\n float. Otherwise it will be an array.\n\nSee Also\n--------\nncfdtri : Quantile function; inverse of `ncfdtr` with respect to `f`.\nncfdtridfd : Inverse of `ncfdtr` with respect to `dfd`.\nncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.\nncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.\n\nNotes\n-----\nWrapper for the CDFLIB [1]_ Fortran routine `cdffnc`.\n\nThe cumulative distribution function is computed using Formula 26.6.20 of\n[2]_:\n\n.. math::\n F(d_n, d_d, n_c, f) = \\sum_{j=0}^\\infty e^{-n_c/2} \\frac{(n_c/2)^j}{j!} I_{x}(\\frac{d_n}{2} + j, \\frac{d_d}{2}),\n\nwhere :math:`I` is the regularized incomplete beta function, and\n:math:`x = f d_n/(f d_n + d_d)`.\n\nThe computation time required for this routine is proportional to the\nnoncentrality parameter `nc`. Very large values of this parameter can\nconsume immense computer resources. This is why the search range is\nbounded by 10,000.\n\nReferences\n----------\n.. [1] Barry Brown, James Lovato, and Kathy Russell,\n CDFLIB: Library of Fortran Routines for Cumulative Distribution\n Functions, Inverses, and Other Parameters.\n.. [2] Milton Abramowitz and Irene A. Stegun, eds.\n Handbook of Mathematical Functions with Formulas,\n Graphs, and Mathematical Tables. New York: Dover, 1972.\n\nExamples\n--------\n>>> from scipy import special\n>>> from scipy import stats\n>>> import matplotlib.pyplot as plt\n\nPlot the CDF of the non-central F distribution, for nc=0. Compare with the\nF-distribution from scipy.stats:\n\n>>> x = np.linspace(-1, 8, num=500)\n>>> dfn = 3\n>>> dfd = 2\n>>> ncf_stats = stats.f.cdf(x, dfn, dfd)\n>>> ncf_special = special.ncfdtr(dfn, dfd, 0, x)\n\n>>> fig = plt.figure()\n>>> ax = fig.add_subplot(111)\n>>> ax.plot(x, ncf_stats, 'b-', lw=3)\n>>> ax.plot(x, ncf_special, 'r-')\n>>> plt.show()" - ... - -def ncfdtri( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "ncfdtri(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nncfdtri(dfn, dfd, nc, p)\n\nInverse with respect to `f` of the CDF of the non-central F distribution.\n\nSee `ncfdtr` for more details.\n\nParameters\n----------\ndfn : array_like\n Degrees of freedom of the numerator sum of squares. Range (0, inf).\ndfd : array_like\n Degrees of freedom of the denominator sum of squares. Range (0, inf).\nnc : array_like\n Noncentrality parameter. Should be in range (0, 1e4).\np : array_like\n Value of the cumulative distribution function. Must be in the\n range [0, 1].\n\nReturns\n-------\nf : float\n Quantiles, i.e., the upper limit of integration.\n\nSee Also\n--------\nncfdtr : CDF of the non-central F distribution.\nncfdtridfd : Inverse of `ncfdtr` with respect to `dfd`.\nncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.\nncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.\n\nExamples\n--------\n>>> from scipy.special import ncfdtr, ncfdtri\n\nCompute the CDF for several values of `f`:\n\n>>> f = [0.5, 1, 1.5]\n>>> p = ncfdtr(2, 3, 1.5, f)\n>>> p\narray([ 0.20782291, 0.36107392, 0.47345752])\n\nCompute the inverse. We recover the values of `f`, as expected:\n\n>>> ncfdtri(2, 3, 1.5, p)\narray([ 0.5, 1. , 1.5])" - ... - -def ncfdtridfd( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "ncfdtridfd(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nncfdtridfd(dfn, p, nc, f)\n\nCalculate degrees of freedom (denominator) for the noncentral F-distribution.\n\nThis is the inverse with respect to `dfd` of `ncfdtr`.\nSee `ncfdtr` for more details.\n\nParameters\n----------\ndfn : array_like\n Degrees of freedom of the numerator sum of squares. Range (0, inf).\np : array_like\n Value of the cumulative distribution function. Must be in the\n range [0, 1].\nnc : array_like\n Noncentrality parameter. Should be in range (0, 1e4).\nf : array_like\n Quantiles, i.e., the upper limit of integration.\n\nReturns\n-------\ndfd : float\n Degrees of freedom of the denominator sum of squares.\n\nSee Also\n--------\nncfdtr : CDF of the non-central F distribution.\nncfdtri : Quantile function; inverse of `ncfdtr` with respect to `f`.\nncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.\nncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.\n\nNotes\n-----\nThe value of the cumulative noncentral F distribution is not necessarily\nmonotone in either degrees of freedom. There thus may be two values that\nprovide a given CDF value. This routine assumes monotonicity and will\nfind an arbitrary one of the two values.\n\nExamples\n--------\n>>> from scipy.special import ncfdtr, ncfdtridfd\n\nCompute the CDF for several values of `dfd`:\n\n>>> dfd = [1, 2, 3]\n>>> p = ncfdtr(2, dfd, 0.25, 15)\n>>> p\narray([ 0.8097138 , 0.93020416, 0.96787852])\n\nCompute the inverse. We recover the values of `dfd`, as expected:\n\n>>> ncfdtridfd(2, p, 0.25, 15)\narray([ 1., 2., 3.])" - ... - -def ncfdtridfn( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "ncfdtridfn(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nncfdtridfn(p, dfd, nc, f)\n\nCalculate degrees of freedom (numerator) for the noncentral F-distribution.\n\nThis is the inverse with respect to `dfn` of `ncfdtr`.\nSee `ncfdtr` for more details.\n\nParameters\n----------\np : array_like\n Value of the cumulative distribution function. Must be in the\n range [0, 1].\ndfd : array_like\n Degrees of freedom of the denominator sum of squares. Range (0, inf).\nnc : array_like\n Noncentrality parameter. Should be in range (0, 1e4).\nf : float\n Quantiles, i.e., the upper limit of integration.\n\nReturns\n-------\ndfn : float\n Degrees of freedom of the numerator sum of squares.\n\nSee Also\n--------\nncfdtr : CDF of the non-central F distribution.\nncfdtri : Quantile function; inverse of `ncfdtr` with respect to `f`.\nncfdtridfd : Inverse of `ncfdtr` with respect to `dfd`.\nncfdtrinc : Inverse of `ncfdtr` with respect to `nc`.\n\nNotes\n-----\nThe value of the cumulative noncentral F distribution is not necessarily\nmonotone in either degrees of freedom. There thus may be two values that\nprovide a given CDF value. This routine assumes monotonicity and will\nfind an arbitrary one of the two values.\n\nExamples\n--------\n>>> from scipy.special import ncfdtr, ncfdtridfn\n\nCompute the CDF for several values of `dfn`:\n\n>>> dfn = [1, 2, 3]\n>>> p = ncfdtr(dfn, 2, 0.25, 15)\n>>> p\narray([ 0.92562363, 0.93020416, 0.93188394])\n\nCompute the inverse. We recover the values of `dfn`, as expected:\n\n>>> ncfdtridfn(p, 2, 0.25, 15)\narray([ 1., 2., 3.])" - ... - -def ncfdtrinc( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "ncfdtrinc(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nncfdtrinc(dfn, dfd, p, f)\n\nCalculate non-centrality parameter for non-central F distribution.\n\nThis is the inverse with respect to `nc` of `ncfdtr`.\nSee `ncfdtr` for more details.\n\nParameters\n----------\ndfn : array_like\n Degrees of freedom of the numerator sum of squares. Range (0, inf).\ndfd : array_like\n Degrees of freedom of the denominator sum of squares. Range (0, inf).\np : array_like\n Value of the cumulative distribution function. Must be in the\n range [0, 1].\nf : array_like\n Quantiles, i.e., the upper limit of integration.\n\nReturns\n-------\nnc : float\n Noncentrality parameter.\n\nSee Also\n--------\nncfdtr : CDF of the non-central F distribution.\nncfdtri : Quantile function; inverse of `ncfdtr` with respect to `f`.\nncfdtridfd : Inverse of `ncfdtr` with respect to `dfd`.\nncfdtridfn : Inverse of `ncfdtr` with respect to `dfn`.\n\nExamples\n--------\n>>> from scipy.special import ncfdtr, ncfdtrinc\n\nCompute the CDF for several values of `nc`:\n\n>>> nc = [0.5, 1.5, 2.0]\n>>> p = ncfdtr(2, 3, nc, 15)\n>>> p\narray([ 0.96309246, 0.94327955, 0.93304098])\n\nCompute the inverse. We recover the values of `nc`, as expected:\n\n>>> ncfdtrinc(2, 3, p, 15)\narray([ 0.5, 1.5, 2. ])" - ... - -def nctdtr( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nctdtr(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnctdtr(df, nc, t)\n\nCumulative distribution function of the non-central `t` distribution.\n\nParameters\n----------\ndf : array_like\n Degrees of freedom of the distribution. Should be in range (0, inf).\nnc : array_like\n Noncentrality parameter. Should be in range (-1e6, 1e6).\nt : array_like\n Quantiles, i.e., the upper limit of integration.\n\nReturns\n-------\ncdf : float or ndarray\n The calculated CDF. If all inputs are scalar, the return will be a\n float. Otherwise, it will be an array.\n\nSee Also\n--------\nnctdtrit : Inverse CDF (iCDF) of the non-central t distribution.\nnctdtridf : Calculate degrees of freedom, given CDF and iCDF values.\nnctdtrinc : Calculate non-centrality parameter, given CDF iCDF values.\n\nExamples\n--------\n>>> from scipy import special\n>>> from scipy import stats\n>>> import matplotlib.pyplot as plt\n\nPlot the CDF of the non-central t distribution, for nc=0. Compare with the\nt-distribution from scipy.stats:\n\n>>> x = np.linspace(-5, 5, num=500)\n>>> df = 3\n>>> nct_stats = stats.t.cdf(x, df)\n>>> nct_special = special.nctdtr(df, 0, x)\n\n>>> fig = plt.figure()\n>>> ax = fig.add_subplot(111)\n>>> ax.plot(x, nct_stats, 'b-', lw=3)\n>>> ax.plot(x, nct_special, 'r-')\n>>> plt.show()" - ... - -def nctdtridf( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nctdtridf(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnctdtridf(p, nc, t)\n\nCalculate degrees of freedom for non-central t distribution.\n\nSee `nctdtr` for more details.\n\nParameters\n----------\np : array_like\n CDF values, in range (0, 1].\nnc : array_like\n Noncentrality parameter. Should be in range (-1e6, 1e6).\nt : array_like\n Quantiles, i.e., the upper limit of integration." - ... - -def nctdtrinc( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nctdtrinc(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnctdtrinc(df, p, t)\n\nCalculate non-centrality parameter for non-central t distribution.\n\nSee `nctdtr` for more details.\n\nParameters\n----------\ndf : array_like\n Degrees of freedom of the distribution. Should be in range (0, inf).\np : array_like\n CDF values, in range (0, 1].\nt : array_like\n Quantiles, i.e., the upper limit of integration." - ... - -def nctdtrit( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nctdtrit(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnctdtrit(df, nc, p)\n\nInverse cumulative distribution function of the non-central t distribution.\n\nSee `nctdtr` for more details.\n\nParameters\n----------\ndf : array_like\n Degrees of freedom of the distribution. Should be in range (0, inf).\nnc : array_like\n Noncentrality parameter. Should be in range (-1e6, 1e6).\np : array_like\n CDF values, in range (0, 1]." - ... - -def ndtr(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "ndtr(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nndtr(x)\n\nGaussian cumulative distribution function.\n\nReturns the area under the standard Gaussian probability\ndensity function, integrated from minus infinity to `x`\n\n.. math::\n\n \\frac{1}{\\sqrt{2\\pi}} \\int_{-\\infty}^x \\exp(-t^2/2) dt\n\nParameters\n----------\nx : array_like, real or complex\n Argument\n\nReturns\n-------\nndarray\n The value of the normal CDF evaluated at `x`\n\nSee Also\n--------\nerf\nerfc\nscipy.stats.norm\nlog_ndtr" - ... - -def ndtri(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "ndtri(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nndtri(y)\n\nInverse of `ndtr` vs x\n\nReturns the argument x for which the area under the Gaussian\nprobability density function (integrated from minus infinity to `x`)\nis equal to y." - ... - -def nrdtrimn( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nrdtrimn(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnrdtrimn(p, x, std)\n\nCalculate mean of normal distribution given other params.\n\nParameters\n----------\np : array_like\n CDF values, in range (0, 1].\nx : array_like\n Quantiles, i.e. the upper limit of integration.\nstd : array_like\n Standard deviation.\n\nReturns\n-------\nmn : float or ndarray\n The mean of the normal distribution.\n\nSee Also\n--------\nnrdtrimn, ndtr" - ... - -def nrdtrisd( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "nrdtrisd(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nnrdtrisd(p, x, mn)\n\nCalculate standard deviation of normal distribution given other params.\n\nParameters\n----------\np : array_like\n CDF values, in range (0, 1].\nx : array_like\n Quantiles, i.e. the upper limit of integration.\nmn : float or ndarray\n The mean of the normal distribution.\n\nReturns\n-------\nstd : array_like\n Standard deviation.\n\nSee Also\n--------\nndtr" - ... - -def obl_ang1( - x1, - x2, - x3, - x4, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "obl_ang1(x1, x2, x3, x4[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nobl_ang1(m, n, c, x)\n\nOblate spheroidal angular function of the first kind and its derivative\n\nComputes the oblate spheroidal angular function of the first kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def obl_ang1_cv( - x1, - x2, - x3, - x4, - x5, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "obl_ang1_cv(x1, x2, x3, x4, x5[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nobl_ang1_cv(m, n, c, cv, x)\n\nOblate spheroidal angular function obl_ang1 for precomputed characteristic value\n\nComputes the oblate spheroidal angular function of the first kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\npre-computed characteristic value.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def obl_cv( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "obl_cv(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nobl_cv(m, n, c)\n\nCharacteristic value of oblate spheroidal function\n\nComputes the characteristic value of oblate spheroidal wave\nfunctions of order `m`, `n` (n>=m) and spheroidal parameter `c`." - ... - -def obl_rad1( - x1, - x2, - x3, - x4, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "obl_rad1(x1, x2, x3, x4[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nobl_rad1(m, n, c, x)\n\nOblate spheroidal radial function of the first kind and its derivative\n\nComputes the oblate spheroidal radial function of the first kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def obl_rad1_cv( - x1, - x2, - x3, - x4, - x5, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "obl_rad1_cv(x1, x2, x3, x4, x5[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nobl_rad1_cv(m, n, c, cv, x)\n\nOblate spheroidal radial function obl_rad1 for precomputed characteristic value\n\nComputes the oblate spheroidal radial function of the first kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\npre-computed characteristic value.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def obl_rad2( - x1, - x2, - x3, - x4, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "obl_rad2(x1, x2, x3, x4[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nobl_rad2(m, n, c, x)\n\nOblate spheroidal radial function of the second kind and its derivative.\n\nComputes the oblate spheroidal radial function of the second kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def obl_rad2_cv( - x1, - x2, - x3, - x4, - x5, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "obl_rad2_cv(x1, x2, x3, x4, x5[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nobl_rad2_cv(m, n, c, cv, x)\n\nOblate spheroidal radial function obl_rad2 for precomputed characteristic value\n\nComputes the oblate spheroidal radial function of the second kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\npre-computed characteristic value.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def owens_t(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "owens_t(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nowens_t(h, a)\n\nOwen's T Function.\n\nThe function T(h, a) gives the probability of the event\n(X > h and 0 < Y < a * X) where X and Y are independent\nstandard normal random variables.\n\nParameters\n----------\nh: array_like\n Input value.\na: array_like\n Input value.\n\nReturns\n-------\nt: scalar or ndarray\n Probability of the event (X > h and 0 < Y < a * X),\n where X and Y are independent standard normal random variables.\n\nExamples\n--------\n>>> from scipy import special\n>>> a = 3.5\n>>> h = 0.78\n>>> special.owens_t(h, a)\n0.10877216734852274\n\nReferences\n----------\n.. [1] M. Patefield and D. Tandy, \"Fast and accurate calculation of\n Owen's T Function\", Statistical Software vol. 5, pp. 1-25, 2000." - ... - -def pbdv( - x1, x2, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "pbdv(x1, x2[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npbdv(v, x)\n\nParabolic cylinder function D\n\nReturns (d, dp) the parabolic cylinder function Dv(x) in d and the\nderivative, Dv'(x) in dp.\n\nReturns\n-------\nd\n Value of the function\ndp\n Value of the derivative vs x" - ... - -def pbvv( - x1, x2, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "pbvv(x1, x2[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npbvv(v, x)\n\nParabolic cylinder function V\n\nReturns the parabolic cylinder function Vv(x) in v and the\nderivative, Vv'(x) in vp.\n\nReturns\n-------\nv\n Value of the function\nvp\n Value of the derivative vs x" - ... - -def pbwa( - x1, x2, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "pbwa(x1, x2[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npbwa(a, x)\n\nParabolic cylinder function W.\n\nThe function is a particular solution to the differential equation\n\n.. math::\n\n y'' + \\left(\\frac{1}{4}x^2 - a\\right)y = 0,\n\nfor a full definition see section 12.14 in [1]_.\n\nParameters\n----------\na : array_like\n Real parameter\nx : array_like\n Real argument\n\nReturns\n-------\nw : scalar or ndarray\n Value of the function\nwp : scalar or ndarray\n Value of the derivative in x\n\nNotes\n-----\nThe function is a wrapper for a Fortran routine by Zhang and Jin\n[2]_. The implementation is accurate only for ``|a|, |x| < 5`` and\nreturns NaN outside that range.\n\nReferences\n----------\n.. [1] Digital Library of Mathematical Functions, 14.30.\n https://dlmf.nist.gov/14.30\n.. [2] Zhang, Shanjie and Jin, Jianming. \"Computation of Special\n Functions\", John Wiley and Sons, 1996.\n https://people.sc.fsu.edu/~jburkardt/f_src/special_functions/special_functions.html" - ... - -def pdtr(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "pdtr(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npdtr(k, m, out=None)\n\nPoisson cumulative distribution function.\n\nDefined as the probability that a Poisson-distributed random\nvariable with event rate :math:`m` is less than or equal to\n:math:`k`. More concretely, this works out to be [1]_\n\n.. math::\n\n \\exp(-m) \\sum_{j = 0}^{\\lfloor{k}\\rfloor} \\frac{m^j}{m!}.\n\nParameters\n----------\nk : array_like\n Nonnegative real argument\nm : array_like\n Nonnegative real shape parameter\nout : ndarray\n Optional output array for the function results\n\nSee Also\n--------\npdtrc : Poisson survival function\npdtrik : inverse of `pdtr` with respect to `k`\npdtri : inverse of `pdtr` with respect to `m`\n\nReturns\n-------\nscalar or ndarray\n Values of the Poisson cumulative distribution function\n\nReferences\n----------\n.. [1] https://en.wikipedia.org/wiki/Poisson_distribution\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is a cumulative distribution function, so it converges to 1\nmonotonically as `k` goes to infinity.\n\n>>> sc.pdtr([1, 10, 100, np.inf], 1)\narray([0.73575888, 0.99999999, 1. , 1. ])\n\nIt is discontinuous at integers and constant between integers.\n\n>>> sc.pdtr([1, 1.5, 1.9, 2], 1)\narray([0.73575888, 0.73575888, 0.73575888, 0.9196986 ])" - ... - -def pdtrc(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "pdtrc(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npdtrc(k, m)\n\nPoisson survival function\n\nReturns the sum of the terms from k+1 to infinity of the Poisson\ndistribution: sum(exp(-m) * m**j / j!, j=k+1..inf) = gammainc(\nk+1, m). Arguments must both be non-negative doubles." - ... - -def pdtri(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "pdtri(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npdtri(k, y)\n\nInverse to `pdtr` vs m\n\nReturns the Poisson variable `m` such that the sum from 0 to `k` of\nthe Poisson density is equal to the given probability `y`:\ncalculated by gammaincinv(k+1, y). `k` must be a nonnegative\ninteger and `y` between 0 and 1." - ... - -def pdtrik(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "pdtrik(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npdtrik(p, m)\n\nInverse to `pdtr` vs k\n\nReturns the quantile k such that ``pdtr(k, m) = p``" - ... - -def poch(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "poch(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npoch(z, m)\n\nPochhammer symbol.\n\nThe Pochhammer symbol (rising factorial) is defined as\n\n.. math::\n\n (z)_m = \\frac{\\Gamma(z + m)}{\\Gamma(z)}\n\nFor positive integer `m` it reads\n\n.. math::\n\n (z)_m = z (z + 1) ... (z + m - 1)\n\nSee [dlmf]_ for more details.\n\nParameters\n----------\nz, m : array_like\n Real-valued arguments.\n\nReturns\n-------\nscalar or ndarray\n The value of the function.\n\nReferences\n----------\n.. [dlmf] Nist, Digital Library of Mathematical Functions\n https://dlmf.nist.gov/5.2#iii\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is 1 when m is 0.\n\n>>> sc.poch([1, 2, 3, 4], 0)\narray([1., 1., 1., 1.])\n\nFor z equal to 1 it reduces to the factorial function.\n\n>>> sc.poch(1, 5)\n120.0\n>>> 1 * 2 * 3 * 4 * 5\n120\n\nIt can be expressed in terms of the gamma function.\n\n>>> z, m = 3.7, 2.1\n>>> sc.poch(z, m)\n20.529581933776953\n>>> sc.gamma(z + m) / sc.gamma(z)\n20.52958193377696" - ... - -def pro_ang1( - x1, - x2, - x3, - x4, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "pro_ang1(x1, x2, x3, x4[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npro_ang1(m, n, c, x)\n\nProlate spheroidal angular function of the first kind and its derivative\n\nComputes the prolate spheroidal angular function of the first kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def pro_ang1_cv( - x1, - x2, - x3, - x4, - x5, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "pro_ang1_cv(x1, x2, x3, x4, x5[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npro_ang1_cv(m, n, c, cv, x)\n\nProlate spheroidal angular function pro_ang1 for precomputed characteristic value\n\nComputes the prolate spheroidal angular function of the first kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\npre-computed characteristic value.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def pro_cv( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "pro_cv(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npro_cv(m, n, c)\n\nCharacteristic value of prolate spheroidal function\n\nComputes the characteristic value of prolate spheroidal wave\nfunctions of order `m`, `n` (n>=m) and spheroidal parameter `c`." - ... - -def pro_rad1( - x1, - x2, - x3, - x4, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "pro_rad1(x1, x2, x3, x4[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npro_rad1(m, n, c, x)\n\nProlate spheroidal radial function of the first kind and its derivative\n\nComputes the prolate spheroidal radial function of the first kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def pro_rad1_cv( - x1, - x2, - x3, - x4, - x5, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "pro_rad1_cv(x1, x2, x3, x4, x5[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npro_rad1_cv(m, n, c, cv, x)\n\nProlate spheroidal radial function pro_rad1 for precomputed characteristic value\n\nComputes the prolate spheroidal radial function of the first kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\npre-computed characteristic value.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def pro_rad2( - x1, - x2, - x3, - x4, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "pro_rad2(x1, x2, x3, x4[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npro_rad2(m, n, c, x)\n\nProlate spheroidal radial function of the second kind and its derivative\n\nComputes the prolate spheroidal radial function of the second kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def pro_rad2_cv( - x1, - x2, - x3, - x4, - x5, - out1=..., - out2=..., - out=..., - *, - where=..., - casting=..., - order=..., - dtype=..., - subok=..., - signature=..., - extobj=..., -) -> typing.Any: - "pro_rad2_cv(x1, x2, x3, x4, x5[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npro_rad2_cv(m, n, c, cv, x)\n\nProlate spheroidal radial function pro_rad2 for precomputed characteristic value\n\nComputes the prolate spheroidal radial function of the second kind\nand its derivative (with respect to `x`) for mode parameters m>=0\nand n>=m, spheroidal parameter `c` and ``|x| < 1.0``. Requires\npre-computed characteristic value.\n\nReturns\n-------\ns\n Value of the function\nsp\n Value of the derivative vs x" - ... - -def pseudo_huber( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "pseudo_huber(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npseudo_huber(delta, r)\n\nPseudo-Huber loss function.\n\n.. math:: \\mathrm{pseudo\\_huber}(\\delta, r) = \\delta^2 \\left( \\sqrt{ 1 + \\left( \\frac{r}{\\delta} \\right)^2 } - 1 \\right)\n\nParameters\n----------\ndelta : ndarray\n Input array, indicating the soft quadratic vs. linear loss changepoint.\nr : ndarray\n Input array, possibly representing residuals.\n\nReturns\n-------\nres : ndarray\n The computed Pseudo-Huber loss function values.\n\nNotes\n-----\nThis function is convex in :math:`r`.\n\n.. versionadded:: 0.15.0" - ... - -def psi(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "psi(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\npsi(z, out=None)\n\nThe digamma function.\n\nThe logarithmic derivative of the gamma function evaluated at ``z``.\n\nParameters\n----------\nz : array_like\n Real or complex argument.\nout : ndarray, optional\n Array for the computed values of ``psi``.\n\nReturns\n-------\ndigamma : ndarray\n Computed values of ``psi``.\n\nNotes\n-----\nFor large values not close to the negative real axis, ``psi`` is\ncomputed using the asymptotic series (5.11.2) from [1]_. For small\narguments not close to the negative real axis, the recurrence\nrelation (5.5.2) from [1]_ is used until the argument is large\nenough to use the asymptotic series. For values close to the\nnegative real axis, the reflection formula (5.5.4) from [1]_ is\nused first. Note that ``psi`` has a family of zeros on the\nnegative real axis which occur between the poles at nonpositive\nintegers. Around the zeros the reflection formula suffers from\ncancellation and the implementation loses precision. The sole\npositive zero and the first negative zero, however, are handled\nseparately by precomputing series expansions using [2]_, so the\nfunction should maintain full accuracy around the origin.\n\nReferences\n----------\n.. [1] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/5\n.. [2] Fredrik Johansson and others.\n \"mpmath: a Python library for arbitrary-precision floating-point arithmetic\"\n (Version 0.19) http://mpmath.org/" - ... - -def radian( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "radian(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nradian(d, m, s, out=None)\n\nConvert from degrees to radians.\n\nReturns the angle given in (d)egrees, (m)inutes, and (s)econds in\nradians.\n\nParameters\n----------\nd : array_like\n Degrees, can be real-valued.\nm : array_like\n Minutes, can be real-valued.\ns : array_like\n Seconds, can be real-valued.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Values of the inputs in radians.\n\nExamples\n--------\n>>> import scipy.special as sc\n\nThere are many ways to specify an angle.\n\n>>> sc.radian(90, 0, 0)\n1.5707963267948966\n>>> sc.radian(0, 60 * 90, 0)\n1.5707963267948966\n>>> sc.radian(0, 0, 60**2 * 90)\n1.5707963267948966\n\nThe inputs can be real-valued.\n\n>>> sc.radian(1.5, 0, 0)\n0.02617993877991494\n>>> sc.radian(1, 30, 0)\n0.02617993877991494" - ... - -def rel_entr( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "rel_entr(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nrel_entr(x, y, out=None)\n\nElementwise function for computing relative entropy.\n\n.. math::\n\n \\mathrm{rel\\_entr}(x, y) =\n \\begin{cases}\n x \\log(x / y) & x > 0, y > 0 \\\\\n 0 & x = 0, y \\ge 0 \\\\\n \\infty & \\text{otherwise}\n \\end{cases}\n\nParameters\n----------\nx, y : array_like\n Input arrays\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Relative entropy of the inputs\n\nSee Also\n--------\nentr, kl_div\n\nNotes\n-----\n.. versionadded:: 0.15.0\n\nThis function is jointly convex in x and y.\n\nThe origin of this function is in convex programming; see\n[1]_. Given two discrete probability distributions :math:`p_1,\n\\ldots, p_n` and :math:`q_1, \\ldots, q_n`, to get the relative\nentropy of statistics compute the sum\n\n.. math::\n\n \\sum_{i = 1}^n \\mathrm{rel\\_entr}(p_i, q_i).\n\nSee [2]_ for details.\n\nReferences\n----------\n.. [1] Grant, Boyd, and Ye, \"CVX: Matlab Software for Disciplined Convex\n Programming\", http://cvxr.com/cvx/\n.. [2] Kullback-Leibler divergence,\n https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" - ... - -def rgamma(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "rgamma(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nrgamma(z, out=None)\n\nReciprocal of the gamma function.\n\nDefined as :math:`1 / \\Gamma(z)`, where :math:`\\Gamma` is the\ngamma function. For more on the gamma function see `gamma`.\n\nParameters\n----------\nz : array_like\n Real or complex valued input\nout : ndarray, optional\n Optional output array for the function results\n\nReturns\n-------\nscalar or ndarray\n Function results\n\nNotes\n-----\nThe gamma function has no zeros and has simple poles at\nnonpositive integers, so `rgamma` is an entire function with zeros\nat the nonpositive integers. See the discussion in [dlmf]_ for\nmore details.\n\nSee Also\n--------\ngamma, gammaln, loggamma\n\nReferences\n----------\n.. [dlmf] Nist, Digital Library of Mathematical functions,\n https://dlmf.nist.gov/5.2#i\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is the reciprocal of the gamma function.\n\n>>> sc.rgamma([1, 2, 3, 4])\narray([1. , 1. , 0.5 , 0.16666667])\n>>> 1 / sc.gamma([1, 2, 3, 4])\narray([1. , 1. , 0.5 , 0.16666667])\n\nIt is zero at nonpositive integers.\n\n>>> sc.rgamma([0, -1, -2, -3])\narray([0., 0., 0., 0.])\n\nIt rapidly underflows to zero along the positive real axis.\n\n>>> sc.rgamma([10, 100, 179])\narray([2.75573192e-006, 1.07151029e-156, 0.00000000e+000])" - ... - -def round(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "round(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nround(x, out=None)\n\nRound to the nearest integer.\n\nReturns the nearest integer to `x`. If `x` ends in 0.5 exactly,\nthe nearest even integer is chosen.\n\nParameters\n----------\nx : array_like\n Real valued input.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n The nearest integers to the elements of `x`. The result is of\n floating type, not integer type.\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt rounds to even.\n\n>>> sc.round([0.5, 1.5])\narray([0., 2.])" - ... - -def seterr() -> typing.Any: - "Set how special-function errors are handled.\n\n Parameters\n ----------\n all : {'ignore', 'warn' 'raise'}, optional\n Set treatment for all type of special-function errors at\n once. The options are:\n\n - 'ignore' Take no action when the error occurs\n - 'warn' Print a `SpecialFunctionWarning` when the error\n occurs (via the Python `warnings` module)\n - 'raise' Raise a `SpecialFunctionError` when the error\n occurs.\n\n The default is to not change the current behavior. If\n behaviors for additional categories of special-function errors\n are specified, then ``all`` is applied first, followed by the\n additional categories.\n singular : {'ignore', 'warn', 'raise'}, optional\n Treatment for singularities.\n underflow : {'ignore', 'warn', 'raise'}, optional\n Treatment for underflow.\n overflow : {'ignore', 'warn', 'raise'}, optional\n Treatment for overflow.\n slow : {'ignore', 'warn', 'raise'}, optional\n Treatment for slow convergence.\n loss : {'ignore', 'warn', 'raise'}, optional\n Treatment for loss of accuracy.\n no_result : {'ignore', 'warn', 'raise'}, optional\n Treatment for failing to find a result.\n domain : {'ignore', 'warn', 'raise'}, optional\n Treatment for an invalid argument to a function.\n arg : {'ignore', 'warn', 'raise'}, optional\n Treatment for an invalid parameter to a function.\n other : {'ignore', 'warn', 'raise'}, optional\n Treatment for an unknown error.\n\n Returns\n -------\n olderr : dict\n Dictionary containing the old settings.\n\n See Also\n --------\n geterr : get the current way of handling special-function errors\n errstate : context manager for special-function error handling\n numpy.seterr : similar numpy function for floating-point errors\n\n Examples\n --------\n >>> import scipy.special as sc\n >>> from pytest import raises\n >>> sc.gammaln(0)\n inf\n >>> olderr = sc.seterr(singular='raise')\n >>> with raises(sc.SpecialFunctionError):\n ... sc.gammaln(0)\n ...\n >>> _ = sc.seterr(**olderr)\n\n We can also raise for every category except one.\n\n >>> olderr = sc.seterr(all='raise', singular='ignore')\n >>> sc.gammaln(0)\n inf\n >>> with raises(sc.SpecialFunctionError):\n ... sc.spence(-1)\n ...\n >>> _ = sc.seterr(**olderr)\n\n" - ... - -def shichi( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "shichi(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nshichi(x, out=None)\n\nHyperbolic sine and cosine integrals.\n\nThe hyperbolic sine integral is\n\n.. math::\n\n \\int_0^x \\frac{\\sinh{t}}{t}dt\n\nand the hyperbolic cosine integral is\n\n.. math::\n\n \\gamma + \\log(x) + \\int_0^x \\frac{\\cosh{t} - 1}{t} dt\n\nwhere :math:`\\gamma` is Euler's constant and :math:`\\log` is the\nprinciple branch of the logarithm.\n\nParameters\n----------\nx : array_like\n Real or complex points at which to compute the hyperbolic sine\n and cosine integrals.\n\nReturns\n-------\nsi : ndarray\n Hyperbolic sine integral at ``x``\nci : ndarray\n Hyperbolic cosine integral at ``x``\n\nNotes\n-----\nFor real arguments with ``x < 0``, ``chi`` is the real part of the\nhyperbolic cosine integral. For such points ``chi(x)`` and ``chi(x\n+ 0j)`` differ by a factor of ``1j*pi``.\n\nFor real arguments the function is computed by calling Cephes'\n[1]_ *shichi* routine. For complex arguments the algorithm is based\non Mpmath's [2]_ *shi* and *chi* routines.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/\n.. [2] Fredrik Johansson and others.\n \"mpmath: a Python library for arbitrary-precision floating-point arithmetic\"\n (Version 0.19) http://mpmath.org/" - ... - -def sici( - x, out1=..., out2=..., out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "sici(x[, out1, out2], / [, out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nsici(x, out=None)\n\nSine and cosine integrals.\n\nThe sine integral is\n\n.. math::\n\n \\int_0^x \\frac{\\sin{t}}{t}dt\n\nand the cosine integral is\n\n.. math::\n\n \\gamma + \\log(x) + \\int_0^x \\frac{\\cos{t} - 1}{t}dt\n\nwhere :math:`\\gamma` is Euler's constant and :math:`\\log` is the\nprinciple branch of the logarithm.\n\nParameters\n----------\nx : array_like\n Real or complex points at which to compute the sine and cosine\n integrals.\n\nReturns\n-------\nsi : ndarray\n Sine integral at ``x``\nci : ndarray\n Cosine integral at ``x``\n\nNotes\n-----\nFor real arguments with ``x < 0``, ``ci`` is the real part of the\ncosine integral. For such points ``ci(x)`` and ``ci(x + 0j)``\ndiffer by a factor of ``1j*pi``.\n\nFor real arguments the function is computed by calling Cephes'\n[1]_ *sici* routine. For complex arguments the algorithm is based\non Mpmath's [2]_ *si* and *ci* routines.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/\n.. [2] Fredrik Johansson and others.\n \"mpmath: a Python library for arbitrary-precision floating-point arithmetic\"\n (Version 0.19) http://mpmath.org/" - ... - -def sindg(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "sindg(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nsindg(x, out=None)\n\nSine of the angle `x` given in degrees.\n\nParameters\n----------\nx : array_like\n Angle, given in degrees.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Sine at the input.\n\nSee Also\n--------\ncosdg, tandg, cotdg\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is more accurate than using sine directly.\n\n>>> x = 180 * np.arange(3)\n>>> sc.sindg(x)\narray([ 0., -0., 0.])\n>>> np.sin(x * np.pi / 180)\narray([ 0.0000000e+00, 1.2246468e-16, -2.4492936e-16])" - ... - -def smirnov(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "smirnov(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nsmirnov(n, d)\n\nKolmogorov-Smirnov complementary cumulative distribution function\n\nReturns the exact Kolmogorov-Smirnov complementary cumulative\ndistribution function,(aka the Survival Function) of Dn+ (or Dn-)\nfor a one-sided test of equality between an empirical and a\ntheoretical distribution. It is equal to the probability that the\nmaximum difference between a theoretical distribution and an empirical\none based on `n` samples is greater than d.\n\nParameters\n----------\nn : int\n Number of samples\nd : float array_like\n Deviation between the Empirical CDF (ECDF) and the target CDF.\n\nReturns\n-------\nfloat\n The value(s) of smirnov(n, d), Prob(Dn+ >= d) (Also Prob(Dn- >= d))\n\nNotes\n-----\n`smirnov` is used by `stats.kstest` in the application of the\nKolmogorov-Smirnov Goodness of Fit test. For historial reasons this\nfunction is exposed in `scpy.special`, but the recommended way to achieve\nthe most accurate CDF/SF/PDF/PPF/ISF computations is to use the\n`stats.ksone` distribution.\n\nSee Also\n--------\nsmirnovi : The Inverse Survival Function for the distribution\nscipy.stats.ksone : Provides the functionality as a continuous distribution\nkolmogorov, kolmogi : Functions for the two-sided distribution\n\nExamples\n--------\n>>> from scipy.special import smirnov\n\nShow the probability of a gap at least as big as 0, 0.5 and 1.0 for a sample of size 5\n\n>>> smirnov(5, [0, 0.5, 1.0])\narray([ 1. , 0.056, 0. ])\n\nCompare a sample of size 5 drawn from a source N(0.5, 1) distribution against\na target N(0, 1) CDF.\n\n>>> from scipy.stats import norm\n>>> n = 5\n>>> gendist = norm(0.5, 1) # Normal distribution, mean 0.5, stddev 1\n>>> np.random.seed(seed=233423) # Set the seed for reproducibility\n>>> x = np.sort(gendist.rvs(size=n))\n>>> x\narray([-0.20946287, 0.71688765, 0.95164151, 1.44590852, 3.08880533])\n>>> target = norm(0, 1)\n>>> cdfs = target.cdf(x)\n>>> cdfs\narray([ 0.41704346, 0.76327829, 0.82936059, 0.92589857, 0.99899518])\n# Construct the Empirical CDF and the K-S statistics (Dn+, Dn-, Dn)\n>>> ecdfs = np.arange(n+1, dtype=float)/n\n>>> cols = np.column_stack([x, ecdfs[1:], cdfs, cdfs - ecdfs[:n], ecdfs[1:] - cdfs])\n>>> np.set_printoptions(precision=3)\n>>> cols\narray([[ -2.095e-01, 2.000e-01, 4.170e-01, 4.170e-01, -2.170e-01],\n [ 7.169e-01, 4.000e-01, 7.633e-01, 5.633e-01, -3.633e-01],\n [ 9.516e-01, 6.000e-01, 8.294e-01, 4.294e-01, -2.294e-01],\n [ 1.446e+00, 8.000e-01, 9.259e-01, 3.259e-01, -1.259e-01],\n [ 3.089e+00, 1.000e+00, 9.990e-01, 1.990e-01, 1.005e-03]])\n>>> gaps = cols[:, -2:]\n>>> Dnpm = np.max(gaps, axis=0)\n>>> print('Dn-=%f, Dn+=%f' % (Dnpm[0], Dnpm[1]))\nDn-=0.563278, Dn+=0.001005\n>>> probs = smirnov(n, Dnpm)\n>>> print(chr(10).join(['For a sample of size %d drawn from a N(0, 1) distribution:' % n,\n... ' Smirnov n=%d: Prob(Dn- >= %f) = %.4f' % (n, Dnpm[0], probs[0]),\n... ' Smirnov n=%d: Prob(Dn+ >= %f) = %.4f' % (n, Dnpm[1], probs[1])]))\nFor a sample of size 5 drawn from a N(0, 1) distribution:\n Smirnov n=5: Prob(Dn- >= 0.563278) = 0.0250\n Smirnov n=5: Prob(Dn+ >= 0.001005) = 0.9990\n\nPlot the Empirical CDF against the target N(0, 1) CDF\n\n>>> import matplotlib.pyplot as plt\n>>> plt.step(np.concatenate([[-3], x]), ecdfs, where='post', label='Empirical CDF')\n>>> x3 = np.linspace(-3, 3, 100)\n>>> plt.plot(x3, target.cdf(x3), label='CDF for N(0, 1)')\n>>> plt.ylim([0, 1]); plt.grid(True); plt.legend();\n# Add vertical lines marking Dn+ and Dn-\n>>> iminus, iplus = np.argmax(gaps, axis=0)\n>>> plt.vlines([x[iminus]], ecdfs[iminus], cdfs[iminus], color='r', linestyle='dashed', lw=4)\n>>> plt.vlines([x[iplus]], cdfs[iplus], ecdfs[iplus+1], color='m', linestyle='dashed', lw=4)\n>>> plt.show()" - ... - -def smirnovi( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "smirnovi(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nsmirnovi(n, p)\n\nInverse to `smirnov`\n\nReturns `d` such that ``smirnov(n, d) == p``, the critical value\ncorresponding to `p`.\n\nParameters\n----------\nn : int\n Number of samples\np : float array_like\n Probability\n\nReturns\n-------\nfloat\n The value(s) of smirnovi(n, p), the critical values.\n\nNotes\n-----\n`smirnov` is used by `stats.kstest` in the application of the\nKolmogorov-Smirnov Goodness of Fit test. For historial reasons this\nfunction is exposed in `scpy.special`, but the recommended way to achieve\nthe most accurate CDF/SF/PDF/PPF/ISF computations is to use the\n`stats.ksone` distribution.\n\nSee Also\n--------\nsmirnov : The Survival Function (SF) for the distribution\nscipy.stats.ksone : Provides the functionality as a continuous distribution\nkolmogorov, kolmogi, scipy.stats.kstwobign : Functions for the two-sided distribution" - ... - -def spence(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "spence(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nspence(z, out=None)\n\nSpence's function, also known as the dilogarithm.\n\nIt is defined to be\n\n.. math::\n \\int_0^z \\frac{\\log(t)}{1 - t}dt\n\nfor complex :math:`z`, where the contour of integration is taken\nto avoid the branch cut of the logarithm. Spence's function is\nanalytic everywhere except the negative real axis where it has a\nbranch cut.\n\nParameters\n----------\nz : array_like\n Points at which to evaluate Spence's function\n\nReturns\n-------\ns : ndarray\n Computed values of Spence's function\n\nNotes\n-----\nThere is a different convention which defines Spence's function by\nthe integral\n\n.. math::\n -\\int_0^z \\frac{\\log(1 - t)}{t}dt;\n\nthis is our ``spence(1 - z)``." - ... - -def sph_harm( - x1, x2, x3, x4, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "sph_harm(x1, x2, x3, x4, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nsph_harm(m, n, theta, phi)\n\nCompute spherical harmonics.\n\nThe spherical harmonics are defined as\n\n.. math::\n\n Y^m_n(\\theta,\\phi) = \\sqrt{\\frac{2n+1}{4\\pi} \\frac{(n-m)!}{(n+m)!}}\n e^{i m \\theta} P^m_n(\\cos(\\phi))\n\nwhere :math:`P_n^m` are the associated Legendre functions; see `lpmv`.\n\nParameters\n----------\nm : array_like\n Order of the harmonic (int); must have ``|m| <= n``.\nn : array_like\n Degree of the harmonic (int); must have ``n >= 0``. This is\n often denoted by ``l`` (lower case L) in descriptions of\n spherical harmonics.\ntheta : array_like\n Azimuthal (longitudinal) coordinate; must be in ``[0, 2*pi]``.\nphi : array_like\n Polar (colatitudinal) coordinate; must be in ``[0, pi]``.\n\nReturns\n-------\ny_mn : complex float\n The harmonic :math:`Y^m_n` sampled at ``theta`` and ``phi``.\n\nNotes\n-----\nThere are different conventions for the meanings of the input\narguments ``theta`` and ``phi``. In SciPy ``theta`` is the\nazimuthal angle and ``phi`` is the polar angle. It is common to\nsee the opposite convention, that is, ``theta`` as the polar angle\nand ``phi`` as the azimuthal angle.\n\nNote that SciPy's spherical harmonics include the Condon-Shortley\nphase [2]_ because it is part of `lpmv`.\n\nWith SciPy's conventions, the first several spherical harmonics\nare\n\n.. math::\n\n Y_0^0(\\theta, \\phi) &= \\frac{1}{2} \\sqrt{\\frac{1}{\\pi}} \\\\\n Y_1^{-1}(\\theta, \\phi) &= \\frac{1}{2} \\sqrt{\\frac{3}{2\\pi}}\n e^{-i\\theta} \\sin(\\phi) \\\\\n Y_1^0(\\theta, \\phi) &= \\frac{1}{2} \\sqrt{\\frac{3}{\\pi}}\n \\cos(\\phi) \\\\\n Y_1^1(\\theta, \\phi) &= -\\frac{1}{2} \\sqrt{\\frac{3}{2\\pi}}\n e^{i\\theta} \\sin(\\phi).\n\nReferences\n----------\n.. [1] Digital Library of Mathematical Functions, 14.30.\n https://dlmf.nist.gov/14.30\n.. [2] https://en.wikipedia.org/wiki/Spherical_harmonics#Condon.E2.80.93Shortley_phase" - ... - -def stdtr(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "stdtr(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nstdtr(df, t)\n\nStudent t distribution cumulative distribution function\n\nReturns the integral from minus infinity to t of the Student t\ndistribution with df > 0 degrees of freedom::\n\n gamma((df+1)/2)/(sqrt(df*pi)*gamma(df/2)) *\n integral((1+x**2/df)**(-df/2-1/2), x=-inf..t)" - ... - -def stdtridf( - x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "stdtridf(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nstdtridf(p, t)\n\nInverse of `stdtr` vs df\n\nReturns the argument df such that stdtr(df, t) is equal to `p`." - ... - -def stdtrit(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "stdtrit(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nstdtrit(df, p)\n\nInverse of `stdtr` vs `t`\n\nReturns the argument `t` such that stdtr(df, t) is equal to `p`." - ... - -def struve(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "struve(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nstruve(v, x)\n\nStruve function.\n\nReturn the value of the Struve function of order `v` at `x`. The Struve\nfunction is defined as,\n\n.. math::\n H_v(x) = (z/2)^{v + 1} \\sum_{n=0}^\\infty \\frac{(-1)^n (z/2)^{2n}}{\\Gamma(n + \\frac{3}{2}) \\Gamma(n + v + \\frac{3}{2})},\n\nwhere :math:`\\Gamma` is the gamma function.\n\nParameters\n----------\nv : array_like\n Order of the Struve function (float).\nx : array_like\n Argument of the Struve function (float; must be positive unless `v` is\n an integer).\n\nReturns\n-------\nH : ndarray\n Value of the Struve function of order `v` at `x`.\n\nNotes\n-----\nThree methods discussed in [1]_ are used to evaluate the Struve function:\n\n- power series\n- expansion in Bessel functions (if :math:`|z| < |v| + 20`)\n- asymptotic large-z expansion (if :math:`z \\geq 0.7v + 12`)\n\nRounding errors are estimated based on the largest terms in the sums, and\nthe result associated with the smallest error is returned.\n\nSee also\n--------\nmodstruve\n\nReferences\n----------\n.. [1] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/11" - ... - -def tandg(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "tandg(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ntandg(x, out=None)\n\nTangent of angle `x` given in degrees.\n\nParameters\n----------\nx : array_like\n Angle, given in degrees.\nout : ndarray, optional\n Optional output array for the function results.\n\nReturns\n-------\nscalar or ndarray\n Tangent at the input.\n\nSee Also\n--------\nsindg, cosdg, cotdg\n\nExamples\n--------\n>>> import scipy.special as sc\n\nIt is more accurate than using tangent directly.\n\n>>> x = 180 * np.arange(3)\n>>> sc.tandg(x)\narray([0., 0., 0.])\n>>> np.tan(x * np.pi / 180)\narray([ 0.0000000e+00, -1.2246468e-16, -2.4492936e-16])" - ... - -def tklmbda(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "tklmbda(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ntklmbda(x, lmbda)\n\nTukey-Lambda cumulative distribution function" - ... - -def voigt_profile( - x1, x2, x3, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=... -) -> typing.Any: - "voigt_profile(x1, x2, x3, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nvoigt_profile(x, sigma, gamma, out=None)\n\nVoigt profile.\n\nThe Voigt profile is a convolution of a 1-D Normal distribution with\nstandard deviation ``sigma`` and a 1-D Cauchy distribution with half-width at\nhalf-maximum ``gamma``.\n\nIf ``sigma = 0``, PDF of Cauchy distribution is returned.\nConversely, if ``gamma = 0``, PDF of Normal distribution is returned.\nIf ``sigma = gamma = 0``, the return value is ``Inf`` for ``x = 0``, and ``0`` for all other ``x``.\n\nParameters\n----------\nx : array_like\n Real argument\nsigma : array_like\n The standard deviation of the Normal distribution part\ngamma : array_like\n The half-width at half-maximum of the Cauchy distribution part\nout : ndarray, optional\n Optional output array for the function values\n\nReturns\n-------\nscalar or ndarray\n The Voigt profile at the given arguments\n\nNotes\n-----\nIt can be expressed in terms of Faddeeva function\n\n.. math:: V(x; \\sigma, \\gamma) = \\frac{Re[w(z)]}{\\sigma\\sqrt{2\\pi}},\n.. math:: z = \\frac{x + i\\gamma}{\\sqrt{2}\\sigma}\n\nwhere :math:`w(z)` is the Faddeeva function.\n\nSee Also\n--------\nwofz : Faddeeva function\n\nReferences\n----------\n.. [1] https://en.wikipedia.org/wiki/Voigt_profile" - ... - -def wofz(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "wofz(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nwofz(z)\n\nFaddeeva function\n\nReturns the value of the Faddeeva function for complex argument::\n\n exp(-z**2) * erfc(-i*z)\n\nSee Also\n--------\ndawsn, erf, erfc, erfcx, erfi\n\nReferences\n----------\n.. [1] Steven G. Johnson, Faddeeva W function implementation.\n http://ab-initio.mit.edu/Faddeeva\n\nExamples\n--------\n>>> from scipy import special\n>>> import matplotlib.pyplot as plt\n\n>>> x = np.linspace(-3, 3)\n>>> z = special.wofz(x)\n\n>>> plt.plot(x, z.real, label='wofz(x).real')\n>>> plt.plot(x, z.imag, label='wofz(x).imag')\n>>> plt.xlabel('$x$')\n>>> plt.legend(framealpha=1, shadow=True)\n>>> plt.grid(alpha=0.25)\n>>> plt.show()" - ... - -def wrightomega(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "wrightomega(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nwrightomega(z, out=None)\n\nWright Omega function.\n\nDefined as the solution to\n\n.. math::\n\n \\omega + \\log(\\omega) = z\n\nwhere :math:`\\log` is the principal branch of the complex logarithm.\n\nParameters\n----------\nz : array_like\n Points at which to evaluate the Wright Omega function\n\nReturns\n-------\nomega : ndarray\n Values of the Wright Omega function\n\nNotes\n-----\n.. versionadded:: 0.19.0\n\nThe function can also be defined as\n\n.. math::\n\n \\omega(z) = W_{K(z)}(e^z)\n\nwhere :math:`K(z) = \\lceil (\\Im(z) - \\pi)/(2\\pi) \\rceil` is the\nunwinding number and :math:`W` is the Lambert W function.\n\nThe implementation here is taken from [1]_.\n\nSee Also\n--------\nlambertw : The Lambert W function\n\nReferences\n----------\n.. [1] Lawrence, Corless, and Jeffrey, \"Algorithm 917: Complex\n Double-Precision Evaluation of the Wright :math:`\\omega`\n Function.\" ACM Transactions on Mathematical Software,\n 2012. :doi:`10.1145/2168773.2168779`." - ... - -def xlog1py(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "xlog1py(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nxlog1py(x, y)\n\nCompute ``x*log1p(y)`` so that the result is 0 if ``x = 0``.\n\nParameters\n----------\nx : array_like\n Multiplier\ny : array_like\n Argument\n\nReturns\n-------\nz : array_like\n Computed x*log1p(y)\n\nNotes\n-----\n\n.. versionadded:: 0.13.0" - ... - -def xlogy(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "xlogy(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nxlogy(x, y)\n\nCompute ``x*log(y)`` so that the result is 0 if ``x = 0``.\n\nParameters\n----------\nx : array_like\n Multiplier\ny : array_like\n Argument\n\nReturns\n-------\nz : array_like\n Computed x*log(y)\n\nNotes\n-----\n\n.. versionadded:: 0.13.0" - ... - -def y0(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "y0(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ny0(x)\n\nBessel function of the second kind of order 0.\n\nParameters\n----------\nx : array_like\n Argument (float).\n\nReturns\n-------\nY : ndarray\n Value of the Bessel function of the second kind of order 0 at `x`.\n\nNotes\n-----\n\nThe domain is divided into the intervals [0, 5] and (5, infinity). In the\nfirst interval a rational approximation :math:`R(x)` is employed to\ncompute,\n\n.. math::\n\n Y_0(x) = R(x) + \\frac{2 \\log(x) J_0(x)}{\\pi},\n\nwhere :math:`J_0` is the Bessel function of the first kind of order 0.\n\nIn the second interval, the Hankel asymptotic expansion is employed with\ntwo rational functions of degree 6/6 and 7/7.\n\nThis function is a wrapper for the Cephes [1]_ routine `y0`.\n\nSee also\n--------\nj0\nyv\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def y1(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "y1(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\ny1(x)\n\nBessel function of the second kind of order 1.\n\nParameters\n----------\nx : array_like\n Argument (float).\n\nReturns\n-------\nY : ndarray\n Value of the Bessel function of the second kind of order 1 at `x`.\n\nNotes\n-----\n\nThe domain is divided into the intervals [0, 8] and (8, infinity). In the\nfirst interval a 25 term Chebyshev expansion is used, and computing\n:math:`J_1` (the Bessel function of the first kind) is required. In the\nsecond, the asymptotic trigonometric representation is employed using two\nrational functions of degree 5/5.\n\nThis function is a wrapper for the Cephes [1]_ routine `y1`.\n\nSee also\n--------\nj1\nyn\nyv\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def yn(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "yn(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nyn(n, x)\n\nBessel function of the second kind of integer order and real argument.\n\nParameters\n----------\nn : array_like\n Order (integer).\nz : array_like\n Argument (float).\n\nReturns\n-------\nY : ndarray\n Value of the Bessel function, :math:`Y_n(x)`.\n\nNotes\n-----\nWrapper for the Cephes [1]_ routine `yn`.\n\nThe function is evaluated by forward recurrence on `n`, starting with\nvalues computed by the Cephes routines `y0` and `y1`. If `n = 0` or 1,\nthe routine for `y0` or `y1` is called directly.\n\nSee also\n--------\nyv : For real order and real or complex argument.\n\nReferences\n----------\n.. [1] Cephes Mathematical Functions Library,\n http://www.netlib.org/cephes/" - ... - -def yv(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "yv(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nyv(v, z)\n\nBessel function of the second kind of real order and complex argument.\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nY : ndarray\n Value of the Bessel function of the second kind, :math:`Y_v(x)`.\n\nNotes\n-----\nFor positive `v` values, the computation is carried out using the\nAMOS [1]_ `zbesy` routine, which exploits the connection to the Hankel\nBessel functions :math:`H_v^{(1)}` and :math:`H_v^{(2)}`,\n\n.. math:: Y_v(z) = \\frac{1}{2\\imath} (H_v^{(1)} - H_v^{(2)}).\n\nFor negative `v` values the formula,\n\n.. math:: Y_{-v}(z) = Y_v(z) \\cos(\\pi v) + J_v(z) \\sin(\\pi v)\n\nis used, where :math:`J_v(z)` is the Bessel function of the first kind,\ncomputed using the AMOS routine `zbesj`. Note that the second term is\nexactly zero for integer `v`; to improve accuracy the second term is\nexplicitly omitted for `v` values such that `v = floor(v)`.\n\nSee also\n--------\nyve : :math:`Y_v` with leading exponential behavior stripped off.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def yve(x1, x2, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "yve(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nyve(v, z)\n\nExponentially scaled Bessel function of the second kind of real order.\n\nReturns the exponentially scaled Bessel function of the second\nkind of real order `v` at complex `z`::\n\n yve(v, z) = yv(v, z) * exp(-abs(z.imag))\n\nParameters\n----------\nv : array_like\n Order (float).\nz : array_like\n Argument (float or complex).\n\nReturns\n-------\nY : ndarray\n Value of the exponentially scaled Bessel function.\n\nNotes\n-----\nFor positive `v` values, the computation is carried out using the\nAMOS [1]_ `zbesy` routine, which exploits the connection to the Hankel\nBessel functions :math:`H_v^{(1)}` and :math:`H_v^{(2)}`,\n\n.. math:: Y_v(z) = \\frac{1}{2\\imath} (H_v^{(1)} - H_v^{(2)}).\n\nFor negative `v` values the formula,\n\n.. math:: Y_{-v}(z) = Y_v(z) \\cos(\\pi v) + J_v(z) \\sin(\\pi v)\n\nis used, where :math:`J_v(z)` is the Bessel function of the first kind,\ncomputed using the AMOS routine `zbesj`. Note that the second term is\nexactly zero for integer `v`; to improve accuracy the second term is\nexplicitly omitted for `v` values such that `v = floor(v)`.\n\nReferences\n----------\n.. [1] Donald E. Amos, \"AMOS, A Portable Package for Bessel Functions\n of a Complex Argument and Nonnegative Order\",\n http://netlib.org/amos/" - ... - -def zetac(x, out=..., *, where=..., casting=..., order=..., dtype=..., subok=..., signature=..., extobj=...) -> typing.Any: - "zetac(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])\n\nzetac(x)\n\nRiemann zeta function minus 1.\n\nThis function is defined as\n\n.. math:: \\zeta(x) = \\sum_{k=2}^{\\infty} 1 / k^x,\n\nwhere ``x > 1``. For ``x < 1`` the analytic continuation is\ncomputed. For more information on the Riemann zeta function, see\n[dlmf]_.\n\nParameters\n----------\nx : array_like of float\n Values at which to compute zeta(x) - 1 (must be real).\n\nReturns\n-------\nout : array_like\n Values of zeta(x) - 1.\n\nSee Also\n--------\nzeta\n\nExamples\n--------\n>>> from scipy.special import zetac, zeta\n\nSome special values:\n\n>>> zetac(2), np.pi**2/6 - 1\n(0.64493406684822641, 0.6449340668482264)\n\n>>> zetac(-1), -1.0/12 - 1\n(-1.0833333333333333, -1.0833333333333333)\n\nCompare ``zetac(x)`` to ``zeta(x) - 1`` for large `x`:\n\n>>> zetac(60), zeta(60) - 1\n(8.673617380119933e-19, 0.0)\n\nReferences\n----------\n.. [dlmf] NIST Digital Library of Mathematical Functions\n https://dlmf.nist.gov/25" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/special/_ufuncs_cxx.pyi b/stubs/scipy-stubs/special/_ufuncs_cxx.pyi deleted file mode 100644 index 13f6c138..00000000 --- a/stubs/scipy-stubs/special/_ufuncs_cxx.pyi +++ /dev/null @@ -1,14 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.special._ufuncs_cxx, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/special/cython_special.pyi b/stubs/scipy-stubs/special/cython_special.pyi deleted file mode 100644 index c39e7361..00000000 --- a/stubs/scipy-stubs/special/cython_special.pyi +++ /dev/null @@ -1,837 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.special.cython_special, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict -__test__: dict - -def _airy_pywrap(x0) -> typing.Any: ... -def _airye_pywrap(x0) -> typing.Any: ... -def _bench_airy_D_cy() -> typing.Any: ... -def _bench_airy_D_py() -> typing.Any: ... -def _bench_airy_d_cy() -> typing.Any: ... -def _bench_airy_d_py() -> typing.Any: ... -def _bench_beta_dd_cy() -> typing.Any: ... -def _bench_beta_dd_py() -> typing.Any: ... -def _bench_erf_D_cy() -> typing.Any: ... -def _bench_erf_D_py() -> typing.Any: ... -def _bench_erf_d_cy() -> typing.Any: ... -def _bench_erf_d_py() -> typing.Any: ... -def _bench_exprel_d_cy() -> typing.Any: ... -def _bench_exprel_d_py() -> typing.Any: ... -def _bench_gamma_D_cy() -> typing.Any: ... -def _bench_gamma_D_py() -> typing.Any: ... -def _bench_gamma_d_cy() -> typing.Any: ... -def _bench_gamma_d_py() -> typing.Any: ... -def _bench_jv_dD_cy() -> typing.Any: ... -def _bench_jv_dD_py() -> typing.Any: ... -def _bench_jv_dd_cy() -> typing.Any: ... -def _bench_jv_dd_py() -> typing.Any: ... -def _bench_loggamma_D_cy() -> typing.Any: ... -def _bench_loggamma_D_py() -> typing.Any: ... -def _bench_logit_d_cy() -> typing.Any: ... -def _bench_logit_d_py() -> typing.Any: ... -def _bench_psi_D_cy() -> typing.Any: ... -def _bench_psi_D_py() -> typing.Any: ... -def _bench_psi_d_cy() -> typing.Any: ... -def _bench_psi_d_py() -> typing.Any: ... -def _ellipj_pywrap() -> typing.Any: ... -def _fresnel_pywrap(x0) -> typing.Any: ... -def _it2i0k0_pywrap() -> typing.Any: ... -def _it2j0y0_pywrap() -> typing.Any: ... -def _itairy_pywrap() -> typing.Any: ... -def _iti0k0_pywrap() -> typing.Any: ... -def _itj0y0_pywrap() -> typing.Any: ... -def _kelvin_pywrap() -> typing.Any: ... -def _mathieu_cem_pywrap() -> typing.Any: ... -def _mathieu_modcem1_pywrap() -> typing.Any: ... -def _mathieu_modcem2_pywrap() -> typing.Any: ... -def _mathieu_modsem1_pywrap() -> typing.Any: ... -def _mathieu_modsem2_pywrap() -> typing.Any: ... -def _mathieu_sem_pywrap() -> typing.Any: ... -def _modfresnelm_pywrap() -> typing.Any: ... -def _modfresnelp_pywrap() -> typing.Any: ... -def _obl_ang1_cv_pywrap() -> typing.Any: ... -def _obl_ang1_pywrap() -> typing.Any: ... -def _obl_rad1_cv_pywrap() -> typing.Any: ... -def _obl_rad1_pywrap() -> typing.Any: ... -def _obl_rad2_cv_pywrap() -> typing.Any: ... -def _obl_rad2_pywrap() -> typing.Any: ... -def _pbdv_pywrap() -> typing.Any: ... -def _pbvv_pywrap() -> typing.Any: ... -def _pbwa_pywrap() -> typing.Any: ... -def _pro_ang1_cv_pywrap() -> typing.Any: ... -def _pro_ang1_pywrap() -> typing.Any: ... -def _pro_rad1_cv_pywrap() -> typing.Any: ... -def _pro_rad1_pywrap() -> typing.Any: ... -def _pro_rad2_cv_pywrap() -> typing.Any: ... -def _pro_rad2_pywrap() -> typing.Any: ... -def _shichi_pywrap(x0) -> typing.Any: ... -def _sici_pywrap(x0) -> typing.Any: ... -def agm() -> typing.Any: - "See the documentation for scipy.special.agm" - ... - -def bdtr(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.bdtr" - ... - -def bdtrc(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.bdtrc" - ... - -def bdtri(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.bdtri" - ... - -def bdtrik() -> typing.Any: - "See the documentation for scipy.special.bdtrik" - ... - -def bdtrin() -> typing.Any: - "See the documentation for scipy.special.bdtrin" - ... - -def bei() -> typing.Any: - "See the documentation for scipy.special.bei" - ... - -def beip() -> typing.Any: - "See the documentation for scipy.special.beip" - ... - -def ber() -> typing.Any: - "See the documentation for scipy.special.ber" - ... - -def berp() -> typing.Any: - "See the documentation for scipy.special.berp" - ... - -def besselpoly() -> typing.Any: - "See the documentation for scipy.special.besselpoly" - ... - -def beta() -> typing.Any: - "See the documentation for scipy.special.beta" - ... - -def betainc() -> typing.Any: - "See the documentation for scipy.special.betainc" - ... - -def betaincinv() -> typing.Any: - "See the documentation for scipy.special.betaincinv" - ... - -def betaln() -> typing.Any: - "See the documentation for scipy.special.betaln" - ... - -def binom() -> typing.Any: - "See the documentation for scipy.special.binom" - ... - -def boxcox() -> typing.Any: - "See the documentation for scipy.special.boxcox" - ... - -def boxcox1p() -> typing.Any: - "See the documentation for scipy.special.boxcox1p" - ... - -def btdtr() -> typing.Any: - "See the documentation for scipy.special.btdtr" - ... - -def btdtri() -> typing.Any: - "See the documentation for scipy.special.btdtri" - ... - -def btdtria() -> typing.Any: - "See the documentation for scipy.special.btdtria" - ... - -def btdtrib() -> typing.Any: - "See the documentation for scipy.special.btdtrib" - ... - -def cbrt() -> typing.Any: - "See the documentation for scipy.special.cbrt" - ... - -def chdtr() -> typing.Any: - "See the documentation for scipy.special.chdtr" - ... - -def chdtrc() -> typing.Any: - "See the documentation for scipy.special.chdtrc" - ... - -def chdtri() -> typing.Any: - "See the documentation for scipy.special.chdtri" - ... - -def chdtriv() -> typing.Any: - "See the documentation for scipy.special.chdtriv" - ... - -def chndtr() -> typing.Any: - "See the documentation for scipy.special.chndtr" - ... - -def chndtridf() -> typing.Any: - "See the documentation for scipy.special.chndtridf" - ... - -def chndtrinc() -> typing.Any: - "See the documentation for scipy.special.chndtrinc" - ... - -def chndtrix() -> typing.Any: - "See the documentation for scipy.special.chndtrix" - ... - -def cosdg() -> typing.Any: - "See the documentation for scipy.special.cosdg" - ... - -def cosm1() -> typing.Any: - "See the documentation for scipy.special.cosm1" - ... - -def cotdg() -> typing.Any: - "See the documentation for scipy.special.cotdg" - ... - -def dawsn(x0) -> typing.Any: - "See the documentation for scipy.special.dawsn" - ... - -def ellipe() -> typing.Any: - "See the documentation for scipy.special.ellipe" - ... - -def ellipeinc() -> typing.Any: - "See the documentation for scipy.special.ellipeinc" - ... - -def ellipk() -> typing.Any: - "See the documentation for scipy.special.ellipk" - ... - -def ellipkinc() -> typing.Any: - "See the documentation for scipy.special.ellipkinc" - ... - -def ellipkm1() -> typing.Any: - "See the documentation for scipy.special.ellipkm1" - ... - -def entr() -> typing.Any: - "See the documentation for scipy.special.entr" - ... - -def erf(x0) -> typing.Any: - "See the documentation for scipy.special.erf" - ... - -def erfc(x0) -> typing.Any: - "See the documentation for scipy.special.erfc" - ... - -def erfcinv() -> typing.Any: - "See the documentation for scipy.special.erfcinv" - ... - -def erfcx(x0) -> typing.Any: - "See the documentation for scipy.special.erfcx" - ... - -def erfi(x0) -> typing.Any: - "See the documentation for scipy.special.erfi" - ... - -def erfinv() -> typing.Any: - "See the documentation for scipy.special.erfinv" - ... - -def eval_chebyc(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_chebyc" - ... - -def eval_chebys(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_chebys" - ... - -def eval_chebyt(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_chebyt" - ... - -def eval_chebyu(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_chebyu" - ... - -def eval_gegenbauer(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.eval_gegenbauer" - ... - -def eval_genlaguerre(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.eval_genlaguerre" - ... - -def eval_hermite() -> typing.Any: - "See the documentation for scipy.special.eval_hermite" - ... - -def eval_hermitenorm() -> typing.Any: - "See the documentation for scipy.special.eval_hermitenorm" - ... - -def eval_jacobi(x0, x1, x2, x3) -> typing.Any: - "See the documentation for scipy.special.eval_jacobi" - ... - -def eval_laguerre(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_laguerre" - ... - -def eval_legendre(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_legendre" - ... - -def eval_sh_chebyt(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_sh_chebyt" - ... - -def eval_sh_chebyu(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_sh_chebyu" - ... - -def eval_sh_jacobi(x0, x1, x2, x3) -> typing.Any: - "See the documentation for scipy.special.eval_sh_jacobi" - ... - -def eval_sh_legendre(x0, x1) -> typing.Any: - "See the documentation for scipy.special.eval_sh_legendre" - ... - -def exp1(x0) -> typing.Any: - "See the documentation for scipy.special.exp1" - ... - -def exp10() -> typing.Any: - "See the documentation for scipy.special.exp10" - ... - -def exp2() -> typing.Any: - "See the documentation for scipy.special.exp2" - ... - -def expi(x0) -> typing.Any: - "See the documentation for scipy.special.expi" - ... - -def expit(x0) -> typing.Any: - "See the documentation for scipy.special.expit" - ... - -def expm1(x0) -> typing.Any: - "See the documentation for scipy.special.expm1" - ... - -def expn(x0, x1) -> typing.Any: - "See the documentation for scipy.special.expn" - ... - -def exprel() -> typing.Any: - "See the documentation for scipy.special.exprel" - ... - -def fdtr() -> typing.Any: - "See the documentation for scipy.special.fdtr" - ... - -def fdtrc() -> typing.Any: - "See the documentation for scipy.special.fdtrc" - ... - -def fdtri() -> typing.Any: - "See the documentation for scipy.special.fdtri" - ... - -def fdtridfd() -> typing.Any: - "See the documentation for scipy.special.fdtridfd" - ... - -def gamma(x0) -> typing.Any: - "See the documentation for scipy.special.gamma" - ... - -def gammainc() -> typing.Any: - "See the documentation for scipy.special.gammainc" - ... - -def gammaincc() -> typing.Any: - "See the documentation for scipy.special.gammaincc" - ... - -def gammainccinv() -> typing.Any: - "See the documentation for scipy.special.gammainccinv" - ... - -def gammaincinv() -> typing.Any: - "See the documentation for scipy.special.gammaincinv" - ... - -def gammaln() -> typing.Any: - "See the documentation for scipy.special.gammaln" - ... - -def gammasgn() -> typing.Any: - "See the documentation for scipy.special.gammasgn" - ... - -def gdtr() -> typing.Any: - "See the documentation for scipy.special.gdtr" - ... - -def gdtrc() -> typing.Any: - "See the documentation for scipy.special.gdtrc" - ... - -def gdtria() -> typing.Any: - "See the documentation for scipy.special.gdtria" - ... - -def gdtrib() -> typing.Any: - "See the documentation for scipy.special.gdtrib" - ... - -def gdtrix() -> typing.Any: - "See the documentation for scipy.special.gdtrix" - ... - -def hankel1() -> typing.Any: - "See the documentation for scipy.special.hankel1" - ... - -def hankel1e() -> typing.Any: - "See the documentation for scipy.special.hankel1e" - ... - -def hankel2() -> typing.Any: - "See the documentation for scipy.special.hankel2" - ... - -def hankel2e() -> typing.Any: - "See the documentation for scipy.special.hankel2e" - ... - -def huber() -> typing.Any: - "See the documentation for scipy.special.huber" - ... - -def hyp0f1(x0, x1) -> typing.Any: - "See the documentation for scipy.special.hyp0f1" - ... - -def hyp1f1(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.hyp1f1" - ... - -def hyp2f1(x0, x1, x2, x3) -> typing.Any: - "See the documentation for scipy.special.hyp2f1" - ... - -def hyperu() -> typing.Any: - "See the documentation for scipy.special.hyperu" - ... - -def i0() -> typing.Any: - "See the documentation for scipy.special.i0" - ... - -def i0e() -> typing.Any: - "See the documentation for scipy.special.i0e" - ... - -def i1() -> typing.Any: - "See the documentation for scipy.special.i1" - ... - -def i1e() -> typing.Any: - "See the documentation for scipy.special.i1e" - ... - -def inv_boxcox() -> typing.Any: - "See the documentation for scipy.special.inv_boxcox" - ... - -def inv_boxcox1p() -> typing.Any: - "See the documentation for scipy.special.inv_boxcox1p" - ... - -def it2struve0() -> typing.Any: - "See the documentation for scipy.special.it2struve0" - ... - -def itmodstruve0() -> typing.Any: - "See the documentation for scipy.special.itmodstruve0" - ... - -def itstruve0() -> typing.Any: - "See the documentation for scipy.special.itstruve0" - ... - -def iv(x0, x1) -> typing.Any: - "See the documentation for scipy.special.iv" - ... - -def ive(x0, x1) -> typing.Any: - "See the documentation for scipy.special.ive" - ... - -def j0() -> typing.Any: - "See the documentation for scipy.special.j0" - ... - -def j1() -> typing.Any: - "See the documentation for scipy.special.j1" - ... - -def jv(x0, x1) -> typing.Any: - "See the documentation for scipy.special.jv" - ... - -def jve(x0, x1) -> typing.Any: - "See the documentation for scipy.special.jve" - ... - -def k0() -> typing.Any: - "See the documentation for scipy.special.k0" - ... - -def k0e() -> typing.Any: - "See the documentation for scipy.special.k0e" - ... - -def k1() -> typing.Any: - "See the documentation for scipy.special.k1" - ... - -def k1e() -> typing.Any: - "See the documentation for scipy.special.k1e" - ... - -def kei() -> typing.Any: - "See the documentation for scipy.special.kei" - ... - -def keip() -> typing.Any: - "See the documentation for scipy.special.keip" - ... - -def ker() -> typing.Any: - "See the documentation for scipy.special.ker" - ... - -def kerp() -> typing.Any: - "See the documentation for scipy.special.kerp" - ... - -def kl_div() -> typing.Any: - "See the documentation for scipy.special.kl_div" - ... - -def kn(x0, x1) -> typing.Any: - "See the documentation for scipy.special.kn" - ... - -def kolmogi() -> typing.Any: - "See the documentation for scipy.special.kolmogi" - ... - -def kolmogorov() -> typing.Any: - "See the documentation for scipy.special.kolmogorov" - ... - -def kv(x0, x1) -> typing.Any: - "See the documentation for scipy.special.kv" - ... - -def kve(x0, x1) -> typing.Any: - "See the documentation for scipy.special.kve" - ... - -def log1p(x0) -> typing.Any: - "See the documentation for scipy.special.log1p" - ... - -def log_ndtr(x0) -> typing.Any: - "See the documentation for scipy.special.log_ndtr" - ... - -def loggamma(x0) -> typing.Any: - "See the documentation for scipy.special.loggamma" - ... - -def logit(x0) -> typing.Any: - "See the documentation for scipy.special.logit" - ... - -def lpmv() -> typing.Any: - "See the documentation for scipy.special.lpmv" - ... - -def mathieu_a() -> typing.Any: - "See the documentation for scipy.special.mathieu_a" - ... - -def mathieu_b() -> typing.Any: - "See the documentation for scipy.special.mathieu_b" - ... - -def modstruve() -> typing.Any: - "See the documentation for scipy.special.modstruve" - ... - -def nbdtr(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.nbdtr" - ... - -def nbdtrc(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.nbdtrc" - ... - -def nbdtri(x0, x1, x2) -> typing.Any: - "See the documentation for scipy.special.nbdtri" - ... - -def nbdtrik() -> typing.Any: - "See the documentation for scipy.special.nbdtrik" - ... - -def nbdtrin() -> typing.Any: - "See the documentation for scipy.special.nbdtrin" - ... - -def ncfdtr() -> typing.Any: - "See the documentation for scipy.special.ncfdtr" - ... - -def ncfdtri() -> typing.Any: - "See the documentation for scipy.special.ncfdtri" - ... - -def ncfdtridfd() -> typing.Any: - "See the documentation for scipy.special.ncfdtridfd" - ... - -def ncfdtridfn() -> typing.Any: - "See the documentation for scipy.special.ncfdtridfn" - ... - -def ncfdtrinc() -> typing.Any: - "See the documentation for scipy.special.ncfdtrinc" - ... - -def nctdtr() -> typing.Any: - "See the documentation for scipy.special.nctdtr" - ... - -def nctdtridf() -> typing.Any: - "See the documentation for scipy.special.nctdtridf" - ... - -def nctdtrinc() -> typing.Any: - "See the documentation for scipy.special.nctdtrinc" - ... - -def nctdtrit() -> typing.Any: - "See the documentation for scipy.special.nctdtrit" - ... - -def ndtr(x0) -> typing.Any: - "See the documentation for scipy.special.ndtr" - ... - -def ndtri() -> typing.Any: - "See the documentation for scipy.special.ndtri" - ... - -def nrdtrimn() -> typing.Any: - "See the documentation for scipy.special.nrdtrimn" - ... - -def nrdtrisd() -> typing.Any: - "See the documentation for scipy.special.nrdtrisd" - ... - -def obl_cv() -> typing.Any: - "See the documentation for scipy.special.obl_cv" - ... - -def owens_t() -> typing.Any: - "See the documentation for scipy.special.owens_t" - ... - -def pdtr() -> typing.Any: - "See the documentation for scipy.special.pdtr" - ... - -def pdtrc() -> typing.Any: - "See the documentation for scipy.special.pdtrc" - ... - -def pdtri(x0, x1) -> typing.Any: - "See the documentation for scipy.special.pdtri" - ... - -def pdtrik() -> typing.Any: - "See the documentation for scipy.special.pdtrik" - ... - -def poch() -> typing.Any: - "See the documentation for scipy.special.poch" - ... - -def pro_cv() -> typing.Any: - "See the documentation for scipy.special.pro_cv" - ... - -def pseudo_huber() -> typing.Any: - "See the documentation for scipy.special.pseudo_huber" - ... - -def psi(x0) -> typing.Any: - "See the documentation for scipy.special.psi" - ... - -def radian() -> typing.Any: - "See the documentation for scipy.special.radian" - ... - -def rel_entr() -> typing.Any: - "See the documentation for scipy.special.rel_entr" - ... - -def rgamma(x0) -> typing.Any: - "See the documentation for scipy.special.rgamma" - ... - -def round() -> typing.Any: - "See the documentation for scipy.special.round" - ... - -def sindg() -> typing.Any: - "See the documentation for scipy.special.sindg" - ... - -def smirnov(x0, x1) -> typing.Any: - "See the documentation for scipy.special.smirnov" - ... - -def smirnovi(x0, x1) -> typing.Any: - "See the documentation for scipy.special.smirnovi" - ... - -def spence(x0) -> typing.Any: - "See the documentation for scipy.special.spence" - ... - -def sph_harm(x0, x1, x2, x3) -> typing.Any: - "See the documentation for scipy.special.sph_harm" - ... - -def spherical_in() -> typing.Any: - "See the documentation for scipy.special.spherical_in" - ... - -def spherical_jn() -> typing.Any: - "See the documentation for scipy.special.spherical_jn" - ... - -def spherical_kn() -> typing.Any: - "See the documentation for scipy.special.spherical_kn" - ... - -def spherical_yn() -> typing.Any: - "See the documentation for scipy.special.spherical_yn" - ... - -def stdtr() -> typing.Any: - "See the documentation for scipy.special.stdtr" - ... - -def stdtridf() -> typing.Any: - "See the documentation for scipy.special.stdtridf" - ... - -def stdtrit() -> typing.Any: - "See the documentation for scipy.special.stdtrit" - ... - -def struve() -> typing.Any: - "See the documentation for scipy.special.struve" - ... - -def tandg() -> typing.Any: - "See the documentation for scipy.special.tandg" - ... - -def tklmbda() -> typing.Any: - "See the documentation for scipy.special.tklmbda" - ... - -def voigt_profile() -> typing.Any: - "See the documentation for scipy.special.voigt_profile" - ... - -def wofz() -> typing.Any: - "See the documentation for scipy.special.wofz" - ... - -def wrightomega(x0) -> typing.Any: - "See the documentation for scipy.special.wrightomega" - ... - -def xlog1py(x0, x1) -> typing.Any: - "See the documentation for scipy.special.xlog1py" - ... - -def xlogy(x0, x1) -> typing.Any: - "See the documentation for scipy.special.xlogy" - ... - -def y0() -> typing.Any: - "See the documentation for scipy.special.y0" - ... - -def y1() -> typing.Any: - "See the documentation for scipy.special.y1" - ... - -def yn(x0, x1) -> typing.Any: - "See the documentation for scipy.special.yn" - ... - -def yv(x0, x1) -> typing.Any: - "See the documentation for scipy.special.yv" - ... - -def yve(x0, x1) -> typing.Any: - "See the documentation for scipy.special.yve" - ... - -def zetac() -> typing.Any: - "See the documentation for scipy.special.zetac" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/special/specfun.pyi b/stubs/scipy-stubs/special/specfun.pyi deleted file mode 100644 index 86d8b470..00000000 --- a/stubs/scipy-stubs/special/specfun.pyi +++ /dev/null @@ -1,113 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.special.specfun, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def airyzo(nt, kf=...) -> typing.Any: - "xa,xb,xc,xd = airyzo(nt,[kf])\n\nWrapper for ``airyzo``.\n\nParameters\n----------\nnt : input int\n\nOther Parameters\n----------------\nkf : input int, optional\n Default: 1\n\nReturns\n-------\nxa : rank-1 array('d') with bounds (nt)\nxb : rank-1 array('d') with bounds (nt)\nxc : rank-1 array('d') with bounds (nt)\nxd : rank-1 array('d') with bounds (nt)\n" - ... - -def bernob(n) -> typing.Any: - "bn = bernob(n)\n\nWrapper for ``bernob``.\n\nParameters\n----------\nn : input int\n\nReturns\n-------\nbn : rank-1 array('d') with bounds (n + 1)\n" - ... - -def cerzo(nt) -> typing.Any: - "zo = cerzo(nt)\n\nWrapper for ``cerzo``.\n\nParameters\n----------\nnt : input int\n\nReturns\n-------\nzo : rank-1 array('D') with bounds (nt)\n" - ... - -def clpmn(m, n, x, y, ntype) -> typing.Any: - "cpm,cpd = clpmn(m,n,x,y,ntype)\n\nWrapper for ``clpmn``.\n\nParameters\n----------\nm : input int\nn : input int\nx : input float\ny : input float\nntype : input int\n\nReturns\n-------\ncpm : rank-2 array('D') with bounds (m + 1,n + 1)\ncpd : rank-2 array('D') with bounds (m + 1,n + 1)\n" - ... - -def clpn(n, z) -> typing.Any: - "cpn,cpd = clpn(n,z)\n\nWrapper for ``clpn``.\n\nParameters\n----------\nn : input int\nz : input complex\n\nReturns\n-------\ncpn : rank-1 array('D') with bounds (n + 1)\ncpd : rank-1 array('D') with bounds (n + 1)\n" - ... - -def clqmn(m, n, z) -> typing.Any: - "cqm,cqd = clqmn(m,n,z)\n\nWrapper for ``clqmn``.\n\nParameters\n----------\nm : input int\nn : input int\nz : input complex\n\nReturns\n-------\ncqm : rank-2 array('D') with bounds (mm + 1,n + 1)\ncqd : rank-2 array('D') with bounds (mm + 1,n + 1)\n" - ... - -def clqn(n, z) -> typing.Any: - "cqn,cqd = clqn(n,z)\n\nWrapper for ``clqn``.\n\nParameters\n----------\nn : input int\nz : input complex\n\nReturns\n-------\ncqn : rank-1 array('D') with bounds (n + 1)\ncqd : rank-1 array('D') with bounds (n + 1)\n" - ... - -def cpbdn(n, z) -> typing.Any: - "cpb,cpd = cpbdn(n,z)\n\nWrapper for ``cpbdn``.\n\nParameters\n----------\nn : input int\nz : input complex\n\nReturns\n-------\ncpb : rank-1 array('D') with bounds (abs(n)+2)\ncpd : rank-1 array('D') with bounds (abs(n)+2)\n" - ... - -def cyzo(nt, kf, kc) -> typing.Any: - "zo,zv = cyzo(nt,kf,kc)\n\nWrapper for ``cyzo``.\n\nParameters\n----------\nnt : input int\nkf : input int\nkc : input int\n\nReturns\n-------\nzo : rank-1 array('D') with bounds (nt)\nzv : rank-1 array('D') with bounds (nt)\n" - ... - -def eulerb(n) -> typing.Any: - "en = eulerb(n)\n\nWrapper for ``eulerb``.\n\nParameters\n----------\nn : input int\n\nReturns\n-------\nen : rank-1 array('d') with bounds (n + 1)\n" - ... - -def fcoef(kd, m, q, a) -> typing.Any: - "fc = fcoef(kd,m,q,a)\n\nWrapper for ``fcoef``.\n\nParameters\n----------\nkd : input int\nm : input int\nq : input float\na : input float\n\nReturns\n-------\nfc : rank-1 array('d') with bounds (251)\n" - ... - -def fcszo(kf, nt) -> typing.Any: - "zo = fcszo(kf,nt)\n\nWrapper for ``fcszo``.\n\nParameters\n----------\nkf : input int\nnt : input int\n\nReturns\n-------\nzo : rank-1 array('D') with bounds (nt)\n" - ... - -def jdzo(nt) -> typing.Any: - "n,m,pcode,zo = jdzo(nt)\n\nWrapper for ``jdzo``.\n\nParameters\n----------\nnt : input int\n\nReturns\n-------\nn : rank-1 array('i') with bounds (1400)\nm : rank-1 array('i') with bounds (1400)\npcode : rank-1 array('i') with bounds (1400)\nzo : rank-1 array('d') with bounds (1401)\n" - ... - -def jyzo(n, nt) -> typing.Any: - "rj0,rj1,ry0,ry1 = jyzo(n,nt)\n\nWrapper for ``jyzo``.\n\nParameters\n----------\nn : input int\nnt : input int\n\nReturns\n-------\nrj0 : rank-1 array('d') with bounds (nt)\nrj1 : rank-1 array('d') with bounds (nt)\nry0 : rank-1 array('d') with bounds (nt)\nry1 : rank-1 array('d') with bounds (nt)\n" - ... - -def klvnzo(nt, kd) -> typing.Any: - "zo = klvnzo(nt,kd)\n\nWrapper for ``klvnzo``.\n\nParameters\n----------\nnt : input int\nkd : input int\n\nReturns\n-------\nzo : rank-1 array('d') with bounds (nt)\n" - ... - -def lamn(n, x) -> typing.Any: - "nm,bl,dl = lamn(n,x)\n\nWrapper for ``lamn``.\n\nParameters\n----------\nn : input int\nx : input float\n\nReturns\n-------\nnm : int\nbl : rank-1 array('d') with bounds (n + 1)\ndl : rank-1 array('d') with bounds (n + 1)\n" - ... - -def lamv(v, x) -> typing.Any: - "vm,vl,dl = lamv(v,x)\n\nWrapper for ``lamv``.\n\nParameters\n----------\nv : input float\nx : input float\n\nReturns\n-------\nvm : float\nvl : rank-1 array('d') with bounds ((int)v+1)\ndl : rank-1 array('d') with bounds ((int)v+1)\n" - ... - -def lpmn(m, n, x) -> typing.Any: - "pm,pd = lpmn(m,n,x)\n\nWrapper for ``lpmn``.\n\nParameters\n----------\nm : input int\nn : input int\nx : input float\n\nReturns\n-------\npm : rank-2 array('d') with bounds (m + 1,n + 1)\npd : rank-2 array('d') with bounds (m + 1,n + 1)\n" - ... - -def lpn(n, x) -> typing.Any: - "pn,pd = lpn(n,x)\n\nWrapper for ``lpn``.\n\nParameters\n----------\nn : input int\nx : input float\n\nReturns\n-------\npn : rank-1 array('d') with bounds (n + 1)\npd : rank-1 array('d') with bounds (n + 1)\n" - ... - -def lqmn(m, n, x) -> typing.Any: - "qm,qd = lqmn(m,n,x)\n\nWrapper for ``lqmn``.\n\nParameters\n----------\nm : input int\nn : input int\nx : input float\n\nReturns\n-------\nqm : rank-2 array('d') with bounds (mm + 1,n + 1)\nqd : rank-2 array('d') with bounds (mm + 1,n + 1)\n" - ... - -def lqnb(n, x) -> typing.Any: - "qn,qd = lqnb(n,x)\n\nWrapper for ``lqnb``.\n\nParameters\n----------\nn : input int\nx : input float\n\nReturns\n-------\nqn : rank-1 array('d') with bounds (n + 1)\nqd : rank-1 array('d') with bounds (n + 1)\n" - ... - -def pbdv(v, x) -> typing.Any: - "dv,dp,pdf,pdd = pbdv(v,x)\n\nWrapper for ``pbdv``.\n\nParameters\n----------\nv : input float\nx : input float\n\nReturns\n-------\ndv : rank-1 array('d') with bounds (abs((int)v)+2)\ndp : rank-1 array('d') with bounds (abs((int)v)+2)\npdf : float\npdd : float\n" - ... - -def rctj(n, x) -> typing.Any: - "nm,rj,dj = rctj(n,x)\n\nWrapper for ``rctj``.\n\nParameters\n----------\nn : input int\nx : input float\n\nReturns\n-------\nnm : int\nrj : rank-1 array('d') with bounds (n + 1)\ndj : rank-1 array('d') with bounds (n + 1)\n" - ... - -def rcty(n, x) -> typing.Any: - "nm,ry,dy = rcty(n,x)\n\nWrapper for ``rcty``.\n\nParameters\n----------\nn : input int\nx : input float\n\nReturns\n-------\nnm : int\nry : rank-1 array('d') with bounds (n + 1)\ndy : rank-1 array('d') with bounds (n + 1)\n" - ... - -def segv(m, n, c, kd) -> typing.Any: - "cv,eg = segv(m,n,c,kd)\n\nWrapper for ``segv``.\n\nParameters\n----------\nm : input int\nn : input int\nc : input float\nkd : input int\n\nReturns\n-------\ncv : float\neg : rank-1 array('d') with bounds (n-m+2)\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/stats/_stats.pyi b/stubs/scipy-stubs/stats/_stats.pyi deleted file mode 100644 index c6a5911e..00000000 --- a/stubs/scipy-stubs/stats/_stats.pyi +++ /dev/null @@ -1,31 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.stats._stats, version: unspecified -import builtins as _mod_builtins -import typing - -__doc__: typing.Any -__file__: str -__name__: str -__package__: str -__pyx_capi__: dict - -def __pyx_unpickle_Enum() -> typing.Any: ... - -__test__: dict - -def _center_distance_matrix() -> typing.Any: ... -def _kendall_dis() -> typing.Any: ... -def _local_correlations() -> typing.Any: ... -def _local_covariance() -> typing.Any: ... -def _rank_distance_matrix() -> typing.Any: ... -def _toint64() -> typing.Any: ... -def _transform_distance_matrix() -> typing.Any: ... -def _weightedrankedtau(x, y, rank, weigher, additive) -> typing.Any: ... -def gaussian_kernel_estimate() -> typing.Any: - "\n def gaussian_kernel_estimate(points, real[:, :] values, xi, precision)\n\n Evaluate a multivariate Gaussian kernel estimate.\n\n Parameters\n ----------\n points : array_like with shape (n, d)\n Data points to estimate from in d dimenions.\n values : real[:, :] with shape (n, p)\n Multivariate values associated with the data points.\n xi : array_like with shape (m, d)\n Coordinates to evaluate the estimate at in d dimensions.\n precision : array_like with shape (d, d)\n Precision matrix for the Gaussian kernel.\n\n Returns\n -------\n estimate : double[:, :] with shape (m, p)\n Multivariate Gaussian kernel estimate evaluated at the input coordinates.\n" - ... - -def geninvgauss_logpdf() -> typing.Any: ... -def von_mises_cdf() -> typing.Any: ... -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/stats/mvn.pyi b/stubs/scipy-stubs/stats/mvn.pyi deleted file mode 100644 index eb5a9caa..00000000 --- a/stubs/scipy-stubs/stats/mvn.pyi +++ /dev/null @@ -1,29 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.stats.mvn, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def dkblck() -> typing.Any: - "'i'-scalar\n" - ... - -def mvndst(lower, upper, infin, correl, maxpts=..., abseps=..., releps=...) -> typing.Any: - "error,value,inform = mvndst(lower,upper,infin,correl,[maxpts,abseps,releps])\n\nWrapper for ``mvndst``.\n\nParameters\n----------\nlower : input rank-1 array('d') with bounds (n)\nupper : input rank-1 array('d') with bounds (n)\ninfin : input rank-1 array('i') with bounds (n)\ncorrel : input rank-1 array('d') with bounds (n*(n-1)/2)\n\nOther Parameters\n----------------\nmaxpts : input int, optional\n Default: 2000\nabseps : input float, optional\n Default: 1e-06\nreleps : input float, optional\n Default: 1e-06\n\nReturns\n-------\nerror : float\nvalue : float\ninform : int\n" - ... - -def mvnun(lower, upper, means, covar, maxpts=..., abseps=..., releps=...) -> typing.Any: - "value,inform = mvnun(lower,upper,means,covar,[maxpts,abseps,releps])\n\nWrapper for ``mvnun``.\n\nParameters\n----------\nlower : input rank-1 array('d') with bounds (d)\nupper : input rank-1 array('d') with bounds (d)\nmeans : input rank-2 array('d') with bounds (d,n)\ncovar : input rank-2 array('d') with bounds (d,d)\n\nOther Parameters\n----------------\nmaxpts : input int, optional\n Default: d*1000\nabseps : input float, optional\n Default: 1e-06\nreleps : input float, optional\n Default: 1e-06\n\nReturns\n-------\nvalue : float\ninform : int\n" - ... - -def mvnun_weighted(lower, upper, means, weights, covar, maxpts=..., abseps=..., releps=...) -> typing.Any: - "value,inform = mvnun_weighted(lower,upper,means,weights,covar,[maxpts,abseps,releps])\n\nWrapper for ``mvnun_weighted``.\n\nParameters\n----------\nlower : input rank-1 array('d') with bounds (d)\nupper : input rank-1 array('d') with bounds (d)\nmeans : input rank-2 array('d') with bounds (d,n)\nweights : input rank-1 array('d') with bounds (n)\ncovar : input rank-2 array('d') with bounds (d,d)\n\nOther Parameters\n----------------\nmaxpts : input int, optional\n Default: d*1000\nabseps : input float, optional\n Default: 1e-06\nreleps : input float, optional\n Default: 1e-06\n\nReturns\n-------\nvalue : float\ninform : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/stubs/scipy-stubs/stats/statlib.pyi b/stubs/scipy-stubs/stats/statlib.pyi deleted file mode 100644 index b9a68338..00000000 --- a/stubs/scipy-stubs/stats/statlib.pyi +++ /dev/null @@ -1,25 +0,0 @@ -# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)] -# Library: scipy, version: 1.6.2 -# Module: scipy.stats.statlib, version: $Revision: $ -import builtins as _mod_builtins -import typing - -__doc__: str -__file__: str -__name__: str -__package__: str -__version__: bytes - -def gscale(test, other) -> typing.Any: - "astart,a1,ifault = gscale(test,other)\n\nWrapper for ``gscale``.\n\nParameters\n----------\ntest : input int\nother : input int\n\nReturns\n-------\nastart : float\na1 : rank-1 array('f') with bounds (l1)\nifault : int\n" - ... - -def prho(n, is_) -> typing.Any: - "ifault = prho(n,is)\n\nWrapper for ``prho``.\n\nParameters\n----------\nn : input int\nis : input int\n\nReturns\n-------\nprho : float\nifault : int\n" - ... - -def swilk(x, a, init=..., n1=...) -> typing.Any: - "a,w,pw,ifault = swilk(x,a,[init,n1])\n\nWrapper for ``swilk``.\n\nParameters\n----------\nx : input rank-1 array('f') with bounds (n)\na : input rank-1 array('f') with bounds (n2)\n\nOther Parameters\n----------------\ninit : input int, optional\n Default: 0\nn1 : input int, optional\n Default: n\n\nReturns\n-------\na : rank-1 array('f') with bounds (n2)\nw : float\npw : float\nifault : int\n" - ... - -def __getattr__(name) -> typing.Any: ... diff --git a/tests/requirements.txt b/tests/requirements.txt index 6d71f9aa..fa91bd51 100644 --- a/tests/requirements.txt +++ b/tests/requirements.txt @@ -3,5 +3,4 @@ mypy==0.950 pyright pytest scikit-learn -scipy typing_extensions==4.2.0