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helpdb.jl
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helpdb.jl
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# automatically generated from files in doc/stdlib/ -- do not edit here
Any[
("Base","ndims","ndims(A) -> Integer
Returns the number of dimensions of A
"),
("Base","size","size(A[, dim...])
Returns a tuple containing the dimensions of A. Optionally you can
specify the dimension(s) you want the length of, and get the length
of that dimension, or a tuple of the lengths of dimensions you
asked for.:
julia> A = rand(2,3,4);
julia> size(A, 2)
3
julia> size(A,3,2)
(4,3)
"),
("Base","iseltype","iseltype(A, T)
Tests whether A or its elements are of type T
"),
("Base","length","length(A) -> Integer
Returns the number of elements in A
"),
("Base","eachindex","eachindex(A...)
Creates an iterable object for visiting each index of an
AbstractArray \"A\" in an efficient manner. For array types that
have opted into fast linear indexing (like \"Array\"), this is
simply the range \"1:length(A)\". For other array types, this
returns a specialized Cartesian range to efficiently index into the
array with indices specified for every dimension. For other
iterables, including strings and dictionaries, this returns an
iterator object supporting arbitrary index types (e.g. unevenly
spaced or non-integer indices).
Example for a sparse 2-d array:
julia> A = sprand(2, 3, 0.5)
2x3 sparse matrix with 4 Float64 entries:
[1, 1] = 0.598888
[1, 2] = 0.0230247
[1, 3] = 0.486499
[2, 3] = 0.809041
julia> for iter in eachindex(A)
@show iter.I_1, iter.I_2
@show A[iter]
end
(iter.I_1,iter.I_2) = (1,1)
A[iter] = 0.5988881393454597
(iter.I_1,iter.I_2) = (2,1)
A[iter] = 0.0
(iter.I_1,iter.I_2) = (1,2)
A[iter] = 0.02302469881746183
(iter.I_1,iter.I_2) = (2,2)
A[iter] = 0.0
(iter.I_1,iter.I_2) = (1,3)
A[iter] = 0.4864987874354343
(iter.I_1,iter.I_2) = (2,3)
A[iter] = 0.8090413606455655
"),
("Base","Base","Base.linearindexing(A)
\"linearindexing\" defines how an AbstractArray most efficiently
accesses its elements. If \"Base.linearindexing(A)\" returns
\"Base.LinearFast()\", this means that linear indexing with only
one index is an efficient operation. If it instead returns
\"Base.LinearSlow()\" (by default), this means that the array
intrinsically accesses its elements with indices specified for
every dimension. Since converting a linear index to multiple
indexing subscripts is typically very expensive, this provides a
traits-based mechanism to enable efficient generic code for all
array types.
An abstract array subtype \"MyArray\" that wishes to opt into fast
linear indexing behaviors should define \"linearindexing\" in the
type-domain:
Base.linearindexing{T<:MyArray}(::Type{T}) = Base.LinearFast()
"),
("Base","countnz","countnz(A)
Counts the number of nonzero values in array A (dense or sparse).
Note that this is not a constant-time operation. For sparse
matrices, one should usually use \"nnz\", which returns the number
of stored values.
"),
("Base","conj!","conj!(A)
Convert an array to its complex conjugate in-place
"),
("Base","stride","stride(A, k)
Returns the distance in memory (in number of elements) between
adjacent elements in dimension k
"),
("Base","strides","strides(A)
Returns a tuple of the memory strides in each dimension
"),
("Base","ind2sub","ind2sub(dims, index) -> subscripts
Returns a tuple of subscripts into an array with dimensions
\"dims\", corresponding to the linear index \"index\"
**Example** \"i, j, ... = ind2sub(size(A), indmax(A))\" provides
the indices of the maximum element
"),
("Base","ind2sub","ind2sub(a, index) -> subscripts
Returns a tuple of subscripts into array \"a\" corresponding to the
linear index \"index\"
"),
("Base","sub2ind","sub2ind(dims, i, j, k...) -> index
The inverse of \"ind2sub\", returns the linear index corresponding
to the provided subscripts
"),
("Base","Array","Array(dims)
\"Array{T}(dims)\" constructs an uninitialized dense array with
element type \"T\". \"dims\" may be a tuple or a series of integer
arguments. The syntax \"Array(T, dims)\" is also available, but
deprecated.
"),
("Base","getindex","getindex(type[, elements...])
Construct a 1-d array of the specified type. This is usually called
with the syntax \"Type[]\". Element values can be specified using
\"Type[a,b,c,...]\".
"),
("Base","cell","cell(dims)
Construct an uninitialized cell array (heterogeneous array).
\"dims\" can be either a tuple or a series of integer arguments.
"),
("Base","zeros","zeros(type, dims)
Create an array of all zeros of specified type. The type defaults
to Float64 if not specified.
"),
("Base","zeros","zeros(A)
Create an array of all zeros with the same element type and shape
as A.
"),
("Base","ones","ones(type, dims)
Create an array of all ones of specified type. The type defaults to
Float64 if not specified.
"),
("Base","ones","ones(A)
Create an array of all ones with the same element type and shape as
A.
"),
("Base","trues","trues(dims)
Create a \"BitArray\" with all values set to true
"),
("Base","falses","falses(dims)
Create a \"BitArray\" with all values set to false
"),
("Base","fill","fill(x, dims)
Create an array filled with the value \"x\". For example,
\"fill(1.0, (10,10))\" returns a 10x10 array of floats, with each
element initialized to 1.0.
If \"x\" is an object reference, all elements will refer to the
same object. \"fill(Foo(), dims)\" will return an array filled with
the result of evaluating \"Foo()\" once.
"),
("Base","fill!","fill!(A, x)
Fill array \"A\" with the value \"x\". If \"x\" is an object
reference, all elements will refer to the same object. \"fill!(A,
Foo())\" will return \"A\" filled with the result of evaluating
\"Foo()\" once.
"),
("Base","reshape","reshape(A, dims)
Create an array with the same data as the given array, but with
different dimensions. An implementation for a particular type of
array may choose whether the data is copied or shared.
"),
("Base","similar","similar(array, element_type, dims)
Create an uninitialized array of the same type as the given array,
but with the specified element type and dimensions. The second and
third arguments are both optional. The \"dims\" argument may be a
tuple or a series of integer arguments. For some special
\"AbstractArray\" objects which are not real containers (like
ranges), this function returns a standard \"Array\" to allow
operating on elements.
"),
("Base","reinterpret","reinterpret(type, A)
Change the type-interpretation of a block of memory. For example,
\"reinterpret(Float32, UInt32(7))\" interprets the 4 bytes
corresponding to \"UInt32(7)\" as a \"Float32\". For arrays, this
constructs an array with the same binary data as the given array,
but with the specified element type.
"),
("Base","eye","eye(n)
n-by-n identity matrix
"),
("Base","eye","eye(m, n)
m-by-n identity matrix
"),
("Base","eye","eye(A)
Constructs an identity matrix of the same dimensions and type as
\"A\".
"),
("Base","linspace","linspace(start, stop, n=100)
Construct a range of \"n\" linearly spaced elements from \"start\"
to \"stop\".
"),
("Base","logspace","logspace(start, stop, n=50)
Construct a vector of \"n\" logarithmically spaced numbers from
\"10^start\" to \"10^stop\".
"),
("Base","broadcast","broadcast(f, As...)
Broadcasts the arrays \"As\" to a common size by expanding
singleton dimensions, and returns an array of the results
\"f(as...)\" for each position.
"),
("Base","broadcast!","broadcast!(f, dest, As...)
Like \"broadcast\", but store the result of \"broadcast(f, As...)\"
in the \"dest\" array. Note that \"dest\" is only used to store the
result, and does not supply arguments to \"f\" unless it is also
listed in the \"As\", as in \"broadcast!(f, A, A, B)\" to perform
\"A[:] = broadcast(f, A, B)\".
"),
("Base","bitbroadcast","bitbroadcast(f, As...)
Like \"broadcast\", but allocates a \"BitArray\" to store the
result, rather then an \"Array\".
"),
("Base","broadcast_function","broadcast_function(f)
Returns a function \"broadcast_f\" such that
\"broadcast_function(f)(As...) === broadcast(f, As...)\". Most
useful in the form \"const broadcast_f = broadcast_function(f)\".
"),
("Base","broadcast!_function","broadcast!_function(f)
Like \"broadcast_function\", but for \"broadcast!\".
"),
("Base","getindex","getindex(A, inds...)
Returns a subset of array \"A\" as specified by \"inds\", where
each \"ind\" may be an \"Int\", a \"Range\", or a \"Vector\". See
the manual section on *array indexing* for details.
"),
("Base","sub","sub(A, inds...)
Like \"getindex()\", but returns a view into the parent array \"A\"
with the given indices instead of making a copy. Calling
\"getindex()\" or \"setindex!()\" on the returned \"SubArray\"
computes the indices to the parent array on the fly without
checking bounds.
"),
("Base","parent","parent(A)
Returns the \"parent array\" of an array view type (e.g.,
SubArray), or the array itself if it is not a view
"),
("Base","parentindexes","parentindexes(A)
From an array view \"A\", returns the corresponding indexes in the
parent
"),
("Base","slicedim","slicedim(A, d, i)
Return all the data of \"A\" where the index for dimension \"d\"
equals \"i\". Equivalent to \"A[:,:,...,i,:,:,...]\" where \"i\" is
in position \"d\".
"),
("Base","slice","slice(A, inds...)
Returns a view of array \"A\" with the given indices like
\"sub()\", but drops all dimensions indexed with scalars.
"),
("Base","setindex!","setindex!(A, X, inds...)
Store values from array \"X\" within some subset of \"A\" as
specified by \"inds\".
"),
("Base","broadcast_getindex","broadcast_getindex(A, inds...)
Broadcasts the \"inds\" arrays to a common size like \"broadcast\",
and returns an array of the results \"A[ks...]\", where \"ks\" goes
over the positions in the broadcast.
"),
("Base","broadcast_setindex!","broadcast_setindex!(A, X, inds...)
Broadcasts the \"X\" and \"inds\" arrays to a common size and
stores the value from each position in \"X\" at the indices given
by the same positions in \"inds\".
"),
("Base","cat","cat(dims, A...)
Concatenate the input arrays along the specified dimensions in the
iterable \"dims\". For dimensions not in \"dims\", all input arrays
should have the same size, which will also be the size of the
output array along that dimension. For dimensions in \"dims\", the
size of the output array is the sum of the sizes of the input
arrays along that dimension. If \"dims\" is a single number, the
different arrays are tightly stacked along that dimension. If
\"dims\" is an iterable containing several dimensions, this allows
to construct block diagonal matrices and their higher-dimensional
analogues by simultaneously increasing several dimensions for every
new input array and putting zero blocks elsewhere. For example,
*cat([1,2], matrices...)* builds a block diagonal matrix, i.e. a
block matrix with *matrices[1]*, *matrices[2]*, ... as diagonal
blocks and matching zero blocks away from the diagonal.
"),
("Base","vcat","vcat(A...)
Concatenate along dimension 1
"),
("Base","hcat","hcat(A...)
Concatenate along dimension 2
"),
("Base","hvcat","hvcat(rows::Tuple{Vararg{Int}}, values...)
Horizontal and vertical concatenation in one call. This function is
called for block matrix syntax. The first argument specifies the
number of arguments to concatenate in each block row. For example,
\"[a b;c d e]\" calls \"hvcat((2,3),a,b,c,d,e)\".
If the first argument is a single integer \"n\", then all block
rows are assumed to have \"n\" block columns.
"),
("Base","flipdim","flipdim(A, d)
Reverse \"A\" in dimension \"d\".
"),
("Base","circshift","circshift(A, shifts)
Circularly shift the data in an array. The second argument is a
vector giving the amount to shift in each dimension.
"),
("Base","find","find(A)
Return a vector of the linear indexes of the non-zeros in \"A\"
(determined by \"A[i]!=0\"). A common use of this is to convert a
boolean array to an array of indexes of the \"true\" elements.
"),
("Base","find","find(f, A)
Return a vector of the linear indexes of \"A\" where \"f\" returns
true.
"),
("Base","findn","findn(A)
Return a vector of indexes for each dimension giving the locations
of the non-zeros in \"A\" (determined by \"A[i]!=0\").
"),
("Base","findnz","findnz(A)
Return a tuple \"(I, J, V)\" where \"I\" and \"J\" are the row and
column indexes of the non-zero values in matrix \"A\", and \"V\" is
a vector of the non-zero values.
"),
("Base","findfirst","findfirst(A)
Return the index of the first non-zero value in \"A\" (determined
by \"A[i]!=0\").
"),
("Base","findfirst","findfirst(A, v)
Return the index of the first element equal to \"v\" in \"A\".
"),
("Base","findfirst","findfirst(predicate, A)
Return the index of the first element of \"A\" for which
\"predicate\" returns true.
"),
("Base","findlast","findlast(A)
Return the index of the last non-zero value in \"A\" (determined by
\"A[i]!=0\").
"),
("Base","findlast","findlast(A, v)
Return the index of the last element equal to \"v\" in \"A\".
"),
("Base","findlast","findlast(predicate, A)
Return the index of the last element of \"A\" for which
\"predicate\" returns true.
"),
("Base","findnext","findnext(A, i)
Find the next index >= \"i\" of a non-zero element of \"A\", or
\"0\" if not found.
"),
("Base","findnext","findnext(predicate, A, i)
Find the next index >= \"i\" of an element of \"A\" for which
\"predicate\" returns true, or \"0\" if not found.
"),
("Base","findnext","findnext(A, v, i)
Find the next index >= \"i\" of an element of \"A\" equal to \"v\"
(using \"==\"), or \"0\" if not found.
"),
("Base","findprev","findprev(A, i)
Find the previous index <= \"i\" of a non-zero element of \"A\", or
0 if not found.
"),
("Base","findprev","findprev(predicate, A, i)
Find the previous index <= \"i\" of an element of \"A\" for which
\"predicate\" returns true, or \"0\" if not found.
"),
("Base","findprev","findprev(A, v, i)
Find the previous index <= \"i\" of an element of \"A\" equal to
\"v\" (using \"==\"), or \"0\" if not found.
"),
("Base","permutedims","permutedims(A, perm)
Permute the dimensions of array \"A\". \"perm\" is a vector
specifying a permutation of length \"ndims(A)\". This is a
generalization of transpose for multi-dimensional arrays. Transpose
is equivalent to \"permutedims(A, [2,1])\".
"),
("Base","ipermutedims","ipermutedims(A, perm)
Like \"permutedims()\", except the inverse of the given permutation
is applied.
"),
("Base","permutedims!","permutedims!(dest, src, perm)
Permute the dimensions of array \"src\" and store the result in the
array \"dest\". \"perm\" is a vector specifying a permutation of
length \"ndims(src)\". The preallocated array \"dest\" should have
\"size(dest) == size(src)[perm]\" and is completely overwritten. No
in-place permutation is supported and unexpected results will
happen if *src* and *dest* have overlapping memory regions.
"),
("Base","squeeze","squeeze(A, dims)
Remove the dimensions specified by \"dims\" from array \"A\".
Elements of \"dims\" must be unique and within the range
\"1:ndims(A)\".
"),
("Base","vec","vec(Array) -> Vector
Vectorize an array using column-major convention.
"),
("Base","promote_shape","promote_shape(s1, s2)
Check two array shapes for compatibility, allowing trailing
singleton dimensions, and return whichever shape has more
dimensions.
"),
("Base","checkbounds","checkbounds(array, indexes...)
Throw an error if the specified indexes are not in bounds for the
given array.
"),
("Base","randsubseq","randsubseq(A, p) -> Vector
Return a vector consisting of a random subsequence of the given
array \"A\", where each element of \"A\" is included (in order)
with independent probability \"p\". (Complexity is linear in
\"p*length(A)\", so this function is efficient even if \"p\" is
small and \"A\" is large.) Technically, this process is known as
\"Bernoulli sampling\" of \"A\".
"),
("Base","randsubseq!","randsubseq!(S, A, p)
Like \"randsubseq\", but the results are stored in \"S\" (which is
resized as needed).
"),
("Base","cumprod","cumprod(A[, dim])
Cumulative product along a dimension \"dim\" (defaults to 1). See
also \"cumprod!()\" to use a preallocated output array, both for
performance and to control the precision of the output (e.g. to
avoid overflow).
"),
("Base","cumprod!","cumprod!(B, A[, dim])
Cumulative product of \"A\" along a dimension, storing the result
in \"B\". The dimension defaults to 1.
"),
("Base","cumsum","cumsum(A[, dim])
Cumulative sum along a dimension \"dim\" (defaults to 1). See also
\"cumsum!()\" to use a preallocated output array, both for
performance and to control the precision of the output (e.g. to
avoid overflow).
"),
("Base","cumsum!","cumsum!(B, A[, dim])
Cumulative sum of \"A\" along a dimension, storing the result in
\"B\". The dimension defaults to 1.
"),
("Base","cumsum_kbn","cumsum_kbn(A[, dim])
Cumulative sum along a dimension, using the Kahan-Babuska-Neumaier
compensated summation algorithm for additional accuracy. The
dimension defaults to 1.
"),
("Base","cummin","cummin(A[, dim])
Cumulative minimum along a dimension. The dimension defaults to 1.
"),
("Base","cummax","cummax(A[, dim])
Cumulative maximum along a dimension. The dimension defaults to 1.
"),
("Base","diff","diff(A[, dim])
Finite difference operator of matrix or vector.
"),
("Base","gradient","gradient(F[, h])
Compute differences along vector \"F\", using \"h\" as the spacing
between points. The default spacing is one.
"),
("Base","rot180","rot180(A)
Rotate matrix \"A\" 180 degrees.
"),
("Base","rot180","rot180(A, k)
Rotate matrix \"A\" 180 degrees an integer \"k\" number of times.
If \"k\" is even, this is equivalent to a \"copy\".
"),
("Base","rotl90","rotl90(A)
Rotate matrix \"A\" left 90 degrees.
"),
("Base","rotl90","rotl90(A, k)
Rotate matrix \"A\" left 90 degrees an integer \"k\" number of
times. If \"k\" is zero or a multiple of four, this is equivalent
to a \"copy\".
"),
("Base","rotr90","rotr90(A)
Rotate matrix \"A\" right 90 degrees.
"),
("Base","rotr90","rotr90(A, k)
Rotate matrix \"A\" right 90 degrees an integer \"k\" number of
times. If \"k\" is zero or a multiple of four, this is equivalent
to a \"copy\".
"),
("Base","reducedim","reducedim(f, A, dims[, initial])
Reduce 2-argument function \"f\" along dimensions of \"A\".
\"dims\" is a vector specifying the dimensions to reduce, and
\"initial\" is the initial value to use in the reductions. For *+*,
***, *max* and *min* the *initial* argument is optional.
The associativity of the reduction is implementation-dependent; if
you need a particular associativity, e.g. left-to-right, you should
write your own loop. See documentation for \"reduce\".
"),
("Base","mapreducedim","mapreducedim(f, op, A, dims[, initial])
Evaluates to the same as *reducedim(op, map(f, A), dims,
f(initial))*, but is generally faster because the intermediate
array is avoided.
"),
("Base","mapslices","mapslices(f, A, dims)
Transform the given dimensions of array \"A\" using function \"f\".
\"f\" is called on each slice of \"A\" of the form
\"A[...,:,...,:,...]\". \"dims\" is an integer vector specifying
where the colons go in this expression. The results are
concatenated along the remaining dimensions. For example, if
\"dims\" is \"[1,2]\" and A is 4-dimensional, \"f\" is called on
\"A[:,:,i,j]\" for all \"i\" and \"j\".
"),
("Base","sum_kbn","sum_kbn(A)
Returns the sum of all array elements, using the Kahan-Babuska-
Neumaier compensated summation algorithm for additional accuracy.
"),
("Base","cartesianmap","cartesianmap(f, dims)
Given a \"dims\" tuple of integers \"(m, n, ...)\", call \"f\" on
all combinations of integers in the ranges \"1:m\", \"1:n\", etc.
julia> cartesianmap(println, (2,2))
11
21
12
22
"),
("Base","nthperm","nthperm(v, k)
Compute the kth lexicographic permutation of a vector.
"),
("Base","nthperm","nthperm(p)
Return the \"k\" that generated permutation \"p\". Note that
\"nthperm(nthperm([1:n], k)) == k\" for \"1 <= k <= factorial(n)\".
"),
("Base","nthperm!","nthperm!(v, k)
In-place version of \"nthperm()\".
"),
("Base","randperm","randperm([rng], n)
Construct a random permutation of length \"n\". The optional
\"rng\" argument specifies a random number generator, see *Random
Numbers*.
"),
("Base","invperm","invperm(v)
Return the inverse permutation of v.
"),
("Base","isperm","isperm(v) -> Bool
Returns true if v is a valid permutation.
"),
("Base","permute!","permute!(v, p)
Permute vector \"v\" in-place, according to permutation \"p\". No
checking is done to verify that \"p\" is a permutation.
To return a new permutation, use \"v[p]\". Note that this is
generally faster than \"permute!(v,p)\" for large vectors.
"),
("Base","ipermute!","ipermute!(v, p)
Like permute!, but the inverse of the given permutation is applied.
"),
("Base","randcycle","randcycle([rng], n)
Construct a random cyclic permutation of length \"n\". The optional
\"rng\" argument specifies a random number generator, see *Random
Numbers*.
"),
("Base","shuffle","shuffle([rng], v)
Return a randomly permuted copy of \"v\". The optional \"rng\"
argument specifies a random number generator, see *Random Numbers*.
"),
("Base","shuffle!","shuffle!([rng], v)
In-place version of \"shuffle()\".
"),
("Base","reverse","reverse(v[, start=1[, stop=length(v)]])
Return a copy of \"v\" reversed from start to stop.
"),
("Base","reverseind","reverseind(v, i)
Given an index \"i\" in \"reverse(v)\", return the corresponding
index in \"v\" so that \"v[reverseind(v,i)] == reverse(v)[i]\".
(This can be nontrivial in the case where \"v\" is a Unicode
string.)
"),
("Base","reverse!","reverse!(v[, start=1[, stop=length(v)]]) -> v
In-place version of \"reverse()\".
"),
("Base","combinations","combinations(array, n)
Generate all combinations of \"n\" elements from an indexable
object. Because the number of combinations can be very large, this
function returns an iterator object. Use
\"collect(combinations(array,n))\" to get an array of all
combinations.
"),
("Base","permutations","permutations(array)
Generate all permutations of an indexable object. Because the
number of permutations can be very large, this function returns an
iterator object. Use \"collect(permutations(array))\" to get an
array of all permutations.
"),
("Base","partitions","partitions(n)
Generate all integer arrays that sum to \"n\". Because the number
of partitions can be very large, this function returns an iterator
object. Use \"collect(partitions(n))\" to get an array of all
partitions. The number of partitions to generate can be efficiently
computed using \"length(partitions(n))\".
"),
("Base","partitions","partitions(n, m)
Generate all arrays of \"m\" integers that sum to \"n\". Because
the number of partitions can be very large, this function returns
an iterator object. Use \"collect(partitions(n,m))\" to get an
array of all partitions. The number of partitions to generate can
be efficiently computed using \"length(partitions(n,m))\".
"),
("Base","partitions","partitions(array)
Generate all set partitions of the elements of an array,
represented as arrays of arrays. Because the number of partitions
can be very large, this function returns an iterator object. Use
\"collect(partitions(array))\" to get an array of all partitions.
The number of partitions to generate can be efficiently computed
using \"length(partitions(array))\".
"),
("Base","partitions","partitions(array, m)
Generate all set partitions of the elements of an array into
exactly m subsets, represented as arrays of arrays. Because the
number of partitions can be very large, this function returns an
iterator object. Use \"collect(partitions(array,m))\" to get an
array of all partitions. The number of partitions into m subsets is
equal to the Stirling number of the second kind and can be
efficiently computed using \"length(partitions(array,m))\".
"),
("Base","bitpack","bitpack(A::AbstractArray{T, N}) -> BitArray
Converts a numeric array to a packed boolean array
"),
("Base","bitunpack","bitunpack(B::BitArray{N}) -> Array{Bool,N}
Converts a packed boolean array to an array of booleans
"),
("Base","flipbits!","flipbits!(B::BitArray{N}) -> BitArray{N}
Performs a bitwise not operation on B. See *~ operator*.
"),
("Base","rol!","rol!(dest::BitArray{1}, src::BitArray{1}, i::Integer) -> BitArray{1}
Performs a left rotation operation on \"src\" and put the result
into \"dest\".
"),
("Base","rol!","rol!(B::BitArray{1}, i::Integer) -> BitArray{1}
Performs a left rotation operation on B.
"),