Migrate full(X) to convert(Array, X) in tests outside of test/sparsedir and test/linalg. Migrate full to convert in some documentation.

Author: Sacha0

base/docs/helpdb/Base.jl

@@ -8665,7 +8665,7 @@ the one with smallest norm is returned.
 
 Multiplication with respect to either thin or full `Q` is allowed, i.e. both `F[:Q]*F[:R]`
 and `F[:Q]*A` are supported. A `Q` matrix can be converted into a regular matrix with
-[`full`](:func:`full`) which has a named argument `thin`.
+[`convert(Array, _)`](:func:`convert`).
 
 **note**
 

doc/manual/arrays.rst

@@ -811,7 +811,7 @@ reference.
 +----------------------------------------+----------------------------------+--------------------------------------------+
 | :func:`speye(n) <speye>`               | :func:`eye(n) <eye>`             | Creates a *n*-by-*n* identity matrix.      |
 +----------------------------------------+----------------------------------+--------------------------------------------+
-| :func:`full(S) <full>`                 | :func:`sparse(A) <sparse>`       | Interconverts between dense                |
+| :func:`convert(Array, S) <convert>`    | :func:`sparse(A) <sparse>`       | Interconverts between dense                |
 |                                        |                                  | and sparse formats.                        |
 +----------------------------------------+----------------------------------+--------------------------------------------+
 | :func:`sprand(m,n,d) <sprand>`         | :func:`rand(m,n) <rand>`         | Creates a *m*-by-*n* random matrix (of     |

doc/stdlib/linalg.rst

@@ -75,7 +75,7 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
    .. Docstring generated from Julia source
 
-   Constructs an upper (``isupper=true``\ ) or lower (``isupper=false``\ ) bidiagonal matrix using the given diagonal (``dv``\ ) and off-diagonal (``ev``\ ) vectors.  The result is of type ``Bidiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with :func:`full`\ . ``ev``\ 's length must be one less than the length of ``dv``\ .
+   Constructs an upper (``isupper=true``\ ) or lower (``isupper=false``\ ) bidiagonal matrix using the given diagonal (``dv``\ ) and off-diagonal (``ev``\ ) vectors.  The result is of type ``Bidiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with :func:`convert`\ . ``ev``\ 's length must be one less than the length of ``dv``\ .
 
    **Example**
 
@@ -90,7 +90,7 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
    .. Docstring generated from Julia source
 
-   Constructs an upper (``uplo='U'``\ ) or lower (``uplo='L'``\ ) bidiagonal matrix using the given diagonal (``dv``\ ) and off-diagonal (``ev``\ ) vectors.  The result is of type ``Bidiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with :func:`full`\ . ``ev``\ 's length must be one less than the length of ``dv``\ .
+   Constructs an upper (``uplo='U'``\ ) or lower (``uplo='L'``\ ) bidiagonal matrix using the given diagonal (``dv``\ ) and off-diagonal (``ev``\ ) vectors.  The result is of type ``Bidiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with :func:`convert`\ . ``ev``\ 's length must be one less than the length of ``dv``\ .
 
    **Example**
 
@@ -119,13 +119,13 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
    .. Docstring generated from Julia source
 
-   Construct a symmetric tridiagonal matrix from the diagonal and first sub/super-diagonal, respectively. The result is of type ``SymTridiagonal`` and provides efficient specialized eigensolvers, but may be converted into a regular matrix with :func:`full`\ .
+   Construct a symmetric tridiagonal matrix from the diagonal and first sub/super-diagonal, respectively. The result is of type ``SymTridiagonal`` and provides efficient specialized eigensolvers, but may be converted into a regular matrix with :func:`convert`\ .
 
 .. function:: Tridiagonal(dl, d, du)
 
    .. Docstring generated from Julia source
 
-   Construct a tridiagonal matrix from the first subdiagonal, diagonal, and first superdiagonal, respectively.  The result is of type ``Tridiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with :func:`full`\ . The lengths of ``dl`` and ``du`` must be one less than the length of ``d``\ .
+   Construct a tridiagonal matrix from the first subdiagonal, diagonal, and first superdiagonal, respectively.  The result is of type ``Tridiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with :func:`convert`\ . The lengths of ``dl`` and ``du`` must be one less than the length of ``d``\ .
 
 .. function:: Symmetric(A, uplo=:U)
 
@@ -461,7 +461,7 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
    The following functions are available for the ``QR`` objects: ``size``\ , ``\``\ . When ``A`` is rectangular, ``\`` will return a least squares solution and if the solution is not unique, the one with smallest norm is returned.
 
-   Multiplication with respect to either thin or full ``Q`` is allowed, i.e. both ``F[:Q]*F[:R]`` and ``F[:Q]*A`` are supported. A ``Q`` matrix can be converted into a regular matrix with :func:`full` which has a named argument ``thin``\ .
+   Multiplication with respect to either thin or full ``Q`` is allowed, i.e. both ``F[:Q]*F[:R]`` and ``F[:Q]*A`` are supported. A ``Q`` matrix can be converted into a regular matrix with :func:`convert`\ .
 
    **note**
 
@@ -626,7 +626,7 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
    .. Docstring generated from Julia source
 
-   Compute the Hessenberg decomposition of ``A`` and return a ``Hessenberg`` object. If ``F`` is the factorization object, the unitary matrix can be accessed with ``F[:Q]`` and the Hessenberg matrix with ``F[:H]``\ . When ``Q`` is extracted, the resulting type is the ``HessenbergQ`` object, and may be converted to a regular matrix with :func:`full`\ .
+   Compute the Hessenberg decomposition of ``A`` and return a ``Hessenberg`` object. If ``F`` is the factorization object, the unitary matrix can be accessed with ``F[:Q]`` and the Hessenberg matrix with ``F[:H]``\ . When ``Q`` is extracted, the resulting type is the ``HessenbergQ`` object, and may be converted to a regular matrix with :func:`convert`\ .
 
 .. function:: hessfact!(A)
 
@@ -942,7 +942,7 @@ Linear algebra functions in Julia are largely implemented by calling functions f
 
    .. Docstring generated from Julia source
 
-   Construct a tridiagonal matrix from the first subdiagonal, diagonal, and first superdiagonal, respectively.  The result is of type ``Tridiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with :func:`full`\ . The lengths of ``dl`` and ``du`` must be one less than the length of ``d``\ .
+   Construct a tridiagonal matrix from the first subdiagonal, diagonal, and first superdiagonal, respectively.  The result is of type ``Tridiagonal`` and provides efficient specialized linear solvers, but may be converted into a regular matrix with :func:`convert`\ . The lengths of ``dl`` and ``du`` must be one less than the length of ``d``\ .
 
 .. function:: rank(M)
 

doc/stdlib/strings.rst

@@ -500,3 +500,4 @@
    .. Docstring generated from Julia source
 
    Create a string from the address of a NUL-terminated UTF-32 string. A copy is made; the pointer can be safely freed. If ``length`` is specified, the string does not have to be NUL-terminated.
+

doc/stdlib/test.rst

@@ -306,7 +306,7 @@ gives a `Broken` `Result`.
 
    .. Docstring generated from Julia source
 
-   For use to indicate a test that should pass but currently intermittently fails. Does not evaluate the expression.
+   For use to indicate a test that should pass but currently intermittently fails. Does not evaluate the expression, which makes it useful for tests of not-yet-implemented functionality.
 
 Creating Custom ``AbstractTestSet`` Types
 -----------------------------------------

test/hashing.jl

@@ -89,7 +89,7 @@ end
 x = sprand(10, 10, 0.5)
 x[1] = 1
 x.nzval[1] = 0
-@test hash(x) == hash(full(x))
+@test hash(x) == hash(convert(Array, x))
 
 let a = QuoteNode(1), b = QuoteNode(1.0)
     @test (hash(a)==hash(b)) == (a==b)

test/perf/threads/stockcorr/pstockcorr.jl

@@ -78,7 +78,7 @@ function pstockcorr(n)
     SimulPriceB[1,:] = CurrentPrice[2]
 
     ## Generating the paths of stock prices by Geometric Brownian Motion
-    const UpperTriangle = full(chol(Corr))    # UpperTriangle Matrix by Cholesky decomposition
+    const UpperTriangle = convert(Array, chol(Corr))    # UpperTriangle Matrix by Cholesky decomposition
 
     # Optimization: pre-allocate these for performance
     # NOTE: the new GC will hopefully fix this, but currently GC time