some doc/manual fixes found by doctest

Sacha0 · Nov 22, 2013 · b995da5 · b995da5
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diff --git a/doc/manual/arrays.rst b/doc/manual/arrays.rst
@@ -111,27 +111,31 @@ scalar.
 The following example computes a weighted average of the current element
 and its left and right neighbor along a 1-d grid. :
 
-.. doctest::
+.. testsetup:: *
+
+ srand(314)
+
+.. doctest:: array-rand
 
  julia> const x = rand(8)
- 8-element Float64 Array:
- 0.276455
- 0.614847
- 0.0601373
- 0.896024
- 0.646236
- 0.143959
- 0.0462343
- 0.730987
+ 8-element Array{Float64,1}:
+ 0.843025
+ 0.869052
+ 0.365105
+ 0.699456
+ 0.977653
+ 0.994953
+ 0.41084 
+ 0.809411
 
  julia> [ 0.25*x[i-1] + 0.5*x[i] + 0.25*x[i+1] for i=2:length(x)-1 ]
- 6-element Float64 Array:
- 0.391572
- 0.407786
- 0.624605
- 0.583114
- 0.245097
- 0.241854
+ 6-element Array{Float64,1}:
+ 0.736559
+ 0.57468
+ 0.685417
+ 0.912429
+ 0.8446 
+ 0.656511
 
 .. note:: In the above example, ``x`` is declared as constant because type
  inference in Julia does not work as well on non-constant global
@@ -150,7 +154,7 @@ array of type ``Any``:
 .. doctest::
 
  julia> { i/2 for i = 1:3 }
- 3-element Any Array:
+ 3-element Array{Any,1}:
  0.5
  1.0
  1.5
@@ -188,16 +192,17 @@ Example:
 .. doctest::
 
  julia> x = reshape(1:16, 4, 4)
- 4x4 Int64 Array
- 1 5 9 13
- 2 6 10 14
- 3 7 11 15
- 4 8 12 16
+ 4x4 Array{Int64,2}:
+ 1 5 9 13
+ 2 6 10 14
+ 3 7 11 15
+ 4 8 12 16
+
 
  julia> x[2:3, 2:end-1]
- 2x2 Int64 Array
- 6 10
- 7 11
+ 2x2 Array{Int64,2}:
+  6  10
+  7  11
 
 Assignment
 ----------
@@ -230,16 +235,19 @@ Example:
 .. doctest::
 
  julia> x = reshape(1:9, 3, 3)
- 3x3 Int64 Array
- 1 4 7
- 2 5 8
- 3 6 9
+ 3x3 Array{Int64,2}:
+  1 4  7
+  2 5  8
+  3 6  9
 
  julia> x[1:2, 2:3] = -1
- 3x3 Int64 Array
- 1 -1 -1
- 2 -1 -1
- 3 6 9
+ -1
+
+ julia> x
+ 3x3 Array{Int64,2}:
+ 1 -1 -1
+ 2 -1 -1
+ 3 6 9
 
 Concatenation
 -------------
@@ -321,9 +329,9 @@ the name of the function to vectorize. Here is a simple example:
 
  julia> methods(square)
  # 4 methods for generic function "square":
- square{T<:Number}(x::AbstractArray{T<:Number,1}) at operators.jl:236
- square{T<:Number}(x::AbstractArray{T<:Number,2}) at operators.jl:237
- square{T<:Number}(x::AbstractArray{T<:Number,N}) at operators.jl:239
+ square{T<:Number}(x::AbstractArray{T<:Number,1}) at operators.jl:248
+ square{T<:Number}(x::AbstractArray{T<:Number,2}) at operators.jl:249
+ square{T<:Number}(x::AbstractArray{T<:Number,N}) at operators.jl:251
  square(x) at none:1
 
  julia> square([1 2 4; 5 6 7])
@@ -495,13 +503,15 @@ you can use the same names with an ``sp`` prefix:
 .. doctest::
 
  julia> spzeros(3,5)
- 3x5 sparse matrix with 0 nonzeros:
+ 3x5 sparse matrix with 0 Float64 nonzeros:
+ <BLANKLINE>
 
  julia> speye(3,5)
- 3x5 sparse matrix with 3 nonzeros:
- [1, 1] = 1.0
- [2, 2] = 1.0
- [3, 3] = 1.0
+ 3x5 sparse matrix with 3 Float64 nonzeros:
+ [1, 1] = 1.0
+ [2, 2] = 1.0
+ [3, 3] = 1.0
+ <BLANKLINE>
 
 The ``sparse`` function is often a handy way to construct sparse
 matrices. It takes as its input a vector ``I`` of row indices, a
@@ -514,35 +524,37 @@ values. ``sparse(I,J,V)`` constructs a sparse matrix such that
  julia> I = [1, 4, 3, 5]; J = [4, 7, 18, 9]; V = [1, 2, -5, 3];
 
  julia> S = sparse(I,J,V)
- 5x18 sparse matrix with 4 nonzeros:
- [1 , 4] = 1
- [4 , 7] = 2
- [5 , 9] = 3
- [3 , 18] = -5
+ 5x18 sparse matrix with 4 Int64 nonzeros:
+ [1 , 4] = 1
+ [4 , 7] = 2
+ [5 , 9] = 3
+ [3 , 18] = -5
+ <BLANKLINE>
 
 The inverse of the ``sparse`` function is ``findn``, which
 retrieves the inputs used to create the sparse matrix.
 
 .. doctest::
 
  julia> findn(S)
- ([1, 4, 5, 3],[4, 7, 9, 18])
+ ([1,4,5,3],[4,7,9,18])
 
  julia> findnz(S)
- ([1, 4, 5, 3],[4, 7, 9, 18],[1, 2, 3, -5])
+ ([1,4,5,3],[4,7,9,18],[1,2,3,-5])
 
 Another way to create sparse matrices is to convert a dense matrix
 into a sparse matrix using the ``sparse`` function:
 
 .. doctest::
 
  julia> sparse(eye(5))
- 5x5 sparse matrix with 5 nonzeros:
- [1, 1] = 1.0
- [2, 2] = 1.0
- [3, 3] = 1.0
- [4, 4] = 1.0
- [5, 5] = 1.0
+ 5x5 sparse matrix with 5 Float64 nonzeros:
+ [1, 1] = 1.0
+ [2, 2] = 1.0
+ [3, 3] = 1.0
+ [4, 4] = 1.0
+ [5, 5] = 1.0
+ <BLANKLINE>
 
 You can go in the other direction using the ``dense`` or the ``full``
 function. The ``issparse`` function can be used to query if a matrix

diff --git a/doc/manual/complex-and-rational-numbers.rst b/doc/manual/complex-and-rational-numbers.rst
@@ -127,7 +127,7 @@ defined for complex numbers:
 
 .. doctest::
 
- julia> sqrt(im)
+ julia> sqrt(1im)
  0.7071067811865476 + 0.7071067811865475im
 
  julia> sqrt(1 + 2im)
@@ -150,8 +150,10 @@ versus ``-1 + 0im`` even though ``-1 == -1 + 0im``:
 .. doctest::
 
  julia> sqrt(-1)
- ERROR: DomainError()
- in sqrt at math.jl:111
+ ERROR: DomainError
+ sqrt will only return a complex result if called with a complex argument.
+ try sqrt(complex(x))
+ in sqrt at math.jl:284
 
  julia> sqrt(-1 + 0im)
  0.0 + 1.0im
@@ -288,14 +290,16 @@ Constructing infinite rational values is acceptable:
  -Inf
 
  julia> typeof(ans)
- Rational{Int64}
+ Rational{Int64} (constructor with 1 method)
 
 Trying to construct a ``NaN`` rational value, however, is not:
 
 .. doctest::
 
  julia> 0//0
- invalid rational: 0//0
+ ERROR: invalid rational: 0//0
+ in Rational at rational.jl:7
+ in // at rational.jl:17
 
 As usual, the promotion system makes interactions with other numeric
 types effortless:
@@ -306,7 +310,7 @@ types effortless:
  8//5
 
  julia> 3//5 - 0.5
- 0.1
+ 0.09999999999999998
 
  julia> 2//7 * (1 + 2im)
  2//7 + 4//7im
@@ -321,7 +325,7 @@ types effortless:
  1//2 + 2//1im
 
  julia> 1 + 2//3im
- 1//1 + 2//3im
+ 1//1 - 2//3im
 
  julia> 0.5 == 1//2
  true