From 42fd6b1879a26cdd75b383f3061da75ffbc61611 Mon Sep 17 00:00:00 2001
From: Rafael Fourquet <fourquet.rafael@gmail.com>
Date: Wed, 3 Dec 2014 09:33:10 +0530
Subject: [PATCH] small fixup for rand(::BigInt) and update docs

Now the runtime version of GMP is checked against the compile time
version, as suggested by @JeffBezanson. He also noted that the limbs
field of RangeGeneratorBigInt was not specific enough.
---
 base/gmp.jl         | 12 ++++++++++--
 base/random.jl      |  2 +-
 doc/stdlib/base.rst |  4 ++--
 3 files changed, 13 insertions(+), 5 deletions(-)

diff --git a/base/gmp.jl b/base/gmp.jl
index ccc491657acf06..ea30a3c38eb134 100644
--- a/base/gmp.jl
+++ b/base/gmp.jl
@@ -18,8 +18,11 @@ else
 end
 typealias CdoubleMax Union(Float16, Float32, Float64)
 
-const GMP_VERSION = VersionNumber(bytestring(unsafe_load(cglobal((:__gmp_version, :libgmp), Ptr{Cchar}))))
-const GMP_BITS_PER_LIMB = Int(unsafe_load(cglobal((:__gmp_bits_per_limb, :libgmp), Cint)))
+gmp_version() = VersionNumber(bytestring(unsafe_load(cglobal((:__gmp_version, :libgmp), Ptr{Cchar}))))
+gmp_bits_per_limb() = Int(unsafe_load(cglobal((:__gmp_bits_per_limb, :libgmp), Cint)))
+
+const GMP_VERSION = gmp_version()
+const GMP_BITS_PER_LIMB = gmp_bits_per_limb()
 
 # GMP's mp_limb_t is by default a typedef of `unsigned long`, but can also be configured to be either
 # `unsigned int` or `unsigned long long int`. The correct unsigned type is here named Limb, and must
@@ -49,6 +52,11 @@ _gmp_clear_func = C_NULL
 _mpfr_clear_func = C_NULL
 
 function __init__()
+    if gmp_version() != GMP_VERSION || gmp_bits_per_limb() != GMP_BITS_PER_LIMB
+        error(string("the dynamically loaded GMP library (version $(gmp_version()) with __gmp_bits_per_limb == $(gmp_bits_per_limb()))\n",
+                     "does not correspond to the compile time version (version $GMP_VERSION with __gmp_bits_per_limb == $GMP_BITS_PER_LIMB)"))
+    end
+
     global _gmp_clear_func = cglobal((:__gmpz_clear, :libgmp))
     global _mpfr_clear_func = cglobal((:mpfr_clear, :libmpfr))
     ccall((:__gmp_set_memory_functions, :libgmp), Void,
diff --git a/base/random.jl b/base/random.jl
index 025b4f7012bdc0..ec4d09687b0ec7 100644
--- a/base/random.jl
+++ b/base/random.jl
@@ -399,7 +399,7 @@ else
     immutable RangeGeneratorBigInt <: RangeGenerator
         a::BigInt             # first
         m::BigInt             # range length - 1
-        limbs::Array{Limb}    # buffer to be copied into generated BigInt's
+        limbs::Vector{Limb}   # buffer to be copied into generated BigInt's
         mask::Limb            # applied to the highest limb
 
         RangeGeneratorBigInt(a, m, nlimbs, mask) = new(a, m, Array(Limb, nlimbs), mask)
diff --git a/doc/stdlib/base.rst b/doc/stdlib/base.rst
index 3ab5822e46ca18..7ea4e7f80fe73b 100644
--- a/doc/stdlib/base.rst
+++ b/doc/stdlib/base.rst
@@ -4083,7 +4083,7 @@ Random number generation in Julia uses the `Mersenne Twister library <http://www
 
 Most functions related to random generation accept an optional ``AbstractRNG`` as the first argument, ``rng`` , which defaults to the global one if not provided. Morever, some of them accept optionally dimension specifications ``dims...`` (which can be given as a tuple) to generate arrays of random values.
 
-A ``MersenneTwister`` RNG can generate random numbers of the following types: ``Float16, Float32, Float64, Bool, Int16, UInt16, Int32, UInt32, Int64, UInt64, Int128, UInt128`` (or complex numbers or arrays of those types). Random floating point numbers are generated uniformly in [0,1).
+A ``MersenneTwister`` RNG can generate random numbers of the following types: ``Float16``, ``Float32``, ``Float64``, ``Bool``, ``Int8``, ``UInt8``, ``Int16``, ``UInt16``, ``Int32``, ``UInt32``, ``Int64``, ``UInt64``, ``Int128``, ``UInt128``, ``BigIng`` (or complex numbers of those types). Random floating point numbers are generated uniformly in [0,1). As ``BigInt`` represents unbounded integers, the interval must be specified (e.g. ``rand(big(1:6))``).
 
 .. function:: srand([rng], [seed])
 
@@ -4099,7 +4099,7 @@ A ``MersenneTwister`` RNG can generate random numbers of the following types: ``
 
    * an indexable collection (for example ``1:n`` or ``['x','y','z']``), or
 
-   * a type: the set of values to pick from is then equivalent to ``typemin(S):typemax(S)`` for integers, and to [0,1) for floating point numbers;
+   * a type: the set of values to pick from is then equivalent to ``typemin(S):typemax(S)`` for integers (this is not applicable to ``BigInt``), and to [0,1) for floating point numbers;
 
    ``S`` defaults to ``Float64``.