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fatal: No names found, cannot describe anything.
fatal: bad revision '^'
    JULIA usr/lib/julia/inference.ji
essentials.jl
generator.jl
reflection.jl
options.jl
promotion.jl
tuple.jl
range.jl

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JULIA_CPU_TARGET:=core2
prefix=/datanumerik/bzfsikor/julia/julia-head

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typealias ColVec Vector{Union{Colon,Int}}
immutable SliceIterator{A<:AbstractArray}
  a::A
  dim::Int
  cols::ColVec
end

function SliceIterator(a, dim::Int)
    cols = ColVec(length(size(a)))
    cols = fill!(cols, :)

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@userplot PlotSolutions
@recipe function f(o::PlotSolutions)
  samples = o.args[1]

  species       --> measuredinds
  t             --> 0:1/3:30
  color_palette --> [colorant"steelblue"]
  linewidth     --> 0.1
  label         --> ""
  seriesalpha   --> 0.1

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heu

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using Plots

rect(x,y,w,h) = Shape(x+[0,w,w,0], y+[0,0,h,h])

@recipe function f(ds::Array{Measurement}; alpha = 1)
  isempty(ds) && return []
  binedges = [0,10,12,14,16,18,20,25]
  nmax = nfollicles(ds)
  a = alpha

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using Sundials
using Sundials: N_Vector, N_Vector_S

using ForwardDiff
#using ReverseDiff

"""
    sens(f!, t0, y0, p, tout; reltol, abstol)

Compute the solution and sensivities to the parametrized ODE problem defined by `f!(ẏ, t, y, p)`, starting at t0, y0, p.

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using Sundials

function test1()
  yS0 = [1. 2. 3.; 4. 5 6]
  N, Ns = size(yS0)

  GC.@preserve yS0n = [Sundials.NVector(yS0[:,j]) for j=1:Ns] # array of julia wrappers
  GC.@preserve yS0nv = [Sundials.N_Vector(n) for n in yS0n]   # array of pointers to sund. structs
  pyS0  = pointer(yS0nv)

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# HEAD FROM lapack.jl
const liblapack = Base.liblapack_name

import Base.LinAlg: BlasFloat, BlasChar, BlasInt, blas_int, chkstride1, chksquare, Schur

# extract to appear in lapack.jl
# Reorder Schur forms
for (trsen, elty) in
    ((:dtrsen_,:Float64),
     (:strsen_,:Float32))

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import numpy as np

t_adopt = .5
p_adopt = .9
p_expl = .1

def timestep(state, influence):
    influenced = (np.abs(np.dot(influence,state)) > t_adopt)
    r = np.random.rand(np.size(state))
    # vectorized: 415us