meggart

type Naive_Sparse_3{T} <: AbstractArray{T,3}
    size::(Int,Int,Int)
    vals::Dict{Int,T}
end 
function getindex(A::Naive_Sparse_3,i::Int)
    A.vals[i]
end 
function getindex(A::Naive_Sparse_3,i1::Int,i2::Int,i3::Int)
    i=((i3-1)*A.size[1]+(i2-1))*A.size[2]+i1
    getindex(A,i)

meggart

if VERSION > v"0.4.0-dev+4268"
	const marcoexpandkeys = Dict{Any,Any}("^I" => function (s,o...)
           if !isempty(s)
               
                   line = parse(Base.LineEdit.input_string(s))
                   s.kill_buffer=Base.LineEdit.input_string(s)
                   Base.LineEdit.edit_clear(s)
                   Base.LineEdit.edit_insert(s,string(macroexpand(line)))
               
           end

meggart

"Get the exact version of a package."
function version(name::AbstractString)
    packages = JSON.parse(readall(`$conda list --json`))
    for package in packages
        if startswith(package, name)
            return package
        end
    end
    warn("Could not find the $name package")
    return "Not found"

meggart

from datetime import datetime, timedelta
from cablab import Cube, CubeConfig, CubeSourceProvider
import numpy

class MiniCubeProvider(CubeSourceProvider):

    def __init__(self, cube_config):
        if cube_config.grid_width != 6 or cube_config.grid_height != 3:
            raise ValueError('illegal cube configuration, cube dimension must be 6x3')

meggart

import cablab
from cablab import cube_gen
from cablab import cube_config
from collections import OrderedDict
from textwrap import TextWrapper

class varInfo:
    def __init__(self):
        plist = cube_gen._load_source_providers()
        plist.pop('test')

meggart

"""
    broadcast_reduce(f,op,v0,A,Bs...)

Should behave like mapreduce, with the only difference that singleton dimension are expanded like in broadcast.
"""
function broadcast_reduce(f,op,v0,A,Bs...)
    shape = Base.Broadcast.broadcast_indices(A,Bs...)
    iter = CartesianRange(shape)
    keeps, Idefaults = Base.Broadcast.map_newindexer(shape, A, Bs)
    _broadcast_reduce(f,op,v0,keeps,Idefaults,A, Bs,Val{length(Bs)}(),iter)

meggart

meggart

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import numpy.fft as fft\n",

meggart

FROM jupyter/datascience-notebook:eb149a8c333a

MAINTAINER [email protected]
LABEL version='0.6.1'

USER root
RUN sudo apt-get update && sudo apt-get upgrade -y
RUN sudo apt-get install -y git unzip apt-utils vim

ADD start.sh /usr/local/bin/start.sh

meggart

import Tokenize.Tokens: IDENTIFIER, LSQUARE, COMMA, RSQUARE, STRING, ENDMARKER
function parsetokens(t,ar)
if t[1].kind==Tokenize.Tokens.IDENTIFIER
    @assert t[2].kind==LSQUARE
    brackcount=1
    i=3
    while true
        t[i].kind==LSQUARE && (brackcount=brackcount+1)
        t[i].kind==RSQUARE && (brackcount=brackcount-1)
        brackcount==0 && break