dask-play.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: $\\hat{A}x=\\hat{b}$\n",
    "- want to solve $\\hat{A}x=\\hat{b}$\n",
    "- $\\hat{A}$ is so big that we can't work on it on a single machine\n",
    "- split $\\hat{A}$ into groups of rows\n",
    "$$\n",
    "\\hat{A}x=\\hat{b}\n",
    "$$\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "-\\ A_1 - \\\\\n",
    "-\\ A_2 - \\\\\n",
    "-\\ A_3 - \n",
    "\\end{bmatrix}x =\n",
    "\\begin{bmatrix}\n",
    "b_1 \\\\\n",
    "b_2 \\\\\n",
    "b_3\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "- $\\hat{A}x=\\hat{b}$ equivalent to set intersection problem\n",
    "$$\n",
    "x \\in \\lbrace z \\mid A_1z = b_1 \\rbrace \\cap \\lbrace z \\mid A_2z = b_2 \\rbrace \\cap \\lbrace z \\mid A_3z = b_3 \\rbrace\n",
    "$$\n",
    "- i.e., find $x$ in the intersection of subspaces\n",
    "- on each machine, **easy** to **project** onto its subspace:\n",
    "$$\n",
    "\\begin{array}{ll}\n",
    "\\mbox{minimize} & \\|x - x_0 \\|_2^2 \\\\\n",
    "\\mbox{subject to} & A_i x = b_i\n",
    "\\end{array}\n",
    "$$\n",
    "- \"easy\" because it's just linear algebra; involves a matrix factorization that can be **reused** at each iteration\n",
    "- let $\\mbox{proj}_i(x_0)$ be the projection of $x_0$ onto the subspace\n",
    "$$\\lbrace z \\mid A_iz = b_i \\rbrace$$\n",
    "\n",
    "# Projection\n",
    "\n",
    "projection of $x_0$ onto $\\lbrace x \\mid Ax = b\\rbrace$ given by\n",
    "$$\n",
    "\\mbox{proj}(x_0) = \\left(I - A^T (AA^T)^{-1}A\\right)x_0 + A^T (AA^T)^{-1}b = \\left(I - A^T (AA^T)^{-1}A\\right)x_0 + c\n",
    "$$\n",
    "\n",
    "## One-time computation\n",
    "- compute $AA^T$ and form its Cholesky factorization once to reuse at each iteration\n",
    "- compute $c = A^T (AA^T)^{-1}b$ to reuse\n",
    "\n",
    "\n",
    "# Iteration\n",
    "- $z_i^{k+1} = \\mbox{proj}_i(x^k)$\n",
    "- $\\bar{x}^{k+1} = \\frac{1}{N} \\sum_{i=1}^N z_i^{k+1}$\n",
    "- the projection operation involves using the Cholesky factorization of $AA^T$ to compute $A^T (AA^T)^{-1}Ax^k$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.linalg import cho_factor, cho_solve\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def factor(A,b):\n",
    "    \"\"\"Return cholesky factorization data to project onto Ax=b.\"\"\"\n",
    "    AAt = A.dot(A.T)\n",
    "    chol = cho_factor(AAt, overwrite_a=True)\n",
    "    \n",
    "    c = cho_solve(chol, b, overwrite_b=False)\n",
    "    c = A.T.dot(c)\n",
    "    \n",
    "    proj_data = dict(A=A, b=b, chol=chol,c=c)\n",
    "    \n",
    "    return proj_data\n",
    "\n",
    "def proj(proj_data, x0):\n",
    "    \"\"\"Use cholesky factorization data to project onto Ax=b.\"\"\"\n",
    "    A, chol, c = (proj_data[k] for k in 'A chol c'.split())\n",
    "\n",
    "    x = A.dot(x0)\n",
    "    x = cho_solve(chol, x, overwrite_b=True)\n",
    "    x = A.T.dot(x)\n",
    "    x = x0 - x + c\n",
    "    return x\n",
    "\n",
    "def average(*vals):\n",
    "    \"\"\" Come to a consensus \"\"\"\n",
    "    return np.mean(vals, axis=0)\n",
    "\n",
    "def make_data(k, rows, seed=0):\n",
    "    \"\"\"Make some random test data.\"\"\"\n",
    "    # each of k chunks gets 'rows' rows of the full matrix\n",
    "    n = rows*k\n",
    "\n",
    "    np.random.seed(seed)\n",
    "\n",
    "    Ahat = np.random.randn(n,n)\n",
    "    bhat = np.random.randn(n)\n",
    "\n",
    "    x_true = np.linalg.solve(Ahat,bhat)\n",
    "\n",
    "    x0 = np.random.randn(n)\n",
    "\n",
    "    As = []\n",
    "    bs = []\n",
    "\n",
    "    for i in range(k):\n",
    "        s = slice(i*rows,(i+1)*rows)\n",
    "        As += [Ahat[s,:]]\n",
    "        bs += [bhat[s]]\n",
    "        \n",
    "    return As, bs, x_true, x0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem data\n",
    "- this algorithm has the same flavor as the thing I'd like to do, but actually converges **very** slowly\n",
    "- will take a very long time to converge anything other than the smallest examples\n",
    "- don't worry if convergence plots look flat when dealing with 100s of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1062928d0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DoUFq2t1SrBQGIinkDpMmxQ13li2DSy6Bfv0UCpI/CgORFHOHxx+PUPjmmwiF/v1jzEEk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\nDEREBIWBiIigMBARERQGIiIC/B+37fg/wMI2lgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104b63978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "As, bs, x_true, x0 = make_data(4, 2, seed=0)\n",
    "\n",
    "proj_data = list(map(factor, As, bs))\n",
    "\n",
    "x = x0\n",
    "\n",
    "r = []\n",
    "for i in range(1000):\n",
    "    z = (proj(d,x) for d in proj_data)\n",
    "    x = average(*z)\n",
    "    r.append(np.linalg.norm(x_true-x))\n",
    "    \n",
    "plt.semilogy(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1069d0cc0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAECCAYAAAAYfWtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQGMiANCYCAA09v+kUFgx6zsTBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10659d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "As, bs, x_true, x0 = make_data(4, 100, seed=0)\n",
    "proj_data = list(map(factor, As, bs))\n",
    "\n",
    "x = x0\n",
    "\n",
    "r = []\n",
    "for i in range(1000):\n",
    "    z = (proj(d,x) for d in proj_data)\n",
    "    x = average(*z)\n",
    "    r.append(np.linalg.norm(x_true-x))\n",
    "    \n",
    "\n",
    "plt.semilogy(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I'll fix the test data to something large enough so that each iteration's computational task is significant\n",
    "- just 10 iterations of the algorithm (along with the setup factorizations) in serial takes about a second on my laptop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "As, bs, x_true, x0 = make_data(4, 1000, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.03 s, sys: 59.2 ms, total: 2.09 s\n",
      "Wall time: 976 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "proj_data = list(map(factor, As, bs))\n",
    "\n",
    "x = x0\n",
    "\n",
    "r = []\n",
    "for i in range(10):\n",
    "    z = (proj(d,x) for d in proj_data)\n",
    "    x = average(*z)\n",
    "    r.append(np.linalg.norm(x_true-x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dask Solution\n",
    "- I create a few weird functions to have pretty names in dask graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import dask\n",
    "from dask import do, value, compute, visualize, get\n",
    "from dask.imperative import Value\n",
    "from dask.dot import dot_graph\n",
    "\n",
    "from itertools import repeat\n",
    "\n",
    "def enum_values(vals, name=None):\n",
    "    \"\"\"Create values with a name and a subscript\"\"\"\n",
    "    if not name:\n",
    "        raise ValueError('Need a name.')\n",
    "    return [value(v,name+'_%d'%i) for i,v in enumerate(vals)]\n",
    "\n",
    "def rename(value, name):\n",
    "    \"\"\"Rename a Value.\"\"\"\n",
    "    d = dict(value.dask)\n",
    "    d[name] = d[value.key]\n",
    "    del d[value.key]\n",
    "    return Value(name, [d])\n",
    "    \n",
    "def enum_map(func, *args, name=None):\n",
    "    \"\"\"Map `func` over `args` to create `Value`s with a name and a subscript.\"\"\"\n",
    "    if not name:\n",
    "        raise ValueError('Need a name.')\n",
    "    \n",
    "    values = (do(func)(*a) for a in  zip(*args))\n",
    "    return [rename(v, name+'_%d'%i) for i, v in enumerate(values)]\n",
    "\n",
    "\n",
    "def step(proj_data, xk, k=None):\n",
    "    \"\"\"One step of the projection iteration.\"\"\"\n",
    "    if k is None:\n",
    "        sufx = '^k+1'\n",
    "    else:\n",
    "        sufx = '^%d'%k\n",
    "    \n",
    "    z = enum_map(proj, proj_data, repeat(xk), name='z'+sufx)\n",
    "    xkk = do(average)(*z)\n",
    "    xkk = rename(xkk, 'x'+sufx)\n",
    "    return xkk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize\n",
    "- the setup step involving the matrix factorizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0f5xFUN5B/8ctDf0fZ4F31ghgPB3nFpDxtMwTd+Z5R9emTZvS6NGjqWPHjset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\nD6pduzaNGDFCTbtJzRTBy9W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7di3VrFlThcXu2LGj73rCm21u27aNvvrqK9+13eYG52oX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\nBEAABEAABKwQ+H+B4cxtbTZMlAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lAs = enum_values(As, 'A')\n",
    "lbs = enum_values(bs, 'b')\n",
    "proj_data = enum_map(factor, lAs, lbs, name='proj_data')\n",
    "\n",
    "visualize(*proj_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- visualize one iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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66SYSIYFAE6DSDbT4g1l4WDF37txZ+vbtazw0BZNCdkvds2dP0VsrSvPmzbOb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\n+N21Y/qiSfwkuIGf261C9+zZo2rWrKnq1q2r8JshdQJPP/2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JdkQXEQeWFUAOkIdTAWbt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\nERABERABESiPwP8CA+qSF3wLLIwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_val = [pd.compute() for pd in proj_data]\n",
    "\n",
    "xk = value(x0,'x^k')\n",
    "xkk = step(pd_val, xk)\n",
    "xkk.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The setup step along with 3 iterations gives the following dask graph. (Which I'm showing mostly because it was satisfying to make.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y5EnZtGmTub9z586SLVs2yZ07t1xyySVSqFAhufzyy6VUqVLmNUsW/tecJjQJEEAAAQRs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\nadOmIeehz2V26dJFdFRTF+cJHLpQkI4WtGnTJnD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fG3dKRQABewjE6SOZ9qgKtUAAAQTC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\nTx9bi20y8lrTp0+37rvvPuvhhx92FX9jdbE9f9wcbr3/ncbf1KZ0/f0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Dbm4xKZRYNzJK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Pq3zwwQfq5cKoUaPU7zv2+Xgx9uabb4pmEVW/99jH9Pxud/4Yq+ZSrxRqbV14vKFr68NF89BQ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\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DBu3aa6/V9OXK2qBBg1T10cYGjpHBsTAFChTQPvzww7QmRxob6BvGCvqpz2arsavv90nL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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = value(x0,'x^0')\n",
    "for k in range(3):\n",
    "    x = step(proj_data, x, k+1)\n",
    "    \n",
    "x.visualize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reuse dask graph\n",
    "\n",
    "Obviously, it's not efficient to make a huge dask graph, especially if I'll be doing thousands of iterations.\n",
    "I really just want to create the dask graph for computing $x^{k+1}$ from $x^k$ and re-apply it at every iteration.\n",
    "Is it more efficient to create that dask graph once and reuse it? Maybe that's a premature optimization... I'll do it anyway for fun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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66SYSIYFAE6DSDbT4g1l4WDF37txZ+vbtazw0BZNCdkvds2dP0VsrSvPmzbOb\nMFMjAZcRoNJ1mUCYnewQuPPOO2XTpk3GB3F2UgxuKtiw/t1335V77rnHLBcKLgmWnAREuJ8u34LA\nEsDcLrwjLV26NGJ7u8ACcajgDzzwgFG62M4vv60NHcoCoyUB1xBgT9c1omBGsk3gscceE+x4g14Y\ngzMEYKyGYfyHHnqICtcZxIzVYwTY0/WYwJhdewncdtttgqUsP/74Ix022IvWxNapUyf58ssvZfny\n5YK5dAYSCDoB9nSD/gYEvPxPPfWUwDtV9+7dA07C/uJ/8803MnjwYHn++eepcO3Hyxg9SoA9XY8K\njtm2jwAsa++//37jiB977jJkTuCff/6RWrVqmdEDzJsXKFAg80gZAwn4gACVrg+EyCJkRmD//v1S\nv359s7k79nqlgsiMJ55+5ZVX5OGHHxZsWF+9evXMI2QMJOATAhxe9okgWYz0CRQqVEjgLQlbzr3w\nwgvpR8QnDYEffvjBKNyOHTtK1apVSYUESCCMAHu6YTD4M3gEli1bJpMmTTI7D02ePNk4y5g6dao0\natQoeDBsKPHOnTulTp06smLFCsEQ82GHHWZ8XTdp0kTOP/98OfXUUzmSYANnRuFdAlS63pUdc54G\ngXXr1gmU6xdffGH21YWDDPR0McR87LHHSo0aNWTRokWyYMECOeqoo9JIIdiPoHeL/Ypffvllue66\n6wwM7J+Lf1DCxYsXN3vpYj9dKOEqVaoEGxhLHzgCVLqBE3mwCrx9+3b56quvTG/2k08+MUtXMGcL\nRQslYAUcz5w5UypXriwwpkKPbPz48fSgZAFK4u+gQYMEvpWxbSLcPcIpRo8ePcwHTfjjYI2ADx3s\n+tS0aVOjiM877zypUKFC+K38TQL+I6AYSMBnBLTnI/X444+revXqKd3DUrrWqoMOOsj8xe/of1oJ\nK234E6Kgla8qUqSI6tKlS+gcfyQm8NlnnxnG2ngqdOO+ffuMDAoXLpyHebgMcN2Skx7WDz3PHyTg\nRwLix0KxTMEm8OijjyZs5KMbfN0rywPsww8/VLpHpvQ63jzXeCKSwJw5c9Thhx+uOnTooA4cOBBx\ncc2aNerII49U+LAJ5x7rN+7RPeWI53lAAn4jwOFlXfsZ/EXgr7/+kpNPPlk2btwoWgnELRzmGbG3\n6+LFi2PO3/bv319uueUWefPNN+Xmm2+OG0+QL2DLPhid1a1bVz766KOYPqwxx3vxxRcnxARZHHPM\nMQLDNmy7yEACfiVQ6Akd/Fo4liuYBPTQsJmXhTek/ALmeeMta4EVLuYf4TijUqVKJs784gvSdbh2\nhFVyxYoVzTwuuMcKMJbas2ePzJgxAyNrsW4x5/XoglSrVi3mdZ4kAb8QoNL1iyRZjggC2LsVlsqw\nQo7V0KNn9fTTT0v79u0jnos+OPfcc80yovvuu8847D/rrLOibwnkMdY0X3DBBXLccccJPlyOOOKI\nhBxgJKXnfQUbIESPPug5XWnQoIE8+OCDEk9xJ4ycF0nAQwToHMNDwmJWkyfw/fffm57VoYcemscC\nGY38OeecI926dUsqwmeffVZeffVVoxSgfGMp8aQi8slNWG4FJYqRACy/KlmyZL4lw4jBqFGjRM/9\nRqzTxccPlmZhwwlYjGPNNAMJ+JkAla6fpRvAsmEZCpTkGWecYdaEwtNUuJJEI49e2fDhw/Mo40S4\nsOn9sGHDpHfv3qZ3vGvXrkS3+/baO++8I5dccom0bt3azOHC+UWyoXz58oZ7uDzQ633vvfdkyZIl\noq3NzXD1rbfeKljqxUACviSgKwADCfiCgN6M3ixR0VvIKe2cQWkFbMrVtWtXY4msK7CxotU9tbTL\n+/nnnyvds1Pan7DSBlhpx+O1B7WnKXXdddcZfnpv3DxWyqmUB8uK9MePwlIhrWAjHh0xYoQqVaqU\n0vPESvd6I67xgAT8QAC9AAYS8DQBKFess4Wy1T1cpXtNEeXRW/cp7fTCLFl55JFHIq6lc/Drr7+q\nhg0bqqJFi6q33347nSg89Yz2paxOOeUUpYeB1YQJEzLOu3ZKovTcuNIewJTu0eaJb8OGDeqyyy4z\nCh5KOdY9eR7iCRLwCAEqXY8IitmMTUD7+FVnn322ccygDaMUHDLECvPnz1d6iFihwbcjIB3tccko\nhquvvlppAyE7onVVHGCl57LNxwU+MvCxYVdAz1m74EwY3fvvv28UvbYcV3ruOOG9vEgCXiFApesV\nSTGfeQi8/vrrSs8pqtNOO01BqeYiaMtdpecqjQOIPn36hIa0c5EXO9PUy3tUzZo1zccMnI3E+5ix\nM81YceFjpk2bNubjBh7CduzYEes2niMBzxCg0vWMqJhRiwC8HOn1oWaeFvODeg2odSknf//88091\n2223mXnK2rVrK7iR9GrQDkXUjTfeaJQc3GhqK3BXFEUbW5m59OOPP15NmTLFFXliJkggHQK0Xval\neZx/C/Xuu++apSV6qFOw4TwslQ8++OCcFhjW0P369TP78WJJDNactmrVSrBsySvhjz/+ED3fLVjf\njHW3eq5aZs2aJXoUwRVF0EP4xnMY8oPdif7zn/8IthFkIAHPEUhHU/MZEsg2AQwzXnrppaYHdvfd\ndyu9ZCfbWUg6PRgb6Z2KjOEWDJAysZZOOtE0b1y5cqWZ64YlMQzRXnjhBYX5VjeHoUOHml6v/kBQ\neu9jN2eVeSOBPATY0/XcZ1LwMjxy5EjRS3TMPrfYYL5nz54CpxduDXCNWLZsWZM99MZwDF/Q2GMW\nHplyHeCScfTo0dKsWTOzleHYsWPlpJNOkr///lt+++0343kr13lMlP61115rer1wpgEnHVhDHdR1\n04k48ZpLCeRRwzxBAi4hsHnzZtWuXTvTY9QbD3hi6Qjml7Vz/9C6YBh7aVeUZptA7LaD9alaUai+\nffsqbEGYraA3gVDYOUlvMq/0cLjJB3ZXwjkYScGyG7v84B8MlqJ3C8pWPlNNZ8iQIapEiRLqxBNP\nVHrf5FQf5/0kkHUCNKTKOnImmAwBvWON0rvOmLWc2KvVC2Hv3r2qRYsWIYWrv7Mj1rViSFy7QlRt\n27Y1W+Hhuu4BK3xQ6M0ZlN5hxzbrZ+13WunN5BUMzbTLS2OFDIWPpT9YBqR7tBFIsc7Z2nPY2mLP\nK4r3999/V9pLlvlgwMeD24fHI8DzIHAEuLWfS0cggpotbQkses5W4G5Q98qkV69exp2j23no3qJo\nhw6CbezgitIK2HBBL72xDkN/tYKWadOmifZwJd98843MmzfP7MSDYXM9Dyy652Z2NoLrRO2hyfg3\nxjVsCABXlngew8Rbt26VLVu2iHYoIatWrRI9R2tcKuo1sCYtDGtjkwZsTnDRRReZuEKZCPuB4eYr\nrrgidAZpaA9UAjeaWgmHzrv5B3aVuuuuu0wZYQiGLQcZSMBtBKh03SaRAOcHzu5vuOEGo1Cwhy0s\ngL0Q4ilc5F0Pkcfcqze6XFCg2vOTLFy40MxXQnlCiepenMCyGGnEClCIxYsXlzJlypgt9vSSGrNV\nIax8oeyxmUAyAbsGwV91eIDixS5MUGD47YUAXtj7GB8/mOt97rnnXD3/7wWmzKO9BKh07eXJ2NIg\nAGMj7d1J9PynXH755eYvendeCFCGyDOW2YT3cJF3LGWCMrUjYAMA7c7SfJBgkwDEjV4vlithB59M\ng16fK0cffXSeaKBssVwHS7XsSCdPAg6dQH4xYlK6dGnz0cAtGR0CzWhTJkClmzIyPmAnAQyxYhgT\nw6TauEiuuuoqO6N3NK5EChcJY6/Z1atXO5oHOyOHEsewdXSA4sXQM3YD8pLihSU2er2ffvqpGXbG\nmm43W71Hc+exPwl4Y8zIn+wDXSosT8HetNgkvlq1amZo1WsKF4ooVg/XEqzeKcf66Ym/5cqVi5lP\n9KyxF662JBftjznmPW48iflwbUxm5qXfeust0WunzR7Lbswr8xQcAlS6wZG1a0o6Z84c0e4SZcCA\nAebf+PHjQ+taXZPJBBlBDxcKFw169JCy9Rh6h5hf9VKAN6p4AYoX63mvvPLKuPPL8Z7N9fnrr7/e\nfNShfHpzDPOxh48+BhLIBQEq3VxQD2iaUFbdu3cXvWxF0KtatGiRMZzyGg7MP+slTXEVLsqjPTxJ\nhQoVPFU0KCXkO17ABwYU71NPPRXvFtee19sIGuMq60MPvV7tI9u1+WXG/EuASte/snVVyWCVC+vY\nHj16iF4nKto1opnzdFUmk8wMDKfQaCPEm+NEzxANvZcChsPjWSnrNbymKHrHH+nUqZOXihWRV1jH\nw0q8UqVKZknR/fffbzxxRdzEAxJwkACVroNwGbWY3iCWbUDhYjMAbAJw++23e2btZywZYv2n3kpQ\ntNMOqV+/vrkluoeIuU+v9XShdKPnbKFssSypdevWRllpD1bGdWQsLl45B7nAuArL0vr37y+1atWS\n2bNneyX7zKfHCVDpelyAbs7+jz/+aBwzYDgSlqNff/215xvscN5wNqG3mTNrZCtXrmwuhStfL/Z0\n0UNHsJQt/BvDIAmWy/B/7afQuXNn8yEBK3MsKXrwwQdtW+LlJ04si70EqHTt5cnYNAHt180MIaMH\ngXnA7777zhivxBu69DI0eEHatm2bGS5HObU7wlAv3os9XcgCcsKmAsuXLzcbI2CThmHDhnlZTHHz\nDhlhxAJrxPEP7+y3334b935eIIGMCQTO8SUL7CgBbBVn+frVPVzjTN/RBHMYuR6KVbqHq3SPKSIX\nS5YsUdpgJ+KcVw6Q7+iNGPQ6alWlShWF8vo56DXV6sILLzS+s3WvV2kLZz8Xl2XLEQFueJAj8H5M\n9o033jCO/GvUqKF0r8+PRYwokx5yNQ207hFGnPfbAcqnDcaU7u36rWgxy4P3uFixYgp7Ieteb8x7\neJIE0iVAj1QZjxUwgrVr18qNN94okydPFliDPvnkk8ZNoZ/J6Aon8G+sPzDMfKefy4qydejQwWzK\nAMtfP04TRMsPnsTwTmP/ZiwRe+KJJ3z/Tkcz4LEzBDin6wzXwMSKOU0oHjjnh0vH//73v4FonLBO\nd/HixaK3zguErB955BGBYRw8UwUhwJIby9pee+016dOnj3Hmgk0hGEggUwLs6WZKMKDPYys5vQ+s\ncRKB3VygbIPk17ZevXrGwQecRQQlwE2nnq82y768st2fHbLBByXW92ILRlg4P/bYY4H4sLSDHePI\nS4BKNy8TnsmHwAcffCBdunQx626x7y38JwcpoAeE5UKwco3eDs/PHDC0jCF17L0LJxlBCphOgHUz\nhprh3hPvfZ06dYKEgGW1iQCVrk0ggxAN9nWFY4vhw4eb3VteeeUVo3iDUPbwMjZu3Nj0dLABfdAC\nfE5jr18sjwpi0JbdpteLqZSHHnrIuDXFNosMJJAsASrdZEkF/L4JEyYYRQsjmoEDB0qzZs0CSWTG\njBnGkQIMbILWw4fAFyxYYNayYk67ZcuWgXwH0Ovt16+fGWqGv2rs3Yv1vQwkkAwBKt1kKAX4nr/+\n+stsBv72229L+/btjVFJ8eLFA0ukRYsW8ueffxqjsaBCaNWqlaxbty7wTiTQ48dc7/Tp06Vbt26m\n12v5qA7qu8Fy50+ASjd/RoG9A0uA0KhgGzT4qYX/3SAH+FvGloTYQ7d58+aBRQErXsxlT5w4MbAj\nHpbw0euFhTOGmuEKFL1eazMM6x7+JYFwAlS64TT42xDYtWuXMRjBENpll11mDEhKly4deDrYS3bF\nihWBnc8MfwHQ49+6dSs3hf9/KHgv8IGK7QKxjAxLrNjrDX9j+NsiQKVrkeBfQwBDZdi6DUZT+IK/\n5pprSEYTWLZsmXH4P2LECLOBfdChzJo1Sxo0aGDWsmrXiUHHYcqPXi/W9GKo+aSTTjIWzjVr1iQb\nEoggQOcYETiCe7Bnzx7jTUr7TZaTTz7ZOH6gwv33fcA6ZDSk6PkziJx55pnSpEkTefrpp4nj/wlg\n7TLWrGP7SmxjiSF47LC1b98+MiKBfwnorzOGgBOYM2eO8TMLf7PaMjngNPIWHxsA6C37lJ6vy3sx\nwGe0swilWxKlLbkDTCF20fXuWqpnz55KO4xR2rJZaUUc+0aeDRwB9nT//f4I3C98gcO7DoYJjznm\nGFm0aJHxNxs4EPkU+MUXXxTsjcuefySoRo0aCdYsozfHEEkAS+vuvvtu0+stWrSo1K1b14wK6J2a\nIm/kUfAIBO4zgwU2BBYuXKi0laXSDYLS81BKb15OMjEI/P7776pIkSJKeyOKcZWnvvzyS9Pb1c4i\nCCMOAfR6e/ToYXq92vpdoe4xBJcAe7oB+87CpvLPP/+8+fLGFzjmn/7zn/+ENl4PGI58iwuvWyVL\nlpTrr78+33uDeMN5550n6PFybje+9NHrveeee4xjkUMOOcTUvWeffVbY643PzNdXgvu9EbyS611i\nlDaAMT03PWSq8AXOEJ/A5s2b1WGHHaZefvnl+DfxitLuME1vd/bs2aSRDwHUObxPWvkq7btZ6Smd\nfJ7gZb8RYE/X159U/yucfmmlV69eZtE+5nHnzZtnLJWDsC9qJuIFM/RMbr311kyi8f2zsGKGNTPn\ndvMXNercvffea3q98NmMTROee+45wQgUQ0AI+O0rguWJJADLW+0jWOmF+kpvxK200o28gUcxCWj3\nl0q7u1RakcS8zpORBD7++GPT29UfdJEXeBSXAHq9L730kun1akMrpXdxinsvL/iHAHu6Pv646t+/\nv9lgfsuWLQJnBo8//rjopS8+LrF9Revbt69o4zK544477IvUxzFdfPHFptfGud3khYxe73333Sdw\nL1qoUCHDD/YW7PUmz9CTd/rn+4ElsQisXbtW6V2AlK7ISm+6rbTvZOsS/yZBQLvBVGXKlFHan24S\nd/MWi8C4ceOUdhDBNakWkBT+aqMq9cILLxh7C+1UQy1evDiFp3mrlwiwp+vJT6X4mR46dKiceuqp\nxkewdl5gLJX1kpf4D/BKHgLYunD79u3G4jTPRZ6ISwBb/WGTe/Z24yKKewE93QceeMD0euHZChtr\naCXMXm9cYt69QKXrXdlF5Hzjxo3Spk0b6dixo/mHpUBwesGQGgEYmul5NrnppptE93ZTezjgd0NZ\ndO/eXUaPHi1LliwJOI30il+tWjWziYS2vzDTQWeddZYsXbo0vcj4lCsJUOm6UiypZQqNHHq32GAc\n2/HB6la7n0stEt5tCAwePFjWr19v5tqIJHUC8E19yimnyDPPPJP6w3zCEECvF1sFfvfdd8auAL1e\nfAhyrtcfLwiVrofliK3Vrr32WrPrDTYW155uBM4KGNIjgEYNhiwYLahQoUJ6kQT8KfR2H330UcFu\nTHpdeMBpZFZ8fLxgq0C4asUIApyQYLcrBm8ToNL1qPywkXr16tVFO5s3m6oPGDBA9IYFHi2NO7I9\ncuRI0UusTC/DHTnyZi7atm1rdmTC+lOGzAig14utArG2Hh6s9OYJop1rmB5wZjHz6VwRoNLNFfk0\n04WBT+fOnQWbiJ9//vmi1/ZJ8+bN04yNj1kEtPWjYPs+KIzKlStbp/k3DQJYCoNN3IcNG2YM+tKI\ngo9EEcAHNnq96PGCLXq9HEmIguSRQypdjwgK2dTO5c26248++sgYq8BSuUSJEh4qgXuzCqb4gHn4\n4Yfdm0kP5ezqq6+W448/3nhb8lC2XZ1VrLHH+4le7969e42HOb2RAnu9rpZa3sxR6eZl4rozet2o\ncdJw4YUXmgX0eg0fN1O3WUoYCr300kuNQZrNUQcyOgyLQkEMGTJEVq1aFUgGThUaRpNwdoMeL4ae\nzz77bPnpp5+cSo7x2kyAStdmoHZHN2PGDKlZs6YZqkMDBkvl0qVL251MoOObNGmSfPvtt+zl2vwW\ndOjQwexDDOM0BnsJoNcLg7W5c+fK7t27Ta+3Z8+e7PXai9mR2Kh0HcGaeaR79uwxi+XxFVulShUz\n9AlLZQb7CWCbNYwi1KtXz/7IAxwjFAN6Ym+//bZoL2kBJuFc0WvUqGE+GLHESHufk3POOUeWL1/u\nXIKMOWMCVLoZI7Q/AszZYPeRN954Q958801jnVyuXDn7E2KMxhEBLMAxVMdgP4FOnTrJ0UcfbZZi\n2R87YwQBfNxgWRF6vTt37jQjY1irD9/hDO4jQKXrIpnAG9IT2hMNtkmDNyS916axVHZRFn2XFfRy\nGzZsKI0bN/Zd2dxQIGxfh17YoEGDZN26dW7Ikm/zABecmCZBj/f+++837/TPP//s2/J6tWBUui6R\nHCxnoWzheQYWifAsVbFiRZfkzp/ZgAcvrHdmL9dZ+d54441SsmRJefHFF51NiLGL3sLTuI+E8tXb\nU5peb+/evQVL4hjcQYBKN8dygBckODbX+2kKNiaAIsB2cvDsw+AsAfRy4WwA29IxOEcA7zV6X5gq\n2bBhg3MJMeYQgdNPP13mzJljerzYPhAjOStWrAhd54/cEaDSzR17Y/AAQynsc/vkk08KdgWC0RSD\n8wTgTu/DDz+kxbLzqE0K2EDiiCOOMN6UspRk4JNBrxfTVbNnz5Zt27aZXu9rr73GXm+O34wC2Icw\n0zzs2LFDPvvsM07cpwASS4H69esn5cuXl9tvv12OO+64mE9jnubkk0+OeS0bJ+H1Bj6d/RZGjRol\n06ZNM0P58KCU7YA0mzZtKocffnhWk86lPMePHy8TJ06UPn36mE3bs1pwhxPLlTyjixVPvnAhieWG\nY8aMkapVq5qPTcy3MyRHwFb5QulmGrTfXyhu/nOAwUUXXZSpeDJ6vkmTJpSrA3JFfUG9yXagPJ1r\np3Ihz+j3h/J1v3xt+cTHVxQMJfQLwH82MrjllluMk/PkvsWcuQtzzsgHZWvvu436kout2ihPe+Vo\n1YtcyTO61lO+7pevLUo3WvA89hcBGnVRnv4i4O/SsL66W75Uuu6WD3NHAiRAAiTgIwJUuj4SJotC\nAiRAAiTgbgJUuu6WD3NHAiRAAiTgIwJUuj4SJotCAiRAAiTgbgJUuu6WD3NHAiRAAiTgIwJUuj4S\nJotCAiRAAiTgbgJUuu6WD3NHAiRAAiTgIwJUuj4SJotCAiRAAiTgbgJUuu6WD3NHAiRAAiTgIwJU\nuj4SJotCAiRAAiTgbgJUuu6WD3NHAiRAAiTgIwJUuj4SJotCAiRAAiTgbgJUuu6WD3NHAiRAAiTg\nIwJUuj4SJotCAiRAAiTgbgKuV7pjxoyRZcuW5aG4YsUK6dOnj+C6E2HcuHHy999/px31tm3b5JVX\nXpG77rpLPv/885zsnZp25rP0YCzZgvmnn35quDmRjfXr18vUqVMzivqXX36R119/Xd555x3ZuHFj\nRnH56WGvytOSQaZ13orHr3+9Kt8ZM2bIE088Ic8++6x8++23ORePq5XugQMH5L777pMXX3wxAhQU\n7sCBA+XOO++UhQsXRlzL9ODjjz+WunXrSuvWrWX37t1pRffHH3+YOL7//nv54YcfpHnz5tKwYcO0\n4vLrQ/FkO2HCBLnnnnukd+/ethZ906ZN5l064YQTMvpQe+GFF+SGG26QCy64QCpXriyNGzeWb775\nxta8ejEyr8oTrO2o816UWSp59qp80em5+OKL5e2335ZHH31UzjzzzDz6JBUOdtzraqWLL6uCBQvK\n6NGjZcOGDaHynnjiifLII4+Eju368euvv0qNGjXkpJNOyijKkSNHmi+qwYMHy+TJk81XFr6wpk+f\nnlG8fno4nmyvuOIKadGihe1FXbVqlXTs2DHtDylkCD3whx9+WHr06GHekUaNGknXrl2lTZs2snbt\nWtvz7KUIvShP8LWrzntJVunk1Yvy/fDDD43+2LJli6D+T5o0SUqUKGF0x8qVK9PBYMszrla6aNzu\nv/9+adWqlfTt2zeiwIULF444TuYAw9QDBgyIe+txxx0n+FepUqW49+R3Ye/evdK0aVMpWbJk6FY0\n9ghHHHFE6FzQf9gt23fffTfhqMcZZ5whVatWzQj7888/L7Vq1TL/rIjat28vO3bskEGDBlmnAvnX\ni/KEoOyo80EQuBflO3PmTHn55ZelUKFCUqBAATM61a5dO/nnn39kzpw5ORNb6prLhqwuWrRI5s2b\nlycmgEEjBkizZ8+WpUuXmuPTTz/d9H66desmhx56aJ7ncAJfMtOmTTNAEU+TJk2kXLlyEffOnTvX\nfOXcdNNNEeftPDj44IPl+OOPj4gSQ+CXXHKJ6UVHXPDhwZAhQ2LOX2MEoU6dOqbEqcoWQ86bN282\nz5YpU8YMF0Wje/rpp02v87TTTou+ZMsx0scwsvUBZUV6yCGHCEZeMLrx+OOPW6d989ev8vSNgDIs\niJ/l+8ADDxhdEo4I7TDsMdDjzVXISU8XBguYl61WrZrUrl1b/vrrL7n++uvl66+/DkHCl1Xnzp2l\naNGiUq9ePcFcHIZr44WKFSvKqFGjBHN3zZo1y6Nw4z3n5HmllGmMH3roISNoJ9NyS9w9e/aU4sWL\nG7miV4j5+C5dukixYsVCWUxVtqVKlZI33nhDTj75ZLnoootC8WTzB4ajMK9VtmzZPMniQ+Dnn38W\nyNtvwa/y9Juc0i2Pn+VbunTpPFjWrFljFC7mdnMVctLTRU/w6quvNuPtu3btMnNixx57rJkrA4jV\nq1cLFPOPP/4Y4nLHHXcY67Obb77ZDBWELugfe/bskbvvvlvuvfdeOfvss8Mv5ez3zp07jUHQsGHD\nBGVETw9WzBjm9HOAHGCEhvDmm2+a0QoM8Vjz5KnKFpbGH3zwgfkgwyhCroJlUxBrpAUfhphWwNwR\nPhD8FPwqTz/JKJOyBE2+I0aMMCNSuZzqy4nSvfbaa0PvCQyi0IuYOHGiHHnkkeY8LFdhcYbeqxXa\ntm1rlOonn3wSYWizdetWueyyy8zYPXrO4QFK+7vvvgudwhg/FPT7778fOgdDLYzz2x0OO+ww6d+/\nv+mhoTywwkaPL5dzCXaXMVZ81vArvigxHw+rbVgjWyEV2UJOmDJ47bXX8nxoYVRj3759VrRmXhXT\nB+GyxVBz9erVQ/dk8uPwww83j2PqIjrs379fihQpktMhq+g82XXsV3naxcfr8QRJvujIYaQKFs25\nDDlRulaBoQTRCGMJBoaErQAL1goVKliH5i96OV988YVp3MIvwKIUyvXyyy83w9Xh17788kvp3r17\n6BQULnqg6DVbAQZZTihdK34odXxNYq0YrOmQBzTQfg+33HKLmV+HqT4YWCEV2WJtHeb3ITNL6Vnx\nQKFv377dOhR8fGFe9aOPPgqdwwedXUrXeh+Rl+iAfKAnj7z6NfhNnn6VU7rl8rt8ly9fLm+99ZZp\nI9JlZNdzOVO6UD5QtjB2whxfeGjQoEH4Yeh3LCMZDFND6d5+++1mHhFGV1a47bbbBP+sMHToUGNs\nkwuHBhdeeKFMmTIlEAoXc+8YuYBzEGtY2ZJBKrKFEsXcCwzfwnuwiAsOKsID1sxi+Q5GE5wIULoY\nvUAPPjrAyArz134NfpSnX2WVTrn8Ll84KsIHPMrphg7Pv12QdKSVwTNPPvmk8TSFJTzWsDKcSvz0\n008px4ph3Ep6mQ/WS1pWrilH4vADixcvlpYtWzqcSu6jh8cn9OwxrIy/Vpg1a5b1M+m/NWvWNEPL\nw4cPNwo86QcduBGV9cYbbxSUAwZVVoARIL6iMf3hx+BXefpRVumUye/yhT0NrJh79eoV0jPgtG7d\nurR0TTqMo5/JidLFPOtLL72UZ1gZk9yAlEyw7sO8HoYeMceHHiwUr3UtmXhi3YOhSoR03EDCixXc\njcETlRVgYDN//nyBpaDfA3qa4BY+rAwjIxiUJRss+WE9HRQd5p0efPBBGTt2bLJRxLwvE7kiQvSk\nEQectVgB7ywMx2BX4MfgZ3mGyyvTdyM8Li/99rN8oRswnQXjRny4wzYE/5566inp0KFDnqWd2ZJb\n1oeXsawCw8poUAEF86s4B4UJd2zRw4axQKBnAYtYBKwzw5Kic8891/zDsCYshDGXe9VVV8V6PO45\nWKhiGBNzrwhY6oN1w1jzm2xALwiNMtKHO0nMVUPoMACLnpdMNk6v3Adu8FyDpT3wi40ABQwDp3jD\nytFlwxQADB4QYHwGIyzIAEND6E1iaRmGimIt3YmOK/wY7wUcaCBAeeMdwZq9Y445Jvy2hL9h2Idl\nbZjKwDrzo48+2ng06tevX8LnvHrR70IWlSkAAEAASURBVPKEXOyo85SvO+srPtZR7/EvOqD3e9BB\nB0Wfzs6xVngZB73YWGkPTBnH46cI9Jez0kY3GRVJGzeo888/P6M4Mn0Y6d96662ZRuO75/V6cKV7\n8GmXC/VFrz1O+/l0H6Q80yWX+LlcyTM6V5RvNBF7ju2Ub9Z7utn5lLA/FfTC8S9RKF++fMgnNBxE\nMHiDQKqyRan8th7XG5JKLpfpyDO5mHmXGwh4Xb5Uukm+RXDocd555yW82zIIS3gTL7qOAGXrOpFk\nlCHKMyN8rn/Y6/Kl0k3yFTvllFME/xj8R4Cy9ZdMKU9/yTO6NF6Xb06sl6Mh8pgESIAESIAEgkCA\nSjcIUmYZSYAESIAEXEGAStcVYmAmSIAESIAEgkCASjcIUmYZSYAESIAEXEGAStcVYmAmSIAESIAE\ngkCASjcIUmYZSYAESIAEXEGAStcVYmAmSIAESIAEgkCASjcIUmYZSYAESIAEXEGAStcVYmAmSIAE\nSIAEgkCASjcIUmYZSYAESIAEXEGAStcVYmAmSIAESIAEgkCASjcIUmYZSYAESIAEXEGAStcVYmAm\nSIAESIAEgkDAtl2G9Ibe8sEHHwSBWdbKuHLlyqyllSihFStWULaJAKVxDfUlV4HytJ98LuUZXRrK\nN5pI5sd2ytcWpVu2bFnZuXOntG3bNvPSMYYIAh06dIg4zvZBuXLlZOjQofLFF19kO2lfp1egQAFB\nvcl2oDydIZ4reUaXhvKNJmLPsZ3yLaB0sCdb7o5l3rx5UrduXalWrZosWbLE3Zll7lIm0KRJE5k0\naZJMmDBBWrRokfLzfMB9BN577z259tprzcf8iBEj3JdB5ihjAtgbd+nSpYL2uXbt2hnH54UIAjOn\n+9prrwm+ViDg33//3QuyYR6TJAB5Tp482dw9atSoJJ/ibW4n8OGHH5os4u8ff/zh9uwyfykSQL1F\ne4x2Ge1zUEIglO7WrVsFX83o1BcsWFAmTpwYFPkGopxDhgyRQoUKmbKigf7nn38CUW4/F3LPnj3y\n8ccfmyKi3mKKg8FfBNAOoz2GfIcNGybbtm3zVwHjlCYQSvedd96R/fv3GwT4qsIQJIN/CPTv3z+k\naP/66y+ZOnWqfwoX0JJg5OLvv/82pUfdff311wNKwr/FHj9+vOnlooSQ8dtvv+3fwoaVzPdKF19R\nvXr1CildCPfzzz+Xffv2hWHgT68SmD17toRbeR900EHCIWavSvPffI8ZM0YgSyssW7ZM5s6dax3y\nr8cJoP2FcabVGcLf3r17m16vx4uWb/Z9r3RhXLN69eoIELt27ZLp06dHnOOBNwng6zi8cUZlxtK1\nAwcOeLNAzLWRHaYJwj+MIeMBAwaQjk8IoP1FOxweVq1aZYwhw8/58bfvlW6fPn2kcOHIlVGowJ98\n8okf5RmoMmH4EXN94Y0zAMDohh9V3n0VZs2alcdwCjKGrKMbau+WMtg5R/sb/rEMGmin0V77Pfha\n6a5Zs8bM30Yb1qACjxs3zu+y9X35IMNYjTAqM4eYvSv+sWPH5mmQUZrdu3fTSYt3xRqRc8g4+mMZ\n7TTsbdauXRtxr98OfK10YWBjWbVGC+6nn34SKGUG7xIYOHCgsX6MLgEqM9Z1Yj6fwXsERo4cmadB\nRilgBPnGG294r0DMcQQBtLvLly+POGcdoL1+8803rUNf/vWt0kXD269fv5BVa7T0IFwOMUdT8c6x\ntTbXMsSIzvmGDRvk22+/jT7NY5cTgOOaaBsMK8uYp8fQMz6YGbxLAO1uvM4QeruwVI/uBXu3tHlz\n7lulC0MMrM+NF9AL4tKheHTcf37w4MFxKy5yjyHm0aNHu78gzGEEgXhDy9ZNkOugQYOsQ/71IAEs\nFUo0CgWbDLTffg2+dQPZsGFDwXKSRFashxxyiPz5559y8MEH+1W+vi3XiSeeGLFUKFZBjz32WE4h\nxALj4nOnn366fP/99wlzeNRRR8n69evzGEgmfIgXXUEAGwcceeSRoTXYsTIFhxn169eXGTNmxLrs\n+XO+7OlieGrmzJlmDggKFf+iLZghOVi/fv31154XYtAKgPWaWJuLISrIFX8x3xcdYJCxYMGC6NM8\ndimB3377LabCRSMMOaOXi79btmzhBhwulWF+2UJ7azk9Cb8XcrXaatRltN+//vpr+C2++R25lsYn\nxUIPB24fN23aZHqy8FIEl2OorDVq1DDDzujhwvIV7uYYvEWgYsWK8sADDxhrVnw549+UKVPMqAZ6\nSpApKjaGsDiK4R3ZQo516tQxH1BFixaVww47zBjcYAezli1bCkamDj30UHO+Vq1a3ikYcxoigHpZ\nvnx5gXzR4y1RooQsWrRIMHrRvHlzOeKII8z50qVLC9pxPwbfDi9HC6tTp05G6XIeN5qMP47btGlj\nKjJ8uDL4h0DXrl2N8ZRfhxr9I6n0S3LJJZcYpfvuu++mH4mHnvTl8HIs/ujt4iuKwZ8E0EvC8COD\nvwigd4v1uQz+JYB2Ge1zUEJglC6GkzGcweBPAlC6HEr2n2wxnEyl6z+5hpeISjecho9+s6frI2HG\nKArW9VHpxgDj8VNQurEMbzxeLGY/jAA6Q+gUBSWwpxsUSfu8nOzp+lPAHF72p1zDS8WebjgNH/1m\nT9dHwoxRFM7pxoDig1McXvaBEPMpAnu6+QDy6mUqXa9KLrl8s6ebHCev3cXhZa9JLPX8sqebOjPX\nP4EGGfNCNKRyvajSziDndNNG5+oHMbwM2cbzse3qzDNzSRGA0kX77Gd/y+EgAjGna5mjQ7gM/iTA\nnq4/5YqeLgItmP0pX5TK6gwFxZgqEErXEqYlXP++vsEtGed0/Sl7S+nSgtmf8kWprM6Q1Tnyb0n/\nV7JAKF1LmJZw/S7UIJaPPV1/Sh3Dywjs6fpTviiV1RmyOkf+Len/Skal63cJB6R8nNP1p6Ctni6V\nrj/li1JZnSGrc+Tfkv6vZIFQutYXlPVF5XehBrF87On6U+pWT5fDy/6UL0pFpetD2eILCt6KihQp\n4sPSsUggwDldf74H7On6U67hpcKHFdpnq3MUfs2PvwPR04XSZS/Xj6/vv2Xi8PK/LPz0y1K67On6\nSap5y4LeLoeX83Lx7Bl8QVlDGJ4tBDMel4C1vo++l+Mi8uwFa3iZc7qeFWFSGUeniD3dpFB54yZ8\nQVHpekNW6eQSQ8sIVLrp0HP3M1ZPl0rX3XLKNHfs6WZK0GXP4wuKw8suE4qN2bGULvfTtRGqS6Iq\nXLiw4B+Hl10iEIeyQaXrENhcRcuebq7IZyddS+myp5sd3tlOBUPM7Olmm3p20+PwcnZ5O54ae7qO\nI85pApzTzSl+xxPHEDOVruOYc5oAe7o5xW9/4uzp2s/UTTGyp+smadifFyhdDi/bz9VNMbKn6yZp\n2JAXKl0bILo4CipdFwvHhqxxeNkGiC6Pgj1dlwso1exxeDlVYt6631K6NKTyltySzS2Hl5Ml5d37\nqHS9K7uYOWdPNyYW35y0lC4NqXwj0oiCcHg5AocvDzi87DOxQulyyZDPhBpWHBpShcHw4U8OL/tQ\nqFFFYk83CoiXD2H1iEYZQmXwJwH2dP0pV6tUHF62SPj3LzpFaKeDYKXue9/L6OUiUOn6t8JaSpdz\nuv6UMYeX/SnX8FJZ7bPVXodf89tv3ytdy58nh5f99ur+Wx5L6XJO918mfvrF4WU/STN2Wah0Y3Px\n5Fnry8kSqicLwUwnJMA53YR4PH+RPV3PizDfAlidIquTlO8DHr7B9z1dS+laQvWwrJj1OATQ0y1Q\noIDx0RvnFp72MAH2dD0svCSzbnWKrPY6ycc8eZvvla715WQJ1ZNSYqYTEoDS5XxuQkSevkhDKk+L\nL6nMW50iq71O6iGP3uR7pYsvJ1Ra7FTC4E8CULqcz/WnbFEqDi/7V7ZWydA+Q85B6On6RhMppWTb\ntm2yY8cOQSOMeT70flatWiXFihUTXMcQJIP3CUC2W7duNcsL9uzZI2vWrJFChQrJ77//LsWLF5ei\nRYt6v5AsgSGwa9cuU5/RGC9btkwKFiwoRYoUMQ10iRIlOMLhk/cE7fPhhx8uq1evlhUrVoTab3xM\n4zzqtV/a7wK6sMorckNWf/rpJ/n+++9l4cKF5jeUKgS1ZcsW2b9/f9yioFE+6qijpGLFilKpUiU5\n+eSTpUaNGnL66adLlSpVfCPQuAA8dgEO7i05L1q0yFREyPq3336T/IagMAd49NFHy/HHH2/+nXLK\nKUbWtWvXltKlS3uMhP+zu2nTJvnuu+8Ecl68eLFAzr/88ots2LAh340OMCxZvnx5U6dPPPFEI+fT\nTjtNatasKXgPGNxDgO33/2TheqULJTtx4kSZPHmyzJgxwyhXKNCTTjpJqlWrZioblCga01KlSple\nLb6O0MtFjwi93u3bt8vmzZsFlRsVGv+WLl1qlDYUNZRxw4YN5YILLpDmzZubuN3zqgYjJ//8849M\nnz5dPv30U/nqq69k3rx5Rnb4yj311FPNhxHkXKFCBSPnkiVLmh4tZI3eD+SMXi96wPgAW7dunWm4\nV65caRpy9IIR8N40atRILrroIvMPvSWG7BKAjD7//HPz75tvvpHly5ebDJQrV87I2vpYKlu2rKmb\nkBF6t5D1gQMHjKzRA/7jjz9MvcZIB+o04vnhhx/MaBfurVOnjpx77rmmTqN+c4opu3JGamy/8zJ3\npdJFxRk+fLiMHDnSVCQMLUAhorE866yzzNesHV+x6E3h6xqN/bRp04xixxA1er5t27aVq666yjQC\nebHxjB0EoGjR+I4YMULGjRtnerCVK1cOyfrMM88U9F7sGFaCIv7222+NrKHUZ82aZaYc0ChD1ldc\ncYVp4O0oF+PISwD8R40aZWT99ddfG5lCvuCPOl2vXj1b+KM3heHJmTNnGlnjY/3nn382bmBbtWol\n7dq1Mx9bVMB5ZWTXGbbf+ZDUL6krgv5yVQMGDFC68mG4W+kejeratavSX8JKN85ZySPSQXpIF+kj\nH8gP8oX8MdhDQA8dqm7duqljjjlGaYWqdOOrevbsqXTjaE8CScSiP66U/qhTV155pdJzwEr3jJRu\nkNWXX36ZxNO8JVkC4Amu4AvO4A3u4J+tgPcK7xfeM7xvugdt3j+8hwz2EGD7nTxHfO3nNOghX9W9\ne3elh3iV7r2qa665Rk2ZMkXpYaSc5gvpIx/ID/KF/CGfyC9DegTmzp1rGmA9PaD0PJx69NFHlR7+\nTS8yG5/SxnfqrbfeUg0aNDAfWnqeXw0dOjRrH3s2FsUVUeHjFfzAER+u4Aq+4JzrgPcN7x3eP7yH\n+CDQUxm5zpZn02f7nbrocqZ08aX7yCOPKD1np/RcrHr88cfVxo0bUy9BFp5AvpA/5BP5Rb6z+aWe\nhSI6moQ2elOtW7c2DbA2ZlLvvfee0vPtjqaZbuT4MNDTCqZB1sZ26v3338/5B2C6Zcn2c/hQBS9w\n08O3hiN4ujHg/Rs2bJjC+4gPA7yfeE8ZkiPA9js5TrHuyonSHTx4sCpTpozSxjDqv//9ryu+gGPB\niT6HL3XkF/nW1rEK5WCIT0BbGas777zTKLBatWqp8ePHx7/ZZVe0UY5q37690kZaSs85skHORz7a\n0txwAi9wAz+vBLyXeD/R88X7iveWIT4Btt/x2SRzJatKV0+wq3POOcc0ZF26dFHaijGZPLruHuQb\n+UcDg/KgXAyRBNCLwJwtRgcGDRrk2d6iXspi5gLRc7vnnnuUXi8aWdCAH4EHuIAP5kzBy4sBvXS8\np3hf8d7i/WWIJMD2O5JHukdZUbqY43nsscdMxTzjjDOUW4ecUoWIcqA8aHBQvmwZfKWaz2zer5fm\nqAsvvNB8kNxyyy1KW61mM3lH0kKD3L9/fzOvr5e1KG1x7Ug6XosUHMAD9g7gA05eD3hfb775ZvP+\n4j3G+xz0wPbb3jfAcaWLl1YvCzDGSH369FF6Xay9JchxbCgPygVjK5QzyJUUjTCmDfRaWDV79uwc\nS8b+5PVabzNPiREOGOME9SML5Ub5wQHz3+Dit6CXlCm9dNC8z0H+yGL7bf+b7ajS/eyzz5R2WmEa\n4QULFtifexfFiPJB2UDpoNxBCmiEYVyGRvjqq6/2/RDsm2++GfrI0h6ygiRqhfJaH9Hg4OeAoXO8\nz3iv8X4H7SOL7bczb7djSvell14ya+Kw5EZ7hHIm9y6LFeVEebEWEOUPQkCZmzRpYpQQhhiDEmA4\nZH1k+WW6JD/ZoZzWSAbKH5RgfWThPQ9KW8b227n223ali3md++67zygeLEgPYujRo4cpPzj4YZ4r\nngyxlEq72jOW3F41oIlXtmTOowFu2rSpWUb2xRdfJPOIZ+9B+bBcrlmzZoFRPOHCwlperFjA++7W\npY3h+U33N9tvpZxuv21Vulj71rFjR6X9Hgfe+g/Wj+AAHm5dk5puxcRzcDKAOS/tplFpt3uZROXp\nZ7XPZ3Xttdcaj0vadamnyxIv8ygXPEqhnChvUAPec7zveO/d4NTFbjmw/f6XqJPtt21KFwJr2bKl\nOuywwwI3p/mvqCJ/YU4EPC699FJfKV7txNy40sPaxvXr10cWOoBH6B1g2Qzm/gYOHOgrAigPyoXy\n+XnUJlmh4X3Hew9XkqgHfglsv/NK0qn22xali8rYoUMHo2C0o/G8uQ/wGfCA4kWP1w+NFqwZ9W4/\nqn79+r43mEr1tcWyMThYGDNmTKqPuvL+Dz/80JQH5WL4lwCcZ8Anu94NyRerFdh+/yvb6F9OtN+2\nKF3MXWIoNWhWu9ECincMLuADTl4OcP2m9ypVVatW9eUyETtkc9tttxmjMr2TkR3R5SwO5B/L4FAe\nhrwEsEwK7i5RH7zuEpbtd175hp+xu/3OWOlaVm7wp8sQnwDmCLxs1bx7927jfQuO4levXh2/oAG/\ngnXbeptApTdXV1618EW+kX+Uw2/r6u18PVEPUB/glQ71w4uB7XdyUrOz/c5I6eILAIokqFbKyYnr\n37ssqzgvjgjcdNNNSu9rTJeX/4oz7i+9T7Nq3LixGX70mqtT5BfTB8g/ysGQmABcI6JewIuV1wLb\n79QkZlf7nbbSxSJ5OL7AulSG5AmAF9Y6eslzFUYxsBOLX+Yqk5dW+ndiWQlcJLZp0yb9SHLwJPKL\nfPt5WYzdWDH3jfrhpdE+tt/pvQV2tN9pKV14ZoFXGjgHCMpi8fRElPcp8AI38POChxtYaBYrVkzd\nddddeQvDMwkJTJ061Rgi9erVK+F9brmIfMIQzOvz0bngid2JUE+8YNHM9jv9N8SO9jstpQtrRhhZ\n+N21Y/qiSfwkuIGf261C9+zZo2rWrKnq1q2r8JshdQJPP/2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JdkQXEQeWFUAOkIdTAWbt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\nERABERABESiPwP8CA+qSF3wLLIwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proj_data = enum_map(factor, As, bs, name='proj_data')\n",
    "proj_data = compute(*proj_data)\n",
    "\n",
    "x = value(0,'x^k')\n",
    "x = step(proj_data, x)\n",
    "\n",
    "dsk_step = x.dask\n",
    "dot_graph(dsk_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dask.context.set_options at 0x1073ee208>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dask.set_options(get=dask.threaded.get)     # multiple threads\n",
    "#dask.set_options(get=dask.async.get_sync)   # single thread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 7.2 s, sys: 146 ms, total: 7.34 s\n",
      "Wall time: 4.64 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# do one-time computation of factorizations\n",
    "proj_data = enum_map(factor, As, bs, name='proj_data')\n",
    "\n",
    "# realize the computations, so they aren't recomputed at each iteration\n",
    "proj_data = compute(*proj_data)\n",
    "\n",
    "# get dask graph for reuse\n",
    "x = value(x0,'x^k')\n",
    "x = step(proj_data, x)\n",
    "dsk_step = x.dask\n",
    "\n",
    "K = 100\n",
    "r = []\n",
    "for k in range(K):\n",
    "    dsk_step['x^k'] = get(dsk_step, 'x^k+1')\n",
    "    r.append(np.linalg.norm(x_true-dsk_step['x^k']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.92 s, sys: 104 ms, total: 7.03 s\n",
      "Wall time: 4.46 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# serial execution\n",
    "proj_data = list(map(factor, As, bs))\n",
    "\n",
    "x = x0\n",
    "\n",
    "K = 100\n",
    "r = []\n",
    "for i in range(K):\n",
    "    z = (proj(d,x) for d in proj_data)\n",
    "    x = average(*z)\n",
    "    r.append(np.linalg.norm(x_true-x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iterative projection algorithm thoughts\n",
    "- I don't see any performance gain in using the threaded scheduler, but I don't see what I'm doing wrong here\n",
    "- I don't see any difference in runtime switching between `dask.set_options(get=dask.threaded.get)` and `dask.set_options(get=dask.async.get_sync)`; not sure if it's actually changing the scheduler, but I haven't looked into it closely\n",
    "\n",
    "# random Dask thoughts\n",
    "- would be nice to be able to rename a `Value` and change the data that it points to\n",
    "- at least for visualizing, I wanted more control over intermediate value names and the ability to enumerate those names with subscripts, which lead to my kludgy functions above. probably not a high-priority item for you tho...\n",
    "- would be nice if `dask.visualize` also worked on Dask graph dictionaries, so I don't have to remember `dask.dot.dot_graph`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dask attempt 2\n",
    "- I tried a different method for using the threaded scheduler, but got similar results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.34 s, sys: 813 ms, total: 10.2 s\n",
      "Wall time: 4.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# do one-time computation of factorizations\n",
    "proj_data = enum_map(factor, As, bs, name='proj_data')\n",
    "\n",
    "# realize the computations, so they aren't recomputed at each iteration\n",
    "proj_data = compute(*proj_data, get=dask.threaded.get, num_workers=2)\n",
    "\n",
    "# get dask graph for reuse\n",
    "x = value(x0,'x^k')\n",
    "x = step(proj_data, x)\n",
    "dsk_step = x.dask\n",
    "\n",
    "K = 100\n",
    "r = []\n",
    "for k in range(K):\n",
    "    dsk_step['x^k'] = dask.threaded.get(dsk_step, 'x^k+1', num_workers=2)\n",
    "    r.append(np.linalg.norm(x_true-dsk_step['x^k']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Runtime error\n",
    "- As I was experimenting and switching schedulers and between my first and second dask attempts, I would very often get the following \"can't start new thread\" error\n",
    "- I would also occasionally get an \"TypeError: get_async() got multiple values for argument 'num_workers'\" even though I had thought I'd set `dask.set_options(get=dask.threaded.get)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "can't start new thread",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-4e7ff52c7a3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\n# do one-time computation of factorizations\\nproj_data = enum_map(factor, As, bs, name='proj_data')\\n\\n# realize the computations, so they aren't recomputed at each iteration\\nproj_data = compute(*proj_data)\\n\\n# get dask graph for reuse\\nx = value(x0,'x^k')\\nx = step(proj_data, x)\\ndsk_step = x.dask\\n\\nK = 100\\nr = []\\nfor k in range(K):\\n    dsk_step['x^k'] = get(dsk_step, 'x^k+1', num_workers=2)\\n    r.append(np.linalg.norm(x_true-dsk_step['x^k']))\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2291\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2292\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2293\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2294\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1167\u001b[0;31m             \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1168\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1169\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/dask/threaded.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(dsk, result, cache, num_workers, **kwargs)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpool\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m             \u001b[0mpool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mThreadPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_workers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     51\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m             \u001b[0mpool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_pool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5/multiprocessing/pool.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, processes, initializer, initargs)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocesses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 749\u001b[0;31m         \u001b[0mPool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocesses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    750\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    751\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setup_queues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5/multiprocessing/pool.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, processes, initializer, initargs, maxtasksperchild, context)\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdaemon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRUN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_task_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         self._result_handler = threading.Thread(\n",
      "\u001b[0;32m/usr/local/Cellar/python3/3.5.0/Frameworks/Python.framework/Versions/3.5/lib/python3.5/threading.py\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    851\u001b[0m             \u001b[0m_limbo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    852\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 853\u001b[0;31m             \u001b[0m_start_new_thread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bootstrap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    854\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    855\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0m_active_limbo_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: can't start new thread"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# do one-time computation of factorizations\n",
    "proj_data = enum_map(factor, As, bs, name='proj_data')\n",
    "\n",
    "# realize the computations, so they aren't recomputed at each iteration\n",
    "proj_data = compute(*proj_data)\n",
    "\n",
    "# get dask graph for reuse\n",
    "x = value(x0,'x^k')\n",
    "x = step(proj_data, x)\n",
    "dsk_step = x.dask\n",
    "\n",
    "K = 100\n",
    "r = []\n",
    "for k in range(K):\n",
    "    dsk_step['x^k'] = get(dsk_step, 'x^k+1', num_workers=2)\n",
    "    r.append(np.linalg.norm(x_true-dsk_step['x^k']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "get_async() got multiple values for argument 'num_workers'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-4e7ff52c7a3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\\n# do one-time computation of factorizations\\nproj_data = enum_map(factor, As, bs, name='proj_data')\\n\\n# realize the computations, so they aren't recomputed at each iteration\\nproj_data = compute(*proj_data)\\n\\n# get dask graph for reuse\\nx = value(x0,'x^k')\\nx = step(proj_data, x)\\ndsk_step = x.dask\\n\\nK = 100\\nr = []\\nfor k in range(K):\\n    dsk_step['x^k'] = get(dsk_step, 'x^k+1', num_workers=2)\\n    r.append(np.linalg.norm(x_true-dsk_step['x^k']))\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2291\u001b[0m             \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2292\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2293\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2294\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m             \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1167\u001b[0;31m             \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1168\u001b[0m             \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1169\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/Users/ajfriend/Dropbox/work/scs_python/env3/lib/python3.5/site-packages/dask/async.py\u001b[0m in \u001b[0;36mget_sync\u001b[0;34m(dsk, keys, **kwargs)\u001b[0m\n\u001b[1;32m    515\u001b[0m     \u001b[0mqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     return get_async(apply_sync, 1, dsk, keys, queue=queue,\n\u001b[0;32m--> 517\u001b[0;31m                      raise_on_exception=True, **kwargs)\n\u001b[0m\u001b[1;32m    518\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: get_async() got multiple values for argument 'num_workers'"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# do one-time computation of factorizations\n",
    "proj_data = enum_map(factor, As, bs, name='proj_data')\n",
    "\n",
    "# realize the computations, so they aren't recomputed at each iteration\n",
    "proj_data = compute(*proj_data)\n",
    "\n",
    "# get dask graph for reuse\n",
    "x = value(x0,'x^k')\n",
    "x = step(proj_data, x)\n",
    "dsk_step = x.dask\n",
    "\n",
    "K = 100\n",
    "r = []\n",
    "for k in range(K):\n",
    "    dsk_step['x^k'] = get(dsk_step, 'x^k+1', num_workers=2)\n",
    "    r.append(np.linalg.norm(x_true-dsk_step['x^k']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "atlas_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_threads_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_3_10_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_3_10_blas_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_blas_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_blas_threads_info:\n",
      "  NOT AVAILABLE\n",
      "blas_opt_info:\n",
      "    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']\n",
      "    define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]\n",
      "    extra_compile_args = ['-msse3', '-I/System/Library/Frameworks/vecLib.framework/Headers']\n",
      "openblas_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_3_10_blas_threads_info:\n",
      "  NOT AVAILABLE\n",
      "atlas_3_10_threads_info:\n",
      "  NOT AVAILABLE\n",
      "lapack_mkl_info:\n",
      "  NOT AVAILABLE\n",
      "blas_mkl_info:\n",
      "  NOT AVAILABLE\n",
      "openblas_lapack_info:\n",
      "  NOT AVAILABLE\n",
      "lapack_opt_info:\n",
      "    extra_link_args = ['-Wl,-framework', '-Wl,Accelerate']\n",
      "    define_macros = [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]\n",
      "    extra_compile_args = ['-msse3']\n",
      "mkl_info:\n",
      "  NOT AVAILABLE\n"
     ]
    }
   ],
   "source": [
    "np.__config__.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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