Map of matter around a filament's center

Map of matter around filaments.ipynb

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      "cell_type": "code",
      "source": "import recipy  # Deactivate if you don't want to save the inputs/outputs!",
      "execution_count": null,
      "outputs": []
    },
    {
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      "cell_type": "code",
      "source": "from __future__ import division, print_function",
      "execution_count": 60,
      "outputs": []
    },
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      "cell_type": "code",
      "source": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import spherical_jn\nfrom scipy.interpolate import interp2d\n\nfrom tqdm import tqdm_notebook as tqdm\n%matplotlib inline\nimport matplotlib\nimport matplotlib.colors as colors\nfrom matplotlib.ticker import MaxNLocator\nmatplotlib.rcParams['figure.figsize'] = (16, 9)\n%config InlineBackend.figure_format = 'png'\n\nfrom numba import jit\nimport labellines\nimport os\nfrom os import path\n\nfrom multiprocessing import Pool",
      "execution_count": 2,
      "outputs": []
    },
    {
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      "cell_type": "code",
      "source": "recipy.log_init()",
      "execution_count": 2,
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'recipy' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-999b624ea10d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mrecipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'recipy' is not defined"
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          "output_type": "error"
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      "cell_type": "code",
      "source": "def launcher(fun, tasks, pool, **kwa):\n    it = pool.imap(fun, tasks)\n    res = list(tqdm(it, total=len(tasks), **kwa))\n    return np.array(res)\n\ndef sym_clone(arr, qrx, qry, qrz):\n    xx, yy, zz = arr.shape\n    newArr = np.zeros([2*xx, 2*yy, 2*zz])\n\n    newArr[xx:,    yy:  , zz:  ] = arr[:,    :,    :   ]\n    newArr[:xx,    yy:  , zz:  ] = arr[::-1, :,    :   ]\n    newArr[xx:,   :yy,    zz:  ] = arr[:,    ::-1, :   ]\n    newArr[:xx,   :yy,    zz:  ] = arr[::-1, ::-1, :   ]\n    newArr[xx:,   yy:  ,  :zz  ] = arr[:,    :,    ::-1]\n    newArr[:xx,   yy:  ,  :zz  ] = arr[::-1, :,    ::-1]\n    newArr[xx:,   :yy,    :zz  ] = arr[:,    ::-1, ::-1]\n    newArr[:xx,   :yy,    :zz  ] = arr[::-1, ::-1, ::-1]\n    #alphastarmap[np.isnan(alphastarmap)] = np.mean(alphastarmap[np.isfinite(alphastarmap)])\n    rx = np.array(list(-qrx[::-1]) + list(qrx))\n    ry = np.array(list(-qry[::-1]) + list(qry))\n    rz = np.array(list(-qrz[::-1]) + list(qrz))\n\n    return rx, ry, rz, newArr",
      "execution_count": 44,
      "outputs": []
    },
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        "collapsed": true,
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      "cell_type": "code",
      "source": "@jit\ndef j0(x):\n    return np.sin(x)/x\n@jit\ndef j1(x):\n    return (np.sin(x)/x - np.cos(x))/x\n@jit\ndef j2(x):\n    return (3./x**2-1.)*(np.sin(x) - 3.*np.cos(x)/x)/x\n#@jit\n#def W1(x) :\n#    return 3.*j1(x)/x\n#@jit\n#def W2(x) :\n#    return -3.*j2(x)/x\ndef W1(x) :\n    return 3.*(np.sin(x) - x*np.cos(x))/x**3\ndef W2(x) :\n    return 3.*(W1(x) - np.sin(x)/x)/x\n@jit\ndef j(n, x):\n    return spherical_jn(n, x)\n    if n == 0:\n        return j0(x)\n    elif n == 1:\n        return j1(x)\n    else:\n        return spherical_jn(n, x)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
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          "start_time": "2017-03-27T18:18:29.857415+02:00",
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      "cell_type": "code",
      "source": "[k, Pk, blip] = np.genfromtxt(\"Pk_lcdm_transfer_out.dat_s8_0.7913_ns_0.9500\").T\n#k = 10**np.linspace(-2, 2, 40000)\n#Pk = k**-1.8\n\nRs = 5 # Mpc\nR  = np.geomspace(1e-2, 1e3, 200) * Rs\nRsparse = np.geomspace(1e-2, 1e1, 10) * Rs\nradii = np.geomspace(1e-2, 1e2, 500) * Rs\n\ndef _sigma(R):\n    s0 = np.array([np.trapz( k**2 * Pk * W1(k*r)**2, k)/(2*np.pi**2)\n                 for r in R])\n    sigma0 = np.sqrt(s0)\n    return sigma0\n\nsigma0 = _sigma(R)\n\ndef _gamma(R, sigma0):\n    var_x = np.array([np.trapz( k**4*Pk*W2(k*r)**2, k)/2/np.pi**2 \n                      for r in R])\n    sigma_x = np.sqrt(var_x)\n\n    gamma = np.array([np.trapz(k**3*Pk*W1(k*r)*W2(k*r), k)/2/np.pi**2 \n                      for r in R])/sigma0/sigma_x\n    return gamma\n\ngamma = _gamma(R, sigma0)\ngamma2 = gamma**2\nGamma2 = gamma2/(1-gamma2)\nGamma = np.sqrt(Gamma2)\n\ndef _dsigma_o_dR(R, sigma0):\n    slope = np.array([np.trapz(-k**3*Pk*W1(k*r)*W2(k*r), k)/2/np.pi**2 \n                 for r in R])/sigma0\n    return slope\n\ndsigma_o_dR = _dsigma_o_dR(R, sigma0)",
      "execution_count": 5,
      "outputs": []
    },
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        "editable": true,
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      "cell_type": "code",
      "source": "output_dir = './data_Rs=5Mpc/'\nxi00, xi11, xi20, xi00prime, xi11prime, xi20prime, radiixi, Rxi = [\n    np.loadtxt(path.join(output_dir, fname))\n    for fname in ['xi00.dat','xi11.dat','xi20.dat',\n                  'xi00prime.dat','xi11prime.dat','xi20prime.dat',\n                  'radii.dat', 'R.dat']]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "editable": true,
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      },
      "cell_type": "markdown",
      "source": "Now we take the $\\xi$ from the output to transform them into functions."
    },
    {
      "metadata": {
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          "start_time": "2017-03-27T18:18:33.609154+02:00",
          "end_time": "2017-03-27T16:18:33.632702Z"
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        "collapsed": false,
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      "cell_type": "code",
      "source": "interp = 'linear'\nxi00fun = interp2d(Rxi, radiixi, xi00, kind=interp)\nxi11fun = interp2d(Rxi, radiixi, xi11, kind=interp)\nxi20fun = interp2d(Rxi, radiixi, xi20, kind=interp)\nxi00pfun = interp2d(Rxi, radiixi, xi00prime, kind=interp)\nxi11pfun = interp2d(Rxi, radiixi, xi11prime, kind=interp)\nxi20pfun = interp2d(Rxi, radiixi, xi20prime, kind=interp)",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "editable": true,
        "deletable": true
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      "cell_type": "markdown",
      "source": "Here we fix the traceless shear at the saddle point to be\n\\begin{equation}\n    \\begin{pmatrix}\n        q_{11} & 0 & 0 \\\\\n        0 & q_{11} & 0 \\\\\n        0 & 0 & q_{33} \n   \\end{pmatrix}.\n\\end{equation}\nWe need first to find the mass scale solving $\\nu_c(M_\\star) = 1 \\Leftrightarrow \\delta(R) = \\delta_c$. \n\n## Set $q_{ij}$, constants and values at the saddle point"
    },
    {
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          "end_time": "2017-03-27T16:18:36.760955Z"
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      "cell_type": "code",
      "source": "delta_c = 1.68\nRs = 5  # Mpc/h\nsigmaRs = _sigma([Rs])[0]\ndeltas = 1.2\nnus = deltas/sigmaRs\nrhobar = 1  # TODO\ndsigma_o_dRs = _dsigma_o_dR([Rs], sigmaRs)\n\nprint(sigmaRs, nus, dsigma_o_dRs)",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": "1.05750355807 1.13474795507 [-0.12231749]\n",
          "name": "stdout"
        }
      ]
    },
    {
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          "start_time": "2017-03-27T18:18:37.978374+02:00",
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        "collapsed": false,
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      "cell_type": "code",
      "source": "q11 = nus*2.0\nq22 = nus*1.0\nq33 = nus*(1-q11/nus-q22/nus)\n\nnuo3 = nus/3\nQbar = (np.array([[q11-nuo3,        0,        0],\n                  [       0, q22-nuo3,        0],\n                  [       0,        0, q33-nuo3]]))\nQbar, np.trace(Qbar)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "(array([[ 1.89124659,  0.        ,  0.        ],\n        [ 0.        ,  0.75649864,  0.        ],\n        [ 0.        ,  0.        , -2.64774523]]), 0.0)"
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "editable": true,
        "deletable": true
      },
      "cell_type": "markdown",
      "source": "Define the quantities at finitie distance from the saddle point."
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:22:59.255310+02:00",
          "end_time": "2017-03-27T20:22:59.436441Z"
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        "collapsed": false,
        "editable": true,
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      "cell_type": "code",
      "source": "@jit\ndef _DeltaMstar(R, r, qbar, dsigma_o_dM):\n    r = np.array(r)\n    r0 = np.sqrt(np.sum(r**2))\n    riqijrj = np.dot(np.dot(r, qbar), r) / r0**2\n    res = -15 / 2 * delta_c * xi20fun(R, r0) * riqijrj / np.abs(dsigma_o_dM)\n    return res\n\n\n@jit\ndef x2_nucS(R, r, qbar, Rstar):\n    r0 = np.sqrt(np.sum(r**2))\n    xi00 = xi00fun(R, r0)\n    xi00p = xi00pfun(R, r0)\n    xi11 = xi11fun(R, r0)\n    xi11p = xi11pfun(R, r0)\n    xi20 = xi20fun(R, r0)\n    xi20p = xi20pfun(R, r0)\n\n    rstar2 = r0**2 / Rstar**2\n\n    res = (1 - 3 * rstar2 * xi11p**2 - 5 * xi20p**2 - xi00p**2 -\n           (3 * rstar2 * xi11 * xi11p + 5 * xi20 * xi20p + xi00 * xi00p)**2 /\n           (1 - rstar2 * xi11**2 - 5 * xi20**2 - xi00**2))\n    return res\n\n\n@jit\ndef x_nucS(R, r, qbar, nus, Rstar):\n    r0 = np.sqrt(np.sum(r**2))\n    xi00 = xi00fun(R, r0)\n    xi00p = xi00pfun(R, r0)\n    xi11 = xi11fun(R, r0)\n    xi11p = xi11pfun(R, r0)\n    xi20 = xi20fun(R, r0)\n    xi20p = xi20pfun(R, r0)\n    riqijrj = np.dot(np.dot(r, qbar), r) / r0**2\n\n    rstar2 = r0**2 / Rstar**2\n\n    res = (-15 / 2 * riqijrj * xi20p + xi00p * nus -\n           (3 * rstar2 * xi11 * xi11p + 5 * xi20 * xi20p + xi00 * xi00p) *\n           (nuc + 15 / 2 * riqijrj * xi20 - xi00 * nus) /\n           (1 - 3 * rstar2 * xi11**2 - 5 * xi20**2 - xi00**2))\n    return res\n\n\n#@jit\ndef nuhalfnu(R, R_half):\n    return (np.trapz(Pk * W1(k * R) * W1(k * R_half) * k**2, k)\n            / _sigma([R]) / _sigma([R_half]) / (2 * np.pi**2))\n\n\n#@jit\ndef nuhalf_nucS(R, r, qbar, nuc, nus, Rstar, R_half, nuhalfnu_mean):\n    r0 = np.sqrt(np.sum(r**2))\n    xi00 = xi00fun(R, r0)\n    xi00half = xi00fun(R_half, r0)\n    xi11 = xi11fun(R, r0)\n    xi11half = xi11fun(R_half, r0)\n    xi20 = xi20fun(R, r0)\n    xi20half = xi20fun(R_half, r0)\n    riqijrj = np.dot(np.dot(r, qbar), r) / r0**2\n\n    rstar2 = r0**2 / Rstar**2\n\n    beta_half = (nuhalfnu_mean -\n                 3 * rstar2 * xi11 * xi11half -\n                 5 * xi20 * xi20half - xi00 * xi00half)\n\n    res = (-15 / 2 * riqijrj * xi20half + xi00half * nus +\n           beta_half * (nuc + 15 / 2 * riqijrj * xi20 - xi00 * nus) /\n           (1 - 3 * rstar2 * xi11**2 - 5 * xi20**2 - xi00**2))\n    return res\n\n\n#@jit\ndef nuhalf2_nucS(R, r, Rstar, R_half, nuhalfnu_mean):\n    r0 = np.sqrt(np.sum(r**2))\n    xi00 = xi00fun(R, r0)\n    xi00half = xi00fun(R_half, r0)\n    xi11 = xi11fun(R, r0)\n    xi11half = xi11fun(R_half, r0)\n    xi20 = xi20fun(R, r0)\n    xi20half = xi20fun(R_half, r0)\n\n    rstar2 = r0**2 / Rstar**2\n\n    beta_half = (nuhalfnu_mean -\n                 3 * rstar2 * xi11 * xi11half -\n                 5 * xi20 * xi20half - xi00 * xi00half)\n\n    res = (1 - 3 * rstar2 * xi11half**2 - 5 * xi20half**2 - xi00half**2 -\n           beta_half**2 / (1 - 3 * rstar2 * xi11**2 - 5 * xi20**2 - xi00**2))\n    return res\n\n\n@jit\ndef _alphastar(R, r, qbar, Gamma, nuc, nus, Rstar):\n    r = np.array(r)\n    return (Gamma * nuc /\n            (Gamma * nuc\n             + np.sqrt(x2_nucS(R, r, qbar, Rstar))\n             + x_nucS(R, r, qbar, nus, Rstar)))\n\n\n#@jit\ndef _Dstar(R, r, qbar, nuc, nus, nu_half, Rstar, R_half, nuhalfnu_mean):\n    r = np.array(r)\n    a2 = nuhalf2_nucS(R, r, Rstar, R_half, nuhalfnu_mean)\n    if a2 < 0:\n        import pdb\n        pdb.set_trace()\n    b = nuhalf_nucS(R, r, qbar, nuc, nus, Rstar, R_half, nuhalfnu_mean)\n    return nu_half / (np.sqrt(a2) + b)",
      "execution_count": 93,
      "outputs": []
    },
    {
      "metadata": {
        "editable": true,
        "deletable": true
      },
      "cell_type": "markdown",
      "source": "## Plot of $\\Delta M_\\star$\nPlot of variation from the caracteristic mass $\\Delta M_\\star$:\n\\begin{equation}\n  \\Delta M_*(\\hat r) = -\n  \\frac{15}{2}\\frac{\\mathrm{d}\\,\\xi_{2,0}(r)}{|\\mathrm{d}\\sigma(M_*)/\\mathrm{d} M|}\n  \\frac{r_i\\bar q_{ij,s}r_j}{r^2}\n\\end{equation}"
    },
    {
      "metadata": {
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          "start_time": "2017-03-21T11:21:49.173163+01:00",
          "end_time": "2017-03-21T10:21:49.180011Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
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      },
      "cell_type": "code",
      "source": "nx = 128\nny = nx\nnz = ny\n\ndmax = 25\nrx = np.linspace(-dmax, dmax, nx)\nry = np.linspace(-dmax, dmax, ny)\nrz = np.linspace(-dmax, dmax, nz)\n#DeltaMstarMap = DeltaMstar(rxg, ryg)",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-21T11:33:18.228148+01:00",
          "end_time": "2017-03-21T10:33:30.882601Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
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      },
      "cell_type": "code",
      "source": "R = 0.5\nsigmaR = _sigma([R])\ndsigma_o_dR = _dsigma_o_dR([R], sigmaR)\ndsigma_o_dM = dsigma_o_dR / (4*np.pi*R**2*rhobar)\n\nDeltaMstarMap = np.array(\n    [[[ _DeltaMstar(R, [_x, _y, _z], Qbar, dsigma_o_dM)[0]\n       for _z in rz]\n      for _y in ry]\n     for _x in tqdm(rx)])",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "fb5d37870d4b4b6db69c486c57dca1d7"
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "\n"
        }
      ]
    },
    {
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          "start_time": "2017-03-21T11:33:30.884898+01:00",
          "end_time": "2017-03-21T10:33:30.946497Z"
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        "collapsed": false,
        "editable": true,
        "deletable": true,
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      },
      "cell_type": "code",
      "source": "np.savez(path.join(output_dir, 'deltaM.dat.npz'), rx=rx, ry=ry, rz=rz, DeltaMstarMap=DeltaMstarMap)",
      "execution_count": 47,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-21T17:05:01.221687+01:00",
          "end_time": "2017-03-21T16:05:01.240389Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
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      },
      "cell_type": "code",
      "source": "with np.load(path.join(output_dir, 'deltaM.dat.npz')) as f:\n    rx, ry, rz, DeltaMstarMap = [f[_k] for _k in ['rx', 'ry', 'rz', 'DeltaMstarMap']]",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
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          "start_time": "2017-03-21T17:05:03.988148+01:00",
          "end_time": "2017-03-21T16:05:03.996902Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "extr = max(np.abs(DeltaMstarMap.min()), np.abs(DeltaMstarMap.max()))\nrx.shape, ry.shape, DeltaMstarMap.shape",
      "execution_count": 12,
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
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        "ExecuteTime": {
          "start_time": "2017-03-21T17:05:09.107593+01:00",
          "end_time": "2017-03-21T16:05:12.071695Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(10,3))\nax1.pcolormesh(rx, ry, DeltaMstarMap[:, :, rz.shape[0]//2].T, vmin=-extr, vmax=extr, cmap='seismic')\nax1.set_xlabel('$x [Mpc/h]$')\nax1.set_ylabel('$y [Mpc/h]$')\n\nax2.pcolormesh(rx, rz, DeltaMstarMap[:, ry.shape[0]//2, :].T, vmin=-extr, vmax=extr, cmap='seismic')\nax2.set_xlabel('$x [Mpc/h]$')\nax2.set_ylabel('$z [Mpc/h]$')\n\nM = ax3.pcolormesh(ry, rz, DeltaMstarMap[rx.shape[0]//2, :, :].T, vmin=-extr, vmax=extr, cmap='seismic')\nax3.set_xlabel('$y [Mpc/h]$')\nax3.set_ylabel('$z [Mpc/h]$')\n\n# plt.colorbar(M)\nplt.tight_layout(rect=(0, 0, 1, 0.95), h_pad=0, w_pad=0)\ncbaxes = fig.add_axes([0.1, .97, 0.8, 0.03]) \ncb = plt.colorbar(M, cax=cbaxes, orientation='horizontal')\nplt.savefig(path.join(output_dir, 'DeltaM.pdf'))",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1sdfgYFooxbHvyFIpHKkgE0IIIYQQ0g0LqYHF0ry1USpLqJzTzMcdq/detrpQSmPnYhYK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\nC5p/qB4OsQAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7cc1d37e90>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "editable": true,
        "deletable": true
      },
      "cell_type": "markdown",
      "source": "## Plot of $\\alpha_\\star$ for $R=0.5\\text{Mpc/h} $ (using symmetries)"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-21T17:51:44.404114+01:00",
          "end_time": "2017-03-21T16:51:44.432252Z"
        },
        "collapsed": true,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "qnx = 100\nqny = qnx\nqnz = qny\n\ndmax = 15\nqrx = np.linspace(0, dmax, qnx)\nqry = np.linspace(0, dmax, qny)\nqrz = np.linspace(0, dmax, qnz)\n\n# Can't compute at 0, set it to small value\neps = 1e-10\nqrx[qrx==0] = eps\nqry[qrz==0] = eps\nqrz[qrz==0] = eps\n#DeltaMstarMap = DeltaMstar(rxg, ryg)",
      "execution_count": 145,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-21T17:43:04.542242+01:00",
          "end_time": "2017-03-21T16:43:04.566472Z"
        },
        "collapsed": true,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "R = .5 # Mpc/h\nsigma0 = _sigma([R])\nnuc = 1.68/sigma0\ngamma2 = _gamma([R], sigma0)**2\nGamma = np.sqrt(gamma2/(1-gamma2))\nRstar = np.trapz(Pk*W1(k*Rs)**2, k)/sigmaRs**2/2/np.pi**2\n\n# Set nus\nnus = 1.2",
      "execution_count": 132,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-21T17:51:09.534038+01:00",
          "end_time": "2017-03-21T16:51:09.558028Z"
        },
        "collapsed": false,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": "eps = 1e-9\n_alphastar(R, [eps, eps, eps], Qbar, Gamma, nuc, nus, Rstar)",
      "execution_count": 144,
      "outputs": [
        {
          "execution_count": 144,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([ 0.20831703])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-21T17:43:05.073346+01:00",
          "end_time": "2017-03-21T16:48:22.658672Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Compute alpha*\nqalphastarmap = np.array(\n    [[[ _alphastar(R, [_x, _y, _z], Qbar, Gamma, nuc, nus, Rstar)[0]\n       for _z in qrz]\n      for _y in qry]\n     for _x in tqdm(qrx)])",
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d3b6aa5a23304a0fb84c80cd502e5cbd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "/home/ccc/.virtualenvs/astrop2/lib/python2.7/site-packages/ipykernel/__main__.py:6: RuntimeWarning: invalid value encountered in double_scalars\n"
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "\n"
        }
      ]
    },
    {
      "metadata": {
        "editable": true,
        "deletable": true
      },
      "cell_type": "markdown",
      "source": "Reconstruct full map"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T19:03:30.009434+02:00",
          "end_time": "2017-03-27T17:03:30.040442Z"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "rx, ry, rz, alphastarmap = sym_clone(qalphastarmap, qrx, qry, qrz)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-21T17:51:48.765842+01:00",
          "end_time": "2017-03-21T16:51:48.809094Z"
        },
        "collapsed": false,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": "alphastarmap = np.zeros([2*qnx, 2*qny, 2*qnz])\nalphastarmap[qnx:,    qny:  , qnz:  ] = qalphastarmap[:,    :,    :   ]\nalphastarmap[:qnx,    qny:  , qnz:  ] = qalphastarmap[::-1, :,    :   ]\nalphastarmap[qnx:,   :qny,    qnz:  ] = qalphastarmap[:,    ::-1, :   ]\nalphastarmap[:qnx,   :qny,    qnz:  ] = qalphastarmap[::-1, ::-1, :   ]\nalphastarmap[qnx:,   qny:  ,  :qnz  ] = qalphastarmap[:,    :,    ::-1]\nalphastarmap[:qnx,   qny:  ,  :qnz  ] = qalphastarmap[::-1, :,    ::-1]\nalphastarmap[qnx:,   :qny,    :qnz  ] = qalphastarmap[:,    ::-1, ::-1]\nalphastarmap[:qnx,   :qny,    :qnz  ] = qalphastarmap[::-1, ::-1, ::-1]\n#alphastarmap[np.isnan(alphastarmap)] = np.mean(alphastarmap[np.isfinite(alphastarmap)])\nrx = np.array(list(-qrx[::-1]) + list(qrx))\nry = np.array(list(-qry[::-1]) + list(qry))\nrz = np.array(list(-qrz[::-1]) + list(qrz))\nnx = 2*qnx\nny = 2*qny\nnz = 2*qnz",
      "execution_count": 146,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-21T17:51:49.776890+01:00",
          "end_time": "2017-03-21T16:51:50.243893Z"
        },
        "collapsed": true,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": "np.savez(path.join(output_dir, 'alphastar.dat.npz'), rx=rx, ry=ry, rz=rz, alphastarmap=alphastarmap, R=R)",
      "execution_count": 147,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-23T16:37:48.068742+01:00",
          "end_time": "2017-03-23T15:37:48.571323Z"
        },
        "collapsed": true,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": " with np.load(path.join(output_dir, 'alphastar.dat.npz')) as f:\n        rx, ry, rz, alphastarmap, R = [f[_k] for _k in ['rx', 'ry', 'rz', 'alphastarmap', 'R']]",
      "execution_count": 216,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-23T16:40:42.032951+01:00",
          "end_time": "2017-03-23T15:40:42.074906Z"
        },
        "collapsed": false,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": "alphastarmap[np.isnan(alphastarmap)] = _alphastar(R, [eps, eps, eps], Qbar, Gamma, nuc, nus, Rstar)\nvmax, vmin = alphastarmap.max(), alphastarmap.min()\nnx, ny, nz = len(rx), len(ry), len(rz)",
      "execution_count": 224,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-23T16:47:15.412401+01:00",
          "end_time": "2017-03-23T15:47:17.291984Z"
        },
        "collapsed": false,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": "size = 6\nfig = plt.figure(figsize=(size, size))\nax1 = plt.subplot2grid((5, 5), (1, 0), rowspan=4, colspan=4)\nax2 = plt.subplot2grid((5, 5), (0, 0), colspan=4)\nax3 = plt.subplot2grid((5, 5), (1, 4), rowspan=4)\nax1.contourf(ry, rz, alphastarmap[nx//2, :, :].T, vmin=vmin, vmax=vmax)\n\nax1.set_xlim(-15, 15)\nax1.set_ylim(-15, 15)\nax1.set_xlabel('$y$ [Mpc/h]')\nax1.set_ylabel('$z$ [Mpc/h]')\n\nax2.plot(ry, alphastarmap[nx//2, :, nz//2] / alphastarmap[nx//2, ny//2, nz//2])\nax2.set_xticklabels([])\nax2.set_xlim(-15, 15)\nax2.set_ylim(0.9, 1.01)\nax2.set_ylabel(r'$\\alpha_\\star/\\alpha_{\\star,s}$')\n\nax3.plot(alphastarmap[nx//2, ny//2, :] / alphastarmap[nx//2, ny//2, nz//2], rz)\nax3.set_yticklabels([])\nax3.set_ylim(-15, 15)\nax3.set_xlim(.99, 1.1)\nax3.set_xlabel(r'$\\alpha_\\star/\\alpha_{\\star,s}$')\n\nax1.xaxis.set_major_locator(MaxNLocator(nbins=7, prune='upper'))\nax1.yaxis.set_major_locator(MaxNLocator(nbins=7, prune='upper'))\n\n#fig.tight_layout()  #(w_pad=-1, h_pad=-1)\nfig.subplots_adjust(left=0.12, right=.98, top=0.99, bottom=0.10, wspace=0, hspace=0)\n\nc = plt.Circle((0, 0), radius=5, facecolor='none', edgecolor='white', linestyle='--', alpha=0.5) \nax1.add_artist(c)\n\n#Mpc_o_h = _Dimension('Mpc/h', latexrepr='$\\mathrm{Mpc/h}$')\n#ax1.add_artist(ScaleBar(1, units='Mpc/h', dimension=Mpc_o_h, box_alpha=0.1, color='white'))\nfig.savefig(path.join(output_dir, 'accretion_yz.pdf'))\nfig.savefig(path.join(output_dir, 'accretion_yz.svg'))\nfig.savefig(path.join(output_dir, 'accretion_yz.png'), dpi=360)",
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hhGESDboOYBPwS6VUk1LqLUAmpAghhLC8RLfR/SGwXGsdUkrVAv8PeD1zZQkhhBDGSLRH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\nM7NKc6IzM7NKc6IzM7NK+x8gRg4099sVRQAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7cb78af890>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-22T18:45:44.274391+01:00",
          "end_time": "2017-03-22T17:45:44.987048Z"
        },
        "collapsed": false,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": "rrr = np.linspace(0, 20, 1000)\nplt.figure(figsize=(4, 3))\nplt.plot(rrr, xi00pfun(.05, rrr), label=r\"$\\xi_{00}'$\")\nplt.plot(rrr, xi20pfun(.05, rrr), label=r\"$\\xi_{20}'$\")\nplt.legend()\nplt.xlabel('$r$ [$\\mathrm{Mpc/h}$]')\nplt.tight_layout()\nplt.savefig('/tmp/xifun2.pdf', transparent=True)",
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xgC+fWpXtrhiTlAWYAWjJ5kbmr9zGdZ+cxJCSvtWrMaY/WIAZYOJx5fZnVzF8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\nAGOMyRgLMMaYjLEAY4zJGAswxpiM+f/2LPVzZ/EK4gAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7cbc26c410>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-23T16:56:51.315267+01:00",
          "end_time": "2017-03-23T15:56:52.843156Z"
        },
        "collapsed": false,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": "size = 6\nfig = plt.figure(figsize=(size, size))\nax1 = plt.subplot2grid((5, 5), (1, 0), rowspan=4, colspan=4)\nax2 = plt.subplot2grid((5, 5), (0, 0), colspan=4)\nax3 = plt.subplot2grid((5, 5), (1, 4), rowspan=4)\nCS = ax1.contourf(rx, rz, alphastarmap[:, ny//2, :].T, vmin=vmin, vmax=vmax)\n#ax3.clabel(CS)\n\nax1.set_xlim(-15, 15)\nax1.set_ylim(-15, 15)\nax1.set_xlabel('$x$ [Mpc/h]')\nax1.set_ylabel('$z$ [Mpc/h]')\n\nax2.plot(ry, alphastarmap[:, ny//2, nz//2] / alphastarmap[nx//2, ny//2, nz//2])\nax2.set_xticklabels([])\nax2.set_xlim(-15, 15)\nax2.set_ylim(0.9, 1.01)\nax2.set_ylabel(r'$\\alpha_\\star/\\alpha_{\\star,s}$')\n\n\nax3.plot(alphastarmap[nx//2, ny//2, :] / alphastarmap[nx//2, ny//2, nz//2], rz)\nax3.set_yticklabels([])\nax3.set_ylim(-15, 15)\nax3.set_xlim(0.99, 1.1)\nax3.set_xlabel(r'$\\alpha_\\star/\\alpha_{\\star,s}$')\n\nax1.xaxis.set_major_locator(MaxNLocator(nbins=7, prune='upper'))\nax1.yaxis.set_major_locator(MaxNLocator(nbins=7, prune='upper'))\n\n#fig.tight_layout()  #(w_pad=-1, h_pad=-1)\nfig.subplots_adjust(left=0.12, right=.98, top=0.99, bottom=0.10, wspace=0, hspace=0)\n\nc = plt.Circle((0, 0), radius=5, facecolor='none', edgecolor='white', linestyle='--', alpha=0.5) \nax1.add_artist(c)\n\n#Mpc_o_h = _Dimension('Mpc/h', latexrepr='$\\mathrm{Mpc/h}$')\n#ax1.add_artist(ScaleBar(1, units='Mpc/h', dimension=Mpc_o_h, box_alpha=0.1, color='white'))\nfig.savefig(path.join(output_dir, 'accretion_xz.pdf'))\nfig.savefig(path.join(output_dir, 'accretion_xz.svg'))\nfig.savefig(path.join(output_dir, 'accretion_xz.png'), dpi=360)",
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TxcYD7WaXYksSdEIIYXFKKS5aVMArdR2M+gJml2M7EnQiKqSlYB/yt7KHixfl\nMzDik2EGEZCgE0IIG7igJp9UZwJP7j5hdim2Y8mgU0r9SinVppTaM+GyHKXUs0qpQ2Pfs82sUUxN\nWgj2I38z60txJnDZ0kL+sucEXr8cvgyHJYMOuAe48pTLvgE8r7WuAZ4f+10IIeaMa1aW0DPk5ZW6\nDrNLsRVLBp3W+mXg1APR1wL3jv18L/DemBYlRJyTVp31XVCTT4Yrkcd3NJtdiq0kml1AGAq11uMH\np1uAwqluqJS6BbgFICldjnDGkuwshZhZ5+7NdO3ZDEC+K/T2hjPRwbuWF/PYzmb+fdhLuispWiXG\nFUu26GaitdbAlHtUrfXdWuu1Wuu1iSnuGFYmhL3JB5XYyF1+DjU3fJWaG75Kfn5+WPe94awKhkb9\n/PnN41GqLv7YKehalVLFAGPfZYoAi5GdpBDRt7I8i+Wlmdy/pZHgZ34xEzsF3WPATWM/3wQ8amIt\nQghhmo+tr+Rg64CMqQuRJYNOKfUAsBlYpJRqUkp9CvgucJlS6hBw6djvQggx57xnZQkZrkTu29Jo\ndim2YMnOKFrrG6a46p0xLUSIOai/UpPeqMwuQ0wjxZnAh86q4Jd/a6Cxc5DKXOmLMB1LtuiE/cj5\nOSFi6+bzq0lMcPCzl+rNLsXyJOiEEMKGCjJcfGBtGQ9tb+JEr8fscixNgk4IIWzqM++YD8D/PnfI\n5EqsTYJOCHEaORRtD+U5qXx0fSV/2HaMQ639ZpdjWRJ0YtZkpyiEeb54SQ1uZyLfe/qA2aVYliV7\nXQphtqTKgbBu721Mi1IlQkwvx+3ksxfP5/tP17Kxto2LFxWYXZLlSItOzHlJlQOnfZnxGEJE6lPn\nVzM/382//nkPnlG/2eVYjgSdmJNiEUgSeiJWkhMT+M77ltPU7eGOF6Rjyqkk6MScYlbwSOCJaFs/\nL5fr15Rx98sNvHm02+xyLEWCTswJVgkaaeWJaPqXq5dSlOHiK7/fwcCIz+xyLEOCTsQ1K4eKVesa\nJ71p7SczJYnbP3gmx7qGuPXxvWaXYxkSdCIuWTngJrJLncI+zqrO4XMXLeAP25r447ZjZpdjCRJ0\nIu7YMTjsWLOwrq9cWsN5C3L55z/tkfN1SNCJOGPnwJDWnTBKYoKDO29YTWFmMp+5fzutfcNml2Qq\nCToRF+IpJOLldQhzZbud/OLGtQyM+PjkPVvpG/aaXZJpJOiE7cVjMMTjaxKxt7gog7s+spraln5u\nvncbw947H642AAAgAElEQVS5OZhcgk7YWjwHQjy/NhE7Fy8q4IcfPJOtR7r4/G/fwOsPmF1SzEnQ\nCduaC0Fg5muUVcbjxzUrS7j12mU8f6CNLz3wJiO+udWyk6ATtjQXQm7cXHqtIno+tr6Sf716KX/Z\n08LN925jaHTuDCiXoBPCBiTshBE+dX4133//CjbVdfCx/3udXs/c6KAiQSdsR3b6QkTuA+vKufPD\nq9nV1MMHf76ZY11DZpcUdbIenbCVaIfc+rLGWd1/S1OlQZWcLqlyQNa9E4Z41/Ji0l2JfP63b3DN\nna9w10dWc+78PLPLihpp0Yk5b31Z48kvKz3WZKQ1K4xyQU0+f/78eeS4nXzs/17n3lePoHV8zm8q\nQSdmLVa984zeyUczkCY+fjSfQ4jZmJefxp8/fx4XLczn24/t5Wt/3BWXqx5I0AlbMDLkzAgfI59T\nWnXCSOmuJH5x41q+9M4aHnmziXff8Td2HOsxuyxDSdCJOcMKrSuznz9UMoZubnE4FF+9bCEPfno9\nPr/m/T99lTtfOIQ/EB+HMiXohOUZ0YKxUsAYEbjSqhPRcPa8XJ768gVctayI2/56kA/dvZn6dvtv\naxJ0Iq5ZoRU3FavWJea2zJQkfnzDKn74gZXUtvRz1Y/+xo+eO2jr2VQk6IQhrHioyw5BMpsapVUn\nokUpxYbVZTz39xdyxbIifvTcIa7637+xpaHT7NIiIkEnLC3SnbkdQm6c1Wq14ocWYY6CdBc/vmEV\n93xiHV5/gA/dvYV/+ONO261vJwPGRdyZbXBsyNse0f0e6VgT8XOuL2uM6mBzIWbjokUF/PUrF/Kj\n5w/yq1cO8/iuZj59wTxuecc80l1JZpc3IxWvAwTHpRaW65obvmp2GXNGf6Wx21O4LbpIQy7ScJtK\npKEXSdgZPVuKtOhiK+mV37Jt2zazywhZY+cgP3imlid2nSDX7eRL76zhhrMqcCZG9wChUmq71npt\nJPeVQ5fCsmIRchvythsecrN5XLMPY0rIiZlU5rq588OrefTz51FTmMa3H9vLZbe/xJO7Tlh2ZhUJ\nOjEnRSvgjHges8NOiFCsLM/igU+v59cfX4crMYHP/+4N3vuTV3nNgh1WJOiEocxqEYQTDrEIOCs8\npxDRppTi4sUFPPXlC/j+dSto7R3mg3dv4eZ7t3Kotd/s8k6SoBNzipmBE07rLpzgNmqYgRy2FJFK\ncCg+sLacjV+7iK9fuYjXGrq44kcv842Hd1mih6YEnbC9UEPBKq0qq9QhhNFSnAl87qIFvPT1i/n4\nudU8/EYTF/5gI7c9U0v/sHmLvEqvSxEVs+19GU4rJZSgiyRc3pMa+oKUjw+lhv34ofTMDLUX5mx7\nXkprzjx263UZjqOdQ9z211oe29lMjtvJN65azPVrylAq/O1Nel2KOcvokHtP6tDJr3BEch9p2Yl4\nV5Gbyh03rOLxL5zPvDw3X39oFx+8ewt1bbGd1UeCTkSFVVoIoYZJJEEVzccZJz0wRTxYXpbJHz5z\nDt/dsJzaln42/GQTbx7tjtnzS9AJ25opBMIJOaOF+phWaNVZ5UOJiG8Oh+JDZ1Xw5JfOJ3tsVfOG\nGK2MIEEnosYOO9BohFwsHlsIu8pKdZLrduILBHBEcK4uErab61IpdQToB/yAL9KTkyK+WaGlFKoN\nedtnNU/mbNjhw4iwt1FfgB3HethU18Gr9R28ebQHX0Dzk4+spirPHZMabBd0Yy7WWneYXYSYWXqj\nMnz+SyM9PpQatZZXJD0xJxOtCZ8l5ITReoZGqW8fpKF9gPr2Qfaf6GPrkS6GRv04FCwvzeTT75jH\npUsKWVOZHbO67Bp0QhjGqEASYi7w+gMc6xqioX2Q+vYBGtoHaegIBlvX4OjJ2yUlKKrz3Lx/dRnn\nLcjjnHm5ZKaas9KBHYNOA39VSmng51rru80uSEwvkladtzFt2rF00eqNqEgkw7mARIebREcqicpN\nosNN72gtPSN7SFAuKtKvRWs/fj2MNzCILzDIoPcoHn9LVGqKBmnNiekMe/0c6xriSOcQjZ2DNHYO\ncWTs+/EeD/7AW//PeWlO5uWlccUZhczLS2Nevpt5+WmUZ6eQmGCNbiB2DLrztdbHlVIFwLNKqQNa\n65cn3kApdQtwC0BSeuyax2JqVjyE6XRk404qxZVQwLC/k+6RnQCkJBaxsbuU4cAww/5hhv29DPkz\nGAmsQaFI627FoRwkO5JxJWSRklDEOvfxscfMoiL9Wjy+Nob97Xh8JxjynQACJr5SYTWduzfTtWcz\nAPkuc8JAa017/wi1rf3UtvRT3z7AkY5gsJ3oG2biXCIZrkSq8tysLM/i2jNLqMx1My/fzfy8NNNa\naeGw9cwoSql/Awa01rdNdRuZGcU6wg06I1p0k3VK2T/yEcpSS1Eo2kba6R7tpmO0k0HfYFj1TSUl\nIYXspCyynFnkJ+fhTkzj9c7X6RjtnLQeI2ZICWdmFGnNWUssZkbp9Xg51Np/MtRqW/o52NpP99Bb\n03LluJ1U5aZSleumMtdNVV5q8HtuKlmpzqjWF4rZzIxiqxadUsoNOLTW/WM/Xw7canJZIkRGtuq2\nNFWGFHaPdKzB5XBR5CrkyFDw9uUpA7zetY1eb++0jx+OibV4/B48fg/NwycAcDlc+LQPgDeGNpCX\nnEf9QAPdXmMGzErIiVMFApqdTT28cKCN5/a3sf9E38nr0pITWViYxpXLillUmMbConQWFaaTm5Zs\nYsXRZaugAwqBP43Nk5YI/E5r/bS5JYlwxPIQZo4zh3nuagpcBTQNNaFQaDTHPE1AZKt5T2WmxxoP\nwuOeZhIciazNWcNoYJSGgQaOe5oJTHNoMxo9LkV82tLQycPbm9hY20bHwCgJDsWaymy+dvlClpZk\nsLAwndKslIjmmrQzWwWd1roBWGl2HSI2ZuqQMhV3gpvV2atwJSTTMHCYnT07+duxUqDC+CJD9Paw\n8nFO2fMUugqY555HRWo5mzo3R70Gac3Fr4Ot/Xz3Lwd44UAb6a5ELlpUwKVLCrhwYb4lDjuazVZB\nJ+KDUa268fAYby2Nt9g2NRVxvGeU2t5jBJ+ldNbPZbTNTeOh20yScuDVlZxffpwcZw5tI21AaC25\nUA9bSsjFp0BA8z/P1vLTF+txJyfyT1ct5qZzq3AlJZhdmqVI0AlTGHkIc9vxas7OryIv2c2jx3YD\nPg70toZ039kubxOKmVqlXh08bLmvrYZ3lZ6Bcozw4omDgPkLVgrrGhzx8eUHd/Dc/lY+sLaMf7pq\nCdluab1NRoJOWNpMhy8LXGlcVrKEjuEBnm0+MONjmWGy553sNXWNDPG7hm2syCnl+qrVbG4/zN6e\nE7N+fmnNxZ9RX4DP3L+dzQ2d/Ps1Z3DjOZVz7rxbOCTohGlm06pTwNq8SlZkl/Byax2H+tpPu41Z\nwRaKibVNDL0Amh1dTTQOdHF56WKynClsamuY8TGmIiEXn775p928UtfBD65bwfVry80ux/Ik6ISp\nQgm7yVp1icpBelIyDx7ezqBv9OTt7OjUupMqB+geHeKPh98kJXHywbgScnPXYzubeWh7E1+6ZIGE\nXIgk6ITpQg07gJyUFPpGRhgKBHjmSDPgHPuKHxNDbAQAJ5dUV9M6OMjetjazyhIW0Ovx8u1H93Bm\neRZfemeN2eXYhjUmIhMiBNVZWVx3xhkUuGOztIeVvHHiBGtKSji/opJQ2mnSmotPP9lYR4/Hy3fe\nt8wy80jagbxTwhJm2jGvKCzkknnzefzAAZr7+2NUlXX0DA/z+917KExz8+6Fi0iYpuOBhFx86h4c\n5b7Njbz3zFLOKMk0uxxbkUOX4m2y6n1h36dnvjGb0VSHMFcWFbG6uJg/7t1D38iIIc9lRyN+H3/a\nv58rFizg8gUL+MuhQ6fdxuiQC3d7MGpbEKd7YOtRPF4//+/C+WaXYjuyVc5xkQTbTI8xm53dqWGX\nmpTEGQUFPLRvH/1zOOTGBbTmmbo6clJSTrvOiJCb7fZg5LYg3qK15pE3jrOuKptFRelml2M7shXO\nUUYEXCiPHcmObmLYDXm9/G7XLsNqiwcBrekYCq6KXp6ZSVNvL2mzCDkrbwsiaG9zH3VtA3znfcvM\nLsWWZMubY6K5U5vu+cLdyS3qzyR5QQq7Wu2zmGmsKeCs0lIqM7PY2Xg0rPvGejuY+JwSeOF7fGcz\nSQmKdy8vNrsUW5LOKHNEVr3PlJ1bJM+f5nJy6coavPUyBdZ0NPDkwYMsc+Qyvyg3pPuYvR1YpQa7\neelgO+uqcmSC5ghJ0MU5q+1UZqonwaG4/MyF7D5ygqbOqdeLE0FJ9X6eefMgFyytJift9PN246y2\nHYA5rUo7ausf5kBLP+fX5Jldim1J0MUxK+9Ipqrt/CXVDAyP8ubhZkC6yk9n/L3p6Bvk1QNHuHL1\noknHVll9O7ByfVbwal0nABcsyDe5EvuSoItDdtl5nFpjXoaboux0Nu6uf9vlEnanO/U9OdjcwQu7\n6vH5376Aqx22A7BPnWZ482g3qc4ElpZkmF2KbUnQxRm77TAm1tvRN8jDr+7G6/efdjsJu5m19AQH\n0tt1Enu7bbuxsqOpl+WlmSQ4bPqHtQAJujhi5x1FRmoyAL5AYIZbiulCP8WZxAfOW0lSQoIttwc7\n1hxNo74A+5v7WFmeZXYptiZBFyfsvIMoz8vk6rVLcczQFJFW3czvgWfUS0t3P+sXVUx7Oyuz87Zs\ntCOdg4z6AywtlsOWsyFBFwfsvGNwOBQXLJ3H3/Y1ENAzr00nYTezzbWNVBdkk5djz2WLwN7btJHq\n24LLUy0osO/f0gok6GzO7juEhfML6R30cKxDhhLMJNSQH/X52VZ/nFUr7NuqA/tv20aobw8GXXXe\n3Fuxw0gSdMI0CQkOVpxRxta6Y2Hdby626sJ9zQea2shId5GdlRqlimJjroddXdsAJZku3Mkym8xs\nyLtnY1bcCaQeaJ/yuqHFbx8HlJ+bRlt7H229g2E/TyiLtcaLSII9oDW/ObSXlJ7TJ8IO528kzNXQ\nMch8OWw5axJ0NmW1kJtu5znxNhN3pC1tfRxIH4pmWXPaiNfP1HOlTG7i39EqoZdV75uz82M293g4\nQ8bPzVrIW49SKieEmwW01j2zqEfYTCgBNxmnM5HR0dmF9Vxo1c32MG1RQQZLFhaz8ZVaILy/1/ht\nrRB4czHsRnx+OgZGKcoI9+OKOFU4W07z2Nd0/3kJgL3PgNuAFVpzkQbcuMsvWsq2nY30EP5hy4nm\nQtjNRnvnABcXZuJOdTI4NBrRY5zaEhex0dobPOxcnOUyuRL7C6czyn6t9TytdfVUX0BntAoVQfEQ\ncjnZblJSkmhtk56W0zGi043fH6DhSDsL5xfO6nFm+zc3ghW2/Vg60esBoDhTgm62wgm6cwy6jbAx\nI3Z4ixYUcrC+lRCGzYUkHnthGvWaeuYncrC+lZp5hbOeGiz1QLslAm+uaOkLLlNVnCmHLmcr5EOX\nWuthAKVUMvB+oGri/bXWt47fRkSH2Z9ojdjJJTgU1ZV5/PnJHQZUJELR3TPEoGeEkqIsuvfP/m9o\n5qHMuXSu7kTveNBJi262ItliHgV6ge3A6X2XRVwy6pN8UWEmPT1DDHlGDd1hxdO5umi0ULe92Yhn\nOLJzdMIcnQMjpCQlyBg6A0TyDpZpra80vBIxLTNbc0YermrvHODVrQ2GPd5E8RR2Rmtt7wPAqOHj\n0qqLvp4hL1mpSWaXERcimRnlVaXUcsMrEZZk9DmZ0VEfPb0ydm4q0TzfmJ+XTmqacYfB5HxddPV4\nvGSmSNAZIeSgU0rtVkrtAs4H3lBK1Sqldk24XIhppWWkMK8quq2AeOyYMlvjrZ95lXmUXL7Y5GqM\nYfb56ljoGRqVFp1Bwmn/Xx21KsS0zPqnNvoTe2l1Hin5GTQckZbAZKId0k3N3SxbUkqdgY8pY+yi\np2fIK6sWGCScoCsBtmhtVKdwMdekrSo/ea4omuRc3eQ6uwbJzTF+FnwJu+jolnN0hgnnHN2NwHal\n1INKqY8rpYqiVZQwXzTOv+Rku+nsDi47Mhc6E4QjFodch0e8+LwB0uJkSql4P3w5MOIlTXpcGiKc\ncXSfBVBKLQauAu5RSmUCG4GngU1aa39UqhS246s/TOL86pO/JyQ6yEh30dMjHVHM1Nk9QE5BOgN9\nnpOX+eoPA7zt7yXMpbVm2BsgJSnB7FLiQti9LrXWB7TWt48NMbgEeAW4HnjN6OKEPT+1ju84x78D\nZGS76e3z4A/E5pCidEqZ3Ktb6zl+pGPS6yb+vcIlPTCNNeILAOByStAZIeygU0rdq5TKAtBae4DN\ngFtrvdbo4oQ5ZrPTmmpn2d3ez5N/lc65k4llKHs8XvxjO1FhXZ7R4MExadEZI5JxdCsmLsWjte4G\nVhlXkrCrmVoEsWrNiamluJJYuLxsyutn06oTxvF4JeiMFEnQOZRS2eO/jK1TJ2dMxbSqFxdTVBDb\nBSTl8OXpEhIcLF0TnXNxZhy+tOOh/VAMjwedHLo0RCQB9T/AZqXUH8d+vx74jnEliXhUWpXHUCA+\nd0qzEesw9gx7SXE7T/4+WQvu1I5EIvbGz9E5EyJpi4hTRdIZ5T5gA9A69rVBa32/0YVNRSl15dis\nLHVKqW/E6nnnikg/lc90yCs1LVkmFbYAvz+Az+sn2SXjs6wsMDZc2eGQoxJGiOiQo9Z6H7DP4Fpm\npJRKAO4CLgOagK1KqcfG6ok78XRYxpmcxPBI/LweOxsZ9pKcksTIsNfsUsQUxqflcMx2EUEBRBB0\nSikX8DmCc15qgsMLfhqjtejOAuq01g1jtTwIXIsJoSvC40hwEAhIbz8rCPgDKNmBWtrJFp38mQwR\nSYvuPqAf+PHY7x8G7id4ri7aSoFjE35vAs6OwfMKAwSk16Ul/PWhrXhHZW4HKwtIi85QkQTdMq31\n0gm/b1RKWapFpZS6BbgFICk9e4Zbi1h47L5NMh+iRYzKIWTTde7eTNeezQDku07vKjHeopOcM0Yk\nQfeGUmq91noLgFLqbGCbsWVN6ThQPuH3srHL3kZrfTdwN0BqYbk0I4SYYPGZFRyuPcGIR87RmSV3\n+TnkLj8HgKRXfnva9TJ1vrEi6bu6huDiq0eUUkcIzoyyLkbr0m0FapRS1UopJ/Ah4LEoP6cwwIqz\n55PmTja7DAEsWllOklOGvlpZUkKwKefzS+IZIZKt/UrDqwiR1tqnlPoC8AyQAPxKa73XrHqirWd+\nYtz0vCwoySK9q5eBwRGzS5nzXClORjwy1MPKksbGz/mkA5chQg46pdS0LSet9TWzL2dmWuungKdi\n8Vxz0dDi/IjG0iXOr552LJ1naITUFOeU14vYSEx0oJQ62Rllpr+bMMd40I1Ki84Q4bToziHY4/EB\ngisVyGlSETLP4Cgp+alml2E5/ZU6prOjpKY48QxN36q206wo8bqu4fiMKF6ZgNsQ4ZyjKwK+CSwD\n/pfgoO0OrfVLWuuXolGcsJfpdpCewRFSYjwbh6wyfroUlxNPlA4fS69a4ySOnaPz+iXojBBy0Gmt\n/Vrrp7XWNwHrgTrgxbFzZkJMyzM4QpLMxG661vY+Nj725pTX26k1F8/eOnQpQWeEsNr9Sqlk4N3A\nDUAVcAfwJ+PLEmaK9DwdvP2cz8Sd5pGDLQw5ZJDyZGJ9+FIGi1tfWnJw1zw4In8rI4TTGeU+goct\nnwL+XWu9J2pViZPs2PPSCh0c5LDl5JYvLaXleD+D/W+fsW+2LTk5bGksV5KDBIdiYETGOhohnHN0\nHwVqgC8THEfXN/bVr5Tqi055wq4m23GuWl5Obo7bhGrEuGVLSk+bis2uhyvjtSMKgFKKtOREBobt\n9SHXqsI5R+fQWqePfWVM+ErXWsd2RU0RddH4hO5yJVGQF9xUotlKtWNrLhY1u1OdBALa8M4o0pqL\njrTkRPplujZDyKp+ImYGtjeRn5dudhlzVn5eOp1dA2aXIUKU7pIWnVFCDjql1BtG3EaEz6xDNEZ/\nUj9xtIPSoiyZqHYK0WrVjbeey0qyaWruNvSxpTUXPWnJifRL0BkinD3okhnmslRA5izrERYzmx6Y\npz3WwAiDQyPk56XT1t5vyGOeyo6HLWMlMyOF2oenHlpgJ/F8fm5cVqqTpu4hs8uIC+FsLYtDuI30\nhY0SO/a+nEz94XZSkqMzcDweQi6aQw2e/OtuUvuNWx9ZWnPRlZ/uZMexHrPLiAshB53WujGahQjr\nMrJVt7e22ZDHEeYyM+TmQmsOIC8tma7BEfwBTYIsNT4r0hlFhMToHVtiosPQFmo8tObGReO1vOvS\nZYYtkyQtudjIS0smoKF7SFaamK2wg04pdZ1S0p3ADGZ/kjVqB7d8aSmrV1QY8ljxysiwKyrIwOlM\nMmSJJLNDzuz/gVjKTQuu9tExIEtbzVYkLbr7gd8ppU5OXKiU+oRxJQkrM2JHd7ixg/lVBSQkGHNA\nIZ5ac0bLqvexcEERtXUts34ss0NurslLC7bAO/qlRTdbkexpDgAvAQ8rpcZ7FXzRuJLEdKzwiXa2\nO7yBwRE6ugaoKs81qKL4ZESAJycnUlaSRf2R2Z1jtULIWWHbj6WiDBcAJ3o9Jldif5EEndZa/wx4\nBHhMKZWCrE0XU1b4hx9anD+rnd/BuhYW1RTNug5pzU2vZl4hx5q6GR2N7HzobP/OInIlWSkoBce6\nJehmK5Kg6wbQWt8H/B/wJCAras5Rke4Ijx7vwpWcSGlO5LPHzYWQm+1rbOvoY9e+ppO/h/q3slrA\nWeHDXaw5Ex0UZ7ho6pKxdLMV9tajtX7nhJ8fUkoNA/cYWZSYmdXG1U3cKU41FGHibbSG517az/Dg\nCFTLOnXTmc3YuskG5k83XMRK4TZuLobcuLKcVI7JoPFZm/UWpLV+AsgzoBYRJquF3bhQd5Z9sxi8\nPBdac7PhTEzgAlcR25l8+KsVA02crjw7lVfqjBnDOpfJODqbs/unXXeqk6tWLwrrPnMx5MJ9zSuq\niklJic4MNLFk9+17tuYXuGntG6HXI+vSzYYEnTDV4NAoLmcSS8oKzC7F8kINu/SUZJZVFLFj97Eo\nVxRdcz3kABYXBVf7ONganblh5woJujhg9x3CS3saOKumnNQQ5sCci625cF14xjx2Hm42ZIC4Wey+\nTRtlUVGws9aBFgm62ZCgixN23jF0DQyx71gr71g6/UrXEnIzvweLSvNxORPZccS+c4raeVs2Wkmm\ni3RXIrUtfWaXYmsSdHHEzjuINxqOk+lOoSxXVnqayXRh19YzwAu76tDantuDHWuOJqUUS4oy2Nss\nQTcbEnRxpmd+oq12FuO1+gOax7fuo6mzd9LbSWsuNN2DHroG7DnA2E7bbSytrsxmz/Fehr2yClqk\nJOjilB12GqfWODQS7FnmTna+bVkSCbnTnfqerFtQxqrqktNuZ5ftwA51mmVdVTZev5a16WZBgi6O\nWXnnMV1taxeUccHY+ToJuamNvzdVBdksLivgwPHJx1tZOUisWpeVrK3MQSnYXN9pdim2JUEX56y2\nkwulnk0HjlCYlU7VWTLkYCaJi5K5aNl8nnnzIJ7R6cda2W07EEGZqUmsrsjmuf2tZpdiWxJ0c4TZ\nO5Zwnt/nD/D0G7WsLyunOisrypXZlzspiWsWL+GF/kbaegdCus/438GsbcHs7dCuLltayN7mPo73\n2PP8q9kk6OaYWO5oZrNTbcr38HjtAS5bsICitLQoVGd/uamp7G1rZW9bW0SHeM3YFkRkLltaCMBf\ndp8wuRJ7ki1vjpq40zFyvkwjdmbjO+2WgQGeqK2l2xP5nJjxSAEaONrby9Het3qpRjr5s5W3BRE0\nPz+N1RVZ/GZLI588rxqHQ1ZGC4dsiWLSHVIoO7xo7MhObZk09wdnhEhQitzUVNoGBw1/TjtJSUwM\nHq483EB7FN6LSLeFqe4rjHPTuVV8+cEdvHyonYsWyfnrcMiWKSZlxk5rusNvOampXLt4Ca8cbWR/\n+9yczT03JYVrFi9mf3vHlCE3myV9piIBZg1XLSvmP9P384u/NUjQhUnO0QlLmOkcU/vgIA/v28vZ\nZWWcW14eo6qsozIziw1Lz2DzsWNsaZp+smYZkhGfnIkOPvOOeWyq6+TF2jazy7EVCTphG10eD4/0\nbqGiOplrzq0gtWpuLEhZukhx5bpynhl8gwMdHSHdR8IuPt14ThXVeW7+5c976B+WpXtCJUEnTBfK\nTjmpcoCkygE8fi9/ProTj99LhjPl5OXjX/Fg/LU4x763Dffz+8PbOeHpC+t1StjFH2eig9uuX0Fz\nj4dv/mkPgYD8jUMhQSdMFWrITeTXmhdbDtE5EjxPtSA9nwSlTt7WbqE3WVjPS8/jhnlrSVLBf9FB\n3+hp9xFz05rKHL52xSIe39nM954+gNYSdjORs8zCNEa0OBwoFmYWsC6vgo0th2jxvDXL+2Rh4G00\nf0zedCHlTnRyTkE1xSmZPNt8AK8OTPs4M72eaHROEeb77IXzae7x8POXG+j1eLn12mU4E6XdMhUJ\nOmGKUENuppZLAM1TTXtZlFHAlaVLafX08WpbA73eycfexTr8Qm15KWBdXiUrc0rZ19PCgw3bpg25\ncEjYxR+lFLdes4ysFCd3bqyjtrWfW69ZxvIyWeZqMhJ0wtbWlzWO/dTIoZE3mJ8+j68UVfFfb9SF\nHBRWOAx4dlkjNWlJ1Hu24XcOsaY0ePmWpkpDHl/CLv44HIqvXbGIxcXpfOvRvbznzle49swSvvTO\nGubnm3/kwkpUvB/fTS0s1zU3fNXsMsQERrXm3gq5t1MoNMHnWJy+iBPDLfR6ew0LDSNcWNFClbuS\nytQKXmx/mdHA6JS3nanuUFukEnTWlPTKb9m2bdusHqN/2MvPX2rgl680MOwNMD/fzaVLCrlkcQFr\nKrNJTLD/YU2l1Hat9dpI7istOhFTsegJOB5yCoVf+1mfcxZDfg+lKYdpGW7Br99awDIW4TcxkHOc\n2Q4JEcoAABg7SURBVFS7qyhyLeW4p5ktna9PG3Lj9zeiTmnVxa90VxJfu2IRN55TyZO7T/DCgTZ+\ntekwP3+5gcyUJM5bkMuSogwWFaWzqCid8uzUOTWNmG2CTin1b8CngfFpMb6ptX7KvIqE1Wk0hwbq\nqBuop9hVRJW7ilVZK9natY3WkeCA26laheNmCpiZ7q9QOB3JjARGcCe6OTPrTI4NHWN3794ZAy4a\nJOziW0GGi0+cV80nzqumf9jLK4c6eG5/G68d7uSp3S0nb5eSlMDCwjQWFaWzsDD95PeC9GSUir/t\nwzZBN+Z2rfVtZhchImNka26mgAHYkLf9tMscvmQuSA/w0MgKKlIrqHZX0j3aQ/doNz3eHvp9bz9c\nGsrzTORyuMhyZpKdlE22M4tsZzb1Aw0sTf5d8AaBl8lywXLXW/d5pGNNWM9xqlB6X4q5J92VxFXL\ni7lqeTEAAyM+DrX2U9vST21rPwdb+3nhQBt/2NZ08j4pSQlU5qZSmZtKVa6bylw3VbmpVOSmUpyZ\nQoJNW4F2CzphU7EevDxZyAEE9MjJ6xU7cSUUsCAtn5TEAlwJpSQ4XDT0/g6/HsadVIEroYBnuyvw\nB/wExg6JOlAo5eDy7MMkOtz49BBdw28CUJV+HT7tYdh3kGF/Ox5fK0uTp19DbEPe9lmHXTikVTc3\npSUnsqoim1UV2W+7vGNghIOt/dS1DdDYOURj5yD17YNsPNDOqP+tDl3OBAflOSknA3Bevpv5+WnM\nz3eTb/GWoN2C7gtKqRuBbcDfa627J7uRUuoW4BaApPTsyW4iLC4WPSE1Pjy+Zjy+ZrqD+UeCcuHX\nwaEJAe1FAe/K7USRgFIJY/cLgA7g0yl4A32M+N/aDI/0PxT1uoX9de7eTNeezQDku8ztKJKXlkxe\nWjLnzs972+X+gKalb5jGzkEaO4c40jlIY0fw+6b6Doa9b4VgenIi1WPBNy/Pzbz8NOYXuKnKdeNK\nSoj1SzqNpXpdKqWeA4omueqfgS1AB8GluP4DKNZaf3Kmx5Rel+aLpDUXStBNd1hxqhadVc3UojOq\n5+VE0qqzBiN6XcZaYCwE69sHaGgfpKF9gPqx7829b41hTXQoVpZncd6CPM6bn8uqiuyIB7bHTa9L\nrfWlodxOKfUL4IkolyPmiPekRm9y6MeHUqP22LMlhzBFpBwORUlWCiVZKVxQk/+264ZGfcHw6xhk\n/4k+Xq3v5M4XDnHH84dISUrgrOocLj+jkA+fVRGzw52WCrrpKKWKtdbj68i/D9hjZj0iNFafWDia\nIWcUK43/E2Imqc5ElpVmsqw0k2tWlgDQ6/GypaGTV+s6eKWug3/+0x56hrx8/uIFManJTqMIv6+U\n2q2U2gVcDPyd2QUJ6wq1c4cVWlyx7IhyKqt/EBHxITMliSvOKOLfr13Gc1+9kEuXFPKDZ2rZ1dQT\nk+e3TYtOa/0xs2sQ4ZnrO1ErhKgQVvPa4S62NHRSnhM89BkLdmrRCfE2Mx3SC6dVZ3Qohfp4Zrbm\nxs31DyQiNvqGvXzr0T3c8IstFKQn84fPnENeWnJMnts2LTphL1bZeT7SsSbkHpiPD6XO+pxdOIEZ\nSsjJ+Tlhd/6A5uHtTdz211raB0a46Zwqvnr5QjJcSTGrQYJO2NqWpsoZZy8JN+zGhRp68XCIUnpg\nCqNprXl+fxvfe/oAh9oGOLM8i1/etJYVZVkxr0WCThjOiNactzHN0EHj4YTduGgGmLTmRDzb3tjF\nd/9ygK1HuqnOc/PTj6zmymVFps2eIkEnbC+UVh1EFnZGs8I5OSGi5VBrP99/ppZn97WSn57Mf753\nGR9cV06SycsESdCJOcXMsAsn5MJpzRk1obMcvhSRau7xcPuzB3n4jSbczkS+dvlCPnl+NalOa0SM\nNaoQccOsTiihturgrcCJZeBFK+SEMFPP0Cg/ebGee149Aho+eV41n794Adlup9mlvY0EnYgb4YQd\nxCbw5FCliEcjPj/3bDrCnRvrGBjxsWFVGX93WQ1l2dbsmCVBJyzL6A4pUzE68GYTbma35uTwpZjJ\ni7Vt/Pvj+zjcMcg7Fxfw9SsXs6go3eyypiVBJwxjhbFz4bbqJpoYUOGGnhEtt0hCThZcFbFytHOI\nW5/Yx3P7W5mX5+beT57FhQvzZ76jBUjQibgzm7AbF+tDjma35ISYyojPz10b6/nZS/UkOhTfuGox\nnzyvOuLldswgQScsLdLDl0aEXaxYLeTk8KUYt/NYD1/7404OtQ1wzcoSvvmuJRRluswuK2wSdMIQ\nVjhseSo7hN1sQk4OW4poGfH5ueP5Q/zspQby05L59SfWcfGiArPLipgEnYhr40FixcCzWktOCIA9\nx3v5+z/spLa1n+vXlPEvVy8lMyV281JGgwSdsDwjel9aqXVnRMBJa04YTWvNb7Y08h9P7CfbncSv\nP76OixfbtxU3kQSdmDPMbt3ZqQUn5+nmloERH//0yG4e39nMxYvy+eEHzrTcoO/ZkKATtmDkmLpY\nB57RASetOWGk2pZ+Pvvb7Rz5/+3de7CcdX3H8feH3O8hF5IYAkkgBAhIhIS7WBg0YDumaGlD6YhF\nBq04rVadgUFLxTqipbUDBRErlUo0g2ggI8hNEaQlIgQwNxKSQEJIIBfI7ZCck+R8+8c+0eVwcs6e\nPXv2ueznNZPJXp/z/T278/vs79lnf78tTXxp1lT+7gNHccghxfqQ46CzbqvXiSi1/gF5TwdenkZw\n1ph+vWITV81dxIC+vZl7xemccdTItEvqEQ46a3htA6na4KtHsHk0Z7Uy7+l1XHvvEo4ZM4T//sTM\nXP5soFIOOsuVekwLltWRmEPOaiEiuPHhFdzy2GrOOWY0t156MoP7FTsK8vPTdrOEO3yz6rS2Bl++\ndwm3PLaaOTMn8P3LZhQ+5MBBZznVaGHXaO212osI/mnBEub+dh2f+sBkvvHRE1NfELVeGqOVVkiN\n0vk3Sjut50QEX7lvCXctXMenzpnM1Rcci1SsMys74qAzy7A0Qy6L07pZdW548EXuWriOK8+ZzNUX\nNlbIgYPOcm7v2sGFHfEUtV1WXz9cuJbvPr6GS087gmsaMOTAQWcFUbRQKFp7LB2PvbiJ6+5bwnnH\nHsZXPzKtIUMOHHRWIEUIhyKPUK2+Vr6xk6t+tIjjxg3l5kveR+8GOfGkPY3bciukPIdEnmu3bGlq\n3sdn5i5iYN9e3PGJmQxqgJ8QdKSxW2+FdCAwevqH5bXkkLNaiQiunb+Y1Zt3MfeTpzFmaHFnPKmU\nR3RWWHkIDx+qtFr7yTPruff5DXz+/GM48+hRaZeTCR7RWaFldXSXh3DzMj3589q23Vz/82WcPnkE\nV517dNrlZIaDzhpCVgIvDwFn+RQRXPOzxbRG8K9/cRK9CrbUTnc46KyhlAdNvULP4Wb1cM+z63li\n5Waunz2NCSMGpl1OpjjorGH1ZOg53Kyetu/eyw2/eJEZRx7K35yWzdU30uSgM+PgwdRZADrQLAtu\n+uVLvPl2C3d+5NTCrQ5eCw46sw44yCzrVm/exZ3/9wpzZk7ghPHD0i4nk/zzAus2n51nlp5/f3gl\n/Xofwhc+NDXtUjLLQWdm7+IPL/mwbMMO7l+8kcvPnsSowf3SLiezHHRmZjn17UdXMqR/b644e3La\npWSag87MLIdWvrGTR5a9weVnTWLYwD5pl5NpDjqrCR/qMquv//rNGvr3OYTLzpyYdimZ56Azs3fw\nh5bs27RzD/c+t4GLT5nAiEF90y4n8zIXdJIulrRUUqukGW3uu0bSKkkrJM1Kq0YzszTNe/pVWva3\ncvnZk9IuJRcyF3TAEuCjwBPlN0o6HpgDTAMuAG6V1Kv+5dnBeCRg1vNaW4O7n3mVM48ayaRRg9Iu\nJxcyF3QRsTwiVrRz12xgXkQ0R8TLwCrg1PpWZ1Zs/rCSfU+t2cr6t3bzVzMnpF1KbuRpZpTxwMKy\n6+uT295F0pXAlQB9hhza85WZmXXB1sVP8eaSpwAY3b9r4415v3uVYQP6MGva2J4orZBSCTpJjwLt\nvUrXRsR93d1+RNwO3A4wcMyE6O72rHJD1oqdR3qXm3Vk5IlnMPLEMwDo8+Tcip+37e0WHlr6OpfM\nnED/Pv7mplKpBF1EnF/F014Dysfqhye3mVkN+LBl9j287A1a9rXysVMOT7uUXMncd3QdWADMkdRP\n0iRgCvB0yjVZO9xhmvWMXyzeyOGHDuBET97cJZkLOkkXSVoPnAHcL+khgIhYCtwNLAMeBK6KiP3p\nVWpWHP5wkn079uzlyVVbuGDaWCS/Xl2RuZNRImI+MP8g930d+Hp9KzIzS9+vlm9i7/7gwhPHpV1K\n7mRuRGfF4BFCfvi1yoeHlr7OmKH9eN+E4WmXkjsOOusx7kDNamPf/laeXLWFc6ce5hXEq+CgM2tg\n/jCSDy+s387OPft4/5TRaZeSSw4661HuSM267zcvbUaCs44emXYpueSgM2tQ/hCSH795aQvvPXw4\nwwd6pYJqOOisx7lDzR6/JvnR1LyP51/dxtkezVXNQWd14Y7VrDovrN/G/tZgxsQRaZeSWw46swbj\nDx35smjtWwCcPMET1FfLQWd14w42fX4N8ufZtW8x5bDBDBvYJ+1ScstBZ3XljjY93vf509oaLFq3\njVOO9GiuOxx0VnfucOvP+zyf1mzZxfbdeznZQdctDjpLhTtes84tWrcNgJOPcNB1h4POUuOwqw/v\n5/xatmEHA/v2YvKoQWmXkmsOOkuVO+Ge5f2bb8s37mDq2CGe37KbHHSWOnfGPcP7Nd8iguUbd3Dc\nuKFpl5J7DjrLBHfKteX9mX8btu9hx559DroacNBZZrhz7r4ha+X9WBDLN+wA4PhxQ1KuJP8cdJYp\n7qSr531XLC++Xgq6qWM9ousuB51ljjvsrvM+K57Vm5t4z7D+DO7XO+1Scs9BZ5nkQ3CV834qppe3\nNDFptH9WUAsOOss0d+IH5w8DxfbyliYmjnTQ1YKDzjLPHfq7eX8U21tNLWzfvZdJ/qF4TTjoLDfc\nuTv0G8WaLU0ADroa8beclisHOvmdR0bKldSXw62xvOKgqykHneVSowSeA64xvbyliV6HiAkjBqZd\nSiE46CzXihp4DrjG9srWJsYPH0CfXv52qRYcdFYIRQk8B5wBbNy+h/HDB6RdRmE46KxQyoMiL6Hn\ncLO2Nm7bzelHjUy7jMJw0FlhZTn0HG52MPtbgzd2NvOeYR7R1YqDzhpC22Cpd/A52KxSm3buYX9r\nMG54/7RLKQwHnTWkgwVPLQLQoWbdsWHbHgCP6GrIQWdWxiFladu4fTeAR3Q15HNXzcwyZGMyohvn\nEV3NOOjMzDJkw/bdDOrbi6H9fcCtVhx0ZmYZ8vr2PYwd1h/Jh9FrxUFnZpYhm3c2c9gQfz9XSw46\nM7MM2drUwsjBfdMuo1AcdGZmGbJ1VzOjBvdLu4xCcdCZmWVEy75WduzZx4hBHtHVkoPOzCwj3mxq\nAfChyxpz0JmZZcTWpmYARnpEV1MOOjOzjNi668CIzt/R1VLmgk7SxZKWSmqVNKPs9omSdkt6Pvl3\nW5p1mpnVmkd0PSOLP71fAnwU+G47962OiOl1rsfMrC7+MKIb5BFdLWUu6CJiOeBZAcys4WxtaqH3\nIWLogMx1zbmWuUOXnZgk6TlJj0t6f9rFmJnV0tZdzYwY1Ncf9GtMEfVfeVnSo8DYdu66NiLuSx7z\na+CLEfFMcr0fMDgitko6BbgXmBYRO9rZ/pXAlcnVEygdDs2zUcCWtIuogSK0owhtgGK0I89tGAWM\nTi4PABZ18Li8trHWpkbEkGqemMr4OCLOr+I5zUBzcvlZSauBY4Bn2nns7cDtAJKeiYgZbR+TJ0Vo\nAxSjHUVoAxSjHUVoQ2caoY2VkvSuvr5SuTl0KWm0pF7J5cnAFGBNulWZmVnWZS7oJF0kaT1wBnC/\npIeSu84Bfi/peeAe4NMR8WZadZqZWT5k7tSeiJgPzG/n9p8CP61ik7d3u6j0FaENUIx2FKENUIx2\nFKENnWmENlaq6n2RyskoZmZm9ZK5Q5dmZma1VMigK8o0YgdrR3LfNZJWSVohaVZaNXaFpH+W9FrZ\n/v9w2jV1haQLkv29StLVaddTDUmvSFqc7P+qz2KrN0l3SNokaUnZbSMkPSLppeT/Q9OssVrtta3N\n/ZJ0U/K++72kk+tdY71UsC+OlfSUpGZJX6x0u4UMOv44jdgT7dy3OiKmJ/8+Xee6uqrddkg6HpgD\nTAMuAG49cEZqDny7bP8/kHYxlUr27y3AhcDxwCXJ65BH5yb7P0+nrf+A0nu93NXALyNiCvDL5Hoe\n/YB3t63chZTOMp9C6ffB36lDTWn5AR3vizeBvwdu7MpGCxl0EbE8IlakXUd3ddCO2cC8iGiOiJeB\nVcCp9a2u4ZwKrIqINRHRAsyj9DpYHUTEE5Q6uXKzgTuTy3cCf17XomrkIG0rNxv4nyhZCAyXNK4+\n1dVXZ/siIjZFxO+AvV3ZbiGDrhNFmEZsPPBq2fX1yW158Nnk8MsdOTvUlOd9Xi6AhyU9m8wglGdj\nImJjcvl1YEyaxfSgorz3UpO5nxdUqpJpxNqxETiifBoxSe1OI1YvVbYjszpqD6VDLl+j1Nl+Dfg3\n4PL6VWfA2RHxmqTDgEckvZh8is61iAhJPoXc2pXboOvpacTqpZp2AK8BE8quH57clrpK2yPpe8DP\ne7icWsrsPu+KiHgt+X+TpPmUDsnmNejekDQuIjYmh/I2pV1QDynEey9NDXXoskDTiC0A5kjqJ2kS\npXY8nXJNnWrzvcJF5Guy7d8BUyRNktSX0slAC1KuqUskDZI05MBl4EPk6zVoawFwWXL5MiB3R0Aq\ntAD4eHL25enA9rJDtlaB3I7oOiLpIuBmSrOD3y/p+YiYRWkasesl7QVayfg0YgdrR0QslXQ3sAzY\nB1wVEfvTrLVC35I0ndKhy1eAT6VbTuUiYp+kzwIPAb2AOyJiacplddUYYL5KS8D0Bn4UEQ+mW1Jl\nJP0Y+BNgVDJF4HXADcDdkj4JrAX+Mr0Kq3eQtvUBiIjbgAeAD1M66ext4G/TqbTndbYvJI2ldARu\nKNAq6XPA8Z19/eSZUczMrNAa6tClmZk1HgedmZkVmoPOzMwKzUFnZmaF5qAzM7NCc9CZmVmhOejM\nzAwASbdJOivtOmrNQWcNqXxtwrLbQtJdZdd7S9osqeqpyg50HMnfe9csJJIGJGvDtUgaVe3fMauR\n04GFaRdRaw46a2SrI2J62fUm4ARJA5LrH6T7cwp22HFExO6khg3d/DtmAEiaJulRSSslfUXSzZJm\nVvC844CVEbG/2m1klYPOCknSY5I+mFz+F0k3V/jUB4A/TS5fAvw42cZESS9KmitpuaR7JA0s+3sf\nT5YfekHSD5Pb/tBxJA/rJel7Kq0a/3BZoJrVhKT+wE+AfwBOAq4AxidruHXmQuDBbm4jkxx0VlTX\nAddKuhR4H/C5Cp83j9KE2f2B9wK/LbtvKnBrRBwH7AA+A6VP0MCXgfMi4iRKHQQkHUfZ86cAt0TE\nNGAb8LFqGmbWgfOB5yJiaUTsBvpSWg6rErMovV+7s41MctBZISVrrAn4R2BOpZNeR8TvgYmURnMP\ntLn71Yj43+TyXcDZyeXzgJ9ExJZkGwcmCj/QcRzwckQc+E7w2eTvmNXSdOA5AEnvAXaVvWdJbj+8\n7ZOSoxPDI2JDZ9to7/nJ7ROSIxY3Sqpm+bEe46CzQpJ0IjAOaImInV18+gLgRpLDlmXazoB+0BnR\n23QcBzSXXd5PQVcPsVS18MfVx79BaTQGQLLMz5eAHyYBVu5c4LGOttHJ8wGOTZ57U0Q8WovG1IqD\nzgonWfduLjAb2CXpgi5u4g7gqxGxuM3tR0g6I7n818CTyeVfARdLGpn8/RG8s+Mwq5cfAedIWgG8\nADwl6T+gtAo7paV+HmnzAQzeeZi93W108nwi4hFKy4r9p6Txbe9Pkz9RWqEkI6mfAV+IiOWSvgZ8\nk3ceQuxQRKwHbmrnrhXAVZLuoLQW4HeSxy+V9HXgcUn7KR322QXc063GmHVR8t49pYP75wPz27nr\nTODznW2j7fMljQH+LCK+L+mblNZqXEfGVnv3enTWkCRNBH4eESf00OMXAadFxN4KH/8KMOPA93xm\neZAcLWmJiF+lXUtHPKKzRrUfGJas2j6900d3UUScXMnjkp8YPEVpFeXWWtdh1pNys0K9R3RmZlZk\nPhnFzMwKzUFnZmaF5qAzM7NCc9CZmVmhOejMzKzQHHRmZlZoDjozMys0B52ZmRWag87MzArt/wFF\nPi9oL67zPAAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f7cbc906250>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Plot of $D_\\star$ for $R=5\\mathrm{Mpc/h}$\n\\begin{equation}\nD_\\star = \\frac{\\delta_c/\\sigma_{1/2}}{\\sqrt{\\left\\langle \\nu_{1/2}^2\\middle| \\nu_c, {\\cal S}\\right\\rangle} + \\left\\langle \\nu_{1/2} \\middle| \\nu_c, {\\cal S}\\right\\rangle}\n\\end{equation}"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:26:00.943102+02:00",
          "end_time": "2017-03-27T20:26:00.968250Z"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "qnx = 100\nqny = qnx\nqnz = qny\n\ndmax = 15\nqrx = np.linspace(0, dmax, qnx)\nqry = np.linspace(0, dmax, qny)\nqrz = np.linspace(0, dmax, qnz)\n\n# Can't compute at 0, set it to small value\neps = 1e-10\nqrx[qrx==0] = eps\nqry[qrz==0] = eps\nqrz[qrz==0] = eps\n#DeltaMstarMap = DeltaMstar(rxg, ryg)",
      "execution_count": 103,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Compute relevant quantities"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:26:03.917273+02:00",
          "end_time": "2017-03-27T20:26:03.940342Z"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "R = .5 # Mpc/h\nR_half = R/2**(1/3)\nsigma0, sigma_half = _sigma([R, R_half])\nnu_half = 1.68/sigma_half\nnuc = 1.68/sigma0\ngamma2 = _gamma([R], sigma0)**2\nGamma = np.sqrt(gamma2/(1-gamma2))\nRstar = np.trapz(Pk*W1(k*Rs)**2, k)/sigmaRs**2/2/np.pi**2\n\n# Set nus\nnus = 1.2\n\n# Compute beta_1/2\nnuhalfnu_mean = nuhalfnu(R, Rhalf)",
      "execution_count": 104,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:26:05.893411+02:00",
          "end_time": "2017-03-27T20:30:37.994516Z"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "# Compute alpha*\nqDstarmap = np.array(\n    [[[ _Dstar(R, [_x, _y, _z], Qbar, nuc, nus, nu_half, Rstar, R_half, nuhalfnu_mean)[0]\n       for _z in qrz]\n      for _y in qry]\n     for _x in tqdm(qrx)])",
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42da510404f34a3eb463ef7b74b23c9d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:32:56.327430+02:00",
          "end_time": "2017-03-27T20:32:56.382317Z"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "rx, ry, rz, Dstarmap = sym_clone(qDstarmap, qrx, qry, qrz)",
      "execution_count": 114,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:31:18.252464+02:00",
          "end_time": "2017-03-27T20:31:18.761787Z"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "np.savez(path.join(output_dir, 'Dstar.dat.npz'), rx=rx, ry=ry, rz=rz, Dstarmap=Dstarmap, R=R)",
      "execution_count": 109,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-23T16:37:48.068742+01:00",
          "end_time": "2017-03-23T15:37:48.571323Z"
        },
        "collapsed": true,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": " with np.load(path.join(output_dir, 'alphastar.dat.npz')) as f:\n        rx, ry, rz, alphastarmap, R = [f[_k] for _k in ['rx', 'ry', 'rz', 'alphastarmap', 'R']]",
      "execution_count": 216,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:33:23.378024+02:00",
          "end_time": "2017-03-27T20:33:23.409018Z"
        },
        "collapsed": false,
        "trusted": true,
        "editable": true,
        "deletable": true
      },
      "cell_type": "code",
      "source": "vmax, vmin = Dstarmap.max(), Dstarmap.min()\nnx, ny, nz = [len(_) for _ in rx, ry, rz]",
      "execution_count": 116,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:33:34.954972+02:00",
          "end_time": "2017-03-27T20:33:36.259863Z"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "size = 6\nfig = plt.figure(figsize=(size, size))\nax1 = plt.subplot2grid((5, 5), (1, 0), rowspan=4, colspan=4)\nax2 = plt.subplot2grid((5, 5), (0, 0), colspan=4)\nax3 = plt.subplot2grid((5, 5), (1, 4), rowspan=4)\nCS = ax1.contourf(rx, rz, Dstarmap[:, ny//2, :].T, vmin=vmin, vmax=vmax)\n#ax3.clabel(CS)\n\nax1.set_xlim(-15, 15)\nax1.set_ylim(-15, 15)\nax1.set_xlabel('$x$ [Mpc/h]')\nax1.set_ylabel('$z$ [Mpc/h]')\n\nax2.plot(ry, Dstarmap[:, ny//2, nz//2] / Dstarmap[nx//2, ny//2, nz//2])\nax2.set_xticklabels([])\nax2.set_xlim(-15, 15)\n#ax2.set_ylim(0.9, 1.01)\nax2.set_ylabel(r'$D_\\star/D_{\\star,s}$')\n\n\nax3.plot(Dstarmap[nx//2, ny//2, :] / Dstarmap[nx//2, ny//2, nz//2], rz)\nax3.set_yticklabels([])\nax3.set_ylim(-15, 15)\n#ax3.set_xlim(0.99, 1.1)\nax3.set_xlabel(r'$D_\\star/D_{\\star,s}$')\n\nax1.xaxis.set_major_locator(MaxNLocator(nbins=7, prune='upper'))\nax1.yaxis.set_major_locator(MaxNLocator(nbins=7, prune='upper'))\n\n#fig.tight_layout()  #(w_pad=-1, h_pad=-1)\nfig.subplots_adjust(left=0.12, right=.98, top=0.99, bottom=0.10, wspace=0, hspace=0)\n\nc = plt.Circle((0, 0), radius=5, facecolor='none', edgecolor='white', linestyle='--', alpha=0.5) \nax1.add_artist(c)\n\n#Mpc_o_h = _Dimension('Mpc/h', latexrepr='$\\mathrm{Mpc/h}$')\n#ax1.add_artist(ScaleBar(1, units='Mpc/h', dimension=Mpc_o_h, box_alpha=0.1, color='white'))\nfig.savefig(path.join(output_dir, 'Dhalf_xz.pdf'))\nfig.savefig(path.join(output_dir, 'Dhalf_xz.svg'))\nfig.savefig(path.join(output_dir, 'Dhalf_xz.png'), dpi=360)",
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ux2sSahaSxiaMIOEvjHbWzBxKcpK4a8UO+lzBdZURCTVhS7KhFkaQHUffREQo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\naz3sMWaH2ZGODLeYiBgmJRYwMT6fN+vep9fdN+T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e3Hp6du/GlR8Y47qULlmoecICLSfr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\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f0dbd68c0d0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-27T22:14:57.822103+02:00",
          "end_time": "2017-03-27T20:14:57.841229Z"
        },
        "trusted": true,
        "collapsed": false
      },
      "cell_type": "code",
      "source": "np.isfinite(qDstarmap).max()",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "False"
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-18T14:58:00.989503+01:00",
          "end_time": "2017-03-18T13:58:01.010804Z"
        },
        "collapsed": true,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "recipy.log_exit()",
      "execution_count": 115,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-18T13:12:58.725735+01:00",
          "end_time": "2017-03-18T12:12:58.764181Z"
        },
        "collapsed": true,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "@jit\ndef bisect(fun, l, r, yl=None, yr=None, depth=0, max_depth=10):\n    if yl is None:\n        yl = fun(l)\n    if yr is None:\n        yr = fun(r)\n    if yl*yr > 0:\n        raise Exception('Left and right bound of the same sign!')\n    m = (l + r)/2\n    ym = fun(m)\n    if depth == max_depth:\n        return m\n    if ym*yl < 0:\n        return bisect(fun, l, m, yl, ym, depth+1, max_depth)\n    else:\n        return bisect(fun, m, r, ym, yr, depth+1, max_depth)",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-18T13:12:59.228648+01:00",
          "end_time": "2017-03-18T12:12:59.691608Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def gen(R):\n    v0 = _alphastar(R, [1e-3, 0, 0], Qbar, Gamma, nuc, nus)\n    @np.vectorize\n    def loc(x):\n        return _alphastar(R, [x, 0, 0], Qbar, Gamma, nuc, nus)-v0\n    return loc\n#loc(3.9), loc(4.25)\nbisect(gen(R), 3.9, 4.25, max_depth=50)",
      "execution_count": 35,
      "outputs": [
        {
          "execution_count": 35,
          "output_type": "execute_result",
          "data": {
            "text/plain": "4.041192254383468"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-18T13:14:25.863129+01:00",
          "end_time": "2017-03-18T12:14:28.536174Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "rrr = np.linspace(0, 100, 200)\nfor _R in np.geomspace(1e-2, 1, 20):\n    plt.plot(rrr, gen(_R)(rrr), label='%.2f' % _R)\nlabellines.labelLines(plt.gca().lines[::])\nplt.ylim(-0.05, 1e-4)\nplt.xlim(xmin=5)\nplt.yscale('symlog', linthreshy=1e-4)\n\nplt.title(r'Plot of $\\alpha_\\star$ for different values of $R$ (labels of the lines)')\nplt.xlabel(r'$r$')\nplt.ylabel(r'$\\alpha_\\star(r)$')\nplt.xscale('log')",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "/home/ccc/.virtualenvs/astrop2/lib/python2.7/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in double_scalars\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fAlzgt621D3Z4WDvC9Tw8P1BDLmmZTpRVO46LMU7PlFUTJ7CNsmock9k5xwqO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Ru5CDH8+Rn5Wc7FIH0BSim45RPw9j+d+Ps/TsbVyklRlK8D3xJCnBZClICXFUU5ANyh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DAwMDAwODWxfjbgAQ4X5YqCQJxs3Xl3V/B+W7dGvymz+HN36qB6ma8yjMfAAcw+NuehXREDSe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            "text/plain": "<matplotlib.figure.Figure at 0x7fe2d77e9b10>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-18T14:07:08.940240+01:00",
          "end_time": "2017-03-18T13:07:10.180107Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "delta_c = 1.68\nRs = 5  # Mpc\nsigmaRs = _sigma([Rs])[0]\ndeltas = 1.2\nnus = deltas/sigmaRs\n\nRstar = np.trapz(Pk*W1(k*Rs)**2, k)/sigmaRs**2/2/np.pi**2\n\nrrr = np.linspace(0, 100, 1000)\nnus = 1.2\nx2 = [x_nucS(0.5, np.array([_, 0, 0])/Rstar, Qbar, nus, Rstar) for _ in rrr]\nplt.plot(rrr, x2)\nplt.xscale('log')",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "/home/ccc/.virtualenvs/astrop2/lib/python2.7/site-packages/ipykernel/__main__.py:11: RuntimeWarning: invalid value encountered in double_scalars\n"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U2LVJZg4yZnaSVYcc97TOvZwXvqfWuj/JliTTDhvz00keqLXuGewCpZT3l1IWl1IW9/b2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\n936LsbK46krtvkrhSde8HwtSOo+HtO6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            "text/plain": "<matplotlib.figure.Figure at 0x7fe2db7cd590>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-18T14:21:04.206889+01:00",
          "end_time": "2017-03-18T13:21:04.743386Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "rrr = np.linspace(0, 100, 1000)\nx2 = [x2_nucS(0.5, np.array([_, 0, 0]), Qbar, Rstar) for _ in rrr]\nplt.plot(rrr, x2)\n# plt.xscale('log')\nplt.xlim(0, 20)",
      "execution_count": 67,
      "outputs": [
        {
          "execution_count": 67,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(0, 20)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rc7Xr1c7rh84xqXOI9UOSdkkqkvRqf/1KekXSbqf9b53zAQAD8PC7u5UaH6NP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\n4AwAAAAAwAQIzgAAAAAATOD/A3wJnj1iJhdpAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fe2db727290>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-18T14:07:44.531602+01:00",
          "end_time": "2017-03-18T13:07:45.067236Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "rrr = np.linspace(0, 100, 1000)\nR = .5\ny = np.array([1 - 3 * _**2/Rstar**2 * xi11fun(R, _)**2 - 5 * xi20fun(R, _)**2 - xi00fun(R, _)**2\n          for _ in rrr])\nplt.plot(rrr, y)",
      "execution_count": 65,
      "outputs": [
        {
          "execution_count": 65,
          "output_type": "execute_result",
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x7fe2dadc32d0>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9CgAAwAWJ1Q5yamQsj+09lo3rXF8VAABobmK1gzyy+2hGxqpsvN77VQEAgOYm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\nAvD3hctCdgqVAAAAAElFTkSuQmCC\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7fe2dadbd2d0>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-03-18T13:50:09.236048+01:00",
          "end_time": "2017-03-18T12:50:09.253740Z"
        },
        "collapsed": false,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "y.min(), y.max()",
      "execution_count": 61,
      "outputs": [
        {
          "execution_count": 61,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(-0.3551977554212376, 0.98262947434150383)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "editable": true,
        "deletable": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2",
      "language": "python"
    },
    "latex_envs": {
      "eqNumInitial": 1,
      "eqLabelWithNumbers": true,
      "current_citInitial": 1,
      "cite_by": "apalike",
      "bibliofile": "biblio.bib",
      "LaTeX_envs_menu_present": true,
      "labels_anchors": false,
      "latex_user_defs": false,
      "user_envs_cfg": false,
      "report_style_numbering": false,
      "autocomplete": true,
      "hotkeys": {
        "equation": "Ctrl-E",
        "itemize": "Ctrl-I"
      }
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "name": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.13",
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 2,
        "name": "ipython"
      }
    },
    "gist": {
      "id": "4b809ef3b4f54b280ad05e8f22e9baa6",
      "data": {
        "description": "Map of matter around a filament's center",
        "public": false
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/4b809ef3b4f54b280ad05e8f22e9baa6"
    },
    "notify_time": "5"
  },
  "nbformat": 4,
  "nbformat_minor": 2
}

Maps around filament.ipynb

Pk_lcdm_transfer_out.dat_s8_0.7913_ns_0.9500

9.96334e-06 68.904 3.46517e-10
1.00635e-05 69.5619 3.56894e-10
1.01646e-05 70.2256 3.67575e-10
1.02668e-05 70.8962 3.78585e-10
1.037e-05 71.5731 3.89922e-10
1.04742e-05 72.2561 4.01594e-10
1.05794e-05 72.9454 4.13609e-10
1.06858e-05 73.6422 4.26001e-10
1.07932e-05 74.3451 4.38756e-10
1.09016e-05 75.0543 4.51883e-10
1.10112e-05 75.7709 4.65417e-10
1.11219e-05 76.4944 4.79356e-10
1.12336e-05 77.2241 4.93698e-10
1.13465e-05 77.9612 5.08479e-10
1.14606e-05 78.705 5.23706e-10
1.15758e-05 79.4564 5.39388e-10
1.16921e-05 80.2146 5.55532e-10
1.18096e-05 80.9802 5.72163e-10
1.19283e-05 81.7533 5.89295e-10
1.20482e-05 82.5338 6.06941e-10
1.21693e-05 83.3217 6.25114e-10
1.22916e-05 84.117 6.43829e-10
1.24151e-05 84.9197 6.631e-10
1.25399e-05 85.7304 6.82957e-10
1.26659e-05 86.5486 7.034e-10
1.27932e-05 87.3747 7.2446e-10
1.29218e-05 88.2089 7.46154e-10
1.30516e-05 89.0505 7.68482e-10
1.31828e-05 89.9007 7.91496e-10
1.33153e-05 90.7589 8.15194e-10
1.34491e-05 91.625 8.39597e-10
1.35843e-05 92.4998 8.6474e-10
1.37208e-05 93.3826 8.90626e-10
1.38587e-05 94.274 9.17291e-10
1.3998e-05 95.174 9.44758e-10
1.41387e-05 96.0826 9.73047e-10
1.42807e-05 96.9991 1.00216e-09
1.44243e-05 97.9255 1.03218e-09
1.45692e-05 98.8598 1.06307e-09
1.47157e-05 99.8039 1.09491e-09
1.48636e-05 100.757 1.1277e-09
1.50129e-05 101.718 1.16144e-09
1.51638e-05 102.689 1.19622e-09
1.53162e-05 103.669 1.23203e-09
1.54702e-05 104.659 1.26893e-09
1.56256e-05 105.658 1.30691e-09
1.57827e-05 106.666 1.34605e-09
1.59413e-05 107.684 1.38634e-09
1.61015e-05 108.712 1.42785e-09
1.62633e-05 109.75 1.47059e-09
1.64268e-05 110.798 1.51463e-09
1.65919e-05 111.855 1.55998e-09
1.67586e-05 112.923 1.60667e-09
1.6927e-05 114 1.65477e-09
1.70972e-05 115.089 1.70433e-09
1.7269e-05 116.188 1.75535e-09
1.74425e-05 117.296 1.80789e-09
1.76178e-05 118.416 1.86202e-09
1.77949e-05 119.546 1.91778e-09
1.79738e-05 120.688 1.97521e-09
1.81544e-05 121.84 2.03433e-09
1.83368e-05 123.002 2.09522e-09
1.85211e-05 124.176 2.15796e-09
1.87073e-05 125.362 2.22259e-09
1.88953e-05 126.559 2.28913e-09
1.90852e-05 127.767 2.35766e-09
1.9277e-05 128.986 2.42824e-09
1.94707e-05 130.217 2.50093e-09
1.96664e-05 131.46 2.57581e-09
1.98641e-05 132.715 2.65295e-09
2.00637e-05 133.982 2.73236e-09
2.02654e-05 135.261 2.81419e-09
2.0469e-05 136.552 2.89842e-09
2.06747e-05 137.855 2.98519e-09
2.08825e-05 139.171 3.07457e-09
2.10924e-05 140.498 3.1666e-09
2.13044e-05 141.84 3.26142e-09
2.15185e-05 143.193 3.35905e-09
2.17348e-05 144.56 3.45964e-09
2.19532e-05 145.94 3.5632e-09
2.21738e-05 147.333 3.66986e-09
2.23967e-05 148.739 3.77976e-09
2.26218e-05 150.159 3.89293e-09
2.28491e-05 151.592 4.00946e-09
2.30788e-05 153.04 4.12953e-09
2.33107e-05 154.5 4.25314e-09
2.3545e-05 155.975 4.38049e-09
2.37816e-05 157.464 4.51162e-09
2.40206e-05 158.967 4.64669e-09
2.4262e-05 160.484 4.78581e-09
2.45059e-05 162.016 4.92913e-09
2.47522e-05 163.563 5.07671e-09
2.50009e-05 165.124 5.22866e-09
2.52522e-05 166.7 5.38523e-09
2.5506e-05 168.291 5.54647e-09
2.57623e-05 169.897 5.7125e-09
2.60212e-05 171.519 5.88352e-09
2.62827e-05 173.156 6.05966e-09
2.65469e-05 174.809 6.24112e-09
2.68137e-05 176.478 6.42797e-09
2.70832e-05 178.163 6.62044e-09
2.73554e-05 179.863 6.81865e-09
2.76303e-05 181.58 7.02278e-09
2.7908e-05 183.311 7.23298e-09
2.81885e-05 185.061 7.44954e-09
2.84718e-05 186.828 7.67258e-09
2.87579e-05 188.611 7.90226e-09
2.90469e-05 190.411 8.13883e-09
2.93388e-05 192.228 8.38248e-09
2.96337e-05 194.063 8.63348e-09
2.99315e-05 195.916 8.89194e-09
3.02323e-05 197.786 9.15814e-09
3.05362e-05 199.674 9.43239e-09
3.08431e-05 201.58 9.71479e-09
3.11531e-05 203.504 1.00057e-08
3.14662e-05 205.447 1.03052e-08
3.17824e-05 207.408 1.06137e-08
3.21018e-05 209.387 1.09315e-08
3.24244e-05 211.386 1.12587e-08
3.27503e-05 213.404 1.15958e-08
3.30795e-05 215.439 1.1943e-08
3.34119e-05 217.495 1.23005e-08
3.37477e-05 219.571 1.26687e-08
3.40869e-05 221.667 1.30481e-08
3.44295e-05 223.783 1.34387e-08
3.47755e-05 225.919 1.38411e-08
3.5125e-05 228.075 1.42555e-08
3.5478e-05 230.252 1.46823e-08
3.58345e-05 232.45 1.51218e-08
3.61947e-05 234.669 1.55746e-08
3.65585e-05 236.909 1.60409e-08
3.69259e-05 239.17 1.65211e-08
3.7297e-05 241.453 1.70158e-08
3.76718e-05 243.755 1.7525e-08
3.80504e-05 246.082 1.80496e-08
3.84328e-05 248.431 1.859e-08
3.88191e-05 250.803 1.91467e-08
3.92092e-05 253.196 1.97198e-08
3.96033e-05 255.613 2.03103e-08
4.00013e-05 258.053 2.09184e-08
4.04033e-05 260.516 2.15446e-08
4.08094e-05 263.003 2.21897e-08
4.12195e-05 265.513 2.2854e-08
4.16338e-05 268.045 2.35381e-08
4.20522e-05 270.604 2.42427e-08
4.24749e-05 273.187 2.49687e-08
4.29017e-05 275.794 2.57161e-08
4.33329e-05 278.427 2.6486e-08
4.37684e-05 281.085 2.7279e-08
4.42083e-05 283.768 2.80958e-08
4.46526e-05 286.476 2.89369e-08
4.51014e-05 289.211 2.98034e-08
4.55546e-05 291.968 3.06952e-08
4.60125e-05 294.756 3.16144e-08
4.64749e-05 297.569 3.25608e-08
4.6942e-05 300.41 3.35357e-08
4.74138e-05 303.277 3.45398e-08
4.78903e-05 306.172 3.55739e-08
4.83716e-05 309.095 3.66389e-08
4.88577e-05 312.042 3.77354e-08
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4.98447e-05 318.027 4.00288e-08
5.03457e-05 321.063 4.12273e-08
5.08516e-05 324.127 4.24615e-08
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5.18789e-05 330.342 4.50417e-08
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5.34589e-05 339.892 4.92098e-08
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5.67646e-05 359.825 5.87377e-08
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5.79113e-05 366.723 6.23068e-08
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6.02747e-05 380.927 7.01104e-08
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6.14923e-05 388.23 7.43705e-08
6.21103e-05 391.936 7.65971e-08
6.27346e-05 395.677 7.88907e-08
6.33651e-05 399.45 8.12518e-08
6.40019e-05 403.263 8.36844e-08
6.46451e-05 407.112 8.61897e-08
6.52948e-05 410.998 8.87702e-08
6.5951e-05 414.917 9.14269e-08
6.66138e-05 418.877 9.41641e-08
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6.86425e-05 430.983 1.02877e-07
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