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Map of matter around a filament's center
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{ | |
"cells": [ | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:13.138382+02:00", | |
"end_time": "2017-04-04T09:04:13.219275Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "# import recipy # Deactivate if you don't want to save the inputs/outputs!", | |
"execution_count": 8, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:13.459875+02:00", | |
"end_time": "2017-04-04T09:04:13.468556Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "from __future__ import division, print_function", | |
"execution_count": 9, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:13.957139+02:00", | |
"end_time": "2017-04-04T09:04:14.071550Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "# %matplotlib\n# %matplotlib notebook\n%matplotlib inline", | |
"execution_count": 10, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:14.386768+02:00", | |
"end_time": "2017-04-04T09:04:14.458254Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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\nfrom matplotlib_colorbar.colorbar import Colorbar", | |
"execution_count": 11, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:14.936326+02:00", | |
"end_time": "2017-04-04T09:04:14.980741Z" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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": 12, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:15.554876+02:00", | |
"end_time": "2017-04-04T09:04:15.594593Z" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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": 13, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:18.802253+02:00", | |
"end_time": "2017-04-04T09:04:19.187474Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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": 14, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:20.079344+02:00", | |
"end_time": "2017-04-04T09:04:21.610786Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "output_dir = './data_Rs=5Mpc/'\nxi00, xi11, xi20, xi00prime, xi11prime, xi20prime, radiixi, Rxi, radiixiprime, Rxiprime = [\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 'xiradii.dat', 'xiR.dat',\n 'xiprimeradii.dat', 'xiprimeR.dat']]", | |
"execution_count": 15, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": true | |
}, | |
"cell_type": "markdown", | |
"source": "Now we take the $\\xi$ from the output to transform them into functions." | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:24.817173+02:00", | |
"end_time": "2017-04-04T09:04:24.974555Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "interp = 'cubic'\nxi00fun = interp2d(Rxi, radiixi, xi00, kind=interp)\nxi11fun = interp2d(Rxi, radiixi, xi11, kind=interp)\nxi20fun = interp2d(Rxi, radiixi, xi20, kind=interp)\nxi00pfun = interp2d(Rxiprime, radiixiprime, xi00prime, kind=interp)\nxi11pfun = interp2d(Rxiprime, radiixiprime, xi11prime, kind=interp)\nxi20pfun = interp2d(Rxiprime, radiixiprime, xi20prime, kind=interp)", | |
"execution_count": 16, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": true | |
}, | |
"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_{22} & 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" | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:29.313261+02:00", | |
"end_time": "2017-04-04T09:04:29.345665Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
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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": 17, | |
"outputs": [ | |
{ | |
"output_type": "stream", | |
"text": "1.05750355807 1.13474795507 [-0.12231749]\n", | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T11:04:30.194816+02:00", | |
"end_time": "2017-04-04T09:04:30.235832Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
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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": 18, | |
"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": 18 | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": true | |
}, | |
"cell_type": "markdown", | |
"source": "Define the quantities at finitie distance from the saddle point." | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T16:34:52.305920+02:00", | |
"end_time": "2017-04-04T14:34:52.522032Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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": 21, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": 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": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T11:57:01.941594Z", | |
"start_time": "2017-03-30T13:57:01.921413+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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": 14, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T11:58:45.790228Z", | |
"start_time": "2017-03-30T13:57:04.250204+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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": 15, | |
"outputs": [ | |
{ | |
"data": { | |
"application/vnd.jupyter.widget-view+json": { | |
"model_id": "8597a5fc16d04592aa678d6ae07f5fb7" | |
} | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": "\n" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T11:58:46.020033Z", | |
"start_time": "2017-03-30T13:58:45.793470+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "np.savez(path.join(output_dir, 'deltaM.dat.npz'), rx=rx, ry=ry, rz=rz, DeltaMstarMap=DeltaMstarMap)", | |
"execution_count": 16, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T11:58:46.068015Z", | |
"start_time": "2017-03-30T13:58:46.022735+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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": 17, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T11:58:46.081642Z", | |
"start_time": "2017-03-30T13:58:46.070503+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": 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": 18, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": "((128,), (128,), (128, 128, 128))" | |
}, | |
"execution_count": 18, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T11:59:05.665261Z", | |
"start_time": "2017-03-30T13:58:46.085393+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": 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": 19, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": true | |
}, | |
"cell_type": "markdown", | |
"source": "## Plot of $\\alpha_\\star$ for $R=0.5\\text{Mpc/h} $ (using symmetries) & $R_s=5\\mathrm{Mpc/h}$" | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:20:49.322689Z", | |
"start_time": "2017-04-03T17:20:49.297144+02:00" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "qnx = 25\nqny = qnx\nqnz = qny\n\ndmax = 20\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": 38, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:34:40.140613Z", | |
"start_time": "2017-04-03T17:34:40.122042+02:00" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": 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.sqrt(np.trapz(Pk*W1(k*Rs)**2, k)/sigmaRs**2/2/np.pi**2)\n\n# Set nus\nnus = 1.2 / sigma0", | |
"execution_count": 79, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:34:40.791619Z", | |
"start_time": "2017-04-03T17:34:40.307592+02:00" | |
}, | |
"collapsed": false, | |
"trusted": true, | |
"editable": true, | |
"deletable": true | |
}, | |
"cell_type": "code", | |
"source": "rrr = np.linspace(0, 4, 200)*Rs\nQbar = np.array([[1, 0, 0],\n [0, 1, 0],\n [0, 0, -1]])\naaa = [_alphastar(R, [_rrr, 0, 0], Qbar, Gamma, nuc, nus, Rstar) for _rrr in rrr]\n#bbb = [_alphastar(R, [0, _rrr, 0], Qbar, Gamma, nuc, nus, Rstar) for _rrr in rrr]\nccc = [_alphastar(R, [0, 0, _rrr], Qbar, Gamma, nuc, nus, Rstar) for _rrr in rrr]\n\nplt.figure(figsize=(6, 4))\nplt.plot(rrr/Rs, aaa)\n#plt.plot(rrr, bbb)\nplt.plot(rrr/Rs, ccc)\n#plt.ylim(0.16, 0.32)\n#plt.xlim(0, 4)\nplt.ylabel(r'$\\alpha_\\star$')\nplt.xlabel(r'$r/R_s$')\nplt.savefig('/tmp/alpha.pdf')", | |
"execution_count": 80, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": "/home/ccc/.virtualenvs/astro/lib/python3.6/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in double_scalars\n/home/ccc/.virtualenvs/astro/lib/python3.6/site-packages/ipykernel/__main__.py:7: RuntimeWarning: invalid value encountered in double_scalars\n" | |
}, | |
{ | |
"data": { | |
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1ZuLIrtz3wVI+LdgYdDki0oKELSycc9uByfhf8guBp5xz883sJjM7OXTYn4Ek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\ngg==\n", | |
"text/plain": "<matplotlib.figure.Figure at 0x7fd7bc976358>" | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:20:55.488175Z", | |
"start_time": "2017-04-03T17:20:50.368503+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": 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": 40, | |
"outputs": [ | |
{ | |
"data": { | |
"application/vnd.jupyter.widget-view+json": { | |
"model_id": "55156eabb7304943a2770478b8b043fd" | |
} | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": "\n" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": true | |
}, | |
"cell_type": "markdown", | |
"source": "Reconstruct full map" | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:20:55.492662Z", | |
"start_time": "2017-04-03T17:20:55.489889+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "rx, ry, rz, alphastarmap = sym_clone(qalphastarmap, qrx, qry, qrz)", | |
"execution_count": 41, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:20:55.517664Z", | |
"start_time": "2017-04-03T17:20:55.494251+02:00" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": 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": 42, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T12:05:28.825857Z", | |
"start_time": "2017-03-30T14:05:28.689722+02:00" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": 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": 27, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:20:55.523885Z", | |
"start_time": "2017-04-03T17:20:55.519052+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": 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": 43, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:20:56.970998Z", | |
"start_time": "2017-04-03T17:20:55.525354+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "size = 6\ndmax = 20\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(ry, rz, alphastarmap[nx//2, :, :].T / alphastarmap[nx//2, ny//2, nz//2], \n vmin=vmin / alphastarmap[nx//2, ny//2, nz//2], vmax=vmax / alphastarmap[nx//2, ny//2, nz//2])\n\nax1.set_xlim(-dmax, dmax)\nax1.set_ylim(-dmax, dmax)\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(-dmax, dmax)\n#ax2.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(-dmax, dmax)\n#ax3.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\ncb = Colorbar(CS, location='upper center', box_alpha=0.5, length_fraction=0.9, orientation='horizontal')\nax1.add_artist(cb)\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": 44, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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p7CjYcMhzsnOHsXLpV5x46oWsXPoVzU2N1NfVUF1ZdqC70fnaNKory9yuwRXV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\n9l2JKoNzBwAAAABJRU5ErkJggg==\n", | |
"text/plain": "<matplotlib.figure.Figure at 0x7fd7bfdfe668>" | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-04-03T15:20:58.633850Z", | |
"start_time": "2017-04-03T17:20:56.976480+02:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": 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 / alphastarmap[nx//2, ny//2, nz//2], \n vmin=vmin / alphastarmap[nx//2, ny//2, nz//2], vmax=vmax / alphastarmap[nx//2, ny//2, nz//2])\n#ax3.clabel(CS)\n\nax1.set_xlim(-dmax, dmax)\nax1.set_ylim(-dmax, dmax)\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(-dmax, dmax)\n#ax2.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(-dmax, dmax)\n#ax3.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#bbox = ax1.get_position()\n#h = 0.015\n#w = 0.9 # relative\n#nbbox = ((bbox.x0+bbox.width*(1-w)/2, bbox.y1-h, bbox.width*w, h))\n#ax3cb = fig.add_axes(nbbox)\n#cb = fig.colorbar(CS, cax=ax3cb, orientation='horizontal')\n#cb.outline.set_color('w')\n#plt.setp(ax3cb.get_xticklabels(), facecolor='w')\n\ncb = Colorbar(CS, location='upper center', box_alpha=0.5, length_fraction=0.9, orientation='horizontal')\nax1.add_artist(cb)\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": 45, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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HnIxOKXUjcCNASGjsmFfqdDiorCjh57+4j/r6Ku6+63v8/s6nDrfUzLJmzQcs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\nu9a032HnAAAAAElFTkSuQmCC\n", | |
"text/plain": "<matplotlib.figure.Figure at 0x7fd7bfe21908>" | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": true | |
}, | |
"cell_type": "markdown", | |
"source": "## Plot of $D_\\star$ for $R=0.5\\mathrm{Mpc/h}$ & $R_s=5\\mathrm{Mpc/h}$,\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": "2017-04-04T16:34:35.985354+02:00", | |
"end_time": "2017-04-04T14:34:36.098611Z" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "qnx = 50\nqny = qnx\nqnz = qny\n\ndmax = 30\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": 19, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": true | |
}, | |
"cell_type": "markdown", | |
"source": "Compute relevant quantities" | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T16:34:57.008970+02:00", | |
"end_time": "2017-04-04T14:34:58.237741Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "R = .5 # Mpc/h\nR_half = R/2**(1/3)\nsigma0, sigma_half, sigmaRs = _sigma([R, R_half, Rs])\nnu_half = 1.68/sigma_half\nnuc = 1.68/sigma0\ngamma2 = _gamma([R], sigma0)**2\nGamma = np.sqrt(gamma2/(1-gamma2))\nRstar = np.sqrt(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, R_half)", | |
"execution_count": 22, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T16:34:58.819029+02:00", | |
"end_time": "2017-04-04T14:35:42.088857Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"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": 23, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"application/vnd.jupyter.widget-view+json": { | |
"model_id": "6f9cafd50a304eecb6818c37e0547dbb" | |
} | |
}, | |
"metadata": {} | |
}, | |
{ | |
"output_type": "stream", | |
"text": "\n", | |
"name": "stdout" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T16:35:42.090327+02:00", | |
"end_time": "2017-04-04T14:35:42.095591Z" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "rx, ry, rz, Dstarmap = sym_clone(qDstarmap, qrx, qry, qrz)", | |
"execution_count": 24, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T16:35:42.097137+02:00", | |
"end_time": "2017-04-04T14:35:42.160781Z" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": 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": 25, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T12:11:20.681196Z", | |
"start_time": "2017-03-30T14:11:20.580075+02:00" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": " with np.load(path.join(output_dir, 'Dstar.dat.npz')) as f:\n rx, ry, rz, Dstarmap, R = [f[_k] for _k in ['rx', 'ry', 'rz', 'Dstarmap', 'R']]", | |
"execution_count": 41, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T16:35:51.216091+02:00", | |
"end_time": "2017-04-04T14:35:51.232927Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "vmax, vmin = Dstarmap.max(), Dstarmap.min()\nnx, ny, nz = [len(_) for _ in [rx, ry, rz]]", | |
"execution_count": 27, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T16:35:52.462590+02:00", | |
"end_time": "2017-04-04T14:35:54.244855Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "fsize = 6\nfig = plt.figure(figsize=(fsize, fsize))\nbound = 15\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[nx//2, :, :].T / Dstarmap[nx//2, ny//2, nz//2], vmin=vmin/ Dstarmap[nx//2, ny//2, nz//2], vmax=vmax/ Dstarmap[nx//2, ny//2, nz//2])\n# ax1.clabel(CS, colors='black')#, manual=[(0, 0), (5, 5)])#, use_clabeltext=True)\n\nax1.set_xlim(-bound, bound)\nax1.set_ylim(-bound, bound)\nax1.set_xlabel('$y$ [Mpc/h]')\nax1.set_ylabel('$z$ [Mpc/h]')\n\nax2.plot(ry, Dstarmap[nx//2, :, nz//2] / Dstarmap[nx//2, ny//2, nz//2])\nax2.set_xticklabels([])\nax2.set_xlim(-bound, bound)\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(-bound, bound)\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\ncb = Colorbar(CS, location='upper center', box_alpha=0.5, length_fraction=0.9, orientation='horizontal')\nax1.add_artist(cb)\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_yz.pdf'))\nfig.savefig(path.join(output_dir, 'Dhalf_yz.svg'))\nfig.savefig(path.join(output_dir, 'Dhalf_yz.png'), dpi=360)", | |
"execution_count": 28, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": "<matplotlib.figure.Figure at 0x7ff61266c358>", | |
"image/png": 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6JZfNW8kPOyqZf/l0ZgxN6ftJis8JIVabYVtqFurO14pP2R1OrntjDYU1zTx34WQVaDqy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\n/weNbzzLEp9B5QAAAABJRU5ErkJggg==\n" | |
}, | |
"metadata": {} | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"start_time": "2017-04-04T16:35:54.257115+02:00", | |
"end_time": "2017-04-04T14:35:56.097639Z" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "fsize = 6\nfig = plt.figure(figsize=(fsize, fsize))\nbound = 15\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(-bound, bound)\nax1.set_ylim(-bound, bound)\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(-bound, bound)\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(-bound, bound)\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\ncb = Colorbar(CS, location='upper center', box_alpha=0.5, length_fraction=0.9, orientation='horizontal')\nax1.add_artist(cb)\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": 29, | |
"outputs": [ | |
{ | |
"output_type": "display_data", | |
"data": { | |
"text/plain": "<matplotlib.figure.Figure at 0x7ff613228438>", | |
"image/png": 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Jyxpqdw197SuqK8pY+NxjNDXUERoWwRU33E90XAIAK79dxrIPXgPgd+dextSZ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\nDy16qDFTcjEmRIhmeueMpm/eOBpb9hz7dySU3Dx3WVJAflb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G8PCbFYwp6cHMEcVeFych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\n" | |
}, | |
"metadata": {} | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-30T12:11:23.354538Z", | |
"start_time": "2017-03-30T14:11:23.321899+02:00" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "recipy.log_exit()", | |
"execution_count": 45, | |
"outputs": [] | |
}, | |
{ | |
"metadata": { | |
"deletable": true, | |
"editable": true | |
}, | |
"cell_type": "markdown", | |
"source": "## Other misc (mostly draft)\nThis section is not meant to be kept." | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-18T12:12:58.764181Z", | |
"start_time": "2017-03-18T13:12:58.725735+01:00" | |
}, | |
"collapsed": true, | |
"deletable": true, | |
"editable": 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": { | |
"end_time": "2017-03-18T12:12:59.691608Z", | |
"start_time": "2017-03-18T13:12:59.228648+01:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": 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": [ | |
{ | |
"data": { | |
"text/plain": "4.041192254383468" | |
}, | |
"execution_count": 35, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-18T12:14:28.536174Z", | |
"start_time": "2017-03-18T13:14:25.863129+01:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": 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": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": "/home/ccc/.virtualenvs/astrop2/lib/python2.7/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in double_scalars\n" | |
}, | |
{ | |
"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\ngrpjuhiuO6bnHo6F9O2WVJi4ok9XaSvwwDe+z9Yff5+dP/8nhKYxbcWqkRn7KCCE4ETzCXZd2sWe\ny3toDDZiN9lZOW4layeuZXnhcqzKwIIkCU3j3KE3eeOp39JWV0P+lKms/9yXKZoxRPmkh5FYSwu+\nHTvxbt9O6NQpUBScK1bg/vrXcK5ahWwbHZdQWVZAkkbFrToaVqk63cqlY01cOtFMuCOGYpIZNy2d\nBfdMYMLsLJSWIE2/PI67JG305obK0qjOOR6MWzVcWxxrqoqvqZG2uhra6uvwNdXjbdQtwN6mesId\n3cWvyWrFnZ2LOyeXwrLpuLNzceXk6uvsHGzOVGMOr4GBgYGBgUG/MMQx17Ec94bJCjM26Yu/AU48\nrQvlnV+BF78BU++BOR+EyXcNXe7kkA8aTnYXwk1nQcTHbnVD/mxY+HHImw35cyBz8nXPb7HZuf+v\nvsfWf/gBO3/xz2iaxoz33Tk0Yx4DCCEobytn16VdvHj5RWraazDLZlYUruCeifdwx7g7cJivnY6n\nr34rj7/L63/8HxouVpA5bjwbv/ItShYsHtM34lokQvsre/Fu3077a69BLIZt+nRyv/HXuDZswJSZ\nOdpDBHTr8Ui5VYc6onF36WaqzrSiRjWsDhPFszKZNCeboukZWGz69yjmDdP0VDlyignLBNeIjK9X\nRtGteigsx4qiEAp0UHniKG11tXjqa2irq6WtrhZvY0M3t2eTxYo7Rxe6+aVluLNzcGXn6uucXOyp\nrjH9nTMwMDAwMDB473DLi2NJktB6zjkeCKm5sOxzsPSzUH8cjv4BTvwfnN4OKTm62/WcRyFvAGl7\nOpp18Vt/vFMMt17o3J6So4vfqev0dd5sSJ9wwwG0zDYbm7/+Hbb/5Ifs/vefoqkqs1avuaG+xgJC\nCM61nWNP5R72XN7DZd9lFElhScESnpjzBKvHrybVcuPRlevOl/PaH/6bqlPHcWXnsO4zf8G0FSvH\ndE7Q0NmzeLZswffc86geD6bcXDL/7KO477sP65QpkkWYYQAAIABJREFUoz28q1BMpuvmcx0M0bDK\n5ePNnDvSwJVTLWiqwJlhZfryAibNySJ/ShqK0t06qnZEaf7Pk2jBGNmfnI3iHL2UOaPtVt1fy7Gm\nqngb62mprqKlporW+BKUHZzev5eL//cbQBfAaXn5ZBUVM2XRUtLyC0jPKyA9vxCHexQt9AYGBgYG\nBga3FLe8OEYGER6CwDCSpAvV/Dlw9w+g4iXdmnzoP+DAv+oCdu4HYdZDkBKPWCwE+Go6LcEJMeyr\n6ew3bbze55xH4/3PhtS8wY+3B2arjY1f/RbP/dOP2PPL/w+hacy+a92Qn2e4EEJwtvUsL1W+xJ7K\nPVT6KpElmQW5C/jw9A9zd/HdpNsGl46qpfoKr//xt1QcOYDd5WbVRz/F7LvWYTKPzQA9qteL94UX\n8D67hdDp00hmM6l334V78/2kLFuKNEjr33Aim8xDbjlWYxpVp1s5d6SBS8ebiYVVUtwWZq0aR+nC\nXLLH9+1+q4VVWn5zilhrkOyPzcRS6BzSsQ2YUXar7mk5FkLgb2miqfIyzVcu01R5iZbqK7TV1XR7\nH53pGWQUFmHGSl7BNO76xCdJzy/EmZ4xovPaDQwMDAwMDAx6wxDHkjRwt+rrYbJA2QZ96WjRg3cd\n/R3s/ivY8y0ouRPUiC6GAy2JgUBWKRQv67QG580aufnLgNliZeOXv8nz//J3vPSrf0VVY9y29t4R\nO/9AEUJwuvU0ey7v4aXKl6jyV6FICgvzFvL49Me5c/ydZNoH5ybsb2nm3MHXKT/wGnXny7HY7Sx7\n+DHmr9+IxT5wd+zhRmgaHQcO4H12C/6XX0ZEIlinTyP3W9/Cfe8GlLS00R5ivxgqt2qhCWorPJw7\n0sCFdxoJd8SwOkyULsqldEEu+VPSrptuScQ0Wn57mkiNn8wPTcc6afRfQ0kePcuxqqqE2n0ce2kX\nTVcu03zlEs1XKrsFwnJl55JVNJ4Jc+eTWVhERmERmeOKsDpSAPjpT39KWl4B42fOGZVrMDAwMDAw\nMDDojVteHEuydHUqp6EkJRMWf0pfGk7Dsd/rLte2NJi6vtPanDsDLCnDN45+YrJYeP9ffoMXfvpj\nXvn1fyBUlXnrN472sJIIITjVcoo9l/ewp3IPNe01KJLC4vzF/PnMP2f1+NWDthC3t7Zw7tAblB94\nndry0wDkTChh+SOPM+vOtThcYy/PdaS6Gu+WrXi2bSVWW4fsdpP28MOk3b8Z2/Tpoz28AaOYTDcc\nkEsIQdMVP+ePNHD+rUY6PGFMFpmJc7IpXZhL0fQMFFP/rJRCE7Q+VU64wkP6Q6XYp4+NOdkoIzPn\nOBoK0Xj5Ig0Xz1N/sYKGC+fpSMnkYtVFauuvYLE7yBo/gbLb30d28QSyxk8kq6gYq+PaD44GE63a\nwMDAwMDAoJPQuTY8z18AAY4FubhWFvW6X/BkMy2/O0PO527DUuhEqIK2LeeJ1rQjNIFjXk6fx95K\n3PLiGEl3mRwRcqfDmr/VlzGMyWzm/X/xV+z42T+y979/haZpLLh386iNJxFlOmEhru2oxSSZWFyw\nmE/N/hSrilaRZhucNa/D0xa3EL9OTflpEILs8RO4/QMfpnTJcjIKxl6eYi0YxP/SS3ie3ULg0CGQ\nJFKWLyf3q1/FuXo1snVgkbfHEjdiOQ61Ryk/VM+p12tpq+tAViTGz8hk2QMlTJydjdk6MDdyIQSe\nrRUETzTj3jCJlPm5Azp+OJFkCaEOrbiMRSI0VV6i/uJ5Gi5U0HDxPC3VVQihn8eZkUnupCnUBDUm\nzVvIhvXfIzUr+4bmAxvi2MDAwMDAYPAITdC2vYLsP5+F4rbS+K/vYp+eiTmn+0NqLaLif6MWS1Fn\nzJ3giSZETCP3S/MQUZX6f3kHx9xsTGmjk61krGCI4+G2HL9HUUxmNnzxa+z8+U949bf/iaaqLNr4\n4IidXxMax5uOs6dSF8T1HfWYZBNL85fyxNwnWFW0Crd1cBbcDk8b5w8f4NyB16g6cxKEIKuomGUP\nfZDSJcvJLBx7T8+EEIROnsTzzLP4duxAa2/HXFRE9pe+iHvjRsz5+aM9xCFBVkz9ynMshKD2nIdT\nr9dy8d0m1JhG7kQXKx+bSsm8HGwpNz4f3Lf7Mh1H6kldVUTqijH2cGQIolUHfF5qyk9TW36GmvLT\nNFyoSAZBc7jTyCuZwpTFy8idNIXcSZNxputTPE798Idk5BXgys654XMrimKIYwMDAwMDg0ESqfZj\nyrRjytAFrX1ONsHTLVeJY9+eSlJXjqP91Wo95hGABCKiIjSBiGpIioRsNaThLf8KSLI0cpbj9xiK\nycSGL3wVSZZ57fe/QVNVltz/gWE7n6qpHG8+nrQQNwQaMMtmbi+4nc/f9nlWFq3EZRlc+pyAz0vF\n4QOUH9hP1amTCKGRUTCOpQ88QumS5WQVFQ/R1QwtqteL97nn8TzzDOHyciSbDdfatbgfuB/HggU3\nXTAj5ToBuQK+CGcP1nHmjTo8DQEsdhPTlxcwfXkBWeMGHyzL/2o1/lerSVmch2vN2PtM6HOO+7+/\nEILW2uqkEK4tP01bXS2gf89zS0qZv2Ej+VOmkldSijMjs+/gZAOIVt0XhuXYwMDAwMBg8KjeCKa0\nTk9BxW0lWuXvtk+kph3VG8Y+NUMXx/H/d/vMbIKnW6n74SFEVCPt3knI9rErDYUQiIgGiGEV8WP3\nFRgppBvIc3wLISsK6z/3ZWRF4Y2nfoumqix76IND1n8wFuRg7UH2Vu3l1epXaQ21YpEt3F54O1+c\n90VWFq0cVNolgKDfp1uID77OlZPHEJpGen4hi+9/mKlLlpNZVDwmU8UIIQgcOYLn6Wfwv/giIhLB\nNmMGed/7Hq4N61FSB/e6jGV6m3MsNEF1eRunX6/l4tEmNFWQP9nN/HumUTIvB7NlaKJvdxypx7vr\nEvbZWaRtnDwmPxsoElzDrVoIgae+lsrjR6k8cZTqs6cI+X0A2FJdFE6dxsxVayicOp3cSZMxWfqf\nlqq3aNUDRZZlVNX43TUwGCmEJnRrkab/PqDpixD03R4/RmidZUTXvuLbRI++uqy7tdFLW3IdP3+X\n/dBEstrnfvHzJwxhXffrbO/eBl36SfTf9djE69XHtl7P16XcbVvXPrq2dS92r/TWLno29db3tfsT\nAzxP93p/PJV6+69MfC7ifSRfm87zih717vuIzsN6vH9d++t2jsRYpLgGlNAL8bXUo9451B7XeK1L\nFn1UrtXFVf31bOgxNjlxDfoYraXpuNcWD1gUCiHw7rhI+sOlV22LVPuRZIn8by5GC0Rp+uVxrJPT\nklboBOHLXjwvXERSZCRFAlN8LUs9vp9Ct0LHBKgaQhWImAbxtVDjvzGyhCTHr1GW9GuMX7N+uRJC\ni++vCoSqISIaIqKCgJTFeaRvHr40pIY4liWIaYiYhtTPID23GrKisO4zX0KWFQ4883uE0Fj20GM3\nLBqag83sr97P3it7OVB3gLAaxml2sqJwBSuLVnLHuDtwWm7M+hcNh2itqaa5qpLmqkoaL1VQdfok\nQtNIy81n0cYHKV2ynOziiWNT9ACx5ma827bhefoZIpWVyKmppD34AGkPPvieDK51I8gmJWk57vCG\nOXugjtOv1+JrDmFNMTFr5Tim315ARsHQBrELnmymbct5rKXpZDw8Fek6kaxHi96iVQd8Xq6cPBYX\nxO/ib24CIDUrm5J5iygsm07B1GlkFIy74c9+wtprWI4NbiaE1veNnH5zdo2bPE3Ej4+vtS5rtUc9\nvt819+mjPpDzdL1JTQjfW4auYgi6iKH4xsRPn9RFPHWpd9tGz756but6TGf/0lXn6tJHct8u5+py\neM/2q/fvLHReF93H3sd5etulz/2v2rGX7V3pS0j29TrSvS5x7e1A5/9xX9u71ns+/Ojt4UpXId6f\na7zW/+a1Nl3rNe6B0MTVYjM+RnOWXdcsPVDcFmKecLKuesPIrs4H3iKsEm0I0PTkCRAC1R+l5b9P\nkfmRGQSONmIrTUeSJRSnBUuxi0iN/ypxLJlkZIc5+VtIMIYWL3cVtkkxb5KQrApyDzEtmeTO90cV\nXX6n6P4wDvTjFAlJlnRRbpH1Pq0K5mFOp3nLi+PEh1YLq/2OYHsrIssKaz/9BWRF5uCzf0SLxVj+\n6Ef6dZMthOCi9yJ7q/ayt2ovJ5pOIBAUpBTwwJQHWFm0kgW5CzAr/Z8fqsaitNXV0lxVSUtcCDdX\nVeJpqE8+/VPMZjIKi1jw/vuZunQFORMmjVlBLFSVjjfe0K3Ee/dCLIZ9wXzyn/g0rrVrke320R7i\niKKYTAT9IXY/eZJLR5vQNEHh1DQWb5zEpLnZmMxDn6M5dL6Nlj+cxTLeReaHpo3th2WyhIhpXD7+\nLpXH3+XKiWM0Xr4AgNWRQtGM2Sza+BDFs+aQllcwZJ/7hLXXEMcGQ4EQAmJxsRnV4msVkWyLl7us\nSe53jbUq9IfecYsDsbjloTehq2ojIx4TFpLEWulSV7q0x+vd9jXLIEt62rmexybXdJYlvS5JUucN\na7I9brFJlJM3tT2O6bGtr2OS1h+px3mgSz3+GvRmvQN97FKnxairwO1u5ettv85tY/X/3cBgOLGM\nSyXWEiTWFkJJtRA81kTGo2XJ7bLNRMG3lyTrTU8ex71hEpZCJ+EKK6ELHhy35aBFVCJX/KQuvzrG\nimVcKtkfmzki1zMWuOXFsZB0ISVCMRhE8J5bAUmWufsTn0OSZQ5vf4Z3dj6H3eXGnurC7nLhcLmx\nu9w4XG6sTid1WjMnA+c45H2HS9EaImaNGVkz+Mzcz7CqaBWl6aXX/TPTNBVvY4Mugq90iuC2utpk\n8CBJlknPLyRnQgnTV6wmq6iYzKJi0nLzkAfp/jncRGtr8Ty7Bc+WLcTq6lAyMsh4/HHSHnwA66RJ\noz28EUfTBJeONdF0JUCoo51IrJU5dxYxfXkBabnDl1c6UuWn5benMWfbyfrIdOQhctEeakLt7Vx4\n+xBSTQfh9g5eeeV3yIqJgqll3P6BD1M8ay65kyYP2+c+IWgH61atKArR6PUDrhmMLkITerCWuDub\nFlF7qWvxNhWtS1mEVbRovB7tXcQSG6QqjVsiJLPcbZ20UphlZJsCSherhaKLS0mRdetGb5YNpbO9\naz15PkVK9nmVSO0pdOUuotPAwMBgiJFkifT7Smj+Tz2wrGNhHuYcB96XKrGMc2Kf1ncKypSlBbQ9\nfY76f3lbry/IxZw3+mllR5tbXhx7ol7AmHfcXyRZ5q6Pf5b8KWW0VF8h6PMS9PsI+Ly01tXQ4W1D\ni3Te9MrAUmSWUoQky9hTTdhSj3PMVcm5pJh2xQW2G7PVSmttTdIa3FJdRSzS6S7izskls6iYkgWL\nySoqJquomPSCcZjM750HGyIaxb93L56nn6Hj9dcBSFm2jNyvf53U1auQBjD/82YhGlY582Ydx16p\nwtcURFMlnGkmHv/RMiy24fuZEkIQPNGMZ1sFstNC1sdm6a5DY4j21hYqjhzk/JEDVJ8+gaaqrB73\nGE53Bvc//j0Kp83AYhsZzwLDcjz20a2xGlpIRQuriFAsvlbRwjFdtIZURDgWX6tooXh71/3Duqgd\nECYZ2SojmRUki6K7wJllZKe5i4BVdFFqVpCS66sFbre1OS54e24bo9MeDAwMDEYS29QM8qZmdGtz\n313c677Zn5ydLMsWhczHpg3r2N6L3PLi2BfVg9SIsJHOqb9IksTMlXcBUN9Rz6tVr/Ja1V4O1x8l\nqkVJN7m5I2MJi9y3UWqbiOgIE/D5CPp9BH1eAj4vQb+XpiuXCfp9hNr9VwVCcKZnkFlUzJy77yGz\naLxuDR43fsREwHAQvnQJ77PP4tm6DbWlBVNuLllPfBr3/Q9gGTfGUgWNEB2eMMf3VXNqfw3hQIy8\nSS6WbS7h1L5DtNZUDaswDlf68O64SOSKH3Oeg8wPT0dxjY0HE231tZw/9CYVRw5Qd74cgPT8Qubf\nu5kpi5Ziei2K1hEl97bbRnRcQ2U5NgJyXRuhCV2kBntfRF/tIV3w9ivNlyIh2xQkqwnZqgtZJdWC\nlGWPtyvIlrjI7SJ0E2XJLHduTwhiQ6waGBgYGLzHueXFsVnRb4Yrmy5TOmnuKI9m7BPVopxsPsnB\n2oPsq97H6ZbTABSlFvFo2aOsKlrF3Jy5mOT+f7Q0VSXU7ifg8xINhUjLL8DuvDkiMWuhEP49e/A8\n/QyBI0dAUXCuWknagw/iXLECaYy7fQ8XzdXtHHv5CueONCA0waS52cy9ezx5k/Tc1Wdf71+e4xsh\n1hLEu/sywRPNyKkW0h+YgmN+7qjf2Ptbmzm170+Uv7mf5qpKAHImlnD7wx9iyuJlZBQWJV0zm984\npQf2GWGMgFwDR089oaJ1xNA6oqiBKFp7FC0Q1dsCUdQO/WGH1hFvD8SuHSlVkZDtpuSiOM2Ys+1I\ndhOyzaQLWZuCbO0sS1ZTp+i1mcb2nHoDAwMDA4NR4pYXxznOHAD+9dDPaW4OsmnyJtZNXDfofLo3\nC0IIznvOc6juEAfrDvJW/VsEYgEkJGZnz+aL877IqqJVTHLfeLArWVFwuNNwuNOGePSjR+jsWTxP\nP4P3+efRfD7M48eT/Zd/iXvTRsw5OaM9vFFBCEHV6VaOvnyFqjNtmKwKM+4oZM7qItzZ3T0Crpfn\n+EbQAlF8r1TRfqAWSZZw3TUe54pxyNbRe0ChxmJcevctTrzyIpfefRshNArLprPy8U8weeES3Dm5\nvR7XW7TqkSBh7R0Ky/F7XRxrERXNH0H1R1D90S7liF5uj+rljmjfDzJkCTnFhOwwo6SYMeelIDtM\nyClmZLu5mwCWHSZd/NpNuluxMYfVwMDAwMBgyLnlxbHFpFuON467l1+ov+VvDv4N/3DkH1g9fjWb\nJ29mcf5iZOnWesJe217LobpDHKg7wOG6w7SEWgAYnzqeeyfdy+L8xSzKW0Sa7eYRs0OB2t6Bb8cO\nPM88Q+jECSSzmdQ1a0h76CEcixYiDdLa9l5FaIKKdxp5a+dlWms7SHFbWLJpEjNWFGLrIwheb3mO\nb/j8MY32A3X4XrmCCMVwzM/FvWbCqLpQt9XXcvKVPZx69U90eNpISc9g0aYHmbnybtLy8q/fgSL1\nz3V2iLkVLMdCFaj+MKonjNoWJuYN9xC+uugV4V7cwiWQnRaUVDNKqkUXu04zisOsi+AUM3JKom5G\nsimGyDUwMDAwMBhD3PLiOJFKYGH6Ap5duYnTLafZWrGVnZd2suvSLvJT8rmv5D42Tt5IUWrRKA92\nePCEPByuP8zBuoMcqjvEFf8VADJtmSzOX8yS/CUszl9MgbNglEc69hBCEDp2jLann8a3azciEMA6\nZTK53/hrXO9/P6b09NEe4qghhKDyRAsHn7tIS3U7GQUp3PnRaUxZkHvdtGmyyTRoy7EQguDJZry7\nLqO2hrCWpuO+ZyKW/NGJxBiNhKk49CYnXtlD1ekTSLLMpHkLmbV6DRPnLhhQhOn3uuVYUZRRE8da\nKIbqCRPzhFE9oS5lXQyrvvBVLs2SRUFxWZCdZswFKdic6cipFpRUXQgnynKKedTd8w0MDAwMDAxu\nHEMcS3oC7VB5K6krxzEjawYzsmbw1YVfZe+VvWyr2MaTx5/kl8d/yYLcBWyespm7xt+Fwzx8aWWG\nm2AsyDsN7yRdpc+2nkUgcJgcLMxbyCNlj7AkfwmT0yYbVo0+iLW14Xv+eTxPP0P4/HkkhwPX+ntI\nf/BBbHPm3PKvW3V5Gwe3XaDhkg93tp27/3w6UwYwr1cZpDjuGWwr/WMzsZWOzoOKluorHN2zkzOv\n7yXc0YE7N4/ljzzOjPfdiTOj7xQL10SWRmXOscfjAcZ2QC4R1Yi1hYg1B/Xcjy26ANZFcAjRMzOB\nLKGkWVHcVqwlbr2cZsWUZku2j6brvYGBgYGBgcHIYYhjIHVlEZ7tFwhXeLBN0W+grYqVdRPXsW7i\nOuo76nnuwnNsq9jGN1//Jj8y/4i1E9ayafIm5mbPHfNCKKbFONl8MimGjzUdI6pFMckm5mTP4Ym5\nT7A0fykzsmZglsdWGpuxhNA0AocP43n6GfwvvYSIRLDNmkXeD76Pa/16FKdztIc46jRc8nFw+wWq\nz7bhTLey6kNlTF2ah6IMzA1XNpnQBhiQS6iC0LlWOg7VEzrbipxqHtVgWy3VVRx45veUH3wdxWRi\nyqJlzFq9lqLpMwftYi8p8oi7VXu9XrZv3056ejoTJ04cVF+DdasWmkBtDRFtDHSK4Oa4EPZ2t/xK\nNgVTug0l3YploqtT9KZZMaVbkZ0Ww9prYGBgYGBgABjiGICUhXn4X63Gu6cS6+S0q8RuXkoen5z9\nST4x6xO80/gOW89vZdelXWw5v4UJrglsnLyR+0ruI8cxNgItBaIBKjwVnGg+wcHag7zV8Bbt0XYA\nyjLKeGzaYyzOX8y8nHnvaQv4SBGtq8O7bRuerduIXrmC7HKR9tBDpD30ILaystEe3pigpaadQ89d\n5NKxZuypZpY/NIUZdxRgMt+YxS1hORZCXPfhU6wtRMeRegJvNaD6IshO86gG22qtrebAM3/g7Jv7\nMVusLN70EPPWb8Thcg/dSWRG1K06Go3y1FNPEY1G+chHPoLNZhtUf/0VxyKmEWsJEm0IEGsMEG0K\nEmsIEG0OQKzz+uUUE6ZMO9aJbkyZNkyZdkxZdkyZtjGXt9rAwMDAwMBg7GKIY0AyybjuHE/bs+cJ\nnWnFPr13V0dJkpifO5/5ufP5xuJv8OLlF9lWsY2fvfMzfv7uz1lWsIzNkzezsmglFmX4g/2omkp1\nezXn285zru1ccqn2VyPippNxznGsm7guGUQrw5ZxnV4NIJ6C6eU/4d2yhY4DB0AIHAsXkv25z5K6\nZg3yIMXBzYKnMcDh5y9x/q0GLDYTi++bxOzV4wadn1hR9OOFpvWa7kqoGqEzrbQfrid8vg0A65R0\n0u4rwTYtQ7esjjBt9bUcfPaPnHltH4rFzML7HmDBvZuHVhQnkEcuIJcQghdeeIHa2loeeeQRcoYg\n2npv4lgLxYjWdRCpbSda0060toNoY6DzOiVQ0m2YcxxYS9Mw5zgw5TgwZzuQ7cZfmYGBgYGBgcHg\nMe4o4jjm5eDbV4XvpUpsZRnXdbNzmB1snrKZzVM2U+mrZHvFdrZf2M6XX/0ybqubDRM3sGnyJqZl\nThuS8XlCHs57OkXw+bbzVHgqCMaCAEhIFLuKKcso4/0l76c0vZRpGdOMIFoDQAhB6PhxPFu24tu5\nE83vx1xQQNYTT+DevAlL0c0ZkO1G8LeGeGvnZc68WYdikpi3tpjb7h7fZ/TpgSKb9J8mNRbtFqgq\n1hKk40gDHW/Xo/mjKC4LqauKSFmYhyl9dB5YeBrqObjlj5ze/wqKYmLeho0suu+BYU1NJikyYoTm\nHB88eJBjx46xcuVKyobIU0JGQoup+PZWEa1tJ1rbTqwl1LndacZc4CS1LB1zXgqmbAembDuyxZj7\na2BgYGBgYDB8GOI4jqTIuO4qpu2pcoKnmnHMyu73scWuYr4w7wt8du5nOVh3kK0VW3n63NP8/uzv\nmZo+lc1TNrN+4nrSbdcPCBRVo1z0XkwK4HOec5xvPU9jsDG5T5o1janpU3lgygOUppdSml7KpLRJ\n2E32a/Rs0Bexpia8zz2HZ+tWIhUXkGw2UtfcTdr99+NYtOiWTcHUG0F/hLd3V3Ly1RoEglnvK2Te\numJS3NYhPY9i0kW2GothUjSCp1voOFxPuMIDEtjKMkhZmIdtagaSMjrzRX1NjRzc+hSn9r2MJMvc\ntvZeFm58EGf6CHhnjJDl+OLFi+zZs4eysjLuuOOOG+4n5gkRuewjXOkjctlHoKkOVVHxvXgZJcOG\nJT8Fx7xczIVOLAXOUU2zZWBgYGBgYHDrYojjLjjmZOPfewXfS5XYZ2QNOEiLIivcXng7txfejjfs\nZeelnWyr2MaPD/+Yn7z1E1YVrWLT5E0sK1iGIik0BBo6RXDcInzZe5mY0KP0mmUzJWklLM5fnBTB\nU9KnkGXPGvNBwMY6IhLBv28f3i1baX/tNVBV7HPn6sG17rkHJTV1tIc4ptA0wan9NRzcfpFoKEbZ\nsnwWbphIasbwWGsVxUSWtRD/riu0nvGhdURR0qy47i7GsSAX0xCL8YEQbPfzxh9/y4lX9iBJMPuu\ne1i06UFSM7JGbAwjkcqptbWVp59+mqysLDZv3tzv3MZCCKL1ASIXPboYrvSheiMASBYZy3gXtpQ0\ntOpKCr6zxJgTbGBgYGBgYDBmMMRxFyRZwnV3Ma2/O0vgWBMpt9343Dq31c2jZY/yaNmjlLeWs61i\nGzsu7uClypfIsGUQ02L4Ir7k/vkp+ZSml7KyaGVSCI93jTeiRw8xoTNndLfp559H9XgwZWeT+bE/\nw715M9ZJk0Z7eGOShks+Xv1DOU1X/IwrS2fFB0rJGIZcwUIThC95CZ5sJuNtJ3cWfIjwu63YyzJI\nWZSHdUr6qEcVrj59kh3/+hMCnjZmrV7Dok0P48rqv5fJkKEMr+U4HA7zxz/+ESEEjzzyCFbrtR9G\naBGVcIWHUHkrobOtSTGsuC1Yil1Yi11YJrgx56UgKRL2fS1QDdgMN2kDAwMDA4PRpqKigt27d6Np\nGrfddhsrVqy4ap/Tp0+zd+9eAPLy8njggQeor69nx44dhMNhJEnijjvuYMaMGSM9/CHFEMc9sM/I\nwpyfgv/lShyzs4YksM/UjKl8fdHX+cv5f8n+6v28WPkiTrMzKYInp0/GZXENwegNekPPSfwCnq1b\nCZ85g2Q247zzTtLu30zKsmVIJuNr0Buh9igHtl/g9Ou1pLgsrPn4DCbPzxlSrwWhaoQvegmeaCZ4\nqgWtI4pkllEz4NCJ57jre18ifXzhkJ3vRtFUlQPP/J5DW58mLS+PR//mJ+SVTBm18UiyBEJ/oDDU\nDwyEEGzbto2mpiYee+wxMjN7D1AYaw0lxXDumNx9AAAgAElEQVToghdiGpJFwTolDdddGVgnp/U5\nDzxhhdY0rd8WaQMDAwMDA4OhRwjBjh07ePzxx3G73Tz55JOUlZWRnd358L+1tZX9+/fzsY99DLvd\nTnu7ngXHbDazadMmMjIy8Pv9/OpXv6KkpGTQWS1GE0MV9CBhPW75n9ME3mkkZWHekPVtVszcWXwn\ndxbfOWR9GvSOFgrR/up+fC+8gH/fPohGsc2YQe63voVrw3pM6def/32rIjTBmQN1HNhygXAwxpw7\ni1h078RBR6BO9h/TCFV4CJ5oJnSmBS0QQ7LI2MoysM/KwjY1g3NHXufKwTNoyo3nwh0qvI317Pj5\nT6g7d5YZK+9i9Z99CottlOf3JwSxJjrLQ8T+/fs5c+YMd999N5MnT+62LdoUIPB2A8EzrcQaAgCY\nMm04F+dhK8vAOtGNZLq+2O0qjg0MDAwMDAxGj5qaGjIzM0mP3xvPnDmTs2fPdhPHb7/9NosWLcJu\n1+9/nE4nQLcH6C6XC4fDQUdHhyGObzZs0zIwF6Xi+9MVHLfl9Otmz2D0EdEoHW++iW/nTvwv/wmt\nowMlM5OMD34Q9/2bsU2dOtpDHPM0VfnZ/4dy6i/6yJ/s5n2PTiWz0DnofrWISvi8h+DJZoJnWhAh\nFcmqYJ+eiX1mFrbSNKQuOZET0aq1WGzQ5x4MZ954lZd/9QskSWLDF79G2bIbD0o1lCSCkAlNMJTS\nuLy8nL179zJr1iyWLVumn0ONB0Q7WEf4ghdkCetEFykLJmIry8CcPfBc6Uo8Arkhjg0MDAwMDEYX\nn8+Hy9Xpwepyuaiuru62T0tLCwC//vWv0TSNVatWUVJS0m2f2tpaVFUlI+O9nTbWEMe9IEkS7ruL\naf71STqO1ONcaqRDGqsIVSVw5C18O3bg37MH1etFdrlIvWcd7g0bcCxcaLhN94NwMMah5y5ycl81\nNqeZOz86jamL8wblQq16wwTPthI600qowqO73dpN2Gdk6RbiyWl9PnhSkqmcRkccR4IBXvmvX3Lq\n1T9RUDqN9Z//Cu6c3FEZS68krMVDmM6pqamJZ599lvz8fO677z5Ub5iOw/V0HImnzUqz4lpbTMqC\nPJTUwUWTTliOVVUdiqEbGBgMACEEmqZ1Ww9VW2JJnOdabf3ZZywdN5Lrru/VQOpjuQ+D6zMSr9mj\njz6atPoOZByaptHS0sJHPvIRfD4fv/nNb3jiiSeSFuL29na2bNnCxo0be713jMViHDx4kJMnTyZ/\nM3rS8zhJkhBCEIvFkks0GmXu3Lls2LBhIJc9IAzV0AfWKWlYJrjwvVJFyoLcblYtg9FFCEHo2DG8\nO3bi270LtakZyeEgdfVqXOvX41x+O5LFSAXTH4QQnDvcwBvPVhD0R5h1RyGLN07CegMRhIUQRGva\nCZ7R56FGa/T5KEq6FeeiPGzTMrBOcvdrHr+ixC3H6siL4/qKc+z4+T/ibWhg6YOPsuT+R7rlWh4L\nJOYZD0XE6kAgwKFDhzh06JA+d2jhOry/P0fobCsAtqkZpCzJx1Y6dAHRDLdqg7GEEAJVVZNLLBa7\nqqxpWrelP23DfVxPkdpfAWugI0lS8mY8Ue6tLVEeyXXP8o3Uh7OPgYxzLPJeGONw0du1u1wuvF5v\nst7TkpzYp7CwEEVRSE9PJyMjg9bWVgoKCgiHw/zud79j1apVFBUV9Xluq9WKy+VCluVun6Ouv0u9\n/UaZTKbkYjabr3mOocAQx30gSRLuNRNoevI47QfrSF0xbrSHdEsjhCBcXo5vx058O3cSralBslhw\nvu8OXRCvXIlsN/I8D4TW2g72/7GcmnMecia4uPezs8kpHlhgOBFVCVV4CJ1pJXi2Fc0XAQksRam4\n1k7APi0DU65jwH9Ecpc8xyOF0DSOPL+FN576LSlpGTz83R8xbtrMETv/gEjkdh6EOG5vb+fgwYMc\nPnyYSCTC5Oxi5vnGE/2/K6hOM6kri0hZlNdnUK3BYIhjgwSqqhKNRrtZBXpb99XWVcj2JWz7s20k\nkCQJWZa7LYqiXLOeaLNYLFe1J24we66Hqu1Gj+mv6OyrbbiPu5WFkYFBbxQUFNDa2kpbWxsul4tT\np05x//33d9unrKyMkydPcttttxEIBGhpaSEtLQ1VVXnqqaeYM2fONaNUm0wmFi5cyMKFC4f7cgaN\nIY6vgXWSG+vkNPz7qkhZlI9sHVvWo1uB8KVL+HbuxLdjJ5GLF0FRSFm2jKzPfY7Uu+408hHfAJFQ\njLd2XObYn6ow2xRWPjaV6bcX9NsqqPrCunX4TCvhCx5EVI9SbCtNw1aWia0sHcU5OMv9SLtVt7e2\nsOsX/8yVk8coXbKcuz/xOWzXcTsaTQZjOfb5fLz55pu89dZbxGIxppeUMbM1H1eVjGWiC+eGAuzT\nM4c11oIhjt87JMRoJBJJLl3r19qWqCeW3kTuYCyZiqJgMplQFOWqcqJuMpmwWq29brvWcT3LNyJo\nexOzBgYGBmMNWZZZv349//u//4sQgttuu42cnBz27t1LQUEBU6dOZfLkyVy4cIFf/OIXyLLMmjVr\ncDgcHD9+nMuXLxMIBDh69CgAmzZtIi9v6AIajzS3vDi+3h+za00xTf92jPY3a3GtGl4zvoFOtLYW\n365deHfsIHz6DEgSjgULyHj8w6SuXWtEmh4EdRe8vPxfp/A1h5h2ez5LN5dgv46QFaogUuUjVN5G\n6Fxbp7t0mhXHglzs0zJ1d+khFFPKCAbkarx8kWf+9ltEI2HWfOoLzFx199i3LNzAnGOPx8Mbb7zB\nO++8g6ZpzJ45i9vMJZgO+ZFtCmmPlmCfnT0i124E5Bp+VFUlHA4TDocJhUI3VI5EIgN6jyRJwmKx\nYLFYMJvN3dYpKSlJl7iu7nG9rfvTlhCfBgYGBgaDZ8qUKUyZ0j1F5apVq7rV165dy9q1a7u1zZ49\nm9mzZwO6phrz90/9wBDH1xHH1vEubGUZ+F+txrkkH9l+y79kQ44QgsjFi7Tv24f/5T8RfPddAGyz\nZ5P7139F6rp1mHPHUDCk9yCaqnFk52Xe3nmZ1Ewbm78yj4LJaX3ur/ojcTHcSui8BxGM6e7S4124\n1hZjn5Z5Q+7S/UVOWo6jw9J/goDPy/af/C2K2cwjP/gHMgreG9MnukarvhZCCBobGzl48CDHjh0D\nYO7cuSwuuQ1eaiRa78M+N5u0eycN2to/EAzLcf+JRCIEg0ECgQDBYLBbube2hMCNRq//3ZFlGZvN\nhtVqTa7T0tKS5YTQ7Spyr1U3mUw3xY2RgYGBgcHAuVl+/295paf1Y66R6+5iGn/+Lv7Xa3DfXTwC\no7r50SIRAoeP0L5vH+379hGNh4y3lpWR/aUv4dqwHsswT7i/VfA2BXjp16dpuOSjbEkeKz5QiqXH\nQ56+rMNyqhn79ExsU9OxTU5DvoFAXTeCMgJzjtVYjBf+5cd0eNp45Ht//54RxkD3PMc90DSNK1eu\nUF5eztmzZ2lra0NRFBYsWMDSRUuQDnto/9/LKKkWMh+fjn165lV9DDe3crRqTdMIBoO0t7fT3t5O\nR0dHsty1LSF2Y9f4DpjNZux2Ow6HA7vdjsvlSgrbroK3r7LZPDLfZwMDAwMDg/cKt7w4jjY0XNcN\nwFLoxD4zk/bXa3AuK0BJMW4oboRoYyMd+/fj37ePjjcPIAIBJKuVlKVLyfz4x3G+7w7M+fmjPcyb\nBiEEZw/U89pT55AViTUfn8GUBZ0W+G7W4XMeRCgGcqd12FaagTk/ZcgiFA+EkXCr3vc//z9Vp09w\nz2f/krzJpcN2nuEgOec47lYdiUS4cOECZ8+e5dy5cwSDQRRFYeLEiSxbtoxp06ZhbozR9l/nibWE\nSFmUh3v9RGTb6PwFjBXLcTBYw6VLP6O09DuYTIObYy6EIBAI4PP58Hq9+Hw+fD7fVaK3vb29V48l\nRVFwOp04nU7cbjf5+flJ0ZtY9ywb4tbAwMDAwGBo+X/svXd8HNd9r/3MbO+LXhbALgCCYCdBUSwi\n1Ytl9WLJltxiWUkcJ6+jFPum+Tq5r53kOk5T4tixYjmO4yLJii2rWpRkWV2URIoUJZIgCSw6Ubdh\n++7M/WMWC4AEQRC9nOfzWc7MmXPOnAXA3fnOr614cZyJRhn6/vcp+o3fmLSf82ov8fcHGX6pE9eH\na+dncUscVVFIvP9B3jqceP99APQVFbhuvgn7pZdi27FDZJmeAxLRNC/+8Bgn9/dR2eDmqs+sw+4y\nkmwJkTgeIHFsiHR3FADZYcSyPmcdbihYFKED8hwn5Dr0/C9595dPcMENt7Lukivm5BpziiwRI8m7\nHxzkxK/8tLS0kMlkMJvNNDQ0sGbNGlatWoXJZEJJZAg93UrwzVPoCs0U37sR8yQu9fOy/EUijpub\n/5KBwRcoLbuO4qLLJu07In4DgUA+q2cgECAUCuXF8OlWXlmWsdls2O12HA4HFRUV2O32fNvYl8lk\nWjYuaQKBQCAQLFUW/i54gZGsNvr+7hta6abbbjtr9mNDmQ3r5hKGX+vGvseDziHq6E5EdjhK9PXX\nNEH80ktk+wdAkrBs2ULJffdhv/wyTKtXi5vAOaTzWIDnvvcB8XCK3dd6qS+1kHyihfDJIGoyO8Y6\n7MPcWKBZhxfZ72Mus1V3HTvC89/9Ft5NTVxy92/M+vxzRTgcpq2tDb/fj/94C4PmALwMLpeLCy64\ngMbGRrxebz7ZFYCaUej/j/dIdw1j3+PBeY0X2bjwWfcXQ0KugYEXGBh8AYB4zA857/JUKsXg4CB9\nfX309/czMDCQF8KpVGrcHHa7HbfbTXl5OY2NjbhcLpxOZ35rs9lE0iiBQCAQCJYQK14cy4UFWJq2\n0Ps3f0vfP/4Tzuuuw33HR7Bs2XKGYHBc5SV2qJ/Iix24b6xfoBUvPlIdHQz/SrMOx956CzWdRnY4\nsO3ZjeOyy7BdfDH6wsKFXuayJ5tR2Pc/J+h6uYu1DiOeaiu80U0I0BWYsG4pwdxQgGmVe8HcaaeK\nnHernt2EXJHBAX7x91/DWVLCDb//v5B1Cy8Uz0YwGMTv99PW1kZbWxtDQ0MAGI1GqoorqBssZOPt\nu6huqj/rw43w8+2kO4cpvHsN1k0l87n8SVloy3E2m+RY8//BZPKSSvVxrPllXn7ZSH9/P4FAYNw6\nCwsLKSgowOfzUVBQkH+53W6MRvGQVCAQCASC5cTivkOeB1TA+4MfkDj8PsFHHiH8xBOE/ud/MDU0\n4L7zTlw33YjO5QLAUGzBurWM4Td7sF9Shd5lWtjFLxBKPE784CGGX3qJ4V//mtTJkwAYa2sp+MQn\nsF92GdatTUgiHm7OUVWVdE+UwP5e+l/vwZNRqLbpQQfmchvmS6owrS5AX2xZdNbhychbjmcxYVM6\nleSxb3yNdDLJnf/7rxdVHeNsNktfXx9dXV20t7fT1tZGKBQCwGw24/V62bZtGz6fj7KyMtL+CAMP\nvEeJu/isv9ekP0TkxQ6s28oWlTCGhUnIFYvF6OzspLOzk1D4RzidHbx36Cp8tREymWYCga1UVFSw\nefNmSkpKKCkpoaioaJwlXiAQCAQCwfJGiONcMi7Lxg1YNm6g9EtfIvzUkwQffoTer32Nvm98A+e1\n1+L+6J1YmppwXlFD7EAfkRfaKbi14dwXWAak+/qI7z9A/MB+YvsPkDhyBDIZMBiwXbiNgo/eif3S\nSzF6RSbv+SA7nCJ5IkiiOUDieAAlollXdSqoa4so3lOJyTe7dYfnm9lOyKWqKnu/86/0thzn5i9+\nmaKqmlmZd7prCQaDdHV15V/d3d35eFWr1YrP5+Oiiy7C6/VSWlp6hmtu5hylnJREhqGHm9EVmHHf\nWDe3b2gazIflOJFI0NzczIkTJ+js7Mxb3s3mKBdse5lMZjOXXPLbKMq/k0wd5SO3/+6crUUgEAgE\nAsHSYMWL49NvznR2GwV33knBnXeS+OADAo88QvgXjxN67DGM9fUU3HkHlk3biL7Vi+PSavSF5gVa\n+dygZrMkT5wgvl8TwvH9+0l3dQEgmUxYNm6k6DOfwbK1CeuFF6JbRNa35YqaVUi1R/JiON01DCpI\nFj1DkkRrLIOh1sUl96zHtky8GWTd7NY5fueJn3Hk5V+x+85PsGrbjlmZc6pEo1G6u7vHieFYLAaA\nXq+noqKCbdu24fF48Hg8FBQUnNvKP5JBPDuxOA4+3kI2kKDkc5uRTYvvY36uxHEkEuHo0aMcPXqU\n1tZWFEXBZrNRXV3N1q1b8Xg8hMJfJxAwsGvnv2I2V3KyZQ1+/wsoShJZXh7/fwQCgUAgEEyPxXfX\nNM9MVFJjBPO6dVR85SuU/fEfE37mGQIPP0zv3/wtsqME2xX/h6GH9lPyuV1Lyl31dJRolPihQ8T2\n79eswwcPogxrNW51JcVYm7ZS8IlPYN3ahHntWiQRYzcvZIYSuazSAZJjE2lVO3Fe5SWgk3n+qVZS\nsSy7bl3FpsurFqTk0lwhSRKyTj8rCbn8777DSz/8T1bv2M2O2z4688VNQjqdpqenZ5wQHhvDWlpa\nSmNjY14Il5aWTsttN1/KaQLLcfzwALF3enFcXo3J65z+m5lDZlMcDw4OcuTIEY4ePUpnrl56YWEh\nO3fuZO3atXg8nvz1BodeoaX1Werr/gizuRIAq7UWUIjHO7DZVs14PQKBQCAQCJYuK14cT+XmTLbZ\ncN9+O+7bbydx7BjBhx8hfuQVVC6m9ZZP4rr5Sly33oK+oGAeVjwz0t3dxA4cIL7/ALED+0kePQaK\nApKEqaEB5w3XY21qwrJ1K4aqqiUt/JcSSipLsiVEsjlAojlAZiAOgM5twrq5BPPqAkz1bjDpeONn\nJzmwt53CShs3//56ijzL03qv089cHAd6unji/q9TXF3Dhz5/36z+PSuKwsDAAF1dXXR2dtLV1UVf\nX1/+M8XpdOLxeLjgggvweDxUVlZiMs2SZXLkQchp4jgbThH4n+MYPHacVy2c6/i5mI1s1d3d3ezd\nu5fW1lYAKioquPzyy1m7di0lJSVn/K4VJUVz819hsXipqflsvt1q8QEQi/mFOBYIBAKBYIWz4sXx\nZJbjiTA3NlL+5b8gPRCm9x/eRV9zBX1f/zr9//iPOK6+Gvedd2LdsX1RiEo1kyFx7Ni4eOHMqVMA\nSFYrlk2bKP7cb2Np2opl8yZ0zsVpZVqOqKpK+lQsJ4aHSPrDkFWRDDKmOhe2nRWYG8cn0krGMzz7\nzYO0vz/Ehks87L5jFXrD8k0WpNPrZxRznIzF+PnffRVJ1nHzF7+M0Tz9etqqqhIOh8+IEx4p7WMy\nmfB4POzevTtvFXacpSzcbCCNxByPcatWVZWhnzajphUKP9qIpFu8MeczsRwHAgFeeOEF3nvvPaxW\nK1dffTXr16/H7Z68dnNHx38Si7WwedN/jHOftlp9AMTi/vNei0AgEAgEguXFihfH07VcGIqdOC6p\nJvJriervP8rwcz8n9NhjhJ96CoO3hoI77sB1663oi4pmecXjUVMp0j09pDo7SXd0ku7qJNXRSbqj\ng2RrK+pIbGN5OdatTZoQ3tqEubERSb/if/3zyvhEWkGUiCasDOVW7LsrtTJLPheS4UxRE+yN8dS3\nDhHqi3Pp3Y1suMQz38ufd2S9ftoxx6qi8NS/foNATxd3/MVXcZWWndf4eDx+Rpzw8Ei4gU5HeXk5\nW7ZsyQvhwsLC+a1nO4HlOPpGD8nmAO6b6zGUWudvLdNgOtmq4/E4L730Evv27UOSJC6++GJ2796N\n2XzuvA+J5Cla/f9KcfGVFBdfPu6cweDGYCggFms9vzchEAgEAoFg2bHi1dH5Wo7H4rikiuHXe0g2\nQ/mf/xmlf/SHRJ59lsDDD9P3jb+n75/vx3HllRTceQfWnTuRpnHzrKoq2cFB0p050dvZMSqEOztJ\nnzqluUXnkAwGDB4Phqoq3E1NWJq2YN26FUNFxbTfp2B6qGmFZFuIxPEgyeMB0t1RAGSrHtMqN+bV\nBZgbCtCdI4lWx5EhfvnAYSRJ4qb7tuBZvfjd92cDnd4wbbfq1x75IS3v7OOKez5H9fpNk/bNZDL0\n9vbmXaO7uroYHBzMny8qKqKuri4vhMvLy9Ev8IOl02OO030xgk+2Ym4swLZz8f9fPx/LcSaTYd++\nfbz00kskEgm2bNnC5ZdfjitXYm8qnDjxf1HVNA2r/nzC8xaLj3jMP+X5BAKBQCAQjGdw8Nc0H/8q\nqqpQWXknPu9vjzvffPxrBAJvICGRVWKkUkNcesl+QPueHhj8FQCFBbtZvfrL877+EVa8OJ5JzJts\nNeC42EP4uXZSnRGMVQ5cN92E66abSJ48SfDhRwj9/OdEnnkGQ3U17jvuwH3rLehLxtccVWIx0l1d\nE4jfDlKdXajx+Lj++pISDNXVWLZdgKuqGkNVFcbqKgzV1ehLS6clwgUzR1VVMr0xLZHW8SCp1hBq\nWgGdhLHGifNDXswNBRgq7VNKnqWqKu+92Mkrj5ygoNzK9Z/fhLN4+q7BSw1Zr5uWW3Xzm6/yxv88\nxMYrrmHLNdePO6coCkNDQ+MswqdOncpbMO12Ox6Ph82bN+fjhC2WRfgz141mq1YzCkMPHUM2yhTc\nvnpRhHSci6mK4/fff5+9e/cSDAZZtWoVV111FeXl5ed1rUBgH729v8Dn+z2s1onLzVmtPgKB189r\nXoFAIBAIBBqqqnCs+S9p2vLfmEylvPX2rZQUX4XNVp/vs7ph9AF1R+d/MRw5AkAotJ9gaD87dzyD\nqqq8884dBAL7KCjYPu/vA4Q4npHlGMC+x0Pk1W7Ce9so/syGfLupvp6yP/0TSv7wD4jsfY7gww/T\n/w//QP/992O/9FJki4V0Rwepri6yAwPj5pStVgzV1RhqvNgu2o2hqgpDdRXG6moMHg/yFNwIBfND\nNpIicUKzDI91ldaXWrBtL8fUUICp1oVsOr/Y4GxG4aWfNPPBK934NhVz9T3rMJpX1n9X3TSzVb/+\n0x9T4q3lint+h+Hh4XFCuKuri2QyCYDRaKSyspKdO3fmrcJOp3NJiMuRB2CqohJ+vp101zBFn1iL\nzrk0sslPJSFXc3MzjzzyCOXl5Xzyk5+kvr7+rH3PhqKkaW7+S8xmDz7v587az2rxcerUz8hm4+h0\ni/BhiEAgEAgEi5hw+CBWiw+LRQv7Kyu9gf6B58aJ47H09j5OXe19uSMJRUmSzSYBBVXNYjQWz8/C\nJ2Bl3W1PgKqqqKo67Rti2azHcWkV4Wf8JNvCZ5ROkU0mXDdcj+uG60m2tBL86U8JP/EEktGIoaoK\nx+WXYfCMEb9VVeimUudUsCCo6SxJf5jE8QDJ5iDpU2NcpRsKMDe4MTUUoJ9BveF4JMUz3zlM9/Eg\nW6/1svOmumVVpmmq6PR6lOzUxXE6nebksaN0D8cpaFzP/f/yL4TDYUArDVVWVsaGDRvyQrikpGR+\n44Rnk9yyk60h4gf7sW4rw7Jh4b5IzpdzWY6TySRPPvkkxcXF3Hvvveftxp7JROju+SkdHd8nkehg\n44Z/m1T0jiblasNhX3Ne1xIIBILFwKixR829RtpGj0Fl1CaknqUvp7VxxhyTX3/Cs9M6N6n56qzX\nm+61Jhk37fd2tulm/2c1X78XSdJjNnuQpPH3T8lkLybzaFiXyVxOOHxwwjkSiW4S8S4KCi4CwOVq\noqBgB6+8uhOAqqpPYrPVTfhewuEDGAyF+e/tuUCIY1Ulk8lgMBimPYf9okqGX+kivLeNkns3nrWf\nqa6Wsi99kbIvfXHa1xLML6qqku6Jaom0jgdItoYho7lKm7xOnNf6NFfpCtusCNjBrmGe/LdDxMIp\nrr5nHau3n58L6XJCniTmWFVVgsEgHR0ddHZ20tnZyalTpzSxVVZNLKtQ4/WNixM2LqMa3SN/a/F3\n+9EVmnHfeOaXyGLmXAm5XnjhBUKhEPfcc895CeN4vIOOzu/T3f0I2ewwLtcFrG74M0pKrpl0nFbr\nGOIxvxDHggVDVRVUNY2qZnOvDEpuqyoZbTtyPNIHBdRsbqzCiNVldF87HruvjZloX9X6qgpqbh5y\n86pMtq/mxme1G/HcvNoa1Nx8ak5gqLlr5ra59z2uH+oUxk7UDxg3tzoq+MbNrWr9xo0nfz732xhz\nHUbbc0Lj9L7jheVo/4nF6en9ziZOJxKyE11vZh6QAsH5YLH42H7hY+j10y8j2tv7OCWl1+YNgbFY\nG7FoC3t2vw6oHDjwSYKFl+B2bzttpMLb79xBZeXHWLvma9N/E+dgxYtj0KwUMxHHslGH49JqQk+2\nkDgZxFw/eUkRweImG05pQvh4gMSJIMqwljFZX2bFvrMCU4Nbc5U2zm4ZpZZ3+3nuex9gMOu49Y+2\nUuZb2aW1xtY5TqVSdHd309nZmRfE0ahmtTcYDHg8Hi666CJOHdhHqKWZz3/le8vb+2KkTJMEhR9t\nRDYtrY/yySzHnZ2dvPnmm2zbto2amnPXalZVlWDobTo6HqS//zkkSaa09Dpqqj+D0zl5MrYRLBYt\nFllkrF7aKEoaRUmiqmkUJYWipEf31TSqkh7Xpm3To+fU3PjT2rRtbr4xbaeLVUWdSMDm2pRz91s6\nIkdCknSAjCTJuX2tbfy+rO0jwci+JOW22rE2hzSun4SsnUKeeCwy5PZlSZ8bM9KPMdcd7Td+nly/\ncedHP1Nz/2hjkHJ9GJ0n/2OQxvcdczyub37O0/tOYc4z+o7+Ds63rzRmbfl1SlJ+hnNfn9PmmIBJ\nv3fPfk6a5Nxk4ya/3vxd6+xzTvdak52azpyz9750Otu4cogjmExlJBLd+eNk4hQm08QGnt7eJ2ls\n/Kv8cf/AszhdW9DptLDRoqJLCYUPTCCOZbZs/t44C/VcsLTuqOaIRCKB3T79JyAA9p3lRF7uJPxs\nG6bPuZb3jfkyQ0llSbVqWaUTxwNkerXyV7LNgKnBjbmhAPMq9zmzSk8XVVV555k23nyshVKvg+t+\nZxM299xcaymgqiqBQICIzkg8A//+7wMd8IQAACAASURBVP/OqVOn8k/bCwsLWbVqFVVVVVRVVVFa\nWopOp0NRsnzrJw9S17Rt2f//k/Qyst2gPazxLr2HKGcTx9lsll/84hc4HA6uuuqqs45XlAzDwx8Q\nDL7Fqd5fEIkcRq934/X+NlWej2M+zy9Ovd6O0Vgiah3PMoqSJpuN5V5xstmotlW0YyWbQFGSKIq2\nzSqp/P7IuaySzPVJjumfJKuM7o+8NAvp7CJJRmTZgCQZkGUDsmRAyh/rcyJwdCvLpjPaJEmnjcuJ\nR2ncOP348SN98v1Pu4Y80l/WtjmBOl6ojtnPic3T90fH5PYlGQkdkjSR8D19f3l/vgoEgvPH6dxE\nPN5GPN6FyVRCb98TbFj/T2f0i0ZPks6Ecbma8m1mUyXd3Q+jej+HqioEgvuoqb7njLGSJFFUdMmc\nvg8Q4hggn6BnJkgGHc4rqgn+/CTJ40HMK6TczlJEVTRX6cTxAMkTQZKtIciqoJcw+VzYtpZiaijA\nUD47rtKTkUlleeEHRzn+Vi8NF5ZxxSfXoJ9li/RiJ5lMnmEVjsVigAFJVfGZzezZs4fq6mo8Hg82\nm23CefpaTpKIhPFt3jq/b2ABkHQSFX+6A0m3NG9SzyaOX3vtNfr6+vjYxz42rn5xNhsnFH6XYPBt\nQsG3CIUPkM1qD7FstgYaG/9/KspvnVEyLau1ltgKL+ekidlhMpkImczINkIm15bNtWtid4zQzcTy\ngnesGFbV869TrolOE7JsQiebkHXm/LEsmzAY3Ke1mbV+Iy+dCVkyIskG5BFhmxO1smzMzW/Mt515\nzjBuK4SgQCAQnBtJ0tG4+i959+CntVJOFXdis62ipeWfcDo3UVx8BQC9fU9SVnbDuLGlpR8mEHid\nN968LieAL6W4+PKFeBuAEMfA7IhjANu2ciIvdhJ61o+pwS2+VBcR2VAybxlOngiiRLWbNkO5FftF\nlZgbCjD6nLPuKj0Zw4EkT3/7EH3tEXbeUsfWD3mX/d+MqqoMDQ2NixXu7e3NW4WLiopYvXo1VVVV\nNO99gkwoyKc+/ekpze0/uB8kCe+mpnN3XgYsVWEM2tNfWZbHieOBgQFefPFF1q5twONR6en5GcPD\nRwiG9hOJHM4JLQm7vZGK8ttxu7fhcm/DfBa3rfPFavHRP/D8rMy10ChKknQ6mHsFSKUDpNMBMmPa\n8ttMKC+CFSVxzrklyYheb0MnW5B1VnQ6CzqdFaOxNL+ff8mnHZ92Pi+Cx4hdzTopEAgEgqVGUdGl\n7Cq6dFxbXd19449rv3DGOEmSWbPmq3O6tvNBiGNmTxxLehnnlTUEHj1O4oMhLOuLZmVewfmjJDIk\nW0IkTwZJHA+S6cu5StsNmFcXaO7SqwoWrPRNb2uYp759iHQiy3Wf20jt5pJzD1qCpNNpuru7aW9v\np729nc7OTuK5ut0mkwmPx8PFF1+ctwpbrdb82O5fP0vgPEo5+Q/tp6y2HqvTNevvQzD7yLJENttH\nf/9zDA8f4/DhZ9jS1I3VGmHfW5p7rCQZcTo3UFN9D273hbhcWzEY5ub3a7H6SKcHyWQi6PWOObnG\nTFBVlXR6iESim2Sqj1Syj2Sqf3Sb6ieVGiCdDuSt6hMhyxYMBjdGQyEGgxuTuQK93qG9dPb8vk5v\nR6/LtetH2yeKNRMIBAKBYLkgxDGzJ44BrFvLiLzYQXivH/PawhVZgmchGCmxlDypCeJUZ0TLbaKX\nMdU6sW0ry7lKWxfcOnvszVP86gdHsbqM3PSlLRR5ZhbvvpiIxWJ0dHTkxXB3d3c+I3FxcTFr1qzJ\nxwqfq5SSrJ96neNkLEp381G23/yRWXkfgtlBVVVS6UHiMT+xmJ9Y3K/tx/1s33EcnS7Dofe0vrLO\nht2+lirPduz2Rmz2RqwWH7I8/WSJ50O+nFPMj9N59qoDc4mipIjF/ERjJ0jEO4gnukjkXvF4J4oS\nP2OMwVCIyViC0VSK1VqLISd6DYaC3Ms9utUXoNMJcSsQCAQCwdkQ4hgtIddsIekknFd7GfrJMeKH\nB7BuWp4WwYVGzSqkOoe1mOGTQZJtYS1uWJYwVjtwXF6Nqd6NqcaJZFgctWxVVeWtJ/289UQrlQ1u\nrv3tDVjsS7e80Eg5pREh3N7eTn9/P6DFlFZWVrJjxw5qamqoqakZZxWeCjqdbsriuP3wQVRFwbdp\n+ccbL0bS6YAmfnPCNxZrJR73E4u1kc0O5/tJkh6LpRqLxUd/v46iog1s2vRh/vsHL1BSUs2Hr/30\ngtWetlp8gJaxeq7FsaqqxON+IsNHiUaP51+xWGsua7GGXu/CYq7Caq2lsHAPZrMHi9mD0VSmCWJj\nMbK8dD9DBAKBQCBYbAhxzOxajgEsm0rQv9BBeG8blvXFSzo2cLEwkkQreTInhltDqCkFJDBU2LDv\nrtTEsM+5KMvaKIrKyz9p5vBLXazZWc5ln1iDTr84RPtUURSFvr4+2tra8mI4EokAmot0dXU1Gzdu\npKamBo/HM6PyaKDVOVYyU0vo4z+4H6PFQsVqUaN2rkinQ8TjbWdYgGMxP5lMaExPGYu5CovVS0XF\nVqwWHxarD6vFh9lchSxr/z+f/eXXcTnX8eorPSSTEjfeeOOCCWMYU84p3jYn8yeSpwgMvcZQ4FWG\nhl4jlerLnZGwmKux2RsoLr4Su201NtsqLJaaReneLRAIBALBcmbxqYh5RpKkWRfHkpyzHv/wCLF3\n+7BdUDar868EVFUl0x8fFcMtIZSYZlHRl1iwXlCGud6NsdaFzjY/bpfTJZtW2Pu99zm5v5+ma2rY\ndWv9grt2T4VsNktPTw9tbW15QTziZeFwOPB6vXmrcGlp6awLG51en3fJngxVVfEfPED1+s3o9Cv+\nI23aaDGtAU0Ax9uIx9py+5oFOJMJjuktYTZVYLH6KCu7fowArsViqZqSNVOn03HixAmCwSCXX345\nxcXFc/fmpoBOZ8ZsqiQ+SxmrFSXF4NDLDA29ytDQq8RiJwDNDbqgYBeFBRfhcKzHZls1oyzbAoFA\nIBAIZo8Vfyc5F+IYwLK+CEOljfDz7Vi3lCDplpaVcL5RVZXsUIJka4jkyRCJk0GUcAoAnduEeW0R\n5lVuTPUudM6lEzOXimd46tvv0XUswO6PrGLLVTULvaSzMpI8q62tDb/fT0dHB+m0ZrktKipi3bp1\neUHsds99NnadXo8yBbfqQE834f5eLrzxtjldz3JgbAzwqBW4LVebsI1MJjKmt4TZ7MFq8VJWdh0W\nixerxYvF4sViqUGnM5/1OlNBlmWCwSClpaXs3r17Zm9slrBYfcTirTOaQ1VVBgZf4PjxvyYe9yPL\nZtzuC6ms/AiFBbux29fkaswKBAKBQCBYbKx4cSzL8qzGHI8wYj0e/P4HRN/pxb69YtavsZRRFZX0\nqSip1pCWSMsfRoloYli2GzDVuzHX58RwoXlJWFpPJxZO8fi/vMtQV5SrfmMtjTsX199AKpWio6Mj\nbxnu7OzMW2pLS0vZsmULXq8Xr9eLwzH/7p1TTcjlP7gfYEXUN54KqqqSTPWOsfxqVuAREZzNRvN9\nJUmnxbFavLicTVisYwVw1ZxmJh7xNLjpppvQLxKLv9Xqo7f3yWmPHx4+xvHjX2Mo8CpWaz2bNn6L\nwsJLZy0J1olYgt8/0s4313nxWZbOQ0KBQCAQCJYKi+OOZAGZK8sxgHlNIcZqB5Hn27E1lS2axFAL\ngZpWSHVESLaFSLaGSbWFUZOaENO5TZjrXRh9Lky1TvSlC59ReqaE+uP84v53iYWSXPf5TXg3LHxZ\nr2QySXt7O36/n7a2Nrq7u1EUBUmSqKioYPv27XnL8Pkmz5oLdHoD2SnEHLcd2o+7rAJ3+eJ6+DCX\nqKpCMnmK2IgFeMT6mxPBY+vVjibB8uJ2X6iJX6s3FwPsmbds0KdTX1+Pw+GgqqpqQa4/EVZLLZlM\niHQ6gMFQMOVxqdQgLa3/TFfXj9HrHaxu+N94PHfP6s82q6r8wZEO3gnHeKo/xOdrSmdtboFAIBAI\nBBorXhzLsjxn4liSJJzXeBn47mGi+3qw7/bMyXUWI0o8Q7ItnLcMpzojWjZpQF9mxbqlBFOtC6PP\nid49M/fMxcZAZ4TH7z9INqtw831NlNctTN3dZDJJR0cHfr8fv99PV1cXqqoiyzIej4eLLroIr9dL\ndXU1ZvPi+x3o9HpURUFRssiybsI+mXSa9vcPsf7Sq+Z5dfNDNhvPZYE+STTWQizWQizaQjTWMq6s\njyQZsVhqsFq9FBTuzsUAa1Zgk6kinwRrMXHDDTcs9BLOYLScUysu17nFsaKk6Oz8Aa3+fyGbjVFV\n9Qnqar9wXsJ6qnyva4C3wlGMksQbwWEhjgUCgUAgmAMW3x3TPDOXlmMA0yotaVT4xQ6sF5YjGye+\nyV/qZEJJUv6cVdgfIt0b0+oMyxLGKjv23R5MPqeWTdq6uBNozYSu5gBP/dshjBY9N//BBRRW2Obt\n2iNu0n6/n9bW1rxleEQM79mzB5/PR3V1NUbj4i//IudcbZVM9qz/b7qPHSGTTC5pl2pVVUml+jTx\nG20hGjupieBYC4lE15ieWgywzVqHu2A7VmsdVosPq9WHyVSGJC3Pz5b5xJIv5+TH5Tr339TBg/cy\nFHiVosJLaGj4c2y2VXOyrvZ4kr9u6eHyQgcVJgNP9ofIqiq6Je5hIxAIBALBYmPFi+O5tByDJr5d\n13jp//dDRN/owXHJ4nEhnC5qViUzENMswv4wSX+IbED7GUpGHUavA+fGEow+J8Zqx7J9IHA6Jw/0\nsfe7H+AsNnPjF7bgKJxba2w6nR4nhru6uvJu0iOW4RExbDItvfjEkczT2UwG/VnEvP/QfmSdjpr1\nc1uXdjZQlCSxWBuxWE4AR1ty+y3jagHrdFas1lpcrguorLhDE8G2eqwW34yTYAkmx2KpQpJ0xOL+\nc/aNx7sYCryKz/d71Nf9wZytSVVVvnSsEwn4emM1bwSH+VHPEEejCdbbRZZrgUAgEAhmkxUvjtNJ\nZU4Sco3FVOvC1OAm8mIHth3li7IO79lQEhnSPVHSPVFS3cOkT0VJn4pBRgFyybN8Tow5y7Chwr4i\n6zq//3IXv/7RMUp9Tm743c2Y7bNvHc9ms3R2dtLS0pIXw9lsFkmSqKysZNeuXfh8PmpqapakGD4d\nWTcijs8ed+w/uJ/KxrUYLQsfIz2CoiSJxlqJDjczHG0mOnyMaOwE8XgnoOT7mUzl2Kz1VFTcitVa\nh81aj9Vah8lUvuRj7pcqsmzEbPYQi507Y/XAwHMAVJTfMqdreujUEC8GIvx1g4dqsxHJbQfg9eCw\nEMcCgUAgEMwyS0elzRHZtEIymURV1Tm9IXVd46Pvm+8y/Go3zisWXzkfVVXJBpKku4dJ5cRw+lSU\n7NDogwPZqsdQace+swJDhQ1jjQN9sWVF38irqso7T/t58xet1Kwv4trf2oDBNDuWclVV6evro6Wl\nhZaWFvx+f760UmVlJTt27MiL4cUYMzxTdHm36okzVkeDAfr9Lez52Kfmc1l5VDVLPN6eE8A5IRw9\nTizWiqpqa5YkPVZrHQ7HBsrLbs5ZgeuwWmrR6+fP5V4wdawWH/FY2zn7DQw8j9Vaj9VaO2dr6Uum\n+cqJbna4bPyGR6sDXWU2UmU28HpwmHurSubs2gKBQCAQrERWvDjWG7QfwVvPnOSCa+rQzVE9YmO1\nA/PaQiIvdWLfWbGgcbdqOkv6VEyzBvcM5y3DI9mjkUBfbMFYZcdwYRmGCjvGChuy07iihfDpqIrK\nyw8f570XO2ncUc7ln1oz47+fYDCYF8Otra1Eo1rZnaKiIrZs2UJdXR0+nw+LZflbjMa6VU9E26ED\nwNyXcBopjaQJ4GNjhPCJcVmhLeYabPbVlBRfjc2+GrttNVZrLbK8+OO7BaNYrLUEQ+9M+sA0nQ4T\nCL5JTfU9c7qWPz3eSUJR+Ps11chj1rLLbeeFwcicP9QVCAQCgWApoKpa0t/Z+E5c8eLYknN/fePx\n45x4c4DddzTgXT83ZXecV3vpu/8AkVe6cF3jm5NrjEVVVZRImnTPGGtwzzCZ/riWLAstRthQYcPa\nVIqhwqa9ym0rJk54umTTCs99/wNOvN3Hlququei2VUjy+f+HjMVi+P3+vCAeGhoCwGazUVdXl3+5\nXAuT8XohyVuOsxOLY//B/VicLkp9dbN2zXQ6yPBwM9GoJoCHh48RjTaTyYTzfYzGUuy21Xg8d2O3\nNWK3r8ZqrReW4GWC1eojm42SSvVjMk2cEXpw6NeoaobikrnLkv5EX5An+0P8eV0Fq6zjPUN2uew8\ncirA8ViS1bbl5zUiEAgEgpXFfUfaeW4wTIlRz6+2r5mwz581d/KroTBWncw/r6lhg0MLqftJzyD3\nt/UhAb/vK+PO8sIZrWXFi2NZ1ix9l9xdz+FfDvLEvxzEu7GIPR9pwF02u3GMxko7lo3FDL/Sjcnn\nAklLbkVGQc0qqBkVsmp+X80q447JKqhZFTUztXYlmkaJjsZr6twmDBU2LBuK89ZgXaF5WqJuJZNK\nZHj62+/ReTTArtvq2XqNd8pj0+k07e3teTHc09MDgNFoxOfzsX37dmprayktLV3xFiFZrz24mshy\nrCoK/kMH8G1qQpLP31qvqllisVYikQ8YHj5CZPgI0eFmkqnefB+93oHN1khZ2Q3YbJol2G5fPSdl\negSLB6tFc5OOxfxnFccD/c9hMBTicm6ZkzUE0xn+9HgnG+0WPld95hp2jYk7FuJYIBAIBEuduyoK\n+WxVMV840j7h+ecGw7TFU7y+cx37Q1G+1NzJUxesZiid4R/9vTx/YSMKcPVbx7i22IVTP30jnxDH\nuRvrIq+Fu76yg0MvdPLWU638+K/eZNMVVWy7vhaTZfZ+TM6rvcQPDzDw4OGpD5IAnYykk5D0EpJO\nBn3uWCfl9rVjDDKy3qCdMzswlGvWYGOFbVmXUJovYuEUT37zIP0dw1z56bWs2VUxaX9FUeju7qa1\ntZWWlhba29vJZrPIskxVVRWXXXYZdXV1eDwedDphrR/LZG7Vff4W4uHQlFyqs9kYw8NHiUSOEBnW\nxPDw8LG8S7QkGbHbGigovAi7vRG7bTU222qRGGuFMlLrOB73U1Cw/YzzipJicOjXlJRcOyflszKK\nypdPdDGUzvCjTXUYJnh46bMYKTPqeSM4zKdzscgCgUAgECxVdrjttMfPXj3o2YEQd1ZoxomtLhuR\nTJb+VJpXAsNcVujAnhPDlxY6eGEwzC1l0zdkrHhxPHLzm0wm0ellmq6poXFnOW8+dpJ3n+/g2Jun\n2HFTHWt3VyLPgoXVUGql9AtbUaLpUaGrk5ByYndU9I5pF5bdRUF4IM4v7n+XaCDJdZ/biG/TmTel\nqqoyODg4LonWSDb0srIytm/fTl1d3bLJKD2XTJaQy39wPwDeTU3j2lOpISKRw0QiH+SFsJZ5WIsj\n0OtdOOxr8XjuxmFfi8OxHqu1DlkWD44EGmZzJZJkPGvG6mDwLTKZCCXFV87aNRNZhZcCEZ4eCPHL\ngRBD6SxfqCllo2Ni7yVJktjltvN6MCrijgXLBlVVUQFFBQU1t82FiAFKfqt9oqu5z3U1f6y1jT8e\nnZf88Zi+ZxnPBOPVCcZzlmuN7zvm+lNY68i1J/wZjft5jbSpE5+foO/Y9vF91Qn7nnveM689tTVM\nPo4J1ju+78TrPeu1J1nvGdee4nonu/Z0mOn4WZtjoj+AWVzDBruFPQV2DOfp9deTTOMxjeZwqTAZ\n6EmmOZVMU3la+6nk2aucTAUhjseI4xGsTiOXf3ItGy6t4uWHm3nxh8c4/FIXF9/ZQGXDzF0qjRUi\nNnGp0d8e4YlvHiSbVrjpviYq6kdjgCORSN4y3NLSQjisxae6XC7Wrl1LXV0dtbW12O32hVr+kkTW\nn72Uk//QfsoaPCSyh+jzH9YEcfgwiWR3vo/ZXI3DsZaysptyQngdJlOFEBKCSZEkHRZLzVlrHfcP\nPIcsmygs3DOj60QyWZ4fDPPUQIjnB8NEswoOnczVxS6uK3ZxXcnkeQZ2uu38vC9IWyKFzyIetM03\niqqSVlUyirZNqyoZVSWtqGRU8scpZWz76EtRIaOqZHMiMDumPYs2R1ZVUVSVbK7v6LnRMdmRLWP2\nR9pPa8tfMyc+R9pGhOeZwlQTb+POT7GvJmonbldygvF0ESwQCJY/n64sYqfbxnRMEud6oDFbrHhx\nPOJWPVYcj1BS4+DWP9rKiXf6eO3RE/zs7w+w6oJSdt1Wj7No+WcLFmi0Hhrg2f84jNlm4KY/3oq9\nyMCxY8fyYri/vx8Ai8VCbW1tPolWQUGBEGIzYKxbdSo1QDgngEPBgzibXsZoT/PuQa3WrMXiw+Xa\nSpXzUzgcG3DY12MwOBdy+YIljNXqIxbzn9GuqioDA89TWLgHnW5q3wGxrMLJWILmaILjsSTN0QTN\nsQSt8SRZFYoNem4rK+DDxS72FNgxTvFp+k639pD19eDwshbHWVUlnlWIKwpJRRObydz+hFtVzfXT\n2hKKctqY0f2UopJWlZzIHRW0Y0Xv2OPUGAG80GJOBnSShF4CWZLQSaBDQjeyL43ZR0KWQJ9rG9kf\nmUNCm0MGZBlkZCQJ5Nw47bx2PLZdzo0be35sv3Hzjhs/tl3b5vuPmUOeaI7cd6o08spdf/QYJEbb\nkMacG7OuicYzwXjpLOOZ4vVHbgGmcv3R9Y6+xxHG3kqMa5+obUznifqOZfy8k487+xrOHHe2vuOu\nPW4dE7zns/ad2npPX8dk6z2974Q/1wnWO1mf6TArd4yzMIk0w0kmG62TwHCWe+PJ7pkrTAa6Eiku\ndGnffT3JNBUmAxUmA68Fh/P9upNp9hTMzBglxHHuRmTE9fV0JEmiYVsZvk3FHHi2nQO/bKP10ABN\nV9ew9UPeWatpK1h8qKrKoRc6efmnx7BXZSnfmubnTz9EZ2cnqqqi1+upqalh8+bN1NXVUV5env97\nEkyfTCZCOHyIQOx5fNd00DrwW5zoH8yf18vlRHssVDbdTU3DNTgc69HrHQu4YsFyw2rxMjT0Mqqq\nIEmj/6eHh4+SSHTh8/1uvi2azdKdSNOdTNOVSNGZTNGVSNOdTNEWT9GRSOWfcOslqLWYaLSZubHE\nzWWFDra5bOim8RCt0Wqm0KDj9eAwd1XMTYWF6ZBUFILpLMFMlmA6QyiTZTirEMsqxLJZYlmF6Mix\nMtI+pi2rEFOy+f2EMnP7gEmWMEoSJlnGJI9uDbl2vSRh1knYJRmDpLXrJQlD7tzYY4M8pl2S0MsS\nhpzYnGicYcw4fa59RNSOCFUdWrssjQheTdTKaFvt3FgBPDvlSgQCgWAxMTYE4nQ+VOziu5393FJW\nwNuhKE69jhKjgcsKHfxNSw/BdAYVeGkowpfrK2e0jhUvjiVJQqfTcfToUbZu3YrZPHHmT4NRx/Yb\nall7UQWv/+wkbz/l58hrPVx0Wz0NF5aJL6plhKIonOo5xQs/f4v2Tj+Z8jAD6Sxt+yQqKyvZs2cP\ndXV1VFVVYTCIWNWZkM0mGR7+gHD4kPaKHCIWa8mfNxcYMek2Uum7DKdjAw7Hel78/n/T9epz3HLP\nn6IXP3/BHKA319GnOHhzoIOw5KY/laE/lebkQAvt/CFSz0YGO47Ql9LE31gkoNxkwGMysNVp5aPl\nhay2mVltM1NrMU7ZMnwuJElip8vOG8HorMw3EaqqEspkOZVK05vM0JNM0ZvMMJTJEExnCeW2wUyW\nUE4Mx6cgZvUSWHUyVlmHTSdr+zqZAoMOj9mQO6e12XQ6rDoZsyxhlmWM8pkid3Q7vs0oSRhlaVyN\naIFAIBAsPn7nfT+vBYcJpLNc8Nr7/HFtOWlFRZLgk5XFXFnk5PnBMDvf+ACrLPPPa2sAcBv0/KGv\nnA+/04yExB/Xls8oUzWANNPA66XOtm3b1AceeIDHH3+cwsJC7r77bgoLz10fq/tEkFcePk5/e4Ty\nOhcXf7SBUq9w41yqBAKBcXHDsVgMAKvJyfpNjdTV1eHz+bBYhDv9dFHVLNHoCcLh9whHDhIOH2R4\n+BiqqiXcMhpLcDo343RuwuncTDrk4od/8qfccN+f0LhrNL7zu1/4TQo9Vdz6v76yUG9FsASJZxX6\nU2kGUhn605m84O3LbQdSubZ0mnBmYqdZG3HcUoxqZw3FRj0lRs2ly2My4DEb8ZiNlBsNE2aYngse\n6Ojnyye6eGfXOjxm47kHTICqqgyms5yIJTgRS3I8luBENElLPEFPMj2h5daqkynQ63DpdbgMOgr0\nelwG7Xhk363X4TbocOn12McIYJtOnrUHBAKBQCAQTBVJkt5RVXXbufqteMsxQFNTE263m4ceeogH\nHniAj33sY3i93knHVK5yc8efbOPI6z288VgLj/zN26zZVc7OW+qxuZZv/NdyIRaLjRPDgUAAAJvV\nhj7uxhGu5uJrt7L9msYFXunSJZUaJBR+l1DoAKHQfiKR98hmtYcOOp0dp3MjNTX3amLYsemM0klD\niS4AlDEJuYKnegj29tD04Zvm980IFiWxnOAdEbr9eYE7XvD2pdIMZycWvC69jhKjnmKDnnV2CyVG\nB4VynGD737Gu8nrWVF5FidGAQxngnTcupr72i/h8s5epeiaMxB2/ERzm9vJzP9QdIZjO8PxgmKcH\nQrwWHGYoPWr9NssS9VYTGx1Wri02UG7KvYzattRowKIT4lYgEAgEyxMhjnPU1tbym7/5m/zoRz/i\n+9//PjfeeCNNTU2TjpFkiXW7K1m1tZS3n/Zz8PkOTu7vZ9t1PjZfUY3OIG4gFgupVIqOjo68GO7p\n6QHAaDTi8/nYsWMHbksZr/2ok0xS4drf3EDN+sUTx7fYUZQM0eixnBA+QCi8n3hcK+QuSTrs9rVU\nlN+etwxbrbXjYjknIp+QKzt64+4/dABgSvWNBUuTaCY7oWW3P5UZZ93tT2WInkXwukcEr1HPBoeF\nUqODEoMh31ZiNOQFsXkCoaeq0X22IgAAIABJREFUCi92voZH52W182YAOrteBKC4ZHEIY4B1dgtO\nvczrweg5xXFXIsUzAyGeGQjxenCYjAplRj3XFLlYazfTYDWzymqiymwUbsgCgUAgWLEIcTyGoqIi\n7r33Xh555BEee+wxBgYGuPLKK8+ZZMlo0XPRbatYt7uSVx89wes/O8n7r3Sz+/ZV1G4uFvHIC0A2\nm6Wnpycvhjs6Oshms8iyTHV1NZdffjl1dXVUVlai0+loebefvd99H7PdwM1fbKLII8ouTUYqNUAo\n9C6hsCaGw+FDKEocAKOxGJezCU/lx3A6m3A6N045s+9YJqpz7D+4H2dJGQUVM0u2IJhfsqrKYCrD\nqZRWk7A3tx2x6uYtvqkMcWViwVto0FGcE7hbHFZKciK32KinNCd2Swya+J2p264kyVitXuJjMlYP\n9O/FYvFis66a0dyziU6S2O6y80Zo+Kx9UorCvYf9PDuolZhrsJr4nepSPlzsYovTKoSwQCAQCARj\nEOL4NCwWCx//+Md5+umnefXVVxkYGOC2227DZDq3q7S7zMr1n99ExwdDvPzIcZ7+9ntUrSlgzx0N\nQmzNMVqJlYG8q3Rra2u+PFdZWRnbt2+nrq4Or9eL0WgcN+7d59p59dETlNY4uO7zm4Rb/GmoqkI0\nepxg6B1CwXdOswrrsdvXUll5By5nEy5XE2Zz1aw8EDq9znE2k6Hj/YOs2X2peOC0SFBUlaF0Ni92\ne5PpXPKmEQGcoTeVpi+VJjtBeotCg5ZtstSoZ5vLlhe3JWOsu5qFd/7ieEewWHxEo8cAyGSGGQq8\nQXXVJxfd394ut53nBsP0JdOUms5MUPfVkz08OxjmPm8Zt5cV0GCbOOmkQCAQCASCZSqOJUm6Bbge\ncALfVVX12fMZr9PpuP766ykpKeGZZ57hwQcf5K677sLtdk9pfPW6Qj72Fxdy+KVu9j3ewkNf3ceG\nSzxsv7EOs11k150tIpFI3jLc0tJCJBIBwO12s379eurq6qitrcVms004XskqvPzQcQ6/1EVdUwlX\nfWYdBqMozZXNJgiHDxEKva0J4tB+MhnN6mQwFOF2bcVTeRdOVxNOx4ZpWYWngk6v/V/J5izHPc1H\nScXjwqV6nlBUlb5Uhu5Eis5kmu5Eiq5kiu6kJoRPJTWX5/QESR0LDTrKcjGqa2xmyk0GykwGyox6\nyo3afuk8Jq6aDlZrLQMDz6EoGQaHXkZVUxQXX7XQyzqDXbmaj2+EotxUOv476om+IN/p7OfeqmL+\npK5iIZYnEAgEAsGSYlGJY0mSHgRuAPpUVd0wpv1a4J8BHfAfqqr+7WTzqKr6c+DnkiQVAN8Azksc\n567Jjh07KCoq4pFHHuGBBx7grrvuoqqqakrjZZ3MpsurWH1hGfseb+Hwy900v9XL9hvr2HBJJbJI\naHLeJBIJ/H5/3jrc398PaNb+2tpa6urqqKurm1K28VQ8wy//4zDt7w/RdHUNu26tR1rEN+pzSSo1\nkLcKB0P7iUQOo6qatdZqXUVpybW43dtwuS7AYvHOm+UsH3OcE8f+Q/uRZJmaDZvn5frLGVVVCWey\ndCfTdCZS+Rq9Y497kukzhK9VJ+MxaRmad7nt+WRNI0K4LOfiPFEc71LDavGhqhkSiS4GBp5Dr3fj\nci2+BzMbHVasOpnXg8PjxHFLLMl9R9vZ6rTyv2dY81EgEAgEgpXCohLHwH8C/wr810iDJEk64JvA\n1UAn8JYkSb9QVfUDSZI2An9z2hz3qKral9v/i9zYabNq1So++9nP8uMf/5jvfe973HLLLWzcuHHK\n4812A5fc1cj6Szy88shxXn6omcMvdbHpMg81G4pwFonSQGcjk8nQ2dmZtwx3dXWhqip6vR6v18uW\nLVuoq6ujrKzsnHHhY4kMJXjymwcZ6olx2ccbWX+xZw7fxeJCVVVisda8VTgYfJt43A+ALBtxODZR\nU30PLvcFuF1bMRgKFmytcq5O3UjMsf/gfipXr8FkndgTQDDKiNW3PZ6kLZHKC97ORIquRJruZOqM\n7M16SavPW2Uyss1lw2MyUGk2jpYpMhlw6XWLzq14rrBafQBEYycYGHiR4uLLkOXF9pUJBlniQqeN\nN4KjccfxrMK9h1sxSBLfWe8TpZMEAoFAIJgii+qbXlXVlyRJ8p3WvB04oapqC4AkST8BbgY+UFX1\nPTRL8zgk7e7tb4GnVVXdP9N1lZaWcu+99/LQQw/x6KOPMjAwwGWXXXZeN4lFHjs3/f4WWg8O8MbP\nT/LrHzcDUFhpw7u+CO/GIsrrXeiWgcVluiiKQm9vb94y3NbWRjqdRpIkKisr2bNnD3V1dVRXV6PX\nT+9Pt68tzJPfPEQmleXG39tM9bqplz9ZiqhqluHhZoLBfQSDbxEI7iOdHgTAYCjA5boAT+VHcbkv\nwOnYgCwvnnhrWdYhyTLZTIZYOERv60l23/HxhV7WoiGSydKeSGkCOJ6iPZHKbZN0JFJn1KctNuip\nNBtYZTVxSaEdj8lIpVkTw5Vmzc1Zt0KE71QYEcc9PY+SyQQpKb56YRc0CTvdNv5v6ymG0hkKDXr+\n7HgnH0QT/HBTHVXTrH8sEAgEAsFKZFGJ47PgATrGHHcCO84x5v8DrgJckiStUlX122NPSpL0W8AX\nAXdJScmUFmGz2fjUpz7FE088wa9//WsGBga45ZZbMBimHkMsSRJ1W0qo3VxMsDdG2+FB2g4PcvCF\nDg7sbcdo1lG9rhDvhmJq1hcu+8RQqqrS399Pa2srfr8fv99PPK5lPC4uLqapqSmfRMtimbmFveVA\nP3sffB+Lw8hN922hqHL5JUlTlDSRyGGCwX0Egm8RCr2Tjxc2mz0UFV2M270dt2sbVmvdorcC6nR6\nspk0bYcOgKquqHjjtKLSnUyNEb7JcQJ4bG1aAIdOxmsxsdpm5soiJ16LiRqzEa/FiMdkFLVpzxOD\noQidzk5//7NIkpHCwj0LvaSzssutfZbtC0YJZjL8uGeI+7xlXFnkXOCVCQQCgUCwtJhXcSxJ0nNA\n+QSn/lxV1cdm6zqqqt4P3D/J+e8A3wHYtm3bBDlUJ0av13PzzTdTUlLC3r17CQQC3HXXXTgcjvNa\nnyRJFJTbKCi3seWqGlKJDJ1HArQdHqDt8CAn92uxtCU1Drwbi/BuKKLU60Re4jGxqqoyODiYjxv2\n+/1Eo1EAXC4XjY2N+Hw+6urqcDpn76ZOVVXe3dvBaz87QanXyfWf34TVuTysKdlsnFD4XYLBtwkG\n9xEKHciXVLJa6yktvY4C93bc7gsxm5de3KGs16NkMvgP7sfscFJaV7/QS5pV0opKeyLJydjoy59z\nhe5OpsZleNZLUGU24jWbuL7EnRO+owLYvYJcnucDSZKwWn1EIocpLNyFXr94H6ZtcVgxyRL/1T3A\n68Fh9rjtfLF2oq9agUAgEAgEkzGv4lhV1emk+uwCqsccV+XaFgRJkti9ezdFRUU8+uijfOc73+Hu\nu++momL6mUCNZj11TSXUNZVoJYk6h2k7PEj74UHeecrP20/6MdsNmvv1hiKq1xViti2NrNeBQCAv\nhFtbW/MZpR0ORz6btM/no6CgYE5u7LNZhZd/0sz7L3dT31TClUs8I3UmM0wo9A6B4D6CwX2Ew+/l\nkmdJuZJKd1Lg3o7LvQ2TsXihlztjdHo92UyGtkMH8G7cgiwvvd+dqqr0pjKcjCU0ARxP0pITwm2J\n5DgBXGjQ4bOY2Oa04rUUUGMx5kVwhdGAfok/IFtqWK21RCKHF2WW6rGYdTJbnVZeGIpQZtTzrfVe\n4SIvEAgEAsE0WApu1W8BDZIk1aKJ4o8Bdy/skmDNmjV89rOf5Uc/+hEPPvggt912G2vXrp3xvJIk\nUVLtoKTawbYP+0gMp2k/Mkjbe5oL9rE3TyFJUF7vwruhCO+GYoo8tkVjMQqFQnkh3NraSigUAjS3\ndJ/PlxfDRUVFc77mwe5hXnn4OJ1HA2z9UA07b156GanzYjjwJoHgm0Qi76GqWSRJj9OxkZrqe3C7\nL8TlugCDYfm5UOr0enpbjhMNBvBtalro5UxKJJOlJT5iAdaEcEtODEfHJL8yyxK1FhNr7WZuLHVT\nZzFRbzVRZzVRaFgKH8krB5t1FSBRXHzFQi/lnFxS4GBfKMq31/soMS6Nh6cCgUAgECw2JHWCGpUL\nhSRJPwYuA4qBXuArqqp+V5Kk64B/Qivl9KCqql+brWtu27ZNffvtt6c9PhKJ8JOf/ISuri6uvPJK\n9uzZM2eiT1FU+vzhfKxyf7tmhbW5TTmhXETVmgKM5vm7wY5EIuPcpIeGhgCtvJLP58sL4pKSknkT\n8AOdEd5+ys/JA/3ojTouvqOBdXuWhktxJhPNWYbfJBB4k0jk0KgYdm6mwL2DgoKduFxN6HTWhV7u\nnPPA732WcH8vAL/1rf/EUbiw1vCsqtKRSNEcTYy6Qse1/b5UJt9PQnOBXmU1UWfRhO8qq5m6/9fe\nnQfHfd73Hf/89r5/uzgIgAAJUhLBQ7xJEaSUJqqdyFcdO/Up12mSeuRk6ritx7Wnsd3G46aeWk6T\n1jNJWnucZjqdsIoa144rZdyMXSeVzVukCFIESVEELxEiAex9H0//2AVMUpQIkgB2gX2/ZjALLMDd\n7wIEZj/7PM/3G/Cq3+uWo0VezMJbq1TSymbPtuQIp1sVqjWNl8pa5V/avSoAALgXlmUdMcbsvOPX\ntVI4bob7DceSVC6X9b3vfU8nTpzQli1b9N73vveeuynfjWyyOLP9+uKpKZULVTlclvrXRDW4sUuD\nGzsV7Zm7AGWM0dTUlC5fvqxLly5pbGxMExMTkiSv16vBwUGtXr1aq1ev1rJly+5qvNJcuHYhpcPP\nj+n8SxNy+5za/PiAtvziCvlDrXu+mDD81v70X/ym4levqGvFoH7t9+9rKttdqdSMLhSKOpMt6Ey2\nqNO5gs5kC3olV7ipC3SH26kH/b5G+K2H4Af8Xq32e5fErF8AAIClYLbhmD18c8DtdusDH/iAurq6\n9OMf/1hTU1P66Ec/qmBwfuexBm2vNjy2XBseW65qpaar55L1VeWRCb3w7Fm98OxZ2d3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| |
"text/plain": "<matplotlib.figure.Figure at 0x7fe2d77e9b10>" | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-18T13:07:10.180107Z", | |
"start_time": "2017-03-18T14:07:08.940240+01:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": 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.sqrt(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": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": "/home/ccc/.virtualenvs/astrop2/lib/python2.7/site-packages/ipykernel/__main__.py:11: RuntimeWarning: invalid value encountered in double_scalars\n" | |
}, | |
{ | |
"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": {}, | |
"output_type": "display_data" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-18T13:21:04.743386Z", | |
"start_time": "2017-03-18T14:21:04.206889+01:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": 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": [ | |
{ | |
"data": { | |
"text/plain": "(0, 20)" | |
}, | |
"execution_count": 67, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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NAQAYgJrGNv38jZ16ctV+Jfq8un3pNC07c6wSY/kRCwBApOCnOgAAp6DdH9Bj7+/V/W8W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\nKu2O9UZJt5nZLZJKJVWZ2f9y99+f5HkAAAAAAEwo26Haj0o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| |
"text/plain": "<matplotlib.figure.Figure at 0x7fe2db727290>" | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-18T13:07:45.067236Z", | |
"start_time": "2017-03-18T14:07:44.531602+01:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": 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": [ | |
{ | |
"data": { | |
"text/plain": "[<matplotlib.lines.Line2D at 0x7fe2dadc32d0>]" | |
}, | |
"execution_count": 65, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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j+YU/ezQff3hP7r3zuvyrv3mH96cCAECTEqt0hKHhsfy9//JwPrflUP7Bd9ycf/jmm12W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\nAAAAGsRqExgfr7KzX6wCAAC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| |
"text/plain": "<matplotlib.figure.Figure at 0x7fe2dadbd2d0>" | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"ExecuteTime": { | |
"end_time": "2017-03-18T12:50:09.253740Z", | |
"start_time": "2017-03-18T13:50:09.236048+01:00" | |
}, | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "y.min(), y.max()", | |
"execution_count": 61, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": "(-0.3551977554212376, 0.98262947434150383)" | |
}, | |
"execution_count": 61, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
] | |
}, | |
{ | |
"metadata": { | |
"collapsed": true, | |
"deletable": true, | |
"editable": true, | |
"trusted": true | |
}, | |
"cell_type": "code", | |
"source": "", | |
"execution_count": null, | |
"outputs": [] | |
} | |
], | |
"metadata": { | |
"_draft": { | |
"nbviewer_url": "https://gist.github.com/4b809ef3b4f54b280ad05e8f22e9baa6" | |
}, | |
"gist": { | |
"id": "4b809ef3b4f54b280ad05e8f22e9baa6", | |
"data": { | |
"description": "Map of matter around a filament's center", | |
"public": false | |
} | |
}, | |
"kernelspec": { | |
"name": "python3", | |
"display_name": "Python 3", | |
"language": "python" | |
}, | |
"language_info": { | |
"name": "python", | |
"version": "3.6.0", | |
"mimetype": "text/x-python", | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"pygments_lexer": "ipython3", | |
"nbconvert_exporter": "python", | |
"file_extension": ".py" | |
}, | |
"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, | |
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0.0438668 14602.9 1.38888 | |
0.0443076 14460.7 1.40244 | |
0.0447529 14321.9 1.41632 | |
0.0452027 14186.4 1.43051 | |
0.045657 14054.1 1.44503 | |
0.0461159 13925 1.4599 | |
0.0465793 13799.1 1.4751 | |
0.0470475 13676.3 1.49067 | |
0.0475203 13556.6 1.50661 | |
0.0479979 13440 1.52293 | |
0.0484803 13326.3 1.53964 | |
0.0489675 13215.7 1.55675 | |
0.0494597 13108.5 1.57433 | |
0.0499567 13003.6 1.59227 | |
0.0504588 12901.4 1.61063 | |
0.0509659 12801.9 1.62941 | |
0.0514782 12705.1 1.64865 | |
0.0519955 12610.7 1.6683 | |
0.0525181 12518.6 1.68839 | |
0.0530459 12428.8 1.70892 | |
0.053579 12341.1 1.72987 | |
0.0541175 12255.2 1.75123 | |
0.0546614 12171.1 1.77299 | |
0.0552107 12088.4 1.79512 | |
0.0557656 12007 1.81761 | |
0.056326 11926.6 1.84042 | |
0.0568921 11847 1.86353 | |
0.0574639 11767.9 1.88689 | |
0.0580415 11688.9 1.91045 | |
0.0586247 11609.8 1.93415 | |
0.0592139 11530.3 1.95796 | |
0.0598091 11450.1 1.98182 | |
0.0604102 11368.9 2.00565 | |
0.0610173 11286.5 2.0294 | |
0.0616305 11202.5 2.05299 | |
0.0622499 11116.8 2.07638 | |
0.0628755 11029 2.09948 | |
0.0635075 10939.1 2.12225 | |
0.0641457 10846.7 2.14459 | |
0.0647904 10751.9 2.16647 | |
0.0654415 10654.4 2.18778 | |
0.0660993 10554.2 2.20853 | |
0.0667635 10451.1 2.22859 | |
0.0674345 10345.1 2.24793 | |
0.0681123 10236.1 2.2665 | |
0.0687968 10124.1 2.28422 | |
0.0694882 10009 2.30102 | |
0.0701866 9890.83 2.31687 | |
0.070892 9769.51 2.33169 | |
0.0716045 9648.26 2.34619 | |
0.0723241 9522.49 2.35922 | |
0.0730509 9393.56 2.37105 | |
0.0737851 9261.44 2.3816 | |
0.0745267 9126.19 2.39083 | |
0.0752757 8987.91 2.39869 | |
0.0760322 8846.76 2.40515 | |
0.0767964 8702.95 2.41021 | |
0.0775682 8556.72 2.41386 | |
0.0783477 8408.4 2.41612 | |
0.0791351 8258.3 2.41705 | |
0.0799305 8106.9 2.41671 | |
0.0807338 7954.59 2.41517 | |
0.0815452 7801.88 2.41253 | |
0.0823647 7649.32 2.40894 | |
0.0831925 7497.3 2.40449 | |
0.0840286 7346.41 2.39934 | |
0.0848731 7197.06 2.39362 | |
0.0857261 7049.77 2.38748 | |
0.0865876 6904.93 2.38108 | |
0.0874579 6762.99 2.37457 | |
0.0883369 6624.28 2.3681 | |
0.0892246 6489.05 2.36178 | |
0.0901213 6357.75 2.3558 | |
0.0910271 6230.47 2.35026 | |
0.091942 6107.53 2.34532 | |
0.0928659 5989.1 2.34109 | |
0.0937993 5875.31 2.33771 | |
0.094742 5766.3 2.33528 | |
0.0956942 5662.17 2.33391 | |
0.0966559 5563 2.33373 | |
0.0976273 5468.82 2.33482 | |
0.0986085 5379.58 2.33725 | |
0.0995995 5295.38 2.34114 | |
0.1006 5216.07 2.3465 | |
0.101612 5141.72 2.35353 | |
0.102633 5072.13 2.36213 | |
0.103664 5007.18 2.37237 | |
0.104706 4946.67 2.38428 | |
0.105758 4890.45 2.39784 | |
0.106821 4838.18 2.413 | |
0.107895 4789.53 2.42967 | |
0.108979 4744.04 2.44765 | |
0.110074 4701.3 2.46683 | |
0.111181 4660.79 2.48703 | |
0.112298 4621.89 2.50786 | |
0.113427 4584.12 2.52917 | |
0.114567 4546.81 2.55055 | |
0.115718 4509.44 2.5717 | |
0.116881 4471.45 2.59233 | |
0.118056 4432.42 2.61214 | |
0.119242 4391.86 2.63075 | |
0.120441 4349.42 2.64796 | |
0.121651 4304.75 2.66341 | |
0.122874 4257.61 2.67691 | |
0.124109 4207.68 2.68814 | |
0.125356 4154.76 2.69686 | |
0.126616 4098.74 2.70291 | |
0.127888 4039.45 2.70601 | |
0.129174 3977.01 2.70619 | |
0.130472 3911.26 2.70314 | |
0.131783 3842.41 2.69691 | |
0.133108 3770.72 2.68756 | |
0.134445 3696.4 2.67509 | |
0.135796 3619.84 2.65969 | |
0.137161 3541.48 2.64161 | |
0.13854 3461.82 2.62111 | |
0.139932 3381.3 2.59845 | |
0.141338 3300.57 2.57407 | |
0.142759 3220.23 2.54845 | |
0.144194 3140.88 2.52201 | |
0.145643 3063.19 2.4953 | |
0.147106 2987.65 2.46877 | |
0.148585 2914.87 2.44301 | |
0.150078 2845.31 2.41842 | |
0.151587 2779.31 2.39545 | |
0.15311 2717.17 2.37439 | |
0.154649 2659.05 2.35556 | |
0.156203 2605.09 2.33917 | |
0.157773 2555.32 2.3254 | |
0.159359 2509.69 2.31434 | |
0.16096 2468.02 2.30591 | |
0.162578 2430.14 2.30013 | |
0.164212 2395.74 2.29679 | |
0.165862 2364.42 2.29562 | |
0.167529 2335.72 2.29626 | |
0.169213 2309.19 2.29836 | |
0.170913 2284.29 2.30141 | |
0.172631 2260.55 2.30499 | |
0.174366 2237.43 2.30856 | |
0.176119 2214.39 2.31158 | |
0.177889 2190.91 2.31346 | |
0.179676 2166.47 2.31362 | |
0.181482 2140.6 2.31152 | |
0.183306 2112.89 2.30663 | |
0.185148 2083.04 2.29854 | |
0.187009 2050.89 2.28698 | |
0.188888 2016.37 2.27177 | |
0.190787 1979.56 2.25295 | |
0.192704 1940.69 2.23063 | |
0.194641 1900.03 2.20509 | |
0.196597 1857.95 2.1767 | |
0.198573 1814.88 2.14589 | |
0.200569 1771.27 2.1132 | |
0.202585 1727.61 2.07917 | |
0.20462 1684.35 2.04435 | |
0.206677 1641.98 2.00937 | |
0.208754 1600.96 1.97483 | |
0.210852 1561.76 1.94136 | |
0.212971 1524.8 1.90953 | |
0.215112 1490.37 1.87979 | |
0.217274 1458.63 1.85241 | |
0.219457 1429.59 1.82747 | |
0.221663 1403.12 1.8049 | |
0.22389 1378.99 1.78445 | |
0.226141 1356.95 1.76585 | |
0.228413 1336.65 1.74869 | |
0.230709 1317.78 1.73259 | |
0.233028 1299.9 1.71704 | |
0.23537 1282.6 1.70146 | |
0.237735 1265.43 1.68529 | |
0.240125 1247.99 1.66799 | |
0.242538 1229.91 1.64906 | |
0.244975 1210.9 1.62812 | |
0.247437 1190.79 1.60492 | |
0.249924 1169.48 1.57934 | |
0.252436 1147.01 1.55143 | |
0.254973 1123.48 1.52133 | |
0.257535 1099.1 1.48935 | |
0.260124 1074.14 1.45589 | |
0.262738 1048.92 1.42139 | |
0.265378 1023.8 1.38637 | |
0.268046 999.146 1.35138 | |
0.27074 975.281 1.31686 | |
0.27346 952.466 1.2832 | |
0.276209 930.909 1.25071 | |
0.278985 910.762 1.2196 | |
0.281789 892.1 1.18999 | |
0.284621 874.886 1.16184 | |
0.287481 858.954 1.13493 | |
0.29037 844.06 1.10893 | |
0.293289 829.951 1.08353 | |
0.296236 816.347 1.05837 | |
0.299213 803 1.03314 | |
0.302221 789.671 1.00754 | |
0.305258 776.144 0.981345 | |
0.308326 762.249 0.954369 | |
0.311424 747.881 0.926531 | |
0.314554 732.998 0.897829 | |
0.317716 717.669 0.868403 | |
0.320909 702.047 0.838501 | |
0.324134 686.318 0.808396 | |
0.327392 670.663 0.778342 | |
0.330682 655.268 0.748595 | |
0.334006 640.336 0.719408 | |
0.337362 626.024 0.690979 | |
0.340753 612.437 0.663417 | |
0.344177 599.606 0.636761 | |
0.347637 587.523 0.610984 | |
0.35113 576.103 0.586002 | |
0.354659 565.211 0.561662 | |
0.358224 554.69 0.537816 | |
0.361824 544.391 0.514336 | |
0.36546 534.176 0.491113 | |
0.369133 523.933 0.468075 | |
0.372843 513.589 0.445197 | |
0.37659 503.121 0.422509 | |
0.380375 492.539 0.40006 | |
0.384198 481.889 0.377936 | |
0.388059 471.279 0.356259 | |
0.391959 460.813 0.335135 | |
0.395898 450.586 0.314653 | |
0.399877 440.678 0.294875 | |
0.403896 431.151 0.275847 | |
0.407955 422.029 0.25758 | |
0.412055 413.296 0.240056 | |
0.416196 404.897 0.223238 | |
0.420379 396.756 0.20708 | |
0.424604 388.791 0.191545 | |
0.428872 380.952 0.176616 | |
0.433181 373.175 0.16228 | |
0.437535 365.422 0.148527 | |
0.441933 357.686 0.135373 | |
0.446374 349.984 0.122842 | |
0.45086 342.356 0.110952 | |
0.455391 334.848 0.0997245 | |
0.459968 327.507 0.0891704 | |
0.464591 320.369 0.0792932 | |
0.46926 313.454 0.0700885 | |
0.473976 306.764 0.0615424 | |
0.47874 300.279 0.0536367 | |
0.483551 293.975 0.0463553 | |
0.488411 287.804 0.0396757 | |
0.493319 281.734 0.0335836 | |
0.498277 275.751 0.0280633 | |
0.503285 269.837 0.0231005 | |
0.508344 263.996 0.018681 | |
0.513452 258.238 0.0147907 | |
0.518613 252.577 0.0114094 | |
0.523825 247.04 0.00851912 | |
0.529089 241.634 0.00609693 | |
0.534407 236.374 0.00411867 | |
0.539778 231.252 0.00255989 | |
0.545202 226.255 0.00139499 | |
0.550682 221.371 0.000597554 | |
0.556216 216.585 0.000141594 | |
0.561806 211.885 7.67952e-08 | |
0.567453 207.263 0.000145713 | |
0.573156 202.719 0.000550623 | |
0.578916 198.256 0.00118661 | |
0.584734 193.883 0.00202552 | |
0.590611 189.605 0.00303941 | |
0.596547 185.424 0.00420049 | |
0.602542 181.343 0.00548145 | |
0.608597 177.358 0.00685562 | |
0.614714 173.459 0.00829708 | |
0.620892 169.641 0.0097798 | |
0.627132 165.897 0.011279 | |
0.633435 162.228 0.0127715 | |
0.639801 158.629 0.0142345 | |
0.646231 155.103 0.0156475 | |
0.652726 151.649 0.0169913 | |
0.659286 148.276 0.0182492 | |
0.665912 144.975 0.0194049 | |
0.672604 141.747 0.020444 | |
0.679364 138.589 0.0213549 | |
0.686192 135.499 0.022127 | |
0.693088 132.472 0.0227512 | |
0.700054 129.508 0.0232218 | |
0.70709 126.607 0.023535 | |
0.714195 123.766 0.0236881 | |
0.721373 120.986 0.0236821 | |
0.728624 118.267 0.0235192 | |
0.735947 115.608 0.0232035 | |
0.743342 113.006 0.0227404 | |
0.750813 110.461 0.0221381 | |
0.758359 107.967 0.0214052 | |
0.765981 105.53 0.0205536 | |
0.773679 103.144 0.0195951 | |
0.781455 100.807 0.0185429 | |
0.789308 98.5256 0.0174128 | |
0.797241 96.2914 0.0162188 | |
0.805254 94.1044 0.0149768 | |
0.813346 91.9675 0.013704 | |
0.821521 89.8757 0.0124155 | |
0.829777 87.8301 0.0111282 | |
0.838117 85.8289 0.00985742 | |
0.846539 83.8701 0.0086186 | |
0.855048 81.9553 0.00742583 | |
0.863641 80.0839 0.00629282 | |
0.872321 78.2519 0.00523107 | |
0.881087 76.461 0.00425157 | |
0.889943 74.7092 0.0033629 | |
0.898887 72.9958 0.00257258 | |
0.907921 71.3201 0.00188594 | |
0.917045 69.6822 0.0013065 | |
0.926262 68.08 0.000835637 | |
0.935571 66.5135 0.000473015 | |
0.944974 64.9814 0.000216192 | |
0.954471 63.4834 6.09312e-05 | |
0.964063 62.019 1.18713e-06 | |
0.973753 60.5863 2.93091e-05 | |
0.983539 59.186 0.000136189 | |
0.993424 57.8167 0.000311496 | |
1.00341 56.4783 0.000543924 | |
1.01349 55.1693 0.000821145 | |
1.02368 53.8897 0.00113105 | |
1.03397 52.6384 0.00146056 | |
1.04436 51.4154 0.00179703 | |
1.05485 50.2194 0.0021282 | |
1.06546 49.0509 0.00244329 | |
1.07616 47.9076 0.00273066 | |
1.08698 46.7911 0.0029821 | |
1.0979 45.6989 0.00318905 | |
1.10894 44.6318 0.00334587 | |
1.12008 43.5885 0.00344765 | |
1.13134 42.569 0.0034922 | |
1.14271 41.5728 0.00347871 | |
1.15419 40.5985 0.0034084 | |
1.16579 39.6466 0.00328437 | |
1.17751 38.7165 0.00311131 | |
1.18934 37.8074 0.00289553 | |
1.2013 36.9188 0.00264428 | |
1.21337 36.0507 0.00236642 | |
1.22556 35.2021 0.002071 | |
1.23788 34.3731 0.00176741 | |
1.25032 33.5626 0.00146545 | |
1.26289 32.771 0.00117422 | |
1.27558 31.9972 0.000902523 | |
1.2884 31.2413 0.000657822 | |
1.30135 30.5026 0.000446514 | |
1.31443 29.781 0.000273509 | |
1.32764 29.0756 0.000142049 | |
1.34098 28.3867 5.36011e-05 | |
1.35446 27.7136 7.7992e-06 | |
1.36807 27.056 2.60805e-06 | |
1.38182 26.4134 3.43028e-05 | |
1.39571 25.7858 9.77418e-05 | |
1.40974 25.1726 0.000186574 | |
1.4239 24.5734 0.000293468 | |
1.43821 23.9882 0.000410815 | |
1.45267 23.4166 0.00053071 | |
1.46727 22.8581 0.000645376 | |
1.48201 22.3124 0.000747755 | |
1.49691 21.7795 0.000831915 | |
1.51195 21.2589 0.000892868 | |
1.52715 20.7505 0.000927392 | |
1.5425 20.2538 0.000933735 | |
1.558 19.7686 0.000911936 | |
1.57366 19.2949 0.000863699 | |
1.58947 18.832 0.000792271 | |
1.60545 18.3801 0.000702153 | |
1.62158 17.9387 0.000599004 | |
1.63788 17.5076 0.000488949 | |
1.65434 17.0867 0.000378531 | |
1.67097 16.6756 0.000273989 | |
1.68776 16.2741 0.000181093 | |
1.70472 15.8819 0.000104538 | |
1.72185 15.4991 4.78014e-05 | |
1.73916 15.1253 1.28508e-05 | |
1.75664 14.7601 9.05467e-08 | |
1.77429 14.4035 8.27376e-06 | |
1.79212 14.0554 3.46839e-05 | |
1.81014 13.7156 7.53832e-05 | |
1.82833 13.3836 0.000125426 | |
1.8467 13.0595 0.000179417 | |
1.86526 12.7431 0.000231895 | |
1.88401 12.4342 0.00027774 | |
1.90294 12.1325 0.000312603 | |
1.92207 11.838 0.000333364 | |
1.94138 11.5504 0.000338225 | |
1.9609 11.2697 0.000326975 | |
1.9806 10.9955 0.00030091 | |
2.00051 10.728 0.000262676 | |
2.02061 10.4667 0.000216054 | |
2.04092 10.2117 0.000165466 | |
2.06143 9.9627 0.00011564 | |
2.08215 9.71964 7.10304e-05 | |
2.10308 9.48249 3.54216e-05 | |
2.12421 9.2509 1.1529e-05 | |
2.14556 9.02482 6.88062e-07 | |
2.16713 8.80419 2.79969e-06 | |
2.18891 8.58876 1.63215e-05 | |
2.2109 8.37856 3.84773e-05 | |
2.23312 8.1733 6.56421e-05 | |
2.25557 7.97308 9.37447e-05 | |
2.27824 7.77758 0.000118763 | |
2.30113 7.58676 0.000137258 | |
2.32426 7.40056 0.000146793 | |
2.34762 7.21883 0.000146181 | |
2.37121 7.04144 0.000135659 | |
2.39504 6.86831 0.000116804 | |
2.41912 6.69934 9.22887e-05 | |
2.44343 6.53445 6.5518e-05 | |
2.46799 6.37357 4.00694e-05 | |
2.49279 6.21649 1.9226e-05 | |
2.51784 6.06319 5.45381e-06 | |
2.54315 5.91367 6.91675e-08 | |
2.56871 5.76771 3.05612e-06 | |
2.59452 5.62529 1.30825e-05 | |
2.6206 5.48627 2.77627e-05 | |
2.64693 5.35065 4.4038e-05 | |
2.67354 5.2183 5.87484e-05 | |
2.70041 5.08917 6.91147e-05 | |
2.72754 4.96315 7.32608e-05 | |
2.75496 4.84019 7.05091e-05 | |
2.78265 4.72023 6.14704e-05 | |
2.81061 4.60318 4.79176e-05 | |
2.83886 4.48903 3.24073e-05 | |
2.86739 4.37761 1.77933e-05 | |
2.89621 4.26891 6.64169e-06 | |
2.92532 4.16283 7.3521e-07 | |
2.95471 4.05934 7.18809e-07 | |
2.98441 3.95838 5.99386e-06 | |
3.0144 3.85991 1.4868e-05 | |
3.0447 3.76379 2.49534e-05 | |
3.0753 3.67006 3.36949e-05 | |
3.10621 3.57858 3.89617e-05 | |
3.13742 3.48937 3.95208e-05 | |
3.16896 3.40233 3.53063e-05 | |
3.2008 3.3174 2.7413e-05 | |
3.23297 3.23458 1.77927e-05 | |
3.26546 3.15374 8.76177e-06 | |
3.29828 3.07492 2.39667e-06 | |
3.33143 2.99809 6.41281e-09 | |
3.36491 2.92306 1.80124e-06 | |
3.39873 2.8499 6.86234e-06 | |
3.43289 2.77854 1.3418e-05 | |
3.46739 2.70894 1.93587e-05 | |
3.50224 2.64106 2.28484e-05 | |
3.53743 2.57483 2.28498e-05 | |
3.57299 2.51025 1.94144e-05 | |
3.6089 2.44726 1.36431e-05 | |
3.64516 2.38582 7.32075e-06 | |
3.6818 2.32589 2.33263e-06 | |
3.7188 2.26744 6.81929e-08 | |
3.75618 2.21043 9.67023e-07 | |
3.79393 2.15484 4.39868e-06 | |
3.83206 2.10061 8.90145e-06 | |
3.87057 2.04772 1.27089e-05 | |
3.90947 1.99614 1.43833e-05 | |
3.94876 1.94585 1.33245e-05 | |
3.98845 1.89679 9.97127e-06 | |
4.02853 1.84895 5.61329e-06 | |
4.06902 1.8023 1.8729e-06 | |
4.10991 1.75681 6.86381e-08 | |
4.15122 1.71245 6.94443e-07 | |
4.19294 1.66919 3.24111e-06 | |
4.23508 1.627 6.43048e-06 | |
4.27764 1.58587 8.77284e-06 | |
4.32063 1.54576 9.21269e-06 | |
4.36406 1.50665 7.57884e-06 | |
4.40792 1.46851 4.64039e-06 | |
4.45222 1.43131 1.73174e-06 | |
4.49696 1.39506 1.19161e-07 | |
4.54216 1.35969 4.02964e-07 | |
4.58781 1.32522 2.24532e-06 | |
4.63392 1.2916 4.55925e-06 | |
4.68048 1.25882 6.07231e-06 | |
4.72752 1.22685 5.98275e-06 | |
4.77504 1.19569 4.36186e-06 | |
4.82303 1.16531 2.09633e-06 | |
4.8715 1.13569 3.85447e-07 | |
4.92046 1.10681 7.19737e-08 | |
4.96991 1.07865 1.178e-06 | |
5.01986 1.0512 2.91362e-06 | |
5.07031 1.02444 4.15915e-06 | |
5.12127 0.998352 4.14017e-06 | |
5.17274 0.972916 2.88751e-06 | |
5.22473 0.948118 1.1965e-06 | |
5.27723 0.923943 1.05738e-07 | |
5.33027 0.900376 2.23038e-07 | |
5.38384 0.877398 1.34098e-06 | |
5.43795 0.855 2.59645e-06 | |
5.4926 0.833164 3.07468e-06 | |
5.5478 0.811879 2.44357e-06 | |
5.60356 0.791126 1.17092e-06 | |
5.65988 0.770899 1.66587e-07 | |
5.71676 0.751182 1.03893e-07 | |
5.77421 0.731961 9.24351e-07 | |
5.83224 0.713229 1.90327e-06 | |
5.89086 0.694969 2.22896e-06 | |
5.95007 0.677169 1.64465e-06 | |
6.00986 0.65982 6.39168e-07 | |
6.07026 0.642906 2.42784e-08 | |
6.13127 0.626422 2.50034e-07 | |
6.19289 0.610353 1.03292e-06 | |
6.25513 0.594691 1.62279e-06 | |
6.31799 0.579423 1.4769e-06 | |
6.38149 0.564545 7.40039e-07 | |
6.44563 0.550043 9.155e-08 | |
6.5104 0.535906 9.7378e-08 | |
6.57584 0.522132 6.77736e-07 | |
6.64193 0.508703 1.20371e-06 | |
6.70868 0.495619 1.13194e-06 | |
6.7761 0.482864 5.44826e-07 | |
6.8442 0.470435 4.67924e-08 | |
6.91299 0.458322 1.21552e-07 | |
6.98247 0.446517 6.25536e-07 | |
7.05264 0.435012 9.62139e-07 | |
7.12352 0.423799 7.48848e-07 | |
7.19511 0.412868 2.35685e-07 | |
7.26743 0.402219 1.17129e-09 | |
7.34047 0.39184 2.7438e-07 | |
7.41423 0.381725 6.77476e-07 | |
7.48875 0.371869 6.97813e-07 | |
7.56401 0.362261 3.15243e-07 | |
7.64004 0.3529 1.03602e-08 | |
7.71681 0.343782 1.4217e-07 | |
7.79437 0.334892 4.92384e-07 | |
7.87271 0.326232 5.70112e-07 | |
7.95183 0.317791 2.7246e-07 | |
8.03174 0.309568 9.41937e-09 | |
8.11246 0.301552 1.22643e-07 | |
8.194 0.293744 4.11069e-07 | |
8.27635 0.286134 4.36079e-07 | |
8.35952 0.278719 1.63982e-07 | |
8.44354 0.271492 1.51133e-10 | |
8.5284 0.264452 1.67054e-07 | |
8.61412 0.257592 3.73066e-07 | |
8.70068 0.250909 2.82717e-07 | |
8.78813 0.244396 4.52606e-08 | |
8.87645 0.238053 3.73779e-08 | |
8.96566 0.231868 2.42581e-07 | |
9.05576 0.225847 2.94127e-07 | |
9.14678 0.219978 1.04268e-07 | |
9.23871 0.214258 2.55614e-09 | |
9.33156 0.208686 1.47537e-07 | |
9.42535 0.203259 2.54652e-07 | |
9.52007 0.19797 1.24011e-07 | |
9.61575 0.192816 2.09184e-10 | |
9.71239 0.187797 1.0143e-07 | |
9.81 0.182904 2.11128e-07 | |
9.90859 0.17814 1.09855e-07 | |
10.0082 0.173498 2.03914e-10 | |
10.1088 0.168977 8.96106e-08 | |
10.2104 0.164571 1.75712e-07 | |
10.313 0.160278 7.56265e-08 | |
10.4166 0.156096 1.36924e-09 | |
10.5213 0.152024 9.82588e-08 | |
10.627 0.148054 1.39919e-07 | |
10.7338 0.144189 3.34124e-08 | |
10.8417 0.14042 1.5752e-08 | |
10.9507 0.136753 1.12229e-07 | |
11.0607 0.133178 8.99761e-08 | |
11.1719 0.129697 2.54948e-09 | |
11.2842 0.126307 5.12311e-08 | |
11.3976 0.123002 1.04642e-07 | |
11.5121 0.119783 2.92396e-08 | |
11.6278 0.116647 1.16774e-08 | |
11.7447 0.113595 8.47014e-08 | |
11.8627 0.110619 5.42451e-08 | |
11.982 0.107723 1.91157e-10 | |
12.1024 0.104898 5.72451e-08 | |
12.224 0.102149 6.38522e-08 | |
12.3469 0.0994714 2.19814e-09 | |
12.471 0.0968631 3.69163e-08 | |
12.5963 0.0943221 6.2149e-08 | |
12.7229 0.0918475 5.83208e-09 | |
12.8507 0.0894364 2.58297e-08 | |
12.9799 0.0870887 5.5776e-08 | |
13.1104 0.0848019 6.47991e-09 | |
13.2421 0.082574 2.19055e-08 | |
13.3752 0.0804044 4.81217e-08 | |
13.5096 0.078291 4.14076e-09 | |
13.6454 0.0762329 2.28811e-08 | |
13.7825 0.0742282 3.93685e-08 | |
13.921 0.0722757 9.21788e-10 | |
14.061 0.0703746 2.69753e-08 | |
14.2023 0.0685226 2.81578e-08 | |
14.345 0.0667187 3.6392e-10 | |
14.4892 0.064962 3.11063e-08 | |
14.6348 0.0632508 1.43848e-08 | |
14.7819 0.0615845 6.03307e-09 | |
14.9304 0.0599615 2.97933e-08 | |
15.0805 0.0583811 2.48008e-09 | |
15.2321 0.056842 1.70971e-08 | |
15.3851 0.0553427 1.85239e-08 | |
15.5398 0.0538831 1.33025e-09 | |
15.6959 0.0524614 2.352e-08 | |
15.8537 0.0510768 3.38239e-09 | |
16.013 0.0497282 1.27676e-08 | |
16.174 0.0484152 1.41409e-08 | |
16.3365 0.0471363 1.9875e-09 | |
16.5007 0.045891 1.86806e-08 | |
16.6665 0.0446782 5.62522e-10 | |
16.834 0.0434972 1.43837e-08 | |
17.0032 0.0423474 5.60267e-09 | |
17.1741 0.0412275 7.08702e-09 | |
17.3467 0.0401368 1.07823e-08 | |
17.521 0.0390748 1.89378e-09 | |
17.6971 0.0380406 1.29216e-08 | |
17.875 0.0370336 5.36488e-11 | |
18.0546 0.036053 1.22528e-08 | |
18.2361 0.0350982 4.12125e-10 | |
18.4193 0.0341683 1.02718e-08 | |
18.6045 0.0332631 1.49352e-09 | |
18.7914 0.0323812 8.20762e-09 | |
18.9803 0.0315229 2.39027e-09 | |
19.1711 0.030687 6.64928e-09 | |
19.3637 0.0298729 2.7681e-09 | |
19.5583 0.0290803 5.71049e-09 | |
19.7549 0.0283086 2.61142e-09 | |
19.9535 0.0275573 5.34158e-09 | |
20.154 0.0268258 2.03363e-09 | |
20.3565 0.0261133 5.37489e-09 | |
20.5611 0.0254197 1.20782e-09 | |
20.7678 0.0247442 5.61074e-09 | |
20.9765 0.0240868 3.94442e-10 | |
21.1873 0.0234464 5.69827e-09 | |
21.4002 0.022823 4.85864e-13 | |
21.6153 0.0222161 5.1892e-09 | |
21.8325 0.0216251 4.6219e-10 | |
22.052 0.0210498 3.74138e-09 | |
22.2736 0.0204896 1.8828e-09 | |
22.4975 0.0199442 1.63204e-09 | |
22.7235 0.0194131 3.4928e-09 | |
22.9519 0.0188961 1.04547e-10 | |
23.1826 0.0183928 3.7619e-09 | |
23.4156 0.0179028 5.65288e-10 | |
23.6509 0.0174257 1.97063e-09 | |
23.8886 0.0169612 2.4828e-09 | |
24.1287 0.016509 9.37142e-11 | |
24.3712 0.0160687 2.89736e-09 | |
24.6161 0.0156401 8.74352e-10 | |
24.8635 0.0152228 8.18499e-10 | |
25.1134 0.0148166 2.55696e-09 | |
25.3658 0.0144211 2.15925e-10 | |
25.6207 0.0140361 1.23402e-09 | |
25.8782 0.0136612 2.05328e-09 | |
26.1383 0.0132964 8.15621e-11 | |
26.401 0.0129411 1.15581e-09 | |
26.6663 0.0125952 1.80699e-09 | |
26.9343 0.0122587 1.43602e-10 | |
27.205 0.0119309 7.37838e-10 | |
27.4784 0.0116119 1.70105e-09 | |
27.7546 0.0113012 4.59362e-10 | |
28.0335 0.0109989 1.84607e-10 | |
28.3153 0.0107046 1.32823e-09 | |
28.5999 0.0104181 1.05214e-09 | |
28.8873 0.0101392 5.11253e-11 | |
29.1776 0.00986773 4.39956e-10 | |
29.4709 0.00960347 1.20262e-09 | |
29.767 0.00934614 7.76817e-10 | |
30.0662 0.00909576 4.50493e-11 | |
30.3684 0.00885204 2.65744e-10 | |
30.6736 0.00861471 9.00274e-10 | |
30.9819 0.00838372 8.72192e-10 | |
31.2932 0.0081589 2.89639e-10 | |
31.6077 0.00794004 9.43002e-13 | |
31.9254 0.00772699 2.95075e-10 | |
32.2463 0.00751967 7.03145e-10 | |
32.5703 0.00731783 7.41641e-10 | |
32.8977 0.0071213 4.22421e-10 | |
33.2283 0.00693014 8.92937e-11 | |
33.5622 0.00674397 7.29958e-12 | |
33.8996 0.00656279 1.72136e-10 | |
34.2403 0.00638648 4.08808e-10 | |
34.5844 0.00621485 5.51183e-10 | |
34.9319 0.00604768 5.405e-10 | |
35.283 0.00588512 4.1583e-10 | |
35.6376 0.00572685 2.53369e-10 | |
35.9958 0.00557277 1.15794e-10 | |
36.3575 0.0054228 3.15838e-11 | |
36.723 0.00527687 1.02338e-12 | |
37.092 0.00513482 8.22074e-12 | |
37.4648 0.00499662 3.46879e-11 | |
37.8413 0.00486202 6.56783e-11 | |
38.2216 0.00473111 9.1883e-11 | |
38.6058 0.00460365 1.08739e-10 | |
38.9938 0.00447959 1.15349e-10 | |
39.3857 0.00435882 1.11939e-10 | |
39.7815 0.00424131 9.97334e-11 | |
40.1813 0.00412696 8.02171e-11 | |
40.5851 0.00401566 5.58724e-11 | |
40.993 0.00390731 3.03302e-11 | |
41.405 0.00380188 9.269e-12 | |
41.8211 0.00369926 1.44295e-14 | |
42.2415 0.00359938 1.01039e-11 | |
42.666 0.00350225 4.36021e-11 | |
43.0948 0.00340764 9.63082e-11 | |
43.5279 0.00331555 1.51534e-10 | |
43.9654 0.00322599 1.82545e-10 | |
44.4072 0.00313881 1.65431e-10 | |
44.8535 0.00305397 1.0064e-10 | |
45.3043 0.0029714 2.64109e-11 | |
45.7596 0.00289104 1.08157e-12 | |
46.2195 0.00281284 4.97004e-11 | |
46.684 0.00273674 1.21045e-10 | |
47.1532 0.00266268 1.2595e-10 | |
47.6271 0.00259061 5.07728e-11 | |
48.1057 0.00252047 1.16389e-15 | |
48.5892 0.00245222 5.13584e-11 | |
49.0776 0.0023858 1.0963e-10 | |
49.5708 0.00232117 5.85714e-11 | |
50.069 0.00225827 2.05976e-16 | |
50.5722 0.00219706 5.73134e-11 | |
51.0805 0.0021375 8.60634e-11 | |
51.5938 0.00207953 1.11736e-11 | |
52.1123 0.00202313 2.95574e-11 | |
52.6361 0.00196825 7.79888e-11 | |
53.1651 0.00191484 9.49064e-12 | |
53.6994 0.00186287 3.4858e-11 | |
54.2391 0.00181229 5.93594e-11 | |
54.7842 0.00176309 3.67428e-14 | |
55.3348 0.0017152 5.7463e-11 | |
55.8909 0.0016686 1.61132e-11 | |
56.4527 0.00162327 2.94758e-11 | |
57.02 0.00157915 3.52353e-11 | |
57.593 0.00153622 1.19735e-11 | |
58.1719 0.00149444 4.09258e-11 | |
58.7565 0.00145379 6.64504e-12 | |
59.347 0.00141424 3.79954e-11 | |
59.9435 0.00137576 8.30541e-12 | |
60.5459 0.00133831 2.90259e-11 | |
61.1544 0.00130188 1.68321e-11 | |
61.769 0.00126643 1.34192e-11 | |
62.3898 0.00123193 3.00676e-11 | |
63.0168 0.00119836 2.66791e-13 | |
63.6502 0.00116571 2.9823e-11 | |
64.2898 0.00113393 1.1775e-11 | |
64.936 0.00110302 5.36514e-12 | |
65.5886 0.00107293 2.7647e-11 | |
66.2478 0.00104367 9.08534e-12 | |
66.9136 0.00101519 2.66382e-12 | |
67.5861 0.000987482 2.14373e-11 | |
68.2653 0.000960519 1.74191e-11 | |
68.9514 0.000934291 1.48403e-12 | |
69.6444 0.000908769 4.37416e-12 | |
70.3443 0.000883943 1.67094e-11 | |
71.0513 0.000859788 1.80867e-11 | |
71.7654 0.000836284 9.01732e-12 | |
72.4866 0.000813414 1.23205e-12 | |
73.2151 0.00079117 5.53079e-13 | |
73.9509 0.000769523 4.72326e-12 | |
74.6942 0.000748463 9.42543e-12 | |
75.4449 0.000727976 1.23262e-11 | |
76.2031 0.000708046 1.3239e-11 | |
76.9689 0.000688651 1.29397e-11 | |
77.7425 0.000669785 1.22279e-11 | |
78.5238 0.000651431 1.1573e-11 | |
79.313 0.000633577 1.11082e-11 | |
80.1101 0.000616205 1.06881e-11 | |
80.9152 0.000599303 9.94618e-12 | |
81.7285 0.00058286 8.38865e-12 | |
82.5498 0.000566864 5.7299e-12 | |
83.3794 0.000551302 2.42532e-12 | |
84.2174 0.000536164 1.43453e-13 | |
85.0639 0.000521436 9.84172e-13 | |
85.9187 0.000507111 4.78905e-12 | |
86.7822 0.000493172 7.28148e-12 | |
87.6544 0.000479612 4.32667e-12 | |
88.5354 0.000466423 1.37404e-13 | |
89.4252 0.00045359 2.56241e-12 | |
90.3239 0.000441108 6.01442e-12 | |
91.2317 0.000428966 1.6463e-12 | |
92.1485 0.000417154 1.19256e-12 | |
93.0746 0.000405663 5.20401e-12 | |
94.01 0.000394484 5.22061e-13 | |
94.9549 0.000383612 3.1151e-12 | |
95.9092 0.000373034 2.52328e-12 | |
96.8731 0.000362744 1.30608e-12 | |
97.8467 0.000352736 3.09464e-12 | |
98.8302 0.000343 1.0069e-12 | |
99.8232 0.00033353 2.45982e-12 | |
100.827 0.000324318 1.8229e-12 |
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