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@jzuhone
Created May 2, 2014 22:33
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{
"metadata": {
"name": "",
"signature": "sha256:669eabfbae30896d2b9bef2d019f93c8b27ce0ff24b4a559bc5378530214fe84"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from yt.mods import *\n",
"from yt.utilities.physical_constants import cm_per_kpc, K_per_keV\n",
"from sympy.utilities.lambdify import lambdify, implemented_function\n",
"from sympy import *\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"font = {'family':'serif',\n",
" 'size':18}\n",
"matplotlib.rc('font', **font)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"yt : [INFO ] 2014-05-02 18:32:37,741 Loading plugins from /Users/jzuhone/.yt/my_plugins.py\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r, rt, a, b, c, rcool, Tmin, T0, acool = symbols(\"r rt a b c rcool Tmin T0 acool\") \n",
"t = (r/rt)**(-a)/((1+(r/rt)**b)**(c/b))\n",
"x = (r/rcool)**acool\n",
"tcool = (x+Tmin/T0)/(x+1)\n",
"T3D = T0*tcool*t"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dTdr = diff(T3D, r)\n",
"d2Tdr = diff(dTdr, r)\n",
"kappa = 0.3*4.6e13*(T3D/1.0e8)**(2.5)\n",
"Q = diff(kappa*dTdr, r)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"temp = lambdify(r, T3D.subs([(rt, 3040), (a, -0.03), (b,1.57), (c,5.9), \n",
" (T0,16.19*K_per_keV), (acool,0.48), (rcool, 93.0), (Tmin, 1.619*K_per_keV)]))\n",
"flux = lambdify(r, Q.subs([(rt, 3040), (a, -0.03), (b,1.57), (c,5.9), \n",
" (T0,16.19*K_per_keV), (acool,0.48), (rcool, 93.0), (Tmin, 1.619*K_per_keV)]))\n",
"K = lambdify(r, kappa.subs([(rt, 3040), (a, -0.03), (b,1.57), (c,5.9), \n",
" (T0,16.19*K_per_keV), (acool,0.48), (rcool, 93.0), (Tmin, 1.619*K_per_keV)]))\n",
"dT = lambdify(r, dTdr.subs([(rt, 3040), (a, -0.03), (b,1.57), (c,5.9), \n",
" (T0,16.19*K_per_keV), (acool,0.48), (rcool, 93.0), (Tmin, 1.619*K_per_keV)]))\n",
"d2T = lambdify(r, d2Tdr.subs([(rt, 3040), (a, -0.03), (b,1.57), (c,5.9), \n",
" (T0,16.19*K_per_keV), (acool,0.48), (rcool, 93.0), (Tmin, 1.619*K_per_keV)]))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rr, TT, dq, kap, dtdr, d2tdr = np.loadtxt(\"brock_test.dat\", unpack=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(rr, np.abs(TT-temp(rr)/K_per_keV)/(temp(rr)/K_per_keV), lw=3)\n",
"plt.xscale(\"log\")\n",
"plt.yscale(\"log\")\n",
"plt.xlabel(\"r (kpc)\")\n",
"plt.ylabel(\"rel. err.\")\n",
"plt.title(r\"$\\mathrm{T(r)}$\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.text.Text at 0x10b556190>"
]
},
{
"metadata": {},
"output_type": "display_data",
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d8osnJISxXbvqf2xLFRWMrVnDWGio9FwdOzKWkyPvuQhjH30kXmMb6UrrJC7O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\nRe71LRw0AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10b543dd0>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(rr,np.abs(kap-K(rr))/K(rr), lw=3)\n",
"plt.xscale(\"log\")\n",
"plt.yscale(\"log\")\n",
"plt.xlabel(\"r (kpc)\")\n",
"plt.ylabel(\"rel. err.\")\n",
"plt.title(r\"$\\mathrm{\\kappa}$\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<matplotlib.text.Text at 0x10b935290>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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1oPkzrNjYWAzPWXF7n+bNm/NZVing6mobDBeQpSwj+e47W7ICgCVLmKxIHYMG\nyedZgPyB58CEFgDsS1dt2pS+ZKUmp55hFTSb8PXr15Genl7sgPSmtA9+m5fAQNuykaoFU1OBiRNt\n6+PG2abBIFKa2Ww/uvr8+bZhvwpSmqsDdTP4bd++fdGwYUN8/PHHMJvNdlWCqampGDduHFJSUrBh\nwwbVgi1pZbFKEDButeD06cDMmXK5enXZLF/LWWup9Lt+Xc4rZx3ya+tWoFevgo/p0QP48Ue5/NVX\nwNCh6saoBc2rBN955x0sXrwYjRs3xpgxY3Dz5k1MnjwZzz77LOrUqYNvvvkGM2bMUDxAKnn3Vwsa\nobXguXPA3Lm29ffeY7Ii9Xl62g8e/cknBe9/5479sGedOqkTV2nlVMfhmJgYjB07Ntc0I02bNsWX\nX36Jx4szEqQOldUSFiB/KfbpI5fr15ezyeq5E/Hw4XICRkCOqr13L/tcUck4dUrOoAzIf3PnzgE+\nPnnvGxNja7HauLGsBSiN1PrudGpuWX9/f/zvf//Db7/9lp20mjZtikceeUTxwEhbAQGyE3FKiuzZ\nf+wY0KqV1lHl7dAhW7IC5KjaTFZUUho3Brp3l9V8WVmyibt17Mr7WasCAXkMOcfpj7WLiwvatm2L\noUOHYujQoUxWpZSbG9Cvn239u++0i6UgQgBTp9rWn3qK1SxU8nI2vli8WE7OmJcdO2zLTFjO4+9Q\nyteTT9qW9ZqwtmwBoqPlstkMzJmjbTxUNvXvD9SrJ5evXgU++CD3PikpwK+/ymWTiZ3Zi4IJi/LV\nt6+tj8hvvwHnz2sbz/0yM+3nEHrxRaBpU+3iobLLbLbNJwcA4eG5m7hv2WKbYfiRR2RLVnIOExbl\nq0oVOfGhld56LKxbB/z+u1yuWBEIDdU2HirbcrYWjI0FvvjC/v1vv7UtDxpUIiGVOkxYhSiLHYdz\nylktqKeElZUFvPuubX3SJNtwN0RaqF5dzkhs9cYbtk73SUly4lCrnINMlya66ThcFpXlZu1W8fFA\nnTpyuVw5OTOxp6e2MQEyeVqTacWKwF9/sYqFtHf+vO1ZFiCntBk1Cpg3D5gyRW575BHg8GF9dxMp\nLs07DlPZVLu2HO8MkKNS52yWqxUh7BtXjB/PZEX64OMjG2BYvfaanOAxZzP3V14p3clKTYolrLi4\nOFy9elWp05GO9O1rW46K0i4Oq717beOxubkBkydrGw9RTqtX2zoOJyUBjz0mayYA4MEHgZEjtYvN\n6BRJWEl0bj08AAAbz0lEQVRJSfDz80NHIww4R06zjngByJZOWteSfvihbXnUKDmbMJFeeHrKjuxu\nbvbbTSY5e4CHhzZxlQaKJCxXV1d4eHigUqVKSpyOdKZjR8DLSy7HxQF//KFdLGfPynmIrCZN0i4W\novx06gRs2mSbfqROHTnQbc+emoZleE4NzZSfSpUqITY2FuXKKXI60ply5eQHzTo3VlQU0LKlNrEs\nWGBredW7N9CihTZxEBWmZ0/5A+vMGdkQg/NeFZ9iz7AqVqwId3d3pU5HOqOH51jJybJKxYrPrkjv\nTCagUSMmK6Uo2kpwDsfFKbVyPsfaswe4caPkY1i82DbvUIsWrF4hKmsUTVgLFixQ8nSkI97esv8I\nICd4LOnm7RkZsjrQatIkNg0mKmvyTVguLi4wm81wcXEp9GXd79KlSyUZO5WwnNWCW7aU7LU3bQIu\nXJDLNWoAI0aU7PWJSHv5tpJwd3fHsGHDnOqtvM4IU9NSkfXpY+uwGxUlm7eXVCln2TLb8tixbBpM\nVBblOzSTt7c3EhISnDpZUY7RMw7NZC8jA3jgATlNAiCbtzdvrv51L12SzYLv3pXrJ0/KSfOISJ9K\nfGimogz4WpYHiS0LXF3tGzqUVGvBNWtsyapzZyYrorIq34TVNJ+JhdLT07F3715E3uu9efPmTdy9\n922S3zFGVtZHa7+fFs3bc1YHhoSUzDWJyHm6Gq196dKlmDZtGq5cuYJKlSohJSUFO3bsQFBQEObM\nmYMXXnhBtUC1wCrB3P7+G6hbVy67ucnZVdUc4OTIEaB1a7ns4QEkJupjtHgiyp/mo7Vv3LgRY8aM\nQYMGDTB+/Hi43usJ17VrVyxcuBDTp0/HN998o3iApC916gAPPSSX09OB3bvVvd7y5bblIUOYrIjK\nMocT1ty5czFnzhzs378fCxcuhNu9kR1dXV3x7LPP4ptvvsEHH3ygWqCkHz162JZ37FDvOunpwKpV\ntnVWBxKVbQ4nrOPHj2NyAWPhPPbYY4iLi1MkKNK3nAlLzQ7EUVG2aRnq1AG6dVPvWkSkf06NdGEq\noNPN7du3kZaWVuyASP+6dJED4gLA0aOy2bkaclYHPvccYDarcx0iMgaHE1bLli0LrPKbPn06Hn74\nYUWCIn2rVElOSme1c6fy17h8Gfj+e9t6cLDy1yAiY3F4PpC33noLffv2xQ8//ICePXsiLS0NCxYs\nQFxcHL777jucPXsW27dvVzNW0pEePWwNLnbsAIYNU/b8a9fa+l517AiUwh4TROQkp5q1r1mzBq+8\n8gquX79ut71q1ar4/PPPERgYqHiAWmKz9vz98ovsxAvI6cBjY5UdpqltW+DwYbn8+efAuHHKnZuI\n1KXWd6dTCQuQHYW3bt2K06dPA5CdhXv27ImKFSsqHpzWmLDyl5EBVKtmm+7j1Ck5748S/vwTaNZM\nLru7y75X1hmPiUj/1PrudLhKcPTo0TCZTBg+fDiefvppxQMpSenp6fDz80OXLl2wdOlSrcMxJFdX\nwN9fjqIOyGpBpRJWzu58ffsyWRGR5HCji+XLlyMjIwMWi0XNeErExx9/jKtXrxbY6pEKp1Z/rJwJ\n65lnlDsvERmbw1WCFosFiYmJasejusuXL2PIkCFo0KABzGZzgSUsVgkW7I8/gJYt5XLVqrJlX3Gb\nnp89CzRsKJfd3GSTeY5uQWQsmg/N1LJlS8THxxe4T7AB2h6HhoZi+vTpMLNTT7E1bw5YC9xJScBv\nvxX/nDlLV716MVkRkY3DCWvBggUYP348Dh48mO8+27ZtK1YwK1asgJeXF0aPHl3gfhEREWjbti1q\n1aoFHx8fTJ061aFOy8ePH0dcXBx69uzJkpMCTCblR72IiLAtszqQiHJyuNFF//79cfPmTXTo0AHl\ny5dHjRo14OJiy3dCCFyxjqPjpMuXL2PcuHE4evQoUlJSCny2FB4ejhdffBGrVq1CUFAQYmNj0atX\nLxw+fBjbt2+3i+l+06ZNw9y5cwHIIiufYRVf9+628f62bwemTSv6uf76CzhwQC6XKwcMGlT8+Iio\n9HC4hBUfH4+WLVuiS5cuaN++PerXrw8fH5/sV7169YpczRYSEoIWLVpg69atBe6XlJSEyZMnIzAw\nEEFBQQCA+vXrY968eYiOjsaKFSsAyAYiAQEBCAgIyE5QW7ZsQZ06deDn5wdAJliWsoqve3fb8s8/\nA3fuFP1c335rW+7RQz4XIyKycriEVa1aNURHRxe4j7e3d5GCWLJkCSwWC2JjYwvcb926dUhJScGQ\nIUPstvfp0wfly5fH4sWLERISguDg4FzP03bu3IkjR44gICAAAPC///0PABAQEICpU6eiX79+RYq9\nrHvwQdmc/fRp4PZtWUJ64ominYvVgURUEIcT1sqVKwvdZ8uWLUUKwtGm8rt27QIAtGrVym67q6sr\n/Pz8sH//fmRkZGTP1ZWTtaRlFRISApPJxH5YCvD3lwkLAKKji5aw/v5bjp4ByJaGgwcrFh4RlRIO\nVwn2yPl0PR9qD3578uRJmEymPEtytWvXRmZmJs6ePVvgOa5fv45nnnkGP/30E2JiYhAYGIikpCS1\nQi4T7hVaAQAxMUU7R2SkbdnfH3jggeJERESlkcMlLD2wjmFYoUKFXO9ZtyUnJxd4Dk9PT0TkrHui\nYvP3ty3v3SurBj08nDsHqwOJqDBOzYdFlJfatYEmTeTy7dvA/v3OHX/xInCvthcmE/DUU8rGR0Sl\ng6FKWJ73epGmpqZmL1ulpqba7aOUsLCw7GV/f3/45yxOUDZ/f+DkSbm8cyfQtavjx0ZGAtYGm088\nAdSqpXh4RKSimJgYxBT1eYATDJWwmjRpgkOHDiEhISFXYoqPj4fZbIavr6+i18yZsCh/3bsDixbJ\n5agoYMYMx4/l2IFExnb/j/kZznwBOMFQVYJd7/1sP3r0qN32jIwMnDhxAh07doSbm5sWoZV5vXrZ\nxhH89Vc5JYgjrl6VLQut7uuxQESUzVAJKzAwEFWqVEFkziZlAKKiopCWloYxY8Yofs2wsLASKeoa\nnZeXfXP2zZsdO27jRiAzUy537AjUqaN8bERUMmJiYlStldJlwspvBIqqVaviww8/REREBNasWQMA\niI2NxZQpU9CtWzdVBt8NCwvjcysHDRhgW7bOk1WYnNWBBp9mjajM8/f3VzVhOT3jsBqWLVuG8ePH\nQwiBjIwMmEwmuLq6wtvbO89+VREREZgzZw7i4uLg5uaGYcOGYebMmfBwti11ITi9iHNyzhRcsaKs\n7nN3z3//lBSgRg0gPV2unz0LNGigfpxEpC61vjt1kbD0ignLOULI5u3WUS9++AEoaMSrtWuB4cPl\ncuvWwOHD6sdIROrTfD6ssorPsBx3fx+q9esL3t86yjvA6kCi0kDtZ1gsYRWAJSzn/for0KGDXPby\nkp2C82q4GRsL+Pra+l+dOSPXicj4WMIiQ2jXDqhfXy4nJwM7duS936JFtmTVuzeTFREVjgmLFGUy\nAYGBtvWvv869T3o6sGSJbX38ePXjIiLjY8IixeVMWN98I1sD5hQZCVy6JJfr1gX69y+52IjIuJiw\nCsFGF85r1w5o0UIu37oFrF5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"text": [
"<matplotlib.figure.Figure at 0x10b52e350>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(rr, np.abs((dtdr-dT(rr)/K_per_keV)/(dT(rr)/K_per_keV)), lw=3)\n",
"plt.xscale(\"log\")\n",
"plt.yscale(\"log\")\n",
"plt.xlabel(\"r (kpc)\")\n",
"plt.ylabel(\"rel. err.\")\n",
"plt.title(r\"$\\mathrm{dT/dr}$\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"<matplotlib.text.Text at 0x10b3f6c90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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hO2toqg579mzRbgmRzIEDfFuuxqFD+lWExPFoegumpKRQwrI1sa4Sbt4EMjO1pa/CQtPr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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10b5c8b50>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(rr, np.abs((d2tdr-d2T(rr)/K_per_keV)/(d2T(rr)/K_per_keV)), lw=3)\n",
"plt.xscale(\"log\")\n",
"plt.yscale(\"log\")\n",
"plt.title(r\"$\\mathrm{d^2T/dr^2}$\")\n",
"plt.xlabel(\"r (kpc)\")\n",
"plt.ylabel(\"rel. err.\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<matplotlib.text.Text at 0x10b97a410>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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t69u3L9555x1s2rRJ0MapUvH3n0f29vattsnLysvLVe7r6uoKmUyGhoYGNDY2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\ngcACABiBwAIAGIHAAgAYgcACABjhCUUJuesvfd3cAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10b9be310>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(rr, np.abs((dq-flux(rr)/(cm_per_kpc**2))/(flux(rr)/(cm_per_kpc**2))), lw=3)\n",
"plt.xscale(\"log\")\n",
"plt.yscale(\"log\")\n",
"plt.title(r\"$\\mathrm{\\nabla\\cdot{\\bf Q}}$\")\n",
"plt.xlabel(\"r (kpc)\")\n",
"plt.ylabel(\"rel. err.\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.text.Text at 0x10cf01c10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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4jO508bOmzm2AFOPuBQUFAQDO1P0NBqBWq5GTk4PAwEA4NLENaPHixZSsrJiT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\nhBBCrAIlLEIIIVbh/wFkgtzKSQPAjwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10cb1c110>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
}
],
"metadata": {}
}
]
}
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