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@xgarrido
Created May 31, 2021 09:21
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"('1.3.4',\n",
" '/home/garrido/Workdir/CMB/development/camb/software/camb/__init__.py')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import camb\n",
"camb.__version__, camb.__file__"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"import camb\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"cosmo_params = {\n",
" \"cosmomc_theta\": 0.0104085,\n",
" \"As\": 1e-9,\n",
" \"ombh2\": 0.02237,\n",
" \"omch2\": 0.1200,\n",
" \"ns\": 0.9649,\n",
" \"tau\": 0.0544,\n",
"}\n",
"pars = camb.set_params(**cosmo_params)\n",
"results = camb.get_results(pars)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def xe(z, tau=0.0544, dz=0.5, f=1.08, with_helium=False):\n",
" y = (1+z)**(3/2)\n",
" zre = camb.get_zre_from_tau(pars, tau)\n",
" yre = (1+zre)**(3/2)\n",
" dy = 3/2 * (1 + z)**(1/2) * dz\n",
" \n",
" xe = 1/2 * (1 + np.tanh((yre - y) / dy))\n",
" \n",
" # Effect of Helium becoming fully ionized is small so details not important\n",
" He_fraction = 8.1884281020483535E-002\n",
" He_redshift = 3.5\n",
" He_delta = 0.4\n",
" xe += He_fraction / 2 * (1 + np.tanh((He_redshift - z)/He_delta))\n",
" \n",
" return xe\n",
"\n",
"z = np.arange(0, 25, 0.1)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.plot(z, xe(z=z), label=r\"$\\Delta_z$ = 0.5\", c=\"black\")\n",
"ax.plot(z, xe(z=z, dz=1.5), label=r\"$\\Delta_z$ = 1.5\", c=\"red\")\n",
"ax.set(ylabel=\"$x_e$\", xlabel=\"$z$\")\n",
"ax.legend();"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"zz = np.arange(6.25, 30, 0.25)\n",
"# dl = 20\n",
"# w = np.hstack([np.ones(len(z)-dl),(np.cos(np.pi*np.arange(dl)/(dl-1))+1)/2])\n",
"# modes = np.loadtxt(\"xepcs.txt\")[1:]*w\n",
"\n",
"fidxe = xe(z=zz)\n",
"fidxe[:5] = 0.0\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.plot(z, xe(z=z), label=r\"$\\Delta_z$ = 0.5\", c=\"black\")\n",
"ax.plot(zz, fidxe)\n",
"ax.set(ylabel=\"$x_e$\", xlabel=\"$z$\");"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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