Created
February 1, 2016 13:32
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "Populating the interactive namespace from numpy and matplotlib\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "pylab inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Load the matter C_ell for the auto-correlation spectra\n", | |
| "ell=loadtxt(\"./0.3/matter_cl/ell.txt\")\n", | |
| "v3=loadtxt(\"./0.3/matter_cl/bin_1_1.txt\")\n", | |
| "v301=loadtxt(\"./0.301/matter_cl/bin_1_1.txt\")\n", | |
| "v31=loadtxt(\"./0.31/matter_cl/bin_1_1.txt\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": { | |
| "collapsed": false, | |
| "scrolled": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x10aa82b90>" | |
| ] | |
| }, | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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| |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10a988990>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "#Plot the ratio of the spectra to the baseline omega_m=0.3\n", | |
| "plot(ell, v301/v3, label='Omega_m = 0.301')\n", | |
| "plot(ell, v31/v3, label='Omega_m = 0.31')\n", | |
| "legend(loc='center right')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "#Now do the cross-spectra\n", | |
| "ell=loadtxt(\"./0.3/matter_cl/ell.txt\")\n", | |
| "v3=loadtxt(\"./0.3/matter_cl/bin_2_1.txt\")\n", | |
| "v301=loadtxt(\"./0.301/matter_cl/bin_2_1.txt\")\n", | |
| "v31=loadtxt(\"./0.31/matter_cl/bin_2_1.txt\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": { | |
| "collapsed": false | |
| }, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.legend.Legend at 0x10ae1c810>" | |
| ] | |
| }, | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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46+u3CHmIsb3GMrbXWM7vdb7OYhKpQwoFaVTcndU7V0cfFPR+9vuclHISY3uN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\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x10aaa4990>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "plot(ell, v301/v3, label='Omega_m = 0.301')\n", | |
| "plot(ell, v31/v3, label='Omega_m = 0.31')\n", | |
| "legend(loc='center right')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "collapsed": true | |
| }, | |
| "outputs": [], | |
| "source": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "IPython (Python 2)", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.10" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 0 | |
| } |
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