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@jbwhit
Created May 29, 2013 01:14
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{
"metadata": {
"name": "2013-04-06-Large-Programme-Paper-2"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jonathan's Comments\n",
"=================\n",
"\n",
"* $\\mu$ should be defined as $\\mu \\equiv m_{p}/m_{e}$ (as an inline eq.)\n",
"* I think Eq. 3 has an error in it (minus sign)\n",
"* reduced redshift needs to be defined see note further down the page \n",
"\n",
"From the paper\n",
"---------\n",
"Eq. 2: \n",
"$$\\lambda_i = \\lambda_i^0 (1 + z_{abs}) \\left( 1 + K_i \\frac{d\\mu}{\\mu}\\right)$$\n",
"\n",
"\n",
"Eq. 3a: \n",
"$$ z_i = z_{abs} + C K_i$$\n",
"\n",
"Eq. 3b:\n",
"$$ C = - (1 + z_{abs}) \\frac{d\\mu}{\\mu}$$\n",
"\n",
"I think that the C factor is off by a sign. \n",
"\n",
"My derivation\n",
"-----------------\n",
"\n",
"Starting from Eq. 2: \n",
"$$\\lambda_i = \\lambda_i^0 (1 + z_{abs}) \\left( 1 + K_i \\frac{d\\mu}{\\mu}\\right)$$\n",
"\n",
"$$\\frac{d\\mu}{\\mu} \\equiv U$$\n",
"$$z_{abs} \\equiv z_a$$\n",
"\n",
"$$\\lambda_i = \\lambda_i^0 (1 + z_a) \\left( 1 + K_i U \\right)$$\n",
"\n",
"Redshift / wavelength relationship: \n",
"\n",
"$$1 + z_i \\equiv \\frac{\\lambda_i}{\\lambda_i^0}$$\n",
"\n",
"$$\\lambda_i = \\lambda_i^0 ( 1 + z_i) $$\n",
"\n",
"Substituting into Eq. 2: \n",
"\n",
"$$\\lambda_i = \\lambda_i^0 ( 1 + z_i) = \\lambda_i^0 (1 + z_a) \\left( 1 + K_i U \\right) $$\n",
"\n",
"$$( 1 + z_i) = (1 + z_a) \\left( 1 + K_i U \\right) $$\n",
"\n",
"Distributing: \n",
"\n",
"$$1 + z_i = 1 + K_i U + z_a + K_i U z_a $$\n",
"\n",
"$$z_i = K_i U + z_a + K_i U z_a $$\n",
"\n",
"$$z_i = z_a + K_i U + K_i U z_a $$\n",
"\n",
"Regrouping: \n",
"\n",
"$$z_i = z_a + (1 + z_a) U K_i $$\n",
"\n",
"Which if we allow\n",
"\n",
"$$ C \\equiv (1 + z_a) U = (1 + z_{abs}) \\frac{d\\mu}{\\mu} $$ \n",
"\n",
"Which is the opposite sign for the relationship than is written in Eq. 3."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Slope to $d\\mu/\\mu$\n",
"------------\n",
"Now, solving for the slope and it's relation to $\\frac{d\\mu}{\\mu}$ I get: \n",
"\n",
"$$ z_i = z_a + (1 + z_a) \\frac{d\\mu}{\\mu} K_i $$\n",
"\n",
"$$ z_i - z_a = (1 + z_a) \\frac{d\\mu}{\\mu} K_i $$\n",
"\n",
"$$ \\frac{z_i - z_a}{(1 + z_a)} = \\frac{d\\mu}{\\mu} K_i $$\n",
"\n",
"$$ \\frac{z_i - z_a}{(1 + z_a)} \\frac{1}{K_i} = \\frac{d\\mu}{\\mu} $$\n",
"\n",
"$$ \\frac{d\\mu}{\\mu} =\\frac{z_i - z_a}{(1 + z_a)} \\frac{1}{K_i} $$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import scipy\n",
"from scipy import optimize\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pylab as pylab\n",
"pylab.rcParams['figure.figsize'] = 16, 8\n",
"# For plotting\n",
"color = ['blue', 'green', 'red', 'orange', 'black', 'purple']\n",
"yfactor = 1.0e6"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kfactors_string = \"+0.041 +0.031 +0.012 +0.006 -0.001 -0.008 +0.038 +0.037 +0.034 -0.006 +0.030 +0.030 +0.021 +0.016 +0.016 +0.005 +0.004 -0.001 -0.003 -0.008 +0.039 +0.038 +0.006 +0.004 +0.035 +0.033 +0.031 -0.005 +0.020 +0.019 +0.015 +0.013 +0.010 +0.004 +0.002 -0.003 +0.036 +0.035 +0.030 -0.009 -0.011 +0.018 +0.013 +0.011 +0.005 +0.001 -0.001 -0.008 -0.000 -0.002 +0.030 +0.028 +0.027 +0.017 +0.014 +0.009 +0.007 +0.001 -0.002 -0.005 -0.012 -0.003 +0.000 +0.020 -0.014 +0.012 +0.007 +0.005 +0.000 -0.003 -0.010\"\n",
"transition_string = \"L10R0 L7R0 L3R0 L2R0 L1R0 L0R0 L9R1 L9P1 L8R1 W0Q1 L7R1 L7P1 L5R1 L4R1 L4P1 L2R1 L2P1 L1R1 L1P1 L0R1 L10R2 L10P2 W1R2 W1Q2 L9P2 L8R2 L8P2 W0R2 L5R2 L5P2 L4R2 L4P2 L3R2 L2R2 L2P2 L1R2 L10R3 L10P3 L8R3 W0Q3 W0P3 L5R3 L4R3 L4P3 L3P3 L2R3 L2P3 L1P3 W1Q4 W1P4 L9R4 L9P4 L8R4 L6P4 L5R4 L4R4 L4P4 L3P4 L2R4 L2P4 L1P4 W1Q5 W0R5 L8P5 W0Q5 L6P5 L5P5 L4R5 L3R5 L3P5 L2P5\"\n",
"redshifts = np.array([2.401853,2.401850,2.401843,2.401845,2.401846,2.401845,2.401855,2.401853,2.401854,2.401845,2.401854,2.401848,2.401851,2.401849,2.401848,2.401850,2.401846,2.401848,2.401850,2.401850,2.401855,2.401855,2.401852,2.401858,2.401851,2.401862,2.401854,2.401856,2.401852,2.401853,2.401851,2.401850,2.401849,2.401849,2.401848,2.401845,2.401842,2.401852,2.401844,2.401850,2.401853,2.401850,2.401856,2.401852,2.401855,2.401853,2.401857,2.401849,2.401857,2.401849,2.401859,2.401858,2.401845,2.401847,2.401853,2.401858,2.401854,2.401845,2.401851,2.401850,2.401849,2.401857,2.401862,2.401858,2.401855,2.401853,2.401854,2.401852,2.401861,2.401845,2.401860])\n",
"wavelengthstr = \"981.4387 1012.8129 1062.8821 1077.1387 1092.1952 1108.1273 992.0163 992.8096 1002.4520 1009.7709 1013.4369 1014.3272 1037.1498 1049.9597 1051.0325 1077.6989 1078.9254 1092.7324 1094.0519 1108.6332 983.5911 984.8640 986.2440 987.9745 994.8740 1003.9854 1005.3931 1009.0249 1038.6902 1040.3672 1051.4985 1053.2842 1064.9948 1079.2254 1081.2660 1094.2446 985.9628 987.7688 1006.4141 1012.6796 1014.5042 1041.1588 1053.9761 1056.4714 1070.1408 1081.7112 1084.5603 1099.7872 992.0508 994.2299 999.2715 1001.6557 1009.7196 1035.1825 1044.5433 1057.3807 1060.5810 1074.3129 1085.1455 1088.7954 1104.0839 994.9244 1014.2425 1017.0043 1017.8315 1040.0587 1052.4970 1061.6972 1075.2441 1079.4004 1093.9550\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kfactors = np.array([float(x) for x in kfactors_string.split()])\n",
"wavelength = [float(x) for x in wavelengthstr.split()]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The paper says it plots \"reduced redshift\" vs \"$K_i$\" with \"reduced redshifts\" calculated relative to z_average = 2.4018517"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# What I'll be calling the \"reduced redshift\" value through this program\n",
"z_average = 2.4018517"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reduced Redshift\n",
"-----------------\n",
"\n",
"Assuming reduced redshift to be: \n",
"\n",
"$$z_{reduced_i} = \\frac{z_i - z_{a}}{1 + z_{a}}$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reducedredshifts = np.array((redshifts - z_average) / (1 + z_average))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"J_level = {}\n",
"levels = range(6)\n",
"for level in levels:\n",
" J_level[level] = {}\n",
" J_level[level]['k'] = []\n",
" J_level[level]['r'] = []\n",
" J_level[level]['reduced_z'] = []\n",
" J_level[level]['w'] = []\n",
" \n",
"for index, transition in enumerate(transition_string.split()):\n",
" level = int(transition[-1])\n",
" J_level[level]['k'].append(kfactors[index])\n",
" J_level[level]['r'].append(reducedredshifts[index])\n",
" J_level[level]['w'].append(wavelength[index])\n",
" J_level[level]['reduced_z'].append(reducedredshifts[index])\n",
"\n",
"for level in range(6):\n",
" J_level[level]['k'] = np.array(J_level[level]['k'])\n",
" J_level[level]['r'] = np.array(J_level[level]['r'])\n",
" J_level[level]['w'] = np.array(J_level[level]['w'])\n",
" J_level[level]['reduced_z'] = np.array(J_level[level]['reduced_z'])\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fitfunc = lambda p, x: p[0] + p[1]*x # Target function\n",
"errfunc = lambda p, x, y: fitfunc(p, x) - y # Distance to the target function\n",
"p0 = [0.01, 0.01] # Initial guess for the parameters\n",
"p1, success = optimize.leastsq(errfunc, p0[:], args=(kfactors, reducedredshifts))\n",
"\n",
"scatter( kfactors, reducedredshifts * yfactor)\n",
"xlabel(\"Sensitivity Coefficient K_i\")\n",
"ylabel('Reduced redshift by: ' + str(yfactor))\n",
"title(\"Figure 12\")\n",
"xarray = np.linspace(np.min(kfactors), np.max(kfactors), 20)\n",
"plot(xarray, fitfunc(p1, xarray) * yfactor, label=\"best fit\")\n",
"slope = p1[1]\n",
"mu = 1e6 * slope \n",
"title(\"Unweighted fit to all transitions. dmu/mu: \" + str(round(mu, 2)) + \" ppm\")\n",
"legend()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.legend.Legend at 0x10ea32a10>"
]
},
{
"output_type": "display_data",
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VHR19x3za7ILdrl27FBERoZSUFLm5ucnJyUmenp7px9061t/fX5cuXVJ0dHSm572b6/N3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\n1AIAAAAAnNb/Ayz3NaWK5/SGAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"redshiftarray = []\n",
"kfactorarray = []\n",
"for level in levels:\n",
" redshiftarray.append(J_level[level]['r'])\n",
" kfactorarray.append(J_level[level]['k'])\n",
"redshiftarray = np.array(redshiftarray)\n",
"kfactorarray = np.array(kfactorarray)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def newfitfunc(p, x):\n",
" temp = []\n",
" for thing in range(6):\n",
" temp.append(p[thing] + p[6]*x[thing])\n",
" return np.hstack(temp)\n",
" \n",
"def newerrfunc(p, x, y):\n",
" return newfitfunc(p, x) - y\n",
"\n",
"\n",
"starting = [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.01]\n",
"\n",
"combined, success = optimize.leastsq(newerrfunc, starting[:], args=(kfactorarray, np.hstack(redshiftarray)), ftol=1.49012e-09, xtol=1.49012e-09)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"for level in levels:\n",
" plot(xarray, fitfunc([combined[level], combined[-1]], xarray) * yfactor, \\\n",
" label=\"J-\" + str(level) + \"; \" + str(round(combined[level] + z_average, 7)), color=color[level])\n",
" scatter(J_level[level]['k'], (J_level[level]['reduced_z']) * yfactor, color=color[level])\n",
"legend(title=\"J-Level; Intercept\")\n",
"ylabel('Reduced redshift by: ' + str(yfactor))\n",
"xlabel('k-sensitivity')\n",
"slope = combined[-1]\n",
"mu = 1e6 * slope \n",
"title(\"Fit by J-level, tied slope gives dmu/mu = \" + str(round(mu, 2)) + \" ppm\")\n",
"#savefig(\"LP-tied-slope.pdf\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<matplotlib.text.Text at 0x10ea634d0>"
]
},
{
"output_type": "display_data",
"png": 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j//79MDIyqnJbV69exdmzZ3HmzBmcPn0awMNk9M033wQATJ48GYsXL8bJkyex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\nTkSuQmCC\n"
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fits = {}\n",
"for level in levels:\n",
" fits[level], success = optimize.leastsq(errfunc, p0[:], args=(J_level[level]['k'], J_level[level]['reduced_z']))\n",
" \n",
"for level in levels:\n",
" slope = fits[level][1]\n",
" mu = 1e6 * slope\n",
" plot(xarray, fitfunc(fits[level], xarray) * yfactor, label=\"J-\" + str(level) + \" \" + str(round(mu, 2)), color=color[level])\n",
" scatter(J_level[level]['k'], J_level[level]['reduced_z'] * yfactor, color=color[level])\n",
"#legend()\n",
"legend(title=\"J-Level; dmu/mu ppm\")\n",
"ylabel('Reduced redshift by: ' + str(yfactor))\n",
"xlabel('k-sensitivity')\n",
"title(\"Fig 12 fit by J-level\")\n",
"#savefig(\"2013-04-06-slope-per-j-level.pdf\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.text.Text at 0x10eab8c10>"
]
},
{
"output_type": "display_data",
"png": 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UfRVJoVZEKgXXW115fPvj+N7ryy3tb6Hr5K6EfRZm67JEREREysWZM2fo0aMH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\ntyCXAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Values used\n",
"print \"index, transition, wavelength, redshift, kfactor\"\n",
"for index, transition in enumerate(transition_string.split()):\n",
" print index, transition, wavelength[index], redshifts[index], kfactors[index]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"index, transition, wavelength, redshift, kfactor\n",
"0 L10R0 981.4387 2.401853 0.041\n",
"1 L7R0 1012.8129 2.40185 0.031\n",
"2 L3R0 1062.8821 2.401843 0.012\n",
"3 L2R0 1077.1387 2.401845 0.006\n",
"4 L1R0 1092.1952 2.401846 -0.001\n",
"5 L0R0 1108.1273 2.401845 -0.008\n",
"6 L9R1 992.0163 2.401855 0.038\n",
"7 L9P1 992.8096 2.401853 0.037\n",
"8 L8R1 1002.452 2.401854 0.034\n",
"9 W0Q1 1009.7709 2.401845 -0.006\n",
"10 L7R1 1013.4369 2.401854 0.03\n",
"11 L7P1 1014.3272 2.401848 0.03\n",
"12 L5R1 1037.1498 2.401851 0.021\n",
"13 L4R1 1049.9597 2.401849 0.016\n",
"14 L4P1 1051.0325 2.401848 0.016\n",
"15 L2R1 1077.6989 2.40185 0.005\n",
"16 L2P1 1078.9254 2.401846 0.004\n",
"17 L1R1 1092.7324 2.401848 -0.001\n",
"18 L1P1 1094.0519 2.40185 -0.003\n",
"19 L0R1 1108.6332 2.40185 -0.008\n",
"20 L10R2 983.5911 2.401855 0.039\n",
"21 L10P2 984.864 2.401855 0.038\n",
"22 W1R2 986.244 2.401852 0.006\n",
"23 W1Q2 987.9745 2.401858 0.004\n",
"24 L9P2 994.874 2.401851 0.035\n",
"25 L8R2 1003.9854 2.401862 0.033\n",
"26 L8P2 1005.3931 2.401854 0.031\n",
"27 W0R2 1009.0249 2.401856 -0.005\n",
"28 L5R2 1038.6902 2.401852 0.02\n",
"29 L5P2 1040.3672 2.401853 0.019\n",
"30 L4R2 1051.4985 2.401851 0.015\n",
"31 L4P2 1053.2842 2.40185 0.013\n",
"32 L3R2 1064.9948 2.401849 0.01\n",
"33 L2R2 1079.2254 2.401849 0.004\n",
"34 L2P2 1081.266 2.401848 0.002\n",
"35 L1R2 1094.2446 2.401845 -0.003\n",
"36 L10R3 985.9628 2.401842 0.036\n",
"37 L10P3 987.7688 2.401852 0.035\n",
"38 L8R3 1006.4141 2.401844 0.03\n",
"39 W0Q3 1012.6796 2.40185 -0.009\n",
"40 W0P3 1014.5042 2.401853 -0.011\n",
"41 L5R3 1041.1588 2.40185 0.018\n",
"42 L4R3 1053.9761 2.401856 0.013\n",
"43 L4P3 1056.4714 2.401852 0.011\n",
"44 L3P3 1070.1408 2.401855 0.005\n",
"45 L2R3 1081.7112 2.401853 0.001\n",
"46 L2P3 1084.5603 2.401857 -0.001\n",
"47 L1P3 1099.7872 2.401849 -0.008\n",
"48 W1Q4 992.0508 2.401857 -0\n",
"49 W1P4 994.2299 2.401849 -0.002\n",
"50 L9R4 999.2715 2.401859 0.03\n",
"51 L9P4 1001.6557 2.401858 0.028\n",
"52 L8R4 1009.7196 2.401845 0.027\n",
"53 L6P4 1035.1825 2.401847 0.017\n",
"54 L5R4 1044.5433 2.401853 0.014\n",
"55 L4R4 1057.3807 2.401858 0.009\n",
"56 L4P4 1060.581 2.401854 0.007\n",
"57 L3P4 1074.3129 2.401845 0.001\n",
"58 L2R4 1085.1455 2.401851 -0.002\n",
"59 L2P4 1088.7954 2.40185 -0.005\n",
"60 L1P4 1104.0839 2.401849 -0.012\n",
"61 W1Q5 994.9244 2.401857 -0.003\n",
"62 W0R5 1014.2425 2.401862 0.0\n",
"63 L8P5 1017.0043 2.401858 0.02\n",
"64 W0Q5 1017.8315 2.401855 -0.014\n",
"65 L6P5 1040.0587 2.401853 0.012\n",
"66 L5P5 1052.497 2.401854 0.007\n",
"67 L4R5 1061.6972 2.401852 0.005\n",
"68 L3R5 1075.2441 2.401861 0.0\n",
"69 L3P5 1079.4004 2.401845 -0.003\n",
"70 L2P5 1093.955 2.40186 -0.01\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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