mirror of
https://asciireactor.com/otho/phy-4660.git
synced 2024-11-22 15:05:07 +00:00
826 lines
157 KiB
Plaintext
826 lines
157 KiB
Plaintext
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"name": "stdout",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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},
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"<Container object of 3 artists>"
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"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f5265f94c10>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import numpy as np\n",
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"import sys\n",
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"import getopt\n",
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"sys.path.insert(1,\"/usr/local/science/clag/\")\n",
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"import clag\n",
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"%pylab inline\n",
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"\n",
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"from scipy.stats import norm\n",
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"from scipy.stats import lognorm\n",
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"\n",
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"ref_file=\"lc/1367A.lc\"\n",
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"echo_file=\"lc/9157A.lc\"\n",
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"\n",
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"\n",
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"dt = 0.01\n",
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"t1, l1, l1e = np.loadtxt(ref_file).T\n",
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"errorbar(t1, l1, yerr=l1e, fmt='o')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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|
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"array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n",
|
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|
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" 0.20739079, 0.32145572, 0.49825637])"
|
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]
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|
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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|
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"source": [
|
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"\n",
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"\n",
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"fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n",
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" 0.25819945, 0.40020915, 0.62032418])\n",
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|
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"# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n",
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"nfq = len(fqL) - 1\n",
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"fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n",
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"\n",
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"\n",
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"fqd\n",
|
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|
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"\n",
|
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|
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"\n"
|
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]
|
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},
|
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|
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{
|
||
|
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"cell_type": "code",
|
||
|
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"execution_count": 3,
|
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|
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"metadata": {
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"collapsed": false
|
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},
|
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|
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"outputs": [
|
||
|
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{
|
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|
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"name": "stdout",
|
||
|
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"output_type": "stream",
|
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|
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"text": [
|
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|
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" 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n",
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|
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" 2 7.674e-01 5.065e+01 8.300e+01 -- -4.700e+02 -- 0.653018 0.587019 0.568277 0.567457 0.566281 0.566085 0.565773 0.566163\n",
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" 3 3.298e+00 5.043e+01 8.075e+01 -- -3.893e+02 -- 0.414806 0.209135 0.141159 0.13784 0.133393 0.132728 0.131612 0.132761\n",
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" 4 1.572e+00 5.010e+01 7.754e+01 -- -3.117e+02 -- 0.322539 -0.0834456 -0.273066 -0.284295 -0.297612 -0.299435 -0.302479 -0.300412\n",
|
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|
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" 5 5.908e-01 4.964e+01 7.386e+01 -- -2.379e+02 -- 0.302357 -0.214604 -0.654658 -0.688754 -0.723838 -0.729279 -0.736418 -0.733888\n",
|
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|
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" 6 3.713e-01 4.877e+01 6.953e+01 -- -1.683e+02 -- 0.284419 -0.200357 -0.96379 -1.05472 -1.13798 -1.15477 -1.17031 -1.16748\n",
|
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|
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" 7 2.709e-01 4.671e+01 6.269e+01 -- -1.056e+02 -- 0.277768 -0.185001 -1.13047 -1.34026 -1.52128 -1.56845 -1.6043 -1.60101\n",
|
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|
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" 8 2.135e-01 4.361e+01 5.281e+01 -- -5.282e+01 -- 0.277012 -0.185189 -1.16375 -1.49463 -1.83211 -1.9477 -2.03737 -2.03476\n",
|
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|
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" 9 1.764e-01 3.767e+01 4.019e+01 -- -1.264e+01 -- 0.282161 -0.184207 -1.17891 -1.53049 -2.01851 -2.24569 -2.46562 -2.46922\n",
|
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|
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" 10 1.508e-01 2.738e+01 2.645e+01 -- 1.382e+01 -- 0.289545 -0.182463 -1.18795 -1.52893 -2.08812 -2.41103 -2.87498 -2.90486\n",
|
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|
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" 11 1.349e-01 1.468e+01 1.390e+01 -- 2.772e+01 -- 0.293547 -0.180898 -1.1897 -1.52803 -2.10944 -2.46526 -3.22702 -3.34288\n",
|
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|
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" 12 1.358e-01 5.365e+00 5.378e+00 -- 3.309e+01 -- 0.295456 -0.179941 -1.19052 -1.52801 -2.11928 -2.48294 -3.46233 -3.79368\n",
|
||
|
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" 13 1.868e-01 1.338e+00 1.633e+00 -- 3.473e+01 -- 0.297315 -0.17923 -1.19104 -1.52666 -2.12491 -2.48975 -3.55567 -4.30889\n",
|
||
|
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" 14 6.248e-01 2.604e-01 4.517e-01 -- 3.518e+01 -- 0.299091 -0.178645 -1.191 -1.52463 -2.12805 -2.4915 -3.5672 -5.11363\n",
|
||
|
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" 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n",
|
||
|
|
" 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n",
|
||
|
|
" 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
|
||
|
|
"********************\n",
|
||
|
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"0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
|
||
|
|
"0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n",
|
||
|
|
"-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n",
|
||
|
|
"p1 = np.ones(nfq)\n",
|
||
|
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"p1, p1e = clag.optimize(P1, p1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\t### errors for param 0 ###\n",
|
||
|
|
"+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n",
|
||
|
|
"+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n",
|
||
|
|
"+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n",
|
||
|
|
"+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n",
|
||
|
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"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n",
|
||
|
|
"+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n",
|
||
|
|
"+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n",
|
||
|
|
"+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n",
|
||
|
|
"+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n",
|
||
|
|
"+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n",
|
||
|
|
"+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n",
|
||
|
|
"+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n",
|
||
|
|
"+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n",
|
||
|
|
"+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n",
|
||
|
|
"+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n",
|
||
|
|
"+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n",
|
||
|
|
"+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n",
|
||
|
|
"+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n",
|
||
|
|
"+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n",
|
||
|
|
"+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n",
|
||
|
|
"+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n",
|
||
|
|
"+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n",
|
||
|
|
"+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n",
|
||
|
|
"+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n",
|
||
|
|
"+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n",
|
||
|
|
"+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n",
|
||
|
|
"+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n",
|
||
|
|
"+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n",
|
||
|
|
"+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n",
|
||
|
|
"+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n",
|
||
|
|
"+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n",
|
||
|
|
"+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n",
|
||
|
|
"+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n",
|
||
|
|
"+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n",
|
||
|
|
"+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n",
|
||
|
|
"\t### errors for param 7 ###\n",
|
||
|
|
"+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n",
|
||
|
|
"+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n",
|
||
|
|
"+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n",
|
||
|
|
"+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n",
|
||
|
|
"+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n",
|
||
|
|
"********************\n",
|
||
|
|
"0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n",
|
||
|
|
"0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"p1, p1e = clag.errors(P1, p1, p1e)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAGp1JREFUeJzt3X9s4/d93/HnxZaj5dxOi12RZ+98bLhdaBhJDbLSHzrb\n5TIn2IY26dBOIbFsiLTCQdoOuHUrcMMgzZCAYS2G9uqua3FbD9kQjNQNaDYH2LXFUKW5UVqnimm7\nbmadUT+W5kxmTXvr6kSpVnt/UHeR1I9+8I7fL389HwAhivx8vp+3cJ+TXvx+P9/vFyRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkvSA/iGwBvwh0AQ+A1zsakWSJKkn3AT+NvA08H7gs8AW\n8K4u1iRJknrQ48BbwHPdLkSSJJ3sHTGONbb39fdjHFOSJPW4M7QON/xqtwuRJEmn83BM4/xz4BmO\nP9Rwbu8hSZLa88beo6PiCAk/DXw38AJw+4g255544onbt28f9bYkSTrGl4EJOhwUogwJZ2gFhI8A\neWD7mLbnbt++zac//WmefvrpCEvqvMuXL3P16tW+HO9BttVu33ban6btSW2Oez/uf7NOca51vr1z\nLcy51vn2Uc611157jY997GNP0tob3zch4WeAIq2Q8CaQ3Hv9DrAT6vD000+TzWYjLKnzxsbGYq25\nk+M9yLba7dtO+9O0PanNce/H/W/WKc61zrd3roU51zrfPuq5FpWHItz2Z4F3AjPA39/3+CLwm4fa\nngM+8YlPfIJz5/pvWcL73ve+vh3vQbbVbt922p+m7Ultjnq/VCpRLBZPXUsvca51vr1zLcy51vn2\nUc21N954g2vXrgFco8N7Es50cmMPIAusr6+v92XqVn/58Ic/zKuvvtrtMjQEnGuKQ7VaJZfLAeSA\naie3Hed1EiRJUh8xJGjo9OvuX/Uf55r6nSFBQ8df3IqLc039zpAgSZKCDAmSJCnIkCBJkoIMCZIk\nKciQIEmSggwJkiQpyJAgSZKCDAmSJCnIkCBJkoIMCZIkKciQIEmSggwJkiQpyJAgSZKCDAmSJCnI\nkCBJkoIMCZIkKciQIEmSggwJkiQpyJAgSZKCDAmSJCnIkCBJkoIMCZIkKciQIEmSggwJkiQpyJAg\nSZKCDAmSJCnIkCBJkoIMCZIkKciQIEmSggwJkiQpyJAgSZKCDAmSJCnIkCBJkoIMCZIkKciQIEmS\nggwJkiQpyJAgSZKCDAmSJCkoypDwAvBZ4MvAW8BHIhxLkiR1WJQh4V3AF4Af2vv+7QjHkiRJHfZw\nhNv+xb2HJEnqQ65JkCRJQYYESZIUZEiQJElBUa5JaNvly5cZGxs78FqxWKRYLHapIkmSekepVKJU\nKh147c6dO5GNdyayLR/0FvC9wKtHvJ8F1tfX18lmszGVJElS/6tWq+RyOYAcUO3ktqPck3AW+Iv7\nvn8P8CzwVeBLEY4rSZI6IMqQMAH8yt7zt4Gf2Hv+KWA2wnElSVIHRBkSPocLIyVJ6lv+EZckSUGG\nBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJ\nkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhT0cLcLkKJQKpUolUoA7OzssL29zYUL\nFxgdHQWgWCxSLBa7WaIk9TxDggbS/hBQrVbJ5XKUSiWy2WyXK5Ok/uHhBkmSFGRIkCRJQYYESZIU\nZEiQJElBhgQNrK2tLWZnZ5mengZgenqa2dlZtra2uluYJPUJz27QwGk2mxQKBWq1Go1G497r9Xqd\ner3OzZs3yWQylMtlEolEFyuVpN5mSNBAaTabTE1NsbGxcWSbRqNBo9Hg0qVLVCoVg4IkHcHDDRoo\nhULh2ICwX71ep1AoRFyRJPUvQ4IGxubmJrVara0+tVrNNQqSdARDggbG4uLigTUIp9FoNFhYWIio\nIknqb4YEDYy1tbVY+0nSoDMkaGDs7u7G2k+SBp0hQQNjZGQk1n6SNOgMCRoYExMT99VvcnKyw5VI\n0mAwJGhgzM/Pk0wm2+qTTCaZm5uLqCJJ6m+GBA2MVCpFJpNpq08mkyGVSkVTkCT1OUOCBkq5XCad\nTp+qbTqdZmlpKeKKJKl/GRI0UBKJBJVKhXw+f+Shh2QyST6fZ2VlhfHx8ZgrlKT+YUjQwEkkEiwv\nL7O6usrMzMy9PQvpdJqZmRlWV1dZXl42IEjSCbzBkwZWKpXi+vXrVKtVcrkcN27cIJvNdrssSeob\nUe9J+EFgE/g68OvAcxGPJ0mSOiTKkPBR4CeBReBZ4BZwEzgf4ZiSJKlDogwJPwL8K+A68DvA3wO+\nBHwywjElSVKHRBUSHgGywC8fev2XgamIxpQkSR0U1cLFx4GHgOah178CtHdJPOk+lEolSqUSADs7\nO1y8eJErV64wOjoKQLFYpFgsdrNESep5nt2ggWQIOJ3DYWp7e5sLFy4YpiQBcCai7T4CvAl8P/Af\n9r3+U8D7gb90qH0WWH/++ecZGxs78Ia/pKR43D1VdH193VNFpR61P9jfdefOHW7dugWQA6qdHC+q\nPQl/DKwDH+JgSPgg8JmjOl29etVfTpIkHSH0wfluwI9ClGc3/ATwA8AM8DSt0yH/PPBzEY4pqU1b\nW1vMzs4yPT0NwPT0NLOzs2xtbXW3MEldF+WahBvAY8A8cA74b8Bfo3UapKQuazabFAoFarUajUbj\n3uv1ep16vc7NmzfJZDKUy2USiUQXK5XULVEvXPzZvYekHtJsNpmammJjY+PINo1Gg0ajwaVLl6hU\nKgYFaQh5gydpCBUKhWMDwn71ep1CoRBxRZJ6kSFBGjKbm5vUarW2+tRqNdcoSEPIkCANmcXFxQNr\nEE6j0WiwsLAQUUWSepUhQRoya2trsfaT1L8MCdKQ2d3djbWfpP5lSJCGzMjISKz9JPUvQ4I0ZCYm\nJu6r3+TkZIcrkdTrDAnSkJmfnyeZbO9mrMlkkrm5uYgqktSrDAnSkEmlUmQymbb6ZDIZUqlUNAVJ\n6lmGBGkIlctl0un0qdqm02mWlpYirkhSLzIkSEMokUhQqVTI5/NHHnpIJpPk83lWVlYYHx+PuUJJ\nvcCQIA2pRCLB8vIyq6urzMzM3NuzkE6nmZmZYXV1leXlZQOCNMSivsGTpB6XSqW4fv36vXvS37hx\ng2w22+2yJPUA9yRIkqQgQ4IkSQrycIM0xEqlEqVSCYCdnR0uXrzIlStXGB0dBaBYLFIsFrtZoqQu\nMiRIQ8wQIOk4Hm6QJElBhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhRkSJAk\nSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElB\nhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUFFVI+EfACvA14A8iGkOSJEUoqpAwAiwB/yKi7UuS\npIg9HNF2X977+vGIti9JkiLmmgRJkhQU1Z4ESeq4UqlEqVQCYGdnh+3tbS5cuMDo6CgAxWKRYrHY\nzRKlgdJOSHgZmD+hzXcC1fuuRpKOsT8EVKtVcrkcpVKJbDbb5cqkwdROSPhp4N+e0Gb7AWrh8uXL\njI2NHXjNTwaSJLXs35t21507dyIbr52Q8NW9R2SuXr3qJwJJko4Q+uB8d69aFKJak/AU8O69rw8B\n3wGcAb4IvBnRmJIkqYOiOrthgdbahJeBs8AXgHUgmqgjaWhsbW0xOzvL9PQ0ANPT08zOzrK1tdXd\nwqQBFNWehI/jNRIkdVCz2aRQKFCr1Wg0Gvder9fr1Ot1bt68SSaToVwuk0gkulipNDg8BVJSz2s2\nm0xNTbGxsXFkm0ajQaPR4NKlS1QqFYOC1AFeTElSzysUCscGhP3q9TqFQiHiiqThYEiQ1NM2Nzep\n1Wpt9anVaq5RkDrAkCC
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f528c4d9050>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"xscale('log'); ylim(-4,2)\n",
|
||
|
|
"errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10, color=\"black\")\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFkCAYAAACn/timAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPQ72MLBnwYPw4ZwP83L
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f5265afdd10>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"t2, l2, l2e = np.loadtxt(echo_file).T\n",
|
||
|
|
"errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n",
|
||
|
|
"errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" 1 4.359e-01 7.161e+01 inf -- -2.181e+02 -- 1 1 1 1 1 1 1 1\n",
|
||
|
|
" 2 7.742e-01 7.111e+01 8.740e+01 -- -1.307e+02 -- 0.564074 0.564103 0.564133 0.564559 0.565233 0.56464 0.564848 0.564132\n",
|
||
|
|
" 3 3.436e+00 7.050e+01 8.697e+01 -- -4.372e+01 -- 0.13087 0.128441 0.128217 0.128912 0.13058 0.129543 0.129728 0.127363\n",
|
||
|
|
" 4 1.429e+00 6.964e+01 8.635e+01 -- 4.262e+01 -- -0.295217 -0.304667 -0.307069 -0.306748 -0.303835 -0.30536 -0.305448 -0.310246\n",
|
||
|
|
" 5 5.887e-01 6.841e+01 8.530e+01 -- 1.279e+02 -- -0.704126 -0.731704 -0.741301 -0.742618 -0.738088 -0.740228 -0.740922 -0.748518\n",
|
||
|
|
" 6 3.715e-01 6.674e+01 8.354e+01 -- 2.115e+02 -- -1.07182 -1.14488 -1.17363 -1.17921 -1.17251 -1.17519 -1.17707 -1.18728\n",
|
||
|
|
" 7 2.720e-01 6.454e+01 8.085e+01 -- 2.923e+02 -- -1.35117 -1.52737 -1.60252 -1.61695 -1.60778 -1.61012 -1.61437 -1.62665\n",
|
||
|
|
" 8 2.151e-01 6.151e+01 7.746e+01 -- 3.698e+02 -- -1.49897 -1.84925 -2.02742 -2.05591 -2.04475 -2.04475 -2.05353 -2.06755\n",
|
||
|
|
" 9 1.795e-01 5.724e+01 7.348e+01 -- 4.433e+02 -- -1.55318 -2.06643 -2.44779 -2.49458 -2.48325 -2.47853 -2.49523 -2.5119\n",
|
||
|
|
" 10 1.552e-01 5.129e+01 6.861e+01 -- 5.119e+02 -- -1.57472 -2.15815 -2.85964 -2.92873 -2.92176 -2.90848 -2.9396 -2.96289\n",
|
||
|
|
" 11 1.358e-01 4.343e+01 6.173e+01 -- 5.736e+02 -- -1.57452 -2.1858 -3.24732 -3.34792 -3.35575 -3.328 -3.38557 -3.42282\n",
|
||
|
|
" 12 1.168e-01 3.415e+01 5.061e+01 -- 6.242e+02 -- -1.56805 -2.20666 -3.56401 -3.72207 -3.774 -3.72255 -3.82733 -3.8877\n",
|
||
|
|
" 13 9.496e-02 2.420e+01 3.428e+01 -- 6.585e+02 -- -1.56623 -2.22075 -3.74568 -3.98216 -4.14916 -4.05587 -4.24428 -4.34169\n",
|
||
|
|
" 14 6.665e-02 1.351e+01 1.640e+01 -- 6.749e+02 -- -1.5696 -2.22354 -3.78328 -4.0829 -4.4261 -4.2599 -4.58517 -4.75399\n",
|
||
|
|
" 15 2.964e-02 4.358e+00 4.427e+00 -- 6.793e+02 -- -1.57425 -2.21978 -3.77127 -4.13056 -4.55417 -4.30434 -4.77899 -5.07083\n",
|
||
|
|
" 16 3.872e-03 6.183e-01 4.700e-01 -- 6.798e+02 -- -1.57487 -2.21895 -3.77396 -4.16245 -4.5755 -4.2897 -4.83862 -5.22114\n",
|
||
|
|
" 17 1.291e-03 1.659e-01 1.113e-02 -- 6.798e+02 -- -1.57331 -2.22027 -3.78214 -4.16768 -4.57631 -4.28246 -4.85735 -5.23199\n",
|
||
|
|
" 18 5.326e-04 6.959e-02 1.219e-03 -- 6.798e+02 -- -1.57311 -2.22061 -3.78349 -4.168 -4.57225 -4.27943 -4.86362 -5.23031\n",
|
||
|
|
" 19 2.494e-04 2.982e-02 2.268e-04 -- 6.798e+02 -- -1.57309 -2.22069 -3.78429 -4.1678 -4.56992 -4.2784 -4.86621 -5.23009\n",
|
||
|
|
" 20 1.140e-04 1.317e-02 4.436e-05 -- 6.798e+02 -- -1.57306 -2.22073 -3.78469 -4.16784 -4.56878 -4.27802 -4.86734 -5.22989\n",
|
||
|
|
"********************\n",
|
||
|
|
"-1.57306 -2.22073 -3.78469 -4.16784 -4.56878 -4.27802 -4.86734 -5.22989\n",
|
||
|
|
"0.237078 0.201135 0.284718 0.248327 0.252841 0.150914 0.188371 0.222107\n",
|
||
|
|
"0.000184037 -0.000586923 -0.00230695 0.00112283 0.00799663 0.00635492 -0.0131691 -0.000270457\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n",
|
||
|
|
"p2 = np.ones(nfq)\n",
|
||
|
|
"p2, p2e = clag.optimize(P2, p2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\t### errors for param 0 ###\n",
|
||
|
|
"+++ 6.798e+02 6.794e+02 -1.573e+00 -1.336e+00 0.88 +++\n",
|
||
|
|
"+++ 6.798e+02 6.789e+02 -1.573e+00 -1.217e+00 1.85 +++\n",
|
||
|
|
"+++ 6.798e+02 6.791e+02 -1.573e+00 -1.277e+00 1.33 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -1.573e+00 -1.306e+00 1.09 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -1.573e+00 -1.321e+00 0.984 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -1.573e+00 -1.314e+00 1.04 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -1.573e+00 -1.317e+00 1.01 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -1.573e+00 -1.319e+00 0.998 +++\n",
|
||
|
|
"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 6.798e+02 6.794e+02 -2.221e+00 -2.020e+00 0.889 +++\n",
|
||
|
|
"+++ 6.798e+02 6.789e+02 -2.221e+00 -1.919e+00 1.88 +++\n",
|
||
|
|
"+++ 6.798e+02 6.791e+02 -2.221e+00 -1.969e+00 1.35 +++\n",
|
||
|
|
"+++ 6.798e+02 6.792e+02 -2.221e+00 -1.994e+00 1.11 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -2.221e+00 -2.007e+00 0.996 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -3.785e+00 -3.500e+00 0.976 +++\n",
|
||
|
|
"+++ 6.798e+02 6.787e+02 -3.785e+00 -3.358e+00 2.11 +++\n",
|
||
|
|
"+++ 6.798e+02 6.791e+02 -3.785e+00 -3.429e+00 1.49 +++\n",
|
||
|
|
"+++ 6.798e+02 6.792e+02 -3.785e+00 -3.465e+00 1.22 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -3.785e+00 -3.482e+00 1.1 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -3.785e+00 -3.491e+00 1.04 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -3.785e+00 -3.496e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 6.798e+02 6.796e+02 -4.168e+00 -4.044e+00 0.34 +++\n",
|
||
|
|
"+++ 6.798e+02 6.794e+02 -4.168e+00 -3.982e+00 0.75 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.168e+00 -3.951e+00 1.01 +++\n",
|
||
|
|
"+++ 6.798e+02 6.794e+02 -4.168e+00 -3.966e+00 0.876 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.168e+00 -3.958e+00 0.942 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.168e+00 -3.954e+00 0.976 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.168e+00 -3.952e+00 0.993 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 6.798e+02 6.794e+02 -4.568e+00 -4.316e+00 0.875 +++\n",
|
||
|
|
"+++ 6.798e+02 6.788e+02 -4.568e+00 -4.189e+00 2.1 +++\n",
|
||
|
|
"+++ 6.798e+02 6.791e+02 -4.568e+00 -4.252e+00 1.41 +++\n",
|
||
|
|
"+++ 6.798e+02 6.792e+02 -4.568e+00 -4.284e+00 1.13 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.568e+00 -4.300e+00 0.996 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 6.798e+02 6.794e+02 -4.278e+00 -4.127e+00 0.875 +++\n",
|
||
|
|
"+++ 6.798e+02 6.789e+02 -4.278e+00 -4.052e+00 1.83 +++\n",
|
||
|
|
"+++ 6.798e+02 6.791e+02 -4.278e+00 -4.089e+00 1.36 +++\n",
|
||
|
|
"+++ 6.798e+02 6.792e+02 -4.278e+00 -4.108e+00 1.1 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.278e+00 -4.118e+00 0.986 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.278e+00 -4.113e+00 1.04 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.278e+00 -4.115e+00 1.02 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.278e+00 -4.116e+00 1 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 6.798e+02 6.794e+02 -4.868e+00 -4.679e+00 0.799 +++\n",
|
||
|
|
"+++ 6.798e+02 6.788e+02 -4.868e+00 -4.585e+00 1.9 +++\n",
|
||
|
|
"+++ 6.798e+02 6.792e+02 -4.868e+00 -4.632e+00 1.28 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.868e+00 -4.656e+00 1.03 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.868e+00 -4.668e+00 0.908 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.868e+00 -4.662e+00 0.966 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -4.868e+00 -4.659e+00 0.996 +++\n",
|
||
|
|
"\t### errors for param 7 ###\n",
|
||
|
|
"+++ 6.798e+02 6.796e+02 -5.230e+00 -5.119e+00 0.305 +++\n",
|
||
|
|
"+++ 6.798e+02 6.795e+02 -5.230e+00 -5.063e+00 0.696 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -5.230e+00 -5.036e+00 0.953 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -5.230e+00 -5.022e+00 1.1 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -5.230e+00 -5.029e+00 1.02 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -5.230e+00 -5.032e+00 0.988 +++\n",
|
||
|
|
"+++ 6.798e+02 6.793e+02 -5.230e+00 -5.030e+00 1.01 +++\n",
|
||
|
|
"********************\n",
|
||
|
|
"-1.57305 -2.22075 -3.78488 -4.16779 -4.56826 -4.27788 -4.86784 -5.22982\n",
|
||
|
|
"0.253747 0.213705 0.289194 0.215344 0.268522 0.161511 0.209074 0.199524\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"p2, p2e = clag.errors(P2, p2, p2e)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSHEh1sDQs0ORjEdqJIY
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f5265b07dd0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"xscale('log'); ylim(-6,2)\n",
|
||
|
|
"errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10, color=\"green\")\n",
|
||
|
|
"errorbar(fqd, p2, yerr=p2e, fmt='o', ms=10, color=\"black\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" 1 4.263e+02 8.743e+00 inf -- 7.200e+02 -- -0.936223 -1.49944 -2.78774 -3.1451 -3.64922 -3.68468 -4.51464 -6.91491 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
|
||
|
|
" 3 4.860e+01 9.447e+00 3.012e+00 -- 7.230e+02 -- -0.89632 -1.47767 -2.84644 -3.16314 -3.6728 -3.70288 -4.53842 -6.61491 0.0812657 0.222203 0.312409 0.206336 0.095992 0.137977 -0.0249432 2.12014\n",
|
||
|
|
" 5 1.834e+02 1.086e+01 2.400e+00 -- 7.254e+02 -- -0.863738 -1.45362 -2.88196 -3.17521 -3.69408 -3.71906 -4.5538 -6.91491 0.0679893 0.320406 0.548492 0.312861 0.089923 0.174248 -0.146165 0.867841\n",
|
||
|
|
" 7 2.704e+01 1.233e+01 1.965e+00 -- 7.273e+02 -- -0.83671 -1.42964 -2.88961 -3.18149 -3.71318 -3.73336 -4.56179 -6.61491 0.0582761 0.398604 0.784091 0.415604 0.0825065 0.208572 -0.259049 -2.48409\n",
|
||
|
|
" 9 8.381e+01 1.445e+01 1.699e+00 -- 7.290e+02 -- -0.814012 -1.40696 -2.87289 -3.18272 -3.73036 -3.74595 -4.56372 -6.91491 0.0510133 0.460993 0.990591 0.511606 0.0740674 0.240773 -0.360623 -2.05029\n",
|
||
|
|
" 11 5.382e+01 1.673e+01 1.479e+00 -- 7.305e+02 -- -0.794769 -1.38613 -2.84208 -3.17997 -3.74567 -3.75695 -4.56136 -7.21491 0.0454691 0.511132 1.15265 0.598735 0.0649178 0.270666 -0.449551 2.56611\n",
|
||
|
|
" 13 5.234e+02 1.913e+01 1.324e+00 -- 7.318e+02 -- -0.778329 -1.36729 -2.80694 -3.1744 -3.75929 -3.7665 -4.55618 -7.51491 0.041178 0.551805 1.27263 0.676139 0.0552576 0.298158 -0.525871 1.32105\n",
|
||
|
|
" 15 2.636e+02 2.168e+01 1.189e+00 -- 7.330e+02 -- -0.764197 -1.35038 -2.77293 -3.16703 -3.77138 -3.7748 -4.54947 -7.21491 0.0378314 0.585121 1.36044 0.743875 0.0453504 0.323244 -0.590433 1.29064\n",
|
||
|
|
" 17 1.149e+02 2.437e+01 1.088e+00 -- 7.341e+02 -- -0.751986 -1.33529 -2.74211 -3.15871 -3.78208 -3.78199 -4.54208 -6.91491 0.03521 0.612663 1.4256 0.802584 0.0353532 0.345957 -0.644618 -1.31496\n",
|
||
|
|
" 19 6.722e+01 2.722e+01 9.849e-01 -- 7.351e+02 -- -0.741387 -1.32184 -2.71491 -3.15002 -3.79154 -3.78825 -4.53464 -6.61491 0.0331641 0.635624 1.47504 0.853226 0.0254728 0.36641 -0.689866 1.22418\n",
|
||
|
|
" 21 2.720e+01 3.023e+01 9.444e-01 -- 7.360e+02 -- -0.732157 -1.30986 -2.69111 -3.14143 -3.79983 -3.79362 -4.52741 -6.31491 0.0315615 0.654913 1.51347 0.896759 0.0156921 0.384625 -0.727661 -0.72188\n",
|
||
|
|
" 23 1.618e+01 3.340e+01 8.070e-01 -- 7.368e+02 -- -0.724091 -1.29919 -2.67035 -3.13314 -3.80709 -3.79838 -4.52086 -6.01491 0.0303421 0.671224 1.54392 0.93425 0.00644024 0.400889 -0.759178 1.24133\n",
|
||
|
|
" 24 8.891e+01 4.172e+02 8.637e+00 -- 7.455e+02 -- -0.65346 -1.20411 -2.48868 -3.0558 -3.86972 -3.83787 -4.45774 -5.36047 0.0208476 0.809948 1.7913 1.25651 -0.0861695 0.542223 -1.02272 0.0117388\n",
|
||
|
|
" 25 3.521e+01 9.060e+00 9.410e-01 -- 7.464e+02 -- -0.661625 -1.20837 -2.49819 -3.05016 -3.85526 -3.84511 -4.44606 -4.62172 0.0636491 0.75819 1.74668 1.16355 0.00391259 0.465285 -0.857723 -1.03192\n",
|
||
|
|
" 26 2.800e-01 2.694e+00 4.013e-01 -- 7.468e+02 -- -0.659925 -1.20808 -2.49692 -3.04136 -3.86296 -3.86865 -4.52444 -4.7803 0.0440885 0.773556 1.74836 1.18011 0.141663 0.501264 -1.01577 -0.968135\n",
|
||
|
|
" 27 1.118e-01 1.396e+00 6.852e-02 -- 7.469e+02 -- -0.660557 -1.20802 -2.49682 -3.04782 -3.86407 -3.85635 -4.50129 -4.79491 0.0494721 0.769251 1.75312 1.18102 0.159998 0.494797 -0.942213 -0.697092\n",
|
||
|
|
" 28 6.066e-02 5.475e-01 7.813e-03 -- 7.469e+02 -- -0.660375 -1.20798 -2.49707 -3.04899 -3.85699 -3.85319 -4.501 -4.80785 0.0481904 0.770454 1.74927 1.18272 0.163201 0.485505 -0.957511 -0.619169\n",
|
||
|
|
" 29 2.205e-02 2.173e-01 1.373e-03 -- 7.469e+02 -- -0.660499 -1.20797 -2.4971 -3.05002 -3.85533 -3.85154 -4.49936 -4.80815 0.048302 0.77034 1.75136 1.18473 0.158919 0.484615 -0.950396 -0.581613\n",
|
||
|
|
" 30 2.005e-02 7.267e-02 2.580e-04 -- 7.469e+02 -- -0.660478 -1.20797 -2.49717 -3.05009 -3.85287 -3.85128 -4.49884 -4.81171 0.0482529 0.770456 1.75045 1.18445 0.155414 0.48385 -0.951932 -0.569748\n",
|
||
|
|
" 31 1.192e-02 5.990e-02 5.760e-05 -- 7.469e+02 -- -0.660494 -1.20797 -2.49717 -3.05021 -3.85235 -3.85102 -4.49839 -4.81176 0.0482126 0.770461 1.75114 1.18511 0.152299 0.483729 -0.950569 -0.563021\n",
|
||
|
|
"********************\n",
|
||
|
|
"-0.660494 -1.20797 -2.49717 -3.05021 -3.85235 -3.85102 -4.49839 -4.81176 0.0482126 0.770461 1.75114 1.18511 0.152299 0.483729 -0.950569 -0.563021\n",
|
||
|
|
"0.015038 0.00527965 0.0219253 0.14394 0.404753 0.263604 0.317912 0.460201 0.139209 0.0769038 0.175352 0.449778 0.985963 0.654083 0.765981 0.99603\n",
|
||
|
|
"0.0218905 0.0599 -0.0352934 0.00081409 0.00482364 -0.00014268 0.0010011 -0.00637734 0.00136757 0.00106954 -0.00732663 -0.00116 -0.00186662 -0.000327004 -0.000366653 0.00318593\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n",
|
||
|
|
"p = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a good starting point generally\n",
|
||
|
|
"p, pe = clag.optimize(Cx, p)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"ERROR:root:Line magic function `%autoreload` not found.\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\t### errors for param 0 ###\n",
|
||
|
|
"+++ 7.469e+02 7.467e+02 -6.605e-01 -6.530e-01 0.383 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 -6.605e-01 -6.492e-01 1.15 +++\n",
|
||
|
|
"+++ 7.469e+02 7.466e+02 -6.605e-01 -6.511e-01 0.685 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 -6.605e-01 -6.502e-01 0.892 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -6.605e-01 -6.497e-01 1.01 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -6.605e-01 -6.499e-01 0.95 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -6.605e-01 -6.498e-01 0.981 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -6.605e-01 -6.497e-01 0.997 +++\n",
|
||
|
|
"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 7.469e+02 7.467e+02 -1.208e+00 -1.205e+00 0.378 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.1 +++\n",
|
||
|
|
"+++ 7.469e+02 7.466e+02 -1.208e+00 -1.205e+00 0.668 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 -1.208e+00 -1.204e+00 0.864 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 0.977 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.04 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 7.469e+02 7.467e+02 -2.497e+00 -2.486e+00 0.424 +++\n",
|
||
|
|
"+++ 7.469e+02 7.461e+02 -2.497e+00 -2.481e+00 1.65 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 -2.497e+00 -2.483e+00 0.847 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 -2.497e+00 -2.482e+00 1.18 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -2.497e+00 -2.483e+00 1 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 7.469e+02 7.467e+02 -3.050e+00 -2.978e+00 0.391 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 -3.050e+00 -2.942e+00 1.19 +++\n",
|
||
|
|
"+++ 7.469e+02 7.466e+02 -3.050e+00 -2.960e+00 0.703 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.050e+00 -2.951e+00 0.918 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.050e+00 -2.947e+00 1.04 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.050e+00 -2.949e+00 0.98 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.050e+00 -2.948e+00 1.01 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.050e+00 -2.948e+00 0.996 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 7.469e+02 7.468e+02 -3.852e+00 -3.650e+00 0.275 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 -3.852e+00 -3.549e+00 0.847 +++\n",
|
||
|
|
"+++ 7.469e+02 7.462e+02 -3.852e+00 -3.498e+00 1.35 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.852e+00 -3.523e+00 1.07 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.852e+00 -3.536e+00 0.948 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.852e+00 -3.530e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 7.469e+02 7.467e+02 -3.851e+00 -3.719e+00 0.406 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 -3.851e+00 -3.653e+00 1.18 +++\n",
|
||
|
|
"+++ 7.469e+02 7.466e+02 -3.851e+00 -3.686e+00 0.714 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.851e+00 -3.670e+00 0.922 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.851e+00 -3.662e+00 1.04 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.851e+00 -3.666e+00 0.981 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.851e+00 -3.664e+00 1.01 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -3.851e+00 -3.665e+00 0.996 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 7.469e+02 7.467e+02 -4.498e+00 -4.339e+00 0.402 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 -4.498e+00 -4.260e+00 1.2 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 -4.498e+00 -4.300e+00 0.719 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -4.498e+00 -4.280e+00 0.934 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -4.498e+00 -4.270e+00 1.06 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -4.498e+00 -4.275e+00 0.995 +++\n",
|
||
|
|
"\t### errors for param 7 ###\n",
|
||
|
|
"+++ 7.469e+02 7.466e+02 -4.813e+00 -4.582e+00 0.567 +++\n",
|
||
|
|
"+++ 7.469e+02 7.458e+02 -4.813e+00 -4.466e+00 2.24 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 -4.813e+00 -4.524e+00 1.15 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 -4.813e+00 -4.553e+00 0.813 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -4.813e+00 -4.539e+00 0.968 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -4.813e+00 -4.531e+00 1.05 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -4.813e+00 -4.535e+00 1.01 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -4.813e+00 -4.537e+00 0.989 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -4.813e+00 -4.536e+00 1 +++\n",
|
||
|
|
"\t### errors for param 8 ###\n",
|
||
|
|
"+++ 7.469e+02 7.468e+02 4.824e-02 1.178e-01 0.265 +++\n",
|
||
|
|
"+++ 7.469e+02 7.466e+02 4.824e-02 1.526e-01 0.589 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 4.824e-02 1.700e-01 0.794 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 4.824e-02 1.787e-01 0.907 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 4.824e-02 1.831e-01 0.963 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 4.824e-02 1.853e-01 0.993 +++\n",
|
||
|
|
"\t### errors for param 9 ###\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 7.705e-01 8.474e-01 1.02 +++\n",
|
||
|
|
"+++ 7.469e+02 7.468e+02 7.705e-01 8.089e-01 0.265 +++\n",
|
||
|
|
"+++ 7.469e+02 7.466e+02 7.705e-01 8.281e-01 0.586 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 7.705e-01 8.378e-01 0.788 +++\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 7.705e-01 8.426e-01 0.899 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 7.705e-01 8.450e-01 0.957 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 7.705e-01 8.462e-01 0.986 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 7.705e-01 8.468e-01 1 +++\n",
|
||
|
|
"\t### errors for param 10 ###\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 1.751e+00 1.926e+00 0.955 +++\n",
|
||
|
|
"+++ 7.469e+02 7.460e+02 1.751e+00 2.014e+00 1.89 +++\n",
|
||
|
|
"+++ 7.469e+02 7.462e+02 1.751e+00 1.970e+00 1.4 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 1.751e+00 1.948e+00 1.17 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 1.751e+00 1.937e+00 1.06 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 1.751e+00 1.932e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 11 ###\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 1.185e+00 1.635e+00 0.84 +++\n",
|
||
|
|
"+++ 7.469e+02 7.461e+02 1.185e+00 1.860e+00 1.62 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 1.185e+00 1.747e+00 1.22 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 1.185e+00 1.691e+00 1.03 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 1.185e+00 1.663e+00 0.932 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 1.185e+00 1.677e+00 0.979 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 1.185e+00 1.684e+00 1 +++\n",
|
||
|
|
"\t### errors for param 12 ###\n",
|
||
|
|
"\t### errors for param 13 ###\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 4.836e-01 1.138e+00 0.903 +++\n",
|
||
|
|
"+++ 7.469e+02 7.461e+02 4.836e-01 1.465e+00 1.67 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 4.836e-01 1.301e+00 1.29 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 4.836e-01 1.219e+00 1.1 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 4.836e-01 1.179e+00 0.999 +++\n",
|
||
|
|
"\t### errors for param 14 ###\n",
|
||
|
|
"+++ 7.469e+02 7.465e+02 -9.510e-01 -1.852e-01 0.856 +++\n",
|
||
|
|
"+++ 7.469e+02 7.462e+02 -9.510e-01 1.976e-01 1.43 +++\n",
|
||
|
|
"+++ 7.469e+02 7.463e+02 -9.510e-01 6.198e-03 1.17 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -9.510e-01 -8.952e-02 1.02 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -9.510e-01 -1.374e-01 0.937 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -9.510e-01 -1.134e-01 0.978 +++\n",
|
||
|
|
"+++ 7.469e+02 7.464e+02 -9.510e-01 -1.015e-01 0.998 +++\n",
|
||
|
|
"\t### errors for param 15 ###\n",
|
||
|
|
"********************\n",
|
||
|
|
"-0.66049 -1.20797 -2.49719 -3.05019 -3.85156 -3.85104 -4.49824 -4.81261 0.048236 0.77047 1.75092 1.18493 0.150483 0.48362 -0.95096 -0.560876\n",
|
||
|
|
"0.0107478 0.0038354 0.014397 0.101761 0.321854 0.186386 0.223452 0.276776 0.137021 0.0762971 0.180879 0.498941 2 0.694997 0.849478 4.9864\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"%autoreload\n",
|
||
|
|
"p, pe = clag.errors(Cx, p, pe)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"phi, phie = p[nfq:], pe[nfq:]\n",
|
||
|
|
"lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd) \n",
|
||
|
|
"cx, cxe = p[:nfq], pe[:nfq]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 0.79565312, 4.2489229 , 5.00370082, 2.18467546, 0.17899829,\n",
|
||
|
|
" 0.37113758, -0.47082688, -0.17915708])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFrCAYAAACE+GArAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n76mnnmpxJdAaly9fzv7
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f5265077890>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"xscale('log'); ylim(-10,10)\n",
|
||
|
|
"errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n",
|
||
|
|
"\n",
|
||
|
|
"lag"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f5261e9ce90>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGYCAYAAACQz+KaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nK4F9SrAvSZJUY+ImI8O
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f526218fb50>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from scipy.optimize import curve_fit\n",
|
||
|
|
"\n",
|
||
|
|
"# Define model function to be used to fit to the data above:\n",
|
||
|
|
"def tophat_time(x, *p):\n",
|
||
|
|
" mean, width = p\n",
|
||
|
|
" if x>(mean+width): y=0\n",
|
||
|
|
" if x<(mean-width): y=0\n",
|
||
|
|
" if x==(mean+width) | x==(mean-width): y=5\n",
|
||
|
|
" return y\n",
|
||
|
|
"\n",
|
||
|
|
"def tophat_freq(f, *pars):\n",
|
||
|
|
" A,T,t0 = pars\n",
|
||
|
|
" #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n",
|
||
|
|
" return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n",
|
||
|
|
"\n",
|
||
|
|
"x=np.logspace(fqd[0],fqd[-1],200)\n",
|
||
|
|
"\n",
|
||
|
|
"# p0 is the initial guess for the fitting coefficients\n",
|
||
|
|
"p0 = [3, 3, 3]\n",
|
||
|
|
"coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n",
|
||
|
|
"fit = tophat_freq(fqd, *coeff)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"mpl.rcParams['xtick.labelsize']=12\n",
|
||
|
|
"mpl.rcParams['ytick.labelsize']=12\n",
|
||
|
|
"xscale('log'); xlim(.009,.6)\n",
|
||
|
|
"xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n",
|
||
|
|
"ylabel(\"Time Lag (days)\",fontsize=20)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n",
|
||
|
|
"plot(fqd,fit)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"scrolled": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f5261ef0f90>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f5261f2d750>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"time_fit = irfft(fit)\n",
|
||
|
|
"\n",
|
||
|
|
"mpl.rcParams['xtick.labelsize']=12\n",
|
||
|
|
"mpl.rcParams['ytick.labelsize']=12\n",
|
||
|
|
"ylabel(\"Response (relative)\",fontsize=20)\n",
|
||
|
|
"xlabel(\"Time (days)\",fontsize=20) \n",
|
||
|
|
"\n",
|
||
|
|
"ylim(-0.5,2)\n",
|
||
|
|
"xlim(0,7)\n",
|
||
|
|
"\n",
|
||
|
|
"plot(time_fit)\n",
|
||
|
|
"plot([3.93,3.93], [-50, 50], color='k', linestyle='-', linewidth=2)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "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": "2.7.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|