mirror of
https://asciireactor.com/otho/phy-4660.git
synced 2024-12-18 11:55:04 +00:00
833 lines
161 KiB
Plaintext
833 lines
161 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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"execution_count": 1,
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAFkCAYAAACn/timAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzJw5yObNyX/esix+/et
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f11415a1c10>"
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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/7647A.lc\"\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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"array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n",
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" 0.20739079, 0.32145572, 0.49825637])"
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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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"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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"# 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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"\n",
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"\n"
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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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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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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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" 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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" 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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" 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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" 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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" 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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" 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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" 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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" 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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" 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",
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" 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",
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" 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",
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"********************\n",
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"0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n",
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|
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"0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n",
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"-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n",
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"********************\n"
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]
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}
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],
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"source": [
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|
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"P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n",
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"p1 = np.ones(nfq)\n",
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|
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"p1, p1e = clag.optimize(P1, p1)"
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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": 4,
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||
|
|
"metadata": {
|
||
|
|
"collapsed": false
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||
|
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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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|
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"output_type": "stream",
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"text": [
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||
|
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"\t### errors for param 0 ###\n",
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|
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"+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n",
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||
|
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"+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n",
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||
|
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"+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n",
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||
|
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"+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n",
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"+++ 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",
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"+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n",
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|
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"+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n",
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"+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n",
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"+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n",
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|
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"+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n",
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|
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"+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n",
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||
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"+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n",
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"\t### errors for param 2 ###\n",
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"+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n",
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|
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"+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n",
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|
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"+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n",
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|
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"+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n",
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|
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"+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n",
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|
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"+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n",
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|
|
"\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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\n1Wpt9anVaq5RkDrAkCC
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f11653021d0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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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\nbW2tcbCX/msW3Asfzv4
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f114110a090>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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 4.352e-01 7.004e+01 inf -- -2.548e+02 -- 1 1 1 1 1 1 1 1\n",
|
||
|
|
" 2 7.709e-01 6.945e+01 8.689e+01 -- -1.680e+02 -- 0.568202 0.566496 0.565546 0.565389 0.565371 0.564829 0.565108 0.56515\n",
|
||
|
|
" 3 3.365e+00 6.850e+01 8.615e+01 -- -8.181e+01 -- 0.138452 0.134629 0.131417 0.131051 0.130581 0.129376 0.130493 0.130733\n",
|
||
|
|
" 4 1.432e+00 6.742e+01 8.494e+01 -- 3.128e+00 -- -0.281314 -0.293481 -0.30187 -0.302706 -0.304046 -0.305979 -0.303068 -0.302158\n",
|
||
|
|
" 5 5.888e-01 6.660e+01 8.345e+01 -- 8.658e+01 -- -0.670439 -0.713504 -0.734283 -0.735609 -0.738394 -0.740978 -0.735503 -0.733408\n",
|
||
|
|
" 6 3.712e-01 6.580e+01 8.170e+01 -- 1.683e+02 -- -0.98186 -1.11559 -1.16661 -1.16796 -1.17265 -1.17538 -1.16791 -1.16475\n",
|
||
|
|
" 7 2.710e-01 6.441e+01 7.924e+01 -- 2.475e+02 -- -1.15319 -1.47413 -1.59924 -1.60004 -1.60675 -1.60901 -1.601 -1.59713\n",
|
||
|
|
" 8 2.138e-01 6.207e+01 7.606e+01 -- 3.236e+02 -- -1.2017 -1.73389 -2.0317 -2.03093 -2.04033 -2.04197 -2.03495 -2.02955\n",
|
||
|
|
" 9 1.767e-01 5.874e+01 7.252e+01 -- 3.961e+02 -- -1.21944 -1.84738 -2.46191 -2.45696 -2.47336 -2.47447 -2.47007 -2.4612\n",
|
||
|
|
" 10 1.505e-01 5.422e+01 6.836e+01 -- 4.645e+02 -- -1.22867 -1.87558 -2.87636 -2.86554 -2.90591 -2.90606 -2.90644 -2.89281\n",
|
||
|
|
" 11 1.304e-01 4.826e+01 6.187e+01 -- 5.263e+02 -- -1.23499 -1.89484 -3.22854 -3.22689 -3.33472 -3.3325 -3.34386 -3.32489\n",
|
||
|
|
" 12 1.163e-01 4.093e+01 5.179e+01 -- 5.781e+02 -- -1.23852 -1.90721 -3.44389 -3.49376 -3.74893 -3.73895 -3.77938 -3.75848\n",
|
||
|
|
" 13 9.980e-02 3.115e+01 3.779e+01 -- 6.159e+02 -- -1.23702 -1.90846 -3.49545 -3.63342 -4.12276 -4.08853 -4.19527 -4.19544\n",
|
||
|
|
" 14 7.004e-02 1.776e+01 2.014e+01 -- 6.360e+02 -- -1.23105 -1.90503 -3.4814 -3.68042 -4.41637 -4.31891 -4.53403 -4.61415\n",
|
||
|
|
" 15 2.773e-02 5.191e+00 5.391e+00 -- 6.414e+02 -- -1.22657 -1.90214 -3.47116 -3.69656 -4.59718 -4.39786 -4.69506 -4.93731\n",
|
||
|
|
" 16 1.057e-02 6.378e-01 4.477e-01 -- 6.419e+02 -- -1.22775 -1.90089 -3.46879 -3.70567 -4.68256 -4.39619 -4.69519 -5.07423\n",
|
||
|
|
" 17 7.888e-03 4.329e-01 3.041e-02 -- 6.419e+02 -- -1.22951 -1.90056 -3.46786 -3.71222 -4.73204 -4.38418 -4.69485 -5.07494\n",
|
||
|
|
" 18 5.819e-03 2.998e-01 1.477e-02 -- 6.419e+02 -- -1.2294 -1.90021 -3.46615 -3.71187 -4.76937 -4.37843 -4.69171 -5.07347\n",
|
||
|
|
" 19 4.291e-03 2.100e-01 7.619e-03 -- 6.419e+02 -- -1.22925 -1.89995 -3.46439 -3.71159 -4.79712 -4.37418 -4.69047 -5.07198\n",
|
||
|
|
" 20 3.153e-03 1.486e-01 3.972e-03 -- 6.419e+02 -- -1.22914 -1.89977 -3.46327 -3.71125 -4.8177 -4.3712 -4.68937 -5.07092\n",
|
||
|
|
" 21 2.314e-03 1.059e-01 2.083e-03 -- 6.419e+02 -- -1.22907 -1.89964 -3.46247 -3.71101 -4.83289 -4.36903 -4.68864 -5.07007\n",
|
||
|
|
" 22 1.695e-03 7.595e-02 1.096e-03 -- 6.419e+02 -- -1.22901 -1.89955 -3.46191 -3.71083 -4.84408 -4.36746 -4.68811 -5.06945\n",
|
||
|
|
" 23 1.239e-03 5.467e-02 5.783e-04 -- 6.419e+02 -- -1.22898 -1.89949 -3.46151 -3.7107 -4.85229 -4.36633 -4.68772 -5.06898\n",
|
||
|
|
" 24 9.051e-04 3.947e-02 3.054e-04 -- 6.419e+02 -- -1.22895 -1.89945 -3.46122 -3.7106 -4.8583 -4.3655 -4.68745 -5.06863\n",
|
||
|
|
" 25 6.605e-04 2.855e-02 1.614e-04 -- 6.419e+02 -- -1.22893 -1.89941 -3.46102 -3.71052 -4.8627 -4.3649 -4.68725 -5.06838\n",
|
||
|
|
" 26 4.816e-04 2.069e-02 8.536e-05 -- 6.419e+02 -- -1.22891 -1.89939 -3.46087 -3.71047 -4.86591 -4.36446 -4.68711 -5.06819\n",
|
||
|
|
"********************\n",
|
||
|
|
"-1.22891 -1.89939 -3.46087 -3.71047 -4.86591 -4.36446 -4.68711 -5.06819\n",
|
||
|
|
"0.234933 0.200019 0.259825 0.196536 0.332059 0.153872 0.153084 0.180761\n",
|
||
|
|
"0.000177336 0.000464495 0.00122062 -0.000734339 -0.0206875 0.00948273 0.00295267 0.00321028\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",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -1.229e+00 -9.940e-01 0.854 +++\n",
|
||
|
|
"+++ 6.419e+02 6.411e+02 -1.229e+00 -8.765e-01 1.78 +++\n",
|
||
|
|
"+++ 6.419e+02 6.413e+02 -1.229e+00 -9.352e-01 1.28 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -1.229e+00 -9.646e-01 1.06 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -1.229e+00 -9.793e-01 0.954 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -1.229e+00 -9.719e-01 1.01 +++\n",
|
||
|
|
"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -1.899e+00 -1.699e+00 0.892 +++\n",
|
||
|
|
"+++ 6.419e+02 6.410e+02 -1.899e+00 -1.599e+00 1.89 +++\n",
|
||
|
|
"+++ 6.419e+02 6.413e+02 -1.899e+00 -1.649e+00 1.35 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -1.899e+00 -1.674e+00 1.11 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -1.899e+00 -1.687e+00 1 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -3.461e+00 -3.201e+00 1.01 +++\n",
|
||
|
|
"+++ 6.419e+02 6.418e+02 -3.461e+00 -3.331e+00 0.268 +++\n",
|
||
|
|
"+++ 6.419e+02 6.417e+02 -3.461e+00 -3.266e+00 0.586 +++\n",
|
||
|
|
"+++ 6.419e+02 6.416e+02 -3.461e+00 -3.233e+00 0.788 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -3.461e+00 -3.217e+00 0.898 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -3.461e+00 -3.209e+00 0.956 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -3.461e+00 -3.205e+00 0.985 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -3.461e+00 -3.203e+00 1 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -3.710e+00 -3.514e+00 0.934 +++\n",
|
||
|
|
"+++ 6.419e+02 6.409e+02 -3.710e+00 -3.416e+00 2.02 +++\n",
|
||
|
|
"+++ 6.419e+02 6.412e+02 -3.710e+00 -3.465e+00 1.43 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -3.710e+00 -3.489e+00 1.17 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -3.710e+00 -3.502e+00 1.05 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -3.710e+00 -3.508e+00 0.991 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 6.419e+02 6.417e+02 -4.868e+00 -4.535e+00 0.545 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -4.868e+00 -4.369e+00 1.17 +++\n",
|
||
|
|
"+++ 6.419e+02 6.416e+02 -4.868e+00 -4.452e+00 0.745 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -4.868e+00 -4.411e+00 0.943 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -4.868e+00 -4.390e+00 1.05 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -4.868e+00 -4.400e+00 0.998 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -4.364e+00 -4.210e+00 0.842 +++\n",
|
||
|
|
"+++ 6.419e+02 6.410e+02 -4.364e+00 -4.133e+00 1.87 +++\n",
|
||
|
|
"+++ 6.419e+02 6.413e+02 -4.364e+00 -4.172e+00 1.32 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -4.364e+00 -4.191e+00 1.07 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -4.364e+00 -4.201e+00 0.951 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -4.364e+00 -4.196e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 6.419e+02 6.418e+02 -4.687e+00 -4.610e+00 0.306 +++\n",
|
||
|
|
"+++ 6.419e+02 6.416e+02 -4.687e+00 -4.572e+00 0.686 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -4.687e+00 -4.553e+00 0.933 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -4.687e+00 -4.544e+00 1.07 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -4.687e+00 -4.548e+00 1 +++\n",
|
||
|
|
"\t### errors for param 7 ###\n",
|
||
|
|
"+++ 6.419e+02 6.418e+02 -5.068e+00 -4.978e+00 0.293 +++\n",
|
||
|
|
"+++ 6.419e+02 6.416e+02 -5.068e+00 -4.932e+00 0.662 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -5.068e+00 -4.910e+00 0.903 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -5.068e+00 -4.899e+00 1.04 +++\n",
|
||
|
|
"+++ 6.419e+02 6.415e+02 -5.068e+00 -4.904e+00 0.969 +++\n",
|
||
|
|
"+++ 6.419e+02 6.414e+02 -5.068e+00 -4.901e+00 1 +++\n",
|
||
|
|
"********************\n",
|
||
|
|
"-1.2289 -1.89937 -3.46076 -3.71043 -4.86825 -4.36415 -4.687 -5.06805\n",
|
||
|
|
"0.256958 0.212521 0.257777 0.202656 0.468138 0.168247 0.138715 0.166617\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\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3X9w3OVh5/G3sYV9Ta44hvOuiYM33p67pggYCblgARUt\nzaVcfvRCq+xeMp1I8ZBr6XncO5jztWMdI990romnSWn6Y1xser3Ayr5pc4UZXGhauVDZ5FSJAMLe\nkFv9wA7edR3XtA0IBPb9sRLI5itLK+93f75fMzuSdp/n+zzGD/Ln+/0+3+cBSZIkSZIkSZIkSZIk\nSZIkSZIkSZIkSZIkSZIkSZIkSZIkXaL/CgwC/wjkgW8CGyraI0mSVBUOAL8EbASuBx4HxoEfqWCf\nJElSFboKOAvcWumOSJKk+V1WxrZWTn89XcY2JUlSlVtC4XbD31S6I5IkaWGWlamdrwM/wcVvNayZ\nfkmSpOKcmH6VVDlCwu8CnwBuB16do8yaq6+++tVXX53rY0mSdBHfB9oocVAIMyQsoRAQPg10ABMX\nKbvm1Vdf5Rvf+AYbN24MsUult23bNr72ta/VZHuXcqxi6xZTfiFl5ytzsc/L/XdWKo610pd3rAVz\nrJW+fJhj7ejRo3z+85//MIWr8TUTEn4PSFEICT8EotPvnwEmgyps3LiRlpaWELtUeitXrixrn0vZ\n3qUcq9i6xZRfSNn5ylzs83L/nZWKY6305R1rwRxrpS8f9lgLy9IQj/04sBzoAv7zrNf3gOcvKLsG\n+NKXvvQl1qypvWkJzc3NNdvepRyr2LrFlF9I2fnKzPV5Op0mlUotuC/VxLFW+vKOtWCOtdKXD2us\nnThxgt27dwPspsRXEpaU8mCXoAUYGhoaqsnUrdryqU99iscee6zS3VADcKypHIaHh2ltbQVoBYZL\neexyrpMgSZJqiCFBDadWL/+q9jjWVOsMCWo4/uJWuTjWVOsMCZIkKZAhQZIkBTIkSJKkQIYESZIU\nyJAgSZICGRIkSVIgQ4IkSQpkSJAkSYEMCZIkKZAhQZIkBTIkSJKkQIYESZIUyJAgSZICGRIkSVIg\nQ4IkSQpkSJAkSYEMCZIkKZAhQZIkBTIkSJKkQIYESZIUyJAgSZICGRIkSVIgQ4IkSQpkSJAkSYEM\nCZIkKVCYIeF24HHg+8BZ4NMhtiVJkkoszJDwI8BzwL3TP58LsS1JklRiy0I89l9MvyRJUg1yToIk\nSQpkSJAkSYEMCZIkKVCYcxKKtm3bNlauXHnee6lUilQqVaEeSZJUPdLpNOl0+rz3zpw5E1p7S0I7\n8vnOAj8PPDbH5y3A0NDQEC0tLWXqkiRJtW94eJjW1laAVmC4lMcO80rCB4B/Pevn9cCNwA+AYyG2\nK0mSSiDMkNAG/PX09+eA357+/o+B7hDblSRJJRBmSDiIEyMlSapZ/iMuSZICGRIkSVIgQ4IkSQpk\nSJAkSYEMCZIkKZAhQZIkBTIkSJKkQIYESZIUyJAgSZICGRIkSVIgQ4IkSQpkSJAkSYHC3OBJqpj0\ni2nSI2kAJt+eZOK1CdZdsY4Vy1YAkLouRao5VckuSlLVMySoLqWa3wsBwyeGad3dSvruNC1rWirc\nM0mqHd5ukCRJgQwJqlvj4+N039tN52c64VHo/Ewn3fd2Mz4+XumuSVJN8HaD6k4+nye5JUnmdIbc\ntTn4eOH9LFmyx7Mc+NwBEqsS9D3URyQSqWxnJamKGRJUV/L5PJvv2szozaNwU0CBtZBbmyN3Mkf7\nXe0MPDFgUJCkOXi7QXUluSVZCAir5ym4GrI3Z0luSZalX5JUiwwJqhtjY2NkTmfmDwgzVkPmdMY5\nCpI0B0OC6sbOXTsLcxCKkNuYo3dXb0g9kqTaZkhQ3Rh8YRDWFllpLQw+PxhKfySp1hkSVDem3pkq\nvtISmDq7iHqS1AAMCaobTUubiq90DpouW0Q9SWoAhgTVjbbr2+B4kZWOw6YbNoXSH0mqdYYE1Y2e\n+3uIHokWVSd6NMqO+3aE1CNJqm2GBNWNWCxGYlUCTi6wwklIrEoQi8XC7JYk1aywQ8KvAGPAG8Df\nAbeG3J4aXN9DfcSfjc8fFE5C/Nk4+/bsK0u/JKkWhRkSPgt8FdgJ3Ag8AxwAPhJim2pwkUiEgScG\n6Hilg+hTUTgGnJv+8BxwDKJPRel4pYNDBw6xevVCV16SpMYTZkj4T8BDwF7gu8CvUfiV/cshtikR\niUTof7yfw48cpmtFF/En4/AoxJ+M07Wii8OPHKb/8X4DgiTNI6wNni4HWoDfvOD9p4DNIbUpnScW\ni7H363sZPjFM6+5W9t+zn5Y1LZXuliTVjLCuJFwFLAXyF7x/Eihu+rkkSaoIt4pWXUq/mCY9kgZg\n8u1JNly5ge3f2s6KZSsASF2XItWcqmQXq8KF/50mXptg3RXr/O8kCYAlIR33cuCHwC8Afz7r/d8B\nrgfuuKB8CzB02223sXLlyvM+SKVSpFL+kpLCMj4+Tu9Xenl6+Gmyp7PEV8W5veV2eu7v8fFQqcqk\n02nS6fR57505c4ZnnnkGoBUYLmV7YYUEgGeBIeDeWe8dAb4J/MYFZVuAoaGhIVpavGcslUM+nye5\nJUnmdKawe+bszbGOQ/RIlMSqBH0P9RGJRCrWT0kXNzw8TGtrK4QQEsK83fDbwP+isD7Cs8A9FH4N\n/WGIbUpagHw+z+a7NjN68yjcFFBgLeTW5sidzNF+VzsDTwwYFKQGFOYjkPuBbUAP8ByFhZTuovAY\npKQKSm5JFgLCfE+BrobszVmSW5Jl6Zek6hL2iot/AHwUWAG0AX8bcnuS5jE2NkbmdGb+gDBjNWRO\nZxgfHw+zW5KqkHs3SA1m566dhTkIRchtzNG7qzekHkmqVoYEqcEMvjB4/iTFhVgLg88PhtIfSdXL\nkCA1mKl3poqvtASmzi6inqSaZkiQGkzT0qbiK52DpssWUU9STTMkSA2m7fo2OF5kpeOw6YZNofRH\nUvUyJEgNpuf+HqJHittCJXo0yo77doTUI0nVypAgNZhYLEZiVaKw3dpCnITEqoRLNEsNyJAgNaC+\nh/qIPxufPyichPizcfbt2VeWfkmqLoYEqQFFIhEGnhig45UOok9FC+ugnpv+8BxwDKJPRel4pYND\nBw6xevVCV16SVE/cKlpqUJFIhP7H+wu7QO7q5eknZ+0C2Xo7PY+4C6TU6AwJUgNLv5gmPZKGdlj/\nk+tZ+tpS1l2xjlPLTrH18FZS/5Qi1exW7VKjMiRIDSzVbAiQNDfnJEiSpECGBEmSFMiQIEmSAhkS\nJElSIEOCJEkKZEiQJEmBDAmSJCmQIUGSJAUyJEiSpECGBEmSFMiQIEmSAhkSJElSIEOCJEkKZEiQ\nJEmBDAmSJCmQIUGSJAUKKyT8BnAIeB34h5DakCRJIQorJDQB+4DfD+n4kiQpZMtCOu4D01+/ENLx\nJUlSyJyTIEmSAhkSJElSoGJuNzwA9MxT5iZgeNG9kUoknU6TTqcBmJycZGJignXr1rFixQoAUqkU\nqVSqkl3UIqRfTJMemf57fXuSidcmWHfFOlYsm/57vS5Fqtm/V6lUlhRR9srp18VMAG/O+vkLwFeB\nD81TrwUYuu2221i5cuV5H/jLXJdqeHiY1tZWhoaGaGlpqXR3dInGx8fp/UovTw8/TfZ0lviqOLe3\n3E7P/T3EYrFKd08K1ewToBlnzpzhmWeeAWilxCfqxYSExfgCRYQEf4krDIaE+pDP50luSZI5nSF3\nbQ7WzvrwOESPREmsStD3UB+RSKRi/ZTKbeZ3HCGEhLDmJFwD3Dj9dSlww/TPHwipPel9xsfH6e7u\nprOzE4DOzk66u7sZHx+vbMdUtHw+z+a7NnPwmoPkPnZBQABYC7mP5Th4zUHa72onn89XpJ9SvQnr\nEche4Jemvz8HPDf99Q7g6ZDalIDpM85kkkwmQy6Xe/f9bDZLNpvlwIEDJBIJ+vo846wVyS1JRm8e\nhdXzFFwN2ZuzJLck6X+
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f1140fb3850>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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 1.149e+03 8.511e+00 inf -- 6.871e+02 -- -0.76415 -1.33875 -2.62568 -2.91642 -3.79922 -3.72781 -4.42422 -6.83403 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
|
||
|
|
" 3 3.083e+01 1.011e+01 2.462e+00 -- 6.895e+02 -- -0.722947 -1.31306 -2.64209 -2.92939 -3.79892 -3.74348 -4.4401 -6.53403 0.112591 0.202668 0.237888 0.184561 0.247154 0.169837 0.149615 -0.977256\n",
|
||
|
|
" 5 2.313e+01 1.184e+01 2.138e+00 -- 6.917e+02 -- -0.689217 -1.28746 -2.64829 -2.93823 -3.79051 -3.75521 -4.45318 -6.83403 0.122108 0.284555 0.367997 0.266931 0.379308 0.236851 0.196816 2.03535\n",
|
||
|
|
" 7 1.836e+01 1.371e+01 1.807e+00 -- 6.935e+02 -- -0.66115 -1.2633 -2.64623 -2.94323 -3.77682 -3.76343 -4.4638 -6.53403 0.129539 0.350049 0.485494 0.345407 0.493126 0.299979 0.241096 -2.67313\n",
|
||
|
|
" 9 2.046e+01 1.572e+01 1.610e+00 -- 6.951e+02 -- -0.637504 -1.24117 -2.63838 -2.94489 -3.76048 -3.76869 -4.47217 -6.83403 0.135502 0.402878 0.588 0.418868 0.588605 0.358702 0.28198 -1.29684\n",
|
||
|
|
" 11 3.541e+01 1.788e+01 1.456e+00 -- 6.966e+02 -- -0.617387 -1.22121 -2.62698 -2.94377 -3.7434 -3.77151 -4.47878 -7.13403 0.140359 0.445949 0.67527 0.486465 0.667539 0.412608 0.319287 1.3562\n",
|
||
|
|
" 13 8.288e+02 2.020e+01 1.291e+00 -- 6.978e+02 -- -0.60014 -1.20337 -2.61379 -2.94045 -3.72685 -3.77239 -4.48389 -6.83403 0.144381 0.48146 0.748601 0.54777 0.732508 0.461524 0.352994 -0.12492\n",
|
||
|
|
" 15 6.267e+01 2.266e+01 1.179e+00 -- 6.990e+02 -- -0.585259 -1.18747 -2.60004 -2.9355 -3.71148 -3.77179 -4.48775 -7.13403 0.147758 0.511044 0.809901 0.602807 0.786107 0.50562 0.383157 -2.33832\n",
|
||
|
|
" 17 5.670e+01 2.527e+01 1.086e+00 -- 7.001e+02 -- -0.572351 -1.17335 -2.58649 -2.92939 -3.6976 -3.77007 -4.49056 -7.43403 0.150636 0.53593 0.861183 0.651797 0.830596 0.545107 0.409928 1.85585\n",
|
||
|
|
" 19 6.344e+01 2.802e+01 9.867e-01 -- 7.011e+02 -- -0.561107 -1.1608 -2.57356 -2.92251 -3.68525 -3.76757 -4.49259 -7.13403 0.153107 0.557037 0.9042 0.695116 0.867766 0.580281 0.433567 -2.38363\n",
|
||
|
|
" 21 7.571e+01 3.089e+01 9.220e-01 -- 7.020e+02 -- -0.551274 -1.14965 -2.56148 -2.91518 -3.6744 -3.76453 -4.49398 -7.43403 0.155257 0.575074 0.940495 0.733284 0.89908 0.611551 0.454317 1.34492\n",
|
||
|
|
" 22 4.003e-01 2.319e+04 1.251e+01 -- 6.895e+02 -- -0.46504 -1.05058 -2.44998 -2.83968 -3.57966 -3.73073 -4.50329 -4.43403 0.174105 0.730184 1.24835 1.06835 1.1647 0.888615 0.636009 2.63401\n",
|
||
|
|
" 23 3.602e-01 6.251e+01 2.137e+01 -- 7.109e+02 -- -0.479175 -1.05499 -2.40759 -2.71044 -3.63212 -3.67843 -4.55868 -4.7139 0.243794 0.662729 1.29622 1.06228 1.10731 0.828249 0.484699 2.85439\n",
|
||
|
|
" 24 1.324e-01 3.847e+01 7.851e-01 -- 7.117e+02 -- -0.473576 -1.05695 -2.43181 -2.73925 -3.6054 -3.70634 -4.40662 -4.87766 0.183146 0.709437 1.27054 0.992921 1.08464 0.839337 0.659286 2.73599\n",
|
||
|
|
" 25 1.821e-01 8.521e+00 1.578e-01 -- 7.118e+02 -- -0.475023 -1.05607 -2.43744 -2.74267 -3.61814 -3.70001 -4.50477 -4.97221 0.199534 0.699185 1.24387 0.993811 1.07721 0.861017 0.572018 2.42745\n",
|
||
|
|
" 26 2.014e-01 5.280e+00 6.998e-02 -- 7.119e+02 -- -0.474604 -1.05634 -2.44031 -2.7453 -3.60481 -3.70497 -4.4716 -5.01385 0.194772 0.702203 1.23578 0.990364 1.07625 0.849061 0.623206 1.98547\n",
|
||
|
|
" 27 1.559e-01 8.470e-01 5.248e-02 -- 7.119e+02 -- -0.474838 -1.05633 -2.44133 -2.7456 -3.61013 -3.70353 -4.4991 -4.96485 0.195398 0.70079 1.23042 0.988953 1.0694 0.8531 0.602048 1.5856\n",
|
||
|
|
" 28 1.154e-01 1.799e+00 3.734e-02 -- 7.120e+02 -- -0.474876 -1.05636 -2.44221 -2.74624 -3.6043 -3.70638 -4.49561 -4.88587 0.194273 0.700465 1.22605 0.986774 1.06378 0.847619 0.626675 1.33843\n",
|
||
|
|
" 29 7.922e-02 1.175e+00 2.365e-02 -- 7.120e+02 -- -0.474959 -1.05637 -2.44284 -2.74635 -3.60609 -3.70691 -4.50684 -4.82345 0.193792 0.699958 1.22274 0.984729 1.05776 0.847888 0.625222 1.18395\n",
|
||
|
|
" 30 5.978e-02 9.905e-01 1.545e-02 -- 7.120e+02 -- -0.475014 -1.05638 -2.44337 -2.74658 -3.60376 -3.70854 -4.50832 -4.7745 0.193235 0.699587 1.22004 0.983324 1.05292 0.845739 0.637938 1.09015\n",
|
||
|
|
" 31 4.332e-02 8.004e-01 9.908e-03 -- 7.120e+02 -- -0.475061 -1.05639 -2.4438 -2.74665 -3.60426 -3.70921 -4.5135 -4.73769 0.192849 0.699306 1.21807 0.982147 1.04865 0.845744 0.640636 1.02498\n",
|
||
|
|
" 32 3.306e-02 6.233e-01 6.372e-03 -- 7.120e+02 -- -0.475099 -1.05639 -2.44413 -2.74676 -3.60331 -3.71016 -4.51536 -4.70979 0.19251 0.699065 1.21644 0.981352 1.04524 0.844821 0.647708 0.980583\n",
|
||
|
|
" 33 2.501e-02 5.124e-01 4.073e-03 -- 7.120e+02 -- -0.475129 -1.0564 -2.4444 -2.74681 -3.6034 -3.71069 -4.51806 -4.68842 0.192252 0.698889 1.21522 0.980704 1.04238 0.844743 0.650651 0.948169\n",
|
||
|
|
" 34 1.931e-02 3.988e-01 2.597e-03 -- 7.120e+02 -- -0.475154 -1.0564 -2.44461 -2.74688 -3.603 -3.71126 -4.51949 -4.67205 0.192037 0.69874 1.21423 0.980247 1.04011 0.844313 0.654788 0.924451\n",
|
||
|
|
" 35 1.493e-02 3.244e-01 1.651e-03 -- 7.120e+02 -- -0.475173 -1.0564 -2.44478 -2.74692 -3.60297 -3.71164 -4.52101 -4.65936 0.19187 0.698628 1.21347 0.97988 1.03824 0.844226 0.657098 0.906602\n",
|
||
|
|
" 36 1.163e-02 2.542e-01 1.047e-03 -- 7.120e+02 -- -0.475189 -1.0564 -2.44491 -2.74696 -3.60279 -3.71199 -4.52198 -4.64952 0.191733 0.698535 1.21286 0.979613 1.03676 0.844016 0.659591 0.893069\n",
|
||
|
|
" 37 9.093e-03 2.048e-01 6.628e-04 -- 7.120e+02 -- -0.475201 -1.0564 -2.44501 -2.74699 -3.60275 -3.71224 -4.52287 -4.64183 0.191626 0.698464 1.21238 0.9794 1.03556 0.843944 0.661216 0.882678\n",
|
||
|
|
" 38 7.134e-03 1.613e-01 4.191e-04 -- 7.120e+02 -- -0.475212 -1.0564 -2.4451 -2.74701 -3.60265 -3.71246 -4.52351 -4.63582 0.191539 0.698406 1.212 0.979241 1.03461 0.843836 0.662747 0.874652\n",
|
||
|
|
" 39 5.608e-03 1.291e-01 2.647e-04 -- 7.120e+02 -- -0.475219 -1.05641 -2.44516 -2.74703 -3.60262 -3.71262 -4.52405 -4.6311 0.191471 0.698362 1.21171 0.979114 1.03385 0.843785 0.663834 0.868413\n",
|
||
|
|
" 40 4.418e-03 1.020e-01 1.670e-04 -- 7.120e+02 -- -0.475226 -1.05641 -2.44521 -2.74705 -3.60257 -3.71276 -4.52445 -4.62739 0.191416 0.698326 1.21147 0.979017 1.03325 0.843726 0.664783 0.863542\n",
|
||
|
|
" 41 3.485e-03 8.130e-02 1.053e-04 -- 7.120e+02 -- -0.475231 -1.05641 -2.44525 -2.74706 -3.60255 -3.71286 -4.52479 -4.62447 0.191373 0.698297 1.21128 0.978941 1.03276 0.843692 0.665491 0.859727\n",
|
||
|
|
" 42 2.753e-03 6.436e-02 6.636e-05 -- 7.120e+02 -- -0.475235 -1.05641 -2.44529 -2.74707 -3.60252 -3.71295 -4.52504 -4.62216 0.191338 0.698275 1.21113 0.978881 1.03238 0.843658 0.666084 0.856731\n",
|
||
|
|
"********************\n",
|
||
|
|
"-0.475235 -1.05641 -2.44529 -2.74707 -3.60252 -3.71295 -4.52504 -4.62216 0.191338 0.698275 1.21113 0.978881 1.03238 0.843658 0.666084 0.856731\n",
|
||
|
|
"0.00699188 0.00954622 0.0841246 0.0745967 0.124782 0.148601 0.368254 0.278607 0.0954532 0.101842 0.352028 0.291475 0.397613 0.407863 0.879459 0.681533\n",
|
||
|
|
"-0.064356 -0.00407965 -0.00327632 -0.000885349 0.00210339 -0.00248053 -0.0014596 0.0217369 -0.00315135 -0.00176922 -0.000952405 -0.000682482 -0.00194583 -0.000139415 0.000655392 -0.00209613\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",
|
||
|
|
"+++ 7.120e+02 7.118e+02 -4.752e-01 -4.717e-01 0.444 +++\n",
|
||
|
|
"+++ 7.120e+02 7.114e+02 -4.752e-01 -4.700e-01 1.35 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -4.752e-01 -4.709e-01 0.796 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.752e-01 -4.704e-01 1.04 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -4.752e-01 -4.706e-01 0.912 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -4.752e-01 -4.705e-01 0.975 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.752e-01 -4.705e-01 1.01 +++\n",
|
||
|
|
"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 7.120e+02 7.118e+02 -1.056e+00 -1.052e+00 0.401 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -1.056e+00 -1.049e+00 1.14 +++\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 -1.056e+00 -1.050e+00 0.702 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.903 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -1.056e+00 -1.050e+00 1.02 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.959 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.988 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -1.056e+00 -1.050e+00 1 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 7.120e+02 7.119e+02 -2.445e+00 -2.403e+00 0.347 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -2.445e+00 -2.382e+00 1.08 +++\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 -2.445e+00 -2.393e+00 0.631 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -2.445e+00 -2.387e+00 0.829 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -2.445e+00 -2.385e+00 0.946 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -2.445e+00 -2.384e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 7.120e+02 7.119e+02 -2.747e+00 -2.710e+00 0.245 +++\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 -2.747e+00 -2.691e+00 0.695 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -2.747e+00 -2.682e+00 1.08 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -2.747e+00 -2.686e+00 0.87 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -2.747e+00 -2.684e+00 0.97 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -2.747e+00 -2.683e+00 1.02 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -2.747e+00 -2.684e+00 0.996 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 -3.603e+00 -3.540e+00 0.596 +++\n",
|
||
|
|
"+++ 7.120e+02 7.111e+02 -3.603e+00 -3.509e+00 1.86 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -3.603e+00 -3.525e+00 1.09 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -3.603e+00 -3.532e+00 0.813 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -3.603e+00 -3.528e+00 0.942 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -3.603e+00 -3.526e+00 1.01 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -3.603e+00 -3.527e+00 0.976 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -3.603e+00 -3.527e+00 0.994 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 7.120e+02 7.119e+02 -3.713e+00 -3.639e+00 0.357 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -3.713e+00 -3.602e+00 0.966 +++\n",
|
||
|
|
"+++ 7.120e+02 7.113e+02 -3.713e+00 -3.583e+00 1.46 +++\n",
|
||
|
|
"+++ 7.120e+02 7.114e+02 -3.713e+00 -3.592e+00 1.19 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -3.713e+00 -3.597e+00 1.08 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -3.713e+00 -3.599e+00 1.02 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -3.713e+00 -3.600e+00 0.992 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 -4.525e+00 -4.341e+00 0.622 +++\n",
|
||
|
|
"+++ 7.120e+02 7.111e+02 -4.525e+00 -4.249e+00 1.92 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.525e+00 -4.295e+00 1.13 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -4.525e+00 -4.318e+00 0.843 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -4.525e+00 -4.306e+00 0.978 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.525e+00 -4.301e+00 1.05 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.525e+00 -4.304e+00 1.01 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.525e+00 -4.305e+00 0.996 +++\n",
|
||
|
|
"\t### errors for param 7 ###\n",
|
||
|
|
"+++ 7.120e+02 7.118e+02 -4.620e+00 -4.343e+00 0.54 +++\n",
|
||
|
|
"+++ 7.120e+02 -inf -4.620e+00 -4.204e+00 inf +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.620e+00 -4.274e+00 1.18 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -4.620e+00 -4.308e+00 0.803 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 -4.620e+00 -4.291e+00 0.974 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.620e+00 -4.282e+00 1.07 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.620e+00 -4.287e+00 1.02 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 -4.620e+00 -4.289e+00 0.998 +++\n",
|
||
|
|
"\t### errors for param 8 ###\n",
|
||
|
|
"+++ 7.120e+02 7.119e+02 1.913e-01 2.390e-01 0.269 +++\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 1.913e-01 2.629e-01 0.595 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 1.913e-01 2.748e-01 0.803 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 1.913e-01 2.808e-01 0.917 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 1.913e-01 2.838e-01 0.977 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 1.913e-01 2.853e-01 1.01 +++\n",
|
||
|
|
"\t### errors for param 9 ###\n",
|
||
|
|
"+++ 7.120e+02 7.119e+02 6.983e-01 7.492e-01 0.276 +++\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 6.983e-01 7.746e-01 0.62 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 6.983e-01 7.874e-01 0.82 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 6.983e-01 7.937e-01 0.936 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 6.983e-01 7.969e-01 0.997 +++\n",
|
||
|
|
"\t### errors for param 10 ###\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 1.211e+00 1.563e+00 0.938 +++\n",
|
||
|
|
"+++ 7.120e+02 7.111e+02 1.211e+00 1.739e+00 1.91 +++\n",
|
||
|
|
"+++ 7.120e+02 7.113e+02 1.211e+00 1.651e+00 1.4 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 1.211e+00 1.607e+00 1.16 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 1.211e+00 1.585e+00 1.05 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 1.211e+00 1.574e+00 0.993 +++\n",
|
||
|
|
"\t### errors for param 11 ###\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 9.788e-01 1.270e+00 0.74 +++\n",
|
||
|
|
"+++ 7.120e+02 7.113e+02 9.788e-01 1.416e+00 1.49 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 9.788e-01 1.343e+00 1.1 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 9.788e-01 1.307e+00 0.912 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 9.788e-01 1.325e+00 1 +++\n",
|
||
|
|
"\t### errors for param 12 ###\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 1.032e+00 1.430e+00 0.828 +++\n",
|
||
|
|
"+++ 7.120e+02 7.111e+02 1.032e+00 1.628e+00 1.84 +++\n",
|
||
|
|
"+++ 7.120e+02 7.114e+02 1.032e+00 1.529e+00 1.29 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 1.032e+00 1.479e+00 1.05 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 1.032e+00 1.455e+00 0.935 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 1.032e+00 1.467e+00 0.99 +++\n",
|
||
|
|
"\t### errors for param 13 ###\n",
|
||
|
|
"+++ 7.120e+02 7.119e+02 8.436e-01 1.048e+00 0.276 +++\n",
|
||
|
|
"+++ 7.120e+02 7.117e+02 8.436e-01 1.150e+00 0.604 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 8.436e-01 1.201e+00 0.808 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 8.436e-01 1.226e+00 0.92 +++\n",
|
||
|
|
"+++ 7.120e+02 7.116e+02 8.436e-01 1.239e+00 0.977 +++\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 8.436e-01 1.245e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 14 ###\n",
|
||
|
|
"+++ 7.120e+02 7.115e+02 6.665e-01 1.546e+00 1 +++\n",
|
||
|
|
"\t### errors for param 15 ###\n",
|
||
|
|
"********************\n",
|
||
|
|
"-0.475238 -1.05641 -2.44531 -2.74708 -3.6025 -3.71301 -4.52525 -4.62034 0.191311 0.698257 1.21102 0.978834 1.03208 0.843636 0.666538 0.854372\n",
|
||
|
|
"0.00475354 0.00682415 0.0617908 0.0635266 0.0755408 0.112636 0.220195 0.331549 0.0939739 0.0986604 0.363072 0.346135 0.434851 0.401558 0.879854 3.38957\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
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 3.15567684, 3.85069114, 3.46079358, 1.80469275, 1.2276475 ,\n",
|
||
|
|
" 0.64741975, 0.33000748, 0.27290686])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAFrCAYAAACE+GArAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntQkSAB1oaRGsiYmJjI6
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f114076d690>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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": 15,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f11404bf410>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGYCAYAAAB/DYmkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKC21pGSffWDvvb1rMRI
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f113e81f150>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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": 17,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f113e6b9290>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f113e6b9f90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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([3.99,3.99], [-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
|
||
|
|
}
|