mirror of
https://asciireactor.com/otho/phy-4660.git
synced 2024-11-24 14:35:07 +00:00
735 lines
121 KiB
Plaintext
735 lines
121 KiB
Plaintext
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"outputs": [
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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 0x7fa848d60a10>"
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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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"ref_file=\"lc/1367A.lc\"\n",
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"echo_file=\"lc/3471A.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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"array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n",
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" 0.16658029, 0.25819945, 0.40020915, 0.62032418])"
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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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"fqL\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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|
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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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"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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||
|
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"execution_count": 4,
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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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|
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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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"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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"+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n",
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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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"+++ 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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"+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n",
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"+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n",
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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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"+++ 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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"+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n",
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"+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n",
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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",
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|
|
"+++ 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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\nW6zUI7wEUlLHq9VqHD6
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7fa86c4d3810>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"xscale('log'); ylim(-4,2)\n",
|
||
|
|
"errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10)\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": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7fa84670c610>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"t2, l2, l2e = np.loadtxt(echo_file).T\n",
|
||
|
|
"errorbar(t1, l1, yerr=l1e, fmt='o')\n",
|
||
|
|
"errorbar(t2, l2, yerr=l2e, fmt='o')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" 1 4.362e-01 5.425e+01 inf -- -3.468e+02 -- 1 1 1 1 1 1 1 1\n",
|
||
|
|
" 2 7.747e-01 5.347e+01 7.019e+01 -- -2.766e+02 -- 0.58045 0.569962 0.564602 0.563764 0.567626 0.565054 0.565279 0.564008\n",
|
||
|
|
" 3 3.447e+00 5.213e+01 6.905e+01 -- -2.076e+02 -- 0.198681 0.140203 0.130333 0.127063 0.134237 0.12998 0.131082 0.127044\n",
|
||
|
|
" 4 1.448e+00 4.998e+01 6.682e+01 -- -1.408e+02 -- -0.0825582 -0.283347 -0.300715 -0.310826 -0.299703 -0.304649 -0.30132 -0.310849\n",
|
||
|
|
" 5 5.884e-01 4.684e+01 6.330e+01 -- -7.746e+01 -- -0.194194 -0.676998 -0.726162 -0.750752 -0.733583 -0.738758 -0.729946 -0.74976\n",
|
||
|
|
" 6 3.739e-01 4.258e+01 5.850e+01 -- -1.895e+01 -- -0.203226 -0.953186 -1.14229 -1.19152 -1.16459 -1.17342 -1.15279 -1.19094\n",
|
||
|
|
" 7 2.741e-01 3.740e+01 5.292e+01 -- 3.397e+01 -- -0.205901 -0.9956 -1.54459 -1.63073 -1.58521 -1.61127 -1.5669 -1.63629\n",
|
||
|
|
" 8 2.128e-01 3.180e+01 4.679e+01 -- 8.076e+01 -- -0.180502 -0.943953 -1.92935 -2.06126 -1.98071 -2.05288 -1.97074 -2.08385\n",
|
||
|
|
" 9 1.675e-01 2.595e+01 3.791e+01 -- 1.187e+02 -- -0.15428 -0.934563 -2.26437 -2.45352 -2.31988 -2.48374 -2.35707 -2.52729\n",
|
||
|
|
" 10 1.286e-01 2.002e+01 2.537e+01 -- 1.440e+02 -- -0.13956 -0.939381 -2.49352 -2.71726 -2.56371 -2.84902 -2.70919 -2.95055\n",
|
||
|
|
" 11 9.197e-02 1.277e+01 1.299e+01 -- 1.570e+02 -- -0.130332 -0.946187 -2.57446 -2.74449 -2.69402 -3.05697 -3.00449 -3.33007\n",
|
||
|
|
" 12 5.076e-02 6.047e+00 5.021e+00 -- 1.621e+02 -- -0.124515 -0.945944 -2.55165 -2.71422 -2.73704 -3.10737 -3.209 -3.63633\n",
|
||
|
|
" 13 1.434e-02 1.485e+00 1.060e+00 -- 1.631e+02 -- -0.12047 -0.945369 -2.54157 -2.70197 -2.76441 -3.10602 -3.30159 -3.8209\n",
|
||
|
|
" 14 4.769e-03 3.557e-01 7.280e-02 -- 1.632e+02 -- -0.119516 -0.946002 -2.53635 -2.69518 -2.78858 -3.10381 -3.32152 -3.8757\n",
|
||
|
|
" 15 2.150e-03 1.530e-01 4.222e-03 -- 1.632e+02 -- -0.119506 -0.945491 -2.53265 -2.69429 -2.80187 -3.10094 -3.32488 -3.87969\n",
|
||
|
|
" 16 1.017e-03 7.109e-02 8.430e-04 -- 1.632e+02 -- -0.119394 -0.945124 -2.53033 -2.69448 -2.8079 -3.09858 -3.32603 -3.87983\n",
|
||
|
|
" 17 4.817e-04 3.356e-02 1.918e-04 -- 1.632e+02 -- -0.119311 -0.944963 -2.52941 -2.69447 -2.81076 -3.09727 -3.32654 -3.87984\n",
|
||
|
|
" 18 2.335e-04 1.618e-02 4.417e-05 -- 1.632e+02 -- -0.119282 -0.944891 -2.52886 -2.69454 -2.81211 -3.09664 -3.32676 -3.87987\n",
|
||
|
|
"********************\n",
|
||
|
|
"-0.119282 -0.944891 -2.52886 -2.69454 -2.81211 -3.09664 -3.32676 -3.87987\n",
|
||
|
|
"0.233661 0.204163 0.319993 0.254151 0.198248 0.179386 0.161786 0.221522\n",
|
||
|
|
"0.000372164 0.000983332 0.00164575 -0.000696472 -0.0161795 0.00744155 -0.00415795 -0.000828998\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": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzt3X9w3OVh5/G3sQVuIMExOe9CDN5aPboyMclI2AYruCJH\ncwkX0l7Sc7RDuKkdjlwRYXQtTLl0rDIySa8p05iC6Y0LTtoLrMxNL3NhBpf0h6g5+UcViV/+sZfc\nSmvswK5LHJOGxEQY3x9fCWTztaWV9rs/36+Z70jafZ7v83h40H70/T7f5wFJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiTN0n8FBoGfAAXg28DlFe2RJEmqCtuB/wi0AFcCTwA54D0V7JMk\nSapCHwDeAj5a6Y5IkqSpnVPGthaMfz1axjYlSVKVm0Nwu+EfK90RSZI0PfPK1M6DwBWc/VbDxeOH\nJEkqzivjR0mVIyQ8AHwKWAO8fIYyF19yySUvv/zymd6WJEln8UNgBSUOClGGhDkEAeE3gA7g4FnK\nXvzyyy/zrW99i5aWlgi7VHrd3d1s2rSpJtubzbmKrVtM+emUnarM2d4v93+zUnGslb68Yy2cY630\n5aMcawcOHODzn//8BwmuxtdMSNgMpAhCwutAfPz1Y8DxsAotLS20trZG2KXSW7BgQVn7XMr2ZnOu\nYusWU346Zacqc7b3y/3frFQca6Uv71gL51grffmox1pU5kZ47ieA84B1wO9NOn4APH9a2YuBL37x\ni1/k4otrb1rC8uXLa7a92Zyr2LrFlJ9O2anKnOn9dDpNKpWadl+qiWOt9OUda+Eca6UvH9VYe+WV\nV9iyZQvAFkp8JWFOKU82C63A0NDQUE2mbtWWT3/603znO9+pdDfUABxrKofh4WHa2toA2oDhUp67\nnOskSJKkGmJIUMOp1cu/qj2ONdU6Q4Iajr+4VS6ONdU6Q4IkSQplSJAkSaEMCZIkKZQhQZIkhTIk\nSJKkUIYESZIUypAgSZJCGRIkSVIoQ4IkSQplSJAkSaEMCZIkKZQhQZIkhTIkSJKkUIYESZIUypAg\nSZJCGRIkSVIoQ4IkSQplSJAkSaEMCZIkKZQhQZIkhTIkSJKkUIYESZIUypAgSZJCGRIkSVIoQ4Ik\nSQoVZUhYAzwB/BB4C/iNCNuSJEklFmVIeA/wLNA1/vPJCNuSJEklNi/Cc//N+CFJkmqQcxIkSVIo\nQ4IkSQplSJAkSaGinJNQtO7ubhYsWHDKa6lUilQqVaEeSZJUPdLpNOl0+pTXjh07Fll7cyI786ne\nAn4T+M4Z3m8FhoaGhmhtbS1TlyRJqn3Dw8O0tbUBtAHDpTx3lFcSzgf+9aSflwIfAX4EHIqwXUmS\nVAJRhoQVwD+Mf38S+NPx778JrI+wXUmSVAJRhoSncWKkJEk1yw9xSZIUypAgSZJCGRIkSVIoQ4Ik\nSQplSJAkSaEMCZIkKZQhQZIkhTIkSJKkUIYESZIUypAgSZJCGRIkSVIoQ4IkSQoV5QZPUsWk08EB\ncPw4HDwIS5bA/PnBa6lUcEiSzsyQoLo0OQQMD0NbWxAaWlsr2y9JqiXebpAkSaEMCapbuVyO9evv\nYu3aG4EbWbv2Rtavv4tcLlfprklSTfB2g+pOoVCgs7ObTKaJfL4LWAVANgvZ7B62b+8hmRyjr28T\nsVissp2VpCpmSFBdKRQKrF6dYmTkQWBZSIlV5POryOf3096eYmAgbVCQpDPwdoPqSmdn91kCwmTL\nyGYfoLOzuxzdkqSaZEhQ3RgdHSWTaWLqgDDhCjKZec5RkKQzMCSobmzc+ND4HITpy+e76O19KKIe\nSVJtMySobgwOZpiYpDh9qxgcPBBFdySp5hkSVDfGxmZSa84M60lS/TMkqG40Nc2k1skZ1pOk+mdI\nUN1YsSIJ7Cmy1h5WrmyJojuSVPMMCaobPT1dxOObi6oTj29mw4bbIuqRJNU2Q4LqRiKRIJkcA/ZP\ns8Y+ksk3SSQSEfZKkmpX1CHhNmAU+DnwPeCjEbenBtfXt4nm5tuBfVOU3Edz85fYtu3+cnRLkmpS\nlCHhc8DXgY3AR4BngO3ApRG2qQYXi8UYGEjT0XEv8fjNwG7g5Pi7J4HdxOM309FxLzt39rFo0aLK\ndVaSqlyUezf8LvAwsHX85/8C/Fvgd4AvR9iuGlwsFuPWP/s0j/zTIzR9//O8+qOf8PPjb/JL8+fx\ngYvex+WXJ/jCyi8YECRpClGFhHOBVuCrp73+XWB1RG1Kb0stT5FangLg0X8Y5vPPtPEX1w5x08da\nK9wzSaodUd1u+AAwFyic9voRIB5Rm9Ipcrkc67vWc/fvrYXH4O7fW8v6rvXu1SBJ0+RW0ao7hUKB\n627sZOSnGd5oz8NvBq8fJss3Dmd57IbtLL0gSf8TfQ2/TXQ6HRwAx4/DwYOwZAnMnx+8lkoFh6TG\nFFVIeBU4AZz+GzgGvHKmSt3d3SxYsOCU11KpFCl/S2maCoUCq29YzcjVIxA25WAxvPG5PAeO5Gm/\noZ2BJwcaOiikUnDNNTl6ezezY0eGbBZOnIA1a5L09HT5eKhUZdLpNOmJZD/u2LFjkbU3J7IzB9PK\nh4DJ2/LtB74N/MFpZVuBoaGhIVpbvWesmbvuxut4+rKnwwPC6Y5Ax0sd9D/RH3GvqlOhUKCzs5tM\npml898zJm2PtIR7fTDI5Rl/fpoYOUlK1Gx4epq2tDaANGC7luaO83fCnwP8gWB9hN3ArsBj47xG2\nqQY2OjpK5mgGrppmhUWQeS5DLpdruL+YC4UCq1enGBl5EFgWUmIV+fwq8vn9tLenGBhIGxSkBhTl\nOgmPA91AD/AswUJKNwCHImxTDWzjfRvJL8sXVSffkqf3vt6IelS9Oju7zxIQJltGNvsAnZ3d5eiW\npCoT9YqLfw78MjAfWAH8n4jbUwMbfGEwuFZVjMUw+PxgJP2pVqOjo2QyTUwdECZcQSYzz6dCpAbk\n3g2qG2MnxoqvNAfG3ppBvRq2ceND43MQpi+f76K396GIeiSpWhkSVDea5jYVX+kkNJ0zg3o1bHAw\nw6mTFKdjFYODB6LojqQqZkhQ3Vhx5Qo4XGSlw7Dywysj6U+1GpvRhZM5M6wnqZYZElQ3eu7qIb6/\nuAU94wfibLhzQ0Q9qk5NM7pwcnKG9STVMkOC6kYikSC5MBks/j0dRyC5MNlwjz+uWJEE9hRZaw8r\nV7ZE0R1JVcyQoLrS93Afzbubpw4KR6B5dzPbHtlWln5Vk56eLuLxzUXVicc3s2HDbRH1SFK1MiSo\nrsRiMQaeHKDjpQ7i340Hq3KcHH/zJHAI4t+N0/FSBzu372zI7aITiQTJ5BjBAqjTsY9k8s2Gu+Ii\nyZCgOhSLxeh/op9dj+5i3fx1ND/VDI9B81PNrJu/jl2P7qL/if6GDAgT+vo20dx8O7BvipL7aG7+\nEtu23V+ObkmqMu4CqbqVSCTY+uBWhl8Zpm1LG4/f+jitF7s3CIxfcRlIj+/dMG/S3g1zCC65TOzd\n8CbbtvU1dKCSGpkhQXUp/WKa9N5gp7Tjbx7n8osu5+6/u5v584I9kFMfSpFa3ti7i8ZiMfr70+Ry\nOXp7H2LHjq+QzUJzM6xZ00JPz0ZvMUgNLspdIIvhLpBSBaTTwQFw/DgcPAhLlsD8IEuRSgWHpOpV\nq7tASqpyhgBJZ+PERUmSFMqQIEmSQhkSJElSKEOCJEkKZUiQJEmhDAmSJCmUIUGSJIUyJEiSpFCG\nBEmSFMqQIEmSQhkSJElSKEOCJEkK5QZPUgM7fUvtg68dZMmFS9xSWxJgSJAaWmp5imveew29f9LL\njuEdZI9mObHwBGta19BzVw+JRKLSXZRUQYYEqUEVCgU6b+kkczRDflkePhG8niVL9nCW7TdtJ7kw\nSd/DfcRiscp2VlJFGBKkBlQoFFh9w2pGrh6Bq0IKLIb84jz5I3nab2hn4MkBg4LUgKKauPgHwE7g\nZ8CPI2pD0gx13tIZBIR
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7fa8466ecf90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"xscale('log'); ylim(-6,2)\n",
|
||
|
|
"errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10)\n",
|
||
|
|
"errorbar(fqd, p2, yerr=p2e, fmt='o', ms=10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\t### errors for param 0 ###\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -1.193e-01 1.144e-01 0.905 +++\n",
|
||
|
|
"+++ 1.632e+02 1.622e+02 -1.193e-01 2.312e-01 1.9 +++\n",
|
||
|
|
"+++ 1.632e+02 1.625e+02 -1.193e-01 1.728e-01 1.37 +++\n",
|
||
|
|
"+++ 1.632e+02 1.626e+02 -1.193e-01 1.436e-01 1.13 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -1.193e-01 1.290e-01 1.01 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -1.193e-01 1.217e-01 0.958 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -1.193e-01 1.254e-01 0.985 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -1.193e-01 1.272e-01 0.999 +++\n",
|
||
|
|
"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -9.449e-01 -7.407e-01 0.961 +++\n",
|
||
|
|
"+++ 1.632e+02 1.622e+02 -9.449e-01 -6.386e-01 2.04 +++\n",
|
||
|
|
"+++ 1.632e+02 1.625e+02 -9.449e-01 -6.897e-01 1.46 +++\n",
|
||
|
|
"+++ 1.632e+02 1.626e+02 -9.449e-01 -7.152e-01 1.2 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -9.449e-01 -7.279e-01 1.08 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -9.449e-01 -7.343e-01 1.02 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -9.449e-01 -7.375e-01 0.989 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -9.449e-01 -7.359e-01 1 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 1.632e+02 1.630e+02 -2.529e+00 -2.369e+00 0.307 +++\n",
|
||
|
|
"+++ 1.632e+02 1.629e+02 -2.529e+00 -2.289e+00 0.681 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.529e+00 -2.249e+00 0.919 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.529e+00 -2.229e+00 1.05 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.529e+00 -2.239e+00 0.984 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.529e+00 -2.234e+00 1.02 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.529e+00 -2.236e+00 1 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 1.632e+02 1.630e+02 -2.695e+00 -2.567e+00 0.306 +++\n",
|
||
|
|
"+++ 1.632e+02 1.629e+02 -2.695e+00 -2.504e+00 0.681 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.695e+00 -2.472e+00 0.922 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.695e+00 -2.456e+00 1.05 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.695e+00 -2.464e+00 0.988 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.695e+00 -2.460e+00 1.02 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.695e+00 -2.462e+00 1 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 1.632e+02 1.629e+02 -2.813e+00 -2.614e+00 0.665 +++\n",
|
||
|
|
"+++ 1.632e+02 1.624e+02 -2.813e+00 -2.515e+00 1.57 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.813e+00 -2.565e+00 1.07 +++\n",
|
||
|
|
"+++ 1.632e+02 1.628e+02 -2.813e+00 -2.590e+00 0.853 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.813e+00 -2.577e+00 0.956 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.813e+00 -2.571e+00 1.01 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.813e+00 -2.574e+00 0.983 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -2.813e+00 -2.573e+00 0.997 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 1.632e+02 1.628e+02 -3.096e+00 -2.917e+00 0.837 +++\n",
|
||
|
|
"+++ 1.632e+02 1.623e+02 -3.096e+00 -2.827e+00 1.87 +++\n",
|
||
|
|
"+++ 1.632e+02 1.625e+02 -3.096e+00 -2.872e+00 1.29 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -3.096e+00 -2.895e+00 1.06 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -3.096e+00 -2.906e+00 0.947 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -3.096e+00 -2.900e+00 1 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -3.327e+00 -3.165e+00 0.992 +++\n",
|
||
|
|
"\t### errors for param 7 ###\n",
|
||
|
|
"+++ 1.632e+02 1.631e+02 -3.880e+00 -3.769e+00 0.278 +++\n",
|
||
|
|
"+++ 1.632e+02 1.629e+02 -3.880e+00 -3.714e+00 0.631 +++\n",
|
||
|
|
"+++ 1.632e+02 1.628e+02 -3.880e+00 -3.686e+00 0.862 +++\n",
|
||
|
|
"+++ 1.632e+02 1.627e+02 -3.880e+00 -3.672e+00 0.991 +++\n",
|
||
|
|
"********************\n",
|
||
|
|
"-0.119263 -0.944854 -2.52866 -2.69453 -2.81277 -3.09632 -3.32687 -3.87987\n",
|
||
|
|
"0.246439 0.208948 0.29245 0.232296 0.24017 0.196142 0.161799 0.207668\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"p2, p2e = clag.errors(P2, p2, p2e)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" 1 2.664e+02 1.060e+01 inf -- 2.187e+02 -- -0.209329 -0.861493 -2.15962 -2.40847 -2.77148 -3.0939 -3.74416 -6.23993 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
|
||
|
|
" 3 2.987e+01 1.264e+01 2.257e+00 -- 2.210e+02 -- -0.16831 -0.824919 -2.12464 -2.39488 -2.7608 -3.0706 -3.74724 -5.93993 0.0804747 0.16472 0.155918 0.19816 0.150032 0.148466 -0.0426696 2.76354\n",
|
||
|
|
" 5 3.330e+01 1.479e+01 2.062e+00 -- 2.230e+02 -- -0.134691 -0.793404 -2.09481 -2.37915 -2.74942 -3.04926 -3.74185 -6.23993 0.0666414 0.2131 0.199386 0.281712 0.193626 0.185727 -0.170109 -2.41599\n",
|
||
|
|
" 7 4.032e+02 1.707e+01 1.889e+00 -- 2.249e+02 -- -0.106698 -0.766368 -2.06929 -2.36269 -2.73774 -3.02999 -3.73105 -6.53993 0.0565128 0.250258 0.234434 0.352126 0.231411 0.214451 -0.279352 -0.654819\n",
|
||
|
|
" 9 1.006e+02 1.948e+01 1.734e+00 -- 2.267e+02 -- -0.0831017 -0.743148 -2.04735 -2.34643 -2.7261 -3.0127 -3.71739 -6.23993 0.0489265 0.27947 0.263536 0.411371 0.264011 0.236678 -0.370557 0.617417\n",
|
||
|
|
" 11 5.264e+01 2.201e+01 1.603e+00 -- 2.283e+02 -- -0.0630157 -0.723142 -2.02838 -2.33091 -2.71472 -2.99725 -3.70277 -5.93993 0.0431576 0.302888 0.288287 0.46139 0.292059 0.253914 -0.445773 0.691506\n",
|
||
|
|
" 13 3.251e+00 2.467e+01 1.448e+00 -- 2.297e+02 -- -0.0457823 -0.705838 -2.0119 -2.31642 -2.70374 -2.98344 -3.68838 -5.63993 0.0387303 0.321962 0.309779 0.503862 0.316158 0.267284 -0.507655 -2.9489\n",
|
||
|
|
" 15 5.697e+00 2.744e+01 1.389e+00 -- 2.311e+02 -- -0.0308979 -0.690819 -1.99751 -2.30307 -2.6933 -2.97112 -3.67473 -5.93993 0.0353185 0.337702 0.32866 0.540271 0.336854 0.27759 -0.559235 -3.12456\n",
|
||
|
|
" 17 9.962e+01 3.030e+01 1.295e+00 -- 2.324e+02 -- -0.0179753 -0.677732 -1.9849 -2.29088 -2.68339 -2.9601 -3.66228 -6.23993 0.0326949 0.350836 0.34562 0.571642 0.354613 0.285527 -0.602072 1.49229\n",
|
||
|
|
" 19 8.259e+01 3.325e+01 1.196e+00 -- 2.336e+02 -- -0.00670527 -0.666291 -1.97381 -2.2798 -2.67406 -2.95025 -3.6511 -5.93993 0.0306947 0.361894 0.361049 0.598897 0.369841 0.291591 -0.63793 -0.807155\n",
|
||
|
|
" 21 5.466e+01 3.625e+01 1.126e+00 -- 2.347e+02 -- 0.00316205 -0.65626 -1.96403 -2.26977 -2.6653 -2.94143 -3.64118 -5.63993 0.0291917 0.371274 0.375204 0.622756 0.382913 0.296138 -0.668602 -0.4239\n",
|
||
|
|
" 23 9.811e+00 3.926e+01 1.030e+00 -- 2.358e+02 -- 0.0118288 -0.647441 -1.95537 -2.26071 -2.65711 -2.93352 -3.63242 -5.33993 0.0280965 0.379279 0.388328 0.643807 0.394112 0.29949 -0.69488 1.89304\n",
|
||
|
|
" 24 1.540e+02 1.799e+03 6.966e+00 -- 2.427e+02 -- 0.0881649 -0.569745 -1.87911 -2.17878 -2.58105 -2.86227 -3.55241 -6.66644 0.0206511 0.447973 0.510379 0.832388 0.489337 0.32364 -0.914155 2.17049\n",
|
||
|
|
" 25 6.954e+03 5.225e+01 3.722e+00 -- 2.464e+02 -- 0.0820074 -0.577455 -1.8985 -2.17524 -2.54532 -2.85023 -3.57378 -8 0.0966827 0.412543 0.647274 0.885367 0.477195 0.280838 -0.833819 0.980251\n",
|
||
|
|
" 26 6.093e+00 2.044e+01 2.548e-01 -- 2.467e+02 -- 0.0836413 -0.576897 -1.8864 -2.18141 -2.55069 -2.85268 -3.54966 -5 0.0710854 0.434328 0.591545 0.904499 0.417485 0.266475 -0.938934 1.34469\n",
|
||
|
|
" 27 1.995e+00 5.009e+00 1.592e-01 -- 2.469e+02 -- 0.0831948 -0.576805 -1.88848 -2.17663 -2.54734 -2.85305 -3.5567 -4.22205 0.0761925 0.426631 0.625953 0.910649 0.424127 0.266244 -0.891405 -0.565813\n",
|
||
|
|
" 28 1.248e+00 1.375e+01 4.599e-02 -- 2.469e+02 -- 0.0833105 -0.576655 -1.88396 -2.17799 -2.5475 -2.85314 -3.57374 -4.03476 0.0730522 0.429262 0.625062 0.905512 0.418149 0.264942 -0.967314 0.563133\n",
|
||
|
|
" 29 2.171e+00 9.396e+00 3.382e-01 -- 2.472e+02 -- 0.0829995 -0.576375 -1.88776 -2.17599 -2.54493 -2.85329 -3.58608 -4.02931 0.0763743 0.428086 0.638377 0.920493 0.411594 0.274496 -0.815157 -0.139608\n",
|
||
|
|
" 30 9.841e-01 1.149e+01 1.388e-01 -- 2.474e+02 -- 0.0832295 -0.57636 -1.88467 -2.17976 -2.54698 -2.8543 -3.58978 -3.89975 0.0738415 0.429686 0.629413 0.910992 0.412336 0.271501 -0.957932 0.163421\n",
|
||
|
|
" 31 2.338e+01 3.752e+00 6.030e-02 -- 2.474e+02 -- 0.0829583 -0.576172 -1.88634 -2.17783 -2.54432 -2.85466 -3.60447 -3.874 0.0761271 0.428633 0.633503 0.910755 0.410289 0.277401 -0.858061 0.00260138\n",
|
||
|
|
" 32 3.920e-01 3.182e+00 1.581e-02 -- 2.475e+02 -- 0.0830523 -0.576117 -1.88559 -2.18042 -2.54532 -2.85537 -3.60659 -3.85372 0.0749087 0.429912 0.628364 0.908088 0.410987 0.278401 -0.917876 0.0634286\n",
|
||
|
|
" 33 3.181e-01 1.292e+00 4.081e-03 -- 2.475e+02 -- 0.0829733 -0.576083 -1.88614 -2.17987 -2.54423 -2.85554 -3.60968 -3.84594 0.0757541 0.429324 0.627903 0.906455 0.410964 0.280705 -0.882158 0.0385634\n",
|
||
|
|
" 34 9.740e-02 4.768e-01 1.118e-03 -- 2.475e+02 -- 0.0830058 -0.576059 -1.88614 -2.18078 -2.54437 -2.85582 -3.61078 -3.84115 0.0755814 0.429797 0.625777 0.906066 0.411713 0.281667 -0.897167 0.0508303\n",
|
||
|
|
" 35 6.139e-02 4.667e-01 3.378e-04 -- 2.475e+02 -- 0.0829936 -0.576051 -1.88632 -2.18077 -2.54398 -2.85585 -3.61168 -3.83901 0.0759106 0.429622 0.624827 0.905205 0.411841 0.282606 -0.888007 0.0458794\n",
|
||
|
|
" 36 2.074e-02 6.100e-02 1.146e-04 -- 2.475e+02 -- 0.0830052 -0.576043 -1.8864 -2.18106 -2.54393 -2.85595 -3.61204 -3.83759 0.0759487 0.42976 0.623936 0.905027 0.412248 0.283153 -0.891435 0.0486959\n",
|
||
|
|
" 37 1.454e-02 1.834e-01 4.331e-05 -- 2.475e+02 -- 0.0830055 -0.57604 -1.88647 -2.18112 -2.54378 -2.85597 -3.61236 -3.83689 0.0760799 0.429719 0.623352 0.904662 0.412343 0.283555 -0.889115 0.047686\n",
|
||
|
|
"********************\n",
|
||
|
|
"0.0830055 -0.57604 -1.88647 -2.18112 -2.54378 -2.85597 -3.61236 -3.83689 0.0760799 0.429719 0.623352 0.904662 0.412343 0.283555 -0.889115 0.047686\n",
|
||
|
|
"0.00496524 0.0080639 0.0332817 0.053869 0.0497847 0.0510814 0.188809 0.22006 0.0806462 0.0938084 0.215752 0.243886 0.211727 0.201233 0.481882 0.380625\n",
|
||
|
|
"0.183432 0.0383088 -0.0465819 -0.037514 0.0169821 -0.0145299 -0.00365015 0.00975964 0.0073746 0.00371696 -0.00866648 -0.0017458 0.00395749 0.0070437 -0.00320102 0.00463012\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": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\t### errors for param 0 ###\n",
|
||
|
|
"+++ 2.475e+02 2.473e+02 8.301e-02 8.549e-02 0.306 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 8.301e-02 8.673e-02 0.912 +++\n",
|
||
|
|
"+++ 2.475e+02 2.467e+02 8.301e-02 8.735e-02 1.46 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 8.301e-02 8.704e-02 1.16 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 8.301e-02 8.689e-02 1.03 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 8.301e-02 8.681e-02 0.969 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 8.301e-02 8.685e-02 0.999 +++\n",
|
||
|
|
"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 2.475e+02 2.473e+02 -5.760e-01 -5.720e-01 0.399 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -5.760e-01 -5.700e-01 1.13 +++\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 -5.760e-01 -5.710e-01 0.698 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -5.760e-01 -5.705e-01 0.897 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -5.760e-01 -5.702e-01 1.01 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 2.475e+02 2.473e+02 -1.887e+00 -1.870e+00 0.291 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -1.887e+00 -1.862e+00 0.915 +++\n",
|
||
|
|
"+++ 2.475e+02 2.467e+02 -1.887e+00 -1.857e+00 1.53 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -1.887e+00 -1.859e+00 1.18 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -1.887e+00 -1.860e+00 1.04 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -1.887e+00 -1.861e+00 0.971 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -1.887e+00 -1.861e+00 1 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 2.475e+02 2.473e+02 -2.181e+00 -2.154e+00 0.29 +++\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 -2.181e+00 -2.141e+00 0.808 +++\n",
|
||
|
|
"+++ 2.475e+02 2.468e+02 -2.181e+00 -2.134e+00 1.25 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.181e+00 -2.137e+00 1.01 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.181e+00 -2.139e+00 0.905 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.957 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.984 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.181e+00 -2.138e+00 0.997 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.544e+00 -2.494e+00 0.973 +++\n",
|
||
|
|
"+++ 2.475e+02 2.459e+02 -2.544e+00 -2.469e+00 3.08 +++\n",
|
||
|
|
"+++ 2.475e+02 2.466e+02 -2.544e+00 -2.482e+00 1.78 +++\n",
|
||
|
|
"+++ 2.475e+02 2.468e+02 -2.544e+00 -2.488e+00 1.32 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -2.544e+00 -2.491e+00 1.14 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -2.544e+00 -2.492e+00 1.05 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.544e+00 -2.493e+00 1.01 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.544e+00 -2.494e+00 0.993 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 2.475e+02 2.473e+02 -2.856e+00 -2.830e+00 0.277 +++\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 -2.856e+00 -2.818e+00 0.713 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -2.856e+00 -2.811e+00 1.04 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.856e+00 -2.814e+00 0.867 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.856e+00 -2.813e+00 0.953 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -2.856e+00 -2.812e+00 0.993 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 2.475e+02 2.473e+02 -3.612e+00 -3.518e+00 0.392 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -3.612e+00 -3.471e+00 1.06 +++\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 -3.612e+00 -3.494e+00 0.67 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -3.612e+00 -3.483e+00 0.845 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -3.612e+00 -3.477e+00 0.947 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -3.612e+00 -3.474e+00 1 +++\n",
|
||
|
|
"\t### errors for param 7 ###\n",
|
||
|
|
"+++ 2.475e+02 2.474e+02 -3.836e+00 -3.727e+00 0.218 +++\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 -3.836e+00 -3.672e+00 0.684 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -3.836e+00 -3.644e+00 1.16 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -3.836e+00 -3.658e+00 0.894 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -3.836e+00 -3.651e+00 1.02 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -3.836e+00 -3.654e+00 0.955 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -3.836e+00 -3.653e+00 0.988 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -3.836e+00 -3.652e+00 1 +++\n",
|
||
|
|
"\t### errors for param 8 ###\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 7.613e-02 1.568e-01 0.869 +++\n",
|
||
|
|
"+++ 2.475e+02 2.465e+02 7.613e-02 1.971e-01 1.9 +++\n",
|
||
|
|
"+++ 2.475e+02 2.468e+02 7.613e-02 1.769e-01 1.35 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 7.613e-02 1.668e-01 1.1 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 7.613e-02 1.618e-01 0.98 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 7.613e-02 1.643e-01 1.04 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 7.613e-02 1.630e-01 1.01 +++\n",
|
||
|
|
"\t### errors for param 9 ###\n",
|
||
|
|
"+++ 2.475e+02 2.473e+02 4.298e-01 4.767e-01 0.29 +++\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 4.298e-01 5.001e-01 0.641 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.298e-01 5.118e-01 0.863 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.298e-01 5.177e-01 0.985 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 4.298e-01 5.206e-01 1.05 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.298e-01 5.192e-01 1.02 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.298e-01 5.184e-01 1 +++\n",
|
||
|
|
"\t### errors for param 10 ###\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 6.230e-01 8.388e-01 0.802 +++\n",
|
||
|
|
"+++ 2.475e+02 2.466e+02 6.230e-01 9.467e-01 1.77 +++\n",
|
||
|
|
"+++ 2.475e+02 2.468e+02 6.230e-01 8.928e-01 1.25 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 6.230e-01 8.658e-01 1.02 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 6.230e-01 8.523e-01 0.906 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 6.230e-01 8.591e-01 0.96 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 6.230e-01 8.624e-01 0.988 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 6.230e-01 8.641e-01 1 +++\n",
|
||
|
|
"\t### errors for param 11 ###\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 9.046e-01 1.149e+00 0.88 +++\n",
|
||
|
|
"+++ 2.475e+02 2.465e+02 9.046e-01 1.271e+00 1.88 +++\n",
|
||
|
|
"+++ 2.475e+02 2.468e+02 9.046e-01 1.210e+00 1.35 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 9.046e-01 1.179e+00 1.1 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 9.046e-01 1.164e+00 0.989 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 9.046e-01 1.171e+00 1.05 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 9.046e-01 1.168e+00 1.02 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 9.046e-01 1.166e+00 1 +++\n",
|
||
|
|
"\t### errors for param 12 ###\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 4.125e-01 6.242e-01 0.737 +++\n",
|
||
|
|
"+++ 2.475e+02 2.467e+02 4.125e-01 7.300e-01 1.59 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 4.125e-01 6.771e-01 1.13 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.125e-01 6.507e-01 0.924 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 4.125e-01 6.639e-01 1.02 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.125e-01 6.573e-01 0.974 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.125e-01 6.606e-01 0.999 +++\n",
|
||
|
|
"\t### errors for param 13 ###\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 2.838e-01 4.851e-01 0.854 +++\n",
|
||
|
|
"+++ 2.475e+02 2.465e+02 2.838e-01 5.857e-01 1.84 +++\n",
|
||
|
|
"+++ 2.475e+02 2.468e+02 2.838e-01 5.354e-01 1.31 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 2.838e-01 5.103e-01 1.07 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 2.838e-01 4.977e-01 0.96 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 2.838e-01 5.040e-01 1.01 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 2.838e-01 5.008e-01 0.987 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 2.838e-01 5.024e-01 1 +++\n",
|
||
|
|
"\t### errors for param 14 ###\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -8.899e-01 -4.078e-01 0.957 +++\n",
|
||
|
|
"+++ 2.475e+02 2.465e+02 -8.899e-01 -1.668e-01 1.91 +++\n",
|
||
|
|
"+++ 2.475e+02 2.468e+02 -8.899e-01 -2.873e-01 1.41 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -8.899e-01 -3.476e-01 1.18 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 -8.899e-01 -3.777e-01 1.07 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -8.899e-01 -3.928e-01 1.01 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -8.899e-01 -4.003e-01 0.984 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 -8.899e-01 -3.965e-01 0.998 +++\n",
|
||
|
|
"\t### errors for param 15 ###\n",
|
||
|
|
"+++ 2.475e+02 2.471e+02 4.838e-02 4.285e-01 0.661 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.838e-02 6.186e-01 0.961 +++\n",
|
||
|
|
"+++ 2.475e+02 2.469e+02 4.838e-02 7.136e-01 1.07 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.838e-02 6.661e-01 1.02 +++\n",
|
||
|
|
"+++ 2.475e+02 2.470e+02 4.838e-02 6.424e-01 0.991 +++\n",
|
||
|
|
"********************\n",
|
||
|
|
"0.0830101 -0.576037 -1.88651 -2.18123 -2.54372 -2.856 -3.61248 -3.8364 0.0761257 0.429759 0.622953 0.90456 0.412522 0.283831 -0.889856 0.0483793\n",
|
||
|
|
"0.00383825 0.00579502 0.025767 0.0436043 0.050153 0.0439107 0.138708 0.1845 0.086924 0.0886716 0.241158 0.261188 0.248061 0.218563 0.493323 0.593987\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"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": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7fa846cbd410>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"xscale('log'); ylim(-10,10)\n",
|
||
|
|
"errorbar(fqd, lag, yerr=lage, fmt='o', ms=10)\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 1.43381244, 0.4889989 , 0.68917289, 0.48155621, 0.29506678,\n",
|
||
|
|
" 0.16772896, 0.24424756, 0.18973346])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"lage"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|