mirror of
https://asciireactor.com/otho/phy-4660.git
synced 2024-11-24 06:45:08 +00:00
705 lines
124 KiB
Plaintext
705 lines
124 KiB
Plaintext
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"name": "stdout",
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"output_type": "stream",
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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 0x7f8268013a10>"
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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/1367A_shifted.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])"
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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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" 0.25819945, 0.40020915])\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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"output_type": "stream",
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"text": [
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" 1 4.337e-01 6.112e+01 inf -- -4.041e+02 -- 1 1 1 1 1 1 1\n",
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" 2 7.647e-01 6.015e+01 6.901e+01 -- -3.351e+02 -- 0.65784 0.58285 0.5699 0.567505 0.56704 0.566344 0.573791\n",
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" 3 3.242e+00 5.939e+01 6.602e+01 -- -2.691e+02 -- 0.42512 0.198095 0.146253 0.137492 0.135449 0.133237 0.150987\n",
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" 4 1.563e+00 5.894e+01 6.233e+01 -- -2.067e+02 -- 0.328446 -0.100568 -0.261985 -0.286046 -0.293214 -0.298752 -0.270142\n",
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" 5 6.151e-01 5.858e+01 5.845e+01 -- -1.483e+02 -- 0.300114 -0.231637 -0.637422 -0.692493 -0.715789 -0.728243 -0.692465\n",
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" 6 3.834e-01 5.758e+01 5.397e+01 -- -9.432e+01 -- 0.288119 -0.218711 -0.948517 -1.05695 -1.12601 -1.152 -1.11841\n",
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" 7 2.764e-01 5.488e+01 4.687e+01 -- -4.745e+01 -- 0.288807 -0.20359 -1.129 -1.33421 -1.50858 -1.56203 -1.54725\n",
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" 8 2.123e-01 4.884e+01 3.703e+01 -- -1.042e+01 -- 0.290741 -0.199424 -1.16697 -1.47951 -1.82761 -1.93862 -1.97495\n",
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" 9 1.660e-01 3.761e+01 2.500e+01 -- 1.458e+01 -- 0.297078 -0.192793 -1.17471 -1.51254 -2.03046 -2.24008 -2.39415\n",
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" 10 1.251e-01 2.218e+01 1.352e+01 -- 2.810e+01 -- 0.304071 -0.185609 -1.18142 -1.51049 -2.10848 -2.41366 -2.79148\n",
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" 11 8.256e-02 9.018e+00 5.492e+00 -- 3.359e+01 -- 0.305677 -0.180534 -1.18507 -1.51162 -2.12584 -2.46833 -3.14066\n",
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" 12 4.067e-02 2.560e+00 1.439e+00 -- 3.503e+01 -- 0.30378 -0.178308 -1.18784 -1.51647 -2.12974 -2.48272 -3.39994\n",
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" 13 1.216e-02 5.340e-01 2.063e-01 -- 3.524e+01 -- 0.301773 -0.177939 -1.18966 -1.52028 -2.13067 -2.48953 -3.5382\n",
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" 14 2.104e-03 8.200e-02 1.337e-02 -- 3.525e+01 -- 0.30075 -0.178052 -1.19052 -1.52208 -2.13075 -2.49214 -3.58123\n",
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" 15 2.833e-04 1.082e-02 3.730e-04 -- 3.525e+01 -- 0.300445 -0.178142 -1.19077 -1.52264 -2.13068 -2.4928 -3.58876\n",
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" 16 3.684e-05 1.402e-03 6.992e-06 -- 3.525e+01 -- 0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
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"********************\n",
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"0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
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"0.23893 0.202426 0.232625 0.177239 0.153017 0.132987 0.308424\n",
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"-0.000180614 -0.000143998 -0.000174906 -0.000705814 0.000447101 -0.000873593 -0.00140183\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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{
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"cell_type": "code",
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||
|
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"execution_count": 4,
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"metadata": {
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|
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\t### errors for param 0 ###\n",
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"+++ 3.525e+01 3.481e+01 3.004e-01 5.393e-01 0.89 +++\n",
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|
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"+++ 3.525e+01 3.432e+01 3.004e-01 6.588e-01 1.87 +++\n",
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"+++ 3.525e+01 3.458e+01 3.004e-01 5.990e-01 1.34 +++\n",
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||
|
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"+++ 3.525e+01 3.470e+01 3.004e-01 5.692e-01 1.11 +++\n",
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"+++ 3.525e+01 3.475e+01 3.004e-01 5.543e-01 0.996 +++\n",
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"\t### errors for param 1 ###\n",
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"+++ 3.525e+01 3.476e+01 -1.782e-01 2.426e-02 0.974 +++\n",
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||
|
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"+++ 3.525e+01 3.422e+01 -1.782e-01 1.255e-01 2.07 +++\n",
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|
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"+++ 3.525e+01 3.451e+01 -1.782e-01 7.486e-02 1.48 +++\n",
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"+++ 3.525e+01 3.464e+01 -1.782e-01 4.956e-02 1.21 +++\n",
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"+++ 3.525e+01 3.471e+01 -1.782e-01 3.691e-02 1.09 +++\n",
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"+++ 3.525e+01 3.474e+01 -1.782e-01 3.058e-02 1.03 +++\n",
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"+++ 3.525e+01 3.475e+01 -1.782e-01 2.742e-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 -1.075e+00 0.275 +++\n",
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|
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"+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.597 +++\n",
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"+++ 3.525e+01 3.485e+01 -1.191e+00 -9.873e-01 0.798 +++\n",
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|
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"+++ 3.525e+01 3.480e+01 -1.191e+00 -9.727e-01 0.909 +++\n",
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"+++ 3.525e+01 3.477e+01 -1.191e+00 -9.655e-01 0.966 +++\n",
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"+++ 3.525e+01 3.475e+01 -1.191e+00 -9.618e-01 0.995 +++\n",
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"\t### errors for param 3 ###\n",
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"+++ 3.525e+01 3.482e+01 -1.523e+00 -1.346e+00 0.861 +++\n",
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|
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"+++ 3.525e+01 3.433e+01 -1.523e+00 -1.257e+00 1.85 +++\n",
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|
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"+++ 3.525e+01 3.459e+01 -1.523e+00 -1.301e+00 1.32 +++\n",
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|
|
"+++ 3.525e+01 3.471e+01 -1.523e+00 -1.323e+00 1.08 +++\n",
|
||
|
|
"+++ 3.525e+01 3.477e+01 -1.523e+00 -1.334e+00 0.967 +++\n",
|
||
|
|
"+++ 3.525e+01 3.474e+01 -1.523e+00 -1.329e+00 1.02 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -1.523e+00 -1.332e+00 0.994 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 3.525e+01 3.482e+01 -2.131e+00 -1.978e+00 0.868 +++\n",
|
||
|
|
"+++ 3.525e+01 3.430e+01 -2.131e+00 -1.901e+00 1.9 +++\n",
|
||
|
|
"+++ 3.525e+01 3.458e+01 -2.131e+00 -1.939e+00 1.34 +++\n",
|
||
|
|
"+++ 3.525e+01 3.471e+01 -2.131e+00 -1.958e+00 1.09 +++\n",
|
||
|
|
"+++ 3.525e+01 3.476e+01 -2.131e+00 -1.968e+00 0.977 +++\n",
|
||
|
|
"+++ 3.525e+01 3.473e+01 -2.131e+00 -1.963e+00 1.03 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -2.131e+00 -1.966e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 3.525e+01 3.476e+01 -2.493e+00 -2.360e+00 0.992 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 3.525e+01 3.511e+01 -3.590e+00 -3.436e+00 0.274 +++\n",
|
||
|
|
"+++ 3.525e+01 3.491e+01 -3.590e+00 -3.358e+00 0.68 +++\n",
|
||
|
|
"+++ 3.525e+01 3.477e+01 -3.590e+00 -3.320e+00 0.971 +++\n",
|
||
|
|
"+++ 3.525e+01 3.468e+01 -3.590e+00 -3.301e+00 1.14 +++\n",
|
||
|
|
"+++ 3.525e+01 3.472e+01 -3.590e+00 -3.310e+00 1.05 +++\n",
|
||
|
|
"+++ 3.525e+01 3.475e+01 -3.590e+00 -3.315e+00 1.01 +++\n",
|
||
|
|
"+++ 3.525e+01 3.476e+01 -3.590e+00 -3.317e+00 0.991 +++\n",
|
||
|
|
"********************\n",
|
||
|
|
"0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n",
|
||
|
|
"0.253863 0.205589 0.22899 0.191086 0.164971 0.132987 0.27228\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"p1, p1e = clag.errors(P1, p1, p1e)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAG7xJREFUeJzt3X9s3Pd93/GnYtHRErfTEpd3tufomtuUo4y0xl0lAlKs\ncp1bbEOVdOim8LCoSJQhQUwb4LoJ8FCIM0h5WI2hpWOLHbxFyLZgR2lAMyTA1BZDlcqjKo7lZe1K\n6ZrsxNPS2HdZkmpdkyihY+6P7zGhuI9IHnXf+/l8AF+Q/N7n8/28BX1Eve6+n+/3C5IkSZIkSZIk\nSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSbpH/xRYAP4cqAGfBfa3tSJJktQRLgK/BAwBPwF8HqgA\nb2tjTZIkqQM9CLwJvK/dhUiSpK29pYVj7a1//WYLx5QkSR1uF9Hpht9rdyGSJGl7drdonJeBx9j8\nVMND9U2SJDXm9frWVK0ICS8BPw8cBV67S5uHHn744ddee+1uL0uSpE18FThIk4NCnCFhF1FA+AAw\nAtzcpO1Dr732Gp/5zGcYGhqKsaTmGx8fZ3p6uivHu5djNdq3kfbbabtVm81eb/XfWbM415rf3rkW\n5lxrfvs459r169f50Ic+9AjRp/FdExLOAnmikPAtIFnffwu4HeowNDRENpuNsaTm27t3b0trbuZ4\n93KsRvs20n47bbdqs9nrrf47axbnWvPbO9fCnGvNbx/3XIvLfTEe+/PAW4GPAP943fZl4A83tH0I\n+PjHP/5xHnqo+5YlvPe97+3a8e7lWI32baT9dtpu1eZurxcKBfL5/LZr6STOtea3d66FOdea3z6u\nufb666/zyiuvALxCkz9J2NXMg92DLLC4uLjYlalb3eX9738/n/vc59pdhvqAc02tUCwWyeVyADmg\n2Mxjt/I+CZIkqYsYEtR3uvXjX3Uf55q6nSFBfcdf3GoV55q6nSFBkiQFGRIkSVKQIUGSJAUZEiRJ\nUpAhQZIkBRkSJElSkCFBkiQFGRIkSVKQIUGSJAUZEiRJUpAhQZIkBRkSJElSkCFBkiQFGRIkSVKQ\nIUGSJAUZEiRJUpAhQZIkBRkSJElSkCFBkiQFGRIkSVKQIUGSJAUZEiRJUpAhQZIkBRkSJElSkCFB\nkiQFGRIkSVKQIUGSJAUZEiRJUpAhQZIkBRkSJElSkCFBkiQFGRIkSVKQIUGSJAUZEiRJUpAhQZIk\nBRkSJElSkCFBkiQFGRIkSVJQnCHhKPB54KvAm8AHYhxLkiQ1WZwh4W3AF4Gx+s+rMY4lSZKabHeM\nx/6t+iZJkrqQaxIkSVKQIUGSJAUZEiRJUlCcaxIaNj4+zt69e+/Yl8/nyefzbapIkqTOUSgUKBQK\nd+y7detWbOPtiu3Id3oT+AXgc3d5PQssLi4uks1mW1SSJEndr1gsksvlAHJAsZnHjvOThLcDf33d\nz+8GHge+AXwlxnElSVITxBkSDgK/W/9+Ffi1+vefBk7GOK4kSWqCOEPCF3BhpCRJXcv/xCVJUpAh\nQZIkBRkSJElSkCFBkiQFGRIkSVKQIUGSJAUZEiRJUpAhQZIkBRkSJElSkCFBkiQFGRIkSVKQIUGS\nJAUZEiRJUpAhQZIkBRkSJElSkCFBkiQFGRIkSVKQIUGSJAXtbncBUhwKhWgDuH0bbt6Efftgz55o\nXz4fbZKkuzMkqCetDwHFIuRyUWjIZttblyR1E083SJKkIEOCJEkKMiRIkqQgQ4IkSQoyJKhnVSoV\nTp48xfHjx4BjHD9+jJMnT1GpVNpdmiR1Ba9uUM+p1WqMjo5TKg1QrY4BwwCUy1Auz3Px4gSZzAqz\ns9MkEon2FitJHcyQoJ5Sq9U4fDjPjRsvAwcCLYapVoepVq9x5EieubmCQUGS7sLTDeopo6PjmwSE\n9Q5QLr/E6Oh4K8qSpK5kSFDPWF5eplQaYOuAsOYxSqXdrlGQpLswJKhnTE3N1NcgbF+1Osbk5ExM\nFUlSdzMkqGcsLJRYW6S4fcMsLFyPoxxJ6nqGBPWMlZWd9Nq1w36S1PsMCeoZAwM76bW6w36S1PsM\nCeoZBw9mgPkGe81z6NBQHOVIUtczJKhnTEyMkUyebahPMnmW06efiqkiSepuhgT1jFQqRSazAlzb\nZo8lMpk3SKVSMVYlSd3LkKCeMjs7TTr9NLC0Rcsl0ulnOH/+xVaUJUldyZCgnpJIJJibKzAycoZk\n8gRwFVitv7oKXCWZPMHIyBmuXJllcHCwfcVKUofz2Q3qOYlEgkuXClQqFSYnZ7h8+XnKZUin4ejR\nISYmpjzFIEnbYEhQz0qlUpw79wLFIuRycOECZLPtrkqSukfcpxueApaB7wB/ALwv5vEkSVKTxBkS\nPgj8OjAFPA68ClwEHo1xTEmS1CRxhoRfBv4NcA74E+AfAV8BPhHjmJIkqUniCgn3A1ngdzbs/x3g\ncExjSpKkJopr4eKDwH1AbcP+rwHJmMaUfqBQiDaA27dh/3549lnYsyfal89HmyTp7ry6QT3JELA9\nG8PUzZuwb59hSlIkrpDwdeD7QGLD/gTw+t06jY+Ps3fv3jv25fN58v6WkmKxPgSsXSpaKHipqNSp\nCoUChbVkX3fr1q3YxtsV25GjW90tAmPr9l0DPgv8yoa2WWBxcXGRrL+dpLZYCwmLi4YEqZsUi0Vy\nuRxADig289hxnm74NeDfE90f4SrwMeCvAv8qxjElNSi6M+VZLl8uAXD8OBw9mmFiYsw7U0p9Ls6Q\ncAF4JzABPAT8D+DvEF0GKanNarUao6PjlEoDVKtjwDAA5TKUy/NcvDhBJrPC7Ow0icTGM4eS+kHc\nCxd/o75J6iC1Wo3Dh/PcuPEycCDQYphqdZhq9RpHjuSZmysYFKQ+5FMgpT40Ojq+SUBY7wDl8kuM\njo63oixJHcaQIPWZ5eVlSqUBtg4Iax6jVNpNpVKJsSpJnciQIPWZqamZ+hqE7atWx5icnImpIkmd\nypAg9ZmFhRJrixS3b5iFhetxlCOpgxkSpD6zsrKTXrt22E9SNzMkSH1mYGAnvVZ32E9SNzMkSH3m\n4MEMMN9gr3kOHRqKoxxJHcyQIPWZiYkxksmzDfVJJs9y+vRTMVUkqVMZEqQ+k0qlyGRWiB6lsh1L\nZDJveItmqQ8ZEqQ+NDs7TTr9NLC0Rcsl0ulnOH/+xVaUJanDGBKkPpRIJJibKzAycoZk8gTRM9hW\n66+uAldJJk8wMnKGK1dmGRwcbF+xktom7mc3SOpQiUSCS5cK9adAznD58vOUy5BOw9GjQ0xMTHmK\nQepzhgSpz6VSKc6de4FiEXI5uHABstl2VyWpE3i6QZIkBRkSJElSkKcbpD5WKEQbwO3bsH8/PPss\n7NkT7cvno01SfzIkSH3MECBpM55ukCRJQYYESZIUZEiQJElBhgRJkhRkSJAkSUGGBEmSFGRIkCRJ\nQYYESZIUZEiQJElBhgRJkhRkSJAkSUGGBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhRkSJAkSUGG\nBEmSFGRIkCRJQYYESZIUZEiQJElBhgRJkhRkSJAkSUGGBEmSFBRXSPgV4ArwbeDPYhpDkiTFKK6Q\nMACcB2ZiOr4kSYrZ7piO+1z964djOr4kSYqZaxIkSVJQXJ8kSFLTFQrRBnD7Nty8Cfv2wZ490b58\nPtokNUcjIeE5YGKLNj8FFHdcjSRtYn0IKBYhl4tCQzbb3rqkXtVISHgJ+A9btLl5D7UwPj7O3r17\n79iXz+fJ+9ZAkiQKhQKFtY/T6m7duhXbeI2EhG/Ut9hMT0+T9S2BJElBoTfOxWKRXC4Xy3hxrUl4\nF/CO+tf7gJ8EdgFfBr4V05iSJKmJ4goJk8Av1b9fBb5Y//o3gMsxjSmpD1QqFSYnz3L5cgmA48fh\n6NEMExNjpFKp9hYn9Zi4QsKH8R4JkpqoVqsxOjpOqTRAtToGDANQLkO5PM/FixNkMivMzk6TSCTa\nW6zUI7wEUlLHq9VqHD6
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f82810f4090>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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": 8,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f825d37ca50>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"errorbar(t1, l1, yerr=l1e, fmt='o')\n",
|
||
|
|
"t2, l2, l2e = np.loadtxt(echo_file).T\n",
|
||
|
|
"errorbar(t2, l2, yerr=l2e, fmt='o')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" 1 4.337e-01 6.129e+01 inf -- -4.045e+02 -- 1 1 1 1 1 1 1\n",
|
||
|
|
" 2 7.642e-01 6.012e+01 6.908e+01 -- -3.354e+02 -- 0.659275 0.584349 0.573488 0.567303 0.567058 0.566313 0.572424\n",
|
||
|
|
" 3 3.228e+00 5.916e+01 6.581e+01 -- -2.696e+02 -- 0.42785 0.202009 0.156574 0.137043 0.135869 0.133522 0.149486\n",
|
||
|
|
" 4 1.558e+00 5.870e+01 6.191e+01 -- -2.077e+02 -- 0.331792 -0.095026 -0.241091 -0.287087 -0.291742 -0.297484 -0.270575\n",
|
||
|
|
" 5 6.161e-01 5.838e+01 5.801e+01 -- -1.497e+02 -- 0.307892 -0.233206 -0.602158 -0.695208 -0.712571 -0.725065 -0.692226\n",
|
||
|
|
" 6 3.835e-01 5.717e+01 5.345e+01 -- -9.623e+01 -- 0.307174 -0.236649 -0.896104 -1.06223 -1.12044 -1.1459 -1.11873\n",
|
||
|
|
" 7 2.763e-01 5.434e+01 4.635e+01 -- -4.988e+01 -- 0.329917 -0.225069 -1.07001 -1.34027 -1.50052 -1.55267 -1.54775\n",
|
||
|
|
" 8 2.125e-01 4.842e+01 3.677e+01 -- -1.311e+01 -- 0.365734 -0.216758 -1.11613 -1.48335 -1.81897 -1.92787 -1.97538\n",
|
||
|
|
" 9 1.662e-01 3.735e+01 2.505e+01 -- 1.194e+01 -- 0.396836 -0.209034 -1.12234 -1.51466 -2.02433 -2.23196 -2.39516\n",
|
||
|
|
" 10 1.251e-01 2.200e+01 1.359e+01 -- 2.553e+01 -- 0.414786 -0.203235 -1.12662 -1.51315 -2.10323 -2.41253 -2.79331\n",
|
||
|
|
" 11 8.212e-02 8.898e+00 5.471e+00 -- 3.100e+01 -- 0.420985 -0.199803 -1.12907 -1.51521 -2.11742 -2.47391 -3.14266\n",
|
||
|
|
" 12 4.003e-02 2.505e+00 1.412e+00 -- 3.241e+01 -- 0.421103 -0.198221 -1.13105 -1.52048 -2.11896 -2.4902 -3.40075\n",
|
||
|
|
" 13 1.174e-02 5.152e-01 1.981e-01 -- 3.261e+01 -- 0.419844 -0.197745 -1.1323 -1.52425 -2.11903 -2.49687 -3.53687\n",
|
||
|
|
" 14 1.974e-03 7.723e-02 1.236e-02 -- 3.262e+01 -- 0.418984 -0.197677 -1.1328 -1.52593 -2.11879 -2.49921 -3.57839\n",
|
||
|
|
" 15 2.574e-04 9.878e-03 3.258e-04 -- 3.262e+01 -- 0.418666 -0.197682 -1.13291 -1.52643 -2.11863 -2.49979 -3.58545\n",
|
||
|
|
" 16 4.291e-05 1.242e-03 5.812e-06 -- 3.262e+01 -- 0.418586 -0.197687 -1.13292 -1.52653 -2.11856 -2.49988 -3.58637\n",
|
||
|
|
"********************\n",
|
||
|
|
"0.418586 -0.197687 -1.13292 -1.52653 -2.11856 -2.49988 -3.58637\n",
|
||
|
|
"0.230614 0.201096 0.230036 0.178274 0.152947 0.133797 0.307332\n",
|
||
|
|
"-0.000341872 -4.95209e-05 -1.00476e-05 -0.000577377 0.000648932 -0.000804247 -0.00124186\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": 10,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgkAAAFrCAYAAABbtho0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOPG41xilYtbZ2clFV1z
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f825d339950>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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": 11,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\t### errors for param 0 ###\n",
|
||
|
|
"+++ 3.262e+01 3.224e+01 4.186e-01 6.492e-01 0.774 +++\n",
|
||
|
|
"+++ 3.262e+01 3.180e+01 4.186e-01 7.645e-01 1.66 +++\n",
|
||
|
|
"+++ 3.262e+01 3.203e+01 4.186e-01 7.069e-01 1.18 +++\n",
|
||
|
|
"+++ 3.262e+01 3.214e+01 4.186e-01 6.780e-01 0.967 +++\n",
|
||
|
|
"+++ 3.262e+01 3.209e+01 4.186e-01 6.924e-01 1.07 +++\n",
|
||
|
|
"+++ 3.262e+01 3.212e+01 4.186e-01 6.852e-01 1.02 +++\n",
|
||
|
|
"+++ 3.262e+01 3.213e+01 4.186e-01 6.816e-01 0.993 +++\n",
|
||
|
|
"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 3.262e+01 3.217e+01 -1.977e-01 3.409e-03 0.913 +++\n",
|
||
|
|
"+++ 3.262e+01 3.166e+01 -1.977e-01 1.040e-01 1.94 +++\n",
|
||
|
|
"+++ 3.262e+01 3.193e+01 -1.977e-01 5.368e-02 1.38 +++\n",
|
||
|
|
"+++ 3.262e+01 3.205e+01 -1.977e-01 2.855e-02 1.14 +++\n",
|
||
|
|
"+++ 3.262e+01 3.211e+01 -1.977e-01 1.598e-02 1.02 +++\n",
|
||
|
|
"+++ 3.262e+01 3.214e+01 -1.977e-01 9.693e-03 0.968 +++\n",
|
||
|
|
"+++ 3.262e+01 3.213e+01 -1.977e-01 1.284e-02 0.995 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 3.262e+01 3.219e+01 -1.133e+00 -9.029e-01 0.868 +++\n",
|
||
|
|
"+++ 3.262e+01 3.170e+01 -1.133e+00 -7.879e-01 1.84 +++\n",
|
||
|
|
"+++ 3.262e+01 3.197e+01 -1.133e+00 -8.454e-01 1.32 +++\n",
|
||
|
|
"+++ 3.262e+01 3.208e+01 -1.133e+00 -8.741e-01 1.08 +++\n",
|
||
|
|
"+++ 3.262e+01 3.214e+01 -1.133e+00 -8.885e-01 0.973 +++\n",
|
||
|
|
"+++ 3.262e+01 3.211e+01 -1.133e+00 -8.813e-01 1.03 +++\n",
|
||
|
|
"+++ 3.262e+01 3.212e+01 -1.133e+00 -8.849e-01 1 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 3.262e+01 3.219e+01 -1.527e+00 -1.348e+00 0.872 +++\n",
|
||
|
|
"+++ 3.262e+01 3.169e+01 -1.527e+00 -1.259e+00 1.88 +++\n",
|
||
|
|
"+++ 3.262e+01 3.196e+01 -1.527e+00 -1.304e+00 1.33 +++\n",
|
||
|
|
"+++ 3.262e+01 3.208e+01 -1.527e+00 -1.326e+00 1.09 +++\n",
|
||
|
|
"+++ 3.262e+01 3.213e+01 -1.527e+00 -1.337e+00 0.979 +++\n",
|
||
|
|
"+++ 3.262e+01 3.211e+01 -1.527e+00 -1.332e+00 1.03 +++\n",
|
||
|
|
"+++ 3.262e+01 3.212e+01 -1.527e+00 -1.334e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 3.262e+01 3.220e+01 -2.119e+00 -1.966e+00 0.845 +++\n",
|
||
|
|
"+++ 3.262e+01 3.170e+01 -2.119e+00 -1.889e+00 1.85 +++\n",
|
||
|
|
"+++ 3.262e+01 3.197e+01 -2.119e+00 -1.927e+00 1.3 +++\n",
|
||
|
|
"+++ 3.262e+01 3.209e+01 -2.119e+00 -1.946e+00 1.06 +++\n",
|
||
|
|
"+++ 3.262e+01 3.215e+01 -2.119e+00 -1.956e+00 0.951 +++\n",
|
||
|
|
"+++ 3.262e+01 3.212e+01 -2.119e+00 -1.951e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 3.262e+01 3.214e+01 -2.500e+00 -2.366e+00 0.978 +++\n",
|
||
|
|
"+++ 3.262e+01 3.155e+01 -2.500e+00 -2.299e+00 2.16 +++\n",
|
||
|
|
"+++ 3.262e+01 3.187e+01 -2.500e+00 -2.333e+00 1.51 +++\n",
|
||
|
|
"+++ 3.262e+01 3.201e+01 -2.500e+00 -2.349e+00 1.23 +++\n",
|
||
|
|
"+++ 3.262e+01 3.207e+01 -2.500e+00 -2.358e+00 1.1 +++\n",
|
||
|
|
"+++ 3.262e+01 3.211e+01 -2.500e+00 -2.362e+00 1.04 +++\n",
|
||
|
|
"+++ 3.262e+01 3.212e+01 -2.500e+00 -2.364e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 3.262e+01 3.249e+01 -3.586e+00 -3.433e+00 0.274 +++\n",
|
||
|
|
"+++ 3.262e+01 3.228e+01 -3.586e+00 -3.356e+00 0.68 +++\n",
|
||
|
|
"+++ 3.262e+01 3.214e+01 -3.586e+00 -3.317e+00 0.97 +++\n",
|
||
|
|
"+++ 3.262e+01 3.205e+01 -3.586e+00 -3.298e+00 1.14 +++\n",
|
||
|
|
"+++ 3.262e+01 3.210e+01 -3.586e+00 -3.308e+00 1.05 +++\n",
|
||
|
|
"+++ 3.262e+01 3.212e+01 -3.586e+00 -3.313e+00 1.01 +++\n",
|
||
|
|
"+++ 3.262e+01 3.213e+01 -3.586e+00 -3.315e+00 0.99 +++\n",
|
||
|
|
"********************\n",
|
||
|
|
"0.418586 -0.197687 -1.13292 -1.52653 -2.11856 -2.49988 -3.58637\n",
|
||
|
|
"0.263044 0.210523 0.248008 0.192202 0.167286 0.135887 0.271316\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"p2, p2e = clag.errors(P2, p2, p2e)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" 1 1.751e+01 2.876e+01 inf -- 5.733e+01 -- 0.0594864 -0.487928 -1.46187 -1.82465 -2.4246 -2.79639 -3.88808 0.1 0.1 0.1 0.1 0.1 0.1 0.1\n",
|
||
|
|
" 2 1.970e+01 3.013e+01 1.798e+01 -- 7.532e+01 -- 0.29683 -1.52364 -1.4649 -2.25166 -2.5651 -3.43969 -4.03355 1.21455 0.923908 -1.61801 1.85071 -0.285296 -0.531157 0.012722\n",
|
||
|
|
" 4 6.537e+00 1.384e+01 2.528e+00 -- 7.784e+01 -- 0.291378 -1.68551 -1.44919 -2.09362 -2.55998 -3.48384 -4.03518 1.14909 2.74383 -1.47933 1.80908 -0.301606 -0.875227 0.0310364\n",
|
||
|
|
" 6 3.785e+00 7.896e+00 1.984e+00 -- 7.983e+01 -- 0.288282 -1.38551 -1.42819 -1.9952 -2.55472 -3.47151 -4.03617 1.0893 2.48495 -1.36763 1.78981 -0.314059 -1.2346 0.0473648\n",
|
||
|
|
" 8 2.247e+00 7.953e+00 1.903e+00 -- 8.173e+01 -- 0.287062 -1.08551 -1.40581 -1.9248 -2.54944 -3.41037 -4.03676 1.03541 2.70301 -1.2801 1.77686 -0.323434 -1.51696 0.0617974\n",
|
||
|
|
" 10 1.524e+00 9.195e+00 1.868e+00 -- 8.360e+01 -- 0.287304 -0.84158 -1.38402 -1.87105 -2.54419 -3.33668 -4.03696 0.987277 2.6684 -1.21194 1.76655 -0.330285 -1.69315 0.0746248\n",
|
||
|
|
" 12 1.161e+00 1.058e+01 1.526e+00 -- 8.513e+01 -- 0.288653 -0.721287 -1.36379 -1.8284 -2.53901 -3.27143 -4.03682 0.944635 2.66148 -1.15906 1.75754 -0.335021 -1.79864 0.0859985\n",
|
||
|
|
" 14 9.236e-01 1.358e+01 1.320e+00 -- 8.645e+01 -- 0.29076 -0.640904 -1.34555 -1.79371 -2.53397 -3.21778 -4.03644 0.907049 2.65848 -1.11758 1.74949 -0.338064 -1.86544 0.0959851\n",
|
||
|
|
" 16 7.887e-01 1.616e+01 1.163e+00 -- 8.761e+01 -- 0.293363 -0.581712 -1.32932 -1.76496 -2.5291 -3.17401 -4.03591 0.874019 2.65704 -1.08472 1.74218 -0.339743 -1.91057 0.104678\n",
|
||
|
|
" 18 6.852e-01 1.848e+01 1.035e+00 -- 8.864e+01 -- 0.296262 -0.535833 -1.31497 -1.7408 -2.52442 -3.13802 -4.03528 0.84503 2.65643 -1.05844 1.73544 -0.340323 -1.94268 0.112177\n",
|
||
|
|
" 20 6.016e-01 2.063e+01 9.268e-01 -- 8.957e+01 -- 0.299311 -0.499119 -1.30233 -1.72029 -2.51992 -3.1081 -4.03459 0.819584 2.65634 -1.03727 1.7292 -0.340017 -1.96645 0.11858\n",
|
||
|
|
" 21 1.026e+00 1.793e+03 1.113e+01 -- 7.844e+01 -- 0.330255 -0.19883 -1.191 -1.54462 -2.4769 -2.85684 -4.0275 0.595995 2.65885 -0.865635 1.67089 -0.329856 -2.14791 0.172608\n",
|
||
|
|
" 22 2.203e+01 4.653e+01 1.881e+01 -- 9.725e+01 -- 0.346046 -0.283463 -1.21608 -1.56969 -2.45327 -2.92337 -4.05674 0.633366 2.66967 -1.02345 1.65017 -0.216181 -2.10193 -0.00456476\n",
|
||
|
|
" 23 9.017e-02 4.157e+01 4.743e-01 -- 9.772e+01 -- 0.350061 -0.2856 -1.19812 -1.56937 -2.44621 -2.91187 -4.02571 0.593047 2.67704 -0.939304 1.64631 -0.234834 -2.06914 0.0960162\n",
|
||
|
|
" 24 1.011e-01 1.709e+01 7.447e-02 -- 9.780e+01 -- 0.351939 -0.286126 -1.19593 -1.56919 -2.44211 -2.91054 -4.02815 0.594891 2.68864 -0.964614 1.63615 -0.233118 -2.07263 0.0873586\n",
|
||
|
|
" 25 7.198e-03 7.315e+00 9.879e-03 -- 9.781e+01 -- 0.352451 -0.285708 -1.19443 -1.5689 -2.44029 -2.90957 -4.02701 0.591849 2.68648 -0.958907 1.63497 -0.229941 -2.07127 0.0961923\n",
|
||
|
|
" 26 7.573e-03 2.607e+00 1.318e-03 -- 9.781e+01 -- 0.352645 -0.285889 -1.19403 -1.56883 -2.43938 -2.90916 -4.02687 0.591744 2.6883 -0.960874 1.63369 -0.228302 -2.0711 0.0968847\n",
|
||
|
|
" 27 1.407e-03 1.002e+00 1.762e-04 -- 9.781e+01 -- 0.35271 -0.285841 -1.19384 -1.5688 -2.43903 -2.909 -4.02679 0.59146 2.68804 -0.960478 1.63339 -0.22751 -2.07101 0.0976184\n",
|
||
|
|
" 28 7.648e-04 3.587e-01 2.373e-05 -- 9.781e+01 -- 0.352735 -0.28586 -1.19379 -1.56879 -2.43888 -2.90894 -4.02677 0.59142 2.68826 -0.960644 1.63322 -0.22719 -2.07099 0.0977376\n",
|
||
|
|
"********************\n",
|
||
|
|
"0.352735 -0.28586 -1.19379 -1.56879 -2.43888 -2.90894 -4.02677 0.59142 2.68826 -0.960644 1.63322 -0.22719 -2.07099 0.0977376\n",
|
||
|
|
"0.00496596 0.0557416 0.0244243 0.0261901 0.169649 0.216583 0.627326 0.0836975 0.256304 0.179223 0.163052 0.470721 0.550365 1.46594\n",
|
||
|
|
"0.358674 0.00129977 0.0426552 0.00780676 0.00197808 0.000483372 2.94351e-05 -0.00466008 -0.000401306 0.00086537 -0.00189251 0.000598726 2.9782e-05 3.40563e-05\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": 13,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\t### errors for param 0 ###\n",
|
||
|
|
"+++ 9.781e+01 9.767e+01 3.527e-01 3.552e-01 0.284 +++\n",
|
||
|
|
"+++ 9.781e+01 9.736e+01 3.527e-01 3.565e-01 0.898 +++\n",
|
||
|
|
"+++ 9.781e+01 9.705e+01 3.527e-01 3.571e-01 1.51 +++\n",
|
||
|
|
"+++ 9.781e+01 9.723e+01 3.527e-01 3.568e-01 1.17 +++\n",
|
||
|
|
"+++ 9.781e+01 9.730e+01 3.527e-01 3.566e-01 1.02 +++\n",
|
||
|
|
"+++ 9.781e+01 9.733e+01 3.527e-01 3.565e-01 0.959 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 3.527e-01 3.566e-01 0.991 +++\n",
|
||
|
|
"\t### errors for param 1 ###\n",
|
||
|
|
"+++ 9.781e+01 9.757e+01 -2.859e-01 -2.580e-01 0.487 +++\n",
|
||
|
|
"+++ 9.781e+01 9.709e+01 -2.859e-01 -2.441e-01 1.43 +++\n",
|
||
|
|
"+++ 9.781e+01 9.738e+01 -2.859e-01 -2.510e-01 0.864 +++\n",
|
||
|
|
"+++ 9.781e+01 9.725e+01 -2.859e-01 -2.475e-01 1.12 +++\n",
|
||
|
|
"+++ 9.781e+01 9.732e+01 -2.859e-01 -2.493e-01 0.985 +++\n",
|
||
|
|
"+++ 9.781e+01 9.728e+01 -2.859e-01 -2.484e-01 1.05 +++\n",
|
||
|
|
"+++ 9.781e+01 9.730e+01 -2.859e-01 -2.488e-01 1.02 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 -2.859e-01 -2.491e-01 1 +++\n",
|
||
|
|
"\t### errors for param 2 ###\n",
|
||
|
|
"+++ 9.781e+01 9.763e+01 -1.194e+00 -1.182e+00 0.354 +++\n",
|
||
|
|
"+++ 9.781e+01 9.727e+01 -1.194e+00 -1.175e+00 1.09 +++\n",
|
||
|
|
"+++ 9.781e+01 9.749e+01 -1.194e+00 -1.179e+00 0.638 +++\n",
|
||
|
|
"+++ 9.781e+01 9.739e+01 -1.194e+00 -1.177e+00 0.837 +++\n",
|
||
|
|
"+++ 9.781e+01 9.733e+01 -1.194e+00 -1.176e+00 0.954 +++\n",
|
||
|
|
"+++ 9.781e+01 9.730e+01 -1.194e+00 -1.176e+00 1.02 +++\n",
|
||
|
|
"+++ 9.781e+01 9.732e+01 -1.194e+00 -1.176e+00 0.986 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 -1.194e+00 -1.176e+00 1 +++\n",
|
||
|
|
"\t### errors for param 3 ###\n",
|
||
|
|
"+++ 9.781e+01 9.765e+01 -1.569e+00 -1.556e+00 0.327 +++\n",
|
||
|
|
"+++ 9.781e+01 9.736e+01 -1.569e+00 -1.549e+00 0.907 +++\n",
|
||
|
|
"+++ 9.781e+01 9.712e+01 -1.569e+00 -1.546e+00 1.39 +++\n",
|
||
|
|
"+++ 9.781e+01 9.725e+01 -1.569e+00 -1.548e+00 1.13 +++\n",
|
||
|
|
"+++ 9.781e+01 9.730e+01 -1.569e+00 -1.548e+00 1.01 +++\n",
|
||
|
|
"+++ 9.781e+01 9.733e+01 -1.569e+00 -1.549e+00 0.958 +++\n",
|
||
|
|
"+++ 9.781e+01 9.732e+01 -1.569e+00 -1.549e+00 0.985 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 -1.569e+00 -1.548e+00 0.998 +++\n",
|
||
|
|
"\t### errors for param 4 ###\n",
|
||
|
|
"+++ 9.781e+01 9.766e+01 -2.439e+00 -2.354e+00 0.291 +++\n",
|
||
|
|
"+++ 9.781e+01 9.739e+01 -2.439e+00 -2.312e+00 0.833 +++\n",
|
||
|
|
"+++ 9.781e+01 9.716e+01 -2.439e+00 -2.290e+00 1.3 +++\n",
|
||
|
|
"+++ 9.781e+01 9.729e+01 -2.439e+00 -2.301e+00 1.04 +++\n",
|
||
|
|
"+++ 9.781e+01 9.734e+01 -2.439e+00 -2.306e+00 0.934 +++\n",
|
||
|
|
"+++ 9.781e+01 9.732e+01 -2.439e+00 -2.304e+00 0.988 +++\n",
|
||
|
|
"+++ 9.781e+01 9.730e+01 -2.439e+00 -2.302e+00 1.02 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 -2.439e+00 -2.303e+00 1 +++\n",
|
||
|
|
"\t### errors for param 5 ###\n",
|
||
|
|
"+++ 9.781e+01 9.760e+01 -2.909e+00 -2.801e+00 0.412 +++\n",
|
||
|
|
"+++ 9.781e+01 9.723e+01 -2.909e+00 -2.746e+00 1.17 +++\n",
|
||
|
|
"+++ 9.781e+01 9.745e+01 -2.909e+00 -2.774e+00 0.72 +++\n",
|
||
|
|
"+++ 9.781e+01 9.735e+01 -2.909e+00 -2.760e+00 0.923 +++\n",
|
||
|
|
"+++ 9.781e+01 9.729e+01 -2.909e+00 -2.753e+00 1.04 +++\n",
|
||
|
|
"+++ 9.781e+01 9.732e+01 -2.909e+00 -2.757e+00 0.98 +++\n",
|
||
|
|
"+++ 9.781e+01 9.730e+01 -2.909e+00 -2.755e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 6 ###\n",
|
||
|
|
"+++ 9.781e+01 -inf -4.027e+00 -3.027e+00 inf +++\n",
|
||
|
|
"+++ 9.781e+01 9.626e+01 -4.027e+00 -3.527e+00 3.1 +++\n",
|
||
|
|
"+++ 9.781e+01 9.763e+01 -4.027e+00 -3.777e+00 0.349 +++\n",
|
||
|
|
"+++ 9.781e+01 9.724e+01 -4.027e+00 -3.652e+00 1.14 +++\n",
|
||
|
|
"+++ 9.781e+01 9.748e+01 -4.027e+00 -3.714e+00 0.653 +++\n",
|
||
|
|
"+++ 9.781e+01 9.737e+01 -4.027e+00 -3.683e+00 0.869 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 -4.027e+00 -3.667e+00 0.996 +++\n",
|
||
|
|
"\t### errors for param 7 ###\n",
|
||
|
|
"+++ 9.781e+01 9.733e+01 5.914e-01 6.750e-01 0.966 +++\n",
|
||
|
|
"+++ 9.781e+01 9.688e+01 5.914e-01 7.169e-01 1.87 +++\n",
|
||
|
|
"+++ 9.781e+01 9.711e+01 5.914e-01 6.960e-01 1.4 +++\n",
|
||
|
|
"+++ 9.781e+01 9.722e+01 5.914e-01 6.855e-01 1.18 +++\n",
|
||
|
|
"+++ 9.781e+01 9.727e+01 5.914e-01 6.803e-01 1.07 +++\n",
|
||
|
|
"+++ 9.781e+01 9.730e+01 5.914e-01 6.777e-01 1.02 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 5.914e-01 6.763e-01 0.992 +++\n",
|
||
|
|
"\t### errors for param 8 ###\n",
|
||
|
|
"+++ 9.781e+01 9.765e+01 2.688e+00 2.816e+00 0.315 +++\n",
|
||
|
|
"+++ 9.781e+01 9.747e+01 2.688e+00 2.880e+00 0.685 +++\n",
|
||
|
|
"+++ 9.781e+01 9.735e+01 2.688e+00 2.912e+00 0.915 +++\n",
|
||
|
|
"+++ 9.781e+01 9.729e+01 2.688e+00 2.929e+00 1.04 +++\n",
|
||
|
|
"+++ 9.781e+01 9.732e+01 2.688e+00 2.921e+00 0.977 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 2.688e+00 2.925e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 9 ###\n",
|
||
|
|
"+++ 9.781e+01 9.767e+01 -9.606e-01 -8.710e-01 0.287 +++\n",
|
||
|
|
"+++ 9.781e+01 9.750e+01 -9.606e-01 -8.262e-01 0.615 +++\n",
|
||
|
|
"+++ 9.781e+01 9.740e+01 -9.606e-01 -8.038e-01 0.814 +++\n",
|
||
|
|
"+++ 9.781e+01 9.735e+01 -9.606e-01 -7.927e-01 0.921 +++\n",
|
||
|
|
"+++ 9.781e+01 9.732e+01 -9.606e-01 -7.871e-01 0.976 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 -9.606e-01 -7.843e-01 1 +++\n",
|
||
|
|
"\t### errors for param 10 ###\n",
|
||
|
|
"+++ 9.781e+01 9.737e+01 1.633e+00 1.796e+00 0.887 +++\n",
|
||
|
|
"+++ 9.781e+01 9.687e+01 1.633e+00 1.878e+00 1.89 +++\n",
|
||
|
|
"+++ 9.781e+01 9.713e+01 1.633e+00 1.837e+00 1.35 +++\n",
|
||
|
|
"+++ 9.781e+01 9.725e+01 1.633e+00 1.817e+00 1.11 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 1.633e+00 1.806e+00 0.995 +++\n",
|
||
|
|
"\t### errors for param 11 ###\n",
|
||
|
|
"+++ 9.781e+01 9.744e+01 -2.271e-01 2.436e-01 0.733 +++\n",
|
||
|
|
"+++ 9.781e+01 9.707e+01 -2.271e-01 4.789e-01 1.47 +++\n",
|
||
|
|
"+++ 9.781e+01 9.726e+01 -2.271e-01 3.612e-01 1.09 +++\n",
|
||
|
|
"+++ 9.781e+01 9.736e+01 -2.271e-01 3.024e-01 0.906 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 -2.271e-01 3.318e-01 0.996 +++\n",
|
||
|
|
"\t### errors for param 12 ###\n",
|
||
|
|
"+++ 9.781e+01 9.655e+01 -2.071e+00 -1.071e+00 2.53 +++\n",
|
||
|
|
"+++ 9.781e+01 9.739e+01 -2.071e+00 -1.571e+00 0.834 +++\n",
|
||
|
|
"+++ 9.781e+01 9.697e+01 -2.071e+00 -1.321e+00 1.68 +++\n",
|
||
|
|
"+++ 9.781e+01 9.719e+01 -2.071e+00 -1.446e+00 1.24 +++\n",
|
||
|
|
"+++ 9.781e+01 9.729e+01 -2.071e+00 -1.508e+00 1.03 +++\n",
|
||
|
|
"+++ 9.781e+01 9.734e+01 -2.071e+00 -1.540e+00 0.932 +++\n",
|
||
|
|
"+++ 9.781e+01 9.732e+01 -2.071e+00 -1.524e+00 0.981 +++\n",
|
||
|
|
"+++ 9.781e+01 9.731e+01 -2.071e+00 -1.516e+00 1.01 +++\n",
|
||
|
|
"\t### errors for param 13 ###\n",
|
||
|
|
"********************\n",
|
||
|
|
"0.352744 -0.285856 -1.19376 -1.56878 -2.43883 -2.90891 -4.02676 0.591388 2.68824 -0.960618 1.63318 -0.22706 -2.07098 0.0978123\n",
|
||
|
|
"0.00383633 0.0367967 0.0178294 0.0203568 0.135817 0.153961 0.359375 0.0849581 0.236274 0.176365 0.173234 0.558872 0.554688 10\n",
|
||
|
|
"********************\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"p, pe = clag.errors(Cx, p, pe)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"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": 15,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Container object of 3 artists>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f825d256650>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"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": 17,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 1.40138477, 1.30298225, 0.50400939, 0.31939362, 0.66477489,\n",
|
||
|
|
" 0.42567588, 4.95106888])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"lage"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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
|
||
|
|
}
|