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Ultraviolet Observations of the Seyfert 1 Galaxy NGC 5548 with the Cosmic Origins Spectrograph on Hubble Space Telescope}", + journal = {\apj}, +archivePrefix = "arXiv", + eprint = {1501.05954}, + keywords = {galaxies: active, galaxies: individual: NGC 5548, galaxies: nuclei, galaxies: Seyfert}, + year = 2015, + month = jun, + volume = 806, + eid = {128}, + pages = {128}, + doi = {10.1088/0004-637X/806/1/128}, + adsurl = {http://adsabs.harvard.edu/abs/2015ApJ...806..128D}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@ARTICLE{1999MNRAS.302L..24C, + author = {{Collier}, S. and {Horne}, K. and {Wanders}, I. and {Peterson}, B.~M. + }, + title = "{A new direct method for measuring the Hubble constant from reverberating accretion discs in active galaxies}", + journal = {\mnras}, + eprint = {astro-ph/9811278}, + year = 1999, + month = jan, + volume = 302, + pages = {L24-L28}, + doi = {10.1046/j.1365-8711.1999.02250.x}, + adsurl = {http://adsabs.harvard.edu/abs/1999MNRAS.302L..24C}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@ARTICLE{2016MNRAS.462..511K, + author = {{Kara}, E. and {Alston}, W.~N. and {Fabian}, A.~C. and {Cackett}, E.~M. and + {Uttley}, P. and {Reynolds}, C.~S. and {Zoghbi}, A.}, + title = "{A global look at X-ray time lags in Seyfert galaxies}", + journal = {\mnras}, + keywords = {black hole physics, galaxies: active, X-rays: galaxies}, + year = 2016, + month = oct, + volume = 462, + pages = {511-531}, + doi = {10.1093/mnras/stw1695}, + adsurl = {http://adsabs.harvard.edu/abs/2016MNRAS.462..511K}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@INPROCEEDINGS{2006pces.conf...89P, + author = {{Peterson}, B.~M. and {Horne}, K.}, + title = "{Reverberation mapping of active galactic nuclei}", +booktitle = {Planets to Cosmology: Essential Science in the Final Years of the Hubble Space Telescope}, + year = 2006, + volume = 18, + editor = {{Livio}, M. and {Casertano}, S.}, + month = jan, + pages = {89}, + adsurl = {http://adsabs.harvard.edu/abs/2006pces.conf...89P}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@ARTICLE{2012ARA&A..50..455F, + author = {{Fabian}, A.~C.}, + title = "{Observational Evidence of Active Galactic Nuclei Feedback}", + journal = {\araa}, +archivePrefix = "arXiv", + eprint = {1204.4114}, + year = 2012, + month = sep, + volume = 50, + pages = {455-489}, + doi = {10.1146/annurev-astro-081811-125521}, + adsurl = {http://adsabs.harvard.edu/abs/2012ARA%26A..50..455F}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + +@ARTICLE{2015PASP..127...67B, + author = {{Bentz}, M.~C. and {Katz}, S.}, + title = "{The AGN Black Hole Mass Database}", + journal = {\pasp}, +archivePrefix = "arXiv", + eprint = {1411.2596}, + year = 2015, + month = jan, + volume = 127, + pages = {67-73}, + doi = {10.1086/679601}, + adsurl = {http://adsabs.harvard.edu/abs/2015PASP..127...67B}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + +@ARTICLE{2014SSRv..183..253P, + author = {{Peterson}, B.~M.}, + title = "{Measuring the Masses of Supermassive Black Holes}", + journal = {\ssr}, + keywords = {Active galactic nuclei, Black hole, Reverberation mapping}, + year = 2014, + month = sep, + volume = 183, + pages = {253-275}, + doi = {10.1007/s11214-013-9987-4}, + adsurl = {http://adsabs.harvard.edu/abs/2014SSRv..183..253P}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + +@ARTICLE{1973A&A....24..337S, + author = {{Shakura}, N.~I. and {Sunyaev}, R.~A.}, + title = "{Black holes in binary systems. Observational appearance.}", + journal = {\aap}, + year = 1973, + volume = 24, + pages = {337-355}, + adsurl = {http://adsabs.harvard.edu/abs/1973A%26A....24..337S}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} + + + + + + + + + @book{CHAOSDYNAMICS, author = {Baker, G.L. and Gollub, J.P.}, @@ -117,4 +438,11 @@ url = {http://www.engineeringtoolbox.com/air-density-specific-weight-d_600.html} month=oct # "~28", publisher={Google Patents}, note={WO Patent App. PCT/GB2004/001,654} -} \ No newline at end of file +} + + + + + + + diff --git a/chaos/report/report.tex b/chaos/report/report.tex index 0977f55..6600ab5 100644 --- a/chaos/report/report.tex +++ b/chaos/report/report.tex @@ -31,9 +31,9 @@ The chaotic behaviour of a driven pendulum is explored. Phase space behaviours o \section{Chaos} \label{sec:chaos} -Chaos is observed in many non-linear physical systems. It is the condition that a system's outcome is strongly sensitive to initial conditions. The changing conditions as the system evolves affect the outcome such that predicting the future state becomes impossible. The motion of a driven oscillator, such as a driven pendulum, for example, becomes unpredictable as the driving frequency and natural frequency of the pendulum interact. Damping can constrain the motion, and we find that while the motion is unpredictable, it still displays certain characteristics that can be analyzed. +Chaos is observed in many non-linear physical systems. It is the condition that a system's evolution is strongly sensitive to initial conditions, such that predicting the future state becomes impossible. The motion of a driven oscillator, such as a driven pendulum, becomes unpredictable as the driving frequency and natural frequency of the pendulum interact. Damping can constrain the motion, and we find that while the motion is unpredictable, it still exhibits some useful features, which will be discussed in section \ref{sec:modeling}. -For a finite period of time, chaotic behaviour isn't completely discernable from periodic behaviour, because the possibility exists that the function may repeat itself at some future time. To identify chaos, one makes a judgment after enough time has elapsed to assume for practical purposes the function will not repeat. Distinctions are seen between a periodic variable and a chaotic variable in phase space and poincar\'{e} sections. \cite{TANGLEDTALE} +For a finite period of time, chaotic behaviour isn't completely discernable from periodic behaviour, because the possibility exists that the function may repeat itself at some future time. Distinctions are seen between a periodic variable and a chaotic variable in phase space and Poincar\'{e} sections. \cite{TANGLEDTALE} \subsection{Model of a Driven Pendulum} @@ -44,7 +44,7 @@ For a finite period of time, chaotic behaviour isn't completely discernable from $\frac{d^2\theta}{dt^2} = \frac{{\omega_0}^2}{I} sin(\theta) - \frac{{\alpha}}{I} \frac{d\theta}{dt} + \frac{f}{I} cos(\omega t + \phi)$. \end{center} - Here, $\omega_0$ represents the natural frequency of the pedulum, also its resonant frequency. The system will respond most strongly to the driver at this frequency. $\alpha$ is a damping term -- this can take a variety of forms, and in the experiment of section \ref{sec:experiment} is produced by a neodymium magnet interacting with the metal wheel of the pendulum. $f$ is the forcing amplitude where $\omega$ is the forcing frequency, offset from the angular coordinate by a phase $\phi$. $I$ is the moment of inertia of the pendulum. + Here, $\omega_0$ represents the natural frequency of the pedulum, also its resonant frequency. $\alpha$ is a damping term -- this can take a variety of forms, and in the experiment of section \ref{sec:experiment} is produced by a neodymium magnet interacting with the metal wheel of the pendulum. $f$ is the forcing amplitude where $\omega$ is the forcing frequency, offset from the angular coordinate by a phase $\phi$. $I$ is the moment of inertia of the pendulum. The system will respond most strongly to the driver at this frequency. \section{Phase Space} @@ -83,7 +83,7 @@ A 2-dimensional phase space is a useful environment in which to identify the cha \end{figure} \subsection{Poincar\'{e} Map} - A Poincar\'{e} section is a 2D phase space cross-section; in the case of the driven pendulum, the cross-section at a phase $\phi$ from the forcing term. One draws a map between each successive point to create a Poincar\'e map, which is a useful representation of a system's behaviour in phase space. A poincar\'e section of the driven pendulum model is shown in figure \ref{fig:model_driven_poincare}. An undriven and undamped oscillator will always return to the same point after one period, so a Poincar\'e map sampled using the period corresponding to the oscillator's natural frequency will consist of a single dot. The existence of multiple points at the same phase indicates chaotic motion. + A Poincar\'{e} section is a 2D phase space cross-section; in the case of the driven pendulum, the cross-section at a phase $\phi$ from the forcing term. One draws a map between each successive point to create a Poincar\'e map, which is a useful representation of a system's behaviour in phase space. A Poincar\'e section of the driven pendulum model is shown in figure \ref{fig:model_driven_poincare}. An undriven and undamped oscillator will always return to the same point after one period, so a Poincar\'e map sampled using the period corresponding to the oscillator's natural frequency will consist of a single dot. The existence of multiple points at the same phase indicates chaotic motion. \begin{figure} \includegraphics[width=6.5in]{model_driven_poincare.png} @@ -117,7 +117,7 @@ An experimental driven pendulum is built in order to determine whether its motio \subsection{Periodic Motion} - To produce periodic motion, the driving arm is run with a driving period $1.45\pm0.02s$. The observed motion is plotted in figure \ref{fig:exp_periodic}. The motion still suggests the possibility of chaos by the variation in the path taken about the critical point, but the poincar\'e section in figure \ref{fig:exp_periodic} indicates that the orbit is likely converging. The period of the observed motion is consistent with the driving period. + To produce periodic motion, the driving arm is run with a driving period $1.45\pm0.02s$. The observed motion is plotted in figure \ref{fig:exp_periodic}. The motion still suggests the possibility of chaos by the variation in the path taken about the critical point, but the Poincar\'e section in figure \ref{fig:exp_periodic} indicates that the orbit is likely converging. The period of the observed motion is consistent with the driving period. \begin{figure} \hfill @@ -133,13 +133,13 @@ An experimental driven pendulum is built in order to determine whether its motio \includegraphics[width=1.8in]{exp_periodic_poincare.png} } \hfill - \caption{[A] The observed motion when the driving arm is run with period $1.45\pm0.02s$ is periodic at $1.48\pm0.08s$. [B] The periodic nature is recognizable in phase space. [C] The poincar\'e section shows the orbit is likely converging.} + \caption{[A] The observed motion when the driving arm is run with period $1.45\pm0.02s$ is periodic at $1.48\pm0.08s$. [B] The periodic nature is recognizable in phase space. [C] The Poincar\'e section shows the orbit is likely converging.} \label{fig:exp_periodic} \end{figure} \subsection{Chaotic Motion} - The driving arm period was increased until the pendulum was exhibiting visibly complex behaviour. The final period was $1.13\pm0.02s$. In figure \ref{fig:exp_chaotic_A} the observed motions are plotted. Two chaotic attractors are strikingly visible in the phase diagram, and the poincar\'e plot shows significant deviation through the phase. + The driving arm period was increased until the pendulum was exhibiting visibly complex behaviour. The final period was $1.13\pm0.02s$. In figure \ref{fig:exp_chaotic_A} the observed motions are plotted. Two chaotic attractors are strikingly visible in the phase diagram, and the Poincar\'e plot shows significant deviation through the phase. \begin{figure} \hfill @@ -155,7 +155,7 @@ An experimental driven pendulum is built in order to determine whether its motio \includegraphics[width=1.8in]{exp_chaotic_A_poincare.png} } \hfill - \caption{[A] The observed motion when the driving arm is run with period $1.13\pm0.02s$ is chaotic. [B] Chaotic attractors are recognizable in phase space. [C] The poincar\'e section shows chaos in the orbit, but that the motions are still constrained to a finite area in phase space.} + \caption{[A] The observed motion when the driving arm is run with period $1.13\pm0.02s$ is chaotic. [B] Chaotic attractors are recognizable in phase space. [C] The Poincar\'e section shows chaos in the orbit, but that the motions are still constrained to a finite area in phase space.} \label{fig:exp_chaotic_A} \end{figure} @@ -175,12 +175,12 @@ An experimental driven pendulum is built in order to determine whether its motio \includegraphics[width=1.8in]{exp_chaotic_B_poincare.png} } \hfill - \caption{[A] The observed motion when the driving arm is run with period $1.26\pm0.02s$ (near the resonance frequency) is chaotic. [B] . [C] The poincar\'e section shows the same integrated area of constraint in variation as the first test, but more evenly distributed.} + \caption{[A] The observed motion when the driving arm is run with period $1.26\pm0.02s$ (near the resonance frequency) is chaotic. [B] . [C] The Poincar\'e section shows the same integrated area of constraint in variation as the first test, but more evenly distributed.} \label{fig:exp_chaotic_B} \end{figure} - One final driving period was chosen for good measure. At $1.15\pm0.02s$, the motion appears closer to sinsuisoidal than in the pervious tests. Nonetheless, the attractors can be observed in the phase diagram and the paths through the poincar\'e section are limited to the same integrated area. + One final driving period was chosen for good measure. At $1.15\pm0.02s$, the motion appears closer to sinsuisoidal than in the pervious tests. Nonetheless, the attractors can be observed in the phase diagram and the paths through the Poincar\'e section are limited to the same integrated area. \begin{figure} \hfill @@ -190,13 +190,13 @@ An experimental driven pendulum is built in order to determine whether its motio \hfill \includegraphics[width=1.8in]{exp_chaotic_C_poincare.png} \hfill - \caption{The observed motion when the driving arm is run with period $1.16\pm0.02s$. The time graph looks significiantly different from the previous tests, but the same attractors and poincar\'e section are observed.} + \caption{The observed motion when the driving arm is run with period $1.16\pm0.02s$. The time graph looks significiantly different from the previous tests, but the same attractors and Poincar\'e section are observed.} \label{fig:exp_chaotic_C} \end{figure} \section{Discussion} \label{sec:discussion} -The periodic motion is observable in the first experimental case, plotted in figure \ref{fig:exp_periodic}. Once the driving arm period was increased from that frequency to one that induced chaotic motion, the predicted spread of intercepts through the poincar\'e section and the orbits about the chaotic attractors were visually confirmed. It would have been useful to produce the phase diagram of the damped, undriven pendulum to compare the locations of those critical points to the chaotic attractors. However, as Poincar\'e predicted so long ago, it is quite evident that constrained chaotic motion does follow complex paths about attractors, and this motion is evident in the observed motion of the damped, driven pendulum. +The periodic motion is observable in the first experimental case, plotted in figure \ref{fig:exp_periodic}. Once the driving arm period was increased from that frequency to one that induced chaotic motion, the predicted spread of intercepts through the Poincar\'e section and the orbits about the chaotic attractors were visually confirmed. It would have been useful to produce the phase diagram of the damped, undriven pendulum to compare the locations of those critical points to the chaotic attractors. However, as Poincar\'e predicted so long ago, it is quite evident that constrained chaotic motion does follow complex paths about attractors, and this motion is evident in the observed motion of the damped, driven pendulum. \printbibliography diff --git a/lag/data/clag_analysis-7bins-3465A.ipynb b/lag/data/clag_analysis-7bins-3465A.ipynb new file mode 100644 index 0000000..9e60550 --- /dev/null +++ b/lag/data/clag_analysis-7bins-3465A.ipynb @@ -0,0 +1,781 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/3465A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.337e-01 6.112e+01 inf -- -4.041e+02 -- 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + "********************\n", + "0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n", + "0.23893 0.202426 0.232625 0.177239 0.153017 0.132987 0.308424\n", + "-0.000180614 -0.000143998 -0.000174906 -0.000705814 0.000447101 -0.000873593 -0.00140183\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.481e+01 3.004e-01 5.393e-01 0.89 +++\n", + "+++ 3.525e+01 3.432e+01 3.004e-01 6.588e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.004e-01 5.990e-01 1.34 +++\n", + "+++ 3.525e+01 3.470e+01 3.004e-01 5.692e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.004e-01 5.543e-01 0.996 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.782e-01 2.426e-02 0.974 +++\n", + "+++ 3.525e+01 3.422e+01 -1.782e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.782e-01 7.486e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.782e-01 4.956e-02 1.21 +++\n", + "+++ 3.525e+01 3.471e+01 -1.782e-01 3.691e-02 1.09 +++\n", + "+++ 3.525e+01 3.474e+01 -1.782e-01 3.058e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.782e-01 2.742e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.075e+00 0.275 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.597 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.873e-01 0.798 +++\n", + "+++ 3.525e+01 3.480e+01 -1.191e+00 -9.727e-01 0.909 +++\n", + "+++ 3.525e+01 3.477e+01 -1.191e+00 -9.655e-01 0.966 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.618e-01 0.995 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.523e+00 -1.346e+00 0.861 +++\n", + "+++ 3.525e+01 3.433e+01 -1.523e+00 -1.257e+00 1.85 +++\n", + "+++ 3.525e+01 3.459e+01 -1.523e+00 -1.301e+00 1.32 +++\n", + "+++ 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": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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JMC7jsU4bMCCQYdGglxT3NBg8DQnPRUZp3D5rYE+W3G/CQYwHMR4ixkpvQbyr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\ncfEwB7+DSYoAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "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 4.360e-01 4.662e+01 inf -- -2.840e+01 -- 1 1 1 1 1 1 1\n", + " 2 7.723e-01 4.603e+01 5.319e+01 -- 2.478e+01 -- 0.595569 0.573902 0.564885 0.564933 0.564693 0.564817 0.564018\n", + " 3 3.411e+00 4.536e+01 5.196e+01 -- 7.675e+01 -- 0.241284 0.160851 0.128635 0.129632 0.129254 0.129464 0.128634\n", + " 4 1.484e+01 4.466e+01 5.009e+01 -- 1.268e+02 -- -0.00703234 -0.219623 -0.310096 -0.306017 -0.306362 -0.306197 -0.306021\n", + " 5 5.921e-01 4.382e+01 4.797e+01 -- 1.748e+02 -- -0.111363 -0.525952 -0.75296 -0.741885 -0.742361 -0.742634 -0.74129\n", + " 6 3.733e-01 4.246e+01 4.584e+01 -- 2.207e+02 -- -0.140827 -0.700007 -1.19878 -1.1757 -1.17861 -1.18043 -1.17869\n", + " 7 2.744e-01 4.002e+01 4.318e+01 -- 2.638e+02 -- -0.163132 -0.756421 -1.63306 -1.59991 -1.61462 -1.6204 -1.61864\n", + " 8 2.214e-01 3.558e+01 3.862e+01 -- 3.024e+02 -- -0.180826 -0.786047 -2.01272 -1.99632 -2.05034 -2.06438 -2.06287\n", + " 9 1.964e-01 2.792e+01 3.104e+01 -- 3.335e+02 -- -0.198109 -0.808078 -2.25955 -2.32516 -2.48547 -2.51643 -2.51957\n", + " 10 2.013e-01 1.674e+01 2.083e+01 -- 3.543e+02 -- -0.213273 -0.817818 -2.33481 -2.52428 -2.9116 -2.9802 -3.01438\n", + " 11 2.834e-01 5.753e+00 1.015e+01 -- 3.645e+02 -- -0.221915 -0.819446 -2.34863 -2.58195 -3.28722 -3.43843 -3.62111\n", + " 12 1.282e+00 5.594e-01 2.962e+00 -- 3.674e+02 -- -0.22692 -0.818447 -2.35934 -2.59 -3.51729 -3.8012 -4.6475\n", + " 13 7.073e+02 9.360e-02 2.736e-01 -- 3.677e+02 -- -0.229484 -0.817489 -2.36471 -2.59372 -3.56061 -3.9224 -7.6475\n", + " 14 1.523e+03 1.144e-01 1.474e-03 -- 3.677e+02 -- -0.229711 -0.817671 -2.36494 -2.59367 -3.55253 -3.91079 -8\n", + " 15 1.523e+03 1.092e-01 3.831e-04 -- 3.677e+02 -- -0.229667 -0.817642 -2.365 -2.59369 -3.55271 -3.9141 -8\n", + " 16 1.523e+03 1.099e-01 6.214e-05 -- 3.677e+02 -- -0.229676 -0.817649 -2.36499 -2.59368 -3.55237 -3.91373 -8\n", + "********************\n", + "-0.229676 -0.817649 -2.36499 -2.59368 -3.55237 -3.91373 -8\n", + "0.26864 0.229014 0.349667 0.273159 0.62553 0.906575 7060.11\n", + "-0.00293561 -0.00849564 -0.0205528 -0.0525552 -0.0608705 -0.109908 -0.000245353\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.677e+02 3.672e+02 -2.297e-01 3.897e-02 0.967 +++\n", + "+++ 3.677e+02 3.667e+02 -2.297e-01 1.733e-01 2.01 +++\n", + "+++ 3.677e+02 3.670e+02 -2.297e-01 1.061e-01 1.45 +++\n", + "+++ 3.677e+02 3.671e+02 -2.297e-01 7.255e-02 1.2 +++\n", + "+++ 3.677e+02 3.672e+02 -2.297e-01 5.576e-02 1.08 +++\n", + "+++ 3.677e+02 3.672e+02 -2.297e-01 4.736e-02 1.02 +++\n", + "+++ 3.677e+02 3.672e+02 -2.297e-01 4.316e-02 0.995 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.886e-01 0.936 +++\n", + "+++ 3.677e+02 3.667e+02 -8.176e-01 -4.741e-01 1.97 +++\n", + "+++ 3.677e+02 3.670e+02 -8.176e-01 -5.314e-01 1.41 +++\n", + "+++ 3.677e+02 3.671e+02 -8.176e-01 -5.600e-01 1.16 +++\n", + "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.743e-01 1.05 +++\n", + "+++ 3.677e+02 3.672e+02 -8.176e-01 -5.815e-01 0.991 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.677e+02 3.675e+02 -2.365e+00 -2.190e+00 0.303 +++\n", + "+++ 3.677e+02 3.674e+02 -2.365e+00 -2.103e+00 0.671 +++\n", + "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.059e+00 0.905 +++\n", + "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.037e+00 1.03 +++\n", + "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.048e+00 0.968 +++\n", + "+++ 3.677e+02 3.672e+02 -2.365e+00 -2.043e+00 1 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.677e+02 3.675e+02 -2.594e+00 -2.457e+00 0.301 +++\n", + "+++ 3.677e+02 3.674e+02 -2.594e+00 -2.389e+00 0.667 +++\n", + "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.355e+00 0.902 +++\n", + "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.338e+00 1.03 +++\n", + "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.346e+00 0.966 +++\n", + "+++ 3.677e+02 3.672e+02 -2.594e+00 -2.342e+00 0.999 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.677e+02 3.675e+02 -3.552e+00 -3.240e+00 0.35 +++\n", + "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.083e+00 0.938 +++\n", + "+++ 3.677e+02 3.670e+02 -3.552e+00 -3.005e+00 1.39 +++\n", + "+++ 3.677e+02 3.671e+02 -3.552e+00 -3.044e+00 1.15 +++\n", + "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.064e+00 1.04 +++\n", + "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.073e+00 0.988 +++\n", + "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.069e+00 1.01 +++\n", + "+++ 3.677e+02 3.672e+02 -3.552e+00 -3.071e+00 1 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.677e+02 3.673e+02 -3.914e+00 -3.460e+00 0.7 +++\n", + "+++ 3.677e+02 3.667e+02 -3.914e+00 -3.234e+00 2.01 +++\n", + "+++ 3.677e+02 3.671e+02 -3.914e+00 -3.347e+00 1.23 +++\n", + "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.404e+00 0.939 +++\n", + "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.375e+00 1.08 +++\n", + "+++ 3.677e+02 3.672e+02 -3.914e+00 -3.390e+00 1.01 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.677e+02 3.677e+02 -8.000e+00 -6.000e+00 0.0117 +++\n", + "+++ 3.677e+02 3.676e+02 -8.000e+00 -5.000e+00 0.203 +++\n", + "+++ 3.677e+02 3.674e+02 -8.000e+00 -4.500e+00 0.665 +++\n", + "+++ 3.677e+02 3.671e+02 -8.000e+00 -4.250e+00 1.19 +++\n", + "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.375e+00 0.891 +++\n", + "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.312e+00 1.03 +++\n", + "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.344e+00 0.958 +++\n", + "+++ 3.677e+02 3.672e+02 -8.000e+00 -4.328e+00 0.994 +++\n", + "********************\n", + "-0.229674 -0.817648 -2.365 -2.59368 -3.55236 -3.91386 -8\n", + "0.272837 0.236171 0.32235 0.251819 0.481357 0.524238 3.67188\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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-0.698635 -1.97153 -2.26256 -3.05584 -3.30885 -6.99489 -0.0118999 0.220073 0.352843 0.149084 0.389017 0.574531 -0.403772\n", + " 9 2.935e+02 1.701e+01 1.600e+00 -- 4.234e+02 -- -0.142929 -0.674898 -1.94419 -2.24018 -3.02852 -3.26097 -7.29489 -0.0319925 0.242138 0.397314 0.158774 0.451737 0.636061 -2.89416\n", + " 11 9.523e+02 1.944e+01 1.478e+00 -- 4.249e+02 -- -0.122901 -0.654512 -1.92069 -2.22102 -3.00314 -3.22181 -6.99489 -0.0475429 0.25942 0.431356 0.166636 0.503311 0.679979 0.381182\n", + " 13 4.366e+02 2.207e+01 1.373e+00 -- 4.262e+02 -- -0.105633 -0.636902 -1.9005 -2.20452 -2.97982 -3.18951 -7.29489 -0.0597637 0.273183 0.4579 0.173119 0.546186 0.712957 1.78135\n", + " 15 1.284e+02 2.490e+01 1.279e+00 -- 4.275e+02 -- -0.0906648 -0.62161 -1.8831 -2.19023 -2.95852 -3.16261 -7.59489 -0.0694848 0.284294 0.478919 0.178532 0.582205 0.73873 -2.12851\n", + " 17 6.684e+01 2.796e+01 1.196e+00 -- 4.287e+02 -- -0.0776289 -0.608271 -1.86808 -2.17781 -2.93913 -3.14003 -7.29489 -0.0772912 0.293368 0.495775 0.183096 0.612767 0.759519 -2.12851\n", + " 19 1.131e+03 3.125e+01 1.119e+00 -- 4.298e+02 -- -0.0662284 -0.596588 -1.85505 -2.16698 -2.92148 -3.12092 -6.99489 -0.0836055 0.300851 0.509435 0.186975 0.638935 0.776775 -0.468363\n", + " 21 7.866e+01 3.476e+01 1.049e+00 -- 4.309e+02 -- -0.0562219 -0.586319 -1.84372 -2.1575 -2.90544 -3.10467 -6.69489 -0.0887399 0.307075 0.5206 0.190292 0.661523 0.791406 3.12663\n", + " 23 4.749e+01 3.851e+01 9.840e-01 -- 4.319e+02 -- -0.0474108 -0.577266 -1.83385 -2.14919 -2.89085 -3.09077 -6.39489 -0.0929293 0.312289 0.529789 0.19314 0.681153 0.80404 2.58773\n", + " 25 9.016e+01 4.250e+01 9.239e-01 -- 4.328e+02 -- -0.0396304 -0.569264 -1.82521 -2.14191 -2.87758 -3.07884 -6.69489 -0.0963531 0.316687 0.537395 0.195593 0.698325 0.815127 2.31075\n", + " 26 2.291e+03 4.856e+03 4.196e+00 -- 4.370e+02 -- 0.0292432 -0.498358 -1.74955 -2.0779 -2.75662 -2.9761 -8 -0.124329 0.354004 0.600746 0.216726 0.849429 0.913805 -2.9851\n", + " 27 3.278e+00 8.497e+01 6.218e+00 -- 4.432e+02 -- 0.0254995 -0.502475 -1.77412 -2.11199 -2.72719 -3.04402 -5 -0.0720726 0.308662 0.702517 0.165496 0.94859 0.890579 1.11967\n", + " 28 3.369e+02 3.689e+01 7.032e-01 -- 4.439e+02 -- 0.02803 -0.501445 -1.76227 -2.08919 -2.72868 -3.00045 -7.18712 -0.103292 0.35578 0.648429 0.1705 0.951458 0.974389 -2.55109\n", + " 29 1.859e-01 1.619e+01 2.026e-01 -- 4.437e+02 -- 0.0284028 -0.501795 -1.76339 -2.09611 -2.722 -3.01268 -4.18712 -0.0900085 0.341162 0.653855 0.173506 0.94415 0.962545 2.3652\n", + " 30 9.710e-01 1.003e+01 2.272e-01 -- 4.439e+02 -- 0.0287103 -0.501625 -1.76349 -2.09457 -2.72212 -3.0105 -4.78581 -0.0929545 0.347387 0.644721 0.172745 0.942888 0.964175 2.80497\n", + " 31 3.231e+01 4.382e+00 4.330e-02 -- 4.440e+02 -- 0.0288105 -0.50173 -1.76305 -2.0945 -2.72206 -3.00972 -5.95917 -0.0912361 0.344716 0.65491 0.172811 0.94407 0.967832 -0.754455\n", + " 33 7.887e+00 3.999e+00 5.730e-03 -- 4.440e+02 -- 0.0288182 -0.501725 -1.76307 -2.09454 -2.72196 -3.00967 -5.65917 -0.0912811 0.344831 0.654757 0.172631 0.944361 0.968167 3.09091\n", + " 34 1.021e+03 1.567e+00 3.306e-03 -- 4.440e+02 -- 0.0288906 -0.501682 -1.76326 -2.09497 -2.72112 -3.00942 -8 -0.0916682 0.345845 0.653313 0.170964 0.94684 0.970952 2.33492\n", + " 35 1.958e+03 8.857e-01 2.455e-04 -- 4.440e+02 -- 0.0289281 -0.501698 -1.76317 -2.09482 -2.72066 -3.00928 -8 -0.0913907 0.345397 0.655119 0.171217 0.948376 0.971555 -1.95846\n", + " 36 2.231e+01 1.235e+00 1.134e-03 -- 4.440e+02 -- 0.0289502 -0.501688 -1.76324 -2.09495 -2.7204 -3.00936 -5 -0.0914472 0.345652 0.654699 0.170847 0.949278 0.972212 0.258881\n", + " 37 8.016e+00 5.056e-01 1.360e-02 -- 4.440e+02 -- 0.0289689 -0.501701 -1.76321 -2.09488 -2.71981 -3.00893 -5.06546 -0.0913367 0.345498 0.655382 0.171555 0.950824 0.972854 0.766704\n", + " 38 1.884e+02 2.121e-01 1.200e-02 -- 4.440e+02 -- 0.0289819 -0.501697 -1.76328 -2.09496 -2.71958 -3.00916 -6.54275 -0.0913617 0.345681 0.654623 0.17157 0.951297 0.972966 0.903638\n", + " 39 1.947e+04 3.463e-01 5.080e-04 -- 4.440e+02 -- 0.0289824 -0.501696 -1.76321 -2.0949 -2.71995 -3.00932 -8 -0.0913416 0.345612 0.655284 0.170969 0.950797 0.973096 0.295641\n", + " 40 3.049e+03 1.048e-01 3.268e-05 -- 4.440e+02 -- 0.0289805 -0.501691 -1.76324 -2.09495 -2.72006 -3.00933 -8 -0.0913641 0.345627 0.655123 0.170779 0.950573 0.972811 -0.79378\n", + "********************\n", + "0.0289805 -0.501691 -1.76324 -2.09495 -2.72006 -3.00933 -8 -0.0913641 0.345627 0.655123 0.170779 0.950573 0.972811 -0.79378\n", + "0.00488089 0.00320555 0.0166349 0.0498124 0.0675131 0.0930602 5801.93 0.082796 0.0625792 0.157936 0.242192 0.285454 0.316561 13418.5\n", + "-0.0761655 -0.104783 0.024591 0.0175272 0.0313802 0.0122834 6.72756e-05 0.000572629 -0.00671213 0.00501444 0.000107792 -0.0065959 -0.0147919 -1.32979e-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": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 4.441e+02 4.440e+02 2.894e-02 3.139e-02 0.184 +++\n", + "+++ 4.441e+02 4.439e+02 2.894e-02 3.262e-02 0.538 +++\n", + "+++ 4.441e+02 4.437e+02 2.894e-02 3.323e-02 0.868 +++\n", + "+++ 4.441e+02 4.436e+02 2.894e-02 3.354e-02 1.1 +++\n", + "+++ 4.441e+02 4.436e+02 2.894e-02 3.339e-02 0.977 +++\n", + "+++ 4.441e+02 4.436e+02 2.894e-02 3.346e-02 1.04 +++\n", + "+++ 4.441e+02 4.436e+02 2.894e-02 3.342e-02 1.01 +++\n", + "\t### errors for param 1 ###\n", + "+++ 4.441e+02 4.440e+02 -5.016e-01 -5.000e-01 0.272 +++\n", + "+++ 4.441e+02 4.437e+02 -5.016e-01 -4.993e-01 0.765 +++\n", + "+++ 4.441e+02 4.435e+02 -5.016e-01 -4.989e-01 1.18 +++\n", + "+++ 4.441e+02 4.437e+02 -5.016e-01 -4.991e-01 0.954 +++\n", + "+++ 4.441e+02 4.436e+02 -5.016e-01 -4.990e-01 1.06 +++\n", + "+++ 4.441e+02 4.436e+02 -5.016e-01 -4.990e-01 1.01 +++\n", + "\t### errors for param 2 ###\n", + "+++ 4.441e+02 4.439e+02 -1.763e+00 -1.754e+00 0.496 +++\n", + "+++ 4.441e+02 4.433e+02 -1.763e+00 -1.750e+00 1.76 +++\n", + "+++ 4.441e+02 4.437e+02 -1.763e+00 -1.752e+00 0.96 +++\n", + "+++ 4.441e+02 4.435e+02 -1.763e+00 -1.751e+00 1.3 +++\n", + "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 1.12 +++\n", + "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 1.03 +++\n", + "+++ 4.441e+02 4.436e+02 -1.763e+00 -1.752e+00 0.994 +++\n", + "\t### errors for param 3 ###\n", + "+++ 4.441e+02 4.439e+02 -2.096e+00 -2.071e+00 0.418 +++\n", + "+++ 4.441e+02 4.435e+02 -2.096e+00 -2.058e+00 1.22 +++\n", + "+++ 4.441e+02 4.438e+02 -2.096e+00 -2.064e+00 0.741 +++\n", + "+++ 4.441e+02 4.437e+02 -2.096e+00 -2.061e+00 0.959 +++\n", + "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.060e+00 1.08 +++\n", + "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.060e+00 1.02 +++\n", + "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.061e+00 0.989 +++\n", + "+++ 4.441e+02 4.436e+02 -2.096e+00 -2.061e+00 1 +++\n", + "\t### errors for param 4 ###\n", + "+++ 4.441e+02 4.440e+02 -2.720e+00 -2.686e+00 0.217 +++\n", + "+++ 4.441e+02 4.438e+02 -2.720e+00 -2.670e+00 0.62 +++\n", + "+++ 4.441e+02 4.436e+02 -2.720e+00 -2.661e+00 0.996 +++\n", + "\t### errors for param 5 ###\n", + "+++ 4.441e+02 4.439e+02 -3.008e+00 -2.962e+00 0.426 +++\n", + "+++ 4.441e+02 4.435e+02 -3.008e+00 -2.939e+00 1.27 +++\n", + "+++ 4.441e+02 4.438e+02 -3.008e+00 -2.951e+00 0.758 +++\n", + "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.945e+00 0.986 +++\n", + "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.942e+00 1.12 +++\n", + "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.944e+00 1.05 +++\n", + "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.944e+00 1.02 +++\n", + "+++ 4.441e+02 4.436e+02 -3.008e+00 -2.945e+00 1 +++\n", + "\t### errors for param 6 ###\n", + "+++ 4.441e+02 4.440e+02 -4.031e+00 -3.728e+00 0.351 +++\n", + "+++ 4.441e+02 4.435e+02 -4.031e+00 -3.577e+00 1.31 +++\n", + "+++ 4.441e+02 4.438e+02 -4.031e+00 -3.653e+00 0.699 +++\n", + "+++ 4.441e+02 4.437e+02 -4.031e+00 -3.615e+00 0.961 +++\n", + "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.596e+00 1.12 +++\n", + "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.605e+00 1.04 +++\n", + "+++ 4.441e+02 4.436e+02 -4.031e+00 -3.610e+00 0.999 +++\n", + "\t### errors for param 7 ###\n", + "+++ 4.441e+02 4.440e+02 -9.183e-02 -5.033e-02 0.277 +++\n", + "+++ 4.441e+02 4.438e+02 -9.183e-02 -2.959e-02 0.608 +++\n", + "+++ 4.441e+02 4.437e+02 -9.183e-02 -1.921e-02 0.805 +++\n", + "+++ 4.441e+02 4.437e+02 -9.183e-02 -1.403e-02 0.914 +++\n", + "+++ 4.441e+02 4.436e+02 -9.183e-02 -1.143e-02 0.97 +++\n", + "+++ 4.441e+02 4.436e+02 -9.183e-02 -1.014e-02 0.998 +++\n", + "\t### errors for param 8 ###\n", + "+++ 4.441e+02 4.440e+02 3.447e-01 3.758e-01 0.288 +++\n", + "+++ 4.441e+02 4.438e+02 3.447e-01 3.913e-01 0.618 +++\n", + "+++ 4.441e+02 4.437e+02 3.447e-01 3.991e-01 0.821 +++\n", + "+++ 4.441e+02 4.437e+02 3.447e-01 4.030e-01 0.931 +++\n", + "+++ 4.441e+02 4.436e+02 3.447e-01 4.049e-01 0.987 +++\n", + "+++ 4.441e+02 4.436e+02 3.447e-01 4.059e-01 1.02 +++\n", + "+++ 4.441e+02 4.436e+02 3.447e-01 4.054e-01 1 +++\n", + "\t### errors for param 9 ###\n", + "+++ 4.441e+02 4.438e+02 6.661e-01 8.218e-01 0.763 +++\n", + "+++ 4.441e+02 4.434e+02 6.661e-01 8.997e-01 1.49 +++\n", + "+++ 4.441e+02 4.436e+02 6.661e-01 8.607e-01 1.11 +++\n", + "+++ 4.441e+02 4.437e+02 6.661e-01 8.413e-01 0.932 +++\n", + "+++ 4.441e+02 4.436e+02 6.661e-01 8.510e-01 1.02 +++\n", + "+++ 4.441e+02 4.436e+02 6.661e-01 8.461e-01 0.976 +++\n", + "+++ 4.441e+02 4.436e+02 6.661e-01 8.486e-01 0.999 +++\n", + "\t### errors for param 10 ###\n", + "+++ 4.441e+02 4.437e+02 1.618e-01 4.046e-01 0.907 +++\n", + "+++ 4.441e+02 4.432e+02 1.618e-01 5.261e-01 1.91 +++\n", + "+++ 4.441e+02 4.434e+02 1.618e-01 4.654e-01 1.38 +++\n", + "+++ 4.441e+02 4.436e+02 1.618e-01 4.350e-01 1.13 +++\n", + "+++ 4.441e+02 4.436e+02 1.618e-01 4.198e-01 1.02 +++\n", + "+++ 4.441e+02 4.437e+02 1.618e-01 4.122e-01 0.962 +++\n", + "+++ 4.441e+02 4.436e+02 1.618e-01 4.160e-01 0.99 +++\n", + "+++ 4.441e+02 4.436e+02 1.618e-01 4.179e-01 1 +++\n", + "\t### errors for param 11 ###\n", + "+++ 4.441e+02 4.438e+02 9.572e-01 1.242e+00 0.573 +++\n", + "+++ 4.441e+02 4.435e+02 9.572e-01 1.384e+00 1.26 +++\n", + "+++ 4.441e+02 4.437e+02 9.572e-01 1.313e+00 0.888 +++\n", + "+++ 4.441e+02 4.436e+02 9.572e-01 1.348e+00 1.07 +++\n", + "+++ 4.441e+02 4.436e+02 9.572e-01 1.330e+00 0.976 +++\n", + "+++ 4.441e+02 4.436e+02 9.572e-01 1.339e+00 1.02 +++\n", + "+++ 4.441e+02 4.436e+02 9.572e-01 1.335e+00 0.999 +++\n", + "\t### errors for param 12 ###\n", + "+++ 4.441e+02 4.437e+02 9.835e-01 1.298e+00 0.846 +++\n", + "+++ 4.441e+02 4.432e+02 9.835e-01 1.456e+00 1.85 +++\n", + "+++ 4.441e+02 4.435e+02 9.835e-01 1.377e+00 1.3 +++\n", + "+++ 4.441e+02 4.436e+02 9.835e-01 1.338e+00 1.06 +++\n", + "+++ 4.441e+02 4.437e+02 9.835e-01 1.318e+00 0.952 +++\n", + "+++ 4.441e+02 4.436e+02 9.835e-01 1.328e+00 1.01 +++\n", + "\t### errors for param 13 ###\n", + "********************\n", + "0.028939 -0.501626 -1.76254 -2.09557 -2.71971 -3.00832 -4.0306 -0.0918281 0.344656 0.666137 0.16175 0.957202 0.983537 -1.17645\n", + "0.0044848 0.00262318 0.0102132 0.0350358 0.0585583 0.0635997 0.42048 0.0816919 0.0607682 0.182442 0.256176 0.377633 0.344174 7.10562\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": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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e4H0U64n8hGLAIkmSWljaYORB4CDgQKLZbqn3JNM1wGPAE0TOyDIiJ2VrynPn\nrq+vjyVLYsDfJUuW0NfXN84ekiRptHqO2jsMnJVMTaevr49ly5YxODgIwLp161i2bBkA3d3deSZN\nkqQpJW3OyETMJQbVe3EO585Mb2/vXwORgsHBQXp7e3NKkSRJU1M9c0YqeSXwdSLnZHoO58/Eli1b\nalouSZLKyyNnZHQX8VPSjBnl47hKyyVJUnl5BCNNoaenh/b29hHL2tvb6enpySlFkiRNTQYjE9Td\n3c2KFSuYP38+APPnz2fFihVWXpUkqUaWKaTQ3d3NokWL6OzsZNWqVXR0dOSdJEmSphxzRiRJUq4M\nRiRJUq4MRiRJUq4MRiRJUq5qqcD6LqKjsrSOzOAYkiSpSdQSjBR6TW2KTsskSVJjqLWYJstAxKBG\nkiTVlDOSdW9eWRT51OqlwDuBFwD7AQ8CvwH+HRjIIT2SJLW8WoKRs+uViEn0XmBP4AvATcnrHuDX\nwCuAy/JLmiRJranVemD9AHDvqGWXAH8C/g2DEUmSJl2rNe0dHYgAPAasAfaf5LRIkiRaLxgpZzbQ\nQRTbSJKkSWYwAl8BdgBOyzshkiS1oqkcjCwGhqqcnlXhGJ8E3g58CLiuvsmVJEnlTOUKrDcDJ1S5\n7Z/LLDsZOJGouHr6WDsvXbqUOXPmjFjW1dVFV1dXlaeXJKl59ff309/fP2LZxo0bq95/KgcjdwN9\nE9z35JLpM+NtvHLlSjo6OiZ4KkmSmlu5H+gDAwN0dnZWtf9ULqaZqI8RQcgnk0mSJOVoKueMTEQP\n8Amib5EfAkeMWv/rSU+RJEktrtWCkdcQ3dC/MplKDQPTJz1FkiS1uFYLRl6SdwIkSdJIWQcjTyEG\nodub6Lvjv4D7Mj6HJElqIlkFI4uALwJ/k/zdRhR7fJeRwcg/AR8HHgIWAk9mdH5JkjRFZdGa5hjg\nV8CLiCCkLVneVmbbc4AdgflE/Q1JktTi0gYjc4Hzge2JweZeDeyarBsus/1DwP8mr49JeW5JktQE\n0gYjS4FdgDuIIpofAY+Os8/lyby6nlAkSVJTSxuMFHI3vgA8WOU+a5L5QSnPLUmSmkDaYORgojjm\nlzXs81Ay3yXluSVJUhNIG4xsl8yfqGGfnZP5YynPLUmSmkDaYOQeotXMgTXs85xkfmfKc0uSpCaQ\nNhj5VTKvtpluG3BC8vr/Up5bkiQ1gbTByHnJ/F3A4VVs/3ngmcnrs1OeW5IkNYG0wcjFwE+Amcn8\ng0RX8AX3b0tHAAAXiElEQVQzgf2AtwK/SNYDfBu4OuW5JUlSE8iiO/i/A35G9BvyBSL3A6JIZqDk\ndcGvKBbVSJKkFpdFd/APAUcCpwEPMzLwKO0e/jHgM8BibEkjSZISWQ2Utxn4GPBZ4CjgucBewHRi\noLzrgJ9T7GNEkiQJyC4YKXiUqEdyccbHlSRJTSqLYhpJkqQJMxiRJEm5yrKYZg/gBcR4NbsQ9UXG\n8+8Znl+SJE1BWQQj+xDNed9MBCBtY2/+V8MYjEiS1PLSBiN7EiP2zpvAvtUGLZIkqYmlrTPyCYqB\nyCrgaKK4ZkZy7PEmSZLU4tLmjBQGyDuXGJ9GkiSpJmlzJ/Yi6n70ZZAWSZLUgtIGIxuS+aNpEyJJ\nklpT2mDkCqIi6rMySEu9LSJ6hl0PPA48QFS+fUeeiZIkqdWlDUZ6gSeBHmBW+uTU1WzgduBfgWOA\ndwK3EfVdTswvWZIktba0FVhvBN4NnA38FDgB+EPKY9bLFclU6mKik7b/R4w6XJX+/n76+/sB2LRp\nEwsWLGD58uXMmhXxWFdXF11dXZkkWpKkZpdFp2fnAeuA/wVuAn4H3EIUhYynO4Pzp/UAURG3agYb\nkiRlJ4tg5JlED6xzkr8XJdN4hsknGGkjeordDXgL8Argn3NIhyRJIn0wcjBwGdBesuxRYCMwNM6+\nwynPPVFnEMUyAFuBjyTLJElSDtIGIx8jApFh4D+A04nWKvW2GLi0ym0XEUVHBacBXyWKZl5H5OrM\nAj6bYfokSVKV0gYjL03mK4F/SXmsWtxMVJatxp/L/F1Ydkky/yTRcdt95Q6wdOlS5syZM2KZ9UYk\nSQqlDTsKNm7cWPX+aYORQg+s3015nFrdTXa9vl4LvI8ociobjKxcuZKOjo6MTidJUnMp9wN9YGCA\nzs7OqvZP28/IXcl8c8rj5OklRN2RW/NOiCRJrShtzsiPgfcChwO/SZ+cuvoq8BCRE3IPMbrwW4C3\nAp8jmvhKkqRJljZn5D+AR4CPArunT05d/ZIImr5MdNB2JlHMdCywPMd0SZLU0tIGI7cCbwZ2Ba4C\nXp46RfVzNnAUEYBsR7QCOhr4Vo5pkiSp5aUtprmMqMB6H7CAaJ3yIPBHquuB9eiU55ckSVNc2mDk\nqDLLdiOKQ8aTV6dnkiSpgaQNRq5Msa/BiCRJSh2MLM4iEZIkqXWlrcAqSZKUisGIJEnKlcGIJEnK\nVbV1Rg4seX17heUTcfv4m0iSpGZWbTByG8XWL9MrLK9FW7Lf9PE2lCRJza2W1jRtNS6f6PEkSVIL\nqTYY6aZ8Dkh3inPbz4gkSao6GDkbGCICiGuB35cslyRJmrBaW9NYtCJJkjJVazBi0YokScqU/YxI\nkqRcGYxIkqRcGYxIkqRcGYxIkqRc1dLpGURrmh8DT6Y8b6EH1vkpjyNJkqa4WoMRgP0yOrctcyRJ\n0oSCkQ3AlgzObTAiSZJqDkaGgVcAN9UhLZIkqQVNpAKrORqSJCkztqaRJEm5MhiRJEm5MhiRJEm5\nauVg5ARgCHgk74RIktTKag1G2uqSism3H/AfRDNlK+RKkpSjWpr2FnpLvaMeCZlk/wVcBmwEluSc\nFkmSWlotOSO3JVMWHZ7l6VjgRcD7aZ6cHkmSpqxWqzMyF1gJLCeKaCRJUs5aLRj5CvB7ophGkiQ1\ngKkajCwmWsJUMz0r2WcJ8BrgPZOcVkmSNIaJDJTXCG4mmuZW43ZgZ+DLwH8C9wBzknXbJfPZRF2Y\nx8odYOnSpcyZM2fEsq6uLrq6umpLtSRJTai/v5/+/v4RyzZu3Fj1/q1SgfMgYO0423wPeNOoZR3A\n6tWrV9PR0VGPdEmSNKkGBgbo7Oyk3t9thfMAncDAWNtO1ZyRWt0FvISRfYq0ERVZjwJeCdyfQ7ok\nSWp5rRKMPAFcUWb58cBW4MrJTY4kSSpolWCkkmHsgVWS1ORK63Rs2rSJBQsWsHz5cmbNmgXkXw+y\n1YOR45NJkqSmlXewMZ6p2rRXkiQ1CYMRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKU\nK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MR\nSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKUK4MRSZKU\nq1YLRhYDQxWmw/NLliRJravVgpGCfwWOGDXdlNXB+/v7szqUJFXks0bNolWDkT8C14yaHsvq4D4g\nJE0GnzVqFq0ajLTlnQBJkhRaNRj5CvAk8BBwCXBkvsnReJrlF2AjvY/JTku9zpflcdMeK83+jfTZ\naFXNcg+m4vtotWBkI7AS+H9EZdYPAgcAlwMvzy1VGtdU/Ocqp5Heh8FI9scyGJnamuUeTMX3MSPv\nBKSwGLi0ym0XAb8Drk+mgquAC4EbgM8CPym385o1a2pK2MaNGxkYGKhpH42tWa5pI72PyU5Lvc6X\n5XHTHivN/hPZt5E+T82gWa5no7yPWr47p3Ldib2BV1W57YXAg2OsPwN4L7AD8ETJ8n2Aa4H9JpJA\nSZJa3J3A84C7xtpoKueM3A30ZXzM4VF/30VcxH0yPo8kSa3gLsYJRBR2A+4AVuedEEmSWtFUzhmZ\niG8C64ABYBB4GtAD7Am8M8d0SZKkFvEvRCDyING09x7gf4DOPBMlSZIkSZIkSZIkSZKklrId8HXg\ndqJr+l8BL8g1RZKa0T8Q9eU2AyfnnBZpG63WHXyjmQGsBV4IzCY6X7uI6HxNkrKyAfg48D227U9J\nkrbxAPDMvBMhqSmdiTkjakDmjDSWZxC5IrfmnRBJkiaLwUjj2BE4F/gk8HjOaZEkadIYjEyudwCP\nJNPFJctnAquAG4FP55AuSc2j0nNG0hS1M/A54CfAfcAQlctbdwZWEiMU/gW4Dvi7Ks4xDTifGFnY\n4FBqPZPxnCk4k6jIKjUUv/zGtgfwHiLn4sJkWaWa6BcQ49ucArwSuBboB7rGOcd/A3OBtxEPIUmt\nZTKeM9OBWUQLvpnJa5//0hS0OxEslPtV8apk3ehfKD8mRgSu9E8/L9nvMYrZqo8AR2aQXklTTz2e\nMxDBy9CoycFBpSloDyo/JM4kOi0b/TAo5HbYkZmkavicUUsymy4bhwFr2LaY5YZkfujkJkdSE/I5\no6ZlMJKN3YHBMssHS9ZLUho+Z9S0DEYkSVKuDEay8QDlf5W0l6yXpDR8zqhpGYxk43fAQra9noUx\nZm6c3ORIakI+Z9S0DEaycSHRGdGSUcuPIzonunqyEySp6ficUdOakXcCpoBjgJ2AXZK/D6X4MLiY\n6AXxEuCnwBnArsRAd13Ay4mumR2yW9JYfM5IGtM6ip0EbR31+sCS7XYiumneAGwiuml+66SmVNJU\n5XNGkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRV7SDgf/JORCOblncC\nJElqYn8LXAG0552QRjYj7wRIktSEOoFPArcDf8k5LZIm2XHAUDIdmG9SpIa3HfAH4v9lyQSPcRz+\nz43ncuDSGrb/MnE9z6lLahq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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "x=np.logspace(fqd[0],fqd[-1],200)\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n", + "fit = tophat_freq(fqd, *coeff)\n", + "\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "xscale('log'); xlim(.009,.4)\n", + "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n", + "ylabel(\"Time Lag (days)\",fontsize=20)\n", + "\n", + "\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n", + "plot(fqd,fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dvPVW5Lr55z/rXSNJUrPLMrDpA+wNXEQM6342WR4k+tbsRbaPvtQDbLhhtNos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\nSZIkSZIkSQ3o/wHwh9tnGRaNgwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_fit = irfft(fit)\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "ylabel(\"Response (relative)\",fontsize=20)\n", + "xlabel(\"Time (days)\",fontsize=20) \n", + "\n", + "ylim(-0.5,2)\n", + "xlim(0,7)\n", + "\n", + "plot(time_fit)\n", + "plot([1.80,1.80], [-50, 50], color='k', linestyle='-', linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lag/data/clag_analysis-ZoghbiCopy.ipynb b/lag/data/clag_analysis-ZoghbiCopy.ipynb new file mode 100644 index 0000000..17e7140 --- /dev/null +++ b/lag/data/clag_analysis-ZoghbiCopy.ipynb @@ -0,0 +1,704 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/1367A_shifted.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " #0.25819945, 0.40020915, 0.62032418])\n", + " 0.25819945, 0.40020915])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.337e-01 6.112e+01 inf -- -4.041e+02 -- 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + "********************\n", + "0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n", + "0.23893 0.202426 0.232625 0.177239 0.153017 0.132987 0.308424\n", + "-0.000180614 -0.000143998 -0.000174906 -0.000705814 0.000447101 -0.000873593 -0.00140183\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.481e+01 3.004e-01 5.393e-01 0.89 +++\n", + "+++ 3.525e+01 3.432e+01 3.004e-01 6.588e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.004e-01 5.990e-01 1.34 +++\n", + "+++ 3.525e+01 3.470e+01 3.004e-01 5.692e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.004e-01 5.543e-01 0.996 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.782e-01 2.426e-02 0.974 +++\n", + "+++ 3.525e+01 3.422e+01 -1.782e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.782e-01 7.486e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.782e-01 4.956e-02 1.21 +++\n", + "+++ 3.525e+01 3.471e+01 -1.782e-01 3.691e-02 1.09 +++\n", + "+++ 3.525e+01 3.474e+01 -1.782e-01 3.058e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.782e-01 2.742e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.075e+00 0.275 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.597 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.873e-01 0.798 +++\n", + "+++ 3.525e+01 3.480e+01 -1.191e+00 -9.727e-01 0.909 +++\n", + "+++ 3.525e+01 3.477e+01 -1.191e+00 -9.655e-01 0.966 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.618e-01 0.995 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.523e+00 -1.346e+00 0.861 +++\n", + "+++ 3.525e+01 3.433e+01 -1.523e+00 -1.257e+00 1.85 +++\n", + "+++ 3.525e+01 3.459e+01 -1.523e+00 -1.301e+00 1.32 +++\n", + "+++ 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": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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qHoR+id6qjWHZc0k0B1Bt9CgZFqGKIm8CZjq/VoPaYV1jxf3bTEvJyXkyojZT\n98n5bZTDkIVqJd2Lijx04hkrvgDfNtNTkHO4a5up57o+ChwEfo3DoeoPjh/vAHaQePLrtKe2wmKU\nfa9agY8OfITNZgv72ruv61er4Rrnz5kJGNqgoxFqK2HLHmjcGz89hjCjMkL/ROtA7VXz0CPEWyQq\nGXWMOR1nO4LQ6/DNj8eu2hjSnrdEcwDVRo94k7cCpBlYDyxBRRzMwGZgG6DaTGEB48Y9EbHD4z45\n2/CIQqUCi1BHilPO/y8HPgeswMvAb9WYca2CQM91fRePA+T9hu+ivXWechw0rsWxmcciUrB0X9dj\neBwgv9ekoBpM3+o14l+CEG/0dh5+CtwCZKNWpq1AGvAbne0IQq8noEQzoOYVxCE/HkKi2SPepKUA\nWYSqby4FMlAOxavA90lJMTB27BZWrzZHpGXgc3L2N+dKYZwEUlAOhQW4GVKSUhj74FhW71rdRcvA\nc12DSGBfuz/otdj6/tawdRLc19XbAfInE5JS36O4ODJZ7VCIHoPQW9E75jUKdXa4FvWrVoo6FpzU\n2Y4g9Hp647yCOW4lQ62iSFfE4SckJT2A3Z7tpWEQXYrFrWSoVRTpijh8CPwxARITyM7OUjUaewNr\nDXiuaxAJ7Ix1YKhU//dTm6QTEtoS2LBwQ1jvyX1dtRwgFwlwffaIuIl/Fa0r4v0dfnoMO0SPQeg5\n9HYepMpREJx0lWj2Jo7zCgLkx5sajNTVFWEyraC5OQvVHJXn90QzcB/XXmvgmmvWM3o0nDsHq1dH\n1h3i6lBondpKyn+k0GJq0S6KTEWJRTVeC/+ridG3j+ac6Ryrd60OqDXgua5Brm+781o0oeKfC/Cp\nfWisbSRvSV5YOgnu6xqiK+TLumSs1jh10MyDcTeNI6khidGDRnPOGPwaCUK8kWqbfkhvbBG8Ghe4\n3jav4I75syheB488MpFXX32fpqa/Q2UU8/CfvaFqG6L/Wbw7FMxpZk7UnlAlFPeiQv9eRZFszYHG\nUviskjED/hRSJ8FzXYN0hbSNgVMVqo7CVafgIkKdBPd1DdEVUpA/S9ffKxA9BqH3orfCZCSIwmQc\niZeEblB7InXrg69Ec9fhWHp3W/hINGsMx9r+u+3cdtvD1Ne71BHPAj9BidNdICVlGDfeOJWtW2O/\nR3w6P1zKiJeAXUCtAVJToDMBHNOhbitqZw5PvdJzXR9FlVn5X9+PSEkpojO9lispx2JWr7TZbEye\nl0f9jGo5slxAAAAgAElEQVSViNXoChnwnom8f7mR052ndXGcxSEXQtHTCpPiPPRD4iWhG9Se/0ah\ng4Ruf6A3OXFFRcWUlNyHdiSklBEjtjFzpjr1xxqdWvXQKkpaS7RP6SeA/Zkw1UvFvtwCRyyMH5/P\n0aPbQ76+67ru3v0JJ06cw2hUOg+jR6eTlzcOSGDfvs850fwePBw4PTR863hOlx8Ny94jjz3Cq2+9\nQVPLJfXFREhNvoY7br6dgSkD2X9kPzUXazAZTQy9fig5X8uJyZkQh1wIhjgP4jzozqpVa4JuEuEM\nNorIXrCNQodBRH2V3pY+OvSbk9S+dYhYZ1aEw6S5k6i4I7DKI7/IhYv+J/7wfgZthUlobYXk5Hou\nX15J2/hbYcoB2PsurLocU+TBWm6l5GAJ7x3aR1vyACDJ82BTK7zeAHd0domwGM8YcaQ7yB4S+aYv\nDrkQip52HqTmoR/iEQLSYjZbtjzJrl1ruHChgra2RAYMaGfIkFxyc4soLDRHvKGFktDdsnkLu+7c\nxYUTF2jrbGOAYQBDrh9C7j25FM4p7LfhV72dg5D2QuTHJ6zLp7u6P0J1fpCopYwYXh1IsOu6atV6\nSkqehSM3wRHAtBJObdZ2bE/B1K9ODW1vioWdPztEW8lTdHHITavAUuJ5/cuoIaILwJHp0BTqCmfT\n99HscBGgVqO3OamSUrk6EOehHxK8RfAszc21NDU9g0v10G7voKmpjPr6FcBmrFZzRAtO0I2iGZrP\nN9M0r8mdQbF32GmqbaJ+fT2sAesRqyw43UB3dn+E6vygvc35D986kEjUK7Xo4jg3Z8CWUVBQq1Go\nOQruGhrd67oY4KetsY+YCzQhspkWvVEGXej/iPPQDwm+SfwbnZ0bUCcoG2pBrAASaWvr5IsvlrNv\n35aIFoCgG8X70JnfqRZTv377tvY2vnj+C/a9tU8WnG6gO7s/QnV+jLs+lfYhy/zqQDy1ONGeprs6\nzseg8RBsWqs2+kSHauNsmwXNP+bTT78V1kk6oEOe6Oc429BlkFUkMy2CyaBv3ryCm2/eTHJyZAeC\nYASTQd88dzM3f+9mktOT5UDQzxHnoR8SeJOoB3YDv3T+eyVK3lctONBBVdVH5OVFNnch4EZxGTUu\n4S7nvzX67atqq8Lutxdio7i4iD17And/xHrq97G1rpg9S/ZQTdfOD9MbOYyd8i6d15lJSkJTSyLa\n03QXx3nyGZjybeejOV7PPAf8A+fPngl5kt61y0xd3Utoesjtfo5zCCGpUIOsXI5MXUNd2DMtfGXQ\nfQ8ELS3RHQiC4ZNS8TsQtNhb5EBwlSDOQz8k8CaxBrWSG1DSw64Fx0UCMMc9dyHcosqAG8XbQLrT\nnE7hXCF6zGYzpaWbnRvyMwFP/brZCqSM+JfQoe1oh4p1cZyPDIcjfybQLjw0d1nwk/TUPdw8tZRr\nrhnL5cuu0eVetM1SehKu+zqC8eKBIh6GZgMdrR1hTxr1pFT0OxAEw51S6aEDgdR49A6k26KfotUi\neP78lzQ0XAe8BnzN+bc+lfdabWXnbedpMDbA/ahhR/cHNBdW1bsQG9256Ma64EbSMeT9vhobbezb\ntwK73eU4rwW+TlclTc/rYDobtK10zEcTaL80hJMnDTB5sericOlcX6nH8PEBOhd3KMd5F0oxM8zO\nI//fm+tTr+fC2QtcuvmSx+HW0Ozw3pgnTMinsnI76nAQ/y6rCXMnULmoEnZG9l71pDe1QPdUhEW6\nLQTdUYupmpQ4bhzu0PC+ffnABJQqX/C5C+fPJ5Kfr/4XaqMJJKG7r3ifijycImQ493jjcfKt+f3S\ns+9VJyVL91zPWJURQ3UMlZU947HlleI4cqQCuz2JtLR/AoxcvjwYo/E1HI7/RktJs7h4M7fec2vg\nOoVMOFZ3Bi6+CHwFjhTDkRYwHYUBFyHRQMoAEyMPDcd+2M7xC8cxVhlxfM3hW6Dp3PS9x4trRTxO\nvHVCDQjzHl3+gXqMNhg3ZFyXlIAnVRP+NYsFd42TTvUdkdIbR91fjSkacR76IYE2o0mT2qmoWIMK\nbQaPrw4d2s6GDeHlnANtFJP+axIVcypUaJOg5hiVMoqMDzP6ZfW2VMNHTiRDxbQ2k8uXlShaUtLj\nzJy5jZMnX+T8+Wdpbu6aqgndVmpHOR4GYA2kz4Hlp9z6C83OUH3KBymM/fZYstKzqH6tmvMfnafZ\n0RxwkJVmO+ZZuo4ud9EBZ14502WmhSdV0z2D2Nw1TjHWd0SLb42Hl0GvUfd66thE0jZ7NSHOw1WE\nWmS+QE1OXI7S2p2j8cz9TJky2ikBHL13P+uGWVRcqFCm/kDgHG4VnD5zWoWO+6Fn35dPSj0VNamb\nvx9m5Hd9QrkFjqygrq7dPYQq2GZit/+IQ4dexGhcz8CBYDDApUvw6aeeAs2QbaWprbDobmX7i52w\nXHsjabmlhYvvXKRxcSMtN7cw0DgQg8PApaRLfJr+aZdNX7MdM8SGPHLQSLZbfBU4PTVOwQ8EerXi\numuc2qrDru/Qk0iiUrrYi6Bt9moioad/AKH7KC4uYtiwx4EqlAPxA1RitcP5jA6glJycJzAYEr0W\nZNfq4Ovdh7S3rphh+4fBeaAAeAclText7iSkfZDGpUWX1ILsbSoLqqcpz74v47u5RXctI7LnfVKK\n8XpaLLBhg42MjDVUVS2lsjKfqqqlZGSsYcMGW1wGQW1YuIHh+83wZjnsrIA398GfD4G1FY68BKzi\na18b47atNpPZGq9mA16hrW0HTU35DBq0lOXL11BRYePgQdi+Xb2/WTfMUo4tqO6BncDvUXU6vwUu\nJIP1v9Wob39dB28GQ2NFI80lzTT9polBrwxiee1yKgorOPjgQbZbtvtE6DQjHq79X4sAG7KrEHbc\nuE7UgUAL/VpxXcWw44aM81w3f/yKOvWkN466vxqRyMNVhNls5sgRVW3/+usV2GxJJCb+fxgMRuA6\nkpNhyJBccnI2s2dPIdoLMoTr3ZvNZo7sPkLRuiJef+d1bAk2Et9MxJBggDRITkxmyPVDuHDNhcAL\ncj/w7PvySSleUZNAEQ2DoZ4Py+fQfFe1msDpJcvMloHQuBeo5qOPvo/NZlNpB/dm4t2m6EB5qhvp\n7FwPkzdRM+Vlalo/YvP3v8rISUMYe+1YBhoH0jq1lWHPDaO+ud4z+OoOb9vNsGU6NH7iq+vg3abY\nDjSC425H2MqSmhGPEJM7tTZkV43T2LFbqK1dQUtLfFtxXdGhsQ+OpfY/amm5pUWzqNO7vkNPetuo\n+3hFWHo7V+e7vsqwWqGkxMaBA8U0NFTQ0ZGIwQBG41TS04sYM8bMqFG+IegJE6Lz7q3lVkr2lVDx\npwpsx2y0dbaR0JmAcbgR010mxmaOJXtwNuAp3JswdwKVhspApvq8Z99XT0pWK3zve09z+vQz6J1f\n1qoDuZB5kssX/0rLXU2aaQEKqmHTY9C8kWPHfuy2rTaLLwELnjbFImCd5+c+YoEjC8FURMuAU1S/\nWUNHZgfzZ87npr+5CR6Gd57dTfsdjgC2a2HTNz26Dk34tim+DdxGRHlxTX2UucAW4Ha6KGKa3szh\npn/tuiF7fm/N2GyRteJG0xXjXeNk+7pNux13R/xqa3rbqPt4RVh6O+I89GNcTkN5+b9y5sy7dHZu\nxHV67OzsoKOjjCFDVrBjx2Z27TK7T4NXrsCxY5F799ZyK8/veJ4Pf/Yh7Uvb3XLU7R3ttNe2M2z7\nMHbv3o3ZbHYvWtYjVmov1vZrz76vnpQWLKjn7NndwC8CPCP6qIlWRMO2+wwMzYJlAb4pE5U2aPa1\nrTaTInx1S/yjPfXOQkfPoCl3ZOBfVGRg3ovzqMwM4MRmgtH0EZ2tJtpPAZ/jq1sSRedB8bpi3rj9\nDeqp95zcU1BlSK8aIMUA7QaMbcmMGZHN9ne2MHFi4A05UJeVvwCXN7EU1wbqsjpnPNelvkNPulPs\nDIILnsUzwtLb6durshCUBQvqefLJlZw+nQW8SKjTo/dJsK2tjmAFlVre/YLrFvDALx5QjoPGCaye\nevcJzHvRMlw2RByq7Uv01ZPS2rXrsdtHEiyMcfx4IosWRV5IqVnkaPo+DA7R+TD4ONypCimPpR4g\n35pP261tJH5yhPa/lHg92S/aY1obsNCxmmqm//106hvOB7XdkQCzJ7/NgT8vw55W7essRNF5sOvM\nLm54+AbK/1CO7Q0bjnaHpwot0wiTvwqOMTjopIqLzPyPm/nZt37Eg3Me1DQTTfFqLMW1sbbjRsuu\nXWZycjbT2lrMhQvP+Az3y8lRByE9a3F2ndlFzgM5tP6plQulvsP9ch7IYdeZXVjMfbeVPFrEeejH\neBbop9HeuMB1gut6EjwLrABcIevQ3v3ap9Zy2Xg5rPoFn0Xrb1AhYP9QbTd79vHqLAh4Upr8NKbZ\nv+T0zTNZ9LtFuulb6HVSUrUaAwgcxviS1tYT7N27lObmyNpPNetABpSpPT+QuUvA5VY1ZtuRht1x\nmYwBGdw0tZg9fy2k2eeb/KI9IQodL+26RGerPWjEpqNtMNWmAub98Ho++Okx2g1eEaMIlCVdWKZY\nWHDdAua8MAfHXQ6/0dt22NIMjRtQhRDQTCkftW/jQS1/Pkr6Yhui+j1UEZZusedykrR9tqsWcR76\nEf6b3/vvuxbo0Dn3ridBM6ojoxh4ArhCdvZQn/ypf770/d3vq70mjBNYl0VrOar4bA/QDmntaRQs\nLYhr7tQfVx6+oKCYAwcqaG5O5OTJdpKScpk2rQir1exuD4zEiQgoCz0zl6Lv7eEf/+0fOfDpAZod\nzZxMPEnS8CSm3TfNndaJ1InoIg395VlMjpEMTc0jc0Qx8+aZIxgy5RIV85+b4ACO4XD8BodD5adc\nhZQvl9/KNXlZXLkygOSUDlrbm0lJMnF9ZoKqrQk0aCrR4Vsw6F+Q2ADc44DMy2C4jL0DSmpL+MPP\n/gBfT4F279bOo1D+NBz5oee1te5L5wjthoUN6m0FioCdAtrmU//OA9yVtY0JmV9S0Vnhec0oCh0h\n+OatajyKoNm1eetTXOu9Tuw+VAbfDvBEnYqVRd65fyLOQz/Cvwitre0ManULnXPX7ghwefcdDBiw\njHHjXvfLn/rmS9sa2iAtqCn3CaxLR4C3IE4HdG7t5Ny8+OZO/fGNvqwB1tPS8hktLYfZu/dWCgpu\n5xe/+GHEzkygXHRdXT0zFzg7C+4C9kFLfQstx1vY+/ReCpYW8Isf/yJyexq56JEpI6iu3MPH5ybT\nfC4Te9XwMIdMuUTFvgf8BwELEgG16+XQdnAmV/7nME2d52lNPY8j6QrXjshi6uzbKP6mcgafMWoM\nmmo3qizZVtTfLnnmYAWJQ6E5pRnj23ZwHIb2a5wTM18FHnJ+8+yuA6xceM9cGYoqVvSXhD4FbM2B\n5mIgg7KyZ5h1q19qaG6A7w0R7QnaGeNT46HesB7Ftd7rRGv7sbi3IWrVVVxI6CC5Yx65Y4rZ2Glm\nncyn6HOI89CP6Jp6+BpqxczFc3r0R+Xc9+07SrBVZMyYRHbu1LDnnS99GbiWoKc31wksVEfAqMGj\nuojhxBtP9GUs/gOGHI4ONm36iI8/jrw9MdBCuOqhtTRnVcMQugwYcnQ42FS7iY+XfByxSJZ/Ltp1\nX5ysfglXFWsN4Q6Z8hYVC1aQCJ7BTI/SlLgXlp/C4QzFH+84TkltiTuPrlkH0jYLvEXFFhK8INFr\nMJMj0w6Gk84NuwK27IHGPwPFGI3fxtHWoH1fer9uKp4ImEsS+oIBho2CBTnw111wREVNuqSGUlFj\nJXaB8TUjjnQH2UNCdx6EVrf03rz1Ka71WScGvwHeERRvdCpW1qqrsHVUQW0VDW/9kSEJMzlxwiSq\nq30MEYnqR3QVI3I5DUXA46gCSG+Fpr3O+oUir44ALbQXrS5iRGYgGyUGddLP1Am45u1rKF6nNhx3\nR4C2uR7psPCIDXlPHPVWWZqjq6hT2WFnHt779BsHkaxoRap8RcVM+A6X0kqFOa+baYunODHA+yku\nLiInx++ebP4xbE9S07KT8K1R0CpIDHLdKKgm8ZrFZGYOY+7c9xg7/BOStud0FSlr93tdVwTsb1GD\n3Dq+Ap+dhD++pdo9nb8LrtRQYXIh2Tuy4WUwvWkic0Qmc5+cy/hHxpNamMqb2W8y5TdTmPrrqUx4\nbgKLfreIGc/PYPhPhzPj+RmeTiMtOlAREzf6FNf63A9ts+Iu9BRMtOzSogZOnM0EtlNT8yolJfeR\nl7cCm80Ws11v6uvryVuSR0lrCTV31sD9ULOkhpLWEvKW5Olu72pAnId+RFelPZfTUAVYgW2oaMQd\nGI1TWLlyk/vUqRal/c7vs6HC1UuBfGABx441sXixzZ0rBa/Nz8Vc1II+B3UwteJW6Uvbmcb+t/a7\nPfxQqn7H6o+x+PnFWMu9DMYZTx4+kGIhqLxzhT72XKdOG8GLTA/HlncOrMAIwd6PS1SssHAbRqMr\nBeZCy9l02glWnOh8P66K+czMbaSmLiMpKZ/U1G+ReH6i0lNoGOBrTkt5Mdh1y4SB6YOYNm096elm\n7iraxW1P5zDy1EiMv0+C3yZASSI0GSLYvAH209iYy3eet7J612rOzTvHuIfHMf4745n72Fym/f00\n0oems27+Osr/qZzyb5ZzZ82dXC65TOUvK6l6roqpn07l3aXvMvXTqZ5OIy1OAaMawbIMJj/ldvRj\nxed+aC6GLRpO1QlI/OMgjlUU+fzOR2XPf50Az+/8B0D6b+H6a2DSaLj3X6lOmd+rVVcFhaQt+hFd\ni9C8ix4rgDOkpAwnKekGpk17maYms7t+wdMR4J/bVuXfzc37qa5ewcKFm3FVf3cJuXqHfc8Cl8GY\nbsQ8wcyUe6ew9vBaLA4VUneHfVuqfXPbzmrz5tpmql+oZuHXF8bvgvnhib50j6iTjx5DHPPO0YpU\neddqJCcvxeHwLhrQSoU57QQqTlTmcODQrJi3lltZ+9KTnDxZDXsHQGdb8ILEENdt1GgH292ZLwv1\n9QuYs34ljupXcIuQmArh1G+CFEkaUE50InCJtLSz7Nz5ByZOnIgSpQpMwDbIqhpemv8SjnyHb6fR\nEFQgph61gV80wJV2rr/Uwu23Xw4o9OSPS9+loqKYCxcquHIlEWgnISEXKMJu974fzNC4HTYtgAEp\nkJikHKa2G2lvXsHJkw/5/M5r2vMShrtw4gJXOq5AByQMS4B5YD9n9/2cvNJN6ne+EzouQ+1l2JIM\njb+lrONbId9nJMh8Cv2RyEM/wGqF/Hyoq9M6DboW6FcZMGA48+a9zvz56jRmsXj0/V0nQZNpPZ72\nTG8XPc8d4raWW8m35lPXUNfVnCvsa4EBgwZw2xO3MfObM0lOT/YpSnL1TpvKTHEN2UeCJ/oSeQon\nKnuu6EsU8wwiIZqUFOC+P7Zvh+XLvSNToJ0Kcyg77dGlpCxTLBx8tJScsiQ4e4vviXwu2umwCOxo\npm+a18OWQZonb7YOUI/zGrAdeIfLl19g2bKHwgpzBzztHkM5DlmoAuPlwKfAb4CJqJTJ3wMPd4Ll\nNLa2Y9TVFbF6tTmsKMCCBfUcPryCU6fuo6npNdrbt9Pe/iJ2ez12+zygEveFm/w8WG6Bu3Pgzsmw\naALcmQN3nwfLr8OKAjSca+DDn33IqVGnaFreRPvKdtqXtWO/ZMf+f+3qIOH6nJrwFJYGSDdheqzX\nqq4KHiTy0A9wVU/PmdNEVVVgYaf7789lY4CWbddJcNKkVCoq8rSf5GwV2zhlPQuHL2TO+jlUnaoK\n2J52/+33s/HvtA26ivom/XYSFZkB0gDdfCLwRF+ygI/wzfG70E/UyR19MVXHVSRLD5EqX62KscBP\nUavxg0ArMJjU1Cs0NZU68+jRiVS52lofeWQdW/9kwnF3s+dEbgBeRfkoCQlgT4BTjrDtBOwoapwJ\nmzJhwMcqatJuVBGH65fCgp9D5QNw6SIkdEKHgeqBJm74t1n89IFng1boBzzt+hd/pqJanO9Ge1rn\n0mpGJoevt7B27Xrq658FclBO3n7gNPB/UN6X6/7OgSOb4chUlGemXTVZ1hFI8lOxf8t+mmc2K9XN\nD4A2lCbHElR77SDU/e0qDDYQNN3EgDKMxjFhvddwkfkU+iNXrB/gqZ5ei5qU6S/b+hE5OT8IS7Y1\nnBC3Oxw7sVqtORG2p/nY60UnAlf0pbl5HWfOfIvOzv9GORDxkb91RV+a/9DMmT+foTO/My4iWXrI\n+XquTVepc/VaZbS3P0pKyvdpaV6ruh0KqiN+P65USe2QeaTfW0XDvnLavzwD93T6DavqgNeT4c0k\nWNISlh3Ne3uyFaYcQRWE5ng9sB+uHIYPD0N+vZ+A02Vsm0ws/GHwlFrAe1sr3RJC3nrLhjL27Qqv\nnVE5SY+iRN4eBXagxoNuQ6Ujc5yPZaHWin9XP9BkK0zpGto4nnqQfGt+wHbGfWX7VHRhAerc8hvU\nUDOXhLerBXaQ8/97NN6/iwQg8VKvVV0VPIjz0A/wDcfORC3qz+DK05pMneTkbAlLtjWcOQw+4dhR\n+La2tYHJaCLn0fBkW3vTicCTh/8VNptLL+PZsAYMRWXPS7nOZovfgCE95Hxd12bVqlRKSjaiJXV+\n5cp6Rox4iZQb/5OLwxro2D8Q3rFDgoEEQzKDM0PL+Xo2RAtgYdVDqyhpLdEWUbqrBfaOgP0ZsOsc\nhgGtmAakBJQN1ry3j1icY77/7Pt18sGUAZadmrbt+c0h1RcD3ttaapQh6jda249RWZnvbme86SaP\naJm/Gurx44moyNCzwCuo1OVNqDXB5fBtRhVi3IQ7Tec1PEypfaoojDHFyNJvLHWLlvnrJHxR94V6\nqSxUEWQqytn6AI/Dtxx4yfn1EGqctH/JW29dYfFiG4WF+khN+7TWDsantsRw2cBbU95i8fOLKZxT\nKHoPYSLOQz/ANxzrL9vawZgxy3jrrfA2oHBC3GWH3/DtjV/k9ZQOGLNrDG89+FZ49nrhiSCaAUMx\n2YvzgCE95XxDjRcfMuQZPnv/9ZjtuO2FElH6cghc/AzoYGLuMj77LLDtwPd2Lj5pqslWmLIf9jYH\nDa+HSqkFvLe1ij9DbKiOK2NQ7Ywd1NTs5403VvDee7+iuPhF91RSl2MxalQrlZWuz+lpVGGFAd9C\nYDPgmluSC5Ofhut3Q9mHcHe7T6SloRZ++PAPee8P71H8XHEXnYSEjgTPdbLhUZn1dohSgWvwtHQH\nVfK0cPri32IyrQhZrBku7ijf5mbOlJ+h825PJKuzo5PTtacxvWDq1gLtvo44D/0APUc+hxPinnfP\ndt1SDb1xYp3ezkFIez00YCgaQt1rx44lku9UidbjOoYUUfIalHX+7JmgrxX43r4NWIV7eNwRCxzZ\nA9f+Jqb7POC9PQb4E6rGwZVuCSGuRpvLiVbFy/X132P27JVcuvQ8rmiCSx48LW0HMByPw+CKuPhH\nXlz/L4Ij98LJT6AgwFC7lnpmL57NpUWXugzQMnQaPC9pwOMI+TtErv8HUuN0K3muB8wxjXz3x/U7\nturTVZSMLelTszx6K+I89AP0HPkccA6DV8hez1RDlzkMOofsBX3peq95z7tIxG6vISNjTdgqgaGG\nkTU1+N1r3vMuDECjHf6cAc0/Zmhu8PY+s9nMmt+u4Ed//jvOnm2gpaUNZ/Wl8z0thiODofw6DIM/\nozPtSkz3ufe9vfvV3Zw4fYLEjkTaO9rVa24HQ4IBkmFg8kDaXm9TE2kDSmN78y6XLv0arfTR5cs/\nISHhX+jocDkME1FFk/7ttV7/N2XBkL2BIy01KMdBY9PtTO70XKdOPI6Qf4TB+//+Sp5twLlxcGkf\nnkiD7yyPaAbXac7fWRHgPfoVaIukdXCkVbMf4Cvw5E9k3QFWK6xebebcufWMG/c6w4dvp6GhhE2b\nYMSIQozGfCqPNOimSmct94jtpBamYiowUTewjt/t+h3X3XwdAycOJOvOrG4XjBK08b3X6lEr8X24\n2hnt9r9EpBJoscCGDTYyMtZQVbWUysp8qqqWkpGxhg0bbNwx30tM7DLqxDoRpf54P/BwG1hKIH0G\nU6eOCWrLaoXXf/wgOWXbadtihDcHw85OeNMMfy4A6xdQfhxM19F53xUV1Y/hPnfd23VT6rBdtsHt\n0G5qVxGHh4CHofMfO+lc1Mk1A6/h5u/dTGZtJqlbUz0iVvszlTT25F1+r/452t1AAEtISxuASuzn\noo74j6MiLN7tta52230w4NPgQ+3OElT4y32dvFVmx+DbXutqtz0BpOBu6WYeYDfBwrFgWa3SRoB/\n1DTUvaIV5bJMsbBh4QYyPsyg6rkqNX8nzGiS//e6RL4yPsxgw8INV7XjAIEvY3cwHTh48OBBpk+f\n3oM/Rt/HZrORlxc41RDpLAZvfOdlOIV1+BLSZ0BBrWaVe6SzGHxsuUR1fKrbY3tdQT9877VXgAK0\n62NKKSzcFjLkrH1/qe6NnJzHefXVX/G/bpmJPd/ZCpiLdmj/BKxkJdYNwR3M+vp6xs0Yz6XFDV3u\nMbbkQGMpDL4V/rlCDaQKEF7PORz+/egu+vwc5fho/fwnoTC50B0ynzAhn8rKdpRTprVM56O0J7Qx\nmRYDdpqbH0UVTz4K7AYOo/S/24BEDIbBJCUlYE//mM4hzcoh0zL3MuoxLZrAUGJQ3UKDUbdFHnAM\nOAO04JYBN6QY6OwAGAjtg6D5MrTd505V+NJBrlcdS6h7RWud67KmuN5HgGhS7q5cPtv7mfb39rL1\n6NChQ8yYMQNgBnCou+1L5KEf4Eo1FBZuIzt7GZBPdvYyCgu3xeQ4QKC5CNdB4yew6S7SSgbDy5C9\nI5vC5MKYfqFEQrb34y0rbTC8Rawy3qHmbsz7xzW035qqTuBVhqAFjJ/+z6eh7T21VjkOgQSKJk1X\ndRQGPIqpn+ORWrdC+jvpEd3nbnnmCGTIVXrIlW7Q4hLBFLKGDDEyb95mhg59l4SEBOAJ4C2giYSE\nYUdGK5sAACAASURBVAwdehdTprzDtGkfsHjxe5iSx3jSDVq0BTGXAqahJjJrM0l+PVmVWbwLHAeD\n0UDy4GQyZ2QyZc0UhtxvhnlzYd5CmH8j3Hkd3F3tF3Fw4Rs1jWZGi+b8nTCjSbIeBUdqHvoB8ewO\n0Kyud/eDJ9CZlsT4qeN16Q4QCdnej3fnxoQJR6msjK1Q1/f+8q2fAAftf6lj3HXTqKzcARlfBUNl\nIHOcbwxdqOtzj/nXT3QAtktgGAWdlR4Hwq+bqOOVjojuc3fRZwQy5Koz5HZUWqGrbovRWIvDEVjI\n7I47ctm4MfwOm1UPzaLkYoW2bsspMF424ggiyLV8wfKwCg0nTVrK+QrvaIoNlfr6Ph5HVFuDJFSn\nj3d9hPt7/NeUCEany3oUHHEe+gHx7A7QrK4/YnFOGIRR4/M5+oI+o7N7k2CUEBo9CnU995drnLfv\nTJWGho9ob38Q+Mgjex0g5Dw0PfRy5r7HusxXwLmJNMDW9qCKnwXzC9hoCb8i311gHKId07sA09MZ\n8igqD+DRbUlLO8uOHRv55jefiEn4yxt3Z8icauW7eRUypjnS2PGnHXzzkW/G3BUVfP7OM8AZsrOH\na2qqRNNVFnT+zgfAZcjO0i7QlvUoOJK2EIIS7VyEqGz14JhuqxUWL7aRlbWGtLSlDBiQT1raUrKy\n1nSZJqqLvXIri59fTNadWaRNSmNA7gDSJqX1qeJQPQp1PfdX4DHoly//hGuu+UdoGxNzoa77Hgs2\nzvuuy/CKqeu8i5POjXJdZO3D7hkmEYTMzWYza9ZsJjPzXUymz4FEjMZ2RoyYyZw5u3nkkbmYTP5T\nSZeRmbnNLfwVCWazmTU/W0Pm+UxMDSYwgNFgZMSEEcz5wRweOfIIJovJXdCZtDmJ1K2pZNZmugW5\nwkF7PfGdvzNu3OucO7e+yyyPaNYizTXFb/7OuIfHcW6eipp6/9715HrUF7i6370QEj3mIoRtqwcF\noxYsqOfJJ1dy6pTn5Gu3d9DUVEZysn5iNW571y3gyRee5NS0U+7aL3uHnabaJpJfSO4TYjV6yF57\n7q9gIekldHT8jBGDxnDmFROdX2+OWsbbfY8Fk4P+CvBhCyNPjaThowbaOtsYYBgQULkyFO5T/VfD\nl3O3WuH1181Mm7Zesy3x0Uf1E/4C+M7zVjZX/JFLI5KxX5sGHal0Jhi4kGTikwoDK3If5ZePWlTd\nZQy4P+/JNfCVEqis8Job0oFxeCKG+xZj0VB6jGYtCrWmBJu/0xsF7HoT0m0hBCWenRyatpbkqSIl\njcU1ntXNq1atoaTkPmLpHIjInpbssgu/yvveiv/oZ2/Z69zcorCkhT33VyLwdsDnjR+fz7qtFp/R\nz96beu49uWFJC7vvsYvVanJlAHLeyKFqf1XwHz4CXPLju0uV3oMRIw6jg9HXjea22bdRvC6wpkk0\n+gaR4tPJMPkLmLIJdey+SNLAT5k7dzrXmK5R9mLQN/B83t+D9H+G5eF3MkSzFsWypvTkehQOPd1t\nIc6DEBLPnIcKP9Go8ISAIra1rsgtgesWjAqyuOrBpElLqagI1Bbn2zKmi725k6i4oyKslrH+jMsB\n2b37ThyOj4n39beWW/n3V0o48vxuOh8MkNPugLQXc3mh+DNdaolcYkO1jbWcaDxBi72FZGMyrY5W\nUpJSuD79ekaljwq6Kcf7d7Cr82zzmnFxibTUSxR87R5dfg9tNhtzFt5G1Y2fRew8R3MdYllTemo9\nCgdxHsR56NV0x6nHbasHFd1UX33gws/x4/M5elSfwlCACXMnULkoQOcAMH7neI7uPaqbvd6ORH4C\nE0zfICXlcUaM2ExOjjmm30lf57ke0udoRgVSPkhhxKoR5GTmRPV76VpP3v1kEpdXR+Y866EwGcma\n0tsVJsV5EOdB6AVI5KFn6c70mNteLw5Je6PtWLnaWg8AV8jOHhpTJMLHeTatUqqd3o6Vq621DnBA\n9sjYTuDiPMdOTzsPUjAphKQ7ow/QMx5/dxaGQvcUY/Wlz02PseGR4Jqy2PqnVi6UXoi5KDKedNU3\n6NrW6hqKtWfPiqgcLZ+22wFlvmJWGm2trqFYe5bsicrR0nM+jtAzSORBCIvurHtw2+vGXGN/Pfl2\nx+fmXTh5/vwntBiOwoALdBoNpCQN58ap89j6W/0+N2u5NebCyb5E15TaGtQ8Ef1SPD7RjYwJ8B2v\nqMBOwpbVDtteH0ob9VZ6OvIgzoMQkmg05WO21wOa8v3NQequz81j51FIfySiCvqo7fXimQN60zWl\ntpTAMy+iS7H5OM+Dv61me7he/veEPQ8iInt9JG3UWxHnQZyHqOmusLT7VDK5xilL7c15srLOcMMN\nubqlE3riVNKXQvzh0l1FiG47pue75spd6Pi5XW2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fPj2CVBQuWV776YzTMS8UbYOuoL5+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\nC2NCzB01BVzVm6ollyuE6BFSmBT9inTuaFABlxBC2IOSrsJWTMdCZw7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+ "text/plain": [ + "" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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i4+oNBgUVFEOCpLyRSPDnU/s9PTBtGnzlK1BeHuyLx4MtXRoun3PsgHC0Sjh0bQ8Nl8+h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\nAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "from scipy.optimize import curve_fit\n", + "import numpy.fft\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/3471A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915, 0.62032418])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jc6zSnYKHDOC5o3IwvvPfCOT25VL1l5FVwsvmOwIzgHI2eH1ov+F3AbO4omAi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\nmWgq4CViGZjL5WLDwxuoXV7LwuULqV1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yuaivr+fo0aNR/ZzddxW9vb1UVFQwODgY8nnl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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.362e-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": [ + { + "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": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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-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": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Line magic function `%autoreload` not found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 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": [ + "%autoreload\n", + "p, pe = clag.errors(Cx, p, pe)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phi, phie = p[nfq:], pe[nfq:]\n", + "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd) \n", + "cx, cxe = p[:nfq], pe[:nfq]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.25569486, 2.37000041, 1.7802513 , 1.66775218, 0.49069246,\n", + " 0.21781609, -0.44057362, 0.01545348])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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s6Om0xRZ+RyXiD7fVTKuBHGC7GM4ZEHRuuFiWuYypKV8DGwF7Af8b47knAv2x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\n+VmR9uSlSe+6guJtKiuDRVgeDVfHdix/xjhm9v8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "x=np.logspace(fqd[0],fqd[-1],200)\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n", + "fit = tophat_freq(fqd, *coeff)\n", + "\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "xscale('log'); xlim(.009,.6)\n", + "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n", + "ylabel(\"Time Lag (days)\",fontsize=20)\n", + "\n", + "\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n", + "plot(fqd,fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MypQkSZpPlqt7/yN59QHWJoZGdyeGRj8LfJBhWdXwIdFq01z/guOSJKmK0gabK4gWmzuB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\nbSl8Xzjrs9pXrrWspcdfuW1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_fit = irfft(fit)\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "ylabel(\"Response (relative)\",fontsize=20)\n", + "xlabel(\"Time (days)\",fontsize=20) \n", + "\n", + "ylim(-0.5,2)\n", + "xlim(0,7)\n", + "\n", + "plot(time_fit)\n", + "plot([2.02,2.02], [-50, 50], color='k', linestyle='-', linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lag/data/clag_analysis-origbins-6175A.ipynb b/lag/data/clag_analysis-origbins-6175A.ipynb new file mode 100644 index 0000000..a073f9d --- /dev/null +++ b/lag/data/clag_analysis-origbins-6175A.ipynb @@ -0,0 +1,816 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/6175A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n", + " 0.20739079, 0.32145572, 0.49825637])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqd\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jlekUPGSBwB2Vi5Gd/4agoKeA6v8RWyW8XL4jsAIo99KgD+3Xwi5gNlcUTIVR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\nAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.356e-01 6.706e+01 inf -- -2.964e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 7.719e-01 6.609e+01 8.091e+01 -- -2.155e+02 -- 0.566297 0.565079 0.565579 0.565108 0.564503 0.565385 0.564367 0.564961\n", + " 3 3.379e+00 6.484e+01 7.996e+01 -- -1.355e+02 -- 0.138579 0.130954 0.131695 0.130103 0.128857 0.130335 0.128712 0.130511\n", + " 4 1.429e+00 6.354e+01 7.865e+01 -- -5.686e+01 -- -0.27205 -0.301257 -0.300578 -0.304918 -0.306545 -0.305024 -0.306246 -0.301896\n", + " 5 5.891e-01 6.225e+01 7.700e+01 -- 2.014e+01 -- -0.639865 -0.728428 -0.729483 -0.7395 -0.741278 -0.740758 -0.740477 -0.732461\n", + " 6 3.717e-01 6.064e+01 7.473e+01 -- 9.487e+01 -- -0.916158 -1.1392 -1.15122 -1.17266 -1.17468 -1.17713 -1.17495 -1.16261\n", + " 7 2.723e-01 5.813e+01 7.142e+01 -- 1.663e+02 -- -1.05542 -1.49675 -1.55907 -1.60318 -1.60661 -1.61473 -1.61106 -1.59295\n", + " 8 2.151e-01 5.427e+01 6.693e+01 -- 2.332e+02 -- -1.09159 -1.71699 -1.94737 -2.02949 -2.03872 -2.0544 -2.04928 -2.02094\n", + " 9 1.766e-01 4.902e+01 6.164e+01 -- 2.949e+02 -- -1.0864 -1.76184 -2.31512 -2.44735 -2.4733 -2.49635 -2.4886 -2.44039\n", + " 10 1.488e-01 4.321e+01 5.498e+01 -- 3.498e+02 -- -1.06321 -1.76835 -2.64945 -2.8386 -2.90987 -2.9371 -2.92734 -2.84392\n", + " 11 1.266e-01 3.774e+01 4.629e+01 -- 3.961e+02 -- -1.05206 -1.78791 -2.90485 -3.15492 -3.34002 -3.36876 -3.363 -3.22984\n", + " 12 1.020e-01 3.183e+01 3.581e+01 -- 4.319e+02 -- -1.04328 -1.80162 -3.05493 -3.33738 -3.73969 -3.77247 -3.78883 -3.60537\n", + " 13 8.417e-02 2.370e+01 2.339e+01 -- 4.553e+02 -- -1.03318 -1.80414 -3.12825 -3.41545 -4.06016 -4.10442 -4.17529 -3.97261\n", + " 14 5.584e-02 1.304e+01 1.058e+01 -- 4.659e+02 -- -1.02449 -1.80162 -3.1555 -3.47184 -4.25708 -4.30813 -4.44309 -4.307\n", + " 15 2.209e-02 4.062e+00 2.538e+00 -- 4.684e+02 -- -1.02032 -1.79854 -3.15803 -3.51038 -4.34263 -4.3778 -4.52295 -4.54752\n", + " 16 5.247e-03 5.023e-01 2.477e-01 -- 4.687e+02 -- -1.02062 -1.79556 -3.15642 -3.51858 -4.37903 -4.39008 -4.51121 -4.64796\n", + " 17 2.880e-03 1.411e-01 1.010e-02 -- 4.687e+02 -- -1.02146 -1.79354 -3.15518 -3.51435 -4.40201 -4.39592 -4.50461 -4.66221\n", + " 18 1.495e-03 7.076e-02 1.728e-03 -- 4.687e+02 -- -1.02146 -1.79301 -3.15424 -3.51272 -4.41468 -4.39804 -4.50083 -4.6632\n", + " 19 7.709e-04 3.584e-02 4.325e-04 -- 4.687e+02 -- -1.02146 -1.79285 -3.15372 -3.51155 -4.42128 -4.39875 -4.49916 -4.66361\n", + " 20 3.958e-04 1.822e-02 1.104e-04 -- 4.687e+02 -- -1.02145 -1.79279 -3.15346 -3.51104 -4.42469 -4.39897 -4.4983 -4.66381\n", + " 21 2.024e-04 9.268e-03 2.840e-05 -- 4.687e+02 -- -1.02145 -1.79276 -3.15333 -3.51076 -4.42644 -4.39904 -4.4979 -4.6639\n", + "********************\n", + "-1.02145 -1.79276 -3.15333 -3.51076 -4.42644 -4.39904 -4.4979 -4.6639\n", + "0.234388 0.20444 0.264527 0.210547 0.308149 0.209858 0.179189 0.173278\n", + "1.75973e-05 0.000353327 0.000863132 0.0021508 -0.00926831 -0.00174289 0.00467078 -0.00131932\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 4.687e+02 4.637e+02 -1.021e+00 -2.145e-02 9.96 +++\n", + "+++ 4.687e+02 4.671e+02 -1.021e+00 -5.214e-01 3.22 +++\n", + "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.714e-01 0.935 +++\n", + "+++ 4.687e+02 4.677e+02 -1.021e+00 -6.464e-01 1.95 +++\n", + "+++ 4.687e+02 4.680e+02 -1.021e+00 -7.089e-01 1.41 +++\n", + "+++ 4.687e+02 4.681e+02 -1.021e+00 -7.402e-01 1.16 +++\n", + "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.558e-01 1.05 +++\n", + "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.636e-01 0.99 +++\n", + "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.597e-01 1.02 +++\n", + "+++ 4.687e+02 4.682e+02 -1.021e+00 -7.617e-01 1 +++\n", + "\t### errors for param 1 ###\n", + "+++ 4.687e+02 4.683e+02 -1.793e+00 -1.589e+00 0.875 +++\n", + "+++ 4.687e+02 4.678e+02 -1.793e+00 -1.487e+00 1.84 +++\n", + "+++ 4.687e+02 4.680e+02 -1.793e+00 -1.538e+00 1.33 +++\n", + "+++ 4.687e+02 4.681e+02 -1.793e+00 -1.563e+00 1.09 +++\n", + "+++ 4.687e+02 4.682e+02 -1.793e+00 -1.576e+00 0.981 +++\n", + "+++ 4.687e+02 4.682e+02 -1.793e+00 -1.569e+00 1.04 +++\n", + "+++ 4.687e+02 4.682e+02 -1.793e+00 -1.573e+00 1.01 +++\n", + "\t### errors for param 2 ###\n", + "+++ 4.687e+02 4.683e+02 -3.153e+00 -2.889e+00 0.804 +++\n", + "+++ 4.687e+02 4.678e+02 -3.153e+00 -2.756e+00 1.76 +++\n", + "+++ 4.687e+02 4.681e+02 -3.153e+00 -2.823e+00 1.24 +++\n", + "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.856e+00 1.01 +++\n", + "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.872e+00 0.904 +++\n", + "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.864e+00 0.957 +++\n", + "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.860e+00 0.983 +++\n", + "+++ 4.687e+02 4.682e+02 -3.153e+00 -2.858e+00 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 4.687e+02 4.686e+02 -3.511e+00 -3.405e+00 0.282 +++\n", + "+++ 4.687e+02 4.684e+02 -3.511e+00 -3.353e+00 0.62 +++\n", + "+++ 4.687e+02 4.683e+02 -3.511e+00 -3.326e+00 0.835 +++\n", + "+++ 4.687e+02 4.682e+02 -3.511e+00 -3.313e+00 0.953 +++\n", + "+++ 4.687e+02 4.682e+02 -3.511e+00 -3.307e+00 1.01 +++\n", + "+++ 4.687e+02 4.682e+02 -3.511e+00 -3.310e+00 0.984 +++\n", + "+++ 4.687e+02 4.682e+02 -3.511e+00 -3.308e+00 0.999 +++\n", + "\t### errors for param 4 ###\n", + "+++ 4.687e+02 4.683e+02 -4.427e+00 -4.119e+00 0.792 +++\n", + "+++ 4.687e+02 4.678e+02 -4.427e+00 -3.965e+00 1.84 +++\n", + "+++ 4.687e+02 4.680e+02 -4.427e+00 -4.042e+00 1.31 +++\n", + "+++ 4.687e+02 4.682e+02 -4.427e+00 -4.080e+00 1.03 +++\n", + "+++ 4.687e+02 4.682e+02 -4.427e+00 -4.100e+00 0.907 +++\n", + "+++ 4.687e+02 4.682e+02 -4.427e+00 -4.090e+00 0.968 +++\n", + "+++ 4.687e+02 4.682e+02 -4.427e+00 -4.085e+00 1 +++\n", + "\t### errors for param 5 ###\n", + "+++ 4.687e+02 4.682e+02 -4.399e+00 -4.189e+00 0.899 +++\n", + "+++ 4.687e+02 4.677e+02 -4.399e+00 -4.084e+00 1.98 +++\n", + "+++ 4.687e+02 4.680e+02 -4.399e+00 -4.137e+00 1.33 +++\n", + "+++ 4.687e+02 4.682e+02 -4.399e+00 -4.163e+00 1.06 +++\n", + "+++ 4.687e+02 4.682e+02 -4.399e+00 -4.176e+00 0.941 +++\n", + "+++ 4.687e+02 4.682e+02 -4.399e+00 -4.170e+00 1 +++\n", + "\t### errors for param 6 ###\n", + "+++ 4.687e+02 4.682e+02 -4.498e+00 -4.319e+00 1.01 +++\n", + "\t### errors for param 7 ###\n", + "+++ 4.687e+02 4.686e+02 -4.664e+00 -4.577e+00 0.267 +++\n", + "+++ 4.687e+02 4.684e+02 -4.664e+00 -4.534e+00 0.602 +++\n", + "+++ 4.687e+02 4.683e+02 -4.664e+00 -4.512e+00 0.82 +++\n", + "+++ 4.687e+02 4.682e+02 -4.664e+00 -4.501e+00 0.942 +++\n", + "+++ 4.687e+02 4.682e+02 -4.664e+00 -4.496e+00 1.01 +++\n", + "********************\n", + "-1.02145 -1.79274 -3.15326 -3.51062 -4.42734 -4.39906 -4.49769 -4.66395\n", + "0.259766 0.220088 0.295514 0.2023 0.342171 0.229522 0.179146 0.167871\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1.62466\n", + " 7 2.140e+01 1.475e+01 1.536e+00 -- 5.204e+02 -- -0.557456 -1.19853 -2.45014 -2.93384 -3.52464 -3.81372 -4.22037 -6.12619 0.175865 0.28851 0.308387 0.323331 0.0151409 0.217034 0.131397 0.0766008\n", + " 9 4.054e+01 1.673e+01 1.375e+00 -- 5.218e+02 -- -0.53338 -1.17617 -2.44091 -2.96736 -3.51007 -3.8326 -4.19508 -5.82619 0.19012 0.327294 0.359004 0.414056 -0.00394175 0.251863 0.139262 -0.0873399\n", + " 11 1.539e+02 1.878e+01 1.257e+00 -- 5.231e+02 -- -0.512807 -1.15664 -2.43168 -2.9966 -3.49691 -3.84978 -4.17339 -5.52619 0.201361 0.359027 0.402429 0.511897 -0.0199226 0.284261 0.146507 0.000430987\n", + " 13 6.674e+00 2.124e+01 1.152e+00 -- 5.242e+02 -- -0.495111 -1.13956 -2.42273 -3.02071 -3.48496 -3.86545 -4.15466 -5.35324 0.210341 0.385373 0.439852 0.614993 -0.0333288 0.314186 0.153466 0.00706348\n", + " 15 3.252e+00 2.410e+01 1.060e+00 -- 5.253e+02 -- -0.479803 -1.1246 -2.41423 -3.03903 -3.47409 -3.87976 -4.13835 -5.24709 0.217599 0.407526 0.472271 0.72086 -0.0446408 0.341603 0.160157 0.0117776\n", + " 17 2.016e+00 2.719e+01 9.825e-01 -- 5.263e+02 -- -0.466499 -1.11148 -2.40628 -3.05126 -3.46421 -3.89282 -4.124 -5.17073 0.223523 0.426356 0.500528 0.826462 -0.0542649 0.366566 0.166562 0.0156079\n", + " 19 1.362e+00 3.054e+01 9.145e-01 -- 5.272e+02 -- -0.454889 -1.09993 -2.3989 -3.05757 -3.45521 -3.90473 -4.1113 -5.1118 0.228399 0.442508 0.525313 0.928622 -0.0625105 0.389157 0.172682 0.0187539\n", + " 21 9.515e-01 3.414e+01 8.538e-01 -- 5.280e+02 -- -0.444723 -1.08976 -2.3921 -3.05859 -3.44701 -3.9156 -4.09998 -5.06443 0.232442 0.456468 0.547196 1.02454 -0.0696201 0.409485 0.178522 0.0213081\n", + " 23 7.093e-01 3.801e+01 7.988e-01 -- 5.288e+02 -- -0.435796 -1.08077 -2.38588 -3.05525 -3.43953 -3.92551 -4.08984 -5.0253 0.235816 0.468611 0.566644 1.11219 -0.0757858 0.427675 0.18409 0.0233356\n", + " 25 5.803e-01 4.215e+01 7.484e-01 -- 5.296e+02 -- -0.427935 -1.07281 -2.38021 -3.04863 -3.43269 -3.93455 -4.0807 -4.99237 0.23865 0.479229 0.584038 1.19049 -0.0811613 0.443868 0.189393 0.0248916\n", + " 27 4.826e-01 4.655e+01 7.016e-01 -- 5.303e+02 -- -0.421 -1.06576 -2.37506 -3.03976 -3.42645 -3.94279 -4.07243 -4.96428 0.241046 0.488555 0.599693 1.25918 -0.085871 0.458207 0.194441 0.0260258\n", + " 29 4.068e-01 5.122e+01 6.579e-01 -- 5.309e+02 -- -0.414869 -1.0595 -2.3704 -3.02952 -3.42074 -3.95028 -4.06492 -4.94006 0.243083 0.496775 0.613868 1.31864 -0.0900155 0.470841 0.199241 0.0267841\n", + " 30 3.534e+01 8.279e+04 1.751e+01 -- 5.134e+02 -- -0.360586 -1.00374 -2.32839 -2.92007 -3.3685 -4.01834 -3.99643 -4.7296 0.260518 0.56944 0.742943 1.82845 -0.126634 0.581593 0.244852 0.0310258\n", + " 31 2.628e+00 5.230e+02 1.385e+01 -- 5.273e+02 -- -0.367347 -0.995704 -2.41817 -2.93715 -3.22222 -3.67509 -3.96857 -4.92098 0.252274 0.493499 0.92368 1.27613 -0.141219 0.150824 0.421865 -1.06553\n", + " 33 1.245e+00 1.834e+02 5.511e+00 -- 5.328e+02 -- -0.366953 -0.996565 -2.41698 -2.92546 -3.2361 -3.70408 -3.95122 -4.90366 0.254103 0.498794 0.945127 1.30107 -0.17833 0.138325 0.416295 -0.885392\n", + " 34 2.632e+00 2.549e+02 4.231e+00 -- 5.370e+02 -- -0.363913 -1.00399 -2.39017 -2.80507 -3.34439 -3.93632 -3.87676 -4.84813 0.268355 0.551262 1.00931 1.53983 -0.400317 0.167888 0.35166 0.103037\n", + " 35 2.312e-01 2.769e+01 1.185e+00 -- 5.382e+02 -- -0.36551 -1.00259 -2.35867 -2.82081 -3.32433 -3.99735 -3.92146 -5.15833 0.258537 0.557539 0.798409 1.50393 -0.115974 0.467843 0.342653 -0.168121\n", + " 36 1.282e-01 1.344e+01 1.168e-01 -- 5.383e+02 -- -0.365155 -1.00375 -2.35465 -2.81558 -3.34281 -3.98947 -3.93824 -5.07587 0.261859 0.557604 0.867168 1.51669 -0.142791 0.48333 0.289755 -0.174609\n", + " 37 2.322e-01 2.131e+00 2.015e-02 -- 5.383e+02 -- -0.365299 -1.00349 -2.35109 -2.82381 -3.34204 -3.98257 -3.95106 -5.06742 0.260301 0.559365 0.842097 1.51206 -0.130381 0.490731 0.287031 -0.152216\n", + " 38 6.590e-02 2.211e+00 4.939e-03 -- 5.383e+02 -- -0.365272 -1.00361 -2.3515 -2.82539 -3.34697 -3.98003 -3.95473 -5.01474 0.260526 0.558922 0.844539 1.51206 -0.138355 0.502691 0.282545 -0.116878\n", + " 39 5.891e-02 3.773e-01 1.420e-03 -- 5.383e+02 -- -0.365297 -1.00355 -2.3514 -2.82741 -3.34661 -3.9783 -3.95801 -4.99087 0.260204 0.559082 0.839989 1.51118 -0.135923 0.505973 0.282768 -0.109176\n", + " 40 2.572e-02 4.637e-01 4.968e-04 -- 5.383e+02 -- -0.365292 -1.00357 -2.35165 -2.82796 -3.34757 -3.97813 -3.95907 -4.96797 0.260227 0.558996 0.840181 1.51095 -0.137625 0.508445 0.282456 -0.102744\n", + " 41 1.649e-02 5.031e-02 1.977e-04 -- 5.383e+02 -- -0.365297 -1.00356 -2.35169 -2.8285 -3.34746 -3.97798 -3.95998 -4.95472 0.260155 0.559001 0.83919 1.51074 -0.137022 0.509266 0.282795 -0.100101\n", + " 42 9.246e-03 1.178e-01 8.567e-05 -- 5.383e+02 -- -0.365297 -1.00356 -2.35177 -2.82872 -3.34765 -3.9781 -3.96035 -4.94463 0.260153 0.558976 0.839165 1.51071 -0.137277 0.509832 0.282928 -0.0984504\n", + "********************\n", + "-0.365297 -1.00356 -2.35177 -2.82872 -3.34765 -3.9781 -3.96035 -4.94463 0.260153 0.558976 0.839165 1.51071 -0.137277 0.509832 0.282928 -0.0984504\n", + "0.0033599 0.00994879 0.12815 0.199699 0.0939759 0.373185 0.0923458 1.11837 0.0669538 0.104719 0.458098 0.560874 0.328106 0.903943 0.28723 1.91846\n", + "-0.117785 0.0115573 -0.00195453 -0.00472824 0.000416438 -0.000751097 -0.0358018 0.00519553 -0.00437069 -0.000802906 -0.00125322 -8.26251e-05 0.00185064 0.000334232 0.00218703 -0.00017406\n", + "********************\n" + ] + } + ], + "source": [ + "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n", + "p = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a good starting point generally\n", + "p, pe = clag.optimize(Cx, p)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Line magic function `%autoreload` not found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 5.384e+02 5.381e+02 -3.653e-01 -3.636e-01 0.473 +++\n", + "+++ 5.384e+02 5.376e+02 -3.653e-01 -3.628e-01 1.49 +++\n", + "+++ 5.384e+02 5.379e+02 -3.653e-01 -3.632e-01 0.867 +++\n", + "+++ 5.384e+02 5.378e+02 -3.653e-01 -3.630e-01 1.14 +++\n", + "+++ 5.384e+02 5.379e+02 -3.653e-01 -3.631e-01 0.998 +++\n", + "\t### errors for param 1 ###\n", + "+++ 5.384e+02 5.382e+02 -1.004e+00 -9.986e-01 0.343 +++\n", + "+++ 5.384e+02 5.379e+02 -1.004e+00 -9.961e-01 0.962 +++\n", + "+++ 5.384e+02 5.376e+02 -1.004e+00 -9.949e-01 1.48 +++\n", + "+++ 5.384e+02 5.378e+02 -1.004e+00 -9.955e-01 1.2 +++\n", + "+++ 5.384e+02 5.378e+02 -1.004e+00 -9.958e-01 1.08 +++\n", + "+++ 5.384e+02 5.378e+02 -1.004e+00 -9.959e-01 1.02 +++\n", + "+++ 5.384e+02 5.379e+02 -1.004e+00 -9.960e-01 0.989 +++\n", + "+++ 5.384e+02 5.378e+02 -1.004e+00 -9.960e-01 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 5.384e+02 5.382e+02 -2.352e+00 -2.288e+00 0.363 +++\n", + "+++ 5.384e+02 5.378e+02 -2.352e+00 -2.256e+00 1.15 +++\n", + "+++ 5.384e+02 5.380e+02 -2.352e+00 -2.272e+00 0.665 +++\n", + "+++ 5.384e+02 5.379e+02 -2.352e+00 -2.264e+00 0.878 +++\n", + "+++ 5.384e+02 5.378e+02 -2.352e+00 -2.260e+00 1.01 +++\n", + "\t### errors for param 3 ###\n", + "+++ 5.384e+02 5.382e+02 -2.829e+00 -2.729e+00 0.379 +++\n", + "+++ 5.384e+02 5.378e+02 -2.829e+00 -2.679e+00 1.17 +++\n", + "+++ 5.384e+02 5.380e+02 -2.829e+00 -2.704e+00 0.689 +++\n", + "+++ 5.384e+02 5.379e+02 -2.829e+00 -2.692e+00 0.902 +++\n", + "+++ 5.384e+02 5.378e+02 -2.829e+00 -2.685e+00 1.03 +++\n", + "+++ 5.384e+02 5.379e+02 -2.829e+00 -2.688e+00 0.964 +++\n", + "+++ 5.384e+02 5.379e+02 -2.829e+00 -2.687e+00 0.996 +++\n", + "\t### errors for param 4 ###\n", + "+++ 5.384e+02 5.382e+02 -3.348e+00 -3.301e+00 0.337 +++\n", + "+++ 5.384e+02 5.379e+02 -3.348e+00 -3.277e+00 0.983 +++\n", + "+++ 5.384e+02 5.376e+02 -3.348e+00 -3.265e+00 1.56 +++\n", + "+++ 5.384e+02 5.377e+02 -3.348e+00 -3.271e+00 1.24 +++\n", + "+++ 5.384e+02 5.378e+02 -3.348e+00 -3.274e+00 1.11 +++\n", + "+++ 5.384e+02 5.378e+02 -3.348e+00 -3.276e+00 1.04 +++\n", + "+++ 5.384e+02 5.378e+02 -3.348e+00 -3.276e+00 1.01 +++\n", + "+++ 5.384e+02 5.379e+02 -3.348e+00 -3.277e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 5.384e+02 5.381e+02 -3.978e+00 -3.792e+00 0.447 +++\n", + "+++ 5.384e+02 5.376e+02 -3.978e+00 -3.698e+00 1.41 +++\n", + "+++ 5.384e+02 5.379e+02 -3.978e+00 -3.745e+00 0.822 +++\n", + "+++ 5.384e+02 5.378e+02 -3.978e+00 -3.722e+00 1.08 +++\n", + "+++ 5.384e+02 5.379e+02 -3.978e+00 -3.733e+00 0.946 +++\n", + "+++ 5.384e+02 5.378e+02 -3.978e+00 -3.727e+00 1.01 +++\n", + "+++ 5.384e+02 5.379e+02 -3.978e+00 -3.730e+00 0.979 +++\n", + "+++ 5.384e+02 5.379e+02 -3.978e+00 -3.729e+00 0.996 +++\n", + "\t### errors for param 6 ###\n", + "+++ 5.384e+02 5.382e+02 -3.961e+00 -3.914e+00 0.206 +++\n", + "+++ 5.384e+02 5.381e+02 -3.961e+00 -3.891e+00 0.527 +++\n", + "+++ 5.384e+02 5.380e+02 -3.961e+00 -3.880e+00 0.771 +++\n", + "+++ 5.384e+02 5.379e+02 -3.961e+00 -3.874e+00 0.919 +++\n", + "+++ 5.384e+02 5.378e+02 -3.961e+00 -3.871e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 5.384e+02 5.382e+02 -4.938e+00 -4.387e+00 0.366 +++\n", + "+++ 5.384e+02 5.370e+02 -4.938e+00 -4.112e+00 2.72 +++\n", + "+++ 5.384e+02 5.379e+02 -4.938e+00 -4.250e+00 0.924 +++\n", + "+++ 5.384e+02 5.376e+02 -4.938e+00 -4.181e+00 1.5 +++\n", + "+++ 5.384e+02 5.378e+02 -4.938e+00 -4.215e+00 1.17 +++\n", + "+++ 5.384e+02 5.378e+02 -4.938e+00 -4.232e+00 1.04 +++\n", + "+++ 5.384e+02 5.379e+02 -4.938e+00 -4.241e+00 0.98 +++\n", + "+++ 5.384e+02 5.378e+02 -4.938e+00 -4.237e+00 1.01 +++\n", + "\t### errors for param 8 ###\n", + "+++ 5.384e+02 5.379e+02 2.601e-01 3.271e-01 0.994 +++\n", + "\t### errors for param 9 ###\n", + "+++ 5.384e+02 5.382e+02 5.590e-01 6.113e-01 0.275 +++\n", + "+++ 5.384e+02 5.380e+02 5.590e-01 6.375e-01 0.603 +++\n", + "+++ 5.384e+02 5.379e+02 5.590e-01 6.506e-01 0.808 +++\n", + "+++ 5.384e+02 5.379e+02 5.590e-01 6.571e-01 0.921 +++\n", + "+++ 5.384e+02 5.379e+02 5.590e-01 6.604e-01 0.979 +++\n", + "+++ 5.384e+02 5.378e+02 5.590e-01 6.620e-01 1.01 +++\n", + "\t### errors for param 10 ###\n", + "+++ 5.384e+02 5.379e+02 8.389e-01 1.297e+00 0.964 +++\n", + "+++ 5.384e+02 5.374e+02 8.389e-01 1.526e+00 1.86 +++\n", + "+++ 5.384e+02 5.376e+02 8.389e-01 1.412e+00 1.4 +++\n", + "+++ 5.384e+02 5.378e+02 8.389e-01 1.354e+00 1.18 +++\n", + "+++ 5.384e+02 5.378e+02 8.389e-01 1.326e+00 1.07 +++\n", + "+++ 5.384e+02 5.378e+02 8.389e-01 1.311e+00 1.02 +++\n", + "+++ 5.384e+02 5.379e+02 8.389e-01 1.304e+00 0.991 +++\n", + "\t### errors for param 11 ###\n", + "+++ 5.384e+02 5.380e+02 1.511e+00 2.072e+00 0.717 +++\n", + "+++ 5.384e+02 5.377e+02 1.511e+00 2.352e+00 1.37 +++\n", + "+++ 5.384e+02 5.378e+02 1.511e+00 2.212e+00 1.04 +++\n", + "+++ 5.384e+02 5.379e+02 1.511e+00 2.142e+00 0.876 +++\n", + "+++ 5.384e+02 5.379e+02 1.511e+00 2.177e+00 0.957 +++\n", + "+++ 5.384e+02 5.379e+02 1.511e+00 2.195e+00 0.999 +++\n", + "\t### errors for param 12 ###\n", + "+++ 5.384e+02 5.380e+02 -1.371e-01 1.910e-01 0.629 +++\n", + "+++ 5.384e+02 5.377e+02 -1.371e-01 3.551e-01 1.31 +++\n", + "+++ 5.384e+02 5.379e+02 -1.371e-01 2.730e-01 0.947 +++\n", + "+++ 5.384e+02 5.378e+02 -1.371e-01 3.141e-01 1.13 +++\n", + "+++ 5.384e+02 5.378e+02 -1.371e-01 2.935e-01 1.03 +++\n", + "+++ 5.384e+02 5.379e+02 -1.371e-01 2.833e-01 0.991 +++\n", + "\t### errors for param 13 ###\n", + "+++ 5.384e+02 5.380e+02 5.100e-01 1.414e+00 0.77 +++\n", + "+++ 5.384e+02 5.378e+02 5.100e-01 1.866e+00 1.16 +++\n", + "+++ 5.384e+02 5.378e+02 5.100e-01 1.640e+00 1 +++\n", + "\t### errors for param 14 ###\n", + "+++ 5.384e+02 5.379e+02 2.831e-01 5.706e-01 0.999 +++\n", + "\t### errors for param 15 ###\n", + "********************\n", + "-0.365298 -1.00356 -2.3518 -2.8289 -3.34762 -3.97818 -3.96065 -4.9382 0.260133 0.55897 0.838914 1.51069 -0.137073 0.510048 0.283115 -0.0975401\n", + "0.00220546 0.00757768 0.0921231 0.142074 0.0708427 0.249334 0.0895957 0.701524 0.0669616 0.10308 0.465309 0.683925 0.420368 1.13017 0.287504 9.44994\n", + "********************\n" + ] + } + ], + "source": [ + "%autoreload\n", + "p, pe = clag.errors(Cx, p, pe)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phi, phie = p[nfq:], pe[nfq:]\n", + "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd) \n", + "cx, cxe = p[:nfq], pe[:nfq]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4.29089818, 3.08255955, 2.39741626, 2.78529154, -0.1630473 ,\n", + " 0.39141847, 0.14017202, -0.03115664])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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hL5JTTgmdo/32t6FOiVTNkkxGci0C3uyifUtSxaqpgdNPD721HnccvPkmXHst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\nXZKkMlNtPbBKkqQyYzIiSZJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "x=np.logspace(fqd[0],fqd[-1],200)\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n", + "fit = tophat_freq(fqd, *coeff)\n", + "\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "xscale('log'); xlim(.009,.6)\n", + "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n", + "ylabel(\"Time Lag (days)\",fontsize=20)\n", + "\n", + "\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n", + "plot(fqd,fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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GjLHkZtQoqFbNanJERERygdvE5j/Y7MG9seao4PKL9wEXA/sDfwP+6hwPjpj6DbjdZdni\nwq23wubNoT43gwb5HZGIiIh7bhObRVhtTWXKjoDa5Bx/EhsSHoi453xCnYjFB4EA3HOP9bm56CKr\nuenXz++oRERE3PFirah3YxxfDhyD9b9p7ZS1FJjvQZnigUAAHnzQkpsBA2xNqT59/I5KRESk/Lxc\n3TuWJc4mWahSJZvjZutWKCy0RTN79fI7KhERkfLRek1CQYHNTty7t9XYvPmm3xGJiIiUjxIbAaBK\nFVtX6oQT4PTTYfZsvyMSERFJXbJNURdgSyd4bUoaninlVLWqrQjeu7c1R82aBV27+h2ViIhI8pJN\nbCZhiU28BS5TVYoSm6xTvTpMmwYnnww9e8Lbb0OHDn5HJSIikpxUmqK8TGrS8TzxSM2a8Prr0LIl\n9OgBixYlvkdERCQbJFtj0yKtUUjWqV0bZsywPjfdu1ufm5Yt/Y5KREQkvmQTm+XpDEKyU/361s/m\nuOMswZk9G1ooxRURkSymUVESV6NGNvy7enU48URYofmiRUQkiymxkYSaNLFOxKWlVnPzo9ZlFxGR\nLOVlYlMbGIyt8v0a8BawX8Q1zYBWqM9OhbPPPpbcbNlifW7WrPE7IhERkd15ldhcDvyAJTWDgVOB\n44CaEdcdjy2CuRio71HZkiEtWsBbb8HatXDSSfDbb35HJCIiUpYXic0Y4EGsxmYbUBzn2iLgZ6Aq\noOUWK6CDD7Y+NytX2jw3Gzf6HZGIiEiI28SmHfB353UR0AToFOf6XcBLzuvuLssWn7RpAzNnwtdf\n2wzFmzb5HZGIiIhxm9hcgU20Nwc4H1ifxD0fOfvDXJYtPurQwea5+fxzW1tqyxa/IxIREXGf2Bzn\n7B8ASpK8Z5mzb+qybPFZ164wfTp8/LGtCr5tm98RiYhIvnOb2DTF1nxanMI9m519NZdlSxbo1g1e\necVGTPXtCzt2+B2RiIjkM7eJzU5nX5DCPQ2c/QaXZZfHnsCdwExgDVbLdGMK9zcGJjv3bsKa1U7w\nNsSK56ST4MUX4bXXYMAA2LXL74hERCRfuU1sVmJ9bA5J4Z5jnP23Lssuj4bAxUAV4GXnWGmS91bF\n5uY5HhgOnI6N8JoBdPM2zIqnd2949ll44QW46CIoSbZhUkRExEPJrhUVyztYUnM+8FQS19cFLnVe\nv+Wy7PJYDtRzXjcALkrh3sFAa+BI4FPn2LvAF1gtUFdPIqzA+vSBKVOgf39bguHBByGgNdxFRCSD\n3NbYPITVeHTHJumLpyHwCrAXsB142GXZbqX6lXsmsIRQUgM2fP0poDM21D3v9esHjz0GEybAyJG2\nDIOIiEimuK2xWQjcBVyLjYzqATznnAsARwGHA0cD/bBJ/ABuAiracoptgPeiHF/o7FsDWkUJGDTI\nhn8PG2Y1N+PG+R2RiIjkC7eJDcD1QA1gGHCGswU9EuX6u4E7PCg30+oD66IcDx5rEOVc3ho61JKb\na66x5GbMGL8jEhGRfOBFYlOKdaadBozG5raJ1sT1IXALNiJJ8sCoUZbcjB0LNWvCVVf5HZGIiOQ6\nLxKboDedrTbW/NQYGwa+Butg+6uHZflhLdEX7qwfdl4ijBlj60mNGmUT+h15pN8RiYhILnOb2EzC\namz+C7zgHNtI9L4oFd1Coi8D0dbZL4p144gRI6hbt26ZY4WFhRQWFnoXXZYKBOD22+GDD+D882H+\nfKhVy++oREQkmxQVFVFUVFTm2Pr1yazStDu3g3FLsMSmFzafS0XSEPgF68h8cxLXXwaMx4Z1z3GO\nVQY+x5K5o6Lc0wGYN2/ePDp06OA23grt22+hXTubnfixx/yORiqC5s2bs2rVKpo1a8bKlSv9DkdE\nMqy4uJiOHTsCdASKk73P7XDvNVhy9JPL52TSKcDZwGnOz62dn88GqjvHJgI7gH3C7nscWzriBaAQ\nG+L+PPAn4Lq0R13BHXgg3HcfTJwIL7+c+HoREZHycJvYfOns93MbSAaNxxKSiVht0znOz88BjZxr\nKjlbeI3WduBEbFLC+7HO0nthidL7mQi8orvwQjjzTLj4YvhRA+NFRCQN3CY2Tzr7gS6fk0kHEEpc\nCiJe/+Bcc2HEz0G/YO+1ITbE/Wjg7bRHnCMCAXjkEahSxZIcTd4nIiJec5vYTMaWRjgD+Dvu++xI\njmvYECZPhjfesCUXREREvOR2VNSfgX9iTThjgXOxJp0FwG/YkgPxzHZZvlRAJ58MV1xhk/edcAK0\nauV3RCIikivcJjbvYv1UgjU1BwM3OK/jNTQEnPMFLsuXCuof/4A334TzzoNPP4U99vA7IhERyQVu\nm6IgdvNTIM4W7z7JA9Wrw9NPw+LFcMMNia8XERFJhtsamxNc3Kuuo3nu8MPh1lth9Gg45RQ49li/\nIxIRkYrOi6YokXIbORKmT4cBA+CLLyBigmYREZGUeNEUJVJuBQUwZQqsXw/DhvkdjYiIVHRKbMR3\n++0H48dbn5uIpUJERERSosRGskK/fraO1OWXw4oVfkcjIiIVVbKJzWvYgo7p0AmYnqZnSwURCFit\nTa1acMEFUFLid0QiIlIRJZvYnArMBV7GlhHwQjdsvaU52HpLkufq1bP+Nu++C//6l9/RiIhIRZRs\nYnMztgjkGdhswd9iSyi0T+EZVYAjgNuA5dhikr2Brc6zRDj+eBsp9de/2igpERGRVCQ73PsmbF2o\nm4D+2EKSY4ExwBZgPraMwq/AOuB3oDZQH1sw8nCgHVCV0MR8u7BFNG9i98UmJY/deivMnGmzEn/2\nGVSr5ndEIiJSUaQyj81ybGXrm4HhwPlAPUKrXCfbRLUWS2juc54pUkbVqjZCqlMnuP56NUuJiEjy\nyjMq6jtgBNAE6AXchfWT2Rnj+p3AJ8CdWF+dpsDVKKmRONq0sfWk7r0XZs3yOxoREako3Mw8vB34\nr7OBLWjZEFvpuw6wHliD1dAkWuVbZDdXXGGzEg8cCAsWQIMGfkckIiLZzst5bHYBPwOLgA+BxcAv\nKKmRcqpUCSZPhq1b4dJLoVSri4mISAKaoE+yWtOm8PDDMHWqDQUXERGJR4mNZL2zz7ZJ+4YNg+++\n8zsaERHJZkpspEK47z5o2NBWAd8Zq5u6iIjkPSU2UiHUrg1PPgkff2yjpURERKJRYiMVxp//bPPa\n3HQTzJ3rdzQiIpKNlNhIhXLjjdC+PfTvD5s2+R2NiIhkGyU2UqFUqQJPPQUrVsA11/gdjYiIZBsl\nNlLhHHww3HMPTJhgE/iJiIgEKbGRCunSS6FXLxg0CH75xe9oREQkWyixkQopEICJE2024osu0qzE\nIiJilNhIhbXXXpbcvPoqPPqo39GIiEg28DKxOR54Evgf8Ae2RlSriGu6AUOA/h6WK3nstNOsWeqq\nq2DpUr+jERERv3mR2NQAngPeAs4DDnSOBaJcWwo8ADwB/MmDskW4+25o1syGgO/Y4Xc0IiLiJy8S\nm2eAc5zXc4F7nNfRej28D3yJJT1neVC2CDVr2hDw4mK45Ra/oxERET+5TWzOAE53Xg8BugCjEtzz\nH2d/rMuyRf6/zp1t8r5x4+Cjj/yORkRE/OI2sRno7J8FHkrynuBk+Ie6LFukjOuvhy5drEnq99/9\njkZERPzgNrHp4uyLUrjnR2ff2GXZImVUrmwLZa5ZA1de6Xc0IiLiB7eJTUOsL80PKdyzy6OyRXZz\n4IFw330waRJMnep3NCIikmluk4tghf+eKdzT3NmvdVm2SFQDB8JZZ8Ell8Dq1X5HIyIimeQ2sfkG\nG+HUMYV7TnH2i12WLRJVIAAPPwxVq1qSU1Lid0QiIpIpbhOb/zr7S4GCJK5vDVzgvNbyhZI2DRta\nc9SsWfDAA35HIyIimeI2sXkQm2X4UGAyUDXOtT2Amc41vwITXZYtEtfJJ8Pw4XDddbBY9YMiInnB\nbWKzBrjIeX0e8B0wwfk5AFwJPIpNyjcDaAKUAOcDm1yWLZLQHXdAixY2BHzbNr+jERGRdPNiZNLz\n2MzDG7HE5dKwcxcDg4FDnJ83YjMOv+FBuSIJVa8OTz9tNTY33OB3NCIikm5eDbmeiq0RdQMwj9CQ\n7qBFwDjgIGCaR2WKJKV9e5uR+K674N13/Y5GRETSqbKHz1oL3OpsBUB9Z78W0NKE4qurr4bp02HA\nAFiwAOrW9TsiERFJh3RNkrcL63/zE0pqJAsUFMCUKbBxIwwd6nc0IiKSLpr9V/LGvvvC+PHwzDO2\niYhI7nGb2OwBtHK2alHOVwfuAVYCW7DRUVe4LFOk3Pr1g8JCGDIEfkhlIRAREakQ3CY2/4d1DH4H\nG8Yd6SVgBNAUm7/mEODfwH0uyxUptwcfhNq1rb/Nrshu7iIiUqG5TWxOdvYvA9sjzvUKO78S+A8Q\nXLlnKHCky7JFyqVePXjiCZg9G+65x+9oRETES24Tm+AaUbOjnLvQ2S/FllI4y9kvwSbvuyjKPSIZ\ncfzxMGoU/O1v8PnnfkcjIiJecZvYNAZKgW+jPPck5/UDhFYB3+D8DHCUy7LLY0/gXmAV1udnPvCX\nJO4biDW1RdsapyNQSb9bboFWreC882DLFr+jERERL7hNbBo6+60Rx9sDtbCkJ3Kxy0XOfh+XZZfH\nS8AA4CagJzAXKAIKk7x/INA1YlvndZCSGVWr2qzE334Lo0f7HY2IiHjB7QR927GRTw0jjndz9iuB\nZRHngrU3yawG7qVTge5YEvOcc+w9YD/gLudYtA7Q4RYBxekKUDKvdWu480648kro1Qt69PA7IhER\nccNtjc1yrL9M14jjpzn796PcU9/Zr3FZdqrOxJKqFyKOT8JGbXVJ4hkBr4MS/w0bZgnNwIGwdq3f\n0YiIiBtuE5t3nP0wbC4bgNOB45zXr0e5p7Wz/9Fl2alqA3zF7rUyC519axJ7DdiJLRMxNcl7JMtV\nqgSTJtnq35dcAqWlfkckIiLl5TaxuR9bMmEvLEH4FRvWHcA66E6Nck+wsn9hlHPp1IDo/WHWhZ2P\n5UdsDazBWNI2FjgC+ARo612I4pemTeGRR+Cll2wouIiIVExuE5ulQH9gM5bMBJuZ1mN9WbZFXL83\nocTmbZdlZ9Ib2MrlrwMfAOOBY7DO0Tf7GJd4qE8fa4664gr47ju/oxERkfLwYnXvF7B5bHphictq\nYBrRa0cOA57BEoJozVTptJbotTL1w86n4nvgQ3bvXyQV2L//De+9B+efb/vKXvyGiIhIxnj1z/bP\nwONJXDfT2fywAKtFqkTZfjbBpqRFu92RnIQ9MkaMGEHdunXLHCssLKSwMNlR5pIptWvDk09Ct25w\nxx0wZozfEYmI5L6ioiKKiorKHFu/fn25npVPo3x6YrVEfYHnw47PwDoB70sSSUqYFliy9AbQJ8Y1\nHYB58+bNo0OHDikHLP4ZOxZuvx0++gg6d/Y7mvzUvHlzVq1aRbNmzVi5cqXf4YhIhhUXF9OxY0ew\nVQ6SnmolnyraZwCzgAlAbWy25EKsz895hJKaidgkfi2AFc6xWVifoMXAH1gtz7XYCKmxmQlfMumG\nG2DGDOjfH+bPh5o1/Y5IRESS4WVi0xBb2PIAbNbhZCbgy3TH27OAcU659bHh35E1OJWcLbw2ayGW\n/OyDTUj4C/AmcAvwTdqjloyrUsVmJT78cBg5Eh56yO+IREQkGV4kNnsB/wLOxpKZZJu3/BhRtAkY\n4WyxXEhoAc+gq9MWkWStli1t9e/LLrNZiU87LfE9IiLiL7fDvethswv3xZKkVPrs5FP/HqmgLrkE\neveGwYPh55/9jkZERBJxm9iMBg5yXs/EOug2xpKcSklsIlktEICJE20/eLBmJRYRyXZuk4sznP10\nLKmZic0+nGgxSZEKo3FjS26mT4eHH/Y7GhERicdtYrMf1lfmQQ9iEclavXtbX5urr4avv/Y7GhER\nicVtYvOHs//JbSAi2e6f/4TmzW0I+I4dfkcjIiLRuE1sFmCdgPfzIBaRrFazpg0Bnz8fbtYKYSIi\nWcltYhPscTDAbSAiFcERR8BNN8Ftt8GHH/odjYiIRHKb2DwPFAFnAte7D0ck+40eDV272kKZGzf6\nHY2IiIRzO0FfN2wJgv2xGX3PxFbvXgJsTuL+2S7LF8m4ypVtocx27eDKK2HSJL8jEhGRILeJzbvY\nqKjgZHvW7JeoAAAgAElEQVSdnA3iLygZcM4ns+yCSNZp0QLuvx8uvNBmJT77bL8jEhER8GaSvFgz\nCAfibPHuE6kQLrgA+vSBSy+FVav8jkZERMB9jc0JLu7VHK5SoQUCNmFf27ZWczNjBlTSfNoiIr7y\noilKJG81aACTJ8PJJ1vT1JVX+h2RiEh+0/8vRVzq0cMSmuuug0WL/I5GRCS/KbER8cDtt8NBB8F5\n58G2bX5HIyKSv9w2RUXqBHQHWgP1nWPrgEXAm8A8j8sTyQrVq9usxJ07w5gxcNddfkckIpKfvEps\nDgMeATrHueY2YA5wKbYUg0hOadcObr3VmqROOQVOcNO1XkREysWLpqjuWMISntTsBH52tp3OsQDQ\nBfjUuUck51x9NRx7rA0F/+03v6MREck/bhObhsALwB5ACfAYlrzUBJo4Ww3n2KPONVWxpRgauCxb\nJOsUFMATT8Dvv8PQoX5HIyKSf9wmNlcCdYAdQC/gEmCu83PQTufYpcCpzs91gREuyxbJSvvuCxMm\nQFERPPOM39GIiOQXt4lNL2f/APBGEtfPBO5zXp/qsmyRrFVYCP36wZAh8P33fkcjIpI/3CY2LbAZ\nhKelcM+rYfeK5KwHH4Tata2/za5dfkcjIpIf3CY21Zz9HyncE1z1u6rLskWyWt26MGUKzJ4Nd9/t\ndzQiIvnBbWLzEzbaqUMK97R39j+7LFsk6x13HFxzjc1tM3++39GIiOQ+t4nN+87+OqB2EtfXdq4F\n+MBl2SIVws03Q+vWNivxli1+RyMiktvcJjYPO/sWWJITb4K+zs41wb41D8e5ViRnVK1qsxIvW2aT\n94mISPq4nXn4A2A8MARoC3wMfIlNwhdsatobm8emVdh941GNjeSRVq3gzjth+HCoUgVuuglq1fI7\nKhGR3OPFkgrDsQ7BI7H+Nq2dLZoS4G5gtAflilQow4bB5s3w97/Dc8/BvfdCnz4QCPgdmYhI7vBi\nSYUS4FqsU/BDwDdRrvkfMMG55jpsiLhIXgkErCnqyy+hUyc45xxbU+qbaL8xIiJSLl4kNkELsSap\nlkB1oKmzVQcOBoZiq3yL5LX994f//AemTYMlS6BNG6vF2brV78hERCo+LxObcNuwoeA/Oa9FJMJp\np1ntzciRMG6cJThvJDN/t4iIxJSuxEZEklCjhiU1CxbYGlM9e1oT1apVfkcmIlIxeZnYVAHOxvrZ\nvA8sdrb3sf41ffCms7JIzjnkEHjrLRsW/v779vM998COHYnvFRGREK8SmzOBZcDz2ArfRwOHOtvR\n2MreLwDLnWtFJEIgYAtnfv01XHihzVjcsSN8+KHfkYmIVBxeJDZXAVOxjsJBy7C5bD7FkpmgpsCL\nzj0iEkWdOnDffTB3LlSvDn/+MwweDL/+6ndkIiLZz21i0xW4y3m9ERvK3Rg4EDjS2VoAeznnNmJz\n3dyJTdonIjF06AAffQQPPQQvvQQHHwyPPgolJX5HJiKSvdwmNlc7z9gIHIUlOdH+X7nGOXekc20B\nNqGfiMRRUACXXmrNU6edBpdcAkcfDZ9/7ndkIiLZyW1ic4yz/we2lEIiXwF3RNwrIgk0bgyTJ8N7\n78Hvv1vfmxEjYONGvyMTEckubhObetgswm+ncM+7zr6uy7JF8k63bjB/PvzjH/DYYzZ66rnnoFRz\neYuIAO4Tmx+xPjPlvVdEUlSlCowaBV99BUceCX37Qo8esHSp35GJiPjPbWIzy9kfl8I9xzr7d1yW\nLZLX9tkHpk6F6dPhu++gbVsYOxa2bPE7MhER/7hNbO7GVva+DlsPKpGWzrWbCY2mEhEXTj0VFi2C\n0aPhzjuhdWt4/XW/oxIR8YfbxOZr4BysOepjbH6a+lGuqw+McK4JAOcCS1yWLSKO6tVtIc2FC+HA\nA6FXLzjrLPjhB78jExHJLLdLHLyDdR7+BfgTVoNzFzZB3y/Oub2AAwglUd8Ao5wtlhNcxiWSl1q2\nhJkz4YUXbNTUoYfCjTfCVVdZ3xwRkVznNrE5NsqxStgEfQfGuOcgZ4tF4ztEXAgE4NxzbUHNG2+E\n66+HJ56ACRNsVJWISC5zm9jM9iSKspTYiHigdm3417/gggtgyBA49lgYMADuusvmxRERyUVuE5vj\nvAhCRNKnfXv44AOYNAmuvRamTYPbbrNZjAsK/I5ORMRbbhMbEakAKlWyhTTPOMNGTw0ZYonOhAk2\ni7GIH375BT79FObMgc8+gz32sDXRDjnEtoMPhgYN/I5SKholNiJ5pGFDm7F40CC4/HLo3NmSnFtu\ngbqaC1zSaPNmKC4OJTKffgrff2/nGje2v4vbt8OLL8Ly5aHZtBs2DCU74fsWLaCyvsEkikz8tagG\n/BlogI2WmpOBMmPZE7gVG6JeHxtyfgfwXBL3NsZWJe8F1AC+AMaQ2nISIlnhqKNg3jy4/3644QYb\nRXX33dCvn3U+FnFj1y6bGTuYwMyZY1MR7NoFNWpYLeHZZ0OXLpbQ7Ltv2b93W7bAN9/AkiW2AOyS\nJbbw63PPwR9/2DVVqtjUBpEJzyGHQL16/rxvyQ5uE5v9gGFYh9/bgd8izncFpgJ7Y/PXlALzgbMA\nP2bYeAnohE0SuBQ4DyjCRnIVxbmvKvAWUBsYjg1lHwbMALqTnk7UImlVubINAz/3XLj6aujf32pz\nxo+3YeIiyVq1qmxNzGefWQISCNiEkV26WA1hly72c6KalurVbSbttm3LHi8thdWrQ8lOcP/ss6Ha\nH4BGjaInPPvvr1qefOD2/2ZXYXPXFGMJQ7hawP+wmo5IXwLtgZ0uy0/FqcBrQCFla2jeAFoD+wIl\nMe4dAjwAHAl86hwrwGpt/sASuGg6APPmzZtHhw4dXAUvkm4zZ8LQofYFMXKkLc9Qo4Z/8TRv3pxV\nq1bRrFkzVq5c6V8gUsbvv1viEp7IrF5t55o3txqYYE1Mx45Qq1Zm4tq8Gf73v7IJT3C/ebNdU6UK\n/OlP0Zu21BSbfYqLi+lonQA7YnlGUtzmric5+1einLuEUFJzH9Zk0wNLEloBA4HHXJafijOB34EX\nIo5PAp4BumAzI8e6dwmhpAZgF/AUcBvQBC3qKRVcjx7WXHDnnTZqqqgI7rsPTj/d78jELzt22HId\n4U1KX35pNSe1asERR9gUAsFEpmlT/2KtUQPatbMtXGmp1SgtWVI22XnqKVixInTdXntFT3j231+j\nBysat4lNC2f/WZRz5zr7l7HlFACmAY2wPi59yGxi0wb4it1rZRY6+9bETmzaAO9FOR5+rxIbqfCq\nVbM+N+edB8OG2Siq006zBGf//f2OTtKptNRq68JrYoqLrb9LQQEcdhgcc4zV5nXpYl/6FeELPxCw\nmqTmzaF797LnNm2CpUvLJjxz5sCTT4YWk61aNXYtT+3amX8/kpjbxKYx1m/m54jjtbGqo1KsRiTc\nc1hiE5FXp10DbDmHSOvCzsdSP+y6VO8VqXAOPNAW0nz5ZbjySmjVypqmRo60IblS8f32G8ydG0pk\n5syx4dcABxxgNTB9+tj+8MP9bZZMl5o17b0dfnjZ4yUlsHLl7rU8kydb7U9QkybRE559960YSV+u\ncpvYBFtPI/8Ij8Y65O4E3o0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_fit = irfft(fit)\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "ylabel(\"Response (relative)\",fontsize=20)\n", + "xlabel(\"Time (days)\",fontsize=20) \n", + "\n", + "ylim(-0.5,2)\n", + "xlim(0,7)\n", + "\n", + "plot(time_fit)\n", + "plot([3.13,3.13], [-50, 50], color='k', linestyle='-', linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lag/data/clag_analysis-origbins-6439A.ipynb b/lag/data/clag_analysis-origbins-6439A.ipynb new file mode 100644 index 0000000..4179b43 --- /dev/null +++ b/lag/data/clag_analysis-origbins-6439A.ipynb @@ -0,0 +1,861 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/6439A.lc\"\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n", + " 0.20739079, 0.32145572, 0.49825637])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqd\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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cLTya9zlWuU7BQx4I3FG5GL3y3zAU9hZS/9/jq4Q3nu8IrADKsyTopP1m2AXM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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.345e-01 5.367e+01 inf -- -2.331e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 7.681e-01 5.318e+01 6.465e+01 -- -1.684e+02 -- 0.582708 0.565919 0.567071 0.565455 0.565472 0.567054 0.565985 0.567541\n", + " 3 3.306e+00 5.243e+01 6.380e+01 -- -1.046e+02 -- 0.185981 0.132028 0.135218 0.13138 0.131144 0.134919 0.132269 0.135939\n", + " 4 1.864e+00 5.146e+01 6.266e+01 -- -4.198e+01 -- -0.181134 -0.300836 -0.294737 -0.302717 -0.302476 -0.296396 -0.30108 -0.294221\n", + " 5 5.957e-01 5.034e+01 6.140e+01 -- 1.942e+01 -- -0.518821 -0.728805 -0.721808 -0.738858 -0.734893 -0.72747 -0.734418 -0.722812\n", + " 6 3.763e-01 4.873e+01 5.988e+01 -- 7.930e+01 -- -0.826205 -1.14111 -1.14324 -1.17897 -1.16599 -1.15914 -1.16891 -1.15053\n", + " 7 2.737e-01 4.601e+01 5.715e+01 -- 1.365e+02 -- -1.07976 -1.51457 -1.54719 -1.6226 -1.59467 -1.59132 -1.60536 -1.57616\n", + " 8 2.126e-01 4.210e+01 5.222e+01 -- 1.887e+02 -- -1.22751 -1.79949 -1.90925 -2.06669 -2.0187 -2.02254 -2.04251 -1.99341\n", + " 9 1.708e-01 3.838e+01 4.572e+01 -- 2.344e+02 -- -1.23813 -1.90084 -2.20766 -2.50544 -2.43783 -2.45004 -2.47673 -2.39386\n", + " 10 1.443e-01 3.612e+01 4.007e+01 -- 2.745e+02 -- -1.15982 -1.77137 -2.45761 -2.92172 -2.8541 -2.86716 -2.89804 -2.78318\n", + " 11 1.465e-01 3.205e+01 3.355e+01 -- 3.080e+02 -- -1.00642 -1.66795 -2.68038 -3.28735 -3.25239 -3.25214 -3.28683 -3.18475\n", + " 12 1.147e-01 2.460e+01 2.338e+01 -- 3.314e+02 -- -0.859013 -1.64077 -2.83751 -3.57526 -3.57815 -3.56594 -3.6097 -3.59927\n", + " 13 9.727e-02 1.530e+01 1.253e+01 -- 3.439e+02 -- -0.770014 -1.63264 -2.92007 -3.72033 -3.73522 -3.78014 -3.82976 -4.01212\n", + " 14 7.615e-02 7.436e+00 5.601e+00 -- 3.495e+02 -- -0.717971 -1.62697 -2.94479 -3.70604 -3.70977 -3.91796 -3.95008 -4.4024\n", + " 15 4.898e-02 2.805e+00 2.111e+00 -- 3.516e+02 -- -0.694246 -1.6192 -2.93083 -3.65405 -3.66478 -4.01526 -4.01935 -4.73764\n", + " 16 2.092e-02 1.546e+00 5.495e-01 -- 3.522e+02 -- -0.686453 -1.6148 -2.9107 -3.61859 -3.64327 -4.06623 -4.07053 -4.96968\n", + " 17 6.268e-03 6.434e-01 9.603e-02 -- 3.523e+02 -- -0.684867 -1.61298 -2.89653 -3.5983 -3.63262 -4.0834 -4.10603 -5.07364\n", + " 18 2.301e-03 2.165e-01 1.331e-02 -- 3.523e+02 -- -0.685248 -1.61215 -2.88778 -3.5897 -3.62755 -4.08853 -4.12306 -5.10544\n", + " 19 7.739e-04 7.392e-02 1.675e-03 -- 3.523e+02 -- -0.685882 -1.61169 -2.88397 -3.58619 -3.6255 -4.0902 -4.1289 -5.11719\n", + " 20 2.809e-04 2.462e-02 2.072e-04 -- 3.523e+02 -- -0.686276 -1.61151 -2.88251 -3.58466 -3.62482 -4.09073 -4.13094 -5.12115\n", + " 21 1.264e-04 8.449e-03 2.600e-05 -- 3.523e+02 -- -0.686469 -1.61146 -2.88191 -3.58405 -3.62462 -4.0909 -4.13161 -5.12257\n", + "********************\n", + "-0.686469 -1.61146 -2.88191 -3.58405 -3.62462 -4.0909 -4.13161 -5.12257\n", + "0.231147 0.207462 0.274193 0.294642 0.222424 0.244627 0.185969 0.531275\n", + "-0.00158514 0.000476266 0.00320976 0.00304574 0.000752612 -0.00194507 -0.00844944 -0.00236823\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.523e+02 3.519e+02 -6.866e-01 -4.554e-01 0.735 +++\n", + "+++ 3.523e+02 3.515e+02 -6.866e-01 -3.398e-01 1.52 +++\n", + "+++ 3.523e+02 3.517e+02 -6.866e-01 -3.976e-01 1.11 +++\n", + "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.265e-01 0.915 +++\n", + "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.121e-01 1.01 +++\n", + "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.193e-01 0.962 +++\n", + "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.157e-01 0.986 +++\n", + "+++ 3.523e+02 3.518e+02 -6.866e-01 -4.139e-01 0.999 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.523e+02 3.518e+02 -1.611e+00 -1.404e+00 0.91 +++\n", + "+++ 3.523e+02 3.513e+02 -1.611e+00 -1.300e+00 1.93 +++\n", + "+++ 3.523e+02 3.516e+02 -1.611e+00 -1.352e+00 1.38 +++\n", + "+++ 3.523e+02 3.517e+02 -1.611e+00 -1.378e+00 1.13 +++\n", + "+++ 3.523e+02 3.518e+02 -1.611e+00 -1.391e+00 1.02 +++\n", + "+++ 3.523e+02 3.518e+02 -1.611e+00 -1.397e+00 0.964 +++\n", + "+++ 3.523e+02 3.518e+02 -1.611e+00 -1.394e+00 0.992 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.523e+02 3.518e+02 -2.882e+00 -2.608e+00 0.933 +++\n", + "+++ 3.523e+02 3.513e+02 -2.882e+00 -2.470e+00 2.04 +++\n", + "+++ 3.523e+02 3.516e+02 -2.882e+00 -2.539e+00 1.44 +++\n", + "+++ 3.523e+02 3.517e+02 -2.882e+00 -2.573e+00 1.17 +++\n", + "+++ 3.523e+02 3.518e+02 -2.882e+00 -2.590e+00 1.05 +++\n", + "+++ 3.523e+02 3.518e+02 -2.882e+00 -2.599e+00 0.991 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.523e+02 3.518e+02 -3.584e+00 -3.289e+00 0.911 +++\n", + "+++ 3.523e+02 3.512e+02 -3.584e+00 -3.142e+00 2.13 +++\n", + "+++ 3.523e+02 3.516e+02 -3.584e+00 -3.216e+00 1.45 +++\n", + "+++ 3.523e+02 3.517e+02 -3.584e+00 -3.252e+00 1.17 +++\n", + "+++ 3.523e+02 3.518e+02 -3.584e+00 -3.271e+00 1.03 +++\n", + "+++ 3.523e+02 3.518e+02 -3.584e+00 -3.280e+00 0.971 +++\n", + "+++ 3.523e+02 3.518e+02 -3.584e+00 -3.275e+00 1 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.523e+02 3.519e+02 -3.625e+00 -3.402e+00 0.838 +++\n", + "+++ 3.523e+02 3.513e+02 -3.625e+00 -3.291e+00 1.88 +++\n", + "+++ 3.523e+02 3.516e+02 -3.625e+00 -3.347e+00 1.31 +++\n", + "+++ 3.523e+02 3.518e+02 -3.625e+00 -3.374e+00 1.06 +++\n", + "+++ 3.523e+02 3.518e+02 -3.625e+00 -3.388e+00 0.946 +++\n", + "+++ 3.523e+02 3.518e+02 -3.625e+00 -3.381e+00 1 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.523e+02 3.521e+02 -4.091e+00 -3.969e+00 0.288 +++\n", + "+++ 3.523e+02 3.520e+02 -4.091e+00 -3.907e+00 0.659 +++\n", + "+++ 3.523e+02 3.518e+02 -4.091e+00 -3.877e+00 0.904 +++\n", + "+++ 3.523e+02 3.518e+02 -4.091e+00 -3.862e+00 1.04 +++\n", + "+++ 3.523e+02 3.518e+02 -4.091e+00 -3.869e+00 0.972 +++\n", + "+++ 3.523e+02 3.518e+02 -4.091e+00 -3.865e+00 1.01 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.523e+02 3.518e+02 -4.132e+00 -3.946e+00 0.976 +++\n", + "+++ 3.523e+02 3.512e+02 -4.132e+00 -3.853e+00 2.17 +++\n", + "+++ 3.523e+02 3.515e+02 -4.132e+00 -3.899e+00 1.49 +++\n", + "+++ 3.523e+02 3.517e+02 -4.132e+00 -3.923e+00 1.24 +++\n", + "+++ 3.523e+02 3.517e+02 -4.132e+00 -3.934e+00 1.1 +++\n", + "+++ 3.523e+02 3.518e+02 -4.132e+00 -3.940e+00 1.04 +++\n", + "+++ 3.523e+02 3.518e+02 -4.132e+00 -3.943e+00 1.01 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.523e+02 3.521e+02 -5.123e+00 -4.857e+00 0.383 +++\n", + "+++ 3.523e+02 3.518e+02 -5.123e+00 -4.724e+00 1 +++\n", + "********************\n", + "-0.686556 -1.61144 -2.88168 -3.58378 -3.62457 -4.09096 -4.13184 -5.12305\n", + "0.272685 0.217188 0.282737 0.308392 0.243262 0.225511 0.188902 0.398741\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0.657906\n", + " 7 1.553e+02 1.490e+01 1.934e+00 -- 4.014e+02 -- -0.450147 -1.10622 -2.30366 -2.86384 -3.15036 -3.59968 -4.44186 -6.56153 0.351939 0.32391 0.403816 0.412369 0.381156 0.372195 0.00381962 2.32595\n", + " 9 7.337e+01 1.721e+01 1.703e+00 -- 4.031e+02 -- -0.433442 -1.08282 -2.28754 -2.85817 -3.13723 -3.59775 -4.56867 -6.86153 0.41207 0.369439 0.474927 0.499989 0.447819 0.447139 -0.0555143 2.71453\n", + " 11 2.642e+02 1.966e+01 1.517e+00 -- 4.046e+02 -- -0.417104 -1.0623 -2.27119 -2.84975 -3.12388 -3.59444 -4.72052 -7.16153 0.462324 0.406445 0.533924 0.577792 0.503961 0.514362 -0.147032 1.64655\n", + " 13 1.134e+02 2.224e+01 1.353e+00 -- 4.060e+02 -- -0.401589 -1.04435 -2.25531 -2.83959 -3.11085 -3.59024 -4.9108 -6.86153 0.504215 0.43699 0.582926 0.646084 0.551409 0.574431 -0.303917 2.12243\n", + " 15 4.422e+01 2.495e+01 1.214e+00 -- 4.072e+02 -- -0.387134 -1.02863 -2.24025 -2.8285 -3.09839 -3.58552 -5.16145 -6.92565 0.539131 0.46254 0.623794 0.705676 0.59172 0.627943 -0.619596 -3.09158\n", + " 17 2.645e+02 2.779e+01 1.104e+00 -- 4.083e+02 -- -0.373836 -1.01484 -2.22621 -2.81703 -3.08666 -3.58053 -5.46145 -7.22565 0.568271 0.484154 0.658085 0.757538 0.626161 0.675551 -1.4082 2.0864\n", + " 19 2.867e+01 3.074e+01 9.808e-01 -- 4.093e+02 -- -0.361705 -1.00274 -2.21326 -2.80561 -3.07569 -3.57544 -5.41113 -6.92565 0.592636 0.502612 0.687049 0.802652 0.655755 0.718052 2.88736 -2.83886\n", + " 21 2.872e+02 3.381e+01 9.306e-01 -- 4.102e+02 -- -0.350685 -0.992095 -2.20141 -2.79451 -3.06542 -3.57021 -5.11113 -7.22565 0.612993 0.518498 0.711608 0.842016 0.681241 0.756352 -2.50407 1.58824\n", + " 23 4.288e+03 3.693e+01 9.343e-01 -- 4.111e+02 -- -0.340752 -0.982709 -2.19051 -2.78383 -3.05601 -3.56539 -4.81113 -6.92565 0.630223 0.532271 0.732844 0.876431 0.703498 0.789821 -2.82897 -0.0394334\n", + " 25 3.415e+01 4.012e+01 8.120e-01 -- 4.119e+02 -- -0.331787 -0.974425 -2.18054 -2.7737 -3.04741 -3.56096 -4.66441 -6.62565 0.644673 0.544307 0.750981 0.90648 0.723128 0.819985 -2.85376 -1.97808\n", + " 26 9.994e+02 4.117e+02 2.909e+00 -- 4.148e+02 -- -0.251075 -0.9012 -2.08945 -2.67816 -2.96888 -3.52102 -3.7456 -8 0.766488 0.649993 0.907611 1.17077 0.897255 1.08952 -2.95842 2.74048\n", + " 27 6.433e+02 1.002e+01 3.929e+00 -- 4.188e+02 -- -0.248558 -0.908886 -2.07891 -2.65067 -2.96976 -3.54662 -3.96146 -8 0.701601 0.621254 0.876275 1.16829 1.0404 1.20006 -2.9623 -2.8748\n", + " 28 8.040e+02 4.356e+00 3.810e-01 -- 4.192e+02 -- -0.24941 -0.908256 -2.07352 -2.6406 -2.96684 -3.53274 -4.09371 -8 0.719662 0.643488 0.90068 1.14499 0.973393 1.15172 -2.91207 1.1996\n", + " 29 6.002e-01 1.402e+00 4.879e-01 -- 4.187e+02 -- -0.249394 -0.90855 -2.07276 -2.63793 -2.96077 -3.52618 -4.14701 -5 0.719126 0.6433 0.900826 1.17205 0.953098 1.1439 -2.86205 -1.93902\n", + " 30 1.613e+02 1.454e+00 5.129e-01 -- 4.192e+02 -- -0.249496 -0.908472 -2.07365 -2.63477 -2.95891 -3.5366 -4.14887 -6.64352 0.723003 0.644781 0.906634 1.17771 0.938562 1.11686 -2.77459 -0.775149\n", + " 31 1.624e+03 1.161e+00 1.714e-02 -- 4.192e+02 -- -0.249667 -0.908595 -2.07175 -2.63868 -2.95842 -3.52179 -4.1679 -8 0.72384 0.64458 0.904311 1.17024 0.944786 1.13843 -2.857 -1.41511\n", + " 32 1.970e+02 3.152e-01 7.158e-04 -- 4.192e+02 -- -0.249609 -0.908562 -2.07305 -2.63658 -2.95704 -3.52378 -4.16495 -8 0.721446 0.644368 0.906308 1.1724 0.941102 1.13792 -2.85898 -2.42836\n", + " 33 8.595e+02 3.798e-01 6.275e-04 -- 4.192e+02 -- -0.24955 -0.908572 -2.07254 -2.6381 -2.95677 -3.52227 -4.16647 -8 0.722642 0.644061 0.90574 1.17222 0.940858 1.13781 -2.8621 2.99735\n", + " 34 9.794e+02 1.202e-01 9.051e-05 -- 4.192e+02 -- -0.249592 -0.908557 -2.07291 -2.63765 -2.95644 -3.52276 -4.16658 -8 0.721993 0.644122 0.906381 1.1716 0.940534 1.13825 -2.86125 2.8441\n", + "********************\n", + "-0.249592 -0.908557 -2.07291 -2.63765 -2.95644 -3.52276 -4.16658 -8 0.721993 0.644122 0.906381 1.1716 0.940534 1.13825 -2.86125 2.8441\n", + "0.0338278 0.0081193 0.0342279 0.0767303 0.0666745 0.170245 0.340225 1391.22 0.211725 0.0953128 0.214457 0.311582 0.260149 0.464275 0.812667 2572.32\n", + "0.0179335 -0.0240087 0.0250454 -0.120246 -0.0820404 -0.0104754 -0.0627678 -0.000341413 0.00675955 -0.0176544 -0.00416412 0.00520845 -0.00874943 0.000211486 -0.0321303 -0.000438528\n", + "********************\n" + ] + } + ], + "source": [ + "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n", + "p = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a good starting point generally\n", + "p, pe = clag.optimize(Cx, p)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Line magic function `%autoreload` not found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 4.199e+02 4.196e+02 -2.495e-01 -2.326e-01 0.408 +++\n", + "+++ 4.199e+02 4.192e+02 -2.495e-01 -2.242e-01 1.23 +++\n", + "+++ 4.199e+02 4.195e+02 -2.495e-01 -2.284e-01 0.733 +++\n", + "+++ 4.199e+02 4.194e+02 -2.495e-01 -2.263e-01 0.956 +++\n", + "+++ 4.199e+02 4.193e+02 -2.495e-01 -2.252e-01 1.09 +++\n", + "+++ 4.199e+02 4.193e+02 -2.495e-01 -2.257e-01 1.02 +++\n", + "+++ 4.199e+02 4.194e+02 -2.495e-01 -2.260e-01 0.988 +++\n", + "+++ 4.199e+02 4.194e+02 -2.495e-01 -2.259e-01 1 +++\n", + "\t### errors for param 1 ###\n", + "+++ 4.199e+02 4.197e+02 -9.086e-01 -9.045e-01 0.351 +++\n", + "+++ 4.199e+02 4.194e+02 -9.086e-01 -9.025e-01 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 4.199e+02 4.197e+02 -2.073e+00 -2.056e+00 0.357 +++\n", + "+++ 4.199e+02 4.192e+02 -2.073e+00 -2.047e+00 1.24 +++\n", + "+++ 4.199e+02 4.195e+02 -2.073e+00 -2.051e+00 0.685 +++\n", + "+++ 4.199e+02 4.194e+02 -2.073e+00 -2.049e+00 0.93 +++\n", + "+++ 4.199e+02 4.193e+02 -2.073e+00 -2.048e+00 1.08 +++\n", + "+++ 4.199e+02 4.194e+02 -2.073e+00 -2.049e+00 1 +++\n", + "\t### errors for param 3 ###\n", + "+++ 4.199e+02 4.197e+02 -2.645e+00 -2.605e+00 0.304 +++\n", + "+++ 4.199e+02 4.195e+02 -2.645e+00 -2.585e+00 0.798 +++\n", + "+++ 4.199e+02 4.192e+02 -2.645e+00 -2.574e+00 1.23 +++\n", + "+++ 4.199e+02 4.194e+02 -2.645e+00 -2.579e+00 0.995 +++\n", + "\t### errors for param 4 ###\n", + "+++ 4.199e+02 4.198e+02 -2.954e+00 -2.922e+00 0.194 +++\n", + "+++ 4.199e+02 4.196e+02 -2.954e+00 -2.905e+00 0.544 +++\n", + "+++ 4.199e+02 4.194e+02 -2.954e+00 -2.897e+00 0.845 +++\n", + "+++ 4.199e+02 4.193e+02 -2.954e+00 -2.893e+00 1.04 +++\n", + "+++ 4.199e+02 4.194e+02 -2.954e+00 -2.895e+00 0.94 +++\n", + "+++ 4.199e+02 4.194e+02 -2.954e+00 -2.894e+00 0.991 +++\n", + "\t### errors for param 5 ###\n", + "+++ 4.199e+02 4.197e+02 -3.498e+00 -3.421e+00 0.356 +++\n", + "+++ 4.199e+02 4.193e+02 -3.498e+00 -3.382e+00 1.02 +++\n", + "+++ 4.199e+02 4.195e+02 -3.498e+00 -3.401e+00 0.628 +++\n", + "+++ 4.199e+02 4.194e+02 -3.498e+00 -3.392e+00 0.807 +++\n", + "+++ 4.199e+02 4.194e+02 -3.498e+00 -3.387e+00 0.909 +++\n", + "+++ 4.199e+02 4.194e+02 -3.498e+00 -3.385e+00 0.964 +++\n", + "+++ 4.199e+02 4.194e+02 -3.498e+00 -3.383e+00 0.992 +++\n", + "\t### errors for param 6 ###\n", + "+++ 4.199e+02 4.197e+02 -4.180e+00 -4.005e+00 0.338 +++\n", + "+++ 4.199e+02 4.193e+02 -4.180e+00 -3.918e+00 1.02 +++\n", + "+++ 4.199e+02 4.195e+02 -4.180e+00 -3.962e+00 0.609 +++\n", + "+++ 4.199e+02 4.195e+02 -4.180e+00 -3.940e+00 0.793 +++\n", + "+++ 4.199e+02 4.194e+02 -4.180e+00 -3.929e+00 0.899 +++\n", + "+++ 4.199e+02 4.194e+02 -4.180e+00 -3.924e+00 0.956 +++\n", + "+++ 4.199e+02 4.194e+02 -4.180e+00 -3.921e+00 0.986 +++\n", + "+++ 4.199e+02 4.194e+02 -4.180e+00 -3.920e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 4.199e+02 4.195e+02 -4.560e+00 -4.379e+00 0.634 +++\n", + "+++ 4.199e+02 4.190e+02 -4.560e+00 -4.288e+00 1.79 +++\n", + "+++ 4.199e+02 4.193e+02 -4.560e+00 -4.333e+00 1.09 +++\n", + "+++ 4.199e+02 4.194e+02 -4.560e+00 -4.356e+00 0.841 +++\n", + "+++ 4.199e+02 4.194e+02 -4.560e+00 -4.345e+00 0.961 +++\n", + "+++ 4.199e+02 4.193e+02 -4.560e+00 -4.339e+00 1.03 +++\n", + "+++ 4.199e+02 4.194e+02 -4.560e+00 -4.342e+00 0.993 +++\n", + "\t### errors for param 8 ###\n", + "+++ 4.199e+02 4.197e+02 7.176e-01 8.234e-01 0.288 +++\n", + "+++ 4.199e+02 4.195e+02 7.176e-01 8.762e-01 0.636 +++\n", + "+++ 4.199e+02 4.194e+02 7.176e-01 9.027e-01 0.85 +++\n", + "+++ 4.199e+02 4.194e+02 7.176e-01 9.159e-01 0.967 +++\n", + "+++ 4.199e+02 4.193e+02 7.176e-01 9.225e-01 1.03 +++\n", + "+++ 4.199e+02 4.194e+02 7.176e-01 9.192e-01 0.997 +++\n", + "\t### errors for param 9 ###\n", + "+++ 4.199e+02 4.194e+02 6.417e-01 7.370e-01 0.917 +++\n", + "+++ 4.199e+02 4.189e+02 6.417e-01 7.847e-01 1.94 +++\n", + "+++ 4.199e+02 4.192e+02 6.417e-01 7.609e-01 1.4 +++\n", + "+++ 4.199e+02 4.193e+02 6.417e-01 7.490e-01 1.15 +++\n", + "+++ 4.199e+02 4.193e+02 6.417e-01 7.430e-01 1.03 +++\n", + "+++ 4.199e+02 4.194e+02 6.417e-01 7.400e-01 0.972 +++\n", + "+++ 4.199e+02 4.194e+02 6.417e-01 7.415e-01 1 +++\n", + "\t### errors for param 10 ###\n", + "+++ 4.199e+02 4.194e+02 9.056e-01 1.120e+00 0.922 +++\n", + "+++ 4.199e+02 4.189e+02 9.056e-01 1.227e+00 1.85 +++\n", + "+++ 4.199e+02 4.192e+02 9.056e-01 1.173e+00 1.37 +++\n", + "+++ 4.199e+02 4.193e+02 9.056e-01 1.147e+00 1.14 +++\n", + "+++ 4.199e+02 4.193e+02 9.056e-01 1.133e+00 1.03 +++\n", + "+++ 4.199e+02 4.194e+02 9.056e-01 1.127e+00 0.975 +++\n", + "+++ 4.199e+02 4.194e+02 9.056e-01 1.130e+00 1 +++\n", + "\t### errors for param 11 ###\n", + "+++ 4.199e+02 4.193e+02 1.164e+00 1.485e+00 1.01 +++\n", + "\t### errors for param 12 ###\n", + "+++ 4.199e+02 4.195e+02 9.406e-01 1.198e+00 0.789 +++\n", + "+++ 4.199e+02 4.191e+02 9.406e-01 1.326e+00 1.57 +++\n", + "+++ 4.199e+02 4.193e+02 9.406e-01 1.262e+00 1.16 +++\n", + "+++ 4.199e+02 4.194e+02 9.406e-01 1.230e+00 0.972 +++\n", + "+++ 4.199e+02 4.193e+02 9.406e-01 1.246e+00 1.07 +++\n", + "+++ 4.199e+02 4.193e+02 9.406e-01 1.238e+00 1.02 +++\n", + "+++ 4.199e+02 4.194e+02 9.406e-01 1.234e+00 0.995 +++\n", + "\t### errors for param 13 ###\n", + "+++ 4.199e+02 4.197e+02 1.187e+00 1.403e+00 0.29 +++\n", + "+++ 4.199e+02 4.195e+02 1.187e+00 1.511e+00 0.642 +++\n", + "+++ 4.199e+02 4.194e+02 1.187e+00 1.565e+00 0.862 +++\n", + "+++ 4.199e+02 4.194e+02 1.187e+00 1.592e+00 0.982 +++\n", + "+++ 4.199e+02 4.193e+02 1.187e+00 1.606e+00 1.04 +++\n", + "+++ 4.199e+02 4.193e+02 1.187e+00 1.599e+00 1.01 +++\n", + "+++ 4.199e+02 4.194e+02 1.187e+00 1.596e+00 0.997 +++\n", + "\t### errors for param 14 ###\n", + "+++ 4.199e+02 4.195e+02 -3.113e+00 -2.282e+00 0.756 +++\n", + "+++ 4.199e+02 4.193e+02 -3.113e+00 -1.866e+00 1.19 +++\n", + "+++ 4.199e+02 4.194e+02 -3.113e+00 -2.074e+00 0.996 +++\n", + "\t### errors for param 15 ###\n", + "+++ 4.199e+02 4.197e+02 1.032e+00 1.598e+00 0.239 +++\n", + "+++ 4.199e+02 4.195e+02 1.032e+00 1.881e+00 0.622 +++\n", + "+++ 4.199e+02 4.194e+02 1.032e+00 2.023e+00 0.848 +++\n", + "+++ 4.199e+02 4.194e+02 1.032e+00 2.094e+00 0.957 +++\n", + "+++ 4.199e+02 4.193e+02 1.032e+00 2.129e+00 1.01 +++\n", + "********************\n", + "-0.249474 -0.908595 -2.07282 -2.6453 -2.95426 -3.49832 -4.17972 -4.56002 0.717619 0.641666 0.90563 1.164 0.940625 1.18717 -3.11332 1.03245\n", + "0.0235989 0.00609752 0.0240131 0.0658289 0.0603077 0.115006 0.260075 0.218293 0.201566 0.0998359 0.224231 0.321435 0.293375 0.408381 1.03966 1.09658\n", + "********************\n" + ] + } + ], + "source": [ + "%autoreload\n", + "p, pe = clag.errors(Cx, p, pe)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phi, phie = p[nfq:], pe[nfq:]\n", + "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd) \n", + "cx, cxe = p[:nfq], pe[:nfq]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 11.83713905, 3.53860689, 2.58807242, 2.14608694,\n", + " 1.11886842, 0.91105232, -1.54142564, 0.32978838])" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SaRGZ5DKmTNQFGA7MYtMkRCRuLVvCpEmWkJSX2/3jj/c7KhGRzJdPw3cfAuqw\nrKwe66w6GugG/M7HuCRHFBbajL0NDXZ65vHH4dhj/Y5KRCSz5dPw3TnAkcBE4GXgOuBjYG9il34X\nSUhhITz0EBx3HJx0Ejz7rN8RiYhktnxKRG7E+nx0AVoBWwEjgBo/g5LcU1gIDz8Mw4fDiSfCc8/5\nHZGISObKp0REJG1atYLqajjqKEtGnn/e74hERDKTEhGRFGndGh59FIYNgxNOgGnT/I5IRCTzKBER\nSaHWreGxx+DQQ63j6ksqmyci0ogSEZEU22wzG0Fz8MGWjLzyit8RiYhkDiUiImmw2WbwxBNQVgbH\nHAOvapyWiAiQX3VEROISXqp4zZo1LF68mB49etCmTRsg+fL+bdrAlCnWKnL00daBtazMy8hFRLJP\nJsz3kslU4j3PBUsUe/k/8Ouv1iryzjvwwguw//6e7FZEJKPEW+Jdp2ZE0qxtW3j6aRg6FI48Et56\ny++IRET843Ui0gs4HbgE+BtWPl1EIrRrB888A4MHwxFHWOuIiEg+8ioRGQS8DnyOzaj7T+AqNk1E\nLgJ+AL7AqpuKZKyqqipGONPojhgxgqqqKk/3v/nmVnW1pMRqjbz3nqe7FxHJCl4kIkcA7wL7YX1O\ngv1OovU/uR9oBxQDR3twbJGUqKqqYsyYMdTV1QFQV1fHmDFjUpKMTJ0Ku+0Ghx8OM2d6unsRkYzn\nNhHZCngE2AyYDxwFdHQeC0RZ/ycgOA3YES6PLZIy48ePp76+vtGy+vp6xo8f7/mx2re3ETQDB8Jh\nh8EHH3h+CBGRjOU2ERkFdAC+BvYFXgBWNrPNDOe61OWxRVJmw4YNCS13q0MHG0Gz886WjNRoKkYR\nyRNuE5Fgq8a/geVxbjPfud7R5bFFUqawMHqJnVjLvdCxoyUj/fpZSfhZs1J2KBGRjOE2EemJnYJJ\npM//T851B5fHFkmZ0aNHU1RU1GhZUVERo0ePTulxO3WyyfF694ZDDoHZs1N6OBER37lNRFo712sT\n2Ka9c73K5bFFUqaiooLKykqKi4sBKC4uprKykoqKipQfu1MnmxyvZ09LRubMSfkhRUR847ad+Ttg\nB+cS72+33Z3rb1weWySlKioqGDRoEKWlpUyePDmt1XU7d4aXX7ZE5OCD4bXXrDNrpkhVGXwRyT9u\nE5F3sSTkaOCZONYvAM52br/p8tgiOa1LF0tGDj4YDjrIkpEBA/yOyoQnGsEyztXV1ZoKQUQS5vbU\nzIPO9RkRXyuuAAAgAElEQVTAkDjW/xewi3N7kstji+S8oiJ45RXYZhtLRj75xO+IRES85bZFZCrw\nEnCYcz0OeDTs8VbAdsA+wMXA3s7yR4H3XR5bJCUiTzv07duXsWPH+nbaoWtXmD7dEpGDDoIZM2Cn\nndJ2eBGRlPJiLOIpwCtYXZB/Y60eYKdhasNuB71L6PSMSMbJxP4NW2xhyciBB9plxgwb5isiku28\nKPH+E9bicT3wM42TjvCS76uAfwBlaMSMSMK6dbNkpEsXS0YWLPA7otTPxyMiuc+r6kzrsNl2bwQO\nAPYAtgRaYpPczQKmE6ohIiJJ2GorePXVxi0jvXv7E0twPp5gKfzgfDxAWoY5u6FRPyKZI9rEdBJS\nAtTU1NRoNIBklGXLoKwMVq+2ZKRXr/THMGDAAD6J0nt25513Zt68eekPKEnBUT96n4t4K/jewrpu\n1MZaz4tTMyKSZttsY8N527a1lhFnkuC0Svd8PCKSm5SIiGSpbbe1ZGSzzSwZWbQovcf3Yz4eEck9\nXn5ibAHshc0/0wHrH9Kcazw8vkje2W47S0YOOCDUZ6RHj/Qce/To0Y36iEB65uPxUlVVFddddx1g\nnW2vuOKKjO/fIpJrvEhEtsGG7J6IJR/x9jsJoERExLXu3S0ZKSuzZOT112H77VN/3OAX9vXXX8+X\nX35JcXExl19+edZ8kWdzZ1uRXOK2s2o3YCaQ7G+wTD81pM6qkjUWL7ZkpGVLaxnp3j09x83Wzp65\n0tlWJFOlq7Pq1YSSkMnAQdgpmkJn381dRMQjPXpYy8j69dYy8o2mlWySOtuKZAa3ycDRzvUDWIXV\nGUA90OByvyKShB13tGRk7VorB79smd8RZS51thXJDG4TkS2xvh4qpyiSIYqLLRlZvdpaRr791u+I\nMtPo0aMpKipqtCzbOtuK5AK3qf9S7NTMSg9iERGP9Oq16Wiarbbybv+ZNjFgMrK9s61IrnDbWfVe\n4AzgLOd2rlFnVclqn39uHVi7dLHEZMst/Y4o82RrZ1uRTJeuzqrjgfXAaKCNy32JiMf69rUEpL4e\nDj4YfvjB74hERBpzm4h8jLWG7AS8DGhicpEM06+fTZT3ww+WjPzvf35HJCIS4kX38AeBOuBZYB4w\nB/gcWB3HtjoZK5IG/ftbMlJWBoccAtOnQ9eufkflr0AA3nwTrr22J/AB//rXdpSXw/77Q4cOfkcn\nkj+8SER2wSqrdnbuD3IuzQmgREQkbXbe2ZKRAw+EQw+FV16BiEEjeWH1ahg16n0efbQbP/9cTLt2\nK+nQ4RueeGIHHnoICgo20KfPck45pRsHHwxDh9p8PiKSGm4TkZ7Aa0D4x9lKYAXN1xIJuDy2iCRo\n4MBNk5EuXfyOKj0WLYLbb4d77oEVK/bkqKPgoovgkEO2p0WL7QkE4IsvYPr0QqZP78Ztt8G119oM\nx/vtZ6e1Dj4YBg2y6rV+ixy5tHjxYnr06JFVI5dEvFCFJRwbgRtJvtR7pioBAjU1NQGRXDJ7diBQ\nVBQI7LFHILB8ud/RpE5DQyDw8suBwDHHBAIFBYFA586BwOjRgcDChc1vu3FjIFBbGwhUVgYCw4YF\nAu3aBQIQCHTpEgiccEIgcNttgcCnn9ox/DRx4sRAz549A0CgZ8+egYkTJ/obkIijpqYmgDU6pHQ4\n2mIsERmfyoP4SImI5KxZs+xLdfDgQGDFCr+j8dbPP1ui0L+/JQ8DBwYCd94ZCKxcmfw+164NBF5/\nPRC48spAYJ99AoHCQtt39+6BwO9+Fwjcd18g8PXX3v0N8Zg4cWKgqKgo+GEfAAJFRUVKRiQjxJuI\nuK0j8ivQGtgPeMflvjKR6ohITquttdMNO+0E06ZBx45+R+TOggVw660waRKsWgXHHQcXXmiF3Qrc\nftpFWLkS3njDOv5Onw4ffWTL+/ULncY58MDUnvrK5on7gqeWlixZwhdffMG6deto3bo1vXv3Zocd\ndtCppRyQrjoiwZks1rncj4j4oKTE+onMnw/DhsEvv/gdUeIaGuD55+GII6xuysMPwwUXQF0dPP64\njRTyOgkBaN8ejjwSxo+H2bPh++/h0Uct6Zk2DU480UYm7bEHXHYZvPSSdZT1UjZP3FdeXs5xxx3H\nV199xapVq1i/fj2rVq3iq6++4rjjjlMSkkfcJiLTsFaVIR7EIiI+KC2Fl1+GefPsyzxbkpEVK2DC\nBGuBOOooq5MyaRJ89RXccANsv3164+nWDU4+Ge680zq9LlpkHWP79YP774fDD7fWkbIy6wT7zjs2\nU7Ib2T5x3/jx46mvr2+0rL6+nvHjc/Vsv0TjNhG5CfgFuBTI86oEItlr8GD7xT5njn2pr8zg2aPm\nzYPzz4fu3WHMGIv9nXfggw/gjDOgTYbUeO7RAyoq4KGHYOlS+PhjqKyETp3gpptgn31s+PTRR8O/\n/23PfUOC85Zn+8R92dyiI95xm4gsBE4EOgJvA4e5jkhEfLHnnnZKYdYs+3JctcrviEI2boSnnrJ+\nFwMH2u1LLoElS+xUzF57peb0i1cKCmDAALj4Ynj6afjxR3jvPfjLX+DXX+16t91g663h1FPh7rvh\nyy+b329FRQWVlZUUFxcDUFxcTGVlZdZM3JftLTriDbdv3dewHrHbAX2cZcuBBcRXWfUgl8dPNXVW\nlbzz9tt2GmHIEHjuOWjXzr9YfvzRTm/cfrslHXvtZbU/TjwRWrf2Ly6v/fqrteoEO75++KG1juy4\nY6jj60EHNZ5BORfqiFRVVTFmzJhGp2eKioqyKpmS2OLtrOo2EUmwIbGRAJABZYGapERE8tKbb1p/\nkaFD4dlnrahXOs2eDbfcYq0dgQCUl9voF/tMy30rVsDrr4cSk+DAmIEDQ4nJAQdk/ygnsGTk+uuv\n58svv6S4uJjLL79cSUiapDqZTVciMsPFtgHgQJfHTzUlIpK33njDkpF99rHTCalORtavhyeftATk\n7betD8gf/gBnn20dQfPZsmVWETeYmCxZYtVdBw8OtZbssIMlJh07Zk4/mabkQotOLgkmDV5+36Ur\nEck27YHrgJOwsvSfAv8AHo2xvhIRyWszZtgQ1f32s2QkFV9w330Hd90F//2vdeosK7PWj2OPBXUV\n2FQgAAsXhpKSV1+1U1jhWrcOJSVuLu3aZXbfG/FGVVUV1113HXV1dfTs2ZMrrrjCk1apeBORfHub\nPwnsAVyGzRB8GlCNddqt9jEukYxUVmb9RI46Ck44AaZM8W4CuPfft+Jjjz1mv+5/+1tLQHbZxZv9\n56qCAujd2y7nnmt9SebPtzomP//c9GXpUvj008bL1qyJfawWLbxJaNq3z4z5eWRTkf106urqGDNm\nDEDaTpHlU657JPAcUE7jFpBpwABgBzbt86IWERGs6Nnw4XYa4Iknkk9G1q61xOOWW2y4bc+eVnys\noiJ/Jt/LNOvWWe2Y5pKY5i7NDflu377pZKV7d5uIcbfd1AqTTqmszqsWkU0dj9U8mRyx/F7gYWBP\n4N10ByWSDQ45xE7NHHMMnHSSVSxNZNTKN9/YqZe77rJf7ocdZp1gjzhCv5T91rq1VYDt6rIS1MaN\nlowkmsB8+61d19XBpZfaEObDDrNKv4ceClts4c3fKdFlQi2XeBORHcJuL4mxPBlLml/FMwOB+Wza\n6jHXuR6AEhGRmA47zOp3HHusVRB97LGmk5FAAN56y1o/nnzSOruOHGmnX/r1S1vYkiYtW1qxtk6d\nktt+3TrrpPzii3a5/35rGRk82IaTDxtmQ8rVb8hbmVDLJd6CZouAOucSa3kil0VR9pVqXYH6KMvr\nwx4XkSYMG2b9RF54wQpvRStRvno1TJwIu+8O++9vFUMnTLBWkVtuURIi0bVubZME3nijTSD4zTdQ\nVWWn72691UZvdetmLXITJ8LXX/sdcW5oXJ3XvgbTXZ03kcqqBUTvU1KQxAXyq3+KSM448kjrJ/Lc\nc1bfI5iMLFpkTevbbw/nnGPX06ZZDYwLL8yNmheSPttuay1ojzxi8wi99x6MGmUJyO9/b/9fu+xi\nFXZfecX6H0n8fvzRWp6++aaC7befRcuW3wOf07Nn+qvzxpsMjMTqfgDcF7E8WYGIfaXau1jitWfE\n8gHY6ZnfA/dEPFYC1Oy333507ty50QMa4y757plnYMQIayVp0cL6fHTsCGedZfU/nKrjIp6rr7fk\nI3gaZ9kyG2p84IGh0zi9e6vTa9CqVVBbax3EZ8606+AUAq1a/ULnzgto3/5T1q59k513rqNtWzvn\nmsj3XHhdmKAVK1bw5ptvgod1RBqw5GEXYNMutpnvTmzETGca9xM5FeusujfwXsQ2GjUj0oSnnrL+\nIv36Wen1006DzTf3OyrJJ4EAzJ1rrW8vvmhVgdevt0R42DBLTA48EDp08DvS9Fi/3p6P8KRj3jwb\n5t22LZSUWL+bIUPsulev1CVsqShoFvzyHkh2JiLDgOexxOOxsOUvEhq+G4jYRomISDNWrLAOivr1\nKZlg5Up47TVLTF54wX75t2oF++4bai3Zddfc+H9taIAFCxonHbNnW22Yli3t1FV40jFgQHo7+6Zq\n+G7kF3U2eRF4GbgDmy14IdZCchhW2Cyb/zYR30SctRTxVfv2VvNm+HC7/8UX1lIybRpccw2MHQvb\nbGNJyeGH2xBht0OX0yEQsA684UnHhx/CTz/Z4336WLJx6ql2PWiQvxNWJiLfBkKdAFwPXIOVeJ/P\npi0kIiKSI3r3ts7SF15oHVrfeiuUmEyaZC0jQ4aETuMMGZIZtW3q6y3RCCYdM2dazRWwjryDB1vn\n8MGDYY89srsgYD71EUmGTs2IiOSor7+Gl16yxOTll+00Y5cu1koSbDHZbrvUx7F6dagzaTDpWLjQ\nHuvc2RKN4OmVwYPTE5MXVFlVRESkCd272/QCFRWwYYMlAcGROGefbadDdtkl1Fqy777u51pavx4+\n/rhx0jFvnlWmbdPGOpMefXSob0evXjYqLZcpERERkbxXWAh77WWXq6+G//0vNET4/vuhstL6XBx0\nUCgx6d276X02NFgflfCkY9asUGfSgQMt2bjgArseMMA61uabRBORAmySuCj1FBPeTwBQpQEREck4\nW2xhHT9PPdUSijlzQkOER42yFpRevRoPEf7pp8ZJx4cf2ukesKRl8GCrDDtkiFUezpbOpKmWTIuI\nV2enNEpFREQyXosWNgpl0CC47DKbrfjVVy0xmToVbrvN1mlwilxsvbUlG6NH2/Uee8D/V1GXTSST\niCwFvJiWT4mIiIhknQ4dbPLHY4+1fiQLFljtki23DHUmzYU6JemSTB2Rw4F5KYhFREQkqxQUQN++\ndpHkJNMXVy0ZIiIi4gmNmhEREUlC+ERva9asYfHixfTo0YM2bdoAmhw1XkpEREREkhCeaASLd1VX\nV6sAZoJyvEyKiIiIZDIlIiIiIi5UVVUxYsQIAEaMGEFVVZXPEWWXZAqaiYiICJaEjBkzhvr6egDq\n6uoYM2YMABUVFX6GljUSaREpdi6fpygWERGRrDJ+/Pj/T0KC6uvrGT9+vE8RZZ9EWkQWpSoIERGR\nbLRhQ/T6nrGWy6bUR0RERCRJhYXRf8/HWi6bUiIiIiKSpNGjR1MUMZFMUVERo0eP9imi7KNERERE\nJEkVFRVUVlZSXGyTyRcXF1NZWamOqglQ25GIiIgLFRUVDBo0iNLSUiZPnqyCZglSi4iIiIj4Ri0i\nIiIiSYica6Zv376MHTtWc80kSImIiIhIEpRoeEOnZkRERMQ3SkRERETEN0pERERExDdKRERERMQ3\nSkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdK\nRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pE\nRERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3SkRERETEN0pERERExDdKRERERMQ3+ZSI\nlAENMS5D/AtLREQkf+VTIhL0F2BoxGWeFzuurq72YjcikmX03hdJXj4mIguAmRGXVV7sWB9GIvlJ\n732R5OVjIlLgdwAiIiJi8jERuQ1YD/wEvAjs4284+S0bf0n6HXM6ju/1MbzYn5t9JLOt369zrsvG\n59fvmLPxvR+PfEpEVgATgN9jHVf/CGwPzAAO8y2qPOf3GzsZfsecjR9GSkQkUjY+v37HnI3v/XgU\npv2I3igDXo1z3UHAHGC2cwl6G5gCzAVuBF6KtYP58+fHdaAVK1ZQW1sbZ1gC2fmc+R1zOo7v9TG8\n2J+bfSSzbSLb+P0/kY2y8TnzO+Zse+/H+92Zrf0ltgaOjHPdKcDyJh6/AzgXaAusjXhsG+ADYLtE\nAxQRERHmAwcDy2KtkK0tIt8CVR7vMxBl2TJgMJaQiIiISGKW0UQSItAF+Bqo8TsQERGRfJStLSLJ\neAioA2qBeqAPMBroBvzOx7hEREQkD1yGJSHLseG73wGPA6V+BiUiIiIiIiIiIiIiIiIiIiIiOas1\ncC+wBCs1/y6wl68RiUi6nI/1V1sHjPM5FhHf5VOJ90xSCHwJ7A10woqqPYMVVROR3LYUuBJ4iuj1\ni0REfPEjsIvfQYhI2tyNWkRE1CKSIXbCWkMW+h2IiIhIOikR8V874AHgWmC1z7GIiIiklRKR9DgN\n+MW5TA1b3gqYDHwM/N2HuEQktWK990VEmtQe+CfwEvAD0EDsc7ntgQnAN8CvwCzglDiO0QJ4BJsd\nWAmhSGZIx3s/6G6s06pIXtMXYHRbAOdgLRZTnGWxerc/ic1VcxUwDPgAqAbKmznGncBWwKnYh52I\n+C8d7/2WQBts9Fwr57Y+i0Ukpq5YohDtl8uRzmORv4KmYbP6xvpw6eFst4pQs+0vwD4exCsi3kjF\nex8scWmIuGjiTRGJaQtifxjdjRUki/zQCbZyqEiZSPbSe18kDdQc6M5AYD6bnlqZ61wPSG84IpIm\neu+LeESJiDtdgfooy+vDHheR3KP3vohHlIiIiIiIb5SIuPMj0X/5FIU9LiK5R+99EY8oEXFnDtCf\nTZ/H4JwxH6c3HBFJE733RTyiRMSdKVhRoxERy0diRY7eT3dAIpIWeu+LeKTQ7wAy2BHA5kAH5/4A\nQh86U7FKii8CLwN3AB2xSevKgcOw0s6a4lsk++i9LyIZoY5QsaGNEbd3CFtvc6zM81JgDVbm+eS0\nRioiXtJ7X0RERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\nRERERETSYkfgcb+DyGQt/A5AREQkRx0KvA4U+R1IJiv0OwAREZEcUwpcCywBfvU5FhFJo5FAg3PZ\nwd9QRDJea+Az7P0yIsl9jETvuebMAF5NYP1bsefz/pREk4F0aiZ3lRH6gIjncoYvUaZGwO8AElRG\n/r5W4p8/AX2AObjvw5Bt77lMdgOwFjgNGOpzLGmhRCR/BOK4ZLtc+BsgP14r8VdnYCz2vzTO51ik\nsaXA3UAB8HefY0kL9RHJD7c7l6Z8k45AUuw+55LN8uW1En/9EegEfAE87XMssql/ARcCBwD7A2/4\nG05qKRHJD98Dn/gdhMRFr5Wk2mbAH5zbD/oZiMS0CHgb2AcYRY4nIjo1IyKSX44GumGnZZSIZK7g\na3MU9nrlLCUi0pTW2C+n14AfgHXAt8BUrCNVQRPbTsI6VtY1c4yRNN3r/qqwx8Gak/8GzAJW0Ljz\nZnP7CrcvUIU1Ta8CVgLzgf8AxU1sl0g86ZJsTMk+B0FdgH8An2JDFL8HXiY0AmMkTb8ek/DmfyTI\nq9e0DTAGqAV+cS7vAxcALZuJNWgf4B5sVMrP2Hvna+BZ7D3VyVmvFfaeagBeiGO/A8NiHRtnLJFO\ndq7nAl82s25zr3E8BgJXANOw52At9toswP4H9oyxnZfPzbbY31EL/ETos2wu8DD2/ugQsc0Q4N0E\nLkfHEWMinnSuWwEneLxvkbQoI/SmvDKJ7XfEPsTDR2tsjLj/BvZBFc0kZ53mPuhGhu27qURkI9Ab\n+9KKjOl3ce4LrFn6vij7CP/b1gJnxtg+kXjiVYa71yrRmNw+BwA7Y53qYm1/D/bh3tTrMQlv/ke8\nfE23BGZH7Cd8v0/TdALeFvtiayqWBhp3EL3RWbYe+8Jsyr+cddcB2zSzbizBL/c7m1kvntd4JE2/\nNmU0/rtjPR83xIjBi+dmPyz5aC6Go5rZf7JmkNjw3XALsdiqPYsmA6mPiETTHpgO9HTuT8F+aS7F\nflkGO1Hti/3C25/Qr8lUKQCewD5g/gM8AyzHhh8uTmA/jwHDsXifACZjX4QtgBLsfOxO2Ifsd8Dz\nScSzJIF4vBRvTG6fg07Yr9utnfuPYInA90A/4M9ABbCLl39cE7x8Tac4696M/W/XO/f/BvR3jnMO\ncFeU7Vtgicohzv3PsY7HHwKrsS/SvYG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "x=np.logspace(fqd[0],fqd[-1],200)\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n", + "fit = tophat_freq(fqd, *coeff)\n", + "\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "xscale('log'); xlim(.009,.6)\n", + "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n", + "ylabel(\"Time Lag (days)\",fontsize=20)\n", + "\n", + "\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n", + "plot(fqd,fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Nfkcrkl6U2IiIJFBWVmj5hiVLYOFCK0T+4QfIy4NGjWD6dL+jFEkfSmxERJIk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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_fit = irfft(fit)\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "ylabel(\"Response (relative)\",fontsize=20)\n", + "xlabel(\"Time (days)\",fontsize=20) \n", + "\n", + "ylim(-0.5,2)\n", + "xlim(0,7)\n", + "\n", + "plot(time_fit)\n", + "plot([3.22,3.22], [-50, 50], color='k', linestyle='-', linewidth=2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Nfkcrkl6U2IiIJFBWVmj5hiVLYOFCK0T+4QfIy4NGjWD6dL+jFEkfSmxERJIk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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_fit = irfft(fit)\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "ylabel(\"Response (relative)\",fontsize=20)\n", + "xlabel(\"Time (days)\",fontsize=20) \n", + "\n", + "ylim(-0.5,2)\n", + "xlim(0,7)\n", + "\n", + "plot(time_fit)\n", + "plot([3.22,3.22], [-50, 50], color='k', linestyle='-', linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lag/data/clag_analysis-origbins-7647A.ipynb b/lag/data/clag_analysis-origbins-7647A.ipynb new file mode 100644 index 0000000..2959a2e --- /dev/null +++ b/lag/data/clag_analysis-origbins-7647A.ipynb @@ -0,0 +1,832 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/7647A.lc\"\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n", + " 0.20739079, 0.32145572, 0.49825637])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqd\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DMGkKot994V16h/XGD5Y6iP/aRExQZAUF1u8SPuuqFNMrOcjZWE6xSuY3vtQY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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.352e-01 7.004e+01 inf -- -2.548e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 7.709e-01 6.945e+01 8.689e+01 -- -1.680e+02 -- 0.568202 0.566496 0.565546 0.565389 0.565371 0.564829 0.565108 0.56515\n", + " 3 3.365e+00 6.850e+01 8.615e+01 -- -8.181e+01 -- 0.138452 0.134629 0.131417 0.131051 0.130581 0.129376 0.130493 0.130733\n", + " 4 1.432e+00 6.742e+01 8.494e+01 -- 3.128e+00 -- -0.281314 -0.293481 -0.30187 -0.302706 -0.304046 -0.305979 -0.303068 -0.302158\n", + " 5 5.888e-01 6.660e+01 8.345e+01 -- 8.658e+01 -- -0.670439 -0.713504 -0.734283 -0.735609 -0.738394 -0.740978 -0.735503 -0.733408\n", + " 6 3.712e-01 6.580e+01 8.170e+01 -- 1.683e+02 -- -0.98186 -1.11559 -1.16661 -1.16796 -1.17265 -1.17538 -1.16791 -1.16475\n", + " 7 2.710e-01 6.441e+01 7.924e+01 -- 2.475e+02 -- -1.15319 -1.47413 -1.59924 -1.60004 -1.60675 -1.60901 -1.601 -1.59713\n", + " 8 2.138e-01 6.207e+01 7.606e+01 -- 3.236e+02 -- -1.2017 -1.73389 -2.0317 -2.03093 -2.04033 -2.04197 -2.03495 -2.02955\n", + " 9 1.767e-01 5.874e+01 7.252e+01 -- 3.961e+02 -- -1.21944 -1.84738 -2.46191 -2.45696 -2.47336 -2.47447 -2.47007 -2.4612\n", + " 10 1.505e-01 5.422e+01 6.836e+01 -- 4.645e+02 -- -1.22867 -1.87558 -2.87636 -2.86554 -2.90591 -2.90606 -2.90644 -2.89281\n", + " 11 1.304e-01 4.826e+01 6.187e+01 -- 5.263e+02 -- -1.23499 -1.89484 -3.22854 -3.22689 -3.33472 -3.3325 -3.34386 -3.32489\n", + " 12 1.163e-01 4.093e+01 5.179e+01 -- 5.781e+02 -- -1.23852 -1.90721 -3.44389 -3.49376 -3.74893 -3.73895 -3.77938 -3.75848\n", + " 13 9.980e-02 3.115e+01 3.779e+01 -- 6.159e+02 -- -1.23702 -1.90846 -3.49545 -3.63342 -4.12276 -4.08853 -4.19527 -4.19544\n", + " 14 7.004e-02 1.776e+01 2.014e+01 -- 6.360e+02 -- -1.23105 -1.90503 -3.4814 -3.68042 -4.41637 -4.31891 -4.53403 -4.61415\n", + " 15 2.773e-02 5.191e+00 5.391e+00 -- 6.414e+02 -- -1.22657 -1.90214 -3.47116 -3.69656 -4.59718 -4.39786 -4.69506 -4.93731\n", + " 16 1.057e-02 6.378e-01 4.477e-01 -- 6.419e+02 -- -1.22775 -1.90089 -3.46879 -3.70567 -4.68256 -4.39619 -4.69519 -5.07423\n", + " 17 7.888e-03 4.329e-01 3.041e-02 -- 6.419e+02 -- -1.22951 -1.90056 -3.46786 -3.71222 -4.73204 -4.38418 -4.69485 -5.07494\n", + " 18 5.819e-03 2.998e-01 1.477e-02 -- 6.419e+02 -- -1.2294 -1.90021 -3.46615 -3.71187 -4.76937 -4.37843 -4.69171 -5.07347\n", + " 19 4.291e-03 2.100e-01 7.619e-03 -- 6.419e+02 -- -1.22925 -1.89995 -3.46439 -3.71159 -4.79712 -4.37418 -4.69047 -5.07198\n", + " 20 3.153e-03 1.486e-01 3.972e-03 -- 6.419e+02 -- -1.22914 -1.89977 -3.46327 -3.71125 -4.8177 -4.3712 -4.68937 -5.07092\n", + " 21 2.314e-03 1.059e-01 2.083e-03 -- 6.419e+02 -- -1.22907 -1.89964 -3.46247 -3.71101 -4.83289 -4.36903 -4.68864 -5.07007\n", + " 22 1.695e-03 7.595e-02 1.096e-03 -- 6.419e+02 -- -1.22901 -1.89955 -3.46191 -3.71083 -4.84408 -4.36746 -4.68811 -5.06945\n", + " 23 1.239e-03 5.467e-02 5.783e-04 -- 6.419e+02 -- -1.22898 -1.89949 -3.46151 -3.7107 -4.85229 -4.36633 -4.68772 -5.06898\n", + " 24 9.051e-04 3.947e-02 3.054e-04 -- 6.419e+02 -- -1.22895 -1.89945 -3.46122 -3.7106 -4.8583 -4.3655 -4.68745 -5.06863\n", + " 25 6.605e-04 2.855e-02 1.614e-04 -- 6.419e+02 -- -1.22893 -1.89941 -3.46102 -3.71052 -4.8627 -4.3649 -4.68725 -5.06838\n", + " 26 4.816e-04 2.069e-02 8.536e-05 -- 6.419e+02 -- -1.22891 -1.89939 -3.46087 -3.71047 -4.86591 -4.36446 -4.68711 -5.06819\n", + "********************\n", + "-1.22891 -1.89939 -3.46087 -3.71047 -4.86591 -4.36446 -4.68711 -5.06819\n", + "0.234933 0.200019 0.259825 0.196536 0.332059 0.153872 0.153084 0.180761\n", + "0.000177336 0.000464495 0.00122062 -0.000734339 -0.0206875 0.00948273 0.00295267 0.00321028\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 6.419e+02 6.415e+02 -1.229e+00 -9.940e-01 0.854 +++\n", + "+++ 6.419e+02 6.411e+02 -1.229e+00 -8.765e-01 1.78 +++\n", + "+++ 6.419e+02 6.413e+02 -1.229e+00 -9.352e-01 1.28 +++\n", + "+++ 6.419e+02 6.414e+02 -1.229e+00 -9.646e-01 1.06 +++\n", + "+++ 6.419e+02 6.415e+02 -1.229e+00 -9.793e-01 0.954 +++\n", + "+++ 6.419e+02 6.414e+02 -1.229e+00 -9.719e-01 1.01 +++\n", + "\t### errors for param 1 ###\n", + "+++ 6.419e+02 6.415e+02 -1.899e+00 -1.699e+00 0.892 +++\n", + "+++ 6.419e+02 6.410e+02 -1.899e+00 -1.599e+00 1.89 +++\n", + "+++ 6.419e+02 6.413e+02 -1.899e+00 -1.649e+00 1.35 +++\n", + "+++ 6.419e+02 6.414e+02 -1.899e+00 -1.674e+00 1.11 +++\n", + "+++ 6.419e+02 6.414e+02 -1.899e+00 -1.687e+00 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 6.419e+02 6.414e+02 -3.461e+00 -3.201e+00 1.01 +++\n", + "+++ 6.419e+02 6.418e+02 -3.461e+00 -3.331e+00 0.268 +++\n", + "+++ 6.419e+02 6.417e+02 -3.461e+00 -3.266e+00 0.586 +++\n", + "+++ 6.419e+02 6.416e+02 -3.461e+00 -3.233e+00 0.788 +++\n", + "+++ 6.419e+02 6.415e+02 -3.461e+00 -3.217e+00 0.898 +++\n", + "+++ 6.419e+02 6.415e+02 -3.461e+00 -3.209e+00 0.956 +++\n", + "+++ 6.419e+02 6.415e+02 -3.461e+00 -3.205e+00 0.985 +++\n", + "+++ 6.419e+02 6.414e+02 -3.461e+00 -3.203e+00 1 +++\n", + "\t### errors for param 3 ###\n", + "+++ 6.419e+02 6.415e+02 -3.710e+00 -3.514e+00 0.934 +++\n", + "+++ 6.419e+02 6.409e+02 -3.710e+00 -3.416e+00 2.02 +++\n", + "+++ 6.419e+02 6.412e+02 -3.710e+00 -3.465e+00 1.43 +++\n", + "+++ 6.419e+02 6.414e+02 -3.710e+00 -3.489e+00 1.17 +++\n", + "+++ 6.419e+02 6.414e+02 -3.710e+00 -3.502e+00 1.05 +++\n", + "+++ 6.419e+02 6.415e+02 -3.710e+00 -3.508e+00 0.991 +++\n", + "\t### errors for param 4 ###\n", + "+++ 6.419e+02 6.417e+02 -4.868e+00 -4.535e+00 0.545 +++\n", + "+++ 6.419e+02 6.414e+02 -4.868e+00 -4.369e+00 1.17 +++\n", + "+++ 6.419e+02 6.416e+02 -4.868e+00 -4.452e+00 0.745 +++\n", + "+++ 6.419e+02 6.415e+02 -4.868e+00 -4.411e+00 0.943 +++\n", + "+++ 6.419e+02 6.414e+02 -4.868e+00 -4.390e+00 1.05 +++\n", + "+++ 6.419e+02 6.414e+02 -4.868e+00 -4.400e+00 0.998 +++\n", + "\t### errors for param 5 ###\n", + "+++ 6.419e+02 6.415e+02 -4.364e+00 -4.210e+00 0.842 +++\n", + "+++ 6.419e+02 6.410e+02 -4.364e+00 -4.133e+00 1.87 +++\n", + "+++ 6.419e+02 6.413e+02 -4.364e+00 -4.172e+00 1.32 +++\n", + "+++ 6.419e+02 6.414e+02 -4.364e+00 -4.191e+00 1.07 +++\n", + "+++ 6.419e+02 6.415e+02 -4.364e+00 -4.201e+00 0.951 +++\n", + "+++ 6.419e+02 6.414e+02 -4.364e+00 -4.196e+00 1.01 +++\n", + "\t### errors for param 6 ###\n", + "+++ 6.419e+02 6.418e+02 -4.687e+00 -4.610e+00 0.306 +++\n", + "+++ 6.419e+02 6.416e+02 -4.687e+00 -4.572e+00 0.686 +++\n", + "+++ 6.419e+02 6.415e+02 -4.687e+00 -4.553e+00 0.933 +++\n", + "+++ 6.419e+02 6.414e+02 -4.687e+00 -4.544e+00 1.07 +++\n", + "+++ 6.419e+02 6.414e+02 -4.687e+00 -4.548e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 6.419e+02 6.418e+02 -5.068e+00 -4.978e+00 0.293 +++\n", + "+++ 6.419e+02 6.416e+02 -5.068e+00 -4.932e+00 0.662 +++\n", + "+++ 6.419e+02 6.415e+02 -5.068e+00 -4.910e+00 0.903 +++\n", + "+++ 6.419e+02 6.414e+02 -5.068e+00 -4.899e+00 1.04 +++\n", + "+++ 6.419e+02 6.415e+02 -5.068e+00 -4.904e+00 0.969 +++\n", + "+++ 6.419e+02 6.414e+02 -5.068e+00 -4.901e+00 1 +++\n", + "********************\n", + "-1.2289 -1.89937 -3.46076 -3.71043 -4.86825 -4.36415 -4.687 -5.06805\n", + "0.256958 0.212521 0.257777 0.202656 0.468138 0.168247 0.138715 0.166617\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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K99xzD3v27KGpqYnTp0/z1ltvcfnll7Nq1So2bNjAF7/4xZIGhHw+TzKZJJPJ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\n", 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2.03535\n", + " 7 1.836e+01 1.371e+01 1.807e+00 -- 6.935e+02 -- -0.66115 -1.2633 -2.64623 -2.94323 -3.77682 -3.76343 -4.4638 -6.53403 0.129539 0.350049 0.485494 0.345407 0.493126 0.299979 0.241096 -2.67313\n", + " 9 2.046e+01 1.572e+01 1.610e+00 -- 6.951e+02 -- -0.637504 -1.24117 -2.63838 -2.94489 -3.76048 -3.76869 -4.47217 -6.83403 0.135502 0.402878 0.588 0.418868 0.588605 0.358702 0.28198 -1.29684\n", + " 11 3.541e+01 1.788e+01 1.456e+00 -- 6.966e+02 -- -0.617387 -1.22121 -2.62698 -2.94377 -3.7434 -3.77151 -4.47878 -7.13403 0.140359 0.445949 0.67527 0.486465 0.667539 0.412608 0.319287 1.3562\n", + " 13 8.288e+02 2.020e+01 1.291e+00 -- 6.978e+02 -- -0.60014 -1.20337 -2.61379 -2.94045 -3.72685 -3.77239 -4.48389 -6.83403 0.144381 0.48146 0.748601 0.54777 0.732508 0.461524 0.352994 -0.12492\n", + " 15 6.267e+01 2.266e+01 1.179e+00 -- 6.990e+02 -- -0.585259 -1.18747 -2.60004 -2.9355 -3.71148 -3.77179 -4.48775 -7.13403 0.147758 0.511044 0.809901 0.602807 0.786107 0.50562 0.383157 -2.33832\n", + " 17 5.670e+01 2.527e+01 1.086e+00 -- 7.001e+02 -- -0.572351 -1.17335 -2.58649 -2.92939 -3.6976 -3.77007 -4.49056 -7.43403 0.150636 0.53593 0.861183 0.651797 0.830596 0.545107 0.409928 1.85585\n", + " 19 6.344e+01 2.802e+01 9.867e-01 -- 7.011e+02 -- -0.561107 -1.1608 -2.57356 -2.92251 -3.68525 -3.76757 -4.49259 -7.13403 0.153107 0.557037 0.9042 0.695116 0.867766 0.580281 0.433567 -2.38363\n", + " 21 7.571e+01 3.089e+01 9.220e-01 -- 7.020e+02 -- -0.551274 -1.14965 -2.56148 -2.91518 -3.6744 -3.76453 -4.49398 -7.43403 0.155257 0.575074 0.940495 0.733284 0.89908 0.611551 0.454317 1.34492\n", + " 22 4.003e-01 2.319e+04 1.251e+01 -- 6.895e+02 -- -0.46504 -1.05058 -2.44998 -2.83968 -3.57966 -3.73073 -4.50329 -4.43403 0.174105 0.730184 1.24835 1.06835 1.1647 0.888615 0.636009 2.63401\n", + " 23 3.602e-01 6.251e+01 2.137e+01 -- 7.109e+02 -- -0.479175 -1.05499 -2.40759 -2.71044 -3.63212 -3.67843 -4.55868 -4.7139 0.243794 0.662729 1.29622 1.06228 1.10731 0.828249 0.484699 2.85439\n", + " 24 1.324e-01 3.847e+01 7.851e-01 -- 7.117e+02 -- -0.473576 -1.05695 -2.43181 -2.73925 -3.6054 -3.70634 -4.40662 -4.87766 0.183146 0.709437 1.27054 0.992921 1.08464 0.839337 0.659286 2.73599\n", + " 25 1.821e-01 8.521e+00 1.578e-01 -- 7.118e+02 -- -0.475023 -1.05607 -2.43744 -2.74267 -3.61814 -3.70001 -4.50477 -4.97221 0.199534 0.699185 1.24387 0.993811 1.07721 0.861017 0.572018 2.42745\n", + " 26 2.014e-01 5.280e+00 6.998e-02 -- 7.119e+02 -- -0.474604 -1.05634 -2.44031 -2.7453 -3.60481 -3.70497 -4.4716 -5.01385 0.194772 0.702203 1.23578 0.990364 1.07625 0.849061 0.623206 1.98547\n", + " 27 1.559e-01 8.470e-01 5.248e-02 -- 7.119e+02 -- -0.474838 -1.05633 -2.44133 -2.7456 -3.61013 -3.70353 -4.4991 -4.96485 0.195398 0.70079 1.23042 0.988953 1.0694 0.8531 0.602048 1.5856\n", + " 28 1.154e-01 1.799e+00 3.734e-02 -- 7.120e+02 -- -0.474876 -1.05636 -2.44221 -2.74624 -3.6043 -3.70638 -4.49561 -4.88587 0.194273 0.700465 1.22605 0.986774 1.06378 0.847619 0.626675 1.33843\n", + " 29 7.922e-02 1.175e+00 2.365e-02 -- 7.120e+02 -- -0.474959 -1.05637 -2.44284 -2.74635 -3.60609 -3.70691 -4.50684 -4.82345 0.193792 0.699958 1.22274 0.984729 1.05776 0.847888 0.625222 1.18395\n", + " 30 5.978e-02 9.905e-01 1.545e-02 -- 7.120e+02 -- -0.475014 -1.05638 -2.44337 -2.74658 -3.60376 -3.70854 -4.50832 -4.7745 0.193235 0.699587 1.22004 0.983324 1.05292 0.845739 0.637938 1.09015\n", + " 31 4.332e-02 8.004e-01 9.908e-03 -- 7.120e+02 -- -0.475061 -1.05639 -2.4438 -2.74665 -3.60426 -3.70921 -4.5135 -4.73769 0.192849 0.699306 1.21807 0.982147 1.04865 0.845744 0.640636 1.02498\n", + " 32 3.306e-02 6.233e-01 6.372e-03 -- 7.120e+02 -- -0.475099 -1.05639 -2.44413 -2.74676 -3.60331 -3.71016 -4.51536 -4.70979 0.19251 0.699065 1.21644 0.981352 1.04524 0.844821 0.647708 0.980583\n", + " 33 2.501e-02 5.124e-01 4.073e-03 -- 7.120e+02 -- -0.475129 -1.0564 -2.4444 -2.74681 -3.6034 -3.71069 -4.51806 -4.68842 0.192252 0.698889 1.21522 0.980704 1.04238 0.844743 0.650651 0.948169\n", + " 34 1.931e-02 3.988e-01 2.597e-03 -- 7.120e+02 -- -0.475154 -1.0564 -2.44461 -2.74688 -3.603 -3.71126 -4.51949 -4.67205 0.192037 0.69874 1.21423 0.980247 1.04011 0.844313 0.654788 0.924451\n", + " 35 1.493e-02 3.244e-01 1.651e-03 -- 7.120e+02 -- -0.475173 -1.0564 -2.44478 -2.74692 -3.60297 -3.71164 -4.52101 -4.65936 0.19187 0.698628 1.21347 0.97988 1.03824 0.844226 0.657098 0.906602\n", + " 36 1.163e-02 2.542e-01 1.047e-03 -- 7.120e+02 -- -0.475189 -1.0564 -2.44491 -2.74696 -3.60279 -3.71199 -4.52198 -4.64952 0.191733 0.698535 1.21286 0.979613 1.03676 0.844016 0.659591 0.893069\n", + " 37 9.093e-03 2.048e-01 6.628e-04 -- 7.120e+02 -- -0.475201 -1.0564 -2.44501 -2.74699 -3.60275 -3.71224 -4.52287 -4.64183 0.191626 0.698464 1.21238 0.9794 1.03556 0.843944 0.661216 0.882678\n", + " 38 7.134e-03 1.613e-01 4.191e-04 -- 7.120e+02 -- -0.475212 -1.0564 -2.4451 -2.74701 -3.60265 -3.71246 -4.52351 -4.63582 0.191539 0.698406 1.212 0.979241 1.03461 0.843836 0.662747 0.874652\n", + " 39 5.608e-03 1.291e-01 2.647e-04 -- 7.120e+02 -- -0.475219 -1.05641 -2.44516 -2.74703 -3.60262 -3.71262 -4.52405 -4.6311 0.191471 0.698362 1.21171 0.979114 1.03385 0.843785 0.663834 0.868413\n", + " 40 4.418e-03 1.020e-01 1.670e-04 -- 7.120e+02 -- -0.475226 -1.05641 -2.44521 -2.74705 -3.60257 -3.71276 -4.52445 -4.62739 0.191416 0.698326 1.21147 0.979017 1.03325 0.843726 0.664783 0.863542\n", + " 41 3.485e-03 8.130e-02 1.053e-04 -- 7.120e+02 -- -0.475231 -1.05641 -2.44525 -2.74706 -3.60255 -3.71286 -4.52479 -4.62447 0.191373 0.698297 1.21128 0.978941 1.03276 0.843692 0.665491 0.859727\n", + " 42 2.753e-03 6.436e-02 6.636e-05 -- 7.120e+02 -- -0.475235 -1.05641 -2.44529 -2.74707 -3.60252 -3.71295 -4.52504 -4.62216 0.191338 0.698275 1.21113 0.978881 1.03238 0.843658 0.666084 0.856731\n", + "********************\n", + "-0.475235 -1.05641 -2.44529 -2.74707 -3.60252 -3.71295 -4.52504 -4.62216 0.191338 0.698275 1.21113 0.978881 1.03238 0.843658 0.666084 0.856731\n", + "0.00699188 0.00954622 0.0841246 0.0745967 0.124782 0.148601 0.368254 0.278607 0.0954532 0.101842 0.352028 0.291475 0.397613 0.407863 0.879459 0.681533\n", + "-0.064356 -0.00407965 -0.00327632 -0.000885349 0.00210339 -0.00248053 -0.0014596 0.0217369 -0.00315135 -0.00176922 -0.000952405 -0.000682482 -0.00194583 -0.000139415 0.000655392 -0.00209613\n", + "********************\n" + ] + } + ], + "source": [ + "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n", + "p = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a good starting point generally\n", + "p, pe = clag.optimize(Cx, p)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Line magic function `%autoreload` not found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 7.120e+02 7.118e+02 -4.752e-01 -4.717e-01 0.444 +++\n", + "+++ 7.120e+02 7.114e+02 -4.752e-01 -4.700e-01 1.35 +++\n", + "+++ 7.120e+02 7.116e+02 -4.752e-01 -4.709e-01 0.796 +++\n", + "+++ 7.120e+02 7.115e+02 -4.752e-01 -4.704e-01 1.04 +++\n", + "+++ 7.120e+02 7.116e+02 -4.752e-01 -4.706e-01 0.912 +++\n", + "+++ 7.120e+02 7.116e+02 -4.752e-01 -4.705e-01 0.975 +++\n", + "+++ 7.120e+02 7.115e+02 -4.752e-01 -4.705e-01 1.01 +++\n", + "\t### errors for param 1 ###\n", + "+++ 7.120e+02 7.118e+02 -1.056e+00 -1.052e+00 0.401 +++\n", + "+++ 7.120e+02 7.115e+02 -1.056e+00 -1.049e+00 1.14 +++\n", + "+++ 7.120e+02 7.117e+02 -1.056e+00 -1.050e+00 0.702 +++\n", + "+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.903 +++\n", + "+++ 7.120e+02 7.115e+02 -1.056e+00 -1.050e+00 1.02 +++\n", + "+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.959 +++\n", + "+++ 7.120e+02 7.116e+02 -1.056e+00 -1.050e+00 0.988 +++\n", + "+++ 7.120e+02 7.115e+02 -1.056e+00 -1.050e+00 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 7.120e+02 7.119e+02 -2.445e+00 -2.403e+00 0.347 +++\n", + "+++ 7.120e+02 7.115e+02 -2.445e+00 -2.382e+00 1.08 +++\n", + "+++ 7.120e+02 7.117e+02 -2.445e+00 -2.393e+00 0.631 +++\n", + "+++ 7.120e+02 7.116e+02 -2.445e+00 -2.387e+00 0.829 +++\n", + "+++ 7.120e+02 7.116e+02 -2.445e+00 -2.385e+00 0.946 +++\n", + "+++ 7.120e+02 7.115e+02 -2.445e+00 -2.384e+00 1.01 +++\n", + "\t### errors for param 3 ###\n", + "+++ 7.120e+02 7.119e+02 -2.747e+00 -2.710e+00 0.245 +++\n", + "+++ 7.120e+02 7.117e+02 -2.747e+00 -2.691e+00 0.695 +++\n", + "+++ 7.120e+02 7.115e+02 -2.747e+00 -2.682e+00 1.08 +++\n", + "+++ 7.120e+02 7.116e+02 -2.747e+00 -2.686e+00 0.87 +++\n", + "+++ 7.120e+02 7.116e+02 -2.747e+00 -2.684e+00 0.97 +++\n", + "+++ 7.120e+02 7.115e+02 -2.747e+00 -2.683e+00 1.02 +++\n", + "+++ 7.120e+02 7.115e+02 -2.747e+00 -2.684e+00 0.996 +++\n", + "\t### errors for param 4 ###\n", + "+++ 7.120e+02 7.117e+02 -3.603e+00 -3.540e+00 0.596 +++\n", + "+++ 7.120e+02 7.111e+02 -3.603e+00 -3.509e+00 1.86 +++\n", + "+++ 7.120e+02 7.115e+02 -3.603e+00 -3.525e+00 1.09 +++\n", + "+++ 7.120e+02 7.116e+02 -3.603e+00 -3.532e+00 0.813 +++\n", + "+++ 7.120e+02 7.116e+02 -3.603e+00 -3.528e+00 0.942 +++\n", + "+++ 7.120e+02 7.115e+02 -3.603e+00 -3.526e+00 1.01 +++\n", + "+++ 7.120e+02 7.116e+02 -3.603e+00 -3.527e+00 0.976 +++\n", + "+++ 7.120e+02 7.115e+02 -3.603e+00 -3.527e+00 0.994 +++\n", + "\t### errors for param 5 ###\n", + "+++ 7.120e+02 7.119e+02 -3.713e+00 -3.639e+00 0.357 +++\n", + "+++ 7.120e+02 7.116e+02 -3.713e+00 -3.602e+00 0.966 +++\n", + "+++ 7.120e+02 7.113e+02 -3.713e+00 -3.583e+00 1.46 +++\n", + "+++ 7.120e+02 7.114e+02 -3.713e+00 -3.592e+00 1.19 +++\n", + "+++ 7.120e+02 7.115e+02 -3.713e+00 -3.597e+00 1.08 +++\n", + "+++ 7.120e+02 7.115e+02 -3.713e+00 -3.599e+00 1.02 +++\n", + "+++ 7.120e+02 7.116e+02 -3.713e+00 -3.600e+00 0.992 +++\n", + "\t### errors for param 6 ###\n", + "+++ 7.120e+02 7.117e+02 -4.525e+00 -4.341e+00 0.622 +++\n", + "+++ 7.120e+02 7.111e+02 -4.525e+00 -4.249e+00 1.92 +++\n", + "+++ 7.120e+02 7.115e+02 -4.525e+00 -4.295e+00 1.13 +++\n", + "+++ 7.120e+02 7.116e+02 -4.525e+00 -4.318e+00 0.843 +++\n", + "+++ 7.120e+02 7.116e+02 -4.525e+00 -4.306e+00 0.978 +++\n", + "+++ 7.120e+02 7.115e+02 -4.525e+00 -4.301e+00 1.05 +++\n", + "+++ 7.120e+02 7.115e+02 -4.525e+00 -4.304e+00 1.01 +++\n", + "+++ 7.120e+02 7.115e+02 -4.525e+00 -4.305e+00 0.996 +++\n", + "\t### errors for param 7 ###\n", + "+++ 7.120e+02 7.118e+02 -4.620e+00 -4.343e+00 0.54 +++\n", + "+++ 7.120e+02 -inf -4.620e+00 -4.204e+00 inf +++\n", + "+++ 7.120e+02 7.115e+02 -4.620e+00 -4.274e+00 1.18 +++\n", + "+++ 7.120e+02 7.116e+02 -4.620e+00 -4.308e+00 0.803 +++\n", + "+++ 7.120e+02 7.116e+02 -4.620e+00 -4.291e+00 0.974 +++\n", + "+++ 7.120e+02 7.115e+02 -4.620e+00 -4.282e+00 1.07 +++\n", + "+++ 7.120e+02 7.115e+02 -4.620e+00 -4.287e+00 1.02 +++\n", + "+++ 7.120e+02 7.115e+02 -4.620e+00 -4.289e+00 0.998 +++\n", + "\t### errors for param 8 ###\n", + "+++ 7.120e+02 7.119e+02 1.913e-01 2.390e-01 0.269 +++\n", + "+++ 7.120e+02 7.117e+02 1.913e-01 2.629e-01 0.595 +++\n", + "+++ 7.120e+02 7.116e+02 1.913e-01 2.748e-01 0.803 +++\n", + "+++ 7.120e+02 7.116e+02 1.913e-01 2.808e-01 0.917 +++\n", + "+++ 7.120e+02 7.116e+02 1.913e-01 2.838e-01 0.977 +++\n", + "+++ 7.120e+02 7.115e+02 1.913e-01 2.853e-01 1.01 +++\n", + "\t### errors for param 9 ###\n", + "+++ 7.120e+02 7.119e+02 6.983e-01 7.492e-01 0.276 +++\n", + "+++ 7.120e+02 7.117e+02 6.983e-01 7.746e-01 0.62 +++\n", + "+++ 7.120e+02 7.116e+02 6.983e-01 7.874e-01 0.82 +++\n", + "+++ 7.120e+02 7.116e+02 6.983e-01 7.937e-01 0.936 +++\n", + "+++ 7.120e+02 7.115e+02 6.983e-01 7.969e-01 0.997 +++\n", + "\t### errors for param 10 ###\n", + "+++ 7.120e+02 7.116e+02 1.211e+00 1.563e+00 0.938 +++\n", + "+++ 7.120e+02 7.111e+02 1.211e+00 1.739e+00 1.91 +++\n", + "+++ 7.120e+02 7.113e+02 1.211e+00 1.651e+00 1.4 +++\n", + "+++ 7.120e+02 7.115e+02 1.211e+00 1.607e+00 1.16 +++\n", + "+++ 7.120e+02 7.115e+02 1.211e+00 1.585e+00 1.05 +++\n", + "+++ 7.120e+02 7.116e+02 1.211e+00 1.574e+00 0.993 +++\n", + "\t### errors for param 11 ###\n", + "+++ 7.120e+02 7.117e+02 9.788e-01 1.270e+00 0.74 +++\n", + "+++ 7.120e+02 7.113e+02 9.788e-01 1.416e+00 1.49 +++\n", + "+++ 7.120e+02 7.115e+02 9.788e-01 1.343e+00 1.1 +++\n", + "+++ 7.120e+02 7.116e+02 9.788e-01 1.307e+00 0.912 +++\n", + "+++ 7.120e+02 7.115e+02 9.788e-01 1.325e+00 1 +++\n", + "\t### errors for param 12 ###\n", + "+++ 7.120e+02 7.116e+02 1.032e+00 1.430e+00 0.828 +++\n", + "+++ 7.120e+02 7.111e+02 1.032e+00 1.628e+00 1.84 +++\n", + "+++ 7.120e+02 7.114e+02 1.032e+00 1.529e+00 1.29 +++\n", + "+++ 7.120e+02 7.115e+02 1.032e+00 1.479e+00 1.05 +++\n", + "+++ 7.120e+02 7.116e+02 1.032e+00 1.455e+00 0.935 +++\n", + "+++ 7.120e+02 7.116e+02 1.032e+00 1.467e+00 0.99 +++\n", + "\t### errors for param 13 ###\n", + "+++ 7.120e+02 7.119e+02 8.436e-01 1.048e+00 0.276 +++\n", + "+++ 7.120e+02 7.117e+02 8.436e-01 1.150e+00 0.604 +++\n", + "+++ 7.120e+02 7.116e+02 8.436e-01 1.201e+00 0.808 +++\n", + "+++ 7.120e+02 7.116e+02 8.436e-01 1.226e+00 0.92 +++\n", + "+++ 7.120e+02 7.116e+02 8.436e-01 1.239e+00 0.977 +++\n", + "+++ 7.120e+02 7.115e+02 8.436e-01 1.245e+00 1.01 +++\n", + "\t### errors for param 14 ###\n", + "+++ 7.120e+02 7.115e+02 6.665e-01 1.546e+00 1 +++\n", + "\t### errors for param 15 ###\n", + "********************\n", + "-0.475238 -1.05641 -2.44531 -2.74708 -3.6025 -3.71301 -4.52525 -4.62034 0.191311 0.698257 1.21102 0.978834 1.03208 0.843636 0.666538 0.854372\n", + "0.00475354 0.00682415 0.0617908 0.0635266 0.0755408 0.112636 0.220195 0.331549 0.0939739 0.0986604 0.363072 0.346135 0.434851 0.401558 0.879854 3.38957\n", + "********************\n" + ] + } + ], + "source": [ + "%autoreload\n", + "p, pe = clag.errors(Cx, p, pe)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phi, phie = p[nfq:], pe[nfq:]\n", + "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd) \n", + "cx, cxe = p[:nfq], pe[:nfq]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.15567684, 3.85069114, 3.46079358, 1.80469275, 1.2276475 ,\n", + " 0.64741975, 0.33000748, 0.27290686])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ww+xyjUiuc5uIBGoGzonyenhn1UXAbGAT4GyX+xYRSdiGDRvimu+VPn2sxkiv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\nlMDjSy/B8uXBYc95eXYjwUi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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "x=np.logspace(fqd[0],fqd[-1],200)\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n", + "fit = tophat_freq(fqd, *coeff)\n", + "\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "xscale('log'); xlim(.009,.6)\n", + "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n", + "ylabel(\"Time Lag (days)\",fontsize=20)\n", + "\n", + "\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n", + "plot(fqd,fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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srrgCWrSwvYiI5KYKLu/fjo18qhd2vJuzXwV8F3YuUHsTz2rgXjoO6IklMU84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\n2eimPff0O7rkq1DB+t60aBH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WsQhVF/tcbvA7EB80iHCsGtbT/7UUx5KO3sdqcXLVC1ji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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_fit = irfft(fit)\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "ylabel(\"Response (relative)\",fontsize=20)\n", + "xlabel(\"Time (days)\",fontsize=20) \n", + "\n", + "ylim(-0.5,2)\n", + "xlim(0,7)\n", + "\n", + "plot(time_fit)\n", + "plot([3.99,3.99], [-50, 50], color='k', linestyle='-', linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lag/data/clag_analysis-origbins-9157A.ipynb b/lag/data/clag_analysis-origbins-9157A.ipynb new file mode 100644 index 0000000..1fa3cb8 --- /dev/null +++ b/lag/data/clag_analysis-origbins-9157A.ipynb @@ -0,0 +1,825 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\nwYOIiIikRcMWUhRCoVA496I7LvfCr9w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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/9157A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n", + " 0.20739079, 0.32145572, 0.49825637])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqd\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Fd+kb0Rc/WOok/nOTMEGRFRRYP0v4qqtSzKhkgqux7GKVzG9+uTn4/twZ58VR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\nAYP9MYqIuElpaWnc5w4ePGj79s6XsCr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.359e-01 7.161e+01 inf -- -2.181e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 7.742e-01 7.111e+01 8.740e+01 -- -1.307e+02 -- 0.564074 0.564103 0.564133 0.564559 0.565233 0.56464 0.564848 0.564132\n", + " 3 3.436e+00 7.050e+01 8.697e+01 -- -4.372e+01 -- 0.13087 0.128441 0.128217 0.128912 0.13058 0.129543 0.129728 0.127363\n", + " 4 1.429e+00 6.964e+01 8.635e+01 -- 4.262e+01 -- -0.295217 -0.304667 -0.307069 -0.306748 -0.303835 -0.30536 -0.305448 -0.310246\n", + " 5 5.887e-01 6.841e+01 8.530e+01 -- 1.279e+02 -- -0.704126 -0.731704 -0.741301 -0.742618 -0.738088 -0.740228 -0.740922 -0.748518\n", + " 6 3.715e-01 6.674e+01 8.354e+01 -- 2.115e+02 -- -1.07182 -1.14488 -1.17363 -1.17921 -1.17251 -1.17519 -1.17707 -1.18728\n", + " 7 2.720e-01 6.454e+01 8.085e+01 -- 2.923e+02 -- -1.35117 -1.52737 -1.60252 -1.61695 -1.60778 -1.61012 -1.61437 -1.62665\n", + " 8 2.151e-01 6.151e+01 7.746e+01 -- 3.698e+02 -- -1.49897 -1.84925 -2.02742 -2.05591 -2.04475 -2.04475 -2.05353 -2.06755\n", + " 9 1.795e-01 5.724e+01 7.348e+01 -- 4.433e+02 -- -1.55318 -2.06643 -2.44779 -2.49458 -2.48325 -2.47853 -2.49523 -2.5119\n", + " 10 1.552e-01 5.129e+01 6.861e+01 -- 5.119e+02 -- -1.57472 -2.15815 -2.85964 -2.92873 -2.92176 -2.90848 -2.9396 -2.96289\n", + " 11 1.358e-01 4.343e+01 6.173e+01 -- 5.736e+02 -- -1.57452 -2.1858 -3.24732 -3.34792 -3.35575 -3.328 -3.38557 -3.42282\n", + " 12 1.168e-01 3.415e+01 5.061e+01 -- 6.242e+02 -- -1.56805 -2.20666 -3.56401 -3.72207 -3.774 -3.72255 -3.82733 -3.8877\n", + " 13 9.496e-02 2.420e+01 3.428e+01 -- 6.585e+02 -- -1.56623 -2.22075 -3.74568 -3.98216 -4.14916 -4.05587 -4.24428 -4.34169\n", + " 14 6.665e-02 1.351e+01 1.640e+01 -- 6.749e+02 -- -1.5696 -2.22354 -3.78328 -4.0829 -4.4261 -4.2599 -4.58517 -4.75399\n", + " 15 2.964e-02 4.358e+00 4.427e+00 -- 6.793e+02 -- -1.57425 -2.21978 -3.77127 -4.13056 -4.55417 -4.30434 -4.77899 -5.07083\n", + " 16 3.872e-03 6.183e-01 4.700e-01 -- 6.798e+02 -- -1.57487 -2.21895 -3.77396 -4.16245 -4.5755 -4.2897 -4.83862 -5.22114\n", + " 17 1.291e-03 1.659e-01 1.113e-02 -- 6.798e+02 -- -1.57331 -2.22027 -3.78214 -4.16768 -4.57631 -4.28246 -4.85735 -5.23199\n", + " 18 5.326e-04 6.959e-02 1.219e-03 -- 6.798e+02 -- -1.57311 -2.22061 -3.78349 -4.168 -4.57225 -4.27943 -4.86362 -5.23031\n", + " 19 2.494e-04 2.982e-02 2.268e-04 -- 6.798e+02 -- -1.57309 -2.22069 -3.78429 -4.1678 -4.56992 -4.2784 -4.86621 -5.23009\n", + " 20 1.140e-04 1.317e-02 4.436e-05 -- 6.798e+02 -- -1.57306 -2.22073 -3.78469 -4.16784 -4.56878 -4.27802 -4.86734 -5.22989\n", + "********************\n", + "-1.57306 -2.22073 -3.78469 -4.16784 -4.56878 -4.27802 -4.86734 -5.22989\n", + "0.237078 0.201135 0.284718 0.248327 0.252841 0.150914 0.188371 0.222107\n", + "0.000184037 -0.000586923 -0.00230695 0.00112283 0.00799663 0.00635492 -0.0131691 -0.000270457\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 6.798e+02 6.794e+02 -1.573e+00 -1.336e+00 0.88 +++\n", + "+++ 6.798e+02 6.789e+02 -1.573e+00 -1.217e+00 1.85 +++\n", + "+++ 6.798e+02 6.791e+02 -1.573e+00 -1.277e+00 1.33 +++\n", + "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.306e+00 1.09 +++\n", + "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.321e+00 0.984 +++\n", + "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.314e+00 1.04 +++\n", + "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.317e+00 1.01 +++\n", + "+++ 6.798e+02 6.793e+02 -1.573e+00 -1.319e+00 0.998 +++\n", + "\t### errors for param 1 ###\n", + "+++ 6.798e+02 6.794e+02 -2.221e+00 -2.020e+00 0.889 +++\n", + "+++ 6.798e+02 6.789e+02 -2.221e+00 -1.919e+00 1.88 +++\n", + "+++ 6.798e+02 6.791e+02 -2.221e+00 -1.969e+00 1.35 +++\n", + "+++ 6.798e+02 6.792e+02 -2.221e+00 -1.994e+00 1.11 +++\n", + "+++ 6.798e+02 6.793e+02 -2.221e+00 -2.007e+00 0.996 +++\n", + "\t### errors for param 2 ###\n", + "+++ 6.798e+02 6.793e+02 -3.785e+00 -3.500e+00 0.976 +++\n", + "+++ 6.798e+02 6.787e+02 -3.785e+00 -3.358e+00 2.11 +++\n", + "+++ 6.798e+02 6.791e+02 -3.785e+00 -3.429e+00 1.49 +++\n", + "+++ 6.798e+02 6.792e+02 -3.785e+00 -3.465e+00 1.22 +++\n", + "+++ 6.798e+02 6.793e+02 -3.785e+00 -3.482e+00 1.1 +++\n", + "+++ 6.798e+02 6.793e+02 -3.785e+00 -3.491e+00 1.04 +++\n", + "+++ 6.798e+02 6.793e+02 -3.785e+00 -3.496e+00 1.01 +++\n", + "\t### errors for param 3 ###\n", + "+++ 6.798e+02 6.796e+02 -4.168e+00 -4.044e+00 0.34 +++\n", + "+++ 6.798e+02 6.794e+02 -4.168e+00 -3.982e+00 0.75 +++\n", + "+++ 6.798e+02 6.793e+02 -4.168e+00 -3.951e+00 1.01 +++\n", + "+++ 6.798e+02 6.794e+02 -4.168e+00 -3.966e+00 0.876 +++\n", + "+++ 6.798e+02 6.793e+02 -4.168e+00 -3.958e+00 0.942 +++\n", + "+++ 6.798e+02 6.793e+02 -4.168e+00 -3.954e+00 0.976 +++\n", + "+++ 6.798e+02 6.793e+02 -4.168e+00 -3.952e+00 0.993 +++\n", + "\t### errors for param 4 ###\n", + "+++ 6.798e+02 6.794e+02 -4.568e+00 -4.316e+00 0.875 +++\n", + "+++ 6.798e+02 6.788e+02 -4.568e+00 -4.189e+00 2.1 +++\n", + "+++ 6.798e+02 6.791e+02 -4.568e+00 -4.252e+00 1.41 +++\n", + "+++ 6.798e+02 6.792e+02 -4.568e+00 -4.284e+00 1.13 +++\n", + "+++ 6.798e+02 6.793e+02 -4.568e+00 -4.300e+00 0.996 +++\n", + "\t### errors for param 5 ###\n", + "+++ 6.798e+02 6.794e+02 -4.278e+00 -4.127e+00 0.875 +++\n", + "+++ 6.798e+02 6.789e+02 -4.278e+00 -4.052e+00 1.83 +++\n", + "+++ 6.798e+02 6.791e+02 -4.278e+00 -4.089e+00 1.36 +++\n", + "+++ 6.798e+02 6.792e+02 -4.278e+00 -4.108e+00 1.1 +++\n", + "+++ 6.798e+02 6.793e+02 -4.278e+00 -4.118e+00 0.986 +++\n", + "+++ 6.798e+02 6.793e+02 -4.278e+00 -4.113e+00 1.04 +++\n", + "+++ 6.798e+02 6.793e+02 -4.278e+00 -4.115e+00 1.02 +++\n", + "+++ 6.798e+02 6.793e+02 -4.278e+00 -4.116e+00 1 +++\n", + "\t### errors for param 6 ###\n", + "+++ 6.798e+02 6.794e+02 -4.868e+00 -4.679e+00 0.799 +++\n", + "+++ 6.798e+02 6.788e+02 -4.868e+00 -4.585e+00 1.9 +++\n", + "+++ 6.798e+02 6.792e+02 -4.868e+00 -4.632e+00 1.28 +++\n", + "+++ 6.798e+02 6.793e+02 -4.868e+00 -4.656e+00 1.03 +++\n", + "+++ 6.798e+02 6.793e+02 -4.868e+00 -4.668e+00 0.908 +++\n", + "+++ 6.798e+02 6.793e+02 -4.868e+00 -4.662e+00 0.966 +++\n", + "+++ 6.798e+02 6.793e+02 -4.868e+00 -4.659e+00 0.996 +++\n", + "\t### errors for param 7 ###\n", + "+++ 6.798e+02 6.796e+02 -5.230e+00 -5.119e+00 0.305 +++\n", + "+++ 6.798e+02 6.795e+02 -5.230e+00 -5.063e+00 0.696 +++\n", + "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.036e+00 0.953 +++\n", + "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.022e+00 1.1 +++\n", + "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.029e+00 1.02 +++\n", + "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.032e+00 0.988 +++\n", + "+++ 6.798e+02 6.793e+02 -5.230e+00 -5.030e+00 1.01 +++\n", + "********************\n", + "-1.57305 -2.22075 -3.78488 -4.16779 -4.56826 -4.27788 -4.86784 -5.22982\n", + "0.253747 0.213705 0.289194 0.215344 0.268522 0.161511 0.209074 0.199524\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0.867841\n", + " 7 2.704e+01 1.233e+01 1.965e+00 -- 7.273e+02 -- -0.83671 -1.42964 -2.88961 -3.18149 -3.71318 -3.73336 -4.56179 -6.61491 0.0582761 0.398604 0.784091 0.415604 0.0825065 0.208572 -0.259049 -2.48409\n", + " 9 8.381e+01 1.445e+01 1.699e+00 -- 7.290e+02 -- -0.814012 -1.40696 -2.87289 -3.18272 -3.73036 -3.74595 -4.56372 -6.91491 0.0510133 0.460993 0.990591 0.511606 0.0740674 0.240773 -0.360623 -2.05029\n", + " 11 5.382e+01 1.673e+01 1.479e+00 -- 7.305e+02 -- -0.794769 -1.38613 -2.84208 -3.17997 -3.74567 -3.75695 -4.56136 -7.21491 0.0454691 0.511132 1.15265 0.598735 0.0649178 0.270666 -0.449551 2.56611\n", + " 13 5.234e+02 1.913e+01 1.324e+00 -- 7.318e+02 -- -0.778329 -1.36729 -2.80694 -3.1744 -3.75929 -3.7665 -4.55618 -7.51491 0.041178 0.551805 1.27263 0.676139 0.0552576 0.298158 -0.525871 1.32105\n", + " 15 2.636e+02 2.168e+01 1.189e+00 -- 7.330e+02 -- -0.764197 -1.35038 -2.77293 -3.16703 -3.77138 -3.7748 -4.54947 -7.21491 0.0378314 0.585121 1.36044 0.743875 0.0453504 0.323244 -0.590433 1.29064\n", + " 17 1.149e+02 2.437e+01 1.088e+00 -- 7.341e+02 -- -0.751986 -1.33529 -2.74211 -3.15871 -3.78208 -3.78199 -4.54208 -6.91491 0.03521 0.612663 1.4256 0.802584 0.0353532 0.345957 -0.644618 -1.31496\n", + " 19 6.722e+01 2.722e+01 9.849e-01 -- 7.351e+02 -- -0.741387 -1.32184 -2.71491 -3.15002 -3.79154 -3.78825 -4.53464 -6.61491 0.0331641 0.635624 1.47504 0.853226 0.0254728 0.36641 -0.689866 1.22418\n", + " 21 2.720e+01 3.023e+01 9.444e-01 -- 7.360e+02 -- -0.732157 -1.30986 -2.69111 -3.14143 -3.79983 -3.79362 -4.52741 -6.31491 0.0315615 0.654913 1.51347 0.896759 0.0156921 0.384625 -0.727661 -0.72188\n", + " 23 1.618e+01 3.340e+01 8.070e-01 -- 7.368e+02 -- -0.724091 -1.29919 -2.67035 -3.13314 -3.80709 -3.79838 -4.52086 -6.01491 0.0303421 0.671224 1.54392 0.93425 0.00644024 0.400889 -0.759178 1.24133\n", + " 24 8.891e+01 4.172e+02 8.637e+00 -- 7.455e+02 -- -0.65346 -1.20411 -2.48868 -3.0558 -3.86972 -3.83787 -4.45774 -5.36047 0.0208476 0.809948 1.7913 1.25651 -0.0861695 0.542223 -1.02272 0.0117388\n", + " 25 3.521e+01 9.060e+00 9.410e-01 -- 7.464e+02 -- -0.661625 -1.20837 -2.49819 -3.05016 -3.85526 -3.84511 -4.44606 -4.62172 0.0636491 0.75819 1.74668 1.16355 0.00391259 0.465285 -0.857723 -1.03192\n", + " 26 2.800e-01 2.694e+00 4.013e-01 -- 7.468e+02 -- -0.659925 -1.20808 -2.49692 -3.04136 -3.86296 -3.86865 -4.52444 -4.7803 0.0440885 0.773556 1.74836 1.18011 0.141663 0.501264 -1.01577 -0.968135\n", + " 27 1.118e-01 1.396e+00 6.852e-02 -- 7.469e+02 -- -0.660557 -1.20802 -2.49682 -3.04782 -3.86407 -3.85635 -4.50129 -4.79491 0.0494721 0.769251 1.75312 1.18102 0.159998 0.494797 -0.942213 -0.697092\n", + " 28 6.066e-02 5.475e-01 7.813e-03 -- 7.469e+02 -- -0.660375 -1.20798 -2.49707 -3.04899 -3.85699 -3.85319 -4.501 -4.80785 0.0481904 0.770454 1.74927 1.18272 0.163201 0.485505 -0.957511 -0.619169\n", + " 29 2.205e-02 2.173e-01 1.373e-03 -- 7.469e+02 -- -0.660499 -1.20797 -2.4971 -3.05002 -3.85533 -3.85154 -4.49936 -4.80815 0.048302 0.77034 1.75136 1.18473 0.158919 0.484615 -0.950396 -0.581613\n", + " 30 2.005e-02 7.267e-02 2.580e-04 -- 7.469e+02 -- -0.660478 -1.20797 -2.49717 -3.05009 -3.85287 -3.85128 -4.49884 -4.81171 0.0482529 0.770456 1.75045 1.18445 0.155414 0.48385 -0.951932 -0.569748\n", + " 31 1.192e-02 5.990e-02 5.760e-05 -- 7.469e+02 -- -0.660494 -1.20797 -2.49717 -3.05021 -3.85235 -3.85102 -4.49839 -4.81176 0.0482126 0.770461 1.75114 1.18511 0.152299 0.483729 -0.950569 -0.563021\n", + "********************\n", + "-0.660494 -1.20797 -2.49717 -3.05021 -3.85235 -3.85102 -4.49839 -4.81176 0.0482126 0.770461 1.75114 1.18511 0.152299 0.483729 -0.950569 -0.563021\n", + "0.015038 0.00527965 0.0219253 0.14394 0.404753 0.263604 0.317912 0.460201 0.139209 0.0769038 0.175352 0.449778 0.985963 0.654083 0.765981 0.99603\n", + "0.0218905 0.0599 -0.0352934 0.00081409 0.00482364 -0.00014268 0.0010011 -0.00637734 0.00136757 0.00106954 -0.00732663 -0.00116 -0.00186662 -0.000327004 -0.000366653 0.00318593\n", + "********************\n" + ] + } + ], + "source": [ + "Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)\n", + "p = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a good starting point generally\n", + "p, pe = clag.optimize(Cx, p)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Line magic function `%autoreload` not found.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 7.469e+02 7.467e+02 -6.605e-01 -6.530e-01 0.383 +++\n", + "+++ 7.469e+02 7.463e+02 -6.605e-01 -6.492e-01 1.15 +++\n", + "+++ 7.469e+02 7.466e+02 -6.605e-01 -6.511e-01 0.685 +++\n", + "+++ 7.469e+02 7.465e+02 -6.605e-01 -6.502e-01 0.892 +++\n", + "+++ 7.469e+02 7.464e+02 -6.605e-01 -6.497e-01 1.01 +++\n", + "+++ 7.469e+02 7.464e+02 -6.605e-01 -6.499e-01 0.95 +++\n", + "+++ 7.469e+02 7.464e+02 -6.605e-01 -6.498e-01 0.981 +++\n", + "+++ 7.469e+02 7.464e+02 -6.605e-01 -6.497e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 7.469e+02 7.467e+02 -1.208e+00 -1.205e+00 0.378 +++\n", + "+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.1 +++\n", + "+++ 7.469e+02 7.466e+02 -1.208e+00 -1.205e+00 0.668 +++\n", + "+++ 7.469e+02 7.465e+02 -1.208e+00 -1.204e+00 0.864 +++\n", + "+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 0.977 +++\n", + "+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.04 +++\n", + "+++ 7.469e+02 7.464e+02 -1.208e+00 -1.204e+00 1.01 +++\n", + "\t### errors for param 2 ###\n", + "+++ 7.469e+02 7.467e+02 -2.497e+00 -2.486e+00 0.424 +++\n", + "+++ 7.469e+02 7.461e+02 -2.497e+00 -2.481e+00 1.65 +++\n", + "+++ 7.469e+02 7.465e+02 -2.497e+00 -2.483e+00 0.847 +++\n", + "+++ 7.469e+02 7.463e+02 -2.497e+00 -2.482e+00 1.18 +++\n", + "+++ 7.469e+02 7.464e+02 -2.497e+00 -2.483e+00 1 +++\n", + "\t### errors for param 3 ###\n", + "+++ 7.469e+02 7.467e+02 -3.050e+00 -2.978e+00 0.391 +++\n", + "+++ 7.469e+02 7.463e+02 -3.050e+00 -2.942e+00 1.19 +++\n", + "+++ 7.469e+02 7.466e+02 -3.050e+00 -2.960e+00 0.703 +++\n", + "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.951e+00 0.918 +++\n", + "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.947e+00 1.04 +++\n", + "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.949e+00 0.98 +++\n", + "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.948e+00 1.01 +++\n", + "+++ 7.469e+02 7.464e+02 -3.050e+00 -2.948e+00 0.996 +++\n", + "\t### errors for param 4 ###\n", + "+++ 7.469e+02 7.468e+02 -3.852e+00 -3.650e+00 0.275 +++\n", + "+++ 7.469e+02 7.465e+02 -3.852e+00 -3.549e+00 0.847 +++\n", + "+++ 7.469e+02 7.462e+02 -3.852e+00 -3.498e+00 1.35 +++\n", + "+++ 7.469e+02 7.464e+02 -3.852e+00 -3.523e+00 1.07 +++\n", + "+++ 7.469e+02 7.464e+02 -3.852e+00 -3.536e+00 0.948 +++\n", + "+++ 7.469e+02 7.464e+02 -3.852e+00 -3.530e+00 1.01 +++\n", + "\t### errors for param 5 ###\n", + "+++ 7.469e+02 7.467e+02 -3.851e+00 -3.719e+00 0.406 +++\n", + "+++ 7.469e+02 7.463e+02 -3.851e+00 -3.653e+00 1.18 +++\n", + "+++ 7.469e+02 7.466e+02 -3.851e+00 -3.686e+00 0.714 +++\n", + "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.670e+00 0.922 +++\n", + "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.662e+00 1.04 +++\n", + "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.666e+00 0.981 +++\n", + "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.664e+00 1.01 +++\n", + "+++ 7.469e+02 7.464e+02 -3.851e+00 -3.665e+00 0.996 +++\n", + "\t### errors for param 6 ###\n", + "+++ 7.469e+02 7.467e+02 -4.498e+00 -4.339e+00 0.402 +++\n", + "+++ 7.469e+02 7.463e+02 -4.498e+00 -4.260e+00 1.2 +++\n", + "+++ 7.469e+02 7.465e+02 -4.498e+00 -4.300e+00 0.719 +++\n", + "+++ 7.469e+02 7.464e+02 -4.498e+00 -4.280e+00 0.934 +++\n", + "+++ 7.469e+02 7.464e+02 -4.498e+00 -4.270e+00 1.06 +++\n", + "+++ 7.469e+02 7.464e+02 -4.498e+00 -4.275e+00 0.995 +++\n", + "\t### errors for param 7 ###\n", + "+++ 7.469e+02 7.466e+02 -4.813e+00 -4.582e+00 0.567 +++\n", + "+++ 7.469e+02 7.458e+02 -4.813e+00 -4.466e+00 2.24 +++\n", + "+++ 7.469e+02 7.463e+02 -4.813e+00 -4.524e+00 1.15 +++\n", + "+++ 7.469e+02 7.465e+02 -4.813e+00 -4.553e+00 0.813 +++\n", + "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.539e+00 0.968 +++\n", + "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.531e+00 1.05 +++\n", + "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.535e+00 1.01 +++\n", + "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.537e+00 0.989 +++\n", + "+++ 7.469e+02 7.464e+02 -4.813e+00 -4.536e+00 1 +++\n", + "\t### errors for param 8 ###\n", + "+++ 7.469e+02 7.468e+02 4.824e-02 1.178e-01 0.265 +++\n", + "+++ 7.469e+02 7.466e+02 4.824e-02 1.526e-01 0.589 +++\n", + "+++ 7.469e+02 7.465e+02 4.824e-02 1.700e-01 0.794 +++\n", + "+++ 7.469e+02 7.465e+02 4.824e-02 1.787e-01 0.907 +++\n", + "+++ 7.469e+02 7.464e+02 4.824e-02 1.831e-01 0.963 +++\n", + "+++ 7.469e+02 7.464e+02 4.824e-02 1.853e-01 0.993 +++\n", + "\t### errors for param 9 ###\n", + "+++ 7.469e+02 7.464e+02 7.705e-01 8.474e-01 1.02 +++\n", + "+++ 7.469e+02 7.468e+02 7.705e-01 8.089e-01 0.265 +++\n", + "+++ 7.469e+02 7.466e+02 7.705e-01 8.281e-01 0.586 +++\n", + "+++ 7.469e+02 7.465e+02 7.705e-01 8.378e-01 0.788 +++\n", + "+++ 7.469e+02 7.465e+02 7.705e-01 8.426e-01 0.899 +++\n", + "+++ 7.469e+02 7.464e+02 7.705e-01 8.450e-01 0.957 +++\n", + "+++ 7.469e+02 7.464e+02 7.705e-01 8.462e-01 0.986 +++\n", + "+++ 7.469e+02 7.464e+02 7.705e-01 8.468e-01 1 +++\n", + "\t### errors for param 10 ###\n", + "+++ 7.469e+02 7.464e+02 1.751e+00 1.926e+00 0.955 +++\n", + "+++ 7.469e+02 7.460e+02 1.751e+00 2.014e+00 1.89 +++\n", + "+++ 7.469e+02 7.462e+02 1.751e+00 1.970e+00 1.4 +++\n", + "+++ 7.469e+02 7.463e+02 1.751e+00 1.948e+00 1.17 +++\n", + "+++ 7.469e+02 7.464e+02 1.751e+00 1.937e+00 1.06 +++\n", + "+++ 7.469e+02 7.464e+02 1.751e+00 1.932e+00 1.01 +++\n", + "\t### errors for param 11 ###\n", + "+++ 7.469e+02 7.465e+02 1.185e+00 1.635e+00 0.84 +++\n", + "+++ 7.469e+02 7.461e+02 1.185e+00 1.860e+00 1.62 +++\n", + "+++ 7.469e+02 7.463e+02 1.185e+00 1.747e+00 1.22 +++\n", + "+++ 7.469e+02 7.464e+02 1.185e+00 1.691e+00 1.03 +++\n", + "+++ 7.469e+02 7.464e+02 1.185e+00 1.663e+00 0.932 +++\n", + "+++ 7.469e+02 7.464e+02 1.185e+00 1.677e+00 0.979 +++\n", + "+++ 7.469e+02 7.464e+02 1.185e+00 1.684e+00 1 +++\n", + "\t### errors for param 12 ###\n", + "\t### errors for param 13 ###\n", + "+++ 7.469e+02 7.465e+02 4.836e-01 1.138e+00 0.903 +++\n", + "+++ 7.469e+02 7.461e+02 4.836e-01 1.465e+00 1.67 +++\n", + "+++ 7.469e+02 7.463e+02 4.836e-01 1.301e+00 1.29 +++\n", + "+++ 7.469e+02 7.464e+02 4.836e-01 1.219e+00 1.1 +++\n", + "+++ 7.469e+02 7.464e+02 4.836e-01 1.179e+00 0.999 +++\n", + "\t### errors for param 14 ###\n", + "+++ 7.469e+02 7.465e+02 -9.510e-01 -1.852e-01 0.856 +++\n", + "+++ 7.469e+02 7.462e+02 -9.510e-01 1.976e-01 1.43 +++\n", + "+++ 7.469e+02 7.463e+02 -9.510e-01 6.198e-03 1.17 +++\n", + "+++ 7.469e+02 7.464e+02 -9.510e-01 -8.952e-02 1.02 +++\n", + "+++ 7.469e+02 7.464e+02 -9.510e-01 -1.374e-01 0.937 +++\n", + "+++ 7.469e+02 7.464e+02 -9.510e-01 -1.134e-01 0.978 +++\n", + "+++ 7.469e+02 7.464e+02 -9.510e-01 -1.015e-01 0.998 +++\n", + "\t### errors for param 15 ###\n", + "********************\n", + "-0.66049 -1.20797 -2.49719 -3.05019 -3.85156 -3.85104 -4.49824 -4.81261 0.048236 0.77047 1.75092 1.18493 0.150483 0.48362 -0.95096 -0.560876\n", + "0.0107478 0.0038354 0.014397 0.101761 0.321854 0.186386 0.223452 0.276776 0.137021 0.0762971 0.180879 0.498941 2 0.694997 0.849478 4.9864\n", + "********************\n" + ] + } + ], + "source": [ + "%autoreload\n", + "p, pe = clag.errors(Cx, p, pe)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phi, phie = p[nfq:], pe[nfq:]\n", + "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd) \n", + "cx, cxe = p[:nfq], pe[:nfq]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.79565312, 4.2489229 , 5.00370082, 2.18467546, 0.17899829,\n", + " 0.37113758, -0.47082688, -0.17915708])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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jU+nu4KwFuJTQJfxBhA7MjomeqwM+AZwL7AD8ELg65rElqSi9K+6NGzeOGTNm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\n4gZSBusAPwZuBl4m1GmZmWh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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "x=np.logspace(fqd[0],fqd[-1],200)\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n", + "fit = tophat_freq(fqd, *coeff)\n", + "\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "xscale('log'); xlim(.009,.6)\n", + "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n", + "ylabel(\"Time Lag (days)\",fontsize=20)\n", + "\n", + "\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n", + "plot(fqd,fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ZGHY+lrXYOljnYEnbeGxNrAVAG+9CFK/ccAOsWgUPPeR3JCIikk5uE5svgcHA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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_fit = irfft(fit)\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "ylabel(\"Response (relative)\",fontsize=20)\n", + "xlabel(\"Time (days)\",fontsize=20) \n", + "\n", + "ylim(-0.5,2)\n", + "xlim(0,7)\n", + "\n", + "plot(time_fit)\n", + "plot([3.93,3.93], [-50, 50], color='k', linestyle='-', linewidth=2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/lag/data/clag_analysis-origbins-noLFerrors-1928A.ipynb b/lag/data/clag_analysis-origbins-noLFerrors-1928A.ipynb new file mode 100644 index 0000000..66c0287 --- /dev/null +++ b/lag/data/clag_analysis-origbins-noLFerrors-1928A.ipynb @@ -0,0 +1,800 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/1928A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915, 0.62032418])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AHXkdWZ9jlekUPGQB7xmVg7Gd/4Yhry+P6r+NrhLeeD4jMAMo10qfH+0DAQcw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\nyQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.344e-01 7.393e+01 inf -- -3.944e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 7.685e-01 7.351e+01 8.622e+01 -- -3.082e+02 -- 0.624092 0.584331 0.567607 0.567004 0.567011 0.567373 0.565602 0.572165\n", + " 3 3.322e+00 7.320e+01 8.468e+01 -- -2.235e+02 -- 0.335696 0.192847 0.135867 0.134225 0.133328 0.135054 0.130916 0.142995\n", + " 4 1.578e+00 7.233e+01 8.258e+01 -- -1.409e+02 -- 0.188517 -0.142584 -0.293749 -0.297289 -0.300771 -0.296754 -0.30398 -0.289444\n", + " 5 6.020e-01 7.051e+01 7.959e+01 -- -6.131e+01 -- 0.148668 -0.367563 -0.714785 -0.72349 -0.733746 -0.726833 -0.738389 -0.724505\n", + " 6 3.768e-01 6.774e+01 7.540e+01 -- 1.409e+01 -- 0.144731 -0.462001 -1.10711 -1.13381 -1.16285 -1.15289 -1.1711 -1.16069\n", + " 7 2.756e-01 6.360e+01 6.924e+01 -- 8.333e+01 -- 0.174356 -0.494567 -1.42737 -1.5044 -1.58181 -1.57067 -1.60086 -1.59803\n", + " 8 2.179e-01 5.635e+01 5.976e+01 -- 1.431e+02 -- 0.217323 -0.511297 -1.61338 -1.7878 -1.97301 -1.97114 -2.02736 -2.03849\n", + " 9 1.801e-01 4.421e+01 4.621e+01 -- 1.893e+02 -- 0.246071 -0.522108 -1.67034 -1.93112 -2.29163 -2.33219 -2.45077 -2.48259\n", + " 10 1.570e-01 2.842e+01 3.021e+01 -- 2.195e+02 -- 0.26181 -0.527692 -1.69937 -1.95215 -2.46868 -2.6103 -2.87208 -2.92966\n", + " 11 1.534e-01 1.394e+01 1.638e+01 -- 2.359e+02 -- 0.270025 -0.532175 -1.71636 -1.93588 -2.49871 -2.76037 -3.29811 -3.38966\n", + " 12 1.967e-01 4.790e+00 7.494e+00 -- 2.434e+02 -- 0.270795 -0.536861 -1.72096 -1.92459 -2.47931 -2.79803 -3.75141 -3.90973\n", + " 13 5.458e-01 9.532e-01 2.865e+00 -- 2.462e+02 -- 0.26859 -0.539948 -1.7231 -1.91925 -2.46297 -2.79554 -4.30331 -4.67867\n", + " 14 1.056e+02 2.730e-01 7.004e-01 -- 2.469e+02 -- 0.26685 -0.541445 -1.72405 -1.91702 -2.45339 -2.78988 -5.27857 -7.23243\n", + " 15 5.573e+02 3.423e-01 3.921e-02 -- 2.470e+02 -- 0.266108 -0.541837 -1.72399 -1.91704 -2.44871 -2.78749 -8 -8\n", + " 16 5.573e+02 3.538e-01 2.319e-04 -- 2.470e+02 -- 0.265884 -0.541836 -1.72403 -1.91735 -2.44747 -2.78719 -8 -8\n", + " 17 5.573e+02 3.544e-01 3.124e-05 -- 2.470e+02 -- 0.265818 -0.541848 -1.72407 -1.91742 -2.44724 -2.78718 -8 -8\n", + "********************\n", + "0.265818 -0.541848 -1.72407 -1.91742 -2.44724 -2.78718 -8 -8\n", + "0.236518 0.210143 0.256686 0.193866 0.180651 0.167701 4558.58 3532.49\n", + "-0.003073 -0.00781145 -0.023497 -0.0612628 -0.14201 -0.354433 -0.000171646 -0.000375339\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 2.470e+02 2.466e+02 2.658e-01 5.023e-01 0.855 +++\n", + "+++ 2.470e+02 2.461e+02 2.658e-01 6.206e-01 1.8 +++\n", + "+++ 2.470e+02 2.463e+02 2.658e-01 5.615e-01 1.29 +++\n", + "+++ 2.470e+02 2.465e+02 2.658e-01 5.319e-01 1.06 +++\n", + "+++ 2.470e+02 2.465e+02 2.658e-01 5.171e-01 0.957 +++\n", + "+++ 2.470e+02 2.465e+02 2.658e-01 5.245e-01 1.01 +++\n", + "+++ 2.470e+02 2.465e+02 2.658e-01 5.208e-01 0.983 +++\n", + "+++ 2.470e+02 2.465e+02 2.658e-01 5.226e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 2.470e+02 2.465e+02 -5.419e-01 -3.317e-01 0.963 +++\n", + "+++ 2.470e+02 2.460e+02 -5.419e-01 -2.266e-01 2.03 +++\n", + "+++ 2.470e+02 2.463e+02 -5.419e-01 -2.792e-01 1.46 +++\n", + "+++ 2.470e+02 2.464e+02 -5.419e-01 -3.054e-01 1.2 +++\n", + "+++ 2.470e+02 2.464e+02 -5.419e-01 -3.186e-01 1.08 +++\n", + "+++ 2.470e+02 2.465e+02 -5.419e-01 -3.251e-01 1.02 +++\n", + "+++ 2.470e+02 2.465e+02 -5.419e-01 -3.284e-01 0.991 +++\n", + "\t### errors for param 2 ###\n", + "+++ 2.470e+02 2.468e+02 -1.724e+00 -1.596e+00 0.302 +++\n", + "+++ 2.470e+02 2.467e+02 -1.724e+00 -1.532e+00 0.653 +++\n", + "+++ 2.470e+02 2.466e+02 -1.724e+00 -1.499e+00 0.873 +++\n", + "+++ 2.470e+02 2.465e+02 -1.724e+00 -1.483e+00 0.993 +++\n", + "\t### errors for param 3 ###\n", + "+++ 2.470e+02 2.465e+02 -1.917e+00 -1.724e+00 0.929 +++\n", + "+++ 2.470e+02 2.460e+02 -1.917e+00 -1.627e+00 1.98 +++\n", + "+++ 2.470e+02 2.463e+02 -1.917e+00 -1.675e+00 1.41 +++\n", + "+++ 2.470e+02 2.464e+02 -1.917e+00 -1.699e+00 1.16 +++\n", + "+++ 2.470e+02 2.465e+02 -1.917e+00 -1.711e+00 1.04 +++\n", + "+++ 2.470e+02 2.465e+02 -1.917e+00 -1.718e+00 0.984 +++\n", + "+++ 2.470e+02 2.465e+02 -1.917e+00 -1.714e+00 1.01 +++\n", + "+++ 2.470e+02 2.465e+02 -1.917e+00 -1.716e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 2.470e+02 2.466e+02 -2.447e+00 -2.267e+00 0.864 +++\n", + "+++ 2.470e+02 2.460e+02 -2.447e+00 -2.176e+00 1.9 +++\n", + "+++ 2.470e+02 2.463e+02 -2.447e+00 -2.221e+00 1.33 +++\n", + "+++ 2.470e+02 2.464e+02 -2.447e+00 -2.244e+00 1.09 +++\n", + "+++ 2.470e+02 2.465e+02 -2.447e+00 -2.255e+00 0.972 +++\n", + "+++ 2.470e+02 2.465e+02 -2.447e+00 -2.250e+00 1.03 +++\n", + "+++ 2.470e+02 2.465e+02 -2.447e+00 -2.252e+00 1 +++\n", + "\t### errors for param 5 ###\n", + "+++ 2.470e+02 2.468e+02 -2.787e+00 -2.703e+00 0.292 +++\n", + "+++ 2.470e+02 2.467e+02 -2.787e+00 -2.661e+00 0.614 +++\n", + "+++ 2.470e+02 2.466e+02 -2.787e+00 -2.640e+00 0.82 +++\n", + "+++ 2.470e+02 2.465e+02 -2.787e+00 -2.630e+00 0.934 +++\n", + "+++ 2.470e+02 2.465e+02 -2.787e+00 -2.625e+00 0.993 +++\n", + "\t### errors for param 6 ###\n", + "+++ 2.470e+02 2.470e+02 -8.000e+00 -6.000e+00 0.0115 +++\n", + "+++ 2.470e+02 2.469e+02 -8.000e+00 -5.000e+00 0.147 +++\n", + "+++ 2.470e+02 2.467e+02 -8.000e+00 -4.500e+00 0.484 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.250e+00 0.884 +++\n", + "+++ 2.470e+02 2.464e+02 -8.000e+00 -4.125e+00 1.2 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.188e+00 1.03 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.219e+00 0.954 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.203e+00 0.991 +++\n", + "\t### errors for param 7 ###\n", + "+++ 2.470e+02 2.470e+02 -8.000e+00 -6.000e+00 0.0294 +++\n", + "+++ 2.470e+02 2.468e+02 -8.000e+00 -5.000e+00 0.323 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.500e+00 1.03 +++\n", + "+++ 2.470e+02 2.467e+02 -8.000e+00 -4.750e+00 0.579 +++\n", + "+++ 2.470e+02 2.466e+02 -8.000e+00 -4.625e+00 0.774 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.562e+00 0.894 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.531e+00 0.962 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.516e+00 0.997 +++\n", + "********************\n", + "0.265804 -0.541852 -1.72407 -1.91743 -2.44719 -2.78718 -8 -8\n", + "0.256844 0.213426 0.240643 0.20144 0.19476 0.162461 2 3.48438\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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-3.03469\n", + " 7 3.420e+03 1.966e+01 1.889e+00 -- 3.134e+02 -- 0.0650135 -0.553404 -1.64618 -1.93385 -2.50984 -2.88274 -5.78072 -8 -0.0424699 0.0913679 0.131869 0.14207 0.0426621 0.133189 -0.940465 1.19045\n", + " 9 2.868e+03 2.272e+01 1.819e+00 -- 3.152e+02 -- 0.0859844 -0.529292 -1.62113 -1.91359 -2.49094 -2.8689 -6.08072 -7.7 -0.0718915 0.0901134 0.137101 0.15086 0.0309294 0.141054 0.754261 2.5184\n", + " 11 5.181e+03 2.608e+01 1.679e+00 -- 3.169e+02 -- 0.104393 -0.508851 -1.59992 -1.8962 -2.4746 -2.85689 -5.78072 -8 -0.0958425 0.0892713 0.140997 0.158143 0.021406 0.147907 -0.44842 2.70944\n", + " 13 4.148e+04 2.974e+01 1.637e+00 -- 3.185e+02 -- 0.120579 -0.491366 -1.5818 -1.88115 -2.46039 -2.84641 -5.48072 -8 -0.115571 0.088729 0.14396 0.164245 0.0136007 0.153954 1.68591 0.219106\n", + " 15 1.364e+03 3.373e+01 1.423e+00 -- 3.199e+02 -- 0.134842 -0.476304 -1.56621 -1.86807 -2.44792 -2.83724 -5.18072 -7.7 -0.131975 0.0884089 0.146209 0.169436 0.00715737 0.159354 2.77837 -1.88001\n", + " 17 3.384e+03 3.807e+01 1.457e+00 -- 3.214e+02 -- 0.147438 -0.463252 -1.55271 -1.85663 -2.43694 -2.82923 -5.48072 -8 -0.145729 0.088245 0.147942 0.173866 0.00170694 0.164237 -0.858069 -3.03191\n", + " 19 5.448e+03 4.279e+01 1.401e+00 -- 3.228e+02 -- 0.15859 -0.451883 -1.54097 -1.84658 -2.42726 -2.82213 -5.78072 -8 -0.157362 0.0882266 0.149341 0.177654 -0.00272343 0.168668 0.243127 1.49491\n", + " 21 5.570e+02 4.789e+01 1.208e+00 -- 3.240e+02 -- 0.168487 -0.441941 -1.53072 -1.83772 -2.41866 -2.81587 -5.48072 -7.7 -0.167244 0.0883118 0.150432 0.180941 -0.00638645 0.172731 -2.75775 -2.43488\n", + " 23 5.770e+04 5.341e+01 1.297e+00 -- 3.253e+02 -- 0.177287 -0.433218 -1.52174 -1.82988 -2.411 -2.81036 -5.78072 -8 -0.175683 0.0884674 0.151298 0.18378 -0.00950607 0.176491 -0.399562 0.166824\n", + " 25 2.578e+03 5.934e+01 1.208e+00 -- 3.265e+02 -- 0.185129 -0.42554 -1.51385 -1.82293 -2.40415 -2.80545 -5.48072 -7.7 -0.182926 0.0886974 0.152004 0.18625 -0.0120039 0.179977 1.74647 1.42211\n", + " 27 4.862e+03 6.571e+01 1.069e+00 -- 3.276e+02 -- 0.192131 -0.418765 -1.5069 -1.81673 -2.39801 -2.8011 -5.18072 -7.4 -0.189156 0.0889782 0.152558 0.188415 -0.0140412 0.18323 2.48031 -0.745704\n", + " 29 9.405e+02 7.253e+01 9.711e-01 -- 3.285e+02 -- 0.198393 -0.412774 -1.50077 -1.81121 -2.39247 -2.79726 -5.17938 -7.7 -0.19453 0.0892935 0.153002 0.190299 -0.0157421 0.186287 -1.33712 -2.59349\n", + " 31 6.968e+03 7.979e+01 1.152e+00 -- 3.297e+02 -- 0.204002 -0.407466 -1.49534 -1.80628 -2.38752 -2.79383 -5.47938 -8 -0.199199 0.0896459 0.15344 0.191883 -0.0170844 0.18917 1.57648 -1.46962\n", + " 33 5.064e+03 8.750e+01 8.071e-01 -- 3.305e+02 -- 0.209036 -0.402755 -1.49055 -1.80185 -2.38303 -2.79076 -5.17938 -8 -0.203237 0.0900308 0.153771 0.193301 -0.0180605 0.19188 -2.99641 -1.64369\n", + " 35 4.661e+03 9.564e+01 1.077e+00 -- 3.316e+02 -- 0.213558 -0.398567 -1.48629 -1.79787 -2.37897 -2.78807 -5.47938 -8 -0.206743 0.0904245 0.154068 0.194501 -0.0188992 0.194463 0.599717 1.32447\n", + " 37 2.185e+04 1.042e+02 7.254e-01 -- 3.323e+02 -- 0.217628 -0.394841 -1.48253 -1.7943 -2.3753 -2.7856 -5.17938 -7.7 -0.209797 0.0908529 0.154345 0.195543 -0.0193431 0.196896 3.07347 -0.309744\n", + " 39 5.370e+02 1.132e+02 9.309e-01 -- 3.332e+02 -- 0.221294 -0.391521 -1.47918 -1.79107 -2.37197 -2.78346 -5.47938 -7.4 -0.212449 0.0912809 0.154587 0.196421 -0.0197202 0.199223 -0.122741 -2.19799\n", + " 41 4.124e+02 1.225e+02 8.464e-01 -- 3.341e+02 -- 0.224602 -0.38856 -1.47621 -1.78815 -2.36895 -2.78149 -5.17938 -7.7 -0.214768 0.0917346 0.154842 0.197156 -0.0197791 0.201426 0.185379 0.875823\n", + " 43 1.140e+04 1.321e+02 5.576e-01 -- 3.346e+02 -- 0.227588 -0.385917 -1.47357 -1.78551 -2.36621 -2.7797 -4.87938 -7.4 -0.216789 0.0922022 0.155086 0.197771 -0.0196213 0.203517 2.76502 -0.295434\n", + " 44 1.202e+03 9.319e+03 6.921e+00 -- 3.415e+02 -- 0.25457 -0.362307 -1.45009 -1.76155 -2.34098 -2.76463 -7.3588 -4.4 -0.234221 0.0967125 0.156919 0.202935 -0.0187407 0.223585 -3.03547 0.658572\n", + " 46 3.348e+03 2.383e+03 2.056e+00 -- 3.436e+02 -- 0.2541 -0.362138 -1.45037 -1.76118 -2.3417 -2.76372 -7.6588 -4.27727 -0.236662 0.0951999 0.162918 0.195741 -0.00804371 0.221694 2.83744 0.887952\n", + " 48 8.279e+03 1.188e+03 7.339e-01 -- 3.443e+02 -- 0.253984 -0.362098 -1.45055 -1.76083 -2.34195 -2.763 -7.9588 -4.18142 -0.238339 0.0952591 0.166595 0.190819 -0.00170333 0.220894 1.62132 0.948638\n", + " 50 5.831e+02 7.367e+02 4.678e-01 -- 3.448e+02 -- 0.25397 -0.362096 -1.45068 -1.76047 -2.34209 -2.76245 -7.6588 -4.11058 -0.239676 0.0957548 0.169216 0.186723 0.00306954 0.220363 -2.34059 0.972914\n", + " 52 1.225e+04 5.114e+02 3.593e-01 -- 3.452e+02 -- 0.254002 -0.36211 -1.45078 -1.76013 -2.34219 -2.76201 -7.9588 -4.05557 -0.240772 0.0964213 0.171173 0.183133 0.00691799 0.219951 -1.3379 0.984989\n", + " 54 2.908e+03 3.804e+02 2.971e-01 -- 3.455e+02 -- 0.254058 -0.362133 -1.45087 -1.75979 -2.34228 -2.76167 -8 -4.01135 -0.241679 0.0971513 0.172659 0.179917 0.0101243 0.219607 -2.58716 0.991938\n", + " 56 2.520e+04 2.966e+02 2.554e-01 -- 3.457e+02 -- 0.254126 -0.362159 -1.45094 -1.75946 -2.34236 -2.76142 -8 -3.975 -0.242428 0.0978921 0.17379 0.177 0.012847 0.219308 -0.893281 0.996473\n", + " 58 1.393e+04 2.392e+02 2.239e-01 -- 3.459e+02 -- 0.2542 -0.362187 -1.451 -1.75914 -2.34244 -2.76123 -8 -3.94463 -0.243046 0.098616 0.174644 0.174336 0.0151899 0.219038 0.548158 0.999814\n", + " 60 8.788e+02 1.978e+02 1.977e-01 -- 3.461e+02 -- 0.254275 -0.362215 -1.45106 -1.75884 -2.34252 -2.7611 -7.7 -3.919 -0.243552 0.0993077 0.175277 0.171891 0.0172264 0.218788 -2.44331 1.00258\n", + " 62 8.149e+03 1.668e+02 1.772e-01 -- 3.463e+02 -- 0.25435 -0.362242 -1.45111 -1.75855 -2.34259 -2.76101 -8 -3.89719 -0.243965 0.0999593 0.175731 0.169641 0.019011 0.218552 2.73951 1.0051\n", + " 64 3.097e+03 1.429e+02 1.584e-01 -- 3.465e+02 -- 0.254423 -0.362268 -1.45115 -1.75828 -2.34266 -2.76097 -8 -3.87855 -0.244298 0.100567 0.176038 0.167566 0.0205874 0.218325 0.780197 1.00758\n", + " 66 1.637e+03 1.238e+02 1.410e-01 -- 3.466e+02 -- 0.254493 -0.362292 -1.45119 -1.75803 -2.34273 -2.76096 -7.7 -3.86256 -0.244565 0.10113 0.176223 0.165649 0.02199 0.218102 -2.63332 1.01014\n", + " 68 1.780e+05 1.083e+02 1.272e-01 -- 3.467e+02 -- 0.254559 -0.362315 -1.45123 -1.75779 -2.34279 -2.76097 -8 -3.84883 -0.244776 0.101648 0.176309 0.163876 0.0232461 0.217881 -0.110744 1.01285\n", + " 70 2.894e+03 9.543e+01 1.126e-01 -- 3.469e+02 -- 0.254622 -0.362337 -1.45126 -1.75757 -2.34285 -2.761 -7.7 -3.83704 -0.244941 0.102124 0.176311 0.162237 0.0243797 0.217658 -1.79891 1.01576\n", + " 72 3.221e+03 8.461e+01 1.009e-01 -- 3.470e+02 -- 0.254681 -0.362357 -1.45129 -1.75736 -2.3429 -2.76105 -8 -3.8269 -0.245067 0.102559 0.176245 0.16072 0.025409 0.217434 -2.61948 1.01891\n", + " 73 3.986e+04 8.840e+04 3.099e+01 -- 3.160e+02 -- 0.255228 -0.362541 -1.45157 -1.75543 -2.3433 -2.76164 -8 -3.73993 -0.246022 0.106522 0.175014 0.146688 0.0348206 0.215163 -1.11659 1.05295\n", + " 75 8.896e+03 2.717e+02 3.098e+01 -- 3.469e+02 -- 0.254563 -0.362134 -1.45184 -1.757 -2.34298 -2.7592 -8 -3.74513 -0.25066 0.107475 0.155394 0.124168 0.0867597 0.167292 1.40399 1.0852\n", + " 76 1.040e+01 7.468e+01 5.524e-01 -- 3.475e+02 -- 0.254408 -0.362427 -1.45159 -1.7568 -2.33986 -2.76088 -5 -3.7742 -0.252304 0.108109 0.154003 0.131005 0.0778512 0.176436 2.13841 1.28287\n", + " 78 1.148e+02 6.144e+01 2.449e-01 -- 3.477e+02 -- 0.254428 -0.362448 -1.45159 -1.75668 -2.33984 -2.7612 -4.7 -3.77154 -0.252011 0.107845 0.155672 0.132885 0.0741505 0.180529 -0.0860208 1.27425\n", + " 80 5.736e-01 5.603e+01 5.946e-01 -- 3.483e+02 -- 0.254453 -0.362466 -1.4516 -1.75658 -2.33986 -2.76138 -4.45762 -3.76935 -0.251748 0.107647 0.157187 0.134499 0.0710198 0.184185 0.901147 1.267\n", + " 82 3.050e-01 5.070e+01 6.261e-01 -- 3.490e+02 -- 0.25448 -0.362483 -1.4516 -1.75645 -2.33989 -2.76144 -4.20194 -3.76751 -0.251481 0.107471 0.158475 0.136032 0.0683814 0.187492 0.910107 1.26066\n", + " 84 2.525e-01 4.649e+01 4.542e-01 -- 3.494e+02 -- 0.254511 -0.362499 -1.45161 -1.75631 -2.33995 -2.76134 -4.07732 -3.76602 -0.251239 0.107333 0.159592 0.137475 0.0662961 0.190508 0.913997 1.25501\n", + " 86 2.077e-01 4.317e+01 3.787e-01 -- 3.498e+02 -- 0.254544 -0.362513 -1.45163 -1.75617 -2.34003 -2.76113 -3.99339 -3.7648 -0.251017 0.107226 0.160556 0.138816 0.0646219 0.193263 0.917195 1.25003\n", + " 88 1.690e-01 4.054e+01 3.278e-01 -- 3.501e+02 -- 0.254579 -0.362526 -1.45164 -1.75602 -2.34015 -2.76083 -3.93087 -3.76381 -0.250811 0.107146 0.161384 0.140062 0.0632798 0.195783 0.92011 1.24565\n", + " 90 1.357e-01 3.844e+01 2.899e-01 -- 3.504e+02 -- 0.254615 -0.362538 -1.45165 -1.75588 -2.34028 -2.76046 -3.88172 -3.76302 -0.25062 0.107088 0.162091 0.141219 0.0622103 0.198094 0.922868 1.24181\n", + " 92 1.073e-01 3.979e+01 2.602e-01 -- 3.507e+02 -- 0.254653 -0.36255 -1.45167 -1.75573 -2.34044 -2.76004 -3.84172 -3.76239 -0.250443 0.107051 0.16269 0.142294 0.0613659 0.200217 0.925524 1.23844\n", + " 94 8.910e-02 4.379e+01 2.364e-01 -- 3.509e+02 -- 0.25469 -0.362561 -1.45169 -1.75559 -2.34061 -2.75957 -3.80836 -3.7619 -0.250278 0.10703 0.163195 0.143295 0.0607075 0.202171 0.928104 1.23549\n", + " 96 8.159e-02 4.824e+01 2.169e-01 -- 3.511e+02 -- 0.254729 -0.362572 -1.4517 -1.75545 -2.34079 -2.75908 -3.78005 -3.76152 -0.250125 0.107023 0.163616 0.144231 0.0602022 0.203972 0.930626 1.23291\n", + " 98 7.491e-02 5.321e+01 2.009e-01 -- 3.513e+02 -- 0.254767 -0.362582 -1.45172 -1.75531 -2.34099 -2.75856 -3.75567 -3.76124 -0.249983 0.107028 0.163963 0.145106 0.0598227 0.205636 0.933099 1.23064\n", + " 100 6.896e-02 5.877e+01 1.874e-01 -- 3.515e+02 -- 0.254805 -0.362593 -1.45174 -1.75518 -2.34119 -2.75803 -3.73444 -3.76104 -0.249849 0.107042 0.164245 0.145927 0.0595455 0.207177 0.935528 1.22866\n", + " 102 6.363e-02 6.501e+01 1.761e-01 -- 3.517e+02 -- 0.254843 -0.362603 -1.45176 -1.75505 -2.3414 -2.75749 -3.71578 -3.76092 -0.249724 0.107065 0.16447 0.1467 0.0593509 0.208605 0.937919 1.22691\n", + " 104 5.885e-02 7.201e+01 1.664e-01 -- 3.519e+02 -- 0.25488 -0.362613 -1.45178 -1.75493 -2.34162 -2.75695 -3.69927 -3.76085 -0.249607 0.107094 0.164646 0.14743 0.0592217 0.209933 0.940273 1.22537\n", + " 106 5.455e-02 7.989e+01 1.581e-01 -- 3.520e+02 -- 0.254916 -0.362622 -1.4518 -1.75482 -2.34183 -2.7564 -3.68456 -3.76083 -0.249496 0.107129 0.164779 0.148121 0.0591435 0.211168 0.942591 1.22401\n", + " 108 5.065e-02 8.875e+01 1.509e-01 -- 3.522e+02 -- 0.254952 -0.362632 -1.45182 -1.75471 -2.34205 -2.75586 -3.67139 -3.76085 -0.24939 0.107168 0.164876 0.148776 0.0591036 0.21232 0.944874 1.22279\n", + " 110 4.711e-02 9.874e+01 1.446e-01 -- 3.523e+02 -- 0.254988 -0.362641 -1.45184 -1.7546 -2.34226 -2.75533 -3.65956 -3.76091 -0.24929 0.10721 0.16494 0.149401 0.0590914 0.213396 0.947122 1.22169\n", + " 112 4.386e-02 1.100e+02 1.390e-01 -- 3.524e+02 -- 0.255022 -0.362651 -1.45186 -1.7545 -2.34248 -2.7548 -3.64889 -3.761 -0.249193 0.107255 0.164978 0.149997 0.0590972 0.214401 0.949333 1.22069\n", + " 114 4.086e-02 1.226e+02 1.340e-01 -- 3.526e+02 -- 0.255056 -0.36266 -1.45188 -1.75441 -2.34269 -2.75428 -3.63925 -3.76111 -0.249101 0.107302 0.164994 0.150567 0.0591131 0.215341 0.951506 1.21977\n", + " 116 3.807e-02 1.369e+02 1.295e-01 -- 3.527e+02 -- 0.255089 -0.362669 -1.4519 -1.75432 -2.3429 -2.75377 -3.63051 -3.76124 -0.24901 0.10735 0.164992 0.151115 0.0591318 0.216221 0.953639 1.21891\n", + " 118 3.543e-02 1.529e+02 1.253e-01 -- 3.528e+02 -- 0.255121 -0.362678 -1.45192 -1.75423 -2.3431 -2.75328 -3.62259 -3.76138 -0.248923 0.107399 0.164974 0.151642 0.0591471 0.217044 0.95573 1.21809\n", + " 120 3.292e-02 1.710e+02 1.213e-01 -- 3.530e+02 -- 0.255152 -0.362687 -1.45193 -1.75415 -2.3433 -2.7528 -3.61541 -3.76153 -0.248837 0.107449 0.164945 0.152149 0.0591535 0.217814 0.957773 1.2173\n", + " 122 3.110e-02 1.912e+02 1.176e-01 -- 3.531e+02 -- 0.255182 -0.362696 -1.45195 -1.75407 -2.34349 -2.75234 -3.60889 -3.76169 -0.248752 0.1075 0.164908 0.15264 0.0591462 0.218531 0.959768 1.21653\n", + " 124 3.007e-02 2.139e+02 1.139e-01 -- 3.532e+02 -- 0.255212 -0.362704 -1.45197 -1.754 -2.34368 -2.7519 -3.60297 -3.76186 -0.248668 0.107551 0.164864 0.153114 0.0591207 0.219197 0.961708 1.21577\n", + " 126 2.912e-02 2.393e+02 1.104e-01 -- 3.533e+02 -- 0.255241 -0.362713 -1.45199 -1.75393 -2.34386 -2.75147 -3.5976 -3.76202 -0.248584 0.107603 0.164817 0.153575 0.0590734 0.219814 0.963589 1.215\n", + " 128 2.825e-02 2.677e+02 1.069e-01 -- 3.534e+02 -- 0.255269 -0.362721 -1.45201 -1.75387 -2.34404 -2.75106 -3.59275 -3.76219 -0.2485 0.107655 0.164769 0.154022 0.0590009 0.220381 0.965407 1.21423\n", + " 130 2.746e-02 2.993e+02 1.034e-01 -- 3.535e+02 -- 0.255297 -0.362729 -1.45203 -1.75381 -2.34421 -2.75067 -3.58835 -3.76235 -0.248416 0.107708 0.164721 0.154457 0.0589 0.220898 0.967157 1.21345\n", + " 132 2.805e-02 3.345e+02 9.997e-02 -- 3.536e+02 -- 0.255324 -0.362737 -1.45205 -1.75375 -2.34437 -2.7503 -3.58439 -3.76251 -0.248331 0.107761 0.164676 0.154881 0.0587683 0.221364 0.968833 1.21265\n", + " 134 3.410e-02 3.735e+02 9.652e-02 -- 3.537e+02 -- 0.25535 -0.362745 -1.45207 -1.7537 -2.34453 -2.74995 -3.58082 -3.76266 -0.248245 0.107815 0.164635 0.155295 0.0586035 0.221779 0.970432 1.21184\n", + " 136 4.046e-02 4.168e+02 9.310e-02 -- 3.538e+02 -- 0.255376 -0.362753 -1.45208 -1.75365 -2.34469 -2.74962 -3.57761 -3.7628 -0.248158 0.10787 0.164599 0.1557 0.0584036 0.222141 0.97195 1.211\n", + " 138 4.708e-02 4.645e+02 8.971e-02 -- 3.539e+02 -- 0.255401 -0.36276 -1.4521 -1.7536 -2.34484 -2.74931 -3.57474 -3.76293 -0.248069 0.107925 0.164571 0.156096 0.0581673 0.222449 0.973382 1.21015\n", + " 140 5.391e-02 5.172e+02 8.638e-02 -- 3.540e+02 -- 0.255426 -0.362767 -1.45212 -1.75356 -2.34499 -2.74902 -3.57218 -3.76306 -0.247979 0.107983 0.16455 0.156484 0.0578935 0.222701 0.974726 1.20929\n", + " 142 6.088e-02 5.752e+02 8.313e-02 -- 3.541e+02 -- 0.25545 -0.362775 -1.45214 -1.75352 -2.34513 -2.74875 -3.56989 -3.76317 -0.247887 0.108041 0.164537 0.156864 0.0575814 0.222897 0.97598 1.2084\n", + " 144 6.792e-02 6.389e+02 7.999e-02 -- 3.541e+02 -- 0.255474 -0.362781 -1.45215 -1.75348 -2.34526 -2.74851 -3.56787 -3.76327 -0.247793 0.108101 0.164534 0.157238 0.0572308 0.223035 0.977144 1.20751\n", + " 146 7.496e-02 7.089e+02 7.699e-02 -- 3.542e+02 -- 0.255498 -0.362788 -1.45217 -1.75345 -2.3454 -2.74829 -3.56608 -3.76337 -0.247697 0.108163 0.164541 0.157606 0.0568421 0.223116 0.978218 1.20662\n", + " 148 8.194e-02 7.858e+02 7.415e-02 -- 3.543e+02 -- 0.255521 -0.362795 -1.45218 -1.75341 -2.34553 -2.74809 -3.56451 -3.76345 -0.247599 0.108227 0.164558 0.157967 0.0564161 0.22314 0.979203 1.20571\n", + " 150 8.877e-02 8.701e+02 7.150e-02 -- 3.544e+02 -- 0.255544 -0.362801 -1.4522 -1.75338 -2.34566 -2.74791 -3.56313 -3.76352 -0.247499 0.108292 0.164584 0.158323 0.0559538 0.223106 0.980101 1.20482\n", + " 152 9.539e-02 9.628e+02 6.905e-02 -- 3.544e+02 -- 0.255566 -0.362807 -1.45222 -1.75336 -2.34579 -2.74775 -3.56192 -3.76359 -0.247398 0.108359 0.16462 0.158673 0.0554571 0.223018 0.980916 1.20392\n", + " 154 1.017e-01 1.065e+03 6.682e-02 -- 3.545e+02 -- 0.255589 -0.362813 -1.45223 -1.75333 -2.34591 -2.74761 -3.56087 -3.76365 -0.247295 0.108428 0.164666 0.159017 0.0549281 0.222877 0.981651 1.20304\n", + " 156 1.077e-01 1.177e+03 6.480e-02 -- 3.546e+02 -- 0.255611 -0.362819 -1.45225 -1.75331 -2.34603 -2.74749 -3.55996 -3.76369 -0.247191 0.108499 0.164721 0.159357 0.0543694 0.222685 0.982311 1.20217\n", + " 158 1.132e-01 1.300e+03 6.299e-02 -- 3.546e+02 -- 0.255633 -0.362825 -1.45226 -1.75329 -2.34615 -2.74739 -3.55918 -3.76373 -0.247085 0.108571 0.164785 0.159691 0.0537841 0.222446 0.982901 1.20132\n", + " 160 1.182e-01 1.436e+03 6.139e-02 -- 3.547e+02 -- 0.255654 -0.36283 -1.45227 -1.75327 -2.34627 -2.74731 -3.55851 -3.76377 -0.246978 0.108645 0.164856 0.160019 0.0531753 0.222164 0.983425 1.20049\n", + " 162 1.227e-01 1.586e+03 6.000e-02 -- 3.547e+02 -- 0.255676 -0.362835 -1.45229 -1.75326 -2.34638 -2.74724 -3.55794 -3.76379 -0.246871 0.108719 0.164934 0.160342 0.0525467 0.221843 0.98389 1.19969\n", + " 164 1.266e-01 1.752e+03 5.878e-02 -- 3.548e+02 -- 0.255697 -0.36284 -1.4523 -1.75324 -2.34649 -2.74719 -3.55745 -3.76382 -0.246763 0.108795 0.165018 0.160659 0.0519018 0.221488 0.9843 1.19892\n", + " 166 1.299e-01 1.936e+03 5.774e-02 -- 3.549e+02 -- 0.255717 -0.362845 -1.45231 -1.75323 -2.3466 -2.74715 -3.55703 -3.76383 -0.246655 0.10887 0.165107 0.16097 0.0512446 0.221103 0.98466 1.19817\n", + " 167 8.335e-01 5.253e+05 1.229e+00 -- 3.561e+02 -- 0.255921 -0.362892 -1.45244 -1.75313 -2.34767 -2.74689 -3.55355 -3.76396 -0.245575 0.109632 0.166041 0.164016 0.0445881 0.217007 0.987818 1.19106\n", + " 169 4.967e-01 6.931e+04 9.078e-01 -- 3.570e+02 -- 0.256778 -0.363023 -1.45248 -1.75314 -2.34788 -2.74729 -3.55391 -3.76392 -0.240168 0.115356 0.168594 0.167645 0.0408715 0.213369 0.987598 1.19065\n", + " 171 3.048e-01 2.023e+05 2.339e-01 -- 3.572e+02 -- 0.257135 -0.363073 -1.4525 -1.75314 -2.348 -2.7475 -3.5541 -3.76389 -0.237882 0.117852 0.16997 0.169576 0.0388412 0.2114 0.98748 1.19042\n", + " 173 1.911e-01 2.661e+05 1.067e-01 -- 3.573e+02 -- 0.257299 -0.363094 -1.45251 -1.75314 -2.34807 -2.74763 -3.55422 -3.76388 -0.236811 0.118996 0.170757 0.170684 0.0376575 0.210262 0.987412 1.19029\n", + " 175 1.242e-01 3.120e+05 7.122e-02 -- 3.574e+02 -- 0.257367 -0.363102 -1.45252 -1.75313 -2.34811 -2.7477 -3.55428 -3.76387 -0.236362 0.119438 0.171223 0.171343 0.036938 0.209578 0.987372 1.19021\n", + " 177 8.518e-02 3.537e+05 6.017e-02 -- 3.575e+02 -- 0.257377 -0.363102 -1.45252 -1.75313 -2.34814 -2.74775 -3.55433 -3.76386 -0.236301 0.119504 0.171509 0.171751 0.0364794 0.209147 0.987347 1.19015\n", + " 178 8.968e+00 4.801e+09 1.203e+01 -- 3.454e+02 -- 0.25718 -0.363074 -1.45256 -1.7531 -2.34833 -2.74807 -3.55461 -3.76383 -0.236925 0.118229 0.173367 0.174411 0.0333719 0.206265 0.987183 1.18976\n", + " 181 2.164e+01 5.609e+09 1.234e+00 -- 3.467e+02 -- 0.254646 -0.363062 -1.45256 -1.7531 -2.34833 -2.74806 -3.55461 -3.76383 -0.215677 0.117722 0.173339 0.174333 0.0334359 0.206333 0.987186 1.18977\n", + " 184 5.483e+01 2.331e+10 1.652e+01 -- 3.302e+02 -- 0.252058 -0.363045 -1.45256 -1.7531 -2.34833 -2.74806 -3.5546 -3.76383 -0.169015 0.117128 0.173323 0.174259 0.0334941 0.206388 0.987189 1.18977\n", + " 188 6.183e+01 1.828e+10 5.801e+00 -- 3.360e+02 -- 0.252061 -0.363046 -1.45256 -1.7531 -2.34833 -2.74806 -3.5546 -3.76383 -0.178282 0.117142 0.173318 0.174255 0.0334983 0.206394 0.987189 1.18977\n", + " 192 5.885e+01 2.436e+10 3.844e+00 -- 3.321e+02 -- 0.253244 -0.363049 -1.45256 -1.7531 -2.34833 -2.74806 -3.5546 -3.76383 -0.189305 0.117135 0.173312 0.174248 0.0335015 0.206395 0.987189 1.18977\n", + " 196 2.129e+01 1.929e+10 7.309e+00 -- 3.394e+02 -- 0.252685 -0.363051 -1.45256 -1.7531 -2.34833 -2.74806 -3.5546 -3.76383 -0.200446 0.117182 0.173307 0.174242 0.0335052 0.206401 0.987189 1.18978\n", + " 200 2.206e+01 2.181e+10 2.023e+00 -- 3.374e+02 -- 0.253371 -0.363052 -1.45256 -1.7531 -2.34833 -2.74806 -3.5546 -3.76383 -0.204712 0.117211 0.173302 0.174232 0.0335083 0.206402 0.987189 1.18978\n", + " 204 8.299e+01 5.939e+10 2.238e+01 -- 3.150e+02 -- 0.254665 -0.363051 -1.45256 -1.7531 -2.34833 -2.74806 -3.5546 -3.76383 -0.200196 0.117131 0.173299 0.174226 0.0335125 0.206401 0.98719 1.18978\n", + " 208 9.303e+01 8.504e+10 5.484e+00 -- 3.095e+02 -- 0.255535 -0.363057 -1.45256 -1.7531 -2.34833 -2.74806 -3.5546 -3.76383 -0.216811 0.117221 0.173291 0.174227 0.0335159 0.206403 0.98719 1.18978\n", + " 212 6.491e+01 2.077e+11 3.885e+01 -- 2.707e+02 -- 0.254951 -0.36305 -1.45256 -1.7531 -2.34833 -2.74806 -3.5546 -3.76383 -0.196641 0.117099 0.173292 0.174214 0.0335247 0.206409 0.987191 1.18978\n", + " 217 1.111e+02 2.167e+11 3.081e+00 -- 2.676e+02 -- 0.25519 -0.363051 -1.45256 -1.7531 -2.34832 -2.74806 -3.5546 -3.76383 -0.197918 0.117105 0.173291 0.174215 0.0335252 0.206409 0.987191 1.18978\n", + " 221 2.876e+02 9.255e+11 1.181e+02 -- 1.495e+02 -- 0.254392 -0.363043 -1.45256 -1.7531 -2.34832 -2.74805 -3.5546 -3.76383 -0.175939 0.116879 0.173293 0.174201 0.0335327 0.206419 0.987192 1.18978\n", + " 225 1.234e+00 2.500e+08 2.070e+02 -- 3.565e+02 -- 0.257392 -0.363058 -1.45256 -1.7531 -2.34832 -2.74805 -3.5546 -3.76383 -0.226547 0.117399 0.173269 0.174192 0.0335295 0.206406 0.987194 1.18978\n", + " 226 5.528e+01 1.532e+11 3.627e+02 -- -6.266e+00 -- 0.253834 -0.36252 -1.45253 -1.75307 -2.34805 -2.74748 -3.5541 -3.76389 0.0531025 0.0821288 0.169868 0.16878 0.0388404 0.211605 0.987527 1.19032\n", + " 229 7.686e+00 1.730e+09 3.323e+02 -- 3.261e+02 -- 0.255597 -0.362547 -1.45253 -1.75307 -2.34807 -2.7475 -3.55413 -3.76389 0.08246 0.0832204 0.170036 0.169033 0.0385988 0.211368 0.987512 1.19029\n", + " 232 3.732e+00 4.680e+08 1.751e+01 -- 3.436e+02 -- 0.257357 -0.362572 -1.45254 -1.75307 -2.34808 -2.74752 -3.55415 -3.76389 0.0887979 0.0841294 0.170166 0.169243 0.0384008 0.211174 0.987501 1.19027\n", + " 235 3.446e+00 2.036e+08 5.719e+00 -- 3.493e+02 -- 0.258519 -0.362592 -1.45254 -1.75307 -2.34808 -2.74754 -3.55416 -3.76388 0.0921117 0.0848699 0.17027 0.169411 0.03824 0.211016 0.987491 1.19026\n", + " 237 2.034e+00 1.556e+08 4.483e-02 -- 3.492e+02 -- 0.26605 -0.36276 -1.45254 -1.75309 -2.34815 -2.74769 -3.55429 -3.76387 0.123855 0.0911334 0.171129 0.170821 0.0369156 0.209732 0.987409 1.19012\n", + " 239 1.466e+00 1.245e+08 2.780e+00 -- 3.520e+02 -- 0.259943 -0.362627 -1.45254 -1.75308 -2.34809 -2.74756 -3.55417 -3.76388 0.0986618 0.085177 0.170356 0.169568 0.0381123 0.210889 0.987481 1.19024\n", + " 241 1.862e+00 2.649e+08 3.442e+00 -- 3.486e+02 -- 0.266437 -0.362754 -1.45254 -1.75309 -2.34814 -2.74767 -3.55427 -3.76387 0.113128 0.0899647 0.170998 0.17062 0.0371147 0.209918 0.987421 1.19014\n", + " 243 1.266e+01 1.952e+09 9.726e+00 -- 3.388e+02 -- 0.259641 -0.362639 -1.45253 -1.75308 -2.34809 -2.74755 -3.55417 -3.76388 0.0920617 0.0850623 0.170303 0.169498 0.038182 0.210959 0.987481 1.19025\n", + " 246 3.739e+00 6.376e+08 9.943e+00 -- 3.488e+02 -- 0.260555 -0.362649 -1.45254 -1.75308 -2.3481 -2.74756 -3.55418 -3.76388 0.103714 0.0855166 0.170377 0.169607 0.0380736 0.210855 0.987475 1.19024\n", + " 249 7.179e+00 2.798e+08 3.620e+00 -- 3.524e+02 -- 0.261275 -0.362658 -1.45254 -1.75308 -2.3481 -2.74757 -3.55419 -3.76388 0.107592 0.0858678 0.170432 0.169696 0.0379917 0.210774 0.987471 1.19023\n", + " 251 6.516e-01 1.445e+07 2.805e+00 -- 3.552e+02 -- 0.260815 -0.362722 -1.45254 -1.75308 -2.34813 -2.74765 -3.55425 -3.76387 0.184829 0.0886738 0.170854 0.170348 0.0373649 0.210209 0.987427 1.19017\n", + " 253 3.810e+00 2.052e+08 1.727e+00 -- 3.535e+02 -- 0.257283 -0.362714 -1.45254 -1.75308 -2.34812 -2.74764 -3.55423 -3.76387 0.172785 0.088281 0.17073 0.170156 0.0375437 0.210382 0.987434 1.19018\n", + " 255 3.064e+00 8.311e+06 1.177e+00 -- 3.547e+02 -- 0.258177 -0.36276 -1.45254 -1.75308 -2.34815 -2.74769 -3.55428 -3.76387 0.238623 0.0899565 0.171066 0.170674 0.0370633 0.209939 0.987402 1.19013\n", + " 257 7.394e+00 3.133e+09 1.172e+01 -- 3.429e+02 -- 0.254324 -0.362757 -1.45254 -1.75308 -2.34814 -2.74767 -3.55426 -3.76387 0.165501 0.0891934 0.170888 0.170414 0.0372912 0.210148 0.987412 1.19016\n", + " 259 4.577e+00 8.174e+09 2.129e+01 -- 3.217e+02 -- 0.25069 -0.36289 -1.45254 -1.7531 -2.34818 -2.74774 -3.55433 -3.76386 0.0431235 0.0911987 0.171302 0.171131 0.0366077 0.209486 0.987358 1.19009\n", + " 261 2.816e+00 1.095e+09 2.508e+01 -- 3.467e+02 -- 0.259954 -0.363017 -1.45255 -1.75311 -2.34824 -2.74786 -3.55443 -3.76385 0.0628627 0.0987668 0.171982 0.172358 0.0355272 0.20841 0.987296 1.18998\n", + " 263 8.570e+00 1.645e+09 2.114e+00 -- 3.489e+02 -- 0.252615 -0.362928 -1.45254 -1.7531 -2.3482 -2.74777 -3.55435 -3.76386 0.0451626 0.0945636 0.171419 0.171452 0.0363838 0.209263 0.987344 1.19006\n", + " 266 6.015e+00 7.015e+08 4.892e+00 -- 3.537e+02 -- 0.254279 -0.362935 -1.45254 -1.7531 -2.3482 -2.74777 -3.55435 -3.76386 0.041292 0.0948982 0.171457 0.171509 0.0363216 0.209197 0.987341 1.19006\n", + " 269 2.525e+01 1.168e+08 3.655e+00 -- 3.574e+02 -- 0.256735 -0.362939 -1.45254 -1.7531 -2.3482 -2.74777 -3.55436 -3.76386 0.0388082 0.0951206 0.171485 0.171557 0.0362756 0.209144 0.98734 1.19005\n", + " 271 1.208e+01 6.119e+09 1.521e+01 -- 3.422e+02 -- 0.257086 -0.362929 -1.45255 -1.7531 -2.34821 -2.7478 -3.55437 -3.76386 0.136783 0.0964349 0.171623 0.17166 0.0360846 0.209008 0.987331 1.19003\n", + " 273 6.524e+01 2.429e+09 1.264e+01 -- 3.548e+02 -- 0.249456 -0.36281 -1.45255 -1.75308 -2.34818 -2.74774 -3.55432 -3.76386 0.302002 0.0935984 0.171321 0.171267 0.0365623 0.209564 0.98736 1.19009\n", + " 277 8.104e+01 1.297e+10 5.058e+00 -- 3.498e+02 -- 0.248213 -0.362807 -1.45255 -1.75308 -2.34818 -2.74775 -3.55432 -3.76386 0.321703 0.0935769 0.171325 0.171264 0.0365626 0.209571 0.987359 1.19009\n", + " 282 2.328e+01 1.055e+10 1.358e+00 -- 3.511e+02 -- 0.248486 -0.362807 -1.45255 -1.75308 -2.34818 -2.74775 -3.55432 -3.76386 0.319096 0.0935799 0.171325 0.171264 0.0365624 0.20957 0.987359 1.19009\n", + " 286 6.803e+00 3.491e+09 3.884e+00 -- 3.550e+02 -- 0.249067 -0.362804 -1.45255 -1.75308 -2.34818 -2.74775 -3.55432 -3.76386 0.326525 0.0936242 0.171326 0.171261 0.0365618 0.209572 0.98736 1.19009\n", + " 291 6.563e+01 1.909e+09 6.548e-01 -- 3.557e+02 -- 0.249237 -0.362804 -1.45255 -1.75308 -2.34818 -2.74775 -3.55432 -3.76386 0.326513 0.0936243 0.171326 0.171261 0.0365619 0.209571 0.98736 1.19009\n", + " 295 2.097e+01 5.540e+08 7.801e-01 -- 3.565e+02 -- 0.249081 -0.362799 -1.45255 -1.75307 -2.34818 -2.74775 -3.55432 -3.76386 0.347942 0.093616 0.171332 0.171258 0.0365647 0.209579 0.98736 1.19009\n", + " 299 5.078e+01 2.673e+09 1.016e+00 -- 3.554e+02 -- 0.248981 -0.362802 -1.45255 -1.75307 -2.34818 -2.74775 -3.55432 -3.76386 0.340645 0.0934478 0.171333 0.171274 0.0365637 0.209578 0.98736 1.19009\n", + " 304 1.217e+02 6.839e+08 7.133e-01 -- 3.562e+02 -- 0.249232 -0.362802 -1.45255 -1.75307 -2.34818 -2.74775 -3.55432 -3.76386 0.338915 0.0934395 0.171333 0.171274 0.0365637 0.209577 0.98736 1.19009\n", + " 308 8.032e-01 2.458e+07 1.069e+00 -- 3.551e+02 -- 0.252232 -0.362801 -1.45255 -1.75308 -2.34818 -2.74774 -3.55432 -3.76386 0.297682 0.0932028 0.17133 0.171276 0.0365613 0.209559 0.987364 1.19009\n", + " 310 2.176e+00 1.190e+08 1.235e+00 -- 3.539e+02 -- 0.24778 -0.362787 -1.45255 -1.75307 -2.34817 -2.74773 -3.55431 -3.76386 0.321591 0.0922355 0.17126 0.171144 0.0366787 0.209695 0.987368 1.1901\n", + " 313 7.459e+00 1.929e+07 9.609e-01 -- 3.548e+02 -- 0.249155 -0.36279 -1.45255 -1.75307 -2.34817 -2.74774 -3.55432 -3.76386 0.314594 0.0923622 0.171282 0.171188 0.0366438 0.209658 0.987366 1.1901\n", + " 315 5.281e+00 2.205e+08 1.301e+00 -- 3.561e+02 -- 0.255257 -0.362875 -1.45254 -1.75309 -2.34819 -2.74775 -3.55434 -3.76386 0.0799459 0.0941447 0.171371 0.171431 0.0364087 0.209347 0.987352 1.19007\n", + " 318 4.407e+00 4.106e+07 1.134e+00 -- 3.572e+02 -- 0.256315 -0.362879 -1.45254 -1.75309 -2.3482 -2.74775 -3.55434 -3.76386 0.0757242 0.0943132 0.171389 0.17146 0.0363794 0.209314 0.987352 1.19007\n", + " 321 7.132e+00 7.906e+07 6.987e-01 -- 3.579e+02 -- 0.25764 -0.362879 -1.45254 -1.75309 -2.3482 -2.74775 -3.55434 -3.76386 0.0723873 0.0944535 0.171398 0.171474 0.0363617 0.209289 0.987352 1.19007\n", + " 324 7.614e+00 3.913e+07 3.547e-01 -- 3.576e+02 -- 0.258611 -0.362877 -1.45254 -1.75309 -2.3482 -2.74775 -3.55434 -3.76386 0.0775498 0.0944425 0.171402 0.17148 0.0363586 0.209281 0.987353 1.19007\n", + " 327 1.263e+00 4.729e+07 5.561e-01 -- 3.570e+02 -- 0.258918 -0.362873 -1.45254 -1.75309 -2.3482 -2.74775 -3.55434 -3.76386 0.0834542 0.0943921 0.171395 0.171469 0.0363714 0.20929 0.987354 1.19007\n", + " 330 1.910e+01 8.393e+07 7.689e-01 -- 3.578e+02 -- 0.258007 -0.362872 -1.45254 -1.75309 -2.34819 -2.74775 -3.55434 -3.76386 0.0845079 0.0943617 0.171384 0.171449 0.036389 0.20931 0.987354 1.19007\n", + " 333 2.525e+01 1.008e+08 2.575e-01 -- 3.581e+02 -- 0.258286 -0.362878 -1.45254 -1.75309 -2.3482 -2.74774 -3.55434 -3.76386 0.0683659 0.0944189 0.171376 0.171441 0.0363934 0.209307 0.987354 1.19007\n", + " 336 5.562e+00 5.143e+07 4.538e-01 -- 3.576e+02 -- 0.25833 -0.362872 -1.45254 -1.75309 -2.34819 -2.74775 -3.55434 -3.76386 0.0856295 0.0943039 0.171382 0.171444 0.036396 0.209312 0.987355 1.19007\n", + " 339 1.157e+01 9.557e+07 2.073e-01 -- 3.578e+02 -- 0.257744 -0.36287 -1.45254 -1.75309 -2.34819 -2.74775 -3.55434 -3.76386 0.0903926 0.0942372 0.171377 0.171435 0.0364077 0.209327 0.987355 1.19007\n", + " 342 1.068e+01 9.556e+08 3.223e+00 -- 3.546e+02 -- 0.260013 -0.36287 -1.45254 -1.75309 -2.34819 -2.74774 -3.55434 -3.76386 0.0799386 0.0943017 0.171369 0.171427 0.0364092 0.20932 0.987357 1.19007\n", + " 345 4.170e+01 1.886e+08 2.403e+00 -- 3.570e+02 -- 0.258928 -0.362866 -1.45254 -1.75309 -2.34819 -2.74774 -3.55433 -3.76386 0.0884755 0.0939485 0.171358 0.171408 0.0364383 0.209351 0.987358 1.19008\n", + " 348 2.879e+01 9.511e+07 1.296e+00 -- 3.583e+02 -- 0.258843 -0.362878 -1.45254 -1.75309 -2.34819 -2.74774 -3.55433 -3.76386 0.0515822 0.0941797 0.171336 0.171408 0.0364491 0.209351 0.987357 1.19008\n", + " 351 4.880e+01 1.238e+08 3.808e-01 -- 3.579e+02 -- 0.259089 -0.362884 -1.45254 -1.75309 -2.34819 -2.74774 -3.55434 -3.76386 0.0367321 0.0942631 0.171336 0.171434 0.0364398 0.209343 0.987356 1.19008\n", + " 354 1.177e+02 4.510e+08 6.040e-01 -- 3.573e+02 -- 0.259363 -0.362893 -1.45254 -1.7531 -2.34819 -2.74774 -3.55434 -3.76386 0.0188061 0.094653 0.171334 0.171438 0.0364246 0.209325 0.987355 1.19007\n", + " 357 1.797e+01 2.155e+08 1.342e+00 -- 3.586e+02 -- 0.259777 -0.362885 -1.45254 -1.7531 -2.34819 -2.74774 -3.55434 -3.76386 0.0409415 0.0945432 0.171355 0.171458 0.0364111 0.209306 0.987355 1.19007\n", + " 359 2.641e+04 1.751e+14 2.177e+03 -- -1.818e+03 -- 0.258935 -0.362902 -1.45254 -1.7531 -2.3482 -2.74773 -3.55433 -3.76386 -0.0326452 0.0965976 0.171306 0.171256 0.0364091 0.20932 0.987355 1.19007\n", + " 368 1.789e+04 2.368e+14 3.546e+02 -- -2.173e+03 -- 0.258935 -0.362902 -1.45254 -1.7531 -2.3482 -2.74773 -3.55433 -3.76386 -0.0326366 0.0965975 0.171306 0.171256 0.0364091 0.20932 0.987355 1.19007\n", + " 372 6.690e-01 1.850e+06 2.525e+03 -- 3.517e+02 -- 0.261935 -0.363089 -1.45251 -1.75324 -2.3482 -2.74767 -3.55432 -3.76386 -0.616453 0.0981103 0.170229 0.17144 0.0374651 0.209113 0.987434 1.19006\n", + " 374 6.163e-01 2.129e+05 2.479e+00 -- 3.542e+02 -- 0.258997 -0.363262 -1.45252 -1.75326 -2.34833 -2.7479 -3.55454 -3.76384 -0.578257 0.104674 0.17185 0.173716 0.0352143 0.206809 0.987308 1.18982\n", + " 376 5.913e-01 6.432e+05 1.228e+00 -- 3.554e+02 -- 0.256225 -0.363323 -1.45253 -1.75326 -2.3484 -2.74802 -3.55465 -3.76382 -0.542617 0.108165 0.172695 0.174865 0.0340199 0.205587 0.987244 1.18969\n", + " 378 5.514e-01 8.533e+05 7.479e-01 -- 3.561e+02 -- 0.254051 -0.363342 -1.45253 -1.75326 -2.34844 -2.74809 -3.55472 -3.76382 -0.510533 0.110241 0.173163 0.175487 0.0333382 0.204888 0.987208 1.18961\n", + " 380 5.180e-01 1.004e+06 4.971e-01 -- 3.566e+02 -- 0.25242 -0.363341 -1.45254 -1.75326 -2.34846 -2.74813 -3.55476 -3.76381 -0.482381 0.111475 0.17342 0.175814 0.0329481 0.204485 0.987189 1.18957\n", + " 382 4.902e-01 1.139e+06 3.585e-01 -- 3.570e+02 -- 0.25124 -0.36333 -1.45254 -1.75326 -2.34847 -2.74815 -3.55478 -3.76381 -0.457393 0.112191 0.173555 0.175975 0.0327287 0.204255 0.987179 1.18954\n", + " 384 4.537e-01 1.276e+06 2.752e-01 -- 3.573e+02 -- 0.250444 -0.363315 -1.45254 -1.75326 -2.34848 -2.74816 -3.55479 -3.76381 -0.434971 0.112595 0.173618 0.176038 0.0326106 0.204128 0.987175 1.18953\n", + " 386 4.177e-01 1.422e+06 2.177e-01 -- 3.575e+02 -- 0.249927 -0.363298 -1.45254 -1.75326 -2.34849 -2.74816 -3.55479 -3.76381 -0.415237 0.112771 0.173639 0.176044 0.0325551 0.204065 0.987174 1.18952\n", + " 388 3.997e-01 1.582e+06 1.784e-01 -- 3.577e+02 -- 0.249603 -0.36328 -1.45254 -1.75325 -2.34849 -2.74816 -3.55479 -3.76381 -0.397894 0.112831 0.173635 0.176017 0.0325368 0.20404 0.987175 1.18952\n", + " 390 3.521e-01 1.759e+06 1.541e-01 -- 3.578e+02 -- 0.24945 -0.363264 -1.45254 -1.75325 -2.34849 -2.74816 -3.55479 -3.76381 -0.381991 0.11281 0.173617 0.175973 0.0325408 0.204038 0.987177 1.18952\n", + " 392 2.781e-01 1.955e+06 1.294e-01 -- 3.580e+02 -- 0.249369 -0.363247 -1.45254 -1.75325 -2.34849 -2.74815 -3.55479 -3.76381 -0.368541 0.112703 0.17359 0.175916 0.0325603 0.204053 0.98718 1.18952\n", + " 394 3.503e-01 2.172e+06 1.069e-01 -- 3.581e+02 -- 0.249517 -0.363233 -1.45254 -1.75325 -2.34849 -2.74815 -3.55479 -3.76381 -0.35829 0.112607 0.173559 0.17586 0.0325849 0.204074 0.987183 1.18952\n", + " 396 2.380e-01 2.413e+06 1.111e-01 -- 3.582e+02 -- 0.249439 -0.363219 -1.45254 -1.75325 -2.34849 -2.74814 -3.55478 -3.76381 -0.345741 0.112463 0.173528 0.175799 0.0326152 0.204099 0.987186 1.18952\n", + " 398 2.527e-01 2.681e+06 9.063e-02 -- 3.583e+02 -- 0.249818 -0.363207 -1.45254 -1.75325 -2.34849 -2.74814 -3.55478 -3.76381 -0.337514 0.112322 0.173495 0.175737 0.0326474 0.204127 0.987189 1.18952\n", + " 400 1.574e-01 2.980e+06 8.735e-02 -- 3.583e+02 -- 0.24992 -0.363195 -1.45254 -1.75324 -2.34849 -2.74813 -3.55478 -3.76381 -0.328983 0.112124 0.17346 0.175677 0.0326852 0.204162 0.987193 1.18953\n", + " 402 4.787e-02 3.312e+06 7.415e-02 -- 3.584e+02 -- 0.250149 -0.363185 -1.45254 -1.75324 -2.34848 -2.74813 -3.55477 -3.76381 -0.323806 0.111956 0.173429 0.175624 0.0327188 0.204194 0.987196 1.18953\n", + " 404 1.565e-01 3.682e+06 6.319e-02 -- 3.585e+02 -- 0.250644 -0.363177 -1.45254 -1.75324 -2.34848 -2.74812 -3.55477 -3.76381 -0.322256 0.111821 0.173403 0.175576 0.0327469 0.204218 0.987198 1.18953\n", + " 406 1.014e-01 4.089e+06 7.156e-02 -- 3.586e+02 -- 0.250855 -0.363169 -1.45254 -1.75324 -2.34848 -2.74812 -3.55477 -3.76381 -0.317211 0.111743 0.17338 0.175533 0.0327711 0.20424 0.9872 1.18953\n", + " 408 6.026e-02 4.546e+06 6.444e-02 -- 3.586e+02 -- 0.250942 -0.363162 -1.45254 -1.75324 -2.34848 -2.74812 -3.55476 -3.76381 -0.313996 0.111664 0.173354 0.175484 0.032799 0.204265 0.987203 1.18954\n", + " 410 5.353e-02 5.051e+06 6.122e-02 -- 3.587e+02 -- 0.251214 -0.363156 -1.45254 -1.75324 -2.34848 -2.74811 -3.55476 -3.76381 -0.312103 0.111601 0.173334 0.175452 0.0328209 0.204283 0.987205 1.18954\n", + " 412 3.313e-01 5.616e+06 5.936e-02 -- 3.587e+02 -- 0.25132 -0.36315 -1.45254 -1.75324 -2.34848 -2.74811 -3.55476 -3.76381 -0.310433 0.111483 0.173315 0.175418 0.0328405 0.204302 0.987206 1.18954\n", + " 414 2.458e-01 6.221e+06 8.104e-02 -- 3.588e+02 -- 0.25152 -0.363144 -1.45254 -1.75324 -2.34848 -2.74811 -3.55476 -3.76381 -0.300149 0.111342 0.173305 0.175396 0.0328536 0.204313 0.987208 1.18954\n", + " 416 3.303e-01 6.940e+06 4.243e-02 -- 3.589e+02 -- 0.252529 -0.363141 -1.45254 -1.75324 -2.34848 -2.7481 -3.55475 -3.76381 -0.307525 0.111245 0.173274 0.175347 0.0328886 0.204345 0.987211 1.18954\n", + " 418 2.732e-01 7.749e+06 7.538e-02 -- 3.589e+02 -- 0.251523 -0.363133 -1.45254 -1.75324 -2.34848 -2.7481 -3.55475 -3.76381 -0.297369 0.111097 0.173256 0.175306 0.0329145 0.204372 0.987212 1.18955\n", + " 420 2.171e-01 8.619e+06 6.585e-02 -- 3.590e+02 -- 0.250986 -0.363128 -1.45254 -1.75324 -2.34848 -2.7481 -3.55475 -3.76381 -0.289245 0.110957 0.173254 0.175304 0.0329193 0.204375 0.987213 1.18955\n", + " 422 1.367e-01 9.558e+06 6.498e-02 -- 3.591e+02 -- 0.25117 -0.363126 -1.45254 -1.75324 -2.34848 -2.7481 -3.55475 -3.76381 -0.282965 0.110892 0.173251 0.175291 0.0329228 0.204377 0.987213 1.18955\n", + " 424 1.078e-01 1.065e+07 5.047e-02 -- 3.591e+02 -- 0.250283 -0.363121 -1.45254 -1.75324 -2.34848 -2.7481 -3.55475 -3.76381 -0.279096 0.110663 0.173235 0.175263 0.0329452 0.204402 0.987215 1.18955\n", + " 426 6.572e-01 1.172e+07 3.144e-02 -- 3.592e+02 -- 0.249336 -0.363121 -1.45254 -1.75324 -2.34848 -2.7481 -3.55475 -3.76381 -0.282104 0.11081 0.173244 0.175268 0.03293 0.204387 0.987213 1.18955\n", + " 428 5.054e-01 1.316e+07 7.386e-02 -- 3.592e+02 -- 0.249036 -0.36312 -1.45254 -1.75324 -2.34848 -2.74811 -3.55475 -3.76381 -0.263564 0.110889 0.173276 0.175312 0.0328876 0.204345 0.987211 1.18954\n", + " 430 1.710e+00 1.428e+07 5.282e-02 -- 3.593e+02 -- 0.249406 -0.363114 -1.45254 -1.75324 -2.34848 -2.7481 -3.55475 -3.76381 -0.250243 0.110635 0.173257 0.175261 0.0329177 0.204379 0.987214 1.18955\n", + " 432 7.772e-01 1.655e+07 5.093e-02 -- 3.593e+02 -- 0.251439 -0.363115 -1.45254 -1.75324 -2.34847 -2.74809 -3.55474 -3.76381 -0.293033 0.1105 0.173206 0.175194 0.0329741 0.20443 0.987218 1.18955\n", + " 434 1.590e+00 1.724e+07 4.105e-02 -- 3.593e+02 -- 0.251114 -0.363123 -1.45254 -1.75324 -2.34848 -2.7481 -3.55475 -3.76381 -0.315808 0.11098 0.173234 0.175245 0.0329205 0.20438 0.987214 1.18955\n", + " 436 2.078e+00 1.442e+07 6.228e-02 -- 3.594e+02 -- 0.252467 -0.36312 -1.45254 -1.75324 -2.34848 -2.74811 -3.55476 -3.76381 -0.265603 0.111067 0.173304 0.17534 0.0328373 0.204281 0.987211 1.18954\n", + " 438 2.622e+00 2.092e+07 1.046e-01 -- 3.595e+02 -- 0.249064 -0.363089 -1.45254 -1.75323 -2.34847 -2.74809 -3.55474 -3.76381 -0.210422 0.109904 0.173201 0.175158 0.0330073 0.204453 0.987222 1.18955\n", + " 440 2.370e-01 2.398e+07 1.058e-01 -- 3.596e+02 -- 0.250133 -0.363091 -1.45254 -1.75324 -2.34847 -2.74808 -3.55473 -3.76381 -0.265588 0.109484 0.173108 0.175038 0.033125 0.204566 0.987227 1.18957\n", + " 442 6.561e-01 2.389e+07 2.821e-02 -- 3.595e+02 -- 0.247097 -0.363105 -1.45254 -1.75324 -2.34847 -2.74809 -3.55474 -3.76381 -0.271884 0.110528 0.173184 0.175113 0.033014 0.204456 0.987219 1.18955\n", + " 444 5.072e+00 5.109e+08 1.835e+00 -- 3.577e+02 -- 0.236997 -0.363105 -1.45255 -1.75323 -2.34848 -2.74811 -3.55476 -3.76381 -0.254045 0.11072 0.173288 0.175291 0.0328836 0.204367 0.987207 1.18954\n", + " 447 1.057e+01 4.593e+08 1.662e-01 -- 3.579e+02 -- 0.237886 -0.363111 -1.45255 -1.75323 -2.34848 -2.74812 -3.55476 -3.76381 -0.266931 0.110974 0.173303 0.175328 0.0328554 0.204341 0.987206 1.18954\n", + " 450 7.338e+01 1.668e+10 1.284e+01 -- 3.450e+02 -- 0.229947 -0.363104 -1.45255 -1.75323 -2.34848 -2.74812 -3.55476 -3.76381 -0.238724 0.11079 0.17333 0.175359 0.032836 0.204334 0.987203 1.18954\n", + " 454 1.669e+01 4.660e+07 1.379e+01 -- 3.588e+02 -- 0.232947 -0.363102 -1.45255 -1.75323 -2.34848 -2.74812 -3.55476 -3.76381 -0.221206 0.110782 0.173331 0.175354 0.0328338 0.204323 0.987204 1.18953\n", + " 457 6.736e+00 7.637e+07 8.954e-03 -- 3.588e+02 -- 0.236984 -0.363112 -1.45255 -1.75323 -2.34848 -2.74812 -3.55476 -3.76381 -0.258136 0.110966 0.173339 0.17538 0.0328089 0.204294 0.987203 1.18953\n", + " 460 6.471e+00 9.704e+06 4.604e-01 -- 3.593e+02 -- 0.236655 -0.363111 -1.45255 -1.75323 -2.34849 -2.74813 -3.55477 -3.76381 -0.240747 0.111111 0.173355 0.1754 0.0327882 0.204273 0.987202 1.18953\n", + " 463 8.613e+00 4.704e+07 4.115e-01 -- 3.597e+02 -- 0.237547 -0.36311 -1.45255 -1.75323 -2.34849 -2.74813 -3.55477 -3.76381 -0.225168 0.111183 0.173367 0.17542 0.032773 0.204255 0.987202 1.18953\n", + " 466 3.362e+00 7.507e+07 1.913e-01 -- 3.599e+02 -- 0.238404 -0.363107 -1.45255 -1.75323 -2.34849 -2.74813 -3.55477 -3.76381 -0.205775 0.111127 0.173375 0.175432 0.0327636 0.204245 0.987202 1.18953\n", + " 468 3.391e+01 6.069e+07 2.465e-01 -- 3.596e+02 -- 0.233164 -0.363093 -1.45255 -1.75323 -2.34849 -2.74813 -3.55477 -3.76381 -0.136596 0.111009 0.173402 0.175399 0.0327685 0.204241 0.987201 1.18953\n", + " 471 1.410e+02 2.370e+07 3.883e-01 -- 3.592e+02 -- 0.228233 -0.363082 -1.45255 -1.75323 -2.34849 -2.74813 -3.55477 -3.76381 -0.0902722 0.110874 0.173404 0.175393 0.0327806 0.204258 0.987201 1.18953\n", + " 472 4.001e+00 6.877e+07 5.523e+01 -- 3.040e+02 -- 0.144854 -0.360368 -1.4527 -1.75287 -2.34832 -2.74795 -3.55454 -3.76384 0.073615 0.0439555 0.174963 0.172283 0.0343795 0.20623 0.987592 1.18965\n", + " 474 4.449e+00 9.477e+04 4.908e+01 -- 3.531e+02 -- 0.178975 -0.361472 -1.45258 -1.7526 -2.34944 -2.74907 -3.55594 -3.76368 0.0676088 0.0602605 0.184173 0.186509 0.0206225 0.191289 0.986849 1.18821\n", + " 476 7.496e+00 1.443e+05 8.590e-01 -- 3.539e+02 -- 0.184083 -0.361709 -1.45256 -1.75266 -2.34954 -2.74923 -3.5561 -3.76366 0.0375291 0.0666604 0.184752 0.186797 0.01903 0.189498 0.986749 1.188\n", + " 478 2.779e+01 1.616e+05 4.958e-01 -- 3.544e+02 -- 0.188654 -0.361864 -1.45255 -1.75273 -2.34955 -2.74927 -3.55613 -3.76365 0.00939594 0.0715689 0.184457 0.186024 0.018875 0.189182 0.986725 1.18796\n", + " 480 1.441e+01 1.748e+05 3.655e-01 -- 3.548e+02 -- 0.192925 -0.361971 -1.45254 -1.7528 -2.34952 -2.74926 -3.55609 -3.76366 -0.0167118 0.075451 0.183801 0.184833 0.0193558 0.189523 0.986735 1.18799\n", + " 482 5.419e+00 1.903e+05 3.047e-01 -- 3.551e+02 -- 0.196991 -0.36205 -1.45252 -1.75286 -2.34949 -2.74921 -3.55603 -3.76366 -0.0407928 0.0786126 0.183025 0.183518 0.020092 0.190136 0.986762 1.18805\n", + " 484 3.210e+00 2.094e+05 2.684e-01 -- 3.554e+02 -- 0.20088 -0.36211 -1.45251 -1.75292 -2.34944 -2.74916 -3.55596 -3.76367 -0.0629001 0.081257 0.18224 0.182224 0.0208981 0.190834 0.986794 1.18811\n", + " 486 2.214e+00 2.319e+05 2.427e-01 -- 3.556e+02 -- 0.204623 -0.362158 -1.4525 -1.75296 -2.3494 -2.74911 -3.55589 -3.76368 -0.0830936 0.0835154 0.181498 0.181014 0.0216866 0.191527 0.986828 1.18818\n", + " 488 1.641e+00 2.576e+05 2.220e-01 -- 3.558e+02 -- 0.2082 -0.362199 -1.45249 -1.753 -2.34936 -2.74906 -3.55583 -3.76369 -0.101492 0.0854718 0.18082 0.179912 0.0224199 0.192178 0.986859 1.18824\n", + " 490 1.279e+00 2.867e+05 2.041e-01 -- 3.560e+02 -- 0.21159 -0.362234 -1.45249 -1.75303 -2.34933 -2.74901 -3.55577 -3.76369 -0.118149 0.0871837 0.18021 0.178923 0.0230854 0.192771 0.986888 1.1883\n", + " 492 1.019e+00 3.190e+05 1.889e-01 -- 3.562e+02 -- 0.214798 -0.362264 -1.45248 -1.75306 -2.3493 -2.74897 -3.55572 -3.7637 -0.133264 0.0886906 0.179665 0.178041 0.0236819 0.193305 0.986915 1.18835\n", + " 494 8.350e-01 3.550e+05 1.755e-01 -- 3.564e+02 -- 0.217839 -0.362291 -1.45247 -1.75309 -2.34927 -2.74893 -3.55567 -3.76371 -0.146847 0.0900198 0.179181 0.177257 0.024214 0.193782 0.986939 1.1884\n", + " 496 6.864e-01 3.949e+05 1.631e-01 -- 3.566e+02 -- 0.220677 -0.362314 -1.45247 -1.75311 -2.34924 -2.74889 -3.55563 -3.76371 -0.159109 0.0911918 0.178751 0.17656 0.0246885 0.19421 0.98696 1.18844\n", + " 498 5.835e-01 4.392e+05 1.520e-01 -- 3.567e+02 -- 0.223333 -0.362335 -1.45246 -1.75313 -2.34922 -2.74886 -3.55559 -3.76371 -0.17003 0.0922269 0.178367 0.175939 0.0251115 0.194591 0.986979 1.18847\n", + " 500 4.877e-01 4.884e+05 1.422e-01 -- 3.569e+02 -- 0.225803 -0.362354 -1.45246 -1.75314 -2.3492 -2.74883 -3.55556 -3.76372 -0.179951 0.0931396 0.178026 0.175387 0.0254897 0.194933 0.986996 1.18851\n", + "********************\n", + "0.248545 -0.362521 -1.45242 -1.75328 -2.34903 -2.74857 -3.55526 -3.76375 -0.267712 0.101175 0.174975 0.17046 0.0288768 0.198006 0.987152 1.18879\n", + "0.0326854 0.00121998 0.000396181 0.00485342 0.00441057 0.00846026 0.00173706 0.000198443 0.208668 0.0333017 0.0139548 0.0472137 0.0310511 0.0388547 0.00686236 0.00229119\n", + "21.6768 960.747 11500.2 1088.5 2531.9 1489.42 54226.9 488371 -1.74721 7.48018 -88.3148 -31.5201 -35.9069 149.822 -1498.85 -399.194\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": 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 + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s, loc, scale = lognorm.fit(lag,loc=.01)\n", + "\n", + "xscale('log'); ylim(-1,5)\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n", + "plot(fqd,lognorm.pdf(fqd,s,loc,scale))" + ] + }, + { + "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 +} diff --git a/lag/data/clag_analysis-origbins-noLFerrors-4368A.ipynb b/lag/data/clag_analysis-origbins-noLFerrors-4368A.ipynb new file mode 100644 index 0000000..a5bf876 --- /dev/null +++ b/lag/data/clag_analysis-origbins-noLFerrors-4368A.ipynb @@ -0,0 +1,868 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "from scipy.optimize import curve_fit\n", + "import numpy.fft\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/4368A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915, 0.62032418])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AG51jNQiHSw4XfI5VvlPwUABCd1Qexq78NwIl/SXU//fkKuFN5DsCK4DyLg87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\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.361e-01 5.503e+01 inf -- -2.230e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 7.719e-01 5.459e+01 6.638e+01 -- -1.566e+02 -- 0.578708 0.567444 0.565767 0.565645 0.565269 0.564635 0.565645 0.563945\n", + " 3 3.383e+00 5.393e+01 6.562e+01 -- -9.097e+01 -- 0.185507 0.1439 0.132758 0.130601 0.130916 0.129667 0.131085 0.12865\n", + " 4 1.445e+00 5.277e+01 6.426e+01 -- -2.671e+01 -- -0.142659 -0.257806 -0.298121 -0.305924 -0.303305 -0.305147 -0.304059 -0.306574\n", + " 5 5.917e-01 5.083e+01 6.196e+01 -- 3.525e+01 -- -0.348804 -0.608981 -0.725147 -0.744945 -0.737788 -0.740133 -0.740511 -0.742464\n", + " 6 3.723e-01 4.792e+01 5.867e+01 -- 9.392e+01 -- -0.417419 -0.869413 -1.14636 -1.18533 -1.17119 -1.17501 -1.17867 -1.1784\n", + " 7 2.715e-01 4.436e+01 5.444e+01 -- 1.484e+02 -- -0.421165 -1.0235 -1.55636 -1.61922 -1.59707 -1.60795 -1.61745 -1.61028\n", + " 8 3.575e-01 4.073e+01 4.955e+01 -- 1.979e+02 -- -0.374308 -1.10343 -1.93497 -2.03239 -2.00811 -2.03747 -2.05653 -2.03339\n", + " 9 5.926e-01 3.677e+01 4.460e+01 -- 2.425e+02 -- -0.240503 -1.1527 -2.25517 -2.39911 -2.40429 -2.4649 -2.49991 -2.44956\n", + " 10 7.482e-01 3.119e+01 3.817e+01 -- 2.807e+02 -- -0.0979917 -1.17663 -2.4937 -2.68072 -2.78672 -2.88942 -2.95412 -2.86404\n", + " 11 1.685e+00 2.383e+01 2.855e+01 -- 3.092e+02 -- -0.0246695 -1.17354 -2.62135 -2.85109 -3.13126 -3.28381 -3.42245 -3.27294\n", + " 12 1.725e+00 1.552e+01 1.678e+01 -- 3.260e+02 -- 0.0169008 -1.15973 -2.6659 -2.93332 -3.39842 -3.56844 -3.89212 -3.66754\n", + " 13 4.932e-01 7.866e+00 6.878e+00 -- 3.329e+02 -- 0.0460619 -1.14068 -2.67864 -2.97196 -3.56721 -3.67273 -4.2811 -4.0398\n", + " 14 1.822e-01 2.994e+00 2.075e+00 -- 3.350e+02 -- 0.0687779 -1.12228 -2.6668 -2.98023 -3.66584 -3.7084 -4.35974 -4.38147\n", + " 15 4.696e-02 1.029e+00 5.962e-01 -- 3.356e+02 -- 0.0813069 -1.11484 -2.65366 -2.96318 -3.74202 -3.72746 -4.24095 -4.67811\n", + " 16 2.197e-02 3.378e-01 1.595e-01 -- 3.357e+02 -- 0.0842152 -1.11741 -2.64665 -2.94344 -3.80682 -3.72328 -4.23445 -4.89778\n", + " 17 1.076e-02 1.040e-01 2.591e-02 -- 3.357e+02 -- 0.0848765 -1.11851 -2.639 -2.93736 -3.84761 -3.73244 -4.19543 -5.00538\n", + " 18 2.514e-03 8.065e-02 3.505e-03 -- 3.357e+02 -- 0.0846132 -1.1201 -2.63699 -2.93391 -3.86186 -3.72437 -4.196 -5.05922\n", + " 19 3.048e-03 6.689e-02 4.875e-04 -- 3.357e+02 -- 0.0844096 -1.12043 -2.63505 -2.93301 -3.87157 -3.72842 -4.18689 -5.06117\n", + " 20 1.543e-03 4.977e-02 1.110e-04 -- 3.357e+02 -- 0.0843879 -1.12064 -2.63495 -2.93266 -3.87045 -3.72409 -4.18999 -5.0766\n", + " 21 1.622e-03 4.180e-02 4.688e-05 -- 3.357e+02 -- 0.084282 -1.1207 -2.63451 -2.93251 -3.87392 -3.72714 -4.1865 -5.06876\n", + "********************\n", + "0.084282 -1.1207 -2.63451 -2.93251 -3.87392 -3.72714 -4.1865 -5.06876\n", + "0.228004 0.210453 0.282191 0.221429 0.392762 0.234543 0.3158 0.979911\n", + "0.000969367 -0.000244794 -0.00155556 0.00155923 0.0136076 0.0418006 -0.0299347 -0.0115783\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.357e+02 3.353e+02 8.433e-02 3.123e-01 0.809 +++\n", + "+++ 3.357e+02 3.349e+02 8.433e-02 4.263e-01 1.68 +++\n", + "+++ 3.357e+02 3.351e+02 8.433e-02 3.693e-01 1.21 +++\n", + "+++ 3.357e+02 3.352e+02 8.433e-02 3.408e-01 1.01 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.357e+02 3.353e+02 -1.121e+00 -9.103e-01 0.833 +++\n", + "+++ 3.357e+02 3.349e+02 -1.121e+00 -8.050e-01 1.76 +++\n", + "+++ 3.357e+02 3.351e+02 -1.121e+00 -8.576e-01 1.26 +++\n", + "+++ 3.357e+02 3.352e+02 -1.121e+00 -8.839e-01 1.04 +++\n", + "+++ 3.357e+02 3.353e+02 -1.121e+00 -8.971e-01 0.933 +++\n", + "+++ 3.357e+02 3.353e+02 -1.121e+00 -8.905e-01 0.985 +++\n", + "+++ 3.357e+02 3.352e+02 -1.121e+00 -8.872e-01 1.01 +++\n", + "+++ 3.357e+02 3.352e+02 -1.121e+00 -8.889e-01 0.998 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.357e+02 3.353e+02 -2.635e+00 -2.352e+00 0.888 +++\n", + "+++ 3.357e+02 3.348e+02 -2.635e+00 -2.211e+00 1.91 +++\n", + "+++ 3.357e+02 3.351e+02 -2.635e+00 -2.282e+00 1.36 +++\n", + "+++ 3.357e+02 3.352e+02 -2.635e+00 -2.317e+00 1.11 +++\n", + "+++ 3.357e+02 3.352e+02 -2.635e+00 -2.335e+00 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.357e+02 3.353e+02 -2.933e+00 -2.711e+00 0.893 +++\n", + "+++ 3.357e+02 3.348e+02 -2.933e+00 -2.600e+00 1.98 +++\n", + "+++ 3.357e+02 3.351e+02 -2.933e+00 -2.656e+00 1.38 +++\n", + "+++ 3.357e+02 3.352e+02 -2.933e+00 -2.683e+00 1.13 +++\n", + "+++ 3.357e+02 3.352e+02 -2.933e+00 -2.697e+00 1.01 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.357e+02 3.356e+02 -3.872e+00 -3.676e+00 0.27 +++\n", + "+++ 3.357e+02 3.354e+02 -3.872e+00 -3.578e+00 0.656 +++\n", + "+++ 3.357e+02 3.353e+02 -3.872e+00 -3.529e+00 0.919 +++\n", + "+++ 3.357e+02 3.352e+02 -3.872e+00 -3.505e+00 1.07 +++\n", + "+++ 3.357e+02 3.352e+02 -3.872e+00 -3.517e+00 0.994 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.357e+02 3.356e+02 -3.725e+00 -3.608e+00 0.326 +++\n", + "+++ 3.357e+02 3.354e+02 -3.725e+00 -3.549e+00 0.747 +++\n", + "+++ 3.357e+02 3.352e+02 -3.725e+00 -3.520e+00 1.03 +++\n", + "+++ 3.357e+02 3.353e+02 -3.725e+00 -3.535e+00 0.881 +++\n", + "+++ 3.357e+02 3.353e+02 -3.725e+00 -3.527e+00 0.952 +++\n", + "+++ 3.357e+02 3.353e+02 -3.725e+00 -3.524e+00 0.989 +++\n", + "+++ 3.357e+02 3.352e+02 -3.725e+00 -3.522e+00 1.01 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.357e+02 3.356e+02 -4.189e+00 -4.030e+00 0.334 +++\n", + "+++ 3.357e+02 3.353e+02 -4.189e+00 -3.951e+00 0.82 +++\n", + "+++ 3.357e+02 3.352e+02 -4.189e+00 -3.912e+00 1.18 +++\n", + "+++ 3.357e+02 3.353e+02 -4.189e+00 -3.931e+00 0.987 +++\n", + "+++ 3.357e+02 3.352e+02 -4.189e+00 -3.922e+00 1.08 +++\n", + "+++ 3.357e+02 3.352e+02 -4.189e+00 -3.927e+00 1.03 +++\n", + "+++ 3.357e+02 3.352e+02 -4.189e+00 -3.929e+00 1.01 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.357e+02 3.355e+02 -5.077e+00 -4.580e+00 0.558 +++\n", + "+++ 3.357e+02 3.348e+02 -5.077e+00 -4.331e+00 1.79 +++\n", + "+++ 3.357e+02 3.352e+02 -5.077e+00 -4.456e+00 1.04 +++\n", + "+++ 3.357e+02 3.354e+02 -5.077e+00 -4.518e+00 0.777 +++\n", + "+++ 3.357e+02 3.353e+02 -5.077e+00 -4.487e+00 0.901 +++\n", + "+++ 3.357e+02 3.353e+02 -5.077e+00 -4.471e+00 0.971 +++\n", + "+++ 3.357e+02 3.352e+02 -5.077e+00 -4.463e+00 1.01 +++\n", + "********************\n", + "0.0843333 -1.12071 -2.63465 -2.93252 -3.87183 -3.72454 -4.18887 -5.07698\n", + "0.256503 0.231827 0.299857 0.235295 0.354897 0.202775 0.259869 0.613662\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0.0713363 1.432\n", + " 7 5.441e+02 1.532e+01 2.037e+00 -- 3.958e+02 -- -0.0584518 -0.853102 -2.14774 -2.456 -3.21197 -3.29032 -4.1871 -7.13849 -0.100019 0.185908 0.435633 0.382521 0.226208 0.192552 0.0562607 1.59836\n", + " 9 1.849e+02 1.731e+01 1.886e+00 -- 3.977e+02 -- -0.0431336 -0.830436 -2.1241 -2.43328 -3.1906 -3.26311 -4.19225 -7.43849 -0.148048 0.203221 0.505675 0.442641 0.251144 0.209088 0.0403995 2.5906\n", + " 11 1.731e+04 1.942e+01 1.753e+00 -- 3.994e+02 -- -0.0288471 -0.810973 -2.10201 -2.41247 -3.17227 -3.23993 -4.19795 -7.73849 -0.189141 0.217465 0.561595 0.491159 0.271137 0.221974 0.0236847 0.232263\n", + " 13 6.528e+03 2.165e+01 1.632e+00 -- 4.011e+02 -- -0.0156722 -0.794137 -2.08185 -2.39369 -3.15646 -3.22006 -4.20417 -7.43849 -0.224437 0.229415 0.606654 0.53056 0.287329 0.232192 0.00612053 0.325107\n", + " 15 4.527e+02 2.400e+01 1.520e+00 -- 4.026e+02 -- -0.003614 -0.779481 -2.06369 -2.37687 -3.14277 -3.20293 -4.21087 -7.13849 -0.25488 0.239608 0.643331 0.562767 0.300521 0.240394 -0.0123632 1.71181\n", + " 17 2.052e+03 2.647e+01 1.426e+00 -- 4.040e+02 -- 0.00736587 -0.766656 -2.04744 -2.36188 -3.13087 -3.1881 -4.21799 -7.43849 -0.281243 0.248428 0.673473 0.58925 0.311289 0.247033 -0.0318346 -0.392158\n", + " 18 3.773e+00 5.593e+03 1.514e+00 -- 4.055e+02 -- 0.106995 -0.653906 -1.90262 -2.22854 -3.02717 -3.05917 -4.29289 -4.43849 -0.510384 0.325715 0.923411 0.807986 0.399154 0.301171 -0.237126 -1.01765\n", + " 20 2.849e+00 3.659e+03 5.484e+00 -- 4.110e+02 -- 0.107323 -0.654528 -1.90254 -2.23194 -3.02323 -3.064 -4.28509 -4.32949 -0.509195 0.328192 0.912135 0.803339 0.389116 0.289516 -0.32659 -0.962776\n", + " 22 1.486e+00 2.444e+03 3.340e+00 -- 4.143e+02 -- 0.108022 -0.654908 -1.90278 -2.23432 -3.01757 -3.06737 -4.25844 -4.25945 -0.507022 0.331633 0.909715 0.784568 0.370291 0.289219 -0.419626 -0.914908\n", + " 24 9.532e-01 1.559e+03 2.394e+00 -- 4.167e+02 -- 0.108513 -0.655239 -1.90317 -2.23664 -3.01314 -3.07039 -4.24064 -4.21341 -0.504547 0.334668 0.90699 0.766684 0.356598 0.288412 -0.48198 -0.887983\n", + " 25 1.060e+00 2.012e+02 1.863e+00 -- 4.149e+02 -- 0.112015 -0.658052 -1.90797 -2.25721 -2.98205 -3.09702 -4.14368 -3.91937 -0.477027 0.36146 0.872856 0.608912 0.263183 0.274467 -0.941403 -0.711503\n", + " 26 6.891e-01 5.834e+02 5.512e+00 -- 4.204e+02 -- 0.112132 -0.654668 -1.90638 -2.25379 -3.06056 -3.07914 -4.76266 -3.9152 -0.42166 0.404325 0.803237 0.671367 0.321541 0.247382 -1.93887 -0.377098\n", + " 28 4.161e-01 2.170e+02 1.164e+00 -- 4.215e+02 -- 0.111002 -0.655061 -1.90694 -2.25331 -3.04803 -3.07818 -4.87251 -3.9149 -0.433955 0.397673 0.809209 0.653739 0.311798 0.264429 -1.87993 -0.373049\n", + " 30 4.034e-01 1.252e+02 5.089e-01 -- 4.221e+02 -- 0.110361 -0.655324 -1.90727 -2.253 -3.04023 -3.0776 -4.95911 -3.91453 -0.440907 0.393109 0.812583 0.641828 0.30727 0.275431 -1.81241 -0.369329\n", + " 32 4.173e-01 1.421e+02 2.508e-01 -- 4.223e+02 -- 0.109998 -0.655506 -1.90746 -2.25281 -3.03521 -3.07721 -5.02214 -3.91419 -0.44479 0.38991 0.814296 0.633472 0.305273 0.282683 -1.7393 -0.365774\n", + " 33 2.844e+01 4.813e+05 3.489e+01 -- 3.874e+02 -- 0.108249 -0.656743 -1.90855 -2.25163 -3.003 -3.0745 -5.3963 -3.91113 -0.462731 0.368115 0.820327 0.574151 0.299794 0.329367 -1.01341 -0.331703\n", + " 36 1.265e+01 2.320e+05 1.439e+01 -- 4.018e+02 -- 0.109865 -0.656548 -1.90848 -2.25209 -3.0047 -3.07472 -5.3663 -3.91105 -0.437958 0.375118 0.819209 0.579752 0.302019 0.324713 -1.30159 -0.331943\n", + " 39 6.733e+00 1.667e+05 5.585e+00 -- 4.074e+02 -- 0.111231 -0.65638 -1.90842 -2.25246 -3.00615 -3.0749 -5.3363 -3.91099 -0.420135 0.380536 0.818248 0.584365 0.30392 0.320786 -1.46618 -0.33213\n", + " 42 3.976e+00 1.333e+05 3.474e+00 -- 4.109e+02 -- 0.112481 -0.65623 -1.90836 -2.25279 -3.00744 -3.07507 -5.3063 -3.91093 -0.405052 0.385134 0.817406 0.588389 0.305606 0.31735 -1.56491 -0.332295\n", + " 45 3.037e+00 1.091e+05 2.625e+00 -- 4.135e+02 -- 0.113648 -0.656095 -1.90831 -2.25307 -3.00859 -3.07521 -5.2763 -3.91087 -0.391718 0.389141 0.816665 0.591957 0.307115 0.314316 -1.62712 -0.332443\n", + " 47 5.206e+00 2.332e+06 1.467e+00 -- 4.120e+02 -- 0.124504 -0.654874 -1.90786 -2.25555 -3.0188 -3.07651 -4.9763 -3.91038 -0.272749 0.424266 0.810137 0.623655 0.320604 0.287511 -2.03571 -0.33378\n", + " 49 3.404e+00 4.336e+05 5.127e+00 -- 4.171e+02 -- 0.114992 -0.655304 -1.90798 -2.25475 -3.01557 -3.07613 -5.08623 -3.9105 -0.414737 0.410238 0.811866 0.611592 0.316059 0.295817 -2.07572 -0.333075\n", + " 51 2.831e+00 3.964e+04 3.716e+00 -- 4.209e+02 -- 0.120998 -0.654743 -1.90782 -2.25581 -3.01975 -3.07664 -4.90259 -3.91033 -0.273559 0.428183 0.809505 0.62668 0.3219 0.284945 -1.99017 -0.333892\n", + " 53 9.648e-01 6.528e+03 2.044e+00 -- 4.229e+02 -- 0.111438 -0.655357 -1.90801 -2.25473 -3.01525 -3.07606 -5.0328 -3.91052 -0.350991 0.411981 0.811898 0.611992 0.315981 0.296109 -2.02358 -0.332869\n", + " 55 6.664e-01 4.533e+03 5.395e-01 -- 4.234e+02 -- 0.115342 -0.655005 -1.90788 -2.25535 -3.01813 -3.07641 -4.91958 -3.91037 -0.317126 0.420377 0.810122 0.620816 0.319972 0.288893 -1.97289 -0.333205\n", + " 57 1.352e-01 5.078e+03 2.047e-01 -- 4.236e+02 -- 0.113136 -0.655183 -1.90793 -2.25501 -3.01693 -3.07625 -4.95555 -3.91041 -0.338259 0.415156 0.810681 0.616936 0.318597 0.291917 -1.97334 -0.332782\n", + " 58 4.391e+00 1.231e+05 9.307e+00 -- 4.143e+02 -- 0.121374 -0.654363 -1.90751 -2.25625 -3.02524 -3.0772 -4.67748 -3.90983 -0.292538 0.427891 0.804793 0.639809 0.331433 0.271874 -1.8091 -0.332351\n", + " 60 7.456e-01 3.599e+03 8.350e+00 -- 4.227e+02 -- 0.106032 -0.655512 -1.90793 -2.25425 -3.01553 -3.07594 -4.84676 -3.91027 -0.421005 0.399168 0.809729 0.61079 0.31849 0.294571 -1.79863 -0.330294\n", + " 62 1.392e-01 2.951e+03 7.707e-01 -- 4.235e+02 -- 0.109207 -0.655134 -1.90775 -2.25492 -3.01931 -3.07641 -4.74803 -3.91008 -0.389616 0.408214 0.807266 0.62061 0.323572 0.285397 -1.79761 -0.330504\n", + " 64 4.741e-02 3.163e+03 7.625e-02 -- 4.235e+02 -- 0.109031 -0.655208 -1.90776 -2.25473 -3.01888 -3.07633 -4.75697 -3.91007 -0.39504 0.405859 0.807295 0.619007 0.323289 0.286476 -1.78732 -0.330095\n", + " 65 7.877e-01 1.498e+04 9.182e-01 -- 4.245e+02 -- 0.112757 -0.655181 -1.90757 -2.25427 -3.02158 -3.07653 -4.69927 -3.9097 -0.391127 0.400991 0.803725 0.621415 0.329113 0.280586 -1.7026 -0.327321\n", + " 67 7.769e-01 7.591e+03 8.463e-03 -- 4.245e+02 -- 0.111405 -0.655282 -1.9076 -2.25407 -3.02073 -3.07642 -4.71485 -3.90973 -0.421937 0.397325 0.804142 0.618422 0.327917 0.282676 -1.69714 -0.327119\n", + " 69 8.439e-01 1.220e+04 6.394e-03 -- 4.245e+02 -- 0.112447 -0.655184 -1.90757 -2.25426 -3.02159 -3.07653 -4.69823 -3.9097 -0.389155 0.400927 0.803682 0.621315 0.329157 0.280545 -1.70166 -0.327268\n", + " 71 8.438e-01 1.705e+03 1.926e-02 -- 4.244e+02 -- 0.111092 -0.655286 -1.9076 -2.25405 -3.02073 -3.07642 -4.71408 -3.90973 -0.421995 0.397181 0.804108 0.618265 0.327936 0.282675 -1.69617 -0.327066\n", + " 73 9.456e-01 3.856e+03 3.704e-02 -- 4.244e+02 -- 0.112145 -0.655184 -1.90756 -2.25425 -3.02163 -3.07653 -4.69678 -3.90969 -0.386386 0.40098 0.80363 0.621306 0.329229 0.280449 -1.70102 -0.327224\n", + " 75 9.484e-01 1.219e+04 5.405e-02 -- 4.243e+02 -- 0.110702 -0.655293 -1.9076 -2.25403 -3.0207 -3.07642 -4.71375 -3.90972 -0.422922 0.396955 0.804089 0.618033 0.327915 0.282736 -1.69523 -0.327011\n", + " 77 1.088e+00 1.133e+04 7.281e-02 -- 4.243e+02 -- 0.111809 -0.655182 -1.90756 -2.25425 -3.02168 -3.07654 -4.69489 -3.90969 -0.382814 0.401142 0.803569 0.621375 0.329327 0.280303 -1.70068 -0.327188\n", + " 79 1.081e+00 3.236e+04 9.055e-02 -- 4.242e+02 -- 0.110185 -0.655303 -1.9076 -2.25401 -3.02065 -3.07641 -4.71376 -3.90972 -0.424473 0.396658 0.804084 0.61773 0.327857 0.282854 -1.69434 -0.326954\n", + " 81 1.256e+00 3.127e+04 1.020e-01 -- 4.241e+02 -- 0.1114 -0.655177 -1.90756 -2.25426 -3.02175 -3.07655 -4.69255 -3.90968 -0.378605 0.401398 0.803501 0.621505 0.329444 0.280115 -1.70061 -0.327157\n", + " 83 1.218e+00 5.401e+04 1.109e-01 -- 4.240e+02 -- 0.1095 -0.655315 -1.9076 -2.25398 -3.02057 -3.0764 -4.71391 -3.90972 -0.426174 0.396317 0.804088 0.61737 0.32777 0.283014 -1.69355 -0.326894\n", + " 85 1.416e+00 4.823e+04 1.019e-01 -- 4.239e+02 -- 0.11089 -0.655173 -1.90755 -2.25427 -3.02182 -3.07655 -4.68987 -3.90967 -0.374267 0.401708 0.803429 0.621658 0.329569 0.279907 -1.70078 -0.327127\n", + " 87 1.324e+00 6.691e+04 9.205e-02 -- 4.238e+02 -- 0.108647 -0.655329 -1.9076 -2.25396 -3.02049 -3.07639 -4.71389 -3.90972 -0.427257 0.39599 0.804093 0.616994 0.327674 0.283184 -1.69292 -0.326831\n", + " 89 1.518e+00 5.336e+04 5.901e-02 -- 4.237e+02 -- 0.110282 -0.655169 -1.90755 -2.25427 -3.02188 -3.07656 -4.68711 -3.90967 -0.370691 0.401992 0.803358 0.621766 0.329676 0.279722 -1.70104 -0.32709\n", + " 91 1.363e+00 6.464e+04 3.320e-02 -- 4.237e+02 -- 0.107719 -0.655341 -1.9076 -2.25393 -3.02043 -3.07638 -4.71329 -3.90972 -0.426953 0.395764 0.804086 0.616671 0.327601 0.283309 -1.69252 -0.326767\n", + " 93 1.526e+00 4.562e+04 6.692e-03 -- 4.237e+02 -- 0.109628 -0.65517 -1.90755 -2.25427 -3.02191 -3.07656 -4.6847 -3.90966 -0.368773 0.402148 0.8033 0.621751 0.329738 0.279613 -1.70118 -0.327043\n", + " 95 1.323e+00 5.120e+04 3.216e-02 -- 4.237e+02 -- 0.106898 -0.655349 -1.9076 -2.25392 -3.0204 -3.07637 -4.71179 -3.90972 -0.425043 0.395712 0.804056 0.616471 0.327583 0.283338 -1.69238 -0.326705\n", + " 97 1.445e+00 3.175e+04 5.704e-02 -- 4.238e+02 -- 0.109016 -0.655177 -1.90754 -2.25426 -3.0219 -3.07656 -4.68302 -3.90966 -0.368792 0.402111 0.80326 0.621574 0.329739 0.27961 -1.70101 -0.326981\n", + " 99 1.229e+00 3.515e+04 6.982e-02 -- 4.239e+02 -- 0.106338 -0.655353 -1.9076 -2.25391 -3.02041 -3.07637 -4.70944 -3.90972 -0.4221 0.395834 0.804 0.616416 0.327632 0.283254 -1.69235 -0.326647\n", + " 101 1.319e+00 1.840e+04 7.455e-02 -- 4.239e+02 -- 0.108527 -0.655187 -1.90754 -2.25424 -3.02185 -3.07655 -4.68208 -3.90966 -0.370206 0.401903 0.803237 0.621265 0.329691 0.279697 -1.70044 -0.326907\n", + " 103 1.120e+00 2.184e+04 7.530e-02 -- 4.240e+02 -- 0.106072 -0.655352 -1.9076 -2.25391 -3.02046 -3.07638 -4.70665 -3.90971 -0.41905 0.396056 0.803924 0.616461 0.327731 0.283092 -1.69227 -0.326593\n", + " 105 1.194e+00 8.302e+03 6.817e-02 -- 4.241e+02 -- 0.108199 -0.655199 -1.90755 -2.25421 -3.02179 -3.07654 -4.68162 -3.90966 -0.372137 0.401611 0.80322 0.620907 0.329627 0.279821 -1.69955 -0.326829\n", + " 107 1.025e+00 1.258e+04 6.301e-02 -- 4.241e+02 -- 0.106023 -0.655349 -1.90759 -2.25392 -3.02053 -3.07638 -4.70391 -3.9097 -0.41658 0.396283 0.803843 0.616536 0.327848 0.282906 -1.69203 -0.326541\n", + " 109 1.099e+00 1.871e+03 5.223e-02 -- 4.242e+02 -- 0.108019 -0.655211 -1.90755 -2.25419 -3.02173 -3.07653 -4.6813 -3.90966 -0.37389 0.401324 0.803202 0.620571 0.329573 0.279934 -1.69853 -0.326753\n", + " 111 9.609e-01 7.026e+03 4.523e-02 -- 4.242e+02 -- 0.106089 -0.655347 -1.90759 -2.25392 -3.02058 -3.07639 -4.70155 -3.9097 -0.414971 0.396455 0.803767 0.616587 0.327956 0.28274 -1.69159 -0.326489\n", + " 113 1.043e+00 2.556e+03 3.426e-02 -- 4.243e+02 -- 0.107944 -0.655219 -1.90755 -2.25416 -3.02169 -3.07653 -4.68086 -3.90965 -0.375095 0.401096 0.803178 0.6203 0.329543 0.280008 -1.69752 -0.326683\n", + " 115 9.324e-01 4.787e+03 2.670e-02 -- 4.243e+02 -- 0.106189 -0.655346 -1.90759 -2.25391 -3.02063 -3.07639 -4.69965 -3.90969 -0.414229 0.396547 0.803702 0.616588 0.32804 0.282617 -1.69102 -0.326437\n", + " 117 1.029e+00 3.041e+03 1.600e-02 -- 4.243e+02 -- 0.107925 -0.655226 -1.90755 -2.25415 -3.02167 -3.07652 -4.6802 -3.90965 -0.375607 0.400949 0.803146 0.620104 0.32954 0.280036 -1.69661 -0.326621\n", + " 119 9.381e-01 5.862e+03 7.637e-03 -- 4.243e+02 -- 0.106265 -0.655346 -1.90758 -2.2539 -3.02065 -3.07639 -4.69822 -3.90969 -0.414257 0.396559 0.803649 0.616532 0.328095 0.282542 -1.69035 -0.326385\n", + " 121 1.053e+00 1.792e+03 3.136e-03 -- 4.243e+02 -- 0.107922 -0.655229 -1.90754 -2.25413 -3.02167 -3.07652 -4.67926 -3.90965 -0.375397 0.400887 0.803107 0.619981 0.329563 0.280018 -1.69585 -0.326566\n", + " 123 9.747e-01 1.055e+04 1.263e-02 -- 4.243e+02 -- 0.106279 -0.655349 -1.90758 -2.25389 -3.02065 -3.07639 -4.69719 -3.90968 -0.414927 0.396498 0.803609 0.616418 0.328121 0.282516 -1.68965 -0.326333\n", + " 125 1.111e+00 5.278e+03 2.340e-02 -- 4.243e+02 -- 0.107901 -0.655231 -1.90754 -2.25413 -3.02169 -3.07652 -4.67804 -3.90964 -0.374485 0.400906 0.803062 0.619925 0.329608 0.279959 -1.69529 -0.326518\n", + " 127 1.036e+00 1.896e+04 3.343e-02 -- 4.242e+02 -- 0.106203 -0.655354 -1.90758 -2.25388 -3.02064 -3.07639 -4.69648 -3.90968 -0.416081 0.396375 0.80358 0.616251 0.328119 0.282535 -1.68894 -0.32628\n", + " 129 1.195e+00 1.424e+04 4.252e-02 -- 4.242e+02 -- 0.107834 -0.655231 -1.90754 -2.25412 -3.02172 -3.07652 -4.67654 -3.90964 -0.372961 0.400999 0.803011 0.619921 0.32967 0.279866 -1.6949 -0.326475\n", + " 131 1.114e+00 3.012e+04 5.112e-02 -- 4.241e+02 -- 0.106017 -0.655361 -1.90758 -2.25386 -3.02061 -3.07638 -4.69598 -3.90968 -0.417517 0.396205 0.80356 0.616037 0.328093 0.28259 -1.68827 -0.326225\n", + " 133 1.291e+00 2.480e+04 5.501e-02 -- 4.241e+02 -- 0.107702 -0.655229 -1.90754 -2.25412 -3.02175 -3.07653 -4.67479 -3.90964 -0.371025 0.401147 0.802956 0.619954 0.329744 0.279748 -1.69469 -0.326435\n", + " 135 1.192e+00 4.132e+04 5.866e-02 -- 4.240e+02 -- 0.105716 -0.655369 -1.90758 -2.25384 -3.02057 -3.07637 -4.69557 -3.90968 -0.418932 0.396012 0.803546 0.615793 0.328051 0.282668 -1.68765 -0.326169\n", + " 137 1.381e+00 3.378e+04 5.372e-02 -- 4.240e+02 -- 0.107499 -0.655228 -1.90753 -2.25412 -3.0218 -3.07653 -4.67287 -3.90963 -0.369006 0.401322 0.802898 0.619996 0.32982 0.279621 -1.6946 -0.326396\n", + " 139 1.253e+00 4.876e+04 4.978e-02 -- 4.239e+02 -- 0.105319 -0.655378 -1.90758 -2.25383 -3.02052 -3.07637 -4.69506 -3.90968 -0.419968 0.395831 0.803531 0.615542 0.328005 0.282751 -1.68713 -0.326112\n", + " 141 1.442e+00 3.792e+04 3.551e-02 -- 4.239e+02 -- 0.107235 -0.655227 -1.90753 -2.25412 -3.02183 -3.07653 -4.67093 -3.90963 -0.367349 0.401482 0.802843 0.620014 0.329885 0.279508 -1.69456 -0.326353\n", + " 143 1.281e+00 4.985e+04 2.509e-02 -- 4.239e+02 -- 0.104881 -0.655386 -1.90758 -2.25381 -3.02048 -3.07636 -4.69427 -3.90968 -0.420306 0.3957 0.803511 0.615319 0.327969 0.282811 -1.68673 -0.326054\n", + " 145 1.457e+00 3.617e+04 6.649e-03 -- 4.239e+02 -- 0.106937 -0.655229 -1.90753 -2.25412 -3.02185 -3.07653 -4.66916 -3.90962 -0.366454 0.401581 0.802793 0.619973 0.329929 0.279431 -1.69448 -0.326305\n", + " 147 1.271e+00 4.504e+04 5.304e-03 -- 4.239e+02 -- 0.104484 -0.655393 -1.90758 -2.2538 -3.02046 -3.07635 -4.69308 -3.90967 -0.419842 0.395651 0.803481 0.615152 0.327958 0.282828 -1.68645 -0.325997\n", + " 149 1.426e+00 3.019e+04 2.056e-02 -- 4.239e+02 -- 0.106651 -0.655233 -1.90753 -2.25411 -3.02185 -3.07653 -4.6677 -3.90962 -0.366499 0.401591 0.802752 0.619855 0.329943 0.279405 -1.69426 -0.32625\n", + " 151 1.228e+00 3.710e+04 2.895e-02 -- 4.239e+02 -- 0.1042 -0.655397 -1.90758 -2.25379 -3.02046 -3.07635 -4.69149 -3.90967 -0.418762 0.395689 0.803437 0.615054 0.327979 0.28279 -1.68624 -0.325941\n", + " 153 1.364e+00 2.275e+04 3.671e-02 -- 4.240e+02 -- 0.106417 -0.655239 -1.90753 -2.2541 -3.02182 -3.07653 -4.66661 -3.90962 -0.367337 0.40151 0.802719 0.619668 0.329931 0.279426 -1.69385 -0.326189\n", + " 155 1.169e+00 2.891e+04 3.977e-02 -- 4.240e+02 -- 0.104061 -0.655399 -1.90758 -2.25378 -3.02048 -3.07635 -4.68964 -3.90967 -0.417432 0.395789 0.803383 0.615013 0.328027 0.282709 -1.68604 -0.325888\n", + " 157 1.292e+00 1.595e+04 4.051e-02 -- 4.240e+02 -- 0.10626 -0.655247 -1.90753 -2.25408 -3.02179 -3.07652 -4.66581 -3.90962 -0.368619 0.40137 0.802691 0.61944 0.329904 0.279475 -1.69327 -0.326124\n", + " 159 1.112e+00 2.222e+04 3.910e-02 -- 4.241e+02 -- 0.104051 -0.655399 -1.90758 -2.25378 -3.02051 -3.07635 -4.68771 -3.90966 -0.41623 0.395911 0.803323 0.615002 0.32809 0.282604 -1.68578 -0.325836\n", + " 161 1.228e+00 1.084e+04 3.547e-02 -- 4.241e+02 -- 0.106183 -0.655255 -1.90753 -2.25406 -3.02176 -3.07651 -4.66515 -3.90961 -0.369965 0.40121 0.802665 0.619207 0.329875 0.279531 -1.69259 -0.326059\n", + " 163 1.067e+00 1.774e+04 3.132e-02 -- 4.241e+02 -- 0.104126 -0.655399 -1.90757 -2.25377 -3.02054 -3.07636 -4.6859 -3.90966 -0.4154 0.396019 0.803264 0.614993 0.328155 0.2825 -1.68543 -0.325784\n", + " 165 1.185e+00 7.799e+03 2.564e-02 -- 4.242e+02 -- 0.106169 -0.655262 -1.90753 -2.25404 -3.02173 -3.07651 -4.6645 -3.90961 -0.37108 0.401067 0.802637 0.618998 0.329856 0.279574 -1.69187 -0.325996\n", + " 167 1.042e+00 1.568e+04 2.005e-02 -- 4.242e+02 -- 0.104234 -0.6554 -1.90757 -2.25377 -3.02057 -3.07636 -4.68431 -3.90965 -0.415037 0.39609 0.803209 0.614964 0.328211 0.282413 -1.685 -0.325733\n", + " 169 1.166e+00 6.928e+03 1.359e-02 -- 4.242e+02 -- 0.106193 -0.655267 -1.90753 -2.25403 -3.02171 -3.0765 -4.66377 -3.90961 -0.371795 0.400963 0.802606 0.618827 0.32985 0.279594 -1.69119 -0.325938\n", + " 171 1.038e+00 1.607e+04 7.134e-03 -- 4.242e+02 -- 0.104336 -0.655401 -1.90757 -2.25376 -3.02059 -3.07636 -4.68298 -3.90965 -0.415146 0.396113 0.803161 0.614903 0.328252 0.282352 -1.6845 -0.325682\n", + " 173 1.173e+00 8.268e+03 6.275e-04 -- 4.242e+02 -- 0.106228 -0.655271 -1.90753 -2.25401 -3.02171 -3.0765 -4.66288 -3.90961 -0.372036 0.400909 0.80257 0.6187 0.329861 0.279586 -1.69059 -0.325883\n", + " 175 1.055e+00 1.886e+04 6.252e-03 -- 4.242e+02 -- 0.104399 -0.655404 -1.90757 -2.25375 -3.02059 -3.07636 -4.6819 -3.90965 -0.415663 0.396089 0.80312 0.614807 0.328275 0.28232 -1.68397 -0.325631\n", + " 177 1.202e+00 1.171e+04 1.208e-02 -- 4.242e+02 -- 0.10625 -0.655274 -1.90752 -2.25401 -3.02171 -3.0765 -4.66183 -3.9096 -0.371805 0.400905 0.80253 0.618615 0.329886 0.279551 -1.69008 -0.325833\n", + " 179 1.088e+00 2.375e+04 1.876e-02 -- 4.242e+02 -- 0.1044 -0.655408 -1.90757 -2.25374 -3.02058 -3.07635 -4.68102 -3.90964 -0.416481 0.396024 0.803087 0.614677 0.328281 0.282317 -1.68342 -0.325578\n", + " 181 1.247e+00 1.681e+04 2.282e-02 -- 4.242e+02 -- 0.106242 -0.655275 -1.90752 -2.254 -3.02172 -3.0765 -4.66061 -3.9096 -0.371173 0.400948 0.802486 0.618565 0.329922 0.279494 -1.68969 -0.325786\n", + " 183 1.130e+00 2.993e+04 2.820e-02 -- 4.241e+02 -- 0.104331 -0.655413 -1.90757 -2.25373 -3.02057 -3.07635 -4.68028 -3.90964 -0.417458 0.395928 0.803059 0.614518 0.328272 0.282337 -1.68288 -0.325526\n", + " 185 1.300e+00 2.258e+04 2.907e-02 -- 4.241e+02 -- 0.106194 -0.655276 -1.90752 -2.25399 -3.02174 -3.0765 -4.65925 -3.9096 -0.370276 0.401025 0.802439 0.618537 0.329966 0.279422 -1.6894 -0.325741\n", + " 187 1.174e+00 3.603e+04 3.195e-02 -- 4.241e+02 -- 0.104189 -0.655419 -1.90757 -2.25371 -3.02054 -3.07634 -4.6796 -3.90964 -0.418419 0.395818 0.803034 0.614341 0.328254 0.282371 -1.68237 -0.325472\n", + " 189 1.350e+00 2.756e+04 2.844e-02 -- 4.240e+02 -- 0.106101 -0.655277 -1.90752 -2.25399 -3.02177 -3.0765 -4.6578 -3.90959 -0.36931 0.401118 0.802391 0.618516 0.330012 0.279345 -1.68917 -0.325696\n", + " 191 1.209e+00 4.041e+04 2.805e-02 -- 4.240e+02 -- 0.103991 -0.655425 -1.90757 -2.2537 -3.02052 -3.07634 -4.67888 -3.90964 -0.419178 0.395713 0.80301 0.614159 0.328232 0.282407 -1.68192 -0.325418\n", + " 193 1.386e+00 3.040e+04 2.035e-02 -- 4.240e+02 -- 0.10597 -0.655278 -1.90752 -2.25399 -3.02178 -3.0765 -4.65634 -3.90959 -0.368498 0.401205 0.802344 0.618484 0.330052 0.279274 -1.68898 -0.32565\n", + " 195 1.228e+00 4.191e+04 1.710e-02 -- 4.240e+02 -- 0.103766 -0.655431 -1.90757 -2.25369 -3.02049 -3.07633 -4.67804 -3.90964 -0.419581 0.395634 0.802983 0.613991 0.328214 0.282435 -1.68153 -0.325363\n", + " 197 1.400e+00 3.045e+04 7.090e-03 -- 4.240e+02 -- 0.105817 -0.65528 -1.90751 -2.25398 -3.02179 -3.0765 -4.65495 -3.90959 -0.368037 0.401264 0.8023 0.618424 0.330081 0.27922 -1.68878 -0.325601\n", + " 199 1.228e+00 4.042e+04 2.670e-03 -- 4.240e+02 -- 0.103554 -0.655436 -1.90757 -2.25367 -3.02048 -3.07633 -4.67701 -3.90964 -0.419574 0.395594 0.802951 0.613849 0.328208 0.282441 -1.6812 -0.325309\n", + " 201 1.391e+00 2.809e+04 6.909e-03 -- 4.240e+02 -- 0.105665 -0.655283 -1.90751 -2.25397 -3.02179 -3.0765 -4.65372 -3.90958 -0.368037 0.40128 0.802261 0.618327 0.330095 0.279192 -1.68851 -0.325549\n", + " 203 1.211e+00 3.681e+04 1.046e-02 -- 4.240e+02 -- 0.10339 -0.65544 -1.90756 -2.25367 -3.02047 -3.07633 -4.67579 -3.90963 -0.419218 0.395598 0.802913 0.613741 0.328217 0.282421 -1.68092 -0.325255\n", + " 205 1.362e+00 2.436e+04 1.738e-02 -- 4.240e+02 -- 0.105534 -0.655288 -1.90751 -2.25396 -3.02178 -3.07649 -4.65266 -3.90958 -0.368467 0.401249 0.802225 0.618193 0.330096 0.279187 -1.68816 -0.325494\n", + " 207 1.182e+00 3.237e+04 1.877e-02 -- 4.240e+02 -- 0.103297 -0.655443 -1.90756 -2.25366 -3.02048 -3.07633 -4.67442 -3.90963 -0.418664 0.395637 0.802869 0.613665 0.328241 0.282377 -1.68064 -0.325203\n", + " 209 1.325e+00 2.040e+04 2.221e-02 -- 4.240e+02 -- 0.105441 -0.655293 -1.90751 -2.25395 -3.02177 -3.07649 -4.65177 -3.90958 -0.369187 0.401182 0.802193 0.618034 0.330086 0.279201 -1.68772 -0.325436\n", + " 211 1.150e+00 2.823e+04 2.124e-02 -- 4.241e+02 -- 0.103274 -0.655445 -1.90756 -2.25365 -3.02049 -3.07632 -4.67299 -3.90963 -0.418095 0.395695 0.802822 0.613609 0.328274 0.282318 -1.68036 -0.325151\n", + " 213 1.288e+00 1.706e+04 2.176e-02 -- 4.241e+02 -- 0.105391 -0.655299 -1.90751 -2.25394 -3.02175 -3.07649 -4.65098 -3.90958 -0.370016 0.401097 0.802163 0.617865 0.330073 0.279223 -1.68722 -0.325378\n", + " 215 1.123e+00 2.514e+04 1.884e-02 -- 4.241e+02 -- 0.103305 -0.655447 -1.90756 -2.25365 -3.02051 -3.07632 -4.67159 -3.90962 -0.417666 0.395753 0.802774 0.61356 0.328311 0.282255 -1.68003 -0.3251\n", + " 217 1.259e+00 1.485e+04 1.739e-02 -- 4.241e+02 -- 0.105376 -0.655304 -1.90751 -2.25392 -3.02173 -3.07648 -4.65022 -3.90958 -0.370776 0.401013 0.802132 0.617703 0.330062 0.279242 -1.68669 -0.32532\n", + " 219 1.105e+00 2.348e+04 1.310e-02 -- 4.241e+02 -- 0.103366 -0.655448 -1.90756 -2.25364 -3.02052 -3.07632 -4.67027 -3.90962 -0.41747 0.395796 0.802727 0.613505 0.328345 0.282197 -1.67967 -0.325049\n", + " 221 1.244e+00 1.401e+04 1.065e-02 -- 4.241e+02 -- 0.105386 -0.655309 -1.90751 -2.25391 -3.02171 -3.07648 -4.64944 -3.90957 -0.371342 0.400944 0.8021 0.617559 0.330058 0.279251 -1.68616 -0.325265\n", + " 223 1.099e+00 2.340e+04 5.564e-03 -- 4.241e+02 -- 0.10343 -0.65545 -1.90756 -2.25363 -3.02053 -3.07632 -4.66909 -3.90962 -0.417537 0.395814 0.802684 0.613436 0.328372 0.282152 -1.67926 -0.324998\n", + " 225 1.243e+00 1.459e+04 2.885e-03 -- 4.241e+02 -- 0.105406 -0.655313 -1.90751 -2.2539 -3.02171 -3.07647 -4.64859 -3.90957 -0.371646 0.400902 0.802065 0.617438 0.330062 0.279246 -1.68566 -0.325211\n", + " 227 1.105e+00 2.483e+04 2.557e-03 -- 4.241e+02 -- 0.103477 -0.655453 -1.90755 -2.25362 -3.02053 -3.07632 -4.66805 -3.90961 -0.417854 0.395806 0.802645 0.613347 0.328389 0.282123 -1.67882 -0.324947\n", + " 229 1.256e+00 1.645e+04 4.768e-03 -- 4.241e+02 -- 0.105422 -0.655316 -1.90751 -2.25389 -3.0217 -3.07647 -4.64766 -3.90957 -0.371669 0.400889 0.802028 0.617342 0.330075 0.279225 -1.68522 -0.32516\n", + " 231 1.121e+00 2.751e+04 9.997e-03 -- 4.241e+02 -- 0.10349 -0.655456 -1.90755 -2.25361 -3.02053 -3.07632 -4.66714 -3.90961 -0.418357 0.395771 0.802609 0.613237 0.328396 0.28211 -1.67838 -0.324896\n", + " 233 1.279e+00 1.926e+04 1.104e-02 -- 4.241e+02 -- 0.105421 -0.655318 -1.90751 -2.25388 -3.02171 -3.07647 -4.64663 -3.90957 -0.371441 0.400903 0.801989 0.617267 0.330095 0.27919 -1.68484 -0.325111\n", + " 235 1.144e+00 3.091e+04 1.553e-02 -- 4.241e+02 -- 0.10346 -0.65546 -1.90755 -2.2536 -3.02051 -3.07631 -4.66631 -3.90961 -0.418965 0.395718 0.802578 0.613111 0.328395 0.282112 -1.67793 -0.324844\n", + " 237 1.308e+00 2.246e+04 1.471e-02 -- 4.241e+02 -- 0.105396 -0.65532 -1.9075 -2.25387 -3.02172 -3.07647 -4.64552 -3.90956 -0.371043 0.400939 0.801948 0.617207 0.33012 0.279146 -1.68451 -0.325063\n", + " 239 1.168e+00 3.433e+04 1.790e-02 -- 4.241e+02 -- 0.103389 -0.655465 -1.90755 -2.25359 -3.0205 -3.07631 -4.66553 -3.90961 -0.419576 0.395655 0.802548 0.612973 0.328387 0.282122 -1.6775 -0.324792\n", + " 241 1.336e+00 2.532e+04 1.481e-02 -- 4.241e+02 -- 0.105346 -0.655322 -1.9075 -2.25387 -3.02173 -3.07647 -4.64436 -3.90956 -0.370581 0.400986 0.801906 0.617153 0.330146 0.279097 -1.68423 -0.325016\n", + " 243 1.188e+00 3.697e+04 1.653e-02 -- 4.240e+02 -- 0.103284 -0.65547 -1.90755 -2.25358 -3.02048 -3.07631 -4.66474 -3.90961 -0.420093 0.395593 0.802519 0.612832 0.328377 0.282136 -1.6771 -0.32474\n", + " 245 1.358e+00 2.720e+04 1.127e-02 -- 4.240e+02 -- 0.105274 -0.655324 -1.9075 -2.25386 -3.02173 -3.07647 -4.6432 -3.90956 -0.370176 0.401032 0.801865 0.617094 0.33017 0.279051 -1.68397 -0.324968\n", + " 247 1.201e+00 3.825e+04 1.163e-02 -- 4.240e+02 -- 0.10316 -0.655475 -1.90755 -2.25357 -3.02047 -3.0763 -4.6639 -3.9096 -0.420433 0.395542 0.802489 0.612697 0.328368 0.282145 -1.67673 -0.324687\n", + " 249 1.369e+00 2.772e+04 4.985e-03 -- 4.240e+02 -- 0.105187 -0.655326 -1.9075 -2.25385 -3.02174 -3.07647 -4.64206 -3.90955 -0.369933 0.401066 0.801825 0.617023 0.330189 0.279013 -1.68371 -0.324919\n", + " 251 1.204e+00 3.800e+04 4.642e-03 -- 4.240e+02 -- 0.103037 -0.655479 -1.90755 -2.25356 -3.02046 -3.0763 -4.66298 -3.9096 -0.42056 0.395511 0.802457 0.612574 0.328364 0.282145 -1.67639 -0.324635\n", + " 253 1.367e+00 2.692e+04 2.208e-03 -- 4.240e+02 -- 0.105097 -0.655329 -1.9075 -2.25385 -3.02174 -3.07646 -4.641 -3.90955 -0.369915 0.401078 0.801787 0.616934 0.330199 0.278988 -1.68342 -0.324869\n", + " 255 1.198e+00 3.649e+04 2.379e-03 -- 4.240e+02 -- 0.102935 -0.655483 -1.90755 -2.25355 -3.02045 -3.0763 -4.66196 -3.9096 -0.420493 0.395502 0.802421 0.612468 0.328367 0.282132 -1.67608 -0.324583\n", + " 257 1.355e+00 2.519e+04 8.313e-03 -- 4.240e+02 -- 0.105017 -0.655333 -1.9075 -2.25384 -3.02173 -3.07646 -4.64003 -3.90955 -0.370125 0.401067 0.801751 0.616826 0.330203 0.278975 -1.6831 -0.324816\n", + " 259 1.184e+00 3.425e+04 7.673e-03 -- 4.240e+02 -- 0.102867 -0.655486 -1.90755 -2.25354 -3.02045 -3.07629 -4.66087 -3.9096 -0.420294 0.395514 0.802383 0.612379 0.328378 0.282107 -1.67578 -0.324532\n", + " 261 1.337e+00 2.308e+04 1.201e-02 -- 4.240e+02 -- 0.104954 -0.655337 -1.9075 -2.25383 -3.02172 -3.07646 -4.63915 -3.90955 -0.370512 0.401035 0.801718 0.616703 0.3302 0.278973 -1.68273 -0.324763\n", + " 263 1.168e+00 3.192e+04 1.022e-02 -- 4.240e+02 -- 0.102836 -0.655489 -1.90755 -2.25353 -3.02045 -3.07629 -4.65973 -3.9096 -0.420052 0.395538 0.802343 0.612303 0.328395 0.282072 -1.67548 -0.324481\n", + " 265 1.317e+00 2.111e+04 1.292e-02 -- 4.241e+02 -- 0.104913 -0.655342 -1.9075 -2.25381 -3.02171 -3.07645 -4.63833 -3.90955 -0.370989 0.40099 0.801685 0.616573 0.330194 0.278977 -1.68232 -0.324708\n", + " 267 1.153e+00 3.000e+04 9.954e-03 -- 4.241e+02 -- 0.102837 -0.655491 -1.90754 -2.25352 -3.02046 -3.07629 -4.65859 -3.90959 -0.419854 0.395565 0.802301 0.612233 0.328414 0.282033 -1.67517 -0.32443\n", + " 269 1.300e+00 1.967e+04 1.134e-02 -- 4.241e+02 -- 0.104892 -0.655346 -1.9075 -2.2538 -3.0217 -3.07645 -4.63755 -3.90954 -0.371464 0.400942 0.801653 0.616443 0.330188 0.278982 -1.6819 -0.324654\n", + " 271 1.141e+00 2.883e+04 7.453e-03 -- 4.241e+02 -- 0.102859 -0.655493 -1.90754 -2.25352 -3.02046 -3.07629 -4.6575 -3.90959 -0.419761 0.395587 0.80226 0.612163 0.328433 0.281995 -1.67484 -0.32438\n", + " 273 1.290e+00 1.899e+04 7.993e-03 -- 4.241e+02 -- 0.104887 -0.65535 -1.90749 -2.25379 -3.02169 -3.07645 -4.63677 -3.90954 -0.371856 0.400899 0.801621 0.61632 0.330185 0.278983 -1.68147 -0.324601\n", + " 275 1.136e+00 2.857e+04 3.456e-03 -- 4.241e+02 -- 0.102888 -0.655496 -1.90754 -2.25351 -3.02047 -3.07629 -4.65646 -3.90959 -0.419808 0.395598 0.802221 0.612086 0.328449 0.281962 -1.67448 -0.324329\n", + " 277 1.287e+00 1.915e+04 3.678e-03 -- 4.241e+02 -- 0.10489 -0.655354 -1.90749 -2.25378 -3.02168 -3.07644 -4.63596 -3.90954 -0.372112 0.400869 0.801587 0.616209 0.330186 0.278977 -1.68105 -0.324549\n", + " 279 1.138e+00 2.921e+04 1.107e-03 -- 4.241e+02 -- 0.102911 -0.655499 -1.90754 -2.2535 -3.02047 -3.07628 -4.6555 -3.90959 -0.419993 0.395594 0.802183 0.612 0.32846 0.281938 -1.67411 -0.324279\n", + " 281 1.292e+00 2.009e+04 7.159e-04 -- 4.241e+02 -- 0.104892 -0.655357 -1.90749 -2.25377 -3.02167 -3.07644 -4.63511 -3.90954 -0.372211 0.400854 0.801552 0.616112 0.330192 0.278962 -1.68066 -0.324498\n", + " 283 1.145e+00 3.061e+04 5.484e-03 -- 4.241e+02 -- 0.102917 -0.655502 -1.90754 -2.25349 -3.02046 -3.07628 -4.65461 -3.90958 -0.420293 0.395575 0.802148 0.611903 0.328466 0.281923 -1.67373 -0.324229\n", + " 285 1.303e+00 2.160e+04 4.414e-03 -- 4.241e+02 -- 0.104887 -0.65536 -1.90749 -2.25376 -3.02167 -3.07644 -4.63421 -3.90954 -0.372166 0.400855 0.801516 0.616028 0.330201 0.27894 -1.6803 -0.324448\n", + " 287 1.157e+00 3.247e+04 8.809e-03 -- 4.241e+02 -- 0.102901 -0.655505 -1.90754 -2.25348 -3.02046 -3.07628 -4.65378 -3.90958 -0.420664 0.395545 0.802115 0.611796 0.328466 0.281916 -1.67334 -0.324178\n", + " 289 1.318e+00 2.338e+04 6.750e-03 -- 4.241e+02 -- 0.104869 -0.655362 -1.90749 -2.25376 -3.02167 -3.07644 -4.63327 -3.90953 -0.372009 0.400869 0.801478 0.615952 0.330214 0.278912 -1.67997 -0.3244\n", + " 291 1.170e+00 3.441e+04 1.052e-02 -- 4.241e+02 -- 0.102861 -0.655509 -1.90754 -2.25347 -3.02045 -3.07628 -4.65299 -3.90958 -0.421049 0.395508 0.802083 0.611682 0.328462 0.281915 -1.67297 -0.324127\n", + " 293 1.334e+00 2.506e+04 7.174e-03 -- 4.241e+02 -- 0.104837 -0.655364 -1.90749 -2.25375 -3.02168 -3.07643 -4.6323 -3.90953 -0.371803 0.40089 0.801441 0.615882 0.330228 0.278881 -1.67967 -0.324351\n", + " 295 1.181e+00 3.602e+04 1.026e-02 -- 4.240e+02 -- 0.102801 -0.655513 -1.90754 -2.25346 -3.02043 -3.07627 -4.65219 -3.90958 -0.421397 0.39547 0.802052 0.611566 0.328457 0.281916 -1.6726 -0.324076\n", + " 297 1.347e+00 2.628e+04 5.695e-03 -- 4.240e+02 -- 0.104792 -0.655367 -1.90749 -2.25374 -3.02168 -3.07643 -4.63133 -3.90953 -0.37161 0.400913 0.801403 0.615811 0.330241 0.27885 -1.67939 -0.324303\n", + " 299 1.190e+00 3.698e+04 8.084e-03 -- 4.240e+02 -- 0.102728 -0.655517 -1.90754 -2.25345 -3.02042 -3.07627 -4.65139 -3.90958 -0.421657 0.395437 0.802021 0.611451 0.328451 0.281917 -1.67226 -0.324025\n", + " 301 1.355e+00 2.683e+04 2.723e-03 -- 4.240e+02 -- 0.104738 -0.655369 -1.90749 -2.25373 -3.02168 -3.07643 -4.63037 -3.90953 -0.371488 0.400931 0.801366 0.615734 0.330251 0.278823 -1.6791 -0.324254\n", + " 303 1.193e+00 3.715e+04 4.763e-03 -- 4.240e+02 -- 0.102653 -0.655521 -1.90754 -2.25344 -3.02041 -3.07626 -4.65054 -3.90957 -0.421811 0.395414 0.801989 0.611342 0.328448 0.281913 -1.67193 -0.323974\n", + " 305 1.356e+00 2.668e+04 9.591e-04 -- 4.240e+02 -- 0.104681 -0.655372 -1.90749 -2.25373 -3.02168 -3.07643 -4.62944 -3.90952 -0.371477 0.40094 0.80133 0.615649 0.330258 0.278801 -1.67881 -0.324204\n", + " 307 1.192e+00 3.660e+04 1.062e-03 -- 4.240e+02 -- 0.102586 -0.655524 -1.90753 -2.25343 -3.02041 -3.07626 -4.64965 -3.90957 -0.421857 0.395402 0.801955 0.611242 0.328448 0.281903 -1.67162 -0.323923\n", + " 309 1.352e+00 2.595e+04 4.356e-03 -- 4.240e+02 -- 0.104627 -0.655375 -1.90748 -2.25372 -3.02167 -3.07643 -4.62856 -3.90952 -0.371584 0.400936 0.801295 0.615554 0.330261 0.278787 -1.67851 -0.324154\n", + " 311 1.186e+00 3.557e+04 2.052e-03 -- 4.240e+02 -- 0.102535 -0.655528 -1.90753 -2.25342 -3.0204 -3.07626 -4.64873 -3.90957 -0.42182 0.395401 0.80192 0.611151 0.328452 0.281886 -1.67132 -0.323873\n", + " 313 1.344e+00 2.491e+04 6.784e-03 -- 4.240e+02 -- 0.104581 -0.655379 -1.90748 -2.25371 -3.02167 -3.07642 -4.62773 -3.90952 -0.371792 0.40092 0.801262 0.615451 0.33026 0.278779 -1.67818 -0.324102\n", + " 315 1.178e+00 3.435e+04 3.937e-03 -- 4.240e+02 -- 0.102503 -0.655531 -1.90753 -2.25341 -3.0204 -3.07626 -4.64777 -3.90957 -0.421744 0.395407 0.801883 0.611068 0.328459 0.281864 -1.67102 -0.323822\n", + " 317 1.333e+00 2.384e+04 7.835e-03 -- 4.241e+02 -- 0.104546 -0.655382 -1.90748 -2.2537 -3.02166 -3.07642 -4.62694 -3.90952 -0.372063 0.400896 0.801229 0.615343 0.330256 0.278774 -1.67783 -0.32405\n", + " 319 1.170e+00 3.326e+04 4.332e-03 -- 4.241e+02 -- 0.102489 -0.655533 -1.90753 -2.25341 -3.0204 -3.07625 -4.64682 -3.90957 -0.421675 0.395418 0.801846 0.610989 0.328467 0.281839 -1.67072 -0.323772\n", + " 321 1.324e+00 2.298e+04 7.501e-03 -- 4.241e+02 -- 0.104523 -0.655386 -1.90748 -2.25369 -3.02165 -3.07642 -4.62618 -3.90952 -0.372353 0.400869 0.801196 0.615234 0.330252 0.278772 -1.67746 -0.323999\n", + " 323 1.163e+00 3.251e+04 3.402e-03 -- 4.241e+02 -- 0.102489 -0.655536 -1.90753 -2.2534 -3.0204 -3.07625 -4.64588 -3.90956 -0.421652 0.395427 0.801809 0.610912 0.328476 0.281813 -1.67041 -0.323723\n", + " 325 1.317e+00 2.251e+04 5.976e-03 -- 4.241e+02 -- 0.104509 -0.65539 -1.90748 -2.25368 -3.02164 -3.07641 -4.62542 -3.90951 -0.372611 0.400843 0.801164 0.615127 0.330249 0.278768 -1.6771 -0.323947\n", + " 327 1.159e+00 3.225e+04 1.455e-03 -- 4.241e+02 -- 0.102495 -0.655539 -1.90753 -2.25339 -3.0204 -3.07625 -4.64497 -3.90956 -0.421696 0.395431 0.801772 0.610833 0.328484 0.28179 -1.67009 -0.323673\n", + " 329 1.315e+00 2.251e+04 3.759e-03 -- 4.241e+02 -- 0.1045 -0.655393 -1.90748 -2.25367 -3.02163 -3.07641 -4.62466 -3.90951 -0.372805 0.400822 0.801131 0.615027 0.330248 0.278761 -1.67674 -0.323896\n", + " 331 1.159e+00 3.251e+04 1.007e-03 -- 4.241e+02 -- 0.102501 -0.655541 -1.90753 -2.25338 -3.0204 -3.07625 -4.6441 -3.90956 -0.421818 0.395428 0.801737 0.610749 0.32849 0.281771 -1.66976 -0.323623\n", + " 333 1.316e+00 2.296e+04 1.324e-03 -- 4.241e+02 -- 0.104493 -0.655396 -1.90748 -2.25366 -3.02163 -3.07641 -4.62388 -3.90951 -0.372917 0.400809 0.801097 0.614933 0.330248 0.278749 -1.67639 -0.323846\n", + " 335 1.163e+00 3.322e+04 3.487e-03 -- 4.241e+02 -- 0.102499 -0.655544 -1.90753 -2.25337 -3.02039 -3.07624 -4.64326 -3.90956 -0.422006 0.395417 0.801702 0.610659 0.328492 0.281756 -1.66942 -0.323574\n", + " 337 1.322e+00 2.376e+04 8.469e-04 -- 4.241e+02 -- 0.104483 -0.655399 -1.90748 -2.25365 -3.02162 -3.0764 -4.62308 -3.90951 -0.372946 0.400805 0.801063 0.614847 0.330252 0.278734 -1.67606 -0.323797\n", + " 339 1.168e+00 3.423e+04 5.485e-03 -- 4.241e+02 -- 0.102486 -0.655547 -1.90753 -2.25336 -3.02039 -3.07624 -4.64247 -3.90956 -0.422237 0.395399 0.801669 0.610564 0.328492 0.281747 -1.66908 -0.323524\n", + " 341 1.330e+00 2.475e+04 2.283e-03 -- 4.241e+02 -- 0.104467 -0.655402 -1.90748 -2.25364 -3.02162 -3.0764 -4.62225 -3.90951 -0.372907 0.400809 0.801028 0.614767 0.330257 0.278714 -1.67574 -0.323748\n", + " 343 1.175e+00 3.533e+04 6.691e-03 -- 4.241e+02 -- 0.10246 -0.655551 -1.90752 -2.25335 -3.02038 -3.07624 -4.6417 -3.90955 -0.422488 0.395377 0.801637 0.610465 0.328489 0.28174 -1.66875 -0.323474\n", + " 345 1.338e+00 2.574e+04 2.776e-03 -- 4.241e+02 -- 0.104443 -0.655404 -1.90748 -2.25364 -3.02162 -3.0764 -4.62141 -3.90951 -0.372833 0.400817 0.800993 0.61469 0.330263 0.278693 -1.67544 -0.323699\n", + " 347 1.182e+00 3.631e+04 6.809e-03 -- 4.241e+02 -- 0.102422 -0.655554 -1.90752 -2.25335 -3.02037 -3.07623 -4.64094 -3.90955 -0.422723 0.395353 0.801606 0.610364 0.328485 0.281736 -1.66842 -0.323424\n", + " 349 1.346e+00 2.653e+04 2.229e-03 -- 4.240e+02 -- 0.104411 -0.655407 -1.90747 -2.25363 -3.02162 -3.0764 -4.62057 -3.9095 -0.372755 0.400828 0.800958 0.614613 0.330268 0.278672 -1.67515 -0.323651\n", + " 351 1.187e+00 3.698e+04 5.968e-03 -- 4.240e+02 -- 0.102376 -0.655558 -1.90752 -2.25334 -3.02036 -3.07623 -4.64017 -3.90955 -0.422919 0.395331 0.801574 0.610264 0.32848 0.281732 -1.6681 -0.323374\n", + " 353 1.351e+00 2.699e+04 8.519e-04 -- 4.240e+02 -- 0.104374 -0.655409 -1.90747 -2.25362 -3.02161 -3.07639 -4.61973 -3.9095 -0.372705 0.400836 0.800923 0.614535 0.330273 0.278652 -1.67487 -0.323602\n", + " 355 1.190e+00 3.726e+04 4.385e-03 -- 4.240e+02 -- 0.102327 -0.655561 -1.90752 -2.25333 -3.02035 -3.07623 -4.63939 -3.90955 -0.423061 0.395314 0.801542 0.610166 0.328476 0.281726 -1.66779 -0.323324\n", + " 357 1.353e+00 2.708e+04 9.742e-04 -- 4.240e+02 -- 0.104335 -0.655412 -1.90747 -2.25361 -3.02161 -3.07639 -4.61891 -3.9095 -0.372705 0.400841 0.800888 0.614453 0.330275 0.278635 -1.67458 -0.323553\n", + " 359 1.191e+00 3.715e+04 2.518e-03 -- 4.240e+02 -- 0.102281 -0.655564 -1.90752 -2.25332 -3.02035 -3.07622 -4.6386 -3.90955 -0.423146 0.395302 0.801509 0.610073 0.328474 0.281717 -1.66749 -0.323274\n", + " 361 1.352e+00 2.684e+04 2.848e-03 -- 4.240e+02 -- 0.104296 -0.655415 -1.90747 -2.2536 -3.02161 -3.07639 -4.61812 -3.9095 -0.372767 0.400839 0.800855 0.614366 0.330276 0.278621 -1.67429 -0.323503\n", + " 363 1.189e+00 3.672e+04 7.728e-04 -- 4.240e+02 -- 0.102241 -0.655568 -1.90752 -2.25331 -3.02034 -3.07622 -4.63778 -3.90955 -0.423181 0.395296 0.801476 0.609984 0.328473 0.281705 -1.6672 -0.323225\n", + " 365 1.349e+00 2.638e+04 4.327e-03 -- 4.240e+02 -- 0.10426 -0.655418 -1.90747 -2.2536 -3.0216 -3.07639 -4.61735 -3.9095 -0.372885 0.400831 0.800822 0.614275 0.330274 0.278611 -1.67399 -0.323453\n", + " 367 1.185e+00 3.614e+04 4.647e-04 -- 4.240e+02 -- 0.102211 -0.65557 -1.90752 -2.2533 -3.02034 -3.07622 -4.63696 -3.90954 -0.423187 0.395295 0.801442 0.609901 0.328474 0.28169 -1.66691 -0.323175\n", + " 369 1.344e+00 2.584e+04 5.155e-03 -- 4.240e+02 -- 0.10423 -0.655421 -1.90747 -2.25359 -3.02159 -3.07638 -4.61661 -3.90949 -0.373044 0.400818 0.800789 0.614181 0.33027 0.278603 -1.67367 -0.323403\n", + " 371 1.181e+00 3.557e+04 9.155e-04 -- 4.240e+02 -- 0.102191 -0.655573 -1.90752 -2.25329 -3.02033 -3.07622 -4.63612 -3.90954 -0.42319 0.395297 0.801407 0.60982 0.328476 0.281673 -1.66662 -0.323126\n", + " 373 1.339e+00 2.538e+04 5.246e-03 -- 4.241e+02 -- 0.104207 -0.655425 -1.90747 -2.25358 -3.02158 -3.07638 -4.61588 -3.90949 -0.373222 0.400803 0.800757 0.614086 0.330266 0.278597 -1.67335 -0.323353\n", + " 375 1.177e+00 3.514e+04 6.996e-04 -- 4.241e+02 -- 0.10218 -0.655576 -1.90752 -2.25329 -3.02033 -3.07621 -4.6353 -3.90954 -0.423208 0.395299 0.801372 0.60974 0.328479 0.281655 -1.66632 -0.323077\n", + " 377 1.336e+00 2.510e+04 4.657e-03 -- 4.241e+02 -- 0.104188 -0.655428 -1.90747 -2.25357 -3.02158 -3.07638 -4.61517 -3.90949 -0.37339 0.400787 0.800725 0.613991 0.330262 0.278591 -1.67303 -0.323303\n", + " 379 1.175e+00 3.496e+04 1.708e-04 -- 4.241e+02 -- 0.102173 -0.655579 -1.90752 -2.25328 -3.02033 -3.07621 -4.63449 -3.90954 -0.423257 0.395299 0.801338 0.609661 0.328481 0.281638 -1.66603 -0.323028\n", + " 381 1.334e+00 2.506e+04 3.566e-03 -- 4.241e+02 -- 0.104174 -0.655431 -1.90747 -2.25356 -3.02157 -3.07637 -4.61446 -3.90949 -0.373529 0.400773 0.800693 0.6139 0.330259 0.278583 -1.67271 -0.323253\n", + " 383 1.174e+00 3.505e+04 1.439e-03 -- 4.241e+02 -- 0.102167 -0.655581 -1.90752 -2.25327 -3.02032 -3.07621 -4.6337 -3.90954 -0.423345 0.395295 0.801304 0.60958 0.328481 0.281623 -1.66572 -0.322979\n", + " 385 1.334e+00 2.528e+04 2.296e-03 -- 4.241e+02 -- 0.104161 -0.655434 -1.90747 -2.25355 -3.02156 -3.07637 -4.61374 -3.90949 -0.373628 0.400763 0.80066 0.613812 0.330257 0.278573 -1.6724 -0.323204\n", + " 387 1.176e+00 3.541e+04 2.759e-03 -- 4.241e+02 -- 0.102158 -0.655584 -1.90751 -2.25326 -3.02032 -3.0762 -4.63294 -3.90953 -0.423468 0.395287 0.801271 0.609496 0.328481 0.28161 -1.66542 -0.32293\n", + " 389 1.337e+00 2.570e+04 1.086e-03 -- 4.241e+02 -- 0.104146 -0.655437 -1.90747 -2.25354 -3.02156 -3.07637 -4.61301 -3.90949 -0.373679 0.400758 0.800627 0.613728 0.330255 0.278562 -1.67209 -0.323155\n", + " 391 1.179e+00 3.596e+04 3.999e-03 -- 4.241e+02 -- 0.102145 -0.655587 -1.90751 -2.25325 -3.02031 -3.0762 -4.63219 -3.90953 -0.423623 0.395276 0.801239 0.609409 0.328478 0.2816 -1.66511 -0.322881\n", + " 393 1.341e+00 2.626e+04 1.867e-04 -- 4.241e+02 -- 0.104129 -0.655439 -1.90746 -2.25353 -3.02155 -3.07636 -4.61227 -3.90948 -0.373694 0.400757 0.800594 0.613647 0.330255 0.278548 -1.67179 -0.323106\n", + " 395 1.182e+00 3.660e+04 4.819e-03 -- 4.241e+02 -- 0.102124 -0.65559 -1.90751 -2.25324 -3.0203 -3.0762 -4.63147 -3.90953 -0.423794 0.395262 0.801207 0.60932 0.328475 0.281592 -1.66481 -0.322832\n", + " 397 1.345e+00 2.685e+04 2.326e-04 -- 4.241e+02 -- 0.104109 -0.655442 -1.90746 -2.25353 -3.02155 -3.07636 -4.61153 -3.90948 -0.373684 0.400759 0.800561 0.613569 0.330256 0.278532 -1.6715 -0.323058\n", + " 399 1.186e+00 3.720e+04 5.033e-03 -- 4.240e+02 -- 0.102097 -0.655593 -1.90751 -2.25324 -3.0203 -3.07619 -4.63075 -3.90953 -0.423959 0.395246 0.801175 0.609229 0.32847 0.281586 -1.66451 -0.322783\n", + " 401 1.350e+00 2.736e+04 5.035e-05 -- 4.240e+02 -- 0.104084 -0.655444 -1.90746 -2.25352 -3.02155 -3.07636 -4.61078 -3.90948 -0.373668 0.400763 0.800528 0.613492 0.330256 0.278517 -1.67122 -0.323009\n", + "********************\n", + "0.104084 -0.655444 -1.90746 -2.25352 -3.02155 -3.07636 -4.61078 -3.90948 -0.373668 0.400763 0.800528 0.613492 0.330256 0.278517 -1.67122 -0.323009\n", + "0.0351976 0.000839204 0.000349775 0.00402504 0.00707581 0.00275589 0.124412 0.000905571 0.189505 0.0199362 0.00701796 0.0326727 0.0352226 0.0205451 0.268968 0.0111152\n", + "-77.2828 -5289.45 -27360.2 -1043.89 -821.646 -3179.45 28.7066 -13573.9 -24.1291 -159.085 170.979 -62.8103 -40.6872 59.9863 5.53304 592.733\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": [], + "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": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-6.16365918, 2.21009502, 2.28771584, 1.13110535, 0.3928378 ,\n", + " 0.21373824, -0.82743402, -0.10317688])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s, loc, scale = lognorm.fit(lag,loc=.008)\n", + "\n", + "xscale('log'); ylim(-10,15)\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n", + "plot(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),lognorm.pdf(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),s,loc,scale))\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.10734722921479302, 2.5047796344038056)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loc, scale = norm.fit(lag,loc=.01)\n", + "\n", + "xscale('log'); ylim(-10,10)\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n", + "plot(np.logspace(np.log(fqd[3]),np.log(fqd[-1])),norm.pdf(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),loc,scale))\n", + "\n", + "norm.fit(lag,loc=.01,scale=.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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arbaKATde0pCk+pLPwzrrxNZoDBFVssoqsOmmhghJqjeNOqgSDBFV5eBKSao/hghVhSFC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\ni8s8agyFQsybYoj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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(irfft(lag))" + ] + }, + { + "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 +} diff --git a/lag/data/clag_analysis-origbins-noLFerrors-4775A.ipynb b/lag/data/clag_analysis-origbins-noLFerrors-4775A.ipynb new file mode 100644 index 0000000..621efeb --- /dev/null +++ b/lag/data/clag_analysis-origbins-noLFerrors-4775A.ipynb @@ -0,0 +1,701 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "from scipy.optimize import curve_fit\n", + "import numpy.fft\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/4775A.lc\"\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915, 0.62032418])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WvXCs6FjO51hlOwUPOSB0R+Vh+Mp/g1DUXUTd/0isEl4+3xFYAZRvSdhJe2/U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\nIiIiIiIiIiKO+v8Bl+sGsAOf55gAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.348e-01 6.523e+01 inf -- -3.028e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 7.699e-01 6.427e+01 8.100e+01 -- -2.218e+02 -- 0.580653 0.565804 0.565201 0.565214 0.565618 0.565947 0.565564 0.568603\n", + " 3 3.354e+00 6.351e+01 8.006e+01 -- -1.417e+02 -- 0.182695 0.132486 0.130058 0.130216 0.131744 0.132399 0.131339 0.138486\n", + " 4 1.556e+00 6.272e+01 7.903e+01 -- -6.268e+01 -- -0.16907 -0.296462 -0.306097 -0.304877 -0.301753 -0.300835 -0.302915 -0.292641\n", + " 5 5.969e-01 6.132e+01 7.726e+01 -- 1.458e+01 -- -0.432204 -0.709149 -0.744405 -0.7396 -0.734995 -0.733546 -0.736839 -0.725614\n", + " 6 3.744e-01 5.885e+01 7.418e+01 -- 8.877e+01 -- -0.574318 -1.06985 -1.18591 -1.17296 -1.16819 -1.16502 -1.16974 -1.15872\n", + " 7 2.704e-01 5.526e+01 6.977e+01 -- 1.585e+02 -- -0.632315 -1.30174 -1.62986 -1.60311 -1.60167 -1.59376 -1.60104 -1.58978\n", + " 8 2.118e-01 5.083e+01 6.476e+01 -- 2.233e+02 -- -0.652594 -1.36692 -2.06821 -2.02717 -2.03482 -2.01697 -2.03052 -2.01815\n", + " 9 1.754e-01 4.545e+01 5.869e+01 -- 2.820e+02 -- -0.636309 -1.38302 -2.45398 -2.43781 -2.46343 -2.42984 -2.45896 -2.44563\n", + " 10 1.490e-01 3.871e+01 4.984e+01 -- 3.318e+02 -- -0.605808 -1.40775 -2.67391 -2.81453 -2.87421 -2.81781 -2.88685 -2.87465\n", + " 11 1.236e-01 3.039e+01 3.772e+01 -- 3.696e+02 -- -0.57763 -1.42753 -2.69296 -3.11171 -3.23381 -3.14653 -3.31141 -3.30288\n", + " 12 9.229e-02 2.069e+01 2.286e+01 -- 3.924e+02 -- -0.556681 -1.44088 -2.69001 -3.27817 -3.48572 -3.34948 -3.71554 -3.7112\n", + " 13 5.640e-02 1.099e+01 9.621e+00 -- 4.020e+02 -- -0.542063 -1.44901 -2.6978 -3.33497 -3.57555 -3.3977 -4.04347 -4.0537\n", + " 14 2.620e-02 4.040e+00 2.310e+00 -- 4.043e+02 -- -0.533703 -1.45096 -2.69258 -3.36521 -3.54261 -3.39935 -4.19655 -4.28234\n", + " 15 9.422e-03 1.233e+00 3.575e-01 -- 4.047e+02 -- -0.531223 -1.44814 -2.69847 -3.37914 -3.48828 -3.41313 -4.19651 -4.39454\n", + " 16 5.169e-03 4.540e-01 5.788e-02 -- 4.048e+02 -- -0.532521 -1.44581 -2.70748 -3.37398 -3.45542 -3.42428 -4.19558 -4.43406\n", + " 17 2.718e-03 2.442e-01 1.172e-02 -- 4.048e+02 -- -0.533779 -1.44399 -2.71046 -3.36898 -3.43756 -3.43204 -4.19666 -4.4455\n", + " 18 1.439e-03 1.307e-01 3.039e-03 -- 4.048e+02 -- -0.534412 -1.44278 -2.71234 -3.36531 -3.42821 -3.43643 -4.19795 -4.44912\n", + " 19 7.592e-04 6.935e-02 8.433e-04 -- 4.048e+02 -- -0.534752 -1.44211 -2.71311 -3.36323 -3.42328 -3.43886 -4.19871 -4.45056\n", + " 20 4.030e-04 3.692e-02 2.371e-04 -- 4.048e+02 -- -0.534926 -1.44173 -2.71356 -3.36205 -3.42068 -3.44015 -4.19914 -4.45124\n", + " 21 2.137e-04 1.961e-02 6.686e-05 -- 4.048e+02 -- -0.53502 -1.44153 -2.71378 -3.36142 -3.4193 -3.44084 -4.19936 -4.45158\n", + "********************\n", + "-0.53502 -1.44153 -2.71378 -3.36142 -3.4193 -3.44084 -4.19936 -4.45158\n", + "0.23132 0.206392 0.257293 0.247733 0.190033 0.14318 0.192361 0.189515\n", + "-0.000780421 0.00249097 -0.00128998 0.00612834 0.0196141 -0.0166488 -0.00464909 -0.00601261\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 4.048e+02 4.043e+02 -5.351e-01 -3.037e-01 0.864 +++\n", + "+++ 4.048e+02 4.039e+02 -5.351e-01 -1.881e-01 1.82 +++\n", + "+++ 4.048e+02 4.041e+02 -5.351e-01 -2.459e-01 1.3 +++\n", + "+++ 4.048e+02 4.042e+02 -5.351e-01 -2.748e-01 1.07 +++\n", + "+++ 4.048e+02 4.043e+02 -5.351e-01 -2.893e-01 0.967 +++\n", + "+++ 4.048e+02 4.043e+02 -5.351e-01 -2.821e-01 1.02 +++\n", + "+++ 4.048e+02 4.043e+02 -5.351e-01 -2.857e-01 0.993 +++\n", + "\t### errors for param 1 ###\n", + "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.235e+00 0.928 +++\n", + "+++ 4.048e+02 4.038e+02 -1.441e+00 -1.132e+00 1.97 +++\n", + "+++ 4.048e+02 4.041e+02 -1.441e+00 -1.183e+00 1.41 +++\n", + "+++ 4.048e+02 4.042e+02 -1.441e+00 -1.209e+00 1.16 +++\n", + "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.222e+00 1.04 +++\n", + "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.229e+00 0.983 +++\n", + "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.225e+00 1.01 +++\n", + "+++ 4.048e+02 4.043e+02 -1.441e+00 -1.227e+00 0.997 +++\n", + "\t### errors for param 2 ###\n", + "+++ 4.048e+02 4.046e+02 -2.714e+00 -2.585e+00 0.296 +++\n", + "+++ 4.048e+02 4.045e+02 -2.714e+00 -2.521e+00 0.647 +++\n", + "+++ 4.048e+02 4.043e+02 -2.714e+00 -2.489e+00 0.868 +++\n", + "+++ 4.048e+02 4.043e+02 -2.714e+00 -2.473e+00 0.989 +++\n", + "+++ 4.048e+02 4.042e+02 -2.714e+00 -2.465e+00 1.05 +++\n", + "+++ 4.048e+02 4.043e+02 -2.714e+00 -2.469e+00 1.02 +++\n", + "+++ 4.048e+02 4.043e+02 -2.714e+00 -2.471e+00 1.01 +++\n", + "\t### errors for param 3 ###\n", + "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.113e+00 0.873 +++\n", + "+++ 4.048e+02 4.038e+02 -3.361e+00 -2.990e+00 1.95 +++\n", + "+++ 4.048e+02 4.041e+02 -3.361e+00 -3.051e+00 1.36 +++\n", + "+++ 4.048e+02 4.042e+02 -3.361e+00 -3.082e+00 1.1 +++\n", + "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.098e+00 0.985 +++\n", + "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.090e+00 1.04 +++\n", + "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.094e+00 1.01 +++\n", + "+++ 4.048e+02 4.043e+02 -3.361e+00 -3.096e+00 1 +++\n", + "\t### errors for param 4 ###\n", + "+++ 4.048e+02 4.045e+02 -3.419e+00 -3.229e+00 0.635 +++\n", + "+++ 4.048e+02 4.040e+02 -3.419e+00 -3.134e+00 1.47 +++\n", + "+++ 4.048e+02 4.043e+02 -3.419e+00 -3.181e+00 1.01 +++\n", + "\t### errors for param 5 ###\n", + "+++ 4.048e+02 4.044e+02 -3.441e+00 -3.298e+00 0.824 +++\n", + "+++ 4.048e+02 4.039e+02 -3.441e+00 -3.226e+00 1.85 +++\n", + "+++ 4.048e+02 4.041e+02 -3.441e+00 -3.262e+00 1.29 +++\n", + "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.280e+00 1.04 +++\n", + "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.289e+00 0.93 +++\n", + "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.285e+00 0.985 +++\n", + "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.282e+00 1.01 +++\n", + "+++ 4.048e+02 4.043e+02 -3.441e+00 -3.283e+00 0.999 +++\n", + "\t### errors for param 6 ###\n", + "+++ 4.048e+02 4.046e+02 -4.199e+00 -4.103e+00 0.303 +++\n", + "+++ 4.048e+02 4.044e+02 -4.199e+00 -4.055e+00 0.684 +++\n", + "+++ 4.048e+02 4.043e+02 -4.199e+00 -4.031e+00 0.933 +++\n", + "+++ 4.048e+02 4.043e+02 -4.199e+00 -4.019e+00 0.966 +++\n", + "+++ 4.048e+02 4.043e+02 -4.199e+00 -4.013e+00 1.03 +++\n", + "+++ 4.048e+02 4.043e+02 -4.199e+00 -4.016e+00 0.999 +++\n", + "\t### errors for param 7 ###\n", + "+++ 4.048e+02 4.043e+02 -4.452e+00 -4.262e+00 0.879 +++\n", + "+++ 4.048e+02 4.038e+02 -4.452e+00 -4.167e+00 2.03 +++\n", + "+++ 4.048e+02 4.041e+02 -4.452e+00 -4.215e+00 1.39 +++\n", + "+++ 4.048e+02 4.042e+02 -4.452e+00 -4.239e+00 1.12 +++\n", + "+++ 4.048e+02 4.043e+02 -4.452e+00 -4.250e+00 0.996 +++\n", + "********************\n", + "-0.535069 -1.44142 -2.7139 -3.36108 -3.41857 -3.44121 -4.19949 -4.45176\n", + "0.249396 0.214451 0.243242 0.265109 0.23744 0.157753 0.183354 0.201379\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ESVJNpTYbAparsC83XA18A3gTuC3ktiRJUgWFHRJ+A/hWyG1IkqQQhBkSPgi8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\n", 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0.625425\n", + " 7 2.952e+01 1.780e+01 1.997e+00 -- 4.683e+02 -- -0.321166 -1.00987 -2.19485 -2.63901 -3.02019 -3.2426 -4.21334 -5.62588 0.00616037 0.202118 0.375333 0.294616 0.162597 0.328567 -0.0750795 -1.84997\n", + " 9 3.498e+01 2.030e+01 1.927e+00 -- 4.702e+02 -- -0.298358 -0.986546 -2.1755 -2.61326 -3.00636 -3.23255 -4.21943 -5.92588 -0.0120242 0.222305 0.43445 0.33256 0.177047 0.383459 -0.125124 0.240961\n", + " 11 2.323e+01 2.295e+01 1.750e+00 -- 4.719e+02 -- -0.278794 -0.966621 -2.15757 -2.59104 -2.99413 -3.22256 -4.22358 -5.62588 -0.0266225 0.238634 0.482619 0.362829 0.189318 0.429938 -0.171714 -0.602008\n", + " 13 9.908e+00 2.576e+01 1.642e+00 -- 4.736e+02 -- -0.261904 -0.949487 -2.14126 -2.57185 -2.98327 -3.21301 -4.22619 -5.32588 -0.0385241 0.252101 0.522228 0.387431 0.199792 0.469237 -0.213944 0.796178\n", + " 15 5.329e+01 2.871e+01 1.637e+00 -- 4.752e+02 -- -0.247262 -0.934659 -2.12658 -2.55525 -2.97361 -3.20401 -4.22742 -5.02588 -0.0483821 0.263296 0.555178 0.407636 0.208732 0.502656 -0.25301 0.00733246\n", + " 17 2.560e+00 3.181e+01 1.529e+00 -- 4.767e+02 -- -0.234505 -0.921766 -2.11346 -2.54086 -2.96485 -3.19577 -4.22761 -4.804 -0.056605 0.27274 0.582856 0.424353 0.216437 0.5308 -0.288552 0.0464091\n", + " 19 1.172e+00 3.504e+01 1.400e+00 -- 4.781e+02 -- -0.223355 -0.910503 -2.10178 -2.52834 -2.9569 -3.18826 -4.2269 -4.68017 -0.0635434 0.28074 0.606367 0.438267 0.223049 0.554643 -0.321322 0.0582901\n", + " 21 7.724e-01 3.838e+01 1.293e+00 -- 4.794e+02 -- -0.21358 -0.900628 -2.09139 -2.51744 -2.94968 -3.18148 -4.22551 -4.59311 -0.0694486 0.287569 0.626536 0.449937 0.228692 0.574921 -0.351228 0.0651221\n", + " 23 6.475e-01 4.182e+01 1.196e+00 -- 4.806e+02 -- -0.204987 -0.891941 -2.08217 -2.5079 -2.94312 -3.17538 -4.22361 -4.5264 -0.0745103 0.293433 0.644002 0.459774 0.233469 0.59222 -0.378357 0.0697841\n", + " 24 6.033e-01 4.904e+02 9.259e+00 -- 4.899e+02 -- -0.129265 -0.815314 -2.00033 -2.42441 -2.88331 -3.12077 -4.20108 -3.99042 -0.118156 0.344039 0.796617 0.542976 0.273449 0.74018 -0.62334 0.104006\n", + " 25 1.633e+00 2.416e+01 2.127e+00 -- 4.920e+02 -- -0.136332 -0.820492 -2.00326 -2.42953 -2.86836 -3.13063 -4.19803 -4.0221 -0.107855 0.34592 0.942222 0.436475 0.11856 0.656515 -0.24726 0.0760793\n", + " 26 2.297e-01 1.685e+01 3.223e-01 -- 4.923e+02 -- -0.136021 -0.81967 -2.01733 -2.4291 -2.86411 -3.12575 -4.28732 -4.0298 -0.106863 0.340823 0.802305 0.502205 0.115029 0.705918 -0.651119 0.0904933\n", + " 27 2.114e-01 1.002e+01 6.323e-02 -- 4.924e+02 -- -0.136189 -0.820198 -2.00806 -2.43011 -2.86374 -3.13176 -4.15965 -4.03428 -0.10583 0.344597 0.86383 0.461694 0.102618 0.695825 -0.501555 0.0943452\n", + " 28 7.282e-02 6.910e+00 4.695e-02 -- 4.925e+02 -- -0.136116 -0.819859 -2.01221 -2.42953 -2.86299 -3.12599 -4.24062 -4.03499 -0.106121 0.343076 0.828348 0.490418 0.10121 0.697346 -0.60756 0.0921811\n", + " 29 6.355e-02 4.447e+00 1.251e-02 -- 4.925e+02 -- -0.136164 -0.820085 -2.00914 -2.42964 -2.86356 -3.12997 -4.19174 -4.03653 -0.106059 0.34408 0.850084 0.472121 0.0987163 0.695127 -0.563315 0.0960744\n", + " 30 3.208e-02 2.630e+00 6.361e-03 -- 4.925e+02 -- -0.136149 -0.819937 -2.01076 -2.4294 -2.86326 -3.12717 -4.2269 -4.03604 -0.106248 0.343798 0.836239 0.482367 0.0971931 0.696096 -0.599112 0.0951975\n", + " 31 2.383e-02 1.888e+00 2.157e-03 -- 4.925e+02 -- -0.136169 -0.820025 -2.00964 -2.42939 -2.86343 -3.12896 -4.20665 -4.03645 -0.106249 0.344186 0.84463 0.475064 0.0961482 0.695566 -0.579893 0.0964022\n", + " 32 1.323e-02 1.058e+00 9.738e-04 -- 4.925e+02 -- -0.136163 -0.819962 -2.01029 -2.42931 -2.86326 -3.12779 -4.22134 -4.03608 -0.106327 0.344123 0.839179 0.479109 0.0951898 0.696016 -0.593714 0.0963018\n", + " 33 9.243e-03 7.911e-01 3.707e-04 -- 4.925e+02 -- -0.136171 -0.819998 -2.00986 -2.4293 -2.86331 -3.12857 -4.21272 -4.03621 -0.106324 0.344261 0.842547 0.476174 0.0946849 0.695851 -0.58586 0.0965931\n", + " 34 5.341e-03 4.358e-01 1.598e-04 -- 4.925e+02 -- -0.136168 -0.819972 -2.01013 -2.42927 -2.86323 -3.12808 -4.21887 -4.03601 -0.106356 0.344247 0.840355 0.477825 0.0941922 0.69603 -0.591275 0.0966528\n", + " 35 3.654e-03 3.303e-01 6.407e-05 -- 4.925e+02 -- -0.136172 -0.819986 -2.00996 -2.42926 -2.86324 -3.12842 -4.2152 -4.03606 -0.106352 0.344296 0.84173 0.476631 0.0939684 0.695975 -0.588117 0.0967043\n", + "********************\n", + "-0.136172 -0.819986 -2.00996 -2.42926 -2.86324 -3.12842 -4.2152 -4.03606 -0.106352 0.344296 0.84173 0.476631 0.0939684 0.695975 -0.588117 0.0967043\n", + "0.0115263 0.0058258 0.0387362 0.00846868 0.0539018 0.0780724 0.334539 0.108003 0.121876 0.0805411 0.223567 0.102776 0.222423 0.256036 0.800481 0.273811\n", + "0.0127444 0.33028 -0.0607609 0.127486 0.0143628 0.0331918 -0.0227012 0.00709893 -0.000657376 -0.00220005 -0.0168474 0.0629554 -0.0038883 0.00145515 -0.00332533 0.000655605\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": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "phi, phie = p[nfq:], pe[nfq:]\n", + "lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd) \n", + "cx, cxe = p[:nfq], pe[:nfq]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1.75428512, 1.89869772, 2.40546134, 0.87877231, 0.11177487,\n", + " 0.53410247, -0.29118093, 0.03088965])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s, loc, scale = lognorm.fit(lag,loc=.008)\n", + "\n", + "xscale('log'); ylim(-10,15)\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n", + "plot(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),lognorm.pdf(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),s,loc,scale))\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.47677903915219444, 1.214804919002201)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loc, scale = norm.fit(lag,loc=.01)\n", + "\n", + "xscale('log'); ylim(-10,10)\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n", + "plot(np.logspace(np.log(fqd[3]),np.log(fqd[-1])),norm.pdf(np.logspace(np.log(fqd[0]),np.log(fqd[-1])),loc,scale))\n", + "\n", + "norm.fit(lag,loc=.01,scale=.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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H9eqpBLaIiN/k58Muu8Bhh7mOxHteJxGDgQeA24AuwGzgLaBdJccfBeQDxwNZwHTg9ci5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\nJ3vt5ToiETj0UEvSX3zRKqW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+++ b/lag/data/clag_analysis-origbins-noLFerrors-5404A.ipynb @@ -0,0 +1,623 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "from scipy.optimize import curve_fit\n", + "import numpy.fft\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/5404A.lc\"\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915, 0.62032418])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tbI7VOThQdCDvc6xynYKHPBC+o/IwcuW/ISjqLaL+vydXCW8i3xFYAZRvacRJ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\nREREREREREREREREREQkq/4vwbptSNrRFIEAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.610e-01 9.098e+01 inf -- 1.744e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 8.066e-01 8.980e+01 1.014e+02 -- 2.758e+02 -- 0.764458 0.592778 0.539049 0.562235 0.572289 0.566582 0.574488 0.570186\n", + " 3 4.133e+00 8.890e+01 1.012e+02 -- 3.771e+02 -- 0.390803 0.167503 0.104265 0.129187 0.143015 0.13475 0.14757 0.142553\n", + " 4 7.028e+01 8.821e+01 1.004e+02 -- 4.775e+02 -- 0.00503816 -0.253372 -0.326662 -0.304159 -0.285875 -0.29734 -0.280456 -0.284431\n", + " 5 8.140e-01 8.698e+01 9.903e+01 -- 5.765e+02 -- -0.34905 -0.657519 -0.755903 -0.73766 -0.713908 -0.729735 -0.710276 -0.71228\n", + " 6 3.770e-01 8.522e+01 9.618e+01 -- 6.727e+02 -- -0.633175 -1.02654 -1.18245 -1.1696 -1.13984 -1.16159 -1.14079 -1.13949\n", + " 7 2.735e-01 8.339e+01 9.226e+01 -- 7.650e+02 -- -0.806027 -1.33498 -1.60319 -1.59717 -1.56166 -1.59163 -1.57085 -1.56469\n", + " 8 2.138e-01 8.120e+01 8.777e+01 -- 8.527e+02 -- -0.862131 -1.54146 -2.00828 -2.01427 -1.9772 -2.01825 -2.00048 -1.98894\n", + " 9 1.754e-01 7.801e+01 8.241e+01 -- 9.351e+02 -- -0.842095 -1.59954 -2.37394 -2.40556 -2.38506 -2.43954 -2.42825 -2.41302\n", + " 10 1.483e-01 7.298e+01 7.502e+01 -- 1.010e+03 -- -0.783896 -1.56622 -2.64497 -2.73499 -2.78365 -2.85462 -2.85094 -2.83628\n", + " 11 1.272e-01 6.498e+01 6.421e+01 -- 1.074e+03 -- -0.728704 -1.54164 -2.75253 -2.94491 -3.15964 -3.25681 -3.26391 -3.25681\n", + " 12 1.111e-01 5.364e+01 4.985e+01 -- 1.124e+03 -- -0.697013 -1.52135 -2.76748 -3.01573 -3.47965 -3.61977 -3.65793 -3.67111\n", + " 13 9.921e-02 3.928e+01 3.311e+01 -- 1.157e+03 -- -0.68286 -1.50549 -2.78198 -3.01943 -3.7022 -3.88826 -4.01576 -4.07908\n", + " 14 8.742e-02 2.316e+01 1.815e+01 -- 1.175e+03 -- -0.67696 -1.49524 -2.79447 -3.00982 -3.82195 -4.00921 -4.30702 -4.48377\n", + " 15 6.880e-02 9.460e+00 7.667e+00 -- 1.183e+03 -- -0.675273 -1.48938 -2.80142 -2.99712 -3.87606 -4.02132 -4.49151 -4.87573\n", + " 16 3.697e-02 2.241e+00 2.003e+00 -- 1.185e+03 -- -0.675444 -1.48628 -2.80573 -2.98594 -3.90153 -4.01347 -4.55974 -5.21119\n", + " 17 6.073e-03 2.318e-01 2.232e-01 -- 1.185e+03 -- -0.675725 -1.48454 -2.80827 -2.9785 -3.91588 -4.0101 -4.56645 -5.40386\n", + " 18 8.426e-04 9.306e-02 4.878e-03 -- 1.185e+03 -- -0.675636 -1.48368 -2.80908 -2.97402 -3.92351 -4.00944 -4.56338 -5.43668\n", + " 19 4.159e-04 3.811e-02 3.790e-04 -- 1.185e+03 -- -0.675401 -1.48333 -2.809 -2.97151 -3.92669 -4.00953 -4.56255 -5.4346\n", + " 20 1.919e-04 1.760e-02 7.212e-05 -- 1.185e+03 -- -0.675286 -1.48317 -2.80889 -2.97028 -3.92799 -4.00961 -4.56253 -5.43514\n", + "********************\n", + "-0.675286 -1.48317 -2.80889 -2.97028 -3.92799 -4.00961 -4.56253 -5.43514\n", + "0.231904 0.203388 0.246283 0.177512 0.182405 0.144335 0.166268 0.423476\n", + "0.000864464 0.00208153 0.00125836 0.0175976 -0.0171085 -0.00187558 -1.56774e-05 2.12549e-05\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.433e-01 0.84 +++\n", + "+++ 1.185e+03 1.184e+03 -6.752e-01 -3.274e-01 1.76 +++\n", + "+++ 1.185e+03 1.185e+03 -6.752e-01 -3.854e-01 1.27 +++\n", + "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.144e-01 1.04 +++\n", + "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.288e-01 0.94 +++\n", + "+++ 1.185e+03 1.185e+03 -6.752e-01 -4.216e-01 0.992 +++\n", + "\t### errors for param 1 ###\n", + "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.280e+00 0.943 +++\n", + "+++ 1.185e+03 1.184e+03 -1.483e+00 -1.178e+00 2 +++\n", + "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.229e+00 1.43 +++\n", + "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.254e+00 1.18 +++\n", + "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.267e+00 1.06 +++\n", + "+++ 1.185e+03 1.185e+03 -1.483e+00 -1.273e+00 0.999 +++\n", + "\t### errors for param 2 ###\n", + "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.563e+00 0.987 +++\n", + "+++ 1.185e+03 1.184e+03 -2.809e+00 -2.439e+00 2.1 +++\n", + "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.501e+00 1.5 +++\n", + "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.532e+00 1.23 +++\n", + "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.547e+00 1.11 +++\n", + "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.555e+00 1.05 +++\n", + "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.559e+00 1.02 +++\n", + "+++ 1.185e+03 1.185e+03 -2.809e+00 -2.561e+00 1 +++\n", + "\t### errors for param 3 ###\n", + "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.792e+00 0.612 +++\n", + "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.703e+00 1.31 +++\n", + "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.748e+00 0.934 +++\n", + "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.726e+00 1.12 +++\n", + "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.737e+00 1.02 +++\n", + "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.742e+00 0.978 +++\n", + "+++ 1.185e+03 1.185e+03 -2.970e+00 -2.740e+00 1 +++\n", + "\t### errors for param 4 ###\n", + "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.746e+00 0.715 +++\n", + "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.655e+00 1.61 +++\n", + "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.701e+00 1.12 +++\n", + "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.723e+00 0.906 +++\n", + "+++ 1.185e+03 1.185e+03 -3.929e+00 -3.712e+00 1.01 +++\n", + "\t### errors for param 5 ###\n", + "+++ 1.185e+03 1.185e+03 -4.010e+00 -3.865e+00 0.991 +++\n", + "\t### errors for param 6 ###\n", + "+++ 1.185e+03 1.185e+03 -4.563e+00 -4.396e+00 1.01 +++\n", + "\t### errors for param 7 ###\n", + "+++ 1.185e+03 1.185e+03 -5.435e+00 -5.223e+00 0.386 +++\n", + "+++ 1.185e+03 1.185e+03 -5.435e+00 -5.118e+00 0.993 +++\n", + "********************\n", + "-0.675245 -1.48309 -2.80886 -2.96971 -3.92857 -4.00963 -4.56252 -5.43512\n", + "0.253646 0.209743 0.248217 0.230185 0.21667 0.144334 0.166265 0.317595\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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-2.11553\n", + " 7 2.105e+01 1.585e+01 2.131e+00 -- 1.251e+03 -- -0.425484 -1.04389 -2.23659 -2.46561 -3.24012 -3.50157 -4.48059 -6.71756 0.283283 0.288529 0.348404 0.27129 0.21583 0.231595 0.0974289 -2.46919\n", + " 9 2.358e+01 1.743e+01 1.909e+00 -- 1.253e+03 -- -0.40737 -1.02151 -2.21687 -2.44489 -3.21894 -3.48846 -4.51886 -6.41756 0.324291 0.326635 0.400676 0.306939 0.238059 0.261125 0.0966177 -1.38312\n", + " 11 4.074e+01 1.911e+01 1.751e+00 -- 1.255e+03 -- -0.390833 -1.00195 -2.19886 -2.42677 -3.20086 -3.47683 -4.55632 -6.71756 0.358383 0.357437 0.443033 0.336042 0.255704 0.285884 0.0957404 1.63909\n", + " 13 1.370e+01 2.129e+01 1.587e+00 -- 1.256e+03 -- -0.375838 -0.984839 -2.18261 -2.41092 -3.18537 -3.46656 -4.59286 -6.41756 0.386914 0.382651 0.477698 0.360091 0.269847 0.306761 0.095098 2.03338\n", + " 15 1.158e+01 2.351e+01 1.408e+00 -- 1.258e+03 -- -0.362294 -0.96986 -2.16805 -2.39704 -3.17203 -3.45749 -4.62837 -6.11756 0.410934 0.403523 0.506367 0.380155 0.281317 0.324474 0.0944395 -1.46371\n", + " 16 1.607e+03 7.266e+02 8.010e+00 -- 1.266e+03 -- -0.240266 -0.83846 -2.03814 -2.27523 -3.05681 -3.37736 -4.97188 -8 0.614202 0.577977 0.745974 0.548634 0.37576 0.476151 0.0813258 0.437418\n", + " 17 9.038e+02 4.128e+00 3.834e+00 -- 1.270e+03 -- -0.232783 -0.852575 -2.04678 -2.29283 -3.08252 -3.39513 -5.04543 -8 0.548929 0.498486 0.710668 0.487552 0.327531 0.450717 -0.157389 -0.565487\n", + " 18 2.647e+02 5.497e-01 2.932e-02 -- 1.270e+03 -- -0.23322 -0.852233 -2.04339 -2.2905 -3.07964 -3.39057 -5.07095 -8 0.552004 0.51698 0.698282 0.491552 0.331349 0.451833 -0.156166 -2.69909\n", + " 19 9.652e-01 2.219e+00 6.531e-01 -- 1.269e+03 -- -0.233246 -0.852217 -2.0438 -2.29075 -3.08035 -3.39107 -5.06163 -5 0.551423 0.515009 0.700327 0.490665 0.330402 0.452209 -0.176384 -0.814953\n", + " 20 7.789e+00 7.955e-01 6.402e-01 -- 1.270e+03 -- -0.233412 -0.852282 -2.04423 -2.29033 -3.07948 -3.38989 -5.09639 -5.98364 0.551849 0.515428 0.700779 0.489124 0.333636 0.45985 -0.205833 -1.60153\n", + " 21 8.557e+01 4.082e-02 1.163e-02 -- 1.270e+03 -- -0.233294 -0.852207 -2.04379 -2.29075 -3.08047 -3.39073 -5.05794 -7.0122 0.551775 0.515347 0.699486 0.490646 0.330835 0.451633 -0.186288 -1.50956\n", + " 22 1.000e+03 9.633e-02 1.325e-03 -- 1.270e+03 -- -0.233263 -0.852211 -2.04374 -2.29072 -3.08027 -3.39085 -5.06491 -8 0.551627 0.515234 0.700132 0.49056 0.330468 0.452479 -0.17545 1.26429\n", + " 23 6.947e+01 7.583e-02 5.012e-04 -- 1.270e+03 -- -0.23325 -0.852208 -2.04374 -2.29072 -3.08029 -3.39091 -5.06372 -8 0.551664 0.515214 0.700074 0.490679 0.330402 0.452326 -0.175132 2.98034\n", + " 24 1.062e+00 2.147e+00 6.150e-01 -- 1.269e+03 -- -0.23325 -0.852208 -2.04374 -2.29072 -3.08028 -3.39091 -5.06387 -5 0.551636 0.515215 0.70009 0.490675 0.330405 0.452326 -0.175244 -0.97755\n", + " 25 5.159e+00 7.693e-01 6.227e-01 -- 1.270e+03 -- -0.233412 -0.852294 -2.04429 -2.29036 -3.07951 -3.38938 -5.09276 -5.9203 0.551804 0.515509 0.700612 0.48922 0.334134 0.458766 -0.237317 -2.01536\n", + " 26 3.256e+00 2.577e+00 8.259e-01 -- 1.269e+03 -- -0.233292 -0.852208 -2.04377 -2.29076 -3.08053 -3.3906 -5.0573 -4.88481 0.551755 0.515396 0.699418 0.490641 0.330803 0.451361 -0.185258 0.154178\n", + " 27 8.306e+00 8.513e-01 7.419e-01 -- 1.269e+03 -- -0.2334 -0.85217 -2.04371 -2.2903 -3.07979 -3.394 -5.12588 -5.6438 0.551608 0.514436 0.70228 0.489167 0.329463 0.464863 0.0259555 0.65621\n", + " 28 3.018e+02 2.512e-01 7.557e-02 -- 1.270e+03 -- -0.23325 -0.852177 -2.04375 -2.29075 -3.08018 -3.3918 -5.06768 -7.58414 0.552082 0.51496 0.699848 0.491275 0.330113 0.452176 -0.189638 -1.62114\n", + " 29 9.648e+02 1.054e-01 5.781e-05 -- 1.270e+03 -- -0.233258 -0.852207 -2.04374 -2.29072 -3.08024 -3.39094 -5.06451 -8 0.551479 0.515219 0.700113 0.49073 0.330409 0.452292 -0.172337 -0.754914\n", + "********************\n", + "-0.233258 -0.852207 -2.04374 -2.29072 -3.08024 -3.39094 -5.06451 -8 0.551479 0.515219 0.700113 0.49073 0.330409 0.452292 -0.172337 -0.754914\n", + "0.0271527 0.0112859 0.0294371 0.0238447 0.0419821 0.0761252 1.60955 678.342 0.189103 0.111853 0.196777 0.152506 0.193957 0.252076 3.72644 1583.62\n", + "0.0121717 -0.0302322 -0.0279866 -0.0393548 -0.105374 -0.0168605 -0.00293106 -0.000917936 0.00679344 -3.13658e-05 -4.94438e-05 -0.00117381 0.00319937 0.00794062 0.00314398 -0.000234878\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": 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": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 9.0966594 , 2.8412865 , 2.00075511, 0.90476609, 0.39301911,\n", + " 0.34709626, -0.08532525, -0.24113739])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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0Pwa+AkwEDgMuy+H49fo48FTZumuIYOsU4IbU+i8BLxJlXJmsWwA8SNROzUrW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\nT6X0z+xqYubSa4DrgO8CmxM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "x=np.logspace(fqd[0],fqd[-1],200)\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n", + "fit = tophat_freq(fqd, *coeff)\n", + "\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "xscale('log'); xlim(.009,.6)\n", + "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n", + "ylabel(\"Time Lag (days)\",fontsize=20)\n", + "\n", + "\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n", + "plot(fqd,fit)" + ] + }, + { + "cell_type": "code", + "execution_count": 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 +} diff --git a/lag/data/clag_analysis-origbins-noLFerrors.ipynb b/lag/data/clag_analysis-origbins-noLFerrors.ipynb new file mode 100644 index 0000000..b70d09b --- /dev/null +++ b/lag/data/clag_analysis-origbins-noLFerrors.ipynb @@ -0,0 +1,887 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['norm']\n", + "`%matplotlib` prevents importing * from pylab and numpy\n", + " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ\nazf8yEXFEl1gk7Xb8O2kXs0xGD5+zUWupOQKa/r0ddbSpcGYC0zioaFjFnzWKiurtWBZyhfIfH4W\nchG4uKVoWb4DGK9T8KDgwTHDT8jRJ/9Ed2G/SjuzvZBF2qCwxp3HOo4eDAatiy+2j5tb446PRBfY\n1LeTPA9kyZgv3PEXo4qKZdb06eus668PWnPmWNYVV1jWBz5gvq68MnK3W1dnHqury/9Mo1zkPLgh\nr8ItAYyX5Tt4UIXJAjK80p4fsCtKdgCbgE8AN1FWdiWrVz/yfvZ2pKIfDK/qt5j9+09z880hV628\n6LTIOHxhrTiavAIjjPT72EXFWlo2UVZ2jNgchURVF1Pfjp0xP336JiorV1Be3khl5QrKyk4meF9b\n8gJAiVYM/djHtnLNNW1MmuTjvvvg97+H3/8+xK23tnLqVKRa5lVXtfLcc69w1VWmimZkplEiu3jn\nndqsfQ4CAT81NdGrgkJ05VcnVu9MfDzYn/mv097+b1RVLWfGjNasfea9XHVV8k89Dw5L3N0a3e1b\nZ02cuMyaNMncjUWPL0buBOPHtlMfK/a6SM9DYQ3hjLUbPnpcurIy/m480d1r5u2Wzbvi5He7W62y\nsrlRj9s9dVssuMuyh0zgBquq6gqru7t7zPuQiky78+36LtOnr7MqK5dZpaUrrNLSZVZ5+TqrvDyY\n4O+U+nCTU9TTmbl89zzkk4IHh9gn+aqqsSc92iecior4sW1vd9mnI52prV7ixIk6tYTE+KGN9LeT\nzWmtyQOTRMmfL1twVYILanaDaCcSCY8fPx4ebored5P/AXMsmBv1+OiL5GXjM68cq8wpeFDw4Ihg\nMGjNnp35SaCY7wgiF67PJriYeDeAcuJuPvaifix8wf1TC64MX5DmW5WVVzlyEUp25/3QQ8GYO+ry\n8hVWZeWyhAmUiSQ/thM9nv+8gLGKTXRdZ8F1Fsy2YKtlAj77+LZ7HG7M+We+mM8zTlHwoOAhY5Hu\n2F9aiRMjU58lUMx3BJFy3l8Ml/PO/loGueDE3Xwqpc7PO+9j1sSJH894O8nuvJcuTXRHnXr3evJj\nO9Hj+b+4jbUXInatj60WXBH+m8QnUNtBRO4/825I2vQ6BQ8KHjIW+0GMndoGN1gVFdendGdmWboj\nsBXSNLL4MfB079ijjXbSnzbti45sZyzbHu2Ck17PgzuC6JF6YZIFFpWVK6IChXVWpGch+vcMWlCf\n4PHcfOar2oiKAAAgAElEQVQT92SZmiolJbXWtGlfcuSYKWT5Dh5UYbIAxFal8wHR1e+GmDVrBU8+\nmVoVQ6+uw+Ake4ljaGP2bCgvJ2trGeSC2d/442JsRquAOHnyel5+eWvG24lmV0R87rnMqi8mP7YT\nLbmd32WfOzrghz/s5le/amZw8IfErnOxi1/+sonnn/8egcCPeeGFbnp7S+nvN1VLL730LHv32m31\ndaAq/HtEV/X0Afa6JWOvxjlW9mybvr7/h2PHnsOyNr7/O1rWEEeP7qaiooklSzrD+yoSoZ4Hhzg5\n1ODVdRgkN0Y71ioqVmSlSmAwGLQmTVqY0XGe/Njeapkkwuh1XO6y8pn3cvz4cauq6ooR9mGLdf75\niRI6d4R/zq6XsSKqZyG+h8H+/+h1YLJFwxdjp54HyZi5C3LmLinf6zCIuw0/1qLXuyilv7+XqVNb\nCQT8KR0rqayzsHixWWnynXcmkslxHn1sP/vs1zh48ASlpe8xOGi/3+cpKRkHnM95553HuXNPMzj4\nA8wd+ThMvYVd4XoLnaP+bpm4++42Tp3ykbg3AOA53n33ByRaf+PUqW8xbtx/Y2jIwvSgXI7pWYjv\nYYj+v72OzXpMD8W7zJ5tsX37o45+5pOvv5NIbG+S1uRwFxWJKgCxBZ7ipdftGF9s54Mf3Mzbb7fz\nyCMwbVoLZWWNWS8gI+4Ve6wFgSZgJbAF2Ex//29pb19JQ0MToVBo1PdrboYNG0JMnWoKNNmFm6ZO\nbWXDhhDNzdEFherJ5Di3j+0jR1oJhYaA+xkcnIZZrv4VoBvL+h2W9SDnn1/Cddc9wvTp/xRTxGr6\n9E3vL/ucTWZ4yB5uSOQVYodZot1CVdV4TKGpWmAxpljcjeHvdgEqu4jcdmAqZljrceArTJxYwmWX\nPcratT5HP+Pxf+9z5+KLj0WLXTo9lWNFioOGLRySzaEGlZGVaLHH2l0ZdzmncnxFEh2d6V6PdJW7\nt8vcDA+NlMg42vDRUquiwp5tYX+/y4LFFlxtQa0FV1olJddZ48dfb02cuNTKRWJw8vV3Rk/W1Lko\nVr6HLfJJwYODsjU7QGOSEi165kZJyRUpn/iTGe34MjM3oi+U8bOJllmTJl2d1nEeCUbcO7PI7ONI\nwdkNI+77pZcus5YuDVpTpqyzxo2zA4YrLFhojRu31JoyZZ115ZVBq64utytbJl9/Z/Tzi85FsfId\nPCjnoQBkc3bAaNn1o2W4S2GJnrkxd+6r7N2bWpdzMrHHV2z+BAzw7rtHuPTSD7B3r4Xp3h4+m2ho\naAVr1/pSPs4ja5hEzz4Y2/5ni5kZ8qeYYYX7MWtR2HkXOykrO8zAQPwMEdsubrqplo0bnZlh46Th\n5xM/Zugr/nccnluic5G7KHgoANlMFIqcaBPJ7wlW8suJRN3I8RUEVgMPYE/ZgyHefnsng4N3MHwq\npW0Xq1bVsnHjWPY7v9MxRxII+Nm2rYmennXAL4hOZKyqeoMnntjI5z53Lz09o1903WT4+cRHbLLm\nMaqrP5gwQVvnIndRwqSMKHKiTSS/J1gndXTAzTeHmDGjlaqq5Ywfr8TQ0TiRqBs5vtowgUP8KouL\nOHXqW5x//n/BqZUmI/vtXKKx03w+H62tnUyf/hwVFa8ApZSVDTJt2kdZtOhZ/vqvP0ZFxfBVSXOV\n0DlWic8ndg/J44wf/0Fmz97KiRNtw5I1i+VcJKNTzoMHFMs4o5Kx0udEom6qi5FVVi52rHJlZL/t\nZMKxl3PPFicWyEp3e05VIR1JJueTYjkXpSrfOQ/5pODBA4qlaJROTOlz4oITOb6WJGl78+V0OWg7\nwfhDHzLJhGVlJplw5sylritDnotAIlfBcybnk2I5F6VKwYOCB9crpHUektGaHvlhByBlZfU5a3/7\nYlxXZ1kXXWRZlZWWNWWK+X7RRdb7MxDctK5Ctj+DuQyeM/ldiuFclKp8Bw/Jsk9yoQ7o6urqoq6u\n6AInzyiWqm5z5zayd+/mpM/PmdPIq68mf14ys2ZNK+3tK0lcUXEHLS2b2LjRXTMHciUYNBU2TaGs\nBdjJpLCbiRPvYdq0TmpqfBl9JufNW0539xaSJY/W1q5wZM2STM4nxXIuStWePXuor68HUz1tT663\nr9kWMqJi+UA6WeJb0heZXeCt2QO5EKmwGR1YnQB+wZkzFq+91ohlTQnPUEitLHi8XM1kyOR8Uizn\nIq9Q8CCjKoaIvxBXE/XS381eZfHs2QAnT67n3LlSxo8fZPLk2vdnD7hlX3NteH2D4dNazWqbu9m2\nrYkdO9Jfg0bBs3iJch48pNDHGgs1GSsXf7dcZeoXq+ErmTqfn6CEYe/Jd85DPil48IhimcZYaAFS\nrv5uxXJ85MvwZF7nk3sLNXguZPkOHlQkysM6OqCx0XwtXQpz55rv9mNOFTaKHXONLuCzkJ6e+/H7\nk5WM9Y741UTnzNmctFiNV+Tq71YMx0c+xRbjCgFv4nR+gr1ceUvLJqqrVwCNTJy4lIqKr3D0aDmX\nXro2b0XTVMBN4qnnwQG5uFvWNEZn5Kp7P9fTH3V8ZFekV2CLZYpa3eh4e0dPX50yJWiVln7Bgj/J\nWW/SSLUsli49bl18sXq24qnnQcYsGAzS0NBEe/tKenu3AJvp7X2c9vaVNDQ0EQqFHNmOaso7Y/Hi\nID09TRw6tJLTp7fQ37+Z06cf59ChlfT0NLFkiTN/L3s7AwOTycXfTcdHdtnJpBUVbZj1H+pxuqx2\nczNs3gz/+q9BJk9uYnDwNPBjctWb1NwMGzaEmDq1lX37lrN3byP79i1n6tRWpkz5OsGgerYkQj0P\nGcpVkpPuLJ2Rjb9Xoju2adNSK/msngdvibRz0EpcVvtXGd+Jp1ou3Om/6Uh5MyUltTq+ElDPg4yZ\nmcK1IMmzC8LPZ86JBZAkO3+vRHdsb7zxRHg7ufm76fjIjUgPj70S5SbA5CfACsrKvpTxoliRYzS6\nNykEtALL39/W3r1HHM03iM2beSO8vRXAA1jWeNSzVVy+jKny8p0kz6vnIUPDp3BlZz0AZWI7Ixt/\nr8R3bPZ2sneHGk3HR27koocncoza2zoePobiewScXTws8rsl2t6t6nlIoFB7Hq4F/gL4HSRdQ1Uy\nlKslau0xV68t/+s22fh7JZ7pYG8n0R3qJygr+0tH/26JMvWrq1fQ0rJpTAWLJLFc9PBEjlF7W8mX\nSncy3yDSq5Joe/NQz5b7ZKPCZBXwM+DzwNey8P4SlquqiKYSoQ/zwS4sHR3Q3h6iuzvAyZPdMZUN\na2v9tLQ4V9kwG3+v4dUHIXLiX4gJIKL/bju4/fZNbNzo3AXdVLI025k9G8rLTSXLEydg7Vp3VbL0\nslyU8I4co36gCRNIJAsQFrB79/qMtwnRQYt9PIfC37uBAWAL8COgAZUuL1w/Af4u/O/ngQeTvE7D\nFhlSd3HmclngKBt/r8RDIcmGK3RceFkupvrGHqPHLFiYk6HRSKLmiiRDF8cs+KwFc6yysmWqYGrl\nf9jC6Z6H1cDVmGEL0JBFVmk9gMwlXnQodhqYU6s5ZuPvlXhNAnu44luUlPwFllVNRcUgH/1oLY89\npmEEr8pFD6A9BOX3B3jhhW56e08x/PiyOTc0GulVsXs64j+TH8Dcl77I7bf/c9GusOomTgYPM4D/\nASwBzoUfK2GUZb/vvPNOLrzwwpjHmpubadZVb1SFPJyQK4m7/W3OdcuCc3+v6AWvTp6MHqKA2O7e\nc4wbN4HKyrlcc42fSZN8GkaQEcUPQZ040co772R/aNQOrI8e/TR9fV0k/4w0sHv3A45s00s6Ojro\niJva8tZbb+Vpb5z3ScxAVH/U1xAme+scw4MIDVtI3uVqxorT7MqiH/rQYstUAvyVBUeTZMYXdyU+\nGbtcD40Gg0GrvPxqT34mcy3fwxZOzrZ4BrgC+Ej462rgJUzy5NVoCENcKFczVpzS0QE33NDNtGlL\naG9fycGDTwP/DvwTptNvParEJ07J9UyaZ57xMX78xXjpM1msnAweTmH6Su2vl4E+zCouzlQrEnGY\nVwoc2YsD3XXXF3nhhUYGBx8iEiT4gG8DMzHZ6Ik4VzRMioe9YNzvftfG22+3U1Y2lwMH4Cc/eZVp\n01qYOtXZxamam+HTn74KL3wmi122K0za3SoirhQI+KmpuQfYgRllI/x9R3gamD9/OxfFXq/i6NHT\nmPSiRGPQWmNCnBW/5sXAwEqGhrZgWZsZHHycN990dl0W8M5nsthlO3i4EfhvWd6GyJh5pQBWZFbI\nG8D5JA4SvDUEI96Ry2XXVXTMG7JRJErEM7wyYyUyK6SUSJAQH0DUAjtJPHSh7l4Zu1zOSlLRMW9Q\n8CDiAZHyvYPA5cROzwQzRbMPaMEspaxKfOKcXC67ruDAG7SqpsfYiXMzZrRSVbWc8eMbqapazowZ\nziYuibvErjmwGIgeEw5iSgl/BtgG/DPwCeAmSkquZNq0h101BCPe47VZSZJ96nnwmMWLg3zta6s5\ndOgBTDdiCf39Q5w+vZsJE5pYsqQTk30vI4kutPTee3DggOkaPe8885jb7n6GrzmwDvgFZmrmEeB7\nRHoiIkMwlrWDm292di0LKT6R46+GSBEyewjtIk6enEVHx8ifmVyuIyOFTUWi0vDww5a1YoVlTZ9u\n14BPVEBlu9XSsi7fu+oZdqGl6uplFqywqquXWS0t61xZTGn4mgPrwksVL7FgjpYslqwKBoPWzJkf\ns6AhQRGyF61Zsz4+6ucml+vIFINCKhIlWdTcDBs2hHj33ecwK+olkt25/IU0ZBIMBmloaKK9fSW9\nvVuAzfT2Pk57+0oaGpoIhZybeuaE2Az0/wy8SnV1CS0tV1NTMxtN0ZRs8vl8NDRcg6knMnx57v37\nvznqjItcztiQ7NOwhUcEg0EWLVrN229PJl8XikIaMsnlglhOGCkD/fjx5eRi8SIpXh0dsGXLa4xc\nhGzkGRfpztjw2tBisVHPg0dELnbjyVfiUiHdOZgTWX56cBIZrVcHTLGezZvhqafg1VfN982bYdUq\nb1TJFO9qboZLLslsxkW6Mzbs3tapU1vZt285e/c2sm/fcqZObWXDhpAChzxT8OARkYtd/i4Ubrvg\nZiKXU89SYVeQPHRoJadPb6G/fzOnTz/OoUOjV/BTRT7JhdgZFyGgFVgONALLeO21gyMOX6Y7Y8Nr\nQ4vFRsGDR0Qudn5ip+kR/v5i1i8UbrvgZsJtU88y6dXxSpVM8bbIOjD21OCVgLmowxYGBr4/YqCb\n7joyhdTTKc7SbIs01NYui8pQDoaz7c0sAbjVKiv7iLV0adB6+OFc7YO3s/pbWtw1a6WQ2lYKU2TG\nz2fDs37S++yku7y3PhMj02wLSUls1G6XU96Kifq/xu2338STT2Z3nrRXVqBMhdu6+gupV0cKk93D\nVVb2MokXZoORhi/T7SHTZ8LdNNvCIwIBP9u2NdHTcz8m7yD3pYfdsA9OsU9kZ88GOHlyfUzBGvtE\nlsuErMgwimZMiDvZ68DMnXsJe/emf1FPdx0ZfSbcTcGDR7jhYueGfXCK2xbEilTwS3RH561eHSls\nubqo6zPhbgoePMINFzs37EMhSFSmt7y8j9LSpxgc/AHmZOndXh0pbLm6qBdST2chUs6DSI4lmpbZ\n1/cUg4PfpKrqDmbOvBVopLp6BS0tm9ixoxOfTzMmxB1ylS+kWUTulmzgKhfqgK6uri7q6vKSLCqS\nF2vWtNLevpLEd247mDZtE5bVxrlzIfr6AvT3m0WIzjtPiwiJO4RCIfz+AC+80E1vbynV1YNcf30t\ngYBfgW6O7Nmzh/r6eoB6YE+ut6+eBykqblifY7RiW5Mnd/Pb3waZPLmJ995byeDgRgYH53L6tMWh\nQ//B00/fwF13/ZXn1hORwtDRAWvX+jhxoo3Zs7cyZ85mZs/eyokTbaxd69MxWSSU8yBFxQ3rc6Qy\nBS1SIOcyYDUQ2V/LGuLo0Z1UVHhrPREpDPaaEnbuTl9fgO3bI0ts/+Y3tbS3q3es0KnnweXccKdc\nSNxQtS5xdUu73O8y9u49xE9+8gSmd6INEzgMX8lQVfYknzIpqS7ep+DB5dz4AY0PaMrKGiktXUJp\n6Q2Ult5MWZl7Axw3rM8xvNhWdLnfrcAeLKsaEyzkf39FEnFDIC75o+DB5dz4AY0PaAYHf8TQ0BBD\nQ99kaOgJBgfzH+Ak44aqdcOz1duA+4n9G9u9E/nfX5FE3BCIS/4oeHA5N35Ahwc0ybrW3XcH4oYF\nseKnoMETDJ95YfdO5H9/RRJxQyAu+aPgweXc+AGNDWhCwHO4LcBJxg3rczQ3w5NP+nj99TZOndrK\nnDn2EEU0e/XUi4CdSd5JVfYkf9wQiEv+KHhwOTd+QCMBjT1WPxm3BTjJuG1BLEj2N/YBnUAl8Bng\nRdyyvyKQeiCupO/CpODB5dxwpxwvcrGzhyvGE7n42bMGlgONwDJee+2ga04SbqpaZ59U9+8/jQlm\n4vmAzzBt2s1Mn/7Ped9fkWipBuJuTPoWb6sDrK6urnwvi+5qwWDQqqm50YLtFgy+v5Y9bLdqam60\ngsFgzveppWWdBTssWGbBkAX2/49bcGP430NR+/pi3vbVzY4fPx7+2/4y3G7xf2O1m7jXww9b1tKl\nQWvKlHXWuHGLLbjagissWGjBDVZJyfXWuHFLw4+/GD6u47+2Wy0t6/L9q3hSV1eXhblry0uJZvU8\nuJyb7pRtkTuOc5jhCnt8vpXhswZUkyCZSOLprZghik3ACkyPzWIqKu5R74K4lp2788orrVRXDwE/\nAH4H/AummJmZfQWXAA1J3sVdOVGSOlWYdDk3rmRpBzQHDtzKwIBFZHz+VhKv1wDmJLE+Z/voBeak\naQdU8X/jIWbNWsGTTypwEHeLnX0FsbOvQNONC5N6HiRt9h3H7bffSCQfw4e5w9BJIlVunEkjkq7R\nZ1+5L+lbMqfgQcZseMKUThLpcONMGpF0jT77yn1J35I5BQ8yZvH5GCUlR0g8awB0khjOjTNpRNI1\n8uwriOREuWd6tGROwYOMWXyxo+PHn6Km5qvoJJEaN9acEElXJAi2hy+ig+IQJq9nHPBF4ErgGsaN\nu5UpUzTd2MuUMCmOsXsizp4NcPLk+veX6J08ufb9k4SW6I1Qe0khCAT8bNvWRE+PPXzhxwxf3AX8\nHdHLyZvgeBfV1feyY4cfn0+Bg1cly9bKhTqgq6uri7q6vExTFRGRDHV0QHt7iGefvZWBgV9jLish\n4NOYwGFRgp/aQUvLJjZudM8sMq/Zs2cP9fX1APXAnlxvX8MWIiIyZslnX1Wi+g6FS8GDiIhkbHgO\nj6YiFzIFD1JUQqEQa9a0Mm/ecubObWTevOWsWdNKKKT6+iKZGL7UfC+aily4FDxI0XjooSCzZjXR\n3r6S7u4t7N27me7ux2lvX8msWU388IcKIETGKn72VUvLLWgqcuFS8CBFY9euNvr67LK50WtvLKSv\n73527tTaGyJO0VTkwqbgQYpGbBndeErgEnGSGxf1E+eozoMUDa0lIZI7blzUT5yjngcpGlpLQkTE\nGQoexHFundGgtSRERJyh4EEc5eYZDUrgEhFxhoIHcZSbZzQogUtExBlKmBRHmRkLyQKEBezevT6X\nuxNDCVwiIs5Qz4M4SjMaREQKn4IHcZRmNIiIFD4FD+Ko2BkNIaAVWA40AovZv/80N98coqMjX3so\nIiKZUvAgjorMaNgKNAErgS3AZuDf6Ov7Bj09TSxZonUkRES8yung4QvAb4G3w1/bgVsc3oa4mD2j\noaKiDVjP8FkXDfT03I/fr3UkRES8yung4XXgbqAOqAeexdxyznN4O+JS9sp6s2ZVAg1JXqV1JERE\nvMzpqZpb4v7/VUxvxHzgZYe3JS6mWRciIoUrm3UeSoFPAxOAbVncjrhQZNZFogBCsy5ERLwsG8HD\nlZj6vxOAM8CfAfuysB1xsfnza+nu3gXUYIpGdWPiyUHgIk6enEVHhxnmEBERb0nWr5yJcmAGcAGm\n5+FLwA3AnrjX1QFd1113HRdeeGHME83NzTTrquJpoVCIa6+9jQMHhoAHgQWYw20I2MmsWV9h9+7H\n8PlUElpEZCQdHR10xM1vf+utt9i2bRuY/ML462vWZSN4iPc0sB/487jH64Curq4u6urqcrAbkksd\nHXDXXX/F0aPNwKIEr9hBS8smNm5UqWgRkXTt2bOH+vp6yFPwkIs6D+NytB1xkeZmmDz5NTTjQkSk\n8Did8/AN4JeYKZvnA6uB64H7Hd6OeIBmXIiIFCangwcf8FNgGqZI1G+BmzH1HqTIaMaFiEhhcjp4\n+LzD7yceFplxsTDBs7vC62CIiIjXKBdBsmbhQj8VFfdgZu4OhR8dAnZQUXEvCxf687dzIiIyZtks\nEiVF7o47fHzqU534/QF2717PwEApZWWDzJ9fSyDQqWmaIiIepeBBssrn82k6pohIgdGwhYiIiKRF\nwYOIiIikRcMWUhRCoVA496I7LvfCr9w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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/2246A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915, 0.62032418])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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V8jlWyU7BQwrwXFE5GN35bxDSO9Mp+u/hVcIbz1cEZgDlWuX1pf32iBOYzRUF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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.379e-01 6.748e+01 inf -- -3.302e+02 -- 1 1 1 1 1 1 1 1\n", + " 2 7.796e-01 6.679e+01 7.758e+01 -- -2.526e+02 -- 0.618991 0.579498 0.564066 0.564121 0.56476 0.563682 0.563867 0.56205\n", + " 3 3.540e+00 6.595e+01 7.576e+01 -- -1.769e+02 -- 0.314779 0.183356 0.127531 0.127909 0.129377 0.127333 0.127748 0.123875\n", + " 4 1.452e+00 6.481e+01 7.321e+01 -- -1.036e+02 -- 0.145822 -0.154058 -0.309225 -0.308235 -0.306082 -0.309022 -0.308575 -0.314603\n", + " 5 5.873e-01 6.313e+01 7.037e+01 -- -3.327e+01 -- 0.101594 -0.377738 -0.742409 -0.742747 -0.740971 -0.745083 -0.745333 -0.753741\n", + " 6 3.716e-01 6.044e+01 6.710e+01 -- 3.383e+01 -- 0.0846307 -0.469976 -1.15278 -1.17038 -1.1732 -1.18022 -1.18308 -1.19385\n", + " 7 3.359e-01 5.601e+01 6.209e+01 -- 9.591e+01 -- 0.0659456 -0.50867 -1.48798 -1.574 -1.5971 -1.61323 -1.62275 -1.63479\n", + " 8 5.149e-01 4.857e+01 5.399e+01 -- 1.499e+02 -- 0.0437973 -0.538092 -1.68322 -1.90776 -2.00001 -2.04069 -2.06646 -2.07709\n", + " 9 7.563e-01 3.652e+01 4.228e+01 -- 1.922e+02 -- 0.0212475 -0.55813 -1.75872 -2.09423 -2.35209 -2.45331 -2.51958 -2.52359\n", + " 10 1.974e+00 2.058e+01 2.756e+01 -- 2.197e+02 -- 0.00517809 -0.569653 -1.80128 -2.12815 -2.59485 -2.83321 -2.9937 -2.97691\n", + " 11 1.071e+00 7.198e+00 1.293e+01 -- 2.327e+02 -- -0.00504406 -0.575918 -1.82651 -2.12905 -2.68784 -3.15505 -3.51079 -3.42807\n", + " 12 2.091e-01 1.531e+00 3.715e+00 -- 2.364e+02 -- -0.0104477 -0.580461 -1.83547 -2.13193 -2.69807 -3.39738 -4.11475 -3.83333\n", + " 13 5.653e-01 3.932e-01 6.297e-01 -- 2.370e+02 -- -0.0124146 -0.583395 -1.83764 -2.13329 -2.69789 -3.54657 -4.97511 -4.11701\n", + " 14 1.852e+02 1.372e-01 6.354e-02 -- 2.371e+02 -- -0.0129899 -0.584709 -1.83799 -2.13471 -2.69697 -3.6137 -7.78741 -4.23122\n", + " 15 2.921e+02 7.159e-02 2.904e-03 -- 2.371e+02 -- -0.0130755 -0.585195 -1.83773 -2.13556 -2.696 -3.63655 -8 -4.24469\n", + " 16 2.917e+02 5.374e-02 4.141e-04 -- 2.371e+02 -- -0.0130579 -0.585335 -1.83758 -2.13596 -2.69543 -3.64347 -8 -4.24419\n", + " 17 2.916e+02 4.858e-02 9.106e-05 -- 2.371e+02 -- -0.0130506 -0.585376 -1.83754 -2.13607 -2.69524 -3.64553 -8 -4.24379\n", + "********************\n", + "-0.0130506 -0.585376 -1.83754 -2.13607 -2.69524 -3.64553 -8 -4.24379\n", + "0.269171 0.232445 0.302922 0.233996 0.234912 0.539942 6377.59 0.989373\n", + "-0.000636424 -0.00149059 -0.00259185 -0.00860622 -0.0213922 -0.0485818 -5.7966e-05 -0.0291693\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 2.371e+02 2.366e+02 -1.305e-02 2.561e-01 0.96 +++\n", + "+++ 2.371e+02 2.361e+02 -1.305e-02 3.907e-01 2 +++\n", + "+++ 2.371e+02 2.364e+02 -1.305e-02 3.234e-01 1.44 +++\n", + "+++ 2.371e+02 2.365e+02 -1.305e-02 2.898e-01 1.19 +++\n", + "+++ 2.371e+02 2.365e+02 -1.305e-02 2.729e-01 1.07 +++\n", + "+++ 2.371e+02 2.366e+02 -1.305e-02 2.645e-01 1.02 +++\n", + "+++ 2.371e+02 2.366e+02 -1.305e-02 2.603e-01 0.987 +++\n", + "+++ 2.371e+02 2.366e+02 -1.305e-02 2.624e-01 1 +++\n", + "\t### errors for param 1 ###\n", + "+++ 2.371e+02 2.366e+02 -5.854e-01 -3.529e-01 0.949 +++\n", + "+++ 2.371e+02 2.361e+02 -5.854e-01 -2.367e-01 1.99 +++\n", + "+++ 2.371e+02 2.364e+02 -5.854e-01 -2.948e-01 1.43 +++\n", + "+++ 2.371e+02 2.365e+02 -5.854e-01 -3.239e-01 1.18 +++\n", + "+++ 2.371e+02 2.365e+02 -5.854e-01 -3.384e-01 1.06 +++\n", + "+++ 2.371e+02 2.366e+02 -5.854e-01 -3.457e-01 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 2.371e+02 2.369e+02 -1.838e+00 -1.686e+00 0.273 +++\n", + "+++ 2.371e+02 2.368e+02 -1.838e+00 -1.610e+00 0.6 +++\n", + "+++ 2.371e+02 2.367e+02 -1.838e+00 -1.572e+00 0.806 +++\n", + "+++ 2.371e+02 2.366e+02 -1.838e+00 -1.554e+00 0.92 +++\n", + "+++ 2.371e+02 2.366e+02 -1.838e+00 -1.544e+00 0.979 +++\n", + "+++ 2.371e+02 2.366e+02 -1.838e+00 -1.539e+00 1.01 +++\n", + "\t### errors for param 3 ###\n", + "+++ 2.371e+02 2.369e+02 -2.136e+00 -2.019e+00 0.267 +++\n", + "+++ 2.371e+02 2.368e+02 -2.136e+00 -1.961e+00 0.589 +++\n", + "+++ 2.371e+02 2.367e+02 -2.136e+00 -1.931e+00 0.793 +++\n", + "+++ 2.371e+02 2.366e+02 -2.136e+00 -1.917e+00 0.905 +++\n", + "+++ 2.371e+02 2.366e+02 -2.136e+00 -1.909e+00 0.964 +++\n", + "+++ 2.371e+02 2.366e+02 -2.136e+00 -1.906e+00 0.994 +++\n", + "\t### errors for param 4 ###\n", + "+++ 2.371e+02 2.370e+02 -2.695e+00 -2.578e+00 0.256 +++\n", + "+++ 2.371e+02 2.368e+02 -2.695e+00 -2.519e+00 0.577 +++\n", + "+++ 2.371e+02 2.367e+02 -2.695e+00 -2.490e+00 0.787 +++\n", + "+++ 2.371e+02 2.366e+02 -2.695e+00 -2.475e+00 0.904 +++\n", + "+++ 2.371e+02 2.366e+02 -2.695e+00 -2.468e+00 0.965 +++\n", + "+++ 2.371e+02 2.366e+02 -2.695e+00 -2.464e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 2.371e+02 2.369e+02 -3.646e+00 -3.376e+00 0.301 +++\n", + "+++ 2.371e+02 2.367e+02 -3.646e+00 -3.241e+00 0.783 +++\n", + "+++ 2.371e+02 2.365e+02 -3.646e+00 -3.173e+00 1.15 +++\n", + "+++ 2.371e+02 2.366e+02 -3.646e+00 -3.207e+00 0.953 +++\n", + "+++ 2.371e+02 2.366e+02 -3.646e+00 -3.190e+00 1.05 +++\n", + "+++ 2.371e+02 2.366e+02 -3.646e+00 -3.199e+00 1 +++\n", + "\t### errors for param 6 ###\n", + "+++ 2.371e+02 2.371e+02 -8.000e+00 -5.000e+00 0.0545 +++\n", + "+++ 2.371e+02 2.353e+02 -8.000e+00 -3.500e+00 3.58 +++\n", + "+++ 2.371e+02 2.369e+02 -8.000e+00 -4.250e+00 0.39 +++\n", + "+++ 2.371e+02 2.365e+02 -8.000e+00 -3.875e+00 1.18 +++\n", + "+++ 2.371e+02 2.367e+02 -8.000e+00 -4.062e+00 0.674 +++\n", + "+++ 2.371e+02 2.366e+02 -8.000e+00 -3.969e+00 0.891 +++\n", + "+++ 2.371e+02 2.366e+02 -8.000e+00 -3.922e+00 1.03 +++\n", + "+++ 2.371e+02 2.366e+02 -8.000e+00 -3.945e+00 0.956 +++\n", + "+++ 2.371e+02 2.366e+02 -8.000e+00 -3.934e+00 0.99 +++\n", + "\t### errors for param 7 ###\n", + "+++ 2.371e+02 2.367e+02 -4.244e+00 -3.749e+00 0.707 +++\n", + "+++ 2.371e+02 2.359e+02 -4.244e+00 -3.502e+00 2.45 +++\n", + "+++ 2.371e+02 2.364e+02 -4.244e+00 -3.625e+00 1.37 +++\n", + "+++ 2.371e+02 2.366e+02 -4.244e+00 -3.687e+00 0.998 +++\n", + "********************\n", + "-0.0130485 -0.585388 -1.83753 -2.1361 -2.69519 -3.64613 -8 -4.24367\n", + "0.275479 0.239709 0.298186 0.230343 0.23123 0.447579 2 0.556379\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2.85855 1.07978\n", + " 7 1.906e+02 1.570e+01 2.338e+00 -- 2.998e+02 -- -0.0590998 -0.577513 -1.69772 -2.02818 -2.60158 -3.1372 -6.38072 -5.52184 -0.0305296 0.110846 0.182461 0.0892265 0.194212 -0.00858619 2.71513 -3.11054\n", + " 9 5.891e+01 1.810e+01 2.317e+00 -- 3.021e+02 -- -0.0354896 -0.55376 -1.67186 -2.0048 -2.57534 -3.09575 -6.30939 -5.82184 -0.0543126 0.112411 0.195339 0.0877287 0.21341 -0.0199574 1.23313 2.11694\n", + " 11 5.912e+01 2.072e+01 2.113e+00 -- 3.042e+02 -- -0.0151439 -0.533554 -1.65012 -1.98485 -2.55283 -3.06281 -6.00939 -5.52184 -0.0727701 0.113507 0.204816 0.0866854 0.229407 -0.0274598 -2.84324 2.21189\n", + " 13 5.318e+02 2.358e+01 2.081e+00 -- 3.063e+02 -- 0.00246155 -0.516217 -1.63169 -1.96771 -2.5334 -3.03613 -6.30939 -5.22184 -0.0873173 0.114241 0.211893 0.0859833 0.242956 -0.0325361 -0.803414 1.81103\n", + " 15 6.000e+01 2.669e+01 1.754e+00 -- 3.080e+02 -- 0.0177641 -0.501232 -1.61594 -1.95288 -2.51654 -3.01421 -6.60939 -4.92184 -0.0989192 0.114745 0.21724 0.085486 0.254699 -0.0358684 -2.05745 -0.439047\n", + " 17 2.760e+02 3.007e+01 2.029e+00 -- 3.101e+02 -- 0.0311238 -0.488205 -1.60242 -1.93999 -2.50187 -2.99581 -6.90939 -4.99492 -0.108184 0.115134 0.221508 0.0852356 0.26512 -0.0377338 1.31525 1.49865\n", + " 19 3.455e+02 3.375e+01 1.932e+00 -- 3.120e+02 -- 0.0428159 -0.476826 -1.59071 -1.92874 -2.48903 -2.98053 -6.60939 -4.69492 -0.115759 0.115347 0.224648 0.0849875 0.274357 -0.0388865 2.71661 0.824234\n", + " 21 5.627e+02 3.773e+01 2.106e+00 -- 3.141e+02 -- 0.0530961 -0.466839 -1.58055 -1.91888 -2.47782 -2.96759 -6.90939 -4.39492 -0.121894 0.115561 0.227171 0.0847372 0.282893 -0.0391746 3.09606 1.24474\n", + " 23 1.412e+03 4.204e+01 2.098e+00 -- 3.162e+02 -- 0.0621536 -0.458045 -1.57168 -1.9102 -2.468 -2.95676 -7.20939 -4.18138 -0.126953 0.115732 0.229039 0.084359 0.29081 -0.0390341 -1.48027 1.23885\n", + " 25 4.162e+04 4.667e+01 1.855e+00 -- 3.181e+02 -- 0.0701599 -0.450276 -1.56393 -1.90253 -2.45941 -2.94767 -7.50939 -4.06761 -0.131108 0.115932 0.230463 0.0838354 0.298357 -0.0385057 0.151626 1.24726\n", + " 27 1.368e+03 5.163e+01 1.694e+00 -- 3.197e+02 -- 0.0772509 -0.443397 -1.55713 -1.89576 -2.45187 -2.94001 -7.20939 -3.99039 -0.134522 0.11614 0.231552 0.0832518 0.30556 -0.0376963 2.85355 1.25729\n", + " 29 3.278e+03 5.692e+01 1.564e+00 -- 3.213e+02 -- 0.0835427 -0.437294 -1.55116 -1.88977 -2.44524 -2.93357 -7.50939 -3.9332 -0.137326 0.116351 0.232387 0.0826471 0.312468 -0.0366776 2.17754 1.26747\n", + " 31 7.767e+02 6.255e+01 1.446e+00 -- 3.228e+02 -- 0.0891349 -0.43187 -1.54591 -1.88447 -2.4394 -2.92816 -7.20939 -3.88882 -0.139623 0.116565 0.233032 0.0820438 0.319124 -0.0354977 -1.72243 1.27737\n", + " 33 6.146e+04 6.851e+01 1.343e+00 -- 3.241e+02 -- 0.0941131 -0.427041 -1.5413 -1.87977 -2.43426 -2.92364 -7.50939 -3.85335 -0.141498 0.116784 0.233532 0.0814561 0.325567 -0.0341934 0.110556 1.28681\n", + " 35 5.141e+02 7.481e+01 1.246e+00 -- 3.253e+02 -- 0.0985508 -0.422736 -1.53723 -1.87561 -2.42973 -2.91988 -7.20939 -3.82441 -0.143017 0.117008 0.233923 0.0808916 0.331826 -0.0327907 0.968192 1.29571\n", + " 37 2.561e+03 8.143e+01 1.157e+00 -- 3.265e+02 -- 0.102512 -0.418892 -1.53365 -1.87193 -2.42573 -2.9168 -6.90939 -3.80046 -0.144236 0.11724 0.234234 0.0803546 0.337926 -0.0313104 0.474092 1.30403\n", + " 39 4.175e+02 8.835e+01 1.071e+00 -- 3.276e+02 -- 0.106051 -0.415457 -1.53049 -1.86867 -2.4222 -2.91429 -6.60939 -3.78043 -0.145202 0.117482 0.234484 0.0798467 0.343885 -0.0297682 2.50013 1.31174\n", + " 41 2.484e+02 9.557e+01 9.861e-01 -- 3.286e+02 -- 0.109218 -0.412381 -1.52772 -1.86578 -2.41908 -2.91229 -6.30939 -3.76351 -0.14595 0.117736 0.234692 0.079367 0.349713 -0.0281813 -1.35794 1.31885\n", + " 43 1.664e+02 1.030e+02 9.286e-01 -- 3.295e+02 -- 0.112053 -0.409626 -1.52527 -1.86322 -2.41632 -2.91073 -6.60939 -3.74914 -0.146514 0.118004 0.234875 0.0789157 0.355418 -0.0265569 0.955065 1.32532\n", + " 45 3.715e+01 1.108e+02 8.360e-01 -- 3.303e+02 -- 0.114595 -0.407155 -1.52313 -1.86096 -2.41388 -2.90956 -6.30939 -3.73686 -0.146919 0.118288 0.235035 0.0784863 0.361005 -0.024906 -2.00648 1.33124\n", + " 47 1.757e+03 1.187e+02 7.899e-01 -- 3.311e+02 -- 0.116875 -0.404936 -1.52125 -1.85897 -2.41171 -2.90871 -6.60939 -3.7263 -0.147187 0.118587 0.235191 0.0780786 0.366467 -0.0232463 -0.606714 1.3366\n", + " 49 2.419e+03 1.268e+02 7.198e-01 -- 3.318e+02 -- 0.118922 -0.402941 -1.5196 -1.85722 -2.40977 -2.90815 -6.90939 -3.71719 -0.147336 0.118903 0.235343 0.0776863 0.371808 -0.0215787 -0.828333 1.34144\n", + " 50 3.758e+03 1.322e+04 4.674e+00 -- 3.272e+02 -- 0.137314 -0.384994 -1.50519 -1.84182 -2.39252 -2.9049 -8 -3.63812 -0.147803 0.122227 0.236936 0.0738852 0.423919 -0.00499483 -1.20255 1.3852\n", + " 52 2.909e+04 6.385e+02 1.073e+01 -- 3.379e+02 -- 0.136943 -0.384318 -1.50651 -1.84446 -2.39241 -2.90988 -8 -3.64036 -0.1468 0.121814 0.239523 0.0607648 0.447789 0.00582078 -0.683535 1.38625\n", + " 53 8.944e+04 2.504e+02 4.851e-01 -- 3.384e+02 -- 0.137108 -0.382857 -1.511 -1.85395 -2.38877 -2.93977 -8 -3.6534 -0.132666 0.131148 0.26174 0.0372861 0.519783 0.0387377 0.222008 1.38794\n", + " 55 1.907e+04 2.097e+02 2.143e-01 -- 3.386e+02 -- 0.137241 -0.382905 -1.51059 -1.85324 -2.38834 -2.93581 -7.7 -3.65133 -0.133058 0.131502 0.26506 0.0380769 0.517294 0.0298916 0.427458 1.38872\n", + " 57 2.701e+03 1.743e+02 1.707e-01 -- 3.388e+02 -- 0.137362 -0.382947 -1.51023 -1.85264 -2.38795 -2.93261 -7.4 -3.64964 -0.133373 0.131817 0.267849 0.0388293 0.515228 0.0226558 -1.3799 1.38928\n", + " 58 1.901e+04 6.807e+03 9.857e+00 -- 3.289e+02 -- 0.138463 -0.383316 -1.50699 -1.84755 -2.38449 -2.90718 -8 -3.63586 -0.135895 0.13461 0.291172 0.0458143 0.498444 -0.0363648 -0.545357 1.39317\n", + " 60 5.237e+04 4.383e+02 8.985e+00 -- 3.379e+02 -- 0.138201 -0.382988 -1.50809 -1.84815 -2.38423 -2.91079 -8 -3.63639 -0.138737 0.135181 0.288716 0.034039 0.510877 -0.0254366 -0.396913 1.39809\n", + " 61 8.766e+03 4.097e+02 8.172e-01 -- 3.387e+02 -- 0.137968 -0.382536 -1.51047 -1.85072 -2.38194 -2.93875 -8 -3.64468 -0.141543 0.135484 0.281935 0.0183923 0.556885 0.045315 1.79478 1.42693\n", + " 63 1.814e+04 3.493e+02 1.618e-01 -- 3.389e+02 -- 0.13803 -0.382587 -1.51013 -1.85062 -2.38179 -2.93566 -7.7 -3.64395 -0.140901 0.135271 0.284223 0.0205977 0.55377 0.0372067 -0.67593 1.42469\n", + " 64 4.667e+03 9.194e+03 7.138e+00 -- 3.317e+02 -- 0.138594 -0.383049 -1.50705 -1.84979 -2.38045 -2.91127 -8 -3.63796 -0.135166 0.133564 0.302461 0.0405415 0.526549 -0.0285933 1.72877 1.40601\n", + " 66 2.229e+03 8.468e+02 6.334e+00 -- 3.381e+02 -- 0.138375 -0.382821 -1.50788 -1.8493 -2.38027 -2.91526 -7.7 -3.63713 -0.138411 0.134331 0.300768 0.0296213 0.534063 -0.0172368 -2.02693 1.41805\n", + " 67 6.383e+03 4.433e+02 6.946e-01 -- 3.388e+02 -- 0.138019 -0.382446 -1.51012 -1.84832 -2.37854 -2.94579 -8 -3.63552 -0.145232 0.136059 0.290585 0.00900103 0.566205 0.0595408 -1.58605 1.49345\n", + " 69 2.791e+04 3.818e+02 1.832e-01 -- 3.389e+02 -- 0.13807 -0.382495 -1.50981 -1.84878 -2.37843 -2.94234 -8 -3.63605 -0.14425 0.135725 0.292234 0.0124456 0.563364 0.0504802 -0.811369 1.48719\n", + " 71 7.592e+03 3.289e+02 1.419e-01 -- 3.391e+02 -- 0.138118 -0.382539 -1.50952 -1.84915 -2.37833 -2.93967 -8 -3.63644 -0.143394 0.135448 0.293502 0.0154512 0.560893 0.0432337 1.73025 1.48211\n", + " 72 9.792e+00 1.238e+04 4.549e+00 -- 3.345e+02 -- 0.138556 -0.382946 -1.50702 -1.85213 -2.37736 -2.91947 -5 -3.63926 -0.135949 0.133151 0.302987 0.0415893 0.539598 -0.0143255 -2.01979 1.44199\n", + " 73 1.790e+03 2.036e+04 5.362e+00 -- 3.292e+02 -- 0.13686 -0.381484 -1.51285 -1.83573 -2.37645 -2.96346 -8 -3.61694 -0.169822 0.139603 0.306242 -0.0616507 0.578831 0.105014 -2.94898 1.60824\n", + " 75 2.000e+04 3.767e+03 7.143e+00 -- 3.363e+02 -- 0.136937 -0.381554 -1.51207 -1.84035 -2.37648 -2.95217 -8 -3.62182 -0.164447 0.137434 0.31164 -0.0445826 0.577957 0.0688955 -1.04621 1.58574\n", + " 77 1.670e+04 2.864e+03 1.308e+00 -- 3.376e+02 -- 0.137027 -0.381619 -1.51152 -1.84295 -2.37643 -2.94676 -8 -3.62459 -0.16153 0.135849 0.316596 -0.035261 0.576777 0.0522889 -1.10409 1.57318\n", + " 79 3.940e+03 2.269e+03 6.679e-01 -- 3.383e+02 -- 0.137114 -0.381682 -1.51105 -1.8448 -2.37637 -2.94335 -8 -3.62651 -0.159205 0.134652 0.320529 -0.028154 0.575644 0.041706 1.23968 1.56483\n", + " 81 3.136e+03 1.843e+03 4.148e-01 -- 3.387e+02 -- 0.137197 -0.381742 -1.51062 -1.84618 -2.3763 -2.9411 -7.7 -3.62789 -0.157267 0.133753 0.323527 -0.0224013 0.574634 0.0345912 2.94827 1.55921\n", + " 83 7.332e+03 1.524e+03 2.814e-01 -- 3.390e+02 -- 0.137277 -0.381798 -1.51025 -1.84723 -2.37623 -2.9396 -8 -3.6289 -0.155627 0.133083 0.325719 -0.0176294 0.573764 0.0297776 1.97421 1.55551\n", + " 84 4.352e+01 2.419e+05 8.521e+00 -- 3.305e+02 -- 0.13802 -0.382337 -1.50692 -1.85513 -2.37556 -2.92991 -5 -3.63632 -0.141645 0.128163 0.340891 0.0224931 0.566412 -0.00221606 2.81464 1.53266\n", + " 86 2.646e+01 1.360e+05 5.846e+00 -- 3.363e+02 -- 0.137542 -0.382135 -1.50734 -1.85175 -2.3755 -2.93273 -4.7 -3.63458 -0.154265 0.130343 0.343497 0.00406723 0.567925 0.0074277 1.76444 1.5434\n", + " 88 3.599e+00 1.247e+03 4.005e+00 -- 3.403e+02 -- 0.137281 -0.382038 -1.50761 -1.84969 -2.37546 -2.93454 -4.44346 -3.63355 -0.162752 0.1317 0.344853 -0.00669382 0.568721 0.0140179 2.19787 1.55056\n", + " 90 1.187e+00 3.038e+03 3.904e-01 -- 3.407e+02 -- 0.13727 -0.382043 -1.50767 -1.84906 -2.37542 -2.93544 -4.35974 -3.63317 -0.162904 0.131742 0.343848 -0.00910299 0.569009 0.0171214 2.09178 1.55417\n", + " 92 7.462e-01 5.052e+03 2.290e-01 -- 3.409e+02 -- 0.13728 -0.382056 -1.50771 -1.84873 -2.3754 -2.93603 -4.30688 -3.63296 -0.16231 0.131703 0.342609 -0.0100924 0.569167 0.0191537 2.00906 1.55662\n", + " 94 5.215e-01 6.434e+03 1.735e-01 -- 3.411e+02 -- 0.137299 -0.382072 -1.50773 -1.84856 -2.37538 -2.93645 -4.26782 -3.63284 -0.161418 0.131643 0.341287 -0.0103928 0.569258 0.0205828 1.9431 1.5584\n", + " 96 3.952e-01 7.618e+03 1.468e-01 -- 3.413e+02 -- 0.137323 -0.38209 -1.50774 -1.84848 -2.37536 -2.93676 -4.23675 -3.63278 -0.16042 0.131584 0.339946 -0.0103403 0.569314 0.0216563 1.89017 1.55979\n", + " 98 3.181e-01 8.762e+03 1.305e-01 -- 3.414e+02 -- 0.137349 -0.382109 -1.50775 -1.84843 -2.37535 -2.93701 -4.21105 -3.63274 -0.159411 0.131539 0.338623 -0.0101102 0.56935 0.0225121 1.84758 1.56093\n", + " 100 3.494e-01 9.944e+03 1.188e-01 -- 3.415e+02 -- 0.137376 -0.382128 -1.50775 -1.84841 -2.37533 -2.93721 -4.18933 -3.63272 -0.158443 0.131511 0.337339 -0.00979793 0.569375 0.0232282 1.81316 1.56189\n", + " 102 3.644e-01 1.121e+04 1.096e-01 -- 3.416e+02 -- 0.137403 -0.382146 -1.50776 -1.8484 -2.37532 -2.93738 -4.17073 -3.63271 -0.157537 0.131501 0.336106 -0.0094556 0.569394 0.023849 1.78523 1.56274\n", + " 104 3.648e-01 1.258e+04 1.021e-01 -- 3.417e+02 -- 0.13743 -0.382164 -1.50776 -1.84839 -2.37531 -2.93754 -4.15467 -3.6327 -0.156705 0.131508 0.33493 -0.00911106 0.569409 0.0244002 1.7624 1.56349\n", + " 106 3.569e-01 1.408e+04 9.589e-02 -- 3.418e+02 -- 0.137456 -0.382182 -1.50777 -1.84838 -2.3753 -2.93768 -4.14072 -3.6327 -0.155947 0.13153 0.333816 -0.00877869 0.569422 0.0248968 1.74363 1.56416\n", + " 108 3.443e-01 1.574e+04 9.062e-02 -- 3.419e+02 -- 0.137481 -0.382199 -1.50777 -1.84838 -2.3753 -2.9378 -4.12855 -3.63269 -0.155261 0.131565 0.332764 -0.00846538 0.569432 0.0253482 1.72809 1.56477\n", + " 110 3.290e-01 1.757e+04 8.614e-02 -- 3.420e+02 -- 0.137504 -0.382216 -1.50778 -1.84837 -2.37529 -2.93791 -4.11789 -3.63269 -0.154643 0.131611 0.331773 -0.00817392 0.569442 0.0257602 1.71513 1.56532\n", + " 112 3.124e-01 1.961e+04 8.229e-02 -- 3.421e+02 -- 0.137527 -0.382232 -1.50779 -1.84837 -2.37528 -2.93801 -4.10852 -3.63269 -0.154086 0.131666 0.330844 -0.00790497 0.569451 0.0261371 1.70424 1.56582\n", + " 114 2.950e-01 2.186e+04 7.896e-02 -- 3.422e+02 -- 0.137548 -0.382247 -1.50779 -1.84836 -2.37527 -2.9381 -4.10026 -3.63269 -0.153586 0.131728 0.329975 -0.00765804 0.569459 0.0264824 1.69505 1.56628\n", + " 116 2.774e-01 2.436e+04 7.607e-02 -- 3.422e+02 -- 0.137569 -0.382262 -1.5078 -1.84835 -2.37527 -2.93819 -4.09297 -3.63269 -0.153136 0.131795 0.329162 -0.00743212 0.569466 0.0267986 1.68724 1.5667\n", + " 118 2.597e-01 2.714e+04 7.354e-02 -- 3.423e+02 -- 0.137588 -0.382276 -1.50781 -1.84835 -2.37526 -2.93826 -4.08651 -3.63268 -0.152733 0.131866 0.328406 -0.00722598 0.569472 0.0270881 1.68057 1.56707\n", + " 120 2.423e-01 3.022e+04 7.133e-02 -- 3.424e+02 -- 0.137606 -0.38229 -1.50781 -1.84834 -2.37526 -2.93833 -4.08078 -3.63268 -0.15237 0.13194 0.327701 -0.0070383 0.569478 0.0273531 1.67485 1.56742\n", + " 122 2.253e-01 3.365e+04 6.937e-02 -- 3.424e+02 -- 0.137623 -0.382303 -1.50782 -1.84833 -2.37525 -2.93839 -4.07569 -3.63268 -0.152044 0.132015 0.327048 -0.00686775 0.569484 0.0275954 1.66993 1.56773\n", + " 124 2.087e-01 3.745e+04 6.764e-02 -- 3.425e+02 -- 0.137638 -0.382316 -1.50783 -1.84833 -2.37525 -2.93845 -4.07116 -3.63268 -0.151752 0.132092 0.326442 -0.00671305 0.569489 0.027817 1.66568 1.56802\n", + " 126 1.926e-01 4.168e+04 6.611e-02 -- 3.426e+02 -- 0.137653 -0.382328 -1.50783 -1.84832 -2.37525 -2.9385 -4.06713 -3.63268 -0.15149 0.132168 0.325881 -0.00657297 0.569493 0.0280195 1.66199 1.56827\n", + " 128 1.773e-01 4.637e+04 6.474e-02 -- 3.426e+02 -- 0.137667 -0.38234 -1.50784 -1.84831 -2.37524 -2.93855 -4.06353 -3.63268 -0.151255 0.132244 0.325363 -0.00644634 0.569497 0.0282044 1.65879 1.56851\n", + " 130 1.626e-01 5.158e+04 6.353e-02 -- 3.427e+02 -- 0.13768 -0.382351 -1.50784 -1.84831 -2.37524 -2.93859 -4.06031 -3.63268 -0.151045 0.132319 0.324886 -0.00633207 0.569501 0.0283732 1.656 1.56872\n", + " 132 1.487e-01 5.737e+04 6.244e-02 -- 3.428e+02 -- 0.137692 -0.382362 -1.50785 -1.8483 -2.37524 -2.93863 -4.05743 -3.63268 -0.150856 0.132392 0.324446 -0.00622911 0.569505 0.0285272 1.65356 1.56892\n", + " 134 1.356e-01 6.381e+04 6.147e-02 -- 3.428e+02 -- 0.137703 -0.382372 -1.50785 -1.84829 -2.37524 -2.93866 -4.05486 -3.63268 -0.150688 0.132464 0.324041 -0.00613648 0.569508 0.0286677 1.65143 1.56909\n", + " 136 1.233e-01 7.096e+04 6.059e-02 -- 3.429e+02 -- 0.137714 -0.382381 -1.50786 -1.84829 -2.37523 -2.9387 -4.05256 -3.63268 -0.150538 0.132535 0.32367 -0.00605327 0.569511 0.0287957 1.64957 1.56925\n", + " 138 1.118e-01 7.890e+04 5.981e-02 -- 3.429e+02 -- 0.137723 -0.382391 -1.50786 -1.84828 -2.37523 -2.93873 -4.05049 -3.63268 -0.150404 0.132603 0.32333 -0.00597864 0.569514 0.0289123 1.64794 1.5694\n", + " 140 1.012e-01 8.773e+04 5.910e-02 -- 3.430e+02 -- 0.137732 -0.382399 -1.50787 -1.84828 -2.37523 -2.93875 -4.04864 -3.63267 -0.150285 0.132669 0.323018 -0.00591178 0.569516 0.0290186 1.6465 1.56953\n", + " 142 9.132e-02 9.753e+04 5.847e-02 -- 3.431e+02 -- 0.137741 -0.382407 -1.50787 -1.84827 -2.37523 -2.93878 -4.04698 -3.63267 -0.150179 0.132732 0.322732 -0.00585198 0.569518 0.0291153 1.64524 1.56965\n", + " 144 8.225e-02 1.084e+05 5.790e-02 -- 3.431e+02 -- 0.137748 -0.382415 -1.50788 -1.84827 -2.37523 -2.9388 -4.04549 -3.63267 -0.150085 0.132794 0.322472 -0.00579854 0.56952 0.0292034 1.64413 1.56976\n", + " 146 7.394e-02 1.205e+05 5.739e-02 -- 3.432e+02 -- 0.137755 -0.382422 -1.50788 -1.84826 -2.37523 -2.93882 -4.04416 -3.63267 -0.150003 0.132852 0.322234 -0.00575085 0.569522 0.0292835 1.64315 1.56985\n", + " 148 6.635e-02 1.340e+05 5.692e-02 -- 3.432e+02 -- 0.137762 -0.382429 -1.50788 -1.84826 -2.37523 -2.93883 -4.04295 -3.63267 -0.14993 0.132909 0.322017 -0.00570833 0.569524 0.0293563 1.64229 1.56994\n", + " 150 5.944e-02 1.489e+05 5.650e-02 -- 3.433e+02 -- 0.137767 -0.382436 -1.50788 -1.84825 -2.37522 -2.93885 -4.04187 -3.63267 -0.149865 0.132963 0.321819 -0.00567045 0.569526 0.0294226 1.64153 1.57002\n", + " 152 5.317e-02 1.655e+05 5.613e-02 -- 3.434e+02 -- 0.137773 -0.382442 -1.50789 -1.84825 -2.37522 -2.93887 -4.0409 -3.63267 -0.149809 0.133014 0.32164 -0.00563675 0.569527 0.0294828 1.64086 1.5701\n", + " 154 4.750e-02 1.840e+05 5.579e-02 -- 3.434e+02 -- 0.137778 -0.382448 -1.50789 -1.84825 -2.37522 -2.93888 -4.04003 -3.63267 -0.14976 0.133063 0.321476 -0.00560678 0.569529 0.0295374 1.64027 1.57016\n", + " 156 4.239e-02 2.045e+05 5.548e-02 -- 3.435e+02 -- 0.137782 -0.382453 -1.50789 -1.84824 -2.37522 -2.93889 -4.03925 -3.63267 -0.149717 0.13311 0.321328 -0.00558015 0.56953 0.0295871 1.63974 1.57022\n", + " 158 3.778e-02 2.273e+05 5.521e-02 -- 3.435e+02 -- 0.137786 -0.382458 -1.50789 -1.84824 -2.37522 -2.9389 -4.03854 -3.63267 -0.149679 0.133154 0.321193 -0.00555649 0.569531 0.0296321 1.63928 1.57028\n", + " 160 3.366e-02 2.526e+05 5.496e-02 -- 3.436e+02 -- 0.13779 -0.382462 -1.5079 -1.84824 -2.37522 -2.93891 -4.03791 -3.63267 -0.149647 0.133196 0.321071 -0.0055355 0.569532 0.029673 1.63887 1.57033\n", + " 162 2.995e-02 2.807e+05 5.473e-02 -- 3.436e+02 -- 0.137794 -0.382467 -1.5079 -1.84823 -2.37522 -2.93892 -4.03734 -3.63267 -0.149619 0.133236 0.32096 -0.00551686 0.569533 0.0297101 1.63851 1.57037\n", + " 164 2.663e-02 3.119e+05 5.453e-02 -- 3.437e+02 -- 0.137797 -0.382471 -1.5079 -1.84823 -2.37522 -2.93893 -4.03683 -3.63267 -0.149594 0.133273 0.32086 -0.00550034 0.569534 0.0297438 1.63819 1.57041\n", + " 166 2.370e-02 3.467e+05 5.435e-02 -- 3.437e+02 -- 0.137799 -0.382475 -1.5079 -1.84823 -2.37522 -2.93894 -4.03636 -3.63266 -0.149574 0.133308 0.320769 -0.0054857 0.569535 0.0297742 1.6379 1.57045\n", + " 168 2.105e-02 3.852e+05 5.418e-02 -- 3.438e+02 -- 0.137802 -0.382478 -1.5079 -1.84823 -2.37522 -2.93894 -4.03595 -3.63266 -0.149556 0.133341 0.320686 -0.00547269 0.569536 0.0298019 1.63765 1.57048\n", + " 170 1.872e-02 4.281e+05 5.403e-02 -- 3.438e+02 -- 0.137804 -0.382481 -1.5079 -1.84823 -2.37522 -2.93895 -4.03557 -3.63266 -0.14954 0.133373 0.320612 -0.00546117 0.569536 0.0298269 1.63743 1.57051\n", + " 172 1.661e-02 4.757e+05 5.390e-02 -- 3.439e+02 -- 0.137806 -0.382484 -1.5079 -1.84822 -2.37522 -2.93895 -4.03524 -3.63266 -0.149527 0.133402 0.320545 -0.00545095 0.569537 0.0298495 1.63723 1.57054\n", + " 174 1.477e-02 5.286e+05 5.378e-02 -- 3.440e+02 -- 0.137808 -0.382487 -1.5079 -1.84822 -2.37522 -2.93896 -4.03493 -3.63266 -0.149516 0.133429 0.320484 -0.00544189 0.569537 0.02987 1.63705 1.57056\n", + " 176 1.314e-02 5.874e+05 5.367e-02 -- 3.440e+02 -- 0.13781 -0.382489 -1.50791 -1.84822 -2.37522 -2.93896 -4.03466 -3.63266 -0.149506 0.133454 0.320429 -0.00543385 0.569538 0.0298886 1.63689 1.57058\n", + " 178 1.169e-02 6.528e+05 5.357e-02 -- 3.441e+02 -- 0.137812 -0.382492 -1.50791 -1.84822 -2.37522 -2.93897 -4.03442 -3.63266 -0.149498 0.133478 0.320379 -0.00542671 0.569538 0.0299054 1.63675 1.5706\n", + " 180 1.036e-02 7.254e+05 5.348e-02 -- 3.441e+02 -- 0.137813 -0.382494 -1.50791 -1.84822 -2.37522 -2.93897 -4.0342 -3.63266 -0.149491 0.1335 0.320335 -0.00542037 0.569539 0.0299206 1.63663 1.57062\n", + " 182 9.239e-03 8.060e+05 5.340e-02 -- 3.442e+02 -- 0.137814 -0.382496 -1.50791 -1.84822 -2.37522 -2.93897 -4.034 -3.63266 -0.149485 0.133521 0.320294 -0.00541475 0.569539 0.0299343 1.63652 1.57064\n", + " 184 8.217e-03 8.957e+05 5.333e-02 -- 3.442e+02 -- 0.137815 -0.382498 -1.50791 -1.84822 -2.37522 -2.93898 -4.03382 -3.63266 -0.149481 0.13354 0.320258 -0.00540975 0.56954 0.0299467 1.63642 1.57065\n", + " 186 7.314e-03 9.952e+05 5.327e-02 -- 3.443e+02 -- 0.137816 -0.382499 -1.50791 -1.84821 -2.37522 -2.93898 -4.03366 -3.63266 -0.149476 0.133557 0.320225 -0.00540531 0.56954 0.0299579 1.63633 1.57066\n", + " 188 6.496e-03 1.106e+06 5.321e-02 -- 3.443e+02 -- 0.137817 -0.382501 -1.50791 -1.84821 -2.37522 -2.93898 -4.03351 -3.63266 -0.149472 0.133573 0.320195 -0.00540135 0.56954 0.029968 1.63625 1.57068\n", + " 190 5.793e-03 1.229e+06 5.315e-02 -- 3.444e+02 -- 0.137818 -0.382502 -1.50791 -1.84821 -2.37522 -2.93899 -4.03338 -3.63266 -0.14947 0.133589 0.320169 -0.00539784 0.56954 0.0299772 1.63618 1.57069\n", + " 192 5.217e-03 1.365e+06 5.311e-02 -- 3.444e+02 -- 0.137819 -0.382503 -1.50791 -1.84821 -2.37522 -2.93899 -4.03326 -3.63266 -0.149468 0.133603 0.320144 -0.00539472 0.569541 0.0299854 1.63612 1.5707\n", + " 194 4.529e-03 1.517e+06 5.306e-02 -- 3.445e+02 -- 0.13782 -0.382504 -1.50791 -1.84821 -2.37522 -2.93899 -4.03316 -3.63266 -0.149465 0.133615 0.320123 -0.0053919 0.569541 0.0299928 1.63606 1.57071\n", + " 196 4.139e-03 1.686e+06 5.303e-02 -- 3.445e+02 -- 0.13782 -0.382505 -1.50791 -1.84821 -2.37522 -2.93899 -4.03306 -3.63266 -0.149464 0.133627 0.320103 -0.00538946 0.569541 0.0299996 1.63601 1.57071\n", + " 198 3.573e-03 1.873e+06 5.299e-02 -- 3.446e+02 -- 0.137821 -0.382506 -1.50791 -1.84821 -2.37522 -2.93899 -4.03298 -3.63266 -0.149462 0.133636 0.320085 -0.00538723 0.569541 0.0300056 1.63597 1.57072\n", + " 200 3.302e-03 2.081e+06 5.296e-02 -- 3.446e+02 -- 0.137821 -0.382507 -1.50791 -1.84821 -2.37522 -2.93899 -4.0329 -3.63266 -0.14946 0.133646 0.32007 -0.00538531 0.569541 0.0300111 1.63593 1.57073\n", + " 202 2.976e-03 2.313e+06 5.293e-02 -- 3.447e+02 -- 0.137822 -0.382508 -1.50791 -1.84821 -2.37521 -2.93899 -4.03283 -3.63266 -0.149459 0.133656 0.320055 -0.00538353 0.569542 0.0300161 1.63589 1.57073\n", + " 204 2.400e-03 2.570e+06 5.291e-02 -- 3.448e+02 -- 0.137822 -0.382509 -1.50791 -1.84821 -2.37521 -2.939 -4.03277 -3.63266 -0.149457 0.133663 0.320042 -0.00538193 0.569542 0.0300205 1.63586 1.57074\n", + " 206 2.292e-03 2.855e+06 5.288e-02 -- 3.448e+02 -- 0.137823 -0.382509 -1.50791 -1.84821 -2.37521 -2.939 -4.03271 -3.63266 -0.149457 0.13367 0.320031 -0.00538063 0.569542 0.0300245 1.63583 1.57074\n", + " 208 2.150e-03 3.173e+06 5.287e-02 -- 3.449e+02 -- 0.137823 -0.38251 -1.50791 -1.84821 -2.37521 -2.939 -4.03266 -3.63266 -0.149456 0.13368 0.32002 -0.0053794 0.569542 0.0300281 1.6358 1.57075\n", + " 210 1.886e-03 3.525e+06 5.285e-02 -- 3.449e+02 -- 0.137823 -0.38251 -1.50791 -1.84821 -2.37521 -2.939 -4.03262 -3.63266 -0.149456 0.133687 0.320011 -0.00537824 0.569542 0.0300314 1.63578 1.57075\n", + " 212 1.670e-03 3.917e+06 5.283e-02 -- 3.450e+02 -- 0.137823 -0.382511 -1.50791 -1.84821 -2.37521 -2.939 -4.03258 -3.63266 -0.149454 0.133693 0.320002 -0.00537723 0.569542 0.0300343 1.63576 1.57075\n", + " 214 1.507e-03 4.352e+06 5.281e-02 -- 3.450e+02 -- 0.137824 -0.382511 -1.50791 -1.84821 -2.37521 -2.939 -4.03254 -3.63266 -0.149455 0.133696 0.319995 -0.00537633 0.569542 0.030037 1.63574 1.57076\n", + " 216 1.104e-03 4.836e+06 5.280e-02 -- 3.451e+02 -- 0.137824 -0.382512 -1.50791 -1.84821 -2.37521 -2.939 -4.03251 -3.63266 -0.149453 0.133698 0.319988 -0.00537552 0.569542 0.0300394 1.63572 1.57076\n", + " 218 1.248e-03 5.374e+06 5.279e-02 -- 3.451e+02 -- 0.137824 -0.382512 -1.50791 -1.84821 -2.37521 -2.939 -4.03247 -3.63266 -0.14945 0.133705 0.319982 -0.00537493 0.569542 0.0300416 1.63571 1.57076\n", + " 220 9.861e-04 5.971e+06 5.278e-02 -- 3.452e+02 -- 0.137824 -0.382512 -1.50791 -1.84821 -2.37521 -2.939 -4.03245 -3.63266 -0.14945 0.13371 0.319976 -0.00537426 0.569542 0.0300435 1.6357 1.57077\n", + " 222 1.025e-03 6.634e+06 5.277e-02 -- 3.452e+02 -- 0.137824 -0.382513 -1.50791 -1.84821 -2.37521 -2.939 -4.03242 -3.63266 -0.149451 0.133714 0.319971 -0.00537373 0.569542 0.0300453 1.63568 1.57077\n", + " 224 7.583e-04 7.371e+06 5.276e-02 -- 3.453e+02 -- 0.137825 -0.382513 -1.50791 -1.84821 -2.37521 -2.939 -4.0324 -3.63266 -0.14945 0.133716 0.319966 -0.00537318 0.569542 0.0300468 1.63567 1.57077\n", + " 226 6.529e-04 8.190e+06 5.275e-02 -- 3.453e+02 -- 0.137825 -0.382513 -1.50791 -1.84821 -2.37521 -2.939 -4.03238 -3.63266 -0.149445 0.133722 0.319962 -0.00537277 0.569542 0.0300482 1.63566 1.57077\n", + " 228 8.712e-04 9.101e+06 5.274e-02 -- 3.454e+02 -- 0.137825 -0.382513 -1.50791 -1.84821 -2.37521 -2.939 -4.03236 -3.63266 -0.14945 0.133724 0.319959 -0.00537242 0.569543 0.0300495 1.63566 1.57077\n", + " 230 7.297e-04 1.011e+07 5.274e-02 -- 3.454e+02 -- 0.137825 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03235 -3.63266 -0.149455 0.133729 0.319955 -0.00537195 0.569543 0.0300506 1.63565 1.57077\n", + " 232 7.713e-04 1.124e+07 5.273e-02 -- 3.455e+02 -- 0.137825 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03233 -3.63266 -0.149461 0.133726 0.319952 -0.00537156 0.569543 0.0300516 1.63564 1.57077\n", + " 234 1.232e-03 1.248e+07 5.273e-02 -- 3.455e+02 -- 0.137825 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03232 -3.63266 -0.14946 0.133736 0.319949 -0.00537133 0.569543 0.0300525 1.63563 1.57078\n", + " 236 8.332e-04 1.387e+07 5.272e-02 -- 3.456e+02 -- 0.137825 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03231 -3.63266 -0.149466 0.133721 0.319947 -0.00537067 0.569543 0.0300532 1.63562 1.57078\n", + " 238 1.904e-03 1.541e+07 5.271e-02 -- 3.456e+02 -- 0.137825 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.0323 -3.63266 -0.149478 0.133724 0.319945 -0.00537055 0.569543 0.0300541 1.63562 1.57078\n", + " 240 1.685e-03 1.712e+07 5.271e-02 -- 3.457e+02 -- 0.137825 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03229 -3.63266 -0.149472 0.13372 0.319943 -0.00536953 0.569543 0.0300545 1.63561 1.57078\n", + " 242 2.904e-03 1.903e+07 5.268e-02 -- 3.458e+02 -- 0.137825 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03228 -3.63266 -0.149485 0.133743 0.319941 -0.00537003 0.569543 0.0300553 1.63561 1.57078\n", + " 244 2.822e-03 2.113e+07 5.268e-02 -- 3.458e+02 -- 0.137826 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03227 -3.63266 -0.149462 0.133738 0.319939 -0.00536847 0.569543 0.0300552 1.63559 1.57078\n", + " 246 3.296e-03 2.349e+07 5.271e-02 -- 3.459e+02 -- 0.137826 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03227 -3.63266 -0.149478 0.133747 0.319938 -0.00536999 0.569543 0.0300566 1.63561 1.57078\n", + " 248 8.922e-04 2.610e+07 5.276e-02 -- 3.459e+02 -- 0.137826 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03226 -3.63266 -0.149469 0.133756 0.319936 -0.00536822 0.569543 0.0300563 1.63559 1.57078\n", + " 250 1.022e-03 2.900e+07 5.270e-02 -- 3.460e+02 -- 0.137826 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03226 -3.63266 -0.149478 0.133751 0.319935 -0.0053687 0.569543 0.0300571 1.63559 1.57078\n", + " 252 1.275e-03 3.223e+07 5.272e-02 -- 3.460e+02 -- 0.137826 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03225 -3.63266 -0.149493 0.133745 0.319934 -0.00536846 0.569543 0.0300572 1.63559 1.57078\n", + " 254 2.477e-03 3.581e+07 5.270e-02 -- 3.461e+02 -- 0.137826 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03225 -3.63266 -0.149483 0.133762 0.319933 -0.00536868 0.569543 0.0300576 1.63558 1.57078\n", + " 256 2.786e-03 3.976e+07 5.253e-02 -- 3.461e+02 -- 0.137826 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03224 -3.63266 -0.149446 0.133745 0.319932 -0.00536896 0.569543 0.0300578 1.63558 1.57078\n", + " 258 2.532e-03 4.421e+07 5.284e-02 -- 3.462e+02 -- 0.137827 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03224 -3.63266 -0.149451 0.133732 0.319933 -0.00537046 0.569543 0.0300589 1.6356 1.57078\n", + " 260 5.032e-03 4.907e+07 5.240e-02 -- 3.462e+02 -- 0.137827 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03223 -3.63266 -0.149413 0.133704 0.319932 -0.00536945 0.569543 0.0300589 1.63559 1.57078\n", + " 262 3.782e-03 5.456e+07 5.281e-02 -- 3.463e+02 -- 0.137827 -0.382514 -1.50791 -1.84821 -2.37521 -2.939 -4.03223 -3.63266 -0.149414 0.133736 0.319934 -0.00537215 0.569543 0.0300602 1.63561 1.57078\n", + " 264 4.099e-03 6.056e+07 5.240e-02 -- 3.463e+02 -- 0.137827 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03223 -3.63266 -0.149357 0.133751 0.319931 -0.00537099 0.569543 0.0300595 1.63559 1.57078\n", + " 266 3.335e-03 6.736e+07 5.298e-02 -- 3.464e+02 -- 0.137827 -0.382514 -1.50791 -1.8482 -2.37521 -2.939 -4.03222 -3.63266 -0.14935 0.133787 0.319931 -0.00537319 0.569543 0.030061 1.63561 1.57079\n", + " 268 2.128e-03 7.485e+07 5.274e-02 -- 3.464e+02 -- 0.137827 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03222 -3.63266 -0.149373 0.133832 0.319929 -0.00537226 0.569543 0.0300605 1.6356 1.57079\n", + " 270 4.362e-03 8.314e+07 5.273e-02 -- 3.465e+02 -- 0.137827 -0.382516 -1.50791 -1.84821 -2.37521 -2.939 -4.03222 -3.63266 -0.149357 0.133842 0.319928 -0.00537111 0.569543 0.0300599 1.63558 1.57078\n", + " 272 7.401e-03 9.241e+07 5.281e-02 -- 3.465e+02 -- 0.137827 -0.382515 -1.50791 -1.84821 -2.37521 -2.939 -4.03222 -3.63266 -0.149297 0.133867 0.319929 -0.00537346 0.569543 0.0300607 1.6356 1.57079\n", + " 273 2.449e-01 9.778e+11 3.957e+01 -- 3.070e+02 -- 0.137829 -0.382514 -1.50792 -1.8482 -2.37521 -2.93901 -4.03219 -3.63266 -0.149811 0.134858 0.319919 -0.00539408 0.569543 0.0300672 1.63566 1.57079\n", + " 275 2.085e-01 1.216e+12 5.713e+01 -- 2.498e+02 -- 0.137838 -0.382547 -1.50791 -1.84822 -2.37521 -2.93901 -4.03229 -3.63267 -0.146142 0.136356 0.319715 -0.00536682 0.569539 0.0300298 1.63519 1.57077\n", + " 278 1.768e-01 1.553e+11 6.527e+01 -- 3.151e+02 -- 0.13783 -0.382542 -1.50791 -1.84822 -2.37521 -2.93901 -4.03228 -3.63266 -0.146083 0.136071 0.319727 -0.00537179 0.56954 0.0300328 1.63531 1.57077\n", + " 280 1.401e-01 2.293e+10 2.877e+01 -- 3.439e+02 -- 0.13781 -0.382487 -1.50792 -1.8482 -2.37521 -2.93902 -4.03218 -3.63266 -0.147503 0.133665 0.319814 -0.00542418 0.569545 0.0300764 1.63632 1.57081\n", + " 282 3.005e-01 1.584e+12 6.015e+01 -- 2.837e+02 -- 0.137818 -0.38251 -1.50792 -1.84821 -2.37521 -2.93902 -4.03219 -3.63266 -0.149569 0.134696 0.319799 -0.00539672 0.56954 0.0300658 1.63602 1.5708\n", + " 285 6.887e-01 3.338e+12 5.965e+01 -- 2.241e+02 -- 0.137808 -0.382511 -1.50792 -1.84821 -2.37521 -2.93901 -4.03218 -3.63266 -0.149948 0.13503 0.319788 -0.0053805 0.569539 0.0300609 1.63597 1.5708\n", + " 286 2.694e+01 1.474e+08 6.253e+01 -- 2.866e+02 -- 0.13812 -0.382806 -1.50771 -1.84841 -2.37523 -2.93893 -4.03632 -3.63262 -0.0891567 0.0420396 0.319568 -0.00184545 0.56953 0.029434 1.62857 1.57017\n", + " 289 1.835e+01 2.710e+07 3.352e+01 -- 3.201e+02 -- 0.137998 -0.382649 -1.50775 -1.84833 -2.37522 -2.93901 -4.03602 -3.63259 -0.0918629 0.0445307 0.319829 -0.00234263 0.56956 0.0297005 1.63399 1.57042\n", + " 291 1.677e+00 4.770e+04 1.799e+01 -- 3.381e+02 -- 0.13705 -0.381771 -1.50801 -1.84766 -2.3752 -2.93967 -4.03323 -3.63229 -0.113651 0.0634674 0.321859 -0.00664201 0.569811 0.0320009 1.68021 1.57254\n", + " 293 8.980e-01 2.894e+05 2.261e+00 -- 3.404e+02 -- 0.137302 -0.381985 -1.50798 -1.84785 -2.37521 -2.93948 -4.03313 -3.63237 -0.110751 0.0693232 0.32106 -0.00552825 0.569729 0.0313773 1.66678 1.57201\n", + " 295 7.368e-01 3.177e+05 9.101e-01 -- 3.413e+02 -- 0.137338 -0.382099 -1.50797 -1.84786 -2.37521 -2.93948 -4.03253 -3.63238 -0.112228 0.0755485 0.320805 -0.00556173 0.569719 0.0313963 1.66607 1.57205\n", + " 297 5.900e-01 3.573e+05 6.175e-01 -- 3.419e+02 -- 0.137345 -0.382178 -1.50797 -1.84784 -2.37521 -2.93949 -4.03194 -3.63237 -0.114034 0.0811151 0.320645 -0.00570323 0.56972 0.0314796 1.66672 1.57213\n", + " 299 4.790e-01 3.999e+05 4.486e-01 -- 3.424e+02 -- 0.137357 -0.382242 -1.50797 -1.84783 -2.37521 -2.9395 -4.03146 -3.63237 -0.11548 0.0859006 0.320493 -0.00578771 0.569719 0.0315348 1.66691 1.57219\n", + " 301 3.950e-01 4.458e+05 3.396e-01 -- 3.427e+02 -- 0.137372 -0.382298 -1.50797 -1.84783 -2.37521 -2.93951 -4.03105 -3.63237 -0.116614 0.0900153 0.320346 -0.00582755 0.569717 0.0315672 1.66675 1.57223\n", + " 303 3.299e-01 4.961e+05 2.661e-01 -- 3.430e+02 -- 0.137388 -0.382345 -1.50797 -1.84783 -2.37521 -2.93951 -4.03071 -3.63237 -0.117524 0.0935712 0.320206 -0.00584073 0.569714 0.0315858 1.6664 1.57225\n", + " 305 2.783e-01 5.518e+05 2.149e-01 -- 3.432e+02 -- 0.137405 -0.382386 -1.50797 -1.84783 -2.37521 -2.93951 -4.03042 -3.63237 -0.118268 0.0966579 0.320075 -0.00583822 0.569712 0.0315961 1.66596 1.57227\n", + " 307 2.366e-01 6.135e+05 1.782e-01 -- 3.434e+02 -- 0.137421 -0.382421 -1.50797 -1.84783 -2.37521 -2.93951 -4.03017 -3.63237 -0.118886 0.0993476 0.319954 -0.00582657 0.569709 0.0316013 1.66548 1.57227\n", + " 309 2.026e-01 6.820e+05 1.512e-01 -- 3.435e+02 -- 0.137436 -0.382452 -1.50797 -1.84784 -2.37521 -2.93951 -4.02995 -3.63238 -0.119407 0.101698 0.319843 -0.00580983 0.569707 0.0316033 1.665 1.57228\n", + " 311 1.745e-01 7.581e+05 1.310e-01 -- 3.436e+02 -- 0.137449 -0.382478 -1.50797 -1.84784 -2.37521 -2.9395 -4.02976 -3.63238 -0.11985 0.103759 0.319742 -0.00579053 0.569705 0.0316034 1.66454 1.57228\n", + " 313 1.511e-01 8.426e+05 1.157e-01 -- 3.438e+02 -- 0.137462 -0.382502 -1.50797 -1.84784 -2.37521 -2.9395 -4.02959 -3.63238 -0.120231 0.10557 0.319649 -0.00577023 0.569703 0.0316024 1.6641 1.57228\n", + " 315 1.314e-01 9.364e+05 1.039e-01 -- 3.439e+02 -- 0.137474 -0.382522 -1.50797 -1.84785 -2.37521 -2.9395 -4.02944 -3.63238 -0.120561 0.107165 0.319566 -0.00574994 0.569701 0.0316007 1.66369 1.57228\n", + " 317 1.146e-01 1.041e+06 9.470e-02 -- 3.440e+02 -- 0.137484 -0.38254 -1.50797 -1.84785 -2.37521 -2.9395 -4.0293 -3.63238 -0.120849 0.108572 0.31949 -0.00573024 0.569699 0.0315987 1.66331 1.57228\n", + " 319 1.002e-01 1.157e+06 8.741e-02 -- 3.440e+02 -- 0.137494 -0.382556 -1.50797 -1.84785 -2.37521 -2.9395 -4.02918 -3.63238 -0.121102 0.109816 0.319421 -0.00571149 0.569698 0.0315965 1.66296 1.57228\n", + " 321 8.813e-02 1.285e+06 8.158e-02 -- 3.441e+02 -- 0.137503 -0.382571 -1.50797 -1.84786 -2.37521 -2.9395 -4.02907 -3.63239 -0.121323 0.110917 0.319359 -0.0056939 0.569696 0.0315944 1.66265 1.57228\n", + " 323 7.747e-02 1.428e+06 7.694e-02 -- 3.442e+02 -- 0.13751 -0.382583 -1.50797 -1.84786 -2.37521 -2.9395 -4.02898 -3.63239 -0.12152 0.111894 0.319303 -0.00567758 0.569695 0.0315923 1.66236 1.57228\n", + " 325 6.841e-02 1.587e+06 7.313e-02 -- 3.443e+02 -- 0.137518 -0.382594 -1.50797 -1.84786 -2.37521 -2.93949 -4.02889 -3.63239 -0.121694 0.112761 0.319252 -0.00566248 0.569694 0.0315904 1.6621 1.57228\n", + " 327 6.037e-02 1.764e+06 7.004e-02 -- 3.443e+02 -- 0.137524 -0.382604 -1.50797 -1.84786 -2.37521 -2.93949 -4.02881 -3.63239 -0.121849 0.113533 0.319207 -0.00564862 0.569693 0.0315885 1.66187 1.57228\n", + " 329 5.354e-02 1.960e+06 6.747e-02 -- 3.444e+02 -- 0.13753 -0.382613 -1.50797 -1.84786 -2.37521 -2.93949 -4.02875 -3.63239 -0.121987 0.114218 0.319166 -0.00563591 0.569692 0.0315868 1.66165 1.57228\n", + " 331 4.752e-02 2.178e+06 6.536e-02 -- 3.445e+02 -- 0.137535 -0.382621 -1.50797 -1.84787 -2.37521 -2.93949 -4.02868 -3.63239 -0.12211 0.114829 0.319129 -0.00562439 0.569691 0.0315853 1.66146 1.57228\n", + " 333 4.222e-02 2.420e+06 6.360e-02 -- 3.445e+02 -- 0.13754 -0.382628 -1.50797 -1.84787 -2.37521 -2.93949 -4.02863 -3.63239 -0.122219 0.115375 0.319095 -0.00561382 0.56969 0.0315839 1.66129 1.57228\n", + " 335 3.769e-02 2.689e+06 6.211e-02 -- 3.446e+02 -- 0.137544 -0.382634 -1.50797 -1.84787 -2.37521 -2.93949 -4.02858 -3.63239 -0.122317 0.115862 0.319065 -0.00560428 0.56969 0.0315826 1.66113 1.57228\n", + " 337 3.311e-02 2.989e+06 6.088e-02 -- 3.447e+02 -- 0.137547 -0.38264 -1.50797 -1.84787 -2.37521 -2.93949 -4.02853 -3.63239 -0.122404 0.116299 0.319038 -0.00559562 0.569689 0.0315815 1.66099 1.57228\n", + " 339 2.982e-02 3.321e+06 5.975e-02 -- 3.447e+02 -- 0.137551 -0.382645 -1.50797 -1.84787 -2.37521 -2.93949 -4.02849 -3.63239 -0.122484 0.116684 0.319014 -0.00558779 0.569689 0.0315804 1.66087 1.57228\n", + " 341 2.639e-02 3.690e+06 5.889e-02 -- 3.448e+02 -- 0.137554 -0.382649 -1.50797 -1.84787 -2.37521 -2.93949 -4.02845 -3.63239 -0.122555 0.117032 0.318992 -0.00558071 0.569688 0.0315795 1.66076 1.57228\n", + " 343 2.409e-02 4.100e+06 5.809e-02 -- 3.448e+02 -- 0.137557 -0.382653 -1.50797 -1.84787 -2.37521 -2.93949 -4.02842 -3.63239 -0.122619 0.117341 0.318972 -0.00557437 0.569688 0.0315786 1.66065 1.57228\n", + " 345 2.129e-02 4.556e+06 5.751e-02 -- 3.449e+02 -- 0.137559 -0.382657 -1.50797 -1.84787 -2.37521 -2.93949 -4.02839 -3.63239 -0.122676 0.117624 0.318955 -0.00556861 0.569687 0.0315779 1.66056 1.57227\n", + " 347 1.889e-02 5.062e+06 5.690e-02 -- 3.450e+02 -- 0.137561 -0.38266 -1.50797 -1.84787 -2.37521 -2.93949 -4.02837 -3.6324 -0.12273 0.117874 0.318939 -0.0055633 0.569687 0.0315772 1.66048 1.57227\n", + " 349 1.666e-02 5.625e+06 5.637e-02 -- 3.450e+02 -- 0.137563 -0.382663 -1.50797 -1.84788 -2.37521 -2.93949 -4.02834 -3.6324 -0.122775 0.118097 0.318924 -0.00555856 0.569687 0.0315765 1.6604 1.57227\n", + " 351 1.548e-02 6.250e+06 5.589e-02 -- 3.451e+02 -- 0.137565 -0.382666 -1.50797 -1.84788 -2.37521 -2.93949 -4.02832 -3.6324 -0.122815 0.118293 0.318911 -0.00555438 0.569686 0.031576 1.66034 1.57227\n", + " 353 1.367e-02 6.945e+06 5.559e-02 -- 3.451e+02 -- 0.137567 -0.382668 -1.50797 -1.84788 -2.37521 -2.93949 -4.0283 -3.6324 -0.122851 0.118477 0.3189 -0.00555074 0.569686 0.0315756 1.66028 1.57227\n", + " 355 1.218e-02 7.717e+06 5.527e-02 -- 3.452e+02 -- 0.137568 -0.38267 -1.50797 -1.84788 -2.37521 -2.93949 -4.02828 -3.6324 -0.122888 0.118639 0.318889 -0.00554714 0.569686 0.0315751 1.66022 1.57227\n", + " 357 1.125e-02 8.574e+06 5.496e-02 -- 3.452e+02 -- 0.13757 -0.382672 -1.50797 -1.84788 -2.37521 -2.93949 -4.02827 -3.6324 -0.122919 0.118783 0.31888 -0.00554391 0.569686 0.0315747 1.66017 1.57227\n", + " 359 9.239e-03 9.527e+06 5.475e-02 -- 3.453e+02 -- 0.137571 -0.382674 -1.50797 -1.84788 -2.37521 -2.93949 -4.02825 -3.6324 -0.122951 0.118917 0.318871 -0.00554137 0.569686 0.0315743 1.66013 1.57227\n", + " 361 8.671e-03 1.059e+07 5.442e-02 -- 3.453e+02 -- 0.137572 -0.382675 -1.50797 -1.84788 -2.37521 -2.93949 -4.02824 -3.6324 -0.122974 0.119027 0.318864 -0.00553843 0.569685 0.0315738 1.66009 1.57227\n", + " 363 7.118e-03 1.176e+07 5.421e-02 -- 3.454e+02 -- 0.137573 -0.382677 -1.50797 -1.84788 -2.37521 -2.93949 -4.02823 -3.6324 -0.122989 0.11913 0.318857 -0.00553662 0.569685 0.0315737 1.66005 1.57227\n", + " 365 6.502e-03 1.307e+07 5.400e-02 -- 3.454e+02 -- 0.137574 -0.382678 -1.50797 -1.84788 -2.37521 -2.93948 -4.02822 -3.6324 -0.123017 0.119215 0.318851 -0.0055347 0.569685 0.0315735 1.66002 1.57227\n", + " 367 5.241e-03 1.452e+07 5.386e-02 -- 3.455e+02 -- 0.137574 -0.382679 -1.50797 -1.84788 -2.37521 -2.93948 -4.02821 -3.6324 -0.123025 0.119292 0.318845 -0.00553265 0.569685 0.0315732 1.65999 1.57227\n", + " 369 4.797e-03 1.614e+07 5.362e-02 -- 3.456e+02 -- 0.137575 -0.382679 -1.50797 -1.84788 -2.37521 -2.93948 -4.0282 -3.6324 -0.123045 0.119355 0.318841 -0.00553154 0.569685 0.0315732 1.65997 1.57227\n", + " 371 4.242e-03 1.793e+07 5.357e-02 -- 3.456e+02 -- 0.137575 -0.38268 -1.50797 -1.84788 -2.37521 -2.93948 -4.0282 -3.6324 -0.123059 0.119412 0.318836 -0.00553001 0.569685 0.031573 1.65995 1.57227\n", + " 373 5.841e-03 1.992e+07 5.347e-02 -- 3.457e+02 -- 0.137576 -0.382681 -1.50797 -1.84788 -2.37521 -2.93948 -4.02819 -3.6324 -0.123078 0.119463 0.318832 -0.00552879 0.569685 0.0315729 1.65993 1.57227\n", + " 375 3.219e-03 2.213e+07 5.366e-02 -- 3.457e+02 -- 0.137577 -0.382682 -1.50797 -1.84788 -2.37521 -2.93948 -4.02818 -3.6324 -0.123089 0.119532 0.318828 -0.00552759 0.569685 0.0315728 1.65991 1.57227\n", + " 377 2.633e-03 2.459e+07 5.334e-02 -- 3.458e+02 -- 0.137577 -0.382683 -1.50797 -1.84788 -2.37521 -2.93948 -4.02818 -3.6324 -0.123107 0.119571 0.318825 -0.00552604 0.569685 0.0315725 1.65989 1.57227\n", + " 379 4.116e-03 2.733e+07 5.319e-02 -- 3.458e+02 -- 0.137578 -0.382683 -1.50797 -1.84788 -2.37521 -2.93948 -4.02817 -3.6324 -0.12312 0.119602 0.318822 -0.00552505 0.569685 0.0315725 1.65988 1.57227\n", + " 380 5.533e+01 8.512e+13 3.580e+01 -- 3.100e+02 -- 0.13758 -0.382687 -1.50797 -1.84788 -2.37521 -2.93948 -4.02813 -3.6324 -0.123171 0.120095 0.318799 -0.0055189 0.569684 0.0315729 1.65977 1.57227\n", + " 382 7.194e+00 3.296e+06 1.119e+02 -- 1.981e+02 -- 0.136564 -0.384498 -1.50899 -1.84727 -2.37514 -2.93869 -4.00752 -3.63339 -0.76763 0.784575 0.315418 -0.0211879 0.568207 0.0342865 1.65043 1.57386\n", + " 385 6.393e+00 7.364e+05 4.355e+01 -- 2.417e+02 -- 0.134399 -0.385404 -1.5086 -1.84755 -2.37516 -2.93833 -4.01279 -3.63358 -0.756411 0.778329 0.313579 -0.0196636 0.5681 0.0330514 1.62964 1.57277\n", + " 387 6.586e+00 2.759e+05 6.486e+01 -- 3.066e+02 -- 0.116998 -0.394901 -1.50618 -1.84965 -2.37527 -2.93545 -4.04221 -3.6351 -0.661407 0.711541 0.301895 -0.00709193 0.567185 0.023737 1.46306 1.56458\n", + " 389 8.793e-01 6.706e+03 1.268e+01 -- 3.192e+02 -- 0.115989 -0.400915 -1.50697 -1.8488 -2.37519 -2.93697 -4.03138 -3.63433 -0.618922 0.64982 0.305913 -0.0117627 0.567838 0.0280683 1.55938 1.56838\n", + " 391 8.721e-01 1.576e+04 4.935e+00 -- 3.242e+02 -- 0.112264 -0.405631 -1.5073 -1.84862 -2.37517 -2.93742 -4.02343 -3.63415 -0.565235 0.59268 0.305403 -0.0120062 0.568 0.0294253 1.57977 1.56959\n", + " 393 8.609e-01 2.268e+04 2.879e+00 -- 3.271e+02 -- 0.110008 -0.408683 -1.50757 -1.84848 -2.37515 -2.93776 -4.01681 -3.63402 -0.51694 0.540991 0.304308 -0.0120189 0.568108 0.0304757 1.59266 1.57052\n", + " 395 8.448e-01 2.811e+04 1.940e+00 -- 3.290e+02 -- 0.108886 -0.410427 -1.50779 -1.8484 -2.37513 -2.938 -4.01122 -3.63393 -0.473875 0.494416 0.302852 -0.011813 0.568178 0.0312724 1.60029 1.57123\n", + " 397 8.241e-01 3.293e+04 1.442e+00 -- 3.304e+02 -- 0.108628 -0.411163 -1.50798 -1.84834 -2.37512 -2.93818 -4.00644 -3.63387 -0.435812 0.452647 0.30122 -0.0114726 0.568224 0.0318892 1.60467 1.57177\n", + " 399 7.993e-01 3.764e+04 1.149e+00 -- 3.316e+02 -- 0.108995 -0.411144 -1.50813 -1.84831 -2.37512 -2.93832 -4.00231 -3.63383 -0.402379 0.415346 0.299533 -0.0110612 0.568256 0.0323797 1.60708 1.5722\n", + " 401 7.711e-01 4.253e+04 9.635e-01 -- 3.325e+02 -- 0.109791 -0.410578 -1.50827 -1.84829 -2.37511 -2.93843 -3.99871 -3.6338 -0.37314 0.382148 0.297866 -0.01062 0.568277 0.0327796 1.60828 1.57255\n", + " 403 7.403e-01 4.778e+04 8.370e-01 -- 3.334e+02 -- 0.110862 -0.409634 -1.50838 -1.84828 -2.3751 -2.93852 -3.99556 -3.63378 -0.347639 0.35268 0.296263 -0.0101744 0.568292 0.0331127 1.60875 1.57284\n", + " 405 7.072e-01 5.351e+04 7.456e-01 -- 3.341e+02 -- 0.11209 -0.408441 -1.50848 -1.84827 -2.3751 -2.93859 -3.9928 -3.63377 -0.325436 0.326572 0.29475 -0.00973915 0.568303 0.0333948 1.60877 1.57308\n", + " 407 6.726e-01 5.984e+04 6.759e-01 -- 3.348e+02 -- 0.11339 -0.407097 -1.50857 -1.84826 -2.3751 -2.93866 -3.99036 -3.63375 -0.306122 0.303476 0.29334 -0.00932269 0.568311 0.0336367 1.60852 1.57328\n", + " 409 6.368e-01 6.686e+04 6.201e-01 -- 3.354e+02 -- 0.114702 -0.405678 -1.50865 -1.84826 -2.37509 -2.93871 -3.98821 -3.63374 -0.289327 0.283064 0.292038 -0.00892928 0.568317 0.033846 1.60811 1.57346\n", + " 411 6.003e-01 7.469e+04 5.735e-01 -- 3.360e+02 -- 0.115982 -0.404238 -1.50873 -1.84826 -2.37509 -2.93876 -3.98632 -3.63374 -0.274719 0.265039 0.290845 -0.00856069 0.568322 0.0340282 1.60761 1.57361\n", + " 413 5.635e-01 8.343e+04 5.332e-01 -- 3.365e+02 -- 0.117205 -0.402814 -1.50879 -1.84826 -2.37509 -2.9388 -3.98465 -3.63373 -0.262009 0.249128 0.289757 -0.0082171 0.568325 0.0341871 1.60706 1.57375\n", + " 415 5.268e-01 9.320e+04 4.976e-01 -- 3.370e+02 -- 0.118353 -0.401435 -1.50885 -1.84826 -2.37509 -2.93884 -3.98317 -3.63373 -0.25094 0.235089 0.288769 -0.00789773 0.568328 0.034326 1.60648 1.57386\n", + " 417 4.906e-01 1.041e+05 4.652e-01 -- 3.375e+02 -- 0.119416 -0.400117 -1.5089 -1.84826 -2.37508 -2.93887 -3.98187 -3.63372 -0.241292 0.222703 0.287874 -0.00760125 0.56833 0.0344471 1.60589 1.57396\n", + " 419 4.551e-01 1.164e+05 4.356e-01 -- 3.379e+02 -- 0.120392 -0.398872 -1.50895 -1.84826 -2.37508 -2.93889 -3.98073 -3.63372 -0.23287 0.211778 0.287065 -0.00732599 0.568331 0.0345524 1.6053 1.57405\n", + " 421 4.207e-01 1.301e+05 4.080e-01 -- 3.383e+02 -- 0.12128 -0.397707 -1.50899 -1.84826 -2.37508 -2.93892 -3.97972 -3.63372 -0.225507 0.202139 0.286336 -0.00707017 0.568332 0.0346434 1.6047 1.57413\n", + " 423 3.877e-01 1.454e+05 3.822e-01 -- 3.387e+02 -- 0.122083 -0.396622 -1.50902 -1.84826 -2.37508 -2.93894 -3.97883 -3.63372 -0.219056 0.193634 0.285679 -0.00683198 0.568332 0.0347216 1.6041 1.57419\n", + " 425 3.562e-01 1.626e+05 3.580e-01 -- 3.391e+02 -- 0.122806 -0.39562 -1.50906 -1.84827 -2.37508 -2.93895 -3.97805 -3.63372 -0.213392 0.186128 0.285086 -0.0066097 0.568332 0.034788 1.60349 1.57425\n", + " 427 3.264e-01 1.818e+05 3.352e-01 -- 3.394e+02 -- 0.123454 -0.394697 -1.50909 -1.84827 -2.37508 -2.93897 -3.97737 -3.63372 -0.208405 0.179498 0.284553 -0.00640172 0.568331 0.0348437 1.60289 1.57429\n", + " 429 2.984e-01 2.032e+05 3.137e-01 -- 3.397e+02 -- 0.124033 -0.39385 -1.50911 -1.84827 -2.37508 -2.93898 -3.97677 -3.63372 -0.204002 0.173639 0.284072 -0.0062066 0.56833 0.0348898 1.60227 1.57433\n", + " 431 2.872e-01 2.272e+05 2.934e-01 -- 3.400e+02 -- 0.124549 -0.393077 -1.50913 -1.84828 -2.37508 -2.93899 -3.97624 -3.63372 -0.200103 0.168457 0.283638 -0.00602309 0.568329 0.034927 1.60166 1.57436\n", + " 433 2.792e-01 2.540e+05 2.744e-01 -- 3.403e+02 -- 0.125009 -0.392371 -1.50915 -1.84828 -2.37508 -2.939 -3.97578 -3.63372 -0.196637 0.163868 0.283246 -0.00585012 0.568328 0.0349564 1.60104 1.57439\n", + " 435 2.715e-01 2.839e+05 2.565e-01 -- 3.406e+02 -- 0.125417 -0.39173 -1.50917 -1.84828 -2.37508 -2.939 -3.97538 -3.63372 -0.193545 0.159801 0.282892 -0.00568681 0.568326 0.0349787 1.60041 1.57441\n", + " 437 2.639e-01 3.173e+05 2.396e-01 -- 3.408e+02 -- 0.12578 -0.391148 -1.50919 -1.84829 -2.37508 -2.939 -3.97503 -3.63373 -0.190778 0.156191 0.282571 -0.00553244 0.568324 0.0349948 1.59979 1.57442\n", + " 439 2.564e-01 3.545e+05 2.239e-01 -- 3.410e+02 -- 0.126102 -0.390621 -1.5092 -1.84829 -2.37508 -2.93901 -3.97472 -3.63373 -0.188292 0.152983 0.282281 -0.00538642 0.568322 0.0350054 1.59916 1.57443\n", + " 441 2.488e-01 3.960e+05 2.092e-01 -- 3.412e+02 -- 0.126388 -0.390144 -1.50921 -1.8483 -2.37508 -2.93901 -3.97446 -3.63373 -0.186054 0.150128 0.282017 -0.00524832 0.56832 0.0350114 1.59854 1.57443\n", + " 443 2.409e-01 4.422e+05 1.954e-01 -- 3.414e+02 -- 0.126643 -0.389713 -1.50922 -1.84831 -2.37508 -2.93901 -3.97422 -3.63373 -0.18403 0.147582 0.281778 -0.00511776 0.568317 0.0350133 1.59793 1.57443\n", + " 445 2.327e-01 4.937e+05 1.827e-01 -- 3.416e+02 -- 0.126869 -0.389324 -1.50923 -1.84831 -2.37508 -2.93901 -3.97402 -3.63374 -0.182195 0.14531 0.281562 -0.00499447 0.568315 0.0350119 1.59733 1.57443\n", + " 447 2.243e-01 5.511e+05 1.709e-01 -- 3.418e+02 -- 0.12707 -0.388974 -1.50923 -1.84832 -2.37508 -2.93901 -3.97384 -3.63374 -0.18053 0.143277 0.281365 -0.00487824 0.568313 0.0350078 1.59674 1.57443\n", + " 449 2.155e-01 6.150e+05 1.600e-01 -- 3.419e+02 -- 0.127249 -0.388658 -1.50924 -1.84833 -2.37508 -2.939 -3.97369 -3.63374 -0.179014 0.141458 0.281186 -0.00476884 0.56831 0.0350015 1.59617 1.57442\n", + " 451 2.064e-01 6.861e+05 1.499e-01 -- 3.421e+02 -- 0.127409 -0.388373 -1.50924 -1.84833 -2.37508 -2.939 -3.97356 -3.63375 -0.177636 0.139827 0.281023 -0.00466608 0.568308 0.0349935 1.59562 1.57441\n", + " 453 1.969e-01 7.653e+05 1.407e-01 -- 3.422e+02 -- 0.127551 -0.388118 -1.50925 -1.84834 -2.37508 -2.939 -3.97344 -3.63375 -0.176375 0.138362 0.280875 -0.00456977 0.568305 0.0349843 1.59509 1.5744\n", + " 455 1.874e-01 8.534e+05 1.322e-01 -- 3.424e+02 -- 0.127678 -0.387888 -1.50925 -1.84835 -2.37508 -2.939 -3.97334 -3.63375 -0.175226 0.137048 0.280741 -0.00447979 0.568303 0.0349741 1.59459 1.57439\n", + " 457 1.775e-01 9.514e+05 1.245e-01 -- 3.425e+02 -- 0.127792 -0.387681 -1.50925 -1.84835 -2.37508 -2.93899 -3.97325 -3.63376 -0.174176 0.135866 0.28062 -0.00439585 0.568301 0.0349634 1.59411 1.57438\n", + " 459 1.678e-01 1.060e+06 1.174e-01 -- 3.426e+02 -- 0.127894 -0.387495 -1.50926 -1.84836 -2.37508 -2.93899 -3.97317 -3.63376 -0.17322 0.134805 0.280509 -0.0043178 0.568299 0.0349524 1.59365 1.57437\n", + " 461 1.581e-01 1.182e+06 1.110e-01 -- 3.427e+02 -- 0.127985 -0.387328 -1.50926 -1.84836 -2.37508 -2.93899 -3.9731 -3.63376 -0.172347 0.13385 0.280409 -0.00424533 0.568297 0.0349413 1.59322 1.57436\n", + " 463 1.484e-01 1.317e+06 1.052e-01 -- 3.428e+02 -- 0.128066 -0.387178 -1.50926 -1.84837 -2.37508 -2.93898 -3.97304 -3.63376 -0.171549 0.132987 0.280318 -0.0041782 0.568295 0.0349303 1.59282 1.57435\n", + " 465 1.387e-01 1.467e+06 9.997e-02 -- 3.429e+02 -- 0.128139 -0.387043 -1.50926 -1.84837 -2.37508 -2.93898 -3.97299 -3.63377 -0.170819 0.132211 0.280236 -0.00411621 0.568293 0.0349196 1.59244 1.57434\n", + " 467 1.295e-01 1.634e+06 9.520e-02 -- 3.430e+02 -- 0.128204 -0.386922 -1.50926 -1.84838 -2.37508 -2.93898 -3.97295 -3.63377 -0.170155 0.131513 0.280162 -0.0040591 0.568292 0.0349092 1.59209 1.57433\n", + " 469 1.206e-01 1.820e+06 9.088e-02 -- 3.431e+02 -- 0.128263 -0.386813 -1.50926 -1.84838 -2.37508 -2.93898 -3.97291 -3.63377 -0.169555 0.130886 0.280094 -0.00400655 0.56829 0.0348992 1.59177 1.57432\n", + " 471 1.118e-01 2.026e+06 8.702e-02 -- 3.432e+02 -- 0.128316 -0.386716 -1.50926 -1.84839 -2.37508 -2.93897 -3.97287 -3.63377 -0.169004 0.130321 0.280033 -0.00395823 0.568289 0.0348897 1.59147 1.57431\n", + " 473 1.033e-01 2.256e+06 8.358e-02 -- 3.433e+02 -- 0.128363 -0.386628 -1.50926 -1.84839 -2.37508 -2.93897 -3.97284 -3.63377 -0.168497 0.129809 0.279978 -0.00391395 0.568287 0.0348807 1.59119 1.57431\n", + " 475 9.579e-02 2.511e+06 8.038e-02 -- 3.433e+02 -- 0.128405 -0.386549 -1.50926 -1.84839 -2.37508 -2.93897 -3.97282 -3.63378 -0.168047 0.129353 0.279928 -0.00387351 0.568286 0.0348723 1.59093 1.5743\n", + " 477 8.818e-02 2.794e+06 7.758e-02 -- 3.434e+02 -- 0.128443 -0.386478 -1.50926 -1.8484 -2.37508 -2.93897 -3.97279 -3.63378 -0.167639 0.128938 0.279883 -0.00383641 0.568285 0.0348643 1.59069 1.57429\n", + " 479 8.091e-02 3.109e+06 7.509e-02 -- 3.435e+02 -- 0.128478 -0.386415 -1.50926 -1.8484 -2.37508 -2.93896 -3.97277 -3.63378 -0.167267 0.128559 0.279842 -0.00380258 0.568284 0.0348569 1.59048 1.57428\n", + " 481 7.424e-02 3.460e+06 7.280e-02 -- 3.436e+02 -- 0.128509 -0.386357 -1.50926 -1.8484 -2.37508 -2.93896 -3.97275 -3.63378 -0.166936 0.128218 0.279805 -0.00377181 0.568283 0.0348501 1.59028 1.57428\n", + " 483 6.806e-02 3.849e+06 7.073e-02 -- 3.436e+02 -- 0.128537 -0.386306 -1.50926 -1.84841 -2.37508 -2.93896 -3.97274 -3.63378 -0.166628 0.127921 0.279772 -0.00374381 0.568282 0.0348438 1.5901 1.57427\n", + " 485 6.227e-02 4.282e+06 6.891e-02 -- 3.437e+02 -- 0.128562 -0.38626 -1.50926 -1.84841 -2.37508 -2.93896 -3.97272 -3.63378 -0.166346 0.127652 0.279742 -0.00371833 0.568281 0.034838 1.58993 1.57426\n", + " 487 5.690e-02 4.762e+06 6.723e-02 -- 3.438e+02 -- 0.128585 -0.386219 -1.50926 -1.84841 -2.37508 -2.93896 -3.97271 -3.63378 -0.166098 0.127408 0.279715 -0.00369517 0.56828 0.0348326 1.58978 1.57426\n", + " 489 5.246e-02 5.297e+06 6.572e-02 -- 3.438e+02 -- 0.128605 -0.386181 -1.50926 -1.84841 -2.37508 -2.93896 -3.9727 -3.63378 -0.165881 0.127186 0.27969 -0.00367415 0.56828 0.0348277 1.58964 1.57426\n", + " 491 4.668e-02 5.891e+06 6.451e-02 -- 3.439e+02 -- 0.128623 -0.386148 -1.50926 -1.84841 -2.37508 -2.93895 -3.97269 -3.63378 -0.165662 0.126983 0.279668 -0.00365488 0.568279 0.0348231 1.58952 1.57425\n", + " 493 4.336e-02 6.551e+06 6.330e-02 -- 3.440e+02 -- 0.12864 -0.386118 -1.50927 -1.84842 -2.37508 -2.93895 -3.97268 -3.63379 -0.165486 0.126802 0.279649 -0.00363782 0.568279 0.0348191 1.58941 1.57425\n", + " 495 3.896e-02 7.284e+06 6.215e-02 -- 3.440e+02 -- 0.128655 -0.386091 -1.50927 -1.84842 -2.37508 -2.93895 -3.97267 -3.63379 -0.165321 0.12665 0.279631 -0.00362204 0.568278 0.0348153 1.5893 1.57424\n", + " 497 3.577e-02 8.098e+06 6.116e-02 -- 3.441e+02 -- 0.128668 -0.386067 -1.50927 -1.84842 -2.37508 -2.93895 -3.97266 -3.63379 -0.165157 0.126529 0.279614 -0.00360793 0.568278 0.0348118 1.58921 1.57424\n", + " 499 3.106e-02 9.005e+06 6.042e-02 -- 3.442e+02 -- 0.12868 -0.386045 -1.50927 -1.84842 -2.37508 -2.93895 -3.97266 -3.63379 -0.164994 0.126396 0.2796 -0.00359503 0.568277 0.0348086 1.58912 1.57424\n", + "********************\n", + "0.128691 -0.386025 -1.50927 -1.84842 -2.37508 -2.93895 -3.97265 -3.63379 -0.164908 0.12629 0.279587 -0.00358386 0.568277 0.034806 1.58905 1.57423\n", + "0.00126105 0.000998709 0.00018594 0.000356512 2.30678e-05 0.000253636 0.00869718 0.000210956 0.0200512 0.0186844 0.00479074 0.00354031 0.000510855 0.00183101 0.0188838 0.0015161\n", + "-10903 199.23 -2.70075 -1.73798 -1.01372 0.00748608 0.0139392 -0.0830399 -358.669 233.027 -1.33222 -0.312077 -0.267079 -0.122273 0.0137976 -0.0193582\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": [], + "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": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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85aiKnQhsBH4K/B/g7WWtRtXua8D/A75a7kJUtS4ABoCfAe8tcy2xmQHMGXl/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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "s, loc, scale = lognorm.fit(lag,loc=.01)\n", + "xscale('log'); ylim(-4,1.5)\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n", + "#plot(fqd,norm.pdf(fqd,mu,sigma))\n", + "plot(fqd,lognorm.pdf(fqd,s,loc,scale))\n", + "mu,sigma\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib/python2.7/dist-packages/numpy/core/numeric.py:460: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return array(a, dtype, copy=False, order=order)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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/v6WsG/bv35+UlJR9zmVkZJCRkRGFMqIvOdk6RE6YAOPG2U6YIiIiXsnJySEnJ2efc/n5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\n68yffx7+8Q/7hXf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(ifft(lag))" + ] + }, + { + "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 +} diff --git a/lag/data/clag_analysis-origbins.ipynb b/lag/data/clag_analysis-origbins.ipynb new file mode 100644 index 0000000..56bfb3f --- /dev/null +++ b/lag/data/clag_analysis-origbins.ipynb @@ -0,0 +1,830 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\npmEEr8pFD6A9BOX3B3jhhW5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/6175A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n", + " 0.20739079, 0.32145572, 0.49825637])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqd\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.337e-01 6.112e+01 inf -- -4.041e+02 -- 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + "********************\n", + "0.300387 -0.178169 -1.19082 -1.52276 -2.13064 -2.4929 -3.58978\n", + "0.23893 0.202426 0.232625 0.177239 0.153017 0.132987 0.308424\n", + "-0.000180614 -0.000143998 -0.000174906 -0.000705814 0.000447101 -0.000873593 -0.00140183\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.481e+01 3.004e-01 5.393e-01 0.89 +++\n", + "+++ 3.525e+01 3.432e+01 3.004e-01 6.588e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.004e-01 5.990e-01 1.34 +++\n", + "+++ 3.525e+01 3.470e+01 3.004e-01 5.692e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.004e-01 5.543e-01 0.996 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.782e-01 2.426e-02 0.974 +++\n", + "+++ 3.525e+01 3.422e+01 -1.782e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.782e-01 7.486e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.782e-01 4.956e-02 1.21 +++\n", + "+++ 3.525e+01 3.471e+01 -1.782e-01 3.691e-02 1.09 +++\n", + "+++ 3.525e+01 3.474e+01 -1.782e-01 3.058e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.782e-01 2.742e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.075e+00 0.275 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.597 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.873e-01 0.798 +++\n", + "+++ 3.525e+01 3.480e+01 -1.191e+00 -9.727e-01 0.909 +++\n", + "+++ 3.525e+01 3.477e+01 -1.191e+00 -9.655e-01 0.966 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.618e-01 0.995 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.523e+00 -1.346e+00 0.861 +++\n", + "+++ 3.525e+01 3.433e+01 -1.523e+00 -1.257e+00 1.85 +++\n", + "+++ 3.525e+01 3.459e+01 -1.523e+00 -1.301e+00 1.32 +++\n", + "+++ 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": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AHXkdWZ9jlekUPGQB7xmVg7Gd/4Yhry+P6r+NrhLeeD4jMAMo10qfH+0DAQcw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\nyQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "t2, l2, l2e = np.loadtxt(echo_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o', color=\"green\")\n", + "errorbar(t2, l2, yerr=l2e, fmt='o', color=\"black\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.366e-01 5.289e+01 inf -- -1.581e+02 -- 1 1 1 1 1 1 1\n", + " 2 7.741e-01 5.224e+01 6.092e+01 -- -9.716e+01 -- 0.64151 0.582988 0.567078 0.568391 0.566833 0.565789 0.56338\n", + " 3 3.418e+00 5.136e+01 5.853e+01 -- -3.863e+01 -- 0.388803 0.191096 0.136557 0.140188 0.135589 0.132301 0.127287\n", + " 4 1.555e+00 5.020e+01 5.546e+01 -- 1.684e+01 -- 0.278504 -0.141428 -0.288802 -0.28161 -0.292287 -0.300033 -0.307779\n", + " 5 5.853e-01 4.862e+01 5.209e+01 -- 6.893e+01 -- 0.251696 -0.361387 -0.704482 -0.692633 -0.715864 -0.730643 -0.741316\n", + " 6 3.638e-01 4.653e+01 4.847e+01 -- 1.174e+02 -- 0.242093 -0.451675 -1.10227 -1.08525 -1.13485 -1.15797 -1.17191\n", + " 7 2.636e-01 4.340e+01 4.346e+01 -- 1.609e+02 -- 0.240629 -0.481083 -1.4522 -1.44125 -1.54556 -1.57775 -1.59823\n", + " 8 2.061e-01 3.824e+01 3.545e+01 -- 1.963e+02 -- 0.243061 -0.504278 -1.67538 -1.722 -1.93248 -1.97969 -2.01957\n", + " 9 1.693e-01 3.015e+01 2.492e+01 -- 2.212e+02 -- 0.246578 -0.524331 -1.72708 -1.87967 -2.25718 -2.33983 -2.4357\n", + " 10 1.458e-01 1.978e+01 1.425e+01 -- 2.355e+02 -- 0.254622 -0.533743 -1.72985 -1.91821 -2.45669 -2.6121 -2.84811\n", + " 11 1.353e-01 1.021e+01 6.810e+00 -- 2.423e+02 -- 0.264297 -0.537479 -1.7301 -1.91543 -2.50815 -2.75364 -3.26334\n", + " 12 1.464e-01 3.997e+00 3.032e+00 -- 2.453e+02 -- 0.268248 -0.539862 -1.72642 -1.91378 -2.49097 -2.79169 -3.705\n", + " 13 2.296e-01 1.073e+00 1.223e+00 -- 2.465e+02 -- 0.268013 -0.541085 -1.72532 -1.91534 -2.4707 -2.79511 -4.24757\n", + " 14 1.191e+00 1.664e-01 4.032e-01 -- 2.469e+02 -- 0.266953 -0.541769 -1.72481 -1.91721 -2.45806 -2.793 -5.22266\n", + " 15 4.403e+02 2.073e-01 4.480e-02 -- 2.470e+02 -- 0.266181 -0.542036 -1.72478 -1.91832 -2.45194 -2.79147 -8\n", + " 16 4.405e+02 2.177e-01 9.908e-05 -- 2.470e+02 -- 0.265853 -0.542082 -1.72492 -1.91871 -2.45034 -2.79121 -8\n", + "********************\n", + "0.265853 -0.542082 -1.72492 -1.91871 -2.45034 -2.79121 -8\n", + "0.236507 0.210135 0.2567 0.193885 0.180857 0.168104 4543.34\n", + "-0.00248404 -0.00270298 -0.00703597 -0.020554 -0.0555367 -0.217652 -0.000171146\n", + "********************\n" + ] + } + ], + "source": [ + "P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)\n", + "p2 = np.ones(nfq)\n", + "p2, p2e = clag.optimize(P2, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 2.470e+02 2.466e+02 2.658e-01 5.023e-01 0.854 +++\n", + "+++ 2.470e+02 2.461e+02 2.658e-01 6.205e-01 1.8 +++\n", + "+++ 2.470e+02 2.463e+02 2.658e-01 5.614e-01 1.29 +++\n", + "+++ 2.470e+02 2.465e+02 2.658e-01 5.318e-01 1.06 +++\n", + "+++ 2.470e+02 2.465e+02 2.658e-01 5.171e-01 0.956 +++\n", + "+++ 2.470e+02 2.465e+02 2.658e-01 5.244e-01 1.01 +++\n", + "\t### errors for param 1 ###\n", + "+++ 2.470e+02 2.465e+02 -5.421e-01 -3.320e-01 0.961 +++\n", + "+++ 2.470e+02 2.460e+02 -5.421e-01 -2.269e-01 2.03 +++\n", + "+++ 2.470e+02 2.463e+02 -5.421e-01 -2.794e-01 1.45 +++\n", + "+++ 2.470e+02 2.464e+02 -5.421e-01 -3.057e-01 1.2 +++\n", + "+++ 2.470e+02 2.465e+02 -5.421e-01 -3.188e-01 1.08 +++\n", + "+++ 2.470e+02 2.465e+02 -5.421e-01 -3.254e-01 1.02 +++\n", + "+++ 2.470e+02 2.465e+02 -5.421e-01 -3.287e-01 0.989 +++\n", + "+++ 2.470e+02 2.465e+02 -5.421e-01 -3.270e-01 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 2.470e+02 2.468e+02 -1.725e+00 -1.597e+00 0.298 +++\n", + "+++ 2.470e+02 2.467e+02 -1.725e+00 -1.532e+00 0.648 +++\n", + "+++ 2.470e+02 2.466e+02 -1.725e+00 -1.500e+00 0.867 +++\n", + "+++ 2.470e+02 2.465e+02 -1.725e+00 -1.484e+00 0.986 +++\n", + "+++ 2.470e+02 2.465e+02 -1.725e+00 -1.476e+00 1.05 +++\n", + "+++ 2.470e+02 2.465e+02 -1.725e+00 -1.480e+00 1.02 +++\n", + "+++ 2.470e+02 2.465e+02 -1.725e+00 -1.482e+00 1 +++\n", + "\t### errors for param 3 ###\n", + "+++ 2.470e+02 2.465e+02 -1.919e+00 -1.725e+00 0.916 +++\n", + "+++ 2.470e+02 2.460e+02 -1.919e+00 -1.628e+00 1.97 +++\n", + "+++ 2.470e+02 2.463e+02 -1.919e+00 -1.676e+00 1.4 +++\n", + "+++ 2.470e+02 2.464e+02 -1.919e+00 -1.701e+00 1.15 +++\n", + "+++ 2.470e+02 2.465e+02 -1.919e+00 -1.713e+00 1.03 +++\n", + "+++ 2.470e+02 2.465e+02 -1.919e+00 -1.719e+00 0.971 +++\n", + "+++ 2.470e+02 2.465e+02 -1.919e+00 -1.716e+00 0.999 +++\n", + "\t### errors for param 4 ###\n", + "+++ 2.470e+02 2.466e+02 -2.450e+00 -2.269e+00 0.84 +++\n", + "+++ 2.470e+02 2.461e+02 -2.450e+00 -2.179e+00 1.87 +++\n", + "+++ 2.470e+02 2.463e+02 -2.450e+00 -2.224e+00 1.3 +++\n", + "+++ 2.470e+02 2.465e+02 -2.450e+00 -2.247e+00 1.06 +++\n", + "+++ 2.470e+02 2.465e+02 -2.450e+00 -2.258e+00 0.947 +++\n", + "+++ 2.470e+02 2.465e+02 -2.450e+00 -2.252e+00 1 +++\n", + "\t### errors for param 5 ###\n", + "+++ 2.470e+02 2.465e+02 -2.791e+00 -2.623e+00 1.01 +++\n", + "+++ 2.470e+02 2.469e+02 -2.791e+00 -2.707e+00 0.27 +++\n", + "+++ 2.470e+02 2.467e+02 -2.791e+00 -2.665e+00 0.583 +++\n", + "+++ 2.470e+02 2.466e+02 -2.791e+00 -2.644e+00 0.784 +++\n", + "+++ 2.470e+02 2.465e+02 -2.791e+00 -2.634e+00 0.895 +++\n", + "+++ 2.470e+02 2.465e+02 -2.791e+00 -2.628e+00 0.954 +++\n", + "+++ 2.470e+02 2.465e+02 -2.791e+00 -2.626e+00 0.984 +++\n", + "+++ 2.470e+02 2.465e+02 -2.791e+00 -2.624e+00 0.999 +++\n", + "\t### errors for param 6 ###\n", + "+++ 2.470e+02 2.470e+02 -8.000e+00 -6.000e+00 0.0147 +++\n", + "+++ 2.470e+02 2.469e+02 -8.000e+00 -5.000e+00 0.151 +++\n", + "+++ 2.470e+02 2.467e+02 -8.000e+00 -4.500e+00 0.489 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.250e+00 0.891 +++\n", + "+++ 2.470e+02 2.464e+02 -8.000e+00 -4.125e+00 1.21 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.188e+00 1.04 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.219e+00 0.961 +++\n", + "+++ 2.470e+02 2.465e+02 -8.000e+00 -4.203e+00 0.998 +++\n", + "********************\n", + "0.265765 -0.5421 -1.72494 -1.9188 -2.45004 -2.79121 -8\n", + "0.258682 0.215059 0.242662 0.202981 0.197785 0.166791 3.79688\n", + "********************\n" + ] + } + ], + "source": [ + "p2, p2e = clag.errors(P2, p2, p2e)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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1.915e+00 -- 3.134e+02 -- 0.064849 -0.55355 -1.64669 -1.9347 -2.51145 -2.88524 -5.79489 -0.0424971 0.0914217 0.131924 0.142018 0.0422318 0.133023 -0.334542\n", + " 9 3.480e+01 2.272e+01 1.812e+00 -- 3.152e+02 -- 0.0858141 -0.529439 -1.62163 -1.91445 -2.49256 -2.87136 -5.49489 -0.0719278 0.0901783 0.137158 0.150801 0.0304433 0.140833 2.03211\n", + " 11 1.002e+01 2.607e+01 1.783e+00 -- 3.170e+02 -- 0.104218 -0.509001 -1.60042 -1.89706 -2.47621 -2.85932 -5.19489 -0.0958829 0.089339 0.141038 0.158083 0.0208371 0.147639 1.24275\n", + " 13 7.968e+03 2.974e+01 1.627e+00 -- 3.186e+02 -- 0.120402 -0.491519 -1.58231 -1.88202 -2.46198 -2.8488 -4.89489 -0.115613 0.088809 0.143973 0.164209 0.0130572 0.153636 -0.00200163\n", + " 15 7.700e+00 3.372e+01 1.748e+00 -- 3.204e+02 -- 0.134664 -0.476455 -1.56672 -1.86894 -2.44956 -2.83954 -4.59489 -0.132041 0.088517 0.146286 0.169375 0.00677842 0.158969 1.59295\n", + " 17 2.375e+01 3.806e+01 2.026e+00 -- 3.224e+02 -- 0.147265 -0.463405 -1.55325 -1.85749 -2.43853 -2.83144 -4.29489 -0.145779 0.0883846 0.147915 0.173914 0.00155918 0.163793 0.96984\n", + " 19 1.222e+01 4.277e+01 1.856e+00 -- 3.243e+02 -- 0.158428 -0.452038 -1.54155 -1.84743 -2.4288 -2.82406 -4.10712 -0.157385 0.0884563 0.149176 0.177908 -0.0021446 0.168153 0.95096\n", + " 21 3.757e+00 4.786e+01 1.686e+00 -- 3.260e+02 -- 0.168341 -0.442099 -1.53136 -1.83856 -2.42015 -2.81739 -3.99633 -0.167232 0.0886703 0.150095 0.181462 -0.00476573 0.172153 0.946458\n", + " 23 1.719e+00 5.335e+01 1.565e+00 -- 3.275e+02 -- 0.17716 -0.433379 -1.52245 -1.8307 -2.41246 -2.81134 -3.9178 -0.175626 0.0889884 0.150755 0.184625 -0.00655603 0.175857 0.945111\n", + " 25 7.670e-01 5.926e+01 1.462e+00 -- 3.290e+02 -- 0.185022 -0.425705 -1.51464 -1.82371 -2.40559 -2.80587 -3.85771 -0.18281 0.0893847 0.151217 0.187442 -0.00768333 0.179309 0.944998\n", + " 27 3.271e-01 6.558e+01 1.371e+00 -- 3.304e+02 -- 0.192044 -0.418936 -1.50777 -1.81749 -2.39945 -2.80089 -3.80967 -0.188981 0.0898403 0.151526 0.189953 -0.00827265 0.182546 0.945473\n", + " 29 2.839e-01 7.234e+01 1.288e+00 -- 3.316e+02 -- 0.198326 -0.412951 -1.50172 -1.81194 -2.39395 -2.79637 -3.77014 -0.194296 0.0903402 0.151717 0.192191 -0.0084207 0.185594 0.946256\n", + " 31 6.344e-01 7.952e+01 1.212e+00 -- 3.328e+02 -- 0.203957 -0.407649 -1.49638 -1.80697 -2.38902 -2.79224 -3.73694 -0.198885 0.0908733 0.151818 0.194187 -0.00820387 0.188473 0.9472\n", + " 33 1.008e+00 8.711e+01 1.140e+00 -- 3.340e+02 -- 0.20901 -0.402946 -1.49167 -1.80251 -2.38459 -2.78847 -3.70862 -0.202854 0.0914304 0.151849 0.195966 -0.00768338 0.1912 0.948221\n", + " 35 1.430e+00 9.512e+01 1.074e+00 -- 3.351e+02 -- 0.213553 -0.398767 -1.4875 -1.79849 -2.3806 -2.78502 -3.68416 -0.206293 0.0920046 0.151828 0.197549 -0.00690885 0.193787 0.949267\n", + " 37 1.972e+00 1.035e+02 1.010e+00 -- 3.361e+02 -- 0.217642 -0.395048 -1.48381 -1.79488 -2.37701 -2.78186 -3.66283 -0.209276 0.0925904 0.151767 0.198956 -0.00592089 0.196245 0.950302\n", + " 38 1.386e+01 2.586e+03 1.075e+01 -- 3.468e+02 -- 0.254492 -0.361933 -1.45115 -1.76223 -2.34466 -2.75279 -3.47522 -0.235177 0.0985198 0.150868 0.211428 0.00575786 0.2196 0.960307\n", + " 40 4.847e+00 1.291e+03 4.830e-01 -- 3.473e+02 -- 0.254339 -0.36196 -1.45129 -1.7616 -2.34565 -2.75065 -3.47325 -0.238184 0.0984132 0.154637 0.207337 0.0137373 0.221423 0.959887\n", + " 42 2.820e+00 7.911e+02 2.689e-01 -- 3.476e+02 -- 0.254314 -0.362014 -1.45138 -1.76102 -2.34642 -2.7488 -3.47177 -0.240479 0.0987825 0.157405 0.204131 0.0203959 0.222522 0.958597\n", + " 44 1.927e+00 5.366e+02 1.843e-01 -- 3.478e+02 -- 0.254352 -0.362079 -1.45146 -1.76045 -2.34705 -2.74718 -3.47058 -0.24227 0.0993579 0.159522 0.201533 0.0261467 0.223117 0.956931\n", + " 45 6.846e-01 2.491e+03 7.144e-01 -- 3.470e+02 -- 0.255087 -0.362762 -1.45206 -1.75508 -2.35235 -2.73284 -3.4608 -0.256273 0.106036 0.17597 0.180236 0.0765365 0.225273 0.938514\n", + " 47 3.859e-01 7.440e+02 1.045e+00 -- 3.481e+02 -- 0.255642 -0.362946 -1.45188 -1.75441 -2.35202 -2.735 -3.46127 -0.253243 0.107817 0.170909 0.185077 0.0713684 0.20985 0.938462\n", + " 49 2.428e-01 4.740e+02 3.238e-01 -- 3.484e+02 -- 0.255958 -0.36305 -1.45177 -1.75394 -2.35178 -2.73606 -3.46158 -0.25133 0.108819 0.167827 0.187945 0.0689457 0.201752 0.937949\n", + " 50 1.845e+00 3.237e+04 3.382e+00 -- 3.450e+02 -- 0.258014 -0.363678 -1.45096 -1.74999 -2.35001 -2.74182 -3.46363 -0.23749 0.114761 0.147522 0.2072 0.0566519 0.152758 0.930353\n", + " 52 5.341e-01 2.738e+03 3.310e+00 -- 3.483e+02 -- 0.256859 -0.363253 -1.45121 -1.74947 -2.35141 -2.73938 -3.46259 -0.241519 0.110174 0.1534 0.199686 0.0671039 0.167388 0.929964\n", + " 54 2.673e-01 4.258e+02 4.126e-01 -- 3.488e+02 -- 0.256513 -0.363117 -1.45131 -1.74933 -2.35166 -2.73828 -3.46219 -0.242924 0.108757 0.155979 0.197112 0.0706881 0.173906 0.929109\n", + " 56 1.484e-01 7.070e+02 1.233e-01 -- 3.489e+02 -- 0.256358 -0.363054 -1.45135 -1.74924 -2.35169 -2.73765 -3.46198 -0.243691 0.108123 0.157394 0.195813 0.0725777 0.177563 0.928277\n", + " 57 2.633e+00 1.761e+05 5.893e+00 -- 3.430e+02 -- 0.255689 -0.362752 -1.45153 -1.74846 -2.35114 -2.73373 -3.46071 -0.248243 0.105285 0.165654 0.188899 0.0833473 0.19847 0.920537\n", + " 59 5.986e-01 4.834e+03 6.298e+00 -- 3.493e+02 -- 0.25881 -0.363072 -1.45138 -1.74823 -2.34996 -2.73665 -3.46097 -0.236008 0.109215 0.161791 0.19747 0.0614059 0.167187 0.920664\n", + " 61 1.056e-01 1.057e+04 3.624e-01 -- 3.497e+02 -- 0.259231 -0.363167 -1.45134 -1.74818 -2.34961 -2.73738 -3.46105 -0.234491 0.110247 0.160667 0.199701 0.0577301 0.160365 0.920596\n", + " 63 9.832e-02 1.198e+04 8.624e-02 -- 3.497e+02 -- 0.259256 -0.363199 -1.45132 -1.74815 -2.34952 -2.73761 -3.46108 -0.234434 0.110544 0.160282 0.200426 0.0572685 0.158672 0.920487\n", + " 65 1.477e-01 1.320e+04 5.481e-02 -- 3.498e+02 -- 0.259155 -0.363209 -1.45131 -1.74812 -2.34952 -2.73767 -3.46109 -0.234836 0.110592 0.160145 0.20064 0.0578316 0.158622 0.920373\n", + " 66 5.254e-01 1.244e+05 1.134e+00 -- 3.509e+02 -- 0.257792 -0.363238 -1.45127 -1.74783 -2.34977 -2.73779 -3.46112 -0.240158 0.110293 0.159591 0.201062 0.0663719 0.163096 0.919291\n", + " 68 2.702e-02 4.790e+05 6.404e-01 -- 3.516e+02 -- 0.256458 -0.363212 -1.45128 -1.74783 -2.34996 -2.7376 -3.4611 -0.248024 0.109945 0.159825 0.200359 0.0698593 0.166007 0.919278\n", + " 70 7.753e-03 5.328e+05 5.502e-02 -- 3.516e+02 -- 0.256548 -0.363213 -1.45128 -1.74783 -2.34995 -2.73761 -3.4611 -0.247354 0.109964 0.159811 0.200397 0.0696895 0.165854 0.919275\n", + " 71 4.990e-02 5.349e+08 2.391e+00 -- 3.540e+02 -- 0.256515 -0.363211 -1.45128 -1.74782 -2.34996 -2.73759 -3.4611 -0.246413 0.109926 0.159825 0.200321 0.0702298 0.166204 0.919243\n", + " 73 1.883e-01 1.908e+08 7.849e-01 -- 3.548e+02 -- 0.256638 -0.363212 -1.45128 -1.74782 -2.34995 -2.7376 -3.4611 -0.245184 0.109938 0.159816 0.200349 0.0700951 0.166094 0.919244\n", + " 75 3.505e-01 1.280e+10 5.592e+00 -- 3.492e+02 -- 0.256429 -0.363212 -1.45128 -1.74782 -2.34996 -2.73759 -3.4611 -0.2498 0.109944 0.15982 0.200324 0.0701695 0.16617 0.919243\n", + " 76 7.399e-01 4.729e+08 1.154e+01 -- 3.377e+02 -- 0.255205 -0.363227 -1.45127 -1.74784 -2.34981 -2.73775 -3.46111 -0.162247 0.110105 0.15966 0.201149 0.0677482 0.164465 0.919251\n", + " 78 1.590e-01 5.754e+06 1.469e+01 -- 3.524e+02 -- 0.253232 -0.363205 -1.45128 -1.74785 -2.34999 -2.73758 -3.46109 -0.174252 0.109807 0.15986 0.200551 0.0708851 0.16699 0.919246\n", + " 80 5.807e-02 1.303e+07 7.214e-01 -- 3.531e+02 -- 0.253726 -0.36321 -1.45127 -1.74785 -2.34994 -2.73762 -3.4611 -0.171481 0.109878 0.159812 0.200693 0.0701441 0.166393 0.919247\n", + " 82 6.557e-03 1.579e+07 9.480e-02 -- 3.532e+02 -- 0.253613 -0.363209 -1.45127 -1.74785 -2.34995 -2.73761 -3.4611 -0.172477 0.109861 0.159824 0.200658 0.0703205 0.166534 0.919247\n", + " 84 6.675e-03 1.765e+07 5.646e-02 -- 3.532e+02 -- 0.253648 -0.363209 -1.45127 -1.74785 -2.34995 -2.73761 -3.4611 -0.172493 0.109866 0.15982 0.200667 0.0702744 0.166497 0.919247\n", + " 86 1.405e-02 1.962e+07 5.316e-02 -- 3.533e+02 -- 0.253645 -0.363209 -1.45127 -1.74785 -2.34995 -2.73761 -3.4611 -0.172608 0.109865 0.159821 0.200664 0.070284 0.166504 0.919247\n", + " 87 2.371e+00 3.577e+10 6.090e-01 -- 3.539e+02 -- 0.25362 -0.36321 -1.45127 -1.74785 -2.34995 -2.73762 -3.4611 -0.175034 0.10987 0.159819 0.200659 0.0702718 0.166492 0.919246\n", + " 91 6.875e-01 6.345e+10 1.055e+00 -- 3.528e+02 -- 0.253591 -0.36321 -1.45127 -1.74785 -2.34995 -2.73762 -3.4611 -0.174619 0.109871 0.159819 0.200661 0.0702715 0.166492 0.919246\n", + " 94 8.161e+00 6.043e+11 1.715e+01 -- 3.357e+02 -- 0.253529 -0.36321 -1.45127 -1.74785 -2.34995 -2.73762 -3.4611 -0.175819 0.109861 0.159819 0.200658 0.0702745 0.16649 0.919246\n", + " 96 2.611e-01 3.717e+08 9.546e+00 -- 3.452e+02 -- 0.257412 -0.363213 -1.45128 -1.74782 -2.34996 -2.73763 -3.4611 -0.319306 0.109793 0.159798 0.200183 0.0701731 0.16621 0.919236\n", + " 98 3.737e-02 1.435e+07 7.911e+00 -- 3.531e+02 -- 0.258178 -0.363226 -1.45127 -1.74781 -2.34985 -2.73772 -3.46111 -0.312069 0.109964 0.159681 0.20055 0.0683407 0.164732 0.919241\n", + " 100 4.006e-03 2.110e+07 1.702e-01 -- 3.533e+02 -- 0.25807 -0.363224 -1.45127 -1.74782 -2.34987 -2.73771 -3.46111 -0.312793 0.10994 0.159697 0.200499 0.0685961 0.164939 0.919241\n", + " 102 5.577e-03 2.351e+07 5.453e-02 -- 3.534e+02 -- 0.258084 -0.363224 -1.45127 -1.74782 -2.34987 -2.73771 -3.46111 -0.312702 0.109943 0.159695 0.200505 0.0685686 0.164916 0.919241\n", + " 104 6.916e-03 2.613e+07 5.344e-02 -- 3.534e+02 -- 0.258079 -0.363224 -1.45127 -1.74782 -2.34987 -2.73771 -3.46111 -0.312527 0.109942 0.159696 0.200505 0.0685733 0.16492 0.919241\n", + " 105 6.197e+00 8.850e+11 1.956e+01 -- 3.339e+02 -- 0.258065 -0.363224 -1.45127 -1.74782 -2.34987 -2.73771 -3.46111 -0.314689 0.109936 0.159698 0.200489 0.0686299 0.164957 0.91924\n", + " 106 2.457e+00 5.060e+05 4.423e+01 -- 2.896e+02 -- 0.25217 -0.363236 -1.45128 -1.74787 -2.35122 -2.73772 -3.46109 -2.26495 0.107765 0.159324 0.193074 0.0661005 0.165589 0.919229\n", + " 108 9.160e-01 1.076e+06 4.129e+01 -- 3.309e+02 -- 0.190213 -0.363421 -1.45121 -1.74789 -2.34962 -2.73898 -3.46119 -2.20299 0.109386 0.15792 0.199777 0.0502385 0.149847 0.919513\n", + " 110 7.545e-01 1.842e+06 3.241e+00 -- 3.342e+02 -- 0.172789 -0.363413 -1.45121 -1.7479 -2.34968 -2.73894 -3.46119 -2.16833 0.109263 0.157968 0.199848 0.0508925 0.150441 0.919501\n", + " 112 6.604e-01 2.413e+06 1.815e+00 -- 3.360e+02 -- 0.159752 -0.363413 -1.45121 -1.74791 -2.34967 -2.73895 -3.46119 -2.14156 0.109248 0.157963 0.200007 0.0508058 0.150387 0.9195\n", + " 114 5.952e-01 3.001e+06 1.236e+00 -- 3.372e+02 -- 0.149202 -0.363414 -1.45121 -1.74791 -2.34966 -2.73896 -3.46119 -2.11865 0.10925 0.157953 0.20014 0.0506565 0.150272 0.9195\n", + " 116 5.401e-01 3.623e+06 9.191e-01 -- 3.381e+02 -- 0.140322 -0.363414 -1.4512 -1.74792 -2.34964 -2.73897 -3.46119 -2.09844 0.109254 0.157944 0.200248 0.0505214 0.150164 0.9195\n", + " 118 4.946e-01 4.287e+06 7.180e-01 -- 3.389e+02 -- 0.132742 -0.363415 -1.4512 -1.74792 -2.34963 -2.73898 -3.46119 -2.08012 0.10926 0.157935 0.200335 0.0504064 0.150071 0.919501\n", + " 120 4.554e-01 5.002e+06 5.840e-01 -- 3.394e+02 -- 0.126177 -0.363416 -1.4512 -1.74792 -2.34962 -2.73898 -3.46119 -2.06319 0.109265 0.157928 0.200408 0.0503087 0.14999 0.919501\n", + " 122 4.163e-01 5.781e+06 4.883e-01 -- 3.399e+02 -- 0.120431 -0.363416 -1.4512 -1.74792 -2.34961 -2.73899 -3.46119 -2.04749 0.10927 0.157922 0.200469 0.0502247 0.14992 0.919501\n", + " 124 3.856e-01 6.633e+06 4.091e-01 -- 3.403e+02 -- 0.115418 -0.363417 -1.4512 -1.74793 -2.34961 -2.739 -3.46119 -2.03366 0.109275 0.157917 0.200521 0.0501519 0.149858 0.919501\n", + " 126 3.459e-01 7.569e+06 3.539e-01 -- 3.407e+02 -- 0.110968 -0.363417 -1.4512 -1.74793 -2.3496 -2.739 -3.46119 -2.02099 0.109279 0.157912 0.200566 0.0500901 0.149806 0.919501\n", + " 128 3.257e-01 8.604e+06 2.868e-01 -- 3.410e+02 -- 0.10713 -0.363417 -1.4512 -1.74793 -2.34959 -2.739 -3.4612 -2.01203 0.109283 0.157908 0.200604 0.0500359 0.14976 0.919502\n", + " 130 2.677e-01 9.741e+06 2.645e-01 -- 3.412e+02 -- 0.10364 -0.363418 -1.4512 -1.74793 -2.34959 -2.73901 -3.4612 -2.00265 0.109286 0.157905 0.200636 0.0499941 0.149724 0.919502\n", + " 132 2.564e-01 1.094e+07 2.420e-01 -- 3.415e+02 -- 0.100866 -0.363418 -1.4512 -1.74793 -2.34959 -2.73901 -3.4612 -1.991 0.109289 0.157902 0.200663 0.0499572 0.14969 0.919502\n", + " 134 2.658e-01 1.231e+07 2.111e-01 -- 3.417e+02 -- 0.0982797 -0.363418 -1.4512 -1.74793 -2.34958 -2.73901 -3.4612 -1.98223 0.109291 0.157899 0.200689 0.0499203 0.149658 0.919502\n", + " 136 2.061e-01 1.381e+07 2.284e-01 -- 3.419e+02 -- 0.095667 -0.363418 -1.4512 -1.74793 -2.34958 -2.73901 -3.4612 -1.97018 0.109293 0.157897 0.200713 0.0498902 0.149631 0.919502\n", + " 138 2.448e-01 1.549e+07 1.683e-01 -- 3.421e+02 -- 0.0936952 -0.363418 -1.4512 -1.74793 -2.34958 -2.73902 -3.4612 -1.96336 0.109297 0.157894 0.200734 0.0498553 0.1496 0.919502\n", + " 140 1.932e-01 1.745e+07 1.492e-01 -- 3.422e+02 -- 0.0914011 -0.363419 -1.4512 -1.74793 -2.34957 -2.73902 -3.4612 -1.96101 0.109298 0.157892 0.200752 0.0498304 0.14958 0.919502\n", + " 142 1.543e-01 1.951e+07 1.616e-01 -- 3.424e+02 -- 0.0896351 -0.363419 -1.4512 -1.74793 -2.34957 -2.73902 -3.4612 -1.95324 0.109299 0.157891 0.200767 0.0498145 0.149565 0.919502\n", + " 144 1.977e-01 2.183e+07 1.088e-01 -- 3.425e+02 -- 0.0882519 -0.363419 -1.4512 -1.74793 -2.34957 -2.73902 -3.4612 -1.95169 0.109301 0.157889 0.200779 0.0497925 0.149546 0.919502\n", + " 146 2.223e-01 2.449e+07 1.198e-01 -- 3.426e+02 -- 0.0865074 -0.363419 -1.4512 -1.74793 -2.34957 -2.73902 -3.4612 -1.95019 0.109302 0.157888 0.200791 0.0497808 0.149537 0.919502\n", + " 148 1.483e-01 2.753e+07 1.474e-01 -- 3.428e+02 -- 0.084584 -0.363419 -1.4512 -1.74793 -2.34956 -2.73902 -3.4612 -1.94508 0.109302 0.157888 0.200805 0.0497691 0.149528 0.919502\n", + " 150 1.690e-01 3.082e+07 8.624e-02 -- 3.429e+02 -- 0.0833299 -0.363419 -1.4512 -1.74793 -2.34956 -2.73902 -3.4612 -1.9461 0.109303 0.157886 0.200815 0.0497502 0.149512 0.919502\n", + " 152 1.762e-01 3.449e+07 1.188e-01 -- 3.430e+02 -- 0.0819216 -0.363419 -1.4512 -1.74793 -2.34956 -2.73902 -3.4612 -1.94249 0.109303 0.157886 0.200824 0.049743 0.149507 0.919502\n", + " 154 1.238e-01 3.866e+07 1.240e-01 -- 3.431e+02 -- 0.0804781 -0.363419 -1.4512 -1.74793 -2.34956 -2.73902 -3.4612 -1.93818 0.109303 0.157885 0.200835 0.0497304 0.149497 0.919502\n", + " 156 1.139e-01 4.323e+07 8.517e-02 -- 3.432e+02 -- 0.079482 -0.363419 -1.4512 -1.74793 -2.34956 -2.73902 -3.4612 -1.93786 0.109305 0.157884 0.200843 0.049715 0.149485 0.919503\n", + " 157 4.942e-02 2.466e+07 9.311e-01 -- 3.441e+02 -- 0.0704267 -0.363418 -1.4512 -1.74794 -2.34957 -2.73903 -3.4612 -1.77759 0.1093 0.15788 0.200952 0.0496793 0.149428 0.919498\n", + " 159 8.388e-02 3.418e+07 1.894e-01 -- 3.443e+02 -- 0.0700786 -0.36342 -1.4512 -1.74794 -2.34955 -2.73904 -3.4612 -1.77245 0.109321 0.157867 0.200998 0.0494922 0.149273 0.9195\n", + " 160 4.666e-01 2.671e+08 9.403e-01 -- 3.453e+02 -- 0.0642006 -0.363422 -1.4512 -1.74794 -2.34953 -2.73906 -3.4612 -1.69928 0.109349 0.157846 0.201123 0.0492057 0.149018 0.9195\n", + " 161 6.172e+00 9.031e+05 6.135e+00 -- 3.391e+02 -- 0.0572531 -0.363415 -1.4512 -1.74794 -2.34959 -2.73898 -3.46119 -2.49218 0.1092 0.157938 0.20046 0.0506396 0.150192 0.919509\n", + " 163 1.463e+01 1.084e+06 1.303e+00 -- 3.404e+02 -- 0.0219138 -0.363415 -1.4512 -1.74794 -2.34957 -2.739 -3.46119 -2.43778 0.109224 0.15792 0.200646 0.0504673 0.149985 0.919507\n", + " 165 2.912e+01 1.297e+06 9.553e-01 -- 3.414e+02 -- -0.0101463 -0.363418 -1.4512 -1.74794 -2.34954 -2.73902 -3.4612 -2.38182 0.109267 0.157892 0.200829 0.0501465 0.149663 0.919507\n", + " 167 6.809e+00 1.524e+06 7.449e-01 -- 3.421e+02 -- -0.0396938 -0.36342 -1.4512 -1.74795 -2.34952 -2.73904 -3.4612 -2.32367 0.109303 0.15787 0.200977 0.0498882 0.1494 0.919507\n", + " 169 3.698e+00 1.765e+06 5.996e-01 -- 3.427e+02 -- -0.0667198 -0.363421 -1.4512 -1.74795 -2.3495 -2.73905 -3.4612 -2.26425 0.109332 0.157852 0.201095 0.0496903 0.149192 0.919507\n", + " 171 2.425e+00 2.021e+06 4.993e-01 -- 3.432e+02 -- -0.0913899 -0.363422 -1.4512 -1.74795 -2.34949 -2.73907 -3.4612 -2.20346 0.109355 0.157838 0.201191 0.0495391 0.149027 0.919507\n", + " 173 1.738e+00 2.292e+06 4.250e-01 -- 3.436e+02 -- -0.113555 -0.363423 -1.4512 -1.74795 -2.34948 -2.73908 -3.4612 -2.14116 0.109374 0.157827 0.20127 0.0494225 0.148895 0.919506\n", + " 175 1.307e+00 2.583e+06 3.649e-01 -- 3.440e+02 -- -0.133292 -0.363424 -1.4512 -1.74795 -2.34947 -2.73908 -3.4612 -2.0796 0.10939 0.157817 0.201335 0.049332 0.148788 0.919506\n", + " 177 1.047e+00 2.895e+06 3.223e-01 -- 3.443e+02 -- -0.150718 -0.363424 -1.4512 -1.74795 -2.34946 -2.73909 -3.4612 -2.01741 0.109403 0.15781 0.201391 0.0492627 0.148701 0.919505\n", + " 179 8.090e-01 3.236e+06 2.859e-01 -- 3.446e+02 -- -0.166497 -0.363425 -1.4512 -1.74795 -2.34945 -2.73909 -3.4612 -1.95757 0.109414 0.157804 0.201439 0.0492083 0.14863 0.919505\n", + " 181 6.080e-01 3.601e+06 2.585e-01 -- 3.449e+02 -- -0.179967 -0.363425 -1.4512 -1.74795 -2.34945 -2.7391 -3.4612 -1.89744 0.109423 0.157799 0.20148 0.0491661 0.148572 0.919504\n", + " 182 2.539e-01 3.660e+07 2.576e+00 -- 3.475e+02 -- -0.289393 -0.363427 -1.45119 -1.74796 -2.34941 -2.73913 -3.46121 -1.30909 0.109498 0.157756 0.201827 0.0488447 0.148088 0.919499\n", + " 184 4.016e-01 5.855e+07 2.221e-01 -- 3.477e+02 -- -0.282045 -0.363425 -1.45119 -1.74796 -2.34943 -2.73912 -3.46121 -1.30097 0.10948 0.157768 0.201786 0.0490279 0.148229 0.919497\n", + " 186 3.091e-01 6.932e+07 2.593e-03 -- 3.477e+02 -- -0.27144 -0.363425 -1.45119 -1.74796 -2.34943 -2.73912 -3.46121 -1.35322 0.10948 0.15777 0.201757 0.0490685 0.148251 0.919497\n", + " 187 2.851e-01 7.479e+06 1.835e+00 -- 3.458e+02 -- -0.321756 -0.363424 -1.4512 -1.74796 -2.34944 -2.73911 -3.46121 -1.77155 0.109467 0.157782 0.201631 0.0492586 0.148401 0.919498\n", + " 188 2.336e-01 2.433e+07 2.960e-01 -- 3.455e+02 -- -0.374916 -0.363436 -1.45119 -1.74795 -2.34932 -2.73919 -3.46121 -1.26642 0.109638 0.157681 0.202071 0.0478576 0.147162 0.919505\n", + " 190 1.438e-01 1.271e+07 1.388e+00 -- 3.469e+02 -- -0.381149 -0.363427 -1.45119 -1.74796 -2.3494 -2.73914 -3.46121 -1.23684 0.109523 0.157749 0.201855 0.0489144 0.148006 0.919497\n", + " 192 1.187e-01 1.489e+07 1.009e-01 -- 3.470e+02 -- -0.380686 -0.363426 -1.45119 -1.74796 -2.34942 -2.73913 -3.46121 -1.21905 0.109507 0.157758 0.201826 0.049072 0.148126 0.919496\n", + " 193 2.616e+00 1.803e+07 9.241e-01 -- 3.480e+02 -- -0.385652 -0.363423 -1.45119 -1.74796 -2.34943 -2.73912 -3.46121 -1.07438 0.109491 0.157771 0.201807 0.0493538 0.148299 0.919493\n", + " 194 5.869e-01 1.719e+09 1.149e+01 -- 3.365e+02 -- -0.106959 -0.363434 -1.45119 -1.74797 -2.34929 -2.73922 -3.46122 1.73606 0.109587 0.15766 0.202786 0.047551 0.147005 0.919497\n", + " 196 1.130e-01 9.258e+07 9.800e+00 -- 3.463e+02 -- -0.113236 -0.363424 -1.45119 -1.74797 -2.34939 -2.73916 -3.46121 1.65362 0.109461 0.157728 0.202605 0.0485995 0.147848 0.919488\n", + " 198 2.772e+00 1.481e+08 2.691e-01 -- 3.465e+02 -- -0.11407 -0.363423 -1.45119 -1.74797 -2.3494 -2.73915 -3.46121 1.63494 0.109445 0.157735 0.20259 0.0487043 0.147935 0.919487\n", + " 199 2.169e+00 1.764e+06 2.451e+00 -- 3.441e+02 -- -0.430263 -0.363426 -1.45119 -1.74795 -2.34941 -2.73915 -3.46121 -3.05269 0.109524 0.157742 0.201897 0.0488874 0.147882 0.919492\n", + " 201 1.913e+00 2.072e+06 3.862e-01 -- 3.445e+02 -- -0.523586 -0.363424 -1.45119 -1.74795 -2.34943 -2.73914 -3.46121 -3.00172 0.109506 0.157754 0.201892 0.049131 0.148027 0.919488\n", + " 203 1.779e+00 2.384e+06 3.220e-01 -- 3.448e+02 -- -0.623769 -0.363423 -1.45119 -1.74795 -2.34943 -2.73914 -3.46121 -2.9345 0.109515 0.15775 0.20193 0.0491329 0.147982 0.919486\n", + " 205 1.694e+00 2.724e+06 2.804e-01 -- 3.451e+02 -- -0.734739 -0.363423 -1.45119 -1.74795 -2.34943 -2.73915 -3.46121 -2.8405 0.109525 0.157746 0.201967 0.0491169 0.147929 0.919485\n", + " 207 1.635e+00 3.084e+06 2.445e-01 -- 3.453e+02 -- -0.85919 -0.363423 -1.45119 -1.74795 -2.34943 -2.73915 -3.46121 -2.70669 0.109533 0.157742 0.201998 0.0491085 0.147887 0.919483\n", + " 209 1.600e+00 3.438e+06 2.122e-01 -- 3.455e+02 -- -0.999683 -0.363423 -1.45119 -1.74795 -2.34943 -2.73915 -3.46121 -2.50076 0.109539 0.157739 0.202024 0.0491084 0.147855 0.919482\n", + " 211 2.671e+00 3.755e+06 1.835e-01 -- 3.457e+02 -- -1.15968 -0.363423 -1.45119 -1.74795 -2.34943 -2.73915 -3.46121 -2.16708 0.109544 0.157737 0.202047 0.0491139 0.147832 0.919481\n", + " 213 5.748e+00 3.882e+06 1.403e-01 -- 3.459e+02 -- -1.29788 -0.363423 -1.45119 -1.74795 -2.34944 -2.73915 -3.46121 -1.58825 0.109548 0.157736 0.202067 0.0491226 0.147813 0.91948\n", + " 215 6.833e+00 3.560e+06 8.682e-02 -- 3.459e+02 -- -1.27817 -0.363423 -1.45119 -1.74795 -2.34944 -2.73916 -3.46121 -0.6754 0.109551 0.157734 0.202086 0.0491335 0.147798 0.919479\n", + " 217 2.427e+00 3.223e+06 1.398e-01 -- 3.461e+02 -- -0.980173 -0.363423 -1.45119 -1.74795 -2.34944 -2.73916 -3.46121 -0.213922 0.109556 0.157733 0.202103 0.0491447 0.14778 0.919478\n", + " 219 1.487e+00 3.453e+06 2.184e-01 -- 3.463e+02 -- -0.786827 -0.363422 -1.45119 -1.74795 -2.34944 -2.73916 -3.46121 -0.162012 0.109559 0.157732 0.202119 0.0491691 0.147769 0.919477\n", + " 221 1.150e+00 3.793e+06 2.018e-01 -- 3.465e+02 -- -0.669794 -0.363422 -1.45119 -1.74795 -2.34944 -2.73916 -3.46121 -0.139676 0.109561 0.157732 0.202129 0.0492111 0.147775 0.919475\n", + " 223 1.019e+00 4.201e+06 1.765e-01 -- 3.467e+02 -- -0.592793 -0.363422 -1.45119 -1.74795 -2.34945 -2.73916 -3.46121 -0.12627 0.109561 0.157733 0.202135 0.0492592 0.147788 0.919474\n", + " 225 8.538e-01 4.676e+06 1.677e-01 -- 3.468e+02 -- -0.532414 -0.363421 -1.45119 -1.74795 -2.34945 -2.73915 -3.46121 -0.119629 0.10956 0.157734 0.202139 0.0493085 0.147804 0.919473\n", + " 227 8.801e-01 5.212e+06 1.505e-01 -- 3.470e+02 -- -0.486958 -0.363421 -1.45119 -1.74795 -2.34945 -2.73915 -3.46121 -0.114455 0.10956 0.157736 0.202142 0.0493586 0.147824 0.919472\n", + " 229 6.974e-01 5.828e+06 1.558e-01 -- 3.472e+02 -- -0.444102 -0.36342 -1.45119 -1.74795 -2.34946 -2.73915 -3.46121 -0.111906 0.109558 0.157737 0.202144 0.0494072 0.147843 0.919471\n", + " 231 7.119e-01 6.497e+06 1.325e-01 -- 3.473e+02 -- -0.41313 -0.36342 -1.45119 -1.74795 -2.34946 -2.73915 -3.46121 -0.105843 0.109556 0.157739 0.202144 0.0494579 0.147868 0.91947\n", + " 233 5.442e-01 7.244e+06 1.335e-01 -- 3.474e+02 -- -0.383719 -0.36342 -1.45119 -1.74795 -2.34946 -2.73915 -3.46121 -0.0984818 0.109554 0.157741 0.202145 0.0495033 0.147889 0.919469\n", + " 235 1.131e+00 8.076e+06 1.137e-01 -- 3.475e+02 -- -0.362839 -0.363419 -1.45119 -1.74795 -2.34947 -2.73915 -3.46121 -0.0938439 0.109552 0.157743 0.202143 0.0495491 0.147913 0.919469\n", + " 237 3.653e-01 8.999e+06 1.361e-01 -- 3.477e+02 -- -0.335364 -0.363419 -1.45119 -1.74795 -2.34947 -2.73915 -3.46121 -0.0832267 0.10955 0.157744 0.202146 0.0495886 0.147933 0.919468\n", + " 239 3.363e-01 1.004e+07 9.350e-02 -- 3.478e+02 -- -0.323113 -0.363418 -1.45119 -1.74795 -2.34947 -2.73914 -3.46121 -0.0832685 0.109547 0.157747 0.202141 0.0496366 0.147961 0.919467\n", + " 241 1.247e+00 1.121e+07 8.989e-02 -- 3.478e+02 -- -0.312392 -0.363418 -1.45119 -1.74795 -2.34947 -2.73914 -3.46121 -0.0860685 0.109546 0.157748 0.202138 0.0496699 0.147977 0.919467\n", + " 243 4.749e-01 1.245e+07 1.061e-01 -- 3.480e+02 -- -0.295953 -0.363418 -1.45119 -1.74795 -2.34948 -2.73914 -3.46121 -0.0753345 0.109544 0.157749 0.202141 0.049698 0.147993 0.919466\n", + " 245 6.925e-01 1.386e+07 8.248e-02 -- 3.480e+02 -- -0.287411 -0.363418 -1.45119 -1.74795 -2.34948 -2.73914 -3.46121 -0.0717567 0.109542 0.157751 0.202138 0.0497334 0.148014 0.919466\n", + " 247 2.983e+00 1.541e+07 5.421e-02 -- 3.481e+02 -- -0.28685 -0.363417 -1.45119 -1.74795 -2.34948 -2.73914 -3.46121 -0.0667875 0.109541 0.157752 0.202135 0.0497581 0.148028 0.919466\n", + " 248 1.312e+00 2.319e+07 5.164e-01 -- 3.486e+02 -- -0.185228 -0.363417 -1.45119 -1.74795 -2.34947 -2.73914 -3.46121 0.132449 0.109541 0.157752 0.202202 0.0498364 0.148059 0.919464\n", + " 250 1.351e+00 2.829e+07 1.424e-01 -- 3.488e+02 -- -0.170989 -0.363415 -1.45119 -1.74795 -2.34949 -2.73913 -3.46121 0.149822 0.109523 0.157762 0.202181 0.0499928 0.148185 0.919463\n", + " 251 4.563e+00 1.588e+08 1.471e+00 -- 3.473e+02 -- -0.220484 -0.36341 -1.4512 -1.74795 -2.34955 -2.7391 -3.46121 -0.0525833 0.109471 0.157803 0.201972 0.0506843 0.148669 0.919457\n", + " 253 4.464e+00 5.169e+07 1.638e+00 -- 3.489e+02 -- -0.228996 -0.363414 -1.45119 -1.74795 -2.34951 -2.73913 -3.46121 -0.0765774 0.109532 0.157768 0.202077 0.050136 0.148227 0.91946\n", + " 254 1.174e+00 1.462e+08 2.584e+00 -- 3.463e+02 -- -0.165269 -0.363424 -1.45119 -1.74795 -2.3494 -2.73918 -3.46121 0.26524 0.109648 0.157698 0.202403 0.0490414 0.147404 0.919469\n", + " 256 3.140e-01 3.210e+07 2.338e+00 -- 3.487e+02 -- -0.184679 -0.363416 -1.45119 -1.74795 -2.34947 -2.73913 -3.46121 0.254255 0.109557 0.157753 0.202214 0.0499032 0.148084 0.919464\n", + " 258 1.020e+00 3.780e+07 5.449e-02 -- 3.487e+02 -- -0.189584 -0.363416 -1.45119 -1.74795 -2.34948 -2.73913 -3.46121 0.262239 0.109548 0.157759 0.202195 0.049992 0.148154 0.919463\n", + " 259 9.690e-01 1.442e+07 3.727e-01 -- 3.484e+02 -- -0.0646749 -0.363416 -1.45119 -1.74796 -2.34945 -2.73913 -3.46121 0.529609 0.109531 0.157755 0.202322 0.0499443 0.148158 0.919466\n", + " 261 5.802e-01 2.148e+07 2.027e-01 -- 3.486e+02 -- -0.0584081 -0.363414 -1.45119 -1.74796 -2.34947 -2.73911 -3.46121 0.526988 0.109501 0.157773 0.202272 0.0502225 0.148376 0.919464\n", + " 263 2.171e+00 2.442e+07 8.307e-02 -- 3.486e+02 -- -0.0550193 -0.363413 -1.45119 -1.74796 -2.34948 -2.73911 -3.46121 0.523608 0.109493 0.157777 0.202259 0.0503048 0.148438 0.919463\n", + " 265 1.244e+00 2.695e+07 7.115e-02 -- 3.487e+02 -- -0.0430724 -0.363413 -1.45119 -1.74796 -2.34948 -2.73911 -3.46121 0.533686 0.109488 0.157779 0.202264 0.0503385 0.148465 0.919463\n", + " 267 5.411e-01 3.028e+07 6.665e-02 -- 3.488e+02 -- -0.037713 -0.363412 -1.45119 -1.74796 -2.34948 -2.7391 -3.46121 0.538519 0.109482 0.157783 0.202258 0.0503975 0.148511 0.919463\n", + " 269 5.807e-01 3.469e+07 1.242e-01 -- 3.489e+02 -- -0.0397535 -0.363412 -1.45119 -1.74796 -2.34949 -2.7391 -3.46121 0.509938 0.109479 0.157785 0.202246 0.0504397 0.148535 0.919462\n", + " 271 9.263e-01 3.892e+07 8.342e-02 -- 3.490e+02 -- -0.0420622 -0.363411 -1.45119 -1.74796 -2.34949 -2.7391 -3.46121 0.49377 0.109477 0.157787 0.202237 0.0504718 0.148554 0.919461\n", + " 273 1.045e+00 4.354e+07 8.479e-02 -- 3.491e+02 -- -0.0381661 -0.363411 -1.45119 -1.74796 -2.3495 -2.7391 -3.46121 0.48762 0.109476 0.157788 0.202238 0.0504836 0.14856 0.919461\n", + " 275 6.628e-01 4.858e+07 4.584e-02 -- 3.491e+02 -- -0.0341795 -0.363411 -1.45119 -1.74796 -2.3495 -2.7391 -3.46121 0.498943 0.109473 0.15779 0.202236 0.0505139 0.148586 0.919461\n", + " 277 3.138e+00 5.585e+07 1.196e-01 -- 3.492e+02 -- -0.031914 -0.363411 -1.45119 -1.74796 -2.3495 -2.7391 -3.46121 0.48038 0.10947 0.157791 0.202233 0.0505361 0.148596 0.91946\n", + " 278 1.106e+00 3.818e+07 1.331e+00 -- 3.479e+02 -- -0.132049 -0.363407 -1.4512 -1.74796 -2.34957 -2.73908 -3.46121 0.209795 0.109439 0.157817 0.202029 0.0510012 0.148874 0.919453\n", + " 280 3.299e-01 3.189e+07 1.021e+00 -- 3.489e+02 -- -0.138042 -0.363412 -1.45119 -1.74795 -2.34952 -2.73911 -3.46121 0.186601 0.109503 0.15778 0.202137 0.0504273 0.148408 0.919456\n", + " 282 1.357e+00 3.797e+07 6.512e-02 -- 3.490e+02 -- -0.142597 -0.363412 -1.45119 -1.74795 -2.34951 -2.73912 -3.46121 0.187456 0.109515 0.157773 0.202156 0.0503221 0.148322 0.919457\n", + " 284 3.374e-01 4.168e+07 2.378e-02 -- 3.490e+02 -- -0.16194 -0.363413 -1.45119 -1.74795 -2.34951 -2.73912 -3.46121 0.171472 0.109522 0.157771 0.202147 0.0502934 0.148293 0.919457\n", + " 286 1.284e+00 4.784e+07 8.469e-02 -- 3.491e+02 -- -0.157165 -0.363413 -1.45119 -1.74795 -2.34951 -2.73912 -3.46121 0.177257 0.109529 0.157766 0.202164 0.0502281 0.14824 0.919457\n", + " 287 5.863e+00 2.746e+07 7.569e-01 -- 3.483e+02 -- -0.0125226 -0.363413 -1.45119 -1.74795 -2.34949 -2.73912 -3.46121 0.404916 0.109511 0.157767 0.202299 0.0502314 0.148274 0.919458\n", + " 289 1.630e+00 3.365e+07 9.013e-01 -- 3.492e+02 -- -0.0198645 -0.363408 -1.4512 -1.74796 -2.34953 -2.73909 -3.46121 0.404159 0.109451 0.157802 0.202181 0.0507898 0.148716 0.919454\n", + " 291 6.748e+00 3.775e+07 7.961e-02 -- 3.493e+02 -- -0.0166256 -0.363408 -1.4512 -1.74796 -2.34954 -2.73909 -3.46121 0.400832 0.109447 0.157805 0.202176 0.0508312 0.148746 0.919454\n", + " 293 2.863e+00 4.073e+07 4.445e-02 -- 3.493e+02 -- -0.00540687 -0.363408 -1.4512 -1.74796 -2.34953 -2.73909 -3.46121 0.422946 0.109441 0.157807 0.202184 0.050862 0.148775 0.919454\n", + " 295 1.589e+01 4.688e+07 9.607e-02 -- 3.494e+02 -- -0.00385862 -0.363407 -1.4512 -1.74796 -2.34954 -2.73908 -3.46121 0.414394 0.109434 0.157811 0.202171 0.0509315 0.148827 0.919453\n", + " 297 9.102e+01 5.176e+07 5.621e-02 -- 3.495e+02 -- 0.00227262 -0.363407 -1.4512 -1.74796 -2.34954 -2.73908 -3.46121 0.424492 0.109429 0.157813 0.202172 0.0509648 0.148855 0.919453\n", + " 298 1.218e+00 1.223e+06 2.212e+00 -- 3.473e+02 -- -0.204579 -0.363401 -1.4512 -1.74795 -2.34966 -2.73907 -3.4612 -0.153771 0.109419 0.157847 0.201763 0.0516137 0.149147 0.91944\n", + " 300 7.366e-01 5.541e+06 7.566e-01 -- 3.480e+02 -- -0.179665 -0.363411 -1.4512 -1.74794 -2.34957 -2.73913 -3.46121 -0.141308 0.109542 0.157774 0.202006 0.0505035 0.148242 0.919447\n", + " 302 6.563e-01 6.257e+06 1.223e-01 -- 3.481e+02 -- -0.166432 -0.363412 -1.4512 -1.74794 -2.34956 -2.73913 -3.46121 -0.136129 0.109556 0.157767 0.202034 0.0504092 0.14815 0.919447\n", + " 304 6.204e-01 6.962e+06 1.049e-01 -- 3.482e+02 -- -0.155509 -0.363411 -1.4512 -1.74794 -2.34956 -2.73913 -3.46121 -0.128246 0.109555 0.157768 0.202033 0.0504491 0.148168 0.919446\n", + " 306 6.510e-01 7.764e+06 1.013e-01 -- 3.483e+02 -- -0.14586 -0.363411 -1.4512 -1.74794 -2.34956 -2.73913 -3.46121 -0.123823 0.109552 0.157771 0.202028 0.050505 0.1482 0.919446\n", + " 308 3.481e-01 8.654e+06 1.011e-01 -- 3.484e+02 -- -0.136364 -0.36341 -1.4512 -1.74794 -2.34957 -2.73913 -3.46121 -0.117368 0.109549 0.157773 0.202024 0.0505599 0.148233 0.919445\n", + " 310 4.054e-01 9.652e+06 7.879e-02 -- 3.485e+02 -- -0.131618 -0.36341 -1.4512 -1.74794 -2.34957 -2.73912 -3.46121 -0.117155 0.109545 0.157776 0.202015 0.0506154 0.148266 0.919444\n", + " 312 5.584e-01 1.075e+07 8.122e-02 -- 3.486e+02 -- -0.126283 -0.36341 -1.4512 -1.74794 -2.34957 -2.73912 -3.46121 -0.115499 0.109544 0.157777 0.202011 0.0506528 0.148287 0.919444\n", + " 314 4.952e-01 1.200e+07 9.239e-02 -- 3.487e+02 -- -0.119231 -0.363409 -1.4512 -1.74794 -2.34958 -2.73912 -3.46121 -0.115658 0.109542 0.157779 0.202007 0.0506895 0.148307 0.919443\n", + " 316 5.287e-01 1.338e+07 8.527e-02 -- 3.488e+02 -- -0.113326 -0.363409 -1.4512 -1.74794 -2.34958 -2.73912 -3.46121 -0.111769 0.109538 0.157781 0.202002 0.0507334 0.148335 0.919443\n", + " 318 8.017e-01 1.491e+07 8.643e-02 -- 3.489e+02 -- -0.107335 -0.363409 -1.4512 -1.74794 -2.34958 -2.73912 -3.46121 -0.108356 0.109536 0.157783 0.201998 0.0507734 0.14836 0.919442\n", + " 319 2.149e+01 1.344e+08 1.285e+00 -- 3.502e+02 -- -0.0771257 -0.363406 -1.4512 -1.74794 -2.3496 -2.7391 -3.46121 -0.0214826 0.109506 0.157804 0.20197 0.0511631 0.148633 0.91944\n", + " 322 5.016e+01 1.083e+08 7.733e-02 -- 3.501e+02 -- -0.0812549 -0.363406 -1.4512 -1.74794 -2.3496 -2.7391 -3.46121 -0.0261001 0.109507 0.157803 0.201968 0.0511544 0.148625 0.91944\n", + " 325 1.441e+01 1.887e+08 2.246e-01 -- 3.503e+02 -- -0.0735921 -0.363406 -1.4512 -1.74794 -2.3496 -2.7391 -3.46121 -0.0130075 0.109508 0.157802 0.201979 0.0511409 0.148617 0.91944\n", + " 328 7.901e+01 1.536e+08 9.576e-02 -- 3.502e+02 -- -0.0797361 -0.363406 -1.4512 -1.74794 -2.3496 -2.7391 -3.46121 -0.0148821 0.109509 0.157802 0.201976 0.0511349 0.148612 0.91944\n", + " 331 1.082e+01 1.784e+08 6.179e-02 -- 3.503e+02 -- -0.0795032 -0.363406 -1.4512 -1.74794 -2.3496 -2.7391 -3.46121 -0.0266401 0.10951 0.157801 0.201974 0.0511287 0.148603 0.91944\n", + " 333 4.675e+01 2.786e+08 1.995e-01 -- 3.505e+02 -- -0.0809661 -0.363407 -1.4512 -1.74794 -2.3496 -2.7391 -3.46121 -0.0554529 0.109514 0.157797 0.201978 0.0510476 0.148535 0.91944\n", + " 334 5.032e+00 2.430e+05 6.301e+00 -- 3.442e+02 -- -0.985862 -0.363409 -1.4512 -1.74792 -2.3498 -2.73912 -3.4612 -2.64765 0.109732 0.157802 0.200524 0.0514617 0.148271 0.919427\n", + " 336 1.413e+01 2.950e+05 3.625e-01 -- 3.445e+02 -- -1.28586 -0.363418 -1.4512 -1.74791 -2.3497 -2.73918 -3.46121 -1.84183 0.109927 0.157714 0.2008 0.0506701 0.147252 0.919428\n", + " 338 4.005e+01 3.176e+05 1.360e-01 -- 3.447e+02 -- -1.46152 -0.363418 -1.4512 -1.74791 -2.34969 -2.73919 -3.46121 0.760727 0.109999 0.157695 0.200857 0.0507901 0.147055 0.919422\n", + " 340 6.766e+00 4.153e+05 6.549e-02 -- 3.446e+02 -- -1.16152 -0.363417 -1.4512 -1.74791 -2.34969 -2.7392 -3.46121 -2.28614 0.110051 0.157686 0.200882 0.0510196 0.146972 0.919414\n", + " 342 2.742e+01 4.570e+05 2.707e-01 -- 3.449e+02 -- -1.46152 -0.363415 -1.4512 -1.7479 -2.3497 -2.73919 -3.46121 -0.739337 0.110077 0.157687 0.200893 0.0513039 0.146999 0.919408\n", + " 344 1.369e+01 4.823e+05 3.346e-02 -- 3.448e+02 -- -1.16152 -0.363414 -1.4512 -1.7479 -2.3497 -2.73919 -3.46121 1.28787 0.110109 0.157683 0.200916 0.0514964 0.146967 0.919402\n", + " 346 9.597e+00 6.439e+05 3.666e-01 -- 3.452e+02 -- -1.08009 -0.363413 -1.45119 -1.7479 -2.3497 -2.73919 -3.46121 -0.475334 0.110141 0.157677 0.200944 0.051654 0.146914 0.919397\n", + " 348 3.843e+01 6.683e+05 2.895e-01 -- 3.455e+02 -- -0.780091 -0.363412 -1.45119 -1.7479 -2.34971 -2.73919 -3.46121 -0.0191557 0.110155 0.157679 0.200954 0.051853 0.146942 0.919392\n", + " 350 2.431e+00 7.534e+05 4.815e-01 -- 3.460e+02 -- -0.516668 -0.363411 -1.45119 -1.7479 -2.34971 -2.73919 -3.46121 -0.0927694 0.110174 0.157677 0.20097 0.0519999 0.146932 0.919388\n", + " 352 2.128e+00 8.462e+05 3.602e-01 -- 3.463e+02 -- -0.391053 -0.363409 -1.4512 -1.7479 -2.34972 -2.73919 -3.46121 -0.102471 0.110182 0.15768 0.200966 0.0522036 0.146984 0.919383\n", + " 354 1.958e+00 9.507e+05 3.104e-01 -- 3.466e+02 -- -0.307832 -0.363408 -1.4512 -1.7479 -2.34973 -2.73918 -3.46121 -0.105727 0.110186 0.157686 0.200953 0.0524185 0.147058 0.919379\n", + " 356 1.882e+00 1.068e+06 2.733e-01 -- 3.469e+02 -- -0.247561 -0.363406 -1.4512 -1.7479 -2.34974 -2.73917 -3.46121 -0.106268 0.110186 0.157693 0.200935 0.0526364 0.147146 0.919376\n", + " 358 1.839e+00 1.199e+06 2.470e-01 -- 3.472e+02 -- -0.200962 -0.363405 -1.4512 -1.7479 -2.34976 -2.73916 -3.46121 -0.105856 0.110183 0.1577 0.200914 0.0528519 0.147243 0.919372\n", + " 360 1.795e+00 1.346e+06 2.241e-01 -- 3.474e+02 -- -0.164003 -0.363403 -1.4512 -1.7479 -2.34977 -2.73916 -3.46121 -0.105114 0.110179 0.157708 0.20089 0.053063 0.147344 0.919369\n", + " 362 1.817e+00 1.511e+06 2.017e-01 -- 3.476e+02 -- -0.134568 -0.363402 -1.4512 -1.7479 -2.34978 -2.73915 -3.46121 -0.103975 0.110173 0.157716 0.200865 0.053267 0.147448 0.919366\n", + " 364 1.856e+00 1.694e+06 1.860e-01 -- 3.478e+02 -- -0.110116 -0.3634 -1.4512 -1.7479 -2.34979 -2.73914 -3.46121 -0.103075 0.110167 0.157724 0.200841 0.0534605 0.14755 0.919364\n", + " 366 1.880e+00 1.899e+06 1.714e-01 -- 3.479e+02 -- -0.089674 -0.363399 -1.4512 -1.7479 -2.3498 -2.73913 -3.46121 -0.101649 0.110159 0.157732 0.200816 0.0536444 0.14765 0.919361\n", + " 368 1.968e+00 2.128e+06 1.562e-01 -- 3.481e+02 -- -0.0728171 -0.363398 -1.4512 -1.74789 -2.34981 -2.73912 -3.46121 -0.100506 0.110152 0.157739 0.200792 0.053818 0.147747 0.919359\n", + " 370 2.101e+00 2.382e+06 1.448e-01 -- 3.483e+02 -- -0.0584857 -0.363396 -1.4512 -1.74789 -2.34982 -2.73912 -3.46121 -0.099281 0.110145 0.157747 0.200769 0.0539785 0.147839 0.919357\n", + " 372 2.314e+00 2.665e+06 1.350e-01 -- 3.484e+02 -- -0.0461955 -0.363395 -1.4512 -1.74789 -2.34983 -2.73911 -3.46121 -0.0980682 0.110138 0.157753 0.200747 0.0541276 0.147925 0.919355\n", + " 374 2.560e+00 2.981e+06 1.272e-01 -- 3.485e+02 -- -0.0355074 -0.363394 -1.4512 -1.74789 -2.34984 -2.7391 -3.46121 -0.0975489 0.110131 0.15776 0.200727 0.0542661 0.148006 0.919354\n", + " 376 2.984e+00 3.332e+06 1.180e-01 -- 3.486e+02 -- -0.0264176 -0.363393 -1.4512 -1.74789 -2.34985 -2.7391 -3.46121 -0.0966277 0.110124 0.157766 0.200707 0.0543948 0.148083 0.919352\n", + " 378 3.745e+00 3.724e+06 1.111e-01 -- 3.487e+02 -- -0.0185355 -0.363392 -1.4512 -1.74789 -2.34985 -2.73909 -3.46121 -0.0961985 0.110118 0.157771 0.200689 0.0545125 0.148154 0.919351\n", + " 380 5.245e+00 4.158e+06 1.050e-01 -- 3.488e+02 -- -0.0115937 -0.363392 -1.4512 -1.74789 -2.34986 -2.73909 -3.46121 -0.0949656 0.110112 0.157777 0.200673 0.0546199 0.148219 0.919349\n", + " 382 9.697e+00 4.642e+06 9.994e-02 -- 3.489e+02 -- -0.00551311 -0.363391 -1.4512 -1.74789 -2.34987 -2.73908 -3.46121 -0.0945279 0.110106 0.157781 0.200657 0.0547192 0.148279 0.919348\n", + " 384 2.565e+02 5.180e+06 9.454e-02 -- 3.490e+02 -- -0.000167303 -0.36339 -1.4512 -1.74789 -2.34987 -2.73908 -3.46121 -0.0931954 0.110101 0.157786 0.200644 0.0548099 0.148335 0.919347\n", + " 386 9.880e+00 5.780e+06 8.805e-02 -- 3.491e+02 -- 0.00412335 -0.36339 -1.4512 -1.74789 -2.34988 -2.73907 -3.46121 -0.093928 0.110096 0.15779 0.20063 0.0548933 0.148386 0.919346\n", + " 388 4.224e+00 6.439e+06 8.467e-02 -- 3.492e+02 -- 0.00819704 -0.363389 -1.4512 -1.74789 -2.34988 -2.73907 -3.46121 -0.0916356 0.110092 0.157793 0.20062 0.054964 0.14843 0.919346\n", + " 390 2.907e+00 7.178e+06 8.143e-02 -- 3.493e+02 -- 0.0116595 -0.363389 -1.4512 -1.74789 -2.34988 -2.73907 -3.46121 -0.0913694 0.110088 0.157797 0.200609 0.0550314 0.148472 0.919345\n", + " 392 1.516e+00 7.995e+06 8.031e-02 -- 3.494e+02 -- 0.0150489 -0.363388 -1.4512 -1.74789 -2.34989 -2.73907 -3.46121 -0.0900351 0.110084 0.1578 0.2006 0.0550912 0.148509 0.919344\n", + " 394 2.049e+00 8.907e+06 7.170e-02 -- 3.494e+02 -- 0.0173306 -0.363388 -1.4512 -1.74789 -2.34989 -2.73906 -3.46121 -0.0887767 0.11008 0.157802 0.200591 0.0551488 0.148546 0.919344\n", + " 396 8.714e-01 9.911e+06 8.151e-02 -- 3.495e+02 -- 0.020882 -0.363387 -1.4512 -1.74789 -2.34989 -2.73906 -3.46121 -0.0870916 0.110078 0.157805 0.200585 0.0551934 0.148573 0.919343\n", + " 398 9.679e-01 1.107e+07 7.197e-02 -- 3.496e+02 -- 0.0227018 -0.363387 -1.4512 -1.74789 -2.3499 -2.73906 -3.46121 -0.0909551 0.110074 0.157808 0.200573 0.0552522 0.14861 0.919343\n", + " 400 1.270e+00 1.232e+07 7.128e-02 -- 3.497e+02 -- 0.024899 -0.363387 -1.4512 -1.74789 -2.3499 -2.73906 -3.46121 -0.0902504 0.110072 0.15781 0.200567 0.0552904 0.148634 0.919342\n", + " 402 6.361e-01 1.367e+07 7.507e-02 -- 3.497e+02 -- 0.0280603 -0.363386 -1.4512 -1.74789 -2.3499 -2.73905 -3.46121 -0.0837997 0.110068 0.157811 0.200565 0.0553281 0.14866 0.919342\n", + " 404 8.794e-01 1.528e+07 6.816e-02 -- 3.498e+02 -- 0.0292014 -0.363386 -1.4512 -1.74789 -2.34991 -2.73905 -3.46121 -0.0891302 0.110065 0.157814 0.200552 0.0553829 0.148695 0.919341\n", + " 406 3.784e-01 1.697e+07 7.227e-02 -- 3.499e+02 -- 0.0317693 -0.363386 -1.4512 -1.74789 -2.34991 -2.73905 -3.46121 -0.0855337 0.110063 0.157816 0.200551 0.0554098 0.148713 0.919341\n", + " 408 8.584e-01 1.892e+07 6.373e-02 -- 3.500e+02 -- 0.0329715 -0.363385 -1.4512 -1.74789 -2.34991 -2.73905 -3.46121 -0.0840924 0.110059 0.157818 0.200544 0.0554524 0.148743 0.919341\n", + " 410 2.281e-01 2.102e+07 7.585e-02 -- 3.500e+02 -- 0.0358016 -0.363385 -1.4512 -1.74789 -2.34991 -2.73905 -3.46121 -0.0818116 0.110057 0.15782 0.200541 0.0554798 0.148761 0.91934\n", + " 412 2.994e-01 2.352e+07 5.761e-02 -- 3.501e+02 -- 0.0358705 -0.363385 -1.4512 -1.74789 -2.34991 -2.73904 -3.46121 -0.0836779 0.110053 0.157822 0.200531 0.0555267 0.148795 0.91934\n", + " 414 7.158e-01 2.615e+07 5.648e-02 -- 3.501e+02 -- 0.0361137 -0.363385 -1.4512 -1.74789 -2.34991 -2.73904 -3.46121 -0.086183 0.110053 0.157823 0.200528 0.0555391 0.148801 0.91934\n", + " 415 4.481e+00 7.672e+07 5.225e-01 -- 3.507e+02 -- 0.027996 -0.363384 -1.4512 -1.74789 -2.34993 -2.73904 -3.46121 -0.147877 0.110054 0.157826 0.200473 0.0556367 0.148828 0.919338\n", + " 418 2.687e+00 9.990e+07 3.995e-02 -- 3.506e+02 -- 0.0267416 -0.363384 -1.4512 -1.74789 -2.34993 -2.73904 -3.46121 -0.148469 0.110055 0.157825 0.200475 0.0556202 0.148816 0.919338\n", + " 421 9.609e+00 8.090e+07 3.507e-02 -- 3.507e+02 -- 0.026023 -0.363384 -1.4512 -1.74789 -2.34992 -2.73904 -3.46121 -0.144961 0.110057 0.157824 0.200481 0.0556034 0.148804 0.919338\n", + " 423 8.329e+00 2.967e+08 1.085e+00 -- 3.496e+02 -- 0.0510295 -0.363386 -1.4512 -1.74789 -2.3499 -2.73905 -3.46121 -0.0496008 0.110067 0.157813 0.200579 0.0554229 0.148684 0.91934\n", + " 425 9.815e-01 4.106e+07 1.153e+00 -- 3.507e+02 -- 0.0367336 -0.363383 -1.4512 -1.74789 -2.34993 -2.73903 -3.4612 -0.0909145 0.110038 0.157835 0.200479 0.055778 0.148952 0.919338\n", + " 427 1.774e+00 2.140e+08 4.961e-01 -- 3.512e+02 -- 0.03879 -0.363384 -1.4512 -1.74789 -2.34992 -2.73904 -3.46121 -0.0998381 0.110055 0.157825 0.200503 0.0556283 0.14883 0.919338\n", + " 428 1.829e+01 3.065e+08 7.322e-02 -- 3.513e+02 -- 0.0449577 -0.363384 -1.4512 -1.74789 -2.3499 -2.73904 -3.46121 0.0772391 0.11003 0.157827 0.200623 0.0555725 0.148873 0.919341\n", + " 430 2.067e+00 2.772e+09 7.286e+00 -- 3.440e+02 -- 0.0871111 -0.363384 -1.4512 -1.74789 -2.34988 -2.73904 -3.46121 0.218532 0.110015 0.157828 0.200722 0.0555747 0.148913 0.919342\n", + " 433 9.668e-01 1.632e+09 2.268e+00 -- 3.463e+02 -- 0.0853101 -0.363384 -1.4512 -1.74789 -2.34989 -2.73903 -3.46121 0.215536 0.110009 0.157832 0.200707 0.0556372 0.148962 0.919342\n", + " 435 2.233e+00 5.145e+08 3.823e+00 -- 3.501e+02 -- 0.0895547 -0.363379 -1.4512 -1.74789 -2.34993 -2.739 -3.4612 0.194698 0.109954 0.157866 0.200601 0.0561718 0.149374 0.919338\n", + " 437 8.471e-01 1.114e+08 8.833e-01 -- 3.510e+02 -- 0.0957779 -0.363377 -1.4512 -1.74789 -2.34994 -2.73899 -3.4612 0.238181 0.109929 0.15788 0.200589 0.0563701 0.149554 0.919338\n", + " 440 2.255e+00 1.662e+08 1.147e-01 -- 3.511e+02 -- 0.0949666 -0.363377 -1.4512 -1.7479 -2.34994 -2.73899 -3.4612 0.239067 0.109928 0.15788 0.200586 0.0563808 0.149563 0.919338\n", + " 443 2.186e+00 2.435e+08 1.498e-01 -- 3.512e+02 -- 0.0928252 -0.363377 -1.4512 -1.7479 -2.34994 -2.73899 -3.4612 0.239188 0.109928 0.157881 0.200582 0.0563891 0.14957 0.919338\n", + " 445 3.207e+00 4.072e+07 7.106e-01 -- 3.505e+02 -- 0.104422 -0.363377 -1.4512 -1.7479 -2.34993 -2.73899 -3.4612 0.291471 0.109923 0.157882 0.200614 0.0563947 0.149592 0.919339\n", + " 446 3.453e+00 3.733e+06 4.698e+00 -- 3.458e+02 -- -0.0763908 -0.363361 -1.45121 -1.74788 -2.35021 -2.73891 -3.46119 -0.643341 0.10981 0.158001 0.199689 0.0585065 0.150869 0.919317\n", + " 448 2.985e+00 1.422e+06 1.924e+00 -- 3.478e+02 -- -0.0500124 -0.36339 -1.4512 -1.74786 -2.34993 -2.7391 -3.46121 -0.583889 0.110183 0.157776 0.200426 0.0551411 0.148099 0.919337\n", + " 450 3.389e+00 1.653e+06 2.223e-01 -- 3.480e+02 -- -0.0350817 -0.363393 -1.4512 -1.74786 -2.34989 -2.73912 -3.46121 -0.545114 0.110235 0.157748 0.200523 0.0547811 0.147759 0.919338\n", + " 452 4.450e+00 1.833e+06 1.695e-01 -- 3.482e+02 -- -0.0231916 -0.363393 -1.4512 -1.74786 -2.34989 -2.73912 -3.46121 -0.515006 0.110238 0.15775 0.200522 0.0548683 0.147787 0.919337\n", + " 454 7.001e+00 2.038e+06 1.566e-01 -- 3.483e+02 -- -0.0128712 -0.363392 -1.4512 -1.74786 -2.3499 -2.73911 -3.46121 -0.489289 0.110232 0.157756 0.200508 0.0550141 0.147866 0.919335\n", + " 456 2.133e+01 2.270e+06 1.442e-01 -- 3.485e+02 -- -0.00386017 -0.36339 -1.4512 -1.74786 -2.34991 -2.7391 -3.46121 -0.467919 0.110226 0.157762 0.200491 0.0551616 0.14795 0.919333\n", + " 458 1.676e+01 2.528e+06 1.371e-01 -- 3.486e+02 -- 0.00437402 -0.363389 -1.4512 -1.74786 -2.34991 -2.7391 -3.46121 -0.449042 0.11022 0.157769 0.200474 0.0553006 0.148032 0.919331\n", + " 460 5.489e+00 2.814e+06 1.297e-01 -- 3.487e+02 -- 0.0117036 -0.363388 -1.4512 -1.74786 -2.34992 -2.73909 -3.46121 -0.431565 0.110213 0.157775 0.200459 0.0554329 0.148111 0.91933\n", + " 462 3.102e+00 3.136e+06 1.197e-01 -- 3.488e+02 -- 0.018128 -0.363387 -1.4512 -1.74786 -2.34993 -2.73908 -3.46121 -0.41801 0.110206 0.157781 0.200442 0.0555588 0.148188 0.919329\n", + " 464 2.085e+00 3.494e+06 1.125e-01 -- 3.490e+02 -- 0.0237512 -0.363387 -1.4512 -1.74786 -2.34993 -2.73908 -3.46121 -0.405779 0.1102 0.157786 0.200427 0.0556737 0.148258 0.919327\n", + " 466 1.578e+00 3.891e+06 1.061e-01 -- 3.491e+02 -- 0.0287038 -0.363386 -1.4512 -1.74786 -2.34994 -2.73907 -3.46121 -0.394935 0.110194 0.157791 0.200413 0.0557793 0.148323 0.919326\n", + " 468 1.215e+00 4.333e+06 1.019e-01 -- 3.492e+02 -- 0.0332333 -0.363385 -1.4512 -1.74786 -2.34994 -2.73907 -3.46121 -0.385103 0.110188 0.157796 0.2004 0.0558765 0.148384 0.919325\n", + " 470 8.728e-01 4.825e+06 9.728e-02 -- 3.493e+02 -- 0.0372705 -0.363384 -1.4512 -1.74786 -2.34995 -2.73906 -3.46121 -0.376014 0.110183 0.1578 0.200388 0.0559674 0.148442 0.919324\n", + " 472 7.791e-01 5.374e+06 8.909e-02 -- 3.494e+02 -- 0.0405235 -0.363384 -1.4512 -1.74786 -2.34995 -2.73906 -3.46121 -0.368803 0.110178 0.157805 0.200376 0.0560518 0.148496 0.919323\n", + " 474 6.891e-01 5.981e+06 8.746e-02 -- 3.494e+02 -- 0.0436808 -0.363383 -1.4512 -1.74786 -2.34996 -2.73906 -3.46121 -0.362446 0.110173 0.157808 0.200366 0.0561246 0.148541 0.919323\n", + " 476 5.921e-01 6.661e+06 8.541e-02 -- 3.495e+02 -- 0.0466909 -0.363383 -1.4512 -1.74786 -2.34996 -2.73905 -3.46121 -0.357513 0.110169 0.157812 0.200356 0.0561939 0.148585 0.919322\n", + " 478 3.144e-01 7.415e+06 8.354e-02 -- 3.496e+02 -- 0.0494557 -0.363382 -1.4512 -1.74786 -2.34996 -2.73905 -3.46121 -0.352194 0.110165 0.157815 0.200346 0.0562588 0.148627 0.919321\n", + " 480 4.451e-01 8.264e+06 7.056e-02 -- 3.497e+02 -- 0.0510106 -0.363382 -1.4512 -1.74786 -2.34997 -2.73905 -3.46121 -0.349714 0.110161 0.157818 0.200336 0.0563208 0.148667 0.919321\n", + " 482 2.834e-01 9.191e+06 7.707e-02 -- 3.498e+02 -- 0.0532813 -0.363381 -1.4512 -1.74786 -2.34997 -2.73905 -3.46121 -0.346633 0.110159 0.15782 0.20033 0.0563633 0.148692 0.91932\n", + " 484 3.966e-01 1.023e+07 7.072e-02 -- 3.498e+02 -- 0.0547914 -0.363381 -1.4512 -1.74786 -2.34997 -2.73904 -3.46121 -0.342721 0.110156 0.157823 0.200323 0.0564112 0.148724 0.91932\n", + " 486 2.514e-01 1.136e+07 7.734e-02 -- 3.499e+02 -- 0.0569644 -0.363381 -1.4512 -1.74786 -2.34997 -2.73904 -3.46121 -0.337696 0.110153 0.157824 0.200319 0.056451 0.148749 0.919319\n", + " 488 2.119e-01 1.266e+07 6.951e-02 -- 3.500e+02 -- 0.0583964 -0.36338 -1.4512 -1.74786 -2.34998 -2.73904 -3.46121 -0.335476 0.11015 0.157827 0.20031 0.0565002 0.148783 0.919319\n", + " 490 1.907e-01 1.409e+07 6.640e-02 -- 3.500e+02 -- 0.0596337 -0.36338 -1.4512 -1.74786 -2.34998 -2.73904 -3.46121 -0.33443 0.110147 0.157829 0.200304 0.0565377 0.148807 0.919318\n", + " 492 2.838e-01 1.566e+07 6.668e-02 -- 3.501e+02 -- 0.060771 -0.36338 -1.4512 -1.74786 -2.34998 -2.73904 -3.46121 -0.330323 0.110145 0.157831 0.200301 0.0565667 0.148826 0.919318\n", + " 494 2.169e-01 1.737e+07 7.289e-02 -- 3.502e+02 -- 0.062496 -0.36338 -1.4512 -1.74786 -2.34998 -2.73903 -3.46121 -0.324585 0.110142 0.157832 0.200299 0.056597 0.148846 0.919318\n", + " 496 1.775e-01 1.940e+07 6.791e-02 -- 3.502e+02 -- 0.0638514 -0.363379 -1.4512 -1.74786 -2.34998 -2.73903 -3.46121 -0.32577 0.11014 0.157835 0.200289 0.0566408 0.148875 0.919317\n", + " 497 4.848e-01 6.026e+09 5.483e+00 -- 3.448e+02 -- 0.0541459 -0.363377 -1.4512 -1.74786 -2.35001 -2.73902 -3.4612 -0.383591 0.110125 0.157851 0.200189 0.056956 0.149081 0.919315\n", + " 499 3.946e+00 2.201e+08 6.636e+00 -- 3.514e+02 -- 0.0518619 -0.36338 -1.4512 -1.74786 -2.34998 -2.73904 -3.46121 -0.364994 0.110159 0.157825 0.200285 0.0565514 0.14877 0.919318\n", + "********************\n", + "0.071793 -0.363381 -1.4512 -1.74785 -2.35 -2.73904 -3.46121 -0.509038 0.110198 0.157822 0.200171 0.0565592 0.148673 0.919314\n", + "0.00680549 6.76709e-06 1.61276e-06 5.24533e-05 0.000285416 0.000268179 6.23129e-06 0.028847 0.000202035 7.49946e-05 0.000565924 0.00181438 0.00135023 0.000137249\n", + "-78607.4 133.971 0.348338 -0.642816 -0.163519 -0.0095348 0.0102973 -2654.2 38.5323 -4.2263 0.532549 -0.374281 -0.0618559 0.00134632\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": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:root:Line magic function `%autoreload` not found.\n" + ] + }, + { + "ename": "NameError", + "evalue": "name 'clag' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'autoreload'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'clag' is not defined" + ] + } + ], + "source": [ + "%autoreload\n", + "p, pe = clag.errors(Cx, p, pe)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'fqd' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m----------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mxscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'log'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0merrorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfqd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"black\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mlag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'fqd' is not defined" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "xscale('log'); ylim(-10,10)\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n", + "\n", + "lag" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "mu,sigma = norm.fit(lag,loc=12e-2)\n", + "xscale('log'); ylim(-10,10)\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color=\"black\")\n", + "plot(fqd,norm.pdf(fqd,mu,sigma))\n", + "mu,sigma\n", + "\n", + "\n" + ] + } + ], + "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 +} diff --git a/lag/data/clag_analysis.ipynb b/lag/data/clag_analysis.ipynb new file mode 100644 index 0000000..26d4ab9 --- /dev/null +++ b/lag/data/clag_analysis.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/3465A.lc\"\n", + "\n", + "dt=0.01\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fqL = np.logspace(np.log10(0.0049999999),np.log10(0.62032418),9)\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227,\n", + " 0.10747115, 0.16658029, 0.25819945, 0.40020915,\n", + " 0.62032418])\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "## load the first light curve\n", + "ref_time, ref_strength, ref_strength_err = np.loadtxt(ref_file,\n", + " skiprows=1).T" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "## initialize the psd class for multiple light curves ##\n", + "P1 = clag.clag('psd10r',\n", + " [ref_time], [ref_strength], [ref_strength_err],\n", + " dt, fqL)\n", + "ref_psd = np.ones(nfq)\n", + "ref_psd, ref_psd_err = clag.optimize(P1, ref_psd)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ref_psd, ref_psd_err = clag.errors(P1, ref_psd, ref_psd_err)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "xscale('log'); ylim(-4,2)\n", + "errorbar(fqd, ref_psd, yerr=ref_psd_err, fmt='o', ms=10)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Load second light curve\n", + "echo_time, echo_strength, echo_strength_err = np.loadtxt(echo_file,skiprows=1).T\n", + "P2 = clag.clag('psd10r', [echo_time], [echo_strength], [echo_strength_err], dt, fqL)\n", + "echo_psd = np.ones(nfq)\n", + "echo_psd, echo_psd_err = clag.optimize(P2, echo_psd)\n", + "echo_psd, echo_psd_err = clag.errors(P2, echo_psd, echo_psd_err)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "xscale('log'); ylim(-4,2)\n", + "errorbar(fqd, echo_psd, yerr=echo_psd_err, fmt='o', ms=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Cx = clag.clag('cxd10r',\n", + "\t\t\t\t[[ref_time,echo_time]], \n", + " \t[[ref_strength,echo_strength]],\n", + " \t[[ref_strength_err,echo_strength_err]], \n", + " dt, fqL, ref_psd, echo_psd)\n", + "\n", + "Cx_vals = np.concatenate( (ref_psd*0.7+echo_psd*0.4-0.3,ref_psd*0+0.1) )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Cx_vals, Cx_err = clag.optimize(Cx, Cx_vals)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "xscale('log'); ylim(-4,2)\n", + "# errorbar(fqd, Cx_vals, yerr=Cx_err, fmt='o', ms=10)\n", + "Cx_vals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Cx_vals, Cx_err = clag.errors(Cx,Cx_vals,Cx_err)" + ] + }, + { + "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 +} diff --git a/lag/data/complete_clag_analysis-3471_origbins.ipynb b/lag/data/complete_clag_analysis-3471_origbins.ipynb new file mode 100644 index 0000000..2440f93 --- /dev/null +++ b/lag/data/complete_clag_analysis-3471_origbins.ipynb @@ -0,0 +1,734 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tvhMr3fCZ8Lp89zzkk4IHhw3vbrVPgqN3hXp1RoCTIhcg+4IZ3239oquHGZIZ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\nwYOIiIikRcMWUhRCoVA496I7LvfCr9w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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import sys\n", + "import getopt\n", + "sys.path.insert(1,\"/usr/local/science/clag/\")\n", + "import clag\n", + "%pylab inline\n", + "\n", + "ref_file=\"lc/1367A.lc\"\n", + "echo_file=\"lc/3471A.lc\"\n", + "\n", + "\n", + "dt = 0.01\n", + "t1, l1, l1e = np.loadtxt(ref_file).T\n", + "errorbar(t1, l1, yerr=l1e, fmt='o')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.005 , 0.01861938, 0.04473305, 0.06933623, 0.10747115,\n", + " 0.16658029, 0.25819945, 0.40020915, 0.62032418])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, \n", + " 0.25819945, 0.40020915, 0.62032418])\n", + "# fqL = np.logspace(np.log10(0.0006),np.log10(1.2),11)\n", + "nfq = len(fqL) - 1\n", + "fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)\n", + "\n", + "\n", + "fqL\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 1 4.342e-01 5.077e+01 inf -- -5.530e+02 -- 1 1 1 1 1 1 1 1\n", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 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", + " 15 2.744e+02 2.611e-01 7.022e-02 -- 3.525e+01 -- 0.300248 -0.178286 -1.19075 -1.52307 -2.12961 -2.49161 -3.56337 -8\n", + " 16 2.745e+02 2.782e-01 4.368e-04 -- 3.525e+01 -- 0.300566 -0.178158 -1.19062 -1.52252 -2.13009 -2.49151 -3.5617 -8\n", + " 17 2.745e+02 2.805e-01 6.410e-05 -- 3.525e+01 -- 0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "********************\n", + "0.300599 -0.178134 -1.19059 -1.52242 -2.13017 -2.49148 -3.56148 -8\n", + "0.238931 0.202434 0.232634 0.177249 0.153039 0.132988 0.297259 3285.23\n", + "-0.000915535 -0.00131061 -0.00195128 -0.00497899 -0.0226059 -0.0776175 -0.280541 -0.000206646\n", + "********************\n" + ] + } + ], + "source": [ + "P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)\n", + "p1 = np.ones(nfq)\n", + "p1, p1e = clag.optimize(P1, p1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\t### errors for param 0 ###\n", + "+++ 3.525e+01 3.480e+01 3.006e-01 5.395e-01 0.891 +++\n", + "+++ 3.525e+01 3.431e+01 3.006e-01 6.590e-01 1.87 +++\n", + "+++ 3.525e+01 3.458e+01 3.006e-01 5.993e-01 1.34 +++\n", + "+++ 3.525e+01 3.469e+01 3.006e-01 5.694e-01 1.11 +++\n", + "+++ 3.525e+01 3.475e+01 3.006e-01 5.545e-01 0.997 +++\n", + "\t### errors for param 1 ###\n", + "+++ 3.525e+01 3.476e+01 -1.781e-01 2.430e-02 0.973 +++\n", + "+++ 3.525e+01 3.421e+01 -1.781e-01 1.255e-01 2.07 +++\n", + "+++ 3.525e+01 3.451e+01 -1.781e-01 7.491e-02 1.48 +++\n", + "+++ 3.525e+01 3.464e+01 -1.781e-01 4.961e-02 1.21 +++\n", + "+++ 3.525e+01 3.470e+01 -1.781e-01 3.696e-02 1.09 +++\n", + "+++ 3.525e+01 3.473e+01 -1.781e-01 3.063e-02 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.781e-01 2.747e-02 1 +++\n", + "\t### errors for param 2 ###\n", + "+++ 3.525e+01 3.511e+01 -1.191e+00 -1.074e+00 0.276 +++\n", + "+++ 3.525e+01 3.495e+01 -1.191e+00 -1.016e+00 0.598 +++\n", + "+++ 3.525e+01 3.485e+01 -1.191e+00 -9.870e-01 0.8 +++\n", + "+++ 3.525e+01 3.479e+01 -1.191e+00 -9.725e-01 0.91 +++\n", + "+++ 3.525e+01 3.476e+01 -1.191e+00 -9.652e-01 0.968 +++\n", + "+++ 3.525e+01 3.475e+01 -1.191e+00 -9.616e-01 0.997 +++\n", + "\t### errors for param 3 ###\n", + "+++ 3.525e+01 3.482e+01 -1.522e+00 -1.345e+00 0.865 +++\n", + "+++ 3.525e+01 3.432e+01 -1.522e+00 -1.257e+00 1.86 +++\n", + "+++ 3.525e+01 3.459e+01 -1.522e+00 -1.301e+00 1.32 +++\n", + "+++ 3.525e+01 3.471e+01 -1.522e+00 -1.323e+00 1.08 +++\n", + "+++ 3.525e+01 3.476e+01 -1.522e+00 -1.334e+00 0.97 +++\n", + "+++ 3.525e+01 3.473e+01 -1.522e+00 -1.329e+00 1.03 +++\n", + "+++ 3.525e+01 3.475e+01 -1.522e+00 -1.331e+00 0.998 +++\n", + "\t### errors for param 4 ###\n", + "+++ 3.525e+01 3.481e+01 -2.130e+00 -1.977e+00 0.874 +++\n", + "+++ 3.525e+01 3.429e+01 -2.130e+00 -1.901e+00 1.91 +++\n", + "+++ 3.525e+01 3.457e+01 -2.130e+00 -1.939e+00 1.35 +++\n", + "+++ 3.525e+01 3.470e+01 -2.130e+00 -1.958e+00 1.1 +++\n", + "+++ 3.525e+01 3.476e+01 -2.130e+00 -1.968e+00 0.983 +++\n", + "+++ 3.525e+01 3.473e+01 -2.130e+00 -1.963e+00 1.04 +++\n", + "+++ 3.525e+01 3.474e+01 -2.130e+00 -1.965e+00 1.01 +++\n", + "+++ 3.525e+01 3.475e+01 -2.130e+00 -1.966e+00 0.997 +++\n", + "\t### errors for param 5 ###\n", + "+++ 3.525e+01 3.474e+01 -2.491e+00 -2.358e+00 1.01 +++\n", + "+++ 3.525e+01 3.512e+01 -2.491e+00 -2.425e+00 0.263 +++\n", + "+++ 3.525e+01 3.496e+01 -2.491e+00 -2.392e+00 0.579 +++\n", + "+++ 3.525e+01 3.486e+01 -2.491e+00 -2.375e+00 0.781 +++\n", + "+++ 3.525e+01 3.480e+01 -2.491e+00 -2.367e+00 0.893 +++\n", + "+++ 3.525e+01 3.477e+01 -2.491e+00 -2.363e+00 0.952 +++\n", + "+++ 3.525e+01 3.476e+01 -2.491e+00 -2.361e+00 0.982 +++\n", + "+++ 3.525e+01 3.475e+01 -2.491e+00 -2.360e+00 0.997 +++\n", + "\t### errors for param 6 ###\n", + "+++ 3.525e+01 3.507e+01 -3.561e+00 -3.413e+00 0.363 +++\n", + "+++ 3.525e+01 3.484e+01 -3.561e+00 -3.339e+00 0.814 +++\n", + "+++ 3.525e+01 3.468e+01 -3.561e+00 -3.301e+00 1.13 +++\n", + "+++ 3.525e+01 3.477e+01 -3.561e+00 -3.320e+00 0.962 +++\n", + "+++ 3.525e+01 3.473e+01 -3.561e+00 -3.311e+00 1.04 +++\n", + "+++ 3.525e+01 3.475e+01 -3.561e+00 -3.315e+00 1 +++\n", + "\t### errors for param 7 ###\n", + "+++ 3.525e+01 3.524e+01 -8.000e+00 -6.000e+00 0.0178 +++\n", + "+++ 3.525e+01 3.515e+01 -8.000e+00 -5.000e+00 0.187 +++\n", + "+++ 3.525e+01 3.494e+01 -8.000e+00 -4.500e+00 0.618 +++\n", + "+++ 3.525e+01 3.466e+01 -8.000e+00 -4.250e+00 1.18 +++\n", + "+++ 3.525e+01 3.482e+01 -8.000e+00 -4.375e+00 0.851 +++\n", + "+++ 3.525e+01 3.475e+01 -8.000e+00 -4.312e+00 1 +++\n", + "********************\n", + "0.300605 -0.178131 -1.19059 -1.52241 -2.13019 -2.49148 -3.56144 -8\n", + "0.253864 0.205597 0.228999 0.191096 0.1638 0.131949 0.246154 3.6875\n", + "********************\n" + ] + } + ], + "source": [ + "p1, p1e = clag.errors(P1, p1, p1e)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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s1gYNsgosKj2W4uaAwRxImCcZM2P2Wa0zWXK/CQUJHiR4CBs7swWxdkMU4hvv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\n", + "text/plain": [ + "" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WM3L1CFwVUmAx5BfnyR/J035DOwNPDjRsUPB2gySpoXTe0hkEhKnuXiyC7NVZ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\n", + "text/plain": [ + "" + ] + }, + "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": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "%pylab inline\n", + "\n", + "from scipy.stats import norm\n", + "from scipy.stats import lognorm\n", + "from scipy.optimize import curve_fit\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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BqAOMrD1SyyjlmZQxozE5sm1beOst4zFFTEz8531a9VMuXr/I8I3DzS9SRNxK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\nRT4DIiLs9goV7Haw29u0sdsjI+M/JzY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "\n", + "\n", + "#x=np.linspace(1,50,200)\n", + "\n", + "# freq bins we agreed on\n", + "x=array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n", + " 0.20739079, 0.32145572, 0.49825637])\n", + "\n", + "# from 3471A\n", + "lag = array([ 1.25569486, 2.37000041, 1.7802513 , 1.66775218, 0.49069246,\n", + " 0.21781609, -0.44057362, 0.01545348])\n", + "coeff, var_matrix = curve_fit(tophat_freq, x, lag, p0=p0)\n", + "\n", + "# Get the fitted curve\n", + "\n", + "\n", + "plot(tophat_freq(x, *coeff))\n", + "plot(lag)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ucOwCJHU6/HD4/vfhu98NM7ivvTZclrj33uEubkOHhqsQ2trCo+vXhd/39Wex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\nkiRJKq//B0U68uCZHjX/AAAAAElFTkSuQmCC\n", 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kzwtSbtRMfPhu795R/UhciPgU70nDd6G4hqNcaqaUIxIKEX+4jhljwqK9vXBC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\nWfWdRK7jJqJKbI9N819vecTVwP7Yt0D/A/um6Lk1jahjTsZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xscale('log')\n", + "plot(np.logspace(0.01,10,200),sinc(pi*x))\n", + "#len(sinc(pi*x))" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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w8WE7JUHDLlpeG7qWLf2dD3ucj4YGuQn26iXzALIOt4hxh13atDHl9+41F0BR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\nAHE6gs7TDsBPAfwhUWX8xEc6cz4YdiGEEEKCkWr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot(np.fft.fft(ifft(sinc(pi*x))))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'A' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m----------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msinc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'A' is not defined" + ] + } + ], + "source": [ + "A*T*sinc(pi*f*T)*cos(2*pi*f*t0)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'np' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msinc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfqd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfqd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;31m# p0 is the initial guess for the fitting coefficients\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" + ] + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# Define model function to be used to fit to the data above:\n", + "def tophat_time(x, *p):\n", + " mean, width = p\n", + " if x>(mean+width): y=0\n", + " if x<(mean-width): y=0\n", + " if x==(mean+width) | x==(mean-width): y=5\n", + " return y\n", + "\n", + "def tophat_freq(f, *pars):\n", + " A,T,t0 = pars\n", + " #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n", + " return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n", + "\n", + "x=np.logspace(fqd[0],fqd[-1],200)\n", + "\n", + "# p0 is the initial guess for the fitting coefficients\n", + "p0 = [3, 3, 3]\n", + "coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n", + "fit = tophat_freq(fqd, *coeff)\n", + "\n", + "\n", + "mpl.rcParams['xtick.labelsize']=12\n", + "mpl.rcParams['ytick.labelsize']=12\n", + "xscale('log'); xlim(.009,.6)\n", + "xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n", + "ylabel(\"Time Lag (days)\",fontsize=20)\n", + "\n", + "\n", + "errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n", + "plot(fqd,fit)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + 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(8909 A) Tj +ET +Q q 52.42 19.66 485.71 626.47 re W n +1.000 0.000 0.000 RG +0.75 w +[] 0 d +1 J +1 j +10.00 M +55.02 75.65 m 55.02 94.25 l S +53.72 94.25 m 56.32 94.25 l S +53.72 75.65 m 56.32 75.65 l S +BT +1.000 0.000 0.000 rg +/F2 1 Tf 8.00 0.00 -0.00 8.00 56.32 82.21 Tm (=5%) Tj +ET +0.000 0.000 1.000 RG +86.63 47.26 m 86.63 52.36 l S +88.37 44.75 m 88.37 55.17 l S +88.81 48.11 m 88.81 48.91 l S +91.36 49.01 m 91.36 50.81 l S +93.80 52.87 m 93.80 54.27 l S +95.22 52.82 m 95.22 62.43 l S +96.45 51.26 m 96.45 53.87 l S +97.13 53.62 m 97.13 61.33 l S +99.13 53.62 m 99.13 54.52 l S +101.35 45.30 m 101.35 67.04 l S +101.63 52.67 m 101.63 53.97 l S +103.08 52.11 m 103.08 61.93 l S +104.22 54.77 m 104.22 58.58 l S +106.53 61.68 m 106.53 68.39 l S +106.76 59.33 m 106.76 60.23 l S +109.04 55.62 m 109.04 58.12 l S +109.14 51.41 m 109.14 65.74 l S +109.74 53.32 m 109.74 59.43 l S +111.83 57.42 m 111.83 58.33 l S +112.82 53.52 m 112.82 62.63 l S +115.07 57.87 m 115.07 62.78 l S +117.56 59.08 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time-variable spectrum from NGC 5548 provide the most detailed look so far at this prototypical Seyfert galaxy. A reverberation mapping of NGC 5548 is attempted using a novel frequency-domain technique. The power spectral densities of lightcurves in 19 wavelength bands are recovered. Time lags are computed from the cross spectra, but results are marred by some computational issues. Empirical fits are obtained for 6 lightcurves, and the time lags are compared with those computed by Fausnaugh et al. in STORM III. Results are mixed but overall protective of the thermal reprocessing by an accretion disk hypothesis. +\end{abstract} + +%───────────── +\section{Active Galactic Nuclei} + \label{sec:agn} + Active Galactic Nuclei, or AGN, are a class of compact objects that are of great interest to modern astronomers. AGN are superluminous ($\sim10^{44}-10^{50} ergs\ s^{-1})$ systems that surround supermassive ($\sim 10^5 - 10^{10} M_{\odot}$) blackholes. Seyfert galaxies (also called active galaxies) are defined by these objects at their center, and the AGN often outshine all the stars in those galaxies. Quasars are too distant to resolve optically, but their spectra are typical of AGN, leading researchers to believe these are also galaxies with AGN at their core. \cite{2014A&ARv..22...72U} AGN have a variable spectrum in time across all observed wavelengths, and with no known period. The physics underlying these variable spectra are not well-understood and astronomers are highly motivated to quantify them for many reasons. + + AGN science is a strong candidate for computing more accurate and precise values of the Hubble constant, which is a cosmological parameter that argues the rate of expansion of the universe \cite{1999MNRAS.302L..24C}. Type IA supernovae are the standard used to calculate this value now, because their spectra are well-predicted with known physics, and they are very bright. AGN are much brighter (arguably the brightest objects in the cosmos in terms of intrinsic luminosity), so if their physics were well-understood, we could use them to probe the universe beyond the supernova horizon. Toward the goal of computing a more accurate and precise Hubble constant, we study the fundamental physics at play in AGN, but that is not the only concern. + + Evidence exists that AGN are the engines that power evolution of their host galaxies; this field of study is called AGN feedback. \cite{2012ARA&A..50..455F} They also present an interesting challenge on their own merits, because they are complex systems of gasses and plasmas in a variety of thermodynamic conditions and spatial configurations. Working out those dynamics is particularly difficult because AGN in Seyfert galaxies, and especially in quasars, are too small and too distant to resolve optically. The standard model AGN structure is shown in figure \ref{fig:AGN_standard}. It is believed that an accretion disk surrounds the supermassive blackhole, and thermal emission from this disk is the primary contributer to the continuum emission. + + \begin{figure}[h] + \center + \includegraphics[width=3in]{ngc5548.png} + \caption{NGC 5548: the most well-studied Seyfert galaxy. The physics underlying the time-variable spectrum of its nucleus remain an object of great interest and study. The nucleus at the center of the galaxy has approximately the same luminosity as the rest of the galaxy. \cite{ngc5548photo}} + \label{fig:ngc5548} + \end{figure} + + \begin{figure}[h] + \centering + \includegraphics[width=3.5in]{AGN_standard.png} + \caption{Standard model of AGN structure. An accretion disk surrounds the supermassive blackhole at the center, and thermal emission from the accretion disk is believed to be the primary contributor to the continuum component of the observed spectrum. The broad line region extends beyond the accretion disk, as does an obscuring torus, narrow line region, and relativistic jet; these features are not covered by this work. \cite{2016ASSL..439..249B}} + \label{fig:AGN_standard} + \end{figure} + + The Seyfert galaxy NGC 5548, shown in figure \ref{fig:ngc5548}, is the most well-studied active galaxy, and the physics of its nucleus are still mysterious. An ongoing study of this object, the Space Telescope and Optical Reverberation Mapping Project (STORM), is being conducted now, and is the most in-depth study of an AGN to date. Data from the third edition of STORM \cite{2016ApJ...821...56F} is used here to quantify the continuum emission variability and time lags seen in NGC 5548. + + + + + +%───────────── +\section{Reverberation Mapping} + \label{sec:reverbmap} + + Reverberation mapping is a popular method for measuring AGN features, and has been employed for about three decades. Since AGN cannot be resolved optically, reverberation mapping uses observed lags between wavelengths in the time-dependent spectrum to infer spatial distributions, thermodynamic properties, and blackhole mass. \cite{2014A&ARv..22...72U} Work is underway to use reveberation mapping to measure blackhole spin, as well. \cite{2016Natur.535..388K}. This study uses reveberation mapping to study the UV/Optical continuum emission in NGC 5548 as an attempt to protect the thermal reprocessing by an accretion disk hypothesis. + + Reverberation mapping is essentially mapping using light echoes. Some light escapes directly to the observer, while other light is reprocessed in the surrounding gas. Assuming that light travels with velocity $c$, additional path length traversed by reprocessed light results in an observed time delay. As an illustrative example, consider a spherically-distributed gas. This geometry results in ellipsoid isodelay surfaces with the source of light at one focus and the observer at the other. Because the distances to AGN are so great, these surfaces can be approximated as paraboloids; see Figure~\ref{fig:isodelay}. The time delay $\tau$ is associated with the geometry of the system by the equation + + \begin{center} + $\tau = (1+cos(\theta)) \frac{r}{c}$. + \end{center} + + If the time delay is observable, this model can be used to compute the radius of the gas cloud. AGN are more complicated than the spherical model, but the concept is the same, and the spherical model has been used to compute upper-limit radii for some AGN. A transfer function can capture the arbitrarily complex reverberation observed in AGN. This will be discussed in detail in section \ref{subsec:transferfunc}. + + \begin{figure} + \hfill + \includegraphics[width=3in]{isodelay.jpg}[A] + \includegraphics[width=3in]{isodelay_detail.jpg}[B] + \caption{[A] Isodelay paraboloids predicted for a gas cloud that is spherically distributed. This is a simple case of reverberation mapping, but AGN systems are more complicated. [B] Radius and angle dependence of a single reprocessed emission. \cite{2001sac..conf....3P}} + \label{fig:isodelay} + \end{figure} + + \subsection{Continuum Reverberation in the Accretion Disk of NGC 5548} + \label{subsec:contreverb} + + The thermal reprocessing hypothesis suggests that a hot accretion disk surrounds and feeds the supermassive blackhole. This disk responds thermally to high-energy EM emission from a corona near the event horizon. The temperature increases along the disk toward the gravitational well of the blackhole, and as the coronal emission varies, the disk responds with an increase in temperature, and in turn an increase in overall emission and Wien wavelength. Collier et al. \cite{1999MNRAS.302L..24C} provide a model for the average time lag $\tau$ as a function of Wien wavelength $\lambda$. This model assumes the accretion disk consists of a distribution of blackbody radiators with temperature distribution $T(R) = T_0 \left(\frac{R}{R_0}\right)^{-\frac{3}{4}}$. The average time lag under these conditions can be predicted numerically, but for these purposes it is sufficient to conclude that $\tau \propto \lambda^{\frac{4}{3}}$. The precise nature of the accretion disk remains a topic of debate, so this should be considered only a rough estimate. A simple picture is presented in Figure~\ref{fig:simple_geometry} + + \begin{figure} + \center + \includegraphics[width=4in]{basic_geometry.png} + \caption{Cross-section of the accretion disk at the center of a standard model AGN. High-energy emission irradiates the accretion disk from a corona just outside the event horizon of the supermassive blackhole. Shorter-wavelength emission emerges from deeper within the system as it responds to that emission. The observer sees longer time delays accompanying longer wavelengths.} + \label{fig:simple_geometry} + \end{figure} + + At the very least, we may conclude two things from these ideas. The Wien wavelength decreases with temperature, and the temperature increases toward the center of the disk, so we expect to see an increase in time delay as wavelength increases. Hotter gas emits more power at every wavelength, so we also expect to see decreasing variability as wavelength increases. If these characteristics are observed, it will protect the thermal reprocessing hypothesis. More robust analysis tools may also shed light on more detailed aspects of this hypothesis. + + +%───────────── +\section{Analysis of Reverberating Lightcurves} + \label{sec:analysis} + + Observational astronomy in the optical and UV bands suffers from unevenly sampled data due mainly to weather effects and periodic orbital blindness of space observatories. Fausnaugh et al., in STORM III \cite{2016ApJ...821...56F}, published the longest and most detailed to date time-variable lightcurves in the optical and UV spectrum of NGC 5548; see figure \ref{fig:lightcurves}. Reverberation mapping in the optical and UV wavelengths has thus far involved time-domain analyses, and Fausnaugh et al. published one in their paper, included here as figure~\ref{fig:reverb_time}. These analyses have the advantage of weak sensitivity to the unevenness of the data sampling, but the disadvantage of returning only the average time lag in each band. More information than the average time lag is present in the observed data, and a frequency-domain analysis has the potential to deconvolve that information. An important test of our approach will be to confirm the average time lags we compute agree with those reported by Fausnaugh et al. + + Fourier-frequency analysis using the Fast Fourier Transform (FFT) is a common method for analysing the correlations between time-dependent signals. However, even a small deviation in the time interval between samples can create artifacts in the frequency-dependent output. X-ray astronomy suffers from unevenly sampled data in the time domain as well. X-ray astronomer Abdu Zoghbi from the University of Michigan is developing the program $psdlag$ to solve this problem by populating frequency-space bins using a maximum likelihood fitting. We apply this program in an attempt to solve the problem of unevenly sampled data in the optical/UV lightcurves reported by Fausnaugh et al. + + \begin{figure} + \center + \includegraphics[width=6.5in]{lightcurves.pdf} + \caption{Optical and UV lightcurves published in STORM III. \cite{2016ApJ...821...56F}. The variable nature is very clear, and by observing the promiment bumps one can see that time delays exist between the curves. The x axis is the observation day and the y axis indicates the wavelength filter and observed flux.} + \label{fig:lightcurves} + \end{figure} + + \begin{figure} + \center + \includegraphics[width=4in]{TCCF_fausnaugh.pdf} + \caption{Time-domain analyses of lightcurves published by Fausnaugh et al. The average time lags are computed from the light curves (left column in legend) and the best fit is compared against competing accretion disk models (right column).} + \label{fig:reverb_time} + \end{figure} + + + + \subsection{Time Delay and Transfer Function Theory} + \label{subsec:transferfunc} + + A transfer function $g(t,\lambda)$ encodes the time-dependent response of each lightcurve relative to a reference lightcurve. By constructing the transfer function from the observed lightcurves, we obtain a description of the reprocessing of the coronal emission by the hypothesized accretion disk. One way to define the transfer function for each wavelength is as the function by which one convolves the reference lightcurve $x(t)$ to return the reprocessed lightcurve $y(t)$, i.e., + + \begin{equation} + \label{eq:time_transfunc} + y(t) = \int_{-\infty}^{\infty} g(\tau) x(t-\tau) {\rm d}\tau. + \end{equation} + + The convolution theorem provides that convolution in the time $t$ domain is equivalent to multiplication in the frequency $\nu$ domain, so we can also say + + \begin{equation} + \label{eq:freq_transfunc} + Y(\nu) = G(\nu) X(\nu). + \end{equation} + + The power spectral density (PSD) provides a measure of the variability of a lightcurve. We define the PSD as $|X(\nu)|^2 = X^*(\nu) X(\nu)$, where $X^*$ is the complex conjugate of $X$. This quantity can be calculated using Fourier transforms, and suggests a useful relationship involving the lightcurves and the transfer function. + + \begin{equation} + |Y(\nu)|^2 = |G(\nu)|^2 |X(\nu)|^2. + \end{equation} + + We also define a cross spectrum between two lightcurves as $C(\nu) = X^*(\nu) Y(\nu)$. The argument $\phi$ of the cross spectrum is the phase between those two signals. The time lag can be computed from the phase using equation \ref{eq:timelag}. An empirical fit of the time delay in frequency space can be produced, and then transformed to the time domain. The average time delay can be obtained from this new function, but this function describes more fully the time-dependent response of the reprocessed lightcurve. Composing the many specific time-dependent responses into a single function $g(t,\lambda)$ provides the desired description of the reveberation in the system. + + \begin{equation} + \tau(\nu) = \frac{\phi(\nu)}{2\pi\nu}. + \label{eq:timelag} + \end{equation} + +% Given equation \ref{eq:freq_transfunc}, the cross-spectrum can be written as + +% \begin{equation} +% C(\nu) = X^*(\nu) G(\nu) X(\nu) = G(\nu) |X(\nu)|^2 +% \end{equation} + +% Discrete samples of the transfer function can therefore be recovered as + +% \begin{equation} +% G(\nu) = \frac{C(\nu)}{|X(\nu)|^2}. +% \end{equation} + + + + \subsection{Computational Details} + \label{subsec:computation} + + 1365\AA\ is chosen to be the reference wavelength. The $psdlag$ program is used to generate the power spectral densities, cross spectra, and time lags, all in the frequency domain. A least-squares method is used to fit an empirical function to the computed time lags. The inverse FFT transforms that function to the time domain. The most-likely average time lag reported by Fausnaugh et al. is plotted over that function for comparison. + + A tophat function describes one possible time-response mode, and examples of this function in the time domain and their corresponding FFTs to the frequency domain are plotted in figure \ref{fig:tophat_theory}. A tophat function is a rectangular function with mean $t_0$ and width $\sigma$ such that when $(t_0-\sigma) < t < (t_0+\sigma)$, this function returns 1, and otherwise it returns 0. In frequency space, it is known that the function takes the form + + \begin{equation} + A T sinc\left(\pi \nu T\right) e^{\left(-i 2 \pi \nu t_0\right)}, + \end{equation} + + where $A$ is a normalization amplitude and $T$ is a factor describing the periodicity in frequency space. We fit the real component of this function to the data, i.e., + + \begin{equation} + A T sinc\left(\pi \nu T\right) cos\left(2 \pi \nu t_0\right) + \end{equation} + + in frequency space, then perform an inverse real FFT to retrieve the response in the time domain. This is perhaps the simplest response function we could use, but it still provides a rough measure for comparison with the time lags reported by Fausnaugh et al. in Storm III. + + + + + \begin{figure} + \centering + \includegraphics[width=0.5\linewidth]{tophat_timedomain.pdf}[A] + \includegraphics[width=0.5\linewidth]{tophat_freqdomain.pdf}[B] + \caption{[A] Tophat transfer functions in the time domain show an average time lag of the reverberating curve and a constant distribution in time over an interval. [B] The frequency-dependent time lags associated with each tophat function. Important features related to the average time lag are observable (maximum, corresponding to the average time delay; value of $\nu$ at steepest change), and complicated relationships with higher frequency waves can be noted.} + \label{fig:tophat_theory} + \end{figure} + + + + + +%───────────── +\section{Results} +\label{sec:results} + Figure~\ref{fig:PSD_ref} shows the power spectral density computed for the reference curve, illustrating its strong variability. The power spectral densities for all wavelengths were computed as a cohort and compiled into a grid, presented in figure~\ref{fig:grids}. The time lags were also computed for all wavelengths, but observation of the output grid indicates significant computational issues. The errors reported in these grids were sampled from the covariance matrix, which does not account for all correlations between frequency bins. These can therefore only be considered lower-limits estimates of the uncertainty. + + Of the 19 lightcurves for which a time lag analysis was attempted, 10 produced possibly meaningful results through this method. A more rigorously computation was attempted for each of these, by sampling the error from the maximum likelihood function, which does account for error correlations. It was also found that a good fit for 3465\AA\ could be obtained by eliminating the highest-frequency bin from the model. The 6 models that ran successfully are compiled in figures \ref{fig:results1} and \ref{fig:results2}. The $psdlag$-computed time delays are reported as black error-barred dots, and the empirical fits are drawn in blue. The average time lag reported in STORM III is drawn as a black vertical line in the time domain plot. + + \begin{figure} + \centering + \includegraphics[width=4in]{psd1367A.pdf} + \caption{The power spectral density for the chosen reference lightcurve. It is strongly variable across all computed Fourier frequencies.} + \label{fig:PSD_ref} + \end{figure} + + \begin{figure} + \includegraphics[width=.46\textwidth]{psd_atlas.pdf}[A] + \includegraphics[width=.46\textwidth]{timelag_atlas.pdf}[B] + \caption{[A] Power spectral densities for all observed light curves, excluding the reference curve. A decrease in variability is observed with increasing wavelength. [B] Time lags as a function of Fourier frequency. Computational issues make many of these results dubious, but ten curves provide at least a lower-limit estimate of the uncertainty. It can nevertheless be noted that time delay increases with wavelength.} + \label{fig:grids} + \end{figure} + + + \begin{figure} + \centering + \includegraphics[width=.45\textwidth]{freq_3465A.png} + \includegraphics[width=.45\textwidth]{time_3465A.png}[3465\AA] + \centering + \includegraphics[width=.45\textwidth]{freq_3471A.png} + \includegraphics[width=.45\textwidth]{time_3471A.png}[3471\AA] + \centering + \includegraphics[width=.45\textwidth]{freq_6175A.png} + \includegraphics[width=.45\textwidth]{time_6175A.png}[6175\AA] + \caption{The shorter wavelengths, corresponding to emission from the inner region of the accretion disk, do not show strong agreement with the average time lags reported by Fausnaugh et al. This may be due to difficulty fitting the function because the computer is unable to distinguish between emission from the source and reprocessing.} + \label{fig:results1} + \end{figure} + + \begin{figure} + \centering + \includegraphics[width=.45\textwidth]{freq_6439A.png} + \includegraphics[width=.45\textwidth]{time_6439A.png}[6439\AA] + \centering + \includegraphics[width=.45\textwidth]{freq_7657A.png} + \includegraphics[width=.45\textwidth]{time_7657A.png}[7657\AA] + \centering + \includegraphics[width=.45\textwidth]{freq_9157A.png} + \includegraphics[width=.45\textwidth]{time_9157A.png}[9157\AA] + \caption{The empirical fits in these wavelengths show a strong agreement with the average time lags reported by Fausnaugh et al. The strong response trending downward from time 0 may be from the source emission, while the bump is from thermal emission further out in the accretion disk. These components appear to overlap in 6439\AA.} + \label{fig:results2} + \end{figure} + +\section{Conclusion} + \label{sec:conclusion} + + Response curves in the time domain were successfully computed for 6 of the 19 lightcurves. All indicate some variable time-dependent response. The functions fit to 3465\AA, 3471\AA, and 6175\AA\ do not show strong agreement with the average time delays reported by Fausnaugh et al. 7657\AA\ and 9157\AA\ show a response bump precisely where Fausnaugh et al. predicted. The response bump in 6439\AA\ overlaps with the driving emission, but still appears to be in agreement with the time lag computed in STORM III. The model used to compute these functions is rudimentary, and a more rigorous analysis may provide better fits and more insight. + + The thermal reprocessing hypothesis predicted that the variability would decrease as the wavelength increases, and this is certainly observed in the PSD grid in figure \ref{fig:grids}. It was also predicted that the time lag would increase with wavelength due to the decreasing temperature along the accretion disk's increasing radius. This feature is observed in a general sense along the grid of time lags in figure \ref{fig:grids}, and also between the fitted curves for 6439\AA\ and 7657\AA/9157\AA, but not in the other fitted curves. This may be due to inaccuracies from the rudimentary fitting function. This information does seem protective of the accretion disk hypothesis. One thing is certain: further study is warranted. + +\printbibliography + + +\end{document} diff --git a/lag/report/time_3465A.png b/lag/report/time_3465A.png new file mode 100644 index 0000000..7abca75 Binary files /dev/null and b/lag/report/time_3465A.png differ diff --git a/lag/report/time_3471A.png b/lag/report/time_3471A.png new file mode 100644 index 0000000..2c06ecb Binary files /dev/null and b/lag/report/time_3471A.png differ diff --git a/lag/report/time_6175A.png b/lag/report/time_6175A.png new file mode 100644 index 0000000..eb1e07f Binary files /dev/null and b/lag/report/time_6175A.png differ diff --git a/lag/report/time_6439A.png b/lag/report/time_6439A.png new file mode 100644 index 0000000..66b3144 Binary files /dev/null and b/lag/report/time_6439A.png differ diff --git a/lag/report/time_7657A.png b/lag/report/time_7657A.png new file mode 100644 index 0000000..38ca26e Binary files /dev/null and b/lag/report/time_7657A.png differ diff --git a/lag/report/time_9157A.png b/lag/report/time_9157A.png new file mode 100644 index 0000000..78e4797 Binary files /dev/null and b/lag/report/time_9157A.png differ diff --git a/lag/report/timelag_atlas.pdf b/lag/report/timelag_atlas.pdf new file mode 100644 index 0000000..8b0c016 Binary files /dev/null and b/lag/report/timelag_atlas.pdf differ diff --git a/lag/report/tophat_freqdomain.pdf b/lag/report/tophat_freqdomain.pdf new file mode 100644 index 0000000..8041846 Binary files /dev/null and b/lag/report/tophat_freqdomain.pdf differ diff --git a/lag/report/tophat_timedomain.pdf b/lag/report/tophat_timedomain.pdf new file mode 100644 index 0000000..969bfba Binary files /dev/null and b/lag/report/tophat_timedomain.pdf differ