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20150727T203000_SDSS_J160036.83+272117.8_combined.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Investigate eclipses of SDSS J160036.83+272117.8\n",
"\n",
"TODO:\n",
"* Encapsulate more cells below within functions to clean up namespace. As of 2015-07-03, arrays are duplicated between `dataframes` and `models` so that it is clear what version of the data was used for each model.\n",
"* Modeling sequence:\n",
" * Find initial best_period from Lomb-Scargle model to CRTS data.\n",
" * As of 2015-07-03, the MCMC models are chained, i.e. the posterior of the Lomb-Scargle models becomes the prior of the segmeted model. Combine MCMC models so that the combined model accepts (best_period, phase_rel_int, phase_rel_ext, flux_pri, flux_out, flux_sec, flux_sigma). Simpler model eleminates several items below. Also initialize with `scipy.optimize.leastsq`\n",
" \n",
" - Not correcting millisecond timestamps: For the McDonald data, I'm using the timestamps from the counter-timer card, which drift by ~-6 microseconds/second. I never finished the timestamp validation module, but what exists is a good starting point for others (https://github.com/ccd-utexas/tsphot/blob/pipeline/verify_timestamps.py). For this project, my uncorrected timestamps are sufficiently accurate (timestamps at end of 4-hour run are off by ~-0.8 seconds). Time estimate to complete the timestamp validation module: 2 days.\n",
" > TODO: Top priority. Do correct this so that data can be used in years since.\n",
"\n",
" - Not converting or separating filters: I'm combining time series data from both McDonald, which is in a BG40 filter (400-550 nm), with that from Catalina Real Time Survey, which is in V (500-550 nm). I'm not converting the relative fluxes from BG40 to V because the light levels for the depths eclipses are not statistically different, so there's nothing to be gained by converting the BG40 relative flux. The other approach is to model the light curves separately, but that requires a more complex model (similar to section 3 of Hogg et al 2010 http://arxiv.org/pdf/1008.4686v1.pdf). Time estimate to convert the data: 2 days.\n",
" > TODO: Show that data are not statistically different.\n",
"\n",
" - Mixed Bayesian/frequentist approach for outliers: I'm removing outliers using Bonferroni-corrected p-values, which is a frequentist approach. I'm doing this in sequence with Bayesian MCMC. This is inconsistent from a statistical perspective (also section 3 of Hogg et al 2010 http://arxiv.org/pdf/1008.4686v1.pdf), but it shouldn't make any difference in the final result. Time estimate to use Bayesian MCMC for outliers: 2 days.\n",
" > TODO: Do use Bayesian techniques consistently.\n",
"\n",
" - Unregularized period model (01_...png, 02_...png attached): The McDonald data is oversampled between phases wrapped 0.8-0.1 (~5000 data points) compared to the CRTS data, which evenly samples the entire phase space (~400 data points). This causes the Lomb-Scargle model for the period to weight the McDonald data points much more than the CRTS. However, I'm only using the model to fit the period over a ~0.1 second range, so the variance in flux will not change the result. Regularization will help fix the flux values because it penalizes large values for Fourier coefficients. (Resampling the data evenly isn't an option because you then lose the tight constraints from the McDonald data set, but the fit is better in 02_...png.) Time estimate to regularize: 2 days.\n",
" > TODO: Do use regularized model. Plot not representative of physical model.\n",
"\n",
" - Limited info for NOV limits, limb darkening (attached pdfs): At McDonald, I didn't collect much data out of eclipse, and all data was collected at high airmass. My choice of polynomial to correct differential extinction has a large effect on the flux values and the light curve shape. For this reason, I would be hesitant to believe much in the way of NOV limits or limb darkening coefficients. Time estimate for each of these: 2 days.\n",
" > TODO: See photo of black board. Limb darkening is for smaller-radius WD being occulted and may not be really interesting. Primary eclipse is flat in totality because is occultation.\n",
" \n",
" * Ephemeris under estimates duration of eclipse because eclipse is not trapezoidal. Change the fitting function to be quadratic in minima, cubic in ingress/egress, constant out of eclipse. BCs are continuity and first-differentiability.\n",
"\n",
"\n",
"Note: This star was already known to eclipse: http://www.aavso.org/vsx/index.php?view=detail.top&oid=385873"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization\n",
"\n",
"### Imports\n",
"\n",
"**Note:** As of 2015-02-21, code folding from https://github.com/ipython-contrib/IPython-notebook-extensions/wiki/Home_3x\n",
"and https://github.com/ipython-contrib/IPython-notebook-extensions/wiki/Codefolding_v3 is not stable for IPython 3x when using a Python 2x kernel. Do not import.\n",
"\n",
"`cd` to directory where https://github.com/stharrold/Harrold_2015_SDSSJ1600 was downloaded in order to import `code`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/samharrold/Documents/GitHub/stharrold/2015_Harrold_SDSSJ1600\n"
]
}
],
"source": [
"cd /Users/samharrold/Documents/GitHub/stharrold/2015_Harrold_SDSSJ1600"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# TODO: remove unnecessary imports\n",
"# Import standard packages.\n",
"from __future__ import absolute_import, division, print_function\n",
"import collections\n",
"import datetime as dt\n",
"import copy\n",
"import csv\n",
"import itertools\n",
"import os\n",
"import json\n",
"import warnings\n",
"# Import third-party installed packages.\n",
"import astroML.density_estimation as astroML_dens\n",
"import astroML.plotting as astroML_plt\n",
"import astroML.stats as astroML_stats\n",
"import astroML.time_series as astroML_ts\n",
"import astropy.constants as astropy_con\n",
"import astropy.time as astropy_time\n",
"import astropy.units as astropy_units\n",
"import emcee\n",
"import gatspy.periodic as gatspy_per\n",
"import gatspy.datasets as gatspy_data\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import pytz\n",
"import scipy.constants as scipy_con\n",
"import scipy.optimize as scipy_opt\n",
"import scipy.signal as scipy_sig\n",
"import seaborn as sns\n",
"import statsmodels.api as sm\n",
"import triangle\n",
"# Import local packages.\n",
"# TODO: remove autoreload after testing\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"# Using pre-release version of binstarsolver from 2015-05-05:\n",
"# https://github.com/ccd-utexas/binstarsolver/commit/f3968617445c017ffacd30b9f6587ff5b028338f\n",
"import binstarsolver as bss\n",
"import code # from https://github.com/stharrold/Harrold_2015_SDSSJ1600\n",
"# IPython magic.\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Globals"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"warnings.simplefilter('once') # Only display warnings once.\n",
"sns.set() # Set matplotlib styles by seaborn."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract, transform, load data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dataframes = code.utils.Container()\n",
"path_project = os.path.abspath(\n",
" r'/Users/samharrold/Google Drive/ccd.utexas/Projects/20140630_SDSS_J160036.83+272117.8')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Catalina Real-Time Transient Survey\n",
"\n",
"About Catalina Real-Time Survey: http://crts.caltech.edu/#research\n",
"\n",
"To fetch the data for SDSS J160036.83+272117.8:\n",
"- Navigate to http://nesssi.cacr.caltech.edu/DataRelease/ > click \"Search for photometry in a single `location`.\" \n",
"- Input coordinates:\n",
" - RA: 16 00 36.8\n",
" - DEC: +27 21 17\n",
"- Click download.\n",
"- **Notes:**\n",
" - CRTS photometry is in Vmag (from http://nesssi.cacr.caltech.edu/DataRelease/).\n",
" - The photometry is from the Catalina Sky Survey Schmidt telescope, Steward Observatory, Tucson, Arizona (from http://nesssi.cacr.caltech.edu/DataRelease/ > \"Check image coverage by `location`.\" and from individual coverage maps http://crts.caltech.edu/Telescopes.html). Telescope location (longitude, latitude): (-110 deg 43.9 min West, +32 deg 25 min North) = (-110.731667 deg, 32.4166667 deg) (from http://www.lpl.arizona.edu/css/css_facilities.html).\n",
" - The data timestamp precision is 1e-5 days = ~0.9 seconds. Using the telescope location when converting to Barycentric Coordinate Time (TCB) does not improve the timestamp accuracy since the light travel time across the Earth's radius is ~0.02 sec, which is much less than the timestamp precision.\n",
" - Using Barycentric Coordinate Time relative to Unix epoch as time coordinates so that CRTS data can be combined with other data in units of seconds.\n",
" - Using relative flux so that CRTS data can be combined with other data that is differential photometry, not absolute.\n",
"\n",
"For web-based data exploration, also see the VAO:\n",
"- http://www.usvao.org/science-tools-services/time-series-search-tool/ > click \"Launch\"\n",
"- http://vao-web.ipac.caltech.edu/applications/VAOTimeSeries/? > enter Location: 16h00m36.83s +27d21m17.8s, Radius: 10 arcsec\n",
"- A single result appears for CACR archive (http://nesssi.cacr.caltech.edu/DataRelease/). Click \"display\".\n",
"- Read the info message about needing to specifiy columns manually. Click \"periodogram\". An error message will appear \"Problem Processing Request\".\n",
"- Choose the correct data columns. Under Input:\n",
" - Periodogram type: Lomb Scargle\n",
" - Time column: ObsTime\n",
" - Data column: Mag\n",
" - Click \"Create Periodogram\".\n",
"\n",
"Related structures for this section:\n",
"```\n",
"dataframes.\n",
" crts.\n",
" all_data\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`dataframes.crts`: Load original data.\n",
"\n",
"`dataframes.crts.all_data`: Convert time and magnitude units.\n",
"\n",
"`dataframes.crts.all_data`: Plot light curve using pandas plotting utilities.\n"
]
},
{
"data": {
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jozxl72GGBqdZOTZadHJEuk4BXEQqa+XYKPsN3V50MkRyoQAuIiIS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\nBgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d503710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"`dataframes.crts.all_data`: First 5 records.\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MasterID</th>\n",
" <th>Mag</th>\n",
" <th>Magerr</th>\n",
" <th>RA</th>\n",
" <th>Dec</th>\n",
" <th>MJD</th>\n",
" <th>Blend</th>\n",
" <th>datetime_TCB</th>\n",
" <th>unixtime_TCB</th>\n",
" <th>flux_rel</th>\n",
" <th>flux_rel_err</th>\n",
" <th>filter</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1126078052790</td>\n",
" <td>17.45</td>\n",
" <td>0.10</td>\n",
" <td>240.15338</td>\n",
" <td>27.35499</td>\n",
" <td>53470.38932</td>\n",
" <td>0</td>\n",
" <td>2005-04-10 09:21:55.267443</td>\n",
" <td>1.113125e+09</td>\n",
" <td>0.972747</td>\n",
" <td>0.087989</td>\n",
" <td>V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1126078052790</td>\n",
" <td>17.41</td>\n",
" <td>0.10</td>\n",
" <td>240.15344</td>\n",
" <td>27.35494</td>\n",
" <td>53470.39657</td>\n",
" <td>0</td>\n",
" <td>2005-04-10 09:32:21.667452</td>\n",
" <td>1.113125e+09</td>\n",
" <td>1.009253</td>\n",
" <td>0.087989</td>\n",
" <td>V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1126078052790</td>\n",
" <td>17.36</td>\n",
" <td>0.10</td>\n",
" <td>240.15339</td>\n",
" <td>27.35497</td>\n",
" <td>53470.40384</td>\n",
" <td>0</td>\n",
" <td>2005-04-10 09:42:49.795462</td>\n",
" <td>1.113126e+09</td>\n",
" <td>1.056818</td>\n",
" <td>0.087989</td>\n",
" <td>V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1126078052790</td>\n",
" <td>17.39</td>\n",
" <td>0.10</td>\n",
" <td>240.15336</td>\n",
" <td>27.35496</td>\n",
" <td>53470.41115</td>\n",
" <td>0</td>\n",
" <td>2005-04-10 09:53:21.379472</td>\n",
" <td>1.113127e+09</td>\n",
" <td>1.028016</td>\n",
" <td>0.087989</td>\n",
" <td>V</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1126078052790</td>\n",
" <td>17.49</td>\n",
" <td>0.09</td>\n",
" <td>240.15342</td>\n",
" <td>27.35490</td>\n",
" <td>53479.38284</td>\n",
" <td>0</td>\n",
" <td>2005-04-19 09:12:35.407440</td>\n",
" <td>1.113902e+09</td>\n",
" <td>0.937562</td>\n",
" <td>0.079550</td>\n",
" <td>V</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MasterID Mag Magerr RA Dec MJD Blend \\\n",
"0 1126078052790 17.45 0.10 240.15338 27.35499 53470.38932 0 \n",
"1 1126078052790 17.41 0.10 240.15344 27.35494 53470.39657 0 \n",
"2 1126078052790 17.36 0.10 240.15339 27.35497 53470.40384 0 \n",
"3 1126078052790 17.39 0.10 240.15336 27.35496 53470.41115 0 \n",
"4 1126078052790 17.49 0.09 240.15342 27.35490 53479.38284 0 \n",
"\n",
" datetime_TCB unixtime_TCB flux_rel flux_rel_err filter \n",
"0 2005-04-10 09:21:55.267443 1.113125e+09 0.972747 0.087989 V \n",
"1 2005-04-10 09:32:21.667452 1.113125e+09 1.009253 0.087989 V \n",
"2 2005-04-10 09:42:49.795462 1.113126e+09 1.056818 0.087989 V \n",
"3 2005-04-10 09:53:21.379472 1.113127e+09 1.028016 0.087989 V \n",
"4 2005-04-19 09:12:35.407440 1.113902e+09 0.937562 0.079550 V "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"`dataframes.crts`: Load original data.\")\n",
"dataframes.crts = code.utils.Container()\n",
"path_crts = os.path.join(\n",
" path_project,\n",
" os.path.relpath(r'Work_Logs/20141203_CRTS_data/result_web_fileovSSl4.csv'))\n",
"dataframes.crts.all_data = \\\n",
" (pd.DataFrame.from_csv(path=path_crts)).sort(columns='MJD', ascending=True)\n",
"dataframes.crts.all_data = dataframes.crts.all_data.reset_index()\n",
"print()\n",
"print(\"`dataframes.crts.all_data`: Convert time and magnitude units.\")\n",
"# For CRTS telescope location, see notes above and http://nesssi.cacr.caltech.edu/DataRelease/\n",
"crts_telescope_location = (-110.731667*astropy_units.deg, 32.4166667*astropy_units.deg)\n",
"dataframes.crts.all_data['datetime_TCB'] = astropy_time.Time(\n",
" dataframes.crts.all_data['MJD'].values, format='mjd', scale='utc',\n",
" location=crts_telescope_location, precision=6).tcb.datetime\n",
"dataframes.crts.all_data['unixtime_TCB'] = astropy_time.Time(\n",
" dataframes.crts.all_data['MJD'].values, format='mjd', scale='utc',\n",
" location=crts_telescope_location, precision=6).tcb.unix\n",
"dataframes.crts.all_data['flux_rel'] = \\\n",
" map(lambda mag_1: bss.utils.calc_flux_intg_ratio_from_mags(\n",
" mag_1=mag_1, mag_2=dataframes.crts.all_data['Mag'].median()),\n",
" dataframes.crts.all_data['Mag'].values)\n",
"dataframes.crts.all_data['flux_rel_err'] = \\\n",
" map(lambda mag_1, mag_2: \\\n",
" abs(1.0 -\n",
" bss.utils.calc_flux_intg_ratio_from_mags(mag_1=mag_1, mag_2=mag_2)),\n",
" (dataframes.crts.all_data['Mag'] + dataframes.crts.all_data['Magerr']).values,\n",
" dataframes.crts.all_data['Mag'])\n",
"dataframes.crts.all_data['filter'] = 'V'\n",
"print()\n",
"print(\"`dataframes.crts.all_data`: Plot light curve using pandas plotting utilities.\")\n",
"df_plot = dataframes.crts.all_data.set_index(keys='datetime_TCB', inplace=False)\n",
"ax = pd.DataFrame.plot(\n",
" df_plot[['flux_rel', 'flux_rel_err']], yerr='flux_rel_err',\n",
" marker='.', linestyle='', ecolor='gray', linewidth=1)\n",
"ax.set_title(\"Flux vs time\")\n",
"ax.set_ylabel(\"Flux (relative)\")\n",
"plt.show(ax)\n",
"print()\n",
"print(\"`dataframes.crts.all_data`: First 5 records.\")\n",
"dataframes.crts.all_data.head(n=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### McDonald 2.1m, ProEM\n",
"\n",
"SDSS J160036.83+272117.8 was observed from 2014-06-29 to 2014-07-06 at the McDonald Observatory 2.1m telescope using a replacement for the Argos camera (Argos: http://www.as.utexas.edu/mcdonald/facilities/2.1m/argos.html). The replacement is a Princeton Instruments ProEM 1024 EMCCD. See the accompanying IPython Notebooks for the data reduction.\n",
"\n",
"* **Notes:**\n",
" * Relative differential photometry was takent at the McDonald 2.1m with a BG40 filter (http://search.newport.com/?q=*&x2=sku&q2=FSQ-BG40).\n",
" * The McDonald 2.1m is located on Mount Locke near Ft. Davis, TX. Latitude: 30° 40' 17.4\" N; Longitude: 104° 01' 21.4\" W; Elevation: 2076 m above the geoid; System: WGS84 (http://www.as.utexas.edu/mcdonald/coordinates.html)\n",
" * The data timestamp precision is ~0.3 seconds. **TODO: correct timestamps.** Using the telescope location when converting to Barycentric Coordinate Time (TCB) does not improve the timestamp accuracy since the light travel time across the Earth's radius is ~0.02 sec, which is much less than the timestamp precision.\n",
" - Using Barycentric Coordinate Time relative to Unix epoch as time coordinates so that data can be combined with other data in units of seconds.\n",
" - The data is in units of relative flux.\n",
" \n",
"Related structures for this section:\n",
"```\n",
"dataframes.\n",
" mcd.\n",
" all_data\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`dataframes.mcd.all_data`: Load original data.\n",
"\n",
"`dataframes.mcd.all_data`: Load steps:\n",
"Convert time units to unixtime TCB.\n",
"Rename the flux columns and calculate flux errors.\n",
"Define the filter column.\n",
"Plot light curve using `pandas` plotting utilities.\n",
"\n",
"`dataframes.mcd`: Loading /Users/samharrold/Google Drive/ccd.utexas/Projects/20140630_SDSS_J160036.83+272117.8/Work_Logs/20141018_lightcurves/20140629/SDSS_J160036.83+272117.8_lightcurves_custom.csv\n"
]
},
{
"data": {
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TK7ZoBzPBRWMixOt5ahbrybHbEA6Ca3RQ2UEC7rk/TPL8o/lNAStcLHJB6B6m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\ni+bufVNcXPCq3B34DoycPMyIjA8Foeu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hwkXXv24Ml3fe/iMAfPofzvDD895x5vNe/8Ji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\nA9OJylGzCc2nVsj2tfpfI0acZ9DYtj8UOA+YSmzGm2l7vKR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"text/plain": [
"<matplotlib.figure.Figure at 0x1042ab850>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"`dataframes.mcd`: Loading /Users/samharrold/Google Drive/ccd.utexas/Projects/20140630_SDSS_J160036.83+272117.8/Work_Logs/20141018_lightcurves/20140630/SDSS_J160036.83+272117.8_lightcurves_custom.csv\n"
]
},
{
"data": {
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kkk4rE94af0/5eCfYLhnNjWsHp4cFi8lA1Psu21E7Uuj9xupFEqPj986uuAVW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\n5OWJZfycaqfc4bnk2bEL/W1BjzyXX0iRBEVSOHG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"text/plain": [
"<matplotlib.figure.Figure at 0x10e76a2d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"`dataframes.mcd`: Loading /Users/samharrold/Google Drive/ccd.utexas/Projects/20140630_SDSS_J160036.83+272117.8/Work_Logs/20141018_lightcurves/20140701/SDSS_J160036.83+272117.8_lightcurves_custom.csv\n"
]
},
{
"data": {
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LSQbwlwBfArDWfg3w54VcAnzTWvsza+0kMARckWBaRCQB2exQ0bSx6255gH25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\nJphv30ZQ6l40F1c1jDFvMsb8lTHmFQR9V1/2+q0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"text/plain": [
"<matplotlib.figure.Figure at 0x10d9e0c90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"`dataframes.mcd`: Loading /Users/samharrold/Google Drive/ccd.utexas/Projects/20140630_SDSS_J160036.83+272117.8/Work_Logs/20141018_lightcurves/20140702/SDSS_J160036.83+272117.8_BG40-g-r-i-z_lightcurves_custom.csv\n"
]
},
{
"data": {
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3xYoVHfP/rZUBfi1wDHBZRCwDbikfiIjHAbdExO8DD1K0wj9b72Jbtz7YwqJO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\nktSB/n8PsvMnRGZOdQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ef08a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"`dataframes.mcd`: Loading /Users/samharrold/Google Drive/ccd.utexas/Projects/20140630_SDSS_J160036.83+272117.8/Work_Logs/20141018_lightcurves/20140703/SDSS_J160036.83+272117.8_BG40-g-r-i-z_lightcurves_custom.csv\n"
]
},
{
"data": {
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VqHORQzTve3oUwEXqqKdn+aSRttGMXEFcRPO+p6vSILYlZnZv7vbTI7cBJtz9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\n0bBtNJdCGAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f31ed50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"`dataframes.mcd`: Loading /Users/samharrold/Google Drive/ccd.utexas/Projects/20140630_SDSS_J160036.83+272117.8/Work_Logs/20141018_lightcurves/20140704/SDSS_J160036.83+272117.8_BG40-g-r-i-z_lightcurves_custom.csv\n"
]
},
{
"data": {
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HZLoUwCVLLLYi05FGw8lEZkdw8pb7EudXOjlSIxTAJUt7ewc9fXGSqaiGk4nM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\nsuRsXvz8Z/L0U0d56lPPzPpZTExMcOa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"text/plain": [
"<matplotlib.figure.Figure at 0x10f563f10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"`dataframes.mcd`: Loading /Users/samharrold/Google Drive/ccd.utexas/Projects/20140630_SDSS_J160036.83+272117.8/Work_Logs/20141018_lightcurves/20140706/SDSS_J160036.83+272117.8 2014-07-06 05_25_43_lightcurves_custom.csv\n"
]
},
{
"data": {
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L+e77v0Fv30nS6XWh6ppG8ixvHA4cOZyAxgOfdyg3xCYzQjKRo5G8/y+XXx44L+M0J4Y5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\nucaxSL2oWgncWjsObIk8/YPA9r3AK6v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"text/plain": [
"<matplotlib.figure.Figure at 0x10f8a3e50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"`dataframes.mcd.all_data`: Merge individual runs and create columns\n",
"for filename and frame_tracking_number.\n",
"\n",
"`dataframes.mcd.all_data`: First 5 records.\n"
]
},
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>filename</th>\n",
" <th>frame_tracking_number</th>\n",
" <th>comparisons_sum_flux</th>\n",
" <th>comparisons_sum_normalized_flux</th>\n",
" <th>datetime_TCB</th>\n",
" <th>datetime_UTC</th>\n",
" <th>exposure_end_timestamp_UTC</th>\n",
" <th>exposure_mid_timestamp_UTC</th>\n",
" <th>exposure_start_timestamp_UTC</th>\n",
" <th>filter</th>\n",
" <th>flux_rel</th>\n",
" <th>flux_rel_err</th>\n",
" <th>target_flux</th>\n",
" <th>target_normalized_flux</th>\n",
" <th>target_relative_flux</th>\n",
" <th>target_relative_normalized_detrended_flux</th>\n",
" <th>target_relative_normalized_flux</th>\n",
" <th>unixtime_TCB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>mcd_20140629</td>\n",
" <td>1</td>\n",
" <td>225148.776342</td>\n",
" <td>1.307918</td>\n",
" <td>2014-06-29 06:50:17.535700</td>\n",
" <td>2014-06-29 06:48:52.007275+00:00</td>\n",
" <td>2014-06-29 06:48:56.999275</td>\n",
" <td>2014-06-29 06:48:52.007275</td>\n",
" <td>2014-06-29 06:48:47.015275</td>\n",
" <td>BG40</td>\n",
" <td>0.970517</td>\n",
" <td>0.010325</td>\n",
" <td>31933.950577</td>\n",
" <td>1.269105</td>\n",
" <td>0.141835</td>\n",
" <td>0.970517</td>\n",
" <td>0.970517</td>\n",
" <td>1.404025e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>mcd_20140629</td>\n",
" <td>2</td>\n",
" <td>221423.508320</td>\n",
" <td>1.286277</td>\n",
" <td>2014-06-29 06:50:27.535541</td>\n",
" <td>2014-06-29 06:49:02.007116+00:00</td>\n",
" <td>2014-06-29 06:49:06.999116</td>\n",
" <td>2014-06-29 06:49:02.007116</td>\n",
" <td>2014-06-29 06:48:57.015116</td>\n",
" <td>BG40</td>\n",
" <td>0.975452</td>\n",
" <td>0.010325</td>\n",
" <td>31565.267221</td>\n",
" <td>1.254453</td>\n",
" <td>0.142556</td>\n",
" <td>0.975452</td>\n",
" <td>0.975452</td>\n",
" <td>1.404025e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>mcd_20140629</td>\n",
" <td>3</td>\n",
" <td>222659.350493</td>\n",
" <td>1.293457</td>\n",
" <td>2014-06-29 06:50:37.535384</td>\n",
" <td>2014-06-29 06:49:12.006959+00:00</td>\n",
" <td>2014-06-29 06:49:16.998959</td>\n",
" <td>2014-06-29 06:49:12.006959</td>\n",
" <td>2014-06-29 06:49:07.014959</td>\n",
" <td>BG40</td>\n",
" <td>0.969970</td>\n",
" <td>0.010325</td>\n",
" <td>31563.041380</td>\n",
" <td>1.254365</td>\n",
" <td>0.141755</td>\n",
" <td>0.969970</td>\n",
" <td>0.969970</td>\n",
" <td>1.404025e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>mcd_20140629</td>\n",
" <td>4</td>\n",
" <td>223471.600866</td>\n",
" <td>1.298175</td>\n",
" <td>2014-06-29 06:50:47.535227</td>\n",
" <td>2014-06-29 06:49:22.006802+00:00</td>\n",
" <td>2014-06-29 06:49:26.998802</td>\n",
" <td>2014-06-29 06:49:22.006802</td>\n",
" <td>2014-06-29 06:49:17.014802</td>\n",
" <td>BG40</td>\n",
" <td>0.963127</td>\n",
" <td>0.010325</td>\n",
" <td>31454.695334</td>\n",
" <td>1.250059</td>\n",
" <td>0.140755</td>\n",
" <td>0.963127</td>\n",
" <td>0.963127</td>\n",
" <td>1.404025e+09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>mcd_20140629</td>\n",
" <td>5</td>\n",
" <td>223730.933450</td>\n",
" <td>1.299681</td>\n",
" <td>2014-06-29 06:50:57.535070</td>\n",
" <td>2014-06-29 06:49:32.006645+00:00</td>\n",
" <td>2014-06-29 06:49:36.998645</td>\n",
" <td>2014-06-29 06:49:32.006645</td>\n",
" <td>2014-06-29 06:49:27.014645</td>\n",
" <td>BG40</td>\n",
" <td>0.962623</td>\n",
" <td>0.010325</td>\n",
" <td>31474.724597</td>\n",
" <td>1.250855</td>\n",
" <td>0.140681</td>\n",
" <td>0.962623</td>\n",
" <td>0.962623</td>\n",
" <td>1.404025e+09</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" filename frame_tracking_number comparisons_sum_flux \\\n",
"0 mcd_20140629 1 225148.776342 \n",
"1 mcd_20140629 2 221423.508320 \n",
"2 mcd_20140629 3 222659.350493 \n",
"3 mcd_20140629 4 223471.600866 \n",
"4 mcd_20140629 5 223730.933450 \n",
"\n",
" comparisons_sum_normalized_flux datetime_TCB \\\n",
"0 1.307918 2014-06-29 06:50:17.535700 \n",
"1 1.286277 2014-06-29 06:50:27.535541 \n",
"2 1.293457 2014-06-29 06:50:37.535384 \n",
"3 1.298175 2014-06-29 06:50:47.535227 \n",
"4 1.299681 2014-06-29 06:50:57.535070 \n",
"\n",
" datetime_UTC exposure_end_timestamp_UTC \\\n",
"0 2014-06-29 06:48:52.007275+00:00 2014-06-29 06:48:56.999275 \n",
"1 2014-06-29 06:49:02.007116+00:00 2014-06-29 06:49:06.999116 \n",
"2 2014-06-29 06:49:12.006959+00:00 2014-06-29 06:49:16.998959 \n",
"3 2014-06-29 06:49:22.006802+00:00 2014-06-29 06:49:26.998802 \n",
"4 2014-06-29 06:49:32.006645+00:00 2014-06-29 06:49:36.998645 \n",
"\n",
" exposure_mid_timestamp_UTC exposure_start_timestamp_UTC filter flux_rel \\\n",
"0 2014-06-29 06:48:52.007275 2014-06-29 06:48:47.015275 BG40 0.970517 \n",
"1 2014-06-29 06:49:02.007116 2014-06-29 06:48:57.015116 BG40 0.975452 \n",
"2 2014-06-29 06:49:12.006959 2014-06-29 06:49:07.014959 BG40 0.969970 \n",
"3 2014-06-29 06:49:22.006802 2014-06-29 06:49:17.014802 BG40 0.963127 \n",
"4 2014-06-29 06:49:32.006645 2014-06-29 06:49:27.014645 BG40 0.962623 \n",
"\n",
" flux_rel_err target_flux target_normalized_flux target_relative_flux \\\n",
"0 0.010325 31933.950577 1.269105 0.141835 \n",
"1 0.010325 31565.267221 1.254453 0.142556 \n",
"2 0.010325 31563.041380 1.254365 0.141755 \n",
"3 0.010325 31454.695334 1.250059 0.140755 \n",
"4 0.010325 31474.724597 1.250855 0.140681 \n",
"\n",
" target_relative_normalized_detrended_flux target_relative_normalized_flux \\\n",
"0 0.970517 0.970517 \n",
"1 0.975452 0.975452 \n",
"2 0.969970 0.969970 \n",
"3 0.963127 0.963127 \n",
"4 0.962623 0.962623 \n",
"\n",
" unixtime_TCB \n",
"0 1.404025e+09 \n",
"1 1.404025e+09 \n",
"2 1.404025e+09 \n",
"3 1.404025e+09 \n",
"4 1.404025e+09 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(\"`dataframes.mcd.all_data`: Load original data.\")\n",
"dataframes.mcd = code.utils.Container()\n",
"dataframes.mcd.all_data = code.utils.Container()\n",
"path_mcd = os.path.join(\n",
" path_project,\n",
" os.path.relpath(r'Work_Logs/20141018_lightcurves'))\n",
"paths_mcd_csvs = dict(\n",
" mcd_20140629=os.path.relpath(\n",
" r'20140629/SDSS_J160036.83+272117.8_lightcurves_custom.csv'),\n",
" mcd_20140630=os.path.relpath(\n",
" r'20140630/SDSS_J160036.83+272117.8_lightcurves_custom.csv'),\n",
" mcd_20140701=os.path.relpath(\n",
" r'20140701/SDSS_J160036.83+272117.8_lightcurves_custom.csv'),\n",
" mcd_20140702=os.path.relpath(\n",
" r'20140702/SDSS_J160036.83+272117.8_BG40-g-r-i-z_lightcurves_custom.csv'),\n",
" mcd_20140703=os.path.relpath(\n",
" r'20140703/SDSS_J160036.83+272117.8_BG40-g-r-i-z_lightcurves_custom.csv'),\n",
" mcd_20140704=os.path.relpath(\n",
" r'20140704/SDSS_J160036.83+272117.8_BG40-g-r-i-z_lightcurves_custom.csv'),\n",
" mcd_20140706=os.path.relpath(\n",
" r'20140706/SDSS_J160036.83+272117.8 2014-07-06 05_25_43_lightcurves_custom.csv'))\n",
"print()\n",
"print(\"`dataframes.mcd.all_data`: Load steps:\\n\" +\n",
" \"Convert time units to unixtime TCB.\\n\" +\n",
" \"Rename the flux columns and calculate flux errors.\\n\" +\n",
" \"Define the filter column.\\n\" +\n",
" \"Plot light curve using `pandas` plotting utilities.\")\n",
"# For McDonald telescope location, see notes above.\n",
"mcd_telescope_location = \\\n",
" (-104.022611*astropy_units.deg, 30.6715*astropy_units.deg, 2076.0*astropy_units.meter)\n",
"df_dict = dict()\n",
"for (key, path_csv) in sorted(paths_mcd_csvs.items()):\n",
" path = os.path.join(path_mcd, path_csv)\n",
" print()\n",
" print(\"`dataframes.mcd`: Loading {path}\".format(path=path))\n",
" df_dict[key] = pd.DataFrame.from_csv(path=path)\n",
" # Convert time units to unixtime TCB.\n",
" # Use only the mid-exposure timestamps.\n",
" df_dict[key]['datetime_UTC'] = \\\n",
" df_dict[key]['exposure_mid_timestamp_UTC'].apply(\n",
" lambda date_str: dt.datetime.strptime(\n",
" date_str, '%Y-%m-%d %H:%M:%S.%f').replace(tzinfo=pytz.utc))\n",
" df_dict[key]['datetime_TCB'] = astropy_time.Time(\n",
" df_dict[key]['datetime_UTC'].values, format='datetime', scale='utc',\n",
" location=mcd_telescope_location, precision=6).tcb.datetime\n",
" df_dict[key]['unixtime_TCB'] = astropy_time.Time(\n",
" df_dict[key]['datetime_UTC'].values, format='datetime', scale='utc',\n",
" location=mcd_telescope_location, precision=6).tcb.unix\n",
" # Rename the flux columns and calculate flux errors.\n",
" # NOTE: Flux errors are estimated using a rolling median of the flux differences.\n",
" # This method avoids having to remove artifacts due to the eclipses.\n",
" # The window for the rolling median is 15 minutes in duration.\n",
" # The method is usually within +/- 10% of a rank-based standard deviation.\n",
" # NOTE: The data are not required to be consecutive since using a rolling median\n",
" # is more robust to outliers than a rolling mean.\n",
" try:\n",
" df_dict[key]['flux_rel'] = \\\n",
" df_dict[key]['target_relative_normalized_detrended_flux'].copy()\n",
" except KeyError:\n",
" df_dict[key]['flux_rel'] = \\\n",
" df_dict[key]['target_relative_normalized_flux'].copy()\n",
" times = df_dict[key]['unixtime_TCB'].values\n",
" times_resolution = np.median(np.diff(times))\n",
" fluxes = df_dict[key]['flux_rel'].values\n",
" fluxes_absdiff = np.abs(np.diff(fluxes))\n",
" window = int(15*scipy_con.minute/times_resolution)\n",
" fluxes_err_roll = np.median(\n",
" code.utils.rolling_window(\n",
" arr=fluxes_absdiff, window=window), axis=1)\n",
" roll_start_idx = int((len(fluxes) - len(fluxes_err_roll))/2.0)\n",
" roll_stop_idx = roll_start_idx + len(fluxes_err_roll)\n",
" fluxes_err = np.interp(\n",
" x=times, xp=times[roll_start_idx:roll_stop_idx], fp=fluxes_err_roll)\n",
" df_dict[key]['flux_rel_err'] = fluxes_err\n",
" # Define the filter column.\n",
" df_dict[key]['filter'] = 'BG40'\n",
" # Plot light curve using pandas plotting utilities.\")\n",
" df_plot = df_dict[key].set_index(keys='datetime_TCB', inplace=False)\n",
" ax = pd.DataFrame.plot(\n",
" df_plot[['flux_rel', 'flux_rel_err']], yerr='flux_rel_err',\n",
" marker='.', linestyle='', ecolor='gray', linewidth=1)\n",
" ax.set_title(\"Flux vs time\")\n",
" ax.set_ylabel(\"Flux (relative)\")\n",
" plt.show(ax)\n",
"# Merge dataframes.\n",
"print()\n",
"print(\"`dataframes.mcd.all_data`: Merge individual runs and create columns\\n\" +\n",
" \"for filename and frame_tracking_number.\")\n",
"dataframes.mcd.all_data = pd.concat(df_dict, axis=0)\n",
"dataframes.mcd.all_data.index.names = ['filename', 'frame_tracking_number']\n",
"dataframes.mcd.all_data.reset_index(inplace=True)\n",
"dataframes.mcd.all_data.sort(columns='unixtime_TCB', ascending=True, inplace=True)\n",
"print()\n",
"print(\"`dataframes.mcd.all_data`: First 5 records.\")\n",
"dataframes.mcd.all_data.head(n=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate light curve period and phase using Lomb-Scargle light curve model.\n",
"\n",
"* Calculate the single period with Fourier terms that accounts for the most variability of the light curve.\n",
"* Using `gatspy` from VanderPlas, Ivezic, 2015 (http://jakevdp.github.io/multiband_LS/, https://github.com/astroML/gatspy/tree/master/examples).\n",
"* Using unixtime as time units and manually defining period range and period sampling (c.f. https://github.com/astroML/gatspy/issues/3)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model CRTS data, outliers and inliers\n",
"\n",
"Related structures for this section:\n",
"```\n",
"models.\n",
" ls.\n",
" crts.\n",
" all_data.\n",
" model\n",
" periods.\n",
" min, max, num, values, delta\n",
" sigs.\n",
" levels, periods.values, shuffles.num, powers.values\n",
" powers.values\n",
" zoom.\n",
" periods.\n",
" num, oversample, halfwidth, min, max, values\n",
" powers.values\n",
" sigs.\n",
" levels, periods.values, shuffles.num, powers.values\n",
" times, phases, fluxes, fluxes_err, filts\n",
" fit.\n",
" best_period, min_flux_time\n",
" times, phases, filts, fluxes\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.all_data` ('ls' for Lomb-Scargle): Calculate the best period using a Lomb-Scargle\n",
"light curve model fit to the CRTS data.\n",
"\n",
"Finding optimal frequency:\n",
" - Using omega_step = 0.00000\n",
" - Computing periods at 5 steps from 86677.54 to 86700.00\n",
"Zooming-in on 5 candidate peaks:\n",
" - Computing periods at 1005 steps\n",
"models.ls.crts.all_data.model.best_period = 86691.1259531 seconds\n"
]
}
],
"source": [
"print(\"`models.ls.crts.all_data` ('ls' for Lomb-Scargle): Calculate the best period using a Lomb-Scargle\\n\" +\n",
" \"light curve model fit to the CRTS data.\")\n",
"# NOTE: The concept of Nyquist limits does not apply to irregularly sampled data:\n",
"# VanderPlas and Ivezic, 2015, http://adsabs.harvard.edu/abs/2015arXiv150201344\n",
"# https://github.com/astroML/gatspy/issues/3\\n\"+\n",
"# However, Nyquist limits are applied as a conservative constraint.\")\n",
"# NOTE: To fit eclipses well often requires ~6 terms, from section 10.3.3 of\n",
"# Ivezic et al, 2014, 'Statistics, Data Mining, and Machine Learning in Astronomy'\n",
"# More data often requires more terms. Eclipses have even parity, so should the model.\n",
"# NOTE: For only the CRTS data with full period range,\n",
"# This cell takes ~10 minutes to execute for a 2.7GHz processor.\n",
"# Initialize gatspy.periodic.optimizer.LinearScanOptimizer\n",
"# Define a period_range to search for periods.\n",
"# NOTE: The data from McDonald heavily samples the primary eclipse and biases the\n",
"# Lomb-Scargle model against other phases. Only use the McDonald data for MCMC.\n",
"models = code.utils.Container()\n",
"models.ls = code.utils.Container()\n",
"models.ls.crts = code.utils.Container()\n",
"models.ls.crts.all_data = code.utils.Container()\n",
"models.ls.crts.all_data.model = gatspy_per.LombScargleMultiband(Nterms_base=6, Nterms_band=1)\n",
"models.ls.crts.all_data.model.fit(\n",
" t=dataframes.crts.all_data['unixtime_TCB'].values,\n",
" y=dataframes.crts.all_data['flux_rel'].values,\n",
" dy=dataframes.crts.all_data['flux_rel_err'].values,\n",
" filts=dataframes.crts.all_data['filter'].values)\n",
"models.ls.crts.all_data.periods = code.utils.Container()\n",
"(models.ls.crts.all_data.periods.min,\n",
" models.ls.crts.all_data.periods.max,\n",
" models.ls.crts.all_data.periods.num) = \\\n",
" code.utils.calc_period_limits(\n",
" times=dataframes.crts.all_data['unixtime_TCB'].values)\n",
"models.ls.crts.all_data.model.optimizer.period_range = \\\n",
" (models.ls.crts.all_data.periods.min, models.ls.crts.all_data.periods.max)\n",
"# TODO: remove speedup when done with testing\n",
"models.ls.crts.all_data.model.optimizer.period_range = (86680, 86700)\n",
"print()\n",
"print(\"models.ls.crts.all_data.model.best_period = {bp} seconds\".format(\n",
" bp=models.ls.crts.all_data.model.best_period))\n",
"# TODO: remove speedup when done with testing\n",
"models.ls.crts.all_data.model.optimizer.period_range = \\\n",
" (models.ls.crts.all_data.periods.min, models.ls.crts.all_data.periods.max)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.all_data`: Estimate significance levels from Lomb-Scargle periodogram.\n",
"Plot annotated periodogram of entire searched period space but at\n",
"low period resolution.\n",
"Dotted vertical line is models.ls.crts.all_data.model.best_period = 86691.1259531 seconds\n",
"Dashed lines are significance levels at (95.0, 99.0, 99.9) percentiles.\n"
]
},
{
"data": {
"image/png": 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G8FRA19E31IZIWePTTz+mRYsidt65J2PGXENx8e0MHTqEUaOup2vXnVIub9Wq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\npktAKua/gm+OihknIOFbNE1rhrer99Z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7iht1dnqOaJG+BlXf+54OiLogb90XHLdrwrx+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\nByn/D/KQAVWL7G4ZAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fdf6ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"`models.ls.crts.all_data`: Estimate significance levels from Lomb-Scargle periodogram.\")\n",
"print(\"Plot annotated periodogram of entire searched period space but at\\n\" +\n",
" \"low period resolution.\")\n",
"# NOTE: This cell takes ~1.5 minutes to execute.\n",
"# NOTE: `gatspy` searches period space linearly in angular frequency.\")\n",
"# NOTE: This periodogram has low period resolution and does not show all\n",
"# periods at their actual power levels.\n",
"models.ls.crts.all_data.sigs = code.utils.Container()\n",
"models.ls.crts.all_data.sigs.levels = (95.0, 99.0, 99.9)\n",
"print(\"Dotted vertical line is models.ls.crts.all_data.model.best_period = {bp} seconds\".format(\n",
" bp=models.ls.crts.all_data.model.best_period))\n",
"print(\"Dashed lines are significance levels at {sigs} percentiles.\".format(\n",
" sigs=models.ls.crts.all_data.sigs.levels))\n",
"models.ls.crts.all_data.sigs.periods = code.utils.Container()\n",
"models.ls.crts.all_data.sigs.periods.values = np.logspace(\n",
" start=np.log10(models.ls.crts.all_data.periods.min),\n",
" stop=np.log10(models.ls.crts.all_data.periods.max),\n",
" num=21, endpoint=True, base=10.0)\n",
"models.ls.crts.all_data.sigs.shuffles = code.utils.Container()\n",
"models.ls.crts.all_data.sigs.shuffles.num = int(1e4)\n",
"# TODO: remove small sig_num_shuffles after done with testing\n",
"models.ls.crts.all_data.sigs.shuffles.num = 100\n",
"# Calculate significance levels for entire period_range.\n",
"models.ls.crts.all_data.periods.values = np.logspace(\n",
" start=np.log10(models.ls.crts.all_data.periods.min),\n",
" stop=np.log10(models.ls.crts.all_data.periods.max),\n",
" num=1001, endpoint=True, base=10.0)\n",
"models.ls.crts.all_data.powers = code.utils.Container()\n",
"models.ls.crts.all_data.powers.values = models.ls.crts.all_data.model.periodogram(\n",
" periods=models.ls.crts.all_data.periods.values)\n",
"models.ls.crts.all_data.sigs.powers = code.utils.Container()\n",
"models.ls.crts.all_data.sigs.powers.values = code.utils.calc_sig_levels(\n",
" model=models.ls.crts.all_data.model,\n",
" sig_periods=models.ls.crts.all_data.sigs.periods.values,\n",
" sigs=models.ls.crts.all_data.sigs.levels,\n",
" num_shuffles=models.ls.crts.all_data.sigs.shuffles.num)\n",
"code.utils.plot_periodogram(\n",
" periods=models.ls.crts.all_data.periods.values,\n",
" powers=models.ls.crts.all_data.powers.values,\n",
" best_period=models.ls.crts.all_data.model.best_period,\n",
" sig_periods=models.ls.crts.all_data.sigs.periods.values,\n",
" sig_powers=models.ls.crts.all_data.sigs.powers.values,\n",
" xscale='log', period_unit='seconds',\n",
" flux_unit='relative', return_ax=False)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.all_data.zoom`: Plot periodogram zoomed in on best_period.\n",
"Ensure that period space is sampled at much greater resolution\n",
"than the data. Period space is sampled linearly in the zoomed periodogram.\n"
]
},
{
"data": {
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1tDOMMZFEq4SPi/Ja5IW2UCwiZ+F04d8uIqdjs/GN2WM0TIiLODkv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\nZiX3C7N++A5KkIBbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f0d7310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"`models.ls.crts.all_data.zoom`: Plot periodogram zoomed in on best_period.\\n\" +\n",
" \"Ensure that period space is sampled at much greater resolution\\n\" +\n",
" \"than the data. Period space is sampled linearly in the zoomed periodogram.\")\n",
"models.ls.crts.all_data.periods.delta = \\\n",
" (models.ls.crts.all_data.periods.max - models.ls.crts.all_data.periods.min) / \\\n",
" models.ls.crts.all_data.periods.num\n",
"models.ls.crts.all_data.zoom = code.utils.Container()\n",
"models.ls.crts.all_data.zoom.periods = code.utils.Container()\n",
"models.ls.crts.all_data.zoom.periods.num = 1001\n",
"models.ls.crts.all_data.zoom.periods.oversample = 0.001\n",
"models.ls.crts.all_data.zoom.periods.halfwidth = (\n",
" (models.ls.crts.all_data.zoom.periods.num/2.0) * \n",
" models.ls.crts.all_data.periods.delta *\n",
" models.ls.crts.all_data.zoom.periods.oversample)\n",
"models.ls.crts.all_data.zoom.periods.min = \\\n",
" models.ls.crts.all_data.model.best_period - models.ls.crts.all_data.zoom.periods.halfwidth\n",
"models.ls.crts.all_data.zoom.periods.max = \\\n",
" models.ls.crts.all_data.model.best_period + models.ls.crts.all_data.zoom.periods.halfwidth\n",
"models.ls.crts.all_data.zoom.periods.values = np.clip(\n",
" np.linspace(\n",
" start=models.ls.crts.all_data.zoom.periods.min,\n",
" stop=models.ls.crts.all_data.zoom.periods.max,\n",
" num=models.ls.crts.all_data.zoom.periods.num, endpoint=True),\n",
" models.ls.crts.all_data.periods.min,\n",
" models.ls.crts.all_data.periods.max)\n",
"models.ls.crts.all_data.zoom.powers = code.utils.Container()\n",
"models.ls.crts.all_data.zoom.powers.values = \\\n",
" models.ls.crts.all_data.model.periodogram(\n",
" periods=models.ls.crts.all_data.zoom.periods.values)\n",
"# Calculate significance levels for zoomed period range.\n",
"models.ls.crts.all_data.zoom.sigs = code.utils.Container()\n",
"models.ls.crts.all_data.zoom.sigs.levels = models.ls.crts.all_data.sigs.levels\n",
"models.ls.crts.all_data.zoom.sigs.periods = code.utils.Container()\n",
"models.ls.crts.all_data.zoom.sigs.periods.values = np.linspace(\n",
" start=models.ls.crts.all_data.zoom.periods.min,\n",
" stop=models.ls.crts.all_data.zoom.periods.max,\n",
" num=21, endpoint=True)\n",
"models.ls.crts.all_data.zoom.sigs.shuffles = code.utils.Container()\n",
"models.ls.crts.all_data.zoom.sigs.shuffles.num = models.ls.crts.all_data.sigs.shuffles.num\n",
"models.ls.crts.all_data.zoom.sigs.powers = code.utils.Container()\n",
"models.ls.crts.all_data.zoom.sigs.powers.values = code.utils.calc_sig_levels(\n",
" model=models.ls.crts.all_data.model,\n",
" sig_periods=models.ls.crts.all_data.zoom.sigs.periods.values,\n",
" sigs=models.ls.crts.all_data.zoom.sigs.levels,\n",
" num_shuffles=models.ls.crts.all_data.zoom.sigs.shuffles.num)\n",
"code.utils.plot_periodogram(\n",
" periods=models.ls.crts.all_data.zoom.periods.values,\n",
" powers=models.ls.crts.all_data.zoom.powers.values,\n",
" best_period=models.ls.crts.all_data.model.best_period,\n",
" sig_periods=models.ls.crts.all_data.zoom.sigs.periods.values,\n",
" sig_powers=models.ls.crts.all_data.zoom.sigs.powers.values,\n",
" xscale='linear', period_unit='seconds',\n",
" flux_unit='relative', return_ax=False)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.all_data.fit`: Save fit variables from the Lomb-Scargle light curve model and\n",
"plot a phased light curve.\n"
]
},
{
"data": {
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OhyAIgiDcFKyoYTcM49+ATJmf/wl4C/By4CW6rr96JfMiCIIgCDcDNzJ47i8Mw7hmGEYa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\nJei4J/x7PQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1042b6610>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Model is only valid for the times fit:\n",
"from 2009-04-02T09:37:41.520Z to 2009-04-03T09:41:05.955Z\n"
]
}
],
"source": [
"print(\"`models.ls.crts.all_data.fit`: Save fit variables from the Lomb-Scargle light curve model and\\n\" +\n",
" \"plot a phased light curve.\")\n",
"# TODO: generalize to fitting multiple filters.\n",
"models.ls.crts.all_data.times = models.ls.crts.all_data.model.t\n",
"models.ls.crts.all_data.fluxes = models.ls.crts.all_data.model.y\n",
"models.ls.crts.all_data.fluxes_err = models.ls.crts.all_data.model.dy\n",
"models.ls.crts.all_data.filts = models.ls.crts.all_data.model.filts\n",
"models.ls.crts.all_data.fit = code.utils.Container()\n",
"models.ls.crts.all_data.fit.best_period = models.ls.crts.all_data.model.best_period\n",
"models.ls.crts.all_data.fit.min_flux_time = code.utils.calc_min_flux_time(\n",
" model=models.ls.crts.all_data.model,\n",
" filt=models.ls.crts.all_data.filts[0],\n",
" best_period=models.ls.crts.all_data.fit.best_period,\n",
" tol=0.1, maxiter=10)\n",
"models.ls.crts.all_data.phases = code.utils.calc_phases(\n",
" times=models.ls.crts.all_data.times,\n",
" best_period=models.ls.crts.all_data.fit.best_period,\n",
" min_flux_time=models.ls.crts.all_data.fit.min_flux_time)\n",
"dataframes.crts.all_data['phase_dec'] = models.ls.crts.all_data.phases\n",
"# The model is most applicable during times when the data was taken.\n",
"models.ls.crts.all_data.fit.times = np.linspace(\n",
" start=np.median(models.ls.crts.all_data.times),\n",
" stop=np.median(models.ls.crts.all_data.times) + models.ls.crts.all_data.fit.best_period,\n",
" num=1000, endpoint=False)\n",
"models.ls.crts.all_data.fit.phases = code.utils.calc_phases(\n",
" times=models.ls.crts.all_data.fit.times,\n",
" best_period=models.ls.crts.all_data.fit.best_period,\n",
" min_flux_time=models.ls.crts.all_data.fit.min_flux_time)\n",
"# Sort the fit data so that phase is monotonically increasing.\n",
"sorted_idxs = np.argsort(models.ls.crts.all_data.fit.phases)\n",
"models.ls.crts.all_data.fit.times = models.ls.crts.all_data.fit.times[sorted_idxs]\n",
"models.ls.crts.all_data.fit.phases = models.ls.crts.all_data.fit.phases[sorted_idxs]\n",
"models.ls.crts.all_data.fit.filts = \\\n",
" [models.ls.crts.all_data.filts[0]]*len(models.ls.crts.all_data.fit.phases)\n",
"models.ls.crts.all_data.fit.fluxes = models.ls.crts.all_data.model.predict(\n",
" t=models.ls.crts.all_data.fit.times,\n",
" filts=models.ls.crts.all_data.fit.filts,\n",
" period=models.ls.crts.all_data.fit.best_period)\n",
"code.utils.plot_phased_light_curve(\n",
" phases=models.ls.crts.all_data.phases,\n",
" fluxes=models.ls.crts.all_data.fluxes,\n",
" fluxes_err=models.ls.crts.all_data.fluxes_err,\n",
" fit_phases=models.ls.crts.all_data.fit.phases,\n",
" fit_fluxes=models.ls.crts.all_data.fit.fluxes,\n",
" flux_unit='relative', return_ax=False)\n",
"print()\n",
"print((\"Model is only valid for the times fit:\\n\"\n",
" \"from {ts_begin}Z to {ts_end}Z\").format(\n",
" ts_begin=astropy_time.Time(min(models.ls.crts.all_data.fit.times), format='unix', scale='tcb').utc.isot,\n",
" ts_end=astropy_time.Time(max(models.ls.crts.all_data.fit.times), format='unix', scale='tcb').utc.isot))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Identify light curve as eclipse\n",
"\n",
"Related structures for this section:\n",
"```\n",
"models.\n",
" ls.\n",
" crts.\n",
" all_data.\n",
" fit.\n",
" tess.\n",
" values\n",
" min\n",
" phases\n",
" fluxes\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.all_data.fit`: Identify primary (deepest) and secondary minima.\n",
"\n",
"Classify the light curve as an eclipse if deepest minimum\n",
"occurs at phase~0 and 2nd deepest minimum occurs at phase~0.5.\n",
"\n",
"Method for determining minima may not apply to stars with\n",
"intrinsic variablity that exceeds the flux varability due\n",
"to an eclipse.\n",
"\n",
"Light curve may be an eclipse.\n",
"Deepest minima occur at phases 0.0 and 0.5\n",
"within phase tolerance 0.01\n"
]
}
],
"source": [
"print(\"`models.ls.crts.all_data.fit`: Identify primary (deepest) and secondary minima.\")\n",
"print()\n",
"print(\"Classify the light curve as an eclipse if deepest minimum\\n\" +\n",
" \"occurs at phase~0 and 2nd deepest minimum occurs at phase~0.5.\")\n",
"print()\n",
"print(\"Method for determining minima may not apply to stars with\\n\" +\n",
" \"intrinsic variablity that exceeds the flux varability due\\n\" +\n",
" \"to an eclipse.\")\n",
"# NOTE: scipy.signal requires a positive slope to identify a previous minimum.\n",
"# Append the fit model to itself (tesselate) 1 times for total of 2 cycles\n",
"# so that scipy.signal can find minima at all phases.\n",
"models.ls.crts.all_data.fit.tess = code.utils.Container()\n",
"models.ls.crts.all_data.fit.tess.values = \\\n",
" np.asarray(\n",
" zip(np.append(models.ls.crts.all_data.fit.phases,\n",
" np.add(models.ls.crts.all_data.fit.phases, 1.0)),\n",
" np.append(models.ls.crts.all_data.fit.fluxes,\n",
" models.ls.crts.all_data.fit.fluxes)),\n",
" dtype=[('phase_dec', float), ('flux_rel', float)])\n",
"min_idxs = scipy_sig.argrelmin(models.ls.crts.all_data.fit.tess.values['flux_rel'])\n",
"models.ls.crts.all_data.fit.tess.min = models.ls.crts.all_data.fit.tess.values[min_idxs]\n",
"# Phase ~1.0 is same as phase ~0.0.\n",
"models.ls.crts.all_data.fit.tess.min['phase_dec'] = \\\n",
" np.mod(models.ls.crts.all_data.fit.tess.min['phase_dec'], 1.0)\n",
"tfmask_phases_gt05 = models.ls.crts.all_data.fit.tess.min['phase_dec'] > 0.5\n",
"models.ls.crts.all_data.fit.tess.min['phase_dec'][tfmask_phases_gt05] = \\\n",
" np.subtract(1.0, models.ls.crts.all_data.fit.tess.min['phase_dec'][tfmask_phases_gt05])\n",
"models.ls.crts.all_data.fit.tess.min = \\\n",
" np.sort(np.unique(models.ls.crts.all_data.fit.tess.min), order=['flux_rel', 'phase_dec'])[:2]\n",
"tol_phase = 0.01\n",
"if (np.isclose(models.ls.crts.all_data.fit.tess.min[0]['phase_dec'], 0.0, atol=tol_phase) and\n",
" np.isclose(models.ls.crts.all_data.fit.tess.min[1]['phase_dec'], 0.5, atol=tol_phase)):\n",
" print()\n",
" print((\"Light curve may be an eclipse.\\n\" +\n",
" \"Deepest minima occur at phases 0.0 and 0.5\\n\" +\n",
" \"within phase tolerance {tol}\").format(tol=tol_phase))\n",
"else:\n",
" print()\n",
" warnings.warn(\n",
" (\"Light curve does NOT appear to be an eclipse.\\n\" +\n",
" \"Deepest minima do not occur at phases 0.0 and 0.5\\n\" +\n",
" \"within phase tolerance {tol}\").format(tol=tol_phase))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Identify outliers\n",
"\n",
"TODO: redo outlier rejection following section 3 of Hogg et al 2010, http://arxiv.org/pdf/1008.4686v1.pdf\n",
"\n",
"Related structures for this section:\n",
"```\n",
"models.\n",
" ls.\n",
" crts.\n",
" all_data.\n",
" fluxes_res, fluxes_interp\n",
" phases, fluxes, fluxes_err\n",
" model, z1, z2\n",
" res.\n",
" model, fit, outlier_test, is_inlier\n",
" fit.phases\n",
" inliers\n",
" z1, z2\n",
"dataframes.\n",
" crts.\n",
" all_data\n",
" inliers\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.all_data.res`, `dataframes.crts.all_data`: Plot phased residuals\n",
"from Lomb-Scargle light curve model.\n"
]
},
{
"data": {
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8zaOjK7psSTLhQKFW0zjuuinm5SpFr1v/UEOGvmb1qDSVQr9p6rGJx2Z+f+zR\n/HOm/X1un5ydoXzf1onc8Xcjj4L+1VRePXXr/rymovG3oinPFPNKHy+R6PCdc1POuXucc892zv3M\nOXd3+FeqFX1IVqkzz3rIvdBvu2nTRpYvX9ltM2a48LLrZ9ItLLzNzlNuvU922cgWhmlegKdXkKaq\nIUlTa9eNsWxky4yeDm5hxfB95u2Y+fzUJSOl2VwGebTQL5rqNcJm92g+FeZRRTVVdR6VZy39viKt\nr6lM4aaVJP1gnLgmndCGXtiitZdK/JsnlvKL8enYAUxhTaqddbiPXXzbjD58nGksG9mS2nfZKU0l\nNRNG4x8bu6nrGXSvaurCy66PPacdPUX1kaf/tVOaynoO0lRr5JmxUFYelVdTeZlzDh+S+5p6QTRR\nG8rqc4oyOrqi6y9pu6xZPTpT2s3bBJ8Hr428fa9pfZfd1lRc/FXZ1O+aCpvvy8Tro8jUNWlqttun\nnzUVRxn304qm8pA2D/+QtL+2Y57DhM2EWc3OWYOKWmX58pWVZRxVbd8Y1nx8Zhg2mQ4y7XRllJWh\nVqWpKrcDjdOUqNGqpjZt2lhaHgXVFCaq1FTWjIVuF9jSSKvh3wisT/kTKWQ16aS9MO0Kpp2XMU8/\nctgMGD2/7JHlVWXY0WbVfqhltKqpMjKgqjQV1/0VnZnRLqGmyp5eFhINO872XtNZmqbS9NRNp5aV\nht3UVNmFyjDs2rK77ZE2aO8w59zhSX9tx9xnxJVq2xmJXPYLEzc2oBWKXjs2dlPDRiBl35cvpRfN\nlPIcD1/aTmdg4TSeEGmqUVPdcCxVaqrK+0myq1VNVWFrGc62FbvmgqbKsDuzD9/Mnmlml5rZlWb2\naTP7rJnd2HbMPUqRUm1ZI5HLGJTRKRGfbLuamgGjG4F42n2540bpR8m676TjnVosJavWXZWmymjS\n7ISmlo1s4YmLh5pqMJ3QVFKfaKuaqrIZOaRoS047mgrXGyjj3ua6psrOp8rWVJ5Be58DHgaOAX4A\nPAH4r9IsKJk3X3xdWwsWZI2+z0r8og8oXGghvK6sfrKyKfLC9sIsBE/4XJLSPI6bx4+qTE/eljQb\nWnnhwyZNf63XUy88iyh+MF0ebfWSpsJnF5fmSVSpqTx6KaKpsbGb+i6PgsHTVF7yOPxh59xFwLXA\nzcDLgFNKib1kbh4/it2TtRHd4QtVRuk0j5Moc2euKvrJyhB0XP9YVvpmrU6VRFLfWNH7yPviRMO9\nefwoppjgAdW5AAAgAElEQVTXpCcfZruayqoZpE3vLIrXU69pKum9akVTeWyJ68Nv5R5uHj8qV6Gx\nk5rKo/MqNNUqYVdgSD9ryudTndRUEfI4/Akz25vanvXHOed2Ao9vOcYOkzcRixDOCW+XqgalhS+T\nF0jRlzOPsFpZWtSTZc+mTRtjpy8VvY/weW2fXJiY5nnD7QdN+W6XKjTVat9+3oyqSAtMSB5b4jRV\nVE+bJ5YyxbyG35LSvFuampjct+0wPFXlUQ88cH/sWJZ+0lTS2vq9oKk48jj8fwG+Vv87z8yuAX7e\ncowV4Etuxy6+jWEmGWaSYxffxtp1Y02JWAZJc8JbneZS5hrVnrC/qswlaYtMOSxCXDNjEbvTminD\nqX3+c5jmcZnG5oml7DNvR4OefDyd0lQ7ad3Owh9JeE2VraeqRjhXqSnPMJMNo7WTNPWOS2+MzaMg\nPrNvhWUjWxga8t+GYrsNWtVUFXkUJI9lKXp9SFWaaldPSWFESdJUGWQ6fOfcJ4BXOue2UltT/5PA\nK0qzoE3ecemNDSW3YxffNvMihYSJWCVVZLR5KdI3l3VuWsk5vMesVcPyxBt2hSQ1waU1x6V1pWye\nWJpZe46+tKGm9pm3I1ZP0L6m8qyz3aqeyuqPLFNTSbQzFTMpzjyaSrM3TVOhnpLWiQg1tXbdGLf/\n7OHMPAra19ReOapw3cqjvCbL0lTe2RL+ezuaStODPz/PeJx2NFUGeUbpXwScX/9/FvBs4G2VWVQi\na1aPzozWTHrB8uCbAUfmTcTubBUKpFsDP7L672644YaZz9HMMM7mqBNMe/HSSv9h10KRMQ5hE1zY\n3Bm+XFn4PvIya+RlaQrgYG7J1FSreiqjr75sTUVpV1NhnEnL5kbvJ9RUaG8RTZXdwrN61YFta8qn\n99EjP2hqQfC0q6l28QMAW9VUXHhR0p5jVZry50e7DLqpqSTyNOkP1f8AFgCnAU+szKKCXHzeCTOZ\nZhwfPOek2IecR/TRvu9lI1uadraKOrFeXWVp/frktZKKrLldtAaXNBUmJNx2Nov7tk40jRnIs5hK\nkS1IO6EpT5am1q1/KDPMbtFLmooum1tEU9snF7asqTx6WrN6lGceekDi/g3Ll69sW1Nhese1IITb\nALejqap3QUzTVBZnX7K+5XEQWZoKu2shew+OuLFNZWiq3cJanib99zrn3lf/WwMcD5S/UW+bTDEv\nc3RqWLotMl0jKax2CGsVWWHlWbGryEAtX0uF6VyDxcJtP9sZXJY1xsE/Q9/36F+OcDOJpB3J4krv\n4QsWOtW0TCtM117SVN4BlDePH5VLm3NBU6GekuxL05S/NqkJNY+m8nLxeScAZDqksjQV1VOr+whE\nNZU2p78KTdUcbLOmouGuXTfGzl3t7zKXpimfZ/mC083jRzXM+c/TZdCuptqtULayec4i4OC2Yu0w\nYWmr3RpTtOTWylKd4XSsPCXSvCt2hX1zWQ5k6/geYCh2elDIpk0bG5xsmDm2Uto82XZlvug+k4bZ\nFfb8i5KV3nEZTDS+5ctXJjr9vC9UpzSVdyXAsPsiTw2nW5ryz6csTeXRE8Rryt9b0XEocZoqo+Zb\nlqbiapetzNzw4XRTUzVHX9PU2ZfM1v7DcKOaaXccRBFNbZ5YysHcMqOJML/yBYFuaipKnj78nwZ/\ndwN3AleWbkkbVDnSNxRT0gCwvCNY0/ost08uZPPE0sIDn+LWiG5nqlyUsbGbEku9cTv/5QkvDu/k\n4ppgo5mCT++0sQdxi4KE4WStPtYPmsrqk02q8RdZ5jNOU+20bvnnk1dTecOLI0lTcU4mTVOeNE3l\nWc2uSk2FhHoKB4bmGayXpam168Z6UlOhntodW5OmqWUjW5gf090ep6lXjO6dq3m+HU0VJU8N/6Tg\n7wTgEOfcB0q3pE2iGWRYOlo2smVmIEueFy1uTmhY2oVphplMLbmdfcn6hlpO2Gd5z4PjM/b4P1+K\nLtqvGYrTD4rJ00QaNz0oiU2bNubKLFptbvIZ6bKRLTNT4XymGB1olTe+6KIg0YF/eQg1FS1tV6Gp\nrNpAqKloP38tY5+eCQdIrJ0VWeazXU0lOYK8mmqFMCONagpmm9XPvmR903vWTU1BsbEmSdNJZweA\nTXMwt6SGkaSpT103MWNPqKk77x9vW1Pr1j+UO38LNXX5BatSz81bM2+FsLC3gInUPMrfW940CfOD\nVjRVhL2SDpjZ6+ofp2OO4Zz7bOnWlEhYOgpfgjxCGxu7qal01Zjh1ZqYfPNrlNpqWpPAPN588XV8\n8h0vaDi+exIIRmWmDSzKO9r5wfp9+WbLYSbZZ94O1qx+AZddFr/1Qdwo3gdj0mds7Ca+4Rbw4MTS\nhlpQTej7MswUxy6+rSFtizTNRh1gEj7ND8gZbmhPu6Ngo3qoQlNphJo6+5L1DU3i2ycXMjE+nRlG\nEdI0lZapRjVVa5rO1pQn1FS0bz2vpnxah8vCxrFz1yQ7qTmuIs3cnjI15btkIP+YhjyaStPm2Zes\nr/d7N2sq7ALJosjiN9snF86km2/tgOTNsSDU1Mkzuow73z/vaHhr141x5/jRTZratGlj7lp0kTzK\nF4aKaCpMlypJq+E/FxgFzgD+CjgWOBp4N/DKyi0rkXDVqbSXKak5PVpqDskS++RkzZlnjRr24o82\nc+ZZi92XyqNzOYuUdrPmsoctD+vWPzRTEvWFn7HxoxtKpnlXqMrC16Q9jz36aFPzWVJNx9sTpktW\ny0xe8mgqrXumUVON5F3OM2nQWbiaYJEaY2hbWZpKqq1ENRXWbrymoteWMQMmTJeoPd3WlCdpYS9o\nXVN5SMujvKb88SJ5lLctbops3u5QyJ7ZEe0i8Jq68/5x4jRV1oyqLE0lNd37e4qmi0/fsrdvroWd\ngHPuz5xz51KbineMc+6tzrm3AcuBJaVb0iZJgz9qmcVszWefeTsSEz5JTI0v99TMAznZdsUOHjl2\n8W3svWBeU3P5mtWjMyPOw4KDz0j9oDR/P1HihBN1NlX3EU5M7ptrbnvWYJw8K2T5MMJR+o9NPNbU\nfJb14u4zb8fMCxl1jkkDY9Lsz6OpYosBTTd0DcTdT6gp37QZDjqL6gkap/wl3c9c0lQccZoCGkbp\n12yoTlOt7jhXpqbiuPyCVbGaSsqjwuM+zjS7kzU1Xfk4hryaKjpmKm4cRqipsNXCa+pT103k0pT3\nK3GzRsoawJenD/+JwHjwfQc95vDzDv7wL0CRkl20du83voDmjDn8fvkFqzL6xocabPKkbagStwxl\n3LKxSffhw4sr8KRNmVs2soW9F/g0mHV0vn9vmElGF98aK/awTys6WC3uWXkHFn2m/vcw40gbPRz3\nYvrzw8xy+fKVTbZEV29Mo6imwpqO75v0tY+suJI0VbsuXk9hnNH7CedvV6WpuNHvvm89XAbWE2oq\nvDZ8L7ym8ugJkjXlbapaU2NjN/WEpuKeUXo+Fa+ptPw2TlObNm1s0NHIvG0z4cTdS/TZetKmzHlN\nzRZSZjVVy7uaNRW2MhXRVNgqEZdPQX5NxU3Ji2sV+4ZbULiwG0ceh/9V4Ftm9mdmdh5wA/Bvbcfc\nIdasHs01OC3PnN5hpkq3z78IXlxF5yeH022geZ7vpk0bG0R54WXXp46UT5ouMzUZNldNzwh0dPGt\nM+ka1zwXZojRfrCiA1PSulbiCO1Ja3YvOlI4j6by7Kuwz7wdpS+jGYbnn2F08yBPkg6yNAWN6Zam\nKf8MwpqU72udDh6hz4xDTSU193pN+alrreopbE6ddZTp9LOmWm3CDsO78LLrU7uzkqbmhYUlyNZU\n0rTEpDxqtv++sfA4Mm+Cyy9Y1TFNxRe+0gkLpHGainZ/tUOehXfeDnwcMODpwAfr2+X2FEl9lX7Z\nwjw1qLhRw6FQwyabdpqj4poZvbjCxSPy2BzaHYrFv5TRFzDPAhxxGdYCZgsiI/O2pd5/2hS7LMKX\nOK3/eWTetsxn4cMKCwp5+lsvPu+E1BH4eTXln0tYa4g++zKmaiU1M/pnGDZrlqGpKFmaysqwvJOr\nQlPRgmuSpvyeCXHPIq7WV1RTaXFD/2jqF5HBoXn15MPMq6ksslp182gqHJNQNO5QV0lpXURTfvyK\nLyh0vA/fzI6t/18F/Ar4AnAVMG5mJ5RuSYv4prIqRziGgi8yyCQrzLL6sOIHeQ3lEmVeWslEfD9n\nVLxhWOHv4ZSU6DONu4+0NAwzhLSNKeIcyTsuvTF3ITEP0UJXWKIvS1Nl6gmSNeWbPuMWBypCtCCd\nl1BTPt4kPYUa8INKo/lE1AnHpWNcy1QRTW2eWMrtP3u41DyqHzUVT/9qyv/58z1FNBU3aC86ODba\n4tZOnpQ4LQ84G3gT8D7i209PaiVCMxsGLqO2Cc9O4I3OuTuD46cBfwnsAT7tnPvHVuLxrFk9yrkX\nfxVIn/qRhG9mCadglfUShE04PnOIjtbME5dvzopmKLXSc600nvQSJYknbgpWdJBK0vdwihFMM7r4\n1qb44vqZkzJE/5Il3UdSmJ4wQw7PCQfChP1si+eNk8YgayrsokkrbIUsG9nCbTuOY/euXbF6Cq8p\nU1Np0x43TyxlYnwcUjQXF6aniKby1CDb0VSY3j6NytaUnyo5Mm+iIV9pJayimmolj4peF/c8ZzUw\nQmuaaqQ2dTa5UJelqaRpr6OjK1i+vKYP/3xPe9tXNn71oy8rvDJPosN3zr2p/v/E8Hcz298590jR\niAJeDixwzq00s98BPlr/DTObD1xCbTrgNmCDmf2Hc+6XSYFdfN4J/Mk7/y8wm5BPetJTGs5ZverA\nhhJxnhLS2nVj/DTjAUbJeqDRc2czsBrRjCFv/25S388wU6lONHQ6fk2BPBlhmKlAViYzuy939Nqs\ndPLOKVxwJ3qfSWH62o7/HEfSHNynTH6f8Xmz10b1FBdmFZoqoid/fpmaKtrcGqepA+q/75yszfeO\ne+5Zmmqmpim/9kN4btgysc+8HbHp0WlNxRUOytLU2ZesZ2dQEMqiHU1NMY/tkwsZmbetKV3zxtuc\nTyWvHxF9XxryqIz4Q+cbplFyvtOqpqZndJHl7OM0dbLt4sEH09ciKHPFvbQaPjBT434+8AFgE/AE\nM7vIOfeJFuM8HrgGwDn3XTMLOyqWAXf4AoWZ/Te11f2+kBTYHQ/dzaEH/g9QKyEAHPN7z+Pu8Xtm\nznnCM5/Kth/t4u7th7BzagFTC3cDcNG/X8uZL17WEN62hbXztv/mHhhplOIwUxw6csdMPAB3bz9k\n5vP2eri3TB7C3eP3sG3hrto5gS3+tx2TOxlid0PcC4d31Oyr97RMD+9oCGPbwl0N4V79nav58fRB\n9Xh3MxS8PMNMYSN3zNzz9PCu2bC2HxJcU2PX/N1c9O/XsmPhrF1h/J7Q7h1MsffwLoamGs9/7cue\nwPs/87MZW6aZ5urvXM30vo82nevv59CF/4ObeDpTweaMC+vnLJzeyvapWkYxxBQ/nj6Iw/appen0\n9GyYO5hi28JdXP2dq2fCBPhx/Rn5a8Ln8U//uZnpfR9lX2pLkB66zz1sg5lrt9Gsp/A5ZmmqIc1z\naOru8Xv48fRBDWHeMnkIew/vatBArB0Li2nKk6SpIWbL9cP1Z+3ZNX93w3NI0tSPpw9qsKsVTd09\nfk+sprYt3NXw/KeHd3Djj/4HFtbu6dADq9HU3dsPaTjfp6EnTlMzz6v+vyxN7Zr/EEPzG4Jp0tRF\n/34tD00fBDTnUT68vJoaqocdDpDL0tSsNmCYnQzVtRjmUdCoqbj3Zdf83dw9fk9mHnX39kOYHnl0\n9j2bP4+h+ZMN53t93L7tGTODR7M0BbV8Ibz3ferhhdfANNPDOxvsiobpj934oxs59EAKa+pz731z\nS6WAoenp9FKhmY0Bf0rN6f8u8L+B9c6541qJ0MyuAK5yzl1T//4z4HDn3JSZPR/4M+fcn9SPvQ+4\nxzmXuHb/H3/u7OxirRBCCDFH+H+vvrylPpXMGj6Ac+52M/sg8K/OucfqTe+tMk5txz3PsHPOz3d7\nJHJsEfBwWmAvfebvs2HDhobfjj9+tvBzw/fv5aHxnQ3Hh5hir6FJXnpiY+3+nnvu5d577wVg98KD\n+fX4dvYamm0SPXB+zZSHds8u7rp7upYU84d2s2d6HtP10uv8od0z53t7bvj+vYw/+ljDdR5//i92\nPX4mDG8nwEtPXMaGDRt5bOiJPLZzuuGah3YfwJ7peew1NDkTp+eh3Qc02Jh0/u6FB8+k0/yh3Q02\nDjE1Y5P/DsTG5+93w4aNTXHHpU/cuWF8YTpGz/Hxh7+nXRPG6Z9H9J6BpntK0lNoZ5KmNmyYbQ7M\no6nFi/ZrCB+ITTPPf9ywuSFd49IuSVM+7k5oKmoXNL4DaZry9xvVSdgcHD5zf80Xr/9Jarocf/xK\n/uOGzbGaiqbZExf8qunew9/jNBWGF6VTmoq+c1l5FGRrCmh636Jxx+UnIbk0NflE9kxOMnrEPtx7\n770cf/zKxLSK3htMM8T0TPqFaebvd8OGjU0aSNMUwEOTT2T35HRs/M150TTzh/a0lEd5GyFeU62S\nx+H/wsw+QW2p3dVm9lHgnoxr0tgAnAZ83sxWAD8Mjt0OLDWzA4AJas35F6cF9qdHv4I7v1wLwvcT\nnfJHJ88cH/vWGL+4vzYIKzoo4pQnn9wQ1mVfvoSD6uWNhxc9m1/cP85uwoFii+r9lLNri/t+m73n\nTXB0Ux/fopl41q4b4xf3z64hvTc09HfuPW+Cg0b28PDEITN9PcNMstsv8vOtxRz06CI2TzyFPfXj\ns9cczu7JEXYDD0cGtR3EHjZPHD5j0+aJY2fsX1C/HuCcc87i7EvWs3vXLo6s92PtaegbDAuUte9h\nfOF9n/Lkk7lz620NcQPsDvq+vO1j3zqAn99fE3Y4zSmaLgAPTxwe2MRM/EeObOHm8WUz10wxyc+p\n9bftjElj/9xPeXJ9zfgHHwBm+42j95SkJz9qdvZ5N2vqzq2zo4XzaGpqwTz21KdmRleH8+f7ONau\nG2P7/c+aSTPfVzzRpI9DGga8+Ub32fssrqmHF63kwQcfSNXUw4uezdb7H26yaypmMGGapnz6h5qK\nrj3u7fbXfPneBezetawhbaLpcsqTT+Zr9++I1dSCwN6ReRM8zCEz/c+17pHGNIxq6qf310blR9Ov\nG5oK0ygtj4J8mloWed/2BPmDv1dvV3hN2I9dRFO/nFrMQY/+hlOefDJj3xpL1FT4XDy7mc07o+l/\n59bbannVb27L1NTss5/No+I0dRB7ImmTnEc9XL+H7cEgyDC/S9NUq4P28iy8czq1vvsTnXOPAVvq\nv7XKl4AdZraB2oC9883sdDN7k3NuN3ABcC2wEbjSOfdAnkDD6RLhQgnRbROzBqtE5+1GyVoMJ+9U\nlmUjWzIHUE0Fj8cvyBNOF/H25okrySY/eGjtujF27pqcmYq2bCRcw36ItAFB4XS6zRONW/zGxe0H\nTm2eWDqz+1Z0WkqRnehgdllLX1jwI3+zBsjFrbYG+fSUNTUwDKtqTeVf5765JbAVTaXtdPekJz2l\n4dmGcYTPJ6+m1q4ba9JU+O6Eeopbz8KnTRFNRdPEa6lmd3pratL86U5pKmkQb3hvedIgTVONeVdz\neoRT7LxNWWTtnuiXFk7SVJF8KrpTYpam4vKoJE0l5evRPGp2Wl9263xZc/LzLLwzDkwCZ5rZfsB2\n59yjGZelhTftnDvbOXd8/e8nzrl/c85dUT/+NefccufcqHPu8lbjCcm7DWf4QkLz3PVwJKYv4UW3\nSUwSdtbmOZ4wo/FLUAINO1l5e9JszRP+spEtvOIVr048t3k5zNllK8PtRsMXYfvkwthFVsJ4k+bI\nRsOMzoNvTJvmzCStEJW2iM5swSDfGt9FtnUtqqmnLhkppKk8i9GEGeMs07HTmarUVPT55NXUfVsn\nmjSVR0/QmHEX1VSaY8yziE6N6VxaKUtToYPyNvr18kPNVaGp6DvWrqayVhX05zU66qng8+wCNtFR\n9Dt3TTYVvPJoau8F8xrSOk1Tce9xK3kUNGpqmElaqd3Xrs3AzD4MvIjaDnnzgdeb2SWtRFYl4cNK\nE0ic2JN2J0t74acYbqhBhi9hdN/2xusat0+MewF8vGEp/b6tEzO2x72s0SbDNOLuK6xl+HCitZzo\nhjFxNfe0Vgsfbyj+8MVIa4Hx85ujNaDoalvRMIstxDE0k+Z59QTJmorTVZamwhpMtOUkibyaqqXB\nrIP1C55Uoakww4575kU0FRZ2k+L0Nvqww1a9vLbn0VQerTYy1BROJzW1fXJhQ8td2ZoK2Wfejhm7\nL7zs+ly6SSNaCIq2hCRrapb4dEluBUjSVLhMb94WonDjqjDNo4XMUFNJhEuuT2W77UTyXHkKtS1y\ndzjnHgZOplYA6AluuOGGmc9pwk8Tu5+jHz6Ik21XUxg+E4v2aW+fXNgwZzlphba4dfKTbE4qkYal\n5bA5Kby3cMOaIvhMKDtDmJ6xMVoqjmb04f34v6j482Se4YYks88yfp543jD9PYe1lPDZZTV9Zmkq\nuoZ4kqbiNiaamNy3qeUkjl2MNJ2TpqnosqhVaspn2HGF4Wb7ZjPhqKbi9rkIHWK0xrtsZEuhVr2w\n4JOlqayd4vx9h/cTPruqNRUWTqLN0GVrKppH1fLBmt2/GJ/Gr0joHVp4PK2gmUaWpsIKxxTDsYWt\n0cW3xrYepGkqfwEvn6bCQmYeTTUWeoc47W1fybe/eIQ8Dj/atrN3zG9dY/369ZnnxC1fmIR/COkb\nTTQOYMvTrweNDy2plBb/EjSXSP0AkynmNa3w5DPyuJaGIi9Z+IJCuIjL0Mzv93JMQ2YwOrqiKbMN\nX55W184O1/effZY+zZvTJ5pxprW6HLv4tpbW0y5XU1Ea9ZTUcnLIQYtnPveDpqI1x6imNk8sZXR0\nRZODiW6aEj7XPJtMxRHmC839qe1pKuyOK7bYUfuaSnZM5Wqqcdvw+NY9r6fogLgwb4mmY9GCQJam\n1q4ba9JUWh6V9lyzCJ9hmZqKVkxaJY/D/zzw78DjzOx84Nv06G55eYWS1FdSVGi1kuu2yK/J/cCN\nJf+hWKHH1bZGF9860w/nS6nRkmwY/+zvjS0N0dp1HGFJOPoCx73Q+y1a1LCcaPaqULN9tXEtAFEb\n8jabhrXQuIwza138uE0uiuihHU2FBcHZ2r4nfVxBVFO3TjynKf44TY3M29bU7VG1puIyrDhNLV++\nMvf+396h9JqmkuLrlKbCdK1KUz7dQ2cfaiqvgwrTMdST719P6sYoqqm8+Gcc9/yig0c7qakwj6qs\nD9859yHg09Qc/8HAe5xza1uJrErihOKJlo7iBJF3S8vo/sWNJcrsneTybrEb1rbGxo+e6YeD2dL9\nbL/ibJiNA6Ead6PKWxMKm5tCQUf7nnyTWNbLFE2jaF9tnNOIq+FlZSS+VJ12n3kdSZqevH1hOsdp\nKo8zhMZ7u/yCVYX0BJGBSvOSa4ehpsKMJvq8q9JUXKEqjDP8PU1TcZl9VFNx73OSptK2MW1HU9G+\n4E5qKnxXq9QUNHYNhZoKn3ftXpL7ztPSN23r5TyayjPCPZpH+d+i+VTc4NHwnMZ3qDpNVdKkbzWe\n7Jy7xjn3dufcBcD3zOxTrURWFdHmpTjK2nfcv6jhQ46O1k/DNx/77Ruj+HCypr14ov100NicGO6a\nF47yzNsfFc20QueQNwyIr0Hn4V6OaYo7/lk2ZpTNL1vteJIjiWakWXoK+8OLpGccUU2FGskTbnj+\n5Resitg4W1PJSx5N+ZpOUU35ZxitfRfpIw2vKUNTxy6+LUZT0/SzpsL7g+o0lTWAzMfV2LTdzBTz\nCu07H42jDE21s/15q5pqLARNz2gsS1PA8woZWCdte9z3At8HfmJmJ5vZXmb2Lmrz8A9rJbIqyLuP\ncPiyxb14ebZmjCvhFel384SDf3wY4X3EEU4HSSO6U1oceQs/o6Mrctco8hAWEvI0t26eWDoz+Cdp\n+kzYXxdO6/JbXoZNk0k1qyjh4KZh4vc6z1OzzdPkl6SpuOk+aUQ1FfYLRhcN8eTN/MvUFOSvpeYh\nSVNJ73Oapvy02dp91tbfj2qqMQ16S1O+VhiXvlVpKnTieSs9niLN/lmUpam4Wn2YTyUNSPaaCuNu\nru03ayo6NdXX/lst/GSRVjx7HbAUWAWcT23Dm9cCf+Sc+/1KrGmBuMUQ4ggzpLjMadOmjYVrrWUQ\nV2DYPrmwaUBM3HSQaBN7uKBDdI/nIv1NXuTLl6/MLCi1c89pTWNpdoXnxz3XMFNqHmMRT2MzceNU\nsTiy9BSG2w1NRfsFo3pKmgaUV1N+0FNcBhhnT5gR9qqm1qwebaqZRTVVZEpUpzVV5q5qUeIHcc52\n7fgWyzi74groXpNhrTqqqSLTqzulqehgv2jT+/bJhU1OP0tTebt5fXj1wsJ3WrilVPWOO+cecM59\nn9qyuj8EnuOcu7aViKomq7aS5fDyjKCOy+Di+hMh/+CcJHGGtYG0+ZlJzevbJxfG9i9lZYDR1ahC\nyuoWSSOpRA3x0wWjzzVMT5+p5O3PixYg0jS1bGQLT1w81FLzXzScTmnKk7UqXxFNZU1/i2aaUfv7\nTVN+NbfoOJYkuqWptH5tT149NS4kFDI7+jxrvYO4Anq4qudc0FRYOI7aFD4TaBwHM8Vw0wDaLE1V\nMWgvLHb8Cnibc65npuN5wkEhWeJNc3hp4g9LcUliDB90keal6GjatL5W3/Tv40gTehHR+/uLvth+\noGDRprowXL/MalqGGt5fVok6Dn88WguB2YJTWn9eUvdClqY+eM5JhWq2IVma8gPs4jSV1dwXdTBR\nTYUOrGpNhXFNTO7b1P3Wiqbu3HVkk52rVx3YMU15R5c3P+mUpqL2p2kq7wI8Ufxzi3ZthF0HcX+h\nXa0044fvS6uaShoIl6SpPPnUuvUPATVNZb0fvvDYPJ5hqGkAbRx5n30aedundjjnenIbWr/oTTv9\nNw2Qd3IAABtxSURBVFkOOqupLNqcVKRJKcyofLN92iyC6GIpUXvjRJ81Mj3+/hoHlyT1F2aF++PH\njojtT4S0+cLNpI2xaGUchSc6mnvzxNKZZ5jXwcbRjqaiTfJQrJky1JQv6MQ57jDdimoqC39/Scue\n5l/7v5FvuAU8vGN+k53fcAuAwdZU1rWNTfL5uybWrB6daXnweopbuyK0L/oXvvvR7sY8hO9Lq5pK\neufiNOWfW5EuuaxW5Lj3Om3mQkjeWWRZpD31I83sp2b2U+C3/ef6310tx1gyeRe98fgHmcdhRc9J\nuiY6fznvyOXR0RUsX74ytuk97OvK84DTMqi8fXt++qKf+5/WP9tqf2GYKURXb8t6YVavOjDz5QuX\nP82TqURXP/RpWCSjr0JTs9QyhKwBXyFxmmqFdpyeZ9nIFubPm+2aanUUdJadfuCjNJVXU8VmA8S1\nPBRtOg/ffShWOAupWlNpeZSPPynOPHqqUesWGl18a2FNtUOaw38GcFL9z4LPJwEvKCX2Usne+CR8\nkL5GAMkPMHRq3jlnkz04Jy78KNFr41ZaCl/W/Ub2y2FbOr5J3xcyimzokUTcsqieuP6uNEcVTa+w\nFB7WaH0tJMvp+R0CIX4Eep5+/yo0VTs/nF0w299XZv9kXPNq2ZraPLGU3ZPJ/bVFSdMTzGoqOgJf\nmmrUlKdsPcWNPWl3imGUTmoqrU8+T0UoqWtndPGtDeMrimqq9Hn4zrm70/5aiawKwhppVm06jnDl\npLTritRo05pA8y788oKnb2t6uaN9s2F/4AfPOSm1pLhq1aqm34qS1/YoYcEhHOASh9+uN4to82b4\n0uTt64puSDFbA5tuaL6MIy0t1q4bS1kzPp+mGnf9mkp9tnmey7KRLRywcHdTGO1oqgyK104b9eSX\nTE3TVF7dRptNvaaK9J32i6ayKkh5NRU3OLCxEDE98z2rtaUsWsmniuRReYnrgilDU/UWknLn4fcL\nfspDVuL5Zs5wFKTfXzmt/zKPow9Ltlkvdd6Cwyte8erYpv6wBF1kGtKJJ57Y8D3upQj76qKDVDZP\nLG3YwCaOvC+an/4Uvvz+2rTtepPiCafCFOnnTNqFLQ/+OSZpKqlmEF6bRnSBpiK11CQ++tZTmsJo\nR1NR4p7/6lUHxtae/EIlUU1F7yXv+JOoptasHm2psO7xmirab94vmsqqIOVNszh9ePt910GreoL8\nmopqqN18Ki2PapVwEGOnNBWl7x1+0cTLI7hw1aQ8+FpGJ6aDQPxKaEVJeqGjfXVh+p59yfrUQUfR\npsUs/LPI310yG0/oqNIywjR8U150nnQ0k8p7H2kU1RTkL8yWQVWaWr58ZexGJX6hklBTWY6siKaS\n7EkjXGo3z/KnSWH0gqaS0qpKTTXHmX9sVRJ5NBX1AVkD3HyYVeZR/tq4KaDlaao4fe/w2yHsuwnn\nSMatxJXGN9yClqe5FMGP9vW0OrWpCqIvT1EH7snqYgmJK2QVbTZsnk4Tv61pXsKX09sQOrg8mmq1\nJtAKc1VTIUU1FdLKiPJe0FRSX3KVmvJxxmmqF/QEjZpqNY8q0hzvCyZhepSjqdb6Gwba4cPsA2ll\n4Mfo6Aru5ZiZ5rYqCafl5ek6iPKOS29s6QUPHejlF6xKXAUr7eXJm+GGo63zjlAOydoPPuu6GrWF\nVSD/DIkovjmwlUwurJ2UtWJYEuEo/KKaKuJEo4Q1n1BT0Xizpi5WoanGqVPTM/YWfZbRbaCLzrqJ\n0u+ayvMut6qpaCE/bRBeUU1F7Q7T0o+pKKqpVv1NRFP5lhCN0PcOv+wlFcPMKG0krW/i2W/Ropnf\nwkVO8gwETPqeRdE5zGvXjXH7zx5uKNUXebnCqSZFR+6H4yTu5ZjUc6PPMk8JPLqyXtEMI3kVsebw\nW6WIpkJ8TSDP4KFOaip0omHtsoimQh21oqm889mLaipa+GulJlyzKbqwSrlZbTc0VZRQU1np3q6m\nog60HU2F8aXZ7fOboppqpVDTrCn2TTg1lb53+LXMur0aWZQ8YgkHC/kXzy8vmdVkFhVIHscW9i+2\nS9LLlURZa3Tvt2hRaibSyqpu0fPKqcE01vLL2Mgir6biaid5xii0oqmyRiNDcU2VSZmaijunPE2V\nS6c1lYfoINB2CCsLndRU2nTUuOnRWcTlUUX9VMw8/E2FAqjT9w4f8u8x326JNYky5quHpNmZd4Ba\ndOnWZx56QMt9s0ml7LyjXcOaSNY+53k2Y4leMzsiOL7Qlz3Vr3m1qyIbWpStq7L1BOVqKm/tMo04\nTRWZ5RGOYi9TU2HzcHvN8O0tTDpXNZW2z3u7msrTJJ9EqKkPnnNS6rl5FvsJ4w0LQXGDjIs+62Em\nK1lLvy/wNes8gyDS9hhuNfGj5M1g0pxQGTXqaBgXn3fCjD3htJaslyut5lZkBb88mc3o6IqWMqba\nUrLJXVpJU/18DSLcsjJcbCV0KmnEpUNZmioyCLGTmoo+pzI0VcTGvH2grWjKh93KrJs0TRUZqDVX\nNRV3jb+X6HOKVhbSCAcjdkJTWedG41296sCmXfPizo17rtGFd7I2Kkqj7x2+T/RWBkFAc99NHoFk\nvWx5Mpg8882jJG0QkrYpRBxxU6V6gSIvZ3S0bdHR+VHi+rB7RVN57cirKR+Xd4ad0lRVLWxp5NVU\nnG1laCoMq1U9Qe9rKowzKd3i7Mma7jZXNBW3xkLcfWWF7adUlr7SXr+Sd6W2dsj7wItM30gjXEAk\n7kVNmle6fPnKtgdzldHUVpQ86RZ3b0Uz1GjfZiemwSWRR1Ot6ilJF2Em1AlNhfH1mqaS7jdJU0n3\nHGoKssfzVEknNRVd5ChNU3G/laGpvC1yZZFXU1HayaeAwV5pL0pWiTQqpLQds9ohqYkpizih5y1g\npL1QRcOC2X7WqloDkpqv8qZb1r0UXaa1FZJWLCxbU+3oKY8ukihTU2G/fb9rqp1aaRb9qKl2u4zK\n0FQ7LShFaTXtohQZY1DGvc05h59F0sYSvdK8XdaI+HbpxKjrsu/15vGjZubGFgnfl9RbacItqxRf\nFb2opypru1Vqqshqa/4eW3HSvaqpdpYqTgqrHTqlqTLZPLG0YQe+ImkZagr4TivxD5zD7xTLRrbM\nrEufp4mpzL6oMsIqY6e0VoiOwI6SdG83jx81szxs6PRHR1ekzuVN2jClF4gb6VukybLXNNUtytJU\n2PycV1PQ+jawVRDVVJE8qowuwrSw+om09zHpvsJFiaKFlKylt6OaGthR+mVRVn97SNwe0kmkjV4t\nShkl8A+ec1LH+1k9aU43z73Nmze7kM433ILcC/+UTbuait5r0cJImbXddsMK++1XrzqwJKuKxd+O\npkLmkqaK5FFx17eTb81VTRW9r7XrxhqWSa6yICSHT3n9MWWTVQquuoRc5Uj+cKR4GYQ7gX3yHS+I\nPSdcFdHTa2M4qiYrvavUlNdTVU3CVWoqaSqUNFXengetUKWmytZTOB8/TRtx91KWpuTwS6SKzDJN\nyGW2CnQab3uZmcOxi29rypjzzOXtpWb8KOGskzKebVZ694um0gYTSlPplD2TqWh696KeILn1okw9\nLRvZ0rQnQt71BsrQVEcdvpntY2ZXmdmNZna1mT0+5pzzzeym+t97qrAjbupNGVM6emGAVC/Y0Gt0\nYs2BqjQVzjrp1rOVppqZK5rqBtJTM51aF6XTNfyzgVudcycAnwX+IjxoZk8DXgM8zzm3Avh9Mzuq\nOZj2iAouz8pJojfpldqCNDV3kKZEmfSKnqDzDv944Jr652uAF0aO3wOc4pzzC1HPB7ZXbVQ/lzir\nEFMvCTSLpGfX7XuQpqoPsyp6tVujXzVVVdr1wjPJQy/lUXtVFbCZvQF4a+TnXwDj9c+PAvuHB51z\ne4Bfm9kQcDFws3PujqpsLJNOP7wy58RGKWNBm27Tr5mjpxtpLE01Mjq6grGxm4D+1xN0T1NVpV1c\nC4h/Xv57VUTjaoVuaKoyh++cuxK4MvzNzK4C/LDWRcBvoteZ2ULg08AjwDlF412ypHnUbCvXr1q1\nqlBYp556StN10f9l4uMrStSWPLZF76OsuIucX+TapHNbfQ7d0FSYxqtWrWL9+vWV6ikaZ17ibMmy\nL+659qKmTj31lJlMXZpqjU5qyj+vTmgqSRvt5lNVPQdPZQ4/gQ3Ai4HvAS8CbgwP1mv2XwG+5Zz7\nSCsRbN36aFsG+uuPPPK4lsIKr4v+L5OiYXohRa/LE05Z91H0+vD8Itcmnduq/b2gqfXr11eqp1bD\njbsmK5xWn2ve+MuKO8+50lQ60lSxeKp6Dp5OO/zLgc+Y2beBndQG6GFm5wN3APOAE4D5Zvai+jUX\nOufaazsRQHebTauO24ffbjObyM+qVau6Gn/VTbb+f9ma6sXui15hEDTVzTyqow7fObcd+OOY3z8W\nfN2ncxZVR5UDVVoVTFyfV6eour/Khz9XHX4v9keeeOKJTTWSuaKpcA522ZrqlfEA0lQzndDUwDj8\nKvB9Ub1Gpwaq9EpYolo6kRH1WlhziV7Mp6SpwaPvV9o78cQTu21Cx+nGCzAozZCDcp9RpKlqUT7V\nGQZBU+3cY987/LIYBKG0w6CUssu8T2kqnUHRVJlIU+kMgqbauUc5/DqDIBTRWaQpUTbSlGgHOXzR\nk3SyJuM3E1HtaW4jTYmy6Yam2kEOX/QknazJPPDA/R2PU3QeaUqUTTc01Q5y+GJOUebWn6qdCSh/\nO1kx2IyOruha3tL30/KECClz688q5mCL/qPb28mKuUU3W33mVA2/3VKTanQCGlf7kqZEu4QaKEMP\n0pRolTnl8NstOam/bXAJM9FwzrQ0JVolbvfBMvQgTQ0u7Rb25pTDF3OTTtSKlIkOFtKUKJtOtAa2\nqyk5fNHzqFYkykaaEmXTD62BcvhCCCHEACCHL4QQQgwAcvhCCCHEACCHL4QQQgwAcvgiFc35FWUi\nPYmykabyI4cvUtFIZFEm0pMoG2kqP3PC4auEJ4QQQqQzJxy+SnhzCxXgRNlIU6Js+lFTc8Lhi7mF\nCnCibKQpUTb9qCk5fCGEEGIAkMMXQgghBgA5fCGEEGIAkMMXQgghBgA5fCGEEGIAkMMXQgghBoC9\num1AWfTjnEjRfdauG+PBiaVNv0tPolWkKVE2SZoqypyp4ffqnEi95L3L2nVj3Hn/OBOTI6xdN9Zw\nTHoSrSBNibJJ01RR5ozD71V69SUX/Yn0JMpGmhocOurwzWwfM7vKzG40s6vN7PEJ5w2b2X+Z2Zs7\naZ8YLNasHuWIpyxmZN4Ea1aPdtscMQeQpkTZlKmpTtfwzwZudc6dAHwW+IuE8z4A/BYw3SnDxGCy\nZvUoy0a2dNsMMYeQpkTZlKWpTjv844Fr6p+vAV4YPcHMXgVM1o8Pdc40IYQQYu5S2Sh9M3sD8NbI\nz78AxuufHwX2j1zzLOB04FXARVXZJoQQQgwalTl859yVwJXhb2Z2FbCo/nUR8JvIZauBpwDXAYcB\nu8zsp865r6fFtWTJorTDoo7SKZlo2iit8qF0Skaaag2lUzLtpk2n5+FvAF4MfA94EXBjeNA59+f+\ns5ldBDyQ5ewBtm59tGQz5x5LlixSOqUQpo3SKh9Kp3SkqeIondLxadOq4++0w78c+IyZfRvYCbwG\nwMzOB+5wzn21w/YIIYQQA0FHHb5zbjvwxzG/fyzmt/d1xCghhBBiANDCO0IIIcQAIIcvhBBCDABy\n+EIIIcQAIIcvhBBCDABy+EIIIcQAIIcvhBBCDABy+EIIIcQAIIcvhBBCDABy+EIIIcQAIIcvhBBC\nDABy+EIIIcQAIIcvhBBCDABy+EIIIcQAIIcvhBBCDABy+EIIIcQAIIcvhBBCDABy+GLgGR1d0W0T\nxBxDmhJlU4am5PDFwLN8+cpumyDmGNKUKJsyNCWHL4QQQgwAcvhCCCHEACCHL4QQQgwAcvhCCCHE\nACCHL4QQQgwAcvhCCCHEACCHL4QQQgwAcvhCCCHEACCHL4QQQgwAcvhCCCHEACCHL4QQQgwAcvhC\nCCHEACCHL4QQQgwAe3UyMjPbB/gXYAnwKPA659yvIue8CHhP/ev3nHPnddJGIYQQYi7S6Rr+2cCt\nzrkTgM8CfxEeNLNFwEeAU51zzwPuN7MlHbZRCCGEmHN02uEfD1xT/3wN8MLI8ZXAbcAlZnYj8IBz\nbmsH7RNCCCHmJJU16ZvZG4C3Rn7+BTBe//wosH/k+OOBk4CjgQng22b2HefclqrsFEIIIQaByhy+\nc+5K4MrwNzO7ClhU/7oI+E3ksl9R67f/Zf38G4HnAGkOf2jJkkUph4VH6ZQfpVU+lE75UVrlQ+lU\nHZ1u0t8AvLj++UXAjZHjtwDPMrMDzWwvYAXwPx20TwghhJiTdHSUPnA58Bkz+zawE3gNgJmdD9zh\nnPuqmV0IXFs//3POuR932EYhhBBizjE0PT3dbRuEEEIIUTFaeEcIIYQYAOTwhRBCiAFADl8IIYQY\nADo9aK9lzGwYuAx4NrUBf290zt0ZHD8N+EtgD/Bp59w/dsXQLpMjnc4H3gD4BY3e7Jz7SccN7RHM\n7HeADznnTor8Lj0FpKST9FTHzOYDnwYOBfYGPuCc+2pwXJoiVzpJU3XMbB5wBfAMYBp4i3Puf4Lj\nhTTVNw4feDmwwDm3sp75fLT+mxfQJcAosA3YYGb/4efzDxiJ6VTnWGC1c+6WrljXQ5jZO4E/BR6L\n/C49BSSlUx3paZbXAludc6vN7ADgB8BXQZqKkJhOdaSpWV4CTDnnnm9mq4C1tOH3+qlJf2ZZXufc\nd6ndpGcZtWl9jzjndgP/DZzQeRN7grR0AjgOeLeZfdvM3tVp43qMO4BXAkOR36WnRpLSCaSnkM8z\nu/HXMLVal0eamiUtnUCamsE59xXgzfWvhwEPB4cLa6qfHP5iZpflBZisN1/7Y48Ex+KW7R0U0tIJ\n4N+oCegFwPPN7NROGtdLOOe+SHNmA9JTAynpBNLTDM65CefcY/VNwD4PrAkOS1N1MtIJpKkGnHOT\nZvbPwKXA/w0OFdZUPzn8cWaX5QUYds5N1T8/Ejm2iMaS0CCRlk4Af+ec+3W9RHg1cExHresPpKf8\nSE8BZnYwcB3wWefcvweHpKmAlHQCaaoJ59zrqfXjX1HfZh5a0FQ/9eFvAE4DPm9mK4AfBsduB5bW\n+4MmqDVrXNx5E3uCxHQys/2BH5rZb1Pr83kBkf0OBCA95UJ6asTMngh8HTjHOXd95LA0VSctnaSp\nRsxsNfBU59wHge3AFLXBe9CCpvrJ4X8JONnMNtS/n2lmpwP7OeeuMLMLqC3JOwxc6Zx7oFuGdpms\ndHoXcD21EfzfdM5dkxTQADENID1lEpdO0tMs76bWpPoeM/N91FcAI9JUA1npJE3N8gXgn81sPTAf\n+D/AK8yspXxKS+sKIYQQA0A/9eELIYQQokXk8IUQQogBQA5fCCGEGADk8IUQQogBQA5fCCGEGADk\n8IUQQogBQA5fiIows/3M7O/NbIuZ/cDMbjSzF6ScH7c5DWZ2hZkda2aLzexLOeKdyjonOPf1ZvZP\nec9vl/p9fCjm91e1YoeZHWdmV5Rg10w6mNlnzOzJ7YYpRK8hhy9EBZjZELUdwHYAy5xzzwHOA9bV\nd72KI3ZRDOfcm5xzNwOPA55TsqmdXojjEqDJ4beKc+77zrk3lRBUmA4fBj5WQphC9BT9tNKeEP3E\nKuCQcP9459wPzOwD1PavXm9mNwAPAUcCrwaGzezT1NYO/yVwlnPu/vp5FwFvA55sZlc55/7QzNZS\nW3r0ccCv+P/tnW2IVVUUhh/JjLLoQ8nCELOx14IyUYmRPrQsKEJJsDLLHxJaiVRkRR9gUhiRlhCU\nfVCIDUEUYUP5oyzzi1QojSxfHM0MKiPFIFDMmn6sPXi8XRWhYWzueuAy+9xz7jpr74G79lr73P3C\nBNs76zkj6UngAmAw0BdYaHseoYDXJOkzYACwzPY0ST2Bl4tv/QATinm9CHGTfsX0HNutkpqAl4A+\nxJaoM21vqPHhGuBn23vK8WTgCUJ2t42YHCFpJDExOKX0a7rt7ZIuA14BTgZ2EzKrg4HZtseUcfoS\nGFuumUnsTHYx8ILtBZL6E1u1ng6cC7xt+1EqSoC2v5U0UNIg29vqjWeS/B/JDD9JOoeRwPo6768s\n5yCyyo22h9jeSASpVtvDgCUczDLby2sm8FMJ9k3AhbabbYsImJOP4tNFwBhCfnS6pA5RkgHAzeX8\nDWUf82Zgn+1RQFPx7UZCi/t72yOAO4Ario1FwMO2hxNKZ7WCKADjgM8BSsl8HjAauLzYby8a368D\nk4qt54ltVwFaiAnGpcX+fRyambcD7eX8YuDF0q8rOSjHehvQYrsZGArcK6lPHV9XEVrkSdJtyAw/\nSTqHv4m9r2vpVXO8ttLeY7tjjf4t4Kmaa6tZaJukWZKmASICdNsR/GkHFtveC+yV9AFRHfgNWFHJ\nurcCfWyvlLRb0gxgCJFJ9wbWAHNLpvwh8LSkU4ERwJuSOu7XW9KZtqvqXU3AJ6U9CljdUZEo8p/j\nCUWwQUBrxdZpJSifY/uj0v+F5XOja/q5tPzdAXxhex+wQ9IZ5XPzJY2R9CBwCfE/6l1nvH4ofU6S\nbkNm+EnSOawFRpTSeJVmYF3leG+lXdWc78HhNeiRNJxQHIPQFH+fyoTgMPxVaZ9QsV+9TzuxtDCO\nmHT8AbwBrAB62G4jJgAtROa8jvge2Wd7WMcLGFUT7CEmQQcq7er3T4dvJwDbKnaGEypgh4yFpJMk\nDarTx/2V9r/GT9J8olKynZhQ7aL+uP1ZfEySbkMG/CTpBGyvAjYBCzqCfgnSj3No5l4NNn0lXVfa\nU4GPa8we4GBV7mpgue1Xge+A64lgeTh6ABMlnVjkNG8iVLYON0m4FnjH9iJgJxF0e0q6myirvwvM\nAM4uNraUNXkkjQWW17G5FRhY2quBZknnlQccJxGTjc3AWZI6lgqmEiX434Efi22AKcAcjv2hw7HA\nc7bfI5Yy+lN/3AYBW47RdpIc12TAT5LOYwIh8fmNpE3AAmCy7RWVa6oB61fgTkkbiID7QI29X4jy\n9DJiDXuopK8ICc2lwPl1bFbvs48ItGuAubY3c/D5gNprXwMmSVpPPCi3hAjWLYAkfU2sx88uwXgy\ncJekjcBc4JY6PrQSzxBQSvn3EFWK9cU3bO8HJgLzi60pRNCHeGZgdunzRGDWUfpbu74P8AzxS4k1\nwO3Ap8S41V5/VfE3SboNKY+bJA2ApNlE2f3ZLvZjFTDe9q6u9ONISBoKPGb71q72JUn+SzLDT5LG\n4XiY3d8PPNLVThyFh4ifQCZJtyIz/CRJkiRpADLDT5IkSZIGIAN+kiRJkjQAGfCTJEmSpAHIgJ8k\nSZIkDUAG/CRJkiRpADLgJ0mSJEkD8A/iIlATk/nmrgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cb53790>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.all_data`: Measure Gaussianity of residuals using rank-based statistics.\n",
"\n",
"Departure from Gaussian core: number of sigma, Z1 = 1.72946253061\n",
"Departure from Gaussian tail: number of sigma, Z2 = 7.76426353946\n",
"\n",
"Z1 <= 2: The model does not appear to under-fit the data.\n",
"Z2 > 2: There may be outliers in the data.\n"
]
}
],
"source": [
"print(\"`models.ls.crts.all_data.res`, `dataframes.crts.all_data`: Plot phased residuals\\n\" +\n",
" \"from Lomb-Scargle light curve model.\")\n",
"(models.ls.crts.all_data.fluxes_res, models.ls.crts.all_data.fluxes_interp) = \\\n",
" code.utils.calc_residual_fluxes(\n",
" phases=models.ls.crts.all_data.phases,\n",
" fluxes=models.ls.crts.all_data.fluxes,\n",
" fit_phases=models.ls.crts.all_data.fit.phases,\n",
" fit_fluxes=models.ls.crts.all_data.fit.fluxes)\n",
"assert np.all(np.isclose(\n",
" models.ls.crts.all_data.fluxes - models.ls.crts.all_data.fluxes_interp,\n",
" models.ls.crts.all_data.fluxes_res))\n",
"dataframes.crts.all_data['flux_rel_res'] = models.ls.crts.all_data.fluxes_res\n",
"dataframes.crts.all_data['flux_rel_fit'] = models.ls.crts.all_data.fluxes_interp\n",
"ax = code.utils.plot_phased_light_curve(\n",
" phases=models.ls.crts.all_data.phases,\n",
" fluxes=models.ls.crts.all_data.fluxes_res,\n",
" fluxes_err=models.ls.crts.all_data.fluxes_err,\n",
" fit_phases=models.ls.crts.all_data.fit.phases,\n",
" fit_fluxes=[0]*len(models.ls.crts.all_data.fit.phases),\n",
" flux_unit='relative', return_ax=True)\n",
"ax.set_ylabel('Residual flux (relative)')\n",
"plt.show()\n",
"print(\"`models.ls.crts.all_data`: Measure Gaussianity of residuals using rank-based statistics.\")\n",
"(models.ls.crts.all_data.z1, models.ls.crts.all_data.z2) = \\\n",
" code.utils.calc_z1_z2(dist=models.ls.crts.all_data.fluxes_res)\n",
"print()\n",
"print((\"Departure from Gaussian core: number of sigma, Z1 = {z1}\\n\" +\n",
" \"Departure from Gaussian tail: number of sigma, Z2 = {z2}\").format(\n",
" z1=models.ls.crts.all_data.z1, z2=models.ls.crts.all_data.z2))\n",
"print()\n",
"if models.ls.crts.all_data.z1 > 2.0:\n",
" print(\"Z1 > 2: The model may under-fit the data, i.e. the model may have high bias.\")\n",
"else:\n",
" print(\"Z1 <= 2: The model does not appear to under-fit the data.\")\n",
"if models.ls.crts.all_data.z2 > 2.0:\n",
" print(\"Z2 > 2: There may be outliers in the data.\")\n",
"else:\n",
" print(\"Z2 <= 2: There do not appear to be any outliers in the data.\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.all_data`, `dataframes.crts.inliers`: Remove outliers from the residuals using\n",
"Bonferroni-corrected p-values.\n",
"Fit a constant to the residuals.\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.000\n",
"Model: OLS Adj. R-squared: 0.000\n",
"Method: Least Squares F-statistic: nan\n",
"Date: Thu, 16 Jul 2015 Prob (F-statistic): nan\n",
"Time: 09:48:45 Log-Likelihood: 348.72\n",
"No. Observations: 388 AIC: -695.4\n",
"Df Residuals: 387 BIC: -691.5\n",
"Df Model: 0 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"const -0.0062 0.005 -1.234 0.218 -0.016 0.004\n",
"==============================================================================\n",
"Omnibus: 289.542 Durbin-Watson: 1.807\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 7276.644\n",
"Skew: 2.830 Prob(JB): 0.00\n",
"Kurtosis: 23.447 Cond. No. 1.00\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1037e66d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of outliers detected: 5\n"
]
}
],
"source": [
"print(\"`models.ls.crts.all_data`, `dataframes.crts.inliers`: Remove outliers from the residuals using\\n\" +\n",
" \"Bonferroni-corrected p-values.\")\n",
"models.ls.crts.all_data.res = code.utils.Container()\n",
"models.ls.crts.all_data.res.model = \\\n",
" sm.OLS(endog=models.ls.crts.all_data.fluxes_res,\n",
" exog=np.ones(len(dataframes.crts.all_data)))\n",
"models.ls.crts.all_data.res.fit = models.ls.crts.all_data.res.model.fit()\n",
"print(\"Fit a constant to the residuals.\")\n",
"print(models.ls.crts.all_data.res.fit.summary())\n",
"models.ls.crts.all_data.res.outlier_test = models.ls.crts.all_data.res.fit.outlier_test()\n",
"astroML_plt.hist(models.ls.crts.all_data.res.fit.outlier_test()[:, 2],\n",
" bins='scott', histtype='stepfilled')\n",
"plt.title(\"Histogram of Bonferroni corrected p-values\")\n",
"plt.xlabel(\"Corrected p-value\")\n",
"plt.ylabel(\"Number of observed data points\")\n",
"plt.yscale('log')\n",
"plt.ylim(0.5, None)\n",
"plt.show()\n",
"models.ls.crts.all_data.res.is_inlier = \\\n",
" np.isclose(models.ls.crts.all_data.res.fit.outlier_test()[:, 2], 1.0)\n",
"dataframes.crts.all_data['is_inlier'] = models.ls.crts.all_data.res.is_inlier\n",
"print((\"Number of outliers detected: {num}\").format(\n",
" num=len(dataframes.crts.all_data.loc[\n",
" np.logical_not(dataframes.crts.all_data['is_inlier'])])))\n",
"# Create a new dataframe for inliers. Create a copy to prevent altering the original.\n",
"dataframes.crts.inliers = dataframes.crts.all_data.loc[\n",
" dataframes.crts.all_data['is_inlier']].copy()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`dataframes.crts.all_data`, `dataframes.crts.inliers`, `models.ls.crts.inliers`:\n",
"Plot phased residuals from Lomb-Scargle light curve model with\n",
"identified outliers.\n"
]
},
{
"data": {
"image/png": 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0ZjkTZ38Sm0eO6AgNPyicoN+6u7uYf8N9oinHsJPE22x6gkpN+bXiasO6/smZ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\nx7ObySVKMAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e456ad0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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YFlgzM2dFxO7AMRSXG87MzK+PsMnB+fMXjqaUShkY6Md2ao5t1RzbqXm2VXNs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\nGPiSJFWAgS9JUgX8f8QKpu+qOIUYAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f581510>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Measure Gaussianity of inlier residuals using rank-based statistics.\n",
"\n",
"Departure from Gaussian core: number of sigma, Z1 = 0.912441145025\n",
"Departure from Gaussian tail: number of sigma, Z2 = 1.00588108096\n",
"\n",
"Z1 <= 2: The model does not appear to under-fit the inlier data.\n",
"Z2 <= 2: There do not appear to be any remaining outliers in the inlier data.\n"
]
}
],
"source": [
"print(\"`dataframes.crts.all_data`, `dataframes.crts.inliers`, `models.ls.crts.inliers`:\\n\" +\n",
" \"Plot phased residuals from Lomb-Scargle light curve model with\\n\" +\n",
" \"identified outliers.\")\n",
"# TODO: Remove 2nd plot\n",
"ax = code.utils.plot_phased_light_curve(\n",
" phases=models.ls.crts.all_data.phases,\n",
" fluxes=models.ls.crts.all_data.fluxes_res,\n",
" fluxes_err=models.ls.crts.all_data.fluxes_err,\n",
" fit_phases=models.ls.crts.all_data.fit.phases,\n",
" fit_fluxes=[0]*len(models.ls.crts.all_data.fit.phases),\n",
" flux_unit='relative', return_ax=True)\n",
"tfmask_outliers = np.logical_not(models.ls.crts.all_data.res.is_inlier)\n",
"ax_outliers = code.utils.plot_phased_light_curve(\n",
" phases=models.ls.crts.all_data.phases[tfmask_outliers],\n",
" fluxes=models.ls.crts.all_data.fluxes_res[tfmask_outliers],\n",
" fluxes_err=models.ls.crts.all_data.fluxes_err[tfmask_outliers],\n",
" fit_phases=models.ls.crts.all_data.phases[tfmask_outliers],\n",
" fit_fluxes=[0.0]*len(tfmask_outliers[tfmask_outliers]),\n",
" flux_unit='relative', return_ax=True)\n",
"ax.plot(*ax_outliers.get_lines()[-2].get_data(), label='outlier', marker='o', linestyle='')\n",
"ax.set_ylabel('Residual flux (relative)')\n",
"ax.legend(loc='upper left')\n",
"ax_outliers.set_title('Phased light curve\\noutliers only')\n",
"ax_outliers.set_ylabel('Residual flux (relative)')\n",
"(outlier_handles, outlier_labels) = ax_outliers.get_legend_handles_labels()\n",
"outlier_labels[-1] = 'outlier'\n",
"ax_outliers.legend(outlier_handles, outlier_labels, loc='upper left')\n",
"plt.show()\n",
"print(\"Measure Gaussianity of inlier residuals using rank-based statistics.\")\n",
"models.ls.crts.inliers = code.utils.Container()\n",
"(models.ls.crts.inliers.z1, models.ls.crts.inliers.z2) = \\\n",
" code.utils.calc_z1_z2(dist=dataframes.crts.inliers['flux_rel_res'])\n",
"print()\n",
"print((\"Departure from Gaussian core: number of sigma, Z1 = {z1}\\n\" +\n",
" \"Departure from Gaussian tail: number of sigma, Z2 = {z2}\").format(\n",
" z1=models.ls.crts.inliers.z1, z2=models.ls.crts.inliers.z2))\n",
"print()\n",
"if models.ls.crts.inliers.z1 > 2.0:\n",
" print(\"Z1 > 2: The model may under-fit the inlier data, i.e. the model may have high bias.\")\n",
"else:\n",
" print(\"Z1 <= 2: The model does not appear to under-fit the inlier data.\")\n",
"if models.ls.crts.inliers.z2 > 2.0:\n",
" print(\"Z2 > 2: There may still be outliers in the inlier data.\")\n",
"else:\n",
" print(\"Z2 <= 2: There do not appear to be any remaining outliers in the inlier data.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model inliers only for MCMC\n",
"\n",
"To calculate the best period, model only the inlier data, calculate the best number of terms, and sample the probability space using Bayesian MCMC.\n",
"\n",
"Related structures for this section:\n",
"```\n",
"models.\n",
" ls.\n",
" crts.\n",
" inliers.\n",
" model\n",
" times, fluxes, fluxes_err, filts\n",
" fit.\n",
" best_period, min_flux_time,\n",
" times, phases, filts, fluxes\n",
" phases\n",
" fluxes_res, fluxes_interp\n",
" z1, z2\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"`models.ls.crts.inliers`: Use the model with the best number of terms for the inlier data.\n",
"\n",
"models.ls.crts.inliers.model.best_period = 86691.1259531 seconds\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 1\n"
]
},
{
"data": {
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CXFKih0XkUNoqEgCLgF1FZCCwCdcVMM1bNhp4NsD+AVjbAzKMlZeXUVcXb3qG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\nBUVKgBBCCFFQpAQIIYQQBeX/AYP6V43lb+0eAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e437250>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 44.9505678381\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 2\n"
]
},
{
"data": {
"image/png": 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BO5M70a/NzJZPPjCz9ck9r4CIlIG2tuJcgBvq61h5zNAQgyoBInkVZ3TAW8A+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\nXz4ULRHUPtsQ++xpYA9CSuML3P0NwIeBK2PnCFE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WBKmPHVMl11IoF41WbbM38BJ3P4mg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e4b58d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 201.655415682\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 3\n"
]
},
{
"data": {
"image/png": 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DbgaWAneJSPfUhm2MybSCFpq+l+5piMa0ZX4+XQNV9Z+RB6r6ErBnkvL3AiFc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\nDMMwOhQzAgzDMAyjQzEjwDAMwzA6lP8HGEpGN5TYCfUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e4b5a10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 236.663821337\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 4\n"
]
},
{
"data": {
"image/png": 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GOCLdKM4Avf+4+xYA7v4Q8FCmRO6+T/R9zbxFJyIlo76hkS9nLgDg4ef+x9Yb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\nHWatluEQ22/fFIJKPgm+0q+EqOW7ZgUH+6zoW1g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"text/plain": [
"<matplotlib.figure.Figure at 0x10ef0f710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 367.687398983\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 5\n"
]
},
{
"data": {
"image/png": 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OUNVy4K/AEaq6iqoOBX4BTE+0nYgMBm4Drge+A+4QkUGpDdsYk0k1tXXUe8lw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\nEpQ9rsPdAlU92n+lGo3RM7JGr/pd8Cb3FRusYrQ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adQCMUtfsB/tbdZI0z2+Znp+4ReXE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\n/wNEZQnOYVZZygAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11006a710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 422.652014286\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 6\n"
]
},
{
"data": {
"image/png": 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j7ecubCQEPR7kZ4zpmbjtaapapqrlwN+Bo1V1DVUdCvwCeCXediIyGPgLcAPw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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f012ad0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 504.461695688\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 7\n"
]
},
{
"data": {
"image/png": 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U1KzIdBhZyc5NYpk+P8tr69mwpLvvGBpXu/S6NT/XpjzuTJ+bbGbnJrHOfn4S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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f046d50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 514.923853655\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 8\n"
]
},
{
"data": {
"image/png": 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Q2BWYBOwP7CciiZbYNcakUWSAnt8V7yKKiwoItYRpbGpJR1jGmCT8VPa/AY7A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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10eb69810>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 536.087487855\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 9\n"
]
},
{
"data": {
"image/png": 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8i4qmj14sGGP6jp/MfqmInCgiFSIySEQmAQ1J0q+rqjfjhuw1q+r5wJo+jjMI\n11QQFfKq9gGGAzsANwA/B/YQkURL7BpjMuiVD74Fej7OvrTYMntjssVPZv9r4ABclfw8YBeSd7hr\nE5GOeTRFZEOSj8uPWgIMjI1NVcPe/R+AT9Vpx9UA2JybxvSx2rp6lja6cfI3PdyzXvWlXrX/y+99\nm/a4jDHJ+emN/wWwfw/2eQHwPLCWiDwMbA8kWiEv1ovecf4hItsB78S8NheoEJH1vU57PwNuS7az\nIUPKKCzsWZtiNlRWDuw+0SrKzk1y2Tg/RTHfqZKSwh7FcNndbwCwpLGNq+59k6sn7Zz2+KLss5OY\nnZvk+uv5CWRinmoRqQS2xdUcvKqqC3xsE2BFb3yA44CtgApVvdWrtr8C1xv/RVX9XbL9NTQszfkB\nPpWVA2loWJrtMHKSnZvksnl+Jl77PK3tYW4/d/cebXfpjHrmfuNa6tYfPShjC+LYZycxOzfJ5fv5\nqawcmHB9yZ41uvkgIhvgMvp7gZuAP4rIZFX9T7LtVDUCTOzy9Mcxr8/y9muMyaLB5SW0tPd8fvua\n8VtxwpWzKCkusJXvjOljftrse+pO3Pj6A3C96M/EDcMzxvQDjS3tPe6cBxAIBKgoLWLYoAEZiMoY\nk0yySXU+S7JdRFXXS/DaAFW9X0RuA/6mqi+ISNprEIwxfS8SidDU0s7qQ0p7tf2A4gLrjW9MFiTL\nhJMNbUvWHt4uIocA+wF/EpED8dcb3xiT41rbw4TCkR6PsY8qKynkux+b0hyVMaY7yWbQ+1xVPwei\na9j/DNgZdxFwQpJ9ngzsA5ymql8DhwET0hWwMSZ7oqXyz79Z0k3K+EpLCmluDRG2CfKN6VN+Ls8f\nxM2NvyHwAi7Df7hrIhGZBcwGnsTNax8GUNWj0hatMSar/nz/2wAsa2qntq6+xx3tvmpYBkBza4iy\nXtYOGGN6zk8HPQF2Bx4Crga2AdaKk25v3Fj5w4AXRORvIvJrbxieMaYfCKcwVLe2rr5jqt2r730z\nXSEZY3zwk9kv8IbFfQSM9armR3ZNpKotqvqMqp6pqjsBf8DNiHeLV+o3xuS56Ep3QweVpDR8LpWL\nBmNMz/nJ7N8XkRuAWcAZIvJ7oKRrIhEZ6f1dy1viNgw8AZyBa8c3xuS5aJt9b5apramuYrUK99Px\n6702Smtcxpjk/GT2E4H7VfUD3FS4I4F47fC3e39fwLXdz8ZNm/s83mp2xpj81uhl9qW9GGcP8POq\nMYAthmNaHJatAAAgAElEQVRMX/Pzjf2fqm4JoKqPAI/ES6Sq+3p/10lbdMaYnNLUHF3LvneZfWmx\nm1u/qcVG4xrTl/x8YxeIyM64Oe5bukssImsD1+M69bXjVeWrarJlcY0xeSDVkn10OyvZG9O3/FTj\nV+Gq4ptEJOzdkl2W3wM8A4wG1gXqgRmpBmqMyb7/vPMNkELJ3jJ7Y7LCzxK3Kw2dE5GVOujFGKiq\nf415/GcRObYXsRljckhtXT1LlrcCcPvjH3LBcVv3eB/RzP7Z1+fxy1508jPG9E63JXsRebnL4wJc\naT2Rt0TkiJj0vwDe7XWExpicEwwmXEkzqXuecQtZLlraQm1dsp8RY0w6JVsIZxawi3c/HPNSiDgz\n6MXYA6gWkZtwbfZDgTYRORi3gE5ZylEbY/pcTXUVp06ZTXNriJrxW/VqH728RjDGpChhZq+quwGI\nyPWqOsnvDlV1TDoCM8bknuGDB7BwSQvBQO9y7bOO3ILf/OU/lA8otDXtjelDfjro3Soi9wGIyCYi\n8h8R2bhrIhEpEZHrRGR9ERmW9kiNMVnX2NLe65744Ja4BVhr9YHpCskY44OfzP42vN70qvohcLH3\nXFeTgJ2AKbhpco0x/UxTS3tKC9gUBIMUFwWtN74xfcxPZl+mqk9GH6jqM0B5nHT/A5qAImBwesIz\nxuSKcCRCU0uIhhTXoy8tLqSp1SbVMaYv+blEbxCRiUAdEACOABbESfcKrgagDmhLW4TGmJxQe5fr\nPd/cGurV8rZRA0oKabaSvTF9yk/J/jhgP+Ab4AtgX2BC10Te7Hr1wJ5ARexrIrJfypEaY7IqHE7P\nSnWlxQU0tVpmb0xf6jazV9UvvHnv1waGqeqBqjqvazoR+S1wN3AS8LGI7BHz8iXpCtgYkx3H7bMJ\nAIPLi1PqSb9gUSOtbWFC4XD3iY0xaeFnUp1xIvIR8DYwRkTmiEi8QbYnAlur6v7Ar4A6b059Y0w/\nEO1Ut/NP1+j1Pmrr6jsWwbms7vW0xGWM6Z6favwbgIOA71X1K+AUYFqcdBFVbQRQ1ZeAI4H7RWSz\ndAVrjMmexuiKdyn0xo+VrmYBY0z3/PbG/yD6wOuNH29u/P+KyH0isomXbjZwKvAsYBPtGJPnoive\n9XYRHHCz8A0uLwbghH03TUtcxpju+cnsfxCRcdEHInI0sDBOutNxGfug6BOq+iCwP/BiinEaY7Is\nXSX7ncaOArBOesb0IT/f2lNxQ+p+IiKLgU+Ao7smUtUQcSbbUdXXgANTjNMYk2VP/e9LILWSPcQu\nc2tj7Y3pK35643+qqjvi1qbfXFWrVFUzH5oxJlfU1tWzaGkLAPc+90lK+yr1psxttpK9MX2m20t0\nrwp/Bq7dPSgiHwDHqOqnmQ7OGJN7eru8bdSAjpK9ZfbG9BU/bfZ3ADWqOkxVhwDXAHcm20BEBonI\nmiKyVvSWjmCNMdlRU11FuddWf9YRW6S0r+hiOI+99EXKcRlj/PGT2aOqj8Xcf4guM+TFEpE/APOA\n/wCzY27GmDy25gj3tS8tKUhpP/+cPReAH5Y0U1tXn3Jcxpju+elpM0tEzsONrQ/hOud9ICIjAFT1\nuy7pJwDrq2pDTwIRkSAwFRgLtAATVHVOzOu/A04Aovs9WVU/7skxjDG919jczoDiAgqCvsoICaXY\nCmCM6QU/mf3BQAQ4ucvzr3rPr9fl+S+ARb2I5UCgWFV3EJFtgWvp3It/S6BaVd/sxb6NMSlqTHF5\n26jTDtqc39/8CgPLilKadtcY41+331xVXaeH+/wUN8HOTFwJHdzsehd3s92OwFPeMV8Vka6/AlsB\nfxCRkcDjqnpFD+MyxqRg4ZJmCgtSK9WDW+IWQNZcLeV9GWP88TM3/rYiMllESkTk3yLSICKHJNlk\nPi7TbvUeB7xbdwYBS2Ieh7yq/ah7cbULuwM7ici+PvZpjEmD2rvqCUegtT2ccjt7tM3f1rQ3pu/4\nqZO7HjgHV53fhCthPwg8EC+xql7Yy1iWAANjHgdVNXZZrOtUdQmAiDwObAE8nmhnQ4aUUViYWkei\nvlBZObD7RKsoOzfJ9eX5CcaU6IsKC1I6diQSobAgQHs4krH3YJ+dxOzcJNdfz4+fzD6oqrNF5B7g\nn6r6pYislIuKyJuquoWIxFu3MqKq3eW8L+Km1v2HiGwHvBOz78HAOyKyKdCIK93fnmxnixY1dnO4\n7KusHEhDw9Jsh5GT7Nwk19fn58T9NuGcaS9TUVrEOUdukfKxBxQXsnR5a0beg312ErNzk1y+n59k\nFyp+MvtGETkL2AP4jbdu/UpnQ1W38P72tlHvIWBPEYnOo3+ciBwJVKjqrd6IgFm4fgDPqupTvTyO\nMaaHovPib7vp6mnZX3Nru02qY0wf8pPZHw0cDxykqgu9DnJHpTsQVY0AE7s8/XHM6/fi2u2NMX2s\nKQ0r3kXV1tXTHop03Lce+cZknp/e+POAi2Me/z6jERljck6617LvYEvaG9MnUh9HY4zp9/4+0y2F\nkY6SfU11Vcd+Jh8+rpvUxph08DP0LuUu7SKyZar7MMZkR21dPd/92ATAk6+mZz77zdcfBthiOMb0\nFT8l+3RMXn1JGvZhjMmyYCA9c91Gl7m1sfbG9A0/mf23IrKziJT09iCqahPgGJOnaqqrGFxeDMCE\n/TdNyz6jy9w2W8nemD7hpwGuCngeQESizyUcNy8ix+C63USLANEuOAFvu7t6G6wxJjs2W28oL777\nbVra7CG2ZG+ZvTF9wU9v/Moe7nMvYBfcLHttwL64lere8163zN6YPLOiN35RWvYXLdnf88zHXH7S\n9mnZpzEmsW4ze6/6/ixAgEne7QpVbU2wyRhgnKp+721/IfCUqnYdQ2+MyRPRjnSprmUf9Vz9PAAW\nLGyysfbG9AE/bfY3AhW4OfHbgQ1JPlXtKODHmMetwODeBmiMyb65Xy8hECDlteyj0rQbY4xPfr5y\nW3kT6bSq6jJgPG5t+UQeA54TkdNFZBKuvb8u5UiNMVlRW1dPa3uYSISUV7yLOnov1/9n6MASK9Ub\n0wf89LYJi0hxzOPhQLzFbqLOBA4FdsatkvcnVX2m9yEaY/qb6Jr2W28yIsuRGLNq8FOyvw54Fhgp\nItcBrwN/SZTYm+P+a+B94I+4hWuMMXnq3KNcRV5pcUHaSuEda9q32Dh7Y/pCt5m9N1RuIlALzAH2\nV9WEbfYicgZuEp3f4danv0VEzk5PuMaYvtbodc7bdJ2hadvnAK9k32xD74zpE36my30XqAbeAv6q\nqm93s8mxwN7AclVtALbGrZpnjMlDy5vaACgvTd8iOFayN6Zv+anG3wtQ4DfAxyJyt4gckSR9SFVj\nq+6bcL34jTF5aHmT+/q+M+eHtO2zpMhl9p/M+7GblMaYdPBTjf8NMAO4GrgN2A24Pskms0XkWqBC\nRA4EHgFmpiFWY0wW3PHEhwD8uKw1bb3xL7v7dQCaW0Np26cxJjE/1fhPAJ8CNUAz8Etg9SSbnAV8\nAryNG6b3BK6HvjEmD4XDtui8MfnOTyPcm7iOdsNwmfxIXObfmCD9U6q6F3BTWiI0xmTV7luN4b7n\nPmHk0NK09cavqa7ilGuepy0UtnH2xvQBP9X4Nar6M2Af4CPcjHqLkmxSKiJrpSk+Y0yWLfM66B2z\n98Zp3e96awwiEoH2ULJpO4wx6eBnbvy9gT28WxB4AHg8ySaVwOci8h2ucx641e7WSzFWY0wWLG/2\neuOnaRGcqPJSt7/G5nYGlRd3k9oYkwo/1fin4abAvU5V5yVKJCKHq+rfccP0GtIUnzEmy1YMvUtz\nZj/A/fwsb26zzN6YDPOT2f8fcApwnYgUALOAG1S1a93bxSLyT+BmVU02d74xJo+8O3chsCJzTpfo\nUL7lzTYy15hM8zPO/ircWPsZwHRgd2BKnHQv4qbGHSci4S43mznDmDxUW1ffsbzt1fe9mdb9/rjM\nrZJ9pze0zxiTOX4u1fcCtlDVEICIPAa81zWRqh4PHC8ij6jqAekN0xjTX4VsaJ8xGeenZF9A54uC\nQpLMiGcZvTH9R011FcEAFBcG0zpErqa6itWHlgLw863GpG2/xpj4/GT29wDPi8hvvPXpZwH3ZjYs\nY0wuCIXDhCOw7qhBad/3r7017Rutzd6YjOu2Gl9VLxORt3DT5AaBS1U12dA7Y0w/Ec2IK9LcEx+g\nwhvKt8wb2meMyRw/JXuAEmCAl741c+EYY3JJdEKdTCxYU+b17n/1/QVp37cxpjM/c+Nfi5vv/mPg\nC+ASEflDTw4iIunrxmuM6TNT/+X64i5pbEv7gjU3Pez2vbQp/fs2xnTmpzf+AcBPVLUVQERuwq1t\nf1kPjrNvL2IzxmRZKJS5nvLBQCBj+zbGdOYns1+AWwgnuph1Ycz9lYjI2kDsL0SEFdPmJiQiQWAq\nMBY3Xn+Cqs6Jk+4W4AdV/b2P2I0xKdh727WY/uRHjFgtfYvgRNWMr+LEq2YRCGCL4RiTYX4y+++A\nt0TkASAE7A80iMg03Jz3p3ZJ/xAuw37He7wZ8K2ItAMnqeqzCY5zIFCsqjuIyLbAtd5zHUTkZG9/\nz/uI2xiToqWNrovOkT/fMCP7HzOigq+/X04kEiFgJX1jMsZPZv+Id4uW1t/z7gfoXIKPmgecqKqv\nA4jI5sBFwBnAP4FEmf2OwFMAqvqqiHS61BeRHYBtgJuB9C6/ZYyJ69l6txzGwLLMzF3fsKiJtvYw\nTS2hjg57xpj08zP0bnoP97leNKP3tn9XRNZX1S+9ufUTGQQsiXkcEpGgqoZFZBTwJ+BXwOE9jMcY\n0wu1dfUsXu5K9nc9/REXHrdN2vff6E3Fe9Xf3uDC49O7f2PMCpm4lJ4jIlcAdbjZ944CPvFK5snm\nyF+C6xsQFYxZbOcQYDjwBDASKBORD1X1rkQ7GzKkjMLCZNcWuaGycmD3iVZRdm6Sy/T5KYr5/gwo\nKUz78WL3TyCQ1v3bZycxOzfJ9dfzE4hE0tvbVkQG40rhP8dl7s8Al+J69X8UW+rvst1BwP6qepyI\nbAf8UVVX6sUvIscAG3fXQa+hYWnOT7hdWTmQhoal2Q4jJ9m5Sa6vzs/Ea5+ntS3MbefulpE29XOm\nvcT3i5s5+YCfsO2mq6dln/bZSczOTXL5fn4qKwcm/JL6KtmLyLrApsC/gTGq+lmitKq6GDgzzkv3\ndHOYh4A9ReRF7/FxInIkUKGqt3ZJm/MZuTH9QUVpEZFSMtZ57vDdN+DGh97raC4wxmRGt5m9iBwB\n1ABluE50L4nIOapalyD9scA1wNCYpyOqmrROXVUjwMQuT38cJ92M7mI2xqTH0sY2Rg0rz9j+B5eX\nAPDUq1+w19ZrZuw4xqzq/EyXey4uk1+iqt8CWwLJqtAvAHYFClQ16N1yv/HcGNNJS2uI1vYw3/3Y\n7TQZvXb3MwrAj8tabRY9YzLIT2YfUtWOXvKq+g3JO9rNU9X3vJK6MSZPXXGP617T1NKesYy4sMDv\n8hzGmFT4abN/X0R+AxSLyDjgVNx0uYm87k3A82/cTHjgqvET9pw3xuSeUDjz1+vnj6/i+CtmAjaL\nnjGZ5Oey+jRgNG7K2ztwQ+S6zpoXazVgGbA9rjp/N+9mjMkjh+y6PgBDB5VkLCOOrTG4ZIZV4xuT\nKX4m1VkGnOd3h6p6bNfnRKSsZ2EZY7JtaaNb3vaAHdftk+PNb1jWJ8cxZlWUMLMXkXCi10jSu15E\nDsGNsy/H1RwUACVAegbRGmP6xL/+40bYDiwtytgxaqqrOPnq52kLhWltD1NbV2/V+cZkQMLMXlV7\n23PmKmACMBmoBX6Bq9Y3xuSJ2rp6fljSDMCDL8xli40qM3asYJCOLr9z5i/h+CtmUlJcwLTJu2Ts\nmMasavyMs7+A+EvWfqiqj8fZZJGqzvSmxx2sqhd6E+Vck5aIjTF9qqAgs6vRjRlRwZz5Szo919Ia\n4oQrZ3L7ubtn9NjGrCr8lN7XB34J/AgsBvbEdbw7UUSuipO+UUQ2Aj4CdhURq8I3Js/UVFdRVuLK\nAuccuWXGjxVPJIKNvTcmTfxk9hsDu6rq9ap6HW7O++GqeiCwd5z05+Oq7x8F9gAWAP9KU7zGmD4y\nclgZBcEApSWZnxPrjvN2J96MvHO/XrLyk8aYHvMzzn41oIgVY+ZLgArv/kpfT1WdDcz2Hm4tIkNV\ndWGqgRpj+taXC9yCIJmaF7+raJV9bV19R7V+tHRvnfaMSY2fkv1fgXoRuVpEpgCvAVNF5Azgne42\ntozemPxTW1dPeyhCKBzp86r0muoqigptZj1j0snPN+pe4DDgG+Bz4GBVnQo8DhyXudCMMdmS5pWv\ne2ytERUrHtjE28akzE81/n9UdWO6lOJV9ZPMhGSMybZT/u8nnDPtZSpKi7JShT7v++Ud97+yyXaM\nSZmfzP4tERkPvIobcgeAqn7p9yAichGu3f+mnmxnjMmO6PryO20+KivHH1NZ3tFuH042vZcxxhc/\n1fjbARcBT+E63sV2wPPrM9xkO6N7uJ0xJgtuf+xDAAaVF2fl+DXVVRR77fbtobANwTMmRX7mxl8n\n1YOo6nTv7sup7ssYk1m1dfV8u7ARgOffms/e266VlTjWHFHBHBt6Z0xa+JlBb2PcKnfluKF2hcA6\nqrpzgvTrALcC6wI7A/cAx6vqZ2mK2RjTRwqDfTPsLq6YQ0esKt+YlPipxv87sAjYAreO/QjgySTp\nb8ZNjbsU+BaX2c9ILUxjTF+pqa7qqL6feOBmWY7GaWkPZTsEY/Kan8w+qKoXAE8DbwD/h1vcJpHh\nqvo0gKqGVfU2YHDKkRpj+swGo91XNltt9uAuOkasVgq4ufKNMb3nJ7Nf7s1v/zGwlaq2AMOTpG8U\nkTHRByKyE9CcWpjGmL70/uduLqzyDC5v60dJsZuq9/vFzdZJz5gU+Mns7wYe826TROQp4Osk6Sfj\nJtzZQETexk3K89tUAzXG9I3auvqOkvTld7+e1VhsJj1j0qPbb5Kq/hU4SFUbgN2BW4BfJdnkM6AK\n2B4YD2ygqq+kIVZjTF/IoRnrOk3Ln0NxGZNvus3sRWQ3XHs9QBlwLTAuySZvAg8BmwHqVfsbY/LE\n5MPd17uspDCnFqBpD1tub0xv+akjmwKcBKCqH+LWtr8uSfp1vNf3AlREpovIz1OM0xjTR6Kz520l\nlVmOxHXSGzKwBIDWNuukZ0xv+cnsS1T1vegDVf2IJOPzVTWkqs+o6vHAscBY4MFUAzXG9I3Fy1xl\n3Huf5caCldGZ9L75odE66RnTS37mxlcRuRKow01zcQSuZ35cIrKVl+YgL901wL9SD9UY0xemP/kR\nAIuWtuTEWvLWSc+Y1Pn5Fp0AVOB61c/AzaR3YpL0twDzgR1V9Zeq+jdVbUw5UmNMnwjlWNv4+ePd\nxUYgQNYvPIzJV3564y9U1dNUdXNcO/xkVV3cNZ2IjPTuHoTroFcsImtFb2mN2hiTMdtuujoAo4eX\n50TmevV9bwIQiWDV+Mb0UsJqfBGpBG4CbsCtcvcgLrP/VkT2V9UPumxyO7CvlzZe0WDdtERsjMmo\nF95202icftDmWY5kZeEcq3UwJl8kK9n/FXgNqAcOA7YERgGHEqc3vqru693dUlXXjb0Bu6U3bGNM\nJtTW1bO0sQ2AWx7tej2fHTXVVQz2pu1ta7cVcYzpjWQd9DZV1cMBROSXwP2qugR4Q0RWWpdeRNbE\nXTw8LiL7xLxUhJtRb+NkgYhIEJiK673fAkxQ1Tkxrx8MnIurNbhHVa/38f6MMb2UzQXvuop20pvX\nsDwnOg0ak2+SlexjL6H3AJ6NeVwaJ/3FwPPAhriq/OjtKZKvkhd1IFCsqjsA5+Em7wFARAqAy704\ntgdOFZGhPvZpjOmBmuoqCoIBigqC1IzPnQy1qMB65BuTimQl+y9F5HBc7/tSYBaAiPwaeL9rYlU9\nznv9PFW9ohex7Ii7MEBVXxWRjl8aVQ2JyMaqGhaR1YECoLUXxzDGJNHWHiYUjrDhWrm1UOXpB29O\nza2vEgwGrFRvTC8ky+xPw61NvzpwtKq2ishfgP2AfZJsd6eITMZdJARwGfO6qjq+m1gGAUtiHodE\nJKiqYXDL5YrIQbi+BI8BNpzPmDSLTqgzr2F5liPp7PbHPwRcBz2rxjem55LNhPclbmrcWBcBZ6pq\nsnkrHwQ+xVW3P4Trwe+nGn8JMDDmcUdGHxPTgyLyEDAdt8jO9EQ7GzKkjMLCAh+Hza7KyoHdJ1pF\n2blJLhPn58LprwGwrKmNq+59k6sn7Zz2Y/RGcdGK73JRYUG3790+O4nZuUmuv54fPzPoxXpOVbfs\nJs1wVd1RRK7FZfaXAQ/42PeLwP7AP0RkO+Cd6AsiMgh4FNjTq2FYDiSdKHvRotwv+FdWDqShYWm2\nw8hJdm6Sy9T5aWlp77jf1h7Kmf/BOUduwWlTZtPUGuKMQ8Ymjcs+O4nZuUku389PsguVnmb2fvrn\nRifUVmCsqr4iIsN9bPcQsKeIvOg9Pk5EjgQqVPVWEbkbeEFE2oC3gbt7GLsxphu7jBvN/bM+ZeTQ\nspyrKi8qDNLUGqJhcTOjh5dnOxyTpyZOmU1La4hAANZbY1DOfc4zJROZ/UwR+QdwFvBvb678bpe5\nVdUIMLHL0x/HvH4rcGsPYjXG9NDT//sSgBP33zTLkXRWW1fPEm/8/40PvstlJ22X5YhMPopm9OBm\nZJwzfwkTp8xm2uRdshxZ5vkezyIiA3Ht8Empag1wnqp+ARwFfISbQtcYk8Nq6+o7lrete1qzHE1i\n7SGbWMf0TjSj7/rcqjANc7clexHZFNcRbn3v8YfAMbET3njPH8OKaXIDIrKTd38h8HPgrjTFbIzJ\nsIJcmlEHN/7/j7e/yvyG5TaLnumVZBn63K+XJHytv/BTsr8VuFBVh6nqMNxkN7fHSbdbzG3XLjeb\nLteYHFdTXUVhQYDCgkBOTagTFZ1YZ/Hy1lWiJGbSKzZDDwSgpHjFCI9VYZElP232par6RPSBqj4k\nIn/qmkhVj419LCJDVXVh13Qmv9XW1TNnvvvSrD961encsioIRyK0hyKUFOXmkNVcq20w+aO2rp5I\nzBpK0Y55J1w5s+P5ed/l1twS6ZawZC8iQ0VkGG4u/N+JyEARKRORE4EXkmw3TkQ+At4WkTVFZI7X\nSc/kuYlTZndk9OA6t/T3q+FVyaUz3P+ypS032zBrxldRWBCkwGbRMykoKSro+PzEzt/Q2pZ0NHfe\nS1aN/wZuxbs9gEm4ce/vAzXAAUm2uwHXIe97Vf0KOAWYlpZoTdbU1tXH7dwSm/mb/NaWBx3figoD\nhMKRfv/DbNJr3nfLOu6PGbFi2OaYyhX3I8DZ1ycsx+a9hJm9qq7Tdala77aOt2xtImWxa92r6jNA\nSTqDNn0v2fSpJ1w5sw8jMZmy/w7rADB88ICcLDnX1tXT1OIy+cvqXs9yNCZf1NbV09IW/0K2prqK\nQEzr0Off9N/Ci5/e+BsDJwFDYp6OqOrxCTb5QUTGxWx/NCsm2jF5KhJe0eBVUlxAa1uoo60rEmGV\nGavan90/61MAjtpzoyxH0r18qIUwuae4MLjShex6awzqqKEMx/zO9Td+euM/BPxI52VrZydJfypw\nI7CpiCwGfoeryjd5qraunlZvuFNRYZBpk3fh9nN375RmVRmr2l/V1tWzcImb++qhF+ZmOZr4aqqr\nGLGaW117r63XzHI0Jl/Elt5vOGPltR5qqqsoLnRZYWt7uN/+jvnpjb9IVS/uwT5/7s2NXwEUqOri\nXsZmckRsFX4wps5r/dGDOrXZ9/ferKuKwhxeO/7YX27MVfe+ySMvfs4u40ZnOxyTBy69a0VP/Kvu\nfSNuE1Ug5netv/6O+flWTxeRWhHZXUR2jt6SpP8NgKous4y+f4jEjFmJ7dzStb3LOk3lr5rqqo65\nsP94TO6110fdN/MTABYtbem3JTCTXrGd8xKJ/V2L/b3rT/xk9rsChwF/xC1xG70l8pWIzBSRy0Xk\nAu+20rh8kx9q6+pp9Tq3FBasPORpvTUGddyPALV32Q9wPqq9q75j+stczkRzudbB5J7YJsh4v19R\nNdVVFPXzqnw/1fhVwEbeQjV+vOL9jU1vs2H0A2vEWWms68QUq8K0k/1Rvsw3f/74Kk68ahaBADk5\nYsDkrtWHliV9fUxlOZ99k7/L23bHT2b/LjAWt6xst1T1wlQCMrmrKEGpKrY3awR3NW0/xPklWvoZ\nOrAk5/93RYVBmltDhMJhCoJW0jf+RDvhJRIMxrbbd1/1n2/8fFPWx82iN19EPvNuudld16Rd7Id+\n3vfxO650bbvvrx1c+qvaunq++aERgKKi3M48a+vqafYmd4rO+GeMH8EeTLfc0tb/qvL9fLMPxGX4\nO7BiYZvdk6Q3/UjssNPY2aa6im27D/fTDi6rguLC3JwXP562kH3OTHJnHbEFAEEfzT411VU5uy5E\nOvjJ7L8E9gGmANfjMv8vMxmUyQ21dfUdy4kWxZmMIlZsB5e2ftrBpb+KrZk5f3xuL2NRU11F5WoD\ngPzpZ2CyJ9phOOxzVbt1Ygot9LNrST9t9lcBGwB34C4OjgPWBc6Il1hEjgWuAYbGPB1R1f57ybQK\nGFNZ0W2a0ZXlfN6PO7j0V7WdxiG/mfNt9tEe+d8tasq5/iETr51NS1uIkqICpp1pM0pmW7Sw4teX\n367oYPxVP2u391Oy3ws4WFUfUdV/AQcDeydJfwGuqr9AVYPezTL6PHTe0VsCbiiFn7HXsUuQWk1+\n/mjPs+rwXB1+d8KVM2nx5ppoaQsx8dpkE42avhDteDpqWJmvi8K1R/bf5kg/35oCOtcAFALtSdLP\nU9X3ejBUz+Soi+58DVjRw74n+mNv1v6qpS3Z1zn3/OHXrqmhtKQgZ0r1XddLBzoyfpMdtXX1LFrq\npoD22xfl6kk7U+J1Um0PRfpVc6SfzP4e4HkR+Y2ITAJmAfcmSf+6iDwgIieJyDHebXxaojV96tuF\njTRfIhEAACAASURBVD1KX1NdRWGBK93314kp+qOGH5uzHUKPlBQXEAwG8qJGwr4DuSH6u+RLzNCi\n/jRvSLeZvapeBlwCrAWsDVyqqrVJNlkNWAZsj6vO3827mTxSW1ff8WMab6WoRLqbuMLkltq6ekLe\nkIuSIv//52yqrasnHI64jqA5MmNjotXSbBhq9nQ0QwagZrz/z3WnNe59duzLB3466KGqTwBPRB+L\nyFRVPTVB2mPTE5rJFWuO6L5zXlRJHg3dMhCJ6b80pgf/51wRypElSWMXiyopLqDFmwugv86zng8u\n8eZhiGbYfi9ka6qrmHjt87S09a/RHr3t6VLd9QkRedz7+1mcm03Ck2dCsVWkPakBi/1E2e9czvuq\nIf/6VtRUVzG4vBiAo/faKMvRrDxEddrkXTo6EVpzVva0ppBZx174frUg/74j8aSzW+uJ3t/d4txs\nEp48Mz/BbHndqamuIjqD6Sn/t1kaIzLpFptJ5Zt9tl8bgNse/SDLkXQWHaI6OskEVKZvRD/bI4eW\nptQ81V8u2NKW2avq197fz+Pd0nUck3k9mUwn3rZhL/+Ycv9bmQjPpEls56N8W1hm1hvzAVjgjbXP\nqpgarOiFbo86hJm0q62r54clruNpUS+aFrtOAd4fOuolbLMXkVlJtivNQCwmByWbIrc7NvQod02c\nMrvTULHY6Y7zQS5lpt1NvhLKg1ED/VlRNwvgJNJpga8etvvnomRn4aIktz0yH5rJmk4llZ79qNZU\nV7HW6q4qM5U2M5M5E6fM7uhABvlXqocVPa3LSgqzGnui9dJrqqsYMrAEsIvebIj9TJzfg574XffR\naYGvhvweWZGwZK+qz/dmhyJyJVCjqu3e41HAraq6X68iTLPaunrmfr2ESMT1mp022aa07CrVaSKj\nS+Eua2rL+6vh/qa2rr5TRg/5V6oHKBtQRDCQW/PjjxjSedhpdFGVb35otO9BH7tkxmsd91M598VF\nMSMrcmTkR2/5GnrXQ0OA/4lINbAlUAtcl4Hj9FhtXX1HtQxAS2uIiVNmW4Yfo3NJpZfjrmMrA/L7\n+5HXYi9sEykpyp1Z6Hqitq6ecMTrPHVXfY/GUadTTXUVE66cSTiyorYhqj+voJbrWtPU8XRMZXlH\nnpErwzx7K+2TTKvqScDVwFu4RXR2VdVru9tORIIicpOIvCQis0Rk/S6vHykir4jIf0Vkmoj0uNEu\n3gQXXUs5ZoU1hqc+QU6+9vbOdxOnzGbO/OQZfSBAv1isJZTFsezRiw6A6x54u9NrudSvYFXT5jUh\nVg4ekNLFbOyyt6Fwfk+f6yuzF5GdROQUERkgIjt3k/Z4XGZfAzwF3C8iW/g4zP+3d+ZxcpRl4v92\nJpnBJBPOiZiAIiw+uCsgMHiAS0g+gqvL5b2IA0IUzKqsJBzBEeTHOpBIiGw84gFxZfDnjRvBz8rq\nBhIORcMqqKsPbrgkAUkgEGTJTDKZ/aOqet6uqa6u6q4+quf5fj79yaS7uvrtt6ve53mf8zSgU1WP\nARYDRQVBRF6CV8XveFV9E7A7kNotUK6xQZ5/wMxxpmhKBg1HhnaaMtVookz1UdxwSX4zYvv7eouN\nl856yyFNG8euOF3WkfV53xXmiYHB9Tz17ItA9cF5Jbh++xz3/Kg4EyLyceDTwEKgG/iKiFwU85YP\nA29W1c+o6tl4XfD+LcFYjsVTDlDVewFXHdsOvFFVgyLek4EXE5yzSDidrN3SKrJih+sDrXJj0t/X\ny8v29qwCFqTXeCpdz12dHaxanF9BD6Vlfr/8w981bRw7YpTZ/r5e9pjuBekNmwWxKUzJwJXiZiTF\nKnctThK15wPAW4AXVHUznhA+J+b4N6jqH4L/qOqPgMMSfM4MwF2lRkRkkn+OUf+zEZGPAdNU9acJ\nzlnEjaScVCiUBCW1U/3jWtm0JV3zm3IEpq+tzw/Z3DaQcPe1QLC7j3aLUdnRxCC9Sr7hoIPaJj9I\nz6g//X29xX3K5Qlacyc5X6dvIdgxkt8CO0kC9EZUdUhEgv9vJ77F7Qbn2IBR4MAKn7MNz3IQMElV\ni3eSL/g/A/wV8M5Kg95zz6lMdoopuJvUV86awTXnH8cpF64uLowPbdpGT083jaYZn1mOi1asK4lu\nnjK5o+rx7dY1dmlVe55WmptWJGp+NjmVDwsF+N7VLZEEkznXLZzLR5fdzqNPbOPYw2eNm4tGXTuB\ndWH/l3Zz3cLx/b6mTe2ErYFJufr7KUtaYQz15MIV64reyGu+9WuuOT/W8zyOqPl55azd0ce2Aq3z\nO6YlibBfKyLXAtNF5DTgXGBNzPHuFT8Fzxe/W4LPuRs4GfiuiLwBeCD0+pfxFI23q2pFB9jWrWM7\n1IHB9Wz3zWiTOwpcfPoRbN78/LiiCR9ffntDI5N7errZvPn5hn1eJVyTZNeUjuI8VcPFpx/B/KVr\nvHl912Gpz9Nqc9NqlJuf7UNjv2HnlI62nsML3n0YH19xF//5i8c47ZgDis836toZGFzP0895nsVJ\nBSI/s+CYWXbsHGn67zER7quHNz1X/DvtnJebn50jY/fV0HDzf8dyxCkhSYT9hXgC/n7gTLzud18q\nd3BEadxrROQ+vAC7OH4AnCAid/v/P1tETgemA+vxXAfrgDW+5eBfVDVJLEAJbi5sf18v5y27wyLG\nfXbuHFuY9ptZW21v15z86RvXc+X819d0vnYjKGxTKHh57lkomQOD60syHWupfpgHPvc9bz/wwvad\nTc9jn1yu+JTz9KgtM3VnYHB9MU7ILXKUJY/nsHkUJBP2nwUGVbWsgHcRkTmMxXQXgNeQYGfv79YX\nhJ5+0Pk7k6TVcO7ry2dOZ4Mf0DTRe09X2/ymEk8+kyqWsq0JV68bHYUNG7dlIqzc6zePVfFS0+R6\nDpeecRQf/MztifulD+c0MyWo1wDZKaaNoGeP7Kq69/f1ct41d7BjZBc7/MY4eZmHgCQBen8ErhOR\n34vIJ0XkgArHu2V1PwXMAc6qaZS1EtGoooizYAztGMlt8EWtDAyuL/rrO1M2v4nCDWrZmeOgliw5\nZ8masilxbrGnanFTS/NYFS8t/X29BBvqf3z7oQ3//Cv9Km1JA3zzWDY3KEQ2OlqqmLYq7jVxyfuO\njD84JS/LoO5IM6ko7FX1835u+9/h+cxXi8hdMccfr6pz/cc8VX2Pqjb16oiL1u3v680mFzPnlO4K\nsykGMrtneuWDJgjnLIkLc/GoZRGtpVNhXnEL2lz77cZ3WEzi/uvv6y26U4ZymIYalcaZhWJaLQOD\n65m/dA0Llq8t+3pwTXzu5nDYV21c/oGjAW9/mMf7K2lRnd2BNwMn4pnTb4s45vaYR+WVro5UKjCy\n/0wTSqOj2fnrA1wrShOLnDWdKEFfKMCqxfNK6j3UYop2lbVJGSlreSIu371eBL7hl1bol97puw63\nvTDc0rviMOE0zvBrjcatCjk0PBJ5X7lxR1lz9U33Ad5t+umv5+d3DKjosxeRW/Bq3N8MXOYXvIni\nCjylZ5Sqy7HUh8B8NmufaZE3peurznOFpGpx6+FPqbYefgUm4rwCzF86fkE6aPaY39PNCHmshjmq\nh7LW6vT39XLZDfeycfMLDS/e5PZL76zQL93VvfJUlMXd1RcKpQp7o+ObylWFPGfJmpICUfWKOwqT\nxyC9JAF6XwH+PehiF8PnVfVQEfmFqr4ug7FlwsDgep79yzBQvjGF2+xgIla1LCk4lKFHww1qGc5p\nUEstRO2M4irXVRv40whlrVUJXHDP+bvmZnz3NGWlw0IiCH7rnNJaHTjD124QAzK2TqZbKBdcu7a4\n6aqm22ic62D+0jXccMm8krijetDf18u519zBzgatZ0HqclYZO2WvUhH5f/6f78Arkfs157Eq4i2b\nRGQjcLiIPBx6PFTTKDOinCBz/fbBgjuhKNkVZuvScOe8nv2gA1/eOUvWMH/pmpb4DcO7nyhB7zba\nAKoy5Zcqay1lVKs7HU36viX90lNUaXPXl7BZOsoK1AoECmRJJbkU6+T8pWtKghODbqNJCc9L2EtV\nDJB0K0fWqZtjUAa83pyzZE1xWQ4CI9PMWRRxKmnwS96Bl9++NvQI81bgjYACx+MV1wkeTSvEfcG7\nXwt4RS/ifvyJ6rcfGFxfDByqS16qc2cO1yka2Y0Yhuxujlpxv29XZ3lTr2t2/1M1pvwJaMIP6O/r\nLV5iWUdfxxHulx7HOIWOaLP06Ki3A241Zjv1Gtzg3SSm/HJ+/6TdRsPvLxS8Bk5hxXnDxm0lbod6\n3QeNCOYup/SlVZLClB25qt7i/zlbVf/VfQDj2kyp6i5VfUxVD1PVR1X1EfdR9QhrZMk3vKCKXRXS\nY/LqV6sVd1dYj12SW9ilXj0Iypn4hoabl0pZbYGb4ZSWpboray1OSfGmBgZNpe2XXqLQ/fkv5a/Z\nVkn/dS7eDqdVr/s9kpjy45oyJfme4XlyOzWGFehGeGBdOVGPoOMFy9fGnrcWgR9nxl8iIl8DFonI\nKseEPwi8q6pPawLVFHTJY/BF1YTV5oxxd16QfdpOpQWjWWlCD20sDW6KE8KueTQt9VbW8sQTT2fT\nxCkJxX7pe6Tvl15JUWiFLpzlfN9pXJ7hXflBs2ek6jYaFmoHzS6tHVHO71/PglL9fb10T50CJEu9\nTEOUtWfV4nnjlJogEyGtUhi3wtyMZ65/gVLz/W3A21J9SpNwAzYq5R5PVL/9yIhjAq5TedVwgZdq\nLtRyhIX5qsXzxplMG23OD+/qkxS4qdaNNHvvsd8s63iLPFBy3zaoeFNpv/RkhT3DSm9AkILpLuit\n0IUzLrthv4T1M1xFNBDAabqNhgVf1PodFQfj7v7rQbC+/Ompv2T6O4WVn0C5WblwTuS1k9ZVGWfG\n/4Vvsj9UVb/umPD/PxDrcBGRbhF5uftIPKI6MXufyoJsVoOCL1qFBdeuZaefflCvgBaIXuiy8KmX\n0/xXLirV+JP6B7MinLKUaF6d+UmTpviniWSFKkMzewCkschEKX2BYAov6M3e3Qelfffda+q467ck\n6DbGbz/LUUSD7x5eC8p9z7DfOryrdwmUpaCdc70pl9VVC1Gtqd15v+GSeZECP42rMsmVeqaIbBOR\nERHZhdfe9pZyB4vIMuBxKgf01Z1A659UgMsSRMwGFZKC97Yznq/XEYJ1tgBHadu1+tTdALiwUA0v\nDo3aKZVLWapEf18vk33f6NCOZDtUt2reRKYkA6EBjlu3X3qSdcV9X3BdBjt6lzS73nri1RAYAqBz\nSryIiIsxKOcOrfQ9o4LyKq3HKxfOaVjqYmcdhH3JBoFoF8UNl8yLVHqS1jxIkme/CHgtMABcihdp\nPy5Az+E0vKC+pm85RnZ5TQu6pnQkKgF7lV8hCbxo28vOOjrm6PwQ5PLGBX40Yne0avG8Yu5oQLU+\n9fCCEFYm+vt6Sz6rlp3SguVrGd4xkijXtapdvc++e01Nl57YgFSjvLGzAYUyBm4cc9NcddN9qea9\nkisxq2s2K6JqCCTpFhqniPb39fLhZXeUjV0If+96m+XTUpLCncHlNm6DEGPFCK4f9zpJmuWUZGf/\nlKo+hNfi9lDflP+3McffT7L+9XXnn/3o3GoiXOuZE95IwmlpUYRNRvUkyhxVzQ7GXRDKmdVq3SkN\nDK4vNq9Jks5X7a4+IK150E3Tm2gpdy79fb3s1d0FNMZlE9dro1ZaYnfvrhVl9kiuib4SUYqoG6Pi\n7kyjgvpaDbfZzoLTXlPz+UpiG0i2QSi5Tkh2nSQR9n8RkbnAb4CTReRlwL4xxw8CfxSRO5tdGz+t\nibMdg/Qq7Q4KhfJRrfUirKmn3cGEF4Rygi6pf7DcZ0RZHeJcD+HjUytQzljdwMkoLlqxLnX6VzsT\nKEpPPvO/XLRiXV0/Kwhe22tGV+ZKsusigOZsOpIoM5Mnx7tO3PTlyPuzTLfRWixjjcJttrPsW7U3\nYBp2FNTOmHocLtWsbUmE/fnAKcC/A3sDfwA+H3P8dcA/AZdR2u624QSR+DP3iG9U4dKoCkmNIK6R\nBXg7+maZyFyNPe0OJo0mXO1OKW6RjVICwjv+uCI6SXhxOL469aNPtv6i2Ei6upLP94LlazlnSfnO\naXEMDK7nyWe8FL9q0yUr4S749SpEFUdaZSYqTa9S+nJYWD2++YWaLWPNYMuz6VO7XaqtxwHp17aK\nPntV/S1wgf/fdyYYw7OqemOC4+rKwOB6Nj/rNaqYnOKmTFPnutUJp760ku+rv6+XBdfeUVXbz9n7\nTCtqspUWhP6+XuYvWVO8oZLulIZD5uBwIxC3AceC5WvHmY9rtZZs8a/dcrg7pzwsivXGrTHwyJPl\ndzlup7SgRG2190XStLu0uL06gkW8UcpcqTKT7PttD137Sdstd07pKN43Q8MjtVvGGoS7du3cNVrT\n71OLJSPtGhpXVCdc3z5prfu7ROT7IvJBETnLf5yZ+BvUgVQCvKSSXs674jjSqR4RpLWyX4nfLkW6\nmXtsgiyCEmGY4CcNa9sHzZ4RKRDOWbKm6NMndHw1uGVVR/xFpNz4gkyKidK7Pg3bh6JdLVFlSNNa\nldwd6SfPrF+qaleFKPhGEBeJ39/XW0xnDgt7l7iU57hdbK2WsXpTUmugSjGRhSXDXUMrVfSLu6Lm\nhh7Hk6zW/XTgeeBY/z3B+xqKe1OmTY/ZY3rjgn3qyYijrDQzFzkJ9Uw36z+zt7jzSxKsWU7bTpLD\nW3OwY4J8e3d8E7F3fRT9fb2xSn2cSyttLEdwns9887/SDDEV1SrCteLGDLipyFEEAjnoOFikTKnd\nyM8q83IrdQCMxBl3tZ32oooO1UKl6ySuqI5b1/5Y4FxgC3BcXK17Vf2Af+xyYAVwrqqenXbgteLe\nlFd/4774g0N0dXrTsunp/81tkJ5XPTAomNOau7+qysRWmW7mmnnjTPmVtO04gV9N684wrlIWZVhK\nGpw4EZkdMxdhgV5txbpKgZP1IKkinAVuWuHVN8Wvm+X6iaRp5hRlMWtEYZws2bSlujLNo86NXK0r\nzg0qH965i5MXrb6n3LEVV1oRWYpXHvcdwBTgbBFZHnN8L/Ag8HVgFfCoiLwh1TfIgNEaApXrUSGp\n0ZQUWmjh3V+5FJxyVJtu5n7OfjGmxSTadlS96oNmz8hkN1KpjWg1aToTBbc2vrvLiapOtnLhnKqi\n3k9/88GApzzWc+6jAtgaQbVFmrb7AaUDg+uLWSKTO5JtMhpdAS8LSu7TKso0DwyuLwZCJp2nciSp\nDgvJiuq8BTgSuE9Vt4rICXhpeAvLHL8CeK+q3gvgC/oVwOsSjSgjzn7bIVx2wy+YMXVK6olshyA9\nV2tsaRN+RApOud/LXUhq+ZzYBS2htl1PM2NcG1E3Ojtpms5EwQ1scy2rYbdM8NsdOGsGG/zX4hRA\nl+tv/b1//toCs5Jw4KwZY4F6DYofGvLL5O6dMq3wqa3jo9Jn7ZM8s6nlzfYR7DdzetWFj9z31drA\nKs5V4pJEqoUd110Rz7lMCwQ9gKr+nCYU2fniD34LwAlH75/+zTkP0qtGu24WrhkqDbW4JjY9Hb1L\napV2sa7FwhXu40z4razENQE3sG2nv9sKtwwtUeBCimYSntkWnyWRJWETbb1N+QOD6/mz3yU0SUCv\nO9/FgFJnrtth0xRHieUlYclaqLsrrmyVnyS/xneBbwF7icgFwJ3AN2OO3yoipwX/EZG3A08nHGhm\nPOGnj9z1wBM1nSfvQXpptOtmMTuh0Cq9QdJ1eCsVBNGR7q3SLtYNkhplLIp8Q4q2uRMWZwXesHFb\nyf0bN2dBulkcA4Pri0GvjYqDcQMwG1k+N2naHSErVNpMmXYhTZXWrF1xoeDU7nLHVRT2qroEz/f+\nXWB/4HJVHYh5y7nAJ0TkaRF5BvgE8OGkA8+aNDn2UTxVY9GEZpMH7doVrHGxFjVHJceYx70PH/uz\n2e1iO0MBZG5+OIC8fM9GDykXxFk7wm6ZJApg2c9p0PXh7vrqXT63NBI/mQBy53t4x0huLIpZUK1V\n0l1nskqJnpRgGLGHiMcsVf2xql6oqguBX4rIV2LeNk9VXwe8AjhAVY9WVU0z8CyYtpsXjnDRPxyR\n+r21LAKtwOM51q7LVY5zXRPV5paXRrqXumfc3PV6B18lIc6HWSjANecf18DR5Ae3brlLuV19idCu\n4LG75H1Hxp6rHjSyfO6nQw1+kuAKPHf6crDHyIS0AcbgNWgLyMqEn0T5jCuqcwVwH/CgiJwgIpNF\nZDHwR+CAmHN+DEBV/6KqTWvbtGNkF4UCdE+dUtX7S4om5IhW8TtXS1SgD5QuctVa2JP2PmiVksnl\nivO0UiXEVmT1slNLMmq6piQrC12pJnzQWKvRDWrc3V89y+dWazmLqvXQbMtYoyhEBBjHMXBj81Ki\n4/Svs4CDgTl45XJ/DJwBvFtVT4x5359EZI2IXC0in/Ifl2c35GQM79jF6GhyDXUcOQ/SA3jpnq0h\ntCqRqHJcRib2/cu9N5S/3wq4/c+BXKUmNZuVi+awavE8Vi2ex8pFySK93dS9KKpNS6uVRpjyS4N6\n020SonanedtkVEtay8sGN+6iwSnRccJ+m6o+oar3AUcDDwCvVdXbog4WkeAX/xmwDgjCVgvkzphc\nSl7b3bZ6yckSYirHZWliL5T5HDf46fEtrfN79/f1jgmtHKYntTppOl0O+2lp++41taHCrJbujdWQ\ndpPgKqWFQv6K4tRKUstLuFxzltk0zm/ws3LHxOXZu2rsFmCRqsZtce/AUwr2VdUFKcZZN2oRDP19\nvZy37A527NxVXATyoK26fqMWrqUzDjdHOs6QMnOPl2T2mW5lsmo7Txn5Z7+eaTz8xPOxxwwMrueZ\nbUNAfM34elGScz/qNV+ql/JXTdBYHtbGerHfzNLGRVHNlcLpdvWI++jv66Wnp/uYcq8nvWq3VxD0\nAN0i8g3gPSKySkS+5jxWJfkQEZkkIl8SkXtE5HYROSjimKkicreISKXzxTUTSULe/PatFmSWhnDl\nuHI9yXfrSlIHKv5zxu2SqizBa7QHk9wgkAQeu2ZkuISv26Hh5KleiXC+d5LIbmOM8G8TZM+4LZTD\nHf2aEXcTt3L+jYg87P89y/kbYFRVDwwdfyJe05s3AWspNd0ndXqfBnSq6jEi8nrgWv85oFiK90vA\nrBTnrBr3oq+l/G4zmLlndjvgRrH/zOmlPi0ft6FPFgtReJfkfqbVmp/YlAvSu/T9R/HBpbd7edF1\n6nZXCfe6hfECpBaSFhUyogn/NuApZOGUWai+K2atxAn7V6U811OqeqOIPKCqv446QER2U9W4ElTH\n4gUCoqr3+sLdpRNP+A8mGdB+PdMy26X9afP4SNVAc6u3L3VgcD0PbdpWYgY6aPaM2O+2W2dtO+Cm\n4KiHDz8xduNszDhmor+vN/ImNCYm/X29XPjFu3lm21CxXnmYK//1l4C3w2iWS6+/r5cFy9eWFAqK\nMhm7uGtHV2cH37v6pMjjkhQVMsrT39fLwOD6RApYsyyHibreRT0i3vINEfkQsCH8gojMEJGP4FXi\ni2MG4M7WiIgUx6iq96jq4xXOUaSqggcOcU1J5i/1+piX096yYsHytWzYuG1ce84NG7eVmImgtCNX\n3k1xgZmympa2SYiKuLeqdBOX4Hp48pnoTpeVIvUbRXhjEfjvo5i/dE3J2jE0PMKpF64ed5zbIbOz\nRTtk5oEgmDYuVqqZwYtZbv/eAyzAK7rzHPA4sBOvuM4+wL8A76pwjm2UlvubpKpVr/RdnZPp6Slb\nPTARkzoK3rfA8+/29HTznk/cOk74nrNkDbdce2rq88eN76IV62LL9Q4Nj/CRz67lO1d52vpGJ4p8\nyuSOmr97o7lu4VxOuXB1cW43bXmBl+87ZvLarauD6xbOzeSzXjlrBn94dGvJcz9clv73ayZ5+30b\nSdq5mfaSTsAT6OF756IV64oKZ+fkSZldg9VyyCv2LLl2w+sAwMmLxgt18IJfw8d2ODEIB87afcJf\nV7V+/x8uO5WLVqzjwce2FoONX9LVUTLnzSAzYa+qI8DnReQLwOF4OfojeDv9BxIE+AHcDZwMfNfv\nlvdAtePZf+Z0Lj79CDZvjo+yrcTsfUojLV1hFOaUC1enCrzo6emOHV9YGEXx4tAIp1y4ms7JHSU7\n4B07R2r+7s3A9X1tHx7hQWcOZu8zLbPvdPHpRxRNnJ1TvJaneZqvStfORKaauRkdLX/v7Ng5pnDv\nu/fUps/7xacfMc6c/+LQCCcvWs1Bs8f7jsO8ODTCuy69tWglePCxZ4uv5XXdyIqs7quLTx9fubUR\n8xqnqBRGy0muJiAiBeCLwGH+U2cDRwHTVfWrznG3A+ep6oPlzrV58/OZfbE0ZvpKvnSXuAtr/tI1\n41I1AkUifKOHcY/NI+XmO83ctjsm7MtTzdy4/tYDXtbN5WcdXfL6/CVrGAWuv2RuZMW4ZlBpHQg4\naPYMHt/8wrhjCwXGbVwm+j2W9/uqp6e77MXZUp5dVR1V1QWqeqz/eFBVv+kKev+4uXGCPmvKFaeJ\nqmiWRcGLcE4mlArvlQvnxBbMievDngei1lLzpxv1pL+vlz2mdwEwHBKKbs34q6utyFkHKq0DMCa8\no44NrzF2j7U3LSXsW5WoaPuuzo7i8wc5wjWLcpZhM1xUUEe5G72rM/954lHKSp4tFUY+6Or0lsNN\nT5cG6dXcbbGOrFw4p2wqV3iXvnLhHF7SVV45sHusvTFhn5BVi+fR1dlBoeDdRK4C0H9mL5M7sjHt\nhUsqxuVkBjd6oUDkuPJKf18vh7xiTwoFqwdvNI6oDI1aasY3iiAK3F2fVi2eFznW71x1UuSaYvdY\n+5PDZOzmESdI95/ZPZYbHhEt4PoECwVv9xqO6l2wfG3qkoqtuPhkwTXnH5dr35mRPypVxtt3r9Zu\nLJVU0Q/WjGBjYTv6iYEJ+4zY9PRY2lvYbx8Othsd9Uz1p164muv9G21gcP24ABq7CQ2jgZR0ba9O\nsQAACxRJREFUuhz/cjU141sZW18mFibsM8Jt5OJW2YqL5N81Gh95bhhGc9g+7BXXyGtjKcMIYz77\njAg3Q9iwcVvVlfUsKtYwmstTW1/MdWMpwwhjwj5DKqW8BYEzlXbtZl4zjMbT39dbDNIb2TXK405P\nhpfmsLGUYbiYsM+Q8O7exY2ODaJnJ4WOtchzw2gyoTayAbW2VjaMZmNXcMbccMk8Fly7tmj+i6tm\nt3rZqRZxbhgthBt74+L2nTCMPGLCvg6sXJT/XHfDmIj09/WOy54BTwkwjDxjZnzDMAyHcOyNBcwa\n7YAJe8MwDIf+vt5iZcquzg4LmDXaAjPjG4ZhhLCdvNFu2M7eMAzDMNocE/aGYRiG0eaYsDcMwzCM\nNseEvWEYhmG0OSbsDcMwDKPNMWFvGIZhGG2OCXvDMAzDaHNM2BuGYRhGm2PC3jAMwzDaHBP2hmEY\nhtHmmLA3DMMwjDbHhL1hGIZhtDkm7A3DMAyjzTFhbxiGYRhtjgl7wzAMw2hzTNgbhmEYRptjwt4w\nDMMw2hwT9oZhGIbR5kxu9gACRGQS8EXgMGAI+KCqbnBePxm4DNgJrFLV65syUMMwDMPIGa20sz8N\n6FTVY4DFwLXBCyIyBVgOnADMAc4VkZlNGaVhGIZh5IxWEvbHAj8GUNV7gV7ntVcD/6Oqz6nqDuAu\n4LjGD9EwDMMw8kcrCfsZwDbn/yO+aT947TnnteeB3Rs1MMMwDMPIMy3js8cT9N3O/yep6i7/7+dC\nr3UDW+NO1tPTXch2ePWhp6e78kETFJubeGx+ymNzUx6bm3jadX5aaWd/N/A2ABF5A/CA89ofgINF\nZE8R6cQz4f+s8UM0DMMwjPxRGB0dbfYYABCRAmPR+ABnA0cB01X1qyJyEnA5noJyg6qubM5IDcMw\nDCNftIywNwzDMAyjPrSSGd8wDMMwjDpgwt4wDMMw2hwT9oZhGIbR5rRS6l1u8esBXA+8CtgFfAgv\nNfCrwB5AAThTVR8RkbfiBRoC/FJVz3fOcwjwc2Cmqg77WQnX4ZUI/g9VvbJR3ylLap0fEenAq6B4\nFNAJXK6qP26H+clgbqYC3/SPHQber6p/nkhzg1dz4zrnrW8ATgXuBG4CevBqc5ylqlvaYW4gk/m5\nF29+uvHuq4Wq+vN2mJ9a50ZV/8M/T9usybazz4YTgWmq+ibgSuAqYCkwqKpz8Bbo14hIN/AZ4O9V\n9Y3ARhHpARCRGXglgrc7510JnO6f9/Ui8tqGfaNsqXV++oDJ/vtPw6uoCPAl8j8/tc7NmcDv/WO/\nDVzkn3fCzI2q3q+qc1V1Ll5Gz/f8xXoBcL+qHgfcCHzSP287zA3UPj8XAD9R1eOBDwBf8M/bDvNT\n69y03Zpswj4bXgR299MHd8fbYR0L7C8iPwHOANYAxwC/AZaLyDrgCVXd7L/vy8Cl/rmCC61LVR/2\nP+M24M0N/E5ZUtP84N24G0XkVjzNfLU/P51tMD+1zs2LwN7+uXYHhn3FYCLNDQAiMg24Avgn/6li\nCW7/3ze30dxA7fPzWeAr/t9TgBfbaH5qmpt2XJNN2GfD3cBueMV/vgysAA4AnlHVE4DHgEvwFuW5\nwMXAW4GPi8jBwKeAH6lqUEiowPjywXkuEVzr/OwDHKSqJ+Fp51/DMz22w/zUOjc/AN4kIr8DFgGr\n8OZhIs1NwHzgO6r6jP9/t8x2MAcT8b4KKJkfv9fIdhHZFxjEE2x27Xi03Zpswj4bLgbuVlUBXotn\nMtwC/NB//Ra8xj5P4/lan1LVF4B1/vFnAPNF5HZgXzyNMVwieAbwbAO+Sz2odX6eBn4EoKrr8Pxw\n4fLKeZ2fWudmGbBcVf8GeAvwfdrn2kk6NwHvw/PTBmzD++7gzceztM91A7XPDyJyKPBT4FJVvZP2\nmZ9a56bt1mQT9tkwjTGNbyte4OPPgL/3n5sD/Bb4Lzz/694iMhkvGOR3qnqw4zd6EjhRVZ/HM8ke\n6JuUTsRb4PNITfOD1+UwKKV8OPBoG81PLXPz36H3bwa6J+DcICK745lYNzrvL5bgxrOGrGujuYEa\n50dE/hr4Lp4P+jYAVd1Ge8xPTXPTjmuyReNnwzXA10TkTjzf16XAPcD1IrIAT/t7n6o+JyKX4mmJ\nAN9W1f8Oncstafhh4BtAB3Cbqv6ynl+ijtQ0PyLyP8BKEQn6IXzY+Tfv81PL3PxORD4BfFVEPoJ3\nP3/If33CzI1/7KuAh0PvXwl83X//kHNsO8wN1D4/V+FF4a8QEYBnVfXttMf81Do3Lm2xJlu5XMMw\nDMNoc8yMbxiGYRhtjgl7wzAMw2hzTNgbhmEYRptjwt4wDMMw2hwT9oZhGIbR5piwNwzDMIw2x/Ls\nDSNHiMgBwIN4xYZG8fKkNwFnhwrKVDrPr1T1iBTH3wpco6prQ893AN8BzlDV7ZFvbiDlxum8/nW8\nanGbGjsyw2gutrM3jPyxUVWPUNUjVfU1wHrgc2lOkEbQ+4xSWlwkYAHw41YQ9D7lxhmwFK8BjGFM\nKGxnbxj5507gFAARORpYDkzFqwV+nqo+IiJ34NXX/2vgH4BfqeokEZmK10nwMLy+38tUdVBEuvA6\nor0Or2nI3oTwS4Z+FDja///78FrsjuBVJHu/qg6JyGLg3YxVHbvEP/4C4Dz/+FtUdbGIvBS4Adgf\nr2f4J1T1NhG5ApgN/BXwCuB6Vb2q3DhFZD+8SmdT/e91vqre61dkPEBEDlTVh2qadcPIEbazN4wc\nIyJTgPcCd/l/X49X6/woPKH/Vf/QUbze7q9W1fudU1wBbFbVQ4F5wBV+c5SPAh2q+mo8gfyqiI8/\nHHjOrxkO8M/ACarai9dt7BAR+TvgSDyF4EhgPxE5Q0Reh2cVOBpP0ThKRI7Es1D8VFUPB94FrBKR\nmf75DwVOAF4PLPZrmkeNswCcg6dAHI3XFOVNzrjvAk5KMr+G0S7Yzt4w8scsEfmV/3cXcC+wGBDg\nQOAWv9Y5lHbpujfiXHPxBCOq+rSIrAaO9x9f9p9/RETWRLz3YOBx5/+3APeIyL8B31fV+0WkD084\n3+cfsxvwCF4nsR86isIJACIyF6/dKKr6sIjc679/FFijqjuBzSLyDF570XLj/Clws4gcgdcx8fPO\nOB/1x24YEwYT9oaRPzZF+dxF5BXAQ8FrIjIJT6gGvBhxrkl4O2H3/5PxhKtr+dsZ8d4R93lV/biI\n3IDXWewm3/Q+CbhOVT/rj2lPYAeeglH8XBF5mT++8HgKjK1TQ87zo/5rUeMcVdV7/K5uJ+FZPj6A\n16UM//N3RXwfw2hbzIxvGO3DH4C9RCQwWZ+D57cOKIx/C2vwd9Iisg9wKnA78BOgT0QKviA+PuK9\nG/D854hIh4gosEVVl+D1Dz/CP3+fiEzzW/PeDLwDL87grc7z3wSOCo3nQOBYvG5lUWOnzDgLInI1\n0KeqNwIfw3MhBBwI/LHM+QyjLTFhbxj5IzLaXFWH8ALhrhWR+4Ez8U30Ee8L/r4ST0F4AFgLfFpV\nf43XHnYL8HvgJuCBiI98ANhHRGao6gjwKeCnIvJL4G+Ba1X1VuD7eC6E3+AFBt6oqr/CM63/DPg1\nsFZV/xM4H5jnj+cHwHxV/TPRUfajZcY5CnwBeKfv7riZsbbIAMfhuRwMY8JgLW4Nw6gaEfkYsEtV\nv9DssSRBRA7Hi/B/b7PHYhiNxHb2hmHUwkrgBBHZrdkDSchFwKJmD8IwGo3t7A3DMAyjzbGdvWEY\nhmG0OSbsDcMwDKPNMWFvGIZhGG2OCXvDMAzDaHNM2BuGYRhGm2PC3jAMwzDanP8DPCkiGX/qTaUA\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f030650>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 532.489300815\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 10\n"
]
},
{
"data": {
"image/png": 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RgcDfgeuAH4FbRWSAj1gOBvJVdUdcTcL1EccMALcD41R1Z+DfwHo+jmmMSbKu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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fd9f910>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bayesian Information Criterion: 538.934125592\n",
"\n",
"--------------------------------------------------------------------------------\n",
"Number of Fourier terms for base model: 11\n"
]
},
{
"data": {
"image/png": 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V7aOqfYF/AYeq6mqqOgj4PfBSou1EZCBwE3AN8DNwi4gM8BHLAUChqu6E60m4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\nUSqDNunhUk0GyM8Lcr7PM+NYzV253m/SIjaPQlFBXlZVoIkMHlCS
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